From 05b6b5a322c4e7a131a58c044c624949102e09b2 Mon Sep 17 00:00:00 2001 From: rafa9811 Date: Mon, 8 Nov 2021 20:23:58 +0100 Subject: [PATCH 001/400] Support for dataframes --- skfda/ml/regression/_linear_regression.py | 73 ++++++++++++++++++++--- 1 file changed, 66 insertions(+), 7 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 5de4d4eb1..34956a9f3 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -5,6 +5,8 @@ from typing import Any, Iterable, List, Optional, Sequence, Tuple, Union import numpy as np +import pandas as pd + from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted @@ -14,7 +16,7 @@ compute_penalty_matrix, ) from ...representation import FData -from ...representation.basis import Basis +from ...representation.basis import Basis, FDataBasis from ._coefficients import CoefficientInfo, coefficient_info_from_covariate RegularizationType = Union[ @@ -49,10 +51,36 @@ ] +class DataFrameMixin: + """Mixin class to add DataFrame funcionality.""" + + def dataframe_conversion(X: pd.DataFrame): + """Converts DataFrames to a list of two elements: first of all, a list with mv + covariates and the second, a FDataBasis object with functional data. + + Args: + X: pandas DataFrame to convert. + + """ + fdb = FDataBasis.concatenate(*X.iloc[:, 1].tolist()) + X = [X.iloc[:, 0].tolist(), fdb] + return X + + class LinearRegression( BaseEstimator, # type: ignore - RegressorMixin, # type: ignore + RegressorMixin, # type: ignore + DataFrameMixin, # type: ignore ): + """ + .. deprecated:: 0.8 + Use covariate parameters of type pandas.FDataFrame in methods fit, predict. + """ + warnings.warn( + "Usage of arguments of type FData, ndarray or a sequence of both is deprecated (fit, predict)." + "Use pandas DataFrame instead", + DeprecationWarning, + ) r"""Linear regression with multivariate response. This is a regression algorithm equivalent to multivariate linear @@ -153,6 +181,34 @@ class LinearRegression( >>> linear.predict([x, x_fd]) array([ 11., 10., 12., 6., 10., 13.]) + Funcionality with pandas Dataframe: + + >>> x_basis = Monomial(n_basis=2) + >>> x_fd = FDataBasis(x_basis, [[0, 2], + ... [0, 4], + ... [1, 0], + ... [2, 0], + ... [1, 2], + ... [2, 2]]) + >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] + >>> cov = {'mv': x, 'fd': x_fd} + >>> df = pd.Dataframe(cov) + >>> y = [11, 10, 12, 6, 10, 13] + >>> linear = LinearRegression( + ... coef_basis=[None, Constant()]) + >>> _ = linear.fit(df, y) + >>> linear.coef_[0] + array([ 2., 1.]) + >>> linear.coef_[1] + FDataBasis( + basis=Constant(domain_range=((0, 1),), n_basis=1), + coefficients=[[ 1.]], + ...) + >>> linear.intercept_ + array([ 1.]) + >>> linear.predict(df) + array([ 11., 10., 12., 6., 10., 13.]) + """ def __init__( @@ -168,11 +224,11 @@ def __init__( def fit( # noqa: D102 self, - X: Union[AcceptedDataType, Sequence[AcceptedDataType]], + X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], y: np.ndarray, sample_weight: Optional[np.ndarray] = None, ) -> LinearRegression: - + X_new, y, sample_weight, coef_info = self._argcheck_X_y( X, y, @@ -244,7 +300,7 @@ def fit( # noqa: D102 def predict( # noqa: D102 self, - X: Union[AcceptedDataType, Sequence[AcceptedDataType]], + X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], ) -> np.ndarray: check_is_fitted(self) @@ -268,11 +324,14 @@ def predict( # noqa: D102 def _argcheck_X( self, - X: Union[AcceptedDataType, Sequence[AcceptedDataType]], + X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], ) -> Sequence[AcceptedDataType]: if isinstance(X, (FData, np.ndarray)): X = [X] + if isinstance(X, pd.DataFrame): + X = DataFrameMixin.dataframe_conversion(X) + X = [x if isinstance(x, FData) else np.asarray(x) for x in X] if all(not isinstance(i, FData) for i in X): @@ -282,7 +341,7 @@ def _argcheck_X( def _argcheck_X_y( self, - X: Union[AcceptedDataType, Sequence[AcceptedDataType]], + X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], y: np.ndarray, sample_weight: Optional[np.ndarray] = None, coef_basis: Optional[BasisCoefsType] = None, From 3027d938daa077f7a39b488df81fa231322fd981 Mon Sep 17 00:00:00 2001 From: rafa9811 Date: Tue, 9 Nov 2021 18:41:45 +0100 Subject: [PATCH 002/400] fixed some style problems --- skfda/ml/regression/_linear_regression.py | 37 ++++++++++++++--------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 34956a9f3..42dd153da 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -6,7 +6,6 @@ import numpy as np import pandas as pd - from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted @@ -54,33 +53,41 @@ class DataFrameMixin: """Mixin class to add DataFrame funcionality.""" - def dataframe_conversion(X: pd.DataFrame): - """Converts DataFrames to a list of two elements: first of all, a list with mv + def dataframe_conversion(self, X: pd.DataFrame) -> List: + """Convert DataFrames to a list with two elements: first of all, a list with mv covariates and the second, a FDataBasis object with functional data. Args: - X: pandas DataFrame to convert. - + - X: pandas DataFrame to convert. + + Returns: + - list with two elements: first of all, a list with mv + covariates and the second, a FDataBasis object with functional data. """ fdb = FDataBasis.concatenate(*X.iloc[:, 1].tolist()) - X = [X.iloc[:, 0].tolist(), fdb] - return X + return [X.iloc[:, 0].tolist(), fdb] class LinearRegression( - BaseEstimator, # type: ignore - RegressorMixin, # type: ignore - DataFrameMixin, # type: ignore + BaseEstimator, # type: ignore + RegressorMixin, # type: ignore + DataFrameMixin, # type: ignore ): + """ - .. deprecated:: 0.8 - Use covariate parameters of type pandas.FDataFrame in methods fit, predict. + .. deprecated:: 0.8 + Use covariate parameters of type pandas.FDataFrame in methods + fit, predict. + """ + warnings.warn( - "Usage of arguments of type FData, ndarray or a sequence of both is deprecated (fit, predict)." + "Usage of arguments of type FData, ndarray or a sequence \ + of both is deprecated (fit, predict)." "Use pandas DataFrame instead", DeprecationWarning, ) + r"""Linear regression with multivariate response. This is a regression algorithm equivalent to multivariate linear @@ -228,7 +235,7 @@ def fit( # noqa: D102 y: np.ndarray, sample_weight: Optional[np.ndarray] = None, ) -> LinearRegression: - + X_new, y, sample_weight, coef_info = self._argcheck_X_y( X, y, @@ -330,7 +337,7 @@ def _argcheck_X( X = [X] if isinstance(X, pd.DataFrame): - X = DataFrameMixin.dataframe_conversion(X) + X = self.dataframe_conversion(X) X = [x if isinstance(x, FData) else np.asarray(x) for x in X] From 45e1a9b42ce4183fe0fc4130c968bf2a305a51ff Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 11 Nov 2021 11:47:35 +0100 Subject: [PATCH 003/400] Changes on _evaluation_transformer --- skfda/misc/feature_construction/__init__.py | 2 + .../_evaluation_trasformer.py | 179 ++++++++++++++++++ skfda/representation/__init__.py | 1 - 3 files changed, 181 insertions(+), 1 deletion(-) create mode 100644 skfda/misc/feature_construction/__init__.py create mode 100644 skfda/misc/feature_construction/_evaluation_trasformer.py diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py new file mode 100644 index 000000000..986f75ea5 --- /dev/null +++ b/skfda/misc/feature_construction/__init__.py @@ -0,0 +1,2 @@ +"""Feature construction.""" +from ._evaluation_trasformer import EvaluationTransformer diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/misc/feature_construction/_evaluation_trasformer.py new file mode 100644 index 000000000..50ce7f3b3 --- /dev/null +++ b/skfda/misc/feature_construction/_evaluation_trasformer.py @@ -0,0 +1,179 @@ +"""Evaluation Transformer Module.""" +from __future__ import annotations + +from typing import Optional, Union, overload + +import numpy as np +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.utils.validation import check_is_fitted +from typing_extensions import Literal + +from ...representation._functional_data import FData +from ...representation._typing import ArrayLike, GridPointsLike +from ...representation.extrapolation import ExtrapolationLike +from ...representation.grid import FDataGrid + + +class EvaluationTransformer( + BaseEstimator, # type:ignore + TransformerMixin, # type:ignore +): + r""" + Transformer returning the evaluations of FData objects as a matrix. + + Args: + eval_points (array_like): List of points where the functions are + evaluated. If `None`, the functions must be `FDatagrid` objects + and all points will be returned. + extrapolation (str or Extrapolation, optional): Controls the + extrapolation mode for elements outside the :term:`domain` range. + By default it is used the mode defined during the instance of the + object. + grid (bool, optional): Whether to evaluate the results on a grid + spanned by the input arrays, or at points specified by the + input arrays. If true the eval_points should be a list of size + dim_domain with the corresponding times for each axis. The + return matrix has shape n_samples x len(t1) x len(t2) x ... x + len(t_dim_domain) x dim_codomain. If the domain dimension is 1 + the parameter has no efect. Defaults to False. + + Attributes: + shape\_ (tuple): original shape of coefficients per sample. + + Examples: + >>> from skfda.representation import (FDataGrid, FDataBasis, + ... EvaluationTransformer) + >>> from skfda.representation.basis import Monomial + + Functional data object with 2 samples + representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. + + >>> data_matrix = [[1, 2], [2, 3]] + >>> grid_points = [2, 4] + >>> fd = FDataGrid(data_matrix, grid_points) + >>> + >>> transformer = EvaluationTransformer() + >>> transformer.fit_transform(fd) + array([[ 1., 2.], + [ 2., 3.]]) + + Functional data object with 2 samples + representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}^2`. + + >>> data_matrix = [[[1, 0.3], [2, 0.4]], [[2, 0.5], [3, 0.6]]] + >>> grid_points = [2, 4] + >>> fd = FDataGrid(data_matrix, grid_points) + >>> + >>> transformer = EvaluationTransformer() + >>> transformer.fit_transform(fd) + array([[ 1. , 0.3, 2. , 0.4], + [ 2. , 0.5, 3. , 0.6]]) + + Representation of a functional data object with 2 samples + representing a function :math:`f : \mathbb{R}^2\longmapsto\mathbb{R}`. + + >>> data_matrix = [[[1, 0.3], [2, 0.4]], [[2, 0.5], [3, 0.6]]] + >>> grid_points = [[2, 4], [3, 6]] + >>> fd = FDataGrid(data_matrix, grid_points) + >>> + >>> transformer = EvaluationTransformer() + >>> transformer.fit_transform(fd) + array([[ 1. , 0.3, 2. , 0.4], + [ 2. , 0.5, 3. , 0.6]]) + + Evaluation of a functional data object at several points. + + >>> basis = Monomial(n_basis=4) + >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] + >>> fd = FDataBasis(basis, coefficients) + >>> + >>> transformer = EvaluationTransformer([0, 0.2, 0.5, 0.7, 1]) + >>> transformer.fit_transform(fd) + array([[ 0.5 , 0.784 , 1.5625, 2.3515, 4. ], + [ 1.5 , 1.864 , 3.0625, 4.3315, 7. ]]) + + """ + + @overload + def __init__( + self, + eval_points: ArrayLike, + *, + extrapolation: Optional[ExtrapolationLike] = None, + grid: Literal[False] = False, + ) -> None: + pass + + @overload + def __init__( + self, + eval_points: GridPointsLike, + *, + extrapolation: Optional[ExtrapolationLike] = None, + grid: Literal[True], + ) -> None: + pass + + def __init__( + self, + eval_points: Union[ArrayLike, GridPointsLike, None] = None, + *, + extrapolation: Optional[ExtrapolationLike] = None, + grid: bool = False, + ): + self.eval_points = eval_points + self.extrapolation = extrapolation + self.grid = grid + + def fit( + self, + X: FData, + y: None = None, + ) -> EvaluationTransformer: + """ + Fit the model by doing some checkings. + + Args: + X: FData with the training data. + y: Target values of shape = (n_samples). By default is None + + Returns: + self + """ + if self.eval_points is None and not isinstance(X, FDataGrid): + raise ValueError( + "If no eval_points are passed, the functions " + "should be FDataGrid objects.", + ) + + self._is_fitted = True + + return self + + def transform( + self, + X: FData, + y: None = None, + ) -> np.ndarray: + """ + Transform the provided data using the already fitted transformer. + + Args: + X: FDataGrid with the test samples. + y: Target values of shape = (n_samples). By default is None + + Returns: + Array of shape (n_samples, G) + """ + check_is_fitted(self, '_is_fitted') + + if self.eval_points is None: + evaluation = X.data_matrix.copy() + else: + evaluation = X( # type: ignore + self.eval_points, + extrapolation=self.extrapolation, + grid=self.grid, + ) + + return evaluation.reshape((X.n_samples, -1)) diff --git a/skfda/representation/__init__.py b/skfda/representation/__init__.py index d6a18fc9b..9565798ee 100644 --- a/skfda/representation/__init__.py +++ b/skfda/representation/__init__.py @@ -2,7 +2,6 @@ from . import extrapolation from . import grid from . import interpolation -from ._evaluation_trasformer import EvaluationTransformer from ._functional_data import FData from .basis import FDataBasis from .grid import FDataGrid From ab3ee1de220ab6ccceccef4fccb1383c99d8b413 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sat, 13 Nov 2021 18:30:17 +0100 Subject: [PATCH 004/400] Local averages and occupation measure --- skfda/misc/feature_construction/__init__.py | 1 + .../_functional_transformers.py | 107 ++++++++++++++++++ skfda/representation/basis/_fdatabasis.py | 33 +++++- skfda/representation/grid.py | 20 +++- 4 files changed, 157 insertions(+), 4 deletions(-) create mode 100644 skfda/misc/feature_construction/_functional_transformers.py diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py index 986f75ea5..c64fc6537 100644 --- a/skfda/misc/feature_construction/__init__.py +++ b/skfda/misc/feature_construction/__init__.py @@ -1,2 +1,3 @@ """Feature construction.""" from ._evaluation_trasformer import EvaluationTransformer +from ._functional_transformers import local_averages, occupation_measure diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py new file mode 100644 index 000000000..168a2d8c2 --- /dev/null +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -0,0 +1,107 @@ +"""Functional Transformers Module.""" + +from __future__ import annotations + +from typing import Sequence, Tuple, Union + +import numpy as np + +from ...representation import FDataBasis, FDataGrid + + +def local_averages( + data: Union[FDataGrid, FDataBasis], + n_intervals: int, +) -> np.ndarray: + r""" + Calculate the local averages of a given data. + + Take functional data as a grid or a basis and performs + the following map: + + .. math:: + f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ + f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt + where {T_1,\dots,T_p} are disjoint intervals of the interval [a,b] + + It is calculated for a given number of intervals, + which are of equal sizes. + Args: + data: FDataGrid or FDataBasis where we want to + calculate the local averages. + n_intervals: number of intervals we want to consider. + Returns: + ndarray of shape (n_intervals, n_samples, n_dimensions)\ + with the transformed data. + """ + domain_range = data.domain_range + + x, y = domain_range[0] + interval_size = (y - x) / n_intervals + + integrated_data = [[]] + for i in np.arange(x, y, interval_size): + interval = (i, i + interval_size) + if isinstance(data, FDataGrid): + data_grid = data.restrict(interval) + integrated_data = integrated_data + [ + data_grid.integrate(), + ] + else: + integrated_data = integrated_data + [ + data.integrate(interval), + ] + return np.asarray(integrated_data[1:]) + + +def occupation_measure( + data: FDataGrid, + intervals: Sequence[Tuple], +) -> np.ndarray: + r""" + Take functional data as a grid and calculate the occupation measure. + + It performs the following map. + ..math: + :math:`f_1(X) = |t: X(t)\in T_p|,\dots,|t: X(t)\in T_p|` + + where :math:`{T_1,\dots,T_p}` are disjoint intervals in + :math:`\mathbb{R}` and | | stands for the Lebesgue measure. + + Args: + data: FDataGrid where we want to calculate + the occupation measure. + intervals: sequence of tuples containing the + intervals we want to consider. + Returns: + ndarray of shape (n_dimensions, n_samples)\ + with the transformed data. + """ + curves = data.data_matrix + grid = data.grid_points[0] + + transformed_intervals = [] + for i, interval in enumerate(intervals): + a, b = interval + curves_intervals = [] + for c in curves: + first = None + last = None + for index, point in enumerate(c): + if a <= point[i] <= b and first is None: + first = grid[index] + elif a <= point[i] <= b: + last = grid[index] + + if first is None: + t = () + elif last is None: + t = (first, first) + else: + t = (first, last) + + curves_intervals = curves_intervals + [t] + + transformed_intervals = transformed_intervals + [curves_intervals] + + return np.asarray(transformed_intervals, dtype=list) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index a9071e7f4..e5ee02248 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -20,10 +20,16 @@ from skfda._utils._utils import _to_array_maybe_ragged -from ..._utils import _check_array_key, _int_to_real, constants +from ..._utils import _check_array_key, _int_to_real, constants, nquad_vec from .. import grid from .._functional_data import FData -from .._typing import ArrayLike, DomainRange, GridPointsLike, LabelTupleLike +from .._typing import ( + ArrayLike, + DomainRange, + GridPointsLike, + LabelTupleLike, + NDArrayFloat, +) from ..extrapolation import ExtrapolationLike from . import Basis @@ -350,6 +356,29 @@ def derivative(self: T, *, order: int = 1) -> T: # noqa: D102 return self.copy(basis=basis, coefficients=coefficients) + def integrate(self: T, interval: DomainRange) -> NDArrayFloat: + """ + Integration for the FDataBasis object. + + It is done on the domain range that is passed as argument. + Args: + interval: domain range where we want to integrate. + + Returns: + ndarray of shape (n_samples, n_dimensions)\ + with the integrated data. + """ + integrated = nquad_vec( + self, + [interval], + ) + integrated_values = np.empty((1, 1)) + + for i in integrated: + integrated_values = np.concatenate([integrated_values, i]) + + return integrated_values[1:] + def sum( # noqa: WPS125 self: T, *, diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 9f3fb1bef..1a846c908 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -25,9 +25,9 @@ import findiff import numpy as np import pandas.api.extensions -from matplotlib.figure import Figure - +import scipy.integrate import scipy.stats.mstats +from matplotlib.figure import Figure from .._utils import ( _check_array_key, @@ -44,6 +44,7 @@ GridPoints, GridPointsLike, LabelTupleLike, + NDArrayFloat, ) from .basis import Basis from .evaluator import Evaluator @@ -469,6 +470,21 @@ def derivative(self: T, *, order: int = 1) -> T: data_matrix=data_matrix, ) + def integrate(self: T) -> NDArrayFloat: + """ + Integration of the FDataGrid object. + + It is done on the whole domain range. + Returns: + ndarray of shape (n_samples, n_dimensions)\ + with the integrated data. + """ + return scipy.integrate.simps( + self.data_matrix, + x=self.grid_points[0], + axis=-2, + ) + def _check_same_dimensions(self: T, other: T) -> None: if self.data_matrix.shape[1:-1] != other.data_matrix.shape[1:-1]: raise ValueError("Error in columns dimensions") From 6e665fec255866bba993457851c109901db75607 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sat, 13 Nov 2021 23:26:16 +0100 Subject: [PATCH 005/400] Number of up crossings to a level --- skfda/misc/feature_construction/__init__.py | 6 +- .../_functional_transformers.py | 56 ++++++++++++++++++- 2 files changed, 60 insertions(+), 2 deletions(-) diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py index c64fc6537..51b930b69 100644 --- a/skfda/misc/feature_construction/__init__.py +++ b/skfda/misc/feature_construction/__init__.py @@ -1,3 +1,7 @@ """Feature construction.""" from ._evaluation_trasformer import EvaluationTransformer -from ._functional_transformers import local_averages, occupation_measure +from ._functional_transformers import ( + local_averages, + number_up_crossings, + occupation_measure, +) diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py index 168a2d8c2..a7310229c 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -59,7 +59,7 @@ def occupation_measure( intervals: Sequence[Tuple], ) -> np.ndarray: r""" - Take functional data as a grid and calculate the occupation measure. + Calculate the occupation measure of a grid. It performs the following map. ..math: @@ -83,6 +83,8 @@ def occupation_measure( transformed_intervals = [] for i, interval in enumerate(intervals): a, b = interval + if b < a: + a, b = b, a curves_intervals = [] for c in curves: first = None @@ -105,3 +107,55 @@ def occupation_measure( transformed_intervals = transformed_intervals + [curves_intervals] return np.asarray(transformed_intervals, dtype=list) + + +def number_up_crossings( # noqa: WPS231 + data: FDataGrid, + intervals: Sequence[Tuple], +) -> np.ndarray: + r""" + Calculate the number of up crossings to a level of a FDataGrid. + + Args: + data: FDataGrid where we want to calculate + the number of up crossings. + intervals: sequence of tuples containing the + intervals we want to consider for the crossings. + Returns: + ndarray of shape (n_dimensions, n_samples)\ + with the values of the counters. + """ + curves = data.data_matrix + transformed_counters = [] + for index, interval in enumerate(intervals): + a, b = interval + if b < a: + a, b = b, a + curves_counters = [] + for c in curves: + counter = 0 + inside_interval = False + size_curve = c.shape[0] + for i in range(0, size_curve - 1): + if ( + # Check that the chunk of function grows + c[i][index] < c[i + 1][index] # noqa: WPS408 + and ( + # First point <= a, second >= a + (c[i][index] <= a and c[i + 1][index] >= a) + # First point inside interval, second >=a + or (a <= c[i][index] <= b and c[i + 1][index] >= a) + ) + ): + # Last pair of points where not inside interval + if inside_interval is False: + counter += 1 + inside_interval = True + else: + inside_interval = False + + curves_counters = curves_counters + [counter] + + transformed_counters = transformed_counters + [curves_counters] + + return np.asarray(transformed_counters, dtype=list) From c037e645351d4e37c328db891b5046fd2a4a8e84 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 14 Nov 2021 18:22:44 +0100 Subject: [PATCH 006/400] Moments of the norm and moments of the process --- skfda/misc/feature_construction/__init__.py | 2 + .../_functional_transformers.py | 72 +++++++++++++++++-- skfda/representation/basis/_fdatabasis.py | 9 ++- skfda/representation/grid.py | 16 ++++- 4 files changed, 90 insertions(+), 9 deletions(-) diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py index 51b930b69..cf07760ad 100644 --- a/skfda/misc/feature_construction/__init__.py +++ b/skfda/misc/feature_construction/__init__.py @@ -4,4 +4,6 @@ local_averages, number_up_crossings, occupation_measure, + moments_of_norm, + moments_of_process, ) diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py index a7310229c..e441e250c 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -2,6 +2,7 @@ from __future__ import annotations +from functools import reduce from typing import Sequence, Tuple, Union import numpy as np @@ -49,7 +50,7 @@ def local_averages( ] else: integrated_data = integrated_data + [ - data.integrate(interval), + data.integrate(interval=interval), ] return np.asarray(integrated_data[1:]) @@ -68,12 +69,12 @@ def occupation_measure( where :math:`{T_1,\dots,T_p}` are disjoint intervals in :math:`\mathbb{R}` and | | stands for the Lebesgue measure. - Args: + Args: data: FDataGrid where we want to calculate the occupation measure. intervals: sequence of tuples containing the intervals we want to consider. - Returns: + Returns: ndarray of shape (n_dimensions, n_samples)\ with the transformed data. """ @@ -116,12 +117,12 @@ def number_up_crossings( # noqa: WPS231 r""" Calculate the number of up crossings to a level of a FDataGrid. - Args: + Args: data: FDataGrid where we want to calculate the number of up crossings. intervals: sequence of tuples containing the intervals we want to consider for the crossings. - Returns: + Returns: ndarray of shape (n_dimensions, n_samples)\ with the values of the counters. """ @@ -159,3 +160,64 @@ def number_up_crossings( # noqa: WPS231 transformed_counters = transformed_counters + [curves_counters] return np.asarray(transformed_counters, dtype=list) + + +def moments_of_norm( + data: FDataGrid, +) -> np.ndarray: + r""" + Calculate the moments of the norm of the process of a FDataGrid. + + It performs the following map: + :math:`f_1(X)=\mathbb{E}(||X||),\dots,f_p(X)=\mathbb{E}(||X||^p)`. + + Args: + data: FDataGrid where we want to calculate + the moments of the norm of the process. + Returns: + ndarray of shape (n_dimensions, n_samples)\ + with the values of the moments. + """ + curves = data.data_matrix + norms = [] + for c in curves: # noqa: WPS426 + x, y = c.shape + curve_norms = [] + for i in range(0, y): # noqa: WPS426 + curve_norms = curve_norms + [ + reduce(lambda a, b: a + b[i], c, 0) / x, + ] + norms = norms + [curve_norms] + return np.asarray(norms, dtype=list) + + +def moments_of_process( + data: FDataGrid, +) -> np.ndarray: + r""" + Calculate the moments of the process of a FDataGrid. + + It performs the following map: + .. math:: + f_1(X)=\int_a^b X(t,\omega)dP(\omega),\dots,f_p(X)=\int_a^b \\ + X^p(t,\omega)dP(\omega). + + Args: + data: FDataGrid where we want to calculate + the moments of the process. + Returns: + ndarray of shape (n_dimensions, n_samples)\ + with the values of the moments. + """ + norm = moments_of_norm(data) + curves = data.data_matrix + moments = [] + for i, c in enumerate(curves): # noqa: WPS426 + x, y = c.shape + curve_moments = [] + for j in range(0, y): # noqa: WPS426 + curve_moments = curve_moments + [ + reduce(lambda a, b: a + ((b[j] - norm[i][j]) ** 2), c, 0) / x, + ] + moments = moments + [curve_moments] + return np.asarray(moments, dtype=list) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index e5ee02248..61c163da5 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -356,18 +356,25 @@ def derivative(self: T, *, order: int = 1) -> T: # noqa: D102 return self.copy(basis=basis, coefficients=coefficients) - def integrate(self: T, interval: DomainRange) -> NDArrayFloat: + def integrate( + self: T, + *, + interval: Optional[DomainRange] = None, + ) -> NDArrayFloat: """ Integration for the FDataBasis object. It is done on the domain range that is passed as argument. Args: interval: domain range where we want to integrate. + By default is None as we integrate on the whole domain. Returns: ndarray of shape (n_samples, n_dimensions)\ with the integrated data. """ + if interval is None: + interval = self.basis.domain_range[0] integrated = nquad_vec( self, [interval], diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 1a846c908..ea9020d91 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -470,18 +470,28 @@ def derivative(self: T, *, order: int = 1) -> T: data_matrix=data_matrix, ) - def integrate(self: T) -> NDArrayFloat: + def integrate( + self: T, + *, + interval: Optional[DomainRange] = None, + ) -> NDArrayFloat: """ Integration of the FDataGrid object. - It is done on the whole domain range. + It is done on the domain range that is passed as argument. + Args: + interval: domain range where we want to integrate. + By default is None as we integrate on the whole domain. Returns: ndarray of shape (n_samples, n_dimensions)\ with the integrated data. """ + if interval is None: + interval = self.grid_points[0] + return scipy.integrate.simps( self.data_matrix, - x=self.grid_points[0], + x=interval, axis=-2, ) From fec4222b59fcf966fbe231bc63fc03038335f61e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 16 Nov 2021 22:16:18 +0100 Subject: [PATCH 007/400] Examples and corrections --- .../_functional_transformers.py | 317 ++++++++++++++++-- 1 file changed, 285 insertions(+), 32 deletions(-) diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py index e441e250c..6c2a6531a 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -34,6 +34,37 @@ def local_averages( Returns: ndarray of shape (n_intervals, n_samples, n_dimensions)\ with the transformed data. + + Example: + + We import the Berkeley Growth Study dataset. + >>> from skfda.datasets import fetch_growth + >>> X = fetch_growth(return_X_y=True)[0] + + Then we decide how many intervals we want to consider (in our case 4) + and call the function with the dataset. + >>> from skfda.misc.feature_construction import local_averages + >>> local_averages(X, 4) + [[[400.97], + [384.42], + ... + [399.46], + [389.384]], + [[474.30], + [450.67], + ... + [472.78], + [467.61]], + [[645.68], + [583.68], + ... + [629.6], + [629.83]], + [[769.], + [678.53], + ... + [670.5], + [673.43]]] """ domain_range = data.domain_range @@ -55,9 +86,110 @@ def local_averages( return np.asarray(integrated_data[1:]) +def _calculate_time_on_interval( + curves: Sequence[Sequence[Sequence[float]]], + interval_dim: int, + a: float, + b: float, + grid: Sequence, +) -> np.ndarray: + for j, c in enumerate(curves): + # TODO: Considerar que la curva pasa varias veces por el intervalo + total_time = 0 + points_a = _get_interpolation_points(c, a, interval_dim) + x, y = points_a + if y is None: + interp1x = None + elif x is None: + index2, y2 = y + interp1x = _calculate_interpolation( + (grid[index2], y2), + (grid[0], c[0][interval_dim]), + a, + ) + else: + index1, y1 = x + index2, y2 = y + first_grid = grid[index1] + if y1 == y2: + interp1x = first_grid + else: + interp1x = _calculate_interpolation( + (first_grid, y1), + (grid[index2], y2), + a, + ) + points_b = _get_interpolation_points(c, b, interval_dim) + x, y = points_b + if x is None: + interp2x = None + elif y is None: + index1, y1 = x + interp2x = _calculate_interpolation( + (grid[index1], y1), + (grid[0], c[0][interval_dim]), + b, + ) + else: + index1, y1 = x + index2, y2 = y + first_grid = grid[index1] + if y1 == y2: + interp2x = first_grid + else: + interp2x = _calculate_interpolation( + (first_grid, y1), + (grid[index2], y2), + b, + ) + if interp1x is not None and interp2x is not None: + total_time += (interp2x - interp1x) + if j == 0: + curves_time = np.array([[total_time]]) + else: + curves_time = np.concatenate( + (curves_time, np.array([[total_time]])), + ) + return curves_time + + +def _get_interpolation_points( + curve: Sequence[Sequence[float]], + y: float, + dimension: int, +) -> Tuple: + less = None + greater = None + for i, p in enumerate(curve): + value = p[dimension] + if value < y: + less = (i, value) + elif value > y: + greater = (i, value) + else: + return ((i, value), (i, value)) + if less is not None and greater is not None: + return (less, greater) + return (less, greater) + + +def _calculate_interpolation(p1: Tuple, p2: Tuple, y: float) -> float: + """ + Calculate the linear interpolation following this formula. + + x = x_1 + (x_2 – x_1)(y_0 – y_1) / (y_2 – y_1). + """ + x1, y1 = p1 + x2, y2 = p2 + if y2 == y1: + return 0 + + return x1 + (x2 - x1) * (y - y1) / (y2 - y1) + + def occupation_measure( data: FDataGrid, - intervals: Sequence[Tuple], + intervals: Sequence[Sequence[Tuple]], ) -> np.ndarray: r""" Calculate the occupation measure of a grid. @@ -72,42 +204,30 @@ def occupation_measure( Args: data: FDataGrid where we want to calculate the occupation measure. - intervals: sequence of tuples containing the - intervals we want to consider. + intervals: sequence of sequence of tuples containing the + intervals we want to consider. The shape should be + (n_sequences,n_dimensions, 2) Returns: - ndarray of shape (n_dimensions, n_samples)\ + ndarray of shape (n_intervals, n_samples, n_dimensions) with the transformed data. """ curves = data.data_matrix grid = data.grid_points[0] - transformed_intervals = [] - for i, interval in enumerate(intervals): - a, b = interval - if b < a: - a, b = b, a - curves_intervals = [] - for c in curves: - first = None - last = None - for index, point in enumerate(c): - if a <= point[i] <= b and first is None: - first = grid[index] - elif a <= point[i] <= b: - last = grid[index] - - if first is None: - t = () - elif last is None: - t = (first, first) + transformed_times = [] + for interval in intervals: + for i, dimension_interval in enumerate(interval): + a, b = dimension_interval + if b < a: + a, b = b, a + time = np.array(_calculate_time_on_interval(curves, i, a, b, grid)) + if i == 0: + curves_time = time else: - t = (first, last) - - curves_intervals = curves_intervals + [t] + curves_time = np.hstack((curves_time, time)) + transformed_times = transformed_times + [curves_time] - transformed_intervals = transformed_intervals + [curves_intervals] - - return np.asarray(transformed_intervals, dtype=list) + return np.asarray(transformed_times, dtype=list) def number_up_crossings( # noqa: WPS231 @@ -117,6 +237,17 @@ def number_up_crossings( # noqa: WPS231 r""" Calculate the number of up crossings to a level of a FDataGrid. + Let f_1(X) = N_i, where N_i is the number of up crossings of X + to a level c_i \in \mathbb{R}, i = 1,\dots,p. + + Recall that the process X(t) is said to have an up crossing of c + at :math:`t_0 > 0` if for some :math:`\epsilon >0`, X(t) $\leq$ + c if t :math:'\in (t_0 - \epsilon, t_0) and X(t) $\geq$ c if + :math:`t\in (t_0, t_0+\epsilon)`. + + If the trajectories are differentiable, then + :math:`N_i = card\{t \in[a,b]: X(t) = c_i, X' (t) > 0\}.` + Args: data: FDataGrid where we want to calculate the number of up crossings. @@ -125,8 +256,39 @@ def number_up_crossings( # noqa: WPS231 Returns: ndarray of shape (n_dimensions, n_samples)\ with the values of the counters. + + Example: + + We import the Phoneme dataset and for simplicity we use + the first 200 samples. + >>> from skfda.datasets import fetch_phoneme + >>> dataset = fetch_phoneme() + >>> X = dataset['data'][:200] + + Then we decide the interval we want to consider (in our case (5.0,7.5)) + and call the function with the dataset. The output will be the number of + times each curve cross the interval (5.0,7.5) growing. + >>> from skfda.misc.feature_construction import number_up_crossings + >>> number_up_crossings(X, [(5.0,7.5)]) + [[1, 20, 69, 64, 42, 33, 14, 3, 35, 31, 0, 4, 67, 6, 12, 16, 22, 1, + 25, 30, 2, 27, 61, 0, 11, 20, 3, 36, 28, 1, 67, 36, 12, 29, 2, + 16, 25, 1, 24, 57, 65, 26, 20, 18, 43, 0, 35, 40, 0, 2, 56, 4, + 21, 28, 1, 0, 19, 24, 1, 2, 8, 63, 0, 2, 3, 3, 0, 8, 3, 2, 10, + 62, 72, 19, 36, 46, 0, 1, 2, 18, 1, 10, 67, 60, 20, 21, 23, 12, + 3, 30, 21, 1, 57, 64, 15, 4, 4, 17, 0, 2, 31, 0, 5, 24, 56, 8, + 11, 14, 17, 1, 25, 1, 3, 61, 10, 33, 17, 1, 12, 18, 0, 2, 57, 4, + 6, 5, 2, 0, 7, 17, 4, 23, 60, 62, 2, 19, 21, 0, 42, 28, 0, 10, + 29, 74, 34, 29, 7, 0, 25, 23, 0, 15, 19, 1, 43, 1, 11, 9, 4, 0, + 0, 2, 1, 54, 55, 14, 14, 6, 1, 24, 20, 2, 27, 55, 62, 32, 26, 24, + 37, 0, 26, 28, 1, 3, 41, 64, 8, 6, 27, 12, 1, 5, 16, 0, 0, 61, + 62, 1, 3, 7]] """ curves = data.data_matrix + if curves.shape[2] != len(intervals): + raise ValueError( + "Sequence of intervals should have the " + + "same number of dimensions as the data samples", + ) transformed_counters = [] for index, interval in enumerate(intervals): a, b = interval @@ -138,14 +300,16 @@ def number_up_crossings( # noqa: WPS231 inside_interval = False size_curve = c.shape[0] for i in range(0, size_curve - 1): + p1 = c[i][index] + p2 = c[i + 1][index] if ( # Check that the chunk of function grows - c[i][index] < c[i + 1][index] # noqa: WPS408 + p1 < p2 # noqa: WPS408 and ( # First point <= a, second >= a - (c[i][index] <= a and c[i + 1][index] >= a) + (p1 <= a and p2 >= a) # First point inside interval, second >=a - or (a <= c[i][index] <= b and c[i + 1][index] >= a) + or (a <= p1 <= b and p2 >= a) ) ): # Last pair of points where not inside interval @@ -177,6 +341,50 @@ def moments_of_norm( Returns: ndarray of shape (n_dimensions, n_samples)\ with the values of the moments. + + Example: + + We import the Canadian Weather dataset + >>> from skfda.datasets import fetch_weather + >>> X = fetch_weather(return_X_y=True)[0] + Then we call the function with the dataset. + >>> from skfda.misc.feature_construction import moments_of_norm + >>> moments_of_norm(X) + [[4.69, 4.06], + [6.15, 3.99], + [5.51, 4.04], + [6.81, 3.46], + [5.23, 3.29], + [5.26, 3.09], + [-5.06, 2.20], + [3.10, 2.46], + [2.25, 2.55], + [4.08, 3.31], + [4.12, 3.04], + [6.13, 2.58], + [5.81, 2.50], + [7.27, 2.14], + [7.31, 2.62], + [2.46, 1.93], + [2.47, 1.40], + [-0.15, 1.23], + [-7.09, 1.20], + [2.75, 1.02], + [0.68, 1.11], + [-3.41, 0.99], + [2.26, 1.27], + [3.99, 1.10], + [8.75, 0.74], + [9.96, 3.16], + [9.62, 2.33], + [3.84, 1.67], + [7.00, 7.10], + [-0.85, 0.74], + [-4.79, 0.90], + [-5.02, 0.73], + [-9.65, 1.14], + [-9.24, 0.71], + [-16.52, 0.39]] """ curves = data.data_matrix norms = [] @@ -208,6 +416,51 @@ def moments_of_process( Returns: ndarray of shape (n_dimensions, n_samples)\ with the values of the moments. + + Example: + + We import the Canadian Weather dataset + >>> from skfda.datasets import fetch_weather + >>> X = fetch_weather(return_X_y=True)[0] + + Then we call the function with the dataset. + >>> from skfda.misc.feature_construction import moments_of_process + >>> moments_of_process(X) + [[52.43, 2.25], + [77.80, 2.92], + [72.15, 2.69], + [51.25, 2.12], + [89.31, 1.59], + [105.04, 1.53], + [162.09, 1.49], + [141.43, 1.45], + [143.21, 1.15], + [125.92, 1.64], + [111.39, 1.39], + [122.14, 1.16], + [125.92, 1.04], + [93.56, 1.02], + [93.08, 1.21], + [128.36, 1.26], + [180.68, 1.19], + [189.07, 0.92], + [196.43, 0.55], + [158.55, 0.94], + [177.39, 0.81], + [230.41, 0.44], + [119.27, 1.26], + [84.89, 1.01], + [79.47, 0.20], + [26.70, 3.52], + [21.67, 3.09], + [77.38, 0.49], + [18.94, 9.94], + [127.83, 0.25], + [252.06, 0.29], + [252.01, 0.27], + [164.01, 0.49], + [254.90, 0.20], + [187.72, 0.12]] """ norm = moments_of_norm(data) curves = data.data_matrix From 1849355d6dd2ecaeadf6f7f14ba080ba99d7cd4e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 23 Nov 2021 17:52:39 +0100 Subject: [PATCH 008/400] Functional transformers --- skfda/misc/feature_construction/__init__.py | 4 +- .../_functional_transformers.py | 576 ++++++++++-------- 2 files changed, 318 insertions(+), 262 deletions(-) diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py index cf07760ad..5369b05ee 100644 --- a/skfda/misc/feature_construction/__init__.py +++ b/skfda/misc/feature_construction/__init__.py @@ -2,8 +2,8 @@ from ._evaluation_trasformer import EvaluationTransformer from ._functional_transformers import ( local_averages, - number_up_crossings, - occupation_measure, moments_of_norm, moments_of_process, + number_up_crossings, + occupation_measure, ) diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py index 6c2a6531a..b08c51451 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -3,10 +3,11 @@ from __future__ import annotations from functools import reduce -from typing import Sequence, Tuple, Union +from typing import Tuple, Union import numpy as np +from ..._utils import constants from ...representation import FDataBasis, FDataGrid @@ -23,6 +24,7 @@ def local_averages( .. math:: f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt + where {T_1,\dots,T_p} are disjoint intervals of the interval [a,b] It is calculated for a given number of intervals, @@ -37,34 +39,79 @@ def local_averages( Example: - We import the Berkeley Growth Study dataset. - >>> from skfda.datasets import fetch_growth - >>> X = fetch_growth(return_X_y=True)[0] - - Then we decide how many intervals we want to consider (in our case 4) - and call the function with the dataset. - >>> from skfda.misc.feature_construction import local_averages - >>> local_averages(X, 4) - [[[400.97], - [384.42], - ... - [399.46], - [389.384]], - [[474.30], - [450.67], - ... - [472.78], - [467.61]], - [[645.68], - [583.68], - ... - [629.6], - [629.83]], - [[769.], - [678.53], - ... - [670.5], - [673.43]]] + We import the Berkeley Growth Study dataset. + We will use only the first 30 samples to make the + example easy. + >>> from skfda.datasets import fetch_growth + >>> dataset = fetch_growth(return_X_y=True)[0] + >>> X = dataset[:30] + + Then we decide how many intervals we want to consider (in our case 2) + and call the function with the dataset. + >>> import numpy as np + >>> from skfda.misc.feature_construction import local_averages + >>> np.around(local_averages(X, 2), decimals=2) + array([[[ 993.98], + [ 950.82], + [ 911.93], + [ 946.44], + [ 887.3 ], + [ 930.18], + [ 927.89], + [ 959.72], + [ 928.14], + [ 1002.57], + [ 953.22], + [ 971.53], + [ 947.54], + [ 976.26], + [ 988.16], + [ 974.07], + [ 943.67], + [ 965.36], + [ 925.48], + [ 931.64], + [ 932.47], + [ 922.56], + [ 927.99], + [ 908.83], + [ 930.23], + [ 933.65], + [ 980.25], + [ 919.39], + [ 1013.98], + [ 940.23]], + + [[ 1506.69], + [ 1339.79], + [ 1317.25], + [ 1392.53], + [ 1331.65], + [ 1340.17], + [ 1320.15], + [ 1436.71], + [ 1310.51], + [ 1482.64], + [ 1371.34], + [ 1446.15], + [ 1394.84], + [ 1445.87], + [ 1416.5 ], + [ 1434.16], + [ 1418.19], + [ 1421.35], + [ 1354.89], + [ 1383.46], + [ 1323.45], + [ 1343.07], + [ 1360.87], + [ 1325.57], + [ 1342.55], + [ 1389.99], + [ 1379.43], + [ 1301.34], + [ 1517.04], + [ 1374.91]]]) """ domain_range = data.domain_range @@ -86,110 +133,40 @@ def local_averages( return np.asarray(integrated_data[1:]) -def _calculate_time_on_interval( - curves: Sequence[Sequence[Sequence[float]]], - interval_dim: int, - a: float, - b: float, - grid: Sequence, +def _calculate_curve_occupation_( + curve_y_coordinates: np.ndarray, + curve_x_coordinates: np.ndarray, + interval: Tuple, ) -> np.ndarray: - for j, c in enumerate(curves): - # TODO: Considerar que la curva pasa varias veces por el intervalo - total_time = 0 - points_a = _get_interpolation_points(c, a, interval_dim) - x, y = points_a - if y is None: - interp1x = None - elif x is None: - index2, y2 = y - interp1x = _calculate_interpolation( - (grid[index2], y2), - (grid[0], c[0][interval_dim]), - a, - ) - else: - index1, y1 = x - index2, y2 = y - first_grid = grid[index1] - if y1 == y2: - interp1x = first_grid - else: - interp1x = _calculate_interpolation( - (first_grid, y1), - (grid[index2], y2), - a, - ) - points_b = _get_interpolation_points(c, b, interval_dim) - x, y = points_b - if x is None: - interp2x = None - elif y is None: - index1, y1 = x - interp2x = _calculate_interpolation( - (grid[index1], y1), - (grid[0], c[0][interval_dim]), - b, - ) - else: - index1, y1 = x - index2, y2 = y - first_grid = grid[index1] - if y1 == y2: - interp2x = first_grid - else: - interp2x = _calculate_interpolation( - (first_grid, y1), - (grid[index2], y2), - b, - ) - if interp1x is not None and interp2x is not None: - total_time += (interp2x - interp1x) - if j == 0: - curves_time = np.array([[total_time]]) - else: - curves_time = np.concatenate( - (curves_time, np.array([[total_time]])), - ) - return curves_time - - -def _get_interpolation_points( - curve: Sequence[Sequence[float]], - y: float, - dimension: int, -) -> Tuple: - less = None - greater = None - for i, p in enumerate(curve): - value = p[dimension] - if value < y: - less = (i, value) - elif value > y: - greater = (i, value) - else: - return ((i, value), (i, value)) - if less is not None and greater is not None: - return (less, greater) - return (less, greater) - - -def _calculate_interpolation(p1: Tuple, p2: Tuple, y: float) -> float: - """ - Calculate the linear interpolation following this formula. - - x = x_1 + (x_2 – x_1)(y_0 – y_1) / (y_2 – y_1). - """ - x1, y1 = p1 - x2, y2 = p2 - if y2 == y1: - return 0 + y1, y2 = interval + first_x_coord = 0 + last_x_coord = 0 + time_x_coord_counter = 0 + inside_interval = False + + for j, y_coordinate in enumerate(curve_y_coordinates[1:]): + y_coordinate = y_coordinate[0] + + if y1 <= y_coordinate <= y2 and not inside_interval: + inside_interval = True + first_x_coord = curve_x_coordinates[j][0] + last_x_coord = curve_x_coordinates[j][0] + elif y1 <= y_coordinate <= y2: + last_x_coord = curve_x_coordinates[j][0] + elif inside_interval: + inside_interval = False + time_x_coord_counter += last_x_coord - first_x_coord + first_x_coord = 0 + last_x_coord = 0 - return x1 + (x2 - x1) * (y - y1) / (y2 - y1) + return np.array([[time_x_coord_counter]]) def occupation_measure( - data: FDataGrid, - intervals: Sequence[Sequence[Tuple]], + data: Union[FDataGrid, FDataBasis], + intervals: np.ndarray, + *, + n_points: Union[int, None] = None, ) -> np.ndarray: r""" Calculate the occupation measure of a grid. @@ -206,33 +183,127 @@ def occupation_measure( the occupation measure. intervals: sequence of sequence of tuples containing the intervals we want to consider. The shape should be - (n_sequences,n_dimensions, 2) + (n_sequences,2) + n_points: Number of points to evaluate in the domain. + By default will be used 501 points. Returns: - ndarray of shape (n_intervals, n_samples, n_dimensions) + ndarray of shape (n_intervals, n_samples) with the transformed data. + + Example: + We will create the FDataGrid that we will use to extract + the occupation measure + >>> from skfda.representation import FDataGrid + >>> import numpy as np + >>> t = np.linspace(0, 10, 100) + >>> fd_grid = FDataGrid( + ... data_matrix=[ + ... t, + ... 2 * t, + ... np.sin(t), + ... ], + ... grid_points=t, + ... ) + + Finally we call to the occupation measure function with the + intervals that we want to consider. In our case (0.0, 1.0) + and (2.0, 3.0) + >>> from skfda.misc.feature_construction import occupation_measure + >>> np.around( + ... occupation_measure(fd_grid, [(0.0, 1.0), (2.0, 3.0)]), + ... decimals=2, + ... ) + array([[[ 0.98], + [ 0.48], + [ 6.25]], + + [[ 0.98], + [ 0.48], + [ 0. ]]]) + """ - curves = data.data_matrix - grid = data.grid_points[0] + if n_points is None: + n_points = constants.N_POINTS_UNIDIMENSIONAL_PLOT_MESH + + lower_functional_limit, upper_functional_limit = data.domain_range[0] + domain_size = upper_functional_limit - lower_functional_limit + + if isinstance(data, FDataGrid): + time_x_coord_cumulative = np.empty((0, data.data_matrix.shape[0], 1)) + else: + time_x_coord_cumulative = np.empty((0, data.coefficients.shape[0], 1)) - transformed_times = [] for interval in intervals: - for i, dimension_interval in enumerate(interval): - a, b = dimension_interval - if b < a: - a, b = b, a - time = np.array(_calculate_time_on_interval(curves, i, a, b, grid)) - if i == 0: - curves_time = time - else: - curves_time = np.hstack((curves_time, time)) - transformed_times = transformed_times + [curves_time] - return np.asarray(transformed_times, dtype=list) + y1, y2 = interval + if y2 < y1: + raise ValueError( + "Interval limits (a,b) should satisfy a <= b. " + + str(interval) + " doesn't", + ) + + function_x_coordinates = np.empty((1, 1)) + + for x_coordinate in np.arange( + lower_functional_limit, + upper_functional_limit, + domain_size / n_points, + ): + function_x_coordinates = np.concatenate( + (function_x_coordinates, np.array([[x_coordinate]])), + ) + + function_y_coordinates = data._evaluate( # noqa: WPS437 + function_x_coordinates, + ) + time_x_coord_interval = np.empty((0, 1)) + for curve_y_coordinates in function_y_coordinates: + + time_x_coord_count = _calculate_curve_occupation_( # noqa: WPS317 + curve_y_coordinates, + function_x_coordinates, + (y1, y2), + ) + time_x_coord_interval = np.concatenate( + (time_x_coord_interval, time_x_coord_count), + ) + + time_x_coord_cumulative = np.concatenate( + (time_x_coord_cumulative, np.array([time_x_coord_interval])), + ) -def number_up_crossings( # noqa: WPS231 + return time_x_coord_cumulative + + +def _n_curve_crossings( + c: np.ndarray, + index: int, + interval: Tuple, +) -> int: + a, b = interval + counter = 0 + inside_interval = False + size_curve = c.shape[0] + + for i in range(0, size_curve - 1): + p1 = c[i][index] + p2 = c[i + 1][index] + if p1 < p2: # Check that the chunk of function grows + if p2 >= a and (p1 <= a or a <= p1 <= b): + # Last pair of points where not inside interval + if inside_interval is False: + counter += 1 + inside_interval = True + else: + inside_interval = False + + return counter + + +def number_up_crossings( data: FDataGrid, - intervals: Sequence[Tuple], + intervals: np.ndarray, ) -> np.ndarray: r""" Calculate the number of up crossings to a level of a FDataGrid. @@ -270,18 +341,18 @@ def number_up_crossings( # noqa: WPS231 times each curve cross the interval (5.0,7.5) growing. >>> from skfda.misc.feature_construction import number_up_crossings >>> number_up_crossings(X, [(5.0,7.5)]) - [[1, 20, 69, 64, 42, 33, 14, 3, 35, 31, 0, 4, 67, 6, 12, 16, 22, 1, - 25, 30, 2, 27, 61, 0, 11, 20, 3, 36, 28, 1, 67, 36, 12, 29, 2, - 16, 25, 1, 24, 57, 65, 26, 20, 18, 43, 0, 35, 40, 0, 2, 56, 4, - 21, 28, 1, 0, 19, 24, 1, 2, 8, 63, 0, 2, 3, 3, 0, 8, 3, 2, 10, - 62, 72, 19, 36, 46, 0, 1, 2, 18, 1, 10, 67, 60, 20, 21, 23, 12, - 3, 30, 21, 1, 57, 64, 15, 4, 4, 17, 0, 2, 31, 0, 5, 24, 56, 8, - 11, 14, 17, 1, 25, 1, 3, 61, 10, 33, 17, 1, 12, 18, 0, 2, 57, 4, - 6, 5, 2, 0, 7, 17, 4, 23, 60, 62, 2, 19, 21, 0, 42, 28, 0, 10, - 29, 74, 34, 29, 7, 0, 25, 23, 0, 15, 19, 1, 43, 1, 11, 9, 4, 0, - 0, 2, 1, 54, 55, 14, 14, 6, 1, 24, 20, 2, 27, 55, 62, 32, 26, 24, - 37, 0, 26, 28, 1, 3, 41, 64, 8, 6, 27, 12, 1, 5, 16, 0, 0, 61, - 62, 1, 3, 7]] + array([[1, 20, 69, 64, 42, 33, 14, 3, 35, 31, 0, 4, 67, 6, 12, 16, 22, 1, + 25, 30, 2, 27, 61, 0, 11, 20, 3, 36, 28, 1, 67, 36, 12, 29, 2, + 16, 25, 1, 24, 57, 65, 26, 20, 18, 43, 0, 35, 40, 0, 2, 56, 4, + 21, 28, 1, 0, 19, 24, 1, 2, 8, 63, 0, 2, 3, 3, 0, 8, 3, 2, 10, + 62, 72, 19, 36, 46, 0, 1, 2, 18, 1, 10, 67, 60, 20, 21, 23, 12, + 3, 30, 21, 1, 57, 64, 15, 4, 4, 17, 0, 2, 31, 0, 5, 24, 56, 8, + 11, 14, 17, 1, 25, 1, 3, 61, 10, 33, 17, 1, 12, 18, 0, 2, 57, 4, + 6, 5, 2, 0, 7, 17, 4, 23, 60, 62, 2, 19, 21, 0, 42, 28, 0, 10, + 29, 74, 34, 29, 7, 0, 25, 23, 0, 15, 19, 1, 43, 1, 11, 9, 4, 0, + 0, 2, 1, 54, 55, 14, 14, 6, 1, 24, 20, 2, 27, 55, 62, 32, 26, 24, + 37, 0, 26, 28, 1, 3, 41, 64, 8, 6, 27, 12, 1, 5, 16, 0, 0, 61, + 62, 1, 3, 7]], dtype=object) """ curves = data.data_matrix if curves.shape[2] != len(intervals): @@ -293,32 +364,13 @@ def number_up_crossings( # noqa: WPS231 for index, interval in enumerate(intervals): a, b = interval if b < a: - a, b = b, a + raise ValueError( + "Interval limits (a,b) should satisfy a <= b. " + + str(interval) + " doesn't", + ) curves_counters = [] for c in curves: - counter = 0 - inside_interval = False - size_curve = c.shape[0] - for i in range(0, size_curve - 1): - p1 = c[i][index] - p2 = c[i + 1][index] - if ( - # Check that the chunk of function grows - p1 < p2 # noqa: WPS408 - and ( - # First point <= a, second >= a - (p1 <= a and p2 >= a) - # First point inside interval, second >=a - or (a <= p1 <= b and p2 >= a) - ) - ): - # Last pair of points where not inside interval - if inside_interval is False: - counter += 1 - inside_interval = True - else: - inside_interval = False - + counter = _n_curve_crossings(c, index, interval) curves_counters = curves_counters + [counter] transformed_counters = transformed_counters + [curves_counters] @@ -347,47 +399,49 @@ def moments_of_norm( We import the Canadian Weather dataset >>> from skfda.datasets import fetch_weather >>> X = fetch_weather(return_X_y=True)[0] + Then we call the function with the dataset. + >>> import numpy as np >>> from skfda.misc.feature_construction import moments_of_norm - >>> moments_of_norm(X) - [[4.69, 4.06], - [6.15, 3.99], - [5.51, 4.04], - [6.81, 3.46], - [5.23, 3.29], - [5.26, 3.09], - [-5.06, 2.20], - [3.10, 2.46], - [2.25, 2.55], - [4.08, 3.31], - [4.12, 3.04], - [6.13, 2.58], - [5.81, 2.50], - [7.27, 2.14], - [7.31, 2.62], - [2.46, 1.93], - [2.47, 1.40], - [-0.15, 1.23], - [-7.09, 1.20], - [2.75, 1.02], - [0.68, 1.11], - [-3.41, 0.99], - [2.26, 1.27], - [3.99, 1.10], - [8.75, 0.74], - [9.96, 3.16], - [9.62, 2.33], - [3.84, 1.67], - [7.00, 7.10], - [-0.85, 0.74], - [-4.79, 0.90], - [-5.02, 0.73], - [-9.65, 1.14], - [-9.24, 0.71], - [-16.52, 0.39]] + >>> np.around(moments_of_norm(X), decimals=2) + array([[ 4.69, 4.06], + [ 6.15, 3.99], + [ 5.51, 4.04], + [ 6.81, 3.46], + [ 5.23, 3.29], + [ 5.26, 3.09], + [ -5.06, 2.2 ], + [ 3.1 , 2.46], + [ 2.25, 2.55], + [ 4.08, 3.31], + [ 4.12, 3.04], + [ 6.13, 2.58], + [ 5.81, 2.5 ], + [ 7.27, 2.14], + [ 7.31, 2.62], + [ 2.46, 1.93], + [ 2.47, 1.4 ], + [ -0.15, 1.23], + [ -7.09, 1.12], + [ 2.75, 1.02], + [ 0.68, 1.11], + [ -3.41, 0.99], + [ 2.26, 1.27], + [ 3.99, 1.1 ], + [ 8.75, 0.74], + [ 9.96, 3.16], + [ 9.62, 2.33], + [ 3.84, 1.67], + [ 7. , 7.1 ], + [ -0.85, 0.74], + [ -4.79, 0.9 ], + [ -5.02, 0.73], + [ -9.65, 1.14], + [ -9.24, 0.71], + [-16.52, 0.39]]) """ curves = data.data_matrix - norms = [] + norms = np.empty((0, curves.shape[2])) for c in curves: # noqa: WPS426 x, y = c.shape curve_norms = [] @@ -395,8 +449,8 @@ def moments_of_norm( curve_norms = curve_norms + [ reduce(lambda a, b: a + b[i], c, 0) / x, ] - norms = norms + [curve_norms] - return np.asarray(norms, dtype=list) + norms = np.concatenate((norms, [curve_norms])) + return np.array(norms) def moments_of_process( @@ -424,47 +478,48 @@ def moments_of_process( >>> X = fetch_weather(return_X_y=True)[0] Then we call the function with the dataset. + >>> import numpy as np >>> from skfda.misc.feature_construction import moments_of_process - >>> moments_of_process(X) - [[52.43, 2.25], - [77.80, 2.92], - [72.15, 2.69], - [51.25, 2.12], - [89.31, 1.59], - [105.04, 1.53], - [162.09, 1.49], - [141.43, 1.45], - [143.21, 1.15], - [125.92, 1.64], - [111.39, 1.39], - [122.14, 1.16], - [125.92, 1.04], - [93.56, 1.02], - [93.08, 1.21], - [128.36, 1.26], - [180.68, 1.19], - [189.07, 0.92], - [196.43, 0.55], - [158.55, 0.94], - [177.39, 0.81], - [230.41, 0.44], - [119.27, 1.26], - [84.89, 1.01], - [79.47, 0.20], - [26.70, 3.52], - [21.67, 3.09], - [77.38, 0.49], - [18.94, 9.94], - [127.83, 0.25], - [252.06, 0.29], - [252.01, 0.27], - [164.01, 0.49], - [254.90, 0.20], - [187.72, 0.12]] + >>> np.around(moments_of_process(X), decimals=2) + array([[ 5.2430e+01, 2.2500e+00], + [ 7.7800e+01, 2.9200e+00], + [ 7.2150e+01, 2.6900e+00], + [ 5.1250e+01, 2.1200e+00], + [ 8.9310e+01, 1.6000e+00], + [ 1.0504e+02, 1.5300e+00], + [ 1.6209e+02, 1.5000e+00], + [ 1.4143e+02, 1.4500e+00], + [ 1.4322e+02, 1.1500e+00], + [ 1.2593e+02, 1.6500e+00], + [ 1.1139e+02, 1.4000e+00], + [ 1.2215e+02, 1.1600e+00], + [ 1.2593e+02, 1.0500e+00], + [ 9.3560e+01, 1.0300e+00], + [ 9.3080e+01, 1.2200e+00], + [ 1.2836e+02, 1.2700e+00], + [ 1.8069e+02, 1.2000e+00], + [ 1.8907e+02, 9.2000e-01], + [ 1.9644e+02, 5.6000e-01], + [ 1.5856e+02, 9.4000e-01], + [ 1.7739e+02, 8.1000e-01], + [ 2.3041e+02, 4.5000e-01], + [ 1.1928e+02, 1.2700e+00], + [ 8.4900e+01, 1.0200e+00], + [ 7.9470e+01, 2.0000e-01], + [ 2.6700e+01, 3.5200e+00], + [ 2.1680e+01, 3.0900e+00], + [ 7.7390e+01, 4.9000e-01], + [ 1.8950e+01, 9.9500e+00], + [ 1.2783e+02, 2.5000e-01], + [ 2.5206e+02, 2.9000e-01], + [ 2.5201e+02, 2.7000e-01], + [ 1.6401e+02, 4.9000e-01], + [ 2.5490e+02, 2.0000e-01], + [ 1.8772e+02, 1.2000e-01]]) """ norm = moments_of_norm(data) curves = data.data_matrix - moments = [] + moments = np.empty((0, curves.shape[2])) for i, c in enumerate(curves): # noqa: WPS426 x, y = c.shape curve_moments = [] @@ -472,5 +527,6 @@ def moments_of_process( curve_moments = curve_moments + [ reduce(lambda a, b: a + ((b[j] - norm[i][j]) ** 2), c, 0) / x, ] - moments = moments + [curve_moments] - return np.asarray(moments, dtype=list) + moments = np.concatenate((moments, [curve_moments])) + + return moments From 1ea1b2d41827d1ec6645d603bdae345101e7a282 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 23 Nov 2021 18:07:15 +0100 Subject: [PATCH 009/400] Integration example --- skfda/misc/feature_construction/_evaluation_trasformer.py | 4 ++-- skfda/representation/grid.py | 7 +++++++ 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/misc/feature_construction/_evaluation_trasformer.py index 50ce7f3b3..62d083a0d 100644 --- a/skfda/misc/feature_construction/_evaluation_trasformer.py +++ b/skfda/misc/feature_construction/_evaluation_trasformer.py @@ -41,8 +41,8 @@ class EvaluationTransformer( shape\_ (tuple): original shape of coefficients per sample. Examples: - >>> from skfda.representation import (FDataGrid, FDataBasis, - ... EvaluationTransformer) + >>> from skfda.representation import (FDataGrid, FDataBasis) + >>> from skfda.misc.feature_construction import EvaluationTransformer >>> from skfda.representation.basis import Monomial Functional data object with 2 samples diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index ea9020d91..ae317ba60 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -485,6 +485,13 @@ def integrate( Returns: ndarray of shape (n_samples, n_dimensions)\ with the integrated data. + Examples: + Integration on the whole domain + + >>> fdata = FDataGrid([1,2,4,5,8], range(5)) + >>> fdata.integrate() + array([[ 15.]]) + """ if interval is None: interval = self.grid_points[0] From a38a4852f6901392a744d7b51de08856921a8bcc Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 23 Nov 2021 18:16:54 +0100 Subject: [PATCH 010/400] evaluation transformer --- skfda/misc/feature_construction/_evaluation_trasformer.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/misc/feature_construction/_evaluation_trasformer.py index 62d083a0d..e8ba5fbea 100644 --- a/skfda/misc/feature_construction/_evaluation_trasformer.py +++ b/skfda/misc/feature_construction/_evaluation_trasformer.py @@ -41,9 +41,9 @@ class EvaluationTransformer( shape\_ (tuple): original shape of coefficients per sample. Examples: - >>> from skfda.representation import (FDataGrid, FDataBasis) + >>> from skfda.representation import FDataGrid, FDataBasis >>> from skfda.misc.feature_construction import EvaluationTransformer - >>> from skfda.representation.basis import Monomial + >>> from skfda.representation.basis import Monomial Functional data object with 2 samples representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. @@ -51,7 +51,7 @@ class EvaluationTransformer( >>> data_matrix = [[1, 2], [2, 3]] >>> grid_points = [2, 4] >>> fd = FDataGrid(data_matrix, grid_points) - >>> + >>> transformer = EvaluationTransformer() >>> transformer.fit_transform(fd) array([[ 1., 2.], From 16199785979a22a99bd934c01d4ace3ab46ab67d Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 23 Nov 2021 18:29:44 +0100 Subject: [PATCH 011/400] Delete duplicate file --- .../representation/_evaluation_trasformer.py | 160 ------------------ 1 file changed, 160 deletions(-) delete mode 100644 skfda/representation/_evaluation_trasformer.py diff --git a/skfda/representation/_evaluation_trasformer.py b/skfda/representation/_evaluation_trasformer.py deleted file mode 100644 index f470057a1..000000000 --- a/skfda/representation/_evaluation_trasformer.py +++ /dev/null @@ -1,160 +0,0 @@ -from __future__ import annotations - -from typing import Optional, Union, overload - -import numpy as np -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.utils.validation import check_is_fitted -from typing_extensions import Literal - -from ._functional_data import FData -from ._typing import ArrayLike, GridPointsLike -from .extrapolation import ExtrapolationLike -from .grid import FDataGrid - - -class EvaluationTransformer( - BaseEstimator, # type:ignore - TransformerMixin, # type:ignore -): - r""" - Transformer returning the evaluations of FData objects as a matrix. - - Args: - eval_points (array_like): List of points where the functions are - evaluated. If `None`, the functions must be `FDatagrid` objects - and all points will be returned. - extrapolation (str or Extrapolation, optional): Controls the - extrapolation mode for elements outside the :term:`domain` range. - By default it is used the mode defined during the instance of the - object. - grid (bool, optional): Whether to evaluate the results on a grid - spanned by the input arrays, or at points specified by the - input arrays. If true the eval_points should be a list of size - dim_domain with the corresponding times for each axis. The - return matrix has shape n_samples x len(t1) x len(t2) x ... x - len(t_dim_domain) x dim_codomain. If the domain dimension is 1 - the parameter has no efect. Defaults to False. - - Attributes: - shape\_ (tuple): original shape of coefficients per sample. - - Examples: - >>> from skfda.representation import (FDataGrid, FDataBasis, - ... EvaluationTransformer) - >>> from skfda.representation.basis import Monomial - - Functional data object with 2 samples - representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. - - >>> data_matrix = [[1, 2], [2, 3]] - >>> grid_points = [2, 4] - >>> fd = FDataGrid(data_matrix, grid_points) - >>> - >>> transformer = EvaluationTransformer() - >>> transformer.fit_transform(fd) - array([[ 1., 2.], - [ 2., 3.]]) - - Functional data object with 2 samples - representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}^2`. - - >>> data_matrix = [[[1, 0.3], [2, 0.4]], [[2, 0.5], [3, 0.6]]] - >>> grid_points = [2, 4] - >>> fd = FDataGrid(data_matrix, grid_points) - >>> - >>> transformer = EvaluationTransformer() - >>> transformer.fit_transform(fd) - array([[ 1. , 0.3, 2. , 0.4], - [ 2. , 0.5, 3. , 0.6]]) - - Representation of a functional data object with 2 samples - representing a function :math:`f : \mathbb{R}^2\longmapsto\mathbb{R}`. - - >>> data_matrix = [[[1, 0.3], [2, 0.4]], [[2, 0.5], [3, 0.6]]] - >>> grid_points = [[2, 4], [3, 6]] - >>> fd = FDataGrid(data_matrix, grid_points) - >>> - >>> transformer = EvaluationTransformer() - >>> transformer.fit_transform(fd) - array([[ 1. , 0.3, 2. , 0.4], - [ 2. , 0.5, 3. , 0.6]]) - - Evaluation of a functional data object at several points. - - >>> basis = Monomial(n_basis=4) - >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] - >>> fd = FDataBasis(basis, coefficients) - >>> - >>> transformer = EvaluationTransformer([0, 0.2, 0.5, 0.7, 1]) - >>> transformer.fit_transform(fd) - array([[ 0.5 , 0.784 , 1.5625, 2.3515, 4. ], - [ 1.5 , 1.864 , 3.0625, 4.3315, 7. ]]) - - """ - - @overload - def __init__( - self, - eval_points: ArrayLike, - *, - extrapolation: Optional[ExtrapolationLike] = None, - grid: Literal[False] = False, - ) -> None: - pass - - @overload - def __init__( - self, - eval_points: GridPointsLike, - *, - extrapolation: Optional[ExtrapolationLike] = None, - grid: Literal[True], - ) -> None: - pass - - def __init__( - self, - eval_points: Union[ArrayLike, GridPointsLike, None] = None, - *, - extrapolation: Optional[ExtrapolationLike] = None, - grid: bool = False, - ): - self.eval_points = eval_points - self.extrapolation = extrapolation - self.grid = grid - - def fit( # noqa: D102 - self, - X: FData, - y: None = None, - ) -> EvaluationTransformer: - - if self.eval_points is None and not isinstance(X, FDataGrid): - raise ValueError( - "If no eval_points are passed, the functions " - "should be FDataGrid objects.", - ) - - self._is_fitted = True - - return self - - def transform( # noqa: D102 - self, - X: FData, - y: None = None, - ) -> np.ndarray: - - check_is_fitted(self, '_is_fitted') - - if self.eval_points is None: - evaluation = X.data_matrix.copy() - else: - evaluation = X( # type: ignore - self.eval_points, - extrapolation=self.extrapolation, - grid=self.grid, - ) - - return evaluation.reshape((X.n_samples, -1)) From 502524553d08ba54af6c58fec583fe0d9729cba0 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Tue, 23 Nov 2021 19:03:57 +0100 Subject: [PATCH 012/400] First version --- docs/modules/misc/metrics.rst | 22 ++- docs/refs.bib | 12 ++ skfda/misc/metrics/__init__.py | 1 + skfda/misc/metrics/_mahalanobis.py | 169 ++++++++++++++++++ .../dim_reduction/feature_extraction/_fpca.py | 12 +- 5 files changed, 203 insertions(+), 13 deletions(-) create mode 100644 skfda/misc/metrics/_mahalanobis.py diff --git a/docs/modules/misc/metrics.rst b/docs/modules/misc/metrics.rst index b0823c15b..7969e0314 100644 --- a/docs/modules/misc/metrics.rst +++ b/docs/modules/misc/metrics.rst @@ -17,7 +17,7 @@ for ``p``, and use this instance to evaluate the norm or distance over skfda.misc.metrics.LpNorm skfda.misc.metrics.LpDistance - + As the :math:`L_1`, :math:`L_2` and :math:`L_{\infty}` norms are very common in :term:`FDA`, instances for these have been created, called respectively ``l1_norm``, ``l2_norm`` and ``linf_norm``. The same is true for metrics, @@ -34,7 +34,7 @@ value of ``p`` must be explicitly passed in each call. skfda.misc.metrics.lp_norm skfda.misc.metrics.lp_distance - + Angular distance ---------------- @@ -45,7 +45,7 @@ by the inner product) is also available, and useful in some contexts. :toctree: autosummary skfda.misc.metrics.angular_distance - + Elastic distances ----------------- @@ -60,6 +60,18 @@ analysis and registration of functional data. skfda.misc.metrics.fisher_rao_amplitude_distance skfda.misc.metrics.fisher_rao_phase_distance +Mahalanobis distance +-------------------- + +The following class implements a functional version of the Mahalanobis +distance: + +.. autosummary:: + :toctree: autosummary + + skfda.misc.metrics.FMahalanobisDistance + + Metric induced by a norm ------------------------ @@ -74,7 +86,7 @@ to construct a metric from a norm in this way: :toctree: autosummary skfda.misc.metrics.NormInducedMetric - + Pairwise metric --------------- @@ -86,7 +98,7 @@ of objets. The following class can compute that efficiently: :toctree: autosummary skfda.misc.metrics.PairwiseMetric - + Transformation metric --------------------- diff --git a/docs/refs.bib b/docs/refs.bib index 50e64219d..d8f9799e6 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -1,3 +1,15 @@ +@article{ + berrendero+bueno-larraz+cuevas_2020_mahalanobis, + author = {José R. Berrendero and Beatriz Bueno-Larraz and Antonio Cuevas}, + title = {On Mahalanobis Distance in Functional Settings}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {9}, + pages = {1-33}, + url = {http://jmlr.org/papers/v21/18-156.html} +} + @article{berrendero+cuevas+torrecilla_2016_hunting, author = {Berrendero, J.R. and Cuevas, Antonio and Torrecilla, José}, year = {2016}, diff --git a/skfda/misc/metrics/__init__.py b/skfda/misc/metrics/__init__.py index b2fc981f2..269541f12 100644 --- a/skfda/misc/metrics/__init__.py +++ b/skfda/misc/metrics/__init__.py @@ -15,6 +15,7 @@ lp_distance, ) from ._lp_norms import LpNorm, l1_norm, l2_norm, linf_norm, lp_norm +from ._mahalanobis import FMahalanobisDistance from ._typing import PRECOMPUTED, Metric, Norm from ._utils import ( NormInducedMetric, diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py new file mode 100644 index 000000000..a5ee5ca1e --- /dev/null +++ b/skfda/misc/metrics/_mahalanobis.py @@ -0,0 +1,169 @@ +"""Functional Mahalanobis Distance Module.""" + +from __future__ import annotations + +from typing import Callable, Optional, Union + +import numpy as np +from sklearn.base import BaseEstimator + +from ...representation import FData +from ...representation._typing import ArrayLike, NDArrayFloat +from ...representation.basis import Basis +from .._math import inner_product +from ..regularization._regularization import TikhonovRegularization + +WeightsCallable = Callable[[np.ndarray], np.ndarray] + + +class FMahalanobisDistance(BaseEstimator): # type: ignore + r"""Functional Mahalanobis distance. + + Class that implements functional Mahalanobis distance for both + basis and grid representations of the data + :footcite:`berrendero+bueno-larraz+cuevas_2020_mahalanobis`. + + Parameters: + n_components: Number of eigenfunctions to keep from + functional principal component analysis. Defaults to 10. + centering: If ``True`` then calculate the mean of the functional + data object and center the data first. Defaults to ``True``. + regularization: Regularization object to be applied. + components_basis: The basis in which we want the eigenfunctions. + We can use a different basis than the basis contained in the + passed FDataBasis object. This parameter is only used when + fitting a FDataBasis. + weights: the weights vector used for discrete integration. + If none then the trapezoidal rule is used for computing the + weights. If a callable object is passed, then the weight + vector will be obtained by evaluating the object at the sample + points of the passed FDataGrid object in the fit method. This + parameter is only used when fitting a FDataGrid. + + Attributes: + ef\_: Eigenfunctions of the covariance operator. + ev\_: Eigenvalues of the covariance operator. + mean\_: Mean of the stochastic process. + + Examples: + >>> from skfda.misc.metrics import FMahalanobisDistance + >>> import numpy as np + >>> from skfda.representation.grid import FDataGrid + >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) + >>> grid_points = [0, 1] + >>> fd = FDataGrid(data_matrix, grid_points) + >>> fmah = FMahalanobisDistance(2) + >>> fmah.fit(fd) + FMahalanobisDistance(n_components=2) + >>> fmah.ef_ + FDataGrid( + array([[[-0.63245553], + [ 1.26491106]], + [[ 1.26491106], + [ 0.63245553]]]), + grid_points=(array([ 0., 1.]),), + domain_range=((0.0, 1.0),), + dataset_name=None, + argument_names=(None,), + coordinate_names=(None,), + extrapolation=None, + interpolation=SplineInterpolation(interpolation_order=1, + smoothness_parameter=0, monotone=False)) + >>> fmah.ev_ + array([ 1.25000000e+00, 1.95219693e-33]) + >>> fmah.mean_ + FDataGrid( + array([[[ 0.5], [ 1. ]]]), + grid_points=(array([ 0., 1.]),), + domain_range=((0.0, 1.0),), + dataset_name=None, + argument_names=(None,), + coordinate_names=(None,), + extrapolation=None, + interpolation=SplineInterpolation(interpolation_order=1, + smoothness_parameter=0, monotone=False)) + + References: + .. footbibliography:: + """ + + def __init__( + self, + n_components: int = 10, + centering: bool = True, + regularization: Optional[TikhonovRegularization[FData]] = None, + weights: Optional[Union[ArrayLike, WeightsCallable]] = None, + components_basis: Optional[Basis] = None, + alpha: float = 0.001, + k: int = 10, + ) -> None: + self.n_components = n_components + self.centering = centering + self.regularization = regularization + self.weights = weights + self.components_basis = components_basis + self.alpha = alpha + self.k = k + + def fit( + self, + X: FData, + y: None = None, + ) -> FMahalanobisDistance: + """Fit the functional Mahalanobis distance to X. + + We extract the eigenfunctions and corresponding eigenvalues + of the covariance operator by using FPCA. + + Args: + X: Stochastic process. + y: Ignored. + + Returns: + self + """ + from ...preprocessing.dim_reduction.feature_extraction import FPCA + + fpca = FPCA( + self.n_components, + self.centering, + self.regularization, + self.weights, + self.components_basis, + ) + fpca.fit(X) + self.ev_ = fpca.explained_variance_ + self.ef_ = fpca.components_ + self.mean_ = fpca.mean_ + + return self + + def mahalanobis_distance( + self, + e1: FData, + e2: FData, + ) -> NDArrayFloat: + """Compute the squared functional Mahalanobis distances of given observations. + + Args: + e1: First object. + e2: Second object. + + Returns: + Squared functional Mahalanobis distances of the observations. + """ + return np.sum( + self.ev_ * inner_product(e1 - e2, self.ef_) ** 2 + / (self.ev_ + self.alpha)**2, + ) + + def mahalanobis_depth(self, e1: FData): + """Compute the Mahalanobis depth of a given observations. + + Args: + e1: First object. + + Returns: + Depth of the observations. + """ + return 1 / (1 + self.mahalanobis(e1, self.mean_)) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 6e2f7cabe..7691ec030 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -10,8 +10,6 @@ from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from ....misc import inner_product_matrix -from ....misc.metrics import l2_norm from ....misc.regularization import ( TikhonovRegularization, compute_penalty_matrix, @@ -152,7 +150,6 @@ def _fit_basis( if self.components_basis else X.basis.n_basis ) - n_samples = X.n_samples # necessary in inverse_transform self.n_samples_ = X.n_samples @@ -224,7 +221,7 @@ def _fit_basis( # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA final_matrix = ( - X.coefficients @ np.transpose(l_inv_j_t) / np.sqrt(n_samples) + X.coefficients @ np.transpose(l_inv_j_t) ) # initialize the pca module provided by scikit-learn @@ -289,9 +286,8 @@ def _fit_grid( please view the referenced book, chapter 8. In summary, we are performing standard multivariate PCA over - :math:`\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\mathbf{X}` is the data - matrix and :math:`\mathbf{W}` is the weight matrix (this matrix + :math:`\mathbf{X} \mathbf{W}^{1/2}` where :math:`\mathbf{X}` is the + data matrix and :math:`\mathbf{W}` is the weight matrix (this matrix defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. @@ -375,7 +371,7 @@ def _fit_grid( ).T # see docstring for more information - final_matrix = fd_data @ np.sqrt(weights_matrix) / np.sqrt(n_samples) + final_matrix = fd_data @ np.sqrt(weights_matrix) pca = PCA(n_components=self.n_components) pca.fit(final_matrix) From c756697710083d7397188a3ff19e566989d4815b Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Tue, 23 Nov 2021 19:10:09 +0100 Subject: [PATCH 013/400] Add missing type --- skfda/misc/metrics/_mahalanobis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index a5ee5ca1e..8fd95ab23 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -157,7 +157,7 @@ def mahalanobis_distance( / (self.ev_ + self.alpha)**2, ) - def mahalanobis_depth(self, e1: FData): + def mahalanobis_depth(self, e1: FData) -> NDArrayFloat: """Compute the Mahalanobis depth of a given observations. Args: From 1dedceec7f59d543d653fe6442c73dfd9b836f46 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Tue, 23 Nov 2021 20:09:52 +0100 Subject: [PATCH 014/400] Test PCA --- skfda/misc/metrics/_mahalanobis.py | 2 +- .../dim_reduction/feature_extraction/_fpca.py | 12 ++++++++++-- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 8fd95ab23..1b05d66b1 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -70,7 +70,7 @@ class FMahalanobisDistance(BaseEstimator): # type: ignore interpolation=SplineInterpolation(interpolation_order=1, smoothness_parameter=0, monotone=False)) >>> fmah.ev_ - array([ 1.25000000e+00, 1.95219693e-33]) + array([ 1.25000000e+00, 2.39701829e-95]) >>> fmah.mean_ FDataGrid( array([[[ 0.5], [ 1. ]]]), diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 7691ec030..60ab1b7ef 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -225,7 +225,11 @@ def _fit_basis( ) # initialize the pca module provided by scikit-learn - pca = PCA(n_components=self.n_components) + pca = PCA( + n_components=self.n_components, + svd_solver='randomized', + random_state=1, + ) pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the @@ -373,7 +377,11 @@ def _fit_grid( # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) - pca = PCA(n_components=self.n_components) + pca = PCA( + n_components=self.n_components, + svd_solver='randomized', + random_state=1, + ) pca.fit(final_matrix) self.components_ = X.copy( data_matrix=np.transpose( From 2b072056c32f7565b23844df8bf2fabeebe23b67 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Tue, 23 Nov 2021 20:24:40 +0100 Subject: [PATCH 015/400] Update docstring --- skfda/misc/metrics/_mahalanobis.py | 29 ++--------------------------- 1 file changed, 2 insertions(+), 27 deletions(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 1b05d66b1..2503feec7 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -55,33 +55,8 @@ class FMahalanobisDistance(BaseEstimator): # type: ignore >>> fmah = FMahalanobisDistance(2) >>> fmah.fit(fd) FMahalanobisDistance(n_components=2) - >>> fmah.ef_ - FDataGrid( - array([[[-0.63245553], - [ 1.26491106]], - [[ 1.26491106], - [ 0.63245553]]]), - grid_points=(array([ 0., 1.]),), - domain_range=((0.0, 1.0),), - dataset_name=None, - argument_names=(None,), - coordinate_names=(None,), - extrapolation=None, - interpolation=SplineInterpolation(interpolation_order=1, - smoothness_parameter=0, monotone=False)) - >>> fmah.ev_ - array([ 1.25000000e+00, 2.39701829e-95]) - >>> fmah.mean_ - FDataGrid( - array([[[ 0.5], [ 1. ]]]), - grid_points=(array([ 0., 1.]),), - domain_range=((0.0, 1.0),), - dataset_name=None, - argument_names=(None,), - coordinate_names=(None,), - extrapolation=None, - interpolation=SplineInterpolation(interpolation_order=1, - smoothness_parameter=0, monotone=False)) + >>> fmah.mahalanobis_distance(fd[0], fd[1]) + 1.9968038359080937 References: .. footbibliography:: From 15b7c11e24f1eff091f9fe8f915a1be9666e8011 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Tue, 23 Nov 2021 20:57:52 +0100 Subject: [PATCH 016/400] Typo --- skfda/misc/metrics/_mahalanobis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 2503feec7..54076c436 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -141,4 +141,4 @@ def mahalanobis_depth(self, e1: FData) -> NDArrayFloat: Returns: Depth of the observations. """ - return 1 / (1 + self.mahalanobis(e1, self.mean_)) + return 1 / (1 + self.mahalanobis_distance(e1, self.mean_)) From 0ef4a1833421c1983a011a9a2a47294717ccb1a0 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 25 Nov 2021 22:40:53 +0100 Subject: [PATCH 017/400] Functional transformers pending to review --- .../_functional_transformers.py | 72 +++++++++++++------ 1 file changed, 49 insertions(+), 23 deletions(-) diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py index b08c51451..1a483d80b 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -7,7 +7,6 @@ import numpy as np -from ..._utils import constants from ...representation import FDataBasis, FDataGrid @@ -179,13 +178,14 @@ def occupation_measure( :math:`\mathbb{R}` and | | stands for the Lebesgue measure. Args: - data: FDataGrid where we want to calculate + data: FDataGrid or FDataBasis where we want to calculate the occupation measure. - intervals: sequence of sequence of tuples containing the + intervals: ndarray of tuples containing the intervals we want to consider. The shape should be (n_sequences,2) n_points: Number of points to evaluate in the domain. - By default will be used 501 points. + By default will be used the points defined on the FDataGrid. + On a FDataBasis this value should be specified. Returns: ndarray of shape (n_intervals, n_samples) with the transformed data. @@ -207,10 +207,16 @@ def occupation_measure( Finally we call to the occupation measure function with the intervals that we want to consider. In our case (0.0, 1.0) - and (2.0, 3.0) + and (2.0, 3.0). We need also to specify the number of points + we want that the function takes into account to interpolate. + We are going to use 501 points. >>> from skfda.misc.feature_construction import occupation_measure >>> np.around( - ... occupation_measure(fd_grid, [(0.0, 1.0), (2.0, 3.0)]), + ... occupation_measure( + ... fd_grid, + ... [(0.0, 1.0), (2.0, 3.0)], + ... n_points=501, + ... ), ... decimals=2, ... ) array([[[ 0.98], @@ -223,17 +229,23 @@ def occupation_measure( """ if n_points is None: - n_points = constants.N_POINTS_UNIDIMENSIONAL_PLOT_MESH - - lower_functional_limit, upper_functional_limit = data.domain_range[0] - domain_size = upper_functional_limit - lower_functional_limit + if isinstance(data, FDataBasis): + raise ValueError( + "Number of points to consider, should be given " + + " as an argument for a FDataBasis. Instead None was passed.", + ) + else: + grid = data.grid_points + else: + lower_functional_limit, upper_functional_limit = data.domain_range[0] + domain_size = upper_functional_limit - lower_functional_limit if isinstance(data, FDataGrid): time_x_coord_cumulative = np.empty((0, data.data_matrix.shape[0], 1)) else: time_x_coord_cumulative = np.empty((0, data.coefficients.shape[0], 1)) - for interval in intervals: + for interval in intervals: # noqa: WPS426 y1, y2 = interval if y2 < y1: @@ -243,20 +255,34 @@ def occupation_measure( ) function_x_coordinates = np.empty((1, 1)) - - for x_coordinate in np.arange( - lower_functional_limit, - upper_functional_limit, - domain_size / n_points, - ): - function_x_coordinates = np.concatenate( - (function_x_coordinates, np.array([[x_coordinate]])), + if n_points is None: + function_x_coordinates = reduce( + lambda a, b: np.concatenate( + ( + a, + np.array([[b]]), + ), + ), + grid[0], + function_x_coordinates, + )[1:] + else: + for x_coordinate in np.arange( + lower_functional_limit, + upper_functional_limit, + domain_size / n_points, + ): + function_x_coordinates = np.concatenate( + (function_x_coordinates, np.array([[x_coordinate]])), + ) + + if n_points is None: + function_y_coordinates = data.data_matrix + else: + function_y_coordinates = data._evaluate( # noqa: WPS437 + function_x_coordinates, ) - function_y_coordinates = data._evaluate( # noqa: WPS437 - function_x_coordinates, - ) - time_x_coord_interval = np.empty((0, 1)) for curve_y_coordinates in function_y_coordinates: From a69b387c830be869b1afbf602b6f7989e225afd2 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Fri, 26 Nov 2021 12:12:56 +0100 Subject: [PATCH 018/400] Final changes --- skfda/misc/metrics/_mahalanobis.py | 5 +++-- .../preprocessing/dim_reduction/feature_extraction/_fpca.py | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 54076c436..a8737b55b 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -6,6 +6,7 @@ import numpy as np from sklearn.base import BaseEstimator +from sklearn.utils.validation import check_is_fitted from ...representation import FData from ...representation._typing import ArrayLike, NDArrayFloat @@ -70,7 +71,6 @@ def __init__( weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, alpha: float = 0.001, - k: int = 10, ) -> None: self.n_components = n_components self.centering = centering @@ -78,7 +78,6 @@ def __init__( self.weights = weights self.components_basis = components_basis self.alpha = alpha - self.k = k def fit( self, @@ -127,6 +126,8 @@ def mahalanobis_distance( Returns: Squared functional Mahalanobis distances of the observations. """ + check_is_fitted(self) + return np.sum( self.ev_ * inner_product(e1 - e2, self.ef_) ** 2 / (self.ev_ + self.alpha)**2, diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 60ab1b7ef..95744c2ca 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -204,6 +204,7 @@ def _fit_basis( # apply regularization if regularization_matrix is not None: + # using += would have a different behavior g_matrix = (g_matrix + regularization_matrix) # obtain triangulation using cholesky From 8d396427d24d4a910265cc54afabc0712c2c609b Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Fri, 26 Nov 2021 12:17:36 +0100 Subject: [PATCH 019/400] Final --- skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 95744c2ca..9020ef99d 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -205,7 +205,7 @@ def _fit_basis( # apply regularization if regularization_matrix is not None: # using += would have a different behavior - g_matrix = (g_matrix + regularization_matrix) + g_matrix = (g_matrix + regularization_matrix) # noqa: WPS350 # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -368,7 +368,7 @@ def _fit_grid( basis_matrix = basis.data_matrix[..., 0] if regularization_matrix is not None: - basis_matrix = basis_matrix + regularization_matrix + basis_matrix += regularization_matrix fd_data = np.linalg.solve( basis_matrix.T, From 548baec202ae32a9a61d31c67fdcfb7453d75c41 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 9 Dec 2021 20:33:12 +0100 Subject: [PATCH 020/400] Corrections on integrate method --- .../_functional_transformers.py | 76 ++++--------------- skfda/representation/_functional_data.py | 18 +++++ skfda/representation/basis/_fdatabasis.py | 4 +- skfda/representation/grid.py | 24 +++--- 4 files changed, 47 insertions(+), 75 deletions(-) diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/misc/feature_construction/_functional_transformers.py index 1a483d80b..0b7e9ea29 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/misc/feature_construction/_functional_transformers.py @@ -33,8 +33,9 @@ def local_averages( calculate the local averages. n_intervals: number of intervals we want to consider. Returns: - ndarray of shape (n_intervals, n_samples, n_dimensions)\ - with the transformed data. + ndarray of shape (n_intervals, n_samples, n_dimensions) + with the transformed data for FDataBasis and (n_intervals, n_samples) + for FDataGrid. Example: @@ -50,67 +51,16 @@ def local_averages( >>> import numpy as np >>> from skfda.misc.feature_construction import local_averages >>> np.around(local_averages(X, 2), decimals=2) - array([[[ 993.98], - [ 950.82], - [ 911.93], - [ 946.44], - [ 887.3 ], - [ 930.18], - [ 927.89], - [ 959.72], - [ 928.14], - [ 1002.57], - [ 953.22], - [ 971.53], - [ 947.54], - [ 976.26], - [ 988.16], - [ 974.07], - [ 943.67], - [ 965.36], - [ 925.48], - [ 931.64], - [ 932.47], - [ 922.56], - [ 927.99], - [ 908.83], - [ 930.23], - [ 933.65], - [ 980.25], - [ 919.39], - [ 1013.98], - [ 940.23]], - - [[ 1506.69], - [ 1339.79], - [ 1317.25], - [ 1392.53], - [ 1331.65], - [ 1340.17], - [ 1320.15], - [ 1436.71], - [ 1310.51], - [ 1482.64], - [ 1371.34], - [ 1446.15], - [ 1394.84], - [ 1445.87], - [ 1416.5 ], - [ 1434.16], - [ 1418.19], - [ 1421.35], - [ 1354.89], - [ 1383.46], - [ 1323.45], - [ 1343.07], - [ 1360.87], - [ 1325.57], - [ 1342.55], - [ 1389.99], - [ 1379.43], - [ 1301.34], - [ 1517.04], - [ 1374.91]]]) + array([[ 993.98, 950.82, 911.93, 946.44, 887.3 , 930.18, + 927.89, 959.72, 928.14, 1002.57, 953.22, 971.53, + 947.54, 976.26, 988.16, 974.07, 943.67, 965.36, + 925.48, 931.64, 932.47, 922.56, 927.99, 908.83, + 930.23, 933.65, 980.25, 919.39, 1013.98, 940.23], + [ 1506.69, 1339.79, 1317.25, 1392.53, 1331.65, 1340.17, + 1320.15, 1436.71, 1310.51, 1482.64, 1371.34, 1446.15, + 1394.84, 1445.87, 1416.5 , 1434.16, 1418.19, 1421.35, + 1354.89, 1383.46, 1323.45, 1343.07, 1360.87, 1325.57, + 1342.55, 1389.99, 1379.43, 1301.34, 1517.04, 1374.91]]) """ domain_range = data.domain_range diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 22ac35a8a..d601db50f 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -33,6 +33,7 @@ GridPointsLike, LabelTuple, LabelTupleLike, + NDArrayFloat, ) from .evaluator import Evaluator from .extrapolation import ExtrapolationLike, _parse_extrapolation @@ -660,6 +661,23 @@ def derivative(self: T, *, order: int = 1) -> T: """ pass + @abstractmethod + def integrate( + self: T, + *, + interval: Optional[DomainRange] = None, + ) -> NDArrayFloat: + """Integration of the FData object. + + Args: + interval: domain range where we want to integrate. + By default is None as we integrate on the whole domain. + + Returns: + ndarray of shape with the integrated data. + """ + pass + @abstractmethod def shift( self, diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 61c163da5..8e9af2f12 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -374,10 +374,10 @@ def integrate( with the integrated data. """ if interval is None: - interval = self.basis.domain_range[0] + interval = self.basis.domain_range integrated = nquad_vec( self, - [interval], + interval, ) integrated_values = np.empty((1, 1)) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index ae317ba60..00f8e2cda 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -483,24 +483,28 @@ def integrate( interval: domain range where we want to integrate. By default is None as we integrate on the whole domain. Returns: - ndarray of shape (n_samples, n_dimensions)\ - with the integrated data. + ndarray of with the integrated data. Examples: Integration on the whole domain >>> fdata = FDataGrid([1,2,4,5,8], range(5)) >>> fdata.integrate() - array([[ 15.]]) + array([ 15.]) """ - if interval is None: - interval = self.grid_points[0] + if interval is not None: + self.restrict(interval) - return scipy.integrate.simps( - self.data_matrix, - x=interval, - axis=-2, - ) + integrand = self.data_matrix + + for g in self.grid_points[::-1]: + integrand = scipy.integrate.simps( + integrand, + x=g, + axis=-2, + ) + + return np.sum(integrand, axis=-1) def _check_same_dimensions(self: T, other: T) -> None: if self.data_matrix.shape[1:-1] != other.data_matrix.shape[1:-1]: From 72885aaefde2f5bf59e91bf1aa82717b9dc53f12 Mon Sep 17 00:00:00 2001 From: rafa9811 Date: Thu, 16 Dec 2021 14:03:04 +0100 Subject: [PATCH 021/400] deleted unnecessary mixin --- skfda/ml/regression/_linear_regression.py | 33 ++++++++++------------- 1 file changed, 14 insertions(+), 19 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 42dd153da..02cd87c8b 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -50,28 +50,9 @@ ] -class DataFrameMixin: - """Mixin class to add DataFrame funcionality.""" - - def dataframe_conversion(self, X: pd.DataFrame) -> List: - """Convert DataFrames to a list with two elements: first of all, a list with mv - covariates and the second, a FDataBasis object with functional data. - - Args: - - X: pandas DataFrame to convert. - - Returns: - - list with two elements: first of all, a list with mv - covariates and the second, a FDataBasis object with functional data. - """ - fdb = FDataBasis.concatenate(*X.iloc[:, 1].tolist()) - return [X.iloc[:, 0].tolist(), fdb] - - class LinearRegression( BaseEstimator, # type: ignore RegressorMixin, # type: ignore - DataFrameMixin, # type: ignore ): """ @@ -399,3 +380,17 @@ def _argcheck_X_y( ) return new_X, y, sample_weight, coef_info + + def dataframe_conversion(self, X: pd.DataFrame) -> List: + """Convert DataFrames to a list with two elements: first of all, a list with mv + covariates and the second, a FDataBasis object with functional data + + Args: + - X: pandas DataFrame to convert + + Returns: + - list with two elements: first of all, a list with mv + covariates and the second, a FDataBasis object with functional data + """ + fdb = FDataBasis.concatenate(*X.iloc[:, 1].tolist()) + return [X.iloc[:, 0].tolist(), fdb] From 5f5d2b5fdba485c5e92d0db258b8c4ab154dd885 Mon Sep 17 00:00:00 2001 From: rafa9811 Date: Thu, 16 Dec 2021 14:53:49 +0100 Subject: [PATCH 022/400] fixed some style errors --- skfda/ml/regression/_linear_regression.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 02cd87c8b..0f2460256 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -54,9 +54,8 @@ class LinearRegression( BaseEstimator, # type: ignore RegressorMixin, # type: ignore ): + """.. deprecated:: 0.8. - """ - .. deprecated:: 0.8 Use covariate parameters of type pandas.FDataFrame in methods fit, predict. @@ -380,16 +379,18 @@ def _argcheck_X_y( ) return new_X, y, sample_weight, coef_info - + def dataframe_conversion(self, X: pd.DataFrame) -> List: - """Convert DataFrames to a list with two elements: first of all, a list with mv - covariates and the second, a FDataBasis object with functional data + """Convert DataFrames to a list with two elements. + + First of all, a list with mv covariates and the second, + a FDataBasis object with functional data Args: - - X: pandas DataFrame to convert + X: pandas DataFrame to convert Returns: - - list with two elements: first of all, a list with mv + list with two elements: first of all, a list with mv covariates and the second, a FDataBasis object with functional data """ fdb = FDataBasis.concatenate(*X.iloc[:, 1].tolist()) From cdd275ae6747daf29cbe54548877df1fddf3f3eb Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Thu, 16 Dec 2021 20:22:07 +0100 Subject: [PATCH 023/400] Mahalanobis distance --- skfda/misc/metrics/__init__.py | 2 +- skfda/misc/metrics/_mahalanobis.py | 35 +++++++------------ skfda/ml/classification/_depth_classifiers.py | 2 +- 3 files changed, 14 insertions(+), 25 deletions(-) diff --git a/skfda/misc/metrics/__init__.py b/skfda/misc/metrics/__init__.py index 269541f12..7f1931000 100644 --- a/skfda/misc/metrics/__init__.py +++ b/skfda/misc/metrics/__init__.py @@ -15,7 +15,7 @@ lp_distance, ) from ._lp_norms import LpNorm, l1_norm, l2_norm, linf_norm, lp_norm -from ._mahalanobis import FMahalanobisDistance +from ._mahalanobis import MahalanobisDistance from ._typing import PRECOMPUTED, Metric, Norm from ._utils import ( NormInducedMetric, diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index a8737b55b..bf1d66e10 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -17,7 +17,7 @@ WeightsCallable = Callable[[np.ndarray], np.ndarray] -class FMahalanobisDistance(BaseEstimator): # type: ignore +class MahalanobisDistance(BaseEstimator): # type: ignore r"""Functional Mahalanobis distance. Class that implements functional Mahalanobis distance for both @@ -25,12 +25,12 @@ class FMahalanobisDistance(BaseEstimator): # type: ignore :footcite:`berrendero+bueno-larraz+cuevas_2020_mahalanobis`. Parameters: - n_components: Number of eigenfunctions to keep from + n_components: Number of eigenvectors to keep from functional principal component analysis. Defaults to 10. centering: If ``True`` then calculate the mean of the functional data object and center the data first. Defaults to ``True``. regularization: Regularization object to be applied. - components_basis: The basis in which we want the eigenfunctions. + components_basis: The basis in which we want the eigenvectors. We can use a different basis than the basis contained in the passed FDataBasis object. This parameter is only used when fitting a FDataBasis. @@ -42,21 +42,21 @@ class FMahalanobisDistance(BaseEstimator): # type: ignore parameter is only used when fitting a FDataGrid. Attributes: - ef\_: Eigenfunctions of the covariance operator. + ef\_: eigenvectors of the covariance operator. ev\_: Eigenvalues of the covariance operator. mean\_: Mean of the stochastic process. Examples: - >>> from skfda.misc.metrics import FMahalanobisDistance + >>> from skfda.misc.metrics import MahalanobisDistance >>> import numpy as np >>> from skfda.representation.grid import FDataGrid >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> grid_points = [0, 1] >>> fd = FDataGrid(data_matrix, grid_points) - >>> fmah = FMahalanobisDistance(2) - >>> fmah.fit(fd) - FMahalanobisDistance(n_components=2) - >>> fmah.mahalanobis_distance(fd[0], fd[1]) + >>> mah = MahalanobisDistance(2) + >>> mah.fit(fd) + MahalanobisDistance(n_components=2) + >>> mah(fd[0], fd[1]) 1.9968038359080937 References: @@ -83,10 +83,10 @@ def fit( self, X: FData, y: None = None, - ) -> FMahalanobisDistance: + ) -> MahalanobisDistance: """Fit the functional Mahalanobis distance to X. - We extract the eigenfunctions and corresponding eigenvalues + We extract the eigenvectors and corresponding eigenvalues of the covariance operator by using FPCA. Args: @@ -112,7 +112,7 @@ def fit( return self - def mahalanobis_distance( + def __call__( self, e1: FData, e2: FData, @@ -132,14 +132,3 @@ def mahalanobis_distance( self.ev_ * inner_product(e1 - e2, self.ef_) ** 2 / (self.ev_ + self.alpha)**2, ) - - def mahalanobis_depth(self, e1: FData) -> NDArrayFloat: - """Compute the Mahalanobis depth of a given observations. - - Args: - e1: First object. - - Returns: - Depth of the observations. - """ - return 1 / (1 + self.mahalanobis_distance(e1, self.mean_)) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index 74cadcb5f..a865c62f5 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -305,7 +305,7 @@ class _ArgMaxClassifier( >>> X = np.array([[1,5], [3,2], [4,1]]) >>> y = np.array([1, 0, 0]) - We will fit am ArgMax classifier + We will fit an ArgMax classifier >>> from skfda.ml.classification._depth_classifiers import \ ... _ArgMaxClassifier From e2d5571caaa06bc92e4edc08b4c0a54076e3bae9 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Thu, 16 Dec 2021 20:44:13 +0100 Subject: [PATCH 024/400] Empty lines --- skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 9020ef99d..3ea0c16e8 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -61,7 +61,6 @@ class FPCA( of variance explained by each principal component. mean\_ (FData): mean of the train data. - Examples: Construct an artificial FDataBasis object and run FPCA with this object. The resulting principal components are not compared because @@ -89,7 +88,6 @@ class FPCA( >>> fd = FDataGrid(data_matrix, grid_points) >>> fpca_grid = FPCA(2) >>> fpca_grid = fpca_grid.fit(fd) - """ def __init__( From 4b859fbff2dec1512b0988677e9bdfd09d09eb4c Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 23 Dec 2021 22:38:51 +0100 Subject: [PATCH 025/400] Some corrections and number_up_crossings well implemented --- skfda/exploratory/stats/__init__.py | 7 + .../stats}/_functional_transformers.py | 178 +++++++++--------- skfda/misc/_math.py | 12 +- skfda/misc/feature_construction/__init__.py | 7 - .../_evaluation_trasformer.py | 30 +-- skfda/representation/_functional_data.py | 3 +- skfda/representation/basis/_fdatabasis.py | 25 ++- skfda/representation/grid.py | 23 +-- 8 files changed, 132 insertions(+), 153 deletions(-) rename skfda/{misc/feature_construction => exploratory/stats}/_functional_transformers.py (78%) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index c7d1e3d66..168dc9c29 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,4 +1,11 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean +from ._functional_transformers import ( + local_averages, + moments_of_norm, + moments_of_process, + number_up_crossings, + occupation_measure, +) from ._stats import ( cov, depth_based_median, diff --git a/skfda/misc/feature_construction/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py similarity index 78% rename from skfda/misc/feature_construction/_functional_transformers.py rename to skfda/exploratory/stats/_functional_transformers.py index 0b7e9ea29..00e10f98c 100644 --- a/skfda/misc/feature_construction/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -49,7 +49,7 @@ def local_averages( Then we decide how many intervals we want to consider (in our case 2) and call the function with the dataset. >>> import numpy as np - >>> from skfda.misc.feature_construction import local_averages + >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, 2), decimals=2) array([[ 993.98, 950.82, 911.93, 946.44, 887.3 , 930.18, 927.89, 959.72, 928.14, 1002.57, 953.22, 971.53, @@ -64,22 +64,15 @@ def local_averages( """ domain_range = data.domain_range - x, y = domain_range[0] - interval_size = (y - x) / n_intervals - - integrated_data = [[]] - for i in np.arange(x, y, interval_size): + left, right = domain_range[0] + interval_size = (right - left) / n_intervals + integrated_data = [] + for i in np.arange(left, right, interval_size): interval = (i, i + interval_size) - if isinstance(data, FDataGrid): - data_grid = data.restrict(interval) - integrated_data = integrated_data + [ - data_grid.integrate(), - ] - else: - integrated_data = integrated_data + [ - data.integrate(interval=interval), - ] - return np.asarray(integrated_data[1:]) + integrated_data = integrated_data + [ + data.integrate(interval=(interval,)), + ] + return np.asarray(integrated_data) def _calculate_curve_occupation_( @@ -160,7 +153,7 @@ def occupation_measure( and (2.0, 3.0). We need also to specify the number of points we want that the function takes into account to interpolate. We are going to use 501 points. - >>> from skfda.misc.feature_construction import occupation_measure + >>> from skfda.exploratory.stats import occupation_measure >>> np.around( ... occupation_measure( ... fd_grid, @@ -229,7 +222,7 @@ def occupation_measure( if n_points is None: function_y_coordinates = data.data_matrix else: - function_y_coordinates = data._evaluate( # noqa: WPS437 + function_y_coordinates = data( function_x_coordinates, ) @@ -252,34 +245,9 @@ def occupation_measure( return time_x_coord_cumulative -def _n_curve_crossings( - c: np.ndarray, - index: int, - interval: Tuple, -) -> int: - a, b = interval - counter = 0 - inside_interval = False - size_curve = c.shape[0] - - for i in range(0, size_curve - 1): - p1 = c[i][index] - p2 = c[i + 1][index] - if p1 < p2: # Check that the chunk of function grows - if p2 >= a and (p1 <= a or a <= p1 <= b): - # Last pair of points where not inside interval - if inside_interval is False: - counter += 1 - inside_interval = True - else: - inside_interval = False - - return counter - - def number_up_crossings( data: FDataGrid, - intervals: np.ndarray, + levels: np.ndarray, ) -> np.ndarray: r""" Calculate the number of up crossings to a level of a FDataGrid. @@ -298,60 +266,92 @@ def number_up_crossings( Args: data: FDataGrid where we want to calculate the number of up crossings. - intervals: sequence of tuples containing the - intervals we want to consider for the crossings. + levels: sequence of numbers including the levels + we want to consider for the crossings. Returns: - ndarray of shape (n_dimensions, n_samples)\ + ndarray of shape (n_levels, n_samples)\ with the values of the counters. Example: - We import the Phoneme dataset and for simplicity we use - the first 200 samples. - >>> from skfda.datasets import fetch_phoneme - >>> dataset = fetch_phoneme() - >>> X = dataset['data'][:200] + We import the Medflies dataset and for simplicity we use + the first 50 samples. + >>> from skfda.datasets import fetch_medflies + >>> dataset = fetch_medflies() + >>> X = dataset['data'][:50] - Then we decide the interval we want to consider (in our case (5.0,7.5)) + Then we decide the level we want to consider (in our case 40) and call the function with the dataset. The output will be the number of - times each curve cross the interval (5.0,7.5) growing. - >>> from skfda.misc.feature_construction import number_up_crossings - >>> number_up_crossings(X, [(5.0,7.5)]) - array([[1, 20, 69, 64, 42, 33, 14, 3, 35, 31, 0, 4, 67, 6, 12, 16, 22, 1, - 25, 30, 2, 27, 61, 0, 11, 20, 3, 36, 28, 1, 67, 36, 12, 29, 2, - 16, 25, 1, 24, 57, 65, 26, 20, 18, 43, 0, 35, 40, 0, 2, 56, 4, - 21, 28, 1, 0, 19, 24, 1, 2, 8, 63, 0, 2, 3, 3, 0, 8, 3, 2, 10, - 62, 72, 19, 36, 46, 0, 1, 2, 18, 1, 10, 67, 60, 20, 21, 23, 12, - 3, 30, 21, 1, 57, 64, 15, 4, 4, 17, 0, 2, 31, 0, 5, 24, 56, 8, - 11, 14, 17, 1, 25, 1, 3, 61, 10, 33, 17, 1, 12, 18, 0, 2, 57, 4, - 6, 5, 2, 0, 7, 17, 4, 23, 60, 62, 2, 19, 21, 0, 42, 28, 0, 10, - 29, 74, 34, 29, 7, 0, 25, 23, 0, 15, 19, 1, 43, 1, 11, 9, 4, 0, - 0, 2, 1, 54, 55, 14, 14, 6, 1, 24, 20, 2, 27, 55, 62, 32, 26, 24, - 37, 0, 26, 28, 1, 3, 41, 64, 8, 6, 27, 12, 1, 5, 16, 0, 0, 61, - 62, 1, 3, 7]], dtype=object) + times each curve cross the level 40 growing. + >>> from skfda.exploratory.stats import number_up_crossings + >>> import numpy as np + >>> number_up_crossings(X, np.asarray([40])) + array([[[6], + [3], + [7], + [7], + [3], + [4], + [5], + [7], + [4], + [6], + [4], + [4], + [5], + [6], + [0], + [5], + [1], + [6], + [0], + [7], + [0], + [6], + [2], + [5], + [6], + [5], + [8], + [4], + [3], + [7], + [1], + [3], + [0], + [5], + [2], + [7], + [2], + [5], + [5], + [5], + [4], + [4], + [1], + [2], + [3], + [5], + [3], + [3], + [5], + [2]]]) """ curves = data.data_matrix - if curves.shape[2] != len(intervals): - raise ValueError( - "Sequence of intervals should have the " - + "same number of dimensions as the data samples", - ) - transformed_counters = [] - for index, interval in enumerate(intervals): - a, b = interval - if b < a: - raise ValueError( - "Interval limits (a,b) should satisfy a <= b. " - + str(interval) + " doesn't", - ) - curves_counters = [] - for c in curves: - counter = _n_curve_crossings(c, index, interval) - curves_counters = curves_counters + [counter] - transformed_counters = transformed_counters + [curves_counters] + distances = np.asarray([ + level - curves + for level in levels + ]) + + points_greater = distances >= 0 + points_smaller = distances <= 0 + points_smaller_rotated = np.roll(points_smaller, -1, axis=2) - return np.asarray(transformed_counters, dtype=list) + return np.sum( + points_greater & points_smaller_rotated, + axis=2, + ) def moments_of_norm( @@ -378,7 +378,7 @@ def moments_of_norm( Then we call the function with the dataset. >>> import numpy as np - >>> from skfda.misc.feature_construction import moments_of_norm + >>> from skfda.exploratory.stats import moments_of_norm >>> np.around(moments_of_norm(X), decimals=2) array([[ 4.69, 4.06], [ 6.15, 3.99], @@ -455,7 +455,7 @@ def moments_of_process( Then we call the function with the dataset. >>> import numpy as np - >>> from skfda.misc.feature_construction import moments_of_process + >>> from skfda.exploratory.stats import moments_of_process >>> np.around(moments_of_process(X), decimals=2) array([[ 5.2430e+01, 2.2500e+00], [ 7.7800e+01, 2.9200e+00], diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index b0f8a482d..e1dc9c675 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -367,16 +367,10 @@ def _inner_product_fdatagrid( [0, 1], ) - integrand = d1 * d2 + integrand = arg1 * arg2 + return integrand.integrate() + - for g in arg1.grid_points[::-1]: - integrand = scipy.integrate.simps( - integrand, - x=g, - axis=-2, - ) - - return np.sum(integrand, axis=-1) @inner_product.register(FDataBasis, FDataBasis) diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py index 5369b05ee..986f75ea5 100644 --- a/skfda/misc/feature_construction/__init__.py +++ b/skfda/misc/feature_construction/__init__.py @@ -1,9 +1,2 @@ """Feature construction.""" from ._evaluation_trasformer import EvaluationTransformer -from ._functional_transformers import ( - local_averages, - moments_of_norm, - moments_of_process, - number_up_crossings, - occupation_measure, -) diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/misc/feature_construction/_evaluation_trasformer.py index e8ba5fbea..b5c7dd498 100644 --- a/skfda/misc/feature_construction/_evaluation_trasformer.py +++ b/skfda/misc/feature_construction/_evaluation_trasformer.py @@ -1,7 +1,7 @@ """Evaluation Transformer Module.""" from __future__ import annotations -from typing import Optional, Union, overload +from typing import Optional, TypeVar, Union, overload import numpy as np from sklearn.base import BaseEstimator, TransformerMixin @@ -9,14 +9,18 @@ from typing_extensions import Literal from ...representation._functional_data import FData -from ...representation._typing import ArrayLike, GridPointsLike +from ...representation._typing import ArrayLike, GridPointsLike, NDArrayInt from ...representation.extrapolation import ExtrapolationLike from ...representation.grid import FDataGrid +Input = TypeVar("Input") +Output = TypeVar("Output") +Target = TypeVar("Target", bound=NDArrayInt) + class EvaluationTransformer( BaseEstimator, # type:ignore - TransformerMixin, # type:ignore + TransformerMixin[Input, Output, Target], ): r""" Transformer returning the evaluations of FData objects as a matrix. @@ -130,16 +134,6 @@ def fit( X: FData, y: None = None, ) -> EvaluationTransformer: - """ - Fit the model by doing some checkings. - - Args: - X: FData with the training data. - y: Target values of shape = (n_samples). By default is None - - Returns: - self - """ if self.eval_points is None and not isinstance(X, FDataGrid): raise ValueError( "If no eval_points are passed, the functions " @@ -155,16 +149,6 @@ def transform( X: FData, y: None = None, ) -> np.ndarray: - """ - Transform the provided data using the already fitted transformer. - - Args: - X: FDataGrid with the test samples. - y: Target values of shape = (n_samples). By default is None - - Returns: - Array of shape (n_samples, G) - """ check_is_fitted(self, '_is_fitted') if self.eval_points is None: diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index d601db50f..6374ed709 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -667,7 +667,8 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """Integration of the FData object. + """ + Integration of the FData object. Args: interval: domain range where we want to integrate. diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 8e9af2f12..4e607e1eb 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -361,20 +361,27 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """ - Integration for the FDataBasis object. + """Examples. + - It is done on the domain range that is passed as argument. - Args: - interval: domain range where we want to integrate. - By default is None as we integrate on the whole domain. + We first create the data basis. + >>> from skfda.representation.basis import FDataBasis, Monomial + >>> basis = Monomial(n_basis=4) + >>> coefficients = [1, 1, 3, .5] + >>> fdata = FDataBasis(basis, coefficients) + + Then we can integrate on the whole domain. + >>> fdata.integrate() + array([[ 2.625]]) + + Or we can do it on a given domain. + >>> fdata.integrate(interval = ((0.5, 1),)) + array([[ 1.8671875]]) - Returns: - ndarray of shape (n_samples, n_dimensions)\ - with the integrated data. """ if interval is None: interval = self.basis.domain_range + integrated = nquad_vec( self, interval, diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 00f8e2cda..ee9536526 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -475,29 +475,22 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """ - Integration of the FDataGrid object. - - It is done on the domain range that is passed as argument. - Args: - interval: domain range where we want to integrate. - By default is None as we integrate on the whole domain. - Returns: - ndarray of with the integrated data. - Examples: - Integration on the whole domain + """Examples. + + Integration on the whole domain. >>> fdata = FDataGrid([1,2,4,5,8], range(5)) >>> fdata.integrate() array([ 15.]) - """ if interval is not None: - self.restrict(interval) + data = self.restrict(interval) + else: + data = self - integrand = self.data_matrix + integrand = data.data_matrix - for g in self.grid_points[::-1]: + for g in data.grid_points[::-1]: integrand = scipy.integrate.simps( integrand, x=g, From 9eb402d080abbdc18cc394d2a3f1810521400811 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 23 Dec 2021 22:41:13 +0100 Subject: [PATCH 026/400] Moments transformers --- .../stats/_functional_transformers.py | 347 +----------------- 1 file changed, 1 insertion(+), 346 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 00e10f98c..c94ee6a13 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -3,355 +3,10 @@ from __future__ import annotations from functools import reduce -from typing import Tuple, Union import numpy as np -from ...representation import FDataBasis, FDataGrid - - -def local_averages( - data: Union[FDataGrid, FDataBasis], - n_intervals: int, -) -> np.ndarray: - r""" - Calculate the local averages of a given data. - - Take functional data as a grid or a basis and performs - the following map: - - .. math:: - f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ - f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt - - where {T_1,\dots,T_p} are disjoint intervals of the interval [a,b] - - It is calculated for a given number of intervals, - which are of equal sizes. - Args: - data: FDataGrid or FDataBasis where we want to - calculate the local averages. - n_intervals: number of intervals we want to consider. - Returns: - ndarray of shape (n_intervals, n_samples, n_dimensions) - with the transformed data for FDataBasis and (n_intervals, n_samples) - for FDataGrid. - - Example: - - We import the Berkeley Growth Study dataset. - We will use only the first 30 samples to make the - example easy. - >>> from skfda.datasets import fetch_growth - >>> dataset = fetch_growth(return_X_y=True)[0] - >>> X = dataset[:30] - - Then we decide how many intervals we want to consider (in our case 2) - and call the function with the dataset. - >>> import numpy as np - >>> from skfda.exploratory.stats import local_averages - >>> np.around(local_averages(X, 2), decimals=2) - array([[ 993.98, 950.82, 911.93, 946.44, 887.3 , 930.18, - 927.89, 959.72, 928.14, 1002.57, 953.22, 971.53, - 947.54, 976.26, 988.16, 974.07, 943.67, 965.36, - 925.48, 931.64, 932.47, 922.56, 927.99, 908.83, - 930.23, 933.65, 980.25, 919.39, 1013.98, 940.23], - [ 1506.69, 1339.79, 1317.25, 1392.53, 1331.65, 1340.17, - 1320.15, 1436.71, 1310.51, 1482.64, 1371.34, 1446.15, - 1394.84, 1445.87, 1416.5 , 1434.16, 1418.19, 1421.35, - 1354.89, 1383.46, 1323.45, 1343.07, 1360.87, 1325.57, - 1342.55, 1389.99, 1379.43, 1301.34, 1517.04, 1374.91]]) - """ - domain_range = data.domain_range - - left, right = domain_range[0] - interval_size = (right - left) / n_intervals - integrated_data = [] - for i in np.arange(left, right, interval_size): - interval = (i, i + interval_size) - integrated_data = integrated_data + [ - data.integrate(interval=(interval,)), - ] - return np.asarray(integrated_data) - - -def _calculate_curve_occupation_( - curve_y_coordinates: np.ndarray, - curve_x_coordinates: np.ndarray, - interval: Tuple, -) -> np.ndarray: - y1, y2 = interval - first_x_coord = 0 - last_x_coord = 0 - time_x_coord_counter = 0 - inside_interval = False - - for j, y_coordinate in enumerate(curve_y_coordinates[1:]): - y_coordinate = y_coordinate[0] - - if y1 <= y_coordinate <= y2 and not inside_interval: - inside_interval = True - first_x_coord = curve_x_coordinates[j][0] - last_x_coord = curve_x_coordinates[j][0] - elif y1 <= y_coordinate <= y2: - last_x_coord = curve_x_coordinates[j][0] - elif inside_interval: - inside_interval = False - time_x_coord_counter += last_x_coord - first_x_coord - first_x_coord = 0 - last_x_coord = 0 - - return np.array([[time_x_coord_counter]]) - - -def occupation_measure( - data: Union[FDataGrid, FDataBasis], - intervals: np.ndarray, - *, - n_points: Union[int, None] = None, -) -> np.ndarray: - r""" - Calculate the occupation measure of a grid. - - It performs the following map. - ..math: - :math:`f_1(X) = |t: X(t)\in T_p|,\dots,|t: X(t)\in T_p|` - - where :math:`{T_1,\dots,T_p}` are disjoint intervals in - :math:`\mathbb{R}` and | | stands for the Lebesgue measure. - - Args: - data: FDataGrid or FDataBasis where we want to calculate - the occupation measure. - intervals: ndarray of tuples containing the - intervals we want to consider. The shape should be - (n_sequences,2) - n_points: Number of points to evaluate in the domain. - By default will be used the points defined on the FDataGrid. - On a FDataBasis this value should be specified. - Returns: - ndarray of shape (n_intervals, n_samples) - with the transformed data. - - Example: - We will create the FDataGrid that we will use to extract - the occupation measure - >>> from skfda.representation import FDataGrid - >>> import numpy as np - >>> t = np.linspace(0, 10, 100) - >>> fd_grid = FDataGrid( - ... data_matrix=[ - ... t, - ... 2 * t, - ... np.sin(t), - ... ], - ... grid_points=t, - ... ) - - Finally we call to the occupation measure function with the - intervals that we want to consider. In our case (0.0, 1.0) - and (2.0, 3.0). We need also to specify the number of points - we want that the function takes into account to interpolate. - We are going to use 501 points. - >>> from skfda.exploratory.stats import occupation_measure - >>> np.around( - ... occupation_measure( - ... fd_grid, - ... [(0.0, 1.0), (2.0, 3.0)], - ... n_points=501, - ... ), - ... decimals=2, - ... ) - array([[[ 0.98], - [ 0.48], - [ 6.25]], - - [[ 0.98], - [ 0.48], - [ 0. ]]]) - - """ - if n_points is None: - if isinstance(data, FDataBasis): - raise ValueError( - "Number of points to consider, should be given " - + " as an argument for a FDataBasis. Instead None was passed.", - ) - else: - grid = data.grid_points - else: - lower_functional_limit, upper_functional_limit = data.domain_range[0] - domain_size = upper_functional_limit - lower_functional_limit - - if isinstance(data, FDataGrid): - time_x_coord_cumulative = np.empty((0, data.data_matrix.shape[0], 1)) - else: - time_x_coord_cumulative = np.empty((0, data.coefficients.shape[0], 1)) - - for interval in intervals: # noqa: WPS426 - - y1, y2 = interval - if y2 < y1: - raise ValueError( - "Interval limits (a,b) should satisfy a <= b. " - + str(interval) + " doesn't", - ) - - function_x_coordinates = np.empty((1, 1)) - if n_points is None: - function_x_coordinates = reduce( - lambda a, b: np.concatenate( - ( - a, - np.array([[b]]), - ), - ), - grid[0], - function_x_coordinates, - )[1:] - else: - for x_coordinate in np.arange( - lower_functional_limit, - upper_functional_limit, - domain_size / n_points, - ): - function_x_coordinates = np.concatenate( - (function_x_coordinates, np.array([[x_coordinate]])), - ) - - if n_points is None: - function_y_coordinates = data.data_matrix - else: - function_y_coordinates = data( - function_x_coordinates, - ) - - time_x_coord_interval = np.empty((0, 1)) - for curve_y_coordinates in function_y_coordinates: - - time_x_coord_count = _calculate_curve_occupation_( # noqa: WPS317 - curve_y_coordinates, - function_x_coordinates, - (y1, y2), - ) - time_x_coord_interval = np.concatenate( - (time_x_coord_interval, time_x_coord_count), - ) - - time_x_coord_cumulative = np.concatenate( - (time_x_coord_cumulative, np.array([time_x_coord_interval])), - ) - - return time_x_coord_cumulative - - -def number_up_crossings( - data: FDataGrid, - levels: np.ndarray, -) -> np.ndarray: - r""" - Calculate the number of up crossings to a level of a FDataGrid. - - Let f_1(X) = N_i, where N_i is the number of up crossings of X - to a level c_i \in \mathbb{R}, i = 1,\dots,p. - - Recall that the process X(t) is said to have an up crossing of c - at :math:`t_0 > 0` if for some :math:`\epsilon >0`, X(t) $\leq$ - c if t :math:'\in (t_0 - \epsilon, t_0) and X(t) $\geq$ c if - :math:`t\in (t_0, t_0+\epsilon)`. - - If the trajectories are differentiable, then - :math:`N_i = card\{t \in[a,b]: X(t) = c_i, X' (t) > 0\}.` - - Args: - data: FDataGrid where we want to calculate - the number of up crossings. - levels: sequence of numbers including the levels - we want to consider for the crossings. - Returns: - ndarray of shape (n_levels, n_samples)\ - with the values of the counters. - - Example: - - We import the Medflies dataset and for simplicity we use - the first 50 samples. - >>> from skfda.datasets import fetch_medflies - >>> dataset = fetch_medflies() - >>> X = dataset['data'][:50] - - Then we decide the level we want to consider (in our case 40) - and call the function with the dataset. The output will be the number of - times each curve cross the level 40 growing. - >>> from skfda.exploratory.stats import number_up_crossings - >>> import numpy as np - >>> number_up_crossings(X, np.asarray([40])) - array([[[6], - [3], - [7], - [7], - [3], - [4], - [5], - [7], - [4], - [6], - [4], - [4], - [5], - [6], - [0], - [5], - [1], - [6], - [0], - [7], - [0], - [6], - [2], - [5], - [6], - [5], - [8], - [4], - [3], - [7], - [1], - [3], - [0], - [5], - [2], - [7], - [2], - [5], - [5], - [5], - [4], - [4], - [1], - [2], - [3], - [5], - [3], - [3], - [5], - [2]]]) - """ - curves = data.data_matrix - - distances = np.asarray([ - level - curves - for level in levels - ]) - - points_greater = distances >= 0 - points_smaller = distances <= 0 - points_smaller_rotated = np.roll(points_smaller, -1, axis=2) - - return np.sum( - points_greater & points_smaller_rotated, - axis=2, - ) +from ...representation import FDataGrid def moments_of_norm( From 0e19b0ebe880d968cf167b036c408075b1a18ed5 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 23 Dec 2021 22:42:15 +0100 Subject: [PATCH 027/400] Delete moment transformers from branch --- .../stats/_functional_transformers.py | 154 ------------------ 1 file changed, 154 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 00e10f98c..c99fa2762 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -352,157 +352,3 @@ def number_up_crossings( points_greater & points_smaller_rotated, axis=2, ) - - -def moments_of_norm( - data: FDataGrid, -) -> np.ndarray: - r""" - Calculate the moments of the norm of the process of a FDataGrid. - - It performs the following map: - :math:`f_1(X)=\mathbb{E}(||X||),\dots,f_p(X)=\mathbb{E}(||X||^p)`. - - Args: - data: FDataGrid where we want to calculate - the moments of the norm of the process. - Returns: - ndarray of shape (n_dimensions, n_samples)\ - with the values of the moments. - - Example: - - We import the Canadian Weather dataset - >>> from skfda.datasets import fetch_weather - >>> X = fetch_weather(return_X_y=True)[0] - - Then we call the function with the dataset. - >>> import numpy as np - >>> from skfda.exploratory.stats import moments_of_norm - >>> np.around(moments_of_norm(X), decimals=2) - array([[ 4.69, 4.06], - [ 6.15, 3.99], - [ 5.51, 4.04], - [ 6.81, 3.46], - [ 5.23, 3.29], - [ 5.26, 3.09], - [ -5.06, 2.2 ], - [ 3.1 , 2.46], - [ 2.25, 2.55], - [ 4.08, 3.31], - [ 4.12, 3.04], - [ 6.13, 2.58], - [ 5.81, 2.5 ], - [ 7.27, 2.14], - [ 7.31, 2.62], - [ 2.46, 1.93], - [ 2.47, 1.4 ], - [ -0.15, 1.23], - [ -7.09, 1.12], - [ 2.75, 1.02], - [ 0.68, 1.11], - [ -3.41, 0.99], - [ 2.26, 1.27], - [ 3.99, 1.1 ], - [ 8.75, 0.74], - [ 9.96, 3.16], - [ 9.62, 2.33], - [ 3.84, 1.67], - [ 7. , 7.1 ], - [ -0.85, 0.74], - [ -4.79, 0.9 ], - [ -5.02, 0.73], - [ -9.65, 1.14], - [ -9.24, 0.71], - [-16.52, 0.39]]) - """ - curves = data.data_matrix - norms = np.empty((0, curves.shape[2])) - for c in curves: # noqa: WPS426 - x, y = c.shape - curve_norms = [] - for i in range(0, y): # noqa: WPS426 - curve_norms = curve_norms + [ - reduce(lambda a, b: a + b[i], c, 0) / x, - ] - norms = np.concatenate((norms, [curve_norms])) - return np.array(norms) - - -def moments_of_process( - data: FDataGrid, -) -> np.ndarray: - r""" - Calculate the moments of the process of a FDataGrid. - - It performs the following map: - .. math:: - f_1(X)=\int_a^b X(t,\omega)dP(\omega),\dots,f_p(X)=\int_a^b \\ - X^p(t,\omega)dP(\omega). - - Args: - data: FDataGrid where we want to calculate - the moments of the process. - Returns: - ndarray of shape (n_dimensions, n_samples)\ - with the values of the moments. - - Example: - - We import the Canadian Weather dataset - >>> from skfda.datasets import fetch_weather - >>> X = fetch_weather(return_X_y=True)[0] - - Then we call the function with the dataset. - >>> import numpy as np - >>> from skfda.exploratory.stats import moments_of_process - >>> np.around(moments_of_process(X), decimals=2) - array([[ 5.2430e+01, 2.2500e+00], - [ 7.7800e+01, 2.9200e+00], - [ 7.2150e+01, 2.6900e+00], - [ 5.1250e+01, 2.1200e+00], - [ 8.9310e+01, 1.6000e+00], - [ 1.0504e+02, 1.5300e+00], - [ 1.6209e+02, 1.5000e+00], - [ 1.4143e+02, 1.4500e+00], - [ 1.4322e+02, 1.1500e+00], - [ 1.2593e+02, 1.6500e+00], - [ 1.1139e+02, 1.4000e+00], - [ 1.2215e+02, 1.1600e+00], - [ 1.2593e+02, 1.0500e+00], - [ 9.3560e+01, 1.0300e+00], - [ 9.3080e+01, 1.2200e+00], - [ 1.2836e+02, 1.2700e+00], - [ 1.8069e+02, 1.2000e+00], - [ 1.8907e+02, 9.2000e-01], - [ 1.9644e+02, 5.6000e-01], - [ 1.5856e+02, 9.4000e-01], - [ 1.7739e+02, 8.1000e-01], - [ 2.3041e+02, 4.5000e-01], - [ 1.1928e+02, 1.2700e+00], - [ 8.4900e+01, 1.0200e+00], - [ 7.9470e+01, 2.0000e-01], - [ 2.6700e+01, 3.5200e+00], - [ 2.1680e+01, 3.0900e+00], - [ 7.7390e+01, 4.9000e-01], - [ 1.8950e+01, 9.9500e+00], - [ 1.2783e+02, 2.5000e-01], - [ 2.5206e+02, 2.9000e-01], - [ 2.5201e+02, 2.7000e-01], - [ 1.6401e+02, 4.9000e-01], - [ 2.5490e+02, 2.0000e-01], - [ 1.8772e+02, 1.2000e-01]]) - """ - norm = moments_of_norm(data) - curves = data.data_matrix - moments = np.empty((0, curves.shape[2])) - for i, c in enumerate(curves): # noqa: WPS426 - x, y = c.shape - curve_moments = [] - for j in range(0, y): # noqa: WPS426 - curve_moments = curve_moments + [ - reduce(lambda a, b: a + ((b[j] - norm[i][j]) ** 2), c, 0) / x, - ] - moments = np.concatenate((moments, [curve_moments])) - - return moments From 6c661b2b737dbcf3810829aa0184e145b1216740 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 23 Dec 2021 22:49:27 +0100 Subject: [PATCH 028/400] Evaluation transformer correction --- skfda/exploratory/stats/__init__.py | 2 -- skfda/misc/feature_construction/_evaluation_trasformer.py | 3 ++- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 168dc9c29..2e401b15e 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,8 +1,6 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean from ._functional_transformers import ( local_averages, - moments_of_norm, - moments_of_process, number_up_crossings, occupation_measure, ) diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/misc/feature_construction/_evaluation_trasformer.py index b5c7dd498..c87430ec2 100644 --- a/skfda/misc/feature_construction/_evaluation_trasformer.py +++ b/skfda/misc/feature_construction/_evaluation_trasformer.py @@ -4,10 +4,11 @@ from typing import Optional, TypeVar, Union, overload import numpy as np -from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.base import BaseEstimator from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal +from ..._utils import TransformerMixin from ...representation._functional_data import FData from ...representation._typing import ArrayLike, GridPointsLike, NDArrayInt from ...representation.extrapolation import ExtrapolationLike From ebb318d54a0125cb7a2b185debd7d861b47a8b4c Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 23 Dec 2021 23:08:34 +0100 Subject: [PATCH 029/400] Changes on evaluation transformer --- skfda/exploratory/stats/__init__.py | 8 +------- skfda/misc/feature_construction/_evaluation_trasformer.py | 3 ++- 2 files changed, 3 insertions(+), 8 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 168dc9c29..def4682e4 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,11 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - moments_of_norm, - moments_of_process, - number_up_crossings, - occupation_measure, -) +from ._functional_transformers import moments_of_norm, moments_of_process from ._stats import ( cov, depth_based_median, diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/misc/feature_construction/_evaluation_trasformer.py index b5c7dd498..c87430ec2 100644 --- a/skfda/misc/feature_construction/_evaluation_trasformer.py +++ b/skfda/misc/feature_construction/_evaluation_trasformer.py @@ -4,10 +4,11 @@ from typing import Optional, TypeVar, Union, overload import numpy as np -from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.base import BaseEstimator from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal +from ..._utils import TransformerMixin from ...representation._functional_data import FData from ...representation._typing import ArrayLike, GridPointsLike, NDArrayInt from ...representation.extrapolation import ExtrapolationLike From 0c45ae06020fb6f79b65cd9d8cb29f4e7ba36f2d Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 26 Dec 2021 12:03:21 +0100 Subject: [PATCH 030/400] Occupation measure corrected --- .../stats/_functional_transformers.py | 144 +++++++----------- 1 file changed, 53 insertions(+), 91 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index c99fa2762..f8d7b452d 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,7 +2,6 @@ from __future__ import annotations -from functools import reduce from typing import Tuple, Union import numpy as np @@ -75,33 +74,40 @@ def local_averages( return np.asarray(integrated_data) -def _calculate_curve_occupation_( +def _calculate_curves_occupation_( curve_y_coordinates: np.ndarray, curve_x_coordinates: np.ndarray, interval: Tuple, ) -> np.ndarray: y1, y2 = interval - first_x_coord = 0 - last_x_coord = 0 - time_x_coord_counter = 0 - inside_interval = False - for j, y_coordinate in enumerate(curve_y_coordinates[1:]): - y_coordinate = y_coordinate[0] + # Reshape original curves so they have one dimension less + new_shape = curve_y_coordinates.shape[1::-1] + curve_y_coordinates = curve_y_coordinates.reshape( + new_shape[::-1], + ) + + # Calculate interval sizes on the X axis + x_rotated = np.roll(curve_x_coordinates, 1) + intervals_x_axis = curve_x_coordinates - x_rotated + + # Calculate which points are inside the interval given (y1,y2) on Y axis + greater_than_y1 = curve_y_coordinates >= y1 + less_than_y2 = curve_y_coordinates <= y2 + inside_interval_bools = greater_than_y1 & less_than_y2 - if y1 <= y_coordinate <= y2 and not inside_interval: - inside_interval = True - first_x_coord = curve_x_coordinates[j][0] - last_x_coord = curve_x_coordinates[j][0] - elif y1 <= y_coordinate <= y2: - last_x_coord = curve_x_coordinates[j][0] - elif inside_interval: - inside_interval = False - time_x_coord_counter += last_x_coord - first_x_coord - first_x_coord = 0 - last_x_coord = 0 + # Correct booleans so they are not repeated + bools_interval = np.roll( + inside_interval_bools, 1, axis=1, + ) & inside_interval_bools - return np.array([[time_x_coord_counter]]) + # Calculate intervals on X axis where the points are inside Y axis interval + intervals_x_inside = np.multiply(bools_interval, intervals_x_axis) + + # Delete first element of each interval as it will be a negative number + intervals_x_inside = np.delete(intervals_x_inside, 0, axis=1) + + return np.sum(intervals_x_inside, axis=1) def occupation_measure( @@ -162,87 +168,43 @@ def occupation_measure( ... ), ... decimals=2, ... ) - array([[[ 0.98], - [ 0.48], - [ 6.25]], - - [[ 0.98], - [ 0.48], - [ 0. ]]]) + array([[ 1. , 0.5 , 6.27], + [ 0.98, 0.48, 0. ]]) """ - if n_points is None: - if isinstance(data, FDataBasis): - raise ValueError( - "Number of points to consider, should be given " - + " as an argument for a FDataBasis. Instead None was passed.", - ) - else: - grid = data.grid_points - else: - lower_functional_limit, upper_functional_limit = data.domain_range[0] - domain_size = upper_functional_limit - lower_functional_limit - - if isinstance(data, FDataGrid): - time_x_coord_cumulative = np.empty((0, data.data_matrix.shape[0], 1)) - else: - time_x_coord_cumulative = np.empty((0, data.coefficients.shape[0], 1)) - - for interval in intervals: # noqa: WPS426 + if isinstance(data, FDataBasis) and n_points is None: + raise ValueError( + "Number of points to consider, should be given " + + " as an argument for a FDataBasis. Instead None was passed.", + ) - y1, y2 = interval + for interval_check in intervals: + y1, y2 = interval_check if y2 < y1: raise ValueError( "Interval limits (a,b) should satisfy a <= b. " - + str(interval) + " doesn't", - ) - - function_x_coordinates = np.empty((1, 1)) - if n_points is None: - function_x_coordinates = reduce( - lambda a, b: np.concatenate( - ( - a, - np.array([[b]]), - ), - ), - grid[0], - function_x_coordinates, - )[1:] - else: - for x_coordinate in np.arange( - lower_functional_limit, - upper_functional_limit, - domain_size / n_points, - ): - function_x_coordinates = np.concatenate( - (function_x_coordinates, np.array([[x_coordinate]])), - ) - - if n_points is None: - function_y_coordinates = data.data_matrix - else: - function_y_coordinates = data( - function_x_coordinates, + + str(interval_check) + " doesn't", ) - time_x_coord_interval = np.empty((0, 1)) - for curve_y_coordinates in function_y_coordinates: - - time_x_coord_count = _calculate_curve_occupation_( # noqa: WPS317 - curve_y_coordinates, - function_x_coordinates, - (y1, y2), - ) - time_x_coord_interval = np.concatenate( - (time_x_coord_interval, time_x_coord_count), - ) - - time_x_coord_cumulative = np.concatenate( - (time_x_coord_cumulative, np.array([time_x_coord_interval])), + if n_points is None: + function_x_coordinates = data.grid_points[0] + function_y_coordinates = data.data_matrix + else: + function_x_coordinates = np.arange( + data.domain_range[0][0], + data.domain_range[0][1], + (data.domain_range[0][1] - data.domain_range[0][0]) / n_points, ) + function_y_coordinates = data(function_x_coordinates) - return time_x_coord_cumulative + return np.asarray([ + _calculate_curves_occupation_( # noqa: WPS317 + function_y_coordinates, + function_x_coordinates, + interval, + ) + for interval in intervals + ]) def number_up_crossings( From 5d2475ef44d41f7a477352c0c7f67ca5426980f7 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 14 Jan 2022 19:14:36 +0100 Subject: [PATCH 031/400] Correction on local averages --- .../stats/_functional_transformers.py | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index f8d7b452d..65c33ca99 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -50,16 +50,16 @@ def local_averages( >>> import numpy as np >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, 2), decimals=2) - array([[ 993.98, 950.82, 911.93, 946.44, 887.3 , 930.18, - 927.89, 959.72, 928.14, 1002.57, 953.22, 971.53, - 947.54, 976.26, 988.16, 974.07, 943.67, 965.36, - 925.48, 931.64, 932.47, 922.56, 927.99, 908.83, - 930.23, 933.65, 980.25, 919.39, 1013.98, 940.23], - [ 1506.69, 1339.79, 1317.25, 1392.53, 1331.65, 1340.17, - 1320.15, 1436.71, 1310.51, 1482.64, 1371.34, 1446.15, - 1394.84, 1445.87, 1416.5 , 1434.16, 1418.19, 1421.35, - 1354.89, 1383.46, 1323.45, 1343.07, 1360.87, 1325.57, - 1342.55, 1389.99, 1379.43, 1301.34, 1517.04, 1374.91]]) + array([[ 116.94, 111.86, 107.29, 111.35, 104.39, 109.43, 109.16, + 112.91, 109.19, 117.95, 112.14, 114.3 , 111.48, 114.85, + 116.25, 114.6 , 111.02, 113.57, 108.88, 109.6 , 109.7 , + 108.54, 109.18, 106.92, 109.44, 109.84, 115.32, 108.16, + 119.29, 110.62], + [ 177.26, 157.62, 154.97, 163.83, 156.66, 157.67, 155.31, + 169.02, 154.18, 174.43, 161.33, 170.14, 164.1 , 170.1 , + 166.65, 168.72, 166.85, 167.22, 159.4 , 162.76, 155.7 , + 158.01, 160.1 , 155.95, 157.95, 163.53, 162.29, 153.1 , + 178.48, 161.75]]) """ domain_range = data.domain_range @@ -69,7 +69,7 @@ def local_averages( for i in np.arange(left, right, interval_size): interval = (i, i + interval_size) integrated_data = integrated_data + [ - data.integrate(interval=(interval,)), + data.integrate(interval=(interval,)) / interval_size, ] return np.asarray(integrated_data) From 813501b80143bdff0b6e0030f667626984c0fa04 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 14 Jan 2022 20:50:09 +0100 Subject: [PATCH 032/400] Fixing conflicts after merging --- skfda/misc/_math.py | 2 -- .../dim_reduction/feature_extraction/_fda_feature_union.py | 2 +- skfda/representation/_functional_data.py | 6 ++---- tests/test_fda_feature_union.py | 2 +- 4 files changed, 4 insertions(+), 8 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index e1dc9c675..1658f6984 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -369,8 +369,6 @@ def _inner_product_fdatagrid( integrand = arg1 * arg2 return integrand.integrate() - - @inner_product.register(FDataBasis, FDataBasis) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py index e772be898..24f0fc8bd 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py @@ -62,7 +62,7 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore ... DDGTransformer, ... ) >>> from skfda.exploratory.depth import ModifiedBandDepth - >>> from skfda.representation import EvaluationTransformer + >>> from skfda.misc.feature_construction import EvaluationTransformer >>> import numpy as np Finally we apply fit and transform. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 4bd284e89..1ac1b3b77 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -40,7 +40,7 @@ from .extrapolation import ExtrapolationLike, _parse_extrapolation if TYPE_CHECKING: - from . import FDataGrid, FDataBasis + from . import FDataBasis, FDataGrid from .basis import Basis T = TypeVar('T', bound='FData') @@ -668,8 +668,7 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """ - Integration of the FData object. + """Integration of the FData object. Args: interval: domain range where we want to integrate. @@ -1077,7 +1076,6 @@ def __array_ufunc__( **kwargs: Any, ) -> Any: """Prevent NumPy from converting to array just to do operations.""" - # Make normal multiplication by scalar use the __mul__ method if ufunc == np.multiply and method == "__call__" and len(inputs) == 2: if isinstance(inputs[0], np.ndarray): diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index 21a174067..2e6ca5f20 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -6,6 +6,7 @@ from pandas.testing import assert_frame_equal from skfda.datasets import fetch_growth +from skfda.misc.feature_construction import EvaluationTransformer from skfda.misc.operators import SRSF from skfda.preprocessing.dim_reduction.feature_extraction import ( FDAFeatureUnion, @@ -13,7 +14,6 @@ from skfda.preprocessing.smoothing.kernel_smoothers import ( NadarayaWatsonSmoother, ) -from skfda.representation import EvaluationTransformer class TestFDAFeatureUnion(unittest.TestCase): From 004f01751dc0859468d5cac97f170cc9cc459ec1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 28 Jan 2022 17:43:26 +0100 Subject: [PATCH 033/400] Fix several documentation errors. --- .../preprocessing/dim_reduction/fpca.rst | 24 -------- examples/plot_interpolation.py | 58 +++++++++++-------- skfda/exploratory/stats/_stats.py | 7 ++- skfda/exploratory/visualization/_baseplot.py | 1 + .../visualization/_parametric_plot.py | 1 + .../visualization/representation.py | 1 + skfda/misc/_math.py | 9 +-- skfda/representation/_functional_data.py | 11 ++-- skfda/representation/grid.py | 8 +-- tutorial/plot_getting_data.py | 7 +-- 10 files changed, 60 insertions(+), 67 deletions(-) delete mode 100644 docs/modules/preprocessing/dim_reduction/fpca.rst diff --git a/docs/modules/preprocessing/dim_reduction/fpca.rst b/docs/modules/preprocessing/dim_reduction/fpca.rst deleted file mode 100644 index 3ba0e5ad5..000000000 --- a/docs/modules/preprocessing/dim_reduction/fpca.rst +++ /dev/null @@ -1,24 +0,0 @@ -Functional Principal Component Analysis (FPCA) -============================================== - -This module provides tools to analyse functional data using FPCA. FPCA is -a common tool used to reduce dimensionality. It can be applied to a functional -data object in either a basis representation or a discretized representation. -The output of FPCA are the projections of the original sample functions into the -directions (principal components) in which most of the variance is conserved. -In multivariate PCA those directions are vectors. However, in FPCA we seek -functions that maximizes the sample variance operator, and then project our data -samples into those principal components. The number of principal components are -at most the number of original features. - -For a detailed example please view :ref:`sphx_glr_auto_examples_plot_fpca.py`, -where the process is applied to several datasets in both discretized and basis -forms. - -FPCA for functional data in both representations ----------------------------------------------------------------- - -.. autosummary:: - :toctree: autosummary - - skfda.preprocessing.dim_reduction.feature_extraction.FPCA diff --git a/examples/plot_interpolation.py b/examples/plot_interpolation.py index 9f3e03352..5cff8265c 100644 --- a/examples/plot_interpolation.py +++ b/examples/plot_interpolation.py @@ -11,13 +11,12 @@ # sphinx_gallery_thumbnail_number = 3 -import skfda -from skfda.representation.interpolation import SplineInterpolation - -from mpl_toolkits.mplot3d import axes3d - +import matplotlib.pyplot as plt import numpy as np +from mpl_toolkits.mplot3d import axes3d +import skfda +from skfda.representation.interpolation import SplineInterpolation ############################################################################## # The :class:`~skfda.representation.grid.FDataGrid` class is used for datasets @@ -26,21 +25,25 @@ # # We will construct an example dataset with two curves with 6 points of # discretization. -# -fd = skfda.datasets.make_sinusoidal_process(n_samples=2, n_features=6, - random_state=1) + +fd = skfda.datasets.make_sinusoidal_process( + n_samples=2, + n_features=6, + random_state=1, +) fig = fd.scatter() fig.legend(["Sample 1", "Sample 2"]) +plt.show() ############################################################################## # By default it is used linear interpolation, which is one of the simplest # methods of interpolation and therefore one of the least computationally # expensive, but has the disadvantage that the interpolant is not # differentiable at the points of discretization. -# fig = fd.plot() fd.scatter(fig=fig) +plt.show() ############################################################################## # The interpolation method of the FDataGrid could be changed setting the @@ -48,24 +51,26 @@ # the evaluation of the object. # # Polynomial spline interpolation could be performed using the interpolation -# :class:`~skfda.representation.interpolation.SplineInterpolation. In the +# :class:`~skfda.representation.interpolation.SplineInterpolation`. In the # following example a cubic interpolation is set. fd.interpolation = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) - +plt.show() ############################################################################## # Smooth interpolation could be performed with the attribute # ``smoothness_parameter`` of the spline interpolation. -# # Sample with noise fd_smooth = skfda.datasets.make_sinusoidal_process( - n_samples=1, n_features=30, - random_state=1, error_std=.3) + n_samples=1, + n_features=30, + random_state=1, + error_std=0.3, +) # Cubic interpolation fd_smooth.interpolation = SplineInterpolation(interpolation_order=3) @@ -73,20 +78,22 @@ fig = fd_smooth.plot(label="Cubic") # Smooth interpolation -fd_smooth.interpolation = SplineInterpolation(interpolation_order=3, - smoothness_parameter=1.5) +fd_smooth.interpolation = SplineInterpolation( + interpolation_order=3, + smoothness_parameter=1.5, +) fd_smooth.plot(fig=fig, label="Cubic smoothed") fd_smooth.scatter(fig=fig) fig.legend() +plt.show() ############################################################################## # Sometimes our samples are required to be monotone, in these cases it is # possible to use monotone cubic interpolation with the attribute # ``monotone``. A piecewise cubic hermite interpolating polynomial (PCHIP) # will be used. -# fd = fd[1] @@ -96,12 +103,15 @@ fig = fd_monotone.plot(linestyle='--', label="cubic") -fd_monotone.interpolation = SplineInterpolation(interpolation_order=3, - monotone=True) +fd_monotone.interpolation = SplineInterpolation( + interpolation_order=3, + monotone=True, +) fd_monotone.plot(fig=fig, label="PCHIP") fd_monotone.scatter(fig=fig, c='C1') fig.legend() +plt.show() ############################################################################## # All the interpolations will work regardless of the dimension of the image, @@ -110,7 +120,6 @@ # For the next examples it is constructed a surface, # :math:`x_i: \mathbb{R}^2 \longmapsto \mathbb{R}`. By default, as in # unidimensional samples, it is used linear interpolation. -# X, Y, Z = axes3d.get_test_data(1.2) data_matrix = [Z.T] @@ -121,6 +130,7 @@ fig = fd.plot() fd.scatter(fig=fig) +plt.show() ############################################################################## # In the following figure it is shown the result of the cubic interpolation @@ -128,18 +138,19 @@ # # The degree of the interpolation polynomial does not have to coincide in both # directions, for example, cubic interpolation in the first -# component and quadratic in the second one could be defined using a tuple +# component and quadratic in the second one could be defined using a tuple # with the values (3,2). -# fd.interpolation = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) +plt.show() ############################################################################## # The following table shows the interpolation methods available by the class -# :class:`SplineInterpolation` depending on the domain dimension. +# :class:`~skfda.representation.interpolation.SplineInterpolation` depending +# on the domain dimension. # # +------------------+--------+----------------+----------+-------------+ # | Domain dimension | Linear | Up to degree 5 | Monotone | Smoothing | @@ -150,4 +161,3 @@ # +------------------+--------+----------------+----------+-------------+ # | 3 or more | ✔ | ✖ | ✖ | ✖ | # +------------------+--------+----------------+----------+-------------+ -# diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index bc946703a..e9d3be2b2 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -173,7 +173,10 @@ def geometric_median( \underset{y \in L(\mathcal{T})}{\arg \min} \sum_{i=1}^N \left \| x_i-y \right \| - It uses the corrected Weiszfeld algorithm to compute the median. + The geometric median in the functional case is also described in + :footcite:`gervini_2008_estimation`. + Instead of the proposed algorithm, however, the current implementation + uses the corrected Weiszfeld algorithm to compute the median. Args: X: Object containing different samples of a @@ -200,8 +203,6 @@ def geometric_median( References: .. footbibliography:: - gervini_2008_estimation - """ weights = np.full(len(X), 1 / len(X)) median = _weighted_average(X, weights) diff --git a/skfda/exploratory/visualization/_baseplot.py b/skfda/exploratory/visualization/_baseplot.py index 54f26628b..45742bc58 100644 --- a/skfda/exploratory/visualization/_baseplot.py +++ b/skfda/exploratory/visualization/_baseplot.py @@ -252,6 +252,7 @@ def hover(self, event: MouseEvent) -> None: Callback method that activates the annotation when hovering a specific point in a graph. The annotation is a description of the point containing its coordinates. + Args: event: event object containing the artist of the point hovered. diff --git a/skfda/exploratory/visualization/_parametric_plot.py b/skfda/exploratory/visualization/_parametric_plot.py index 6ba990bf8..6f3e91144 100644 --- a/skfda/exploratory/visualization/_parametric_plot.py +++ b/skfda/exploratory/visualization/_parametric_plot.py @@ -29,6 +29,7 @@ class ParametricPlot(BasePlot): two different functions as coordinates, this can be done giving one FData, with domain 1 and codomain 2, or giving two FData, both of them with domain 1 and codomain 1. + Args: fdata1: functional data set that we will use for the graph. If it has a dim_codomain = 1, the fdata2 will be needed. diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index c9c12052d..ee64535c0 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -119,6 +119,7 @@ class GraphPlot(BasePlot): a group of colors for the representations. Besides, we can use a list of variables (depths, scalar regression targets...) can be used as an argument to display the functions wtih a gradient of colors. + Args: fdata: functional data set that we want to plot. gradient_criteria: list of real values used to determine the color diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index b0f8a482d..68c98d169 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -263,9 +263,10 @@ def inner_product( >>> inner_product(array1, array2) array([32, 9]) - The inner product of the :math:'f(x) = x` and the constant - :math:`y=1` defined over the interval [0,1] is the area of the - triangle delimited by the the lines y = 0, x = 1 and y = x; 0.5. + The inner product of the :math:`f(x) = x` and the constant + :math:`y=1` defined over the interval :math:`[0,1]` is the area of + the triangle delimited by the the lines :math:`y = 0`, :math:`x = 1` + and :math:`y = x`, that is, :math:`0.5`. >>> import skfda >>> @@ -588,7 +589,7 @@ def cosine_similarity( >>> cosine_similarity(array1, array2) array([ 0.97463185, 0.96490128]) - The cosine similarity of the :math:'f(x) = x` and the constant + The cosine similarity of the :math:`f(x) = x` and the constant :math:`y=1` defined over the interval [0,1] is the area of the triangle delimited by the the lines y = 0, x = 1 and y = x; 0.5, multiplied by :math:`\sqrt{3}`. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 9f25ac2cd..4376f5078 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -24,6 +24,7 @@ import numpy as np import pandas.api.extensions +from matplotlib.figure import Figure from typing_extensions import Literal from .._utils import _evaluate_grid, _reshape_eval_points, _to_grid_points @@ -769,15 +770,17 @@ def shift( return shifted - def plot(self, *args: Any, **kwargs: Any) -> Any: + def plot(self, *args: Any, **kwargs: Any) -> Figure: """Plot the FDatGrid object. Args: - args: positional arguments for :func:`plot_graph`. - kwargs: keyword arguments for :func:`plot_graph`. + args: Positional arguments to be passed to the class + :class:`~skfda.exploratory.visualization.representation.GraphPlot`. + kwargs: Keyword arguments to be passed to the class + :class:`~skfda.exploratory.visualization.representation.GraphPlot`. Returns: - fig (figure object): figure object in which the graphs are plotted. + Figure object in which the graphs are plotted. """ from ..exploratory.visualization.representation import GraphPlot diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 2a1ef1092..75b16c5a3 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -803,10 +803,10 @@ def scatter(self, *args: Any, **kwargs: Any) -> Figure: """Scatter plot of the FDatGrid object. Args: - args: positional arguments to be passed to the - matplotlib.pyplot.scatter function. - kwargs: keyword arguments to be passed to the - matplotlib.pyplot.scatter function. + args: Positional arguments to be passed to the class + :class:`~skfda.exploratory.visualization.representation.ScatterPlot`. + kwargs: Keyword arguments to be passed to the class + :class:`~skfda.exploratory.visualization.representation.ScatterPlot`. Returns: Figure object in which the graphs are plotted. diff --git a/tutorial/plot_getting_data.py b/tutorial/plot_getting_data.py index 4ab4b4d33..ba424405d 100644 --- a/tutorial/plot_getting_data.py +++ b/tutorial/plot_getting_data.py @@ -36,8 +36,10 @@ # number of domain dimensions (that is, one for curves, two for surfaces...). # Each of its elements is a 1D numpy :class:`~numpy.ndarray` containing the # grid points for that particular dimension, -# .. math:: +# +# .. math:: # ((t_1, \ldots, t_{M_i}))_{i=1}^p, +# # where :math:`M_i` is the number of measurement points for each "argument" # or domain coordinate of the function :math:`i` and :math:`p` is the domain # dimension. @@ -54,7 +56,6 @@ # :class:`~numpy.ndarray` when necessary. # # .. note:: -# # The grid points can be omitted, # and in that case their number is inferred from the dimensions of # ``data_matrix`` and they are automatically assigned as equispaced points @@ -165,7 +166,6 @@ ############################################################################## # .. note:: -# # :class:`Pandas DataFrames ` are also popular as # datasets containers in the Python scientific ecosystem. If you have # data in a Pandas DataFrame, you can extract its content as a Numpy @@ -226,7 +226,6 @@ ############################################################################## # .. note:: -# # Functional data objects from some packages, such as # `fda.usc `_ # are automatically recognized as such and converted to From f7cfca31ae9da75d0fe704e401ab337435900c3d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 31 Jan 2022 03:46:36 +0100 Subject: [PATCH 034/400] Update smoothing validation notation. --- skfda/preprocessing/smoothing/validation.py | 28 ++++++++++----------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py index 411f547d7..4fffefda4 100644 --- a/skfda/preprocessing/smoothing/validation.py +++ b/skfda/preprocessing/smoothing/validation.py @@ -24,22 +24,22 @@ class LinearSmootherLeaveOneOutScorer(): r"""Leave-one-out cross validation scoring method for linear smoothers. It calculates the cross validation score for every sample in a FDataGrid - object given a linear smoother with a smoothing matrix :math:`\hat{H}^\nu` - calculated with a parameter :math:`\nu`: + object given a linear smoother with a smoothing matrix + :math:`\mathbf{S}(h)` calculated with a parameter :math:`h`: .. math:: - CV(\nu)=\frac{1}{n} \sum_i \left(y_i - \hat{y}_i^{\nu( - -i)}\right)^2 + CV_{loo}(h)=\frac{1}{M} \sum_m \left(x(t_m) - \hat{x}(t_m; h)^{(-m)} + \right)^2, - Where :math:`\hat{y}_i^{\nu(-i)}` is the adjusted :math:`y_i` when the - the pair of values :math:`(x_i,y_i)` are excluded in the smoothing. This + where :math:`\hat{x}(t_m; h)^{(-m)}` is the estimated :math:`x(t_m)` when + the point :math:`t_m` is excluded in the smoothing. This would require to recalculate the smoothing matrix n times. Fortunately the above formula can be expressed in a way where the smoothing matrix does not need to be calculated again. .. math:: - CV(\nu)=\frac{1}{n} \sum_i \left(\frac{y_i - \hat{y}_i^\nu}{1 - - \hat{H}_{ii}^\nu}\right)^2 + CV_{loo}(h)=\frac{1}{M} \sum_{m=1}^{M} + \left(\frac{x(t_m) - \hat{x}(t_m; h)}{1 - S_{mm}(h)}\right)^2, Args: estimator (Estimator): Linear smoothing estimator. @@ -64,19 +64,19 @@ class LinearSmootherGeneralizedCVScorer(): r"""Generalized cross validation scoring method for linear smoothers. It calculates the general cross validation score for every sample in a - FDataGrid object given a smoothing matrix :math:`\hat{H}^\nu` - calculated with a parameter :math:`\nu`: + FDataGrid object given a smoothing matrix :math:`\mathbf{S}(h)` + calculated with a parameter :math:`h`: .. math:: - GCV(\nu)=\Xi(\nu,n)\frac{1}{n} \sum_i \left(y_i - \hat{ - y}_i^\nu\right)^2 + GCV(h)=\Xi(\mathbf{S}(h))\frac{1}{M} \sum_{m=1}^{M} + \left(x(t_m) - \hat{x}(t_m; h)\right)^2, - Where :math:`\hat{y}_i^{\nu}` is the adjusted :math:`y_i` and + Where :math:`\hat{x}(t_m; h)` is the adjusted :math:`x(t_m)` and :math:`\Xi` is a penalization function. By default the penalization function is: .. math:: - \Xi(\nu,n) = \left( 1 - \frac{tr(\hat{H}^\nu)}{n} \right)^{-2} + \Xi(\mathbf{S}(h)) = \frac{1}{(1 - \text{tr}(\mathbf{S}(h))/M)^2}. but others such as the Akaike's information criterion can be considered. From 3b5ae3887e64fe30e7ef9fb62e1773eeb219002e Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Tue, 1 Feb 2022 13:22:04 +0100 Subject: [PATCH 035/400] Kernel Regression and Hat Matrix --- docs/modules/misc.rst | 2 + docs/modules/misc/hat_matrix.rst | 13 + docs/modules/ml/regression.rst | 8 + docs/modules/preprocessing/smoothing.rst | 8 +- docs/refs.bib | 27 +- examples/plot_kernel_regression.py | 265 ++++++++++ examples/plot_kernel_smoothing.py | 33 +- skfda/misc/__init__.py | 7 + skfda/misc/hat_matrix.py | 470 ++++++++++++++++++ skfda/misc/lstsq.py | 13 +- skfda/ml/regression/__init__.py | 1 + skfda/ml/regression/_kernel_regression.py | 106 ++++ .../_per_class_transformer.py | 7 +- skfda/preprocessing/smoothing/__init__.py | 3 +- .../smoothing/kernel_smoothers.py | 409 +++------------ skfda/preprocessing/smoothing/validation.py | 161 ++++-- tests/test_fda_feature_union.py | 13 +- tests/test_kernel_regression.py | 231 +++++++++ tests/test_smoothing.py | 158 ++++-- tutorial/plot_skfda_sklearn.py | 9 +- 20 files changed, 1469 insertions(+), 475 deletions(-) create mode 100644 docs/modules/misc/hat_matrix.rst create mode 100644 examples/plot_kernel_regression.py create mode 100644 skfda/misc/hat_matrix.py create mode 100644 skfda/ml/regression/_kernel_regression.py create mode 100644 tests/test_kernel_regression.py diff --git a/docs/modules/misc.rst b/docs/modules/misc.rst index bf12d16ba..c1a53d05a 100644 --- a/docs/modules/misc.rst +++ b/docs/modules/misc.rst @@ -56,3 +56,5 @@ functional data: misc/metrics misc/operators misc/regularization + misc/hat_matrix + misc/score_functions diff --git a/docs/modules/misc/hat_matrix.rst b/docs/modules/misc/hat_matrix.rst new file mode 100644 index 000000000..d9e1fa857 --- /dev/null +++ b/docs/modules/misc/hat_matrix.rst @@ -0,0 +1,13 @@ +Hat Matrix +========== + +Hat matrix is used in kernel smoothing (:class:`~skfda.preprocessing.smoothing.KernelSmoother`) and +kernel regression (:class:`~skfda.ml.regression.KernelRegression`) algorithms. See the links below for more information of how the matrix are calculated. + + +.. autosummary:: + :toctree: autosummary + + skfda.misc.hat_matrix.NadarayaWatsonHatMatrix + skfda.misc.hat_matrix.LocalLinearRegressionHatMatrix + skfda.misc.hat_matrix.KNeighborsHatMatrix diff --git a/docs/modules/ml/regression.rst b/docs/modules/ml/regression.rst index ea582013a..4af4337d0 100644 --- a/docs/modules/ml/regression.rst +++ b/docs/modules/ml/regression.rst @@ -34,3 +34,11 @@ it is explained the basic usage of these estimators. skfda.ml.regression.KNeighborsRegressor skfda.ml.regression.RadiusNeighborsRegressor + +Kernel regression +----------------- + +.. autosummary:: + :toctree: autosummary + + skfda.ml.regression.KernelRegression diff --git a/docs/modules/preprocessing/smoothing.rst b/docs/modules/preprocessing/smoothing.rst index d279405e0..28e736f7e 100644 --- a/docs/modules/preprocessing/smoothing.rst +++ b/docs/modules/preprocessing/smoothing.rst @@ -20,16 +20,14 @@ the influence of each input point over it. For doing this, it considers a kernel function placed at the desired point. The influence of each input point will be related with the value of the kernel function at that input point. -There are several kernel smoothers provided in this library. All of them are -also *linear* smoothers, meaning that they compute a smoothing matrix (or hat +The kernel smoother provided in this library is +also a *linear* smoother, meaning that it computes a smoothing matrix (or hat matrix) that performs the smoothing as a linear transformation. .. autosummary:: :toctree: autosummary - skfda.preprocessing.smoothing.kernel_smoothers.NadarayaWatsonSmoother - skfda.preprocessing.smoothing.kernel_smoothers.LocalLinearRegressionSmoother - skfda.preprocessing.smoothing.kernel_smoothers.KNeighborsSmoother + skfda.preprocessing.smoothing.KernelSmoother Basis smoother -------------- diff --git a/docs/refs.bib b/docs/refs.bib index 7a774f61c..53dd6b7e7 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -1,4 +1,14 @@ - +@article{baillo+grane+2008+llr, + author = {Amparo Baíllo, Aurea Grané}, + title = {Local linear regression for functional predictor and scalar response}, + journal = {Journal of Multivariate Analysis}, + volume = {100}, + number = {1}, + pages = {102 -- 111}, + year = {2009}, + doi = {https://doi.org/10.1016/j.jmva.2008.03.008}, + url = {https://www.sciencedirect.com/science/article/pii/S0047259X08000973} +} @article{berrendero+cuevas+torrecilla_2016_hunting, author = {Berrendero, J.R. and Cuevas, Antonio and Torrecilla, José}, @@ -106,8 +116,19 @@ @article{dai+genton_2018_visualization @inbook{ferraty+vieu_2006_nonparametric_knn, author = {Frédéric Ferraty and Philippe Vieu}, title = {Nonparametric Functional Data Analysis. Theory and Practice}, - chapter = {Functional Nonparametric Supervised Classification}, - pages = {116}, + chapter = {Computational Issues}, + pages = {101}, + publisher = {Springer-Verlag New York}, + year = {2006}, + isbn = {978-0-387-30369-7}, + doi = {10.1007/0-387-36620-2} +} + +@inbook{ferraty+vieu_2006_nonparametric_nw, + author = {Frédéric Ferraty and Philippe Vieu}, + title = {Nonparametric Functional Data Analysis. Theory and Practice}, + chapter = {Functional Nonparametric Prediction Methodologies}, + pages = {55}, publisher = {Springer-Verlag New York}, year = {2006}, isbn = {978-0-387-30369-7}, diff --git a/examples/plot_kernel_regression.py b/examples/plot_kernel_regression.py new file mode 100644 index 000000000..dbca9b636 --- /dev/null +++ b/examples/plot_kernel_regression.py @@ -0,0 +1,265 @@ +""" +Kernel Regression. +================== + +In this example we will see and compare the performance of different kernel +regression methods. +""" + +import numpy as np +from sklearn.metrics import r2_score +from sklearn.model_selection import GridSearchCV, train_test_split + +import skfda +from skfda.misc.hat_matrix import ( + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) +from skfda.ml.regression._kernel_regression import KernelRegression + +############################################################################## +# +# For this example, we will use the +# :func:`tecator ` dataset. This data set +# contains 215 samples. For each sample the data consists of a spectrum of +# absorbances and the contents of water, fat and protein. + + +X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) +fd = X.iloc[:, 0].values +fat = y['fat'].values + +############################################################################## +# +# Fat percentages will be estimated from the spectrum. +# All curves are shown in the image above. The color of these depends on the +# amount of fat, from least (yellow) to highest (red). + +fd.plot(gradient_criteria=fat, legend=True) + +############################################################################## +# +# The data set is splitted into train and test sets with 80% and 20% of the +# samples respectively. + +fd_train, fd_test, y_train, y_test = train_test_split( + fd, + fat, + test_size=0.2, + random_state=1, +) + +############################################################################## +# +# The KNN algorithm will be tried first. We will use the default kernel +# function, i.e. uniform kernel. To find the most suitable number of +# neighbours GridSearchCV will be used, testing with any number from 1 to 100 +# (approximate number of samples in the test set). + +n_neighbors = np.array(range(1, 100)) +knn = GridSearchCV( + KernelRegression(kernel_estimator=KNeighborsHatMatrix()), + param_grid={'kernel_estimator__bandwidth': n_neighbors}, +) + +############################################################################## +# +# The best performance for the train set is obtained with the following number +# of neighbours + +knn.fit(fd_train, y_train) +print( + 'KNN bandwidth:', + knn.best_params_['kernel_estimator__bandwidth'], +) + +############################################################################## +# +# The accuracy of the estimation using r2_score measurement on the test set is +# shown below. + +y_pred = knn.predict(fd_test) +knn_res = r2_score(y_pred, y_test) +print('Score KNN:', knn_res) + + +############################################################################## +# +# Following a similar procedure for Nadaraya-Watson, the optimal parameter is +# chosen from the interval (0.01, 1). + +bandwidth = np.logspace(-2, 0, num=100) +nw = GridSearchCV( + KernelRegression(kernel_estimator=NadarayaWatsonHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +############################################################################## +# +# The best performance is obtained with the following bandwidth + +nw.fit(fd_train, y_train) +print( + 'Nadaraya-Watson bandwidth:', + nw.best_params_['kernel_estimator__bandwidth'], +) + +############################################################################## +# +# The accuracy of the estimation is shown below and should be similar to that +# obtained with the KNN method. + +y_pred = nw.predict(fd_test) +nw_res = r2_score(y_pred, y_test) +print('Score NW:', nw_res) + +############################################################################## +# +# For Local Linear Regression, FDataBasis representation with an orthonormal +# basis should be used (for the previous cases it is possible to use either +# FDataGrid or FDataBasis). +# +# For basis, BSpline with 10 elements has been selected. Note that the number +# of functions in the basis affects the estimation result and should +# ideally also be chosen using cross-validation. + +bspline = skfda.representation.basis.BSpline(n_basis=10) + +fd_basis = fd.to_basis(basis=bspline) +fd_basis_train, fd_basis_test, y_train, y_test = train_test_split( + fd_basis, + fat, + test_size=0.2, + random_state=1, +) + +############################################################################## +# +# As this procedure is slower than the previous ones, an interval with fewer +# values is taken. +bandwidth = np.logspace(0.3, 1, num=50) + +llr = GridSearchCV( + KernelRegression(kernel_estimator=LocalLinearRegressionHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +############################################################################## +# +# The bandwidth obtained by cross-validation is indicated below. + +llr.fit(fd_basis_train, y_train) +print( + 'LLR bandwidth:', + llr.best_params_['kernel_estimator__bandwidth'], +) + +############################################################################## +# +# Although it is a slower method, the result obtained should be much better +# than in the case of Nadaraya Watson and KNN. + +y_pred = llr.predict(fd_basis_test) +llr_res = r2_score(y_pred, y_test) +print('Score LLR:', llr_res) + +############################################################################## +# +# In the case of this data set, using the derivative of the functions should +# give a better performance. +# +# Below the plot of all the derivatives can be found. The same scheme as before +# is followed: yellow les fat, red more. + +fdd = fd.derivative() +fdd.plot(gradient_criteria=fat, legend=True) + + +fdd_train, fdd_test, y_train, y_test = train_test_split( + fdd, + fat, + test_size=0.2, + random_state=1, +) + +############################################################################## +# +# Exactly the same operations are repeated, but now with the derivatives of the +# functions. + +############################################################################## +# +# K-Nearest Neighbours +knn = GridSearchCV( + KernelRegression(kernel_estimator=KNeighborsHatMatrix()), + param_grid={'kernel_estimator__bandwidth': n_neighbors}, +) + +knn.fit(fdd_train, y_train) +print( + 'KNN bandwidth:', + knn.best_params_['kernel_estimator__bandwidth'], +) + +y_pred = knn.predict(fdd_test) +dknn_res = r2_score(y_pred, y_test) +print('Score KNN:', dknn_res) + + +############################################################################## +# +# Nadaraya-Watson +bandwidth = np.logspace(-3, -1, num=100) + +nw = GridSearchCV( + KernelRegression(kernel_estimator=NadarayaWatsonHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +nw.fit(fdd_train, y_train) +print( + 'Nadara-Watson bandwidth:', + nw.best_params_['kernel_estimator__bandwidth'], +) + +y_pred = nw.predict(fdd_test) +dnw_res = r2_score(y_pred, y_test) +print('Score NW:', dnw_res) + +############################################################################## +# +# For both Nadaraya-Watson and KNN the accuracy has improved significantly +# and should be higher than 0.9. + +############################################################################## +# +# Local Linear Regression +fdd_basis = fdd.to_basis(basis=bspline) +fdd_basis_train, fdd_basis_test, y_train, y_test = train_test_split( + fdd_basis, + fat, + test_size=0.2, + random_state=1, +) + +bandwidth = np.logspace(-2, 1, 50) +llr = GridSearchCV( + KernelRegression(kernel_estimator=LocalLinearRegressionHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +llr.fit(fdd_basis_train, y_train) +print( + 'LLR bandwidth:', + llr.best_params_['kernel_estimator__bandwidth'], +) + +y_pred = llr.predict(fdd_basis_test) +dllr_res = r2_score(y_pred, y_test) +print('Score LLR:', dllr_res) + +############################################################################## +# +# LLR accuracy has also improved, but the difference with Nadaraya-Watson and +# KNN in the case of derivatives is less significant than in the previous case. diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index bda9d1689..fb8f6d294 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -1,6 +1,6 @@ """ -Kernel Smoothing -================ +Kernel Smoothing. +================= This example uses different kernel smoothing methods over the phoneme data set and shows how cross validations scores vary over a range of different @@ -15,6 +15,7 @@ import numpy as np import skfda +import skfda.misc.hat_matrix as hm import skfda.preprocessing.smoothing.kernel_smoothers as ks import skfda.preprocessing.smoothing.validation as val @@ -31,7 +32,7 @@ dataset = skfda.datasets.fetch_phoneme() fd = dataset['data'][:300] -fd[0:5].plot() +fd[:5].plot() ############################################################################## # Here we show the general cross validation scores for different values of the @@ -58,32 +59,35 @@ # and we could compare the results of the different smoothers. scale_factor = ( - (fd.domain_range[0][1] - fd.domain_range[0][0]) - / len(fd.grid_points[0]) + (fd.domain_range[0][1] - fd.domain_range[0][0]) / len(fd.grid_points[0]) ) bandwidth = n_neighbors * scale_factor # K-nearest neighbours kernel smoothing. + knn = val.SmoothingParameterSearch( - ks.KNeighborsSmoother(), + ks.KernelSmoother(kernel_estimator=hm.KNeighborsHatMatrix()), n_neighbors, + param_name='kernel_estimator__bandwidth', ) knn.fit(fd) knn_fd = knn.transform(fd) # Local linear regression kernel smoothing. llr = val.SmoothingParameterSearch( - ks.LocalLinearRegressionSmoother(), + ks.KernelSmoother(kernel_estimator=hm.LocalLinearRegressionHatMatrix()), bandwidth, + param_name='kernel_estimator__bandwidth', ) llr.fit(fd) llr_fd = llr.transform(fd) # Nadaraya-Watson kernel smoothing. nw = val.SmoothingParameterSearch( - ks.NadarayaWatsonSmoother(), + ks.KernelSmoother(kernel_estimator=hm.NadarayaWatsonHatMatrix()), bandwidth, + param_name='kernel_estimator__bandwidth', ) nw.fit(fd) nw_fd = nw.transform(fd) @@ -112,7 +116,6 @@ label='Nadaraya-Watson', ) ax.legend() -fig ############################################################################## # We can plot the smoothed curves corresponding to the 11th element of the @@ -138,7 +141,6 @@ ], title='Smoothing method', ) -fig ############################################################################## # We can compare the curve before and after the smoothing. @@ -153,24 +155,23 @@ fig = fd[10].scatter(s=0.5) nw_fd[10].plot(fig=fig, color='green') -fig ############################################################################## # Now, we can see the effects of a proper smoothing. We can plot the same 5 # samples from the beginning using the Nadaraya-Watson kernel smoother with # the best choice of parameter. -nw_fd[0:5].plot() +nw_fd[:5].plot() ############################################################################## # We can also appreciate the effects of undersmoothing and oversmoothing in # the following plots. -fd_us = ks.NadarayaWatsonSmoother( - smoothing_parameter=2 * scale_factor, +fd_us = ks.KernelSmoother( + kernel_estimator=hm.NadarayaWatsonHatMatrix(bandwidth=2 * scale_factor), ).fit_transform(fd[10]) -fd_os = ks.NadarayaWatsonSmoother( - smoothing_parameter=15 * scale_factor, +fd_os = ks.KernelSmoother( + kernel_estimator=hm.NadarayaWatsonHatMatrix(bandwidth=15 * scale_factor), ).fit_transform(fd[10]) ############################################################################## diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 46e52bc3a..9fd10fe4c 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -1,3 +1,4 @@ +"""Miscellaneous functions and objects.""" from . import covariances, kernels, lstsq, metrics, operators, regularization from ._math import ( cosine_similarity, @@ -11,3 +12,9 @@ log10, sqrt, ) +from .hat_matrix import ( + HatMatrix, + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py new file mode 100644 index 000000000..4078701b4 --- /dev/null +++ b/skfda/misc/hat_matrix.py @@ -0,0 +1,470 @@ +# -*- coding: utf-8 -*- + +"""Hat Matrix. + +This module include implementation to create Nadaraya-Watson, +Local Linear Regression and K-Nearest Neighbours hat matrices used in +kernel smoothing and kernel regression. + +""" + +import abc +from typing import Callable, Optional + +import numpy as np +from sklearn.base import BaseEstimator, RegressorMixin + +from skfda.representation._functional_data import FData +from skfda.representation.basis import FDataBasis + +from . import kernels + + +class HatMatrix( + BaseEstimator, + RegressorMixin, +): + """ + Kernel estimators. + + This module includes three types of kernel estimators that are used in + KernelSmoother and KernelRegression classes. + """ + + def __init__( + self, + *, + bandwidth: Optional[float] = None, + kernel: Optional[Callable] = None, + ): + self.bandwidth = bandwidth + self.kernel = kernel + + def __call__( + self, + delta_x: np.ndarray, + weights: Optional[np.ndarray] = None, + _cv: bool = False, + ) -> np.ndarray: + """ + Hat matrix. + + Calculate and return matrix for smoothing (for all methods) and + regression (for Nadaraya-Watson and K-Nearest Neighbours) + + Args: + delta_x (np.ndarray): Matrix of distances between points or + functions + weights (np.ndarray, optional): Weights to be applied to the + resulting matrix columns + + Returns: + hat matrix (np.ndarray) + + """ + # Obtain the non-normalized matrix + matrix = self._hat_matrix_function_not_normalized(delta_x=delta_x) + + # Adjust weights + if weights is not None: + matrix *= weights + + # Set diagonal to zero if requested (for testing purposes only) + if _cv: + np.fill_diagonal(matrix, 0) + + # Renormalize weights + rs = np.sum(matrix, axis=1) + rs[rs == 0] = 1 + return (matrix.T / rs).T + + def prediction( + self, + *, + delta_x: np.ndarray, + y_train: np.ndarray, + X_train: Optional[FData] = None, + X: Optional[FData] = None, + weights: Optional[np.ndarray] = None, + ) -> np.ndarray: + """ + Prediction. + + Return the resulting :math:`y` array, by default calculates the hat + matrix and multiplies it by y_train (except for regression using + Local Linear Regression method). + + Args: + delta_x (np.ndarray): Matrix of distances between points or + functions + y_train (np.ndarray): Scalar response from for functional data + X_train (FData , optional): Functional data. Only used in + regression with Local Linear Regression method + X (FData, optional): Functional data. Only used in regression + with Local Linear Regression method + weights (np.ndarray, optional): Weights to be applied to the + resulting matrix columns + + Returns: + prediction (np.ndarray) + + """ + return np.dot(self.__call__(delta_x=delta_x, weights=weights), y_train) + + @abc.abstractmethod + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: np.ndarray, + ) -> np.ndarray: + pass + + +class NadarayaWatsonHatMatrix(HatMatrix): + r"""Nadaraya-Watson method. + + Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel + regression algorithms, as explained below. + + For **kernel smoothing** algorithm the matrix :math:`\hat{H}` has the + following expression for each cell + :footcite:`wasserman_2006_nonparametric_nw`: + + .. math:: + \hat{H}_{i,j} = \frac{K\left(\frac{t_j-t_i'}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{t_k-t_i'}{h}\right)} + + where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and + :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired + to estimate the smoothed value. + + The result can be obtained as + + .. math:: + \hat{X} = \hat{H}X + + where :math:`X = (x_1, x_2, ..., x_n)` is the vector of observations at the + points :math:`t` and + :math:`\hat{X} = (\hat{x}_1, \hat{x}_2, ..., \hat{x}_m)` are the estimated + values for the points :math:`t'`. + + For **kernel regression** algorithm + :footcite:`ferraty+vieu_2006_nonparametric_nw`:, + + .. math:: + \hat{H}_{i,j} = \frac{K\left(\frac{d(f_j-f_i')}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{d(f_k-f_i')}{h}\right)} + + where :math:`d(\cdot, \cdot)` is some functional distance + (see :class:`~skfda.misc.metrics.LpDistance`), + :math:`(f_1, f_2, ..., f_n)` is the functional data and the functions + :math:`(f_1', f_2', ..., f_m')` are the functions for which it is desired + to estimate the scalar value. + + The result can be obtained as + + .. math:: + \hat{Y} = \hat{H}Y + + where :math:`Y = (y_1, y_2, ..., y_n)` is the vector of scalar values + corresponding to the dataset and + :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` are the estimated + values + + In both cases, :math:`K(\cdot)` is a kernel function and :math:`h` the + kernel window width or smoothing parameter. + + Args: + bandwidth (float, optional): Window width of the kernel + (also called h or bandwidth). + kernel (function, optional): Kernel function. By default a normal + kernel. + + References: + .. footbibliography:: + + """ + + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: np.ndarray, + ) -> np.ndarray: + + if self.bandwidth is None: + percentage = 15 + self.bandwidth = np.percentile(delta_x, percentage) + + if self.kernel is None: + self.kernel = kernels.normal + + return self.kernel(delta_x / self.bandwidth) + + +class LocalLinearRegressionHatMatrix(HatMatrix): + r"""Local linear regression method. + + Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel + regression algorithms, as explained below. + + For **kernel smoothing** algorithm the matrix :math:`\hat{H}` has the + following expression for each cell + :footcite:`wasserman_2006_nonparametric_llr`: + + .. math:: + \hat{H}_{i,j} = \frac{b_j(t_i')}{\sum_{k=1}^{n}b_k(t_i')} + + .. math:: + b_j(e') = K\left(\frac{t_j - t'}{h}\right) S_{n,2}(t') - + (t_j - t')S_{n,1}(t') + + .. math:: + S_{n,k}(t') = \sum_{j=1}^{n}K\left(\frac{t_j-t'}{h}\right)(t_j-t')^k + + where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and + :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired + to estimate the smoothed value. + + The result can be obtained as + + .. math:: + \hat{X} = \hat{H}X + + where :math:`X = (x_1, x_2, ..., x_n)` is the vector of observations at the + points :math:`t` and + :math:`\hat{X} = (\hat{x}_1, \hat{x}_2, ..., \hat{x}_m)` are the estimated + values for the points :math:`t'`. + + For **kernel regression** algorithm + :footcite:`baillo+grane+2008+llr`: + + Given functional data, :math:`(f_1, f_2, ..., f_n)` where each function + is expressed in a orthonormal basis with :math:`J` elements and scalar + response :math:`Y = (y_1, y_2, ..., y_n)`. + + It is desired to estimate the values + :math:`\hat{Y} = (\hat{y}_1, \hat{y}'_2, ..., \hat{y}'_m)` + for the data :math:`(f'_1, f'_2, ..., f'_m)` (expressed in the same basis). + + For each :math:`f'_k` the estimation :math:`\hat{y}_k` is obtained by + taking the value :math:`a^k` from the vector + :math:`(a^k, b_1^k, ..., b_J^k)` which minimizes the following expression + + .. math:: + AWSE(a^k, b_1^k, ..., b_J^k) = \sum_{i=1}^n \left(y_i - + \left(a + \sum_{j=1}^J b_j^k c_{ij}^k \right) \right)^2 + K \left( \frac {d(f_i - f'_k)}{h} \right) 1/h + + Where: + + - :math:`K(\cdot)` is a kernel function, :math:`h` + the kernel window width or bandwidth and :math:`d` some + functional distance + - :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis + expansion of :math:`f_i - f'_k = \sum_{j=1}^J c_{ij}^k` + + Args: + bandwidth (float, optional): Window width of the kernel + (also called h or bandwidth). + kernel (function, optional): Kernel function. By default a normal + kernel. + + References: + .. footbibliography:: + + """ + + def prediction( + self, + *, + delta_x: np.ndarray, + X_train: FDataBasis, + y_train: np.ndarray, + X: FDataBasis, + weights: Optional[np.ndarray] = None, + ) -> np.ndarray: + """ + Prediction. + + Return the resulting :math:`y` array + + Args: + delta_x: np.ndarray + X_train: np.ndarray + y_train: np.ndarray + X: np.ndarray + weights: np.ndarray + + Returns: + np.ndarray + """ + if self.bandwidth is None: + percentage = 15 + self.bandwidth = np.percentile(delta_x, percentage) + + if self.kernel is None: + self.kernel = kernels.normal + + W = np.sqrt(self.kernel(delta_x / self.bandwidth)) + + # Creating the matrices of coefficients + m1 = X_train.coefficients + m2 = X.coefficients + + # Adding a column of ones to X_train coefficients + m1 = np.concatenate( + ( + np.ones(X_train.n_samples)[:, np.newaxis], + m1, + ), + axis=1, + ) + + # Adding a column of zeros to X coefficients + m2 = np.concatenate((np.zeros(X.n_samples)[:, np.newaxis], m2), axis=1) + + # Subtract previous matrices obtaining a 3D matrix + # The i-th element contains the matrix X_train - X[i] + C = m1 - m2[:, np.newaxis] + + # A x = b + # Where x = (a, b_1, ..., b_J) + A = (C.T * W.T).T + b = W * y_train + + # From https://stackoverflow.com/questions/42534237/broadcasted-lstsq-least-squares # noqa: E501 + u, s, vT = np.linalg.svd(A, full_matrices=False) + + uTb = np.einsum('ijk,ij->ik', u, b) + x = np.einsum('ijk,ij->ik', vT, uTb / s) + + return x[:, 0] + + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: np.ndarray, + ) -> np.ndarray: + + if self.bandwidth is None: + percentage = 15 + self.bandwidth = np.percentile(delta_x, percentage) + + if self.kernel is None: + self.kernel = kernels.normal + + k = self.kernel(delta_x / self.bandwidth) + + s1 = np.sum(k * delta_x, axis=1, keepdims=True) # S_n_1 + s2 = np.sum(k * delta_x ** 2, axis=1, keepdims=True) # S_n_2 + b = (k * (s2 - delta_x * s1)) # b_i(x_j) + + return b # noqa: WPS331 + + +class KNeighborsHatMatrix(HatMatrix): + r"""K-nearest neighbour kernel method. + + Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel + regression algorithms, as explained below. + + For **kernel smoothing** algorithm the matrix :math:`\hat{H}` has the + following expression for each cell + + .. math:: + \hat{H}_{i,j} = \frac{K\left(\frac{t_j-t_i'}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{t_k-t_i'}{h}\right)} + + where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and + :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired + to estimate the smoothed value. + + The result can be obtained as + + .. math:: + \hat{X} = \hat{H}X + + where :math:`X = (x_1, x_2, ..., x_n)` is the vector of observations at the + points :math:`t` and + :math:`\hat{X} = (\hat{x}_1, \hat{x}_2, ..., \hat{x}_m)` are the estimated + values for the points :math:`t'`. + + + For **kernel regression** algorithm + :footcite:`ferraty+vieu_2006_nonparametric_knn`, + + .. math:: + \hat{H}_{i,j} = \frac{K\left(\frac{d(f_j-f_i')}{h_{ik}}\right)} + {\sum_{k=1}^{n}K\left(\frac{d(f_k-f_i')}{h_{ik}}\right)} + + where :math:`d(\cdot, \cdot)` is some functional distance + (see :class:`~skfda.misc.metrics.LpDistance`), + :math:`F = (f_1, f_2, ..., f_n)` is the functional data and the functions + :math:`F' = (f_1', f_2', ..., f_m')` are the functions for which it is + wanted to estimate the scalar value. + + The result can be obtained as + + .. math:: + \hat{Y} = \hat{H}Y + + where :math:`Y = (y_1, y_2, ..., y_n)` is the vector of scalar values + corresponding to the dataset and + :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` are the estimated + values + + In both cases, :math:`K(\cdot)` is a kernel function, for smoothing, + :math:`h_{ik}` is calculated as the distance from :math:`t_i'` to it's 𝑘-th + nearest neighbor in :math:`t`, and for regression, :math:`h_{ik}` is + calculated as the distance from :math:`f_i'` to it's 𝑘-th nearest neighbor + in :math:`F`. + + Usually used with the uniform kernel, it takes the average of the closest k + points to a given point. + + Args: + bandwidth (int, optional): Number of nearest neighbours. By + default it takes the 5% closest points. + kernel (function, optional): Kernel function. By default a uniform + kernel to perform a 'usual' k nearest neighbours estimation. + + References: + .. footbibliography:: + + """ + + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: np.ndarray, + ) -> np.ndarray: + + if self.kernel is None: + self.kernel = kernels.uniform + + input_points_len = delta_x.shape[1] + + if self.bandwidth is None: + self.bandwidth = np.floor( + np.percentile( + range(1, input_points_len), + 5, + ), + ) + elif self.bandwidth <= 0: + raise ValueError('h must be greater than 0') + + # Tolerance to avoid points landing outside the kernel window due to + # computation error + tol = 1.0e-15 + + # For each row in the distances matrix, it calculates the furthest + # point within the k nearest neighbours + vec = np.percentile( + np.abs(delta_x), + self.bandwidth / input_points_len * 100, + axis=1, + interpolation='lower', + ) + tol + + return self.kernel((delta_x.T / vec).T) diff --git a/skfda/misc/lstsq.py b/skfda/misc/lstsq.py index 9d96cb03d..75538ee03 100644 --- a/skfda/misc/lstsq.py +++ b/skfda/misc/lstsq.py @@ -4,9 +4,8 @@ from typing import Callable, Optional, Union import numpy as np -from typing_extensions import Final, Literal - import scipy.linalg +from typing_extensions import Final, Literal LstsqMethodCallable = Callable[[np.ndarray, np.ndarray], np.ndarray] LstsqMethodName = Literal["cholesky", "qr", "svd"] @@ -64,6 +63,9 @@ def solve_regularized_weighted_lstsq( """ Solve a regularized and weighted least squares problem. + If weights is a 1-D array it is converted to 2-D array with weights on the + diagonal. + If the penalty matrix is not ``None`` and nonzero, there is a closed solution. Otherwise the problem can be reduced to a least squares problem. @@ -76,7 +78,12 @@ def solve_regularized_weighted_lstsq( ): # Weighted least squares case if weights is not None: - weights_chol = scipy.linalg.cholesky(weights) + + if weights.ndim == 1: + weights_chol = np.diag(np.sqrt(weights)) + else: + weights_chol = scipy.linalg.cholesky(weights) + coefs = weights_chol @ coefs result = weights_chol @ result diff --git a/skfda/ml/regression/__init__.py b/skfda/ml/regression/__init__.py index 485151c9b..87964e07c 100644 --- a/skfda/ml/regression/__init__.py +++ b/skfda/ml/regression/__init__.py @@ -1,5 +1,6 @@ """Regression.""" from ._historical_linear_model import HistoricalLinearRegression +from ._kernel_regression import KernelRegression from ._linear_regression import LinearRegression from ._neighbors_regression import ( KNeighborsRegressor, diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py new file mode 100644 index 000000000..f6e979e42 --- /dev/null +++ b/skfda/ml/regression/_kernel_regression.py @@ -0,0 +1,106 @@ +from __future__ import annotations + +from typing import Optional + +import numpy as np +from sklearn.base import BaseEstimator, RegressorMixin +from sklearn.utils.validation import check_is_fitted + +from skfda.misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix +from skfda.misc.metrics import PairwiseMetric, l2_distance +from skfda.misc.metrics._typing import Metric +from skfda.representation._functional_data import FData + + +class KernelRegression( + BaseEstimator, + RegressorMixin, +): + r"""Kernel regression with scalar response. + + Let :math:`fd_1 = (f_1, f_2, ..., f_n)` be the functional data set and + :math:`y = (y_1, y_2, ..., y_n)` be the scalar response corresponding + to each function in :math:`fd_1`. Then, the estimation for the + functions in :math:`fd_2 = (g_1, g_2, ..., g_n)` can be calculated as + + .. math:: + \hat{y} = \hat{H}y + + Where :math:`\hat{H}` is the matrix described in + :class:`~skfda.misc.HatMatrix`. + + Args: + kernel_estimator (:class:`~skfda.misc.HatMatrix`, optional): + Method used to calculate the hat matrix + (default = :class:`~skfda.misc.NadarayaWatsonHatMatrix`). + metric (function, optional): The metric used to calculate the distances + (default = :func:`L2 distance `). + + Examples: + >>> from skfda import FDataGrid + >>> from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix + >>> from skfda.misc.hat_matrix import KNeighborsHatMatrix + + >>> grid_points = np.linspace(0, 1, num=11) + >>> data1 = np.array([i + grid_points for i in range(1, 9, 2)]) + >>> data2 = np.array([i + grid_points for i in range(2, 7, 2)]) + + >>> fd_1 = FDataGrid(grid_points=grid_points, data_matrix=data1) + >>> y = np.array([1, 3, 5, 7]) + >>> fd_2 = FDataGrid(grid_points=grid_points, data_matrix=data2) + + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=1) + >>> estimator = KernelRegression(kernel_estimator=kernel_estimator) + >>> _ = estimator.fit(fd_1, y) + >>> estimator.predict(fd_2) + array([ 2.02723928, 4. , 5.97276072]) + + >>> kernel_estimator = KNeighborsHatMatrix(bandwidth=2) + >>> estimator = KernelRegression(kernel_estimator=kernel_estimator) + >>> _ = estimator.fit(fd_1, y) + >>> estimator.predict(fd_2) + array([ 2., 4., 6.]) + + """ + + def __init__( + self, + *, + kernel_estimator: Optional[HatMatrix] = None, + metric: Metric = l2_distance, + ): + + self.kernel_estimator = kernel_estimator + self.metric = metric + + def fit( # noqa: D102 + self, + X: FData, + y: np.ndarray, + weight: Optional[np.ndarray] = None, + ) -> KernelRegression: + + self.X_train_ = X + self.y_train_ = y + self.weights_ = weight + + if not self.kernel_estimator: + self.kernel_estimator = NadarayaWatsonHatMatrix() + + return self + + def predict( # noqa: D102 + self, + X: FData, + ) -> np.ndarray: + + check_is_fitted(self) + delta_x = PairwiseMetric(self.metric)(X, self.X_train_) + + return self.kernel_estimator.prediction( + delta_x=delta_x, + X_train=self.X_train_, + y_train=self.y_train_, + X=X, + weights=self.weights_, + ) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py index e00b887d5..6305663e8 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py @@ -99,10 +99,13 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): In our example we are going to use the Nadaraya Watson Smoother. >>> from skfda.preprocessing.smoothing.kernel_smoothers import ( - ... NadarayaWatsonSmoother, + ... KernelSmoother, + ... ) + >>> from skfda.misc.hat_matrix import ( + ... NadarayaWatsonHatMatrix, ... ) >>> t2 = PerClassTransformer( - ... NadarayaWatsonSmoother(), + ... KernelSmoother(kernel_estimator=NadarayaWatsonHatMatrix()), ... ) >>> x_transformed2 = t2.fit_transform(X, y) diff --git a/skfda/preprocessing/smoothing/__init__.py b/skfda/preprocessing/smoothing/__init__.py index 3fd39f483..ff726e135 100644 --- a/skfda/preprocessing/smoothing/__init__.py +++ b/skfda/preprocessing/smoothing/__init__.py @@ -1,3 +1,4 @@ -from . import kernel_smoothers +"""Smoothing.""" from . import validation from ._basis import BasisSmoother +from .kernel_smoothers import KernelSmoother diff --git a/skfda/preprocessing/smoothing/kernel_smoothers.py b/skfda/preprocessing/smoothing/kernel_smoothers.py index fe3b0a584..f724c8d5a 100644 --- a/skfda/preprocessing/smoothing/kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/kernel_smoothers.py @@ -1,114 +1,45 @@ # -*- coding: utf-8 -*- -"""Kernel smoother functions. -This module includes the most commonly used kernel smoother methods for FDA. - So far only non parametric methods are implemented because we are only - relying on a discrete representation of functional data. +"""Kernel Smoother. + +This module contains the class for kernel smoothing. """ -import abc +from typing import Optional import numpy as np -from ...misc import kernels -from ._linear import _LinearSmoother - -__author__ = "Miguel Carbajo Berrocal" -__email__ = "miguel.carbajo@estudiante.uam.es" - - -class _LinearKernelSmoother(_LinearSmoother): - - def __init__(self, *, smoothing_parameter=None, - kernel=kernels.normal, weights=None, - output_points=None): - self.smoothing_parameter = smoothing_parameter - self.kernel = kernel - self.weights = weights - self.output_points = output_points - self._cv = False # For testing purposes only - - def _hat_matrix(self, input_points, output_points): - return self._hat_matrix_function( - input_points=input_points[0], - output_points=output_points[0], - smoothing_parameter=self.smoothing_parameter, - kernel=self.kernel, - weights=self.weights, - _cv=self._cv - ) - - def _hat_matrix_function(self, *, input_points, output_points, - smoothing_parameter, - kernel, weights, _cv=False): - - # Time deltas - delta_x = np.abs(np.subtract.outer(output_points, input_points)) +from skfda.misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix +from skfda.representation._typing import GridPointsLike - # Obtain the non-normalized matrix - matrix = self._hat_matrix_function_not_normalized( - delta_x=delta_x, - smoothing_parameter=smoothing_parameter, - kernel=kernel) - - # Adjust weights - if weights is not None: - matrix = matrix * weights - - # Set diagonal to cero if requested (for testing purposes only) - if _cv: - np.fill_diagonal(matrix, 0) - - # Renormalize weights - rs = np.sum(matrix, 1) - rs[rs == 0] = 1 - return (matrix.T / rs).T - - @abc.abstractmethod - def _hat_matrix_function_not_normalized(self, *, delta_x, - smoothing_parameter, kernel): - pass +from ._linear import _LinearSmoother - def _more_tags(self): - return { - 'X_types': [] - } +class KernelSmoother(_LinearSmoother): + r"""Kernel smoothing method. -class NadarayaWatsonSmoother(_LinearKernelSmoother): - r"""Nadaraya-Watson smoothing method. + This module allows to perform functional data smoothing. - It is a linear kernel smoothing method. - Uses an smoothing matrix :math:`\hat{H}` for the discretisation - points in argvals by the Nadaraya-Watson estimator. The smoothed - values :math:`\hat{X}` at the points :math:`(t_1', t_2', ..., t_m')` - can be calculated as :math:`\hat{X} = \hat{H}X` where :math:`X` is the - vector of observations at the points of discretisation - :math:`(t_1, t_2, ..., t_n)` and + Let :math:`t = (t_1, t_2, ..., t_n)` be the + points of discretisation and :math:`X` the vector of observations at that + points. Then, the smoothed values, :math:`\hat{X}`, at the points + :math:`t' = (t_1', t_2', ..., t_m')` are obtained as .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{t_j-t_i'}{h}\right)}{\sum_{k=1}^{ - n}K\left(\frac{t_k-t_i'}{h}\right)} - - where :math:`K(\cdot)` is a kernel function and :math:`h` the kernel - window width or smoothing parameter. + \hat{X} = \hat{H} X - Args: - argvals (ndarray): Vector of discretisation points. - smoothing_parameter (float, optional): Window width of the kernel - (also called h or bandwidth). - kernel (function, optional): kernel function. By default a normal - kernel. - weights (ndarray, optional): Case weights matrix (in order to modify - the importance of each point). - output_points (ndarray, optional): The output points. If ommited, - the input points are used. + where :math:`\hat{H}` is the matrix described in + :class:`~skfda.misc.HatMatrix`. Examples: >>> from skfda import FDataGrid - >>> fd = FDataGrid(grid_points=[1, 2, 4, 5, 7], - ... data_matrix=[[1, 2, 3, 4, 5]]) - >>> smoother = NadarayaWatsonSmoother(smoothing_parameter=3.5) + >>> from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix + >>> fd = FDataGrid( + ... grid_points=[1, 2, 4, 5, 7], + ... data_matrix=[[1, 2, 3, 4, 5]], + ... ) + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=3.5) + >>> smoother = KernelSmoother(kernel_estimator=kernel_estimator) >>> fd_smoothed = smoother.fit_transform(fd) >>> fd_smoothed.data_matrix.round(2) array([[[ 2.42], @@ -122,7 +53,8 @@ class NadarayaWatsonSmoother(_LinearKernelSmoother): [ 0.165, 0.202, 0.238, 0.229, 0.165], [ 0.129, 0.172, 0.239, 0.249, 0.211], [ 0.073, 0.115, 0.221, 0.271, 0.319]]) - >>> smoother = NadarayaWatsonSmoother(smoothing_parameter=2) + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=2) + >>> smoother = KernelSmoother(kernel_estimator=kernel_estimator) >>> fd_smoothed = smoother.fit_transform(fd) >>> fd_smoothed.data_matrix.round(2) array([[[ 1.84], @@ -139,9 +71,11 @@ class NadarayaWatsonSmoother(_LinearKernelSmoother): The output points can be changed: - >>> smoother = NadarayaWatsonSmoother( - ... smoothing_parameter=2, - ... output_points=[1, 2, 3, 4, 5, 6, 7]) + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=2) + >>> smoother = KernelSmoother( + ... kernel_estimator=kernel_estimator, + ... output_points=[1, 2, 3, 4, 5, 6, 7], + ... ) >>> fd_smoothed = smoother.fit_transform(fd) >>> fd_smoothed.data_matrix.round(2) array([[[ 1.84], @@ -160,261 +94,44 @@ class NadarayaWatsonSmoother(_LinearKernelSmoother): [ 0.017, 0.053, 0.238, 0.346, 0.346], [ 0.006, 0.022, 0.163, 0.305, 0.503]]) - References: - Wasserman, L. (2006). Local Regression. - In *All of Nonparametric Statistics* (pp. 71). Springer. - """ - - def _hat_matrix_function_not_normalized(self, *, delta_x, - smoothing_parameter, - kernel): - if smoothing_parameter is None: - smoothing_parameter = np.percentile(delta_x, 15) - - k = kernel(delta_x / smoothing_parameter) - return k - - -class LocalLinearRegressionSmoother(_LinearKernelSmoother): - r"""Local linear regression smoothing method. - - It is a linear kernel smoothing method. - Uses an smoothing matrix :math:`\hat{H}` for the discretisation - points in argvals by the local linear regression estimator. The smoothed - values :math:`\hat{X}` at the points :math:`(t_1', t_2', ..., t_m')` - can be calculated as :math:`\hat{X} = \hat{H}X` where :math:`X` is the - vector of observations at the points of discretisation - :math:`(t_1, t_2, ..., t_n)` and - - .. math:: - \hat{H}_{i,j} = \frac{b_j(t_i')}{\sum_{k=1}^{n}b_k(t_i')} - - .. math:: - b_j(t') = K\left(\frac{t_j - t'}{h}\right) S_{n,2}(t') - - (t_j - t')S_{n,1}(t') - - .. math:: - S_{n,k}(t') = \sum_{j=1}^{n}K\left(\frac{t_j-t'}{h}\right)(t_j-t')^k - - where :math:`K(\cdot)` is a kernel function and :math:`h` the kernel - window width. - Args: - argvals (ndarray): Vector of discretisation points. - smoothing_parameter (float, optional): Window width of the kernel - (also called h or bandwidth). - kernel (function, optional): kernel function. By default a normal - kernel. - weights (ndarray, optional): Case weights matrix (in order to modify - the importance of each point). - output_points (ndarray, optional): The output points. If ommited, - the input points are used. + kernel_estimator (:class:`~skfda.misc.HatMatrix`): Method used to + calculate the hat matrix (default = + :class:`~skfda.misc.NadarayaWatsonHatMatrix`) + weights (iterable): weight coefficients for each point. + output_points (GridPointsLike) : The output points. If omitted, the + input points are used. - Examples: - >>> from skfda import FDataGrid - >>> fd = FDataGrid(grid_points=[1, 2, 4, 5, 7], - ... data_matrix=[[1, 2, 3, 4, 5]]) - >>> smoother = LocalLinearRegressionSmoother(smoothing_parameter=3.5) - >>> fd_smoothed = smoother.fit_transform(fd) - >>> fd_smoothed.data_matrix.round(2) - array([[[ 1.13], - [ 1.36], - [ 3.29], - [ 4.27], - [ 5.08]]]) - >>> smoother.hat_matrix().round(3) - array([[ 0.614, 0.429, 0.077, -0.03 , -0.09 ], - [ 0.381, 0.595, 0.168, -0. , -0.143], - [-0.104, 0.112, 0.697, 0.398, -0.104], - [-0.147, -0.036, 0.392, 0.639, 0.152], - [-0.095, -0.079, 0.117, 0.308, 0.75 ]]) - >>> smoother = LocalLinearRegressionSmoother(smoothing_parameter=2) - >>> fd_smoothed = smoother.fit_transform(fd) - >>> fd_smoothed.data_matrix.round(2) - array([[[ 1.11], - [ 1.41], - [ 3.31], - [ 4.04], - [ 5.04]]]) - >>> smoother.hat_matrix().round(3) - array([[ 0.714, 0.386, -0.037, -0.053, -0.01 ], - [ 0.352, 0.724, 0.045, -0.081, -0.04 ], - [-0.078, 0.052, 0.74 , 0.364, -0.078], - [-0.07 , -0.067, 0.36 , 0.716, 0.061], - [-0.012, -0.032, -0.025, 0.154, 0.915]]) + So far only non parametric methods are implemented because we are only + relying on a discrete representation of functional data. - The output points can be changed: - - >>> smoother = LocalLinearRegressionSmoother( - ... smoothing_parameter=2, - ... output_points=[1, 2, 3, 4, 5, 6, 7]) - >>> fd_smoothed = smoother.fit_transform(fd) - >>> fd_smoothed.data_matrix.round(2) - array([[[ 1.11], - [ 1.41], - [ 1.81], - [ 3.31], - [ 4.04], - [ 5.35], - [ 5.04]]]) - >>> smoother.hat_matrix().round(3) - array([[ 0.714, 0.386, -0.037, -0.053, -0.01 ], - [ 0.352, 0.724, 0.045, -0.081, -0.04 ], - [-0.084, 0.722, 0.722, -0.084, -0.278], - [-0.078, 0.052, 0.74 , 0.364, -0.078], - [-0.07 , -0.067, 0.36 , 0.716, 0.061], - [-0.098, -0.202, -0.003, 0.651, 0.651], - [-0.012, -0.032, -0.025, 0.154, 0.915]]) - - References: - Wasserman, L. (2006). Local Regression. - In *All of Nonparametric Statistics* (pp. 77). Springer. - """ - - def _hat_matrix_function_not_normalized(self, *, delta_x, - smoothing_parameter, kernel): - k = kernel(delta_x / smoothing_parameter) - - s1 = np.sum(k * delta_x, 1, keepdims=True) # S_n_1 - s2 = np.sum(k * delta_x ** 2, 1, keepdims=True) # S_n_2 - b = (k * (s2 - delta_x * s1)) # b_i(x_j) - return b - - -class KNeighborsSmoother(_LinearKernelSmoother): - r"""K-nearest neighbour kernel smoother. - - It is a linear kernel smoothing method. - Uses an smoothing matrix S for the discretisation points in argvals by - the :math:`k` nearest neighbours estimator. - - The smoothed values :math:`\hat{X}` at the points - :math:`(t_1', t_2', ..., t_m')` can be calculated as - :math:`\hat{X} = \hat{H}X` where :math:`X` is the vector of observations - at the points of discretisation :math:`(t_1, t_2, ..., t_n)` and - - .. math:: - - H_{i,j} =\frac{K\left(\frac{t_j-t_i'}{h_{ik}}\right)}{\sum_{r=1}^n - K\left(\frac{t_r-t_i'}{h_{ik}}\right)} - - :math:`K(\cdot)` is a kernel function and :math:`h_{ik}` the is the - distance from :math:`t_i'` to the 𝑘-th nearest neighbor of :math:`t_i'`. - - Usually used with the uniform kernel, it takes the average of the closest k - points to a given point. - - Args: - argvals (ndarray): Vector of discretisation points. - smoothing_parameter (int, optional): Number of nearest neighbours. By - default it takes the 5% closest points. - kernel (function, optional): kernel function. By default a uniform - kernel to perform a 'usual' k nearest neighbours estimation. - weights (ndarray, optional): Case weights matrix (in order to modify - the importance of each point). - output_points (ndarray, optional): The output points. If ommited, - the input points are used. - - Examples: - >>> from skfda import FDataGrid - >>> fd = FDataGrid(grid_points=[1, 2, 4, 5, 7], - ... data_matrix=[[1, 2, 3, 4, 5]]) - >>> smoother = KNeighborsSmoother(smoothing_parameter=2) - >>> fd_smoothed = smoother.fit_transform(fd) - >>> fd_smoothed.data_matrix.round(2) - array([[[ 1.5], - [ 1.5], - [ 3.5], - [ 3.5], - [ 4.5]]]) - - >>> smoother.hat_matrix().round(3) - array([[ 0.5, 0.5, 0. , 0. , 0. ], - [ 0.5, 0.5, 0. , 0. , 0. ], - [ 0. , 0. , 0.5, 0.5, 0. ], - [ 0. , 0. , 0.5, 0.5, 0. ], - [ 0. , 0. , 0. , 0.5, 0.5]]) - - In case there are two points at the same distance it will take both. - - >>> fd = FDataGrid(grid_points=[1, 2, 3, 5, 7], - ... data_matrix=[[1, 2, 3, 4, 5]]) - >>> smoother = KNeighborsSmoother(smoothing_parameter=2) - >>> fd_smoothed = smoother.fit_transform(fd) - >>> fd_smoothed.data_matrix.round(2) - array([[[ 1.5], - [ 2. ], - [ 2.5], - [ 4. ], - [ 4.5]]]) - >>> smoother.hat_matrix().round(3) - array([[ 0.5 , 0.5 , 0. , 0. , 0. ], - [ 0.333, 0.333, 0.333, 0. , 0. ], - [ 0. , 0.5 , 0.5 , 0. , 0. ], - [ 0. , 0. , 0.333, 0.333, 0.333], - [ 0. , 0. , 0. , 0.5 , 0.5 ]]) - - The output points can be changed: - - >>> smoother = KNeighborsSmoother( - ... smoothing_parameter=2, - ... output_points=[1, 2, 3, 4, 5, 6, 7]) - >>> fd_smoothed = smoother.fit_transform(fd) - >>> fd_smoothed.data_matrix.round(2) - array([[[ 1.5], - [ 2. ], - [ 2.5], - [ 3.5], - [ 4. ], - [ 4.5], - [ 4.5]]]) - - >>> smoother.hat_matrix().round(3) - array([[ 0.5 , 0.5 , 0. , 0. , 0. ], - [ 0.333, 0.333, 0.333, 0. , 0. ], - [ 0. , 0.5 , 0.5 , 0. , 0. ], - [ 0. , 0. , 0.5 , 0.5 , 0. ], - [ 0. , 0. , 0.333, 0.333, 0.333], - [ 0. , 0. , 0. , 0.5 , 0.5 ], - [ 0. , 0. , 0. , 0.5 , 0.5 ]]) - - References: - Frederic Ferraty, Philippe Vieu (2006). kNN Estimator. - In *Nonparametric Functional Data Analysis: Theory and Practice* - (pp. 116). Springer. """ - def __init__(self, *, smoothing_parameter=None, - kernel=kernels.uniform, weights=None, - output_points=None): - super().__init__( - smoothing_parameter=smoothing_parameter, - kernel=kernel, - weights=weights, - output_points=output_points - ) - - def _hat_matrix_function_not_normalized(self, *, delta_x, - smoothing_parameter, kernel): - - input_points_len = delta_x.shape[1] - - if smoothing_parameter is None: - smoothing_parameter = np.floor(np.percentile( - range(1, input_points_len), 5)) - elif smoothing_parameter <= 0: - raise ValueError('h must be greater than 0') + def __init__( + self, + *, + kernel_estimator: Optional[HatMatrix] = None, + weights: Optional[np.ndarray] = None, + output_points: Optional[GridPointsLike] = None, + ): + self.kernel_estimator = kernel_estimator + self.weights = weights + self.output_points = output_points + self._cv = False # For testing purposes only - # Tolerance to avoid points landing outside the kernel window due to - # computation error - tol = 1.0e-19 + def _hat_matrix( + self, + input_points: Optional[GridPointsLike] = None, + output_points: Optional[GridPointsLike] = None, + ) -> np.ndarray: - # For each row in the distances matrix, it calculates the furthest - # point within the k nearest neighbours - vec = np.percentile(delta_x, smoothing_parameter - / input_points_len * 100, - axis=1, interpolation='lower') + tol + if not self.kernel_estimator: + self.kernel_estimator = NadarayaWatsonHatMatrix() - rr = kernel((delta_x.T / vec).T) + delta_x = np.subtract.outer(output_points[0], input_points[0]) + return self.kernel_estimator(delta_x, self.weights, _cv=self._cv) - return rr + def _more_tags(self): + return { + 'X_types': [], + } diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py index 4fffefda4..5491f738c 100644 --- a/skfda/preprocessing/smoothing/validation.py +++ b/skfda/preprocessing/smoothing/validation.py @@ -1,14 +1,19 @@ """Defines methods for the validation of the smoothing.""" +from typing import Callable, Iterable, Optional, Tuple, Union + import numpy as np import sklearn from sklearn.model_selection import GridSearchCV -__author__ = "Miguel Carbajo Berrocal" -__email__ = "miguel.carbajo@estudiante.uam.es" +from skfda import FDataGrid +from skfda.preprocessing.smoothing._linear import _LinearSmoother -def _get_input_estimation_and_matrix(estimator, X): - """Returns the smoothed data evaluated at the input points & the matrix""" +def _get_input_estimation_and_matrix( + estimator: _LinearSmoother, + X: FDataGrid, +) -> Tuple[FDataGrid, np.ndarray]: + """Return the smoothed data evaluated at the input points & the matrix.""" if estimator.output_points is not None: estimator = sklearn.base.clone(estimator) estimator.output_points = None @@ -20,7 +25,7 @@ def _get_input_estimation_and_matrix(estimator, X): return y_est, hat_matrix -class LinearSmootherLeaveOneOutScorer(): +class LinearSmootherLeaveOneOutScorer: r"""Leave-one-out cross validation scoring method for linear smoothers. It calculates the cross validation score for every sample in a FDataGrid @@ -52,15 +57,24 @@ class LinearSmootherLeaveOneOutScorer(): """ - def __call__(self, estimator, X, y): - + def __call__( + self, + estimator: _LinearSmoother, + X: FDataGrid, + y: FDataGrid, + ) -> float: + """Calculate Leave-One-Out score for linear smoothers.""" y_est, hat_matrix = _get_input_estimation_and_matrix(estimator, X) - return -np.mean(((y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) - / (1 - hat_matrix.diagonal())) ** 2) + return -np.mean( + ( + (y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) + / (1 - hat_matrix.diagonal()) + ) ** 2, + ) -class LinearSmootherGeneralizedCVScorer(): +class LinearSmootherGeneralizedCVScorer: r"""Generalized cross validation scoring method for linear smoothers. It calculates the general cross validation score for every sample in a @@ -91,21 +105,34 @@ class LinearSmootherGeneralizedCVScorer(): """ - def __init__(self, penalization_function=None): + def __init__( + self, + penalization_function: Optional[Callable] = None, + ): self.penalization_function = penalization_function - def __call__(self, estimator, X, y): + def __call__( + self, + estimator: _LinearSmoother, + X: FDataGrid, + y: FDataGrid, + ) -> float: + """Calculate Leave-One-Out score with penalization function.""" y_est, hat_matrix = _get_input_estimation_and_matrix(estimator, X) if self.penalization_function is None: - def penalization_function(hat_matrix): - return (1 - hat_matrix.diagonal().mean()) ** -2 - else: - penalization_function = self.penalization_function + self.penalization_function = lambda matrix: ( + 1 - matrix.diagonal().mean() + ) ** -2 - return -(np.mean(((y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) - / (1 - hat_matrix.diagonal())) ** 2) - * penalization_function(hat_matrix)) + return -( + np.mean( + ( + (y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) + / (1 - hat_matrix.diagonal()) + ) ** 2, + ) * self.penalization_function(hat_matrix) + ) class SmoothingParameterSearch(GridSearchCV): @@ -163,16 +190,20 @@ class SmoothingParameterSearch(GridSearchCV): >>> import skfda >>> from skfda.preprocessing.smoothing import kernel_smoothers + >>> from skfda.misc.hat_matrix import KNeighborsHatMatrix >>> x = np.linspace(-2, 2, 5) >>> fd = skfda.FDataGrid(x ** 2, x) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother(), [2,3]) + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix()), + ... [2,3], + ... param_name='kernel_estimator__bandwidth') >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-11.67, -12.37]) >>> round(grid.best_score_, 2) -11.67 - >>> grid.best_params_['smoothing_parameter'] + >>> grid.best_params_['kernel_estimator__bandwidth'] 2 >>> grid.best_estimator_.hat_matrix().round(2) array([[ 0.5 , 0.5 , 0. , 0. , 0. ], @@ -195,33 +226,48 @@ class SmoothingParameterSearch(GridSearchCV): general cross validation using other penalization functions. >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother(), [2,3], + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix()), + ... [2,3], + ... param_name='kernel_estimator__bandwidth', ... scoring=LinearSmootherLeaveOneOutScorer()) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-4.2, -5.5]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother(), [2,3], + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix()), + ... [2,3], + ... param_name='kernel_estimator__bandwidth', ... scoring=LinearSmootherGeneralizedCVScorer( ... akaike_information_criterion)) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([ -9.35, -10.71]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother(), [2,3], + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix()), + ... [2,3], + ... param_name='kernel_estimator__bandwidth', ... scoring=LinearSmootherGeneralizedCVScorer( ... finite_prediction_error)) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([ -9.8, -11. ]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother(), [2,3], + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix()), + ... [2,3], + ... param_name='kernel_estimator__bandwidth', ... scoring=LinearSmootherGeneralizedCVScorer(shibata)) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-7.56, -9.17]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother(), [2,3], + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix()), + ... [2,3], + ... param_name='kernel_estimator__bandwidth', ... scoring=LinearSmootherGeneralizedCVScorer(rice)) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) @@ -232,9 +278,11 @@ class SmoothingParameterSearch(GridSearchCV): >>> output_points = np.linspace(-2, 2, 9) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KNeighborsSmoother( - ... output_points=output_points - ... ), [2,3]) + ... kernel_smoothers.KernelSmoother( + ... kernel_estimator=KNeighborsHatMatrix(), + ... output_points=output_points), + ... [2,3], + ... param_name='kernel_estimator__bandwidth') >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-11.67, -12.37]) @@ -250,25 +298,46 @@ class SmoothingParameterSearch(GridSearchCV): [ 2.5 ]]]) """ - def __init__(self, estimator, param_values, *, scoring=None, n_jobs=None, - verbose=0, pre_dispatch='2*n_jobs', - error_score=np.nan): - super().__init__(estimator=estimator, scoring=scoring, - param_grid={'smoothing_parameter': param_values}, - n_jobs=n_jobs, - refit=True, cv=[(slice(None), slice(None))], - verbose=verbose, pre_dispatch=pre_dispatch, - error_score=error_score, return_train_score=False) + def __init__( + self, + estimator: _LinearSmoother, + param_values: Iterable, + *, + param_name: str = 'smoothing_parameter', + scoring: Callable = None, + n_jobs: Optional[int] = None, + verbose: int = 0, + pre_dispatch: Optional[Union[int, str]] = '2*n_jobs', + error_score: Union[str, float] = np.nan, + ): + super().__init__( + estimator=estimator, + scoring=scoring, + param_grid={param_name: param_values}, + n_jobs=n_jobs, + refit=True, + cv=[(slice(None), slice(None))], + verbose=verbose, + pre_dispatch=pre_dispatch, + error_score=error_score, + return_train_score=False, + ) self.param_values = param_values - def fit(self, X, y=None, groups=None, **fit_params): + def fit( # noqa: D102 + self, + X, + y=None, + groups=None, + **fit_params, + ): if y is None: y = X return super().fit(X, y=y, groups=groups, **fit_params) -def akaike_information_criterion(hat_matrix): +def akaike_information_criterion(hat_matrix: np.ndarray) -> float: r"""Akaike's information criterion for cross validation. .. math:: @@ -285,7 +354,7 @@ def akaike_information_criterion(hat_matrix): return np.exp(2 * hat_matrix.diagonal().mean()) -def finite_prediction_error(hat_matrix): +def finite_prediction_error(hat_matrix: np.ndarray) -> float: r"""Finite prediction error for cross validation. .. math:: @@ -300,11 +369,13 @@ def finite_prediction_error(hat_matrix): float: penalization given by the finite prediction error. """ - return ((1 + hat_matrix.diagonal().mean()) - / (1 - hat_matrix.diagonal().mean())) + return ( + (1 + hat_matrix.diagonal().mean()) + / (1 - hat_matrix.diagonal().mean()) + ) -def shibata(hat_matrix): +def shibata(hat_matrix: np.ndarray) -> float: r"""Shibata's model selector for cross validation. .. math:: @@ -321,7 +392,7 @@ def shibata(hat_matrix): return 1 + 2 * hat_matrix.diagonal().mean() -def rice(hat_matrix): +def rice(hat_matrix: np.ndarray) -> np.ndarray: r"""Rice's bandwidth selector for cross validation. .. math:: diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index 21a174067..a28bd2db6 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -6,13 +6,12 @@ from pandas.testing import assert_frame_equal from skfda.datasets import fetch_growth +from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix from skfda.misc.operators import SRSF from skfda.preprocessing.dim_reduction.feature_extraction import ( FDAFeatureUnion, ) -from skfda.preprocessing.smoothing.kernel_smoothers import ( - NadarayaWatsonSmoother, -) +from skfda.preprocessing.smoothing.kernel_smoothers import KernelSmoother from skfda.representation import EvaluationTransformer @@ -36,13 +35,17 @@ def test_correct_transformation_concat(self) -> None: u = FDAFeatureUnion( [ ("srsf1", SRSF()), - ("smooth", NadarayaWatsonSmoother()), # type: ignore + ("smooth", KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(), + )), # type: ignore ], ) created_frame = u.fit_transform(self.X) t1 = SRSF().fit_transform(self.X) - t2 = NadarayaWatsonSmoother().fit_transform(self.X) # type: ignore + t2 = KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(), + ).fit_transform(self.X) # type: ignore true_frame = DataFrame({"Transformed data": [t1, t2]}) assert_frame_equal(true_frame, created_frame) diff --git a/tests/test_kernel_regression.py b/tests/test_kernel_regression.py new file mode 100644 index 000000000..7159aae99 --- /dev/null +++ b/tests/test_kernel_regression.py @@ -0,0 +1,231 @@ +"""Test kernel regression method.""" +import unittest +from typing import Tuple + +import numpy as np +import sklearn + +import skfda +from skfda.misc.hat_matrix import ( + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) +from skfda.misc.kernels import normal, uniform +from skfda.misc.metrics import l2_distance +from skfda.ml.regression._kernel_regression import KernelRegression +from skfda.representation.basis import FDataBasis +from skfda.representation.grid import FDataGrid + + +def _nw_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): + if kernel is None: + kernel = normal + + y = np.zeros(fd_test.n_samples) + for i in range(fd_test.n_samples): + w = kernel(l2_distance(fd_train, fd_test[i]) / bandwidth) + y[i] = (w @ y_train) / sum(w) + + return y + + +def _knn_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): + if kernel is None: + kernel = uniform + + y = np.zeros(fd_test.n_samples) + + for i in range(fd_test.n_samples): + d = l2_distance(fd_train, fd_test[i]) + h = sorted(d)[bandwidth - 1] + 1.0e-15 + w = kernel(d / h) + y[i] = (w @ y_train) / sum(w) + + return y + + +def _llr_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): + if kernel is None: + kernel = normal + + y = np.zeros(fd_test.n_samples) + + for i in range(fd_test.n_samples): + d = l2_distance(fd_train, fd_test[i]) + W = np.diag(kernel(d / bandwidth)) + + C = np.concatenate( + ( + (np.ones(fd_train.n_samples))[:, np.newaxis], + (fd_train - fd_test[i]).coefficients, + ), + axis=1, + ) + + M = np.linalg.inv(np.linalg.multi_dot([C.T, W, C])) + y[i] = np.linalg.multi_dot([M, C.T, W, y_train])[0] + + return y + + +def _create_data_basis() -> Tuple[FDataBasis, FDataBasis, np.ndarray]: + X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + fat = y['fat'].values + + basis = skfda.representation.basis.BSpline( + n_basis=10, + domain_range=fd.domain_range, + ) + + fd_basis = fd.to_basis(basis=basis) + + fd_train, fd_test, y_train, _ = sklearn.model_selection.train_test_split( + fd_basis, + fat, + test_size=0.2, + random_state=10, + ) + return fd_train, fd_test, y_train + + +def _create_data_grid() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: + X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + fat = y['fat'].values + + fd_train, fd_test, y_train, _ = sklearn.model_selection.train_test_split( + fd, + fat, + test_size=0.2, + random_state=10, + ) + + return fd_train, fd_test, y_train + + +class TestKernelRegression(unittest.TestCase): + """Test Nadaraya-Watson, KNNeighbours and LocalLinearRegression methods.""" + + def test_nadaraya_watson(self) -> None: + """Test Nadaraya-Watson method.""" + # Creating data + fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() + fd_train_grid, fd_test_grid, y_train_grid = _create_data_grid() + + # Test NW method with basis representation and bandwidth=1 + nw_basis = KernelRegression( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), + ) + nw_basis.fit(fd_train_basis, y_train_basis) + y_basis = nw_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _nw_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=1, + ), + y_basis, + ) + + # Test NW method with grid representation and bandwidth=1 + nw_grid = KernelRegression( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), + ) + nw_grid.fit(fd_train_grid, y_train_grid) + y_grid = nw_grid.predict(fd_test_grid) + + np.testing.assert_allclose( + _nw_alt( + fd_train_grid, + fd_test_grid, + y_train_grid, + bandwidth=1, + ), + y_grid, + ) + + def test_knn(self) -> None: + """Test K-Nearest Neighbours method.""" + # Creating data + fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() + fd_train_grid, fd_test_grid, y_train_grid = _create_data_grid() + + # Test KNN method with basis representation, n_neighbours=3 and + # uniform kernel + knn_basis = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(bandwidth=3), + ) + knn_basis.fit(fd_train_basis, y_train_basis) + y_basis = knn_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _knn_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=3, + ), + y_basis, + ) + + # Test KNN method with grid representation, n_neighbours=3 and + # uniform kernel + knn_grid = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(bandwidth=3), + ) + knn_grid.fit(fd_train_grid, y_train_grid) + y_grid = knn_grid.predict(fd_test_grid) + + np.testing.assert_allclose( + _knn_alt( + fd_train_grid, + fd_test_grid, + y_train_grid, + bandwidth=3, + ), + y_grid, + ) + + # Test KNN method with basis representation, n_neighbours=10 and + # normal kernel + knn_basis = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(bandwidth=10, kernel=normal), + ) + knn_basis.fit(fd_train_basis, y_train_basis) + y_basis = knn_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _knn_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=10, + kernel=normal, + ), + y_basis, + ) + + def test_llr(self) -> None: + """Test Local Linear Regression method.""" + # Creating data + fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() + + llr_basis = KernelRegression( + kernel_estimator=LocalLinearRegressionHatMatrix(bandwidth=1), + ) + llr_basis.fit(fd_train_basis, y_train_basis) + y_basis = llr_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _llr_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=1, + ), + y_basis, + ) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index d7fccd137..e6d728c5b 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -1,3 +1,4 @@ +"""Test smoothing methods.""" import unittest import numpy as np @@ -8,6 +9,11 @@ import skfda.preprocessing.smoothing.kernel_smoothers as kernel_smoothers import skfda.preprocessing.smoothing.validation as validation from skfda._utils import _check_estimator +from skfda.misc.hat_matrix import ( + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.representation.basis import BSpline, Monomial @@ -15,127 +21,181 @@ class TestSklearnEstimators(unittest.TestCase): + """Test for sklearn estimators.""" - def test_nadaraya_watson(self): - _check_estimator(kernel_smoothers.NadarayaWatsonSmoother) + def test_kernel_smoothing(self): + """Test if estimator adheres to scikit-learn conventions.""" + _check_estimator(kernel_smoothers.KernelSmoother) - def test_local_linear_regression(self): - _check_estimator(kernel_smoothers.LocalLinearRegressionSmoother) - def test_knn(self): - _check_estimator(kernel_smoothers.KNeighborsSmoother) +class _LinearSmootherLeaveOneOutScorerAlternative: + """Alternative implementation of the LinearSmootherLeaveOneOutScorer.""" - -class _LinearSmootherLeaveOneOutScorerAlternative(): - r"""Alternative implementation of the LinearSmootherLeaveOneOutScorer""" - - def __call__(self, estimator, X, y): + def __call__( + self, + estimator: kernel_smoothers.KernelSmoother, + X: FDataGrid, + y: FDataGrid, + ) -> None: + """Calculate Leave-One-Out score.""" estimator_clone = sklearn.base.clone(estimator) - estimator_clone._cv = True + estimator_clone._cv = True # noqa: WPS437 y_est = estimator_clone.fit_transform(X) return -np.mean((y.data_matrix[..., 0] - y_est.data_matrix[..., 0])**2) class TestLeaveOneOut(unittest.TestCase): + """Tests of Leave-One-Out score for kernel smoothing.""" - def _test_generic(self, estimator_class): + def _test_generic( + self, + estimator: kernel_smoothers.KernelSmoother, + ) -> None: loo_scorer = validation.LinearSmootherLeaveOneOutScorer() loo_scorer_alt = _LinearSmootherLeaveOneOutScorerAlternative() + x = np.linspace(-2, 2, 5) fd = skfda.FDataGrid(x ** 2, x) - estimator = estimator_class() - grid = validation.SmoothingParameterSearch( - estimator, [2, 3], - scoring=loo_scorer) + estimator, + [2, 3], + param_name='kernel_estimator__bandwidth', + scoring=loo_scorer, + ) + grid.fit(fd) score = np.array(grid.cv_results_['mean_test_score']) grid_alt = validation.SmoothingParameterSearch( - estimator, [2, 3], - scoring=loo_scorer_alt) + estimator, + [2, 3], + param_name='kernel_estimator__bandwidth', + scoring=loo_scorer_alt, + ) + grid_alt.fit(fd) score_alt = np.array(grid_alt.cv_results_['mean_test_score']) np.testing.assert_array_almost_equal(score, score_alt) - def test_nadaraya_watson(self): - self._test_generic(kernel_smoothers.NadarayaWatsonSmoother) + def test_nadaraya_watson(self) -> None: + """Test Leave-One-Out with Nadaraya Watson method.""" + self._test_generic( + kernel_smoothers.KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(), + ), + ) - def test_local_linear_regression(self): - self._test_generic(kernel_smoothers.LocalLinearRegressionSmoother) + def test_local_linear_regression(self) -> None: + """Test Leave-One-Out with Local Linear Regression method.""" + self._test_generic( + kernel_smoothers.KernelSmoother( + kernel_estimator=LocalLinearRegressionHatMatrix(), + ), + ) - def test_knn(self): - self._test_generic(kernel_smoothers.KNeighborsSmoother) + def test_knn(self) -> None: + """Test Leave-One-Out with KNNeighbours method.""" + self._test_generic( + kernel_smoothers.KernelSmoother( + kernel_estimator=KNeighborsHatMatrix(), + ), + ) class TestBasisSmoother(unittest.TestCase): + """Test Basis Smoother.""" - def test_cholesky(self): + def test_cholesky(self) -> None: + """Test Basis Smoother using BSpline basis and Cholesky method.""" t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) basis = BSpline((0, 1), n_basis=5) + fd = FDataGrid(data_matrix=x, grid_points=t) + smoother = smoothing.BasisSmoother( basis=basis, smoothing_parameter=10, regularization=L2Regularization( - LinearDifferentialOperator(2)), + LinearDifferentialOperator(2), + ), method='cholesky', - return_basis=True) + return_basis=True, + ) + fd_basis = smoother.fit_transform(fd) + np.testing.assert_array_almost_equal( fd_basis.coefficients.round(2), - np.array([[0.60, 0.47, 0.20, -0.07, -0.20]]) + np.array([[0.6, 0.47, 0.2, -0.07, -0.2]]), ) - def test_qr(self): + def test_qr(self) -> None: + """Test Basis Smoother using BSpline basis and QR method.""" t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) basis = BSpline((0, 1), n_basis=5) + fd = FDataGrid(data_matrix=x, grid_points=t) + smoother = smoothing.BasisSmoother( basis=basis, smoothing_parameter=10, regularization=L2Regularization( - LinearDifferentialOperator(2)), + LinearDifferentialOperator(2), + ), method='qr', - return_basis=True) + return_basis=True, + ) + fd_basis = smoother.fit_transform(fd) + np.testing.assert_array_almost_equal( fd_basis.coefficients.round(2), - np.array([[0.60, 0.47, 0.20, -0.07, -0.20]]) + np.array([[0.6, 0.47, 0.2, -0.07, -0.2]]), ) - def test_monomial_smoothing(self): + def test_monomial_smoothing(self) -> None: + """Test Basis Smoother using Monomial basis.""" # It does not have much sense to apply smoothing in this basic case # where the fit is very good but its just for testing purposes t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) basis = Monomial(n_basis=4) + fd = FDataGrid(data_matrix=x, grid_points=t) + smoother = smoothing.BasisSmoother( basis=basis, smoothing_parameter=1, regularization=L2Regularization( - LinearDifferentialOperator(2)), - return_basis=True) + LinearDifferentialOperator(2), + ), + return_basis=True, + ) + fd_basis = smoother.fit_transform(fd) + # These results where extracted from the R package fda np.testing.assert_array_almost_equal( fd_basis.coefficients.round(2), - np.array([[0.61, -0.88, 0.06, 0.02]])) + np.array([[0.61, -0.88, 0.06, 0.02]]), + ) def test_vector_valued_smoothing(self) -> None: + """Test Basis Smoother for vector values functions.""" X, _ = skfda.datasets.fetch_weather(return_X_y=True) basis_dim = skfda.representation.basis.Fourier( - n_basis=7, domain_range=X.domain_range) + n_basis=7, domain_range=X.domain_range, + ) + basis = skfda.representation.basis.VectorValued( - [basis_dim] * 2 + [basis_dim] * 2, ) for method in ('cholesky', 'qr', 'svd'): @@ -144,18 +204,22 @@ def test_vector_valued_smoothing(self) -> None: basis_smoother = smoothing.BasisSmoother( basis, regularization=L2Regularization( - LinearDifferentialOperator(2)), + LinearDifferentialOperator(2), + ), return_basis=True, smoothing_parameter=1, - method=method) + method=method, + ) basis_smoother_dim = smoothing.BasisSmoother( basis_dim, regularization=L2Regularization( - LinearDifferentialOperator(2)), + LinearDifferentialOperator(2), + ), return_basis=True, smoothing_parameter=1, - method=method) + method=method, + ) X_basis = basis_smoother.fit_transform(X) @@ -165,10 +229,14 @@ def test_vector_valued_smoothing(self) -> None: np.testing.assert_allclose( X_basis.coordinates[0].coefficients, basis_smoother_dim.fit_transform( - X.coordinates[0]).coefficients) + X.coordinates[0], + ).coefficients, + ) self.assertEqual(X_basis.coordinates[1].basis, basis_dim) np.testing.assert_allclose( X_basis.coordinates[1].coefficients, basis_smoother_dim.fit_transform( - X.coordinates[1]).coefficients) + X.coordinates[1], + ).coefficients, + ) diff --git a/tutorial/plot_skfda_sklearn.py b/tutorial/plot_skfda_sklearn.py index ed67e4f59..5ab3c317a 100644 --- a/tutorial/plot_skfda_sklearn.py +++ b/tutorial/plot_skfda_sklearn.py @@ -102,13 +102,14 @@ ############################################################################## # As an example consider the smoothing method -# :class:`skfda.preprocessing.smoothing.NadarayaWatson`. Smoothing methods -# attempt to remove noise from the data leveraging its continuous nature. +# :class:`skfda.preprocessing.smoothing.NadarayaWatsonHatMatrix`. Smoothing +# methods attempt to remove noise from the data leveraging its continuous +# nature. # As these methods discard information of the original data they usually are # not reversible. import skfda.preprocessing.smoothing.kernel_smoothers as ks - +from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix X, y = skfda.datasets.fetch_phoneme(return_X_y=True) # Keep the first 5 functions @@ -116,7 +117,7 @@ X.plot() -smoother = ks.NadarayaWatsonSmoother() +smoother = ks.KernelSmoother(kernel_estimator=NadarayaWatsonHatMatrix()) X_smooth = smoother.fit_transform(X) X_smooth.plot() From 0fd968d7b3a29eefca820af1a6ab368061867349 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 3 Feb 2022 22:47:22 +0100 Subject: [PATCH 036/400] Gaussian process classifier fit --- skfda/ml/classification/__init__.py | 1 + .../ml/classification/_gaussian_classifier.py | 46 +++++++++++++++++++ 2 files changed, 47 insertions(+) create mode 100644 skfda/ml/classification/_gaussian_classifier.py diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index c7eada4b7..fbd573815 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -5,6 +5,7 @@ DDGClassifier, MaximumDepthClassifier, ) +from ._gaussian_classifier import GaussianClassifier from ._logistic_regression import LogisticRegression from ._neighbors_classifiers import ( KNeighborsClassifier, diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py new file mode 100644 index 000000000..d2ed6d82b --- /dev/null +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import numpy as np +from GPy.kern import Kern +from GPy.models import GPRegression +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.utils.validation import check_is_fitted + +from ..._utils import _classifier_get_classes +from ...representation import FDataGrid + + +class GaussianClassifier( + BaseEstimator, # type: ignore + ClassifierMixin, # type: ignore +): + + def __init__(self, kernel: Kern) -> None: + self._kernel = kernel + + def fit(self, X: FDataGrid, y: np.ndarray): + + grid = X.grid_points[0][:, np.newaxis] + classes, y_ind = _classifier_get_classes(y) + + self._classes = classes + + for cur_class in range(0, self._classes.size): + class_n = X[y_ind == cur_class] + class_n -= class_n.mean() + data = class_n.data_matrix[:, :, 0] + + reg_n = GPRegression(grid, data.T, kernel=self._kernel) + reg_n.optimize() + if cur_class == 0: + self._covariance_kern_zero = reg_n.kern + self._mean_zero = X[y_ind == cur_class].gmean() + else: + self._covariance_kern_one = reg_n.kern + self._mean_one = X[y_ind == cur_class].gmean() + + return self + + def predict(self, X: FDataGrid): + + check_is_fitted(self) From 66d033a83c4aac64cacc0b8aacf16dd22d27b698 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 3 Feb 2022 22:54:32 +0100 Subject: [PATCH 037/400] Local Averages Construction --- skfda/exploratory/stats/__init__.py | 6 +- .../stats/_functional_transformers.py | 244 +----------------- 2 files changed, 2 insertions(+), 248 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 2e401b15e..4aa0fda2b 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,9 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - number_up_crossings, - occupation_measure, -) +from ._functional_transformers import local_averages from ._stats import ( cov, depth_based_median, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 65c33ca99..65220edbc 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import Tuple, Union +from typing import Union import numpy as np @@ -72,245 +72,3 @@ def local_averages( data.integrate(interval=(interval,)) / interval_size, ] return np.asarray(integrated_data) - - -def _calculate_curves_occupation_( - curve_y_coordinates: np.ndarray, - curve_x_coordinates: np.ndarray, - interval: Tuple, -) -> np.ndarray: - y1, y2 = interval - - # Reshape original curves so they have one dimension less - new_shape = curve_y_coordinates.shape[1::-1] - curve_y_coordinates = curve_y_coordinates.reshape( - new_shape[::-1], - ) - - # Calculate interval sizes on the X axis - x_rotated = np.roll(curve_x_coordinates, 1) - intervals_x_axis = curve_x_coordinates - x_rotated - - # Calculate which points are inside the interval given (y1,y2) on Y axis - greater_than_y1 = curve_y_coordinates >= y1 - less_than_y2 = curve_y_coordinates <= y2 - inside_interval_bools = greater_than_y1 & less_than_y2 - - # Correct booleans so they are not repeated - bools_interval = np.roll( - inside_interval_bools, 1, axis=1, - ) & inside_interval_bools - - # Calculate intervals on X axis where the points are inside Y axis interval - intervals_x_inside = np.multiply(bools_interval, intervals_x_axis) - - # Delete first element of each interval as it will be a negative number - intervals_x_inside = np.delete(intervals_x_inside, 0, axis=1) - - return np.sum(intervals_x_inside, axis=1) - - -def occupation_measure( - data: Union[FDataGrid, FDataBasis], - intervals: np.ndarray, - *, - n_points: Union[int, None] = None, -) -> np.ndarray: - r""" - Calculate the occupation measure of a grid. - - It performs the following map. - ..math: - :math:`f_1(X) = |t: X(t)\in T_p|,\dots,|t: X(t)\in T_p|` - - where :math:`{T_1,\dots,T_p}` are disjoint intervals in - :math:`\mathbb{R}` and | | stands for the Lebesgue measure. - - Args: - data: FDataGrid or FDataBasis where we want to calculate - the occupation measure. - intervals: ndarray of tuples containing the - intervals we want to consider. The shape should be - (n_sequences,2) - n_points: Number of points to evaluate in the domain. - By default will be used the points defined on the FDataGrid. - On a FDataBasis this value should be specified. - Returns: - ndarray of shape (n_intervals, n_samples) - with the transformed data. - - Example: - We will create the FDataGrid that we will use to extract - the occupation measure - >>> from skfda.representation import FDataGrid - >>> import numpy as np - >>> t = np.linspace(0, 10, 100) - >>> fd_grid = FDataGrid( - ... data_matrix=[ - ... t, - ... 2 * t, - ... np.sin(t), - ... ], - ... grid_points=t, - ... ) - - Finally we call to the occupation measure function with the - intervals that we want to consider. In our case (0.0, 1.0) - and (2.0, 3.0). We need also to specify the number of points - we want that the function takes into account to interpolate. - We are going to use 501 points. - >>> from skfda.exploratory.stats import occupation_measure - >>> np.around( - ... occupation_measure( - ... fd_grid, - ... [(0.0, 1.0), (2.0, 3.0)], - ... n_points=501, - ... ), - ... decimals=2, - ... ) - array([[ 1. , 0.5 , 6.27], - [ 0.98, 0.48, 0. ]]) - - """ - if isinstance(data, FDataBasis) and n_points is None: - raise ValueError( - "Number of points to consider, should be given " - + " as an argument for a FDataBasis. Instead None was passed.", - ) - - for interval_check in intervals: - y1, y2 = interval_check - if y2 < y1: - raise ValueError( - "Interval limits (a,b) should satisfy a <= b. " - + str(interval_check) + " doesn't", - ) - - if n_points is None: - function_x_coordinates = data.grid_points[0] - function_y_coordinates = data.data_matrix - else: - function_x_coordinates = np.arange( - data.domain_range[0][0], - data.domain_range[0][1], - (data.domain_range[0][1] - data.domain_range[0][0]) / n_points, - ) - function_y_coordinates = data(function_x_coordinates) - - return np.asarray([ - _calculate_curves_occupation_( # noqa: WPS317 - function_y_coordinates, - function_x_coordinates, - interval, - ) - for interval in intervals - ]) - - -def number_up_crossings( - data: FDataGrid, - levels: np.ndarray, -) -> np.ndarray: - r""" - Calculate the number of up crossings to a level of a FDataGrid. - - Let f_1(X) = N_i, where N_i is the number of up crossings of X - to a level c_i \in \mathbb{R}, i = 1,\dots,p. - - Recall that the process X(t) is said to have an up crossing of c - at :math:`t_0 > 0` if for some :math:`\epsilon >0`, X(t) $\leq$ - c if t :math:'\in (t_0 - \epsilon, t_0) and X(t) $\geq$ c if - :math:`t\in (t_0, t_0+\epsilon)`. - - If the trajectories are differentiable, then - :math:`N_i = card\{t \in[a,b]: X(t) = c_i, X' (t) > 0\}.` - - Args: - data: FDataGrid where we want to calculate - the number of up crossings. - levels: sequence of numbers including the levels - we want to consider for the crossings. - Returns: - ndarray of shape (n_levels, n_samples)\ - with the values of the counters. - - Example: - - We import the Medflies dataset and for simplicity we use - the first 50 samples. - >>> from skfda.datasets import fetch_medflies - >>> dataset = fetch_medflies() - >>> X = dataset['data'][:50] - - Then we decide the level we want to consider (in our case 40) - and call the function with the dataset. The output will be the number of - times each curve cross the level 40 growing. - >>> from skfda.exploratory.stats import number_up_crossings - >>> import numpy as np - >>> number_up_crossings(X, np.asarray([40])) - array([[[6], - [3], - [7], - [7], - [3], - [4], - [5], - [7], - [4], - [6], - [4], - [4], - [5], - [6], - [0], - [5], - [1], - [6], - [0], - [7], - [0], - [6], - [2], - [5], - [6], - [5], - [8], - [4], - [3], - [7], - [1], - [3], - [0], - [5], - [2], - [7], - [2], - [5], - [5], - [5], - [4], - [4], - [1], - [2], - [3], - [5], - [3], - [3], - [5], - [2]]]) - """ - curves = data.data_matrix - - distances = np.asarray([ - level - curves - for level in levels - ]) - - points_greater = distances >= 0 - points_smaller = distances <= 0 - points_smaller_rotated = np.roll(points_smaller, -1, axis=2) - - return np.sum( - points_greater & points_smaller_rotated, - axis=2, - ) From 563d195b55921a209df7a27a204bf11beab08196 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 3 Feb 2022 23:25:38 +0100 Subject: [PATCH 038/400] Number of up crossings construction --- skfda/exploratory/stats/__init__.py | 6 +- .../stats/_functional_transformers.py | 202 +----------------- 2 files changed, 2 insertions(+), 206 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 2e401b15e..d139fb437 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,9 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - number_up_crossings, - occupation_measure, -) +from ._functional_transformers import number_up_crossings from ._stats import ( cov, depth_based_median, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 65c33ca99..fa2ee7424 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,209 +2,9 @@ from __future__ import annotations -from typing import Tuple, Union - import numpy as np -from ...representation import FDataBasis, FDataGrid - - -def local_averages( - data: Union[FDataGrid, FDataBasis], - n_intervals: int, -) -> np.ndarray: - r""" - Calculate the local averages of a given data. - - Take functional data as a grid or a basis and performs - the following map: - - .. math:: - f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ - f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt - - where {T_1,\dots,T_p} are disjoint intervals of the interval [a,b] - - It is calculated for a given number of intervals, - which are of equal sizes. - Args: - data: FDataGrid or FDataBasis where we want to - calculate the local averages. - n_intervals: number of intervals we want to consider. - Returns: - ndarray of shape (n_intervals, n_samples, n_dimensions) - with the transformed data for FDataBasis and (n_intervals, n_samples) - for FDataGrid. - - Example: - - We import the Berkeley Growth Study dataset. - We will use only the first 30 samples to make the - example easy. - >>> from skfda.datasets import fetch_growth - >>> dataset = fetch_growth(return_X_y=True)[0] - >>> X = dataset[:30] - - Then we decide how many intervals we want to consider (in our case 2) - and call the function with the dataset. - >>> import numpy as np - >>> from skfda.exploratory.stats import local_averages - >>> np.around(local_averages(X, 2), decimals=2) - array([[ 116.94, 111.86, 107.29, 111.35, 104.39, 109.43, 109.16, - 112.91, 109.19, 117.95, 112.14, 114.3 , 111.48, 114.85, - 116.25, 114.6 , 111.02, 113.57, 108.88, 109.6 , 109.7 , - 108.54, 109.18, 106.92, 109.44, 109.84, 115.32, 108.16, - 119.29, 110.62], - [ 177.26, 157.62, 154.97, 163.83, 156.66, 157.67, 155.31, - 169.02, 154.18, 174.43, 161.33, 170.14, 164.1 , 170.1 , - 166.65, 168.72, 166.85, 167.22, 159.4 , 162.76, 155.7 , - 158.01, 160.1 , 155.95, 157.95, 163.53, 162.29, 153.1 , - 178.48, 161.75]]) - """ - domain_range = data.domain_range - - left, right = domain_range[0] - interval_size = (right - left) / n_intervals - integrated_data = [] - for i in np.arange(left, right, interval_size): - interval = (i, i + interval_size) - integrated_data = integrated_data + [ - data.integrate(interval=(interval,)) / interval_size, - ] - return np.asarray(integrated_data) - - -def _calculate_curves_occupation_( - curve_y_coordinates: np.ndarray, - curve_x_coordinates: np.ndarray, - interval: Tuple, -) -> np.ndarray: - y1, y2 = interval - - # Reshape original curves so they have one dimension less - new_shape = curve_y_coordinates.shape[1::-1] - curve_y_coordinates = curve_y_coordinates.reshape( - new_shape[::-1], - ) - - # Calculate interval sizes on the X axis - x_rotated = np.roll(curve_x_coordinates, 1) - intervals_x_axis = curve_x_coordinates - x_rotated - - # Calculate which points are inside the interval given (y1,y2) on Y axis - greater_than_y1 = curve_y_coordinates >= y1 - less_than_y2 = curve_y_coordinates <= y2 - inside_interval_bools = greater_than_y1 & less_than_y2 - - # Correct booleans so they are not repeated - bools_interval = np.roll( - inside_interval_bools, 1, axis=1, - ) & inside_interval_bools - - # Calculate intervals on X axis where the points are inside Y axis interval - intervals_x_inside = np.multiply(bools_interval, intervals_x_axis) - - # Delete first element of each interval as it will be a negative number - intervals_x_inside = np.delete(intervals_x_inside, 0, axis=1) - - return np.sum(intervals_x_inside, axis=1) - - -def occupation_measure( - data: Union[FDataGrid, FDataBasis], - intervals: np.ndarray, - *, - n_points: Union[int, None] = None, -) -> np.ndarray: - r""" - Calculate the occupation measure of a grid. - - It performs the following map. - ..math: - :math:`f_1(X) = |t: X(t)\in T_p|,\dots,|t: X(t)\in T_p|` - - where :math:`{T_1,\dots,T_p}` are disjoint intervals in - :math:`\mathbb{R}` and | | stands for the Lebesgue measure. - - Args: - data: FDataGrid or FDataBasis where we want to calculate - the occupation measure. - intervals: ndarray of tuples containing the - intervals we want to consider. The shape should be - (n_sequences,2) - n_points: Number of points to evaluate in the domain. - By default will be used the points defined on the FDataGrid. - On a FDataBasis this value should be specified. - Returns: - ndarray of shape (n_intervals, n_samples) - with the transformed data. - - Example: - We will create the FDataGrid that we will use to extract - the occupation measure - >>> from skfda.representation import FDataGrid - >>> import numpy as np - >>> t = np.linspace(0, 10, 100) - >>> fd_grid = FDataGrid( - ... data_matrix=[ - ... t, - ... 2 * t, - ... np.sin(t), - ... ], - ... grid_points=t, - ... ) - - Finally we call to the occupation measure function with the - intervals that we want to consider. In our case (0.0, 1.0) - and (2.0, 3.0). We need also to specify the number of points - we want that the function takes into account to interpolate. - We are going to use 501 points. - >>> from skfda.exploratory.stats import occupation_measure - >>> np.around( - ... occupation_measure( - ... fd_grid, - ... [(0.0, 1.0), (2.0, 3.0)], - ... n_points=501, - ... ), - ... decimals=2, - ... ) - array([[ 1. , 0.5 , 6.27], - [ 0.98, 0.48, 0. ]]) - - """ - if isinstance(data, FDataBasis) and n_points is None: - raise ValueError( - "Number of points to consider, should be given " - + " as an argument for a FDataBasis. Instead None was passed.", - ) - - for interval_check in intervals: - y1, y2 = interval_check - if y2 < y1: - raise ValueError( - "Interval limits (a,b) should satisfy a <= b. " - + str(interval_check) + " doesn't", - ) - - if n_points is None: - function_x_coordinates = data.grid_points[0] - function_y_coordinates = data.data_matrix - else: - function_x_coordinates = np.arange( - data.domain_range[0][0], - data.domain_range[0][1], - (data.domain_range[0][1] - data.domain_range[0][0]) / n_points, - ) - function_y_coordinates = data(function_x_coordinates) - - return np.asarray([ - _calculate_curves_occupation_( # noqa: WPS317 - function_y_coordinates, - function_x_coordinates, - interval, - ) - for interval in intervals - ]) +from ...representation import FDataGrid def number_up_crossings( From 9f1f5839773eeb9e9935365bac7178243efd64ed Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 3 Feb 2022 23:27:59 +0100 Subject: [PATCH 039/400] Occupation measure construction --- skfda/exploratory/stats/__init__.py | 6 +- .../stats/_functional_transformers.py | 174 ------------------ 2 files changed, 1 insertion(+), 179 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 2e401b15e..5bae28d9a 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,9 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - number_up_crossings, - occupation_measure, -) +from ._functional_transformers import occupation_measure from ._stats import ( cov, depth_based_median, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 65c33ca99..38778b01e 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -9,71 +9,6 @@ from ...representation import FDataBasis, FDataGrid -def local_averages( - data: Union[FDataGrid, FDataBasis], - n_intervals: int, -) -> np.ndarray: - r""" - Calculate the local averages of a given data. - - Take functional data as a grid or a basis and performs - the following map: - - .. math:: - f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ - f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt - - where {T_1,\dots,T_p} are disjoint intervals of the interval [a,b] - - It is calculated for a given number of intervals, - which are of equal sizes. - Args: - data: FDataGrid or FDataBasis where we want to - calculate the local averages. - n_intervals: number of intervals we want to consider. - Returns: - ndarray of shape (n_intervals, n_samples, n_dimensions) - with the transformed data for FDataBasis and (n_intervals, n_samples) - for FDataGrid. - - Example: - - We import the Berkeley Growth Study dataset. - We will use only the first 30 samples to make the - example easy. - >>> from skfda.datasets import fetch_growth - >>> dataset = fetch_growth(return_X_y=True)[0] - >>> X = dataset[:30] - - Then we decide how many intervals we want to consider (in our case 2) - and call the function with the dataset. - >>> import numpy as np - >>> from skfda.exploratory.stats import local_averages - >>> np.around(local_averages(X, 2), decimals=2) - array([[ 116.94, 111.86, 107.29, 111.35, 104.39, 109.43, 109.16, - 112.91, 109.19, 117.95, 112.14, 114.3 , 111.48, 114.85, - 116.25, 114.6 , 111.02, 113.57, 108.88, 109.6 , 109.7 , - 108.54, 109.18, 106.92, 109.44, 109.84, 115.32, 108.16, - 119.29, 110.62], - [ 177.26, 157.62, 154.97, 163.83, 156.66, 157.67, 155.31, - 169.02, 154.18, 174.43, 161.33, 170.14, 164.1 , 170.1 , - 166.65, 168.72, 166.85, 167.22, 159.4 , 162.76, 155.7 , - 158.01, 160.1 , 155.95, 157.95, 163.53, 162.29, 153.1 , - 178.48, 161.75]]) - """ - domain_range = data.domain_range - - left, right = domain_range[0] - interval_size = (right - left) / n_intervals - integrated_data = [] - for i in np.arange(left, right, interval_size): - interval = (i, i + interval_size) - integrated_data = integrated_data + [ - data.integrate(interval=(interval,)) / interval_size, - ] - return np.asarray(integrated_data) - - def _calculate_curves_occupation_( curve_y_coordinates: np.ndarray, curve_x_coordinates: np.ndarray, @@ -205,112 +140,3 @@ def occupation_measure( ) for interval in intervals ]) - - -def number_up_crossings( - data: FDataGrid, - levels: np.ndarray, -) -> np.ndarray: - r""" - Calculate the number of up crossings to a level of a FDataGrid. - - Let f_1(X) = N_i, where N_i is the number of up crossings of X - to a level c_i \in \mathbb{R}, i = 1,\dots,p. - - Recall that the process X(t) is said to have an up crossing of c - at :math:`t_0 > 0` if for some :math:`\epsilon >0`, X(t) $\leq$ - c if t :math:'\in (t_0 - \epsilon, t_0) and X(t) $\geq$ c if - :math:`t\in (t_0, t_0+\epsilon)`. - - If the trajectories are differentiable, then - :math:`N_i = card\{t \in[a,b]: X(t) = c_i, X' (t) > 0\}.` - - Args: - data: FDataGrid where we want to calculate - the number of up crossings. - levels: sequence of numbers including the levels - we want to consider for the crossings. - Returns: - ndarray of shape (n_levels, n_samples)\ - with the values of the counters. - - Example: - - We import the Medflies dataset and for simplicity we use - the first 50 samples. - >>> from skfda.datasets import fetch_medflies - >>> dataset = fetch_medflies() - >>> X = dataset['data'][:50] - - Then we decide the level we want to consider (in our case 40) - and call the function with the dataset. The output will be the number of - times each curve cross the level 40 growing. - >>> from skfda.exploratory.stats import number_up_crossings - >>> import numpy as np - >>> number_up_crossings(X, np.asarray([40])) - array([[[6], - [3], - [7], - [7], - [3], - [4], - [5], - [7], - [4], - [6], - [4], - [4], - [5], - [6], - [0], - [5], - [1], - [6], - [0], - [7], - [0], - [6], - [2], - [5], - [6], - [5], - [8], - [4], - [3], - [7], - [1], - [3], - [0], - [5], - [2], - [7], - [2], - [5], - [5], - [5], - [4], - [4], - [1], - [2], - [3], - [5], - [3], - [3], - [5], - [2]]]) - """ - curves = data.data_matrix - - distances = np.asarray([ - level - curves - for level in levels - ]) - - points_greater = distances >= 0 - points_smaller = distances <= 0 - points_smaller_rotated = np.roll(points_smaller, -1, axis=2) - - return np.sum( - points_greater & points_smaller_rotated, - axis=2, - ) From 657863fcff5decd03feb2cfa48a75c9fca2b33ae Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 6 Feb 2022 23:01:18 +0100 Subject: [PATCH 040/400] Corrections --- .../stats/_functional_transformers.py | 21 +++++++++++-------- skfda/representation/basis/_fdatabasis.py | 7 +------ 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 65220edbc..1683aa0d9 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -61,14 +61,17 @@ def local_averages( 158.01, 160.1 , 155.95, 157.95, 163.53, 162.29, 153.1 , 178.48, 161.75]]) """ - domain_range = data.domain_range + left, right = data.domain_range[0] - left, right = domain_range[0] - interval_size = (right - left) / n_intervals - integrated_data = [] - for i in np.arange(left, right, interval_size): - interval = (i, i + interval_size) - integrated_data = integrated_data + [ - data.integrate(interval=(interval,)) / interval_size, - ] + intervals, step = np.linspace( + left, + right, + num=n_intervals + 1, + retstep=True, + ) + + integrated_data = [ + data.integrate(interval=((intervals[i], intervals[i + 1]))) / step + for i in np.arange(0, n_intervals) + ] return np.asarray(integrated_data) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 55af9ddd6..65aa6e160 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -364,7 +364,6 @@ def integrate( ) -> NDArrayFloat: """Examples. - We first create the data basis. >>> from skfda.representation.basis import FDataBasis, Monomial >>> basis = Monomial(n_basis=4) @@ -387,12 +386,8 @@ def integrate( self, interval, ) - integrated_values = np.empty((1, 1)) - - for i in integrated: - integrated_values = np.concatenate([integrated_values, i]) - return integrated_values[1:] + return integrated[0] def sum( # noqa: WPS125 self: T, From c9c0030ea6e71ed50e5b44f7e11de66fb76163b8 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 7 Feb 2022 20:50:00 +0100 Subject: [PATCH 041/400] Close local averages --- skfda/exploratory/stats/_functional_transformers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 1683aa0d9..1d77f0ae3 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -72,6 +72,6 @@ def local_averages( integrated_data = [ data.integrate(interval=((intervals[i], intervals[i + 1]))) / step - for i in np.arange(0, n_intervals) + for i in range(n_intervals) ] return np.asarray(integrated_data) From 55c7a48bc2d354edeab128709becf52e2838ca3f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 8 Feb 2022 22:25:37 +0100 Subject: [PATCH 042/400] First typing for recursive maxima hunting. --- skfda/_utils/_sklearn_adapter.py | 42 +- .../variable_selection/maxima_hunting.py | 5 +- .../recursive_maxima_hunting.py | 632 ++++++++++++------ tests/test_recursive_maxima_hunting.py | 46 +- 4 files changed, 494 insertions(+), 231 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 266970f71..39f9632d9 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -6,9 +6,13 @@ import sklearn.base SelfType = TypeVar("SelfType") +TransformerNoTarget = TypeVar( + "TransformerNoTarget", + bound="TransformerMixin[Any, Any, None]", +) Input = TypeVar("Input") Output = TypeVar("Output") -Target = TypeVar("Target") +Target = TypeVar("Target", contravariant=True) class BaseEstimator( @@ -21,22 +25,43 @@ class BaseEstimator( class TransformerMixin( ABC, Generic[Input, Output, Target], - sklearn.base.TransformerMixin, # type: ignore[misc] + # sklearn.base.TransformerMixin, # Inherit in the future ): + @overload + def fit( + self: TransformerNoTarget, + X: Input, + ) -> TransformerNoTarget: + pass + + @overload def fit( self: SelfType, X: Input, - y: Optional[Target] = None, + y: Target, ) -> SelfType: + pass + def fit( + self: SelfType, + X: Input, + y: Optional[Target] = None, + ) -> SelfType: return self - @overload # type: ignore[misc] + @overload + def fit_transform( + self: TransformerNoTarget, + X: Input, + ) -> Output: + pass + + @overload def fit_transform( self, X: Input, - y: Optional[Target] = None, + y: Target, ) -> Output: pass @@ -46,8 +71,10 @@ def fit_transform( y: Optional[Target] = None, **fit_params: Any, ) -> Output: - - return super().fit_transform(X, y, **fit_params) + if y is None: + return self.fit(X, **fit_params).transform(X) # type: ignore + else: + return self.fit(X, y, **fit_params).transform(X) # type: ignore class InductiveTransformerMixin( @@ -59,5 +86,4 @@ def transform( self: SelfType, X: Input, ) -> Output: - pass diff --git a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py index 57097f1cf..38e37ed59 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py @@ -7,7 +7,6 @@ import scipy.signal import sklearn.base import sklearn.utils - from dcor import u_distance_correlation_sqr from ...._utils import _compute_dependence, _DependenceMeasure @@ -167,11 +166,11 @@ def __init__( self.dependence_measure = dependence_measure self.local_maxima_selector = local_maxima_selector - def fit( + def fit( # noqa: D102 self, X: FDataGrid, y: Union[NDArrayInt, NDArrayFloat], - ) -> MaximaHunting: # noqa: D102 + ) -> MaximaHunting: self.features_shape_ = X.data_matrix.shape[1:] self.dependence_ = _compute_dependence( diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py index 762fa0d2c..0aad65af2 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py @@ -1,22 +1,46 @@ +"""Recursive Maxima Hunting implementation.""" +from __future__ import annotations + import abc import copy import numbers -import random +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Iterable, + Mapping, + Optional, + Sequence, + Tuple, + Union, + overload, +) +import dcor import numpy as np import numpy.linalg as linalg import numpy.ma as ma import scipy.stats -import sklearn.base import sklearn.utils - -import dcor +from numpy.typing import ArrayLike +from typing_extensions import Literal from ...._utils import _compute_dependence +from ...._utils._sklearn_adapter import ( + BaseEstimator, + InductiveTransformerMixin, +) from ....representation import FDataGrid +from ....representation._typing import NDArrayBool, NDArrayFloat, NDArrayInt + +if TYPE_CHECKING: + from ....misc.covariances import CovarianceLike + import GPy + from .maxima_hunting import _DependenceMeasure as _DepMeasure -def _transform_to_2d(t): +def _transform_to_2d(t: ArrayLike) -> NDArrayFloat: t = np.asarray(t) dim = len(t.shape) @@ -28,40 +52,47 @@ def _transform_to_2d(t): return t -def _execute_kernel(kernel, t_0, t_1): +def _execute_kernel( + kernel: CovarianceLike, + t_0: ArrayLike, + t_1: ArrayLike, +) -> NDArrayFloat: from ....misc.covariances import _execute_covariance return _execute_covariance(kernel, t_0, t_1) class _PicklableKernel(): - """ Class used to pickle GPy kernels.""" + """Class used to pickle GPy kernels.""" - def __init__(self, kernel): + def __init__(self, kernel: GPy.kern.Kern) -> None: super().__setattr__('_PicklableKernel__kernel', kernel) - def __getattr__(self, name): + def __getattr__(self, name: str) -> Any: if name != '__deepcopy__': return getattr(self.__kernel, name) - def __setattr__(self, name, value): + def __setattr__(self, name: str, value: Any) -> None: setattr(self.__kernel, name, value) - def __getstate__(self): - return {'class': self.__kernel.__class__, - 'input_dim': self.__kernel.input_dim, - 'values': self.__kernel.param_array} + def __getstate__(self) -> Mapping[str, Any]: + return { + 'class': self.__kernel.__class__, + 'input_dim': self.__kernel.input_dim, + 'values': self.__kernel.param_array, + } - def __setstate__(self, state): + def __setstate__(self, state: Mapping[str, Any]) -> None: super().__setattr__('_PicklableKernel__kernel', state['class']( - input_dim=state['input_dim'])) + input_dim=state['input_dim']), + ) self.__kernel.param_array[...] = state['values'] - def __call__(self, *args, **kwargs): + def __call__(self, *args: Any, **kwargs: Any) -> NDArrayFloat: return self.__kernel.K(*args, **kwargs) -def make_kernel(k): +def make_kernel(k: CovarianceLike) -> CovarianceLike: try: import GPy except ImportError: @@ -73,50 +104,59 @@ def make_kernel(k): return k -def _absolute_argmax(function, *, mask): +def _absolute_argmax( + function: NDArrayFloat, + *, + mask: NDArrayBool, +) -> Tuple[int, ...]: """ - Computes the absolute maximum of a discretized function. + Compute the absolute maximum of a discretized function. Some values of the function may be masked in order not to consider them as maximum. Parameters: - function (numpy array): Discretized function. - mask (numpy boolean array): Masked values. + function: Discretized function. + mask: Masked values. Returns: - int: Index of the absolute maximum. + Index of the absolute maximum. """ masked_function = ma.array(function, mask=mask) t_max = ma.argmax(masked_function) - t_max = np.unravel_index(t_max, function.shape) - - return t_max + return np.unravel_index(t_max, function.shape) -class Correction(abc.ABC, sklearn.base.BaseEstimator): +class Correction(BaseEstimator): """ - Base class for applying a correction after a point is taken, eliminating + Base class for corrections. + + A correction applies a modification after a point is taken, eliminating its influence over the rest. """ - def begin(self, X: FDataGrid, Y): + def begin(self, X: FDataGrid, Y: NDArrayFloat) -> None: """ - Initialization for a particular application of Recursive Maxima - Hunting. + Initialize the correction for a run. The initial parameters of Recursive Maxima Hunting can be used there. """ pass - def conditioned(self, **kwargs): + def conditioned( + self, + *, + X: NDArrayFloat, + T: NDArrayFloat, + t_0: float, + ) -> Correction: """ - Returns a correction object conditioned to the value of a point. + Return a correction object conditioned to the value of a point. This method is necessary because after the RMH correction step, the functions follow a different model. @@ -125,7 +165,11 @@ def conditioned(self, **kwargs): return self @abc.abstractmethod - def correct(self, X, selected_index): + def correct( + self, + X: FDataGrid, + selected_index: Tuple[int, ...], + ) -> None: """ Correct the trajectories. @@ -133,15 +177,14 @@ def correct(self, X, selected_index): other points in the function. Parameters: - - X (FDataGrid): Functions in the current iteration of the algorithm. - selected_index (int or tuple of int): Index of the selected point + X: Functions in the current iteration of the algorithm. + selected_index: Index of the selected point in the ``data_matrix``. """ pass - def __call__(self, *args, **kwargs): + def __call__(self, *args: Any, **kwargs: Any) -> None: self.correct(*args, **kwargs) @@ -157,24 +200,32 @@ class ConditionalMeanCorrection(Correction): """ @abc.abstractmethod - def conditional_mean(self, X, selected_index): + def conditional_mean( + self, + X: FDataGrid, + selected_index: Tuple[int, ...], + ) -> NDArrayFloat: """ - Mean of the process conditioned to the value observed at the - selected point. + Mean of the process conditioned to the value observed. Parameters: - - X (FDataGrid): Functions in the current iteration of the algorithm. + X: Functions in the current iteration of the algorithm. selected_index (int or tuple of int): Index of the selected point in the ``data_matrix``. """ pass - def correct(self, X, selected_index): + def correct( + self, + X: FDataGrid, + selected_index: Tuple[int, ...], + ) -> None: X.data_matrix[...] -= self.conditional_mean( - X, selected_index).T + X, + selected_index, + ).T X.data_matrix[:, selected_index] = 0 @@ -195,22 +246,27 @@ class GaussianCorrection(ConditionalMeanCorrection): :class:`GaussianConditionedCorrection`. Parameters: - - mean (number or function): Mean function of the Gaussian process. - cov (number or function): Covariance function of the Gaussian process. - fit_hyperparameters (boolean): If ``True`` the hyperparameters of the + mean: Mean function of the Gaussian process. + cov: Covariance function of the Gaussian process. + fit_hyperparameters: If ``True`` the hyperparameters of the covariance function are optimized for the data. """ - def __init__(self, *, mean=0, cov=1, fit_hyperparameters=False): - super(GaussianCorrection, self).__init__() + def __init__( + self, + *, + mean: Union[float, Callable[[NDArrayFloat], NDArrayFloat]] = 0, + cov: Union[float, CovarianceLike] = 1, + fit_hyperparameters: bool = False, + ) -> None: + super().__init__() self.mean = mean self.cov = make_kernel(cov) self.fit_hyperparameters = fit_hyperparameters - def begin(self, X, y): + def begin(self, X: FDataGrid, y: NDArrayFloat) -> None: if self.fit_hyperparameters: import GPy @@ -232,7 +288,7 @@ def begin(self, X, y): self.cov_ = copy.deepcopy(make_kernel(m.kern)) - def _evaluate_mean(self, t): + def _evaluate_mean(self, t: NDArrayFloat) -> NDArrayFloat: mean = self.mean @@ -243,12 +299,22 @@ def _evaluate_mean(self, t): return expectation - def _evaluate_cov(self, t_0, t_1): + def _evaluate_cov( + self, + t_0: NDArrayFloat, + t_1: NDArrayFloat, + ) -> NDArrayFloat: cov = getattr(self, "cov_", self.cov) return _execute_kernel(cov, t_0, t_1) - def conditioned(self, X, t_0, **kwargs): + def conditioned( + self, + *, + X: NDArrayFloat, + T: NDArrayFloat, + t_0: float, + ) -> Correction: # If the point makes the matrix singular, don't change the correction cov = getattr(self, "cov_", self.cov) @@ -258,7 +324,7 @@ def conditioned(self, X, t_0, **kwargs): correction = GaussianConditionedCorrection( mean=self.mean, cov=cov, - conditioning_points=t_0) + conditioning_points=np.asarray(t_0)) correction._covariance_matrix_inv() @@ -268,7 +334,11 @@ def conditioned(self, X, t_0, **kwargs): return self - def conditional_mean(self, X, selected_index): + def conditional_mean( + self, + X: FDataGrid, + selected_index: Tuple[int, ...], + ) -> NDArrayFloat: T = X.grid_points[0] @@ -289,44 +359,52 @@ def conditional_mean(self, X, selected_index): b_T = self._evaluate_cov(T, t_0) assert b_T.shape == T.shape - cond_expectation = (expectation + - b_T / var * - (x_0.T - t_0_expectation) - ) if var else expectation + np.zeros_like(x_0.T) + cond_expectation = ( + expectation + + b_T / var + * (x_0.T - t_0_expectation) + ) if var else expectation + np.zeros_like(x_0.T) return cond_expectation class GaussianConditionedCorrection(GaussianCorrection): - r""" + """ + Correction for a conditioned Gaussian process. + Correction assuming that the underlying process is Gaussian, with several values conditioned to 0. - The conditional mean is inherited from :class:`GaussianCorrection`, with - the conditioned mean and covariance. + The conditional mean is inherited from :class:`GaussianCorrection`, with + the conditioned mean and covariance. The corrections after this is applied are of type :class:`GaussianConditionedCorrection`, adding additional points. Parameters: - - conditioning_points (iterable of ints or tuples of ints): Points where - the process is conditioned to have the value 0. - mean (number or function): Mean function of the (unconditioned) - Gaussian process. - cov (number or function): Covariance function of the (unconditioned) - Gaussian process. + conditioning_points: Points where the process is conditioned to + have the value 0. + mean: Mean function of the (unconditioned) Gaussian process. + cov: Covariance function of the (unconditioned) Gaussian process. """ - def __init__(self, conditioning_points, *, mean=0, cov=1): + def __init__( + self, + conditioning_points: NDArrayFloat, + *, + mean: Union[float, Callable[[NDArrayFloat], NDArrayFloat]] = 0, + cov: CovarianceLike = 1, + ) -> None: - super(GaussianConditionedCorrection, self).__init__( - mean=mean, cov=cov) + super().__init__( + mean=mean, + cov=cov, + ) self.conditioning_points = conditioning_points - def _covariance_matrix_inv(self): + def _covariance_matrix_inv(self) -> NDArrayFloat: cond_points = self._conditioning_points() @@ -334,7 +412,7 @@ def _covariance_matrix_inv(self): if cov_matrix_inv is None: cov_matrix = super()._evaluate_cov( - cond_points, cond_points + cond_points, cond_points, ) self._cov_matrix_inv = np.linalg.inv(cov_matrix) @@ -342,10 +420,16 @@ def _covariance_matrix_inv(self): return cov_matrix_inv - def _conditioning_points(self): + def _conditioning_points(self) -> NDArrayFloat: return _transform_to_2d(self.conditioning_points) - def conditioned(self, X, t_0, **kwargs): + def conditioned( + self, + *, + X: NDArrayFloat, + T: NDArrayFloat, + t_0: float, + ) -> Correction: # If the point makes the matrix singular, don't change the correction try: @@ -354,7 +438,8 @@ def conditioned(self, X, t_0, **kwargs): mean=self.mean, cov=self.cov, conditioning_points=np.concatenate( - (self._conditioning_points(), [[t_0]])) + (self._conditioning_points(), np.array([[t_0]])), + ), ) correction._covariance_matrix_inv() @@ -365,7 +450,7 @@ def conditioned(self, X, t_0, **kwargs): return self - def _evaluate_mean(self, t): + def _evaluate_mean(self, t: NDArrayFloat) -> NDArrayFloat: cond_points = self._conditioning_points() @@ -387,7 +472,11 @@ def _evaluate_mean(self, t): return expectation - def _evaluate_cov(self, t_0, t_1): + def _evaluate_cov( + self, + t_0: NDArrayFloat, + t_1: NDArrayFloat, + ) -> NDArrayFloat: cond_points = self._conditioning_points() @@ -397,18 +486,22 @@ def _evaluate_cov(self, t_0, t_1): b_t_1 = super()._evaluate_cov(cond_points, t_1) - return (super()._evaluate_cov(t_0, t_1) - - b_t_0_T @ A_inv @ b_t_1) + return ( + super()._evaluate_cov(t_0, t_1) + - b_t_0_T @ A_inv @ b_t_1 + ) class GaussianSampleCorrection(ConditionalMeanCorrection): """ + Gaussian correction with sample covariance. + Correction assuming that the process is Gaussian and using as the kernel the sample covariance. """ - def begin(self, X: FDataGrid, Y): + def begin(self, X: FDataGrid, Y: NDArrayFloat) -> None: X_copy = np.copy(X.data_matrix[..., 0]) @@ -422,30 +515,52 @@ def begin(self, X: FDataGrid, Y): self.cov_matrix_ = np.cov(X_copy, rowvar=False) self.t_ = np.ravel(X.grid_points) self.gaussian_correction_ = GaussianCorrection( - cov=self.cov) - - def cov(self, t_0, t_1): + cov=self.cov_fun, + ) + + def cov_fun( + self, + t_0: ArrayLike, + t_1: ArrayLike, + ) -> NDArrayFloat: i = np.searchsorted(self.t_, t_0) j = np.searchsorted(self.t_, t_1) - i = np.ravel(i) - j = np.ravel(j) + i_r = np.ravel(i) + j_r = np.ravel(j) - return self.cov_matrix_[np.ix_(i, j)] + return self.cov_matrix_[np.ix_(i_r, j_r)] - def conditioned(self, t_0, **kwargs): + def conditioned( + self, + *, + X: NDArrayFloat, + T: NDArrayFloat, + t_0: float, + ) -> Correction: self.gaussian_correction_ = self.gaussian_correction_.conditioned( - t_0=t_0, **kwargs) + X=X, + T=T, + t_0=t_0, + ) return self - def conditional_mean(self, X, selected_index): + def conditional_mean( + self, + X: FDataGrid, + selected_index: Tuple[int, ...], + ) -> NDArrayFloat: return self.gaussian_correction_.conditional_mean( - X, selected_index) + X, + selected_index, + ) class UniformCorrection(Correction): """ + Correction for uniform process. + Correction assuming that the underlying process is an Ornstein-Uhlenbeck process with infinite lengthscale. @@ -456,12 +571,21 @@ class UniformCorrection(Correction): """ - def conditioned(self, X, t_0, **kwargs): + def conditioned( + self, + X: NDArrayFloat, + T: NDArrayFloat, + t_0: float, + ) -> Correction: from ....misc.covariances import Brownian return GaussianCorrection(cov=Brownian(origin=t_0)) - def correct(self, X, selected_index): + def correct( + self, + X: FDataGrid, + selected_index: Tuple[int, ...], + ) -> None: x_index = (slice(None),) + tuple(selected_index) + (np.newaxis,) # Have to copy it because otherwise is a view and shouldn't be @@ -471,7 +595,7 @@ def correct(self, X, selected_index): X.data_matrix[...] -= x_0 -class StoppingCondition(abc.ABC, sklearn.base.BaseEstimator): +class StoppingCondition(BaseEstimator): """ Stopping condition for RMH. @@ -481,8 +605,16 @@ class StoppingCondition(abc.ABC, sklearn.base.BaseEstimator): """ @abc.abstractmethod - def __call__(self, **kwargs): - pass + def __call__( + self, + *, + selected_index: Tuple[int, ...], + dependences: NDArrayFloat, + selected_variable: NDArrayFloat, + X: FDataGrid, + y: NDArrayFloat, + ) -> bool: + """Whether the algorithm should stop.""" class ScoreThresholdStop(StoppingCondition): @@ -497,20 +629,25 @@ class ScoreThresholdStop(StoppingCondition): points chosen and can vary per problem. Parameters: - - threshold (float): Value compared with the score. If the score - of the selected point is not higher than that, - the point will not be selected (unless it is - the first iteration) and RMH will end. + threshold: Value compared with the score. If the score + of the selected point is not higher than that, + the point will not be selected (unless it is + the first iteration) and RMH will end. """ - def __init__(self, threshold=0.2): + def __init__(self, threshold: float = 0.2) -> None: super().__init__() self.threshold = threshold - def __call__(self, *, selected_index, dependences, **kwargs): + def __call__( + self, + *, + selected_index: Tuple[int, ...], + dependences: NDArrayFloat, + **kwargs: Any, + ) -> bool: score = dependences[selected_index] @@ -538,20 +675,24 @@ class AsymptoticIndependenceTestStop(StoppingCondition): distance covariance. Parameters: - - significance (float): Significance used in the independence test. By - default is 0.01 (1%). + significance: Significance used in the independence test. By + default is 0.01 (1%). References: .. footbibliography:: """ - def __init__(self, significance=0.01): + def __init__(self, significance: float = 0.01) -> None: super().__init__() self.significance = significance - def chi_bound(self, x, y, significance): + def chi_bound( + self, + x: NDArrayFloat, + y: NDArrayFloat, + significance: float, + ) -> float: x_dist = dcor.distances.pairwise_distances(x) y_dist = dcor.distances.pairwise_distances(y) @@ -562,14 +703,20 @@ def chi_bound(self, x, y, significance): return chi_quant * t2 / x_dist.shape[0] - def __call__(self, *, selected_variable, y, **kwargs): + def __call__( + self, + *, + selected_variable: NDArrayFloat, + y: NDArrayFloat, + **kwargs: Any, + ) -> bool: bound = self.chi_bound(selected_variable, y, self.significance) return dcor.u_distance_covariance_sqr(selected_variable, y) < bound -class RedundancyCondition(abc.ABC, sklearn.base.BaseEstimator): +class RedundancyCondition(BaseEstimator): """ Redundancy condition for RMH. @@ -579,58 +726,77 @@ class RedundancyCondition(abc.ABC, sklearn.base.BaseEstimator): """ @abc.abstractmethod - def __call__(self, max_point, test_point, **kwargs): + def __call__( + self, + *, + max_point: NDArrayFloat, + test_point: NDArrayFloat, + **kwargs: Any, + ) -> bool: pass class DependenceThresholdRedundancy(RedundancyCondition): """ - The points are redundant if their dependency is above a given - threshold. + The points are redundant if their dependency is above a given threshold. This stopping condition requires that the dependency has a known bound, for example that it takes values in the interval :math:`[0, 1]`. Parameters: - - threshold (float): Value compared with the score. If the score + threshold: Value compared with the score. If the score of the selected point is not higher than that, the point will not be selected (unless it is the first iteration) and RMH will end. - dependence_measure (callable): Dependence measure to use. By default, + dependence_measure: Dependence measure to use. By default, it uses the bias corrected squared distance correlation. """ - def __init__(self, threshold=0.9, *, - dependence_measure=dcor.u_distance_correlation_sqr): - + def __init__( + self, + threshold: float = 0.9, + *, + dependence_measure: _DepMeasure = dcor.u_distance_correlation_sqr, + ) -> None: super().__init__() self.threshold = threshold self.dependence_measure = dependence_measure - def __call__(self, *, max_point, test_point, **kwargs): - - return self.dependence_measure(max_point, test_point) > self.threshold + def __call__( + self, + *, + max_point: NDArrayFloat, + test_point: NDArrayFloat, + **kwargs: Any, + ) -> bool: + return bool( + self.dependence_measure(max_point, test_point) > self.threshold, + ) class RMHResult(object): - def __init__(self, index, score): + def __init__(self, index: Tuple[int, ...], score: float) -> None: self.index = index self.score = score - self.matrix_after_correction = None - self.original_dependence = None - self.influence_mask = None - self.current_mask = None - - def __repr__(self): - return (self.__class__.__name__ + - "(index={index}, score={score})" - .format(index=self.index, score=self.score)) - - -def _get_influence_mask(X, t_max_index, redundancy_condition, old_mask): + self.matrix_after_correction: Optional[NDArrayFloat] = None + self.original_dependence: Optional[NDArrayFloat] = None + self.influence_mask: Optional[NDArrayBool] = None + self.current_mask: Optional[NDArrayBool] = None + + def __repr__(self) -> str: + return ( + f"{type(self).__name__}(index={self.index}, score={self.score})" + ) + + +def _get_influence_mask( + X: NDArrayFloat, + t_max_index: Tuple[int, ...], + redundancy_condition: RedundancyCondition, + old_mask: NDArrayBool, +) -> NDArrayBool: """ Get the mask of the points that have a large dependence with the selected point. @@ -638,18 +804,22 @@ def _get_influence_mask(X, t_max_index, redundancy_condition, old_mask): """ sl = slice(None) - def get_index(index): + def get_index( + index: Tuple[int, ...], + ) -> Tuple[Union[slice, int, None], ...]: return (sl,) + tuple(index) + (np.newaxis,) - def is_redundant(index): + def is_redundant(index: Tuple[int, ...]) -> bool: max_point = np.squeeze(X[get_index(t_max_index)], axis=1) test_point = np.squeeze(X[get_index(index)], axis=1) - return redundancy_condition(max_point=max_point, - test_point=test_point) + return redundancy_condition( + max_point=max_point, + test_point=test_point, + ) - def adjacent_indexes(index): + def adjacent_indexes(index: Tuple[int, ...]) -> Iterable[Tuple[int, ...]]: for i, coord in enumerate(index): # Out of bounds right check if coord < (X.shape[i + 1] - 1): @@ -662,7 +832,10 @@ def adjacent_indexes(index): new_index[i] -= 1 yield tuple(new_index) - def update_mask(new_mask, index): + def update_mask( + new_mask: NDArrayBool, + index: Tuple[int, ...], + ) -> None: indexes = [index] while indexes: @@ -687,33 +860,35 @@ def update_mask(new_mask, index): def _rec_maxima_hunting_gen_no_copy( - X: FDataGrid, y, *, - dependence_measure=dcor.u_distance_correlation_sqr, - correction=None, - redundancy_condition=None, - stopping_condition=None, - mask=None, - get_intermediate_results=False): + X: FDataGrid, + y: Union[NDArrayInt, NDArrayFloat], + *, + dependence_measure: _DepMeasure = dcor.u_distance_correlation_sqr, + correction: Optional[Correction] = None, + redundancy_condition: Optional[RedundancyCondition] = None, + stopping_condition: Optional[StoppingCondition] = None, + mask: Optional[NDArrayBool] = None, + get_intermediate_results: bool = False, +) -> Iterable[RMHResult]: """ + Recursive maxima hunting algorithm. + Find the most relevant features of a function using recursive maxima hunting. It changes the original matrix. Parameters: - - dependence_measure (callable): Dependence measure to use. By default, + dependence_measure: Dependence measure to use. By default, it uses the bias corrected squared distance correlation. - max_features (int): Maximum number of features to select. - correction (Correction): Correction used to subtract the information + correction: Correction used to subtract the information of each selected point in each iteration. - redundancy_condition (callable): Condition to consider a point + redundancy_condition: Condition to consider a point redundant with the selected maxima and discard it from future consideration as a maximum. - stopping_condition (callable): Condition to stop the algorithm. - mask (boolean array): Masked values. - get_intermediate_results (boolean): Return additional debug info. + stopping_condition: Condition to stop the algorithm. + mask: Masked values. + get_intermediate_results: Return additional debug info. """ - # X = np.asfarray(X) y = np.asfarray(y) if correction is None: @@ -734,11 +909,15 @@ def _rec_maxima_hunting_gen_no_copy( while True: dependences = _compute_dependence( - X=X.data_matrix, y=y, - dependence_measure=dependence_measure) - - t_max_index = _absolute_argmax(dependences, - mask=mask) + X=X.data_matrix, + y=y, + dependence_measure=dependence_measure, + ) + + t_max_index = _absolute_argmax( + dependences, + mask=mask, + ) score = dependences[t_max_index] repeated_point = mask[t_max_index] @@ -746,24 +925,33 @@ def _rec_maxima_hunting_gen_no_copy( stopping_condition_reached = stopping_condition( selected_index=t_max_index, dependences=dependences, - selected_variable=X.data_matrix[(slice(None),) + - tuple(t_max_index)], - X=X, y=y) - - if ((repeated_point or stopping_condition_reached) and - not first_pass): + selected_variable=X.data_matrix[ + (slice(None),) + tuple(t_max_index) + ], + X=X, + y=y, + ) + + if ( + (repeated_point or stopping_condition_reached) + and not first_pass + ): return influence_mask = _get_influence_mask( - X=X.data_matrix, t_max_index=t_max_index, + X=X.data_matrix, + t_max_index=t_max_index, redundancy_condition=redundancy_condition, - old_mask=mask) + old_mask=mask, + ) mask |= influence_mask # Correct the influence of t_max - correction(X=X, - selected_index=t_max_index) + correction( + X=X, + selected_index=t_max_index, + ) result = RMHResult(index=t_max_index, score=score) # Additional info, useful for debugging @@ -780,19 +968,33 @@ def _rec_maxima_hunting_gen_no_copy( correction = correction.conditioned( X=X.data_matrix, T=X.grid_points[0], - t_0=X.grid_points[0][t_max_index]) + t_0=X.grid_points[0][t_max_index], + ) first_pass = False -def _rec_maxima_hunting_gen(X, *args, **kwargs): - yield from _rec_maxima_hunting_gen_no_copy(copy.copy(X), - *args, **kwargs) +def _rec_maxima_hunting_gen( + X: FDataGrid, + *args: Any, + **kwargs: Any, +) -> Iterable[RMHResult]: + yield from _rec_maxima_hunting_gen_no_copy( + copy.copy(X), + *args, + **kwargs, + ) class RecursiveMaximaHunting( - sklearn.base.BaseEstimator, sklearn.base.TransformerMixin): - r""" + BaseEstimator, + InductiveTransformerMixin[ + FDataGrid, + NDArrayFloat, + Union[NDArrayInt, NDArrayFloat], + ], +): + """ Recursive Maxima Hunting variable selection. This is a filter variable selection method for problems with a target @@ -814,24 +1016,22 @@ class RecursiveMaximaHunting( :doc:`/modules/preprocessing/dim_reduction/recursive_maxima_hunting`. Parameters: - - dependence_measure (callable): Dependence measure to use. By default, + dependence_measure: Dependence measure to use. By default, it uses the bias corrected squared distance correlation. - max_features (int): Maximum number of features to select. By default + max_features: Maximum number of features to select. By default there is no limit. - correction (Correction): Correction used to subtract the information + correction: Correction used to subtract the information of each selected point in each iteration. By default it is a :class:`.UniformCorrection` object. - redundancy_condition (callable): Condition to consider a point + redundancy_condition: Condition to consider a point redundant with the selected maxima and discard it from future consideration as a maximum. By default it is a - :class:`.DependenceThresholdRedundancy` object. - stopping_condition (callable): Condition to stop the algorithm. By + :class:`DependenceThresholdRedundancy` object. + stopping_condition: Condition to stop the algorithm. By default it is a :class:`.AsymptoticIndependenceTestStop` object. Examples: - >>> from skfda.preprocessing.dim_reduction import variable_selection >>> from skfda.datasets import make_gaussian_process >>> import skfda @@ -888,19 +1088,26 @@ class RecursiveMaximaHunting( """ - def __init__(self, *, - dependence_measure=dcor.u_distance_correlation_sqr, - max_features=None, - correction=None, - redundancy_condition=None, - stopping_condition=None): + def __init__( + self, + *, + dependence_measure: _DepMeasure = dcor.u_distance_correlation_sqr, + max_features: Optional[int] = None, + correction: Optional[Correction] = None, + redundancy_condition: Optional[RedundancyCondition] = None, + stopping_condition: Optional[StoppingCondition] = None, + ) -> None: self.dependence_measure = dependence_measure self.max_features = max_features self.correction = correction self.redundancy_condition = redundancy_condition self.stopping_condition = stopping_condition - def fit(self, X, y): + def fit( + self, + X: FDataGrid, + y: Union[NDArrayInt, NDArrayFloat], + ) -> RecursiveMaximaHunting: self.features_shape_ = X.data_matrix.shape[1:] @@ -912,32 +1119,53 @@ def fit(self, X, y): dependence_measure=self.dependence_measure, correction=self.correction, redundancy_condition=self.redundancy_condition, - stopping_condition=self.stopping_condition)): + stopping_condition=self.stopping_condition, + ), + ): indexes.append(result.index) - if self.max_features is not None and i >= self.max_features: + if self.max_features is not None and i + 1 >= self.max_features: break self.indexes_ = tuple(np.transpose(indexes).tolist()) return self - def transform(self, X): + def transform(self, X: FDataGrid) -> NDArrayFloat: X_matrix = X.data_matrix sklearn.utils.validation.check_is_fitted(self) if X_matrix.shape[1:] != self.features_shape_: - raise ValueError("The trajectories have a different number of " - "points than the ones fitted") + raise ValueError( + "The trajectories have a different number of " + "points than the ones fitted", + ) output = X_matrix[(slice(None),) + self.indexes_] return output.reshape(X.n_samples, -1) - def get_support(self, indices: bool=False): + @overload + def get_support( + self, + indices: Literal[True], + ) -> Sequence[Tuple[int, ...]]: + pass + + @overload + def get_support( + self, + indices: Literal[False] = False, + ) -> NDArrayBool: + pass + + def get_support( + self, + indices: bool = False, + ) -> Union[Sequence[Tuple[int, ...]], NDArrayBool]: if indices: return self.indexes_ diff --git a/tests/test_recursive_maxima_hunting.py b/tests/test_recursive_maxima_hunting.py index 3f73c4201..6f07bb151 100644 --- a/tests/test_recursive_maxima_hunting.py +++ b/tests/test_recursive_maxima_hunting.py @@ -1,29 +1,37 @@ -import skfda -from skfda.datasets import make_gaussian_process -from skfda.preprocessing.dim_reduction import variable_selection as vs import unittest import numpy as np +import skfda +from skfda.datasets import make_gaussian_process +from skfda.preprocessing.dim_reduction import variable_selection as vs +from skfda.representation._typing import NDArrayFloat + class TestRMH(unittest.TestCase): - def test_rmh(self): + def test_rmh(self) -> None: n_samples = 10000 n_features = 100 - def mean_1(t): - return (np.abs(t - 0.25) - - 2 * np.abs(t - 0.5) - + np.abs(t - 0.75)) - - X_0 = make_gaussian_process(n_samples=n_samples // 2, - n_features=n_features, - random_state=0) - X_1 = make_gaussian_process(n_samples=n_samples // 2, - n_features=n_features, - mean=mean_1, - random_state=1) + def mean_1(t: NDArrayFloat) -> NDArrayFloat: + return ( + np.abs(t - 0.25) + - 2 * np.abs(t - 0.5) + + np.abs(t - 0.75) + ) + + X_0 = make_gaussian_process( + n_samples=n_samples // 2, + n_features=n_features, + random_state=0, + ) + X_1 = make_gaussian_process( + n_samples=n_samples // 2, + n_features=n_features, + mean=mean_1, + random_state=1, + ) X = skfda.concatenate((X_0, X_1)) y = np.zeros(n_samples) @@ -31,11 +39,13 @@ def mean_1(t): correction = vs.recursive_maxima_hunting.GaussianSampleCorrection() stopping_condition = vs.recursive_maxima_hunting.ScoreThresholdStop( - threshold=0.05) + threshold=0.05, + ) rmh = vs.RecursiveMaximaHunting( correction=correction, - stopping_condition=stopping_condition) + stopping_condition=stopping_condition, + ) _ = rmh.fit(X, y) point_mask = rmh.get_support() points = X.grid_points[0][point_mask] From 70dcc10499187d73fcf41c9327cc872b615fd90c Mon Sep 17 00:00:00 2001 From: rafa9811 Date: Thu, 10 Feb 2022 17:47:55 +0100 Subject: [PATCH 043/400] intercalated covariance types in dataframe rows --- skfda/ml/regression/_linear_regression.py | 98 +++++++++++++++++------ 1 file changed, 74 insertions(+), 24 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 0f2460256..a4adc1fa8 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -61,13 +61,6 @@ class LinearRegression( """ - warnings.warn( - "Usage of arguments of type FData, ndarray or a sequence \ - of both is deprecated (fit, predict)." - "Use pandas DataFrame instead", - DeprecationWarning, - ) - r"""Linear regression with multivariate response. This is a regression algorithm equivalent to multivariate linear @@ -168,18 +161,39 @@ class LinearRegression( >>> linear.predict([x, x_fd]) array([ 11., 10., 12., 6., 10., 13.]) - Funcionality with pandas Dataframe: + Funcionality with pandas Dataframe. + + First example: + + >>> x_basis = Monomial(n_basis=3) + >>> cov_list = [ ( FDataBasis(x_basis, [0, 0, 1]) ), + ... ( FDataBasis(x_basis, [0, 1, 0]) ), + ... ( FDataBasis(x_basis, [0, 1, 1]) ), + ... ( FDataBasis(x_basis, [1, 0, 1]) )] + >>> y = [2, 3, 4, 5] + >>> df = pd.DataFrame(cov_list) + >>> linear = LinearRegression() + >>> _ = linear.fit(df, y) + >>> linear.coef_[0] + FDataBasis( + basis=Monomial(domain_range=((0, 1),), n_basis=3), + coefficients=[[-15. 96. -90.]], + ...) + >>> linear.intercept_ + array([ 1.]) + >>> linear.predict(df) + array([ 2., 3., 4., 5.]) + + Second example: >>> x_basis = Monomial(n_basis=2) - >>> x_fd = FDataBasis(x_basis, [[0, 2], - ... [0, 4], - ... [1, 0], - ... [2, 0], - ... [1, 2], - ... [2, 2]]) - >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] - >>> cov = {'mv': x, 'fd': x_fd} - >>> df = pd.Dataframe(cov) + >>> cov_list = [ ( 1, 7, FDataBasis(x_basis, [0, 2]) ), + ... ( 2, FDataBasis(x_basis, [0, 4]), 3 ), + ... ( 4, FDataBasis(x_basis, [1, 0]), 2 ), + ... ( 1, FDataBasis(x_basis, [2, 0]), 1 ), + ... ( FDataBasis(x_basis, [1, 2]), 3, 1 ), + ... ( 2, FDataBasis(x_basis, [2, 2]), 5 ) ] + >>> df = pd.DataFrame(cov_list) >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( ... coef_basis=[None, Constant()]) @@ -313,11 +327,20 @@ def _argcheck_X( self, X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], ) -> Sequence[AcceptedDataType]: + + if not isinstance(X, pd.DataFrame): + warnings.warn( + "Usage of arguments of type FData, ndarray or a sequence \ + of both is deprecated (fit, predict)." + "Use pandas DataFrame instead", + DeprecationWarning, + ) + if isinstance(X, (FData, np.ndarray)): X = [X] if isinstance(X, pd.DataFrame): - X = self.dataframe_conversion(X) + X = self.__dataframe_conversion(X) X = [x if isinstance(x, FData) else np.asarray(x) for x in X] @@ -334,7 +357,6 @@ def _argcheck_X_y( coef_basis: Optional[BasisCoefsType] = None, ) -> ArgcheckResultType: """Do some checks to types and shapes.""" - # TODO: Add support for Dataframes new_X = self._argcheck_X(X) @@ -380,7 +402,7 @@ def _argcheck_X_y( return new_X, y, sample_weight, coef_info - def dataframe_conversion(self, X: pd.DataFrame) -> List: + def __dataframe_conversion(self, X: pd.DataFrame) -> List: """Convert DataFrames to a list with two elements. First of all, a list with mv covariates and the second, @@ -390,8 +412,36 @@ def dataframe_conversion(self, X: pd.DataFrame) -> List: X: pandas DataFrame to convert Returns: - list with two elements: first of all, a list with mv - covariates and the second, a FDataBasis object with functional data + List: first of all, a list with mv covariates + and the second, a list of FDataBasis object with functional data """ - fdb = FDataBasis.concatenate(*X.iloc[:, 1].tolist()) - return [X.iloc[:, 0].tolist(), fdb] + mv_final = [] + fdb_list = [] + final = [] + + for obs in X.values.tolist(): + mv = [] + fdb = [] + for i in range(len(obs)): + if (isinstance(obs[i], FData)): + fdb.append(obs[i]) + else: + mv.append(obs[i]) + + if (len(mv) != 0): + mv_final.append(mv) + + if (len(fdb) != 0): + fdb_list.append(fdb) + + if (len(mv_final) != 0): + final.append(mv_final) + + if (len(fdb_list) != 0): + fdb_df = pd.DataFrame(fdb_list) + + for c in range(fdb_df.shape[1]): + column = FDataBasis.concatenate(*fdb_df.iloc[:, c].tolist()) + final.append(column) + + return final From d903f02b942d92d919fd111e89abcbbdfdbe43d0 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 13 Feb 2022 23:20:37 +0100 Subject: [PATCH 044/400] Adding functionality to gaussian classifier --- .../ml/classification/_gaussian_classifier.py | 191 ++++++++++++++++-- 1 file changed, 172 insertions(+), 19 deletions(-) diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index d2ed6d82b..5cb21457b 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -3,7 +3,9 @@ import numpy as np from GPy.kern import Kern from GPy.models import GPRegression +from scipy.linalg import logm from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.model_selection import GridSearchCV from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes @@ -15,32 +17,183 @@ class GaussianClassifier( ClassifierMixin, # type: ignore ): - def __init__(self, kernel: Kern) -> None: - self._kernel = kernel + def __init__(self, kernel: Kern, regularizer: float) -> None: + self.kernel = kernel + self.regularizer = regularizer - def fit(self, X: FDataGrid, y: np.ndarray): + def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: + """Fit the model using X as training data and y as target values. - grid = X.grid_points[0][:, np.newaxis] - classes, y_ind = _classifier_get_classes(y) + Args: + X: FDataGrid with the training data. + y: Target values of shape (n_samples). - self._classes = classes + Returns: + self + """ + self._classes, self._y_ind = _classifier_get_classes(y) - for cur_class in range(0, self._classes.size): - class_n = X[y_ind == cur_class] - class_n -= class_n.mean() - data = class_n.data_matrix[:, :, 0] + self._cov_kernels_, self._means = self._fit_kernels_and_means(X) - reg_n = GPRegression(grid, data.T, kernel=self._kernel) - reg_n.optimize() - if cur_class == 0: - self._covariance_kern_zero = reg_n.kern - self._mean_zero = X[y_ind == cur_class].gmean() - else: - self._covariance_kern_one = reg_n.kern - self._mean_one = X[y_ind == cur_class].gmean() + self.X = X + self.y = y + + self._priors = self._calculate_priors(y) + self._log_priors = np.log(self._priors) # Calculates prior logartithms + self._covariances = self._calculate_covariances(X, y) + + # Calculates logarithmic covariance -> -1/2 * log|sum| + self._log_cov = self._calculate_log_covariances() return self - def predict(self, X: FDataGrid): + def predict(self, X: FDataGrid) -> np.ndarray: + """Predict the class labels for the provided data. + + Args: + X: FDataGrid with the test samples. + Returns: + Array of shape (n_samples) with class labels + for each data sample. + """ check_is_fitted(self) + return np.asarray([ + self._calculate_log_likelihood_ratio(curve) + for curve in X.data_matrix + ]) + + def _calculate_priors(self, y: np.ndarray) -> np.ndarray: + """ + Calculate the prior probability of each class. + + Args: + y: ndarray with the labels of the training data. + + Returns: + Numpy array with the respective prior of each class. + """ + return np.asarray([ + np.count_nonzero(y == cur_class) / y.size + for cur_class in range(0, self._classes.size) + ]) + + def _calculate_covariances( + self, + X: FDataGrid, + ) -> np.ndarray: + """ + Calculate the covariance matrices for each class. + + It bases the calculation on the kernels that where already + fitted with data of the corresponding classes. + + Args: + X: FDataGrid with the training data. + + Returns: + Numpy array with the covariance matrices. + """ + covariance = [] + for i in range(0, self._classes.size): + class_data = X[self._y_ind == i].data_matrix + # Data needs to be two-dimensional + class_data_r = class_data.reshape( + class_data.shape[0], + class_data.shape[1], + ) + # Caculate covariance matrix + covariance = covariance + [self._cov_kernels_[i].K(class_data_r.T)] + return np.asarray(covariance) + + def _calculate_log_covariances(self) -> np.ndarray: + """ + Calculate the logarithm of the covariance matrices for each class. + + A regularizer parameter has been used to avoid singular matrices. + + Returns: + Numpy array with the logarithmic computation of the + covariance matrices. + """ + return np.asarray([ + -0.5 * np.trace( + logm(cov + self.regularizer * np.eye(cov.shape[0])), + ) + for cov in self._covariances + ]) + + def _fit_kernels_and_means( + self, + X: FDataGrid, + ) -> np.ndarray: + """ + Fit the kernel to the data in each class. + + For each class the initial kernel passed as parameter is + adjusted and the mean is calculated. + + Args: + X: FDataGrid with the training data. + + Returns: + Tuple containing a ndarray of fitted kernels and + another ndarray with the means of each class. + """ + grid = X.grid_points[0][:, np.newaxis] + kernels = [] + means = [] + for cur_class in range(0, self._classes.size): + class_n = X[self._y_ind == cur_class] + class_n_centered = class_n - class_n.mean() + data = class_n_centered.data_matrix[:, :, 0] + + reg_n = GPRegression(grid, data.T, kernel=self.kernel) + reg_n.optimize() + + kernels = kernels + [reg_n.kern] + means = means + [class_n.gmean().data_matrix[0]] + return np.asarray(kernels), np.asarray(means) + + def _calculate_log_likelihood_ratio(self, curve: np.ndarray) -> np.ndarray: + """ + Calculate the log likelihood quadratic discriminant analysis ratio. + + Args: + curve: sample where we want to calculate the ratio. + + Returns: + A ndarray with the ratios corresponding to the output classes. + """ + # Calculates difference wrt. the mean (x - un) + data_mean = curve - self._means + + """ + ¿COMO SE REALIZA EL GRID SEARCH? + """ + param_gr = { + 'regularizer': [10, 1, 0.1, 0.01, 0.001, 0.0001], + } + cross_val = GridSearchCV(self, param_gr) + cross_val = cross_val.fit(self.X, self.y) + print(cross_val.best_score_) + + # Calculates mahalanobis distance (-1/2*(x - un).T*inv(sum)*(x - un)) + mahalanobis = [] + for j in range(0, self._classes.size): + mh = -0.5 * data_mean[j].T @ np.linalg.solve( + self._covariances[j] + self.regularizer * np.eye( + self._covariances[j].shape[0], + ), + data_mean[j], + ) + mahalanobis = mahalanobis + [mh[0][0]] + + # Calculates the log_likelihood + log_likelihood = self._log_cov + np.asarray( + mahalanobis, + ) + self._log_priors + """ + ¿COMO SE CALCULAN TODOS LOS RATIOS? + """ + return log_likelihood[1] / log_likelihood[0] From 83258336a90b705bf40ae22bbdba94927c0729bc Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Tue, 15 Feb 2022 17:11:06 +0100 Subject: [PATCH 045/400] Documentation and test changes --- docs/modules/misc/hat_matrix.rst | 9 +- docs/refs.bib | 16 +- examples/plot_kernel_regression.py | 109 +++--- examples/plot_kernel_smoothing.py | 17 +- skfda/misc/__init__.py | 16 +- skfda/misc/hat_matrix.py | 359 ++++++++---------- skfda/ml/regression/_kernel_regression.py | 13 +- skfda/preprocessing/smoothing/__init__.py | 2 +- ...rnel_smoothers.py => _kernel_smoothers.py} | 26 +- skfda/preprocessing/smoothing/validation.py | 66 ++-- tests/test_fda_feature_union.py | 2 +- tests/test_kernel_regression.py | 103 ++++- tests/test_smoothing.py | 93 ++++- 13 files changed, 459 insertions(+), 372 deletions(-) rename skfda/preprocessing/smoothing/{kernel_smoothers.py => _kernel_smoothers.py} (88%) diff --git a/docs/modules/misc/hat_matrix.rst b/docs/modules/misc/hat_matrix.rst index d9e1fa857..0cb35302d 100644 --- a/docs/modules/misc/hat_matrix.rst +++ b/docs/modules/misc/hat_matrix.rst @@ -1,8 +1,13 @@ Hat Matrix ========== -Hat matrix is used in kernel smoothing (:class:`~skfda.preprocessing.smoothing.KernelSmoother`) and -kernel regression (:class:`~skfda.ml.regression.KernelRegression`) algorithms. See the links below for more information of how the matrix are calculated. +A hat matrix is an object used in kernel smoothing (:class:`~skfda.preprocessing.smoothing.KernelSmoother`) and +kernel regression (:class:`~skfda.ml.regression.KernelRegression`) algorithms. + +Those algorithms estimate the desired values as a weighted mean of train data. The different Hat matrix types define how +these weights are calculated. + +See the links below for more information. .. autosummary:: diff --git a/docs/refs.bib b/docs/refs.bib index 53dd6b7e7..64fe6e0e5 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -1,4 +1,4 @@ -@article{baillo+grane+2008+llr, +@article{baillo+grane_2008_llr, author = {Amparo Baíllo, Aurea Grané}, title = {Local linear regression for functional predictor and scalar response}, journal = {Journal of Multivariate Analysis}, @@ -113,6 +113,18 @@ @article{dai+genton_2018_visualization URL = {https://doi.org/10.1080/10618600.2018.1473781} } +@article{febrero-bande+oviedo_2012_fda.usc, + title={Statistical Computing in Functional Data Analysis: The R Package fda.usc}, + volume={51}, + url={https://www.jstatsoft.org/index.php/jss/article/view/v051i04}, + doi={10.18637/jss.v051.i04}, + number={4}, + journal={Journal of Statistical Software}, + author={Manuel Febrero-Bande and Manuel Oviedo de la Fuente}, + year={2012}, + pages={6-7} +} + @inbook{ferraty+vieu_2006_nonparametric_knn, author = {Frédéric Ferraty and Philippe Vieu}, title = {Nonparametric Functional Data Analysis. Theory and Practice}, @@ -446,4 +458,4 @@ @inbook{wasserman_2006_nonparametric_llr year = {2006}, isbn = {978-0-387-25145-5}, doi = {10.1007/0-387-30623-4} -} \ No newline at end of file +} diff --git a/examples/plot_kernel_regression.py b/examples/plot_kernel_regression.py index dbca9b636..e7efbb4ea 100644 --- a/examples/plot_kernel_regression.py +++ b/examples/plot_kernel_regression.py @@ -1,11 +1,14 @@ """ -Kernel Regression. -================== +Kernel Regression +================= In this example we will see and compare the performance of different kernel regression methods. """ +# Author: Elena Petrunina +# License: MIT + import numpy as np from sklearn.metrics import r2_score from sklearn.model_selection import GridSearchCV, train_test_split @@ -19,7 +22,6 @@ from skfda.ml.regression._kernel_regression import KernelRegression ############################################################################## -# # For this example, we will use the # :func:`tecator ` dataset. This data set # contains 215 samples. For each sample the data consists of a spectrum of @@ -27,35 +29,31 @@ X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) -fd = X.iloc[:, 0].values +X = X.iloc[:, 0].values fat = y['fat'].values ############################################################################## -# # Fat percentages will be estimated from the spectrum. # All curves are shown in the image above. The color of these depends on the # amount of fat, from least (yellow) to highest (red). -fd.plot(gradient_criteria=fat, legend=True) +X.plot(gradient_criteria=fat, legend=True) ############################################################################## -# # The data set is splitted into train and test sets with 80% and 20% of the # samples respectively. -fd_train, fd_test, y_train, y_test = train_test_split( - fd, +X_train, X_test, y_train, y_test = train_test_split( + X, fat, test_size=0.2, random_state=1, ) ############################################################################## -# -# The KNN algorithm will be tried first. We will use the default kernel +# The KNN hat matrix will be tried first. We will use the default kernel # function, i.e. uniform kernel. To find the most suitable number of -# neighbours GridSearchCV will be used, testing with any number from 1 to 100 -# (approximate number of samples in the test set). +# neighbours GridSearchCV will be used, testing with any number from 1 to 100. n_neighbors = np.array(range(1, 100)) knn = GridSearchCV( @@ -63,29 +61,27 @@ param_grid={'kernel_estimator__bandwidth': n_neighbors}, ) + ############################################################################## -# # The best performance for the train set is obtained with the following number # of neighbours -knn.fit(fd_train, y_train) +knn.fit(X_train, y_train) print( 'KNN bandwidth:', knn.best_params_['kernel_estimator__bandwidth'], ) ############################################################################## -# # The accuracy of the estimation using r2_score measurement on the test set is # shown below. -y_pred = knn.predict(fd_test) +y_pred = knn.predict(X_test) knn_res = r2_score(y_pred, y_test) print('Score KNN:', knn_res) ############################################################################## -# # Following a similar procedure for Nadaraya-Watson, the optimal parameter is # chosen from the interval (0.01, 1). @@ -96,49 +92,43 @@ ) ############################################################################## -# # The best performance is obtained with the following bandwidth -nw.fit(fd_train, y_train) +nw.fit(X_train, y_train) print( 'Nadaraya-Watson bandwidth:', nw.best_params_['kernel_estimator__bandwidth'], ) ############################################################################## -# # The accuracy of the estimation is shown below and should be similar to that # obtained with the KNN method. -y_pred = nw.predict(fd_test) +y_pred = nw.predict(X_test) nw_res = r2_score(y_pred, y_test) print('Score NW:', nw_res) ############################################################################## -# # For Local Linear Regression, FDataBasis representation with an orthonormal # basis should be used (for the previous cases it is possible to use either # FDataGrid or FDataBasis). # -# For basis, BSpline with 10 elements has been selected. Note that the number -# of functions in the basis affects the estimation result and should +# For basis, Fourier basis with 10 elements has been selected. Note that the +# number of functions in the basis affects the estimation result and should # ideally also be chosen using cross-validation. -bspline = skfda.representation.basis.BSpline(n_basis=10) +fourier = skfda.representation.basis.Fourier(n_basis=10) -fd_basis = fd.to_basis(basis=bspline) -fd_basis_train, fd_basis_test, y_train, y_test = train_test_split( - fd_basis, +X_basis = X.to_basis(basis=fourier) +X_basis_train, X_basis_test, y_train, y_test = train_test_split( + X_basis, fat, test_size=0.2, random_state=1, ) -############################################################################## -# -# As this procedure is slower than the previous ones, an interval with fewer -# values is taken. -bandwidth = np.logspace(0.3, 1, num=50) + +bandwidth = np.logspace(0.3, 1, num=100) llr = GridSearchCV( KernelRegression(kernel_estimator=LocalLinearRegressionHatMatrix()), @@ -146,120 +136,107 @@ ) ############################################################################## -# # The bandwidth obtained by cross-validation is indicated below. - -llr.fit(fd_basis_train, y_train) +llr.fit(X_basis_train, y_train) print( 'LLR bandwidth:', llr.best_params_['kernel_estimator__bandwidth'], ) ############################################################################## -# -# Although it is a slower method, the result obtained should be much better -# than in the case of Nadaraya Watson and KNN. +# Although it is a slower method, the result obtained in this example should be +# better than in the case of Nadaraya-Watson and KNN. -y_pred = llr.predict(fd_basis_test) +y_pred = llr.predict(X_basis_test) llr_res = r2_score(y_pred, y_test) print('Score LLR:', llr_res) ############################################################################## -# -# In the case of this data set, using the derivative of the functions should -# give a better performance. +# For this data set using the derivative should give a better performance. # # Below the plot of all the derivatives can be found. The same scheme as before # is followed: yellow les fat, red more. -fdd = fd.derivative() -fdd.plot(gradient_criteria=fat, legend=True) - +Xd = X.derivative() +Xd.plot(gradient_criteria=fat, legend=True) -fdd_train, fdd_test, y_train, y_test = train_test_split( - fdd, +Xd_train, Xd_test, y_train, y_test = train_test_split( + Xd, fat, test_size=0.2, random_state=1, ) ############################################################################## -# # Exactly the same operations are repeated, but now with the derivatives of the # functions. ############################################################################## -# # K-Nearest Neighbours knn = GridSearchCV( KernelRegression(kernel_estimator=KNeighborsHatMatrix()), param_grid={'kernel_estimator__bandwidth': n_neighbors}, ) -knn.fit(fdd_train, y_train) +knn.fit(Xd_train, y_train) print( 'KNN bandwidth:', knn.best_params_['kernel_estimator__bandwidth'], ) -y_pred = knn.predict(fdd_test) +y_pred = knn.predict(Xd_test) dknn_res = r2_score(y_pred, y_test) print('Score KNN:', dknn_res) ############################################################################## -# # Nadaraya-Watson bandwidth = np.logspace(-3, -1, num=100) - nw = GridSearchCV( KernelRegression(kernel_estimator=NadarayaWatsonHatMatrix()), param_grid={'kernel_estimator__bandwidth': bandwidth}, ) -nw.fit(fdd_train, y_train) +nw.fit(Xd_train, y_train) print( 'Nadara-Watson bandwidth:', nw.best_params_['kernel_estimator__bandwidth'], ) -y_pred = nw.predict(fdd_test) +y_pred = nw.predict(Xd_test) dnw_res = r2_score(y_pred, y_test) print('Score NW:', dnw_res) ############################################################################## -# # For both Nadaraya-Watson and KNN the accuracy has improved significantly # and should be higher than 0.9. ############################################################################## -# # Local Linear Regression -fdd_basis = fdd.to_basis(basis=bspline) -fdd_basis_train, fdd_basis_test, y_train, y_test = train_test_split( - fdd_basis, +Xd_basis = Xd.to_basis(basis=fourier) +Xd_basis_train, Xd_basis_test, y_train, y_test = train_test_split( + Xd_basis, fat, test_size=0.2, random_state=1, ) -bandwidth = np.logspace(-2, 1, 50) +bandwidth = np.logspace(-2, 1, 100) llr = GridSearchCV( KernelRegression(kernel_estimator=LocalLinearRegressionHatMatrix()), param_grid={'kernel_estimator__bandwidth': bandwidth}, ) -llr.fit(fdd_basis_train, y_train) +llr.fit(Xd_basis_train, y_train) print( 'LLR bandwidth:', llr.best_params_['kernel_estimator__bandwidth'], ) -y_pred = llr.predict(fdd_basis_test) +y_pred = llr.predict(Xd_basis_test) dllr_res = r2_score(y_pred, y_test) print('Score LLR:', dllr_res) ############################################################################## -# # LLR accuracy has also improved, but the difference with Nadaraya-Watson and # KNN in the case of derivatives is less significant than in the previous case. diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index fb8f6d294..9a2ee4e92 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -1,6 +1,6 @@ """ -Kernel Smoothing. -================= +Kernel Smoothing +================ This example uses different kernel smoothing methods over the phoneme data set and shows how cross validations scores vary over a range of different @@ -16,11 +16,10 @@ import skfda import skfda.misc.hat_matrix as hm -import skfda.preprocessing.smoothing.kernel_smoothers as ks import skfda.preprocessing.smoothing.validation as val +from skfda.preprocessing.smoothing import KernelSmoother ############################################################################## -# # For this example, we will use the # :func:`phoneme ` dataset. This dataset # contains the log-periodograms of several phoneme pronunciations. The phoneme @@ -67,7 +66,7 @@ # K-nearest neighbours kernel smoothing. knn = val.SmoothingParameterSearch( - ks.KernelSmoother(kernel_estimator=hm.KNeighborsHatMatrix()), + KernelSmoother(kernel_estimator=hm.KNeighborsHatMatrix()), n_neighbors, param_name='kernel_estimator__bandwidth', ) @@ -76,7 +75,7 @@ # Local linear regression kernel smoothing. llr = val.SmoothingParameterSearch( - ks.KernelSmoother(kernel_estimator=hm.LocalLinearRegressionHatMatrix()), + KernelSmoother(kernel_estimator=hm.LocalLinearRegressionHatMatrix()), bandwidth, param_name='kernel_estimator__bandwidth', ) @@ -85,7 +84,7 @@ # Nadaraya-Watson kernel smoothing. nw = val.SmoothingParameterSearch( - ks.KernelSmoother(kernel_estimator=hm.NadarayaWatsonHatMatrix()), + KernelSmoother(kernel_estimator=hm.NadarayaWatsonHatMatrix()), bandwidth, param_name='kernel_estimator__bandwidth', ) @@ -167,10 +166,10 @@ # We can also appreciate the effects of undersmoothing and oversmoothing in # the following plots. -fd_us = ks.KernelSmoother( +fd_us = KernelSmoother( kernel_estimator=hm.NadarayaWatsonHatMatrix(bandwidth=2 * scale_factor), ).fit_transform(fd[10]) -fd_os = ks.KernelSmoother( +fd_os = KernelSmoother( kernel_estimator=hm.NadarayaWatsonHatMatrix(bandwidth=15 * scale_factor), ).fit_transform(fd[10]) diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 9fd10fe4c..139d717a5 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -1,5 +1,13 @@ """Miscellaneous functions and objects.""" -from . import covariances, kernels, lstsq, metrics, operators, regularization +from . import ( + covariances, + hat_matrix, + kernels, + lstsq, + metrics, + operators, + regularization, +) from ._math import ( cosine_similarity, cosine_similarity_matrix, @@ -12,9 +20,3 @@ log10, sqrt, ) -from .hat_matrix import ( - HatMatrix, - KNeighborsHatMatrix, - LocalLinearRegressionHatMatrix, - NadarayaWatsonHatMatrix, -) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 4078701b4..dbc01a79c 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -9,7 +9,7 @@ """ import abc -from typing import Callable, Optional +from typing import Callable, Optional, Union import numpy as np from sklearn.base import BaseEstimator, RegressorMixin @@ -17,6 +17,7 @@ from skfda.representation._functional_data import FData from skfda.representation.basis import FDataBasis +from ..representation._typing import GridPoints, GridPointsLike from . import kernels @@ -35,35 +36,45 @@ def __init__( self, *, bandwidth: Optional[float] = None, - kernel: Optional[Callable] = None, + kernel: Callable[[np.ndarray], np.ndarray] = kernels.normal, ): self.bandwidth = bandwidth self.kernel = kernel def __call__( self, + *, delta_x: np.ndarray, + X_train: Optional[Union[FData, GridPointsLike]] = None, + X: Optional[Union[FData, GridPointsLike]] = None, + y_train: Optional[np.ndarray] = None, weights: Optional[np.ndarray] = None, _cv: bool = False, ) -> np.ndarray: - """ - Hat matrix. + r""" + Calculate the hat matrix or the prediction. - Calculate and return matrix for smoothing (for all methods) and - regression (for Nadaraya-Watson and K-Nearest Neighbours) + If y_train is given, the estimation for X is calculated, otherwise, + hat matrix, :math:`\hat{H]`, is returned. The prediction, :math:`y`, + can be calculated as :math:`y = \hat{H} \dot y_train`. Args: - delta_x (np.ndarray): Matrix of distances between points or - functions - weights (np.ndarray, optional): Weights to be applied to the - resulting matrix columns + delta_x: Matrix of distances between points or + functions. + X_train: Training data. + X: Test samples. + y_train: Target values for X_train. + weights: Weights to be applied to the + resulting matrix columns. Returns: - hat matrix (np.ndarray) + The prediction if y_train is given, hat matrix otherwise. """ # Obtain the non-normalized matrix - matrix = self._hat_matrix_function_not_normalized(delta_x=delta_x) + matrix = self._hat_matrix_function_not_normalized( + delta_x=delta_x, + ) # Adjust weights if weights is not None: @@ -76,40 +87,13 @@ def __call__( # Renormalize weights rs = np.sum(matrix, axis=1) rs[rs == 0] = 1 - return (matrix.T / rs).T - - def prediction( - self, - *, - delta_x: np.ndarray, - y_train: np.ndarray, - X_train: Optional[FData] = None, - X: Optional[FData] = None, - weights: Optional[np.ndarray] = None, - ) -> np.ndarray: - """ - Prediction. - Return the resulting :math:`y` array, by default calculates the hat - matrix and multiplies it by y_train (except for regression using - Local Linear Regression method). + matrix = (matrix.T / rs).T - Args: - delta_x (np.ndarray): Matrix of distances between points or - functions - y_train (np.ndarray): Scalar response from for functional data - X_train (FData , optional): Functional data. Only used in - regression with Local Linear Regression method - X (FData, optional): Functional data. Only used in regression - with Local Linear Regression method - weights (np.ndarray, optional): Weights to be applied to the - resulting matrix columns - - Returns: - prediction (np.ndarray) + if y_train is None: + return matrix - """ - return np.dot(self.__call__(delta_x=delta_x, weights=weights), y_train) + return matrix @ y_train @abc.abstractmethod def _hat_matrix_function_not_normalized( @@ -124,60 +108,29 @@ class NadarayaWatsonHatMatrix(HatMatrix): r"""Nadaraya-Watson method. Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel - regression algorithms, as explained below. - - For **kernel smoothing** algorithm the matrix :math:`\hat{H}` has the - following expression for each cell - :footcite:`wasserman_2006_nonparametric_nw`: + regression algorithms, as explained below .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{t_j-t_i'}{h}\right)}{\sum_{k=1}^{ - n}K\left(\frac{t_k-t_i'}{h}\right)} + \hat{H}_{i,j} = \frac{K\left(\frac{d(e_j-e_i')}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{d(e_k-e_i')}{h}\right)} - where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and - :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired - to estimate the smoothed value. - - The result can be obtained as - - .. math:: - \hat{X} = \hat{H}X + For smoothing, :math:`e_i` are the points of discretisation + and :math:`e'_i` are the points for which it is desired to estimate the + smoothed value. The distance :math:`d` is the absolute value + function :footcite:`wasserman_2006_nonparametric_nw`. - where :math:`X = (x_1, x_2, ..., x_n)` is the vector of observations at the - points :math:`t` and - :math:`\hat{X} = (\hat{x}_1, \hat{x}_2, ..., \hat{x}_m)` are the estimated - values for the points :math:`t'`. + For regression, :math:`e_i` is the functional data and :math:`e_i'` + are the functions for which it is desired to estimate the scalar value. + Here, :math:`d` is some functional distance + :footcite:`ferraty+vieu_2006_nonparametric_nw`. - For **kernel regression** algorithm - :footcite:`ferraty+vieu_2006_nonparametric_nw`:, - - .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{d(f_j-f_i')}{h}\right)}{\sum_{k=1}^{ - n}K\left(\frac{d(f_k-f_i')}{h}\right)} - - where :math:`d(\cdot, \cdot)` is some functional distance - (see :class:`~skfda.misc.metrics.LpDistance`), - :math:`(f_1, f_2, ..., f_n)` is the functional data and the functions - :math:`(f_1', f_2', ..., f_m')` are the functions for which it is desired - to estimate the scalar value. - - The result can be obtained as - - .. math:: - \hat{Y} = \hat{H}Y - - where :math:`Y = (y_1, y_2, ..., y_n)` is the vector of scalar values - corresponding to the dataset and - :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` are the estimated - values - - In both cases, :math:`K(\cdot)` is a kernel function and :math:`h` the - kernel window width or smoothing parameter. + In both cases :math:`K(\cdot)` is a kernel function and :math:`h` is the + bandwidth. Args: - bandwidth (float, optional): Window width of the kernel + bandwidth: Window width of the kernel (also called h or bandwidth). - kernel (function, optional): Kernel function. By default a normal + kernel: Kernel function. By default a normal kernel. References: @@ -195,9 +148,6 @@ def _hat_matrix_function_not_normalized( percentage = 15 self.bandwidth = np.percentile(delta_x, percentage) - if self.kernel is None: - self.kernel = kernels.normal - return self.kernel(delta_x / self.bandwidth) @@ -207,9 +157,15 @@ class LocalLinearRegressionHatMatrix(HatMatrix): Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel regression algorithms, as explained below. - For **kernel smoothing** algorithm the matrix :math:`\hat{H}` has the - following expression for each cell - :footcite:`wasserman_2006_nonparametric_llr`: + For **kernel smoothing** algorithm to estimate the smoothed value for + :math:`t_j` the following error must be minimised + + .. math:: + AWSE(a, b) = \sum_{i=1}^n \left[ \left(y_i - + \left(a + b (t_i - t'_j) \right) \right)^2 + K \left( \frac {|t_i - t'_j|}{h} \right) \right ] + + which gives the following expression for each cell .. math:: \hat{H}_{i,j} = \frac{b_j(t_i')}{\sum_{k=1}^{n}b_k(t_i')} @@ -223,50 +179,39 @@ class LocalLinearRegressionHatMatrix(HatMatrix): where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired - to estimate the smoothed value. - - The result can be obtained as - - .. math:: - \hat{X} = \hat{H}X - - where :math:`X = (x_1, x_2, ..., x_n)` is the vector of observations at the - points :math:`t` and - :math:`\hat{X} = (\hat{x}_1, \hat{x}_2, ..., \hat{x}_m)` are the estimated - values for the points :math:`t'`. + to estimate the smoothed value + :footcite:`wasserman_2006_nonparametric_llr`. - For **kernel regression** algorithm - :footcite:`baillo+grane+2008+llr`: + For **kernel regression** algorithm: - Given functional data, :math:`(f_1, f_2, ..., f_n)` where each function + Given functional data, :math:`(x_1, x_2, ..., x_n)` where each function is expressed in a orthonormal basis with :math:`J` elements and scalar response :math:`Y = (y_1, y_2, ..., y_n)`. It is desired to estimate the values :math:`\hat{Y} = (\hat{y}_1, \hat{y}'_2, ..., \hat{y}'_m)` - for the data :math:`(f'_1, f'_2, ..., f'_m)` (expressed in the same basis). + for the data :math:`(x'_1, x'_2, ..., x'_m)` (expressed in the same basis). For each :math:`f'_k` the estimation :math:`\hat{y}_k` is obtained by - taking the value :math:`a^k` from the vector - :math:`(a^k, b_1^k, ..., b_J^k)` which minimizes the following expression + taking the value :math:`a_k` from the vector + :math:`(a_k, b_{1k}, ..., b_{Jk})` which minimizes the following expression .. math:: - AWSE(a^k, b_1^k, ..., b_J^k) = \sum_{i=1}^n \left(y_i - - \left(a + \sum_{j=1}^J b_j^k c_{ij}^k \right) \right)^2 - K \left( \frac {d(f_i - f'_k)}{h} \right) 1/h + AWSE(a_k, b_{1k}, ..., b_{Jk}) = \sum_{i=1}^n \left(y_i - + \left(a + \sum_{j=1}^J b_{jk} c_{ijk} \right) \right)^2 + K \left( \frac {d(x_i - x'_k)}{h} \right) - Where: + Where :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis + expansion of :math:`x_i - x'_k = \sum_{j=1}^J c_{ij}^k` and :math:`d` some + functional distance :footcite:`baillo+grane_2008_llr` - - :math:`K(\cdot)` is a kernel function, :math:`h` - the kernel window width or bandwidth and :math:`d` some - functional distance - - :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis - expansion of :math:`f_i - f'_k = \sum_{j=1}^J c_{ij}^k` + For both cases, :math:`K(\cdot)` is a kernel function and :math:`h` the + bandwidth. Args: - bandwidth (float, optional): Window width of the kernel - (also called h or bandwidth). - kernel (function, optional): Kernel function. By default a normal + bandwidth: Window width of the kernel + (also called h). + kernel: Kernel function. By default a normal kernel. References: @@ -274,54 +219,76 @@ class LocalLinearRegressionHatMatrix(HatMatrix): """ - def prediction( + def __call__( # noqa: D102 self, *, delta_x: np.ndarray, - X_train: FDataBasis, - y_train: np.ndarray, - X: FDataBasis, + X_train: Optional[Union[FDataBasis, GridPoints]] = None, + X: Optional[Union[FDataBasis, GridPoints]] = None, + y_train: Optional[np.ndarray] = None, weights: Optional[np.ndarray] = None, + _cv: bool = False, ) -> np.ndarray: - """ - Prediction. - - Return the resulting :math:`y` array - Args: - delta_x: np.ndarray - X_train: np.ndarray - y_train: np.ndarray - X: np.ndarray - weights: np.ndarray - - Returns: - np.ndarray - """ if self.bandwidth is None: percentage = 15 self.bandwidth = np.percentile(delta_x, percentage) - if self.kernel is None: - self.kernel = kernels.normal + # Regression + if isinstance(X_train, FDataBasis): - W = np.sqrt(self.kernel(delta_x / self.bandwidth)) + if y_train is None: + y_train = np.identity(X_train.n_samples) + + m1 = X_train.coefficients + m2 = X.coefficients + + return self._solve_least_squares( + delta_x=delta_x, + m1=m1, + m2=m2, + y_train=y_train, + ) + + # Smoothing + else: + + return super().__call__( + delta_x=delta_x, + X_train=X_train, + X=X, + y_train=y_train, + weights=weights, + _cv=_cv, + ) - # Creating the matrices of coefficients - m1 = X_train.coefficients - m2 = X.coefficients + def _solve_least_squares( + self, + delta_x: np.ndarray, + m1: np.ndarray, + m2: np.ndarray, + y_train: np.ndarray, + ) -> np.ndarray: + + W = np.sqrt(self.kernel(delta_x / self.bandwidth)) - # Adding a column of ones to X_train coefficients + # Adding a column of ones to m1 m1 = np.concatenate( ( - np.ones(X_train.n_samples)[:, np.newaxis], + np.ones(m1.shape[0])[:, np.newaxis], m1, ), axis=1, ) - # Adding a column of zeros to X coefficients - m2 = np.concatenate((np.zeros(X.n_samples)[:, np.newaxis], m2), axis=1) + # Adding a column of zeros to m2 + m2 = np.concatenate( + ( + np.zeros(m2.shape[0])[:, np.newaxis], + m2, + ), + axis=1, + ) # Subtract previous matrices obtaining a 3D matrix # The i-th element contains the matrix X_train - X[i] @@ -330,13 +297,15 @@ def prediction( # A x = b # Where x = (a, b_1, ..., b_J) A = (C.T * W.T).T - b = W * y_train + b = np.einsum('ij, j... -> ij...', W, y_train) + # For Ax = b calculates x that minimize the square error # From https://stackoverflow.com/questions/42534237/broadcasted-lstsq-least-squares # noqa: E501 u, s, vT = np.linalg.svd(A, full_matrices=False) - uTb = np.einsum('ijk,ij->ik', u, b) - x = np.einsum('ijk,ij->ik', vT, uTb / s) + uTb = np.einsum('ijk, ij...->ik...', u, b) + uTbs = (uTb.T / s.T).T + x = np.einsum('ijk,ij...->ik...', vT, uTbs) return x[:, 0] @@ -350,9 +319,6 @@ def _hat_matrix_function_not_normalized( percentage = 15 self.bandwidth = np.percentile(delta_x, percentage) - if self.kernel is None: - self.kernel = kernels.normal - k = self.kernel(delta_x / self.bandwidth) s1 = np.sum(k * delta_x, axis=1, keepdims=True) # S_n_1 @@ -368,64 +334,30 @@ class KNeighborsHatMatrix(HatMatrix): Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel regression algorithms, as explained below. - For **kernel smoothing** algorithm the matrix :math:`\hat{H}` has the - following expression for each cell - .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{t_j-t_i'}{h}\right)}{\sum_{k=1}^{ - n}K\left(\frac{t_k-t_i'}{h}\right)} - - where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and - :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired - to estimate the smoothed value. - - The result can be obtained as + \hat{H}_{i,j} = \frac{K\left(\frac{d(e_j-e_i')}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{d(e_k-e_i')}{h_{ik}}\right)} - .. math:: - \hat{X} = \hat{H}X - - where :math:`X = (x_1, x_2, ..., x_n)` is the vector of observations at the - points :math:`t` and - :math:`\hat{X} = (\hat{x}_1, \hat{x}_2, ..., \hat{x}_m)` are the estimated - values for the points :math:`t'`. - - - For **kernel regression** algorithm - :footcite:`ferraty+vieu_2006_nonparametric_knn`, - - .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{d(f_j-f_i')}{h_{ik}}\right)} - {\sum_{k=1}^{n}K\left(\frac{d(f_k-f_i')}{h_{ik}}\right)} + For smoothing, :math:`e_i` are the points of discretisation + and :math:`e'_i` are the points for which it is desired to estimate the + smoothed value. The distance :math:`d` is the absolute value. - where :math:`d(\cdot, \cdot)` is some functional distance - (see :class:`~skfda.misc.metrics.LpDistance`), - :math:`F = (f_1, f_2, ..., f_n)` is the functional data and the functions - :math:`F' = (f_1', f_2', ..., f_m')` are the functions for which it is - wanted to estimate the scalar value. + For regression, :math:`e_i` are the functional data and :math:`e_i'` + are the functions for which it is desired to estimate the scalar value. + Here, :math:`d` is some functional distance. - The result can be obtained as + In both cases, :math:`K(\cdot)` is a kernel function and + :math:`h_{ik}` is calculated as the distance from :math:`e_i'` to it's + :math:`k`-th nearest neighbor in :math:`\{e_1, ..., e_n\}` + :footcite:`ferraty+vieu_2006_nonparametric_knn`. - .. math:: - \hat{Y} = \hat{H}Y - - where :math:`Y = (y_1, y_2, ..., y_n)` is the vector of scalar values - corresponding to the dataset and - :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` are the estimated - values - - In both cases, :math:`K(\cdot)` is a kernel function, for smoothing, - :math:`h_{ik}` is calculated as the distance from :math:`t_i'` to it's 𝑘-th - nearest neighbor in :math:`t`, and for regression, :math:`h_{ik}` is - calculated as the distance from :math:`f_i'` to it's 𝑘-th nearest neighbor - in :math:`F`. - - Usually used with the uniform kernel, it takes the average of the closest k + Used with the uniform kernel, it takes the average of the closest k points to a given point. Args: - bandwidth (int, optional): Number of nearest neighbours. By + bandwidth: Number of nearest neighbours. By default it takes the 5% closest points. - kernel (function, optional): Kernel function. By default a uniform + kernel: Kernel function. By default a uniform kernel to perform a 'usual' k nearest neighbours estimation. References: @@ -433,15 +365,20 @@ class KNeighborsHatMatrix(HatMatrix): """ + def __init__( + self, + *, + bandwidth: Optional[int] = None, + kernel: Callable[[np.ndarray], np.ndarray] = kernels.uniform, + ): + super().__init__(bandwidth=bandwidth, kernel=kernel) + def _hat_matrix_function_not_normalized( self, *, delta_x: np.ndarray, ) -> np.ndarray: - if self.kernel is None: - self.kernel = kernels.uniform - input_points_len = delta_x.shape[1] if self.bandwidth is None: @@ -456,7 +393,7 @@ def _hat_matrix_function_not_normalized( # Tolerance to avoid points landing outside the kernel window due to # computation error - tol = 1.0e-15 + tol = np.finfo(np.float64).eps # For each row in the distances matrix, it calculates the furthest # point within the k nearest neighbours diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py index f6e979e42..0cd26be25 100644 --- a/skfda/ml/regression/_kernel_regression.py +++ b/skfda/ml/regression/_kernel_regression.py @@ -26,14 +26,13 @@ class KernelRegression( .. math:: \hat{y} = \hat{H}y - Where :math:`\hat{H}` is the matrix described in + Where :math:`\hat{H}` is a matrix described in :class:`~skfda.misc.HatMatrix`. Args: - kernel_estimator (:class:`~skfda.misc.HatMatrix`, optional): - Method used to calculate the hat matrix + kernel_estimator: Method used to calculate the hat matrix (default = :class:`~skfda.misc.NadarayaWatsonHatMatrix`). - metric (function, optional): The metric used to calculate the distances + metric: Metric used to calculate the distances (default = :func:`L2 distance `). Examples: @@ -67,7 +66,7 @@ def __init__( self, *, kernel_estimator: Optional[HatMatrix] = None, - metric: Metric = l2_distance, + metric: Metric[FData] = l2_distance, ): self.kernel_estimator = kernel_estimator @@ -84,7 +83,7 @@ def fit( # noqa: D102 self.y_train_ = y self.weights_ = weight - if not self.kernel_estimator: + if self.kernel_estimator is None: self.kernel_estimator = NadarayaWatsonHatMatrix() return self @@ -97,7 +96,7 @@ def predict( # noqa: D102 check_is_fitted(self) delta_x = PairwiseMetric(self.metric)(X, self.X_train_) - return self.kernel_estimator.prediction( + return self.kernel_estimator( delta_x=delta_x, X_train=self.X_train_, y_train=self.y_train_, diff --git a/skfda/preprocessing/smoothing/__init__.py b/skfda/preprocessing/smoothing/__init__.py index ff726e135..d75eb30ab 100644 --- a/skfda/preprocessing/smoothing/__init__.py +++ b/skfda/preprocessing/smoothing/__init__.py @@ -1,4 +1,4 @@ """Smoothing.""" from . import validation from ._basis import BasisSmoother -from .kernel_smoothers import KernelSmoother +from ._kernel_smoothers import KernelSmoother diff --git a/skfda/preprocessing/smoothing/kernel_smoothers.py b/skfda/preprocessing/smoothing/_kernel_smoothers.py similarity index 88% rename from skfda/preprocessing/smoothing/kernel_smoothers.py rename to skfda/preprocessing/smoothing/_kernel_smoothers.py index f724c8d5a..72d2439ea 100644 --- a/skfda/preprocessing/smoothing/kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/_kernel_smoothers.py @@ -28,7 +28,7 @@ class KernelSmoother(_LinearSmoother): .. math:: \hat{X} = \hat{H} X - where :math:`\hat{H}` is the matrix described in + where :math:`\hat{H}` is a matrix described in :class:`~skfda.misc.HatMatrix`. Examples: @@ -95,11 +95,11 @@ class KernelSmoother(_LinearSmoother): [ 0.006, 0.022, 0.163, 0.305, 0.503]]) Args: - kernel_estimator (:class:`~skfda.misc.HatMatrix`): Method used to + kernel_estimator: Method used to calculate the hat matrix (default = :class:`~skfda.misc.NadarayaWatsonHatMatrix`) - weights (iterable): weight coefficients for each point. - output_points (GridPointsLike) : The output points. If omitted, the + weights: weight coefficients for each point. + output_points: The output points. If omitted, the input points are used. So far only non parametric methods are implemented because we are only @@ -121,17 +121,19 @@ def __init__( def _hat_matrix( self, - input_points: Optional[GridPointsLike] = None, - output_points: Optional[GridPointsLike] = None, + input_points: GridPointsLike, + output_points: GridPointsLike, ) -> np.ndarray: - if not self.kernel_estimator: + if self.kernel_estimator is None: self.kernel_estimator = NadarayaWatsonHatMatrix() delta_x = np.subtract.outer(output_points[0], input_points[0]) - return self.kernel_estimator(delta_x, self.weights, _cv=self._cv) - def _more_tags(self): - return { - 'X_types': [], - } + return self.kernel_estimator( + delta_x=delta_x, + weights=self.weights, + X_train=input_points, + X=output_points, + _cv=self._cv, + ) diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py index 5491f738c..e36c766c9 100644 --- a/skfda/preprocessing/smoothing/validation.py +++ b/skfda/preprocessing/smoothing/validation.py @@ -107,7 +107,7 @@ class LinearSmootherGeneralizedCVScorer: def __init__( self, - penalization_function: Optional[Callable] = None, + penalization_function: Callable[[np.ndarray], float] = None, ): self.penalization_function = penalization_function @@ -121,9 +121,7 @@ def __call__( y_est, hat_matrix = _get_input_estimation_and_matrix(estimator, X) if self.penalization_function is None: - self.penalization_function = lambda matrix: ( - 1 - matrix.diagonal().mean() - ) ** -2 + self.penalization_function = _default_penalization_function return -( np.mean( @@ -189,12 +187,12 @@ class SmoothingParameterSearch(GridSearchCV): smoothing by means of the k-nearest neighbours method. >>> import skfda - >>> from skfda.preprocessing.smoothing import kernel_smoothers + >>> from skfda.preprocessing.smoothing import KernelSmoother >>> from skfda.misc.hat_matrix import KNeighborsHatMatrix >>> x = np.linspace(-2, 2, 5) >>> fd = skfda.FDataGrid(x ** 2, x) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], ... param_name='kernel_estimator__bandwidth') @@ -226,7 +224,7 @@ class SmoothingParameterSearch(GridSearchCV): general cross validation using other penalization functions. >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], ... param_name='kernel_estimator__bandwidth', @@ -235,7 +233,7 @@ class SmoothingParameterSearch(GridSearchCV): >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-4.2, -5.5]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], ... param_name='kernel_estimator__bandwidth', @@ -245,7 +243,7 @@ class SmoothingParameterSearch(GridSearchCV): >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([ -9.35, -10.71]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], ... param_name='kernel_estimator__bandwidth', @@ -255,7 +253,7 @@ class SmoothingParameterSearch(GridSearchCV): >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([ -9.8, -11. ]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], ... param_name='kernel_estimator__bandwidth', @@ -264,7 +262,7 @@ class SmoothingParameterSearch(GridSearchCV): >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-7.56, -9.17]) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], ... param_name='kernel_estimator__bandwidth', @@ -278,7 +276,7 @@ class SmoothingParameterSearch(GridSearchCV): >>> output_points = np.linspace(-2, 2, 9) >>> grid = SmoothingParameterSearch( - ... kernel_smoothers.KernelSmoother( + ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix(), ... output_points=output_points), ... [2,3], @@ -304,7 +302,7 @@ def __init__( param_values: Iterable, *, param_name: str = 'smoothing_parameter', - scoring: Callable = None, + scoring: Optional[Callable] = None, n_jobs: Optional[int] = None, verbose: int = 0, pre_dispatch: Optional[Union[int, str]] = '2*n_jobs', @@ -337,36 +335,46 @@ def fit( # noqa: D102 return super().fit(X, y=y, groups=groups, **fit_params) +def _default_penalization_function(hat_matrix: np.ndarray) -> float: + return (1 - hat_matrix.diagonal().mean()) ** -2 + + def akaike_information_criterion(hat_matrix: np.ndarray) -> float: - r"""Akaike's information criterion for cross validation. + r"""Akaike's information criterion for cross validation + :footcite:`febrero-bande+oviedo_2012_fda.usc`. .. math:: \Xi(\nu,n) = \exp\left(2 * \frac{tr(\hat{H}^\nu)}{n}\right) Args: - hat_matrix (numpy.darray): Smoothing matrix whose penalization + hat_matrix: Smoothing matrix whose penalization score is desired. Returns: - float: penalization given by the Akaike's information criterion. + Penalization given by the Akaike's information criterion. + + .. footbibliography:: """ return np.exp(2 * hat_matrix.diagonal().mean()) def finite_prediction_error(hat_matrix: np.ndarray) -> float: - r"""Finite prediction error for cross validation. + r"""Finite prediction error for cross validation + :footcite:`febrero-bande+oviedo_2012_fda.usc`. .. math:: \Xi(\nu,n) = \frac{1 + \frac{tr(\hat{H}^\nu)}{n}}{1 - \frac{tr(\hat{H}^\nu)}{n}} Args: - hat_matrix (numpy.darray): Smoothing matrix whose penalization + hat_matrix: Smoothing matrix whose penalization score is desired. Returns: - float: penalization given by the finite prediction error. + Penalization given by the finite prediction error. + + .. footbibliography:: """ return ( @@ -376,34 +384,40 @@ def finite_prediction_error(hat_matrix: np.ndarray) -> float: def shibata(hat_matrix: np.ndarray) -> float: - r"""Shibata's model selector for cross validation. + r"""Shibata's model selector for cross validation + :footcite:`febrero-bande+oviedo_2012_fda.usc`. .. math:: \Xi(\nu,n) = 1 + 2 * \frac{tr(\hat{H}^\nu)}{n} Args: - hat_matrix (numpy.darray): Smoothing matrix whose penalization + hat_matrix: Smoothing matrix whose penalization score is desired. Returns: - float: penalization given by the Shibata's model selector. + Penalization given by the Shibata's model selector. + + .. footbibliography:: """ return 1 + 2 * hat_matrix.diagonal().mean() -def rice(hat_matrix: np.ndarray) -> np.ndarray: - r"""Rice's bandwidth selector for cross validation. +def rice(hat_matrix: np.ndarray) -> float: + r"""Rice's bandwidth selector for cross validation + :footcite:`febrero-bande+oviedo_2012_fda.usc`. .. math:: \Xi(\nu,n) = \left(1 - 2 * \frac{tr(\hat{H}^\nu)}{n}\right)^{-1} Args: - hat_matrix (numpy.darray): Smoothing matrix whose penalization + hat_matrix: Smoothing matrix whose penalization score is desired. Returns: - float: penalization given by the Rice's bandwidth selector. + Penalization given by the Rice's bandwidth selector. + + .. footbibliography:: """ return (1 - 2 * hat_matrix.diagonal().mean()) ** -1 diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index a28bd2db6..7a8e20429 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -11,7 +11,7 @@ from skfda.preprocessing.dim_reduction.feature_extraction import ( FDAFeatureUnion, ) -from skfda.preprocessing.smoothing.kernel_smoothers import KernelSmoother +from skfda.preprocessing.smoothing import KernelSmoother from skfda.representation import EvaluationTransformer diff --git a/tests/test_kernel_regression.py b/tests/test_kernel_regression.py index 7159aae99..fc65ad9d4 100644 --- a/tests/test_kernel_regression.py +++ b/tests/test_kernel_regression.py @@ -1,11 +1,12 @@ """Test kernel regression method.""" import unittest -from typing import Tuple +from typing import Callable, Optional, Tuple import numpy as np import sklearn -import skfda +from skfda import FData +from skfda.datasets import fetch_tecator from skfda.misc.hat_matrix import ( KNeighborsHatMatrix, LocalLinearRegressionHatMatrix, @@ -13,12 +14,19 @@ ) from skfda.misc.kernels import normal, uniform from skfda.misc.metrics import l2_distance -from skfda.ml.regression._kernel_regression import KernelRegression -from skfda.representation.basis import FDataBasis +from skfda.ml.regression import KernelRegression +from skfda.representation.basis import FDataBasis, Fourier from skfda.representation.grid import FDataGrid -def _nw_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): +def _nw_alt( + fd_train: FData, + fd_test: FData, + y_train: np.ndarray, + *, + bandwidth: float, + kernel: Optional[Callable] = None, +) -> np.ndarray: if kernel is None: kernel = normal @@ -30,7 +38,14 @@ def _nw_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): return y -def _knn_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): +def _knn_alt( + fd_train: FData, + fd_test: FData, + y_train: np.ndarray, + *, + bandwidth: int, + kernel: Optional[Callable] = None, +) -> np.ndarray: if kernel is None: kernel = uniform @@ -38,14 +53,22 @@ def _knn_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): for i in range(fd_test.n_samples): d = l2_distance(fd_train, fd_test[i]) - h = sorted(d)[bandwidth - 1] + 1.0e-15 + tol = np.finfo(np.float64).eps + h = sorted(d)[bandwidth - 1] + tol w = kernel(d / h) y[i] = (w @ y_train) / sum(w) return y -def _llr_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): +def _llr_alt( + fd_train: FDataBasis, + fd_test: FDataBasis, + y_train: np.ndarray, + *, + bandwidth: float, + kernel: Optional[Callable] = None, +) -> np.ndarray: if kernel is None: kernel = normal @@ -70,11 +93,11 @@ def _llr_alt(fd_train, fd_test, y_train, *, bandwidth, kernel=None): def _create_data_basis() -> Tuple[FDataBasis, FDataBasis, np.ndarray]: - X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) + X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values fat = y['fat'].values - basis = skfda.representation.basis.BSpline( + basis = Fourier( n_basis=10, domain_range=fd.domain_range, ) @@ -91,7 +114,7 @@ def _create_data_basis() -> Tuple[FDataBasis, FDataBasis, np.ndarray]: def _create_data_grid() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: - X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) + X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values fat = y['fat'].values @@ -105,6 +128,14 @@ def _create_data_grid() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: return fd_train, fd_test, y_train +def _create_data_r() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: + X, y = fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + fat = y['fat'].values + + return fd[:100], fd[100:110], fat[:100] + + class TestKernelRegression(unittest.TestCase): """Test Nadaraya-Watson, KNNeighbours and LocalLinearRegression methods.""" @@ -229,3 +260,53 @@ def test_llr(self) -> None: ), y_basis, ) + + def test_nw_r(self) -> None: + """Comparison of NW's results with results from fda.usc.""" + X_train, X_test, y_train = _create_data_r() + + nw = KernelRegression( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), + ) + nw.fit(X_train, y_train) + + y = nw.predict(X_test) + result_R = [ + 18.245093, + 22.976695, + 9.429236, + 16.852003, + 16.568529, + 8.520466, + 14.943808, + 15.344949, + 8.646862, + 16.576900, + ] + + np.testing.assert_almost_equal(y, result_R, decimal=3) + + def test_knn_r(self) -> None: + """Comparison of NW's results with results from fda.usc.""" + X_train, X_test, y_train = _create_data_r() + + knn = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(bandwidth=3), + ) + knn.fit(X_train, y_train) + + y = knn.predict(X_test) + result_R = [ + 20.400000, + 24.166667, + 10.900000, + 20.466667, + 16.900000, + 5.433333, + 14.400000, + 11.966667, + 9.033333, + 19.633333, + ] + + np.testing.assert_almost_equal(y, result_R, decimal=6) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index e6d728c5b..e4864dd8e 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -6,16 +6,17 @@ import skfda import skfda.preprocessing.smoothing as smoothing -import skfda.preprocessing.smoothing.kernel_smoothers as kernel_smoothers import skfda.preprocessing.smoothing.validation as validation from skfda._utils import _check_estimator from skfda.misc.hat_matrix import ( + HatMatrix, KNeighborsHatMatrix, LocalLinearRegressionHatMatrix, NadarayaWatsonHatMatrix, ) from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization +from skfda.preprocessing.smoothing import KernelSmoother from skfda.representation.basis import BSpline, Monomial from skfda.representation.grid import FDataGrid @@ -23,9 +24,9 @@ class TestSklearnEstimators(unittest.TestCase): """Test for sklearn estimators.""" - def test_kernel_smoothing(self): + def test_kernel_smoothing(self) -> None: """Test if estimator adheres to scikit-learn conventions.""" - _check_estimator(kernel_smoothers.KernelSmoother) + _check_estimator(KernelSmoother) class _LinearSmootherLeaveOneOutScorerAlternative: @@ -33,7 +34,7 @@ class _LinearSmootherLeaveOneOutScorerAlternative: def __call__( self, - estimator: kernel_smoothers.KernelSmoother, + estimator: KernelSmoother, X: FDataGrid, y: FDataGrid, ) -> None: @@ -51,7 +52,7 @@ class TestLeaveOneOut(unittest.TestCase): def _test_generic( self, - estimator: kernel_smoothers.KernelSmoother, + estimator: KernelSmoother, ) -> None: loo_scorer = validation.LinearSmootherLeaveOneOutScorer() loo_scorer_alt = _LinearSmootherLeaveOneOutScorerAlternative() @@ -83,28 +84,86 @@ def _test_generic( def test_nadaraya_watson(self) -> None: """Test Leave-One-Out with Nadaraya Watson method.""" - self._test_generic( - kernel_smoothers.KernelSmoother( - kernel_estimator=NadarayaWatsonHatMatrix(), - ), + self._test_generic(KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(), + ), ) def test_local_linear_regression(self) -> None: """Test Leave-One-Out with Local Linear Regression method.""" - self._test_generic( - kernel_smoothers.KernelSmoother( - kernel_estimator=LocalLinearRegressionHatMatrix(), - ), + self._test_generic(KernelSmoother( + kernel_estimator=LocalLinearRegressionHatMatrix(), + ), ) def test_knn(self) -> None: """Test Leave-One-Out with KNNeighbours method.""" - self._test_generic( - kernel_smoothers.KernelSmoother( - kernel_estimator=KNeighborsHatMatrix(), - ), + self._test_generic(KernelSmoother( + kernel_estimator=KNeighborsHatMatrix(), + ), + ) + + +class TestKernelSmoother(unittest.TestCase): + """Test Kernel Smoother. + + Comparison of results with fda.usc R library + """ + + def _test_hat_matrix( + self, + kernel_estimator: HatMatrix, + ) -> np.ndarray: + return KernelSmoother( # noqa: WPS437 + kernel_estimator=kernel_estimator, + )._hat_matrix( + input_points=[[1, 2, 3, 4, 5]], + output_points=[[1, 2, 3, 4, 5]], ) + def test_nw(self) -> None: + """Comparison of NW hat matrix with the one obtained from fda.usc.""" + hat_matrix = self._test_hat_matrix( + NadarayaWatsonHatMatrix(bandwidth=10), + ) + hat_matrix_r = [ + [0.206001865, 0.204974427, 0.201922755, 0.196937264, 0.190163689], + [0.201982911, 0.202995354, 0.201982911, 0.198975777, 0.194063047], + [0.198003042, 0.200995474, 0.202002968, 0.200995474, 0.198003042], + [0.194063047, 0.198975777, 0.201982911, 0.202995354, 0.201982911], + [0.190163689, 0.196937264, 0.201922755, 0.204974427, 0.206001865], + ] + np.testing.assert_allclose(hat_matrix, hat_matrix_r) + + def test_llr(self) -> None: + """Test LLR.""" + hat_matrix = self._test_hat_matrix( + LocalLinearRegressionHatMatrix(bandwidth=10), + ) + + # For a straight line the estimated results should coincide with + # the real values + # r(x) = 3x + 2 + np.testing.assert_allclose( + np.dot(hat_matrix, [5, 8, 11, 14, 17]), + [5, 8, 11, 14, 17], + ) + + def test_knn(self) -> None: + """Comparison of KNN hat matrix with the one obtained from fda.usc.""" + hat_matrix = self._test_hat_matrix( + KNeighborsHatMatrix(bandwidth=2), + ) + + hat_matrix_r = [ + [0.500000000, 0.500000000, 0.000000000, 0.000000000, 0.000000000], + [0.333333333, 0.333333333, 0.333333333, 0.000000000, 0.000000000], + [0.000000000, 0.333333333, 0.333333333, 0.333333333, 0.000000000], + [0.000000000, 0.000000000, 0.333333333, 0.333333333, 0.333333333], + [0.000000000, 0.000000000, 0.000000000, 0.500000000, 0.500000000], + ] + np.testing.assert_allclose(hat_matrix, hat_matrix_r) + class TestBasisSmoother(unittest.TestCase): """Test Basis Smoother.""" From 472dac116c1fb00b8f7da0fd400dd2fcbef4e5ca Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 16 Feb 2022 20:56:25 +0100 Subject: [PATCH 046/400] Error corrections --- docs/modules/ml/regression.rst | 3 ++ examples/plot_kernel_regression.py | 8 ++--- examples/plot_kernel_smoothing.py | 2 +- skfda/misc/hat_matrix.py | 39 +++++++++++---------- skfda/ml/regression/_kernel_regression.py | 12 ++++--- skfda/preprocessing/smoothing/validation.py | 16 ++++----- tests/test_fda_feature_union.py | 2 +- tests/test_kernel_regression.py | 11 +++--- tests/test_smoothing.py | 15 ++++---- 9 files changed, 60 insertions(+), 48 deletions(-) diff --git a/docs/modules/ml/regression.rst b/docs/modules/ml/regression.rst index 4af4337d0..a5f9077b7 100644 --- a/docs/modules/ml/regression.rst +++ b/docs/modules/ml/regression.rst @@ -37,6 +37,9 @@ it is explained the basic usage of these estimators. Kernel regression ----------------- +This module includes the implementation of Kernel Regression for FData with a scalar as a response variable. It is a +non-parametric technique that uses :class:`~skfda.misc.hat_matrix.HatMatrix` object. + .. autosummary:: :toctree: autosummary diff --git a/examples/plot_kernel_regression.py b/examples/plot_kernel_regression.py index e7efbb4ea..39273791e 100644 --- a/examples/plot_kernel_regression.py +++ b/examples/plot_kernel_regression.py @@ -58,7 +58,7 @@ n_neighbors = np.array(range(1, 100)) knn = GridSearchCV( KernelRegression(kernel_estimator=KNeighborsHatMatrix()), - param_grid={'kernel_estimator__bandwidth': n_neighbors}, + param_grid={'kernel_estimator__n_neighbors': n_neighbors}, ) @@ -69,7 +69,7 @@ knn.fit(X_train, y_train) print( 'KNN bandwidth:', - knn.best_params_['kernel_estimator__bandwidth'], + knn.best_params_['kernel_estimator__n_neighbors'], ) ############################################################################## @@ -175,13 +175,13 @@ # K-Nearest Neighbours knn = GridSearchCV( KernelRegression(kernel_estimator=KNeighborsHatMatrix()), - param_grid={'kernel_estimator__bandwidth': n_neighbors}, + param_grid={'kernel_estimator__n_neighbors': n_neighbors}, ) knn.fit(Xd_train, y_train) print( 'KNN bandwidth:', - knn.best_params_['kernel_estimator__bandwidth'], + knn.best_params_['kernel_estimator__n_neighbors'], ) y_pred = knn.predict(Xd_test) diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index 9a2ee4e92..088eac333 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -68,7 +68,7 @@ knn = val.SmoothingParameterSearch( KernelSmoother(kernel_estimator=hm.KNeighborsHatMatrix()), n_neighbors, - param_name='kernel_estimator__bandwidth', + param_name='kernel_estimator__n_neighbors', ) knn.fit(fd) knn_fd = knn.transform(fd) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index dbc01a79c..ae0e9e693 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -146,7 +146,7 @@ def _hat_matrix_function_not_normalized( if self.bandwidth is None: percentage = 15 - self.bandwidth = np.percentile(delta_x, percentage) + self.bandwidth = np.percentile(np.abs(delta_x), percentage) return self.kernel(delta_x / self.bandwidth) @@ -184,25 +184,25 @@ class LocalLinearRegressionHatMatrix(HatMatrix): For **kernel regression** algorithm: - Given functional data, :math:`(x_1, x_2, ..., x_n)` where each function + Given functional data, :math:`(X_1, X_2, ..., X_n)` where each function is expressed in a orthonormal basis with :math:`J` elements and scalar response :math:`Y = (y_1, y_2, ..., y_n)`. It is desired to estimate the values - :math:`\hat{Y} = (\hat{y}_1, \hat{y}'_2, ..., \hat{y}'_m)` - for the data :math:`(x'_1, x'_2, ..., x'_m)` (expressed in the same basis). + :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` + for the data :math:`(X'_1, X'_2, ..., X'_m)` (expressed in the same basis). - For each :math:`f'_k` the estimation :math:`\hat{y}_k` is obtained by + For each :math:`X'_k` the estimation :math:`\hat{y}_k` is obtained by taking the value :math:`a_k` from the vector :math:`(a_k, b_{1k}, ..., b_{Jk})` which minimizes the following expression .. math:: AWSE(a_k, b_{1k}, ..., b_{Jk}) = \sum_{i=1}^n \left(y_i - \left(a + \sum_{j=1}^J b_{jk} c_{ijk} \right) \right)^2 - K \left( \frac {d(x_i - x'_k)}{h} \right) + K \left( \frac {d(X_i - X'_k)}{h} \right) Where :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis - expansion of :math:`x_i - x'_k = \sum_{j=1}^J c_{ij}^k` and :math:`d` some + expansion of :math:`X_i - X'_k = \sum_{j=1}^J c_{ij}^k` and :math:`d` some functional distance :footcite:`baillo+grane_2008_llr` For both cases, :math:`K(\cdot)` is a kernel function and :math:`h` the @@ -232,7 +232,7 @@ def __call__( # noqa: D102 if self.bandwidth is None: percentage = 15 - self.bandwidth = np.percentile(delta_x, percentage) + self.bandwidth = np.percentile(np.abs(delta_x), percentage) # Regression if isinstance(X_train, FDataBasis): @@ -253,7 +253,7 @@ def __call__( # noqa: D102 # Smoothing else: - return super().__call__( + return super().__call__( # noqa: WPS503 delta_x=delta_x, X_train=X_train, X=X, @@ -317,7 +317,7 @@ def _hat_matrix_function_not_normalized( if self.bandwidth is None: percentage = 15 - self.bandwidth = np.percentile(delta_x, percentage) + self.bandwidth = np.percentile(np.abs(delta_x), percentage) k = self.kernel(delta_x / self.bandwidth) @@ -348,15 +348,15 @@ class KNeighborsHatMatrix(HatMatrix): In both cases, :math:`K(\cdot)` is a kernel function and :math:`h_{ik}` is calculated as the distance from :math:`e_i'` to it's - :math:`k`-th nearest neighbor in :math:`\{e_1, ..., e_n\}` + :math:`k`-th nearest neighbour in :math:`\{e_1, ..., e_n\}` :footcite:`ferraty+vieu_2006_nonparametric_knn`. Used with the uniform kernel, it takes the average of the closest k points to a given point. Args: - bandwidth: Number of nearest neighbours. By - default it takes the 5% closest points. + n_neighbors: Number of nearest neighbours. By + default it takes the 5% closest elements. kernel: Kernel function. By default a uniform kernel to perform a 'usual' k nearest neighbours estimation. @@ -368,10 +368,11 @@ class KNeighborsHatMatrix(HatMatrix): def __init__( self, *, - bandwidth: Optional[int] = None, + n_neighbors: Optional[int] = None, kernel: Callable[[np.ndarray], np.ndarray] = kernels.uniform, ): - super().__init__(bandwidth=bandwidth, kernel=kernel) + self.n_neighbors = n_neighbors + self.kernel = kernel def _hat_matrix_function_not_normalized( self, @@ -381,14 +382,14 @@ def _hat_matrix_function_not_normalized( input_points_len = delta_x.shape[1] - if self.bandwidth is None: - self.bandwidth = np.floor( + if self.n_neighbors is None: + self.n_neighbors = np.floor( np.percentile( range(1, input_points_len), 5, ), ) - elif self.bandwidth <= 0: + elif self.n_neighbors <= 0: raise ValueError('h must be greater than 0') # Tolerance to avoid points landing outside the kernel window due to @@ -399,7 +400,7 @@ def _hat_matrix_function_not_normalized( # point within the k nearest neighbours vec = np.percentile( np.abs(delta_x), - self.bandwidth / input_points_len * 100, + self.n_neighbors / input_points_len * 100, axis=1, interpolation='lower', ) + tol diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py index 0cd26be25..2834cae86 100644 --- a/skfda/ml/regression/_kernel_regression.py +++ b/skfda/ml/regression/_kernel_regression.py @@ -18,20 +18,22 @@ class KernelRegression( ): r"""Kernel regression with scalar response. - Let :math:`fd_1 = (f_1, f_2, ..., f_n)` be the functional data set and + Let :math:`fd_1 = (X_1, X_2, ..., X_n)` be the functional data set and :math:`y = (y_1, y_2, ..., y_n)` be the scalar response corresponding to each function in :math:`fd_1`. Then, the estimation for the - functions in :math:`fd_2 = (g_1, g_2, ..., g_n)` can be calculated as + functions in :math:`fd_2 = (X'_1, X'_2, ..., X'_n)` can be + calculated as .. math:: \hat{y} = \hat{H}y Where :math:`\hat{H}` is a matrix described in - :class:`~skfda.misc.HatMatrix`. + :class:`~skfda.misc.hat_matrix.HatMatrix`. Args: kernel_estimator: Method used to calculate the hat matrix - (default = :class:`~skfda.misc.NadarayaWatsonHatMatrix`). + (default = + :class:`~skfda.misc.hat_matrix.NadarayaWatsonHatMatrix`). metric: Metric used to calculate the distances (default = :func:`L2 distance `). @@ -54,7 +56,7 @@ class KernelRegression( >>> estimator.predict(fd_2) array([ 2.02723928, 4. , 5.97276072]) - >>> kernel_estimator = KNeighborsHatMatrix(bandwidth=2) + >>> kernel_estimator = KNeighborsHatMatrix(n_neighbors=2) >>> estimator = KernelRegression(kernel_estimator=kernel_estimator) >>> _ = estimator.fit(fd_1, y) >>> estimator.predict(fd_2) diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py index e36c766c9..562d81f43 100644 --- a/skfda/preprocessing/smoothing/validation.py +++ b/skfda/preprocessing/smoothing/validation.py @@ -195,13 +195,13 @@ class SmoothingParameterSearch(GridSearchCV): ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], - ... param_name='kernel_estimator__bandwidth') + ... param_name='kernel_estimator__n_neighbors') >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-11.67, -12.37]) >>> round(grid.best_score_, 2) -11.67 - >>> grid.best_params_['kernel_estimator__bandwidth'] + >>> grid.best_params_['kernel_estimator__n_neighbors'] 2 >>> grid.best_estimator_.hat_matrix().round(2) array([[ 0.5 , 0.5 , 0. , 0. , 0. ], @@ -227,7 +227,7 @@ class SmoothingParameterSearch(GridSearchCV): ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], - ... param_name='kernel_estimator__bandwidth', + ... param_name='kernel_estimator__n_neighbors', ... scoring=LinearSmootherLeaveOneOutScorer()) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) @@ -236,7 +236,7 @@ class SmoothingParameterSearch(GridSearchCV): ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], - ... param_name='kernel_estimator__bandwidth', + ... param_name='kernel_estimator__n_neighbors', ... scoring=LinearSmootherGeneralizedCVScorer( ... akaike_information_criterion)) >>> _ = grid.fit(fd) @@ -246,7 +246,7 @@ class SmoothingParameterSearch(GridSearchCV): ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], - ... param_name='kernel_estimator__bandwidth', + ... param_name='kernel_estimator__n_neighbors', ... scoring=LinearSmootherGeneralizedCVScorer( ... finite_prediction_error)) >>> _ = grid.fit(fd) @@ -256,7 +256,7 @@ class SmoothingParameterSearch(GridSearchCV): ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], - ... param_name='kernel_estimator__bandwidth', + ... param_name='kernel_estimator__n_neighbors', ... scoring=LinearSmootherGeneralizedCVScorer(shibata)) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) @@ -265,7 +265,7 @@ class SmoothingParameterSearch(GridSearchCV): ... KernelSmoother( ... kernel_estimator=KNeighborsHatMatrix()), ... [2,3], - ... param_name='kernel_estimator__bandwidth', + ... param_name='kernel_estimator__n_neighbors', ... scoring=LinearSmootherGeneralizedCVScorer(rice)) >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) @@ -280,7 +280,7 @@ class SmoothingParameterSearch(GridSearchCV): ... kernel_estimator=KNeighborsHatMatrix(), ... output_points=output_points), ... [2,3], - ... param_name='kernel_estimator__bandwidth') + ... param_name='kernel_estimator__n_neighbors') >>> _ = grid.fit(fd) >>> np.array(grid.cv_results_['mean_test_score']).round(2) array([-11.67, -12.37]) diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index 7a8e20429..d8df986bf 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -6,13 +6,13 @@ from pandas.testing import assert_frame_equal from skfda.datasets import fetch_growth +from skfda.misc.feature_construction import EvaluationTransformer from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix from skfda.misc.operators import SRSF from skfda.preprocessing.dim_reduction.feature_extraction import ( FDAFeatureUnion, ) from skfda.preprocessing.smoothing import KernelSmoother -from skfda.representation import EvaluationTransformer class TestFDAFeatureUnion(unittest.TestCase): diff --git a/tests/test_kernel_regression.py b/tests/test_kernel_regression.py index fc65ad9d4..01e3d9748 100644 --- a/tests/test_kernel_regression.py +++ b/tests/test_kernel_regression.py @@ -188,7 +188,7 @@ def test_knn(self) -> None: # Test KNN method with basis representation, n_neighbours=3 and # uniform kernel knn_basis = KernelRegression( - kernel_estimator=KNeighborsHatMatrix(bandwidth=3), + kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), ) knn_basis.fit(fd_train_basis, y_train_basis) y_basis = knn_basis.predict(fd_test_basis) @@ -206,7 +206,7 @@ def test_knn(self) -> None: # Test KNN method with grid representation, n_neighbours=3 and # uniform kernel knn_grid = KernelRegression( - kernel_estimator=KNeighborsHatMatrix(bandwidth=3), + kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), ) knn_grid.fit(fd_train_grid, y_train_grid) y_grid = knn_grid.predict(fd_test_grid) @@ -224,7 +224,10 @@ def test_knn(self) -> None: # Test KNN method with basis representation, n_neighbours=10 and # normal kernel knn_basis = KernelRegression( - kernel_estimator=KNeighborsHatMatrix(bandwidth=10, kernel=normal), + kernel_estimator=KNeighborsHatMatrix( + n_neighbors=10, + kernel=normal, + ), ) knn_basis.fit(fd_train_basis, y_train_basis) y_basis = knn_basis.predict(fd_test_basis) @@ -291,7 +294,7 @@ def test_knn_r(self) -> None: X_train, X_test, y_train = _create_data_r() knn = KernelRegression( - kernel_estimator=KNeighborsHatMatrix(bandwidth=3), + kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), ) knn.fit(X_train, y_train) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index e4864dd8e..368c44abd 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -53,6 +53,7 @@ class TestLeaveOneOut(unittest.TestCase): def _test_generic( self, estimator: KernelSmoother, + smoothing_param_name: str = 'kernel_estimator__bandwidth', ) -> None: loo_scorer = validation.LinearSmootherLeaveOneOutScorer() loo_scorer_alt = _LinearSmootherLeaveOneOutScorerAlternative() @@ -63,7 +64,7 @@ def _test_generic( grid = validation.SmoothingParameterSearch( estimator, [2, 3], - param_name='kernel_estimator__bandwidth', + param_name=smoothing_param_name, scoring=loo_scorer, ) @@ -73,7 +74,7 @@ def _test_generic( grid_alt = validation.SmoothingParameterSearch( estimator, [2, 3], - param_name='kernel_estimator__bandwidth', + param_name=smoothing_param_name, scoring=loo_scorer_alt, ) @@ -98,9 +99,11 @@ def test_local_linear_regression(self) -> None: def test_knn(self) -> None: """Test Leave-One-Out with KNNeighbours method.""" - self._test_generic(KernelSmoother( - kernel_estimator=KNeighborsHatMatrix(), - ), + self._test_generic( + KernelSmoother( + kernel_estimator=KNeighborsHatMatrix(), + ), + smoothing_param_name='kernel_estimator__n_neighbors', ) @@ -152,7 +155,7 @@ def test_llr(self) -> None: def test_knn(self) -> None: """Comparison of KNN hat matrix with the one obtained from fda.usc.""" hat_matrix = self._test_hat_matrix( - KNeighborsHatMatrix(bandwidth=2), + KNeighborsHatMatrix(n_neighbors=2), ) hat_matrix_r = [ From da41db76f6ccadbe519455d0341f5de52c889594 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 16 Feb 2022 21:06:00 +0100 Subject: [PATCH 047/400] Update _per_class_transformer.py --- .../_per_class_transformer.py | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py index 6305663e8..24867c2c2 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py @@ -6,6 +6,7 @@ import numpy as np from pandas import DataFrame +from sklearn.model_selection import train_test_split from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from ...._utils import TransformerMixin, _fit_feature_transformer @@ -85,22 +86,19 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): >>> neigh1 = neigh1.fit(X_train1, y_train1) Finally we can predict and check the score: + >>> neigh1.predict(X_test1) - array([0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, - 1, 1, 1, 1, 1, 1, 1], dtype=int8) + array([0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, + 1, 1, 1], dtype=int8) >>> round(neigh1.score(X_test1, y_test1), 3) - 0.958 - - + 0.958 We can also use a transformer that returns a FData object when predicting. In our example we are going to use the Nadaraya Watson Smoother. - >>> from skfda.preprocessing.smoothing.kernel_smoothers import ( - ... KernelSmoother, - ... ) + >>> from skfda.preprocessing.smoothing import KernelSmoother >>> from skfda.misc.hat_matrix import ( ... NadarayaWatsonHatMatrix, ... ) @@ -123,13 +121,13 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): ... X_transformed_grid = X_transformed_grid.concatenate( ... curve_grid, ... ) - >>> y = np.concatenate((y,y)) ``X_transformed_grid`` contains a FDataGrid with all the transformed curves. Now we are able to use it to fit a KNN classifier. Again we split the data into train and test. + >>> X_train2, X_test2, y_train2, y_test2 = train_test_split( ... X_transformed_grid, ... y, @@ -140,16 +138,17 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): This time we need a functional data classifier. We fit the classifier and predict. + >>> from skfda.ml.classification import KNeighborsClassifier >>> neigh2 = KNeighborsClassifier() >>> neigh2 = neigh2.fit(X_train2, y_train2) >>> neigh2.predict(X_test2) - array([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, - 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, - 1, 1, 1, 0, 0], dtype=int8) + array([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, + 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, + 1, 1, 1, 0, 0], dtype=int8) >>> round(neigh2.score(X_test2, y_test2), 3) - 0.957 + 0.957 """ From f8a4ff0fde286c8a61d6888ab1030545b271f98f Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 16 Feb 2022 22:37:59 +0100 Subject: [PATCH 048/400] Small updates --- .../dim_reduction/feature_extraction/_per_class_transformer.py | 1 - tutorial/plot_skfda_sklearn.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py index 24867c2c2..0d02941b5 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py @@ -6,7 +6,6 @@ import numpy as np from pandas import DataFrame -from sklearn.model_selection import train_test_split from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from ...._utils import TransformerMixin, _fit_feature_transformer diff --git a/tutorial/plot_skfda_sklearn.py b/tutorial/plot_skfda_sklearn.py index 5ab3c317a..c408589b0 100644 --- a/tutorial/plot_skfda_sklearn.py +++ b/tutorial/plot_skfda_sklearn.py @@ -108,7 +108,7 @@ # As these methods discard information of the original data they usually are # not reversible. -import skfda.preprocessing.smoothing.kernel_smoothers as ks +import skfda.preprocessing.smoothing as ks from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix X, y = skfda.datasets.fetch_phoneme(return_X_y=True) From 82d5d4ac6b9fe189fa82461d36cd20c3d873a0bd Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 16 Feb 2022 23:21:43 +0100 Subject: [PATCH 049/400] Style errors fixed --- skfda/exploratory/stats/__init__.py | 5 +---- skfda/exploratory/stats/_functional_transformers.py | 3 ++- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index e4afa70ef..5dfd0fcd4 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,8 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - number_up_crossings, -) +from ._functional_transformers import local_averages, number_up_crossings from ._stats import ( cov, depth_based_median, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 7c7ae49f8..9b9c50da7 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -6,7 +6,7 @@ import numpy as np -from ...representation import FDataGrid +from ...representation import FDataBasis, FDataGrid def local_averages( @@ -76,6 +76,7 @@ def local_averages( ] return np.asarray(integrated_data) + def number_up_crossings( data: FDataGrid, levels: np.ndarray, From 10c2da299c125ae056db50991843481af96e995f Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 16 Feb 2022 23:22:50 +0100 Subject: [PATCH 050/400] Style erros fixed --- skfda/exploratory/stats/__init__.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 2e401b15e..19860bd6c 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,9 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - number_up_crossings, - occupation_measure, -) +from ._functional_transformers import local_averages, occupation_measure from ._stats import ( cov, depth_based_median, From 393597f4351d6524c2524ab3d8483d01038ac0bd Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 17 Feb 2022 23:31:57 +0100 Subject: [PATCH 051/400] Change example of per class transformer --- .../_per_class_transformer.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py index 0d02941b5..796ec0f68 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py @@ -95,14 +95,13 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): We can also use a transformer that returns a FData object when predicting. - In our example we are going to use the Nadaraya Watson Smoother. + In our example we are going to use the Fisher Rao Elastic Registration. - >>> from skfda.preprocessing.smoothing import KernelSmoother - >>> from skfda.misc.hat_matrix import ( - ... NadarayaWatsonHatMatrix, + >>> from skfda.preprocessing.registration import ( + ... FisherRaoElasticRegistration, ... ) >>> t2 = PerClassTransformer( - ... KernelSmoother(kernel_estimator=NadarayaWatsonHatMatrix()), + ... FisherRaoElasticRegistration(), ... ) >>> x_transformed2 = t2.fit_transform(X, y) @@ -142,12 +141,12 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): >>> neigh2 = KNeighborsClassifier() >>> neigh2 = neigh2.fit(X_train2, y_train2) >>> neigh2.predict(X_test2) - array([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, - 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, - 1, 1, 1, 0, 0], dtype=int8) + array([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, + 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, + 1, 0, 0], dtype=int8) >>> round(neigh2.score(X_test2, y_test2), 3) - 0.957 + 0.851 """ @@ -205,7 +204,7 @@ def _validate_transformer( if tags['stateless']: warnings.warn( - f"Parameter 'transformer' with type " + f"Parameter 'transformer' with type " # noqa:WPS237 f"{type(self.transformer)} should use the data for " f" fitting." f"It should have the 'stateless' tag set to 'False'", From 0a3bd72140b35a1ceb0133f21f6f7d00e6d4a6e3 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 18 Feb 2022 00:23:37 +0100 Subject: [PATCH 052/400] Functional Gaussian Classifier --- .../ml/classification/_gaussian_classifier.py | 53 ++++++++----------- 1 file changed, 21 insertions(+), 32 deletions(-) diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index 5cb21457b..7ea87cd73 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -5,7 +5,6 @@ from GPy.models import GPRegression from scipy.linalg import logm from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.model_selection import GridSearchCV from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes @@ -18,8 +17,8 @@ class GaussianClassifier( ): def __init__(self, kernel: Kern, regularizer: float) -> None: - self.kernel = kernel - self.regularizer = regularizer + self._kernel_ = kernel + self._regularizer_ = regularizer def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: """Fit the model using X as training data and y as target values. @@ -31,16 +30,13 @@ def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: Returns: self """ - self._classes, self._y_ind = _classifier_get_classes(y) + self._classes, self._y_ind = _classifier_get_classes(y) # noqa:WPS414 self._cov_kernels_, self._means = self._fit_kernels_and_means(X) - self.X = X - self.y = y - self._priors = self._calculate_priors(y) self._log_priors = np.log(self._priors) # Calculates prior logartithms - self._covariances = self._calculate_covariances(X, y) + self._covariances = self._calculate_covariances(X) # Calculates logarithmic covariance -> -1/2 * log|sum| self._log_cov = self._calculate_log_covariances() @@ -58,10 +54,16 @@ def predict(self, X: FDataGrid) -> np.ndarray: for each data sample. """ check_is_fitted(self) - return np.asarray([ - self._calculate_log_likelihood_ratio(curve) + likelihoods = [ + self._calculate_log_likelihood(curve) for curve in X.data_matrix + ] + + predictions = np.asarray([ + np.where(likelihood == max(likelihood)) + for likelihood in likelihoods ]) + return np.reshape(predictions, predictions.size) def _calculate_priors(self, y: np.ndarray) -> np.ndarray: """ @@ -118,7 +120,7 @@ def _calculate_log_covariances(self) -> np.ndarray: """ return np.asarray([ -0.5 * np.trace( - logm(cov + self.regularizer * np.eye(cov.shape[0])), + logm(cov + self._regularizer_ * np.eye(cov.shape[0])), ) for cov in self._covariances ]) @@ -148,41 +150,32 @@ def _fit_kernels_and_means( class_n_centered = class_n - class_n.mean() data = class_n_centered.data_matrix[:, :, 0] - reg_n = GPRegression(grid, data.T, kernel=self.kernel) + reg_n = GPRegression(grid, data.T, kernel=self._kernel_) reg_n.optimize() kernels = kernels + [reg_n.kern] means = means + [class_n.gmean().data_matrix[0]] return np.asarray(kernels), np.asarray(means) - def _calculate_log_likelihood_ratio(self, curve: np.ndarray) -> np.ndarray: + def _calculate_log_likelihood(self, curve: np.ndarray) -> np.ndarray: """ - Calculate the log likelihood quadratic discriminant analysis ratio. + Calculate the log likelihood quadratic discriminant analysis. Args: - curve: sample where we want to calculate the ratio. + curve: sample where we want to calculate the discriminant. Returns: - A ndarray with the ratios corresponding to the output classes. + A ndarray with the log likelihoods corresponding to the + output classes. """ # Calculates difference wrt. the mean (x - un) data_mean = curve - self._means - """ - ¿COMO SE REALIZA EL GRID SEARCH? - """ - param_gr = { - 'regularizer': [10, 1, 0.1, 0.01, 0.001, 0.0001], - } - cross_val = GridSearchCV(self, param_gr) - cross_val = cross_val.fit(self.X, self.y) - print(cross_val.best_score_) - # Calculates mahalanobis distance (-1/2*(x - un).T*inv(sum)*(x - un)) mahalanobis = [] for j in range(0, self._classes.size): mh = -0.5 * data_mean[j].T @ np.linalg.solve( - self._covariances[j] + self.regularizer * np.eye( + self._covariances[j] + self._regularizer_ * np.eye( self._covariances[j].shape[0], ), data_mean[j], @@ -190,10 +183,6 @@ def _calculate_log_likelihood_ratio(self, curve: np.ndarray) -> np.ndarray: mahalanobis = mahalanobis + [mh[0][0]] # Calculates the log_likelihood - log_likelihood = self._log_cov + np.asarray( + return self._log_cov + np.asarray( mahalanobis, ) + self._log_priors - """ - ¿COMO SE CALCULAN TODOS LOS RATIOS? - """ - return log_likelihood[1] / log_likelihood[0] From 17e93e79e85e5a73742cccac40666ea1fd9735d3 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 18 Feb 2022 17:19:07 +0100 Subject: [PATCH 053/400] Add additional project urls. --- setup.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/setup.py b/setup.py index 53f91eff9..863927c55 100644 --- a/setup.py +++ b/setup.py @@ -39,6 +39,11 @@ description=DOCLINES[1], long_description="\n".join(DOCLINES[3:]), url='https://fda.readthedocs.io', + project_urls={ + "Bug Tracker": "https://github.com/GAA-UAM/scikit-fda/issues", + "Documentation": "https://fda.readthedocs.io", + "Source Code": "https://github.com/GAA-UAM/scikit-fda", + }, maintainer='Carlos Ramos Carreño', maintainer_email='vnmabus@gmail.com', include_package_data=True, From 3ca8c01f9fb4896c1c2f55e2d72c494c21444943 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 20 Feb 2022 19:40:49 +0100 Subject: [PATCH 054/400] Add deprecated classes and modules for Kernel Smoothing. --- skfda/preprocessing/smoothing/__init__.py | 15 +++ .../smoothing/_kernel_smoothers.py | 13 ++- .../smoothing/kernel_smoothers.py | 110 ++++++++++++++++++ 3 files changed, 133 insertions(+), 5 deletions(-) create mode 100644 skfda/preprocessing/smoothing/kernel_smoothers.py diff --git a/skfda/preprocessing/smoothing/__init__.py b/skfda/preprocessing/smoothing/__init__.py index d75eb30ab..db38c1471 100644 --- a/skfda/preprocessing/smoothing/__init__.py +++ b/skfda/preprocessing/smoothing/__init__.py @@ -1,4 +1,19 @@ """Smoothing.""" +import warnings +from typing import Any + from . import validation from ._basis import BasisSmoother from ._kernel_smoothers import KernelSmoother + +__kernel_smoothers__imported__ = False + + +def __getattr__(name: str) -> Any: + global __kernel_smoothers__imported__ + if name == "kernel_smoothers" and not __kernel_smoothers__imported__: + __kernel_smoothers__imported__ = True + from . import kernel_smoothers + return kernel_smoothers + + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/skfda/preprocessing/smoothing/_kernel_smoothers.py b/skfda/preprocessing/smoothing/_kernel_smoothers.py index 72d2439ea..6dc7d82e3 100644 --- a/skfda/preprocessing/smoothing/_kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/_kernel_smoothers.py @@ -9,9 +9,9 @@ import numpy as np -from skfda.misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix -from skfda.representation._typing import GridPointsLike - +from ..._utils._utils import _to_grid_points +from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix +from ...representation._typing import GridPointsLike, NDArrayFloat from ._linear import _LinearSmoother @@ -111,7 +111,7 @@ def __init__( self, *, kernel_estimator: Optional[HatMatrix] = None, - weights: Optional[np.ndarray] = None, + weights: Optional[NDArrayFloat] = None, output_points: Optional[GridPointsLike] = None, ): self.kernel_estimator = kernel_estimator @@ -123,7 +123,10 @@ def _hat_matrix( self, input_points: GridPointsLike, output_points: GridPointsLike, - ) -> np.ndarray: + ) -> NDArrayFloat: + + input_points = _to_grid_points(input_points) + output_points = _to_grid_points(output_points) if self.kernel_estimator is None: self.kernel_estimator = NadarayaWatsonHatMatrix() diff --git a/skfda/preprocessing/smoothing/kernel_smoothers.py b/skfda/preprocessing/smoothing/kernel_smoothers.py new file mode 100644 index 000000000..92f47ab9d --- /dev/null +++ b/skfda/preprocessing/smoothing/kernel_smoothers.py @@ -0,0 +1,110 @@ +import abc +import warnings +from typing import Callable, Optional + +from ...misc import kernels +from ...misc.hat_matrix import ( + HatMatrix, + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) +from ...representation._typing import GridPointsLike, NDArrayFloat +from . import KernelSmoother +from ._linear import _LinearSmoother + +warnings.warn( + "The \"kernel_smoothers\" module is deprecated. " + "Use the \"KernelSmoother\" class instead", + DeprecationWarning, +) + + +class _DeprecatedLinearKernelSmoother(_LinearSmoother): + + def __init__( + self, + *, + smoothing_parameter: Optional[float] = None, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.normal, + weights: Optional[NDArrayFloat] = None, + output_points: Optional[GridPointsLike] = None, + ): + self.smoothing_parameter = smoothing_parameter + self.kernel = kernel + self.weights = weights + self.output_points = output_points + + warnings.warn( + f"Class \"{type(self)}\" is deprecated. " + "Use the \"KernelSmoother\" class instead", + DeprecationWarning, + ) + + def _hat_matrix( + self, + input_points: GridPointsLike, + output_points: GridPointsLike, + ) -> NDArrayFloat: + + return KernelSmoother( + kernel_estimator=self._get_kernel_estimator(), + weights=self.weights, + output_points=output_points, + )._hat_matrix( + input_points=input_points, + output_points=output_points, + ) + + @abc.abstractmethod + def _get_kernel_estimator(self) -> HatMatrix: + raise NotImplementedError + + +class NadarayaWatsonSmoother(_DeprecatedLinearKernelSmoother): + """Nadaraya-Watson smoother (deprecated).""" + + def _get_kernel_estimator(self) -> HatMatrix: + return NadarayaWatsonHatMatrix( + bandwidth=self.smoothing_parameter, + kernel=self.kernel, + ) + + +class LocalLinearRegressionSmoother(_DeprecatedLinearKernelSmoother): + """Local linear regression smoother (deprecated).""" + + def _get_kernel_estimator(self) -> HatMatrix: + return LocalLinearRegressionHatMatrix( + bandwidth=self.smoothing_parameter, + kernel=self.kernel, + ) + + +class KNeighborsSmoother(_DeprecatedLinearKernelSmoother): + """Local linear regression smoother (deprecated).""" + + def __init__( + self, + *, + smoothing_parameter: Optional[int] = None, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.uniform, + weights: Optional[NDArrayFloat] = None, + output_points: Optional[GridPointsLike] = None, + ): + super().__init__( + smoothing_parameter=smoothing_parameter, + kernel=kernel, + weights=weights, + output_points=output_points, + ) + + def _get_kernel_estimator(self) -> HatMatrix: + return KNeighborsHatMatrix( + n_neighbors=( + int(self.smoothing_parameter) + if self.smoothing_parameter is not None + else None + ), + kernel=self.kernel, + ) From 6322dacbd9b1e21dc2b9e441aa85f7cdc235de20 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 20 Feb 2022 22:53:29 +0100 Subject: [PATCH 055/400] Typing grid. --- skfda/representation/grid.py | 59 +++++++++++++++++++++++++++--------- 1 file changed, 44 insertions(+), 15 deletions(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index bc42e6488..d8d0d1f30 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -44,6 +44,7 @@ GridPoints, GridPointsLike, LabelTupleLike, + NDArrayBool, NDArrayFloat, NDArrayInt, ) @@ -404,7 +405,7 @@ def _evaluate( eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: return self.interpolation( # type: ignore self, @@ -413,7 +414,8 @@ def _evaluate( ) def derivative(self: T, *, order: int = 1) -> T: - """Differentiate a FDataGrid object. + """ + Differentiate a FDataGrid object. It is calculated using central finite differences when possible. In the extremes, forward and backward finite differences with accuracy @@ -650,7 +652,7 @@ def equals(self, other: object) -> bool: return self.interpolation == other.interpolation - def __eq__(self, other: object) -> np.ndarray: # type: ignore[override] + def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] """Elementwise equality of FDataGrid.""" if not isinstance(other, type(self)) or self.dtype != other.dtype: if other is pandas.NA: @@ -676,8 +678,8 @@ def __eq__(self, other: object) -> np.ndarray: # type: ignore[override] def _get_op_matrix( self, - other: Union[T, np.ndarray, float], - ) -> Union[None, float, np.ndarray]: + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> Union[None, float, NDArrayFloat, NDArrayInt]: if isinstance(other, numbers.Real): return float(other) elif isinstance(other, np.ndarray): @@ -698,7 +700,10 @@ def _get_op_matrix( return None - def __add__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __add__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: data_matrix = self._get_op_matrix(other) if data_matrix is None: @@ -706,11 +711,17 @@ def __add__(self: T, other: Union[T, np.ndarray, float]) -> T: return self._copy_op(other, data_matrix=self.data_matrix + data_matrix) - def __radd__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __radd__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: return self.__add__(other) - def __sub__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __sub__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: data_matrix = self._get_op_matrix(other) if data_matrix is None: @@ -718,7 +729,10 @@ def __sub__(self: T, other: Union[T, np.ndarray, float]) -> T: return self._copy_op(other, data_matrix=self.data_matrix - data_matrix) - def __rsub__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __rsub__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: data_matrix = self._get_op_matrix(other) if data_matrix is None: @@ -726,7 +740,10 @@ def __rsub__(self: T, other: Union[T, np.ndarray, float]) -> T: return self.copy(data_matrix=data_matrix - self.data_matrix) - def __mul__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __mul__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: data_matrix = self._get_op_matrix(other) if data_matrix is None: @@ -734,11 +751,17 @@ def __mul__(self: T, other: Union[T, np.ndarray, float]) -> T: return self._copy_op(other, data_matrix=self.data_matrix * data_matrix) - def __rmul__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __rmul__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: return self.__mul__(other) - def __truediv__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __truediv__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: data_matrix = self._get_op_matrix(other) if data_matrix is None: @@ -746,7 +769,10 @@ def __truediv__(self: T, other: Union[T, np.ndarray, float]) -> T: return self._copy_op(other, data_matrix=self.data_matrix / data_matrix) - def __rtruediv__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __rtruediv__( + self: T, + other: Union[T, NDArrayFloat, NDArrayInt, float], + ) -> T: data_matrix = self._get_op_matrix(other) if data_matrix is None: @@ -1261,7 +1287,10 @@ def __repr__(self) -> str: '\n ', ) - def __getitem__(self: T, key: Union[int, slice, np.ndarray]) -> T: + def __getitem__( + self: T, + key: Union[int, slice, NDArrayInt, NDArrayBool], + ) -> T: """Return self[key].""" key = _check_array_key(self.data_matrix, key) @@ -1363,7 +1392,7 @@ def nbytes(self) -> int: p.nbytes for p in self.grid_points ) - def isna(self) -> np.ndarray: + def isna(self) -> NDArrayBool: """ Return a 1-D array indicating if each value is missing. From b3b441d9c03d06cbc585b5c1d22460e2f3756497 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 21 Feb 2022 02:01:22 +0100 Subject: [PATCH 056/400] Typing improvements. --- skfda/representation/_functional_data.py | 78 +++++++++++-------- skfda/representation/basis/_fdatabasis.py | 92 +++++++++++++---------- 2 files changed, 98 insertions(+), 72 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index cdce0ab43..a0a5f739e 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -34,6 +34,7 @@ GridPointsLike, LabelTuple, LabelTupleLike, + NDArrayBool, NDArrayFloat, NDArrayInt, ) @@ -278,7 +279,7 @@ def domain_range(self) -> DomainRange: """ pass - def _extrapolation_index(self, eval_points: np.ndarray) -> np.ndarray: + def _extrapolation_index(self, eval_points: NDArrayFloat) -> NDArrayBool: """Check the points that need to be extrapolated. Args: @@ -304,12 +305,12 @@ def _extrapolation_index(self, eval_points: np.ndarray) -> np.ndarray: def _join_evaluation( self, - index_matrix: np.ndarray, - index_ext: np.ndarray, - index_ev: np.ndarray, - res_extrapolation: np.ndarray, - res_evaluation: np.ndarray, - ) -> np.ndarray: + index_matrix: NDArrayBool, + index_ext: NDArrayBool, + index_ev: NDArrayBool, + res_extrapolation: NDArrayFloat, + res_evaluation: NDArrayFloat, + ) -> NDArrayFloat: """Join the points evaluated. This method is used internally by :func:`evaluate` to join the result @@ -353,7 +354,7 @@ def _evaluate( eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: """Define the evaluation of the FData. Evaluates the samples of an FData object at several points. @@ -386,7 +387,7 @@ def evaluate( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[False] = False, aligned: Literal[True] = True, - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -398,7 +399,7 @@ def evaluate( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[False] = False, aligned: Literal[False], - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -410,7 +411,7 @@ def evaluate( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[True], aligned: Literal[True] = True, - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -422,7 +423,7 @@ def evaluate( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[True], aligned: Literal[False], - ) -> np.ndarray: + ) -> NDArrayFloat: pass def evaluate( @@ -433,7 +434,7 @@ def evaluate( extrapolation: Optional[ExtrapolationLike] = None, grid: bool = False, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: """Evaluate the object at a list of values or a grid. Args: @@ -564,7 +565,7 @@ def __call__( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[False] = False, aligned: Literal[True] = True, - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -576,7 +577,7 @@ def __call__( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[False] = False, aligned: Literal[False], - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -588,7 +589,7 @@ def __call__( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[True], aligned: Literal[True] = True, - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -600,7 +601,7 @@ def __call__( extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[True], aligned: Literal[False], - ) -> np.ndarray: + ) -> NDArrayFloat: pass def __call__( @@ -611,7 +612,7 @@ def __call__( extrapolation: Optional[ExtrapolationLike] = None, grid: bool = False, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: """Evaluate the :term:`functional object`. Evaluate the object or its derivatives at a list of values or a @@ -953,7 +954,7 @@ def compose( self: T, fd: T, *, - eval_points: Optional[np.ndarray] = None, + eval_points: Optional[NDArrayFloat] = None, ) -> FData: """Composition of functions. @@ -969,7 +970,10 @@ def compose( pass @abstractmethod - def __getitem__(self: T, key: Union[int, slice, np.ndarray]) -> T: + def __getitem__( + self: T, + key: Union[int, slice, NDArrayInt], + ) -> T: """Return self[key].""" pass @@ -984,10 +988,10 @@ def equals(self, other: object) -> bool: ) @abstractmethod - def __eq__(self, other: object) -> np.ndarray: # type: ignore[override] + def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] pass - def __ne__(self, other: object) -> np.ndarray: # type: ignore[override] + def __ne__(self, other: object) -> NDArrayBool: # type: ignore[override] """Return for `self != other` (element-wise in-equality).""" result = self.__eq__(other) if result is NotImplemented: @@ -997,7 +1001,7 @@ def __ne__(self, other: object) -> np.ndarray: # type: ignore[override] def _copy_op( self: T, - other: Union[T, np.ndarray, float], + other: Union[T, NDArrayFloat, NDArrayInt, float], **kwargs: Any, ) -> T: @@ -1010,42 +1014,54 @@ def _copy_op( return base_copy.copy(**kwargs) @abstractmethod - def __add__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __add__(self: T, other: T) -> T: """Addition for FData object.""" pass @abstractmethod - def __radd__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __radd__(self: T, other: T) -> T: """Addition for FData object.""" pass @abstractmethod - def __sub__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __sub__(self: T, other: T) -> T: """Subtraction for FData object.""" pass @abstractmethod - def __rsub__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __rsub__(self: T, other: T) -> T: """Right subtraction for FData object.""" pass @abstractmethod - def __mul__(self: T, other: Union[np.ndarray, float]) -> T: + def __mul__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Multiplication for FData object.""" pass @abstractmethod - def __rmul__(self: T, other: Union[np.ndarray, float]) -> T: + def __rmul__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Multiplication for FData object.""" pass @abstractmethod - def __truediv__(self: T, other: Union[np.ndarray, float]) -> T: + def __truediv__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Division for FData object.""" pass @abstractmethod - def __rtruediv__(self: T, other: Union[np.ndarray, float]) -> T: + def __rtruediv__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Right division for FData object.""" pass diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 65aa6e160..be758d04a 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -28,6 +28,7 @@ DomainRange, GridPointsLike, LabelTupleLike, + NDArrayBool, NDArrayFloat, NDArrayInt, ) @@ -123,7 +124,7 @@ def __init__( @classmethod def from_data( cls, - data_matrix: np.ndarray, + data_matrix: Union[NDArrayFloat, NDArrayInt], *, basis: Basis, grid_points: Optional[GridPointsLike] = None, @@ -262,7 +263,7 @@ def _evaluate( eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: if aligned: @@ -442,28 +443,7 @@ def sum( # noqa: WPS125 sample_names=(None,), ) - def gmean(self: T, eval_points: Optional[np.ndarray] = None) -> T: - """Compute the geometric mean of the functional data object. - - A numerical approach its used. The object its transformed into its - discrete representation and then the geometric mean is computed and - then the object is taken back to the basis representation. - - Args: - eval_points: Set of points where the - functions are evaluated to obtain the discrete - representation of the object. If none are passed it calls - numpy.linspace with bounds equal to the ones defined in - self.domain_range and the number of points the maximum - between 501 and 10 times the number of basis. - - Returns: - Geometric mean of the original object. - - """ - return self.to_grid(eval_points).gmean().to_basis(self.basis) - - def var(self: T, eval_points: Optional[np.ndarray] = None) -> T: + def var(self: T, eval_points: Optional[NDArrayFloat] = None) -> T: """Compute the variance of the functional data object. A numerical approach its used. The object its transformed into its @@ -484,7 +464,7 @@ def var(self: T, eval_points: Optional[np.ndarray] = None) -> T: """ return self.to_grid(eval_points).var().to_basis(self.basis) - def cov(self, eval_points: Optional[np.ndarray] = None) -> FData: + def cov(self, eval_points: Optional[NDArrayFloat] = None) -> FData: """Compute the covariance of the functional data object. A numerical approach its used. The object its transformed into its @@ -564,7 +544,7 @@ def to_grid( def to_basis( self, basis: Optional[Basis] = None, - eval_points: Optional[np.ndarray] = None, + eval_points: Optional[NDArrayFloat] = None, **kwargs: Any, ) -> FDataBasis: """ @@ -592,7 +572,7 @@ def copy( *, deep: bool = False, # For Pandas compatibility basis: Optional[Basis] = None, - coefficients: Optional[np.ndarray] = None, + coefficients: Optional[NDArrayFloat] = None, dataset_name: Optional[str] = None, argument_names: Optional[LabelTupleLike] = None, coordinate_names: Optional[LabelTupleLike] = None, @@ -651,7 +631,7 @@ def _to_R(self) -> str: # noqa: N802 def _array_to_R( # noqa: N802 self, - coefficients: np.ndarray, + coefficients: NDArrayFloat, transpose: bool = False, ) -> str: if len(coefficients.shape) == 1: @@ -707,7 +687,7 @@ def equals(self, other: object) -> bool: and np.array_equal(self.coefficients, other.coefficients) ) - def __eq__(self, other: object) -> np.ndarray: # type: ignore[override] + def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] """Elementwise equality of FDataBasis.""" if not isinstance(other, type(self)) or self.dtype != other.dtype: if other is pandas.NA: @@ -776,7 +756,7 @@ def compose( self, fd: FData, *, - eval_points: Optional[np.ndarray] = None, + eval_points: Optional[NDArrayFloat] = None, **kwargs: Any, ) -> FData: """ @@ -806,7 +786,10 @@ def compose( return composition - def __getitem__(self: T, key: Union[int, slice, np.ndarray]) -> T: + def __getitem__( + self: T, + key: Union[int, slice, NDArrayInt, NDArrayBool], + ) -> T: """Return self[key].""" key = _check_array_key(self.coefficients, key) @@ -815,7 +798,10 @@ def __getitem__(self: T, key: Union[int, slice, np.ndarray]) -> T: sample_names=np.array(self.sample_names)[key], ) - def __add__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __add__( + self: T, + other: T, + ) -> T: """Addition for FDataBasis object.""" if isinstance(other, FDataBasis) and self.basis == other.basis: @@ -827,7 +813,10 @@ def __add__(self: T, other: Union[T, np.ndarray, float]) -> T: return NotImplemented - def __radd__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __radd__( + self: T, + other: T, + ) -> T: """Addition for FDataBasis object.""" if isinstance(other, FDataBasis) and self.basis == other.basis: @@ -839,7 +828,10 @@ def __radd__(self: T, other: Union[T, np.ndarray, float]) -> T: return NotImplemented - def __sub__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __sub__( + self: T, + other: T, + ) -> T: """Subtraction for FDataBasis object.""" if isinstance(other, FDataBasis) and self.basis == other.basis: @@ -851,7 +843,10 @@ def __sub__(self: T, other: Union[T, np.ndarray, float]) -> T: return NotImplemented - def __rsub__(self: T, other: Union[T, np.ndarray, float]) -> T: + def __rsub__( + self: T, + other: T, + ) -> T: """Right subtraction for FDataBasis object.""" if isinstance(other, FDataBasis) and self.basis == other.basis: @@ -863,7 +858,10 @@ def __rsub__(self: T, other: Union[T, np.ndarray, float]) -> T: return NotImplemented - def _mul_scalar(self: T, other: Union[np.ndarray, float]) -> T: + def _mul_scalar( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Multiplication by scalar.""" try: vector = np.atleast_1d(other) @@ -881,15 +879,24 @@ def _mul_scalar(self: T, other: Union[np.ndarray, float]) -> T: coefficients=self.coefficients * vector, ) - def __mul__(self: T, other: Union[np.ndarray, float]) -> T: + def __mul__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Multiplication for FDataBasis object.""" return self._mul_scalar(other) - def __rmul__(self: T, other: Union[np.ndarray, float]) -> T: + def __rmul__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Multiplication for FDataBasis object.""" return self._mul_scalar(other) - def __truediv__(self: T, other: Union[np.ndarray, float]) -> T: + def __truediv__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Division for FDataBasis object.""" try: other = 1 / np.asarray(other) @@ -898,7 +905,10 @@ def __truediv__(self: T, other: Union[np.ndarray, float]) -> T: return self._mul_scalar(other) - def __rtruediv__(self: T, other: Union[np.ndarray, float]) -> T: + def __rtruediv__( + self: T, + other: Union[NDArrayFloat, NDArrayInt, float], + ) -> T: """Right division for FDataBasis object.""" return NotImplemented @@ -938,7 +948,7 @@ def nbytes(self) -> int: """ return self.coefficients.nbytes - def isna(self) -> np.ndarray: + def isna(self) -> NDArrayBool: """ Return a 1-D array indicating if each value is missing. From 8ae17b532dfbf18af0ce2c1efe3f9a7784d8f73d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 21 Feb 2022 02:04:23 +0100 Subject: [PATCH 057/400] Allow derivative via basis expansion. --- skfda/representation/grid.py | 38 ++++++++++++++++++++++++++---------- 1 file changed, 28 insertions(+), 10 deletions(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index d8d0d1f30..399414cfa 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -413,16 +413,26 @@ def _evaluate( aligned=aligned, ) - def derivative(self: T, *, order: int = 1) -> T: + def derivative( + self: T, + *, + order: int = 1, + method: Optional[Basis] = None, + ) -> T: """ Differentiate a FDataGrid object. - It is calculated using central finite differences when possible. In - the extremes, forward and backward finite differences with accuracy - 2 are used. + By default, it is calculated using central finite differences when + possible. In the extremes, forward and backward finite differences + with accuracy 2 are used. Args: order: Order of the derivative. Defaults to one. + method: Method to use to compute the derivative. If ``None`` + (the default), finite differences are used. In a basis + object is passed the grid is converted to a basis + representation and the derivative is evaluated using that + representation. Returns: Derivative function. @@ -461,13 +471,21 @@ def derivative(self: T, *, order: int = 1) -> T: if order_list.ndim != 1 or len(order_list) != self.dim_domain: raise ValueError("The order for each partial should be specified.") - operator = findiff.FinDiff(*[ - (1 + i, *z) - for i, z in enumerate( - zip(self.grid_points, order_list), + if method is None: + operator = findiff.FinDiff(*[ + (1 + i, *z) + for i, z in enumerate( + zip(self.grid_points, order_list), + ) + ]) + data_matrix = operator(self.data_matrix.astype(float)) + else: + data_matrix = self.to_basis(method).derivative( + order=order, + )( + self.grid_points, + grid=True, ) - ]) - data_matrix = operator(self.data_matrix.astype(float)) return self.copy( data_matrix=data_matrix, From 67d7fecace729304b118dc1d63362584fcdeb6c2 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 21 Feb 2022 16:50:04 +0100 Subject: [PATCH 058/400] Remove alpha parameter of Magnitude-Shape plot and outlier detector. - This alpha parameter was only used in the asymptotic case, when the number of samples was too high. - Add instead a cutoff_factor parameter that multiplies the cutoff and can be used for an arbitrary number of samples, to consider more or less functions as outliers. --- .../outliers/_directional_outlyingness.py | 30 +++++++++---------- .../visualization/_magnitude_shape_plot.py | 17 +++++------ 2 files changed, 22 insertions(+), 25 deletions(-) diff --git a/skfda/exploratory/outliers/_directional_outlyingness.py b/skfda/exploratory/outliers/_directional_outlyingness.py index 86313af68..979d7eaea 100644 --- a/skfda/exploratory/outliers/_directional_outlyingness.py +++ b/skfda/exploratory/outliers/_directional_outlyingness.py @@ -287,29 +287,27 @@ class MSPlotOutlierDetector( detecting outliers under a normal distribution. Parameters: - multivariate_depth (:ref:`depth measure `, optional): - Method used to order the data. Defaults to :class:`projection - depth `. - pointwise_weights (array_like, optional): an array containing the + multivariate_depth: Method used to order the data. Defaults + to :class:`projection depth + `. + pointwise_weights: an array containing the weights of each points of discretisati on where values have been recorded. - alpha (float, optional): Denotes the quantile to choose the cutoff - value for detecting outliers Defaults to 0.993, which is used - in the classical boxplot. - assume_centered (boolean, optional): If True, the support of the + cutoff_factor: Factor that multiplies the cutoff value, in order to + consider more or less curves as outliers. + assume_centered: If True, the support of the robust location and the covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, default value, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment. - support_fraction (float, 0 < support_fraction < 1, optional): The - proportion of points to be included in the support of the - raw MCD estimate. + support_fraction: The proportion of points to be included in the + support of the raw MCD estimate. Default is None, which implies that the minimum value of support_fraction will be used within the algorithm: [n_sample + n_features + 1] / 2 - random_state (int, RandomState instance or None, optional): If int, + random_state: If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the @@ -345,7 +343,7 @@ def __init__( support_fraction: Optional[float] = None, num_resamples: int = 1000, random_state: RandomStateLike = 0, - alpha: float = 0.993, + cutoff_factor: float = 1, _force_asymptotic: bool = False, ) -> None: self.multivariate_depth = multivariate_depth @@ -354,7 +352,7 @@ def __init__( self.support_fraction = support_fraction self.num_resamples = num_resamples self.random_state = random_state - self.alpha = alpha + self.cutoff_factor = cutoff_factor self._force_asymptotic = _force_asymptotic def _compute_points(self, X: FDataGrid) -> NDArrayFloat: @@ -445,7 +443,7 @@ def _parameters_asymptotic( # Calculation of the cutoff value and scaling factor to identify # outliers. scaling = estimated_c * dfd / estimated_m / dfn - cutoff_value = scipy.stats.f.ppf(self.alpha, dfn, dfd, loc=0, scale=1) + cutoff_value = scipy.stats.f.ppf(0.993, dfn, dfd, loc=0, scale=1) return scaling, cutoff_value @@ -515,7 +513,7 @@ def fit_predict(self, X: FDataGrid, y: None = None) -> NDArrayInt: ) self.scaling_ = scaling - self.cutoff_value_ = cutoff_value + self.cutoff_value_ = cutoff_value * self.cutoff_factor rmd_2 = self.cov_.mahalanobis(self.points_) diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 42d3b31d6..3942a43cd 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -44,30 +44,29 @@ class MagnitudeShapePlot(BasePlot): For more information see :footcite:ts:`dai+genton_2018_visualization`. Args: - fdata (FDataGrid): Object containing the data. - multivariate_depth (:ref:`depth measure `, optional): + fdata: Object containing the data. + multivariate_depth: Method used to order the data. Defaults to :class:`projection depth `. - pointwise_weights (array_like, optional): an array containing the + pointwise_weights: an array containing the weights of each points of discretisation where values have been recorded. - alpha (float, optional): Denotes the quantile to choose the cutoff - value for detecting outliers Defaults to 0.993, which is used - in the classical boxplot. - assume_centered (boolean, optional): If True, the support of the + cutoff_factor: Factor that multiplies the cutoff value, in order to + consider more or less curves as outliers. + assume_centered: If True, the support of the robust location and the covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, default value, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment. - support_fraction (float, 0 < support_fraction < 1, optional): The + support_fraction: The proportion of points to be included in the support of the raw MCD estimate. Default is None, which implies that the minimum value of support_fraction will be used within the algorithm: [n_sample + n_features + 1] / 2 - random_state (int, RandomState instance or None, optional): If int, + random_state: If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the From 84d0c2b9f5cac67dc209f7c4cac23f86e4bce616 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 21 Feb 2022 18:26:44 +0100 Subject: [PATCH 059/400] Fix MSPlot --- skfda/exploratory/visualization/_magnitude_shape_plot.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 3942a43cd..3016c6f9b 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -144,7 +144,7 @@ class MagnitudeShapePlot(BasePlot): ...), multivariate_depth=None, pointwise_weights=None, - alpha=0.993, + cutoff_factor=1, points=array([[ 1.66666667, 0.12777778], [ 0. , 0. ], [-0.8 , 0.17666667], @@ -213,8 +213,8 @@ def pointwise_weights(self) -> Optional[NDArrayFloat]: return self.outlier_detector.pointwise_weights @property - def alpha(self) -> float: - return self.outlier_detector.alpha + def cutoff_factor(self) -> float: + return self.outlier_detector.cutoff_factor @property def points(self) -> NDArrayFloat: @@ -328,7 +328,7 @@ def __repr__(self) -> str: f"\nfdata={repr(self.fdata)}," f"\nmultivariate_depth={self.multivariate_depth}," f"\npointwise_weights={repr(self.pointwise_weights)}," - f"\nalpha={repr(self.alpha)}," + f"\ncutoff_factor={repr(self.cutoff_factor)}," f"\npoints={repr(self.points)}," f"\noutliers={repr(self.outliers)}," f"\ncolormap={self.colormap.name}," From ebb90fc1ed8f3bfbbb798276e068ca86d823bfea Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 22 Feb 2022 01:10:13 +0100 Subject: [PATCH 060/400] Type lstsq. --- skfda/misc/lstsq.py | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/skfda/misc/lstsq.py b/skfda/misc/lstsq.py index 75538ee03..48015f64e 100644 --- a/skfda/misc/lstsq.py +++ b/skfda/misc/lstsq.py @@ -7,15 +7,17 @@ import scipy.linalg from typing_extensions import Final, Literal +from ..representation._typing import NDArrayFloat + LstsqMethodCallable = Callable[[np.ndarray, np.ndarray], np.ndarray] LstsqMethodName = Literal["cholesky", "qr", "svd"] LstsqMethod = Union[LstsqMethodCallable, LstsqMethodName] def lstsq_cholesky( - coefs: np.ndarray, - result: np.ndarray, -) -> np.ndarray: + coefs: NDArrayFloat, + result: NDArrayFloat, +) -> NDArrayFloat: """Solve OLS problem using a Cholesky decomposition.""" left = coefs.T @ coefs right = coefs.T @ result @@ -23,17 +25,17 @@ def lstsq_cholesky( def lstsq_qr( - coefs: np.ndarray, - result: np.ndarray, -) -> np.ndarray: + coefs: NDArrayFloat, + result: NDArrayFloat, +) -> NDArrayFloat: """Solve OLS problem using a QR decomposition.""" return scipy.linalg.lstsq(coefs, result, lapack_driver="gelsy")[0] def lstsq_svd( - coefs: np.ndarray, - result: np.ndarray, -) -> np.ndarray: + coefs: NDArrayFloat, + result: NDArrayFloat, +) -> NDArrayFloat: """Solve OLS problem using a SVD decomposition.""" return scipy.linalg.lstsq(coefs, result, lapack_driver="gelsd")[0] @@ -53,13 +55,13 @@ def _get_lstsq_method( def solve_regularized_weighted_lstsq( - coefs: np.ndarray, - result: np.ndarray, + coefs: NDArrayFloat, + result: NDArrayFloat, *, - weights: Optional[np.ndarray] = None, - penalty_matrix: Optional[np.ndarray] = None, + weights: Optional[NDArrayFloat] = None, + penalty_matrix: Optional[NDArrayFloat] = None, lstsq_method: LstsqMethod = lstsq_svd, -) -> np.ndarray: +) -> NDArrayFloat: """ Solve a regularized and weighted least squares problem. From acad23d62fd858b9e5a9f71cb38ed4df21f0adc5 Mon Sep 17 00:00:00 2001 From: Jorge Duque Date: Mon, 21 Feb 2022 19:02:23 -0600 Subject: [PATCH 061/400] Implemented unary minus for FData subclasses --- skfda/representation/_functional_data.py | 5 +++++ skfda/representation/basis/_fdatabasis.py | 5 +++++ skfda/representation/grid.py | 4 ++++ 3 files changed, 14 insertions(+) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index a0a5f739e..b38a953b6 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -1065,6 +1065,11 @@ def __rtruediv__( """Right division for FData object.""" pass + @abstractmethod + def __neg__(self: T) -> T: + """Negation of FData object.""" + pass + def __iter__(self: T) -> Iterator[T]: """Iterate over the samples.""" yield from (self[i] for i in range(self.n_samples)) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index be758d04a..d7b1319c3 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -912,6 +912,11 @@ def __rtruediv__( """Right division for FDataBasis object.""" return NotImplemented + def __neg__(self: T) -> T: + """Negation of FData object.""" + return self._copy_op(other=None, coefficients=-self.coefficients) + + ##################################################################### # Pandas ExtensionArray methods ##################################################################### diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 399414cfa..84192eaeb 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -798,6 +798,10 @@ def __rtruediv__( return self._copy_op(other, data_matrix=data_matrix / self.data_matrix) + def __neg__(self: T) -> T: + """Negation of FData object.""" + return self._copy_op(other=None, data_matrix=-self.data_matrix) + def concatenate(self: T, *others: T, as_coordinates: bool = False) -> T: """Join samples from a similar FDataGrid object. From 2504030d6b19d892d214d197cf08ffbfd6a4d3e2 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 22 Feb 2022 02:15:00 +0100 Subject: [PATCH 062/400] Add derivative method to SRSF and Fisher Rao registration. --- skfda/misc/operators/_srvf.py | 11 +++++++++- .../preprocessing/registration/_fisher_rao.py | 22 ++++++++++++++----- 2 files changed, 27 insertions(+), 6 deletions(-) diff --git a/skfda/misc/operators/_srvf.py b/skfda/misc/operators/_srvf.py index 1b1af8232..d8720653c 100644 --- a/skfda/misc/operators/_srvf.py +++ b/skfda/misc/operators/_srvf.py @@ -9,6 +9,7 @@ from ..._utils import check_is_univariate from ...representation import FDataGrid from ...representation._typing import ArrayLike +from ...representation.basis import Basis from ._operators import Operator @@ -49,6 +50,11 @@ class SRSF( inverse transformation. If `None` there are stored the initial values of the functions during the transformation to apply during the inverse transformation. Defaults None. + method: Method to use to compute the derivative. If ``None`` + (the default), finite differences are used. In a basis + object is passed the grid is converted to a basis + representation and the derivative is evaluated using that + representation. Attributes: eval_points: Set of points where the @@ -95,11 +101,14 @@ class SRSF( def __init__( self, + *, output_points: Optional[ArrayLike] = None, initial_value: Optional[float] = None, + method: Optional[Basis] = None, ) -> None: self.output_points = output_points self.initial_value = initial_value + self.method = method def __call__(self, vector: FDataGrid) -> FDataGrid: return self.fit_transform(vector) @@ -148,7 +157,7 @@ def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: else: output_points = np.asarray(self.output_points) - g = X.derivative() + g = X.derivative(method=self.method) # Evaluation with the corresponding interpolation data_matrix = g(output_points)[..., 0] diff --git a/skfda/preprocessing/registration/_fisher_rao.py b/skfda/preprocessing/registration/_fisher_rao.py index 6f836ce60..715114aaa 100644 --- a/skfda/preprocessing/registration/_fisher_rao.py +++ b/skfda/preprocessing/registration/_fisher_rao.py @@ -17,6 +17,7 @@ from ...exploratory.stats._fisher_rao import _elastic_alignment_array from ...misc.operators import SRSF from ...representation._typing import ArrayLike +from ...representation.basis import Basis from ...representation.interpolation import SplineInterpolation from .base import InductiveRegistrationTransformer @@ -27,7 +28,8 @@ class FisherRaoElasticRegistration( InductiveRegistrationTransformer[FDataGrid, FDataGrid], ): - r"""Align a FDatagrid using the SRSF framework. + r""" + Align a FDatagrid using the SRSF framework. Let :math:`f` be a function of the functional data object wich will be aligned to the template :math:`g`. Calculates the warping wich minimises @@ -76,6 +78,11 @@ class FisherRaoElasticRegistration( fdatagrid which will be transformed. grid_dim: Dimension of the grid used in the DP alignment algorithm. Defaults 7. + derivative_method: Method to use to compute the derivative. If ``None`` + (the default), finite differences are used. In a basis + object is passed the grid is converted to a basis + representation and the derivative is evaluated using that + representation. Attributes: template\_: Template learned during fitting, @@ -117,11 +124,13 @@ def __init__( penalty: float = 0, output_points: Optional[ArrayLike] = None, grid_dim: int = 7, + derivative_method: Optional[Basis] = None, ) -> None: self.template = template self.penalty = penalty self.output_points = output_points self.grid_dim = grid_dim + self.derivative_method = derivative_method def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: @@ -139,8 +148,12 @@ def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: _check_compatible_fdatagrid(X, self.template_) # Constructs the SRSF of the template - srsf = SRSF(output_points=self._output_points, initial_value=0) - self._template_srsf = srsf.fit_transform(self.template_) + self._srsf = SRSF( + output_points=self._output_points, + initial_value=0, + method=self.derivative_method, + ) + self._template_srsf = self._srsf.fit_transform(self.template_) return self @@ -161,8 +174,7 @@ def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: "same number of samples than X.", ) - srsf = SRSF(output_points=self.output_points, initial_value=0) - fdatagrid_srsf = srsf.fit_transform(X) + fdatagrid_srsf = self._srsf.fit_transform(X) # Points of discretization if self.output_points is None: From 1edbb06547308a448377ceff54ff5ffb9c1fcacb Mon Sep 17 00:00:00 2001 From: Jorge Duque Date: Mon, 21 Feb 2022 20:08:07 -0600 Subject: [PATCH 063/400] used proper copy method --- skfda/representation/basis/_fdatabasis.py | 2 +- skfda/representation/grid.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index d7b1319c3..b8c0cb201 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -914,7 +914,7 @@ def __rtruediv__( def __neg__(self: T) -> T: """Negation of FData object.""" - return self._copy_op(other=None, coefficients=-self.coefficients) + return self.copy(coefficients=-self.coefficients) ##################################################################### diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 84192eaeb..dcf815594 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -800,7 +800,7 @@ def __rtruediv__( def __neg__(self: T) -> T: """Negation of FData object.""" - return self._copy_op(other=None, data_matrix=-self.data_matrix) + return self.copy(data_matrix=-self.data_matrix) def concatenate(self: T, *others: T, as_coordinates: bool = False) -> T: """Join samples from a similar FDataGrid object. From 578167dd90a90301a65a34db48de3eef4e39530e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 22 Feb 2022 12:13:14 +0100 Subject: [PATCH 064/400] Corrections --- .../stats/_functional_transformers.py | 47 ++++++++----------- 1 file changed, 20 insertions(+), 27 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index def2efbaa..cb63175b3 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,11 +2,13 @@ from __future__ import annotations -from typing import Tuple, Union +from typing import Optional, Sequence, Tuple, Union import numpy as np +from ..._utils import check_is_univariate from ...representation import FDataBasis, FDataGrid +from ...representation._typing import NDArrayFloat def local_averages( @@ -80,10 +82,16 @@ def local_averages( def _calculate_curves_occupation_( curve_y_coordinates: np.ndarray, curve_x_coordinates: np.ndarray, - interval: Tuple, -) -> np.ndarray: + interval: Tuple[float, float], +) -> NDArrayFloat: y1, y2 = interval + if y2 < y1: + raise ValueError( + "Interval limits (a,b) should satisfy a <= b. " + + str(interval) + " doesn't", + ) + # Reshape original curves so they have one dimension less new_shape = curve_y_coordinates.shape[1::-1] curve_y_coordinates = curve_y_coordinates.reshape( @@ -91,34 +99,25 @@ def _calculate_curves_occupation_( ) # Calculate interval sizes on the X axis - x_rotated = np.roll(curve_x_coordinates, 1) - intervals_x_axis = curve_x_coordinates - x_rotated + intervals_x_axis = curve_x_coordinates[1:] - curve_x_coordinates[:-1] # Calculate which points are inside the interval given (y1,y2) on Y axis greater_than_y1 = curve_y_coordinates >= y1 less_than_y2 = curve_y_coordinates <= y2 inside_interval_bools = greater_than_y1 & less_than_y2 - # Correct booleans so they are not repeated - bools_interval = np.roll( - inside_interval_bools, 1, axis=1, - ) & inside_interval_bools - # Calculate intervals on X axis where the points are inside Y axis interval - intervals_x_inside = np.multiply(bools_interval, intervals_x_axis) - - # Delete first element of each interval as it will be a negative number - intervals_x_inside = np.delete(intervals_x_inside, 0, axis=1) + intervals_x_inside = inside_interval_bools * intervals_x_axis return np.sum(intervals_x_inside, axis=1) def occupation_measure( data: Union[FDataGrid, FDataBasis], - intervals: np.ndarray, + intervals: Sequence[Tuple[float, float]], *, - n_points: Union[int, None] = None, -) -> np.ndarray: + n_points: Optional[int] = None, +) -> NDArrayFloat: r""" Calculate the occupation measure of a grid. @@ -171,8 +170,8 @@ def occupation_measure( ... ), ... decimals=2, ... ) - array([[ 1. , 0.5 , 6.27], - [ 0.98, 0.48, 0. ]]) + array([[ 1. , 0.5 , 6.29], + [ 1. , 0.5 , 0. ]]) """ if isinstance(data, FDataBasis) and n_points is None: @@ -181,13 +180,7 @@ def occupation_measure( + " as an argument for a FDataBasis. Instead None was passed.", ) - for interval_check in intervals: - y1, y2 = interval_check - if y2 < y1: - raise ValueError( - "Interval limits (a,b) should satisfy a <= b. " - + str(interval_check) + " doesn't", - ) + check_is_univariate(data) if n_points is None: function_x_coordinates = data.grid_points[0] @@ -198,7 +191,7 @@ def occupation_measure( data.domain_range[0][1], (data.domain_range[0][1] - data.domain_range[0][0]) / n_points, ) - function_y_coordinates = data(function_x_coordinates) + function_y_coordinates = data(function_x_coordinates[1:]) return np.asarray([ _calculate_curves_occupation_( # noqa: WPS317 From a3d6700180e86cd6afc9634526e0a40adbd0ffbe Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 25 Feb 2022 17:58:33 +0100 Subject: [PATCH 065/400] Added comment about n_points --- skfda/exploratory/stats/_functional_transformers.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index cb63175b3..7f510cf67 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -128,6 +128,15 @@ def occupation_measure( where :math:`{T_1,\dots,T_p}` are disjoint intervals in :math:`\mathbb{R}` and | | stands for the Lebesgue measure. + The calculations are based on the grid of points of the x axis. In case of + FDataGrid the original grid is taken unless n_points is specified. In case + of FDataBasis the number of points of the x axis to be considered is passed + through the n_points parameter compulsory. + If the result of this function is not accurate enough try to increase the + grid of points of the x axis. Either by increasing n_points or passing a + FDataGrid with more x grid points per curve. + + Args: data: FDataGrid or FDataBasis where we want to calculate the occupation measure. From 68b1900d1e275a73699fc20c5c452ee2965c7110 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 25 Feb 2022 20:22:21 +0100 Subject: [PATCH 066/400] Docstring gaussian classifier --- .../ml/classification/_gaussian_classifier.py | 80 ++++++++++++++++++- 1 file changed, 77 insertions(+), 3 deletions(-) diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index 7ea87cd73..a8ff0acd9 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -15,6 +15,78 @@ class GaussianClassifier( BaseEstimator, # type: ignore ClassifierMixin, # type: ignore ): + """Gaussian process based classifer for functional data. + + This classifier is based on the assumption that the data is part + of a gaussian process and depending on the output label, the covariance + and mean parameters are different for each class. This means that curves + classified with one determined label come from a distinct gaussian process + compared with data that is classified with a different label. + + The training phase of the classifier will try to approximate the two + main parameters of a gaussian process for each class. The covariance + will be estimated by fitting the initial kernel passed on the creation of + the GaussianClassifier object. + The result of the training function will be two arrays, one of means and + another one of covariances. Both with length (n_classes). + + The prediction phase instead uses a quadratic discriminant classifier to + predict which gaussian process of the fitted ones correspond the most with + each curve passed. + + + Parameters: + kernel: initial kernel to be fitted with the training data. + + regularizer: parameter that regularizes the covariance matrices + in order to avoid Singular matrices. It is multiplied by a numpy + eye matrix and then added to the covariance one. + + + Examples: + Firstly, we will import and split the Berkeley Growth Study dataset + + >>> from skfda.datasets import fetch_growth + >>> from sklearn.model_selection import train_test_split + >>> X, y = fetch_growth(return_X_y=True, as_frame=True) + >>> X = X.iloc[:, 0].values + >>> y = y.values.codes + >>> X_train, X_test, y_train, y_test = train_test_split( + ... X, + ... y, + ... test_size=0.3, + ... stratify=y, + ... random_state=0, + ... ) + + Then we need to choose and import a kernel so it can be fitted with + the data in the training phase. As we know the Growth dataset tends + to be approximately linear, we will use a linear kernel. We create + the kernel with mean 1 and variance 6 as an example. + + >>> from GPy.kern import Linear + >>> linear = Linear(1, variances=6) + + We will fit the Gaussian Process classifier with training data. We + use as regularizer parameter a low value as 0.05. + + + >>> gaussian = GaussianClassifier(linear, 0.05) + >>> gaussian = gaussian.fit(X_train, y_train) + + + We can predict the class of new samples + + >>> gaussian.predict(X_test) + array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, + 0, 1, 1, 0, 1, 0, 0, 0, 1, 1]) + + Finally, we calculate the mean accuracy for the test data + + >>> round(gaussian.score(X_test, y_test), 2) + 0.93 + + """ def __init__(self, kernel: Kern, regularizer: float) -> None: self._kernel_ = kernel @@ -32,7 +104,9 @@ def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: """ self._classes, self._y_ind = _classifier_get_classes(y) # noqa:WPS414 - self._cov_kernels_, self._means = self._fit_kernels_and_means(X) + self._cov_kernels_, self._means = self._fit_kernels_and_means( + X, + ) self._priors = self._calculate_priors(y) self._log_priors = np.log(self._priors) # Calculates prior logartithms @@ -148,9 +222,9 @@ def _fit_kernels_and_means( for cur_class in range(0, self._classes.size): class_n = X[self._y_ind == cur_class] class_n_centered = class_n - class_n.mean() - data = class_n_centered.data_matrix[:, :, 0] + data_matrix = class_n_centered.data_matrix[:, :, 0] - reg_n = GPRegression(grid, data.T, kernel=self._kernel_) + reg_n = GPRegression(grid, data_matrix.T, kernel=self._kernel_) reg_n.optimize() kernels = kernels + [reg_n.kern] From 51ce53f9998730cd48d0b6d3a23a2ab04cdc2008 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Mon, 28 Feb 2022 16:34:12 +0100 Subject: [PATCH 067/400] The only deprecated case is passing a sequence. Added import to example --- skfda/ml/regression/_linear_regression.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index a2de1bad8..f6fce18e7 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -110,6 +110,7 @@ class LinearRegression( >>> from skfda.ml.regression import LinearRegression >>> from skfda.representation.basis import (FDataBasis, Monomial, ... Constant) + >>> import pandas as pd Multivariate linear regression can be used with functions expressed in a basis. Also, a functional basis for the weights can be specified: @@ -325,10 +326,10 @@ def _argcheck_X( X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], ) -> Sequence[AcceptedDataType]: - if not isinstance(X, pd.DataFrame): + if isinstance(X, List) and any(isinstance(x, FData) for x in X): warnings.warn( - "Usage of arguments of type FData, ndarray or a sequence \ - of both is deprecated (fit, predict)." + "Usage of arguments of type sequence of " + "FData, ndarray is deprecated (fit, predict). " "Use pandas DataFrame instead", DeprecationWarning, ) @@ -399,7 +400,7 @@ def _argcheck_X_y( return new_X, y, sample_weight, coef_info - def __dataframe_conversion(self, X: pd.DataFrame) -> List: + def __dataframe_conversion(self, X: pd.DataFrame) -> List[AcceptedDataType]: """Convert DataFrames to a list with two elements. First of all, a list with mv covariates and the second, From be01cbdd2dbc090db8e97c44c12e5ce892b604a1 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 2 Mar 2022 22:26:50 +0100 Subject: [PATCH 068/400] Updates --- .../ml/classification/_gaussian_classifier.py | 20 +++++++++---------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index a8ff0acd9..b482247d0 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -128,16 +128,14 @@ def predict(self, X: FDataGrid) -> np.ndarray: for each data sample. """ check_is_fitted(self) - likelihoods = [ - self._calculate_log_likelihood(curve) - for curve in X.data_matrix - ] - - predictions = np.asarray([ - np.where(likelihood == max(likelihood)) - for likelihood in likelihoods - ]) - return np.reshape(predictions, predictions.size) + + return np.argmax( + [ + self._calculate_log_likelihood(curve) + for curve in X.data_matrix + ], + axis=1, + ) def _calculate_priors(self, y: np.ndarray) -> np.ndarray: """ @@ -228,7 +226,7 @@ def _fit_kernels_and_means( reg_n.optimize() kernels = kernels + [reg_n.kern] - means = means + [class_n.gmean().data_matrix[0]] + means = means + [class_n.mean().data_matrix[0]] return np.asarray(kernels), np.asarray(means) def _calculate_log_likelihood(self, curve: np.ndarray) -> np.ndarray: From 0c6a931cecf1319dbdd9ffebe39d5f892916a117 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 3 Mar 2022 00:54:14 +0100 Subject: [PATCH 069/400] Correction of example --- skfda/ml/classification/_gaussian_classifier.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index b482247d0..9f89e0a50 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -77,9 +77,9 @@ class GaussianClassifier( We can predict the class of new samples - >>> gaussian.predict(X_test) - array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, - 0, 1, 1, 0, 1, 0, 0, 0, 1, 1]) + >>> list(gaussian.predict(X_test)) + [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, + 1, 0, 1, 0, 0, 0, 1, 1] Finally, we calculate the mean accuracy for the test data From 7ab9c785ae825f7fe3c21e1a342f05dac6ed4c75 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 3 Mar 2022 18:25:20 +0100 Subject: [PATCH 070/400] Fix integral for FDataGrid. --- .../stats/_functional_transformers.py | 71 ++++++++++++++++--- skfda/misc/_math.py | 2 +- skfda/representation/_functional_data.py | 13 +++- skfda/representation/basis/_fdatabasis.py | 41 +++++++---- skfda/representation/grid.py | 22 ++++-- 5 files changed, 119 insertions(+), 30 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 1d77f0ae3..b6bc2ff1a 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -50,16 +50,67 @@ def local_averages( >>> import numpy as np >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, 2), decimals=2) - array([[ 116.94, 111.86, 107.29, 111.35, 104.39, 109.43, 109.16, - 112.91, 109.19, 117.95, 112.14, 114.3 , 111.48, 114.85, - 116.25, 114.6 , 111.02, 113.57, 108.88, 109.6 , 109.7 , - 108.54, 109.18, 106.92, 109.44, 109.84, 115.32, 108.16, - 119.29, 110.62], - [ 177.26, 157.62, 154.97, 163.83, 156.66, 157.67, 155.31, - 169.02, 154.18, 174.43, 161.33, 170.14, 164.1 , 170.1 , - 166.65, 168.72, 166.85, 167.22, 159.4 , 162.76, 155.7 , - 158.01, 160.1 , 155.95, 157.95, 163.53, 162.29, 153.1 , - 178.48, 161.75]]) + array([[[ 116.94], + [ 111.86], + [ 107.29], + [ 111.35], + [ 104.39], + [ 109.43], + [ 109.16], + [ 112.91], + [ 109.19], + [ 117.95], + [ 112.14], + [ 114.3 ], + [ 111.48], + [ 114.85], + [ 116.25], + [ 114.6 ], + [ 111.02], + [ 113.57], + [ 108.88], + [ 109.6 ], + [ 109.7 ], + [ 108.54], + [ 109.18], + [ 106.92], + [ 109.44], + [ 109.84], + [ 115.32], + [ 108.16], + [ 119.29], + [ 110.62]], + [[ 177.26], + [ 157.62], + [ 154.97], + [ 163.83], + [ 156.66], + [ 157.67], + [ 155.31], + [ 169.02], + [ 154.18], + [ 174.43], + [ 161.33], + [ 170.14], + [ 164.1 ], + [ 170.1 ], + [ 166.65], + [ 168.72], + [ 166.85], + [ 167.22], + [ 159.4 ], + [ 162.76], + [ 155.7 ], + [ 158.01], + [ 160.1 ], + [ 155.95], + [ 157.95], + [ 163.53], + [ 162.29], + [ 153.1 ], + [ 178.48], + [ 161.75]]]) + """ left, right = data.domain_range[0] diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 673d6fbdc..72436aa02 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -369,7 +369,7 @@ def _inner_product_fdatagrid( ) integrand = arg1 * arg2 - return integrand.integrate() + return integrand.integrate().sum(axis=-1) @inner_product.register(FDataBasis, FDataBasis) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index b38a953b6..d8e08fcdf 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -670,14 +670,23 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """Integration of the FData object. + """ + Integration of the FData object. + + The integration is performed over the whole domain. Thus, for a + function of several variables this will be a multiple integral. + + For a vector valued function the vector of integrals will be + returned. Args: interval: domain range where we want to integrate. By default is None as we integrate on the whole domain. Returns: - ndarray of shape with the integrated data. + NumPy array of size (``n_samples``, ``dim_codomain``) + with the integrated data. + """ pass diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index b8c0cb201..ff71d4d8b 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -363,21 +363,37 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """Examples. + """ + Integration of the FData object. - We first create the data basis. - >>> from skfda.representation.basis import FDataBasis, Monomial - >>> basis = Monomial(n_basis=4) - >>> coefficients = [1, 1, 3, .5] - >>> fdata = FDataBasis(basis, coefficients) + The integration is performed over the whole domain. Thus, for a + function of several variables this will be a multiple integral. - Then we can integrate on the whole domain. - >>> fdata.integrate() - array([[ 2.625]]) + For a vector valued function the vector of integrals will be + returned. - Or we can do it on a given domain. - >>> fdata.integrate(interval = ((0.5, 1),)) - array([[ 1.8671875]]) + Args: + interval: domain range where we want to integrate. + By default is None as we integrate on the whole domain. + + Returns: + NumPy array of size (``n_samples``, ``dim_codomain``) + with the integrated data. + + Examples: + We first create the data basis. + >>> from skfda.representation.basis import FDataBasis, Monomial + >>> basis = Monomial(n_basis=4) + >>> coefficients = [1, 1, 3, .5] + >>> fdata = FDataBasis(basis, coefficients) + + Then we can integrate on the whole domain. + >>> fdata.integrate() + array([[ 2.625]]) + + Or we can do it on a given domain. + >>> fdata.integrate(interval = ((0.5, 1),)) + array([[ 1.8671875]]) """ if interval is None: @@ -916,7 +932,6 @@ def __neg__(self: T) -> T: """Negation of FData object.""" return self.copy(coefficients=-self.coefficients) - ##################################################################### # Pandas ExtensionArray methods ##################################################################### diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index dcf815594..affec3af2 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -496,13 +496,27 @@ def integrate( *, interval: Optional[DomainRange] = None, ) -> NDArrayFloat: - """Examples. + """ + Integration of the FData object. + + The integration is performed over the whole domain. Thus, for a + function of several variables this will be a multiple integral. - Integration on the whole domain. + For a vector valued function the vector of integrals will be + returned. + + Args: + interval: domain range where we want to integrate. + By default is None as we integrate on the whole domain. + Returns: + NumPy array of size (``n_samples``, ``dim_codomain``) + with the integrated data. + + Examples: >>> fdata = FDataGrid([1,2,4,5,8], range(5)) >>> fdata.integrate() - array([ 15.]) + array([[ 15.]]) """ if interval is not None: data = self.restrict(interval) @@ -518,7 +532,7 @@ def integrate( axis=-2, ) - return np.sum(integrand, axis=-1) + return integrand def _check_same_dimensions(self: T, other: T) -> None: if self.data_matrix.shape[1:-1] != other.data_matrix.shape[1:-1]: From 53d9e1a9c2c5405f7653ebcbad768da5d6ee5fd7 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 4 Mar 2022 02:04:18 +0100 Subject: [PATCH 071/400] Score functions for FData Close #378 --- docs/modules/misc/score_functions.rst | 18 + skfda/exploratory/stats/_stats.py | 22 +- skfda/misc/__init__.py | 1 + skfda/misc/score_functions.py | 912 ++++++++++++++++++ .../dim_reduction/feature_extraction/_fpca.py | 2 - tests/test_regression_scores.py | 567 +++++++++++ 6 files changed, 1510 insertions(+), 12 deletions(-) create mode 100644 docs/modules/misc/score_functions.rst create mode 100644 skfda/misc/score_functions.py create mode 100644 tests/test_regression_scores.py diff --git a/docs/modules/misc/score_functions.rst b/docs/modules/misc/score_functions.rst new file mode 100644 index 000000000..7080b36b2 --- /dev/null +++ b/docs/modules/misc/score_functions.rst @@ -0,0 +1,18 @@ +Scoring methods for regression with functional response. +======================================================== + +The functions in this module are a generalisation for functional data of +the regression metrics of the sklearn library +(https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics). +Only scores that support multioutput are included. + + +.. autosummary:: + :toctree: autosummary + + skfda.misc.score_functions.explained_variance_score + skfda.misc.score_functions.mean_absolute_error + skfda.misc.score_functions.mean_absolute_percentage_error + skfda.misc.score_functions.mean_squared_error + skfda.misc.score_functions.mean_squared_log_error + skfda.misc.score_functions.r2_score diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index e9d3be2b2..edc0382d3 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -14,31 +14,33 @@ F = TypeVar('F', bound=FData) -def mean(X: F) -> F: +def mean( + X: F, + weight: Optional[np.ndarray] = None, +) -> F: """Compute the mean of all the samples in a FData object. - Args: X: Object containing all the samples whose mean is wanted. - - + weight: Sample weight. By default, uniform weight are + used. Returns: A :term:`functional data object` with just one sample representing the mean of all the samples in the original object. - """ - return X.mean() + if weight is None: + return X.mean() + else: + weight = (X.n_samples / sum(weight)) * weight + return (X * weight).mean() -def var(X: FData) -> FDataGrid: +def var(X: FDataGrid) -> FDataGrid: """Compute the variance of a set of samples in a FDataGrid object. - Args: X: Object containing all the set of samples whose variance is desired. - Returns: A :term:`functional data object` with just one sample representing the variance of all the samples in the original object. - """ return X.var() diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 139d717a5..72f4db4b6 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -7,6 +7,7 @@ metrics, operators, regularization, + score_functions, ) from ._math import ( cosine_similarity, diff --git a/skfda/misc/score_functions.py b/skfda/misc/score_functions.py new file mode 100644 index 000000000..cc495f84d --- /dev/null +++ b/skfda/misc/score_functions.py @@ -0,0 +1,912 @@ +"""Score functions for FData.""" +import warnings +from functools import singledispatch +from typing import Optional, Union + +import numpy as np +import scipy.integrate +import sklearn +from typing_extensions import Literal, Protocol + +from .. import FData +from ..exploratory.stats import mean, var +from ..representation._typing import NDArrayFloat +from ..representation.basis import FDataBasis +from ..representation.grid import FDataGrid + + +class ScoreFunction(Protocol): + """Type definition for score functions.""" + + def __call__( + self, + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] + = 'uniform_average', + squared: Optional[bool] = None, + ) -> Union[NDArrayFloat, FDataGrid, float]: ... + + +def _domain_measure(fd: FData) -> float: + measure = 1.0 + for interval in fd.domain_range: + measure = measure * (interval[1] - interval[0]) + return measure + + +def _var( + x: FDataGrid, + weight: Optional[NDArrayFloat] = None, +) -> FDataGrid: + if weight is None: + return var(x) + + return mean( + np.power(x - mean(x, weight=weight), 2), + weight=weight, + ) + + +@singledispatch +def explained_variance_score( + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid, NDArrayFloat]: + r"""Explained variance score for :class:`~skfda.representation.FData`. + + With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, + :math:`t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)` being the + estimated and :math:`sample\_weight = (w_1, w_2, ..., w_n)`, the score is + calculated as + + .. math:: + EV(y\_true, y\_pred)(t) = 1 - + \frac{Var(y\_true(t) - y\_pred(t), sample\_weight)} + {Var(y\_true(t), sample\_weight)} + + where :math:`Var` is a weighted variance. + + Weighted variance is defined as below + + .. math:: + Var(y\_true, sample\_weight)(t) = \sum_{i=1}^n w_i + (X_i(t) - Mean(fd(t), sample\_weight))^2. + + Here, :math:`Mean` is a weighted mean. + + For :math:`y\_true` and :math:`y\_pred` of type + :class:`~skfda.representation.FDataGrid`, :math:`EV` is + also a :class:`~skfda.representation.FDataGrid` object with + the same grid points. + + If multioutput = 'raw_values', the function :math:`EV` is returned. + Otherwise, if multioutput = 'uniform_average', the mean of :math:`EV` is + calculated: + + .. math:: + mean(EV) = \frac{1}{V}\int_{D} EV(t) dt + + where :math:`D` is the function domain and :math:`V` the volume of that + domain. + + For :class:`~skfda.representation.FDataBasis` only + 'uniform_average' is available. + + If :math:`y\_true` and :math:`y\_pred` are numpy arrays, sklearn function + is called. + + The best possible score is 1.0, lower values are worse. + + Args: + y_true: Correct target values. + y_pred: Estimated values. + sample_weight: Sample weights. By default, uniform weights + are taken. + multioutput: Defines format of the return. + + Returns: + Explained variance score. + + float: + if multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects. + FDataGrid: + if both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values'. + ndarray: + if both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values'. + + """ + return sklearn.metrics.explained_variance_score( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + ) + + +@explained_variance_score.register +def _explained_variance_score_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid]: + + num = _var(y_true - y_pred, weight=sample_weight) + den = _var(y_true, weight=sample_weight) + + # Divisions by zero allowed + with np.errstate(divide='ignore', invalid='ignore'): + score = 1 - num / den + + # 0 / 0 divisions should be 1 in this context and the score, 0 + score.data_matrix[np.isnan(score.data_matrix)] = 0 + + if multioutput == 'raw_values': + return score + + # Score only contains 1 function + # If the dimension of the codomain is > 1, + # the mean of the integrals is taken + return np.mean(score.integrate()[0] / _domain_measure(score)) + + +@explained_variance_score.register +def _explaied_variance_score_fdatabasis( + y_true: FDataBasis, + y_pred: FDataBasis, + *, + sample_weight: Optional[NDArrayFloat] = None, +) -> float: + + start, end = y_true.domain_range[0] + + def _ev_func(x): # noqa: WPS430 + num = np.average( + np.power( + ( + (y_true(x) - y_pred(x)) + - np.average( + y_true(x) - y_pred(x), + weights=sample_weight, + axis=0, + ) + ), + 2, + ), + weights=sample_weight, + axis=0, + ) + + den = np.average( + np.power( + ( + y_true(x) + - np.average(y_true(x), weights=sample_weight, axis=0) + ), + 2, + ), + weights=sample_weight, + axis=0, + ) + # Divisions by zero allowed + with np.errstate(divide='ignore', invalid='ignore'): + score = 1 - num / den + + # 0 / 0 divisions should be 1 in this context and the score, 0 + score[np.isnan(score)] = 0 + + return score[0][0] + + integral = scipy.integrate.quad_vec( + _ev_func, + start, + end, + ) + + return integral[0] / (end - start) + + +@singledispatch +def mean_absolute_error( + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid, NDArrayFloat]: + r"""Mean Absolute Error for :class:`~skfda.representation.FData`. + + With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, + :math:`t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)` being the + estimated and :math:`sample\_weight = (w_1, w_2, ..., w_n)`, the error is + calculated as + + .. math:: + MAE(y\_true, y\_pred)(t) = \frac{1}{\sum w_i} + \sum_{i=1}^n w_i|X_i(t) - \hat{X}_i(t)| + + For :math:`y\_true` and :math:`y\_pred` of type + :class:`~skfda.representation.FDataGrid`, :math:`MAE` is + also a :class:`~skfda.representation.FDataGrid` object with + the same grid points. + + If multioutput = 'raw_values', the function :math:`MAE` is returned. + Otherwise, if multioutput = 'uniform_average', the mean of :math:`MAE` is + calculated: + + .. math:: + mean(MAE) = \frac{1}{V}\int_{D} MAE(t) dt + + where :math:`D` is the function domain and :math:`V` the volume of that + domain. + + For :class:`~skfda.representation.FDataBasis` only + 'uniform_average' is available. + + If :math:`y\_true` and :math:`y\_pred` are numpy arrays, sklearn function + is called. + + Args: + y_true: Correct target values. + y_pred: Estimated values. + sample_weight: Sample weights. By default, uniform weights + are taken. + multioutput: Defines format of the return. + + Returns: + Mean absolute error. + + float: + if multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects. + FDataGrid: + if both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values'. + ndarray: + if both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values'. + + """ + return sklearn.metrics.mean_absolute_error( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + ) + + +@mean_absolute_error.register +def _mean_absolute_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid]: + + error = mean(np.abs(y_true - y_pred), weight=sample_weight) + + if multioutput == 'raw_values': + return error + + # Score only contains 1 function + # If the dimension of the codomain is > 1, + # the mean of the integrals is taken + return np.mean(error.integrate()[0] / _domain_measure(error)) + + +@mean_absolute_error.register +def _mean_absolute_error_fdatabasis( + y_true: FDataBasis, + y_pred: FDataBasis, + *, + sample_weight: Optional[NDArrayFloat] = None, +) -> float: + + start, end = y_true.domain_range[0] + + def _mae_func(x): # noqa: WPS430 + return np.average( + np.abs(y_true(x) - y_pred(x)), + weights=sample_weight, + axis=0, + )[0][0] + + integral = scipy.integrate.quad_vec( + _mae_func, + start, + end, + ) + + return integral[0] / (end - start) + + +@singledispatch +def mean_absolute_percentage_error( + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid]: + r"""Mean Absolute Percentage Error for :class:`~skfda.representation.FData`. + + With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, + :math:`t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)` being the + estimated and :math:`sample\_weight = (w_1, w_2, ..., w_n)`, the error is + calculated as + + .. math:: + MAPE(y\_true, y\_pred)(t) = \frac{1}{\sum w_i} + \sum_{i=1}^n w_i\frac{|X_i(t) - \hat{X}_i(t)|}{|X_i(t)|} + + For :math:`y\_true` and :math:`y\_pred` of type + :class:`~skfda.representation.FDataGrid`, :math:`MAPE` is + also a :class:`~skfda.representation.FDataGrid` object with + the same grid points. + + If multioutput = 'raw_values', the function :math:`MAPE` is returned. + Otherwise, if multioutput = 'uniform_average', the mean of :math:`MAPE` is + calculated: + + .. math:: + mean(MAPE) = \frac{1}{V}\int_{D} MAPE(t) dt + + where :math:`D` is the function domain and :math:`V` the volume of that + domain. + + For :class:`~skfda.representation.FDataBasis` only + 'uniform_average' is available. + + If :math:`y\_true` and :math:`y\_pred` are numpy arrays, sklearn function + is called. + + This function should not be used if for some :math:`t` and some :math:`i`, + :math:`X_i(t) = 0`. + + Args: + y_true: Correct target values. + y_pred: Estimated values. + sample_weight: Sample weights. By default, uniform weights + are taken. + multioutput: Defines format of the return. + + Returns: + Mean absolute percentage error. + + float: + if multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects. + FDataGrid: + if both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values'. + ndarray: + if both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values'. + + """ + return sklearn.metrics.mean_absolute_percentage_error( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + ) + + +@mean_absolute_percentage_error.register +def _mean_absolute_percentage_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid]: + + epsilon = np.finfo(np.float64).eps + + if np.any(np.abs(y_true.data_matrix) < epsilon): + warnings.warn('Zero denominator', RuntimeWarning) + + mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon) + + error = mean(mape, weight=sample_weight) + + if multioutput == 'raw_values': + return error + + # Score only contains 1 function + # If the dimension of the codomain is > 1, + # the mean of the integrals is taken + return np.mean(error.integrate()[0] / _domain_measure(error)) + + +@mean_absolute_percentage_error.register +def _mean_absolute_percentage_error_fdatabasis( + y_true: FDataBasis, + y_pred: FDataBasis, + *, + sample_weight: Optional[NDArrayFloat] = None, +) -> float: + + def _mape_func(x): # noqa: WPS430 + + epsilon = np.finfo(np.float64).eps + if np.any(np.abs(y_true(x)) < epsilon): + warnings.warn('Zero denominator', RuntimeWarning) + + error = np.average( + ( + np.abs(y_true(x) - y_pred(x)) + / np.maximum(np.abs(y_true(x)), epsilon) + ), + weights=sample_weight, + axis=0, + ) + return error[0][0] + + start, end = y_true.domain_range[0] + integral = scipy.integrate.quad_vec( + _mape_func, + start, + end, + ) + + return integral[0] / (end - start) + + +@singledispatch +def mean_squared_error( + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + squared: bool = True, +) -> Union[float, FDataGrid]: + r"""Mean Squared Error for :class:`~skfda.representation.FData`. + + With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, + :math:`t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)` being the + estimated and :math:`sample\_weight = (w_1, w_2, ..., w_n)`, the error is + calculated as + + .. math:: + MSE(y\_true, y\_pred)(t) = \frac{1}{\sum w_i} + \sum_{i=1}^n w_i(X_i(t) - \hat{X}_i(t))^2 + + For :math:`y\_true` and :math:`y\_pred` of type + :class:`~skfda.representation.FDataGrid`, :math:`MSE` is + also a :class:`~skfda.representation.FDataGrid` object with + the same grid points. + + If multioutput = 'raw_values', the function :math:`MSE` is returned. + Otherwise, if multioutput = 'uniform_average', the mean of :math:`MSE` is + calculated: + + .. math:: + mean(MSE) = \frac{1}{V}\int_{D} MSE(t) dt + + where :math:`D` is the function domain and :math:`V` the volume of that + domain. + + For :class:`~skfda.representation.FDataBasis` only + 'uniform_average' is available. + + If :math:`y\_true` and :math:`y\_pred` are numpy arrays, sklearn function + is called. + + Args: + y_true: Correct target values. + y_pred: Estimated values. + sample_weight: Sample weights. By default, uniform weights + are taken. + multioutput: Defines format of the return. + squared: If True returns MSE value, if False returns RMSE value. + + Returns: + Mean squared error. + + float: + if multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects. + FDataGrid: + if both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values'. + ndarray: + if both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values'. + + """ + return mean_squared_error( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + squared=squared, + ) + + +@mean_squared_error.register +def _mean_squared_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + squared: bool = True, +) -> Union[float, FDataGrid]: + + error = mean( + np.power(y_true - y_pred, 2), + weight=sample_weight, + ) + + if not squared: + error = np.sqrt(error) + + if multioutput == 'raw_values': + return error + + # Score only contains 1 function + # If the dimension of the codomain is > 1, + # the mean of the integrals is taken + return np.mean(error.integrate()[0] / _domain_measure(error)) + + +@mean_squared_error.register +def _mean_squared_error_fdatabasis( + y_true: FDataBasis, + y_pred: FDataBasis, + *, + sample_weight: Optional[NDArrayFloat] = None, + squared: bool = True, +) -> float: + + start, end = y_true.domain_range[0] + + def _mse_func(x): # noqa: WPS430 + + error = np.average( + np.power(y_true(x) - y_pred(x), 2), + weights=sample_weight, + axis=0, + ) + + if not squared: + return np.sqrt(error) + + return error[0][0] + + integral = scipy.integrate.quad_vec( + _mse_func, + start, + end, + ) + + return integral[0] / (end - start) + + +@singledispatch +def mean_squared_log_error( + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + squared: bool = True, +) -> Union[float, FDataGrid]: + r"""Mean Squared Log Error for :class:`~skfda.representation.FData`. + + With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, + :math:`t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)` being the + estimated and :math:`sample\_weight = (w_1, w_2, ..., w_n)`, the error is + calculated as + + .. math:: + MSLE(y\_true, y\_pred)(t) = \frac{1}{\sum w_i} + \sum_{i=1}^n w_i(\log(1 + X_i(t)) - \log(1 + \hat{X}_i(t)))^2 + + where :math:`\log` is the natural logarithm. + + For :math:`y\_true` and :math:`y\_pred` of type + :class:`~skfda.representation.FDataGrid`, :math:`MSLE` is + also a :class:`~skfda.representation.FDataGrid` object with + the same grid points. + + If multioutput = 'raw_values', the function :math:`MSLE` is returned. + Otherwise, if multioutput = 'uniform_average', the mean of :math:`MSLE` is + calculated: + + .. math:: + mean(MSLE) = \frac{1}{V}\int_{D} MSLE(t) dt + + where :math:`D` is the function domain and :math:`V` the volume of that + domain. + + For :class:`~skfda.representation.FDataBasis` only + 'uniform_average' is available. + + If :math:`y\_true` and :math:`y\_pred` are numpy arrays, sklearn function + is called. + + This function should not be used if for some :math:`t` and some :math:`i`, + :math:`X_i(t) < 0`. + + Args: + y_true: Correct target values. + y_pred: Estimated values. + sample_weight: Sample weights. By default, uniform weights + are taken. + multioutput: Defines format of the return. + squared: default True. If False, square root is taken. + + Returns: + Mean squared log error. + + float: + if multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects. + FDataGrid: + if both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values'. + ndarray: + if both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values'. + + """ + return sklearn.metrics.mean_squared_log_error( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + squared=squared, + ) + + +@mean_squared_log_error.register +def _mean_squared_log_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + squared: bool = True, +) -> Union[float, FDataGrid]: + + if np.any(y_true.data_matrix < 0) or np.any(y_pred.data_matrix < 0): + raise ValueError( + "Mean Squared Logarithmic Error cannot be used when " + "targets functions have negative values.", + ) + + return mean_squared_error( + np.log1p(y_true), + np.log1p(y_pred), + sample_weight=sample_weight, + multioutput=multioutput, + squared=squared, + ) + + +@mean_squared_log_error.register +def _mean_squared_log_error_fdatabasis( + y_true: FDataBasis, + y_pred: FDataBasis, + *, + sample_weight: Optional[NDArrayFloat] = None, + squared: bool = True, +) -> float: + + start, end = y_true.domain_range[0] + + def _msle_func(x): # noqa: WPS430 + + if np.any(y_true(x) < 0) or np.any(y_pred(x) < 0): + raise ValueError( + "Mean Squared Logarithmic Error cannot be used when " + "targets functions have negative values.", + ) + + error = np.average( + np.power(np.log1p(y_true(x)) - np.log1p(y_pred(x)), 2), + weights=sample_weight, + axis=0, + )[0][0] + + if not squared: + return np.sqrt(error) + + return error + + integral = scipy.integrate.quad_vec( + _msle_func, + start, + end, + ) + + return integral[0] / (end - start) + + +@singledispatch +def r2_score( + y_true: Union[FData, NDArrayFloat], + y_pred: Union[FData, NDArrayFloat], + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid]: + r"""R^2 score for :class:`~skfda.representation.FData`. + + With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, + :math:`t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)` being the + estimated and :math:`sample\_weight = (w_1, w_2, ..., w_n)`, the score is + calculated as + + .. math:: + R^2(y\_true, y\_pred)(t) = 1 - + \frac{\sum_{i=1}^n w_i (X_i(t) - \hat{X}_i(t))^2} + {\sum_{i=1}^n w_i (X_i(t) - Mean(y\_true, sample\_weight)(t))^2} + + where :math:`Mean` is a weighted mean. + + For :math:`y\_true` and :math:`y\_pred` of type + :class:`~skfda.representation.FDataGrid`, :math:`R^2` is + also a :class:`~skfda.representation.FDataGrid` object with + the same grid points. + + If multioutput = 'raw_values', the function :math:`R^2` is returned. + Otherwise, if multioutput = 'uniform_average', the mean of :math:`R^2` is + calculated: + + .. math:: + mean(R^2) = \frac{1}{V}\int_{D} R^2(t) dt + + where :math:`D` is the function domain and :math:`V` the volume of that + domain. + + For :class:`~skfda.representation.FDataBasis` only + 'uniform_average' is available. + + If :math:`y\_true` and :math:`y\_pred` are numpy arrays, sklearn function + is called. + + Args: + y_true: Correct target values. + y_pred: Estimated values. + sample_weight: Sample weights. By default, uniform weights + are taken. + multioutput: Defines format of the return. + + Returns: + R2 score + + float: + if multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects. + FDataGrid: + if both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values'. + ndarray: + if both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values'. + + """ + return sklearn.metrics.r2_score( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + ) + + +@r2_score.register +def _r2_score_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', +) -> Union[float, FDataGrid]: + + if y_pred.n_samples < 2: + raise ValueError( + 'R^2 score is not well-defined with less than two samples.', + ) + + ss_res = mean( + np.power(y_true - y_pred, 2), + weight=sample_weight, + ) + + ss_tot = mean( + (y_true - mean(y_true, weight=sample_weight)) + * (y_true - mean(y_true, weight=sample_weight)), + weight=sample_weight, + ) + + # Divisions by zero allowed + with np.errstate(divide='ignore', invalid='ignore'): + score = 1 - ss_res / ss_tot + + # 0 / 0 divisions should be 1 in this context and the score, 0 + score.data_matrix[np.isnan(score.data_matrix)] = 0 + + if multioutput == 'raw_values': + return score + + # Score only contains 1 function + # If the dimension of the codomain is > 1, + # the mean of the integrals is taken + return np.mean(score.integrate()[0] / _domain_measure(score)) + + +@r2_score.register +def _r2_score_fdatabasis( + y_true: FDataBasis, + y_pred: FDataBasis, + *, + sample_weight: Optional[NDArrayFloat] = None, +) -> float: + + start, end = y_true.domain_range[0] + + if y_pred.n_samples < 2: + raise ValueError( + 'R^2 score is not well-defined with less than two samples.', + ) + + def _r2_func(x): # noqa: WPS430 + ss_res = np.average( + np.power(y_true(x) - y_pred(x), 2), + weights=sample_weight, + axis=0, + ) + + ss_tot = np.average( + np.power( + ( + y_true(x) + - np.average(y_true(x), weights=sample_weight, axis=0) + ), + 2, + ), + weights=sample_weight, + axis=0, + ) + # Divisions by zero allowed + with np.errstate(divide='ignore', invalid='ignore'): + score = 1 - ss_res / ss_tot + + # 0 / 0 divisions should be 1 in this context and the score, 0 + score[np.isnan(score)] = 0 + + return score[0][0] + + integral = scipy.integrate.quad_vec( + _r2_func, + start, + end, + ) + print(integral) + return integral[0] / (end - start) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 9d81298ae..16c9a331b 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -10,8 +10,6 @@ from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from ....misc import inner_product_matrix -from ....misc.metrics import l2_norm from ....misc.regularization import L2Regularization, compute_penalty_matrix from ....representation import FData from ....representation._typing import ArrayLike diff --git a/tests/test_regression_scores.py b/tests/test_regression_scores.py new file mode 100644 index 000000000..0e1a81039 --- /dev/null +++ b/tests/test_regression_scores.py @@ -0,0 +1,567 @@ +"""Test for Score Functions module.""" +import unittest +from typing import Optional, Tuple + +import numpy as np +import sklearn + +from skfda import FDataBasis, FDataGrid +from skfda.datasets import fetch_tecator +from skfda.misc.score_functions import ( + ScoreFunction, + explained_variance_score, + mean_absolute_error, + mean_absolute_percentage_error, + mean_squared_error, + mean_squared_log_error, + r2_score, +) +from skfda.representation._typing import NDArrayFloat +from skfda.representation.basis import BSpline, Fourier, Monomial + + +def _create_data_grid(n: int) -> Tuple[FDataGrid, FDataGrid]: + X, y = fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + + y_true = fd[:n] + y_pred = fd[n:2 * n] + + return y_true, y_pred + + +def _create_data_basis() -> Tuple[FDataBasis, FDataBasis]: + coef_true = [[1, 2, 3], [4, 5, 6]] + coef_pred = [[1, 2, 3], [4, 6, 5]] + + # y_true: 1) 1 + 2x + 3x^2 + # 2) 4 + 5x + 6x^2 + y_true = FDataBasis( + basis=Monomial(domain_range=((0, 3),), n_basis=3), + coefficients=coef_true, + ) + + # y_true: 1) 1 + 2x + 3x^2 + # 2) 4 + 6x + 5x^2 + y_pred = FDataBasis( + basis=Monomial(domain_range=((0, 3),), n_basis=3), + coefficients=coef_pred, + ) + + # y_true - y_pred: 1) 0 + # 2) -x + x^2 + return y_true, y_pred + + +class TestScoreFunctionsGrid(unittest.TestCase): + """Tests for score functions with FDataGrid representation.""" + + n = 10 + + def _test_generic_grid( + self, + function: ScoreFunction, + sklearn_function: ScoreFunction, + weight: Optional[NDArrayFloat] = None, + squared: bool = True, + ) -> None: + y_true, y_pred = _create_data_grid(self.n) + + if squared: + score = function( + y_true, + y_pred, + multioutput='raw_values', + sample_weight=weight, + ) + + score_sklearn = sklearn_function( + y_true.data_matrix.reshape( + (y_true.data_matrix.shape[0], -1), + ), + y_pred.data_matrix.reshape( + (y_pred.data_matrix.shape[0], -1), + ), + multioutput='raw_values', + sample_weight=weight, + ) + else: + score = function( + y_true, + y_pred, + multioutput='raw_values', + sample_weight=weight, + squared=False, + ) + + score_sklearn = sklearn_function( + y_true.data_matrix.reshape( + (y_true.data_matrix.shape[0], -1), + ), + y_pred.data_matrix.reshape( + (y_pred.data_matrix.shape[0], -1), + ), + multioutput='raw_values', + sample_weight=weight, + squared=False, + ) + + np.testing.assert_allclose( + score.data_matrix.reshape( + (score.data_matrix.shape[0], -1), + )[0], + score_sklearn, + ) + + def test_explained_variance_score_grid(self) -> None: + """Test Explained Variance Score for FDataGrid.""" + self._test_generic_grid( + explained_variance_score, + sklearn.metrics.explained_variance_score, + ) + + self._test_generic_grid( + explained_variance_score, + sklearn.metrics.explained_variance_score, + np.random.random_sample(self.n), + ) + + def test_mean_absolute_error_grid(self) -> None: + """Test Mean Absolute Error for FDataGrid.""" + self._test_generic_grid( + mean_absolute_error, + sklearn.metrics.mean_absolute_error, + ) + + self._test_generic_grid( + mean_absolute_error, + sklearn.metrics.mean_absolute_error, + np.random.random_sample(self.n), + ) + + def test_mean_absolute_percentage_error_grid(self) -> None: + """Test Mean Absolute Percentage Error for FDataGrid.""" + self._test_generic_grid( + mean_absolute_percentage_error, + sklearn.metrics.mean_absolute_percentage_error, + ) + + self._test_generic_grid( + mean_absolute_percentage_error, + sklearn.metrics.mean_absolute_percentage_error, + np.random.random_sample(self.n), + ) + + def test_mean_squared_error_grid(self) -> None: + """Test Mean Squared Error for FDataGrid.""" + self._test_generic_grid( + mean_squared_error, + sklearn.metrics.mean_squared_error, + ) + + self._test_generic_grid( + mean_squared_error, + sklearn.metrics.mean_squared_error, + squared=False, + ) + + self._test_generic_grid( + mean_squared_error, + sklearn.metrics.mean_squared_error, + np.random.random_sample(self.n), + ) + + def test_mean_squared_log_error_grid(self) -> None: + """Test Mean Squared Log Error for FDataGrid.""" + self._test_generic_grid( + mean_squared_log_error, + sklearn.metrics.mean_squared_log_error, + ) + + self._test_generic_grid( + mean_squared_log_error, + sklearn.metrics.mean_squared_log_error, + squared=False, + ) + + self._test_generic_grid( + mean_squared_log_error, + sklearn.metrics.mean_squared_log_error, + np.random.random_sample(self.n), + ) + + def test_r2_score_grid(self) -> None: + """Test R2 Score for FDataGrid.""" + self._test_generic_grid( + r2_score, + sklearn.metrics.r2_score, + ) + + self._test_generic_grid( + r2_score, + sklearn.metrics.r2_score, + np.random.random_sample(self.n), + ) + + +class TestScoreFunctionGridBasis(unittest.TestCase): + """Compare the results obtained for FDataGrid and FDataBasis.""" + + n = 10 + + def _test_grid_basis_generic( + self, + score_function: ScoreFunction, + sample_weight: Optional[NDArrayFloat] = None, + squared: bool = True, + ) -> None: + y_true_grid, y_pred_grid = _create_data_grid(self.n) + + y_true_basis = y_true_grid.to_basis(basis=BSpline(n_basis=10)) + y_pred_basis = y_pred_grid.to_basis(basis=BSpline(n_basis=10)) + + # The results should be close but not equal as the two representations + # do not give same functions. + precision = 2 + + if squared: + score_grid = score_function( + y_true_grid, + y_pred_grid, + sample_weight=sample_weight, + ) + score_basis = score_function( + y_true_basis, + y_pred_basis, + sample_weight=sample_weight, + ) + else: + score_grid = score_function( + y_true_grid, + y_pred_grid, + sample_weight=sample_weight, + squared=False, + ) + score_basis = score_function( + y_true_basis, + y_pred_basis, + sample_weight=sample_weight, + squared=False, + ) + + np.testing.assert_almost_equal( + score_grid, + score_basis, + decimal=precision, + ) + + def test_explained_variance_score(self) -> None: + """Explained variance score for FDataGrid and FDataBasis.""" + self._test_grid_basis_generic(explained_variance_score) + self._test_grid_basis_generic( + explained_variance_score, + np.random.random_sample((self.n,)), + ) + + def test_mean_absolute_error(self) -> None: + """Mean Absolute Error for FDataGrid and FDataBasis.""" + self._test_grid_basis_generic(mean_absolute_error) + self._test_grid_basis_generic( + mean_absolute_error, + np.random.random_sample((self.n,)), + ) + + def test_mean_absolute_percentage_error(self) -> None: + """Mean Absolute Percentage Error for FDataGrid and FDataBasis.""" + self._test_grid_basis_generic(mean_absolute_percentage_error) + self._test_grid_basis_generic( + mean_absolute_percentage_error, + np.random.random_sample((self.n,)), + ) + + def test_mean_squared_error(self) -> None: + """Mean Squared Error for FDataGrid and FDataBasis.""" + self._test_grid_basis_generic(mean_squared_error) + self._test_grid_basis_generic( + mean_squared_error, + np.random.random_sample((self.n,)), + ) + self._test_grid_basis_generic(mean_squared_error, squared=False) + + def test_mean_squared_log_error(self) -> None: + """Mean Squared Log Error for FDataGrid and FDataBasis.""" + self._test_grid_basis_generic(mean_squared_log_error) + self._test_grid_basis_generic( + mean_squared_log_error, + np.random.random_sample((self.n,)), + ) + self._test_grid_basis_generic(mean_squared_log_error, squared=False) + + def test_r2_score(self) -> None: + """R2 Score for FDataGrid and FDataBasis.""" + self._test_grid_basis_generic(r2_score) + self._test_grid_basis_generic( + r2_score, + np.random.random_sample((self.n,)), + ) + + +class TestScoreFunctionsBasis(unittest.TestCase): + """Tests for score functions with FDataBasis representation.""" + + def test_explained_variance_basis(self) -> None: + """Test Explain Variance Score for FDataBasis.""" + y_true, y_pred = _create_data_basis() + + ev = explained_variance_score(y_true, y_pred) + + # integrate 1 - num/den + # where num = (1/2x -1/2x^2)^2 + # and den = (1.5 + 1.5x + 1.5x^2)^2 + np.testing.assert_almost_equal(ev, 0.992968) + + def test_mean_absolut_error_basis(self) -> None: + """Test Mean Absolute Error for FDataBasis.""" + y_true, y_pred = _create_data_basis() + + mae = mean_absolute_error(y_true, y_pred) + + # integrate 1/2 * | -x + x^2| + np.testing.assert_almost_equal(mae, 0.8055555555) + + def test_mean_absolute_percentage_error_basis(self) -> None: + """Test Mean Absolute Percentage Error for FDataBasis.""" + y_true, y_pred = _create_data_basis() + + mape = mean_absolute_percentage_error(y_true, y_pred) + + # integrate |1/2 * (-x + x^2) / (4 + 5x + 6x^2)| + np.testing.assert_almost_equal(mape, 0.0199192187) + + def test_mean_squared_error_basis(self) -> None: + """Test Mean Squared Error for FDataBasis.""" + y_true, y_pred = _create_data_basis() + + mse = mean_squared_error(y_true, y_pred) + + # integrate 1/2 * (-x + x^2)^2 + np.testing.assert_almost_equal(mse, 2.85) + + def test_mean_squared_log_error_basis(self) -> None: + """Test Mean Squared Log Error for FDataBasis.""" + y_true, y_pred = _create_data_basis() + + msle = mean_squared_log_error(y_true, y_pred) + + # integrate 1/2*(log(1 + 4 + 5x + 6x^2) - log(1 + 4 + 6x + 5x^2))^2 + np.testing.assert_almost_equal(msle, 0.00107583) + + def test_r2_score_basis(self) -> None: + """Test R2 Score for FDataBasis.""" + y_true, y_pred = _create_data_basis() + + r2 = r2_score(y_true, y_pred) + + # integrate 1 - num/den + # where num = 1/2*(-x + x^2)^2, + # and den = (1.5 + 1.5x + 1.5x^2)^2 + np.testing.assert_almost_equal(r2, 0.9859362) + + +class TestScoreZeroDenominator(unittest.TestCase): + """Tests Score Functions with edge cases.""" + + def test_zero_r2(self) -> None: + """Test R2 Score when the denominator is zero.""" + # Case when both numerator and denominator is zero (in t = 1) + basis_coef_true = [[0, 1], [-1, 2], [-2, 3]] + basis_coef_pred = [[1, 0], [2, -1], [3, -2]] + + # y_true and y_pred are 2 sets of 3 straight lines + # for all f, f(1) = 1 + y_true_basis = FDataBasis( + basis=Monomial(domain_range=((0, 2),), n_basis=2), + coefficients=basis_coef_true, + ) + + y_pred_basis = FDataBasis( + basis=Monomial(domain_range=((0, 2),), n_basis=2), + coefficients=basis_coef_pred, + ) + + grid_points = np.linspace(0, 2, 9) + + y_true_grid = y_true_basis.to_grid(grid_points=grid_points) + y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + + np.testing.assert_almost_equal( + r2_score( + y_true_grid, + y_pred_grid, + multioutput='raw_values', + ).evaluate(1), + [[[0]]], + ) + + np.testing.assert_almost_equal( + r2_score(y_true_basis, y_pred_basis), + -16.5, + ) + + # Case when numerator is non-zero and denominator is zero (in t = 1) + # for all f in y_true, f(1) = 1 + # for all f in y_pred, f(1) = 2 + basis_coef_pred = [[2, 0], [3, -1], [4, -2]] + y_pred_basis = FDataBasis( + basis=Monomial(domain_range=((0, 2),), n_basis=2), + coefficients=basis_coef_pred, + ) + + y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + np.testing.assert_almost_equal( + r2_score( + y_true_grid, + y_pred_grid, + multioutput='raw_values', + ).evaluate(1), + [[[float('-inf')]]], + ) + + def test_zero_ev(self) -> None: + """Test R2 Score when the denominator is zero.""" + basis_coef_true = [[0, 1], [-1, 2], [-2, 3]] + basis_coef_pred = [[1, 0], [2, -1], [3, -2]] + # Case when both numerator and denominator is zero (in t = 1) + + # y_true and y_pred are 2 sets of 3 straight lines + # for all f, f(1) = 1 + # var(y_true(1)) = 0 and var(y_true(1) - y_pred(1)) = 0 + y_true_basis = FDataBasis( + basis=Monomial(domain_range=((0, 2),), n_basis=2), + coefficients=basis_coef_true, + ) + + y_pred_basis = FDataBasis( + basis=Monomial(domain_range=((0, 2),), n_basis=2), + coefficients=basis_coef_pred, + ) + + grid_points = np.linspace(0, 2, 9) + + y_true_grid = y_true_basis.to_grid(grid_points=grid_points) + y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + + np.testing.assert_almost_equal( + explained_variance_score( + y_true_grid, + y_pred_grid, + multioutput='raw_values', + ).evaluate(1), + [[[0]]], + ) + + # Case when numerator is non-zero and denominator is zero (in t = 1) + basis_coef_pred = [[2, 0], [2, -1], [2, -2]] + y_pred_basis = FDataBasis( + basis=Monomial(domain_range=((0, 2),), n_basis=2), + coefficients=basis_coef_pred, + ) + y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + np.testing.assert_almost_equal( + explained_variance_score( + y_true_grid, + y_pred_grid, + multioutput='raw_values', + ).evaluate(1), + [[[float('-inf')]]], + ) + + def test_zero_mape(self) -> None: + """Test Mean Absolute Percentage Error when y_true can be zero.""" + basis_coef_true = [[3, 0, 0], [0, 0, 1]] + basis_coef_pred = [[1, 0, 0], [1, 0, 1]] + + # Fourier basis defined in (0, 2) with 3 elements + # The functions are + # y_true(t) = 1) 3/sqrt(2) + # 2) 1/sqrt(2) * cos(pi t) + # + # y_pred(t) = 1) 1/sqrt(2) + # 2) 1/sqrt(2) + 1/sqrt(2) * cos(pi t) + # The second function in y_true should be zero at t = 0.5 and t = 1.5 + + y_true_basis = FDataBasis( + basis=Fourier(domain_range=((0, 2),), n_basis=3), + coefficients=basis_coef_true, + ) + + y_pred_basis = FDataBasis( + basis=Fourier(domain_range=((0, 2),), n_basis=3), + coefficients=basis_coef_pred, + ) + + self.assertWarns( + RuntimeWarning, + mean_absolute_percentage_error, + y_true_basis, + y_pred_basis, + ) + + grid_points = np.linspace(0, 2, 9) + + # The set of points in which the functions are evaluated + # includes 0.5 and 1.5 + y_true_grid = y_true_basis.to_grid(grid_points=grid_points) + y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + + self.assertWarns( + RuntimeWarning, + mean_absolute_percentage_error, + y_true_grid, + y_pred_grid, + ) + + def test_negative_msle(self) -> None: + """Test Mean Squared Log Error when there are negative data.""" + basis_coef_true = [[3, 0, 0], [0, 0, 1]] + basis_coef_pred = [[1, 0, 0], [np.sqrt(2), 0, 1]] + + # Fourier basis defined in (0, 2) with 3 elements + # The functions are + # y_true(t) = 1) 3/sqrt(2) + # 2) 1/sqrt(2) * cos(pi t) + # + # y_pred(t) = 1) 1/sqrt(2) + # 2) 1 + 1/sqrt(2) * cos(pi t) + # The second function in y_true should be negative + # between t = 0.5 and t = 1.5 + # All functions in y_pred should be always positive + + y_true_basis = FDataBasis( + basis=Fourier(domain_range=((0, 2),), n_basis=3), + coefficients=basis_coef_true, + ) + + y_pred_basis = FDataBasis( + basis=Fourier(domain_range=((0, 2),), n_basis=3), + coefficients=basis_coef_pred, + ) + + self.assertRaises( + ValueError, + mean_squared_log_error, + y_true_basis, + y_pred_basis, + ) + + grid_points = np.linspace(0, 2, 9) + + y_true_grid = y_true_basis.to_grid(grid_points=grid_points) + y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + + self.assertRaises( + ValueError, + mean_squared_log_error, + y_true_grid, + y_pred_grid, + ) From 730da152d7c96a1f9b89df0ddd6d14e64a12f437 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 4 Mar 2022 02:48:48 +0100 Subject: [PATCH 072/400] Update score_functions.py --- skfda/misc/score_functions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/score_functions.py b/skfda/misc/score_functions.py index cc495f84d..016f44337 100644 --- a/skfda/misc/score_functions.py +++ b/skfda/misc/score_functions.py @@ -908,5 +908,5 @@ def _r2_func(x): # noqa: WPS430 start, end, ) - print(integral) + return integral[0] / (end - start) From af8c31551b26b1902ea7d076c496e2232773dbeb Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 4 Mar 2022 04:06:12 +0100 Subject: [PATCH 073/400] LLR for non orthonormal basis Close #421 --- examples/plot_kernel_regression.py | 4 +- skfda/misc/hat_matrix.py | 101 ++++++++++------------ skfda/ml/regression/_kernel_regression.py | 8 +- tests/test_kernel_regression.py | 26 +++++- 4 files changed, 77 insertions(+), 62 deletions(-) diff --git a/examples/plot_kernel_regression.py b/examples/plot_kernel_regression.py index 39273791e..d3cb895bb 100644 --- a/examples/plot_kernel_regression.py +++ b/examples/plot_kernel_regression.py @@ -109,8 +109,8 @@ print('Score NW:', nw_res) ############################################################################## -# For Local Linear Regression, FDataBasis representation with an orthonormal -# basis should be used (for the previous cases it is possible to use either +# For Local Linear Regression, FDataBasis representation with a basis should be +# used (for the previous cases it is possible to use either # FDataGrid or FDataBasis). # # For basis, Fourier basis with 10 elements has been selected. Note that the diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index ae0e9e693..82d53f444 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -14,10 +14,9 @@ import numpy as np from sklearn.base import BaseEstimator, RegressorMixin -from skfda.representation._functional_data import FData -from skfda.representation.basis import FDataBasis - -from ..representation._typing import GridPoints, GridPointsLike +from ..representation._functional_data import FData +from ..representation._typing import GridPoints, GridPointsLike, NDArrayFloat +from ..representation.basis import FDataBasis from . import kernels @@ -36,7 +35,7 @@ def __init__( self, *, bandwidth: Optional[float] = None, - kernel: Callable[[np.ndarray], np.ndarray] = kernels.normal, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.normal, ): self.bandwidth = bandwidth self.kernel = kernel @@ -44,13 +43,13 @@ def __init__( def __call__( self, *, - delta_x: np.ndarray, + delta_x: NDArrayFloat, X_train: Optional[Union[FData, GridPointsLike]] = None, X: Optional[Union[FData, GridPointsLike]] = None, - y_train: Optional[np.ndarray] = None, - weights: Optional[np.ndarray] = None, + y_train: Optional[NDArrayFloat] = None, + weights: Optional[NDArrayFloat] = None, _cv: bool = False, - ) -> np.ndarray: + ) -> NDArrayFloat: r""" Calculate the hat matrix or the prediction. @@ -99,8 +98,8 @@ def __call__( def _hat_matrix_function_not_normalized( self, *, - delta_x: np.ndarray, - ) -> np.ndarray: + delta_x: NDArrayFloat, + ) -> NDArrayFloat: pass @@ -141,8 +140,8 @@ class NadarayaWatsonHatMatrix(HatMatrix): def _hat_matrix_function_not_normalized( self, *, - delta_x: np.ndarray, - ) -> np.ndarray: + delta_x: NDArrayFloat, + ) -> NDArrayFloat: if self.bandwidth is None: percentage = 15 @@ -185,7 +184,7 @@ class LocalLinearRegressionHatMatrix(HatMatrix): For **kernel regression** algorithm: Given functional data, :math:`(X_1, X_2, ..., X_n)` where each function - is expressed in a orthonormal basis with :math:`J` elements and scalar + is expressed in a basis with :math:`J` elements and scalar response :math:`Y = (y_1, y_2, ..., y_n)`. It is desired to estimate the values @@ -222,13 +221,13 @@ class LocalLinearRegressionHatMatrix(HatMatrix): def __call__( # noqa: D102 self, *, - delta_x: np.ndarray, + delta_x: NDArrayFloat, X_train: Optional[Union[FDataBasis, GridPoints]] = None, X: Optional[Union[FDataBasis, GridPoints]] = None, - y_train: Optional[np.ndarray] = None, - weights: Optional[np.ndarray] = None, + y_train: Optional[NDArrayFloat] = None, + weights: Optional[NDArrayFloat] = None, _cv: bool = False, - ) -> np.ndarray: + ) -> NDArrayFloat: if self.bandwidth is None: percentage = 15 @@ -243,10 +242,27 @@ def __call__( # noqa: D102 m1 = X_train.coefficients m2 = X.coefficients + # Subtract previous matrices obtaining a 3D matrix + # The i-th element contains the matrix X_train - X[i] + C = m1 - m2[:, np.newaxis] + + inner_product_matrix = X_train.basis.inner_product_matrix() + + # Calculate new coefficients taking into account cross-products + # if the basis is orthonormal, C would not change + C = np.einsum( + 'ijk, kl -> ijl', + C, + inner_product_matrix, + ) + + # Adding a column of ones in the first position of all matrices + dims = (C.shape[0], C.shape[1], 1) + C = np.c_[np.ones(dims), C] + return self._solve_least_squares( delta_x=delta_x, - m1=m1, - m2=m2, + coefs=C, y_train=y_train, ) @@ -264,39 +280,16 @@ def __call__( # noqa: D102 def _solve_least_squares( self, - delta_x: np.ndarray, - m1: np.ndarray, - m2: np.ndarray, - y_train: np.ndarray, - ) -> np.ndarray: + delta_x: NDArrayFloat, + coefs: NDArrayFloat, + y_train: NDArrayFloat, + ) -> NDArrayFloat: W = np.sqrt(self.kernel(delta_x / self.bandwidth)) - # Adding a column of ones to m1 - m1 = np.concatenate( - ( - np.ones(m1.shape[0])[:, np.newaxis], - m1, - ), - axis=1, - ) - - # Adding a column of zeros to m2 - m2 = np.concatenate( - ( - np.zeros(m2.shape[0])[:, np.newaxis], - m2, - ), - axis=1, - ) - - # Subtract previous matrices obtaining a 3D matrix - # The i-th element contains the matrix X_train - X[i] - C = m1 - m2[:, np.newaxis] - # A x = b - # Where x = (a, b_1, ..., b_J) - A = (C.T * W.T).T + # Where x = (a, b_1, ..., b_J). + A = (coefs.T * W.T).T b = np.einsum('ij, j... -> ij...', W, y_train) # For Ax = b calculates x that minimize the square error @@ -312,8 +305,8 @@ def _solve_least_squares( def _hat_matrix_function_not_normalized( self, *, - delta_x: np.ndarray, - ) -> np.ndarray: + delta_x: NDArrayFloat, + ) -> NDArrayFloat: if self.bandwidth is None: percentage = 15 @@ -369,7 +362,7 @@ def __init__( self, *, n_neighbors: Optional[int] = None, - kernel: Callable[[np.ndarray], np.ndarray] = kernels.uniform, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.uniform, ): self.n_neighbors = n_neighbors self.kernel = kernel @@ -377,8 +370,8 @@ def __init__( def _hat_matrix_function_not_normalized( self, *, - delta_x: np.ndarray, - ) -> np.ndarray: + delta_x: NDArrayFloat, + ) -> NDArrayFloat: input_points_len = delta_x.shape[1] diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py index 2834cae86..aecc64c60 100644 --- a/skfda/ml/regression/_kernel_regression.py +++ b/skfda/ml/regression/_kernel_regression.py @@ -6,10 +6,10 @@ from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted -from skfda.misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix -from skfda.misc.metrics import PairwiseMetric, l2_distance -from skfda.misc.metrics._typing import Metric -from skfda.representation._functional_data import FData +from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix +from ...misc.metrics import PairwiseMetric, l2_distance +from ...misc.metrics._typing import Metric +from ...representation._functional_data import FData class KernelRegression( diff --git a/tests/test_kernel_regression.py b/tests/test_kernel_regression.py index 01e3d9748..c7016a2a3 100644 --- a/tests/test_kernel_regression.py +++ b/tests/test_kernel_regression.py @@ -15,7 +15,7 @@ from skfda.misc.kernels import normal, uniform from skfda.misc.metrics import l2_distance from skfda.ml.regression import KernelRegression -from skfda.representation.basis import FDataBasis, Fourier +from skfda.representation.basis import FDataBasis, Fourier, Monomial from skfda.representation.grid import FDataGrid @@ -80,7 +80,7 @@ def _llr_alt( C = np.concatenate( ( - (np.ones(fd_train.n_samples))[:, np.newaxis], + np.ones(fd_train.n_samples)[:, np.newaxis], (fd_train - fd_test[i]).coefficients, ), axis=1, @@ -313,3 +313,25 @@ def test_knn_r(self) -> None: ] np.testing.assert_almost_equal(y, result_R, decimal=6) + + +class TestNonOthonormalBasisLLR(unittest.TestCase): + """Test LocalLinearRegression method with non orthonormal basis.""" + + def test_llr_non_orthonormal(self): + """Test LocalLinearRegression with monomial basis.""" + coef1 = [[1, 5, 8], [4, 6, 6], [9, 4, 1]] + coef2 = [[6, 3, 5]] + basis = Monomial(n_basis=3, domain_range=(0, 3)) + + X_train = FDataBasis(coefficients=coef1, basis=basis) + X = FDataBasis(coefficients=coef2, basis=basis) + y_train = np.array([8, 6, 1]) + + llr = LocalLinearRegressionHatMatrix( + bandwidth=100, + kernel=uniform, + ) + kr = KernelRegression(kernel_estimator=llr) + kr.fit(X_train, y_train) + np.testing.assert_almost_equal(kr.predict(X), 4.35735166) From e26df2d1ca24e66f8bffb2769bb8ea6b3139807e Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 4 Mar 2022 04:14:31 +0100 Subject: [PATCH 074/400] Update _fourier.py Change to indicate that the Fourier base is orthonormal. --- skfda/representation/basis/_fourier.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index f1f5e619d..6365154e0 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -21,13 +21,16 @@ class Fourier(Basis): \phi_0(t) = \frac{1}{\sqrt{2}} .. math:: - \phi_{2n -1}(t) = sin\left(\frac{2 \pi n}{T} t\right) + \phi_{2n -1}(t) = \frac{sin\left(\frac{2 \pi n}{T} t\right)} + {\sqrt{\frac{T}{2}}} .. math:: - \phi_{2n}(t) = cos\left(\frac{2 \pi n}{T} t\right) + \phi_{2n}(t) = \frac{cos\left(\frac{2 \pi n}{T} t\right)} + {\sqrt{\frac{T}{2}}} - Actually this basis functions are not orthogonal but not orthonormal. To - achieve this they are divided by its norm: :math:`\sqrt{\frac{T}{2}}`. + + This basis will be orthonormal if the period coincides with the length + of the interval in which it is defined. Parameters: domain_range: A tuple of length 2 containing the initial and From d6f0732c8c80ef9a9b794f5f4bc2b488a5b59c85 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 4 Mar 2022 04:37:14 +0100 Subject: [PATCH 075/400] Update test_kernel_regression.py --- tests/test_kernel_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_kernel_regression.py b/tests/test_kernel_regression.py index c7016a2a3..afda97610 100644 --- a/tests/test_kernel_regression.py +++ b/tests/test_kernel_regression.py @@ -318,7 +318,7 @@ def test_knn_r(self) -> None: class TestNonOthonormalBasisLLR(unittest.TestCase): """Test LocalLinearRegression method with non orthonormal basis.""" - def test_llr_non_orthonormal(self): + def test_llr_non_orthonormal(self) -> None: """Test LocalLinearRegression with monomial basis.""" coef1 = [[1, 5, 8], [4, 6, 6], [9, 4, 1]] coef2 = [[6, 3, 5]] From fecb4b2d65d5b9478c970dcf4de440064d748763 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 8 Mar 2022 15:50:09 +0100 Subject: [PATCH 076/400] Fixing DataFrame creation --- .../feature_extraction/_fda_feature_union.py | 12 ++++-- .../_per_class_transformer.py | 41 +++++++------------ tests/test_fda_feature_union.py | 10 ++++- 3 files changed, 31 insertions(+), 32 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py index 24f0fc8bd..03ed03b8f 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py @@ -3,8 +3,8 @@ from typing import Union +import pandas as pd from numpy import ndarray -from pandas import DataFrame from sklearn.pipeline import FeatureUnion from ....representation import FData @@ -109,7 +109,7 @@ def __init__( verbose=verbose, ) - def _hstack(self, Xs: ndarray) -> Union[DataFrame, ndarray]: + def _hstack(self, Xs: ndarray) -> Union[pd.DataFrame, ndarray]: if self.array_output: for i in Xs: @@ -121,4 +121,10 @@ def _hstack(self, Xs: ndarray) -> Union[DataFrame, ndarray]: ) return super()._hstack(Xs) - return DataFrame({'Transformed data': Xs}) + return pd.concat( + [ + pd.DataFrame(data) + for data in Xs + ], + axis=1, + ) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py index 796ec0f68..bbe78e38b 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py @@ -5,7 +5,7 @@ from typing import Any, Mapping, TypeVar, Union import numpy as np -from pandas import DataFrame +import pandas as pd from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from ...._utils import TransformerMixin, _fit_feature_transformer @@ -15,7 +15,7 @@ from ....representation.grid import FDataGrid Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) -Output = TypeVar("Output", bound=Union[DataFrame, NDArrayFloat]) +Output = TypeVar("Output", bound=Union[pd.DataFrame, NDArrayFloat]) Target = TypeVar("Target", bound=NDArrayInt) TransformerOutput = Union[FData, NDArrayFloat] @@ -106,28 +106,12 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): >>> x_transformed2 = t2.fit_transform(X, y) ``x_transformed2`` will be a DataFrame with the transformed data. - Each row on the frame contains a FDataGrid describing a transformed - curve. - We need to convert the DataFrame into a FDataGrid with all the - samples, so we can train a classifier. We also need to duplicate - the outputs as we have the double amount of data curves: - - >>> for i, curve_grid in enumerate(x_transformed2.iloc[:,0].values): - ... if i == 0: - ... X_transformed_grid = curve_grid - ... else: - ... X_transformed_grid = X_transformed_grid.concatenate( - ... curve_grid, - ... ) - >>> y = np.concatenate((y,y)) - - - ``X_transformed_grid`` contains a FDataGrid with all the transformed - curves. Now we are able to use it to fit a KNN classifier. + Each column of the frame contains a FDataGrid describing a transformed + curve. Now we are able to use it to fit a KNN classifier. Again we split the data into train and test. >>> X_train2, X_test2, y_train2, y_test2 = train_test_split( - ... X_transformed_grid, + ... x_transformed2.iloc[:, 0].values, ... y, ... test_size=0.25, ... stratify=y, @@ -141,12 +125,11 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): >>> neigh2 = KNeighborsClassifier() >>> neigh2 = neigh2.fit(X_train2, y_train2) >>> neigh2.predict(X_test2) - array([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, - 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, - 1, 0, 0], dtype=int8) + array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, + 1, 1, 1], dtype=int8) >>> round(neigh2.score(X_test2, y_test2), 3) - 0.851 + 0.917 """ @@ -274,8 +257,12 @@ def transform(self, X: Input) -> Output: ) return np.hstack(transformed_data) - return DataFrame( - {'Transformed data': transformed_data}, + return pd.concat( + [ + pd.DataFrame(data) # noqa: WPS441 + for data in transformed_data + ], + axis=1, ) def fit_transform( # type: ignore[override] diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index d8df986bf..75fb07407 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -2,7 +2,7 @@ import unittest -from pandas import DataFrame +import pandas as pd from pandas.testing import assert_frame_equal from skfda.datasets import fetch_growth @@ -47,7 +47,13 @@ def test_correct_transformation_concat(self) -> None: kernel_estimator=NadarayaWatsonHatMatrix(), ).fit_transform(self.X) # type: ignore - true_frame = DataFrame({"Transformed data": [t1, t2]}) + true_frame = pd.concat( + [ + pd.DataFrame(t1), + pd.DataFrame(t2), + ], + axis=1, + ) assert_frame_equal(true_frame, created_frame) From 7da56029176c45d24e0cdaa37c8fc8862d797a90 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 8 Mar 2022 22:33:37 +0100 Subject: [PATCH 077/400] MInor fix Data frame --- .../dim_reduction/feature_extraction/_fda_feature_union.py | 2 +- .../dim_reduction/feature_extraction/_per_class_transformer.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py index 03ed03b8f..4968d2592 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py @@ -123,7 +123,7 @@ def _hstack(self, Xs: ndarray) -> Union[pd.DataFrame, ndarray]: return pd.concat( [ - pd.DataFrame(data) + pd.DataFrame({0: data}) for data in Xs ], axis=1, diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py index bbe78e38b..bddb17fca 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py @@ -259,7 +259,7 @@ def transform(self, X: Input) -> Output: return pd.concat( [ - pd.DataFrame(data) # noqa: WPS441 + pd.DataFrame({'0': data}) # noqa: WPS441 for data in transformed_data ], axis=1, From f607b547ca62a60b133657122124a052fe4536b9 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 8 Mar 2022 22:48:17 +0100 Subject: [PATCH 078/400] Python 3.7 fix --- tests/test_fda_feature_union.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index 75fb07407..933df6d07 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -49,8 +49,8 @@ def test_correct_transformation_concat(self) -> None: true_frame = pd.concat( [ - pd.DataFrame(t1), - pd.DataFrame(t2), + pd.DataFrame({0: t1}), + pd.DataFrame({0: t2}), ], axis=1, ) From 728213481ed0ae88f1eebefaca7b3c43ad326c84 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 8 Mar 2022 23:27:20 +0100 Subject: [PATCH 079/400] Gaussian fixes and example --- examples/plot_classification_methods.py | 131 ++++++++++++++++ .../ml/classification/_gaussian_classifier.py | 146 ++++++++---------- 2 files changed, 192 insertions(+), 85 deletions(-) create mode 100644 examples/plot_classification_methods.py diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py new file mode 100644 index 000000000..440c8100c --- /dev/null +++ b/examples/plot_classification_methods.py @@ -0,0 +1,131 @@ +""" +Classification methods. + +================================== + +Shows a comparison between different classification methods. +The Berkeley Growth Study dataset is used as input data. +Classification methods KNN, Maximum Depth, Nearest Centroid and +Gaussian classifier are compared. +""" + +# Author:Álvaro Castillo García +# License: MIT + +from GPy.kern import Linear +from sklearn.model_selection import train_test_split + +from skfda.datasets import fetch_growth +from skfda.exploratory.depth import ModifiedBandDepth +from skfda.ml.classification import ( + GaussianClassifier, + KNeighborsClassifier, + MaximumDepthClassifier, + NearestCentroid, +) + +############################################################################## +# The Berkeley Growth Study data contains the heights of 39 boys and 54 girls +# from age 1 to 18 and the ages at which they were collected. Males are +# assigned the numeric value 0 while females are assigned a 1. In our +# comparison of the different methods, we will try to learn the sex of a person +# by using its growth curve. +X, y = fetch_growth(return_X_y=True, as_frame=True) +X = X.iloc[:, 0].values +categories = y.values.categories +y = y.values.codes + + +############################################################################## +# As in many ML algorithms, we split the dataset into train and test. In this +# graph, we can see the training dataset. These growth curves will be used to +# train the model. Hence, the predictions will be data-driven. +X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=0.3, + stratify=y, + random_state=0, +) + +# Plot samples grouped by sex +X_train.plot(group=y_train, group_names=categories).show() + +############################################################################## +# Below are the growth graphs of those individuals that we would like to +# classify. Some of them will be male and some female. +X_test.plot().show() + + +############################################################################## +# As said above, we are trying to compare four different methods: +# :class:`~skfda.ml.classification.MaximumDepthClassifier`, +# :class:`~skfda.ml.classification.KNeighborsClassifier`, +# :class:`~skfda.ml.classification.NearestCentroid` and +# :class:`~skfda.ml.classification.GaussianClassifier` + + +############################################################################## +# The first method we are going to use is the Maximum Depth Classifier. +# As depth method we will consider the Modified Band Depth. + +depth = MaximumDepthClassifier(depth_method=ModifiedBandDepth()) +depth.fit(X_train, y_train) +depth_pred = depth.predict(X_test) +print(depth_pred) +print('The score of Maximum Depth Classifier is {0:2.2%}'.format( + depth.score(X_test, y_test), +)) + +# Plot the prediction +X_test.plot(group=depth_pred, group_names=categories).show() + + +############################################################################## +# The second method to consider is the K-Nearest Neighbours Classifier. + + +knn = KNeighborsClassifier() +knn.fit(X_train, y_train) +knn_pred = knn.predict(X_test) +print(knn_pred) +print('The score of KNN is {0:2.2%}'.format(knn.score(X_test, y_test))) + +# Plot the prediction +X_test.plot(group=knn_pred, group_names=categories).show() + + +############################################################################## +# The third method we are going to use is the Nearest Centroid Classifier + +centroid = NearestCentroid() +centroid.fit(X_train, y_train) +centroid_pred = centroid.predict(X_test) +print(centroid_pred) +print('The score of Nearest Centroid Classifier is {0:2.2%}'.format( + centroid.score(X_test, y_test), +)) + +# Plot the prediction +X_test.plot(group=centroid_pred, group_names=categories).show() + + +############################################################################## +# The fourth method considered is a Gaussian Process based Classifier. +# As the data set tends to be linear we have selected a linear kernel with +# initial parameters: variance=6 and mean=1 +# As regularizer a small value 0.05 has been chosen. + +gaussian = GaussianClassifier( + kernel=Linear(1, variances=6), + regularizer=0.05, +) +gaussian.fit(X_train, y_train) +gaussian_pred = gaussian.predict(X_test) +print(gaussian_pred) +print('The score of Gaussian Process Classifier is {0:2.2%}'.format( + gaussian.score(X_test, y_test), +)) + +# Plot the prediction +X_test.plot(group=gaussian_pred, group_names=categories).show() diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index 9f89e0a50..c5d239ef3 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -65,7 +65,7 @@ class GaussianClassifier( the kernel with mean 1 and variance 6 as an example. >>> from GPy.kern import Linear - >>> linear = Linear(1, variances=6) + >>> linear = Linear(input_dim=1, variances=6) We will fit the Gaussian Process classifier with training data. We use as regularizer parameter a low value as 0.05. @@ -89,8 +89,8 @@ class GaussianClassifier( """ def __init__(self, kernel: Kern, regularizer: float) -> None: - self._kernel_ = kernel - self._regularizer_ = regularizer + self.kernel = kernel + self.regularizer = regularizer def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: """Fit the model using X as training data and y as target values. @@ -102,18 +102,27 @@ def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: Returns: self """ - self._classes, self._y_ind = _classifier_get_classes(y) # noqa:WPS414 + classes, y_ind = _classifier_get_classes(y) + self.classes = classes + self.y_ind = y_ind - self._cov_kernels_, self._means = self._fit_kernels_and_means( - X, - ) + cov_kernels, means = self._fit_gaussian_process(X) + self.cov_kernels = cov_kernels + self.means = means + + self.priors_ = self._calculate_priors(y) + self._log_priors = np.log(self.priors_) - self._priors = self._calculate_priors(y) - self._log_priors = np.log(self._priors) # Calculates prior logartithms - self._covariances = self._calculate_covariances(X) + self._regularized_covariances = ( + self._covariances + + self.regularizer * np.eye(len(X.grid_points[0])) + ) # Calculates logarithmic covariance -> -1/2 * log|sum| - self._log_cov = self._calculate_log_covariances() + self._log_determinant_covariances = np.asarray([ + np.trace(logm(regularized_covariance)) + for regularized_covariance in self._regularized_covariances + ]) return self @@ -147,57 +156,10 @@ def _calculate_priors(self, y: np.ndarray) -> np.ndarray: Returns: Numpy array with the respective prior of each class. """ - return np.asarray([ - np.count_nonzero(y == cur_class) / y.size - for cur_class in range(0, self._classes.size) - ]) + _, counts = np.unique(y, return_counts=True) + return counts / len(y) - def _calculate_covariances( - self, - X: FDataGrid, - ) -> np.ndarray: - """ - Calculate the covariance matrices for each class. - - It bases the calculation on the kernels that where already - fitted with data of the corresponding classes. - - Args: - X: FDataGrid with the training data. - - Returns: - Numpy array with the covariance matrices. - """ - covariance = [] - for i in range(0, self._classes.size): - class_data = X[self._y_ind == i].data_matrix - # Data needs to be two-dimensional - class_data_r = class_data.reshape( - class_data.shape[0], - class_data.shape[1], - ) - # Caculate covariance matrix - covariance = covariance + [self._cov_kernels_[i].K(class_data_r.T)] - return np.asarray(covariance) - - def _calculate_log_covariances(self) -> np.ndarray: - """ - Calculate the logarithm of the covariance matrices for each class. - - A regularizer parameter has been used to avoid singular matrices. - - Returns: - Numpy array with the logarithmic computation of the - covariance matrices. - """ - return np.asarray([ - -0.5 * np.trace( - logm(cov + self._regularizer_ * np.eye(cov.shape[0])), - ) - for cov in self._covariances - ]) - - def _fit_kernels_and_means( + def _fit_gaussian_process( self, X: FDataGrid, ) -> np.ndarray: @@ -217,44 +179,58 @@ def _fit_kernels_and_means( grid = X.grid_points[0][:, np.newaxis] kernels = [] means = [] - for cur_class in range(0, self._classes.size): - class_n = X[self._y_ind == cur_class] - class_n_centered = class_n - class_n.mean() - data_matrix = class_n_centered.data_matrix[:, :, 0] + covariance = [] + for class_index, _ in enumerate(self.classes): + X_class = X[self.y_ind == class_index] + X_class_mean = X_class.mean() + X_class_centered = X_class - X_class_mean + # OJO! Datos sin centrar y centrados + data_matrix = X_class_centered.data_matrix[:, :, 0] + data_matrix_2 = X_class.data_matrix[:, :, 0] + + regressor = GPRegression(grid, data_matrix.T, kernel=self.kernel) + regressor.optimize() + + data_matrix_2d = data_matrix_2.reshape( + data_matrix_2.shape[0], + data_matrix_2.shape[1], + ) - reg_n = GPRegression(grid, data_matrix.T, kernel=self._kernel_) - reg_n.optimize() + kernels = kernels + [regressor.kern] + means = means + [X_class_mean.data_matrix[0]] + covariance = covariance + [ + regressor.kern.K(data_matrix_2d.T), + ] + + self._covariances = np.asarray(covariance) - kernels = kernels + [reg_n.kern] - means = means + [class_n.mean().data_matrix[0]] return np.asarray(kernels), np.asarray(means) - def _calculate_log_likelihood(self, curve: np.ndarray) -> np.ndarray: + def _calculate_log_likelihood(self, X: np.ndarray) -> np.ndarray: """ Calculate the log likelihood quadratic discriminant analysis. Args: - curve: sample where we want to calculate the discriminant. + X: sample where we want to calculate the discriminant. Returns: A ndarray with the log likelihoods corresponding to the output classes. """ # Calculates difference wrt. the mean (x - un) - data_mean = curve - self._means + X_centered = X - self.means # Calculates mahalanobis distance (-1/2*(x - un).T*inv(sum)*(x - un)) - mahalanobis = [] - for j in range(0, self._classes.size): - mh = -0.5 * data_mean[j].T @ np.linalg.solve( - self._covariances[j] + self._regularizer_ * np.eye( - self._covariances[j].shape[0], - ), - data_mean[j], - ) - mahalanobis = mahalanobis + [mh[0][0]] + mahalanobis_distance = np.ravel( + np.transpose(X_centered, axes=(0, 2, 1)) + @ np.linalg.solve( + self._regularized_covariances, + X_centered, + ), + ) - # Calculates the log_likelihood - return self._log_cov + np.asarray( - mahalanobis, - ) + self._log_priors + return ( + -0.5 * self._log_determinant_covariances + - 0.5 * mahalanobis_distance + + self._log_priors + ) From 45bce765650a62940369a16f5999547d2bcb98ee Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 9 Mar 2022 16:05:21 +0100 Subject: [PATCH 080/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 82d53f444..211baac42 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -250,11 +250,7 @@ def __call__( # noqa: D102 # Calculate new coefficients taking into account cross-products # if the basis is orthonormal, C would not change - C = np.einsum( - 'ijk, kl -> ijl', - C, - inner_product_matrix, - ) + C = C @ inner_product_matrix # Adding a column of ones in the first position of all matrices dims = (C.shape[0], C.shape[1], 1) From 01a639c59afdd22d2512f7d2fd1489a5e52075cc Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 10 Mar 2022 22:49:12 +0100 Subject: [PATCH 081/400] Fix HatMatrix documentation --- skfda/misc/hat_matrix.py | 66 ++++++++++++++++++++-------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index ae0e9e693..83a793da6 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -111,17 +111,17 @@ class NadarayaWatsonHatMatrix(HatMatrix): regression algorithms, as explained below .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{d(e_j-e_i')}{h}\right)}{\sum_{k=1}^{ - n}K\left(\frac{d(e_k-e_i')}{h}\right)} + \hat{H}_{i,j} = \frac{K\left(\frac{d(x_j-x_i')}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{d(x_k-x_i')}{h}\right)} - For smoothing, :math:`e_i` are the points of discretisation - and :math:`e'_i` are the points for which it is desired to estimate the - smoothed value. The distance :math:`d` is the absolute value + For smoothing, :math:`\{x_1, ..., x_n\}` are the points with known value + and :math:`\{x_1', ..., x_m'\}` are the points for which it is desired to + estimate the smoothed value. The distance :math:`d` is the absolute value function :footcite:`wasserman_2006_nonparametric_nw`. - For regression, :math:`e_i` is the functional data and :math:`e_i'` - are the functions for which it is desired to estimate the scalar value. - Here, :math:`d` is some functional distance + For regression, :math:`\{x_1, ..., x_n\}` is the functional data and + :math:`\{x_1', ..., x_m'\}` are the functions for which it is desired to + estimate the scalar value. Here, :math:`d` is some functional distance :footcite:`ferraty+vieu_2006_nonparametric_nw`. In both cases :math:`K(\cdot)` is a kernel function and :math:`h` is the @@ -158,12 +158,12 @@ class LocalLinearRegressionHatMatrix(HatMatrix): regression algorithms, as explained below. For **kernel smoothing** algorithm to estimate the smoothed value for - :math:`t_j` the following error must be minimised + :math:`t_i'` the following error must be minimised .. math:: - AWSE(a, b) = \sum_{i=1}^n \left[ \left(y_i - - \left(a + b (t_i - t'_j) \right) \right)^2 - K \left( \frac {|t_i - t'_j|}{h} \right) \right ] + AWSE(a, b) = \sum_{j=1}^n \left[ \left(y_j - + \left(a + b (t_j - t'_i) \right) \right)^2 + K \left( \frac {|t_j - t'_i|}{h} \right) \right ] which gives the following expression for each cell @@ -171,26 +171,26 @@ class LocalLinearRegressionHatMatrix(HatMatrix): \hat{H}_{i,j} = \frac{b_j(t_i')}{\sum_{k=1}^{n}b_k(t_i')} .. math:: - b_j(e') = K\left(\frac{t_j - t'}{h}\right) S_{n,2}(t') - + b_j(t') = K\left(\frac{t_j - t'}{h}\right) S_{n,2}(t') - (t_j - t')S_{n,1}(t') .. math:: S_{n,k}(t') = \sum_{j=1}^{n}K\left(\frac{t_j-t'}{h}\right)(t_j-t')^k - where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and - :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired - to estimate the smoothed value + where :math:`t = \{t_1, t_2, ..., t_n\}` are points with known value and + :math:`t' = \{t_1', t_2', ..., t_m'\}` are the points for which it is + desired to estimate the smoothed value :footcite:`wasserman_2006_nonparametric_llr`. For **kernel regression** algorithm: - Given functional data, :math:`(X_1, X_2, ..., X_n)` where each function + Given functional data, :math:`\{X_1, X_2, ..., X_n\}` where each function is expressed in a orthonormal basis with :math:`J` elements and scalar - response :math:`Y = (y_1, y_2, ..., y_n)`. + response :math:`Y = \{y_1, y_2, ..., y_n\}`. It is desired to estimate the values - :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` - for the data :math:`(X'_1, X'_2, ..., X'_m)` (expressed in the same basis). + :math:`\hat{Y} = \{\hat{y}_1, \hat{y}_2, ..., \hat{y}_m\}` + for the data :math:`\{X'_1, X'_2, ..., X'_m\}` (expressed in the same basis). For each :math:`X'_k` the estimation :math:`\hat{y}_k` is obtained by taking the value :math:`a_k` from the vector @@ -198,7 +198,7 @@ class LocalLinearRegressionHatMatrix(HatMatrix): .. math:: AWSE(a_k, b_{1k}, ..., b_{Jk}) = \sum_{i=1}^n \left(y_i - - \left(a + \sum_{j=1}^J b_{jk} c_{ijk} \right) \right)^2 + \left(a + \sum_{j=1}^J b_{jk} c_{ij}^k \right) \right)^2 K \left( \frac {d(X_i - X'_k)}{h} \right) Where :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis @@ -335,24 +335,24 @@ class KNeighborsHatMatrix(HatMatrix): regression algorithms, as explained below. .. math:: - \hat{H}_{i,j} = \frac{K\left(\frac{d(e_j-e_i')}{h}\right)}{\sum_{k=1}^{ - n}K\left(\frac{d(e_k-e_i')}{h_{ik}}\right)} + \hat{H}_{i,j} = \frac{K\left(\frac{d(x_j-x_i')}{h_i}\right)} + {\sum_{k=1}^{n}K\left(\frac{d(x_k-x_i')}{h_{i}}\right)} - For smoothing, :math:`e_i` are the points of discretisation - and :math:`e'_i` are the points for which it is desired to estimate the - smoothed value. The distance :math:`d` is the absolute value. + For smoothing, :math:`\{x_1, ..., x_n\}` are the points with known value + and :math:`\{x_1', ..., x_m'\}` are the points for which it is desired to + estimate the smoothed value. The distance :math:`d` is the absolute value. - For regression, :math:`e_i` are the functional data and :math:`e_i'` - are the functions for which it is desired to estimate the scalar value. - Here, :math:`d` is some functional distance. + For regression, :math:`\{x_1, ..., x_n\}` are the functional data and + :math:`\{x_1', ..., x_m'\}` are the functions for which it is desired to + estimate the scalar value. Here, :math:`d` is some functional distance. In both cases, :math:`K(\cdot)` is a kernel function and - :math:`h_{ik}` is calculated as the distance from :math:`e_i'` to it's - :math:`k`-th nearest neighbour in :math:`\{e_1, ..., e_n\}` + :math:`h_{i}` is calculated as the distance from :math:`x_i'` to its + :math:`n\_neighbors`-th nearest neighbor in :math:`\{x_1, ..., x_n\}` :footcite:`ferraty+vieu_2006_nonparametric_knn`. - Used with the uniform kernel, it takes the average of the closest k - points to a given point. + Used with the uniform kernel, it takes the average of the closest + :math:`n\_neighbors` points to a given point. Args: n_neighbors: Number of nearest neighbours. By From 2aebfbd19384fa06e82b6d84106efb99c73c31d0 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 10 Mar 2022 23:05:33 +0100 Subject: [PATCH 082/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 211baac42..67f51810e 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -254,7 +254,7 @@ def __call__( # noqa: D102 # Adding a column of ones in the first position of all matrices dims = (C.shape[0], C.shape[1], 1) - C = np.c_[np.ones(dims), C] + C = np.concatenate((np.ones(dims), C), axis=-1) return self._solve_least_squares( delta_x=delta_x, From f9c647ca1cd18831fc5d21ffe3cae52b2d77ea8d Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 11 Mar 2022 00:37:20 +0100 Subject: [PATCH 083/400] Classification fixes --- examples/plot_classification_methods.py | 8 +-- .../ml/classification/_gaussian_classifier.py | 61 +++++++++---------- 2 files changed, 32 insertions(+), 37 deletions(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index 440c8100c..1f0318fdc 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -12,7 +12,7 @@ # Author:Álvaro Castillo García # License: MIT -from GPy.kern import Linear +from GPy.kern import RBF from sklearn.model_selection import train_test_split from skfda.datasets import fetch_growth @@ -112,12 +112,12 @@ ############################################################################## # The fourth method considered is a Gaussian Process based Classifier. -# As the data set tends to be linear we have selected a linear kernel with -# initial parameters: variance=6 and mean=1 +# We have selected a gaussian kernel with initial parameters: variance=6 and +# mean=1 # As regularizer a small value 0.05 has been chosen. gaussian = GaussianClassifier( - kernel=Linear(1, variances=6), + kernel=RBF(input_dim=1, variance=6, lengthscale=1), regularizer=0.05, ) gaussian.fit(X_train, y_train) diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_gaussian_classifier.py index c5d239ef3..4219649b5 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_gaussian_classifier.py @@ -60,31 +60,30 @@ class GaussianClassifier( ... ) Then we need to choose and import a kernel so it can be fitted with - the data in the training phase. As we know the Growth dataset tends - to be approximately linear, we will use a linear kernel. We create - the kernel with mean 1 and variance 6 as an example. + the data in the training phase. We will use a gaussian kernel with + mean 1 and variance 6 as an example. - >>> from GPy.kern import Linear - >>> linear = Linear(input_dim=1, variances=6) + >>> from GPy.kern import RBF + >>> rbf = RBF(input_dim=1, variance=6, lengthscale=1) We will fit the Gaussian Process classifier with training data. We use as regularizer parameter a low value as 0.05. - >>> gaussian = GaussianClassifier(linear, 0.05) + >>> gaussian = GaussianClassifier(rbf, 0.05) >>> gaussian = gaussian.fit(X_train, y_train) We can predict the class of new samples >>> list(gaussian.predict(X_test)) - [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, - 1, 0, 1, 0, 0, 0, 1, 1] + [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, + 1, 0, 1, 0, 1, 0, 1, 1] Finally, we calculate the mean accuracy for the test data >>> round(gaussian.score(X_test, y_test), 2) - 0.93 + 0.96 """ @@ -107,8 +106,8 @@ def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: self.y_ind = y_ind cov_kernels, means = self._fit_gaussian_process(X) - self.cov_kernels = cov_kernels - self.means = means + self.cov_kernels_ = cov_kernels + self.means_ = means self.priors_ = self._calculate_priors(y) self._log_priors = np.log(self.priors_) @@ -139,10 +138,7 @@ def predict(self, X: FDataGrid) -> np.ndarray: check_is_fitted(self) return np.argmax( - [ - self._calculate_log_likelihood(curve) - for curve in X.data_matrix - ], + self._calculate_log_likelihood(X.data_matrix), axis=1, ) @@ -184,22 +180,18 @@ def _fit_gaussian_process( X_class = X[self.y_ind == class_index] X_class_mean = X_class.mean() X_class_centered = X_class - X_class_mean - # OJO! Datos sin centrar y centrados + data_matrix = X_class_centered.data_matrix[:, :, 0] - data_matrix_2 = X_class.data_matrix[:, :, 0] + + grid_points = X_class.grid_points[0][:, np.newaxis] regressor = GPRegression(grid, data_matrix.T, kernel=self.kernel) regressor.optimize() - data_matrix_2d = data_matrix_2.reshape( - data_matrix_2.shape[0], - data_matrix_2.shape[1], - ) - - kernels = kernels + [regressor.kern] - means = means + [X_class_mean.data_matrix[0]] - covariance = covariance + [ - regressor.kern.K(data_matrix_2d.T), + kernels += [regressor.kern] + means += [X_class_mean.data_matrix[0]] + covariance += [ + regressor.kern.K(grid_points), ] self._covariances = np.asarray(covariance) @@ -218,19 +210,22 @@ def _calculate_log_likelihood(self, X: np.ndarray) -> np.ndarray: output classes. """ # Calculates difference wrt. the mean (x - un) - X_centered = X - self.means + X_centered = X[:, np.newaxis, :, :] - self.means_[np.newaxis, :, :, :] # Calculates mahalanobis distance (-1/2*(x - un).T*inv(sum)*(x - un)) - mahalanobis_distance = np.ravel( - np.transpose(X_centered, axes=(0, 2, 1)) + mahalanobis_distances = np.reshape( + np.transpose(X_centered, axes=(0, 1, 3, 2)) @ np.linalg.solve( self._regularized_covariances, X_centered, ), + (-1, self.classes.size), ) - return ( - -0.5 * self._log_determinant_covariances - - 0.5 * mahalanobis_distance - + self._log_priors + return np.asarray( + ( + -0.5 * self._log_determinant_covariances + - 0.5 * mahalanobis_distances + + self._log_priors + ), ) From 474239ba95c258fd865713fdb5abe00c217dd30f Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 11 Mar 2022 14:24:16 +0100 Subject: [PATCH 084/400] Coding fixes --- .../stats/_functional_transformers.py | 78 +++++-------------- 1 file changed, 20 insertions(+), 58 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 9b9c50da7..f9e50f8af 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -7,6 +7,7 @@ import numpy as np from ...representation import FDataBasis, FDataGrid +from ...representation._typing import NDArrayFloat, NDArrayInt def local_averages( @@ -79,8 +80,8 @@ def local_averages( def number_up_crossings( data: FDataGrid, - levels: np.ndarray, -) -> np.ndarray: + levels: NDArrayFloat, +) -> NDArrayInt: r""" Calculate the number of up crossings to a level of a FDataGrid. @@ -101,16 +102,16 @@ def number_up_crossings( levels: sequence of numbers including the levels we want to consider for the crossings. Returns: - ndarray of shape (n_levels, n_samples)\ + ndarray of shape (n_samples, len(levels))\ with the values of the counters. Example: We import the Medflies dataset and for simplicity we use - the first 50 samples. + the first 5 samples. >>> from skfda.datasets import fetch_medflies >>> dataset = fetch_medflies() - >>> X = dataset['data'][:50] + >>> X = dataset['data'][:5] Then we decide the level we want to consider (in our case 40) and call the function with the dataset. The output will be the number of @@ -118,58 +119,13 @@ def number_up_crossings( >>> from skfda.exploratory.stats import number_up_crossings >>> import numpy as np >>> number_up_crossings(X, np.asarray([40])) - array([[[6], - [3], - [7], - [7], - [3], - [4], - [5], - [7], - [4], - [6], - [4], - [4], - [5], - [6], - [0], - [5], - [1], - [6], - [0], - [7], - [0], - [6], - [2], - [5], - [6], - [5], - [8], - [4], - [3], - [7], - [1], - [3], - [0], - [5], - [2], - [7], - [2], - [5], - [5], - [5], - [4], - [4], - [1], - [2], - [3], - [5], - [3], - [3], - [5], - [2]]]) + array([[6], + [3], + [7], + [7], + [3]]) """ - curves = data.data_matrix + curves = data.data_matrix[:, :, 0] distances = np.asarray([ level - curves @@ -178,9 +134,15 @@ def number_up_crossings( points_greater = distances >= 0 points_smaller = distances <= 0 - points_smaller_rotated = np.roll(points_smaller, -1, axis=2) + points_smaller_rotated = np.concatenate( + [ + points_smaller[:, :, 1:], + points_smaller[:, :, :1], + ], + axis=2, + ) return np.sum( points_greater & points_smaller_rotated, axis=2, - ) + ).T From 11327461fa6e42a14277a769e0a7a777d4a1074d Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 11 Mar 2022 16:45:04 +0100 Subject: [PATCH 085/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index a16f1be06..4fc302e18 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -337,11 +337,11 @@ class KNeighborsHatMatrix(HatMatrix): In both cases, :math:`K(\cdot)` is a kernel function and :math:`h_{i}` is calculated as the distance from :math:`x_i'` to its - :math:`n\_neighbors`-th nearest neighbor in :math:`\{x_1, ..., x_n\}` + ``n\_neighbors``-th nearest neighbor in :math:`\{x_1, ..., x_n\}` :footcite:`ferraty+vieu_2006_nonparametric_knn`. Used with the uniform kernel, it takes the average of the closest - :math:`n\_neighbors` points to a given point. + ``n\_neighbors`` points to a given point. Args: n_neighbors: Number of nearest neighbours. By From 63a1a859bea63cc995f9dc4574f4611d3e80e8f7 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Fri, 11 Mar 2022 18:20:19 +0100 Subject: [PATCH 086/400] overload + changes in r2_score and ev_score 1. Added overload from typing 2. Changed case r/0 in r2_score and explained_variance_score for FDataBasis 3. Added test for r/0 --- docs/modules/misc/score_functions.rst | 2 +- skfda/exploratory/stats/_stats.py | 10 +- skfda/misc/score_functions.py | 498 ++++++++++++++++++++------ tests/test_regression_scores.py | 49 ++- 4 files changed, 442 insertions(+), 117 deletions(-) diff --git a/docs/modules/misc/score_functions.rst b/docs/modules/misc/score_functions.rst index 7080b36b2..c6ba92ab2 100644 --- a/docs/modules/misc/score_functions.rst +++ b/docs/modules/misc/score_functions.rst @@ -1,7 +1,7 @@ Scoring methods for regression with functional response. ======================================================== -The functions in this module are a generalisation for functional data of +The functions in this module are a generalization for functional data of the regression metrics of the sklearn library (https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics). Only scores that support multioutput are included. diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index edc0382d3..12a81289d 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -16,22 +16,22 @@ def mean( X: F, - weight: Optional[np.ndarray] = None, + weights: Optional[np.ndarray] = None, ) -> F: """Compute the mean of all the samples in a FData object. Args: X: Object containing all the samples whose mean is wanted. - weight: Sample weight. By default, uniform weight are + weights: Sample weight. By default, uniform weight are used. Returns: A :term:`functional data object` with just one sample representing the mean of all the samples in the original object. """ - if weight is None: + if weights is None: return X.mean() else: - weight = (X.n_samples / sum(weight)) * weight - return (X * weight).mean() + weight = (1 / sum(weights)) * weights + return sum(X * weight) def var(X: FDataGrid) -> FDataGrid: diff --git a/skfda/misc/score_functions.py b/skfda/misc/score_functions.py index 016f44337..0718a38ce 100644 --- a/skfda/misc/score_functions.py +++ b/skfda/misc/score_functions.py @@ -1,7 +1,7 @@ """Score functions for FData.""" import warnings from functools import singledispatch -from typing import Optional, Union +from typing import Optional, Union, overload import numpy as np import scipy.integrate @@ -26,7 +26,8 @@ def __call__( multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', squared: Optional[bool] = None, - ) -> Union[NDArrayFloat, FDataGrid, float]: ... + ) -> Union[NDArrayFloat, FDataGrid, float]: + ... def _domain_measure(fd: FData) -> float: @@ -38,17 +39,39 @@ def _domain_measure(fd: FData) -> float: def _var( x: FDataGrid, - weight: Optional[NDArrayFloat] = None, + weights: Optional[NDArrayFloat] = None, ) -> FDataGrid: - if weight is None: + if weights is None: return var(x) return mean( - np.power(x - mean(x, weight=weight), 2), - weight=weight, + np.power(x - mean(x, weights=weights), 2), + weights=weights, ) +@overload +def explained_variance_score( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def explained_variance_score( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> NDArrayFloat: + ... + + @singledispatch def explained_variance_score( y_true: Union[FData, NDArrayFloat], @@ -112,17 +135,17 @@ def explained_variance_score( Returns: Explained variance score. - float: - if multioutput = 'uniform_average' or - :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataBasis` objects. - FDataGrid: - if both :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataGrid` - objects and multioutput = 'raw_values'. - ndarray: - if both :math:`y\_pred` and :math:`y\_true` are ndarray and - multioutput = 'raw_values'. + If multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects, float is returned. + + If both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values', + :class:`~skfda.representation.FDataGrid` is returned. + + If both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values', ndarray. """ return sklearn.metrics.explained_variance_score( @@ -133,6 +156,28 @@ def explained_variance_score( ) +@overload +def _explained_variance_score_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def _explained_variance_score_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> FDataGrid: + ... + + @explained_variance_score.register def _explained_variance_score_fdatagrid( y_true: FDataGrid, @@ -142,15 +187,15 @@ def _explained_variance_score_fdatagrid( multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', ) -> Union[float, FDataGrid]: - num = _var(y_true - y_pred, weight=sample_weight) - den = _var(y_true, weight=sample_weight) + num = _var(y_true - y_pred, weights=sample_weight) + den = _var(y_true, weights=sample_weight) # Divisions by zero allowed with np.errstate(divide='ignore', invalid='ignore'): score = 1 - num / den - # 0 / 0 divisions should be 1 in this context and the score, 0 - score.data_matrix[np.isnan(score.data_matrix)] = 0 + # 0 / 0 divisions should be 0 in this context, and the score, 1 + score.data_matrix[np.isnan(score.data_matrix)] = 1 if multioutput == 'raw_values': return score @@ -199,24 +244,53 @@ def _ev_func(x): # noqa: WPS430 weights=sample_weight, axis=0, ) - # Divisions by zero allowed - with np.errstate(divide='ignore', invalid='ignore'): - score = 1 - num / den - # 0 / 0 divisions should be 1 in this context and the score, 0 - score[np.isnan(score)] = 0 + # 0/0 case, the score is 1. + if num == 0 and den == 0: + return 1 + + # r/0 case, r!= 0. Return -inf outside this function + if num != 0 and den == 0: + raise ValueError + + score = 1 - num / den return score[0][0] - integral = scipy.integrate.quad_vec( - _ev_func, - start, - end, - ) + try: + integral = scipy.integrate.quad_vec( + _ev_func, + start, + end, + ) + except ValueError: + return float('-inf') return integral[0] / (end - start) +@overload +def mean_absolute_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def mean_absolute_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> NDArrayFloat: + ... + + @singledispatch def mean_absolute_error( y_true: Union[FData, NDArrayFloat], @@ -267,17 +341,17 @@ def mean_absolute_error( Returns: Mean absolute error. - float: - if multioutput = 'uniform_average' or - :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataBasis` objects. - FDataGrid: - if both :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataGrid` - objects and multioutput = 'raw_values'. - ndarray: - if both :math:`y\_pred` and :math:`y\_true` are ndarray and - multioutput = 'raw_values'. + If multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects, float is returned. + + If both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values', + :class:`~skfda.representation.FDataGrid` is returned. + + If both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values', ndarray. """ return sklearn.metrics.mean_absolute_error( @@ -288,6 +362,28 @@ def mean_absolute_error( ) +@overload +def _mean_absolute_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def _mean_absolute_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> FDataGrid: + ... + + @mean_absolute_error.register def _mean_absolute_error_fdatagrid( y_true: FDataGrid, @@ -297,7 +393,7 @@ def _mean_absolute_error_fdatagrid( multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', ) -> Union[float, FDataGrid]: - error = mean(np.abs(y_true - y_pred), weight=sample_weight) + error = mean(np.abs(y_true - y_pred), weights=sample_weight) if multioutput == 'raw_values': return error @@ -334,6 +430,28 @@ def _mae_func(x): # noqa: WPS430 return integral[0] / (end - start) +@overload +def mean_absolute_percentage_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def mean_absolute_percentage_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> NDArrayFloat: + ... + + @singledispatch def mean_absolute_percentage_error( y_true: Union[FData, NDArrayFloat], @@ -387,17 +505,17 @@ def mean_absolute_percentage_error( Returns: Mean absolute percentage error. - float: - if multioutput = 'uniform_average' or - :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataBasis` objects. - FDataGrid: - if both :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataGrid` - objects and multioutput = 'raw_values'. - ndarray: - if both :math:`y\_pred` and :math:`y\_true` are ndarray and - multioutput = 'raw_values'. + If multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects, float is returned. + + If both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values', + :class:`~skfda.representation.FDataGrid` is returned. + + If both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values', ndarray. """ return sklearn.metrics.mean_absolute_percentage_error( @@ -408,6 +526,28 @@ def mean_absolute_percentage_error( ) +@overload +def _mean_absolute_percentage_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def _mean_absolute_percentage_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> FDataGrid: + ... + + @mean_absolute_percentage_error.register def _mean_absolute_percentage_error_fdatagrid( y_true: FDataGrid, @@ -424,7 +564,7 @@ def _mean_absolute_percentage_error_fdatagrid( mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon) - error = mean(mape, weight=sample_weight) + error = mean(mape, weights=sample_weight) if multioutput == 'raw_values': return error @@ -469,6 +609,30 @@ def _mape_func(x): # noqa: WPS430 return integral[0] / (end - start) +@overload +def mean_squared_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', + squared: bool = True, +) -> float: + ... + + +@overload +def mean_squared_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], + squared: bool = True, +) -> NDArrayFloat: + ... + + @singledispatch def mean_squared_error( y_true: Union[FData, NDArrayFloat], @@ -521,17 +685,17 @@ def mean_squared_error( Returns: Mean squared error. - float: - if multioutput = 'uniform_average' or - :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataBasis` objects. - FDataGrid: - if both :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataGrid` - objects and multioutput = 'raw_values'. - ndarray: - if both :math:`y\_pred` and :math:`y\_true` are ndarray and - multioutput = 'raw_values'. + If multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects, float is returned. + + If both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values', + :class:`~skfda.representation.FDataGrid` is returned. + + If both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values', ndarray. """ return mean_squared_error( @@ -543,6 +707,30 @@ def mean_squared_error( ) +@overload +def _mean_squared_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', + squared: bool = True, +) -> float: + ... + + +@overload +def _mean_squared_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], + squared: bool = True, +) -> FDataGrid: + ... + + @mean_squared_error.register def _mean_squared_error_fdatagrid( y_true: FDataGrid, @@ -555,7 +743,7 @@ def _mean_squared_error_fdatagrid( error = mean( np.power(y_true - y_pred, 2), - weight=sample_weight, + weights=sample_weight, ) if not squared: @@ -603,6 +791,30 @@ def _mse_func(x): # noqa: WPS430 return integral[0] / (end - start) +@overload +def mean_squared_log_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', + squared: bool = True, +) -> float: + ... + + +@overload +def mean_squared_log_error( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], + squared: bool = True, +) -> NDArrayFloat: + ... + + @singledispatch def mean_squared_log_error( y_true: Union[FData, NDArrayFloat], @@ -660,17 +872,17 @@ def mean_squared_log_error( Returns: Mean squared log error. - float: - if multioutput = 'uniform_average' or - :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataBasis` objects. - FDataGrid: - if both :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataGrid` - objects and multioutput = 'raw_values'. - ndarray: - if both :math:`y\_pred` and :math:`y\_true` are ndarray and - multioutput = 'raw_values'. + If multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects, float is returned. + + If both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values', + :class:`~skfda.representation.FDataGrid` is returned. + + If both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values', ndarray. """ return sklearn.metrics.mean_squared_log_error( @@ -682,6 +894,30 @@ def mean_squared_log_error( ) +@overload +def _mean_squared_log_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', + squared: bool = True, +) -> float: + ... + + +@overload +def _mean_squared_log_error_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], + squared: bool = True, +) -> FDataGrid: + ... + + @mean_squared_log_error.register def _mean_squared_log_error_fdatagrid( y_true: FDataGrid, @@ -746,6 +982,28 @@ def _msle_func(x): # noqa: WPS430 return integral[0] / (end - start) +@overload +def r2_score( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def r2_score( + y_true: NDArrayFloat, + y_pred: NDArrayFloat, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> NDArrayFloat: + ... + + @singledispatch def r2_score( y_true: Union[FData, NDArrayFloat], @@ -799,17 +1057,17 @@ def r2_score( Returns: R2 score - float: - if multioutput = 'uniform_average' or - :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataBasis` objects. - FDataGrid: - if both :math:`y\_pred` and :math:`y\_true` are - :class:`~skfda.representation.FDataGrid` - objects and multioutput = 'raw_values'. - ndarray: - if both :math:`y\_pred` and :math:`y\_true` are ndarray and - multioutput = 'raw_values'. + If multioutput = 'uniform_average' or + :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataBasis` objects, float is returned. + + If both :math:`y\_pred` and :math:`y\_true` are + :class:`~skfda.representation.FDataGrid` + objects and multioutput = 'raw_values', + :class:`~skfda.representation.FDataGrid` is returned. + + If both :math:`y\_pred` and :math:`y\_true` are ndarray and + multioutput = 'raw_values', ndarray. """ return sklearn.metrics.r2_score( @@ -820,6 +1078,28 @@ def r2_score( ) +@overload +def _r2_score_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', +) -> float: + ... + + +@overload +def _r2_score_fdatagrid( + y_true: FDataGrid, + y_pred: FDataGrid, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], +) -> FDataGrid: + ... + + @r2_score.register def _r2_score_fdatagrid( y_true: FDataGrid, @@ -836,21 +1116,17 @@ def _r2_score_fdatagrid( ss_res = mean( np.power(y_true - y_pred, 2), - weight=sample_weight, + weights=sample_weight, ) - ss_tot = mean( - (y_true - mean(y_true, weight=sample_weight)) - * (y_true - mean(y_true, weight=sample_weight)), - weight=sample_weight, - ) + ss_tot = _var(y_true, weights=sample_weight) # Divisions by zero allowed with np.errstate(divide='ignore', invalid='ignore'): score = 1 - ss_res / ss_tot - # 0 / 0 divisions should be 1 in this context and the score, 0 - score.data_matrix[np.isnan(score.data_matrix)] = 0 + # 0 / 0 divisions should be 0 in this context and the score, 1 + score.data_matrix[np.isnan(score.data_matrix)] = 1 if multioutput == 'raw_values': return score @@ -894,19 +1170,27 @@ def _r2_func(x): # noqa: WPS430 weights=sample_weight, axis=0, ) - # Divisions by zero allowed - with np.errstate(divide='ignore', invalid='ignore'): - score = 1 - ss_res / ss_tot - # 0 / 0 divisions should be 1 in this context and the score, 0 - score[np.isnan(score)] = 0 + # 0/0 case, the score is 1. + if ss_res == 0 and ss_tot == 0: + return 1 + + # r/0 case, r!= 0. Return -inf outside this function + if ss_res != 0 and ss_tot == 0: + raise ValueError + + score = 1 - ss_res/ss_tot return score[0][0] - integral = scipy.integrate.quad_vec( - _r2_func, - start, - end, - ) + try: + integral = scipy.integrate.quad_vec( + _r2_func, + start, + end, + ) + + except ValueError: + return float('-inf') return integral[0] / (end - start) diff --git a/tests/test_regression_scores.py b/tests/test_regression_scores.py index 0e1a81039..176754efe 100644 --- a/tests/test_regression_scores.py +++ b/tests/test_regression_scores.py @@ -394,15 +394,22 @@ def test_zero_r2(self) -> None: y_true_grid = y_true_basis.to_grid(grid_points=grid_points) y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + # 0/0 for FDataGrid np.testing.assert_almost_equal( r2_score( y_true_grid, y_pred_grid, multioutput='raw_values', - ).evaluate(1), - [[[0]]], + ).data_matrix.flatten(), + + sklearn.metrics.r2_score( + y_true_grid.data_matrix.squeeze(), + y_pred_grid.data_matrix.squeeze(), + multioutput='raw_values', + ) ) + # 0/0 for FDataBasis np.testing.assert_almost_equal( r2_score(y_true_basis, y_pred_basis), -16.5, @@ -418,6 +425,8 @@ def test_zero_r2(self) -> None: ) y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + + # r/0 for FDataGrid (r != 0) np.testing.assert_almost_equal( r2_score( y_true_grid, @@ -427,6 +436,15 @@ def test_zero_r2(self) -> None: [[[float('-inf')]]], ) + # r/0 for FDataBasis (r != 0) + np.testing.assert_almost_equal( + r2_score( + y_true_basis, + y_pred_basis, + ), + float('-inf'), + ) + def test_zero_ev(self) -> None: """Test R2 Score when the denominator is zero.""" basis_coef_true = [[0, 1], [-1, 2], [-2, 3]] @@ -451,13 +469,25 @@ def test_zero_ev(self) -> None: y_true_grid = y_true_basis.to_grid(grid_points=grid_points) y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + # 0/0 for FDataGrid np.testing.assert_almost_equal( explained_variance_score( y_true_grid, y_pred_grid, multioutput='raw_values', - ).evaluate(1), - [[[0]]], + ).data_matrix.flatten(), + + sklearn.metrics.explained_variance_score( + y_true_grid.data_matrix.squeeze(), + y_pred_grid.data_matrix.squeeze(), + multioutput='raw_values', + ) + ) + + # 0/0 for FDataBasis + np.testing.assert_almost_equal( + explained_variance_score(y_true_basis, y_pred_basis), + -3, ) # Case when numerator is non-zero and denominator is zero (in t = 1) @@ -467,6 +497,8 @@ def test_zero_ev(self) -> None: coefficients=basis_coef_pred, ) y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) + + # r/0 for FDataGrid (r != 0) np.testing.assert_almost_equal( explained_variance_score( y_true_grid, @@ -476,6 +508,15 @@ def test_zero_ev(self) -> None: [[[float('-inf')]]], ) + # r/0 for FDataBasis (r != 0) + np.testing.assert_almost_equal( + explained_variance_score( + y_true_basis, + y_pred_basis, + ), + float('-inf'), + ) + def test_zero_mape(self) -> None: """Test Mean Absolute Percentage Error when y_true can be zero.""" basis_coef_true = [[3, 0, 0], [0, 0, 1]] From 3cb923a522dc085444af4c9489b05e0488418358 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sat, 12 Mar 2022 02:09:23 +0100 Subject: [PATCH 087/400] Coding fixes and vectorization --- .../stats/_functional_transformers.py | 56 +++++++++---------- 1 file changed, 25 insertions(+), 31 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 7f510cf67..f54c49c8c 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -80,36 +80,33 @@ def local_averages( def _calculate_curves_occupation_( - curve_y_coordinates: np.ndarray, - curve_x_coordinates: np.ndarray, - interval: Tuple[float, float], + curve_y_coordinates: NDArrayFloat, + curve_x_coordinates: NDArrayFloat, + intervals: Sequence[Tuple[float, float]], ) -> NDArrayFloat: - y1, y2 = interval - if y2 < y1: + y1, y2 = np.asarray(intervals).T + + if any(np.greater(y1, y2)): raise ValueError( - "Interval limits (a,b) should satisfy a <= b. " - + str(interval) + " doesn't", + "Interval limits (a,b) should satisfy a <= b.", ) # Reshape original curves so they have one dimension less - new_shape = curve_y_coordinates.shape[1::-1] - curve_y_coordinates = curve_y_coordinates.reshape( - new_shape[::-1], - ) + curve_y_coordinates = curve_y_coordinates[:, :, 0] # Calculate interval sizes on the X axis intervals_x_axis = curve_x_coordinates[1:] - curve_x_coordinates[:-1] # Calculate which points are inside the interval given (y1,y2) on Y axis - greater_than_y1 = curve_y_coordinates >= y1 - less_than_y2 = curve_y_coordinates <= y2 + greater_than_y1 = curve_y_coordinates >= y1[:, np.newaxis, np.newaxis] + less_than_y2 = curve_y_coordinates <= y2[:, np.newaxis, np.newaxis] inside_interval_bools = greater_than_y1 & less_than_y2 # Calculate intervals on X axis where the points are inside Y axis interval intervals_x_inside = inside_interval_bools * intervals_x_axis - return np.sum(intervals_x_inside, axis=1) + return np.sum(intervals_x_inside, axis=2) def occupation_measure( @@ -139,13 +136,13 @@ def occupation_measure( Args: data: FDataGrid or FDataBasis where we want to calculate - the occupation measure. + the occupation measure. intervals: ndarray of tuples containing the - intervals we want to consider. The shape should be - (n_sequences,2) + intervals we want to consider. The shape should be + (n_sequences,2) n_points: Number of points to evaluate in the domain. - By default will be used the points defined on the FDataGrid. - On a FDataBasis this value should be specified. + By default will be used the points defined on the FDataGrid. + On a FDataBasis this value should be specified. Returns: ndarray of shape (n_intervals, n_samples) with the transformed data. @@ -179,8 +176,8 @@ def occupation_measure( ... ), ... decimals=2, ... ) - array([[ 1. , 0.5 , 6.29], - [ 1. , 0.5 , 0. ]]) + array([[ 0.98, 0.5 , 6.28], + [ 1.02, 0.52, 0. ]]) """ if isinstance(data, FDataBasis) and n_points is None: @@ -195,18 +192,15 @@ def occupation_measure( function_x_coordinates = data.grid_points[0] function_y_coordinates = data.data_matrix else: - function_x_coordinates = np.arange( + function_x_coordinates = np.linspace( data.domain_range[0][0], data.domain_range[0][1], - (data.domain_range[0][1] - data.domain_range[0][0]) / n_points, + num=n_points, ) function_y_coordinates = data(function_x_coordinates[1:]) - return np.asarray([ - _calculate_curves_occupation_( # noqa: WPS317 - function_y_coordinates, - function_x_coordinates, - interval, - ) - for interval in intervals - ]) + return _calculate_curves_occupation_( # noqa: WPS317 + function_y_coordinates, + function_x_coordinates, + intervals, + ) From 400cc4bab9b8b739c5ec1d68f5663fed22f9eae1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 15 Mar 2022 17:54:33 +0100 Subject: [PATCH 088/400] Update citation information. --- .zenodo.json | 9 +++++++ CITATION.cff | 69 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 78 insertions(+) create mode 100644 CITATION.cff diff --git a/.zenodo.json b/.zenodo.json index bdc8ff1cc..745629812 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -41,6 +41,15 @@ }, { "name": "Petrunina, Elena" + }, + { + "name": "Castillo, Álvaro" + }, + { + "name": "Serna, Diego" + }, + { + "name": "Hidalgo, Rafael" } ], "license": "BSD-3-Clause" diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 000000000..e827b4d3c --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,69 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: + - family-names: "Ramos-Carreño" + given-names: "Carlos" + orcid: "https://orcid.org/0000-0003-2566-7058" + affiliation: "Universidad Autónoma de Madrid" + email: vnmabus@gmail.com + - family-names: "Suárez" + given-names: "Alberto" + orcid: "https://orcid.org/0000-0003-4534-0909" + affiliation: "Universidad Autónoma de Madrid" + - family-names: "Torrecilla" + given-names: "José Luis" + orcid: "https://orcid.org/0000-0003-3719-5190" + affiliation: "Universidad Autónoma de Madrid" + - family-names: "Carbajo Berrocal" + given-names: "Miguel" + - family-names: "Marcos Manchón" + given-names: "Pablo" + - family-names: "Pérez Manso" + given-names: "Pablo" + - family-names: "Hernando Bernabé" + given-names: "Amanda" + - family-names: "García Fernández" + given-names: "David" + - family-names: "Hong" + given-names: "Yujian" + - family-names: "Rodríguez-Ponga Eyriès" + given-names: "Pedro Martín" + - family-names: "Sánchez Romero" + given-names: "Álvaro" + - family-names: "Petrunina" + given-names: "Elena" + - family-names: "Castillo" + given-names: "Álvaro" + - family-names: "Serna" + given-names: "Diego" + - family-names: "Hidalgo" + given-names: "Rafael" +title: "GAA-UAM/scikit-fda: Functional Data Analysis in Python" +doi: 10.5281/zenodo.3468127 +date-released: 2019-10-01 +url: "https://github.com/GAA-UAM/scikit-fda" +license: BSD-3-Clause +keywords: + - functional data analysis + - machine learning + - Python + - scikit +identifiers: + - description: "This is the collection of archived snapshots of all versions of scikit-fda" + type: doi + value: 10.5281/zenodo.3468127 + - description: "This is the archived snapshot of version 0.3 of scikit-fda" + type: doi + value: 10.5281/zenodo.3468128 + - description: "This is the archived snapshot of version 0.4 of scikit-fda" + type: doi + value: 10.5281/zenodo.3957915 + - description: "This is the archived snapshot of version 0.5 of scikit-fda" + type: doi + value: 10.5281/zenodo.4406983 + - description: "This is the archived snapshot of version 0.6 of scikit-fda" + type: doi + value: 10.5281/zenodo.5502108 + - description: "This is the archived snapshot of version 0.7.1 of scikit-fda" + type: doi + value: 10.5281/zenodo.5903557 \ No newline at end of file From a5ca541bcecf7df7a9f1d7ba04193a3559ad766c Mon Sep 17 00:00:00 2001 From: dSerna4 <91683791+dSerna4@users.noreply.github.com> Date: Wed, 16 Mar 2022 18:21:12 +0100 Subject: [PATCH 089/400] Add "solver" and "max_iter" parameters to class LogisticRegression --- .../ml/classification/_logistic_regression.py | 401 +++++++++--------- 1 file changed, 206 insertions(+), 195 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 10d2b222a..0da77ee9f 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -1,195 +1,206 @@ -from __future__ import annotations - -from typing import Callable, Tuple - -import numpy as np -from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.linear_model import LogisticRegression as mvLogisticRegression -from sklearn.utils.validation import check_is_fitted - -from ..._utils import _classifier_get_classes -from ...representation import FDataGrid -from ...representation._typing import NDArrayAny, NDArrayInt - - -class LogisticRegression( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore -): - r"""Logistic Regression classifier for functional data. - - This class implements the sequential “greedy” algorithm - for functional logistic regression proposed in - :footcite:ts:`bueno++_2021_functional`. - - .. warning:: - For now, only binary classification for functional - data with one dimensional domains is supported. - - Args: - p: - number of points (and coefficients) to be selected by - the algorithm. - - Attributes: - classes\_: A list containing the name of the classes - points\_: A list containing the selected points. - coef\_: A list containing the coefficient for each selected point. - intercept\_: Independent term. - - Examples: - >>> from numpy import array - >>> from skfda.datasets import make_gaussian_process - >>> from skfda.ml.classification import LogisticRegression - >>> fd1 = make_gaussian_process( - ... n_samples=50, - ... n_features=100, - ... noise=0.7, - ... random_state=0, - ... ) - >>> fd2 = make_gaussian_process( - ... n_samples=50, - ... n_features = 100, - ... mean = array([1]*100), - ... noise = 0.7, - ... random_state=0 - ... ) - >>> fd = fd1.concatenate(fd2) - >>> y = 50*[0] + 50*[1] - >>> lr = LogisticRegression() - >>> _ = lr.fit(fd[::2], y[::2]) - >>> lr.coef_.round(2) - array([[ 1.28, 1.17, 1.27, 1.27, 0.96]]) - >>> lr.points_.round(2) - array([ 0.11, 0.06, 0.07, 0.03, 0. ]) - >>> lr.score(fd[1::2],y[1::2]) - 0.94 - - References: - .. footbibliography:: - - """ - - def __init__( - self, - p: int = 5, - ) -> None: - - self.p = p - - def fit( # noqa: D102 - self, - X: FDataGrid, - y: NDArrayAny, - ) -> LogisticRegression: - - X, classes, y_ind = self._argcheck_X_y(X, y) - - self.classes_ = classes - - n_samples = len(y) - n_features = len(X.grid_points[0]) - - selected_indexes = np.zeros(self.p, dtype=np.intc) - - # multivariate logistic regression - mvlr = mvLogisticRegression(penalty='l2') - - x_mv = np.zeros((n_samples, self.p)) - LL = np.zeros(n_features) - for q in range(self.p): - for t in range(n_features): - - x_mv[:, q] = X.data_matrix[:, t, 0] - mvlr.fit(x_mv[:, :q + 1], y_ind) - - # log-likelihood function at t - log_probs = mvlr.predict_log_proba(x_mv[:, :q + 1]) - log_probs = np.concatenate( - (log_probs[y_ind == 0, 0], log_probs[y_ind == 1, 1]), - ) - LL[t] = np.mean(log_probs) - - tmax = np.argmax(LL) - selected_indexes[q] = tmax - x_mv[:, q] = X.data_matrix[:, tmax, 0] - - # fit for the complete set of points - mvlr.fit(x_mv, y_ind) - - self.coef_ = mvlr.coef_ - self.intercept_ = mvlr.intercept_ - self._mvlr = mvlr - - self._selected_indexes = selected_indexes - self.points_ = X.grid_points[0][selected_indexes] - - return self - - def predict(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 - check_is_fitted(self) - return self._wrapper(self._mvlr.predict, X) - - def predict_log_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 - check_is_fitted(self) - return self._wrapper(self._mvlr.predict_log_proba, X) - - def predict_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 - check_is_fitted(self) - return self._wrapper(self._mvlr.predict_proba, X) - - def _argcheck_X( # noqa: N802 - self, - X: FDataGrid, - ) -> FDataGrid: - - if X.dim_domain > 1: - raise ValueError( - f'The dimension of the domain has to be one' - f'; got {X.dim_domain} dimensions', - ) - - return X - - def _argcheck_X_y( # noqa: N802 - self, - X: FDataGrid, - y: NDArrayAny, - ) -> Tuple[FDataGrid, NDArrayAny, NDArrayAny]: - - X = self._argcheck_X(X) - - classes, y_ind = _classifier_get_classes(y) - - if classes.size > 2: - raise ValueError( - f'The number of classes has to be two' - f'; got {classes.size} classes', - ) - - if (len(y) != len(X)): - raise ValueError( - "The number of samples on independent variables" - " and classes should be the same", - ) - - return (X, classes, y_ind) - - def _wrapper( - self, - method: Callable[[NDArrayAny], NDArrayAny], - X: FDataGrid, - ) -> NDArrayAny: - """Wrap multivariate logistic regression method. - - This function transforms functional data in order to pass - them to a multivariate logistic regression method. - - .. warning:: - This function can't be called before fit. - """ - X = self._argcheck_X(X) - - x_mv = X.data_matrix[:, self._selected_indexes, 0] - - return method(x_mv) +from __future__ import annotations +import string + +from typing import Callable, Tuple + +import numpy as np +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.linear_model import LogisticRegression as mvLogisticRegression +from sklearn.utils.validation import check_is_fitted + +from ..._utils import _classifier_get_classes +from ...representation import FDataGrid +from ...representation._typing import NDArrayAny, NDArrayInt + + +class LogisticRegression( + BaseEstimator, # type: ignore + ClassifierMixin, # type: ignore +): + r"""Logistic Regression classifier for functional data. + + This class implements the sequential “greedy” algorithm + for functional logistic regression proposed in + :footcite:ts:`bueno++_2021_functional`. + + .. warning:: + For now, only binary classification for functional + data with one dimensional domains is supported. + + Args: + p: + number of points (and coefficients) to be selected by + the algorithm. + solver: + Algorithm to use in the multivariate logistic regresion + optimization problem. For more info check the parameter + "solver" in sklearn.linear_model.LogisticRegression. + max_iter: + Maximum number of iterations taken for the solver to converge. + + Attributes: + classes\_: A list containing the name of the classes + points\_: A list containing the selected points. + coef\_: A list containing the coefficient for each selected point. + intercept\_: Independent term. + + Examples: + >>> from numpy import array + >>> from skfda.datasets import make_gaussian_process + >>> from skfda.ml.classification import LogisticRegression + >>> fd1 = make_gaussian_process( + ... n_samples=50, + ... n_features=100, + ... noise=0.7, + ... random_state=0, + ... ) + >>> fd2 = make_gaussian_process( + ... n_samples=50, + ... n_features = 100, + ... mean = array([1]*100), + ... noise = 0.7, + ... random_state=0 + ... ) + >>> fd = fd1.concatenate(fd2) + >>> y = 50*[0] + 50*[1] + >>> lr = LogisticRegression() + >>> _ = lr.fit(fd[::2], y[::2]) + >>> lr.coef_.round(2) + array([[ 1.28, 1.17, 1.27, 1.27, 0.96]]) + >>> lr.points_.round(2) + array([ 0.11, 0.06, 0.07, 0.03, 0. ]) + >>> lr.score(fd[1::2],y[1::2]) + 0.94 + + References: + .. footbibliography:: + + """ + + def __init__( + self, + p: int = 5, + solver: string = 'lbfgs', + max_iter: int = 100, + ) -> None: + + self.p = p + self.max_iter = 100 + self.solver = solver + + def fit( # noqa: D102 + self, + X: FDataGrid, + y: NDArrayAny, + ) -> LogisticRegression: + + X, classes, y_ind = self._argcheck_X_y(X, y) + + self.classes_ = classes + + n_samples = len(y) + n_features = len(X.grid_points[0]) + + selected_indexes = np.zeros(self.p, dtype=np.intc) + + # multivariate logistic regression + mvlr = mvLogisticRegression(penalty='l2', solver=self.solver, max_iter=self.max_iter) + + x_mv = np.zeros((n_samples, self.p)) + LL = np.zeros(n_features) + for q in range(self.p): + for t in range(n_features): + + x_mv[:, q] = X.data_matrix[:, t, 0] + mvlr.fit(x_mv[:, :q + 1], y_ind) + + # log-likelihood function at t + log_probs = mvlr.predict_log_proba(x_mv[:, :q + 1]) + log_probs = np.concatenate( + (log_probs[y_ind == 0, 0], log_probs[y_ind == 1, 1]), + ) + LL[t] = np.mean(log_probs) + + tmax = np.argmax(LL) + selected_indexes[q] = tmax + x_mv[:, q] = X.data_matrix[:, tmax, 0] + + # fit for the complete set of points + mvlr.fit(x_mv, y_ind) + + self.coef_ = mvlr.coef_ + self.intercept_ = mvlr.intercept_ + self._mvlr = mvlr + + self._selected_indexes = selected_indexes + self.points_ = X.grid_points[0][selected_indexes] + + return self + + def predict(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 + check_is_fitted(self) + return self._wrapper(self._mvlr.predict, X) + + def predict_log_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 + check_is_fitted(self) + return self._wrapper(self._mvlr.predict_log_proba, X) + + def predict_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 + check_is_fitted(self) + return self._wrapper(self._mvlr.predict_proba, X) + + def _argcheck_X( # noqa: N802 + self, + X: FDataGrid, + ) -> FDataGrid: + + if X.dim_domain > 1: + raise ValueError( + f'The dimension of the domain has to be one' + f'; got {X.dim_domain} dimensions', + ) + + return X + + def _argcheck_X_y( # noqa: N802 + self, + X: FDataGrid, + y: NDArrayAny, + ) -> Tuple[FDataGrid, NDArrayAny, NDArrayAny]: + + X = self._argcheck_X(X) + + classes, y_ind = _classifier_get_classes(y) + + if classes.size > 2: + raise ValueError( + f'The number of classes has to be two' + f'; got {classes.size} classes', + ) + + if (len(y) != len(X)): + raise ValueError( + "The number of samples on independent variables" + " and classes should be the same", + ) + + return (X, classes, y_ind) + + def _wrapper( + self, + method: Callable[[NDArrayAny], NDArrayAny], + X: FDataGrid, + ) -> NDArrayAny: + """Wrap multivariate logistic regression method. + + This function transforms functional data in order to pass + them to a multivariate logistic regression method. + + .. warning:: + This function can't be called before fit. + """ + X = self._argcheck_X(X) + + x_mv = X.data_matrix[:, self._selected_indexes, 0] + + return method(x_mv) From 178df117e20c906ede32755093a87dda49a68557 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 16 Mar 2022 22:34:54 +0100 Subject: [PATCH 090/400] Links to HatMatrix --- docs/modules/misc/hat_matrix.rst | 1 + skfda/misc/hat_matrix.py | 11 ++++++----- skfda/preprocessing/smoothing/_kernel_smoothers.py | 2 +- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/modules/misc/hat_matrix.rst b/docs/modules/misc/hat_matrix.rst index 0cb35302d..7dde09b73 100644 --- a/docs/modules/misc/hat_matrix.rst +++ b/docs/modules/misc/hat_matrix.rst @@ -13,6 +13,7 @@ See the links below for more information. .. autosummary:: :toctree: autosummary + skfda.misc.hat_matrix.HatMatrix skfda.misc.hat_matrix.NadarayaWatsonHatMatrix skfda.misc.hat_matrix.LocalLinearRegressionHatMatrix skfda.misc.hat_matrix.KNeighborsHatMatrix diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 4fc302e18..ebbd45d66 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -25,10 +25,11 @@ class HatMatrix( RegressorMixin, ): """ - Kernel estimators. + Hat Matrix. - This module includes three types of kernel estimators that are used in - KernelSmoother and KernelRegression classes. + Base class for :class:`~skfda.misc.hat_matrix.NadarayaWatsonHatMatrix`, + :class:`~skfda.misc.hat_matrix.LocalLinearRegressionHatMatrix` and + :class:`~skfda.misc.hat_matrix.KNeighborsHatMatrix`. """ def __init__( @@ -337,11 +338,11 @@ class KNeighborsHatMatrix(HatMatrix): In both cases, :math:`K(\cdot)` is a kernel function and :math:`h_{i}` is calculated as the distance from :math:`x_i'` to its - ``n\_neighbors``-th nearest neighbor in :math:`\{x_1, ..., x_n\}` + ``n_neighbors``-th nearest neighbor in :math:`\{x_1, ..., x_n\}` :footcite:`ferraty+vieu_2006_nonparametric_knn`. Used with the uniform kernel, it takes the average of the closest - ``n\_neighbors`` points to a given point. + ``n_neighbors`` points to a given point. Args: n_neighbors: Number of nearest neighbours. By diff --git a/skfda/preprocessing/smoothing/_kernel_smoothers.py b/skfda/preprocessing/smoothing/_kernel_smoothers.py index 6dc7d82e3..f51c3aeab 100644 --- a/skfda/preprocessing/smoothing/_kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/_kernel_smoothers.py @@ -29,7 +29,7 @@ class KernelSmoother(_LinearSmoother): \hat{X} = \hat{H} X where :math:`\hat{H}` is a matrix described in - :class:`~skfda.misc.HatMatrix`. + :class:`~skfda.misc.hat_matrix.HatMatrix`. Examples: >>> from skfda import FDataGrid From da7c1d4936efa6533c925ac7b6d709590398d5ab Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 16 Mar 2022 22:38:12 +0100 Subject: [PATCH 091/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index ebbd45d66..189bb8d84 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -190,7 +190,8 @@ class LocalLinearRegressionHatMatrix(HatMatrix): It is desired to estimate the values :math:`\hat{Y} = \{\hat{y}_1, \hat{y}_2, ..., \hat{y}_m\}` - for the data :math:`\{X'_1, X'_2, ..., X'_m\}` (expressed in the same basis). + for the data :math:`\{X'_1, X'_2, ..., X'_m\}` + (expressed in the same basis). For each :math:`X'_k` the estimation :math:`\hat{y}_k` is obtained by taking the value :math:`a_k` from the vector From 242485abc1588101d3587a09a78c992ce2ef422f Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 16 Mar 2022 23:34:37 +0100 Subject: [PATCH 092/400] Merge with develop --- CITATION.cff | 69 +++ docs/modules/misc/hat_matrix.rst | 18 + examples/plot_kernel_regression.py | 242 +++++++++++ skfda/misc/hat_matrix.py | 397 ++++++++++++++++++ skfda/ml/regression/_kernel_regression.py | 107 +++++ .../smoothing/_kernel_smoothers.py | 142 +++++++ tests/test_kernel_regression.py | 337 +++++++++++++++ 7 files changed, 1312 insertions(+) create mode 100644 CITATION.cff create mode 100644 docs/modules/misc/hat_matrix.rst create mode 100644 examples/plot_kernel_regression.py create mode 100644 skfda/misc/hat_matrix.py create mode 100644 skfda/ml/regression/_kernel_regression.py create mode 100644 skfda/preprocessing/smoothing/_kernel_smoothers.py create mode 100644 tests/test_kernel_regression.py diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 000000000..e827b4d3c --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,69 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: + - family-names: "Ramos-Carreño" + given-names: "Carlos" + orcid: "https://orcid.org/0000-0003-2566-7058" + affiliation: "Universidad Autónoma de Madrid" + email: vnmabus@gmail.com + - family-names: "Suárez" + given-names: "Alberto" + orcid: "https://orcid.org/0000-0003-4534-0909" + affiliation: "Universidad Autónoma de Madrid" + - family-names: "Torrecilla" + given-names: "José Luis" + orcid: "https://orcid.org/0000-0003-3719-5190" + affiliation: "Universidad Autónoma de Madrid" + - family-names: "Carbajo Berrocal" + given-names: "Miguel" + - family-names: "Marcos Manchón" + given-names: "Pablo" + - family-names: "Pérez Manso" + given-names: "Pablo" + - family-names: "Hernando Bernabé" + given-names: "Amanda" + - family-names: "García Fernández" + given-names: "David" + - family-names: "Hong" + given-names: "Yujian" + - family-names: "Rodríguez-Ponga Eyriès" + given-names: "Pedro Martín" + - family-names: "Sánchez Romero" + given-names: "Álvaro" + - family-names: "Petrunina" + given-names: "Elena" + - family-names: "Castillo" + given-names: "Álvaro" + - family-names: "Serna" + given-names: "Diego" + - family-names: "Hidalgo" + given-names: "Rafael" +title: "GAA-UAM/scikit-fda: Functional Data Analysis in Python" +doi: 10.5281/zenodo.3468127 +date-released: 2019-10-01 +url: "https://github.com/GAA-UAM/scikit-fda" +license: BSD-3-Clause +keywords: + - functional data analysis + - machine learning + - Python + - scikit +identifiers: + - description: "This is the collection of archived snapshots of all versions of scikit-fda" + type: doi + value: 10.5281/zenodo.3468127 + - description: "This is the archived snapshot of version 0.3 of scikit-fda" + type: doi + value: 10.5281/zenodo.3468128 + - description: "This is the archived snapshot of version 0.4 of scikit-fda" + type: doi + value: 10.5281/zenodo.3957915 + - description: "This is the archived snapshot of version 0.5 of scikit-fda" + type: doi + value: 10.5281/zenodo.4406983 + - description: "This is the archived snapshot of version 0.6 of scikit-fda" + type: doi + value: 10.5281/zenodo.5502108 + - description: "This is the archived snapshot of version 0.7.1 of scikit-fda" + type: doi + value: 10.5281/zenodo.5903557 \ No newline at end of file diff --git a/docs/modules/misc/hat_matrix.rst b/docs/modules/misc/hat_matrix.rst new file mode 100644 index 000000000..0cb35302d --- /dev/null +++ b/docs/modules/misc/hat_matrix.rst @@ -0,0 +1,18 @@ +Hat Matrix +========== + +A hat matrix is an object used in kernel smoothing (:class:`~skfda.preprocessing.smoothing.KernelSmoother`) and +kernel regression (:class:`~skfda.ml.regression.KernelRegression`) algorithms. + +Those algorithms estimate the desired values as a weighted mean of train data. The different Hat matrix types define how +these weights are calculated. + +See the links below for more information. + + +.. autosummary:: + :toctree: autosummary + + skfda.misc.hat_matrix.NadarayaWatsonHatMatrix + skfda.misc.hat_matrix.LocalLinearRegressionHatMatrix + skfda.misc.hat_matrix.KNeighborsHatMatrix diff --git a/examples/plot_kernel_regression.py b/examples/plot_kernel_regression.py new file mode 100644 index 000000000..d3cb895bb --- /dev/null +++ b/examples/plot_kernel_regression.py @@ -0,0 +1,242 @@ +""" +Kernel Regression +================= + +In this example we will see and compare the performance of different kernel +regression methods. +""" + +# Author: Elena Petrunina +# License: MIT + +import numpy as np +from sklearn.metrics import r2_score +from sklearn.model_selection import GridSearchCV, train_test_split + +import skfda +from skfda.misc.hat_matrix import ( + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) +from skfda.ml.regression._kernel_regression import KernelRegression + +############################################################################## +# For this example, we will use the +# :func:`tecator ` dataset. This data set +# contains 215 samples. For each sample the data consists of a spectrum of +# absorbances and the contents of water, fat and protein. + + +X, y = skfda.datasets.fetch_tecator(return_X_y=True, as_frame=True) +X = X.iloc[:, 0].values +fat = y['fat'].values + +############################################################################## +# Fat percentages will be estimated from the spectrum. +# All curves are shown in the image above. The color of these depends on the +# amount of fat, from least (yellow) to highest (red). + +X.plot(gradient_criteria=fat, legend=True) + +############################################################################## +# The data set is splitted into train and test sets with 80% and 20% of the +# samples respectively. + +X_train, X_test, y_train, y_test = train_test_split( + X, + fat, + test_size=0.2, + random_state=1, +) + +############################################################################## +# The KNN hat matrix will be tried first. We will use the default kernel +# function, i.e. uniform kernel. To find the most suitable number of +# neighbours GridSearchCV will be used, testing with any number from 1 to 100. + +n_neighbors = np.array(range(1, 100)) +knn = GridSearchCV( + KernelRegression(kernel_estimator=KNeighborsHatMatrix()), + param_grid={'kernel_estimator__n_neighbors': n_neighbors}, +) + + +############################################################################## +# The best performance for the train set is obtained with the following number +# of neighbours + +knn.fit(X_train, y_train) +print( + 'KNN bandwidth:', + knn.best_params_['kernel_estimator__n_neighbors'], +) + +############################################################################## +# The accuracy of the estimation using r2_score measurement on the test set is +# shown below. + +y_pred = knn.predict(X_test) +knn_res = r2_score(y_pred, y_test) +print('Score KNN:', knn_res) + + +############################################################################## +# Following a similar procedure for Nadaraya-Watson, the optimal parameter is +# chosen from the interval (0.01, 1). + +bandwidth = np.logspace(-2, 0, num=100) +nw = GridSearchCV( + KernelRegression(kernel_estimator=NadarayaWatsonHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +############################################################################## +# The best performance is obtained with the following bandwidth + +nw.fit(X_train, y_train) +print( + 'Nadaraya-Watson bandwidth:', + nw.best_params_['kernel_estimator__bandwidth'], +) + +############################################################################## +# The accuracy of the estimation is shown below and should be similar to that +# obtained with the KNN method. + +y_pred = nw.predict(X_test) +nw_res = r2_score(y_pred, y_test) +print('Score NW:', nw_res) + +############################################################################## +# For Local Linear Regression, FDataBasis representation with a basis should be +# used (for the previous cases it is possible to use either +# FDataGrid or FDataBasis). +# +# For basis, Fourier basis with 10 elements has been selected. Note that the +# number of functions in the basis affects the estimation result and should +# ideally also be chosen using cross-validation. + +fourier = skfda.representation.basis.Fourier(n_basis=10) + +X_basis = X.to_basis(basis=fourier) +X_basis_train, X_basis_test, y_train, y_test = train_test_split( + X_basis, + fat, + test_size=0.2, + random_state=1, +) + + +bandwidth = np.logspace(0.3, 1, num=100) + +llr = GridSearchCV( + KernelRegression(kernel_estimator=LocalLinearRegressionHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +############################################################################## +# The bandwidth obtained by cross-validation is indicated below. +llr.fit(X_basis_train, y_train) +print( + 'LLR bandwidth:', + llr.best_params_['kernel_estimator__bandwidth'], +) + +############################################################################## +# Although it is a slower method, the result obtained in this example should be +# better than in the case of Nadaraya-Watson and KNN. + +y_pred = llr.predict(X_basis_test) +llr_res = r2_score(y_pred, y_test) +print('Score LLR:', llr_res) + +############################################################################## +# For this data set using the derivative should give a better performance. +# +# Below the plot of all the derivatives can be found. The same scheme as before +# is followed: yellow les fat, red more. + +Xd = X.derivative() +Xd.plot(gradient_criteria=fat, legend=True) + +Xd_train, Xd_test, y_train, y_test = train_test_split( + Xd, + fat, + test_size=0.2, + random_state=1, +) + +############################################################################## +# Exactly the same operations are repeated, but now with the derivatives of the +# functions. + +############################################################################## +# K-Nearest Neighbours +knn = GridSearchCV( + KernelRegression(kernel_estimator=KNeighborsHatMatrix()), + param_grid={'kernel_estimator__n_neighbors': n_neighbors}, +) + +knn.fit(Xd_train, y_train) +print( + 'KNN bandwidth:', + knn.best_params_['kernel_estimator__n_neighbors'], +) + +y_pred = knn.predict(Xd_test) +dknn_res = r2_score(y_pred, y_test) +print('Score KNN:', dknn_res) + + +############################################################################## +# Nadaraya-Watson +bandwidth = np.logspace(-3, -1, num=100) +nw = GridSearchCV( + KernelRegression(kernel_estimator=NadarayaWatsonHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +nw.fit(Xd_train, y_train) +print( + 'Nadara-Watson bandwidth:', + nw.best_params_['kernel_estimator__bandwidth'], +) + +y_pred = nw.predict(Xd_test) +dnw_res = r2_score(y_pred, y_test) +print('Score NW:', dnw_res) + +############################################################################## +# For both Nadaraya-Watson and KNN the accuracy has improved significantly +# and should be higher than 0.9. + +############################################################################## +# Local Linear Regression +Xd_basis = Xd.to_basis(basis=fourier) +Xd_basis_train, Xd_basis_test, y_train, y_test = train_test_split( + Xd_basis, + fat, + test_size=0.2, + random_state=1, +) + +bandwidth = np.logspace(-2, 1, 100) +llr = GridSearchCV( + KernelRegression(kernel_estimator=LocalLinearRegressionHatMatrix()), + param_grid={'kernel_estimator__bandwidth': bandwidth}, +) + +llr.fit(Xd_basis_train, y_train) +print( + 'LLR bandwidth:', + llr.best_params_['kernel_estimator__bandwidth'], +) + +y_pred = llr.predict(Xd_basis_test) +dllr_res = r2_score(y_pred, y_test) +print('Score LLR:', dllr_res) + +############################################################################## +# LLR accuracy has also improved, but the difference with Nadaraya-Watson and +# KNN in the case of derivatives is less significant than in the previous case. diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py new file mode 100644 index 000000000..67f51810e --- /dev/null +++ b/skfda/misc/hat_matrix.py @@ -0,0 +1,397 @@ +# -*- coding: utf-8 -*- + +"""Hat Matrix. + +This module include implementation to create Nadaraya-Watson, +Local Linear Regression and K-Nearest Neighbours hat matrices used in +kernel smoothing and kernel regression. + +""" + +import abc +from typing import Callable, Optional, Union + +import numpy as np +from sklearn.base import BaseEstimator, RegressorMixin + +from ..representation._functional_data import FData +from ..representation._typing import GridPoints, GridPointsLike, NDArrayFloat +from ..representation.basis import FDataBasis +from . import kernels + + +class HatMatrix( + BaseEstimator, + RegressorMixin, +): + """ + Kernel estimators. + + This module includes three types of kernel estimators that are used in + KernelSmoother and KernelRegression classes. + """ + + def __init__( + self, + *, + bandwidth: Optional[float] = None, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.normal, + ): + self.bandwidth = bandwidth + self.kernel = kernel + + def __call__( + self, + *, + delta_x: NDArrayFloat, + X_train: Optional[Union[FData, GridPointsLike]] = None, + X: Optional[Union[FData, GridPointsLike]] = None, + y_train: Optional[NDArrayFloat] = None, + weights: Optional[NDArrayFloat] = None, + _cv: bool = False, + ) -> NDArrayFloat: + r""" + Calculate the hat matrix or the prediction. + + If y_train is given, the estimation for X is calculated, otherwise, + hat matrix, :math:`\hat{H]`, is returned. The prediction, :math:`y`, + can be calculated as :math:`y = \hat{H} \dot y_train`. + + Args: + delta_x: Matrix of distances between points or + functions. + X_train: Training data. + X: Test samples. + y_train: Target values for X_train. + weights: Weights to be applied to the + resulting matrix columns. + + Returns: + The prediction if y_train is given, hat matrix otherwise. + + """ + # Obtain the non-normalized matrix + matrix = self._hat_matrix_function_not_normalized( + delta_x=delta_x, + ) + + # Adjust weights + if weights is not None: + matrix *= weights + + # Set diagonal to zero if requested (for testing purposes only) + if _cv: + np.fill_diagonal(matrix, 0) + + # Renormalize weights + rs = np.sum(matrix, axis=1) + rs[rs == 0] = 1 + + matrix = (matrix.T / rs).T + + if y_train is None: + return matrix + + return matrix @ y_train + + @abc.abstractmethod + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: NDArrayFloat, + ) -> NDArrayFloat: + pass + + +class NadarayaWatsonHatMatrix(HatMatrix): + r"""Nadaraya-Watson method. + + Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel + regression algorithms, as explained below + + .. math:: + \hat{H}_{i,j} = \frac{K\left(\frac{d(e_j-e_i')}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{d(e_k-e_i')}{h}\right)} + + For smoothing, :math:`e_i` are the points of discretisation + and :math:`e'_i` are the points for which it is desired to estimate the + smoothed value. The distance :math:`d` is the absolute value + function :footcite:`wasserman_2006_nonparametric_nw`. + + For regression, :math:`e_i` is the functional data and :math:`e_i'` + are the functions for which it is desired to estimate the scalar value. + Here, :math:`d` is some functional distance + :footcite:`ferraty+vieu_2006_nonparametric_nw`. + + In both cases :math:`K(\cdot)` is a kernel function and :math:`h` is the + bandwidth. + + Args: + bandwidth: Window width of the kernel + (also called h or bandwidth). + kernel: Kernel function. By default a normal + kernel. + + References: + .. footbibliography:: + + """ + + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: NDArrayFloat, + ) -> NDArrayFloat: + + if self.bandwidth is None: + percentage = 15 + self.bandwidth = np.percentile(np.abs(delta_x), percentage) + + return self.kernel(delta_x / self.bandwidth) + + +class LocalLinearRegressionHatMatrix(HatMatrix): + r"""Local linear regression method. + + Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel + regression algorithms, as explained below. + + For **kernel smoothing** algorithm to estimate the smoothed value for + :math:`t_j` the following error must be minimised + + .. math:: + AWSE(a, b) = \sum_{i=1}^n \left[ \left(y_i - + \left(a + b (t_i - t'_j) \right) \right)^2 + K \left( \frac {|t_i - t'_j|}{h} \right) \right ] + + which gives the following expression for each cell + + .. math:: + \hat{H}_{i,j} = \frac{b_j(t_i')}{\sum_{k=1}^{n}b_k(t_i')} + + .. math:: + b_j(e') = K\left(\frac{t_j - t'}{h}\right) S_{n,2}(t') - + (t_j - t')S_{n,1}(t') + + .. math:: + S_{n,k}(t') = \sum_{j=1}^{n}K\left(\frac{t_j-t'}{h}\right)(t_j-t')^k + + where :math:`t = (t_1, t_2, ..., t_n)` are points of discretisation and + :math:`t' = (t_1', t_2', ..., t_m')` are the points for which it is desired + to estimate the smoothed value + :footcite:`wasserman_2006_nonparametric_llr`. + + For **kernel regression** algorithm: + + Given functional data, :math:`(X_1, X_2, ..., X_n)` where each function + is expressed in a basis with :math:`J` elements and scalar + response :math:`Y = (y_1, y_2, ..., y_n)`. + + It is desired to estimate the values + :math:`\hat{Y} = (\hat{y}_1, \hat{y}_2, ..., \hat{y}_m)` + for the data :math:`(X'_1, X'_2, ..., X'_m)` (expressed in the same basis). + + For each :math:`X'_k` the estimation :math:`\hat{y}_k` is obtained by + taking the value :math:`a_k` from the vector + :math:`(a_k, b_{1k}, ..., b_{Jk})` which minimizes the following expression + + .. math:: + AWSE(a_k, b_{1k}, ..., b_{Jk}) = \sum_{i=1}^n \left(y_i - + \left(a + \sum_{j=1}^J b_{jk} c_{ijk} \right) \right)^2 + K \left( \frac {d(X_i - X'_k)}{h} \right) + + Where :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis + expansion of :math:`X_i - X'_k = \sum_{j=1}^J c_{ij}^k` and :math:`d` some + functional distance :footcite:`baillo+grane_2008_llr` + + For both cases, :math:`K(\cdot)` is a kernel function and :math:`h` the + bandwidth. + + Args: + bandwidth: Window width of the kernel + (also called h). + kernel: Kernel function. By default a normal + kernel. + + References: + .. footbibliography:: + + """ + + def __call__( # noqa: D102 + self, + *, + delta_x: NDArrayFloat, + X_train: Optional[Union[FDataBasis, GridPoints]] = None, + X: Optional[Union[FDataBasis, GridPoints]] = None, + y_train: Optional[NDArrayFloat] = None, + weights: Optional[NDArrayFloat] = None, + _cv: bool = False, + ) -> NDArrayFloat: + + if self.bandwidth is None: + percentage = 15 + self.bandwidth = np.percentile(np.abs(delta_x), percentage) + + # Regression + if isinstance(X_train, FDataBasis): + + if y_train is None: + y_train = np.identity(X_train.n_samples) + + m1 = X_train.coefficients + m2 = X.coefficients + + # Subtract previous matrices obtaining a 3D matrix + # The i-th element contains the matrix X_train - X[i] + C = m1 - m2[:, np.newaxis] + + inner_product_matrix = X_train.basis.inner_product_matrix() + + # Calculate new coefficients taking into account cross-products + # if the basis is orthonormal, C would not change + C = C @ inner_product_matrix + + # Adding a column of ones in the first position of all matrices + dims = (C.shape[0], C.shape[1], 1) + C = np.concatenate((np.ones(dims), C), axis=-1) + + return self._solve_least_squares( + delta_x=delta_x, + coefs=C, + y_train=y_train, + ) + + # Smoothing + else: + + return super().__call__( # noqa: WPS503 + delta_x=delta_x, + X_train=X_train, + X=X, + y_train=y_train, + weights=weights, + _cv=_cv, + ) + + def _solve_least_squares( + self, + delta_x: NDArrayFloat, + coefs: NDArrayFloat, + y_train: NDArrayFloat, + ) -> NDArrayFloat: + + W = np.sqrt(self.kernel(delta_x / self.bandwidth)) + + # A x = b + # Where x = (a, b_1, ..., b_J). + A = (coefs.T * W.T).T + b = np.einsum('ij, j... -> ij...', W, y_train) + + # For Ax = b calculates x that minimize the square error + # From https://stackoverflow.com/questions/42534237/broadcasted-lstsq-least-squares # noqa: E501 + u, s, vT = np.linalg.svd(A, full_matrices=False) + + uTb = np.einsum('ijk, ij...->ik...', u, b) + uTbs = (uTb.T / s.T).T + x = np.einsum('ijk,ij...->ik...', vT, uTbs) + + return x[:, 0] + + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: NDArrayFloat, + ) -> NDArrayFloat: + + if self.bandwidth is None: + percentage = 15 + self.bandwidth = np.percentile(np.abs(delta_x), percentage) + + k = self.kernel(delta_x / self.bandwidth) + + s1 = np.sum(k * delta_x, axis=1, keepdims=True) # S_n_1 + s2 = np.sum(k * delta_x ** 2, axis=1, keepdims=True) # S_n_2 + b = (k * (s2 - delta_x * s1)) # b_i(x_j) + + return b # noqa: WPS331 + + +class KNeighborsHatMatrix(HatMatrix): + r"""K-nearest neighbour kernel method. + + Creates the matrix :math:`\hat{H}`, used in the kernel smoothing and kernel + regression algorithms, as explained below. + + .. math:: + \hat{H}_{i,j} = \frac{K\left(\frac{d(e_j-e_i')}{h}\right)}{\sum_{k=1}^{ + n}K\left(\frac{d(e_k-e_i')}{h_{ik}}\right)} + + For smoothing, :math:`e_i` are the points of discretisation + and :math:`e'_i` are the points for which it is desired to estimate the + smoothed value. The distance :math:`d` is the absolute value. + + For regression, :math:`e_i` are the functional data and :math:`e_i'` + are the functions for which it is desired to estimate the scalar value. + Here, :math:`d` is some functional distance. + + In both cases, :math:`K(\cdot)` is a kernel function and + :math:`h_{ik}` is calculated as the distance from :math:`e_i'` to it's + :math:`k`-th nearest neighbour in :math:`\{e_1, ..., e_n\}` + :footcite:`ferraty+vieu_2006_nonparametric_knn`. + + Used with the uniform kernel, it takes the average of the closest k + points to a given point. + + Args: + n_neighbors: Number of nearest neighbours. By + default it takes the 5% closest elements. + kernel: Kernel function. By default a uniform + kernel to perform a 'usual' k nearest neighbours estimation. + + References: + .. footbibliography:: + + """ + + def __init__( + self, + *, + n_neighbors: Optional[int] = None, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.uniform, + ): + self.n_neighbors = n_neighbors + self.kernel = kernel + + def _hat_matrix_function_not_normalized( + self, + *, + delta_x: NDArrayFloat, + ) -> NDArrayFloat: + + input_points_len = delta_x.shape[1] + + if self.n_neighbors is None: + self.n_neighbors = np.floor( + np.percentile( + range(1, input_points_len), + 5, + ), + ) + elif self.n_neighbors <= 0: + raise ValueError('h must be greater than 0') + + # Tolerance to avoid points landing outside the kernel window due to + # computation error + tol = np.finfo(np.float64).eps + + # For each row in the distances matrix, it calculates the furthest + # point within the k nearest neighbours + vec = np.percentile( + np.abs(delta_x), + self.n_neighbors / input_points_len * 100, + axis=1, + interpolation='lower', + ) + tol + + return self.kernel((delta_x.T / vec).T) diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py new file mode 100644 index 000000000..aecc64c60 --- /dev/null +++ b/skfda/ml/regression/_kernel_regression.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +from typing import Optional + +import numpy as np +from sklearn.base import BaseEstimator, RegressorMixin +from sklearn.utils.validation import check_is_fitted + +from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix +from ...misc.metrics import PairwiseMetric, l2_distance +from ...misc.metrics._typing import Metric +from ...representation._functional_data import FData + + +class KernelRegression( + BaseEstimator, + RegressorMixin, +): + r"""Kernel regression with scalar response. + + Let :math:`fd_1 = (X_1, X_2, ..., X_n)` be the functional data set and + :math:`y = (y_1, y_2, ..., y_n)` be the scalar response corresponding + to each function in :math:`fd_1`. Then, the estimation for the + functions in :math:`fd_2 = (X'_1, X'_2, ..., X'_n)` can be + calculated as + + .. math:: + \hat{y} = \hat{H}y + + Where :math:`\hat{H}` is a matrix described in + :class:`~skfda.misc.hat_matrix.HatMatrix`. + + Args: + kernel_estimator: Method used to calculate the hat matrix + (default = + :class:`~skfda.misc.hat_matrix.NadarayaWatsonHatMatrix`). + metric: Metric used to calculate the distances + (default = :func:`L2 distance `). + + Examples: + >>> from skfda import FDataGrid + >>> from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix + >>> from skfda.misc.hat_matrix import KNeighborsHatMatrix + + >>> grid_points = np.linspace(0, 1, num=11) + >>> data1 = np.array([i + grid_points for i in range(1, 9, 2)]) + >>> data2 = np.array([i + grid_points for i in range(2, 7, 2)]) + + >>> fd_1 = FDataGrid(grid_points=grid_points, data_matrix=data1) + >>> y = np.array([1, 3, 5, 7]) + >>> fd_2 = FDataGrid(grid_points=grid_points, data_matrix=data2) + + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=1) + >>> estimator = KernelRegression(kernel_estimator=kernel_estimator) + >>> _ = estimator.fit(fd_1, y) + >>> estimator.predict(fd_2) + array([ 2.02723928, 4. , 5.97276072]) + + >>> kernel_estimator = KNeighborsHatMatrix(n_neighbors=2) + >>> estimator = KernelRegression(kernel_estimator=kernel_estimator) + >>> _ = estimator.fit(fd_1, y) + >>> estimator.predict(fd_2) + array([ 2., 4., 6.]) + + """ + + def __init__( + self, + *, + kernel_estimator: Optional[HatMatrix] = None, + metric: Metric[FData] = l2_distance, + ): + + self.kernel_estimator = kernel_estimator + self.metric = metric + + def fit( # noqa: D102 + self, + X: FData, + y: np.ndarray, + weight: Optional[np.ndarray] = None, + ) -> KernelRegression: + + self.X_train_ = X + self.y_train_ = y + self.weights_ = weight + + if self.kernel_estimator is None: + self.kernel_estimator = NadarayaWatsonHatMatrix() + + return self + + def predict( # noqa: D102 + self, + X: FData, + ) -> np.ndarray: + + check_is_fitted(self) + delta_x = PairwiseMetric(self.metric)(X, self.X_train_) + + return self.kernel_estimator( + delta_x=delta_x, + X_train=self.X_train_, + y_train=self.y_train_, + X=X, + weights=self.weights_, + ) diff --git a/skfda/preprocessing/smoothing/_kernel_smoothers.py b/skfda/preprocessing/smoothing/_kernel_smoothers.py new file mode 100644 index 000000000..6dc7d82e3 --- /dev/null +++ b/skfda/preprocessing/smoothing/_kernel_smoothers.py @@ -0,0 +1,142 @@ +# -*- coding: utf-8 -*- + +"""Kernel Smoother. + +This module contains the class for kernel smoothing. + +""" +from typing import Optional + +import numpy as np + +from ..._utils._utils import _to_grid_points +from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix +from ...representation._typing import GridPointsLike, NDArrayFloat +from ._linear import _LinearSmoother + + +class KernelSmoother(_LinearSmoother): + r"""Kernel smoothing method. + + This module allows to perform functional data smoothing. + + Let :math:`t = (t_1, t_2, ..., t_n)` be the + points of discretisation and :math:`X` the vector of observations at that + points. Then, the smoothed values, :math:`\hat{X}`, at the points + :math:`t' = (t_1', t_2', ..., t_m')` are obtained as + + .. math:: + \hat{X} = \hat{H} X + + where :math:`\hat{H}` is a matrix described in + :class:`~skfda.misc.HatMatrix`. + + Examples: + >>> from skfda import FDataGrid + >>> from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix + >>> fd = FDataGrid( + ... grid_points=[1, 2, 4, 5, 7], + ... data_matrix=[[1, 2, 3, 4, 5]], + ... ) + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=3.5) + >>> smoother = KernelSmoother(kernel_estimator=kernel_estimator) + >>> fd_smoothed = smoother.fit_transform(fd) + >>> fd_smoothed.data_matrix.round(2) + array([[[ 2.42], + [ 2.61], + [ 3.03], + [ 3.24], + [ 3.65]]]) + >>> smoother.hat_matrix().round(3) + array([[ 0.294, 0.282, 0.204, 0.153, 0.068], + [ 0.249, 0.259, 0.22 , 0.179, 0.093], + [ 0.165, 0.202, 0.238, 0.229, 0.165], + [ 0.129, 0.172, 0.239, 0.249, 0.211], + [ 0.073, 0.115, 0.221, 0.271, 0.319]]) + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=2) + >>> smoother = KernelSmoother(kernel_estimator=kernel_estimator) + >>> fd_smoothed = smoother.fit_transform(fd) + >>> fd_smoothed.data_matrix.round(2) + array([[[ 1.84], + [ 2.18], + [ 3.09], + [ 3.55], + [ 4.28]]]) + >>> smoother.hat_matrix().round(3) + array([[ 0.425, 0.375, 0.138, 0.058, 0.005], + [ 0.309, 0.35 , 0.212, 0.114, 0.015], + [ 0.103, 0.193, 0.319, 0.281, 0.103], + [ 0.046, 0.11 , 0.299, 0.339, 0.206], + [ 0.006, 0.022, 0.163, 0.305, 0.503]]) + + The output points can be changed: + + >>> kernel_estimator = NadarayaWatsonHatMatrix(bandwidth=2) + >>> smoother = KernelSmoother( + ... kernel_estimator=kernel_estimator, + ... output_points=[1, 2, 3, 4, 5, 6, 7], + ... ) + >>> fd_smoothed = smoother.fit_transform(fd) + >>> fd_smoothed.data_matrix.round(2) + array([[[ 1.84], + [ 2.18], + [ 2.61], + [ 3.09], + [ 3.55], + [ 3.95], + [ 4.28]]]) + >>> smoother.hat_matrix().round(3) + array([[ 0.425, 0.375, 0.138, 0.058, 0.005], + [ 0.309, 0.35 , 0.212, 0.114, 0.015], + [ 0.195, 0.283, 0.283, 0.195, 0.043], + [ 0.103, 0.193, 0.319, 0.281, 0.103], + [ 0.046, 0.11 , 0.299, 0.339, 0.206], + [ 0.017, 0.053, 0.238, 0.346, 0.346], + [ 0.006, 0.022, 0.163, 0.305, 0.503]]) + + Args: + kernel_estimator: Method used to + calculate the hat matrix (default = + :class:`~skfda.misc.NadarayaWatsonHatMatrix`) + weights: weight coefficients for each point. + output_points: The output points. If omitted, the + input points are used. + + So far only non parametric methods are implemented because we are only + relying on a discrete representation of functional data. + + """ + + def __init__( + self, + *, + kernel_estimator: Optional[HatMatrix] = None, + weights: Optional[NDArrayFloat] = None, + output_points: Optional[GridPointsLike] = None, + ): + self.kernel_estimator = kernel_estimator + self.weights = weights + self.output_points = output_points + self._cv = False # For testing purposes only + + def _hat_matrix( + self, + input_points: GridPointsLike, + output_points: GridPointsLike, + ) -> NDArrayFloat: + + input_points = _to_grid_points(input_points) + output_points = _to_grid_points(output_points) + + if self.kernel_estimator is None: + self.kernel_estimator = NadarayaWatsonHatMatrix() + + delta_x = np.subtract.outer(output_points[0], input_points[0]) + + return self.kernel_estimator( + delta_x=delta_x, + weights=self.weights, + X_train=input_points, + X=output_points, + _cv=self._cv, + ) diff --git a/tests/test_kernel_regression.py b/tests/test_kernel_regression.py new file mode 100644 index 000000000..afda97610 --- /dev/null +++ b/tests/test_kernel_regression.py @@ -0,0 +1,337 @@ +"""Test kernel regression method.""" +import unittest +from typing import Callable, Optional, Tuple + +import numpy as np +import sklearn + +from skfda import FData +from skfda.datasets import fetch_tecator +from skfda.misc.hat_matrix import ( + KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, +) +from skfda.misc.kernels import normal, uniform +from skfda.misc.metrics import l2_distance +from skfda.ml.regression import KernelRegression +from skfda.representation.basis import FDataBasis, Fourier, Monomial +from skfda.representation.grid import FDataGrid + + +def _nw_alt( + fd_train: FData, + fd_test: FData, + y_train: np.ndarray, + *, + bandwidth: float, + kernel: Optional[Callable] = None, +) -> np.ndarray: + if kernel is None: + kernel = normal + + y = np.zeros(fd_test.n_samples) + for i in range(fd_test.n_samples): + w = kernel(l2_distance(fd_train, fd_test[i]) / bandwidth) + y[i] = (w @ y_train) / sum(w) + + return y + + +def _knn_alt( + fd_train: FData, + fd_test: FData, + y_train: np.ndarray, + *, + bandwidth: int, + kernel: Optional[Callable] = None, +) -> np.ndarray: + if kernel is None: + kernel = uniform + + y = np.zeros(fd_test.n_samples) + + for i in range(fd_test.n_samples): + d = l2_distance(fd_train, fd_test[i]) + tol = np.finfo(np.float64).eps + h = sorted(d)[bandwidth - 1] + tol + w = kernel(d / h) + y[i] = (w @ y_train) / sum(w) + + return y + + +def _llr_alt( + fd_train: FDataBasis, + fd_test: FDataBasis, + y_train: np.ndarray, + *, + bandwidth: float, + kernel: Optional[Callable] = None, +) -> np.ndarray: + if kernel is None: + kernel = normal + + y = np.zeros(fd_test.n_samples) + + for i in range(fd_test.n_samples): + d = l2_distance(fd_train, fd_test[i]) + W = np.diag(kernel(d / bandwidth)) + + C = np.concatenate( + ( + np.ones(fd_train.n_samples)[:, np.newaxis], + (fd_train - fd_test[i]).coefficients, + ), + axis=1, + ) + + M = np.linalg.inv(np.linalg.multi_dot([C.T, W, C])) + y[i] = np.linalg.multi_dot([M, C.T, W, y_train])[0] + + return y + + +def _create_data_basis() -> Tuple[FDataBasis, FDataBasis, np.ndarray]: + X, y = fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + fat = y['fat'].values + + basis = Fourier( + n_basis=10, + domain_range=fd.domain_range, + ) + + fd_basis = fd.to_basis(basis=basis) + + fd_train, fd_test, y_train, _ = sklearn.model_selection.train_test_split( + fd_basis, + fat, + test_size=0.2, + random_state=10, + ) + return fd_train, fd_test, y_train + + +def _create_data_grid() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: + X, y = fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + fat = y['fat'].values + + fd_train, fd_test, y_train, _ = sklearn.model_selection.train_test_split( + fd, + fat, + test_size=0.2, + random_state=10, + ) + + return fd_train, fd_test, y_train + + +def _create_data_r() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: + X, y = fetch_tecator(return_X_y=True, as_frame=True) + fd = X.iloc[:, 0].values + fat = y['fat'].values + + return fd[:100], fd[100:110], fat[:100] + + +class TestKernelRegression(unittest.TestCase): + """Test Nadaraya-Watson, KNNeighbours and LocalLinearRegression methods.""" + + def test_nadaraya_watson(self) -> None: + """Test Nadaraya-Watson method.""" + # Creating data + fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() + fd_train_grid, fd_test_grid, y_train_grid = _create_data_grid() + + # Test NW method with basis representation and bandwidth=1 + nw_basis = KernelRegression( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), + ) + nw_basis.fit(fd_train_basis, y_train_basis) + y_basis = nw_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _nw_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=1, + ), + y_basis, + ) + + # Test NW method with grid representation and bandwidth=1 + nw_grid = KernelRegression( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), + ) + nw_grid.fit(fd_train_grid, y_train_grid) + y_grid = nw_grid.predict(fd_test_grid) + + np.testing.assert_allclose( + _nw_alt( + fd_train_grid, + fd_test_grid, + y_train_grid, + bandwidth=1, + ), + y_grid, + ) + + def test_knn(self) -> None: + """Test K-Nearest Neighbours method.""" + # Creating data + fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() + fd_train_grid, fd_test_grid, y_train_grid = _create_data_grid() + + # Test KNN method with basis representation, n_neighbours=3 and + # uniform kernel + knn_basis = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), + ) + knn_basis.fit(fd_train_basis, y_train_basis) + y_basis = knn_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _knn_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=3, + ), + y_basis, + ) + + # Test KNN method with grid representation, n_neighbours=3 and + # uniform kernel + knn_grid = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), + ) + knn_grid.fit(fd_train_grid, y_train_grid) + y_grid = knn_grid.predict(fd_test_grid) + + np.testing.assert_allclose( + _knn_alt( + fd_train_grid, + fd_test_grid, + y_train_grid, + bandwidth=3, + ), + y_grid, + ) + + # Test KNN method with basis representation, n_neighbours=10 and + # normal kernel + knn_basis = KernelRegression( + kernel_estimator=KNeighborsHatMatrix( + n_neighbors=10, + kernel=normal, + ), + ) + knn_basis.fit(fd_train_basis, y_train_basis) + y_basis = knn_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _knn_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=10, + kernel=normal, + ), + y_basis, + ) + + def test_llr(self) -> None: + """Test Local Linear Regression method.""" + # Creating data + fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() + + llr_basis = KernelRegression( + kernel_estimator=LocalLinearRegressionHatMatrix(bandwidth=1), + ) + llr_basis.fit(fd_train_basis, y_train_basis) + y_basis = llr_basis.predict(fd_test_basis) + + np.testing.assert_allclose( + _llr_alt( + fd_train_basis, + fd_test_basis, + y_train_basis, + bandwidth=1, + ), + y_basis, + ) + + def test_nw_r(self) -> None: + """Comparison of NW's results with results from fda.usc.""" + X_train, X_test, y_train = _create_data_r() + + nw = KernelRegression( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), + ) + nw.fit(X_train, y_train) + + y = nw.predict(X_test) + result_R = [ + 18.245093, + 22.976695, + 9.429236, + 16.852003, + 16.568529, + 8.520466, + 14.943808, + 15.344949, + 8.646862, + 16.576900, + ] + + np.testing.assert_almost_equal(y, result_R, decimal=3) + + def test_knn_r(self) -> None: + """Comparison of NW's results with results from fda.usc.""" + X_train, X_test, y_train = _create_data_r() + + knn = KernelRegression( + kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), + ) + knn.fit(X_train, y_train) + + y = knn.predict(X_test) + result_R = [ + 20.400000, + 24.166667, + 10.900000, + 20.466667, + 16.900000, + 5.433333, + 14.400000, + 11.966667, + 9.033333, + 19.633333, + ] + + np.testing.assert_almost_equal(y, result_R, decimal=6) + + +class TestNonOthonormalBasisLLR(unittest.TestCase): + """Test LocalLinearRegression method with non orthonormal basis.""" + + def test_llr_non_orthonormal(self) -> None: + """Test LocalLinearRegression with monomial basis.""" + coef1 = [[1, 5, 8], [4, 6, 6], [9, 4, 1]] + coef2 = [[6, 3, 5]] + basis = Monomial(n_basis=3, domain_range=(0, 3)) + + X_train = FDataBasis(coefficients=coef1, basis=basis) + X = FDataBasis(coefficients=coef2, basis=basis) + y_train = np.array([8, 6, 1]) + + llr = LocalLinearRegressionHatMatrix( + bandwidth=100, + kernel=uniform, + ) + kr = KernelRegression(kernel_estimator=llr) + kr.fit(X_train, y_train) + np.testing.assert_almost_equal(kr.predict(X), 4.35735166) From d169662dc8a7e775aa4cf8ac14ad03eed298e8c5 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 16 Mar 2022 23:36:44 +0100 Subject: [PATCH 093/400] Changing example --- .../stats/_functional_transformers.py | 32 ++++++++++--------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index f9e50f8af..fdcd003ea 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -107,23 +107,25 @@ def number_up_crossings( Example: - We import the Medflies dataset and for simplicity we use - the first 5 samples. - >>> from skfda.datasets import fetch_medflies - >>> dataset = fetch_medflies() - >>> X = dataset['data'][:5] - - Then we decide the level we want to consider (in our case 40) - and call the function with the dataset. The output will be the number of - times each curve cross the level 40 growing. + For this example we will use a well known function so the correct + functioning of this method can be checked. + We will create and use a DataFrame with a sample extracted from + the Bessel Function of first type and order 0. + First of all we import the Bessel Function and create the X axis + data grid. Then we create the FdataGrid >>> from skfda.exploratory.stats import number_up_crossings + >>> from scipy.special import jv >>> import numpy as np - >>> number_up_crossings(X, np.asarray([40])) - array([[6], - [3], - [7], - [7], - [3]]) + >>> x_grid = np.linspace(0, 14, 14) + >>> fd_grid = FDataGrid( + ... data_matrix=[jv([0], x_grid)], + ... grid_points=x_grid, + ... ) + + Finall we evaluate the number of up crossings method with the FDataGrid + created. + >>> number_up_crossings(fd_grid, np.asarray([0])) + array([[2]]) """ curves = data.data_matrix[:, :, 0] From cf85d4a784c3a8c8629ce868e460c508daedfc19 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 16 Mar 2022 23:48:13 +0100 Subject: [PATCH 094/400] Style fix --- skfda/exploratory/stats/__init__.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index e5ceeb706..2e401b15e 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,5 +1,9 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import local_averages, occupation_measure, number_up_crossings +from ._functional_transformers import ( + local_averages, + number_up_crossings, + occupation_measure, +) from ._stats import ( cov, depth_based_median, From a7b036344cae4b51fc187cdea207f49117bedf03 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Wed, 16 Mar 2022 23:59:16 +0100 Subject: [PATCH 095/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 189bb8d84..144f28b35 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -190,7 +190,7 @@ class LocalLinearRegressionHatMatrix(HatMatrix): It is desired to estimate the values :math:`\hat{Y} = \{\hat{y}_1, \hat{y}_2, ..., \hat{y}_m\}` - for the data :math:`\{X'_1, X'_2, ..., X'_m\}` + for the data :math:`\{X'_1, X'_2, ..., X'_m\}` (expressed in the same basis). For each :math:`X'_k` the estimation :math:`\hat{y}_k` is obtained by From 94b6998d8e2b909cb3950132cf2eb45af6039f6f Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 01:43:25 +0100 Subject: [PATCH 096/400] Overload changes --- skfda/exploratory/stats/_stats.py | 4 +- skfda/misc/score_functions.py | 278 +++++++++--------------------- tests/test_regression_scores.py | 49 ++---- 3 files changed, 103 insertions(+), 228 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 12a81289d..ce6882f44 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -30,8 +30,8 @@ def mean( if weights is None: return X.mean() else: - weight = (1 / sum(weights)) * weights - return sum(X * weight) + weight = (1 / np.sum(weights)) * weights + return np.sum(X * weight) def var(X: FDataGrid) -> FDataGrid: diff --git a/skfda/misc/score_functions.py b/skfda/misc/score_functions.py index 0718a38ce..90d2c0024 100644 --- a/skfda/misc/score_functions.py +++ b/skfda/misc/score_functions.py @@ -1,7 +1,7 @@ """Score functions for FData.""" import warnings from functools import singledispatch -from typing import Optional, Union, overload +from typing import Optional, Union, overload, TypeVar import numpy as np import scipy.integrate @@ -14,6 +14,9 @@ from ..representation.basis import FDataBasis from ..representation.grid import FDataGrid +DataType = TypeVar('DataType', FDataGrid, FDataBasis, NDArrayFloat) +DataTypeRawValues = TypeVar('DataTypeRawValues', FDataGrid, NDArrayFloat) + class ScoreFunction(Protocol): """Type definition for score functions.""" @@ -52,8 +55,8 @@ def _var( @overload def explained_variance_score( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataType, + y_pred: DataType, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', @@ -63,12 +66,12 @@ def explained_variance_score( @overload def explained_variance_score( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], -) -> NDArrayFloat: +) -> DataTypeRawValues: ... @@ -156,28 +159,6 @@ def explained_variance_score( ) -@overload -def _explained_variance_score_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average'] = 'uniform_average', -) -> float: - ... - - -@overload -def _explained_variance_score_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['raw_values'], -) -> FDataGrid: - ... - - @explained_variance_score.register def _explained_variance_score_fdatagrid( y_true: FDataGrid, @@ -202,12 +183,12 @@ def _explained_variance_score_fdatagrid( # Score only contains 1 function # If the dimension of the codomain is > 1, - # the mean of the integrals is taken - return np.mean(score.integrate()[0] / _domain_measure(score)) + # the mean of the scores is taken + return np.mean(score.integrate()[0]) / _domain_measure(score) @explained_variance_score.register -def _explaied_variance_score_fdatabasis( +def _explained_variance_score_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, *, @@ -255,7 +236,8 @@ def _ev_func(x): # noqa: WPS430 score = 1 - num / den - return score[0][0] + # Score only contains 1 function + return score[0] try: integral = scipy.integrate.quad_vec( @@ -266,13 +248,15 @@ def _ev_func(x): # noqa: WPS430 except ValueError: return float('-inf') - return integral[0] / (end - start) + # If the dimension of the codomain is > 1, + # the mean of the scores is taken + return np.mean(integral[0]) / (end - start) @overload def mean_absolute_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataType, + y_pred: DataType, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', @@ -282,12 +266,12 @@ def mean_absolute_error( @overload def mean_absolute_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], -) -> NDArrayFloat: +) -> DataTypeRawValues: ... @@ -362,28 +346,6 @@ def mean_absolute_error( ) -@overload -def _mean_absolute_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average'] = 'uniform_average', -) -> float: - ... - - -@overload -def _mean_absolute_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['raw_values'], -) -> FDataGrid: - ... - - @mean_absolute_error.register def _mean_absolute_error_fdatagrid( y_true: FDataGrid, @@ -398,10 +360,10 @@ def _mean_absolute_error_fdatagrid( if multioutput == 'raw_values': return error - # Score only contains 1 function + # Error only contains 1 function # If the dimension of the codomain is > 1, - # the mean of the integrals is taken - return np.mean(error.integrate()[0] / _domain_measure(error)) + # the mean of the errors is taken + return np.mean(error.integrate()[0]) / _domain_measure(error) @mean_absolute_error.register @@ -415,11 +377,14 @@ def _mean_absolute_error_fdatabasis( start, end = y_true.domain_range[0] def _mae_func(x): # noqa: WPS430 - return np.average( + error = np.average( np.abs(y_true(x) - y_pred(x)), weights=sample_weight, axis=0, - )[0][0] + ) + + # Error only contains 1 function + return error[0] integral = scipy.integrate.quad_vec( _mae_func, @@ -427,13 +392,15 @@ def _mae_func(x): # noqa: WPS430 end, ) - return integral[0] / (end - start) + # If the dimension of the codomain is > 1, + # the mean of the errors is taken + return np.mean(integral[0]) / (end - start) @overload def mean_absolute_percentage_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataType, + y_pred: DataType, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', @@ -443,12 +410,12 @@ def mean_absolute_percentage_error( @overload def mean_absolute_percentage_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], -) -> NDArrayFloat: +) -> DataTypeRawValues: ... @@ -526,28 +493,6 @@ def mean_absolute_percentage_error( ) -@overload -def _mean_absolute_percentage_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average'] = 'uniform_average', -) -> float: - ... - - -@overload -def _mean_absolute_percentage_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['raw_values'], -) -> FDataGrid: - ... - - @mean_absolute_percentage_error.register def _mean_absolute_percentage_error_fdatagrid( y_true: FDataGrid, @@ -569,10 +514,10 @@ def _mean_absolute_percentage_error_fdatagrid( if multioutput == 'raw_values': return error - # Score only contains 1 function + # Error only contains 1 function # If the dimension of the codomain is > 1, - # the mean of the integrals is taken - return np.mean(error.integrate()[0] / _domain_measure(error)) + # the mean of the errors is taken + return np.mean(error.integrate()[0]) / _domain_measure(error) @mean_absolute_percentage_error.register @@ -597,7 +542,9 @@ def _mape_func(x): # noqa: WPS430 weights=sample_weight, axis=0, ) - return error[0][0] + + # Error only contains 1 function + return error[0] start, end = y_true.domain_range[0] integral = scipy.integrate.quad_vec( @@ -606,13 +553,15 @@ def _mape_func(x): # noqa: WPS430 end, ) - return integral[0] / (end - start) + # If the dimension of the codomain is > 1, + # the mean of the errors is taken + return np.mean(integral[0]) / (end - start) @overload def mean_squared_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataType, + y_pred: DataType, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', @@ -623,13 +572,13 @@ def mean_squared_error( @overload def mean_squared_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], squared: bool = True, -) -> NDArrayFloat: +) -> DataTypeRawValues: ... @@ -707,30 +656,6 @@ def mean_squared_error( ) -@overload -def _mean_squared_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average'] = 'uniform_average', - squared: bool = True, -) -> float: - ... - - -@overload -def _mean_squared_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['raw_values'], - squared: bool = True, -) -> FDataGrid: - ... - - @mean_squared_error.register def _mean_squared_error_fdatagrid( y_true: FDataGrid, @@ -752,10 +677,10 @@ def _mean_squared_error_fdatagrid( if multioutput == 'raw_values': return error - # Score only contains 1 function + # Error only contains 1 function # If the dimension of the codomain is > 1, - # the mean of the integrals is taken - return np.mean(error.integrate()[0] / _domain_measure(error)) + # the mean of the errors is taken + return np.mean(error.integrate()[0]) / _domain_measure(error) @mean_squared_error.register @@ -780,7 +705,8 @@ def _mse_func(x): # noqa: WPS430 if not squared: return np.sqrt(error) - return error[0][0] + # Error only contains 1 function + return error[0] integral = scipy.integrate.quad_vec( _mse_func, @@ -788,13 +714,15 @@ def _mse_func(x): # noqa: WPS430 end, ) - return integral[0] / (end - start) + # If the dimension of the codomain is > 1, + # the mean of the errors is taken + return np.mean(integral[0]) / (end - start) @overload def mean_squared_log_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataType, + y_pred: DataType, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', @@ -805,13 +733,13 @@ def mean_squared_log_error( @overload def mean_squared_log_error( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], squared: bool = True, -) -> NDArrayFloat: +) -> DataTypeRawValues: ... @@ -894,30 +822,6 @@ def mean_squared_log_error( ) -@overload -def _mean_squared_log_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average'] = 'uniform_average', - squared: bool = True, -) -> float: - ... - - -@overload -def _mean_squared_log_error_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['raw_values'], - squared: bool = True, -) -> FDataGrid: - ... - - @mean_squared_log_error.register def _mean_squared_log_error_fdatagrid( y_true: FDataGrid, @@ -966,12 +870,13 @@ def _msle_func(x): # noqa: WPS430 np.power(np.log1p(y_true(x)) - np.log1p(y_pred(x)), 2), weights=sample_weight, axis=0, - )[0][0] + ) if not squared: return np.sqrt(error) - return error + # Error only contains 1 function + return error[0] integral = scipy.integrate.quad_vec( _msle_func, @@ -979,13 +884,15 @@ def _msle_func(x): # noqa: WPS430 end, ) - return integral[0] / (end - start) + # If the dimension of the codomain is > 1, + # the mean of the errors is taken + return np.mean(integral[0]) / (end - start) @overload def r2_score( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataType, + y_pred: DataType, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', @@ -995,12 +902,12 @@ def r2_score( @overload def r2_score( - y_true: NDArrayFloat, - y_pred: NDArrayFloat, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], -) -> NDArrayFloat: +) -> DataTypeRawValues: ... @@ -1078,28 +985,6 @@ def r2_score( ) -@overload -def _r2_score_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average'] = 'uniform_average', -) -> float: - ... - - -@overload -def _r2_score_fdatagrid( - y_true: FDataGrid, - y_pred: FDataGrid, - *, - sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['raw_values'], -) -> FDataGrid: - ... - - @r2_score.register def _r2_score_fdatagrid( y_true: FDataGrid, @@ -1133,8 +1018,8 @@ def _r2_score_fdatagrid( # Score only contains 1 function # If the dimension of the codomain is > 1, - # the mean of the integrals is taken - return np.mean(score.integrate()[0] / _domain_measure(score)) + # the mean of the scores is taken + return np.mean(score.integrate()[0]) / _domain_measure(score) @r2_score.register @@ -1181,7 +1066,8 @@ def _r2_func(x): # noqa: WPS430 score = 1 - ss_res/ss_tot - return score[0][0] + # Score only contains 1 function + return score[0] try: integral = scipy.integrate.quad_vec( @@ -1193,4 +1079,6 @@ def _r2_func(x): # noqa: WPS430 except ValueError: return float('-inf') - return integral[0] / (end - start) + # If the dimension of the codomain is > 1, + # the mean of the scores is taken + return np.mean(integral[0]) / (end - start) diff --git a/tests/test_regression_scores.py b/tests/test_regression_scores.py index 176754efe..67275e070 100644 --- a/tests/test_regression_scores.py +++ b/tests/test_regression_scores.py @@ -20,6 +20,7 @@ from skfda.representation.basis import BSpline, Fourier, Monomial + def _create_data_grid(n: int) -> Tuple[FDataGrid, FDataGrid]: X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values @@ -76,12 +77,8 @@ def _test_generic_grid( ) score_sklearn = sklearn_function( - y_true.data_matrix.reshape( - (y_true.data_matrix.shape[0], -1), - ), - y_pred.data_matrix.reshape( - (y_pred.data_matrix.shape[0], -1), - ), + y_true.data_matrix.squeeze(), + y_pred.data_matrix.squeeze(), multioutput='raw_values', sample_weight=weight, ) @@ -95,21 +92,15 @@ def _test_generic_grid( ) score_sklearn = sklearn_function( - y_true.data_matrix.reshape( - (y_true.data_matrix.shape[0], -1), - ), - y_pred.data_matrix.reshape( - (y_pred.data_matrix.shape[0], -1), - ), + y_true.data_matrix.squeeze(), + y_pred.data_matrix.squeeze(), multioutput='raw_values', sample_weight=weight, squared=False, ) np.testing.assert_allclose( - score.data_matrix.reshape( - (score.data_matrix.shape[0], -1), - )[0], + score.data_matrix.squeeze(), score_sklearn, ) @@ -249,11 +240,7 @@ def _test_grid_basis_generic( squared=False, ) - np.testing.assert_almost_equal( - score_grid, - score_basis, - decimal=precision, - ) + self.assertAlmostEqual(score_basis, score_grid, places=precision) def test_explained_variance_score(self) -> None: """Explained variance score for FDataGrid and FDataBasis.""" @@ -318,7 +305,7 @@ def test_explained_variance_basis(self) -> None: # integrate 1 - num/den # where num = (1/2x -1/2x^2)^2 # and den = (1.5 + 1.5x + 1.5x^2)^2 - np.testing.assert_almost_equal(ev, 0.992968) + self.assertAlmostEqual(ev, 0.992968, places=6) def test_mean_absolut_error_basis(self) -> None: """Test Mean Absolute Error for FDataBasis.""" @@ -327,7 +314,7 @@ def test_mean_absolut_error_basis(self) -> None: mae = mean_absolute_error(y_true, y_pred) # integrate 1/2 * | -x + x^2| - np.testing.assert_almost_equal(mae, 0.8055555555) + self.assertAlmostEqual(mae, 0.8055555555) def test_mean_absolute_percentage_error_basis(self) -> None: """Test Mean Absolute Percentage Error for FDataBasis.""" @@ -336,7 +323,7 @@ def test_mean_absolute_percentage_error_basis(self) -> None: mape = mean_absolute_percentage_error(y_true, y_pred) # integrate |1/2 * (-x + x^2) / (4 + 5x + 6x^2)| - np.testing.assert_almost_equal(mape, 0.0199192187) + self.assertAlmostEqual(mape, 0.0199192187) def test_mean_squared_error_basis(self) -> None: """Test Mean Squared Error for FDataBasis.""" @@ -345,7 +332,7 @@ def test_mean_squared_error_basis(self) -> None: mse = mean_squared_error(y_true, y_pred) # integrate 1/2 * (-x + x^2)^2 - np.testing.assert_almost_equal(mse, 2.85) + self.assertAlmostEqual(mse, 2.85) def test_mean_squared_log_error_basis(self) -> None: """Test Mean Squared Log Error for FDataBasis.""" @@ -354,7 +341,7 @@ def test_mean_squared_log_error_basis(self) -> None: msle = mean_squared_log_error(y_true, y_pred) # integrate 1/2*(log(1 + 4 + 5x + 6x^2) - log(1 + 4 + 6x + 5x^2))^2 - np.testing.assert_almost_equal(msle, 0.00107583) + self.assertAlmostEqual(msle, 0.00107583) def test_r2_score_basis(self) -> None: """Test R2 Score for FDataBasis.""" @@ -365,7 +352,7 @@ def test_r2_score_basis(self) -> None: # integrate 1 - num/den # where num = 1/2*(-x + x^2)^2, # and den = (1.5 + 1.5x + 1.5x^2)^2 - np.testing.assert_almost_equal(r2, 0.9859362) + self.assertAlmostEqual(r2, 0.9859362) class TestScoreZeroDenominator(unittest.TestCase): @@ -400,7 +387,7 @@ def test_zero_r2(self) -> None: y_true_grid, y_pred_grid, multioutput='raw_values', - ).data_matrix.flatten(), + ).data_matrix.squeeze(), sklearn.metrics.r2_score( y_true_grid.data_matrix.squeeze(), @@ -410,7 +397,7 @@ def test_zero_r2(self) -> None: ) # 0/0 for FDataBasis - np.testing.assert_almost_equal( + self.assertAlmostEqual( r2_score(y_true_basis, y_pred_basis), -16.5, ) @@ -437,7 +424,7 @@ def test_zero_r2(self) -> None: ) # r/0 for FDataBasis (r != 0) - np.testing.assert_almost_equal( + self.assertAlmostEqual( r2_score( y_true_basis, y_pred_basis, @@ -485,7 +472,7 @@ def test_zero_ev(self) -> None: ) # 0/0 for FDataBasis - np.testing.assert_almost_equal( + self.assertAlmostEqual( explained_variance_score(y_true_basis, y_pred_basis), -3, ) @@ -509,7 +496,7 @@ def test_zero_ev(self) -> None: ) # r/0 for FDataBasis (r != 0) - np.testing.assert_almost_equal( + self.assertAlmostEqual( explained_variance_score( y_true_basis, y_pred_basis, From cda8ea4847dc1eca3f0cb35b3bf641fe11b16981 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 02:25:19 +0100 Subject: [PATCH 097/400] Change file name --- skfda/misc/__init__.py | 2 +- skfda/misc/{score_functions.py => scoring.py} | 50 +++++++++---------- ...t_regression_scores.py => test_scoring.py} | 11 ++-- 3 files changed, 30 insertions(+), 33 deletions(-) rename skfda/misc/{score_functions.py => scoring.py} (96%) rename tests/{test_regression_scores.py => test_scoring.py} (99%) diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 72f4db4b6..7ca3a962a 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -7,7 +7,7 @@ metrics, operators, regularization, - score_functions, + scoring, ) from ._math import ( cosine_similarity, diff --git a/skfda/misc/score_functions.py b/skfda/misc/scoring.py similarity index 96% rename from skfda/misc/score_functions.py rename to skfda/misc/scoring.py index 90d2c0024..3ed40582a 100644 --- a/skfda/misc/score_functions.py +++ b/skfda/misc/scoring.py @@ -1,7 +1,7 @@ -"""Score functions for FData.""" +"""Scoring methods for FData.""" import warnings from functools import singledispatch -from typing import Optional, Union, overload, TypeVar +from typing import Optional, TypeVar, Union, overload import numpy as np import scipy.integrate @@ -10,9 +10,9 @@ from .. import FData from ..exploratory.stats import mean, var +from ..representation import FDataBasis, FDataGrid +from ..representation._functional_data import EvalPointsType from ..representation._typing import NDArrayFloat -from ..representation.basis import FDataBasis -from ..representation.grid import FDataGrid DataType = TypeVar('DataType', FDataGrid, FDataBasis, NDArrayFloat) DataTypeRawValues = TypeVar('DataTypeRawValues', FDataGrid, NDArrayFloat) @@ -21,7 +21,7 @@ class ScoreFunction(Protocol): """Type definition for score functions.""" - def __call__( + def __call__( # noqa: D102 self, y_true: Union[FData, NDArrayFloat], y_pred: Union[FData, NDArrayFloat], @@ -30,7 +30,7 @@ def __call__( = 'uniform_average', squared: Optional[bool] = None, ) -> Union[NDArrayFloat, FDataGrid, float]: - ... + ... # noqa: WPS428 def _domain_measure(fd: FData) -> float: @@ -61,7 +61,7 @@ def explained_variance_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... + ... # noqa: WPS428 @overload @@ -72,7 +72,7 @@ def explained_variance_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... + ... # noqa: WPS428 @singledispatch @@ -197,7 +197,7 @@ def _explained_variance_score_fdatabasis( start, end = y_true.domain_range[0] - def _ev_func(x): # noqa: WPS430 + def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 num = np.average( np.power( ( @@ -261,7 +261,7 @@ def mean_absolute_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... + ... # noqa: WPS428 @overload @@ -272,7 +272,7 @@ def mean_absolute_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... + ... # noqa: WPS428 @singledispatch @@ -376,7 +376,7 @@ def _mean_absolute_error_fdatabasis( start, end = y_true.domain_range[0] - def _mae_func(x): # noqa: WPS430 + def _mae_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 error = np.average( np.abs(y_true(x) - y_pred(x)), weights=sample_weight, @@ -405,7 +405,7 @@ def mean_absolute_percentage_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... + ... # noqa: WPS428 @overload @@ -416,7 +416,7 @@ def mean_absolute_percentage_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... + ... # noqa: WPS428 @singledispatch @@ -528,7 +528,7 @@ def _mean_absolute_percentage_error_fdatabasis( sample_weight: Optional[NDArrayFloat] = None, ) -> float: - def _mape_func(x): # noqa: WPS430 + def _mape_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 epsilon = np.finfo(np.float64).eps if np.any(np.abs(y_true(x)) < epsilon): @@ -567,7 +567,7 @@ def mean_squared_error( multioutput: Literal['uniform_average'] = 'uniform_average', squared: bool = True, ) -> float: - ... + ... # noqa: WPS428 @overload @@ -579,7 +579,7 @@ def mean_squared_error( multioutput: Literal['raw_values'], squared: bool = True, ) -> DataTypeRawValues: - ... + ... # noqa: WPS428 @singledispatch @@ -694,7 +694,7 @@ def _mean_squared_error_fdatabasis( start, end = y_true.domain_range[0] - def _mse_func(x): # noqa: WPS430 + def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 error = np.average( np.power(y_true(x) - y_pred(x), 2), @@ -728,7 +728,7 @@ def mean_squared_log_error( multioutput: Literal['uniform_average'] = 'uniform_average', squared: bool = True, ) -> float: - ... + ... # noqa: WPS428 @overload @@ -740,7 +740,7 @@ def mean_squared_log_error( multioutput: Literal['raw_values'], squared: bool = True, ) -> DataTypeRawValues: - ... + ... # noqa: WPS428 @singledispatch @@ -858,7 +858,7 @@ def _mean_squared_log_error_fdatabasis( start, end = y_true.domain_range[0] - def _msle_func(x): # noqa: WPS430 + def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 if np.any(y_true(x) < 0) or np.any(y_pred(x) < 0): raise ValueError( @@ -897,7 +897,7 @@ def r2_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... + ... # noqa: WPS428 @overload @@ -908,7 +908,7 @@ def r2_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... + ... # noqa: WPS428 @singledispatch @@ -1037,7 +1037,7 @@ def _r2_score_fdatabasis( 'R^2 score is not well-defined with less than two samples.', ) - def _r2_func(x): # noqa: WPS430 + def _r2_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 ss_res = np.average( np.power(y_true(x) - y_pred(x), 2), weights=sample_weight, @@ -1064,7 +1064,7 @@ def _r2_func(x): # noqa: WPS430 if ss_res != 0 and ss_tot == 0: raise ValueError - score = 1 - ss_res/ss_tot + score = 1 - ss_res / ss_tot # Score only contains 1 function return score[0] diff --git a/tests/test_regression_scores.py b/tests/test_scoring.py similarity index 99% rename from tests/test_regression_scores.py rename to tests/test_scoring.py index 67275e070..972ebb6d9 100644 --- a/tests/test_regression_scores.py +++ b/tests/test_scoring.py @@ -1,4 +1,4 @@ -"""Test for Score Functions module.""" +"""Test for scoring module.""" import unittest from typing import Optional, Tuple @@ -7,7 +7,7 @@ from skfda import FDataBasis, FDataGrid from skfda.datasets import fetch_tecator -from skfda.misc.score_functions import ( +from skfda.misc.scoring import ( ScoreFunction, explained_variance_score, mean_absolute_error, @@ -20,7 +20,6 @@ from skfda.representation.basis import BSpline, Fourier, Monomial - def _create_data_grid(n: int) -> Tuple[FDataGrid, FDataGrid]: X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values @@ -388,12 +387,11 @@ def test_zero_r2(self) -> None: y_pred_grid, multioutput='raw_values', ).data_matrix.squeeze(), - sklearn.metrics.r2_score( y_true_grid.data_matrix.squeeze(), y_pred_grid.data_matrix.squeeze(), multioutput='raw_values', - ) + ), ) # 0/0 for FDataBasis @@ -463,12 +461,11 @@ def test_zero_ev(self) -> None: y_pred_grid, multioutput='raw_values', ).data_matrix.flatten(), - sklearn.metrics.explained_variance_score( y_true_grid.data_matrix.squeeze(), y_pred_grid.data_matrix.squeeze(), multioutput='raw_values', - ) + ), ) # 0/0 for FDataBasis From 780829db5a3af271441b30c1e0faecdb2d64d2f7 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 02:43:52 +0100 Subject: [PATCH 098/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 144f28b35..e19a0b362 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -171,14 +171,15 @@ class LocalLinearRegressionHatMatrix(HatMatrix): \hat{H}_{i,j} = \frac{b_j(t_i')}{\sum_{k=1}^{n}b_k(t_i')} .. math:: - b_j(t') = K\left(\frac{t_j - t'}{h}\right) S_{n,2}(t') - - (t_j - t')S_{n,1}(t') + b_j(t_i') = K\left(\frac{t_j - t_i'}{h}\right) S_{n,2}(t_i') - + (t_j - t_i')S_{n,1}(t_i') .. math:: - S_{n,k}(t') = \sum_{j=1}^{n}K\left(\frac{t_j-t'}{h}\right)(t_j-t')^k + S_{n,k}(t_i') = \sum_{j=1}^{n}K\left(\frac{t_j-t_i'}{h}\right) + (t_j-t_i')^k - where :math:`t = \{t_1, t_2, ..., t_n\}` are points with known value and - :math:`t' = \{t_1', t_2', ..., t_m'\}` are the points for which it is + where :math:`\{t_1, t_2, ..., t_n\}` are points with known value and + :math:`\{t_1', t_2', ..., t_m'\}` are the points for which it is desired to estimate the smoothed value :footcite:`wasserman_2006_nonparametric_llr`. @@ -199,11 +200,11 @@ class LocalLinearRegressionHatMatrix(HatMatrix): .. math:: AWSE(a_k, b_{1k}, ..., b_{Jk}) = \sum_{i=1}^n \left(y_i - - \left(a + \sum_{j=1}^J b_{jk} c_{ij}^k \right) \right)^2 + \left(a_k + \sum_{j=1}^J b_{jk} c_{ik}^j \right) \right)^2 K \left( \frac {d(X_i - X'_k)}{h} \right) - Where :math:`c_{ij}^k` is the :math:`j`-th coefficient in a truncated basis - expansion of :math:`X_i - X'_k = \sum_{j=1}^J c_{ij}^k` and :math:`d` some + Where :math:`c_{ik}^j` is the :math:`j`-th coefficient in a truncated basis + expansion of :math:`X_i - X'_k = \sum_{j=1}^J c_{ik}^j` and :math:`d` some functional distance :footcite:`baillo+grane_2008_llr` For both cases, :math:`K(\cdot)` is a kernel function and :math:`h` the From 841b113e28848cd38928981a853c69f0cdc864d5 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 02:46:27 +0100 Subject: [PATCH 099/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index e19a0b362..36f1cce4c 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -253,7 +253,7 @@ def __call__( # noqa: D102 # Calculate new coefficients taking into account cross-products # if the basis is orthonormal, C would not change - C = C @ inner_product_matrix + C = C @ inner_product_matrix # noqa: WPS350 # Adding a column of ones in the first position of all matrices dims = (C.shape[0], C.shape[1], 1) From 8ea7aaf77516e79deaa4293e3f25d021b43f82f5 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 02:53:06 +0100 Subject: [PATCH 100/400] Update _stats.py --- skfda/exploratory/stats/_stats.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index ce6882f44..3ed8adc06 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -19,6 +19,7 @@ def mean( weights: Optional[np.ndarray] = None, ) -> F: """Compute the mean of all the samples in a FData object. + Args: X: Object containing all the samples whose mean is wanted. weights: Sample weight. By default, uniform weight are @@ -26,21 +27,24 @@ def mean( Returns: A :term:`functional data object` with just one sample representing the mean of all the samples in the original object. + """ if weights is None: return X.mean() - else: - weight = (1 / np.sum(weights)) * weights - return np.sum(X * weight) + + weight = (1 / np.sum(weights)) * weights + return (X * weight).sum() def var(X: FDataGrid) -> FDataGrid: """Compute the variance of a set of samples in a FDataGrid object. + Args: X: Object containing all the set of samples whose variance is desired. Returns: A :term:`functional data object` with just one sample representing the variance of all the samples in the original object. + """ return X.var() From 9e018a95c4ca675cbf35c8dedb91f792ee94d6f2 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 03:08:26 +0100 Subject: [PATCH 101/400] Update _stats.py --- skfda/exploratory/stats/_stats.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 3ed8adc06..21c89a9ee 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -22,8 +22,8 @@ def mean( Args: X: Object containing all the samples whose mean is wanted. - weights: Sample weight. By default, uniform weight are - used. + weights: Sample weight. By default, uniform weight are used. + Returns: A :term:`functional data object` with just one sample representing the mean of all the samples in the original object. @@ -41,6 +41,7 @@ def var(X: FDataGrid) -> FDataGrid: Args: X: Object containing all the set of samples whose variance is desired. + Returns: A :term:`functional data object` with just one sample representing the variance of all the samples in the original object. From cf2840eaa3be3f7ba2fd585951c82f5e1f610a0b Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 19:49:50 +0100 Subject: [PATCH 102/400] Update hat_matrix.py --- skfda/misc/hat_matrix.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 36f1cce4c..03112f1b1 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -27,9 +27,12 @@ class HatMatrix( """ Hat Matrix. - Base class for :class:`~skfda.misc.hat_matrix.NadarayaWatsonHatMatrix`, - :class:`~skfda.misc.hat_matrix.LocalLinearRegressionHatMatrix` and - :class:`~skfda.misc.hat_matrix.KNeighborsHatMatrix`. + Base class for different hat matrices. + + See also: + :class:`~skfda.misc.hat_matrix.NadarayaWatsonHatMatrix` + :class:`~skfda.misc.hat_matrix.LocalLinearRegressionHatMatrix` + :class:`~skfda.misc.hat_matrix.KNeighborsHatMatrix` """ def __init__( From 265f45a97efbe3a0fc9f0d214b6bdef345dfa07a Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 17 Mar 2022 20:42:45 +0100 Subject: [PATCH 103/400] Update scoring.py --- skfda/misc/scoring.py | 47 +++++++++++++++++-------------------------- 1 file changed, 19 insertions(+), 28 deletions(-) diff --git a/skfda/misc/scoring.py b/skfda/misc/scoring.py index 3ed40582a..cd3a15ecd 100644 --- a/skfda/misc/scoring.py +++ b/skfda/misc/scoring.py @@ -199,29 +199,23 @@ def _explained_variance_score_fdatabasis( def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 num = np.average( - np.power( - ( - (y_true(x) - y_pred(x)) - - np.average( - y_true(x) - y_pred(x), - weights=sample_weight, - axis=0, - ) - ), - 2, - ), + ( + (y_true(x) - y_pred(x)) + - np.average( + y_true(x) - y_pred(x), + weights=sample_weight, + axis=0, + ) + ) ** 2, weights=sample_weight, axis=0, ) den = np.average( - np.power( - ( - y_true(x) - - np.average(y_true(x), weights=sample_weight, axis=0) - ), - 2, - ), + ( + y_true(x) + - np.average(y_true(x), weights=sample_weight, axis=0) + ) ** 2, weights=sample_weight, axis=0, ) @@ -697,7 +691,7 @@ def _mean_squared_error_fdatabasis( def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 error = np.average( - np.power(y_true(x) - y_pred(x), 2), + (y_true(x) - y_pred(x)) ** 2, weights=sample_weight, axis=0, ) @@ -867,7 +861,7 @@ def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 ) error = np.average( - np.power(np.log1p(y_true(x)) - np.log1p(y_pred(x)), 2), + (np.log1p(y_true(x)) - np.log1p(y_pred(x))) ** 2, weights=sample_weight, axis=0, ) @@ -1039,19 +1033,16 @@ def _r2_score_fdatabasis( def _r2_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 ss_res = np.average( - np.power(y_true(x) - y_pred(x), 2), + (y_true(x) - y_pred(x)) ** 2, weights=sample_weight, axis=0, ) ss_tot = np.average( - np.power( - ( - y_true(x) - - np.average(y_true(x), weights=sample_weight, axis=0) - ), - 2, - ), + ( + y_true(x) + - np.average(y_true(x), weights=sample_weight, axis=0) + ) ** 2, weights=sample_weight, axis=0, ) From 30a529d6657b37428ecfb833c85ecb84f1c09d5f Mon Sep 17 00:00:00 2001 From: dSerna4 <91683791+dSerna4@users.noreply.github.com> Date: Tue, 22 Mar 2022 18:50:24 +0100 Subject: [PATCH 104/400] Change end of line character to \n --- .../ml/classification/_logistic_regression.py | 412 +++++++++--------- 1 file changed, 206 insertions(+), 206 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 0da77ee9f..80f1bb94a 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -1,206 +1,206 @@ -from __future__ import annotations -import string - -from typing import Callable, Tuple - -import numpy as np -from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.linear_model import LogisticRegression as mvLogisticRegression -from sklearn.utils.validation import check_is_fitted - -from ..._utils import _classifier_get_classes -from ...representation import FDataGrid -from ...representation._typing import NDArrayAny, NDArrayInt - - -class LogisticRegression( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore -): - r"""Logistic Regression classifier for functional data. - - This class implements the sequential “greedy” algorithm - for functional logistic regression proposed in - :footcite:ts:`bueno++_2021_functional`. - - .. warning:: - For now, only binary classification for functional - data with one dimensional domains is supported. - - Args: - p: - number of points (and coefficients) to be selected by - the algorithm. - solver: - Algorithm to use in the multivariate logistic regresion - optimization problem. For more info check the parameter - "solver" in sklearn.linear_model.LogisticRegression. - max_iter: - Maximum number of iterations taken for the solver to converge. - - Attributes: - classes\_: A list containing the name of the classes - points\_: A list containing the selected points. - coef\_: A list containing the coefficient for each selected point. - intercept\_: Independent term. - - Examples: - >>> from numpy import array - >>> from skfda.datasets import make_gaussian_process - >>> from skfda.ml.classification import LogisticRegression - >>> fd1 = make_gaussian_process( - ... n_samples=50, - ... n_features=100, - ... noise=0.7, - ... random_state=0, - ... ) - >>> fd2 = make_gaussian_process( - ... n_samples=50, - ... n_features = 100, - ... mean = array([1]*100), - ... noise = 0.7, - ... random_state=0 - ... ) - >>> fd = fd1.concatenate(fd2) - >>> y = 50*[0] + 50*[1] - >>> lr = LogisticRegression() - >>> _ = lr.fit(fd[::2], y[::2]) - >>> lr.coef_.round(2) - array([[ 1.28, 1.17, 1.27, 1.27, 0.96]]) - >>> lr.points_.round(2) - array([ 0.11, 0.06, 0.07, 0.03, 0. ]) - >>> lr.score(fd[1::2],y[1::2]) - 0.94 - - References: - .. footbibliography:: - - """ - - def __init__( - self, - p: int = 5, - solver: string = 'lbfgs', - max_iter: int = 100, - ) -> None: - - self.p = p - self.max_iter = 100 - self.solver = solver - - def fit( # noqa: D102 - self, - X: FDataGrid, - y: NDArrayAny, - ) -> LogisticRegression: - - X, classes, y_ind = self._argcheck_X_y(X, y) - - self.classes_ = classes - - n_samples = len(y) - n_features = len(X.grid_points[0]) - - selected_indexes = np.zeros(self.p, dtype=np.intc) - - # multivariate logistic regression - mvlr = mvLogisticRegression(penalty='l2', solver=self.solver, max_iter=self.max_iter) - - x_mv = np.zeros((n_samples, self.p)) - LL = np.zeros(n_features) - for q in range(self.p): - for t in range(n_features): - - x_mv[:, q] = X.data_matrix[:, t, 0] - mvlr.fit(x_mv[:, :q + 1], y_ind) - - # log-likelihood function at t - log_probs = mvlr.predict_log_proba(x_mv[:, :q + 1]) - log_probs = np.concatenate( - (log_probs[y_ind == 0, 0], log_probs[y_ind == 1, 1]), - ) - LL[t] = np.mean(log_probs) - - tmax = np.argmax(LL) - selected_indexes[q] = tmax - x_mv[:, q] = X.data_matrix[:, tmax, 0] - - # fit for the complete set of points - mvlr.fit(x_mv, y_ind) - - self.coef_ = mvlr.coef_ - self.intercept_ = mvlr.intercept_ - self._mvlr = mvlr - - self._selected_indexes = selected_indexes - self.points_ = X.grid_points[0][selected_indexes] - - return self - - def predict(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 - check_is_fitted(self) - return self._wrapper(self._mvlr.predict, X) - - def predict_log_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 - check_is_fitted(self) - return self._wrapper(self._mvlr.predict_log_proba, X) - - def predict_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 - check_is_fitted(self) - return self._wrapper(self._mvlr.predict_proba, X) - - def _argcheck_X( # noqa: N802 - self, - X: FDataGrid, - ) -> FDataGrid: - - if X.dim_domain > 1: - raise ValueError( - f'The dimension of the domain has to be one' - f'; got {X.dim_domain} dimensions', - ) - - return X - - def _argcheck_X_y( # noqa: N802 - self, - X: FDataGrid, - y: NDArrayAny, - ) -> Tuple[FDataGrid, NDArrayAny, NDArrayAny]: - - X = self._argcheck_X(X) - - classes, y_ind = _classifier_get_classes(y) - - if classes.size > 2: - raise ValueError( - f'The number of classes has to be two' - f'; got {classes.size} classes', - ) - - if (len(y) != len(X)): - raise ValueError( - "The number of samples on independent variables" - " and classes should be the same", - ) - - return (X, classes, y_ind) - - def _wrapper( - self, - method: Callable[[NDArrayAny], NDArrayAny], - X: FDataGrid, - ) -> NDArrayAny: - """Wrap multivariate logistic regression method. - - This function transforms functional data in order to pass - them to a multivariate logistic regression method. - - .. warning:: - This function can't be called before fit. - """ - X = self._argcheck_X(X) - - x_mv = X.data_matrix[:, self._selected_indexes, 0] - - return method(x_mv) +from __future__ import annotations +import string + +from typing import Callable, Tuple + +import numpy as np +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.linear_model import LogisticRegression as mvLogisticRegression +from sklearn.utils.validation import check_is_fitted + +from ..._utils import _classifier_get_classes +from ...representation import FDataGrid +from ...representation._typing import NDArrayAny, NDArrayInt + + +class LogisticRegression( + BaseEstimator, # type: ignore + ClassifierMixin, # type: ignore +): + r"""Logistic Regression classifier for functional data. + + This class implements the sequential “greedy” algorithm + for functional logistic regression proposed in + :footcite:ts:`bueno++_2021_functional`. + + .. warning:: + For now, only binary classification for functional + data with one dimensional domains is supported. + + Args: + p: + number of points (and coefficients) to be selected by + the algorithm. + solver: + Algorithm to use in the multivariate logistic regresion + optimization problem. For more info check the parameter + "solver" in sklearn.linear_model.LogisticRegression. + max_iter: + Maximum number of iterations taken for the solver to converge. + + Attributes: + classes\_: A list containing the name of the classes + points\_: A list containing the selected points. + coef\_: A list containing the coefficient for each selected point. + intercept\_: Independent term. + + Examples: + >>> from numpy import array + >>> from skfda.datasets import make_gaussian_process + >>> from skfda.ml.classification import LogisticRegression + >>> fd1 = make_gaussian_process( + ... n_samples=50, + ... n_features=100, + ... noise=0.7, + ... random_state=0, + ... ) + >>> fd2 = make_gaussian_process( + ... n_samples=50, + ... n_features = 100, + ... mean = array([1]*100), + ... noise = 0.7, + ... random_state=0 + ... ) + >>> fd = fd1.concatenate(fd2) + >>> y = 50*[0] + 50*[1] + >>> lr = LogisticRegression() + >>> _ = lr.fit(fd[::2], y[::2]) + >>> lr.coef_.round(2) + array([[ 1.28, 1.17, 1.27, 1.27, 0.96]]) + >>> lr.points_.round(2) + array([ 0.11, 0.06, 0.07, 0.03, 0. ]) + >>> lr.score(fd[1::2],y[1::2]) + 0.94 + + References: + .. footbibliography:: + + """ + + def __init__( + self, + p: int = 5, + solver: string = 'lbfgs', + max_iter: int = 100, + ) -> None: + + self.p = p + self.max_iter = 100 + self.solver = solver + + def fit( # noqa: D102 + self, + X: FDataGrid, + y: NDArrayAny, + ) -> LogisticRegression: + + X, classes, y_ind = self._argcheck_X_y(X, y) + + self.classes_ = classes + + n_samples = len(y) + n_features = len(X.grid_points[0]) + + selected_indexes = np.zeros(self.p, dtype=np.intc) + + # multivariate logistic regression + mvlr = mvLogisticRegression(penalty='l2', solver=self.solver, max_iter=self.max_iter) + + x_mv = np.zeros((n_samples, self.p)) + LL = np.zeros(n_features) + for q in range(self.p): + for t in range(n_features): + + x_mv[:, q] = X.data_matrix[:, t, 0] + mvlr.fit(x_mv[:, :q + 1], y_ind) + + # log-likelihood function at t + log_probs = mvlr.predict_log_proba(x_mv[:, :q + 1]) + log_probs = np.concatenate( + (log_probs[y_ind == 0, 0], log_probs[y_ind == 1, 1]), + ) + LL[t] = np.mean(log_probs) + + tmax = np.argmax(LL) + selected_indexes[q] = tmax + x_mv[:, q] = X.data_matrix[:, tmax, 0] + + # fit for the complete set of points + mvlr.fit(x_mv, y_ind) + + self.coef_ = mvlr.coef_ + self.intercept_ = mvlr.intercept_ + self._mvlr = mvlr + + self._selected_indexes = selected_indexes + self.points_ = X.grid_points[0][selected_indexes] + + return self + + def predict(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 + check_is_fitted(self) + return self._wrapper(self._mvlr.predict, X) + + def predict_log_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 + check_is_fitted(self) + return self._wrapper(self._mvlr.predict_log_proba, X) + + def predict_proba(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 + check_is_fitted(self) + return self._wrapper(self._mvlr.predict_proba, X) + + def _argcheck_X( # noqa: N802 + self, + X: FDataGrid, + ) -> FDataGrid: + + if X.dim_domain > 1: + raise ValueError( + f'The dimension of the domain has to be one' + f'; got {X.dim_domain} dimensions', + ) + + return X + + def _argcheck_X_y( # noqa: N802 + self, + X: FDataGrid, + y: NDArrayAny, + ) -> Tuple[FDataGrid, NDArrayAny, NDArrayAny]: + + X = self._argcheck_X(X) + + classes, y_ind = _classifier_get_classes(y) + + if classes.size > 2: + raise ValueError( + f'The number of classes has to be two' + f'; got {classes.size} classes', + ) + + if (len(y) != len(X)): + raise ValueError( + "The number of samples on independent variables" + " and classes should be the same", + ) + + return (X, classes, y_ind) + + def _wrapper( + self, + method: Callable[[NDArrayAny], NDArrayAny], + X: FDataGrid, + ) -> NDArrayAny: + """Wrap multivariate logistic regression method. + + This function transforms functional data in order to pass + them to a multivariate logistic regression method. + + .. warning:: + This function can't be called before fit. + """ + X = self._argcheck_X(X) + + x_mv = X.data_matrix[:, self._selected_indexes, 0] + + return method(x_mv) From 94840a8915ad91deb171193bf74f862441cf5675 Mon Sep 17 00:00:00 2001 From: dSerna4 <91683791+dSerna4@users.noreply.github.com> Date: Tue, 22 Mar 2022 19:00:23 +0100 Subject: [PATCH 105/400] Update _logistic_regression.py --- skfda/ml/classification/_logistic_regression.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 80f1bb94a..08582ad42 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -104,7 +104,11 @@ def fit( # noqa: D102 selected_indexes = np.zeros(self.p, dtype=np.intc) # multivariate logistic regression - mvlr = mvLogisticRegression(penalty='l2', solver=self.solver, max_iter=self.max_iter) + mvlr = mvLogisticRegression( + penalty='l2', + solver=self.solver, + max_iter=self.max_iter, + ) x_mv = np.zeros((n_samples, self.p)) LL = np.zeros(n_features) From f4e5c1d866bada37b420465abbdca43061f3e308 Mon Sep 17 00:00:00 2001 From: dSerna4 <91683791+dSerna4@users.noreply.github.com> Date: Tue, 22 Mar 2022 19:08:10 +0100 Subject: [PATCH 106/400] Correct str type hint --- skfda/ml/classification/_logistic_regression.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 08582ad42..6494f2d68 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -1,5 +1,4 @@ from __future__ import annotations -import string from typing import Callable, Tuple @@ -80,7 +79,7 @@ class LogisticRegression( def __init__( self, p: int = 5, - solver: string = 'lbfgs', + solver: str = 'lbfgs', max_iter: int = 100, ) -> None: From 8f047b2858c31dc57d68d09f0662f28af398f502 Mon Sep 17 00:00:00 2001 From: dSerna4 <91683791+dSerna4@users.noreply.github.com> Date: Tue, 22 Mar 2022 19:24:59 +0100 Subject: [PATCH 107/400] Correct format issues (hopefully) --- skfda/ml/classification/_logistic_regression.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 6494f2d68..8638c6f1d 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -104,8 +104,8 @@ def fit( # noqa: D102 # multivariate logistic regression mvlr = mvLogisticRegression( - penalty='l2', - solver=self.solver, + penalty='l2', + solver=self.solver, max_iter=self.max_iter, ) From 93fdfadc32b20d9a7c21a4086fca1c3a4cecb98e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 24 Mar 2022 00:01:11 +0100 Subject: [PATCH 108/400] FIxing docstring example --- skfda/exploratory/stats/__init__.py | 1 + .../stats/_functional_transformers.py | 21 ++++++++++++++++--- 2 files changed, 19 insertions(+), 3 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 2e401b15e..b33502e2f 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,3 +1,4 @@ +"""Statistics.""" from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean from ._functional_transformers import ( local_averages, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 8ae6a2355..466bbf2f9 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -291,7 +291,7 @@ def number_up_crossings( We will create and use a DataFrame with a sample extracted from the Bessel Function of first type and order 0. First of all we import the Bessel Function and create the X axis - data grid. Then we create the FdataGrid + data grid. Then we create the FdataGrid. >>> from skfda.exploratory.stats import number_up_crossings >>> from scipy.special import jv >>> import numpy as np @@ -300,8 +300,23 @@ def number_up_crossings( ... data_matrix=[jv([0], x_grid)], ... grid_points=x_grid, ... ) - - Finall we evaluate the number of up crossings method with the FDataGrid + >>> fd_grid.data_matrix + array([[[ 1. ], + [ 0.73041066], + [ 0.13616752], + [-0.32803875], + [-0.35967936], + [-0.04652559], + [ 0.25396879], + [ 0.26095573], + [ 0.01042895], + [-0.22089135], + [-0.2074856 ], + [ 0.0126612 ], + [ 0.20089319], + [ 0.17107348]]]) + + Finally we evaluate the number of up crossings method with the FDataGrid created. >>> number_up_crossings(fd_grid, np.asarray([0])) array([[2]]) From b060adb91478c1ef9591f413228886982bb7d019 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 24 Mar 2022 00:34:38 +0100 Subject: [PATCH 109/400] Reorganization dim_reduction directory --- examples/plot_depth_classification.py | 2 +- examples/plot_fpca.py | 2 +- .../plot_fpca_inverse_transform_outl_detection.py | 2 +- skfda/misc/feature_construction/__init__.py | 2 -- skfda/ml/classification/_depth_classifiers.py | 2 +- skfda/preprocessing/dim_reduction/__init__.py | 3 ++- .../{feature_extraction => }/_fpca.py | 14 +++++++------- .../__init__.py | 2 +- .../_ddg_transformer.py | 2 +- .../_evaluation_trasformer.py | 15 +++++++++------ .../_fda_feature_union.py | 8 +++++--- .../_per_class_transformer.py | 3 ++- .../dim_reduction/projection/__init__.py | 4 ++-- tests/test_fda_feature_union.py | 4 ++-- tests/test_fpca.py | 2 +- tests/test_per_class_transformer.py | 2 +- 16 files changed, 37 insertions(+), 32 deletions(-) delete mode 100644 skfda/misc/feature_construction/__init__.py rename skfda/preprocessing/dim_reduction/{feature_extraction => }/_fpca.py (98%) rename skfda/preprocessing/dim_reduction/{feature_extraction => feature_construction}/__init__.py (75%) rename skfda/preprocessing/dim_reduction/{feature_extraction => feature_construction}/_ddg_transformer.py (98%) rename skfda/{misc => preprocessing/dim_reduction}/feature_construction/_evaluation_trasformer.py (92%) rename skfda/preprocessing/dim_reduction/{feature_extraction => feature_construction}/_fda_feature_union.py (94%) rename skfda/preprocessing/dim_reduction/{feature_extraction => feature_construction}/_per_class_transformer.py (99%) diff --git a/examples/plot_depth_classification.py b/examples/plot_depth_classification.py index 3fb395b47..28756421b 100644 --- a/examples/plot_depth_classification.py +++ b/examples/plot_depth_classification.py @@ -30,7 +30,7 @@ DDGClassifier, MaximumDepthClassifier, ) -from skfda.preprocessing.dim_reduction.feature_extraction import DDGTransformer +from skfda.preprocessing.dim_reduction.feature_construction import DDGTransformer from skfda.representation.grid import FDataGrid ############################################################################## diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index f3f78bfdd..556b99577 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -13,7 +13,7 @@ import skfda from skfda.datasets import fetch_growth from skfda.exploratory.visualization import FPCAPlot -from skfda.preprocessing.dim_reduction.feature_extraction import FPCA +from skfda.preprocessing.dim_reduction import FPCA from skfda.representation.basis import BSpline, Fourier, Monomial ############################################################################## diff --git a/examples/plot_fpca_inverse_transform_outl_detection.py b/examples/plot_fpca_inverse_transform_outl_detection.py index 99de96621..4a6fca82b 100644 --- a/examples/plot_fpca_inverse_transform_outl_detection.py +++ b/examples/plot_fpca_inverse_transform_outl_detection.py @@ -32,7 +32,7 @@ from skfda.datasets import make_gaussian_process from skfda.misc.covariances import Exponential, Gaussian from skfda.misc.metrics import l2_distance, l2_norm -from skfda.preprocessing.dim_reduction.feature_extraction import FPCA +from skfda.preprocessing.dim_reduction.feature_construction import FPCA ############################################################################## # We proceed as follows: diff --git a/skfda/misc/feature_construction/__init__.py b/skfda/misc/feature_construction/__init__.py deleted file mode 100644 index 986f75ea5..000000000 --- a/skfda/misc/feature_construction/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Feature construction.""" -from ._evaluation_trasformer import EvaluationTransformer diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index fe202fcec..a49d5ba67 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -13,7 +13,7 @@ from ..._utils import _classifier_fit_depth_methods, _classifier_get_classes from ...exploratory.depth import Depth, ModifiedBandDepth -from ...preprocessing.dim_reduction.feature_extraction import DDGTransformer +from ...preprocessing.dim_reduction.feature_construction import DDGTransformer from ...representation._typing import NDArrayFloat, NDArrayInt from ...representation.grid import FData diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 765694079..ef7ee4542 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -4,7 +4,8 @@ import importlib from typing import Any -from . import feature_extraction, variable_selection +from . import feature_construction, variable_selection +from ._fpca import FPCA def __getattr__(name: str) -> Any: diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py similarity index 98% rename from skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py rename to skfda/preprocessing/dim_reduction/_fpca.py index 9d81298ae..ae3d61932 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -10,13 +10,13 @@ from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from ....misc import inner_product_matrix -from ....misc.metrics import l2_norm -from ....misc.regularization import L2Regularization, compute_penalty_matrix -from ....representation import FData -from ....representation._typing import ArrayLike -from ....representation.basis import Basis, FDataBasis -from ....representation.grid import FDataGrid +from ...misc import inner_product_matrix +from ...misc.metrics import l2_norm +from ...misc.regularization import L2Regularization, compute_penalty_matrix +from ...representation import FData +from ...representation._typing import ArrayLike +from ...representation.basis import Basis, FDataBasis +from ...representation.grid import FDataGrid Function = TypeVar("Function", bound=FData) WeightsCallable = Callable[[np.ndarray], np.ndarray] diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py b/skfda/preprocessing/dim_reduction/feature_construction/__init__.py similarity index 75% rename from skfda/preprocessing/dim_reduction/feature_extraction/__init__.py rename to skfda/preprocessing/dim_reduction/feature_construction/__init__.py index 0e9556127..ed0606d72 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py +++ b/skfda/preprocessing/dim_reduction/feature_construction/__init__.py @@ -1,5 +1,5 @@ """Feature extraction.""" from ._ddg_transformer import DDGTransformer +from ._evaluation_trasformer import EvaluationTransformer from ._fda_feature_union import FDAFeatureUnion -from ._fpca import FPCA from ._per_class_transformer import PerClassTransformer diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_ddg_transformer.py b/skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py similarity index 98% rename from skfda/preprocessing/dim_reduction/feature_extraction/_ddg_transformer.py rename to skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py index 4e7ba706c..9b8db7804 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_ddg_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py @@ -53,7 +53,7 @@ class DDGTransformer( >>> X_train, X_test, y_train, y_test = train_test_split( ... fd, y, test_size=0.25, stratify=y, random_state=0) - >>> from skfda.preprocessing.dim_reduction.feature_extraction import \ + >>> from skfda.preprocessing.dim_reduction.feature_construction import\ ... DDGTransformer >>> from sklearn.pipeline import make_pipeline >>> from sklearn.neighbors import KNeighborsClassifier diff --git a/skfda/misc/feature_construction/_evaluation_trasformer.py b/skfda/preprocessing/dim_reduction/feature_construction/_evaluation_trasformer.py similarity index 92% rename from skfda/misc/feature_construction/_evaluation_trasformer.py rename to skfda/preprocessing/dim_reduction/feature_construction/_evaluation_trasformer.py index c87430ec2..11d058c18 100644 --- a/skfda/misc/feature_construction/_evaluation_trasformer.py +++ b/skfda/preprocessing/dim_reduction/feature_construction/_evaluation_trasformer.py @@ -8,11 +8,11 @@ from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal -from ..._utils import TransformerMixin -from ...representation._functional_data import FData -from ...representation._typing import ArrayLike, GridPointsLike, NDArrayInt -from ...representation.extrapolation import ExtrapolationLike -from ...representation.grid import FDataGrid +from ...._utils import TransformerMixin +from ....representation._functional_data import FData +from ....representation._typing import ArrayLike, GridPointsLike, NDArrayInt +from ....representation.extrapolation import ExtrapolationLike +from ....representation.grid import FDataGrid Input = TypeVar("Input") Output = TypeVar("Output") @@ -47,7 +47,10 @@ class EvaluationTransformer( Examples: >>> from skfda.representation import FDataGrid, FDataBasis - >>> from skfda.misc.feature_construction import EvaluationTransformer + >>> from skfda.preprocessing.dim_reduction.feature_construction import\ + ... ( + ... EvaluationTransformer + ... ) >>> from skfda.representation.basis import Monomial Functional data object with 2 samples diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py b/skfda/preprocessing/dim_reduction/feature_construction/_fda_feature_union.py similarity index 94% rename from skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py rename to skfda/preprocessing/dim_reduction/feature_construction/_fda_feature_union.py index 4968d2592..0bc555950 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fda_feature_union.py +++ b/skfda/preprocessing/dim_reduction/feature_construction/_fda_feature_union.py @@ -55,14 +55,16 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore will use Generalized depth-versus-depth transformer. Evaluation Transformer returns the original curve, and as it is helpful, we will concatenate it to the already metioned transformer. - >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + >>> from skfda.preprocessing.dim_reduction.feature_construction import ( ... FDAFeatureUnion, ... ) - >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + >>> from skfda.preprocessing.dim_reduction.feature_construction import ( ... DDGTransformer, ... ) >>> from skfda.exploratory.depth import ModifiedBandDepth - >>> from skfda.misc.feature_construction import EvaluationTransformer + >>> from skfda.preprocessing.dim_reduction.feature_construction import ( + ... EvaluationTransformer + ... ) >>> import numpy as np Finally we apply fit and transform. diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py b/skfda/preprocessing/dim_reduction/feature_construction/_per_class_transformer.py similarity index 99% rename from skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py rename to skfda/preprocessing/dim_reduction/feature_construction/_per_class_transformer.py index bddb17fca..8250279a3 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_per_class_transformer.py +++ b/skfda/preprocessing/dim_reduction/feature_construction/_per_class_transformer.py @@ -53,7 +53,8 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): >>> X = X.iloc[:, 0].values >>> y = y.values.codes - >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + >>> from skfda.preprocessing.dim_reduction.feature_construction\ + ... import ( ... PerClassTransformer, ... ) diff --git a/skfda/preprocessing/dim_reduction/projection/__init__.py b/skfda/preprocessing/dim_reduction/projection/__init__.py index b6b3116cb..f028b2627 100644 --- a/skfda/preprocessing/dim_reduction/projection/__init__.py +++ b/skfda/preprocessing/dim_reduction/projection/__init__.py @@ -1,8 +1,8 @@ import warnings -from ..feature_extraction import FPCA +from .. import FPCA warnings.warn( - 'The module "projection" is deprecated. Please use "feature_extraction"', + 'The module "projection" is deprecated. Please use "dim_reduction"', category=DeprecationWarning, ) diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index 933df6d07..3feecc7a7 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -6,10 +6,10 @@ from pandas.testing import assert_frame_equal from skfda.datasets import fetch_growth -from skfda.misc.feature_construction import EvaluationTransformer from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix from skfda.misc.operators import SRSF -from skfda.preprocessing.dim_reduction.feature_extraction import ( +from skfda.preprocessing.dim_reduction.feature_construction import ( + EvaluationTransformer, FDAFeatureUnion, ) from skfda.preprocessing.smoothing import KernelSmoother diff --git a/tests/test_fpca.py b/tests/test_fpca.py index f7e790f5f..6e79a2116 100644 --- a/tests/test_fpca.py +++ b/tests/test_fpca.py @@ -9,7 +9,7 @@ from skfda.datasets import fetch_weather from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization -from skfda.preprocessing.dim_reduction.feature_extraction import FPCA +from skfda.preprocessing.dim_reduction import FPCA from skfda.representation.basis import Basis, BSpline, Fourier diff --git a/tests/test_per_class_transformer.py b/tests/test_per_class_transformer.py index 666b914f9..3310b608e 100644 --- a/tests/test_per_class_transformer.py +++ b/tests/test_per_class_transformer.py @@ -7,7 +7,7 @@ from skfda._utils import _classifier_get_classes from skfda.datasets import fetch_growth from skfda.ml.classification import KNeighborsClassifier -from skfda.preprocessing.dim_reduction.feature_extraction import ( +from skfda.preprocessing.dim_reduction.feature_construction import ( PerClassTransformer, ) from skfda.preprocessing.dim_reduction.variable_selection import ( From 12ebaa9a058b50f5b1241ba02d5c332aa7edcd99 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Mon, 28 Mar 2022 13:23:41 +0200 Subject: [PATCH 110/400] Old errors --- docs/modules/misc/metrics.rst | 2 +- skfda/misc/metrics/_mahalanobis.py | 19 ++++++++++--------- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/docs/modules/misc/metrics.rst b/docs/modules/misc/metrics.rst index 7969e0314..836fa27fa 100644 --- a/docs/modules/misc/metrics.rst +++ b/docs/modules/misc/metrics.rst @@ -69,7 +69,7 @@ distance: .. autosummary:: :toctree: autosummary - skfda.misc.metrics.FMahalanobisDistance + skfda.misc.metrics.MahalanobisDistance diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index bf1d66e10..ed1da204e 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -42,8 +42,8 @@ class MahalanobisDistance(BaseEstimator): # type: ignore parameter is only used when fitting a FDataGrid. Attributes: - ef\_: eigenvectors of the covariance operator. - ev\_: Eigenvalues of the covariance operator. + eigen_vectors\_: Eigenvectors of the covariance operator. + eigen_values\_: Eigenvalues of the covariance operator. mean\_: Mean of the stochastic process. Examples: @@ -53,10 +53,10 @@ class MahalanobisDistance(BaseEstimator): # type: ignore >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> grid_points = [0, 1] >>> fd = FDataGrid(data_matrix, grid_points) - >>> mah = MahalanobisDistance(2) - >>> mah.fit(fd) + >>> mahalanobis = MahalanobisDistance(2) + >>> mahalanobis.fit(fd) MahalanobisDistance(n_components=2) - >>> mah(fd[0], fd[1]) + >>> mahalanobis(fd[0], fd[1]) 1.9968038359080937 References: @@ -106,8 +106,8 @@ def fit( self.components_basis, ) fpca.fit(X) - self.ev_ = fpca.explained_variance_ - self.ef_ = fpca.components_ + self.eigen_values_ = fpca.explained_variance_ + self.eigen_vectors_ = fpca.components_ self.mean_ = fpca.mean_ return self @@ -129,6 +129,7 @@ def __call__( check_is_fitted(self) return np.sum( - self.ev_ * inner_product(e1 - e2, self.ef_) ** 2 - / (self.ev_ + self.alpha)**2, + self.eigen_values_ + * inner_product(e1 - e2, self.eigen_vectors_) ** 2 + / (self.eigen_values_ + self.alpha)**2, ) From facc6e22861c6790cf361103e9d0e64d7e25eddd Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Mon, 28 Mar 2022 13:29:26 +0200 Subject: [PATCH 111/400] More old changes --- docs/refs.bib | 3 +-- skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py | 4 ++-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/refs.bib b/docs/refs.bib index e1564604c..08badbaa3 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -10,8 +10,7 @@ @article{baillo+grane_2008_llr url = {https://www.sciencedirect.com/science/article/pii/S0047259X08000973} } -@article{ - berrendero+bueno-larraz+cuevas_2020_mahalanobis, +@article{berrendero+bueno-larraz+cuevas_2020_mahalanobis, author = {José R. Berrendero and Beatriz Bueno-Larraz and Antonio Cuevas}, title = {On Mahalanobis Distance in Functional Settings}, journal = {Journal of Machine Learning Research}, diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index a215f4818..9ceeac2eb 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -59,7 +59,7 @@ class FPCA( explained_variance_ratio\_ (array_like): this contains the percentage of variance explained by each principal component. singular_values\_: The singular values corresponding to each of the - selected components. + selected components. mean\_ (FData): mean of the train data. Examples: @@ -368,7 +368,7 @@ def _fit_grid( basis_matrix = basis.data_matrix[..., 0] if regularization_matrix is not None: - basis_matrix += regularization_matrix + basis_matrix = basis_matrix + regularization_matrix fd_data = np.linalg.solve( basis_matrix.T, From e2625c7f9a0203a737e0e52d3c7cef178c54a783 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 31 Mar 2022 00:23:46 +0200 Subject: [PATCH 112/400] ParametrizedFunctionalQDA --- skfda/ml/classification/__init__.py | 2 +- ...ier.py => _parametrized_functional_qda.py} | 19 +++++++++---------- 2 files changed, 10 insertions(+), 11 deletions(-) rename skfda/ml/classification/{_gaussian_classifier.py => _parametrized_functional_qda.py} (93%) diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index fbd573815..78bb7db78 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -5,7 +5,7 @@ DDGClassifier, MaximumDepthClassifier, ) -from ._gaussian_classifier import GaussianClassifier +from ._parametrized_functional_qda import ParametrizedFunctionalQDA from ._logistic_regression import LogisticRegression from ._neighbors_classifiers import ( KNeighborsClassifier, diff --git a/skfda/ml/classification/_gaussian_classifier.py b/skfda/ml/classification/_parametrized_functional_qda.py similarity index 93% rename from skfda/ml/classification/_gaussian_classifier.py rename to skfda/ml/classification/_parametrized_functional_qda.py index 4219649b5..d7df4ee61 100644 --- a/skfda/ml/classification/_gaussian_classifier.py +++ b/skfda/ml/classification/_parametrized_functional_qda.py @@ -11,11 +11,11 @@ from ...representation import FDataGrid -class GaussianClassifier( +class ParametrizedFunctionalQDA( BaseEstimator, # type: ignore ClassifierMixin, # type: ignore ): - """Gaussian process based classifer for functional data. + """Parametrized functional quadratic discriminant analysis. This classifier is based on the assumption that the data is part of a gaussian process and depending on the output label, the covariance @@ -26,7 +26,7 @@ class GaussianClassifier( The training phase of the classifier will try to approximate the two main parameters of a gaussian process for each class. The covariance will be estimated by fitting the initial kernel passed on the creation of - the GaussianClassifier object. + the ParametrizedFunctionalQDA object. The result of the training function will be two arrays, one of means and another one of covariances. Both with length (n_classes). @@ -66,23 +66,22 @@ class GaussianClassifier( >>> from GPy.kern import RBF >>> rbf = RBF(input_dim=1, variance=6, lengthscale=1) - We will fit the Gaussian Process classifier with training data. We + We will fit the ParametrizedFunctionalQDA with training data. We use as regularizer parameter a low value as 0.05. - - >>> gaussian = GaussianClassifier(rbf, 0.05) - >>> gaussian = gaussian.fit(X_train, y_train) + >>> pfqda = ParametrizedFunctionalQDA(rbf, 0.05) + >>> pfqda = pfqda.fit(X_train, y_train) We can predict the class of new samples - >>> list(gaussian.predict(X_test)) + >>> list(pfqda.predict(X_test)) [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1] Finally, we calculate the mean accuracy for the test data - >>> round(gaussian.score(X_test, y_test), 2) + >>> round(pfqda.score(X_test, y_test), 2) 0.96 """ @@ -91,7 +90,7 @@ def __init__(self, kernel: Kern, regularizer: float) -> None: self.kernel = kernel self.regularizer = regularizer - def fit(self, X: FDataGrid, y: np.ndarray) -> GaussianClassifier: + def fit(self, X: FDataGrid, y: np.ndarray) -> ParametrizedFunctionalQDA: """Fit the model using X as training data and y as target values. Args: From be147d124745f66cb91c54f9d83f4766ddc8d6b5 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 1 Apr 2022 19:03:48 +0200 Subject: [PATCH 113/400] Corrections on the example --- examples/plot_classification_methods.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index 1f0318fdc..a96bada11 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -18,10 +18,10 @@ from skfda.datasets import fetch_growth from skfda.exploratory.depth import ModifiedBandDepth from skfda.ml.classification import ( - GaussianClassifier, KNeighborsClassifier, MaximumDepthClassifier, NearestCentroid, + ParametrizedFunctionalQDA, ) ############################################################################## @@ -111,12 +111,13 @@ ############################################################################## -# The fourth method considered is a Gaussian Process based Classifier. +# The fourth method considered is a Parametrized functional quadratic +# discriminant. # We have selected a gaussian kernel with initial parameters: variance=6 and # mean=1 # As regularizer a small value 0.05 has been chosen. -gaussian = GaussianClassifier( +gaussian = ParametrizedFunctionalQDA( kernel=RBF(input_dim=1, variance=6, lengthscale=1), regularizer=0.05, ) From d34b5b47a231f13cbc85c7a78fe3e510c05eae2f Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 3 Apr 2022 00:39:00 +0200 Subject: [PATCH 114/400] Fixing a typo on Predictors sklearn vs skfda --- tutorial/plot_skfda_sklearn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorial/plot_skfda_sklearn.py b/tutorial/plot_skfda_sklearn.py index c408589b0..2b728bae2 100644 --- a/tutorial/plot_skfda_sklearn.py +++ b/tutorial/plot_skfda_sklearn.py @@ -136,7 +136,7 @@ # # Predictors should implement the ``fit_predict`` method for fitting the # estimators and predicting the targets in one step and/or the ``predict`` -# method for predicting the targets of possibly non previously bserved data. +# method for predicting the targets of possibly non previously observed data. # Usually :term:`sklearn:transductive` estimators implement only the former # one, while :term:`sklearn:inductive` estimators implement the latter one (or # both). From 2e17b66eea4cfc06d8b958279323a274358b881b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 3 Apr 2022 22:57:04 +0200 Subject: [PATCH 115/400] Style correction --- skfda/ml/classification/__init__.py | 2 +- skfda/ml/classification/_parametrized_functional_qda.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index 78bb7db78..9a265d67f 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -5,9 +5,9 @@ DDGClassifier, MaximumDepthClassifier, ) -from ._parametrized_functional_qda import ParametrizedFunctionalQDA from ._logistic_regression import LogisticRegression from ._neighbors_classifiers import ( KNeighborsClassifier, RadiusNeighborsClassifier, ) +from ._parametrized_functional_qda import ParametrizedFunctionalQDA diff --git a/skfda/ml/classification/_parametrized_functional_qda.py b/skfda/ml/classification/_parametrized_functional_qda.py index d7df4ee61..7b6689daa 100644 --- a/skfda/ml/classification/_parametrized_functional_qda.py +++ b/skfda/ml/classification/_parametrized_functional_qda.py @@ -116,7 +116,6 @@ def fit(self, X: FDataGrid, y: np.ndarray) -> ParametrizedFunctionalQDA: + self.regularizer * np.eye(len(X.grid_points[0])) ) - # Calculates logarithmic covariance -> -1/2 * log|sum| self._log_determinant_covariances = np.asarray([ np.trace(logm(regularized_covariance)) for regularized_covariance in self._regularized_covariances From 97e1b2515488070baa9bc94e7651abf2a8e7c0dd Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Tue, 5 Apr 2022 13:04:10 +0200 Subject: [PATCH 116/400] Suggestion to adapt classifiers to Mahalanobis --- skfda/misc/metrics/_mahalanobis.py | 7 ++++--- skfda/ml/classification/_centroid_classifiers.py | 11 +++++++++-- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index ed1da204e..0f61848a8 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -11,7 +11,7 @@ from ...representation import FData from ...representation._typing import ArrayLike, NDArrayFloat from ...representation.basis import Basis -from .._math import inner_product +from .._math import inner_product_matrix from ..regularization._regularization import TikhonovRegularization WeightsCallable = Callable[[np.ndarray], np.ndarray] @@ -57,7 +57,7 @@ class MahalanobisDistance(BaseEstimator): # type: ignore >>> mahalanobis.fit(fd) MahalanobisDistance(n_components=2) >>> mahalanobis(fd[0], fd[1]) - 1.9968038359080937 + array([ 1.99680384]) References: .. footbibliography:: @@ -130,6 +130,7 @@ def __call__( return np.sum( self.eigen_values_ - * inner_product(e1 - e2, self.eigen_vectors_) ** 2 + * inner_product_matrix(e1 - e2, self.eigen_vectors_) ** 2 / (self.eigen_values_ + self.alpha)**2, + axis=1, ) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 7ed6c92f2..bb7dc73ad 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -4,7 +4,8 @@ from typing import Callable, Generic, Optional, TypeVar from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted +from sklearn.exceptions import NotFittedError +from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes from ...exploratory.depth import Depth, ModifiedBandDepth @@ -86,6 +87,12 @@ def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: Returns: self """ + if hasattr(self.metric, 'fit'): # noqa: WPS421 + try: + check_is_fitted(self.metric) + except NotFittedError: + self.metric.fit(X) + classes, y_ind = _classifier_get_classes(y) self._classes = classes @@ -107,7 +114,7 @@ def predict(self, X: T) -> NDArrayInt: Array of shape (n_samples) or (n_samples, n_outputs) with class labels for each data sample. """ - sklearn_check_is_fitted(self) + check_is_fitted(self) return self._classes[PairwiseMetric(self.metric)( X, From 752c143cdd96092e44d2c6cda5dfd0d87b511327 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Wed, 6 Apr 2022 20:30:29 +0200 Subject: [PATCH 117/400] Mahalanobis depth and other fixes --- docs/modules/exploratory/depth.rst | 9 ++- docs/refs.bib | 12 +++ skfda/exploratory/depth/__init__.py | 10 ++- skfda/exploratory/depth/_depth.py | 76 ++++++++++++++++++- skfda/misc/metrics/_mahalanobis.py | 52 ++++++++----- .../classification/_centroid_classifiers.py | 6 +- 6 files changed, 131 insertions(+), 34 deletions(-) diff --git a/docs/modules/exploratory/depth.rst b/docs/modules/exploratory/depth.rst index 944165477..d9d83ed68 100644 --- a/docs/modules/exploratory/depth.rst +++ b/docs/modules/exploratory/depth.rst @@ -35,10 +35,11 @@ The following classes implement depth functions for functional data: skfda.exploratory.depth.IntegratedDepth skfda.exploratory.depth.BandDepth skfda.exploratory.depth.ModifiedBandDepth + skfda.exploratory.depth.DistanceBasedDepth Most of them support functional data with more than one dimension on the :term:`domain` and on the :term:`codomain`. - + Multivariate depths ^^^^^^^^^^^^^^^^^^^ @@ -52,7 +53,7 @@ Thus we also provide some multivariate depth functions: .. autosummary:: :toctree: autosummary - + skfda.exploratory.depth.multivariate.ProjectionDepth skfda.exploratory.depth.multivariate.SimplicialDepth @@ -78,7 +79,7 @@ data case: .. autosummary:: :toctree: autosummary - + skfda.exploratory.depth.multivariate.StahelDonohoOutlyingness Conversion @@ -90,7 +91,7 @@ The following class define a depth based on an outlyingness measure. .. autosummary:: :toctree: autosummary - + skfda.exploratory.depth.OutlyingnessBasedDepth diff --git a/docs/refs.bib b/docs/refs.bib index 08badbaa3..70c50dd6a 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -308,6 +308,18 @@ @inbook{ramsay+silverman_2005_functional_basis doi = {110.1007/b98888} } +@article{serfling+zuo_2000_depth_function, + author = {R. Serfling and Y. Zuo}, + title = {General notions of statistical depth function}, + volume = {28}, + journal = {The Annals of Statistics}, + number = {2}, + publisher = {Institute of Mathematical Statistics}, + pages = {461 -- 482}, + keywords = {halfspace depth, multivariate symmetry, simplicial depth, Statistical depth functions}, + year = {2000} +} + @inbook{srivastava+klassen_2016_analysis_elastic, author = {Srivastava, Anuj and Klassen, Eric}, title = {Functional and Shape Data Analysis}, diff --git a/skfda/exploratory/depth/__init__.py b/skfda/exploratory/depth/__init__.py index b1eb7aff0..4424a4376 100644 --- a/skfda/exploratory/depth/__init__.py +++ b/skfda/exploratory/depth/__init__.py @@ -1,5 +1,9 @@ +"""Depth.""" from . import multivariate -from ._depth import (IntegratedDepth, - ModifiedBandDepth, - BandDepth) +from ._depth import ( + BandDepth, + DistanceBasedDepth, + IntegratedDepth, + ModifiedBandDepth, +) from .multivariate import Depth, Outlyingness, OutlyingnessBasedDepth diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index d474c0568..e7b94b436 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -8,14 +8,19 @@ from __future__ import annotations import itertools -from typing import Optional +from typing import Optional, TypeVar import numpy as np import scipy.integrate from ... import FDataGrid +from ...misc.metrics import Metric, l2_distance +from ...representation import FData +from ...representation._typing import NDArrayFloat from .multivariate import Depth, SimplicialDepth, _UnivariateFraimanMuniz +T = TypeVar("T", bound=FData) + class IntegratedDepth(Depth[FDataGrid]): r""" @@ -211,3 +216,72 @@ def transform(self, X: FDataGrid) -> np.ndarray: # noqa: D102 n_total += 1 return num_in / n_total + + +class DistanceBasedDepth(Depth[FDataGrid]): + r""" + Functional depth based on a metric. + + Parameters: + metric: + The metric to use as M in the following depth calculation + .. math:: + D(x) = [1 + M^2(x, \mu)]^{-1}. + + as explained in :footcite:`serfling+zuo_2000_depth_function`. + + Examples: + >>> import skfda + >>> from skfda.exploratory.depth import DistanceBasedDepth + >>> from skfda.misc.metrics import MahalanobisDistance + >>> data_matrix = [[1, 1, 2, 3, 2.5, 2], + ... [0.5, 0.5, 1, 2, 1.5, 1], + ... [-1, -1, -0.5, 1, 1, 0.5], + ... [-0.5, -0.5, -0.5, -1, -1, -1]] + >>> grid_points = [0, 2, 4, 6, 8, 10] + >>> fd = skfda.FDataGrid(data_matrix, grid_points) + >>> depth = DistanceBasedDepth(MahalanobisDistance(2)) + >>> depth(fd) + array([ 0.41897777, 0.8058132 , 0.31097392, 0.31723619]) + + References: + .. footbibliography:: + """ + + def __init__( + self, + metric: Metric[T] = l2_distance, + ) -> None: + self.metric = metric + + def fit( # noqa: D102 + self, + X: T, + y: None = None, + ) -> DistanceBasedDepth: + """Fit the model using X as training data. + + Args: + X: FDataGrid with the training data or array matrix with shape + (n_samples, n_samples) if metric='precomputed'. + y: Ignored. + + Returns: + self + """ + if hasattr(self.metric, 'fit'): # noqa: WPS421 + self.metric.fit(X) + self.mean_ = X.mean() + + return self + + def transform(self, X: T) -> NDArrayFloat: # noqa: D102 + """Compute the depth of given observations. + + Args: + X: FDataGrid with the observations to use in the calculation. + + Returns: + Array with the depths. + """ + return 1 / (1 + self.metric(X, self.mean_)) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 0f61848a8..ea7563505 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -30,21 +30,20 @@ class MahalanobisDistance(BaseEstimator): # type: ignore centering: If ``True`` then calculate the mean of the functional data object and center the data first. Defaults to ``True``. regularization: Regularization object to be applied. - components_basis: The basis in which we want the eigenvectors. - We can use a different basis than the basis contained in the - passed FDataBasis object. This parameter is only used when - fitting a FDataBasis. - weights: the weights vector used for discrete integration. + weights: The weights vector used for discrete integration. If none then the trapezoidal rule is used for computing the weights. If a callable object is passed, then the weight vector will be obtained by evaluating the object at the sample points of the passed FDataGrid object in the fit method. This parameter is only used when fitting a FDataGrid. - - Attributes: - eigen_vectors\_: Eigenvectors of the covariance operator. - eigen_values\_: Eigenvalues of the covariance operator. - mean\_: Mean of the stochastic process. + components_basis: The basis in which we want the eigenvectors. + We can use a different basis than the basis contained in the + passed FDataBasis object. This parameter is only used when + fitting a FDataBasis. + alpha: Hyperparameter that controls the smoothness of the + aproximation. + eigen_vectors: Eigenvectors of the covariance operator. + eigen_values: Eigenvalues of the covariance operator. Examples: >>> from skfda.misc.metrics import MahalanobisDistance @@ -71,6 +70,8 @@ def __init__( weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, alpha: float = 0.001, + eigen_values: Optional[NDArrayFloat] = None, + eigen_vectors: Optional[FData] = None, ) -> None: self.n_components = n_components self.centering = centering @@ -78,6 +79,8 @@ def __init__( self.weights = weights self.components_basis = components_basis self.alpha = alpha + self.eigen_values = eigen_values + self.eigen_vectors = eigen_vectors def fit( self, @@ -98,17 +101,24 @@ def fit( """ from ...preprocessing.dim_reduction.feature_extraction import FPCA - fpca = FPCA( - self.n_components, - self.centering, - self.regularization, - self.weights, - self.components_basis, - ) - fpca.fit(X) - self.eigen_values_ = fpca.explained_variance_ - self.eigen_vectors_ = fpca.components_ - self.mean_ = fpca.mean_ + if self.eigen_values is None or self.eigen_vectors is None: + fpca = FPCA( + self.n_components, + self.centering, + self.regularization, + self.weights, + self.components_basis, + ) + fpca.fit(X) + self.eigen_values_ = fpca.explained_variance_ + self.eigen_vectors_ = fpca.components_ + + elif not ( + hasattr(self, 'eigen_values_') # noqa: WPS421 + and hasattr(self, 'eigen_vectors_') # noqa: WPS421 + ): + self.eigen_values_ = self.eigen_values + self.eigen_vectors_ = self.eigen_vectors return self diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index bb7dc73ad..f437b19e5 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -4,7 +4,6 @@ from typing import Callable, Generic, Optional, TypeVar from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.exceptions import NotFittedError from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes @@ -88,10 +87,7 @@ def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: self """ if hasattr(self.metric, 'fit'): # noqa: WPS421 - try: - check_is_fitted(self.metric) - except NotFittedError: - self.metric.fit(X) + self.metric.fit(X) classes, y_ind = _classifier_get_classes(y) From c7b13398f4a1c13fd53c71f7322031ed3394d749 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Wed, 6 Apr 2022 20:45:47 +0200 Subject: [PATCH 118/400] Added fix method to Metric type --- skfda/misc/metrics/_mahalanobis.py | 2 +- skfda/misc/metrics/_typing.py | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index ea7563505..06df300dc 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -18,7 +18,7 @@ class MahalanobisDistance(BaseEstimator): # type: ignore - r"""Functional Mahalanobis distance. + """Functional Mahalanobis distance. Class that implements functional Mahalanobis distance for both basis and grid representations of the data diff --git a/skfda/misc/metrics/_typing.py b/skfda/misc/metrics/_typing.py index 8b44bd216..d000ece8a 100644 --- a/skfda/misc/metrics/_typing.py +++ b/skfda/misc/metrics/_typing.py @@ -6,6 +6,7 @@ from typing_extensions import Final, Literal, Protocol +from ...representation import FData from ...representation._typing import NDArrayFloat, Vector VectorType = TypeVar("VectorType", contravariant=True, bound=Vector) @@ -35,6 +36,13 @@ def __call__(self, __vector: VectorType) -> NDArrayFloat: # noqa: WPS112 class Metric(Protocol[MetricElementType]): """Protocol for a metric between two elements of a metric space.""" + def fit( + self, + X: FData, + y: None, + ) -> Metric[Any]: + """Fit the metric using some data.""" + @abstractmethod def __call__( self, From c33272f0af89bf52c6f530f197cd2c4e4b9bf6a3 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Wed, 6 Apr 2022 21:00:27 +0200 Subject: [PATCH 119/400] Removing fix method from Metric type --- skfda/misc/metrics/_typing.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/skfda/misc/metrics/_typing.py b/skfda/misc/metrics/_typing.py index d000ece8a..8b44bd216 100644 --- a/skfda/misc/metrics/_typing.py +++ b/skfda/misc/metrics/_typing.py @@ -6,7 +6,6 @@ from typing_extensions import Final, Literal, Protocol -from ...representation import FData from ...representation._typing import NDArrayFloat, Vector VectorType = TypeVar("VectorType", contravariant=True, bound=Vector) @@ -36,13 +35,6 @@ def __call__(self, __vector: VectorType) -> NDArrayFloat: # noqa: WPS112 class Metric(Protocol[MetricElementType]): """Protocol for a metric between two elements of a metric space.""" - def fit( - self, - X: FData, - y: None, - ) -> Metric[Any]: - """Fit the metric using some data.""" - @abstractmethod def __call__( self, From 1b107247b88d959da4e45201d675b955fc7a21eb Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Wed, 6 Apr 2022 21:26:12 +0200 Subject: [PATCH 120/400] Small change --- skfda/misc/metrics/_mahalanobis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 06df300dc..744900e71 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -134,7 +134,7 @@ def __call__( e2: Second object. Returns: - Squared functional Mahalanobis distances of the observations. + Squared functional Mahalanobis distance between two observations. """ check_is_fitted(self) From b1149e3264aed3d8dd565acddaad411ee9e857e7 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 6 Apr 2022 22:10:11 +0200 Subject: [PATCH 121/400] Corrections on the classification example --- examples/plot_classification_methods.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index a96bada11..2f123a2b1 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -6,7 +6,7 @@ Shows a comparison between different classification methods. The Berkeley Growth Study dataset is used as input data. Classification methods KNN, Maximum Depth, Nearest Centroid and -Gaussian classifier are compared. +Parametric Functional QDA are compared. """ # Author:Álvaro Castillo García @@ -117,16 +117,16 @@ # mean=1 # As regularizer a small value 0.05 has been chosen. -gaussian = ParametrizedFunctionalQDA( +pfqda = ParametrizedFunctionalQDA( kernel=RBF(input_dim=1, variance=6, lengthscale=1), regularizer=0.05, ) -gaussian.fit(X_train, y_train) -gaussian_pred = gaussian.predict(X_test) -print(gaussian_pred) -print('The score of Gaussian Process Classifier is {0:2.2%}'.format( - gaussian.score(X_test, y_test), +pfqda.fit(X_train, y_train) +pfqda_pred = pfqda.predict(X_test) +print(pfqda_pred) +print('The score of Parametrized Functional QDA is {0:2.2%}'.format( + pfqda_pred.score(X_test, y_test), )) # Plot the prediction -X_test.plot(group=gaussian_pred, group_names=categories).show() +X_test.plot(group=pfqda_pred, group_names=categories).show() From 126cd21019c9afdab4de14c6dc194681d62021b7 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 6 Apr 2022 23:21:57 +0200 Subject: [PATCH 122/400] Construction of moments --- skfda/exploratory/stats/__init__.py | 2 +- .../stats/_functional_transformers.py | 202 ++++++++---------- 2 files changed, 93 insertions(+), 111 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index def4682e4..7dc0450da 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,5 +1,5 @@ from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import moments_of_norm, moments_of_process +from ._functional_transformers import moments, moments_of_norm from ._stats import ( cov, depth_based_median, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index c94ee6a13..2557062c2 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,7 +2,7 @@ from __future__ import annotations -from functools import reduce +from typing import Callable import numpy as np @@ -11,6 +11,7 @@ def moments_of_norm( data: FDataGrid, + p: int, ) -> np.ndarray: r""" Calculate the moments of the norm of the process of a FDataGrid. @@ -34,130 +35,111 @@ def moments_of_norm( Then we call the function with the dataset. >>> import numpy as np >>> from skfda.exploratory.stats import moments_of_norm - >>> np.around(moments_of_norm(X), decimals=2) - array([[ 4.69, 4.06], - [ 6.15, 3.99], - [ 5.51, 4.04], - [ 6.81, 3.46], - [ 5.23, 3.29], - [ 5.26, 3.09], - [ -5.06, 2.2 ], - [ 3.1 , 2.46], - [ 2.25, 2.55], - [ 4.08, 3.31], - [ 4.12, 3.04], - [ 6.13, 2.58], - [ 5.81, 2.5 ], - [ 7.27, 2.14], - [ 7.31, 2.62], - [ 2.46, 1.93], - [ 2.47, 1.4 ], - [ -0.15, 1.23], - [ -7.09, 1.12], - [ 2.75, 1.02], - [ 0.68, 1.11], - [ -3.41, 0.99], - [ 2.26, 1.27], - [ 3.99, 1.1 ], - [ 8.75, 0.74], + >>> np.around(moments_of_norm(X, 1), decimals=2) + array([[ 7.05, 4.06], + [ 8.98, 3.99], + [ 8.38, 4.04], + [ 8.13, 3.46], + [ 9.17, 3.29], + [ 9.95, 3.09], + [ 11.55, 2.2 ], + [ 10.92, 2.46], + [ 10.84, 2.55], + [ 10.53, 3.31], + [ 10.02, 3.04], + [ 10.97, 2.58], + [ 11.01, 2.5 ], + [ 10.15, 2.14], + [ 10.18, 2.62], + [ 10.36, 1.93], + [ 12.36, 1.4 ], + [ 12.28, 1.23], + [ 13.13, 1.12], + [ 11.62, 1.02], + [ 12.05, 1.11], + [ 13.5 , 0.99], + [ 10.16, 1.27], + [ 8.83, 1.1 ], + [ 10.32, 0.74], [ 9.96, 3.16], [ 9.62, 2.33], - [ 3.84, 1.67], - [ 7. , 7.1 ], - [ -0.85, 0.74], - [ -4.79, 0.9 ], - [ -5.02, 0.73], - [ -9.65, 1.14], - [ -9.24, 0.71], - [-16.52, 0.39]]) + [ 8.4 , 1.67], + [ 7.02, 7.1 ], + [ 10.06, 0.74], + [ 14.29, 0.9 ], + [ 14.47, 0.73], + [ 13.05, 1.14], + [ 15.97, 0.71], + [ 17.65, 0.39]]) """ - curves = data.data_matrix - norms = np.empty((0, curves.shape[2])) - for c in curves: # noqa: WPS426 - x, y = c.shape - curve_norms = [] - for i in range(0, y): # noqa: WPS426 - curve_norms = curve_norms + [ - reduce(lambda a, b: a + b[i], c, 0) / x, - ] - norms = np.concatenate((norms, [curve_norms])) - return np.array(norms) - - -def moments_of_process( + return moments(data, lambda x: pow(np.abs(x), 1)) + + +def moments( data: FDataGrid, + f: Callable[[np.ndarray], np.ndarray], ) -> np.ndarray: - r""" - Calculate the moments of the process of a FDataGrid. - - It performs the following map: - .. math:: - f_1(X)=\int_a^b X(t,\omega)dP(\omega),\dots,f_p(X)=\int_a^b \\ - X^p(t,\omega)dP(\omega). + """ + Calculate the moments of a FDataGrid. Args: data: FDataGrid where we want to calculate - the moments of the process. + the moments. + f: function that specifies the moments to be calculated. Returns: ndarray of shape (n_dimensions, n_samples)\ with the values of the moments. Example: - - We import the Canadian Weather dataset + We will use this funtion to calculate the moments of the + norm of a FDataGrid. + We will first import the Canadian Weather dataset. >>> from skfda.datasets import fetch_weather >>> X = fetch_weather(return_X_y=True)[0] - Then we call the function with the dataset. + We will define a function that calculates the moments of the norm. + >>> f = lambda x: pow(np.abs(x), 1) + + Then we call the function with the dataset and the function. >>> import numpy as np - >>> from skfda.exploratory.stats import moments_of_process - >>> np.around(moments_of_process(X), decimals=2) - array([[ 5.2430e+01, 2.2500e+00], - [ 7.7800e+01, 2.9200e+00], - [ 7.2150e+01, 2.6900e+00], - [ 5.1250e+01, 2.1200e+00], - [ 8.9310e+01, 1.6000e+00], - [ 1.0504e+02, 1.5300e+00], - [ 1.6209e+02, 1.5000e+00], - [ 1.4143e+02, 1.4500e+00], - [ 1.4322e+02, 1.1500e+00], - [ 1.2593e+02, 1.6500e+00], - [ 1.1139e+02, 1.4000e+00], - [ 1.2215e+02, 1.1600e+00], - [ 1.2593e+02, 1.0500e+00], - [ 9.3560e+01, 1.0300e+00], - [ 9.3080e+01, 1.2200e+00], - [ 1.2836e+02, 1.2700e+00], - [ 1.8069e+02, 1.2000e+00], - [ 1.8907e+02, 9.2000e-01], - [ 1.9644e+02, 5.6000e-01], - [ 1.5856e+02, 9.4000e-01], - [ 1.7739e+02, 8.1000e-01], - [ 2.3041e+02, 4.5000e-01], - [ 1.1928e+02, 1.2700e+00], - [ 8.4900e+01, 1.0200e+00], - [ 7.9470e+01, 2.0000e-01], - [ 2.6700e+01, 3.5200e+00], - [ 2.1680e+01, 3.0900e+00], - [ 7.7390e+01, 4.9000e-01], - [ 1.8950e+01, 9.9500e+00], - [ 1.2783e+02, 2.5000e-01], - [ 2.5206e+02, 2.9000e-01], - [ 2.5201e+02, 2.7000e-01], - [ 1.6401e+02, 4.9000e-01], - [ 2.5490e+02, 2.0000e-01], - [ 1.8772e+02, 1.2000e-01]]) + >>> from skfda.exploratory.stats import moments + + >>> np.around(moments(X, f), decimals=2) + array([[ 7.05, 4.06], + [ 8.98, 3.99], + [ 8.38, 4.04], + [ 8.13, 3.46], + [ 9.17, 3.29], + [ 9.95, 3.09], + [ 11.55, 2.2 ], + [ 10.92, 2.46], + [ 10.84, 2.55], + [ 10.53, 3.31], + [ 10.02, 3.04], + [ 10.97, 2.58], + [ 11.01, 2.5 ], + [ 10.15, 2.14], + [ 10.18, 2.62], + [ 10.36, 1.93], + [ 12.36, 1.4 ], + [ 12.28, 1.23], + [ 13.13, 1.12], + [ 11.62, 1.02], + [ 12.05, 1.11], + [ 13.5 , 0.99], + [ 10.16, 1.27], + [ 8.83, 1.1 ], + [ 10.32, 0.74], + [ 9.96, 3.16], + [ 9.62, 2.33], + [ 8.4 , 1.67], + [ 7.02, 7.1 ], + [ 10.06, 0.74], + [ 14.29, 0.9 ], + [ 14.47, 0.73], + [ 13.05, 1.14], + [ 15.97, 0.71], + [ 17.65, 0.39]]) + """ - norm = moments_of_norm(data) - curves = data.data_matrix - moments = np.empty((0, curves.shape[2])) - for i, c in enumerate(curves): # noqa: WPS426 - x, y = c.shape - curve_moments = [] - for j in range(0, y): # noqa: WPS426 - curve_moments = curve_moments + [ - reduce(lambda a, b: a + ((b[j] - norm[i][j]) ** 2), c, 0) / x, - ] - moments = np.concatenate((moments, [curve_moments])) - - return moments + return np.mean(f(data.data_matrix), axis=1) From a548c9479ae78187c79b6aec7f83ee20a1d5519b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 6 Apr 2022 23:43:20 +0200 Subject: [PATCH 123/400] Minor fix --- examples/plot_classification_methods.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index 2f123a2b1..dc4fe4081 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -125,7 +125,7 @@ pfqda_pred = pfqda.predict(X_test) print(pfqda_pred) print('The score of Parametrized Functional QDA is {0:2.2%}'.format( - pfqda_pred.score(X_test, y_test), + pfqda.score(X_test, y_test), )) # Plot the prediction From e88fe490e88ccaac8fdaf601e5d6ec4c6f5f4e49 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 6 Apr 2022 23:49:56 +0200 Subject: [PATCH 124/400] Fixing style errors --- skfda/exploratory/stats/_functional_transformers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index c0850b135..162997b0e 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -9,7 +9,6 @@ from ..._utils import check_is_univariate from ...representation import FDataBasis, FDataGrid from ...representation._typing import NDArrayFloat, NDArrayInt -from ...representation import FDataGrid def local_averages( @@ -344,6 +343,7 @@ def number_up_crossings( axis=2, ).T + def moments_of_norm( data: FDataGrid, p: int, From 61000e78a227d492565700d78769dd834a7438c0 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 6 Apr 2022 23:52:22 +0200 Subject: [PATCH 125/400] Change files path --- skfda/preprocessing/__init__.py | 3 +- skfda/preprocessing/dim_reduction/__init__.py | 4 +- .../feature_extraction/__init__.py | 9 +++++ .../_evaluation_trasformer.py | 12 +++--- .../_fda_feature_union.py | 38 +++++++------------ .../_per_class_transformer.py | 13 +++---- tests/test_fda_feature_union.py | 2 +- tests/test_per_class_transformer.py | 2 +- 8 files changed, 40 insertions(+), 43 deletions(-) create mode 100644 skfda/preprocessing/dim_reduction/feature_extraction/__init__.py rename skfda/preprocessing/{dim_reduction => }/feature_construction/_evaluation_trasformer.py (93%) rename skfda/preprocessing/{dim_reduction => }/feature_construction/_fda_feature_union.py (76%) rename skfda/preprocessing/{dim_reduction => }/feature_construction/_per_class_transformer.py (96%) diff --git a/skfda/preprocessing/__init__.py b/skfda/preprocessing/__init__.py index 6eb81f352..a09e80474 100644 --- a/skfda/preprocessing/__init__.py +++ b/skfda/preprocessing/__init__.py @@ -1,3 +1,4 @@ +from . import feature_construction from . import registration from . import smoothing -from . import dim_reduction +from . import dim_reduction \ No newline at end of file diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index ef7ee4542..e7f3268d3 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -4,11 +4,11 @@ import importlib from typing import Any -from . import feature_construction, variable_selection +from . import variable_selection from ._fpca import FPCA def __getattr__(name: str) -> Any: - if name == "projection": + if name in {"projection", "feature_extraction"}: return importlib.import_module(f".{name}", __name__) raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py new file mode 100644 index 000000000..de9068399 --- /dev/null +++ b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py @@ -0,0 +1,9 @@ +import warnings + +from ... import feature_construction + +warnings.warn( + 'The module "feature_extraction" is deprecated.' + 'Please use "feature_construction"', + category=DeprecationWarning, +) diff --git a/skfda/preprocessing/dim_reduction/feature_construction/_evaluation_trasformer.py b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py similarity index 93% rename from skfda/preprocessing/dim_reduction/feature_construction/_evaluation_trasformer.py rename to skfda/preprocessing/feature_construction/_evaluation_trasformer.py index 11d058c18..01551827c 100644 --- a/skfda/preprocessing/dim_reduction/feature_construction/_evaluation_trasformer.py +++ b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py @@ -8,11 +8,11 @@ from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal -from ...._utils import TransformerMixin -from ....representation._functional_data import FData -from ....representation._typing import ArrayLike, GridPointsLike, NDArrayInt -from ....representation.extrapolation import ExtrapolationLike -from ....representation.grid import FDataGrid +from ..._utils import TransformerMixin +from ...representation._functional_data import FData +from ...representation._typing import ArrayLike, GridPointsLike, NDArrayInt +from ...representation.extrapolation import ExtrapolationLike +from ...representation.grid import FDataGrid Input = TypeVar("Input") Output = TypeVar("Output") @@ -47,7 +47,7 @@ class EvaluationTransformer( Examples: >>> from skfda.representation import FDataGrid, FDataBasis - >>> from skfda.preprocessing.dim_reduction.feature_construction import\ + >>> from skfda.preprocessing.feature_construction import\ ... ( ... EvaluationTransformer ... ) diff --git a/skfda/preprocessing/dim_reduction/feature_construction/_fda_feature_union.py b/skfda/preprocessing/feature_construction/_fda_feature_union.py similarity index 76% rename from skfda/preprocessing/dim_reduction/feature_construction/_fda_feature_union.py rename to skfda/preprocessing/feature_construction/_fda_feature_union.py index 0bc555950..99feca519 100644 --- a/skfda/preprocessing/dim_reduction/feature_construction/_fda_feature_union.py +++ b/skfda/preprocessing/feature_construction/_fda_feature_union.py @@ -7,7 +7,7 @@ from numpy import ndarray from sklearn.pipeline import FeatureUnion -from ....representation import FData +from ...representation import FData class FDAFeatureUnion(FeatureUnion): # type: ignore @@ -52,17 +52,14 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore >>> X,y = fetch_growth(return_X_y=True) Then we need to import the transformers we want to use. In our case we - will use Generalized depth-versus-depth transformer. + will use the Coefficients transformer. Evaluation Transformer returns the original curve, and as it is helpful, we will concatenate it to the already metioned transformer. - >>> from skfda.preprocessing.dim_reduction.feature_construction import ( + >>> from skfda.preprocessing.feature_construction import ( ... FDAFeatureUnion, ... ) - >>> from skfda.preprocessing.dim_reduction.feature_construction import ( - ... DDGTransformer, - ... ) - >>> from skfda.exploratory.depth import ModifiedBandDepth - >>> from skfda.preprocessing.dim_reduction.feature_construction import ( + >>> from skfda.preprocessing.dim_reduction import FPCA + >>> from skfda.preprocessing.feature_construction import ( ... EvaluationTransformer ... ) >>> import numpy as np @@ -70,28 +67,19 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore Finally we apply fit and transform. >>> union = FDAFeatureUnion( ... [ - ... ( - ... 'ddgtransformer', - ... DDGTransformer(depth_method=[ModifiedBandDepth()]), - ... ), + ... ("fpca", FPCA()), ... ("eval", EvaluationTransformer()), ... ], ... array_output=True, ... ) >>> np.around(union.fit_transform(X,y), decimals=2) - array([[ 2.100e-01, 9.000e-02, 8.130e+01, ..., 1.938e+02, 1.943e+02, - 1.951e+02], - [ 4.600e-01, 3.800e-01, 7.620e+01, ..., 1.761e+02, 1.774e+02, - 1.787e+02], - [ 2.000e-01, 3.300e-01, 7.680e+01, ..., 1.709e+02, 1.712e+02, - 1.715e+02], - ..., - [ 3.900e-01, 5.100e-01, 6.860e+01, ..., 1.660e+02, 1.663e+02, - 1.668e+02], - [ 2.600e-01, 2.700e-01, 7.990e+01, ..., 1.683e+02, 1.684e+02, - 1.686e+02], - [ 3.300e-01, 3.200e-01, 7.610e+01, ..., 1.686e+02, 1.689e+02, - 1.692e+02]]) + array([[ 52.92, -12.67, -6.18, ..., 193.8 , 194.3 , 195.1 ], + [ -5.89, -7.33, 12.59, ..., 176.1 , 177.4 , 178.7 ], + [-18.82, -13.11, 0.97, ..., 170.9 , 171.2 , 171.5 ], + ..., + [ -8.54, 5.99, 2.17, ..., 166. , 166.3 , 166.8 ], + [ 12.06, 17.22, 6.26, ..., 168.3 , 168.4 , 168.6 ], + [ 10.87, 14.92, 1.8 , ..., 168.6 , 168.9 , 169.2 ]]) """ def __init__( diff --git a/skfda/preprocessing/dim_reduction/feature_construction/_per_class_transformer.py b/skfda/preprocessing/feature_construction/_per_class_transformer.py similarity index 96% rename from skfda/preprocessing/dim_reduction/feature_construction/_per_class_transformer.py rename to skfda/preprocessing/feature_construction/_per_class_transformer.py index 8250279a3..ffdd20401 100644 --- a/skfda/preprocessing/dim_reduction/feature_construction/_per_class_transformer.py +++ b/skfda/preprocessing/feature_construction/_per_class_transformer.py @@ -8,11 +8,11 @@ import pandas as pd from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted -from ...._utils import TransformerMixin, _fit_feature_transformer -from ....representation import FData -from ....representation._typing import NDArrayFloat, NDArrayInt -from ....representation.basis import FDataBasis -from ....representation.grid import FDataGrid +from ..._utils import TransformerMixin, _fit_feature_transformer +from ...representation import FData +from ...representation._typing import NDArrayFloat, NDArrayInt +from ...representation.basis import FDataBasis +from ...representation.grid import FDataGrid Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) Output = TypeVar("Output", bound=Union[pd.DataFrame, NDArrayFloat]) @@ -53,8 +53,7 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): >>> X = X.iloc[:, 0].values >>> y = y.values.codes - >>> from skfda.preprocessing.dim_reduction.feature_construction\ - ... import ( + >>> from skfda.preprocessing.feature_construction import ( ... PerClassTransformer, ... ) diff --git a/tests/test_fda_feature_union.py b/tests/test_fda_feature_union.py index 3feecc7a7..af94ab04d 100644 --- a/tests/test_fda_feature_union.py +++ b/tests/test_fda_feature_union.py @@ -8,7 +8,7 @@ from skfda.datasets import fetch_growth from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix from skfda.misc.operators import SRSF -from skfda.preprocessing.dim_reduction.feature_construction import ( +from skfda.preprocessing.feature_construction import ( EvaluationTransformer, FDAFeatureUnion, ) diff --git a/tests/test_per_class_transformer.py b/tests/test_per_class_transformer.py index 3310b608e..908d5d238 100644 --- a/tests/test_per_class_transformer.py +++ b/tests/test_per_class_transformer.py @@ -7,7 +7,7 @@ from skfda._utils import _classifier_get_classes from skfda.datasets import fetch_growth from skfda.ml.classification import KNeighborsClassifier -from skfda.preprocessing.dim_reduction.feature_construction import ( +from skfda.preprocessing.feature_construction import ( PerClassTransformer, ) from skfda.preprocessing.dim_reduction.variable_selection import ( From 96675fc5b10de2bf23fd99614548bb2cb390f8a4 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 11 Apr 2022 23:47:10 +0200 Subject: [PATCH 126/400] Explanation of the example and fixes --- examples/plot_classification_methods.py | 71 +++++++++++++------ skfda/ml/classification/__init__.py | 2 +- ...da.py => _parameterized_functional_qda.py} | 53 ++++++++------ 3 files changed, 81 insertions(+), 45 deletions(-) rename skfda/ml/classification/{_parametrized_functional_qda.py => _parameterized_functional_qda.py} (78%) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index dc4fe4081..68b0c1099 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -1,17 +1,22 @@ """ Classification methods. - ================================== -Shows a comparison between different classification methods. +Shows a comparison between the accuracies of different classification +methods. +There has been selected one method of each kind present in the library. +In particular, there is one based on depths, Maximum Depth Classifier, +another one based on centroids, Nearest Centroid Classifier, another one +based on the K-Nearest Neighbors, K-Nearest Neighbors Classifier, and +finally, one based on the quadratic discriminant analysis, Parameterized +Functional QDA. + The Berkeley Growth Study dataset is used as input data. -Classification methods KNN, Maximum Depth, Nearest Centroid and -Parametric Functional QDA are compared. """ # Author:Álvaro Castillo García # License: MIT - +import matplotlib.pyplot as plt from GPy.kern import RBF from sklearn.model_selection import train_test_split @@ -21,7 +26,7 @@ KNeighborsClassifier, MaximumDepthClassifier, NearestCentroid, - ParametrizedFunctionalQDA, + ParameterizedFunctionalQDA, ) ############################################################################## @@ -62,7 +67,7 @@ # :class:`~skfda.ml.classification.MaximumDepthClassifier`, # :class:`~skfda.ml.classification.KNeighborsClassifier`, # :class:`~skfda.ml.classification.NearestCentroid` and -# :class:`~skfda.ml.classification.GaussianClassifier` +# :class:`~skfda.ml.classification.ParameterizedFunctionalQDA` ############################################################################## @@ -77,9 +82,6 @@ depth.score(X_test, y_test), )) -# Plot the prediction -X_test.plot(group=depth_pred, group_names=categories).show() - ############################################################################## # The second method to consider is the K-Nearest Neighbours Classifier. @@ -91,9 +93,6 @@ print(knn_pred) print('The score of KNN is {0:2.2%}'.format(knn.score(X_test, y_test))) -# Plot the prediction -X_test.plot(group=knn_pred, group_names=categories).show() - ############################################################################## # The third method we are going to use is the Nearest Centroid Classifier @@ -106,27 +105,55 @@ centroid.score(X_test, y_test), )) -# Plot the prediction -X_test.plot(group=centroid_pred, group_names=categories).show() - ############################################################################## -# The fourth method considered is a Parametrized functional quadratic +# The fourth method considered is a Parameterized functional quadratic # discriminant. # We have selected a gaussian kernel with initial parameters: variance=6 and -# mean=1 +# mean=1. The selection of the initial parameters does not really affect the +# results as the algorithm will automatically optimize them. # As regularizer a small value 0.05 has been chosen. -pfqda = ParametrizedFunctionalQDA( +pfqda = ParameterizedFunctionalQDA( kernel=RBF(input_dim=1, variance=6, lengthscale=1), regularizer=0.05, ) pfqda.fit(X_train, y_train) pfqda_pred = pfqda.predict(X_test) print(pfqda_pred) -print('The score of Parametrized Functional QDA is {0:2.2%}'.format( +print('The score of Parameterized Functional QDA is {0:2.2%}'.format( pfqda.score(X_test, y_test), )) -# Plot the prediction -X_test.plot(group=pfqda_pred, group_names=categories).show() + +############################################################################## +# As it can be seen, the classifier with the lowest score is the Maximum +# Depth Classifier. It obtains a 82.14% accuracy for the test set. +# KNN and Parameterized Functional QDA can be seen as the best classifiers +# for this problem, with an accuracy of 96.43%. +# Instead, the Nearest Centroid Classifier is not as good as the others. +# However, it obtains an accuracy of 85.71% for the test set. +# It can be concluded that all classifiers work well for this problem, as they +# achieve more than an 80% of score, but the most robust ones are KNN and +# Parameterized Functional QDA. +# The figure below shows the results of the classification for the test set on +# the four methods considered. +# It can be seen that the curves are similarly classified by all of them. + + +fig, axs = plt.subplots(2, 2) +plt.subplots_adjust(hspace=0.45, bottom=0.06) + +X_test.plot(group=centroid_pred, group_names=categories, axes=axs[0][1]) +axs[0][1].set_title('Nearest Centroid Classifier', loc='left') + +X_test.plot(group=depth_pred, group_names=categories, axes=axs[0][0]) +axs[0][0].set_title('Maximum Depth Classifier', loc='left') + +X_test.plot(group=knn_pred, group_names=categories, axes=axs[1][0]) +axs[1][0].set_title('KNN', loc='left') + +X_test.plot(group=pfqda_pred, group_names=categories, axes=axs[1][1]) +axs[1][1].set_title('Parameterized Functional QDA', loc='left') + +plt.show() diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index 9a265d67f..ac822f9c9 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -10,4 +10,4 @@ KNeighborsClassifier, RadiusNeighborsClassifier, ) -from ._parametrized_functional_qda import ParametrizedFunctionalQDA +from ._parameterized_functional_qda import ParameterizedFunctionalQDA diff --git a/skfda/ml/classification/_parametrized_functional_qda.py b/skfda/ml/classification/_parameterized_functional_qda.py similarity index 78% rename from skfda/ml/classification/_parametrized_functional_qda.py rename to skfda/ml/classification/_parameterized_functional_qda.py index 7b6689daa..380a1a45a 100644 --- a/skfda/ml/classification/_parametrized_functional_qda.py +++ b/skfda/ml/classification/_parameterized_functional_qda.py @@ -1,5 +1,7 @@ from __future__ import annotations +from typing import Tuple + import numpy as np from GPy.kern import Kern from GPy.models import GPRegression @@ -9,26 +11,27 @@ from ..._utils import _classifier_get_classes from ...representation import FDataGrid +from ...representation._typing import NDArrayFloat, NDArrayInt -class ParametrizedFunctionalQDA( +class ParameterizedFunctionalQDA( BaseEstimator, # type: ignore ClassifierMixin, # type: ignore ): """Parametrized functional quadratic discriminant analysis. This classifier is based on the assumption that the data is part - of a gaussian process and depending on the output label, the covariance + of a Gaussian process and depending on the output label, the covariance and mean parameters are different for each class. This means that curves - classified with one determined label come from a distinct gaussian process + classified with one determined label come from a distinct Gaussian process compared with data that is classified with a different label. The training phase of the classifier will try to approximate the two - main parameters of a gaussian process for each class. The covariance + main parameters of a Gaussian process for each class. The covariance will be estimated by fitting the initial kernel passed on the creation of - the ParametrizedFunctionalQDA object. + the ParameterizedFunctionalQDA object. The result of the training function will be two arrays, one of means and - another one of covariances. Both with length (n_classes). + another one of covariances. Both with length (``n_classes``). The prediction phase instead uses a quadratic discriminant classifier to predict which gaussian process of the fitted ones correspond the most with @@ -36,11 +39,12 @@ class ParametrizedFunctionalQDA( Parameters: - kernel: initial kernel to be fitted with the training data. + kernel: Initial kernel to be fitted with the training data. For now, + only kernels that belongs to the GPy module are allowed. - regularizer: parameter that regularizes the covariance matrices - in order to avoid Singular matrices. It is multiplied by a numpy - eye matrix and then added to the covariance one. + regularizer: Parameter that regularizes the covariance matrices in + order to avoid Singular matrices. It is multiplied by the identity + matrix and then added to the covariance one. Examples: @@ -60,16 +64,19 @@ class ParametrizedFunctionalQDA( ... ) Then we need to choose and import a kernel so it can be fitted with - the data in the training phase. We will use a gaussian kernel with - mean 1 and variance 6 as an example. + the data in the training phase. We will use a Gaussian kernel. The + variance and lengthscale parameters will be optimized during the + training phase. Therefore, the initial values do not matter too much. + We will use random values such as 1 for the mean and 6 for the + variance. >>> from GPy.kern import RBF >>> rbf = RBF(input_dim=1, variance=6, lengthscale=1) - We will fit the ParametrizedFunctionalQDA with training data. We - use as regularizer parameter a low value as 0.05. + We will fit the ParameterizedFunctionalQDA with training data. We + use as regularizer parameter a low value such as 0.05. - >>> pfqda = ParametrizedFunctionalQDA(rbf, 0.05) + >>> pfqda = ParameterizedFunctionalQDA(rbf, 0.05) >>> pfqda = pfqda.fit(X_train, y_train) @@ -90,8 +97,9 @@ def __init__(self, kernel: Kern, regularizer: float) -> None: self.kernel = kernel self.regularizer = regularizer - def fit(self, X: FDataGrid, y: np.ndarray) -> ParametrizedFunctionalQDA: - """Fit the model using X as training data and y as target values. + def fit(self, X: FDataGrid, y: np.ndarray) -> ParameterizedFunctionalQDA: + """ + Fit the model using X as training data and y as target values. Args: X: FDataGrid with the training data. @@ -123,8 +131,9 @@ def fit(self, X: FDataGrid, y: np.ndarray) -> ParametrizedFunctionalQDA: return self - def predict(self, X: FDataGrid) -> np.ndarray: - """Predict the class labels for the provided data. + def predict(self, X: FDataGrid) -> NDArrayInt: + """ + Predict the class labels for the provided data. Args: X: FDataGrid with the test samples. @@ -140,7 +149,7 @@ def predict(self, X: FDataGrid) -> np.ndarray: axis=1, ) - def _calculate_priors(self, y: np.ndarray) -> np.ndarray: + def _calculate_priors(self, y: np.ndarray) -> NDArrayFloat: """ Calculate the prior probability of each class. @@ -156,7 +165,7 @@ def _calculate_priors(self, y: np.ndarray) -> np.ndarray: def _fit_gaussian_process( self, X: FDataGrid, - ) -> np.ndarray: + ) -> Tuple[NDArrayFloat, NDArrayFloat]: """ Fit the kernel to the data in each class. @@ -196,7 +205,7 @@ def _fit_gaussian_process( return np.asarray(kernels), np.asarray(means) - def _calculate_log_likelihood(self, X: np.ndarray) -> np.ndarray: + def _calculate_log_likelihood(self, X: np.ndarray) -> NDArrayFloat: """ Calculate the log likelihood quadratic discriminant analysis. From 629edcbf90100a7321613b7abcba74fef5aee676 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 18 Apr 2022 00:22:19 +0200 Subject: [PATCH 127/400] Deletion of ddg_transformer --- examples/plot_depth_classification.py | 5 +- skfda/ml/classification/_depth_classifiers.py | 16 ++- .../feature_construction/_ddg_transformer.py | 132 ------------------ .../feature_construction/__init__.py | 1 - 4 files changed, 13 insertions(+), 141 deletions(-) delete mode 100644 skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py rename skfda/preprocessing/{dim_reduction => }/feature_construction/__init__.py (80%) diff --git a/examples/plot_depth_classification.py b/examples/plot_depth_classification.py index 28756421b..2094d09c9 100644 --- a/examples/plot_depth_classification.py +++ b/examples/plot_depth_classification.py @@ -30,8 +30,7 @@ DDGClassifier, MaximumDepthClassifier, ) -from skfda.preprocessing.dim_reduction.feature_construction import DDGTransformer -from skfda.representation.grid import FDataGrid +from skfda.preprocessing.feature_construction import FDAFeatureUnion ############################################################################## # The Berkeley Growth Study data contains the heights of 39 boys and 54 girls @@ -210,7 +209,7 @@ def _plot_boundaries(axis): # | NearestClass | DDGClassifier with nearest neighbors | # +--------------+--------------------------------------+ -ddg: DDGTransformer[FDataGrid] = DDGTransformer( +ddg = FDAFeatureUnion( depth_method=ModifiedBandDepth(), ) X_train_trans = ddg.fit_transform(X_train, y_train) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index a49d5ba67..f79b30c28 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -5,6 +5,7 @@ from typing import Generic, Optional, Sequence, TypeVar, Union import numpy as np +import pandas as pd from scipy.interpolate import lagrange from sklearn.base import BaseEstimator, ClassifierMixin, clone from sklearn.metrics import accuracy_score @@ -13,7 +14,7 @@ from ..._utils import _classifier_fit_depth_methods, _classifier_get_classes from ...exploratory.depth import Depth, ModifiedBandDepth -from ...preprocessing.dim_reduction.feature_construction import DDGTransformer +from ...preprocessing.feature_construction import PerClassTransformer from ...representation._typing import NDArrayFloat, NDArrayInt from ...representation.grid import FData @@ -257,7 +258,10 @@ def __init__( # noqa: WPS234 depth_method: Union[Depth[T], Sequence[Depth[T]], None] = None, ) -> None: self.multivariate_classifier = multivariate_classifier - self.depth_method = depth_method + if depth_method is None: + self.depth_method = ModifiedBandDepth() + else: + self.depth_method = depth_method def fit(self, X: T, y: NDArrayInt) -> DDGClassifier[T]: """Fit the model using X as training data and y as target values. @@ -270,7 +274,7 @@ def fit(self, X: T, y: NDArrayInt) -> DDGClassifier[T]: self """ self._pipeline = make_pipeline( - DDGTransformer(self.depth_method), + PerClassTransformer(self.depth_method), clone(self.multivariate_classifier), ) @@ -333,16 +337,18 @@ def fit(self, X: NDArrayFloat, y: NDArrayInt) -> _ArgMaxClassifier: self._classes = classes return self - def predict(self, X: NDArrayFloat) -> NDArrayInt: + def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> NDArrayInt: """Predict the class labels for the provided data. Args: - X: Array with the test samples. + X: Array with the test samples or a pandas DataFrame. Returns: Array of shape (n_samples) with class labels for each data sample. """ + if isinstance(X, pd.DataFrame): + X = X.to_numpy() return self._classes[np.argmax(X, axis=1)] diff --git a/skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py b/skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py deleted file mode 100644 index 9b8db7804..000000000 --- a/skfda/preprocessing/dim_reduction/feature_construction/_ddg_transformer.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Feature extraction transformers for dimensionality reduction.""" -from __future__ import annotations - -from typing import Generic, Sequence, TypeVar, Union - -import numpy as np -from numpy import ndarray -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted - -from ...._utils import _classifier_fit_depth_methods -from ....exploratory.depth import Depth, ModifiedBandDepth -from ....representation.grid import FData - -T = TypeVar("T", bound=FData) - - -class DDGTransformer( - BaseEstimator, # type: ignore - TransformerMixin, # type: ignore - Generic[T], -): - r"""Generalized depth-versus-depth (DD) transformer for functional data. - - This transformer takes a list of k depths and performs the following map: - - .. math:: - \mathcal{X} &\rightarrow \mathbb{R}^G \\ - x &\rightarrow \textbf{d} = (D_1^1(x), D_1^2(x),...,D_g^k(x)) - - Where :math:`D_i^j(x)` is the depth of the point :math:`x` with respect to - the data in the :math:`i`-th group using the :math:`j`-th depth of the - provided list. - - Note that :math:`\mathcal{X}` is possibly multivariate, that is, - :math:`\mathcal{X} = \mathcal{X}_1 \times ... \times \mathcal{X}_p`. - - Parameters: - depth_method: - The depth class or sequence of depths to use when calculating - the depth of a test sample in a class. See the documentation of - the depths module for a list of available depths. By default it - is ModifiedBandDepth. - - Examples: - Firstly, we will import and split the Berkeley Growth Study dataset - - >>> from skfda.datasets import fetch_growth - >>> from sklearn.model_selection import train_test_split - >>> dataset = fetch_growth() - >>> fd = dataset['data'] - >>> y = dataset['target'] - >>> X_train, X_test, y_train, y_test = train_test_split( - ... fd, y, test_size=0.25, stratify=y, random_state=0) - - >>> from skfda.preprocessing.dim_reduction.feature_construction import\ - ... DDGTransformer - >>> from sklearn.pipeline import make_pipeline - >>> from sklearn.neighbors import KNeighborsClassifier - - We classify by first transforming our data using the defined map - and then using KNN - - >>> pipe = make_pipeline(DDGTransformer(), KNeighborsClassifier()) - >>> pipe.fit(X_train, y_train) - Pipeline(steps=[('ddgtransformer', - DDGTransformer(depth_method=[ModifiedBandDepth()])), - ('kneighborsclassifier', KNeighborsClassifier())]) - - We can predict the class of new samples - - >>> pipe.predict(X_test) - array([1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, - 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1]) - - Finally, we calculate the mean accuracy for the test data - - >>> pipe.score(X_test, y_test) - 0.875 - - References: - Cuesta-Albertos, J. A., Febrero-Bande, M. and Oviedo de la Fuente, M. - (2017). The DDG-classifier in the functional setting. - TEST, 26. 119-142. - """ - - def __init__( # noqa: WPS234 - self, - depth_method: Union[Depth[T], Sequence[Depth[T]], None] = None, - ) -> None: - self.depth_method = depth_method - - def fit(self, X: T, y: ndarray) -> DDGTransformer[T]: - """Fit the model using X as training data and y as target values. - - Args: - X: FDataGrid with the training data. - y: Target values of shape = (n_samples). - - Returns: - self - """ - if self.depth_method is None: - self.depth_method = ModifiedBandDepth() - - if isinstance(self.depth_method, Depth): - self.depth_method = [self.depth_method] - - classes, class_depth_methods = _classifier_fit_depth_methods( - X, y, self.depth_method, - ) - - self._classes = classes - self.class_depth_methods_ = class_depth_methods - - return self - - def transform(self, X: T) -> ndarray: - """Transform the provided data using the defined map. - - Args: - X: FDataGrid with the test samples. - - Returns: - Array of shape (n_samples, G). - """ - sklearn_check_is_fitted(self) - - return np.transpose([ - depth_method.transform(X) - for depth_method in self.class_depth_methods_ - ]) diff --git a/skfda/preprocessing/dim_reduction/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py similarity index 80% rename from skfda/preprocessing/dim_reduction/feature_construction/__init__.py rename to skfda/preprocessing/feature_construction/__init__.py index ed0606d72..db8402e8b 100644 --- a/skfda/preprocessing/dim_reduction/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -1,5 +1,4 @@ """Feature extraction.""" -from ._ddg_transformer import DDGTransformer from ._evaluation_trasformer import EvaluationTransformer from ._fda_feature_union import FDAFeatureUnion from ._per_class_transformer import PerClassTransformer From 275d3342feced7ce1e6cb553f6cc74ba0ae59ad6 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 20 Apr 2022 00:53:37 +0200 Subject: [PATCH 128/400] Init corrections --- .../dim_reduction/feature_extraction/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py index de9068399..aebe7e812 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py @@ -1,9 +1,10 @@ import warnings -from ... import feature_construction +from .. import FPCA warnings.warn( 'The module "feature_extraction" is deprecated.' - 'Please use "feature_construction"', + 'Please use "dim_reduction" for FPCA' + 'or "feature_construction" for feature construction techniques', category=DeprecationWarning, ) From 384933d513da505be8521a4a7837e082e6f555ad Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 20 Apr 2022 00:57:01 +0200 Subject: [PATCH 129/400] Style fix on imports --- tests/test_per_class_transformer.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/test_per_class_transformer.py b/tests/test_per_class_transformer.py index 908d5d238..ad824be2e 100644 --- a/tests/test_per_class_transformer.py +++ b/tests/test_per_class_transformer.py @@ -7,12 +7,10 @@ from skfda._utils import _classifier_get_classes from skfda.datasets import fetch_growth from skfda.ml.classification import KNeighborsClassifier -from skfda.preprocessing.feature_construction import ( - PerClassTransformer, -) from skfda.preprocessing.dim_reduction.variable_selection import ( RecursiveMaximaHunting, ) +from skfda.preprocessing.feature_construction import PerClassTransformer class TestPerClassTransformer(unittest.TestCase): From c428d1186f5f1c7222702e1570c37242ffed307d Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 20 Apr 2022 19:54:40 +0200 Subject: [PATCH 130/400] Added warning --- .../classification/_parameterized_functional_qda.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/skfda/ml/classification/_parameterized_functional_qda.py b/skfda/ml/classification/_parameterized_functional_qda.py index 380a1a45a..f1af22c4c 100644 --- a/skfda/ml/classification/_parameterized_functional_qda.py +++ b/skfda/ml/classification/_parameterized_functional_qda.py @@ -20,11 +20,11 @@ class ParameterizedFunctionalQDA( ): """Parametrized functional quadratic discriminant analysis. - This classifier is based on the assumption that the data is part - of a Gaussian process and depending on the output label, the covariance - and mean parameters are different for each class. This means that curves - classified with one determined label come from a distinct Gaussian process - compared with data that is classified with a different label. + It is based on the assumption that the data is part of a Gaussian process + and depending on the output label, the covariance and mean parameters are + different for each class. This means that curves classified with one + determined label come from a distinct Gaussian process compared with data + that is classified with a different label. The training phase of the classifier will try to approximate the two main parameters of a Gaussian process for each class. The covariance @@ -37,6 +37,9 @@ class ParameterizedFunctionalQDA( predict which gaussian process of the fitted ones correspond the most with each curve passed. + Warning: + This classifier is experimental as it does not come from a + peer-published paper. Parameters: kernel: Initial kernel to be fitted with the training data. For now, From d01da4674bf24a37435d7a4d41a9c3670b232e8d Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 22 Apr 2022 23:43:41 +0200 Subject: [PATCH 131/400] ddg_transform deletion corrections --- examples/plot_depth_classification.py | 8 ++---- ...t_fpca_inverse_transform_outl_detection.py | 2 +- .../_evaluation_trasformer.py | 5 ++-- .../_fda_feature_union.py | 28 ++++++++++--------- 4 files changed, 21 insertions(+), 22 deletions(-) diff --git a/examples/plot_depth_classification.py b/examples/plot_depth_classification.py index 2094d09c9..affb70937 100644 --- a/examples/plot_depth_classification.py +++ b/examples/plot_depth_classification.py @@ -30,7 +30,7 @@ DDGClassifier, MaximumDepthClassifier, ) -from skfda.preprocessing.feature_construction import FDAFeatureUnion +from skfda.preprocessing.feature_construction import PerClassTransformer ############################################################################## # The Berkeley Growth Study data contains the heights of 39 boys and 54 girls @@ -209,10 +209,8 @@ def _plot_boundaries(axis): # | NearestClass | DDGClassifier with nearest neighbors | # +--------------+--------------------------------------+ -ddg = FDAFeatureUnion( - depth_method=ModifiedBandDepth(), -) -X_train_trans = ddg.fit_transform(X_train, y_train) +pct = PerClassTransformer(ModifiedBandDepth()) +X_train_trans = pct.fit_transform(X_train, y_train) clf = KNeighborsClassifier(n_neighbors=5) clf.fit(X_train_trans, y_train) diff --git a/examples/plot_fpca_inverse_transform_outl_detection.py b/examples/plot_fpca_inverse_transform_outl_detection.py index 4a6fca82b..37b98dba1 100644 --- a/examples/plot_fpca_inverse_transform_outl_detection.py +++ b/examples/plot_fpca_inverse_transform_outl_detection.py @@ -32,7 +32,7 @@ from skfda.datasets import make_gaussian_process from skfda.misc.covariances import Exponential, Gaussian from skfda.misc.metrics import l2_distance, l2_norm -from skfda.preprocessing.dim_reduction.feature_construction import FPCA +from skfda.preprocessing.dim_reduction import FPCA ############################################################################## # We proceed as follows: diff --git a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py index 01551827c..93f6102cc 100644 --- a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py +++ b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py @@ -47,9 +47,8 @@ class EvaluationTransformer( Examples: >>> from skfda.representation import FDataGrid, FDataBasis - >>> from skfda.preprocessing.feature_construction import\ - ... ( - ... EvaluationTransformer + >>> from skfda.preprocessing.feature_construction import ( + ... EvaluationTransformer, ... ) >>> from skfda.representation.basis import Monomial diff --git a/skfda/preprocessing/feature_construction/_fda_feature_union.py b/skfda/preprocessing/feature_construction/_fda_feature_union.py index 99feca519..a461432df 100644 --- a/skfda/preprocessing/feature_construction/_fda_feature_union.py +++ b/skfda/preprocessing/feature_construction/_fda_feature_union.py @@ -52,34 +52,36 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore >>> X,y = fetch_growth(return_X_y=True) Then we need to import the transformers we want to use. In our case we - will use the Coefficients transformer. - Evaluation Transformer returns the original curve, and as it is helpful, - we will concatenate it to the already metioned transformer. + will use the Minimum redundancy maximum relevance method to select + important features. We will concatenate to the results of the previous + method the original curves with an Evaluation Transfomer. >>> from skfda.preprocessing.feature_construction import ( ... FDAFeatureUnion, ... ) - >>> from skfda.preprocessing.dim_reduction import FPCA + >>> from skfda.preprocessing.dim_reduction.variable_selection import ( + ... MinimumRedundancyMaximumRelevance, + ... ) >>> from skfda.preprocessing.feature_construction import ( - ... EvaluationTransformer + ... EvaluationTransformer, ... ) >>> import numpy as np Finally we apply fit and transform. >>> union = FDAFeatureUnion( ... [ - ... ("fpca", FPCA()), + ... ("mrmr", MinimumRedundancyMaximumRelevance()), ... ("eval", EvaluationTransformer()), ... ], ... array_output=True, ... ) >>> np.around(union.fit_transform(X,y), decimals=2) - array([[ 52.92, -12.67, -6.18, ..., 193.8 , 194.3 , 195.1 ], - [ -5.89, -7.33, 12.59, ..., 176.1 , 177.4 , 178.7 ], - [-18.82, -13.11, 0.97, ..., 170.9 , 171.2 , 171.5 ], - ..., - [ -8.54, 5.99, 2.17, ..., 166. , 166.3 , 166.8 ], - [ 12.06, 17.22, 6.26, ..., 168.3 , 168.4 , 168.6 ], - [ 10.87, 14.92, 1.8 , ..., 168.6 , 168.9 , 169.2 ]]) + array([[ 194.3, 81.3, 84.2, ..., 193.8, 194.3, 195.1], + [ 177.4, 76.2, 80.4, ..., 176.1, 177.4, 178.7], + [ 171.2, 76.8, 79.8, ..., 170.9, 171.2, 171.5], + ..., + [ 166.3, 68.6, 73.6, ..., 166. , 166.3, 166.8], + [ 168.4, 79.9, 82.6, ..., 168.3, 168.4, 168.6], + [ 168.9, 76.1, 78.4, ..., 168.6, 168.9, 169.2]]) """ def __init__( From b36e265e27cb519a3cc611fef1bf5752ecf16a0b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 24 Apr 2022 23:00:17 +0200 Subject: [PATCH 132/400] Plot depth classification fix --- examples/plot_depth_classification.py | 3 ++- .../_fda_feature_union.py | 24 +++++++++---------- 2 files changed, 14 insertions(+), 13 deletions(-) diff --git a/examples/plot_depth_classification.py b/examples/plot_depth_classification.py index affb70937..84ef388fb 100644 --- a/examples/plot_depth_classification.py +++ b/examples/plot_depth_classification.py @@ -209,8 +209,9 @@ def _plot_boundaries(axis): # | NearestClass | DDGClassifier with nearest neighbors | # +--------------+--------------------------------------+ -pct = PerClassTransformer(ModifiedBandDepth()) +pct = PerClassTransformer(ModifiedBandDepth(), array_output=True) X_train_trans = pct.fit_transform(X_train, y_train) +X_train_trans = X_train_trans.reshape(len(categories), X_train.shape[0]).T clf = KNeighborsClassifier(n_neighbors=5) clf.fit(X_train_trans, y_train) diff --git a/skfda/preprocessing/feature_construction/_fda_feature_union.py b/skfda/preprocessing/feature_construction/_fda_feature_union.py index a461432df..402e12abf 100644 --- a/skfda/preprocessing/feature_construction/_fda_feature_union.py +++ b/skfda/preprocessing/feature_construction/_fda_feature_union.py @@ -52,14 +52,14 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore >>> X,y = fetch_growth(return_X_y=True) Then we need to import the transformers we want to use. In our case we - will use the Minimum redundancy maximum relevance method to select - important features. We will concatenate to the results of the previous - method the original curves with an Evaluation Transfomer. + will use the Recursive Maxima Hunting method to select important features. + We will concatenate to the results of the previous method the original + curves with an Evaluation Transfomer. >>> from skfda.preprocessing.feature_construction import ( ... FDAFeatureUnion, ... ) >>> from skfda.preprocessing.dim_reduction.variable_selection import ( - ... MinimumRedundancyMaximumRelevance, + ... RecursiveMaximaHunting, ... ) >>> from skfda.preprocessing.feature_construction import ( ... EvaluationTransformer, @@ -69,19 +69,19 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore Finally we apply fit and transform. >>> union = FDAFeatureUnion( ... [ - ... ("mrmr", MinimumRedundancyMaximumRelevance()), + ... ("rmh", RecursiveMaximaHunting()), ... ("eval", EvaluationTransformer()), ... ], ... array_output=True, ... ) >>> np.around(union.fit_transform(X,y), decimals=2) - array([[ 194.3, 81.3, 84.2, ..., 193.8, 194.3, 195.1], - [ 177.4, 76.2, 80.4, ..., 176.1, 177.4, 178.7], - [ 171.2, 76.8, 79.8, ..., 170.9, 171.2, 171.5], - ..., - [ 166.3, 68.6, 73.6, ..., 166. , 166.3, 166.8], - [ 168.4, 79.9, 82.6, ..., 168.3, 168.4, 168.6], - [ 168.9, 76.1, 78.4, ..., 168.6, 168.9, 169.2]]) + array([[ 195.1, 141.1, 163.8, ..., 193.8, 194.3, 195.1], + [ 178.7, 133. , 148.1, ..., 176.1, 177.4, 178.7], + [ 171.5, 126.5, 143.6, ..., 170.9, 171.2, 171.5], + ..., + [ 166.8, 132.8, 152.2, ..., 166. , 166.3, 166.8], + [ 168.6, 139.4, 161.6, ..., 168.3, 168.4, 168.6], + [ 169.2, 138.1, 161.7, ..., 168.6, 168.9, 169.2]]) """ def __init__( From 11a96eeacb5d48455f9ea81ce006dc96c8122cd3 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 25 Apr 2022 00:02:54 +0200 Subject: [PATCH 133/400] Funcion transformers --- .../feature_extraction/__init__.py | 5 + .../_function_transformers.py | 216 ++++++++++++++++++ 2 files changed, 221 insertions(+) create mode 100644 skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py index 0e9556127..5e5faf75d 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py @@ -2,4 +2,9 @@ from ._ddg_transformer import DDGTransformer from ._fda_feature_union import FDAFeatureUnion from ._fpca import FPCA +from ._function_transformers import ( + LocalAveragesTransformer, + NumberUpCrossingsTransformer, + OccupationMeasureTransformer, +) from ._per_class_transformer import PerClassTransformer diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py b/skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py new file mode 100644 index 000000000..64f6bbb7a --- /dev/null +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py @@ -0,0 +1,216 @@ +"""Function transformers for feature construction techniques.""" +from typing import Optional, Sequence, Tuple + +from sklearn.preprocessing import FunctionTransformer + +from ....exploratory.stats._functional_transformers import ( + local_averages, + number_up_crossings, + occupation_measure, +) +from ....representation._typing import NDArrayFloat + + +class LocalAveragesTransformer(FunctionTransformer): + """ + Transformer that works as an adapter for the local_averages function. + + Args: + n_intervals: number of intervals we want to consider. + + Example: + We import the Berkeley Growth Study dataset. + We will use only the first 30 samples to make the + example easy. + >>> from skfda.datasets import fetch_growth + >>> dataset = fetch_growth(return_X_y=True)[0] + >>> X = dataset[:30] + + Then we decide how many intervals we want to consider (in our case 2) + and call the function with the dataset. + >>> import numpy as np + >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + ... LocalAveragesTransformer, + ... ) + >>> local_averages = LocalAveragesTransformer(2) + >>> np.around(local_averages.fit_transform(X), decimals=2) + array([[[ 116.94], + [ 111.86], + [ 107.29], + [ 111.35], + [ 104.39], + [ 109.43], + [ 109.16], + [ 112.91], + [ 109.19], + [ 117.95], + [ 112.14], + [ 114.3 ], + [ 111.48], + [ 114.85], + [ 116.25], + [ 114.6 ], + [ 111.02], + [ 113.57], + [ 108.88], + [ 109.6 ], + [ 109.7 ], + [ 108.54], + [ 109.18], + [ 106.92], + [ 109.44], + [ 109.84], + [ 115.32], + [ 108.16], + [ 119.29], + [ 110.62]], + [[ 177.26], + [ 157.62], + [ 154.97], + [ 163.83], + [ 156.66], + [ 157.67], + [ 155.31], + [ 169.02], + [ 154.18], + [ 174.43], + [ 161.33], + [ 170.14], + [ 164.1 ], + [ 170.1 ], + [ 166.65], + [ 168.72], + [ 166.85], + [ 167.22], + [ 159.4 ], + [ 162.76], + [ 155.7 ], + [ 158.01], + [ 160.1 ], + [ 155.95], + [ 157.95], + [ 163.53], + [ 162.29], + [ 153.1 ], + [ 178.48], + [ 161.75]]]) + + """ + + def __init__(self, n_intervals: int): + super().__init__( + func=local_averages, + kw_args={'n_intervals': n_intervals}, + ) + + +class OccupationMeasureTransformer(FunctionTransformer): + """ + Transformer that works as an adapter for the occupation_measure function. + + Args: + intervals: ndarray of tuples containing the + intervals we want to consider. The shape should be + (n_sequences,2) + n_points: Number of points to evaluate in the domain. + By default will be used the points defined on the FDataGrid. + On a FDataBasis this value should be specified. + + Example: + We will create the FDataGrid that we will use to extract + the occupation measure + >>> from skfda.representation import FDataGrid + >>> import numpy as np + >>> t = np.linspace(0, 10, 100) + >>> fd_grid = FDataGrid( + ... data_matrix=[ + ... t, + ... 2 * t, + ... np.sin(t), + ... ], + ... grid_points=t, + ... ) + + Finally we call to the occupation measure function with the + intervals that we want to consider. In our case (0.0, 1.0) + and (2.0, 3.0). We need also to specify the number of points + we want that the function takes into account to interpolate. + We are going to use 501 points. + >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + ... OccupationMeasureTransformer, + ... ) + >>> occupation_measure = OccupationMeasureTransformer( + ... intervals=[(0.0, 1.0), (2.0, 3.0)], + ... n_points=501, + ... ) + + >>> np.around(occupation_measure.fit_transform(fd_grid), decimals=2) + array([[ 0.98, 0.5 , 6.28], + [ 1.02, 0.52, 0. ]]) + """ + + def __init__( + self, + intervals: Sequence[Tuple[float, float]], + *, + n_points: Optional[int] = None, + ): + super().__init__( + func=occupation_measure, + kw_args={'intervals': intervals, 'n_points': n_points}, + ) + + +class NumberUpCrossingsTransformer(FunctionTransformer): + """ + Transformer that works as an adapter for the number_up_crossings function. + + Args: + levels: sequence of numbers including the levels + we want to consider for the crossings. + Example: + For this example we will use a well known function so the correct + functioning of this method can be checked. + We will create and use a DataFrame with a sample extracted from + the Bessel Function of first type and order 0. + First of all we import the Bessel Function and create the X axis + data grid. Then we create the FdataGrid. + >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + ... NumberUpCrossingsTransformer, + ... ) + >>> from scipy.special import jv + >>> from skfda.representation import FDataGrid + >>> import numpy as np + >>> x_grid = np.linspace(0, 14, 14) + >>> fd_grid = FDataGrid( + ... data_matrix=[jv([0], x_grid)], + ... grid_points=x_grid, + ... ) + >>> fd_grid.data_matrix + array([[[ 1. ], + [ 0.73041066], + [ 0.13616752], + [-0.32803875], + [-0.35967936], + [-0.04652559], + [ 0.25396879], + [ 0.26095573], + [ 0.01042895], + [-0.22089135], + [-0.2074856 ], + [ 0.0126612 ], + [ 0.20089319], + [ 0.17107348]]]) + + Finally we evaluate the number of up crossings method with the FDataGrid + created. + >>> NumberUpCrossingsTransformer(np.asarray([0])).fit_transform(fd_grid) + array([[2]]) + """ + + def __init__(self, levels: NDArrayFloat): + self.levels = levels + super().__init__( + func=number_up_crossings, + kw_args={'levels': levels}, + ) From 4526e50b61b6d75fcce3a150177401e3f9f6432f Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 25 Apr 2022 00:02:54 +0200 Subject: [PATCH 134/400] Function transformers --- .../feature_extraction/__init__.py | 5 + .../_function_transformers.py | 216 ++++++++++++++++++ 2 files changed, 221 insertions(+) create mode 100644 skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py index 0e9556127..5e5faf75d 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py @@ -2,4 +2,9 @@ from ._ddg_transformer import DDGTransformer from ._fda_feature_union import FDAFeatureUnion from ._fpca import FPCA +from ._function_transformers import ( + LocalAveragesTransformer, + NumberUpCrossingsTransformer, + OccupationMeasureTransformer, +) from ._per_class_transformer import PerClassTransformer diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py b/skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py new file mode 100644 index 000000000..64f6bbb7a --- /dev/null +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_function_transformers.py @@ -0,0 +1,216 @@ +"""Function transformers for feature construction techniques.""" +from typing import Optional, Sequence, Tuple + +from sklearn.preprocessing import FunctionTransformer + +from ....exploratory.stats._functional_transformers import ( + local_averages, + number_up_crossings, + occupation_measure, +) +from ....representation._typing import NDArrayFloat + + +class LocalAveragesTransformer(FunctionTransformer): + """ + Transformer that works as an adapter for the local_averages function. + + Args: + n_intervals: number of intervals we want to consider. + + Example: + We import the Berkeley Growth Study dataset. + We will use only the first 30 samples to make the + example easy. + >>> from skfda.datasets import fetch_growth + >>> dataset = fetch_growth(return_X_y=True)[0] + >>> X = dataset[:30] + + Then we decide how many intervals we want to consider (in our case 2) + and call the function with the dataset. + >>> import numpy as np + >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + ... LocalAveragesTransformer, + ... ) + >>> local_averages = LocalAveragesTransformer(2) + >>> np.around(local_averages.fit_transform(X), decimals=2) + array([[[ 116.94], + [ 111.86], + [ 107.29], + [ 111.35], + [ 104.39], + [ 109.43], + [ 109.16], + [ 112.91], + [ 109.19], + [ 117.95], + [ 112.14], + [ 114.3 ], + [ 111.48], + [ 114.85], + [ 116.25], + [ 114.6 ], + [ 111.02], + [ 113.57], + [ 108.88], + [ 109.6 ], + [ 109.7 ], + [ 108.54], + [ 109.18], + [ 106.92], + [ 109.44], + [ 109.84], + [ 115.32], + [ 108.16], + [ 119.29], + [ 110.62]], + [[ 177.26], + [ 157.62], + [ 154.97], + [ 163.83], + [ 156.66], + [ 157.67], + [ 155.31], + [ 169.02], + [ 154.18], + [ 174.43], + [ 161.33], + [ 170.14], + [ 164.1 ], + [ 170.1 ], + [ 166.65], + [ 168.72], + [ 166.85], + [ 167.22], + [ 159.4 ], + [ 162.76], + [ 155.7 ], + [ 158.01], + [ 160.1 ], + [ 155.95], + [ 157.95], + [ 163.53], + [ 162.29], + [ 153.1 ], + [ 178.48], + [ 161.75]]]) + + """ + + def __init__(self, n_intervals: int): + super().__init__( + func=local_averages, + kw_args={'n_intervals': n_intervals}, + ) + + +class OccupationMeasureTransformer(FunctionTransformer): + """ + Transformer that works as an adapter for the occupation_measure function. + + Args: + intervals: ndarray of tuples containing the + intervals we want to consider. The shape should be + (n_sequences,2) + n_points: Number of points to evaluate in the domain. + By default will be used the points defined on the FDataGrid. + On a FDataBasis this value should be specified. + + Example: + We will create the FDataGrid that we will use to extract + the occupation measure + >>> from skfda.representation import FDataGrid + >>> import numpy as np + >>> t = np.linspace(0, 10, 100) + >>> fd_grid = FDataGrid( + ... data_matrix=[ + ... t, + ... 2 * t, + ... np.sin(t), + ... ], + ... grid_points=t, + ... ) + + Finally we call to the occupation measure function with the + intervals that we want to consider. In our case (0.0, 1.0) + and (2.0, 3.0). We need also to specify the number of points + we want that the function takes into account to interpolate. + We are going to use 501 points. + >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + ... OccupationMeasureTransformer, + ... ) + >>> occupation_measure = OccupationMeasureTransformer( + ... intervals=[(0.0, 1.0), (2.0, 3.0)], + ... n_points=501, + ... ) + + >>> np.around(occupation_measure.fit_transform(fd_grid), decimals=2) + array([[ 0.98, 0.5 , 6.28], + [ 1.02, 0.52, 0. ]]) + """ + + def __init__( + self, + intervals: Sequence[Tuple[float, float]], + *, + n_points: Optional[int] = None, + ): + super().__init__( + func=occupation_measure, + kw_args={'intervals': intervals, 'n_points': n_points}, + ) + + +class NumberUpCrossingsTransformer(FunctionTransformer): + """ + Transformer that works as an adapter for the number_up_crossings function. + + Args: + levels: sequence of numbers including the levels + we want to consider for the crossings. + Example: + For this example we will use a well known function so the correct + functioning of this method can be checked. + We will create and use a DataFrame with a sample extracted from + the Bessel Function of first type and order 0. + First of all we import the Bessel Function and create the X axis + data grid. Then we create the FdataGrid. + >>> from skfda.preprocessing.dim_reduction.feature_extraction import ( + ... NumberUpCrossingsTransformer, + ... ) + >>> from scipy.special import jv + >>> from skfda.representation import FDataGrid + >>> import numpy as np + >>> x_grid = np.linspace(0, 14, 14) + >>> fd_grid = FDataGrid( + ... data_matrix=[jv([0], x_grid)], + ... grid_points=x_grid, + ... ) + >>> fd_grid.data_matrix + array([[[ 1. ], + [ 0.73041066], + [ 0.13616752], + [-0.32803875], + [-0.35967936], + [-0.04652559], + [ 0.25396879], + [ 0.26095573], + [ 0.01042895], + [-0.22089135], + [-0.2074856 ], + [ 0.0126612 ], + [ 0.20089319], + [ 0.17107348]]]) + + Finally we evaluate the number of up crossings method with the FDataGrid + created. + >>> NumberUpCrossingsTransformer(np.asarray([0])).fit_transform(fd_grid) + array([[2]]) + """ + + def __init__(self, levels: NDArrayFloat): + self.levels = levels + super().__init__( + func=number_up_crossings, + kw_args={'levels': levels}, + ) From 1d6832b00ecae9cbce87c238887e31bc83f475e6 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 27 Apr 2022 00:22:41 +0200 Subject: [PATCH 135/400] Moments --- skfda/exploratory/stats/__init__.py | 2 +- .../stats/_functional_transformers.py | 233 ++++++++++-------- 2 files changed, 133 insertions(+), 102 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 918d113b1..3a0b6433f 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,9 +1,9 @@ """Statistics.""" from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean from ._functional_transformers import ( + expected_value, local_averages, moments, - moments_of_norm, number_up_crossings, occupation_measure, ) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 162997b0e..3561ae11e 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -5,8 +5,9 @@ from typing import Callable, Optional, Sequence, Tuple, Union import numpy as np +import scipy.integrate -from ..._utils import check_is_univariate +from ..._utils import check_is_univariate, nquad_vec from ...representation import FDataBasis, FDataGrid from ...representation._typing import NDArrayFloat, NDArrayInt @@ -344,137 +345,167 @@ def number_up_crossings( ).T -def moments_of_norm( - data: FDataGrid, +def moments( + data: Union[FDataBasis, FDataGrid], p: int, + c: Optional[float] = 0, ) -> np.ndarray: r""" - Calculate the moments of the norm of the process of a FDataGrid. + Calculate the moments of a dataset. It performs the following map: - :math:`f_1(X)=\mathbb{E}(||X||),\dots,f_p(X)=\mathbb{E}(||X||^p)`. + :math:`f_1(X)=\int_{-\infty}^\infty\left(x - c \right)^n f(x) dx`. Args: - data: FDataGrid where we want to calculate - the moments of the norm of the process. + data: FDataGrid or FDataBasis where we want to calculate + a particular moment. + p: order of the moment. + c: value around which we want to calculate the moments. + Returns: - ndarray of shape (n_dimensions, n_samples)\ - with the values of the moments. + ndarray of shape (n_dimensions, n_samples) + with the values of the specified moment. Example: - We import the Canadian Weather dataset + For this example, we will calculate the first moment of the + curves around the value 0. We will use the Canadian Weather + data set. + And so, first we import it. >>> from skfda.datasets import fetch_weather >>> X = fetch_weather(return_X_y=True)[0] - Then we call the function with the dataset. + Then we call the function with the dataset and the specified moment + order. >>> import numpy as np - >>> from skfda.exploratory.stats import moments_of_norm - >>> np.around(moments_of_norm(X, 1), decimals=2) - array([[ 7.05, 4.06], - [ 8.98, 3.99], - [ 8.38, 4.04], - [ 8.13, 3.46], - [ 9.17, 3.29], - [ 9.95, 3.09], - [ 11.55, 2.2 ], - [ 10.92, 2.46], - [ 10.84, 2.55], - [ 10.53, 3.31], - [ 10.02, 3.04], - [ 10.97, 2.58], - [ 11.01, 2.5 ], - [ 10.15, 2.14], - [ 10.18, 2.62], - [ 10.36, 1.93], - [ 12.36, 1.4 ], - [ 12.28, 1.23], - [ 13.13, 1.12], - [ 11.62, 1.02], - [ 12.05, 1.11], - [ 13.5 , 0.99], - [ 10.16, 1.27], - [ 8.83, 1.1 ], - [ 10.32, 0.74], - [ 9.96, 3.16], - [ 9.62, 2.33], - [ 8.4 , 1.67], - [ 7.02, 7.1 ], - [ 10.06, 0.74], - [ 14.29, 0.9 ], - [ 14.47, 0.73], - [ 13.05, 1.14], - [ 15.97, 0.71], - [ 17.65, 0.39]]) + >>> from skfda.exploratory.stats import moments + >>> np.around(moments(X, 1), decimals=2) + array([[ 1175.73, 751.62], + [ 1461.09, 739.39], + [ 1384.83, 750.58], + [ 1539.13, 644.97], + [ 1339.4 , 626.75], + [ 1290.5 , 581.54], + [ -467.02, 437.43], + [ 950.21, 488.32], + [ 790.6 , 499.32], + [ 1100.51, 634.14], + [ 1088.21, 586.63], + [ 1467.25, 492.74], + [ 1399.31, 484.8 ], + [ 1647.07, 419.07], + [ 1652.64, 504.02], + [ 789.49, 382.02], + [ 803.42, 262.25], + [ 333.59, 244.46], + [ -767.34, 233.5 ], + [ 796.91, 187.26], + [ 433.72, 210.78], + [ -207.19, 199.69], + [ 637.24, 238.12], + [ 916.25, 208.4 ], + [ 1733.84, 145.82], + [ 1922.21, 591.72], + [ 1852.65, 430.36], + [ 849.19, 322. ], + [ 1398.36, 1405.86], + [ 53.39, 149.07], + [ -617.71, 180.6 ], + [ -484.52, 149.91], + [-1219. , 225.17], + [-1284.82, 144.51], + [-2552.93, 82.2 ]]) """ - return moments(data, lambda x: pow(np.abs(x), 1)) + return expected_value( + data, + lambda x: x, + lambda x: pow(np.abs(x - c), p), + ) -def moments( - data: FDataGrid, +def expected_value( + data: Union[FDataBasis, FDataGrid], f: Callable[[np.ndarray], np.ndarray], + g: Callable[[np.ndarray], np.ndarray], ) -> np.ndarray: """ - Calculate the moments of a FDataGrid. + Calculate the expected value of a function. Args: - data: FDataGrid where we want to calculate - the moments. - f: function that specifies the moments to be calculated. + data: FDataGrid or FDataBasis where we want to calculate + the expectation. + f: function that specifies the weights to be applied. + g: function that specifies the moments to be calculated. Returns: - ndarray of shape (n_dimensions, n_samples)\ - with the values of the moments. + ndarray of shape (n_dimensions, n_samples) + with the values of the expectations. Example: - We will use this funtion to calculate the moments of the - norm of a FDataGrid. - We will first import the Canadian Weather dataset. + We will use this funtion to calculate the first moment of the + Canadian Weather dataset. + We will start by importing it. >>> from skfda.datasets import fetch_weather >>> X = fetch_weather(return_X_y=True)[0] - We will define a function that calculates the moments of the norm. - >>> f = lambda x: pow(np.abs(x), 1) + We will define a function that calculates the first moment around + the value c = 0. + >>> f = lambda x: x + >>> g = lambda x: pow(np.abs(x - 0), 1) Then we call the function with the dataset and the function. >>> import numpy as np - >>> from skfda.exploratory.stats import moments - - >>> np.around(moments(X, f), decimals=2) - array([[ 7.05, 4.06], - [ 8.98, 3.99], - [ 8.38, 4.04], - [ 8.13, 3.46], - [ 9.17, 3.29], - [ 9.95, 3.09], - [ 11.55, 2.2 ], - [ 10.92, 2.46], - [ 10.84, 2.55], - [ 10.53, 3.31], - [ 10.02, 3.04], - [ 10.97, 2.58], - [ 11.01, 2.5 ], - [ 10.15, 2.14], - [ 10.18, 2.62], - [ 10.36, 1.93], - [ 12.36, 1.4 ], - [ 12.28, 1.23], - [ 13.13, 1.12], - [ 11.62, 1.02], - [ 12.05, 1.11], - [ 13.5 , 0.99], - [ 10.16, 1.27], - [ 8.83, 1.1 ], - [ 10.32, 0.74], - [ 9.96, 3.16], - [ 9.62, 2.33], - [ 8.4 , 1.67], - [ 7.02, 7.1 ], - [ 10.06, 0.74], - [ 14.29, 0.9 ], - [ 14.47, 0.73], - [ 13.05, 1.14], - [ 15.97, 0.71], - [ 17.65, 0.39]]) + >>> from skfda.exploratory.stats import expected_value + + >>> np.around(expected_value(X, f, g), decimals=2) + array([[ 1175.73, 751.62], + [ 1461.09, 739.39], + [ 1384.83, 750.58], + [ 1539.13, 644.97], + [ 1339.4 , 626.75], + [ 1290.5 , 581.54], + [ -467.02, 437.43], + [ 950.21, 488.32], + [ 790.6 , 499.32], + [ 1100.51, 634.14], + [ 1088.21, 586.63], + [ 1467.25, 492.74], + [ 1399.31, 484.8 ], + [ 1647.07, 419.07], + [ 1652.64, 504.02], + [ 789.49, 382.02], + [ 803.42, 262.25], + [ 333.59, 244.46], + [ -767.34, 233.5 ], + [ 796.91, 187.26], + [ 433.72, 210.78], + [ -207.19, 199.69], + [ 637.24, 238.12], + [ 916.25, 208.4 ], + [ 1733.84, 145.82], + [ 1922.21, 591.72], + [ 1852.65, 430.36], + [ 849.19, 322. ], + [ 1398.36, 1405.86], + [ 53.39, 149.07], + [ -617.71, 180.6 ], + [ -484.52, 149.91], + [-1219. , 225.17], + [-1284.82, 144.51], + [-2552.93, 82.2 ]]) """ - return np.mean(f(data.data_matrix), axis=1) + # TODO: decidir nombre de la funcion + domain_range = data.domain_range[0][1] - data.domain_range[0][0] + + if isinstance(data, FDataGrid): + return scipy.integrate.simpson( + g(data.grid_points[0][:, np.newaxis]) * f(data.data_matrix), + x=data.grid_points[0], + axis=1, + ) / domain_range + + integrated = nquad_vec( + data, + data.domain_range, + ) / domain_range + return integrated.reshape(integrated.shape[0], integrated.shape[2]) From 7e2b62867b8993ad9a779d37fa01bf1bd671af3a Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 27 Apr 2022 20:47:46 +0200 Subject: [PATCH 136/400] Parameterized word fix --- skfda/ml/classification/_parameterized_functional_qda.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_parameterized_functional_qda.py b/skfda/ml/classification/_parameterized_functional_qda.py index f1af22c4c..840fbace2 100644 --- a/skfda/ml/classification/_parameterized_functional_qda.py +++ b/skfda/ml/classification/_parameterized_functional_qda.py @@ -18,7 +18,7 @@ class ParameterizedFunctionalQDA( BaseEstimator, # type: ignore ClassifierMixin, # type: ignore ): - """Parametrized functional quadratic discriminant analysis. + """Parameterized functional quadratic discriminant analysis. It is based on the assumption that the data is part of a Gaussian process and depending on the output label, the covariance and mean parameters are From ee2cecd0166c3020966f3cd240005431ed0a8b4b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 29 Apr 2022 15:09:07 +0200 Subject: [PATCH 137/400] Function transformers --- .../stats/_functional_transformers.py | 63 +-------- .../_function_transformers.py | 132 ++++++++---------- 2 files changed, 60 insertions(+), 135 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 466bbf2f9..24971ae80 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -41,11 +41,11 @@ def local_averages( Example: We import the Berkeley Growth Study dataset. - We will use only the first 30 samples to make the + We will use only the first 3 samples to make the example easy. >>> from skfda.datasets import fetch_growth >>> dataset = fetch_growth(return_X_y=True)[0] - >>> X = dataset[:30] + >>> X = dataset[:3] Then we decide how many intervals we want to consider (in our case 2) and call the function with the dataset. @@ -54,65 +54,10 @@ def local_averages( >>> np.around(local_averages(X, 2), decimals=2) array([[[ 116.94], [ 111.86], - [ 107.29], - [ 111.35], - [ 104.39], - [ 109.43], - [ 109.16], - [ 112.91], - [ 109.19], - [ 117.95], - [ 112.14], - [ 114.3 ], - [ 111.48], - [ 114.85], - [ 116.25], - [ 114.6 ], - [ 111.02], - [ 113.57], - [ 108.88], - [ 109.6 ], - [ 109.7 ], - [ 108.54], - [ 109.18], - [ 106.92], - [ 109.44], - [ 109.84], - [ 115.32], - [ 108.16], - [ 119.29], - [ 110.62]], + [ 107.29]], [[ 177.26], [ 157.62], - [ 154.97], - [ 163.83], - [ 156.66], - [ 157.67], - [ 155.31], - [ 169.02], - [ 154.18], - [ 174.43], - [ 161.33], - [ 170.14], - [ 164.1 ], - [ 170.1 ], - [ 166.65], - [ 168.72], - [ 166.85], - [ 167.22], - [ 159.4 ], - [ 162.76], - [ 155.7 ], - [ 158.01], - [ 160.1 ], - [ 155.95], - [ 157.95], - [ 163.53], - [ 162.29], - [ 153.1 ], - [ 178.48], - [ 161.75]]]) - + [ 154.97]]]) """ left, right = data.domain_range[0] diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index d1b38e131..b8034296b 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -1,17 +1,20 @@ """Function transformers for feature construction techniques.""" from typing import Optional, Sequence, Tuple -from sklearn.preprocessing import FunctionTransformer +from sklearn.base import BaseEstimator +from ..._utils import TransformerMixin from ...exploratory.stats._functional_transformers import ( local_averages, number_up_crossings, occupation_measure, ) -from ...representation._typing import NDArrayFloat +from ...representation._typing import NDArrayFloat, Union +from ...representation.basis import FDataBasis +from ...representation.grid import FDataGrid -class LocalAveragesTransformer(FunctionTransformer): +class LocalAveragesTransformer(BaseEstimator, TransformerMixin): """ Transformer that works as an adapter for the local_averages function. @@ -20,11 +23,11 @@ class LocalAveragesTransformer(FunctionTransformer): Example: We import the Berkeley Growth Study dataset. - We will use only the first 30 samples to make the + We will use only the first 3 samples to make the example easy. >>> from skfda.datasets import fetch_growth >>> dataset = fetch_growth(return_X_y=True)[0] - >>> X = dataset[:30] + >>> X = dataset[:3] Then we decide how many intervals we want to consider (in our case 2) and call the function with the dataset. @@ -36,75 +39,31 @@ class LocalAveragesTransformer(FunctionTransformer): >>> np.around(local_averages.fit_transform(X), decimals=2) array([[[ 116.94], [ 111.86], - [ 107.29], - [ 111.35], - [ 104.39], - [ 109.43], - [ 109.16], - [ 112.91], - [ 109.19], - [ 117.95], - [ 112.14], - [ 114.3 ], - [ 111.48], - [ 114.85], - [ 116.25], - [ 114.6 ], - [ 111.02], - [ 113.57], - [ 108.88], - [ 109.6 ], - [ 109.7 ], - [ 108.54], - [ 109.18], - [ 106.92], - [ 109.44], - [ 109.84], - [ 115.32], - [ 108.16], - [ 119.29], - [ 110.62]], + [ 107.29]], [[ 177.26], [ 157.62], - [ 154.97], - [ 163.83], - [ 156.66], - [ 157.67], - [ 155.31], - [ 169.02], - [ 154.18], - [ 174.43], - [ 161.33], - [ 170.14], - [ 164.1 ], - [ 170.1 ], - [ 166.65], - [ 168.72], - [ 166.85], - [ 167.22], - [ 159.4 ], - [ 162.76], - [ 155.7 ], - [ 158.01], - [ 160.1 ], - [ 155.95], - [ 157.95], - [ 163.53], - [ 162.29], - [ 153.1 ], - [ 178.48], - [ 161.75]]]) - + [ 154.97]]]) """ def __init__(self, n_intervals: int): - super().__init__( - func=local_averages, - kw_args={'n_intervals': n_intervals}, - ) + self.n_intervals = n_intervals + + def transform(self, X: Union[FDataGrid, FDataBasis]) -> NDArrayFloat: + """ + Transform the provided data using the local_averages function. + + Args: + X: FDataGrid or FDataBasis with the samples that are going to be + transformed. + Returns: + Array of shape (n_intervals, n_samples, n_dimensions) including + the transformed data. + """ + return local_averages(X, self.n_intervals) -class OccupationMeasureTransformer(FunctionTransformer): + +class OccupationMeasureTransformer(BaseEstimator, TransformerMixin): """ Transformer that works as an adapter for the occupation_measure function. @@ -155,13 +114,25 @@ def __init__( *, n_points: Optional[int] = None, ): - super().__init__( - func=occupation_measure, - kw_args={'intervals': intervals, 'n_points': n_points}, - ) + self.intervals = intervals + self.n_points = n_points + + def transform(self, X: Union[FDataGrid, FDataBasis]) -> NDArrayFloat: + """ + Transform the provided data using the occupation_measure function. + Args: + X: FDataGrid or FDataBasis with the samples that are going to be + transformed. -class NumberUpCrossingsTransformer(FunctionTransformer): + Returns: + Array of shape (n_intervals, n_samples) including the transformed + data. + """ + return occupation_measure(X, self.intervals, n_points=self.n_points) + + +class NumberUpCrossingsTransformer(BaseEstimator, TransformerMixin): """ Transformer that works as an adapter for the number_up_crossings function. @@ -210,7 +181,16 @@ class NumberUpCrossingsTransformer(FunctionTransformer): def __init__(self, levels: NDArrayFloat): self.levels = levels - super().__init__( - func=number_up_crossings, - kw_args={'levels': levels}, - ) + + def transform(self, X: FDataGrid) -> NDArrayFloat: + """ + Transform the provided data using the number_up_crossings function. + + Args: + X: FDataGrid with the samples that are going to be transformed. + + Returns: + Array of shape (n_samples, len(levels)) including the transformed + data. + """ + return number_up_crossings(X, self.levels) From 1c0950ffc4055bd42d87dbb5f9beff53efeb6609 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 29 Apr 2022 16:03:44 +0200 Subject: [PATCH 138/400] Moments and unconditional expected value --- skfda/exploratory/stats/__init__.py | 5 +- .../stats/_functional_transformers.py | 200 ++++++++---------- 2 files changed, 90 insertions(+), 115 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 3a0b6433f..83e5c1001 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,11 +1,12 @@ """Statistics.""" from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean from ._functional_transformers import ( - expected_value, local_averages, - moments, number_up_crossings, occupation_measure, + unconditional_central_moments, + unconditional_expected_value, + unconditional_moments, ) from ._stats import ( cov, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 3561ae11e..339cfdc83 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -345,161 +345,135 @@ def number_up_crossings( ).T -def moments( +def unconditional_central_moments( + data: Union[FDataBasis, FDataGrid], + p: int, +) -> np.ndarray: + """ + Calculate the unconditional central moments of a dataset. + + Args: + data: FDataGrid or FDataBasis where we want to calculate + a particular central moment. + p: order of the moment. + + Returns: + ndarray of shape (n_dimensions, n_samples) with the values of the + specified moment. + + Example: + + We will calculate the first unconditional central moment of the Canadian + Weather data set. In order to simplify the example, we will use only the + first five samples. + First we proceed to import the data set. + >>> from skfda.datasets import fetch_weather + >>> X = fetch_weather(return_X_y=True)[0] + + Then we call the function with the samples that we want to consider and the + specified moment order. + >>> import numpy as np + >>> from skfda.exploratory.stats import unconditional_central_moments + >>> np.around(unconditional_central_moments(X[:5], 1), decimals=2) + array([[ 0.02, -0.02], + [ 0.03, -0.02], + [ 0.03, -0.01], + [ 0.02, -0. ], + [ 0.03, 0.01]]) + """ + mean = np.mean(data.data_matrix, axis=1) + return unconditional_expected_value( + data, + lambda x: pow((x - mean[:, np.newaxis, :]), p), + ) + + +def unconditional_moments( data: Union[FDataBasis, FDataGrid], p: int, - c: Optional[float] = 0, ) -> np.ndarray: r""" - Calculate the moments of a dataset. + Calculate the specified unconditional moment of a dataset. It performs the following map: - :math:`f_1(X)=\int_{-\infty}^\infty\left(x - c \right)^n f(x) dx`. + :math:`f(X)=\int_a^b f_1(X(t))dt,\dots,f_p(X(t))dt=\int_a^b f^p(X(t))dt`. Args: data: FDataGrid or FDataBasis where we want to calculate - a particular moment. + a particular unconditional moment. p: order of the moment. - c: value around which we want to calculate the moments. Returns: - ndarray of shape (n_dimensions, n_samples) - with the values of the specified moment. + ndarray of shape (n_dimensions, n_samples) with the values of the + specified moment. Example: - For this example, we will calculate the first moment of the - curves around the value 0. We will use the Canadian Weather - data set. - And so, first we import it. + We will calculate the first unconditional moment of the Canadian Weather + data set. In order to simplify the example, we will use only the first + five samples. + First we proceed to import the data set. >>> from skfda.datasets import fetch_weather >>> X = fetch_weather(return_X_y=True)[0] - Then we call the function with the dataset and the specified moment - order. + Then we call the function with the samples that we want to consider and the + specified moment order. >>> import numpy as np - >>> from skfda.exploratory.stats import moments - >>> np.around(moments(X, 1), decimals=2) - array([[ 1175.73, 751.62], - [ 1461.09, 739.39], - [ 1384.83, 750.58], - [ 1539.13, 644.97], - [ 1339.4 , 626.75], - [ 1290.5 , 581.54], - [ -467.02, 437.43], - [ 950.21, 488.32], - [ 790.6 , 499.32], - [ 1100.51, 634.14], - [ 1088.21, 586.63], - [ 1467.25, 492.74], - [ 1399.31, 484.8 ], - [ 1647.07, 419.07], - [ 1652.64, 504.02], - [ 789.49, 382.02], - [ 803.42, 262.25], - [ 333.59, 244.46], - [ -767.34, 233.5 ], - [ 796.91, 187.26], - [ 433.72, 210.78], - [ -207.19, 199.69], - [ 637.24, 238.12], - [ 916.25, 208.4 ], - [ 1733.84, 145.82], - [ 1922.21, 591.72], - [ 1852.65, 430.36], - [ 849.19, 322. ], - [ 1398.36, 1405.86], - [ 53.39, 149.07], - [ -617.71, 180.6 ], - [ -484.52, 149.91], - [-1219. , 225.17], - [-1284.82, 144.51], - [-2552.93, 82.2 ]]) + >>> from skfda.exploratory.stats import unconditional_moments + >>> np.around(unconditional_moments(X[:5], 1), decimals=2) + array([[ 4.7 , 4.03], + [ 6.16, 3.96], + [ 5.52, 4.01], + [ 6.82, 3.44], + [ 5.25, 3.29]]) """ - return expected_value( + return unconditional_expected_value( data, - lambda x: x, - lambda x: pow(np.abs(x - c), p), + lambda x: pow(x, p), ) -def expected_value( +def unconditional_expected_value( data: Union[FDataBasis, FDataGrid], - f: Callable[[np.ndarray], np.ndarray], - g: Callable[[np.ndarray], np.ndarray], + function: Callable[[np.ndarray], np.ndarray], ) -> np.ndarray: """ - Calculate the expected value of a function. + Calculate the unconditional expected value of a function. Args: data: FDataGrid or FDataBasis where we want to calculate - the expectation. - f: function that specifies the weights to be applied. - g: function that specifies the moments to be calculated. + the expected value. + f: function that specifies how the expected value to is calculated. + It has to be a function of X(t). Returns: - ndarray of shape (n_dimensions, n_samples) - with the values of the expectations. + ndarray of shape (n_dimensions, n_samples) with the values of the + expectations. Example: - We will use this funtion to calculate the first moment of the - Canadian Weather dataset. + We will use this funtion to calculate the logarithmic first moment + of the first 5 samples of the Berkeley Growth dataset. We will start by importing it. - >>> from skfda.datasets import fetch_weather - >>> X = fetch_weather(return_X_y=True)[0] + >>> from skfda.datasets import fetch_growth + >>> X = fetch_growth(return_X_y=True)[0] - We will define a function that calculates the first moment around - the value c = 0. - >>> f = lambda x: x - >>> g = lambda x: pow(np.abs(x - 0), 1) - - Then we call the function with the dataset and the function. + We will define a function that calculates the inverse first moment. >>> import numpy as np - >>> from skfda.exploratory.stats import expected_value - - >>> np.around(expected_value(X, f, g), decimals=2) - array([[ 1175.73, 751.62], - [ 1461.09, 739.39], - [ 1384.83, 750.58], - [ 1539.13, 644.97], - [ 1339.4 , 626.75], - [ 1290.5 , 581.54], - [ -467.02, 437.43], - [ 950.21, 488.32], - [ 790.6 , 499.32], - [ 1100.51, 634.14], - [ 1088.21, 586.63], - [ 1467.25, 492.74], - [ 1399.31, 484.8 ], - [ 1647.07, 419.07], - [ 1652.64, 504.02], - [ 789.49, 382.02], - [ 803.42, 262.25], - [ 333.59, 244.46], - [ -767.34, 233.5 ], - [ 796.91, 187.26], - [ 433.72, 210.78], - [ -207.19, 199.69], - [ 637.24, 238.12], - [ 916.25, 208.4 ], - [ 1733.84, 145.82], - [ 1922.21, 591.72], - [ 1852.65, 430.36], - [ 849.19, 322. ], - [ 1398.36, 1405.86], - [ 53.39, 149.07], - [ -617.71, 180.6 ], - [ -484.52, 149.91], - [-1219. , 225.17], - [-1284.82, 144.51], - [-2552.93, 82.2 ]]) + >>> f = lambda x: pow(np.log(x), 1) + Then we call the function with the dataset and the function. + >>> from skfda.exploratory.stats import unconditional_expected_value + >>> np.around(unconditional_expected_value(X[:5], f), decimals=2) + array([[ 4.96], + [ 4.88], + [ 4.85], + [ 4.9 ], + [ 4.84]]) """ - # TODO: decidir nombre de la funcion domain_range = data.domain_range[0][1] - data.domain_range[0][0] if isinstance(data, FDataGrid): return scipy.integrate.simpson( - g(data.grid_points[0][:, np.newaxis]) * f(data.data_matrix), + function(data.data_matrix), x=data.grid_points[0], axis=1, ) / domain_range From b45f98ecef4076817d4f155034077cc4b7c90825 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 1 May 2022 17:22:06 +0200 Subject: [PATCH 139/400] Fixing types --- .../stats/_functional_transformers.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 339cfdc83..4cd5da689 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -347,15 +347,15 @@ def number_up_crossings( def unconditional_central_moments( data: Union[FDataBasis, FDataGrid], - p: int, -) -> np.ndarray: + n: int, +) -> NDArrayFloat: """ Calculate the unconditional central moments of a dataset. Args: data: FDataGrid or FDataBasis where we want to calculate a particular central moment. - p: order of the moment. + n: order of the moment. Returns: ndarray of shape (n_dimensions, n_samples) with the values of the @@ -384,14 +384,14 @@ def unconditional_central_moments( mean = np.mean(data.data_matrix, axis=1) return unconditional_expected_value( data, - lambda x: pow((x - mean[:, np.newaxis, :]), p), + lambda x: pow((x - mean[:, np.newaxis, :]), n), ) def unconditional_moments( data: Union[FDataBasis, FDataGrid], - p: int, -) -> np.ndarray: + n: int, +) -> NDArrayFloat: r""" Calculate the specified unconditional moment of a dataset. @@ -401,7 +401,7 @@ def unconditional_moments( Args: data: FDataGrid or FDataBasis where we want to calculate a particular unconditional moment. - p: order of the moment. + n: order of the moment. Returns: ndarray of shape (n_dimensions, n_samples) with the values of the @@ -429,14 +429,14 @@ def unconditional_moments( """ return unconditional_expected_value( data, - lambda x: pow(x, p), + lambda x: pow(x, n), ) def unconditional_expected_value( data: Union[FDataBasis, FDataGrid], function: Callable[[np.ndarray], np.ndarray], -) -> np.ndarray: +) -> NDArrayFloat: """ Calculate the unconditional expected value of a function. From 73bfcc98e1daefdfd0a8e5cc1e2d3f50a56e17fb Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Tue, 3 May 2022 17:40:06 +0200 Subject: [PATCH 140/400] refactoring dataFrame conversion --- skfda/ml/regression/_linear_regression.py | 50 ++++++++++++----------- 1 file changed, 27 insertions(+), 23 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index f6fce18e7..d470e7310 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -164,12 +164,13 @@ class LinearRegression( First example: >>> x_basis = Monomial(n_basis=3) - >>> cov_list = [ ( FDataBasis(x_basis, [0, 0, 1]) ), - ... ( FDataBasis(x_basis, [0, 1, 0]) ), - ... ( FDataBasis(x_basis, [0, 1, 1]) ), - ... ( FDataBasis(x_basis, [1, 0, 1]) )] + >>> x_fd = FDataBasis(x_basis, [[0, 0, 1], + ... [0, 1, 0], + ... [0, 1, 1], + ... [1, 0, 1]]) + >>> cov_dict = { "fd": x_fd } >>> y = [2, 3, 4, 5] - >>> df = pd.DataFrame(cov_list) + >>> df = pd.DataFrame(cov_dict) >>> linear = LinearRegression() >>> _ = linear.fit(df, y) >>> linear.coef_[0] @@ -185,11 +186,11 @@ class LinearRegression( Second example: >>> x_basis = Monomial(n_basis=2) - >>> cov_list = [ ( 1, 7, FDataBasis(x_basis, [0, 2]) ), + >>> cov_list = [ ( 1, FDataBasis(x_basis, [0, 2]), 7 ), ... ( 2, FDataBasis(x_basis, [0, 4]), 3 ), ... ( 4, FDataBasis(x_basis, [1, 0]), 2 ), ... ( 1, FDataBasis(x_basis, [2, 0]), 1 ), - ... ( FDataBasis(x_basis, [1, 2]), 3, 1 ), + ... ( 3, FDataBasis(x_basis, [1, 2]), 1 ), ... ( 2, FDataBasis(x_basis, [2, 2]), 5 ) ] >>> df = pd.DataFrame(cov_list) >>> y = [11, 10, 12, 6, 10, 13] @@ -337,7 +338,7 @@ def _argcheck_X( if isinstance(X, (FData, np.ndarray)): X = [X] - if isinstance(X, pd.DataFrame): + elif isinstance(X, pd.DataFrame): X = self.__dataframe_conversion(X) X = [x if isinstance(x, FData) else np.asarray(x) for x in X] @@ -400,7 +401,10 @@ def _argcheck_X_y( return new_X, y, sample_weight, coef_info - def __dataframe_conversion(self, X: pd.DataFrame) -> List[AcceptedDataType]: + def __dataframe_conversion( + self, + X: pd.DataFrame + ) -> List[AcceptedDataType]: """Convert DataFrames to a list with two elements. First of all, a list with mv covariates and the second, @@ -413,30 +417,30 @@ def __dataframe_conversion(self, X: pd.DataFrame) -> List[AcceptedDataType]: List: first of all, a list with mv covariates and the second, a list of FDataBasis object with functional data """ - mv_final = [] - fdb_list = [] + + mv_list = [] + fd_list = [] final = [] for obs in X.values.tolist(): mv = [] - fdb = [] for i in range(len(obs)): - if (isinstance(obs[i], FData)): - fdb.append(obs[i]) + cov = obs[i] + if (isinstance(cov, FData)): + fd_list.append(cov) + elif (isinstance(cov, (np.ndarray, list))): + mv_list.append(cov) else: - mv.append(obs[i]) + mv.append(cov) if (len(mv) != 0): - mv_final.append(mv) - - if (len(fdb) != 0): - fdb_list.append(fdb) + mv_list.append(mv) - if (len(mv_final) != 0): - final.append(mv_final) + if (len(mv_list) != 0): + final.append(mv_list) - if (len(fdb_list) != 0): - fdb_df = pd.DataFrame(fdb_list) + if (len(fd_list) != 0): + fdb_df = pd.DataFrame(fd_list) for c in range(fdb_df.shape[1]): column = FDataBasis.concatenate(*fdb_df.iloc[:, c].tolist()) From 55d801846d20d3a1ae70564ea6760c67b6d86ddf Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Tue, 3 May 2022 18:05:12 +0200 Subject: [PATCH 141/400] style --- skfda/ml/regression/_linear_regression.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index d470e7310..c9995045a 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -403,7 +403,7 @@ def _argcheck_X_y( def __dataframe_conversion( self, - X: pd.DataFrame + X: pd.DataFrame, ) -> List[AcceptedDataType]: """Convert DataFrames to a list with two elements. @@ -417,7 +417,6 @@ def __dataframe_conversion( List: first of all, a list with mv covariates and the second, a list of FDataBasis object with functional data """ - mv_list = [] fd_list = [] final = [] From a011bd07cfaf71a7b3046a854a4fbe58394fb01b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 3 May 2022 22:47:01 +0200 Subject: [PATCH 142/400] 2D Matrices on local averages --- .../exploratory/stats/_functional_transformers.py | 15 +++++---------- .../_function_transformers.py | 8 ++------ 2 files changed, 7 insertions(+), 16 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 24971ae80..3123a6b20 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -34,9 +34,8 @@ def local_averages( calculate the local averages. n_intervals: number of intervals we want to consider. Returns: - ndarray of shape (n_intervals, n_samples, n_dimensions) - with the transformed data for FDataBasis and (n_intervals, n_samples) - for FDataGrid. + ndarray of shape (n_intervals, n_samples) with the + transformed data. Example: @@ -52,12 +51,8 @@ def local_averages( >>> import numpy as np >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, 2), decimals=2) - array([[[ 116.94], - [ 111.86], - [ 107.29]], - [[ 177.26], - [ 157.62], - [ 154.97]]]) + array([[ 116.94, 111.86, 107.29], + [ 177.26, 157.62, 154.97]]) """ left, right = data.domain_range[0] @@ -72,7 +67,7 @@ def local_averages( data.integrate(interval=((intervals[i], intervals[i + 1]))) / step for i in range(n_intervals) ] - return np.asarray(integrated_data) + return np.asarray(integrated_data)[:, :, 0] def _calculate_curves_occupation_( diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index b8034296b..4210bf384 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -37,12 +37,8 @@ class LocalAveragesTransformer(BaseEstimator, TransformerMixin): ... ) >>> local_averages = LocalAveragesTransformer(2) >>> np.around(local_averages.fit_transform(X), decimals=2) - array([[[ 116.94], - [ 111.86], - [ 107.29]], - [[ 177.26], - [ 157.62], - [ 154.97]]]) + array([[ 116.94, 111.86, 107.29], + [ 177.26, 157.62, 154.97]]) """ def __init__(self, n_intervals: int): From 4fac790f629cbe3672e3120fc7e6819318d2c51e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 4 May 2022 23:42:54 +0200 Subject: [PATCH 143/400] 2D matrices reshape --- .../stats/_functional_transformers.py | 21 +++++++++++------- .../_function_transformers.py | 22 +++++++++++-------- 2 files changed, 26 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 3123a6b20..7b4e75339 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -34,8 +34,8 @@ def local_averages( calculate the local averages. n_intervals: number of intervals we want to consider. Returns: - ndarray of shape (n_intervals, n_samples) with the - transformed data. + ndarray of shape (n_samples, n_intervals, n_dimensions) with + the transformed data. Example: @@ -51,8 +51,12 @@ def local_averages( >>> import numpy as np >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, 2), decimals=2) - array([[ 116.94, 111.86, 107.29], - [ 177.26, 157.62, 154.97]]) + array([[[ 116.94], + [ 177.26]], + [[ 111.86], + [ 157.62]], + [[ 107.29], + [ 154.97]]]) """ left, right = data.domain_range[0] @@ -67,7 +71,7 @@ def local_averages( data.integrate(interval=((intervals[i], intervals[i + 1]))) / step for i in range(n_intervals) ] - return np.asarray(integrated_data)[:, :, 0] + return np.transpose(integrated_data, (1, 0, 2)) def _calculate_curves_occupation_( @@ -167,8 +171,9 @@ def occupation_measure( ... ), ... decimals=2, ... ) - array([[ 0.98, 0.5 , 6.28], - [ 1.02, 0.52, 0. ]]) + array([[ 0.98, 1.02], + [ 0.5 , 0.52], + [ 6.28, 0. ]]) """ if isinstance(data, FDataBasis) and n_points is None: @@ -194,7 +199,7 @@ def occupation_measure( function_y_coordinates, function_x_coordinates, intervals, - ) + ).T def number_up_crossings( diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index 4210bf384..ad188e57d 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -37,8 +37,9 @@ class LocalAveragesTransformer(BaseEstimator, TransformerMixin): ... ) >>> local_averages = LocalAveragesTransformer(2) >>> np.around(local_averages.fit_transform(X), decimals=2) - array([[ 116.94, 111.86, 107.29], - [ 177.26, 157.62, 154.97]]) + array([[ 116.94, 177.26], + [ 111.86, 157.62], + [ 107.29, 154.97]]) """ def __init__(self, n_intervals: int): @@ -49,14 +50,16 @@ def transform(self, X: Union[FDataGrid, FDataBasis]) -> NDArrayFloat: Transform the provided data using the local_averages function. Args: - X: FDataGrid or FDataBasis with the samples that are going to be - transformed. + X: FDataGrid with the samples that are going to be transformed. Returns: - Array of shape (n_intervals, n_samples, n_dimensions) including + Array of shape (n_samples, n_intervals) including the transformed data. """ - return local_averages(X, self.n_intervals) + return local_averages( + X, + self.n_intervals, + ).reshape(X.data_matrix.shape[0], -1) class OccupationMeasureTransformer(BaseEstimator, TransformerMixin): @@ -66,7 +69,7 @@ class OccupationMeasureTransformer(BaseEstimator, TransformerMixin): Args: intervals: ndarray of tuples containing the intervals we want to consider. The shape should be - (n_sequences,2) + (n_sequences, 2) n_points: Number of points to evaluate in the domain. By default will be used the points defined on the FDataGrid. On a FDataBasis this value should be specified. @@ -100,8 +103,9 @@ class OccupationMeasureTransformer(BaseEstimator, TransformerMixin): ... ) >>> np.around(occupation_measure.fit_transform(fd_grid), decimals=2) - array([[ 0.98, 0.5 , 6.28], - [ 1.02, 0.52, 0. ]]) + array([[ 0.98, 1.02], + [ 0.5 , 0.52], + [ 6.28, 0. ]]) """ def __init__( From 18daaafe9f65736a7e9d7282aa4059ae42f9cf4e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 6 May 2022 11:09:31 +0200 Subject: [PATCH 144/400] Style fixes --- examples/plot_classification_methods.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index 68b0c1099..ae2948c1e 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -1,5 +1,5 @@ """ -Classification methods. +Classification methods ================================== Shows a comparison between the accuracies of different classification @@ -95,7 +95,7 @@ ############################################################################## -# The third method we are going to use is the Nearest Centroid Classifier +# The third method we are going to use is the Nearest Centroid Classifier. centroid = NearestCentroid() centroid.fit(X_train, y_train) @@ -136,6 +136,9 @@ # It can be concluded that all classifiers work well for this problem, as they # achieve more than an 80% of score, but the most robust ones are KNN and # Parameterized Functional QDA. + + +############################################################################## # The figure below shows the results of the classification for the test set on # the four methods considered. # It can be seen that the curves are similarly classified by all of them. From 8e08a8e6517411de22241170fdc06048c9e8b205 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sat, 7 May 2022 01:01:32 +0200 Subject: [PATCH 145/400] Update classification rst --- docs/modules/ml/classification.rst | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/modules/ml/classification.rst b/docs/modules/ml/classification.rst index 73002a078..56082355a 100644 --- a/docs/modules/ml/classification.rst +++ b/docs/modules/ml/classification.rst @@ -55,3 +55,12 @@ Classifier based on logistic regression. :toctree: autosummary skfda.ml.classification.LogisticRegression + +Parameterized functional quadratic discriminant analysis +-------------------------------------------------------- +Classifier based on the quadratic discriminant analysis. + +.. autosummary:: + :toctree: autosummary + + skfda.ml.classification.ParameterizedFunctionalQDA From 61c766c1ebd9b755ac0f9b04b65ac2beb005b670 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Mon, 9 May 2022 15:02:52 +0200 Subject: [PATCH 146/400] making df with a dict instead of list of tuples. Call enumerate(...) instead of range(len(...)) --- skfda/ml/regression/_linear_regression.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index c9995045a..7f31806fd 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -186,13 +186,15 @@ class LinearRegression( Second example: >>> x_basis = Monomial(n_basis=2) - >>> cov_list = [ ( 1, FDataBasis(x_basis, [0, 2]), 7 ), - ... ( 2, FDataBasis(x_basis, [0, 4]), 3 ), - ... ( 4, FDataBasis(x_basis, [1, 0]), 2 ), - ... ( 1, FDataBasis(x_basis, [2, 0]), 1 ), - ... ( 3, FDataBasis(x_basis, [1, 2]), 1 ), - ... ( 2, FDataBasis(x_basis, [2, 2]), 5 ) ] - >>> df = pd.DataFrame(cov_list) + >>> x_fd = FDataBasis(x_basis, [[0, 2], + ... [0, 4], + ... [1, 0], + ... [2, 0], + ... [1, 2], + ... [2, 2]]) + >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] + >>> cov_dict = { "fd": x_fd, "mult": x } + >>> df = pd.DataFrame(cov_dict) >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( ... coef_basis=[None, Constant()]) @@ -423,7 +425,7 @@ def __dataframe_conversion( for obs in X.values.tolist(): mv = [] - for i in range(len(obs)): + for i in enumerate(obs): cov = obs[i] if (isinstance(cov, FData)): fd_list.append(cov) From 189c9243b2b0d910ed87c45aba2cb7ac1817f49a Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Mon, 9 May 2022 17:02:44 +0200 Subject: [PATCH 147/400] dataframe tests --- skfda/ml/regression/_linear_regression.py | 2 +- tests/test_regression.py | 87 +++++++++++++++++++++++ 2 files changed, 88 insertions(+), 1 deletion(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 7f31806fd..968aed096 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -426,7 +426,7 @@ def __dataframe_conversion( for obs in X.values.tolist(): mv = [] for i in enumerate(obs): - cov = obs[i] + cov = obs[i[0]] if (isinstance(cov, FData)): fd_list.append(cov) elif (isinstance(cov, (np.ndarray, list))): diff --git a/tests/test_regression.py b/tests/test_regression.py index 4680b9d0b..80a6f5cff 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -1,6 +1,7 @@ import unittest import numpy as np +import pandas as pd from scipy.integrate import cumtrapz from skfda.datasets import make_gaussian, make_gaussian_process @@ -111,6 +112,92 @@ def test_regression_mixed(self): y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) + def test_regression_dataframe_multivariate(self): + + multivariate1 = [0, 2, 1, 3, 4, 2, 3] + + multivariate2 = [0, 7, 7, 9, 16, 14, 5] + + multivariate = [list(obs) for obs in zip(multivariate1, multivariate2)] + + x_fd = FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], + [0, 0, 1], [1, 0, 1], + [1, 0, 0], [0, 1, 0], + [0, 0, 1]]) + + cov_dict = {"fd": x_fd, + "mult1": multivariate1, + "mult2": multivariate2, + } + + df = pd.DataFrame(cov_dict) + + # y = 2 + sum([3, 1] * array) + int(3 * function) + intercept = 2 + coefs_multivariate = np.array([3, 1]) + coefs_functions = FDataBasis( + Monomial(n_basis=3), [[3, 0, 0]]) + y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) + y_sum = multivariate @ coefs_multivariate + y = 2 + y_sum + y_integral + + scalar = LinearRegression() + scalar.fit(df, y) + + np.testing.assert_allclose(scalar.intercept_, + intercept, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[0], + coefs_multivariate, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[1].coefficients, + coefs_functions.coefficients, atol=0.01) + + y_pred = scalar.predict(df) + np.testing.assert_allclose(y_pred, y, atol=0.01) + + def test_regression_dataframe_grouped_multivariate(self): + + multivariate = [[0, 0], [2, 7], [1, 7], [3, 9], + [4, 16], [2, 14], [3, 5]] + + x_fd = FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], + [0, 0, 1], [1, 0, 1], + [1, 0, 0], [0, 1, 0], + [0, 0, 1]]) + + cov_dict = {"fd": x_fd, "mult": multivariate} + + df = pd.DataFrame(cov_dict) + + # y = 2 + sum([3, 1] * array) + int(3 * function) + intercept = 2 + coefs_multivariate = np.array([3, 1]) + coefs_functions = FDataBasis( + Monomial(n_basis=3), [[3, 0, 0]]) + y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) + y_sum = multivariate @ coefs_multivariate + y = 2 + y_sum + y_integral + + scalar = LinearRegression() + scalar.fit(df, y) + + np.testing.assert_allclose(scalar.intercept_, + intercept, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[0], + coefs_multivariate, atol=0.01) + + np.testing.assert_allclose( + scalar.coef_[1].coefficients, + coefs_functions.coefficients, atol=0.01) + + y_pred = scalar.predict(df) + np.testing.assert_allclose(y_pred, y, atol=0.01) + def test_regression_mixed_regularization(self): multivariate = np.array([[0, 0], [2, 7], [1, 7], [3, 9], From 08fb026afaee330e2b58e4dd00057f5d62733f26 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Mon, 9 May 2022 18:12:12 +0200 Subject: [PATCH 148/400] style fixes --- tests/test_regression.py | 116 +++++++++++++++++++++++++++------------ 1 file changed, 82 insertions(+), 34 deletions(-) diff --git a/tests/test_regression.py b/tests/test_regression.py index 80a6f5cff..825db2410 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -112,31 +112,43 @@ def test_regression_mixed(self): y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) - def test_regression_dataframe_multivariate(self): + def test_regression_df_multivariate(self): # noqa: D102 multivariate1 = [0, 2, 1, 3, 4, 2, 3] - + multivariate2 = [0, 7, 7, 9, 16, 14, 5] multivariate = [list(obs) for obs in zip(multivariate1, multivariate2)] - x_fd = FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], - [0, 0, 1], [1, 0, 1], - [1, 0, 0], [0, 1, 0], - [0, 0, 1]]) - - cov_dict = {"fd": x_fd, - "mult1": multivariate1, - "mult2": multivariate2, - } + x_fd = FDataBasis( + Monomial(n_basis=3), [ + [ + 1, 0, 0, + ], [ + 0, 1, 0, + ], [ + 0, 0, 1, + ], [ + 1, 0, 1, + ], [ + 1, 0, 0, + ], [ + 0, 1, 0, + ], [ + 0, 0, 1, + ], + ]) + + cov_dict = {"fd": x_fd, "mult1": multivariate1, "mult2": multivariate2} df = pd.DataFrame(cov_dict) - # y = 2 + sum([3, 1] * array) + int(3 * function) + # y = 2 + sum([3, 1] * array) + int(3 * function) # noqa: E800 intercept = 2 coefs_multivariate = np.array([3, 1]) coefs_functions = FDataBasis( - Monomial(n_basis=3), [[3, 0, 0]]) + Monomial(n_basis=3), [[3, 0, 0]], + ) y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) y_sum = multivariate @ coefs_multivariate y = 2 + y_sum + y_integral @@ -144,39 +156,72 @@ def test_regression_dataframe_multivariate(self): scalar = LinearRegression() scalar.fit(df, y) - np.testing.assert_allclose(scalar.intercept_, - intercept, atol=0.01) + np.testing.assert_allclose( + scalar.intercept_, intercept, atol=0.01, + ) np.testing.assert_allclose( - scalar.coef_[0], - coefs_multivariate, atol=0.01) + scalar.coef_[0], coefs_multivariate, atol=0.01, + ) np.testing.assert_allclose( scalar.coef_[1].coefficients, - coefs_functions.coefficients, atol=0.01) + coefs_functions.coefficients, + atol=0.01, + ) y_pred = scalar.predict(df) np.testing.assert_allclose(y_pred, y, atol=0.01) - def test_regression_dataframe_grouped_multivariate(self): - - multivariate = [[0, 0], [2, 7], [1, 7], [3, 9], - [4, 16], [2, 14], [3, 5]] - - x_fd = FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], - [0, 0, 1], [1, 0, 1], - [1, 0, 0], [0, 1, 0], - [0, 0, 1]]) + def test_regression_df_grouped_multivariate(self): # noqa: D102 + + multivariate = [ + [ + 0, 0, + ], [ + 2, 7, + ], [ + 1, 7, + ], [ + 3, 9, + ], [ + 4, 16, + ], [ + 2, 14, + ], [ + 3, 5, + ], + ] + + x_fd = FDataBasis( + Monomial(n_basis=3), [ + [ + 1, 0, 0, + ], [ + 0, 1, 0, + ], [ + 0, 0, 1, + ], [ + 1, 0, 1, + ], [ + 1, 0, 0, + ], [ + 0, 1, 0, + ], [ + 0, 0, 1, + ], + ]) cov_dict = {"fd": x_fd, "mult": multivariate} df = pd.DataFrame(cov_dict) - # y = 2 + sum([3, 1] * array) + int(3 * function) + # y = 2 + sum([3, 1] * array) + int(3 * function) # noqa: E800 intercept = 2 coefs_multivariate = np.array([3, 1]) coefs_functions = FDataBasis( - Monomial(n_basis=3), [[3, 0, 0]]) + Monomial(n_basis=3), [[3, 0, 0]], + ) y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) y_sum = multivariate @ coefs_multivariate y = 2 + y_sum + y_integral @@ -184,16 +229,19 @@ def test_regression_dataframe_grouped_multivariate(self): scalar = LinearRegression() scalar.fit(df, y) - np.testing.assert_allclose(scalar.intercept_, - intercept, atol=0.01) + np.testing.assert_allclose( + scalar.intercept_, intercept, atol=0.01, + ) np.testing.assert_allclose( - scalar.coef_[0], - coefs_multivariate, atol=0.01) + scalar.coef_[0], coefs_multivariate, atol=0.01, + ) np.testing.assert_allclose( scalar.coef_[1].coefficients, - coefs_functions.coefficients, atol=0.01) + coefs_functions.coefficients, + atol=0.01, + ) y_pred = scalar.predict(df) np.testing.assert_allclose(y_pred, y, atol=0.01) From a8e739d0d867a3009ad7a72f16465b5a78e98af9 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 10 May 2022 00:41:03 +0200 Subject: [PATCH 149/400] Fix docstrings --- .../classification/_parameterized_functional_qda.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/ml/classification/_parameterized_functional_qda.py b/skfda/ml/classification/_parameterized_functional_qda.py index 840fbace2..5e1c5c52e 100644 --- a/skfda/ml/classification/_parameterized_functional_qda.py +++ b/skfda/ml/classification/_parameterized_functional_qda.py @@ -31,7 +31,7 @@ class ParameterizedFunctionalQDA( will be estimated by fitting the initial kernel passed on the creation of the ParameterizedFunctionalQDA object. The result of the training function will be two arrays, one of means and - another one of covariances. Both with length (``n_classes``). + another one of covariances. Both with length ``n_classes``. The prediction phase instead uses a quadratic discriminant classifier to predict which gaussian process of the fitted ones correspond the most with @@ -43,11 +43,11 @@ class ParameterizedFunctionalQDA( Parameters: kernel: Initial kernel to be fitted with the training data. For now, - only kernels that belongs to the GPy module are allowed. + only kernels that belongs to the GPy module are allowed. regularizer: Parameter that regularizes the covariance matrices in - order to avoid Singular matrices. It is multiplied by the identity - matrix and then added to the covariance one. + order to avoid Singular matrices. It is multiplied by the identity + matrix and then added to the covariance one. Examples: @@ -83,13 +83,13 @@ class ParameterizedFunctionalQDA( >>> pfqda = pfqda.fit(X_train, y_train) - We can predict the class of new samples + We can predict the class of new samples. >>> list(pfqda.predict(X_test)) [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1] - Finally, we calculate the mean accuracy for the test data + Finally, we calculate the mean accuracy for the test data. >>> round(pfqda.score(X_test, y_test), 2) 0.96 From 0632ab0581c36d7119a3fcce953ca6c479e3ce2d Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sat, 14 May 2022 01:32:46 +0200 Subject: [PATCH 150/400] Comment fix --- examples/plot_classification_methods.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index ae2948c1e..f02a9ceb8 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -2,7 +2,7 @@ Classification methods ================================== -Shows a comparison between the accuracies of different classification +It shows a comparison between the accuracies of different classification methods. There has been selected one method of each kind present in the library. In particular, there is one based on depths, Maximum Depth Classifier, @@ -16,6 +16,7 @@ # Author:Álvaro Castillo García # License: MIT + import matplotlib.pyplot as plt from GPy.kern import RBF from sklearn.model_selection import train_test_split From bdad5ce12d5cf763ffbe90272b068c7bc34e7aaf Mon Sep 17 00:00:00 2001 From: Alvaro Date: Sun, 15 May 2022 00:45:59 +0200 Subject: [PATCH 151/400] Fixing dimensions --- .../stats/_functional_transformers.py | 25 +++++++++++++------ 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 7b4e75339..fc30458de 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -74,7 +74,7 @@ def local_averages( return np.transpose(integrated_data, (1, 0, 2)) -def _calculate_curves_occupation_( +def _calculate_curves_occupation( curve_y_coordinates: NDArrayFloat, curve_x_coordinates: NDArrayFloat, intervals: Sequence[Tuple[float, float]], @@ -94,14 +94,23 @@ def _calculate_curves_occupation_( intervals_x_axis = curve_x_coordinates[1:] - curve_x_coordinates[:-1] # Calculate which points are inside the interval given (y1,y2) on Y axis - greater_than_y1 = curve_y_coordinates >= y1[:, np.newaxis, np.newaxis] - less_than_y2 = curve_y_coordinates <= y2[:, np.newaxis, np.newaxis] + greater_than_y1 = ( + curve_y_coordinates[:, :, np.newaxis] + >= y1[np.newaxis, np.newaxis, :] + ) + less_than_y2 = ( + curve_y_coordinates[:, :, np.newaxis] + <= y2[np.newaxis, np.newaxis, :] + ) inside_interval_bools = greater_than_y1 & less_than_y2 # Calculate intervals on X axis where the points are inside Y axis interval - intervals_x_inside = inside_interval_bools * intervals_x_axis + intervals_x_inside = ( + inside_interval_bools + * intervals_x_axis[:, np.newaxis] + ) - return np.sum(intervals_x_inside, axis=2) + return np.sum(intervals_x_inside, axis=1) def occupation_measure( @@ -139,7 +148,7 @@ def occupation_measure( By default will be used the points defined on the FDataGrid. On a FDataBasis this value should be specified. Returns: - ndarray of shape (n_intervals, n_samples) + ndarray of shape (n_samples, n_intervals) with the transformed data. Example: @@ -195,11 +204,11 @@ def occupation_measure( ) function_y_coordinates = data(function_x_coordinates[1:]) - return _calculate_curves_occupation_( # noqa: WPS317 + return _calculate_curves_occupation( # noqa: WPS317 function_y_coordinates, function_x_coordinates, intervals, - ).T + ) def number_up_crossings( From 7b2d1629789effd3602dafd0d1e1c9a87d75917b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 17 May 2022 23:35:45 +0200 Subject: [PATCH 152/400] Add rst --- docs/modules/exploratory/stats.rst | 3 ++ docs/modules/preprocessing.rst | 12 ++++++- .../preprocessing/feature_construction.rst | 32 +++++++++++++++++++ 3 files changed, 46 insertions(+), 1 deletion(-) create mode 100644 docs/modules/preprocessing/feature_construction.rst diff --git a/docs/modules/exploratory/stats.rst b/docs/modules/exploratory/stats.rst index bd36cfc0e..1988e77d1 100644 --- a/docs/modules/exploratory/stats.rst +++ b/docs/modules/exploratory/stats.rst @@ -41,4 +41,7 @@ The following statistics can be used to estimate additional properties of the da :toctree: autosummary skfda.exploratory.stats.modified_epigraph_index + skfda.exploratory.stats.local_averages + skfda.exploratory.stats.occupation_measure + skfda.exploratory.stats.number_up_crossings diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index ae14a2938..097e78a7d 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -39,4 +39,14 @@ the data with clarity. To better understand the data, we need to use *dimensionality reduction* methods that can reduce the number of features while still preserving the most relevant information. :doc:`Here ` you can learn more about the -dimension reduction methods available in the library. \ No newline at end of file +dimension reduction methods available in the library. + +Feature construction +-------------------- + +It is crucial to create features from the original ones so they can be +used as input for the machine learning techniques developed, improving +the quality of the predictions. To construct new features from the curves, +we need to use *feature construction* methods. +:doc:`Here ` you can learn more about the +feature construction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/feature_construction.rst b/docs/modules/preprocessing/feature_construction.rst new file mode 100644 index 000000000..4f9fb2287 --- /dev/null +++ b/docs/modules/preprocessing/feature_construction.rst @@ -0,0 +1,32 @@ +Feature construction +==================== + +When dealing with functional data we might want to construct new features +that can be used as additional inputs to the machine learning algorithms. +The expectation is that these features make explicit characteristics that +facilitate the learning process. + + +FDA Feature union +----------------- + +This transformer defines a way of extracting a high number of distinct +features in parallel. + +.. autosummary:: + :toctree: autosummary + + skfda.preprocessing.feature_construction.FDAFeatureUnion + + +Per class transformer +--------------------- + +This method deals with the extraction of features depending on the class +label of each observation. It will apply a different transformation to each +set of observations belonging to different classes. + +.. autosummary:: + :toctree: autosummary + + skfda.preprocessing.feature_construction.PerClassTransformer \ No newline at end of file From 3af1982bf465c35fd4f6f31c0373454280b1a85e Mon Sep 17 00:00:00 2001 From: Alvaro Date: Thu, 19 May 2022 00:07:09 +0200 Subject: [PATCH 153/400] Changes on rst --- docs/modules/preprocessing.rst | 9 +++++---- docs/modules/preprocessing/feature_construction.rst | 7 ++++--- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index 097e78a7d..5c861a72b 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -44,9 +44,10 @@ dimension reduction methods available in the library. Feature construction -------------------- -It is crucial to create features from the original ones so they can be -used as input for the machine learning techniques developed, improving -the quality of the predictions. To construct new features from the curves, -we need to use *feature construction* methods. +When dealing with functional data we might want to construct new features +that can be used as additional inputs to the machine learning algorithms. +The expectation is that these features make explicit characteristics that +facilitate the learning process. To construct new features from the curves, +*feature construction* methods are available. :doc:`Here ` you can learn more about the feature construction methods available in the library. \ No newline at end of file diff --git a/docs/modules/preprocessing/feature_construction.rst b/docs/modules/preprocessing/feature_construction.rst index 4f9fb2287..e33b6bf21 100644 --- a/docs/modules/preprocessing/feature_construction.rst +++ b/docs/modules/preprocessing/feature_construction.rst @@ -22,9 +22,10 @@ features in parallel. Per class transformer --------------------- -This method deals with the extraction of features depending on the class -label of each observation. It will apply a different transformation to each -set of observations belonging to different classes. +This method deals with the extraction of features using the information of +the target classes It applies as many transformations as classes +to every observation. Each transformation is fitted using only the training +data of a particular class. .. autosummary:: :toctree: autosummary From d740f46b0e2164d269edcf838f80b933af45520a Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 13:57:05 -0500 Subject: [PATCH 154/400] Easy fixes --- skfda/misc/metrics/_mahalanobis.py | 32 +++++++++---------- .../dim_reduction/feature_extraction/_fpca.py | 6 +--- 2 files changed, 17 insertions(+), 21 deletions(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 744900e71..b2c3f4b4d 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -42,8 +42,8 @@ class MahalanobisDistance(BaseEstimator): # type: ignore fitting a FDataBasis. alpha: Hyperparameter that controls the smoothness of the aproximation. - eigen_vectors: Eigenvectors of the covariance operator. - eigen_values: Eigenvalues of the covariance operator. + eigenvectors: Eigenvectors of the covariance operator. + eigenvalues: Eigenvalues of the covariance operator. Examples: >>> from skfda.misc.metrics import MahalanobisDistance @@ -70,8 +70,8 @@ def __init__( weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, alpha: float = 0.001, - eigen_values: Optional[NDArrayFloat] = None, - eigen_vectors: Optional[FData] = None, + eigenvalues: Optional[NDArrayFloat] = None, + eigenvectors: Optional[FData] = None, ) -> None: self.n_components = n_components self.centering = centering @@ -79,8 +79,8 @@ def __init__( self.weights = weights self.components_basis = components_basis self.alpha = alpha - self.eigen_values = eigen_values - self.eigen_vectors = eigen_vectors + self.eigenvalues = eigenvalues + self.eigenvectors = eigenvectors def fit( self, @@ -101,7 +101,7 @@ def fit( """ from ...preprocessing.dim_reduction.feature_extraction import FPCA - if self.eigen_values is None or self.eigen_vectors is None: + if self.eigenvalues is None or self.eigenvectors is None: fpca = FPCA( self.n_components, self.centering, @@ -110,15 +110,15 @@ def fit( self.components_basis, ) fpca.fit(X) - self.eigen_values_ = fpca.explained_variance_ - self.eigen_vectors_ = fpca.components_ + self.eigenvalues_ = fpca.explained_variance_ + self.eigenvectors_ = fpca.components_ elif not ( - hasattr(self, 'eigen_values_') # noqa: WPS421 - and hasattr(self, 'eigen_vectors_') # noqa: WPS421 + hasattr(self, 'eigenvalues_') # noqa: WPS421 + and hasattr(self, 'eigenvectors_') # noqa: WPS421 ): - self.eigen_values_ = self.eigen_values - self.eigen_vectors_ = self.eigen_vectors + self.eigenvalues_ = self.eigenvalues + self.eigenvectors_ = self.eigenvectors return self @@ -139,8 +139,8 @@ def __call__( check_is_fitted(self) return np.sum( - self.eigen_values_ - * inner_product_matrix(e1 - e2, self.eigen_vectors_) ** 2 - / (self.eigen_values_ + self.alpha)**2, + self.eigenvalues_ + * inner_product_matrix(e1 - e2, self.eigenvectors_) ** 2 + / (self.eigenvalues_ + self.alpha)**2, axis=1, ) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index 9ceeac2eb..c857cb075 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -225,11 +225,7 @@ def _fit_basis( ) # initialize the pca module provided by scikit-learn - pca = PCA( - n_components=self.n_components, - svd_solver='randomized', - random_state=1, - ) + pca = PCA(n_components=self.n_components) pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the From 9b06103fb9eabc574d83ca04eb692f9c60ab7599 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 14:56:57 -0500 Subject: [PATCH 155/400] more fixes --- skfda/exploratory/depth/_depth.py | 7 ++++--- skfda/ml/classification/_centroid_classifiers.py | 4 ++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index e7b94b436..3268ca8f7 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -226,7 +226,7 @@ class DistanceBasedDepth(Depth[FDataGrid]): metric: The metric to use as M in the following depth calculation .. math:: - D(x) = [1 + M^2(x, \mu)]^{-1}. + D(x) = [1 + M(x, \mu)]^{-1}. as explained in :footcite:`serfling+zuo_2000_depth_function`. @@ -269,8 +269,9 @@ def fit( # noqa: D102 Returns: self """ - if hasattr(self.metric, 'fit'): # noqa: WPS421 - self.metric.fit(X) + fit = getattr(self.metric, 'fit', lambda: None) + fit(X) + self.mean_ = X.mean() return self diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index f437b19e5..2280ff404 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -86,8 +86,8 @@ def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: Returns: self """ - if hasattr(self.metric, 'fit'): # noqa: WPS421 - self.metric.fit(X) + fit = getattr(self.metric, 'fit', lambda: None) + fit(X) classes, y_ind = _classifier_get_classes(y) From 902bfbb4b7c6c03cd9dd092db711ac1e6af21113 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 16:34:44 -0500 Subject: [PATCH 156/400] Final fixes --- skfda/exploratory/depth/_depth.py | 2 +- skfda/misc/metrics/_mahalanobis.py | 13 +++++-------- skfda/ml/classification/_centroid_classifiers.py | 2 +- 3 files changed, 7 insertions(+), 10 deletions(-) diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 3268ca8f7..4988777e9 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -269,7 +269,7 @@ def fit( # noqa: D102 Returns: self """ - fit = getattr(self.metric, 'fit', lambda: None) + fit = getattr(self.metric, 'fit', lambda X: None) fit(X) self.mean_ = X.mean() diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index b2c3f4b4d..4a2203771 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -113,13 +113,6 @@ def fit( self.eigenvalues_ = fpca.explained_variance_ self.eigenvectors_ = fpca.components_ - elif not ( - hasattr(self, 'eigenvalues_') # noqa: WPS421 - and hasattr(self, 'eigenvectors_') # noqa: WPS421 - ): - self.eigenvalues_ = self.eigenvalues - self.eigenvectors_ = self.eigenvectors - return self def __call__( @@ -136,7 +129,11 @@ def __call__( Returns: Squared functional Mahalanobis distance between two observations. """ - check_is_fitted(self) + if self.eigenvalues is not None and self.eigenvectors is not None: + self.eigenvalues_ = self.eigenvalues + self.eigenvectors_ = self.eigenvectors + else: + check_is_fitted(self) return np.sum( self.eigenvalues_ diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 2280ff404..1ed1a7164 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -86,7 +86,7 @@ def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: Returns: self """ - fit = getattr(self.metric, 'fit', lambda: None) + fit = getattr(self.metric, 'fit', lambda X: None) fit(X) classes, y_ind = _classifier_get_classes(y) From 521910a1b7700bd8fecf301c58c4ae28db7b7f94 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 18:18:52 -0500 Subject: [PATCH 157/400] undo changes in fpca --- .../dim_reduction/feature_extraction/_fpca.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index c857cb075..be18d9659 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -62,6 +62,7 @@ class FPCA( selected components. mean\_ (FData): mean of the train data. + Examples: Construct an artificial FDataBasis object and run FPCA with this object. The resulting principal components are not compared because @@ -89,6 +90,7 @@ class FPCA( >>> fd = FDataGrid(data_matrix, grid_points) >>> fpca_grid = FPCA(2) >>> fpca_grid = fpca_grid.fit(fd) + """ def __init__( @@ -149,6 +151,7 @@ def _fit_basis( if self.components_basis else X.basis.n_basis ) + n_samples = X.n_samples # necessary in inverse_transform self.n_samples_ = X.n_samples @@ -203,8 +206,7 @@ def _fit_basis( # apply regularization if regularization_matrix is not None: - # using += would have a different behavior - g_matrix = (g_matrix + regularization_matrix) # noqa: WPS350 + g_matrix = (g_matrix + regularization_matrix) # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -287,8 +289,9 @@ def _fit_grid( please view the referenced book, chapter 8. In summary, we are performing standard multivariate PCA over - :math:`\mathbf{X} \mathbf{W}^{1/2}` where :math:`\mathbf{X}` is the - data matrix and :math:`\mathbf{W}` is the weight matrix (this matrix + :math:`\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` + is the number of samples in the dataset, :math:`\mathbf{X}` is the data + matrix and :math:`\mathbf{W}` is the weight matrix (this matrix defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. @@ -374,11 +377,7 @@ def _fit_grid( # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) - pca = PCA( - n_components=self.n_components, - svd_solver='randomized', - random_state=1, - ) + pca = PCA(n_components=self.n_components) pca.fit(final_matrix) self.components_ = X.copy( data_matrix=np.transpose( From f9ad051d89dd482c39549c52d4828e254db9fa8b Mon Sep 17 00:00:00 2001 From: pedrorponga <32200195+pedrorponga@users.noreply.github.com> Date: Sun, 22 May 2022 18:20:54 -0500 Subject: [PATCH 158/400] Update _fpca.py --- skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py index be18d9659..9d81298ae 100644 --- a/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/feature_extraction/_fpca.py @@ -59,7 +59,7 @@ class FPCA( explained_variance_ratio\_ (array_like): this contains the percentage of variance explained by each principal component. singular_values\_: The singular values corresponding to each of the - selected components. + selected components. mean\_ (FData): mean of the train data. From 08a6c473514882296910e92c95bff5fa0a566044 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 18:54:16 -0500 Subject: [PATCH 159/400] Update mahalanobis --- skfda/misc/metrics/_mahalanobis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 4a2203771..23fb458d3 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -99,7 +99,7 @@ def fit( Returns: self """ - from ...preprocessing.dim_reduction.feature_extraction import FPCA + from ...preprocessing.dim_reduction import FPCA if self.eigenvalues is None or self.eigenvectors is None: fpca = FPCA( From 96a0ba9737c46db5ffc72dc3f992f85ad2e47d6e Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 19:28:36 -0500 Subject: [PATCH 160/400] Small changes in FPCA --- skfda/preprocessing/dim_reduction/_fpca.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index ae3d61932..09fee65b7 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -59,10 +59,9 @@ class FPCA( explained_variance_ratio\_ (array_like): this contains the percentage of variance explained by each principal component. singular_values\_: The singular values corresponding to each of the - selected components. + selected components. mean\_ (FData): mean of the train data. - Examples: Construct an artificial FDataBasis object and run FPCA with this object. The resulting principal components are not compared because @@ -90,7 +89,6 @@ class FPCA( >>> fd = FDataGrid(data_matrix, grid_points) >>> fpca_grid = FPCA(2) >>> fpca_grid = fpca_grid.fit(fd) - """ def __init__( @@ -151,7 +149,6 @@ def _fit_basis( if self.components_basis else X.basis.n_basis ) - n_samples = X.n_samples # necessary in inverse_transform self.n_samples_ = X.n_samples @@ -206,7 +203,8 @@ def _fit_basis( # apply regularization if regularization_matrix is not None: - g_matrix = (g_matrix + regularization_matrix) + # using += would have a different behavior + g_matrix = (g_matrix + regularization_matrix) # noqa: WPS350 # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -289,9 +287,8 @@ def _fit_grid( please view the referenced book, chapter 8. In summary, we are performing standard multivariate PCA over - :math:`\frac{1}{\sqrt{N}} \mathbf{X} \mathbf{W}^{1/2}` where :math:`N` - is the number of samples in the dataset, :math:`\mathbf{X}` is the data - matrix and :math:`\mathbf{W}` is the weight matrix (this matrix + :math:`\mathbf{X} \mathbf{W}^{1/2}` where :math:`\mathbf{X}` is the + data matrix and :math:`\mathbf{W}` is the weight matrix (this matrix defines the numerical integration). By default the weight matrix is obtained using the trapezoidal rule. From 5a744703be93d8114f480e6f18b17850b6a1442b Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Sun, 22 May 2022 19:50:09 -0500 Subject: [PATCH 161/400] Simpson --- skfda/misc/metrics/_mahalanobis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 23fb458d3..d9cd36f8b 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -31,7 +31,7 @@ class MahalanobisDistance(BaseEstimator): # type: ignore data object and center the data first. Defaults to ``True``. regularization: Regularization object to be applied. weights: The weights vector used for discrete integration. - If none then the trapezoidal rule is used for computing the + If none then Simpson's rule is used for computing the weights. If a callable object is passed, then the weight vector will be obtained by evaluating the object at the sample points of the passed FDataGrid object in the fit method. This From 63160a342616d5a5981dfdfdc4f47a88799df6bb Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Mon, 30 May 2022 13:32:35 +0200 Subject: [PATCH 162/400] _fit_metric --- skfda/exploratory/depth/_depth.py | 4 ++-- skfda/misc/metrics/_utils.py | 11 +++++++++++ skfda/ml/classification/_centroid_classifiers.py | 4 ++-- 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 4988777e9..925646b88 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -15,6 +15,7 @@ from ... import FDataGrid from ...misc.metrics import Metric, l2_distance +from ...misc.metrics._utils import _fit_metric from ...representation import FData from ...representation._typing import NDArrayFloat from .multivariate import Depth, SimplicialDepth, _UnivariateFraimanMuniz @@ -269,8 +270,7 @@ def fit( # noqa: D102 Returns: self """ - fit = getattr(self.metric, 'fit', lambda X: None) - fit(X) + _fit_metric(self.metric, X) self.mean_ = X.mean() diff --git a/skfda/misc/metrics/_utils.py b/skfda/misc/metrics/_utils.py index 460fa1526..84c4ad801 100644 --- a/skfda/misc/metrics/_utils.py +++ b/skfda/misc/metrics/_utils.py @@ -265,3 +265,14 @@ def _pairwise_metric_optimization_transformation_distance( pairwise = PairwiseMetric(metric.metric) return pairwise(e1_trans, e2_trans) + + +def _fit_metric(metric: Metric[T], X: T) -> None: + """Fits a metric if it has a fit method. + + Args: + metric: The metric to fit. + X: FData with the training data. + """ + fit = getattr(metric, 'fit', lambda X: None) + fit(X) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 1ed1a7164..f18efd395 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -10,6 +10,7 @@ from ...exploratory.depth import Depth, ModifiedBandDepth from ...exploratory.stats import mean, trim_mean from ...misc.metrics import Metric, PairwiseMetric, l2_distance +from ...misc.metrics._utils import _fit_metric from ...representation import FData from ...representation._typing import NDArrayInt @@ -86,8 +87,7 @@ def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: Returns: self """ - fit = getattr(self.metric, 'fit', lambda X: None) - fit(X) + _fit_metric(self.metric, X) classes, y_ind = _classifier_get_classes(y) From c84aa363c406f39e693a1e835cbf87b50939e171 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Mon, 30 May 2022 17:19:58 +0200 Subject: [PATCH 163/400] fix __call__ --- skfda/misc/metrics/_mahalanobis.py | 24 +++++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index d9cd36f8b..8ba287096 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -6,6 +6,7 @@ import numpy as np from sklearn.base import BaseEstimator +from sklearn.exceptions import NotFittedError from sklearn.utils.validation import check_is_fitted from ...representation import FData @@ -113,6 +114,10 @@ def fit( self.eigenvalues_ = fpca.explained_variance_ self.eigenvectors_ = fpca.components_ + else: + self.eigenvalues_ = self.eigenvalues + self.eigenvectors_ = self.eigenvectors + return self def __call__( @@ -129,15 +134,20 @@ def __call__( Returns: Squared functional Mahalanobis distance between two observations. """ - if self.eigenvalues is not None and self.eigenvectors is not None: - self.eigenvalues_ = self.eigenvalues - self.eigenvectors_ = self.eigenvectors - else: + try: check_is_fitted(self) + eigenvalues = self.eigenvalues_ + eigenvectors = self.eigenvectors_ + except NotFittedError: + if self.eigenvalues is not None and self.eigenvectors is not None: + eigenvalues = self.eigenvalues + eigenvectors = self.eigenvectors + else: + raise NotFittedError return np.sum( - self.eigenvalues_ - * inner_product_matrix(e1 - e2, self.eigenvectors_) ** 2 - / (self.eigenvalues_ + self.alpha)**2, + eigenvalues + * inner_product_matrix(e1 - e2, eigenvectors) ** 2 + / (eigenvalues + self.alpha)**2, axis=1, ) From 92f144259b1dd9e4958f9aad53362904ce7d23f0 Mon Sep 17 00:00:00 2001 From: pedrorponga Date: Wed, 1 Jun 2022 13:06:12 +0200 Subject: [PATCH 164/400] simplify raise --- skfda/misc/metrics/_mahalanobis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 8ba287096..5ebf63304 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -143,7 +143,7 @@ def __call__( eigenvalues = self.eigenvalues eigenvectors = self.eigenvectors else: - raise NotFittedError + raise return np.sum( eigenvalues From 769757046a7060d0732e1dda0ecd56540bcc41b3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Wed, 1 Jun 2022 15:53:15 +0200 Subject: [PATCH 165/400] Add action to publish in PyPI --- .github/workflows/python-publish.yml | 39 ++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 .github/workflows/python-publish.yml diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 000000000..ec703542b --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,39 @@ +# This workflow will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + release: + types: [published] + +permissions: + contents: read + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build + - name: Build package + run: python -m build + - name: Publish package + uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 + with: + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} From 2f527ec96c7db9a48febfc94ad0534fc5e8673a6 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 1 Jun 2022 20:42:58 +0200 Subject: [PATCH 166/400] Swap axes --- skfda/exploratory/stats/_functional_transformers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index fc30458de..1c34f7680 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -71,7 +71,7 @@ def local_averages( data.integrate(interval=((intervals[i], intervals[i + 1]))) / step for i in range(n_intervals) ] - return np.transpose(integrated_data, (1, 0, 2)) + return np.swapaxes(integrated_data, 0, 1) def _calculate_curves_occupation( From 800ce0a1f1ac55316cb0cb1c277851d5436f9710 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Wed, 1 Jun 2022 21:19:59 +0200 Subject: [PATCH 167/400] Unsupported operand operation --- .../stats/_functional_transformers.py | 58 +++++++++++-------- 1 file changed, 34 insertions(+), 24 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 4cd5da689..0bd4945bb 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -5,7 +5,6 @@ from typing import Callable, Optional, Sequence, Tuple, Union import numpy as np -import scipy.integrate from ..._utils import check_is_univariate, nquad_vec from ...representation import FDataBasis, FDataGrid @@ -346,15 +345,23 @@ def number_up_crossings( def unconditional_central_moments( - data: Union[FDataBasis, FDataGrid], + data: FDataGrid, n: int, ) -> NDArrayFloat: - """ + r""" Calculate the unconditional central moments of a dataset. + The unconditional central moments are defined as the unconditional + moments where the mean is subtracted from each sample before the + integration. They are calculated as follows: + + .. math:: + f(X)=\int_a^b \left(X_1(t) - \mu_1\right)^ndt,\dots, + \left(X_p(t) - \mu_p\right)^ndt=\int_a^b \left(X(t) - \mu\right)^ndt + Args: - data: FDataGrid or FDataBasis where we want to calculate - a particular central moment. + data: FDataGrid where we want to calculate + a particular unconditional central moment. n: order of the moment. Returns: @@ -375,16 +382,19 @@ def unconditional_central_moments( >>> import numpy as np >>> from skfda.exploratory.stats import unconditional_central_moments >>> np.around(unconditional_central_moments(X[:5], 1), decimals=2) - array([[ 0.02, -0.02], - [ 0.03, -0.02], - [ 0.03, -0.01], - [ 0.02, -0. ], - [ 0.03, 0.01]]) + array([[0.01, 0.01], + [0.02, 0.01], + [0.02, 0.01], + [0.02, 0.01], + [0.01, 0.01]]) """ - mean = np.mean(data.data_matrix, axis=1) + mean = data.integrate() / ( + data.domain_range[0][1] - data.domain_range[0][0] + ) + return unconditional_expected_value( data, - lambda x: pow((x - mean[:, np.newaxis, :]), n), + lambda x: np.power(x - mean[:, np.newaxis, :], n), ) @@ -429,7 +439,7 @@ def unconditional_moments( """ return unconditional_expected_value( data, - lambda x: pow(x, n), + lambda x: np.power(x, n), ) @@ -458,7 +468,7 @@ def unconditional_expected_value( We will define a function that calculates the inverse first moment. >>> import numpy as np - >>> f = lambda x: pow(np.log(x), 1) + >>> f = lambda x: np.power(np.log(x), 1) Then we call the function with the dataset and the function. >>> from skfda.exploratory.stats import unconditional_expected_value @@ -469,17 +479,17 @@ def unconditional_expected_value( [ 4.9 ], [ 4.84]]) """ - domain_range = data.domain_range[0][1] - data.domain_range[0][0] + domain_range = np.prod( + [ + (iterval[1] - iterval[0]) + for iterval in data.domain_range + ], + ) if isinstance(data, FDataGrid): - return scipy.integrate.simpson( - function(data.data_matrix), - x=data.grid_points[0], - axis=1, - ) / domain_range + return function(data).integrate() / domain_range - integrated = nquad_vec( - data, - data.domain_range, + return nquad_vec( + function, + data.integrate(), ) / domain_range - return integrated.reshape(integrated.shape[0], integrated.shape[2]) From 23cee08d6c07a3519ffce7878507d0faa073cadd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 6 Jun 2022 12:45:50 +0200 Subject: [PATCH 168/400] Fix operations with codomain dimension greater than one. --- .../stats/_functional_transformers.py | 22 +++++++++---------- skfda/representation/grid.py | 11 ++++++++++ 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 0bd4945bb..c29414b23 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -382,11 +382,11 @@ def unconditional_central_moments( >>> import numpy as np >>> from skfda.exploratory.stats import unconditional_central_moments >>> np.around(unconditional_central_moments(X[:5], 1), decimals=2) - array([[0.01, 0.01], - [0.02, 0.01], - [0.02, 0.01], - [0.02, 0.01], - [0.01, 0.01]]) + array([[ 0.01, 0.01], + [ 0.02, 0.01], + [ 0.02, 0.01], + [ 0.02, 0.01], + [ 0.01, 0.01]]) """ mean = data.integrate() / ( data.domain_range[0][1] - data.domain_range[0][0] @@ -394,7 +394,7 @@ def unconditional_central_moments( return unconditional_expected_value( data, - lambda x: np.power(x - mean[:, np.newaxis, :], n), + lambda x: np.power(x - mean, n), ) @@ -431,11 +431,11 @@ def unconditional_moments( >>> import numpy as np >>> from skfda.exploratory.stats import unconditional_moments >>> np.around(unconditional_moments(X[:5], 1), decimals=2) - array([[ 4.7 , 4.03], - [ 6.16, 3.96], - [ 5.52, 4.01], - [ 6.82, 3.44], - [ 5.25, 3.29]]) + array([[ 4.7 , 4.03], + [ 6.16, 3.96], + [ 5.52, 4.01], + [ 6.82, 3.44], + [ 5.25, 3.29]]) """ return unconditional_expected_value( data, diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index affec3af2..1d9598121 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -724,6 +724,17 @@ def _get_op_matrix( * (self.data_matrix.ndim - 1) ) + return other[other_index] + elif other.shape == ( + self.n_samples, + self.dim_codomain, + ): + other_index = ( + (slice(None),) + (np.newaxis,) + * (self.data_matrix.ndim - 2) + + (slice(None),) + ) + return other[other_index] elif isinstance(other, FDataGrid): From d371f42c42be9daec85d8188247400be54c0b7e2 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 7 Jun 2022 11:45:06 +0200 Subject: [PATCH 169/400] Integration with binder. --- binder/requirements.txt | 3 +++ docs/conf.py | 44 ++++++++++++++++++++++++++++++++++++ jupytext.toml | 2 ++ readthedocs-requirements.txt | 9 +------- 4 files changed, 50 insertions(+), 8 deletions(-) create mode 100644 binder/requirements.txt create mode 100644 jupytext.toml diff --git a/binder/requirements.txt b/binder/requirements.txt new file mode 100644 index 000000000..cd77b966b --- /dev/null +++ b/binder/requirements.txt @@ -0,0 +1,3 @@ +-r ../readthedocs-requirements.txt +jupytext +. \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 13ef76f12..be875b91c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,6 +23,9 @@ import warnings import pkg_resources +# Patch sphinx_gallery.binder.gen_binder_rst so as to point to .py file in +# repository +import sphinx_gallery.binder # -- Extensions to the Napoleon GoogleDocstring class --------------------- from sphinx.ext.napoleon.docstring import GoogleDocstring @@ -261,6 +264,9 @@ def __call__(self, filename: str) -> str: return f"zzz{filename}" +rtd_version = os.environ.get("READTHEDOCS_VERSION", "latest") +branch = "master" if rtd_version == "stable" else "develop" + sphinx_gallery_conf = { # path to your examples scripts 'examples_dirs': ['../examples', '../tutorial'], @@ -273,6 +279,14 @@ def __call__(self, filename: str) -> str: 'backreferences_dir': 'backreferences', 'doc_module': 'skfda', 'within_subsection_order': SkfdaExplicitSubOrder, + 'binder': { + 'org': 'GAA-UAM', + 'repo': 'scikit-fda', + 'branch': branch, + 'binderhub_url': 'https://mybinder.org', + 'dependencies': ['../binder/requirements.txt'], + 'notebooks_dir': '../examples', + }, } warnings.filterwarnings( @@ -331,3 +345,33 @@ def patched_parse(self): GoogleDocstring._unpatched_parse = GoogleDocstring._parse GoogleDocstring._parse = patched_parse + + +# Binder integration +# Taken from +# https://stanczakdominik.github.io/posts/simple-binder-usage-with-sphinx-gallery-through-jupytext/ +def patched_gen_binder_rst(fpath, binder_conf, gallery_conf): + """Generate the RST + link for the Binder badge. + ... + """ + binder_conf = sphinx_gallery.binder.check_binder_conf(binder_conf) + binder_url = sphinx_gallery.binder.gen_binder_url( + fpath, binder_conf, gallery_conf) + + # I added the line below: + binder_url = binder_url.replace( + gallery_conf['gallery_dirs'] + os.path.sep, "").replace("ipynb", "py") + + rst = ( + "\n" + " .. container:: binder-badge\n\n" + " .. image:: https://mybinder.org/badge_logo.svg\n" + " :target: {}\n" + " :width: 150 px\n").format(binder_url) + return rst + + +# # And then we finish our monkeypatching misdeed by redirecting + +# sphinx-gallery to use our function: +sphinx_gallery.binder.gen_binder_rst = patched_gen_binder_rst diff --git a/jupytext.toml b/jupytext.toml new file mode 100644 index 000000000..bce362a2e --- /dev/null +++ b/jupytext.toml @@ -0,0 +1,2 @@ +preferred_jupytext_formats_read = "py:sphinx" +sphinx_convert_rst2md = true diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index 3b1d1b9d5..2aec4238f 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -1,16 +1,9 @@ -matplotlib -numpy -scipy -Cython -sklearn +-r ./requirements.txt Sphinx>=3 sphinx_rtd_theme sphinx-gallery pillow -matplotlib setuptools>=41.2 -multimethod>=1.2 -findiff jupyter-sphinx pytest sphinxcontrib-bibtex \ No newline at end of file From 9cf75207024cf1dfc3cd92848b267b4ebd00de6e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 7 Jun 2022 13:29:27 +0200 Subject: [PATCH 170/400] Fix patched_gen_binder_rst. --- docs/conf.py | 30 +++++++++++------------------- 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index be875b91c..627ddfde2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -350,25 +350,17 @@ def patched_parse(self): # Binder integration # Taken from # https://stanczakdominik.github.io/posts/simple-binder-usage-with-sphinx-gallery-through-jupytext/ -def patched_gen_binder_rst(fpath, binder_conf, gallery_conf): - """Generate the RST + link for the Binder badge. - ... - """ - binder_conf = sphinx_gallery.binder.check_binder_conf(binder_conf) - binder_url = sphinx_gallery.binder.gen_binder_url( - fpath, binder_conf, gallery_conf) - - # I added the line below: - binder_url = binder_url.replace( - gallery_conf['gallery_dirs'] + os.path.sep, "").replace("ipynb", "py") - - rst = ( - "\n" - " .. container:: binder-badge\n\n" - " .. image:: https://mybinder.org/badge_logo.svg\n" - " :target: {}\n" - " :width: 150 px\n").format(binder_url) - return rst +original_gen_binder_rst = sphinx_gallery.binder.gen_binder_rst + + +def patched_gen_binder_rst(*args, **kwargs): + return original_gen_binder_rst(*args, **kwargs).replace( + "auto_", + ".py", + ).replace( + ".ipynb", + ".py", + ) # # And then we finish our monkeypatching misdeed by redirecting From 0587c851db906ef016da7e080cc9bee185eeb72e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 7 Jun 2022 16:20:32 +0200 Subject: [PATCH 171/400] Fix monkey patching. --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 627ddfde2..ada4f0d72 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -355,8 +355,8 @@ def patched_parse(self): def patched_gen_binder_rst(*args, **kwargs): return original_gen_binder_rst(*args, **kwargs).replace( - "auto_", - ".py", + "../examples/auto_", + "", ).replace( ".ipynb", ".py", From ab629fb5f30a3b42a86135b8f0352022b16ee078 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 7 Jun 2022 18:08:31 +0200 Subject: [PATCH 172/400] Docstrings fixes --- .../stats/_functional_transformers.py | 27 ++++++++++++++----- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index c29414b23..d17d1de66 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -353,11 +353,14 @@ def unconditional_central_moments( The unconditional central moments are defined as the unconditional moments where the mean is subtracted from each sample before the - integration. They are calculated as follows: + integration. The n-th unconditional central moment is calculated as + follows, where p is the number of observations: .. math:: - f(X)=\int_a^b \left(X_1(t) - \mu_1\right)^ndt,\dots, - \left(X_p(t) - \mu_p\right)^ndt=\int_a^b \left(X(t) - \mu\right)^ndt + f_1(x(t))=\frac{1}{\left(b-a\right)}\int_a^b + \left(x_1(t) - \mu_1\right)^n dt, \dots, + f_p(x(t))=\frac{1}{\left(b-a\right)}\int_a^b + \left(x_p(t) - \mu_p\right)^n dt Args: data: FDataGrid where we want to calculate @@ -405,9 +408,12 @@ def unconditional_moments( r""" Calculate the specified unconditional moment of a dataset. - It performs the following map: - :math:`f(X)=\int_a^b f_1(X(t))dt,\dots,f_p(X(t))dt=\int_a^b f^p(X(t))dt`. - + The n-th unconditional moments of p real-valued continuous functions + are calculated as: + .. math:: + f_1(x(t))=\frac{1}{\left( b-a\right)}\int_a^b \left(x_1(t)\right)^ndt, + \dots, + f_p(x(t))=\frac{1}{\left( b-a\right)}\int_a^b \left(x_p(t)\right)^n dt Args: data: FDataGrid or FDataBasis where we want to calculate a particular unconditional moment. @@ -447,9 +453,16 @@ def unconditional_expected_value( data: Union[FDataBasis, FDataGrid], function: Callable[[np.ndarray], np.ndarray], ) -> NDArrayFloat: - """ + r""" Calculate the unconditional expected value of a function. + Next formula shows for a defined transformation :math: `g(x(t))` + and p observations, how the unconditional expected values are calculated: + .. math:: + f_1(x(t))=\frac{1}{\left( b-a\right)}\int_a^b g + \left(x_1(t)\right)dt,\dots, + f_p(x(t))=\frac{1}{\left( b-a\right)}\int_a^b g + \left(x_p(t)\right) dt Args: data: FDataGrid or FDataBasis where we want to calculate the expected value. From ad1912b3c625f4a1b5cfa86cab4edfbdc30cc74b Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 7 Jun 2022 18:28:16 +0200 Subject: [PATCH 173/400] fix examples --- docs/modules/preprocessing.rst | 1 + .../stats/_functional_transformers.py | 86 ++++++++++--------- .../_fda_feature_union.py | 74 ++++++++-------- 3 files changed, 85 insertions(+), 76 deletions(-) diff --git a/docs/modules/preprocessing.rst b/docs/modules/preprocessing.rst index 5c861a72b..4c378908a 100644 --- a/docs/modules/preprocessing.rst +++ b/docs/modules/preprocessing.rst @@ -13,6 +13,7 @@ this category deal with this problem. preprocessing/smoothing preprocessing/registration preprocessing/dim_reduction + preprocessing/feature_construction Smoothing --------- diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 1c34f7680..f68614bbf 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -37,17 +37,18 @@ def local_averages( ndarray of shape (n_samples, n_intervals, n_dimensions) with the transformed data. - Example: - + Examples: We import the Berkeley Growth Study dataset. We will use only the first 3 samples to make the - example easy. + example easy + >>> from skfda.datasets import fetch_growth >>> dataset = fetch_growth(return_X_y=True)[0] >>> X = dataset[:3] Then we decide how many intervals we want to consider (in our case 2) and call the function with the dataset. + >>> import numpy as np >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, 2), decimals=2) @@ -151,9 +152,10 @@ def occupation_measure( ndarray of shape (n_samples, n_intervals) with the transformed data. - Example: + Examples: We will create the FDataGrid that we will use to extract - the occupation measure + the occupation measure. + >>> from skfda.representation import FDataGrid >>> import numpy as np >>> t = np.linspace(0, 10, 100) @@ -171,6 +173,7 @@ def occupation_measure( and (2.0, 3.0). We need also to specify the number of points we want that the function takes into account to interpolate. We are going to use 501 points. + >>> from skfda.exploratory.stats import occupation_measure >>> np.around( ... occupation_measure( @@ -238,42 +241,43 @@ def number_up_crossings( ndarray of shape (n_samples, len(levels))\ with the values of the counters. - Example: - - For this example we will use a well known function so the correct - functioning of this method can be checked. - We will create and use a DataFrame with a sample extracted from - the Bessel Function of first type and order 0. - First of all we import the Bessel Function and create the X axis - data grid. Then we create the FdataGrid. - >>> from skfda.exploratory.stats import number_up_crossings - >>> from scipy.special import jv - >>> import numpy as np - >>> x_grid = np.linspace(0, 14, 14) - >>> fd_grid = FDataGrid( - ... data_matrix=[jv([0], x_grid)], - ... grid_points=x_grid, - ... ) - >>> fd_grid.data_matrix - array([[[ 1. ], - [ 0.73041066], - [ 0.13616752], - [-0.32803875], - [-0.35967936], - [-0.04652559], - [ 0.25396879], - [ 0.26095573], - [ 0.01042895], - [-0.22089135], - [-0.2074856 ], - [ 0.0126612 ], - [ 0.20089319], - [ 0.17107348]]]) - - Finally we evaluate the number of up crossings method with the FDataGrid - created. - >>> number_up_crossings(fd_grid, np.asarray([0])) - array([[2]]) + Examples: + For this example we will use a well known function so the correct + functioning of this method can be checked. + We will create and use a DataFrame with a sample extracted from + the Bessel Function of first type and order 0. + First of all we import the Bessel Function and create the X axis + data grid. Then we create the FdataGrid. + + >>> from skfda.exploratory.stats import number_up_crossings + >>> from scipy.special import jv + >>> import numpy as np + >>> x_grid = np.linspace(0, 14, 14) + >>> fd_grid = FDataGrid( + ... data_matrix=[jv([0], x_grid)], + ... grid_points=x_grid, + ... ) + >>> fd_grid.data_matrix + array([[[ 1. ], + [ 0.73041066], + [ 0.13616752], + [-0.32803875], + [-0.35967936], + [-0.04652559], + [ 0.25396879], + [ 0.26095573], + [ 0.01042895], + [-0.22089135], + [-0.2074856 ], + [ 0.0126612 ], + [ 0.20089319], + [ 0.17107348]]]) + + Finally we evaluate the number of up crossings method with the + FDataGrid created. + + >>> number_up_crossings(fd_grid, np.asarray([0])) + array([[2]]) """ curves = data.data_matrix[:, :, 0] diff --git a/skfda/preprocessing/feature_construction/_fda_feature_union.py b/skfda/preprocessing/feature_construction/_fda_feature_union.py index 402e12abf..2e5ede7f4 100644 --- a/skfda/preprocessing/feature_construction/_fda_feature_union.py +++ b/skfda/preprocessing/feature_construction/_fda_feature_union.py @@ -47,41 +47,45 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore output. By default the value is False. Examples: - Firstly we will import the Berkeley Growth Study data set - >>> from skfda.datasets import fetch_growth - >>> X,y = fetch_growth(return_X_y=True) - - Then we need to import the transformers we want to use. In our case we - will use the Recursive Maxima Hunting method to select important features. - We will concatenate to the results of the previous method the original - curves with an Evaluation Transfomer. - >>> from skfda.preprocessing.feature_construction import ( - ... FDAFeatureUnion, - ... ) - >>> from skfda.preprocessing.dim_reduction.variable_selection import ( - ... RecursiveMaximaHunting, - ... ) - >>> from skfda.preprocessing.feature_construction import ( - ... EvaluationTransformer, - ... ) - >>> import numpy as np - - Finally we apply fit and transform. - >>> union = FDAFeatureUnion( - ... [ - ... ("rmh", RecursiveMaximaHunting()), - ... ("eval", EvaluationTransformer()), - ... ], - ... array_output=True, - ... ) - >>> np.around(union.fit_transform(X,y), decimals=2) - array([[ 195.1, 141.1, 163.8, ..., 193.8, 194.3, 195.1], - [ 178.7, 133. , 148.1, ..., 176.1, 177.4, 178.7], - [ 171.5, 126.5, 143.6, ..., 170.9, 171.2, 171.5], - ..., - [ 166.8, 132.8, 152.2, ..., 166. , 166.3, 166.8], - [ 168.6, 139.4, 161.6, ..., 168.3, 168.4, 168.6], - [ 169.2, 138.1, 161.7, ..., 168.6, 168.9, 169.2]]) + Firstly we will import the Berkeley Growth Study data set: + + >>> from skfda.datasets import fetch_growth + >>> X,y = fetch_growth(return_X_y=True) + + Then we need to import the transformers we want to use. In our case we + will use the Recursive Maxima Hunting method to select important + features. + We will concatenate to the results of the previous method the original + curves with an Evaluation Transfomer. + + >>> from skfda.preprocessing.feature_construction import ( + ... FDAFeatureUnion, + ... ) + >>> from skfda.preprocessing.dim_reduction.variable_selection import ( + ... RecursiveMaximaHunting, + ... ) + >>> from skfda.preprocessing.feature_construction import ( + ... EvaluationTransformer, + ... ) + >>> import numpy as np + + Finally we apply fit and transform. + + >>> union = FDAFeatureUnion( + ... [ + ... ("rmh", RecursiveMaximaHunting()), + ... ("eval", EvaluationTransformer()), + ... ], + ... array_output=True, + ... ) + >>> np.around(union.fit_transform(X,y), decimals=2) + array([[ 195.1, 141.1, 163.8, ..., 193.8, 194.3, 195.1], + [ 178.7, 133. , 148.1, ..., 176.1, 177.4, 178.7], + [ 171.5, 126.5, 143.6, ..., 170.9, 171.2, 171.5], + ..., + [ 166.8, 132.8, 152.2, ..., 166. , 166.3, 166.8], + [ 168.6, 139.4, 161.6, ..., 168.3, 168.4, 168.6], + [ 169.2, 138.1, 161.7, ..., 168.6, 168.9, 169.2]]) """ def __init__( From 64553b981c3cfc6bec6c3081cb4ad1dac78f9eb1 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Tue, 7 Jun 2022 19:24:33 +0200 Subject: [PATCH 174/400] Adding a comparison table --- examples/plot_classification_methods.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index f02a9ceb8..3fc87cd1a 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -138,6 +138,19 @@ # achieve more than an 80% of score, but the most robust ones are KNN and # Parameterized Functional QDA. +############################################################################## +# +--------------------------------+--------------------+ +# | Classification method | Accuracy | +# +================================+====================+ +# | Maximum Depth Classifier | 82.14% | +# +--------------------------------+--------------------+ +# | Nearest Centroid Classifier | 85.71% | +# +--------------------------------+--------------------+ +# | K-Nearest-Neighbors | 96.43% | +# +--------------------------------+--------------------+ +# | Parameterized Functional QDA | 96.43% | +# +--------------------------------+--------------------+ + ############################################################################## # The figure below shows the results of the classification for the test set on From 19e803c4fd6e4ba2774b58b84699fab4f81635a4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 8 Jun 2022 06:39:29 +0200 Subject: [PATCH 175/400] Fix error in Jupytext. --- binder/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/binder/requirements.txt b/binder/requirements.txt index cd77b966b..b913cb3e4 100644 --- a/binder/requirements.txt +++ b/binder/requirements.txt @@ -1,3 +1,4 @@ -r ../readthedocs-requirements.txt jupytext +sphinx-gallery<=0.7.0 . \ No newline at end of file From 17f9cd6d7203aa5d11ba536255110dde72caaba9 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 8 Jun 2022 10:26:20 +0200 Subject: [PATCH 176/400] Add `return_X_y` parameter for UCR datasets. --- skfda/datasets/_real_datasets.py | 35 +++++++++++++++++++++++++------- 1 file changed, 28 insertions(+), 7 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 933fa68d9..eff2566eb 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -146,7 +146,32 @@ def _ucr_to_fdatagrid(name: str, data: np.ndarray) -> FDataGrid: return FDataGrid(data, grid_points=grid_points, dataset_name=name) -def fetch_ucr(name: str, **kwargs: Any) -> Bunch: +@overload +def fetch_ucr( + name: str, + *, + return_X_y: Literal[False] = False, + **kwargs: Any, +) -> Bunch: + pass + + +@overload +def fetch_ucr( + name: str, + *, + return_X_y: Literal[True], + **kwargs: Any, +) -> Tuple[FDataGrid, ndarray]: + pass + + +def fetch_ucr( + name: str, + *, + return_X_y: bool = False, + **kwargs: Any, +) -> Union[Bunch, Tuple[FDataGrid, ndarray]]: """ Fetch a dataset from the UCR. @@ -180,12 +205,8 @@ def fetch_ucr(name: str, **kwargs: Any) -> Bunch: ) dataset.pop('feature_names') - data_test = dataset.get('data_test', None) - if data_test is not None: - dataset['data_test'] = _ucr_to_fdatagrid( - name=dataset['name'], - data=data_test, - ) + if return_X_y: + return dataset['data'], dataset['target'] return dataset From 049faeeea8b952c73725af6aefeb2bc201ed4530 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Fri, 10 Jun 2022 23:21:25 +0200 Subject: [PATCH 177/400] Adding dynamic table --- examples/plot_classification_methods.py | 39 +++++++++++++++++-------- 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index 3fc87cd1a..3634484b1 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -18,6 +18,7 @@ # License: MIT import matplotlib.pyplot as plt +import pandas as pd from GPy.kern import RBF from sklearn.model_selection import train_test_split @@ -138,18 +139,32 @@ # achieve more than an 80% of score, but the most robust ones are KNN and # Parameterized Functional QDA. -############################################################################## -# +--------------------------------+--------------------+ -# | Classification method | Accuracy | -# +================================+====================+ -# | Maximum Depth Classifier | 82.14% | -# +--------------------------------+--------------------+ -# | Nearest Centroid Classifier | 85.71% | -# +--------------------------------+--------------------+ -# | K-Nearest-Neighbors | 96.43% | -# +--------------------------------+--------------------+ -# | Parameterized Functional QDA | 96.43% | -# +--------------------------------+--------------------+ +accuracies = pd.DataFrame({ + 'Classification methods': + [ + 'Maximum Depth Classifier', + 'K-Nearest-Neighbors', + 'Nearest Centroid Classifier', + 'Parameterized Functional QDA', + ], + 'Accuracy': + [ + '{0:2.2%}'.format( + depth.score(X_test, y_test), + ), + '{0:2.2%}'.format( + knn.score(X_test, y_test), + ), + '{0:2.2%}'.format( + centroid.score(X_test, y_test), + ), + '{0:2.2%}'.format( + pfqda.score(X_test, y_test), + ), + ], +}) + +print(accuracies) ############################################################################## From 8b0049e3d5adedd8714d55849ad47a4352b44c18 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 13 Jun 2022 18:52:16 +0200 Subject: [PATCH 178/400] Accuracies table --- examples/plot_classification_methods.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index 3634484b1..a55931c83 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -164,7 +164,7 @@ ], }) -print(accuracies) +accuracies ############################################################################## From d26581859840345617b09e132877e29436567f18 Mon Sep 17 00:00:00 2001 From: Alvaro Date: Mon, 13 Jun 2022 18:56:43 +0200 Subject: [PATCH 179/400] Statistical measures --- .../stats/_functional_transformers.py | 18 ++++++----- tests/test_functional_transformers.py | 31 +++++++++++++++++++ 2 files changed, 42 insertions(+), 7 deletions(-) create mode 100644 tests/test_functional_transformers.py diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index d17d1de66..ad079893b 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -7,7 +7,7 @@ import numpy as np from ..._utils import check_is_univariate, nquad_vec -from ...representation import FDataBasis, FDataGrid +from ...representation import FData, FDataBasis, FDataGrid from ...representation._typing import NDArrayFloat, NDArrayInt @@ -450,7 +450,7 @@ def unconditional_moments( def unconditional_expected_value( - data: Union[FDataBasis, FDataGrid], + data: FData, function: Callable[[np.ndarray], np.ndarray], ) -> NDArrayFloat: r""" @@ -492,7 +492,7 @@ def unconditional_expected_value( [ 4.9 ], [ 4.84]]) """ - domain_range = np.prod( + lebesgue_measure = np.prod( [ (iterval[1] - iterval[0]) for iterval in data.domain_range @@ -500,9 +500,13 @@ def unconditional_expected_value( ) if isinstance(data, FDataGrid): - return function(data).integrate() / domain_range + return function(data).integrate() / lebesgue_measure + + def integrand(*args: NDArrayFloat): + f1 = data(args)[:, 0, :] + return function(f1) return nquad_vec( - function, - data.integrate(), - ) / domain_range + integrand, + data.domain_range, + ) / lebesgue_measure diff --git a/tests/test_functional_transformers.py b/tests/test_functional_transformers.py new file mode 100644 index 000000000..c212152ef --- /dev/null +++ b/tests/test_functional_transformers.py @@ -0,0 +1,31 @@ +"""Test to check the per functional transformers.""" + +import unittest + +import numpy as np + +import skfda.representation.basis as basis +from skfda.datasets import fetch_growth +from skfda.exploratory.stats import unconditional_expected_value + + +class TestUncondExpectedVals(unittest.TestCase): + """Tests for unconditional expected values method.""" + + def test_transform(self) -> None: + """Check the data transformation is done correctly.""" + X = fetch_growth(return_X_y=True)[0] + + def f(x: np.ndarray) -> np.ndarray: # noqa: WPS430 + return np.log(x) + + data_grid = unconditional_expected_value(X[:5], f) + data_basis = unconditional_expected_value( + X[:5].to_basis(basis.BSpline(n_basis=7)), + f, + ) + np.testing.assert_allclose(data_basis, data_grid, rtol=1e-3) + + +if __name__ == '__main__': + unittest.main() From 7149d0d67ef0186a54ad0c7ff9bb17a20b55afd9 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Wed, 15 Jun 2022 10:53:22 +0200 Subject: [PATCH 180/400] moved deprecated comment into original docstring --- skfda/ml/regression/_linear_regression.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 968aed096..26cfd571c 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -51,13 +51,6 @@ class LinearRegression( BaseEstimator, # type: ignore RegressorMixin, # type: ignore ): - """.. deprecated:: 0.8. - - Use covariate parameters of type pandas.FDataFrame in methods - fit, predict. - - """ - r"""Linear regression with multivariate response. This is a regression algorithm equivalent to multivariate linear @@ -73,6 +66,10 @@ class LinearRegression( where the covariates can be either multivariate or functional and the response is multivariate. + .. deprecated:: 0.8. + Use covariate parameters of type pandas.DataFrame in methods + fit, predict. + .. warning:: For now, only scalar responses are supported. From ac2685a843b876b76025c0576af24f8ce6e76128 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Jun 2022 17:42:26 +0200 Subject: [PATCH 181/400] Add MCO dataset. --- docs/refs.bib | 18 ++++++ skfda/datasets/__init__.py | 1 + skfda/datasets/_real_datasets.py | 100 +++++++++++++++++++++++++++++++ 3 files changed, 119 insertions(+) diff --git a/docs/refs.bib b/docs/refs.bib index 70c50dd6a..029b003c8 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -308,6 +308,24 @@ @inbook{ramsay+silverman_2005_functional_basis doi = {110.1007/b98888} } + +@article{ruiz++_2003_cariporide, + title = {Cariporide Preserves Mitochondrial Proton Gradient and Delays {{ATP}} Depletion in Cardiomyocytes during Ischemic Conditions}, + author = {{Ruiz-Meana}, Marisol and {Garcia-Dorado}, David and Pina, Pilar and Inserte, Javier and Agull{\'o}, Luis and {Soler-Soler}, Jordi}, + year = {2003}, + month = sep, + journal = {American Journal of Physiology. Heart and Circulatory Physiology}, + volume = {285}, + number = {3}, + pages = {H999-1006}, + issn = {0363-6135}, + doi = {10.1152/ajpheart.00035.2003}, + abstract = {The mechanism by which inhibition of Na+/H+ exchanger (NHE) reduces cell death in ischemic-reperfused myocardium remains controversial. This study investigated whether cariporide could inhibit mitochondrial NHE during ischemia, delaying H+ gradient dissipation and ATP exhaustion. Mouse cardiac myocytes (HL-1) were submitted to 1 h of simulated ischemia (SI) with NaCN/deoxyglucose (pH 6.4), with or without 7 microM cariporide, and mitochondrial concentration of Ca2+ (Rhod-2), 2', 7'-bis(2-carboxyethyl)-5(6)-carboxyfluorescein (BCECF) and the charge difference across the mitochondrial membrane potential (Deltapsim, JC-1) were assessed. ATP content was measured by bioluminescence and mitochondrial swelling by spectrophotometry in isolated mitochondria. Cariporide significantly attenuated the acidification of the mitochondrial matrix induced by SI without modifying Deltapsim decay, and this effect was associated to a delayed ATP exhaustion and increased mitochondrial Ca2+ load. These effects were reproduced in sarcolemma-permeabilized cells exposed to SI. In these cells, cariporide markedly attenuated the fall in mitochondrial pH induced by removal of Na+ from the medium. In isolated mitochondria, cariporide significantly reduced the rate and magnitude of passive matrix swelling induced by Na+ acetate. In isolated rat hearts submitted to 40-min ischemia at different temperatures (35.5 degrees, 37 degrees, or 38.5 degrees C) pretreatment with cariporide limited ATP depletion during the first 10 min of ischemia and cell death (lactate dehydrogenase release) during reperfusion. These effects were mimicked when a similar ATP preservation was achieved by hypothermia and were abolished when the sparing effect of cariporide on ATP was suppressed by hyperthermia. We conclude that cariporide acts at the mitochondrial level, delaying mitochondrial matrix acidification and delaying ATP exhaustion during ischemia. These effects can contribute to reduce cell death secondary to ischemia-reperfusion.}, + langid = {english}, + pmid = {12915386}, + keywords = {Acids,Adenosine Triphosphate,Animals,Anti-Arrhythmia Agents,Cardiotonic Agents,Cell Death,Cells; Cultured,Energy Metabolism,Guanidines,Mice,Mitochondria,Myocardial Infarction,Myocardial Reperfusion Injury,Myocytes; Cardiac,Protons,Sodium-Hydrogen Exchangers,Sulfones} +} + @article{serfling+zuo_2000_depth_function, author = {R. Serfling and Y. Zuo}, title = {General notions of statistical depth function}, diff --git a/skfda/datasets/__init__.py b/skfda/datasets/__init__.py index d5f6f0cd7..783d3a5f1 100644 --- a/skfda/datasets/__init__.py +++ b/skfda/datasets/__init__.py @@ -5,6 +5,7 @@ fetch_gait, fetch_growth, fetch_handwriting, + fetch_mco, fetch_medflies, fetch_octane, fetch_phoneme, diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index eff2566eb..08f94f285 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1329,3 +1329,103 @@ def fetch_handwriting( if fetch_handwriting.__doc__ is not None: # docstrings can be stripped off fetch_handwriting.__doc__ += _handwriting_descr + _param_descr + +_mco_descr_template = """ + The mithochondiral calcium overload (MCO) was measured in two groups + (control and treatment) every 10 seconds during an hour in isolated mouse + cardiac cells. In fact, due to technical reasons, the original experiment + [see {cite}] was performed twice, using both the + "intact", original cells and "permeabilized" cells (a condition related + to the mitochondrial membrane). + + References: + {bibliography} + +""" + +_mco_descr = _mco_descr_template.format( + cite="Ruiz-Meana et. al. (2003)", + bibliography="[1] M. Ruiz-Meana, D. Garcia-Dorado, P. Pina, J. Inserte, " + "L. Agulló, and J. Soler-Soler, “Cariporide preserves mitochondrial " + "proton gradient and delays ATP depletion in cardiomyocytes during " + "ischemic conditions,” Am. J. Physiol. Heart Circ. Physiol., vol. 285, " + "no. 3, pp. H999-1006, Sep. 2003, doi: 10.1152/ajpheart.00035.2003.", +) + + +@overload +def fetch_mco( + *, + return_X_y: Literal[False] = False, + as_frame: bool = False, +) -> Bunch: + pass + + +@overload +def fetch_mco( + *, + return_X_y: Literal[True], + as_frame: Literal[False] = False, +) -> Tuple[FDataGrid, ndarray]: + pass + + +@overload +def fetch_mco( + *, + return_X_y: Literal[True], + as_frame: Literal[True], +) -> Tuple[DataFrame, DataFrame]: + pass + + +def fetch_mco( + return_X_y: bool = False, + as_frame: bool = False, +) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, DataFrame]]: + """ + Load the mithochondiral calcium overload (MCO) dataset. + + The data is obtained from the R package 'fda.usc', which takes it from + http://lib.stat.cmu.edu/datasets/tecator. + + """ + descr = _mco_descr + + raw_dataset = _fetch_fda_usc("MCO") + + data = raw_dataset["MCO"] + + curves = data['intact'] + target = pd.Series(data['classintact']) + feature_name = curves.dataset_name.lower() + target_names = target.values.tolist() + + frame = None + + if as_frame: + curves = pd.DataFrame({feature_name: curves}) + frame = pd.concat([curves, target], axis=1) + else: + target = target.values.to_numpy().astype(np.int_) + + if return_X_y: + return curves, target + + return Bunch( + data=curves, + target=target, + frame=frame, + categories={}, + feature_names=[feature_name], + target_names=target_names, + DESCR=descr, + ) + + +if fetch_mco.__doc__ is not None: # docstrings can be stripped off + fetch_mco.__doc__ += _mco_descr_template.format( + cite=":footcite:`ruiz++_2003_cariporide`", + bibliography=":footbibliography:" + ) + _param_descr From b29a9b0ec57efd5278580717a8f0f2141cf1c4d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Mon, 20 Jun 2022 17:50:14 +0200 Subject: [PATCH 182/400] Update _real_datasets.py --- skfda/datasets/_real_datasets.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 08f94f285..5604d79af 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1387,8 +1387,7 @@ def fetch_mco( """ Load the mithochondiral calcium overload (MCO) dataset. - The data is obtained from the R package 'fda.usc', which takes it from - http://lib.stat.cmu.edu/datasets/tecator. + The data is obtained from the R package 'fda.usc'. """ descr = _mco_descr From 00291e706b71626b26adae6e075f203ce0c4bb9b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Jun 2022 18:36:04 +0200 Subject: [PATCH 183/400] Add NOx dataset. --- docs/refs.bib | 19 ++++++ skfda/datasets/__init__.py | 1 + skfda/datasets/_real_datasets.py | 101 +++++++++++++++++++++++++++++++ 3 files changed, 121 insertions(+) diff --git a/docs/refs.bib b/docs/refs.bib index 70c50dd6a..3cde66e93 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -124,6 +124,25 @@ @article{dai+genton_2018_visualization URL = {https://doi.org/10.1080/10618600.2018.1473781} } +@article{febrero++_2008_outlier, + title = {Outlier Detection in Functional Data by Depth Measures, with Application to Identify Abnormal {{NOx}} Levels}, + author = {Febrero, Manuel and Galeano, Pedro and Gonz{\'a}lez-Manteiga, Wenceslao}, + year = {2008}, + month = jun, + journal = {Environmetrics}, + volume = {19}, + number = {4}, + pages = {331--345}, + issn = {1099-095X}, + doi = {10.1002/env.878}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/env.878}, + urldate = {2018-12-10}, + abstract = {This paper analyzes outlier detection for functional data by means of functional depths, which measures the centrality of a given curve within a group of trajectories providing center-outward orderings of the set of curves. We give some insights of the usefulness of looking for outliers in functional datasets and propose a method based in depths for the functional outlier detection. The performance of the proposed procedure is analyzed by several Monte Carlo experiments. Finally, we illustrate the procedure by finding outliers in a dataset of NOx (nitrogen oxides) emissions taken from a control station near an industrial area. Copyright \textcopyright{} 2007 John Wiley \& Sons, Ltd.}, + copyright = {Copyright \textcopyright{} 2007 John Wiley \& Sons, Ltd.}, + langid = {english}, + keywords = {depths,functional median,functional trimmed mean,nitrogen oxides,outliers,smoothed bootstrap} +} + @article{febrero-bande+oviedo_2012_fda.usc, title={Statistical Computing in Functional Data Analysis: The R Package fda.usc}, volume={51}, diff --git a/skfda/datasets/__init__.py b/skfda/datasets/__init__.py index d5f6f0cd7..c217aa256 100644 --- a/skfda/datasets/__init__.py +++ b/skfda/datasets/__init__.py @@ -6,6 +6,7 @@ fetch_growth, fetch_handwriting, fetch_medflies, + fetch_nox, fetch_octane, fetch_phoneme, fetch_tecator, diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index eff2566eb..40771f133 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1329,3 +1329,104 @@ def fetch_handwriting( if fetch_handwriting.__doc__ is not None: # docstrings can be stripped off fetch_handwriting.__doc__ += _handwriting_descr + _param_descr + +_nox_descr_template = """ + NOx levels measured every hour by a control station in Poblenou in + Barcelona (Spain) {cite}. + + References: + {bibliography} + +""" + +_nox_descr = _nox_descr_template.format( + cite="[1]", + bibliography="[1] M. Febrero, P. Galeano, and W. González‐Manteiga, " + "“Outlier detection in functional data by depth measures, with " + "application to identify abnormal NOx levels,” Environmetrics, vol. 19, " + "no. 4, pp. 331–345, Jun. 2008, doi: 10.1002/env.878.", +) + + +@overload +def fetch_nox( + *, + return_X_y: Literal[False] = False, + as_frame: bool = False, +) -> Bunch: + pass + + +@overload +def fetch_nox( + *, + return_X_y: Literal[True], + as_frame: Literal[False] = False, +) -> Tuple[FDataGrid, ndarray]: + pass + + +@overload +def fetch_nox( + *, + return_X_y: Literal[True], + as_frame: Literal[True], +) -> Tuple[DataFrame, DataFrame]: + pass + + +def fetch_nox( + return_X_y: bool = False, + as_frame: bool = False, +) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, DataFrame]]: + """ + Load the NOx dataset. + + The data is obtained from the R package 'fda.usc'. + + """ + descr = _nox_descr + + raw_dataset = _fetch_fda_usc("poblenou") + + data = raw_dataset["poblenou"] + + curves = data['nox'] + target = data['df'].iloc[:, 2] + weekend = ( + (data['df'].iloc[:, 1] == "6") + | (data['df'].iloc[:, 1] == "7") + ) + target[weekend] = "1" + target = pd.Series( + target.values.codes.astype(np.bool_), + name="festive day", + ) + curves.coordinate_names = ["$mglm^3$"] + feature_name = curves.dataset_name.lower() + target_names = target.values.tolist() + + frame = None + + if as_frame: + curves = pd.DataFrame({feature_name: curves}) + frame = pd.concat([curves, target], axis=1) + else: + target = target.values + + if return_X_y: + return curves, target + + return Bunch( + data=curves, + target=target, + frame=frame, + categories={}, + feature_names=[feature_name], + target_names=target_names, + DESCR=descr, + ) + + +if fetch_tecator.__doc__ is not None: # docstrings can be stripped off + fetch_tecator.__doc__ += _tecator_descr + _param_descr From 7e87d0b8fcda21828ce17f458fc93a359c1a7eb9 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Jun 2022 18:45:37 +0200 Subject: [PATCH 184/400] Small fixes. --- docs/modules/datasets.rst | 10 ++++++---- skfda/datasets/_real_datasets.py | 7 +++++-- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/docs/modules/datasets.rst b/docs/modules/datasets.rst index b4968fdd0..6022c052b 100644 --- a/docs/modules/datasets.rst +++ b/docs/modules/datasets.rst @@ -11,14 +11,16 @@ The following functions are used to retrieve specific functional datasets: .. autosummary:: :toctree: autosummary + skfda.datasets.fetch_aemet + skfda.datasets.fetch_gait skfda.datasets.fetch_growth + skfda.datasets.fetch_mco + skfda.datasets.fetch_medflies + skfda.datasets.fetch_nox + skfda.datasets.fetch_octane skfda.datasets.fetch_phoneme skfda.datasets.fetch_tecator - skfda.datasets.fetch_medflies skfda.datasets.fetch_weather - skfda.datasets.fetch_aemet - skfda.datasets.fetch_octane - skfda.datasets.fetch_gait Those functions return a dictionary with at least a "data" field containing the instance data, and a "target" field containing the class labels or regression values, diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 3bfe8e344..abee1dfcd 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1501,7 +1501,10 @@ def fetch_mco( data = raw_dataset["MCO"] curves = data['intact'] - target = pd.Series(data['classintact']) + target = pd.Series( + data['classintact'].rename_categories(["control", "treatment"]), + name="group", + ) feature_name = curves.dataset_name.lower() target_names = target.values.tolist() @@ -1511,7 +1514,7 @@ def fetch_mco( curves = pd.DataFrame({feature_name: curves}) frame = pd.concat([curves, target], axis=1) else: - target = target.values.to_numpy().astype(np.int_) + target = target.values.codes if return_X_y: return curves, target From 6b06eaa3c7789515d1e11e8aba2e6459c99a6388 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 20 Jun 2022 19:00:52 +0200 Subject: [PATCH 185/400] Fix docs. --- skfda/datasets/_real_datasets.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index abee1dfcd..bfef6cbfd 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1331,7 +1331,7 @@ def fetch_handwriting( fetch_handwriting.__doc__ += _handwriting_descr + _param_descr _nox_descr_template = """ - NOx levels measured every hour by a control station in Poblenou in + NOx levels measured every hour by a control station in Poblenou in Barcelona (Spain) {cite}. References: @@ -1431,7 +1431,7 @@ def fetch_nox( if fetch_nox.__doc__ is not None: # docstrings can be stripped off fetch_nox.__doc__ += _nox_descr_template.format( cite=":footcite:`febrero++_2008_outlier`", - bibliography=":footbibliography:" + bibliography=".. footbibliography::" ) + _param_descr _mco_descr_template = """ @@ -1441,7 +1441,7 @@ def fetch_nox( [see {cite}] was performed twice, using both the "intact", original cells and "permeabilized" cells (a condition related to the mitochondrial membrane). - + References: {bibliography} @@ -1533,5 +1533,5 @@ def fetch_mco( if fetch_mco.__doc__ is not None: # docstrings can be stripped off fetch_mco.__doc__ += _mco_descr_template.format( cite=":footcite:`ruiz++_2003_cariporide`", - bibliography=":footbibliography:" + bibliography=".. footbibliography::", ) + _param_descr From 8da1b1ca3586a8ed3d1255822b8de5f5c6ee44a7 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Wed, 22 Jun 2022 19:00:21 +0200 Subject: [PATCH 186/400] completed deprecation docstring --- skfda/ml/regression/_linear_regression.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 26cfd571c..83c347b58 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -67,8 +67,9 @@ class LinearRegression( response is multivariate. .. deprecated:: 0.8. - Use covariate parameters of type pandas.DataFrame in methods - fit, predict. + Usage of arguments of type sequence of FData, ndarray is deprecated + in methods fit, predict. + Use covariate parameters of type pandas.DataFrame instead. .. warning:: For now, only scalar responses are supported. From 73920901b5f889a401291c1ce72136a13af632bb Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 27 Jun 2022 12:37:05 +0200 Subject: [PATCH 187/400] Small changes. --- skfda/exploratory/depth/_depth.py | 3 ++- skfda/misc/lstsq.py | 10 ++++++---- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 925646b88..cd5273fdf 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -14,6 +14,7 @@ import scipy.integrate from ... import FDataGrid +from ..._utils._sklearn_adapter import BaseEstimator from ...misc.metrics import Metric, l2_distance from ...misc.metrics._utils import _fit_metric from ...representation import FData @@ -219,7 +220,7 @@ def transform(self, X: FDataGrid) -> np.ndarray: # noqa: D102 return num_in / n_total -class DistanceBasedDepth(Depth[FDataGrid]): +class DistanceBasedDepth(Depth[FDataGrid], BaseEstimator): r""" Functional depth based on a metric. diff --git a/skfda/misc/lstsq.py b/skfda/misc/lstsq.py index 48015f64e..8044041ac 100644 --- a/skfda/misc/lstsq.py +++ b/skfda/misc/lstsq.py @@ -3,9 +3,10 @@ from typing import Callable, Optional, Union +from typing_extensions import Final, Literal + import numpy as np import scipy.linalg -from typing_extensions import Final, Literal from ..representation._typing import NDArrayFloat @@ -83,11 +84,12 @@ def solve_regularized_weighted_lstsq( if weights.ndim == 1: weights_chol = np.diag(np.sqrt(weights)) + coefs = weights_chol * coefs + result = weights_chol * result else: weights_chol = scipy.linalg.cholesky(weights) - - coefs = weights_chol @ coefs - result = weights_chol @ result + coefs = weights_chol @ coefs + result = weights_chol @ result return lstsq_method(coefs, result) From 9abd38d6e9447643f1fb851e6abf58e271a44bba Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 27 Jun 2022 15:11:37 +0200 Subject: [PATCH 188/400] Remove assert in clip as it depends on the data. --- skfda/misc/_math.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 72436aa02..fedac0988 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -527,11 +527,6 @@ def inner_product_matrix( def _clip_cosine(array: NDArrayFloat) -> NDArrayFloat: """Clip cosine values to prevent numerical errors.""" - small_val = 1e-6 - - # If the difference is too large, there could be a problem - assert np.all((-1 - small_val < array) & (array < 1 + small_val)) - return np.clip(array, -1, 1) From 80f4f23e26bdf5ece47cdc0b0e617f81459ddd97 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 28 Jun 2022 13:24:10 +0200 Subject: [PATCH 189/400] Fit metric in k-NN. --- skfda/ml/_neighbors_base.py | 73 ++++++++++++++++++++++++++++--------- 1 file changed, 56 insertions(+), 17 deletions(-) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 5d2ffebd0..e949f5d88 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -1,16 +1,22 @@ """Base classes for the neighbor estimators""" +from __future__ import annotations from abc import ABC +from typing import Any, Callable, Mapping, Tuple import numpy as np from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted +from typing_extensions import Literal from .. import FData, FDataGrid from ..misc.metrics import l2_distance +from ..misc.metrics._typing import Metric +from ..misc.metrics._utils import _fit_metric +from ..representation._typing import GridPoints, NDArrayFloat, NDArrayInt -def _to_multivariate(fdatagrid): +def _to_multivariate(fdatagrid: FDataGrid) -> NDArrayFloat: r"""Returns the data matrix of a fdatagrid in flatten form compatible with sklearn. @@ -25,7 +31,12 @@ def _to_multivariate(fdatagrid): return fdatagrid.data_matrix.reshape(fdatagrid.n_samples, -1) -def _from_multivariate(data_matrix, grid_points, shape, **kwargs): +def _from_multivariate( + data_matrix: NDArrayFloat, + grid_points: GridPoints, + shape: Tuple[int, ...], + **kwargs: Any, +) -> FDataGrid: r"""Constructs a FDatagrid from the data matrix flattened. Args: @@ -42,7 +53,10 @@ def _from_multivariate(data_matrix, grid_points, shape, **kwargs): return FDataGrid(data_matrix.reshape(shape), grid_points, **kwargs) -def _to_multivariate_metric(metric, grid_points): +def _to_multivariate_metric( + metric: Metric[FDataGrid], + grid_points: GridPoints, +) -> Metric[NDArrayFloat]: r"""Transform a metric between FDatagrid in a sklearn compatible one. Given a metric between FDatagrids returns a compatible metric used to @@ -82,11 +96,17 @@ def _to_multivariate_metric(metric, grid_points): # Shape -> (n_samples = 1, domain_dims...., image_dimension (-1)) shape = [1] + [len(axis) for axis in grid_points] + [-1] - def multivariate_metric(x, y, **kwargs): + def multivariate_metric( + x: NDArrayFloat, + y: NDArrayFloat, + **kwargs: Any, + ) -> NDArrayFloat: - return metric(_from_multivariate(x, grid_points, shape), - _from_multivariate(y, grid_points, shape), - **kwargs) + return metric( + _from_multivariate(x, grid_points, shape), + _from_multivariate(y, grid_points, shape), + **kwargs, + ) return multivariate_metric @@ -94,10 +114,18 @@ def multivariate_metric(x, y, **kwargs): class NeighborsBase(ABC, BaseEstimator): """Base class for nearest neighbors estimators.""" - def __init__(self, n_neighbors=None, radius=None, - weights='uniform', algorithm='auto', - leaf_size=30, metric='l2', metric_params=None, - n_jobs=None, multivariate_metric=False): + def __init__( + self, + n_neighbors: int | None = None, + radius: float | None = None, + weights: Literal["uniform", "distance"] | Callable[[NDArrayFloat], NDArrayFloat] = "uniform", + algorithm: Literal["auto", "ball_tree", "kd_tree", "brute"] = "auto", + leaf_size: int = 30, + metric: Literal["precomputed", "l2"] | Metric[FDataGrid] = 'l2', + metric_params: Mapping[str, Any] | None = None, + n_jobs: int | None = None, + multivariate_metric: bool = False, + ): """Initializes the nearest neighbors estimator""" self.n_neighbors = n_neighbors @@ -110,7 +138,7 @@ def __init__(self, n_neighbors=None, radius=None, self.n_jobs = n_jobs self.multivariate_metric = multivariate_metric - def _check_is_fitted(self): + def _check_is_fitted(self) -> None: """Check if the estimator is fitted. Raises: @@ -119,7 +147,10 @@ def _check_is_fitted(self): """ sklearn_check_is_fitted(self, ['estimator_']) - def _transform_to_multivariate(self, X): + def _transform_to_multivariate( + self, + X: FDataGrid | None, + ) -> NDArrayFloat | None: """Transform the input data to array form. If the metric is precomputed it is not transformed. @@ -131,9 +162,13 @@ def _transform_to_multivariate(self, X): class NeighborsMixin: - """Mixin class to train the neighbors models""" + """Mixin class to train the neighbors models.""" - def fit(self, X, y=None): + def fit( + self, + X: FDataGrid | NDArrayFloat, + y: NDArrayFloat | NDArrayInt | None = None, + ) -> NeighborsMixin: """Fit the model using X as training data and y as target values. Args: @@ -164,9 +199,13 @@ def fit(self, X, y=None): else: metric = self.metric - sklearn_metric = _to_multivariate_metric(metric, - self._grid_points) + _fit_metric(metric, X) + sklearn_metric = _to_multivariate_metric( + metric, + self._grid_points, + ) else: + _fit_metric(self.metric, X) sklearn_metric = self.metric self.estimator_ = self._init_estimator(sklearn_metric) From 4837bc25b9f46e6668625b073bdb925ea4fdeb14 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 28 Jun 2022 17:18:19 +0200 Subject: [PATCH 190/400] Make logistic regression unpenalized by default. --- skfda/ml/classification/_logistic_regression.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 8638c6f1d..4ac0e99cf 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -6,11 +6,14 @@ from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.linear_model import LogisticRegression as mvLogisticRegression from sklearn.utils.validation import check_is_fitted +from typing_extensions import Literal from ..._utils import _classifier_get_classes from ...representation import FDataGrid from ...representation._typing import NDArrayAny, NDArrayInt +Solver = Literal["newton-cg", "lbfgs", "liblinear", "sag", "saga"] + class LogisticRegression( BaseEstimator, # type: ignore @@ -28,7 +31,7 @@ class LogisticRegression( Args: p: - number of points (and coefficients) to be selected by + Number of points (and coefficients) to be selected by the algorithm. solver: Algorithm to use in the multivariate logistic regresion @@ -79,11 +82,15 @@ class LogisticRegression( def __init__( self, p: int = 5, - solver: str = 'lbfgs', + penalty: Literal["l1", "l2", "elasticnet", None] = None, + C: float = 1, + solver: Solver = 'lbfgs', max_iter: int = 100, ) -> None: self.p = p + self.penalty = penalty + self.C = C self.max_iter = 100 self.solver = solver @@ -102,9 +109,12 @@ def fit( # noqa: D102 selected_indexes = np.zeros(self.p, dtype=np.intc) + penalty = 'none' if self.penalty is None else self.penalty + # multivariate logistic regression mvlr = mvLogisticRegression( - penalty='l2', + penalty=penalty, + C=self.C, solver=self.solver, max_iter=self.max_iter, ) From 0f7122e8db024cea432c6e74004d0af48253396c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 29 Jun 2022 11:16:42 +0200 Subject: [PATCH 191/400] Fix tests. --- skfda/ml/classification/_logistic_regression.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 4ac0e99cf..a9798a1d3 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -68,11 +68,11 @@ class LogisticRegression( >>> lr = LogisticRegression() >>> _ = lr.fit(fd[::2], y[::2]) >>> lr.coef_.round(2) - array([[ 1.28, 1.17, 1.27, 1.27, 0.96]]) + array([[ 18.91, 19.69, 19.9 , 6.09, 12.49]]) >>> lr.points_.round(2) - array([ 0.11, 0.06, 0.07, 0.03, 0. ]) + array([ 0.11, 0.06, 0.07, 0.02, 0.03]) >>> lr.score(fd[1::2],y[1::2]) - 0.94 + 0.92 References: .. footbibliography:: From fbbba10b65bb27e300cebf19514457932e9246a8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 1 Jul 2022 09:19:18 +0200 Subject: [PATCH 192/400] Fix DDG classifier for multiple depths. --- examples/plot_depth_classification.py | 6 +- skfda/_utils/_sklearn_adapter.py | 16 +++ skfda/exploratory/depth/_depth.py | 1 + skfda/ml/classification/_depth_classifiers.py | 132 ++++++++++++++++-- tests/test_classification.py | 8 +- 5 files changed, 148 insertions(+), 15 deletions(-) diff --git a/examples/plot_depth_classification.py b/examples/plot_depth_classification.py index 84ef388fb..dd9e906f7 100644 --- a/examples/plot_depth_classification.py +++ b/examples/plot_depth_classification.py @@ -184,8 +184,8 @@ def _plot_boundaries(axis): # :class:`~skfda.ml.classification.DDClassifier` used with # :class:`~sklearn.neighbors.KNeighborsClassifier`. clf = DDGClassifier( - KNeighborsClassifier(n_neighbors=5), depth_method=ModifiedBandDepth(), + multivariate_classifier=KNeighborsClassifier(n_neighbors=5), ) clf.fit(X_train, y_train) print(clf.predict(X_test)) @@ -259,13 +259,13 @@ def _plot_boundaries(axis): # Next, we will use :class:`~skfda.ml.classification.DDGClassifier` together # with a neural network: :class:`~sklearn.neural_network.MLPClassifier`. clf = DDGClassifier( - MLPClassifier( + depth_method=ModifiedBandDepth(), + multivariate_classifier=MLPClassifier( solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(6, 2), random_state=1, ), - depth_method=ModifiedBandDepth(), ) clf.fit(X_train, y_train) print(clf.predict(X_test)) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 39f9632d9..99159b756 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -5,6 +5,8 @@ import sklearn.base +from ..representation._typing import NDArrayFloat + SelfType = TypeVar("SelfType") TransformerNoTarget = TypeVar( "TransformerNoTarget", @@ -87,3 +89,17 @@ def transform( X: Input, ) -> Output: pass + + +class ClassifierMixin( + ABC, + Generic[Input, Target], + sklearn.base.ClassifierMixin, # type: ignore[misc] +): + def score( + self, + X: Input, + y: Target, + sample_weight: NDArrayFloat | None = None, + ) -> NDArrayFloat: + return super().score(X, y, sample_weight=sample_weight) diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index cd5273fdf..c600a260c 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -227,6 +227,7 @@ class DistanceBasedDepth(Depth[FDataGrid], BaseEstimator): Parameters: metric: The metric to use as M in the following depth calculation + .. math:: D(x) = [1 + M(x, \mu)]^{-1}. diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index f6582209c..3db927cea 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -1,15 +1,26 @@ """Depth-based models for supervised classification.""" from __future__ import annotations +from collections import defaultdict +from contextlib import suppress from itertools import combinations -from typing import Generic, Optional, Sequence, TypeVar, Union +from typing import ( + Any, + Generic, + Mapping, + Optional, + Sequence, + Tuple, + TypeVar, + Union, +) import numpy as np import pandas as pd from scipy.interpolate import lagrange from sklearn.base import BaseEstimator, ClassifierMixin, clone from sklearn.metrics import accuracy_score -from sklearn.pipeline import make_pipeline +from sklearn.pipeline import FeatureUnion, make_pipeline from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from ..._utils import _classifier_fit_depth_methods, _classifier_get_classes @@ -214,18 +225,26 @@ class DDGClassifier( >>> from skfda.datasets import fetch_growth >>> from sklearn.model_selection import train_test_split + >>> dataset = fetch_growth() >>> fd = dataset['data'] >>> y = dataset['target'] >>> X_train, X_test, y_train, y_test = train_test_split( ... fd, y, test_size=0.25, stratify=y, random_state=0) + >>> from skfda.exploratory.depth import ( + ... ModifiedBandDepth, + ... IntegratedDepth, + ... ) >>> from sklearn.neighbors import KNeighborsClassifier We will fit a DDG-classifier using KNN >>> from skfda.ml.classification import DDGClassifier - >>> clf = DDGClassifier(KNeighborsClassifier()) + >>> clf = DDGClassifier( + ... depth_method=ModifiedBandDepth(), + ... multivariate_classifier=KNeighborsClassifier(), + ... ) >>> clf.fit(X_train, y_train) DDGClassifier(...) @@ -240,6 +259,24 @@ class DDGClassifier( >>> clf.score(X_test, y_test) 0.875 + It is also possible to use several depth functions to increase the + number of features available to the classifier + + >>> clf = DDGClassifier( + ... depth_method=[ + ... ("mbd", ModifiedBandDepth()), + ... ("id", IntegratedDepth()), + ... ], + ... multivariate_classifier=KNeighborsClassifier(), + ... ) + >>> clf.fit(X_train, y_train) + DDGClassifier(...) + >>> clf.predict(X_test) + array([1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, + 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1]) + >>> clf.score(X_test, y_test) + 0.875 + See also: :class:`~skfda.ml.classification.DDClassifier` :class:`~skfda.ml.classification.MaximumDepthClassifier` @@ -256,14 +293,72 @@ class DDGClassifier( def __init__( # noqa: WPS234 self, + *, multivariate_classifier: ClassifierMixin = None, - depth_method: Union[Depth[T], Sequence[Depth[T]], None] = None, + depth_method: Union[Depth[T], Sequence[Tuple[str, Depth[T]]], None] = None, ) -> None: self.multivariate_classifier = multivariate_classifier - if depth_method is None: - self.depth_method = ModifiedBandDepth() - else: - self.depth_method = depth_method + self.depth_method = depth_method + + def get_params(self, deep: bool = True) -> Mapping[str, Any]: + params = BaseEstimator.get_params(self, deep=deep) + if deep and isinstance(self.depth_method, Sequence): + for name, depth in self.depth_method: + depth_params = depth.get_params(deep=deep) + + for key, value in depth_params.items(): + params[f"depth_method__{name}__{key}"] = value + + return params + + # Copied from scikit-learn's _BaseComposition with minor modifications + def _set_params(self, attr, **params): + # Ensure strict ordering of parameter setting: + # 1. All steps + if attr in params: + setattr(self, attr, params.pop(attr)) + # 2. Replace items with estimators in params + items = getattr(self, attr) + if isinstance(items, list) and items: + # Get item names used to identify valid names in params + # `zip` raises a TypeError when `items` does not contains + # elements of length 2 + with suppress(TypeError): + item_names, _ = zip(*items) + item_params = defaultdict(dict) + for name in list(params.keys()): + if name.startswith(f"{attr}__"): + key, delim, sub_key = name.partition("__") + if "__" not in sub_key and sub_key in item_names: + self._replace_estimator( + attr, + sub_key, + params.pop(name), + ) + else: + key, delim, sub_key = sub_key.partition("__") + item_params[key][sub_key] = params.pop(name) + + for name, estimator in items: + estimator.set_params(**item_params[name]) + + # 3. Step parameters and other initialisation arguments + super().set_params(**params) + return self + + # Copied from scikit-learn's _BaseComposition + def _replace_estimator(self, attr, name, new_val): + # assumes `name` is a valid estimator name + new_estimators = list(getattr(self, attr)) + for i, (estimator_name, _) in enumerate(new_estimators): + if estimator_name == name: + new_estimators[i] = (name, new_val) + break + setattr(self, attr, new_estimators) + + def set_params(self, **params: Mapping[str, Any]): + + return self._set_params("depth_method", **params) def fit(self, X: T, y: NDArrayInt) -> DDGClassifier[T]: """Fit the model using X as training data and y as target values. @@ -275,8 +370,22 @@ def fit(self, X: T, y: NDArrayInt) -> DDGClassifier[T]: Returns: self """ + depth_method = ( + ModifiedBandDepth() + if self.depth_method is None + else self.depth_method + ) + + if isinstance(depth_method, Sequence): + transformer = FeatureUnion([ + (name, PerClassTransformer(depth)) + for name, depth in depth_method + ]) + else: + transformer = PerClassTransformer(depth_method) + self._pipeline = make_pipeline( - PerClassTransformer(self.depth_method), + transformer, clone(self.multivariate_classifier), ) @@ -403,4 +512,7 @@ class MaximumDepthClassifier(DDGClassifier[T]): """ def __init__(self, depth_method: Optional[Depth[T]] = None) -> None: - super().__init__(_ArgMaxClassifier(), depth_method) + super().__init__( + multivariate_classifier=_ArgMaxClassifier(), + depth_method=depth_method, + ) diff --git a/tests/test_classification.py b/tests/test_classification.py index f74a538b7..6c18cf0f0 100644 --- a/tests/test_classification.py +++ b/tests/test_classification.py @@ -124,7 +124,9 @@ def test_dd_classifier(self) -> None: def test_ddg_classifier(self) -> None: """Check DDG classifier.""" - clf: DDGClassifier[FData] = DDGClassifier(_KNeighborsClassifier()) + clf: DDGClassifier[FData] = DDGClassifier( + multivariate_classifier=_KNeighborsClassifier(), + ) clf.fit(self._X_train, self._y_train) np.testing.assert_array_equal( @@ -137,7 +139,9 @@ def test_ddg_classifier(self) -> None: def test_maximumdepth_inheritance(self) -> None: """Check that MaximumDepth is a subclass of DDG.""" - clf1: DDGClassifier[FData] = DDGClassifier(_ArgMaxClassifier()) + clf1: DDGClassifier[FData] = DDGClassifier( + multivariate_classifier=_ArgMaxClassifier(), + ) clf2: MaximumDepthClassifier[FData] = MaximumDepthClassifier() clf1.fit(self._X_train, self._y_train) clf2.fit(self._X_train, self._y_train) From 6b27ed59670c5003fa4b5ed46d3d95b0f0c3685a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 1 Jul 2022 21:30:39 +0200 Subject: [PATCH 193/400] Add experimental precompute_metric parameter for KNeighborClassifiers. --- skfda/ml/_neighbors_base.py | 42 ++++++++++++++----- .../classification/_neighbors_classifiers.py | 6 ++- 2 files changed, 35 insertions(+), 13 deletions(-) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index e949f5d88..fe67f7153 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -9,6 +9,8 @@ from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from typing_extensions import Literal +from skfda.misc.metrics._utils import PairwiseMetric + from .. import FData, FDataGrid from ..misc.metrics import l2_distance from ..misc.metrics._typing import Metric @@ -124,9 +126,10 @@ def __init__( metric: Literal["precomputed", "l2"] | Metric[FDataGrid] = 'l2', metric_params: Mapping[str, Any] | None = None, n_jobs: int | None = None, + precompute_metric: bool = False, multivariate_metric: bool = False, ): - """Initializes the nearest neighbors estimator""" + """Initializes the nearest neighbors estimator.""" self.n_neighbors = n_neighbors self.radius = radius @@ -136,6 +139,7 @@ def __init__( self.metric = metric self.metric_params = metric_params self.n_jobs = n_jobs + self.precompute_metric = precompute_metric self.multivariate_metric = multivariate_metric def _check_is_fitted(self) -> None: @@ -192,7 +196,17 @@ def fit( self._grid_points = X.grid_points self._shape = X.data_matrix.shape[1:] - if not self.multivariate_metric: + X_transform = self._transform_to_multivariate + + if self.multivariate_metric: + _fit_metric(self.metric, X) + sklearn_metric = self.metric + elif self.precompute_metric: + sklearn_metric = "precomputed" + self._fit_X = X.copy() + + def X_transform(X): return np.zeros(shape=(len(X), len(X))) + else: # Constructs sklearn metric to manage vector if self.metric == 'l2': metric = l2_distance @@ -204,12 +218,9 @@ def fit( metric, self._grid_points, ) - else: - _fit_metric(self.metric, X) - sklearn_metric = self.metric self.estimator_ = self._init_estimator(sklearn_metric) - self.estimator_.fit(self._transform_to_multivariate(X), y) + self.estimator_.fit(X_transform(X), y) return self @@ -468,7 +479,12 @@ def predict(self, X): """ self._check_is_fitted() - X = self._transform_to_multivariate(X) + if self.precompute_metric: + X = PairwiseMetric( + l2_distance if self.metric == "l2" else self.metric, + )(X, self._fit_X) + else: + X = self._transform_to_multivariate(X) return self.estimator_.predict(X) @@ -522,8 +538,10 @@ def _functional_fit(self, X, y): """ if len(X) != y.n_samples: - raise ValueError("The response and dependent variable must " - "contain the same number of samples,") + raise ValueError( + "The response and dependent variable must " + "contain the same number of samples.", + ) # If metric is precomputed no different with the Sklearn stimator if self.metric == 'precomputed': @@ -540,8 +558,10 @@ def _functional_fit(self, X, y): else: metric = self.metric - sklearn_metric = _to_multivariate_metric(metric, - self._grid_points) + sklearn_metric = _to_multivariate_metric( + metric, + self._grid_points, + ) else: sklearn_metric = self.metric diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index e2348b402..adb2062bc 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -127,8 +127,9 @@ def __init__( metric='l2', metric_params=None, n_jobs=1, - multivariate_metric=False, - ): + precompute_metric: bool = False, + multivariate_metric: bool = False, + ) -> None: super().__init__( n_neighbors=n_neighbors, weights=weights, @@ -137,6 +138,7 @@ def __init__( metric=metric, metric_params=metric_params, n_jobs=n_jobs, + precompute_metric=precompute_metric, multivariate_metric=multivariate_metric, ) From c92d4d563e3c06e52c9a96f50469840fce06958e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 2 Jul 2022 20:37:24 +0200 Subject: [PATCH 194/400] Add missing fit_metric for precomputed distance. --- skfda/ml/_neighbors_base.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index fe67f7153..3849f2827 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -203,6 +203,7 @@ def fit( sklearn_metric = self.metric elif self.precompute_metric: sklearn_metric = "precomputed" + _fit_metric(self.metric, X) self._fit_X = X.copy() def X_transform(X): return np.zeros(shape=(len(X), len(X))) From 3fede3178b237bc4e5c6d7a9e1a61c56f985bf3c Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Mon, 11 Jul 2022 13:12:03 +0200 Subject: [PATCH 195/400] deleted double underscore. More legible test format. --- skfda/ml/regression/_linear_regression.py | 4 +- tests/test_regression.py | 67 ++++------------------- 2 files changed, 13 insertions(+), 58 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 83c347b58..9aff5e232 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -339,7 +339,7 @@ def _argcheck_X( X = [X] elif isinstance(X, pd.DataFrame): - X = self.__dataframe_conversion(X) + X = self._dataframe_conversion(X) X = [x if isinstance(x, FData) else np.asarray(x) for x in X] @@ -401,7 +401,7 @@ def _argcheck_X_y( return new_X, y, sample_weight, coef_info - def __dataframe_conversion( + def _dataframe_conversion( self, X: pd.DataFrame, ) -> List[AcceptedDataType]: diff --git a/tests/test_regression.py b/tests/test_regression.py index 825db2410..54a45a8ad 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -115,29 +115,13 @@ def test_regression_mixed(self): def test_regression_df_multivariate(self): # noqa: D102 multivariate1 = [0, 2, 1, 3, 4, 2, 3] - multivariate2 = [0, 7, 7, 9, 16, 14, 5] - multivariate = [list(obs) for obs in zip(multivariate1, multivariate2)] - x_fd = FDataBasis( - Monomial(n_basis=3), [ - [ - 1, 0, 0, - ], [ - 0, 1, 0, - ], [ - 0, 0, 1, - ], [ - 1, 0, 1, - ], [ - 1, 0, 0, - ], [ - 0, 1, 0, - ], [ - 0, 0, 1, - ], - ]) + x_basis = Monomial(n_basis=3) + x_fd = FDataBasis(x_basis, [[1, 0, 0], [0, 1, 0], [0, 0, 1], + [1, 0, 1], [1, 0, 0], [0, 1, 0], + [0, 0, 1]]) cov_dict = {"fd": x_fd, "mult1": multivariate1, "mult2": multivariate2} @@ -175,42 +159,13 @@ def test_regression_df_multivariate(self): # noqa: D102 def test_regression_df_grouped_multivariate(self): # noqa: D102 - multivariate = [ - [ - 0, 0, - ], [ - 2, 7, - ], [ - 1, 7, - ], [ - 3, 9, - ], [ - 4, 16, - ], [ - 2, 14, - ], [ - 3, 5, - ], - ] - - x_fd = FDataBasis( - Monomial(n_basis=3), [ - [ - 1, 0, 0, - ], [ - 0, 1, 0, - ], [ - 0, 0, 1, - ], [ - 1, 0, 1, - ], [ - 1, 0, 0, - ], [ - 0, 1, 0, - ], [ - 0, 0, 1, - ], - ]) + multivariate = [[0, 0], [2, 7], [1, 7], [3, 9], + [4, 16], [2, 14], [3, 5]] + + x_basis = Monomial(n_basis=3) + x_fd = FDataBasis(x_basis, [[1, 0, 0], [0, 1, 0], [0, 0, 1], + [1, 0, 1], [1, 0, 0], [0, 1, 0], + [0, 0, 1]]) cov_dict = {"fd": x_fd, "mult": multivariate} From 81033c2f4ffeda04af1967cfd9cd0bfe5d71c4ce Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 11 Jul 2022 20:08:57 +0200 Subject: [PATCH 196/400] Improve logistic regression. --- .../ml/classification/_logistic_regression.py | 78 ++++++++++++++----- 1 file changed, 57 insertions(+), 21 deletions(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index a9798a1d3..691abb992 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -3,12 +3,12 @@ from typing import Callable, Tuple import numpy as np -from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.linear_model import LogisticRegression as mvLogisticRegression from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal from ..._utils import _classifier_get_classes +from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin from ...representation import FDataGrid from ...representation._typing import NDArrayAny, NDArrayInt @@ -16,8 +16,8 @@ class LogisticRegression( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore + BaseEstimator, + ClassifierMixin[FDataGrid, NDArrayAny], ): r"""Logistic Regression classifier for functional data. @@ -30,13 +30,24 @@ class LogisticRegression( data with one dimensional domains is supported. Args: - p: + n_features_to_select: Number of points (and coefficients) to be selected by the algorithm. + penalty: + Penalty to use in the multivariate logistic regresion + optimization problem. For more info check the parameter + "penalty" in + :external:class:`sklearn.linear_model.LogisticRegression`. + C: + Inverse of the regularization parameter in the multivariate + logistic regresion optimization problem. For more info + check the parameter "C" in + :external:class:`sklearn.linear_model.LogisticRegression`. solver: - Algorithm to use in the multivariate logistic regresion + Algorithm to use in the multivariate logistic regresion optimization problem. For more info check the parameter - "solver" in sklearn.linear_model.LogisticRegression. + "solver" in + :external:class:`sklearn.linear_model.LogisticRegression`. max_iter: Maximum number of iterations taken for the solver to converge. @@ -81,18 +92,18 @@ class LogisticRegression( def __init__( self, - p: int = 5, + max_features: int = 5, penalty: Literal["l1", "l2", "elasticnet", None] = None, C: float = 1, solver: Solver = 'lbfgs', max_iter: int = 100, ) -> None: - self.p = p + self.max_features = max_features self.penalty = penalty self.C = C - self.max_iter = 100 self.solver = solver + self.max_iter = max_iter def fit( # noqa: D102 self, @@ -107,7 +118,12 @@ def fit( # noqa: D102 n_samples = len(y) n_features = len(X.grid_points[0]) - selected_indexes = np.zeros(self.p, dtype=np.intc) + selected_indexes = np.empty(self.max_features, dtype=np.int_) + selected_values = np.empty((n_samples, self.max_features)) + + likelihood_curves_data = np.empty( + (self.max_features, n_features), + ) penalty = 'none' if self.penalty is None else self.penalty @@ -119,30 +135,50 @@ def fit( # noqa: D102 max_iter=self.max_iter, ) - x_mv = np.zeros((n_samples, self.p)) - LL = np.zeros(n_features) - for q in range(self.p): + max_features = min(self.max_features, len(X.grid_points[0])) + + last_max_likelihood = -np.inf + + for n_selected in range(max_features): for t in range(n_features): - x_mv[:, q] = X.data_matrix[:, t, 0] - mvlr.fit(x_mv[:, :q + 1], y_ind) + selected_values[:, n_selected] = X.data_matrix[:, t, 0] + mvlr.fit(selected_values[:, :n_selected + 1], y_ind) # log-likelihood function at t - log_probs = mvlr.predict_log_proba(x_mv[:, :q + 1]) + with np.errstate(divide='ignore'): + log_probs = mvlr.predict_log_proba( + selected_values[:, :n_selected + 1], + ) + log_probs = np.concatenate( (log_probs[y_ind == 0, 0], log_probs[y_ind == 1, 1]), ) - LL[t] = np.mean(log_probs) + likelihood_curves_data[n_selected, t] = np.mean(log_probs) + + tmax = np.argmax(likelihood_curves_data[n_selected]) + max_likelihood = likelihood_curves_data[n_selected, tmax] + if max_likelihood == last_max_likelihood: + # This does not improve + selected_indexes = selected_indexes[:n_selected] + selected_values = selected_values[:, :n_selected] + likelihood_curves_data = likelihood_curves_data[:n_selected, t] + break - tmax = np.argmax(LL) - selected_indexes[q] = tmax - x_mv[:, q] = X.data_matrix[:, tmax, 0] + last_max_likelihood = max_likelihood + + selected_indexes[n_selected] = tmax + selected_values[:, n_selected] = X.data_matrix[:, tmax, 0] # fit for the complete set of points - mvlr.fit(x_mv, y_ind) + mvlr.fit(selected_values, y_ind) self.coef_ = mvlr.coef_ self.intercept_ = mvlr.intercept_ + self._likelihood_curves = FDataGrid( + likelihood_curves_data, + grid_points=X.grid_points, + ) self._mvlr = mvlr self._selected_indexes = selected_indexes From ed2880a9534573ab9445d5bfbf107c56cccc9c61 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 11 Jul 2022 20:20:06 +0200 Subject: [PATCH 197/400] Move tests inside package. --- setup.py | 2 +- {tests => skfda/tests}/__init__.py | 0 {tests => skfda/tests}/test_basis.py | 0 {tests => skfda/tests}/test_basis_evaluation.py | 0 {tests => skfda/tests}/test_classification.py | 0 {tests => skfda/tests}/test_clustering.py | 0 {tests => skfda/tests}/test_covariances.py | 0 {tests => skfda/tests}/test_depth.py | 0 {tests => skfda/tests}/test_elastic.py | 0 {tests => skfda/tests}/test_extrapolation.py | 0 {tests => skfda/tests}/test_fda_feature_union.py | 0 {tests => skfda/tests}/test_fdata_boxplot.py | 0 {tests => skfda/tests}/test_fpca.py | 0 {tests => skfda/tests}/test_functional_transformers.py | 0 {tests => skfda/tests}/test_grid.py | 0 {tests => skfda/tests}/test_hotelling.py | 0 {tests => skfda/tests}/test_interpolation.py | 0 {tests => skfda/tests}/test_kernel_regression.py | 0 {tests => skfda/tests}/test_linear_differential_operator.py | 0 {tests => skfda/tests}/test_magnitude_shape.py | 0 {tests => skfda/tests}/test_math.py | 0 {tests => skfda/tests}/test_metrics.py | 0 {tests => skfda/tests}/test_neighbors.py | 0 {tests => skfda/tests}/test_oneway_anova.py | 0 {tests => skfda/tests}/test_outliers.py | 0 {tests => skfda/tests}/test_pandas.py | 0 {tests => skfda/tests}/test_pandas_fdatabasis.py | 0 {tests => skfda/tests}/test_pandas_fdatagrid.py | 0 {tests => skfda/tests}/test_per_class_transformer.py | 0 {tests => skfda/tests}/test_recursive_maxima_hunting.py | 0 {tests => skfda/tests}/test_registration.py | 0 {tests => skfda/tests}/test_regression.py | 0 {tests => skfda/tests}/test_regularization.py | 0 {tests => skfda/tests}/test_smoothing.py | 0 {tests => skfda/tests}/test_stats.py | 0 {tests => skfda/tests}/test_ufunc_numpy.py | 0 36 files changed, 1 insertion(+), 1 deletion(-) rename {tests => skfda/tests}/__init__.py (100%) rename {tests => skfda/tests}/test_basis.py (100%) rename {tests => skfda/tests}/test_basis_evaluation.py (100%) rename {tests => skfda/tests}/test_classification.py (100%) rename {tests => skfda/tests}/test_clustering.py (100%) rename {tests => skfda/tests}/test_covariances.py (100%) rename {tests => skfda/tests}/test_depth.py (100%) rename {tests => skfda/tests}/test_elastic.py (100%) rename {tests => skfda/tests}/test_extrapolation.py (100%) rename {tests => skfda/tests}/test_fda_feature_union.py (100%) rename {tests => skfda/tests}/test_fdata_boxplot.py (100%) rename {tests => skfda/tests}/test_fpca.py (100%) rename {tests => skfda/tests}/test_functional_transformers.py (100%) rename {tests => skfda/tests}/test_grid.py (100%) rename {tests => skfda/tests}/test_hotelling.py (100%) rename {tests => skfda/tests}/test_interpolation.py (100%) rename {tests => skfda/tests}/test_kernel_regression.py (100%) rename {tests => skfda/tests}/test_linear_differential_operator.py (100%) rename {tests => skfda/tests}/test_magnitude_shape.py (100%) rename {tests => skfda/tests}/test_math.py (100%) rename {tests => skfda/tests}/test_metrics.py (100%) rename {tests => skfda/tests}/test_neighbors.py (100%) rename {tests => skfda/tests}/test_oneway_anova.py (100%) rename {tests => skfda/tests}/test_outliers.py (100%) rename {tests => skfda/tests}/test_pandas.py (100%) rename {tests => skfda/tests}/test_pandas_fdatabasis.py (100%) rename {tests => skfda/tests}/test_pandas_fdatagrid.py (100%) rename {tests => skfda/tests}/test_per_class_transformer.py (100%) rename {tests => skfda/tests}/test_recursive_maxima_hunting.py (100%) rename {tests => skfda/tests}/test_registration.py (100%) rename {tests => skfda/tests}/test_regression.py (100%) rename {tests => skfda/tests}/test_regularization.py (100%) rename {tests => skfda/tests}/test_smoothing.py (100%) rename {tests => skfda/tests}/test_stats.py (100%) rename {tests => skfda/tests}/test_ufunc_numpy.py (100%) diff --git a/setup.py b/setup.py index 863927c55..ad015fbc6 100644 --- a/setup.py +++ b/setup.py @@ -81,5 +81,5 @@ ], setup_requires=pytest_runner, tests_require=['pytest'], - test_suite='tests', + test_suite='skfda.tests', zip_safe=False) diff --git a/tests/__init__.py b/skfda/tests/__init__.py similarity index 100% rename from tests/__init__.py rename to skfda/tests/__init__.py diff --git a/tests/test_basis.py b/skfda/tests/test_basis.py similarity index 100% rename from tests/test_basis.py rename to skfda/tests/test_basis.py diff --git a/tests/test_basis_evaluation.py b/skfda/tests/test_basis_evaluation.py similarity index 100% rename from tests/test_basis_evaluation.py rename to skfda/tests/test_basis_evaluation.py diff --git a/tests/test_classification.py b/skfda/tests/test_classification.py similarity index 100% rename from tests/test_classification.py rename to skfda/tests/test_classification.py diff --git a/tests/test_clustering.py b/skfda/tests/test_clustering.py similarity index 100% rename from tests/test_clustering.py rename to skfda/tests/test_clustering.py diff --git a/tests/test_covariances.py b/skfda/tests/test_covariances.py similarity index 100% rename from tests/test_covariances.py rename to skfda/tests/test_covariances.py diff --git a/tests/test_depth.py b/skfda/tests/test_depth.py similarity index 100% rename from tests/test_depth.py rename to skfda/tests/test_depth.py diff --git a/tests/test_elastic.py b/skfda/tests/test_elastic.py similarity index 100% rename from tests/test_elastic.py rename to skfda/tests/test_elastic.py diff --git a/tests/test_extrapolation.py b/skfda/tests/test_extrapolation.py similarity index 100% rename from tests/test_extrapolation.py rename to skfda/tests/test_extrapolation.py diff --git a/tests/test_fda_feature_union.py b/skfda/tests/test_fda_feature_union.py similarity index 100% rename from tests/test_fda_feature_union.py rename to skfda/tests/test_fda_feature_union.py diff --git a/tests/test_fdata_boxplot.py b/skfda/tests/test_fdata_boxplot.py similarity index 100% rename from tests/test_fdata_boxplot.py rename to skfda/tests/test_fdata_boxplot.py diff --git a/tests/test_fpca.py b/skfda/tests/test_fpca.py similarity index 100% rename from tests/test_fpca.py rename to skfda/tests/test_fpca.py diff --git a/tests/test_functional_transformers.py b/skfda/tests/test_functional_transformers.py similarity index 100% rename from tests/test_functional_transformers.py rename to skfda/tests/test_functional_transformers.py diff --git a/tests/test_grid.py b/skfda/tests/test_grid.py similarity index 100% rename from tests/test_grid.py rename to skfda/tests/test_grid.py diff --git a/tests/test_hotelling.py b/skfda/tests/test_hotelling.py similarity index 100% rename from tests/test_hotelling.py rename to skfda/tests/test_hotelling.py diff --git a/tests/test_interpolation.py b/skfda/tests/test_interpolation.py similarity index 100% rename from tests/test_interpolation.py rename to skfda/tests/test_interpolation.py diff --git a/tests/test_kernel_regression.py b/skfda/tests/test_kernel_regression.py similarity index 100% rename from tests/test_kernel_regression.py rename to skfda/tests/test_kernel_regression.py diff --git a/tests/test_linear_differential_operator.py b/skfda/tests/test_linear_differential_operator.py similarity index 100% rename from tests/test_linear_differential_operator.py rename to skfda/tests/test_linear_differential_operator.py diff --git a/tests/test_magnitude_shape.py b/skfda/tests/test_magnitude_shape.py similarity index 100% rename from tests/test_magnitude_shape.py rename to skfda/tests/test_magnitude_shape.py diff --git a/tests/test_math.py b/skfda/tests/test_math.py similarity index 100% rename from tests/test_math.py rename to skfda/tests/test_math.py diff --git a/tests/test_metrics.py b/skfda/tests/test_metrics.py similarity index 100% rename from tests/test_metrics.py rename to skfda/tests/test_metrics.py diff --git a/tests/test_neighbors.py b/skfda/tests/test_neighbors.py similarity index 100% rename from tests/test_neighbors.py rename to skfda/tests/test_neighbors.py diff --git a/tests/test_oneway_anova.py b/skfda/tests/test_oneway_anova.py similarity index 100% rename from tests/test_oneway_anova.py rename to skfda/tests/test_oneway_anova.py diff --git a/tests/test_outliers.py b/skfda/tests/test_outliers.py similarity index 100% rename from tests/test_outliers.py rename to skfda/tests/test_outliers.py diff --git a/tests/test_pandas.py b/skfda/tests/test_pandas.py similarity index 100% rename from tests/test_pandas.py rename to skfda/tests/test_pandas.py diff --git a/tests/test_pandas_fdatabasis.py b/skfda/tests/test_pandas_fdatabasis.py similarity index 100% rename from tests/test_pandas_fdatabasis.py rename to skfda/tests/test_pandas_fdatabasis.py diff --git a/tests/test_pandas_fdatagrid.py b/skfda/tests/test_pandas_fdatagrid.py similarity index 100% rename from tests/test_pandas_fdatagrid.py rename to skfda/tests/test_pandas_fdatagrid.py diff --git a/tests/test_per_class_transformer.py b/skfda/tests/test_per_class_transformer.py similarity index 100% rename from tests/test_per_class_transformer.py rename to skfda/tests/test_per_class_transformer.py diff --git a/tests/test_recursive_maxima_hunting.py b/skfda/tests/test_recursive_maxima_hunting.py similarity index 100% rename from tests/test_recursive_maxima_hunting.py rename to skfda/tests/test_recursive_maxima_hunting.py diff --git a/tests/test_registration.py b/skfda/tests/test_registration.py similarity index 100% rename from tests/test_registration.py rename to skfda/tests/test_registration.py diff --git a/tests/test_regression.py b/skfda/tests/test_regression.py similarity index 100% rename from tests/test_regression.py rename to skfda/tests/test_regression.py diff --git a/tests/test_regularization.py b/skfda/tests/test_regularization.py similarity index 100% rename from tests/test_regularization.py rename to skfda/tests/test_regularization.py diff --git a/tests/test_smoothing.py b/skfda/tests/test_smoothing.py similarity index 100% rename from tests/test_smoothing.py rename to skfda/tests/test_smoothing.py diff --git a/tests/test_stats.py b/skfda/tests/test_stats.py similarity index 100% rename from tests/test_stats.py rename to skfda/tests/test_stats.py diff --git a/tests/test_ufunc_numpy.py b/skfda/tests/test_ufunc_numpy.py similarity index 100% rename from tests/test_ufunc_numpy.py rename to skfda/tests/test_ufunc_numpy.py From 3d26baa0ae3ce69e389df8de3c7bcaecc3139e53 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 12 Jul 2022 14:06:46 +0200 Subject: [PATCH 198/400] Refactor tests for FDataBasis evaluation. - Add typing. - Add style. - Remove tests useless with typing. - Extract repeated tests. - Add proper names and docstrings. --- setup.cfg | 2 +- skfda/tests/test_basis_evaluation.py | 482 ---------------------- skfda/tests/test_fdatabasis_evaluation.py | 396 ++++++++++++++++++ 3 files changed, 397 insertions(+), 483 deletions(-) delete mode 100644 skfda/tests/test_basis_evaluation.py create mode 100644 skfda/tests/test_fdatabasis_evaluation.py diff --git a/setup.cfg b/setup.cfg index 6b04b4ec5..9c1345348 100644 --- a/setup.cfg +++ b/setup.cfg @@ -94,7 +94,7 @@ per-file-ignores = _real_datasets.py: WPS202 # Tests benefit from meaningless zeros, magic numbers and fixtures - test_*.py: WPS339, WPS432, WPS442 + test_*.py: WPS339, WPS432, WPS442, WPS446 # Examples are allowed to have imports in the middle, "commented code", call print and have magic numbers plot_*.py: E402, E800, WPS421, WPS432 diff --git a/skfda/tests/test_basis_evaluation.py b/skfda/tests/test_basis_evaluation.py deleted file mode 100644 index 0ff5727a0..000000000 --- a/skfda/tests/test_basis_evaluation.py +++ /dev/null @@ -1,482 +0,0 @@ - -from skfda.representation.basis import ( - FDataBasis, Monomial, BSpline, Fourier, Constant, VectorValued, Tensor) -import unittest - -import numpy as np - - -class TestBasisEvaluationFourier(unittest.TestCase): - - def test_evaluation_simple_fourier(self): - """Test the evaluation of FDataBasis""" - fourier = Fourier(domain_range=(0, 2), n_basis=5) - - coefficients = np.array([[1, 2, 3, 4, 5], - [6, 7, 8, 9, 10]]) - - f = FDataBasis(fourier, coefficients) - - t = np.linspace(0, 2, 11) - - # Results in R package fda - res = np.array([[8.71, 9.66, 1.84, -4.71, -2.80, 2.71, - 2.45, -3.82, -6.66, -0.30, 8.71], - [22.24, 26.48, 10.57, -4.95, -3.58, 6.24, - 5.31, -7.69, -13.32, 1.13, 22.24]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f(t).round(2), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) - - def test_evaluation_point_fourier(self): - """Test the evaluation of a single point FDataBasis""" - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - - # Test different ways of call f with a point - res = np.array([-0.903918107989282, -0.267163981229459] - ).reshape((2, 1, 1)).round(4) - - np.testing.assert_array_almost_equal(f([0.5]).round(4), res) - np.testing.assert_array_almost_equal(f((0.5,)).round(4), res) - np.testing.assert_array_almost_equal(f(0.5).round(4), res) - np.testing.assert_array_almost_equal(f(np.array([0.5])).round(4), res) - - # Problematic case, should be accepted or no? - #np.testing.assert_array_almost_equal(f(np.array(0.5)).round(4), res) - - def test_evaluation_derivative_fourier(self): - """Test the evaluation of the derivative of a FDataBasis""" - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - - t = np.linspace(0, 1, 4) - - res = np.array([4.34138447771721, -7.09352774867064, 2.75214327095343, - 4.34138447771721, 6.52573053999253, - -4.81336320468984, -1.7123673353027, 6.52573053999253] - ).reshape((2, 4, 1)).round(3) - - f_deriv = f.derivative() - np.testing.assert_array_almost_equal( - f_deriv(t).round(3), res - ) - - def test_evaluation_grid_fourier(self): - """Test the evaluation of FDataBasis with the grid option set to - true. Nothing should be change due to the domain dimension is 1, - but can accept the """ - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - t = np.linspace(0, 1, 4) - - res_test = f(t) - - # Different ways to pass the axes - np.testing.assert_array_almost_equal(f(t, grid=True), res_test) - np.testing.assert_array_almost_equal(f((t,), grid=True), res_test) - np.testing.assert_array_almost_equal(f([t], grid=True), res_test) - np.testing.assert_array_almost_equal(f(np.atleast_2d(t), grid=True), - res_test) - - # Number of axis different than the domain dimension (1) - with np.testing.assert_raises(ValueError): - f((t, t), grid=True) - - def test_evaluation_composed_fourier(self): - """Test the evaluation of FDataBasis the a matrix of times instead of - a list of times """ - fourier = Fourier(domain_range=(0, 1), n_basis=3) - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - t = np.linspace(0, 1, 4) - - # Test same result than evaluation standart - np.testing.assert_array_almost_equal(f([1]), - f([[1], [1]], - aligned=False)) - np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), - aligned=False)) - - # Different evaluation times - t_multiple = [[0, 0.5], [0.2, 0.7]] - np.testing.assert_array_almost_equal(f(t_multiple[0])[0], - f(t_multiple, - aligned=False)[0]) - np.testing.assert_array_almost_equal(f(t_multiple[1])[1], - f(t_multiple, - aligned=False)[1]) - - def test_domain_in_list_fourier(self): - """Test the evaluation of FDataBasis""" - for fourier in (Fourier(domain_range=[(0, 1)], n_basis=3), - Fourier(domain_range=((0, 1),), n_basis=3), - Fourier(domain_range=np.array((0, 1)), n_basis=3), - Fourier(domain_range=np.array([(0, 1)]), n_basis=3)): - - coefficients = np.array([[0.00078238, 0.48857741, 0.63971985], - [0.01778079, 0.73440271, 0.20148638]]) - - f = FDataBasis(fourier, coefficients) - - t = np.linspace(0, 1, 4) - - res = np.array([0.905, 0.147, -1.05, 0.905, 0.303, - 0.775, -1.024, 0.303]).reshape((2, 4, 1)) - - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) - - -class TestBasisEvaluationBSpline(unittest.TestCase): - - def test_evaluation_simple_bspline(self): - """Test the evaluation of FDataBasis""" - bspline = BSpline(domain_range=(0, 2), n_basis=5) - - coefficients = np.array([[1, 2, 3, 4, 5], - [6, 7, 8, 9, 10]]) - - f = FDataBasis(bspline, coefficients) - - t = np.linspace(0, 2, 11) - - # Results in R package fda - res = np.array([[1, 1.54, 1.99, 2.37, 2.7, 3, - 3.3, 3.63, 4.01, 4.46, 5], - [6, 6.54, 6.99, 7.37, 7.7, 8, - 8.3, 8.63, 9.01, 9.46, 10]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f(t).round(2), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) - - def test_evaluation_point_bspline(self): - """Test the evaluation of a single point FDataBasis""" - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - - # Test different ways of call f with a point - res = np.array([[0.5696], [0.3104]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f([0.5]).round(4), res) - np.testing.assert_array_almost_equal(f((0.5,)).round(4), res) - np.testing.assert_array_almost_equal(f(0.5).round(4), res) - np.testing.assert_array_almost_equal(f(np.array([0.5])).round(4), res) - - # Problematic case, should be accepted or no? - #np.testing.assert_array_almost_equal(f(np.array(0.5)).round(4), res) - - def test_evaluation_derivative_bspline(self): - """Test the evaluation of the derivative of a FDataBasis""" - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - - t = np.linspace(0, 1, 4) - - f_deriv = f.derivative() - np.testing.assert_array_almost_equal( - f_deriv(t).round(3), - np.array([[2.927, 0.453, -1.229, 0.6], - [4.3, -1.599, 1.016, -2.52]])[..., np.newaxis] - ) - - def test_evaluation_grid_bspline(self): - """Test the evaluation of FDataBasis with the grid option set to - true. Nothing should be change due to the domain dimension is 1, - but can accept the """ - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - t = np.linspace(0, 1, 4) - - res_test = f(t) - - # Different ways to pass the axes - np.testing.assert_array_almost_equal(f(t, grid=True), res_test) - np.testing.assert_array_almost_equal(f((t,), grid=True), res_test) - np.testing.assert_array_almost_equal(f([t], grid=True), res_test) - np.testing.assert_array_almost_equal( - f(np.atleast_2d(t), grid=True), res_test) - - # Number of axis different than the domain dimension (1) - with np.testing.assert_raises(ValueError): - f((t, t), grid=True) - - def test_evaluation_composed_bspline(self): - """Test the evaluation of FDataBasis the a matrix of times instead of - a list of times """ - bspline = BSpline(domain_range=(0, 1), n_basis=5, order=3) - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - t = np.linspace(0, 1, 4) - - # Test same result than evaluation standart - np.testing.assert_array_almost_equal(f([1]), - f([[1], [1]], - aligned=False)) - np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), - aligned=False)) - - # Different evaluation times - t_multiple = [[0, 0.5], [0.2, 0.7]] - np.testing.assert_array_almost_equal(f(t_multiple[0])[0], - f(t_multiple, - aligned=False)[0]) - np.testing.assert_array_almost_equal(f(t_multiple[1])[1], - f(t_multiple, - aligned=False)[1]) - - def test_domain_in_list_bspline(self): - """Test the evaluation of FDataBasis""" - - for bspline in (BSpline(domain_range=[(0, 1)], n_basis=5, order=3), - BSpline(domain_range=((0, 1),), n_basis=5, order=3), - BSpline(domain_range=np.array((0, 1)), n_basis=5, - order=3), - BSpline(domain_range=np.array([(0, 1)]), n_basis=5, - order=3) - ): - - coefficients = [[0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], - [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12]] - - f = FDataBasis(bspline, coefficients) - - t = np.linspace(0, 1, 4) - - res = np.array([[0.001, 0.564, 0.435, 0.33], - [0.018, 0.468, 0.371, 0.12]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) - - # Check error - with np.testing.assert_raises(ValueError): - BSpline(domain_range=[(0, 1), (0, 1)]) - - -class TestBasisEvaluationMonomial(unittest.TestCase): - - def test_evaluation_simple_monomial(self): - """Test the evaluation of FDataBasis""" - - monomial = Monomial(domain_range=(0, 2), n_basis=5) - - coefficients = np.array([[1, 2, 3, 4, 5], - [6, 7, 8, 9, 10]]) - - f = FDataBasis(monomial, coefficients) - - t = np.linspace(0, 2, 11) - - # Results in R package fda - res = np.array( - [[1.00, 1.56, 2.66, 4.79, 8.62, 15.00, - 25.00, 39.86, 61.03, 90.14, 129.00], - [6.00, 7.81, 10.91, 16.32, 25.42, 40.00, - 62.21, 94.59, 140.08, 201.98, 284.00]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f(t).round(2), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(2), res) - - def test_evaluation_point_monomial(self): - """Test the evaluation of a single point FDataBasis""" - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - - # Test different ways of call f with a point - res = np.array([[2.75], [1.525]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f([0.5]).round(4), res) - np.testing.assert_array_almost_equal(f((0.5,)).round(4), res) - np.testing.assert_array_almost_equal(f(0.5).round(4), res) - np.testing.assert_array_almost_equal(f(np.array([0.5])).round(4), res) - - # Problematic case, should be accepted or no? - #np.testing.assert_array_almost_equal(f(np.array(0.5)).round(4), res) - - def test_evaluation_derivative_monomial(self): - """Test the evaluation of the derivative of a FDataBasis""" - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - - t = np.linspace(0, 1, 4) - - f_deriv = f.derivative() - np.testing.assert_array_almost_equal( - f_deriv(t).round(3), - np.array([[2., 4., 6., 8.], - [1.4, 2.267, 3.133, 4.]])[..., np.newaxis] - ) - - def test_evaluation_grid_monomial(self): - """Test the evaluation of FDataBasis with the grid option set to - true. Nothing should be change due to the domain dimension is 1, - but can accept the """ - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - t = np.linspace(0, 1, 4) - - res_test = f(t) - - # Different ways to pass the axes - np.testing.assert_array_almost_equal(f(t, grid=True), res_test) - np.testing.assert_array_almost_equal(f((t,), grid=True), res_test) - np.testing.assert_array_almost_equal(f([t], grid=True), res_test) - np.testing.assert_array_almost_equal( - f(np.atleast_2d(t), grid=True), res_test) - - # Number of axis different than the domain dimension (1) - with np.testing.assert_raises(ValueError): - f((t, t), grid=True) - - def test_evaluation_composed_monomial(self): - """Test the evaluation of FDataBasis the a matrix of times instead of - a list of times """ - monomial = Monomial(domain_range=(0, 1), n_basis=3) - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - t = np.linspace(0, 1, 4) - - # Test same result than evaluation standart - np.testing.assert_array_almost_equal(f([1]), - f([[1], [1]], - aligned=False)) - np.testing.assert_array_almost_equal(f(t), f(np.vstack((t, t)), - aligned=False)) - - # Different evaluation times - t_multiple = [[0, 0.5], [0.2, 0.7]] - np.testing.assert_array_almost_equal(f(t_multiple[0])[0], - f(t_multiple, - aligned=False)[0]) - np.testing.assert_array_almost_equal(f(t_multiple[1])[1], - f(t_multiple, - aligned=False)[1]) - - def test_domain_in_list_monomial(self): - """Test the evaluation of FDataBasis""" - - for monomial in (Monomial(domain_range=[(0, 1)], n_basis=3), - Monomial(domain_range=((0, 1),), n_basis=3), - Monomial(domain_range=np.array((0, 1)), n_basis=3), - Monomial(domain_range=np.array([(0, 1)]), n_basis=3)): - - coefficients = [[1, 2, 3], [0.5, 1.4, 1.3]] - - f = FDataBasis(monomial, coefficients) - - t = np.linspace(0, 1, 4) - - res = np.array([[1., 2., 3.667, 6.], - [0.5, 1.111, 2.011, 3.2]])[..., np.newaxis] - - np.testing.assert_array_almost_equal(f(t).round(3), res) - np.testing.assert_array_almost_equal(f.evaluate(t).round(3), res) - - -class TestBasisEvaluationVectorValued(unittest.TestCase): - - def test_vector_valued_constant(self): - - basis_first = Constant() - basis_second = Constant() - - basis = VectorValued([basis_first, basis_second]) - - fd = FDataBasis(basis=basis, coefficients=[[1, 2], [3, 4]]) - - self.assertEqual(fd.dim_codomain, 2) - - res = np.array([[[1, 2]], [[3, 4]]]) - - np.testing.assert_allclose(fd(0), res) - - def test_vector_valued_constant_monomial(self): - - basis_first = Constant(domain_range=(0, 5)) - basis_second = Monomial(n_basis=3, domain_range=(0, 5)) - - basis = VectorValued([basis_first, basis_second]) - - fd = FDataBasis(basis=basis, coefficients=[ - [1, 2, 3, 4], [3, 4, 5, 6]]) - - self.assertEqual(fd.dim_codomain, 2) - - np.testing.assert_allclose(fd.domain_range[0], (0, 5)) - - res = np.array([[[1, 2], [1, 9], [1, 24]], - [[3, 4], [3, 15], [3, 38]]]) - - np.testing.assert_allclose(fd([0, 1, 2]), res) - - -class TestBasisEvaluationTensor(unittest.TestCase): - - def test_tensor_monomial_constant(self): - - basis = Tensor([Monomial(n_basis=2), Constant()]) - - fd = FDataBasis(basis=basis, coefficients=[1, 1]) - - self.assertEqual(fd.dim_domain, 2) - self.assertEqual(fd.dim_codomain, 1) - - np.testing.assert_allclose(fd([0., 0.]), [[[1.]]]) - - np.testing.assert_allclose(fd([0.5, 0.5]), [[[1.5]]]) - - np.testing.assert_allclose( - fd([(0., 0.), (0.5, 0.5)]), [[[1.0], [1.5]]]) - - fd_grid = fd.to_grid() - - fd2 = fd_grid.to_basis(basis) - - np.testing.assert_allclose(fd.coefficients, fd2.coefficients) - - -if __name__ == '__main__': - print() - unittest.main() diff --git a/skfda/tests/test_fdatabasis_evaluation.py b/skfda/tests/test_fdatabasis_evaluation.py new file mode 100644 index 000000000..cf8a47ae2 --- /dev/null +++ b/skfda/tests/test_fdatabasis_evaluation.py @@ -0,0 +1,396 @@ +"""Tests for FDataBasis evaluation.""" +import unittest + +import numpy as np + +from skfda.representation.basis import ( + BSpline, + Constant, + FDataBasis, + Fourier, + Monomial, + Tensor, + VectorValued, +) + + +class TestFDataBasisEvaluation(unittest.TestCase): + """ + Test FDataBasis evaluation. + + This class includes tests that don't depend on the particular based used. + + """ + + def test_evaluation_single_point(self) -> None: + """Test the evaluation of a single point FDataBasis.""" + fourier = Fourier( + domain_range=(0, 1), + n_basis=3, + ) + + coefficients = np.array([ + [0.00078238, 0.48857741, 0.63971985], + [0.01778079, 0.73440271, 0.20148638], + ]) + + f = FDataBasis(fourier, coefficients) + + # Test different ways of call f with a point + res = np.array( + [-0.903918107989282, -0.267163981229459], + ).reshape((2, 1, 1)) + + np.testing.assert_allclose(f([0.5]), res) + np.testing.assert_allclose(f((0.5,)), res) + np.testing.assert_allclose(f(0.5), res) + np.testing.assert_allclose(f(np.array([0.5])), res) + np.testing.assert_allclose(f(np.array(0.5)), res) + + def test_evaluation_grid_1d(self) -> None: + """ + Test the evaluation of FDataBasis with grid=True. + + Nothing should change as the domain dimension is 1. + + """ + fourier = Fourier( + domain_range=(0, 1), + n_basis=3, + ) + + coefficients = np.array([ + [0.00078238, 0.48857741, 0.63971985], + [0.01778079, 0.73440271, 0.20148638], + ]) + + f = FDataBasis(fourier, coefficients) + t = np.linspace(0, 1, 4) + + res_test = f(t) + + # Different ways to pass the axes + np.testing.assert_allclose( + f(t, grid=True), + res_test, + ) + np.testing.assert_allclose( + f((t,), grid=True), + res_test, + ) + np.testing.assert_allclose( + f([t], grid=True), + res_test, + ) + np.testing.assert_allclose( + f(np.atleast_2d(t), grid=True), + res_test, + ) + + # Number of axis different than the domain dimension (1) + with np.testing.assert_raises(ValueError): + f((t, t), grid=True) + + def test_evaluation_unaligned(self) -> None: + """Test the unaligned evaluation.""" + fourier = Fourier( + domain_range=(0, 1), + n_basis=3, + ) + + coefficients = np.array([ + [0.00078238, 0.48857741, 0.63971985], + [0.01778079, 0.73440271, 0.20148638], + ]) + + f = FDataBasis(fourier, coefficients) + t = np.linspace(0, 1, 4) + + # Test same result than normal evaluation + np.testing.assert_allclose( + f([1]), + f([[1], [1]], aligned=False), + ) + np.testing.assert_allclose( + f(t), + f(np.vstack((t, t)), aligned=False), + ) + + # Different evaluation times + t_multiple = [[0, 0.5], [0.2, 0.7]] + np.testing.assert_allclose( + f(t_multiple[0])[0], + f(t_multiple, aligned=False)[0], + ) + + np.testing.assert_allclose( + f(t_multiple[1])[1], + f(t_multiple, aligned=False)[1], + ) + + +class TestBasisEvaluationFourier(unittest.TestCase): + """Test FDataBasis with Fourier basis.""" + + def test_simple_evaluation(self) -> None: + """Compare Fourier evaluation with R package fda.""" + fourier = Fourier( + domain_range=(0, 2), + n_basis=5, + ) + + coefficients = np.array([ + [1, 2, 3, 4, 5], + [6, 7, 8, 9, 10], + ]) + + f = FDataBasis(fourier, coefficients) + + t = np.linspace(0, 2, 11) + + # Results in R package fda + res = np.array([ + [ # noqa: WPS317 + 8.71, 9.66, 1.84, -4.71, -2.80, 2.71, + 2.45, -3.82, -6.66, -0.30, 8.71, + ], + [ # noqa: WPS317 + 22.24, 26.48, 10.57, -4.95, -3.58, 6.24, + 5.31, -7.69, -13.32, 1.13, 22.24, + ], + ])[..., np.newaxis] + + np.testing.assert_allclose(f(t), res, atol=1e-2) + np.testing.assert_allclose(f.evaluate(t), res, atol=1e-2) + + def test_evaluation_derivative(self) -> None: + """Test the evaluation of the derivative of Fourier.""" + fourier = Fourier(domain_range=(0, 1), n_basis=3) + + coefficients = np.array([ + [0.00078238, 0.48857741, 0.63971985], + [0.01778079, 0.73440271, 0.20148638], + ]) + + f = FDataBasis(fourier, coefficients) + + t = np.linspace(0, 1, 4) + + res = np.array([ # noqa: WPS317 + 4.34138447771721, -7.09352774867064, 2.75214327095343, + 4.34138447771721, 6.52573053999253, + -4.81336320468984, -1.7123673353027, 6.52573053999253, + ]).reshape((2, 4, 1)) + + f_deriv = f.derivative() + np.testing.assert_allclose( + f_deriv(t), + res, + ) + + +class TestBasisEvaluationBSpline(unittest.TestCase): + """Test FDataBasis with B-spline basis.""" + + def test_simple_evaluation(self) -> None: + """Compare BSpline evaluation with R package fda.""" + bspline = BSpline( + domain_range=(0, 2), + n_basis=5, + ) + + coefficients = np.array([ + [1, 2, 3, 4, 5], + [6, 7, 8, 9, 10], + ]) + + f = FDataBasis(bspline, coefficients) + + t = np.linspace(0, 2, 11) + + # Results in R package fda + res = np.array([ + [ # noqa: WPS317 + 1, 1.54, 1.99, 2.37, 2.7, 3, + 3.3, 3.63, 4.01, 4.46, 5, + ], + [ # noqa: WPS317 + 6, 6.54, 6.99, 7.37, 7.7, 8, + 8.3, 8.63, 9.01, 9.46, 10, + ], + ])[..., np.newaxis] + + np.testing.assert_allclose(f(t), res, atol=1e-2) + np.testing.assert_allclose(f.evaluate(t), res, atol=1e-2) + + def test_evaluation_derivative(self) -> None: + """Test the evaluation of the derivative of BSpline.""" + bspline = BSpline( + domain_range=(0, 1), + n_basis=5, + order=3, + ) + + coefficients = [ + [0.00078238, 0.48857741, 0.63971985, 0.23, 0.33], + [0.01778079, 0.73440271, 0.20148638, 0.54, 0.12], + ] + + f = FDataBasis(bspline, coefficients) + + t = np.linspace(0, 1, 4) + + f_deriv = f.derivative() + np.testing.assert_allclose( + f_deriv(t).round(3), + np.array([ + [2.927, 0.453, -1.229, 0.6], + [4.3, -1.599, 1.016, -2.52], + ])[..., np.newaxis], + ) + + +class TestBasisEvaluationMonomial(unittest.TestCase): + """Test FDataBasis with Monomial basis.""" + + def test_evaluation_simple_monomial(self) -> None: + """Compare Monomial evaluation with R package fda.""" + monomial = Monomial( + domain_range=(0, 2), + n_basis=5, + ) + + coefficients = np.array([ + [1, 2, 3, 4, 5], + [6, 7, 8, 9, 10], + ]) + + f = FDataBasis(monomial, coefficients) + + t = np.linspace(0, 2, 11) + + # Results in R package fda + res = np.array([ + [ # noqa: WPS317 + 1.00, 1.56, 2.66, 4.79, 8.62, 15.00, + 25.00, 39.86, 61.03, 90.14, 129.00, + ], + [ # noqa: WPS317 + 6.00, 7.81, 10.91, 16.32, 25.42, 40.00, + 62.21, 94.59, 140.08, 201.98, 284.00, + ], + ])[..., np.newaxis] + + np.testing.assert_allclose(f(t), res, atol=1e-2) + np.testing.assert_allclose(f.evaluate(t), res, atol=1e-2) + + def test_evaluation_derivative(self) -> None: + """Test the evaluation of the derivative of Monomial.""" + monomial = Monomial( + domain_range=(0, 1), + n_basis=3, + ) + + coefficients = [ + [1.0, 2.0, 3.0], + [0.5, 1.4, 1.3], + ] + + f = FDataBasis(monomial, coefficients) + + t = np.linspace(0, 1, 4) + + f_deriv = f.derivative() + np.testing.assert_allclose( + f_deriv(t).round(3), + np.array([ + [2.0, 4.0, 6.0, 8.0], + [1.4, 2.267, 3.133, 4.0], + ])[..., np.newaxis], + ) + + +class TestBasisEvaluationVectorValued(unittest.TestCase): + """Test basis for vector-valued functions.""" + + def test_constant(self) -> None: + """Test vector-valued constant basis.""" + basis_first = Constant() + basis_second = Constant() + + basis = VectorValued([basis_first, basis_second]) + + fd = FDataBasis(basis=basis, coefficients=[[1, 2], [3, 4]]) + + self.assertEqual(fd.dim_codomain, 2) + + res = np.array([[[1, 2]], [[3, 4]]]) + + np.testing.assert_allclose(fd(0), res) + + def test_monomial(self) -> None: + """Test vector-valued monomial basis.""" + basis_first = Constant(domain_range=(0, 5)) + basis_second = Monomial( + n_basis=3, + domain_range=(0, 5), + ) + + basis = VectorValued([basis_first, basis_second]) + + fd = FDataBasis( + basis=basis, + coefficients=[ + [1, 2, 3, 4], + [3, 4, 5, 6], + ], + ) + + self.assertEqual(fd.dim_codomain, 2) + + np.testing.assert_allclose(fd.domain_range[0], (0, 5)) + + res = np.array([ + [[1, 2], [1, 9], [1, 24]], + [[3, 4], [3, 15], [3, 38]], + ]) + + np.testing.assert_allclose(fd([0, 1, 2]), res) + + +class TestBasisEvaluationTensor(unittest.TestCase): + """Test tensor basis for multivariable functions.""" + + def test_tensor_monomial_constant(self) -> None: + """Test monomial ⊗ constant basis.""" + basis = Tensor([ + Monomial(n_basis=2), + Constant(), + ]) + + fd = FDataBasis( + basis=basis, + coefficients=[1, 1], + ) + + self.assertEqual(fd.dim_domain, 2) + self.assertEqual(fd.dim_codomain, 1) + + np.testing.assert_allclose(fd([0, 0]), [[[1]]]) + + np.testing.assert_allclose(fd([0.5, 0.5]), [[[1.5]]]) + + np.testing.assert_allclose( + fd([(0, 0), (0.5, 0.5)]), + [[[1.0], [1.5]]], + ) + + fd_grid = fd.to_grid() + + fd2 = fd_grid.to_basis(basis) + + np.testing.assert_allclose(fd.coefficients, fd2.coefficients) + + +if __name__ == '__main__': + unittest.main() From eaad8fc6e0e3c001559f44e627e5cd911f88a451 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 12 Jul 2022 16:19:33 +0200 Subject: [PATCH 199/400] Refactor clustering tests. --- skfda/tests/test_clustering.py | 71 +++++++++++++++++++++------------- 1 file changed, 44 insertions(+), 27 deletions(-) diff --git a/skfda/tests/test_clustering.py b/skfda/tests/test_clustering.py index 66f2024f0..91b4b26aa 100644 --- a/skfda/tests/test_clustering.py +++ b/skfda/tests/test_clustering.py @@ -1,3 +1,4 @@ +"""Tests for clustering methods.""" import unittest import numpy as np @@ -6,15 +7,15 @@ from skfda.representation.grid import FDataGrid -class TestClustering(unittest.TestCase): +class TestKMeans(unittest.TestCase): + """Test the KMeans clustering method.""" - # def setUp(self): could be defined for set up before any test - - def test_kmeans_univariate(self) -> None: + def test_univariate(self) -> None: + """Test with univariate functional data.""" data_matrix = [ [1, 1, 2, 3, 2.5, 2], [0.5, 0.5, 1, 2, 1.5, 1], - [-1, -1, -0.5, 1, 1, 0.5], + [-1, -1, -0.5, 1, 1, 0.5], # noqa: WPS204 [-0.5, -0.5, -0.5, -1, -1, -1], ] grid_points = [0, 2, 4, 6, 8, 10] @@ -29,7 +30,7 @@ def test_kmeans_univariate(self) -> None: [2.98142397, 9.23534876], [0.68718427, 6.50960828], [3.31243449, 4.39222798], - [6.49679408, 0.0], + [6.49679408, 0], ]), ) np.testing.assert_array_equal( @@ -38,29 +39,40 @@ def test_kmeans_univariate(self) -> None: ) np.testing.assert_allclose( kmeans.transform(fd), - np.array([[2.98142397, 9.23534876], - [0.68718427, 6.50960828], - [3.31243449, 4.39222798], - [6.49679408, 0.]]), + np.array([ + [2.98142397, 9.23534876], + [0.68718427, 6.50960828], + [3.31243449, 4.39222798], + [6.49679408, 0], + ]), ) centers = FDataGrid( data_matrix=np.array([ - [0.16666667, 0.16666667, 0.83333333, 2., 1.66666667, 1.16666667], - [-0.5, -0.5, -0.5, -1., -1., -1.], + [ # noqa: WPS317 + 0.16666667, 0.16666667, 0.83333333, + 2.0, 1.66666667, 1.16666667, + ], + [-0.5, -0.5, -0.5, -1.0, -1.0, -1.0], ]), - grid_points=grid_points) + grid_points=grid_points, + ) np.testing.assert_array_almost_equal( kmeans.cluster_centers_.data_matrix, centers.data_matrix, ) np.testing.assert_allclose(kmeans.score(fd), np.array([-20.33333333])) - np.testing.assert_array_equal(kmeans.n_iter_, np.array([3.])) + np.testing.assert_array_equal(kmeans.n_iter_, np.array([3.0])) - def test_fuzzy_kmeans_univariate(self) -> None: + +class TestFuzzyCMeans(unittest.TestCase): + """Test the FuzzyCMeans clustering method.""" + + def test_univariate(self) -> None: + """Test with univariate functional data.""" data_matrix = [ [1, 1, 2, 3, 2.5, 2], [0.5, 0.5, 1, 2, 1.5, 1], - [-1, -1, -0.5, 1, 1, 0.5], + [-1, -1, -0.5, 1, 1, 0.5], # noqa: WPS204 [-0.5, -0.5, -0.5, -1, -1, -1], ] grid_points = [0, 2, 4, 6, 8, 10] @@ -69,25 +81,31 @@ def test_fuzzy_kmeans_univariate(self) -> None: fuzzy_kmeans.fit(fd) np.testing.assert_array_equal( fuzzy_kmeans.predict_proba(fd).round(3), - np.array([[0.965, 0.035], - [0.94, 0.06], - [0.227, 0.773], - [0.049, 0.951]]), + np.array([ + [0.965, 0.035], + [0.94, 0.06], + [0.227, 0.773], + [0.049, 0.951], + ]), ) np.testing.assert_allclose( fuzzy_kmeans.transform(fd).round(3), - np.array([[1.492, 7.879], - [1.294, 5.127], - [4.856, 2.633], - [7.775, 1.759]]), + np.array([ + [1.492, 7.879], + [1.294, 5.127], + [4.856, 2.633], + [7.775, 1.759], + ]), ) centers = np.array([ [0.707, 0.707, 1.455, 2.467, 1.981, 1.482], [-0.695, -0.695, -0.494, -0.197, -0.199, -0.398], ]) np.testing.assert_allclose( - fuzzy_kmeans.cluster_centers_.data_matrix[..., 0].round(3), - centers) + fuzzy_kmeans.cluster_centers_.data_matrix[..., 0], + centers, + atol=1e-3, + ) np.testing.assert_allclose( fuzzy_kmeans.score(fd), np.array([-12.025179]), @@ -96,5 +114,4 @@ def test_fuzzy_kmeans_univariate(self) -> None: if __name__ == '__main__': - print() unittest.main() From 27dbfd26481993c8fdce8752c317445823c497d5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 12 Jul 2022 16:30:03 +0200 Subject: [PATCH 200/400] Refactor depth tests. --- skfda/tests/test_depth.py | 39 +++++++++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/skfda/tests/test_depth.py b/skfda/tests/test_depth.py index fe746d792..438e8fe55 100644 --- a/skfda/tests/test_depth.py +++ b/skfda/tests/test_depth.py @@ -1,30 +1,45 @@ -import skfda -from skfda.exploratory.depth import IntegratedDepth, ModifiedBandDepth +"""Tests for depth measures.""" import unittest + import numpy as np +import skfda +from skfda.exploratory.depth import IntegratedDepth, ModifiedBandDepth + class TestsDepthSameCurves(unittest.TestCase): + """Test behavior when all curves in the distribution are the same.""" - def setUp(self): - data_matrix = [[1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4]] + def setUp(self) -> None: + """Define the dataset.""" + data_matrix = np.tile([1, 2, 3, 4], (5, 1)) self.fd = skfda.FDataGrid(data_matrix) - def test_integrated_equal(self): + def test_integrated_equal(self) -> None: + """ + Test the Fraiman-Muñiz depth. + Results should be equal to the minimum depth (0.5). + + """ depth = IntegratedDepth() np.testing.assert_almost_equal( - depth(self.fd), [0.5, 0.5, 0.5, 0.5, 0.5]) + depth(self.fd), + [0.5, 0.5, 0.5, 0.5, 0.5], + ) + + def test_modified_band_depth_equal(self) -> None: + """ + Test MBD. - def test_modified_band_depth_equal(self): + Results should be equal to the maximum depth (1). + """ depth = ModifiedBandDepth() np.testing.assert_almost_equal( - depth(self.fd), [1, 1, 1, 1, 1]) + depth(self.fd), + [1, 1, 1, 1, 1], + ) From ca52c31de73acf31c1fc23a3679a15ff3f86fe48 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 12 Jul 2022 16:53:23 +0200 Subject: [PATCH 201/400] Refactor elastic tests. --- setup.cfg | 2 +- skfda/tests/test_elastic.py | 35 ++++++++++++++++++++--------------- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/setup.cfg b/setup.cfg index 9c1345348..44899870a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -94,7 +94,7 @@ per-file-ignores = _real_datasets.py: WPS202 # Tests benefit from meaningless zeros, magic numbers and fixtures - test_*.py: WPS339, WPS432, WPS442, WPS446 + test_*.py: WPS339, WPS358, WPS432, WPS442, WPS446 # Examples are allowed to have imports in the middle, "commented code", call print and have magic numbers plot_*.py: E402, E800, WPS421, WPS432 diff --git a/skfda/tests/test_elastic.py b/skfda/tests/test_elastic.py index 048d9cde1..1aee58679 100644 --- a/skfda/tests/test_elastic.py +++ b/skfda/tests/test_elastic.py @@ -93,16 +93,16 @@ def test_from_srsf(self) -> None: data_matrix = [ [ # noqa: WPS317 - [0], [-0.23449228], [-0.83464009], + [0.0], [-0.23449228], [-0.83464009], [-1.38200046], [-1.55623723], [-1.38200046], - [-0.83464009], [-0.23449228], [0], + [-0.83464009], [-0.23449228], [0.0], ], ] np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) def test_from_srsf_with_output_points(self) -> None: - """Test from srsf.""" + """Test from srsf and explicit output points.""" # Checks SRSF conversion srsf_transformer = SRSF( initial_value=0, @@ -112,16 +112,16 @@ def test_from_srsf_with_output_points(self) -> None: data_matrix = [ [ # noqa: WPS317 - [0], [-0.23449228], [-0.83464009], + [0.0], [-0.23449228], [-0.83464009], [-1.38200046], [-1.55623723], [-1.38200046], - [-0.83464009], [-0.23449228], [0], + [-0.83464009], [-0.23449228], [0.0], ], ] np.testing.assert_almost_equal(data_matrix, srsf.data_matrix) def test_srsf_conversion(self) -> None: - """Converts to srsf and pull backs.""" + """Convert to srsf and pull back.""" srsf = SRSF() converted = srsf.fit_transform(self.unimodal_samples) @@ -180,7 +180,7 @@ def test_default_alignment(self) -> None: np.testing.assert_allclose(values, expected, atol=1e-4) def test_callable_alignment(self) -> None: - """Test alignment by default.""" + """Test callable template.""" # Should give same result than test_template_alignment reg = FisherRaoElasticRegistration(template=fisher_rao_karcher_mean) register = reg.fit_transform(self.unimodal_samples) @@ -243,11 +243,17 @@ def test_warping_mean(self) -> None: warping = make_random_warping(start=-1, random_state=0) mean = _fisher_rao_warping_mean(warping) values = mean([-1, -0.5, 0, 0.5, 1]) - expected = [[[-1], [-0.376241], [0.136193], [0.599291], [1]]] + expected = [[[-1.0], [-0.376241], [0.136193], [0.599291], [1.0]]] np.testing.assert_array_almost_equal(values, expected) def test_linear(self) -> None: - grid_points = [i for i in range(10)] + """ + Test alignment of two equal (linear) functions. + + In this case no alignment should take place. + + """ + grid_points = list(range(10)) data_matrix = np.array([grid_points, grid_points]) fd = FDataGrid( data_matrix=data_matrix, @@ -279,11 +285,11 @@ def test_fisher_rao(self) -> None: def test_fisher_rao_invariance(self) -> None: """Test invariance of fisher rao metric: d(f,g)= d(foh, goh).""" t = np.linspace(0, np.pi, 1000) - id = FDataGrid([t], t) - cos = np.cos(id) - sin = np.sin(id) - gamma = normalize_warping(np.sqrt(id), (0, np.pi)) - gamma2 = normalize_warping(np.square(id), (0, np.pi)) + identity = FDataGrid([t], t) + cos = np.cos(identity) + sin = np.sin(identity) + gamma = normalize_warping(np.sqrt(identity), (0, np.pi)) + gamma2 = normalize_warping(np.square(identity), (0, np.pi)) distance_original = fisher_rao_distance(cos, sin) @@ -342,5 +348,4 @@ def test_fisher_rao_warping_distance(self) -> None: if __name__ == '__main__': - print() unittest.main() From 006268e3aa1ac9396c2be62f7834ee1154d9aa76 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 Jul 2022 11:48:54 +0200 Subject: [PATCH 202/400] Refactor extrapolation tests. --- skfda/tests/test_extrapolation.py | 240 +++++++++++++++++------------- 1 file changed, 135 insertions(+), 105 deletions(-) diff --git a/skfda/tests/test_extrapolation.py b/skfda/tests/test_extrapolation.py index 56281e702..d3ccf54d4 100644 --- a/skfda/tests/test_extrapolation.py +++ b/skfda/tests/test_extrapolation.py @@ -1,179 +1,209 @@ -"""Test to check the extrapolation module""" +"""Test to check the extrapolation module.""" -from skfda import FDataGrid, FDataBasis -from skfda.datasets import make_sinusoidal_process -from skfda.representation.basis import Fourier -from skfda.representation.extrapolation import ( - PeriodicExtrapolation, BoundaryExtrapolation, ExceptionExtrapolation, - FillExtrapolation) import unittest import numpy as np +from skfda import FDataBasis, FDataGrid +from skfda.datasets import make_sinusoidal_process +from skfda.representation.basis import Fourier +from skfda.representation.extrapolation import ( + BoundaryExtrapolation, + ExceptionExtrapolation, + FillExtrapolation, + PeriodicExtrapolation, +) + -class TestBasis(unittest.TestCase): +class TestExtrapolation(unittest.TestCase): + """Extrapolation tests.""" - def setUp(self): + def setUp(self) -> None: + """Create example data.""" self.grid = make_sinusoidal_process(n_samples=2, random_state=0) self.basis = self.grid.to_basis(Fourier()) self.dummy_data = [[1, 2, 3], [2, 3, 4]] - def test_constructor_FDataBasis_setting(self): + def test_constructor_fdatabasis_setting(self) -> None: + """Check argument normalization in constructor for FDataBasis.""" coeff = self.dummy_data basis = Fourier(n_basis=3) a = FDataBasis(basis, coeff) - np.testing.assert_equal(a.extrapolation, None) + self.assertEqual(a.extrapolation, None) - a = FDataBasis(basis, coeff, extrapolation=PeriodicExtrapolation()) - np.testing.assert_equal(a.extrapolation, PeriodicExtrapolation()) + a = FDataBasis(basis, coeff, extrapolation="periodic") + self.assertEqual(a.extrapolation, PeriodicExtrapolation()) - np.testing.assert_equal( - a.extrapolation == BoundaryExtrapolation(), False) + self.assertNotEqual(a.extrapolation, BoundaryExtrapolation()) a = FDataBasis(basis, coeff, extrapolation=BoundaryExtrapolation()) - np.testing.assert_equal(a.extrapolation, BoundaryExtrapolation()) + self.assertEqual(a.extrapolation, BoundaryExtrapolation()) - a = FDataBasis(basis, coeff, extrapolation=ExceptionExtrapolation()) - np.testing.assert_equal(a.extrapolation, ExceptionExtrapolation()) + a = FDataBasis(basis, coeff, extrapolation="exception") + self.assertEqual(a.extrapolation, ExceptionExtrapolation()) a = FDataBasis(basis, coeff, extrapolation=FillExtrapolation(0)) - np.testing.assert_equal(a.extrapolation, FillExtrapolation(0)) - - np.testing.assert_equal(a.extrapolation == FillExtrapolation(1), False) - - def test_FDataBasis_setting(self): - coeff = self.dummy_data - basis = Fourier(n_basis=3) - a = FDataBasis(basis, coeff) - - a.extrapolation = "periodic" - np.testing.assert_equal(a.extrapolation, PeriodicExtrapolation()) + self.assertEqual(a.extrapolation, FillExtrapolation(0)) + self.assertNotEqual(a.extrapolation, FillExtrapolation(1)) - a.extrapolation = "bounds" - np.testing.assert_equal(a.extrapolation, BoundaryExtrapolation()) - - a.extrapolation = "exception" - np.testing.assert_equal(a.extrapolation, ExceptionExtrapolation()) - - a.extrapolation = "zeros" - np.testing.assert_equal(a.extrapolation, FillExtrapolation(0)) - - a.extrapolation = "nan" - np.testing.assert_equal(a.extrapolation, FillExtrapolation(np.nan)) - - def test_constructor_FDataGrid_setting(self): + def test_constructor_fdatagrid_setting(self) -> None: + """Check argument normalization in constructor for FDataGrid.""" data = self.dummy_data a = FDataGrid(data) - np.testing.assert_equal(a.extrapolation, None) + self.assertEqual(a.extrapolation, None) - a = FDataGrid(data, extrapolation=PeriodicExtrapolation()) - np.testing.assert_equal(a.extrapolation, PeriodicExtrapolation()) + a = FDataGrid(data, extrapolation="periodic") + self.assertEqual(a.extrapolation, PeriodicExtrapolation()) a = FDataGrid(data, extrapolation=BoundaryExtrapolation()) - np.testing.assert_equal(a.extrapolation, BoundaryExtrapolation()) + self.assertEqual(a.extrapolation, BoundaryExtrapolation()) - np.testing.assert_equal( - a.extrapolation == ExceptionExtrapolation(), False) + self.assertNotEqual(a.extrapolation, ExceptionExtrapolation()) - a = FDataGrid(data, extrapolation=ExceptionExtrapolation()) - np.testing.assert_equal(a.extrapolation, ExceptionExtrapolation()) + a = FDataGrid(data, extrapolation="exception") + self.assertEqual(a.extrapolation, ExceptionExtrapolation()) a = FDataGrid(data, extrapolation=FillExtrapolation(0)) - np.testing.assert_equal(a.extrapolation, FillExtrapolation(0)) - np.testing.assert_equal(a.extrapolation == FillExtrapolation(1), False) + self.assertEqual(a.extrapolation, FillExtrapolation(0)) + self.assertNotEqual(a.extrapolation, FillExtrapolation(1)) - def test_FDataGrid_setting(self): + def test_setting(self) -> None: + """Check argument in setter.""" data = self.dummy_data a = FDataGrid(data) a.extrapolation = PeriodicExtrapolation() - np.testing.assert_equal(a.extrapolation, PeriodicExtrapolation()) + self.assertEqual(a.extrapolation, PeriodicExtrapolation()) a.extrapolation = "bounds" - np.testing.assert_equal(a.extrapolation, BoundaryExtrapolation()) + self.assertEqual(a.extrapolation, BoundaryExtrapolation()) - a.extrapolation = "exception" - np.testing.assert_equal(a.extrapolation, ExceptionExtrapolation()) + a.extrapolation = ExceptionExtrapolation() + self.assertEqual(a.extrapolation, ExceptionExtrapolation()) a.extrapolation = "zeros" - np.testing.assert_equal(a.extrapolation, FillExtrapolation(0)) + self.assertEqual(a.extrapolation, FillExtrapolation(0)) - np.testing.assert_equal(a.extrapolation == FillExtrapolation(1), False) + self.assertNotEqual(a.extrapolation, FillExtrapolation(1)) - def test_periodic(self): + def test_periodic(self) -> None: + """Test periodic extrapolation.""" self.grid.extrapolation = PeriodicExtrapolation() - data = self.grid([-.5, 0, 1.5]).round(3) + data = self.grid([-0.5, 0, 1.5]) - np.testing.assert_almost_equal(data[..., 0], - [[-0.724, 0.976, -0.724], - [-1.086, 0.759, -1.086]]) + np.testing.assert_allclose( + data[..., 0], + [ + [-0.723516, 0.976450, -0.723516], + [-1.085999, 0.759385, -1.085999], + ], + rtol=1e-6, + ) self.basis.extrapolation = "periodic" - data = self.basis([-.5, 0, 1.5]).round(3) - - np.testing.assert_almost_equal(data[..., 0], - [[-0.69, 0.692, -0.69], - [-1.021, 1.056, -1.021]]) - - def test_boundary(self): + data = self.basis([-0.5, 0, 1.5]) + + np.testing.assert_allclose( + data[..., 0], + [ + [-0.690170, 0.691735, -0.690170], + [-1.020821, 1.056383, -1.020821], + ], + rtol=1e-6, + ) + + def test_boundary(self) -> None: + """Test boundary-copying extrapolation.""" self.grid.extrapolation = "bounds" - data = self.grid([-.5, 0, 1.5]).round(3) + data = self.grid([-0.5, 0, 1.5]) - np.testing.assert_almost_equal(data[..., 0], - [[0.976, 0.976, 0.797], - [0.759, 0.759, 1.125]]) + np.testing.assert_allclose( + data[..., 0], + [ + [0.976450, 0.976450, 0.796817], + [0.759385, 0.759385, 1.125063], + ], + rtol=1e-6, + ) self.basis.extrapolation = "bounds" - data = self.basis([-.5, 0, 1.5]).round(3) - - np.testing.assert_almost_equal(data[..., 0], - [[0.692, 0.692, 0.692], - [1.056, 1.056, 1.056]]) - - def test_exception(self): + data = self.basis([-0.5, 0, 1.5]) + + np.testing.assert_allclose( + data[..., 0], + [ + [0.691735, 0.691735, 0.691735], + [1.056383, 1.056383, 1.056383], + ], + rtol=1e-6, + ) + + def test_exception(self) -> None: + """Test no extrapolation (exception).""" self.grid.extrapolation = "exception" with np.testing.assert_raises(ValueError): - self.grid([-.5, 0, 1.5]) + self.grid([-0.5, 0, 1.5]) self.basis.extrapolation = "exception" with np.testing.assert_raises(ValueError): - self.basis([-.5, 0, 1.5]) + self.basis([-0.5, 0, 1.5]) - def test_zeros(self): + def test_zeros(self) -> None: + """Test zeros extrapolation.""" self.grid.extrapolation = "zeros" - data = self.grid([-.5, 0, 1.5]).round(3) + data = self.grid([-0.5, 0, 1.5]) - np.testing.assert_almost_equal(data[..., 0], - [[0., 0.976, 0.], - [0., 0.759, 0.]]) + np.testing.assert_allclose( + data[..., 0], + [ + [0, 0.976450, 0], + [0, 0.759385, 0], + ], + rtol=1e-6, + ) self.basis.extrapolation = "zeros" - data = self.basis([-.5, 0, 1.5]).round(3) - - np.testing.assert_almost_equal(data[..., 0], - [[0, 0.692, 0], - [0, 1.056, 0]]) - - def test_nan(self): + data = self.basis([-0.5, 0, 1.5]) + + np.testing.assert_allclose( + data[..., 0], + [ + [0, 0.691735, 0], + [0, 1.056383, 0], + ], + rtol=1e-6, + ) + + def test_nan(self) -> None: + """Test nan extrapolation.""" self.grid.extrapolation = "nan" - data = self.grid([-.5, 0, 1.5]).round(3) + data = self.grid([-0.5, 0, 1.5]) - np.testing.assert_almost_equal(data[..., 0], - [[np.nan, 0.976, np.nan], - [np.nan, 0.759, np.nan]]) + np.testing.assert_allclose( + data[..., 0], + [ + [np.nan, 0.976450, np.nan], + [np.nan, 0.759385, np.nan], + ], + rtol=1e-6, + ) self.basis.extrapolation = "nan" - data = self.basis([-.5, 0, 1.5]).round(3) + data = self.basis([-0.5, 0, 1.5]) - np.testing.assert_almost_equal(data[..., 0], - [[np.nan, 0.692, np.nan], - [np.nan, 1.056, np.nan]]) + np.testing.assert_allclose( + data[..., 0], + [ + [np.nan, 0.691735, np.nan], + [np.nan, 1.056383, np.nan], + ], + rtol=1e-6, + ) if __name__ == '__main__': - print() unittest.main() From 6ebfeac8c5549915bd040ef5196707334aeb79e5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 Jul 2022 13:31:53 +0200 Subject: [PATCH 203/400] Fix EvaluatorTransformer typing. --- .../_evaluation_trasformer.py | 56 +++++++++++-------- skfda/representation/_functional_data.py | 12 ++++ skfda/tests/test_fda_feature_union.py | 17 ++++-- 3 files changed, 56 insertions(+), 29 deletions(-) diff --git a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py index 93f6102cc..6bb35f3d9 100644 --- a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py +++ b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py @@ -1,27 +1,27 @@ """Evaluation Transformer Module.""" from __future__ import annotations -from typing import Optional, TypeVar, Union, overload +from typing import TypeVar, overload -import numpy as np -from sklearn.base import BaseEstimator from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal -from ..._utils import TransformerMixin +from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin from ...representation._functional_data import FData -from ...representation._typing import ArrayLike, GridPointsLike, NDArrayInt +from ...representation._typing import ArrayLike, GridPointsLike, NDArrayFloat from ...representation.extrapolation import ExtrapolationLike from ...representation.grid import FDataGrid -Input = TypeVar("Input") -Output = TypeVar("Output") -Target = TypeVar("Target", bound=NDArrayInt) +Input = TypeVar("Input", bound=FData) +SelfType = TypeVar( + "SelfType", + bound="EvaluationTransformer", # type: ignore[type-arg] +) class EvaluationTransformer( - BaseEstimator, # type:ignore - TransformerMixin[Input, Output, Target], + BaseEstimator, + InductiveTransformerMixin[Input, NDArrayFloat, object], ): r""" Transformer returning the evaluations of FData objects as a matrix. @@ -101,12 +101,21 @@ class EvaluationTransformer( """ + @overload + def __init__( + self: EvaluationTransformer[FDataGrid], + eval_points: None = None, + *, + extrapolation: ExtrapolationLike | None = None, + ) -> None: + pass + @overload def __init__( self, eval_points: ArrayLike, *, - extrapolation: Optional[ExtrapolationLike] = None, + extrapolation: ExtrapolationLike | None = None, grid: Literal[False] = False, ) -> None: pass @@ -116,27 +125,27 @@ def __init__( self, eval_points: GridPointsLike, *, - extrapolation: Optional[ExtrapolationLike] = None, + extrapolation: ExtrapolationLike | None = None, grid: Literal[True], ) -> None: pass def __init__( self, - eval_points: Union[ArrayLike, GridPointsLike, None] = None, + eval_points: ArrayLike | GridPointsLike | None = None, *, - extrapolation: Optional[ExtrapolationLike] = None, + extrapolation: ExtrapolationLike | None = None, grid: bool = False, - ): + ) -> None: self.eval_points = eval_points self.extrapolation = extrapolation self.grid = grid - def fit( - self, + def fit( # noqa: D102 + self: SelfType, X: FData, - y: None = None, - ) -> EvaluationTransformer: + y: object = None, + ) -> SelfType: if self.eval_points is None and not isinstance(X, FDataGrid): raise ValueError( "If no eval_points are passed, the functions " @@ -147,17 +156,18 @@ def fit( return self - def transform( + def transform( # noqa: D102 self, X: FData, - y: None = None, - ) -> np.ndarray: + y: object = None, + ) -> NDArrayFloat: check_is_fitted(self, '_is_fitted') if self.eval_points is None: + assert isinstance(X, FDataGrid) evaluation = X.data_matrix.copy() else: - evaluation = X( # type: ignore + evaluation = X( self.eval_points, extrapolation=self.extrapolation, grid=self.grid, diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index d8e08fcdf..74db54eba 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -604,6 +604,18 @@ def __call__( ) -> NDArrayFloat: pass + @overload + def __call__( + self, + eval_points: EvalPointsType, + *, + derivative: int = 0, + extrapolation: Optional[ExtrapolationLike] = None, + grid: bool = False, + aligned: bool = True, + ) -> NDArrayFloat: + pass + def __call__( self, eval_points: EvalPointsType, diff --git a/skfda/tests/test_fda_feature_union.py b/skfda/tests/test_fda_feature_union.py index af94ab04d..cfe5ab9f0 100644 --- a/skfda/tests/test_fda_feature_union.py +++ b/skfda/tests/test_fda_feature_union.py @@ -23,21 +23,26 @@ def setUp(self) -> None: self.X = fetch_growth(return_X_y=True)[0] def test_incompatible_fdatagrid_output(self) -> None: - """Check that the transformer returns a fdatagrid.""" + """Check that array_output fails for FData features.""" u = FDAFeatureUnion( [("eval", EvaluationTransformer(None)), ("srsf", SRSF())], array_output=True, ) - self.assertRaises(TypeError, u.fit_transform, self.X) + + with self.assertRaises(TypeError): + u.fit_transform(self.X) def test_correct_transformation_concat(self) -> None: """Check that the transformation is done correctly.""" u = FDAFeatureUnion( [ ("srsf1", SRSF()), - ("smooth", KernelSmoother( - kernel_estimator=NadarayaWatsonHatMatrix(), - )), # type: ignore + ( + "smooth", + KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(), + ), + ), ], ) created_frame = u.fit_transform(self.X) @@ -45,7 +50,7 @@ def test_correct_transformation_concat(self) -> None: t1 = SRSF().fit_transform(self.X) t2 = KernelSmoother( kernel_estimator=NadarayaWatsonHatMatrix(), - ).fit_transform(self.X) # type: ignore + ).fit_transform(self.X) true_frame = pd.concat( [ From cf4350c71c05eeea0ebd6278acf5f68f832ac189 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 13 Jul 2022 15:24:16 +0200 Subject: [PATCH 204/400] Refactor some tests. --- skfda/tests/test_fdata_boxplot.py | 47 ++-- skfda/tests/test_fpca.py | 224 +++++++++++--------- skfda/tests/test_functional_transformers.py | 11 +- 3 files changed, 158 insertions(+), 124 deletions(-) diff --git a/skfda/tests/test_fdata_boxplot.py b/skfda/tests/test_fdata_boxplot.py index ee39bd7cf..650414008 100644 --- a/skfda/tests/test_fdata_boxplot.py +++ b/skfda/tests/test_fdata_boxplot.py @@ -1,44 +1,57 @@ -from skfda import FDataGrid -from skfda.exploratory.depth import IntegratedDepth -from skfda.exploratory.visualization import Boxplot, SurfaceBoxplot +"""Tests for boxplot functionality.""" import unittest import numpy as np +from skfda import FDataGrid +from skfda.exploratory.depth import IntegratedDepth +from skfda.exploratory.visualization import Boxplot + class TestBoxplot(unittest.TestCase): + """Test Boxplot.""" - def test_fdboxplot_univariate(self): - data_matrix = [[1, 1, 2, 3, 2.5, 2], - [0.5, 0.5, 1, 2, 1.5, 1], - [-1, -1, -0.5, 1, 1, 0.5], - [-0.5, -0.5, -0.5, -1, -1, -1]] + def test_univariate(self) -> None: + """Test univariate case.""" + data_matrix = [ + [1, 1, 2, 3, 2.5, 2], + [0.5, 0.5, 1, 2, 1.5, 1], + [-1, -1, -0.5, 1, 1, 0.5], # noqa: WPS204 + [-0.5, -0.5, -0.5, -1, -1, -1], + ] grid_points = [0, 2, 4, 6, 8, 10] fd = FDataGrid(data_matrix, grid_points) fdataBoxplot = Boxplot(fd, depth_method=IntegratedDepth()) np.testing.assert_array_equal( fdataBoxplot.median.ravel(), - np.array([-1., -1., -0.5, 1., 1., 0.5])) + np.array([-1, -1, -0.5, 1, 1, 0.5]), + ) np.testing.assert_array_equal( fdataBoxplot.central_envelope[0].ravel(), - np.array([-1., -1., -0.5, -1., -1., -1.])) + np.array([-1, -1, -0.5, -1, -1, -1]), + ) np.testing.assert_array_equal( fdataBoxplot.central_envelope[1].ravel(), - np.array([-0.5, -0.5, -0.5, 1., 1., 0.5])) + np.array([-0.5, -0.5, -0.5, 1, 1, 0.5]), + ) np.testing.assert_array_equal( fdataBoxplot.non_outlying_envelope[0].ravel(), - np.array([-1., -1., -0.5, -1., -1., -1.])) + np.array([-1, -1, -0.5, -1, -1, -1]), + ) np.testing.assert_array_equal( fdataBoxplot.non_outlying_envelope[1].ravel(), - np.array([-0.5, -0.5, -0.5, 1., 1., 0.5])) + np.array([-0.5, -0.5, -0.5, 1, 1, 0.5]), + ) self.assertEqual(len(fdataBoxplot.envelopes), 1) np.testing.assert_array_equal( fdataBoxplot.envelopes[0], - fdataBoxplot.central_envelope) - np.testing.assert_array_equal(fdataBoxplot.outliers, - np.array([True, True, False, False])) + fdataBoxplot.central_envelope, + ) + np.testing.assert_array_equal( + fdataBoxplot.outliers, + np.array([True, True, False, False]), + ) if __name__ == '__main__': - print() unittest.main() diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index 6e79a2116..e315c42ba 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -4,7 +4,6 @@ import numpy as np from sklearn.decomposition import PCA -import skfda from skfda import FDataBasis, FDataGrid from skfda.datasets import fetch_weather from skfda.misc.operators import LinearDifferentialOperator @@ -176,21 +175,21 @@ def test_basis_fpca_noregularization_fit_result(self) -> None: Replication code: - # library(fda) - # - # data("CanadianWeather") - # temp = CanadianWeather$dailyAv[,,1] - # - # basis = create.fourier.basis(c(0,365), 9) - # fdata_temp = Data2fd(1:365, temp, basis) - # fpca = pca.fd(fdata_temp, nharm = 3) - # - # paste( - # round(fpca$harmonics$coefs[,1], 8), - # collapse=", " - # ) # first component, others are analogous - # - # fpca$varprop # explained variance ratio + ... library(fda) + ... + ... data("CanadianWeather") + ... temp = CanadianWeather$dailyAv[,,1] + ... + ... basis = create.fourier.basis(c(0,365), 9) + ... fdata_temp = Data2fd(1:365, temp, basis) + ... fpca = pca.fd(fdata_temp, nharm = 3) + ... + ... paste( + ... round(fpca$harmonics$coefs[,1], 8), + ... collapse=", " + ... ) # first component, others are analogous + ... + ... fpca$varprop # explained variance ratio """ n_basis = 9 @@ -274,24 +273,24 @@ def test_grid_fpca_fit_result(self) -> None: Replication code: - # library(fda) - # library(fda.usc) - # - # data("CanadianWeather") - # temp = CanadianWeather$dailyAv[,,1] - # - # fdata_temp = fdata(t(temp)) - # fpca = fdata2pc(fdata_temp, ncomp = 1) - # - # paste( - # round(fpca$rotation$data[1,], 8), - # collapse=", " - # ) # components - # fpca$d[1] # singular value - # paste( - # round(fpca$x[,1], 8), - # collapse=", " - # ) # transform + ... library(fda) + ... library(fda.usc) + ... + ... data("CanadianWeather") + ... temp = CanadianWeather$dailyAv[,,1] + ... + ... fdata_temp = fdata(t(temp)) + ... fpca = fdata2pc(fdata_temp, ncomp = 1) + ... + ... paste( + ... round(fpca$rotation$data[1,], 8), + ... collapse=", " + ... ) # components + ... fpca$d[1] # singular value + ... paste( + ... round(fpca$x[,1], 8), + ... collapse=", " + ... ) # transform """ n_components = 1 @@ -549,47 +548,49 @@ def draw_one_random_fun( coefficients=coef, ) + def _test_vs_dim_grid( + self, + random_state: np.random.RandomState, + n_samples: int, + n_grid: int, + base_fun: FDataBasis, + ) -> None: + """Test function w.r.t n_samples, n_grid.""" + # Random offsetting base_fun and form dataset fd_random + offset = random_state.uniform(-5, 5, size=n_samples) + + fd_random = FDataBasis( + basis=base_fun.basis, + coefficients=base_fun.coefficients * offset[:, np.newaxis], + ).to_grid(np.linspace(0, 1, n_grid)) + + # Take the allowed maximum number of components + # In almost high dimension: n_components=n_samples-1 < n_samples + # In low dimension: n_components=n_grid << n_samples + fpca = FPCA( + n_components=min(n_samples - 1, n_grid), + ) + + # Project the non-random dataset on FPCs + pc_scores_fd_random_all_equal = fpca.fit_transform( + fd_random, + ) + + # Project the pc scores back to the input functional space + fd_random_hat = fpca.inverse_transform( + pc_scores_fd_random_all_equal, + ) + + # Compare fitting data to the reconstructed ones + np.testing.assert_allclose( + fd_random.data_matrix, + fd_random_hat.data_matrix, + ) + def test_grid_fpca_inverse_transform(self) -> None: """Compare the reconstructions.data_matrix to fitting data.""" random_state = np.random.RandomState(seed=42) - def test_vs_dim( - n_samples: int, - n_grid: int, - base_fun: FDataBasis, - ) -> None: - """Test function w.r.t n_samples, n_grid.""" - # Random offsetting base_fun and form dataset fd_random - offset = random_state.uniform(-5, 5, size=n_samples) - - fd_random = FDataBasis( - basis=base_fun.basis, - coefficients=base_fun.coefficients * offset[:, np.newaxis], - ).to_grid(np.linspace(0, 1, n_grid)) - - # Take the allowed maximum number of components - # In almost high dimension: n_components=n_samples-1 < n_samples - # In low dimension: n_components=n_grid << n_samples - fpca = FPCA( - n_components=min(n_samples - 1, n_grid), - ) - - # Project the non-random dataset on FPCs - pc_scores_fd_random_all_equal = fpca.fit_transform( - fd_random, - ) - - # Project the pc scores back to the input functional space - fd_random_hat = fpca.inverse_transform( - pc_scores_fd_random_all_equal, - ) - - # Compare fitting data to the reconstructed ones - np.testing.assert_allclose( - fd_random.data_matrix, - fd_random_hat.data_matrix, - ) - # Low dimensional case (n_samples>n_grid) n_samples = 1000 n_grid = 100 @@ -599,7 +600,12 @@ def test_vs_dim( order=3, ) true_fun = self.draw_one_random_fun(bsp, random_state) - test_vs_dim(n_samples=n_samples, n_grid=n_grid, base_fun=true_fun) + self._test_vs_dim_grid( + random_state=random_state, + n_samples=n_samples, + n_grid=n_grid, + base_fun=true_fun, + ) # (almost) High dimensional case (n_samples None: - """Compare the coef reconstructions to fitting data.""" - random_state = np.random.RandomState(seed=42) + self._test_vs_dim_grid( + random_state=random_state, + n_samples=n_samples, + n_grid=n_grid, + base_fun=true_fun, + ) - def test_vs_dim(n_samples: int, base_fun: FDataBasis) -> None: - """Test function w.t.t n_samples and basis.""" - # Random offsetting base_fun and form dataset fd_random - offset = random_state.uniform(-5, 5, size=n_samples) + def _test_vs_dim_basis( + self, + random_state: np.random.RandomState, + n_samples: int, + base_fun: FDataBasis, + ) -> None: + """Test function w.t.t n_samples and basis.""" + # Random offsetting base_fun and form dataset fd_random + offset = random_state.uniform(-5, 5, size=n_samples) + + fd_random = FDataBasis( + basis=base_fun.basis, + coefficients=base_fun.coefficients * offset[:, np.newaxis], + ) - fd_random = FDataBasis( - basis=base_fun.basis, - coefficients=base_fun.coefficients * offset[:, np.newaxis], - ) + # Take the allowed maximum number of components + # In almost high dimension: n_components=n_samples-1 < n_samples + # In low dimension: n_components=n_basis< None: + """Compare the coef reconstructions to fitting data.""" + random_state = np.random.RandomState(seed=42) # Low dimensional case: n_basis< None: order=3, ) true_fun = self.draw_one_random_fun(bsp, random_state) - test_vs_dim(n_samples=n_samples, base_fun=true_fun) + self._test_vs_dim_basis( + random_state=random_state, + n_samples=n_samples, + base_fun=true_fun, + ) # Case n_samples None: order=3, ) true_fun = self.draw_one_random_fun(bsp, random_state) - test_vs_dim(n_samples=n_samples, base_fun=true_fun) + self._test_vs_dim_basis( + random_state=random_state, + n_samples=n_samples, + base_fun=true_fun, + ) if __name__ == '__main__': diff --git a/skfda/tests/test_functional_transformers.py b/skfda/tests/test_functional_transformers.py index c212152ef..bf9e32434 100644 --- a/skfda/tests/test_functional_transformers.py +++ b/skfda/tests/test_functional_transformers.py @@ -9,20 +9,17 @@ from skfda.exploratory.stats import unconditional_expected_value -class TestUncondExpectedVals(unittest.TestCase): +class TestUnconditionalExpectedValues(unittest.TestCase): """Tests for unconditional expected values method.""" def test_transform(self) -> None: - """Check the data transformation is done correctly.""" + """Compare results with grid and basis representations.""" X = fetch_growth(return_X_y=True)[0] - def f(x: np.ndarray) -> np.ndarray: # noqa: WPS430 - return np.log(x) - - data_grid = unconditional_expected_value(X[:5], f) + data_grid = unconditional_expected_value(X[:5], np.log) data_basis = unconditional_expected_value( X[:5].to_basis(basis.BSpline(n_basis=7)), - f, + np.log, ) np.testing.assert_allclose(data_basis, data_grid, rtol=1e-3) From a415f96c190421d12bd2a36ef0453d2fd20afa6d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 14 Jul 2022 11:35:01 +0200 Subject: [PATCH 205/400] Refactor grid tests. --- skfda/representation/grid.py | 7 +- skfda/tests/test_grid.py | 347 ++++++++++++++++++++++------------- 2 files changed, 225 insertions(+), 129 deletions(-) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 1d9598121..66be7f207 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -1529,7 +1529,10 @@ def __init__(self, fdatagrid: T) -> None: """Create an iterator through the image coordinates.""" self._fdatagrid = fdatagrid - def __getitem__(self, key: Union[int, slice]) -> T: + def __getitem__( + self, + key: Union[int, slice, NDArrayInt, NDArrayBool], + ) -> T: """Get a specific coordinate.""" s_key = key if isinstance(s_key, int): @@ -1539,7 +1542,7 @@ def __getitem__(self, key: Union[int, slice]) -> T: return self._fdatagrid.copy( data_matrix=self._fdatagrid.data_matrix[..., key], - coordinate_names=coordinate_names, + coordinate_names=tuple(coordinate_names), ) def __len__(self) -> int: diff --git a/skfda/tests/test_grid.py b/skfda/tests/test_grid.py index a0daa09e4..749e8700a 100644 --- a/skfda/tests/test_grid.py +++ b/skfda/tests/test_grid.py @@ -1,137 +1,190 @@ -from skfda import FDataGrid, concatenate -from skfda.exploratory import stats +"""Test FDataGrid behaviour.""" import unittest -from mpl_toolkits.mplot3d import axes3d +import numpy as np import scipy.stats.mstats +from mpl_toolkits.mplot3d import axes3d -import numpy as np +from skfda import FDataGrid, concatenate +from skfda.exploratory import stats class TestFDataGrid(unittest.TestCase): + """Test the FDataGrid representation.""" - # def setUp(self): could be defined for set up before any test - - def test_init(self): + def test_init(self) -> None: + """Test creation.""" fd = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) np.testing.assert_array_equal( fd.data_matrix[..., 0], - np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]])) + np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]), + ) np.testing.assert_array_equal(fd.sample_range, [(0, 1)]) np.testing.assert_array_equal( - fd.grid_points, np.array([[0., 0.25, 0.5, 0.75, 1.]])) + fd.grid_points, np.array([[0, 0.25, 0.5, 0.75, 1]]), + ) - def test_copy_equals(self): + def test_copy_equals(self) -> None: + """Test that copies compare equals.""" fd = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) self.assertTrue(fd.equals(fd.copy())) - def test_mean(self): + def test_mean(self) -> None: + """Test aritmetic mean.""" fd = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) mean = stats.mean(fd) np.testing.assert_array_equal( mean.data_matrix[0, ..., 0], - np.array([1.5, 2.5, 3.5, 4.5, 5.5])) + np.array([1.5, 2.5, 3.5, 4.5, 5.5]), + ) np.testing.assert_array_equal(fd.sample_range, [(0, 1)]) np.testing.assert_array_equal( fd.grid_points, - np.array([[0., 0.25, 0.5, 0.75, 1.]])) + np.array([[0, 0.25, 0.5, 0.75, 1]]), + ) - def test_gmean(self): + def test_gmean(self) -> None: + """Test geometric mean.""" fd = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) mean = stats.gmean(fd) np.testing.assert_array_equal( mean.data_matrix[0, ..., 0], scipy.stats.mstats.gmean( - np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]))) + np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]), + ), + ) np.testing.assert_array_equal(fd.sample_range, [(0, 1)]) np.testing.assert_array_equal( fd.grid_points, - np.array([[0., 0.25, 0.5, 0.75, 1.]])) + np.array([[0, 0.25, 0.5, 0.75, 1]]), + ) - def test_slice(self): + def test_slice(self) -> None: + """Test slicing behaviour.""" t = (5, 3) fd = FDataGrid(data_matrix=np.ones(t)) fd = fd[1:3] np.testing.assert_array_equal( fd.data_matrix[..., 0], - np.array([[1, 1, 1], [1, 1, 1]])) + np.array([[1, 1, 1], [1, 1, 1]]), + ) + + def test_concatenate(self) -> None: + """ + Test concatenation. - def test_concatenate(self): + Ensure that the original argument and coordinate names are kept. + + """ fd1 = FDataGrid([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6]]) fd2 = FDataGrid([[3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) - fd1.argument_names = ["x"] - fd1.coordinate_names = ["y"] + fd1.argument_names = ("x",) + fd1.coordinate_names = ("y",) fd = fd1.concatenate(fd2) - np.testing.assert_equal(fd.n_samples, 4) - np.testing.assert_equal(fd.dim_codomain, 1) - np.testing.assert_equal(fd.dim_domain, 1) - np.testing.assert_array_equal(fd.data_matrix[..., 0], - [[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], - [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) - np.testing.assert_array_equal(fd1.argument_names, fd.argument_names) + self.assertEqual(fd.n_samples, 4) + self.assertEqual(fd.dim_codomain, 1) + self.assertEqual(fd.dim_domain, 1) np.testing.assert_array_equal( - fd1.coordinate_names, fd.coordinate_names) - - def test_concatenate_coordinates(self): + fd.data_matrix[..., 0], + [ + [1, 2, 3, 4, 5], + [2, 3, 4, 5, 6], + [3, 4, 5, 6, 7], + [4, 5, 6, 7, 8], + ], + ) + self.assertEqual( + fd1.argument_names, + fd.argument_names, + ) + self.assertEqual( + fd1.coordinate_names, + fd.coordinate_names, + ) + + def test_concatenate_coordinates(self) -> None: + """ + Test concatenation as coordinates. + + Ensure that the coordinate names are concatenated. + + """ fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) fd2 = FDataGrid([[3, 4, 5, 6], [4, 5, 6, 7]]) - fd1.argument_names = ["x"] - fd1.coordinate_names = ["y"] - fd2.argument_names = ["w"] - fd2.coordinate_names = ["t"] + fd1.argument_names = ("x",) + fd1.coordinate_names = ("y",) + fd2.argument_names = ("w",) + fd2.coordinate_names = ("t",) fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_equal(fd.n_samples, 2) - np.testing.assert_equal(fd.dim_codomain, 2) - np.testing.assert_equal(fd.dim_domain, 1) + self.assertEqual(fd.n_samples, 2) + self.assertEqual(fd.dim_codomain, 2) + self.assertEqual(fd.dim_domain, 1) - np.testing.assert_array_equal(fd.data_matrix, - [[[1, 3], [2, 4], [3, 5], [4, 6]], - [[2, 4], [3, 5], [4, 6], [5, 7]]]) + np.testing.assert_array_equal( + fd.data_matrix, + [ + [[1, 3], [2, 4], [3, 5], [4, 6]], + [[2, 4], [3, 5], [4, 6], [5, 7]], + ], + ) # Testing labels - np.testing.assert_array_equal(("y", "t"), fd.coordinate_names) + self.assertEqual(("y", "t"), fd.coordinate_names) fd2.coordinate_names = None fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_array_equal(("y", None), fd.coordinate_names) + self.assertEqual(("y", None), fd.coordinate_names) fd1.coordinate_names = None fd = fd1.concatenate(fd2, as_coordinates=True) - np.testing.assert_equal((None, None), fd.coordinate_names) + self.assertEqual((None, None), fd.coordinate_names) - def test_concatenate2(self): + def test_concatenate_function(self) -> None: + """Test the concatenate function (as opposed to method).""" sample1 = np.arange(0, 10) sample2 = np.arange(10, 20) fd1 = FDataGrid([sample1]) fd2 = FDataGrid([sample2]) - fd1.argument_names = ["x"] - fd1.coordinate_names = ["y"] + fd1.argument_names = ("x",) + fd1.coordinate_names = ("y",) fd = concatenate([fd1, fd2]) - np.testing.assert_equal(fd.n_samples, 2) - np.testing.assert_equal(fd.dim_codomain, 1) - np.testing.assert_equal(fd.dim_domain, 1) - np.testing.assert_array_equal(fd.data_matrix[..., 0], [sample1, - sample2]) - np.testing.assert_array_equal(fd1.argument_names, fd.argument_names) + self.assertEqual(fd.n_samples, 2) + self.assertEqual(fd.dim_codomain, 1) + self.assertEqual(fd.dim_domain, 1) np.testing.assert_array_equal( - fd1.coordinate_names, fd.coordinate_names) - - def test_coordinates(self): + fd.data_matrix[..., 0], + [sample1, sample2], + ) + self.assertEqual( + fd1.argument_names, + fd.argument_names, + ) + self.assertEqual( + fd1.coordinate_names, + fd.coordinate_names, + ) + + def test_coordinates(self) -> None: + """Test coordinate access and iteration.""" fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) - fd1.argument_names = ["x"] - fd1.coordinate_names = ["y"] + fd1.argument_names = ("x",) + fd1.coordinate_names = ("y",) fd2 = FDataGrid([[3, 4, 5, 6], [4, 5, 6, 7]]) fd = fd1.concatenate(fd2, as_coordinates=True) # Indexing with number - np.testing.assert_array_equal(fd.coordinates[0].data_matrix, - fd1.data_matrix) - np.testing.assert_array_equal(fd.coordinates[1].data_matrix, - fd2.data_matrix) + np.testing.assert_array_equal( + fd.coordinates[0].data_matrix, + fd1.data_matrix, + ) + np.testing.assert_array_equal( + fd.coordinates[1].data_matrix, + fd2.data_matrix, + ) # Iteration for fd_j, fd_i in zip([fd1, fd2], fd.coordinates): @@ -139,41 +192,58 @@ def test_coordinates(self): fd3 = fd1.concatenate(fd2, fd1, fd, as_coordinates=True) - # Multiple indexation - np.testing.assert_equal(fd3.dim_codomain, 5) - np.testing.assert_array_equal(fd3.coordinates[:2].data_matrix, - fd.data_matrix) - np.testing.assert_array_equal(fd3.coordinates[-2:].data_matrix, - fd.data_matrix) + # Multiple indexation + self.assertEqual(fd3.dim_codomain, 5) + np.testing.assert_array_equal( + fd3.coordinates[:2].data_matrix, + fd.data_matrix, + ) + np.testing.assert_array_equal( + fd3.coordinates[-2:].data_matrix, + fd.data_matrix, + ) + index_original = np.array((False, False, True, False, True)) np.testing.assert_array_equal( - fd3.coordinates[np.array( - (False, False, True, False, True))].data_matrix, - fd.data_matrix) + fd3.coordinates[index_original].data_matrix, + fd.data_matrix, + ) - def test_add(self): + def test_add(self) -> None: + """Test addition with different objects.""" fd1 = FDataGrid([[1, 2, 3, 4], [2, 3, 4, 5]]) fd2 = fd1 + fd1 - np.testing.assert_array_equal(fd2.data_matrix[..., 0], - [[2, 4, 6, 8], [4, 6, 8, 10]]) + np.testing.assert_array_equal( + fd2.data_matrix[..., 0], # noqa: WPS204 + [[2, 4, 6, 8], [4, 6, 8, 10]], + ) fd2 = fd1 + 2 - np.testing.assert_array_equal(fd2.data_matrix[..., 0], - [[3, 4, 5, 6], [4, 5, 6, 7]]) + np.testing.assert_array_equal( + fd2.data_matrix[..., 0], + [[3, 4, 5, 6], [4, 5, 6, 7]], + ) fd2 = fd1 + np.array(2) - np.testing.assert_array_equal(fd2.data_matrix[..., 0], - [[3, 4, 5, 6], [4, 5, 6, 7]]) + np.testing.assert_array_equal( + fd2.data_matrix[..., 0], + [[3, 4, 5, 6], [4, 5, 6, 7]], + ) fd2 = fd1 + np.array([2]) - np.testing.assert_array_equal(fd2.data_matrix[..., 0], - [[3, 4, 5, 6], [4, 5, 6, 7]]) + np.testing.assert_array_equal( + fd2.data_matrix[..., 0], + [[3, 4, 5, 6], [4, 5, 6, 7]], + ) fd2 = fd1 + np.array([1, 2]) - np.testing.assert_array_equal(fd2.data_matrix[..., 0], - [[2, 3, 4, 5], [4, 5, 6, 7]]) + np.testing.assert_array_equal( + fd2.data_matrix[..., 0], + [[2, 3, 4, 5], [4, 5, 6, 7]], + ) - def test_composition(self): + def test_composition(self) -> None: + """Test function composition.""" X, Y, Z = axes3d.get_test_data(1.2) data_matrix = [Z.T] @@ -196,18 +266,14 @@ def test_composition(self): class TestEvaluateFDataGrid(unittest.TestCase): + """Test FDataGrid evaluation.""" - def setUp(self): + def setUp(self) -> None: + """Create bidimensional FDataGrid.""" data_matrix = np.array( [ - [ - [[0, 1, 2], [0, 1, 2]], - [[0, 1, 2], [0, 1, 2]] - ], - [ - [[3, 4, 5], [3, 4, 5]], - [[3, 4, 5], [3, 4, 5]] - ] + np.tile([0, 1, 2], (2, 2, 1)), + np.tile([3, 4, 5], (2, 2, 1)), ]) grid_points = [[0, 1], [0, 1]] @@ -219,65 +285,92 @@ def setUp(self): self.fd = fd - def test_evaluate_aligned(self): - + def test_evaluate_aligned(self) -> None: + """Check normal evaluation.""" res = self.fd([(0, 0), (1, 1), (2, 2), (3, 3)]) - expected = np.array([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], - [[3, 4, 5], [3, 4, 5], [3, 4, 5], [3, 4, 5]]]) + expected = np.array([ + np.tile([0, 1, 2], (4, 1)), + np.tile([3, 4, 5], (4, 1)), + ]) np.testing.assert_allclose(res, expected) - def test_evaluate_unaligned(self): - - res = self.fd([[(0, 0), (1, 1), (2, 2), (3, 3)], - [(1, 7), (5, 2), (3, 4), (6, 1)]], - aligned=False) - expected = np.array([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], - [[3, 4, 5], [3, 4, 5], [3, 4, 5], [3, 4, 5]]]) + def test_evaluate_unaligned(self) -> None: + """Check unaligned evaluation.""" + res = self.fd( + [ + [(0, 0), (1, 1), (2, 2), (3, 3)], + [(1, 7), (5, 2), (3, 4), (6, 1)], + ], + aligned=False, + ) + expected = np.array( + [ + np.tile([0, 1, 2], (4, 1)), + np.tile([3, 4, 5], (4, 1)), + ], + ) np.testing.assert_allclose(res, expected) - def test_evaluate_unaligned_ragged(self): - - res = self.fd([[(0, 0), (1, 1), (2, 2), (3, 3)], - [(1, 7), (5, 2), (3, 4)]], - aligned=False) - expected = ([[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]], - [[3, 4, 5], [3, 4, 5], [3, 4, 5]]]) + def test_evaluate_unaligned_ragged(self) -> None: + """Check evaluation with different number of points per curve.""" + res = self.fd( + [ + [(0, 0), (1, 1), (2, 2), (3, 3)], + [(1, 7), (5, 2), (3, 4)], + ], + aligned=False, + ) + expected = ([ + np.tile([0, 1, 2], (4, 1)), + np.tile([3, 4, 5], (3, 1)), + ]) self.assertEqual(len(res), self.fd.n_samples) for r, e in zip(res, expected): np.testing.assert_allclose(r, e) - def test_evaluate_grid_aligned(self): - + def test_evaluate_grid_aligned(self) -> None: + """Test evaluation in aligned grid.""" res = self.fd([[0, 1], [1, 2]], grid=True) - expected = np.array([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], - [[[3, 4, 5], [3, 4, 5]], [[3, 4, 5], [3, 4, 5]]]]) + expected = np.array([ + np.tile([0, 1, 2], (2, 2, 1)), + np.tile([3, 4, 5], (2, 2, 1)), + ]) np.testing.assert_allclose(res, expected) - def test_evaluate_grid_unaligned(self): - - res = self.fd([[[0, 1], [1, 2]], [[3, 4], [5, 6]]], - grid=True, aligned=False) - expected = np.array([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], - [[[3, 4, 5], [3, 4, 5]], [[3, 4, 5], [3, 4, 5]]]]) + def test_evaluate_grid_unaligned(self) -> None: + """Test evaluation with a different grid per curve.""" + res = self.fd( + [[[0, 1], [1, 2]], [[3, 4], [5, 6]]], + grid=True, + aligned=False, + ) + expected = np.array([ + np.tile([0, 1, 2], (2, 2, 1)), + np.tile([3, 4, 5], (2, 2, 1)), + ]) np.testing.assert_allclose(res, expected) - def test_evaluate_grid_unaligned_ragged(self): - - res = self.fd([[[0, 1], [1, 2]], [[3, 4], [5]]], - grid=True, aligned=False) - expected = ([[[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]], - [[[3, 4, 5]], [[3, 4, 5]]]]) + def test_evaluate_grid_unaligned_ragged(self) -> None: + """Test different grid points per curve.""" + res = self.fd( + [[[0, 1], [1, 2]], [[3, 4], [5]]], + grid=True, + aligned=False, + ) + expected = ([ + np.tile([0, 1, 2], (2, 2, 1)), + np.tile([3, 4, 5], (2, 1, 1)), + ]) for r, e in zip(res, expected): np.testing.assert_allclose(r, e) if __name__ == '__main__': - print() unittest.main() From e7e3cf2f8ced6f41ee79ae6d4d1ed5821c87591c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 15 Jul 2022 16:30:59 +0200 Subject: [PATCH 206/400] Refactor tests. --- skfda/inference/hotelling/_hotelling.py | 22 +- skfda/tests/test_hotelling.py | 47 +- skfda/tests/test_interpolation.py | 544 ++++++++++++++---------- 3 files changed, 362 insertions(+), 251 deletions(-) diff --git a/skfda/inference/hotelling/_hotelling.py b/skfda/inference/hotelling/_hotelling.py index 854617790..72b5ad041 100644 --- a/skfda/inference/hotelling/_hotelling.py +++ b/skfda/inference/hotelling/_hotelling.py @@ -1,14 +1,16 @@ +from __future__ import annotations + import itertools -from typing import Optional, Tuple, Union, overload +from typing import Tuple, overload import numpy as np +import scipy.special from sklearn.utils import check_random_state from typing_extensions import Literal -import scipy.special - from ..._utils import RandomStateLike from ...representation import FData, FDataBasis +from ...representation._typing import NDArrayFloat def hotelling_t2( @@ -122,7 +124,7 @@ def hotelling_test_ind( fd1: FData, fd2: FData, *, - n_reps: Optional[int] = None, + n_reps: int | None = None, random_state: RandomStateLike = None, return_dist: Literal[False] = False, ) -> Tuple[float, float]: @@ -134,10 +136,10 @@ def hotelling_test_ind( fd1: FData, fd2: FData, *, - n_reps: Optional[int] = None, + n_reps: int | None = None, random_state: RandomStateLike = None, return_dist: Literal[True], -) -> Tuple[float, float, np.ndarray]: +) -> Tuple[float, float, NDArrayFloat]: pass @@ -145,10 +147,10 @@ def hotelling_test_ind( fd1: FData, fd2: FData, *, - n_reps: Optional[int] = None, + n_reps: int | None = None, random_state: RandomStateLike = None, return_dist: bool = False, -) -> Union[Tuple[float, float], Tuple[float, float, np.ndarray]]: +) -> Tuple[float, float] | Tuple[float, float, NDArrayFloat]: """ Compute Hotelling :math:`T^2`-test. @@ -214,9 +216,9 @@ def hotelling_test_ind( if n_reps is not None and n_reps < 1: raise ValueError("Number of repetitions must be positive.") - n1, n2 = fd1.n_samples, fd2.n_samples + n1 = fd1.n_samples t2_0 = hotelling_t2(fd1, fd2) - n = n1 + n2 + n = n1 + fd2.n_samples sample = fd1.concatenate(fd2) indices = np.arange(n) diff --git a/skfda/tests/test_hotelling.py b/skfda/tests/test_hotelling.py index fdea10d27..6953890a7 100644 --- a/skfda/tests/test_hotelling.py +++ b/skfda/tests/test_hotelling.py @@ -1,19 +1,18 @@ +"""Tests for Hotelling functions.""" +import unittest + from skfda.inference.hotelling import hotelling_t2, hotelling_test_ind from skfda.representation import FDataGrid from skfda.representation.basis import Fourier -import unittest - -import pytest class HotellingTests(unittest.TestCase): + """Tests for Hotelling statistic and test.""" - def test_hotelling_test_ind_args(self): + def test_hotelling_test_ind_args(self) -> None: + """Test that invalid arguments are rejected in test.""" fd1 = FDataGrid([[1, 1, 1]]) - with self.assertRaises(TypeError): - hotelling_test_ind(fd1, []) - with self.assertRaises(TypeError): - hotelling_test_ind([], fd1) + with self.assertRaises(TypeError): hotelling_test_ind(fd1.to_basis(Fourier(n_basis=3)), fd1) with self.assertRaises(TypeError): @@ -21,18 +20,17 @@ def test_hotelling_test_ind_args(self): with self.assertRaises(ValueError): hotelling_test_ind(fd1, fd1, n_reps=0) - def test_hotelling_t2_args(self): + def test_hotelling_t2_args(self) -> None: + """Test that invalid arguments are rejected in statistic.""" fd1 = FDataGrid([[1, 1, 1]]) - with self.assertRaises(TypeError): - hotelling_t2(fd1, []) - with self.assertRaises(TypeError): - hotelling_t2([], fd1) + with self.assertRaises(TypeError): hotelling_t2(fd1.to_basis(Fourier(n_basis=3)), fd1) with self.assertRaises(TypeError): hotelling_t2(fd1, fd1.to_basis(Fourier(n_basis=3))) - def test_hotelling_t2(self): + def test_hotelling_t2(self) -> None: + """Trivial checks for the statistic.""" fd1 = FDataGrid([[1, 1, 1], [1, 1, 1]]) fd2 = FDataGrid([[1, 1, 1], [2, 2, 2]]) self.assertAlmostEqual(hotelling_t2(fd1, fd1), 0) @@ -43,20 +41,29 @@ def test_hotelling_t2(self): self.assertAlmostEqual(hotelling_t2(fd1, fd1), 0) self.assertAlmostEqual(hotelling_t2(fd1, fd2), 1) - def test_hotelling_test(self): + def test_hotelling_test(self) -> None: + """Trivial checks for the test.""" fd1 = FDataGrid([[1, 1, 1], [1, 1, 1]]) fd2 = FDataGrid([[3, 3, 3], [2, 2, 2]]) - t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True, - random_state=0) + t2, pval, dist = hotelling_test_ind( + fd1, + fd2, + return_dist=True, + random_state=0, + ) self.assertAlmostEqual(t2, 9) self.assertAlmostEqual(pval, 0) self.assertEqual(len(dist), 6) reps = 5 - t2, pval, dist = hotelling_test_ind(fd1, fd2, return_dist=True, - n_reps=reps, random_state=1) + t2, pval, dist = hotelling_test_ind( + fd1, + fd2, + return_dist=True, + n_reps=reps, + random_state=1, + ) self.assertEqual(len(dist), reps) if __name__ == '__main__': - print() unittest.main() diff --git a/skfda/tests/test_interpolation.py b/skfda/tests/test_interpolation.py index be9f89e4b..b2f735904 100644 --- a/skfda/tests/test_interpolation.py +++ b/skfda/tests/test_interpolation.py @@ -1,341 +1,443 @@ +"""Tests for FDataGrid's interpolation.""" +import unittest + +import numpy as np from skfda import FDataGrid from skfda.representation.interpolation import SplineInterpolation -import unittest -import numpy as np +class TestEvaluationSplineUnivariate(unittest.TestCase): + """Test the evaluation of univariate spline interpolation.""" -# TODO: Unitest for grids with domain dimension > 1 -class TestEvaluationSpline1_1(unittest.TestCase): - """Test the evaluation of a grid spline interpolation with - domain and image dimension equal to 1. - """ + def setUp(self) -> None: + """ + Define the data. - def setUp(self): - # Data matrix of a datagrid with a dimension of domain and image equal - # to 1. + The data matrix consists on functions (x**2, (9-x)**2). - # Matrix of functions (x**2, (9-x)**2) - self.data_matrix_1_1 = [np.arange(10)**2, - np.arange(start=9, stop=-1, step=-1)**2] + """ + self.data_matrix_1_1 = [ + np.arange(10)**2, + np.arange(start=9, stop=-1, step=-1)**2, + ] - def test_evaluation_linear_simple(self): - """Test basic usage of evaluation""" + self.grid_points = np.arange(10) - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10)) + def test_evaluation_linear_simple(self) -> None: + """Test basic usage of linear evaluation.""" + f = FDataGrid(self.data_matrix_1_1, grid_points=self.grid_points) # Test interpolation in nodes - np.testing.assert_array_almost_equal( - f(np.arange(10))[..., 0], self.data_matrix_1_1) + np.testing.assert_allclose( + f(self.grid_points)[..., 0], + self.data_matrix_1_1, + ) # Test evaluation in a list of times - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( f([0.5, 1.5, 2.5]), - np.array([[[0.5], [2.5], [6.5]], - [[72.5], [56.5], [42.5]]])) + np.array([ + [[0.5], [2.5], [6.5]], + [[72.5], [56.5], [42.5]], + ]), + ) - def test_evaluation_linear_point(self): - """Test the evaluation of a single point""" - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10)) + def test_evaluation_linear_point(self) -> None: + """Test the evaluation of a single point.""" + f = FDataGrid(self.data_matrix_1_1, grid_points=self.grid_points) # Test a single point - np.testing.assert_array_almost_equal(f(5.3).round(1), - np.array([[[28.3]], [[13.9]]])) - np.testing.assert_array_almost_equal( - f([3]), np.array([[[9.]], [[36.]]])) - np.testing.assert_array_almost_equal( - f((2,)), np.array([[[4.]], [[49.]]])) - - def test_evaluation_linear_grid(self): - """Test grid evaluation. With domain dimension = 1""" - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10)) + np.testing.assert_allclose( + f(5.3), + np.array([[[28.3]], [[13.9]]]), + ) + np.testing.assert_allclose( + f([3]), np.array([[[9]], [[36]]]), + ) + np.testing.assert_allclose( + f((2,)), np.array([[[4]], [[49]]]), + ) + + def test_evaluation_linear_grid(self) -> None: + """Test grid evaluation. With domain dimension = 1.""" + f = FDataGrid(self.data_matrix_1_1, grid_points=self.grid_points) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(np.arange(10))[..., 0], - self.data_matrix_1_1) + np.testing.assert_allclose( + f(self.grid_points)[..., 0], + self.data_matrix_1_1, + ) - res = np.array([[[0.5], [2.5], [6.5]], [[72.5], [56.5], [42.5]]]) + res = np.array([[[0.5], [2.5], [6.5]], [[72.5], [56.5], [42.5]]]) t = [0.5, 1.5, 2.5] # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f(t, grid=True), res) - np.testing.assert_array_almost_equal(f((t,), grid=True), res) - np.testing.assert_array_almost_equal(f([t], grid=True), res) + np.testing.assert_allclose(f(t, grid=True), res) + np.testing.assert_allclose(f((t,), grid=True), res) + np.testing.assert_allclose(f([t], grid=True), res) # Single point with grid - np.testing.assert_array_almost_equal(f(3, grid=True), - np.array([[[9.]], [[36.]]])) + np.testing.assert_allclose( + f(3, grid=True), + np.array([[[9]], [[36]]]), + ) # Check erroneous axis - with np.testing.assert_raises(ValueError): + with self.assertRaises(ValueError): f((t, t), grid=True) - def test_evaluation_linear_composed(self): - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10)) + def test_evaluation_linear_unaligned(self) -> None: + """Test unaligned evaluation.""" + f = FDataGrid(self.data_matrix_1_1, grid_points=self.grid_points) - # Evaluate (x**2, (9-x)**2) in (1,8) - np.testing.assert_array_almost_equal(f([[1], [8]], - aligned=False), - np.array([[[1.]], [[1.]]])) + np.testing.assert_allclose( + f([[1], [8]], aligned=False), + np.array([[[1]], [[1]]]), + ) t = np.linspace(4, 6, 4) - np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned=False).round(2), - np.array([[[16.], [22.], [28.67], [36.]], - [[16.], [22.], [28.67], [36.]]])) + np.testing.assert_allclose( + f([t, 9 - t], aligned=False), + np.array([ + [[16], [22], [28.67], [36]], + [[16], [22], [28.67], [36]], + ]), + rtol=1e-3, + ) # Same length than nsample t = np.linspace(4, 6, 2) - np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned=False).round(2), - np.array([[[16.], [36.]], [[16.], [36.]]])) - - def test_evaluation_cubic_simple(self): - """Test basic usage of evaluation""" - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=SplineInterpolation(3)) + np.testing.assert_allclose( + f([t, 9 - t], aligned=False), + np.array([[[16], [36]], [[16], [36]]]), + ) + + def test_evaluation_cubic_simple(self) -> None: + """Test basic usage of cubic evaluation.""" + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=SplineInterpolation(3), + ) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(np.arange(10)).round(1)[..., 0], - self.data_matrix_1_1) + np.testing.assert_allclose( + f(self.grid_points)[..., 0], + self.data_matrix_1_1, + atol=1e-8, + ) # Test evaluation in a list of times - np.testing.assert_array_almost_equal( - f([0.5, 1.5, 2.5]).round(2), - np.array([[[0.25], [2.25], [6.25]], - [[72.25], [56.25], [42.25]]])) - - def test_evaluation_cubic_point(self): - """Test the evaluation of a single point""" - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=SplineInterpolation(3)) + np.testing.assert_allclose( + f([0.5, 1.5, 2.5]), + np.array([ + [[0.25], [2.25], [6.25]], + [[72.25], [56.25], [42.25]], + ]), + ) + + def test_evaluation_cubic_point(self) -> None: + """Test the evaluation of a single point.""" + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=SplineInterpolation(3), + ) # Test a single point - np.testing.assert_array_almost_equal(f(5.3).round(3), - np.array([[[28.09]], [[13.69]]])) - - np.testing.assert_array_almost_equal( - f([3]).round(3), np.array([[[9.]], [[36.]]])) - np.testing.assert_array_almost_equal( - f((2,)).round(3), np.array([[[4.]], [[49.]]])) - - def test_evaluation_cubic_grid(self): - """Test grid evaluation. With domain dimension = 1""" - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=SplineInterpolation(3)) + np.testing.assert_allclose( + f(5.3), + np.array([[[28.09]], [[13.69]]]), + ) + + np.testing.assert_allclose( + f([3]), + np.array([[[9]], [[36]]]), + ) + np.testing.assert_allclose( + f((2,)), + np.array([[[4]], [[49]]]), + ) + + def test_evaluation_cubic_grid(self) -> None: + """Test cubic grid evaluation.""" + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=SplineInterpolation(3), + ) t = [0.5, 1.5, 2.5] - res = np.array([[[0.25], [2.25], [6.25]], - [[72.25], [56.25], [42.25]]]) + res = np.array([ + [[0.25], [2.25], [6.25]], + [[72.25], [56.25], [42.25]], + ]) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f(t, grid=True).round(3), res) - np.testing.assert_array_almost_equal(f((t,), grid=True).round(3), res) - np.testing.assert_array_almost_equal(f([t], grid=True).round(3), res) + np.testing.assert_allclose(f(t, grid=True), res) + np.testing.assert_allclose(f((t,), grid=True), res) + np.testing.assert_allclose(f([t], grid=True), res) + # Single point with grid - np.testing.assert_array_almost_equal( - f(3, grid=True), np.array([[[9.]], [[36.]]])) + np.testing.assert_allclose( + f(3, grid=True), + np.array([[[9]], [[36]]]), + ) # Check erroneous axis - with np.testing.assert_raises(ValueError): + with self.assertRaises(ValueError): f((t, t), grid=True) - def test_evaluation_cubic_composed(self): - - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=SplineInterpolation(3)) + def test_evaluation_cubic_unaligned(self) -> None: + """Test cubic unaligned evaluation.""" + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=SplineInterpolation(3), + ) - # Evaluate (x**2, (9-x)**2) in (1,8) - np.testing.assert_array_almost_equal( - f([[1], [8]], aligned=False).round(3), - np.array([[[1.]], [[1.]]])) + np.testing.assert_allclose( + f([[1], [8]], aligned=False), + np.array([[[1]], [[1]]]), + ) t = np.linspace(4, 6, 4) - np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned=False).round(2), - np.array([[[16.], [21.78], [28.44], [36.]], - [[16.], [21.78], [28.44], [36.]]])) + np.testing.assert_allclose( + f([t, 9 - t], aligned=False), + np.array([ + [[16], [21.78], [28.44], [36]], + [[16], [21.78], [28.44], [36]], + ]), + rtol=1e-3, + ) # Same length than nsample t = np.linspace(4, 6, 2) - np.testing.assert_array_almost_equal( - f([t, 9 - t], aligned=False).round(3), - np.array([[[16.], [36.]], [[16.], [36.]]])) - - def test_evaluation_nodes(self): - """Test interpolation in nodes for all dimensions""" - + np.testing.assert_allclose( + f([t, 9 - t], aligned=False), + np.array([ + [[16], [36]], [[16], [36]], + ]), + ) + + def test_evaluation_nodes(self) -> None: + """Test interpolation in nodes for all dimensions.""" for degree in range(1, 6): interpolation = SplineInterpolation(degree) - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=interpolation) + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=interpolation, + ) # Test interpolation in nodes - np.testing.assert_array_almost_equal( - f(np.arange(10)).round(5)[..., 0], - self.data_matrix_1_1) - - def test_error_degree(self): - - with np.testing.assert_raises(ValueError): + np.testing.assert_allclose( + f(self.grid_points)[..., 0], + self.data_matrix_1_1, + atol=1e-8, + ) + + def test_error_degree(self) -> None: + """Check unsupported spline degrees.""" + with self.assertRaises(ValueError): interpolation = SplineInterpolation(7) - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=interpolation) + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=interpolation, + ) f(1) - with np.testing.assert_raises(ValueError): + with self.assertRaises(ValueError): interpolation = SplineInterpolation(0) - f = FDataGrid(self.data_matrix_1_1, grid_points=np.arange(10), - interpolation=interpolation) + f = FDataGrid( + self.data_matrix_1_1, + grid_points=self.grid_points, + interpolation=interpolation, + ) f(1) -class TestEvaluationSpline1_n(unittest.TestCase): - """Test the evaluation of a grid spline interpolation with - domain dimension equal to 1 and arbitary image dimension. - """ +class TestEvaluationSplineArbitraryImage(unittest.TestCase): + """Test spline for arbitary image dimension.""" - def setUp(self): - # Data matrix of a datagrid with a dimension of domain and image equal - # to 1. + def setUp(self) -> None: + """ + Define the data. - # Matrix of functions (x**2, (9-x)**2) + The data matrix consists on functions (x**2, (9-x)**2). + """ self.t = np.arange(10) - data_1 = np.array([np.arange(10)**2, - np.arange(start=9, stop=-1, step=-1)**2]) + data_1 = np.array([ + np.arange(10)**2, + np.arange(start=9, stop=-1, step=-1)**2, + ]) data_2 = np.sin(np.pi / 81 * data_1) self.data_matrix_1_n = np.dstack((data_1, data_2)) self.interpolation = SplineInterpolation(interpolation_order=2) - def test_evaluation_simple(self): - """Test basic usage of evaluation""" - - f = FDataGrid(self.data_matrix_1_n, grid_points=np.arange(10), - interpolation=self.interpolation) + def test_evaluation_simple(self) -> None: + """Test basic usage of evaluation.""" + f = FDataGrid( + self.data_matrix_1_n, + grid_points=np.arange(10), + interpolation=self.interpolation, + ) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(self.t), self.data_matrix_1_n) + np.testing.assert_allclose( + f(self.t), + self.data_matrix_1_n, + atol=1e-8, + ) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f([1.5, 2.5, 3.5]), - np.array([[[2.25, 0.087212], - [6.25, 0.240202], - [12.25, 0.45773]], - [[56.25, 0.816142], - [42.25, 0.997589], - [30.25, 0.922146]]] - ) - ) - - def test_evaluation_point(self): - """Test the evaluation of a single point""" - - f = FDataGrid(self.data_matrix_1_n, grid_points=np.arange(10), - interpolation=self.interpolation) + np.testing.assert_allclose( + f([1.5, 2.5, 3.5]), + np.array([ + [ + [2.25, 0.087212], + [6.25, 0.240202], + [12.25, 0.45773], + ], + [ + [56.25, 0.816142], + [42.25, 0.997589], + [30.25, 0.922146], + ], + ]), + rtol=1e-5, + ) + + def test_evaluation_point(self) -> None: + """Test the evaluation of a single point.""" + f = FDataGrid( + self.data_matrix_1_n, + grid_points=np.arange(10), + interpolation=self.interpolation, + ) # Test a single point - np.testing.assert_array_almost_equal(f(5.3), - np.array([[[28.09, 0.885526]], - [[13.69, 0.50697]]] - ) - ) - - def test_evaluation_grid(self): - """Test grid evaluation. With domain dimension = 1""" - - f = FDataGrid(self.data_matrix_1_n, grid_points=np.arange(10), - interpolation=SplineInterpolation(2)) + np.testing.assert_allclose( + f(5.3), + np.array([ + [[28.09, 0.885526]], + [[13.69, 0.50697]], + ]), + rtol=1e-6, + ) + + def test_evaluation_grid(self) -> None: + """Test grid evaluation.""" + f = FDataGrid( + self.data_matrix_1_n, + grid_points=np.arange(10), + interpolation=SplineInterpolation(2), + ) t = [1.5, 2.5, 3.5] - res = np.array([[[2.25, 0.08721158], - [6.25, 0.24020233], - [12.25, 0.4577302]], - [[56.25, 0.81614206], - [42.25, 0.99758925], - [30.25, 0.92214607]]]) + res = np.array([ + [ + [2.25, 0.08721158], + [6.25, 0.24020233], + [12.25, 0.4577302], + ], + [ + [56.25, 0.81614206], + [42.25, 0.99758925], + [30.25, 0.92214607], + ], + ]) # Test evaluation in a list of times - np.testing.assert_array_almost_equal(f(t, grid=True), res) - np.testing.assert_array_almost_equal(f((t,), grid=True), res) - np.testing.assert_array_almost_equal(f([t], grid=True), res) + np.testing.assert_allclose(f(t, grid=True), res) + np.testing.assert_allclose(f((t,), grid=True), res) + np.testing.assert_allclose(f([t], grid=True), res) # Check erroneous axis - with np.testing.assert_raises(ValueError): + with self.assertRaises(ValueError): f((t, t), grid=True) - def test_evaluation_composed(self): - - f = FDataGrid(self.data_matrix_1_n, grid_points=self.t, - interpolation=self.interpolation) - - # Evaluate (x**2, (9-x)**2) in (1,8) - np.testing.assert_array_almost_equal(f([[1], [4]], - aligned=False)[0], - f(1)[0]) - np.testing.assert_array_almost_equal(f([[1], [4]], - aligned=False)[1], - f(4)[1]) - - def test_evaluation_nodes(self): - """Test interpolation in nodes for all dimensions""" - + def test_evaluation_unaligned(self) -> None: + """Test unaligned evaluation.""" + f = FDataGrid( + self.data_matrix_1_n, + grid_points=self.t, + interpolation=self.interpolation, + ) + + np.testing.assert_allclose( + f([[1], [4]], aligned=False)[0], + f(1)[0], + ) + np.testing.assert_allclose( + f([[1], [4]], aligned=False)[1], + f(4)[1], + ) + + def test_evaluation_nodes(self) -> None: + """Test interpolation in nodes for all dimensions.""" for degree in range(1, 6): interpolation = SplineInterpolation(degree) - f = FDataGrid(self.data_matrix_1_n, grid_points=np.arange(10), - interpolation=interpolation) + f = FDataGrid( + self.data_matrix_1_n, + grid_points=np.arange(10), + interpolation=interpolation, + ) # Test interpolation in nodes - np.testing.assert_array_almost_equal(f(np.arange(10)), - self.data_matrix_1_n) + np.testing.assert_allclose( + f(np.arange(10)), + self.data_matrix_1_n, + atol=1e-8, + ) -class TestEvaluationSplinem_n(unittest.TestCase): - """Test the evaluation of a grid spline interpolation with - arbitrary domain dimension and arbitary image dimension. - """ +@np.vectorize +def _coordinate_function( + *args: float, +) -> np.typing.NDArray[float]: + _, *domain_indexes, _ = args + return np.sum(domain_indexes) - def test_evaluation_center_and_extreme_points_linear(self): - """Test linear interpolation in the middle point of a grid square.""" +class TestEvaluationSplineArbitraryDim(unittest.TestCase): + """Test spline for arbitrary domain and image dimensions.""" + + def test_evaluation_middle_linear(self) -> None: + """Test linear interpolation in the middle point of a grid square.""" dim_codomain = 4 n_samples = 2 - @np.vectorize - def coordinate_function(*args): - _, *domain_indexes, _ = args - return np.sum(domain_indexes) - for dim_domain in range(1, 6): grid_points = [np.array([0, 1]) for _ in range(dim_domain)] data_matrix = np.fromfunction( - function=coordinate_function, - shape=(n_samples,) + (2,) * dim_domain + (dim_codomain,)) + function=_coordinate_function, + shape=(n_samples,) + (2,) * dim_domain + (dim_codomain,), + ) f = FDataGrid(data_matrix, grid_points=grid_points) - evaluation = f([[0.] * dim_domain, [0.5] * - dim_domain, [1.] * dim_domain]) + evaluation = f([ + [0] * dim_domain, + [0.5] * dim_domain, + [1] * dim_domain, + ]) self.assertEqual(evaluation.shape, (n_samples, 3, dim_codomain)) for i in range(n_samples): for j in range(dim_codomain): - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( evaluation[i, ..., j], - [0, dim_domain * 0.5, dim_domain]) + [0, dim_domain * 0.5, dim_domain], + ) if __name__ == '__main__': - print() unittest.main() From 23a0f60ced94479abcc02dc43a2681a8ab0d4d25 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Fri, 15 Jul 2022 17:31:00 +0200 Subject: [PATCH 207/400] Improve k_neighbors example --- examples/plot_k_neighbors_classification.py | 139 +++++++++++--------- 1 file changed, 76 insertions(+), 63 deletions(-) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index 16491bc95..d957a7b18 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -8,30 +8,30 @@ # Author: Pablo Marcos Manchón # License: MIT -import skfda -from skfda.ml.classification import KNeighborsClassifier - -from sklearn.model_selection import (train_test_split, GridSearchCV, - StratifiedShuffleSplit) - import matplotlib.pyplot as plt import numpy as np +from sklearn.model_selection import ( + GridSearchCV, + train_test_split, +) +import skfda +from skfda.ml.classification import KNeighborsClassifier ############################################################################## # # In this example we are going to show the usage of the K-nearest neighbors # classifier in their functional version, which is a extension of the -# multivariate one, but using functional metrics between the observations. +# multivariate one, but using functional metrics. # # Firstly, we are going to fetch a functional dataset, such as the Berkeley # Growth Study. This dataset contains the height of several boys and girls # measured until the 18 years of age. -# We will try to predict the sex by using its growth curves. +# We will try to predict sex from their growth curves. # # The following figure shows the growth curves grouped by sex. # -# Loads dataset + X, y = skfda.datasets.fetch_growth(return_X_y=True, as_frame=True) X = X.iloc[:, 0].values y = y.values @@ -44,9 +44,8 @@ ############################################################################## # -# In this case, the class labels are stored in an array with 0's in the male -# samples and 1's in the positions with female ones. -# +# The class labels are stored in an array. Zeros represent male +# samples while ones represent female samples. print(y) @@ -58,10 +57,14 @@ # The function will return two # :class:`~skfda.representation.grid.FDataGrid`'s, ``X_train`` and ``X_test`` # with the corresponding partitions, and arrays with their class labels. -# -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, - stratify=y, random_state=0) +X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=0.25, + stratify=y, + random_state=0, +) ############################################################################## @@ -70,12 +73,12 @@ # :class:`~skfda.ml.classification.KNeighborsClassifier` # with the training partition. This classifier works exactly like the sklearn # multivariate classifier -# :class:`~sklearn.neighbors.KNeighborsClassifier`, but -# will accept as input a :class:`~skfda.representation.grid.FDataGrid` with +# :class:`~sklearn.neighbors.KNeighborsClassifier`, but it's input is +# a :class:`~skfda.representation.grid.FDataGrid` with # functional observations instead of an array with multivariate data. # -knn = KNeighborsClassifier() +knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train) ############################################################################## @@ -83,12 +86,14 @@ # Once it is fitted, we can predict labels for the test samples. # # To predict the label of a test sample, the classifier will calculate the -# k-nearest neighbors and will asign the majority class. By default, it is -# used the :math:`\mathbb{L}^2` distance between functions, to determine the -# neighbourhood of a sample, with 5 neighbors. -# Can be used any of the functional metrics described in +# k-nearest neighbors and will asign the class shared by most of those k +# neighbors. In this case, we have set the number of neighbours to 5 +# (:math:`k=5`). +# By default, it will use the +# :math:`\mathbb{L}^2` distance between functions, to determine the +# neighbourhood of a sample. However, it can be used with +# any of the functional metrics described in # :doc:`/modules/misc/metrics`. -# pred = knn.predict(X_test) print(pred) @@ -98,7 +103,6 @@ # The :func:`~skfda.ml.classification.KNeighborsClassifier.score` method # allows us to calculate the mean accuracy for the test data. In this case we # obtained around 96% of accuracy. -# score = knn.score(X_test, y_test) print(score) @@ -109,7 +113,6 @@ # using :func:`~skfda.ml.classification.KNeighborsClassifier.predict_proba`, # which will return an array with the probabilities of the classes, in # lexicographic order, for each test sample. -# probs = knn.predict_proba(X_test[:5]) # Predict first 5 samples print(probs) @@ -121,50 +124,57 @@ # :class:`~sklearn.model_selection.GridSearchCV` to perform a # grid search to select the best hyperparams, using cross-validation. # -# In this case, we will vary the number of neighbors between 1 and 11. -# +# In this case, we will vary the number of neighbors between 1 and 17. # Only odd numbers, to prevent ties -param_grid = {'n_neighbors': np.arange(1, 12, 2)} +param_grid = {"n_neighbors": range(1, 18, 2)} knn = KNeighborsClassifier() # Perform grid search with cross-validation -ss = StratifiedShuffleSplit(n_splits=5, test_size=.25, random_state=0) -gscv = GridSearchCV(knn, param_grid, cv=ss) -gscv.fit(X, y) +gscv = GridSearchCV(knn, param_grid, cv=5) +gscv.fit(X_train, y_train) print("Best params:", gscv.best_params_) -print("Best score:", gscv.best_score_) +print("Best cross-validation score:", gscv.best_score_) ############################################################################## # # We have obtained the greatest mean accuracy using 11 neighbors. The # following figure shows the score depending on the number of neighbors. -# fig = plt.figure() ax = fig.add_subplot(1, 1, 1) -ax.bar(param_grid['n_neighbors'], gscv.cv_results_['mean_test_score']) -ax.set_xticks(param_grid['n_neighbors']) +ax.bar(param_grid["n_neighbors"], gscv.cv_results_["mean_test_score"]) +ax.set_xticks(param_grid["n_neighbors"]) ax.set_ylabel("Number of Neighbors") -ax.set_xlabel("Test score") +ax.set_xlabel("Cross-validation score") ax.set_ylim((0.9, 1)) +############################################################################## +# +# We can also check the accuracy of the classifier for number of neighbors +# selected (11) using the +# :func:`~skfda.ml.classification.KNeighborsClassifier.score` method. + +knn = KNeighborsClassifier(n_neighbors=11) +knn.fit(X_train, y_train) +score = knn.score(X_test, y_test) +print(score) ############################################################################## # -# When the functional data have been sampled in an equispaced way, or +# When the functional data has been sampled in an equispaced way, or # approximately equispaced, it is possible to use the scikit-learn vector # metrics with similar results. # # For example, in the case of the :math:`\mathbb{L}^2` distance, -# if the integral of the distance it is approximated as a -# Riemann sum, we obtain that it is proportional to the euclidean -# distance between vectors. +# by appoximating the integral as a Riemann sum, +# we can derive that the value of said integral is proportional to the +# eucleadian distance between vectors. # # .. math:: # \|f - g \|_{\mathbb{L}^2} = \left ( \int_a^b |f(x) - g(x)|^2 dx \right ) @@ -173,45 +183,48 @@ # = \sqrt{\bigtriangleup h} \, d_{euclidean}(\vec{f}, \vec{g}) # # -# So, in this case, it is roughly equivalent to use this metric instead of the -# functional one, due to the constant multiplication not affecting the +# Therefore, in this case, it is roughly equivalent to use this metric instead +# of the functional one, since multiplying by a constant does not affect the # order of the neighbors. # -# Setting the parameter ``sklearn_metric`` of the classifier to ``True``, -# a vectorial metric of sklearn can be passed. In -# :class:`~sklearn.neighbors.DistanceMetric` there are listed all the metrics -# supported. +# By setting the parameter ``sklearn_metric`` of the classifier to ``True``, +# a vectorial metric of sklearn can be provided. The list of supported +# metrics can be found in :class:`~sklearn.neighbors.DistanceMetric` # # We will fit the model with the sklearn distance and search for the best -# parameter. The results can vary slightly, due to the approximation during -# the integration, but the result should be similar. -# +# parameter. The results can vary slightly, due to the approximation of +# the integral, but the result should be similar. -knn = KNeighborsClassifier(metric='euclidean', multivariate_metric=True) -gscv2 = GridSearchCV(knn, param_grid, cv=ss) -gscv2.fit(X, y) +knn = KNeighborsClassifier(metric="euclidean", multivariate_metric=True) +gscv2 = GridSearchCV(knn, param_grid, cv=5) +gscv2.fit(X_train, y_train) print("Best params:", gscv2.best_params_) print("Best score:", gscv2.best_score_) ############################################################################## # -# The advantage of use the sklearn metrics is the computational speed, three -# orders of magnitude faster. But it is not always possible to have -# equispaced samples nor do all functional metrics have a vector equivalent -# in this way. +# Using sklearn metrics results in a speedup of three orders of magnitude. +# However, it is not always possible to have equispaced sample and not all +# functional metrics have the vector equivalent required to do this +# approximation. # # The mean score time depending on the metric is shown below. -# print("Mean score time (milliseconds)") -print("L2 distance:", 1000 * - np.mean(gscv.cv_results_['mean_score_time']), "(ms)") -print("Euclidean distance:", 1000 * - np.mean(gscv2.cv_results_['mean_score_time']), "(ms)") +print( + "L2 distance:{time}(ms)".format( + time=1000 * np.mean(gscv.cv_results_["mean_score_time"]) + ) +) + +print( + "Euclidean distance:{time}(ms)".format( + time=1000 * np.mean(gscv2.cv_results_["mean_score_time"]) + ) +) ############################################################################## # # This classifier can be used with multivariate funcional data, as surfaces -# or curves in :math:`\mathbb{R}^N`, if the metric support it too. -# +# or curves in :math:`\mathbb{R}^N`, if the metric supports it too. From bdf720bca0e2993c94d8534764d0eafcdf33485b Mon Sep 17 00:00:00 2001 From: Ddelval Date: Fri, 15 Jul 2022 17:45:51 +0200 Subject: [PATCH 208/400] Fix style issues --- examples/plot_k_neighbors_classification.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index d957a7b18..77e34b664 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -10,10 +10,7 @@ import matplotlib.pyplot as plt import numpy as np -from sklearn.model_selection import ( - GridSearchCV, - train_test_split, -) +from sklearn.model_selection import GridSearchCV, train_test_split import skfda from skfda.ml.classification import KNeighborsClassifier @@ -30,7 +27,6 @@ # We will try to predict sex from their growth curves. # # The following figure shows the growth curves grouped by sex. -# X, y = skfda.datasets.fetch_growth(return_X_y=True, as_frame=True) X = X.iloc[:, 0].values @@ -76,7 +72,6 @@ # :class:`~sklearn.neighbors.KNeighborsClassifier`, but it's input is # a :class:`~skfda.representation.grid.FDataGrid` with # functional observations instead of an array with multivariate data. -# knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train) @@ -214,14 +209,14 @@ print("Mean score time (milliseconds)") print( "L2 distance:{time}(ms)".format( - time=1000 * np.mean(gscv.cv_results_["mean_score_time"]) - ) + time=1000 * np.mean(gscv.cv_results_["mean_score_time"]), + ), ) print( "Euclidean distance:{time}(ms)".format( - time=1000 * np.mean(gscv2.cv_results_["mean_score_time"]) - ) + time=1000 * np.mean(gscv2.cv_results_["mean_score_time"]), + ), ) ############################################################################## From 1b8becaf3d8ec80668c3c48391a4575736082843 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Fri, 15 Jul 2022 20:12:33 +0200 Subject: [PATCH 209/400] Update Mypy action version --- .github/workflows/mypy.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index 82b4b8d1c..ab848718b 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -9,7 +9,7 @@ jobs: name: Mypy steps: - uses: actions/checkout@v2 - - uses: tsuyoshicho/action-mypy@v1 + - uses: tsuyoshicho/action-mypy@v3 with: github_token: ${{ secrets.github_token }} # Change reviewdog reporter if you need [github-pr-check,github-check,github-pr-review]. @@ -17,4 +17,4 @@ jobs: # Change reporter level if you need. # GitHub Status Check won't become failure with warning. level: warning - mypy_flags: '' \ No newline at end of file + mypy_flags: '' From 05f591b14820437ae9c9028a6b5cf4c9d1c1a900 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Fri, 15 Jul 2022 20:30:56 +0200 Subject: [PATCH 210/400] Remove useless errors in examples. --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 44899870a..1974b293e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -97,7 +97,7 @@ per-file-ignores = test_*.py: WPS339, WPS358, WPS432, WPS442, WPS446 # Examples are allowed to have imports in the middle, "commented code", call print and have magic numbers - plot_*.py: E402, E800, WPS421, WPS432 + plot_*.py: D205, D400, E402, E800, WPS421, WPS432 rst-directives = # These are sorted alphabetically - but that does not matter From af4d967464083ee52e0c88c4c1554e873d421167 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 15 Jul 2022 21:09:02 +0200 Subject: [PATCH 211/400] Refactor tests. --- skfda/inference/anova/_anova_oneway.py | 46 +++++------ skfda/tests/test_interpolation.py | 2 +- skfda/tests/test_kernel_regression.py | 31 ++++--- skfda/tests/test_magnitude_shape.py | 110 ++++++++++++++----------- skfda/tests/test_math.py | 13 +-- skfda/tests/test_oneway_anova.py | 91 +++++++++++++------- skfda/tests/test_outliers.py | 87 ++++++++++--------- 7 files changed, 219 insertions(+), 161 deletions(-) diff --git a/skfda/inference/anova/_anova_oneway.py b/skfda/inference/anova/_anova_oneway.py index 6b38d53f2..3cd1e2a66 100644 --- a/skfda/inference/anova/_anova_oneway.py +++ b/skfda/inference/anova/_anova_oneway.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Tuple, Union, overload +from typing import Sequence, Tuple, TypeVar, overload import numpy as np from sklearn.utils import check_random_state @@ -11,7 +11,7 @@ from ...datasets import make_gaussian_process from ...misc.metrics import lp_distance from ...representation import FData, FDataGrid -from ...representation._typing import ArrayLike +from ...representation._typing import ArrayLike, NDArrayFloat def v_sample_stat(fd: FData, weights: ArrayLike, p: int = 2) -> float: @@ -169,12 +169,12 @@ def v_asymptotic_stat(fd: FData, weights: ArrayLike, p: int = 2) -> float: def _anova_bootstrap( - fd_grouped: Tuple[FData, ...], + fd_grouped: Sequence[FData], n_reps: int, random_state: RandomStateLike = None, p: int = 2, equal_var: bool = True, -) -> np.ndarray: +) -> NDArrayFloat: n_groups = len(fd_grouped) if n_groups < 2: @@ -221,14 +221,18 @@ def _anova_bootstrap( v_samples = np.empty(n_reps) for i in range(n_reps): - fd = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) - v_samples[i] = v_asymptotic_stat(fd, sizes, p=p) + fdatagrid = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) + v_samples[i] = v_asymptotic_stat(fdatagrid, sizes, p=p) return v_samples +T = TypeVar("T", bound=FData) + + @overload def oneway_anova( - *args: FData, + first: T, + *rest: T, n_reps: int = 2000, return_dist: Literal[False] = False, random_state: RandomStateLike = None, @@ -240,24 +244,26 @@ def oneway_anova( @overload def oneway_anova( - *args: FData, + first: T, + *rest: T, n_reps: int = 2000, return_dist: Literal[True], random_state: RandomStateLike = None, p: int = 2, equal_var: bool = True, -) -> Tuple[float, float, np.ndarray]: +) -> Tuple[float, float, NDArrayFloat]: pass def oneway_anova( - *args: FData, + first: T, + *rest: T, n_reps: int = 2000, return_dist: bool = False, random_state: RandomStateLike = None, p: int = 2, equal_var: bool = True, -) -> Union[Tuple[float, float], Tuple[float, float, np.ndarray]]: +) -> Tuple[float, float] | Tuple[float, float, NDArrayFloat]: r""" Perform one-way functional ANOVA. @@ -289,7 +295,8 @@ def oneway_anova( This procedure is from Cuevas :footcite:`cuevas++_2004_anova`. Args: - args: The sample measurements for each each group. + first: First group of functions. + rest: Remaining groups. n_reps: Number of simulations for the bootstrap procedure. Defaults to 2000 (This value may change in future versions). @@ -329,22 +336,15 @@ def oneway_anova( .. footbibliography:: """ - if len(args) < 2: - raise ValueError("At least two groups must be passed as parameter.") - if not all(isinstance(fd, FData) for fd in args): - raise ValueError("Argument type must inherit FData.") if n_reps < 1: raise ValueError("Number of simulations must be positive.") - fd_groups = args - if not all(isinstance(fd, type(fd_groups[0])) for fd in fd_groups[1:]): - raise TypeError('Found mixed FData types in arguments.') - - for fd in fd_groups[1:]: - if not np.array_equal(fd.domain_range, fd_groups[0].domain_range): + for fd in rest: + if not np.array_equal(fd.domain_range, first.domain_range): raise ValueError("Domain range must match for every FData passed.") - if isinstance(fd_groups[0], FDataGrid): + fd_groups = [first, *rest] + if isinstance(first, FDataGrid): # Creating list with all the sample points list_sample = [fd.grid_points[0].tolist() for fd in fd_groups] # Checking that the all the entries in the list are the same diff --git a/skfda/tests/test_interpolation.py b/skfda/tests/test_interpolation.py index b2f735904..15536e3cc 100644 --- a/skfda/tests/test_interpolation.py +++ b/skfda/tests/test_interpolation.py @@ -401,7 +401,7 @@ def test_evaluation_nodes(self) -> None: @np.vectorize def _coordinate_function( *args: float, -) -> np.typing.NDArray[float]: +) -> np.typing.NDArray[np.float_]: _, *domain_indexes, _ = args return np.sum(domain_indexes) diff --git a/skfda/tests/test_kernel_regression.py b/skfda/tests/test_kernel_regression.py index afda97610..f7b8d7cbf 100644 --- a/skfda/tests/test_kernel_regression.py +++ b/skfda/tests/test_kernel_regression.py @@ -3,7 +3,7 @@ from typing import Callable, Optional, Tuple import numpy as np -import sklearn +import sklearn.model_selection from skfda import FData from skfda.datasets import fetch_tecator @@ -18,15 +18,17 @@ from skfda.representation.basis import FDataBasis, Fourier, Monomial from skfda.representation.grid import FDataGrid +FloatArray = np.typing.NDArray[np.float_] + def _nw_alt( fd_train: FData, fd_test: FData, - y_train: np.ndarray, + y_train: FloatArray, *, bandwidth: float, - kernel: Optional[Callable] = None, -) -> np.ndarray: + kernel: Optional[Callable[[FloatArray], FloatArray]] = None, +) -> FloatArray: if kernel is None: kernel = normal @@ -41,11 +43,11 @@ def _nw_alt( def _knn_alt( fd_train: FData, fd_test: FData, - y_train: np.ndarray, + y_train: FloatArray, *, bandwidth: int, - kernel: Optional[Callable] = None, -) -> np.ndarray: + kernel: Optional[Callable[[FloatArray], FloatArray]] = None, +) -> FloatArray: if kernel is None: kernel = uniform @@ -64,11 +66,11 @@ def _knn_alt( def _llr_alt( fd_train: FDataBasis, fd_test: FDataBasis, - y_train: np.ndarray, + y_train: FloatArray, *, bandwidth: float, - kernel: Optional[Callable] = None, -) -> np.ndarray: + kernel: Optional[Callable[[FloatArray], FloatArray]] = None, +) -> FloatArray: if kernel is None: kernel = normal @@ -92,7 +94,8 @@ def _llr_alt( return y -def _create_data_basis() -> Tuple[FDataBasis, FDataBasis, np.ndarray]: +def _create_data_basis( +) -> Tuple[FDataBasis, FDataBasis, FloatArray]: X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values fat = y['fat'].values @@ -113,7 +116,8 @@ def _create_data_basis() -> Tuple[FDataBasis, FDataBasis, np.ndarray]: return fd_train, fd_test, y_train -def _create_data_grid() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: +def _create_data_grid( +) -> Tuple[FDataGrid, FDataGrid, FloatArray]: X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values fat = y['fat'].values @@ -128,7 +132,8 @@ def _create_data_grid() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: return fd_train, fd_test, y_train -def _create_data_r() -> Tuple[FDataGrid, FDataGrid, np.ndarray]: +def _create_data_r( +) -> Tuple[FDataGrid, FDataGrid, FloatArray]: X, y = fetch_tecator(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].values fat = y['fat'].values diff --git a/skfda/tests/test_magnitude_shape.py b/skfda/tests/test_magnitude_shape.py index bd701e72e..ed7d3fa63 100644 --- a/skfda/tests/test_magnitude_shape.py +++ b/skfda/tests/test_magnitude_shape.py @@ -1,63 +1,75 @@ -from skfda import FDataGrid -from skfda.datasets import fetch_weather -from skfda.exploratory.depth.multivariate import SimplicialDepth -from skfda.exploratory.visualization import MagnitudeShapePlot +"""Tests for Magnitude-Shape plot.""" import unittest import numpy as np +from skfda.datasets import fetch_weather +from skfda.exploratory.depth.multivariate import SimplicialDepth +from skfda.exploratory.visualization import MagnitudeShapePlot + class TestMagnitudeShapePlot(unittest.TestCase): + """Test MS plot.""" - def test_magnitude_shape_plot(self): + def test_magnitude_shape_plot(self) -> None: + """Test MS plot properties.""" fd = fetch_weather()["data"] fd_temperatures = fd.coordinates[0] msplot = MagnitudeShapePlot( - fd_temperatures, multivariate_depth=SimplicialDepth()) - np.testing.assert_allclose(msplot.points, - np.array([[0.2112587, 3.0322570], - [1.2823448, 0.8272850], - [0.8646544, 1.8619370], - [1.9862512, 5.5287354], - [0.7534918, 0.7203502], - [1.1325291, 0.2808455], - [-2.650529, 0.9702889], - [0.1434387, 0.9159834], - [-0.402844, 0.6413531], - [0.6354411, 0.6934311], - [0.5727553, 0.4628254], - [3.0524899, 8.8008899], - [2.7355803, 10.338497], - [3.1179374, 7.0686220], - [3.4944047, 11.479432], - [-0.402532, 0.5253690], - [0.5782190, 5.5400704], - [-0.839887, 0.7350041], - [-3.456470, 1.1156415], - [0.2260207, 1.5071672], - [-0.561562, 0.8836978], - [-1.690263, 0.6392155], - [-0.385394, 0.7401909], - [0.1467050, 0.9090058], - [7.1811993, 39.003407], - [6.8943132, 30.968126], - [6.6227164, 41.448548], - [0.0726709, 1.5960063], - [1.4450617, 8.7183435], - [-1.459836, 0.2719813], - [-2.824349, 4.5729382], - [-2.390462, 1.5464775], - [-5.869571, 5.3517279], - [-5.426019, 5.1817219], - [-16.34459, 0.9397117]]), rtol=1e-5) - np.testing.assert_array_almost_equal(msplot.outliers, - np.array( - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, - 0, 0, 0, 0, 1])) + fd_temperatures, + multivariate_depth=SimplicialDepth(), + ) + np.testing.assert_allclose( + msplot.points, + np.array([ + [0.2112587, 3.0322570], + [1.2823448, 0.8272850], + [0.8646544, 1.8619370], + [1.9862512, 5.5287354], + [0.7534918, 0.7203502], + [1.1325291, 0.2808455], + [-2.650529, 0.9702889], + [0.1434387, 0.9159834], + [-0.402844, 0.6413531], + [0.6354411, 0.6934311], + [0.5727553, 0.4628254], + [3.0524899, 8.8008899], + [2.7355803, 10.338497], + [3.1179374, 7.0686220], + [3.4944047, 11.479432], + [-0.402532, 0.5253690], + [0.5782190, 5.5400704], + [-0.839887, 0.7350041], + [-3.456470, 1.1156415], + [0.2260207, 1.5071672], + [-0.561562, 0.8836978], + [-1.690263, 0.6392155], + [-0.385394, 0.7401909], + [0.1467050, 0.9090058], + [7.1811993, 39.003407], + [6.8943132, 30.968126], + [6.6227164, 41.448548], + [0.0726709, 1.5960063], + [1.4450617, 8.7183435], + [-1.459836, 0.2719813], + [-2.824349, 4.5729382], + [-2.390462, 1.5464775], + [-5.869571, 5.3517279], + [-5.426019, 5.1817219], + [-16.34459, 0.9397117], + ]), + rtol=1e-5, + ) + np.testing.assert_array_almost_equal( + msplot.outliers, + np.array([ # noqa: WPS317 + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, + 0, 0, 0, 0, 1, + ]), + ) if __name__ == '__main__': - print() unittest.main() diff --git a/skfda/tests/test_math.py b/skfda/tests/test_math.py index 20ccc486e..fc17896a7 100644 --- a/skfda/tests/test_math.py +++ b/skfda/tests/test_math.py @@ -22,7 +22,7 @@ class InnerProductTest(unittest.TestCase): def test_several_variables(self) -> None: """Test inner_product with functions of several variables.""" - def f( + def f( # noqa: WPS430 x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloat, @@ -65,10 +65,10 @@ def f( def test_vector_valued(self) -> None: """Test inner_product with vector valued functions.""" - def f(x: NDArrayFloat) -> NDArrayFloat: + def f(x: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 return x**2 - def g(y: NDArrayFloat) -> NDArrayFloat: + def g(y: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 return 3 * y t = np.linspace(0, 1, 100) @@ -128,7 +128,10 @@ def test_matrix(self) -> None: np.testing.assert_allclose(gram, gram_basis, rtol=1e-2) gram_pairwise = _pairwise_symmetric( - skfda.misc.inner_product, X, Y) + skfda.misc.inner_product, + X, + Y, + ) np.testing.assert_allclose(gram, gram_pairwise) @@ -137,6 +140,7 @@ class CosineSimilarityVectorTest(unittest.TestCase): """Tests for cosine similarity for vectors.""" def setUp(self) -> None: + """Create examples.""" self.arr = np.array([ [0, 0, 1], [1, 1, 1], @@ -175,7 +179,6 @@ def test_cosine_similarity_elementwise(self) -> None: def test_cosine_similarity_matrix_one(self) -> None: """Matrix example for vectors with one input.""" - for arr2 in (None, self.arr): np.testing.assert_allclose( diff --git a/skfda/tests/test_oneway_anova.py b/skfda/tests/test_oneway_anova.py index a08af91b4..04ed0e386 100644 --- a/skfda/tests/test_oneway_anova.py +++ b/skfda/tests/test_oneway_anova.py @@ -1,38 +1,39 @@ -from skfda.datasets import fetch_gait -from skfda.inference.anova import oneway_anova, v_asymptotic_stat, \ - v_sample_stat -from skfda.representation import FDataGrid -from skfda.representation.basis import Fourier +"""Tests for ANOVA.""" import unittest -import pytest - import numpy as np +from skfda.datasets import fetch_gait +from skfda.inference.anova import ( + oneway_anova, + v_asymptotic_stat, + v_sample_stat, +) +from skfda.representation import FDataGrid +from skfda.representation.basis import Fourier + class OnewayAnovaTests(unittest.TestCase): + """Tests for ANOVA.""" - def test_oneway_anova_args(self): + def test_oneway_anova_args(self) -> None: + """Check behavior of test with invalid args.""" with self.assertRaises(ValueError): oneway_anova() - with self.assertRaises(ValueError): - oneway_anova(1, '2') with self.assertRaises(ValueError): oneway_anova(FDataGrid([0]), n_reps=-2) - def test_v_stats_args(self): - with self.assertRaises(ValueError): - v_sample_stat(1, [1]) + def test_v_stats_args(self) -> None: + """Check behavior of statistic with invalid args.""" with self.assertRaises(ValueError): v_sample_stat(FDataGrid([0]), [0, 1]) - with self.assertRaises(ValueError): - v_asymptotic_stat(1, [1]) with self.assertRaises(ValueError): v_asymptotic_stat(FDataGrid([0]), [0, 1]) with self.assertRaises(ValueError): v_asymptotic_stat(FDataGrid([[1, 1, 1], [1, 1, 1]]), [0, 0]) - def test_v_stats(self): + def test_v_stats(self) -> None: + """Test statistic behaviour.""" n_features = 50 weights = [1, 2, 3] t = np.linspace(0, 1, n_features) @@ -41,42 +42,68 @@ def test_v_stats(self): m3 = [3 for _ in range(n_features)] fd = FDataGrid([m1, m2, m3], grid_points=t) self.assertEqual(v_sample_stat(fd, weights), 7.0) - self.assertAlmostEqual(v_sample_stat(fd.to_basis(Fourier(n_basis=5)), - weights), 7.0) - res = (1 - 2 * np.sqrt(1 / 2)) ** 2 + (1 - 3 * np.sqrt(1 / 3)) ** 2 \ - + (2 - 3 * np.sqrt(2 / 3)) ** 2 + self.assertAlmostEqual( + v_sample_stat( + fd.to_basis(Fourier(n_basis=5)), + weights, + ), + 7.0, + ) + res = ( + (1 - 2 * np.sqrt(1 / 2)) ** 2 + + (1 - 3 * np.sqrt(1 / 3)) ** 2 + + (2 - 3 * np.sqrt(2 / 3)) ** 2 + ) self.assertAlmostEqual(v_asymptotic_stat(fd, weights), res) - self.assertAlmostEqual(v_asymptotic_stat(fd.to_basis(Fourier( - n_basis=5)), weights), res) + self.assertAlmostEqual( + v_asymptotic_stat( + fd.to_basis(Fourier(n_basis=5)), + weights, + ), + res, + ) - def test_asymptotic_behaviour(self): + def test_asymptotic_behaviour(self) -> None: + """Test asymptotic behaviour.""" dataset = fetch_gait() fd = dataset['data'].coordinates[1] - fd1 = fd[0:5] + fd1 = fd[:5] fd2 = fd[5:10] fd3 = fd[10:15] n_little_sim = 10 - sims = np.array([oneway_anova( - fd1, fd2, fd3, n_reps=500, random_state=i)[1] - for i in range(n_little_sim)]) + sims = np.array([ + oneway_anova( + fd1, + fd2, + fd3, + n_reps=500, + random_state=i, + )[1] + for i in range(n_little_sim) + ]) little_sim = np.mean(sims) big_sim = oneway_anova(fd1, fd2, fd3, n_reps=2000, random_state=100)[1] self.assertAlmostEqual(little_sim, big_sim, delta=0.05) fd = fd.to_basis(Fourier(n_basis=5)) - fd1 = fd[0:5] + fd1 = fd[:5] fd2 = fd[5:10] - sims = np.array([oneway_anova( - fd1, fd2, n_reps=500, random_state=i)[1] - for i in range(n_little_sim)]) + sims = np.array([ + oneway_anova( + fd1, + fd2, + n_reps=500, + random_state=i, + )[1] + for i in range(n_little_sim) + ]) little_sim = np.mean(sims) big_sim = oneway_anova(fd1, fd2, n_reps=2000, random_state=100)[1] self.assertAlmostEqual(little_sim, big_sim, delta=0.05) if __name__ == '__main__': - print() unittest.main() diff --git a/skfda/tests/test_outliers.py b/skfda/tests/test_outliers.py index ba351822b..c7624565d 100644 --- a/skfda/tests/test_outliers.py +++ b/skfda/tests/test_outliers.py @@ -11,53 +11,64 @@ class TestsDirectionalOutlyingness(unittest.TestCase): + """Tests for directional outlyingness.""" - def test_directional_outlyingness(self): - data_matrix = [[[0.3], [0.4], [0.5], [0.6]], - [[0.5], [0.6], [0.7], [0.7]], - [[0.2], [0.3], [0.4], [0.5]]] + def test_directional_outlyingness(self) -> None: + """Test non-asymptotic behaviour.""" + data_matrix = [ + [[0.3], [0.4], [0.5], [0.6]], + [[0.5], [0.6], [0.7], [0.7]], + [[0.2], [0.3], [0.4], [0.5]], + ] grid_points = [2, 4, 6, 8] fd = FDataGrid(data_matrix, grid_points) stats = directional_outlyingness_stats( - fd, multivariate_depth=SimplicialDepth()) - np.testing.assert_allclose(stats.directional_outlyingness, - np.array([[[0.], - [0.], - [0.], - [0.]], - - [[0.5], - [0.5], - [0.5], - [0.5]], - - [[-0.5], - [-0.5], - [-0.5], - [-0.5]]]), - rtol=1e-06) - np.testing.assert_allclose(stats.mean_directional_outlyingness, - np.array([[0.], - [0.5], - [-0.5]]), - rtol=1e-06) - np.testing.assert_allclose(stats.variation_directional_outlyingness, - np.array([0., 0., 0.]), atol=1e-6) - - def test_asymptotic_formula(self): - data_matrix = [[1, 1, 2, 3, 2.5, 2], - [0.5, 0.5, 1, 2, 1.5, 1], - [-1, -1, -0.5, 1, 1, 0.5], - [-0.5, -0.5, -0.5, -1, -1, -1]] + fd, + multivariate_depth=SimplicialDepth(), + ) + np.testing.assert_allclose( + stats.directional_outlyingness, + np.array([ + np.tile(0.0, (4, 1)), + np.tile(0.5, (4, 1)), + np.tile(-0.5, (4, 1)), + ]), + rtol=1e-06, + ) + np.testing.assert_allclose( + stats.mean_directional_outlyingness, + np.array([ + [0.0], + [0.5], + [-0.5], + ]), + rtol=1e-06, + ) + np.testing.assert_allclose( + stats.variation_directional_outlyingness, + np.array([0, 0, 0]), + atol=1e-6, + ) + + def test_asymptotic_formula(self) -> None: + """Test the asymptotic behaviour.""" + data_matrix = [ + [1, 1, 2, 3, 2.5, 2], + [0.5, 0.5, 1, 2, 1.5, 1], + [-1, -1, -0.5, 1, 1, 0.5], # noqa: WPS204 + [-0.5, -0.5, -0.5, -1, -1, -1], + ] grid_points = [0, 2, 4, 6, 8, 10] fd = FDataGrid(data_matrix, grid_points) out_detector = MSPlotOutlierDetector( - _force_asymptotic=True) + _force_asymptotic=True, + ) prediction = out_detector.fit_predict(fd) - np.testing.assert_allclose(prediction, - np.array([1, 1, 1, 1])) + np.testing.assert_allclose( + prediction, + np.array([1, 1, 1, 1]), + ) if __name__ == '__main__': - print() unittest.main() From bc39cd5e93789e022d85a09833714681ce91cc0a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 15 Jul 2022 21:54:42 +0200 Subject: [PATCH 212/400] Refactor tests. --- skfda/tests/test_oneway_anova.py | 2 -- skfda/tests/test_outliers.py | 1 + skfda/tests/test_pandas.py | 37 ++++++++++++++++---- skfda/tests/test_per_class_transformer.py | 2 +- skfda/tests/test_recursive_maxima_hunting.py | 22 +++++++++--- 5 files changed, 50 insertions(+), 14 deletions(-) diff --git a/skfda/tests/test_oneway_anova.py b/skfda/tests/test_oneway_anova.py index 04ed0e386..fbeda4980 100644 --- a/skfda/tests/test_oneway_anova.py +++ b/skfda/tests/test_oneway_anova.py @@ -18,8 +18,6 @@ class OnewayAnovaTests(unittest.TestCase): def test_oneway_anova_args(self) -> None: """Check behavior of test with invalid args.""" - with self.assertRaises(ValueError): - oneway_anova() with self.assertRaises(ValueError): oneway_anova(FDataGrid([0]), n_reps=-2) diff --git a/skfda/tests/test_outliers.py b/skfda/tests/test_outliers.py index c7624565d..ab9ab3740 100644 --- a/skfda/tests/test_outliers.py +++ b/skfda/tests/test_outliers.py @@ -1,3 +1,4 @@ +"""Tests for outlying measures.""" import unittest import numpy as np diff --git a/skfda/tests/test_pandas.py b/skfda/tests/test_pandas.py index 320533189..1d79a1be4 100644 --- a/skfda/tests/test_pandas.py +++ b/skfda/tests/test_pandas.py @@ -1,3 +1,4 @@ +"""Tests for Pandas integration.""" import unittest import pandas as pd @@ -6,41 +7,63 @@ class TestPandas(unittest.TestCase): + """Test basic Pandas integration.""" def setUp(self) -> None: + """Create FDataGrid instance.""" self.fd = skfda.FDataGrid( - [[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 9]]) - self.fd_basis = self.fd.to_basis(skfda.representation.basis.BSpline( - n_basis=5)) + [ + [1, 2, 3, 4, 5, 6, 7], + [2, 3, 4, 5, 6, 7, 9], + ], + ) + self.fd_basis = self.fd.to_basis( + skfda.representation.basis.BSpline( + n_basis=5, + ), + ) def test_fdatagrid_series(self) -> None: + """Test creation of a Series with FDataGrid dtype.""" series = pd.Series(self.fd) self.assertIsInstance( - series.dtype, skfda.representation.grid.FDataGridDType) + series.dtype, + skfda.representation.grid.FDataGridDType, + ) self.assertEqual(len(series), self.fd.n_samples) self.assertTrue(series[0].equals(self.fd[0])) def test_fdatabasis_series(self) -> None: + """Test creation of a Series with FDataBasis dtype.""" series = pd.Series(self.fd_basis) self.assertIsInstance( - series.dtype, skfda.representation.basis.FDataBasisDType) + series.dtype, + skfda.representation.basis.FDataBasisDType, + ) self.assertEqual(len(series), self.fd_basis.n_samples) self.assertTrue(series[0].equals(self.fd_basis[0])) def test_fdatagrid_dataframe(self) -> None: + """Test creation of a DataFrame with FDataGrid dtype.""" df = pd.DataFrame({"function": self.fd}) self.assertIsInstance( - df["function"].dtype, skfda.representation.grid.FDataGridDType) + df["function"].dtype, + skfda.representation.grid.FDataGridDType, + ) self.assertEqual(len(df["function"]), self.fd.n_samples) self.assertTrue(df["function"][0].equals(self.fd[0])) def test_fdatabasis_dataframe(self) -> None: + """Test creation of a DataFrame with FDataBasis dtype.""" df = pd.DataFrame({"function": self.fd_basis}) self.assertIsInstance( - df["function"].dtype, skfda.representation.basis.FDataBasisDType) + df["function"].dtype, + skfda.representation.basis.FDataBasisDType, + ) self.assertEqual(len(df["function"]), self.fd_basis.n_samples) self.assertTrue(df["function"][0].equals(self.fd_basis[0])) def test_take(self) -> None: + """Test behaviour of take function.""" self.assertTrue(self.fd.take(0).equals(self.fd[0])) self.assertTrue(self.fd.take(0, axis=0).equals(self.fd[0])) diff --git a/skfda/tests/test_per_class_transformer.py b/skfda/tests/test_per_class_transformer.py index ad824be2e..6df4ace7b 100644 --- a/skfda/tests/test_per_class_transformer.py +++ b/skfda/tests/test_per_class_transformer.py @@ -34,7 +34,7 @@ def test_transform(self) -> None: manual = np.empty((93, 0)) classes, y_ind = _classifier_get_classes(self.y) for cur_class in range(classes.size): - feature_transformer = RecursiveMaximaHunting().fit( # type: ignore + feature_transformer = RecursiveMaximaHunting().fit( self.X[y_ind == cur_class], self.y[y_ind == cur_class], ) diff --git a/skfda/tests/test_recursive_maxima_hunting.py b/skfda/tests/test_recursive_maxima_hunting.py index 6f07bb151..6628aea45 100644 --- a/skfda/tests/test_recursive_maxima_hunting.py +++ b/skfda/tests/test_recursive_maxima_hunting.py @@ -1,3 +1,4 @@ +"""Tests for Recursive Maxima Hunting (RMH).""" import unittest import numpy as np @@ -5,16 +6,29 @@ import skfda from skfda.datasets import make_gaussian_process from skfda.preprocessing.dim_reduction import variable_selection as vs -from skfda.representation._typing import NDArrayFloat class TestRMH(unittest.TestCase): + """Tests for RMH.""" - def test_rmh(self) -> None: + def test_gaussian_homoscedastic(self) -> None: + """ + Test the case for which RMH is optimal. + + It tests a case with two homoscedastic Brownian processes where the + difference of means is piecewise linear. + + In this case RMH should return the points where the linear parts + join. + + """ n_samples = 10000 n_features = 100 - def mean_1(t: NDArrayFloat) -> NDArrayFloat: + def mean_1( # noqa: WPS430 + t: np.typing.NDArray[np.float_], + ) -> np.typing.NDArray[np.float_]: + return ( np.abs(t - 0.25) - 2 * np.abs(t - 0.5) @@ -46,7 +60,7 @@ def mean_1(t: NDArrayFloat) -> NDArrayFloat: correction=correction, stopping_condition=stopping_condition, ) - _ = rmh.fit(X, y) + rmh.fit(X, y) point_mask = rmh.get_support() points = X.grid_points[0][point_mask] np.testing.assert_allclose(points, [0.25, 0.5, 0.75], rtol=1e-1) From b473df1eb940b92fbaac6562f2a964bb7b116d3c Mon Sep 17 00:00:00 2001 From: Ddelval Date: Fri, 15 Jul 2022 22:02:41 +0200 Subject: [PATCH 213/400] Fix spelling and avoid unnecessary repeated training --- examples/plot_k_neighbors_classification.py | 25 +++++++++++---------- 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index 77e34b664..9522bcf50 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -81,12 +81,12 @@ # Once it is fitted, we can predict labels for the test samples. # # To predict the label of a test sample, the classifier will calculate the -# k-nearest neighbors and will asign the class shared by most of those k -# neighbors. In this case, we have set the number of neighbours to 5 +# k-nearest neighbors and will assign the class shared by most of those k +# neighbors. In this case, we have set the number of neighbors to 5 # (:math:`k=5`). # By default, it will use the # :math:`\mathbb{L}^2` distance between functions, to determine the -# neighbourhood of a sample. However, it can be used with +# neighborhood of a sample. However, it can be used with # any of the functional metrics described in # :doc:`/modules/misc/metrics`. @@ -151,13 +151,14 @@ ############################################################################## # -# We can also check the accuracy of the classifier for number of neighbors -# selected (11) using the -# :func:`~skfda.ml.classification.KNeighborsClassifier.score` method. +# By default, after performing the cross validation, the classifier will +# be fitted to the whole training data provided in the call to +# :func:`~skfda.ml.classification.KNeighborsClassifier.fit`. +# Therefore, to check the accuracy of the classifier for the number of +# neighbors selected (11), we can simply call the +# :func:`~sklearn.model_selection.GridSearchCV.score` method. -knn = KNeighborsClassifier(n_neighbors=11) -knn.fit(X_train, y_train) -score = knn.score(X_test, y_test) +score = gscv.score(X_test, y_test) print(score) ############################################################################## @@ -167,9 +168,9 @@ # metrics with similar results. # # For example, in the case of the :math:`\mathbb{L}^2` distance, -# by appoximating the integral as a Riemann sum, +# by approximating the integral as a Riemann sum, # we can derive that the value of said integral is proportional to the -# eucleadian distance between vectors. +# Euclidian distance between vectors. # # .. math:: # \|f - g \|_{\mathbb{L}^2} = \left ( \int_a^b |f(x) - g(x)|^2 dx \right ) @@ -221,5 +222,5 @@ ############################################################################## # -# This classifier can be used with multivariate funcional data, as surfaces +# This classifier can be used with multivariate functional data, as surfaces # or curves in :math:`\mathbb{R}^N`, if the metric supports it too. From e341c405559f176e80a9b18e3f6e9b0f653e96ce Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 15 Jul 2022 23:21:33 +0200 Subject: [PATCH 214/400] More tests refactor. --- setup.cfg | 2 ++ skfda/tests/test_registration.py | 10 +++++++--- skfda/tests/test_regularization.py | 12 ++++++++++-- 3 files changed, 19 insertions(+), 5 deletions(-) diff --git a/setup.cfg b/setup.cfg index 1974b293e..38807e20e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -134,6 +134,8 @@ strictness = long format = wemake show-source = true +doctests = true + [coverage:run] omit = # Omit reporting for dataset module diff --git a/skfda/tests/test_registration.py b/skfda/tests/test_registration.py index 6bdbac645..66cc3377d 100644 --- a/skfda/tests/test_registration.py +++ b/skfda/tests/test_registration.py @@ -1,3 +1,4 @@ +"""Tests for registration (alignment).""" import unittest import numpy as np @@ -197,7 +198,10 @@ def test_landmark_elastic_registration_warping(self) -> None: # Fixed location center = [0.3, 0.6] warping = landmark_elastic_registration_warping( - fd, landmarks, location=center) + fd, + landmarks, + location=center, + ) np.testing.assert_almost_equal( warping(center)[..., 0], landmarks, @@ -226,7 +230,7 @@ def test_landmark_elastic_registration(self) -> None: ) # Fixed location - center = [.3, .6] + center = [0.3, 0.6] fd_reg = landmark_elastic_registration(fd, landmarks, location=center) np.testing.assert_array_almost_equal( fd_reg(center), @@ -249,7 +253,7 @@ def setUp(self) -> None: def test_fit_transform(self) -> None: - reg = LeastSquaresShiftRegistration() + reg = LeastSquaresShiftRegistration[FDataGrid]() # Test fit transform with FDataGrid fd_reg = reg.fit_transform(self.fd) diff --git a/skfda/tests/test_regularization.py b/skfda/tests/test_regularization.py index 13aa9dc79..092f2c322 100644 --- a/skfda/tests/test_regularization.py +++ b/skfda/tests/test_regularization.py @@ -32,7 +32,15 @@ LinearDifferentialOperatorInput = Union[ int, - Sequence[Union[float, Callable[[np.ndarray], np.ndarray]]], + Sequence[ + Union[ + float, + Callable[ + [np.typing.NDArray[np.float_]], + np.typing.NDArray[np.float_], + ], + ], + ], None, ] @@ -45,7 +53,7 @@ def _test_penalty( basis: Basis, linear_diff_op: LinearDifferentialOperatorInput, atol: float = 0, - result: Optional[np.ndarray] = None, + result: Optional[np.typing.NDArray[np.float_]] = None, ) -> None: operator = LinearDifferentialOperator(linear_diff_op) From 7c31c183c8f78e71227dee7d0f46964a6b9605a7 Mon Sep 17 00:00:00 2001 From: David del Val Date: Sat, 16 Jul 2022 12:35:36 +0200 Subject: [PATCH 215/400] Update examples/plot_k_neighbors_classification.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Carlos Ramos Carreño --- examples/plot_k_neighbors_classification.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index 9522bcf50..64584e202 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -170,7 +170,7 @@ # For example, in the case of the :math:`\mathbb{L}^2` distance, # by approximating the integral as a Riemann sum, # we can derive that the value of said integral is proportional to the -# Euclidian distance between vectors. +# Euclidean distance between vectors. # # .. math:: # \|f - g \|_{\mathbb{L}^2} = \left ( \int_a^b |f(x) - g(x)|^2 dx \right ) From 700e25a7764eae39c78e36906638150793993b72 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sat, 16 Jul 2022 13:42:26 +0200 Subject: [PATCH 216/400] Ignore the entire .vscode folder --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index c9ab507ca..8f00f745c 100644 --- a/.gitignore +++ b/.gitignore @@ -111,4 +111,4 @@ pip-wheel-metadata/ # macOS DS_Store .DS_Store .gitignore -.vscode/settings.json +.vscode From 787f54133c1a2be59176cd5d2a8968a7d369154f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 18 Jul 2022 20:18:53 +0200 Subject: [PATCH 217/400] First version of refactor. --- skfda/_utils/_sklearn_adapter.py | 57 +- .../exploratory/outliers/neighbors_outlier.py | 19 +- skfda/ml/_neighbors_base.py | 550 +++++++++++------- .../classification/_neighbors_classifiers.py | 8 - skfda/ml/clustering/_neighbors_clustering.py | 10 +- skfda/ml/regression/_neighbors_regression.py | 4 +- 6 files changed, 390 insertions(+), 258 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 99159b756..d83cdbf16 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any, Generic, Optional, TypeVar, overload +from typing import Any, Generic, TypeVar, overload import sklearn.base @@ -12,9 +12,10 @@ "TransformerNoTarget", bound="TransformerMixin[Any, Any, None]", ) -Input = TypeVar("Input") -Output = TypeVar("Output") +Input = TypeVar("Input", contravariant=True) +Output = TypeVar("Output", covariant=True) Target = TypeVar("Target", contravariant=True) +TargetPrediction = TypeVar("TargetPrediction") class BaseEstimator( @@ -48,7 +49,7 @@ def fit( def fit( self: SelfType, X: Input, - y: Optional[Target] = None, + y: Target | None = None, ) -> SelfType: return self @@ -70,7 +71,7 @@ def fit_transform( def fit_transform( self, X: Input, - y: Optional[Target] = None, + y: Target | None = None, **fit_params: Any, ) -> Output: if y is None: @@ -93,13 +94,55 @@ def transform( class ClassifierMixin( ABC, - Generic[Input, Target], + Generic[Input, TargetPrediction], sklearn.base.ClassifierMixin, # type: ignore[misc] ): + def fit( + self: SelfType, + X: Input, + y: TargetPrediction, + ) -> SelfType: + return self + + @abstractmethod + def predict( + self: SelfType, + X: Input, + ) -> TargetPrediction: + pass + + def score( + self, + X: Input, + y: Target, + sample_weight: NDArrayFloat | None = None, + ) -> float: + return super().score(X, y, sample_weight=sample_weight) + + +class RegressorMixin( + ABC, + Generic[Input, TargetPrediction], + sklearn.base.RegressorMixin, # type: ignore[misc] +): + def fit( + self: SelfType, + X: Input, + y: TargetPrediction, + ) -> SelfType: + return self + + @abstractmethod + def predict( + self: SelfType, + X: Input, + ) -> TargetPrediction: + pass + def score( self, X: Input, y: Target, sample_weight: NDArrayFloat | None = None, - ) -> NDArrayFloat: + ) -> float: return super().score(X, y, sample_weight=sample_weight) diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index ce12611d9..328735821 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -2,17 +2,12 @@ from sklearn.base import OutlierMixin from ...misc.metrics import l2_distance -from ...ml._neighbors_base import ( - KNeighborsMixin, - NeighborsBase, - NeighborsMixin, - _to_multivariate_metric, -) +from ...ml._neighbors_base import KNeighborsMixin, _to_multivariate_metric -class LocalOutlierFactor(NeighborsBase, NeighborsMixin, KNeighborsMixin, - OutlierMixin): - """Unsupervised Outlier Detection. +class LocalOutlierFactor(KNeighborsMixin, OutlierMixin): + """ + Unsupervised Outlier Detection. Unsupervised Outlier Detection using Local Outlier Factor (LOF). @@ -289,8 +284,10 @@ def fit_predict(self, X, y=None): metric = l2_distance else: metric = self.metric - sklearn_metric = _to_multivariate_metric(metric, - self._grid_points) + sklearn_metric = _to_multivariate_metric( + metric, + self._grid_points, + ) else: sklearn_metric = self.metric diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 3849f2827..d493e638e 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -1,22 +1,63 @@ """Base classes for the neighbor estimators""" from __future__ import annotations -from abc import ABC -from typing import Any, Callable, Mapping, Tuple +from typing import ( + Any, + Callable, + Generic, + Mapping, + Tuple, + TypeVar, + Union, + overload, +) import numpy as np -from sklearn.base import BaseEstimator, RegressorMixin +import sklearn.neighbors +from scipy.sparse import csr_matrix from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from typing_extensions import Literal from skfda.misc.metrics._utils import PairwiseMetric from .. import FData, FDataGrid +from .._utils._sklearn_adapter import ( + BaseEstimator, + ClassifierMixin, + RegressorMixin, +) from ..misc.metrics import l2_distance from ..misc.metrics._typing import Metric from ..misc.metrics._utils import _fit_metric from ..representation._typing import GridPoints, NDArrayFloat, NDArrayInt +FDataType = TypeVar("FDataType", bound="FData") +SelfType = TypeVar("SelfType", bound="NeighborsBase[Any, Any]") +SelfTypeRegressor = TypeVar( + "SelfTypeRegressor", + bound="NeighborsRegressorMixin[Any, Any]", +) +Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +Target = TypeVar("Target") +TargetClassification = TypeVar("TargetClassification", bound=NDArrayInt) +TargetRegression = TypeVar( + "TargetRegression", + bound=Union[NDArrayFloat, FData], +) +TargetRegressionMultivariate = TypeVar( + "TargetRegressionMultivariate", + bound=NDArrayFloat, +) +TargetRegressionFData = TypeVar( + "TargetRegressionFData", + bound=FData, +) + +WeightsType = Union[ + Literal["uniform", "distance"], + Callable[[NDArrayFloat], NDArrayFloat], +] + def _to_multivariate(fdatagrid: FDataGrid) -> NDArrayFloat: r"""Returns the data matrix of a fdatagrid in flatten form compatible with @@ -59,19 +100,20 @@ def _to_multivariate_metric( metric: Metric[FDataGrid], grid_points: GridPoints, ) -> Metric[NDArrayFloat]: - r"""Transform a metric between FDatagrid in a sklearn compatible one. + """ + Transform a metric between FDatagrid in a sklearn compatible one. Given a metric between FDatagrids returns a compatible metric used to wrap the sklearn routines. Args: - metric (pyfunc): Metric of the module `mics.metrics`. Must accept + metric: Metric of the module `mics.metrics`. Must accept two FDataGrids and return a float representing the distance. - grid_points (array_like): Array of arrays with the sample points of + grid_points: Array of arrays with the sample points of the FDataGrids. Returns: - (pyfunc): sklearn vector metric. + Scikit-learn vector metric. Examples: @@ -96,7 +138,7 @@ def _to_multivariate_metric( """ # Shape -> (n_samples = 1, domain_dims...., image_dimension (-1)) - shape = [1] + [len(axis) for axis in grid_points] + [-1] + shape = (1,) + tuple(len(axis) for axis in grid_points) + (-1,) def multivariate_metric( x: NDArrayFloat, @@ -113,14 +155,14 @@ def multivariate_metric( return multivariate_metric -class NeighborsBase(ABC, BaseEstimator): +class NeighborsBase(BaseEstimator, Generic[Input, Target]): """Base class for nearest neighbors estimators.""" def __init__( self, n_neighbors: int | None = None, radius: float | None = None, - weights: Literal["uniform", "distance"] | Callable[[NDArrayFloat], NDArrayFloat] = "uniform", + weights: WeightsType = "uniform", algorithm: Literal["auto", "ball_tree", "kd_tree", "brute"] = "auto", leaf_size: int = 30, metric: Literal["precomputed", "l2"] | Metric[FDataGrid] = 'l2', @@ -129,8 +171,6 @@ def __init__( precompute_metric: bool = False, multivariate_metric: bool = False, ): - """Initializes the nearest neighbors estimator.""" - self.n_neighbors = n_neighbors self.radius = radius self.weights = weights @@ -143,7 +183,8 @@ def __init__( self.multivariate_metric = multivariate_metric def _check_is_fitted(self) -> None: - """Check if the estimator is fitted. + """ + Check if the estimator is fitted. Raises: NotFittedError: If the estimator is not fitted. @@ -164,25 +205,23 @@ def _transform_to_multivariate( return X - -class NeighborsMixin: - """Mixin class to train the neighbors models.""" - def fit( - self, - X: FDataGrid | NDArrayFloat, - y: NDArrayFloat | NDArrayInt | None = None, - ) -> NeighborsMixin: - """Fit the model using X as training data and y as target values. + self: SelfType, + X: Input, + y: Target | None = None, + ) -> SelfType: + """ + Fit the model using X as training data and y as target values. Args: - X (:class:`FDataGrid`, array_matrix): Training data. FDataGrid - with the training data or array matrix with shape - [n_samples, n_samples] if metric='precomputed'. - y (array-like or sparse matrix): Target values of - shape = [n_samples] or [n_samples, n_outputs]. + X: Training data. FDataGrid with the training data or array matrix + with shape [n_samples, n_samples] if metric='precomputed'. + y: Target values of shape = [n_samples] or [n_samples, n_outputs]. In the case of unsupervised search, this parameter is ignored. + Returns: + Self. + Note: This method wraps the corresponding sklearn routine in the module ``sklearn.neighbors``. @@ -226,23 +265,50 @@ def X_transform(X): return np.zeros(shape=(len(X), len(X))) return self -class KNeighborsMixin: - """Mixin class for K-Neighbors""" +class KNeighborsMixin(NeighborsBase[Input, Target]): + """Mixin class for K-Neighbors.""" + + @overload + def kneighbors( + self, + X: FDataGrid | NDArrayFloat | None = None, + n_neighbors: int | None = None, + *, + return_distance: Literal[True] = True, + ) -> Tuple[NDArrayFloat, NDArrayInt]: + pass + + @overload + def kneighbors( + self, + X: FDataGrid | NDArrayFloat | None = None, + n_neighbors: int | None = None, + *, + return_distance: Literal[False], + ) -> NDArrayInt: + pass + + def kneighbors( + self, + X: FDataGrid | NDArrayFloat | None = None, + n_neighbors: int | None = None, + *, + return_distance: bool = True, + ) -> NDArrayInt | Tuple[NDArrayFloat, NDArrayInt]: + """ + Find the K-neighbors of a point. - def kneighbors(self, X=None, n_neighbors=None, return_distance=True): - """Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. Args: - X (:class:`FDataGrid` or matrix): FDatagrid with the query - functions or matrix (n_query, n_indexed) if - metric == 'precomputed'. If not provided, neighbors of each - indexed point are returned. In this case, the query point is - not considered its own neighbor. - n_neighbors (int): Number of neighbors to get (default is the value + X: FDatagrid with the query functions or matrix + (n_query, n_indexed) if metric == 'precomputed'. If not + provided, neighbors of each indexed point are returned. In + this case, the query point is not considered its own neighbor. + n_neighbors: Number of neighbors to get (default is the value passed to the constructor). - return_distance (boolean, optional): Defaults to True. If False, - distances will not be returned. + return_distance: Defaults to True. If False, + distances will not be returned. Returns: dist : array @@ -287,21 +353,25 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True): return self.estimator_.kneighbors(X, n_neighbors, return_distance) - def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'): - """Computes the (weighted) graph of k-Neighbors for points in X + def kneighbors_graph( + self, + X: FDataGrid | NDArrayFloat | None = None, + n_neighbors: int | None = None, + mode: Literal["connectivity", "distance"] = "connectivity", + ) -> csr_matrix: + """ + Compute the (weighted) graph of k-Neighbors for points in X. Args: - X (:class:`FDataGrid` or matrix): FDatagrid with the query - functions or matrix (n_query, n_indexed) if - metric == 'precomputed'. If not provided, neighbors of each - indexed point are returned. In this case, the query point is - not considered its own neighbor. - n_neighbors (int): Number of neighbors to get (default is the value + X: FDatagrid with the query functions or matrix + (n_query, n_indexed) if metric == 'precomputed'. If not + provided, neighbors of each indexed point are returned. In + this case, the query point is not considered its own neighbor. + n_neighbors: Number of neighbors to get (default is the value passed to the constructor). - mode ('connectivity' or 'distance', optional): Type of returned - matrix: 'connectivity' will return the connectivity matrix with - ones and zeros, in 'distance' the edges are distance between - points. + mode: Type of returned matrix: 'connectivity' will return the + connectivity matrix with ones and zeros, in 'distance' the + edges are distance between points. Returns: Sparse matrix in CSR format, shape = [n_samples, n_samples_fit] @@ -346,12 +416,18 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'): return self.estimator_.kneighbors_graph(X, n_neighbors, mode) -class RadiusNeighborsMixin: - """Mixin Class for Raius Neighbors""" +class RadiusNeighborsMixin(NeighborsBase[Input, Target]): + """Mixin Class for Raius Neighbors.""" + + def radius_neighbors( + self, + X: FDataGrid | NDArrayFloat | None = None, + radius: float | None = None, + return_distance: bool = True, + ) -> NDArrayInt | Tuple[NDArrayFloat, NDArrayInt]: # TODO: Fix return type + """ + Find the neighbors within a given radius of a fdatagrid. - def radius_neighbors(self, X=None, radius=None, return_distance=True): - """Finds the neighbors within a given radius of a fdatagrid or - fdatagrids. Return the indices and distances of each point from the dataset lying in a ball with size ``radius`` around the points of the query array. Points lying on the boundary are included in the results. @@ -359,16 +435,15 @@ def radius_neighbors(self, X=None, radius=None, return_distance=True): query point. Args: - X (:class:`FDataGrid`, optional): fdatagrid with the sample or - samples whose neighbors will be returned. If not provided, - neighbors of each indexed point are returned. In this case, the - query point is not considered its own neighbor. - radius (float, optional): Limiting distance of neighbors to return. + X: Sample or samples whose neighbors will be returned. If not + provided, neighbors of each indexed point are returned. In this + case, the query point is not considered its own neighbor. + radius: Limiting distance of neighbors to return. (default is the value passed to the constructor). - return_distance (boolean, optional). Defaults to True. If False, - distances will not be returned + return_distance: Defaults to True. If False, distances will not be + returned. - Returns + Returns: (array, shape (n_samples): dist : array of arrays representing the distances to each point, only present if return_distance=True. The distance values are computed according to the ``metric`` @@ -407,7 +482,6 @@ def radius_neighbors(self, X=None, radius=None, return_distance=True): kneighbors Notes: - Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. @@ -423,23 +497,32 @@ def radius_neighbors(self, X=None, radius=None, return_distance=True): X = self._transform_to_multivariate(X) return self.estimator_.radius_neighbors( - X=X, radius=radius, return_distance=return_distance) + X=X, + radius=radius, + return_distance=return_distance, + ) + + def radius_neighbors_graph( + self, + X: FDataGrid | NDArrayFloat | None = None, + radius: float | None = None, + mode: Literal["connectivity", "distance"] = 'connectivity', + ) -> csr_matrix: + """ + Compute the (weighted) graph of Neighbors for points in X. - def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'): - """Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Args: - X (:class:`FDataGrid`): The query sample or samples. If not - provided, neighbors of each indexed point are returned. In this - case, the query point is not considered its own neighbor. - radius (float): Radius of neighborhoods. (default is the value - passed to the constructor). - mode ('connectivity' or 'distance', optional): Type of returned - matrix: 'connectivity' will return the connectivity matrix with - ones and zeros, in 'distance' the edges are distance between - points. + X: The query sample or samples. If not provided, neighbors of + each indexed point are returned. In this case, the query + point is not considered its own neighbor. + radius: Radius of neighborhoods. (default is the value passed + to the constructor). + mode: Type of returned matrix: 'connectivity' will return the + connectivity matrix with ones and zeros, in 'distance' + the edges are distance between points. Returns: sparse matrix in CSR format, shape = [n_samples, n_samples] @@ -448,30 +531,46 @@ def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'): Notes: This method wraps the corresponding sklearn routine in the module ``sklearn.neighbors``. + """ self._check_is_fitted() X = self._transform_to_multivariate(X) - return self.estimator_.radius_neighbors_graph(X=X, radius=radius, - mode=mode) + return self.estimator_.radius_neighbors_graph( + X=X, + radius=radius, + mode=mode, + ) -class NeighborsClassifierMixin: - """Mixin class for classifiers based in nearest neighbors""" +class NeighborsClassifierMixin( + NeighborsBase[Input, Target], + ClassifierMixin[Input, Target], +): + """Mixin class for classifiers based in nearest neighbors.""" - def predict(self, X): - """Predict the class labels for the provided data. + def fit( + self: SelfType, + X: Input, + y: Target, + ) -> SelfType: + return super().fit(X, y) + + def predict( + self, + X: Input, + ) -> Target: + """ + Predict the class labels for the provided data. Args: - X (:class:`FDataGrid` or array-like): FDataGrid with the test - samples or array (n_query, n_indexed) if metric == + X: Test samples or array (n_query, n_indexed) if metric == 'precomputed'. Returns: - - (np.array): y : array of shape [n_samples] or - [n_samples, n_outputs] with class labels for each data sample. + Array of shape [n_samples] or [n_samples, n_outputs] with class + labels for each data sample. Notes: This method wraps the corresponding sklearn routine in the module @@ -490,35 +589,59 @@ def predict(self, X): return self.estimator_.predict(X) -class NeighborsRegressorMixin(NeighborsMixin, RegressorMixin): - """Mixin class for the regressors based on neighbors""" +class NeighborsRegressorMixin( + NeighborsBase[Input, TargetRegression], + RegressorMixin[Input, TargetRegression], +): + """Mixin class for the regressors based on neighbors.""" - def _mean_regressor(self, X, weights=None): - """ - Default regressor using weighted average. + def _average( + self, + X: TargetRegression, + weights: NDArrayFloat | None = None, + ) -> TargetRegression: + """Compute weighted average.""" + if weights is None: + return np.mean(X, axis=0) - """ + weights /= np.sum(weights) - if weights is None: - return X.mean() + return np.sum(X * weights, axis=0) + + def _prediction_from_neighbors( + self, + neighbors: TargetRegression, + distance: NDArrayFloat, + ) -> TargetRegression: + + if self.weights == 'uniform': + weights = None + elif self.weights == 'distance': + weights = self._distance_weights(distance) else: - weights /= np.sum(weights) + weights = self.weights(distance) - return (X * weights).sum() + return self._average(neighbors, weights) - def fit(self, X, y): - """Fit the model using X as training data and y as responses. + def fit( + self: SelfTypeRegressor, + X: Input, + y: TargetRegression, + ) -> SelfTypeRegressor: + """ + Fit the model using X as training data and y as responses. Args: - X (:class:`FDataGrid`, array_matrix): Training data. FDataGrid + X: Training data. FDataGrid with the training data or array matrix with shape [n_samples, n_samples] if metric='precomputed'. - Y (:class:`FData` or array_like): Training data. FData + y: Training data. FData with the training respones (functional response case) or array matrix with length `n_samples` in the multivariate response case. + Returns: - Estimator: self. + Self. """ self._functional = isinstance(y, FData) @@ -528,16 +651,12 @@ def fit(self, X, y): else: return super().fit(X, y) - def _functional_fit(self, X, y): - """Fit the model using X as training data. - - Args: - X (:class:`FDataGrid`, array_matrix): Training data. FDataGrid - with the training data or array matrix with shape - [n_samples, n_samples] if metric='precomputed'. - - - """ + def _functional_fit( + self: SelfTypeRegressor, + X: Input, + y: Target, + ) -> SelfTypeRegressor: + """Fit the functional response model.""" if len(X) != y.n_samples: raise ValueError( "The response and dependent variable must " @@ -569,59 +688,41 @@ def _functional_fit(self, X, y): self.estimator_ = self._init_estimator(sklearn_metric) self.estimator_.fit(self._transform_to_multivariate(X)) - if self.regressor == 'mean': - self._regressor = self._mean_regressor - else: - self._regressor = self.regressor - - # Choose proper local regressor - if self.weights == 'uniform': - self._local_regressor = self._uniform_local_regression - elif self.weights == 'distance': - self._local_regressor = self._distance_local_regression - else: - self._local_regressor = self._weighted_local_regression - # Store the responses self._y = y return self - def _uniform_local_regression(self, neighbors, distance=None): - """Perform local regression with uniform weights""" - return self._regressor(neighbors) - - def _distance_local_regression(self, neighbors, distance): - """Perform local regression using distances as weights""" - idx = distance == 0. + def _distance_weights( + self, + distance: NDArrayFloat, + ) -> NDArrayFloat: + """Return weights based on distance reciprocal.""" + idx = (distance == 0) if np.any(idx): weights = distance - weights[idx] = 1. / np.sum(idx) - weights[~idx] = 0. + weights[idx] = 1 + weights[~idx] = 0 else: - weights = 1. / distance - weights /= np.sum(weights) - - return self._regressor(neighbors, weights) + weights = 1 / distance - def _weighted_local_regression(self, neighbors, distance): - """Perform local regression using custom weights""" + return weights - weights = self.weights(distance) - - return self._regressor(neighbors, weights) - - def predict(self, X): - """Predict the target for the provided data + def predict( + self, + X: Input, + ) -> TargetRegression: + """ + Predict the target for the provided data. Args: - X (:class:`FDataGrid` or array-like): FDataGrid with the test + X: FDataGrid with the test samples or array (n_query, n_indexed) if metric == 'precomputed'. Returns: - y : array of shape = [n_samples] or [n_samples, n_outputs] - or :class:`FData` containing as many samples as X. + array of shape = [n_samples] or [n_samples, n_outputs] + or :class:`FData` containing as many samples as X. """ self._check_is_fitted() @@ -629,32 +730,24 @@ def predict(self, X): # Choose type of prediction if self._functional: return self._functional_predict(X) - else: - return self._multivariate_predict(X) - - def _multivariate_predict(self, X): - """Predict the target for the provided data - Parameters - ---------- - X (:class:`FDataGrid` or array-like): FDataGrid with the test - samples or array (n_query, n_indexed) if metric == - 'precomputed'. - Returns - ------- - y : array of int, shape = [n_samples] or [n_samples, n_outputs] - Target values - Notes - ----- - This method wraps the corresponding sklearn routine in the module - ``sklearn.neighbors``. - """ + return self._multivariate_predict(X) + + def _multivariate_predict( + self: NeighborsRegressorMixin[Input, TargetRegressionMultivariate], + X: FDataGrid | NDArrayFloat, + ) -> TargetRegressionMultivariate: + """Predict a multivariate target.""" X = self._transform_to_multivariate(X) return self.estimator_.predict(X) - def _init_estimator(self, sklearn_metric): - """Initialize the sklearn nearest neighbors estimator. + def _init_estimator( + self, + sklearn_metric: Literal["precomputed"] | Metric[NDArrayFloat], + ) -> sklearn.neighbors.NearestNeighbors: + """ + Initialize the sklearn nearest neighbors estimator. Args: sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with @@ -666,30 +759,24 @@ def _init_estimator(self, sklearn_metric): """ if self._functional: - from sklearn.neighbors import NearestNeighbors as _NearestNeighbors - - return _NearestNeighbors( - n_neighbors=self.n_neighbors, radius=self.radius, - algorithm=self.algorithm, leaf_size=self.leaf_size, - metric=sklearn_metric, metric_params=self.metric_params, - n_jobs=self.n_jobs) - else: - return self._init_multivariate_estimator(sklearn_metric) - - def _functional_predict(self, X): - """Predict functional responses. - - Args: - X (:class:`FDataGrid` or array-like): FDataGrid with the test - samples or array (n_query, n_indexed) if metric == - 'precomputed'. - - Returns - y : :class:`FDataGrid` containing as many samples as X. + return sklearn.neighbors.NearestNeighbors( + n_neighbors=self.n_neighbors, + radius=self.radius, + algorithm=self.algorithm, + leaf_size=self.leaf_size, + metric=sklearn_metric, + metric_params=self.metric_params, + n_jobs=self.n_jobs, + ) - """ + return self._init_multivariate_estimator(sklearn_metric) + def _functional_predict( + self, + X: Input, + ) -> TargetRegression: + """Predict functional responses.""" X = self._transform_to_multivariate(X) distances, neighbors = self._query(X) @@ -697,34 +784,47 @@ def _functional_predict(self, X): if len(neighbors[0]) == 0: pred = self._outlier_response(neighbors) else: - pred = self._local_regressor(self._y[neighbors[0]], distances[0]) + pred = self._prediction_from_neighbors( + self._y[neighbors[0]], distances[0]) for i, idx in enumerate(neighbors[1:]): if len(idx) == 0: new_pred = self._outlier_response(neighbors) else: - new_pred = self._local_regressor(self._y[idx], - distances[i + 1]) + new_pred = self._prediction_from_neighbors( + self._y[idx], + distances[i + 1], + ) pred = pred.concatenate(new_pred) return pred - def _outlier_response(self, neighbors): - """Response in case of no neighbors""" + def _outlier_response( + self, + neighbors: TargetRegression, + ) -> TargetRegression: + """Response in case of no neighbors.""" + outlier_response = getattr(self, "outlier_response", None) - if (not hasattr(self, "outlier_response") or - self.outlier_response is None): + if outlier_response is None: index = np.where([len(n) == 0 for n in neighbors])[0] - raise ValueError(f"No neighbors found for test samples {index}, " - "you can try using larger radius, give a reponse " - "for outliers, or consider removing them from " - "your dataset.") - else: - return self.outlier_response + raise ValueError( + f"No neighbors found for test samples {index}, " + "you can try using larger radius, give a reponse " + "for outliers, or consider removing them from " + "your dataset.", + ) + + return outlier_response - def score(self, X, y, sample_weight=None): + def score( + self, + X: Input, + y: TargetRegression, + sample_weight: NDArrayFloat | None = None, + ) -> float: r"""Return the coefficient of determination R^2 of the prediction. In the multivariate response case, the coefficient :math:`R^2` is @@ -754,23 +854,28 @@ def score(self, X, y, sample_weight=None): features, would get a R^2 score of 0.0. Args: - X (FDataGrid): Test samples to be predicted. - y (FData or array-like): True responses of the test samples. - sample_weight (array_like, shape = [n_samples], optional): Sample - weights. + X: Test samples to be predicted. + y: True responses of the test samples. + sample_weight: Sample weights. Returns: - (float): Coefficient of determination. + Coefficient of determination. """ if self._functional: return self._functional_score(X, y, sample_weight=sample_weight) - else: - # Default sklearn multivariate score - return super().score(X, y, sample_weight=sample_weight) - def _functional_score(self, X, y, sample_weight=None): - r"""Return an extension of the coefficient of determination R^2. + # Default sklearn multivariate score + return super().score(X, y, sample_weight=sample_weight) + + def _functional_score( + self: NeighborsRegressorMixin[Input, TargetRegressionFData], + X: Input, + y: TargetRegressionFData, + sample_weight: NDArrayFloat | None = None, + ) -> float: + r""" + Return an extension of the coefficient of determination R^2. The coefficient is defined as @@ -787,23 +892,24 @@ def _functional_score(self, X, y, sample_weight=None): features, would get a R^2 score of 0.0. Args: - X (FDataGrid): Test samples to be predicted. - y (FData): True responses of the test samples. + X: Test samples to be predicted. + y: True responses of the test samples. sample_weight (array_like, shape = [n_samples], optional): Sample weights. Returns: - (float): Coefficient of determination. + Coefficient of determination. """ - # TODO: If it is created a module in ml.regression with other # score metrics, move it. from scipy.integrate import simps if y.dim_codomain != 1 or y.dim_domain != 1: - raise ValueError("Score not implemented for multivariate " - "functional data.") + raise ValueError( + "Score not implemented for multivariate " + "functional data.", + ) # Make prediction pred = self.predict(X) diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index adb2062bc..8ee838b4b 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -8,18 +8,13 @@ from .._neighbors_base import ( KNeighborsMixin, - NeighborsBase, NeighborsClassifierMixin, - NeighborsMixin, RadiusNeighborsMixin, ) class KNeighborsClassifier( - NeighborsBase, - NeighborsMixin, KNeighborsMixin, - ClassifierMixin, NeighborsClassifierMixin, ): """Classifier implementing the k-nearest neighbors vote. @@ -185,10 +180,7 @@ def _init_estimator(self, sklearn_metric): class RadiusNeighborsClassifier( - NeighborsBase, - NeighborsMixin, RadiusNeighborsMixin, - ClassifierMixin, NeighborsClassifierMixin, ): """Classifier implementing a vote among neighbors within a given radius. diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 143f62338..855c18289 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -1,15 +1,9 @@ """Unsupervised learner for implementing neighbor searches.""" -from .._neighbors_base import ( - KNeighborsMixin, - NeighborsBase, - NeighborsMixin, - RadiusNeighborsMixin, -) +from .._neighbors_base import KNeighborsMixin, RadiusNeighborsMixin -class NearestNeighbors(NeighborsBase, NeighborsMixin, KNeighborsMixin, - RadiusNeighborsMixin): +class NearestNeighbors(KNeighborsMixin, RadiusNeighborsMixin): """Unsupervised learner for implementing neighbor searches. Parameters diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index 7da0e4316..1d1e4a3fe 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -13,7 +13,7 @@ ) -class KNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, +class KNeighborsRegressor(NeighborsRegressorMixin, KNeighborsMixin): """Regression based on k-nearest neighbors. @@ -173,7 +173,7 @@ def _query(self, X): return self.estimator_.kneighbors(X) -class RadiusNeighborsRegressor(NeighborsBase, NeighborsRegressorMixin, +class RadiusNeighborsRegressor(NeighborsRegressorMixin, RadiusNeighborsMixin): """Regression based on neighbors within a fixed radius. From 1b7793aab792a705cebde2020b29608936599640 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 19 Jul 2022 18:15:29 +0200 Subject: [PATCH 218/400] Big refactor (some errors still). --- skfda/_utils/_sklearn_adapter.py | 2 +- .../exploratory/outliers/neighbors_outlier.py | 405 ++++++------ skfda/ml/_neighbors_base.py | 367 ++++------- .../classification/_neighbors_classifiers.py | 189 ++---- skfda/ml/clustering/_neighbors_clustering.py | 233 ++++--- skfda/ml/regression/_neighbors_regression.py | 606 +++++++++--------- skfda/tests/test_neighbors.py | 45 +- 7 files changed, 799 insertions(+), 1048 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index d83cdbf16..5b8502570 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -142,7 +142,7 @@ def predict( def score( self, X: Input, - y: Target, + y: TargetPrediction, sample_weight: NDArrayFloat | None = None, ) -> float: return super().score(X, y, sample_weight=sample_weight) diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index 328735821..e9037d993 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -1,12 +1,28 @@ """Neighbors outlier detection methods.""" +from __future__ import annotations + +from typing import Any, TypeVar, Union + from sklearn.base import OutlierMixin +from sklearn.neighbors import LocalOutlierFactor as _LocalOutlierFactor +from typing_extensions import Literal -from ...misc.metrics import l2_distance -from ...ml._neighbors_base import KNeighborsMixin, _to_multivariate_metric +from skfda.misc.metrics._typing import Metric +from ...misc.metrics import PairwiseMetric, l2_distance +from ...ml._neighbors_base import AlgorithmType, KNeighborsMixin +from ...representation import FData +from ...representation._typing import NDArrayFloat, NDArrayInt -class LocalOutlierFactor(KNeighborsMixin, OutlierMixin): - """ +SelfType = TypeVar("SelfType", bound="LocalOutlierFactor[Any]") +Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) + + +class LocalOutlierFactor( + KNeighborsMixin[Input, None], + OutlierMixin, # type: ignore +): + r""" Unsupervised Outlier Detection. Unsupervised Outlier Detection using Local Outlier Factor (LOF). @@ -25,79 +41,64 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin): its neighbors, one can identify samples that have a substantially lower density than their neighbors. These are considered outliers. - Parameters - ---------- - n_neighbors : int, optional (default=20) - Number of neighbors to use by default for :meth:`kneighbors` queries. - If n_neighbors is larger than the number of samples provided, - all samples will be used. - algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional - Algorithm used to compute the nearest neighbors: - - - 'ball_tree' will use :class:`BallTree` - - 'kd_tree' will use :class:`KDTree` - - 'brute' will use a brute-force search. - - 'auto' will attempt to decide the most appropriate algorithm - based on the values passed to :meth:`fit` method. - - leaf_size : int, optional (default=30) - Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can - affect the speed of the construction and query, as well as the memory - required to store the tree. The optimal value depends on the - nature of the problem. - metric : string or callable, (default - :func:`l2_distance `) - the distance metric to use for the tree. The default metric is - the L2 distance. See the documentation of the metrics module - for a list of available metrics. - metric_params : dict, optional (default=None) - Additional keyword arguments for the metric function. - contamination : float in (0., 0.5), optional (default='auto') - The amount of contamination of the data set, i.e. the proportion - of outliers in the data set. When fitting this is used to define the - threshold on the decision function. If "auto", the decision function - threshold is determined as in the original paper [BKNS2000]_. - novelty : boolean, default False - By default, LocalOutlierFactor is only meant to be used for outlier - detection (novelty=False). Set novelty to True if you want to use - LocalOutlierFactor for novelty detection. In this case be aware that - that you should only use predict, decision_function and score_samples - on new unseen data and not on the training set. - n_jobs : int or None, optional (default=None) - The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` - for more details. - Affects only :meth:`kneighbors` and :meth:`kneighbors_graph` methods. - multivariate_metric : boolean, optional (default = False) - Indicates if the metric used is a sklearn distance between vectors (see - :class:`~sklearn.neighbors.DistanceMetric`) or a functional metric of - the module `skfda.misc.metrics` if ``False``. - - Attributes - ---------- - negative_outlier_factor_ : numpy array, shape (n_samples,) - The opposite LOF of the training samples. The higher, the more normal. - Inliers tend to have a LOF score close to 1 - (``negative_outlier_factor_`` close to -1), while outliers tend to have - a larger LOF score. - The local outlier factor (LOF) of a sample captures its - supposed 'degree of abnormality'. - It is the average of the ratio of the local reachability density of - a sample and those of its k-nearest neighbors. - n_neighbors_ : integer - The actual number of neighbors used for :meth:`kneighbors` queries. - offset_ : float - Offset used to obtain binary labels from the raw scores. - Observations having a negative_outlier_factor smaller than `offset_` - are detected as abnormal. - The offset is set to -1.5 (inliers score around -1), except when a - contamination parameter different than "auto" is provided. In that - case, the offset is defined in such a way we obtain the expected - number of outliers in training. + Parameters: + n_neighbors: Number of neighbors to use by default for + :meth:`kneighbors` queries. + If n_neighbors is larger than the number of samples provided, + all samples will be used. + algorithm: Algorithm used to compute the nearest neighbors: + + - 'ball_tree' will use :class:`BallTree` + - 'kd_tree' will use :class:`KDTree` + - 'brute' will use a brute-force search. + - 'auto' will attempt to decide the most appropriate algorithm + based on the values passed to :meth:`fit` method. + + leaf_size: Leaf size passed to :class:`BallTree` or :class:`KDTree`. + This can affect the speed of the construction and query, as well as + the memory required to store the tree. The optimal value depends on + the nature of the problem. + metric: The distance metric to use for the tree. The default metric is + the L2 distance. See the documentation of the metrics module + for a list of available metrics. + contamination: The amount of contamination of the data set, i.e. the + proportion of outliers in the data set. When fitting this is used + to define the threshold on the decision function. If "auto", the + decision function threshold is determined as in the original paper + [BKNS2000]_. + novelty: By default, LocalOutlierFactor is only meant to be used for + outlier detection (novelty=False). Set novelty to True if you want + to use LocalOutlierFactor for novelty detection. In this case be + aware that that you should only use predict, decision_function and + score_samples on new unseen data and not on the training set. + n_jobs: The number of parallel jobs to run for neighbors search. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + Affects only :meth:`kneighbors` and :meth:`kneighbors_graph` + methods. + + Attributes: + negative_outlier_factor\_ : The opposite LOF of the training samples. + The higher, the more normal. Inliers tend to have a LOF score close + to 1 (``negative_outlier_factor_`` close to -1), while outliers + tend to have a larger LOF score. + The local outlier factor (LOF) of a sample captures its + supposed 'degree of abnormality'. + It is the average of the ratio of the local reachability density of + a sample and those of its k-nearest neighbors. + n_neighbors\_ : The actual number of neighbors used for + :meth:`kneighbors` queries. + offset\_ : Offset used to obtain binary labels from the raw scores. + Observations having a negative_outlier_factor smaller than + `offset_` are detected as abnormal. + The offset is set to -1.5 (inliers score around -1), except when a + contamination parameter different than "auto" is provided. In that + case, the offset is defined in such a way we obtain the expected + number of outliers in training. Examples: - **Local Outlier Factor (LOF) for outlier detection**. >>> from skfda.exploratory.outliers import LocalOutlierFactor @@ -127,8 +128,12 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin): Creation of a dataset without outliers. - >>> fd_train = make_sinusoidal_process(n_samples=25, error_std=0, - ... phase_std=0.1, random_state=9) + >>> fd_train = make_sinusoidal_process( + ... n_samples=25, + ... error_std=0, + ... phase_std=0.1, + ... random_state=9, + ... ) Fit of LOF using the dataset without outliers. @@ -142,89 +147,78 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin): array([-1, -1, 1, 1, 1, 1, 1, 1, ..., 1, 1, 1, 1]) - References - ---------- - .. [BKNS2000] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, - J. (2000, May). LOF: identifying density-based local outliers. In ACM - sigmod record. + References: + .. [BKNS2000] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, + J. (2000, May). LOF: identifying density-based local outliers. + In ACM sigmod record. - Notes - ----- + Notes: This estimator wraps the scikit-learn class :class:`~sklearn.neighbors.LocalOutlierFactor` employing functional metrics and data instead of the multivariate ones. - See also - -------- - :class:`~skfda.ml.classification.KNeighborsClassifier` - :class:`~skfda.ml.classification.RadiusNeighborsClassifier` - :class:`~skfda.ml.classification.NearestCentroids` - :class:`~skfda.ml.regression.KNeighborsRegressor` - :class:`~skfda.ml.regression.RadiusNeighborsRegressor` - :class:`~skfda.ml.clustering.NearestNeighbors` + See also: + :class:`~skfda.ml.classification.KNeighborsClassifier` + :class:`~skfda.ml.classification.RadiusNeighborsClassifier` + :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.regression.KNeighborsRegressor` + :class:`~skfda.ml.regression.RadiusNeighborsRegressor` + :class:`~skfda.ml.clustering.NearestNeighbors` """ - def __init__(self, n_neighbors=20, algorithm='auto', - leaf_size=30, metric='l2', metric_params=None, - contamination='auto', novelty=False, - n_jobs=1, multivariate_metric=False): - """Initialize the Local Outlier Factor estimator.""" - - super().__init__(n_neighbors=n_neighbors, algorithm=algorithm, - leaf_size=leaf_size, metric=metric, - metric_params=metric_params, n_jobs=n_jobs, - multivariate_metric=multivariate_metric) + def __init__( + self, + n_neighbors: int = 20, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + contamination: float | Literal["auto"] = "auto", + novelty: bool = False, + n_jobs: int | None = None, + ) -> None: + super().__init__( + n_neighbors=n_neighbors, + algorithm=algorithm, + leaf_size=leaf_size, + metric=metric, + n_jobs=n_jobs, + ) self.contamination = contamination self.novelty = novelty - def _init_estimator(self, sklearn_metric): - """Initialize the sklearn nearest neighbors estimator. - - Args: - sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. - - Returns: - Sklearn LocalOutlierFactor estimator initialized. - - """ - from sklearn.neighbors import LocalOutlierFactor as _LocalOutlierFactor - + def _init_estimator(self) -> _LocalOutlierFactor: return _LocalOutlierFactor( - n_neighbors=self.n_neighbors, algorithm=self.algorithm, - leaf_size=self.leaf_size, metric=sklearn_metric, - metric_params=self.metric_params, contamination=self.contamination, - novelty=self.novelty, n_jobs=self.n_jobs) - - def _store_fit_data(self): + n_neighbors=self.n_neighbors, + algorithm=self.algorithm, + leaf_size=self.leaf_size, + metric="precomputed", + contamination=self.contamination, + novelty=self.novelty, + n_jobs=self.n_jobs, + ) + + def _store_fit_data(self) -> None: """Store the parameters created during the fit.""" - self.negative_outlier_factor_ = self.estimator_.negative_outlier_factor_ - self.n_neighbors_ = self.estimator_.n_neighbors_ - self.offset_ = self.estimator_.offset_ - - def fit(self, X, y=None): - """Fit the model using X as training data. - - Parameters - ---------- - X : :class:`~skfda.FDataGrid` or array_like - Training data. FDataGrid containing the samples, - or array with shape [n_samples, n_samples] if metric='precomputed'. - y : Ignored - not used, present for API consistency by convention. - Returns - ------- - self : object - """ - + self.negative_outlier_factor_ = self._estimator.negative_outlier_factor_ + self.n_neighbors_ = self._estimator.n_neighbors_ + self.offset_ = self._estimator.offset_ + + def fit( + self: SelfType, + X: Input, + y: None = None, + ) -> SelfType: super().fit(X, y) self._store_fit_data() return self - def predict(self, X=None): - """Predict the labels (1 inlier, -1 outlier) of X according to LOF. + def predict( + self, + X: Input, + ) -> NDArrayInt: + """ + Predict the labels (1 inlier, -1 outlier) of X according to LOF. This method allows to generalize prediction to *new observations* (not in the training set). Only available for novelty detection (when @@ -232,75 +226,62 @@ def predict(self, X=None): If X is None, returns the same as fit_predict(X_train). - Parameters - ---------- - X : :class:`~skfda.FDataGrid` or array_like - FDataGrid containing the query sample or samples to compute the - Local Outlier Factor w.r.t. to the training samples or array with - the distances to the training samples if metric='precomputed'. + Parameters: + X: FDataGrid containing the query sample or samples to compute the + Local Outlier Factor w.r.t. to the training samples or array with + the distances to the training samples if metric='precomputed'. - Returns - ------- - is_inlier : array, shape (n_samples,) + Returns: Returns -1 for anomalies/outliers and +1 for inliers. - """ + """ self._check_is_fitted() - X_multivariate = self._transform_to_multivariate(X) + X_dist = self._X_to_distances(X) - return self.estimator_.predict(X_multivariate) + return self._estimator.predict(X_dist) - def fit_predict(self, X, y=None): - """Fits the model to the training set X and returns the labels. + def fit_predict( + self, + X: Input, + y: None = None, + ) -> NDArrayInt: + """ + Fits the model to the training set X and returns the labels. Label is 1 for an inlier and -1 for an outlier according to the LOF score and the contamination parameter. - Parameters - ---------- - X : :class:`~skfda.FDataGrid` or array_like - Training data. FDataGrid containing the samples, - or array with shape [n_samples, n_samples] if metric='precomputed'. - y : Ignored - not used, present for API consistency by convention. - Returns - ------- - is_inlier : array, shape (n_samples,) + Parameters: + X: Training data. Samples, or array with shape + [n_samples, n_samples] if metric='precomputed'. + y : Ignored. Not used, present for API consistency by convention. + + Returns: Returns -1 for anomalies/outliers and 1 for inliers. - """ + """ # In this estimator fit_predict cannot be wrapped as fit().predict() + self._estimator = self._init_estimator() + + self._fitted_with_distances = False + if self.metric == 'precomputed': - self.estimator_ = self._init_estimator(self.metric) - res = self.estimator_.fit_predict(X, y) + res = self._estimator.fit_predict(X, y) else: - self._grid_points = X.grid_points - self._shape = X.data_matrix.shape[1:] - - if not self.multivariate_metric: - # Constructs sklearn metric to manage vector - if self.metric == 'l2': - metric = l2_distance - else: - metric = self.metric - sklearn_metric = _to_multivariate_metric( - metric, - self._grid_points, - ) - else: - sklearn_metric = self.metric - - self.estimator_ = self._init_estimator(sklearn_metric) - X_multivariate = self._transform_to_multivariate(X) - res = self.estimator_.fit_predict(X_multivariate, y) + X_dist = PairwiseMetric(self.metric)(X) + res = self._estimator.fit_predict(X_dist, y) self._store_fit_data() return res - def decision_function(self, X): - """Shifted opposite of the Local Outlier Factor of X. + def decision_function( + self, + X: Input, + ) -> NDArrayFloat: + """ + Shifted opposite of the Local Outlier Factor of X. Bigger is better, i.e. large values correspond to inliers. The shift offset allows a zero threshold for being an outlier. @@ -310,26 +291,27 @@ def decision_function(self, X): Also, the samples in X are not considered in the neighborhood of any point. - Parameters - ---------- - X : :class:`~skfda.FDataGrid` or array_like - FDataGrid containing the query sample or samples to compute the - Local Outlier Factor w.r.t. to the training samples. + Parameters: + X: Query sample or samples to compute the + Local Outlier Factor w.r.t. to the training samples. - Returns - ------- - shifted_opposite_lof_scores : array, shape (n_samples,) + Returns: The shifted opposite of the Local Outlier Factor of each input samples. The lower, the more abnormal. Negative scores represent outliers, positive scores represent inliers. + """ self._check_is_fitted() - X_multivariate = self._transform_to_multivariate(X) + X_dist = self._X_to_distances(X) - return self.estimator_.decision_function(X_multivariate) + return self._estimator.decision_function(X_dist) - def score_samples(self, X): - """Opposite of the Local Outlier Factor of X. + def score_samples( + self, + X: Input, + ) -> NDArrayFloat: + """ + Opposite of the Local Outlier Factor of X. It is the opposite as bigger is better, i.e. large values correspond to inliers. @@ -343,19 +325,16 @@ def score_samples(self, X): The score_samples on training data is available by considering the the ``negative_outlier_factor_`` attribute. - Parameters - ---------- - X : :class:`~skfda.FDataGrid` or array_like - FDataGrid containing the query sample or samples to compute the - Local Outlier Factor w.r.t. to the training samples. + Parameters: + X: Query sample or samples to compute the + Local Outlier Factor w.r.t. to the training samples. - Returns - ------- - opposite_lof_scores : array, shape (n_samples,) + Returns: The opposite of the Local Outlier Factor of each input samples. The lower, the more abnormal. + """ self._check_is_fitted() - X_multivariate = self._transform_to_multivariate(X) + X_dist = self._X_to_distances(X) - return self.estimator_.score_samples(X_multivariate) + return self._estimator.score_samples(X_dist) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index d493e638e..dba9c89cf 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -57,102 +57,7 @@ Literal["uniform", "distance"], Callable[[NDArrayFloat], NDArrayFloat], ] - - -def _to_multivariate(fdatagrid: FDataGrid) -> NDArrayFloat: - r"""Returns the data matrix of a fdatagrid in flatten form compatible with - sklearn. - - Args: - fdatagrid (:class:`FDataGrid`): Grid to be converted to matrix - - Returns: - (np.array): Numpy array with size (n_samples, points), where - points = prod([len(d) for d in fdatagrid.grid_points] - - """ - return fdatagrid.data_matrix.reshape(fdatagrid.n_samples, -1) - - -def _from_multivariate( - data_matrix: NDArrayFloat, - grid_points: GridPoints, - shape: Tuple[int, ...], - **kwargs: Any, -) -> FDataGrid: - r"""Constructs a FDatagrid from the data matrix flattened. - - Args: - data_matrix (np.array): Data Matrix flattened as multivariate vector - compatible with sklearn. - grid_points (array_like): List with sample points for each dimension. - shape (tuple): Shape of the data_matrix. - **kwargs: Named params to be passed to the FDataGrid constructor. - - Returns: - (:class:`FDataGrid`): FDatagrid with the data. - - """ - return FDataGrid(data_matrix.reshape(shape), grid_points, **kwargs) - - -def _to_multivariate_metric( - metric: Metric[FDataGrid], - grid_points: GridPoints, -) -> Metric[NDArrayFloat]: - """ - Transform a metric between FDatagrid in a sklearn compatible one. - - Given a metric between FDatagrids returns a compatible metric used to - wrap the sklearn routines. - - Args: - metric: Metric of the module `mics.metrics`. Must accept - two FDataGrids and return a float representing the distance. - grid_points: Array of arrays with the sample points of - the FDataGrids. - - Returns: - Scikit-learn vector metric. - - Examples: - - >>> import numpy as np - >>> from skfda import FDataGrid - >>> from skfda.misc.metrics import l2_distance - >>> from skfda.ml._neighbors_base import _to_multivariate_metric - - Calculate the Lp distance between fd and fd2. - - >>> x = np.linspace(0, 1, 101) - >>> fd = FDataGrid([np.ones(len(x))], x) - >>> fd2 = FDataGrid([np.zeros(len(x))], x) - >>> l2_distance(fd, fd2).round(2) - array([ 1.]) - - Creation of the sklearn-style metric. - - >>> sklearn_l2_distance = _to_multivariate_metric(l2_distance, [x]) - >>> sklearn_l2_distance(np.ones(len(x)), np.zeros(len(x))).round(2) - array([ 1.]) - - """ - # Shape -> (n_samples = 1, domain_dims...., image_dimension (-1)) - shape = (1,) + tuple(len(axis) for axis in grid_points) + (-1,) - - def multivariate_metric( - x: NDArrayFloat, - y: NDArrayFloat, - **kwargs: Any, - ) -> NDArrayFloat: - - return metric( - _from_multivariate(x, grid_points, shape), - _from_multivariate(y, grid_points, shape), - **kwargs, - ) - - return multivariate_metric +AlgorithmType = Literal["auto", "ball_tree", "kd_tree", "brute"] class NeighborsBase(BaseEstimator, Generic[Input, Target]): @@ -163,12 +68,11 @@ def __init__( n_neighbors: int | None = None, radius: float | None = None, weights: WeightsType = "uniform", - algorithm: Literal["auto", "ball_tree", "kd_tree", "brute"] = "auto", + algorithm: AlgorithmType = "auto", leaf_size: int = 30, - metric: Literal["precomputed", "l2"] | Metric[FDataGrid] = 'l2', + metric: Literal["precomputed"] | Metric[Input] = l2_distance, metric_params: Mapping[str, Any] | None = None, n_jobs: int | None = None, - precompute_metric: bool = False, multivariate_metric: bool = False, ): self.n_neighbors = n_neighbors @@ -179,7 +83,6 @@ def __init__( self.metric = metric self.metric_params = metric_params self.n_jobs = n_jobs - self.precompute_metric = precompute_metric self.multivariate_metric = multivariate_metric def _check_is_fitted(self) -> None: @@ -190,20 +93,7 @@ def _check_is_fitted(self) -> None: NotFittedError: If the estimator is not fitted. """ - sklearn_check_is_fitted(self, ['estimator_']) - - def _transform_to_multivariate( - self, - X: FDataGrid | None, - ) -> NDArrayFloat | None: - """Transform the input data to array form. If the metric is - precomputed it is not transformed. - - """ - if X is not None and self.metric != 'precomputed': - X = _to_multivariate(X) - - return X + sklearn_check_is_fitted(self, ['_estimator']) def fit( self: SelfType, @@ -227,42 +117,52 @@ def fit( ``sklearn.neighbors``. """ - # If metric is precomputed no diferences with the Sklearn stimator + # If metric is precomputed no diferences with the Sklearn estimator + self._estimator = self._init_estimator() + + self._fitted_with_distances = False + if self.metric == 'precomputed': - self.estimator_ = self._init_estimator(self.metric) - self.estimator_.fit(X, y) + self._fitted_with_distances = True + self._estimator.fit(X, y) else: - self._grid_points = X.grid_points - self._shape = X.data_matrix.shape[1:] + self._fit_X = X.copy() + self._estimator.fit(np.zeros(shape=(len(X), len(X))), y) - X_transform = self._transform_to_multivariate + if isinstance(y, FData): + self._fit_y = y.copy() - if self.multivariate_metric: - _fit_metric(self.metric, X) - sklearn_metric = self.metric - elif self.precompute_metric: - sklearn_metric = "precomputed" - _fit_metric(self.metric, X) - self._fit_X = X.copy() + return self - def X_transform(X): return np.zeros(shape=(len(X), len(X))) - else: - # Constructs sklearn metric to manage vector - if self.metric == 'l2': - metric = l2_distance - else: - metric = self.metric - - _fit_metric(metric, X) - sklearn_metric = _to_multivariate_metric( - metric, - self._grid_points, - ) + def _refit_with_distances(self) -> None: + if not self._fitted_with_distances: + distances = PairwiseMetric(self.metric)(self._fit_X) + self._estimator.fit(distances, self._fit_y) + self._fitted_with_distances = True - self.estimator_ = self._init_estimator(sklearn_metric) - self.estimator_.fit(X_transform(X), y) + def _X_to_distances( + self, + X: Input, + ) -> NDArrayFloat: - return self + if self.metric == 'precomputed': + return X + + return PairwiseMetric(self.metric)(X, self._fit_X) + + def _init_estimator( + self, + ) -> sklearn.neighbors.NearestNeighbors: + """Initialize the sklearn nearest neighbors estimator.""" + return sklearn.neighbors.NearestNeighbors( + n_neighbors=self.n_neighbors, + radius=self.radius, + algorithm=self.algorithm, + leaf_size=self.leaf_size, + metric="precomputed", + metric_params=self.metric_params, + n_jobs=self.n_jobs, + ) class KNeighborsMixin(NeighborsBase[Input, Target]): @@ -349,9 +249,12 @@ def kneighbors( """ self._check_is_fitted() - X = self._transform_to_multivariate(X) + if X is None: + self._refit_with_distances() + else: + X = self._X_to_distances(X) - return self.estimator_.kneighbors(X, n_neighbors, return_distance) + return self._estimator.kneighbors(X, n_neighbors, return_distance) def kneighbors_graph( self, @@ -410,19 +313,42 @@ def kneighbors_graph( """ self._check_is_fitted() + if X is None: + self._refit_with_distances() + else: + X = self._X_to_distances(X) - X = self._transform_to_multivariate(X) - - return self.estimator_.kneighbors_graph(X, n_neighbors, mode) + return self._estimator.kneighbors_graph(X, n_neighbors, mode) class RadiusNeighborsMixin(NeighborsBase[Input, Target]): """Mixin Class for Raius Neighbors.""" + @overload + def radius_neighbors( + self, + X: FDataGrid | NDArrayFloat | None = None, + radius: float | None = None, + *, + return_distance: Literal[True] = True, + ) -> Tuple[NDArrayFloat, NDArrayInt]: # TODO: Fix return type + pass + + @overload + def radius_neighbors( + self, + X: FDataGrid | NDArrayFloat | None = None, + radius: float | None = None, + *, + return_distance: Literal[False], + ) -> NDArrayInt: + pass + def radius_neighbors( self, X: FDataGrid | NDArrayFloat | None = None, radius: float | None = None, + *, return_distance: bool = True, ) -> NDArrayInt | Tuple[NDArrayFloat, NDArrayInt]: # TODO: Fix return type """ @@ -493,10 +419,12 @@ def radius_neighbors( """ self._check_is_fitted() + if X is None: + self._refit_with_distances() + else: + X = self._X_to_distances(X) - X = self._transform_to_multivariate(X) - - return self.estimator_.radius_neighbors( + return self._estimator.radius_neighbors( X=X, radius=radius, return_distance=return_distance, @@ -534,10 +462,12 @@ def radius_neighbors_graph( """ self._check_is_fitted() + if X is None: + self._refit_with_distances() + else: + X = self._X_to_distances(X) - X = self._transform_to_multivariate(X) - - return self.estimator_.radius_neighbors_graph( + return self._estimator.radius_neighbors_graph( X=X, radius=radius, mode=mode, @@ -579,14 +509,30 @@ def predict( """ self._check_is_fitted() - if self.precompute_metric: - X = PairwiseMetric( - l2_distance if self.metric == "l2" else self.metric, - )(X, self._fit_X) - else: - X = self._transform_to_multivariate(X) + X_dist = self._X_to_distances(X) - return self.estimator_.predict(X) + return self._estimator.predict(X_dist) + + def predict_proba( + self, + X: Input, + ) -> NDArrayFloat: + """Calculate probability estimates for the test data X. + + Args: + X: FDataGrid with the test samples or array (n_query, n_indexed) + if metric == 'precomputed'. + + Returns: + p: The class probabilities of the input samples. Classes are + ordered by lexicographic order. + + """ + self._check_is_fitted() + + X_dist = self._X_to_distances(X) + + return self._estimator.predict_proba(X_dist) class NeighborsRegressorMixin( @@ -635,63 +581,16 @@ def fit( X: Training data. FDataGrid with the training data or array matrix with shape [n_samples, n_samples] if metric='precomputed'. - y: Training data. FData - with the training respones (functional response case) - or array matrix with length `n_samples` in the multivariate - response case. + y: Training data. FData with the training respones (functional + response case) or array matrix with length `n_samples` in + the multivariate response case. Returns: Self. """ self._functional = isinstance(y, FData) - - if self._functional: - return self._functional_fit(X, y) - else: - return super().fit(X, y) - - def _functional_fit( - self: SelfTypeRegressor, - X: Input, - y: Target, - ) -> SelfTypeRegressor: - """Fit the functional response model.""" - if len(X) != y.n_samples: - raise ValueError( - "The response and dependent variable must " - "contain the same number of samples.", - ) - - # If metric is precomputed no different with the Sklearn stimator - if self.metric == 'precomputed': - self.estimator_ = self._init_estimator(self.metric) - self.estimator_.fit(X) - else: - self._grid_points = X.grid_points - self._shape = X.data_matrix.shape[1:] - - if not self.multivariate_metric: - - if self.metric == 'l2': - metric = l2_distance - else: - metric = self.metric - - sklearn_metric = _to_multivariate_metric( - metric, - self._grid_points, - ) - else: - sklearn_metric = self.metric - - self.estimator_ = self._init_estimator(sklearn_metric) - self.estimator_.fit(self._transform_to_multivariate(X)) - - # Store the responses - self._y = y - - return self + return super().fit(X, y) def _distance_weights( self, @@ -735,64 +634,34 @@ def predict( def _multivariate_predict( self: NeighborsRegressorMixin[Input, TargetRegressionMultivariate], - X: FDataGrid | NDArrayFloat, + X: Input, ) -> TargetRegressionMultivariate: """Predict a multivariate target.""" - X = self._transform_to_multivariate(X) - - return self.estimator_.predict(X) + X_dist = self._X_to_distances(X) - def _init_estimator( - self, - sklearn_metric: Literal["precomputed"] | Metric[NDArrayFloat], - ) -> sklearn.neighbors.NearestNeighbors: - """ - Initialize the sklearn nearest neighbors estimator. - - Args: - sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. - - Returns: - Sklearn K Neighbors estimator initialized. - - """ - if self._functional: - - return sklearn.neighbors.NearestNeighbors( - n_neighbors=self.n_neighbors, - radius=self.radius, - algorithm=self.algorithm, - leaf_size=self.leaf_size, - metric=sklearn_metric, - metric_params=self.metric_params, - n_jobs=self.n_jobs, - ) - - return self._init_multivariate_estimator(sklearn_metric) + return self._estimator.predict(X_dist) def _functional_predict( self, X: Input, ) -> TargetRegression: """Predict functional responses.""" - X = self._transform_to_multivariate(X) - distances, neighbors = self._query(X) if len(neighbors[0]) == 0: pred = self._outlier_response(neighbors) else: pred = self._prediction_from_neighbors( - self._y[neighbors[0]], distances[0]) + self._fit_y[neighbors[0]], + distances[0], + ) for i, idx in enumerate(neighbors[1:]): if len(idx) == 0: new_pred = self._outlier_response(neighbors) else: new_pred = self._prediction_from_neighbors( - self._y[idx], + self._fit_y[idx], distances[i + 1], ) diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index 8ee838b4b..cf231e6bf 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -1,67 +1,73 @@ """Neighbor models for supervised classification.""" -from sklearn.base import ClassifierMixin +from __future__ import annotations + +from typing import TypeVar, Union + from sklearn.neighbors import ( KNeighborsClassifier as _KNeighborsClassifier, RadiusNeighborsClassifier as _RadiusNeighborsClassifier, ) +from typing_extensions import Literal + +from skfda.misc.metrics._typing import Metric +from ...misc.metrics import l2_distance +from ...representation import FData +from ...representation._typing import NDArrayFloat, NDArrayInt from .._neighbors_base import ( + AlgorithmType, KNeighborsMixin, NeighborsClassifierMixin, RadiusNeighborsMixin, + WeightsType, ) +Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +Target = TypeVar("Target", bound=NDArrayInt) + class KNeighborsClassifier( - KNeighborsMixin, - NeighborsClassifierMixin, + KNeighborsMixin[Input, Target], + NeighborsClassifierMixin[Input, Target], ): - """Classifier implementing the k-nearest neighbors vote. + """ + Classifier implementing the k-nearest neighbors vote. Parameters: - n_neighbors (int, default = 5): - Number of neighbors to use by default for :meth:`kneighbors` - queries. - weights (str or callable, default = 'uniform'): - Weight function used in prediction. + n_neighbors: Number of neighbors to use by default for + :meth:`kneighbors` queries. + weights: Weight function used in prediction. Possible values: + - 'uniform': uniform weights. All points in each neighborhood - are weighted equally. + are weighted equally. - 'distance': weight points by the inverse of their distance. - in this case, closer neighbors of a query point will have a - greater influence than neighbors which are further away. + in this case, closer neighbors of a query point will have a + greater influence than neighbors which are further away. - [callable]: a user-defined function which accepts an - array of distances, and returns an array of the same shape - containing the weights. - algorithm (string, optional): - Algorithm used to compute the nearest neighbors: + array of distances, and returns an array of the same shape + containing the weights. + + algorithm: Algorithm used to compute the nearest neighbors: + - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm - based on the values passed to :meth:`fit` method. - leaf_size (int, default = 30): - Leaf size passed to BallTree or KDTree. This can affect the + based on the values passed to :meth:`fit` method. + + leaf_size: Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. - metric (string or callable, default - :func:`l2_distance `): - the distance metric to use for the tree. The default metric is + metric: The distance metric to use for the tree. The default metric is the L2 distance. See the documentation of the metrics module for a list of available metrics. - metric_params (dict, optional): - Additional keyword arguments for the metric function. - n_jobs (int or None, optional): - The number of parallel jobs to run for neighbors search. + n_jobs: The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. Doesn't affect :meth:`fit` method. - multivariate_metric (boolean, default = False): - Indicates if the metric used is a sklearn distance between vectors - (see :class:`~sklearn.neighbors.DistanceMetric`) or a functional - metric of the module `skfda.misc.metrics` if ``False``. Examples: Firstly, we will create a toy dataset with 2 classes @@ -115,15 +121,12 @@ class KNeighborsClassifier( def __init__( self, - n_neighbors=5, - weights='uniform', - algorithm='auto', - leaf_size=30, - metric='l2', - metric_params=None, - n_jobs=1, - precompute_metric: bool = False, - multivariate_metric: bool = False, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + n_jobs: int | None = None, ) -> None: super().__init__( n_neighbors=n_neighbors, @@ -131,67 +134,34 @@ def __init__( algorithm=algorithm, leaf_size=leaf_size, metric=metric, - metric_params=metric_params, n_jobs=n_jobs, - precompute_metric=precompute_metric, - multivariate_metric=multivariate_metric, ) - def predict_proba(self, X): - """Calculate probability estimates for the test data X. + def _init_estimator(self) -> _KNeighborsClassifier: - Args: - X (:class:`FDataGrid` or array-like): FDataGrid with the test - samples or array (n_query, n_indexed) if metric == - 'precomputed'. - - Returns: - p (array of shape = (n_samples, n_classes), or a list of n_outputs - of such arrays if n_outputs > 1): - The class probabilities of the input samples. Classes are - ordered by lexicographic order. - """ - self._check_is_fitted() - - X = self._transform_to_multivariate(X) - - return self.estimator_.predict_proba(X) - - def _init_estimator(self, sklearn_metric): - """Initialize the sklearn K neighbors estimator. - - Args: - sklearn_metric (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. - - Returns: - Sklearn K Neighbors estimator initialized. - """ return _KNeighborsClassifier( n_neighbors=self.n_neighbors, weights=self.weights, algorithm=self.algorithm, leaf_size=self.leaf_size, - metric=sklearn_metric, - metric_params=self.metric_params, + metric="precomputed", n_jobs=self.n_jobs, ) class RadiusNeighborsClassifier( - RadiusNeighborsMixin, - NeighborsClassifierMixin, + RadiusNeighborsMixin[Input, Target], + NeighborsClassifierMixin[Input, Target], ): - """Classifier implementing a vote among neighbors within a given radius. + """ + Classifier implementing a vote among neighbors within a given radius. Parameters: - radius (float, default = 1.0): - Range of parameter space to use by default for + radius: Range of parameter space to use by default for :meth:`radius_neighbors` queries. - weights (str or callable, default = 'uniform'): - Weight function used in prediction. + weights: Weight function used in prediction. Possible values: + - 'uniform': uniform weights. All points in each neighborhood are weighted equally. - 'distance': weight points by the inverse of their distance. @@ -200,37 +170,29 @@ class RadiusNeighborsClassifier( - [callable]: a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights. - algorithm (string, optional): - Algorithm used to compute the nearest neighbors: + + algorithm: Algorithm used to compute the nearest neighbors: + - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. - 'brute' will use a brute-force search. - - 'auto' will attempt to decide the most appropriate algorithm + - 'auto' will attempt to decide the most appropriate algorithm. based on the values passed to :meth:`fit` method. - leaf_size (int, default = 30): - Leaf size passed to BallTree or KDTree. This can affect the + + leaf_size: Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. - metric (string or callable, default - :func:`l2_distance `): - the distance metric to use for the tree. The default metric is + metric: The distance metric to use for the tree. The default metric is the L2 distance. See the documentation of the metrics module for a list of available metrics. outlier_label (int, optional): Label, which is given for outlier samples (samples with no neighbors on given radius). If set to None, ValueError is raised, when outlier is detected. - metric_params (dict, optional): - Additional keyword arguments for the metric function. - n_jobs (int or None, optional): - The number of parallel jobs to run for neighbors search. + n_jobs: The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. - multivariate_metric (boolean, default = False): - Indicates if the metric used is a sklearn distance between vectors - (see :class:`~sklearn.neighbors.DistanceMetric`) or a functional - metric of the module `skfda.misc.metrics` if ``False``. Examples: Firstly, we will create a toy dataset with 2 classes. @@ -273,47 +235,32 @@ class RadiusNeighborsClassifier( def __init__( self, - radius=1.0, - weights='uniform', - algorithm='auto', - leaf_size=30, - metric='l2', - metric_params=None, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, outlier_label=None, - n_jobs=1, - multivariate_metric=False, - ): + n_jobs: int | None = None, + ) -> None: super().__init__( radius=radius, weights=weights, algorithm=algorithm, leaf_size=leaf_size, metric=metric, - metric_params=metric_params, n_jobs=n_jobs, - multivariate_metric=multivariate_metric, ) self.outlier_label = outlier_label - def _init_estimator(self, sklearn_metric): - """Initialize the sklearn radius neighbors estimator. - - Args: - sklearn_metric (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. - - Returns: - Sklearn Radius Neighbors estimator initialized. - """ + def _init_estimator(self) -> _RadiusNeighborsClassifier: return _RadiusNeighborsClassifier( radius=self.radius, weights=self.weights, algorithm=self.algorithm, leaf_size=self.leaf_size, - metric=sklearn_metric, - metric_params=self.metric_params, + metric="precomputed", outlier_label=self.outlier_label, n_jobs=self.n_jobs, ) diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 855c18289..9fe3040cc 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -1,131 +1,124 @@ """Unsupervised learner for implementing neighbor searches.""" +from __future__ import annotations -from .._neighbors_base import KNeighborsMixin, RadiusNeighborsMixin - - -class NearestNeighbors(KNeighborsMixin, RadiusNeighborsMixin): - """Unsupervised learner for implementing neighbor searches. - - Parameters - ---------- - n_neighbors : int, optional (default = 5) - Number of neighbors to use by default for :meth:`kneighbors` queries. - radius : float, optional (default = 1.0) - Range of parameter space to use by default for :meth:`radius_neighbors` - queries. - algorithm : {'auto', 'ball_tree', 'brute'}, optional - Algorithm used to compute the nearest neighbors: - - - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. - - 'brute' will use a brute-force search. - - 'auto' will attempt to decide the most appropriate algorithm based on - the values passed to :meth:`fit` method. - - leaf_size : int, optional (default = 30) - Leaf size passed to BallTree or KDTree. This can affect the - speed of the construction and query, as well as the memory - required to store the tree. The optimal value depends on the - nature of the problem. - metric : string or callable, (default - :func:`l2_distance `) - the distance metric to use for the tree. The default metric is - the L2 distance. See the documentation of the metrics module - for a list of available metrics. - metric_params : dict, optional (default = None) - Additional keyword arguments for the metric function. - n_jobs : int or None, optional (default=None) - The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. - Doesn't affect :meth:`fit` method. - multivariate_metric : boolean, optional (default = False) - Indicates if the metric used is a sklearn distance between vectors (see - :class:`sklearn.neighbors.DistanceMetric`) or a functional metric of - the module :mod:`skfda.misc.metrics`. - Examples - -------- - Firstly, we will create a toy dataset with 2 classes - - >>> from skfda.datasets import make_sinusoidal_process - >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0) - >>> fd2 = make_sinusoidal_process(phase_mean=1.8, error_std=0., - ... phase_std=.25, random_state=0) - >>> fd = fd1.concatenate(fd2) - - We will fit a Nearest Neighbors estimator - - >>> from skfda.ml.clustering import NearestNeighbors - >>> neigh = NearestNeighbors(radius=.3) - >>> neigh.fit(fd) - NearestNeighbors(...radius=0.3...) - - Now we can query the k-nearest neighbors. - - >>> distances, index = neigh.kneighbors(fd[:2]) - >>> index # Index of k-neighbors of samples 0 and 1 - array([[ 0, 7, 6, 11, 2],...) - - >>> distances.round(2) # Distances to k-neighbors - array([[ 0. , 0.28, 0.29, 0.29, 0.3 ], - [ 0. , 0.27, 0.28, 0.29, 0.3 ]]) - - We can query the neighbors in a given radius too. - - >>> distances, index = neigh.radius_neighbors(fd[:2]) - >>> index[0] - array([ 0, 2, 6, 7, 11]...) - - >>> distances[0].round(2) # Distances to neighbors of the sample 0 - array([ 0. , 0.3 , 0.29, 0.28, 0.29]) - - See also - -------- - :class:`~skfda.ml.classification.KNeighborsClassifier` - :class:`~skfda.ml.classification.RadiusNeighborsClassifier` - :class:`~skfda.ml.classification.NearestCentroids` - :class:`~skfda.ml.regression.KNeighborsRegressor` - :class:`~skfda.ml.regression.RadiusNeighborsRegressor` - - - Notes - ----- - See Nearest Neighbors in the sklearn online documentation for a discussion - of the choice of ``algorithm`` and ``leaf_size``. - - This class wraps the sklearn classifier - `sklearn.neighbors.KNeighborsClassifier`. - - https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm +from typing import TypeVar, Union +from typing_extensions import Literal + +from skfda.misc.metrics._typing import Metric + +from ...misc.metrics import l2_distance +from ...representation import FData +from ...representation._typing import NDArrayFloat +from .._neighbors_base import ( + AlgorithmType, + KNeighborsMixin, + RadiusNeighborsMixin, +) + +Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) + + +class NearestNeighbors( + KNeighborsMixin[Input, None], + RadiusNeighborsMixin[Input, None], +): """ + Unsupervised learner for implementing neighbor searches. + + Parameters: + n_neighbors: Number of neighbors to use by default for + :meth:`kneighbors` queries. + radius: Range of parameter space to use by default for + :meth:`radius_neighbors` queries. + algorithm: Algorithm used to compute the nearest neighbors: + + - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. + - 'brute' will use a brute-force search. + - 'auto' will attempt to decide the most appropriate algorithm + based on the values passed to :meth:`fit` method. + + leaf_size: Leaf size passed to BallTree or KDTree. This can affect the + speed of the construction and query, as well as the memory + required to store the tree. The optimal value depends on the + nature of the problem. + metric: The distance metric to use for the tree. The default metric is + the L2 distance. See the documentation of the metrics module + for a list of available metrics. + n_jobs: The number of parallel jobs to run for neighbors search. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. + ``-1`` means using all processors. + Doesn't affect :meth:`fit` method. + + Examples: + Firstly, we will create a toy dataset with 2 classes + + >>> from skfda.datasets import make_sinusoidal_process + >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0) + >>> fd2 = make_sinusoidal_process(phase_mean=1.8, error_std=0., + ... phase_std=.25, random_state=0) + >>> fd = fd1.concatenate(fd2) - def __init__(self, n_neighbors=5, radius=1.0, algorithm='auto', - leaf_size=30, metric='l2', metric_params=None, - n_jobs=1, multivariate_metric=False): - """Initialize the nearest neighbors searcher.""" + We will fit a Nearest Neighbors estimator - super().__init__(n_neighbors=n_neighbors, radius=radius, - algorithm=algorithm, leaf_size=leaf_size, - metric=metric, metric_params=metric_params, - n_jobs=n_jobs, - multivariate_metric=multivariate_metric) + >>> from skfda.ml.clustering import NearestNeighbors + >>> neigh = NearestNeighbors(radius=.3) + >>> neigh.fit(fd) + NearestNeighbors(...radius=0.3...) - def _init_estimator(self, sklearn_metric): - """Initialize the sklearn nearest neighbors estimator. + Now we can query the k-nearest neighbors. - Args: - sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. + >>> distances, index = neigh.kneighbors(fd[:2]) + >>> index # Index of k-neighbors of samples 0 and 1 + array([[ 0, 7, 6, 11, 2],...) - Returns: - Sklearn K Neighbors estimator initialized. + >>> distances.round(2) # Distances to k-neighbors + array([[ 0. , 0.28, 0.29, 0.29, 0.3 ], + [ 0. , 0.27, 0.28, 0.29, 0.3 ]]) - """ - from sklearn.neighbors import NearestNeighbors as _NearestNeighbors + We can query the neighbors in a given radius too. + + >>> distances, index = neigh.radius_neighbors(fd[:2]) + >>> index[0] + array([ 0, 2, 6, 7, 11]...) + + >>> distances[0].round(2) # Distances to neighbors of the sample 0 + array([ 0. , 0.3 , 0.29, 0.28, 0.29]) + + See also: + :class:`~skfda.ml.classification.KNeighborsClassifier` + :class:`~skfda.ml.classification.RadiusNeighborsClassifier` + :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.regression.KNeighborsRegressor` + :class:`~skfda.ml.regression.RadiusNeighborsRegressor` + + + Notes: + See Nearest Neighbors in the sklearn online documentation for a + discussion of the choice of ``algorithm`` and ``leaf_size``. + + This class wraps the sklearn classifier + `sklearn.neighbors.KNeighborsClassifier`. + + https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm + + """ - return _NearestNeighbors( - n_neighbors=self.n_neighbors, radius=self.radius, - algorithm=self.algorithm, leaf_size=self.leaf_size, - metric=sklearn_metric, metric_params=self.metric_params, - n_jobs=self.n_jobs) + def __init__( + self, + n_neighbors: int = 5, + radius: float = 1.0, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + n_jobs: int | None = None, + ) -> None: + super().__init__( + n_neighbors=n_neighbors, + radius=radius, + algorithm=algorithm, + leaf_size=leaf_size, + metric=metric, + n_jobs=n_jobs, + ) diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index 1d1e4a3fe..fd0317e5a 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -1,342 +1,346 @@ """Neighbor models for regression.""" +from __future__ import annotations + +from typing import Tuple, TypeVar, Union + from sklearn.neighbors import ( KNeighborsRegressor as _KNeighborsRegressor, RadiusNeighborsRegressor as _RadiusNeighborsRegressor, ) +from typing_extensions import Literal + +from skfda.misc.metrics._typing import Metric +from ...misc.metrics import l2_distance +from ...representation import FData +from ...representation._typing import NDArrayFloat, NDArrayInt from .._neighbors_base import ( + AlgorithmType, KNeighborsMixin, - NeighborsBase, NeighborsRegressorMixin, RadiusNeighborsMixin, + WeightsType, ) +Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +Target = TypeVar("Target", bound=Union[NDArrayFloat, FData]) -class KNeighborsRegressor(NeighborsRegressorMixin, - KNeighborsMixin): - """Regression based on k-nearest neighbors. + +class KNeighborsRegressor( + NeighborsRegressorMixin[Input, Target], + KNeighborsMixin[Input, Target], +): + """ + Regression based on k-nearest neighbors. Regression with scalar, multivariate or functional response. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set. - Parameters - ---------- - n_neighbors : int, optional (default = 5) - Number of neighbors to use by default for :meth:`kneighbors` queries. - weights : str or callable, optional (default = 'uniform') - weight function used in prediction. Possible values: - - - 'uniform' : uniform weights. All points in each neighborhood - are weighted equally. - - 'distance' : weight points by the inverse of their distance. - in this case, closer neighbors of a query point will have a - greater influence than neighbors which are further away. - - [callable] : a user-defined function which accepts an - array of distances, and returns an array of the same shape - containing the weights. - - regressor : callable, optional ((default = - :func:`mean `)) - Function to perform the local regression in the functional response - case. By default used the mean. Can the neighbors of a test sample, - and if weights != 'uniform' an array of weights as second parameter. - algorithm : {'auto', 'ball_tree', 'brute'}, optional - Algorithm used to compute the nearest neighbors: - - - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. - - 'brute' will use a brute-force search. - - 'auto' will attempt to decide the most appropriate algorithm based on - the values passed to :meth:`fit` method. - - leaf_size : int, optional (default = 30) - Leaf size passed to BallTree or KDTree. This can affect the - speed of the construction and query, as well as the memory - required to store the tree. The optimal value depends on the - nature of the problem. - metric : string or callable, (default - :func:`l2_distance `) - the distance metric to use for the tree. The default metric is - the L2 distance. See the documentation of the metrics module - for a list of available metrics. - metric_params : dict, optional (default = None) - Additional keyword arguments for the metric function. - n_jobs : int or None, optional (default=None) - The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. - Doesn't affect :meth:`fit` method. - multivariate_metric : boolean, optional (default = False) - Indicates if the metric used is a sklearn distance between vectors (see - :class:`sklearn.neighbors.DistanceMetric`) or a functional metric of - the module :mod:`skfda.misc.metrics`. - Examples - -------- - Firstly, we will create a toy dataset with gaussian-like samples shifted. - - >>> from skfda.ml.regression import KNeighborsRegressor - >>> from skfda.datasets import make_multimodal_samples - >>> from skfda.datasets import make_multimodal_landmarks - >>> y = make_multimodal_landmarks(n_samples=30, std=.5, random_state=0) - >>> y_train = y.flatten() - >>> X_train = make_multimodal_samples(n_samples=30, std=.5, random_state=0) - >>> X_test = make_multimodal_samples(n_samples=5, std=.05, random_state=0) - - We will fit a K-Nearest Neighbors regressor to regress a scalar response. - - >>> neigh = KNeighborsRegressor() - >>> neigh.fit(X_train, y_train) - KNeighborsRegressor(...) - - We can predict the modes of new samples - - >>> neigh.predict(X_test).round(2) # Predict test data - array([ 0.38, 0.14, 0.27, 0.52, 0.38]) - - - Now we will create a functional response to train the model - - >>> y_train = 5 * X_train + 1 - >>> y_train - FDataGrid(...) - - We train the estimator with the functional response - - >>> neigh.fit(X_train, y_train) - KNeighborsRegressor(...) - - And predict the responses as in the first case. - - >>> neigh.predict(X_test) - FDataGrid(...) - - See also - -------- - :class:`~skfda.ml.classification.KNeighborsClassifier` - :class:`~skfda.ml.classification.RadiusNeighborsClassifier` - :class:`~skfda.ml.classification.NearestCentroids` - :class:`~skfda.ml.regression.RadiusNeighborsRegressor` - :class:`~skfda.ml.clustering.NearestNeighbors` - - - Notes - ----- - See Nearest Neighbors in the sklearn online documentation for a discussion - of the choice of ``algorithm`` and ``leaf_size``. - - This class wraps the sklearn regressor - `sklearn.neighbors.KNeighborsRegressor`. - - .. warning:: - Regarding the Nearest Neighbors algorithms, if it is found that two - neighbors, neighbor `k+1` and `k`, have identical distances - but different labels, the results will depend on the ordering of the - training data. - - https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm + Parameters: + n_neighbors: Number of neighbors to use by default for + :meth:`kneighbors` queries. + weights: Weight function used in prediction. Possible values: + + - 'uniform' : uniform weights. All points in each neighborhood + are weighted equally. + - 'distance' : weight points by the inverse of their distance. + in this case, closer neighbors of a query point will have a + greater influence than neighbors which are further away. + - [callable] : a user-defined function which accepts an + array of distances, and returns an array of the same shape + containing the weights. + + algorithm: Algorithm used to compute the nearest neighbors: + + - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. + - 'brute' will use a brute-force search. + - 'auto' will attempt to decide the most appropriate algorithm + based on the values passed to :meth:`fit` method. + + leaf_size: Leaf size passed to BallTree or KDTree. This can affect the + speed of the construction and query, as well as the memory + required to store the tree. The optimal value depends on the + nature of the problem. + metric: The distance metric to use for the tree. The default metric is + the L2 distance. See the documentation of the metrics module + for a list of available metrics. + n_jobs: The number of parallel jobs to run for neighbors search. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. + ``-1`` means using all processors. + Doesn't affect :meth:`fit` method. + + Examples: + Firstly, we will create a toy dataset with gaussian-like samples + shifted. + + >>> from skfda.ml.regression import KNeighborsRegressor + >>> from skfda.datasets import make_multimodal_samples + >>> from skfda.datasets import make_multimodal_landmarks + >>> y = make_multimodal_landmarks( + ... n_samples=30, + ... std=0.5, + ... random_state=0, + ... ) + >>> y_train = y.flatten() + >>> X_train = make_multimodal_samples( + ... n_samples=30, + ... std=0.5, + ... random_state=0, + ... ) + >>> X_test = make_multimodal_samples( + ... n_samples=5, + ... std=0.05, + ... random_state=0, + ... ) + + We will fit a K-Nearest Neighbors regressor to regress a scalar + response. + + >>> neigh = KNeighborsRegressor() + >>> neigh.fit(X_train, y_train) + KNeighborsRegressor(...) + + We can predict the modes of new samples + + >>> neigh.predict(X_test).round(2) # Predict test data + array([ 0.38, 0.14, 0.27, 0.52, 0.38]) + + + Now we will create a functional response to train the model + + >>> y_train = 5 * X_train + 1 + >>> y_train + FDataGrid(...) + + We train the estimator with the functional response + + >>> neigh.fit(X_train, y_train) + KNeighborsRegressor(...) + + And predict the responses as in the first case. + + >>> neigh.predict(X_test) + FDataGrid(...) + + See also: + :class:`~skfda.ml.classification.KNeighborsClassifier` + :class:`~skfda.ml.classification.RadiusNeighborsClassifier` + :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.regression.RadiusNeighborsRegressor` + :class:`~skfda.ml.clustering.NearestNeighbors` + + + Notes: + See Nearest Neighbors in the sklearn online documentation for a + discussion of the choice of ``algorithm`` and ``leaf_size``. + + This class wraps the sklearn regressor + `sklearn.neighbors.KNeighborsRegressor`. + + .. warning:: + Regarding the Nearest Neighbors algorithms, if it is found that two + neighbors, neighbor `k+1` and `k`, have identical distances + but different labels, the results will depend on the ordering of the + training data. + + https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ - def __init__(self, n_neighbors=5, weights='uniform', regressor='mean', - algorithm='auto', leaf_size=30, metric='l2', - metric_params=None, n_jobs=1, multivariate_metric=False): + def __init__( + self, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + n_jobs: int | None = None, + ) -> None: """Initialize the regressor.""" - super().__init__(n_neighbors=n_neighbors, - weights=weights, algorithm=algorithm, - leaf_size=leaf_size, metric=metric, - metric_params=metric_params, n_jobs=n_jobs, - multivariate_metric=multivariate_metric) - self.regressor = regressor - - def _init_multivariate_estimator(self, sklearn_metric): - """Initialize the sklearn K neighbors estimator. - - Args: - sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. - - Returns: - Sklearn K Neighbors estimator initialized. - - """ + super().__init__( + n_neighbors=n_neighbors, + weights=weights, + algorithm=algorithm, + leaf_size=leaf_size, + metric=metric, + n_jobs=n_jobs, + ) + + def _init_estimator(self) -> _KNeighborsRegressor: return _KNeighborsRegressor( - n_neighbors=self.n_neighbors, weights=self.weights, - algorithm=self.algorithm, leaf_size=self.leaf_size, - metric=sklearn_metric, metric_params=self.metric_params, - n_jobs=self.n_jobs) - - def _query(self, X): + n_neighbors=self.n_neighbors, + weights=self.weights, + algorithm=self.algorithm, + leaf_size=self.leaf_size, + metric="precomputed", + n_jobs=self.n_jobs, + ) + + def _query( + self, + X: Input, + ) -> Tuple[NDArrayFloat, NDArrayInt]: """Return distances and neighbors of given sample.""" - return self.estimator_.kneighbors(X) + return self.kneighbors(X) -class RadiusNeighborsRegressor(NeighborsRegressorMixin, - RadiusNeighborsMixin): - """Regression based on neighbors within a fixed radius. +class RadiusNeighborsRegressor( + NeighborsRegressorMixin[Input, Target], + RadiusNeighborsMixin[Input, Target], +): + """ + Regression based on neighbors within a fixed radius. Regression with scalar, multivariate or functional response. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set. - Parameters - ---------- - radius : float, optional (default = 1.0) - Range of parameter space to use by default for :meth:`radius_neighbors` - queries. - weights : str or callable - weight function used in prediction. Possible values: - - - 'uniform' : uniform weights. All points in each neighborhood - are weighted equally. - - 'distance' : weight points by the inverse of their distance. - in this case, closer neighbors of a query point will have a - greater influence than neighbors which are further away. - - [callable] : a user-defined function which accepts an - array of distances, and returns an array of the same shape - containing the weights. - - Uniform weights are used by default. - regressor : callable, optional ((default = - :func:`mean `)) - Function to perform the local regression in the functional response - case. By default used the mean. Can the neighbors of a test sample, - and if weights != 'uniform' an array of weights as second parameter. - algorithm : {'auto', 'ball_tree', 'brute'}, optional - Algorithm used to compute the nearest neighbors: - - - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. - - 'brute' will use a brute-force search. - - 'auto' will attempt to decide the most appropriate algorithm - based on the values passed to :meth:`fit` method. - - leaf_size : int, optional (default = 30) - Leaf size passed to BallTree. This can affect the - speed of the construction and query, as well as the memory - required to store the tree. The optimal value depends on the - nature of the problem. - metric : string or callable, (default - :func:`l2_distance `) - the distance metric to use for the tree. The default metric is - the L2 distance. See the documentation of the metrics module - for a list of available metrics. - metric_params : dict, optional (default = None) - Additional keyword arguments for the metric function. - outlier_response : :class:`FData`, optional (default = None) - Default response in the functional response case for test samples - without neighbors. - n_jobs : int or None, optional (default=None) - The number of parallel jobs to run for neighbors search. - ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. - multivariate_metric : boolean, optional (default = False) - Indicates if the metric used is a sklearn distance between vectors (see - :class:`sklearn.neighbors.DistanceMetric`) or a functional metric of - the module :mod:`skfda.misc.metrics`. - - Examples - -------- - Firstly, we will create a toy dataset with gaussian-like samples shifted. - - >>> from skfda.ml.regression import RadiusNeighborsRegressor - >>> from skfda.datasets import make_multimodal_samples - >>> from skfda.datasets import make_multimodal_landmarks - >>> y = make_multimodal_landmarks(n_samples=30, std=.5, random_state=0) - >>> y_train = y.flatten() - >>> X_train = make_multimodal_samples(n_samples=30, std=.5, random_state=0) - >>> X_test = make_multimodal_samples(n_samples=5, std=.05, random_state=0) - - We will fit a Radius-Nearest Neighbors regressor to regress a scalar - response. - - >>> neigh = RadiusNeighborsRegressor(radius=0.2) - >>> neigh.fit(X_train, y_train) - RadiusNeighborsRegressor(...radius=0.2...) - - We can predict the modes of new samples - - >>> neigh.predict(X_test).round(2) # Predict test data - array([ 0.39, 0.07, 0.26, 0.5 , 0.46]) - - - Now we will create a functional response to train the model - - >>> y_train = 5 * X_train + 1 - >>> y_train - FDataGrid(...) - - We train the estimator with the functional response - - >>> neigh.fit(X_train, y_train) - RadiusNeighborsRegressor(...radius=0.2...) - - And predict the responses as in the first case. - - >>> neigh.predict(X_test) - FDataGrid(...) - - See also - -------- - :class:`~skfda.ml.classification.KNeighborsClassifier` - :class:`~skfda.ml.classification.RadiusNeighborsClassifier` - :class:`~skfda.ml.classification.NearestCentroids` - :class:`~skfda.ml.regression.KNeighborsRegressor` - :class:`~skfda.ml.clustering.NearestNeighbors` - - - Notes - ----- - See Nearest Neighbors in the sklearn online documentation for a discussion - of the choice of ``algorithm`` and ``leaf_size``. - - This class wraps the sklearn classifier - `sklearn.neighbors.RadiusNeighborsClassifier`. - - https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm + Parameters: + radius: Range of parameter space to use by default for + :meth:`radius_neighbors` queries. + weights: Weight function used in prediction. Possible values: + + - 'uniform' : uniform weights. All points in each neighborhood + are weighted equally. + - 'distance' : weight points by the inverse of their distance. + in this case, closer neighbors of a query point will have a + greater influence than neighbors which are further away. + - [callable] : a user-defined function which accepts an + array of distances, and returns an array of the same shape + containing the weights. + + Uniform weights are used by default. + algorithm: Algorithm used to compute the nearest neighbors: + + - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. + - 'brute' will use a brute-force search. + - 'auto' will attempt to decide the most appropriate algorithm + based on the values passed to :meth:`fit` method. + + leaf_size: Leaf size passed to BallTree. This can affect the + speed of the construction and query, as well as the memory + required to store the tree. The optimal value depends on the + nature of the problem. + metric: The distance metric to use for the tree. The default metric is + the L2 distance. See the documentation of the metrics module + for a list of available metrics. + outlier_response: Default response in the functional response case for + test samples without neighbors. + n_jobs: The number of parallel jobs to run for neighbors search. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. + ``-1`` means using all processors. + + Examples: + Firstly, we will create a toy dataset with gaussian-like samples + shifted. + + >>> from skfda.ml.regression import RadiusNeighborsRegressor + >>> from skfda.datasets import make_multimodal_samples + >>> from skfda.datasets import make_multimodal_landmarks + >>> y = make_multimodal_landmarks( + ... n_samples=30, + ... std=0.5, + ... random_state=0, + ... ) + >>> y_train = y.flatten() + >>> X_train = make_multimodal_samples( + ... n_samples=30, + ... std=0.5, + ... random_state=0, + ... ) + >>> X_test = make_multimodal_samples( + ... n_samples=5, + ... std=0.05, + ... random_state=0, + ... ) + + We will fit a Radius-Nearest Neighbors regressor to regress a scalar + response. + + >>> neigh = RadiusNeighborsRegressor(radius=0.2) + >>> neigh.fit(X_train, y_train) + RadiusNeighborsRegressor(...radius=0.2...) + + We can predict the modes of new samples + + >>> neigh.predict(X_test).round(2) # Predict test data + array([ 0.39, 0.07, 0.26, 0.5 , 0.46]) + + + Now we will create a functional response to train the model + + >>> y_train = 5 * X_train + 1 + >>> y_train + FDataGrid(...) + + We train the estimator with the functional response + + >>> neigh.fit(X_train, y_train) + RadiusNeighborsRegressor(...radius=0.2...) + + And predict the responses as in the first case. + + >>> neigh.predict(X_test) + FDataGrid(...) + + See also: + :class:`~skfda.ml.classification.KNeighborsClassifier` + :class:`~skfda.ml.classification.RadiusNeighborsClassifier` + :class:`~skfda.ml.classification.NearestCentroids` + :class:`~skfda.ml.regression.KNeighborsRegressor` + :class:`~skfda.ml.clustering.NearestNeighbors` + + + Notes: + See Nearest Neighbors in the sklearn online documentation for a + discussion of the choice of ``algorithm`` and ``leaf_size``. + + This class wraps the sklearn classifier + `sklearn.neighbors.RadiusNeighborsClassifier`. + + https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ - def __init__(self, radius=1.0, weights='uniform', regressor='mean', - algorithm='auto', leaf_size=30, metric='l2', - metric_params=None, outlier_response=None, n_jobs=1, - multivariate_metric=False): + def __init__( + self, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + outlier_response=None, + n_jobs: int | None = None, + ) -> None: """Initialize the classifier.""" - super().__init__(radius=radius, weights=weights, algorithm=algorithm, - leaf_size=leaf_size, metric=metric, - metric_params=metric_params, n_jobs=n_jobs, - multivariate_metric=multivariate_metric) - self.regressor = regressor + super().__init__( + radius=radius, + weights=weights, + algorithm=algorithm, + leaf_size=leaf_size, + metric=metric, + n_jobs=n_jobs, + ) self.outlier_response = outlier_response - def _init_multivariate_estimator(self, sklearn_metric): - """Initialize the sklearn radius neighbors estimator. - - Args: - sklearn_metric: (pyfunc or 'precomputed'): Metric compatible with - sklearn API or matrix (n_samples, n_samples) with precomputed - distances. - - Returns: - Sklearn Radius Neighbors estimator initialized. - - """ + def _init_estimator(self) -> _RadiusNeighborsRegressor: return _RadiusNeighborsRegressor( - radius=self.radius, weights=self.weights, - algorithm=self.algorithm, leaf_size=self.leaf_size, - metric=sklearn_metric, metric_params=self.metric_params, - n_jobs=self.n_jobs) - - def _query(self, X): - """Return distances and neighbors of given sample. - - Args: - X: the sample - - Returns: - Distances and neighbors of a given sample - - """ - return self.estimator_.radius_neighbors(X) + radius=self.radius, + weights=self.weights, + algorithm=self.algorithm, + leaf_size=self.leaf_size, + metric="precomputed", + n_jobs=self.n_jobs, + ) + + def _query( + self, + X: Input, + ) -> Tuple[NDArrayFloat, NDArrayInt]: + return self.radius_neighbors(X) diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index 903a9528d..f5c82f0b0 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -20,7 +20,7 @@ class TestNeighbors(unittest.TestCase): - def setUp(self): + def setUp(self) -> None: """Create test data.""" random_state = np.random.RandomState(0) modes_location = np.concatenate(( @@ -188,21 +188,6 @@ def test_knn_functional_response(self): self.X.data_matrix, ) - def test_knn_functional_response_sklearn(self): - # Check sklearn metric - knnr = KNeighborsRegressor( - n_neighbors=1, - metric='euclidean', - multivariate_metric=True, - ) - knnr.fit(self.X, self.X) - - res = knnr.predict(self.X) - np.testing.assert_array_almost_equal( - res.data_matrix, - self.X.data_matrix, - ) - def test_knn_functional_response_precomputed(self): knnr = KNeighborsRegressor( n_neighbors=4, @@ -239,7 +224,7 @@ def test_functional_response_custom_weights(self): knnr.fit(self.X, response) res = knnr.predict(self.X) - np.testing.assert_array_almost_equal( + np.testing.assert_allclose( res.coefficients, response.coefficients, ) @@ -324,22 +309,6 @@ def test_search_neighbors_precomputed(self): np.array([[0, 3], [1, 2], [2, 1], [3, 0]]), ) - def test_search_neighbors_sklearn(self): - - nn = NearestNeighbors( - metric='euclidean', - multivariate_metric=True, - n_neighbors=2, - ) - nn.fit(self.X[:4], self.y[:4]) - - _, neighbors = nn.kneighbors(self.X[:4]) - - np.testing.assert_array_almost_equal( - neighbors, - np.array([[0, 3], [1, 2], [2, 1], [3, 0]]), - ) - def test_score_scalar_response(self): neigh = KNeighborsRegressor() @@ -409,16 +378,6 @@ def test_lof_fit_predict(self): res3 = lof3.fit_predict(distances) np.testing.assert_array_equal(expected, res3) - # With multivariate sklearn - lof4 = LocalOutlierFactor(metric='euclidean', multivariate_metric=True) - res4 = lof4.fit_predict(self.fd_lof) - np.testing.assert_array_equal(expected, res4) - - # Other way of call fit_predict, undocumented in sklearn - lof5 = LocalOutlierFactor(novelty=True) - res5 = lof5.fit(self.fd_lof).predict() - np.testing.assert_array_equal(expected, res5) - # Check values of negative outlier factor negative_lof = [ # noqa: WPS317 -7.1068, -1.5412, -0.9961, From 44ee0cc6be462a2bf5ecce4df8301406e0891b24 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 20 Jul 2022 10:34:51 +0200 Subject: [PATCH 219/400] Fix tests. --- .../exploratory/outliers/neighbors_outlier.py | 5 ++- skfda/ml/_neighbors_base.py | 32 ++++++++++++------- skfda/tests/test_neighbors.py | 4 +-- 3 files changed, 24 insertions(+), 17 deletions(-) diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index e9037d993..29190bca9 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -208,7 +208,7 @@ def fit( X: Input, y: None = None, ) -> SelfType: - super().fit(X, y) + super()._fit(X, y, fit_with_zeros=False) self._store_fit_data() return self @@ -264,8 +264,6 @@ def fit_predict( self._estimator = self._init_estimator() - self._fitted_with_distances = False - if self.metric == 'precomputed': res = self._estimator.fit_predict(X, y) else: @@ -273,6 +271,7 @@ def fit_predict( res = self._estimator.fit_predict(X_dist, y) self._store_fit_data() + self._fitted_with_distances = True return res diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index dba9c89cf..910e3a927 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -1,6 +1,7 @@ """Base classes for the neighbor estimators""" from __future__ import annotations +import copy from typing import ( Any, Callable, @@ -71,9 +72,7 @@ def __init__( algorithm: AlgorithmType = "auto", leaf_size: int = 30, metric: Literal["precomputed"] | Metric[Input] = l2_distance, - metric_params: Mapping[str, Any] | None = None, n_jobs: int | None = None, - multivariate_metric: bool = False, ): self.n_neighbors = n_neighbors self.radius = radius @@ -81,9 +80,7 @@ def __init__( self.algorithm = algorithm self.leaf_size = leaf_size self.metric = metric - self.metric_params = metric_params self.n_jobs = n_jobs - self.multivariate_metric = multivariate_metric def _check_is_fitted(self) -> None: """ @@ -117,20 +114,32 @@ def fit( ``sklearn.neighbors``. """ + return self._fit(X, y) + + def _fit( + self: SelfType, + X: Input, + y: Target | None = None, + fit_with_zeros: bool = True, + ) -> SelfType: # If metric is precomputed no diferences with the Sklearn estimator self._estimator = self._init_estimator() - self._fitted_with_distances = False + self._fitted_with_distances = True if self.metric == 'precomputed': - self._fitted_with_distances = True + if isinstance(y, FData): # For functional response regression + self._fit_y = copy.deepcopy(y) self._estimator.fit(X, y) else: - self._fit_X = X.copy() - self._estimator.fit(np.zeros(shape=(len(X), len(X))), y) - - if isinstance(y, FData): - self._fit_y = y.copy() + self._fit_X = copy.deepcopy(X) + self._fit_y = copy.deepcopy(y) + if fit_with_zeros: + self._fitted_with_distances = False + self._estimator.fit(np.zeros(shape=(len(X), len(X))), y) + else: + distances = PairwiseMetric(self.metric)(X) + self._estimator.fit(distances, y) return self @@ -160,7 +169,6 @@ def _init_estimator( algorithm=self.algorithm, leaf_size=self.leaf_size, metric="precomputed", - metric_params=self.metric_params, n_jobs=self.n_jobs, ) diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index f5c82f0b0..a7c35d5da 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -441,8 +441,8 @@ def test_lof_exceptions(self): with np.testing.assert_raises(AttributeError): lof.predict(self.fd_lof[5:]) - def _weights(self, weights_): - return np.array([w == 0 for w in weights_], dtype=float) + def _weights(self, weights): + return np.array([w == np.min(weights) for w in weights], dtype=float) if __name__ == '__main__': From fe646c89b304544125ce98aa9cd7959acf9da3de Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 22 Jul 2022 18:19:07 +0200 Subject: [PATCH 220/400] Neighbors tests. --- setup.cfg | 1 + .../exploratory/outliers/neighbors_outlier.py | 58 ++++- skfda/ml/_neighbors_base.py | 103 ++++----- .../classification/_neighbors_classifiers.py | 79 ++++++- skfda/ml/clustering/_neighbors_clustering.py | 37 ++- skfda/ml/regression/_neighbors_regression.py | 57 ++++- skfda/tests/test_neighbors.py | 211 +++++++++++------- 7 files changed, 389 insertions(+), 157 deletions(-) diff --git a/setup.cfg b/setup.cfg index 38807e20e..6363131cd 100644 --- a/setup.cfg +++ b/setup.cfg @@ -154,6 +154,7 @@ skip_glob = **/plot_*.py plot_*.py strict = True strict_equality = True implicit_reexport = True +enable_error_code = ignore-without-code [mypy-dcor.*] ignore_missing_imports = True diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index 29190bca9..8457280a7 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -1,7 +1,7 @@ """Neighbors outlier detection methods.""" from __future__ import annotations -from typing import Any, TypeVar, Union +from typing import Any, TypeVar, Union, overload from sklearn.base import OutlierMixin from sklearn.neighbors import LocalOutlierFactor as _LocalOutlierFactor @@ -20,7 +20,7 @@ class LocalOutlierFactor( KNeighborsMixin[Input, None], - OutlierMixin, # type: ignore + OutlierMixin, # type: ignore[misc] ): r""" Unsupervised Outlier Detection. @@ -166,8 +166,37 @@ class LocalOutlierFactor( :class:`~skfda.ml.clustering.NearestNeighbors` """ + @overload + def __init__( + self: LocalOutlierFactor[NDArrayFloat], + *, + n_neighbors: int = 20, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"], + contamination: float | Literal["auto"] = "auto", + novelty: bool = False, + n_jobs: int | None = None, + ) -> None: + pass + + @overload + def __init__( + self, + *, + n_neighbors: int = 20, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Metric[Input] = l2_distance, + contamination: float | Literal["auto"] = "auto", + novelty: bool = False, + n_jobs: int | None = None, + ) -> None: + pass + def __init__( self, + *, n_neighbors: int = 20, algorithm: AlgorithmType = 'auto', leaf_size: int = 30, @@ -199,16 +228,18 @@ def _init_estimator(self) -> _LocalOutlierFactor: def _store_fit_data(self) -> None: """Store the parameters created during the fit.""" - self.negative_outlier_factor_ = self._estimator.negative_outlier_factor_ + self.negative_outlier_factor_ = ( + self._estimator.negative_outlier_factor_ + ) self.n_neighbors_ = self._estimator.n_neighbors_ self.offset_ = self._estimator.offset_ - def fit( + def fit( # noqa: D102 self: SelfType, X: Input, y: None = None, ) -> SelfType: - super()._fit(X, y, fit_with_zeros=False) + self._fit(X, y, fit_with_zeros=False) self._store_fit_data() return self @@ -228,8 +259,9 @@ def predict( Parameters: X: FDataGrid containing the query sample or samples to compute the - Local Outlier Factor w.r.t. to the training samples or array with - the distances to the training samples if metric='precomputed'. + Local Outlier Factor w.r.t. to the training samples or array + with the distances to the training samples if + metric='precomputed'. Returns: Returns -1 for anomalies/outliers and +1 for inliers. @@ -238,7 +270,7 @@ def predict( self._check_is_fitted() X_dist = self._X_to_distances(X) - return self._estimator.predict(X_dist) + return self._estimator.predict(X_dist) # type: ignore[no-any-return] def fit_predict( self, @@ -273,7 +305,7 @@ def fit_predict( self._store_fit_data() self._fitted_with_distances = True - return res + return res # type: ignore[no-any-return] def decision_function( self, @@ -303,7 +335,9 @@ def decision_function( self._check_is_fitted() X_dist = self._X_to_distances(X) - return self._estimator.decision_function(X_dist) + return ( # type: ignore[no-any-return] + self._estimator.decision_function(X_dist) + ) def score_samples( self, @@ -336,4 +370,6 @@ def score_samples( self._check_is_fitted() X_dist = self._X_to_distances(X) - return self._estimator.score_samples(X_dist) + return ( # type: ignore[no-any-return] + self._estimator.score_samples(X_dist) + ) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 910e3a927..4faf88c72 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -1,17 +1,8 @@ -"""Base classes for the neighbor estimators""" +"""Base classes for the neighbor estimators.""" from __future__ import annotations import copy -from typing import ( - Any, - Callable, - Generic, - Mapping, - Tuple, - TypeVar, - Union, - overload, -) +from typing import Any, Callable, Generic, Tuple, TypeVar, Union, overload import numpy as np import sklearn.neighbors @@ -30,7 +21,7 @@ from ..misc.metrics import l2_distance from ..misc.metrics._typing import Metric from ..misc.metrics._utils import _fit_metric -from ..representation._typing import GridPoints, NDArrayFloat, NDArrayInt +from ..representation._typing import NDArrayFloat, NDArrayInt FDataType = TypeVar("FDataType", bound="FData") SelfType = TypeVar("SelfType", bound="NeighborsBase[Any, Any]") @@ -95,7 +86,7 @@ def _check_is_fitted(self) -> None: def fit( self: SelfType, X: Input, - y: Target | None = None, + y: Target, ) -> SelfType: """ Fit the model using X as training data and y as target values. @@ -119,7 +110,7 @@ def fit( def _fit( self: SelfType, X: Input, - y: Target | None = None, + y: Target, fit_with_zeros: bool = True, ) -> SelfType: # If metric is precomputed no diferences with the Sklearn estimator @@ -129,9 +120,10 @@ def _fit( if self.metric == 'precomputed': if isinstance(y, FData): # For functional response regression - self._fit_y = copy.deepcopy(y) + self._fit_y: Target = copy.deepcopy(y) self._estimator.fit(X, y) else: + _fit_metric(self.metric, X) self._fit_X = copy.deepcopy(X) self._fit_y = copy.deepcopy(y) if fit_with_zeros: @@ -145,6 +137,7 @@ def _fit( def _refit_with_distances(self) -> None: if not self._fitted_with_distances: + assert self.metric != "precomputed" distances = PairwiseMetric(self.metric)(self._fit_X) self._estimator.fit(distances, self._fit_y) self._fitted_with_distances = True @@ -179,7 +172,7 @@ class KNeighborsMixin(NeighborsBase[Input, Target]): @overload def kneighbors( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, n_neighbors: int | None = None, *, return_distance: Literal[True] = True, @@ -189,7 +182,7 @@ def kneighbors( @overload def kneighbors( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, n_neighbors: int | None = None, *, return_distance: Literal[False], @@ -198,7 +191,7 @@ def kneighbors( def kneighbors( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, n_neighbors: int | None = None, *, return_distance: bool = True, @@ -259,14 +252,18 @@ def kneighbors( self._check_is_fitted() if X is None: self._refit_with_distances() - else: - X = self._X_to_distances(X) - return self._estimator.kneighbors(X, n_neighbors, return_distance) + X_dist = None if X is None else self._X_to_distances(X) + + return self._estimator.kneighbors( # type: ignore [no-any-return] + X_dist, + n_neighbors, + return_distance, + ) def kneighbors_graph( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, n_neighbors: int | None = None, mode: Literal["connectivity", "distance"] = "connectivity", ) -> csr_matrix: @@ -323,10 +320,10 @@ def kneighbors_graph( self._check_is_fitted() if X is None: self._refit_with_distances() - else: - X = self._X_to_distances(X) - return self._estimator.kneighbors_graph(X, n_neighbors, mode) + X_dist = None if X is None else self._X_to_distances(X) + + return self._estimator.kneighbors_graph(X_dist, n_neighbors, mode) class RadiusNeighborsMixin(NeighborsBase[Input, Target]): @@ -335,7 +332,7 @@ class RadiusNeighborsMixin(NeighborsBase[Input, Target]): @overload def radius_neighbors( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, radius: float | None = None, *, return_distance: Literal[True] = True, @@ -345,7 +342,7 @@ def radius_neighbors( @overload def radius_neighbors( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, radius: float | None = None, *, return_distance: Literal[False], @@ -354,7 +351,7 @@ def radius_neighbors( def radius_neighbors( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, radius: float | None = None, *, return_distance: bool = True, @@ -429,18 +426,20 @@ def radius_neighbors( self._check_is_fitted() if X is None: self._refit_with_distances() - else: - X = self._X_to_distances(X) - return self._estimator.radius_neighbors( - X=X, - radius=radius, - return_distance=return_distance, + X_dist = None if X is None else self._X_to_distances(X) + + return ( # type: ignore [no-any-return] + self._estimator.radius_neighbors( + X_dist, + radius=radius, + return_distance=return_distance, + ) ) def radius_neighbors_graph( self, - X: FDataGrid | NDArrayFloat | None = None, + X: Input | None = None, radius: float | None = None, mode: Literal["connectivity", "distance"] = 'connectivity', ) -> csr_matrix: @@ -472,11 +471,11 @@ def radius_neighbors_graph( self._check_is_fitted() if X is None: self._refit_with_distances() - else: - X = self._X_to_distances(X) + + X_dist = None if X is None else self._X_to_distances(X) return self._estimator.radius_neighbors_graph( - X=X, + X_dist, radius=radius, mode=mode, ) @@ -488,13 +487,6 @@ class NeighborsClassifierMixin( ): """Mixin class for classifiers based in nearest neighbors.""" - def fit( - self: SelfType, - X: Input, - y: Target, - ) -> SelfType: - return super().fit(X, y) - def predict( self, X: Input, @@ -519,28 +511,31 @@ def predict( X_dist = self._X_to_distances(X) - return self._estimator.predict(X_dist) + return self._estimator.predict(X_dist) # type: ignore [no-any-return] def predict_proba( self, X: Input, ) -> NDArrayFloat: - """Calculate probability estimates for the test data X. + """ + Calculate probability estimates for the test data X. Args: X: FDataGrid with the test samples or array (n_query, n_indexed) if metric == 'precomputed'. Returns: - p: The class probabilities of the input samples. Classes are - ordered by lexicographic order. + The class probabilities of the input samples. Classes are + ordered by lexicographic order. """ self._check_is_fitted() X_dist = self._X_to_distances(X) - return self._estimator.predict_proba(X_dist) + return ( # type: ignore [no-any-return] + self._estimator.predict_proba(X_dist) + ) class NeighborsRegressorMixin( @@ -556,11 +551,11 @@ def _average( ) -> TargetRegression: """Compute weighted average.""" if weights is None: - return np.mean(X, axis=0) + return np.mean(X, axis=0) # type: ignore [no-any-return] weights /= np.sum(weights) - return np.sum(X * weights, axis=0) + return np.sum(X * weights, axis=0) # type: ignore [no-any-return] def _prediction_from_neighbors( self, @@ -647,10 +642,10 @@ def _multivariate_predict( """Predict a multivariate target.""" X_dist = self._X_to_distances(X) - return self._estimator.predict(X_dist) + return self._estimator.predict(X_dist) # type: ignore [no-any-return] def _functional_predict( - self, + self: NeighborsRegressorMixin[FData, Any], X: Input, ) -> TargetRegression: """Predict functional responses.""" diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index cf231e6bf..f3e5d8b17 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import TypeVar, Union +from typing import TypeVar, Union, overload from sklearn.neighbors import ( KNeighborsClassifier as _KNeighborsClassifier, @@ -24,12 +24,11 @@ ) Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) -Target = TypeVar("Target", bound=NDArrayInt) class KNeighborsClassifier( - KNeighborsMixin[Input, Target], - NeighborsClassifierMixin[Input, Target], + KNeighborsMixin[Input, NDArrayInt], + NeighborsClassifierMixin[Input, NDArrayInt], ): """ Classifier implementing the k-nearest neighbors vote. @@ -119,8 +118,35 @@ class KNeighborsClassifier( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload + def __init__( + self: KNeighborsClassifier[NDArrayFloat], + *, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"], + n_jobs: int | None = None, + ) -> None: + pass + + @overload def __init__( self, + *, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + n_jobs: int | None = None, + ) -> None: + pass + + def __init__( + self, + *, n_neighbors: int = 5, weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', @@ -150,8 +176,8 @@ def _init_estimator(self) -> _KNeighborsClassifier: class RadiusNeighborsClassifier( - RadiusNeighborsMixin[Input, Target], - NeighborsClassifierMixin[Input, Target], + RadiusNeighborsMixin[Input, NDArrayInt], + NeighborsClassifierMixin[Input, NDArrayInt], ): """ Classifier implementing a vote among neighbors within a given radius. @@ -163,20 +189,20 @@ class RadiusNeighborsClassifier( Possible values: - 'uniform': uniform weights. All points in each neighborhood - are weighted equally. + are weighted equally. - 'distance': weight points by the inverse of their distance. - in this case, closer neighbors of a query point will have a - greater influence than neighbors which are further away. + in this case, closer neighbors of a query point will have a + greater influence than neighbors which are further away. - [callable]: a user-defined function which accepts an - array of distances, and returns an array of the same shape - containing the weights. + array of distances, and returns an array of the same shape + containing the weights. algorithm: Algorithm used to compute the nearest neighbors: - 'ball_tree' will use :class:`sklearn.neighbors.BallTree`. - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm. - based on the values passed to :meth:`fit` method. + based on the values passed to :meth:`fit` method. leaf_size: Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory @@ -233,8 +259,37 @@ class RadiusNeighborsClassifier( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload + def __init__( + self: RadiusNeighborsClassifier[NDArrayFloat], + *, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"], + outlier_label=None, + n_jobs: int | None = None, + ) -> None: + pass + + @overload + def __init__( + self, + *, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Metric[Input] = l2_distance, + outlier_label=None, + n_jobs: int | None = None, + ) -> None: + pass + def __init__( self, + *, radius: float = 1.0, weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 9fe3040cc..470507e23 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -1,7 +1,7 @@ """Unsupervised learner for implementing neighbor searches.""" from __future__ import annotations -from typing import TypeVar, Union +from typing import Any, TypeVar, Union, overload from typing_extensions import Literal @@ -17,6 +17,7 @@ ) Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +SelfType = TypeVar("SelfType", bound="NearestNeighbors[Any]") class NearestNeighbors( @@ -104,9 +105,35 @@ class NearestNeighbors( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload + def __init__( + self: NearestNeighbors[NDArrayFloat], + *, + n_neighbors: int = 5, + radius: float = 1.0, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"], + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, + *, + n_neighbors: int = 5, + radius: float = 1.0, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Metric[Input] = l2_distance, + n_jobs: int | None = None, + ) -> None: + pass + + def __init__( + self, + *, n_neighbors: int = 5, radius: float = 1.0, algorithm: AlgorithmType = 'auto', @@ -122,3 +149,11 @@ def __init__( metric=metric, n_jobs=n_jobs, ) + + # There is actually a change here: the default parameter!! + def fit( # noqa: WPS612, D102 + self: SelfType, + X: Input, + y: None = None, + ) -> SelfType: + return super().fit(X, y) diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index fd0317e5a..822b9f82e 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import Tuple, TypeVar, Union +from typing import Tuple, TypeVar, Union, overload from sklearn.neighbors import ( KNeighborsRegressor as _KNeighborsRegressor, @@ -150,9 +150,36 @@ class KNeighborsRegressor( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload + def __init__( + self: KNeighborsRegressor[NDArrayFloat, Target], + *, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"], + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, + *, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Metric[Input] = l2_distance, + n_jobs: int | None = None, + ) -> None: + pass + + # Not useless: it restrict the inputs. + def __init__( # noqa: WPS612 + self, + *, n_neighbors: int = 5, weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', @@ -307,9 +334,37 @@ class RadiusNeighborsRegressor( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload + def __init__( + self: RadiusNeighborsRegressor[NDArrayFloat, Target], + *, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"], + outlier_response=None, + n_jobs: int | None = None, + ) -> None: + pass + + @overload + def __init__( + self, + *, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Literal["precomputed"] | Metric[Input] = l2_distance, + outlier_response=None, + n_jobs: int | None = None, + ) -> None: + pass def __init__( self, + *, radius: float = 1.0, weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index a7c35d5da..9d5525d55 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -1,12 +1,14 @@ """Test neighbors classifiers and regressors.""" +from __future__ import annotations import unittest +from typing import Any, Sequence import numpy as np +from sklearn.neighbors._base import KNeighborsMixin, RadiusNeighborsMixin from skfda.datasets import make_multimodal_samples, make_sinusoidal_process from skfda.exploratory.outliers import LocalOutlierFactor # Pending theory -from skfda.exploratory.stats import mean from skfda.misc.metrics import PairwiseMetric, l2_distance from skfda.ml.classification import ( KNeighborsClassifier, @@ -15,10 +17,12 @@ ) from skfda.ml.clustering import NearestNeighbors from skfda.ml.regression import KNeighborsRegressor, RadiusNeighborsRegressor +from skfda.representation import FDataBasis, FDataGrid from skfda.representation.basis import Fourier class TestNeighbors(unittest.TestCase): + """Tests for neighbors methods.""" def setUp(self) -> None: """Create test data.""" @@ -65,24 +69,29 @@ def setUp(self) -> None: ) self.fd_lof = fd_outliers.concatenate(fd_clean) - def test_predict_classifier(self): + def test_predict_classifier(self) -> None: """Tests predict for neighbors classifier.""" - for neigh in ( + classifiers: Sequence[ + KNeighborsClassifier[FDataGrid] + | RadiusNeighborsClassifier[FDataGrid] + | NearestCentroid[FDataGrid] + ] = ( KNeighborsClassifier(), RadiusNeighborsClassifier(radius=0.1), NearestCentroid(), - NearestCentroid(metric=l2_distance, centroid=mean), - ): + NearestCentroid[FDataGrid](metric=l2_distance, centroid=np.mean), + ) + for neigh in classifiers: neigh.fit(self.X, self.y) pred = neigh.predict(self.X) np.testing.assert_array_equal( pred, self.y, - err_msg=f'fail in {type(neigh)}', + err_msg=f'fail in {type(neigh)}', # noqa: WPS237 ) - def test_predict_proba_classifier(self): + def test_predict_proba_classifier(self) -> None: """Tests predict proba for k neighbors classifier.""" neigh = KNeighborsClassifier(metric=l2_distance) @@ -91,12 +100,20 @@ def test_predict_proba_classifier(self): np.testing.assert_array_almost_equal(probs, self.probs) - def test_predict_regressor(self): + def test_predict_regressor(self) -> None: """Test scalar regression, predicts mode location.""" # Dummy test, with weight = distance, only the sample with distance 0 # will be returned, obtaining the exact location - knnr = KNeighborsRegressor(weights='distance') - rnnr = RadiusNeighborsRegressor(weights='distance', radius=0.1) + knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float_]]( + weights='distance', + ) + rnnr = RadiusNeighborsRegressor[ + FDataGrid, + np.typing.NDArray[np.float_], + ]( + weights='distance', + radius=0.1, + ) knnr.fit(self.X, self.modes_location) rnnr.fit(self.X, self.modes_location) @@ -110,20 +127,21 @@ def test_predict_regressor(self): self.modes_location, ) - def test_kneighbors(self): + def test_kneighbors(self) -> None: """Test k neighbor searches for all k-neighbors estimators.""" - nn = NearestNeighbors() + nn = NearestNeighbors[FDataGrid]() nn.fit(self.X) - lof = LocalOutlierFactor(n_neighbors=5) + lof = LocalOutlierFactor[FDataGrid](n_neighbors=5) lof.fit(self.X) - knn = KNeighborsClassifier() + knn = KNeighborsClassifier[FDataGrid]() knn.fit(self.X, self.y) - knnr = KNeighborsRegressor() + knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float_]]() knnr.fit(self.X, self.modes_location) + neigh: KNeighborsMixin[FDataGrid, Any] for neigh in (nn, knn, knnr, lof): dist, links = neigh.kneighbors(self.X[:4]) @@ -144,20 +162,24 @@ def test_kneighbors(self): np.testing.assert_array_almost_equal(dist[0, 1], dist_kneigh) for i in range(30): - self.assertEqual(graph[0, i] == 1.0, i in links[0]) - self.assertEqual(graph[0, i] == 0.0, i not in links[0]) + self.assertEqual(graph[0, i] == 1, i in links[0]) + self.assertEqual(graph[0, i] == 0, i not in links[0]) - def test_radius_neighbors(self): + def test_radius_neighbors(self) -> None: """Test query with radius.""" - nn = NearestNeighbors(radius=0.1) + nn = NearestNeighbors[FDataGrid](radius=0.1) nn.fit(self.X) - knn = RadiusNeighborsClassifier(radius=0.1) + knn = RadiusNeighborsClassifier[FDataGrid](radius=0.1) knn.fit(self.X, self.y) - knnr = RadiusNeighborsRegressor(radius=0.1) + knnr = RadiusNeighborsRegressor[ + FDataGrid, + np.typing.NDArray[np.float_], + ](radius=0.1) knnr.fit(self.X, self.modes_location) + neigh: RadiusNeighborsMixin[FDataGrid, Any] for neigh in (nn, knn, knnr): dist, links = neigh.radius_neighbors(self.X[:4]) @@ -174,11 +196,12 @@ def test_radius_neighbors(self): graph = neigh.radius_neighbors_graph(self.X[:4]) for i in range(30): - self.assertEqual(graph[0, i] == 1.0, i in links[0]) - self.assertEqual(graph[0, i] == 0.0, i not in links[0]) + self.assertEqual(graph[0, i] == 1, i in links[0]) + self.assertEqual(graph[0, i] == 0, i not in links[0]) - def test_knn_functional_response(self): - knnr = KNeighborsRegressor(n_neighbors=1) + def test_knn_functional_response(self) -> None: + """Test prediction of functional response.""" + knnr = KNeighborsRegressor[FDataGrid, FDataGrid](n_neighbors=1) knnr.fit(self.X, self.X) @@ -188,8 +211,12 @@ def test_knn_functional_response(self): self.X.data_matrix, ) - def test_knn_functional_response_precomputed(self): - knnr = KNeighborsRegressor( + def test_knn_functional_response_precomputed(self) -> None: + """Test that precomputed distances work for functional response.""" + knnr = KNeighborsRegressor[ + np.typing.NDArray[np.float_], + FDataGrid, + ]( n_neighbors=4, weights='distance', metric='precomputed', @@ -204,8 +231,12 @@ def test_knn_functional_response_precomputed(self): res.data_matrix, self.X[:4].data_matrix, ) - def test_radius_functional_response(self): - knnr = RadiusNeighborsRegressor( + def test_radius_functional_response(self) -> None: + """Test that radius regression work with functional response.""" + knnr = RadiusNeighborsRegressor[ + FDataGrid, + FDataGrid, + ]( metric=l2_distance, weights='distance', ) @@ -217,9 +248,12 @@ def test_radius_functional_response(self): res.data_matrix, self.X.data_matrix, ) - def test_functional_response_custom_weights(self): - - knnr = KNeighborsRegressor(weights=self._weights, n_neighbors=5) + def test_functional_response_custom_weights(self) -> None: + """Test that custom weights work with functional response.""" + knnr = KNeighborsRegressor[ + FDataGrid, + FDataGrid, + ](weights=self._weights, n_neighbors=5) response = self.X.to_basis(Fourier(domain_range=(-1, 1), n_basis=10)) knnr.fit(self.X, response) @@ -228,10 +262,14 @@ def test_functional_response_custom_weights(self): res.coefficients, response.coefficients, ) - def test_functional_regression_distance_weights(self): - - knnr = KNeighborsRegressor( - weights='distance', n_neighbors=10, + def test_functional_response_distance_weights(self) -> None: + """Test that distance weights work with functional response.""" + knnr = KNeighborsRegressor[ + FDataGrid, + FDataGrid, + ]( + weights='distance', + n_neighbors=10, ) knnr.fit(self.X[:10], self.X[:10]) res = knnr.predict(self.X[11]) @@ -247,8 +285,12 @@ def test_functional_regression_distance_weights(self): res.data_matrix, response.data_matrix, ) - def test_functional_response_basis(self): - knnr = KNeighborsRegressor(weights='distance', n_neighbors=5) + def test_functional_response_basis(self) -> None: + """Test FDataBasis response.""" + knnr = KNeighborsRegressor[ + FDataGrid, + FDataBasis, + ](weights='distance', n_neighbors=5) response = self.X.to_basis(Fourier(domain_range=(-1, 1), n_basis=10)) knnr.fit(self.X, response) @@ -257,8 +299,12 @@ def test_functional_response_basis(self): res.coefficients, response.coefficients, ) - def test_radius_outlier_functional_response(self): - knnr = RadiusNeighborsRegressor(radius=0.001) + def test_radius_outlier_functional_response(self) -> None: + """Test response with no neighbors.""" + knnr = RadiusNeighborsRegressor[ + FDataGrid, + FDataBasis, + ](radius=0.001) knnr.fit(self.X[3:6], self.X[3:6]) # No value given @@ -267,7 +313,8 @@ def test_radius_outlier_functional_response(self): # Test response knnr = RadiusNeighborsRegressor( - radius=0.001, outlier_response=self.X[0], + radius=0.001, + outlier_response=self.X[0], ) knnr.fit(self.X[:6], self.X[:6]) @@ -276,26 +323,18 @@ def test_radius_outlier_functional_response(self): self.X[0].data_matrix, res[6].data_matrix, ) - def test_nearest_centroids_exceptions(self): - - # Test more than one class - nn = NearestCentroid() - with np.testing.assert_raises(ValueError): - nn.fit(self.X[:3], 3 * [0]) - - # Precomputed not supported - nn = NearestCentroid(metric='precomputed') - with np.testing.assert_raises(ValueError): - nn.fit(self.X[:3], 3 * [0]) - - def test_functional_regressor_exceptions(self): - - knnr = RadiusNeighborsRegressor() + def test_functional_regressor_exceptions(self) -> None: + """Test exception with unequal sizes.""" + knnr = RadiusNeighborsRegressor[ + FDataGrid, + FDataBasis, + ]() with np.testing.assert_raises(ValueError): knnr.fit(self.X[:3], self.X[:4]) - def test_search_neighbors_precomputed(self): + def test_search_neighbors_precomputed(self) -> None: + """Test search neighbors with precomputed distances.""" d = PairwiseMetric(l2_distance) distances = d(self.X[:4], self.X[:4]) @@ -309,17 +348,23 @@ def test_search_neighbors_precomputed(self): np.array([[0, 3], [1, 2], [2, 1], [3, 0]]), ) - def test_score_scalar_response(self): - - neigh = KNeighborsRegressor() + def test_score_scalar_response(self) -> None: + """Test regression with scalar response.""" + neigh = KNeighborsRegressor[ + FDataGrid, + np.typing.NDArray[np.float_], + ]() neigh.fit(self.X, self.modes_location) r = neigh.score(self.X, self.modes_location) np.testing.assert_almost_equal(r, 0.9975889963743335) - def test_score_functional_response(self): - - neigh = KNeighborsRegressor() + def test_score_functional_response(self) -> None: + """Test functional score.""" + neigh = KNeighborsRegressor[ + FDataGrid, + FDataGrid, + ]() y = 5 * self.X + 1 neigh.fit(self.X, y) @@ -333,20 +378,27 @@ def test_score_functional_response(self): r = neigh.score( self.X[:7], y[:7], - sample_weight=4 * [1.0 / 5] + 3 * [1.0 / 15], + sample_weight=np.array(4 * [1.0 / 5] + 3 * [1.0 / 15]), ) np.testing.assert_almost_equal(r, 0.9982527586114364) - def test_score_functional_response_exceptions(self): - neigh = RadiusNeighborsRegressor() + def test_score_functional_response_exceptions(self) -> None: + """Test weights with invalid length.""" + neigh = RadiusNeighborsRegressor[ + FDataGrid, + FDataGrid, + ]() neigh.fit(self.X, self.X) with np.testing.assert_raises(ValueError): - neigh.score(self.X, self.X, sample_weight=[1, 2, 3]) - - def test_multivariate_response_score(self): - - neigh = RadiusNeighborsRegressor() + neigh.score(self.X, self.X, sample_weight=np.array([1, 2, 3])) + + def test_multivariate_response_score(self) -> None: + """Test multivariate score.""" + neigh = RadiusNeighborsRegressor[ + FDataGrid, + np.typing.NDArray[np.float_], + ]() y = make_multimodal_samples(n_samples=5, dim_domain=2, random_state=0) neigh.fit(self.X[:5], y) @@ -354,14 +406,14 @@ def test_multivariate_response_score(self): with np.testing.assert_raises(ValueError): neigh.score(self.X[:5], y) - def test_lof_fit_predict(self): + def test_lof_fit_predict(self) -> None: """Test same results with different forms to call fit_predict.""" # Outliers expected = np.ones(len(self.fd_lof)) expected[:2] = -1 # With default l2 distance - lof = LocalOutlierFactor() + lof = LocalOutlierFactor[FDataGrid]() res = lof.fit_predict(self.fd_lof) np.testing.assert_array_equal(expected, res) @@ -406,9 +458,9 @@ def test_lof_fit_predict(self): lof3.negative_outlier_factor_, ) - def test_lof_decision_function(self): + def test_lof_decision_function(self) -> None: """Test decision function and score samples of LOF.""" - lof = LocalOutlierFactor(novelty=True) + lof = LocalOutlierFactor[FDataGrid](novelty=True) lof.fit(self.fd_lof[5:]) score = lof.score_samples(self.fd_lof[:5]) @@ -426,9 +478,9 @@ def test_lof_decision_function(self): err_msg='Error in LocalOutlierFactor.decision_function', ) - def test_lof_exceptions(self): + def test_lof_exceptions(self) -> None: """Test error due to novelty attribute.""" - lof = LocalOutlierFactor(novelty=True) + lof = LocalOutlierFactor[FDataGrid](novelty=True) # Error in fit_predict function with np.testing.assert_raises(AttributeError): @@ -441,7 +493,10 @@ def test_lof_exceptions(self): with np.testing.assert_raises(AttributeError): lof.predict(self.fd_lof[5:]) - def _weights(self, weights): + def _weights( + self, + weights: np.typing.NDArray[np.float_], + ) -> np.typing.NDArray[np.float_]: return np.array([w == np.min(weights) for w in weights], dtype=float) From 8d6d82315f1052a0cd577daf6ab2d4608745130b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 22 Jul 2022 19:03:27 +0200 Subject: [PATCH 221/400] Add default types. --- .../exploratory/outliers/neighbors_outlier.py | 16 ++++++++- .../classification/_neighbors_classifiers.py | 30 ++++++++++++++-- skfda/ml/clustering/_neighbors_clustering.py | 16 ++++++++- skfda/ml/regression/_neighbors_regression.py | 35 +++++++++++++++++-- skfda/tests/test_neighbors.py | 16 ++++----- 5 files changed, 98 insertions(+), 15 deletions(-) diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index 8457280a7..71446595e 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -15,7 +15,8 @@ from ...representation._typing import NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType", bound="LocalOutlierFactor[Any]") -Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +InputBound = Union[NDArrayFloat, FData] +Input = TypeVar("Input", contravariant=True, bound=InputBound) class LocalOutlierFactor( @@ -180,6 +181,19 @@ def __init__( ) -> None: pass + @overload + def __init__( + self: LocalOutlierFactor[InputBound], + *, + n_neighbors: int = 20, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + contamination: float | Literal["auto"] = "auto", + novelty: bool = False, + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index f3e5d8b17..9793bcb99 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -23,7 +23,8 @@ WeightsType, ) -Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +InputBound = Union[NDArrayFloat, FData] +Input = TypeVar("Input", contravariant=True, bound=InputBound) class KNeighborsClassifier( @@ -131,6 +132,18 @@ def __init__( ) -> None: pass + @overload + def __init__( + self: KNeighborsClassifier[InputBound], + *, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, @@ -139,7 +152,7 @@ def __init__( weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', leaf_size: int = 30, - metric: Literal["precomputed"] | Metric[Input] = l2_distance, + metric: Metric[Input] = l2_distance, n_jobs: int | None = None, ) -> None: pass @@ -273,6 +286,19 @@ def __init__( ) -> None: pass + @overload + def __init__( + self: RadiusNeighborsClassifier[InputBound], + *, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + outlier_label=None, + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 470507e23..579acdbe0 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -16,7 +16,8 @@ RadiusNeighborsMixin, ) -Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) +InputBound = Union[NDArrayFloat, FData] +Input = TypeVar("Input", contravariant=True, bound=InputBound) SelfType = TypeVar("SelfType", bound="NearestNeighbors[Any]") @@ -118,6 +119,19 @@ def __init__( ) -> None: pass + @overload + def __init__( + self: NearestNeighbors[InputBound], + *, + n_neighbors: int = 5, + radius: float = 1.0, + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + metric: Metric[Input] = l2_distance, + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index 822b9f82e..a937bd2e5 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -23,8 +23,10 @@ WeightsType, ) -Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) -Target = TypeVar("Target", bound=Union[NDArrayFloat, FData]) +InputBound = Union[NDArrayFloat, FData] +Input = TypeVar("Input", contravariant=True, bound=InputBound) +TargetBound = Union[NDArrayFloat, FData] +Target = TypeVar("Target", bound=TargetBound) class KNeighborsRegressor( @@ -150,6 +152,7 @@ class KNeighborsRegressor( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload def __init__( self: KNeighborsRegressor[NDArrayFloat, Target], @@ -163,6 +166,18 @@ def __init__( ) -> None: pass + @overload + def __init__( + self: KNeighborsRegressor[InputBound, Target], + *, + n_neighbors: int = 5, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, @@ -334,6 +349,7 @@ class RadiusNeighborsRegressor( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload def __init__( self: RadiusNeighborsRegressor[NDArrayFloat, Target], @@ -348,6 +364,19 @@ def __init__( ) -> None: pass + @overload + def __init__( + self: RadiusNeighborsRegressor[InputBound, Target], + *, + radius: float = 1.0, + weights: WeightsType = 'uniform', + algorithm: AlgorithmType = 'auto', + leaf_size: int = 30, + outlier_response=None, + n_jobs: int | None = None, + ) -> None: + pass + @overload def __init__( self, @@ -356,7 +385,7 @@ def __init__( weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', leaf_size: int = 30, - metric: Literal["precomputed"] | Metric[Input] = l2_distance, + metric: Metric[Input] = l2_distance, outlier_response=None, n_jobs: int | None = None, ) -> None: diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index 9d5525d55..6af441015 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -129,13 +129,13 @@ def test_predict_regressor(self) -> None: def test_kneighbors(self) -> None: """Test k neighbor searches for all k-neighbors estimators.""" - nn = NearestNeighbors[FDataGrid]() + nn = NearestNeighbors() nn.fit(self.X) - lof = LocalOutlierFactor[FDataGrid](n_neighbors=5) + lof = LocalOutlierFactor(n_neighbors=5) lof.fit(self.X) - knn = KNeighborsClassifier[FDataGrid]() + knn = KNeighborsClassifier() knn.fit(self.X, self.y) knnr = KNeighborsRegressor[FDataGrid, np.typing.NDArray[np.float_]]() @@ -167,10 +167,10 @@ def test_kneighbors(self) -> None: def test_radius_neighbors(self) -> None: """Test query with radius.""" - nn = NearestNeighbors[FDataGrid](radius=0.1) + nn = NearestNeighbors(radius=0.1) nn.fit(self.X) - knn = RadiusNeighborsClassifier[FDataGrid](radius=0.1) + knn = RadiusNeighborsClassifier(radius=0.1) knn.fit(self.X, self.y) knnr = RadiusNeighborsRegressor[ @@ -413,7 +413,7 @@ def test_lof_fit_predict(self) -> None: expected[:2] = -1 # With default l2 distance - lof = LocalOutlierFactor[FDataGrid]() + lof = LocalOutlierFactor() res = lof.fit_predict(self.fd_lof) np.testing.assert_array_equal(expected, res) @@ -460,7 +460,7 @@ def test_lof_fit_predict(self) -> None: def test_lof_decision_function(self) -> None: """Test decision function and score samples of LOF.""" - lof = LocalOutlierFactor[FDataGrid](novelty=True) + lof = LocalOutlierFactor(novelty=True) lof.fit(self.fd_lof[5:]) score = lof.score_samples(self.fd_lof[:5]) @@ -480,7 +480,7 @@ def test_lof_decision_function(self) -> None: def test_lof_exceptions(self) -> None: """Test error due to novelty attribute.""" - lof = LocalOutlierFactor[FDataGrid](novelty=True) + lof = LocalOutlierFactor(novelty=True) # Error in fit_predict function with np.testing.assert_raises(AttributeError): From c5b5d12ff5e863c35b6a27c4198f5f66d1a19082 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 23 Jul 2022 02:07:54 +0200 Subject: [PATCH 222/400] Remove outlier functions in knn-regression. --- skfda/ml/_neighbors_base.py | 24 +++---------------- .../classification/_neighbors_classifiers.py | 16 +++++++------ skfda/ml/regression/_neighbors_regression.py | 7 ------ skfda/tests/test_neighbors.py | 20 ++++------------ 4 files changed, 16 insertions(+), 51 deletions(-) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 4faf88c72..8e76148ec 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -652,7 +652,8 @@ def _functional_predict( distances, neighbors = self._query(X) if len(neighbors[0]) == 0: - pred = self._outlier_response(neighbors) + # TODO: Make public somehow + pred = self._fit_y.dtype._na_repr() # noqa: WPS437 else: pred = self._prediction_from_neighbors( self._fit_y[neighbors[0]], @@ -661,7 +662,7 @@ def _functional_predict( for i, idx in enumerate(neighbors[1:]): if len(idx) == 0: - new_pred = self._outlier_response(neighbors) + new_pred = self._fit_y.dtype._na_repr() # noqa: WPS437 else: new_pred = self._prediction_from_neighbors( self._fit_y[idx], @@ -672,25 +673,6 @@ def _functional_predict( return pred - def _outlier_response( - self, - neighbors: TargetRegression, - ) -> TargetRegression: - """Response in case of no neighbors.""" - outlier_response = getattr(self, "outlier_response", None) - - if outlier_response is None: - index = np.where([len(n) == 0 for n in neighbors])[0] - - raise ValueError( - f"No neighbors found for test samples {index}, " - "you can try using larger radius, give a reponse " - "for outliers, or consider removing them from " - "your dataset.", - ) - - return outlier_response - def score( self, X: Input, diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index 9793bcb99..fc7e24880 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import TypeVar, Union, overload +from typing import Sequence, TypeVar, Union, overload from sklearn.neighbors import ( KNeighborsClassifier as _KNeighborsClassifier, @@ -25,6 +25,7 @@ InputBound = Union[NDArrayFloat, FData] Input = TypeVar("Input", contravariant=True, bound=InputBound) +OutlierLabelType = Union[int, str, Sequence[int], Sequence[str], None] class KNeighborsClassifier( @@ -157,7 +158,8 @@ def __init__( ) -> None: pass - def __init__( + # Not useless, it restricts parameters + def __init__( # noqa: WPS612 self, *, n_neighbors: int = 5, @@ -224,7 +226,7 @@ class RadiusNeighborsClassifier( metric: The distance metric to use for the tree. The default metric is the L2 distance. See the documentation of the metrics module for a list of available metrics. - outlier_label (int, optional): + outlier_label: Label, which is given for outlier samples (samples with no neighbors on given radius). If set to None, ValueError is raised, when outlier is detected. @@ -281,7 +283,7 @@ def __init__( algorithm: AlgorithmType = 'auto', leaf_size: int = 30, metric: Literal["precomputed"], - outlier_label=None, + outlier_label: OutlierLabelType = None, n_jobs: int | None = None, ) -> None: pass @@ -294,7 +296,7 @@ def __init__( weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', leaf_size: int = 30, - outlier_label=None, + outlier_label: OutlierLabelType = None, n_jobs: int | None = None, ) -> None: pass @@ -308,7 +310,7 @@ def __init__( algorithm: AlgorithmType = 'auto', leaf_size: int = 30, metric: Metric[Input] = l2_distance, - outlier_label=None, + outlier_label: OutlierLabelType = None, n_jobs: int | None = None, ) -> None: pass @@ -321,7 +323,7 @@ def __init__( algorithm: AlgorithmType = 'auto', leaf_size: int = 30, metric: Literal["precomputed"] | Metric[Input] = l2_distance, - outlier_label=None, + outlier_label: OutlierLabelType = None, n_jobs: int | None = None, ) -> None: super().__init__( diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index a937bd2e5..f6bf1412a 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -271,8 +271,6 @@ class RadiusNeighborsRegressor( metric: The distance metric to use for the tree. The default metric is the L2 distance. See the documentation of the metrics module for a list of available metrics. - outlier_response: Default response in the functional response case for - test samples without neighbors. n_jobs: The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. @@ -359,7 +357,6 @@ def __init__( algorithm: AlgorithmType = 'auto', leaf_size: int = 30, metric: Literal["precomputed"], - outlier_response=None, n_jobs: int | None = None, ) -> None: pass @@ -372,7 +369,6 @@ def __init__( weights: WeightsType = 'uniform', algorithm: AlgorithmType = 'auto', leaf_size: int = 30, - outlier_response=None, n_jobs: int | None = None, ) -> None: pass @@ -386,7 +382,6 @@ def __init__( algorithm: AlgorithmType = 'auto', leaf_size: int = 30, metric: Metric[Input] = l2_distance, - outlier_response=None, n_jobs: int | None = None, ) -> None: pass @@ -399,7 +394,6 @@ def __init__( algorithm: AlgorithmType = 'auto', leaf_size: int = 30, metric: Literal["precomputed"] | Metric[Input] = l2_distance, - outlier_response=None, n_jobs: int | None = None, ) -> None: """Initialize the classifier.""" @@ -411,7 +405,6 @@ def __init__( metric=metric, n_jobs=n_jobs, ) - self.outlier_response = outlier_response def _init_estimator(self) -> _RadiusNeighborsRegressor: return _RadiusNeighborsRegressor( diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index 6af441015..e3da51452 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -12,7 +12,6 @@ from skfda.misc.metrics import PairwiseMetric, l2_distance from skfda.ml.classification import ( KNeighborsClassifier, - NearestCentroid, RadiusNeighborsClassifier, ) from skfda.ml.clustering import NearestNeighbors @@ -74,12 +73,9 @@ def test_predict_classifier(self) -> None: classifiers: Sequence[ KNeighborsClassifier[FDataGrid] | RadiusNeighborsClassifier[FDataGrid] - | NearestCentroid[FDataGrid] ] = ( KNeighborsClassifier(), RadiusNeighborsClassifier(radius=0.1), - NearestCentroid(), - NearestCentroid[FDataGrid](metric=l2_distance, centroid=np.mean), ) for neigh in classifiers: @@ -301,26 +297,18 @@ def test_functional_response_basis(self) -> None: def test_radius_outlier_functional_response(self) -> None: """Test response with no neighbors.""" + # Test response knnr = RadiusNeighborsRegressor[ FDataGrid, - FDataBasis, - ](radius=0.001) - knnr.fit(self.X[3:6], self.X[3:6]) - - # No value given - with np.testing.assert_raises(ValueError): - knnr.predict(self.X[:10]) - - # Test response - knnr = RadiusNeighborsRegressor( + FDataGrid, + ]( radius=0.001, - outlier_response=self.X[0], ) knnr.fit(self.X[:6], self.X[:6]) res = knnr.predict(self.X[:7]) np.testing.assert_array_almost_equal( - self.X[0].data_matrix, res[6].data_matrix, + res[6].data_matrix, np.nan, ) def test_functional_regressor_exceptions(self) -> None: From a7d164601c93f224caa0ba37cd5261b499df1a53 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 23 Jul 2022 02:21:39 +0200 Subject: [PATCH 223/400] Fix examples. --- examples/plot_k_neighbors_classification.py | 59 ------------- .../plot_neighbors_functional_regression.py | 18 ++-- examples/plot_neighbors_scalar_regression.py | 55 ++++++------ .../plot_radius_neighbors_classification.py | 87 +++++++------------ 4 files changed, 64 insertions(+), 155 deletions(-) diff --git a/examples/plot_k_neighbors_classification.py b/examples/plot_k_neighbors_classification.py index 64584e202..b7b1fcd68 100644 --- a/examples/plot_k_neighbors_classification.py +++ b/examples/plot_k_neighbors_classification.py @@ -161,65 +161,6 @@ score = gscv.score(X_test, y_test) print(score) -############################################################################## -# -# When the functional data has been sampled in an equispaced way, or -# approximately equispaced, it is possible to use the scikit-learn vector -# metrics with similar results. -# -# For example, in the case of the :math:`\mathbb{L}^2` distance, -# by approximating the integral as a Riemann sum, -# we can derive that the value of said integral is proportional to the -# Euclidean distance between vectors. -# -# .. math:: -# \|f - g \|_{\mathbb{L}^2} = \left ( \int_a^b |f(x) - g(x)|^2 dx \right ) -# ^{\frac{1}{2}} \approx \left ( \sum_{n=0}^{N}\bigtriangleup h \,|f(x_n) -# - g(x_n)|^2 \right ) ^ {\frac{1}{2}}\\ -# = \sqrt{\bigtriangleup h} \, d_{euclidean}(\vec{f}, \vec{g}) -# -# -# Therefore, in this case, it is roughly equivalent to use this metric instead -# of the functional one, since multiplying by a constant does not affect the -# order of the neighbors. -# -# By setting the parameter ``sklearn_metric`` of the classifier to ``True``, -# a vectorial metric of sklearn can be provided. The list of supported -# metrics can be found in :class:`~sklearn.neighbors.DistanceMetric` -# -# We will fit the model with the sklearn distance and search for the best -# parameter. The results can vary slightly, due to the approximation of -# the integral, but the result should be similar. - -knn = KNeighborsClassifier(metric="euclidean", multivariate_metric=True) -gscv2 = GridSearchCV(knn, param_grid, cv=5) -gscv2.fit(X_train, y_train) - -print("Best params:", gscv2.best_params_) -print("Best score:", gscv2.best_score_) - -############################################################################## -# -# Using sklearn metrics results in a speedup of three orders of magnitude. -# However, it is not always possible to have equispaced sample and not all -# functional metrics have the vector equivalent required to do this -# approximation. -# -# The mean score time depending on the metric is shown below. - -print("Mean score time (milliseconds)") -print( - "L2 distance:{time}(ms)".format( - time=1000 * np.mean(gscv.cv_results_["mean_score_time"]), - ), -) - -print( - "Euclidean distance:{time}(ms)".format( - time=1000 * np.mean(gscv2.cv_results_["mean_score_time"]), - ), -) - ############################################################################## # # This classifier can be used with multivariate functional data, as surfaces diff --git a/examples/plot_neighbors_functional_regression.py b/examples/plot_neighbors_functional_regression.py index 44acf8fd1..aa70fa65d 100644 --- a/examples/plot_neighbors_functional_regression.py +++ b/examples/plot_neighbors_functional_regression.py @@ -11,13 +11,11 @@ # sphinx_gallery_thumbnail_number = 4 from sklearn.model_selection import train_test_split -import matplotlib.pyplot as plt import skfda from skfda.ml.regression import KNeighborsRegressor from skfda.representation.basis import Fourier - ############################################################################## # # In this example we are going to show the usage of the nearest neighbors @@ -32,7 +30,7 @@ # precipitation at 35 different locations in Canada averaged over 1960 to 1994. # The following figure shows the different temperature and precipitation # curves. -# + data = skfda.datasets.fetch_weather() fd = data['data'] @@ -55,7 +53,6 @@ # We will try to predict the precipitation curves. First of all we are going # to make a smoothing of the precipitation curves using a basis # representation, employing for it a fourier basis with 5 elements. -# y = y.to_basis(Fourier(n_basis=5)) @@ -66,10 +63,13 @@ # We will split the dataset in two partitions, for training and test, # using the sklearn function # :func:`~sklearn.model_selection.train_test_split`. -# -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.1, - random_state=28) +X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=0.1, + random_state=28, +) ############################################################################## # @@ -77,8 +77,6 @@ # distance. In this case, to calculate # the response we will use a mean of the response, weighted by their distance # to the test sample. -# - knn = KNeighborsRegressor(n_neighbors=5, weights='distance') knn.fit(X_train, y_train) @@ -89,7 +87,6 @@ # :meth:`~skfda.ml.regression.KNeighborsFunctionalRegressor.predict`. The # following figure shows the real precipitation curves, in dashed line, and # the predicted ones. -# y_pred = knn.predict(X_test) @@ -126,4 +123,3 @@ # # * Ramsay, James O., and Silverman, Bernard W. (2002). Applied Functional # Data Analysis, Springer, New York\n' -# diff --git a/examples/plot_neighbors_scalar_regression.py b/examples/plot_neighbors_scalar_regression.py index 025121fb3..d474a5624 100644 --- a/examples/plot_neighbors_scalar_regression.py +++ b/examples/plot_neighbors_scalar_regression.py @@ -10,16 +10,13 @@ # sphinx_gallery_thumbnail_number = 3 -from sklearn.model_selection import train_test_split, GridSearchCV, KFold - import matplotlib.pyplot as plt import numpy as np +from sklearn.model_selection import GridSearchCV, train_test_split import skfda from skfda.ml.regression import KNeighborsRegressor - - ############################################################################## # # In this example, we are going to show the usage of the nearest neighbors @@ -37,7 +34,7 @@ # # The following figure shows the different temperature and precipitation # curves. -# + data = skfda.datasets.fetch_weather() fd = data['data'] @@ -60,7 +57,7 @@ # We will try to predict the total log precipitation, i.e, # :math:`logPrecTot_i = \log \sum_{t=0}^{365} prec_i(t)` using the temperature # curves. -# + # Sum directly from the data matrix prec = y_func.data_matrix.sum(axis=1)[:, 0] @@ -73,24 +70,24 @@ # As in the nearest neighbors classifier examples, we will split the dataset # in two partitions, for training and test, using the sklearn function # :func:`~sklearn.model_selection.train_test_split`. -# -X_train, X_test, y_train, y_test = train_test_split(X, log_prec, - random_state=7) +X_train, X_test, y_train, y_test = train_test_split( + X, + log_prec, + random_state=7, +) ############################################################################## # # Firstly we will try make a prediction with the default values of the # estimator, using 5 neighbors and the :math:`\mathbb{L}^2` distance. # -# We can fit the :class:`~skfda.ml.regression.KNeighborsRegressor` in the +# We can fit the :class:`~skfda.ml.regression.KNeighborsRegressor` in the # same way than the # sklearn estimators. This estimator is an extension of the sklearn # :class:`~sklearn.neighbors.KNeighborsRegressor`, but accepting a -# :class:`~skfda.representation.grid.FDataGrid` as input instead of an array with -# multivariate data. -# - +# :class:`~skfda.representation.grid.FDataGrid` as input instead of an array +# with multivariate data. knn = KNeighborsRegressor(weights='distance') knn.fit(X_train, y_train) @@ -99,7 +96,7 @@ # # We can predict values for the test partition using # :meth:`~skfda.ml.regression.KNeighborsScalarRegressor.predict`. -# + pred = knn.predict(X_test) print(pred) @@ -108,7 +105,6 @@ # # The following figure compares the real precipitations with the predicted # values. -# fig = plt.figure() @@ -128,8 +124,7 @@ # The coefficient :math:`R^2` is defined as :math:`(1 - u/v)`, where :math:`u` # is the residual sum of squares :math:`\sum_i (y_i - y_{pred_i})^ 2` # and :math:`v` is the total sum of squares :math:`\sum_i (y_i - \bar y )^2`. -# -# + score = knn.score(X_test, y_test) print(score) @@ -144,28 +139,29 @@ # # We will perform cross-validation to test more robustly our model. # -# As in the neighbors classifiers examples, we can use a sklearn metric to -# approximate the :math:`\mathbb{L}^2` metric between function, but with a -# much lower computational cost. -# # Also, we can make a grid search, using # :class:`~sklearn.model_selection.GridSearchCV`, to determine the optimal # number of neighbors and the best way to weight their votes. -# -param_grid = {'n_neighbors': np.arange(1, 12, 2), - 'weights': ['uniform', 'distance']} + +param_grid = { + 'n_neighbors': range(1, 12, 2), + 'weights': ['uniform', 'distance'], +} -knn = KNeighborsRegressor(metric='euclidean', multivariate_metric=True) -gscv = GridSearchCV(knn, param_grid, cv=KFold(n_splits=3, - shuffle=True, random_state=0)) +knn = KNeighborsRegressor() +gscv = GridSearchCV( + knn, + param_grid, + cv=5, +) gscv.fit(X, log_prec) ############################################################################## # # We obtain that 7 is the optimal number of neighbors. -# + print("Best params", gscv.best_params_) print("Best score", gscv.best_score_) @@ -180,4 +176,3 @@ # # * Ramsay, James O., and Silverman, Bernard W. (2002). Applied Functional # Data Analysis, Springer, New York\n' -# diff --git a/examples/plot_radius_neighbors_classification.py b/examples/plot_radius_neighbors_classification.py index 5a34c267d..6e00b0e1b 100644 --- a/examples/plot_radius_neighbors_classification.py +++ b/examples/plot_radius_neighbors_classification.py @@ -11,7 +11,6 @@ # sphinx_gallery_thumbnail_number = 2 -import matplotlib.pyplot as plt import numpy as np from sklearn.model_selection import train_test_split @@ -30,10 +29,16 @@ # We will create two classes of sinusoidal samples, with different phases. # # Make toy dataset -fd1 = skfda.datasets.make_sinusoidal_process(error_std=.0, phase_std=.35, - random_state=0) -fd2 = skfda.datasets.make_sinusoidal_process(phase_mean=1.9, error_std=.0, - random_state=1) +fd1 = skfda.datasets.make_sinusoidal_process( + error_std=0, + phase_std=0.35, + random_state=0, +) +fd2 = skfda.datasets.make_sinusoidal_process( + phase_mean=1.9, + error_std=0, + random_state=1, +) X = fd1.concatenate(fd2) y = np.array(15 * [0] + 15 * [1]) @@ -49,9 +54,14 @@ # Concatenate the two classes in the same FDataGrid. -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, - shuffle=True, stratify=y, - random_state=0) +X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=0.25, + shuffle=True, + stratify=y, + random_state=0, +) ############################################################################## # @@ -63,7 +73,6 @@ # as a bandwidth with a fixed radius around a function. # The following figure shows the ball centered in the first sample of the test # partition. -# radius = 0.3 sample = X_test[0] # Center of the ball @@ -75,15 +84,18 @@ lower = sample - radius upper = sample + radius fig.axes[0].fill_between( - sample.grid_points[0], lower.data_matrix.flatten(), - upper.data_matrix[0].flatten(), alpha=.25, color='C1') + sample.grid_points[0], + lower.data_matrix.flatten(), + upper.data_matrix[0].flatten(), + alpha=0.25, + color='C1', +) ############################################################################## # # In this case, all the neighbors in the ball belong to the first class, so # this will be the class predicted. -# # Creation of pairwise distance @@ -94,8 +106,12 @@ fig = X_train[distances <= radius].plot(color='C0') sample.plot(fig=fig, color='red', linewidth=3) fig.axes[0].fill_between( - sample.grid_points[0], lower.data_matrix.flatten(), - upper.data_matrix[0].flatten(), alpha=.25, color='C1') + sample.grid_points[0], + lower.data_matrix.flatten(), + upper.data_matrix[0].flatten(), + alpha=0.25, + color='C1', +) ############################################################################## @@ -110,9 +126,8 @@ # # The vote of the neighbors can be weighted using the paramenter ``weights``. # In this case we will weight the vote inversely proportional to the distance. -# -radius_nn = RadiusNeighborsClassifier(radius=radius, weights='distance') +radius_nn = RadiusNeighborsClassifier(radius=radius, weights='distance') radius_nn.fit(X_train, y_train) @@ -120,7 +135,6 @@ # # We can predict labels for the test partition with # :meth:`~skfda.ml.classification.RadiusNeighborsClassifier.predict`. -# pred = radius_nn.predict(X_test) print(pred) @@ -128,51 +142,16 @@ ############################################################################## # # In this case, we get 100% accuracy, although it is a toy dataset. -# - -test_score = radius_nn.score(X_test, y_test) -print(test_score) - -############################################################################## -# -# As in the K-nearest neighbor example, we can use the euclidean sklearn -# metric approximately equivalent to the functional :math:`\mathbb{L}^2` one, -# but computationally faster. -# -# We saw that :math:`\|f -g \|_{\mathbb{L}^2} \approx \sqrt{\bigtriangleup h} -# \, d_{euclidean}(\vec{f}, \vec{g})` if the samples are equiespaced (or -# almost). -# -# In the KNN case, the constant :math:`\sqrt{\bigtriangleup h}` does not -# matter, but in this case will affect the value of the radius, dividing by -# :math:`\sqrt{\bigtriangleup h}`. -# -# In this dataset :math:`\bigtriangleup h=0.001`, so, we have to multiply the -# radius by :math:`\frac{1}{\bigtriangleup h}=10` to achieve the same result. -# -# The computation using this metric it is 1000 times faster. See the -# K-neighbors classifier example and the API documentation to get detailled -# information. -# -# We obtain 100% accuracy with this metric too. -# - -radius_nn = RadiusNeighborsClassifier(radius=3, metric='euclidean', - weights='distance', multivariate_metric=True) - -radius_nn.fit(X_train, y_train) test_score = radius_nn.score(X_test, y_test) - print(test_score) ############################################################################## # # If the radius is too small, it is possible to get samples with no neighbors. # The classifier will raise and exception in this case. -# -radius_nn.set_params(radius=.5) #  Radius 0.05 in the L2 distance +radius_nn.set_params(radius=0.5) #  Radius 0.05 in the L2 distance radius_nn.fit(X_train, y_train) try: @@ -183,7 +162,6 @@ ############################################################################## # # A label to these oulier samples can be provided to avoid this problem. -# radius_nn.set_params(outlier_label=2) radius_nn.fit(X_train, y_train) @@ -195,4 +173,3 @@ # # This classifier can be used with multivariate funcional data, as surfaces # or curves in :math:`\mathbb{R}^N`, if the metric support it too. -# From 296189e1e4ed48ef4a1f7d3d763726e655860c6f Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 25 Jul 2022 22:00:07 +0200 Subject: [PATCH 224/400] Fix style errors. --- skfda/ml/_neighbors_base.py | 34 +++++++------------- skfda/ml/clustering/_neighbors_clustering.py | 4 ++- skfda/ml/regression/_neighbors_regression.py | 3 +- 3 files changed, 17 insertions(+), 24 deletions(-) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 8e76148ec..8424c0b6d 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -12,7 +12,7 @@ from skfda.misc.metrics._utils import PairwiseMetric -from .. import FData, FDataGrid +from .. import FData, FDataGrid, concatenate from .._utils._sklearn_adapter import ( BaseEstimator, ClassifierMixin, @@ -142,7 +142,7 @@ def _refit_with_distances(self) -> None: self._estimator.fit(distances, self._fit_y) self._fitted_with_distances = True - def _X_to_distances( + def _X_to_distances( # noqa: N802 self, X: Input, ) -> NDArrayFloat: @@ -651,27 +651,17 @@ def _functional_predict( """Predict functional responses.""" distances, neighbors = self._query(X) - if len(neighbors[0]) == 0: - # TODO: Make public somehow - pred = self._fit_y.dtype._na_repr() # noqa: WPS437 - else: - pred = self._prediction_from_neighbors( - self._fit_y[neighbors[0]], - distances[0], + iterable = ( + self._fit_y.dtype._na_repr() # noqa: WPS437 + if len(idx) == 0 + else self._prediction_from_neighbors( + self._fit_y[idx], + dist, ) + for idx, dist in zip(neighbors, distances) + ) - for i, idx in enumerate(neighbors[1:]): - if len(idx) == 0: - new_pred = self._fit_y.dtype._na_repr() # noqa: WPS437 - else: - new_pred = self._prediction_from_neighbors( - self._fit_y[idx], - distances[i + 1], - ) - - pred = pred.concatenate(new_pred) - - return pred + return concatenate(iterable) # type: ignore[no-any-return] def score( self, @@ -772,7 +762,7 @@ def _functional_score( v = y - y.mean() # Discretize to integrate and make squares if needed - if type(u) != FDataGrid: + if not isinstance(u, FDataGrid): u = u.to_grid() v = v.to_grid() diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 579acdbe0..6dd6e2756 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -106,6 +106,7 @@ class NearestNeighbors( https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm """ + @overload def __init__( self: NearestNeighbors[NDArrayFloat], @@ -145,7 +146,8 @@ def __init__( ) -> None: pass - def __init__( + # Parameters are important + def __init__( # noqa: WPS612 self, *, n_neighbors: int = 5, diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index f6bf1412a..fac598926 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -386,7 +386,8 @@ def __init__( ) -> None: pass - def __init__( + # Parameters are important + def __init__( # noqa: WPS612 self, *, radius: float = 1.0, From 6363ad36fcbf70f4d98e3a451de5fa65f9c81e05 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 27 Jul 2022 10:31:55 +0200 Subject: [PATCH 225/400] Fix broken links to FPCA in docs. --- docs/modules/exploratory/visualization.rst | 2 +- docs/modules/preprocessing/dim_reduction.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/modules/exploratory/visualization.rst b/docs/modules/exploratory/visualization.rst index 5affcead0..854d989f5 100644 --- a/docs/modules/exploratory/visualization.rst +++ b/docs/modules/exploratory/visualization.rst @@ -125,7 +125,7 @@ the following class is implemented: .. autosummary:: :toctree: autosummary - skfda.exploratory.visualization.fpca.FPCAPlot + skfda.exploratory.visualization.FPCAPlot See the example :ref:`sphx_glr_auto_examples_plot_fpca.py` for detailed explanation. \ No newline at end of file diff --git a/docs/modules/preprocessing/dim_reduction.rst b/docs/modules/preprocessing/dim_reduction.rst index 119dcfaba..e35e5675f 100644 --- a/docs/modules/preprocessing/dim_reduction.rst +++ b/docs/modules/preprocessing/dim_reduction.rst @@ -41,4 +41,4 @@ variance. .. autosummary:: :toctree: autosummary - skfda.preprocessing.dim_reduction.feature_extraction.FPCA \ No newline at end of file + skfda.preprocessing.dim_reduction.FPCA \ No newline at end of file From b1d5f20c27220cc46d313c777c4800940deeccaa Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Fri, 29 Jul 2022 19:02:12 +0200 Subject: [PATCH 226/400] implemented _check_and_convert. implemented 1d and 2d test. Refactoring dataframe conversion. --- skfda/ml/regression/_linear_regression.py | 73 +++++++++----------- tests/test_regression.py | 81 +++++++++++------------ 2 files changed, 72 insertions(+), 82 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 9aff5e232..08d48e7cb 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -190,19 +190,22 @@ class LinearRegression( ... [2, 0], ... [1, 2], ... [2, 2]]) - >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] - >>> cov_dict = { "fd": x_fd, "mult": x } + >>> mult1 = np.asarray([1, 2, 4, 1, 3, 2]) + >>> mult2 = np.asarray([7, 3, 2, 1, 1, 5]) + >>> cov_dict = {"fd": x_fd, "m1": mult1, "m2": mult2} >>> df = pd.DataFrame(cov_dict) >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( ... coef_basis=[None, Constant()]) >>> _ = linear.fit(df, y) >>> linear.coef_[0] - array([ 2., 1.]) + array([2.]) >>> linear.coef_[1] + array([1.]) + >>> linear.coef_[2] FDataBasis( basis=Constant(domain_range=((0, 1),), n_basis=1), - coefficients=[[ 1.]], + coefficients=[[1.]], ...) >>> linear.intercept_ array([ 1.]) @@ -341,13 +344,33 @@ def _argcheck_X( elif isinstance(X, pd.DataFrame): X = self._dataframe_conversion(X) - X = [x if isinstance(x, FData) else np.asarray(x) for x in X] + X = [ + x if isinstance(x, FData) + else self._check_and_convert(x) for x in X + ] if all(not isinstance(i, FData) for i in X): warnings.warn("All the covariates are scalar.") return X + def _check_and_convert( + self, + X: AcceptedDataType, + ) -> np.ndarray: + """Check if the input array is 1D and converts it to a 2D array. + + Args: + X: multivariate array to check and convert. + + Returns: + np.ndarray: numpy 2D array. + """ + new_X = np.asarray(X) + if len(new_X.shape) == 1: + new_X = new_X[:, np.newaxis] + return new_X + def _argcheck_X_y( self, X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], @@ -405,44 +428,12 @@ def _dataframe_conversion( self, X: pd.DataFrame, ) -> List[AcceptedDataType]: - """Convert DataFrames to a list with two elements. - - First of all, a list with mv covariates and the second, - a FDataBasis object with functional data + """Convert DataFrames to a list with input columns. Args: - X: pandas DataFrame to convert + X: pandas DataFrame to convert. Returns: - List: first of all, a list with mv covariates - and the second, a list of FDataBasis object with functional data + List: list which elements are the input DataFrame columns. """ - mv_list = [] - fd_list = [] - final = [] - - for obs in X.values.tolist(): - mv = [] - for i in enumerate(obs): - cov = obs[i[0]] - if (isinstance(cov, FData)): - fd_list.append(cov) - elif (isinstance(cov, (np.ndarray, list))): - mv_list.append(cov) - else: - mv.append(cov) - - if (len(mv) != 0): - mv_list.append(mv) - - if (len(mv_list) != 0): - final.append(mv_list) - - if (len(fd_list) != 0): - fdb_df = pd.DataFrame(fd_list) - - for c in range(fdb_df.shape[1]): - column = FDataBasis.concatenate(*fdb_df.iloc[:, c].tolist()) - final.append(column) - - return final + return [v.values for k, v in X.items()] diff --git a/tests/test_regression.py b/tests/test_regression.py index 54a45a8ad..537b6f2b5 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -9,7 +9,7 @@ from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.ml.regression import HistoricalLinearRegression, LinearRegression -from skfda.representation.basis import BSpline, FDataBasis, Fourier, Monomial +from skfda.representation.basis import BSpline, FDataBasis, Fourier, Monomial, Constant from skfda.representation.grid import FDataGrid @@ -112,62 +112,57 @@ def test_regression_mixed(self): y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) - def test_regression_df_multivariate(self): # noqa: D102 - - multivariate1 = [0, 2, 1, 3, 4, 2, 3] - multivariate2 = [0, 7, 7, 9, 16, 14, 5] - multivariate = [list(obs) for obs in zip(multivariate1, multivariate2)] + def test_same_result_1d_2d_multivariate_arrays(self): + """Test if the results using 1D and 2D arrays are the same. - x_basis = Monomial(n_basis=3) - x_fd = FDataBasis(x_basis, [[1, 0, 0], [0, 1, 0], [0, 0, 1], - [1, 0, 1], [1, 0, 0], [0, 1, 0], - [0, 0, 1]]) - - cov_dict = {"fd": x_fd, "mult1": multivariate1, "mult2": multivariate2} + 1D and 2D multivariate arrays are allowed in LinearRegression + interface, and the result must be the same. + """ + multivariate1 = np.asarray([1, 2, 4, 1, 3, 2]) + multivariate2 = np.asarray([7, 3, 2, 1, 1, 5]) + multivariate = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] + + x_basis = Monomial(n_basis=2) + x_fd = FDataBasis( + x_basis, + [[0, 2], [0, 4], [1, 0], [2, 0], [1, 2], [2, 2]], + ) - df = pd.DataFrame(cov_dict) + y = [11, 10, 12, 6, 10, 13] - # y = 2 + sum([3, 1] * array) + int(3 * function) # noqa: E800 - intercept = 2 - coefs_multivariate = np.array([3, 1]) - coefs_functions = FDataBasis( - Monomial(n_basis=3), [[3, 0, 0]], - ) - y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) - y_sum = multivariate @ coefs_multivariate - y = 2 + y_sum + y_integral + linear = LinearRegression(coef_basis=[None, Constant()]) + linear.fit([multivariate, x_fd], y) - scalar = LinearRegression() - scalar.fit(df, y) + linear2 = LinearRegression(coef_basis=[None, None, Constant()]) + linear2.fit([multivariate1, multivariate2, x_fd], y) - np.testing.assert_allclose( - scalar.intercept_, intercept, atol=0.01, + np.testing.assert_equal( + linear.coef_[0][0], + linear2.coef_[0], ) - np.testing.assert_allclose( - scalar.coef_[0], coefs_multivariate, atol=0.01, + np.testing.assert_equal( + linear.coef_[0][1], + linear2.coef_[1], ) - np.testing.assert_allclose( - scalar.coef_[1].coefficients, - coefs_functions.coefficients, - atol=0.01, + np.testing.assert_equal( + linear.coef_[1], + linear2.coef_[2], ) - y_pred = scalar.predict(df) - np.testing.assert_allclose(y_pred, y, atol=0.01) - - def test_regression_df_grouped_multivariate(self): # noqa: D102 + def test_regression_df_multivariate(self): # noqa: D102 - multivariate = [[0, 0], [2, 7], [1, 7], [3, 9], - [4, 16], [2, 14], [3, 5]] + multivariate1 = [0, 2, 1, 3, 4, 2, 3] + multivariate2 = [0, 7, 7, 9, 16, 14, 5] + multivariate = [list(obs) for obs in zip(multivariate1, multivariate2)] x_basis = Monomial(n_basis=3) x_fd = FDataBasis(x_basis, [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]) - cov_dict = {"fd": x_fd, "mult": multivariate} + cov_dict = {"fd": x_fd, "mult1": multivariate1, "mult2": multivariate2} df = pd.DataFrame(cov_dict) @@ -189,11 +184,15 @@ def test_regression_df_grouped_multivariate(self): # noqa: D102 ) np.testing.assert_allclose( - scalar.coef_[0], coefs_multivariate, atol=0.01, + scalar.coef_[1], coefs_multivariate[0], atol=0.01, ) np.testing.assert_allclose( - scalar.coef_[1].coefficients, + scalar.coef_[2], coefs_multivariate[1], atol=0.01, + ) + + np.testing.assert_allclose( + scalar.coef_[0].coefficients, coefs_functions.coefficients, atol=0.01, ) From e29d72880b203297e3c26d71efe3f9c79155a7a2 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 1 Aug 2022 21:15:50 +0200 Subject: [PATCH 227/400] Fix MS-Plot title. --- skfda/datasets/_real_datasets.py | 12 ++++++------ .../visualization/_magnitude_shape_plot.py | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index bfef6cbfd..2f7fb3b4c 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -256,16 +256,16 @@ def _fetch_fda_usc(name: str) -> Any: The data were extracted from the TIMIT database (TIMIT Acoustic-Phonetic Continuous Speech Corpus, NTIS, US Dept of Commerce) - which is a widely used resource for research in speech recognition. A + which is a widely used resource for research in speech recognition. A dataset was formed by selecting five phonemes for - classification based on digitized speech from this database. The + classification based on digitized speech from this database. phonemes are transcribed as follows: "sh" as in "she", "dcl" as in "dark", "iy" as the vowel in "she", "aa" as the vowel in "dark", and - "ao" as the first vowel in "water". From continuous speech of 50 male + "ao" as the first vowel in "water". From continuous speech of 50 male speakers, 4509 speech frames of 32 msec duration were selected, approximately 2 examples of each phoneme from each speaker. Each speech frame is represented by 512 samples at a 16kHz sampling rate, - and each frame represents one of the above five phonemes. The + and each frame represents one of the above five phonemes. The breakdown of the 4509 speech frames into phoneme frequencies is as follows: @@ -277,13 +277,13 @@ def _fetch_fda_usc(name: str) -> Any: From each speech frame, a log-periodogram was computed, which is one of several widely used methods for casting speech data in a form suitable - for speech recognition. Thus the data used in what follows consist of + for speech recognition. Thus the data used in what follows consist of 4509 log-periodograms of length 256, with known class (phoneme) memberships. The data contain curves sampled at 256 points, a response variable, and a column labelled "speaker" identifying the - diffferent speakers. + different speakers. References: Hastie, Trevor; Buja, Andreas; Tibshirani, Robert. Penalized diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 3016c6f9b..e2c8cc2fe 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -198,7 +198,7 @@ def __init__( self._outliercol = 0.8 self.xlabel = 'MO' self.ylabel = 'VO' - self.title = 'MS-Plot' + self.title = "" if self.fdata.dataset_name is None else self.fdata.dataset_name @property def fdata(self) -> FDataGrid: From 250dc1be894c55af0c7a893c593fbbef3273f493 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 1 Aug 2022 22:13:09 +0200 Subject: [PATCH 228/400] Fix tests. --- skfda/exploratory/visualization/_magnitude_shape_plot.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index e2c8cc2fe..9958f61f7 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -155,7 +155,7 @@ class MagnitudeShapePlot(BasePlot): outliercol=0.8, xlabel='MO', ylabel='VO', - title='MS-Plot') + title='') References: .. footbibliography:: @@ -198,7 +198,9 @@ def __init__( self._outliercol = 0.8 self.xlabel = 'MO' self.ylabel = 'VO' - self.title = "" if self.fdata.dataset_name is None else self.fdata.dataset_name + self.title = ( + "" if self.fdata.dataset_name is None else self.fdata.dataset_name + ) @property def fdata(self) -> FDataGrid: From 1675ba7c80b9854bc92a8859e2dbe1de2361ffe8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 5 Aug 2022 00:27:03 +0200 Subject: [PATCH 229/400] Improve Maxima Hunting. --- .../variable_selection/maxima_hunting.py | 135 +++++++++++++----- 1 file changed, 96 insertions(+), 39 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py index 38e37ed59..470b4d06e 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py @@ -1,13 +1,18 @@ """Maxima Hunting dimensionality reduction and related methods.""" from __future__ import annotations -from typing import Callable, Optional, Union +from typing import Callable, Union import numpy as np import scipy.signal -import sklearn.base import sklearn.utils +from sklearn.base import clone + from dcor import u_distance_correlation_sqr +from skfda._utils._sklearn_adapter import ( + BaseEstimator, + InductiveTransformerMixin, +) from ...._utils import _compute_dependence, _DependenceMeasure from ....representation import FDataGrid @@ -16,6 +21,27 @@ _LocalMaximaSelector = Callable[[FDataGrid], NDArrayInt] +def _select_relative_maxima(X: FDataGrid, *, order: int = 1) -> NDArrayInt: + + X_array = X.data_matrix[0, ..., 0] + + indexes = scipy.signal.argrelextrema( + X_array, + comparator=np.greater_equal, + order=order, + )[0] + + # Discard flat + maxima = X_array[indexes] + + left_points = np.take(X_array, indexes - 1, mode='clip') + right_points = np.take(X_array, indexes + 1, mode='clip') + + is_not_flat = (maxima > left_points) | (maxima > right_points) + + return indexes[is_not_flat] # type: ignore [no-any-return] + + def select_local_maxima(X: FDataGrid, *, order: int = 1) -> NDArrayInt: r""" Compute local maxima of an array. @@ -52,28 +78,44 @@ def select_local_maxima(X: FDataGrid, *, order: int = 1) -> NDArrayInt: array([ 0, 5, 10]) """ - X_array = X.data_matrix[0, ..., 0] + return _select_relative_maxima(X, order=order) - indexes = scipy.signal.argrelextrema( - X_array, - comparator=np.greater_equal, - order=order, - )[0] - # Discard flat - maxima = X_array[indexes] +class RelativeLocalMaximaSelector(BaseEstimator): - left_points = np.take(X_array, indexes - 1, mode='clip') - right_points = np.take(X_array, indexes + 1, mode='clip') + def __init__( + self, + smoothing_parameter: int = 1, + max_points: int | None = None, + ): + self.smoothing_parameter = smoothing_parameter + self.max_points = max_points + + def __call__(self, X: FDataGrid) -> NDArrayInt: + indexes = _select_relative_maxima( + X, + order=self.smoothing_parameter, + ) - is_not_flat = (maxima > left_points) | (maxima > right_points) + if self.max_points is not None: + values = X.data_matrix[:, indexes] + partition_indexes = np.argpartition( + values, + -self.max_points, + axis=None, + ) + indexes = indexes[np.sort(partition_indexes[-self.max_points:])] - return indexes[is_not_flat] + return indexes class MaximaHunting( - sklearn.base.BaseEstimator, # type: ignore - sklearn.base.TransformerMixin, # type: ignore + BaseEstimator, + InductiveTransformerMixin[ + FDataGrid, + NDArrayFloat, + Union[NDArrayInt, NDArrayFloat], + ], ): r""" Maxima Hunting variable selection. @@ -105,7 +147,7 @@ class MaximaHunting( Examples: >>> from skfda.preprocessing.dim_reduction import variable_selection >>> from skfda.preprocessing.dim_reduction.variable_selection.\ - ... maxima_hunting import select_local_maxima + ... maxima_hunting import RelativeLocalMaximaSelector >>> from skfda.datasets import make_gaussian_process >>> from functools import partial >>> import skfda @@ -122,13 +164,17 @@ class MaximaHunting( ... - 2 * np.abs(t - 0.5) ... + np.abs(t - 0.75)) >>> - >>> X_0 = make_gaussian_process(n_samples=n_samples // 2, - ... n_features=n_features, - ... random_state=0) - >>> X_1 = make_gaussian_process(n_samples=n_samples // 2, - ... n_features=n_features, - ... mean=mean_1, - ... random_state=1) + >>> X_0 = make_gaussian_process( + ... n_samples=n_samples // 2, + ... n_features=n_features, + ... random_state=0, + ... ) + >>> X_1 = make_gaussian_process( + ... n_samples=n_samples // 2, + ... n_features=n_features, + ... mean=mean_1, + ... random_state=1, + ... ) >>> X = skfda.concatenate((X_0, X_1)) >>> >>> y = np.zeros(n_samples) @@ -136,9 +182,12 @@ class MaximaHunting( Select the relevant points to distinguish the two classes - >>> local_maxima_selector = partial(select_local_maxima, order=10) + >>> local_maxima_selector = RelativeLocalMaximaSelector( + ... smoothing_parameter=10, + ... ) >>> mh = variable_selection.MaximaHunting( - ... local_maxima_selector=local_maxima_selector) + ... local_maxima_selector=local_maxima_selector, + ... ) >>> _ = mh.fit(X, y) >>> point_mask = mh.get_support() >>> points = X.grid_points[0][point_mask] @@ -161,7 +210,7 @@ class MaximaHunting( def __init__( self, dependence_measure: _DependenceMeasure = u_distance_correlation_sqr, - local_maxima_selector: _LocalMaximaSelector = select_local_maxima, + local_maxima_selector: _LocalMaximaSelector | None = None, ) -> None: self.dependence_measure = dependence_measure self.local_maxima_selector = local_maxima_selector @@ -169,24 +218,32 @@ def __init__( def fit( # noqa: D102 self, X: FDataGrid, - y: Union[NDArrayInt, NDArrayFloat], + y: NDArrayInt | NDArrayFloat, ) -> MaximaHunting: - self.features_shape_ = X.data_matrix.shape[1:] - self.dependence_ = _compute_dependence( - X.data_matrix, - y, - dependence_measure=self.dependence_measure, + self._maxima_selector = ( + RelativeLocalMaximaSelector() + if self.local_maxima_selector is None + else clone(self.local_maxima_selector, safe=False) ) - self.indexes_ = self.local_maxima_selector( - FDataGrid( - self.dependence_, - grid_points=X.grid_points, + self.features_shape_ = X.data_matrix.shape[1:] + self.dependence_ = FDataGrid( + data_matrix=_compute_dependence( + X.data_matrix, + y, + dependence_measure=self.dependence_measure, ), + grid_points=X.grid_points, + domain_range=X.domain_range, + ) + + self.indexes_ = self._maxima_selector( + self.dependence_, ) - sorting_indexes = np.argsort(self.dependence_[self.indexes_])[::-1] + sorting_indexes = np.argsort( + self.dependence_.data_matrix[0, self.indexes_, 0])[::-1] self.sorted_indexes_ = self.indexes_[sorting_indexes] return self @@ -202,7 +259,7 @@ def get_support(self, indices: bool = False) -> NDArrayInt: # noqa: D102 def transform( # noqa: D102 self, X: FDataGrid, - y: Optional[Union[NDArrayInt, NDArrayFloat]] = None, + y: NDArrayInt | NDArrayFloat | None = None, ) -> NDArrayFloat: sklearn.utils.validation.check_is_fitted(self) From 71723dd00f4c100dea0edb8b83a8509e547ccab3 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sat, 6 Aug 2022 23:25:19 +0200 Subject: [PATCH 230/400] Add first showcasing example. --- docs/.gitignore | 3 +- docs/conf.py | 4 +- full_examples/README.txt | 4 + full_examples/plot_aemet_unsupervised.py | 249 +++++++++++++++++++++++ skfda/datasets/_real_datasets.py | 2 +- 5 files changed, 258 insertions(+), 4 deletions(-) create mode 100644 full_examples/README.txt create mode 100644 full_examples/plot_aemet_unsupervised.py diff --git a/docs/.gitignore b/docs/.gitignore index 6efaba914..ff2ab8c70 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -1,4 +1,5 @@ /auto_examples/ /auto_tutorial/ /backreferences/ -**/autosummary/ \ No newline at end of file +**/autosummary/ +/auto_full_examples/ diff --git a/docs/conf.py b/docs/conf.py index ada4f0d72..29a88f602 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -269,9 +269,9 @@ def __call__(self, filename: str) -> str: sphinx_gallery_conf = { # path to your examples scripts - 'examples_dirs': ['../examples', '../tutorial'], + 'examples_dirs': ['../examples', '../tutorial', '../full_examples'], # path where to save gallery generated examples - 'gallery_dirs': ['auto_examples', 'auto_tutorial'], + 'gallery_dirs': ['auto_examples', 'auto_tutorial', 'auto_full_examples'], 'reference_url': { # The module you locally document uses None 'skfda': None, diff --git a/full_examples/README.txt b/full_examples/README.txt new file mode 100644 index 000000000..97afa5eb9 --- /dev/null +++ b/full_examples/README.txt @@ -0,0 +1,4 @@ +Full examples +============= + +Examples of complete analyses showcasing the main functionalities of the scikit-fda package. diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py new file mode 100644 index 000000000..95f137148 --- /dev/null +++ b/full_examples/plot_aemet_unsupervised.py @@ -0,0 +1,249 @@ +""" +Meteorological data: data visualization, clustering, and functional PCA +======================================================================= + +Shows the use of unsupervised tools +""" + +# License: MIT + +# sphinx_gallery_thumbnail_number = 2 + +from __future__ import annotations + +from typing import Any, Mapping, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import sklearn.cluster +from matplotlib.axes import Axes +from mpl_toolkits.basemap import Basemap + +from skfda.datasets import fetch_aemet +from skfda.exploratory.depth import ModifiedBandDepth +from skfda.exploratory.visualization import Boxplot, MagnitudeShapePlot +from skfda.exploratory.visualization.fpca import FPCAPlot +from skfda.misc.metrics import l2_distance +from skfda.ml.clustering import KMeans +from skfda.preprocessing.dim_reduction import FPCA + +############################################################################## +# We will first load the AEMET dataset and plot it. +dataset = fetch_aemet() +X = dataset["data"] +X = X.coordinates[0] + +X.plot() +plt.show() + +############################################################################## +# A boxplot can show magnitude outliers, in this case Navacerrada. +Boxplot( + X, + depth_method=ModifiedBandDepth(), +).plot() +plt.show() + +############################################################################## +# A magnitude-shape plot can be used to detect shape outliers, such as the +# Canary islands, with a less steeper temperature curve. +MagnitudeShapePlot( + X, +).plot() +plt.show() + +############################################################################## +# We now attempt to cluster the curves using functional k-means. +n_clusters = 5 +n_init = 10 + +fda_kmeans = KMeans( + n_clusters=n_clusters, + n_init=n_init, + metric=l2_distance, + random_state=0, +) +fda_clusters = fda_kmeans.fit_predict(X) + +############################################################################## +# We want to plot the cluster of each station in the map of Spain. We need to +# define first auxiliary variables and functions for plotting. +coords_spain = (-10, 34.98, 5, 44.8) +coords_canary = (-18.5, 27.5, -13, 29.5) + +# It is easier to obtain the longitudes and latitudes from the data in +# a Pandas dataframe. +aemet, _ = fetch_aemet(return_X_y=True, as_frame=True) + +station_longitudes = aemet.loc[:, "longitude"].values +station_latitudes = aemet.loc[:, "latitude"].values + + +def create_map( + coords: Tuple[float, float, float, float], + ax: Axes, +) -> Basemap: + """Create a map for a region of the world.""" + basemap = Basemap( + *coords, + projection='merc', + resolution="h", + epsg=4326, + ax=ax, + fix_aspect=False, + ) + basemap.arcgisimage( + service='World_Imagery', + xpixels=1000, + dpi=100, + ) + + return basemap + + +def plot_cluster_points( + longitudes: np.typing.NDArray[np.floating[Any]], + latitudes: np.typing.NDArray[np.floating[Any]], + clusters: np.typing.NDArray[np.integer[Any]], + color_map: Mapping[int, str], + basemap: Basemap, + ax: Axes, +) -> None: + """Plot the stations in a map with their cluster color.""" + x, y = basemap(longitudes, latitudes) + for cluster in range(n_clusters): + selection = (clusters == cluster) + ax.scatter( + x[selection], + y[selection], + color=color_map[cluster], + edgecolors='white', + ) + + +# Colors for each cluster +fda_color_map = { + 0: "purple", + 1: "yellow", + 2: "green", + 3: "red", + 4: "orange", + 5: "cyan", +} + +############################################################################## +# We now plot the obtained clustering in the maps. + +# Mainland +fig_spain = plt.figure(figsize=(8, 6)) +ax_spain = fig_spain.add_axes([0, 0, 1, 1]) +map_spain = create_map(coords_spain, ax=ax_spain) +plot_cluster_points( + longitudes=station_longitudes, + latitudes=station_latitudes, + clusters=fda_clusters, + color_map=fda_color_map, + basemap=map_spain, + ax=ax_spain, +) + +# Canary Islands +fig_canary = plt.figure(figsize=(8, 3)) +ax_canary = fig_canary.add_axes([0, 0, 1, 1]) +map_canary = create_map(coords_canary, ax=ax_canary) +plot_cluster_points( + longitudes=station_longitudes, + latitudes=station_latitudes, + clusters=fda_clusters, + color_map=fda_color_map, + basemap=map_canary, + ax=ax_canary, +) +plt.show() + +############################################################################## +# We now can compute the first two principal components for interpretability, +# and project the data over these directions. +fpca = FPCA(n_components=2) +fpca.fit(X) + +# The sign of the components is arbitrary, but this way it is easier to +# understand. +fpca.components_ *= -1 + +X_red = fpca.transform(X) + +############################################################################## +# We now plot the first two principal components. +fig = plt.figure(figsize=(8, 4)) +FPCAPlot( + fpca.mean_, + fpca.components_, + multiple=50, + fig=fig, +).plot() +plt.show() + +############################################################################## +# We also plot the projections over the first two principal components. +fig, ax = plt.subplots(1, 1) +for cluster in range(n_clusters): + selection = fda_clusters == cluster + ax.scatter( + X_red[selection, 0], + X_red[selection, 1], + color=fda_color_map[cluster], + ) + +ax.set_xlabel('First principal component') +ax.set_ylabel('Second principal component') +plt.show() + +############################################################################## +# We now attempt a multivariate clustering using only these projections. +mv_kmeans = sklearn.cluster.KMeans( + n_clusters=n_clusters, + n_init=n_init, + random_state=0, +) +mv_clusters = mv_kmeans.fit_predict(X_red) + +############################################################################## +# We now plot the multivariate clustering in the maps. We define a different +# color map to match cluster colors with the previously obtained ones. + +mv_color_map = { + 0: "red", + 1: "purple", + 2: "yellow", + 3: "green", + 4: "orange", + 5: "cyan", +} + +# Mainland +fig_spain = plt.figure(figsize=(8, 6)) +ax_spain = fig_spain.add_axes([0, 0, 1, 1]) +map_spain = create_map(coords_spain, ax=ax_spain) +plot_cluster_points( + longitudes=station_longitudes, + latitudes=station_latitudes, + clusters=mv_clusters, + color_map=mv_color_map, + basemap=map_spain, + ax=ax_spain, +) + +# Canary Islands +fig_canary = plt.figure(figsize=(8, 3)) +ax_canary = fig_canary.add_axes([0, 0, 1, 1]) +map_canary = create_map(coords_canary, ax=ax_canary) +plot_cluster_points( + longitudes=station_longitudes, + latitudes=station_latitudes, + clusters=mv_clusters, + color_map=mv_color_map, + basemap=map_canary, + ax=ax_canary, +) +plt.show() diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 2f7fb3b4c..a6ecc87cb 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -945,7 +945,7 @@ def fetch_aemet( # noqa: WPS210 data_matrix=data_matrix, grid_points=np.arange(0, days_in_year) + 0.5, domain_range=(0, days_in_year), - dataset_name="aemet", + dataset_name="AEMET", sample_names=data["df"].iloc[:, 1], argument_names=("day",), coordinate_names=( From a30efb4c91dd3a33c17fa821701480de3e23cecc Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 7 Aug 2022 00:26:35 +0200 Subject: [PATCH 231/400] Add phonemes full example. --- full_examples/plot_aemet_unsupervised.py | 5 +- full_examples/plot_phonemes_classification.py | 140 ++++++++++++++++++ .../smoothing/_kernel_smoothers.py | 2 +- 3 files changed, 144 insertions(+), 3 deletions(-) create mode 100644 full_examples/plot_phonemes_classification.py diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index 95f137148..5fedd5a8a 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -2,12 +2,13 @@ Meteorological data: data visualization, clustering, and functional PCA ======================================================================= -Shows the use of unsupervised tools +Shows the use of data visualization tools, clustering and functional +principal component analysis (FPCA). """ # License: MIT -# sphinx_gallery_thumbnail_number = 2 +# sphinx_gallery_thumbnail_number = 4 from __future__ import annotations diff --git a/full_examples/plot_phonemes_classification.py b/full_examples/plot_phonemes_classification.py new file mode 100644 index 000000000..75d0fd8e7 --- /dev/null +++ b/full_examples/plot_phonemes_classification.py @@ -0,0 +1,140 @@ +""" +Voice signals: smoothing, registration, and classification +========================================================== + +Shows the use of functional preprocessing tools such as +smoothing and registration, and functional classification +methods. +""" + +# License: MIT + +# sphinx_gallery_thumbnail_number = 3 +import matplotlib.pyplot as plt +import numpy as np +from sklearn.metrics import accuracy_score +from sklearn.model_selection import GridSearchCV, train_test_split +from sklearn.pipeline import Pipeline + +from skfda.datasets import fetch_phoneme +from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix +from skfda.misc.kernels import normal +from skfda.misc.metrics import MahalanobisDistance +from skfda.ml.classification import KNeighborsClassifier +from skfda.preprocessing.registration import FisherRaoElasticRegistration +from skfda.preprocessing.smoothing import KernelSmoother + +############################################################################## +# We will first load the (binary) Phoneme dataset, restricted to the first +# 150 variables, and plot the first 20 functions. +X, y = fetch_phoneme(return_X_y=True) + +X = X[(y == 0) | (y == 1)] +y = y[(y == 0) | (y == 1)] + +n_points = 150 + +new_points = X.grid_points[0][:n_points] +new_data = X.data_matrix[:, :n_points] + +X = X.copy( + grid_points=new_points, + data_matrix=new_data, + domain_range=(np.min(new_points), np.max(new_points)), +) + +n_plot = 20 +X[:n_plot].plot(group=y) +plt.show() + +############################################################################## +# We now smooth and plot the data again, as well as the class means. +smoother = KernelSmoother( + NadarayaWatsonHatMatrix( + bandwidth=0.1, + kernel=normal, + ), +) +X_smooth = smoother.fit_transform(X) + +fig = X_smooth[:n_plot].plot(group=y) + +X_smooth_aa = X_smooth[:n_plot][y[:n_plot] == 0] +X_smooth_ao = X_smooth[:n_plot][y[:n_plot] == 1] + +X_smooth_aa.mean().plot(fig=fig, color="blue", linewidth=3) +X_smooth_ao.mean().plot(fig=fig, color="red", linewidth=3) +plt.show() + +############################################################################## +# We now register the data per class. As Fisher-Rao elastic registration is +# very slow, we only register the plotted curves as an approximation. +reg = FisherRaoElasticRegistration( + penalty=0.01, +) + +X_reg_aa = reg.fit_transform(X_smooth[:n_plot][y[:n_plot] == 0]) +fig = X_reg_aa.plot(color="C0") + +X_reg_ao = reg.fit_transform(X_smooth[:n_plot][y[:n_plot] == 1]) +X_reg_ao.plot(fig=fig, color="C1") + +X_reg_aa.mean().plot(fig=fig, color="blue", linewidth=3) +X_reg_ao.mean().plot(fig=fig, color="red", linewidth=3) +plt.show() + +############################################################################## +# We now split the smoothed data in train and test datasets. +# Note that there is no data leakage because no parameters are fitted in +# the smoothing step, but normally you would want to do all preprocessing in +# a pipeline to guarantee that. + +X_train, X_test, y_train, y_test = train_test_split( + X_smooth, + y, + test_size=0.25, + random_state=0, + stratify=y, +) + +############################################################################## +# We use a k-nn classifier with a functional analog to the Mahalanobis +# distance and a fixed number of neighbors. +n_neighbors = int(np.sqrt(X_smooth.n_samples)) +n_neighbors += n_neighbors % 2 - 1 # Round to an odd integer + +classifier = KNeighborsClassifier( + n_neighbors=n_neighbors, + metric=MahalanobisDistance( + alpha=0.001, + ), +) + +classifier.fit(X_train, y_train) +y_pred = classifier.predict(X_test) +score = accuracy_score(y_test, y_pred) +print(score) + +############################################################################## +# If we wanted to optimize hyperparameters, we can use scikit-learn tools. +pipeline = Pipeline([ + ("smoother", smoother), + ("classifier", classifier), +]) + +grid_search = GridSearchCV( + pipeline, + param_grid={ + "smoother__kernel_estimator__bandwidth": [1, 1e-1, 1e-2, 1e-3], + "classifier__n_neighbors": range(3, n_neighbors, 2), + "classifier__metric__alpha": [1, 1e-1, 1e-2, 1e-3, 1e-4], + }, +) + +# The grid search is too slow for a example. Uncomment it if you want, but it +# will take a while. + +# grid_search.fit(X_train, y_train) +# y_pred = grid_search.predict(X_test) +# score = accuracy_score(y_test, y_pred) +# print(score) diff --git a/skfda/preprocessing/smoothing/_kernel_smoothers.py b/skfda/preprocessing/smoothing/_kernel_smoothers.py index f51c3aeab..5162b06c4 100644 --- a/skfda/preprocessing/smoothing/_kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/_kernel_smoothers.py @@ -109,8 +109,8 @@ class KernelSmoother(_LinearSmoother): def __init__( self, - *, kernel_estimator: Optional[HatMatrix] = None, + *, weights: Optional[NDArrayFloat] = None, output_points: Optional[GridPointsLike] = None, ): From 14e08abda0641cfe7739f45334f6c169e7f30e3d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 7 Aug 2022 00:42:34 +0200 Subject: [PATCH 232/400] Fix tests. --- readthedocs-requirements.txt | 2 ++ setup.cfg | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index 2aec4238f..ad83c07c6 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -1,4 +1,6 @@ -r ./requirements.txt +basemap +basemap-data Sphinx>=3 sphinx_rtd_theme sphinx-gallery diff --git a/setup.cfg b/setup.cfg index 6363131cd..1496b3537 100644 --- a/setup.cfg +++ b/setup.cfg @@ -4,7 +4,7 @@ test=pytest [tool:pytest] addopts = --doctest-modules doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS -norecursedirs = .* build dist *.egg venv .svn _build docs/auto_examples examples docs/auto_tutorial tutorial +norecursedirs = .* build dist *.egg venv .svn _build docs/auto_examples examples docs/auto_tutorial tutorial docs/auto_full_examples full_examples [flake8] ignore = From 4532f9a09751a464e6d5b944ec0962e0795b5292 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 7 Aug 2022 00:54:55 +0200 Subject: [PATCH 233/400] Fix readthedocs. --- readthedocs-requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index ad83c07c6..4328bdbec 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -1,6 +1,7 @@ -r ./requirements.txt basemap basemap-data +basemap-data-hires Sphinx>=3 sphinx_rtd_theme sphinx-gallery From 34897762d3c06dc0bd42061c519395d963f01d4b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 7 Aug 2022 01:46:12 +0200 Subject: [PATCH 234/400] Add tecator full example. --- full_examples/plot_tecator_regression.py | 127 +++++++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 full_examples/plot_tecator_regression.py diff --git a/full_examples/plot_tecator_regression.py b/full_examples/plot_tecator_regression.py new file mode 100644 index 000000000..24028fe02 --- /dev/null +++ b/full_examples/plot_tecator_regression.py @@ -0,0 +1,127 @@ +""" +Spectrometric data: derivatives, regression, and variable selection +=================================================================== + +Shows the use of functional preprocessing tools such as +smoothing and registration, and functional classification +methods. +""" + +# License: MIT + +# sphinx_gallery_thumbnail_number = 4 + +import matplotlib.pyplot as plt +import sklearn.linear_model +from sklearn.metrics import r2_score +from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline +from sklearn.tree import DecisionTreeRegressor, plot_tree + +from skfda.datasets import fetch_tecator +from skfda.ml.regression import LinearRegression +from skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting import ( + MaximaHunting, + RelativeLocalMaximaSelector, +) +from skfda.representation.basis import BSpline + +############################################################################## +# We will first load the Tecator data, keeping only the fat content target, +# and plot it. +X, y = fetch_tecator(return_X_y=True) +y = y[:, 0] + +X.plot(gradient_criteria=y) +plt.show() + +############################################################################## +# We compute numerically the second derivative and plot it. +X_der = X.derivative(order=2) +X_der.plot(gradient_criteria=y) +plt.show() + +############################################################################## +# In order to compute functional linear regression we first convert the data +# to a basis expansion. +basis = BSpline( + domain_range=X.domain_range, + n_basis=10, +) +X_der_basis = X_der.to_basis(basis) + +############################################################################## +# We split the data in train and test, and compute the regression score using +# the linear regression model. +X_train, X_test, y_train, y_test = train_test_split( + X_der_basis, + y, + random_state=0, +) + +regressor = LinearRegression() +regressor.fit(X_train, y_train) +y_pred = regressor.predict(X_test) +score = r2_score(y_test, y_pred) +print(score) + +############################################################################## +# Another approach is to use variable selection using maxima hunting. +var_sel = MaximaHunting( + local_maxima_selector=RelativeLocalMaximaSelector(max_points=2), +) +X_mv = var_sel.fit_transform(X_der, y) + +print(var_sel.indexes_) + +############################################################################## +# We can visualize the relevance function and the selected points. +var_sel.dependence_.plot() +for p in var_sel.indexes_: + plt.axvline(X_der.grid_points[0][p], color="black") +plt.show() + +############################################################################## +# We also can visualize the selected points on the curves. +X_der.plot(gradient_criteria=y) +for p in var_sel.indexes_: + plt.axvline(X_der.grid_points[0][p], color="black") +plt.show() + +############################################################################## +# We split the data again (using the same seed), but this time without the +# basis expansion. +X_train, X_test, y_train, y_test = train_test_split( + X_der, + y, + random_state=0, +) + +############################################################################## +# We now make a pipeline with the variable selection and a multivariate linear +# regression method for comparison. +pipeline = Pipeline([ + ("variable_selection", var_sel), + ("classifier", sklearn.linear_model.LinearRegression()), +]) +pipeline.fit(X_train, y_train) +y_predicted = pipeline.predict(X_test) +score = r2_score(y_test, y_predicted) +print(score) + +############################################################################## +# We can use a tree regressor instead to improve both the score and the +# interpretability. +pipeline = Pipeline([ + ("variable_selection", var_sel), + ("classifier", DecisionTreeRegressor(max_depth=3)), +]) +pipeline.fit(X_train, y_train) +y_predicted = pipeline.predict(X_test) +score = r2_score(y_test, y_predicted) +print(score) + +############################################################################## +# We can plot the final version of the tree to explain every prediction. +plot_tree(pipeline.named_steps["classifier"], precision=6) +plt.show() From 95db3ffa35ba8546fa5c987977ff3886167d14a3 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 7 Aug 2022 01:57:13 +0200 Subject: [PATCH 235/400] Fix example description. --- full_examples/plot_tecator_regression.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/full_examples/plot_tecator_regression.py b/full_examples/plot_tecator_regression.py index 24028fe02..351c5be77 100644 --- a/full_examples/plot_tecator_regression.py +++ b/full_examples/plot_tecator_regression.py @@ -2,9 +2,8 @@ Spectrometric data: derivatives, regression, and variable selection =================================================================== -Shows the use of functional preprocessing tools such as -smoothing and registration, and functional classification -methods. +Shows the use of derivatives, functional regression and +variable selection for functional data. """ # License: MIT From 22033ef2e2b97840e7f633fbf0c3ac94298962a1 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Sun, 7 Aug 2022 02:09:46 +0200 Subject: [PATCH 236/400] Color the tree. --- full_examples/plot_tecator_regression.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/full_examples/plot_tecator_regression.py b/full_examples/plot_tecator_regression.py index 351c5be77..df023360a 100644 --- a/full_examples/plot_tecator_regression.py +++ b/full_examples/plot_tecator_regression.py @@ -122,5 +122,6 @@ ############################################################################## # We can plot the final version of the tree to explain every prediction. -plot_tree(pipeline.named_steps["classifier"], precision=6) +fig, ax = plt.subplots(figsize=(10, 10)) +plot_tree(pipeline.named_steps["classifier"], precision=6, filled=True, ax=ax) plt.show() From 683dcb52f51402ae7c741b49159287a751b56155 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 8 Aug 2022 13:12:11 +0200 Subject: [PATCH 237/400] Fix factor name in FPCAPlot. --- examples/plot_fpca.py | 2 +- full_examples/plot_aemet_unsupervised.py | 8 +++++-- skfda/exploratory/visualization/fpca.py | 28 ++++++++++++++++++++---- 3 files changed, 31 insertions(+), 7 deletions(-) diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index 556b99577..f20d99ea3 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -76,7 +76,7 @@ FPCAPlot( basis_fd.mean(), fpca.components_, - 30, + factor=30, fig=plt.figure(figsize=(6, 2 * 4)), n_rows=2, ).plot() diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index 5fedd5a8a..2b18d5ccb 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -175,12 +175,16 @@ def plot_cluster_points( X_red = fpca.transform(X) ############################################################################## -# We now plot the first two principal components. +# We now plot the first two principal components as perturbations over the +# mean. +# +# The ``factor`` parameters is a number that multiplies each component in +# order to make their effect more noticeable. fig = plt.figure(figsize=(8, 4)) FPCAPlot( fpca.mean_, fpca.components_, - multiple=50, + factor=50, fig=fig, ).plot() plt.show() diff --git a/skfda/exploratory/visualization/fpca.py b/skfda/exploratory/visualization/fpca.py index 8da05b16b..87a9b2861 100644 --- a/skfda/exploratory/visualization/fpca.py +++ b/skfda/exploratory/visualization/fpca.py @@ -1,3 +1,6 @@ +from __future__ import annotations + +import warnings from typing import Optional, Sequence, Union from matplotlib.axes import Axes @@ -17,7 +20,7 @@ class FPCAPlot(BasePlot): mean: The functional data object containing the mean function. If len(mean) > 1, the mean is computed. components: The principal components - multiple: Multiple of the principal component curve to be added or + factor: Multiple of the principal component curve to be added or subtracted. fig: Figure over which the graph is plotted. If not specified it will be initialized @@ -31,9 +34,10 @@ def __init__( self, mean: FData, components: FData, - multiple: float, - chart: Union[Figure, Axes, None] = None, *, + factor: float = 1, + multiple: float | None = None, + chart: Union[Figure, Axes, None] = None, fig: Optional[Figure] = None, axes: Optional[Axes] = None, n_rows: Optional[int] = None, @@ -48,7 +52,23 @@ def __init__( ) self.mean = mean self.components = components - self.multiple = multiple + if multiple is None: + self.factor = factor + else: + warnings.warn( + "The 'multiple' parameter is deprecated, " + "use 'factor' instead.", + DeprecationWarning, + ) + self.factor = multiple + + @property + def multiple(self) -> float: + warnings.warn( + "The 'multiple' attribute is deprecated, use 'factor' instead.", + DeprecationWarning, + ) + return self.factor @property def n_subplots(self) -> int: From 11faac72d281ef4c4844b2d0814ab9e0630dbabf Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 8 Aug 2022 18:29:43 +0200 Subject: [PATCH 238/400] Change points size. --- full_examples/plot_aemet_unsupervised.py | 1 + 1 file changed, 1 insertion(+) diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index 2b18d5ccb..2c5f9af95 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -117,6 +117,7 @@ def plot_cluster_points( ax.scatter( x[selection], y[selection], + s=64, color=color_map[cluster], edgecolors='white', ) From 28efd01b2ca38ba0c08a0fd2ebc5d05eefe87973 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 8 Aug 2022 20:06:03 +0200 Subject: [PATCH 239/400] Changes in full examples. --- full_examples/plot_aemet_unsupervised.py | 13 +++++++++++-- full_examples/plot_tecator_regression.py | 1 - 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index 2c5f9af95..2b8dc804b 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -130,7 +130,15 @@ def plot_cluster_points( 2: "green", 3: "red", 4: "orange", - 5: "cyan", +} + +# Names of each climate (for this particular seed) +climate_names = { + 0: "Cold climate", + 1: "Mediterranean", + 2: "Atlantic", + 3: "Subtropical", + 4: "Continental", } ############################################################################## @@ -199,10 +207,12 @@ def plot_cluster_points( X_red[selection, 0], X_red[selection, 1], color=fda_color_map[cluster], + label=climate_names[cluster], ) ax.set_xlabel('First principal component') ax.set_ylabel('Second principal component') +ax.legend() plt.show() ############################################################################## @@ -224,7 +234,6 @@ def plot_cluster_points( 2: "yellow", 3: "green", 4: "orange", - 5: "cyan", } # Mainland diff --git a/full_examples/plot_tecator_regression.py b/full_examples/plot_tecator_regression.py index df023360a..e7d58ee8f 100644 --- a/full_examples/plot_tecator_regression.py +++ b/full_examples/plot_tecator_regression.py @@ -44,7 +44,6 @@ # In order to compute functional linear regression we first convert the data # to a basis expansion. basis = BSpline( - domain_range=X.domain_range, n_basis=10, ) X_der_basis = X_der.to_basis(basis) From fbd5ecf57138d6746c1e54efd74208b2524b8a42 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Mon, 8 Aug 2022 20:52:44 +0200 Subject: [PATCH 240/400] Documentation fixes, weights deprecation and linter warnings --- skfda/preprocessing/dim_reduction/_fpca.py | 104 +++++++++++---------- 1 file changed, 54 insertions(+), 50 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index 09fee65b7..f807345a1 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -2,6 +2,7 @@ from __future__ import annotations +import warnings from typing import Callable, Optional, TypeVar, Union import numpy as np @@ -10,8 +11,6 @@ from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from ...misc import inner_product_matrix -from ...misc.metrics import l2_norm from ...misc.regularization import L2Regularization, compute_penalty_matrix from ...representation import FData from ...representation._typing import ArrayLike @@ -37,23 +36,26 @@ class FPCA( Parameters: n_components: Number of principal components to keep from functional principal component analysis. Defaults to 3. - centering: If ``True`` then calculate the mean of the functional - data object and center the data first. Defaults to ``True``. + centering: Set to ``False`` when the functional data is already known + to be centered and there is no need to center it. Otherwise, + the mean of the functional data object is calculated and, the data, + centered before fitting . Defaults to ``True``. regularization: Regularization object to be applied. components_basis: The basis in which we want the principal components. We can use a different basis than the basis contained in the passed FDataBasis object. This parameter is only used when fitting a FDataBasis. weights: the weights vector used for - discrete integration. If none then the trapezoidal rule is used for + discrete integration. If none then Simpson's rule is used for computing the weights. If a callable object is passed, then the weight vector will be obtained by evaluating the object at the sample points of the passed FDataGrid object in the fit method. This parameter is only used when fitting a FDataGrid. + ..deprecated:: 0.7.2 + Attributes: - components\_ (FData): this contains the principal components in a - basis representation. + components\_ (FData): this contains the principal components. explained_variance\_ (array_like): The amount of variance explained by each of the selected components. explained_variance_ratio\_ (array_like): this contains the percentage @@ -99,11 +101,16 @@ def __init__( weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, ) -> None: - self.n_components = n_components - self.centering = centering - self.regularization = regularization - self.weights = weights - self.components_basis = components_basis + self._n_components = n_components + self._centering = centering + self._regularization = regularization + self._weights = weights + self._components_basis = components_basis + if weights is not None: + warnings.warn( + "The weights parameter is deprecated and will be removed.", + DeprecationWarning, + ) def _center_if_necessary( self, @@ -115,7 +122,7 @@ def _center_if_necessary( if learn_mean: self.mean_ = X.mean() - return X - self.mean_ if self.centering else X + return X - self.mean_ if self._centering else X def _fit_basis( self, @@ -145,15 +152,15 @@ def _fit_basis( # the maximum number of components is established by the target basis # if the target basis is available. n_basis = ( - self.components_basis.n_basis - if self.components_basis + self._components_basis.n_basis + if self._components_basis else X.basis.n_basis ) # necessary in inverse_transform self.n_samples_ = X.n_samples # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: + if self._n_components > X.n_samples: raise AttributeError( "The sample size must be bigger than the " "number of components", @@ -161,7 +168,7 @@ def _fit_basis( # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - if self.n_components > n_basis: + if self._n_components > n_basis: raise AttributeError( "The number of components should be " "smaller than the number of attributes of " @@ -173,7 +180,7 @@ def _fit_basis( X = self._center_if_necessary(X) # setup principal component basis if not given - components_basis = self.components_basis + components_basis = self._components_basis if components_basis is not None: # First fix domain range if not already done components_basis = components_basis.copy( @@ -198,13 +205,13 @@ def _fit_basis( regularization_matrix = compute_penalty_matrix( basis_iterable=(components_basis,), regularization_parameter=1, - regularization=self.regularization, + regularization=self._regularization, ) # apply regularization if regularization_matrix is not None: # using += would have a different behavior - g_matrix = (g_matrix + regularization_matrix) # noqa: WPS350 + g_matrix = g_matrix + regularization_matrix # noqa: WPS350 # obtain triangulation using cholesky l_matrix = np.linalg.cholesky(g_matrix) @@ -220,12 +227,10 @@ def _fit_basis( ) # the final matrix, C(L-1Jt)t for svd or (L-1Jt)-1CtC(L-1Jt)t for PCA - final_matrix = ( - X.coefficients @ np.transpose(l_inv_j_t) - ) + final_matrix = X.coefficients @ np.transpose(l_inv_j_t) # initialize the pca module provided by scikit-learn - pca = PCA(n_components=self.n_components) + pca = PCA(n_components=self._n_components) pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the @@ -242,7 +247,7 @@ def _fit_basis( self.components_ = X.copy( basis=components_basis, coefficients=component_coefficients.T, - sample_names=(None,) * self.n_components, + sample_names=(None,) * self._n_components, ) return self @@ -270,8 +275,7 @@ def _transform_basis( # in this case it is the inner product of our data with the components return ( - X.coefficients @ self._j_matrix - @ self.components_.coefficients.T + X.coefficients @ self._j_matrix @ self.components_.coefficients.T ) def _fit_grid( @@ -306,7 +310,7 @@ def _fit_grid( """ # check that the number of components is smaller than the sample size - if self.n_components > X.n_samples: + if self._n_components > X.n_samples: raise AttributeError( "The sample size must be bigger than the " "number of components", @@ -314,7 +318,7 @@ def _fit_grid( # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the funcional data object - if self.n_components > X.data_matrix.shape[1]: + if self._n_components > X.data_matrix.shape[1]: raise AttributeError( "The number of components should be " "smaller than the number of discretization " @@ -335,21 +339,18 @@ def _fit_grid( X = self._center_if_necessary(X) # establish weights for each point of discretization - if not self.weights: + if not self._weights: # grid_points is a list with one array in the 1D case - # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the - # weight vector is as follows: [\deltax_1/2, \deltax_1/2 + - # \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] identity = np.eye(len(X.grid_points[0])) - self.weights = scipy.integrate.simps(identity, X.grid_points[0]) - elif callable(self.weights): - self.weights = self.weights(X.grid_points[0]) + self._weights = scipy.integrate.simps(identity, X.grid_points[0]) + elif callable(self._weights): + self._weights = self._weights(X.grid_points[0]) # if its a FDataGrid then we need to reduce the dimension to 1-D # array - if isinstance(self.weights, FDataGrid): - self.weights = np.squeeze(self.weights.data_matrix) + if isinstance(self._weights, FDataGrid): + self._weights = np.squeeze(self._weights.data_matrix) - weights_matrix = np.diag(self.weights) + weights_matrix = np.diag(self._weights) basis = FDataGrid( data_matrix=np.identity(n_points_discretization), @@ -359,12 +360,12 @@ def _fit_grid( regularization_matrix = compute_penalty_matrix( basis_iterable=(basis,), regularization_parameter=1, - regularization=self.regularization, + regularization=self._regularization, ) basis_matrix = basis.data_matrix[..., 0] if regularization_matrix is not None: - basis_matrix = basis_matrix + regularization_matrix + basis_matrix += regularization_matrix fd_data = np.linalg.solve( basis_matrix.T, @@ -374,7 +375,7 @@ def _fit_grid( # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) - pca = PCA(n_components=self.n_components) + pca = PCA(n_components=self._n_components) pca.fit(final_matrix) self.components_ = X.copy( data_matrix=np.transpose( @@ -383,7 +384,7 @@ def _fit_grid( np.transpose(pca.components_), ), ), - sample_names=(None,) * self.n_components, + sample_names=(None,) * self._n_components, ) self.explained_variance_ratio_ = pca.explained_variance_ratio_ @@ -413,7 +414,7 @@ def _transform_grid( return ( X.data_matrix.reshape(X.data_matrix.shape[:-1]) - * self.weights + * self._weights @ np.transpose( self.components_.data_matrix.reshape( self.components_.data_matrix.shape[:-1], @@ -513,7 +514,7 @@ def inverse_transform( if pc_scores.ndim == 1: pc_scores = pc_scores[np.newaxis, :] - if pc_scores.shape[1] != self.n_components: + if pc_scores.shape[1] != self._n_components: raise AttributeError( "pc_scores must be a numpy array " "with n_samples rows and n_components columns.", @@ -528,7 +529,7 @@ def inverse_transform( additional_args = { "data_matrix": np.einsum( - 'nc,c...->n...', + "nc,c...->n...", pc_scores, self.components_.data_matrix, ), @@ -540,7 +541,10 @@ def inverse_transform( "coefficients": pc_scores @ self.components_.coefficients, } - return self.mean_.copy( - **additional_args, - sample_names=(None,) * len(pc_scores), - ) + self.mean_ + return ( + self.mean_.copy( + **additional_args, + sample_names=(None,) * len(pc_scores), + ) + + self.mean_ + ) From 7f1b471900793e5fbc01a75f5fb61cb5c748d170 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Mon, 8 Aug 2022 21:33:13 +0200 Subject: [PATCH 241/400] Finish weight deprecation --- skfda/preprocessing/dim_reduction/_fpca.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index f807345a1..692000542 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -52,7 +52,7 @@ class FPCA( sample points of the passed FDataGrid object in the fit method. This parameter is only used when fitting a FDataGrid. - ..deprecated:: 0.7.2 + .. deprecated:: 0.7.2 Attributes: components\_ (FData): this contains the principal components. @@ -101,6 +101,20 @@ def __init__( weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, ) -> None: + """ + Initialize the FPCA object. + + Args: + n_components: Number of principal components + centering: If False, the data is assumed to be centered. + regularization: Regularization object to be applied. + components_basis: The basis in which we want the principal + components + weights: The weights vector used for discrete integration. + + .. deprecated:: 0.7.2 + + """ self._n_components = n_components self._centering = centering self._regularization = regularization From ac045be8334f81ae54f48ff5ad9bfb68237d798a Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 8 Aug 2022 21:44:43 +0200 Subject: [PATCH 242/400] Change scatter legend. --- full_examples/plot_aemet_unsupervised.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index 2b8dc804b..d509cff93 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -134,7 +134,7 @@ def plot_cluster_points( # Names of each climate (for this particular seed) climate_names = { - 0: "Cold climate", + 0: "Cold-mountain", 1: "Mediterranean", 2: "Atlantic", 3: "Subtropical", From 97ae01bf7abff739159fd54bd35f2b8c2a2d5775 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 11 Aug 2022 09:00:00 +0200 Subject: [PATCH 243/400] Ignore the too many public attributes warning and minor fixes --- skfda/preprocessing/dim_reduction/_fpca.py | 70 +++++++++++----------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index 692000542..ce02d6772 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -21,7 +21,7 @@ WeightsCallable = Callable[[np.ndarray], np.ndarray] -class FPCA( +class FPCA( # noqa: WPS230 (too many public attributes) BaseEstimator, # type: ignore TransformerMixin, # type: ignore ): @@ -38,7 +38,7 @@ class FPCA( functional principal component analysis. Defaults to 3. centering: Set to ``False`` when the functional data is already known to be centered and there is no need to center it. Otherwise, - the mean of the functional data object is calculated and, the data, + the mean of the functional data object is calculated and the data centered before fitting . Defaults to ``True``. regularization: Regularization object to be applied. components_basis: The basis in which we want the principal @@ -55,14 +55,14 @@ class FPCA( .. deprecated:: 0.7.2 Attributes: - components\_ (FData): this contains the principal components. - explained_variance\_ (array_like): The amount of variance explained by + components\_: this contains the principal components. + explained_variance\_ : The amount of variance explained by each of the selected components. - explained_variance_ratio\_ (array_like): this contains the percentage + explained_variance_ratio\_ : this contains the percentage of variance explained by each principal component. singular_values\_: The singular values corresponding to each of the selected components. - mean\_ (FData): mean of the train data. + mean\_: mean of the train data. Examples: Construct an artificial FDataBasis object and run FPCA with this @@ -115,14 +115,14 @@ def __init__( .. deprecated:: 0.7.2 """ - self._n_components = n_components - self._centering = centering - self._regularization = regularization - self._weights = weights - self._components_basis = components_basis + self.n_components = n_components + self.centering = centering + self.regularization = regularization + self.weights = weights + self.components_basis = components_basis if weights is not None: warnings.warn( - "The weights parameter is deprecated and will be removed.", + "The 'weights' parameter is deprecated and will be removed.", DeprecationWarning, ) @@ -136,7 +136,7 @@ def _center_if_necessary( if learn_mean: self.mean_ = X.mean() - return X - self.mean_ if self._centering else X + return X - self.mean_ if self.centering else X def _fit_basis( self, @@ -166,15 +166,15 @@ def _fit_basis( # the maximum number of components is established by the target basis # if the target basis is available. n_basis = ( - self._components_basis.n_basis - if self._components_basis + self.components_basis.n_basis + if self.components_basis else X.basis.n_basis ) # necessary in inverse_transform self.n_samples_ = X.n_samples # check that the number of components is smaller than the sample size - if self._n_components > X.n_samples: + if self.n_components > X.n_samples: raise AttributeError( "The sample size must be bigger than the " "number of components", @@ -182,7 +182,7 @@ def _fit_basis( # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the basis - if self._n_components > n_basis: + if self.n_components > n_basis: raise AttributeError( "The number of components should be " "smaller than the number of attributes of " @@ -194,7 +194,7 @@ def _fit_basis( X = self._center_if_necessary(X) # setup principal component basis if not given - components_basis = self._components_basis + components_basis = self.components_basis if components_basis is not None: # First fix domain range if not already done components_basis = components_basis.copy( @@ -219,7 +219,7 @@ def _fit_basis( regularization_matrix = compute_penalty_matrix( basis_iterable=(components_basis,), regularization_parameter=1, - regularization=self._regularization, + regularization=self.regularization, ) # apply regularization @@ -244,7 +244,7 @@ def _fit_basis( final_matrix = X.coefficients @ np.transpose(l_inv_j_t) # initialize the pca module provided by scikit-learn - pca = PCA(n_components=self._n_components) + pca = PCA(n_components=self.n_components) pca.fit(final_matrix) # we choose solve to obtain the component coefficients for the @@ -261,7 +261,7 @@ def _fit_basis( self.components_ = X.copy( basis=components_basis, coefficients=component_coefficients.T, - sample_names=(None,) * self._n_components, + sample_names=(None,) * self.n_components, ) return self @@ -324,7 +324,7 @@ def _fit_grid( """ # check that the number of components is smaller than the sample size - if self._n_components > X.n_samples: + if self.n_components > X.n_samples: raise AttributeError( "The sample size must be bigger than the " "number of components", @@ -332,7 +332,7 @@ def _fit_grid( # check that we do not exceed limits for n_components as it should # be smaller than the number of attributes of the funcional data object - if self._n_components > X.data_matrix.shape[1]: + if self.n_components > X.data_matrix.shape[1]: raise AttributeError( "The number of components should be " "smaller than the number of discretization " @@ -353,18 +353,18 @@ def _fit_grid( X = self._center_if_necessary(X) # establish weights for each point of discretization - if not self._weights: + if not self.weights: # grid_points is a list with one array in the 1D case identity = np.eye(len(X.grid_points[0])) - self._weights = scipy.integrate.simps(identity, X.grid_points[0]) - elif callable(self._weights): - self._weights = self._weights(X.grid_points[0]) + self.weights = scipy.integrate.simps(identity, X.grid_points[0]) + elif callable(self.weights): + self.weights = self.weights(X.grid_points[0]) # if its a FDataGrid then we need to reduce the dimension to 1-D # array - if isinstance(self._weights, FDataGrid): - self._weights = np.squeeze(self._weights.data_matrix) + if isinstance(self.weights, FDataGrid): + self.weights = np.squeeze(self.weights.data_matrix) - weights_matrix = np.diag(self._weights) + weights_matrix = np.diag(self.weights) basis = FDataGrid( data_matrix=np.identity(n_points_discretization), @@ -374,7 +374,7 @@ def _fit_grid( regularization_matrix = compute_penalty_matrix( basis_iterable=(basis,), regularization_parameter=1, - regularization=self._regularization, + regularization=self.regularization, ) basis_matrix = basis.data_matrix[..., 0] @@ -389,7 +389,7 @@ def _fit_grid( # see docstring for more information final_matrix = fd_data @ np.sqrt(weights_matrix) - pca = PCA(n_components=self._n_components) + pca = PCA(n_components=self.n_components) pca.fit(final_matrix) self.components_ = X.copy( data_matrix=np.transpose( @@ -398,7 +398,7 @@ def _fit_grid( np.transpose(pca.components_), ), ), - sample_names=(None,) * self._n_components, + sample_names=(None,) * self.n_components, ) self.explained_variance_ratio_ = pca.explained_variance_ratio_ @@ -428,7 +428,7 @@ def _transform_grid( return ( X.data_matrix.reshape(X.data_matrix.shape[:-1]) - * self._weights + * self.weights @ np.transpose( self.components_.data_matrix.reshape( self.components_.data_matrix.shape[:-1], @@ -528,7 +528,7 @@ def inverse_transform( if pc_scores.ndim == 1: pc_scores = pc_scores[np.newaxis, :] - if pc_scores.shape[1] != self._n_components: + if pc_scores.shape[1] != self.n_components: raise AttributeError( "pc_scores must be a numpy array " "with n_samples rows and n_components columns.", From 41e7309655f74322ad237f30ee19ca7ea1544689 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Fri, 12 Aug 2022 16:46:45 +0200 Subject: [PATCH 244/400] Remove init documentation --- skfda/preprocessing/dim_reduction/_fpca.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index ce02d6772..d92da31ad 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -101,20 +101,6 @@ def __init__( weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, ) -> None: - """ - Initialize the FPCA object. - - Args: - n_components: Number of principal components - centering: If False, the data is assumed to be centered. - regularization: Regularization object to be applied. - components_basis: The basis in which we want the principal - components - weights: The weights vector used for discrete integration. - - .. deprecated:: 0.7.2 - - """ self.n_components = n_components self.centering = centering self.regularization = regularization From bc738096de169c6216b2a08d3c47617535f04aea Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 19 Aug 2022 18:58:24 +0200 Subject: [PATCH 245/400] Improve typing. --- skfda/_utils/_sklearn_adapter.py | 55 ++++++++++++++++++++------------ skfda/_utils/_warping.py | 3 +- skfda/_utils/constants.py | 6 ---- 3 files changed, 37 insertions(+), 27 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 5b8502570..c982700ce 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -18,17 +18,17 @@ TargetPrediction = TypeVar("TargetPrediction") -class BaseEstimator( +class BaseEstimator( # noqa: D101 ABC, sklearn.base.BaseEstimator, # type: ignore[misc] ): pass -class TransformerMixin( +class TransformerMixin( # noqa: D101 ABC, Generic[Input, Output, Target], - # sklearn.base.TransformerMixin, # Inherit in the future + sklearn.base.TransformerMixin, # type: ignore[misc] ): @overload @@ -46,7 +46,7 @@ def fit( ) -> SelfType: pass - def fit( + def fit( # noqa: D102 self: SelfType, X: Input, y: Target | None = None, @@ -68,36 +68,43 @@ def fit_transform( ) -> Output: pass - def fit_transform( + def fit_transform( # noqa: D102 self, X: Input, y: Target | None = None, **fit_params: Any, ) -> Output: if y is None: - return self.fit(X, **fit_params).transform(X) # type: ignore - else: - return self.fit(X, y, **fit_params).transform(X) # type: ignore + return self.fit( # type: ignore[no-any-return] + X, + **fit_params, + ).transform(X) + + return self.fit( # type: ignore[no-any-return] + X, + y, + **fit_params, + ).transform(X) -class InductiveTransformerMixin( +class InductiveTransformerMixin( # noqa: D101 TransformerMixin[Input, Output, Target], ): @abstractmethod - def transform( + def transform( # noqa: D102 self: SelfType, X: Input, ) -> Output: pass -class ClassifierMixin( +class ClassifierMixin( # noqa: D101 ABC, Generic[Input, TargetPrediction], sklearn.base.ClassifierMixin, # type: ignore[misc] ): - def fit( + def fit( # noqa: D102 self: SelfType, X: Input, y: TargetPrediction, @@ -105,27 +112,31 @@ def fit( return self @abstractmethod - def predict( + def predict( # noqa: D102 self: SelfType, X: Input, ) -> TargetPrediction: pass - def score( + def score( # noqa: D102 self, X: Input, y: Target, sample_weight: NDArrayFloat | None = None, ) -> float: - return super().score(X, y, sample_weight=sample_weight) + return super().score( # type: ignore[no-any-return] + X, + y, + sample_weight=sample_weight, + ) -class RegressorMixin( +class RegressorMixin( # noqa: D101 ABC, Generic[Input, TargetPrediction], sklearn.base.RegressorMixin, # type: ignore[misc] ): - def fit( + def fit( # noqa: D102 self: SelfType, X: Input, y: TargetPrediction, @@ -133,16 +144,20 @@ def fit( return self @abstractmethod - def predict( + def predict( # noqa: D102 self: SelfType, X: Input, ) -> TargetPrediction: pass - def score( + def score( # noqa: D102 self, X: Input, y: TargetPrediction, sample_weight: NDArrayFloat | None = None, ) -> float: - return super().score(X, y, sample_weight=sample_weight) + return super().score( # type: ignore[no-any-return] + X, + y, + sample_weight=sample_weight, + ) diff --git a/skfda/_utils/_warping.py b/skfda/_utils/_warping.py index 5247c3941..82dfd149e 100644 --- a/skfda/_utils/_warping.py +++ b/skfda/_utils/_warping.py @@ -110,7 +110,8 @@ def normalize_scale( """ t = t.T # Broadcast to normalize multiple arrays - t1 = (t - t[0]).astype(float) # Translation to [0, t[-1] - t[0]] + t1 = np.array(t, copy=True) + t1 -= t[0] # Translation to [0, t[-1] - t[0]] t1 *= (b - a) / (t[-1] - t[0]) # Scale to [0, b-a] t1 += a # Translation to [a, b] t1[0] = a # Fix possible round errors diff --git a/skfda/_utils/constants.py b/skfda/_utils/constants.py index 634049f3a..42ed5bdbd 100644 --- a/skfda/_utils/constants.py +++ b/skfda/_utils/constants.py @@ -1,10 +1,6 @@ """ This module contains the definition of the constants used in the package. The following constants are defined: -.. data:: BASIS_MIN_FACTOR - Constant used in the discretization of a basis object, by default de - number of points used are the maximum between - BASIS_MIN_FACTOR * n_basis + 1 and N_POINTS_FINE_MESH. .. data:: N_POINTS_FINE_MESH Constant used in the discretization of a basis object, by default de number of points used are the maximum between @@ -18,8 +14,6 @@ plotted. """ -BASIS_MIN_FACTOR = 10 - N_POINTS_COARSE_MESH = 201 N_POINTS_FINE_MESH = 501 From 374f96699a3bf5f25e6a54ec20eccb57c913ecae Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Aug 2022 12:31:57 +0200 Subject: [PATCH 246/400] Improve typing of depth modules. --- skfda/exploratory/depth/_depth.py | 24 ++--- skfda/exploratory/depth/multivariate.py | 118 +++++++++++++----------- skfda/representation/_typing.py | 12 +-- 3 files changed, 83 insertions(+), 71 deletions(-) diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index c600a260c..9f47bfcfd 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -58,17 +58,17 @@ class IntegratedDepth(Depth[FDataGrid]): def __init__( self, *, - multivariate_depth: Optional[Depth[np.ndarray]] = None, + multivariate_depth: Optional[Depth[NDArrayFloat]] = None, ) -> None: self.multivariate_depth = multivariate_depth def fit( # noqa: D102 self, X: FDataGrid, - y: None = None, + y: object = None, ) -> IntegratedDepth: - self.multivariate_depth_: Depth[np.ndarray] + self.multivariate_depth_: Depth[NDArrayFloat] if self.multivariate_depth is None: self.multivariate_depth_ = _UnivariateFraimanMuniz() @@ -80,7 +80,7 @@ def fit( # noqa: D102 self.multivariate_depth_.fit(X.data_matrix) return self - def transform(self, X: FDataGrid) -> np.ndarray: # noqa: D102 + def transform(self, X: FDataGrid) -> NDArrayFloat: # noqa: D102 pointwise_depth = self.multivariate_depth_.transform(X.data_matrix) @@ -102,15 +102,15 @@ def transform(self, X: FDataGrid) -> np.ndarray: # noqa: D102 return integrand - @property # noqa: WPS125 - def max(self) -> float: # noqa: WPS125 + @property + def max(self) -> float: if self.multivariate_depth is None: return 1 return self.multivariate_depth.max - @property # noqa: WPS125 - def min(self) -> float: # noqa: WPS125 + @property + def min(self) -> float: if self.multivariate_depth is None: return 1 / 2 @@ -183,7 +183,7 @@ class BandDepth(Depth[FDataGrid]): """ - def fit(self, X: FDataGrid, y: None = None) -> BandDepth: # noqa: D102 + def fit(self, X: FDataGrid, y: object = None) -> BandDepth: # noqa: D102 if X.dim_codomain != 1: raise NotImplementedError( @@ -193,9 +193,9 @@ def fit(self, X: FDataGrid, y: None = None) -> BandDepth: # noqa: D102 self._distribution = X return self - def transform(self, X: FDataGrid) -> np.ndarray: # noqa: D102 + def transform(self, X: FDataGrid) -> NDArrayFloat: # noqa: D102 - num_in = 0 + num_in = np.zeros(shape=len(X), dtype=X.data_matrix.dtype) n_total = 0 for f1, f2 in itertools.combinations(self._distribution, 2): @@ -260,7 +260,7 @@ def __init__( def fit( # noqa: D102 self, X: T, - y: None = None, + y: object = None, ) -> DistanceBasedDepth: """Fit the model using X as training data. diff --git a/skfda/exploratory/depth/multivariate.py b/skfda/exploratory/depth/multivariate.py index 62e90c9e1..314a7d99e 100644 --- a/skfda/exploratory/depth/multivariate.py +++ b/skfda/exploratory/depth/multivariate.py @@ -4,7 +4,7 @@ import abc import math -from typing import Generic, Optional, TypeVar +from typing import Optional, TypeVar import numpy as np import scipy.stats @@ -12,19 +12,23 @@ from scipy.special import comb from typing_extensions import Literal +from skfda.representation._typing import NDArrayFloat, NDArrayInt + +from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin + T = TypeVar("T", contravariant=True) SelfType = TypeVar("SelfType") _Side = Literal["left", "right"] +Input = TypeVar("Input", contravariant=True) class _DepthOrOutlyingness( - abc.ABC, - sklearn.base.BaseEstimator, # type: ignore - Generic[T], + BaseEstimator, + InductiveTransformerMixin[Input, NDArrayFloat, object], ): """Abstract class representing a depth or outlyingness function.""" - def fit(self: SelfType, X: T, y: None = None) -> SelfType: + def fit(self: SelfType, X: Input, y: object = None) -> SelfType: """ Learn the distribution from the observations. @@ -40,7 +44,7 @@ def fit(self: SelfType, X: T, y: None = None) -> SelfType: return self @abc.abstractmethod - def transform(self, X: T) -> np.ndarray: + def transform(self, X: Input) -> NDArrayFloat: """ Compute the depth or outlyingness inside the learned distribution. @@ -53,7 +57,7 @@ def transform(self, X: T) -> np.ndarray: """ pass - def fit_transform(self, X: T, y: None = None) -> np.ndarray: + def fit_transform(self, X: Input, y: object = None) -> NDArrayFloat: """ Compute the depth or outlyingness of each observation. @@ -71,10 +75,10 @@ def fit_transform(self, X: T, y: None = None) -> np.ndarray: def __call__( self, - X: T, + X: Input, *, - distribution: Optional[T] = None, - ) -> np.ndarray: + distribution: Optional[Input] = None, + ) -> NDArrayFloat: """ Allow the depth or outlyingness to be used as a function. @@ -87,15 +91,15 @@ def __call__( Depth of each observation. """ - copy = sklearn.base.clone(self) + copy: _DepthOrOutlyingness[Input] = sklearn.base.clone(self) if distribution is None: return copy.fit_transform(X) return copy.fit(distribution).transform(X) - @property # noqa: WPS125 - def max(self) -> float: # noqa: WPS125 + @property + def max(self) -> float: """ Maximum (or supremum if there is no maximum) of the possibly predicted values. @@ -103,8 +107,8 @@ def max(self) -> float: # noqa: WPS125 """ return 1 - @property # noqa: WPS125 - def min(self) -> float: # noqa: WPS125 + @property + def min(self) -> float: """ Minimum (or infimum if there is no maximum) of the possibly predicted values. @@ -122,11 +126,11 @@ class Outlyingness(_DepthOrOutlyingness[T]): def _searchsorted_one_dim( - array: np.ndarray, - values: np.ndarray, + array: NDArrayFloat, + values: NDArrayFloat, *, side: _Side = 'left', -) -> np.ndarray: +) -> NDArrayInt: return np.searchsorted(array, values, side=side) @@ -138,24 +142,29 @@ def _searchsorted_one_dim( def _searchsorted_ordered( - array: np.ndarray, - values: np.ndarray, + array: NDArrayFloat, + values: NDArrayFloat, *, side: _Side = 'left', -) -> np.ndarray: - return _searchsorted_vectorized(array, values, side=side) +) -> NDArrayInt: + return _searchsorted_vectorized( # type: ignore[no-any-return] + array, + values, + side=side, + ) -def _cumulative_distribution(column: np.ndarray) -> np.ndarray: - """Calculate the cumulative distribution function at each point. +def _cumulative_distribution(column: NDArrayFloat) -> NDArrayFloat: + """ + Calculate the cumulative distribution function at each point. Args: - column (numpy.darray): Array containing the values over which the + column: Array containing the values over which the distribution function is calculated. Returns: - numpy.darray: Array containing the evaluation at each point of the - distribution function. + Array containing the evaluation at each point of the + distribution function. Examples: >>> _cumulative_distribution(np.array([1, 4, 5, 1, 2, 2, 4, 1, 1, 3])) @@ -169,7 +178,7 @@ def _cumulative_distribution(column: np.ndarray) -> np.ndarray: ) / len(column) -class _UnivariateFraimanMuniz(Depth[np.ndarray]): +class _UnivariateFraimanMuniz(Depth[NDArrayFloat]): r""" Univariate depth used to compute the Fraiman an Muniz depth. @@ -185,26 +194,30 @@ class _UnivariateFraimanMuniz(Depth[np.ndarray]): """ - def fit(self: SelfType, X: np.ndarray, y: None = None) -> SelfType: + def fit(self: SelfType, X: NDArrayFloat, y: object = None) -> SelfType: self._sorted_values = np.sort(X, axis=0) return self - def transform(self, X: np.ndarray) -> np.ndarray: + def transform(self, X: NDArrayFloat) -> NDArrayFloat: cum_dist = _searchsorted_ordered( np.moveaxis(self._sorted_values, 0, -1), np.moveaxis(X, 0, -1), side='right', - ) / len(self._sorted_values) + ).astype(X.dtype) / len(self._sorted_values) assert cum_dist.shape[-2] == 1 - return 1 - np.abs(0.5 - np.moveaxis(cum_dist, -1, 0)[..., 0]) + ret = 0.5 - np.moveaxis(cum_dist, -1, 0)[..., 0] + ret = - np.abs(ret) + ret += 1 - @property # noqa: WPS125 - def min(self) -> float: # noqa: WPS125 + return ret + + @property + def min(self) -> float: return 1 / 2 -class SimplicialDepth(Depth[np.ndarray]): +class SimplicialDepth(Depth[NDArrayFloat]): r""" Simplicial depth. @@ -221,8 +234,8 @@ class SimplicialDepth(Depth[np.ndarray]): def fit( # noqa: D102 self, - X: np.ndarray, - y: None = None, + X: NDArrayFloat, + y: object = None, ) -> SimplicialDepth: self._dim = X.shape[-1] @@ -236,7 +249,7 @@ def fit( # noqa: D102 return self - def transform(self, X: np.ndarray) -> np.ndarray: # noqa: D102 + def transform(self, X: NDArrayFloat) -> NDArrayFloat: # noqa: D102 assert self._dim == X.shape[-1] @@ -261,7 +274,7 @@ def transform(self, X: np.ndarray) -> np.ndarray: # noqa: D102 total_pairs = comb(len(self.sorted_values), 2) - return ( + return ( # type: ignore[no-any-return] total_pairs - comb(num_strictly_below, 2) - comb(num_strictly_above, 2) ) / total_pairs @@ -301,13 +314,13 @@ def __init__(self, outlyingness: Outlyingness[T]): def fit( # noqa: D102 self, X: T, - y: None = None, + y: object = None, ) -> OutlyingnessBasedDepth[T]: self.outlyingness.fit(X) return self - def transform(self, X: np.ndarray) -> np.ndarray: # noqa: D102 + def transform(self, X: T) -> NDArrayFloat: # noqa: D102 outlyingness_values = self.outlyingness.transform(X) min_val = self.outlyingness.min @@ -319,7 +332,7 @@ def transform(self, X: np.ndarray) -> np.ndarray: # noqa: D102 return 1 - (outlyingness_values - min_val) / (max_val - min_val) -class StahelDonohoOutlyingness(Outlyingness[np.ndarray]): +class StahelDonohoOutlyingness(Outlyingness[NDArrayFloat]): r""" Computes Stahel-Donoho outlyingness. @@ -341,8 +354,8 @@ class StahelDonohoOutlyingness(Outlyingness[np.ndarray]): def fit( # noqa: D102 self, - X: np.ndarray, - y: None = None, + X: NDArrayFloat, + y: object = None, ) -> StahelDonohoOutlyingness: dim = X.shape[-1] @@ -355,26 +368,25 @@ def fit( # noqa: D102 return self - def transform(self, X: np.ndarray) -> np.ndarray: # noqa: D102 + def transform(self, X: NDArrayFloat) -> NDArrayFloat: # noqa: D102 dim = X.shape[-1] if dim == 1: # Special case, can be computed exactly - return ( - np.abs(X - self._location) - / self._scale - )[..., 0] + diff: NDArrayFloat = np.abs(X - self._location) / self._scale + + return diff[..., 0] raise NotImplementedError("Only implemented for one dimension") - @property # noqa: WPS125 - def max(self) -> float: # noqa: WPS125 + @property + def max(self) -> float: return math.inf -class ProjectionDepth(OutlyingnessBasedDepth[np.ndarray]): - r""" +class ProjectionDepth(OutlyingnessBasedDepth[NDArrayFloat]): + """ Computes Projection depth. It is defined as the depth induced by the diff --git a/skfda/representation/_typing.py b/skfda/representation/_typing.py index d74ead4b5..5132170d8 100644 --- a/skfda/representation/_typing.py +++ b/skfda/representation/_typing.py @@ -8,7 +8,7 @@ try: from numpy.typing import ArrayLike except ImportError: - ArrayLike = np.ndarray # type:ignore + ArrayLike = np.ndarray # type:ignore[misc] try: from numpy.typing import NDArray @@ -17,11 +17,11 @@ NDArrayFloat = NDArray[np.float_] NDArrayBool = NDArray[np.bool_] except ImportError: - NDArray = np.ndarray # type:ignore - NDArrayAny = np.ndarray # type:ignore - NDArrayInt = np.ndarray # type:ignore - NDArrayFloat = np.ndarray # type:ignore - NDArrayBool = np.ndarray # type:ignore + NDArray = np.ndarray # type:ignore[misc] + NDArrayAny = np.ndarray # type:ignore[misc] + NDArrayInt = np.ndarray # type:ignore[misc] + NDArrayFloat = np.ndarray # type:ignore[misc] + NDArrayBool = np.ndarray # type:ignore[misc] VectorType = TypeVar("VectorType") From ab9da5c17522ad1b4ef7493bd908d0f1de6f2aec Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 22 Aug 2022 18:29:14 +0200 Subject: [PATCH 247/400] Typing outlier detectors. --- skfda/_utils/_sklearn_adapter.py | 18 ++- skfda/exploratory/depth/_depth.py | 4 +- skfda/exploratory/depth/multivariate.py | 4 +- skfda/exploratory/outliers/_boxplot.py | 36 ++--- .../outliers/_directional_outlyingness.py | 124 ++++++++++-------- skfda/exploratory/outliers/_envelopes.py | 4 +- skfda/exploratory/outliers/_outliergram.py | 35 +++-- 7 files changed, 133 insertions(+), 92 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index c982700ce..1bd989db5 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -5,7 +5,7 @@ import sklearn.base -from ..representation._typing import NDArrayFloat +from ..representation._typing import NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType") TransformerNoTarget = TypeVar( @@ -22,7 +22,7 @@ class BaseEstimator( # noqa: D101 ABC, sklearn.base.BaseEstimator, # type: ignore[misc] ): - pass + pass # noqa: WPS604 class TransformerMixin( # noqa: D101 @@ -99,6 +99,20 @@ def transform( # noqa: D102 pass +class OutlierMixin( # noqa: D101 + ABC, + Generic[Input], + sklearn.base.OutlierMixin, # type: ignore[misc] +): + + def fit_predict( # noqa: D102 + self, + X: Input, + y: object = None, + ) -> NDArrayInt: + return self.fit(X, y).predict(X) # type: ignore[no-any-return] + + class ClassifierMixin( # noqa: D101 ABC, Generic[Input, TargetPrediction], diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 9f47bfcfd..42bcde790 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -8,7 +8,7 @@ from __future__ import annotations import itertools -from typing import Optional, TypeVar +from typing import TypeVar import numpy as np import scipy.integrate @@ -58,7 +58,7 @@ class IntegratedDepth(Depth[FDataGrid]): def __init__( self, *, - multivariate_depth: Optional[Depth[NDArrayFloat]] = None, + multivariate_depth: Depth[NDArrayFloat] | None = None, ) -> None: self.multivariate_depth = multivariate_depth diff --git a/skfda/exploratory/depth/multivariate.py b/skfda/exploratory/depth/multivariate.py index 314a7d99e..9ef2b2246 100644 --- a/skfda/exploratory/depth/multivariate.py +++ b/skfda/exploratory/depth/multivariate.py @@ -4,7 +4,7 @@ import abc import math -from typing import Optional, TypeVar +from typing import TypeVar import numpy as np import scipy.stats @@ -77,7 +77,7 @@ def __call__( self, X: Input, *, - distribution: Optional[Input] = None, + distribution: Input | None = None, ) -> NDArrayFloat: """ Allow the depth or outlyingness to be used as a function. diff --git a/skfda/exploratory/outliers/_boxplot.py b/skfda/exploratory/outliers/_boxplot.py index e7262a55a..9e541aed8 100644 --- a/skfda/exploratory/outliers/_boxplot.py +++ b/skfda/exploratory/outliers/_boxplot.py @@ -1,20 +1,19 @@ from __future__ import annotations -from typing import Optional - -import numpy as np -from sklearn.base import BaseEstimator, OutlierMixin +from skfda.representation._typing import NDArrayInt +from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin from ...representation import FDataGrid from ..depth import Depth, ModifiedBandDepth from . import _envelopes class BoxplotOutlierDetector( - BaseEstimator, # type: ignore - OutlierMixin, # type: ignore + BaseEstimator, + OutlierMixin[FDataGrid], ): - r"""Outlier detector using the interquartile range. + r""" + Outlier detector using the interquartile range. Detects as outliers functions that have one or more points outside ``factor`` times the interquartile range plus or minus the central @@ -44,13 +43,17 @@ class BoxplotOutlierDetector( def __init__( self, *, - depth_method: Optional[Depth[FDataGrid]] = None, + depth_method: Depth[FDataGrid] | None = None, factor: float = 1.5, ) -> None: self.depth_method = depth_method self.factor = factor - def fit(self, X: FDataGrid, y: None = None) -> BoxplotOutlierDetector: + def fit( # noqa: D102 + self, + X: FDataGrid, + y: None = None, + ) -> BoxplotOutlierDetector: depth_method = ( self.depth_method @@ -62,22 +65,25 @@ def fit(self, X: FDataGrid, y: None = None) -> BoxplotOutlierDetector: # Central region and envelope must be computed for outlier detection central_region = _envelopes.compute_region( - X, indices_descending_depth, 0.5) + X, + indices_descending_depth, + 0.5, + ) self._central_envelope = _envelopes.compute_envelope(central_region) # Non-outlying envelope self.non_outlying_threshold_ = _envelopes.non_outlying_threshold( - self._central_envelope, self.factor) + self._central_envelope, + self.factor, + ) return self - def predict(self, X: FDataGrid) -> np.ndarray: + def predict(self, X: FDataGrid) -> NDArrayInt: # noqa: D102 outliers = _envelopes.predict_outliers( X, self.non_outlying_threshold_, ) # Predict as scikit-learn outlier detectors - predicted = ~outliers + outliers * -1 - - return predicted + return ~outliers + outliers * -1 diff --git a/skfda/exploratory/outliers/_directional_outlyingness.py b/skfda/exploratory/outliers/_directional_outlyingness.py index 979d7eaea..ea76fe84f 100644 --- a/skfda/exploratory/outliers/_directional_outlyingness.py +++ b/skfda/exploratory/outliers/_directional_outlyingness.py @@ -1,33 +1,38 @@ from __future__ import annotations -from typing import NamedTuple, Optional, Tuple +from dataclasses import dataclass +from typing import Tuple import numpy as np import scipy.integrate import scipy.stats from numpy import linalg as la -from sklearn.base import BaseEstimator, OutlierMixin from sklearn.covariance import MinCovDet - -from skfda.exploratory.depth.multivariate import Depth, ProjectionDepth +from sklearn.utils.validation import check_random_state from ... import FDataGrid from ..._utils import RandomStateLike +from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin from ...representation._typing import NDArrayFloat, NDArrayInt +from ..depth.multivariate import Depth, ProjectionDepth +from . import _directional_outlyingness_experiment_results as experiments + +@dataclass +class DirectionalOutlyingnessStats: + """Directional outlyingness statistical measures.""" -class DirectionalOutlyingnessStats(NamedTuple): directional_outlyingness: NDArrayFloat functional_directional_outlyingness: NDArrayFloat mean_directional_outlyingness: NDArrayFloat variation_directional_outlyingness: NDArrayFloat -def directional_outlyingness_stats( +def directional_outlyingness_stats( # noqa: WPS218 fdatagrid: FDataGrid, *, - multivariate_depth: Optional[Depth[NDArrayFloat]] = None, - pointwise_weights: Optional[NDArrayFloat] = None, + multivariate_depth: Depth[NDArrayFloat] | None = None, + pointwise_weights: NDArrayFloat | None = None, ) -> DirectionalOutlyingnessStats: r""" Compute the directional outlyingness of the functional data. @@ -246,9 +251,9 @@ def directional_outlyingness_stats( ) -class MSPlotOutlierDetector( - BaseEstimator, # type: ignore - OutlierMixin, # type: ignore +class MSPlotOutlierDetector( # noqa: WPS230 + BaseEstimator, + OutlierMixin[FDataGrid], ): r"""Outlier detector using directional outlyingness. @@ -337,10 +342,10 @@ class MSPlotOutlierDetector( def __init__( self, *, - multivariate_depth: Optional[Depth[NDArrayFloat]] = None, - pointwise_weights: Optional[NDArrayFloat] = None, + multivariate_depth: Depth[NDArrayFloat] | None = None, + pointwise_weights: NDArrayFloat | None = None, assume_centered: bool = False, - support_fraction: Optional[float] = None, + support_fraction: float | None = None, num_resamples: int = 1000, random_state: RandomStateLike = 0, cutoff_factor: float = 1, @@ -361,19 +366,18 @@ def _compute_points(self, X: FDataGrid) -> NDArrayFloat: multivariate_depth = ProjectionDepth() # The depths of the samples are calculated giving them an ordering. - *_, mean_dir_outl, variation_dir_outl = directional_outlyingness_stats( + stats = directional_outlyingness_stats( X, multivariate_depth=multivariate_depth, - pointwise_weights=self.pointwise_weights) - - points = np.concatenate( - (mean_dir_outl, variation_dir_outl[:, np.newaxis]), - axis=1, + pointwise_weights=self.pointwise_weights, ) - return points + mean = stats.mean_directional_outlyingness + variation = stats.variation_directional_outlyingness[:, np.newaxis] - def _parameters_asymptotic( + return np.concatenate((mean, variation), axis=1) + + def _parameters_asymptotic( # noqa: WPS210 self, sample_size: int, dimension: int, @@ -404,23 +408,24 @@ def _parameters_asymptotic( # m estimation alpha = (n - h) / n - q_alpha = scipy.stats.chi2.ppf(1 - alpha, df=p) + alpha_compl = 1 - alpha + q_alpha = scipy.stats.chi2.ppf(alpha_compl, df=p) dist_p2 = scipy.stats.chi2.cdf(q_alpha, df=p + 2) dist_p4 = scipy.stats.chi2.cdf(q_alpha, df=p + 4) - c_alpha = (1 - alpha) / dist_p2 + c_alpha = alpha_compl / dist_p2 c2 = -dist_p2 / 2 c3 = -dist_p4 / 2 c4 = 3 * c3 - b1 = (c_alpha * (c3 - c4)) / (1 - alpha) + b1 = (c3 - c4) / dist_p2 b2 = ( - 0.5 + c_alpha / (1 - alpha) - * (c3 - q_alpha / p * (c2 + (1 - alpha) / 2)) + 0.5 + 1 / dist_p2 + * (c3 - q_alpha / p * (c2 + alpha_compl / 2)) ) v1 = ( - (1 - alpha) * b1**2 + alpha_compl * b1**2 * (alpha * (c_alpha * q_alpha / p - 1) ** 2 - 1) - 2 * c3 * c_alpha**2 * ( @@ -428,13 +433,14 @@ def _parameters_asymptotic( + (p + 2) * b2 * (2 * b1 - p * b2) ) ) - v2 = n * (b1 * (b1 - p * b2) * (1 - alpha))**2 * c_alpha**2 + v2 = n * (b1 * (b1 - p * b2) * alpha_compl)**2 * c_alpha**2 v = v1 / v2 m_asympt = 2 / (c_alpha**2 * v) estimated_m = ( - m_asympt * np.exp(0.725 - 0.00663 * p - 0.0780 * np.log(n)) + m_asympt + * np.exp(0.725 - 0.00663 * p - 0.078 * np.log(n)) # noqa: WPS432 ) dfn = p @@ -443,7 +449,13 @@ def _parameters_asymptotic( # Calculation of the cutoff value and scaling factor to identify # outliers. scaling = estimated_c * dfd / estimated_m / dfn - cutoff_value = scipy.stats.f.ppf(0.993, dfn, dfd, loc=0, scale=1) + cutoff_value = scipy.stats.f.ppf( + 0.993, # noqa: WPS432 + dfn, + dfd, + loc=0, + scale=1, + ) return scaling, cutoff_value @@ -452,39 +464,38 @@ def _parameters_numeric( sample_size: int, dimension: int, ) -> Tuple[float, float]: - from . import \ - _directional_outlyingness_experiment_results as experiments key = sample_size // 5 use_asympt = True - if dimension == 2: - scaling_list = experiments.dim2_scaling_list - cutoff_list = experiments.dim2_cutoff_list - assert len(scaling_list) == len(cutoff_list) - if key < len(scaling_list): - use_asympt = False - - elif dimension == 3: - scaling_list = experiments.dim3_scaling_list - cutoff_list = experiments.dim3_cutoff_list - assert len(scaling_list) == len(cutoff_list) - if key < len(scaling_list): - use_asympt = False + if not self._force_asymptotic: + if dimension == 2: + scaling_list = experiments.dim2_scaling_list + cutoff_list = experiments.dim2_cutoff_list + assert len(scaling_list) == len(cutoff_list) + if key < len(scaling_list): + use_asympt = False + + elif dimension == 3: + scaling_list = experiments.dim3_scaling_list + cutoff_list = experiments.dim3_cutoff_list + assert len(scaling_list) == len(cutoff_list) + if key < len(scaling_list): + use_asympt = False if use_asympt: return self._parameters_asymptotic(sample_size, dimension) return scaling_list[key], cutoff_list[key] - def fit_predict(self, X: FDataGrid, y: None = None) -> NDArrayInt: - - try: - self.random_state_ = np.random.RandomState(self.random_state) - except ValueError: - self.random_state_ = self.random_state + def fit_predict( # noqa: D102 + self, + X: FDataGrid, + y: object = None, + ) -> NDArrayInt: + self.random_state_ = check_random_state(self.random_state) self.points_ = self._compute_points(X) # The square mahalanobis distances of the samples are @@ -494,7 +505,8 @@ def fit_predict(self, X: FDataGrid, y: None = None) -> NDArrayInt: assume_centered=self.assume_centered, support_fraction=self.support_fraction, random_state=self.random_state_, - ).fit(self.points_) + ) + self.cov_.fit(self.points_) # Calculation of the degrees of freedom of the F-distribution # (approximation of the tail of the distance distribution). @@ -515,11 +527,9 @@ def fit_predict(self, X: FDataGrid, y: None = None) -> NDArrayInt: self.scaling_ = scaling self.cutoff_value_ = cutoff_value * self.cutoff_factor - rmd_2 = self.cov_.mahalanobis(self.points_) + rmd_2: NDArrayFloat = self.cov_.mahalanobis(self.points_) outliers = self.scaling_ * rmd_2 > self.cutoff_value_ # Predict as scikit-learn outlier detectors - predicted = ~outliers + outliers * -1 - - return predicted + return ~outliers + outliers * -1 diff --git a/skfda/exploratory/outliers/_envelopes.py b/skfda/exploratory/outliers/_envelopes.py index 22f8a3a46..97f8284ce 100644 --- a/skfda/exploratory/outliers/_envelopes.py +++ b/skfda/exploratory/outliers/_envelopes.py @@ -44,11 +44,11 @@ def predict_outliers( or_axes = tuple(i for i in range(1, fdatagrid.data_matrix.ndim)) - below_outliers = np.any( + below_outliers: NDArrayBool = np.any( fdatagrid.data_matrix < min_threshold, axis=or_axes, ) - above_outliers = np.any( + above_outliers: NDArrayBool = np.any( fdatagrid.data_matrix > max_threshold, axis=or_axes, ) diff --git a/skfda/exploratory/outliers/_outliergram.py b/skfda/exploratory/outliers/_outliergram.py index a8dc80275..bf67bb2d9 100644 --- a/skfda/exploratory/outliers/_outliergram.py +++ b/skfda/exploratory/outliers/_outliergram.py @@ -1,18 +1,20 @@ from __future__ import annotations import numpy as np -from sklearn.base import BaseEstimator, OutlierMixin +from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin from ...representation import FDataGrid +from ...representation._typing import NDArrayFloat, NDArrayInt from ..depth._depth import ModifiedBandDepth from ..stats import modified_epigraph_index class OutliergramOutlierDetector( - BaseEstimator, # type: ignore - OutlierMixin, # type: ignore + BaseEstimator, + OutlierMixin[FDataGrid], ): - r"""Outlier detector using the relation between MEI and MBD. + r""" + Outlier detector using the relation between MEI and MBD. Detects as outliers functions that have the vertical distance to the outliergram parabola greater than ``factor`` times the interquartile @@ -52,7 +54,7 @@ class OutliergramOutlierDetector( def __init__(self, *, factor: float = 1.5) -> None: self.factor = factor - def _compute_parabola(self, X: FDataGrid) -> np.ndarray: + def _compute_parabola(self, X: FDataGrid) -> NDArrayFloat: """Compute the parabola in which pairs (mei, mbd) should lie.""" a_0 = -2 / (X.n_samples * (X.n_samples - 1)) a_1 = (2 * (X.n_samples + 1)) / (X.n_samples - 1) @@ -63,14 +65,21 @@ def _compute_parabola(self, X: FDataGrid) -> np.ndarray: + X.n_samples**2 * a_2 * self.mei_**2 ) - def _compute_maximum_inlier_distance(self, distances: np.ndarray) -> float: + def _compute_maximum_inlier_distance( + self, + distances: NDArrayFloat, + ) -> float: """Compute the distance above which data are considered outliers.""" first_quartile = np.percentile(distances, 25) # noqa: WPS432 third_quartile = np.percentile(distances, 75) # noqa: WPS432 iqr = third_quartile - first_quartile - return third_quartile + self.factor * iqr + return float(third_quartile + self.factor * iqr) - def fit(self, X: FDataGrid, y: None = None) -> OutliergramOutlierDetector: + def fit( # noqa: D102 + self, + X: FDataGrid, + y: object = None, + ) -> OutliergramOutlierDetector: self.mbd_ = ModifiedBandDepth()(X) self.mei_ = modified_epigraph_index(X) self.parabola_ = self._compute_parabola(X) @@ -81,12 +90,14 @@ def fit(self, X: FDataGrid, y: None = None) -> OutliergramOutlierDetector: return self - def fit_predict(self, X: FDataGrid, y: None = None) -> np.ndarray: + def fit_predict( # noqa: D102 + self, + X: FDataGrid, + y: object = None, + ) -> NDArrayInt: self.fit(X, y) outliers = self.distances_ > self.max_inlier_distance_ # Predict as scikit-learn outlier detectors - predicted = ~outliers + outliers * -1 - - return predicted + return ~outliers + outliers * -1 From bf31a8c9ead3984953381340aed771671795099e Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 23 Aug 2022 11:37:49 +0200 Subject: [PATCH 248/400] Typing stats. --- skfda/exploratory/stats/_fisher_rao.py | 6 +- .../stats/_functional_transformers.py | 54 ++++++++----- skfda/exploratory/stats/_stats.py | 78 +++++++++++-------- 3 files changed, 80 insertions(+), 58 deletions(-) diff --git a/skfda/exploratory/stats/_fisher_rao.py b/skfda/exploratory/stats/_fisher_rao.py index bc07de515..fb0167ec7 100644 --- a/skfda/exploratory/stats/_fisher_rao.py +++ b/skfda/exploratory/stats/_fisher_rao.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Optional +from typing import Any import numpy as np import scipy.integrate @@ -45,7 +45,7 @@ def _elastic_alignment_array( the functions aligned to the template(s). """ - return optimum_reparam( + return optimum_reparam( # type: ignore[no-any-return] np.ascontiguousarray(template_data.T), np.ascontiguousarray(eval_points), np.ascontiguousarray(q_data.T), @@ -186,7 +186,7 @@ def fisher_rao_karcher_mean( center: bool = True, max_iter: int = 20, tol: float = 1e-3, - initial: Optional[float] = None, + initial: float | None = None, grid_dim: int = 7, **kwargs: Any, ) -> FDataGrid: diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 7043e614d..06d262262 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,19 +2,34 @@ from __future__ import annotations -from typing import Callable, Optional, Sequence, Tuple, Union +from typing import Optional, Sequence, Tuple, TypeVar, Union + +from typing_extensions import Protocol import numpy as np from ..._utils import check_is_univariate, nquad_vec from ...representation import FData, FDataBasis, FDataGrid -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...representation._typing import ( + ArrayLike, + NDArrayBool, + NDArrayFloat, + NDArrayInt, +) + +T = TypeVar("T", bound=Union[NDArrayFloat, FDataGrid]) + + +class _BasicUfuncProtocol(Protocol): + + def __call__(self, __arg: T) -> T: # noqa: WPS112 + pass def local_averages( data: Union[FDataGrid, FDataBasis], n_intervals: int, -) -> np.ndarray: +) -> NDArrayFloat: r""" Calculate the local averages of a given data. @@ -111,7 +126,7 @@ def _calculate_curves_occupation( * intervals_x_axis[:, np.newaxis] ) - return np.sum(intervals_x_inside, axis=1) + return np.sum(intervals_x_inside, axis=1) # type: ignore[no-any-return] def occupation_measure( @@ -216,7 +231,7 @@ def occupation_measure( def number_up_crossings( data: FDataGrid, - levels: NDArrayFloat, + levels: ArrayLike, ) -> NDArrayInt: r""" Calculate the number of up crossings to a level of a FDataGrid. @@ -234,9 +249,10 @@ def number_up_crossings( Args: data: FDataGrid where we want to calculate - the number of up crossings. + the number of up crossings. levels: sequence of numbers including the levels - we want to consider for the crossings. + we want to consider for the crossings. + Returns: ndarray of shape (n_samples, len(levels))\ with the values of the counters. @@ -279,25 +295,21 @@ def number_up_crossings( >>> number_up_crossings(fd_grid, np.asarray([0])) array([[2]]) """ + levels = np.asarray(levels) curves = data.data_matrix[:, :, 0] - distances = np.asarray([ - level - curves - for level in levels - ]) + distances = np.subtract.outer(levels, curves) points_greater = distances >= 0 points_smaller = distances <= 0 - points_smaller_rotated = np.concatenate( - [ - points_smaller[:, :, 1:], - points_smaller[:, :, :1], - ], - axis=2, + + growing = distances[:, :, :-1] < points_greater[:, :, 1:] + upcrossing_positions: NDArrayBool = ( + points_smaller[:, :, :-1] & points_greater[:, :, 1:] & growing ) - return np.sum( - points_greater & points_smaller_rotated, + return np.sum( # type: ignore[no-any-return] + upcrossing_positions, axis=2, ).T @@ -409,7 +421,7 @@ def unconditional_moments( def unconditional_expected_value( data: FData, - function: Callable[[np.ndarray], np.ndarray], + function: _BasicUfuncProtocol, ) -> NDArrayFloat: r""" Calculate the unconditional expected value of a function. @@ -460,7 +472,7 @@ def unconditional_expected_value( if isinstance(data, FDataGrid): return function(data).integrate() / lebesgue_measure - def integrand(*args: NDArrayFloat): + def integrand(*args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 f1 = data(args)[:, 0, :] return function(f1) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index e9d3be2b2..6a54c9071 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -1,64 +1,71 @@ """Functional data descriptive statistics.""" +from __future__ import annotations from builtins import isinstance -from typing import Optional, TypeVar, Union +from typing import TypeVar, Union import numpy as np from scipy import integrate from scipy.stats import rankdata -from ...misc.metrics import Metric, l2_distance, l2_norm +from ...misc.metrics import Metric, l2_distance from ...representation import FData, FDataGrid +from ...representation._typing import NDArrayFloat from ..depth import Depth, ModifiedBandDepth F = TypeVar('F', bound=FData) +T = TypeVar('T', bound=Union[NDArrayFloat, FData]) def mean(X: F) -> F: - """Compute the mean of all the samples in a FData object. + """ + Compute the mean of all the samples in a FData object. Args: X: Object containing all the samples whose mean is wanted. Returns: - A :term:`functional data object` with just one sample representing - the mean of all the samples in the original object. + Mean of all the samples in the original object, as a + :term:`functional data object` with just one sample. """ return X.mean() def var(X: FData) -> FDataGrid: - """Compute the variance of a set of samples in a FDataGrid object. + """ + Compute the variance of a set of samples in a FDataGrid object. Args: X: Object containing all the set of samples whose variance is desired. Returns: - A :term:`functional data object` with just one sample representing the - variance of all the samples in the original object. + Variance of all the samples in the original object, as a + :term:`functional data object` with just one sample. """ return X.var() def gmean(X: FDataGrid) -> FDataGrid: - """Compute the geometric mean of all the samples in a FDataGrid object. + """ + Compute the geometric mean of all the samples in a FDataGrid object. Args: X: Object containing all the samples whose geometric mean is wanted. Returns: - A :term:`functional data object` with just one sample representing the - geometric mean of all the samples in the original object. + Geometric mean of all the samples in the original object, as a + :term:`functional data object` with just one sample. """ return X.gmean() def cov(X: FData) -> FDataGrid: - """Compute the covariance. + """ + Compute the covariance. Calculates the covariance matrix representing the covariance of the functional samples at the observation points. @@ -67,14 +74,14 @@ def cov(X: FData) -> FDataGrid: X: Object containing different samples of a functional variable. Returns: - A :term:`functional data object` with just one sample representing the - covariance of all the samples in the original object. + Covariance of all the samples in the original object, as a + :term:`functional data object` with just one sample. """ return X.cov() -def modified_epigraph_index(X: FDataGrid) -> np.ndarray: +def modified_epigraph_index(X: FDataGrid) -> NDArrayFloat: """ Calculate the Modified Epigraph Index of a FDataGrid. @@ -90,7 +97,7 @@ def modified_epigraph_index(X: FDataGrid) -> np.ndarray: # Array containing at each point the number of curves # are above it. - num_functions_above = rankdata( + num_functions_above: NDArrayFloat = rankdata( -X.data_matrix, method='max', axis=0, @@ -113,10 +120,11 @@ def modified_epigraph_index(X: FDataGrid) -> np.ndarray: def depth_based_median( - X: FDataGrid, - depth_method: Optional[Depth] = None, -) -> FDataGrid: - """Compute the median based on a depth measure. + X: T, + depth_method: Depth[T] | None = None, +) -> T: + """ + Compute the median based on a depth measure. The depth based median is the deepest curve given a certain depth measure. @@ -124,7 +132,7 @@ def depth_based_median( Args: X: Object containing different samples of a functional variable. - depth_method: Method used to order the data. Defaults to + depth_method: Depth method used to order the data. Defaults to :func:`modified band depth `. @@ -135,26 +143,27 @@ def depth_based_median( :func:`geometric_median` """ + depth_method_used: Depth[T] + if depth_method is None: - depth_method = ModifiedBandDepth() + assert isinstance(X, FDataGrid) + depth_method_used = ModifiedBandDepth() + else: + depth_method_used = depth_method - depth = depth_method(X) + depth = depth_method_used(X) indices_descending_depth = (-depth).argsort(axis=0) # The median is the deepest curve return X[indices_descending_depth[0]] -T = TypeVar('T', bound=Union[np.ndarray, FData]) - +def _weighted_average(X: T, weights: NDArrayFloat) -> T: -def _weighted_average(X: T, weights: np.ndarray) -> T: + if isinstance(X, FData): + return (X * weights).sum() - return ( - (X * weights).sum() if isinstance(X, FData) - # To support also multivariate data - else (X.T * weights).T.sum(axis=0) - ) + return (X.T * weights).T.sum(axis=0) # type: ignore[no-any-return] def geometric_median( @@ -163,7 +172,8 @@ def geometric_median( tol: float = 1.e-8, metric: Metric[T] = l2_distance, ) -> T: - r"""Compute the geometric median. + r""" + Compute the geometric median. The sample geometric median is the point that minimizes the :math:`L_1` norm of the vector of distances to all observations: @@ -218,7 +228,7 @@ def geometric_median( median_new = _weighted_average(X, weights_new) - if l2_norm(median_new - median) < tol: + if l2_distance(median_new, median) < tol: return median_new distances = metric(X, median_new) @@ -230,7 +240,7 @@ def trim_mean( X: F, proportiontocut: float, *, - depth_method: Optional[Depth[F]] = None, + depth_method: Depth[F] | None = None, ) -> FDataGrid: """Compute the trimmed means based on a depth measure. From d0bf7b598d740e858850e396d63fb865162cdd98 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 24 Aug 2022 10:49:34 +0200 Subject: [PATCH 249/400] Generalize local averages. --- .../preprocessing/feature_construction.rst | 22 +- skfda/_utils/_utils.py | 4 +- skfda/exploratory/stats/__init__.py | 4 +- .../stats/_functional_transformers.py | 273 ++++++++++++------ .../variable_selection/maxima_hunting.py | 7 +- .../_function_transformers.py | 75 +++-- skfda/representation/_functional_data.py | 9 +- skfda/representation/basis/_fdatabasis.py | 15 +- skfda/representation/grid.py | 10 +- 9 files changed, 279 insertions(+), 140 deletions(-) diff --git a/docs/modules/preprocessing/feature_construction.rst b/docs/modules/preprocessing/feature_construction.rst index e33b6bf21..e8795144d 100644 --- a/docs/modules/preprocessing/feature_construction.rst +++ b/docs/modules/preprocessing/feature_construction.rst @@ -7,8 +7,8 @@ The expectation is that these features make explicit characteristics that facilitate the learning process. -FDA Feature union ------------------ +Feature union +------------- This transformer defines a way of extracting a high number of distinct features in parallel. @@ -30,4 +30,20 @@ data of a particular class. .. autosummary:: :toctree: autosummary - skfda.preprocessing.feature_construction.PerClassTransformer \ No newline at end of file + skfda.preprocessing.feature_construction.PerClassTransformer + + +Functional features +------------------- + +The following transformers can be used to extract specific functional features +for each functional datum, which can then be used for prediction. + +.. autosummary:: + :toctree: autosummary + + skfda.preprocessing.feature_construction.LocalAveragesTransformer + skfda.preprocessing.feature_construction.OccupationMeasureTransformer + skfda.preprocessing.feature_construction.NumberUpCrossingsTransformer + + \ No newline at end of file diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 10a49b625..0284ae5a3 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -19,6 +19,8 @@ overload, ) +from typing_extensions import Literal, Protocol + import numpy as np import scipy.integrate from numpy import ndarray @@ -26,9 +28,7 @@ from sklearn.base import clone from sklearn.preprocessing import LabelEncoder from sklearn.utils.multiclass import check_classification_targets -from typing_extensions import Literal, Protocol -from .._utils._sklearn_adapter import TransformerMixin from ..representation._typing import ( ArrayLike, DomainRange, diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 83e5c1001..1587fab47 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -4,9 +4,9 @@ local_averages, number_up_crossings, occupation_measure, - unconditional_central_moments, + unconditional_central_moment, unconditional_expected_value, - unconditional_moments, + unconditional_moment, ) from ._stats import ( cov, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 06d262262..e11fc6816 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -2,16 +2,18 @@ from __future__ import annotations -from typing import Optional, Sequence, Tuple, TypeVar, Union +import itertools +from typing import Optional, Sequence, Tuple, TypeVar, Union, cast -from typing_extensions import Protocol +from typing_extensions import Protocol, TypeGuard import numpy as np -from ..._utils import check_is_univariate, nquad_vec +from ..._utils import _to_domain_range, check_is_univariate, nquad_vec from ...representation import FData, FDataBasis, FDataGrid from ...representation._typing import ( ArrayLike, + DomainRangeLike, NDArrayBool, NDArrayFloat, NDArrayInt, @@ -26,32 +28,43 @@ def __call__(self, __arg: T) -> T: # noqa: WPS112 pass +def _sequence_of_ints(data: Sequence[object]) -> TypeGuard[Sequence[int]]: + """Check that every element is an integer.""" + return all(isinstance(d, int) for d in data) + + def local_averages( - data: Union[FDataGrid, FDataBasis], - n_intervals: int, + data: FData, + *, + domains: int | Sequence[int] | Sequence[DomainRangeLike], ) -> NDArrayFloat: r""" - Calculate the local averages of a given data. + Calculate the local averages of given data in the desired domains. - Take functional data as a grid or a basis and performs - the following map: + It takes functional data and performs the following map: .. math:: f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt - where {T_1,\dots,T_p} are disjoint intervals of the interval [a,b] + where :math:`T_1, \dots, T_p` are subregions of the original + :term:`domain`. - It is calculated for a given number of intervals, - which are of equal sizes. Args: data: FDataGrid or FDataBasis where we want to - calculate the local averages. - n_intervals: number of intervals we want to consider. + calculate the local averages. + domains: Domains for each local average. It is possible to + pass a number or a list of numbers to automatically split + each dimension in that number of intervals and use them for + the averages. + Returns: - ndarray of shape (n_samples, n_intervals, n_dimensions) with + ndarray of shape (n_samples, n_domains, codomain_dimension) with the transformed data. + See also: + :class:`~skfda.preprocessing.feature_construction.LocalAveragesTransformer` + Examples: We import the Berkeley Growth Study dataset. We will use only the first 3 samples to make the @@ -61,12 +74,35 @@ def local_averages( >>> dataset = fetch_growth(return_X_y=True)[0] >>> X = dataset[:3] - Then we decide how many intervals we want to consider (in our case 2) - and call the function with the dataset. + We can choose the intervals used for the local averages. For example, + we could in this case use the averages at different stages of + development of the child: from 1 to 3 years, from 3 to 10 and from + 10 to 18: + + >>> import numpy as np + >>> from skfda.exploratory.stats import local_averages + >>> averages = local_averages( + ... X, + ... domains=[(1, 3), (3, 10), (10, 18)], + ... ) + >>> np.around(averages, decimals=2) + array([[[ 91.37], + [ 126.52], + [ 179.02]], + [[ 87.51], + [ 120.71], + [ 158.81]], + [[ 86.36], + [ 115.04], + [ 156.37]]]) + + A different possibility is to decide how many intervals we want to + consider. For example, we could want to split the domain in 2 + intervals of the same length. >>> import numpy as np >>> from skfda.exploratory.stats import local_averages - >>> np.around(local_averages(X, 2), decimals=2) + >>> np.around(local_averages(X, domains=2), decimals=2) array([[[ 116.94], [ 177.26]], [[ 111.86], @@ -74,18 +110,36 @@ def local_averages( [[ 107.29], [ 154.97]]]) """ - left, right = data.domain_range[0] + if isinstance(domains, int): + domains = [domains] * data.dim_domain + + if _sequence_of_ints(domains): + # Get a list of arrays with the interval endpoints + interval_endpoints = [ + np.linspace( + domain_range[0], + domain_range[1], + num=n_intervals + 1, + ) + for n_intervals, domain_range in zip(domains, data.domain_range) + ] + + # Get all combinations of intervals as ranges + domains = list( + itertools.product( + *[zip(p, p[1:]) for p in interval_endpoints], + ), + ) - intervals, step = np.linspace( - left, - right, - num=n_intervals + 1, - retstep=True, - ) + domains = cast(Sequence[DomainRangeLike], domains) integrated_data = [ - data.integrate(interval=((intervals[i], intervals[i + 1]))) / step - for i in range(n_intervals) + unconditional_expected_value( + data, + lambda x: x, + domain=domain, + ) + for domain in domains ] return np.swapaxes(integrated_data, 0, 1) @@ -314,12 +368,14 @@ def number_up_crossings( ).T -def unconditional_central_moments( +def unconditional_central_moment( data: FDataGrid, n: int, + *, + domain: DomainRangeLike | None = None, ) -> NDArrayFloat: r""" - Calculate the unconditional central moments of a dataset. + Calculate a specified unconditional central moment. The unconditional central moments are defined as the unconditional moments where the mean is subtracted from each sample before the @@ -334,49 +390,57 @@ def unconditional_central_moments( Args: data: FDataGrid where we want to calculate - a particular unconditional central moment. + a particular unconditional central moment. n: order of the moment. + domain: Integration domain. By default, the whole domain is used. Returns: ndarray of shape (n_dimensions, n_samples) with the values of the specified moment. Example: + We will calculate the first unconditional central moment of the + Canadian Weather data set. In order to simplify the example, we will + use only the first five samples. + First we proceed to import the data set. + + >>> from skfda.datasets import fetch_weather + >>> X = fetch_weather(return_X_y=True)[0] + + Then we call the function with the samples that we want to consider and + the specified moment order. + + >>> import numpy as np + >>> from skfda.exploratory.stats import unconditional_central_moment + >>> np.around(unconditional_central_moment(X[:5], 1), decimals=2) + array([[ 0.01, 0.01], + [ 0.02, 0.01], + [ 0.02, 0.01], + [ 0.02, 0.01], + [ 0.01, 0.01]]) - We will calculate the first unconditional central moment of the Canadian - Weather data set. In order to simplify the example, we will use only the - first five samples. - First we proceed to import the data set. - >>> from skfda.datasets import fetch_weather - >>> X = fetch_weather(return_X_y=True)[0] - - Then we call the function with the samples that we want to consider and the - specified moment order. - >>> import numpy as np - >>> from skfda.exploratory.stats import unconditional_central_moments - >>> np.around(unconditional_central_moments(X[:5], 1), decimals=2) - array([[ 0.01, 0.01], - [ 0.02, 0.01], - [ 0.02, 0.01], - [ 0.02, 0.01], - [ 0.01, 0.01]]) """ - mean = data.integrate() / ( - data.domain_range[0][1] - data.domain_range[0][0] + mean = unconditional_expected_value( + data, + lambda x: x, + domain=domain, ) return unconditional_expected_value( data, lambda x: np.power(x - mean, n), + domain=domain, ) -def unconditional_moments( +def unconditional_moment( data: Union[FDataBasis, FDataGrid], n: int, + *, + domain: DomainRangeLike | None = None, ) -> NDArrayFloat: r""" - Calculate the specified unconditional moment of a dataset. + Calculate a specified unconditional moment. The n-th unconditional moments of p real-valued continuous functions are calculated as: @@ -386,91 +450,110 @@ def unconditional_moments( f_p(x(t))=\frac{1}{\left( b-a\right)}\int_a^b \left(x_p(t)\right)^n dt Args: data: FDataGrid or FDataBasis where we want to calculate - a particular unconditional moment. - n: order of the moment. + a particular unconditional moment. + n: Order of the moment. + domain: Integration domain. By default, the whole domain is used. Returns: ndarray of shape (n_dimensions, n_samples) with the values of the specified moment. - Example: + Examples: + We will calculate the first unconditional moment of the Canadian + Weather data set. In order to simplify the example, we will use only + the first five samples. + First we proceed to import the data set. + + >>> from skfda.datasets import fetch_weather + >>> X = fetch_weather(return_X_y=True)[0] + + Then we call the function with the samples that we want to consider and + the specified moment order. + + >>> import numpy as np + >>> from skfda.exploratory.stats import unconditional_moment + >>> np.around(unconditional_moment(X[:5], 1), decimals=2) + array([[ 4.7 , 4.03], + [ 6.16, 3.96], + [ 5.52, 4.01], + [ 6.82, 3.44], + [ 5.25, 3.29]]) - We will calculate the first unconditional moment of the Canadian Weather - data set. In order to simplify the example, we will use only the first - five samples. - First we proceed to import the data set. - >>> from skfda.datasets import fetch_weather - >>> X = fetch_weather(return_X_y=True)[0] - - Then we call the function with the samples that we want to consider and the - specified moment order. - >>> import numpy as np - >>> from skfda.exploratory.stats import unconditional_moments - >>> np.around(unconditional_moments(X[:5], 1), decimals=2) - array([[ 4.7 , 4.03], - [ 6.16, 3.96], - [ 5.52, 4.01], - [ 6.82, 3.44], - [ 5.25, 3.29]]) """ return unconditional_expected_value( data, lambda x: np.power(x, n), + domain=domain, ) def unconditional_expected_value( data: FData, function: _BasicUfuncProtocol, + *, + domain: DomainRangeLike | None = None, ) -> NDArrayFloat: r""" Calculate the unconditional expected value of a function. Next formula shows for a defined transformation :math: `g(x(t))` and p observations, how the unconditional expected values are calculated: + .. math:: f_1(x(t))=\frac{1}{\left( b-a\right)}\int_a^b g \left(x_1(t)\right)dt,\dots, f_p(x(t))=\frac{1}{\left( b-a\right)}\int_a^b g \left(x_p(t)\right) dt + Args: data: FDataGrid or FDataBasis where we want to calculate - the expected value. - f: function that specifies how the expected value to is calculated. - It has to be a function of X(t). + the expected value. + function: function that specifies how the expected value to is + calculated. It has to be a function of X(t). + domain: Integration domain. By default, the whole domain is used. + Returns: ndarray of shape (n_dimensions, n_samples) with the values of the expectations. - Example: - We will use this funtion to calculate the logarithmic first moment - of the first 5 samples of the Berkeley Growth dataset. - We will start by importing it. - >>> from skfda.datasets import fetch_growth - >>> X = fetch_growth(return_X_y=True)[0] - - We will define a function that calculates the inverse first moment. - >>> import numpy as np - >>> f = lambda x: np.power(np.log(x), 1) - - Then we call the function with the dataset and the function. - >>> from skfda.exploratory.stats import unconditional_expected_value - >>> np.around(unconditional_expected_value(X[:5], f), decimals=2) - array([[ 4.96], - [ 4.88], - [ 4.85], - [ 4.9 ], - [ 4.84]]) + Examples: + We will use this funtion to calculate the logarithmic first moment + of the first 5 samples of the Berkeley Growth dataset. + We will start by importing it. + + >>> from skfda.datasets import fetch_growth + >>> X = fetch_growth(return_X_y=True)[0] + + We will define a function that calculates the inverse first moment. + + >>> import numpy as np + >>> f = lambda x: np.power(np.log(x), 1) + + Then we call the function with the dataset and the function. + + >>> from skfda.exploratory.stats import unconditional_expected_value + >>> np.around(unconditional_expected_value(X[:5], f), decimals=2) + array([[ 4.96], + [ 4.88], + [ 4.85], + [ 4.9 ], + [ 4.84]]) + """ + if domain is None: + domain = data.domain_range + else: + domain = _to_domain_range(domain) + lebesgue_measure = np.prod( [ (iterval[1] - iterval[0]) - for iterval in data.domain_range + for iterval in domain ], ) if isinstance(data, FDataGrid): - return function(data).integrate() / lebesgue_measure + return function(data).integrate(domain=domain) / lebesgue_measure def integrand(*args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 f1 = data(args)[:, 0, :] @@ -478,5 +561,5 @@ def integrand(*args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 return nquad_vec( integrand, - data.domain_range, + domain, ) / lebesgue_measure diff --git a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py index 470b4d06e..221ac35f7 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py @@ -6,15 +6,14 @@ import numpy as np import scipy.signal import sklearn.utils +from dcor import u_distance_correlation_sqr from sklearn.base import clone -from dcor import u_distance_correlation_sqr -from skfda._utils._sklearn_adapter import ( +from ...._utils import _compute_dependence, _DependenceMeasure +from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, ) - -from ...._utils import _compute_dependence, _DependenceMeasure from ....representation import FDataGrid from ....representation._typing import NDArrayFloat, NDArrayInt diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index ad188e57d..c46d312f0 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -1,64 +1,105 @@ """Function transformers for feature construction techniques.""" -from typing import Optional, Sequence, Tuple +from __future__ import annotations -from sklearn.base import BaseEstimator +from typing import Optional, Sequence, Tuple -from ..._utils import TransformerMixin +from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from ...exploratory.stats._functional_transformers import ( local_averages, number_up_crossings, occupation_measure, ) -from ...representation._typing import NDArrayFloat, Union +from ...representation import FData +from ...representation._typing import DomainRangeLike, NDArrayFloat, Union from ...representation.basis import FDataBasis from ...representation.grid import FDataGrid -class LocalAveragesTransformer(BaseEstimator, TransformerMixin): - """ - Transformer that works as an adapter for the local_averages function. +class LocalAveragesTransformer( + BaseEstimator, + TransformerMixin[FData, NDArrayFloat, object], +): + r""" + Transforms functional data to its local averages. + + It takes functional data and performs the following map: + + .. math:: + f_1(X) = \frac{1}{|T_1|} \int_{T_1} X(t) dt,\dots, \\ + f_p(X) = \frac{1}{|T_p|} \int_{T_p} X(t) dt + + where :math:`T_1, \dots, T_p` are subregions of the original + :term:`domain`. Args: - n_intervals: number of intervals we want to consider. + domains: Domains for each local average. It is possible to + pass a number or a list of numbers to automatically split + each dimension in that number of intervals and use them for + the averages. - Example: + See also: + :func:`local_averages` + + Examples: We import the Berkeley Growth Study dataset. We will use only the first 3 samples to make the example easy. + >>> from skfda.datasets import fetch_growth >>> dataset = fetch_growth(return_X_y=True)[0] >>> X = dataset[:3] - Then we decide how many intervals we want to consider (in our case 2) - and call the function with the dataset. + We can choose the intervals used for the local averages. For example, + we could in this case use the averages at different stages of + development of the child: from 1 to 3 years, from 3 to 10 and from + 10 to 18: + >>> import numpy as np >>> from skfda.preprocessing.feature_construction import ( ... LocalAveragesTransformer, ... ) - >>> local_averages = LocalAveragesTransformer(2) + >>> local_averages = LocalAveragesTransformer( + ... domains=[(1, 3), (3, 10), (10, 18)], + ... ) + >>> np.round(local_averages.fit_transform(X), decimals=2) + array([[ 91.37, 126.52, 179.02], + [ 87.51, 120.71, 158.81], + [ 86.36, 115.04, 156.37]]) + + A different possibility is to decide how many intervals we want to + consider. For example, we could want to split the domain in 2 + intervals of the same length. + + >>> local_averages = LocalAveragesTransformer(domains=2) >>> np.around(local_averages.fit_transform(X), decimals=2) array([[ 116.94, 177.26], [ 111.86, 157.62], [ 107.29, 154.97]]) """ - def __init__(self, n_intervals: int): - self.n_intervals = n_intervals + def __init__( + self, + *, + domains: int | Sequence[int] | Sequence[DomainRangeLike], + ) -> None: + self.domains = domains - def transform(self, X: Union[FDataGrid, FDataBasis]) -> NDArrayFloat: + def transform(self, X: FData, y: object = None) -> NDArrayFloat: """ - Transform the provided data using the local_averages function. + Transform the provided data to its local averages. Args: X: FDataGrid with the samples that are going to be transformed. + y: Unused. Returns: Array of shape (n_samples, n_intervals) including the transformed data. + """ return local_averages( X, - self.n_intervals, + domains=self.domains, ).reshape(X.data_matrix.shape[0], -1) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 74db54eba..37ccee841 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -22,10 +22,11 @@ overload, ) +from typing_extensions import Literal + import numpy as np import pandas.api.extensions from matplotlib.figure import Figure -from typing_extensions import Literal from .._utils import _evaluate_grid, _reshape_eval_points, _to_grid_points from ._typing import ( @@ -680,7 +681,7 @@ def derivative(self: T, *, order: int = 1) -> T: def integrate( self: T, *, - interval: Optional[DomainRange] = None, + domain: Optional[DomainRange] = None, ) -> NDArrayFloat: """ Integration of the FData object. @@ -692,8 +693,8 @@ def integrate( returned. Args: - interval: domain range where we want to integrate. - By default is None as we integrate on the whole domain. + domain: Domain range where we want to integrate. + By default is None as we integrate on the whole domain. Returns: NumPy array of size (``n_samples``, ``dim_codomain``) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index ff71d4d8b..5a2160b6c 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -17,7 +17,6 @@ import numpy as np import pandas.api.extensions - from skfda._utils._utils import _to_array_maybe_ragged from ..._utils import _check_array_key, _int_to_real, constants, nquad_vec @@ -361,7 +360,7 @@ def derivative(self: T, *, order: int = 1) -> T: # noqa: D102 def integrate( self: T, *, - interval: Optional[DomainRange] = None, + domain: Optional[DomainRange] = None, ) -> NDArrayFloat: """ Integration of the FData object. @@ -373,8 +372,8 @@ def integrate( returned. Args: - interval: domain range where we want to integrate. - By default is None as we integrate on the whole domain. + domain: Domain range where we want to integrate. + By default is None as we integrate on the whole domain. Returns: NumPy array of size (``n_samples``, ``dim_codomain``) @@ -392,16 +391,16 @@ def integrate( array([[ 2.625]]) Or we can do it on a given domain. - >>> fdata.integrate(interval = ((0.5, 1),)) + >>> fdata.integrate(domain=((0.5, 1),)) array([[ 1.8671875]]) """ - if interval is None: - interval = self.basis.domain_range + if domain is None: + domain = self.basis.domain_range integrated = nquad_vec( self, - interval, + domain, ) return integrated[0] diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 66be7f207..e05b97277 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -494,7 +494,7 @@ def derivative( def integrate( self: T, *, - interval: Optional[DomainRange] = None, + domain: Optional[DomainRange] = None, ) -> NDArrayFloat: """ Integration of the FData object. @@ -506,8 +506,8 @@ def integrate( returned. Args: - interval: domain range where we want to integrate. - By default is None as we integrate on the whole domain. + domain: Domain range where we want to integrate. + By default is None as we integrate on the whole domain. Returns: NumPy array of size (``n_samples``, ``dim_codomain``) @@ -518,8 +518,8 @@ def integrate( >>> fdata.integrate() array([[ 15.]]) """ - if interval is not None: - data = self.restrict(interval) + if domain is not None: + data = self.restrict(domain) else: data = self From e3cc9cae0f6bc5ab49dfcd481c726913c58f744d Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 24 Aug 2022 13:15:56 +0200 Subject: [PATCH 250/400] Add validation tools. --- skfda/_utils/__init__.py | 3 - skfda/_utils/_utils.py | 57 ------- skfda/_utils/_warping.py | 10 +- skfda/exploratory/stats/_fisher_rao.py | 9 +- .../stats/_functional_transformers.py | 61 +++---- skfda/exploratory/visualization/clustering.py | 8 +- skfda/misc/__init__.py | 1 + skfda/misc/operators/_srvf.py | 14 +- skfda/misc/validation.py | 153 ++++++++++++++++++ skfda/ml/clustering/_kmeans.py | 9 +- .../preprocessing/registration/_fisher_rao.py | 18 +-- .../registration/_lstsq_shift_registration.py | 8 +- .../preprocessing/registration/validation.py | 51 ++++-- 13 files changed, 278 insertions(+), 124 deletions(-) create mode 100644 skfda/misc/validation.py diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index c80e233b5..504f2facd 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -8,8 +8,6 @@ RandomStateLike, _cartesian_product, _check_array_key, - _check_compatible_fdata, - _check_compatible_fdatagrid, _check_estimator, _classifier_fit_depth_methods, _classifier_get_classes, @@ -26,7 +24,6 @@ _to_domain_range, _to_grid, _to_grid_points, - check_is_univariate, nquad_vec, ) from ._warping import invert_warping, normalize_scale, normalize_warping diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 0284ae5a3..d4a67abb3 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -54,63 +54,6 @@ Target = TypeVar("Target", bound=NDArrayInt) -def check_is_univariate(fd: FData) -> None: - """Check if an FData is univariate and raises an error. - - Args: - fd: Functional object to check if is univariate. - - Raises: - ValueError: If it is not univariate, i.e., `fd.dim_domain != 1` or - `fd.dim_codomain != 1`. - - """ - if fd.dim_domain != 1 or fd.dim_codomain != 1: - domain_str = ( - "" if fd.dim_domain == 1 - else f"(currently is {fd.dim_domain}) " - ) - - codomain_str = ( - "" if fd.dim_codomain == 1 - else f"(currently is {fd.dim_codomain})" - ) - - raise ValueError( - f"The functional data must be univariate, i.e., " - f"with dim_domain=1 {domain_str}" - f"and dim_codomain=1 {codomain_str}", - ) - - -def _check_compatible_fdata(fdata1: FData, fdata2: FData) -> None: - """Check that two FData are compatible.""" - if (fdata1.dim_domain != fdata2.dim_domain): - raise ValueError( - f"Functional data has incompatible domain dimensions: " - f"{fdata1.dim_domain} != {fdata2.dim_domain}", - ) - - if (fdata1.dim_codomain != fdata2.dim_codomain): - raise ValueError( - f"Functional data has incompatible codomain dimensions: " - f"{fdata1.dim_codomain} != {fdata2.dim_codomain}", - ) - - -def _check_compatible_fdatagrid(fdata1: FDataGrid, fdata2: FDataGrid) -> None: - """Check that two FDataGrid are compatible.""" - _check_compatible_fdata(fdata1, fdata2) - if not all( - np.array_equal(g1, g2) - for g1, g2 in zip(fdata1.grid_points, fdata2.grid_points) - ): - raise ValueError( - f"Incompatible grid points between template and " - f"data: {fdata1.grid_points} != {fdata2.grid_points}", - ) - - def _to_grid( X: FData, y: FData, diff --git a/skfda/_utils/_warping.py b/skfda/_utils/_warping.py index 82dfd149e..c960234bc 100644 --- a/skfda/_utils/_warping.py +++ b/skfda/_utils/_warping.py @@ -10,7 +10,7 @@ from scipy.interpolate import PchipInterpolator from ..representation._typing import ArrayLike, DomainRangeLike, NDArrayFloat -from ._utils import _to_domain_range, check_is_univariate +from ._utils import _to_domain_range if TYPE_CHECKING: from ..representation import FDataGrid @@ -74,7 +74,13 @@ def invert_warping( [ 1. ]]]) """ - check_is_univariate(warping) + from ..misc.validation import check_fdata_dimensions + + check_fdata_dimensions( + warping, + dim_domain=1, + dim_codomain=1, + ) output_points = ( warping.grid_points[0] diff --git a/skfda/exploratory/stats/_fisher_rao.py b/skfda/exploratory/stats/_fisher_rao.py index fb0167ec7..023312488 100644 --- a/skfda/exploratory/stats/_fisher_rao.py +++ b/skfda/exploratory/stats/_fisher_rao.py @@ -6,8 +6,9 @@ import scipy.integrate from fdasrsf.utility_functions import optimum_reparam -from ..._utils import check_is_univariate, invert_warping, normalize_scale +from ..._utils import invert_warping, normalize_scale from ...misc.operators import SRSF +from ...misc.validation import check_fdata_dimensions from ...representation import FDataGrid from ...representation._typing import NDArrayFloat from ...representation.interpolation import SplineInterpolation @@ -239,7 +240,11 @@ def fisher_rao_karcher_mean( .. footbibliography:: """ - check_is_univariate(fdatagrid) + check_fdata_dimensions( + fdatagrid, + dim_domain=1, + dim_codomain=1, + ) srsf_transformer = SRSF(initial_value=0) fdatagrid_srsf = srsf_transformer.fit_transform(fdatagrid) diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index e11fc6816..dd4cd8a33 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -5,11 +5,11 @@ import itertools from typing import Optional, Sequence, Tuple, TypeVar, Union, cast -from typing_extensions import Protocol, TypeGuard - import numpy as np +from typing_extensions import Protocol, TypeGuard -from ..._utils import _to_domain_range, check_is_univariate, nquad_vec +from ..._utils import _to_domain_range, nquad_vec +from ...misc.validation import check_fdata_dimensions from ...representation import FData, FDataBasis, FDataGrid from ...representation._typing import ( ArrayLike, @@ -184,7 +184,7 @@ def _calculate_curves_occupation( def occupation_measure( - data: Union[FDataGrid, FDataBasis], + data: FData, intervals: Sequence[Tuple[float, float]], *, n_points: Optional[int] = None, @@ -192,34 +192,33 @@ def occupation_measure( r""" Calculate the occupation measure of a grid. - It performs the following map. - ..math: - :math:`f_1(X) = |t: X(t)\in T_p|,\dots,|t: X(t)\in T_p|` + It performs the following map: - where :math:`{T_1,\dots,T_p}` are disjoint intervals in - :math:`\mathbb{R}` and | | stands for the Lebesgue measure. + ..math: + :math:`f_1(X) = |t: X(t)\in T_p|,\dots,|t: X(t)\in T_p|` - The calculations are based on the grid of points of the x axis. In case of - FDataGrid the original grid is taken unless n_points is specified. In case - of FDataBasis the number of points of the x axis to be considered is passed - through the n_points parameter compulsory. - If the result of this function is not accurate enough try to increase the - grid of points of the x axis. Either by increasing n_points or passing a - FDataGrid with more x grid points per curve. + where :math:`{T_1,\dots,T_p}` are disjoint intervals in + :math:`\mathbb{R}` and | | stands for the Lebesgue measure. + The calculations are based on evaluation at a grid of points. In case of + :class:`FDataGrid` the original grid is taken unless ``n_points`` is + specified. In case of :class:`FDataBasis` it is mandatory to pass the + number of points. If the result of this function is not accurate enough + try to increase the grid of points. - Args: - data: FDataGrid or FDataBasis where we want to calculate - the occupation measure. - intervals: ndarray of tuples containing the - intervals we want to consider. The shape should be - (n_sequences,2) - n_points: Number of points to evaluate in the domain. - By default will be used the points defined on the FDataGrid. - On a FDataBasis this value should be specified. - Returns: - ndarray of shape (n_samples, n_intervals) - with the transformed data. + Args: + data: Functional data where we want to calculate the occupation + measure. + intervals: ndarray of tuples containing the + intervals we want to consider. The shape should be + (n_sequences,2) + n_points: Number of points to evaluate in the domain. + By default will be used the points defined on the FDataGrid. + On a FDataBasis this value should be specified. + + Returns: + ndarray of shape (n_samples, n_intervals) + with the transformed data. Examples: We will create the FDataGrid that we will use to extract @@ -263,7 +262,11 @@ def occupation_measure( + " as an argument for a FDataBasis. Instead None was passed.", ) - check_is_univariate(data) + check_fdata_dimensions( + data, + dim_domain=1, + dim_codomain=1, + ) if n_points is None: function_x_coordinates = data.grid_points[0] diff --git a/skfda/exploratory/visualization/clustering.py b/skfda/exploratory/visualization/clustering.py index 603bbd7c5..d7a494db9 100644 --- a/skfda/exploratory/visualization/clustering.py +++ b/skfda/exploratory/visualization/clustering.py @@ -18,7 +18,7 @@ from sklearn.utils.validation import check_is_fitted from typing_extensions import Protocol -from ..._utils import _check_compatible_fdata +from ...misc.validation import check_fdata_same_dimensions from ...representation import FData, FDataGrid from ...representation._typing import NDArrayFloat, NDArrayInt from ._baseplot import BasePlot @@ -325,7 +325,7 @@ def _plot( try: check_is_fitted(self.estimator) - _check_compatible_fdata( + check_fdata_same_dimensions( self.estimator.cluster_centers_, self.fdata, ) @@ -414,7 +414,7 @@ def _plot( try: check_is_fitted(self.estimator) - _check_compatible_fdata( + check_fdata_same_dimensions( self.estimator.cluster_centers_, self.fdata, ) @@ -565,7 +565,7 @@ def _plot( try: check_is_fitted(self.estimator) - _check_compatible_fdata( + check_fdata_same_dimensions( self.estimator.cluster_centers_, self.fdata, ) diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 139d717a5..6e0045c10 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -7,6 +7,7 @@ metrics, operators, regularization, + validation, ) from ._math import ( cosine_similarity, diff --git a/skfda/misc/operators/_srvf.py b/skfda/misc/operators/_srvf.py index d8720653c..bf974caba 100644 --- a/skfda/misc/operators/_srvf.py +++ b/skfda/misc/operators/_srvf.py @@ -6,7 +6,7 @@ import scipy.integrate from sklearn.base import BaseEstimator, TransformerMixin -from ..._utils import check_is_univariate +from ...misc.validation import check_fdata_dimensions from ...representation import FDataGrid from ...representation._typing import ArrayLike from ...representation.basis import Basis @@ -150,7 +150,11 @@ def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: ValueError: If functions are not univariate. """ - check_is_univariate(X) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) if self.output_points is None: output_points = X.grid_points[0] @@ -200,7 +204,11 @@ def inverse_transform(self, X: FDataGrid, y: None = None) -> FDataGrid: Raises: ValueError: If functions are multidimensional. """ - check_is_univariate(X) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) stored_initial_value = getattr(self, 'initial_value_', None) diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py new file mode 100644 index 000000000..25dc72dfd --- /dev/null +++ b/skfda/misc/validation.py @@ -0,0 +1,153 @@ +"""Validation utilities.""" + +from __future__ import annotations + +import functools +from typing import Container + +import numpy as np + +from ..representation import FData, FDataBasis, FDataGrid + + +def check_fdata_dimensions( + fd: FData, + *, + dim_domain: int | Container[int] | None = None, + dim_codomain: int | Container[int] | None = None, +) -> None: + """ + Check that a functional data object have appropriate dimensions. + + Args: + fd: Functional data object to check. + dim_domain: Allowed dimension(s) of the :term:`domain`. + dim_codomain: Allowed dimension(s) of the :term:`codomain`. + + Raises: + ValueError: If the data has not the requested dimensions. + + """ + if isinstance(dim_domain, int): + dim_domain = {dim_domain} + + if isinstance(dim_codomain, int): + dim_codomain = {dim_codomain} + + if dim_domain is not None: + + if fd.dim_domain not in dim_domain: + raise ValueError( + "Invalid domain dimension for functional data object:" + f"{fd.dim_domain} not in {dim_domain}.", + ) + + if dim_codomain is not None: + + if fd.dim_codomain not in dim_codomain: + raise ValueError( + "Invalid domain dimension for functional data object:" + f"{fd.dim_codomain} not in {dim_codomain}.", + ) + + +def check_fdata_same_dimensions( + fdata1: FData, + fdata2: FData, +) -> None: + """ + Check that two FData have the same dimensions. + + Args: + fdata1: First functional data object. + fdata2: Second functional data object. + + Raises: + ValueError: If the dimensions don't agree. + + """ + if (fdata1.dim_domain != fdata2.dim_domain): + raise ValueError( + f"Functional data has incompatible domain dimensions: " + f"{fdata1.dim_domain} != {fdata2.dim_domain}", + ) + + if (fdata1.dim_codomain != fdata2.dim_codomain): + raise ValueError( + f"Functional data has incompatible codomain dimensions: " + f"{fdata1.dim_codomain} != {fdata2.dim_codomain}", + ) + + +@functools.singledispatch +def _check_fdata_same_kind_specific( + fdata1: FData, + fdata2: FData, +) -> None: + """Specific comparisons for subclasses.""" + + +@_check_fdata_same_kind_specific.register +def _check_fdatagrid_same_kind_specific( + fdata1: FDataGrid, + fdata2: FData, +) -> None: + + # First we do an identity comparison to speed up the common case + if fdata1.grid_points is not fdata2.grid_points: + if not all( + np.array_equal(g1, g2) + for g1, g2 in zip(fdata1.grid_points, fdata2.grid_points) + ): + raise ValueError( + f"Incompatible grid points between functional data objects:" + f"{fdata1.grid_points} != {fdata2.grid_points}", + ) + + +@_check_fdata_same_kind_specific.register +def _check_fdatabasis_same_kind_specific( + fdata1: FDataBasis, + fdata2: FData, +) -> None: + + if fdata1.basis != fdata2.basis: + raise ValueError( + f"Incompatible basis between functional data objects:" + f"{fdata1.basis} != {fdata2.basis}", + ) + + +def check_fdata_same_kind( + fdata1: FData, + fdata2: FData, +) -> None: + """ + Check that two functional objects are of the same kind. + + This compares everything to ensure that the data is compatible: dimensions, + grids, basis, when applicable. Every parameter that must be fixed for all + samples should be the same. + + In other words: it should be possible to concatenate the two functional + objects together. + + Args: + fdata1: First functional data object. + fdata2: Second functional data object. + + Raises: + ValueError: If some attributes don't agree. + + """ + check_fdata_same_dimensions(fdata1, fdata2) + + # If the second is a subclass, reverse the order + if isinstance(fdata2, type(fdata1)): + fdata1, fdata2 = fdata2, fdata1 + + _check_fdata_same_kind_specific(fdata1, fdata2) + + # If there is no subclassing, execute both checks + if not isinstance(fdata1, type(fdata2)): + _check_fdata_same_kind_specific(fdata2, fdata1) diff --git a/skfda/ml/clustering/_kmeans.py b/skfda/ml/clustering/_kmeans.py index d5138c034..3e90e3cd8 100644 --- a/skfda/ml/clustering/_kmeans.py +++ b/skfda/ml/clustering/_kmeans.py @@ -11,8 +11,9 @@ from sklearn.utils import check_random_state from sklearn.utils.validation import check_is_fitted -from ..._utils import RandomStateLike, _check_compatible_fdata +from ..._utils import RandomStateLike from ...misc.metrics import Metric, PairwiseMetric, l2_distance +from ...misc.validation import check_fdata_same_dimensions from ...representation import FDataGrid from ...representation._typing import NDArrayAny, NDArrayFloat, NDArrayInt @@ -364,7 +365,7 @@ def _predict_membership( """ check_is_fitted(self) - _check_compatible_fdata(self.cluster_centers_, X) + check_fdata_same_dimensions(self.cluster_centers_, X) membership_matrix = self._create_membership(X.n_samples) centroids = self.cluster_centers_.copy() @@ -421,7 +422,7 @@ def transform(self, X: FDataGrid) -> NDArrayFloat: """ check_is_fitted(self) - _check_compatible_fdata(self.cluster_centers_, X) + check_fdata_same_dimensions(self.cluster_centers_, X) return self._distances_to_centers def fit_transform( @@ -464,7 +465,7 @@ def score( """ check_is_fitted(self) - _check_compatible_fdata(self.cluster_centers_, X) + check_fdata_same_dimensions(self.cluster_centers_, X) return -self.inertia_ diff --git a/skfda/preprocessing/registration/_fisher_rao.py b/skfda/preprocessing/registration/_fisher_rao.py index 715114aaa..89f11ef4e 100644 --- a/skfda/preprocessing/registration/_fisher_rao.py +++ b/skfda/preprocessing/registration/_fisher_rao.py @@ -7,15 +7,11 @@ from sklearn.utils.validation import check_is_fitted from ... import FDataGrid -from ..._utils import ( - _check_compatible_fdatagrid, - check_is_univariate, - invert_warping, - normalize_scale, -) +from ..._utils import invert_warping, normalize_scale from ...exploratory.stats import fisher_rao_karcher_mean from ...exploratory.stats._fisher_rao import _elastic_alignment_array from ...misc.operators import SRSF +from ...misc.validation import check_fdata_dimensions, check_fdata_same_kind from ...representation._typing import ArrayLike from ...representation.basis import Basis from ...representation.interpolation import SplineInterpolation @@ -145,7 +141,7 @@ def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: else: self.template_ = self.template(X) - _check_compatible_fdatagrid(X, self.template_) + check_fdata_same_kind(X, self.template_) # Constructs the SRSF of the template self._srsf = SRSF( @@ -160,8 +156,12 @@ def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: check_is_fitted(self) - check_is_univariate(X) - _check_compatible_fdatagrid(X, self.template_) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) + check_fdata_same_kind(X, self.template_) if ( len(self._template_srsf) != 1 diff --git a/skfda/preprocessing/registration/_lstsq_shift_registration.py b/skfda/preprocessing/registration/_lstsq_shift_registration.py index 0df35017d..efced65ce 100644 --- a/skfda/preprocessing/registration/_lstsq_shift_registration.py +++ b/skfda/preprocessing/registration/_lstsq_shift_registration.py @@ -9,9 +9,9 @@ from typing_extensions import Literal from ... import FData, FDataGrid -from ..._utils import check_is_univariate from ...misc._math import inner_product from ...misc.metrics._lp_norms import l2_norm +from ...misc.validation import check_fdata_dimensions from ...representation._typing import ArrayLike, GridPointsLike, NDArrayFloat from ...representation.extrapolation import ExtrapolationLike from .base import InductiveRegistrationTransformer @@ -173,7 +173,11 @@ def _compute_deltas( A tuple with an array of deltas and an FDataGrid with the template. """ - check_is_univariate(fd) + check_fdata_dimensions( + fd, + dim_domain=1, + dim_codomain=1, + ) domain_range = fd.domain_range[0] diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 61e99eb3d..4ddcd4501 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -7,7 +7,8 @@ import numpy as np -from ..._utils import _to_grid, check_is_univariate +from ..._utils import _to_grid +from ...misc.validation import check_fdata_dimensions from ...representation import FData from .base import RegistrationTransformer @@ -272,8 +273,16 @@ def stats( """ from ...misc.metrics import l2_distance, l2_norm - check_is_univariate(X) - check_is_univariate(y) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) + check_fdata_dimensions( + y, + dim_domain=1, + dim_codomain=1, + ) if len(y) != len(X): raise ValueError( @@ -410,8 +419,16 @@ def score_function(self, X: FData, y: FData) -> float: """ from ...misc.metrics import l2_distance - check_is_univariate(X) - check_is_univariate(y) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) + check_fdata_dimensions( + y, + dim_domain=1, + dim_codomain=1, + ) # Instead of compute f_i - 1/(N-1) sum(j!=i)f_j for each i = 1 ... N # It is used (1 + 1/(N-1))f_i - 1/(N-1) sum(j=1 ... N) f_j = @@ -523,8 +540,16 @@ def score_function(self, X: FData, y: FData) -> float: """ from ...misc.metrics import l2_distance - check_is_univariate(X) - check_is_univariate(y) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) + check_fdata_dimensions( + y, + dim_domain=1, + dim_codomain=1, + ) # Compute derivative X = X.derivative() @@ -619,8 +644,16 @@ def score_function(self, X: FData, y: FData) -> float: float: Score of the transformation. """ - check_is_univariate(X) - check_is_univariate(y) + check_fdata_dimensions( + X, + dim_domain=1, + dim_codomain=1, + ) + check_fdata_dimensions( + y, + dim_domain=1, + dim_codomain=1, + ) # Discretize functional data if needed X, y = _to_grid(X, y, eval_points=self.eval_points) From 7ad5d7cf6083b531e30271f9faf4c39826acef64 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 24 Aug 2022 21:08:17 +0200 Subject: [PATCH 251/400] Fix Mypy errors. --- setup.cfg | 2 + skfda/_utils/__init__.py | 3 - skfda/_utils/_utils.py | 120 +++----- skfda/ml/classification/_depth_classifiers.py | 29 +- .../_per_class_transformer.py | 29 +- skfda/preprocessing/smoothing/_basis.py | 4 +- skfda/representation/_functional_data.py | 283 +++++++----------- skfda/representation/_typing.py | 5 +- skfda/representation/basis/_basis.py | 111 ++++--- skfda/representation/basis/_bspline.py | 53 ++-- .../basis/_coefficients_transformer.py | 10 +- skfda/representation/basis/_constant.py | 10 +- skfda/representation/basis/_fdatabasis.py | 13 +- skfda/representation/basis/_finite_element.py | 12 +- skfda/representation/basis/_fourier.py | 25 +- skfda/representation/basis/_monomial.py | 11 +- skfda/representation/basis/_tensor_basis.py | 8 +- skfda/representation/basis/_vector_basis.py | 24 +- skfda/representation/evaluator.py | 25 +- skfda/representation/extrapolation.py | 14 +- skfda/representation/grid.py | 6 +- skfda/tests/test_fdatabasis_evaluation.py | 3 - skfda/tests/test_neighbors.py | 2 +- 23 files changed, 405 insertions(+), 397 deletions(-) diff --git a/setup.cfg b/setup.cfg index 1496b3537..574192ca9 100644 --- a/setup.cfg +++ b/setup.cfg @@ -45,6 +45,8 @@ ignore = WPS232, # The number of imported things may be large, especially for typing WPS235, + # The allowed complexity in f-strings is too low + WPS237, # We like local imports, thanks WPS300, # Dotted imports are ok diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 504f2facd..ac002cd94 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -9,13 +9,10 @@ _cartesian_product, _check_array_key, _check_estimator, - _classifier_fit_depth_methods, _classifier_get_classes, - _classifier_get_depth_methods, _compute_dependence, _DependenceMeasure, _evaluate_grid, - _fit_feature_transformer, _int_to_real, _pairwise_symmetric, _reshape_eval_points, diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index d4a67abb3..a8da14aab 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -19,15 +19,13 @@ overload, ) -from typing_extensions import Literal, Protocol - import numpy as np import scipy.integrate from numpy import ndarray from pandas.api.indexers import check_array_indexer -from sklearn.base import clone from sklearn.preprocessing import LabelEncoder from sklearn.utils.multiclass import check_classification_targets +from typing_extensions import Literal, Protocol from ..representation._typing import ( ArrayLike, @@ -38,13 +36,15 @@ NDArrayAny, NDArrayFloat, NDArrayInt, + NDArrayObject, ) from ..representation.extrapolation import ExtrapolationLike RandomStateLike = Optional[Union[int, np.random.RandomState]] +ArrayT = TypeVar("ArrayT", bound=NDArrayAny) + if TYPE_CHECKING: - from ..exploratory.depth import Depth from ..representation import FData, FDataGrid from ..representation.basis import Basis T = TypeVar("T", bound=FData) @@ -127,7 +127,7 @@ def _to_array_maybe_ragged( array: Iterable[ArrayLike], *, row_shape: Optional[Sequence[int]] = None, -) -> np.ndarray: +) -> NDArrayFloat | NDArrayObject: """ Convert to an array where each element may or may not be of equal length. @@ -135,7 +135,7 @@ def _to_array_maybe_ragged( Otherwise it is a ragged array. """ - def convert_row(row: ArrayLike) -> np.ndarray: + def convert_row(row: ArrayLike) -> NDArrayFloat: r = np.array(row) if row_shape is not None: @@ -159,30 +159,30 @@ def convert_row(row: ArrayLike) -> np.ndarray: @overload def _cartesian_product( - axes: Sequence[np.ndarray], + axes: Sequence[ArrayT], *, flatten: bool = True, return_shape: Literal[False] = False, -) -> np.ndarray: +) -> ArrayT: pass @overload def _cartesian_product( - axes: Sequence[np.ndarray], + axes: Sequence[ArrayT], *, flatten: bool = True, return_shape: Literal[True], -) -> Tuple[np.ndarray, Tuple[int, ...]]: +) -> Tuple[ArrayT, Tuple[int, ...]]: pass def _cartesian_product( # noqa: WPS234 - axes: Sequence[np.ndarray], + axes: Sequence[ArrayT], *, flatten: bool = True, return_shape: bool = False, -) -> Union[np.ndarray, Tuple[np.ndarray, Tuple[int, ...]]]: +) -> Union[ArrayT, Tuple[ArrayT, Tuple[int, ...]]]: """ Compute the cartesian product of the axes. @@ -234,7 +234,7 @@ def _cartesian_product( # noqa: WPS234 if return_shape: return cartesian, shape - return cartesian + return cartesian # type: ignore[no-any-return] def _same_domain(fd: Union[Basis, FData], fd2: Union[Basis, FData]) -> bool: @@ -257,10 +257,10 @@ def _reshape_eval_points( def _reshape_eval_points( eval_points: Sequence[ArrayLike], *, - aligned: Literal[True], + aligned: Literal[False], n_samples: int, dim_domain: int, -) -> NDArrayFloat: +) -> NDArrayFloat | NDArrayObject: pass @@ -271,7 +271,7 @@ def _reshape_eval_points( aligned: bool, n_samples: int, dim_domain: int, -) -> NDArrayFloat: +) -> NDArrayFloat | NDArrayObject: pass @@ -281,7 +281,7 @@ def _reshape_eval_points( aligned: bool, n_samples: int, dim_domain: int, -) -> NDArrayFloat: +) -> NDArrayFloat | NDArrayObject: """Convert and reshape the eval_points to ndarray. Args: @@ -397,7 +397,21 @@ def _evaluate_grid( dim_codomain: int, extrapolation: Optional[ExtrapolationLike] = None, aligned: Literal[False], -) -> NDArrayFloat: +) -> NDArrayFloat | NDArrayObject: + pass + + +@overload +def _evaluate_grid( + axes: Union[GridPointsLike, Iterable[GridPointsLike]], + *, + evaluate_method: EvaluateMethod, + n_samples: int, + dim_domain: int, + dim_codomain: int, + extrapolation: Optional[ExtrapolationLike] = None, + aligned: bool, +) -> NDArrayFloat | NDArrayObject: pass @@ -410,7 +424,7 @@ def _evaluate_grid( # noqa: WPS234 dim_codomain: int, extrapolation: Optional[ExtrapolationLike] = None, aligned: bool = True, -) -> NDArrayFloat: +) -> NDArrayFloat | NDArrayObject: """ Evaluate the functional object in the cartesian grid. @@ -526,14 +540,14 @@ def integrate(*args: Any, depth: int) -> NDArrayFloat: # noqa: WPS430 else: f = functools.partial(integrate, *args, depth=depth - 1) - return scipy.integrate.quad_vec(f, *ranges[initial_depth - depth])[0] + return scipy.integrate.quad_vec( # type: ignore[no-any-return] + f, + *ranges[initial_depth - depth], + )[0] return integrate(depth=initial_depth) -ArrayT = TypeVar("ArrayT", bound=NDArrayAny) - - def _map_in_batches( function: Callable[..., ArrayT], arguments: Tuple[Union[FData, NDArrayAny], ...], @@ -571,7 +585,7 @@ def _map_in_batches( batches.append(function(*batch_args, **kwargs)) - return np.concatenate(batches, axis=0) + return np.concatenate(batches, axis=0) # type: ignore[return-value] def _pairwise_symmetric( @@ -586,8 +600,6 @@ def _pairwise_symmetric( if arg2 is None or arg2 is arg1: indices = np.triu_indices(dim1) - matrix = np.empty((dim1, dim1)) - triang_vec = _map_in_batches( function, (arg1, arg1), @@ -596,6 +608,8 @@ def _pairwise_symmetric( **kwargs, ) + matrix = np.empty((dim1, dim1), dtype=triang_vec.dtype) + # Set upper matrix matrix[indices] = triang_vec @@ -674,58 +688,12 @@ def _classifier_get_classes(y: ndarray) -> Tuple[ndarray, NDArrayInt]: return classes, y_ind -def _classifier_get_depth_methods( - classes: ndarray, - X: T, - y_ind: ndarray, - depth_methods: Sequence[Depth[T]], -) -> Sequence[Depth[T]]: - return [ - clone(depth_method).fit(X[y_ind == cur_class]) - for cur_class in range(classes.size) - for depth_method in depth_methods - ] - - -def _classifier_fit_depth_methods( - X: T, - y: ndarray, - depth_methods: Sequence[Depth[T]], -) -> Tuple[ndarray, Sequence[Depth[T]]]: - classes, y_ind = _classifier_get_classes(y) - - class_depth_methods_ = _classifier_get_depth_methods( - classes, X, y_ind, depth_methods, - ) - - return classes, class_depth_methods_ - - -def _fit_feature_transformer( # noqa: WPS320 WPS234 - X: Input, - y: Target, - transformer: TransformerMixin[Input, Output, Target], -) -> Tuple[ - Union[NDArrayInt, NDArrayFloat], - Sequence[TransformerMixin[Input, Output, Target]], -]: - - classes, y_ind = _classifier_get_classes(y) - - class_feature_transformers = [ - clone(transformer).fit(X[y_ind == cur_class], y[y_ind == cur_class]) - for cur_class, _ in enumerate(classes) - ] - - return classes, class_feature_transformers - - _DependenceMeasure = Callable[[np.ndarray, np.ndarray], np.ndarray] def _compute_dependence( - X: Union[NDArrayInt, NDArrayFloat], - y: Union[NDArrayInt, NDArrayFloat], + X: NDArrayFloat, + y: NDArrayFloat, *, dependence_measure: _DependenceMeasure, ) -> NDArrayFloat: @@ -753,4 +721,6 @@ def _compute_dependence( dependence_results = rowwise(dependence_measure, X, Y) - return dependence_results.reshape(input_shape) + return dependence_results.reshape( # type: ignore[no-any-return] + input_shape, + ) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index 3db927cea..790f8bfbd 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -23,7 +23,7 @@ from sklearn.pipeline import FeatureUnion, make_pipeline from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted -from ..._utils import _classifier_fit_depth_methods, _classifier_get_classes +from ..._utils import _classifier_get_classes from ...exploratory.depth import Depth, ModifiedBandDepth from ...preprocessing.feature_construction._per_class_transformer import ( PerClassTransformer, @@ -34,6 +34,33 @@ T = TypeVar("T", bound=FData) +def _classifier_get_depth_methods( + classes: NDArrayInt, + X: T, + y_ind: NDArrayInt, + depth_methods: Sequence[Depth[T]], +) -> Sequence[Depth[T]]: + return [ + clone(depth_method).fit(X[y_ind == cur_class]) + for cur_class in range(classes.size) + for depth_method in depth_methods + ] + + +def _classifier_fit_depth_methods( + X: T, + y: NDArrayInt, + depth_methods: Sequence[Depth[T]], +) -> Tuple[NDArrayInt, Sequence[Depth[T]]]: + classes, y_ind = _classifier_get_classes(y) + + class_depth_methods_ = _classifier_get_depth_methods( + classes, X, y_ind, depth_methods, + ) + + return classes, class_depth_methods_ + + class DDClassifier( BaseEstimator, # type: ignore ClassifierMixin, # type: ignore diff --git a/skfda/preprocessing/feature_construction/_per_class_transformer.py b/skfda/preprocessing/feature_construction/_per_class_transformer.py index ffdd20401..b53683f2b 100644 --- a/skfda/preprocessing/feature_construction/_per_class_transformer.py +++ b/skfda/preprocessing/feature_construction/_per_class_transformer.py @@ -2,13 +2,15 @@ from __future__ import annotations import warnings -from typing import Any, Mapping, TypeVar, Union +from typing import Any, Mapping, Sequence, Tuple, TypeVar, Union import numpy as np import pandas as pd +from sklearn.base import clone from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted -from ..._utils import TransformerMixin, _fit_feature_transformer +from ..._utils import _classifier_get_classes +from ..._utils._sklearn_adapter import TransformerMixin from ...representation import FData from ...representation._typing import NDArrayFloat, NDArrayInt from ...representation.basis import FDataBasis @@ -21,6 +23,25 @@ TransformerOutput = Union[FData, NDArrayFloat] +def _fit_feature_transformer( # noqa: WPS320 WPS234 + X: Input, + y: Target, + transformer: TransformerMixin[Input, Output, Target], +) -> Tuple[ + Union[NDArrayInt, NDArrayFloat], + Sequence[TransformerMixin[Input, Output, Target]], +]: + + classes, y_ind = _classifier_get_classes(y) + + class_feature_transformers = [ + clone(transformer).fit(X[y_ind == cur_class], y[y_ind == cur_class]) + for cur_class, _ in enumerate(classes) + ] + + return classes, class_feature_transformers + + class PerClassTransformer(TransformerMixin[Input, Output, Target]): r"""Per class feature transformer for functional data. @@ -187,7 +208,7 @@ def _validate_transformer( if tags['stateless']: warnings.warn( - f"Parameter 'transformer' with type " # noqa:WPS237 + f"Parameter 'transformer' with type " f"{type(self.transformer)} should use the data for " f" fitting." f"It should have the 'stateless' tag set to 'False'", @@ -195,7 +216,7 @@ def _validate_transformer( if tags['requires_y']: warnings.warn( - f"Parameter 'transformer' with type " # noqa: WPS237 + f"Parameter 'transformer' with type " f"{type(self.transformer)} should not use the class label." f"It should have the 'requires_y' tag set to 'False'", ) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 21b089a9c..a9b00e27a 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -231,7 +231,7 @@ def _coef_matrix( """Get the matrix that gives the coefficients.""" from ...misc.regularization import compute_penalty_matrix - basis_values_input = self.basis.evaluate( + basis_values_input = self.basis( _cartesian_product(_to_grid_points(input_points)), ).reshape((self.basis.n_basis, -1)).T @@ -259,7 +259,7 @@ def _hat_matrix( input_points: GridPointsLike, output_points: GridPointsLike, ) -> NDArrayFloat: - basis_values_output = self.basis.evaluate( + basis_values_output = self.basis( _cartesian_product( _to_grid_points(output_points), ), diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 37ccee841..c79800adc 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -22,11 +22,10 @@ overload, ) -from typing_extensions import Literal - import numpy as np import pandas.api.extensions from matplotlib.figure import Figure +from typing_extensions import Literal from .._utils import _evaluate_grid, _reshape_eval_points, _to_grid_points from ._typing import ( @@ -38,6 +37,7 @@ NDArrayBool, NDArrayFloat, NDArrayInt, + NDArrayObject, ) from .evaluator import Evaluator from .extrapolation import ExtrapolationLike, _parse_extrapolation @@ -58,7 +58,7 @@ class FData( # noqa: WPS214 ABC, - pandas.api.extensions.ExtensionArray, # type: ignore + pandas.api.extensions.ExtensionArray, # type: ignore[misc] ): """Defines the structure of a functional data object. @@ -80,42 +80,17 @@ def __init__( *, extrapolation: Optional[ExtrapolationLike] = None, dataset_name: Optional[str] = None, - dataset_label: Optional[str] = None, - axes_labels: Optional[LabelTupleLike] = None, argument_names: Optional[LabelTupleLike] = None, coordinate_names: Optional[LabelTupleLike] = None, sample_names: Optional[LabelTupleLike] = None, ) -> None: - self.extrapolation = extrapolation # type: ignore + self.extrapolation = extrapolation # type: ignore[assignment] self.dataset_name = dataset_name - if dataset_label is not None: - self.dataset_label = dataset_label - - self.argument_names = argument_names # type: ignore - self.coordinate_names = coordinate_names # type: ignore - if axes_labels is not None: - self.axes_labels = axes_labels # type: ignore - self.sample_names = sample_names # type: ignore - - @property - def dataset_label(self) -> Optional[str]: - warnings.warn( - "Parameter dataset_label is deprecated. Use the " - "parameter dataset_name instead.", - DeprecationWarning, - ) - return self.dataset_name - - @dataset_label.setter - def dataset_label(self, name: Optional[str]) -> None: - warnings.warn( - "Parameter dataset_label is deprecated. Use the " - "parameter dataset_name instead.", - DeprecationWarning, - ) - self.dataset_name = name + self.argument_names = argument_names # type: ignore[assignment] + self.coordinate_names = coordinate_names # type: ignore[assignment] + self.sample_names = sample_names # type: ignore[assignment] @property def argument_names(self) -> LabelTuple: @@ -159,45 +134,6 @@ def coordinate_names( self._coordinate_names = names - @property - def axes_labels(self) -> LabelTuple: - warnings.warn( - "Parameter axes_labels is deprecated. Use the " - "parameters argument_names and " - "coordinate_names instead.", - DeprecationWarning, - ) - - return self.argument_names + self.coordinate_names - - @axes_labels.setter - def axes_labels(self, labels: LabelTupleLike) -> None: - """Set the list of labels.""" - if labels is not None: - - warnings.warn( - "Parameter axes_labels is deprecated. Use the " - "parameters argument_names and " - "coordinate_names instead.", - DeprecationWarning, - ) - - labels_array = np.asarray(labels) - if len(labels_array) > (self.dim_domain + self.dim_codomain): - raise ValueError( - "There must be a label for each of the " - "dimensions of the domain and the image.", - ) - if len(labels_array) < (self.dim_domain + self.dim_codomain): - diff = ( - (self.dim_domain + self.dim_codomain) - - len(labels_array) - ) - labels_array = np.concatenate((labels_array, diff * [None])) - - self.argument_names = labels_array[:self.dim_domain] - self.coordinate_names = labels_array[self.dim_domain:] - @property def sample_names(self) -> LabelTuple: return self._sample_names @@ -436,7 +372,11 @@ def evaluate( grid: bool = False, aligned: bool = True, ) -> NDArrayFloat: - """Evaluate the object at a list of values or a grid. + """ + Evaluate the object at a list of values or a grid. + + .. deprecated:: 0.8 + Use normal calling notation instead. Args: eval_points: List of points where the functions are @@ -464,96 +404,19 @@ def evaluate( function at the values specified in eval_points. """ - if derivative != 0: - warnings.warn( - "Parameter derivative is deprecated. Use the " - "derivative function instead.", - DeprecationWarning, - ) - return self.derivative(order=derivative)( # type: ignore - eval_points, - extrapolation=extrapolation, - grid=grid, - aligned=aligned, - ) - - if grid: # Evaluation of a grid performed in auxiliar function - - return _evaluate_grid( # type: ignore - eval_points, - evaluate_method=self.evaluate, - n_samples=self.n_samples, - dim_domain=self.dim_domain, - dim_codomain=self.dim_codomain, - extrapolation=extrapolation, - aligned=aligned, - ) - - eval_points = cast(Union[ArrayLike, Iterable[ArrayLike]], eval_points) - - if extrapolation is None: - extrapolation = self.extrapolation - else: - # Gets the function to perform extrapolation or None - extrapolation = _parse_extrapolation(extrapolation) - - eval_points = cast( - Union[ArrayLike, Sequence[ArrayLike]], - eval_points, - ) - - # Convert to array and check dimensions of eval points - eval_points = _reshape_eval_points( - eval_points, - aligned=aligned, - n_samples=self.n_samples, - dim_domain=self.dim_domain, + warnings.warn( + "The method 'evaluate' is deprecated. " + "Please use the normal calling notation on the functional data " + "object instead.", + DeprecationWarning, + stacklevel=2, ) - if extrapolation is not None: - - index_matrix = self._extrapolation_index(eval_points) - - if index_matrix.any(): - - # Partition of eval points - if aligned: - - index_ext = index_matrix - index_ev = ~index_matrix - - eval_points_extrapolation = eval_points[index_ext] - eval_points_evaluation = eval_points[index_ev] - - else: - index_ext = np.logical_or.reduce(index_matrix, axis=0) - eval_points_extrapolation = eval_points[:, index_ext] - - index_ev = np.logical_or.reduce(~index_matrix, axis=0) - eval_points_evaluation = eval_points[:, index_ev] - - # Direct evaluation - res_evaluation = self._evaluate( - eval_points_evaluation, - aligned=aligned, - ) - - res_extrapolation = extrapolation( # type: ignore - self, - eval_points_extrapolation, - aligned=aligned, - ) - - return self._join_evaluation( - index_matrix, - index_ext, - index_ev, - res_extrapolation, - res_evaluation, - ) - - return self._evaluate( - eval_points, + return self( + eval_points=eval_points, + derivative=derivative, + extrapolation=extrapolation, + grid=grid, aligned=aligned, ) @@ -626,7 +489,8 @@ def __call__( grid: bool = False, aligned: bool = True, ) -> NDArrayFloat: - """Evaluate the :term:`functional object`. + """ + Evaluate the :term:`functional object`. Evaluate the object or its derivatives at a list of values or a grid. This method is a wrapper of :meth:`evaluate`. @@ -656,11 +520,96 @@ def __call__( function at the values specified in eval_points. """ - return self.evaluate( # type: ignore + if derivative != 0: + warnings.warn( + "Parameter derivative is deprecated. Use the " + "derivative function instead.", + DeprecationWarning, + ) + return self.derivative(order=derivative)( + eval_points, + extrapolation=extrapolation, + grid=grid, + aligned=aligned, + ) + + if grid: # Evaluation of a grid performed in auxiliar function + + return _evaluate_grid( + eval_points, + evaluate_method=self, + n_samples=self.n_samples, + dim_domain=self.dim_domain, + dim_codomain=self.dim_codomain, + extrapolation=extrapolation, + aligned=aligned, + ) + + eval_points = cast(Union[ArrayLike, Iterable[ArrayLike]], eval_points) + + if extrapolation is None: + extrapolation = self.extrapolation + else: + # Gets the function to perform extrapolation or None + extrapolation = _parse_extrapolation(extrapolation) + + eval_points = cast( + Union[ArrayLike, Sequence[ArrayLike]], + eval_points, + ) + + # Convert to array and check dimensions of eval points + eval_points = _reshape_eval_points( + eval_points, + aligned=aligned, + n_samples=self.n_samples, + dim_domain=self.dim_domain, + ) + + if extrapolation is not None: + + index_matrix = self._extrapolation_index(eval_points) + + if index_matrix.any(): + + # Partition of eval points + if aligned: + + index_ext = index_matrix + index_ev = ~index_matrix + + eval_points_extrapolation = eval_points[index_ext] + eval_points_evaluation = eval_points[index_ev] + + else: + index_ext = np.logical_or.reduce(index_matrix, axis=0) + eval_points_extrapolation = eval_points[:, index_ext] + + index_ev = np.logical_or.reduce(~index_matrix, axis=0) + eval_points_evaluation = eval_points[:, index_ev] + + # Direct evaluation + res_evaluation = self._evaluate( + eval_points_evaluation, + aligned=aligned, + ) + + res_extrapolation = extrapolation( + self, + eval_points_extrapolation, + aligned=aligned, + ) + + return self._join_evaluation( + index_matrix, + index_ext, + index_ev, + res_extrapolation, + res_evaluation, + ) + + return self._evaluate( eval_points, - derivative=derivative, - extrapolation=extrapolation, - grid=grid, aligned=aligned, ) @@ -1104,7 +1053,7 @@ def __len__(self) -> int: # Numpy methods ##################################################################### - def __array__(self, *args: Any, **kwargs: Any) -> np.ndarray: + def __array__(self, *args: Any, **kwargs: Any) -> NDArrayObject: """Return a numpy array with the objects.""" # This is to prevent numpy to access inner dimensions array = np.empty(shape=len(self), dtype=np.object_) @@ -1190,7 +1139,7 @@ def _take_allow_fill( ) -> T: pass - def take( + def take( # noqa: WPS238 self: T, indices: Union[int, Sequence[int], NDArrayInt], allow_fill: bool = False, diff --git a/skfda/representation/_typing.py b/skfda/representation/_typing.py index 5132170d8..30be3e81f 100644 --- a/skfda/representation/_typing.py +++ b/skfda/representation/_typing.py @@ -1,9 +1,8 @@ """Common types.""" from typing import Any, Optional, Sequence, Tuple, TypeVar, Union -from typing_extensions import Protocol - import numpy as np +from typing_extensions import Protocol try: from numpy.typing import ArrayLike @@ -16,12 +15,14 @@ NDArrayInt = NDArray[np.int_] NDArrayFloat = NDArray[np.float_] NDArrayBool = NDArray[np.bool_] + NDArrayObject = NDArray[np.object_] except ImportError: NDArray = np.ndarray # type:ignore[misc] NDArrayAny = np.ndarray # type:ignore[misc] NDArrayInt = np.ndarray # type:ignore[misc] NDArrayFloat = np.ndarray # type:ignore[misc] NDArrayBool = np.ndarray # type:ignore[misc] + NDArrayObject = np.ndarray # type:ignore[misc] VectorType = TypeVar("VectorType") diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index d5ba326b9..e41ccb1f2 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -5,13 +5,13 @@ import copy import warnings from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Optional, Tuple, TypeVar, Union +from typing import TYPE_CHECKING, Any, Tuple, TypeVar import numpy as np from matplotlib.figure import Figure from ..._utils import _reshape_eval_points, _same_domain, _to_domain_range -from .._typing import ArrayLike, DomainRange, DomainRangeLike +from .._typing import ArrayLike, DomainRange, DomainRangeLike, NDArrayFloat if TYPE_CHECKING: from . import FDataBasis @@ -32,7 +32,7 @@ class Basis(ABC): def __init__( self, *, - domain_range: Optional[DomainRangeLike] = None, + domain_range: DomainRangeLike | None = None, n_basis: int = 1, ) -> None: """Basis constructor.""" @@ -55,7 +55,7 @@ def __call__( eval_points: ArrayLike, *, derivative: int = 0, - ) -> np.ndarray: + ) -> NDArrayFloat: """Evaluate Basis objects. Evaluates the basis functions at a list of given values. @@ -74,7 +74,26 @@ def __call__( eval_points. """ - return self.evaluate(eval_points, derivative=derivative) + if derivative < 0: + raise ValueError("derivative only takes non-negative values.") + elif derivative != 0: + warnings.warn( + "Parameter derivative is deprecated. Use the " + "derivative method instead.", + DeprecationWarning, + ) + return self.derivative(order=derivative)(eval_points) + + eval_points = _reshape_eval_points( + eval_points, + aligned=True, + n_samples=self.n_basis, + dim_domain=self.dim_domain, + ) + + return self._evaluate(eval_points).reshape( + (self.n_basis, len(eval_points), self.dim_codomain), + ) @property def dim_domain(self) -> int: @@ -114,8 +133,8 @@ def is_domain_range_fixed(self) -> bool: @abstractmethod def _evaluate( self, - eval_points: np.ndarray, - ) -> np.ndarray: + eval_points: NDArrayFloat, + ) -> NDArrayFloat: """ Evaluate Basis object. @@ -129,11 +148,15 @@ def evaluate( eval_points: ArrayLike, *, derivative: int = 0, - ) -> np.ndarray: - """Evaluate Basis objects and its derivatives. + ) -> NDArrayFloat: + """ + Evaluate Basis objects and its derivatives. Evaluates the basis functions at a list of given values. + .. deprecated:: 0.8 + Use normal calling notation instead. + Args: eval_points: List of points where the basis is evaluated. @@ -148,25 +171,17 @@ def evaluate( eval_points. """ - if derivative < 0: - raise ValueError("derivative only takes non-negative values.") - elif derivative != 0: - warnings.warn( - "Parameter derivative is deprecated. Use the " - "derivative method instead.", - DeprecationWarning, - ) - return self.derivative(order=derivative)(eval_points) - - eval_points = _reshape_eval_points( - eval_points, - aligned=True, - n_samples=self.n_basis, - dim_domain=self.dim_domain, + warnings.warn( + "The method 'evaluate' is deprecated. " + "Please use the normal calling notation on the basis " + "object instead.", + DeprecationWarning, + stacklevel=2, ) - return self._evaluate(eval_points).reshape( - (self.n_basis, len(eval_points), self.dim_codomain), + return self( + eval_points=eval_points, + derivative=derivative, ) def __len__(self) -> int: @@ -186,9 +201,9 @@ def derivative(self, *, order: int = 1) -> FDataBasis: def _derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: """ Return basis and coefficients of the derivative. @@ -209,9 +224,9 @@ def _derivative_basis_and_coefs( def derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: """ Return basis and coefficients of the derivative. @@ -242,9 +257,9 @@ def plot(self, *args: Any, **kwargs: Any) -> Figure: def _coordinate_nonfull( self, - coefs: np.ndarray, - key: Union[int, slice], - ) -> Tuple[Basis, np.ndarray]: + coefs: NDArrayFloat, + key: int | slice, + ) -> Tuple[Basis, NDArrayFloat]: """ Return a basis and coefficients for the indexed coordinate functions. @@ -255,9 +270,9 @@ def _coordinate_nonfull( def coordinate_basis_and_coefs( self, - coefs: np.ndarray, - key: Union[int, slice], - ) -> Tuple[Basis, np.ndarray]: + coefs: NDArrayFloat, + key: int | slice, + ) -> Tuple[Basis, NDArrayFloat]: """Return a fdatabasis for the coordinate functions indexed by key.""" # Raises error if not in range and normalize key r_key = range(self.dim_codomain)[key] @@ -277,7 +292,7 @@ def coordinate_basis_and_coefs( key=key, ) - def rescale(self: T, domain_range: Optional[DomainRangeLike] = None) -> T: + def rescale(self: T, domain_range: DomainRangeLike | None = None) -> T: """ Return a copy of the basis with a new :term:`domain` range. @@ -292,7 +307,7 @@ def rescale(self: T, domain_range: Optional[DomainRangeLike] = None) -> T: """ return self.copy(domain_range=domain_range) - def copy(self: T, domain_range: Optional[DomainRangeLike] = None) -> T: + def copy(self: T, domain_range: DomainRangeLike | None = None) -> T: """Basis copy.""" new_copy = copy.deepcopy(self) @@ -312,7 +327,7 @@ def to_basis(self) -> FDataBasis: as observations. """ - from . import FDataBasis + from . import FDataBasis # noqa: WPS442 return FDataBasis(self.copy(), np.identity(self.n_basis)) def _to_R(self) -> str: # noqa: N802 @@ -320,8 +335,8 @@ def _to_R(self) -> str: # noqa: N802 def inner_product_matrix( self, - other: Optional[Basis] = None, - ) -> np.ndarray: + other: Basis | None = None, + ) -> NDArrayFloat: r""" Return the Inner Product Matrix of a pair of basis. @@ -351,13 +366,13 @@ def inner_product_matrix( return inner_product_matrix(self, other) - def _gram_matrix_numerical(self) -> np.ndarray: + def _gram_matrix_numerical(self) -> NDArrayFloat: """Compute the Gram matrix numerically.""" from ...misc import inner_product_matrix return inner_product_matrix(self, force_numerical=True) - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: """ Compute the Gram matrix. @@ -367,7 +382,7 @@ def _gram_matrix(self) -> np.ndarray: """ return self._gram_matrix_numerical() - def gram_matrix(self) -> np.ndarray: + def gram_matrix(self) -> NDArrayFloat: r""" Return the Gram Matrix of a basis. @@ -393,12 +408,12 @@ def gram_matrix(self) -> np.ndarray: def _mul_constant( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, other: float, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: coefs = coefs.copy() - other = np.atleast_2d(other).reshape(-1, 1) - coefs *= other + other_array = np.atleast_2d(other).reshape(-1, 1) + coefs *= other_array return self.copy(), coefs diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 9054de46e..0d0499739 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -1,12 +1,13 @@ -from typing import Any, Optional, Sequence, Tuple, Type, TypeVar +from __future__ import annotations + +from typing import Any, Sequence, Tuple, Type, TypeVar import numpy as np -import scipy.interpolate from numpy import polyint, polymul, polyval -from scipy.interpolate import BSpline as SciBSpline, PPoly +from scipy.interpolate import BSpline as SciBSpline, PPoly, splev from ..._utils import _to_domain_range -from .._typing import DomainRangeLike +from .._typing import DomainRangeLike, NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='BSpline') @@ -88,12 +89,12 @@ class BSpline(Basis): """ - def __init__( + def __init__( # noqa: WPS238 self, - domain_range: Optional[DomainRangeLike] = None, - n_basis: Optional[int] = None, + domain_range: DomainRangeLike | None = None, + n_basis: int | None = None, order: int = 4, - knots: Optional[Sequence[float]] = None, + knots: Sequence[float] | None = None, ) -> None: """Bspline basis constructor.""" if domain_range is not None: @@ -174,7 +175,7 @@ def _evaluation_knots(self) -> Tuple[float, ...]: + (self.knots[-1],) * (self.order - 1), ) - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: # Input is scalar eval_points = eval_points[..., 0] @@ -194,7 +195,7 @@ def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: # iteration c[i] = 1 # compute the spline - mat[i] = scipy.interpolate.splev( + mat[i] = splev( eval_points, (knots, c, self.order - 1), ) @@ -204,9 +205,9 @@ def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: def _derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: if order >= self.order: return ( type(self)(n_basis=1, domain_range=self.domain_range, order=1), @@ -229,7 +230,7 @@ def _derivative_basis_and_coefs( def rescale( # noqa: D102 self: T, - domain_range: Optional[DomainRangeLike] = None, + domain_range: DomainRangeLike | None = None, ) -> T: knots = np.array(self.knots, dtype=np.dtype('float')) @@ -251,7 +252,7 @@ def rescale( # noqa: D102 # TODO: Allow multiple dimensions domain_range = self.domain_range[0] - return type(self)(domain_range, self.n_basis, self.order, knots) + return type(self)(domain_range, self.n_basis, self.order, tuple(knots)) def __repr__(self) -> str: """Representation of a BSpline basis.""" @@ -261,7 +262,7 @@ def __repr__(self) -> str: f"knots={self.knots})" ) - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: # Places m knots at the boundaries knots = self._evaluation_knots() @@ -339,12 +340,22 @@ def _gram_matrix(self) -> np.ndarray: return matrix - def _to_scipy_bspline(self, coefs: np.ndarray) -> SciBSpline: + def _to_scipy_bspline(self, coefs: NDArrayFloat) -> SciBSpline: + + repeated_initial: NDArrayFloat = np.repeat( + self.knots[0], + self.order - 1, + ) + knots_array = np.asarray(self.knots) + repeated_final: NDArrayFloat = np.repeat( + self.knots[-1], + self.order - 1, + ) - knots = np.concatenate(( - np.repeat(self.knots[0], self.order - 1), - self.knots, - np.repeat(self.knots[-1], self.order - 1), + knots: NDArrayFloat = np.concatenate(( + repeated_initial, + knots_array, + repeated_final, )) return SciBSpline(knots, coefs, self.order - 1) @@ -353,7 +364,7 @@ def _to_scipy_bspline(self, coefs: np.ndarray) -> SciBSpline: def _from_scipy_bspline( cls: Type[T], bspline: SciBSpline, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: order = bspline.k knots = bspline.t diff --git a/skfda/representation/basis/_coefficients_transformer.py b/skfda/representation/basis/_coefficients_transformer.py index 17fbc5952..39da3f297 100644 --- a/skfda/representation/basis/_coefficients_transformer.py +++ b/skfda/representation/basis/_coefficients_transformer.py @@ -2,16 +2,16 @@ from typing import Optional -from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted +from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from .._typing import NDArrayFloat from ._fdatabasis import FDataBasis class CoefficientsTransformer( - BaseEstimator, # type:ignore - TransformerMixin, # type:ignore + BaseEstimator, + TransformerMixin[FDataBasis, NDArrayFloat, object], ): r""" Transformer returning the coefficients of FDataBasis objects as a matrix. @@ -44,7 +44,7 @@ def __init__(self, n_components: Optional[int] = None) -> None: def fit( # noqa: D102 self, X: FDataBasis, - y: None = None, + y: object = None, ) -> CoefficientsTransformer: self.basis_ = X.basis @@ -54,7 +54,7 @@ def fit( # noqa: D102 def transform( # noqa: D102 self, X: FDataBasis, - y: None = None, + y: object = None, ) -> NDArrayFloat: check_is_fitted(self) diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 3d49af323..2f111c746 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -2,7 +2,7 @@ import numpy as np -from .._typing import DomainRangeLike +from .._typing import DomainRangeLike, NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='Constant') @@ -29,20 +29,20 @@ def __init__(self, domain_range: Optional[DomainRangeLike] = None) -> None: """Constant basis constructor.""" super().__init__(domain_range=domain_range, n_basis=1) - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: return np.ones((1, len(eval_points))) def _derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: return ( (self.copy(), coefs.copy()) if order == 0 else (self.copy(), np.zeros(coefs.shape)) ) - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: return np.array( [[self.domain_range[0][1] - self.domain_range[0][0]]], ) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 5a2160b6c..930e95f8d 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -17,6 +17,7 @@ import numpy as np import pandas.api.extensions + from skfda._utils._utils import _to_array_maybe_ragged from ..._utils import _check_array_key, _int_to_real, constants, nquad_vec @@ -91,9 +92,7 @@ def __init__( basis: Basis, coefficients: ArrayLike, *, - dataset_label: Optional[str] = None, dataset_name: Optional[str] = None, - axes_labels: Optional[LabelTupleLike] = None, argument_names: Optional[LabelTupleLike] = None, coordinate_names: Optional[LabelTupleLike] = None, sample_names: Optional[LabelTupleLike] = None, @@ -112,9 +111,7 @@ def __init__( super().__init__( extrapolation=extrapolation, - dataset_label=dataset_label, dataset_name=dataset_name, - axes_labels=axes_labels, argument_names=argument_names, coordinate_names=coordinate_names, sample_names=sample_names, @@ -269,7 +266,7 @@ def _evaluate( eval_points = np.asarray(eval_points) # Each row contains the values of one element of the basis - basis_values = self.basis.evaluate(eval_points) + basis_values = self.basis(eval_points) res = np.tensordot(self.coefficients, basis_values, axes=(1, 0)) @@ -280,7 +277,7 @@ def _evaluate( eval_points = cast(Iterable[ArrayLike], eval_points) res_list = [ - np.sum((c * self.basis.evaluate(np.asarray(p)).T).T, axis=0) + np.sum((c * self.basis(np.asarray(p)).T).T, axis=0) for c, p in zip(self.coefficients, eval_points) ] @@ -403,7 +400,7 @@ def integrate( domain, ) - return integrated[0] + return integrated[:, 0, :] def sum( # noqa: WPS125 self: T, @@ -551,7 +548,7 @@ def to_grid( grid_points = self._default_grid_points() return grid.FDataGrid( - self.evaluate(grid_points, grid=True), + self(grid_points, grid=True), grid_points=grid_points, domain_range=self.domain_range, ) diff --git a/skfda/representation/basis/_finite_element.py b/skfda/representation/basis/_finite_element.py index 0e860c06f..c195a54cc 100644 --- a/skfda/representation/basis/_finite_element.py +++ b/skfda/representation/basis/_finite_element.py @@ -2,7 +2,7 @@ import numpy as np -from .._typing import ArrayLike, DomainRangeLike +from .._typing import ArrayLike, DomainRangeLike, NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='FiniteElement') @@ -71,18 +71,20 @@ def __init__( cells: ArrayLike, domain_range: Optional[DomainRangeLike] = None, ) -> None: + vertices = np.asarray(vertices) + super().__init__( domain_range=domain_range, n_basis=len(vertices), ) - self.vertices = np.asarray(vertices) + self.vertices = vertices self.cells = np.asarray(cells) @property def dim_domain(self) -> int: return self.vertices.shape[-1] - def _barycentric_coords(self, points: np.ndarray) -> np.ndarray: + def _barycentric_coords(self, points: NDArrayFloat) -> NDArrayFloat: """ Find the barycentric coordinates of each point in each cell. @@ -110,7 +112,7 @@ def _barycentric_coords(self, points: np.ndarray) -> np.ndarray: return np.swapaxes(coords, -2, -1) - def _cell_points_values(self, points: np.ndarray) -> np.ndarray: + def _cell_points_values(self, points: NDArrayFloat) -> NDArrayFloat: """ Compute the values of each point in each of the vertices of each cell. @@ -136,7 +138,7 @@ def _cell_points_values(self, points: np.ndarray) -> np.ndarray: return barycentric_coords - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: points_values_per_cell = self._cell_points_values(eval_points) diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 6365154e0..6ab4bb0a9 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -1,14 +1,25 @@ -from typing import Any, Optional, Tuple, TypeVar +from typing import Any, Optional, Sequence, Tuple, TypeVar import numpy as np +from typing_extensions import Protocol from ..._utils import _to_domain_range -from .._typing import DomainRangeLike +from .._typing import DomainRangeLike, NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='Fourier') +class _SinCos(Protocol): + + def __call__( + self, + __array: NDArrayFloat, # noqa: WPS112 + out: NDArrayFloat, + ) -> NDArrayFloat: + pass + + class Fourier(Basis): r"""Fourier basis. @@ -115,12 +126,12 @@ def period(self) -> float: return self._period - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: # Input is scalar eval_points = eval_points[..., 0] - functions = [np.sin, np.cos] + functions: Sequence[_SinCos] = [np.sin, np.cos] omega = 2 * np.pi / self.period normalization_denominator = np.sqrt(self.period / 2) @@ -148,9 +159,9 @@ def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: def _derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: omega = 2 * np.pi / self.period deriv_factor = (np.arange(1, (self.n_basis + 1) / 2) * omega) ** order @@ -172,7 +183,7 @@ def _derivative_basis_and_coefs( # normalise return self.copy(), deriv_coefs - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: # Orthogonal in this case if self.period == (self.domain_range[0][1] - self.domain_range[0][0]): diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index 811fc4d36..954e79bc9 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -3,6 +3,7 @@ import numpy as np import scipy.linalg +from .._typing import NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='Monomial') @@ -67,7 +68,7 @@ class Monomial(Basis): [ 2.]]]) """ - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: # Input is scalar eval_points = eval_points[..., 0] @@ -79,9 +80,9 @@ def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: def _derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: if order >= self.n_basis: return ( type(self)(domain_range=self.domain_range, n_basis=1), @@ -96,7 +97,7 @@ def _derivative_basis_and_coefs( np.array([np.polyder(x[::-1], order)[::-1] for x in coefs]), ) - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: integral_coefs = np.polyint(np.ones(2 * self.n_basis - 1)) # We obtain the powers of both extremes in the domain range @@ -117,7 +118,7 @@ def _gram_matrix(self) -> np.ndarray: ordered_evaluated_points = evaluated_points[-2::-1] # Build the matrix - return scipy.linalg.hankel( + return scipy.linalg.hankel( # type: ignore[no-any-return] ordered_evaluated_points[:self.n_basis], ordered_evaluated_points[self.n_basis - 1:], ) diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index 8d6a07191..f345ebe89 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -1,8 +1,10 @@ import itertools +import math from typing import Any, Iterable, Tuple import numpy as np +from .._typing import NDArrayFloat from ._basis import Basis @@ -73,14 +75,14 @@ def __init__(self, basis_list: Iterable[Basis]): super().__init__( domain_range=[b.domain_range[0] for b in basis_list], - n_basis=np.prod([b.n_basis for b in basis_list]), + n_basis=math.prod([b.n_basis for b in basis_list]), ) @property def basis_list(self) -> Tuple[Basis, ...]: return self._basis_list - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: matrix = np.zeros((self.n_basis, len(eval_points), self.dim_codomain)) @@ -95,7 +97,7 @@ def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: return matrix - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: gram_matrices = [b.gram_matrix() for b in self.basis_list] diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index 2f6e69a72..93e66bbd6 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -1,16 +1,14 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any, Iterable, Tuple, TypeVar, Union +from typing import Any, Iterable, Tuple, TypeVar, Union import numpy as np import scipy.linalg from ..._utils import _same_domain +from .._typing import NDArrayFloat from ._basis import Basis -if TYPE_CHECKING: - from .. import FDataBasis - T = TypeVar("T", bound='VectorValued') @@ -104,12 +102,12 @@ def dim_domain(self) -> int: def dim_codomain(self) -> int: return len(self.basis_list) - def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: + def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: matrix = np.zeros((self.n_basis, len(eval_points), self.dim_codomain)) n_basis_eval = 0 - basis_evaluations = [b.evaluate(eval_points) for b in self.basis_list] + basis_evaluations = [b(eval_points) for b in self.basis_list] for i, ev in enumerate(basis_evaluations): @@ -120,9 +118,9 @@ def _evaluate(self, eval_points: np.ndarray) -> np.ndarray: def _derivative_basis_and_coefs( self: T, - coefs: np.ndarray, + coefs: NDArrayFloat, order: int = 1, - ) -> Tuple[T, np.ndarray]: + ) -> Tuple[T, NDArrayFloat]: n_basis_list = [b.n_basis for b in self.basis_list] indexes = np.cumsum(n_basis_list) @@ -141,17 +139,19 @@ def _derivative_basis_and_coefs( return new_basis, new_coefs - def _gram_matrix(self) -> np.ndarray: + def _gram_matrix(self) -> NDArrayFloat: gram_matrices = [b.gram_matrix() for b in self.basis_list] - return scipy.linalg.block_diag(*gram_matrices) + return scipy.linalg.block_diag( # type: ignore[no-any-return] + *gram_matrices, + ) def _coordinate_nonfull( self, - coefs: np.ndarray, + coefs: NDArrayFloat, key: Union[int, slice], - ) -> Tuple[Basis, np.ndarray]: + ) -> Tuple[Basis, NDArrayFloat]: basis_sizes = [b.n_basis for b in self.basis_list] basis_indexes = np.cumsum(basis_sizes) diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index eb1fe9d94..c954b04c6 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -10,10 +10,9 @@ from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any, Iterable, Union, overload -import numpy as np from typing_extensions import Literal, Protocol -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArrayFloat if TYPE_CHECKING: from . import FData @@ -40,7 +39,7 @@ def _evaluate( eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: """ Evaluate the samples at evaluation points. @@ -56,7 +55,7 @@ def __call__( eval_points: ArrayLike, *, aligned: Literal[True] = True, - ) -> np.ndarray: + ) -> NDArrayFloat: pass @overload @@ -66,7 +65,17 @@ def __call__( eval_points: Iterable[ArrayLike], *, aligned: Literal[False], - ) -> np.ndarray: + ) -> NDArrayFloat: + pass + + @overload + def __call__( + self, + fdata: FData, + eval_points: Union[ArrayLike, Iterable[ArrayLike]], + *, + aligned: bool, + ) -> NDArrayFloat: pass def __call__( @@ -75,7 +84,7 @@ def __call__( eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: """ Evaluate the samples at evaluation points. @@ -123,7 +132,7 @@ def __call__( eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: """ Evaluate the samples at evaluation points. @@ -169,6 +178,6 @@ def _evaluate( # noqa: D102 eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: return self.evaluate_function(fdata, eval_points, aligned=aligned) diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 6217014a1..1bf92e718 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -19,7 +19,7 @@ import numpy as np from typing_extensions import Literal -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArrayFloat from .evaluator import Evaluator if TYPE_CHECKING: @@ -69,7 +69,7 @@ def _evaluate( # noqa: D102 eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: domain_range = np.asarray(fdata.domain_range) @@ -78,7 +78,7 @@ def _evaluate( # noqa: D102 eval_points %= domain_range[:, 1] - domain_range[:, 0] eval_points += domain_range[:, 0] - return fdata(eval_points, aligned=aligned) # type: ignore + return fdata(eval_points, aligned=aligned) class BoundaryExtrapolation(Evaluator): @@ -119,7 +119,7 @@ def _evaluate( # noqa: D102 eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: domain_range = fdata.domain_range @@ -142,7 +142,7 @@ def _evaluate( # noqa: D102 points_per_sample[points_per_sample[..., i] < a, i] = a points_per_sample[points_per_sample[..., i] > b, i] = b - return fdata(eval_points, aligned=aligned) # type: ignore + return fdata(eval_points, aligned=aligned) class ExceptionExtrapolation(Evaluator): @@ -223,7 +223,7 @@ class FillExtrapolation(Evaluator): def __init__(self, fill_value: float) -> None: self.fill_value = fill_value - def _fill(self, fdata: FData, eval_points: ArrayLike) -> np.ndarray: + def _fill(self, fdata: FData, eval_points: ArrayLike) -> NDArrayFloat: eval_points = np.asarray(eval_points) shape = ( @@ -239,7 +239,7 @@ def _evaluate( # noqa: D102 eval_points: Union[ArrayLike, Iterable[ArrayLike]], *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: from .._utils import _to_array_maybe_ragged if aligned: diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index e05b97277..20b978a8f 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -143,12 +143,10 @@ def __init__( # noqa: WPS211 *, sample_points: Optional[GridPointsLike] = None, domain_range: Optional[DomainRangeLike] = None, - dataset_label: Optional[str] = None, dataset_name: Optional[str] = None, argument_names: Optional[LabelTupleLike] = None, coordinate_names: Optional[LabelTupleLike] = None, sample_names: Optional[LabelTupleLike] = None, - axes_labels: Optional[LabelTupleLike] = None, extrapolation: Optional[ExtrapolationLike] = None, interpolation: Optional[Evaluator] = None, ): @@ -219,9 +217,7 @@ def __init__( # noqa: WPS211 super().__init__( extrapolation=extrapolation, - dataset_label=dataset_label, dataset_name=dataset_name, - axes_labels=axes_labels, argument_names=argument_names, coordinate_names=coordinate_names, sample_names=sample_names, @@ -1000,7 +996,7 @@ def to_grid( # noqa: D102 ) return self.copy( - data_matrix=self.evaluate(grid_points, grid=True), + data_matrix=self(grid_points, grid=True), grid_points=grid_points, ) diff --git a/skfda/tests/test_fdatabasis_evaluation.py b/skfda/tests/test_fdatabasis_evaluation.py index cf8a47ae2..889caeda0 100644 --- a/skfda/tests/test_fdatabasis_evaluation.py +++ b/skfda/tests/test_fdatabasis_evaluation.py @@ -161,7 +161,6 @@ def test_simple_evaluation(self) -> None: ])[..., np.newaxis] np.testing.assert_allclose(f(t), res, atol=1e-2) - np.testing.assert_allclose(f.evaluate(t), res, atol=1e-2) def test_evaluation_derivative(self) -> None: """Test the evaluation of the derivative of Fourier.""" @@ -221,7 +220,6 @@ def test_simple_evaluation(self) -> None: ])[..., np.newaxis] np.testing.assert_allclose(f(t), res, atol=1e-2) - np.testing.assert_allclose(f.evaluate(t), res, atol=1e-2) def test_evaluation_derivative(self) -> None: """Test the evaluation of the derivative of BSpline.""" @@ -282,7 +280,6 @@ def test_evaluation_simple_monomial(self) -> None: ])[..., np.newaxis] np.testing.assert_allclose(f(t), res, atol=1e-2) - np.testing.assert_allclose(f.evaluate(t), res, atol=1e-2) def test_evaluation_derivative(self) -> None: """Test the evaluation of the derivative of Monomial.""" diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index e3da51452..2ffb38e5b 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -84,7 +84,7 @@ def test_predict_classifier(self) -> None: np.testing.assert_array_equal( pred, self.y, - err_msg=f'fail in {type(neigh)}', # noqa: WPS237 + err_msg=f'fail in {type(neigh)}', ) def test_predict_proba_classifier(self) -> None: From d9c91a5313f082058544365b51b1a784007ce8dd Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 25 Aug 2022 09:12:28 +0200 Subject: [PATCH 252/400] Remove ragged array support. --- skfda/_utils/__init__.py | 1 - skfda/_utils/_utils.py | 48 +++-------------------- skfda/representation/basis/_fdatabasis.py | 8 +--- skfda/representation/extrapolation.py | 3 +- skfda/representation/interpolation.py | 4 +- skfda/tests/test_grid.py | 34 ---------------- 6 files changed, 10 insertions(+), 88 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index ac002cd94..e04baf82e 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -17,7 +17,6 @@ _pairwise_symmetric, _reshape_eval_points, _same_domain, - _to_array_maybe_ragged, _to_domain_range, _to_grid, _to_grid_points, diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index a8da14aab..ef0cdad85 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -123,40 +123,6 @@ def _to_domain_range(sequence: DomainRangeLike) -> DomainRange: return cast(DomainRange, tuple_aux) -def _to_array_maybe_ragged( - array: Iterable[ArrayLike], - *, - row_shape: Optional[Sequence[int]] = None, -) -> NDArrayFloat | NDArrayObject: - """ - Convert to an array where each element may or may not be of equal length. - - If each element is of equal length the array is multidimensional. - Otherwise it is a ragged array. - - """ - def convert_row(row: ArrayLike) -> NDArrayFloat: - r = np.array(row) - - if row_shape is not None: - r = r.reshape(row_shape) - - return r - - array_list = [convert_row(a) for a in array] - shapes = [a.shape for a in array_list] - - if all(s == shapes[0] for s in shapes): - return np.array(array_list) - - res = np.empty(len(array_list), dtype=np.object_) - - for i, a in enumerate(array_list): - res[i] = a - - return res - - @overload def _cartesian_product( axes: Sequence[ArrayT], @@ -276,7 +242,7 @@ def _reshape_eval_points( def _reshape_eval_points( - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: ArrayLike, *, aligned: bool, n_samples: int, @@ -302,11 +268,9 @@ def _reshape_eval_points( if aligned: eval_points = np.asarray(eval_points) else: - eval_points = cast(Iterable[ArrayLike], eval_points) - - eval_points = _to_array_maybe_ragged( - eval_points, - row_shape=(-1, dim_domain), + eval_points = np.asarray(eval_points) + eval_points = eval_points.reshape( + (eval_points.shape[0], -1, dim_domain), ) # Case evaluation of a single value, i.e., f(0) @@ -500,7 +464,7 @@ def _evaluate_grid( # noqa: WPS234 "Should be provided a list of axis per sample", ) - eval_points = _to_array_maybe_ragged(eval_points_tuple) + eval_points = np.asarray(eval_points_tuple) # Evaluate the points evaluated = evaluate_method( @@ -518,7 +482,7 @@ def _evaluate_grid( # noqa: WPS234 else: - res = _to_array_maybe_ragged([ + res = np.asarray([ r.reshape(list(s) + [dim_codomain]) for r, s in zip(evaluated, shape_tuple) ]) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 930e95f8d..8b90ac652 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -18,8 +18,6 @@ import numpy as np import pandas.api.extensions -from skfda._utils._utils import _to_array_maybe_ragged - from ..._utils import _check_array_key, _int_to_real, constants, nquad_vec from .. import grid from .._functional_data import FData @@ -256,7 +254,7 @@ def domain_range(self) -> DomainRange: def _evaluate( self, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: ArrayLike, *, aligned: bool = True, ) -> NDArrayFloat: @@ -274,14 +272,12 @@ def _evaluate( (self.n_samples, len(eval_points), self.dim_codomain), ) - eval_points = cast(Iterable[ArrayLike], eval_points) - res_list = [ np.sum((c * self.basis(np.asarray(p)).T).T, axis=0) for c, p in zip(self.coefficients, eval_points) ] - return _to_array_maybe_ragged(res_list) + return np.asarray(res_list) def shift( self, diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 1bf92e718..d573558bd 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -240,7 +240,6 @@ def _evaluate( # noqa: D102 *, aligned: bool = True, ) -> NDArrayFloat: - from .._utils import _to_array_maybe_ragged if aligned: eval_points = cast(ArrayLike, eval_points) @@ -250,7 +249,7 @@ def _evaluate( # noqa: D102 res_list = [self._fill(fdata, p) for p in eval_points] - return _to_array_maybe_ragged(res_list) + return np.asarray(res_list) def __repr__(self) -> str: return ( diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 5ba146f8d..7d9c148fb 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -16,7 +16,6 @@ ) import numpy as np - from scipy.interpolate import ( PchipInterpolator, RectBivariateSpline, @@ -24,7 +23,6 @@ UnivariateSpline, ) -from .._utils import _to_array_maybe_ragged from ._typing import ArrayLike from .evaluator import Evaluator @@ -107,7 +105,7 @@ def evaluate( else: eval_points = cast(Iterable[ArrayLike], eval_points) - res = _to_array_maybe_ragged([ + res = np.asarray([ self._evaluate_codomain(s, np.asarray(e)) for s, e in zip(self.splines, eval_points) ]) diff --git a/skfda/tests/test_grid.py b/skfda/tests/test_grid.py index 749e8700a..e1c0c899a 100644 --- a/skfda/tests/test_grid.py +++ b/skfda/tests/test_grid.py @@ -313,25 +313,6 @@ def test_evaluate_unaligned(self) -> None: np.testing.assert_allclose(res, expected) - def test_evaluate_unaligned_ragged(self) -> None: - """Check evaluation with different number of points per curve.""" - res = self.fd( - [ - [(0, 0), (1, 1), (2, 2), (3, 3)], - [(1, 7), (5, 2), (3, 4)], - ], - aligned=False, - ) - expected = ([ - np.tile([0, 1, 2], (4, 1)), - np.tile([3, 4, 5], (3, 1)), - ]) - - self.assertEqual(len(res), self.fd.n_samples) - - for r, e in zip(res, expected): - np.testing.assert_allclose(r, e) - def test_evaluate_grid_aligned(self) -> None: """Test evaluation in aligned grid.""" res = self.fd([[0, 1], [1, 2]], grid=True) @@ -356,21 +337,6 @@ def test_evaluate_grid_unaligned(self) -> None: np.testing.assert_allclose(res, expected) - def test_evaluate_grid_unaligned_ragged(self) -> None: - """Test different grid points per curve.""" - res = self.fd( - [[[0, 1], [1, 2]], [[3, 4], [5]]], - grid=True, - aligned=False, - ) - expected = ([ - np.tile([0, 1, 2], (2, 2, 1)), - np.tile([3, 4, 5], (2, 1, 1)), - ]) - - for r, e in zip(res, expected): - np.testing.assert_allclose(r, e) - if __name__ == '__main__': unittest.main() From b15acf3a6037304c5b5607e6f2154ac94424b49c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 25 Aug 2022 09:44:34 +0200 Subject: [PATCH 253/400] Remove unused domain comparisons. --- skfda/_utils/_utils.py | 33 ------------------- skfda/misc/_math.py | 6 ++-- .../_linear_differential_operator.py | 4 +-- skfda/misc/validation.py | 6 ++++ 4 files changed, 10 insertions(+), 39 deletions(-) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index ef0cdad85..14eac6b5d 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -208,39 +208,6 @@ def _same_domain(fd: Union[Basis, FData], fd2: Union[Basis, FData]) -> bool: return np.array_equal(fd.domain_range, fd2.domain_range) -@overload -def _reshape_eval_points( - eval_points: ArrayLike, - *, - aligned: Literal[True], - n_samples: int, - dim_domain: int, -) -> NDArrayFloat: - pass - - -@overload -def _reshape_eval_points( - eval_points: Sequence[ArrayLike], - *, - aligned: Literal[False], - n_samples: int, - dim_domain: int, -) -> NDArrayFloat | NDArrayObject: - pass - - -@overload -def _reshape_eval_points( - eval_points: Union[ArrayLike, Sequence[ArrayLike]], - *, - aligned: bool, - n_samples: int, - dim_domain: int, -) -> NDArrayFloat | NDArrayObject: - pass - - def _reshape_eval_points( eval_points: ArrayLike, *, diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index fedac0988..7c14f128e 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -12,10 +12,11 @@ import numpy as np import scipy.integrate -from .._utils import _same_domain, nquad_vec +from .._utils import nquad_vec from ..representation import FData, FDataBasis, FDataGrid from ..representation._typing import ArrayLike, DomainRange, NDArrayFloat from ..representation.basis import Basis +from .validation import check_fdata_same_dimensions Vector = TypeVar( "Vector", @@ -385,8 +386,7 @@ def _inner_product_fdatabasis( force_numerical: bool = False, ) -> NDArrayFloat: - if not _same_domain(arg1, arg2): - raise ValueError("Both Objects should have the same domain_range") + check_fdata_same_dimensions(arg1, arg2) if isinstance(arg1, Basis): arg1 = arg1.to_basis() diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index aaef7c1e5..9eafdf0e1 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -4,12 +4,10 @@ from typing import Callable, Optional, Sequence, Tuple, Union, cast import numpy as np -from numpy import polyder, polyint, polymul, polyval - import scipy.integrate +from numpy import polyder, polyint, polymul, polyval from scipy.interpolate import PPoly -from ..._utils import _same_domain from ...representation import FData, FDataGrid from ...representation._typing import DomainRangeLike from ...representation.basis import ( diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index 25dc72dfd..d78f3b2fc 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -78,6 +78,12 @@ def check_fdata_same_dimensions( f"{fdata1.dim_codomain} != {fdata2.dim_codomain}", ) + if (fdata1.domain_range != fdata2.domain_range): + raise ValueError( + f"Functional data has incompatible domain range: " + f"{fdata1.domain_range} != {fdata2.domain_range}", + ) + @functools.singledispatch def _check_fdata_same_kind_specific( From 999231a05a426e67364f857055cef83e51fe176b Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 26 Aug 2022 13:43:58 +0200 Subject: [PATCH 254/400] Refactor. - Fix error in ANOVA (wrong grid points used for bootstrap samples). - Speed up ANOVA. - Simplify BSpline evaluation and derivative. --- skfda/inference/anova/_anova_oneway.py | 76 +++++++++++-------- .../dim_reduction/variable_selection/_rkvs.py | 4 +- skfda/representation/basis/_bspline.py | 46 +++-------- skfda/tests/test_oneway_anova.py | 9 ++- 4 files changed, 61 insertions(+), 74 deletions(-) diff --git a/skfda/inference/anova/_anova_oneway.py b/skfda/inference/anova/_anova_oneway.py index 3cd1e2a66..13fcca68c 100644 --- a/skfda/inference/anova/_anova_oneway.py +++ b/skfda/inference/anova/_anova_oneway.py @@ -8,7 +8,7 @@ from ... import concatenate from ..._utils import RandomStateLike -from ...datasets import make_gaussian_process +from ...datasets import make_gaussian from ...misc.metrics import lp_distance from ...representation import FData, FDataGrid from ...representation._typing import ArrayLike, NDArrayFloat @@ -92,7 +92,36 @@ def v_sample_stat(fd: FData, weights: ArrayLike, p: int = 2) -> float: )) -def v_asymptotic_stat(fd: FData, weights: ArrayLike, p: int = 2) -> float: +def _v_asymptotic_stat_with_reps( + *fds: FData, + weights: ArrayLike, + p: int = 2, +) -> float: + """Vectorized version of v_asymptotic_stat for repetitions.""" + weights = np.asarray(weights) + if len(weights) != len(fds): + raise ValueError("Number of weights must match number of groups.") + if np.count_nonzero(weights) != len(weights): + raise ValueError("All weights must be non-zero.") + + t_ind = np.tril_indices(len(fds), -1) + + results = np.zeros(shape=(len(t_ind[0]), fds[0].n_samples)) + for i, pair in enumerate(zip(*t_ind)): + left_fd = fds[pair[1]] + coef = np.sqrt(weights[pair[1]] / weights[pair[0]]) + right_fd = fds[pair[0]] * coef + results[i] = lp_distance(left_fd, right_fd, p=p) ** p + + return np.sum(results, axis=0) + + +def v_asymptotic_stat( + fd: FData, + *, + weights: ArrayLike, + p: int = 2, +) -> float: r""" Compute asymptitic statistic. @@ -146,26 +175,14 @@ def v_asymptotic_stat(fd: FData, weights: ArrayLike, p: int = 2) -> float: Finally the value of the statistic is calculated: - >>> v_asymptotic_stat(fd, weights) + >>> v_asymptotic_stat(fd, weights=weights) 0.0018159320335885969 References: .. footbibliography:: """ - weights = np.asarray(weights) - if not isinstance(fd, FData): - raise ValueError("Argument type must inherit FData.") - if len(weights) != fd.n_samples: - raise ValueError("Number of weights must match number of samples.") - if np.count_nonzero(weights) != len(weights): - raise ValueError("All weights must be non-zero.") - - t_ind = np.tril_indices(fd.n_samples, -1) - coef = np.sqrt(weights[t_ind[1]] / weights[t_ind[0]]) - left_fd = fd[t_ind[1]] - right_fd = fd[t_ind[0]] * coef - return float(np.sum(lp_distance(left_fd, right_fd, p=p) ** p)) + return float(_v_asymptotic_stat_with_reps(*fd, weights=weights, p=p)) def _anova_bootstrap( @@ -186,8 +203,6 @@ def _anova_bootstrap( "Domain range must match for every FData in fd_grouped.", ) - start, stop = fd_grouped[0].domain_range[0] - # List with sizes of each group sizes = [fd.n_samples for fd in fd_grouped] @@ -201,18 +216,18 @@ def _anova_bootstrap( # Estimating covariances for each group k_est = [fd.cov().data_matrix[0, ..., 0] for fd in fd_grouped] - # Number of sample points for gaussian processes have to match - # the features of the covariances. - n_features = k_est[0].shape[0] - # Simulating n_reps observations for each of the n_groups gaussian # processes + grid_points = getattr(fd_grouped[0], "grid_points", None) + if grid_points is None: + start, stop = fd_grouped[0].domain_range[0] + n_features = k_est[0].shape[0] + grid_points = np.linspace(start, stop, n_features) + sim = [ - make_gaussian_process( + make_gaussian( n_reps, - n_features=n_features, - start=start, - stop=stop, + grid_points=grid_points, cov=k_est[i], random_state=random_state, ) @@ -220,10 +235,7 @@ def _anova_bootstrap( ] v_samples = np.empty(n_reps) - for i in range(n_reps): - fdatagrid = FDataGrid([s.data_matrix[i, ..., 0] for s in sim]) - v_samples[i] = v_asymptotic_stat(fdatagrid, sizes, p=p) - return v_samples + return _v_asymptotic_stat_with_reps(*sim, weights=sizes, p=p) T = TypeVar("T", bound=FData) @@ -324,13 +336,13 @@ def oneway_anova( >>> fd = fetch_gait()["data"].coordinates[1] >>> fd1, fd2, fd3 = fd[:13], fd[13:26], fd[26:] >>> oneway_anova(fd1, fd2, fd3, random_state=RandomState(42)) - (179.52499999999998, 0.5945) + (179.52499999999998, 0.56) >>> _, _, dist = oneway_anova(fd1, fd2, fd3, n_reps=3, ... random_state=RandomState(42), ... return_dist=True) >>> with printoptions(precision=4): ... print(dist) - [ 184.0698 212.7395 195.3663] + [ 174.8663 202.1025 185.598 ] References: .. footbibliography:: diff --git a/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py b/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py index e85b0cf76..df167a3a1 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py @@ -146,7 +146,7 @@ class RKHSVariableSelection( other with a peak-like mean. Both have Brownian covariance. >>> n_samples = 10000 - >>> n_features = 1000 + >>> n_features = 200 >>> >>> def mean_1(t): ... return (np.abs(t - 0.25) @@ -179,7 +179,7 @@ class RKHSVariableSelection( >>> X_dimred = rkvs.transform(X) >>> len(X.grid_points[0]) - 1000 + 200 >>> X_dimred.shape (10000, 3) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 0d0499739..462400f62 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -180,53 +180,24 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: # Input is scalar eval_points = eval_points[..., 0] - # Places m knots at the boundaries - knots = self._evaluation_knots() - - # c is used the select which spline the function splev below computes - c = np.zeros(len(knots)) - - # Initialise empty matrix - mat = np.empty((self.n_basis, len(eval_points))) - - # For each basis computes its value for each evaluation point - for i in range(self.n_basis): - # write a 1 in c in the position of the spline calculated in each - # iteration - c[i] = 1 - # compute the spline - mat[i] = splev( - eval_points, - (knots, c, self.order - 1), - ) - c[i] = 0 - - return mat + return self._to_scipy_bspline( # type: ignore[no-any-return] + np.eye(self.n_basis), + )(eval_points).T def _derivative_basis_and_coefs( self: T, coefs: NDArrayFloat, order: int = 1, ) -> Tuple[T, NDArrayFloat]: + if order >= self.order: return ( type(self)(n_basis=1, domain_range=self.domain_range, order=1), np.zeros((len(coefs), 1)), ) - deriv_splines = [ - self._to_scipy_bspline(coefs[i]).derivative(order) - for i in range(coefs.shape[0]) - ] - - deriv_coefs = [ - self._from_scipy_bspline(spline)[1] - for spline in deriv_splines - ] - - deriv_basis = self._from_scipy_bspline(deriv_splines[0])[0] - - return deriv_basis, np.array(deriv_coefs)[:, 0:deriv_basis.n_basis] + deriv_splines = self._to_scipy_bspline(coefs).derivative(order) + return self._from_scipy_bspline(deriv_splines) def rescale( # noqa: D102 self: T, @@ -358,7 +329,7 @@ def _to_scipy_bspline(self, coefs: NDArrayFloat) -> SciBSpline: repeated_final, )) - return SciBSpline(knots, coefs, self.order - 1) + return SciBSpline(knots, coefs.T, self.order - 1) @classmethod def _from_scipy_bspline( @@ -375,6 +346,9 @@ def _from_scipy_bspline( coefs = bspline.c domain_range = [knots[0], knots[-1]] + n_basis = len(knots) + order - 1 + coefs = coefs[:n_basis, ...].T + return cls(domain_range, order=order + 1, knots=knots), coefs def __eq__(self, other: Any) -> bool: diff --git a/skfda/tests/test_oneway_anova.py b/skfda/tests/test_oneway_anova.py index fbeda4980..20e6e9dc6 100644 --- a/skfda/tests/test_oneway_anova.py +++ b/skfda/tests/test_oneway_anova.py @@ -26,9 +26,10 @@ def test_v_stats_args(self) -> None: with self.assertRaises(ValueError): v_sample_stat(FDataGrid([0]), [0, 1]) with self.assertRaises(ValueError): - v_asymptotic_stat(FDataGrid([0]), [0, 1]) + v_asymptotic_stat(FDataGrid([0]), weights=[0, 1]) with self.assertRaises(ValueError): - v_asymptotic_stat(FDataGrid([[1, 1, 1], [1, 1, 1]]), [0, 0]) + v_asymptotic_stat( + FDataGrid([[1, 1, 1], [1, 1, 1]]), weights=[0, 0]) def test_v_stats(self) -> None: """Test statistic behaviour.""" @@ -52,11 +53,11 @@ def test_v_stats(self) -> None: + (1 - 3 * np.sqrt(1 / 3)) ** 2 + (2 - 3 * np.sqrt(2 / 3)) ** 2 ) - self.assertAlmostEqual(v_asymptotic_stat(fd, weights), res) + self.assertAlmostEqual(v_asymptotic_stat(fd, weights=weights), res) self.assertAlmostEqual( v_asymptotic_stat( fd.to_basis(Fourier(n_basis=5)), - weights, + weights=weights, ), res, ) From 5a5625f3d39d99a1ec07353115244a28257028e6 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 26 Aug 2022 14:17:57 +0200 Subject: [PATCH 255/400] Speed up covariance tests. --- skfda/_utils/_utils.py | 9 ++++----- skfda/tests/test_covariances.py | 12 +++++++----- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 14eac6b5d..63a28d9aa 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -36,7 +36,6 @@ NDArrayAny, NDArrayFloat, NDArrayInt, - NDArrayObject, ) from ..representation.extrapolation import ExtrapolationLike @@ -214,7 +213,7 @@ def _reshape_eval_points( aligned: bool, n_samples: int, dim_domain: int, -) -> NDArrayFloat | NDArrayObject: +) -> NDArrayFloat: """Convert and reshape the eval_points to ndarray. Args: @@ -328,7 +327,7 @@ def _evaluate_grid( dim_codomain: int, extrapolation: Optional[ExtrapolationLike] = None, aligned: Literal[False], -) -> NDArrayFloat | NDArrayObject: +) -> NDArrayFloat: pass @@ -342,7 +341,7 @@ def _evaluate_grid( dim_codomain: int, extrapolation: Optional[ExtrapolationLike] = None, aligned: bool, -) -> NDArrayFloat | NDArrayObject: +) -> NDArrayFloat: pass @@ -355,7 +354,7 @@ def _evaluate_grid( # noqa: WPS234 dim_codomain: int, extrapolation: Optional[ExtrapolationLike] = None, aligned: bool = True, -) -> NDArrayFloat | NDArrayObject: +) -> NDArrayFloat: """ Evaluate the functional object in the cartesian grid. diff --git a/skfda/tests/test_covariances.py b/skfda/tests/test_covariances.py index 3f6b1be1f..7dd0da9e9 100644 --- a/skfda/tests/test_covariances.py +++ b/skfda/tests/test_covariances.py @@ -33,8 +33,9 @@ def test_linear(self) -> None: def test_polynomial(self) -> None: - for variance in (1, 2): - for intercept in (0, 1, 2): + # Test a couple of non-default parameters only for speed + for variance in (2,): + for intercept in (0, 2): for slope in (1, 2): for degree in (1, 2, 3): with self.subTest( @@ -81,9 +82,10 @@ def test_exponential(self) -> None: def test_matern(self) -> None: - for variance in (1, 2): - for length_scale in (0.5, 1, 2): - for nu in (0.5, 1, 1.5, 2, 2.5, 3.5, 4.5, np.inf): + # Test a couple of non-default parameters only for speed + for variance in (2,): + for length_scale in (0.5,): + for nu in (0.5, 1, 1.5, 2.5, 3.5, np.inf): with self.subTest( variance=variance, length_scale=length_scale, From 2b805aac86acec4f81e95b0015ea4b9c5f55fb21 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 26 Aug 2022 14:28:29 +0200 Subject: [PATCH 256/400] Make inverse FPCA transform test faster. --- skfda/tests/test_fpca.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index e315c42ba..d93484c86 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -661,7 +661,7 @@ def test_basis_fpca_inverse_transform(self) -> None: # Low dimensional case: n_basis< None: # Case n_samples Date: Fri, 26 Aug 2022 16:57:02 +0200 Subject: [PATCH 257/400] Improve validation and fix type hints. --- skfda/_utils/__init__.py | 1 - skfda/_utils/_utils.py | 106 ++++++----------------- skfda/misc/validation.py | 73 +++++++++++++++- skfda/representation/_functional_data.py | 25 ++---- skfda/representation/_typing.py | 2 + skfda/representation/basis/_basis.py | 6 +- 6 files changed, 111 insertions(+), 102 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index e04baf82e..c43b8792a 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -15,7 +15,6 @@ _evaluate_grid, _int_to_real, _pairwise_symmetric, - _reshape_eval_points, _same_domain, _to_domain_range, _to_grid, diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 63a28d9aa..72717a6a8 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -21,14 +21,12 @@ import numpy as np import scipy.integrate -from numpy import ndarray from pandas.api.indexers import check_array_indexer from sklearn.preprocessing import LabelEncoder from sklearn.utils.multiclass import check_classification_targets from typing_extensions import Literal, Protocol from ..representation._typing import ( - ArrayLike, DomainRange, DomainRangeLike, GridPoints, @@ -36,12 +34,14 @@ NDArrayAny, NDArrayFloat, NDArrayInt, + NDArrayStr, ) from ..representation.extrapolation import ExtrapolationLike +from ._sklearn_adapter import BaseEstimator RandomStateLike = Optional[Union[int, np.random.RandomState]] -ArrayT = TypeVar("ArrayT", bound=NDArrayAny) +ArrayDTypeT = TypeVar("ArrayDTypeT", bound="np.generic") if TYPE_CHECKING: from ..representation import FData, FDataGrid @@ -124,30 +124,33 @@ def _to_domain_range(sequence: DomainRangeLike) -> DomainRange: @overload def _cartesian_product( - axes: Sequence[ArrayT], + axes: Sequence[np.typing.NDArray[ArrayDTypeT]], *, flatten: bool = True, return_shape: Literal[False] = False, -) -> ArrayT: +) -> np.typing.NDArray[ArrayDTypeT]: pass @overload def _cartesian_product( - axes: Sequence[ArrayT], + axes: Sequence[np.typing.NDArray[ArrayDTypeT]], *, flatten: bool = True, return_shape: Literal[True], -) -> Tuple[ArrayT, Tuple[int, ...]]: +) -> Tuple[np.typing.NDArray[ArrayDTypeT], Tuple[int, ...]]: pass def _cartesian_product( # noqa: WPS234 - axes: Sequence[ArrayT], + axes: Sequence[np.typing.NDArray[ArrayDTypeT]], *, flatten: bool = True, return_shape: bool = False, -) -> Union[ArrayT, Tuple[ArrayT, Tuple[int, ...]]]: +) -> ( + np.typing.NDArray[ArrayDTypeT] + | Tuple[np.typing.NDArray[ArrayDTypeT], Tuple[int, ...]] +): """ Compute the cartesian product of the axes. @@ -207,63 +210,6 @@ def _same_domain(fd: Union[Basis, FData], fd2: Union[Basis, FData]) -> bool: return np.array_equal(fd.domain_range, fd2.domain_range) -def _reshape_eval_points( - eval_points: ArrayLike, - *, - aligned: bool, - n_samples: int, - dim_domain: int, -) -> NDArrayFloat: - """Convert and reshape the eval_points to ndarray. - - Args: - eval_points: Evaluation points to be reshaped. - aligned: Boolean flag. True if all the samples - will be evaluated at the same evaluation_points. - n_samples: Number of observations. - dim_domain: Dimension of the domain. - - Returns: - Numpy array with the eval_points, if - evaluation_aligned is True with shape `number of evaluation points` - x `dim_domain`. If the points are not aligned the shape of the - points will be `n_samples` x `number of evaluation points` - x `dim_domain`. - - """ - if aligned: - eval_points = np.asarray(eval_points) - else: - eval_points = np.asarray(eval_points) - eval_points = eval_points.reshape( - (eval_points.shape[0], -1, dim_domain), - ) - - # Case evaluation of a single value, i.e., f(0) - # Only allowed for aligned evaluation - if aligned and ( - eval_points.shape == (dim_domain,) - or (eval_points.ndim == 0 and dim_domain == 1) - ): - eval_points = np.array([eval_points]) - - if aligned: # Samples evaluated at same eval points - eval_points = eval_points.reshape( - (eval_points.shape[0], dim_domain), - ) - - else: # Different eval_points for each sample - - if eval_points.shape[0] != n_samples: - raise ValueError( - f"eval_points should be a list " - f"of length {n_samples} with the " - f"evaluation points for each sample.", - ) - - return eval_points - - def _one_grid_to_points( axes: GridPointsLike, *, @@ -479,12 +425,12 @@ def integrate(*args: Any, depth: int) -> NDArrayFloat: # noqa: WPS430 def _map_in_batches( - function: Callable[..., ArrayT], + function: Callable[..., np.typing.NDArray[ArrayDTypeT]], arguments: Tuple[Union[FData, NDArrayAny], ...], indexes: Tuple[NDArrayInt, ...], memory_per_batch: Optional[int] = None, **kwargs: Any, -) -> ArrayT: +) -> np.typing.NDArray[ArrayDTypeT]: """ Map a function over samples of FData or ndarray tuples efficiently. @@ -505,7 +451,7 @@ def _map_in_batches( assert all(n_indexes == len(i) for i in indexes) - batches: List[ArrayT] = [] + batches: List[np.typing.NDArray[ArrayDTypeT]] = [] for pos in range(0, n_indexes, n_elements_per_batch_allowed): batch_args = tuple( @@ -515,25 +461,25 @@ def _map_in_batches( batches.append(function(*batch_args, **kwargs)) - return np.concatenate(batches, axis=0) # type: ignore[return-value] + return np.concatenate(batches, axis=0) def _pairwise_symmetric( - function: Callable[..., ArrayT], + function: Callable[..., np.typing.NDArray[ArrayDTypeT]], arg1: Union[FData, NDArrayAny], arg2: Optional[Union[FData, NDArrayAny]] = None, memory_per_batch: Optional[int] = None, **kwargs: Any, -) -> ArrayT: +) -> np.typing.NDArray[ArrayDTypeT]: """Compute pairwise a commutative function.""" dim1 = len(arg1) if arg2 is None or arg2 is arg1: - indices = np.triu_indices(dim1) + triu_indices = np.triu_indices(dim1) triang_vec = _map_in_batches( function, (arg1, arg1), - indices, + triu_indices, memory_per_batch=memory_per_batch, **kwargs, ) @@ -541,10 +487,10 @@ def _pairwise_symmetric( matrix = np.empty((dim1, dim1), dtype=triang_vec.dtype) # Set upper matrix - matrix[indices] = triang_vec + matrix[triu_indices] = triang_vec # Set lower matrix - matrix[(indices[1], indices[0])] = triang_vec + matrix[(triu_indices[1], triu_indices[0])] = triang_vec return matrix @@ -559,7 +505,7 @@ def _pairwise_symmetric( **kwargs, ) - return vec.reshape((dim1, dim2)) + return np.reshape(vec, (dim1, dim2)) def _int_to_real(array: Union[NDArrayInt, NDArrayFloat]) -> NDArrayFloat: @@ -589,7 +535,7 @@ def _check_array_key(array: NDArrayAny, key: Any) -> Any: return key -def _check_estimator(estimator): +def _check_estimator(estimator: BaseEstimator) -> None: from sklearn.utils.estimator_checks import ( check_get_params_invariance, check_set_params, @@ -601,7 +547,9 @@ def _check_estimator(estimator): check_set_params(name, instance) -def _classifier_get_classes(y: ndarray) -> Tuple[ndarray, NDArrayInt]: +def _classifier_get_classes( + y: NDArrayStr | NDArrayInt, +) -> Tuple[NDArrayStr | NDArrayInt, NDArrayInt]: check_classification_targets(y) diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index d78f3b2fc..462b729e2 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -3,11 +3,12 @@ from __future__ import annotations import functools -from typing import Container +from typing import Container, Sequence import numpy as np from ..representation import FData, FDataBasis, FDataGrid +from ..representation._typing import ArrayLike, NDArrayFloat def check_fdata_dimensions( @@ -157,3 +158,73 @@ def check_fdata_same_kind( # If there is no subclassing, execute both checks if not isinstance(fdata1, type(fdata2)): _check_fdata_same_kind_specific(fdata2, fdata1) + + +def _valid_eval_points_shape( + shape: Sequence[int], + *, + dim_domain: int, +) -> bool: + """Check that the shape for aligned evaluation points is ok.""" + return ( + (len(shape) == 2 and shape[-1] == dim_domain) # noqa: WPS222 + or (len(shape) <= 1 and dim_domain == 1) # Domain ommited + or (len(shape) == 1 and shape == (dim_domain,)) # Num. points ommited + ) + + +def validate_evaluation_points( + eval_points: ArrayLike, + *, + aligned: bool, + n_samples: int, + dim_domain: int, +) -> NDArrayFloat: + """Convert and reshape the eval_points to ndarray. + + Args: + eval_points: Evaluation points to be reshaped. + aligned: Boolean flag. True if all the samples + will be evaluated at the same evaluation_points. + n_samples: Number of observations. + dim_domain: Dimension of the domain. + + Returns: + Numpy array with the eval_points, if + evaluation_aligned is True with shape `number of evaluation points` + x `dim_domain`. If the points are not aligned the shape of the + points will be `n_samples` x `number of evaluation points` + x `dim_domain`. + + """ + eval_points = np.asarray(eval_points) + + shape: Sequence[int] + if aligned: + if _valid_eval_points_shape(eval_points.shape, dim_domain=dim_domain): + shape = (-1, dim_domain) + else: + raise ValueError( + "Invalid shape for evaluation points." + f"An array with size (n_points, dim_domain (={dim_domain})) " + "was expected (both can be ommited if they are 1)." + "Instead, the received evaluation points have shape " + f"{eval_points.shape}.", + ) + else: + if eval_points.shape[0] == n_samples and _valid_eval_points_shape( + eval_points.shape[1:], + dim_domain=dim_domain, + ): + shape = (n_samples, -1, dim_domain) + else: + raise ValueError( + "Invalid shape for unaligned evaluation points." + f"An array with size (n_samples (={n_samples}), " + f"n_points, dim_domain (={dim_domain})) " + "was expected (the last two can be ommited if they are 1)." + "Instead, the received evaluation points have shape " + f"{eval_points.shape}.", + ) + + return eval_points.reshape(shape) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index c79800adc..84846aa8e 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -27,7 +27,7 @@ from matplotlib.figure import Figure from typing_extensions import Literal -from .._utils import _evaluate_grid, _reshape_eval_points, _to_grid_points +from .._utils import _evaluate_grid, _to_grid_points from ._typing import ( ArrayLike, DomainRange, @@ -50,7 +50,6 @@ EvalPointsType = Union[ ArrayLike, - Iterable[ArrayLike], GridPointsLike, Iterable[GridPointsLike], ] @@ -428,19 +427,7 @@ def __call__( derivative: int = 0, extrapolation: Optional[ExtrapolationLike] = None, grid: Literal[False] = False, - aligned: Literal[True] = True, - ) -> NDArrayFloat: - pass - - @overload - def __call__( - self, - eval_points: Iterable[ArrayLike], - *, - derivative: int = 0, - extrapolation: Optional[ExtrapolationLike] = None, - grid: Literal[False] = False, - aligned: Literal[False], + aligned: bool = True, ) -> NDArrayFloat: pass @@ -520,6 +507,8 @@ def __call__( function at the values specified in eval_points. """ + from ..misc.validation import validate_evaluation_points + if derivative != 0: warnings.warn( "Parameter derivative is deprecated. Use the " @@ -545,8 +534,6 @@ def __call__( aligned=aligned, ) - eval_points = cast(Union[ArrayLike, Iterable[ArrayLike]], eval_points) - if extrapolation is None: extrapolation = self.extrapolation else: @@ -554,12 +541,12 @@ def __call__( extrapolation = _parse_extrapolation(extrapolation) eval_points = cast( - Union[ArrayLike, Sequence[ArrayLike]], + ArrayLike, eval_points, ) # Convert to array and check dimensions of eval points - eval_points = _reshape_eval_points( + eval_points = validate_evaluation_points( eval_points, aligned=aligned, n_samples=self.n_samples, diff --git a/skfda/representation/_typing.py b/skfda/representation/_typing.py index 30be3e81f..f4a841413 100644 --- a/skfda/representation/_typing.py +++ b/skfda/representation/_typing.py @@ -15,6 +15,7 @@ NDArrayInt = NDArray[np.int_] NDArrayFloat = NDArray[np.float_] NDArrayBool = NDArray[np.bool_] + NDArrayStr = NDArray[np.str_] NDArrayObject = NDArray[np.object_] except ImportError: NDArray = np.ndarray # type:ignore[misc] @@ -22,6 +23,7 @@ NDArrayInt = np.ndarray # type:ignore[misc] NDArrayFloat = np.ndarray # type:ignore[misc] NDArrayBool = np.ndarray # type:ignore[misc] + NDArrayStr = np.ndarray # type:ignore[misc] NDArrayObject = np.ndarray # type:ignore[misc] VectorType = TypeVar("VectorType") diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index e41ccb1f2..996f46c12 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -10,7 +10,7 @@ import numpy as np from matplotlib.figure import Figure -from ..._utils import _reshape_eval_points, _same_domain, _to_domain_range +from ..._utils import _same_domain, _to_domain_range from .._typing import ArrayLike, DomainRange, DomainRangeLike, NDArrayFloat if TYPE_CHECKING: @@ -74,6 +74,8 @@ def __call__( eval_points. """ + from ...misc.validation import validate_evaluation_points + if derivative < 0: raise ValueError("derivative only takes non-negative values.") elif derivative != 0: @@ -84,7 +86,7 @@ def __call__( ) return self.derivative(order=derivative)(eval_points) - eval_points = _reshape_eval_points( + eval_points = validate_evaluation_points( eval_points, aligned=True, n_samples=self.n_basis, From 1637c69515db847e9ea95174df511000626e193c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 26 Aug 2022 18:44:10 +0200 Subject: [PATCH 258/400] Upgrade minimum version to 3.8. Fix Mypy errors. --- .github/workflows/tests.yml | 2 +- .travis.yml | 53 ----------------------- readthedocs.yml | 2 +- skfda/preprocessing/smoothing/_linear.py | 7 ++- skfda/representation/_functional_data.py | 31 ++++++++++++- skfda/representation/basis/_fdatabasis.py | 49 +++++++-------------- skfda/representation/grid.py | 47 ++++++++------------ 7 files changed, 67 insertions(+), 124 deletions(-) delete mode 100644 .travis.yml diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 3b14b1de2..2d910db96 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -11,7 +11,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ['3.7', '3.8'] + python-version: ['3.8', '3.9'] steps: - uses: actions/checkout@v2 diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index db248d6a7..000000000 --- a/.travis.yml +++ /dev/null @@ -1,53 +0,0 @@ -language: python - -matrix: - include: - - name: "Python 3.6 on Linux" - python: 3.6 - - name: "Python 3.7.1 on Xenial Linux" - python: 3.7 # this works for Linux but is ignored on macOS or Windows - dist: xenial # required for Python >= 3.7 - env: - - COVERAGE=true # coverage test are only run in python 3.7 - - PEP8=true # pep8 checks are only run in python 3.7 - - name: "Python 3.7.2 on macOS" - os: osx - osx_image: xcode10.2 # Python 3.7.2 running on macOS 10.14.3 - language: shell # 'language: python' is an error on Travis CI macOS - - name: "Python 3.7.3 on Windows" - os: windows # Windows 10.0.17134 N/A Build 17134 - language: shell # 'language: python' is an error on Travis CI Windows - before_install: - - choco install python3 --version=3.7.3 - env: PATH=/c/Python37:/c/Python37/Scripts:$PATH - - name: "Coverage and pep 8 tests on Python 3.7.1 on Xenial Linux" - python: 3.7 # this works for Linux but is ignored on macOS or Windows - dist: xenial # required for Python >= 3.7 - env: - - PEP8COVERAGE=true # coverage test are only - -install: - - pip3 install --upgrade pip cython numpy || pip3 install --upgrade --user pip cython numpy # all three OSes agree about 'pip3' - - | - if [[ $PEP8COVERAGE == true ]]; then - pip3 install flake8 || pip3 install --user flake8 - pip3 install codecov pytest-cov || pip3 install --user codecov pytest-cov - fi - -# 'python' points to Python 2.7 on macOS but points to Python 3.7 on Linux and Windows -# 'python3' is a 'command not found' error on Windows but 'py' works on Windows only -script: - - | - pip3 install . - if [[ $PEP8COVERAGE == true ]]; then - flake8 --exit-zero skfda; - coverage run --source=skfda/ setup.py test; - else - python3 setup.py test || python setup.py test; - fi - -after_success: - - | - if [[ $PEP8COVERAGE == true ]]; then - codecov - fi diff --git a/readthedocs.yml b/readthedocs.yml index 08775369f..6e905e528 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -18,7 +18,7 @@ sphinx: # Optionally set the version of Python and requirements required to build your docs python: - version: 3.7 + version: 3.8 install: - requirements: readthedocs-requirements.txt - method: pip diff --git a/skfda/preprocessing/smoothing/_linear.py b/skfda/preprocessing/smoothing/_linear.py index f9d787d73..d4a5d69b0 100644 --- a/skfda/preprocessing/smoothing/_linear.py +++ b/skfda/preprocessing/smoothing/_linear.py @@ -10,17 +10,16 @@ from typing import Any, Mapping, Optional import numpy as np -from sklearn.base import BaseEstimator, TransformerMixin from ... import FDataGrid from ..._utils import _to_grid_points +from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from ...representation._typing import GridPointsLike class _LinearSmoother( - abc.ABC, - BaseEstimator, # type: ignore - TransformerMixin, # type: ignore + BaseEstimator, + TransformerMixin, ): """Linear smoother. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 84846aa8e..17a342628 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -287,7 +287,7 @@ def _join_evaluation( @abstractmethod def _evaluate( self, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: NDArrayFloat, *, aligned: bool = True, ) -> NDArrayFloat: @@ -946,9 +946,32 @@ def equals(self, other: object) -> bool: ) @abstractmethod - def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] + def _eq_elemenwise(self: T, other: T) -> NDArrayBool: + """Elementwise equality.""" pass + def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] + """Elementwise equality, as with arrays.""" + if not isinstance(other, type(self)) or self.dtype != other.dtype: + if other is pandas.NA: + return self.isna() + if pandas.api.types.is_list_like(other) and not isinstance( + other, (pandas.Series, pandas.Index, pandas.DataFrame), + ): + other = cast(Iterable[object], other) + return np.concatenate([x == y for x, y in zip(self, other)]) + + return NotImplemented + + if len(self) != len(other) and len(self) != 1 and len(other) != 1: + raise ValueError( + f"Different lengths: " + f"len(self)={len(self)} and " + f"len(other)={len(other)}", + ) + + return self._eq_elemenwise(other) + def __ne__(self, other: object) -> NDArrayBool: # type: ignore[override] """Return for `self != other` (element-wise in-equality).""" result = self.__eq__(other) @@ -1126,6 +1149,10 @@ def _take_allow_fill( ) -> T: pass + @abstractmethod + def isna(self) -> NDArrayBool: # noqa: D102 + pass + def take( # noqa: WPS238 self: T, indices: Union[int, Sequence[int], NDArrayInt], diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 8b90ac652..ea2051745 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -6,7 +6,6 @@ from typing import ( TYPE_CHECKING, Any, - Iterable, Optional, Sequence, Type, @@ -208,13 +207,6 @@ def from_data( ) grid_points = sample_points - # n is the samples - # m is the observations - # k is the number of elements of the basis - - # Each sample in a column (m x n) - data_matrix = np.atleast_2d(data_matrix) - fd = grid.FDataGrid(data_matrix=data_matrix, grid_points=grid_points) return fd.to_basis(basis=basis, method=method) @@ -254,7 +246,7 @@ def domain_range(self) -> DomainRange: def _evaluate( self, - eval_points: ArrayLike, + eval_points: NDArrayFloat, *, aligned: bool = True, ) -> NDArrayFloat: @@ -695,26 +687,12 @@ def equals(self, other: object) -> bool: and np.array_equal(self.coefficients, other.coefficients) ) - def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] + def _eq_elemenwise(self: T, other: T) -> NDArrayBool: """Elementwise equality of FDataBasis.""" - if not isinstance(other, type(self)) or self.dtype != other.dtype: - if other is pandas.NA: - return self.isna() - if pandas.api.types.is_list_like(other) and not isinstance( - other, (pandas.Series, pandas.Index, pandas.DataFrame), - ): - return np.concatenate([x == y for x, y in zip(self, other)]) - - return NotImplemented - - if len(self) != len(other) and len(self) != 1 and len(other) != 1: - raise ValueError( - f"Different lengths: " - f"len(self)={len(self)} and " - f"len(other)={len(other)}", - ) - - return np.all(self.coefficients == other.coefficients, axis=1) + return np.all( # type: ignore[no-any-return] + self.coefficients == other.coefficients, + axis=1, + ) def concatenate( self: T, @@ -774,9 +752,9 @@ def compose( compute the composition. Args: - fd (:class:`FData`): FData object to make the composition. Should + fd: FData object to make the composition. Should have the same number of samples and image dimension equal to 1. - eval_points (array_like): Points to perform the evaluation. + eval_points: Points to perform the evaluation. kwargs: Named arguments to be passed to :func:`from_data`. Returns: @@ -803,7 +781,7 @@ def __getitem__( return self.copy( coefficients=self.coefficients[key], - sample_names=np.array(self.sample_names)[key], + sample_names=list(np.array(self.sample_names)[key]), ) def __add__( @@ -967,10 +945,15 @@ def isna(self) -> NDArrayBool: Returns: na_values (np.ndarray): Positions of NA. """ - return np.all(np.isnan(self.coefficients), axis=1) + return np.all( # type: ignore[no-any-return] + np.isnan(self.coefficients), + axis=1, + ) -class FDataBasisDType(pandas.api.extensions.ExtensionDtype): # type: ignore +class FDataBasisDType( + pandas.api.extensions.ExtensionDtype, # type: ignore[misc] +): """DType corresponding to FDataBasis in Pandas.""" kind = 'O' diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 20b978a8f..5457df25f 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -13,7 +13,6 @@ from typing import ( TYPE_CHECKING, Any, - Iterable, Optional, Sequence, Type, @@ -213,7 +212,7 @@ def __init__( # noqa: WPS211 if self.data_matrix.ndim == 1 + self.dim_domain: self.data_matrix = self.data_matrix[..., np.newaxis] - self.interpolation = interpolation # type: ignore + self.interpolation = interpolation # type: ignore[assignment] super().__init__( extrapolation=extrapolation, @@ -398,12 +397,12 @@ def interpolation(self, new_interpolation: Optional[Evaluator]) -> None: def _evaluate( self, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: NDArrayFloat, *, aligned: bool = True, ) -> NDArrayFloat: - return self.interpolation( # type: ignore + return self.interpolation( self, eval_points, aligned=aligned, @@ -680,26 +679,9 @@ def equals(self, other: object) -> bool: return self.interpolation == other.interpolation - def __eq__(self, other: object) -> NDArrayBool: # type: ignore[override] + def _eq_elemenwise(self: T, other: T) -> NDArrayBool: """Elementwise equality of FDataGrid.""" - if not isinstance(other, type(self)) or self.dtype != other.dtype: - if other is pandas.NA: - return self.isna() - if pandas.api.types.is_list_like(other) and not isinstance( - other, (pandas.Series, pandas.Index, pandas.DataFrame), - ): - return np.concatenate([x == y for x, y in zip(self, other)]) - - return NotImplemented - - if len(self) != len(other) and len(self) != 1 and len(other) != 1: - raise ValueError( - f"Different lengths: " - f"len(self)={len(self)} and " - f"len(other)={len(other)}", - ) - - return np.all( + return np.all( # type: ignore[no-any-return] self.data_matrix == other.data_matrix, axis=tuple(range(1, self.data_matrix.ndim)), ) @@ -1113,7 +1095,7 @@ def restrict( grid_points[keep_index], ) - data_matrix = self.data_matrix[tuple([slice(None)] + index_list)] + data_matrix = self.data_matrix[(slice(None),) + tuple(index_list)] return self.copy( domain_range=domain_range, @@ -1170,7 +1152,7 @@ def shift( >>> >>> t = np.linspace(0, 1, 6) >>> x = np.array([t, t**2, t**3]) - >>> fd = FDataGrid(x, t) + >>> fd = skfda.FDataGrid(x, t) >>> fd.domain_range[0] (0.0, 1.0) >>> fd.grid_points[0] @@ -1275,8 +1257,11 @@ def compose( aligned=False, ) else: - if eval_points is None: - eval_points = fd.grid_points + eval_points = ( + fd.grid_points + if eval_points is None + else _to_grid_points(eval_points) + ) grid_transformation = fd(eval_points, grid=True) @@ -1339,7 +1324,7 @@ def __getitem__( return self.copy( data_matrix=self.data_matrix[key], - sample_names=np.array(self.sample_names)[key], + sample_names=list(np.array(self.sample_names)[key]), ) ##################################################################### @@ -1442,13 +1427,15 @@ def isna(self) -> NDArrayBool: Returns: na_values: Positions of NA. """ - return np.all( + return np.all( # type: ignore[no-any-return] np.isnan(self.data_matrix), axis=tuple(range(1, self.data_matrix.ndim)), ) -class FDataGridDType(pandas.api.extensions.ExtensionDtype): # type: ignore +class FDataGridDType( + pandas.api.extensions.ExtensionDtype, # type: ignore[misc] +): """DType corresponding to FDataGrid in Pandas.""" name = 'FDataGrid' From c7e4e64de229398bf4a295fdfc5f86e619289f18 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 26 Aug 2022 23:23:06 +0200 Subject: [PATCH 259/400] Fix Mypy errors. --- skfda/preprocessing/smoothing/_basis.py | 4 ++-- skfda/preprocessing/smoothing/_linear.py | 12 ++++++------ 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index a9b00e27a..0d5707c60 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -270,7 +270,7 @@ def _hat_matrix( def fit( self, X: FDataGrid, - y: None = None, + y: object = None, ) -> BasisSmoother: """Compute the hat matrix for the desired output points. @@ -297,7 +297,7 @@ def fit( def transform( self, X: FDataGrid, - y: None = None, + y: object = None, ) -> FData: """ Smooth the data. diff --git a/skfda/preprocessing/smoothing/_linear.py b/skfda/preprocessing/smoothing/_linear.py index d4a5d69b0..8c5bf0062 100644 --- a/skfda/preprocessing/smoothing/_linear.py +++ b/skfda/preprocessing/smoothing/_linear.py @@ -14,12 +14,12 @@ from ... import FDataGrid from ..._utils import _to_grid_points from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin -from ...representation._typing import GridPointsLike +from ...representation._typing import GridPointsLike, NDArrayFloat class _LinearSmoother( BaseEstimator, - TransformerMixin, + TransformerMixin[FDataGrid, FDataGrid, object], ): """Linear smoother. @@ -39,7 +39,7 @@ def hat_matrix( self, input_points: Optional[GridPointsLike] = None, output_points: Optional[GridPointsLike] = None, - ) -> np.ndarray: + ) -> NDArrayFloat: # Use the fitted points if they are not provided if input_points is None: @@ -57,7 +57,7 @@ def _hat_matrix( self, input_points: GridPointsLike, output_points: GridPointsLike, - ) -> np.ndarray: + ) -> NDArrayFloat: pass def _more_tags(self) -> Mapping[str, Any]: @@ -68,7 +68,7 @@ def _more_tags(self) -> Mapping[str, Any]: def fit( self, X: FDataGrid, - y: None = None, + y: object = None, ) -> _LinearSmoother: """Compute the hat matrix for the desired output points. @@ -94,7 +94,7 @@ def fit( def transform( self, X: FDataGrid, - y: None = None, + y: object = None, ) -> FDataGrid: """Multiply the hat matrix with the function values to smooth them. From 88e52251c55b48b91b438410983343a3b2e3d5d0 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 29 Aug 2022 07:10:27 +0200 Subject: [PATCH 260/400] Fix more Mypy errors. Simplify extrapolation logic. --- skfda/_utils/_utils.py | 2 +- skfda/misc/validation.py | 4 +- skfda/representation/_typing.py | 4 +- skfda/representation/evaluator.py | 51 ++++++-------------- skfda/representation/extrapolation.py | 68 +++++++-------------------- skfda/representation/interpolation.py | 32 ++++++------- 6 files changed, 54 insertions(+), 107 deletions(-) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 72717a6a8..f86db2af2 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -36,7 +36,6 @@ NDArrayInt, NDArrayStr, ) -from ..representation.extrapolation import ExtrapolationLike from ._sklearn_adapter import BaseEstimator RandomStateLike = Optional[Union[int, np.random.RandomState]] @@ -46,6 +45,7 @@ if TYPE_CHECKING: from ..representation import FData, FDataGrid from ..representation.basis import Basis + from ..representation.extrapolation import ExtrapolationLike T = TypeVar("T", bound=FData) Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index 462b729e2..e01351a79 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -8,7 +8,7 @@ import numpy as np from ..representation import FData, FDataBasis, FDataGrid -from ..representation._typing import ArrayLike, NDArrayFloat +from ..representation._typing import ArrayLike, EvaluationPoints def check_fdata_dimensions( @@ -179,7 +179,7 @@ def validate_evaluation_points( aligned: bool, n_samples: int, dim_domain: int, -) -> NDArrayFloat: +) -> EvaluationPoints: """Convert and reshape the eval_points to ndarray. Args: diff --git a/skfda/representation/_typing.py b/skfda/representation/_typing.py index f4a841413..acad6c15e 100644 --- a/skfda/representation/_typing.py +++ b/skfda/representation/_typing.py @@ -1,5 +1,5 @@ """Common types.""" -from typing import Any, Optional, Sequence, Tuple, TypeVar, Union +from typing import Any, NewType, Optional, Sequence, Tuple, TypeVar, Union import numpy as np from typing_extensions import Protocol @@ -41,6 +41,8 @@ GridPoints = Tuple[np.ndarray, ...] GridPointsLike = Union[ArrayLike, Sequence[ArrayLike]] +EvaluationPoints = NDArrayFloat + class Vector(Protocol): """ diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index c954b04c6..ffd9087a6 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -8,11 +8,11 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Iterable, Union, overload +from typing import TYPE_CHECKING, Any -from typing_extensions import Literal, Protocol +from typing_extensions import Protocol -from ._typing import ArrayLike, NDArrayFloat +from ._typing import ArrayLike, EvaluationPoints, NDArrayFloat if TYPE_CHECKING: from . import FData @@ -36,7 +36,7 @@ class Evaluator(ABC): def _evaluate( self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NDArrayFloat: @@ -48,41 +48,11 @@ def _evaluate( """ pass - @overload def __call__( self, fdata: FData, eval_points: ArrayLike, *, - aligned: Literal[True] = True, - ) -> NDArrayFloat: - pass - - @overload - def __call__( - self, - fdata: FData, - eval_points: Iterable[ArrayLike], - *, - aligned: Literal[False], - ) -> NDArrayFloat: - pass - - @overload - def __call__( - self, - fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], - *, - aligned: bool, - ) -> NDArrayFloat: - pass - - def __call__( - self, - fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], - *, aligned: bool = True, ) -> NDArrayFloat: """ @@ -109,6 +79,15 @@ def __call__( j-th evaluation point. """ + from ..misc.validation import validate_evaluation_points + + eval_points = validate_evaluation_points( + eval_points, + aligned=aligned, + n_samples=fdata.n_samples, + dim_domain=fdata.dim_domain, + ) + return self._evaluate( fdata=fdata, eval_points=eval_points, @@ -129,7 +108,7 @@ class EvaluateFunction(Protocol): def __call__( self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NDArrayFloat: @@ -175,7 +154,7 @@ def __init__(self, evaluate_function: EvaluateFunction) -> None: def _evaluate( # noqa: D102 self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NDArrayFloat: diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index d573558bd..934919a5f 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -5,21 +5,12 @@ """ from __future__ import annotations -from typing import ( - TYPE_CHECKING, - Any, - Iterable, - NoReturn, - Optional, - Union, - cast, - overload, -) +from typing import TYPE_CHECKING, Any, NoReturn, Optional, Union, overload import numpy as np from typing_extensions import Literal -from ._typing import ArrayLike, NDArrayFloat +from ._typing import ArrayLike, EvaluationPoints, NDArrayFloat from .evaluator import Evaluator if TYPE_CHECKING: @@ -66,7 +57,7 @@ class PeriodicExtrapolation(Evaluator): def _evaluate( # noqa: D102 self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NDArrayFloat: @@ -116,31 +107,19 @@ class BoundaryExtrapolation(Evaluator): def _evaluate( # noqa: D102 self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NDArrayFloat: domain_range = fdata.domain_range - if aligned: - eval_points = np.asarray(eval_points) - - for i in range(fdata.dim_domain): - a, b = domain_range[i] - eval_points[eval_points[..., i] < a, i] = a - eval_points[eval_points[..., i] > b, i] = b - else: - eval_points = cast(Iterable[ArrayLike], eval_points) - - for points_per_sample in eval_points: - - points_per_sample = np.asarray(points_per_sample) + eval_points = np.asarray(eval_points) - for i in range(fdata.dim_domain): - a, b = domain_range[i] - points_per_sample[points_per_sample[..., i] < a, i] = a - points_per_sample[points_per_sample[..., i] > b, i] = b + for i in range(fdata.dim_domain): + a, b = domain_range[i] + eval_points[eval_points[..., i] < a, i] = a + eval_points[eval_points[..., i] > b, i] = b return fdata(eval_points, aligned=aligned) @@ -177,7 +156,7 @@ class ExceptionExtrapolation(Evaluator): def _evaluate( # noqa: D102 self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NoReturn: @@ -223,33 +202,20 @@ class FillExtrapolation(Evaluator): def __init__(self, fill_value: float) -> None: self.fill_value = fill_value - def _fill(self, fdata: FData, eval_points: ArrayLike) -> NDArrayFloat: - eval_points = np.asarray(eval_points) - - shape = ( - fdata.n_samples, - eval_points.shape[-2], - fdata.dim_codomain, - ) - return np.full(shape, self.fill_value) - def _evaluate( # noqa: D102 self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: EvaluationPoints, *, aligned: bool = True, ) -> NDArrayFloat: - if aligned: - eval_points = cast(ArrayLike, eval_points) - return self._fill(fdata, eval_points) - - eval_points = cast(Iterable[ArrayLike], eval_points) - - res_list = [self._fill(fdata, p) for p in eval_points] - - return np.asarray(res_list) + shape = ( + fdata.n_samples, + eval_points.shape[-2], + fdata.dim_codomain, + ) + return np.full(shape, self.fill_value) def __repr__(self) -> str: return ( diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 7d9c148fb..d812d3b2c 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -23,7 +23,7 @@ UnivariateSpline, ) -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArrayFloat from .evaluator import Evaluator if TYPE_CHECKING: @@ -58,16 +58,16 @@ def __init__( def _evaluate_one( self, spline: SplineCallable, - eval_points: np.ndarray, - ) -> np.ndarray: + eval_points: NDArrayFloat, + ) -> NDArrayFloat: """Evaluate one spline of the list.""" pass def _evaluate_codomain( self, spline_list: Sequence[SplineCallable], - eval_points: np.ndarray, - ) -> np.ndarray: + eval_points: NDArrayFloat, + ) -> NDArrayFloat: """Evaluate a multidimensional sample.""" return np.array([ self._evaluate_one(spl, eval_points) @@ -77,12 +77,12 @@ def _evaluate_codomain( def evaluate( self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: ArrayLike, *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: - res: np.ndarray + res: NDArrayFloat if aligned: @@ -199,7 +199,7 @@ def __init__( if monotone: def constructor( # noqa: WPS430 - data: np.ndarray, + data: NDArrayFloat, ) -> SplineCallable: """Construct an unidimensional cubic monotone interpolation.""" return PchipInterpolator(grid_points, data) @@ -207,7 +207,7 @@ def constructor( # noqa: WPS430 else: def constructor( # noqa: WPS430, WPS440 - data: np.ndarray, + data: NDArrayFloat, ) -> SplineCallable: """Construct an unidimensional interpolation.""" return UnivariateSpline( @@ -226,8 +226,8 @@ def constructor( # noqa: WPS430, WPS440 def _evaluate_one( self, spline: SplineCallable, - eval_points: np.ndarray, - ) -> np.ndarray: + eval_points: NDArrayFloat, + ) -> NDArrayFloat: try: return spline(eval_points)[:, 0] except ValueError: @@ -405,8 +405,8 @@ def __init__( def _evaluate_one( self, spline: SplineCallable, - eval_points: np.ndarray, - ) -> np.ndarray: + eval_points: NDArrayFloat, + ) -> NDArrayFloat: return spline(eval_points) @@ -505,10 +505,10 @@ def _build_interpolator( def _evaluate( # noqa: D102 self, fdata: FData, - eval_points: Union[ArrayLike, Iterable[ArrayLike]], + eval_points: ArrayLike, *, aligned: bool = True, - ) -> np.ndarray: + ) -> NDArrayFloat: spline_list = self._build_interpolator(fdata) From 3dfa78550a2d3643f521283da3856f5b26d76397 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Mon, 29 Aug 2022 20:20:19 +0200 Subject: [PATCH 261/400] Update plot_kernel_smoothing.py * Fix runtime warning (division by zero) * Change bandwidth values in CV parameter search * Reorder sections --- examples/plot_kernel_smoothing.py | 188 ++++++++++++++++++------------ 1 file changed, 116 insertions(+), 72 deletions(-) diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index 088eac333..ac8decc08 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -3,47 +3,113 @@ ================ This example uses different kernel smoothing methods over the phoneme data -set and shows how cross validations scores vary over a range of different -parameters used in the smoothing methods. It also show examples of -undersmoothing and oversmoothing. +set (:func:`phoneme `) and shows how cross +validations scores vary over a range of different parameters used in the +smoothing methods. It also shows examples of undersmoothing and oversmoothing. """ # Author: Miguel Carbajo Berrocal +# Modified: Elena Petrunina # License: MIT -import matplotlib.pylab as plt +import matplotlib.pyplot as plt import numpy as np import skfda -import skfda.misc.hat_matrix as hm import skfda.preprocessing.smoothing.validation as val from skfda.preprocessing.smoothing import KernelSmoother +from skfda.misc.kernels import uniform +from skfda.misc.hat_matrix import ( + NadarayaWatsonHatMatrix, + LocalLinearRegressionHatMatrix, + KNeighborsHatMatrix, +) ############################################################################## -# For this example, we will use the -# :func:`phoneme ` dataset. This dataset -# contains the log-periodograms of several phoneme pronunciations. The phoneme -# curves are very irregular and noisy, so we usually will want to smooth them -# as a preprocessing step. +# This dataset contains the log-periodograms of several phoneme pronunciations. +# The phoneme curves are very irregular and noisy, so we usually will want to +# smooth them as a preprocessing step. # # As an example, we will smooth the first 300 curves only. In the following # plot, the first five curves are shown. + dataset = skfda.datasets.fetch_phoneme() fd = dataset['data'][:300] -fd[:5].plot() +out = fd[:5].plot() + +############################################################################# +# To better illustrate the smoothing effects and the influence of different +# values of the bandwidth parameter, all results will be plotted for a single +# curve. However, note that the smoothing is performed at the same time for all +# functions contained in a FData object. + +############################################################################# +# We will take the curve found at index 10 (a random choice). +# Below the original (without any smoothing) curve is plotted. + +out = fd[10].plot() + +############################################################################# +# The library currently has three smoothing methods available: +# Nadaraya-Watson, Local Linear Regression and K-Neigbors. + + +############################################################################# +# The bandwith parameter controls the influence of more distant points on the +# final estimation. So, it is to be expected that with larger bandwidth +# values, the resulting function will be smoother. + +############################################################################# +# Below are examples of oversmoothing (with bandwidth = 1) and undersmoothing +# (with bandwidth = 0.05) using the Nadaraya-Watson method with normal kernel. + +fd_os = KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), +).fit_transform(fd) + +fd_us = KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=0.05), +).fit_transform(fd) + +############################################################################## +# Over-smoothed + +fig = fd[10].plot() +out = fd_os[10].plot(fig=fig) + +############################################################################## +# Under-smoothed + +fig = fd[10].plot() +out = fd_us[10].plot(fig=fig) + +############################################################################# +# The same could be done, for example, with different kernel. For example, +# over-smoothed case with uniform kernel: + +fd_os = KernelSmoother( + kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1, kernel=uniform), +).fit_transform(fd) + +fig = fd[10].plot() +out = fd_os[10].plot(fig=fig) + +############################################################################## +# The values for which the undersmoothing and oversmoothing occur are different +# for each dataset and that is why the library also has parameter search +# methods. + ############################################################################## # Here we show the general cross validation scores for different values of the -# parameters given to the different smoothing methods. Currently we have -# three kernel smoothing methods implemented: Nadaraya Watson, Local Linear -# Regression and K Nearest Neighbors (k-NN) +# parameters given to the different smoothing methods. ############################################################################## # The smoothing parameter for k-NN is the number of neighbors. We will choose -# this parameter between 1 and 23 in this example. +# this parameter between 2 and 23 in this example. -n_neighbors = np.arange(1, 24) +n_neighbors = np.arange(2, 24) ############################################################################## # The smoothing parameter for Nadaraya Watson and Local Linear Regression is @@ -51,22 +117,27 @@ # As we want to compare the results of these smoothers with k-NN, with uses # as the smoothing parameter the number of neighbors, we want to use a # comparable range of values. In this case, we know that our grid points are -# equispaced, so a given bandwidth ``B`` will include -# ``B * N / D`` grid points, where ``N`` is the total number of grid points -# and ``D`` the size of the whole domain range. Thus, if we pick -# ``B = n_neighbors * D / N``, ``B`` will include ``n_neighbors`` grid points -# and we could compare the results of the different smoothers. - -scale_factor = ( - (fd.domain_range[0][1] - fd.domain_range[0][0]) / len(fd.grid_points[0]) -) +# equispaced and the distance between two contiguous points is approximately +# 0.03. -bandwidth = n_neighbors * scale_factor +############################################################################## +# As we want to obtain the estimate at the same points where the function is +# already defined (input_points equals output_points), the nearest neighbor +# will be the point itself, and the second nearest neighbor, the contiguous +# point. So to get equivalent bandwidth values for each value of n_neigbors we +# have to take 22 values in the range from approximately 0.3 to 0.3*11 + +dist = fd.grid_points[0][1] - fd.grid_points[0][0] +bandwidth = np.linspace( + dist, + dist*((n_neighbors[-1] - 1)//2), + len(n_neighbors) +) # K-nearest neighbours kernel smoothing. knn = val.SmoothingParameterSearch( - KernelSmoother(kernel_estimator=hm.KNeighborsHatMatrix()), + KernelSmoother(kernel_estimator=KNeighborsHatMatrix()), n_neighbors, param_name='kernel_estimator__n_neighbors', ) @@ -75,7 +146,7 @@ # Local linear regression kernel smoothing. llr = val.SmoothingParameterSearch( - KernelSmoother(kernel_estimator=hm.LocalLinearRegressionHatMatrix()), + KernelSmoother(kernel_estimator=LocalLinearRegressionHatMatrix()), bandwidth, param_name='kernel_estimator__bandwidth', ) @@ -84,18 +155,23 @@ # Nadaraya-Watson kernel smoothing. nw = val.SmoothingParameterSearch( - KernelSmoother(kernel_estimator=hm.NadarayaWatsonHatMatrix()), + KernelSmoother(kernel_estimator=NadarayaWatsonHatMatrix()), bandwidth, param_name='kernel_estimator__bandwidth', ) nw.fit(fd) nw_fd = nw.transform(fd) +############################################################################## +# For more information on how the parameter search is performed and how score +# is calculated see +# :class:`~skfda.preprocessing.smoothing.validation.LinearSmootherGeneralizedCVScorer` + ############################################################################## # The plot of the mean test scores for all smoothers is shown below. # As the X axis we will use the neighbors for all the smoothers in order -# to compare k-NN with the others, but remember that the bandwidth is -# this quantity scaled by ``scale_factor``! +# to compare k-NN with the others, but remember that the bandwidth for the +# other two estimators is the distance to the k-th neighbor. fig = plt.figure() ax = fig.add_subplot(1, 1, 1) @@ -114,12 +190,11 @@ nw.cv_results_['mean_test_score'], label='Nadaraya-Watson', ) -ax.legend() +out = ax.legend() ############################################################################## # We can plot the smoothed curves corresponding to the 11th element of the -# data set (this is a random choice) for the three different smoothing -# methods. +# data set for the three different smoothing methods. fig = plt.figure() ax = fig.add_subplot(1, 1, 1) @@ -131,7 +206,7 @@ knn_fd[10].plot(fig=fig) llr_fd[10].plot(fig=fig) nw_fd[10].plot(fig=fig) -ax.legend( +out = ax.legend( [ 'original data', 'k-nearest neighbors', @@ -141,46 +216,15 @@ title='Smoothing method', ) -############################################################################## -# We can compare the curve before and after the smoothing. - -############################################################################## -# Not smoothed - -fd[10].plot() - -############################################################################## -# Smoothed - -fig = fd[10].scatter(s=0.5) -nw_fd[10].plot(fig=fig, color='green') ############################################################################## # Now, we can see the effects of a proper smoothing. We can plot the same 5 # samples from the beginning using the Nadaraya-Watson kernel smoother with # the best choice of parameter. -nw_fd[:5].plot() - -############################################################################## -# We can also appreciate the effects of undersmoothing and oversmoothing in -# the following plots. - -fd_us = KernelSmoother( - kernel_estimator=hm.NadarayaWatsonHatMatrix(bandwidth=2 * scale_factor), -).fit_transform(fd[10]) -fd_os = KernelSmoother( - kernel_estimator=hm.NadarayaWatsonHatMatrix(bandwidth=15 * scale_factor), -).fit_transform(fd[10]) - -############################################################################## -# Under-smoothed - -fig = fd[10].scatter(s=0.5) -fd_us.plot(fig=fig) - -############################################################################## -# Over-smoothed - -fig = fd[10].scatter(s=0.5) -fd_os.plot(fig=fig) +fig, ax = plt.subplots(2) +fd[:5].plot(ax[0]) +nw_fd[:5].plot(ax[1]) +# Disable xticks and xlabel of first image +ax[0].set_xticks([]) +out = ax[0].set_xlabel('') From 76527aea717b88139580547120c7e8d958f8bb74 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Mon, 29 Aug 2022 20:59:15 +0200 Subject: [PATCH 262/400] Update plot_kernel_smoothing.py Style fixes --- examples/plot_kernel_smoothing.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index ac8decc08..fdb708e8b 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -11,19 +11,20 @@ # Author: Miguel Carbajo Berrocal # Modified: Elena Petrunina # License: MIT +import math import matplotlib.pyplot as plt import numpy as np import skfda import skfda.preprocessing.smoothing.validation as val -from skfda.preprocessing.smoothing import KernelSmoother -from skfda.misc.kernels import uniform from skfda.misc.hat_matrix import ( - NadarayaWatsonHatMatrix, - LocalLinearRegressionHatMatrix, KNeighborsHatMatrix, + LocalLinearRegressionHatMatrix, + NadarayaWatsonHatMatrix, ) +from skfda.misc.kernels import uniform +from skfda.preprocessing.smoothing import KernelSmoother ############################################################################## # This dataset contains the log-periodograms of several phoneme pronunciations. @@ -130,8 +131,8 @@ dist = fd.grid_points[0][1] - fd.grid_points[0][0] bandwidth = np.linspace( dist, - dist*((n_neighbors[-1] - 1)//2), - len(n_neighbors) + dist * (math.ceil((n_neighbors[-1] - 1) / 2)), + len(n_neighbors), ) # K-nearest neighbours kernel smoothing. @@ -171,7 +172,7 @@ # The plot of the mean test scores for all smoothers is shown below. # As the X axis we will use the neighbors for all the smoothers in order # to compare k-NN with the others, but remember that the bandwidth for the -# other two estimators is the distance to the k-th neighbor. +# other two estimators is related to the distance to the k-th neighbor. fig = plt.figure() ax = fig.add_subplot(1, 1, 1) From 21bca8297d87ad59d64535c0641e34c6b70caa36 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Mon, 29 Aug 2022 22:03:09 +0200 Subject: [PATCH 263/400] Speed up evaluation of FDataGrid - Vectorizes interpolation of FDataGrid using the "new" SciPy API. - Removes spline smoothing functionality from interpolators, as it was not true interpolation and was not vectorizable. - Due to some Scipy limitations not all interpolation orders are available in oldest SciPy versions for every domain dimension. As interpolation is rarely changed, though, and most algorithms do not interpolate, we will break backwards compatibility here to achieve vectorization. - In the rare cases where a user needs the old behaviour, they can always create their own interpolation class from the old code. --- examples/plot_composition.py | 15 +- examples/plot_interpolation.py | 51 +- .../stats/_functional_transformers.py | 2 +- .../_function_transformers.py | 2 +- skfda/representation/interpolation.py | 522 ++++-------------- skfda/tests/test_interpolation.py | 4 +- 6 files changed, 125 insertions(+), 471 deletions(-) diff --git a/examples/plot_composition.py b/examples/plot_composition.py index 5dded0d1b..ae618e528 100644 --- a/examples/plot_composition.py +++ b/examples/plot_composition.py @@ -10,12 +10,10 @@ # sphinx_gallery_thumbnail_number = 3 -import skfda - -from mpl_toolkits.mplot3d import axes3d - import numpy as np +from mpl_toolkits.mplot3d import axes3d +import skfda ############################################################################## # Function composition can be applied to our data once is in functional @@ -44,7 +42,8 @@ # Sets cubic interpolation g.interpolation = skfda.representation.interpolation.SplineInterpolation( - interpolation_order=3) + interpolation_order=3, +) # Plots the surface g.plot() @@ -55,7 +54,7 @@ # :math:`g \circ f:\mathbb{R} \rightarrow \mathbb{R}` will be another # functional object with the values of :math:`g` along the path given by # :math:`f`. -# + # Creation of circunference in parametric form t = np.linspace(0, 2 * np.pi, 100) @@ -71,10 +70,9 @@ ############################################################################## # In the following chart it is plotted the curve # :math:`(10 \, \cos(t), 10 \, sin(t), g \circ f (t))` and the surface. -# # Plots surface -fig = g.plot(alpha=.8) +fig = g.plot(alpha=0.8) # Plots path along the surface path = f(t)[0] @@ -85,4 +83,3 @@ ############################################################################## # [1] Function composition `https://en.wikipedia.org/wiki/Function_composition # `_. -# diff --git a/examples/plot_interpolation.py b/examples/plot_interpolation.py index 5cff8265c..fd599a22d 100644 --- a/examples/plot_interpolation.py +++ b/examples/plot_interpolation.py @@ -60,35 +60,6 @@ fd.scatter(fig=fig) plt.show() -############################################################################## -# Smooth interpolation could be performed with the attribute -# ``smoothness_parameter`` of the spline interpolation. - -# Sample with noise -fd_smooth = skfda.datasets.make_sinusoidal_process( - n_samples=1, - n_features=30, - random_state=1, - error_std=0.3, -) - -# Cubic interpolation -fd_smooth.interpolation = SplineInterpolation(interpolation_order=3) - -fig = fd_smooth.plot(label="Cubic") - -# Smooth interpolation -fd_smooth.interpolation = SplineInterpolation( - interpolation_order=3, - smoothness_parameter=1.5, -) - -fd_smooth.plot(fig=fig, label="Cubic smoothed") - -fd_smooth.scatter(fig=fig) -fig.legend() -plt.show() - ############################################################################## # Sometimes our samples are required to be monotone, in these cases it is # possible to use monotone cubic interpolation with the attribute @@ -134,30 +105,10 @@ ############################################################################## # In the following figure it is shown the result of the cubic interpolation -# applied to the surface. -# -# The degree of the interpolation polynomial does not have to coincide in both -# directions, for example, cubic interpolation in the first -# component and quadratic in the second one could be defined using a tuple -# with the values (3,2). +# applied to the surface (requires SciPy >= 1.9). fd.interpolation = SplineInterpolation(interpolation_order=3) fig = fd.plot() fd.scatter(fig=fig) plt.show() - -############################################################################## -# The following table shows the interpolation methods available by the class -# :class:`~skfda.representation.interpolation.SplineInterpolation` depending -# on the domain dimension. -# -# +------------------+--------+----------------+----------+-------------+ -# | Domain dimension | Linear | Up to degree 5 | Monotone | Smoothing | -# +==================+========+================+==========+=============+ -# | 1 | ✔ | ✔ | ✔ | ✔ | -# +------------------+--------+----------------+----------+-------------+ -# | 2 | ✔ | ✔ | ✖ | ✔ | -# +------------------+--------+----------------+----------+-------------+ -# | 3 or more | ✔ | ✖ | ✖ | ✖ | -# +------------------+--------+----------------+----------+-------------+ diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index dd4cd8a33..4b4013e10 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -251,7 +251,7 @@ def occupation_measure( ... ), ... decimals=2, ... ) - array([[ 0.98, 1.02], + array([[ 0.98, 1. ], [ 0.5 , 0.52], [ 6.28, 0. ]]) diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index c46d312f0..74669069b 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -144,7 +144,7 @@ class OccupationMeasureTransformer(BaseEstimator, TransformerMixin): ... ) >>> np.around(occupation_measure.fit_transform(fd_grid), decimals=2) - array([[ 0.98, 1.02], + array([[ 0.98, 1. ], [ 0.5 , 0.52], [ 6.28, 0. ]]) """ diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index d812d3b2c..74510fec6 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -4,414 +4,111 @@ from __future__ import annotations import abc -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Iterable, - Sequence, - Tuple, - Union, - cast, -) +from typing import TYPE_CHECKING, Any, Callable, Sequence import numpy as np from scipy.interpolate import ( PchipInterpolator, - RectBivariateSpline, RegularGridInterpolator, - UnivariateSpline, + make_interp_spline, ) -from ._typing import ArrayLike, NDArrayFloat +from ._typing import ArrayLike, EvaluationPoints, NDArrayFloat from .evaluator import Evaluator if TYPE_CHECKING: - from . import FData - -SplineCallable = Callable[..., np.ndarray] - - -class _SplineList(abc.ABC): - """ABC for list of interpolations.""" - - def __init__( - self, - fdatagrid: FData, - interpolation_order: Union[int, Sequence[int]] = 1, - smoothness_parameter: float = 0, - ): - - super().__init__() + from . import FDataGrid - self.fdatagrid = fdatagrid - self.interpolation_order = interpolation_order - self.smoothness_parameter = smoothness_parameter - self.splines: Sequence[Sequence[SplineCallable]] - # @abc.abstractmethod - # @property - # def splines(self) -> Sequence[SplineCallable]: - # pass +class _BaseInterpolation(Evaluator): + """ABC for interpolations.""" @abc.abstractmethod - def _evaluate_one( + def _evaluate_aligned( self, - spline: SplineCallable, - eval_points: NDArrayFloat, + fdata: FDataGrid, + eval_points: EvaluationPoints, ) -> NDArrayFloat: - """Evaluate one spline of the list.""" + """Evaluate at aligned points.""" pass - def _evaluate_codomain( + def _evaluate_unaligned( self, - spline_list: Sequence[SplineCallable], - eval_points: NDArrayFloat, + fdata: FDataGrid, + eval_points: EvaluationPoints, ) -> NDArrayFloat: - """Evaluate a multidimensional sample.""" - return np.array([ - self._evaluate_one(spl, eval_points) - for spl in spline_list - ]).T + """Evaluate at unaligned points.""" + return np.vstack([ + self._evaluate_aligned(f, e) + for f, e in zip(fdata, eval_points) + ]) - def evaluate( + def _evaluate( # noqa: D102 self, - fdata: FData, + fdata: FDataGrid, eval_points: ArrayLike, *, aligned: bool = True, ) -> NDArrayFloat: - res: NDArrayFloat - - if aligned: - - eval_points = np.asarray(eval_points) - - # Points evaluated inside the domain - res = np.apply_along_axis( - self._evaluate_codomain, - 1, - self.splines, - eval_points, - ) - - res = res.reshape( - fdata.n_samples, - eval_points.shape[0], - fdata.dim_codomain, - ) - - else: - eval_points = cast(Iterable[ArrayLike], eval_points) - - res = np.asarray([ - self._evaluate_codomain(s, np.asarray(e)) - for s, e in zip(self.splines, eval_points) - ]) - - return res - - -class _SplineList1D(_SplineList): - """List of interpolations for curves. - - List of interpolations for objects with domain - dimension = 1. Calling internally during the creation of the - evaluator. - - Uses internally the scipy interpolation UnivariateSpline or - PchipInterpolator. - - Args: - fdatagrid (FDatagrid): Fdatagrid to interpolate. - interpolation_order (int, optional): Order of the interpolation, 1 - for linear interpolation, 2 for cuadratic, 3 for cubic and so - on. In case of curves and surfaces there is available - interpolation up to degree 5. For higher dimensional objects - only linear or nearest interpolation is available. Default - lineal interpolation. - smoothness_parameter (float, optional): Penalisation to perform - smoothness interpolation. Option only available for curves and - surfaces. If 0 the residuals of the interpolation will be 0. - Defaults 0. - monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolator. Only valid for curves (domain - dimension equal to 1) and interpolation order equal to 1 or 3. - Defaults false. - - Returns: - (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolation of the sample i, and image dimension j - in the entry (i,j) of the array. - - Raises: - ValueError: If the value of the interpolation k is not valid. - - """ - - def __init__( - self, - fdatagrid: FData, - interpolation_order: Union[int, Sequence[int]] = 1, - smoothness_parameter: float = 0, - monotone: bool = False, - ): - - super().__init__( - fdatagrid=fdatagrid, - interpolation_order=interpolation_order, - smoothness_parameter=smoothness_parameter, - ) - - self.monotone = monotone - - if ( - isinstance(self.interpolation_order, Sequence) - or not 1 <= self.interpolation_order <= 5 - ): - raise ValueError( - f"Invalid degree of interpolation " - f"({self.interpolation_order}). Must be " - f"an integer greater than 0 and lower or " - f"equal than 5.", - ) - - if self.monotone and self.smoothness_parameter != 0: - raise ValueError( - "Smoothing interpolation is not supported with " - "monotone interpolation", - ) - - if self.monotone and self.interpolation_order in {2, 4}: - raise ValueError( - f"monotone interpolation of degree " - f"{self.interpolation_order}" - f"not supported.", - ) - - # Monotone interpolation of degree 1 is performed with linear spline - monotone = self.monotone - if self.monotone and self.interpolation_order == 1: - monotone = False - - grid_points = fdatagrid.grid_points[0] + from ..misc.validation import validate_evaluation_points - if monotone: - def constructor( # noqa: WPS430 - data: NDArrayFloat, - ) -> SplineCallable: - """Construct an unidimensional cubic monotone interpolation.""" - return PchipInterpolator(grid_points, data) - - else: - - def constructor( # noqa: WPS430, WPS440 - data: NDArrayFloat, - ) -> SplineCallable: - """Construct an unidimensional interpolation.""" - return UnivariateSpline( - grid_points, - data, - s=self.smoothness_parameter, - k=self.interpolation_order, - ) - - self.splines = np.apply_along_axis( - constructor, - 1, - fdatagrid.data_matrix, + eval_points = validate_evaluation_points( + eval_points, + aligned=aligned, + n_samples=fdata.n_samples, + dim_domain=fdata.dim_domain, ) - def _evaluate_one( - self, - spline: SplineCallable, - eval_points: NDArrayFloat, - ) -> NDArrayFloat: - try: - return spline(eval_points)[:, 0] - except ValueError: - return np.zeros_like(eval_points) - - -class _SplineList2D(_SplineList): - """List of interpolations for surfaces. - - List of interpolations for objects with domain - dimension = 2. Calling internally during the creationg of the - evaluator. - - Uses internally the scipy interpolation RectBivariateSpline. - - Args: - fdatagrid (FDatagrid): Fdatagrid to interpolate. - interpolation_order (int, optional): Order of the interpolation, 1 - for linear interpolation, 2 for cuadratic, 3 for cubic and so - on. In case of curves and surfaces there is available - interpolation up to degree 5. For higher dimensional objects - only linear or nearest interpolation is available. Default - lineal interpolation. - smoothness_parameter (float, optional): Penalisation to perform - smoothness interpolation. Option only available for curves and - surfaces. If 0 the residuals of the interpolation will be 0. - Defaults 0. - monotone (boolean, optional): Performs monotone interpolation in - curves using a PCHIP interpolator. Only valid for curves (domain - dimension equal to 1) and interpolation order equal to 1 or 3. - Defaults false. - - Returns: - (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolation of the sample i, and image dimension j - in the entry (i,j) of the array. - - Raises: - ValueError: If the value of the interpolation k is not valid. - - """ - - def __init__( - self, - fdatagrid: FData, - interpolation_order: Union[int, Sequence[int]] = 1, - smoothness_parameter: float = 0, - ): - - super().__init__( - fdatagrid=fdatagrid, - interpolation_order=interpolation_order, - smoothness_parameter=smoothness_parameter, - ) - - if isinstance(self.interpolation_order, int): - kx = self.interpolation_order - ky = kx - elif len(self.interpolation_order) == 2: - kx = self.interpolation_order[0] - ky = self.interpolation_order[1] - else: - raise ValueError("k should be numeric or a tuple of length 2.") - - if kx > 5 or kx <= 0 or ky > 5 or ky <= 0: - raise ValueError( - f"Invalid degree of interpolation ({kx},{ky}). " - f"Must be an integer greater than 0 and lower or " - f"equal than 5.", - ) - - # Matrix of splines - splines = np.empty( - (fdatagrid.n_samples, fdatagrid.dim_codomain), - dtype=object, - ) - - for i in range(fdatagrid.n_samples): - for j in range(fdatagrid.dim_codomain): - splines[i, j] = RectBivariateSpline( - fdatagrid.grid_points[0], - fdatagrid.grid_points[1], - fdatagrid.data_matrix[i, :, :, j], - kx=kx, - ky=ky, - s=self.smoothness_parameter, - ) - - self.splines = splines - - def _evaluate_one( - self, - spline: SplineCallable, - eval_points: np.ndarray, - ) -> np.ndarray: - - return spline( - eval_points[:, 0], - eval_points[:, 1], - grid=False, + return ( + self._evaluate_aligned(fdata, eval_points) + if aligned + else self._evaluate_unaligned(fdata, eval_points) ) -class _SplineListND(_SplineList): - """ - List of interpolations. - - List of interpolations for objects with domain - dimension > 2. Calling internally during the creationg of the - evaluator. - - Only linear and nearest interpolations are available for objects with - domain dimension >= 3. Uses internally the scipy interpolation - RegularGridInterpolator. - - Args: - grid_points (np.ndarray): Sample points of the fdatagrid. - data_matrix (np.ndarray): Data matrix of the fdatagrid. - k (integer): Order of the spline interpolations. - - Returns: - (np.ndarray): Array of size n_samples x dim_codomain with the - corresponding interpolation of the sample i, and image dimension j - in the entry (i,j) of the array. - - Raises: - ValueError: If the value of the interpolation k is not valid. - - """ +class _RegularGridInterpolatorWrapper(): def __init__( self, - fdatagrid: FData, - interpolation_order: Union[int, Sequence[int]] = 1, - smoothness_parameter: float = 0, + fdatagrid: FDataGrid, + interpolation_order: int, ) -> None: - super().__init__( - fdatagrid=fdatagrid, - interpolation_order=interpolation_order, - smoothness_parameter=smoothness_parameter, - ) - - if self.smoothness_parameter != 0: - raise ValueError( - "Smoothing interpolation is only supported with " - "domain dimension up to 2.", - ) + self.fdatagrid = fdatagrid + self.interpolation_order = interpolation_order - # Parses method of interpolation if self.interpolation_order == 0: method = 'nearest' elif self.interpolation_order == 1: method = 'linear' + elif self.interpolation_order == 3: + method = 'cubic' + elif self.interpolation_order == 5: + method = 'quintic' else: raise ValueError( - "interpolation order should be 0 (nearest) or 1 (linear).", + f"Invalid interpolation order: {self.interpolation_order}.", ) - - splines = np.empty( - (fdatagrid.n_samples, fdatagrid.dim_codomain), - dtype=object, + self.interpolator = RegularGridInterpolator( + self.fdatagrid.grid_points, + np.moveaxis(self.fdatagrid.data_matrix, 0, -2), + method=method, + bounds_error=False, + fill_value=None, ) - for i in range(fdatagrid.n_samples): - for j in range(fdatagrid.dim_codomain): - splines[i, j] = RegularGridInterpolator( - fdatagrid.grid_points, - fdatagrid.data_matrix[i, ..., j], - method=method, - bounds_error=False, - ) - - self.splines = splines - - def _evaluate_one( + def __call__( self, - spline: SplineCallable, - eval_points: NDArrayFloat, + eval_points: EvaluationPoints, ) -> NDArrayFloat: - - return spline(eval_points) + return np.moveaxis( + self.interpolator(eval_points), + 0, + 1, + ) -class SplineInterpolation(Evaluator): +class SplineInterpolation(_BaseInterpolation): """ Spline interpolation. @@ -441,47 +138,69 @@ class SplineInterpolation(Evaluator): def __init__( self, - interpolation_order: Union[int, Sequence[int]] = 1, + interpolation_order: int = 1, *, - smoothness_parameter: float = 0, monotone: bool = False, ) -> None: - self._interpolation_order = interpolation_order - self._smoothness_parameter = smoothness_parameter - self._monotone = monotone + self.interpolation_order = interpolation_order + self.monotone = monotone - @property - def interpolation_order(self) -> Union[int, Tuple[int, ...]]: - """Interpolation order.""" + def _get_interpolator_1d( + self, + fdatagrid: FDataGrid, + ) -> Callable[[EvaluationPoints], NDArrayFloat]: + if ( + isinstance(self.interpolation_order, Sequence) + or not 1 <= self.interpolation_order <= 5 + ): + raise ValueError( + f"Invalid degree of interpolation " + f"({self.interpolation_order}). Must be " + f"an integer greater than 0 and lower or " + f"equal than 5.", + ) - return ( - self._interpolation_order - if isinstance(self._interpolation_order, int) - else tuple(self._interpolation_order) + if self.monotone and self.interpolation_order not in {1, 3}: + raise ValueError( + f"monotone interpolation of degree " + f"{self.interpolation_order}" + f"not supported.", + ) + + # Monotone interpolation of degree 1 is performed with linear spline + monotone = self.monotone and self.interpolation_order > 1 + + if monotone: + return PchipInterpolator( # type: ignore[no-any-return] + fdatagrid.grid_points[0], + fdatagrid.data_matrix, + axis=1, + ) + + return make_interp_spline( # type: ignore[no-any-return] + fdatagrid.grid_points[0], + fdatagrid.data_matrix, + k=self.interpolation_order, + axis=1, ) - @property - def smoothness_parameter(self) -> float: - """Smoothness parameter.""" - return self._smoothness_parameter + def _get_interpolator_nd( + self, + fdatagrid: FDataGrid, + ) -> Callable[[EvaluationPoints], NDArrayFloat]: - @property - def monotone(self) -> bool: - """Flag to perform monotone interpolation.""" - return self._monotone + return _RegularGridInterpolatorWrapper( + fdatagrid, + interpolation_order=self.interpolation_order, + ) - def _build_interpolator( + def _get_interpolator( self, - fdatagrid: FData, - ) -> _SplineList: + fdatagrid: FDataGrid, + ) -> Callable[[EvaluationPoints], NDArrayFloat]: if fdatagrid.dim_domain == 1: - return _SplineList1D( - fdatagrid=fdatagrid, - interpolation_order=self.interpolation_order, - smoothness_parameter=self.smoothness_parameter, - monotone=self.monotone, - ) + return self._get_interpolator_1d(fdatagrid) elif self.monotone: raise ValueError( @@ -489,36 +208,24 @@ def _build_interpolator( "domain dimension equal to 1.", ) - elif fdatagrid.dim_domain == 2: - return _SplineList2D( - fdatagrid=fdatagrid, - interpolation_order=self.interpolation_order, - smoothness_parameter=self.smoothness_parameter, - ) - - return _SplineListND( - fdatagrid=fdatagrid, - interpolation_order=self.interpolation_order, - smoothness_parameter=self.smoothness_parameter, - ) + return self._get_interpolator_nd(fdatagrid) - def _evaluate( # noqa: D102 + def _evaluate_aligned( # noqa: D102 self, - fdata: FData, - eval_points: ArrayLike, - *, - aligned: bool = True, + fdata: FDataGrid, + eval_points: EvaluationPoints, ) -> NDArrayFloat: - spline_list = self._build_interpolator(fdata) - - return spline_list.evaluate(fdata, eval_points, aligned=aligned) + interpolator = self._get_interpolator(fdata) + return np.reshape( + interpolator(eval_points), + (fdata.n_samples, -1, fdata.dim_codomain), + ) def __repr__(self) -> str: return ( f"{type(self).__name__}(" f"interpolation_order={self.interpolation_order}, " - f"smoothness_parameter={self.smoothness_parameter}, " f"monotone={self.monotone})" ) @@ -526,6 +233,5 @@ def __eq__(self, other: Any) -> bool: return ( super().__eq__(other) and self.interpolation_order == other.interpolation_order - and self.smoothness_parameter == other.smoothness_parameter and self.monotone == other.monotone ) diff --git a/skfda/tests/test_interpolation.py b/skfda/tests/test_interpolation.py index 15536e3cc..43032d276 100644 --- a/skfda/tests/test_interpolation.py +++ b/skfda/tests/test_interpolation.py @@ -222,7 +222,7 @@ def test_evaluation_cubic_unaligned(self) -> None: def test_evaluation_nodes(self) -> None: """Test interpolation in nodes for all dimensions.""" - for degree in range(1, 6): + for degree in range(1, 6, 2): interpolation = SplineInterpolation(degree) f = FDataGrid( @@ -381,7 +381,7 @@ def test_evaluation_unaligned(self) -> None: def test_evaluation_nodes(self) -> None: """Test interpolation in nodes for all dimensions.""" - for degree in range(1, 6): + for degree in range(1, 6, 2): interpolation = SplineInterpolation(degree) f = FDataGrid( From e607b3f8dbde1521b23118279694fba9410f77a4 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 30 Aug 2022 08:59:18 +0200 Subject: [PATCH 264/400] Change examples because of error https://github.com/scipy/scipy/issues/16905 --- examples/plot_composition.py | 5 ----- examples/plot_interpolation.py | 6 +++--- 2 files changed, 3 insertions(+), 8 deletions(-) diff --git a/examples/plot_composition.py b/examples/plot_composition.py index ae618e528..ac5c09885 100644 --- a/examples/plot_composition.py +++ b/examples/plot_composition.py @@ -40,11 +40,6 @@ g = skfda.FDataGrid(data_matrix, grid_points) -# Sets cubic interpolation -g.interpolation = skfda.representation.interpolation.SplineInterpolation( - interpolation_order=3, -) - # Plots the surface g.plot() diff --git a/examples/plot_interpolation.py b/examples/plot_interpolation.py index fd599a22d..242523e01 100644 --- a/examples/plot_interpolation.py +++ b/examples/plot_interpolation.py @@ -104,10 +104,10 @@ plt.show() ############################################################################## -# In the following figure it is shown the result of the cubic interpolation -# applied to the surface (requires SciPy >= 1.9). +# In the following figure it is shown the result of the constant interpolation +# applied to the surface. -fd.interpolation = SplineInterpolation(interpolation_order=3) +fd.interpolation = SplineInterpolation(interpolation_order=0) fig = fd.plot() fd.scatter(fig=fig) From 6b91b82e7d78118fda94d0ac4f336d84fba3dea5 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 30 Aug 2022 10:52:27 +0200 Subject: [PATCH 265/400] Fix circular imports. --- skfda/__init__.py | 65 +++++++++---------- skfda/datasets/_real_datasets.py | 2 +- skfda/datasets/_samples_generators.py | 2 +- skfda/exploratory/depth/_depth.py | 3 +- .../outliers/_directional_outlyingness.py | 2 +- skfda/exploratory/visualization/_boxplot.py | 2 +- .../visualization/_magnitude_shape_plot.py | 2 +- .../exploratory/visualization/_outliergram.py | 2 +- .../visualization/representation.py | 2 +- skfda/inference/anova/_anova_oneway.py | 3 +- skfda/ml/_neighbors_base.py | 2 +- .../preprocessing/registration/_fisher_rao.py | 2 +- .../registration/_lstsq_shift_registration.py | 2 +- skfda/preprocessing/registration/base.py | 2 +- skfda/preprocessing/smoothing/_linear.py | 2 +- skfda/preprocessing/smoothing/validation.py | 4 +- skfda/representation/__init__.py | 7 +- 17 files changed, 48 insertions(+), 58 deletions(-) diff --git a/skfda/__init__.py b/skfda/__init__.py index eecd71b6c..586e3201e 100644 --- a/skfda/__init__.py +++ b/skfda/__init__.py @@ -1,43 +1,38 @@ -"""fda package. - -It includes the modules: - - basis: For functional data manipulation in a function basis system. - - grid: For functional data manipulation as a discrete set of measures. - - math: Mean, variance, covariance, logarithms, square roots, distances... - - kernels: kernels for kernel smoothing. - - kernel_smoothers: kernel smoothers for smoothing FDataGrid objects. - - validation: cross validation methods for finding the parameter that - best smooths a FDataGrid object. - - depth_measures: depth methods to order he samples of FDataGrid objects. - - magnitude-shape plot: visualization tool. - - fdata_boxplot: informative exploratory tool for visualizing functional - data. - - clustering: K-Means and Fuzzy-KMeans algorithms implemented to cluster - data in the FDataGrid, along with plotting methods. -and the following classes: - - FDataGrid: Discrete representation of functional data. - - FDataBasis: Basis representation for functional data. - - Boxplot: Implements the functional boxplot for FDataGrid with domain - dimension 1. - - SurfaceBoxplot: Implements the functional boxplot for FDataGrid with - domain dimension 2. - - MagnitudeShapePlot: Implements the magnitude shape plot for FDataGrid - with domain dimension 1. - -Kmeans: Implements the K-Means clustering algorithm. - -FuzzyKmeans: Implements the Fuzzy K-Means clustering algorithm. - -""" +"""scikit-fda package.""" import errno as _errno +import os as _os -from .representation import FData -from .representation import FDataBasis -from .representation import FDataGrid +import lazy_loader as lazy + +from . import ( + datasets, + exploratory, + inference, + misc, + ml, + preprocessing, + representation, +) +from .representation import FData, FDataBasis, FDataGrid from .representation._functional_data import concatenate -from . import representation, datasets, preprocessing, exploratory, misc, ml, \ - inference +# __getattr__, __dir__, __all__ = lazy.attach( +# __name__, +# submodules=[ +# "datasets", +# "exploratory", +# "inference", +# "misc", +# "ml", +# "preprocessing", +# "representation", +# ], +# submod_attrs={ +# 'representation': ["FData", "FDataBasis", "FDataGrid"], +# 'representation._functional_data': ['concatenate'], +# }, +# ) -import os as _os try: with open(_os.path.join(_os.path.dirname(__file__), diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index a6ecc87cb..a85986e95 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -10,7 +10,7 @@ import rdata -from .. import FDataGrid +from ..representation import FDataGrid def _get_skdatasets_repositories() -> Any: diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index e3d2f5518..b267f5e09 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -6,7 +6,6 @@ import sklearn.utils from scipy.stats import multivariate_normal -from .. import FDataGrid from .._utils import ( RandomStateLike, _cartesian_product, @@ -14,6 +13,7 @@ normalize_warping, ) from ..misc import covariances +from ..representation import FDataGrid from ..representation._typing import ( DomainRangeLike, GridPointsLike, diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 42bcde790..72baecace 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -13,11 +13,10 @@ import numpy as np import scipy.integrate -from ... import FDataGrid from ..._utils._sklearn_adapter import BaseEstimator from ...misc.metrics import Metric, l2_distance from ...misc.metrics._utils import _fit_metric -from ...representation import FData +from ...representation import FData, FDataGrid from ...representation._typing import NDArrayFloat from .multivariate import Depth, SimplicialDepth, _UnivariateFraimanMuniz diff --git a/skfda/exploratory/outliers/_directional_outlyingness.py b/skfda/exploratory/outliers/_directional_outlyingness.py index ea76fe84f..c02463776 100644 --- a/skfda/exploratory/outliers/_directional_outlyingness.py +++ b/skfda/exploratory/outliers/_directional_outlyingness.py @@ -10,9 +10,9 @@ from sklearn.covariance import MinCovDet from sklearn.utils.validation import check_random_state -from ... import FDataGrid from ..._utils import RandomStateLike from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin +from ...representation import FDataGrid from ...representation._typing import NDArrayFloat, NDArrayInt from ..depth.multivariate import Depth, ProjectionDepth from . import _directional_outlyingness_experiment_results as experiments diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index 73c6161ad..adace8ca0 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -20,7 +20,7 @@ from skfda.exploratory.depth.multivariate import Depth -from ... import FData, FDataGrid +from ...representation import FData, FDataGrid from ...representation._typing import NDArrayBool, NDArrayFloat from ..depth import ModifiedBandDepth from ..outliers import _envelopes diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 9958f61f7..71ba5ec85 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -18,7 +18,7 @@ from matplotlib.figure import Figure from matplotlib.patches import Ellipse -from ... import FDataGrid +from ...representation import FDataGrid from ...representation._typing import NDArrayFloat, NDArrayInt from ..depth import Depth from ..outliers import MSPlotOutlierDetector diff --git a/skfda/exploratory/visualization/_outliergram.py b/skfda/exploratory/visualization/_outliergram.py index 7b45e76ff..d27e300b4 100644 --- a/skfda/exploratory/visualization/_outliergram.py +++ b/skfda/exploratory/visualization/_outliergram.py @@ -14,7 +14,7 @@ from matplotlib.axes import Axes from matplotlib.figure import Figure -from ... import FDataGrid +from ...representation import FDataGrid from ..outliers import OutliergramOutlierDetector from ._baseplot import BasePlot diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index ee64535c0..88bf99ef2 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -18,8 +18,8 @@ from matplotlib.figure import Figure from typing_extensions import Protocol -from ... import FDataGrid from ..._utils import _to_domain_range, _to_grid_points, constants +from ...representation import FDataGrid from ...representation._functional_data import FData from ...representation._typing import DomainRangeLike, GridPointsLike from ._baseplot import BasePlot diff --git a/skfda/inference/anova/_anova_oneway.py b/skfda/inference/anova/_anova_oneway.py index 13fcca68c..ee0655177 100644 --- a/skfda/inference/anova/_anova_oneway.py +++ b/skfda/inference/anova/_anova_oneway.py @@ -6,11 +6,10 @@ from sklearn.utils import check_random_state from typing_extensions import Literal -from ... import concatenate from ..._utils import RandomStateLike from ...datasets import make_gaussian from ...misc.metrics import lp_distance -from ...representation import FData, FDataGrid +from ...representation import FData, FDataGrid, concatenate from ...representation._typing import ArrayLike, NDArrayFloat diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 8424c0b6d..5745eb146 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -12,7 +12,6 @@ from skfda.misc.metrics._utils import PairwiseMetric -from .. import FData, FDataGrid, concatenate from .._utils._sklearn_adapter import ( BaseEstimator, ClassifierMixin, @@ -21,6 +20,7 @@ from ..misc.metrics import l2_distance from ..misc.metrics._typing import Metric from ..misc.metrics._utils import _fit_metric +from ..representation import FData, FDataGrid, concatenate from ..representation._typing import NDArrayFloat, NDArrayInt FDataType = TypeVar("FDataType", bound="FData") diff --git a/skfda/preprocessing/registration/_fisher_rao.py b/skfda/preprocessing/registration/_fisher_rao.py index 89f11ef4e..825f061fd 100644 --- a/skfda/preprocessing/registration/_fisher_rao.py +++ b/skfda/preprocessing/registration/_fisher_rao.py @@ -6,12 +6,12 @@ from sklearn.utils.validation import check_is_fitted -from ... import FDataGrid from ..._utils import invert_warping, normalize_scale from ...exploratory.stats import fisher_rao_karcher_mean from ...exploratory.stats._fisher_rao import _elastic_alignment_array from ...misc.operators import SRSF from ...misc.validation import check_fdata_dimensions, check_fdata_same_kind +from ...representation import FDataGrid from ...representation._typing import ArrayLike from ...representation.basis import Basis from ...representation.interpolation import SplineInterpolation diff --git a/skfda/preprocessing/registration/_lstsq_shift_registration.py b/skfda/preprocessing/registration/_lstsq_shift_registration.py index efced65ce..db422fd16 100644 --- a/skfda/preprocessing/registration/_lstsq_shift_registration.py +++ b/skfda/preprocessing/registration/_lstsq_shift_registration.py @@ -8,10 +8,10 @@ from sklearn.utils.validation import check_is_fitted from typing_extensions import Literal -from ... import FData, FDataGrid from ...misc._math import inner_product from ...misc.metrics._lp_norms import l2_norm from ...misc.validation import check_fdata_dimensions +from ...representation import FData, FDataGrid from ...representation._typing import ArrayLike, GridPointsLike, NDArrayFloat from ...representation.extrapolation import ExtrapolationLike from .base import InductiveRegistrationTransformer diff --git a/skfda/preprocessing/registration/base.py b/skfda/preprocessing/registration/base.py index 2d2c6892b..4c25d951b 100644 --- a/skfda/preprocessing/registration/base.py +++ b/skfda/preprocessing/registration/base.py @@ -9,12 +9,12 @@ from abc import abstractmethod from typing import Any, TypeVar, overload -from ... import FData from ..._utils import ( BaseEstimator, InductiveTransformerMixin, TransformerMixin, ) +from ...representation import FData SelfType = TypeVar("SelfType") Input = TypeVar("Input", bound=FData) diff --git a/skfda/preprocessing/smoothing/_linear.py b/skfda/preprocessing/smoothing/_linear.py index 8c5bf0062..546f2e13d 100644 --- a/skfda/preprocessing/smoothing/_linear.py +++ b/skfda/preprocessing/smoothing/_linear.py @@ -11,9 +11,9 @@ import numpy as np -from ... import FDataGrid from ..._utils import _to_grid_points from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin +from ...representation import FDataGrid from ...representation._typing import GridPointsLike, NDArrayFloat diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py index 562d81f43..4e93ecce2 100644 --- a/skfda/preprocessing/smoothing/validation.py +++ b/skfda/preprocessing/smoothing/validation.py @@ -5,8 +5,8 @@ import sklearn from sklearn.model_selection import GridSearchCV -from skfda import FDataGrid -from skfda.preprocessing.smoothing._linear import _LinearSmoother +from ...representation import FDataGrid +from ._linear import _LinearSmoother def _get_input_estimation_and_matrix( diff --git a/skfda/representation/__init__.py b/skfda/representation/__init__.py index 9565798ee..304499671 100644 --- a/skfda/representation/__init__.py +++ b/skfda/representation/__init__.py @@ -1,7 +1,4 @@ -from . import basis -from . import extrapolation -from . import grid -from . import interpolation -from ._functional_data import FData +from . import basis, extrapolation, grid, interpolation +from ._functional_data import FData, concatenate from .basis import FDataBasis from .grid import FDataGrid From c05a547fd279687f7d534e9acf8d457c6bd0ca26 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 30 Aug 2022 12:01:47 +0200 Subject: [PATCH 266/400] First version with lazy imports. --- setup.cfg | 5 ++ setup.py | 3 +- skfda/__init__.py | 52 ++++++++----------- skfda/_utils/__init__.py | 1 - skfda/_utils/_sklearn_adapter.py | 5 +- skfda/_utils/_utils.py | 38 ++++---------- skfda/_utils/_warping.py | 5 +- .../stats/_functional_transformers.py | 6 +-- .../visualization/representation.py | 7 +-- skfda/misc/validation.py | 34 +++++++++++- .../feature_construction/__init__.py | 1 + .../_coefficients_transformer.py | 14 +++-- skfda/representation/basis/__init__.py | 1 - skfda/representation/basis/_basis.py | 10 ++-- skfda/representation/basis/_bspline.py | 8 +-- skfda/representation/basis/_fourier.py | 5 +- skfda/representation/basis/_vector_basis.py | 2 +- skfda/representation/grid.py | 22 ++++---- 18 files changed, 122 insertions(+), 97 deletions(-) rename skfda/{representation/basis => preprocessing/feature_construction}/_coefficients_transformer.py (81%) diff --git a/setup.cfg b/setup.cfg index 574192ca9..93a19a1fe 100644 --- a/setup.cfg +++ b/setup.cfg @@ -2,6 +2,8 @@ test=pytest [tool:pytest] +env = + EAGER_IMPORT=1 addopts = --doctest-modules doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS norecursedirs = .* build dist *.egg venv .svn _build docs/auto_examples examples docs/auto_tutorial tutorial docs/auto_full_examples full_examples @@ -170,6 +172,9 @@ ignore_missing_imports = True [mypy-joblib.*] ignore_missing_imports = True +[mypy-lazy_loader.*] +ignore_missing_imports = True + [mypy-matplotlib.*] ignore_missing_imports = True diff --git a/setup.py b/setup.py index ad015fbc6..dc2a2568b 100644 --- a/setup.py +++ b/setup.py @@ -69,6 +69,7 @@ 'dcor', 'fdasrsf>=2.2.0', 'findiff', + 'lazy_loader', 'matplotlib', 'multimethod>=1.5', 'numpy>=1.16', @@ -80,6 +81,6 @@ 'typing-extensions', ], setup_requires=pytest_runner, - tests_require=['pytest'], + tests_require=['pytest', 'pytest-env'], test_suite='skfda.tests', zip_safe=False) diff --git a/skfda/__init__.py b/skfda/__init__.py index 586e3201e..0a165fb20 100644 --- a/skfda/__init__.py +++ b/skfda/__init__.py @@ -4,39 +4,33 @@ import lazy_loader as lazy -from . import ( - datasets, - exploratory, - inference, - misc, - ml, - preprocessing, - representation, +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "datasets", + "exploratory", + "inference", + "misc", + "ml", + "preprocessing", + "representation", + ], + submod_attrs={ + 'representation': ["FData", "FDataBasis", "FDataGrid"], + 'representation._functional_data': ['concatenate'], + }, ) -from .representation import FData, FDataBasis, FDataGrid -from .representation._functional_data import concatenate - -# __getattr__, __dir__, __all__ = lazy.attach( -# __name__, -# submodules=[ -# "datasets", -# "exploratory", -# "inference", -# "misc", -# "ml", -# "preprocessing", -# "representation", -# ], -# submod_attrs={ -# 'representation': ["FData", "FDataBasis", "FDataGrid"], -# 'representation._functional_data': ['concatenate'], -# }, -# ) try: - with open(_os.path.join(_os.path.dirname(__file__), - '..', 'VERSION'), 'r') as version_file: + with open( + _os.path.join( + _os.path.dirname(__file__), + '..', + 'VERSION', + ), + 'r', + ) as version_file: __version__ = version_file.read().strip() except IOError as e: if e.errno != _errno.ENOENT: diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index c43b8792a..15d709ad8 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -16,7 +16,6 @@ _int_to_real, _pairwise_symmetric, _same_domain, - _to_domain_range, _to_grid, _to_grid_points, nquad_vec, diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 1bd989db5..6acafc8ef 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -1,11 +1,12 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any, Generic, TypeVar, overload +from typing import TYPE_CHECKING, Any, Generic, TypeVar, overload import sklearn.base -from ..representation._typing import NDArrayFloat, NDArrayInt +if TYPE_CHECKING: + from ..representation._typing import NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType") TransformerNoTarget = TypeVar( diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index f86db2af2..99215a2bc 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -26,16 +26,6 @@ from sklearn.utils.multiclass import check_classification_targets from typing_extensions import Literal, Protocol -from ..representation._typing import ( - DomainRange, - DomainRangeLike, - GridPoints, - GridPointsLike, - NDArrayAny, - NDArrayFloat, - NDArrayInt, - NDArrayStr, -) from ._sklearn_adapter import BaseEstimator RandomStateLike = Optional[Union[int, np.random.RandomState]] @@ -46,6 +36,14 @@ from ..representation import FData, FDataGrid from ..representation.basis import Basis from ..representation.extrapolation import ExtrapolationLike + from ..representation._typing import ( + GridPoints, + GridPointsLike, + NDArrayAny, + NDArrayFloat, + NDArrayInt, + NDArrayStr, + ) T = TypeVar("T", bound=FData) Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) @@ -104,24 +102,6 @@ def _to_grid_points(grid_points_like: GridPointsLike) -> GridPoints: return tuple(_int_to_real(np.asarray(i)) for i in grid_points_like) -def _to_domain_range(sequence: DomainRangeLike) -> DomainRange: - """Convert sequence to a proper domain range.""" - seq_aux = cast( - Sequence[Sequence[float]], - (sequence,) if isinstance(sequence[0], numbers.Real) else sequence, - ) - - tuple_aux = tuple(tuple(s) for s in seq_aux) - - if not all(len(s) == 2 and s[0] <= s[1] for s in tuple_aux): - raise ValueError( - "Domain intervals should have 2 bounds for " - "dimension: (lower, upper).", - ) - - return cast(DomainRange, tuple_aux) - - @overload def _cartesian_product( axes: Sequence[np.typing.NDArray[ArrayDTypeT]], @@ -351,6 +331,8 @@ def _evaluate_grid( # noqa: WPS234 dimension. """ + from ..representation._typing import GridPointsLike + # Compute intersection points and resulting shapes if aligned: diff --git a/skfda/_utils/_warping.py b/skfda/_utils/_warping.py index c960234bc..67c4153c8 100644 --- a/skfda/_utils/_warping.py +++ b/skfda/_utils/_warping.py @@ -10,7 +10,6 @@ from scipy.interpolate import PchipInterpolator from ..representation._typing import ArrayLike, DomainRangeLike, NDArrayFloat -from ._utils import _to_domain_range if TYPE_CHECKING: from ..representation import FDataGrid @@ -147,10 +146,12 @@ def normalize_warping( Normalized warpings. """ + from ..misc.validation import validate_domain_range + domain_range_tuple = ( warping.domain_range[0] if domain_range is None - else _to_domain_range(domain_range)[0] + else validate_domain_range(domain_range)[0] ) data_matrix = normalize_scale( diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 4b4013e10..7fd2d906c 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -8,8 +8,8 @@ import numpy as np from typing_extensions import Protocol, TypeGuard -from ..._utils import _to_domain_range, nquad_vec -from ...misc.validation import check_fdata_dimensions +from ..._utils import nquad_vec +from ...misc.validation import check_fdata_dimensions, validate_domain_range from ...representation import FData, FDataBasis, FDataGrid from ...representation._typing import ( ArrayLike, @@ -546,7 +546,7 @@ def unconditional_expected_value( if domain is None: domain = data.domain_range else: - domain = _to_domain_range(domain) + domain = validate_domain_range(domain) lebesgue_measure = np.prod( [ diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index 88bf99ef2..93142faa6 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -18,7 +18,8 @@ from matplotlib.figure import Figure from typing_extensions import Protocol -from ..._utils import _to_domain_range, _to_grid_points, constants +from ..._utils import _to_grid_points, constants +from ...misc.validation import validate_domain_range from ...representation import FDataGrid from ...representation._functional_data import FData from ...representation._typing import DomainRangeLike, GridPointsLike @@ -258,7 +259,7 @@ def __init__( if domain_range is None: self.domain_range = self.fdata.domain_range else: - self.domain_range = _to_domain_range(domain_range) + self.domain_range = validate_domain_range(domain_range) if self.gradient_list is None: sample_colors, patches = _get_color_info( @@ -463,7 +464,7 @@ def __init__( if self.domain_range is None: self.domain_range = self.fdata.domain_range else: - self.domain_range = _to_domain_range(self.domain_range) + self.domain_range = validate_domain_range(self.domain_range) sample_colors, patches = _get_color_info( self.fdata, diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index e01351a79..b1a6db988 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -3,12 +3,17 @@ from __future__ import annotations import functools -from typing import Container, Sequence +from typing import TYPE_CHECKING, Container, Sequence, Tuple, cast import numpy as np from ..representation import FData, FDataBasis, FDataGrid -from ..representation._typing import ArrayLike, EvaluationPoints +from ..representation._typing import ( + ArrayLike, + DomainRange, + DomainRangeLike, + EvaluationPoints, +) def check_fdata_dimensions( @@ -228,3 +233,28 @@ def validate_evaluation_points( ) return eval_points.reshape(shape) + + +def _validate_domain_range_limits( + limits: Sequence[float], +) -> Tuple[float, float]: + if len(limits) != 2 or limits[0] > limits[1]: + raise ValueError( + f"Invalid domain interval {limits}. " + "Domain intervals should have 2 bounds for " + "dimension: (lower, upper).", + ) + + lower, upper = limits + return (lower, upper) # noqa: WPS331 + + +def validate_domain_range(domain_range: DomainRangeLike) -> DomainRange: + """Convert sequence to a proper domain range.""" + if isinstance(domain_range[0], (int, float)): + domain_range = cast(Sequence[float], domain_range) + domain_range = (domain_range,) + + domain_range = cast(Sequence[Sequence[float]], domain_range) + + return tuple(_validate_domain_range_limits(s) for s in domain_range) diff --git a/skfda/preprocessing/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py index ca1f6d109..2a7522a32 100644 --- a/skfda/preprocessing/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -1,4 +1,5 @@ """Feature extraction.""" +from ._coefficients_transformer import CoefficientsTransformer from ._evaluation_trasformer import EvaluationTransformer from ._fda_feature_union import FDAFeatureUnion from ._function_transformers import ( diff --git a/skfda/representation/basis/_coefficients_transformer.py b/skfda/preprocessing/feature_construction/_coefficients_transformer.py similarity index 81% rename from skfda/representation/basis/_coefficients_transformer.py rename to skfda/preprocessing/feature_construction/_coefficients_transformer.py index 39da3f297..cd287c3a2 100644 --- a/skfda/representation/basis/_coefficients_transformer.py +++ b/skfda/preprocessing/feature_construction/_coefficients_transformer.py @@ -5,8 +5,8 @@ from sklearn.utils.validation import check_is_fitted from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin -from .._typing import NDArrayFloat -from ._fdatabasis import FDataBasis +from ...representation import FDataBasis +from ...representation._typing import NDArrayFloat class CoefficientsTransformer( @@ -20,9 +20,13 @@ class CoefficientsTransformer( basis\_ (tuple): Basis used. Examples: - >>> from skfda.representation.basis import (FDataBasis, Monomial, - ... CoefficientsTransformer) - >>> + >>> from skfda.preprocessing.feature_construction import ( + ... CoefficientsTransformer, + ... ) + >>> from skfda.representation.basis import ( + ... FDataBasis, + ... Monomial, + ... ) >>> basis = Monomial(n_basis=4) >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] >>> fd = FDataBasis(basis, coefficients) diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 28c891554..fb117201f 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -1,6 +1,5 @@ from ._basis import Basis from ._bspline import BSpline -from ._coefficients_transformer import CoefficientsTransformer from ._constant import Constant from ._fdatabasis import FDataBasis, FDataBasisDType from ._finite_element import FiniteElement diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 996f46c12..b820a2595 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -10,7 +10,6 @@ import numpy as np from matplotlib.figure import Figure -from ..._utils import _same_domain, _to_domain_range from .._typing import ArrayLike, DomainRange, DomainRangeLike, NDArrayFloat if TYPE_CHECKING: @@ -36,9 +35,11 @@ def __init__( n_basis: int = 1, ) -> None: """Basis constructor.""" + from ...misc.validation import validate_domain_range + if domain_range is not None: - domain_range = _to_domain_range(domain_range) + domain_range = validate_domain_range(domain_range) if n_basis < 1: raise ValueError( @@ -311,10 +312,12 @@ def rescale(self: T, domain_range: DomainRangeLike | None = None) -> T: def copy(self: T, domain_range: DomainRangeLike | None = None) -> T: """Basis copy.""" + from ...misc.validation import validate_domain_range + new_copy = copy.deepcopy(self) if domain_range is not None: - domain_range = _to_domain_range(domain_range) + domain_range = validate_domain_range(domain_range) new_copy._domain_range = domain_range # noqa: WPS437 @@ -429,6 +432,7 @@ def __repr__(self) -> str: def __eq__(self, other: Any) -> bool: """Test equality of Basis.""" + from ..._utils import _same_domain return ( isinstance(other, type(self)) and _same_domain(self, other) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 462400f62..50b8edcae 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -6,7 +6,6 @@ from numpy import polyint, polymul, polyval from scipy.interpolate import BSpline as SciBSpline, PPoly, splev -from ..._utils import _to_domain_range from .._typing import DomainRangeLike, NDArrayFloat from ._basis import Basis @@ -97,8 +96,10 @@ def __init__( # noqa: WPS238 knots: Sequence[float] | None = None, ) -> None: """Bspline basis constructor.""" + from ...misc.validation import validate_domain_range + if domain_range is not None: - domain_range = _to_domain_range(domain_range) + domain_range = validate_domain_range(domain_range) if len(domain_range) != 1: raise ValueError("Domain range should be unidimensional.") @@ -203,11 +204,12 @@ def rescale( # noqa: D102 self: T, domain_range: DomainRangeLike | None = None, ) -> T: + from ...misc.validation import validate_domain_range knots = np.array(self.knots, dtype=np.dtype('float')) if domain_range is not None: # Rescales the knots - domain_range = _to_domain_range(domain_range)[0] + domain_range = validate_domain_range(domain_range)[0] knots -= knots[0] knots *= ( (domain_range[1] - domain_range[0]) diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index 6ab4bb0a9..f4f1c1e6e 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -3,7 +3,6 @@ import numpy as np from typing_extensions import Protocol -from ..._utils import _to_domain_range from .._typing import DomainRangeLike, NDArrayFloat from ._basis import Basis @@ -106,8 +105,10 @@ def __init__( define the basis. """ + from ...misc.validation import validate_domain_range + if domain_range is not None: - domain_range = _to_domain_range(domain_range) + domain_range = validate_domain_range(domain_range) if len(domain_range) != 1: raise ValueError("Domain range should be unidimensional.") diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index 93e66bbd6..fe7460905 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -5,7 +5,6 @@ import numpy as np import scipy.linalg -from ..._utils import _same_domain from .._typing import NDArrayFloat from ._basis import Basis @@ -65,6 +64,7 @@ class VectorValued(Basis): """ def __init__(self, basis_list: Iterable[Basis]) -> None: + from ..._utils import _same_domain basis_list = tuple(basis_list) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 5457df25f..ad80e9bff 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -28,13 +28,7 @@ import scipy.stats.mstats from matplotlib.figure import Figure -from .._utils import ( - _check_array_key, - _int_to_real, - _to_domain_range, - _to_grid_points, - constants, -) +from .._utils import _check_array_key, _int_to_real, _to_grid_points, constants from ._functional_data import FData from ._typing import ( ArrayLike, @@ -53,7 +47,7 @@ from .interpolation import SplineInterpolation if TYPE_CHECKING: - from .. import FDataBasis + from . import FDataBasis T = TypeVar("T", bound='FDataGrid') @@ -150,6 +144,8 @@ def __init__( # noqa: WPS211 interpolation: Optional[Evaluator] = None, ): """Construct a FDataGrid object.""" + from ..misc.validation import validate_domain_range + if sample_points is not None: warnings.warn( "Parameter sample_points is deprecated. Use the " @@ -191,7 +187,7 @@ def __init__( # noqa: WPS211 # Default value for domain_range is a list of tuples with # the first and last element of each list of the grid_points. - self._domain_range = _to_domain_range(domain_range) + self._domain_range = validate_domain_range(domain_range) if len(self._domain_range) != self.dim_domain: raise ValueError("Incorrect shape of domain_range.") @@ -1070,7 +1066,9 @@ def restrict( Restricted function. """ - domain_range = _to_domain_range(domain_range) + from ..misc.validation import validate_domain_range + + domain_range = validate_domain_range(domain_range) assert all( c <= a < b <= d # noqa: WPS228 for ((a, b), (c, d)) in zip(domain_range, self.domain_range) @@ -1449,6 +1447,8 @@ def __init__( dim_codomain: int, domain_range: Optional[DomainRangeLike] = None, ) -> None: + from ..misc.validation import validate_domain_range + grid_points = _to_grid_points(grid_points) self.grid_points = tuple(tuple(s) for s in grid_points) @@ -1456,7 +1456,7 @@ def __init__( if domain_range is None: domain_range = tuple((s[0], s[-1]) for s in self.grid_points) - self.domain_range = _to_domain_range(domain_range) + self.domain_range = validate_domain_range(domain_range) self.dim_codomain = dim_codomain @classmethod From 381b728942f1d475cf49c46b54c258b17df2dd66 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 30 Aug 2022 14:06:05 +0200 Subject: [PATCH 267/400] Lazy import of representation and preprocessing. --- .../_linear_differential_operator.py | 6 +-- skfda/misc/validation.py | 7 +-- skfda/ml/regression/_linear_regression.py | 4 +- skfda/preprocessing/__init__.py | 17 ++++-- skfda/preprocessing/dim_reduction/__init__.py | 20 +++++-- .../variable_selection/__init__.py | 30 +++++++++-- .../feature_construction/__init__.py | 36 ++++++++++--- skfda/preprocessing/registration/__init__.py | 53 ++++++++++++++----- skfda/preprocessing/smoothing/__init__.py | 23 ++++++-- skfda/representation/__init__.py | 28 ++++++++-- skfda/representation/basis/__init__.py | 39 ++++++++++---- skfda/representation/basis/_basis.py | 2 +- skfda/representation/basis/_fdatabasis.py | 8 +-- skfda/representation/grid.py | 2 +- 14 files changed, 209 insertions(+), 66 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 9eafdf0e1..fdca3635a 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -94,15 +94,15 @@ class LinearDifferentialOperator( >>> LinearDifferentialOperator(weights=fdlist) LinearDifferentialOperator( weights=(FDataBasis( - basis=Constant(domain_range=((0, 1),), n_basis=1), + basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 0.]], ...), FDataBasis( - basis=Constant(domain_range=((0, 1),), n_basis=1), + basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 0.]], ...), FDataBasis( - basis=Monomial(domain_range=((0, 1),), n_basis=3), + basis=Monomial(domain_range=((0.0, 1.0),), n_basis=3), coefficients=[[ 1. 2. 3.]], ...)), ) diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index b1a6db988..5c3f5e88f 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -3,7 +3,8 @@ from __future__ import annotations import functools -from typing import TYPE_CHECKING, Container, Sequence, Tuple, cast +import numbers +from typing import Container, Sequence, Tuple, cast import numpy as np @@ -246,12 +247,12 @@ def _validate_domain_range_limits( ) lower, upper = limits - return (lower, upper) # noqa: WPS331 + return (float(lower), float(upper)) def validate_domain_range(domain_range: DomainRangeLike) -> DomainRange: """Convert sequence to a proper domain range.""" - if isinstance(domain_range[0], (int, float)): + if isinstance(domain_range[0], numbers.Real): domain_range = cast(Sequence[float], domain_range) domain_range = (domain_range,) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 016c70015..cabe023f1 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -116,7 +116,7 @@ class LinearRegression( >>> _ = linear.fit(x_fd, y) >>> linear.coef_[0] FDataBasis( - basis=Monomial(domain_range=((0, 1),), n_basis=3), + basis=Monomial(domain_range=((0.0, 1.0),), n_basis=3), coefficients=[[-15. 96. -90.]], ...) >>> linear.intercept_ @@ -142,7 +142,7 @@ class LinearRegression( array([ 2., 1.]) >>> linear.coef_[1] FDataBasis( - basis=Constant(domain_range=((0, 1),), n_basis=1), + basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 1.]], ...) >>> linear.intercept_ diff --git a/skfda/preprocessing/__init__.py b/skfda/preprocessing/__init__.py index a09e80474..914d2edbc 100644 --- a/skfda/preprocessing/__init__.py +++ b/skfda/preprocessing/__init__.py @@ -1,4 +1,13 @@ -from . import feature_construction -from . import registration -from . import smoothing -from . import dim_reduction \ No newline at end of file +"""Preprocessing methods for functional data.""" + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "feature_construction", + "registration", + "smoothing", + "dim_reduction", + ], +) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index e7f3268d3..17facc60e 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -2,13 +2,25 @@ from __future__ import annotations import importlib -from typing import Any +from typing import TYPE_CHECKING, Any -from . import variable_selection -from ._fpca import FPCA +import lazy_loader as lazy + +_normal_getattr, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "variable_selection", + ], + submod_attrs={ + "_fpca": ["FPCA"], + }, +) + +if TYPE_CHECKING: + from ._fpca import FPCA def __getattr__(name: str) -> Any: if name in {"projection", "feature_extraction"}: return importlib.import_module(f".{name}", __name__) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + return _normal_getattr(name) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/__init__.py b/skfda/preprocessing/dim_reduction/variable_selection/__init__.py index 6d85fff30..8a61ccaa0 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/__init__.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/__init__.py @@ -1,5 +1,25 @@ -from . import maxima_hunting, mrmr, recursive_maxima_hunting -from ._rkvs import RKHSVariableSelection -from .maxima_hunting import MaximaHunting -from .mrmr import MinimumRedundancyMaximumRelevance -from .recursive_maxima_hunting import RecursiveMaximaHunting +"""Variable selection methods for functional data.""" +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "maxima_hunting", + "mrmr", + "recursive_maxima_hunting", + ], + submod_attrs={ + "_rkvs": ["RKHSVariableSelection"], + "maxima_hunting": ["MaximaHunting"], + "mrmr": ["MinimumRedundancyMaximumRelevance"], + "recursive_maxima_hunting": ["RecursiveMaximaHunting"], + }, +) + +if TYPE_CHECKING: + from ._rkvs import RKHSVariableSelection + from .maxima_hunting import MaximaHunting + from .mrmr import MinimumRedundancyMaximumRelevance + from .recursive_maxima_hunting import RecursiveMaximaHunting diff --git a/skfda/preprocessing/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py index 2a7522a32..5f7f3ff94 100644 --- a/skfda/preprocessing/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -1,10 +1,30 @@ """Feature extraction.""" -from ._coefficients_transformer import CoefficientsTransformer -from ._evaluation_trasformer import EvaluationTransformer -from ._fda_feature_union import FDAFeatureUnion -from ._function_transformers import ( - LocalAveragesTransformer, - NumberUpCrossingsTransformer, - OccupationMeasureTransformer, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_coefficients_transformer": ["CoefficientsTransformer"], + "_evaluation_trasformer": ["EvaluationTransformer"], + "_fda_feature_union": ["FDAFeatureUnion"], + "_function_transformers": [ + "LocalAveragesTransformer", + "NumberUpCrossingsTransformer", + "OccupationMeasureTransformer", + ], + "_per_class_transformer": ["PerClassTransformer"], + }, ) -from ._per_class_transformer import PerClassTransformer + +if TYPE_CHECKING: + from ._coefficients_transformer import CoefficientsTransformer + from ._evaluation_trasformer import EvaluationTransformer + from ._fda_feature_union import FDAFeatureUnion + from ._function_transformers import ( + LocalAveragesTransformer, + NumberUpCrossingsTransformer, + OccupationMeasureTransformer, + ) + from ._per_class_transformer import PerClassTransformer diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 8f857ae08..2b9964104 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -3,19 +3,46 @@ This module contains methods to perform the registration of functional data, in basis as well in discretized form. """ +from typing import TYPE_CHECKING +import lazy_loader as lazy + +# This cannot be made lazy for now from ..._utils import invert_warping, normalize_warping -from . import validation -from ._fisher_rao import ElasticRegistration, FisherRaoElasticRegistration -from ._landmark_registration import ( - landmark_elastic_registration, - landmark_elastic_registration_warping, - landmark_registration, - landmark_shift, - landmark_shift_deltas, - landmark_shift_registration, -) -from ._lstsq_shift_registration import ( - LeastSquaresShiftRegistration, - ShiftRegistration, + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "validation", + ], + submod_attrs={ + "_fisher_rao": ["ElasticRegistration", "FisherRaoElasticRegistration"], + "_landmark_registration": [ + "landmark_elastic_registration", + "landmark_elastic_registration_warping", + "landmark_registration", + "landmark_shift", + "landmark_shift_deltas", + "landmark_shift_registration", + ], + "_lstsq_shift_registration": [ + "LeastSquaresShiftRegistration", + "ShiftRegistration", + ], + }, ) + +if TYPE_CHECKING: + from ._fisher_rao import ElasticRegistration, FisherRaoElasticRegistration + from ._landmark_registration import ( + landmark_elastic_registration, + landmark_elastic_registration_warping, + landmark_registration, + landmark_shift, + landmark_shift_deltas, + landmark_shift_registration, + ) + from ._lstsq_shift_registration import ( + LeastSquaresShiftRegistration, + ShiftRegistration, + ) diff --git a/skfda/preprocessing/smoothing/__init__.py b/skfda/preprocessing/smoothing/__init__.py index db38c1471..487c819aa 100644 --- a/skfda/preprocessing/smoothing/__init__.py +++ b/skfda/preprocessing/smoothing/__init__.py @@ -1,10 +1,23 @@ """Smoothing.""" import warnings -from typing import Any +from typing import TYPE_CHECKING, Any -from . import validation -from ._basis import BasisSmoother -from ._kernel_smoothers import KernelSmoother +import lazy_loader as lazy + +_normal_getattr, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "validation", + ], + submod_attrs={ + "_basis": ["BasisSmoother"], + "_kernel_smoothers": ["KernelSmoother"], + }, +) + +if TYPE_CHECKING: + from ._basis import BasisSmoother + from ._kernel_smoothers import KernelSmoother __kernel_smoothers__imported__ = False @@ -16,4 +29,4 @@ def __getattr__(name: str) -> Any: from . import kernel_smoothers return kernel_smoothers - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + return _normal_getattr(name) diff --git a/skfda/representation/__init__.py b/skfda/representation/__init__.py index 304499671..0dec1723b 100644 --- a/skfda/representation/__init__.py +++ b/skfda/representation/__init__.py @@ -1,4 +1,24 @@ -from . import basis, extrapolation, grid, interpolation -from ._functional_data import FData, concatenate -from .basis import FDataBasis -from .grid import FDataGrid +"""Representation of functional data.""" +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "basis", + "extrapolation", + "grid", + "interpolation", + ], + submod_attrs={ + '_functional_data': ["FData", "concatenate"], + 'basis': ["FDataBasis"], + 'grid': ["FDataGrid"], + }, +) + +if TYPE_CHECKING: + from ._functional_data import FData, concatenate + from .basis import FDataBasis + from .grid import FDataGrid diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index fb117201f..0c74a8058 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -1,9 +1,30 @@ -from ._basis import Basis -from ._bspline import BSpline -from ._constant import Constant -from ._fdatabasis import FDataBasis, FDataBasisDType -from ._finite_element import FiniteElement -from ._fourier import Fourier -from ._monomial import Monomial -from ._tensor_basis import Tensor -from ._vector_basis import VectorValued +"""Basis representation.""" +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + '_basis': ["Basis"], + "_bspline": ["BSpline"], + "_constant": ["Constant"], + "_fdatabasis": ["FDataBasis", "FDataBasisDType"], + "_finite_element": ["FiniteElement"], + "_fourier": ["Fourier"], + "_monomial": ["Monomial"], + "_tensor_basis": ["Tensor"], + "_vector_basis": ["VectorValued"], + }, +) + +if TYPE_CHECKING: + from ._basis import Basis + from ._bspline import BSpline + from ._constant import Constant + from ._fdatabasis import FDataBasis, FDataBasisDType + from ._finite_element import FiniteElement + from ._fourier import Fourier + from ._monomial import Monomial + from ._tensor_basis import Tensor + from ._vector_basis import VectorValued diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index b820a2595..873b5c5b4 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -111,7 +111,7 @@ def dim_codomain(self) -> int: @property def domain_range(self) -> DomainRange: if self._domain_range is None: - return ((0, 1),) * self.dim_domain + return ((0.0, 1.0),) * self.dim_domain return self._domain_range diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index ea2051745..2116e730e 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -30,10 +30,10 @@ NDArrayInt, ) from ..extrapolation import ExtrapolationLike -from . import Basis if TYPE_CHECKING: from .. import FDataGrid + from . import Basis T = TypeVar('T', bound='FDataBasis') @@ -78,7 +78,7 @@ class FDataBasis(FData): # noqa: WPS214 >>> coefficients = [1, 1, 3, .5] >>> FDataBasis(basis, coefficients) FDataBasis( - basis=Monomial(domain_range=((0, 1),), n_basis=4), + basis=Monomial(domain_range=((0.0, 1.0),), n_basis=4), coefficients=[[ 1. 1. 3. 0.5]], ...) @@ -421,7 +421,7 @@ def sum( # noqa: WPS125 >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] >>> FDataBasis(basis, coefficients).sum() FDataBasis( - basis=Monomial(domain_range=((0, 1),), n_basis=4), + basis=Monomial(domain_range=((0.0, 1.0),), n_basis=4), coefficients=[[ 2. 2. 6. 1.]], ...) @@ -520,7 +520,7 @@ def to_grid( [ 2.], [ 5.]]]), grid_points=(array([ 0., 1., 2.]),), - domain_range=((0, 5),), + domain_range=((0.0, 5.0),), ...) """ diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index ad80e9bff..23ddae297 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -41,13 +41,13 @@ NDArrayFloat, NDArrayInt, ) -from .basis import Basis from .evaluator import Evaluator from .extrapolation import ExtrapolationLike from .interpolation import SplineInterpolation if TYPE_CHECKING: from . import FDataBasis + from .basis import Basis T = TypeVar("T", bound='FDataGrid') From 27decf0a5d3b67bae0056d3ef289eceac8ca7398 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 30 Aug 2022 15:04:15 +0200 Subject: [PATCH 268/400] Add more lazy imports. --- skfda/_utils/__init__.py | 75 +++++++++++++++++++++++++---------- skfda/exploratory/__init__.py | 15 +++++-- skfda/misc/__init__.py | 66 ++++++++++++++++++++---------- 3 files changed, 109 insertions(+), 47 deletions(-) diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 15d709ad8..2c15207fb 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -1,23 +1,54 @@ -from . import constants -from ._sklearn_adapter import ( - BaseEstimator, - InductiveTransformerMixin, - TransformerMixin, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "constants", + ], + submod_attrs={ + "_utils": [ + "RandomStateLike", + "_cartesian_product", + "_check_array_key", + "_check_estimator", + "_classifier_get_classes", + "_compute_dependence", + "_DependenceMeasure", + "_evaluate_grid", + "_int_to_real", + "_pairwise_symmetric", + "_same_domain", + "_to_grid", + "_to_grid_points", + "nquad_vec", + ], + '_warping': [ + "invert_warping", + "normalize_scale", + "normalize_warping", + ], + }, ) -from ._utils import ( - RandomStateLike, - _cartesian_product, - _check_array_key, - _check_estimator, - _classifier_get_classes, - _compute_dependence, - _DependenceMeasure, - _evaluate_grid, - _int_to_real, - _pairwise_symmetric, - _same_domain, - _to_grid, - _to_grid_points, - nquad_vec, -) -from ._warping import invert_warping, normalize_scale, normalize_warping + +if TYPE_CHECKING: + + from ._utils import ( + RandomStateLike, + _cartesian_product, + _check_array_key, + _check_estimator, + _classifier_get_classes, + _compute_dependence, + _DependenceMeasure, + _evaluate_grid, + _int_to_real, + _pairwise_symmetric, + _same_domain, + _to_grid, + _to_grid_points, + nquad_vec, + ) + + from ._warping import invert_warping, normalize_scale, normalize_warping diff --git a/skfda/exploratory/__init__.py b/skfda/exploratory/__init__.py index 7d58f75c6..e69a33f09 100644 --- a/skfda/exploratory/__init__.py +++ b/skfda/exploratory/__init__.py @@ -1,4 +1,11 @@ -from . import depth -from . import outliers -from . import stats -from . import visualization +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "depth", + "outliers", + "stats", + "visualization", + ], +) diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 6e0045c10..607585215 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -1,23 +1,47 @@ """Miscellaneous functions and objects.""" -from . import ( - covariances, - hat_matrix, - kernels, - lstsq, - metrics, - operators, - regularization, - validation, -) -from ._math import ( - cosine_similarity, - cosine_similarity_matrix, - cumsum, - exp, - inner_product, - inner_product_matrix, - log, - log2, - log10, - sqrt, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "covariances", + "hat_matrix", + "kernels", + "lstsq", + "metrics", + "operators", + "regularization", + "validation", + ], + submod_attrs={ + '_math': [ + "cosine_similarity", + "cosine_similarity_matrix", + "cumsum", + "exp", + "inner_product", + "inner_product_matrix", + "log", + "log2", + "log10", + "sqrt", + ], + }, ) + +if TYPE_CHECKING: + + from ._math import ( + cosine_similarity, + cosine_similarity_matrix, + cumsum, + exp, + inner_product, + inner_product_matrix, + log, + log2, + log10, + sqrt, + ) From 7f7ebcc9ff6e82bb85ce989b7bb24f8ba24a35b8 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Tue, 30 Aug 2022 16:16:12 +0200 Subject: [PATCH 269/400] Move typing definitions. --- skfda/_utils/_sklearn_adapter.py | 2 +- skfda/_utils/_utils.py | 10 ++++--- skfda/_utils/_warping.py | 3 ++- skfda/datasets/_samples_generators.py | 7 ++--- skfda/exploratory/depth/_depth.py | 2 +- skfda/exploratory/depth/multivariate.py | 3 +-- skfda/exploratory/outliers/_boxplot.py | 3 +-- .../outliers/_directional_outlyingness.py | 2 +- skfda/exploratory/outliers/_envelopes.py | 2 +- skfda/exploratory/outliers/_outliergram.py | 2 +- .../exploratory/outliers/neighbors_outlier.py | 5 ++-- skfda/exploratory/stats/_fisher_rao.py | 2 +- .../stats/_functional_transformers.py | 9 ++----- skfda/exploratory/stats/_stats.py | 2 +- skfda/exploratory/visualization/_baseplot.py | 2 +- skfda/exploratory/visualization/_boxplot.py | 2 +- skfda/exploratory/visualization/_ddplot.py | 2 +- .../visualization/_magnitude_shape_plot.py | 2 +- skfda/exploratory/visualization/clustering.py | 2 +- .../visualization/representation.py | 2 +- skfda/inference/anova/_anova_oneway.py | 2 +- skfda/inference/hotelling/_hotelling.py | 2 +- skfda/misc/_math.py | 3 ++- skfda/misc/covariances.py | 2 +- skfda/misc/hat_matrix.py | 3 ++- skfda/misc/lstsq.py | 5 ++-- skfda/misc/metrics/_angular.py | 2 +- skfda/misc/metrics/_fisher_rao.py | 2 +- skfda/misc/metrics/_lp_distances.py | 2 +- skfda/misc/metrics/_lp_norms.py | 4 +-- skfda/misc/metrics/_mahalanobis.py | 2 +- skfda/misc/metrics/_typing.py | 3 ++- skfda/misc/metrics/_utils.py | 3 ++- skfda/misc/operators/_identity.py | 2 +- .../_linear_differential_operator.py | 2 +- skfda/misc/operators/_srvf.py | 2 +- skfda/misc/regularization/_regularization.py | 2 +- skfda/misc/validation.py | 3 ++- skfda/ml/_neighbors_base.py | 2 +- .../classification/_centroid_classifiers.py | 2 +- skfda/ml/classification/_depth_classifiers.py | 2 +- .../ml/classification/_logistic_regression.py | 2 +- .../classification/_neighbors_classifiers.py | 2 +- .../_parameterized_functional_qda.py | 2 +- skfda/ml/clustering/_kmeans.py | 2 +- skfda/ml/clustering/_neighbors_clustering.py | 2 +- skfda/ml/regression/_neighbors_regression.py | 2 +- skfda/preprocessing/dim_reduction/_fpca.py | 4 +-- .../variable_selection/maxima_hunting.py | 5 ++-- .../dim_reduction/variable_selection/mrmr.py | 2 +- .../recursive_maxima_hunting.py | 6 ++--- .../_coefficients_transformer.py | 2 +- .../_evaluation_trasformer.py | 3 ++- .../_function_transformers.py | 5 ++-- .../_per_class_transformer.py | 2 +- .../preprocessing/registration/_fisher_rao.py | 2 +- .../registration/_landmark_registration.py | 3 ++- .../registration/_lstsq_shift_registration.py | 3 ++- skfda/preprocessing/registration/base.py | 2 +- skfda/preprocessing/smoothing/_basis.py | 3 ++- .../smoothing/_kernel_smoothers.py | 3 ++- skfda/preprocessing/smoothing/_linear.py | 3 ++- .../smoothing/kernel_smoothers.py | 3 ++- skfda/representation/_functional_data.py | 10 ++++--- skfda/representation/basis/_basis.py | 5 ++-- skfda/representation/basis/_bspline.py | 5 ++-- skfda/representation/basis/_constant.py | 3 ++- skfda/representation/basis/_fdatabasis.py | 11 ++------ skfda/representation/basis/_finite_element.py | 3 ++- skfda/representation/basis/_fourier.py | 3 ++- skfda/representation/basis/_monomial.py | 2 +- skfda/representation/basis/_tensor_basis.py | 2 +- skfda/representation/basis/_vector_basis.py | 2 +- skfda/representation/evaluator.py | 5 ++-- skfda/representation/extrapolation.py | 5 ++-- skfda/representation/grid.py | 12 +++------ skfda/representation/interpolation.py | 5 ++-- skfda/tests/test_math.py | 21 +++++++++------ skfda/typing/__init__.py | 0 .../_typing.py => typing/_base.py} | 24 ++--------------- skfda/typing/_numpy.py | 27 +++++++++++++++++++ 81 files changed, 169 insertions(+), 157 deletions(-) create mode 100644 skfda/typing/__init__.py rename skfda/{representation/_typing.py => typing/_base.py} (54%) create mode 100644 skfda/typing/_numpy.py diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 6acafc8ef..ce0347ba9 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -6,7 +6,7 @@ import sklearn.base if TYPE_CHECKING: - from ..representation._typing import NDArrayFloat, NDArrayInt + from ..typing._numpy import NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType") TransformerNoTarget = TypeVar( diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 99215a2bc..0f5cb6939 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -36,14 +36,16 @@ from ..representation import FData, FDataGrid from ..representation.basis import Basis from ..representation.extrapolation import ExtrapolationLike - from ..representation._typing import ( - GridPoints, - GridPointsLike, + from ..typing._numpy import ( NDArrayAny, NDArrayFloat, NDArrayInt, NDArrayStr, ) + from ..typing._base import ( + GridPoints, + GridPointsLike, + ) T = TypeVar("T", bound=FData) Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) @@ -331,7 +333,7 @@ def _evaluate_grid( # noqa: WPS234 dimension. """ - from ..representation._typing import GridPointsLike + from ..typing._base import GridPointsLike # Compute intersection points and resulting shapes if aligned: diff --git a/skfda/_utils/_warping.py b/skfda/_utils/_warping.py index 67c4153c8..f3fe7829d 100644 --- a/skfda/_utils/_warping.py +++ b/skfda/_utils/_warping.py @@ -9,7 +9,8 @@ import numpy as np from scipy.interpolate import PchipInterpolator -from ..representation._typing import ArrayLike, DomainRangeLike, NDArrayFloat +from ..typing._base import ArrayLike, DomainRangeLike +from ..typing._numpy import NDArrayFloat if TYPE_CHECKING: from ..representation import FDataGrid diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index b267f5e09..01ffe04ee 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -14,12 +14,9 @@ ) from ..misc import covariances from ..representation import FDataGrid -from ..representation._typing import ( - DomainRangeLike, - GridPointsLike, - NDArrayFloat, -) from ..representation.interpolation import SplineInterpolation +from ..typing._base import DomainRangeLike, GridPointsLike +from ..typing._numpy import NDArrayFloat MeanCallable = Callable[[np.ndarray], np.ndarray] CovarianceCallable = Callable[[np.ndarray, np.ndarray], np.ndarray] diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 72baecace..7b63a5245 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -17,7 +17,7 @@ from ...misc.metrics import Metric, l2_distance from ...misc.metrics._utils import _fit_metric from ...representation import FData, FDataGrid -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from .multivariate import Depth, SimplicialDepth, _UnivariateFraimanMuniz T = TypeVar("T", bound=FData) diff --git a/skfda/exploratory/depth/multivariate.py b/skfda/exploratory/depth/multivariate.py index 9ef2b2246..dd598f1b5 100644 --- a/skfda/exploratory/depth/multivariate.py +++ b/skfda/exploratory/depth/multivariate.py @@ -12,9 +12,8 @@ from scipy.special import comb from typing_extensions import Literal -from skfda.representation._typing import NDArrayFloat, NDArrayInt - from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin +from ...typing._numpy import NDArrayFloat, NDArrayInt T = TypeVar("T", contravariant=True) SelfType = TypeVar("SelfType") diff --git a/skfda/exploratory/outliers/_boxplot.py b/skfda/exploratory/outliers/_boxplot.py index 9e541aed8..271876a49 100644 --- a/skfda/exploratory/outliers/_boxplot.py +++ b/skfda/exploratory/outliers/_boxplot.py @@ -1,9 +1,8 @@ from __future__ import annotations -from skfda.representation._typing import NDArrayInt - from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin from ...representation import FDataGrid +from ...typing._numpy import NDArrayInt from ..depth import Depth, ModifiedBandDepth from . import _envelopes diff --git a/skfda/exploratory/outliers/_directional_outlyingness.py b/skfda/exploratory/outliers/_directional_outlyingness.py index c02463776..eb7ffa7bb 100644 --- a/skfda/exploratory/outliers/_directional_outlyingness.py +++ b/skfda/exploratory/outliers/_directional_outlyingness.py @@ -13,7 +13,7 @@ from ..._utils import RandomStateLike from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin from ...representation import FDataGrid -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt from ..depth.multivariate import Depth, ProjectionDepth from . import _directional_outlyingness_experiment_results as experiments diff --git a/skfda/exploratory/outliers/_envelopes.py b/skfda/exploratory/outliers/_envelopes.py index 97f8284ce..e14d397ae 100644 --- a/skfda/exploratory/outliers/_envelopes.py +++ b/skfda/exploratory/outliers/_envelopes.py @@ -6,7 +6,7 @@ import numpy as np from ...representation import FDataGrid -from ...representation._typing import NDArrayBool, NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayBool, NDArrayFloat, NDArrayInt def compute_region( diff --git a/skfda/exploratory/outliers/_outliergram.py b/skfda/exploratory/outliers/_outliergram.py index bf67bb2d9..6d67d60c8 100644 --- a/skfda/exploratory/outliers/_outliergram.py +++ b/skfda/exploratory/outliers/_outliergram.py @@ -4,7 +4,7 @@ from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin from ...representation import FDataGrid -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt from ..depth._depth import ModifiedBandDepth from ..stats import modified_epigraph_index diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index 71446595e..04c0bb5bf 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -7,12 +7,11 @@ from sklearn.neighbors import LocalOutlierFactor as _LocalOutlierFactor from typing_extensions import Literal -from skfda.misc.metrics._typing import Metric - from ...misc.metrics import PairwiseMetric, l2_distance +from ...misc.metrics._typing import Metric from ...ml._neighbors_base import AlgorithmType, KNeighborsMixin from ...representation import FData -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType", bound="LocalOutlierFactor[Any]") InputBound = Union[NDArrayFloat, FData] diff --git a/skfda/exploratory/stats/_fisher_rao.py b/skfda/exploratory/stats/_fisher_rao.py index 023312488..e6c7cd268 100644 --- a/skfda/exploratory/stats/_fisher_rao.py +++ b/skfda/exploratory/stats/_fisher_rao.py @@ -10,8 +10,8 @@ from ...misc.operators import SRSF from ...misc.validation import check_fdata_dimensions from ...representation import FDataGrid -from ...representation._typing import NDArrayFloat from ...representation.interpolation import SplineInterpolation +from ...typing._numpy import NDArrayFloat ############################################################################### # Based on the original implementation of J. Derek Tucker in # diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 7fd2d906c..fc2a1771d 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -11,13 +11,8 @@ from ..._utils import nquad_vec from ...misc.validation import check_fdata_dimensions, validate_domain_range from ...representation import FData, FDataBasis, FDataGrid -from ...representation._typing import ( - ArrayLike, - DomainRangeLike, - NDArrayBool, - NDArrayFloat, - NDArrayInt, -) +from ...typing._base import DomainRangeLike +from ...typing._numpy import ArrayLike, NDArrayBool, NDArrayFloat, NDArrayInt T = TypeVar("T", bound=Union[NDArrayFloat, FDataGrid]) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 6a54c9071..bc13cc51b 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -10,7 +10,7 @@ from ...misc.metrics import Metric, l2_distance from ...representation import FData, FDataGrid -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ..depth import Depth, ModifiedBandDepth F = TypeVar('F', bound=FData) diff --git a/skfda/exploratory/visualization/_baseplot.py b/skfda/exploratory/visualization/_baseplot.py index 45742bc58..c94caf81f 100644 --- a/skfda/exploratory/visualization/_baseplot.py +++ b/skfda/exploratory/visualization/_baseplot.py @@ -19,7 +19,7 @@ from matplotlib.text import Annotation from ...representation import FData -from ...representation._typing import NDArrayInt +from ...typing._numpy import NDArrayInt from ._utils import _figure_to_svg, _get_figure_and_axes, _set_figure_layout diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index adace8ca0..8538317b6 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -21,7 +21,7 @@ from skfda.exploratory.depth.multivariate import Depth from ...representation import FData, FDataGrid -from ...representation._typing import NDArrayBool, NDArrayFloat +from ...typing._numpy import NDArrayBool, NDArrayFloat from ..depth import ModifiedBandDepth from ..outliers import _envelopes from ._baseplot import BasePlot diff --git a/skfda/exploratory/visualization/_ddplot.py b/skfda/exploratory/visualization/_ddplot.py index 2c71184a2..140f2aa25 100644 --- a/skfda/exploratory/visualization/_ddplot.py +++ b/skfda/exploratory/visualization/_ddplot.py @@ -15,7 +15,7 @@ from ...exploratory.depth.multivariate import Depth from ...representation._functional_data import FData -from ...representation._typing import NDArrayInt +from ...typing._numpy import NDArrayInt from ._baseplot import BasePlot T = TypeVar('T', bound=FData) diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 71ba5ec85..97e674fd6 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -19,7 +19,7 @@ from matplotlib.patches import Ellipse from ...representation import FDataGrid -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt from ..depth import Depth from ..outliers import MSPlotOutlierDetector from ._baseplot import BasePlot diff --git a/skfda/exploratory/visualization/clustering.py b/skfda/exploratory/visualization/clustering.py index d7a494db9..8d1fdb979 100644 --- a/skfda/exploratory/visualization/clustering.py +++ b/skfda/exploratory/visualization/clustering.py @@ -20,7 +20,7 @@ from ...misc.validation import check_fdata_same_dimensions from ...representation import FData, FDataGrid -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt from ._baseplot import BasePlot from ._utils import ColorLike, _darken, _set_labels diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index 93142faa6..6b1da7a15 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -22,7 +22,7 @@ from ...misc.validation import validate_domain_range from ...representation import FDataGrid from ...representation._functional_data import FData -from ...representation._typing import DomainRangeLike, GridPointsLike +from ...typing._base import DomainRangeLike, GridPointsLike from ._baseplot import BasePlot from ._utils import ColorLike, _set_labels diff --git a/skfda/inference/anova/_anova_oneway.py b/skfda/inference/anova/_anova_oneway.py index ee0655177..54c74a93e 100644 --- a/skfda/inference/anova/_anova_oneway.py +++ b/skfda/inference/anova/_anova_oneway.py @@ -10,7 +10,7 @@ from ...datasets import make_gaussian from ...misc.metrics import lp_distance from ...representation import FData, FDataGrid, concatenate -from ...representation._typing import ArrayLike, NDArrayFloat +from ...typing._numpy import ArrayLike, NDArrayFloat def v_sample_stat(fd: FData, weights: ArrayLike, p: int = 2) -> float: diff --git a/skfda/inference/hotelling/_hotelling.py b/skfda/inference/hotelling/_hotelling.py index 72b5ad041..bae282e1a 100644 --- a/skfda/inference/hotelling/_hotelling.py +++ b/skfda/inference/hotelling/_hotelling.py @@ -10,7 +10,7 @@ from ..._utils import RandomStateLike from ...representation import FData, FDataBasis -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat def hotelling_t2( diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 7c14f128e..37659b803 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -14,8 +14,9 @@ from .._utils import nquad_vec from ..representation import FData, FDataBasis, FDataGrid -from ..representation._typing import ArrayLike, DomainRange, NDArrayFloat from ..representation.basis import Basis +from ..typing._base import DomainRange +from ..typing._numpy import ArrayLike, NDArrayFloat from .validation import check_fdata_same_dimensions Vector = TypeVar( diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index 5358a57fa..c2b4db1c1 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -9,7 +9,7 @@ from matplotlib.figure import Figure from scipy.special import gamma, kv -from ..representation._typing import ArrayLike, NDArrayFloat +from ..typing._numpy import ArrayLike, NDArrayFloat def _squared_norms(x: NDArrayFloat, y: NDArrayFloat) -> NDArrayFloat: diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index 03112f1b1..de9482690 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -15,8 +15,9 @@ from sklearn.base import BaseEstimator, RegressorMixin from ..representation._functional_data import FData -from ..representation._typing import GridPoints, GridPointsLike, NDArrayFloat from ..representation.basis import FDataBasis +from ..typing._base import GridPoints, GridPointsLike +from ..typing._numpy import NDArrayFloat from . import kernels diff --git a/skfda/misc/lstsq.py b/skfda/misc/lstsq.py index 8044041ac..3c4f555bc 100644 --- a/skfda/misc/lstsq.py +++ b/skfda/misc/lstsq.py @@ -3,12 +3,11 @@ from typing import Callable, Optional, Union -from typing_extensions import Final, Literal - import numpy as np import scipy.linalg +from typing_extensions import Final, Literal -from ..representation._typing import NDArrayFloat +from ..typing._numpy import NDArrayFloat LstsqMethodCallable = Callable[[np.ndarray, np.ndarray], np.ndarray] LstsqMethodName = Literal["cholesky", "qr", "svd"] diff --git a/skfda/misc/metrics/_angular.py b/skfda/misc/metrics/_angular.py index b0cf3e519..127e7bce2 100644 --- a/skfda/misc/metrics/_angular.py +++ b/skfda/misc/metrics/_angular.py @@ -6,7 +6,7 @@ from typing_extensions import Final from ...representation import FData -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from .._math import cosine_similarity, cosine_similarity_matrix from ._utils import pairwise_metric_optimization diff --git a/skfda/misc/metrics/_fisher_rao.py b/skfda/misc/metrics/_fisher_rao.py index 469ed0479..bfed1db50 100644 --- a/skfda/misc/metrics/_fisher_rao.py +++ b/skfda/misc/metrics/_fisher_rao.py @@ -8,7 +8,7 @@ from ..._utils import normalize_scale, normalize_warping from ...representation import FData, FDataGrid -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ..operators import SRSF from ._lp_distances import l2_distance from ._utils import PairwiseMetric, _cast_to_grid, pairwise_metric_optimization diff --git a/skfda/misc/metrics/_lp_distances.py b/skfda/misc/metrics/_lp_distances.py index b1d25d8b2..37e8928a3 100644 --- a/skfda/misc/metrics/_lp_distances.py +++ b/skfda/misc/metrics/_lp_distances.py @@ -9,7 +9,7 @@ from typing_extensions import Final from ...representation import FData -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ._lp_norms import LpNorm from ._typing import Norm from ._utils import NormInducedMetric, pairwise_metric_optimization diff --git a/skfda/misc/metrics/_lp_norms.py b/skfda/misc/metrics/_lp_norms.py index b7a1c796c..40baa8a6d 100644 --- a/skfda/misc/metrics/_lp_norms.py +++ b/skfda/misc/metrics/_lp_norms.py @@ -7,10 +7,8 @@ import scipy.integrate from typing_extensions import Final -from skfda.representation._typing import NDArrayFloat - from ...representation import FData, FDataBasis -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ._typing import Norm diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 5ebf63304..4d3e95cb9 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -10,8 +10,8 @@ from sklearn.utils.validation import check_is_fitted from ...representation import FData -from ...representation._typing import ArrayLike, NDArrayFloat from ...representation.basis import Basis +from ...typing._numpy import ArrayLike, NDArrayFloat from .._math import inner_product_matrix from ..regularization._regularization import TikhonovRegularization diff --git a/skfda/misc/metrics/_typing.py b/skfda/misc/metrics/_typing.py index 8b44bd216..b8e1dc4ca 100644 --- a/skfda/misc/metrics/_typing.py +++ b/skfda/misc/metrics/_typing.py @@ -6,7 +6,8 @@ from typing_extensions import Final, Literal, Protocol -from ...representation._typing import NDArrayFloat, Vector +from ...typing._base import Vector +from ...typing._numpy import NDArrayFloat VectorType = TypeVar("VectorType", contravariant=True, bound=Vector) MetricElementType = TypeVar("MetricElementType", contravariant=True) diff --git a/skfda/misc/metrics/_utils.py b/skfda/misc/metrics/_utils.py index 84c4ad801..9da2db240 100644 --- a/skfda/misc/metrics/_utils.py +++ b/skfda/misc/metrics/_utils.py @@ -6,7 +6,8 @@ from ..._utils import _pairwise_symmetric from ...representation import FData, FDataGrid -from ...representation._typing import NDArrayFloat, Vector +from ...typing._base import Vector +from ...typing._numpy import NDArrayFloat from ._typing import Metric, MetricElementType, Norm, VectorType T = TypeVar("T", bound=FData) diff --git a/skfda/misc/operators/_identity.py b/skfda/misc/operators/_identity.py index 4ef00b66e..b8cc2adf7 100644 --- a/skfda/misc/operators/_identity.py +++ b/skfda/misc/operators/_identity.py @@ -5,8 +5,8 @@ import numpy as np from ...representation import FDataGrid -from ...representation._typing import Vector from ...representation.basis import Basis +from ...typing._base import Vector from ._operators import Operator, gramian_matrix_optimization T = TypeVar("T", bound=Vector) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index fdca3635a..13bee47b4 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -9,7 +9,6 @@ from scipy.interpolate import PPoly from ...representation import FData, FDataGrid -from ...representation._typing import DomainRangeLike from ...representation.basis import ( BSpline, Constant, @@ -17,6 +16,7 @@ Fourier, Monomial, ) +from ...typing._base import DomainRangeLike from ._operators import Operator, gramian_matrix_optimization Order = int diff --git a/skfda/misc/operators/_srvf.py b/skfda/misc/operators/_srvf.py index bf974caba..f240b0dc3 100644 --- a/skfda/misc/operators/_srvf.py +++ b/skfda/misc/operators/_srvf.py @@ -8,8 +8,8 @@ from ...misc.validation import check_fdata_dimensions from ...representation import FDataGrid -from ...representation._typing import ArrayLike from ...representation.basis import Basis +from ...typing._numpy import ArrayLike from ._operators import Operator diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 9485161ea..1f4ef9e28 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -11,8 +11,8 @@ from skfda.misc.operators import Identity, gramian_matrix from ...representation import FData -from ...representation._typing import NDArrayFloat from ...representation.basis import Basis +from ...typing._numpy import NDArrayFloat from ..operators import Operator from ..operators._operators import OperatorInput diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index 5c3f5e88f..a5798b3a3 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -9,12 +9,13 @@ import numpy as np from ..representation import FData, FDataBasis, FDataGrid -from ..representation._typing import ( +from ..typing._base import ( ArrayLike, DomainRange, DomainRangeLike, EvaluationPoints, ) +from ..typing._numpy import ArrayLike def check_fdata_dimensions( diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 5745eb146..db2b6bcdd 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -21,7 +21,7 @@ from ..misc.metrics._typing import Metric from ..misc.metrics._utils import _fit_metric from ..representation import FData, FDataGrid, concatenate -from ..representation._typing import NDArrayFloat, NDArrayInt +from ..typing._numpy import NDArrayFloat, NDArrayInt FDataType = TypeVar("FDataType", bound="FData") SelfType = TypeVar("SelfType", bound="NeighborsBase[Any, Any]") diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index f18efd395..9bf0dd287 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -12,7 +12,7 @@ from ...misc.metrics import Metric, PairwiseMetric, l2_distance from ...misc.metrics._utils import _fit_metric from ...representation import FData -from ...representation._typing import NDArrayInt +from ...typing._numpy import NDArrayInt T = TypeVar("T", bound=FData) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index 790f8bfbd..63747c728 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -28,8 +28,8 @@ from ...preprocessing.feature_construction._per_class_transformer import ( PerClassTransformer, ) -from ...representation._typing import NDArrayFloat, NDArrayInt from ...representation.grid import FData +from ...typing._numpy import NDArrayFloat, NDArrayInt T = TypeVar("T", bound=FData) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 691abb992..34fe3296c 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -10,7 +10,7 @@ from ..._utils import _classifier_get_classes from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin from ...representation import FDataGrid -from ...representation._typing import NDArrayAny, NDArrayInt +from ...typing._numpy import NDArrayAny, NDArrayInt Solver = Literal["newton-cg", "lbfgs", "liblinear", "sag", "saga"] diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index fc7e24880..1db3042af 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -14,7 +14,7 @@ from ...misc.metrics import l2_distance from ...representation import FData -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt from .._neighbors_base import ( AlgorithmType, KNeighborsMixin, diff --git a/skfda/ml/classification/_parameterized_functional_qda.py b/skfda/ml/classification/_parameterized_functional_qda.py index 5e1c5c52e..e5442f262 100644 --- a/skfda/ml/classification/_parameterized_functional_qda.py +++ b/skfda/ml/classification/_parameterized_functional_qda.py @@ -11,7 +11,7 @@ from ..._utils import _classifier_get_classes from ...representation import FDataGrid -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt class ParameterizedFunctionalQDA( diff --git a/skfda/ml/clustering/_kmeans.py b/skfda/ml/clustering/_kmeans.py index 3e90e3cd8..3fa9b69c8 100644 --- a/skfda/ml/clustering/_kmeans.py +++ b/skfda/ml/clustering/_kmeans.py @@ -15,7 +15,7 @@ from ...misc.metrics import Metric, PairwiseMetric, l2_distance from ...misc.validation import check_fdata_same_dimensions from ...representation import FDataGrid -from ...representation._typing import NDArrayAny, NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType", bound="BaseKMeans[Any]") MembershipType = TypeVar("MembershipType", bound=NDArrayAny) diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 6dd6e2756..0be3450f4 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -9,7 +9,7 @@ from ...misc.metrics import l2_distance from ...representation import FData -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from .._neighbors_base import ( AlgorithmType, KNeighborsMixin, diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index fac598926..a4368f0b4 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -14,7 +14,7 @@ from ...misc.metrics import l2_distance from ...representation import FData -from ...representation._typing import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt from .._neighbors_base import ( AlgorithmType, KNeighborsMixin, diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index 09fee65b7..cd6706e54 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -10,13 +10,11 @@ from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA -from ...misc import inner_product_matrix -from ...misc.metrics import l2_norm from ...misc.regularization import L2Regularization, compute_penalty_matrix from ...representation import FData -from ...representation._typing import ArrayLike from ...representation.basis import Basis, FDataBasis from ...representation.grid import FDataGrid +from ...typing._numpy import ArrayLike Function = TypeVar("Function", bound=FData) WeightsCallable = Callable[[np.ndarray], np.ndarray] diff --git a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py index 221ac35f7..14ae59620 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py @@ -6,16 +6,17 @@ import numpy as np import scipy.signal import sklearn.utils -from dcor import u_distance_correlation_sqr from sklearn.base import clone +from dcor import u_distance_correlation_sqr + from ...._utils import _compute_dependence, _DependenceMeasure from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, ) from ....representation import FDataGrid -from ....representation._typing import NDArrayFloat, NDArrayInt +from ....typing._numpy import NDArrayFloat, NDArrayInt _LocalMaximaSelector = Callable[[FDataGrid], NDArrayInt] diff --git a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py index 22badb531..7fa917153 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py @@ -22,8 +22,8 @@ from typing_extensions import Final, Literal from ...._utils import RandomStateLike, _compute_dependence, _DependenceMeasure -from ....representation._typing import NDArrayFloat, NDArrayInt from ....representation.grid import FDataGrid +from ....typing._numpy import NDArrayFloat, NDArrayInt _Criterion = Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat] _CriterionLike = Union[ diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py index 0aad65af2..0ff743bcc 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py @@ -17,22 +17,22 @@ overload, ) -import dcor import numpy as np import numpy.linalg as linalg import numpy.ma as ma import scipy.stats import sklearn.utils -from numpy.typing import ArrayLike from typing_extensions import Literal +import dcor + from ...._utils import _compute_dependence from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, ) from ....representation import FDataGrid -from ....representation._typing import NDArrayBool, NDArrayFloat, NDArrayInt +from ....typing._numpy import ArrayLike, NDArrayBool, NDArrayFloat, NDArrayInt if TYPE_CHECKING: from ....misc.covariances import CovarianceLike diff --git a/skfda/preprocessing/feature_construction/_coefficients_transformer.py b/skfda/preprocessing/feature_construction/_coefficients_transformer.py index cd287c3a2..cb838bef3 100644 --- a/skfda/preprocessing/feature_construction/_coefficients_transformer.py +++ b/skfda/preprocessing/feature_construction/_coefficients_transformer.py @@ -6,7 +6,7 @@ from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from ...representation import FDataBasis -from ...representation._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat class CoefficientsTransformer( diff --git a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py index 6bb35f3d9..d41e34294 100644 --- a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py +++ b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py @@ -8,9 +8,10 @@ from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin from ...representation._functional_data import FData -from ...representation._typing import ArrayLike, GridPointsLike, NDArrayFloat from ...representation.extrapolation import ExtrapolationLike from ...representation.grid import FDataGrid +from ...typing._base import GridPointsLike +from ...typing._numpy import ArrayLike, NDArrayFloat Input = TypeVar("Input", bound=FData) SelfType = TypeVar( diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index 74669069b..196387f2b 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -10,9 +10,10 @@ occupation_measure, ) from ...representation import FData -from ...representation._typing import DomainRangeLike, NDArrayFloat, Union from ...representation.basis import FDataBasis from ...representation.grid import FDataGrid +from ...typing._base import DomainRangeLike, NDArrayFloat +from ...typing._numpy import NDArrayFloat class LocalAveragesTransformer( @@ -158,7 +159,7 @@ def __init__( self.intervals = intervals self.n_points = n_points - def transform(self, X: Union[FDataGrid, FDataBasis]) -> NDArrayFloat: + def transform(self, X: FData) -> NDArrayFloat: """ Transform the provided data using the occupation_measure function. diff --git a/skfda/preprocessing/feature_construction/_per_class_transformer.py b/skfda/preprocessing/feature_construction/_per_class_transformer.py index b53683f2b..b31b2357e 100644 --- a/skfda/preprocessing/feature_construction/_per_class_transformer.py +++ b/skfda/preprocessing/feature_construction/_per_class_transformer.py @@ -12,9 +12,9 @@ from ..._utils import _classifier_get_classes from ..._utils._sklearn_adapter import TransformerMixin from ...representation import FData -from ...representation._typing import NDArrayFloat, NDArrayInt from ...representation.basis import FDataBasis from ...representation.grid import FDataGrid +from ...typing._numpy import NDArrayFloat, NDArrayInt Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) Output = TypeVar("Output", bound=Union[pd.DataFrame, NDArrayFloat]) diff --git a/skfda/preprocessing/registration/_fisher_rao.py b/skfda/preprocessing/registration/_fisher_rao.py index 825f061fd..7e9592502 100644 --- a/skfda/preprocessing/registration/_fisher_rao.py +++ b/skfda/preprocessing/registration/_fisher_rao.py @@ -12,9 +12,9 @@ from ...misc.operators import SRSF from ...misc.validation import check_fdata_dimensions, check_fdata_same_kind from ...representation import FDataGrid -from ...representation._typing import ArrayLike from ...representation.basis import Basis from ...representation.interpolation import SplineInterpolation +from ...typing._numpy import ArrayLike from .base import InductiveRegistrationTransformer _MeanType = Callable[[FDataGrid], FDataGrid] diff --git a/skfda/preprocessing/registration/_landmark_registration.py b/skfda/preprocessing/registration/_landmark_registration.py index cc02eec6d..1f44a962e 100644 --- a/skfda/preprocessing/registration/_landmark_registration.py +++ b/skfda/preprocessing/registration/_landmark_registration.py @@ -10,9 +10,10 @@ import numpy as np from ...representation import FData, FDataGrid -from ...representation._typing import ArrayLike, GridPointsLike, NDArrayFloat from ...representation.extrapolation import ExtrapolationLike from ...representation.interpolation import SplineInterpolation +from ...typing._base import GridPointsLike +from ...typing._numpy import ArrayLike, NDArrayFloat _FixedLocation = Union[float, Sequence[float]] _LocationCallable = Callable[[np.ndarray], _FixedLocation] diff --git a/skfda/preprocessing/registration/_lstsq_shift_registration.py b/skfda/preprocessing/registration/_lstsq_shift_registration.py index db422fd16..b7ea00565 100644 --- a/skfda/preprocessing/registration/_lstsq_shift_registration.py +++ b/skfda/preprocessing/registration/_lstsq_shift_registration.py @@ -12,8 +12,9 @@ from ...misc.metrics._lp_norms import l2_norm from ...misc.validation import check_fdata_dimensions from ...representation import FData, FDataGrid -from ...representation._typing import ArrayLike, GridPointsLike, NDArrayFloat from ...representation.extrapolation import ExtrapolationLike +from ...typing._base import GridPointsLike +from ...typing._numpy import ArrayLike, NDArrayFloat from .base import InductiveRegistrationTransformer SelfType = TypeVar("SelfType", bound="LeastSquaresShiftRegistration[FData]") diff --git a/skfda/preprocessing/registration/base.py b/skfda/preprocessing/registration/base.py index 4c25d951b..7b09fceb3 100644 --- a/skfda/preprocessing/registration/base.py +++ b/skfda/preprocessing/registration/base.py @@ -9,7 +9,7 @@ from abc import abstractmethod from typing import Any, TypeVar, overload -from ..._utils import ( +from ..._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, TransformerMixin, diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index 0d5707c60..c5eab609b 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -15,8 +15,9 @@ from ...misc.lstsq import LstsqMethod, solve_regularized_weighted_lstsq from ...misc.regularization import L2Regularization from ...representation import FData, FDataBasis, FDataGrid -from ...representation._typing import GridPointsLike, NDArrayFloat from ...representation.basis import Basis +from ...typing._base import GridPointsLike +from ...typing._numpy import NDArrayFloat from ._linear import _LinearSmoother diff --git a/skfda/preprocessing/smoothing/_kernel_smoothers.py b/skfda/preprocessing/smoothing/_kernel_smoothers.py index 5162b06c4..c07489235 100644 --- a/skfda/preprocessing/smoothing/_kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/_kernel_smoothers.py @@ -11,7 +11,8 @@ from ..._utils._utils import _to_grid_points from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix -from ...representation._typing import GridPointsLike, NDArrayFloat +from ...typing._base import GridPointsLike +from ...typing._numpy import NDArrayFloat from ._linear import _LinearSmoother diff --git a/skfda/preprocessing/smoothing/_linear.py b/skfda/preprocessing/smoothing/_linear.py index 546f2e13d..e515ead5c 100644 --- a/skfda/preprocessing/smoothing/_linear.py +++ b/skfda/preprocessing/smoothing/_linear.py @@ -14,7 +14,8 @@ from ..._utils import _to_grid_points from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from ...representation import FDataGrid -from ...representation._typing import GridPointsLike, NDArrayFloat +from ...typing._base import GridPointsLike +from ...typing._numpy import NDArrayFloat class _LinearSmoother( diff --git a/skfda/preprocessing/smoothing/kernel_smoothers.py b/skfda/preprocessing/smoothing/kernel_smoothers.py index 92f47ab9d..ae3b7ac85 100644 --- a/skfda/preprocessing/smoothing/kernel_smoothers.py +++ b/skfda/preprocessing/smoothing/kernel_smoothers.py @@ -9,7 +9,8 @@ LocalLinearRegressionHatMatrix, NadarayaWatsonHatMatrix, ) -from ...representation._typing import GridPointsLike, NDArrayFloat +from ...typing._base import GridPointsLike +from ...typing._numpy import NDArrayFloat from . import KernelSmoother from ._linear import _LinearSmoother diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 17a342628..1dbaccee6 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -28,12 +28,14 @@ from typing_extensions import Literal from .._utils import _evaluate_grid, _to_grid_points -from ._typing import ( - ArrayLike, +from ..typing._base import ( DomainRange, GridPointsLike, LabelTuple, LabelTupleLike, +) +from ..typing._numpy import ( + ArrayLike, NDArrayBool, NDArrayFloat, NDArrayInt, @@ -43,8 +45,8 @@ from .extrapolation import ExtrapolationLike, _parse_extrapolation if TYPE_CHECKING: - from . import FDataBasis, FDataGrid - from .basis import Basis + from .grid import FDataGrid + from .basis import Basis, FDataBasis T = TypeVar('T', bound='FData') diff --git a/skfda/representation/basis/_basis.py b/skfda/representation/basis/_basis.py index 873b5c5b4..599b3c4fb 100644 --- a/skfda/representation/basis/_basis.py +++ b/skfda/representation/basis/_basis.py @@ -10,10 +10,11 @@ import numpy as np from matplotlib.figure import Figure -from .._typing import ArrayLike, DomainRange, DomainRangeLike, NDArrayFloat +from ...typing._base import DomainRange, DomainRangeLike +from ...typing._numpy import ArrayLike, NDArrayFloat if TYPE_CHECKING: - from . import FDataBasis + from ._fdatabasis import FDataBasis T = TypeVar("T", bound='Basis') diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py index 50b8edcae..8b41d301c 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline.py @@ -4,9 +4,10 @@ import numpy as np from numpy import polyint, polymul, polyval -from scipy.interpolate import BSpline as SciBSpline, PPoly, splev +from scipy.interpolate import BSpline as SciBSpline, PPoly -from .._typing import DomainRangeLike, NDArrayFloat +from ...typing._base import DomainRangeLike +from ...typing._numpy import NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='BSpline') diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py index 2f111c746..b5e8f9cda 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant.py @@ -2,7 +2,8 @@ import numpy as np -from .._typing import DomainRangeLike, NDArrayFloat +from ...typing._base import DomainRangeLike +from ...typing._numpy import NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='Constant') diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 2116e730e..fb5af17fb 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -18,17 +18,10 @@ import pandas.api.extensions from ..._utils import _check_array_key, _int_to_real, constants, nquad_vec +from ...typing._base import DomainRange, GridPointsLike, LabelTupleLike +from ...typing._numpy import ArrayLike, NDArrayBool, NDArrayFloat, NDArrayInt from .. import grid from .._functional_data import FData -from .._typing import ( - ArrayLike, - DomainRange, - GridPointsLike, - LabelTupleLike, - NDArrayBool, - NDArrayFloat, - NDArrayInt, -) from ..extrapolation import ExtrapolationLike if TYPE_CHECKING: diff --git a/skfda/representation/basis/_finite_element.py b/skfda/representation/basis/_finite_element.py index c195a54cc..ea5129161 100644 --- a/skfda/representation/basis/_finite_element.py +++ b/skfda/representation/basis/_finite_element.py @@ -2,7 +2,8 @@ import numpy as np -from .._typing import ArrayLike, DomainRangeLike, NDArrayFloat +from ...typing._base import DomainRangeLike +from ...typing._numpy import ArrayLike, NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='FiniteElement') diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py index f4f1c1e6e..6021ee8fe 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier.py @@ -3,7 +3,8 @@ import numpy as np from typing_extensions import Protocol -from .._typing import DomainRangeLike, NDArrayFloat +from ...typing._base import DomainRangeLike +from ...typing._numpy import NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='Fourier') diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py index 954e79bc9..00188b759 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial.py @@ -3,7 +3,7 @@ import numpy as np import scipy.linalg -from .._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='Monomial') diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index f345ebe89..84d7e393e 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -4,7 +4,7 @@ import numpy as np -from .._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ._basis import Basis diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index fe7460905..4b00cc975 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -5,7 +5,7 @@ import numpy as np import scipy.linalg -from .._typing import NDArrayFloat +from ...typing._numpy import NDArrayFloat from ._basis import Basis T = TypeVar("T", bound='VectorValued') diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index ffd9087a6..0dfdf9888 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -12,10 +12,11 @@ from typing_extensions import Protocol -from ._typing import ArrayLike, EvaluationPoints, NDArrayFloat +from ..typing._base import ArrayLike, EvaluationPoints +from ..typing._numpy import NDArrayFloat if TYPE_CHECKING: - from . import FData + from ._functional_data import FData class Evaluator(ABC): diff --git a/skfda/representation/extrapolation.py b/skfda/representation/extrapolation.py index 934919a5f..3d863b00e 100644 --- a/skfda/representation/extrapolation.py +++ b/skfda/representation/extrapolation.py @@ -10,11 +10,12 @@ import numpy as np from typing_extensions import Literal -from ._typing import ArrayLike, EvaluationPoints, NDArrayFloat +from ..typing._base import EvaluationPoints +from ..typing._numpy import NDArrayFloat from .evaluator import Evaluator if TYPE_CHECKING: - from . import FData + from ._functional_data import FData ExtrapolationLike = Union[ Evaluator, diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 23ddae297..4df8e6cfe 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -29,25 +29,21 @@ from matplotlib.figure import Figure from .._utils import _check_array_key, _int_to_real, _to_grid_points, constants -from ._functional_data import FData -from ._typing import ( - ArrayLike, +from ..typing._base import ( DomainRange, DomainRangeLike, GridPoints, GridPointsLike, LabelTupleLike, - NDArrayBool, - NDArrayFloat, - NDArrayInt, ) +from ..typing._numpy import ArrayLike, NDArrayBool, NDArrayFloat, NDArrayInt +from ._functional_data import FData from .evaluator import Evaluator from .extrapolation import ExtrapolationLike from .interpolation import SplineInterpolation if TYPE_CHECKING: - from . import FDataBasis - from .basis import Basis + from .basis import Basis, FDataBasis T = TypeVar("T", bound='FDataGrid') diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index 74510fec6..b546766d4 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -13,11 +13,12 @@ make_interp_spline, ) -from ._typing import ArrayLike, EvaluationPoints, NDArrayFloat +from ..typing._base import EvaluationPoints +from ..typing._numpy import ArrayLike, NDArrayFloat from .evaluator import Evaluator if TYPE_CHECKING: - from . import FDataGrid + from .grid import FDataGrid class _BaseInterpolation(Evaluator): diff --git a/skfda/tests/test_math.py b/skfda/tests/test_math.py index fc17896a7..7e3781f12 100644 --- a/skfda/tests/test_math.py +++ b/skfda/tests/test_math.py @@ -6,11 +6,12 @@ import skfda from skfda._utils import _pairwise_symmetric -from skfda.representation._typing import NDArrayFloat from skfda.representation.basis import Monomial, Tensor, VectorValued -def _ndm(*args: NDArrayFloat) -> Sequence[NDArrayFloat]: +def _ndm( + *args: np.typing.NDArray[np.float_], +) -> Sequence[np.typing.NDArray[np.float_]]: return [ x[(None,) * i + (slice(None),) + (None,) * (len(args) - i - 1)] for i, x in enumerate(args) @@ -23,10 +24,10 @@ class InnerProductTest(unittest.TestCase): def test_several_variables(self) -> None: """Test inner_product with functions of several variables.""" def f( # noqa: WPS430 - x: NDArrayFloat, - y: NDArrayFloat, - z: NDArrayFloat, - ) -> NDArrayFloat: + x: np.typing.NDArray[np.float_], + y: np.typing.NDArray[np.float_], + z: np.typing.NDArray[np.float_], + ) -> np.typing.NDArray[np.float_]: return x * y * z t = np.linspace(0, 1, 30) @@ -65,10 +66,14 @@ def f( # noqa: WPS430 def test_vector_valued(self) -> None: """Test inner_product with vector valued functions.""" - def f(x: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 + def f( # noqa: WPS430 + x: np.typing.NDArray[np.float_], + ) -> np.typing.NDArray[np.float_]: return x**2 - def g(y: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 + def g( # noqa: WPS430 + y: np.typing.NDArray[np.float_], + ) -> np.typing.NDArray[np.float_]: return 3 * y t = np.linspace(0, 1, 100) diff --git a/skfda/typing/__init__.py b/skfda/typing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/skfda/representation/_typing.py b/skfda/typing/_base.py similarity index 54% rename from skfda/representation/_typing.py rename to skfda/typing/_base.py index acad6c15e..4ed6b2f6f 100644 --- a/skfda/representation/_typing.py +++ b/skfda/typing/_base.py @@ -1,30 +1,10 @@ """Common types.""" -from typing import Any, NewType, Optional, Sequence, Tuple, TypeVar, Union +from typing import Optional, Sequence, Tuple, TypeVar, Union import numpy as np from typing_extensions import Protocol -try: - from numpy.typing import ArrayLike -except ImportError: - ArrayLike = np.ndarray # type:ignore[misc] - -try: - from numpy.typing import NDArray - NDArrayAny = NDArray[Any] - NDArrayInt = NDArray[np.int_] - NDArrayFloat = NDArray[np.float_] - NDArrayBool = NDArray[np.bool_] - NDArrayStr = NDArray[np.str_] - NDArrayObject = NDArray[np.object_] -except ImportError: - NDArray = np.ndarray # type:ignore[misc] - NDArrayAny = np.ndarray # type:ignore[misc] - NDArrayInt = np.ndarray # type:ignore[misc] - NDArrayFloat = np.ndarray # type:ignore[misc] - NDArrayBool = np.ndarray # type:ignore[misc] - NDArrayStr = np.ndarray # type:ignore[misc] - NDArrayObject = np.ndarray # type:ignore[misc] +from ._numpy import ArrayLike, NDArrayFloat VectorType = TypeVar("VectorType") diff --git a/skfda/typing/_numpy.py b/skfda/typing/_numpy.py new file mode 100644 index 000000000..57cc6e430 --- /dev/null +++ b/skfda/typing/_numpy.py @@ -0,0 +1,27 @@ +"""NumPy aliases for compatibility.""" + +from typing import Any + +import numpy as np + +try: + from numpy.typing import ArrayLike +except ImportError: + ArrayLike = np.ndarray # type:ignore[misc] + +try: + from numpy.typing import NDArray + NDArrayAny = NDArray[Any] + NDArrayInt = NDArray[np.int_] + NDArrayFloat = NDArray[np.float_] + NDArrayBool = NDArray[np.bool_] + NDArrayStr = NDArray[np.str_] + NDArrayObject = NDArray[np.object_] +except ImportError: + NDArray = np.ndarray # type:ignore[misc] + NDArrayAny = np.ndarray # type:ignore[misc] + NDArrayInt = np.ndarray # type:ignore[misc] + NDArrayFloat = np.ndarray # type:ignore[misc] + NDArrayBool = np.ndarray # type:ignore[misc] + NDArrayStr = np.ndarray # type:ignore[misc] + NDArrayObject = np.ndarray # type:ignore[misc] From 6c6a9ce072b1fcd987276fc24c6a6edb43cef39c Mon Sep 17 00:00:00 2001 From: vnmabus Date: Wed, 31 Aug 2022 12:00:26 +0200 Subject: [PATCH 270/400] Fix more typing errors. --- mypy.out | 4037 +++++++++++++++++ setup.cfg | 3 +- skfda/__init__.py | 8 + skfda/_utils/__init__.py | 28 +- skfda/_utils/_utils.py | 97 +- skfda/exploratory/depth/_depth.py | 3 +- .../exploratory/outliers/neighbors_outlier.py | 2 +- skfda/exploratory/stats/_stats.py | 3 +- skfda/misc/__init__.py | 20 +- skfda/misc/_math.py | 31 +- skfda/misc/kernels.py | 26 +- skfda/misc/lstsq.py | 24 +- skfda/misc/metrics/__init__.py | 2 +- skfda/misc/metrics/_fisher_rao.py | 17 +- skfda/misc/metrics/_lp_distances.py | 4 +- skfda/misc/metrics/_lp_norms.py | 12 +- skfda/misc/metrics/_parse.py | 49 + skfda/misc/metrics/_typing.py | 75 - skfda/misc/metrics/_utils.py | 7 +- skfda/misc/operators/_operators.py | 18 +- skfda/misc/validation.py | 7 +- skfda/ml/_neighbors_base.py | 2 +- .../classification/_centroid_classifiers.py | 3 +- .../classification/_neighbors_classifiers.py | 3 +- skfda/ml/clustering/_hierarchical.py | 5 +- skfda/ml/clustering/_kmeans.py | 3 +- skfda/ml/clustering/_neighbors_clustering.py | 3 +- skfda/ml/regression/_kernel_regression.py | 2 +- skfda/ml/regression/_neighbors_regression.py | 3 +- skfda/preprocessing/smoothing/__init__.py | 4 +- skfda/representation/__init__.py | 6 +- skfda/representation/basis/__init__.py | 21 +- skfda/representation/evaluator.py | 4 +- skfda/typing/_metric.py | 31 + skfda/typing/_numpy.py | 8 +- 35 files changed, 4353 insertions(+), 218 deletions(-) create mode 100644 mypy.out create mode 100644 skfda/misc/metrics/_parse.py delete mode 100644 skfda/misc/metrics/_typing.py create mode 100644 skfda/typing/_metric.py diff --git 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skfda.misc.metrics._utils +LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py (skfda.misc.metrics._utils) +TRACE: Looking for skfda.misc.operators at skfda/misc/operators/__init__.meta.json +LOG: Could not load cache for skfda.misc.operators: skfda/misc/operators/__init__.meta.json +LOG: Metadata not found for skfda.misc.operators +LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py (skfda.misc.operators) +TRACE: Looking for skfda.misc.operators._identity at skfda/misc/operators/_identity.meta.json +LOG: Could not load cache for skfda.misc.operators._identity: skfda/misc/operators/_identity.meta.json +LOG: Metadata not found for skfda.misc.operators._identity +LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py (skfda.misc.operators._identity) +TRACE: Looking for skfda.misc.operators._integral_transform at skfda/misc/operators/_integral_transform.meta.json +LOG: Could not load cache for 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Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/builtins.pyi (builtins) +TRACE: Looking for warnings at warnings.meta.json +TRACE: Meta warnings {"data_mtime": 1661927675, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "_warnings", "collections.abc", "types", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "e72e04279472228d418ae020a487af968a7ebc9c654460e83124849b4274ce91", "id": "warnings", "ignore_all": true, "interface_hash": "bd641060920364d4c34069b1b2e8d939a753654a23a6728823981bb0a0ade70a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, 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"size": 6363, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for functools: options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for functools +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/functools.pyi (functools) +TRACE: Looking for errno at errno.meta.json +TRACE: Meta errno {"data_mtime": 1661927674, "dep_lines": [1, 1, 1, 1], "dep_prios": [5, 5, 30, 30], "dependencies": ["collections.abc", "builtins", "abc", "typing"], "hash": "a6baa869c92171967523355853b42557c003724d799009f25d165d0546068aa5", "id": "errno", "ignore_all": true, "interface_hash": "b7a8f72ecfb026aa43c49d4e7eaef47ecad4764f038500f0f3753bb64de4aeb6", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": 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"version_id": "0.971"} +LOG: Metadata abandoned for errno: options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for errno +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/errno.pyi (errno) +TRACE: Looking for os at os/__init__.meta.json +TRACE: Meta os {"data_mtime": 1661927674, "dep_lines": [1, 26, 2, 17, 18, 19, 20, 21, 22, 23, 24, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30], "dependencies": ["sys", "os.path", "_typeshed", "abc", "builtins", "collections.abc", "contextlib", "io", "subprocess", "typing", "typing_extensions", "array", "ctypes", "mmap", "pickle", "types"], "hash": "5ef19fad3a07c051d77b7c2281a5877a53208a288d10a14179f1d242bb1d500c", "id": "os", "ignore_all": true, "interface_hash": "0050d609575d1c3e4a59f7a1a8c8e6fb7ea27aabf93dccd8e3bd8322422ce553", "mtime": 1658479475, "options": 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/lib/histograms.pyi (numpy.lib.histograms) +TRACE: Looking for numpy.lib.index_tricks at numpy/lib/index_tricks.meta.json +TRACE: Meta numpy.lib.index_tricks {"data_mtime": 1661927676, "dep_lines": [1, 2, 11, 29, 45, 1, 1], "dep_prios": [5, 5, 5, 5, 5, 5, 30], "dependencies": ["collections.abc", "typing", "numpy", "numpy._typing", "numpy.core.multiarray", "builtins", "abc"], "hash": "d730027ff5fc95b1d63a47e4c4b259e68e4883fc264574ff11b03bc27a8e96fc", "id": "numpy.lib.index_tricks", "ignore_all": true, "interface_hash": "8d4b98e38e0320f4c6f16614da931aa8dd396c736a941c874cf23cddcb8b8039", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, 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skfda/representation/extrapolation.meta.json +LOG: Metadata not found for skfda.representation.extrapolation +LOG: Parsing /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py (skfda.representation.extrapolation) +TRACE: Looking for skfda.exploratory.visualization.representation at skfda/exploratory/visualization/representation.meta.json +LOG: Could not load cache for skfda.exploratory.visualization.representation: skfda/exploratory/visualization/representation.meta.json +LOG: Metadata not found for skfda.exploratory.visualization.representation +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py (skfda.exploratory.visualization.representation) +TRACE: Looking for skfda.preprocessing at skfda/preprocessing/__init__.meta.json +TRACE: Meta skfda.preprocessing {"data_mtime": 1661927674, "dep_lines": [1, 1, 1, 3], "dep_prios": [5, 30, 30, 10], "dependencies": ["builtins", "abc", "typing"], "hash": 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Metadata not found for skfda.preprocessing.registration._lstsq_shift_registration +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py (skfda.preprocessing.registration._lstsq_shift_registration) +TRACE: Looking for importlib at importlib/__init__.meta.json +TRACE: Meta importlib {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["collections.abc", "importlib.abc", "types", "builtins", "abc", "typing"], "hash": "5d991f55cfdc58d18e38d479666e5c73bc0eea2f9cc01f07cfbe9452aba97a48", "id": "importlib", "ignore_all": true, "interface_hash": "0c40f65f30f4d30ad6d6e30eecd5e6953d8534c5c37cd74b977a8a08840b8812", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for importlib +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi (importlib) +TRACE: Looking for skfda.preprocessing.dim_reduction._fpca at skfda/preprocessing/dim_reduction/_fpca.meta.json +LOG: Could not load cache for skfda.preprocessing.dim_reduction._fpca: skfda/preprocessing/dim_reduction/_fpca.meta.json +LOG: Metadata not found for skfda.preprocessing.dim_reduction._fpca +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py (skfda.preprocessing.dim_reduction._fpca) +TRACE: Looking for os.path at os/path.meta.json +TRACE: Meta os.path {"data_mtime": 1661927674, "dep_lines": [1, 7, 1, 1, 1], "dep_prios": [10, 5, 5, 30, 30], "dependencies": ["sys", "posixpath", "builtins", "abc", "typing"], "hash": 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/home/carlos/git/scikit-fda/skfda/representation/interpolation.py (skfda.representation.interpolation) +TRACE: Looking for skfda.preprocessing.smoothing at skfda/preprocessing/smoothing/__init__.meta.json +LOG: Could not load cache for skfda.preprocessing.smoothing: skfda/preprocessing/smoothing/__init__.meta.json +LOG: Metadata not found for skfda.preprocessing.smoothing +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py (skfda.preprocessing.smoothing) +TRACE: Looking for numpy._typing._add_docstring at numpy/_typing/_add_docstring.meta.json +TRACE: Meta numpy._typing._add_docstring {"data_mtime": 1661927676, "dep_lines": [3, 4, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30], "dependencies": ["re", "textwrap", "numpy._typing._generic_alias", "builtins", "abc", "array", "ctypes", "enum", "mmap", "numpy._typing._dtype_like", "pickle", "typing", "typing_extensions"], "hash": 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skfda.exploratory.visualization.clustering: skfda/exploratory/visualization/clustering.meta.json +LOG: Metadata not found for skfda.exploratory.visualization.clustering +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py (skfda.exploratory.visualization.clustering) +TRACE: Looking for skfda.exploratory.visualization._baseplot at skfda/exploratory/visualization/_baseplot.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._baseplot: skfda/exploratory/visualization/_baseplot.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._baseplot +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py (skfda.exploratory.visualization._baseplot) +TRACE: Looking for skfda.exploratory.visualization._boxplot at skfda/exploratory/visualization/_boxplot.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._boxplot: skfda/exploratory/visualization/_boxplot.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._boxplot +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_boxplot.py (skfda.exploratory.visualization._boxplot) +TRACE: Looking for skfda.exploratory.visualization._ddplot at skfda/exploratory/visualization/_ddplot.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._ddplot: skfda/exploratory/visualization/_ddplot.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._ddplot +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py (skfda.exploratory.visualization._ddplot) +TRACE: Looking for skfda.exploratory.visualization._magnitude_shape_plot at skfda/exploratory/visualization/_magnitude_shape_plot.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._magnitude_shape_plot: skfda/exploratory/visualization/_magnitude_shape_plot.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._magnitude_shape_plot +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_magnitude_shape_plot.py (skfda.exploratory.visualization._magnitude_shape_plot) +TRACE: Looking for skfda.exploratory.visualization._multiple_display at skfda/exploratory/visualization/_multiple_display.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._multiple_display: skfda/exploratory/visualization/_multiple_display.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._multiple_display +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_multiple_display.py (skfda.exploratory.visualization._multiple_display) +TRACE: Looking for skfda.exploratory.visualization._outliergram at skfda/exploratory/visualization/_outliergram.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._outliergram: skfda/exploratory/visualization/_outliergram.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._outliergram +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_outliergram.py (skfda.exploratory.visualization._outliergram) +TRACE: Looking for skfda.exploratory.visualization._parametric_plot at skfda/exploratory/visualization/_parametric_plot.meta.json +LOG: Could not load cache for skfda.exploratory.visualization._parametric_plot: skfda/exploratory/visualization/_parametric_plot.meta.json +LOG: Metadata not found for skfda.exploratory.visualization._parametric_plot +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py (skfda.exploratory.visualization._parametric_plot) +TRACE: Looking for skfda.exploratory.visualization.fpca at skfda/exploratory/visualization/fpca.meta.json +LOG: Could not load cache for skfda.exploratory.visualization.fpca: skfda/exploratory/visualization/fpca.meta.json +LOG: Metadata not found for skfda.exploratory.visualization.fpca +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py (skfda.exploratory.visualization.fpca) +TRACE: Looking for sre_compile at sre_compile.meta.json +TRACE: Meta sre_compile {"data_mtime": 1661927675, "dep_lines": [1, 3, 4, 1], "dep_prios": [5, 5, 5, 5], "dependencies": ["sre_constants", "sre_parse", "typing", "builtins"], "hash": "2aafd8a2ad6b888f60d11c50cb8dace30ca3fbc63ece6d12fd0efdc17246a9e1", "id": "sre_compile", "ignore_all": true, "interface_hash": "2d1e14083d24ada8238013f9c6e707425f5a25629bbdd80657d5f3890452e9f8", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sre_compile.pyi (sre_compile) +TRACE: Looking for sre_constants at sre_constants.meta.json +TRACE: Meta sre_constants {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 1, 1], "dep_prios": [10, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "typing", "builtins", "abc"], "hash": "b1dc632f9eee2e42a0697caf5a84c4c0aaf7585d7ebc63fda5a42291096636cb", "id": "sre_constants", "ignore_all": true, "interface_hash": "d15b307f74bd7544ef745c274e77b28cc50dec55390d82db90f0ca10164cb208", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, 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sre_constants +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sre_constants.pyi (sre_constants) +TRACE: Looking for rdata at rdata/__init__.meta.json +TRACE: Meta rdata {"data_mtime": 1661922062, "dep_lines": [2, 3, 4, 6, 6, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 10, 10, 10, 5, 30, 30, 30, 30], "dependencies": ["errno", "os", "pathlib", "rdata.conversion", "rdata.parser", "builtins", "abc", "io", "typing", "posixpath"], "hash": "946ff91d4da6c457e2a2d5a21fdf35af0538aef3e80270d3301de04a91fa3b1d", "id": "rdata", "ignore_all": true, "interface_hash": "43c58b1f73b5f1161718566d0323e985e17f5aea096ab0cc6d65a0356663cfd9", "mtime": 1648125155, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": 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skfda.preprocessing.smoothing._basis: skfda/preprocessing/smoothing/_basis.meta.json +LOG: Metadata not found for skfda.preprocessing.smoothing._basis +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py (skfda.preprocessing.smoothing._basis) +TRACE: Looking for skfda.preprocessing.smoothing._kernel_smoothers at skfda/preprocessing/smoothing/_kernel_smoothers.meta.json +LOG: Could not load cache for skfda.preprocessing.smoothing._kernel_smoothers: skfda/preprocessing/smoothing/_kernel_smoothers.meta.json +LOG: Metadata not found for skfda.preprocessing.smoothing._kernel_smoothers +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py (skfda.preprocessing.smoothing._kernel_smoothers) +TRACE: Looking for textwrap at textwrap.meta.json +TRACE: Meta textwrap {"data_mtime": 1661927674, "dep_lines": [1, 2, 1, 1], "dep_prios": [5, 5, 5, 30], "dependencies": ["collections.abc", "typing", "builtins", "abc"], "hash": 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Metadata not found for skfda.exploratory.outliers._envelopes +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py (skfda.exploratory.outliers._envelopes) +TRACE: Looking for skfda.exploratory.outliers at skfda/exploratory/outliers/__init__.meta.json +LOG: Could not load cache for skfda.exploratory.outliers: skfda/exploratory/outliers/__init__.meta.json +LOG: Metadata not found for skfda.exploratory.outliers +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/outliers/__init__.py (skfda.exploratory.outliers) +TRACE: Looking for skfda.exploratory.depth.multivariate at skfda/exploratory/depth/multivariate.meta.json +TRACE: Meta skfda.exploratory.depth.multivariate {"data_mtime": 1661927677, "dep_lines": [5, 6, 9, 3, 7, 13, 15, 16, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 10, 11, 12], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 10, 20, 10, 5], "dependencies": ["abc", "math", "numpy", "__future__", 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/logging/__init__.pyi (logging) +TRACE: Looking for json.decoder at json/decoder.meta.json +TRACE: Meta json.decoder {"data_mtime": 1661927674, "dep_lines": [1, 2, 1, 1], "dep_prios": [5, 5, 5, 30], "dependencies": ["collections.abc", "typing", "builtins", "abc"], "hash": "94d69bd74fbb9158ed4d0253c7134792103ba5d53da327b8fda39343b4082490", "id": "json.decoder", "ignore_all": true, "interface_hash": "658170dc0c67eba99e3cdac028c0bac5b959831f61e997a92649d99a0c388364", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, 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/home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py (skfda.exploratory.outliers._outliergram) +TRACE: Looking for skfda.exploratory.outliers.neighbors_outlier at skfda/exploratory/outliers/neighbors_outlier.meta.json +LOG: Could not load cache for skfda.exploratory.outliers.neighbors_outlier: skfda/exploratory/outliers/neighbors_outlier.meta.json +LOG: Metadata not found for skfda.exploratory.outliers.neighbors_outlier +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py (skfda.exploratory.outliers.neighbors_outlier) +TRACE: Looking for skfda.exploratory.depth._depth at skfda/exploratory/depth/_depth.meta.json +LOG: Could not load cache for skfda.exploratory.depth._depth: skfda/exploratory/depth/_depth.meta.json +LOG: Metadata not found for skfda.exploratory.depth._depth +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py (skfda.exploratory.depth._depth) +TRACE: Looking for rdata.conversion._conversion at 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"no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi", "plugin_data": null, "size": 1067, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for email.charset: options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for email.charset +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi (email.charset) +TRACE: Looking for email.contentmanager at email/contentmanager.meta.json +TRACE: Meta email.contentmanager {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 1], "dep_prios": [5, 5, 5, 5], "dependencies": ["collections.abc", "email.message", "typing", "builtins"], "hash": "25ee805b5770376d2f6d9ed4297c02748c5791f27beb2df113404eb0fa6471a4", "id": "email.contentmanager", "ignore_all": true, "interface_hash": "f44cb75e55e5494622954876c0e21f4cf784ca3bfb528bbe662935a829f585af", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": true, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi", "plugin_data": null, "size": 516, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for email.contentmanager: options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for email.contentmanager +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi (email.contentmanager) +TRACE: Looking for email.errors at email/errors.meta.json +TRACE: Meta email.errors {"data_mtime": 1661927674, "dep_lines": [1, 1, 1, 1, 1], "dep_prios": [10, 5, 30, 30, 30], "dependencies": ["sys", "builtins", "_typeshed", "abc", "typing"], "hash": "2dcb55e1cef7b1dbd0e58056aae9eb2dba5e70d83fca907196faef9f7ea0527c", "id": "email.errors", "ignore_all": true, "interface_hash": "8826bc9a109fb37f0fed685e6abba0077070de28722db20e3e0642da885f9803", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, 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(email.errors) +TRACE: Looking for email.policy at email/policy.meta.json +TRACE: Meta email.policy {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 1], "dep_prios": [5, 5, 5, 5, 5, 5, 5, 5], "dependencies": ["abc", "collections.abc", "email.contentmanager", "email.errors", "email.header", "email.message", "typing", "builtins"], "hash": "2122e2c085231b4505c48fb54f55b2fb36ead76660699bc3d800fffbd4f0c6f4", "id": "email.policy", "ignore_all": true, "interface_hash": "ae20d3778f6f632dabba8b722916b3237817e2416f1ede0b88d880462ecbc84a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi (email.policy) +TRACE: Looking for string at string.meta.json +TRACE: Meta string {"data_mtime": 1661927675, "dep_lines": [1, 2, 3, 4, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["sys", "collections.abc", "re", "typing", "builtins", "_typeshed", "abc", "enum"], "hash": "32482313d12016c3bf5da60f49cfcbe04c26ceda3bf16241ce093dce69b4ee1b", "id": "string", "ignore_all": true, "interface_hash": "bb7a35e9ad66797443782a36c5cdd45eac609b1487db3959ba928667316e6284", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, 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"suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for numpy.compat._pep440: options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for numpy.compat._pep440 +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/compat/_pep440.py (numpy.compat._pep440) +TRACE: Looking for numpy.compat.py3k at numpy/compat/py3k.meta.json +TRACE: Meta numpy.compat.py3k {"data_mtime": 1661927674, "dep_lines": [19, 20, 22, 26, 21, 132, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 24], "dep_prios": [10, 10, 10, 10, 5, 20, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 10], "dependencies": ["sys", "os", "io", "pickle", "pathlib", "importlib.machinery", "builtins", "_typeshed", "abc", "array", "ctypes", "importlib", "importlib.abc", "mmap", "types", "typing", "typing_extensions"], "hash": "8db30d6e7fdc5772435832897d83bbf20ea5097920fbff799a0409961e2a3fcd", "id": "numpy.compat.py3k", 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/home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py (skfda.preprocessing.smoothing.validation) +TRACE: Looking for skfda.exploratory.outliers._directional_outlyingness_experiment_results at skfda/exploratory/outliers/_directional_outlyingness_experiment_results.meta.json +TRACE: Meta skfda.exploratory.outliers._directional_outlyingness_experiment_results {"data_mtime": 1661927674, "dep_lines": [1, 1, 1], "dep_prios": [5, 30, 30], "dependencies": ["builtins", "abc", "typing"], "hash": "41e26e416f6f80eb4d4e9ffe5f524a415b50d468c75ec07d2491fcd0b84b8745", "id": "skfda.exploratory.outliers._directional_outlyingness_experiment_results", "ignore_all": true, "interface_hash": "7a7d7684bf9843583681d747ba1539cf6abb6ce4ff8973bfe1baf11f70ef3241", "mtime": 1580729651, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": 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skfda.exploratory.outliers._directional_outlyingness_experiment_results: options differ +TRACE: follow_imports: silent != normal +TRACE: implicit_reexport: True != False +LOG: Metadata not found for skfda.exploratory.outliers._directional_outlyingness_experiment_results +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness_experiment_results.py (skfda.exploratory.outliers._directional_outlyingness_experiment_results) +TRACE: Looking for skfda.ml._neighbors_base at skfda/ml/_neighbors_base.meta.json +LOG: Could not load cache for skfda.ml._neighbors_base: skfda/ml/_neighbors_base.meta.json +LOG: Metadata not found for skfda.ml._neighbors_base +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py (skfda.ml._neighbors_base) +TRACE: Looking for xarray at xarray/__init__.meta.json +TRACE: Meta xarray {"data_mtime": 1661922062, "dep_lines": [1, 1, 1, 2, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 34, 31, 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Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py (skfda.ml.clustering._neighbors_clustering) +TRACE: Looking for skfda.ml.regression._historical_linear_model at skfda/ml/regression/_historical_linear_model.meta.json +LOG: Could not load cache for skfda.ml.regression._historical_linear_model: skfda/ml/regression/_historical_linear_model.meta.json +LOG: Metadata not found for skfda.ml.regression._historical_linear_model +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py (skfda.ml.regression._historical_linear_model) +TRACE: Looking for skfda.ml.regression._kernel_regression at skfda/ml/regression/_kernel_regression.meta.json +LOG: Could not load cache for skfda.ml.regression._kernel_regression: skfda/ml/regression/_kernel_regression.meta.json +LOG: Metadata not found for skfda.ml.regression._kernel_regression +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi +TRACE: Looking for multiprocessing.reduction at multiprocessing/reduction.meta.json +TRACE: Meta multiprocessing.reduction {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["pickle", "sys", "abc", "copyreg", "typing", "typing_extensions", "builtins", "array", "ctypes", "mmap"], "hash": "30ab326562cf82254f55d7c9f59e11159fbac4f767058f24cb69a2fe013a48e9", "id": "multiprocessing.reduction", "ignore_all": true, "interface_hash": "bbd4eacb98803ecd8041a117ca2b4e3abe2c467a2392ff056525e356dd449d0d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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multiprocessing.reduction: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi +TRACE: Looking for multiprocessing.synchronize at multiprocessing/synchronize.meta.json +TRACE: Meta multiprocessing.synchronize {"data_mtime": 1661922054, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "threading", "collections.abc", "contextlib", "multiprocessing.context", "types", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "f28836537a5963d10f30e158b6f5fa9e513b13c44cfe7ebce1a5eaad723e3e0d", "id": "multiprocessing.synchronize", "ignore_all": true, "interface_hash": "f2d28555b342d19d8f5975080246ffa4f55c04c8591f35788df98fb011c89862", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, 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"plugin_data": null, "size": 2278, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for multiprocessing.synchronize: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi +TRACE: Looking for multiprocessing.managers at multiprocessing/managers.meta.json +TRACE: Meta multiprocessing.managers {"data_mtime": 1661922054, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 1, 1], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["queue", "sys", "threading", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", "multiprocessing.connection", "multiprocessing.context", "multiprocessing.shared_memory", "builtins", "abc"], "hash": "c6594c0969fae2cafce4ec5d4426dbdb8d13b4f0feeb0dcc4c154d22a813c6df", "id": "multiprocessing.managers", "ignore_all": true, "interface_hash": "c8390ff11fdd920a46da7a69ebef949851d0cae7d7d77f5bee2f746aac2f03db", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi", "plugin_data": null, "size": 8556, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for multiprocessing.managers: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi +TRACE: Looking for multiprocessing.process at multiprocessing/process.meta.json +TRACE: Meta multiprocessing.process {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "builtins", "_typeshed", "abc"], "hash": "fa47cbb61f64a2309b6d7043a2c42c4c096b85f17dbd51181d9c83455a89e5c3", "id": "multiprocessing.process", "ignore_all": true, "interface_hash": "77c612e6cb8b00cd8763eb96f60de3ddd7c40a47a2ea78f5710fb059dea117d2", "mtime": 1658479475, "options": {"allow_redefinition": 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"/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi", "plugin_data": null, "size": 1371, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for multiprocessing.process: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi +TRACE: Looking for multiprocessing.queues at multiprocessing/queues.meta.json +TRACE: Meta multiprocessing.queues {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30], "dependencies": ["queue", "sys", "typing", "builtins", "_typeshed", "abc"], "hash": "daf7a7147647126f56cf864be94a85c23c7801d703f87280168714c3eda8a44c", "id": "multiprocessing.queues", "ignore_all": true, "interface_hash": "38370111a855e180ba5a53c74c70d2ab11961893618ccd17ecc4b397487fd0ae", "mtime": 1658479475, "options": {"allow_redefinition": false, 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false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/sharedctypes.pyi", "plugin_data": null, "size": 4000, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for multiprocessing.sharedctypes: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/sharedctypes.pyi +TRACE: Looking for copyreg at copyreg.meta.json +TRACE: Meta copyreg {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["collections.abc", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "c8e7eb44af5f86e716196833d6f8354e14841e515371684c2ec89cf651880761", "id": "copyreg", "ignore_all": true, "interface_hash": "48f6f68ec1b032cdea1cfa5544ffdf3992c28280f3ad14db1b61cbe69a2a7929", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi", "plugin_data": null, "size": 995, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for copyreg: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi +TRACE: Looking for queue at queue.meta.json +TRACE: Meta queue {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "threading", "typing", "builtins", "_typeshed", "abc"], "hash": "c4a619456a709a215e0ff105d23bbac933ce66b9d6a2136a6ee24a9211fecb03", "id": "queue", "ignore_all": true, "interface_hash": "dc4656ee3cd7fe4685ede94d32384c558b2478f4ee4b82f7f15503b8417837b1", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/queue.pyi", "plugin_data": null, "size": 2219, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for queue: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/queue.pyi +TRACE: Looking for multiprocessing.shared_memory at multiprocessing/shared_memory.meta.json +TRACE: Meta multiprocessing.shared_memory {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 4, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "typing", "builtins", "abc"], "hash": "e0f8eb9aabdd634db1a9c49503aa744d9aae5fcad59fc3a481d7f23250fa7f11", "id": "multiprocessing.shared_memory", "ignore_all": true, "interface_hash": "2cae982a6b3a93fc494238eeb3e5d4e9c284687a2c0f48bbdf4ea30528cfd9b1", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi", "plugin_data": null, "size": 1348, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for multiprocessing.shared_memory: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi +TRACE: Looking for skfda.preprocessing.feature_construction._coefficients_transformer at skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json +LOG: Could not load cache for skfda.preprocessing.feature_construction._coefficients_transformer: skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json +LOG: Metadata not found for skfda.preprocessing.feature_construction._coefficients_transformer +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py (skfda.preprocessing.feature_construction._coefficients_transformer) +TRACE: Looking for skfda.preprocessing.feature_construction._evaluation_trasformer at skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json +LOG: Could not load cache for skfda.preprocessing.feature_construction._evaluation_trasformer: skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json +LOG: Metadata not found for skfda.preprocessing.feature_construction._evaluation_trasformer +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py (skfda.preprocessing.feature_construction._evaluation_trasformer) +TRACE: Looking for skfda.preprocessing.feature_construction._fda_feature_union at skfda/preprocessing/feature_construction/_fda_feature_union.meta.json +LOG: Could not load cache for skfda.preprocessing.feature_construction._fda_feature_union: skfda/preprocessing/feature_construction/_fda_feature_union.meta.json +LOG: Metadata not found for skfda.preprocessing.feature_construction._fda_feature_union +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py (skfda.preprocessing.feature_construction._fda_feature_union) +TRACE: Looking for skfda.preprocessing.feature_construction._function_transformers at skfda/preprocessing/feature_construction/_function_transformers.meta.json +LOG: Could not load cache for skfda.preprocessing.feature_construction._function_transformers: skfda/preprocessing/feature_construction/_function_transformers.meta.json +LOG: Metadata not found for skfda.preprocessing.feature_construction._function_transformers +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py (skfda.preprocessing.feature_construction._function_transformers) +TRACE: Looking for _socket at _socket.meta.json +TRACE: Meta _socket {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 4, 5, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "typing", "typing_extensions", "builtins", "abc", "array", "ctypes", "mmap", "pickle"], "hash": "7b12284aac0dd6754c2fc1ba409d438349ab12978515dfc6637799c75626cbaf", "id": "_socket", "ignore_all": true, "interface_hash": "4b8de99821f40c7ef1a8362f7fb84495a4d1b4f73f4955172322d6e465910067", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi", "plugin_data": null, "size": 16847, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for _socket: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi +LOG: Loaded graph with 422 nodes (1.843 sec) +LOG: Found 164 SCCs; largest has 73 nodes +TRACE: Priorities for typing_extensions: abc:10 collections:10 sys:10 _typeshed:5 collections.abc:5 typing:5 +TRACE: Priorities for typing: collections:10 sys:10 _typeshed:5 abc:5 types:5 typing_extensions:5 +TRACE: Priorities for types: sys:10 _typeshed:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 typing:5 typing_extensions:5 +TRACE: Priorities for sys: _typeshed:5 builtins:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 io:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for subprocess: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for posixpath: sys:10 _typeshed:5 collections.abc:5 genericpath:5 os:5 typing:5 typing_extensions:5 +TRACE: Priorities for pickle: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for pathlib: sys:10 _typeshed:5 collections.abc:5 io:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for os.path: sys:10 posixpath:5 +TRACE: Priorities for os: sys:10 os.path:10 _typeshed:5 abc:5 builtins:5 collections.abc:5 contextlib:5 io:5 subprocess:5 typing:5 typing_extensions:5 +TRACE: Priorities for mmap: sys:10 _typeshed:5 collections.abc:5 typing:5 +TRACE: Priorities for io: builtins:10 codecs:10 sys:10 _typeshed:5 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for importlib.metadata: abc:10 pathlib:5 sys:10 _typeshed:5 collections.abc:5 email.message:5 importlib.abc:5 os:5 typing:5 +TRACE: Priorities for importlib.machinery: importlib.abc:10 importlib:20 sys:10 types:10 collections.abc:5 typing:5 importlib.metadata:5 +TRACE: Priorities for importlib.abc: sys:10 types:10 _typeshed:5 abc:5 collections.abc:5 importlib.machinery:5 io:5 typing:5 typing_extensions:5 +TRACE: Priorities for importlib: collections.abc:5 importlib.abc:5 types:5 +TRACE: Priorities for genericpath: os:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for email.policy: abc:5 collections.abc:5 email.contentmanager:5 email.errors:5 email.header:5 email.message:5 typing:5 +TRACE: Priorities for email.message: collections.abc:5 email:5 email.charset:5 email.contentmanager:5 email.errors:5 email.policy:5 typing:5 typing_extensions:5 +TRACE: Priorities for email.header: email.charset:5 +TRACE: Priorities for email.errors: sys:10 +TRACE: Priorities for email.contentmanager: collections.abc:5 email.message:5 typing:5 +TRACE: Priorities for email.charset: collections.abc:5 +TRACE: Priorities for email: collections.abc:5 email.message:5 email.policy:5 typing:5 typing_extensions:5 +TRACE: Priorities for ctypes: sys:10 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for contextlib: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for collections.abc: _collections_abc:5 +TRACE: Priorities for collections: sys:10 _collections_abc:5 _typeshed:5 typing:5 typing_extensions:5 +TRACE: Priorities for codecs: types:10 _codecs:5 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for array: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for abc: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for _typeshed: array:10 ctypes:10 mmap:10 pickle:10 sys:10 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for _collections_abc: sys:10 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for _codecs: codecs:10 sys:10 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for _ast: sys:10 typing:5 typing_extensions:5 +TRACE: Priorities for builtins: sys:10 types:5 _ast:5 _collections_abc:5 _typeshed:5 collections.abc:5 io:5 typing:5 typing_extensions:5 +LOG: Processing SCC of size 36 (typing_extensions typing types sys subprocess posixpath pickle pathlib os.path os mmap io importlib.metadata importlib.machinery importlib.abc importlib genericpath email.policy email.message email.header email.errors email.contentmanager email.charset email ctypes contextlib collections.abc collections codecs array abc _typeshed _collections_abc _codecs _ast builtins) as inherently stale +LOG: Writing typing_extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing_extensions.pyi typing_extensions.meta.json typing_extensions.data.json +TRACE: Interface for typing_extensions has changed +LOG: Cached module typing_extensions has changed interface +LOG: Writing typing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing.pyi typing.meta.json typing.data.json +TRACE: Interface for typing has changed +LOG: Cached module typing has changed interface +LOG: Writing types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/types.pyi types.meta.json types.data.json +TRACE: Interface for types has changed +LOG: Cached module types has changed interface +LOG: Writing sys /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sys.pyi sys.meta.json sys.data.json +TRACE: Interface for sys has changed +LOG: Cached module sys has changed interface +LOG: Writing subprocess /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/subprocess.pyi subprocess.meta.json subprocess.data.json +TRACE: Interface for subprocess has changed +LOG: Cached module subprocess has changed interface +LOG: Writing posixpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/posixpath.pyi posixpath.meta.json posixpath.data.json +TRACE: Interface for posixpath has changed +LOG: Cached module posixpath has changed interface +LOG: Writing pickle /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pickle.pyi pickle.meta.json pickle.data.json +TRACE: Interface for pickle has changed +LOG: Cached module pickle has changed interface +LOG: Writing pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pathlib.pyi pathlib.meta.json pathlib.data.json +TRACE: Interface for pathlib has changed +LOG: Cached module pathlib has changed interface +LOG: Writing os.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/path.pyi os/path.meta.json os/path.data.json +TRACE: Interface for os.path has changed +LOG: Cached module os.path has changed interface +LOG: Writing os /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/__init__.pyi os/__init__.meta.json os/__init__.data.json +TRACE: Interface for os has changed +LOG: Cached module os has changed interface +LOG: Writing mmap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/mmap.pyi mmap.meta.json mmap.data.json +TRACE: Interface for mmap has changed +LOG: Cached module mmap has changed interface +LOG: Writing io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/io.pyi io.meta.json io.data.json +TRACE: Interface for io has changed +LOG: Cached module io has changed interface +LOG: Writing importlib.metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi importlib/metadata/__init__.meta.json importlib/metadata/__init__.data.json +TRACE: Interface for importlib.metadata has changed +LOG: Cached module importlib.metadata has changed interface +LOG: Writing importlib.machinery /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi importlib/machinery.meta.json importlib/machinery.data.json +TRACE: Interface for importlib.machinery has changed +LOG: Cached module importlib.machinery has changed interface +LOG: Writing importlib.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/abc.pyi importlib/abc.meta.json importlib/abc.data.json +TRACE: Interface for importlib.abc has changed +LOG: Cached module importlib.abc has changed interface +LOG: Writing importlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi importlib/__init__.meta.json importlib/__init__.data.json +TRACE: Interface for importlib has changed +LOG: Cached module importlib has changed interface +LOG: Writing genericpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/genericpath.pyi genericpath.meta.json genericpath.data.json +TRACE: Interface for genericpath has changed +LOG: Cached module genericpath has changed interface +LOG: Writing email.policy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi email/policy.meta.json email/policy.data.json +TRACE: Interface for email.policy has changed +LOG: Cached module email.policy has changed interface +LOG: Writing email.message /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/message.pyi email/message.meta.json email/message.data.json +TRACE: Interface for email.message has changed +LOG: Cached module email.message has changed interface +LOG: Writing email.header /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/header.pyi email/header.meta.json email/header.data.json +TRACE: Interface for email.header has changed +LOG: Cached module email.header has changed interface +LOG: Writing email.errors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/errors.pyi email/errors.meta.json email/errors.data.json +TRACE: Interface for email.errors has changed +LOG: Cached module email.errors has changed interface +LOG: Writing email.contentmanager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi email/contentmanager.meta.json email/contentmanager.data.json +TRACE: Interface for email.contentmanager has changed +LOG: Cached module email.contentmanager has changed interface +LOG: Writing email.charset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi email/charset.meta.json email/charset.data.json +TRACE: Interface for email.charset has changed +LOG: Cached module email.charset has changed interface +LOG: Writing email /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/__init__.pyi email/__init__.meta.json email/__init__.data.json +TRACE: Interface for email has changed +LOG: Cached module email has changed interface +LOG: Writing ctypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ctypes/__init__.pyi ctypes/__init__.meta.json ctypes/__init__.data.json +TRACE: Interface for ctypes has changed +LOG: Cached module ctypes has changed interface +LOG: Writing contextlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextlib.pyi contextlib.meta.json contextlib.data.json +TRACE: Interface for contextlib has changed +LOG: Cached module contextlib has changed interface +LOG: Writing collections.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/abc.pyi collections/abc.meta.json collections/abc.data.json +TRACE: Interface for collections.abc has changed +LOG: Cached module collections.abc has changed interface +LOG: Writing collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/__init__.pyi collections/__init__.meta.json collections/__init__.data.json +TRACE: Interface for collections has changed +LOG: Cached module collections has changed interface +LOG: Writing codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/codecs.pyi codecs.meta.json codecs.data.json +TRACE: Interface for codecs has changed +LOG: Cached module codecs has changed interface +LOG: Writing array /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/array.pyi array.meta.json array.data.json +TRACE: Interface for array has changed +LOG: Cached module array has changed interface +LOG: Writing abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/abc.pyi abc.meta.json abc.data.json +TRACE: Interface for abc has changed +LOG: Cached module abc has changed interface +LOG: Writing _typeshed /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_typeshed/__init__.pyi _typeshed/__init__.meta.json _typeshed/__init__.data.json +TRACE: Interface for _typeshed has changed +LOG: Cached module _typeshed has changed interface +LOG: Writing _collections_abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_collections_abc.pyi _collections_abc.meta.json _collections_abc.data.json +TRACE: Interface for _collections_abc has changed +LOG: Cached module _collections_abc has changed interface +LOG: Writing _codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_codecs.pyi _codecs.meta.json _codecs.data.json +TRACE: Interface for _codecs has changed +LOG: Cached module _codecs has changed interface +LOG: Writing _ast /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_ast.pyi _ast.meta.json _ast.data.json +TRACE: Interface for _ast has changed +LOG: Cached module _ast has changed interface +LOG: Writing builtins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/builtins.pyi builtins.meta.json builtins.data.json +TRACE: Interface for builtins has changed +LOG: Cached module builtins has changed interface +TRACE: Priorities for _socket: +LOG: Processing SCC singleton (_socket) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi (_socket) +LOG: Writing _socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi _socket.meta.json _socket.data.json +TRACE: Interface for _socket is unchanged +LOG: Cached module _socket has same interface +TRACE: Priorities for multiprocessing.shared_memory: +LOG: Processing SCC singleton (multiprocessing.shared_memory) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi (multiprocessing.shared_memory) +LOG: Writing multiprocessing.shared_memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi multiprocessing/shared_memory.meta.json multiprocessing/shared_memory.data.json +TRACE: Interface for multiprocessing.shared_memory is unchanged +LOG: Cached module multiprocessing.shared_memory has same interface +TRACE: Priorities for copyreg: +LOG: Processing SCC singleton (copyreg) as stale due to deps (_typeshed abc builtins collections.abc typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi (copyreg) +LOG: Writing copyreg /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi copyreg.meta.json copyreg.data.json +TRACE: Interface for copyreg is unchanged +LOG: Cached module copyreg has same interface +TRACE: Priorities for _weakref: +LOG: Processing SCC singleton (_weakref) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakref.pyi (_weakref) +LOG: Writing _weakref /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakref.pyi _weakref.meta.json _weakref.data.json +TRACE: Interface for _weakref is unchanged +LOG: Cached module _weakref has same interface +TRACE: Priorities for _weakrefset: +LOG: Processing SCC singleton (_weakrefset) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi (_weakrefset) +LOG: Writing _weakrefset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi _weakrefset.meta.json _weakrefset.data.json +TRACE: Interface for _weakrefset is unchanged +LOG: Cached module _weakrefset has same interface +TRACE: Priorities for multiprocessing.spawn: +LOG: Processing SCC singleton (multiprocessing.spawn) as stale due to deps (abc builtins collections.abc types typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi (multiprocessing.spawn) +LOG: Writing multiprocessing.spawn /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi multiprocessing/spawn.meta.json multiprocessing/spawn.data.json +TRACE: Interface for multiprocessing.spawn is unchanged +LOG: Cached module multiprocessing.spawn has same interface +TRACE: Priorities for multiprocessing.process: +LOG: Processing SCC singleton (multiprocessing.process) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi (multiprocessing.process) +LOG: Writing multiprocessing.process /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi multiprocessing/process.meta.json multiprocessing/process.data.json +TRACE: Interface for multiprocessing.process is unchanged +LOG: Cached module multiprocessing.process has same interface +TRACE: Priorities for multiprocessing.pool: +LOG: Processing SCC singleton (multiprocessing.pool) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi (multiprocessing.pool) +LOG: Writing multiprocessing.pool /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi multiprocessing/pool.meta.json multiprocessing/pool.data.json +TRACE: Interface for multiprocessing.pool is unchanged +LOG: Cached module multiprocessing.pool has same interface +TRACE: Priorities for _csv: +LOG: Processing SCC singleton (_csv) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_csv.pyi (_csv) +LOG: Writing _csv /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_csv.pyi _csv.meta.json _csv.data.json +TRACE: Interface for _csv is unchanged +LOG: Cached module _csv has same interface +TRACE: Priorities for unicodedata: +LOG: Processing SCC singleton (unicodedata) as stale due to deps (_typeshed abc builtins sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unicodedata.pyi (unicodedata) +LOG: Writing unicodedata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unicodedata.pyi unicodedata.meta.json unicodedata.data.json +TRACE: Interface for unicodedata is unchanged +LOG: Cached module unicodedata has same interface +TRACE: Priorities for importlib.resources: +LOG: Processing SCC singleton (importlib.resources) as stale due to deps (_typeshed abc array builtins collections.abc contextlib ctypes mmap os pathlib pickle sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/resources.pyi (importlib.resources) +LOG: Writing importlib.resources /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/resources.pyi importlib/resources.meta.json importlib/resources.data.json +TRACE: Interface for importlib.resources is unchanged +LOG: Cached module importlib.resources has same interface +TRACE: Priorities for _operator: +LOG: Processing SCC singleton (_operator) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_operator.pyi (_operator) +LOG: Writing _operator /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_operator.pyi _operator.meta.json _operator.data.json +TRACE: Interface for _operator is unchanged +LOG: Cached module _operator has same interface +TRACE: Priorities for distutils: +LOG: Processing SCC singleton (distutils) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/__init__.pyi (distutils) +LOG: Writing distutils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/__init__.pyi distutils/__init__.meta.json distutils/__init__.data.json +TRACE: Interface for distutils is unchanged +LOG: Cached module distutils has same interface +TRACE: Priorities for traceback: +LOG: Processing SCC singleton (traceback) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/traceback.pyi (traceback) +LOG: Writing traceback /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/traceback.pyi traceback.meta.json traceback.data.json +TRACE: Interface for traceback is unchanged +LOG: Cached module traceback has same interface +TRACE: Priorities for importlib_metadata._collections: +LOG: Processing SCC singleton (importlib_metadata._collections) as stale due to deps (abc array builtins collections ctypes mmap pickle typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_collections.py (importlib_metadata._collections) +LOG: Writing importlib_metadata._collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_collections.py importlib_metadata/_collections.meta.json importlib_metadata/_collections.data.json +TRACE: Interface for importlib_metadata._collections is unchanged +LOG: Cached module importlib_metadata._collections has same interface +TRACE: Priorities for struct: +LOG: Processing SCC singleton (struct) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/struct.pyi (struct) +LOG: Writing struct /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/struct.pyi struct.meta.json struct.data.json +TRACE: Interface for struct is unchanged +LOG: Cached module struct has same interface +TRACE: Priorities for platform: +LOG: Processing SCC singleton (platform) as stale due to deps (_typeshed abc builtins sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/platform.pyi (platform) +LOG: Writing platform /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/platform.pyi platform.meta.json platform.data.json +TRACE: Interface for platform is unchanged +LOG: Cached module platform has same interface +TRACE: Priorities for xarray.util: +LOG: Processing SCC singleton (xarray.util) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/util/__init__.py (xarray.util) +LOG: Writing xarray.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/util/__init__.py xarray/util/__init__.meta.json xarray/util/__init__.data.json +TRACE: Interface for xarray.util is unchanged +LOG: Cached module xarray.util has same interface +TRACE: Priorities for html: +LOG: Processing SCC singleton (html) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/html/__init__.pyi (html) +LOG: Writing html /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/html/__init__.pyi html/__init__.meta.json html/__init__.data.json +TRACE: Interface for html is unchanged +LOG: Cached module html has same interface +TRACE: Priorities for distutils.version: +LOG: Processing SCC singleton (distutils.version) as stale due to deps (_typeshed abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/version.pyi (distutils.version) +LOG: Writing distutils.version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/version.pyi distutils/version.meta.json distutils/version.data.json +TRACE: Interface for distutils.version is unchanged +LOG: Cached module distutils.version has same interface +TRACE: Priorities for glob: +LOG: Processing SCC singleton (glob) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/glob.pyi (glob) +LOG: Writing glob /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/glob.pyi glob.meta.json glob.data.json +TRACE: Interface for glob is unchanged +LOG: Cached module glob has same interface +TRACE: Priorities for xarray.coding: +LOG: Processing SCC singleton (xarray.coding) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/__init__.py (xarray.coding) +LOG: Writing xarray.coding /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/__init__.py xarray/coding/__init__.meta.json xarray/coding/__init__.data.json +TRACE: Interface for xarray.coding is unchanged +LOG: Cached module xarray.coding has same interface +TRACE: Priorities for xarray.core: +LOG: Processing SCC singleton (xarray.core) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/__init__.py (xarray.core) +LOG: Writing xarray.core /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/__init__.py xarray/core/__init__.meta.json xarray/core/__init__.data.json +TRACE: Interface for xarray.core is unchanged +LOG: Cached module xarray.core has same interface +TRACE: Priorities for zlib: +LOG: Processing SCC singleton (zlib) as stale due to deps (_typeshed abc array builtins sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/zlib.pyi (zlib) +LOG: Writing zlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/zlib.pyi zlib.meta.json zlib.data.json +TRACE: Interface for zlib is unchanged +LOG: Cached module zlib has same interface +TRACE: Priorities for _compression: +LOG: Processing SCC singleton (_compression) as stale due to deps (_typeshed builtins collections.abc io typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_compression.pyi (_compression) +LOG: Writing _compression /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_compression.pyi _compression.meta.json _compression.data.json +TRACE: Interface for _compression is unchanged +LOG: Cached module _compression has same interface +TRACE: Priorities for xdrlib: +LOG: Processing SCC singleton (xdrlib) as stale due to deps (abc builtins collections.abc typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/xdrlib.pyi (xdrlib) +LOG: Writing xdrlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/xdrlib.pyi xdrlib.meta.json xdrlib.data.json +TRACE: Interface for xdrlib is unchanged +LOG: Cached module xdrlib has same interface +TRACE: Priorities for lzma: +LOG: Processing SCC singleton (lzma) as stale due to deps (_typeshed abc builtins collections.abc io typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/lzma.pyi (lzma) +LOG: Writing lzma /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/lzma.pyi lzma.meta.json lzma.data.json +TRACE: Interface for lzma is unchanged +LOG: Cached module lzma has same interface +TRACE: Priorities for skfda.exploratory.outliers._directional_outlyingness_experiment_results: +LOG: Processing SCC singleton 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typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/socket.pyi (socket) +LOG: Writing socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/socket.pyi socket.meta.json socket.data.json +TRACE: Interface for socket is unchanged +LOG: Cached module socket has same interface +TRACE: Priorities for multiprocessing.reduction: +LOG: Processing SCC singleton (multiprocessing.reduction) as stale due to deps (abc array builtins ctypes mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi (multiprocessing.reduction) +LOG: Writing multiprocessing.reduction 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typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/operator.pyi (operator) +LOG: Writing operator /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/operator.pyi operator.meta.json operator.data.json +TRACE: Interface for operator is unchanged +LOG: Cached module operator has same interface +TRACE: Priorities for xarray.core.pdcompat: +LOG: Processing SCC singleton (xarray.core.pdcompat) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/pdcompat.py (xarray.core.pdcompat) +LOG: Writing xarray.core.pdcompat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/pdcompat.py xarray/core/pdcompat.meta.json xarray/core/pdcompat.data.json +TRACE: Interface for xarray.core.pdcompat is unchanged +LOG: Cached module xarray.core.pdcompat has same interface +TRACE: Priorities for gzip: +LOG: Processing SCC singleton (gzip) as stale due to deps (_typeshed abc builtins io sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/gzip.pyi (gzip) +LOG: Writing gzip /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/gzip.pyi gzip.meta.json gzip.data.json +TRACE: Interface for gzip is unchanged +LOG: Cached module gzip has same interface +TRACE: Priorities for bz2: +LOG: Processing SCC singleton (bz2) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bz2.pyi (bz2) +LOG: Writing bz2 /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bz2.pyi bz2.meta.json bz2.data.json +TRACE: Interface for bz2 is unchanged +LOG: Cached module bz2 has same interface +TRACE: Priorities for dataclasses: +LOG: Processing SCC singleton (dataclasses) as inherently stale with stale deps (builtins collections.abc enum sys types typing typing_extensions) +LOG: Writing dataclasses /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/dataclasses.pyi dataclasses.meta.json dataclasses.data.json +TRACE: Interface for dataclasses has changed +LOG: Cached module dataclasses has changed interface +TRACE: Priorities for sre_parse: +LOG: Processing SCC singleton (sre_parse) as inherently stale with stale deps (builtins collections.abc sre_constants sys typing typing_extensions) +LOG: Writing sre_parse /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sre_parse.pyi sre_parse.meta.json sre_parse.data.json +TRACE: Interface for sre_parse has changed +LOG: Cached module sre_parse has changed interface +TRACE: Priorities for json: +LOG: Processing SCC singleton (json) as inherently stale with stale deps (_typeshed builtins collections.abc json.decoder json.encoder typing) +LOG: Writing json /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/json/__init__.pyi json/__init__.meta.json json/__init__.data.json +TRACE: Interface for json has changed +LOG: Cached module json has changed interface +TRACE: Priorities for numpy.core.umath: +LOG: Processing SCC singleton (numpy.core.umath) as inherently stale with stale deps (builtins numpy.core) +LOG: Writing numpy.core.umath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/umath.py numpy/core/umath.meta.json numpy/core/umath.data.json +TRACE: Interface for numpy.core.umath has changed +LOG: Cached module numpy.core.umath has changed interface +TRACE: Priorities for numpy._typing._nested_sequence: +LOG: Processing SCC singleton (numpy._typing._nested_sequence) as inherently stale with stale deps (__future__ builtins typing) +LOG: Writing numpy._typing._nested_sequence /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/_typing/_nested_sequence.py numpy/_typing/_nested_sequence.meta.json numpy/_typing/_nested_sequence.data.json +TRACE: Interface for numpy._typing._nested_sequence has changed +LOG: Cached module numpy._typing._nested_sequence has changed interface +TRACE: Priorities for numpy.core.overrides: +LOG: Processing SCC singleton (numpy.core.overrides) as inherently stale with stale deps (builtins collections functools numpy.compat._inspect os) +LOG: Writing numpy.core.overrides /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/overrides.py numpy/core/overrides.meta.json numpy/core/overrides.data.json +TRACE: Interface for numpy.core.overrides has changed +LOG: Cached module numpy.core.overrides has changed interface +TRACE: Priorities for dis: +LOG: Processing SCC singleton (dis) as inherently stale with stale deps (_typeshed builtins collections.abc opcode sys types typing typing_extensions) +LOG: Writing dis /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/dis.pyi dis.meta.json dis.data.json +TRACE: Interface for dis has changed +LOG: Cached module dis has changed interface +TRACE: Priorities for datetime: +LOG: Processing SCC singleton (datetime) as inherently stale with stale deps (_typeshed builtins sys time typing typing_extensions) +LOG: Writing datetime /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/datetime.pyi datetime.meta.json datetime.data.json +TRACE: Interface for datetime has changed +LOG: Cached module datetime has changed interface +TRACE: Priorities for warnings: +LOG: Processing SCC singleton (warnings) as inherently stale with stale deps (_warnings builtins collections.abc sys types typing typing_extensions) +LOG: Writing warnings /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/warnings.pyi warnings.meta.json warnings.data.json +TRACE: Interface for warnings has changed +LOG: Cached module warnings has changed interface +TRACE: Priorities for multiprocessing.queues: +LOG: Processing SCC singleton (multiprocessing.queues) as stale due to deps (_typeshed abc builtins sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi (multiprocessing.queues) +LOG: Writing multiprocessing.queues /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi multiprocessing/queues.meta.json multiprocessing/queues.data.json +TRACE: Interface for multiprocessing.queues is unchanged +LOG: Cached module multiprocessing.queues has same interface +TRACE: Priorities for multiprocessing.connection: +LOG: Processing SCC singleton (multiprocessing.connection) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/connection.pyi (multiprocessing.connection) +LOG: Writing multiprocessing.connection /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/connection.pyi multiprocessing/connection.meta.json multiprocessing/connection.data.json +TRACE: Interface for multiprocessing.connection is unchanged +LOG: Cached module multiprocessing.connection has same interface +TRACE: Priorities for importlib_metadata._meta: +LOG: Processing SCC singleton (importlib_metadata._meta) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_meta.py (importlib_metadata._meta) +LOG: Writing importlib_metadata._meta /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_meta.py importlib_metadata/_meta.meta.json importlib_metadata/_meta.data.json +TRACE: Interface for importlib_metadata._meta is unchanged +LOG: Cached module importlib_metadata._meta has same interface +TRACE: Priorities for decimal: +LOG: Processing SCC singleton (decimal) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/decimal.pyi (decimal) +LOG: Writing decimal /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/decimal.pyi decimal.meta.json decimal.data.json +TRACE: Interface for decimal is unchanged +LOG: Cached module decimal has same interface +TRACE: Priorities for sre_compile: +LOG: Processing SCC singleton (sre_compile) as inherently stale with stale deps (builtins sre_constants sre_parse typing) +LOG: Writing sre_compile /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sre_compile.pyi sre_compile.meta.json sre_compile.data.json +TRACE: Interface for sre_compile has changed +LOG: Cached module sre_compile has changed interface +TRACE: Priorities for numpy._version: +LOG: Processing SCC singleton (numpy._version) as inherently stale with stale deps (builtins json) +LOG: Writing numpy._version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/_version.py numpy/_version.meta.json numpy/_version.data.json +TRACE: Interface for numpy._version has changed +LOG: Cached module numpy._version has changed interface +TRACE: Priorities for inspect: +LOG: Processing SCC singleton (inspect) as inherently stale with stale deps (_typeshed builtins collections collections.abc dis enum sys types typing typing_extensions) +LOG: Writing inspect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/inspect.pyi inspect.meta.json inspect.data.json +TRACE: Interface for inspect has changed +LOG: Cached module inspect has changed interface +TRACE: Priorities for locale: +LOG: Processing SCC singleton (locale) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/locale.pyi (locale) +LOG: Writing locale /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/locale.pyi locale.meta.json locale.data.json +TRACE: Interface for locale is unchanged +LOG: Cached module locale has same interface +TRACE: Priorities for fractions: +LOG: Processing SCC singleton (fractions) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap numbers pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/fractions.pyi (fractions) +LOG: Writing fractions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/fractions.pyi fractions.meta.json fractions.data.json +TRACE: Interface for fractions is unchanged +LOG: Cached module fractions has same interface +TRACE: Priorities for re: +LOG: Processing SCC singleton (re) as inherently stale with stale deps (_typeshed builtins collections.abc enum sre_compile sre_constants sys typing typing_extensions) +LOG: Writing re /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/re.pyi re.meta.json re.data.json +TRACE: Interface for re has changed +LOG: Cached module re has changed interface +TRACE: Priorities for numpy.version: +LOG: Processing SCC singleton (numpy.version) as inherently stale with stale deps (__future__ builtins numpy._version) +LOG: Writing numpy.version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/version.py numpy/version.meta.json numpy/version.data.json +TRACE: Interface for numpy.version has changed +LOG: Cached module numpy.version has changed interface +TRACE: Priorities for multimethod: +LOG: Processing SCC singleton (multimethod) as inherently stale with stale deps (abc builtins collections contextlib functools inspect itertools types typing) +LOG: Writing multimethod /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/multimethod/__init__.py multimethod/__init__.meta.json multimethod/__init__.data.json +TRACE: Interface for multimethod has changed +LOG: Cached module multimethod has changed interface +TRACE: Priorities for importlib_metadata._text: +LOG: Processing SCC singleton (importlib_metadata._text) as stale due to deps (abc array builtins ctypes enum mmap pickle re typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_text.py (importlib_metadata._text) +LOG: Writing importlib_metadata._text /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_text.py importlib_metadata/_text.meta.json importlib_metadata/_text.data.json +TRACE: Interface for importlib_metadata._text is unchanged +LOG: Cached module importlib_metadata._text has same interface +TRACE: Priorities for xarray.util.print_versions: +LOG: Processing SCC singleton (xarray.util.print_versions) as stale due to deps (_typeshed abc array builtins ctypes genericpath importlib mmap os pickle subprocess sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/util/print_versions.py (xarray.util.print_versions) +LOG: Writing xarray.util.print_versions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/util/print_versions.py xarray/util/print_versions.meta.json xarray/util/print_versions.data.json +TRACE: Interface for xarray.util.print_versions is unchanged +LOG: Cached module xarray.util.print_versions has same interface +TRACE: Priorities for numpy.compat._pep440: +LOG: Processing SCC singleton (numpy.compat._pep440) as inherently stale with stale deps (builtins collections itertools re) +LOG: Writing numpy.compat._pep440 /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/compat/_pep440.py numpy/compat/_pep440.meta.json numpy/compat/_pep440.data.json +TRACE: Interface for numpy.compat._pep440 has changed +LOG: Cached module numpy.compat._pep440 has changed interface +TRACE: Priorities for string: +LOG: Processing SCC singleton (string) as inherently stale with stale deps (builtins collections.abc re sys typing) +LOG: Writing string /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/string.pyi string.meta.json string.data.json +TRACE: Interface for string has changed +LOG: Cached module string has changed interface +TRACE: Priorities for importlib_metadata._adapters: +LOG: Processing SCC singleton (importlib_metadata._adapters) as stale due to deps (_typeshed abc array builtins ctypes email email.message enum mmap pickle re textwrap typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_adapters.py (importlib_metadata._adapters) +LOG: Writing importlib_metadata._adapters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_adapters.py importlib_metadata/_adapters.meta.json importlib_metadata/_adapters.data.json +TRACE: Interface for importlib_metadata._adapters is unchanged +LOG: Cached module importlib_metadata._adapters has same interface +TRACE: Priorities for numpy.compat: +LOG: Processing SCC singleton (numpy.compat) as inherently stale with stale deps (builtins numpy.compat._inspect numpy.compat._pep440 numpy.compat.py3k) +LOG: Writing numpy.compat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/compat/__init__.py numpy/compat/__init__.meta.json numpy/compat/__init__.data.json +TRACE: Interface for numpy.compat has changed +LOG: Cached module numpy.compat has changed interface +TRACE: Priorities for logging: +LOG: Processing SCC singleton (logging) as inherently stale with stale deps (_typeshed builtins collections.abc io string sys threading time types typing typing_extensions) +LOG: Writing logging /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/logging/__init__.pyi logging/__init__.meta.json logging/__init__.data.json +TRACE: Interface for logging has changed +LOG: Cached module logging has changed interface +TRACE: Priorities for multiprocessing.sharedctypes: multiprocessing.context:5 multiprocessing.synchronize:5 +TRACE: Priorities for multiprocessing.synchronize: multiprocessing.context:5 +TRACE: Priorities for multiprocessing.context: multiprocessing:10 multiprocessing.synchronize:10 multiprocessing.sharedctypes:5 +TRACE: Priorities for multiprocessing.managers: multiprocessing.context:5 +TRACE: Priorities for multiprocessing: multiprocessing.context:5 multiprocessing.synchronize:10 multiprocessing.managers:5 multiprocessing.sharedctypes:30 +LOG: Processing SCC of size 5 (multiprocessing.sharedctypes multiprocessing.synchronize multiprocessing.context multiprocessing.managers multiprocessing) as stale due to deps (_typeshed abc builtins collections.abc contextlib ctypes logging sys threading types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/sharedctypes.pyi (multiprocessing.sharedctypes) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi (multiprocessing.synchronize) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/context.pyi (multiprocessing.context) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi (multiprocessing.managers) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/__init__.pyi (multiprocessing) +LOG: Writing multiprocessing.sharedctypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/sharedctypes.pyi multiprocessing/sharedctypes.meta.json multiprocessing/sharedctypes.data.json +TRACE: Interface for multiprocessing.sharedctypes is unchanged +LOG: Cached module multiprocessing.sharedctypes has same interface +LOG: Writing multiprocessing.synchronize /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi multiprocessing/synchronize.meta.json multiprocessing/synchronize.data.json +TRACE: Interface for multiprocessing.synchronize is unchanged +LOG: Cached module multiprocessing.synchronize has same interface +LOG: Writing multiprocessing.context /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/context.pyi multiprocessing/context.meta.json multiprocessing/context.data.json +TRACE: Interface for multiprocessing.context is unchanged +LOG: Cached module multiprocessing.context has same interface +LOG: Writing multiprocessing.managers /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi multiprocessing/managers.meta.json multiprocessing/managers.data.json +TRACE: Interface for multiprocessing.managers is unchanged +LOG: Cached module multiprocessing.managers has same interface +LOG: Writing multiprocessing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/__init__.pyi multiprocessing/__init__.meta.json multiprocessing/__init__.data.json +TRACE: Interface for multiprocessing is unchanged +LOG: Cached module multiprocessing has same interface +TRACE: Priorities for importlib_metadata: +LOG: Processing SCC singleton (importlib_metadata) as stale due to deps (_collections_abc _typeshed _warnings abc array builtins collections contextlib ctypes email email.message email.policy enum functools importlib importlib.abc itertools mmap os pathlib pickle posixpath re sys textwrap types typing typing_extensions warnings) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/__init__.py (importlib_metadata) +LOG: Writing importlib_metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/__init__.py importlib_metadata/__init__.meta.json importlib_metadata/__init__.data.json +TRACE: Interface for importlib_metadata is unchanged +LOG: Cached module importlib_metadata has same interface +TRACE: Priorities for unittest.result: unittest.case:10 unittest:20 +TRACE: Priorities for unittest.case: unittest.result:10 unittest:20 +TRACE: Priorities for unittest.suite: unittest.case:10 unittest:20 unittest.result:10 +TRACE: Priorities for unittest.signals: unittest.result:10 unittest:20 +TRACE: Priorities for unittest.async_case: unittest.case:5 +TRACE: Priorities for unittest.runner: unittest.case:10 unittest:20 unittest.result:10 unittest.suite:10 +TRACE: Priorities for unittest.loader: unittest.case:10 unittest:20 unittest.result:10 unittest.suite:10 +TRACE: Priorities for unittest.main: unittest.case:10 unittest:20 unittest.loader:10 unittest.result:10 unittest.suite:10 +TRACE: Priorities for unittest: unittest.async_case:5 unittest.case:5 unittest.loader:5 unittest.main:5 unittest.result:5 unittest.runner:5 unittest.signals:5 unittest.suite:5 +LOG: Processing SCC of size 9 (unittest.result unittest.case unittest.suite unittest.signals unittest.async_case unittest.runner unittest.loader unittest.main unittest) as inherently stale with stale deps (_typeshed builtins collections.abc contextlib logging sys types typing typing_extensions warnings) +LOG: Writing unittest.result /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unittest/result.pyi unittest/result.meta.json unittest/result.data.json +TRACE: Interface for 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numpy.lib.arraypad numpy.core.shape_base numpy.core.numerictypes numpy.core.numeric numpy.core.multiarray numpy.core.einsumfunc numpy.core.arrayprint numpy.core._ufunc_config numpy.core._type_aliases numpy.core._asarray numpy.core.fromnumeric numpy.core.function_base numpy._typing._extended_precision numpy._typing._callable numpy._typing numpy.core._internal numpy.matrixlib numpy.ma numpy.lib numpy.ctypeslib numpy numpy.testing._private.utils numpy.random.bit_generator numpy.polynomial.polynomial numpy.polynomial.legendre numpy.polynomial.laguerre numpy.polynomial.hermite_e numpy.polynomial.hermite numpy.polynomial.chebyshev numpy.lib.scimath numpy.lib.mixins numpy.fft.helper numpy.fft._pocketfft numpy.core.records numpy.core.defchararray numpy.linalg.linalg numpy.linalg numpy.random.mtrand numpy.random._sfc64 numpy.random._philox numpy.random._pcg64 numpy.random._mt19937 numpy.testing numpy.polynomial numpy.fft numpy.random._generator numpy.random numpy._typing._add_docstring numpy.typing numpy._typing._ufunc) as inherently stale with stale deps (__future__ abc array ast builtins collections.abc contextlib ctypes datetime enum math mmap numpy._pytesttester numpy._typing._char_codes numpy._typing._nbit numpy._typing._nested_sequence numpy._typing._shape numpy.core numpy.core.overrides numpy.core.umath numpy.lib._version numpy.lib.format numpy.polynomial._polybase numpy.polynomial.polyutils numpy.version os re sys textwrap threading types typing typing_extensions unittest unittest.case warnings zipfile) +LOG: Writing numpy._typing._generic_alias /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/_typing/_generic_alias.py numpy/_typing/_generic_alias.meta.json numpy/_typing/_generic_alias.data.json +TRACE: Interface for numpy._typing._generic_alias has changed +LOG: Cached module numpy._typing._generic_alias has changed interface +LOG: Writing numpy._typing._scalars 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xarray.core.dataarray:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core.common: xarray.core.dtypes:10 xarray.core.duck_array_ops:10 xarray.core.formatting:10 xarray.core.formatting_html:10 xarray.core.ops:10 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.rolling_exp:5 xarray.core.utils:5 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core.types:25 xarray.core.variable:20 xarray.core.weighted:25 xarray.core.computation:20 xarray.coding.cftimeindex:20 xarray.core.resample:20 xarray.core.resample_cftime:20 xarray.core.alignment:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.coordinates:30 +TRACE: Priorities for xarray.core.accessor_dt: xarray.core.common:5 xarray.core.pycompat:5 xarray.coding.cftimeindex:20 xarray.core.dataset:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core._typed_ops: xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.groupby:5 xarray.core.types:5 xarray.core.variable:5 +TRACE: Priorities for xarray.plot.dataset_plot: xarray.core.alignment:5 xarray.plot.facetgrid:5 xarray.plot.utils:5 +TRACE: Priorities for xarray.core.arithmetic: xarray.core._typed_ops:5 xarray.core.common:5 xarray.core.ops:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.computation:20 +TRACE: Priorities for xarray.core.accessor_str: xarray.core.computation:5 +TRACE: Priorities for xarray.plot.plot: xarray.core.alignment:5 xarray.plot.facetgrid:5 xarray.plot.utils:5 +TRACE: Priorities for xarray.core.missing: xarray.core.utils:5 xarray.core.common:5 xarray.core.computation:5 xarray.core.duck_array_ops:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.variable:5 xarray.coding.cftimeindex:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.dask_array_ops:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core.coordinates: xarray.core.formatting:10 xarray.core.indexing:10 xarray.core.indexes:5 xarray.core.merge:5 xarray.core.utils:5 xarray.core.variable:5 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.coding.variables: xarray.core.dtypes:10 xarray.core.duck_array_ops:10 xarray.core.indexing:10 xarray.core.pycompat:5 xarray.core.variable:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.coding.times: xarray.core.indexing:10 xarray.core.common:5 xarray.core.formatting:5 xarray.core.variable:5 xarray.coding.variables:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.core.groupby: xarray.core.dtypes:10 xarray.core.duck_array_ops:10 xarray.core.nputils:10 xarray.core.ops:10 xarray.core._reductions:5 xarray.core.arithmetic:5 xarray.core.concat:5 xarray.core.formatting:5 xarray.core.indexes:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.utils:5 xarray.core.variable:5 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core.resample_cftime:20 xarray.core._typed_ops:30 xarray.core.common:30 xarray.core.coordinates:30 +TRACE: Priorities for xarray.core.variable: xarray:10 xarray.core.common:5 xarray.core.dtypes:10 xarray.core.duck_array_ops:10 xarray.core.indexing:5 xarray.core.nputils:10 xarray.core.ops:10 xarray.core.utils:5 xarray.core.arithmetic:5 xarray.core.indexes:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.types:25 xarray.core.dataarray:20 xarray.core.computation:20 xarray.core.merge:20 xarray.core._typed_ops:30 xarray.core.dataset:30 +TRACE: Priorities for xarray.core.merge: xarray.core.dtypes:10 xarray.core.alignment:5 xarray.core.duck_array_ops:5 xarray.core.indexes:5 xarray.core.utils:5 xarray.core.variable:5 xarray.core.coordinates:25 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core.dataset: xarray:10 xarray.core.alignment:5 xarray.core.dtypes:10 xarray.core.duck_array_ops:5 xarray.core.formatting:10 xarray.core.formatting_html:10 xarray.core.groupby:10 xarray.core.ops:10 xarray.core.resample:10 xarray.core.rolling:10 xarray.core.utils:5 xarray.core.weighted:10 xarray.core.missing:5 xarray.coding.cftimeindex:5 xarray.plot.dataset_plot:5 xarray.core.arithmetic:5 xarray.core.common:5 xarray.core.computation:5 xarray.core.coordinates:5 xarray.core.indexes:5 xarray.core.indexing:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.variable:5 xarray.backends:25 xarray.core.dataarray:20 xarray.core.types:25 xarray.backends.api:20 xarray.core.parallel:20 xarray.backends.common:30 xarray.core._reductions:30 xarray.core._typed_ops:30 xarray.core.concat:30 xarray.core.dask_array_ops:30 xarray.plot:30 +TRACE: Priorities for xarray.core.dataarray: xarray.core.computation:5 xarray.core.dtypes:10 xarray.core.groupby:10 xarray.core.indexing:5 xarray.core.ops:10 xarray.core.resample:10 xarray.core.rolling:10 xarray.core.utils:5 xarray.core.weighted:10 xarray.plot.plot:5 xarray.plot.utils:5 xarray.core.accessor_dt:5 xarray.core.accessor_str:5 xarray.core.alignment:5 xarray.core.arithmetic:5 xarray.core.common:5 xarray.core.coordinates:5 xarray.core.dataset:5 xarray.core.formatting:5 xarray.core.indexes:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.variable:5 xarray.core.types:25 xarray.core.missing:20 xarray.backends.api:20 xarray.convert:20 xarray.core.parallel:20 xarray.core._reductions:30 xarray.core._typed_ops:30 xarray.plot:30 +TRACE: Priorities for xarray.core.concat: xarray.core.dtypes:10 xarray.core.utils:10 xarray.core.alignment:5 xarray.core.duck_array_ops:5 xarray.core.merge:5 xarray.core.variable:5 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.coordinates:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core.computation: xarray.core.dtypes:10 xarray.core.duck_array_ops:10 xarray.core.utils:5 xarray.core.alignment:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.variable:5 xarray.core.coordinates:25 xarray.core.dataset:20 xarray.core.types:25 xarray.core.dataarray:20 xarray.core.groupby:20 xarray.core.missing:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.indexes:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core.alignment: xarray.core.dtypes:10 xarray.core.indexes:5 xarray.core.utils:5 xarray.core.variable:5 xarray.core.common:25 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.ops:30 +TRACE: Priorities for xarray.coding.cftimeindex: xarray.core.utils:5 xarray.core.common:5 xarray.core.options:5 xarray.coding.times:5 xarray.coding.cftime_offsets:20 xarray.core.resample_cftime:20 xarray.coding.frequencies:20 +TRACE: Priorities for xarray.backends.netcdf3: xarray:20 xarray.core.variable:5 xarray.coding.strings:30 xarray.coding.variables:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.core.rolling: xarray.core.dtypes:10 xarray.core.duck_array_ops:10 xarray.core.utils:5 xarray.core.arithmetic:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core._typed_ops:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.variable:30 +TRACE: Priorities for xarray.core.resample: xarray.core._reductions:5 xarray.core.groupby:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 +TRACE: Priorities for xarray.core.weighted: xarray.core.duck_array_ops:10 xarray.core.computation:5 xarray.core.pycompat:5 xarray.core.types:5 xarray.core.dataarray:20 xarray.core.dataset:25 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.coding.strings: xarray.core.indexing:10 xarray.core.pycompat:5 xarray.core.variable:5 xarray.coding.variables:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.core.parallel: xarray.core.alignment:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.pycompat:5 xarray.core.types:25 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.coordinates:30 xarray.core.indexes:30 xarray.core.ops:30 xarray.core.utils:30 xarray.core.variable:30 +TRACE: Priorities for xarray.core.extensions: xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.core.combine: xarray.core.dtypes:10 xarray.core.concat:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.merge:5 xarray.core.utils:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.variable:30 +TRACE: Priorities for xarray.conventions: xarray.coding.strings:10 xarray.coding.times:10 xarray.coding.variables:5 xarray.core.duck_array_ops:10 xarray.core.indexing:10 xarray.core.common:5 xarray.core.pycompat:5 xarray.core.variable:5 xarray.backends.common:20 xarray.core.dataset:20 xarray.backends:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.coordinates:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.coding.cftime_offsets: xarray.coding.cftimeindex:5 xarray.coding.times:5 +TRACE: Priorities for xarray.ufuncs: xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.groupby:5 xarray.core.pycompat:5 xarray.core.variable:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.testing: xarray.core.duck_array_ops:10 xarray.core.formatting:10 xarray.core.utils:10 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.indexes:5 xarray.core.variable:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.coordinates:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.common: xarray.core.indexing:10 xarray.conventions:5 xarray.core.pycompat:5 xarray.core.utils:5 +TRACE: Priorities for xarray.coding.frequencies: xarray.core.common:5 xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 xarray.core.dataarray:20 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.memory: xarray.core.variable:5 xarray.backends.common:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.backends.store: xarray.conventions:10 xarray:20 xarray.core.dataset:5 xarray.backends.common:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.plugins: xarray.backends.common:5 +TRACE: Priorities for xarray.backends.rasterio_: xarray.core.indexing:10 xarray.core.dataarray:5 xarray.core.utils:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.api: xarray.backends:10 xarray.conventions:10 xarray:20 xarray.core.indexing:10 xarray.backends.plugins:10 xarray.core.combine:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.utils:5 xarray.backends.common:5 xarray.backends.cfgrib_:30 xarray.backends.h5netcdf_:30 xarray.backends.netCDF4_:30 xarray.backends.pseudonetcdf_:30 xarray.backends.pydap_:30 xarray.backends.pynio_:30 xarray.backends.scipy_:30 xarray.backends.zarr:30 xarray.coding.strings:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 xarray.core.variable:30 +TRACE: Priorities for xarray.backends.scipy_: xarray.core.indexing:5 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.netcdf3:5 xarray.backends.store:5 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.pynio_: xarray.core.indexing:10 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.store:5 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.pydap_: xarray.core.indexing:10 xarray.core.pycompat:5 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.store:5 xarray.backends.rasterio_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.pseudonetcdf_: xarray.core.indexing:10 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.store:5 xarray.backends.pynio_:30 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.netCDF4_: xarray:20 xarray.core.indexing:10 xarray.coding.variables:5 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.netcdf3:5 xarray.backends.store:5 xarray.backends.pynio_:30 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.coding.strings:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.cfgrib_: xarray.core.indexing:10 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.store:5 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.zarr: xarray.conventions:10 xarray:20 xarray.core.indexing:10 xarray.core.pycompat:5 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.store:5 xarray.backends.api:20 xarray.coding.strings:30 xarray.coding.variables:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.tutorial: xarray.backends.api:5 xarray.backends.rasterio_:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.backends:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray.backends.h5netcdf_: xarray.core.indexing:10 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.netCDF4_:5 xarray.backends.store:5 xarray.backends.pynio_:30 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 +TRACE: Priorities for xarray: xarray.testing:10 xarray.tutorial:10 xarray.ufuncs:10 xarray.backends.api:5 xarray.backends.rasterio_:5 xarray.backends.zarr:5 xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 xarray.coding.frequencies:5 xarray.conventions:5 xarray.core.alignment:5 xarray.core.combine:5 xarray.core.common:5 xarray.core.computation:5 xarray.core.concat:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.extensions:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.parallel:5 xarray.core.variable:5 +TRACE: Priorities for xarray.backends: xarray.backends.cfgrib_:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.h5netcdf_:5 xarray.backends.memory:5 xarray.backends.netCDF4_:5 xarray.backends.plugins:5 xarray.backends.pseudonetcdf_:5 xarray.backends.pydap_:5 xarray.backends.pynio_:5 xarray.backends.scipy_:5 xarray.backends.zarr:5 +TRACE: Priorities for xarray.convert: xarray.core.duck_array_ops:10 xarray.coding.times:5 xarray.conventions:5 xarray.core.dataarray:5 xarray.core.dtypes:5 xarray.core.pycompat:5 xarray.coding.strings:30 xarray.coding.variables:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.dataset:30 xarray.core.ops:30 xarray.core.utils:30 +TRACE: Priorities for xarray.core.resample_cftime: xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 +TRACE: Priorities for xarray.plot: xarray.plot.dataset_plot:5 xarray.plot.facetgrid:5 xarray.plot.plot:5 +LOG: Processing SCC of size 73 (xarray.core.types xarray.core.utils xarray.core.dtypes xarray.core.pycompat xarray.core.options xarray.core.dask_array_compat xarray.core.nputils xarray.plot.utils xarray.core.rolling_exp xarray.backends.file_manager xarray.core.dask_array_ops xarray.core.duck_array_ops xarray.core._reductions xarray.core.nanops xarray.core.ops xarray.core.indexing xarray.core.formatting xarray.plot.facetgrid xarray.core.formatting_html xarray.core.indexes xarray.core.common xarray.core.accessor_dt xarray.core._typed_ops xarray.plot.dataset_plot xarray.core.arithmetic xarray.core.accessor_str xarray.plot.plot xarray.core.missing xarray.core.coordinates xarray.coding.variables xarray.coding.times xarray.core.groupby xarray.core.variable xarray.core.merge xarray.core.dataset xarray.core.dataarray xarray.core.concat xarray.core.computation xarray.core.alignment xarray.coding.cftimeindex xarray.backends.netcdf3 xarray.core.rolling xarray.core.resample xarray.core.weighted xarray.coding.strings xarray.core.parallel xarray.core.extensions xarray.core.combine xarray.conventions xarray.coding.cftime_offsets xarray.ufuncs xarray.testing xarray.backends.common xarray.coding.frequencies xarray.backends.memory xarray.backends.store xarray.backends.plugins xarray.backends.rasterio_ xarray.backends.api xarray.backends.scipy_ xarray.backends.pynio_ xarray.backends.pydap_ xarray.backends.pseudonetcdf_ xarray.backends.netCDF4_ xarray.backends.cfgrib_ xarray.backends.zarr xarray.tutorial xarray.backends.h5netcdf_ xarray xarray.backends xarray.convert xarray.core.resample_cftime xarray.plot) as stale due to deps (__future__ _collections_abc _typeshed _warnings abc array builtins codecs collections collections.abc contextlib copy ctypes datetime enum functools genericpath importlib importlib.metadata inspect io itertools logging mmap numbers numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.arrayprint numpy.core.defchararray numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.core.numerictypes numpy.core.shape_base numpy.lib numpy.lib.arraypad 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dask_array_ops.py (xarray.core.dask_array_ops) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/duck_array_ops.py (xarray.core.duck_array_ops) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/_reductions.py (xarray.core._reductions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/nanops.py (xarray.core.nanops) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/ops.py (xarray.core.ops) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/indexing.py (xarray.core.indexing) +LOG: Parsing 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/cfgrib_.py (xarray.backends.cfgrib_) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/zarr.py (xarray.backends.zarr) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/tutorial.py (xarray.tutorial) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/h5netcdf_.py (xarray.backends.h5netcdf_) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/__init__.py (xarray) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/__init__.py (xarray.backends) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/convert.py (xarray.convert) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/resample_cftime.py (xarray.core.resample_cftime) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py (xarray.plot) +LOG: Writing xarray.core.types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/types.py xarray/core/types.meta.json xarray/core/types.data.json +TRACE: Interface for xarray.core.types is unchanged +LOG: Cached module xarray.core.types has same interface +LOG: Writing xarray.core.utils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/utils.py xarray/core/utils.meta.json xarray/core/utils.data.json +TRACE: Interface for xarray.core.utils is unchanged +LOG: Cached module xarray.core.utils has same interface +LOG: Writing xarray.core.dtypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dtypes.py xarray/core/dtypes.meta.json xarray/core/dtypes.data.json +TRACE: Interface for xarray.core.dtypes is unchanged +LOG: Cached module xarray.core.dtypes has same interface +LOG: Writing xarray.core.pycompat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/pycompat.py xarray/core/pycompat.meta.json xarray/core/pycompat.data.json +TRACE: Interface for xarray.core.pycompat is unchanged +LOG: Cached module xarray.core.pycompat has same interface +LOG: Writing xarray.core.options /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/options.py xarray/core/options.meta.json xarray/core/options.data.json +TRACE: Interface for xarray.core.options is unchanged +LOG: Cached 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is unchanged +LOG: Cached module xarray.plot.utils has same interface +LOG: Writing xarray.core.rolling_exp /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/rolling_exp.py xarray/core/rolling_exp.meta.json xarray/core/rolling_exp.data.json +TRACE: Interface for xarray.core.rolling_exp is unchanged +LOG: Cached module xarray.core.rolling_exp has same interface +LOG: Writing xarray.backends.file_manager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/file_manager.py xarray/backends/file_manager.meta.json xarray/backends/file_manager.data.json +TRACE: Interface for xarray.backends.file_manager is unchanged +LOG: Cached module xarray.backends.file_manager has same interface +LOG: Writing xarray.core.dask_array_ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dask_array_ops.py xarray/core/dask_array_ops.meta.json xarray/core/dask_array_ops.data.json +TRACE: Interface for xarray.core.dask_array_ops is unchanged +LOG: Cached module xarray.core.dask_array_ops has same interface +LOG: Writing xarray.core.duck_array_ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/duck_array_ops.py xarray/core/duck_array_ops.meta.json xarray/core/duck_array_ops.data.json +TRACE: Interface for xarray.core.duck_array_ops is unchanged +LOG: Cached module xarray.core.duck_array_ops has same interface +LOG: Writing xarray.core._reductions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/_reductions.py xarray/core/_reductions.meta.json xarray/core/_reductions.data.json +TRACE: Interface for xarray.core._reductions is unchanged +LOG: Cached module xarray.core._reductions has same interface +LOG: Writing xarray.core.nanops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/nanops.py xarray/core/nanops.meta.json xarray/core/nanops.data.json +TRACE: Interface for xarray.core.nanops is unchanged +LOG: Cached module xarray.core.nanops has same interface +LOG: Writing xarray.core.ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/ops.py xarray/core/ops.meta.json xarray/core/ops.data.json +TRACE: Interface for xarray.core.ops is unchanged +LOG: Cached module xarray.core.ops has same interface +LOG: Writing xarray.core.indexing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/indexing.py xarray/core/indexing.meta.json xarray/core/indexing.data.json +TRACE: Interface for xarray.core.indexing is unchanged +LOG: Cached module xarray.core.indexing has same interface +LOG: Writing xarray.core.formatting /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/formatting.py xarray/core/formatting.meta.json xarray/core/formatting.data.json +TRACE: Interface for xarray.core.formatting is unchanged +LOG: Cached module xarray.core.formatting has same interface +LOG: Writing xarray.plot.facetgrid /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/facetgrid.py xarray/plot/facetgrid.meta.json xarray/plot/facetgrid.data.json +TRACE: Interface for xarray.plot.facetgrid is unchanged +LOG: Cached module xarray.plot.facetgrid has same interface +LOG: Writing xarray.core.formatting_html /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/formatting_html.py xarray/core/formatting_html.meta.json xarray/core/formatting_html.data.json +TRACE: Interface for xarray.core.formatting_html is unchanged +LOG: Cached module xarray.core.formatting_html has same interface +LOG: Writing xarray.core.indexes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/indexes.py xarray/core/indexes.meta.json xarray/core/indexes.data.json +TRACE: Interface for xarray.core.indexes is unchanged +LOG: Cached module xarray.core.indexes has same interface +LOG: Writing xarray.core.common /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/common.py xarray/core/common.meta.json xarray/core/common.data.json +TRACE: Interface for xarray.core.common is unchanged +LOG: Cached module xarray.core.common has same interface +LOG: Writing xarray.core.accessor_dt /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/accessor_dt.py xarray/core/accessor_dt.meta.json xarray/core/accessor_dt.data.json +TRACE: Interface for xarray.core.accessor_dt is unchanged +LOG: Cached module xarray.core.accessor_dt has same interface +LOG: Writing xarray.core._typed_ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/_typed_ops.pyi xarray/core/_typed_ops.meta.json xarray/core/_typed_ops.data.json +TRACE: Interface for xarray.core._typed_ops is unchanged +LOG: Cached module xarray.core._typed_ops has same interface +LOG: Writing xarray.plot.dataset_plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/dataset_plot.py xarray/plot/dataset_plot.meta.json xarray/plot/dataset_plot.data.json +TRACE: Interface for xarray.plot.dataset_plot is unchanged +LOG: Cached module xarray.plot.dataset_plot has same interface +LOG: Writing xarray.core.arithmetic /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/arithmetic.py xarray/core/arithmetic.meta.json xarray/core/arithmetic.data.json +TRACE: Interface for xarray.core.arithmetic is unchanged +LOG: Cached module xarray.core.arithmetic has same interface +LOG: Writing xarray.core.accessor_str /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/accessor_str.py xarray/core/accessor_str.meta.json xarray/core/accessor_str.data.json +TRACE: Interface for xarray.core.accessor_str is unchanged +LOG: Cached module xarray.core.accessor_str has same interface +LOG: Writing xarray.plot.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/plot.py xarray/plot/plot.meta.json xarray/plot/plot.data.json +TRACE: Interface for xarray.plot.plot is unchanged +LOG: Cached module xarray.plot.plot has same interface +LOG: Writing xarray.core.missing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/missing.py xarray/core/missing.meta.json xarray/core/missing.data.json +TRACE: Interface for xarray.core.missing is unchanged +LOG: Cached module xarray.core.missing has same interface +LOG: Writing xarray.core.coordinates /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/coordinates.py xarray/core/coordinates.meta.json xarray/core/coordinates.data.json +TRACE: Interface for xarray.core.coordinates is unchanged +LOG: Cached module xarray.core.coordinates has same interface +LOG: Writing xarray.coding.variables /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/variables.py xarray/coding/variables.meta.json xarray/coding/variables.data.json +TRACE: Interface for xarray.coding.variables is unchanged +LOG: Cached module xarray.coding.variables has same interface +LOG: Writing xarray.coding.times /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/times.py xarray/coding/times.meta.json xarray/coding/times.data.json +TRACE: Interface for xarray.coding.times is unchanged +LOG: Cached module xarray.coding.times has same interface +LOG: Writing xarray.core.groupby /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/groupby.py xarray/core/groupby.meta.json xarray/core/groupby.data.json +TRACE: Interface for xarray.core.groupby is unchanged +LOG: Cached module xarray.core.groupby has same interface +LOG: Writing xarray.core.variable /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/variable.py xarray/core/variable.meta.json xarray/core/variable.data.json +TRACE: Interface for xarray.core.variable is unchanged +LOG: Cached module xarray.core.variable has same interface +LOG: Writing xarray.core.merge /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/merge.py xarray/core/merge.meta.json xarray/core/merge.data.json +TRACE: Interface for xarray.core.merge is unchanged +LOG: Cached module xarray.core.merge has same interface +LOG: Writing xarray.core.dataset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dataset.py xarray/core/dataset.meta.json xarray/core/dataset.data.json +TRACE: Interface for xarray.core.dataset is unchanged +LOG: Cached module xarray.core.dataset has same interface +LOG: Writing xarray.core.dataarray /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dataarray.py xarray/core/dataarray.meta.json xarray/core/dataarray.data.json +TRACE: Interface for xarray.core.dataarray is unchanged +LOG: Cached module xarray.core.dataarray has same interface +LOG: Writing xarray.core.concat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/concat.py xarray/core/concat.meta.json xarray/core/concat.data.json +TRACE: Interface for xarray.core.concat is unchanged +LOG: Cached module xarray.core.concat has same interface +LOG: Writing xarray.core.computation /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/computation.py xarray/core/computation.meta.json xarray/core/computation.data.json +TRACE: Interface for xarray.core.computation is unchanged +LOG: Cached module xarray.core.computation has same interface +LOG: Writing xarray.core.alignment /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/alignment.py xarray/core/alignment.meta.json xarray/core/alignment.data.json +TRACE: Interface for xarray.core.alignment is unchanged +LOG: Cached module xarray.core.alignment has same interface +LOG: Writing xarray.coding.cftimeindex /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/cftimeindex.py xarray/coding/cftimeindex.meta.json xarray/coding/cftimeindex.data.json +TRACE: Interface for xarray.coding.cftimeindex is unchanged +LOG: Cached module xarray.coding.cftimeindex has same interface +LOG: Writing xarray.backends.netcdf3 /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/netcdf3.py xarray/backends/netcdf3.meta.json xarray/backends/netcdf3.data.json +TRACE: Interface for xarray.backends.netcdf3 is unchanged +LOG: Cached module xarray.backends.netcdf3 has same interface +LOG: Writing xarray.core.rolling /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/rolling.py xarray/core/rolling.meta.json xarray/core/rolling.data.json +TRACE: Interface for xarray.core.rolling is unchanged +LOG: Cached module xarray.core.rolling has same interface +LOG: Writing xarray.core.resample /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/resample.py xarray/core/resample.meta.json xarray/core/resample.data.json +TRACE: Interface for xarray.core.resample is unchanged +LOG: Cached module xarray.core.resample has same interface +LOG: Writing xarray.core.weighted /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/weighted.py xarray/core/weighted.meta.json xarray/core/weighted.data.json +TRACE: Interface for xarray.core.weighted is unchanged +LOG: Cached module xarray.core.weighted has same interface +LOG: Writing xarray.coding.strings /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/strings.py xarray/coding/strings.meta.json xarray/coding/strings.data.json +TRACE: Interface for xarray.coding.strings is unchanged +LOG: Cached module xarray.coding.strings has same interface +LOG: Writing xarray.core.parallel /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/parallel.py xarray/core/parallel.meta.json xarray/core/parallel.data.json +TRACE: Interface for xarray.core.parallel is unchanged +LOG: Cached module xarray.core.parallel has same interface +LOG: Writing xarray.core.extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/extensions.py xarray/core/extensions.meta.json xarray/core/extensions.data.json +TRACE: Interface for xarray.core.extensions is unchanged +LOG: Cached module xarray.core.extensions has same interface +LOG: Writing xarray.core.combine /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/combine.py xarray/core/combine.meta.json xarray/core/combine.data.json +TRACE: Interface for xarray.core.combine is unchanged +LOG: Cached module xarray.core.combine has same interface +LOG: Writing xarray.conventions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/conventions.py xarray/conventions.meta.json xarray/conventions.data.json +TRACE: Interface for xarray.conventions is unchanged +LOG: Cached module xarray.conventions has same interface +LOG: Writing xarray.coding.cftime_offsets /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/cftime_offsets.py xarray/coding/cftime_offsets.meta.json xarray/coding/cftime_offsets.data.json +TRACE: Interface for xarray.coding.cftime_offsets is unchanged +LOG: Cached module xarray.coding.cftime_offsets has same interface +LOG: Writing xarray.ufuncs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/ufuncs.py xarray/ufuncs.meta.json xarray/ufuncs.data.json +TRACE: Interface for xarray.ufuncs is unchanged +LOG: Cached module xarray.ufuncs has same interface +LOG: Writing xarray.testing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/testing.py xarray/testing.meta.json xarray/testing.data.json +TRACE: Interface for xarray.testing is unchanged +LOG: Cached module xarray.testing has same interface +LOG: Writing xarray.backends.common /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/common.py xarray/backends/common.meta.json xarray/backends/common.data.json +TRACE: Interface for xarray.backends.common is unchanged +LOG: Cached module xarray.backends.common has same interface +LOG: Writing xarray.coding.frequencies /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/frequencies.py xarray/coding/frequencies.meta.json xarray/coding/frequencies.data.json +TRACE: Interface for xarray.coding.frequencies is unchanged +LOG: Cached module xarray.coding.frequencies has same interface +LOG: Writing xarray.backends.memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/memory.py xarray/backends/memory.meta.json xarray/backends/memory.data.json +TRACE: Interface for xarray.backends.memory is unchanged +LOG: Cached module xarray.backends.memory has same interface +LOG: Writing xarray.backends.store /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/store.py xarray/backends/store.meta.json xarray/backends/store.data.json +TRACE: Interface for xarray.backends.store is unchanged +LOG: Cached module xarray.backends.store has same interface +LOG: Writing xarray.backends.plugins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/plugins.py xarray/backends/plugins.meta.json xarray/backends/plugins.data.json +TRACE: Interface for xarray.backends.plugins is unchanged +LOG: Cached module xarray.backends.plugins has same interface +LOG: Writing xarray.backends.rasterio_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/rasterio_.py xarray/backends/rasterio_.meta.json xarray/backends/rasterio_.data.json +TRACE: Interface for xarray.backends.rasterio_ is unchanged +LOG: Cached module xarray.backends.rasterio_ has same interface +LOG: Writing xarray.backends.api /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/api.py xarray/backends/api.meta.json xarray/backends/api.data.json +TRACE: Interface for xarray.backends.api is unchanged +LOG: Cached module xarray.backends.api has same interface +LOG: Writing xarray.backends.scipy_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/scipy_.py xarray/backends/scipy_.meta.json xarray/backends/scipy_.data.json +TRACE: Interface for xarray.backends.scipy_ is unchanged +LOG: Cached module xarray.backends.scipy_ has same interface +LOG: Writing xarray.backends.pynio_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/pynio_.py xarray/backends/pynio_.meta.json xarray/backends/pynio_.data.json +TRACE: Interface for xarray.backends.pynio_ is unchanged +LOG: Cached module xarray.backends.pynio_ has same interface +LOG: Writing xarray.backends.pydap_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/pydap_.py xarray/backends/pydap_.meta.json xarray/backends/pydap_.data.json +TRACE: Interface for xarray.backends.pydap_ is unchanged +LOG: Cached module xarray.backends.pydap_ has same interface +LOG: Writing xarray.backends.pseudonetcdf_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/pseudonetcdf_.py xarray/backends/pseudonetcdf_.meta.json xarray/backends/pseudonetcdf_.data.json +TRACE: Interface for xarray.backends.pseudonetcdf_ is unchanged +LOG: Cached module xarray.backends.pseudonetcdf_ has same interface +LOG: Writing xarray.backends.netCDF4_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/netCDF4_.py xarray/backends/netCDF4_.meta.json xarray/backends/netCDF4_.data.json +TRACE: Interface for xarray.backends.netCDF4_ is unchanged +LOG: Cached module xarray.backends.netCDF4_ has same interface +LOG: Writing xarray.backends.cfgrib_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/cfgrib_.py xarray/backends/cfgrib_.meta.json xarray/backends/cfgrib_.data.json +TRACE: Interface for xarray.backends.cfgrib_ is unchanged +LOG: Cached module xarray.backends.cfgrib_ has same interface +LOG: Writing xarray.backends.zarr /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/zarr.py xarray/backends/zarr.meta.json xarray/backends/zarr.data.json +TRACE: Interface for xarray.backends.zarr is unchanged +LOG: Cached module xarray.backends.zarr has same interface +LOG: Writing xarray.tutorial /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/tutorial.py xarray/tutorial.meta.json xarray/tutorial.data.json +TRACE: Interface for xarray.tutorial is unchanged +LOG: Cached module xarray.tutorial has same interface +LOG: Writing xarray.backends.h5netcdf_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/h5netcdf_.py xarray/backends/h5netcdf_.meta.json xarray/backends/h5netcdf_.data.json +TRACE: Interface for xarray.backends.h5netcdf_ is unchanged +LOG: Cached module xarray.backends.h5netcdf_ has same interface +LOG: Writing xarray /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/__init__.py xarray/__init__.meta.json xarray/__init__.data.json +TRACE: Interface for xarray is unchanged +LOG: Cached module xarray has same interface +LOG: Writing xarray.backends /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/__init__.py xarray/backends/__init__.meta.json xarray/backends/__init__.data.json +TRACE: Interface for xarray.backends is unchanged +LOG: Cached module xarray.backends has same interface +LOG: Writing xarray.convert /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/convert.py xarray/convert.meta.json xarray/convert.data.json +TRACE: Interface for xarray.convert is unchanged +LOG: Cached module xarray.convert has same interface +LOG: Writing xarray.core.resample_cftime /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/resample_cftime.py xarray/core/resample_cftime.meta.json xarray/core/resample_cftime.data.json +TRACE: Interface for xarray.core.resample_cftime is unchanged +LOG: Cached module xarray.core.resample_cftime has same interface +LOG: Writing xarray.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py xarray/plot/__init__.meta.json xarray/plot/__init__.data.json +TRACE: Interface for xarray.plot is unchanged +LOG: Cached module xarray.plot has same interface +TRACE: Priorities for rdata.parser: +LOG: Processing SCC singleton (rdata.parser) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/rdata/rdata/parser/__init__.py (rdata.parser) +LOG: Writing rdata.parser /home/carlos/git/rdata/rdata/parser/__init__.py rdata/parser/__init__.meta.json rdata/parser/__init__.data.json +TRACE: Interface for rdata.parser is unchanged +LOG: Cached module rdata.parser has same interface +TRACE: Priorities for skfda._utils._sklearn_adapter: +LOG: Processing SCC singleton (skfda._utils._sklearn_adapter) as inherently stale with stale deps (__future__ abc builtins skfda.typing._numpy typing) +LOG: Writing skfda._utils._sklearn_adapter /home/carlos/git/scikit-fda/skfda/_utils/_sklearn_adapter.py skfda/_utils/_sklearn_adapter.meta.json skfda/_utils/_sklearn_adapter.data.json +TRACE: Interface for skfda._utils._sklearn_adapter has changed +LOG: Cached module skfda._utils._sklearn_adapter has changed interface +TRACE: Priorities for skfda.typing._base: +LOG: Processing SCC singleton (skfda.typing._base) as inherently stale with stale deps (builtins numpy skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.typing._base /home/carlos/git/scikit-fda/skfda/typing/_base.py skfda/typing/_base.meta.json skfda/typing/_base.data.json +TRACE: Interface for skfda.typing._base has changed +LOG: Cached module skfda.typing._base has changed interface +TRACE: Priorities for skfda.misc.lstsq: +LOG: Processing SCC singleton (skfda.misc.lstsq) as inherently stale with stale deps (__future__ builtins numpy skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.misc.lstsq /home/carlos/git/scikit-fda/skfda/misc/lstsq.py skfda/misc/lstsq.meta.json skfda/misc/lstsq.data.json +TRACE: Interface for skfda.misc.lstsq has changed +LOG: Cached module skfda.misc.lstsq has changed interface +TRACE: Priorities for skfda.misc.kernels: +LOG: Processing SCC singleton (skfda.misc.kernels) as inherently stale with stale deps (builtins math numpy skfda.typing._numpy) +LOG: Writing skfda.misc.kernels /home/carlos/git/scikit-fda/skfda/misc/kernels.py skfda/misc/kernels.meta.json skfda/misc/kernels.data.json +TRACE: Interface for skfda.misc.kernels has changed +LOG: Cached module skfda.misc.kernels has changed interface +TRACE: Priorities for skfda.exploratory.depth.multivariate: +LOG: Processing SCC singleton (skfda.exploratory.depth.multivariate) as inherently stale with stale deps (__future__ abc builtins math numpy skfda._utils._sklearn_adapter skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.exploratory.depth.multivariate /home/carlos/git/scikit-fda/skfda/exploratory/depth/multivariate.py skfda/exploratory/depth/multivariate.meta.json skfda/exploratory/depth/multivariate.data.json +TRACE: Interface for skfda.exploratory.depth.multivariate has changed +LOG: Cached module skfda.exploratory.depth.multivariate has changed interface +TRACE: Priorities for rdata.conversion._conversion: rdata:20 +TRACE: Priorities for rdata.conversion: rdata.conversion._conversion:5 +TRACE: Priorities for rdata: rdata.conversion:10 +LOG: Processing SCC of size 3 (rdata.conversion._conversion rdata.conversion rdata) as stale due to deps (__future__ _typeshed _warnings abc array builtins collections ctypes dataclasses enum errno io mmap numbers numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray os pathlib pickle posixpath types typing typing_extensions warnings) +LOG: Parsing /home/carlos/git/rdata/rdata/conversion/_conversion.py (rdata.conversion._conversion) +LOG: Parsing /home/carlos/git/rdata/rdata/conversion/__init__.py (rdata.conversion) +LOG: Parsing /home/carlos/git/rdata/rdata/__init__.py (rdata) +LOG: Writing rdata.conversion._conversion /home/carlos/git/rdata/rdata/conversion/_conversion.py rdata/conversion/_conversion.meta.json rdata/conversion/_conversion.data.json +TRACE: Interface for rdata.conversion._conversion is unchanged +LOG: Cached module rdata.conversion._conversion has same interface +LOG: Writing rdata.conversion /home/carlos/git/rdata/rdata/conversion/__init__.py rdata/conversion/__init__.meta.json rdata/conversion/__init__.data.json +TRACE: Interface for rdata.conversion is unchanged +LOG: Cached module rdata.conversion has same interface +LOG: Writing rdata /home/carlos/git/rdata/rdata/__init__.py rdata/__init__.meta.json rdata/__init__.data.json +TRACE: Interface for rdata is unchanged +LOG: Cached module rdata has same interface +TRACE: Priorities for skfda.misc.metrics._typing: +LOG: Processing SCC singleton (skfda.misc.metrics._typing) as inherently stale with stale deps (abc builtins enum skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.misc.metrics._typing /home/carlos/git/scikit-fda/skfda/misc/metrics/_typing.py skfda/misc/metrics/_typing.meta.json skfda/misc/metrics/_typing.data.json +TRACE: Interface for skfda.misc.metrics._typing has changed +LOG: Cached module skfda.misc.metrics._typing has changed interface +TRACE: Priorities for skfda.preprocessing.dim_reduction: skfda.preprocessing.dim_reduction._fpca:25 +TRACE: Priorities for skfda.representation.basis: skfda.representation.basis._basis:25 skfda.representation.basis._bspline:25 skfda.representation.basis._constant:25 skfda.representation.basis._fdatabasis:25 skfda.representation.basis._finite_element:25 skfda.representation.basis._fourier:25 skfda.representation.basis._monomial:25 skfda.representation.basis._tensor_basis:25 skfda.representation.basis._vector_basis:25 +TRACE: Priorities for skfda.representation: skfda.representation._functional_data:25 skfda.representation.basis:25 skfda.representation.grid:25 +TRACE: Priorities for skfda._utils: skfda._utils._utils:25 skfda._utils._warping:25 +TRACE: Priorities for skfda: skfda.representation:25 +TRACE: Priorities for skfda.misc: skfda.misc._math:25 +TRACE: Priorities for skfda.preprocessing.smoothing._linear: skfda._utils:5 skfda.representation:5 skfda.preprocessing.smoothing.validation:20 +TRACE: Priorities for skfda.preprocessing.smoothing.validation: skfda.representation:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda._utils._utils: skfda.representation:25 skfda.representation.basis:25 skfda.representation.extrapolation:25 skfda:20 +TRACE: Priorities for skfda.preprocessing.registration: skfda._utils:5 skfda.preprocessing.registration._fisher_rao:25 skfda.preprocessing.registration._landmark_registration:25 skfda.preprocessing.registration._lstsq_shift_registration:25 +TRACE: Priorities for skfda.misc.validation: skfda.representation:5 +TRACE: Priorities for skfda.misc.operators._operators: skfda.representation:5 skfda.representation.basis:5 skfda.misc:20 +TRACE: Priorities for skfda.misc.metrics._utils: skfda._utils:5 skfda.representation:5 +TRACE: Priorities for skfda.misc.metrics._lp_norms: skfda.representation:5 skfda.misc:20 +TRACE: Priorities for skfda.exploratory.stats._functional_transformers: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 +TRACE: Priorities for skfda.representation.evaluator: skfda.representation._functional_data:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.basis._basis: skfda.representation.basis._fdatabasis:25 skfda.misc.validation:20 skfda.representation.basis:20 skfda.misc:20 skfda._utils:20 +TRACE: Priorities for skfda._utils._warping: skfda.representation:25 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Priorities for skfda.exploratory.depth: skfda.exploratory.depth._depth:5 +TRACE: Priorities for skfda.preprocessing.smoothing._kernel_smoothers: skfda._utils._utils:5 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.smoothing._basis: skfda._utils:5 skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.smoothing: skfda.preprocessing.smoothing.kernel_smoothers:20 skfda.preprocessing.smoothing._basis:25 skfda.preprocessing.smoothing._kernel_smoothers:25 +TRACE: Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 +TRACE: Priorities for skfda.exploratory.stats._stats: skfda.misc.metrics:5 skfda.representation:5 skfda.exploratory.depth:5 +TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 +TRACE: Priorities for skfda.exploratory.stats: skfda.exploratory.stats._fisher_rao:5 skfda.exploratory.stats._functional_transformers:5 skfda.exploratory.stats._stats:5 +TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 +TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 +TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 +LOG: Processing SCC of size 62 (skfda.preprocessing.dim_reduction skfda.representation.basis skfda.representation skfda._utils skfda skfda.misc skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda._utils._utils skfda.preprocessing.registration skfda.misc.validation skfda.misc.operators._operators skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda.exploratory.stats._functional_transformers skfda.representation.evaluator skfda.representation.basis._basis skfda._utils._warping 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skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.exploratory.stats._fisher_rao skfda.misc.regularization skfda.misc.hat_matrix skfda.exploratory.depth skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing._basis skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.preprocessing.registration._lstsq_shift_registration skfda.exploratory.stats._stats skfda.representation.grid skfda.exploratory.stats skfda.preprocessing.dim_reduction._fpca skfda.representation.basis._fdatabasis skfda.preprocessing.registration._fisher_rao) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._typing skfda.typing._base skfda.typing._numpy typing typing_extensions 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skfda/misc/__init__.data.json +LOG: Deleting skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json skfda/preprocessing/smoothing/_linear.data.json +LOG: Deleting skfda.preprocessing.smoothing.validation /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json +LOG: Deleting skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json +LOG: Deleting skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json +LOG: Deleting skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json +LOG: Deleting skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json +LOG: Deleting skfda.misc.metrics._utils /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py skfda/misc/metrics/_utils.meta.json skfda/misc/metrics/_utils.data.json +LOG: Deleting skfda.misc.metrics._lp_norms /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py skfda/misc/metrics/_lp_norms.meta.json skfda/misc/metrics/_lp_norms.data.json +LOG: Deleting skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json +LOG: Deleting skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json +LOG: Deleting skfda.representation.basis._basis /home/carlos/git/scikit-fda/skfda/representation/basis/_basis.py skfda/representation/basis/_basis.meta.json skfda/representation/basis/_basis.data.json +LOG: Deleting skfda._utils._warping /home/carlos/git/scikit-fda/skfda/_utils/_warping.py skfda/_utils/_warping.meta.json skfda/_utils/_warping.data.json +LOG: Deleting skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json +LOG: Deleting skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json +LOG: Deleting skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json +LOG: Deleting skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json +LOG: Deleting skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json +LOG: Deleting skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json +LOG: Deleting skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json +LOG: Deleting skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json +LOG: Deleting skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json +LOG: Deleting skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json +LOG: Deleting skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json +LOG: Deleting skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json +LOG: 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skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json +LOG: Deleting skfda.misc.operators /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py skfda/misc/operators/__init__.meta.json skfda/misc/operators/__init__.data.json +LOG: Deleting skfda.misc.regularization._regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py skfda/misc/regularization/_regularization.meta.json skfda/misc/regularization/_regularization.data.json +LOG: Deleting skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json +LOG: Deleting skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json +LOG: Deleting skfda.misc.metrics /home/carlos/git/scikit-fda/skfda/misc/metrics/__init__.py 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skfda/preprocessing/registration/_fisher_rao.data.json +TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) +LOG: Deleting skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json +TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as 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/home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json +TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) +LOG: Deleting skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json +TRACE: Priorities for skfda.ml.regression._coefficients: +LOG: Processing SCC singleton 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skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json +TRACE: Priorities for skfda.ml.regression._kernel_regression: +LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as inherently stale with stale deps (__future__ builtins numpy skfda.misc.hat_matrix skfda.misc.metrics skfda.misc.metrics._typing skfda.representation._functional_data typing) +LOG: Deleting skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json +TRACE: Priorities for skfda.ml.regression._historical_linear_model: +LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as inherently stale with stale deps (__future__ builtins math numpy skfda._utils skfda.representation skfda.representation.basis typing) +LOG: Deleting skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json +TRACE: Priorities for skfda.ml.clustering._kmeans: +LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as inherently stale with stale deps (__future__ abc builtins numpy skfda._utils skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._numpy typing warnings) +LOG: Deleting skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json +TRACE: Priorities for skfda.ml.clustering._hierarchical: +LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as inherently stale with stale deps (__future__ builtins enum numpy skfda.misc.metrics skfda.misc.metrics._typing skfda.representation typing typing_extensions) +LOG: 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skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing typing_extensions) +LOG: Deleting skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json skfda/ml/classification/_logistic_regression.data.json +TRACE: Priorities for skfda.ml.classification._centroid_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._centroid_classifiers) as inherently stale with stale deps (__future__ builtins skfda._utils skfda.exploratory.depth skfda.exploratory.stats skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._numpy typing) +LOG: Deleting skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json +TRACE: Priorities for 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skfda/exploratory/outliers/_outliergram.data.json +TRACE: Priorities for skfda.exploratory.outliers._envelopes: +LOG: Processing SCC singleton (skfda.exploratory.outliers._envelopes) as inherently stale with stale deps (__future__ builtins math numpy skfda.representation skfda.typing._numpy typing) +LOG: Deleting skfda.exploratory.outliers._envelopes /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py skfda/exploratory/outliers/_envelopes.meta.json skfda/exploratory/outliers/_envelopes.data.json +TRACE: Priorities for skfda.exploratory.visualization.fpca: +LOG: Processing SCC singleton (skfda.exploratory.visualization.fpca) as inherently stale with stale deps (__future__ builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.representation typing warnings) +LOG: Deleting skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json +TRACE: Priorities for skfda.exploratory.visualization._parametric_plot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._parametric_plot) as inherently stale with stale deps (builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.exploratory.visualization.representation skfda.representation typing) +LOG: Deleting skfda.exploratory.visualization._parametric_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py skfda/exploratory/visualization/_parametric_plot.meta.json skfda/exploratory/visualization/_parametric_plot.data.json +TRACE: Priorities for skfda.exploratory.visualization._multiple_display: +LOG: Processing SCC singleton (skfda.exploratory.visualization._multiple_display) as inherently stale with stale deps (builtins copy functools itertools numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils typing) +LOG: Deleting skfda.exploratory.visualization._multiple_display /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_multiple_display.py skfda/exploratory/visualization/_multiple_display.meta.json skfda/exploratory/visualization/_multiple_display.data.json +TRACE: Priorities for skfda.exploratory.visualization._ddplot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._ddplot) as inherently stale with stale deps (builtins numpy skfda.exploratory.depth.multivariate skfda.exploratory.visualization._baseplot skfda.representation._functional_data skfda.typing._numpy typing) +LOG: Deleting skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json +TRACE: Priorities for skfda.exploratory.visualization.clustering: +LOG: Processing SCC singleton (skfda.exploratory.visualization.clustering) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.misc.validation skfda.representation skfda.typing._numpy typing typing_extensions) +LOG: Deleting skfda.exploratory.visualization.clustering /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py skfda/exploratory/visualization/clustering.meta.json skfda/exploratory/visualization/clustering.data.json +TRACE: Priorities for skfda.datasets._real_datasets: +LOG: Processing SCC singleton (skfda.datasets._real_datasets) as inherently stale with stale deps (builtins numpy skfda.representation typing typing_extensions warnings) +LOG: Deleting skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json +TRACE: Priorities for skfda.preprocessing.feature_construction: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction) as 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skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json +TRACE: Priorities for skfda.ml.regression._linear_regression: +LOG: Processing SCC singleton (skfda.ml.regression._linear_regression) as inherently stale with stale deps (__future__ builtins itertools numpy skfda.misc.lstsq skfda.misc.regularization skfda.ml.regression._coefficients skfda.representation skfda.representation.basis typing warnings) +LOG: Deleting skfda.ml.regression._linear_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py skfda/ml/regression/_linear_regression.meta.json skfda/ml/regression/_linear_regression.data.json +TRACE: Priorities for skfda.ml.clustering._neighbors_clustering: +LOG: Processing SCC singleton (skfda.ml.clustering._neighbors_clustering) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.misc.metrics._typing skfda.ml._neighbors_base skfda.representation skfda.typing._numpy typing typing_extensions) +LOG: Deleting skfda.ml.clustering._neighbors_clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py skfda/ml/clustering/_neighbors_clustering.meta.json skfda/ml/clustering/_neighbors_clustering.data.json +TRACE: Priorities for skfda.ml.classification._neighbors_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.misc.metrics._typing skfda.ml._neighbors_base skfda.representation skfda.typing._numpy typing typing_extensions) +LOG: Deleting skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json +TRACE: Priorities for 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skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json +TRACE: Priorities for skfda.misc.covariances: skfda.datasets:20 +TRACE: Priorities for skfda.datasets._samples_generators: skfda.misc.covariances:10 +TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:5 +LOG: Processing SCC of size 3 (skfda.misc.covariances skfda.datasets._samples_generators skfda.datasets) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) +LOG: Deleting skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json +LOG: 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skfda.exploratory.outliers._boxplot: skfda.exploratory.outliers:20 +TRACE: Priorities for skfda.exploratory.outliers: skfda.exploratory.outliers._boxplot:5 skfda.exploratory.outliers._directional_outlyingness:5 +LOG: Processing SCC of size 3 (skfda.exploratory.outliers._directional_outlyingness skfda.exploratory.outliers._boxplot skfda.exploratory.outliers) as inherently stale with stale deps (__future__ builtins dataclasses numpy numpy.linalg skfda._utils skfda._utils._sklearn_adapter skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers._directional_outlyingness_experiment_results skfda.exploratory.outliers._envelopes skfda.exploratory.outliers._outliergram skfda.exploratory.outliers.neighbors_outlier skfda.representation skfda.typing._numpy typing) +LOG: Deleting skfda.exploratory.outliers._directional_outlyingness /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py skfda/exploratory/outliers/_directional_outlyingness.meta.json skfda/exploratory/outliers/_directional_outlyingness.data.json +LOG: Deleting skfda.exploratory.outliers._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_boxplot.py skfda/exploratory/outliers/_boxplot.meta.json skfda/exploratory/outliers/_boxplot.data.json +LOG: Deleting skfda.exploratory.outliers /home/carlos/git/scikit-fda/skfda/exploratory/outliers/__init__.py skfda/exploratory/outliers/__init__.meta.json skfda/exploratory/outliers/__init__.data.json +TRACE: Priorities for skfda.ml: +LOG: Processing SCC singleton (skfda.ml) as inherently stale with stale deps (builtins skfda.ml.classification skfda.ml.clustering skfda.ml.regression) +LOG: Deleting skfda.ml /home/carlos/git/scikit-fda/skfda/ml/__init__.py skfda/ml/__init__.meta.json skfda/ml/__init__.data.json +TRACE: Priorities for skfda.exploratory.visualization._outliergram: +LOG: Processing SCC singleton (skfda.exploratory.visualization._outliergram) as inherently stale with stale deps (builtins numpy skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation typing) +LOG: Deleting skfda.exploratory.visualization._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_outliergram.py skfda/exploratory/visualization/_outliergram.meta.json skfda/exploratory/visualization/_outliergram.data.json +TRACE: Priorities for skfda.exploratory.visualization._magnitude_shape_plot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._magnitude_shape_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.depth skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation skfda.typing._numpy typing) +LOG: Deleting skfda.exploratory.visualization._magnitude_shape_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_magnitude_shape_plot.py skfda/exploratory/visualization/_magnitude_shape_plot.meta.json skfda/exploratory/visualization/_magnitude_shape_plot.data.json +TRACE: Priorities for skfda.exploratory.visualization._boxplot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._boxplot) as inherently stale with stale deps (__future__ abc builtins math numpy skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._envelopes skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.representation skfda.typing._numpy typing) +LOG: Deleting skfda.exploratory.visualization._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_boxplot.py skfda/exploratory/visualization/_boxplot.meta.json skfda/exploratory/visualization/_boxplot.data.json +TRACE: Priorities for skfda.exploratory.visualization: +LOG: Processing SCC singleton (skfda.exploratory.visualization) as inherently stale with stale deps (builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.exploratory.visualization._ddplot skfda.exploratory.visualization._magnitude_shape_plot skfda.exploratory.visualization._multiple_display skfda.exploratory.visualization._outliergram skfda.exploratory.visualization._parametric_plot skfda.exploratory.visualization.clustering skfda.exploratory.visualization.fpca skfda.exploratory.visualization.representation) +LOG: Deleting skfda.exploratory.visualization /home/carlos/git/scikit-fda/skfda/exploratory/visualization/__init__.py skfda/exploratory/visualization/__init__.meta.json skfda/exploratory/visualization/__init__.data.json +LOG: No fresh SCCs left in queue +LOG: Build finished in 45.057 seconds with 422 modules, and 169 errors diff --git a/setup.cfg b/setup.cfg index 93a19a1fe..fc4e01a7b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -89,6 +89,8 @@ per-file-ignores = __init__.py: # Unused modules are allowed in `__init__.py`, to reduce imports F401, + # Explicit re-exports allowed in __init__ + WPS113, # Import multiple names is allowed in `__init__.py` WPS235, # Logic is allowec in `__init__.py` @@ -157,7 +159,6 @@ skip_glob = **/plot_*.py plot_*.py [mypy] strict = True strict_equality = True -implicit_reexport = True enable_error_code = ignore-without-code [mypy-dcor.*] diff --git a/skfda/__init__.py b/skfda/__init__.py index 0a165fb20..99d72a19b 100644 --- a/skfda/__init__.py +++ b/skfda/__init__.py @@ -1,6 +1,7 @@ """scikit-fda package.""" import errno as _errno import os as _os +from typing import TYPE_CHECKING import lazy_loader as lazy @@ -21,6 +22,13 @@ }, ) +if TYPE_CHECKING: + from .representation import ( + FData as FData, + FDataBasis as FDataBasis, + FDataGrid as FDataGrid, + concatenate as concatenate, + ) try: with open( diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 2c15207fb..45896f096 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -35,20 +35,20 @@ if TYPE_CHECKING: from ._utils import ( - RandomStateLike, - _cartesian_product, - _check_array_key, - _check_estimator, - _classifier_get_classes, - _compute_dependence, - _DependenceMeasure, - _evaluate_grid, - _int_to_real, - _pairwise_symmetric, - _same_domain, - _to_grid, - _to_grid_points, - nquad_vec, + RandomStateLike as RandomStateLike, + _cartesian_product as _cartesian_product, + _check_array_key as _check_array_key, + _check_estimator as _check_estimator, + _classifier_get_classes as _classifier_get_classes, + _compute_dependence as _compute_dependence, + _DependenceMeasure as _DependenceMeasure, + _evaluate_grid as _evaluate_grid, + _int_to_real as _int_to_real, + _pairwise_symmetric as _pairwise_symmetric, + _same_domain as _same_domain, + _to_grid as _to_grid, + _to_grid_points as _to_grid_points, + nquad_vec as nquad_vec, ) from ._warping import invert_warping, normalize_scale, normalize_warping diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 0f5cb6939..743465bed 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -12,6 +12,7 @@ List, Optional, Sequence, + Sized, Tuple, TypeVar, Union, @@ -24,8 +25,10 @@ from pandas.api.indexers import check_array_indexer from sklearn.preprocessing import LabelEncoder from sklearn.utils.multiclass import check_classification_targets -from typing_extensions import Literal, Protocol +from typing_extensions import Literal, ParamSpec, Protocol +from ..typing._base import GridPoints, GridPointsLike +from ..typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt, NDArrayStr from ._sklearn_adapter import BaseEstimator RandomStateLike = Optional[Union[int, np.random.RandomState]] @@ -36,16 +39,7 @@ from ..representation import FData, FDataGrid from ..representation.basis import Basis from ..representation.extrapolation import ExtrapolationLike - from ..typing._numpy import ( - NDArrayAny, - NDArrayFloat, - NDArrayInt, - NDArrayStr, - ) - from ..typing._base import ( - GridPoints, - GridPointsLike, - ) + T = TypeVar("T", bound=FData) Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) @@ -53,6 +47,57 @@ Target = TypeVar("Target", bound=NDArrayInt) +_MapAcceptableSelf = TypeVar( + "_MapAcceptableSelf", + bound="_MapAcceptable", +) + + +class _MapAcceptable(Protocol, Sized): + + def __getitem__( + self: _MapAcceptableSelf, + __key: Union[slice, NDArrayInt], # noqa: WPS112 + ) -> _MapAcceptableSelf: + pass + + @property + def nbytes(self) -> int: + pass + + +_MapAcceptableT = TypeVar( + "_MapAcceptableT", + bound=_MapAcceptable, + contravariant=True, +) +MapFunctionT = TypeVar("MapFunctionT", covariant=True) +P = ParamSpec("P") + + +class _MapFunction(Protocol[_MapAcceptableT, P, MapFunctionT]): + """Protocol for functions that can be mapped over several arrays.""" + + def __call__( + self, + *args: _MapAcceptableT, + **kwargs: P.kwargs, + ) -> MapFunctionT: + pass + + +class _PairwiseFunction(Protocol[_MapAcceptableT, P, MapFunctionT]): + """Protocol for pairwise array functions.""" + + def __call__( + self, + __arg1: _MapAcceptableT, # noqa: WPS112 + __arg2: _MapAcceptableT, # noqa: WPS112 + **kwargs: P.kwargs, # type: ignore[name-defined] + ) -> MapFunctionT: + pass + + def _to_grid( X: FData, y: FData, @@ -333,7 +378,6 @@ def _evaluate_grid( # noqa: WPS234 dimension. """ - from ..typing._base import GridPointsLike # Compute intersection points and resulting shapes if aligned: @@ -409,11 +453,11 @@ def integrate(*args: Any, depth: int) -> NDArrayFloat: # noqa: WPS430 def _map_in_batches( - function: Callable[..., np.typing.NDArray[ArrayDTypeT]], - arguments: Tuple[Union[FData, NDArrayAny], ...], + function: _MapFunction[_MapAcceptableT, P, np.typing.NDArray[ArrayDTypeT]], + arguments: Tuple[_MapAcceptableT, ...], indexes: Tuple[NDArrayInt, ...], memory_per_batch: Optional[int] = None, - **kwargs: Any, + **kwargs: P.kwargs, # type: ignore[name-defined] ) -> np.typing.NDArray[ArrayDTypeT]: """ Map a function over samples of FData or ndarray tuples efficiently. @@ -449,19 +493,30 @@ def _map_in_batches( def _pairwise_symmetric( - function: Callable[..., np.typing.NDArray[ArrayDTypeT]], - arg1: Union[FData, NDArrayAny], - arg2: Optional[Union[FData, NDArrayAny]] = None, + function: _PairwiseFunction[ + _MapAcceptableT, + P, + np.typing.NDArray[ArrayDTypeT], + ], + arg1: _MapAcceptableT, + arg2: Optional[_MapAcceptableT] = None, memory_per_batch: Optional[int] = None, - **kwargs: Any, + **kwargs: P.kwargs, # type: ignore[name-defined] ) -> np.typing.NDArray[ArrayDTypeT]: """Compute pairwise a commutative function.""" + def map_function( + *args: _MapAcceptableT, + **kwargs: P.kwargs, # type: ignore[name-defined] + ) -> np.typing.NDArray[ArrayDTypeT]: + """Just to keep Mypy happy.""" + return function(args[0], args[1], **kwargs) + dim1 = len(arg1) if arg2 is None or arg2 is arg1: triu_indices = np.triu_indices(dim1) triang_vec = _map_in_batches( - function, + map_function, (arg1, arg1), triu_indices, memory_per_batch=memory_per_batch, @@ -482,7 +537,7 @@ def _pairwise_symmetric( indices = np.indices((dim1, dim2)) vec = _map_in_batches( - function, + map_function, (arg1, arg2), (indices[0].ravel(), indices[1].ravel()), memory_per_batch=memory_per_batch, diff --git a/skfda/exploratory/depth/_depth.py b/skfda/exploratory/depth/_depth.py index 7b63a5245..04702b1c6 100644 --- a/skfda/exploratory/depth/_depth.py +++ b/skfda/exploratory/depth/_depth.py @@ -14,9 +14,10 @@ import scipy.integrate from ..._utils._sklearn_adapter import BaseEstimator -from ...misc.metrics import Metric, l2_distance +from ...misc.metrics import l2_distance from ...misc.metrics._utils import _fit_metric from ...representation import FData, FDataGrid +from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat from .multivariate import Depth, SimplicialDepth, _UnivariateFraimanMuniz diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index 04c0bb5bf..93b414469 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -8,9 +8,9 @@ from typing_extensions import Literal from ...misc.metrics import PairwiseMetric, l2_distance -from ...misc.metrics._typing import Metric from ...ml._neighbors_base import AlgorithmType, KNeighborsMixin from ...representation import FData +from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType", bound="LocalOutlierFactor[Any]") diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index bc13cc51b..99d7a8485 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -8,8 +8,9 @@ from scipy import integrate from scipy.stats import rankdata -from ...misc.metrics import Metric, l2_distance +from ...misc.metrics import l2_distance from ...representation import FData, FDataGrid +from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat from ..depth import Depth, ModifiedBandDepth diff --git a/skfda/misc/__init__.py b/skfda/misc/__init__.py index 607585215..cdd16e6d4 100644 --- a/skfda/misc/__init__.py +++ b/skfda/misc/__init__.py @@ -34,14 +34,14 @@ if TYPE_CHECKING: from ._math import ( - cosine_similarity, - cosine_similarity_matrix, - cumsum, - exp, - inner_product, - inner_product_matrix, - log, - log2, - log10, - sqrt, + cosine_similarity as cosine_similarity, + cosine_similarity_matrix as cosine_similarity_matrix, + cumsum as cumsum, + exp as exp, + inner_product as inner_product, + inner_product_matrix as inner_product_matrix, + log as log, + log2 as log2, + log10 as log10, + sqrt as sqrt, ) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 37659b803..c4ccfcd56 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -21,7 +21,7 @@ Vector = TypeVar( "Vector", - bound=Union[NDArrayFloat, Basis, Callable[[ArrayLike], NDArrayFloat]], + bound=Union[NDArrayFloat, Basis, Callable[[NDArrayFloat], NDArrayFloat]], ) @@ -325,10 +325,15 @@ def inner_product( _matrix=_matrix, _domain_range=_domain_range, ) + elif isinstance(arg1, np.ndarray) and isinstance(arg2, np.ndarray): + return ( # type: ignore[no-any-return] + np.einsum('n...,m...->nm...', arg1, arg2).sum(axis=-1) + if _matrix else (arg1 * arg2).sum(axis=-1) + ) - return ( - np.einsum('n...,m...->nm...', arg1, arg2).sum(axis=-1) - if _matrix else (arg1 * arg2).sum(axis=-1) + raise ValueError( + "Cannot compute inner product between " + f"{type(arg1)} and {type(arg2)}", ) @@ -362,7 +367,7 @@ def _inner_product_fdatagrid( index = (slice(None),) + (np.newaxis,) * (i + 1) d1 *= weights[index] - return np.einsum( + return np.einsum( # type: ignore[call-overload, no-any-return] d1, [0] + einsum_broadcast_list, d2, @@ -371,7 +376,7 @@ def _inner_product_fdatagrid( ) integrand = arg1 * arg2 - return integrand.integrate().sum(axis=-1) + return integrand.integrate().sum(axis=-1) # type: ignore[no-any-return] @inner_product.register(FDataBasis, FDataBasis) @@ -387,14 +392,14 @@ def _inner_product_fdatabasis( force_numerical: bool = False, ) -> NDArrayFloat: - check_fdata_same_dimensions(arg1, arg2) - if isinstance(arg1, Basis): arg1 = arg1.to_basis() if isinstance(arg2, Basis): arg2 = arg2.to_basis() + check_fdata_same_dimensions(arg1, arg2) + # Now several cases where computing the matrix is preferrable # # First, if force_numerical is True, the matrix is NOT used @@ -425,14 +430,14 @@ def _inner_product_fdatabasis( coef2 = arg2.coefficients if _matrix: - return np.einsum( + return np.einsum( # type: ignore[no-any-return] 'nb,bc,mc->nm', coef1, inner_product_matrix, coef2, ) - return ( + return ( # type: ignore[no-any-return] coef1 @ inner_product_matrix * coef2 @@ -477,7 +482,9 @@ def integrand(*args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 if _matrix: ret = np.einsum('n...,m...->nm...', f1, f2) - return ret.reshape((-1,) + ret.shape[2:]) + return ret.reshape( # type: ignore[no-any-return] + (-1,) + ret.shape[2:], + ) return f1 * f2 @@ -491,7 +498,7 @@ def integrand(*args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 if _matrix: summation = summation.reshape((len_arg1, len_arg2)) - return summation + return summation # type: ignore[no-any-return] def inner_product_matrix( diff --git a/skfda/misc/kernels.py b/skfda/misc/kernels.py index 9895c3b48..70fefcd98 100644 --- a/skfda/misc/kernels.py +++ b/skfda/misc/kernels.py @@ -1,24 +1,23 @@ """Defines the most commonly used kernels.""" import math -from scipy import stats -import numpy as np +import numpy as np +from scipy import stats -__author__ = "Miguel Carbajo Berrocal" -__email__ = "miguel.carbajo@estudiante.uam.es" +from ..typing._numpy import NDArrayFloat -def normal(u): +def normal(u: NDArrayFloat) -> NDArrayFloat: r"""Evaluate a normal kernel. .. math:: K(x) = \frac{1}{\sqrt{2\pi}}e^{-\frac{x^2}{2}} """ - return stats.norm.pdf(u) + return stats.norm.pdf(u) # type: ignore[no-any-return] -def cosine(u): +def cosine(u: NDArrayFloat) -> NDArrayFloat: r"""Cosine kernel. .. math:: @@ -32,15 +31,16 @@ def cosine(u): """ if isinstance(u, np.ndarray): res = np.zeros(u.shape) - res[abs(u) <= 1] = math.pi / 4 * (math.cos(math.pi * u[abs(u) <= 1] - / 2)) + res[abs(u) <= 1] = math.pi / 4 * ( + math.cos(math.pi * u[abs(u) <= 1] / 2) + ) return res if abs(u) <= 1: return math.pi / 4 * (math.cos(math.pi * u / 2)) return 0 -def epanechnikov(u): +def epanechnikov(u: NDArrayFloat) -> NDArrayFloat: r"""Epanechnikov kernel. .. math:: @@ -60,7 +60,7 @@ def epanechnikov(u): return 0 -def tri_weight(u): +def tri_weight(u: NDArrayFloat) -> NDArrayFloat: r"""Tri-weight kernel. .. math:: @@ -80,7 +80,7 @@ def tri_weight(u): return 0 -def quartic(u): +def quartic(u: NDArrayFloat) -> NDArrayFloat: r"""Quartic kernel. .. math:: @@ -100,7 +100,7 @@ def quartic(u): return 0 -def uniform(u): +def uniform(u: NDArrayFloat) -> NDArrayFloat: r"""Uniform kernel. .. math:: diff --git a/skfda/misc/lstsq.py b/skfda/misc/lstsq.py index 3c4f555bc..51694ede0 100644 --- a/skfda/misc/lstsq.py +++ b/skfda/misc/lstsq.py @@ -21,7 +21,11 @@ def lstsq_cholesky( """Solve OLS problem using a Cholesky decomposition.""" left = coefs.T @ coefs right = coefs.T @ result - return scipy.linalg.solve(left, right, assume_a="pos") + return scipy.linalg.solve( # type: ignore[no-any-return] + left, + right, + assume_a="pos", + ) def lstsq_qr( @@ -29,7 +33,11 @@ def lstsq_qr( result: NDArrayFloat, ) -> NDArrayFloat: """Solve OLS problem using a QR decomposition.""" - return scipy.linalg.lstsq(coefs, result, lapack_driver="gelsy")[0] + return scipy.linalg.lstsq( # type: ignore[no-any-return] + coefs, + result, + lapack_driver="gelsy", + )[0] def lstsq_svd( @@ -37,7 +45,11 @@ def lstsq_svd( result: NDArrayFloat, ) -> NDArrayFloat: """Solve OLS problem using a SVD decomposition.""" - return scipy.linalg.lstsq(coefs, result, lapack_driver="gelsd")[0] + return scipy.linalg.lstsq( # type: ignore[no-any-return] + coefs, + result, + lapack_driver="gelsd", + )[0] method_dict: Final = { @@ -103,4 +115,8 @@ def solve_regularized_weighted_lstsq( if penalty_matrix is not None: left += penalty_matrix - return scipy.linalg.solve(left, right, assume_a="pos") + return scipy.linalg.solve( # type: ignore[no-any-return] + left, + right, + assume_a="pos", + ) diff --git a/skfda/misc/metrics/__init__.py b/skfda/misc/metrics/__init__.py index 7f1931000..3fc6f9acc 100644 --- a/skfda/misc/metrics/__init__.py +++ b/skfda/misc/metrics/__init__.py @@ -16,7 +16,7 @@ ) from ._lp_norms import LpNorm, l1_norm, l2_norm, linf_norm, lp_norm from ._mahalanobis import MahalanobisDistance -from ._typing import PRECOMPUTED, Metric, Norm +from ._parse import PRECOMPUTED from ._utils import ( NormInducedMetric, PairwiseMetric, diff --git a/skfda/misc/metrics/_fisher_rao.py b/skfda/misc/metrics/_fisher_rao.py index bfed1db50..7fb7233d6 100644 --- a/skfda/misc/metrics/_fisher_rao.py +++ b/skfda/misc/metrics/_fisher_rao.py @@ -1,6 +1,7 @@ """Elastic metrics.""" +from __future__ import annotations -from typing import Any, Optional, Tuple, TypeVar, Union +from typing import Any, Optional, Tuple, TypeVar import numpy as np import scipy.integrate @@ -20,7 +21,7 @@ def _transformation_for_fisher_rao( fdata1: T, fdata2: T, *, - eval_points: Optional[NDArrayFloat] = None, + eval_points: NDArrayFloat | None = None, _check: bool = True, ) -> Tuple[FDataGrid, FDataGrid]: fdata1, fdata2 = _cast_to_grid( @@ -92,7 +93,7 @@ def __call__( fdata1: T, fdata2: T, *, - eval_points: Optional[NDArrayFloat] = None, + eval_points: NDArrayFloat | None = None, _check: bool = True, ) -> NDArrayFloat: """Compute the distance.""" @@ -137,7 +138,7 @@ def fisher_rao_amplitude_distance( fdata2: T, *, lam: float = 0, - eval_points: Optional[NDArrayFloat] = None, + eval_points: NDArrayFloat | None = None, _check: bool = True, **kwargs: Any, ) -> NDArrayFloat: @@ -245,7 +246,7 @@ def fisher_rao_phase_distance( fdata2: T, *, lam: float = 0, - eval_points: Optional[NDArrayFloat] = None, + eval_points: NDArrayFloat | None = None, _check: bool = True, ) -> NDArrayFloat: r""" @@ -324,14 +325,14 @@ def fisher_rao_phase_distance( d = scipy.integrate.simps(derivative_warping, x=eval_points_normalized) d = np.clip(d, -1, 1) - return np.arccos(d) + return np.arccos(d) # type: ignore[no-any-return] def _fisher_rao_warping_distance( warping1: T, warping2: T, *, - eval_points: Optional[NDArrayFloat] = None, + eval_points: NDArrayFloat | None = None, _check: bool = True, ) -> NDArrayFloat: r""" @@ -396,4 +397,4 @@ def _fisher_rao_warping_distance( d = scipy.integrate.simps(product, x=warping1.grid_points[0]) d = np.clip(d, -1, 1) - return np.arccos(d) + return np.arccos(d) # type: ignore[no-any-return] diff --git a/skfda/misc/metrics/_lp_distances.py b/skfda/misc/metrics/_lp_distances.py index 37e8928a3..eba60e349 100644 --- a/skfda/misc/metrics/_lp_distances.py +++ b/skfda/misc/metrics/_lp_distances.py @@ -9,9 +9,9 @@ from typing_extensions import Final from ...representation import FData +from ...typing._metric import Norm from ...typing._numpy import NDArrayFloat from ._lp_norms import LpNorm -from ._typing import Norm from ._utils import NormInducedMetric, pairwise_metric_optimization T = TypeVar("T", NDArrayFloat, FData) @@ -138,7 +138,7 @@ def _pairwise_metric_optimization_lp_fdata( out=distance_matrix_sqr, ) - return np.sqrt(distance_matrix_sqr) + return np.sqrt(distance_matrix_sqr) # type: ignore[no-any-return] return NotImplemented diff --git a/skfda/misc/metrics/_lp_norms.py b/skfda/misc/metrics/_lp_norms.py index 40baa8a6d..2a6c2f9a6 100644 --- a/skfda/misc/metrics/_lp_norms.py +++ b/skfda/misc/metrics/_lp_norms.py @@ -8,8 +8,8 @@ from typing_extensions import Final from ...representation import FData, FDataBasis +from ...typing._metric import Norm from ...typing._numpy import NDArrayFloat -from ._typing import Norm class LpNorm(): @@ -108,7 +108,11 @@ def __call__(self, vector: Union[NDArrayFloat, FData]) -> NDArrayFloat: from ...misc import inner_product if isinstance(vector, np.ndarray): - return np.linalg.norm(vector, ord=self.p, axis=-1) + return np.linalg.norm( # type: ignore[no-any-return] + vector, + ord=self.p, + axis=-1, + ) vector_norm = self.vector_norm @@ -167,9 +171,9 @@ def __call__(self, vector: Union[NDArrayFloat, FData]) -> NDArrayFloat: return NotImplemented if len(res) == 1: - return res[0] + return res[0] # type: ignore[no-any-return] - return res + return res # type: ignore[no-any-return] l1_norm: Final = LpNorm(1) diff --git a/skfda/misc/metrics/_parse.py b/skfda/misc/metrics/_parse.py new file mode 100644 index 000000000..042fc6712 --- /dev/null +++ b/skfda/misc/metrics/_parse.py @@ -0,0 +1,49 @@ +"""Typing for norms and metrics.""" +import enum +from builtins import isinstance +from typing import Any, TypeVar, Union, overload + +from typing_extensions import Final, Literal + +from ...typing._metric import Metric + + +class _MetricSingletons(enum.Enum): + PRECOMPUTED = "precomputed" + + +PRECOMPUTED: Final = _MetricSingletons.PRECOMPUTED + +_PrecomputedTypes = Literal[ + _MetricSingletons.PRECOMPUTED, + "precomputed", +] + +_NonStringMetric = TypeVar( + "_NonStringMetric", + bound=Union[ + Metric[Any], + _MetricSingletons, + ], +) + + +@overload +def _parse_metric( + metric: str, +) -> _MetricSingletons: + pass + + +@overload +def _parse_metric( + metric: _NonStringMetric, +) -> _NonStringMetric: + pass + + +def _parse_metric( + metric: Union[Metric[Any], _MetricSingletons, str], +) -> Union[Metric[Any], _MetricSingletons]: + + return _MetricSingletons(metric) if isinstance(metric, str) else metric diff --git a/skfda/misc/metrics/_typing.py b/skfda/misc/metrics/_typing.py deleted file mode 100644 index b8e1dc4ca..000000000 --- a/skfda/misc/metrics/_typing.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Typing for norms and metrics.""" -import enum -from abc import abstractmethod -from builtins import isinstance -from typing import Any, TypeVar, Union, overload - -from typing_extensions import Final, Literal, Protocol - -from ...typing._base import Vector -from ...typing._numpy import NDArrayFloat - -VectorType = TypeVar("VectorType", contravariant=True, bound=Vector) -MetricElementType = TypeVar("MetricElementType", contravariant=True) - - -class _MetricSingletons(enum.Enum): - PRECOMPUTED = "precomputed" - - -PRECOMPUTED: Final = _MetricSingletons.PRECOMPUTED - -_PrecomputedTypes = Literal[ - _MetricSingletons.PRECOMPUTED, - "precomputed", -] - - -class Norm(Protocol[VectorType]): - """Protocol for a norm of a vector.""" - - @abstractmethod - def __call__(self, __vector: VectorType) -> NDArrayFloat: # noqa: WPS112 - """Compute the norm of a vector.""" - - -class Metric(Protocol[MetricElementType]): - """Protocol for a metric between two elements of a metric space.""" - - @abstractmethod - def __call__( - self, - __e1: MetricElementType, # noqa: WPS112 - __e2: MetricElementType, # noqa: WPS112 - ) -> NDArrayFloat: - """Compute the metric between two vectors.""" - - -_NonStringMetric = TypeVar( - "_NonStringMetric", - bound=Union[ - Metric[Any], - _MetricSingletons, - ], -) - - -@overload -def _parse_metric( - metric: str, -) -> _MetricSingletons: - pass - - -@overload -def _parse_metric( - metric: _NonStringMetric, -) -> _NonStringMetric: - pass - - -def _parse_metric( - metric: Union[Metric[Any], _MetricSingletons, str], -) -> Union[Metric[Any], _MetricSingletons]: - - return _MetricSingletons(metric) if isinstance(metric, str) else metric diff --git a/skfda/misc/metrics/_utils.py b/skfda/misc/metrics/_utils.py index 9da2db240..752190fd7 100644 --- a/skfda/misc/metrics/_utils.py +++ b/skfda/misc/metrics/_utils.py @@ -7,8 +7,8 @@ from ..._utils import _pairwise_symmetric from ...representation import FData, FDataGrid from ...typing._base import Vector +from ...typing._metric import Metric, MetricElementType, Norm, VectorType from ...typing._numpy import NDArrayFloat -from ._typing import Metric, MetricElementType, Norm, VectorType T = TypeVar("T", bound=FData) @@ -141,7 +141,7 @@ def pairwise_metric_optimization( elem1: Any, elem2: Optional[Any], ) -> NDArrayFloat: - r""" + """ Optimized computation of a pairwise metric. This is a generic function that can be subclassed for different @@ -152,7 +152,8 @@ def pairwise_metric_optimization( class PairwiseMetric(Generic[MetricElementType]): - r"""Pairwise metric function. + """ + Pairwise metric function. Computes a given metric pairwise. The matrix returned by the pairwise metric is a matrix with as many rows as observations in the first object diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index c95e4d32d..9c30f8ed0 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -4,19 +4,19 @@ from typing import Any, Callable, TypeVar, Union import multimethod -import numpy as np from typing_extensions import Protocol from ...representation import FData from ...representation.basis import Basis +from ...typing._numpy import NDArrayFloat OperatorInput = TypeVar( "OperatorInput", - bound=Union[np.ndarray, FData, Basis], + bound=Union[NDArrayFloat, FData, Basis], contravariant=True, ) -OutputType = Union[np.ndarray, Callable[[np.ndarray], np.ndarray]] +OutputType = Union[NDArrayFloat, Callable[[NDArrayFloat], NDArrayFloat]] OperatorOutput = TypeVar( "OperatorOutput", @@ -38,7 +38,7 @@ def __call__(self, vector: OperatorInput) -> OperatorOutput: def gramian_matrix_optimization( linear_operator: Any, basis: OperatorInput, -) -> np.ndarray: +) -> NDArrayFloat: """ Efficient implementation of gramian_matrix. @@ -52,7 +52,7 @@ def gramian_matrix_optimization( def gramian_matrix_numerical( linear_operator: Operator[OperatorInput, OutputType], basis: OperatorInput, -) -> np.ndarray: +) -> NDArrayFloat: """ Return the gramian matrix given a basis, computed numerically. @@ -71,7 +71,7 @@ def gramian_matrix_numerical( def gramian_matrix( linear_operator: Operator[OperatorInput, OutputType], basis: OperatorInput, -) -> np.ndarray: +) -> NDArrayFloat: r""" Return the gramian matrix given a basis. @@ -96,7 +96,7 @@ def gramian_matrix( return gramian_matrix_numerical(linear_operator, basis) -class MatrixOperator(Operator[np.ndarray, np.ndarray]): +class MatrixOperator(Operator[NDArrayFloat, NDArrayFloat]): """Linear operator for finite spaces. Between finite dimensional spaces, every linear operator can be expressed @@ -107,8 +107,8 @@ class MatrixOperator(Operator[np.ndarray, np.ndarray]): """ - def __init__(self, matrix: np.ndarray) -> None: + def __init__(self, matrix: NDArrayFloat) -> None: self.matrix = matrix - def __call__(self, f: np.ndarray) -> np.ndarray: # noqa: D102 + def __call__(self, f: NDArrayFloat) -> NDArrayFloat: # noqa: D102 return self.matrix @ f diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index a5798b3a3..d37f476e1 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -9,12 +9,7 @@ import numpy as np from ..representation import FData, FDataBasis, FDataGrid -from ..typing._base import ( - ArrayLike, - DomainRange, - DomainRangeLike, - EvaluationPoints, -) +from ..typing._base import DomainRange, DomainRangeLike, EvaluationPoints from ..typing._numpy import ArrayLike diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index db2b6bcdd..e2646c2af 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -18,9 +18,9 @@ RegressorMixin, ) from ..misc.metrics import l2_distance -from ..misc.metrics._typing import Metric from ..misc.metrics._utils import _fit_metric from ..representation import FData, FDataGrid, concatenate +from ..typing._metric import Metric from ..typing._numpy import NDArrayFloat, NDArrayInt FDataType = TypeVar("FDataType", bound="FData") diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 9bf0dd287..79ec3ecaf 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -9,9 +9,10 @@ from ..._utils import _classifier_get_classes from ...exploratory.depth import Depth, ModifiedBandDepth from ...exploratory.stats import mean, trim_mean -from ...misc.metrics import Metric, PairwiseMetric, l2_distance +from ...misc.metrics import PairwiseMetric, l2_distance from ...misc.metrics._utils import _fit_metric from ...representation import FData +from ...typing._metric import Metric from ...typing._numpy import NDArrayInt T = TypeVar("T", bound=FData) diff --git a/skfda/ml/classification/_neighbors_classifiers.py b/skfda/ml/classification/_neighbors_classifiers.py index 1db3042af..7f40e8d8e 100644 --- a/skfda/ml/classification/_neighbors_classifiers.py +++ b/skfda/ml/classification/_neighbors_classifiers.py @@ -10,10 +10,9 @@ ) from typing_extensions import Literal -from skfda.misc.metrics._typing import Metric - from ...misc.metrics import l2_distance from ...representation import FData +from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat, NDArrayInt from .._neighbors_base import ( AlgorithmType, diff --git a/skfda/ml/clustering/_hierarchical.py b/skfda/ml/clustering/_hierarchical.py index 0f7230412..d5057d6fe 100644 --- a/skfda/ml/clustering/_hierarchical.py +++ b/skfda/ml/clustering/_hierarchical.py @@ -9,9 +9,10 @@ from sklearn.base import BaseEstimator, ClusterMixin from typing_extensions import Literal -from ...misc.metrics import PRECOMPUTED, Metric, PairwiseMetric, l2_distance -from ...misc.metrics._typing import _parse_metric, _PrecomputedTypes +from ...misc.metrics import PRECOMPUTED, PairwiseMetric, l2_distance +from ...misc.metrics._parse import _parse_metric, _PrecomputedTypes from ...representation import FData +from ...typing._metric import Metric kk = ["ward", "average", "complete"] diff --git a/skfda/ml/clustering/_kmeans.py b/skfda/ml/clustering/_kmeans.py index 3fa9b69c8..649e4d56b 100644 --- a/skfda/ml/clustering/_kmeans.py +++ b/skfda/ml/clustering/_kmeans.py @@ -12,9 +12,10 @@ from sklearn.utils.validation import check_is_fitted from ..._utils import RandomStateLike -from ...misc.metrics import Metric, PairwiseMetric, l2_distance +from ...misc.metrics import PairwiseMetric, l2_distance from ...misc.validation import check_fdata_same_dimensions from ...representation import FDataGrid +from ...typing._metric import Metric from ...typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt SelfType = TypeVar("SelfType", bound="BaseKMeans[Any]") diff --git a/skfda/ml/clustering/_neighbors_clustering.py b/skfda/ml/clustering/_neighbors_clustering.py index 0be3450f4..448799729 100644 --- a/skfda/ml/clustering/_neighbors_clustering.py +++ b/skfda/ml/clustering/_neighbors_clustering.py @@ -5,10 +5,9 @@ from typing_extensions import Literal -from skfda.misc.metrics._typing import Metric - from ...misc.metrics import l2_distance from ...representation import FData +from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat from .._neighbors_base import ( AlgorithmType, diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py index aecc64c60..4ed08985f 100644 --- a/skfda/ml/regression/_kernel_regression.py +++ b/skfda/ml/regression/_kernel_regression.py @@ -8,8 +8,8 @@ from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix from ...misc.metrics import PairwiseMetric, l2_distance -from ...misc.metrics._typing import Metric from ...representation._functional_data import FData +from ...typing._metric import Metric class KernelRegression( diff --git a/skfda/ml/regression/_neighbors_regression.py b/skfda/ml/regression/_neighbors_regression.py index a4368f0b4..256bd904a 100644 --- a/skfda/ml/regression/_neighbors_regression.py +++ b/skfda/ml/regression/_neighbors_regression.py @@ -10,10 +10,9 @@ ) from typing_extensions import Literal -from skfda.misc.metrics._typing import Metric - from ...misc.metrics import l2_distance from ...representation import FData +from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat, NDArrayInt from .._neighbors_base import ( AlgorithmType, diff --git a/skfda/preprocessing/smoothing/__init__.py b/skfda/preprocessing/smoothing/__init__.py index 487c819aa..4d3840d1e 100644 --- a/skfda/preprocessing/smoothing/__init__.py +++ b/skfda/preprocessing/smoothing/__init__.py @@ -16,8 +16,8 @@ ) if TYPE_CHECKING: - from ._basis import BasisSmoother - from ._kernel_smoothers import KernelSmoother + from ._basis import BasisSmoother as BasisSmoother + from ._kernel_smoothers import KernelSmoother as KernelSmoother __kernel_smoothers__imported__ = False diff --git a/skfda/representation/__init__.py b/skfda/representation/__init__.py index 0dec1723b..fd5afe3c5 100644 --- a/skfda/representation/__init__.py +++ b/skfda/representation/__init__.py @@ -19,6 +19,6 @@ ) if TYPE_CHECKING: - from ._functional_data import FData, concatenate - from .basis import FDataBasis - from .grid import FDataGrid + from ._functional_data import FData as FData, concatenate as concatenate + from .basis import FDataBasis as FDataBasis + from .grid import FDataGrid as FDataGrid diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 0c74a8058..33e344783 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -19,12 +19,15 @@ ) if TYPE_CHECKING: - from ._basis import Basis - from ._bspline import BSpline - from ._constant import Constant - from ._fdatabasis import FDataBasis, FDataBasisDType - from ._finite_element import FiniteElement - from ._fourier import Fourier - from ._monomial import Monomial - from ._tensor_basis import Tensor - from ._vector_basis import VectorValued + from ._basis import Basis as Basis + from ._bspline import BSpline as BSpline + from ._constant import Constant as Constant + from ._fdatabasis import ( + FDataBasis as FDataBasis, + FDataBasisDType as FDataBasisDType, + ) + from ._finite_element import FiniteElement as FiniteElement + from ._fourier import Fourier as Fourier + from ._monomial import Monomial as Monomial + from ._tensor_basis import Tensor as Tensor + from ._vector_basis import VectorValued as VectorValued diff --git a/skfda/representation/evaluator.py b/skfda/representation/evaluator.py index 0dfdf9888..89e42a77e 100644 --- a/skfda/representation/evaluator.py +++ b/skfda/representation/evaluator.py @@ -12,8 +12,8 @@ from typing_extensions import Protocol -from ..typing._base import ArrayLike, EvaluationPoints -from ..typing._numpy import NDArrayFloat +from ..typing._base import EvaluationPoints +from ..typing._numpy import ArrayLike, NDArrayFloat if TYPE_CHECKING: from ._functional_data import FData diff --git a/skfda/typing/_metric.py b/skfda/typing/_metric.py new file mode 100644 index 000000000..bcafe0c49 --- /dev/null +++ b/skfda/typing/_metric.py @@ -0,0 +1,31 @@ +"""Typing for norms and metrics.""" +from abc import abstractmethod +from typing import TypeVar + +from typing_extensions import Protocol + +from ._base import Vector +from ._numpy import NDArrayFloat + +VectorType = TypeVar("VectorType", contravariant=True, bound=Vector) +MetricElementType = TypeVar("MetricElementType", contravariant=True) + + +class Norm(Protocol[VectorType]): + """Protocol for a norm of a vector.""" + + @abstractmethod + def __call__(self, __vector: VectorType) -> NDArrayFloat: # noqa: WPS112 + """Compute the norm of a vector.""" + + +class Metric(Protocol[MetricElementType]): + """Protocol for a metric between two elements of a metric space.""" + + @abstractmethod + def __call__( + self, + __e1: MetricElementType, # noqa: WPS112 + __e2: MetricElementType, # noqa: WPS112 + ) -> NDArrayFloat: + """Compute the metric between two vectors.""" diff --git a/skfda/typing/_numpy.py b/skfda/typing/_numpy.py index 57cc6e430..472daccc4 100644 --- a/skfda/typing/_numpy.py +++ b/skfda/typing/_numpy.py @@ -5,11 +5,11 @@ import numpy as np try: - from numpy.typing import ArrayLike + from numpy.typing import ArrayLike as ArrayLike # noqa: WPS113 except ImportError: - ArrayLike = np.ndarray # type:ignore[misc] + ArrayLike = np.ndarray # type:ignore[misc] # noqa: WPS440 -try: +try: # noqa: WPS229 from numpy.typing import NDArray NDArrayAny = NDArray[Any] NDArrayInt = NDArray[np.int_] @@ -18,7 +18,7 @@ NDArrayStr = NDArray[np.str_] NDArrayObject = NDArray[np.object_] except ImportError: - NDArray = np.ndarray # type:ignore[misc] + NDArray = np.ndarray # type:ignore[misc] # noqa: WPS440 NDArrayAny = np.ndarray # type:ignore[misc] NDArrayInt = np.ndarray # type:ignore[misc] NDArrayFloat = np.ndarray # type:ignore[misc] From 2fae831c21f10156422f56ceb4ccd8a58fdd8627 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Thu, 1 Sep 2022 12:53:06 +0200 Subject: [PATCH 271/400] Fix Mypy errors in misc module. --- mypy.out | 1353 +++++++++-------- skfda/_utils/__init__.py | 8 +- skfda/_utils/_utils.py | 14 +- skfda/_utils/_warping.py | 4 +- skfda/datasets/__init__.py | 81 +- skfda/exploratory/stats/__init__.py | 73 +- skfda/misc/_math.py | 8 +- skfda/misc/covariances.py | 27 +- skfda/misc/hat_matrix.py | 98 +- skfda/misc/metrics/__init__.py | 90 +- skfda/misc/metrics/_mahalanobis.py | 6 +- skfda/misc/metrics/_utils.py | 21 +- skfda/misc/operators/__init__.py | 41 +- skfda/misc/operators/_identity.py | 10 +- skfda/misc/operators/_integral_transform.py | 21 +- .../_linear_differential_operator.py | 88 +- skfda/misc/operators/_operators.py | 4 +- skfda/misc/operators/_srvf.py | 14 +- skfda/misc/regularization/__init__.py | 25 +- skfda/misc/regularization/_regularization.py | 12 +- skfda/preprocessing/dim_reduction/__init__.py | 2 +- skfda/preprocessing/dim_reduction/_fpca.py | 62 +- .../variable_selection/__init__.py | 12 +- .../feature_construction/__init__.py | 20 +- skfda/preprocessing/registration/__init__.py | 24 +- .../preprocessing/registration/_fisher_rao.py | 18 +- skfda/typing/_base.py | 2 +- 27 files changed, 1206 insertions(+), 932 deletions(-) diff --git a/mypy.out b/mypy.out index 738df3708..e993724fe 100644 --- a/mypy.out +++ b/mypy.out @@ -33,7 +33,7 @@ TRACE: Options({'allow_redefinition': False, 'dump_type_stats': False, 'enable_error_code': ['ignore-without-code'], 'enable_incomplete_features': False, - 'enabled_error_codes': {}, + 'enabled_error_codes': {}, 'error_summary': True, 'exclude': [], 'explicit_package_bases': False, @@ -136,7 +136,7 @@ LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfd LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py', module='skfda.misc.metrics._lp_distances', has_text=False, base_dir=None) LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py', module='skfda.misc.metrics._lp_norms', has_text=False, base_dir=None) LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py', module='skfda.misc.metrics._mahalanobis', has_text=False, base_dir=None) -LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/metrics/_typing.py', module='skfda.misc.metrics._typing', has_text=False, base_dir=None) +LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/metrics/_parse.py', module='skfda.misc.metrics._parse', has_text=False, base_dir=None) LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py', module='skfda.misc.metrics._utils', has_text=False, base_dir=None) LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py', module='skfda.misc.operators', has_text=False, base_dir=None) LOG: Found source: BuildSource(path='/home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py', module='skfda.misc.operators._identity', has_text=False, base_dir=None) @@ -178,17 +178,15 @@ LOG: Could not load cache for skfda.misc.hat_matrix: skfda/misc/hat_matrix.meta LOG: Metadata not found for skfda.misc.hat_matrix LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py (skfda.misc.hat_matrix) TRACE: Looking for skfda.misc.kernels at skfda/misc/kernels.meta.json -TRACE: Meta skfda.misc.kernels {"data_mtime": 1661927677, "dep_lines": [2, 4, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5], "dep_prios": [10, 10, 5, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 5], "dependencies": ["math", "numpy", "skfda.typing._numpy", "builtins", "abc", "array", "mmap", "numpy._typing", "numpy._typing._array_like", "numpy._typing._dtype_like", "numpy.core", "numpy.core.multiarray", "typing", "typing_extensions"], "hash": "ed30ffb69f97bc24aef39e6fccaebd5cc740b1e006b21a55373d87e36da4a474", "id": "skfda.misc.kernels", "ignore_all": true, "interface_hash": "2bb474ff4112d8d2ad0e982bae1d5f5d599ffb1de9eded49242061cc956860d8", "mtime": 1661922602, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": true, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/misc/kernels.py", "plugin_data": null, "size": 2734, "suppressed": ["scipy"], "version_id": "0.971"} +TRACE: Meta skfda.misc.kernels {"data_mtime": 1662028583, "dep_lines": [2, 4, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5], "dep_prios": [10, 10, 5, 5, 30, 30, 30, 30, 30, 30, 30, 30, 5], "dependencies": ["math", "numpy", "skfda.typing._numpy", "builtins", "abc", "array", "mmap", "numpy._typing", "numpy._typing._array_like", "numpy._typing._dtype_like", "typing", "typing_extensions"], "hash": "ed30ffb69f97bc24aef39e6fccaebd5cc740b1e006b21a55373d87e36da4a474", "id": "skfda.misc.kernels", "ignore_all": true, "interface_hash": "899bae857a20fc246947d68d2595c8afac58a0b2875626d1f7df6ba702710b20", "mtime": 1661922602, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/misc/kernels.py", "plugin_data": null, "size": 2734, "suppressed": ["scipy"], "version_id": "0.971"} LOG: Metadata abandoned for skfda.misc.kernels: options differ TRACE: follow_imports: silent != normal -TRACE: implicit_reexport: True != False LOG: Metadata not found for skfda.misc.kernels LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/kernels.py (skfda.misc.kernels) TRACE: Looking for skfda.misc.lstsq at skfda/misc/lstsq.meta.json -TRACE: Meta skfda.misc.lstsq {"data_mtime": 1661927677, "dep_lines": [6, 2, 4, 8, 10, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30, 30, 30, 10, 20], "dependencies": ["numpy", "__future__", "typing", "typing_extensions", "skfda.typing._numpy", "builtins", "abc", "numpy._typing", "numpy._typing._array_like", "numpy._typing._nested_sequence", "numpy._typing._ufunc", "numpy.lib", "numpy.lib.twodim_base"], "hash": "c554c53c68bd3fce0e8fbd4ba5c4cca1927246ae1a9a86c39b3f603f2b17bc3f", "id": "skfda.misc.lstsq", "ignore_all": true, "interface_hash": "bd1ea1e7aeb867c3b25759402a18bf20c59c502592482a9f8c46614ac7f76363", "mtime": 1661922212, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": true, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/misc/lstsq.py", "plugin_data": null, "size": 3229, "suppressed": ["scipy.linalg", "scipy"], "version_id": "0.971"} +TRACE: Meta skfda.misc.lstsq {"data_mtime": 1662028583, "dep_lines": [6, 2, 4, 8, 10, 1, 1, 1, 1, 1, 1, 7, 7], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30, 10, 20], "dependencies": ["numpy", "__future__", "typing", "typing_extensions", "skfda.typing._numpy", "builtins", "abc", "numpy._typing", "numpy._typing._array_like", "numpy._typing._nested_sequence", "numpy._typing._ufunc"], "hash": "c554c53c68bd3fce0e8fbd4ba5c4cca1927246ae1a9a86c39b3f603f2b17bc3f", "id": "skfda.misc.lstsq", "ignore_all": true, "interface_hash": "7ab063a409ff0154b3a02b5edc33ca0da2050786de43e8addb045e2664edff91", "mtime": 1661922212, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/misc/lstsq.py", "plugin_data": null, "size": 3229, "suppressed": ["scipy.linalg", "scipy"], "version_id": "0.971"} LOG: Metadata abandoned for skfda.misc.lstsq: options differ TRACE: follow_imports: silent != normal -TRACE: implicit_reexport: True != False LOG: Metadata not found for skfda.misc.lstsq LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/lstsq.py (skfda.misc.lstsq) TRACE: Looking for skfda.misc.metrics at skfda/misc/metrics/__init__.meta.json @@ -215,13 +213,12 @@ TRACE: Looking for skfda.misc.metrics._mahalanobis at skfda/misc/metrics/_mahala LOG: Could not load cache for skfda.misc.metrics._mahalanobis: skfda/misc/metrics/_mahalanobis.meta.json LOG: Metadata not found for skfda.misc.metrics._mahalanobis LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py (skfda.misc.metrics._mahalanobis) -TRACE: Looking for skfda.misc.metrics._typing at skfda/misc/metrics/_typing.meta.json -TRACE: Meta skfda.misc.metrics._typing {"data_mtime": 1661927677, "dep_lines": [2, 3, 4, 5, 7, 9, 10, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30], "dependencies": ["enum", "abc", "builtins", "typing", "typing_extensions", "skfda.typing._base", "skfda.typing._numpy", "array", "ctypes", "mmap", "pickle"], "hash": "fe85e23748cf9a6860b75137be0dcb5f5f44fcf2851378003c04271b52a10f5e", "id": "skfda.misc.metrics._typing", "ignore_all": true, "interface_hash": "953682f0e508ad69dd20718e60c576b0169b74b15eebeb67618295f2e3637d49", "mtime": 1661867655, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": true, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/misc/metrics/_typing.py", "plugin_data": null, "size": 1709, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for skfda.misc.metrics._typing: options differ +TRACE: Looking for skfda.misc.metrics._parse at skfda/misc/metrics/_parse.meta.json +TRACE: Meta skfda.misc.metrics._parse {"data_mtime": 1662028583, "dep_lines": [2, 3, 4, 6, 8, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30, 30, 30, 30, 30, 30], "dependencies": ["enum", "builtins", "typing", "typing_extensions", "skfda.typing._metric", "abc", "array", "ctypes", "mmap", "pickle", "skfda.typing"], "hash": "e1fc6966b357b2300b25fbbddafaea061afc51ef1677c9e57c5de8d757138406", "id": "skfda.misc.metrics._parse", "ignore_all": true, "interface_hash": "61a3e9ef71c34709806bd7b103bd4e5eb696f73c5c19c376d9b6195dc69644b5", "mtime": 1661928689, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/misc/metrics/_parse.py", "plugin_data": null, "size": 921, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for skfda.misc.metrics._parse: options differ TRACE: follow_imports: silent != normal -TRACE: implicit_reexport: True != False -LOG: Metadata not found for skfda.misc.metrics._typing -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_typing.py (skfda.misc.metrics._typing) +LOG: Metadata not found for skfda.misc.metrics._parse +LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_parse.py (skfda.misc.metrics._parse) TRACE: Looking for skfda.misc.metrics._utils at skfda/misc/metrics/_utils.meta.json LOG: Could not load cache for skfda.misc.metrics._utils: skfda/misc/metrics/_utils.meta.json LOG: Metadata not found for skfda.misc.metrics._utils @@ -267,38 +264,33 @@ LOG: Could not load cache for skfda: skfda/__init__.meta.json LOG: Metadata not found for skfda LOG: Parsing /home/carlos/git/scikit-fda/skfda/__init__.py (skfda) TRACE: Looking for typing at typing.meta.json -TRACE: Meta typing {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 30, 30, 30, 30], "dependencies": ["collections", "sys", "_typeshed", "abc", "types", "typing_extensions", "builtins", 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TRACE: follow_imports: silent != normal -TRACE: implicit_reexport: True != False LOG: Metadata not found for numpy.linalg LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/linalg/__init__.pyi (numpy.linalg) TRACE: Looking for numpy.ma at numpy/ma/__init__.meta.json -TRACE: Meta numpy.ma {"data_mtime": 1661927676, "dep_lines": [3, 1, 5, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["numpy.ma.extras", "numpy._pytesttester", "numpy.ma.core", "builtins", "abc", "typing"], "hash": "f3d6d2dd99d5ed4385c748a81995bbf44fc640e81f049e926b50d6b2f6d1ec14", "id": "numpy.ma", "ignore_all": true, "interface_hash": "3f9bdf3c59a49cfaec96c6dae8fb372099f35273e590ca170c6daf13e2fa59ed", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, 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"mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": 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"/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/colorsys.pyi", "plugin_data": null, "size": 648, "suppressed": [], "version_id": "0.971"} +TRACE: Meta colorsys {"data_mtime": 1662028333, "dep_lines": [1, 1, 1], "dep_prios": [5, 30, 30], "dependencies": ["builtins", "abc", "typing"], "hash": "a359a986bd38d55896d08c3e762608dfa737c133f60fcc77299f688b6fd23e80", "id": "colorsys", "ignore_all": true, "interface_hash": "b98da01d6d5246f41986bf85f177efaf66bdc61886a6d757690580ebd529113a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/colorsys.pyi", "plugin_data": null, "size": 648, "suppressed": [], "version_id": "0.971"} LOG: Metadata abandoned for colorsys: options differ TRACE: follow_imports: silent != normal -TRACE: implicit_reexport: True != False LOG: Metadata not found for colorsys LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/colorsys.pyi (colorsys) TRACE: Looking for skfda.datasets._real_datasets at skfda/datasets/_real_datasets.meta.json @@ -938,10 +866,9 @@ LOG: Could not load cache for skfda.exploratory.visualization.representation: s LOG: Metadata not found for skfda.exploratory.visualization.representation LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py (skfda.exploratory.visualization.representation) TRACE: Looking for skfda.preprocessing at skfda/preprocessing/__init__.meta.json -TRACE: Meta skfda.preprocessing {"data_mtime": 1661927674, "dep_lines": [1, 1, 1, 3], "dep_prios": [5, 30, 30, 10], "dependencies": ["builtins", "abc", "typing"], "hash": "dad871df3023d8ce4eff2f0a91594f4fa62252731c02403eb5db3a99b2548533", "id": "skfda.preprocessing", "ignore_all": true, "interface_hash": 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Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/__init__.py (skfda.preprocessing) TRACE: Looking for skfda.preprocessing.registration._fisher_rao at skfda/preprocessing/registration/_fisher_rao.meta.json @@ -957,10 +884,9 @@ LOG: Could not load cache for skfda.preprocessing.registration._lstsq_shift_reg LOG: Metadata not found for skfda.preprocessing.registration._lstsq_shift_registration LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py (skfda.preprocessing.registration._lstsq_shift_registration) TRACE: Looking for importlib at importlib/__init__.meta.json -TRACE: Meta importlib {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["collections.abc", "importlib.abc", "types", "builtins", "abc", "typing"], "hash": "5d991f55cfdc58d18e38d479666e5c73bc0eea2f9cc01f07cfbe9452aba97a48", "id": "importlib", "ignore_all": true, "interface_hash": 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"warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi", "plugin_data": null, "size": 800, "suppressed": [], "version_id": "0.971"} +TRACE: Meta importlib {"data_mtime": 1662028333, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["collections.abc", "importlib.abc", "types", "builtins", "abc", "typing"], "hash": "5d991f55cfdc58d18e38d479666e5c73bc0eea2f9cc01f07cfbe9452aba97a48", "id": "importlib", "ignore_all": true, "interface_hash": "0c40f65f30f4d30ad6d6e30eecd5e6953d8534c5c37cd74b977a8a08840b8812", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/random/_pcg64.pyi (numpy.random._pcg64) TRACE: Looking for numpy.random._philox at numpy/random/_philox.meta.json -TRACE: Meta numpy.random._philox {"data_mtime": 1661927676, "dep_lines": [1, 3, 4, 5, 1], "dep_prios": [5, 5, 5, 5, 5], "dependencies": ["typing", "numpy", "numpy.random.bit_generator", "numpy._typing", "builtins"], "hash": "38a95a88853e863ef6069d38ce33627f0bac383ff7fa663121fbf2bb2b3c73fa", "id": "numpy.random._philox", "ignore_all": true, "interface_hash": "d2714d4ac3c72d37b242baa03c3f2c902c0e45217590a11913dad01c5d5af389", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/_typing/_nbit.py (numpy._typing._nbit) TRACE: Looking for numpy._typing._char_codes at numpy/_typing/_char_codes.meta.json -TRACE: Meta numpy._typing._char_codes {"data_mtime": 1661927674, "dep_lines": [1, 1], "dep_prios": [5, 5], "dependencies": ["typing", "builtins"], "hash": "2d1e753b90140436c2989be5328c7252fb1fbdbd69ed61eb7b182d4c6b6e5937", "id": "numpy._typing._char_codes", "ignore_all": true, "interface_hash": "9081edb4a1e40ddad009945ea8da6e8f22b8bbb84cfe8bc0d1fcc5805ef509bb", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, 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"/home/carlos/git/scikit-fda/skfda/_utils/constants.py", "plugin_data": null, "size": 800, "suppressed": [], "version_id": "0.971"} +TRACE: Meta skfda._utils.constants {"data_mtime": 1662028333, "dep_lines": [1, 1, 1], "dep_prios": [5, 30, 30], "dependencies": ["builtins", "abc", "typing"], "hash": "3ab7dac7f5a0c7ae48def217414ac40abe143c3bc398603e338dde71e36974f3", "id": "skfda._utils.constants", "ignore_all": true, "interface_hash": "a0ca9f2a4427121fd817f8019ad208f249823a23bbaf7882a176d48cc3987fba", "mtime": 1660923560, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, 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skfda.representation.interpolation at skfda/representation/interpolation.meta.json @@ -1333,10 +1202,9 @@ LOG: Could not load cache for skfda.preprocessing.smoothing: skfda/preprocessin LOG: Metadata not found for skfda.preprocessing.smoothing LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py (skfda.preprocessing.smoothing) TRACE: Looking for numpy._typing._add_docstring at numpy/_typing/_add_docstring.meta.json -TRACE: Meta numpy._typing._add_docstring {"data_mtime": 1661927676, "dep_lines": [3, 4, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30], "dependencies": ["re", "textwrap", "numpy._typing._generic_alias", "builtins", "abc", "array", "ctypes", "enum", "mmap", "numpy._typing._dtype_like", "pickle", "typing", "typing_extensions"], "hash": "7ef899031081079c35398e4f9e73fd1e3d836bb92b12cf22c698fcca4ff468b3", "id": "numpy._typing._add_docstring", "ignore_all": true, "interface_hash": 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Looking for sre_compile at sre_compile.meta.json -TRACE: Meta sre_compile {"data_mtime": 1661927675, "dep_lines": [1, 3, 4, 1], "dep_prios": [5, 5, 5, 5], "dependencies": ["sre_constants", "sre_parse", "typing", "builtins"], "hash": "2aafd8a2ad6b888f60d11c50cb8dace30ca3fbc63ece6d12fd0efdc17246a9e1", "id": "sre_compile", "ignore_all": true, "interface_hash": "2d1e14083d24ada8238013f9c6e707425f5a25629bbdd80657d5f3890452e9f8", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, 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skfda.preprocessing.registration.base LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py (skfda.preprocessing.registration.base) TRACE: Looking for posixpath at posixpath.meta.json -TRACE: Meta posixpath {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 4, 17, 18, 19, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "genericpath", "os", "typing", "typing_extensions", "builtins", "abc"], "hash": "9ade5ce9176036cba29e7a315902abf6fa29891aacc1d02c7a92affa72efd83e", "id": "posixpath", "ignore_all": true, "interface_hash": "e1e50e65ca097b43ae107aeaa9eebf6d17685ab8320b4e5e24839000c0861880", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, 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at importlib/metadata/__init__.meta.json -TRACE: Meta importlib.metadata {"data_mtime": 1661927674, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 1], "dep_prios": [10, 5, 10, 5, 5, 5, 5, 5, 5, 5], "dependencies": ["abc", "pathlib", "sys", "_typeshed", "collections.abc", "email.message", "importlib.abc", "os", "typing", "builtins"], "hash": "134cdf9583757d465e5475aa2d79a708e9838a296dcd9a62eddb7fa1759ecb14", "id": "importlib.metadata", "ignore_all": true, "interface_hash": "69e25360fb9578149d25a169467ba8d8b81ab2d7bfa34d3e9b9015460b500370", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, 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unittest.runner: options differ TRACE: follow_imports: silent != normal -TRACE: implicit_reexport: True != False LOG: Metadata not found for unittest.runner LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unittest/runner.pyi (unittest.runner) TRACE: Looking for unittest.signals at unittest/signals.meta.json -TRACE: Meta unittest.signals {"data_mtime": 1661927675, "dep_lines": [1, 1, 2, 3, 4, 1], "dep_prios": [10, 20, 5, 5, 5, 5], "dependencies": ["unittest.result", "unittest", "collections.abc", "typing", "typing_extensions", "builtins"], "hash": "1fabced2f8502a2aaaddec795ae151a5cdaccf9181d7526d0578c40b07204ca8", "id": "unittest.signals", "ignore_all": true, "interface_hash": "c886376ceb1c0b334d2d44a331ba59639522eece62775e5676f0e4ba984f3d5a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, 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-TRACE: implicit_reexport: True != False LOG: Metadata not found for unittest.signals LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unittest/signals.pyi (unittest.signals) TRACE: Looking for unittest.suite at unittest/suite.meta.json -TRACE: Meta unittest.suite {"data_mtime": 1661927675, "dep_lines": [1, 1, 2, 3, 4, 1, 1, 1], "dep_prios": [10, 20, 10, 5, 5, 5, 30, 30], "dependencies": ["unittest.case", "unittest", "unittest.result", "collections.abc", "typing_extensions", "builtins", "abc", "typing"], "hash": "b798bd39ca70b909dda2f7623d2f79252840459f9b795fc07573c4ed26b40b2b", "id": "unittest.suite", "ignore_all": true, "interface_hash": "253358ac3195422f54147b999fe02921ede94ce6355ee30f6f9bdf74dde81723", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": 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"version_id": "0.971"} +TRACE: Meta unittest.suite {"data_mtime": 1662028334, "dep_lines": [1, 1, 2, 3, 4, 1, 1, 1], "dep_prios": [10, 20, 10, 5, 5, 5, 30, 30], "dependencies": ["unittest.case", "unittest", "unittest.result", "collections.abc", "typing_extensions", "builtins", "abc", "typing"], "hash": "b798bd39ca70b909dda2f7623d2f79252840459f9b795fc07573c4ed26b40b2b", "id": "unittest.suite", "ignore_all": true, "interface_hash": "253358ac3195422f54147b999fe02921ede94ce6355ee30f6f9bdf74dde81723", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unittest/suite.pyi (unittest.suite) TRACE: Looking for numpy.testing._private at numpy/testing/_private/__init__.meta.json -TRACE: Meta numpy.testing._private {"data_mtime": 1661927674, "dep_lines": [1], "dep_prios": [5], "dependencies": ["builtins"], "hash": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "id": "numpy.testing._private", "ignore_all": true, "interface_hash": "17c0b44f9246846a30794fc2bc2d4558f0168809cebccdf0a5194100d93253a6", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, 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Meta email.contentmanager {"data_mtime": 1662028333, "dep_lines": [1, 2, 3, 1], "dep_prios": [5, 5, 5, 5], "dependencies": ["collections.abc", "email.message", "typing", "builtins"], "hash": "25ee805b5770376d2f6d9ed4297c02748c5791f27beb2df113404eb0fa6471a4", "id": "email.contentmanager", "ignore_all": true, "interface_hash": "f44cb75e55e5494622954876c0e21f4cf784ca3bfb528bbe662935a829f585af", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, 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(email.contentmanager) TRACE: Looking for email.errors at email/errors.meta.json -TRACE: Meta email.errors {"data_mtime": 1661927674, "dep_lines": [1, 1, 1, 1, 1], "dep_prios": [10, 5, 30, 30, 30], "dependencies": ["sys", "builtins", "_typeshed", "abc", "typing"], "hash": "2dcb55e1cef7b1dbd0e58056aae9eb2dba5e70d83fca907196faef9f7ea0527c", "id": "email.errors", "ignore_all": true, "interface_hash": "8826bc9a109fb37f0fed685e6abba0077070de28722db20e3e0642da885f9803", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi TRACE: Looking for multiprocessing.reduction at multiprocessing/reduction.meta.json -TRACE: Meta multiprocessing.reduction {"data_mtime": 1661922053, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["pickle", "sys", "abc", "copyreg", "typing", "typing_extensions", "builtins", "array", "ctypes", "mmap"], "hash": "30ab326562cf82254f55d7c9f59e11159fbac4f767058f24cb69a2fe013a48e9", "id": "multiprocessing.reduction", "ignore_all": true, "interface_hash": "bbd4eacb98803ecd8041a117ca2b4e3abe2c467a2392ff056525e356dd449d0d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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"version_id": "0.971"} +TRACE: Meta _socket {"data_mtime": 1662025658, "dep_lines": [1, 2, 3, 4, 5, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "typing", "typing_extensions", "builtins", "abc", "array", "ctypes", "mmap", "pickle"], "hash": "7b12284aac0dd6754c2fc1ba409d438349ab12978515dfc6637799c75626cbaf", "id": "_socket", "ignore_all": true, "interface_hash": "4b8de99821f40c7ef1a8362f7fb84495a4d1b4f73f4955172322d6e465910067", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi", "plugin_data": null, "size": 16847, "suppressed": [], "version_id": "0.971"} LOG: Metadata fresh for _socket: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi -LOG: Loaded graph with 422 nodes (1.843 sec) -LOG: Found 164 SCCs; largest has 73 nodes +LOG: Loaded graph with 423 nodes (1.540 sec) +LOG: Found 165 SCCs; largest has 73 nodes TRACE: Priorities for typing_extensions: abc:10 collections:10 sys:10 _typeshed:5 collections.abc:5 typing:5 TRACE: Priorities for typing: collections:10 sys:10 _typeshed:5 abc:5 types:5 typing_extensions:5 TRACE: Priorities for types: sys:10 _typeshed:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 typing:5 typing_extensions:5 @@ -3778,34 +3604,50 @@ LOG: Cached module rdata.conversion has same interface LOG: Writing rdata /home/carlos/git/rdata/rdata/__init__.py rdata/__init__.meta.json rdata/__init__.data.json TRACE: Interface for rdata is unchanged LOG: Cached module rdata has same interface -TRACE: Priorities for skfda.misc.metrics._typing: -LOG: Processing SCC singleton (skfda.misc.metrics._typing) as inherently stale with stale deps (abc builtins enum skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.misc.metrics._typing /home/carlos/git/scikit-fda/skfda/misc/metrics/_typing.py skfda/misc/metrics/_typing.meta.json skfda/misc/metrics/_typing.data.json -TRACE: Interface for skfda.misc.metrics._typing has changed -LOG: Cached module skfda.misc.metrics._typing has changed interface +TRACE: Priorities for skfda.typing._metric: +LOG: Processing SCC singleton (skfda.typing._metric) as inherently stale with stale deps (abc builtins skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.typing._metric /home/carlos/git/scikit-fda/skfda/typing/_metric.py skfda/typing/_metric.meta.json skfda/typing/_metric.data.json +TRACE: Interface for skfda.typing._metric has changed +LOG: Cached module skfda.typing._metric has changed interface +TRACE: Priorities for skfda.misc.metrics._parse: +LOG: Processing SCC singleton (skfda.misc.metrics._parse) as inherently stale with stale deps (builtins enum skfda.typing._metric typing typing_extensions) +LOG: Writing skfda.misc.metrics._parse /home/carlos/git/scikit-fda/skfda/misc/metrics/_parse.py skfda/misc/metrics/_parse.meta.json skfda/misc/metrics/_parse.data.json +TRACE: Interface for skfda.misc.metrics._parse has changed +LOG: Cached module skfda.misc.metrics._parse has changed interface +TRACE: Priorities for skfda.exploratory.stats: skfda.exploratory.stats._fisher_rao:25 skfda.exploratory.stats._functional_transformers:25 skfda.exploratory.stats._stats:25 TRACE: Priorities for skfda.preprocessing.dim_reduction: skfda.preprocessing.dim_reduction._fpca:25 +TRACE: Priorities for skfda.preprocessing.registration: skfda.preprocessing.registration._fisher_rao:25 skfda.preprocessing.registration._landmark_registration:25 skfda.preprocessing.registration._lstsq_shift_registration:25 TRACE: Priorities for skfda.representation.basis: skfda.representation.basis._basis:25 skfda.representation.basis._bspline:25 skfda.representation.basis._constant:25 skfda.representation.basis._fdatabasis:25 skfda.representation.basis._finite_element:25 skfda.representation.basis._fourier:25 skfda.representation.basis._monomial:25 skfda.representation.basis._tensor_basis:25 skfda.representation.basis._vector_basis:25 TRACE: Priorities for skfda.representation: skfda.representation._functional_data:25 skfda.representation.basis:25 skfda.representation.grid:25 TRACE: Priorities for skfda._utils: skfda._utils._utils:25 skfda._utils._warping:25 TRACE: Priorities for skfda: skfda.representation:25 +TRACE: Priorities for skfda.misc.regularization: skfda.misc.regularization._regularization:25 +TRACE: Priorities for skfda.misc.operators: skfda.misc.operators._identity:25 skfda.misc.operators._integral_transform:25 skfda.misc.operators._linear_differential_operator:25 skfda.misc.operators._operators:25 skfda.misc.operators._srvf:25 +TRACE: Priorities for skfda.misc.metrics: skfda.misc.metrics._angular:25 skfda.misc.metrics._fisher_rao:25 skfda.misc.metrics._lp_distances:25 skfda.misc.metrics._lp_norms:25 skfda.misc.metrics._mahalanobis:25 skfda.misc.metrics._utils:25 TRACE: Priorities for skfda.misc: skfda.misc._math:25 TRACE: Priorities for skfda.preprocessing.smoothing._linear: skfda._utils:5 skfda.representation:5 skfda.preprocessing.smoothing.validation:20 TRACE: Priorities for skfda.preprocessing.smoothing.validation: skfda.representation:5 skfda.preprocessing.smoothing._linear:5 TRACE: Priorities for skfda._utils._utils: skfda.representation:25 skfda.representation.basis:25 skfda.representation.extrapolation:25 skfda:20 -TRACE: Priorities for skfda.preprocessing.registration: skfda._utils:5 skfda.preprocessing.registration._fisher_rao:25 skfda.preprocessing.registration._landmark_registration:25 skfda.preprocessing.registration._lstsq_shift_registration:25 TRACE: Priorities for skfda.misc.validation: skfda.representation:5 TRACE: Priorities for skfda.misc.operators._operators: skfda.representation:5 skfda.representation.basis:5 skfda.misc:20 TRACE: Priorities for skfda.misc.metrics._utils: skfda._utils:5 skfda.representation:5 TRACE: Priorities for skfda.misc.metrics._lp_norms: skfda.representation:5 skfda.misc:20 +TRACE: Priorities for skfda.exploratory.depth._depth: skfda.misc.metrics:5 skfda.misc.metrics._utils:5 skfda.representation:5 TRACE: Priorities for skfda.exploratory.stats._functional_transformers: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 +TRACE: Priorities for skfda.preprocessing.smoothing._basis: skfda._utils:5 skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.registration.base: skfda.representation:5 skfda.preprocessing.registration.validation:20 +TRACE: Priorities for skfda.preprocessing.registration.validation: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 skfda.preprocessing.registration.base:5 skfda.misc.metrics:20 TRACE: Priorities for skfda.representation.evaluator: skfda.representation._functional_data:25 skfda.misc.validation:20 TRACE: Priorities for skfda.representation.basis._basis: skfda.representation.basis._fdatabasis:25 skfda.misc.validation:20 skfda.representation.basis:20 skfda.misc:20 skfda._utils:20 TRACE: Priorities for skfda._utils._warping: skfda.representation:25 skfda.misc.validation:20 -TRACE: Priorities for skfda.misc.operators._srvf: skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.regularization._regularization: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._srvf: skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 skfda.misc.operators._operators:5 TRACE: Priorities for skfda.misc.operators._linear_differential_operator: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 TRACE: Priorities for skfda.misc.operators._integral_transform: skfda.representation:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._identity: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 skfda.misc.metrics:20 TRACE: Priorities for skfda.misc.metrics._lp_distances: skfda.representation:5 skfda.misc.metrics._lp_norms:5 skfda.misc.metrics._utils:5 skfda.misc:20 TRACE: Priorities for skfda.misc._math: skfda._utils:5 skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 +TRACE: Priorities for skfda.exploratory.depth: skfda.exploratory.depth._depth:5 TRACE: Priorities for skfda.representation.interpolation: skfda.representation.evaluator:5 skfda.representation.grid:25 skfda.misc.validation:20 TRACE: Priorities for skfda.representation.extrapolation: skfda.representation.evaluator:5 skfda.representation._functional_data:25 TRACE: Priorities for skfda.representation.basis._vector_basis: skfda.representation.basis._basis:5 skfda._utils:20 @@ -3815,223 +3657,422 @@ TRACE: Priorities for skfda.representation.basis._fourier: skfda.representation. TRACE: Priorities for skfda.representation.basis._finite_element: skfda.representation.basis._basis:5 TRACE: Priorities for skfda.representation.basis._constant: skfda.representation.basis._basis:5 TRACE: Priorities for skfda.representation.basis._bspline: skfda.representation.basis._basis:5 skfda.misc.validation:20 +TRACE: Priorities for skfda.misc.metrics._mahalanobis: skfda.representation:5 skfda.representation.basis:5 skfda.misc._math:5 skfda.misc.regularization._regularization:5 skfda.preprocessing.dim_reduction:20 +TRACE: Priorities for skfda.misc.metrics._fisher_rao: skfda._utils:5 skfda.representation:5 skfda.misc.operators:5 skfda.misc.metrics._lp_distances:5 skfda.misc.metrics._utils:5 skfda.preprocessing.registration:20 TRACE: Priorities for skfda.misc.metrics._angular: skfda.representation:5 skfda.misc._math:5 skfda.misc.metrics._utils:5 +TRACE: Priorities for skfda.exploratory.stats._stats: skfda.misc.metrics:5 skfda.representation:5 skfda.exploratory.depth:5 +TRACE: Priorities for skfda.exploratory.stats._fisher_rao: skfda._utils:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.interpolation:5 +TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 TRACE: Priorities for skfda.preprocessing.registration._landmark_registration: skfda.representation:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 TRACE: Priorities for skfda.representation._functional_data: skfda._utils:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.grid:25 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 TRACE: Priorities for skfda.exploratory.visualization._utils: skfda.representation._functional_data:5 TRACE: Priorities for skfda.exploratory.visualization._baseplot: skfda.representation:5 skfda.exploratory.visualization._utils:5 TRACE: Priorities for skfda.exploratory.visualization.representation: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation._functional_data:5 skfda.exploratory.visualization._baseplot:5 skfda.exploratory.visualization._utils:5 -TRACE: Priorities for skfda.misc.operators._identity: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 skfda.misc.metrics:20 -TRACE: Priorities for skfda.misc.operators: skfda.misc.operators._identity:5 skfda.misc.operators._integral_transform:5 skfda.misc.operators._linear_differential_operator:5 skfda.misc.operators._operators:5 skfda.misc.operators._srvf:5 -TRACE: Priorities for skfda.misc.regularization._regularization: skfda.misc.operators:5 skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 -TRACE: Priorities for skfda.misc.metrics._fisher_rao: skfda._utils:5 skfda.representation:5 skfda.misc.operators:5 skfda.misc.metrics._lp_distances:5 skfda.misc.metrics._utils:5 skfda.preprocessing.registration:20 -TRACE: Priorities for skfda.misc.metrics._mahalanobis: skfda.representation:5 skfda.representation.basis:5 skfda.misc._math:5 skfda.misc.regularization._regularization:5 skfda.preprocessing.dim_reduction:20 -TRACE: Priorities for skfda.misc.metrics: skfda.misc.metrics._angular:5 skfda.misc.metrics._fisher_rao:5 skfda.misc.metrics._lp_distances:5 skfda.misc.metrics._lp_norms:5 skfda.misc.metrics._mahalanobis:5 skfda.misc.metrics._utils:5 -TRACE: Priorities for skfda.exploratory.depth._depth: skfda.misc.metrics:5 skfda.misc.metrics._utils:5 skfda.representation:5 -TRACE: Priorities for skfda.preprocessing.registration.base: skfda.representation:5 skfda.preprocessing.registration.validation:20 -TRACE: Priorities for skfda.preprocessing.registration.validation: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 skfda.preprocessing.registration.base:5 skfda.misc.metrics:20 -TRACE: Priorities for skfda.exploratory.stats._fisher_rao: skfda._utils:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.interpolation:5 -TRACE: Priorities for skfda.misc.regularization: skfda.misc.regularization._regularization:5 +TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 TRACE: Priorities for skfda.misc.hat_matrix: skfda.misc:20 skfda.representation._functional_data:5 skfda.representation.basis:5 -TRACE: Priorities for skfda.exploratory.depth: skfda.exploratory.depth._depth:5 TRACE: Priorities for skfda.preprocessing.smoothing._kernel_smoothers: skfda._utils._utils:5 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.preprocessing.smoothing._basis: skfda._utils:5 skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.preprocessing.smoothing._linear:5 TRACE: Priorities for skfda.preprocessing.smoothing: skfda.preprocessing.smoothing.kernel_smoothers:20 skfda.preprocessing.smoothing._basis:25 skfda.preprocessing.smoothing._kernel_smoothers:25 TRACE: Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 -TRACE: Priorities for skfda.exploratory.stats._stats: skfda.misc.metrics:5 skfda.representation:5 skfda.exploratory.depth:5 TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 -TRACE: Priorities for skfda.exploratory.stats: skfda.exploratory.stats._fisher_rao:5 skfda.exploratory.stats._functional_transformers:5 skfda.exploratory.stats._stats:5 TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 -TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 -LOG: Processing SCC of size 62 (skfda.preprocessing.dim_reduction skfda.representation.basis skfda.representation skfda._utils skfda skfda.misc skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda._utils._utils skfda.preprocessing.registration skfda.misc.validation skfda.misc.operators._operators skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda.exploratory.stats._functional_transformers skfda.representation.evaluator skfda.representation.basis._basis skfda._utils._warping skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.metrics._lp_distances skfda.misc._math skfda.representation.interpolation skfda.representation.extrapolation skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.misc.metrics._angular skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.misc.operators._identity skfda.misc.operators skfda.misc.regularization._regularization skfda.misc.metrics._fisher_rao skfda.misc.metrics._mahalanobis skfda.misc.metrics skfda.exploratory.depth._depth skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.exploratory.stats._fisher_rao skfda.misc.regularization skfda.misc.hat_matrix skfda.exploratory.depth skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing._basis skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.preprocessing.registration._lstsq_shift_registration skfda.exploratory.stats._stats skfda.representation.grid skfda.exploratory.stats skfda.preprocessing.dim_reduction._fpca skfda.representation.basis._fdatabasis skfda.preprocessing.registration._fisher_rao) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._typing skfda.typing._base skfda.typing._numpy typing typing_extensions warnings) -LOG: Deleting skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json skfda/preprocessing/dim_reduction/__init__.data.json -LOG: Deleting skfda.representation.basis /home/carlos/git/scikit-fda/skfda/representation/basis/__init__.py skfda/representation/basis/__init__.meta.json skfda/representation/basis/__init__.data.json -LOG: Deleting skfda.representation /home/carlos/git/scikit-fda/skfda/representation/__init__.py skfda/representation/__init__.meta.json skfda/representation/__init__.data.json -LOG: Deleting skfda._utils /home/carlos/git/scikit-fda/skfda/_utils/__init__.py skfda/_utils/__init__.meta.json skfda/_utils/__init__.data.json -LOG: Deleting skfda /home/carlos/git/scikit-fda/skfda/__init__.py skfda/__init__.meta.json skfda/__init__.data.json -LOG: Deleting skfda.misc /home/carlos/git/scikit-fda/skfda/misc/__init__.py skfda/misc/__init__.meta.json skfda/misc/__init__.data.json -LOG: Deleting skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json skfda/preprocessing/smoothing/_linear.data.json -LOG: Deleting skfda.preprocessing.smoothing.validation /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json -LOG: Deleting skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json -LOG: Deleting skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json -LOG: Deleting skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json -LOG: Deleting skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json 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skfda/exploratory/stats/__init__.data.json -LOG: Deleting skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json -LOG: Deleting skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json -LOG: Deleting skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json +LOG: Processing SCC of size 62 (skfda.exploratory.stats skfda.preprocessing.dim_reduction skfda.preprocessing.registration skfda.representation.basis skfda.representation skfda._utils skfda skfda.misc.regularization skfda.misc.operators skfda.misc.metrics skfda.misc skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda._utils._utils skfda.misc.validation skfda.misc.operators._operators skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda.exploratory.depth._depth skfda.exploratory.stats._functional_transformers skfda.preprocessing.smoothing._basis skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.representation.evaluator skfda.representation.basis._basis skfda._utils._warping skfda.misc.regularization._regularization skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.operators._identity skfda.misc.metrics._lp_distances skfda.misc._math skfda.exploratory.depth skfda.representation.interpolation skfda.representation.extrapolation skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.misc.metrics._mahalanobis skfda.misc.metrics._fisher_rao skfda.misc.metrics._angular skfda.exploratory.stats._stats skfda.exploratory.stats._fisher_rao skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.preprocessing.registration._fisher_rao skfda.misc.hat_matrix skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.representation.grid skfda.preprocessing.dim_reduction._fpca skfda.representation.basis._fdatabasis) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._parse skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) +LOG: Writing skfda.exploratory.stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/__init__.py skfda/exploratory/stats/__init__.meta.json skfda/exploratory/stats/__init__.data.json +TRACE: Interface for skfda.exploratory.stats has changed +LOG: Cached module skfda.exploratory.stats has changed interface +LOG: Writing skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json skfda/preprocessing/dim_reduction/__init__.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction has changed +LOG: Cached module skfda.preprocessing.dim_reduction has changed interface +LOG: Writing skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json +TRACE: Interface for skfda.preprocessing.registration has changed +LOG: Cached module skfda.preprocessing.registration has changed interface +LOG: Writing skfda.representation.basis /home/carlos/git/scikit-fda/skfda/representation/basis/__init__.py skfda/representation/basis/__init__.meta.json skfda/representation/basis/__init__.data.json +TRACE: Interface for skfda.representation.basis has changed +LOG: Cached module skfda.representation.basis has changed interface +LOG: Writing skfda.representation /home/carlos/git/scikit-fda/skfda/representation/__init__.py skfda/representation/__init__.meta.json skfda/representation/__init__.data.json +TRACE: Interface for skfda.representation has changed +LOG: Cached module skfda.representation has changed interface +LOG: Writing skfda._utils 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/home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json +TRACE: Interface for skfda.preprocessing.smoothing.validation has changed +LOG: Cached module skfda.preprocessing.smoothing.validation has changed interface +LOG: Writing skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json +TRACE: Interface for skfda._utils._utils has changed +LOG: Cached module skfda._utils._utils has changed interface +LOG: Writing skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json +TRACE: Interface for skfda.misc.validation has changed +LOG: Cached module skfda.misc.validation has changed interface +LOG: Writing skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json +TRACE: Interface for skfda.misc.operators._operators has changed +LOG: Cached module skfda.misc.operators._operators has changed interface +LOG: Writing skfda.misc.metrics._utils /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py skfda/misc/metrics/_utils.meta.json skfda/misc/metrics/_utils.data.json +TRACE: Interface for skfda.misc.metrics._utils has changed +LOG: Cached module skfda.misc.metrics._utils has changed interface +LOG: Writing skfda.misc.metrics._lp_norms /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py skfda/misc/metrics/_lp_norms.meta.json skfda/misc/metrics/_lp_norms.data.json +TRACE: Interface for skfda.misc.metrics._lp_norms has changed +LOG: Cached module skfda.misc.metrics._lp_norms has changed interface +LOG: Writing skfda.exploratory.depth._depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py skfda/exploratory/depth/_depth.meta.json skfda/exploratory/depth/_depth.data.json +TRACE: Interface for skfda.exploratory.depth._depth has changed +LOG: Cached module skfda.exploratory.depth._depth has changed interface +LOG: Writing skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json +TRACE: Interface for skfda.exploratory.stats._functional_transformers has changed +LOG: Cached module skfda.exploratory.stats._functional_transformers has changed interface +LOG: Writing skfda.preprocessing.smoothing._basis /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py skfda/preprocessing/smoothing/_basis.meta.json skfda/preprocessing/smoothing/_basis.data.json +TRACE: Interface for skfda.preprocessing.smoothing._basis has changed +LOG: Cached module skfda.preprocessing.smoothing._basis has changed interface +LOG: Writing skfda.preprocessing.registration.base /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py skfda/preprocessing/registration/base.meta.json skfda/preprocessing/registration/base.data.json +TRACE: Interface for skfda.preprocessing.registration.base has changed +LOG: Cached module skfda.preprocessing.registration.base has changed interface +LOG: Writing skfda.preprocessing.registration.validation /home/carlos/git/scikit-fda/skfda/preprocessing/registration/validation.py skfda/preprocessing/registration/validation.meta.json skfda/preprocessing/registration/validation.data.json +TRACE: Interface for skfda.preprocessing.registration.validation has changed +LOG: Cached module skfda.preprocessing.registration.validation has changed interface +LOG: Writing skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json +TRACE: Interface for 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skfda.misc.regularization._regularization has changed +LOG: Cached module skfda.misc.regularization._regularization has changed interface +LOG: Writing skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json +TRACE: Interface for skfda.misc.operators._srvf has changed +LOG: Cached module skfda.misc.operators._srvf has changed interface +LOG: Writing skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json +TRACE: Interface for skfda.misc.operators._linear_differential_operator has changed +LOG: Cached module skfda.misc.operators._linear_differential_operator has changed interface +LOG: Writing skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json +TRACE: Interface for skfda.misc.operators._integral_transform has changed +LOG: Cached module skfda.misc.operators._integral_transform has changed interface +LOG: Writing skfda.misc.operators._identity /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json +TRACE: Interface for skfda.misc.operators._identity has changed +LOG: Cached module skfda.misc.operators._identity has changed interface +LOG: Writing skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json +TRACE: Interface for skfda.misc.metrics._lp_distances has changed +LOG: Cached module skfda.misc.metrics._lp_distances has changed interface +LOG: Writing skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json +TRACE: Interface for skfda.misc._math has changed +LOG: Cached module skfda.misc._math has changed interface +LOG: Writing skfda.exploratory.depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/__init__.py skfda/exploratory/depth/__init__.meta.json skfda/exploratory/depth/__init__.data.json +TRACE: Interface for skfda.exploratory.depth has changed +LOG: Cached module skfda.exploratory.depth has changed interface +LOG: Writing skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json +TRACE: Interface for skfda.representation.interpolation has changed +LOG: Cached module skfda.representation.interpolation has changed interface +LOG: Writing skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json +TRACE: Interface for skfda.representation.extrapolation has changed +LOG: Cached module skfda.representation.extrapolation has changed interface +LOG: Writing skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json +TRACE: Interface for skfda.representation.basis._vector_basis has changed +LOG: Cached module skfda.representation.basis._vector_basis has changed interface +LOG: Writing skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json +TRACE: Interface for skfda.representation.basis._tensor_basis has changed +LOG: Cached module skfda.representation.basis._tensor_basis has changed interface +LOG: Writing skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json +TRACE: Interface for skfda.representation.basis._monomial has changed +LOG: Cached module skfda.representation.basis._monomial has changed interface +LOG: Writing skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json +TRACE: Interface for skfda.representation.basis._fourier has changed +LOG: Cached module skfda.representation.basis._fourier has changed interface +LOG: Writing skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json +TRACE: Interface for skfda.representation.basis._finite_element has changed +LOG: Cached module skfda.representation.basis._finite_element has changed interface +LOG: Writing skfda.representation.basis._constant /home/carlos/git/scikit-fda/skfda/representation/basis/_constant.py skfda/representation/basis/_constant.meta.json skfda/representation/basis/_constant.data.json +TRACE: Interface for skfda.representation.basis._constant has changed +LOG: Cached module skfda.representation.basis._constant has changed interface +LOG: Writing skfda.representation.basis._bspline /home/carlos/git/scikit-fda/skfda/representation/basis/_bspline.py skfda/representation/basis/_bspline.meta.json skfda/representation/basis/_bspline.data.json +TRACE: Interface for skfda.representation.basis._bspline has changed +LOG: Cached module skfda.representation.basis._bspline has changed interface +LOG: Writing skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json +TRACE: Interface for skfda.misc.metrics._mahalanobis has changed +LOG: Cached module skfda.misc.metrics._mahalanobis has changed interface +LOG: Writing skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json +TRACE: Interface for skfda.misc.metrics._fisher_rao has changed +LOG: Cached module skfda.misc.metrics._fisher_rao has changed interface +LOG: Writing skfda.misc.metrics._angular /home/carlos/git/scikit-fda/skfda/misc/metrics/_angular.py skfda/misc/metrics/_angular.meta.json skfda/misc/metrics/_angular.data.json +TRACE: Interface for skfda.misc.metrics._angular has changed +LOG: Cached module skfda.misc.metrics._angular has changed interface +LOG: Writing skfda.exploratory.stats._stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/_stats.py skfda/exploratory/stats/_stats.meta.json skfda/exploratory/stats/_stats.data.json +TRACE: Interface for skfda.exploratory.stats._stats has changed +LOG: Cached module skfda.exploratory.stats._stats has changed interface +LOG: Writing skfda.exploratory.stats._fisher_rao /home/carlos/git/scikit-fda/skfda/exploratory/stats/_fisher_rao.py skfda/exploratory/stats/_fisher_rao.meta.json skfda/exploratory/stats/_fisher_rao.data.json +TRACE: Interface for skfda.exploratory.stats._fisher_rao has changed +LOG: Cached module skfda.exploratory.stats._fisher_rao has changed interface +LOG: Writing skfda.preprocessing.registration._lstsq_shift_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py skfda/preprocessing/registration/_lstsq_shift_registration.meta.json skfda/preprocessing/registration/_lstsq_shift_registration.data.json +TRACE: Interface for skfda.preprocessing.registration._lstsq_shift_registration has changed +LOG: Cached module skfda.preprocessing.registration._lstsq_shift_registration has changed interface +LOG: Writing skfda.preprocessing.registration._landmark_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py skfda/preprocessing/registration/_landmark_registration.meta.json skfda/preprocessing/registration/_landmark_registration.data.json +TRACE: Interface for skfda.preprocessing.registration._landmark_registration has changed +LOG: Cached module skfda.preprocessing.registration._landmark_registration has changed interface +LOG: Writing skfda.representation._functional_data /home/carlos/git/scikit-fda/skfda/representation/_functional_data.py skfda/representation/_functional_data.meta.json skfda/representation/_functional_data.data.json +TRACE: Interface for skfda.representation._functional_data has changed +LOG: Cached module skfda.representation._functional_data has changed interface +LOG: Writing skfda.exploratory.visualization._utils /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_utils.py skfda/exploratory/visualization/_utils.meta.json skfda/exploratory/visualization/_utils.data.json +TRACE: Interface for skfda.exploratory.visualization._utils has changed +LOG: Cached module skfda.exploratory.visualization._utils has changed interface +LOG: Writing skfda.exploratory.visualization._baseplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py skfda/exploratory/visualization/_baseplot.meta.json skfda/exploratory/visualization/_baseplot.data.json +TRACE: Interface for skfda.exploratory.visualization._baseplot has changed +LOG: Cached module skfda.exploratory.visualization._baseplot has changed interface +LOG: Writing skfda.exploratory.visualization.representation /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py skfda/exploratory/visualization/representation.meta.json skfda/exploratory/visualization/representation.data.json +TRACE: Interface for skfda.exploratory.visualization.representation has changed +LOG: Cached module skfda.exploratory.visualization.representation has changed interface +LOG: Writing skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json +TRACE: Interface for skfda.preprocessing.registration._fisher_rao has changed +LOG: Cached module skfda.preprocessing.registration._fisher_rao has changed interface +LOG: Writing skfda.misc.hat_matrix /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py skfda/misc/hat_matrix.meta.json skfda/misc/hat_matrix.data.json +TRACE: Interface for skfda.misc.hat_matrix has changed +LOG: Cached module skfda.misc.hat_matrix has changed interface +LOG: Writing skfda.preprocessing.smoothing._kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py skfda/preprocessing/smoothing/_kernel_smoothers.meta.json skfda/preprocessing/smoothing/_kernel_smoothers.data.json +TRACE: Interface for skfda.preprocessing.smoothing._kernel_smoothers has changed +LOG: Cached module skfda.preprocessing.smoothing._kernel_smoothers has changed interface +LOG: Writing skfda.preprocessing.smoothing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py skfda/preprocessing/smoothing/__init__.meta.json skfda/preprocessing/smoothing/__init__.data.json +TRACE: Interface for skfda.preprocessing.smoothing has changed +LOG: Cached module skfda.preprocessing.smoothing has changed interface +LOG: Writing skfda.preprocessing.smoothing.kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/kernel_smoothers.py skfda/preprocessing/smoothing/kernel_smoothers.meta.json skfda/preprocessing/smoothing/kernel_smoothers.data.json +TRACE: Interface for skfda.preprocessing.smoothing.kernel_smoothers has changed +LOG: Cached module skfda.preprocessing.smoothing.kernel_smoothers has changed interface +LOG: Writing skfda.representation.grid /home/carlos/git/scikit-fda/skfda/representation/grid.py skfda/representation/grid.meta.json skfda/representation/grid.data.json +TRACE: Interface for skfda.representation.grid has changed +LOG: Cached module skfda.representation.grid has changed interface +LOG: Writing skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction._fpca has changed +LOG: Cached module skfda.preprocessing.dim_reduction._fpca has changed interface +LOG: Writing skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json +TRACE: Interface for skfda.representation.basis._fdatabasis has changed +LOG: Cached module skfda.representation.basis._fdatabasis has changed interface TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) -LOG: Deleting skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json +LOG: Writing skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._function_transformers has changed +LOG: Cached module skfda.preprocessing.feature_construction._function_transformers has changed interface TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as inherently stale with stale deps (__future__ builtins numpy skfda.representation typing) -LOG: Deleting skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json +LOG: Writing skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._fda_feature_union has changed +LOG: Cached module skfda.preprocessing.feature_construction._fda_feature_union has changed interface TRACE: Priorities for skfda.preprocessing.feature_construction._evaluation_trasformer: LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._evaluation_trasformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation._functional_data skfda.representation.extrapolation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json +LOG: Writing skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._evaluation_trasformer has changed +LOG: Cached module skfda.preprocessing.feature_construction._evaluation_trasformer has changed interface TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) -LOG: Deleting skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json +LOG: Writing skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._coefficients_transformer has changed +LOG: Cached module skfda.preprocessing.feature_construction._coefficients_transformer has changed interface TRACE: Priorities for skfda.ml.regression._coefficients: LOG: Processing SCC singleton (skfda.ml.regression._coefficients) as inherently stale with stale deps (__future__ abc builtins functools numpy skfda.misc._math skfda.representation.basis typing) -LOG: Deleting skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json +LOG: Writing skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json +TRACE: Interface for skfda.ml.regression._coefficients has changed +LOG: Cached module skfda.ml.regression._coefficients has changed interface TRACE: Priorities for skfda.preprocessing.feature_construction._per_class_transformer: LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._per_class_transformer) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._numpy typing warnings) -LOG: Deleting skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json +LOG: Writing skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._per_class_transformer has changed +LOG: Cached module skfda.preprocessing.feature_construction._per_class_transformer has changed interface TRACE: Priorities for skfda.ml.regression._kernel_regression: -LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as inherently stale with stale deps (__future__ builtins numpy skfda.misc.hat_matrix skfda.misc.metrics skfda.misc.metrics._typing skfda.representation._functional_data typing) -LOG: Deleting skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json +LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as inherently stale with stale deps (__future__ builtins numpy skfda.misc.hat_matrix skfda.misc.metrics skfda.representation._functional_data skfda.typing._metric typing) +LOG: Writing skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json +TRACE: Interface for skfda.ml.regression._kernel_regression has changed +LOG: Cached module skfda.ml.regression._kernel_regression has changed interface TRACE: Priorities for skfda.ml.regression._historical_linear_model: LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as inherently stale with stale deps (__future__ builtins math numpy skfda._utils skfda.representation skfda.representation.basis typing) -LOG: Deleting skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json +LOG: Writing skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json +TRACE: Interface for skfda.ml.regression._historical_linear_model has changed +LOG: Cached module skfda.ml.regression._historical_linear_model has changed interface TRACE: Priorities for skfda.ml.clustering._kmeans: -LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as inherently stale with stale deps (__future__ abc builtins numpy skfda._utils skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._numpy typing warnings) -LOG: Deleting skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json +LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as inherently stale with stale deps (__future__ abc builtins numpy skfda._utils skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._metric skfda.typing._numpy typing warnings) +LOG: Writing skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json +TRACE: Interface for skfda.ml.clustering._kmeans has changed +LOG: Cached module skfda.ml.clustering._kmeans has changed interface TRACE: Priorities for skfda.ml.clustering._hierarchical: -LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as inherently stale with stale deps (__future__ builtins enum numpy skfda.misc.metrics skfda.misc.metrics._typing skfda.representation typing typing_extensions) -LOG: Deleting skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json +LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as inherently stale with stale deps (__future__ builtins enum numpy skfda.misc.metrics skfda.misc.metrics._parse skfda.representation skfda.typing._metric typing typing_extensions) +LOG: Writing skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json +TRACE: Interface for skfda.ml.clustering._hierarchical has changed +LOG: Cached module skfda.ml.clustering._hierarchical has changed interface TRACE: Priorities for skfda.ml.classification._parameterized_functional_qda: LOG: Processing SCC singleton (skfda.ml.classification._parameterized_functional_qda) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda.representation skfda.typing._numpy typing) -LOG: Deleting skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json +LOG: Writing skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json +TRACE: Interface for skfda.ml.classification._parameterized_functional_qda has changed +LOG: Cached module skfda.ml.classification._parameterized_functional_qda has changed interface TRACE: Priorities for skfda.ml.classification._logistic_regression: LOG: Processing SCC singleton (skfda.ml.classification._logistic_regression) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json skfda/ml/classification/_logistic_regression.data.json +LOG: Writing skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json skfda/ml/classification/_logistic_regression.data.json +TRACE: Interface for skfda.ml.classification._logistic_regression has changed +LOG: Cached module skfda.ml.classification._logistic_regression has changed interface TRACE: Priorities for skfda.ml.classification._centroid_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._centroid_classifiers) as inherently stale with stale deps (__future__ builtins skfda._utils skfda.exploratory.depth skfda.exploratory.stats skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._numpy typing) -LOG: Deleting skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json +LOG: Processing SCC singleton (skfda.ml.classification._centroid_classifiers) as inherently stale with stale deps (__future__ builtins skfda._utils skfda.exploratory.depth skfda.exploratory.stats skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing) +LOG: Writing skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json +TRACE: Interface for skfda.ml.classification._centroid_classifiers has changed +LOG: Cached module skfda.ml.classification._centroid_classifiers has changed interface TRACE: Priorities for skfda.ml._neighbors_base: -LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._typing skfda.misc.metrics._utils skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.ml._neighbors_base /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py skfda/ml/_neighbors_base.meta.json skfda/ml/_neighbors_base.data.json +LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml._neighbors_base /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py skfda/ml/_neighbors_base.meta.json skfda/ml/_neighbors_base.data.json +TRACE: Interface for skfda.ml._neighbors_base has changed +LOG: Cached module skfda.ml._neighbors_base has changed interface TRACE: Priorities for skfda.exploratory.outliers._outliergram: LOG: Processing SCC singleton (skfda.exploratory.outliers._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda._utils._sklearn_adapter skfda.exploratory.depth._depth skfda.exploratory.stats skfda.representation skfda.typing._numpy) -LOG: Deleting skfda.exploratory.outliers._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py skfda/exploratory/outliers/_outliergram.meta.json skfda/exploratory/outliers/_outliergram.data.json +LOG: Writing skfda.exploratory.outliers._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py skfda/exploratory/outliers/_outliergram.meta.json skfda/exploratory/outliers/_outliergram.data.json +TRACE: Interface for skfda.exploratory.outliers._outliergram has changed +LOG: Cached module skfda.exploratory.outliers._outliergram has changed interface TRACE: Priorities for skfda.exploratory.outliers._envelopes: LOG: Processing SCC singleton (skfda.exploratory.outliers._envelopes) as inherently stale with stale deps (__future__ builtins math numpy skfda.representation skfda.typing._numpy typing) -LOG: Deleting skfda.exploratory.outliers._envelopes /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py skfda/exploratory/outliers/_envelopes.meta.json skfda/exploratory/outliers/_envelopes.data.json +LOG: Writing skfda.exploratory.outliers._envelopes /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py skfda/exploratory/outliers/_envelopes.meta.json skfda/exploratory/outliers/_envelopes.data.json +TRACE: Interface for skfda.exploratory.outliers._envelopes has changed +LOG: Cached module skfda.exploratory.outliers._envelopes has changed interface TRACE: Priorities for skfda.exploratory.visualization.fpca: LOG: Processing SCC singleton (skfda.exploratory.visualization.fpca) as inherently stale with stale deps (__future__ builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.representation typing warnings) -LOG: Deleting skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json +LOG: Writing skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json +TRACE: Interface for skfda.exploratory.visualization.fpca has changed +LOG: Cached module skfda.exploratory.visualization.fpca has changed interface TRACE: Priorities for skfda.exploratory.visualization._parametric_plot: LOG: Processing SCC singleton (skfda.exploratory.visualization._parametric_plot) as inherently stale with stale deps (builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.exploratory.visualization.representation skfda.representation typing) -LOG: Deleting skfda.exploratory.visualization._parametric_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py skfda/exploratory/visualization/_parametric_plot.meta.json skfda/exploratory/visualization/_parametric_plot.data.json +LOG: Writing skfda.exploratory.visualization._parametric_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py skfda/exploratory/visualization/_parametric_plot.meta.json skfda/exploratory/visualization/_parametric_plot.data.json +TRACE: Interface for skfda.exploratory.visualization._parametric_plot has changed +LOG: Cached module skfda.exploratory.visualization._parametric_plot has changed interface TRACE: Priorities for skfda.exploratory.visualization._multiple_display: LOG: Processing SCC singleton (skfda.exploratory.visualization._multiple_display) as inherently stale with stale deps (builtins copy functools itertools numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils typing) -LOG: Deleting skfda.exploratory.visualization._multiple_display /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_multiple_display.py skfda/exploratory/visualization/_multiple_display.meta.json skfda/exploratory/visualization/_multiple_display.data.json +LOG: Writing skfda.exploratory.visualization._multiple_display /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_multiple_display.py skfda/exploratory/visualization/_multiple_display.meta.json skfda/exploratory/visualization/_multiple_display.data.json +TRACE: Interface for skfda.exploratory.visualization._multiple_display has changed +LOG: Cached module skfda.exploratory.visualization._multiple_display has changed interface TRACE: Priorities for skfda.exploratory.visualization._ddplot: LOG: Processing SCC singleton (skfda.exploratory.visualization._ddplot) as inherently stale with stale deps (builtins numpy skfda.exploratory.depth.multivariate skfda.exploratory.visualization._baseplot skfda.representation._functional_data skfda.typing._numpy typing) -LOG: Deleting skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json +LOG: Writing skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json +TRACE: Interface for skfda.exploratory.visualization._ddplot has changed +LOG: Cached module skfda.exploratory.visualization._ddplot has changed interface TRACE: Priorities for skfda.exploratory.visualization.clustering: LOG: Processing SCC singleton (skfda.exploratory.visualization.clustering) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.misc.validation skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.exploratory.visualization.clustering /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py skfda/exploratory/visualization/clustering.meta.json skfda/exploratory/visualization/clustering.data.json +LOG: Writing skfda.exploratory.visualization.clustering /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py skfda/exploratory/visualization/clustering.meta.json skfda/exploratory/visualization/clustering.data.json +TRACE: Interface for skfda.exploratory.visualization.clustering has changed +LOG: Cached module skfda.exploratory.visualization.clustering has changed interface TRACE: Priorities for skfda.datasets._real_datasets: LOG: Processing SCC singleton (skfda.datasets._real_datasets) as inherently stale with stale deps (builtins numpy skfda.representation typing typing_extensions warnings) -LOG: Deleting skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json +LOG: Writing skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json +TRACE: Interface for skfda.datasets._real_datasets has changed +LOG: Cached module skfda.datasets._real_datasets has changed interface TRACE: Priorities for skfda.preprocessing.feature_construction: LOG: Processing SCC singleton (skfda.preprocessing.feature_construction) as inherently stale with stale deps (builtins skfda.preprocessing.feature_construction._coefficients_transformer skfda.preprocessing.feature_construction._evaluation_trasformer skfda.preprocessing.feature_construction._fda_feature_union skfda.preprocessing.feature_construction._function_transformers skfda.preprocessing.feature_construction._per_class_transformer typing) -LOG: Deleting skfda.preprocessing.feature_construction /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py skfda/preprocessing/feature_construction/__init__.meta.json skfda/preprocessing/feature_construction/__init__.data.json +LOG: Writing skfda.preprocessing.feature_construction /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py skfda/preprocessing/feature_construction/__init__.meta.json skfda/preprocessing/feature_construction/__init__.data.json +TRACE: Interface for skfda.preprocessing.feature_construction has changed +LOG: Cached module skfda.preprocessing.feature_construction has changed interface TRACE: Priorities for skfda.ml.regression._neighbors_regression: -LOG: Processing SCC singleton (skfda.ml.regression._neighbors_regression) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.misc.metrics._typing skfda.ml._neighbors_base skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json +LOG: Processing SCC singleton (skfda.ml.regression._neighbors_regression) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json +TRACE: Interface for skfda.ml.regression._neighbors_regression has changed +LOG: Cached module skfda.ml.regression._neighbors_regression has changed interface TRACE: Priorities 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+TRACE: Interface for skfda.ml.clustering._neighbors_clustering has changed +LOG: Cached module skfda.ml.clustering._neighbors_clustering has changed interface TRACE: Priorities for skfda.ml.classification._neighbors_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.misc.metrics._typing skfda.ml._neighbors_base skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json +LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json +TRACE: Interface for skfda.ml.classification._neighbors_classifiers has changed +LOG: Cached module skfda.ml.classification._neighbors_classifiers has changed interface TRACE: Priorities for skfda.ml.classification._depth_classifiers: LOG: Processing SCC singleton (skfda.ml.classification._depth_classifiers) as inherently stale with stale deps (__future__ builtins collections contextlib itertools numpy skfda._utils skfda.exploratory.depth skfda.preprocessing.feature_construction._per_class_transformer skfda.representation.grid skfda.typing._numpy typing) -LOG: Deleting skfda.ml.classification._depth_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py skfda/ml/classification/_depth_classifiers.meta.json skfda/ml/classification/_depth_classifiers.data.json +LOG: Writing skfda.ml.classification._depth_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py skfda/ml/classification/_depth_classifiers.meta.json skfda/ml/classification/_depth_classifiers.data.json +TRACE: Interface for skfda.ml.classification._depth_classifiers has changed +LOG: Cached module skfda.ml.classification._depth_classifiers has changed interface TRACE: Priorities for skfda.exploratory.outliers.neighbors_outlier: -LOG: Processing SCC singleton (skfda.exploratory.outliers.neighbors_outlier) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.misc.metrics._typing skfda.ml._neighbors_base skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Deleting skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json +LOG: Processing SCC singleton (skfda.exploratory.outliers.neighbors_outlier) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json +TRACE: Interface for skfda.exploratory.outliers.neighbors_outlier has changed +LOG: Cached module skfda.exploratory.outliers.neighbors_outlier has changed interface +TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:25 TRACE: Priorities for skfda.misc.covariances: skfda.datasets:20 TRACE: Priorities for skfda.datasets._samples_generators: skfda.misc.covariances:10 -TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:5 -LOG: Processing SCC of size 3 (skfda.misc.covariances skfda.datasets._samples_generators skfda.datasets) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) -LOG: Deleting skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json -LOG: Deleting skfda.datasets._samples_generators /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py skfda/datasets/_samples_generators.meta.json skfda/datasets/_samples_generators.data.json -LOG: Deleting skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json +LOG: Processing SCC of size 3 (skfda.datasets skfda.misc.covariances skfda.datasets._samples_generators) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) +LOG: Writing skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json +TRACE: Interface for skfda.datasets has changed +LOG: Cached module skfda.datasets has changed interface +LOG: Writing skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json +TRACE: Interface for skfda.misc.covariances has changed +LOG: Cached module skfda.misc.covariances has changed interface +LOG: Writing skfda.datasets._samples_generators 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_DependenceMeasure, _evaluate_grid as _evaluate_grid, _int_to_real as _int_to_real, + _MapAcceptable as _MapAcceptable, _pairwise_symmetric as _pairwise_symmetric, _same_domain as _same_domain, _to_grid as _to_grid, @@ -51,4 +53,8 @@ nquad_vec as nquad_vec, ) - from ._warping import invert_warping, normalize_scale, normalize_warping + from ._warping import ( + invert_warping as invert_warping, + normalize_scale as normalize_scale, + normalize_warping as normalize_warping, + ) diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 743465bed..009c3969d 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -457,7 +457,8 @@ def _map_in_batches( arguments: Tuple[_MapAcceptableT, ...], indexes: Tuple[NDArrayInt, ...], memory_per_batch: Optional[int] = None, - **kwargs: P.kwargs, # type: ignore[name-defined] + *args: P.args, # Should be empty + **kwargs: P.kwargs, ) -> np.typing.NDArray[ArrayDTypeT]: """ Map a function over samples of FData or ndarray tuples efficiently. @@ -501,12 +502,13 @@ def _pairwise_symmetric( arg1: _MapAcceptableT, arg2: Optional[_MapAcceptableT] = None, memory_per_batch: Optional[int] = None, - **kwargs: P.kwargs, # type: ignore[name-defined] + *args: P.args, # Should be empty + **kwargs: P.kwargs, ) -> np.typing.NDArray[ArrayDTypeT]: """Compute pairwise a commutative function.""" def map_function( *args: _MapAcceptableT, - **kwargs: P.kwargs, # type: ignore[name-defined] + **kwargs: P.kwargs, ) -> np.typing.NDArray[ArrayDTypeT]: """Just to keep Mypy happy.""" return function(args[0], args[1], **kwargs) @@ -519,8 +521,8 @@ def map_function( map_function, (arg1, arg1), triu_indices, - memory_per_batch=memory_per_batch, - **kwargs, + memory_per_batch, + **kwargs, # type: ignore[arg-type] ) matrix = np.empty((dim1, dim1), dtype=triang_vec.dtype) @@ -541,7 +543,7 @@ def map_function( (arg1, arg2), (indices[0].ravel(), indices[1].ravel()), memory_per_batch=memory_per_batch, - **kwargs, + **kwargs, # type: ignore[arg-type] ) return np.reshape(vec, (dim1, dim2)) diff --git a/skfda/_utils/_warping.py b/skfda/_utils/_warping.py index f3fe7829d..092a68f57 100644 --- a/skfda/_utils/_warping.py +++ b/skfda/_utils/_warping.py @@ -9,8 +9,8 @@ import numpy as np from scipy.interpolate import PchipInterpolator -from ..typing._base import ArrayLike, DomainRangeLike -from ..typing._numpy import NDArrayFloat +from ..typing._base import DomainRangeLike +from ..typing._numpy import ArrayLike, NDArrayFloat if TYPE_CHECKING: from ..representation import FDataGrid diff --git a/skfda/datasets/__init__.py b/skfda/datasets/__init__.py index 90e48255b..666495cef 100644 --- a/skfda/datasets/__init__.py +++ b/skfda/datasets/__init__.py @@ -1,24 +1,59 @@ -from ._real_datasets import ( - fdata_constructor, - fetch_aemet, - fetch_cran, - fetch_gait, - fetch_growth, - fetch_handwriting, - fetch_mco, - fetch_medflies, - fetch_nox, - fetch_octane, - fetch_phoneme, - fetch_tecator, - fetch_ucr, - fetch_weather, -) -from ._samples_generators import ( - make_gaussian, - make_gaussian_process, - make_multimodal_landmarks, - make_multimodal_samples, - make_random_warping, - make_sinusoidal_process, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_real_datasets": [ + "fdata_constructor", + "fetch_aemet", + "fetch_cran", + "fetch_gait", + "fetch_growth", + "fetch_handwriting", + "fetch_mco", + "fetch_medflies", + "fetch_nox", + "fetch_octane", + "fetch_phoneme", + "fetch_tecator", + "fetch_ucr", + "fetch_weather", + ], + "_samples_generators": [ + "make_gaussian", + "make_gaussian_process", + "make_multimodal_landmarks", + "make_multimodal_samples", + "make_random_warping", + "make_sinusoidal_process", + ], + }, ) + +if TYPE_CHECKING: + from ._real_datasets import ( + fdata_constructor as fdata_constructor, + fetch_aemet as fetch_aemet, + fetch_cran as fetch_cran, + fetch_gait as fetch_gait, + fetch_growth as fetch_growth, + fetch_handwriting as fetch_handwriting, + fetch_mco as fetch_mco, + fetch_medflies as fetch_medflies, + fetch_nox as fetch_nox, + fetch_octane as fetch_octane, + fetch_phoneme as fetch_phoneme, + fetch_tecator as fetch_tecator, + fetch_ucr as fetch_ucr, + fetch_weather as fetch_weather, + ) + from ._samples_generators import ( + make_gaussian as make_gaussian, + make_gaussian_process as make_gaussian_process, + make_multimodal_landmarks as make_multimodal_landmarks, + make_multimodal_samples as make_multimodal_samples, + make_random_warping as make_random_warping, + make_sinusoidal_process as make_sinusoidal_process, + ) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 1587fab47..1a051f72c 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -1,20 +1,57 @@ """Statistics.""" -from ._fisher_rao import _fisher_rao_warping_mean, fisher_rao_karcher_mean -from ._functional_transformers import ( - local_averages, - number_up_crossings, - occupation_measure, - unconditional_central_moment, - unconditional_expected_value, - unconditional_moment, -) -from ._stats import ( - cov, - depth_based_median, - geometric_median, - gmean, - mean, - modified_epigraph_index, - trim_mean, - var, + +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_fisher_rao": [ + "_fisher_rao_warping_mean", + "fisher_rao_karcher_mean", + ], + "_functional_transformers": [ + "local_averages", + "number_up_crossings", + "occupation_measure", + "unconditional_central_moment", + "unconditional_expected_value", + "unconditional_moment", + ], + "_stats": [ + "cov", + "depth_based_median", + "geometric_median", + "gmean", + "mean", + "modified_epigraph_index", + "trim_mean", + "var", + ], + }, ) + +if TYPE_CHECKING: + from ._fisher_rao import ( + _fisher_rao_warping_mean as _fisher_rao_warping_mean, + fisher_rao_karcher_mean as fisher_rao_karcher_mean, + ) + from ._functional_transformers import ( + local_averages as local_averages, + number_up_crossings as number_up_crossings, + occupation_measure as occupation_measure, + unconditional_central_moment as unconditional_central_moment, + unconditional_expected_value as unconditional_expected_value, + unconditional_moment as unconditional_moment, + ) + from ._stats import ( + cov as cov, + depth_based_median as depth_based_median, + geometric_median as geometric_median, + gmean as gmean, + mean as mean, + modified_epigraph_index as modified_epigraph_index, + trim_mean as trim_mean, + var as var, + ) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index c4ccfcd56..68751d10f 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -16,7 +16,7 @@ from ..representation import FData, FDataBasis, FDataGrid from ..representation.basis import Basis from ..typing._base import DomainRange -from ..typing._numpy import ArrayLike, NDArrayFloat +from ..typing._numpy import NDArrayFloat from .validation import check_fdata_same_dimensions Vector = TypeVar( @@ -447,8 +447,8 @@ def _inner_product_fdatabasis( def _inner_product_integrate( - arg1: Callable[[ArrayLike], NDArrayFloat], - arg2: Callable[[ArrayLike], NDArrayFloat], + arg1: Callable[[NDArrayFloat], NDArrayFloat], + arg2: Callable[[NDArrayFloat], NDArrayFloat], *, _matrix: bool = False, _domain_range: Optional[DomainRange] = None, @@ -476,7 +476,7 @@ def _inner_product_integrate( len_arg1 = len(arg1(left_domain)) len_arg2 = len(arg2(left_domain)) - def integrand(*args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 + def integrand(args: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 f1 = arg1(args)[:, 0, :] f2 = arg2(args)[:, 0, :] diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index c2b4db1c1..8c7c8fa78 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -13,7 +13,9 @@ def _squared_norms(x: NDArrayFloat, y: NDArrayFloat) -> NDArrayFloat: - return ((x[np.newaxis, :, :] - y[:, np.newaxis, :]) ** 2).sum(2) + return ( # type: ignore[no-any-return] + (x[np.newaxis, :, :] - y[:, np.newaxis, :]) ** 2 + ).sum(2) CovarianceLike = Union[ @@ -25,7 +27,7 @@ def _squared_norms(x: NDArrayFloat, y: NDArrayFloat) -> NDArrayFloat: def _transform_to_2d(t: ArrayLike) -> NDArrayFloat: """Transform 1d arrays in column vectors.""" - t = np.asarray(t) + t = np.asfarray(t) dim = len(t.shape) assert dim <= 2 @@ -254,7 +256,9 @@ def __call__(self, x: ArrayLike, y: ArrayLike) -> NDArrayFloat: axis=-1, ) - return self.variance * (sum_norms - norm_sub) / 2 + return ( # type: ignore[no-any-return] + self.variance * (sum_norms - norm_sub) / 2 + ) class Linear(Covariance): @@ -713,7 +717,7 @@ def __call__(self, x: ArrayLike, y: ArrayLike) -> NDArrayFloat: power_list = np.full(shape=(p,) + body.shape, fill_value=2 * body) power_list = np.cumprod(power_list, axis=0) power_list = np.concatenate( - (power_list[::-1], [np.ones_like(body)]), + (power_list[::-1], np.asarray([np.ones_like(body)])), ) power_list = np.moveaxis(power_list, 0, -1) numerator = np.cumprod(np.arange(p, 0, -1)) @@ -724,9 +728,15 @@ def __call__(self, x: ArrayLike, y: ArrayLike) -> NDArrayFloat: denom2 = np.concatenate(([1], denom2)) sum_terms = power_list * numerator / (denom1 * denom2) - return self.variance * exponential * np.sum(sum_terms, axis=-1) + return ( # type: ignore[no-any-return] + self.variance * exponential * np.sum(sum_terms, axis=-1) + ) elif self.nu == np.inf: - return self.variance * np.exp(-x_y_squared / (2 * self.length_scale ** 2)) + return ( + self.variance * np.exp( + -x_y_squared / (2 * self.length_scale ** 2), + ) + ) else: # General formula scaling = 2**(1 - self.nu) / gamma(self.nu) @@ -738,7 +748,10 @@ def __call__(self, x: ArrayLike, y: ArrayLike) -> NDArrayFloat: eval_cov = self.variance * scaling * power * bessel # Values with nan are where the distance is 0 - return np.nan_to_num(eval_cov, nan=self.variance) + return np.nan_to_num( # type: ignore[no-any-return] + eval_cov, + nan=self.variance, + ) def to_sklearn(self) -> sklearn_kern.Kernel: return ( diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index de9482690..e1645673a 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -7,23 +7,24 @@ kernel smoothing and kernel regression. """ +from __future__ import annotations import abc -from typing import Callable, Optional, Union +import math +from typing import Callable import numpy as np -from sklearn.base import BaseEstimator, RegressorMixin +from .._utils._sklearn_adapter import BaseEstimator from ..representation._functional_data import FData from ..representation.basis import FDataBasis -from ..typing._base import GridPoints, GridPointsLike +from ..typing._base import GridPointsLike from ..typing._numpy import NDArrayFloat from . import kernels class HatMatrix( BaseEstimator, - RegressorMixin, ): """ Hat Matrix. @@ -39,20 +40,18 @@ class HatMatrix( def __init__( self, *, - bandwidth: Optional[float] = None, kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.normal, ): - self.bandwidth = bandwidth self.kernel = kernel def __call__( self, *, delta_x: NDArrayFloat, - X_train: Optional[Union[FData, GridPointsLike]] = None, - X: Optional[Union[FData, GridPointsLike]] = None, - y_train: Optional[NDArrayFloat] = None, - weights: Optional[NDArrayFloat] = None, + X_train: FData | GridPointsLike | None = None, + X: FData | GridPointsLike | None = None, + y_train: NDArrayFloat | None = None, + weights: NDArrayFloat | None = None, _cv: bool = False, ) -> NDArrayFloat: r""" @@ -142,17 +141,28 @@ class NadarayaWatsonHatMatrix(HatMatrix): """ + def __init__( + self, + *, + bandwidth: float | None = None, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.normal, + ): + super().__init__(kernel=kernel) + self.bandwidth = bandwidth + def _hat_matrix_function_not_normalized( self, *, delta_x: NDArrayFloat, ) -> NDArrayFloat: - if self.bandwidth is None: - percentage = 15 - self.bandwidth = np.percentile(np.abs(delta_x), percentage) + bandwidth = ( + np.percentile(np.abs(delta_x), 15) + if self.bandwidth is None + else self.bandwidth + ) - return self.kernel(delta_x / self.bandwidth) + return self.kernel(delta_x / bandwidth) class LocalLinearRegressionHatMatrix(HatMatrix): @@ -225,23 +235,40 @@ class LocalLinearRegressionHatMatrix(HatMatrix): """ + def __init__( + self, + *, + bandwidth: float | None = None, + kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.normal, + ): + super().__init__(kernel=kernel) + self.bandwidth = bandwidth + def __call__( # noqa: D102 self, *, delta_x: NDArrayFloat, - X_train: Optional[Union[FDataBasis, GridPoints]] = None, - X: Optional[Union[FDataBasis, GridPoints]] = None, - y_train: Optional[NDArrayFloat] = None, - weights: Optional[NDArrayFloat] = None, + X_train: FData | GridPointsLike | None = None, + X: FData | GridPointsLike | None = None, + y_train: NDArrayFloat | None = None, + weights: NDArrayFloat | None = None, _cv: bool = False, ) -> NDArrayFloat: - if self.bandwidth is None: - percentage = 15 - self.bandwidth = np.percentile(np.abs(delta_x), percentage) + bandwidth = ( + np.percentile(np.abs(delta_x), 15) + if self.bandwidth is None + else self.bandwidth + ) # Regression - if isinstance(X_train, FDataBasis): + if isinstance(X_train, FData) and isinstance(X, FData): + + if not ( + isinstance(X_train, FDataBasis) + and isinstance(X, FDataBasis) + ): + raise ValueError("Only FDataBasis is supported for now.") if y_train is None: y_train = np.identity(X_train.n_samples) @@ -267,6 +294,7 @@ def __call__( # noqa: D102 delta_x=delta_x, coefs=C, y_train=y_train, + bandwidth=bandwidth, ) # Smoothing @@ -286,9 +314,11 @@ def _solve_least_squares( delta_x: NDArrayFloat, coefs: NDArrayFloat, y_train: NDArrayFloat, + *, + bandwidth: float, ) -> NDArrayFloat: - W = np.sqrt(self.kernel(delta_x / self.bandwidth)) + W = np.sqrt(self.kernel(delta_x / bandwidth)) # A x = b # Where x = (a, b_1, ..., b_J). @@ -303,7 +333,7 @@ def _solve_least_squares( uTbs = (uTb.T / s.T).T x = np.einsum('ijk,ij...->ik...', vT, uTbs) - return x[:, 0] + return x[:, 0] # type: ignore[no-any-return] def _hat_matrix_function_not_normalized( self, @@ -321,7 +351,7 @@ def _hat_matrix_function_not_normalized( s2 = np.sum(k * delta_x ** 2, axis=1, keepdims=True) # S_n_2 b = (k * (s2 - delta_x * s1)) # b_i(x_j) - return b # noqa: WPS331 + return b # type: ignore[no-any-return] # noqa: WPS331 class KNeighborsHatMatrix(HatMatrix): @@ -364,7 +394,7 @@ class KNeighborsHatMatrix(HatMatrix): def __init__( self, *, - n_neighbors: Optional[int] = None, + n_neighbors: int | None = None, kernel: Callable[[NDArrayFloat], NDArrayFloat] = kernels.uniform, ): self.n_neighbors = n_neighbors @@ -378,14 +408,18 @@ def _hat_matrix_function_not_normalized( input_points_len = delta_x.shape[1] - if self.n_neighbors is None: - self.n_neighbors = np.floor( + n_neighbors = ( + math.floor( np.percentile( range(1, input_points_len), 5, ), ) - elif self.n_neighbors <= 0: + if self.n_neighbors is None + else self.n_neighbors + ) + + if n_neighbors <= 0: raise ValueError('h must be greater than 0') # Tolerance to avoid points landing outside the kernel window due to @@ -394,11 +428,9 @@ def _hat_matrix_function_not_normalized( # For each row in the distances matrix, it calculates the furthest # point within the k nearest neighbours - vec = np.percentile( + vec = np.sort( np.abs(delta_x), - self.n_neighbors / input_points_len * 100, axis=1, - interpolation='lower', - ) + tol + )[:, n_neighbors - 1] + tol return self.kernel((delta_x.T / vec).T) diff --git a/skfda/misc/metrics/__init__.py b/skfda/misc/metrics/__init__.py index 3fc6f9acc..517a63190 100644 --- a/skfda/misc/metrics/__init__.py +++ b/skfda/misc/metrics/__init__.py @@ -1,25 +1,71 @@ """Metrics, norms and related utilities.""" -from ._angular import angular_distance -from ._fisher_rao import ( - _fisher_rao_warping_distance, - fisher_rao_amplitude_distance, - fisher_rao_distance, - fisher_rao_phase_distance, -) -from ._lp_distances import ( - LpDistance, - l1_distance, - l2_distance, - linf_distance, - lp_distance, -) -from ._lp_norms import LpNorm, l1_norm, l2_norm, linf_norm, lp_norm -from ._mahalanobis import MahalanobisDistance -from ._parse import PRECOMPUTED -from ._utils import ( - NormInducedMetric, - PairwiseMetric, - TransformationMetric, - pairwise_metric_optimization, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_angular": ["angular_distance"], + "_fisher_rao": [ + "_fisher_rao_warping_distance", + "fisher_rao_amplitude_distance", + "fisher_rao_distance", + "fisher_rao_phase_distance", + ], + "_lp_distances": [ + "LpDistance", + "l1_distance", + "l2_distance", + "linf_distance", + "lp_distance", + ], + "_lp_norms": [ + "LpNorm", + "l1_norm", + "l2_norm", + "linf_norm", + "lp_norm", + ], + "_mahalanobis": ["MahalanobisDistance"], + "_parse": ["PRECOMPUTED"], + "_utils": [ + "NormInducedMetric", + "PairwiseMetric", + "TransformationMetric", + "pairwise_metric_optimization", + ], + }, ) + +if TYPE_CHECKING: + from ._angular import angular_distance as angular_distance + from ._fisher_rao import ( + _fisher_rao_warping_distance as _fisher_rao_warping_distance, + fisher_rao_amplitude_distance as fisher_rao_amplitude_distance, + fisher_rao_distance as fisher_rao_distance, + fisher_rao_phase_distance as fisher_rao_phase_distance, + ) + from ._lp_distances import ( + LpDistance as LpDistance, + l1_distance as l1_distance, + l2_distance as l2_distance, + linf_distance as linf_distance, + lp_distance as lp_distance, + ) + from ._lp_norms import ( + LpNorm as LpNorm, + l1_norm as l1_norm, + l2_norm as l2_norm, + linf_norm as linf_norm, + lp_norm as lp_norm, + ) + from ._mahalanobis import MahalanobisDistance as MahalanobisDistance + from ._parse import PRECOMPUTED as PRECOMPUTED + from ._utils import ( + NormInducedMetric as NormInducedMetric, + PairwiseMetric as PairwiseMetric, + TransformationMetric as TransformationMetric, + pairwise_metric_optimization as pairwise_metric_optimization, + ) diff --git a/skfda/misc/metrics/_mahalanobis.py b/skfda/misc/metrics/_mahalanobis.py index 4d3e95cb9..cb04149d0 100644 --- a/skfda/misc/metrics/_mahalanobis.py +++ b/skfda/misc/metrics/_mahalanobis.py @@ -5,10 +5,10 @@ from typing import Callable, Optional, Union import numpy as np -from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError from sklearn.utils.validation import check_is_fitted +from ..._utils._sklearn_adapter import BaseEstimator from ...representation import FData from ...representation.basis import Basis from ...typing._numpy import ArrayLike, NDArrayFloat @@ -18,7 +18,7 @@ WeightsCallable = Callable[[np.ndarray], np.ndarray] -class MahalanobisDistance(BaseEstimator): # type: ignore +class MahalanobisDistance(BaseEstimator): """Functional Mahalanobis distance. Class that implements functional Mahalanobis distance for both @@ -145,7 +145,7 @@ def __call__( else: raise - return np.sum( + return np.sum( # type: ignore[no-any-return] eigenvalues * inner_product_matrix(e1 - e2, eigenvectors) ** 2 / (eigenvalues + self.alpha)**2, diff --git a/skfda/misc/metrics/_utils.py b/skfda/misc/metrics/_utils.py index 752190fd7..9850057a0 100644 --- a/skfda/misc/metrics/_utils.py +++ b/skfda/misc/metrics/_utils.py @@ -4,12 +4,13 @@ import multimethod import numpy as np -from ..._utils import _pairwise_symmetric +from ..._utils import _MapAcceptable, _pairwise_symmetric from ...representation import FData, FDataGrid from ...typing._base import Vector -from ...typing._metric import Metric, MetricElementType, Norm, VectorType +from ...typing._metric import Metric, Norm, VectorType from ...typing._numpy import NDArrayFloat +_MapAcceptableT = TypeVar("_MapAcceptableT", bound=_MapAcceptable) T = TypeVar("T", bound=FData) @@ -151,7 +152,7 @@ def pairwise_metric_optimization( return NotImplemented -class PairwiseMetric(Generic[MetricElementType]): +class PairwiseMetric(Generic[_MapAcceptableT]): """ Pairwise metric function. @@ -169,20 +170,24 @@ class PairwiseMetric(Generic[MetricElementType]): def __init__( self, - metric: Metric[MetricElementType], + metric: Metric[_MapAcceptableT], ): self.metric = metric def __call__( self, - elem1: MetricElementType, - elem2: Optional[MetricElementType] = None, + elem1: _MapAcceptableT, + elem2: Optional[_MapAcceptableT] = None, ) -> NDArrayFloat: """Evaluate the pairwise metric.""" optimized = pairwise_metric_optimization(self.metric, elem1, elem2) return ( - _pairwise_symmetric(self.metric, elem1, elem2) + _pairwise_symmetric( + self.metric, # type: ignore[arg-type] + elem1, + elem2, + ) if optimized is NotImplemented else optimized ) @@ -255,7 +260,7 @@ def __repr__(self) -> str: @pairwise_metric_optimization.register -def _pairwise_metric_optimization_transformation_distance( +def _pairwise_metric_optimization_transformation_dist( metric: TransformationMetric[Any, Any], e1: T, e2: Optional[T], diff --git a/skfda/misc/operators/__init__.py b/skfda/misc/operators/__init__.py index 3825aea7c..605b3a933 100644 --- a/skfda/misc/operators/__init__.py +++ b/skfda/misc/operators/__init__.py @@ -1,11 +1,34 @@ """Operators applicable to functional data.""" -from ._identity import Identity -from ._integral_transform import IntegralTransform -from ._linear_differential_operator import LinearDifferentialOperator -from ._operators import ( - MatrixOperator, - Operator, - gramian_matrix, - gramian_matrix_optimization, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_identity": ["Identity"], + "_integral_transform": ["IntegralTransform"], + "_linear_differential_operator": ["LinearDifferentialOperator"], + "_operators": [ + "MatrixOperator", + "Operator", + "gramian_matrix", + "gramian_matrix_optimization", + ], + "_srvf": ["SRSF"], + }, ) -from ._srvf import SRSF + +if TYPE_CHECKING: + from ._identity import Identity as Identity + from ._integral_transform import IntegralTransform as IntegralTransform + from ._linear_differential_operator import ( + LinearDifferentialOperator as LinearDifferentialOperator + ) + from ._operators import ( + MatrixOperator as MatrixOperator, + Operator as Operator, + gramian_matrix as gramian_matrix, + gramian_matrix_optimization as gramian_matrix_optimization, + ) + from ._srvf import SRSF as SRSF diff --git a/skfda/misc/operators/_identity.py b/skfda/misc/operators/_identity.py index b8cc2adf7..11f2084a2 100644 --- a/skfda/misc/operators/_identity.py +++ b/skfda/misc/operators/_identity.py @@ -6,10 +6,10 @@ from ...representation import FDataGrid from ...representation.basis import Basis -from ...typing._base import Vector -from ._operators import Operator, gramian_matrix_optimization +from ...typing._numpy import NDArrayFloat +from ._operators import InputType, Operator, gramian_matrix_optimization -T = TypeVar("T", bound=Vector) +T = TypeVar("T", bound=InputType) class Identity(Operator[T, T]): @@ -32,7 +32,7 @@ def __call__(self, f: T) -> T: # noqa: D102 def basis_penalty_matrix_optimized( linear_operator: Identity[Any], basis: Basis, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version of the penalty matrix for Basis.""" return basis.gram_matrix() @@ -41,7 +41,7 @@ def basis_penalty_matrix_optimized( def fdatagrid_penalty_matrix_optimized( linear_operator: Identity[Any], basis: FDataGrid, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version of the penalty matrix for FDataGrid.""" from ..metrics import l2_norm diff --git a/skfda/misc/operators/_integral_transform.py b/skfda/misc/operators/_integral_transform.py index 943718de8..fd5d585a4 100644 --- a/skfda/misc/operators/_integral_transform.py +++ b/skfda/misc/operators/_integral_transform.py @@ -2,15 +2,16 @@ from typing import Callable -import numpy as np - import scipy.integrate from ...representation import FData +from ...typing._numpy import NDArrayFloat from ._operators import Operator -class IntegralTransform(Operator[FData, Callable[[np.ndarray], np.ndarray]]): +class IntegralTransform( + Operator[FData, Callable[[NDArrayFloat], NDArrayFloat]], +): """Integral operator. Parameters: @@ -20,22 +21,22 @@ class IntegralTransform(Operator[FData, Callable[[np.ndarray], np.ndarray]]): def __init__( self, - kernel_function: Callable[[np.ndarray, np.ndarray], np.ndarray], + kernel_function: Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat], ) -> None: self.kernel_function = kernel_function def __call__( # noqa: D102 self, f: FData, - ) -> Callable[[np.ndarray], np.ndarray]: + ) -> Callable[[NDArrayFloat], NDArrayFloat]: def evaluate_covariance( # noqa: WPS430 - points: np.ndarray, - ) -> np.ndarray: + points: NDArrayFloat, + ) -> NDArrayFloat: def integral_body( # noqa: WPS430 - integration_var: np.ndarray, - ) -> np.ndarray: + integration_var: NDArrayFloat, + ) -> NDArrayFloat: return ( f(integration_var) * self.kernel_function(integration_var, points) @@ -43,7 +44,7 @@ def integral_body( # noqa: WPS430 domain_range = f.domain_range[0] - return scipy.integrate.quad_vec( + return scipy.integrate.quad_vec( # type: ignore[no-any-return] integral_body, domain_range[0], domain_range[1], diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 13bee47b4..16ae88e71 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -1,7 +1,7 @@ from __future__ import annotations import numbers -from typing import Callable, Optional, Sequence, Tuple, Union, cast +from typing import Callable, Sequence, Union import numpy as np import scipy.integrate @@ -9,14 +9,9 @@ from scipy.interpolate import PPoly from ...representation import FData, FDataGrid -from ...representation.basis import ( - BSpline, - Constant, - FDataBasis, - Fourier, - Monomial, -) +from ...representation.basis import BSpline, Constant, Fourier, Monomial from ...typing._base import DomainRangeLike +from ...typing._numpy import NDArrayFloat from ._operators import Operator, gramian_matrix_optimization Order = int @@ -24,20 +19,20 @@ WeightSequence = Sequence[ Union[ float, - Callable[[np.ndarray], np.ndarray], + Callable[[NDArrayFloat], NDArrayFloat], ], ] class LinearDifferentialOperator( - Operator[FData, Callable[[np.ndarray], np.ndarray]], + Operator[FData, Callable[[NDArrayFloat], NDArrayFloat]], ): r""" Defines the structure of a linear differential operator function system. .. math:: - Lx(t) = b_0(t) x(t) + b_1(t) x'(x) + - \dots + b_{n-1}(t) d^{n-1}(x(t)) + b_n(t) d^n(x(t)) + Lx(t) = b_0(t) x(t) + b_1(t) x'(t) + + \dots + b_{n-1}(t) D^{n-1}(x(t)) + b_n(t) D^n(x(t)) Can only be applied to functional data, as multivariate data has no derivatives. @@ -48,17 +43,13 @@ class LinearDifferentialOperator( order if it is an integral type and the weights otherwise. Parameters: - order (int, optional): the order of the operator. It's the highest - derivative order of the operator - - weights (list, optional): A FDataBasis objects list of length - order + 1 items - - domain_range (tuple or list of tuples, optional): Definition - of the interval where the weight functions are - defined. If the functional weights are specified - and this is not, takes the domain range from them. - Otherwise, defaults to (0,1). + order: the order of the operator. It's the highest derivative order + of the operator. + weights: A FDataBasis objects list of length order + 1 items. + domain_range: Definition of the interval where the weight functions + are defined. If the functional weights are specified and this is + not, takes the domain range from them. Otherwise, defaults to + (0,1). Attributes: weights (list): A list of callables. @@ -111,11 +102,11 @@ class LinearDifferentialOperator( def __init__( self, - order_or_weights: Union[Order, WeightSequence, None] = None, + order_or_weights: Order | WeightSequence | None = None, *, - order: Optional[int] = None, - weights: Optional[WeightSequence] = None, - domain_range: Optional[DomainRangeLike] = None, + order: int | None = None, + weights: WeightSequence | None = None, + domain_range: DomainRangeLike | None = None, ) -> None: num_args = sum( @@ -178,7 +169,7 @@ def __eq__(self, other: object) -> bool: and self.weights == other.weights ) - def constant_weights(self) -> Optional[np.ndarray]: + def constant_weights(self) -> NDArrayFloat | None: """ Return constant weights. @@ -195,20 +186,22 @@ def constant_weights(self) -> Optional[np.ndarray]: return None if weights.dtype == np.object_ else weights - def __call__(self, f: FData) -> Callable[[np.ndarray], np.ndarray]: + def __call__(self, f: FData) -> Callable[[NDArrayFloat], NDArrayFloat]: """Return the function that results of applying the operator.""" function_derivatives = [ f.derivative(order=i) for i, _ in enumerate(self.weights) ] def applied_linear_diff_op( - t: np.ndarray, - ) -> np.ndarray: - return sum( - (w(t) if callable(w) else w) + t: NDArrayFloat, + ) -> NDArrayFloat: + result = sum( + (w(t) if callable(w) else np.asarray(w)) * function_derivatives[i](t) for i, w in enumerate(self.weights) ) + assert not isinstance(result, int) + return result return applied_linear_diff_op @@ -224,7 +217,7 @@ def applied_linear_diff_op( def constant_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: Constant, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version for Constant basis.""" coefs = linear_operator.constant_weights() if coefs is None: @@ -238,8 +231,8 @@ def constant_penalty_matrix_optimized( def _monomial_evaluate_constant_linear_diff_op( basis: Monomial, - weights: np.ndarray, -) -> np.ndarray: + weights: NDArrayFloat, +) -> NDArrayFloat: """Evaluate constant weights over the monomial basis.""" max_derivative = len(weights) - 1 @@ -293,14 +286,14 @@ def _monomial_evaluate_constant_linear_diff_op( # that is the result of applying the linear differential operator # to each element of the basis - return polynomials + return polynomials # type: ignore[no-any-return] @gramian_matrix_optimization.register def monomial_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: Monomial, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version for Monomial basis.""" weights = linear_operator.constant_weights() if weights is None: @@ -363,8 +356,8 @@ def monomial_penalty_matrix_optimized( def _fourier_penalty_matrix_optimized_orthonormal( basis: Fourier, - weights: np.ndarray, -) -> np.ndarray: + weights: NDArrayFloat, +) -> NDArrayFloat: """Return the penalty when the basis is orthonormal.""" signs = np.array([1, 1, -1, -1]) signs_expanded = np.tile(signs, len(weights) // 4 + 1) @@ -428,7 +421,7 @@ def _fourier_penalty_matrix_optimized_orthonormal( def fourier_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: Fourier, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version for Fourier basis.""" weights = linear_operator.constant_weights() if weights is None: @@ -446,7 +439,7 @@ def fourier_penalty_matrix_optimized( def bspline_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: BSpline, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version for BSpline basis.""" coefs = linear_operator.constant_weights() if coefs is None: @@ -476,7 +469,9 @@ def bspline_penalty_matrix_optimized( constants = basis_deriv(mid_inter)[..., 0].T knots_intervals = np.diff(basis.knots) # Integration of product of constants - return constants.T @ np.diag(knots_intervals) @ constants + return ( # type: ignore[no-any-return] + constants.T @ np.diag(knots_intervals) @ constants + ) # We only deal with the case without zero length intervals # for now @@ -540,7 +535,7 @@ def bspline_penalty_matrix_optimized( penalty_matrix[i, i] += np.diff( polyval( integral, - basis.knots[interval: interval + 2] + np.asarray(basis.knots[interval: interval + 2]) - basis.knots[interval], ), )[0] @@ -563,7 +558,7 @@ def bspline_penalty_matrix_optimized( penalty_matrix[i, j] += np.diff( polyval( integral, - basis.knots[interval: interval + 2] + np.asarray(basis.knots[interval: interval + 2]) - basis.knots[interval], ), )[0] @@ -575,13 +570,14 @@ def bspline_penalty_matrix_optimized( def fdatagrid_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: FDataGrid, -) -> np.ndarray: +) -> NDArrayFloat: """Optimized version for FDatagrid.""" evaluated_basis = sum( w(basis.grid_points[0]) if callable(w) else w * basis.derivative(order=i)(basis.grid_points[0]) for i, w in enumerate(linear_operator.weights) ) + assert not isinstance(evaluated_basis, int) indices = np.triu_indices(basis.n_samples) product = evaluated_basis[indices[0]] * evaluated_basis[indices[1]] diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 9c30f8ed0..770401d7a 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -10,9 +10,11 @@ from ...representation.basis import Basis from ...typing._numpy import NDArrayFloat +InputType = Union[NDArrayFloat, FData, Basis] + OperatorInput = TypeVar( "OperatorInput", - bound=Union[NDArrayFloat, FData, Basis], + bound=InputType, contravariant=True, ) diff --git a/skfda/misc/operators/_srvf.py b/skfda/misc/operators/_srvf.py index f240b0dc3..b574d6043 100644 --- a/skfda/misc/operators/_srvf.py +++ b/skfda/misc/operators/_srvf.py @@ -4,19 +4,19 @@ import numpy as np import scipy.integrate -from sklearn.base import BaseEstimator, TransformerMixin -from ...misc.validation import check_fdata_dimensions +from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin from ...representation import FDataGrid from ...representation.basis import Basis from ...typing._numpy import ArrayLike +from ..validation import check_fdata_dimensions from ._operators import Operator class SRSF( Operator[FDataGrid, FDataGrid], - BaseEstimator, # type: ignore - TransformerMixin, # type: ignore + BaseEstimator, + InductiveTransformerMixin[FDataGrid, FDataGrid, object], ): r"""Square-Root Slope Function (SRSF) transform. @@ -110,10 +110,10 @@ def __init__( self.initial_value = initial_value self.method = method - def __call__(self, vector: FDataGrid) -> FDataGrid: + def __call__(self, vector: FDataGrid) -> FDataGrid: # noqa: D102 return self.fit_transform(vector) - def fit(self, X: FDataGrid, y: None = None) -> SRSF: + def fit(self, X: FDataGrid, y: object = None) -> SRSF: """ Return self. This transformer does not need to be fitted. @@ -127,7 +127,7 @@ def fit(self, X: FDataGrid, y: None = None) -> SRSF: """ return self - def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: + def transform(self, X: FDataGrid, y: object = None) -> FDataGrid: r""" Compute the square-root slope function (SRSF) transform. diff --git a/skfda/misc/regularization/__init__.py b/skfda/misc/regularization/__init__.py index 769366cff..0e977299f 100644 --- a/skfda/misc/regularization/__init__.py +++ b/skfda/misc/regularization/__init__.py @@ -1,5 +1,22 @@ -from ._regularization import ( - L2Regularization, - TikhonovRegularization, - compute_penalty_matrix, +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_regularization": [ + "L2Regularization", + "TikhonovRegularization", + "compute_penalty_matrix", + ], + + }, ) + +if TYPE_CHECKING: + from ._regularization import ( + L2Regularization as L2Regularization, + TikhonovRegularization as TikhonovRegularization, + compute_penalty_matrix as compute_penalty_matrix, + ) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 1f4ef9e28..47c3a5432 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -6,19 +6,17 @@ import numpy as np import scipy.linalg -from sklearn.base import BaseEstimator - -from skfda.misc.operators import Identity, gramian_matrix +from ..._utils._sklearn_adapter import BaseEstimator from ...representation import FData from ...representation.basis import Basis from ...typing._numpy import NDArrayFloat -from ..operators import Operator +from ..operators import Identity, Operator, gramian_matrix from ..operators._operators import OperatorInput class L2Regularization( - BaseEstimator, # type: ignore + BaseEstimator, Generic[OperatorInput], ): r""" @@ -174,4 +172,6 @@ def compute_penalty_matrix( regularization_parameter, )] - return scipy.linalg.block_diag(*penalty_blocks) + return scipy.linalg.block_diag( # type: ignore[no-any-return] + *penalty_blocks, + ) diff --git a/skfda/preprocessing/dim_reduction/__init__.py b/skfda/preprocessing/dim_reduction/__init__.py index 17facc60e..79df95a6b 100644 --- a/skfda/preprocessing/dim_reduction/__init__.py +++ b/skfda/preprocessing/dim_reduction/__init__.py @@ -17,7 +17,7 @@ ) if TYPE_CHECKING: - from ._fpca import FPCA + from ._fpca import FPCA as FPCA def __getattr__(name: str) -> Any: diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index cd6706e54..e917b4659 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -7,22 +7,22 @@ import numpy as np import scipy.integrate from scipy.linalg import solve_triangular -from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA +from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin from ...misc.regularization import L2Regularization, compute_penalty_matrix from ...representation import FData from ...representation.basis import Basis, FDataBasis from ...representation.grid import FDataGrid -from ...typing._numpy import ArrayLike +from ...typing._numpy import ArrayLike, NDArrayFloat Function = TypeVar("Function", bound=FData) WeightsCallable = Callable[[np.ndarray], np.ndarray] class FPCA( - BaseEstimator, # type: ignore - TransformerMixin, # type: ignore + BaseEstimator, + InductiveTransformerMixin[FData, NDArrayFloat, object], ): r""" Principal component analysis. @@ -118,7 +118,7 @@ def _center_if_necessary( def _fit_basis( self, X: FDataBasis, - y: None = None, + y: object = None, ) -> FPCA: """ Compute the first n_components principal components and saves them. @@ -234,10 +234,12 @@ def _fit_basis( lower=False, ) - self.explained_variance_ratio_ = pca.explained_variance_ratio_ - self.explained_variance_ = pca.explained_variance_ - self.singular_values_ = pca.singular_values_ - self.components_ = X.copy( + self.explained_variance_ratio_: NDArrayFloat = ( + pca.explained_variance_ratio_ + ) + self.explained_variance_: NDArrayFloat = pca.explained_variance_ + self.singular_values_: NDArrayFloat = pca.singular_values_ + self.components_: FData = X.copy( basis=components_basis, coefficients=component_coefficients.T, sample_names=(None,) * self.n_components, @@ -248,8 +250,8 @@ def _fit_basis( def _transform_basis( self, X: FDataBasis, - y: None = None, - ) -> np.ndarray: + y: object = None, + ) -> NDArrayFloat: """Compute the n_components first principal components score. Args: @@ -275,7 +277,7 @@ def _transform_basis( def _fit_grid( self, X: FDataGrid, - y: None = None, + y: object = None, ) -> FPCA: r""" Compute the n_components first principal components and saves them. @@ -333,21 +335,23 @@ def _fit_grid( X = self._center_if_necessary(X) # establish weights for each point of discretization - if not self.weights: + if self.weights is None: # grid_points is a list with one array in the 1D case # in trapezoidal rule, suppose \deltax_k = x_k - x_{k-1}, the # weight vector is as follows: [\deltax_1/2, \deltax_1/2 + # \deltax_2/2, \deltax_2/2 + \deltax_3/2, ... , \deltax_n/2] identity = np.eye(len(X.grid_points[0])) - self.weights = scipy.integrate.simps(identity, X.grid_points[0]) + self.weights_ = scipy.integrate.simps(identity, X.grid_points[0]) elif callable(self.weights): - self.weights = self.weights(X.grid_points[0]) + self.weights_ = self.weights(X.grid_points[0]) # if its a FDataGrid then we need to reduce the dimension to 1-D # array if isinstance(self.weights, FDataGrid): - self.weights = np.squeeze(self.weights.data_matrix) + self.weights_ = np.squeeze(self.weights.data_matrix) + else: + self.weights_ = self.weights - weights_matrix = np.diag(self.weights) + weights_matrix = np.diag(self.weights_) basis = FDataGrid( data_matrix=np.identity(n_points_discretization), @@ -384,7 +388,9 @@ def _fit_grid( sample_names=(None,) * self.n_components, ) - self.explained_variance_ratio_ = pca.explained_variance_ratio_ + self.explained_variance_ratio = ( + pca.explained_variance_ratio_ + ) self.explained_variance_ = pca.explained_variance_ self.singular_values_ = pca.singular_values_ @@ -393,8 +399,8 @@ def _fit_grid( def _transform_grid( self, X: FDataGrid, - y: None = None, - ) -> np.ndarray: + y: object = None, + ) -> NDArrayFloat: """ Compute the ``n_components`` first principal components score. @@ -409,9 +415,9 @@ def _transform_grid( # in this case its the coefficient matrix multiplied by the principal # components as column vectors - return ( + return ( # type: ignore[no-any-return] X.data_matrix.reshape(X.data_matrix.shape[:-1]) - * self.weights + * self.weights_ @ np.transpose( self.components_.data_matrix.reshape( self.components_.data_matrix.shape[:-1], @@ -422,7 +428,7 @@ def _transform_grid( def fit( self, X: FData, - y: None = None, + y: object = None, ) -> FPCA: """ Compute the n_components first principal components and saves them. @@ -445,8 +451,8 @@ def fit( def transform( self, X: FData, - y: None = None, - ) -> np.ndarray: + y: object = None, + ) -> NDArrayFloat: """ Compute the ``n_components`` first principal components scores. @@ -470,8 +476,8 @@ def transform( def fit_transform( self, X: FData, - y: None = None, - ) -> np.ndarray: + y: object = None, + ) -> NDArrayFloat: """ Compute the n_components first principal components and their scores. @@ -487,7 +493,7 @@ def fit_transform( def inverse_transform( self, - pc_scores: np.ndarray, + pc_scores: NDArrayFloat, ) -> FData: """ Compute the recovery from the fitted principal components scores. diff --git a/skfda/preprocessing/dim_reduction/variable_selection/__init__.py b/skfda/preprocessing/dim_reduction/variable_selection/__init__.py index 8a61ccaa0..1661e894f 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/__init__.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/__init__.py @@ -19,7 +19,11 @@ ) if TYPE_CHECKING: - from ._rkvs import RKHSVariableSelection - from .maxima_hunting import MaximaHunting - from .mrmr import MinimumRedundancyMaximumRelevance - from .recursive_maxima_hunting import RecursiveMaximaHunting + from ._rkvs import RKHSVariableSelection as RKHSVariableSelection + from .maxima_hunting import MaximaHunting as MaximaHunting + from .mrmr import ( + MinimumRedundancyMaximumRelevance as MinimumRedundancyMaximumRelevance + ) + from .recursive_maxima_hunting import ( + RecursiveMaximaHunting as RecursiveMaximaHunting + ) diff --git a/skfda/preprocessing/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py index 5f7f3ff94..bcaed4ebf 100644 --- a/skfda/preprocessing/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -19,12 +19,18 @@ ) if TYPE_CHECKING: - from ._coefficients_transformer import CoefficientsTransformer - from ._evaluation_trasformer import EvaluationTransformer - from ._fda_feature_union import FDAFeatureUnion + from ._coefficients_transformer import ( + CoefficientsTransformer as CoefficientsTransformer + ) + from ._evaluation_trasformer import ( + EvaluationTransformer as EvaluationTransformer + ) + from ._fda_feature_union import FDAFeatureUnion as FDAFeatureUnion from ._function_transformers import ( - LocalAveragesTransformer, - NumberUpCrossingsTransformer, - OccupationMeasureTransformer, + LocalAveragesTransformer as LocalAveragesTransformer, + NumberUpCrossingsTransformer as NumberUpCrossingsTransformer, + OccupationMeasureTransformer as OccupationMeasureTransformer, + ) + from ._per_class_transformer import ( + PerClassTransformer as PerClassTransformer ) - from ._per_class_transformer import PerClassTransformer diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index 2b9964104..c9ab29e42 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -7,9 +7,6 @@ import lazy_loader as lazy -# This cannot be made lazy for now -from ..._utils import invert_warping, normalize_warping - __getattr__, __dir__, __all__ = lazy.attach( __name__, submodules=[ @@ -33,16 +30,19 @@ ) if TYPE_CHECKING: - from ._fisher_rao import ElasticRegistration, FisherRaoElasticRegistration + from ._fisher_rao import ( + ElasticRegistration as ElasticRegistration, + FisherRaoElasticRegistration as FisherRaoElasticRegistration, + ) from ._landmark_registration import ( - landmark_elastic_registration, - landmark_elastic_registration_warping, - landmark_registration, - landmark_shift, - landmark_shift_deltas, - landmark_shift_registration, + landmark_elastic_registration as landmark_elastic_registration, + landmark_elastic_registration_warping as landmark_elastic_registration_warping, + landmark_registration as landmark_registration, + landmark_shift as landmark_shift, + landmark_shift_deltas as landmark_shift_deltas, + landmark_shift_registration as landmark_shift_registration, ) from ._lstsq_shift_registration import ( - LeastSquaresShiftRegistration, - ShiftRegistration, + LeastSquaresShiftRegistration as LeastSquaresShiftRegistration, + ShiftRegistration as ShiftRegistration, ) diff --git a/skfda/preprocessing/registration/_fisher_rao.py b/skfda/preprocessing/registration/_fisher_rao.py index 7e9592502..733422fd6 100644 --- a/skfda/preprocessing/registration/_fisher_rao.py +++ b/skfda/preprocessing/registration/_fisher_rao.py @@ -4,6 +4,7 @@ import warnings from typing import Callable, Optional, TypeVar, Union +import numpy as np from sklearn.utils.validation import check_is_fitted from ..._utils import invert_warping, normalize_scale @@ -113,6 +114,9 @@ class FisherRaoElasticRegistration( """ + template_: FDataGrid + warping_: FDataGrid + def __init__( self, *, @@ -131,10 +135,11 @@ def __init__( def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: # Points of discretization - if self.output_points is None: - self._output_points = X.grid_points[0] - else: - self._output_points = self.output_points + self._output_points = ( + X.grid_points[0] + if self.output_points is None + else np.asarray(self.output_points) + ) if isinstance(self.template, FDataGrid): self.template_ = self.template # Template already constructed @@ -177,10 +182,7 @@ def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: fdatagrid_srsf = self._srsf.fit_transform(X) # Points of discretization - if self.output_points is None: - output_points = fdatagrid_srsf.grid_points[0] - else: - output_points = self.output_points + output_points = self._output_points # Discretizacion in evaluation points q_data = fdatagrid_srsf(output_points)[..., 0] diff --git a/skfda/typing/_base.py b/skfda/typing/_base.py index 4ed6b2f6f..e1c6ac6b8 100644 --- a/skfda/typing/_base.py +++ b/skfda/typing/_base.py @@ -18,7 +18,7 @@ LabelTuple = Tuple[Optional[str], ...] LabelTupleLike = Sequence[Optional[str]] -GridPoints = Tuple[np.ndarray, ...] +GridPoints = Tuple[NDArrayFloat, ...] GridPointsLike = Union[ArrayLike, Sequence[ArrayLike]] EvaluationPoints = NDArrayFloat From d361529000522fa09fdcc99e3a707f6582017914 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 2 Sep 2022 10:58:19 +0200 Subject: [PATCH 272/400] Typing preprocessing. --- mypy.out | 4078 ----------------- setup.cfg | 6 +- skfda/_utils/_utils.py | 27 +- skfda/preprocessing/dim_reduction/_fpca.py | 2 +- .../variable_selection/__init__.py | 4 +- .../dim_reduction/variable_selection/_rkvs.py 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/home/carlos/git/scikit-fda/skfda/misc/kernels.py (skfda.misc.kernels) -TRACE: Looking for skfda.misc.lstsq at skfda/misc/lstsq.meta.json -TRACE: Meta skfda.misc.lstsq {"data_mtime": 1662028583, "dep_lines": [6, 2, 4, 8, 10, 1, 1, 1, 1, 1, 1, 7, 7], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30, 10, 20], "dependencies": ["numpy", "__future__", "typing", "typing_extensions", "skfda.typing._numpy", "builtins", "abc", "numpy._typing", "numpy._typing._array_like", "numpy._typing._nested_sequence", "numpy._typing._ufunc"], "hash": "c554c53c68bd3fce0e8fbd4ba5c4cca1927246ae1a9a86c39b3f603f2b17bc3f", "id": "skfda.misc.lstsq", "ignore_all": true, "interface_hash": "7ab063a409ff0154b3a02b5edc33ca0da2050786de43e8addb045e2664edff91", "mtime": 1661922212, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": 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Metadata not found for skfda.misc.metrics._fisher_rao -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py (skfda.misc.metrics._fisher_rao) -TRACE: Looking for skfda.misc.metrics._lp_distances at skfda/misc/metrics/_lp_distances.meta.json -LOG: Could not load cache for skfda.misc.metrics._lp_distances: skfda/misc/metrics/_lp_distances.meta.json -LOG: Metadata not found for skfda.misc.metrics._lp_distances -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py (skfda.misc.metrics._lp_distances) -TRACE: Looking for skfda.misc.metrics._lp_norms at skfda/misc/metrics/_lp_norms.meta.json -LOG: Could not load cache for skfda.misc.metrics._lp_norms: skfda/misc/metrics/_lp_norms.meta.json -LOG: Metadata not found for skfda.misc.metrics._lp_norms -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py (skfda.misc.metrics._lp_norms) -TRACE: Looking for skfda.misc.metrics._mahalanobis at skfda/misc/metrics/_mahalanobis.meta.json 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skfda.misc.operators._linear_differential_operator: skfda/misc/operators/_linear_differential_operator.meta.json -LOG: Metadata not found for skfda.misc.operators._linear_differential_operator -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py (skfda.misc.operators._linear_differential_operator) -TRACE: Looking for skfda.misc.operators._operators at skfda/misc/operators/_operators.meta.json -LOG: Could not load cache for skfda.misc.operators._operators: skfda/misc/operators/_operators.meta.json -LOG: Metadata not found for skfda.misc.operators._operators -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py (skfda.misc.operators._operators) -TRACE: Looking for skfda.misc.operators._srvf at skfda/misc/operators/_srvf.meta.json -LOG: Could not load cache for skfda.misc.operators._srvf: skfda/misc/operators/_srvf.meta.json -LOG: Metadata not found for skfda.misc.operators._srvf -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py (skfda.misc.operators._srvf) -TRACE: Looking for skfda.misc.regularization at skfda/misc/regularization/__init__.meta.json -LOG: Could not load cache for skfda.misc.regularization: skfda/misc/regularization/__init__.meta.json -LOG: Metadata not found for skfda.misc.regularization -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/regularization/__init__.py (skfda.misc.regularization) -TRACE: Looking for skfda.misc.regularization._regularization at skfda/misc/regularization/_regularization.meta.json -LOG: Could not load cache for skfda.misc.regularization._regularization: skfda/misc/regularization/_regularization.meta.json -LOG: Metadata not found for skfda.misc.regularization._regularization -LOG: Parsing /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py (skfda.misc.regularization._regularization) -TRACE: Looking for skfda.misc.validation at skfda/misc/validation.meta.json -LOG: Could not load cache for 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numpy/matrixlib/__init__.meta.json -TRACE: Meta numpy.matrixlib {"data_mtime": 1662028335, "dep_lines": [1, 3, 7, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["numpy._pytesttester", "numpy", "numpy.matrixlib.defmatrix", "builtins", "abc", "typing"], "hash": "faddd9baf6f346e47059f64e78de3194d59d9bb810129ae152c5b3bbc311bd41", "id": "numpy.matrixlib", "ignore_all": true, "interface_hash": "f421c2ebbaedafebf009918a07505deeeff4746dc20ff25098f4ba44bc45159f", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": 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-TRACE: Looking for numpy.polynomial at numpy/polynomial/__init__.meta.json -TRACE: Meta numpy.polynomial {"data_mtime": 1662028335, "dep_lines": [3, 3, 3, 3, 3, 3, 1, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["numpy.polynomial.chebyshev", "numpy.polynomial.hermite", "numpy.polynomial.hermite_e", "numpy.polynomial.laguerre", "numpy.polynomial.legendre", "numpy.polynomial.polynomial", "numpy._pytesttester", "builtins", "abc", "typing"], "hash": "5bcb3362d554cb44548bcde39852cae7c04df022954a81c0d825bb21c7549757", "id": "numpy.polynomial", "ignore_all": true, "interface_hash": "214492f35decb4e2e27904b92ca2ef7fe354c83c81f6abbf1d3d46c4ad2eb819", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, 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follow_imports: silent != normal -LOG: Metadata not found for numpy.polynomial -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/polynomial/__init__.pyi (numpy.polynomial) -TRACE: Looking for numpy.random at numpy/random/__init__.meta.json -TRACE: Meta numpy.random {"data_mtime": 1662028335, "dep_lines": [1, 3, 5, 6, 10, 11, 12, 14, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["numpy._pytesttester", "numpy.random._generator", "numpy.random._mt19937", "numpy.random._pcg64", "numpy.random._philox", "numpy.random._sfc64", "numpy.random.bit_generator", "numpy.random.mtrand", "builtins", "abc", "typing"], "hash": "dc68aff16d842dfbbc9fe07b6a23187577a02c856b4bdbaef87a73a4bab2336b", "id": "numpy.random", "ignore_all": true, "interface_hash": "79ccefef564283c2d45ee35e74238b8bb38e14877daefefbdaece2e6d30a489a", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": 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"mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": true, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": 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"warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/defchararray.pyi", "plugin_data": null, "size": 9216, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for numpy.core.defchararray: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for numpy.core.defchararray -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/defchararray.pyi (numpy.core.defchararray) -TRACE: Looking for numpy.core.records at numpy/core/records.meta.json -TRACE: Meta numpy.core.records {"data_mtime": 1662028335, "dep_lines": [1, 2, 3, 10, 21, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30], "dependencies": ["os", "collections.abc", "typing", "numpy", "numpy._typing", "builtins", "abc", "array", "mmap", "numpy._typing._dtype_like"], "hash": 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"strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/records.pyi", "plugin_data": null, "size": 5692, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for numpy.core.records: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for numpy.core.records -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/records.pyi (numpy.core.records) -TRACE: Looking for numpy.core at numpy/core/__init__.meta.json -TRACE: Meta numpy.core {"data_mtime": 1662028333, "dep_lines": [1], "dep_prios": [5], "dependencies": ["builtins"], "hash": "c6d77d3856336be646de3c8426b97344f4fe4a456807fa9899509ee85c5192cd", "id": 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"strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/__init__.pyi", "plugin_data": null, "size": 126, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for numpy.core: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for numpy.core -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/__init__.pyi (numpy.core) -TRACE: Looking for numpy._pytesttester at numpy/_pytesttester.meta.json -TRACE: Meta numpy._pytesttester {"data_mtime": 1662028333, "dep_lines": [1, 2, 1, 1], "dep_prios": [5, 5, 5, 30], "dependencies": ["collections.abc", "typing", "builtins", "abc"], "hash": "3adc974a2b92cbca3fefcc3740d4118cc2e9bef37211d0b468cb31e93faf4715", "id": "numpy._pytesttester", "ignore_all": 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/home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py (skfda.representation.basis._tensor_basis) -TRACE: Looking for skfda.representation.basis._vector_basis at skfda/representation/basis/_vector_basis.meta.json -LOG: Could not load cache for skfda.representation.basis._vector_basis: skfda/representation/basis/_vector_basis.meta.json -LOG: Metadata not found for skfda.representation.basis._vector_basis -LOG: Parsing /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py (skfda.representation.basis._vector_basis) -TRACE: Looking for skfda.typing at skfda/typing/__init__.meta.json -TRACE: Meta skfda.typing {"data_mtime": 1662028333, "dep_lines": [1], "dep_prios": [5], "dependencies": ["builtins"], "hash": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "id": "skfda.typing", "ignore_all": true, "interface_hash": "5dc3de0a00137f1e3ad802800a8ddedd2480c9ff114ce4d664dc7da3a9c13aa8", "mtime": 1661864757, "options": {"allow_redefinition": 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-LOG: Parsing /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py (skfda.datasets._real_datasets) -TRACE: Looking for skfda.datasets._samples_generators at skfda/datasets/_samples_generators.meta.json -LOG: Could not load cache for skfda.datasets._samples_generators: skfda/datasets/_samples_generators.meta.json -LOG: Metadata not found for skfda.datasets._samples_generators -LOG: Parsing /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py (skfda.datasets._samples_generators) -TRACE: Looking for skfda.representation.evaluator at skfda/representation/evaluator.meta.json -LOG: Could not load cache for skfda.representation.evaluator: skfda/representation/evaluator.meta.json -LOG: Metadata not found for skfda.representation.evaluator -LOG: Parsing /home/carlos/git/scikit-fda/skfda/representation/evaluator.py (skfda.representation.evaluator) -TRACE: Looking for skfda.representation.extrapolation at skfda/representation/extrapolation.meta.json -LOG: Could not load cache for skfda.representation.extrapolation: skfda/representation/extrapolation.meta.json -LOG: Metadata not found for skfda.representation.extrapolation -LOG: Parsing /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py (skfda.representation.extrapolation) -TRACE: Looking for skfda.exploratory.visualization.representation at skfda/exploratory/visualization/representation.meta.json -LOG: Could not load cache for skfda.exploratory.visualization.representation: skfda/exploratory/visualization/representation.meta.json -LOG: Metadata not found for skfda.exploratory.visualization.representation -LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py (skfda.exploratory.visualization.representation) -TRACE: Looking for skfda.preprocessing at skfda/preprocessing/__init__.meta.json -TRACE: Meta skfda.preprocessing {"data_mtime": 1662028333, "dep_lines": [1, 1, 1, 3], "dep_prios": [5, 30, 30, 10], "dependencies": ["builtins", "abc", "typing"], "hash": 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/home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py (skfda.preprocessing.registration._fisher_rao) -TRACE: Looking for skfda.preprocessing.registration._landmark_registration at skfda/preprocessing/registration/_landmark_registration.meta.json -LOG: Could not load cache for skfda.preprocessing.registration._landmark_registration: skfda/preprocessing/registration/_landmark_registration.meta.json -LOG: Metadata not found for skfda.preprocessing.registration._landmark_registration -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py (skfda.preprocessing.registration._landmark_registration) -TRACE: Looking for skfda.preprocessing.registration._lstsq_shift_registration at skfda/preprocessing/registration/_lstsq_shift_registration.meta.json -LOG: Could not load cache for skfda.preprocessing.registration._lstsq_shift_registration: skfda/preprocessing/registration/_lstsq_shift_registration.meta.json -LOG: Metadata not found for skfda.preprocessing.registration._lstsq_shift_registration -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py (skfda.preprocessing.registration._lstsq_shift_registration) -TRACE: Looking for importlib at importlib/__init__.meta.json -TRACE: Meta importlib {"data_mtime": 1662028333, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["collections.abc", "importlib.abc", "types", "builtins", "abc", "typing"], "hash": "5d991f55cfdc58d18e38d479666e5c73bc0eea2f9cc01f07cfbe9452aba97a48", "id": "importlib", "ignore_all": true, "interface_hash": "0c40f65f30f4d30ad6d6e30eecd5e6953d8534c5c37cd74b977a8a08840b8812", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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importlib: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for importlib -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi (importlib) -TRACE: Looking for skfda.preprocessing.dim_reduction._fpca at skfda/preprocessing/dim_reduction/_fpca.meta.json -LOG: Could not load cache for skfda.preprocessing.dim_reduction._fpca: skfda/preprocessing/dim_reduction/_fpca.meta.json -LOG: Metadata not found for skfda.preprocessing.dim_reduction._fpca -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py (skfda.preprocessing.dim_reduction._fpca) -TRACE: Looking for os.path at os/path.meta.json -TRACE: Meta os.path {"data_mtime": 1662028333, "dep_lines": [1, 7, 1, 1, 1], "dep_prios": [10, 5, 5, 30, 30], "dependencies": ["sys", "posixpath", "builtins", "abc", "typing"], "hash": 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(importlib.abc) -TRACE: Looking for importlib.machinery at importlib/machinery.meta.json -TRACE: Meta importlib.machinery {"data_mtime": 1662028333, "dep_lines": [1, 1, 2, 3, 4, 5, 8, 1, 1, 1], "dep_prios": [10, 20, 10, 10, 5, 5, 5, 5, 30, 30], "dependencies": ["importlib.abc", "importlib", "sys", "types", "collections.abc", "typing", "importlib.metadata", "builtins", "_typeshed", "abc"], "hash": "0ffa622a78f8f59b5bf4a64ea8bfb28a51fdd678dae90267a08114ffff997a47", "id": "importlib.machinery", "ignore_all": true, "interface_hash": "7bdf6f958d22202d70feadcd87088fdc58d1914ca6e2e59ec67e36ff5769ba44", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": 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Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi (importlib.machinery) -TRACE: Looking for pickle at pickle.meta.json -TRACE: Meta pickle {"data_mtime": 1662028333, "dep_lines": [1, 2, 3, 4, 5, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 30, 30, 30, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "typing", "typing_extensions", "builtins", "abc", "array", "ctypes", "mmap"], "hash": "6208dcdc7bb69e9bc9406662ec119f1cbe9c2c1acf8ba601c4ad9fc37080339d", "id": "pickle", "ignore_all": true, "interface_hash": "d621c6a0b11267cd7a25164855631e6fb06576505544302a6665cf01c2a1aac4", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, 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Looking for email.message at email/message.meta.json -TRACE: Meta email.message {"data_mtime": 1662028333, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 1, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30], "dependencies": ["collections.abc", "email", "email.charset", "email.contentmanager", "email.errors", "email.policy", "typing", "typing_extensions", "builtins", "abc", "array", "ctypes", "mmap", "pickle"], "hash": "a4535caf3504ff59acd4e99050298ef22023e6a6a261ee7b577c09e91e23118a", "id": "email.message", "ignore_all": true, "interface_hash": "17bbba77b65d038d63c88ca00dcfad8a715f8a49d17cd8b663d84668ebbcaf33", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": 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Looking for skfda.exploratory.outliers._boxplot at skfda/exploratory/outliers/_boxplot.meta.json -LOG: Could not load cache for skfda.exploratory.outliers._boxplot: skfda/exploratory/outliers/_boxplot.meta.json -LOG: Metadata not found for skfda.exploratory.outliers._boxplot -LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_boxplot.py (skfda.exploratory.outliers._boxplot) -TRACE: Looking for skfda.exploratory.outliers._directional_outlyingness at skfda/exploratory/outliers/_directional_outlyingness.meta.json -LOG: Could not load cache for skfda.exploratory.outliers._directional_outlyingness: skfda/exploratory/outliers/_directional_outlyingness.meta.json -LOG: Metadata not found for skfda.exploratory.outliers._directional_outlyingness -LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py (skfda.exploratory.outliers._directional_outlyingness) -TRACE: Looking for skfda.exploratory.outliers._outliergram at 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-TRACE: Priorities for importlib: collections.abc:5 importlib.abc:5 types:5 -TRACE: Priorities for genericpath: os:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for email.policy: abc:5 collections.abc:5 email.contentmanager:5 email.errors:5 email.header:5 email.message:5 typing:5 -TRACE: Priorities for email.message: collections.abc:5 email:5 email.charset:5 email.contentmanager:5 email.errors:5 email.policy:5 typing:5 typing_extensions:5 -TRACE: Priorities for email.header: email.charset:5 -TRACE: Priorities for email.errors: sys:10 -TRACE: Priorities for email.contentmanager: collections.abc:5 email.message:5 typing:5 -TRACE: Priorities for email.charset: collections.abc:5 -TRACE: Priorities for email: collections.abc:5 email.message:5 email.policy:5 typing:5 typing_extensions:5 -TRACE: Priorities for ctypes: sys:10 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for contextlib: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for collections.abc: _collections_abc:5 -TRACE: Priorities for collections: sys:10 _collections_abc:5 _typeshed:5 typing:5 typing_extensions:5 -TRACE: Priorities for codecs: types:10 _codecs:5 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for array: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for abc: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for _typeshed: array:10 ctypes:10 mmap:10 pickle:10 sys:10 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for _collections_abc: sys:10 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for _codecs: codecs:10 sys:10 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for _ast: sys:10 typing:5 typing_extensions:5 -TRACE: Priorities for builtins: sys:10 types:5 _ast:5 _collections_abc:5 _typeshed:5 collections.abc:5 io:5 typing:5 typing_extensions:5 -LOG: Processing SCC of size 36 (typing_extensions typing types sys subprocess posixpath pickle pathlib os.path os mmap io importlib.metadata importlib.machinery importlib.abc importlib genericpath email.policy email.message email.header email.errors email.contentmanager email.charset email ctypes contextlib collections.abc collections codecs array abc _typeshed _collections_abc _codecs _ast builtins) as inherently stale -LOG: Writing typing_extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing_extensions.pyi typing_extensions.meta.json typing_extensions.data.json -TRACE: Interface for typing_extensions has changed -LOG: Cached module typing_extensions has changed interface -LOG: Writing typing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing.pyi typing.meta.json typing.data.json 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/posixpath.pyi posixpath.meta.json posixpath.data.json -TRACE: Interface for posixpath has changed -LOG: Cached module posixpath has changed interface -LOG: Writing pickle /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pickle.pyi pickle.meta.json pickle.data.json -TRACE: Interface for pickle has changed -LOG: Cached module pickle has changed interface -LOG: Writing pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pathlib.pyi pathlib.meta.json pathlib.data.json -TRACE: Interface for pathlib has changed -LOG: Cached module pathlib has changed interface -LOG: Writing os.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/path.pyi os/path.meta.json os/path.data.json -TRACE: Interface for os.path has changed -LOG: Cached module os.path has changed interface -LOG: Writing os /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/__init__.pyi os/__init__.meta.json os/__init__.data.json -TRACE: Interface for os has changed -LOG: Cached module os has changed interface -LOG: Writing mmap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/mmap.pyi mmap.meta.json mmap.data.json -TRACE: Interface for mmap has changed -LOG: Cached module mmap has changed interface -LOG: Writing io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/io.pyi io.meta.json io.data.json -TRACE: Interface for io has changed -LOG: Cached module io has changed interface -LOG: Writing importlib.metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi importlib/metadata/__init__.meta.json importlib/metadata/__init__.data.json -TRACE: Interface for importlib.metadata has changed -LOG: Cached module importlib.metadata has changed interface -LOG: Writing importlib.machinery /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi importlib/machinery.meta.json importlib/machinery.data.json -TRACE: Interface for importlib.machinery has changed -LOG: Cached module importlib.machinery has changed interface -LOG: Writing importlib.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/abc.pyi importlib/abc.meta.json importlib/abc.data.json -TRACE: Interface for importlib.abc has changed -LOG: Cached module importlib.abc has changed interface -LOG: Writing importlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi importlib/__init__.meta.json importlib/__init__.data.json -TRACE: Interface for importlib has changed -LOG: Cached module importlib has changed interface -LOG: Writing genericpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/genericpath.pyi genericpath.meta.json genericpath.data.json -TRACE: Interface for genericpath has changed -LOG: Cached module genericpath has changed interface -LOG: Writing email.policy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi email/policy.meta.json email/policy.data.json -TRACE: Interface for email.policy has changed -LOG: Cached module email.policy has changed interface -LOG: Writing email.message /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/message.pyi email/message.meta.json email/message.data.json -TRACE: Interface for email.message has changed -LOG: Cached module email.message has changed interface -LOG: Writing email.header /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/header.pyi email/header.meta.json email/header.data.json -TRACE: Interface for email.header has changed -LOG: Cached module email.header has changed interface -LOG: Writing email.errors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/errors.pyi email/errors.meta.json email/errors.data.json -TRACE: Interface for email.errors has changed -LOG: Cached module email.errors has changed interface -LOG: Writing email.contentmanager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi email/contentmanager.meta.json email/contentmanager.data.json -TRACE: Interface for email.contentmanager has changed -LOG: Cached module email.contentmanager has changed interface -LOG: Writing email.charset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi email/charset.meta.json email/charset.data.json -TRACE: Interface for email.charset has changed -LOG: Cached module email.charset has changed interface -LOG: Writing email /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/__init__.pyi email/__init__.meta.json email/__init__.data.json -TRACE: Interface for email has changed -LOG: Cached module email has changed interface -LOG: Writing ctypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ctypes/__init__.pyi ctypes/__init__.meta.json ctypes/__init__.data.json -TRACE: Interface for ctypes has changed -LOG: Cached module ctypes has changed interface -LOG: Writing contextlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextlib.pyi contextlib.meta.json contextlib.data.json -TRACE: Interface for contextlib has changed -LOG: Cached module contextlib has changed interface -LOG: Writing collections.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/abc.pyi collections/abc.meta.json collections/abc.data.json -TRACE: Interface for collections.abc has changed -LOG: Cached module collections.abc has changed interface -LOG: Writing collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/__init__.pyi collections/__init__.meta.json collections/__init__.data.json -TRACE: Interface for collections has changed -LOG: Cached module collections has changed interface -LOG: Writing codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/codecs.pyi codecs.meta.json codecs.data.json -TRACE: Interface for codecs has changed -LOG: Cached module codecs has changed interface -LOG: Writing array /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/array.pyi array.meta.json array.data.json -TRACE: Interface for array has changed -LOG: Cached module array has changed interface -LOG: Writing abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/abc.pyi abc.meta.json abc.data.json -TRACE: Interface for abc has changed -LOG: Cached module abc has changed interface -LOG: Writing _typeshed /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_typeshed/__init__.pyi _typeshed/__init__.meta.json _typeshed/__init__.data.json -TRACE: Interface for _typeshed has changed -LOG: Cached module _typeshed has changed interface -LOG: Writing _collections_abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_collections_abc.pyi _collections_abc.meta.json _collections_abc.data.json -TRACE: Interface for _collections_abc has changed -LOG: Cached module _collections_abc has changed interface -LOG: Writing _codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_codecs.pyi _codecs.meta.json _codecs.data.json -TRACE: Interface for _codecs has changed -LOG: Cached module _codecs has changed interface -LOG: Writing _ast /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_ast.pyi _ast.meta.json _ast.data.json -TRACE: Interface for _ast has changed -LOG: Cached module _ast has changed interface -LOG: Writing builtins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/builtins.pyi builtins.meta.json builtins.data.json -TRACE: Interface for builtins has changed -LOG: Cached module builtins has changed interface -TRACE: Priorities for _socket: -LOG: Processing SCC singleton (_socket) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi (_socket) -LOG: Writing _socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi _socket.meta.json _socket.data.json -TRACE: Interface for _socket is unchanged -LOG: Cached module _socket has same interface -TRACE: Priorities for multiprocessing.shared_memory: -LOG: Processing SCC singleton (multiprocessing.shared_memory) as stale due to deps (_typeshed abc builtins collections.abc sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi (multiprocessing.shared_memory) -LOG: Writing multiprocessing.shared_memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi multiprocessing/shared_memory.meta.json multiprocessing/shared_memory.data.json -TRACE: Interface for multiprocessing.shared_memory is unchanged -LOG: Cached module multiprocessing.shared_memory has same interface -TRACE: Priorities for copyreg: -LOG: Processing SCC singleton (copyreg) as stale due to deps (_typeshed abc builtins collections.abc typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi (copyreg) -LOG: Writing copyreg /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi copyreg.meta.json copyreg.data.json -TRACE: Interface for copyreg is unchanged -LOG: Cached module copyreg has same interface -TRACE: Priorities for _weakref: -LOG: Processing SCC singleton (_weakref) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakref.pyi (_weakref) -LOG: Writing _weakref /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakref.pyi _weakref.meta.json _weakref.data.json -TRACE: Interface for _weakref is unchanged -LOG: Cached module _weakref has same interface -TRACE: Priorities for _weakrefset: -LOG: Processing SCC singleton (_weakrefset) as stale due to deps (_typeshed abc builtins collections.abc sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi (_weakrefset) -LOG: Writing _weakrefset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi _weakrefset.meta.json _weakrefset.data.json -TRACE: Interface for _weakrefset is unchanged -LOG: Cached module _weakrefset has same interface -TRACE: Priorities for multiprocessing.spawn: -LOG: Processing SCC singleton (multiprocessing.spawn) as stale due to deps (abc builtins collections.abc types typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi (multiprocessing.spawn) -LOG: Writing multiprocessing.spawn /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi multiprocessing/spawn.meta.json multiprocessing/spawn.data.json -TRACE: Interface for multiprocessing.spawn is unchanged -LOG: Cached module multiprocessing.spawn has same interface -TRACE: Priorities for multiprocessing.process: -LOG: Processing SCC singleton (multiprocessing.process) as stale due to deps (_typeshed abc builtins collections.abc sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi (multiprocessing.process) -LOG: Writing multiprocessing.process /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi multiprocessing/process.meta.json multiprocessing/process.data.json -TRACE: Interface for multiprocessing.process is unchanged -LOG: Cached module multiprocessing.process has same interface -TRACE: Priorities for multiprocessing.pool: -LOG: Processing SCC singleton (multiprocessing.pool) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi (multiprocessing.pool) -LOG: Writing multiprocessing.pool /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi multiprocessing/pool.meta.json multiprocessing/pool.data.json -TRACE: Interface for multiprocessing.pool is unchanged -LOG: Cached module multiprocessing.pool has same interface -TRACE: Priorities for _csv: -LOG: Processing SCC singleton (_csv) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_csv.pyi (_csv) -LOG: Writing _csv /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_csv.pyi _csv.meta.json _csv.data.json -TRACE: Interface for _csv is unchanged -LOG: Cached module _csv has same interface -TRACE: Priorities for unicodedata: -LOG: Processing SCC singleton (unicodedata) as stale due to deps (_typeshed abc builtins sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unicodedata.pyi (unicodedata) -LOG: Writing unicodedata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unicodedata.pyi unicodedata.meta.json unicodedata.data.json -TRACE: Interface for unicodedata is unchanged -LOG: Cached module unicodedata has same interface -TRACE: Priorities for importlib.resources: -LOG: Processing SCC singleton (importlib.resources) as stale due to deps (_typeshed abc array builtins collections.abc contextlib ctypes mmap os pathlib pickle sys types typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/resources.pyi (importlib.resources) -LOG: Writing importlib.resources /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/resources.pyi importlib/resources.meta.json importlib/resources.data.json -TRACE: Interface for importlib.resources is unchanged -LOG: Cached module importlib.resources has same interface -TRACE: Priorities for _operator: -LOG: Processing SCC singleton (_operator) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_operator.pyi (_operator) -LOG: Writing _operator /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_operator.pyi _operator.meta.json _operator.data.json -TRACE: Interface for _operator is unchanged -LOG: Cached module _operator has same interface -TRACE: Priorities for distutils: -LOG: Processing SCC singleton (distutils) as stale due to deps (builtins) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/__init__.pyi (distutils) -LOG: Writing distutils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/__init__.pyi distutils/__init__.meta.json distutils/__init__.data.json -TRACE: Interface for distutils is unchanged -LOG: Cached module distutils has same interface -TRACE: Priorities for traceback: -LOG: Processing SCC singleton (traceback) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/traceback.pyi (traceback) -LOG: Writing traceback /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/traceback.pyi traceback.meta.json traceback.data.json -TRACE: Interface for traceback is unchanged -LOG: Cached module traceback has same interface -TRACE: Priorities for importlib_metadata._collections: -LOG: Processing SCC singleton 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struct /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/struct.pyi struct.meta.json struct.data.json -TRACE: Interface for struct is unchanged -LOG: Cached module struct has same interface -TRACE: Priorities for platform: -LOG: Processing SCC singleton (platform) as stale due to deps (_typeshed abc builtins sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/platform.pyi (platform) -LOG: Writing platform /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/platform.pyi platform.meta.json platform.data.json -TRACE: Interface for platform is unchanged -LOG: Cached module platform has same interface -TRACE: Priorities for xarray.util: -LOG: Processing SCC singleton (xarray.util) as stale due to deps (builtins) -LOG: Parsing 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typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/socket.pyi (socket) -LOG: Writing socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/socket.pyi socket.meta.json socket.data.json -TRACE: Interface for socket is unchanged -LOG: Cached module socket has same interface -TRACE: Priorities for multiprocessing.reduction: -LOG: Processing SCC singleton (multiprocessing.reduction) as stale due to deps (abc array builtins ctypes mmap pickle sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi (multiprocessing.reduction) -LOG: Writing multiprocessing.reduction 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xarray.core.variable:5 xarray.backends.common:5 xarray.backends.store:5 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 -TRACE: Priorities for xarray.backends.zarr: xarray.conventions:10 xarray:20 xarray.core.indexing:10 xarray.core.pycompat:5 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.store:5 xarray.backends.api:20 xarray.coding.strings:30 xarray.coding.variables:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 -TRACE: Priorities for xarray.tutorial: xarray.backends.api:5 xarray.backends.rasterio_:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.backends:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 -TRACE: Priorities for xarray.backends.h5netcdf_: xarray.core.indexing:10 xarray.core.utils:5 xarray.core.variable:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.netCDF4_:5 xarray.backends.store:5 xarray.backends.pynio_:30 xarray.backends.rasterio_:30 xarray.backends.scipy_:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.ops:30 -TRACE: Priorities for xarray: xarray.testing:10 xarray.tutorial:10 xarray.ufuncs:10 xarray.backends.api:5 xarray.backends.rasterio_:5 xarray.backends.zarr:5 xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 xarray.coding.frequencies:5 xarray.conventions:5 xarray.core.alignment:5 xarray.core.combine:5 xarray.core.common:5 xarray.core.computation:5 xarray.core.concat:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.extensions:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.parallel:5 xarray.core.variable:5 -TRACE: Priorities for xarray.backends: xarray.backends.cfgrib_:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.h5netcdf_:5 xarray.backends.memory:5 xarray.backends.netCDF4_:5 xarray.backends.plugins:5 xarray.backends.pseudonetcdf_:5 xarray.backends.pydap_:5 xarray.backends.pynio_:5 xarray.backends.scipy_:5 xarray.backends.zarr:5 -TRACE: Priorities for xarray.convert: xarray.core.duck_array_ops:10 xarray.coding.times:5 xarray.conventions:5 xarray.core.dataarray:5 xarray.core.dtypes:5 xarray.core.pycompat:5 xarray.coding.strings:30 xarray.coding.variables:30 xarray.core._typed_ops:30 xarray.core.arithmetic:30 xarray.core.common:30 xarray.core.dataset:30 xarray.core.ops:30 xarray.core.utils:30 -TRACE: Priorities for xarray.core.resample_cftime: xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 -TRACE: Priorities for xarray.plot: xarray.plot.dataset_plot:5 xarray.plot.facetgrid:5 xarray.plot.plot:5 -LOG: Processing SCC of size 73 (xarray.core.types xarray.core.utils xarray.core.dtypes xarray.core.pycompat xarray.core.options xarray.core.dask_array_compat xarray.core.nputils xarray.plot.utils xarray.core.rolling_exp xarray.backends.file_manager 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xarray.backends.store xarray.backends.plugins xarray.backends.rasterio_ xarray.backends.api xarray.backends.scipy_ xarray.backends.pynio_ xarray.backends.pydap_ xarray.backends.pseudonetcdf_ xarray.backends.netCDF4_ xarray.backends.cfgrib_ xarray.backends.zarr xarray.tutorial xarray.backends.h5netcdf_ xarray xarray.backends xarray.convert xarray.core.resample_cftime xarray.plot) as stale due to deps (__future__ _collections_abc _typeshed _warnings abc array builtins codecs collections collections.abc contextlib copy ctypes datetime enum functools genericpath importlib importlib.metadata inspect io itertools logging mmap numbers numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.arrayprint numpy.core.defchararray numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.core.numerictypes numpy.core.shape_base numpy.lib numpy.lib.arraypad 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/convert.py (xarray.convert) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/resample_cftime.py (xarray.core.resample_cftime) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py (xarray.plot) -LOG: Writing xarray.core.types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/types.py xarray/core/types.meta.json xarray/core/types.data.json -TRACE: Interface for xarray.core.types is unchanged -LOG: Cached module xarray.core.types has same interface -LOG: Writing xarray.core.utils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/utils.py xarray/core/utils.meta.json xarray/core/utils.data.json -TRACE: Interface for xarray.core.utils is unchanged -LOG: Cached module xarray.core.utils has same interface -LOG: Writing xarray.core.dtypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dtypes.py xarray/core/dtypes.meta.json xarray/core/dtypes.data.json -TRACE: Interface for xarray.core.dtypes is unchanged -LOG: Cached module xarray.core.dtypes has same interface -LOG: Writing xarray.core.pycompat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/pycompat.py xarray/core/pycompat.meta.json xarray/core/pycompat.data.json -TRACE: Interface for xarray.core.pycompat is unchanged -LOG: Cached module xarray.core.pycompat has same interface -LOG: Writing xarray.core.options /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/options.py xarray/core/options.meta.json xarray/core/options.data.json -TRACE: Interface for xarray.core.options is unchanged -LOG: Cached 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is unchanged -LOG: Cached module xarray.plot.utils has same interface -LOG: Writing xarray.core.rolling_exp /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/rolling_exp.py xarray/core/rolling_exp.meta.json xarray/core/rolling_exp.data.json -TRACE: Interface for xarray.core.rolling_exp is unchanged -LOG: Cached module xarray.core.rolling_exp has same interface -LOG: Writing xarray.backends.file_manager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/file_manager.py xarray/backends/file_manager.meta.json xarray/backends/file_manager.data.json -TRACE: Interface for xarray.backends.file_manager is unchanged -LOG: Cached module xarray.backends.file_manager has same interface -LOG: Writing xarray.core.dask_array_ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dask_array_ops.py xarray/core/dask_array_ops.meta.json xarray/core/dask_array_ops.data.json -TRACE: Interface for xarray.core.dask_array_ops is unchanged -LOG: Cached module xarray.core.dask_array_ops has same interface -LOG: Writing xarray.core.duck_array_ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/duck_array_ops.py xarray/core/duck_array_ops.meta.json xarray/core/duck_array_ops.data.json -TRACE: Interface for xarray.core.duck_array_ops is unchanged -LOG: Cached module xarray.core.duck_array_ops has same interface -LOG: Writing xarray.core._reductions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/_reductions.py xarray/core/_reductions.meta.json xarray/core/_reductions.data.json -TRACE: Interface for xarray.core._reductions is unchanged -LOG: Cached module xarray.core._reductions has same interface -LOG: Writing xarray.core.nanops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/nanops.py xarray/core/nanops.meta.json xarray/core/nanops.data.json -TRACE: Interface for xarray.core.nanops is unchanged -LOG: Cached module xarray.core.nanops has same interface -LOG: Writing xarray.core.ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/ops.py xarray/core/ops.meta.json xarray/core/ops.data.json -TRACE: Interface for xarray.core.ops is unchanged -LOG: Cached module xarray.core.ops has same interface -LOG: Writing xarray.core.indexing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/indexing.py xarray/core/indexing.meta.json xarray/core/indexing.data.json -TRACE: Interface for xarray.core.indexing is unchanged -LOG: Cached module xarray.core.indexing has same interface -LOG: Writing xarray.core.formatting /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/formatting.py xarray/core/formatting.meta.json xarray/core/formatting.data.json -TRACE: Interface for xarray.core.formatting is unchanged -LOG: Cached module xarray.core.formatting has same interface -LOG: Writing xarray.plot.facetgrid /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/facetgrid.py xarray/plot/facetgrid.meta.json xarray/plot/facetgrid.data.json -TRACE: Interface for xarray.plot.facetgrid is unchanged -LOG: Cached module xarray.plot.facetgrid has same interface -LOG: Writing xarray.core.formatting_html /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/formatting_html.py xarray/core/formatting_html.meta.json xarray/core/formatting_html.data.json -TRACE: Interface for xarray.core.formatting_html is unchanged -LOG: Cached module xarray.core.formatting_html has same interface -LOG: Writing xarray.core.indexes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/indexes.py xarray/core/indexes.meta.json xarray/core/indexes.data.json -TRACE: Interface for xarray.core.indexes is unchanged -LOG: Cached module xarray.core.indexes has same interface -LOG: Writing xarray.core.common /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/common.py xarray/core/common.meta.json xarray/core/common.data.json -TRACE: Interface for xarray.core.common is unchanged -LOG: Cached module xarray.core.common has same interface -LOG: Writing xarray.core.accessor_dt /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/accessor_dt.py xarray/core/accessor_dt.meta.json xarray/core/accessor_dt.data.json -TRACE: Interface for xarray.core.accessor_dt is unchanged -LOG: Cached module xarray.core.accessor_dt has same interface -LOG: Writing xarray.core._typed_ops /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/_typed_ops.pyi xarray/core/_typed_ops.meta.json xarray/core/_typed_ops.data.json -TRACE: Interface for xarray.core._typed_ops is unchanged -LOG: Cached module xarray.core._typed_ops has same interface -LOG: Writing xarray.plot.dataset_plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/dataset_plot.py xarray/plot/dataset_plot.meta.json xarray/plot/dataset_plot.data.json -TRACE: Interface for xarray.plot.dataset_plot is unchanged -LOG: Cached module xarray.plot.dataset_plot has same interface -LOG: Writing xarray.core.arithmetic /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/arithmetic.py xarray/core/arithmetic.meta.json xarray/core/arithmetic.data.json -TRACE: Interface for xarray.core.arithmetic is unchanged -LOG: Cached module xarray.core.arithmetic has same interface -LOG: Writing xarray.core.accessor_str /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/accessor_str.py xarray/core/accessor_str.meta.json xarray/core/accessor_str.data.json -TRACE: Interface for xarray.core.accessor_str is unchanged -LOG: Cached module xarray.core.accessor_str has same interface -LOG: Writing xarray.plot.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/plot.py xarray/plot/plot.meta.json xarray/plot/plot.data.json -TRACE: Interface for xarray.plot.plot is unchanged -LOG: Cached module xarray.plot.plot has same interface -LOG: Writing xarray.core.missing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/missing.py xarray/core/missing.meta.json xarray/core/missing.data.json -TRACE: Interface for xarray.core.missing is unchanged -LOG: Cached module xarray.core.missing has same interface -LOG: Writing xarray.core.coordinates /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/coordinates.py xarray/core/coordinates.meta.json xarray/core/coordinates.data.json -TRACE: Interface for xarray.core.coordinates is unchanged -LOG: Cached module xarray.core.coordinates has same interface -LOG: Writing xarray.coding.variables /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/variables.py xarray/coding/variables.meta.json xarray/coding/variables.data.json -TRACE: Interface for xarray.coding.variables is unchanged -LOG: Cached module xarray.coding.variables has same interface -LOG: Writing xarray.coding.times /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/times.py xarray/coding/times.meta.json xarray/coding/times.data.json -TRACE: Interface for xarray.coding.times is unchanged -LOG: Cached module xarray.coding.times has same interface -LOG: Writing xarray.core.groupby /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/groupby.py xarray/core/groupby.meta.json xarray/core/groupby.data.json -TRACE: Interface for xarray.core.groupby is unchanged -LOG: Cached module xarray.core.groupby has same interface -LOG: Writing xarray.core.variable /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/variable.py xarray/core/variable.meta.json xarray/core/variable.data.json -TRACE: Interface for xarray.core.variable is unchanged -LOG: Cached module xarray.core.variable has same interface -LOG: Writing xarray.core.merge /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/merge.py xarray/core/merge.meta.json xarray/core/merge.data.json -TRACE: Interface for xarray.core.merge is unchanged -LOG: Cached module xarray.core.merge has same interface -LOG: Writing xarray.core.dataset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dataset.py xarray/core/dataset.meta.json xarray/core/dataset.data.json -TRACE: Interface for xarray.core.dataset is unchanged -LOG: Cached module xarray.core.dataset has same interface -LOG: Writing xarray.core.dataarray /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/dataarray.py xarray/core/dataarray.meta.json xarray/core/dataarray.data.json -TRACE: Interface for xarray.core.dataarray is unchanged -LOG: Cached module xarray.core.dataarray has same interface -LOG: Writing xarray.core.concat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/concat.py xarray/core/concat.meta.json xarray/core/concat.data.json -TRACE: Interface for xarray.core.concat is unchanged -LOG: Cached module xarray.core.concat has same interface -LOG: Writing xarray.core.computation /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/computation.py xarray/core/computation.meta.json xarray/core/computation.data.json -TRACE: Interface for xarray.core.computation is unchanged -LOG: Cached module xarray.core.computation has same interface -LOG: Writing xarray.core.alignment /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/alignment.py xarray/core/alignment.meta.json xarray/core/alignment.data.json -TRACE: Interface for xarray.core.alignment is unchanged -LOG: Cached module xarray.core.alignment has same interface -LOG: Writing xarray.coding.cftimeindex /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/cftimeindex.py xarray/coding/cftimeindex.meta.json xarray/coding/cftimeindex.data.json -TRACE: Interface for xarray.coding.cftimeindex is unchanged 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unchanged -LOG: Cached module xarray.core.resample has same interface -LOG: Writing xarray.core.weighted /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/weighted.py xarray/core/weighted.meta.json xarray/core/weighted.data.json -TRACE: Interface for xarray.core.weighted is unchanged -LOG: Cached module xarray.core.weighted has same interface -LOG: Writing xarray.coding.strings /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/strings.py xarray/coding/strings.meta.json xarray/coding/strings.data.json -TRACE: Interface for xarray.coding.strings is unchanged -LOG: Cached module xarray.coding.strings has same interface -LOG: Writing xarray.core.parallel /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/parallel.py xarray/core/parallel.meta.json xarray/core/parallel.data.json -TRACE: Interface for xarray.core.parallel is unchanged -LOG: Cached module xarray.core.parallel has same interface -LOG: Writing xarray.core.extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/extensions.py xarray/core/extensions.meta.json xarray/core/extensions.data.json -TRACE: Interface for xarray.core.extensions is unchanged -LOG: Cached module xarray.core.extensions has same interface -LOG: Writing xarray.core.combine /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/combine.py xarray/core/combine.meta.json xarray/core/combine.data.json -TRACE: Interface for xarray.core.combine is unchanged -LOG: Cached module xarray.core.combine has same interface -LOG: Writing xarray.conventions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/conventions.py xarray/conventions.meta.json xarray/conventions.data.json -TRACE: Interface for xarray.conventions is unchanged 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xarray.testing has same interface -LOG: Writing xarray.backends.common /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/common.py xarray/backends/common.meta.json xarray/backends/common.data.json -TRACE: Interface for xarray.backends.common is unchanged -LOG: Cached module xarray.backends.common has same interface -LOG: Writing xarray.coding.frequencies /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/frequencies.py xarray/coding/frequencies.meta.json xarray/coding/frequencies.data.json -TRACE: Interface for xarray.coding.frequencies is unchanged -LOG: Cached module xarray.coding.frequencies has same interface -LOG: Writing xarray.backends.memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/memory.py xarray/backends/memory.meta.json xarray/backends/memory.data.json -TRACE: Interface for xarray.backends.memory is unchanged -LOG: Cached module xarray.backends.memory has same interface -LOG: Writing xarray.backends.store /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/store.py xarray/backends/store.meta.json xarray/backends/store.data.json -TRACE: Interface for xarray.backends.store is unchanged -LOG: Cached module xarray.backends.store has same interface -LOG: Writing xarray.backends.plugins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/plugins.py xarray/backends/plugins.meta.json xarray/backends/plugins.data.json -TRACE: Interface for xarray.backends.plugins is unchanged -LOG: Cached module xarray.backends.plugins has same interface -LOG: Writing xarray.backends.rasterio_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/rasterio_.py xarray/backends/rasterio_.meta.json xarray/backends/rasterio_.data.json -TRACE: Interface for xarray.backends.rasterio_ is unchanged -LOG: Cached module xarray.backends.rasterio_ has same interface -LOG: Writing xarray.backends.api /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/api.py xarray/backends/api.meta.json xarray/backends/api.data.json -TRACE: Interface for xarray.backends.api is unchanged -LOG: Cached module xarray.backends.api has same interface -LOG: Writing xarray.backends.scipy_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/scipy_.py xarray/backends/scipy_.meta.json xarray/backends/scipy_.data.json -TRACE: Interface for xarray.backends.scipy_ is unchanged -LOG: Cached module xarray.backends.scipy_ has same interface -LOG: Writing xarray.backends.pynio_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/pynio_.py xarray/backends/pynio_.meta.json xarray/backends/pynio_.data.json -TRACE: Interface for xarray.backends.pynio_ is unchanged -LOG: Cached module xarray.backends.pynio_ has same interface -LOG: Writing xarray.backends.pydap_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/pydap_.py xarray/backends/pydap_.meta.json xarray/backends/pydap_.data.json -TRACE: Interface for xarray.backends.pydap_ is unchanged -LOG: Cached module xarray.backends.pydap_ has same interface -LOG: Writing xarray.backends.pseudonetcdf_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/pseudonetcdf_.py xarray/backends/pseudonetcdf_.meta.json xarray/backends/pseudonetcdf_.data.json -TRACE: Interface for xarray.backends.pseudonetcdf_ is unchanged -LOG: Cached module xarray.backends.pseudonetcdf_ has same interface -LOG: Writing xarray.backends.netCDF4_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/netCDF4_.py xarray/backends/netCDF4_.meta.json xarray/backends/netCDF4_.data.json -TRACE: Interface for xarray.backends.netCDF4_ is unchanged -LOG: Cached module xarray.backends.netCDF4_ has same interface -LOG: Writing xarray.backends.cfgrib_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/cfgrib_.py xarray/backends/cfgrib_.meta.json xarray/backends/cfgrib_.data.json -TRACE: Interface for xarray.backends.cfgrib_ is unchanged -LOG: Cached module xarray.backends.cfgrib_ has same interface -LOG: Writing xarray.backends.zarr /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/zarr.py xarray/backends/zarr.meta.json xarray/backends/zarr.data.json -TRACE: Interface for xarray.backends.zarr is unchanged -LOG: Cached module xarray.backends.zarr has same interface -LOG: Writing xarray.tutorial /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/tutorial.py xarray/tutorial.meta.json xarray/tutorial.data.json -TRACE: Interface for xarray.tutorial is unchanged -LOG: Cached module xarray.tutorial has same interface -LOG: Writing xarray.backends.h5netcdf_ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/h5netcdf_.py xarray/backends/h5netcdf_.meta.json xarray/backends/h5netcdf_.data.json -TRACE: Interface for xarray.backends.h5netcdf_ is unchanged -LOG: Cached module xarray.backends.h5netcdf_ has same interface -LOG: Writing xarray /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/__init__.py xarray/__init__.meta.json xarray/__init__.data.json -TRACE: Interface for xarray is unchanged -LOG: Cached module xarray has same interface -LOG: Writing xarray.backends /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/__init__.py xarray/backends/__init__.meta.json xarray/backends/__init__.data.json -TRACE: Interface for xarray.backends is unchanged -LOG: Cached module xarray.backends has same interface -LOG: Writing xarray.convert /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/convert.py xarray/convert.meta.json xarray/convert.data.json -TRACE: Interface for xarray.convert is unchanged -LOG: Cached module xarray.convert has same interface -LOG: Writing xarray.core.resample_cftime /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/resample_cftime.py xarray/core/resample_cftime.meta.json xarray/core/resample_cftime.data.json -TRACE: Interface for xarray.core.resample_cftime is unchanged -LOG: Cached module xarray.core.resample_cftime has same interface -LOG: Writing xarray.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py xarray/plot/__init__.meta.json xarray/plot/__init__.data.json -TRACE: Interface for xarray.plot is unchanged -LOG: Cached module xarray.plot has same interface -TRACE: Priorities for rdata.parser: -LOG: Processing SCC singleton (rdata.parser) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/git/rdata/rdata/parser/__init__.py (rdata.parser) -LOG: Writing rdata.parser /home/carlos/git/rdata/rdata/parser/__init__.py rdata/parser/__init__.meta.json rdata/parser/__init__.data.json -TRACE: Interface for rdata.parser is unchanged -LOG: Cached module rdata.parser has same interface -TRACE: Priorities for skfda._utils._sklearn_adapter: -LOG: Processing SCC singleton (skfda._utils._sklearn_adapter) as inherently stale with stale deps (__future__ abc builtins skfda.typing._numpy typing) -LOG: Writing skfda._utils._sklearn_adapter /home/carlos/git/scikit-fda/skfda/_utils/_sklearn_adapter.py skfda/_utils/_sklearn_adapter.meta.json skfda/_utils/_sklearn_adapter.data.json -TRACE: Interface for skfda._utils._sklearn_adapter has changed -LOG: Cached module skfda._utils._sklearn_adapter has changed interface -TRACE: Priorities for skfda.typing._base: -LOG: Processing SCC singleton (skfda.typing._base) as inherently stale with stale deps (builtins numpy skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.typing._base /home/carlos/git/scikit-fda/skfda/typing/_base.py skfda/typing/_base.meta.json skfda/typing/_base.data.json -TRACE: Interface for skfda.typing._base has changed -LOG: Cached module skfda.typing._base has changed interface -TRACE: Priorities for skfda.misc.lstsq: -LOG: Processing SCC singleton (skfda.misc.lstsq) as inherently stale with stale deps (__future__ builtins numpy skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.misc.lstsq /home/carlos/git/scikit-fda/skfda/misc/lstsq.py skfda/misc/lstsq.meta.json skfda/misc/lstsq.data.json -TRACE: Interface for skfda.misc.lstsq has changed -LOG: Cached module skfda.misc.lstsq has changed interface -TRACE: Priorities for skfda.misc.kernels: -LOG: Processing SCC singleton (skfda.misc.kernels) as inherently stale with stale deps (builtins math numpy skfda.typing._numpy) -LOG: Writing skfda.misc.kernels /home/carlos/git/scikit-fda/skfda/misc/kernels.py skfda/misc/kernels.meta.json skfda/misc/kernels.data.json -TRACE: Interface for skfda.misc.kernels has changed -LOG: Cached module skfda.misc.kernels has changed interface -TRACE: Priorities for skfda.exploratory.depth.multivariate: -LOG: Processing SCC singleton (skfda.exploratory.depth.multivariate) as inherently stale with stale deps (__future__ abc builtins math numpy skfda._utils._sklearn_adapter skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.exploratory.depth.multivariate /home/carlos/git/scikit-fda/skfda/exploratory/depth/multivariate.py skfda/exploratory/depth/multivariate.meta.json skfda/exploratory/depth/multivariate.data.json -TRACE: Interface for skfda.exploratory.depth.multivariate has changed -LOG: Cached module skfda.exploratory.depth.multivariate has changed interface -TRACE: Priorities for rdata.conversion._conversion: rdata:20 -TRACE: Priorities for rdata.conversion: rdata.conversion._conversion:5 -TRACE: Priorities for rdata: rdata.conversion:10 -LOG: Processing SCC of size 3 (rdata.conversion._conversion rdata.conversion rdata) as stale due to deps (__future__ _typeshed _warnings abc array builtins collections ctypes dataclasses enum errno io mmap numbers numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray os pathlib pickle posixpath types typing typing_extensions warnings) -LOG: Parsing /home/carlos/git/rdata/rdata/conversion/_conversion.py (rdata.conversion._conversion) -LOG: Parsing /home/carlos/git/rdata/rdata/conversion/__init__.py (rdata.conversion) -LOG: Parsing /home/carlos/git/rdata/rdata/__init__.py (rdata) -LOG: Writing rdata.conversion._conversion /home/carlos/git/rdata/rdata/conversion/_conversion.py rdata/conversion/_conversion.meta.json rdata/conversion/_conversion.data.json -TRACE: Interface for rdata.conversion._conversion is unchanged -LOG: Cached module rdata.conversion._conversion has same interface -LOG: Writing rdata.conversion /home/carlos/git/rdata/rdata/conversion/__init__.py rdata/conversion/__init__.meta.json rdata/conversion/__init__.data.json -TRACE: Interface for rdata.conversion is unchanged -LOG: Cached module rdata.conversion has same interface -LOG: Writing rdata /home/carlos/git/rdata/rdata/__init__.py rdata/__init__.meta.json rdata/__init__.data.json -TRACE: Interface for rdata is unchanged -LOG: Cached module rdata has same 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Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 -TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 -TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 -LOG: Processing SCC of size 62 (skfda.exploratory.stats skfda.preprocessing.dim_reduction skfda.preprocessing.registration skfda.representation.basis skfda.representation skfda._utils skfda skfda.misc.regularization skfda.misc.operators skfda.misc.metrics skfda.misc skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda._utils._utils skfda.misc.validation skfda.misc.operators._operators skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda.exploratory.depth._depth skfda.exploratory.stats._functional_transformers skfda.preprocessing.smoothing._basis skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.representation.evaluator skfda.representation.basis._basis skfda._utils._warping skfda.misc.regularization._regularization skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.operators._identity skfda.misc.metrics._lp_distances skfda.misc._math skfda.exploratory.depth skfda.representation.interpolation skfda.representation.extrapolation skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.misc.metrics._mahalanobis skfda.misc.metrics._fisher_rao skfda.misc.metrics._angular skfda.exploratory.stats._stats skfda.exploratory.stats._fisher_rao skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.preprocessing.registration._fisher_rao skfda.misc.hat_matrix skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.representation.grid skfda.preprocessing.dim_reduction._fpca skfda.representation.basis._fdatabasis) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._parse skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) -LOG: Writing skfda.exploratory.stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/__init__.py skfda/exploratory/stats/__init__.meta.json skfda/exploratory/stats/__init__.data.json -TRACE: Interface for skfda.exploratory.stats has changed -LOG: Cached module skfda.exploratory.stats has changed interface -LOG: Writing skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json 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skfda.misc.operators /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py skfda/misc/operators/__init__.meta.json skfda/misc/operators/__init__.data.json -TRACE: Interface for skfda.misc.operators has changed -LOG: Cached module skfda.misc.operators has changed interface -LOG: Writing skfda.misc.metrics /home/carlos/git/scikit-fda/skfda/misc/metrics/__init__.py skfda/misc/metrics/__init__.meta.json skfda/misc/metrics/__init__.data.json -TRACE: Interface for skfda.misc.metrics has changed -LOG: Cached module skfda.misc.metrics has changed interface -LOG: Writing skfda.misc /home/carlos/git/scikit-fda/skfda/misc/__init__.py skfda/misc/__init__.meta.json skfda/misc/__init__.data.json -TRACE: Interface for skfda.misc has changed -LOG: Cached module skfda.misc has changed interface -LOG: Writing skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json 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Cached module skfda.misc.metrics._lp_norms has changed interface -LOG: Writing skfda.exploratory.depth._depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py skfda/exploratory/depth/_depth.meta.json skfda/exploratory/depth/_depth.data.json -TRACE: Interface for skfda.exploratory.depth._depth has changed -LOG: Cached module skfda.exploratory.depth._depth has changed interface -LOG: Writing skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json -TRACE: Interface for skfda.exploratory.stats._functional_transformers has changed -LOG: Cached module skfda.exploratory.stats._functional_transformers has changed interface -LOG: Writing skfda.preprocessing.smoothing._basis /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py skfda/preprocessing/smoothing/_basis.meta.json skfda/preprocessing/smoothing/_basis.data.json -TRACE: Interface for skfda.preprocessing.smoothing._basis has changed -LOG: Cached module skfda.preprocessing.smoothing._basis has changed interface -LOG: Writing skfda.preprocessing.registration.base /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py skfda/preprocessing/registration/base.meta.json skfda/preprocessing/registration/base.data.json -TRACE: Interface for skfda.preprocessing.registration.base has changed -LOG: Cached module skfda.preprocessing.registration.base has changed interface -LOG: Writing skfda.preprocessing.registration.validation /home/carlos/git/scikit-fda/skfda/preprocessing/registration/validation.py skfda/preprocessing/registration/validation.meta.json skfda/preprocessing/registration/validation.data.json -TRACE: Interface for skfda.preprocessing.registration.validation has changed -LOG: Cached module skfda.preprocessing.registration.validation has changed interface -LOG: Writing skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json -TRACE: Interface for skfda.representation.evaluator has changed -LOG: Cached module skfda.representation.evaluator has changed interface -LOG: Writing skfda.representation.basis._basis /home/carlos/git/scikit-fda/skfda/representation/basis/_basis.py skfda/representation/basis/_basis.meta.json skfda/representation/basis/_basis.data.json -TRACE: Interface for skfda.representation.basis._basis has changed -LOG: Cached module skfda.representation.basis._basis has changed interface -LOG: Writing skfda._utils._warping /home/carlos/git/scikit-fda/skfda/_utils/_warping.py skfda/_utils/_warping.meta.json skfda/_utils/_warping.data.json -TRACE: Interface for skfda._utils._warping has changed -LOG: Cached module skfda._utils._warping has changed interface -LOG: Writing skfda.misc.regularization._regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py skfda/misc/regularization/_regularization.meta.json skfda/misc/regularization/_regularization.data.json -TRACE: Interface for skfda.misc.regularization._regularization has changed -LOG: Cached module skfda.misc.regularization._regularization has changed interface -LOG: Writing skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json -TRACE: Interface for skfda.misc.operators._srvf has changed -LOG: Cached module skfda.misc.operators._srvf has changed interface -LOG: Writing skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json -TRACE: Interface for skfda.misc.operators._linear_differential_operator has changed -LOG: Cached module skfda.misc.operators._linear_differential_operator has changed interface -LOG: Writing skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json -TRACE: Interface for skfda.misc.operators._integral_transform has changed -LOG: Cached module skfda.misc.operators._integral_transform has changed interface -LOG: Writing skfda.misc.operators._identity /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json -TRACE: Interface for skfda.misc.operators._identity has changed -LOG: Cached module skfda.misc.operators._identity has changed interface -LOG: Writing skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json -TRACE: Interface for skfda.misc.metrics._lp_distances has changed -LOG: Cached module skfda.misc.metrics._lp_distances has changed interface -LOG: Writing skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json -TRACE: Interface for skfda.misc._math has changed -LOG: Cached module skfda.misc._math has changed interface -LOG: Writing skfda.exploratory.depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/__init__.py skfda/exploratory/depth/__init__.meta.json skfda/exploratory/depth/__init__.data.json -TRACE: Interface for skfda.exploratory.depth has changed -LOG: Cached module skfda.exploratory.depth has changed interface -LOG: Writing skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json -TRACE: Interface for skfda.representation.interpolation has changed -LOG: Cached module skfda.representation.interpolation has changed interface -LOG: Writing skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json -TRACE: Interface for skfda.representation.extrapolation has changed -LOG: Cached module skfda.representation.extrapolation has changed interface -LOG: Writing skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json -TRACE: Interface for skfda.representation.basis._vector_basis has changed -LOG: Cached module skfda.representation.basis._vector_basis has changed interface -LOG: Writing skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json -TRACE: Interface for skfda.representation.basis._tensor_basis has changed -LOG: Cached module skfda.representation.basis._tensor_basis has changed interface -LOG: Writing skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json -TRACE: Interface for skfda.representation.basis._monomial has changed -LOG: Cached module skfda.representation.basis._monomial has changed interface -LOG: Writing skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json -TRACE: Interface for skfda.representation.basis._fourier has changed -LOG: Cached module skfda.representation.basis._fourier has changed interface -LOG: Writing skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json -TRACE: Interface for skfda.representation.basis._finite_element has changed -LOG: Cached module skfda.representation.basis._finite_element has changed interface -LOG: Writing skfda.representation.basis._constant /home/carlos/git/scikit-fda/skfda/representation/basis/_constant.py skfda/representation/basis/_constant.meta.json skfda/representation/basis/_constant.data.json -TRACE: Interface for skfda.representation.basis._constant has changed -LOG: Cached module skfda.representation.basis._constant has changed interface -LOG: Writing skfda.representation.basis._bspline /home/carlos/git/scikit-fda/skfda/representation/basis/_bspline.py skfda/representation/basis/_bspline.meta.json skfda/representation/basis/_bspline.data.json -TRACE: Interface for skfda.representation.basis._bspline has changed -LOG: Cached module skfda.representation.basis._bspline has changed interface -LOG: Writing skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json -TRACE: Interface for skfda.misc.metrics._mahalanobis has changed -LOG: Cached module skfda.misc.metrics._mahalanobis has changed interface -LOG: Writing skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json -TRACE: Interface for skfda.misc.metrics._fisher_rao has changed -LOG: Cached module skfda.misc.metrics._fisher_rao has changed interface -LOG: Writing skfda.misc.metrics._angular /home/carlos/git/scikit-fda/skfda/misc/metrics/_angular.py skfda/misc/metrics/_angular.meta.json skfda/misc/metrics/_angular.data.json -TRACE: Interface for skfda.misc.metrics._angular has changed -LOG: Cached module skfda.misc.metrics._angular has changed interface -LOG: Writing skfda.exploratory.stats._stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/_stats.py skfda/exploratory/stats/_stats.meta.json skfda/exploratory/stats/_stats.data.json -TRACE: Interface for skfda.exploratory.stats._stats has changed -LOG: Cached module skfda.exploratory.stats._stats has changed interface -LOG: Writing skfda.exploratory.stats._fisher_rao /home/carlos/git/scikit-fda/skfda/exploratory/stats/_fisher_rao.py skfda/exploratory/stats/_fisher_rao.meta.json skfda/exploratory/stats/_fisher_rao.data.json -TRACE: Interface for skfda.exploratory.stats._fisher_rao has changed -LOG: Cached module skfda.exploratory.stats._fisher_rao has changed interface -LOG: Writing skfda.preprocessing.registration._lstsq_shift_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py skfda/preprocessing/registration/_lstsq_shift_registration.meta.json skfda/preprocessing/registration/_lstsq_shift_registration.data.json -TRACE: Interface for skfda.preprocessing.registration._lstsq_shift_registration has changed -LOG: Cached module skfda.preprocessing.registration._lstsq_shift_registration has changed interface -LOG: Writing skfda.preprocessing.registration._landmark_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py skfda/preprocessing/registration/_landmark_registration.meta.json skfda/preprocessing/registration/_landmark_registration.data.json -TRACE: Interface for skfda.preprocessing.registration._landmark_registration has changed -LOG: Cached module skfda.preprocessing.registration._landmark_registration has changed interface -LOG: Writing skfda.representation._functional_data /home/carlos/git/scikit-fda/skfda/representation/_functional_data.py skfda/representation/_functional_data.meta.json skfda/representation/_functional_data.data.json -TRACE: Interface for skfda.representation._functional_data has changed -LOG: Cached module skfda.representation._functional_data has changed interface -LOG: Writing skfda.exploratory.visualization._utils /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_utils.py skfda/exploratory/visualization/_utils.meta.json skfda/exploratory/visualization/_utils.data.json -TRACE: Interface for skfda.exploratory.visualization._utils has changed -LOG: Cached module skfda.exploratory.visualization._utils has changed interface -LOG: Writing skfda.exploratory.visualization._baseplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py skfda/exploratory/visualization/_baseplot.meta.json skfda/exploratory/visualization/_baseplot.data.json -TRACE: Interface for skfda.exploratory.visualization._baseplot has changed -LOG: Cached module skfda.exploratory.visualization._baseplot has changed interface -LOG: Writing skfda.exploratory.visualization.representation /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py skfda/exploratory/visualization/representation.meta.json skfda/exploratory/visualization/representation.data.json -TRACE: Interface for skfda.exploratory.visualization.representation has changed -LOG: Cached module skfda.exploratory.visualization.representation has changed interface -LOG: Writing skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json -TRACE: Interface for skfda.preprocessing.registration._fisher_rao has changed -LOG: Cached module skfda.preprocessing.registration._fisher_rao has changed interface -LOG: Writing skfda.misc.hat_matrix /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py skfda/misc/hat_matrix.meta.json skfda/misc/hat_matrix.data.json -TRACE: Interface for skfda.misc.hat_matrix has changed -LOG: Cached module skfda.misc.hat_matrix has changed interface -LOG: Writing skfda.preprocessing.smoothing._kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py skfda/preprocessing/smoothing/_kernel_smoothers.meta.json skfda/preprocessing/smoothing/_kernel_smoothers.data.json -TRACE: Interface for skfda.preprocessing.smoothing._kernel_smoothers has changed -LOG: Cached module skfda.preprocessing.smoothing._kernel_smoothers has changed interface -LOG: Writing skfda.preprocessing.smoothing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py skfda/preprocessing/smoothing/__init__.meta.json skfda/preprocessing/smoothing/__init__.data.json -TRACE: Interface for skfda.preprocessing.smoothing has changed -LOG: Cached module skfda.preprocessing.smoothing has changed interface -LOG: Writing skfda.preprocessing.smoothing.kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/kernel_smoothers.py skfda/preprocessing/smoothing/kernel_smoothers.meta.json skfda/preprocessing/smoothing/kernel_smoothers.data.json -TRACE: Interface for skfda.preprocessing.smoothing.kernel_smoothers has changed -LOG: Cached module skfda.preprocessing.smoothing.kernel_smoothers has changed interface -LOG: Writing skfda.representation.grid /home/carlos/git/scikit-fda/skfda/representation/grid.py skfda/representation/grid.meta.json skfda/representation/grid.data.json -TRACE: Interface for skfda.representation.grid has changed -LOG: Cached module skfda.representation.grid has changed interface -LOG: Writing skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction._fpca has changed -LOG: Cached module skfda.preprocessing.dim_reduction._fpca has changed interface -LOG: Writing skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json -TRACE: Interface for skfda.representation.basis._fdatabasis has changed -LOG: Cached module skfda.representation.basis._fdatabasis has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) -LOG: Writing skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._function_transformers has changed -LOG: Cached module skfda.preprocessing.feature_construction._function_transformers has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as inherently stale with stale deps (__future__ builtins numpy skfda.representation typing) -LOG: Writing skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._fda_feature_union has changed -LOG: Cached module skfda.preprocessing.feature_construction._fda_feature_union has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._evaluation_trasformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._evaluation_trasformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation._functional_data skfda.representation.extrapolation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._evaluation_trasformer has changed -LOG: Cached module skfda.preprocessing.feature_construction._evaluation_trasformer has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._coefficients_transformer has changed -LOG: Cached module skfda.preprocessing.feature_construction._coefficients_transformer has changed interface -TRACE: Priorities for skfda.ml.regression._coefficients: -LOG: Processing SCC singleton (skfda.ml.regression._coefficients) as inherently stale with stale deps (__future__ abc builtins functools numpy skfda.misc._math skfda.representation.basis typing) -LOG: Writing skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json -TRACE: Interface for skfda.ml.regression._coefficients has changed -LOG: Cached module skfda.ml.regression._coefficients has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._per_class_transformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._per_class_transformer) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._numpy typing warnings) -LOG: Writing skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._per_class_transformer has changed -LOG: Cached module skfda.preprocessing.feature_construction._per_class_transformer has changed interface -TRACE: Priorities for skfda.ml.regression._kernel_regression: -LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as inherently stale with stale deps (__future__ builtins numpy skfda.misc.hat_matrix skfda.misc.metrics skfda.representation._functional_data skfda.typing._metric typing) -LOG: Writing skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json -TRACE: Interface for skfda.ml.regression._kernel_regression has changed -LOG: Cached module skfda.ml.regression._kernel_regression has changed interface -TRACE: Priorities for skfda.ml.regression._historical_linear_model: -LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as inherently stale with stale deps (__future__ builtins math numpy skfda._utils skfda.representation skfda.representation.basis typing) -LOG: Writing skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json -TRACE: Interface for skfda.ml.regression._historical_linear_model has changed -LOG: Cached module skfda.ml.regression._historical_linear_model has changed interface -TRACE: Priorities for skfda.ml.clustering._kmeans: -LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as inherently stale with stale deps (__future__ abc builtins numpy skfda._utils skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._metric skfda.typing._numpy typing warnings) -LOG: Writing skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json -TRACE: Interface for skfda.ml.clustering._kmeans has changed -LOG: Cached module skfda.ml.clustering._kmeans has changed interface -TRACE: Priorities for skfda.ml.clustering._hierarchical: -LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as inherently stale with stale deps (__future__ builtins enum numpy skfda.misc.metrics skfda.misc.metrics._parse skfda.representation skfda.typing._metric typing typing_extensions) -LOG: Writing skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json -TRACE: Interface for skfda.ml.clustering._hierarchical has changed -LOG: Cached module skfda.ml.clustering._hierarchical has changed interface -TRACE: Priorities for skfda.ml.classification._parameterized_functional_qda: -LOG: Processing SCC singleton (skfda.ml.classification._parameterized_functional_qda) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json -TRACE: Interface for skfda.ml.classification._parameterized_functional_qda has changed -LOG: Cached module 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(__future__ builtins skfda._utils skfda.exploratory.depth skfda.exploratory.stats skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing) -LOG: Writing skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json -TRACE: Interface for skfda.ml.classification._centroid_classifiers has changed -LOG: Cached module skfda.ml.classification._centroid_classifiers has changed interface -TRACE: Priorities for skfda.ml._neighbors_base: -LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml._neighbors_base 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typing) -LOG: Writing skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json -TRACE: Interface for skfda.datasets has changed -LOG: Cached module skfda.datasets has changed interface -LOG: Writing skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json -TRACE: Interface for skfda.misc.covariances has changed -LOG: Cached module skfda.misc.covariances has changed interface -LOG: Writing skfda.datasets._samples_generators /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py skfda/datasets/_samples_generators.meta.json skfda/datasets/_samples_generators.data.json -TRACE: Interface for skfda.datasets._samples_generators has changed -LOG: Cached module skfda.datasets._samples_generators has changed interface -TRACE: Priorities for skfda.ml.regression: -LOG: Processing SCC singleton (skfda.ml.regression) as 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skfda.exploratory.outliers._boxplot:5 skfda.exploratory.outliers._directional_outlyingness:5 -LOG: Processing SCC of size 3 (skfda.exploratory.outliers._directional_outlyingness skfda.exploratory.outliers._boxplot skfda.exploratory.outliers) as inherently stale with stale deps (__future__ builtins dataclasses numpy numpy.linalg skfda._utils skfda._utils._sklearn_adapter skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers._directional_outlyingness_experiment_results skfda.exploratory.outliers._envelopes skfda.exploratory.outliers._outliergram skfda.exploratory.outliers.neighbors_outlier skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.outliers._directional_outlyingness /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py skfda/exploratory/outliers/_directional_outlyingness.meta.json skfda/exploratory/outliers/_directional_outlyingness.data.json -TRACE: Interface for 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abc builtins math numpy skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._envelopes skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.visualization._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_boxplot.py skfda/exploratory/visualization/_boxplot.meta.json skfda/exploratory/visualization/_boxplot.data.json -TRACE: Interface for skfda.exploratory.visualization._boxplot has changed -LOG: Cached module skfda.exploratory.visualization._boxplot has changed interface -TRACE: Priorities for skfda.exploratory.visualization: -LOG: Processing SCC singleton (skfda.exploratory.visualization) as inherently stale with stale deps (builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.exploratory.visualization._ddplot skfda.exploratory.visualization._magnitude_shape_plot skfda.exploratory.visualization._multiple_display skfda.exploratory.visualization._outliergram skfda.exploratory.visualization._parametric_plot skfda.exploratory.visualization.clustering skfda.exploratory.visualization.fpca skfda.exploratory.visualization.representation) -LOG: Writing skfda.exploratory.visualization /home/carlos/git/scikit-fda/skfda/exploratory/visualization/__init__.py skfda/exploratory/visualization/__init__.meta.json skfda/exploratory/visualization/__init__.data.json -TRACE: Interface for skfda.exploratory.visualization has changed -LOG: Cached module skfda.exploratory.visualization has changed interface -LOG: No fresh SCCs left in queue -LOG: Build finished in 55.580 seconds with 423 modules, and 0 errors diff --git a/setup.cfg b/setup.cfg index fc4e01a7b..1ce2af7e8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -161,15 +161,15 @@ strict = True strict_equality = True enable_error_code = ignore-without-code -[mypy-dcor.*] -ignore_missing_imports = True - [mypy-fdasrsf.*] ignore_missing_imports = True [mypy-findiff.*] ignore_missing_imports = True +[mypy-GPy.*] +ignore_missing_imports = True + [mypy-joblib.*] ignore_missing_imports = True diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 009c3969d..9d7c2bd89 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -607,14 +607,27 @@ def _classifier_get_classes( return classes, y_ind -_DependenceMeasure = Callable[[np.ndarray, np.ndarray], np.ndarray] +dtype_bound = np.number +dtype_X_T = TypeVar("dtype_X_T", bound="dtype_bound[Any]") +dtype_y_T = TypeVar("dtype_y_T", bound="dtype_bound[Any]") + +depX_T = TypeVar("depX_T", bound=NDArrayAny) +depy_T = TypeVar("depy_T", bound=NDArrayAny) + +_DependenceMeasure = Callable[ + [depX_T, depy_T], + NDArrayFloat, +] def _compute_dependence( - X: NDArrayFloat, - y: NDArrayFloat, + X: np.typing.NDArray[dtype_X_T], + y: np.typing.NDArray[dtype_y_T], *, - dependence_measure: _DependenceMeasure, + dependence_measure: _DependenceMeasure[ + np.typing.NDArray[dtype_X_T], + np.typing.NDArray[dtype_y_T], + ], ) -> NDArrayFloat: """ Compute dependence between points and target. @@ -638,7 +651,11 @@ def _compute_dependence( y = np.atleast_2d(y).T Y = np.array([y] * len(X)) - dependence_results = rowwise(dependence_measure, X, Y) + dependence_results = rowwise( # type: ignore[no-untyped-call] + dependence_measure, + X, + Y, + ) return dependence_results.reshape( # type: ignore[no-any-return] input_shape, diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index e917b4659..aa6195b9b 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -269,7 +269,7 @@ def _transform_basis( ) # in this case it is the inner product of our data with the components - return ( + return ( # type: ignore[no-any-return] X.coefficients @ self._j_matrix @ self.components_.coefficients.T ) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/__init__.py b/skfda/preprocessing/dim_reduction/variable_selection/__init__.py index 1661e894f..17390f488 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/__init__.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/__init__.py @@ -22,8 +22,8 @@ from ._rkvs import RKHSVariableSelection as RKHSVariableSelection from .maxima_hunting import MaximaHunting as MaximaHunting from .mrmr import ( - MinimumRedundancyMaximumRelevance as MinimumRedundancyMaximumRelevance + MinimumRedundancyMaximumRelevance as MinimumRedundancyMaximumRelevance, ) from .recursive_maxima_hunting import ( - RecursiveMaximaHunting as RecursiveMaximaHunting + RecursiveMaximaHunting as RecursiveMaximaHunting, ) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py b/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py index df167a3a1..61fdc4294 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py @@ -7,14 +7,19 @@ import sklearn.utils.validation from ...._utils import _classifier_get_classes +from ...._utils._sklearn_adapter import ( + BaseEstimator, + InductiveTransformerMixin, +) from ....representation import FDataGrid +from ....typing._numpy import NDArrayFloat, NDArrayInt def _rkhs_vs( - X: np.ndarray, - Y: np.ndarray, + X: NDArrayFloat, + Y: NDArrayInt, n_features_to_select: int = 1, -) -> Tuple[np.ndarray, np.ndarray]: +) -> Tuple[NDArrayInt, NDArrayFloat]: """ RKHS-VS implementation. @@ -97,8 +102,8 @@ def _rkhs_vs( class RKHSVariableSelection( - sklearn.base.BaseEstimator, # type: ignore - sklearn.base.TransformerMixin, # type: ignore + BaseEstimator, + InductiveTransformerMixin[FDataGrid, NDArrayFloat, NDArrayInt], ): r""" Reproducing kernel variable selection. @@ -195,10 +200,10 @@ class RKHSVariableSelection( def __init__(self, n_features_to_select: int = 1) -> None: self.n_features_to_select = n_features_to_select - def fit( # noqa: D102 + def fit( # type: ignore[override] # noqa: D102 self, X: FDataGrid, - y: np.ndarray, + y: NDArrayInt, ) -> RKHSVariableSelection: n_unique_labels = len(np.unique(y)) @@ -231,7 +236,7 @@ def transform( # noqa: D102 self, X: FDataGrid, Y: None = None, - ) -> np.ndarray: + ) -> NDArrayFloat: sklearn.utils.validation.check_is_fitted(self) @@ -243,9 +248,9 @@ def transform( # noqa: D102 "points than the ones fitted", ) - return X_matrix[:, self._features_] + return X_matrix[:, self._features_] # type: ignore[no-any-return] - def get_support(self, indices: bool = False) -> np.ndarray: + def get_support(self, indices: bool = False) -> NDArrayInt: """ Get a mask, or integer index, of the features selected. diff --git a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py index 14ae59620..66d1b4ce4 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py @@ -1,7 +1,7 @@ """Maxima Hunting dimensionality reduction and related methods.""" from __future__ import annotations -from typing import Callable, Union +from typing import Callable import numpy as np import scipy.signal @@ -16,7 +16,7 @@ InductiveTransformerMixin, ) from ....representation import FDataGrid -from ....typing._numpy import NDArrayFloat, NDArrayInt +from ....typing._numpy import NDArrayFloat, NDArrayInt, NDArrayReal _LocalMaximaSelector = Callable[[FDataGrid], NDArrayInt] @@ -114,7 +114,7 @@ class MaximaHunting( InductiveTransformerMixin[ FDataGrid, NDArrayFloat, - Union[NDArrayInt, NDArrayFloat], + NDArrayReal, ], ): r""" @@ -149,7 +149,6 @@ class MaximaHunting( >>> from skfda.preprocessing.dim_reduction.variable_selection.\ ... maxima_hunting import RelativeLocalMaximaSelector >>> from skfda.datasets import make_gaussian_process - >>> from functools import partial >>> import skfda >>> import numpy as np @@ -209,19 +208,22 @@ class MaximaHunting( def __init__( self, - dependence_measure: _DependenceMeasure = u_distance_correlation_sqr, + dependence_measure: _DependenceMeasure[ + NDArrayFloat, + NDArrayFloat, + ] = u_distance_correlation_sqr, local_maxima_selector: _LocalMaximaSelector | None = None, ) -> None: self.dependence_measure = dependence_measure self.local_maxima_selector = local_maxima_selector - def fit( # noqa: D102 + def fit( # type: ignore[override] # noqa: D102 self, X: FDataGrid, - y: NDArrayInt | NDArrayFloat, + y: NDArrayReal, ) -> MaximaHunting: - self._maxima_selector = ( + self._maxima_selector: Callable[[NDArrayFloat], NDArrayInt] = ( RelativeLocalMaximaSelector() if self.local_maxima_selector is None else clone(self.local_maxima_selector, safe=False) @@ -231,7 +233,7 @@ def fit( # noqa: D102 self.dependence_ = FDataGrid( data_matrix=_compute_dependence( X.data_matrix, - y, + y.astype(np.float_), dependence_measure=self.dependence_measure, ), grid_points=X.grid_points, diff --git a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py index 7fa917153..f05e9dd55 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py @@ -5,15 +5,15 @@ Any, Callable, Dict, + Generic, NamedTuple, - Optional, Tuple, + TypeVar, Union, overload, ) import numpy as np -import sklearn.base import sklearn.utils.validation from sklearn.feature_selection import ( mutual_info_classif, @@ -22,8 +22,12 @@ from typing_extensions import Final, Literal from ...._utils import RandomStateLike, _compute_dependence, _DependenceMeasure +from ...._utils._sklearn_adapter import ( + BaseEstimator, + InductiveTransformerMixin, +) from ....representation.grid import FDataGrid -from ....typing._numpy import NDArrayFloat, NDArrayInt +from ....typing._numpy import NDArrayFloat, NDArrayInt, NDArrayReal _Criterion = Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat] _CriterionLike = Union[ @@ -31,19 +35,38 @@ Literal["difference", "quotient"], ] +SelfType = TypeVar( + "SelfType", + bound="MinimumRedundancyMaximumRelevance[Any]", +) + +dtype_X_T = TypeVar("dtype_X_T", bound=np.float_, covariant=True) +dtype_y_T = TypeVar( + "dtype_y_T", + bound=Union[np.int_, np.float_], + covariant=True, +) -class Method(NamedTuple): + +class Method(NamedTuple, Generic[dtype_y_T]): """Predefined mRMR method.""" - relevance_dependence_measure: _DependenceMeasure - redundancy_dependence_measure: _DependenceMeasure + relevance_dependence_measure: _DependenceMeasure[ + NDArrayFloat, + np.typing.NDArray[dtype_y_T], + ] + redundancy_dependence_measure: _DependenceMeasure[ + NDArrayFloat, + NDArrayFloat, + ] criterion: _Criterion def mutual_information( x: NDArrayFloat, - y: Union[NDArrayInt, NDArrayFloat], - n_neighbors: Optional[int] = None, + y: NDArrayReal, + *, + n_neighbors: int | None = None, random_state: RandomStateLike = None, ) -> NDArrayFloat: """Compute mutual information.""" @@ -58,7 +81,12 @@ def mutual_information( if n_neighbors is not None: extra_args['n_neighbors'] = n_neighbors - return method(x, y, random_state=random_state, **extra_args) + return method( # type: ignore[no-any-return] + x, + y, + random_state=random_state, + **extra_args, + ) MID: Final = Method( @@ -86,11 +114,17 @@ def _parse_method(name: MethodName) -> Method: def _mrmr( - X: NDArrayFloat, - Y: Union[NDArrayInt, NDArrayFloat], + X: np.typing.NDArray[dtype_X_T], + Y: np.typing.NDArray[dtype_y_T], n_features_to_select: int = 1, - relevance_dependence_measure: _DependenceMeasure = mutual_information, - redundancy_dependence_measure: _DependenceMeasure = mutual_information, + relevance_dependence_measure: _DependenceMeasure[ + np.typing.NDArray[dtype_X_T], + np.typing.NDArray[dtype_y_T], + ] = mutual_information, + redundancy_dependence_measure: _DependenceMeasure[ + np.typing.NDArray[dtype_X_T], + np.typing.NDArray[dtype_X_T], + ] = mutual_information, criterion: _Criterion = operator.truediv, ) -> Tuple[NDArrayInt, NDArrayFloat, NDArrayFloat]: indexes = list(range(X.shape[1])) @@ -149,8 +183,13 @@ def _mrmr( class MinimumRedundancyMaximumRelevance( - sklearn.base.BaseEstimator, # type: ignore - sklearn.base.TransformerMixin, # type: ignore + BaseEstimator, + InductiveTransformerMixin[ + FDataGrid, + NDArrayFloat, + Union[NDArrayInt, NDArrayFloat], + ], + Generic[dtype_y_T], ): r""" Minimum redundancy maximum relevance (mRMR) method. @@ -194,7 +233,6 @@ class MinimumRedundancyMaximumRelevance( >>> from skfda.datasets import make_gaussian_process >>> import skfda >>> import numpy as np - >>> import operator >>> import dcor We create trajectories from two classes, one with zero mean and the @@ -286,7 +324,7 @@ def __init__( self, *, n_features_to_select: int = 1, - method: Union[Method, MethodName], + method: Method | MethodName, ) -> None: pass @@ -295,7 +333,10 @@ def __init__( self, *, n_features_to_select: int = 1, - dependence_measure: _DependenceMeasure, + dependence_measure: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[np.float_ | dtype_y_T], + ], criterion: _CriterionLike, ) -> None: pass @@ -305,8 +346,14 @@ def __init__( self, *, n_features_to_select: int = 1, - relevance_dependence_measure: _DependenceMeasure, - redundancy_dependence_measure: _DependenceMeasure, + relevance_dependence_measure: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[dtype_y_T], + ], + redundancy_dependence_measure: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[np.float_], + ], criterion: _CriterionLike, ) -> None: pass @@ -315,11 +362,20 @@ def __init__( self, *, n_features_to_select: int = 1, - method: Union[Method, MethodName, None] = None, - dependence_measure: Optional[_DependenceMeasure] = None, - relevance_dependence_measure: Optional[_DependenceMeasure] = None, - redundancy_dependence_measure: Optional[_DependenceMeasure] = None, - criterion: Optional[_CriterionLike] = None, + method: Method | MethodName | None = None, + dependence_measure: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[np.float_ | dtype_y_T], + ] | None = None, + relevance_dependence_measure: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[dtype_y_T], + ] | None = None, + redundancy_dependence_measure: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[np.float_], + ] | None = None, + criterion: _CriterionLike | None = None, ) -> None: self.n_features_to_select = n_features_to_select self.method = method @@ -358,8 +414,11 @@ def _validate_parameters(self) -> None: if isinstance(method, str) else method ) - self.relevance_dependence_measure_ = ( - method.relevance_dependence_measure + self.relevance_dependence_measure_: _DependenceMeasure[ + np.typing.NDArray[np.float_], + np.typing.NDArray[dtype_y_T], + ] = ( + method.relevance_dependence_measure # type: ignore[assignment] ) self.redundancy_dependence_measure_ = ( method.redundancy_dependence_measure @@ -413,11 +472,11 @@ def _validate_parameters(self) -> None: self.redundancy_dependence_measure ) - def fit( - self, + def fit( # type: ignore[override] # noqa: D102 + self: SelfType, X: FDataGrid, - y: Union[NDArrayInt, NDArrayFloat], - ) -> MinimumRedundancyMaximumRelevance: + y: np.typing.NDArray[dtype_y_T], + ) -> SelfType: self._validate_parameters() @@ -441,7 +500,7 @@ def fit( def transform( self, X: FDataGrid, - y: None = None, + y: NDArrayInt | NDArrayFloat | None = None, ) -> NDArrayFloat: X_array = X.data_matrix[..., 0] diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py index 0ff743bcc..86a38e2f5 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py @@ -3,7 +3,6 @@ import abc import copy -import numbers from typing import ( TYPE_CHECKING, Any, @@ -26,7 +25,7 @@ import dcor -from ...._utils import _compute_dependence +from ...._utils import _compute_dependence, _DependenceMeasure as _DepMeasure from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, @@ -37,11 +36,10 @@ if TYPE_CHECKING: from ....misc.covariances import CovarianceLike import GPy - from .maxima_hunting import _DependenceMeasure as _DepMeasure def _transform_to_2d(t: ArrayLike) -> NDArrayFloat: - t = np.asarray(t) + t = np.asfarray(t) dim = len(t.shape) assert dim <= 2 @@ -89,7 +87,7 @@ def __setstate__(self, state: Mapping[str, Any]) -> None: self.__kernel.param_array[...] = state['values'] def __call__(self, *args: Any, **kwargs: Any) -> NDArrayFloat: - return self.__kernel.K(*args, **kwargs) + return self.__kernel.K(*args, **kwargs) # type: ignore[no-any-return] def make_kernel(k: CovarianceLike) -> CovarianceLike: @@ -123,7 +121,10 @@ def _absolute_argmax( Index of the absolute maximum. """ - masked_function = ma.array(function, mask=mask) + masked_function = ma.array( # type: ignore[no-untyped-call] + function, + mask=mask, + ) t_max = ma.argmax(masked_function) @@ -268,6 +269,7 @@ def __init__( def begin(self, X: FDataGrid, y: NDArrayFloat) -> None: if self.fit_hyperparameters: + # TODO: Migrate this to scikit-learn import GPy T = X.grid_points[0] @@ -280,9 +282,13 @@ def begin(self, X: FDataGrid, y: NDArrayFloat) -> None: mean = np.mean(trajectories, axis=0) X_copy[y == class_label, :] -= mean + gpy_kernel = getattr(self.cov, "_PicklableKernel__kernel") + m = GPy.models.GPRegression( - T[:, None], X_copy.T, - kernel=self.cov._PicklableKernel__kernel) + T[:, None], + X_copy.T, + kernel=gpy_kernel, + ) m.constrain_positive('') m.optimize() @@ -292,7 +298,7 @@ def _evaluate_mean(self, t: NDArrayFloat) -> NDArrayFloat: mean = self.mean - if isinstance(mean, numbers.Number): + if isinstance(mean, (int, float)): expectation = np.ones_like(t, dtype=float) * mean else: expectation = mean(t) @@ -365,7 +371,7 @@ def conditional_mean( * (x_0.T - t_0_expectation) ) if var else expectation + np.zeros_like(x_0.T) - return cond_expectation + return cond_expectation # type: ignore[no-any-return] class GaussianConditionedCorrection(GaussianCorrection): @@ -470,7 +476,7 @@ def _evaluate_mean(self, t: NDArrayFloat) -> NDArrayFloat: expectation = original_expect + modified_expect assert expectation.shape == t.shape - return expectation + return expectation # type: ignore[no-any-return] def _evaluate_cov( self, @@ -529,7 +535,9 @@ def cov_fun( i_r = np.ravel(i) j_r = np.ravel(j) - return self.cov_matrix_[np.ix_(i_r, j_r)] + return self.cov_matrix_[ # type: ignore[no-any-return] + np.ix_(i_r, j_r) + ] def conditioned( self, @@ -651,7 +659,7 @@ def __call__( score = dependences[selected_index] - return score < self.threshold + return score < self.threshold # type: ignore[no-any-return] class AsymptoticIndependenceTestStop(StoppingCondition): @@ -701,7 +709,7 @@ def chi_bound( chi_quant = scipy.stats.chi2.ppf(1 - significance, df=1) - return chi_quant * t2 / x_dist.shape[0] + return float(chi_quant * t2 / x_dist.shape[0]) def __call__( self, @@ -713,7 +721,9 @@ def __call__( bound = self.chi_bound(selected_variable, y, self.significance) - return dcor.u_distance_covariance_sqr(selected_variable, y) < bound + return bool( + dcor.u_distance_covariance_sqr(selected_variable, y) < bound, + ) class RedundancyCondition(BaseEstimator): @@ -757,7 +767,10 @@ def __init__( self, threshold: float = 0.9, *, - dependence_measure: _DepMeasure = dcor.u_distance_correlation_sqr, + dependence_measure: _DepMeasure[ + NDArrayFloat, + NDArrayFloat, + ] = dcor.u_distance_correlation_sqr, ) -> None: super().__init__() self.threshold = threshold @@ -842,8 +855,8 @@ def update_mask( index = indexes.pop() # Check if it wasn't masked before if ( - not old_mask[index] and not new_mask[index] and - is_redundant(index) + not old_mask[index] and not new_mask[index] + and is_redundant(index) ): new_mask[index] = True for i in adjacent_indexes(index): @@ -863,7 +876,10 @@ def _rec_maxima_hunting_gen_no_copy( X: FDataGrid, y: Union[NDArrayInt, NDArrayFloat], *, - dependence_measure: _DepMeasure = dcor.u_distance_correlation_sqr, + dependence_measure: _DepMeasure[ + NDArrayFloat, + NDArrayFloat, + ] = dcor.u_distance_correlation_sqr, correction: Optional[Correction] = None, redundancy_condition: Optional[RedundancyCondition] = None, stopping_condition: Optional[StoppingCondition] = None, @@ -1091,7 +1107,10 @@ class RecursiveMaximaHunting( def __init__( self, *, - dependence_measure: _DepMeasure = dcor.u_distance_correlation_sqr, + dependence_measure: _DepMeasure[ + NDArrayFloat, + NDArrayFloat, + ] = dcor.u_distance_correlation_sqr, max_features: Optional[int] = None, correction: Optional[Correction] = None, redundancy_condition: Optional[RedundancyCondition] = None, @@ -1103,7 +1122,7 @@ def __init__( self.redundancy_condition = redundancy_condition self.stopping_condition = stopping_condition - def fit( + def fit( # type: ignore[override] # noqa: D102 self, X: FDataGrid, y: Union[NDArrayInt, NDArrayFloat], @@ -1146,7 +1165,10 @@ def transform(self, X: FDataGrid) -> NDArrayFloat: output = X_matrix[(slice(None),) + self.indexes_] - return output.reshape(X.n_samples, -1) + return output.reshape( # type: ignore[no-any-return] + X.n_samples, + -1, + ) @overload def get_support( diff --git a/skfda/preprocessing/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py index bcaed4ebf..25f190847 100644 --- a/skfda/preprocessing/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -20,10 +20,10 @@ if TYPE_CHECKING: from ._coefficients_transformer import ( - CoefficientsTransformer as CoefficientsTransformer + CoefficientsTransformer as CoefficientsTransformer, ) from ._evaluation_trasformer import ( - EvaluationTransformer as EvaluationTransformer + EvaluationTransformer as EvaluationTransformer, ) from ._fda_feature_union import FDAFeatureUnion as FDAFeatureUnion from ._function_transformers import ( @@ -32,5 +32,5 @@ OccupationMeasureTransformer as OccupationMeasureTransformer, ) from ._per_class_transformer import ( - PerClassTransformer as PerClassTransformer + PerClassTransformer as PerClassTransformer, ) diff --git a/skfda/preprocessing/feature_construction/_fda_feature_union.py b/skfda/preprocessing/feature_construction/_fda_feature_union.py index 2e5ede7f4..f9cb56bfd 100644 --- a/skfda/preprocessing/feature_construction/_fda_feature_union.py +++ b/skfda/preprocessing/feature_construction/_fda_feature_union.py @@ -1,16 +1,17 @@ """Feature extraction union for dimensionality reduction.""" from __future__ import annotations -from typing import Union +from typing import Any, Mapping, Sequence, Union import pandas as pd -from numpy import ndarray from sklearn.pipeline import FeatureUnion +from ..._utils._sklearn_adapter import TransformerMixin from ...representation import FData +from ...typing._numpy import NDArrayAny -class FDAFeatureUnion(FeatureUnion): # type: ignore +class FDAFeatureUnion(FeatureUnion): # type: ignore[misc] """Concatenates results of multiple functional transformer objects. This estimator applies a list of transformer objects in parallel to the @@ -90,10 +91,10 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore def __init__( self, - transformer_list: list, # type: ignore + transformer_list: Sequence[TransformerMixin[Any, Any, Any]], *, n_jobs: int = 1, - transformer_weights: dict = None, # type: ignore + transformer_weights: Mapping[str, float] | None = None, verbose: bool = False, array_output: bool = False, ) -> None: @@ -105,7 +106,7 @@ def __init__( verbose=verbose, ) - def _hstack(self, Xs: ndarray) -> Union[pd.DataFrame, ndarray]: + def _hstack(self, Xs: NDArrayAny) -> Union[pd.DataFrame, NDArrayAny]: if self.array_output: for i in Xs: diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index 196387f2b..49a08050b 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -10,10 +10,9 @@ occupation_measure, ) from ...representation import FData -from ...representation.basis import FDataBasis from ...representation.grid import FDataGrid -from ...typing._base import DomainRangeLike, NDArrayFloat -from ...typing._numpy import NDArrayFloat +from ...typing._base import DomainRangeLike +from ...typing._numpy import NDArrayFloat, NDArrayInt class LocalAveragesTransformer( @@ -104,7 +103,10 @@ def transform(self, X: FData, y: object = None) -> NDArrayFloat: ).reshape(X.data_matrix.shape[0], -1) -class OccupationMeasureTransformer(BaseEstimator, TransformerMixin): +class OccupationMeasureTransformer( + BaseEstimator, + TransformerMixin[FData, NDArrayFloat, object], +): """ Transformer that works as an adapter for the occupation_measure function. @@ -159,7 +161,7 @@ def __init__( self.intervals = intervals self.n_points = n_points - def transform(self, X: FData) -> NDArrayFloat: + def transform(self, X: FData, y: object = None) -> NDArrayFloat: """ Transform the provided data using the occupation_measure function. @@ -174,7 +176,10 @@ def transform(self, X: FData) -> NDArrayFloat: return occupation_measure(X, self.intervals, n_points=self.n_points) -class NumberUpCrossingsTransformer(BaseEstimator, TransformerMixin): +class NumberUpCrossingsTransformer( + BaseEstimator, + TransformerMixin[FDataGrid, NDArrayInt, object], +): """ Transformer that works as an adapter for the number_up_crossings function. @@ -224,7 +229,7 @@ class NumberUpCrossingsTransformer(BaseEstimator, TransformerMixin): def __init__(self, levels: NDArrayFloat): self.levels = levels - def transform(self, X: FDataGrid) -> NDArrayFloat: + def transform(self, X: FDataGrid, y: object = None) -> NDArrayInt: """ Transform the provided data using the number_up_crossings function. diff --git a/skfda/preprocessing/feature_construction/_per_class_transformer.py b/skfda/preprocessing/feature_construction/_per_class_transformer.py index b31b2357e..da687f56f 100644 --- a/skfda/preprocessing/feature_construction/_per_class_transformer.py +++ b/skfda/preprocessing/feature_construction/_per_class_transformer.py @@ -14,7 +14,7 @@ from ...representation import FData from ...representation.basis import FDataBasis from ...representation.grid import FDataGrid -from ...typing._numpy import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) Output = TypeVar("Output", bound=Union[pd.DataFrame, NDArrayFloat]) @@ -28,7 +28,7 @@ def _fit_feature_transformer( # noqa: WPS320 WPS234 y: Target, transformer: TransformerMixin[Input, Output, Target], ) -> Tuple[ - Union[NDArrayInt, NDArrayFloat], + Union[NDArrayAny, NDArrayFloat], Sequence[TransformerMixin[Input, Output, Target]], ]: @@ -276,9 +276,10 @@ def transform(self, X: Input) -> Output: "FDataBasis that can't be concatenated on a NumPy " "array.", ) - return np.hstack(transformed_data) - return pd.concat( + return np.hstack(transformed_data) # type: ignore[return-value] + + return pd.concat( # type: ignore[no-any-return] [ pd.DataFrame({'0': data}) # noqa: WPS441 for data in transformed_data diff --git a/skfda/preprocessing/registration/_fisher_rao.py b/skfda/preprocessing/registration/_fisher_rao.py index 733422fd6..55c23323e 100644 --- a/skfda/preprocessing/registration/_fisher_rao.py +++ b/skfda/preprocessing/registration/_fisher_rao.py @@ -132,7 +132,7 @@ def __init__( self.grid_dim = grid_dim self.derivative_method = derivative_method - def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: + def fit(self: SelfType, X: FDataGrid, y: object = None) -> SelfType: # Points of discretization self._output_points = ( @@ -158,7 +158,7 @@ def fit(self: SelfType, X: FDataGrid, y: None = None) -> SelfType: return self - def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: + def transform(self, X: FDataGrid, y: object = None) -> FDataGrid: check_is_fitted(self) check_fdata_dimensions( @@ -224,7 +224,7 @@ def transform(self, X: FDataGrid, y: None = None) -> FDataGrid: return X.compose(self.warping_, eval_points=output_points) - def inverse_transform(self, X: FDataGrid, y: None = None) -> FDataGrid: + def inverse_transform(self, X: FDataGrid, y: object = None) -> FDataGrid: r""" Reverse the registration procedure previosly applied. diff --git a/skfda/preprocessing/registration/_lstsq_shift_registration.py b/skfda/preprocessing/registration/_lstsq_shift_registration.py index b7ea00565..5587debcf 100644 --- a/skfda/preprocessing/registration/_lstsq_shift_registration.py +++ b/skfda/preprocessing/registration/_lstsq_shift_registration.py @@ -227,8 +227,8 @@ def _compute_deltas( # Updates the limits for non periodic functions ignoring the ends if self.restrict_domain: # Calculates the new limits - a = domain_range[0] - min(np.min(delta), 0) - b = domain_range[1] - max(np.max(delta), 0) + a = domain_range[0] - min(float(np.min(delta)), 0) + b = domain_range[1] - max(float(np.max(delta)), 0) restricted_domain = ( max(a, template_iter.domain_range[0][0]), @@ -259,7 +259,7 @@ def _compute_deltas( return delta, template_iter - def fit_transform(self, X: T, y: None = None) -> T: + def fit_transform(self, X: T, y: object = None) -> T: deltas, template = self._compute_deltas(X, self.template) @@ -272,13 +272,13 @@ def fit_transform(self, X: T, y: None = None) -> T: extrapolation=self.extrapolation, grid_points=self.grid_points, ) - shifted.argument_names = None + shifted.argument_names = None # type: ignore[assignment] return shifted def fit( self: SelfType, X: FData, - y: None = None, + y: object = None, ) -> SelfType: # If the template is an FData, fit doesnt learn anything @@ -292,7 +292,7 @@ def fit( return self - def transform(self, X: FData, y: None = None) -> FDataGrid: + def transform(self, X: FData, y: object = None) -> FDataGrid: if self.restrict_domain: raise AttributeError( @@ -315,10 +315,10 @@ def transform(self, X: FData, y: None = None) -> FDataGrid: extrapolation=self.extrapolation, grid_points=self.grid_points, ) - shifted.argument_names = None + shifted.argument_names = None # type: ignore[assignment] return shifted - def inverse_transform(self, X: FData, y: None = None) -> FDataGrid: + def inverse_transform(self, X: FData, y: object = None) -> FDataGrid: """ Apply the inverse transformation. diff --git a/skfda/preprocessing/registration/base.py b/skfda/preprocessing/registration/base.py index 7b09fceb3..086a7cb08 100644 --- a/skfda/preprocessing/registration/base.py +++ b/skfda/preprocessing/registration/base.py @@ -23,14 +23,14 @@ class RegistrationTransformer( BaseEstimator, - TransformerMixin[Input, Output, None], + TransformerMixin[Input, Output, object], ): """Base class for the registration methods.""" def fit( self: SelfType, X: Input, - y: None = None, + y: object = None, ) -> SelfType: """ Fit the registration model. @@ -49,14 +49,14 @@ def fit( def fit_transform( self, X: Input, - y: None = None, + y: object = None, ) -> Output: pass - def fit_transform( + def fit_transform( # noqa: WPS612 self, X: Input, - y: None = None, + y: object = None, **fit_params: Any, ) -> Output: """ @@ -71,13 +71,13 @@ def fit_transform( Registered training data. """ - return super().fit_transform( # type: ignore[call-arg] + return super().fit_transform( X, y, **fit_params, ) - def score(self, X: Input, y: None = None) -> float: + def score(self, X: Input, y: object = None) -> float: r""" Return the percentage of total variation removed. @@ -109,12 +109,12 @@ def score(self, X: Input, y: None = None) -> float: """ from .validation import AmplitudePhaseDecomposition - return AmplitudePhaseDecomposition()(self, X, y) + return AmplitudePhaseDecomposition()(self, X, X) class InductiveRegistrationTransformer( RegistrationTransformer[Input, Output], - InductiveTransformerMixin[Input, Output, None], + InductiveTransformerMixin[Input, Output, object], ): @abstractmethod diff --git a/skfda/preprocessing/registration/validation.py b/skfda/preprocessing/registration/validation.py index 4ddcd4501..bce3491c8 100644 --- a/skfda/preprocessing/registration/validation.py +++ b/skfda/preprocessing/registration/validation.py @@ -3,17 +3,21 @@ from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import Optional +from typing import Generic, TypeVar import numpy as np from ..._utils import _to_grid from ...misc.validation import check_fdata_dimensions from ...representation import FData +from ...typing._numpy import NDArrayFloat from .base import RegistrationTransformer +Input = TypeVar("Input", bound=FData) +Output = TypeVar("Output", bound=FData) -class RegistrationScorer(ABC): + +class RegistrationScorer(ABC, Generic[Input, Output]): """Cross validation scoring for registration procedures. It calculates the score of a registration procedure, used to perform @@ -48,9 +52,9 @@ class RegistrationScorer(ABC): def __call__( self, - estimator: RegistrationTransformer, - X: FData, - y: Optional[FData] = None, + estimator: RegistrationTransformer[Input, Output], + X: Input, + y: Output | None = None, ) -> float: """Compute the score of the transformation. @@ -75,8 +79,8 @@ def __call__( @abstractmethod def score_function( self, - X: FData, - y: FData, + X: Input, + y: Output, ) -> float: """Compute the score of the transformation performed. @@ -114,7 +118,7 @@ class AmplitudePhaseDecompositionStats(): class AmplitudePhaseDecomposition( - RegistrationScorer, + RegistrationScorer[FData, FData], ): r"""Compute mean square error measures for amplitude and phase variation. @@ -329,7 +333,7 @@ def score_function( return float(self.stats(X, y).r_squared) -class LeastSquares(RegistrationScorer): +class LeastSquares(RegistrationScorer[FData, FData]): r"""Cross-validated measure of the registration procedure. Computes a cross-validated measure of the level of synchronization @@ -453,7 +457,7 @@ def score_function(self, X: FData, y: FData) -> float: return float(1 - np.mean(quotient)) -class SobolevLeastSquares(RegistrationScorer): +class SobolevLeastSquares(RegistrationScorer[FData, FData]): r"""Cross-validated measure of the registration procedure. Computes a cross-validated measure of the level of synchronization @@ -562,7 +566,7 @@ def score_function(self, X: FData, y: FData) -> float: return float(1 - sls_y.sum() / sls_x.sum()) -class PairwiseCorrelation(RegistrationScorer): +class PairwiseCorrelation(RegistrationScorer[FData, FData]): r"""Cross-validated measure of pairwise correlation between functions. Computes a cross-validated pairwise correlation between functions @@ -630,7 +634,7 @@ class PairwiseCorrelation(RegistrationScorer): """ - def __init__(self, eval_points: Optional[np.ndarray] = None) -> None: + def __init__(self, eval_points: NDArrayFloat | None = None) -> None: self.eval_points = eval_points def score_function(self, X: FData, y: FData) -> float: diff --git a/skfda/preprocessing/smoothing/_linear.py b/skfda/preprocessing/smoothing/_linear.py index e515ead5c..8759704c9 100644 --- a/skfda/preprocessing/smoothing/_linear.py +++ b/skfda/preprocessing/smoothing/_linear.py @@ -14,7 +14,7 @@ from ..._utils import _to_grid_points from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from ...representation import FDataGrid -from ...typing._base import GridPointsLike +from ...typing._base import GridPoints, GridPointsLike from ...typing._numpy import NDArrayFloat @@ -28,6 +28,8 @@ class _LinearSmoother( ``hat_matrix`` to define the smoothing or 'hat' matrix. """ + input_points_: GridPoints + output_points_: GridPoints def __init__( self, diff --git a/skfda/preprocessing/smoothing/validation.py b/skfda/preprocessing/smoothing/validation.py index 4e93ecce2..fae56d456 100644 --- a/skfda/preprocessing/smoothing/validation.py +++ b/skfda/preprocessing/smoothing/validation.py @@ -1,18 +1,21 @@ """Defines methods for the validation of the smoothing.""" -from typing import Callable, Iterable, Optional, Tuple, Union +from __future__ import annotations + +from typing import Any, Callable, Iterable, Tuple import numpy as np import sklearn from sklearn.model_selection import GridSearchCV from ...representation import FDataGrid +from ...typing._numpy import NDArrayFloat, NDArrayInt from ._linear import _LinearSmoother def _get_input_estimation_and_matrix( estimator: _LinearSmoother, X: FDataGrid, -) -> Tuple[FDataGrid, np.ndarray]: +) -> Tuple[FDataGrid, NDArrayFloat]: """Return the smoothed data evaluated at the input points & the matrix.""" if estimator.output_points is not None: estimator = sklearn.base.clone(estimator) @@ -25,6 +28,9 @@ def _get_input_estimation_and_matrix( return y_est, hat_matrix +Scorer = Callable[[_LinearSmoother, FDataGrid, FDataGrid], float] + + class LinearSmootherLeaveOneOutScorer: r"""Leave-one-out cross validation scoring method for linear smoothers. @@ -66,11 +72,13 @@ def __call__( """Calculate Leave-One-Out score for linear smoothers.""" y_est, hat_matrix = _get_input_estimation_and_matrix(estimator, X) - return -np.mean( - ( - (y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) - / (1 - hat_matrix.diagonal()) - ) ** 2, + return -float( + np.mean( + ( + (y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) + / (1 - hat_matrix.diagonal()) + ) ** 2, + ), ) @@ -107,7 +115,7 @@ class LinearSmootherGeneralizedCVScorer: def __init__( self, - penalization_function: Callable[[np.ndarray], float] = None, + penalization_function: Callable[[NDArrayFloat], float] | None = None, ): self.penalization_function = penalization_function @@ -123,17 +131,19 @@ def __call__( if self.penalization_function is None: self.penalization_function = _default_penalization_function - return -( + return -float( np.mean( ( (y.data_matrix[..., 0] - y_est.data_matrix[..., 0]) / (1 - hat_matrix.diagonal()) ) ** 2, - ) * self.penalization_function(hat_matrix) + ) * self.penalization_function(hat_matrix), ) -class SmoothingParameterSearch(GridSearchCV): +class SmoothingParameterSearch( + GridSearchCV, # type: ignore[misc] +): """Chooses the best smoothing parameter and performs smoothing. Performs the smoothing of a FDataGrid object choosing the best @@ -299,14 +309,14 @@ class SmoothingParameterSearch(GridSearchCV): def __init__( self, estimator: _LinearSmoother, - param_values: Iterable, + param_values: Iterable[float], *, param_name: str = 'smoothing_parameter', - scoring: Optional[Callable] = None, - n_jobs: Optional[int] = None, + scoring: Scorer | None = None, + n_jobs: int | None = None, verbose: int = 0, - pre_dispatch: Optional[Union[int, str]] = '2*n_jobs', - error_score: Union[str, float] = np.nan, + pre_dispatch: int | str | None = '2*n_jobs', + error_score: str | float = np.nan, ): super().__init__( estimator=estimator, @@ -324,22 +334,23 @@ def __init__( def fit( # noqa: D102 self, - X, - y=None, - groups=None, - **fit_params, - ): + X: FDataGrid, + y: FDataGrid | None = None, + groups: NDArrayInt | None = None, + **fit_params: Any, + ) -> SmoothingParameterSearch: if y is None: y = X - return super().fit(X, y=y, groups=groups, **fit_params) + super().fit(X, y=y, groups=groups, **fit_params) + return self -def _default_penalization_function(hat_matrix: np.ndarray) -> float: - return (1 - hat_matrix.diagonal().mean()) ** -2 +def _default_penalization_function(hat_matrix: NDArrayFloat) -> float: + return float(1 - hat_matrix.diagonal().mean()) ** -2 -def akaike_information_criterion(hat_matrix: np.ndarray) -> float: +def akaike_information_criterion(hat_matrix: NDArrayFloat) -> float: r"""Akaike's information criterion for cross validation :footcite:`febrero-bande+oviedo_2012_fda.usc`. @@ -356,10 +367,10 @@ def akaike_information_criterion(hat_matrix: np.ndarray) -> float: .. footbibliography:: """ - return np.exp(2 * hat_matrix.diagonal().mean()) + return float(np.exp(2 * hat_matrix.diagonal().mean())) -def finite_prediction_error(hat_matrix: np.ndarray) -> float: +def finite_prediction_error(hat_matrix: NDArrayFloat) -> float: r"""Finite prediction error for cross validation :footcite:`febrero-bande+oviedo_2012_fda.usc`. @@ -377,13 +388,13 @@ def finite_prediction_error(hat_matrix: np.ndarray) -> float: .. footbibliography:: """ - return ( + return float( (1 + hat_matrix.diagonal().mean()) / (1 - hat_matrix.diagonal().mean()) ) -def shibata(hat_matrix: np.ndarray) -> float: +def shibata(hat_matrix: NDArrayFloat) -> float: r"""Shibata's model selector for cross validation :footcite:`febrero-bande+oviedo_2012_fda.usc`. @@ -400,10 +411,10 @@ def shibata(hat_matrix: np.ndarray) -> float: .. footbibliography:: """ - return 1 + 2 * hat_matrix.diagonal().mean() + return float(1 + 2 * hat_matrix.diagonal().mean()) -def rice(hat_matrix: np.ndarray) -> float: +def rice(hat_matrix: NDArrayFloat) -> float: r"""Rice's bandwidth selector for cross validation :footcite:`febrero-bande+oviedo_2012_fda.usc`. @@ -420,4 +431,4 @@ def rice(hat_matrix: np.ndarray) -> float: .. footbibliography:: """ - return (1 - 2 * hat_matrix.diagonal().mean()) ** -1 + return float(1 - 2 * hat_matrix.diagonal().mean()) ** -1 diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 1dbaccee6..f975393c4 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -75,6 +75,7 @@ class FData( # noqa: WPS214 coordinate functions. """ + dataset_name: Optional[str] def __init__( self, diff --git a/skfda/typing/_numpy.py b/skfda/typing/_numpy.py index 472daccc4..774511cc4 100644 --- a/skfda/typing/_numpy.py +++ b/skfda/typing/_numpy.py @@ -1,6 +1,6 @@ """NumPy aliases for compatibility.""" -from typing import Any +from typing import Any, Union import numpy as np @@ -14,6 +14,7 @@ NDArrayAny = NDArray[Any] NDArrayInt = NDArray[np.int_] NDArrayFloat = NDArray[np.float_] + NDArrayReal = NDArray[Union[np.float_, np.int_]] NDArrayBool = NDArray[np.bool_] NDArrayStr = NDArray[np.str_] NDArrayObject = NDArray[np.object_] @@ -22,6 +23,7 @@ NDArrayAny = np.ndarray # type:ignore[misc] NDArrayInt = np.ndarray # type:ignore[misc] NDArrayFloat = np.ndarray # type:ignore[misc] + NDArrayReal = np.ndarray # type:ignore[misc] NDArrayBool = np.ndarray # type:ignore[misc] NDArrayStr = np.ndarray # type:ignore[misc] NDArrayObject = np.ndarray # type:ignore[misc] From 2f7bdc948be77b095473b93f55172cd91353c046 Mon Sep 17 00:00:00 2001 From: vnmabus Date: Fri, 2 Sep 2022 15:38:42 +0200 Subject: [PATCH 273/400] Typing exploratory submodule. --- skfda/exploratory/depth/__init__.py | 43 +++++-- skfda/exploratory/outliers/__init__.py | 33 +++++- .../exploratory/outliers/neighbors_outlier.py | 5 +- skfda/exploratory/stats/_stats.py | 6 +- skfda/exploratory/visualization/__init__.py | 40 +++++-- skfda/exploratory/visualization/_baseplot.py | 43 +++---- skfda/exploratory/visualization/_boxplot.py | 41 ++++--- skfda/exploratory/visualization/_ddplot.py | 11 +- .../visualization/_magnitude_shape_plot.py | 14 +-- .../visualization/_multiple_display.py | 54 ++++----- .../exploratory/visualization/_outliergram.py | 9 +- .../visualization/_parametric_plot.py | 19 +-- skfda/exploratory/visualization/_utils.py | 26 +++-- skfda/exploratory/visualization/clustering.py | 110 ++++++++++-------- skfda/exploratory/visualization/fpca.py | 12 +- .../visualization/representation.py | 82 ++++++------- .../dim_reduction/variable_selection/mrmr.py | 13 ++- skfda/typing/_base.py | 1 - 18 files changed, 326 insertions(+), 236 deletions(-) diff --git a/skfda/exploratory/depth/__init__.py b/skfda/exploratory/depth/__init__.py index 4424a4376..e04dba73f 100644 --- a/skfda/exploratory/depth/__init__.py +++ b/skfda/exploratory/depth/__init__.py @@ -1,9 +1,38 @@ """Depth.""" -from . import multivariate -from ._depth import ( - BandDepth, - DistanceBasedDepth, - IntegratedDepth, - ModifiedBandDepth, + +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "multivariate", + ], + submod_attrs={ + '_depth': [ + "BandDepth", + "DistanceBasedDepth", + "IntegratedDepth", + "ModifiedBandDepth", + ], + "multivariate": [ + "Depth", + "Outlyingness", + "OutlyingnessBasedDepth", + ], + }, ) -from .multivariate import Depth, Outlyingness, OutlyingnessBasedDepth + +if TYPE_CHECKING: + from ._depth import ( + BandDepth as BandDepth, + DistanceBasedDepth as DistanceBasedDepth, + IntegratedDepth as IntegratedDepth, + ModifiedBandDepth as ModifiedBandDepth, + ) + from .multivariate import ( + Depth as Depth, + Outlyingness as Outlyingness, + OutlyingnessBasedDepth as OutlyingnessBasedDepth, + ) diff --git a/skfda/exploratory/outliers/__init__.py b/skfda/exploratory/outliers/__init__.py index d33e35798..3934b97e8 100644 --- a/skfda/exploratory/outliers/__init__.py +++ b/skfda/exploratory/outliers/__init__.py @@ -1,7 +1,28 @@ -from ._boxplot import BoxplotOutlierDetector -from ._directional_outlyingness import ( - MSPlotOutlierDetector, - directional_outlyingness_stats, +"""Outlier detection methods.""" +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + '_boxplot': ["BoxplotOutlierDetector"], + "_directional_outlyingness": [ + "MSPlotOutlierDetector", + "directional_outlyingness_stats", + ], + "_outliergram": ["OutliergramOutlierDetector"], + "neighbors_outlier": ["LocalOutlierFactor"], + }, ) -from ._outliergram import OutliergramOutlierDetector -from .neighbors_outlier import LocalOutlierFactor + +if TYPE_CHECKING: + from ._boxplot import BoxplotOutlierDetector as BoxplotOutlierDetector + from ._directional_outlyingness import ( + MSPlotOutlierDetector as MSPlotOutlierDetector, + directional_outlyingness_stats as directional_outlyingness_stats, + ) + from ._outliergram import ( + OutliergramOutlierDetector as OutliergramOutlierDetector + ) + from .neighbors_outlier import LocalOutlierFactor as LocalOutlierFactor diff --git a/skfda/exploratory/outliers/neighbors_outlier.py b/skfda/exploratory/outliers/neighbors_outlier.py index 93b414469..df1bd024a 100644 --- a/skfda/exploratory/outliers/neighbors_outlier.py +++ b/skfda/exploratory/outliers/neighbors_outlier.py @@ -308,11 +308,12 @@ def fit_predict( # In this estimator fit_predict cannot be wrapped as fit().predict() self._estimator = self._init_estimator() + metric = self.metric - if self.metric == 'precomputed': + if metric == 'precomputed': res = self._estimator.fit_predict(X, y) else: - X_dist = PairwiseMetric(self.metric)(X) + X_dist = PairwiseMetric(metric)(X) res = self._estimator.fit_predict(X_dist, y) self._store_fit_data() diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index 99d7a8485..27eeac78b 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -8,7 +8,7 @@ from scipy import integrate from scipy.stats import rankdata -from ...misc.metrics import l2_distance +from ...misc.metrics._lp_distances import l2_distance from ...representation import FData, FDataGrid from ...typing._metric import Metric from ...typing._numpy import NDArrayFloat @@ -46,7 +46,7 @@ def var(X: FData) -> FDataGrid: :term:`functional data object` with just one sample. """ - return X.var() + return X.var() # type: ignore[no-any-return] def gmean(X: FDataGrid) -> FDataGrid: @@ -79,7 +79,7 @@ def cov(X: FData) -> FDataGrid: :term:`functional data object` with just one sample. """ - return X.cov() + return X.cov() # type: ignore[no-any-return] def modified_epigraph_index(X: FDataGrid) -> NDArrayFloat: diff --git a/skfda/exploratory/visualization/__init__.py b/skfda/exploratory/visualization/__init__.py index 05bad49a9..e50004c6f 100644 --- a/skfda/exploratory/visualization/__init__.py +++ b/skfda/exploratory/visualization/__init__.py @@ -1,11 +1,33 @@ """Initialization module of visualization folder.""" -from . import clustering, representation -from ._baseplot import BasePlot -from ._boxplot import Boxplot, SurfaceBoxplot -from ._ddplot import DDPlot -from ._magnitude_shape_plot import MagnitudeShapePlot -from ._multiple_display import MultipleDisplay -from ._outliergram import Outliergram -from ._parametric_plot import ParametricPlot -from .fpca import FPCAPlot +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "clustering", + "representation", + ], + submod_attrs={ + "_baseplot": ["BasePlot"], + "_boxplot": ["Boxplot", "SurfaceBoxplot"], + "_ddplot": ["DDPlot"], + "_magnitude_shape_plot": ["MagnitudeShapePlot"], + "_multiple_display": ["MultipleDisplay"], + "_outliergram": ["Outliergram"], + "_parametric_plot": ["ParametricPlot"], + "fpca": ["FPCAPlot"], + }, +) + +if TYPE_CHECKING: + from ._baseplot import BasePlot as BasePlot + from ._boxplot import Boxplot as Boxplot, SurfaceBoxplot as SurfaceBoxplot + from ._ddplot import DDPlot as DDPlot + from ._magnitude_shape_plot import MagnitudeShapePlot as MagnitudeShapePlot + from ._multiple_display import MultipleDisplay as MultipleDisplay + from ._outliergram import Outliergram as Outliergram + from ._parametric_plot import ParametricPlot as ParametricPlot + from .fpca import FPCAPlot as FPCAPlot diff --git a/skfda/exploratory/visualization/_baseplot.py b/skfda/exploratory/visualization/_baseplot.py index c94caf81f..58930e396 100644 --- a/skfda/exploratory/visualization/_baseplot.py +++ b/skfda/exploratory/visualization/_baseplot.py @@ -4,12 +4,12 @@ the visualization modules, containing the basic functionality common to all of them. """ +from __future__ import annotations from abc import ABC, abstractmethod -from typing import Optional, Sequence, Tuple, Union +from typing import Sequence, Tuple import matplotlib.pyplot as plt -import numpy as np from matplotlib.artist import Artist from matplotlib.axes import Axes from matplotlib.backend_bases import LocationEvent, MouseEvent @@ -19,7 +19,7 @@ from matplotlib.text import Annotation from ...representation import FData -from ...typing._numpy import NDArrayInt +from ...typing._numpy import NDArrayInt, NDArrayObject from ._utils import _figure_to_svg, _get_figure_and_axes, _set_figure_layout @@ -38,18 +38,18 @@ class BasePlot(ABC): @abstractmethod def __init__( self, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Union[Axes, Sequence[Axes], None] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, - c: Optional[NDArrayInt] = None, + fig: Figure | None = None, + axes: Axes | Sequence[Axes] | None = None, + n_rows: int | None = None, + n_cols: int | None = None, + c: NDArrayInt | None = None, cmap_bold: ListedColormap = None, - x_label: Optional[str] = None, - y_label: Optional[str] = None, + x_label: str | None = None, + y_label: str | None = None, ) -> None: - self.artists: Optional[np.ndarray] = None + self.artists: NDArrayObject | None = None self.chart = chart self.fig = fig self.axes = axes @@ -78,8 +78,8 @@ def plot( Figure: figure object in which the displays and widgets will be plotted. """ - fig = getattr(self, "fig_", None) - axes = getattr(self, "axes_", None) + fig: Figure | None = getattr(self, "fig_", None) + axes: Sequence[Axes] | None = getattr(self, "axes_", None) if fig is None: fig, axes = self._set_figure_and_axes( @@ -87,6 +87,9 @@ def plot( fig=self.fig, axes=self.axes, ) + + assert axes is not None + if self.x_label is not None: axes[0].set_xlabel(self.x_label) if self.y_label is not None: @@ -112,16 +115,16 @@ def n_subplots(self) -> int: return 1 @property - def n_samples(self) -> Optional[int]: + def n_samples(self) -> int | None: """Get the number of instances that will be used for interactivity.""" return None def _set_figure_and_axes( self, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Union[Axes, Sequence[Axes], None] = None, + fig: Figure | None = None, + axes: Axes | Sequence[Axes] | None = None, ) -> Tuple[Figure, Sequence[Axes]]: fig, axes = _get_figure_and_axes(chart, fig, axes) fig, axes = _set_figure_layout( @@ -173,7 +176,7 @@ def _update_annotation( *, axes: Axes, sample_number: int, - fdata: Optional[FData], + fdata: FData | None, position: Tuple[float, float], ) -> None: """ @@ -226,7 +229,7 @@ def _update_annotation( def _sample_artist_from_event( self, event: LocationEvent, - ) -> Optional[Tuple[int, Optional[FData], Artist]]: + ) -> Tuple[int, FData | None, Artist] | None: """Get the number, fdata and artist under a location event.""" if self.artists is None: return None diff --git a/skfda/exploratory/visualization/_boxplot.py b/skfda/exploratory/visualization/_boxplot.py index 8538317b6..df0728023 100644 --- a/skfda/exploratory/visualization/_boxplot.py +++ b/skfda/exploratory/visualization/_boxplot.py @@ -8,12 +8,11 @@ import math from abc import abstractmethod -from typing import Optional, Sequence, Tuple, Union +from typing import Sequence, Tuple import matplotlib import matplotlib.pyplot as plt import numpy as np -from matplotlib.artist import Artist from matplotlib.axes import Axes from matplotlib.colors import Colormap from matplotlib.figure import Figure @@ -45,13 +44,13 @@ class FDataBoxplot(BasePlot): @abstractmethod def __init__( self, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, factor: float = 1.5, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, + fig: Figure | None = None, + axes: Axes | None = None, + n_rows: int | None = None, + n_cols: int | None = None, ) -> None: if factor < 0: raise ValueError( @@ -269,15 +268,15 @@ class Boxplot(FDataBoxplot): def __init__( self, fdatagrid: FData, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - depth_method: Optional[Depth[FDataGrid]] = None, + depth_method: Depth[FDataGrid] | None = None, prob: Sequence[float] = (0.5,), factor: float = 1.5, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, + fig: Figure | None = None, + axes: Axes | None = None, + n_rows: int | None = None, + n_cols: int | None = None, ): """Initialize the Boxplot class. @@ -389,7 +388,7 @@ def fdatagrid(self) -> FDataGrid: @property def median(self) -> NDArrayFloat: - return self._median + return self._median # type: ignore[no-any-return] @property def central_envelope(self) -> Tuple[NDArrayFloat, NDArrayFloat]: @@ -648,14 +647,14 @@ class SurfaceBoxplot(FDataBoxplot): def __init__( self, fdatagrid: FDataGrid, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - depth_method: Optional[Depth[FDataGrid]] = None, + depth_method: Depth[FDataGrid] | None = None, factor: float = 1.5, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, + fig: Figure | None = None, + axes: Axes | None = None, + n_rows: int | None = None, + n_cols: int | None = None, ) -> None: super().__init__( @@ -712,7 +711,7 @@ def fdatagrid(self) -> FDataGrid: @property def median(self) -> NDArrayFloat: - return self._median + return self._median # type: ignore[no-any-return] @property def central_envelope(self) -> Tuple[NDArrayFloat, NDArrayFloat]: diff --git a/skfda/exploratory/visualization/_ddplot.py b/skfda/exploratory/visualization/_ddplot.py index 140f2aa25..b1beb36f2 100644 --- a/skfda/exploratory/visualization/_ddplot.py +++ b/skfda/exploratory/visualization/_ddplot.py @@ -4,8 +4,9 @@ To do this depth is calculated for the two chosen distributions, and then a scatter plot is created of this two variables. """ +from __future__ import annotations -from typing import Optional, TypeVar, Union +from typing import TypeVar import numpy as np from matplotlib.artist import Artist @@ -56,12 +57,12 @@ def __init__( fdata: T, dist1: T, dist2: T, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, depth_method: Depth[T], - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - c: Optional[NDArrayInt] = None, + fig: Figure | None = None, + axes: Axes | None = None, + c: NDArrayInt | None = None, cmap_bold: ListedColormap = None, x_label: str = "X depth", y_label: str = "Y depth", diff --git a/skfda/exploratory/visualization/_magnitude_shape_plot.py b/skfda/exploratory/visualization/_magnitude_shape_plot.py index 97e674fd6..58d036d56 100644 --- a/skfda/exploratory/visualization/_magnitude_shape_plot.py +++ b/skfda/exploratory/visualization/_magnitude_shape_plot.py @@ -7,7 +7,7 @@ """ from __future__ import annotations -from typing import Any, Optional, Sequence, Union +from typing import Any, Sequence import matplotlib import matplotlib.pyplot as plt @@ -165,10 +165,10 @@ class MagnitudeShapePlot(BasePlot): def __init__( self, fdata: FDataGrid, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Optional[Sequence[Axes]] = None, + fig: Figure | None = None, + axes: Sequence[Axes] | None = None, ellipsoid: bool = True, **kwargs: Any, ) -> None: @@ -207,11 +207,11 @@ def fdata(self) -> FDataGrid: return self._fdata @property - def multivariate_depth(self) -> Optional[Depth[NDArrayFloat]]: + def multivariate_depth(self) -> Depth[NDArrayFloat] | None: return self.outlier_detector.multivariate_depth @property - def pointwise_weights(self) -> Optional[NDArrayFloat]: + def pointwise_weights(self) -> NDArrayFloat | None: return self.outlier_detector.pointwise_weights @property @@ -224,7 +224,7 @@ def points(self) -> NDArrayFloat: @property def outliers(self) -> NDArrayInt: - return self._outliers + return self._outliers # type: ignore[no-any-return] @property def colormap(self) -> Colormap: diff --git a/skfda/exploratory/visualization/_multiple_display.py b/skfda/exploratory/visualization/_multiple_display.py index d592979df..d8cc54d47 100644 --- a/skfda/exploratory/visualization/_multiple_display.py +++ b/skfda/exploratory/visualization/_multiple_display.py @@ -1,24 +1,15 @@ +from __future__ import annotations + import copy import itertools from functools import partial -from typing import ( - Generator, - List, - Optional, - Sequence, - Tuple, - Type, - Union, - cast, -) +from typing import Generator, List, Sequence, Tuple, Type, cast import numpy as np from matplotlib.artist import Artist from matplotlib.axes import Axes -from matplotlib.backend_bases import Event, LocationEvent, MouseEvent -from matplotlib.collections import PathCollection +from matplotlib.backend_bases import Event from matplotlib.figure import Figure -from matplotlib.text import Annotation from matplotlib.widgets import Slider, Widget from ._baseplot import BasePlot @@ -67,13 +58,13 @@ class MultipleDisplay: def __init__( self, - displays: Union[BasePlot, Sequence[BasePlot]], - criteria: Union[Sequence[float], Sequence[Sequence[float]]] = (), - sliders: Union[Type[Widget], Sequence[Type[Widget]]] = (), - label_sliders: Union[str, Sequence[str], None] = None, - chart: Union[Figure, Axes, None] = None, - fig: Optional[Figure] = None, - axes: Optional[Sequence[Axes]] = None, + displays: BasePlot | Sequence[BasePlot], + criteria: Sequence[float] | Sequence[Sequence[float]] = (), + sliders: Type[Widget] | Sequence[Type[Widget]] = (), + label_sliders: str | Sequence[str] | None = None, + chart: Figure | Axes | None = None, + fig: Figure | None = None, + axes: Sequence[Axes] | None = None, ): if isinstance(displays, BasePlot): displays = (displays,) @@ -86,9 +77,10 @@ def __init__( if d.n_samples is not None ) self.sliders: List[Widget] = [] - self.selected_sample: Optional[int] = None + self.selected_sample: int | None = None if len(criteria) != 0 and not isinstance(criteria[0], Sequence): + criteria = cast(Sequence[float], criteria) criteria = (criteria,) criteria = cast(Sequence[Sequence[float]], criteria) @@ -121,10 +113,10 @@ def __init__( def _init_axes( self, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Optional[Sequence[Axes]] = None, + fig: Figure | None = None, + axes: Sequence[Axes] | None = None, extra: int = 0, ) -> None: """ @@ -178,7 +170,7 @@ def _create_sliders( *, criteria: Sequence[Sequence[float]], sliders: Sequence[Type[Widget]], - label_sliders: Optional[Sequence[str]] = None, + label_sliders: Sequence[str] | None = None, ) -> None: """ Create the sliders with the criteria selected. @@ -267,7 +259,7 @@ def pick(self, event: Event) -> None: else: self._select_sample(selected_sample) - def _sample_from_artist(self, artist: Artist) -> Optional[int]: + def _sample_from_artist(self, artist: Artist) -> int | None: """Return the sample corresponding to an artist.""" for d in self.displays: @@ -277,9 +269,13 @@ def _sample_from_artist(self, artist: Artist) -> Optional[int]: for i, a in enumerate(d.axes_): if a == artist.axes: if len(d.axes_) == 1: - return np.where(d.artists == artist)[0][0] + return np.where( # type: ignore[no-any-return] + d.artists == artist, + )[0][0] else: - return np.where(d.artists[:, i] == artist)[0][0] + return np.where( # type: ignore[no-any-return] + d.artists[:, i] == artist, + )[0][0] return None @@ -316,7 +312,7 @@ def add_slider( axes: Axes, criterion: Sequence[float], widget_class: Type[Widget] = Slider, - label: Optional[str] = None, + label: str | None = None, ) -> None: """ Add the slider to the MultipleDisplay object. diff --git a/skfda/exploratory/visualization/_outliergram.py b/skfda/exploratory/visualization/_outliergram.py index d27e300b4..9120e7abc 100644 --- a/skfda/exploratory/visualization/_outliergram.py +++ b/skfda/exploratory/visualization/_outliergram.py @@ -6,8 +6,7 @@ these outliers. The motivation of the method is that it is easy to find magnitude outliers, but there is a necessity of capturing this other type. """ - -from typing import Optional, Sequence, Union +from __future__ import annotations import numpy as np from matplotlib.artist import Artist @@ -63,10 +62,10 @@ class Outliergram(BasePlot): def __init__( self, fdata: FDataGrid, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, + fig: Figure | None = None, + axes: Axes | None = None, factor: float = 1.5, ) -> None: BasePlot.__init__( diff --git a/skfda/exploratory/visualization/_parametric_plot.py b/skfda/exploratory/visualization/_parametric_plot.py index 6f3e91144..d6227c38f 100644 --- a/skfda/exploratory/visualization/_parametric_plot.py +++ b/skfda/exploratory/visualization/_parametric_plot.py @@ -5,8 +5,9 @@ one FData, with domain 1 and codomain 2, or giving two FData, both of them with domain 1 and codomain 1. """ +from __future__ import annotations -from typing import Mapping, Optional, Sequence, TypeVar, Union +from typing import Dict, Sequence, TypeVar import numpy as np from matplotlib.artist import Artist @@ -47,14 +48,14 @@ class ParametricPlot(BasePlot): def __init__( self, fdata1: FData, - fdata2: Optional[FData] = None, - chart: Union[Figure, Axes, None] = None, + fdata2: FData | None = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - group: Optional[Sequence[K]] = None, - group_colors: Optional[Indexable[K, ColorLike]] = None, - group_names: Optional[Indexable[K, str]] = None, + fig: Figure | None = None, + axes: Axes | None = None, + group: Sequence[K] | None = None, + group_colors: Indexable[K, ColorLike] | None = None, + group_names: Indexable[K, str] | None = None, legend: bool = False, ) -> None: BasePlot.__init__( @@ -98,7 +99,7 @@ def _plot( self.legend, ) - color_dict: Mapping[str, Union[ColorLike, None]] = {} + color_dict: Dict[str, ColorLike | None] = {} if ( self.fd_final.dim_domain == 1 diff --git a/skfda/exploratory/visualization/_utils.py b/skfda/exploratory/visualization/_utils.py index e9aa766cc..3d288270a 100644 --- a/skfda/exploratory/visualization/_utils.py +++ b/skfda/exploratory/visualization/_utils.py @@ -1,14 +1,16 @@ +from __future__ import annotations + import io import math import re from itertools import repeat -from typing import Optional, Sequence, Tuple, TypeVar, Union +from typing import Sequence, Tuple, TypeVar, Union import matplotlib.backends.backend_svg import matplotlib.pyplot as plt from matplotlib.axes import Axes from matplotlib.figure import Figure -from typing_extensions import Protocol +from typing_extensions import Protocol, TypeAlias from ...representation._functional_data import FData @@ -22,7 +24,7 @@ ) svg_height_replacement = r'\g<1>\g<2>' -ColorLike = Union[ +ColorLike: TypeAlias = Union[ Tuple[float, float, float], Tuple[float, float, float, float], str, @@ -72,9 +74,9 @@ def _figure_to_svg(figure: Figure) -> str: def _get_figure_and_axes( - chart: Union[Figure, Axes, Sequence[Axes], None] = None, - fig: Optional[Figure] = None, - axes: Union[Axes, Sequence[Axes], None] = None, + chart: Figure | Axes | Sequence[Axes] | None = None, + fig: Figure | None = None, + axes: Axes | Sequence[Axes] | None = None, ) -> Tuple[Figure, Sequence[Axes]]: """Obtain the figure and axes from the arguments.""" num_defined = sum(e is not None for e in (chart, fig, axes)) @@ -112,8 +114,8 @@ def _get_figure_and_axes( def _get_axes_shape( n_axes: int, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, + n_rows: int | None = None, + n_cols: int | None = None, ) -> Tuple[int, int]: """Get the number of rows and columns of the subplots.""" if ( @@ -155,10 +157,10 @@ def _projection_from_dim(dim: int) -> str: def _set_figure_layout( fig: Figure, axes: Sequence[Axes], - dim: Union[int, Sequence[int]] = 2, + dim: int | Sequence[int] = 2, n_axes: int = 1, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, + n_rows: int | None = None, + n_cols: int | None = None, ) -> Tuple[Figure, Sequence[Axes]]: """ Set the figure axes for plotting. @@ -242,7 +244,7 @@ def _set_labels( fdata: FData, fig: Figure, axes: Sequence[Axes], - patches: Optional[Sequence[matplotlib.patches.Patch]] = None, + patches: Sequence[matplotlib.patches.Patch] | None = None, ) -> None: """Set labels if any. diff --git a/skfda/exploratory/visualization/clustering.py b/skfda/exploratory/visualization/clustering.py index 8d1fdb979..5758f52d3 100644 --- a/skfda/exploratory/visualization/clustering.py +++ b/skfda/exploratory/visualization/clustering.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import Optional, Sequence, Tuple, Union +from typing import Sequence, Tuple import matplotlib import matplotlib.patches as mpatches @@ -55,12 +55,12 @@ def predict_proba(self, X: FDataGrid) -> NDArrayFloat: def _plot_clustering_checks( estimator: ClusteringEstimator, fdata: FData, - sample_colors: Optional[Sequence[ColorLike]], - sample_labels: Optional[Sequence[str]], - cluster_colors: Optional[Sequence[ColorLike]], - cluster_labels: Optional[Sequence[str]], - center_colors: Optional[Sequence[ColorLike]], - center_labels: Optional[Sequence[str]], + sample_colors: Sequence[ColorLike] | None, + sample_labels: Sequence[str] | None, + cluster_colors: Sequence[ColorLike] | None, + cluster_labels: Sequence[str] | None, + center_colors: Sequence[ColorLike] | None, + center_labels: Sequence[str] | None, ) -> None: """Check the arguments.""" if ( @@ -113,9 +113,9 @@ def _plot_clustering_checks( def _get_labels( - x_label: Optional[str], - y_label: Optional[str], - title: Optional[str], + x_label: str | None, + y_label: str | None, + title: str | None, xlabel_str: str, ) -> Tuple[str, str, str]: """ @@ -195,16 +195,16 @@ def __init__( self, estimator: ClusteringEstimator, fdata: FDataGrid, - chart: Union[Figure, Axes, None] = None, - fig: Optional[Figure] = None, - axes: Union[Axes, Sequence[Axes], None] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, - sample_labels: Optional[Sequence[str]] = None, - cluster_colors: Optional[Sequence[ColorLike]] = None, - cluster_labels: Optional[Sequence[str]] = None, - center_colors: Optional[Sequence[ColorLike]] = None, - center_labels: Optional[Sequence[str]] = None, + chart: Figure | Axes | None = None, + fig: Figure | None = None, + axes: Axes | Sequence[Axes] | None = None, + n_rows: int | None = None, + n_cols: int | None = None, + sample_labels: Sequence[str] | None = None, + cluster_colors: Sequence[ColorLike] | None = None, + cluster_labels: Sequence[str] | None = None, + center_colors: Sequence[ColorLike] | None = None, + center_labels: Sequence[str] | None = None, center_width: int = 3, colormap: matplotlib.colors.Colormap = None, ) -> None: @@ -278,7 +278,7 @@ def _plot_clusters( f'$CENTER: {i}$' for i in range(self.estimator.n_clusters) ] - colors_by_cluster = self.cluster_colors[self.labels] + colors_by_cluster = np.asarray(self.cluster_colors)[self.labels] patches = [ mpatches.Patch( @@ -372,16 +372,16 @@ def __init__( estimator: FuzzyClusteringEstimator, fdata: FDataGrid, *, - chart: Union[Figure, Axes, None] = None, - fig: Optional[Figure] = None, - axes: Union[Axes, Sequence[Axes], None] = None, - sample_colors: Optional[Sequence[ColorLike]] = None, - sample_labels: Optional[Sequence[str]] = None, - cluster_labels: Optional[Sequence[str]] = None, + chart: Figure | Axes | None = None, + fig: Figure | None = None, + axes: Axes | Sequence[Axes] | None = None, + sample_colors: Sequence[ColorLike] | None = None, + sample_labels: Sequence[str] | None = None, + cluster_labels: Sequence[str] | None = None, colormap: matplotlib.colors.Colormap = None, - x_label: Optional[str] = None, - y_label: Optional[str] = None, - title: Optional[str] = None, + x_label: str | None = None, + y_label: str | None = None, + title: str | None = None, ) -> None: if colormap is None: @@ -514,18 +514,18 @@ def __init__( self, estimator: FuzzyClusteringEstimator, fdata: FData, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Union[Axes, Sequence[Axes], None] = None, + fig: Figure | None = None, + axes: Axes | Sequence[Axes] | None = None, sort: int = -1, - sample_labels: Optional[Sequence[str]] = None, - cluster_colors: Optional[Sequence[ColorLike]] = None, - cluster_labels: Optional[Sequence[str]] = None, + sample_labels: Sequence[str] | None = None, + cluster_colors: Sequence[ColorLike] | None = None, + cluster_labels: Sequence[str] | None = None, colormap: matplotlib.colors.Colormap = None, - x_label: Optional[str] = None, - y_label: Optional[str] = None, - title: Optional[str] = None, + x_label: str | None = None, + y_label: str | None = None, + title: str | None = None, ) -> None: if colormap is None: @@ -539,7 +539,11 @@ def __init__( self.fdata = fdata self.estimator = estimator self.sample_labels = sample_labels - self.cluster_colors = cluster_colors + self.cluster_colors = ( + None + if cluster_colors is None + else list(cluster_colors) + ) self.cluster_labels = cluster_labels self.x_label = x_label self.y_label = y_label @@ -599,12 +603,18 @@ def _plot( ) if self.sample_labels is None: - self.sample_labels = np.arange(self.fdata.n_samples) + self.sample_labels = list( + np.arange( + self.fdata.n_samples, + ).astype(np.str_), + ) if self.cluster_colors is None: - self.cluster_colors = self.colormap( - np.arange(self.estimator.n_clusters) - / (self.estimator.n_clusters - 1), + self.cluster_colors = list( + self.colormap( + np.arange(self.estimator.n_clusters) + / (self.estimator.n_clusters - 1), + ), ) if self.cluster_labels is None: @@ -625,16 +635,20 @@ def _plot( labels_dim = membership else: sample_indices = np.argsort(-membership[:, self.sort]) - self.sample_labels = np.copy(self.sample_labels[sample_indices]) + self.sample_labels = list( + np.array(self.sample_labels)[sample_indices], + ) labels_dim = np.copy(membership[sample_indices]) temp_labels = np.copy(labels_dim[:, 0]) labels_dim[:, 0] = labels_dim[:, self.sort] labels_dim[:, self.sort] = temp_labels - temp_color = np.copy(self.cluster_colors[0]) - self.cluster_colors[0] = self.cluster_colors[self.sort] - self.cluster_colors[self.sort] = temp_color + # Swap + self.cluster_colors[0], self.cluster_colors[self.sort] = ( + self.cluster_colors[self.sort], + self.cluster_colors[0], + ) conc = np.zeros((self.fdata.n_samples, 1)) labels_dim = np.concatenate((conc, labels_dim), axis=-1) diff --git a/skfda/exploratory/visualization/fpca.py b/skfda/exploratory/visualization/fpca.py index 87a9b2861..415dba2f7 100644 --- a/skfda/exploratory/visualization/fpca.py +++ b/skfda/exploratory/visualization/fpca.py @@ -1,7 +1,7 @@ from __future__ import annotations import warnings -from typing import Optional, Sequence, Union +from typing import Sequence from matplotlib.axes import Axes from matplotlib.figure import Figure @@ -37,11 +37,11 @@ def __init__( *, factor: float = 1, multiple: float | None = None, - chart: Union[Figure, Axes, None] = None, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, + chart: Figure | Axes | None = None, + fig: Figure | None = None, + axes: Axes | None = None, + n_rows: int | None = None, + n_cols: int | None = None, ): super().__init__( chart, diff --git a/skfda/exploratory/visualization/representation.py b/skfda/exploratory/visualization/representation.py index 6b1da7a15..a9e1addee 100644 --- a/skfda/exploratory/visualization/representation.py +++ b/skfda/exploratory/visualization/representation.py @@ -6,8 +6,9 @@ be set manually or automatically depending on values like depth measures. """ +from __future__ import annotations -from typing import Any, Dict, Optional, Sequence, Tuple, TypeVar, Union +from typing import Any, Dict, Sequence, Sized, Tuple, TypeVar import matplotlib.cm import matplotlib.patches @@ -20,7 +21,6 @@ from ..._utils import _to_grid_points, constants from ...misc.validation import validate_domain_range -from ...representation import FDataGrid from ...representation._functional_data import FData from ...typing._base import DomainRangeLike, GridPointsLike from ._baseplot import BasePlot @@ -28,7 +28,6 @@ K = TypeVar('K', contravariant=True) V = TypeVar('V', covariant=True) -T = TypeVar('T', FDataGrid, np.ndarray) class Indexable(Protocol[K, V]): @@ -42,15 +41,15 @@ def __len__(self) -> int: def _get_color_info( - fdata: T, - group: Optional[Sequence[K]] = None, - group_names: Optional[Indexable[K, str]] = None, - group_colors: Optional[Indexable[K, ColorLike]] = None, + fdata: Sized, + group: Sequence[K] | None = None, + group_names: Indexable[K, str] | None = None, + group_colors: Indexable[K, ColorLike] | None = None, legend: bool = False, - kwargs: Optional[Dict[str, Any]] = None, + kwargs: Dict[str, Any] | None = None, ) -> Tuple[ - Optional[ColorLike], - Optional[Sequence[matplotlib.patches.Patch]], + Sequence[ColorLike] | None, + Sequence[matplotlib.patches.Patch] | None, ]: if kwargs is None: @@ -62,7 +61,10 @@ def _get_color_info( # In this case, each curve has a label, and all curves with the same # label should have the same color - group_unique, group_indexes = np.unique(group, return_inverse=True) + group_unique, group_indexes = np.unique( + np.asarray(group), + return_inverse=True, + ) n_labels = len(group_unique) if group_colors is not None: @@ -77,7 +79,7 @@ def _get_color_info( cycle_colors, np.arange(n_labels), mode='wrap', ) - sample_colors = group_colors_array[group_indexes] + sample_colors = list(group_colors_array[group_indexes]) group_names_array = None @@ -193,21 +195,21 @@ class GraphPlot(BasePlot): def __init__( self, fdata: FData, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, - n_points: Union[int, Tuple[int, int], None] = None, - domain_range: Optional[DomainRangeLike] = None, - group: Optional[Sequence[K]] = None, - group_colors: Optional[Indexable[K, ColorLike]] = None, - group_names: Optional[Indexable[K, str]] = None, - gradient_criteria: Optional[Sequence[float]] = None, - max_grad: Optional[float] = None, - min_grad: Optional[float] = None, - colormap: Union[Colormap, str, None] = None, + fig: Figure | None = None, + axes: Axes | None = None, + n_rows: int | None = None, + n_cols: int | None = None, + n_points: int | Tuple[int, int] | None = None, + domain_range: DomainRangeLike | None = None, + group: Sequence[K] | None = None, + group_colors: Indexable[K, ColorLike] | None = None, + group_names: Indexable[K, str] | None = None, + gradient_criteria: Sequence[float] | None = None, + max_grad: float | None = None, + min_grad: float | None = None, + colormap: Colormap | str | None = None, legend: bool = False, **kwargs: Any, ) -> None: @@ -238,7 +240,7 @@ def __init__( else: self.max_grad = max_grad - self.gradient_list: Optional[Sequence[float]] = ( + self.gradient_list: Sequence[float] | None = ( [ (grad_color - self.min_grad) / (self.max_grad - self.min_grad) @@ -306,7 +308,7 @@ def _plot( dtype=Artist, ) - color_dict: Dict[str, Optional[ColorLike]] = {} + color_dict: Dict[str, ColorLike | None] = {} if self.fdata.dim_domain == 1: @@ -422,17 +424,17 @@ class ScatterPlot(BasePlot): def __init__( self, fdata: FData, - chart: Union[Figure, Axes, None] = None, + chart: Figure | Axes | None = None, *, - fig: Optional[Figure] = None, - axes: Optional[Axes] = None, - n_rows: Optional[int] = None, - n_cols: Optional[int] = None, - grid_points: Optional[GridPointsLike] = None, - domain_range: Union[Tuple[int, int], DomainRangeLike, None] = None, - group: Optional[Sequence[K]] = None, - group_colors: Optional[Indexable[K, ColorLike]] = None, - group_names: Optional[Indexable[K, str]] = None, + fig: Figure | None = None, + axes: Axes | None = None, + n_rows: int | None = None, + n_cols: int | None = None, + grid_points: GridPointsLike | None = None, + domain_range: Tuple[int, int] | DomainRangeLike | None = None, + group: Sequence[K] | None = None, + group_colors: Indexable[K, ColorLike] | None = None, + group_names: Indexable[K, str] | None = None, legend: bool = False, **kwargs: Any, ) -> None: @@ -505,7 +507,7 @@ def _plot( dtype=Artist, ) - color_dict: Dict[str, Optional[ColorLike]] = {} + color_dict: Dict[str, ColorLike | None] = {} if self.fdata.dim_domain == 1: @@ -548,7 +550,7 @@ def _plot( def set_color_dict( sample_colors: Any, ind: int, - color_dict: Dict[str, Optional[ColorLike]], + color_dict: Dict[str, ColorLike | None], ) -> None: """ Auxiliary method used to update color_dict. diff --git a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py index f05e9dd55..1b91c50ac 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py @@ -1,12 +1,12 @@ from __future__ import annotations import operator +from dataclasses import dataclass from typing import ( Any, Callable, Dict, Generic, - NamedTuple, Tuple, TypeVar, Union, @@ -48,7 +48,8 @@ ) -class Method(NamedTuple, Generic[dtype_y_T]): +@dataclass +class Method(Generic[dtype_y_T]): """Predefined mRMR method.""" relevance_dependence_measure: _DependenceMeasure[ @@ -106,7 +107,7 @@ def mutual_information( MethodName = Literal["MID", "MIQ"] -def _parse_method(name: MethodName) -> Method: +def _parse_method(name: MethodName) -> Method[Union[np.int_, np.float_]]: if name == "MID": return MID elif name == "MIQ": @@ -324,7 +325,7 @@ def __init__( self, *, n_features_to_select: int = 1, - method: Method | MethodName, + method: Method[dtype_y_T] | MethodName, ) -> None: pass @@ -362,7 +363,7 @@ def __init__( self, *, n_features_to_select: int = 1, - method: Method | MethodName | None = None, + method: Method[dtype_y_T] | MethodName | None = None, dependence_measure: _DependenceMeasure[ np.typing.NDArray[np.float_], np.typing.NDArray[np.float_ | dtype_y_T], @@ -418,7 +419,7 @@ def _validate_parameters(self) -> None: np.typing.NDArray[np.float_], np.typing.NDArray[dtype_y_T], ] = ( - method.relevance_dependence_measure # type: ignore[assignment] + method.relevance_dependence_measure ) self.redundancy_dependence_measure_ = ( method.redundancy_dependence_measure diff --git a/skfda/typing/_base.py b/skfda/typing/_base.py index e1c6ac6b8..0bf16b404 100644 --- a/skfda/typing/_base.py +++ b/skfda/typing/_base.py @@ -1,7 +1,6 @@ """Common types.""" from typing import Optional, Sequence, Tuple, TypeVar, Union -import numpy as np from 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email.contentmanager {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 1], "dep_prios": [5, 5, 5, 5], "dependencies": ["collections.abc", "email.message", "typing", "builtins"], "hash": "25ee805b5770376d2f6d9ed4297c02748c5791f27beb2df113404eb0fa6471a4", "id": "email.contentmanager", "ignore_all": true, "interface_hash": "f44cb75e55e5494622954876c0e21f4cf784ca3bfb528bbe662935a829f585af", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, 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email.errors at email/errors.meta.json +TRACE: Meta email.errors {"data_mtime": 1662126099, "dep_lines": [1, 1, 1, 1, 1], "dep_prios": [10, 5, 30, 30, 30], "dependencies": ["sys", "builtins", "_typeshed", "abc", "typing"], "hash": "2dcb55e1cef7b1dbd0e58056aae9eb2dba5e70d83fca907196faef9f7ea0527c", "id": "email.errors", "ignore_all": true, "interface_hash": "8826bc9a109fb37f0fed685e6abba0077070de28722db20e3e0642da885f9803", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, 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email/policy.meta.json +TRACE: Meta email.policy {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 1], "dep_prios": [5, 5, 5, 5, 5, 5, 5, 5], "dependencies": ["abc", "collections.abc", "email.contentmanager", "email.errors", "email.header", "email.message", "typing", "builtins"], "hash": "2122e2c085231b4505c48fb54f55b2fb36ead76660699bc3d800fffbd4f0c6f4", "id": "email.policy", "ignore_all": true, "interface_hash": "ae20d3778f6f632dabba8b722916b3237817e2416f1ede0b88d880462ecbc84a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi (email.policy) +TRACE: Looking for string at string.meta.json +TRACE: Meta string {"data_mtime": 1662126100, "dep_lines": [1, 2, 3, 4, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["sys", "collections.abc", "re", "typing", "builtins", "_typeshed", "abc", "enum"], "hash": "32482313d12016c3bf5da60f49cfcbe04c26ceda3bf16241ce093dce69b4ee1b", "id": "string", "ignore_all": true, "interface_hash": "bb7a35e9ad66797443782a36c5cdd45eac609b1487db3959ba928667316e6284", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/string.pyi (string) +TRACE: Looking for numpy.compat._pep440 at numpy/compat/_pep440.meta.json +TRACE: Meta numpy.compat._pep440 {"data_mtime": 1662126100, "dep_lines": [32, 33, 34, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 10, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30], "dependencies": ["collections", "itertools", "re", "builtins", "_typeshed", "abc", "array", "ctypes", "enum", "mmap", "pickle", "typing", "typing_extensions"], "hash": "56bec1dd0b228d1e69ea1f18033d8b8cd194cb31d4279819e2fdba3696402837", "id": "numpy.compat._pep440", "ignore_all": true, "interface_hash": "f9e20e78bf0ca05186e66d67f75f0b9d44268b26038275f344247cb6776cebba", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": 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true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi", "plugin_data": null, "size": 2278, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.synchronize: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.synchronize +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi (multiprocessing.synchronize) +TRACE: Looking for multiprocessing.managers at multiprocessing/managers.meta.json +TRACE: Meta multiprocessing.managers {"data_mtime": 1662126100, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 1, 1], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["queue", "sys", "threading", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", 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false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi", "plugin_data": null, "size": 8556, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.managers: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.managers +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi (multiprocessing.managers) +TRACE: Looking for multiprocessing.process at multiprocessing/process.meta.json +TRACE: Meta multiprocessing.process {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "builtins", "_typeshed", "abc"], "hash": "fa47cbb61f64a2309b6d7043a2c42c4c096b85f17dbd51181d9c83455a89e5c3", "id": "multiprocessing.process", "ignore_all": true, "interface_hash": "77c612e6cb8b00cd8763eb96f60de3ddd7c40a47a2ea78f5710fb059dea117d2", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, 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(multiprocessing.process) +TRACE: Looking for multiprocessing.queues at multiprocessing/queues.meta.json +TRACE: Meta multiprocessing.queues {"data_mtime": 1662126100, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30], "dependencies": ["queue", "sys", "typing", "builtins", "_typeshed", "abc"], "hash": "daf7a7147647126f56cf864be94a85c23c7801d703f87280168714c3eda8a44c", "id": "multiprocessing.queues", "ignore_all": true, "interface_hash": "38370111a855e180ba5a53c74c70d2ab11961893618ccd17ecc4b397487fd0ae", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi", "plugin_data": null, "size": 1398, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.queues: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.queues +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi (multiprocessing.queues) +TRACE: Looking for multiprocessing.spawn at multiprocessing/spawn.meta.json +TRACE: Meta multiprocessing.spawn {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 1, 1], "dep_prios": [5, 5, 5, 5, 30], "dependencies": ["collections.abc", "types", "typing", "builtins", "abc"], "hash": "b49c0c45edfea0381227f3a4dd3408fe75dd8c69e824da53e7271f0005371a6a", "id": "multiprocessing.spawn", "ignore_all": true, "interface_hash": "864e86b58a2b7fe05e14bfb866bac09cbeff6f212bcefb318fedda6f40e77af4", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, 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normal +LOG: Metadata not found for multiprocessing.spawn +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi (multiprocessing.spawn) +TRACE: Looking for _weakrefset at _weakrefset.meta.json +TRACE: Meta _weakrefset {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 4, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "typing", "builtins", "abc"], "hash": "b782eb1547d8f9158e20866c201b6753d853647d67d993ccd2e266b1ae292828", "id": "_weakrefset", "ignore_all": true, "interface_hash": "454315f7522e84d316cc85d5404e0d35f6174236661489ccb7cebf12f69b03ea", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": 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follow_imports: silent != normal +LOG: Metadata not found for _weakrefset +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi (_weakrefset) +TRACE: Looking for _weakref at _weakref.meta.json +TRACE: Meta _weakref {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 4, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "2eb8b487360e720252edcc10dc29f1d3ecb8948a81c02e9a6693df62b92e592c", "id": "_weakref", "ignore_all": true, "interface_hash": "60fad058d2e4209acdc3f2e2722c4eea7a895c8f8586a9f8724b37cab26f2e8a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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1658259053, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, 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"mtime": 1631470905, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py", "plugin_data": null, "size": 174, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for skfda.ml.clustering: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for skfda.ml.clustering +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py (skfda.ml.clustering) +TRACE: Looking for skfda.ml.regression at skfda/ml/regression/__init__.meta.json +TRACE: Meta skfda.ml.regression {"data_mtime": 1662127955, "dep_lines": [2, 3, 4, 5, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 5, 30, 30], "dependencies": ["skfda.ml.regression._historical_linear_model", "skfda.ml.regression._kernel_regression", "skfda.ml.regression._linear_regression", "skfda.ml.regression._neighbors_regression", "builtins", "abc", "typing"], "hash": "96a60ad58cd0e0b85e98e956dfc4c2a1b5c696019623a4cc5338772740ea65d8", "id": "skfda.ml.regression", "ignore_all": true, "interface_hash": 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/home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py (skfda.preprocessing.feature_construction._function_transformers) +LOG: Loaded graph with 440 nodes (2.813 sec) +LOG: Found 176 SCCs; largest has 72 nodes +TRACE: Priorities for typing_extensions: abc:10 collections:10 sys:10 _typeshed:5 collections.abc:5 typing:5 +TRACE: Priorities for typing: collections:10 sys:10 _typeshed:5 abc:5 types:5 typing_extensions:5 +TRACE: Priorities for types: sys:10 _typeshed:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 typing:5 typing_extensions:5 +TRACE: Priorities for sys: _typeshed:5 builtins:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 io:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for subprocess: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for posixpath: sys:10 _typeshed:5 collections.abc:5 genericpath:5 os:5 typing:5 typing_extensions:5 +TRACE: Priorities for pickle: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for pathlib: sys:10 _typeshed:5 collections.abc:5 io:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for os.path: sys:10 posixpath:5 +TRACE: Priorities for os: sys:10 os.path:10 _typeshed:5 abc:5 builtins:5 collections.abc:5 contextlib:5 io:5 subprocess:5 typing:5 typing_extensions:5 +TRACE: Priorities for mmap: sys:10 _typeshed:5 collections.abc:5 typing:5 +TRACE: Priorities for io: builtins:10 codecs:10 sys:10 _typeshed:5 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for importlib.metadata: abc:10 pathlib:5 sys:10 _typeshed:5 collections.abc:5 email.message:5 importlib.abc:5 os:5 typing:5 +TRACE: Priorities for importlib.machinery: importlib.abc:10 importlib:20 sys:10 types:10 collections.abc:5 typing:5 importlib.metadata:5 +TRACE: Priorities for importlib.abc: sys:10 types:10 _typeshed:5 abc:5 collections.abc:5 importlib.machinery:5 io:5 typing:5 typing_extensions:5 +TRACE: Priorities for importlib: collections.abc:5 importlib.abc:5 types:5 +TRACE: Priorities for genericpath: os:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for email.policy: abc:5 collections.abc:5 email.contentmanager:5 email.errors:5 email.header:5 email.message:5 typing:5 +TRACE: Priorities for email.message: collections.abc:5 email:5 email.charset:5 email.contentmanager:5 email.errors:5 email.policy:5 typing:5 typing_extensions:5 +TRACE: Priorities for email.header: email.charset:5 +TRACE: Priorities for email.errors: sys:10 +TRACE: Priorities for email.contentmanager: collections.abc:5 email.message:5 typing:5 +TRACE: Priorities for email.charset: collections.abc:5 +TRACE: Priorities for email: collections.abc:5 email.message:5 email.policy:5 typing:5 typing_extensions:5 +TRACE: Priorities for ctypes: sys:10 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for contextlib: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for collections.abc: _collections_abc:5 +TRACE: Priorities for collections: sys:10 _collections_abc:5 _typeshed:5 typing:5 typing_extensions:5 +TRACE: Priorities for codecs: types:10 _codecs:5 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for array: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for abc: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for _typeshed: array:10 ctypes:10 mmap:10 pickle:10 sys:10 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for _collections_abc: sys:10 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for _codecs: codecs:10 sys:10 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for _ast: sys:10 typing:5 typing_extensions:5 +TRACE: Priorities for builtins: sys:10 types:5 _ast:5 _collections_abc:5 _typeshed:5 collections.abc:5 io:5 typing:5 typing_extensions:5 +LOG: Processing SCC of size 36 (typing_extensions typing types sys subprocess posixpath pickle pathlib os.path os mmap io importlib.metadata importlib.machinery importlib.abc importlib genericpath email.policy email.message email.header email.errors email.contentmanager email.charset email ctypes contextlib collections.abc collections codecs array abc _typeshed _collections_abc _codecs _ast builtins) as inherently stale +LOG: Writing typing_extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing_extensions.pyi typing_extensions.meta.json typing_extensions.data.json +TRACE: Interface for typing_extensions has changed +LOG: Cached module typing_extensions has changed interface +LOG: Writing typing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing.pyi typing.meta.json typing.data.json +TRACE: Interface for typing has changed +LOG: Cached module typing has changed interface +LOG: Writing types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/types.pyi types.meta.json types.data.json +TRACE: Interface for types has changed +LOG: Cached module types has changed interface +LOG: Writing sys /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sys.pyi sys.meta.json sys.data.json +TRACE: Interface for sys has changed +LOG: Cached module sys has changed interface +LOG: Writing subprocess /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/subprocess.pyi subprocess.meta.json subprocess.data.json +TRACE: Interface for subprocess has changed +LOG: Cached module subprocess has changed interface +LOG: Writing posixpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/posixpath.pyi posixpath.meta.json posixpath.data.json +TRACE: Interface for posixpath has changed +LOG: Cached module posixpath has changed interface +LOG: Writing pickle /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pickle.pyi pickle.meta.json pickle.data.json +TRACE: Interface for pickle has changed +LOG: Cached module pickle has changed interface +LOG: Writing pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pathlib.pyi pathlib.meta.json pathlib.data.json +TRACE: Interface for pathlib has changed +LOG: Cached module pathlib has changed interface +LOG: Writing os.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/path.pyi os/path.meta.json os/path.data.json +TRACE: Interface for os.path has changed +LOG: Cached module os.path has changed interface +LOG: Writing os /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/__init__.pyi os/__init__.meta.json os/__init__.data.json +TRACE: Interface for os has changed +LOG: Cached module os has changed interface +LOG: Writing mmap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/mmap.pyi mmap.meta.json mmap.data.json +TRACE: Interface for mmap has changed +LOG: Cached module mmap has changed interface +LOG: Writing io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/io.pyi io.meta.json io.data.json +TRACE: Interface for io has changed +LOG: Cached module io has changed interface +LOG: Writing importlib.metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi importlib/metadata/__init__.meta.json importlib/metadata/__init__.data.json +TRACE: Interface for importlib.metadata has changed +LOG: Cached module importlib.metadata has changed interface +LOG: Writing importlib.machinery /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi importlib/machinery.meta.json importlib/machinery.data.json +TRACE: Interface for importlib.machinery has changed +LOG: Cached module importlib.machinery has changed interface +LOG: Writing importlib.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/abc.pyi importlib/abc.meta.json importlib/abc.data.json +TRACE: Interface for importlib.abc has changed +LOG: Cached module importlib.abc has changed interface +LOG: Writing importlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi importlib/__init__.meta.json importlib/__init__.data.json +TRACE: Interface for importlib has changed +LOG: Cached module importlib has changed interface +LOG: Writing genericpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/genericpath.pyi genericpath.meta.json genericpath.data.json +TRACE: Interface for genericpath has changed +LOG: Cached module genericpath has changed interface +LOG: Writing email.policy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi email/policy.meta.json email/policy.data.json +TRACE: Interface for email.policy has changed +LOG: Cached module email.policy has changed interface +LOG: Writing email.message /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/message.pyi email/message.meta.json email/message.data.json +TRACE: Interface for email.message has changed +LOG: Cached module email.message has changed interface +LOG: Writing email.header /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/header.pyi email/header.meta.json email/header.data.json +TRACE: Interface for email.header has changed +LOG: Cached module email.header has changed interface +LOG: Writing email.errors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/errors.pyi email/errors.meta.json email/errors.data.json +TRACE: Interface for email.errors has changed +LOG: Cached module email.errors has changed interface +LOG: Writing email.contentmanager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi email/contentmanager.meta.json email/contentmanager.data.json +TRACE: Interface for email.contentmanager has changed +LOG: Cached module email.contentmanager has changed interface +LOG: Writing email.charset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi email/charset.meta.json email/charset.data.json +TRACE: Interface for email.charset has changed +LOG: Cached module email.charset has changed interface +LOG: Writing email /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/__init__.pyi email/__init__.meta.json email/__init__.data.json +TRACE: Interface for email has changed +LOG: Cached module email has changed interface +LOG: Writing ctypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ctypes/__init__.pyi ctypes/__init__.meta.json ctypes/__init__.data.json +TRACE: Interface for ctypes has changed +LOG: Cached module ctypes has changed interface +LOG: Writing contextlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextlib.pyi contextlib.meta.json contextlib.data.json +TRACE: Interface for contextlib has changed +LOG: Cached module contextlib has changed interface +LOG: Writing collections.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/abc.pyi collections/abc.meta.json collections/abc.data.json +TRACE: Interface for collections.abc has changed +LOG: Cached module collections.abc has changed interface +LOG: Writing collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/__init__.pyi collections/__init__.meta.json collections/__init__.data.json +TRACE: Interface for collections has changed +LOG: Cached module collections has changed interface +LOG: Writing codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/codecs.pyi codecs.meta.json codecs.data.json +TRACE: Interface for codecs has changed +LOG: Cached module codecs has changed interface +LOG: Writing array /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/array.pyi array.meta.json array.data.json +TRACE: Interface for array has changed +LOG: Cached module array has changed interface +LOG: Writing abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/abc.pyi abc.meta.json abc.data.json +TRACE: Interface for abc has changed +LOG: Cached module abc has changed interface +LOG: Writing _typeshed /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_typeshed/__init__.pyi _typeshed/__init__.meta.json _typeshed/__init__.data.json +TRACE: Interface for _typeshed has changed +LOG: Cached module _typeshed has changed interface +LOG: Writing _collections_abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_collections_abc.pyi _collections_abc.meta.json _collections_abc.data.json +TRACE: Interface for _collections_abc has changed +LOG: Cached module _collections_abc has changed interface +LOG: Writing _codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_codecs.pyi _codecs.meta.json _codecs.data.json +TRACE: Interface for _codecs has changed +LOG: Cached module _codecs has changed interface +LOG: Writing _ast /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_ast.pyi _ast.meta.json _ast.data.json +TRACE: Interface for _ast has changed +LOG: Cached module _ast has changed interface +LOG: Writing builtins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/builtins.pyi builtins.meta.json builtins.data.json +TRACE: Interface for builtins has changed +LOG: Cached module builtins has changed interface +TRACE: Priorities for _socket: +LOG: Processing SCC singleton (_socket) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing typing_extensions) +LOG: Writing _socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi _socket.meta.json _socket.data.json +TRACE: Interface for _socket has changed +LOG: Cached module _socket has changed interface +TRACE: Priorities for multiprocessing.shared_memory: +LOG: Processing SCC singleton (multiprocessing.shared_memory) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing) +LOG: Writing multiprocessing.shared_memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi multiprocessing/shared_memory.meta.json multiprocessing/shared_memory.data.json +TRACE: Interface for multiprocessing.shared_memory has changed +LOG: Cached module multiprocessing.shared_memory has changed interface +TRACE: Priorities for copyreg: +LOG: Processing SCC singleton (copyreg) as inherently stale with stale deps (builtins collections.abc typing typing_extensions) +LOG: Writing copyreg /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi copyreg.meta.json copyreg.data.json +TRACE: Interface for 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collections.abc sys types typing typing_extensions) +LOG: Writing enum /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/enum.pyi enum.meta.json enum.data.json +TRACE: Interface for enum has changed +LOG: Cached module enum has changed interface +TRACE: Priorities for errno: +LOG: Processing SCC singleton (errno) as inherently stale with stale deps (builtins collections.abc) +LOG: Writing errno /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/errno.pyi errno.meta.json errno.data.json +TRACE: Interface for errno has changed +LOG: Cached module errno has changed interface +TRACE: Priorities for itertools: +LOG: Processing SCC singleton (itertools) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing typing_extensions) +LOG: Writing itertools 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importlib_metadata/_compat.meta.json importlib_metadata/_compat.data.json +TRACE: Interface for importlib_metadata._compat has changed +LOG: Cached module importlib_metadata._compat has changed interface +TRACE: Priorities for csv: +LOG: Processing SCC singleton (csv) as inherently stale with stale deps (_csv _typeshed builtins collections.abc sys typing typing_extensions) +LOG: Writing csv /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/csv.pyi csv.meta.json csv.data.json +TRACE: Interface for csv has changed +LOG: Cached module csv has changed interface +TRACE: Priorities for operator: +LOG: Processing SCC singleton (operator) as inherently stale with stale deps (_operator builtins sys) +LOG: Writing operator /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/operator.pyi operator.meta.json operator.data.json +TRACE: Interface for operator has changed 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email.message importlib_metadata._text re textwrap) +LOG: Writing importlib_metadata._adapters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_adapters.py importlib_metadata/_adapters.meta.json importlib_metadata/_adapters.data.json +TRACE: Interface for importlib_metadata._adapters has changed +LOG: Cached module importlib_metadata._adapters has changed interface +TRACE: Priorities for numpy.compat: +LOG: Processing SCC singleton (numpy.compat) as inherently stale with stale deps (builtins numpy.compat._inspect numpy.compat._pep440 numpy.compat.py3k) +LOG: Writing numpy.compat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/compat/__init__.py numpy/compat/__init__.meta.json numpy/compat/__init__.data.json +TRACE: Interface for numpy.compat has changed +LOG: Cached module numpy.compat has changed interface +TRACE: Priorities for logging: +LOG: Processing SCC singleton 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numpy._typing._add_docstring:5 +TRACE: Priorities for numpy._typing._ufunc: numpy:5 numpy.typing:5 numpy._typing._scalars:5 numpy._typing._array_like:5 numpy._typing._dtype_like:5 +LOG: Processing SCC of size 72 (numpy._typing._generic_alias numpy._typing._scalars numpy._typing._dtype_like numpy.ma.mrecords numpy._typing._array_like numpy.matrixlib.defmatrix numpy.ma.core numpy.ma.extras numpy.lib.utils numpy.lib.ufunclike numpy.lib.type_check numpy.lib.twodim_base numpy.lib.stride_tricks numpy.lib.shape_base numpy.lib.polynomial numpy.lib.npyio numpy.lib.nanfunctions numpy.lib.index_tricks numpy.lib.histograms numpy.lib.function_base numpy.lib.arrayterator numpy.lib.arraysetops numpy.lib.arraypad numpy.core.shape_base numpy.core.numerictypes numpy.core.numeric numpy.core.multiarray numpy.core.einsumfunc numpy.core.arrayprint numpy.core._ufunc_config numpy.core._type_aliases numpy.core._asarray numpy.core.fromnumeric numpy.core.function_base numpy._typing._extended_precision numpy._typing._callable numpy._typing numpy.core._internal numpy.matrixlib numpy.ma numpy.lib numpy.ctypeslib numpy numpy.testing._private.utils numpy.random.bit_generator numpy.polynomial.polynomial numpy.polynomial.legendre numpy.polynomial.laguerre numpy.polynomial.hermite_e numpy.polynomial.hermite numpy.polynomial.chebyshev numpy.lib.scimath numpy.lib.mixins numpy.fft.helper numpy.fft._pocketfft numpy.core.records numpy.core.defchararray numpy.linalg.linalg numpy.linalg numpy.random.mtrand numpy.random._sfc64 numpy.random._philox numpy.random._pcg64 numpy.random._mt19937 numpy.testing numpy.polynomial numpy.fft numpy.random._generator numpy.random numpy._typing._add_docstring numpy.typing numpy._typing._ufunc) as inherently stale with stale deps (__future__ abc array ast builtins collections.abc contextlib ctypes datetime enum math mmap numpy._pytesttester numpy._typing._char_codes numpy._typing._nbit numpy._typing._nested_sequence numpy._typing._shape numpy.core 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xarray.core.common:5 xarray.core.computation:5 xarray.core.concat:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.extensions:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.parallel:5 xarray.core.variable:5 +TRACE: Priorities for xarray.backends: xarray.backends.cfgrib_:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.h5netcdf_:5 xarray.backends.memory:5 xarray.backends.netCDF4_:5 xarray.backends.plugins:5 xarray.backends.pseudonetcdf_:5 xarray.backends.pydap_:5 xarray.backends.pynio_:5 xarray.backends.scipy_:5 xarray.backends.zarr:5 +TRACE: Priorities for xarray.convert: xarray.core.duck_array_ops:10 xarray.coding.times:5 xarray.conventions:5 xarray.core.dataarray:5 xarray.core.dtypes:5 xarray.core.pycompat:5 +TRACE: Priorities for xarray.core.resample_cftime: xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 +LOG: Processing SCC of size 72 (xarray.core.types xarray.core.utils xarray.core.dtypes xarray.core.pycompat xarray.core.options xarray.core.dask_array_compat xarray.core.nputils xarray.plot.utils xarray.core.rolling_exp xarray.backends.file_manager xarray.core.dask_array_ops xarray.core.duck_array_ops xarray.core._reductions xarray.core.nanops xarray.core.ops xarray.core.indexing xarray.core.formatting xarray.plot.facetgrid xarray.core.formatting_html xarray.core.indexes xarray.core.common xarray.core.accessor_dt xarray.core._typed_ops xarray.plot.dataset_plot xarray.core.arithmetic xarray.core.accessor_str xarray.plot.plot xarray.core.missing xarray.core.coordinates xarray.coding.variables xarray.coding.times xarray.core.groupby xarray.core.variable xarray.core.merge xarray.core.dataset xarray.core.dataarray xarray.core.concat xarray.core.computation xarray.core.alignment xarray.coding.cftimeindex xarray.backends.netcdf3 xarray.core.rolling xarray.core.resample xarray.core.weighted xarray.coding.strings xarray.core.parallel xarray.core.extensions xarray.core.combine xarray.conventions xarray.coding.cftime_offsets xarray.ufuncs xarray.testing xarray.backends.common xarray.coding.frequencies xarray.backends.memory xarray.backends.store xarray.backends.plugins xarray.backends.rasterio_ xarray.backends.api xarray.backends.scipy_ xarray.backends.pynio_ xarray.backends.pydap_ xarray.backends.pseudonetcdf_ xarray.backends.netCDF4_ xarray.backends.cfgrib_ xarray.backends.zarr xarray.tutorial xarray.backends.h5netcdf_ xarray xarray.backends xarray.convert xarray.core.resample_cftime) as inherently stale with stale deps (__future__ builtins codecs collections collections.abc contextlib copy datetime distutils.version enum functools glob gzip html importlib importlib.metadata importlib.resources importlib_metadata inspect io itertools logging numbers numpy numpy.core.multiarray numpy.core.numeric operator os pathlib re sys textwrap threading time traceback typing typing_extensions unicodedata uuid warnings xarray.backends.locks xarray.backends.lru_cache xarray.coding 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xarray.plot.facetgrid /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/facetgrid.py xarray/plot/facetgrid.meta.json xarray/plot/facetgrid.data.json +TRACE: Interface for xarray.plot.facetgrid has changed +LOG: Cached module xarray.plot.facetgrid has changed interface +LOG: Writing xarray.core.formatting_html /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/formatting_html.py xarray/core/formatting_html.meta.json xarray/core/formatting_html.data.json +TRACE: Interface for xarray.core.formatting_html has changed +LOG: Cached module xarray.core.formatting_html has changed interface +LOG: Writing xarray.core.indexes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/indexes.py xarray/core/indexes.meta.json xarray/core/indexes.data.json +TRACE: Interface for xarray.core.indexes has changed +LOG: Cached module 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+LOG: Writing rdata.parser /home/carlos/git/rdata/rdata/parser/__init__.py rdata/parser/__init__.meta.json rdata/parser/__init__.data.json +TRACE: Interface for rdata.parser has changed +LOG: Cached module rdata.parser has changed interface +TRACE: Priorities for skfda._utils._sklearn_adapter: +LOG: Processing SCC singleton (skfda._utils._sklearn_adapter) as inherently stale with stale deps (__future__ abc builtins skfda.typing._numpy typing) +LOG: Writing skfda._utils._sklearn_adapter /home/carlos/git/scikit-fda/skfda/_utils/_sklearn_adapter.py skfda/_utils/_sklearn_adapter.meta.json skfda/_utils/_sklearn_adapter.data.json +TRACE: Interface for skfda._utils._sklearn_adapter has changed +LOG: Cached module skfda._utils._sklearn_adapter has changed interface +TRACE: Priorities for skfda.typing._base: +LOG: Processing SCC singleton (skfda.typing._base) as inherently stale with stale deps (builtins numpy skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.typing._base /home/carlos/git/scikit-fda/skfda/typing/_base.py skfda/typing/_base.meta.json skfda/typing/_base.data.json +TRACE: Interface for skfda.typing._base has changed +LOG: Cached module skfda.typing._base has changed interface +TRACE: Priorities for xarray.plot: +LOG: Processing SCC singleton (xarray.plot) as inherently stale with stale deps (builtins xarray.plot.dataset_plot xarray.plot.facetgrid xarray.plot.plot) +LOG: Writing xarray.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py xarray/plot/__init__.meta.json xarray/plot/__init__.data.json +TRACE: Interface for xarray.plot has changed +LOG: Cached module xarray.plot has changed interface +TRACE: Priorities for skfda.exploratory.depth.multivariate: +LOG: Processing SCC singleton (skfda.exploratory.depth.multivariate) as inherently stale with stale deps (__future__ abc builtins math numpy skfda._utils._sklearn_adapter skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.exploratory.depth.multivariate /home/carlos/git/scikit-fda/skfda/exploratory/depth/multivariate.py skfda/exploratory/depth/multivariate.meta.json skfda/exploratory/depth/multivariate.data.json +TRACE: Interface for skfda.exploratory.depth.multivariate has changed +LOG: Cached module skfda.exploratory.depth.multivariate has changed interface +TRACE: Priorities for dcor._energy: dcor:20 +TRACE: Priorities for dcor._dcor_internals: dcor:20 +TRACE: Priorities for dcor._partial_dcor: dcor._dcor_internals:5 +TRACE: Priorities for dcor._dcor: dcor._dcor_internals:5 +TRACE: Priorities for dcor.homogeneity: dcor:20 dcor._energy:5 +TRACE: Priorities for dcor._rowwise: dcor._dcor:10 dcor:20 +TRACE: Priorities for dcor.independence: dcor._dcor:5 dcor._dcor_internals:5 +TRACE: Priorities for dcor: dcor.homogeneity:10 dcor.independence:10 dcor._dcor:5 dcor._dcor_internals:5 dcor._energy:5 dcor._partial_dcor:5 dcor._rowwise:5 +LOG: Processing SCC of size 8 (dcor._energy dcor._dcor_internals dcor._partial_dcor dcor._dcor dcor.homogeneity dcor._rowwise dcor.independence dcor) as inherently stale with stale deps (__future__ builtins dataclasses dcor._fast_dcov_avl dcor._fast_dcov_mergesort dcor._hypothesis dcor._utils dcor.distances enum errno numpy os pathlib typing typing_extensions warnings) +LOG: Writing dcor._energy /home/carlos/git/dcor/dcor/_energy.py dcor/_energy.meta.json dcor/_energy.data.json +TRACE: Interface for dcor._energy has changed +LOG: Cached module dcor._energy has changed interface +LOG: Writing dcor._dcor_internals /home/carlos/git/dcor/dcor/_dcor_internals.py dcor/_dcor_internals.meta.json dcor/_dcor_internals.data.json +TRACE: Interface for dcor._dcor_internals has changed +LOG: Cached module dcor._dcor_internals has changed interface +LOG: Writing dcor._partial_dcor /home/carlos/git/dcor/dcor/_partial_dcor.py dcor/_partial_dcor.meta.json dcor/_partial_dcor.data.json +TRACE: Interface for dcor._partial_dcor has changed +LOG: Cached module dcor._partial_dcor has changed interface +LOG: Writing dcor._dcor /home/carlos/git/dcor/dcor/_dcor.py dcor/_dcor.meta.json dcor/_dcor.data.json +TRACE: Interface for dcor._dcor has changed +LOG: Cached module dcor._dcor has changed interface +LOG: Writing dcor.homogeneity /home/carlos/git/dcor/dcor/homogeneity.py dcor/homogeneity.meta.json dcor/homogeneity.data.json +TRACE: Interface for dcor.homogeneity has changed +LOG: Cached module dcor.homogeneity has changed interface +LOG: Writing dcor._rowwise /home/carlos/git/dcor/dcor/_rowwise.py dcor/_rowwise.meta.json dcor/_rowwise.data.json +TRACE: Interface for dcor._rowwise has changed +LOG: Cached module dcor._rowwise has changed interface +LOG: Writing dcor.independence /home/carlos/git/dcor/dcor/independence.py dcor/independence.meta.json dcor/independence.data.json +TRACE: Interface for dcor.independence has changed +LOG: Cached module dcor.independence has changed interface +LOG: Writing dcor /home/carlos/git/dcor/dcor/__init__.py dcor/__init__.meta.json dcor/__init__.data.json +TRACE: Interface for dcor has changed +LOG: Cached module dcor has changed interface +TRACE: Priorities for skfda.typing._metric: +LOG: Processing SCC singleton (skfda.typing._metric) as inherently stale with stale deps (abc builtins skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.typing._metric /home/carlos/git/scikit-fda/skfda/typing/_metric.py skfda/typing/_metric.meta.json skfda/typing/_metric.data.json +TRACE: Interface for skfda.typing._metric has changed +LOG: Cached module skfda.typing._metric has changed interface +TRACE: Priorities for rdata.conversion._conversion: rdata:20 +TRACE: Priorities for rdata.conversion: rdata.conversion._conversion:5 +TRACE: Priorities for rdata: rdata.conversion:10 +LOG: Processing SCC of size 3 (rdata.conversion._conversion rdata.conversion rdata) as inherently stale with stale deps (__future__ abc builtins dataclasses errno fractions numpy os pathlib rdata.parser types typing warnings xarray) +LOG: Writing rdata.conversion._conversion /home/carlos/git/rdata/rdata/conversion/_conversion.py rdata/conversion/_conversion.meta.json rdata/conversion/_conversion.data.json +TRACE: Interface for rdata.conversion._conversion has changed +LOG: Cached module rdata.conversion._conversion has changed interface +LOG: Writing rdata.conversion /home/carlos/git/rdata/rdata/conversion/__init__.py rdata/conversion/__init__.meta.json rdata/conversion/__init__.data.json +TRACE: Interface for rdata.conversion has changed +LOG: Cached module rdata.conversion has changed interface +LOG: Writing rdata /home/carlos/git/rdata/rdata/__init__.py rdata/__init__.meta.json rdata/__init__.data.json +TRACE: Interface for rdata has changed +LOG: Cached module rdata has changed interface +TRACE: Priorities for skfda.misc.metrics._parse: +LOG: Processing SCC singleton (skfda.misc.metrics._parse) as inherently stale with stale deps 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skfda.representation.basis:5 skfda.misc.validation:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._linear_differential_operator: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._integral_transform: skfda.representation:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._identity: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 skfda.misc.metrics:20 +TRACE: Priorities for skfda._utils._warping: skfda.representation:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.basis._basis: skfda.representation.basis._fdatabasis:25 skfda.misc.validation:20 skfda.representation.basis:20 skfda.misc:20 skfda._utils:20 +TRACE: Priorities for skfda.representation.evaluator: skfda.representation._functional_data:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.misc.regularization._regularization: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc._math: skfda._utils:5 skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 +TRACE: Priorities for skfda.misc.metrics._lp_distances: skfda.representation:5 skfda.misc.metrics._lp_norms:5 skfda.misc.metrics._utils:5 skfda.misc:20 +TRACE: Priorities for skfda.exploratory.stats._stats: skfda.misc.metrics._lp_distances:5 skfda.representation:5 skfda.exploratory.depth:5 +TRACE: Priorities for skfda.representation.basis._vector_basis: skfda.representation.basis._basis:5 skfda._utils:20 +TRACE: Priorities for skfda.representation.basis._tensor_basis: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._monomial: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._fourier: skfda.representation.basis._basis:5 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.basis._finite_element: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._constant: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._bspline: skfda.representation.basis._basis:5 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.extrapolation: skfda.representation.evaluator:5 skfda.representation._functional_data:25 +TRACE: Priorities for skfda.representation.interpolation: skfda.representation.evaluator:5 skfda.representation.grid:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.misc.metrics._mahalanobis: skfda.representation:5 skfda.representation.basis:5 skfda.misc._math:5 skfda.misc.regularization._regularization:5 skfda.preprocessing.dim_reduction:20 +TRACE: Priorities for skfda.misc.metrics._fisher_rao: skfda._utils:5 skfda.representation:5 skfda.misc.operators:5 skfda.misc.metrics._lp_distances:5 skfda.misc.metrics._utils:5 skfda.preprocessing.registration:20 +TRACE: Priorities for skfda.misc.metrics._angular: skfda.representation:5 skfda.misc._math:5 skfda.misc.metrics._utils:5 +TRACE: Priorities for skfda.exploratory.stats._fisher_rao: skfda._utils:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.interpolation:5 +TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 +TRACE: Priorities for skfda.preprocessing.registration._landmark_registration: skfda.representation:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 +TRACE: Priorities for skfda.representation._functional_data: skfda._utils:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.grid:25 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 +TRACE: Priorities for skfda.exploratory.visualization._utils: skfda.representation._functional_data:5 +TRACE: Priorities for skfda.exploratory.visualization._baseplot: skfda.representation:5 skfda.exploratory.visualization._utils:5 +TRACE: Priorities for skfda.exploratory.visualization.representation: skfda._utils:5 skfda.misc.validation:5 skfda.representation._functional_data:5 skfda.exploratory.visualization._baseplot:5 skfda.exploratory.visualization._utils:5 +TRACE: Priorities for skfda.misc.hat_matrix: skfda.misc:20 skfda.representation._functional_data:5 skfda.representation.basis:5 +TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 +TRACE: Priorities for skfda.preprocessing.smoothing._kernel_smoothers: skfda._utils._utils:5 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.smoothing: skfda.preprocessing.smoothing.kernel_smoothers:20 skfda.preprocessing.smoothing._basis:25 skfda.preprocessing.smoothing._kernel_smoothers:25 +TRACE: Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 +TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 +TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 +LOG: Processing SCC of size 62 (skfda.exploratory.depth skfda.exploratory.stats skfda.misc.regularization skfda.preprocessing.dim_reduction skfda.preprocessing.registration skfda.misc.operators skfda._utils skfda.representation.basis skfda.misc skfda.representation skfda.misc.metrics skfda skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda.misc.operators._operators skfda._utils._utils skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda.misc.validation skfda.exploratory.depth._depth skfda.exploratory.stats._functional_transformers skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.preprocessing.smoothing._basis skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.operators._identity skfda._utils._warping skfda.representation.basis._basis skfda.representation.evaluator skfda.misc.regularization._regularization skfda.misc._math skfda.misc.metrics._lp_distances skfda.exploratory.stats._stats skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.representation.extrapolation skfda.representation.interpolation skfda.misc.metrics._mahalanobis skfda.misc.metrics._fisher_rao skfda.misc.metrics._angular skfda.exploratory.stats._fisher_rao skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.misc.hat_matrix skfda.preprocessing.registration._fisher_rao skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.representation.grid skfda.preprocessing.dim_reduction._fpca skfda.representation.basis._fdatabasis) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses dcor errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._parse skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) +LOG: Writing skfda.exploratory.depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/__init__.py skfda/exploratory/depth/__init__.meta.json skfda/exploratory/depth/__init__.data.json +TRACE: Interface for skfda.exploratory.depth has changed +LOG: Cached module skfda.exploratory.depth has changed interface +LOG: Writing skfda.exploratory.stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/__init__.py skfda/exploratory/stats/__init__.meta.json skfda/exploratory/stats/__init__.data.json +TRACE: Interface for skfda.exploratory.stats has changed +LOG: Cached module skfda.exploratory.stats has changed interface +LOG: Writing skfda.misc.regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/__init__.py skfda/misc/regularization/__init__.meta.json skfda/misc/regularization/__init__.data.json +TRACE: Interface for skfda.misc.regularization has changed +LOG: Cached module skfda.misc.regularization has changed interface +LOG: Writing skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json skfda/preprocessing/dim_reduction/__init__.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction has changed +LOG: Cached module skfda.preprocessing.dim_reduction has changed interface +LOG: Writing skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json +TRACE: Interface for skfda.preprocessing.registration has changed +LOG: Cached module skfda.preprocessing.registration has changed interface +LOG: Writing skfda.misc.operators /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py skfda/misc/operators/__init__.meta.json skfda/misc/operators/__init__.data.json +TRACE: Interface for skfda.misc.operators has changed +LOG: Cached module skfda.misc.operators has changed interface +LOG: Writing skfda._utils /home/carlos/git/scikit-fda/skfda/_utils/__init__.py skfda/_utils/__init__.meta.json skfda/_utils/__init__.data.json +TRACE: Interface for skfda._utils has changed +LOG: Cached module skfda._utils has changed interface +LOG: Writing skfda.representation.basis /home/carlos/git/scikit-fda/skfda/representation/basis/__init__.py skfda/representation/basis/__init__.meta.json skfda/representation/basis/__init__.data.json +TRACE: Interface for skfda.representation.basis has changed +LOG: Cached module skfda.representation.basis has changed interface +LOG: Writing skfda.misc /home/carlos/git/scikit-fda/skfda/misc/__init__.py skfda/misc/__init__.meta.json skfda/misc/__init__.data.json +TRACE: Interface for skfda.misc has changed +LOG: Cached module skfda.misc has changed interface +LOG: Writing skfda.representation /home/carlos/git/scikit-fda/skfda/representation/__init__.py skfda/representation/__init__.meta.json skfda/representation/__init__.data.json +TRACE: Interface for skfda.representation has changed +LOG: Cached module skfda.representation has changed interface +LOG: Writing skfda.misc.metrics /home/carlos/git/scikit-fda/skfda/misc/metrics/__init__.py skfda/misc/metrics/__init__.meta.json skfda/misc/metrics/__init__.data.json +TRACE: Interface for skfda.misc.metrics has changed +LOG: Cached module skfda.misc.metrics has changed interface +LOG: Writing skfda /home/carlos/git/scikit-fda/skfda/__init__.py skfda/__init__.meta.json skfda/__init__.data.json +TRACE: Interface for skfda has changed +LOG: Cached module skfda has changed interface +LOG: Writing skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json skfda/preprocessing/smoothing/_linear.data.json +TRACE: Interface for skfda.preprocessing.smoothing._linear has changed +LOG: Cached module skfda.preprocessing.smoothing._linear has changed interface +LOG: Writing skfda.preprocessing.smoothing.validation /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json +TRACE: Interface for skfda.preprocessing.smoothing.validation has changed +LOG: Cached module skfda.preprocessing.smoothing.validation has changed interface +LOG: Writing skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json +TRACE: Interface for skfda.misc.operators._operators has changed +LOG: Cached module skfda.misc.operators._operators has changed interface +LOG: Writing skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json +TRACE: Interface for skfda._utils._utils has changed +LOG: Cached module skfda._utils._utils has changed interface +LOG: Writing skfda.misc.metrics._utils /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py skfda/misc/metrics/_utils.meta.json skfda/misc/metrics/_utils.data.json +TRACE: Interface for skfda.misc.metrics._utils has changed +LOG: Cached module skfda.misc.metrics._utils has changed interface +LOG: Writing skfda.misc.metrics._lp_norms /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py skfda/misc/metrics/_lp_norms.meta.json skfda/misc/metrics/_lp_norms.data.json +TRACE: Interface for skfda.misc.metrics._lp_norms has changed +LOG: Cached module skfda.misc.metrics._lp_norms has changed interface +LOG: Writing skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json +TRACE: Interface for skfda.misc.validation has changed +LOG: Cached module skfda.misc.validation has changed interface +LOG: Writing skfda.exploratory.depth._depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py skfda/exploratory/depth/_depth.meta.json skfda/exploratory/depth/_depth.data.json +TRACE: Interface for skfda.exploratory.depth._depth has changed +LOG: Cached module skfda.exploratory.depth._depth has changed interface +LOG: Writing skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json +TRACE: Interface for skfda.exploratory.stats._functional_transformers has changed +LOG: Cached module skfda.exploratory.stats._functional_transformers has changed interface +LOG: Writing skfda.preprocessing.registration.base /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py skfda/preprocessing/registration/base.meta.json skfda/preprocessing/registration/base.data.json +TRACE: Interface for skfda.preprocessing.registration.base has changed +LOG: Cached module skfda.preprocessing.registration.base has changed interface +LOG: Writing skfda.preprocessing.registration.validation /home/carlos/git/scikit-fda/skfda/preprocessing/registration/validation.py skfda/preprocessing/registration/validation.meta.json skfda/preprocessing/registration/validation.data.json +TRACE: Interface for skfda.preprocessing.registration.validation has changed +LOG: Cached module skfda.preprocessing.registration.validation has changed interface +LOG: Writing skfda.preprocessing.smoothing._basis /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py skfda/preprocessing/smoothing/_basis.meta.json skfda/preprocessing/smoothing/_basis.data.json +TRACE: Interface for skfda.preprocessing.smoothing._basis has changed +LOG: Cached module skfda.preprocessing.smoothing._basis has changed interface +LOG: Writing skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json +TRACE: Interface for skfda.misc.operators._srvf has changed +LOG: Cached module skfda.misc.operators._srvf has changed interface +LOG: Writing skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json +TRACE: Interface for skfda.misc.operators._linear_differential_operator has changed +LOG: Cached module skfda.misc.operators._linear_differential_operator has changed interface +LOG: Writing skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json +TRACE: Interface for skfda.misc.operators._integral_transform has changed +LOG: Cached module skfda.misc.operators._integral_transform has changed interface +LOG: Writing skfda.misc.operators._identity /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json +TRACE: Interface for skfda.misc.operators._identity has changed +LOG: Cached module skfda.misc.operators._identity has changed interface +LOG: Writing skfda._utils._warping /home/carlos/git/scikit-fda/skfda/_utils/_warping.py skfda/_utils/_warping.meta.json skfda/_utils/_warping.data.json +TRACE: Interface for skfda._utils._warping has changed +LOG: Cached module skfda._utils._warping has changed interface +LOG: Writing skfda.representation.basis._basis /home/carlos/git/scikit-fda/skfda/representation/basis/_basis.py skfda/representation/basis/_basis.meta.json skfda/representation/basis/_basis.data.json +TRACE: Interface for skfda.representation.basis._basis has changed +LOG: Cached module skfda.representation.basis._basis has changed interface +LOG: Writing skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json +TRACE: Interface for skfda.representation.evaluator has changed +LOG: Cached module skfda.representation.evaluator has changed interface +LOG: Writing skfda.misc.regularization._regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py skfda/misc/regularization/_regularization.meta.json skfda/misc/regularization/_regularization.data.json +TRACE: Interface for skfda.misc.regularization._regularization has changed +LOG: Cached module skfda.misc.regularization._regularization has changed interface +LOG: Writing skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json +TRACE: Interface for skfda.misc._math has changed +LOG: Cached module skfda.misc._math has changed interface +LOG: Writing skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json +TRACE: Interface for skfda.misc.metrics._lp_distances has changed +LOG: Cached module skfda.misc.metrics._lp_distances has changed interface +LOG: Writing skfda.exploratory.stats._stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/_stats.py skfda/exploratory/stats/_stats.meta.json skfda/exploratory/stats/_stats.data.json +TRACE: Interface for skfda.exploratory.stats._stats has changed +LOG: Cached module skfda.exploratory.stats._stats has changed interface +LOG: Writing skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json +TRACE: Interface for skfda.representation.basis._vector_basis has changed +LOG: Cached module skfda.representation.basis._vector_basis has changed interface +LOG: Writing skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json +TRACE: Interface for skfda.representation.basis._tensor_basis has changed +LOG: Cached module skfda.representation.basis._tensor_basis has changed interface +LOG: Writing skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json +TRACE: Interface for skfda.representation.basis._monomial has changed +LOG: Cached module skfda.representation.basis._monomial has changed interface +LOG: Writing skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json +TRACE: Interface for skfda.representation.basis._fourier has changed +LOG: Cached module skfda.representation.basis._fourier has changed interface +LOG: Writing skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json +TRACE: Interface for skfda.representation.basis._finite_element has changed +LOG: Cached module skfda.representation.basis._finite_element has changed interface +LOG: Writing skfda.representation.basis._constant /home/carlos/git/scikit-fda/skfda/representation/basis/_constant.py skfda/representation/basis/_constant.meta.json skfda/representation/basis/_constant.data.json +TRACE: Interface for skfda.representation.basis._constant has changed +LOG: Cached module skfda.representation.basis._constant has changed interface +LOG: Writing skfda.representation.basis._bspline /home/carlos/git/scikit-fda/skfda/representation/basis/_bspline.py skfda/representation/basis/_bspline.meta.json skfda/representation/basis/_bspline.data.json +TRACE: Interface for skfda.representation.basis._bspline has changed +LOG: Cached module skfda.representation.basis._bspline has changed interface +LOG: Writing skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json +TRACE: Interface for skfda.representation.extrapolation has changed +LOG: Cached module skfda.representation.extrapolation has changed interface +LOG: Writing skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json +TRACE: Interface for skfda.representation.interpolation has changed +LOG: Cached module skfda.representation.interpolation has changed interface +LOG: Writing skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json +TRACE: Interface for skfda.misc.metrics._mahalanobis has changed +LOG: Cached module skfda.misc.metrics._mahalanobis has changed interface +LOG: Writing skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json +TRACE: Interface for skfda.misc.metrics._fisher_rao has changed +LOG: Cached module skfda.misc.metrics._fisher_rao has changed interface +LOG: Writing skfda.misc.metrics._angular /home/carlos/git/scikit-fda/skfda/misc/metrics/_angular.py skfda/misc/metrics/_angular.meta.json skfda/misc/metrics/_angular.data.json +TRACE: Interface for skfda.misc.metrics._angular has changed +LOG: Cached module skfda.misc.metrics._angular has changed interface +LOG: Writing skfda.exploratory.stats._fisher_rao /home/carlos/git/scikit-fda/skfda/exploratory/stats/_fisher_rao.py skfda/exploratory/stats/_fisher_rao.meta.json skfda/exploratory/stats/_fisher_rao.data.json +TRACE: Interface for skfda.exploratory.stats._fisher_rao has changed +LOG: Cached module skfda.exploratory.stats._fisher_rao has changed interface +LOG: Writing skfda.preprocessing.registration._lstsq_shift_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py skfda/preprocessing/registration/_lstsq_shift_registration.meta.json skfda/preprocessing/registration/_lstsq_shift_registration.data.json +TRACE: Interface for skfda.preprocessing.registration._lstsq_shift_registration has changed +LOG: Cached module skfda.preprocessing.registration._lstsq_shift_registration has changed interface +LOG: Writing skfda.preprocessing.registration._landmark_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py skfda/preprocessing/registration/_landmark_registration.meta.json skfda/preprocessing/registration/_landmark_registration.data.json +TRACE: Interface for skfda.preprocessing.registration._landmark_registration has changed +LOG: Cached module skfda.preprocessing.registration._landmark_registration has changed interface +LOG: Writing skfda.representation._functional_data /home/carlos/git/scikit-fda/skfda/representation/_functional_data.py skfda/representation/_functional_data.meta.json skfda/representation/_functional_data.data.json +TRACE: Interface for skfda.representation._functional_data has changed +LOG: Cached module skfda.representation._functional_data has changed interface +LOG: Writing skfda.exploratory.visualization._utils /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_utils.py skfda/exploratory/visualization/_utils.meta.json skfda/exploratory/visualization/_utils.data.json +TRACE: Interface for skfda.exploratory.visualization._utils has changed +LOG: Cached module skfda.exploratory.visualization._utils has changed interface +LOG: Writing skfda.exploratory.visualization._baseplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py skfda/exploratory/visualization/_baseplot.meta.json skfda/exploratory/visualization/_baseplot.data.json +TRACE: Interface for skfda.exploratory.visualization._baseplot has changed +LOG: Cached module skfda.exploratory.visualization._baseplot has changed interface +LOG: Writing skfda.exploratory.visualization.representation /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py skfda/exploratory/visualization/representation.meta.json skfda/exploratory/visualization/representation.data.json +TRACE: Interface for skfda.exploratory.visualization.representation has changed +LOG: Cached module skfda.exploratory.visualization.representation has changed interface +LOG: Writing skfda.misc.hat_matrix /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py skfda/misc/hat_matrix.meta.json skfda/misc/hat_matrix.data.json +TRACE: Interface for skfda.misc.hat_matrix has changed +LOG: Cached module skfda.misc.hat_matrix has changed interface +LOG: Writing skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json +TRACE: Interface for skfda.preprocessing.registration._fisher_rao has changed +LOG: Cached module skfda.preprocessing.registration._fisher_rao has changed interface +LOG: Writing skfda.preprocessing.smoothing._kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py skfda/preprocessing/smoothing/_kernel_smoothers.meta.json skfda/preprocessing/smoothing/_kernel_smoothers.data.json +TRACE: Interface for skfda.preprocessing.smoothing._kernel_smoothers has changed +LOG: Cached module skfda.preprocessing.smoothing._kernel_smoothers has changed interface +LOG: Writing skfda.preprocessing.smoothing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py skfda/preprocessing/smoothing/__init__.meta.json skfda/preprocessing/smoothing/__init__.data.json +TRACE: Interface for skfda.preprocessing.smoothing has changed +LOG: Cached module skfda.preprocessing.smoothing has changed interface +LOG: Writing skfda.preprocessing.smoothing.kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/kernel_smoothers.py skfda/preprocessing/smoothing/kernel_smoothers.meta.json skfda/preprocessing/smoothing/kernel_smoothers.data.json +TRACE: Interface for skfda.preprocessing.smoothing.kernel_smoothers has changed +LOG: Cached module skfda.preprocessing.smoothing.kernel_smoothers has changed interface +LOG: Writing skfda.representation.grid /home/carlos/git/scikit-fda/skfda/representation/grid.py skfda/representation/grid.meta.json skfda/representation/grid.data.json +TRACE: Interface for skfda.representation.grid has changed +LOG: Cached module skfda.representation.grid has changed interface +LOG: Writing skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction._fpca has changed +LOG: Cached module skfda.preprocessing.dim_reduction._fpca has changed interface +LOG: Writing skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json +TRACE: Interface for skfda.representation.basis._fdatabasis has changed +LOG: Cached module skfda.representation.basis._fdatabasis has changed interface +TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) +LOG: Writing skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._function_transformers has changed +LOG: Cached module skfda.preprocessing.feature_construction._function_transformers has changed interface +TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._fda_feature_union has changed +LOG: Cached module skfda.preprocessing.feature_construction._fda_feature_union has changed interface +TRACE: Priorities for skfda.preprocessing.feature_construction._evaluation_trasformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._evaluation_trasformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation._functional_data skfda.representation.extrapolation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._evaluation_trasformer has changed +LOG: Cached module skfda.preprocessing.feature_construction._evaluation_trasformer has changed interface +TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._coefficients_transformer has changed +LOG: Cached module skfda.preprocessing.feature_construction._coefficients_transformer has changed interface +TRACE: Priorities for skfda.ml.regression._coefficients: +LOG: Processing SCC singleton (skfda.ml.regression._coefficients) as inherently stale with stale deps (__future__ abc builtins functools numpy skfda.misc._math skfda.representation.basis typing) +LOG: Writing skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json +TRACE: Interface for skfda.ml.regression._coefficients has changed +LOG: Cached module skfda.ml.regression._coefficients has changed interface +TRACE: Priorities for skfda.preprocessing.feature_construction._per_class_transformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._per_class_transformer) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._numpy typing warnings) +LOG: Writing skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._per_class_transformer has changed +LOG: Cached module skfda.preprocessing.feature_construction._per_class_transformer has changed interface +TRACE: Priorities for skfda.ml.regression._kernel_regression: +LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as inherently stale with stale deps (__future__ builtins numpy skfda.misc.hat_matrix skfda.misc.metrics skfda.representation._functional_data skfda.typing._metric typing) +LOG: Writing skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json +TRACE: Interface for skfda.ml.regression._kernel_regression has changed +LOG: Cached module skfda.ml.regression._kernel_regression has changed interface +TRACE: Priorities for skfda.ml.regression._historical_linear_model: +LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as inherently stale with stale deps (__future__ builtins math numpy skfda._utils skfda.representation skfda.representation.basis typing) +LOG: Writing skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json +TRACE: Interface for skfda.ml.regression._historical_linear_model has changed +LOG: Cached module skfda.ml.regression._historical_linear_model has changed interface +TRACE: Priorities for skfda.ml.clustering._kmeans: +LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as inherently stale with stale deps (__future__ abc builtins numpy skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._metric skfda.typing._numpy typing warnings) +LOG: Writing skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json +TRACE: Interface for skfda.ml.clustering._kmeans has changed +LOG: Cached module skfda.ml.clustering._kmeans has changed interface +TRACE: Priorities for skfda.ml.clustering._hierarchical: +LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as inherently stale with stale deps (__future__ builtins enum numpy skfda.misc.metrics skfda.misc.metrics._parse skfda.representation skfda.typing._metric typing typing_extensions) +LOG: Writing skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json +TRACE: Interface for skfda.ml.clustering._hierarchical has changed +LOG: Cached module skfda.ml.clustering._hierarchical has changed interface +TRACE: Priorities for skfda.ml.classification._parameterized_functional_qda: +LOG: Processing SCC singleton (skfda.ml.classification._parameterized_functional_qda) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json +TRACE: Interface for skfda.ml.classification._parameterized_functional_qda has changed +LOG: Cached module skfda.ml.classification._parameterized_functional_qda has changed interface +TRACE: Priorities for skfda.ml.classification._logistic_regression: +LOG: Processing SCC singleton (skfda.ml.classification._logistic_regression) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json skfda/ml/classification/_logistic_regression.data.json +TRACE: Interface for skfda.ml.classification._logistic_regression has changed +LOG: Cached module skfda.ml.classification._logistic_regression has changed interface +TRACE: Priorities for skfda.ml.classification._centroid_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._centroid_classifiers) as inherently stale with stale deps (__future__ builtins skfda._utils skfda.exploratory.depth skfda.exploratory.stats skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing) +LOG: Writing skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json +TRACE: Interface for skfda.ml.classification._centroid_classifiers has changed +LOG: Cached module skfda.ml.classification._centroid_classifiers has changed interface +TRACE: Priorities for skfda.ml._neighbors_base: +LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml._neighbors_base /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py skfda/ml/_neighbors_base.meta.json skfda/ml/_neighbors_base.data.json +TRACE: Interface for skfda.ml._neighbors_base has changed +LOG: Cached module skfda.ml._neighbors_base has changed interface +TRACE: Priorities for skfda.exploratory.outliers._outliergram: +LOG: Processing SCC singleton (skfda.exploratory.outliers._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda._utils._sklearn_adapter skfda.exploratory.depth._depth skfda.exploratory.stats skfda.representation skfda.typing._numpy) +LOG: Writing skfda.exploratory.outliers._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py skfda/exploratory/outliers/_outliergram.meta.json skfda/exploratory/outliers/_outliergram.data.json +TRACE: Interface for skfda.exploratory.outliers._outliergram has changed +LOG: Cached module skfda.exploratory.outliers._outliergram has changed interface +TRACE: Priorities for skfda.exploratory.outliers._envelopes: +LOG: Processing SCC singleton (skfda.exploratory.outliers._envelopes) as inherently stale with stale deps (__future__ builtins math numpy skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.outliers._envelopes /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py skfda/exploratory/outliers/_envelopes.meta.json skfda/exploratory/outliers/_envelopes.data.json +TRACE: Interface for skfda.exploratory.outliers._envelopes has changed +LOG: Cached module skfda.exploratory.outliers._envelopes has changed interface +TRACE: Priorities for skfda.exploratory.visualization.fpca: +LOG: Processing SCC singleton (skfda.exploratory.visualization.fpca) as inherently stale with stale deps (__future__ builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.representation typing warnings) +LOG: Writing skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json +TRACE: Interface for skfda.exploratory.visualization.fpca has changed +LOG: Cached module skfda.exploratory.visualization.fpca has changed interface +TRACE: Priorities for skfda.exploratory.visualization._parametric_plot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._parametric_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.exploratory.visualization.representation skfda.representation typing) +LOG: Writing skfda.exploratory.visualization._parametric_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py skfda/exploratory/visualization/_parametric_plot.meta.json skfda/exploratory/visualization/_parametric_plot.data.json +TRACE: Interface for skfda.exploratory.visualization._parametric_plot has changed +LOG: Cached module skfda.exploratory.visualization._parametric_plot has changed interface +TRACE: Priorities for skfda.exploratory.visualization._multiple_display: +LOG: Processing SCC singleton (skfda.exploratory.visualization._multiple_display) as inherently stale with stale deps (__future__ builtins copy functools itertools numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils typing) +LOG: Writing skfda.exploratory.visualization._multiple_display /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_multiple_display.py skfda/exploratory/visualization/_multiple_display.meta.json skfda/exploratory/visualization/_multiple_display.data.json +TRACE: Interface for skfda.exploratory.visualization._multiple_display has changed +LOG: Cached module skfda.exploratory.visualization._multiple_display has changed interface +TRACE: Priorities for skfda.exploratory.visualization._ddplot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._ddplot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.depth.multivariate skfda.exploratory.visualization._baseplot skfda.representation._functional_data skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json +TRACE: Interface for skfda.exploratory.visualization._ddplot has changed +LOG: Cached module skfda.exploratory.visualization._ddplot has changed interface +TRACE: Priorities for skfda.datasets._real_datasets: +LOG: Processing SCC singleton (skfda.datasets._real_datasets) as inherently stale with stale deps (builtins numpy rdata skfda.representation typing typing_extensions warnings) +LOG: Writing skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json +TRACE: Interface for skfda.datasets._real_datasets has changed +LOG: Cached module skfda.datasets._real_datasets has changed interface +TRACE: Priorities for skfda.inference.hotelling._hotelling: +LOG: Processing SCC singleton (skfda.inference.hotelling._hotelling) as inherently stale with stale deps (__future__ builtins itertools numpy skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.inference.hotelling._hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/_hotelling.py skfda/inference/hotelling/_hotelling.meta.json skfda/inference/hotelling/_hotelling.data.json +TRACE: Interface for skfda.inference.hotelling._hotelling has changed +LOG: Cached module skfda.inference.hotelling._hotelling has changed interface +TRACE: Priorities for skfda.preprocessing.feature_construction: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction) as inherently stale with stale deps (builtins skfda.preprocessing.feature_construction._coefficients_transformer skfda.preprocessing.feature_construction._evaluation_trasformer skfda.preprocessing.feature_construction._fda_feature_union skfda.preprocessing.feature_construction._function_transformers skfda.preprocessing.feature_construction._per_class_transformer typing) +LOG: Writing skfda.preprocessing.feature_construction /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py skfda/preprocessing/feature_construction/__init__.meta.json skfda/preprocessing/feature_construction/__init__.data.json +TRACE: Interface for skfda.preprocessing.feature_construction has changed +LOG: Cached module skfda.preprocessing.feature_construction has changed interface +TRACE: Priorities for skfda.ml.regression._neighbors_regression: +LOG: Processing SCC singleton (skfda.ml.regression._neighbors_regression) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json +TRACE: Interface for skfda.ml.regression._neighbors_regression has changed +LOG: Cached module skfda.ml.regression._neighbors_regression has changed interface +TRACE: Priorities for skfda.ml.regression._linear_regression: +LOG: Processing SCC singleton (skfda.ml.regression._linear_regression) as inherently stale with stale deps (__future__ builtins itertools numpy skfda.misc.lstsq skfda.misc.regularization skfda.ml.regression._coefficients skfda.representation skfda.representation.basis typing warnings) +LOG: Writing skfda.ml.regression._linear_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py skfda/ml/regression/_linear_regression.meta.json skfda/ml/regression/_linear_regression.data.json +TRACE: Interface for skfda.ml.regression._linear_regression has changed +LOG: Cached module skfda.ml.regression._linear_regression has changed interface +TRACE: Priorities for skfda.ml.clustering._neighbors_clustering: +LOG: Processing SCC singleton (skfda.ml.clustering._neighbors_clustering) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml.clustering._neighbors_clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py skfda/ml/clustering/_neighbors_clustering.meta.json skfda/ml/clustering/_neighbors_clustering.data.json +TRACE: Interface for skfda.ml.clustering._neighbors_clustering has changed +LOG: Cached module skfda.ml.clustering._neighbors_clustering has changed interface +TRACE: Priorities for skfda.ml.classification._neighbors_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json +TRACE: Interface for skfda.ml.classification._neighbors_classifiers has changed +LOG: Cached module skfda.ml.classification._neighbors_classifiers has changed interface +TRACE: Priorities for skfda.ml.classification._depth_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._depth_classifiers) as inherently stale with stale deps (__future__ builtins collections contextlib itertools numpy skfda._utils skfda.exploratory.depth skfda.preprocessing.feature_construction._per_class_transformer skfda.representation.grid skfda.typing._numpy typing) +LOG: Writing skfda.ml.classification._depth_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py skfda/ml/classification/_depth_classifiers.meta.json skfda/ml/classification/_depth_classifiers.data.json +TRACE: Interface for skfda.ml.classification._depth_classifiers has changed +LOG: Cached module skfda.ml.classification._depth_classifiers has changed interface +TRACE: Priorities for skfda.exploratory.outliers.neighbors_outlier: +LOG: Processing SCC singleton (skfda.exploratory.outliers.neighbors_outlier) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json +TRACE: Interface for skfda.exploratory.outliers.neighbors_outlier has changed +LOG: Cached module skfda.exploratory.outliers.neighbors_outlier has changed interface +TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:25 +TRACE: Priorities for skfda.misc.covariances: skfda.datasets:20 +TRACE: Priorities for skfda.datasets._samples_generators: skfda.misc.covariances:10 +LOG: Processing SCC of size 3 (skfda.datasets skfda.misc.covariances skfda.datasets._samples_generators) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc skfda.misc.validation skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) +LOG: Writing skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json +TRACE: Interface for skfda.datasets has changed +LOG: Cached module skfda.datasets has changed interface +LOG: Writing skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json +TRACE: Interface for skfda.misc.covariances has changed +LOG: Cached module skfda.misc.covariances has changed interface +LOG: Writing skfda.datasets._samples_generators /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py skfda/datasets/_samples_generators.meta.json skfda/datasets/_samples_generators.data.json +TRACE: Interface for skfda.datasets._samples_generators has changed +LOG: Cached module skfda.datasets._samples_generators has changed interface +TRACE: Priorities for skfda.inference.hotelling: +LOG: Processing SCC singleton (skfda.inference.hotelling) as inherently stale with stale deps (builtins skfda.inference.hotelling._hotelling) +LOG: Writing skfda.inference.hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/__init__.py skfda/inference/hotelling/__init__.meta.json skfda/inference/hotelling/__init__.data.json +TRACE: Interface for skfda.inference.hotelling has changed +LOG: Cached module skfda.inference.hotelling has changed interface +TRACE: Priorities for skfda.ml.regression: +LOG: Processing SCC singleton (skfda.ml.regression) as inherently stale with stale deps (builtins skfda.ml.regression._historical_linear_model skfda.ml.regression._kernel_regression skfda.ml.regression._linear_regression skfda.ml.regression._neighbors_regression) +LOG: Writing skfda.ml.regression /home/carlos/git/scikit-fda/skfda/ml/regression/__init__.py skfda/ml/regression/__init__.meta.json skfda/ml/regression/__init__.data.json +TRACE: Interface for skfda.ml.regression has changed +LOG: Cached module skfda.ml.regression has changed interface +TRACE: Priorities for skfda.ml.clustering: +LOG: Processing SCC singleton (skfda.ml.clustering) as inherently stale with stale deps (builtins skfda.ml.clustering._hierarchical skfda.ml.clustering._kmeans skfda.ml.clustering._neighbors_clustering) +LOG: Writing skfda.ml.clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py skfda/ml/clustering/__init__.meta.json skfda/ml/clustering/__init__.data.json +TRACE: Interface for skfda.ml.clustering has changed +LOG: Cached module skfda.ml.clustering has changed interface +TRACE: Priorities for skfda.ml.classification: +LOG: Processing SCC singleton (skfda.ml.classification) as inherently stale with stale deps (builtins skfda.ml.classification._centroid_classifiers skfda.ml.classification._depth_classifiers skfda.ml.classification._logistic_regression skfda.ml.classification._neighbors_classifiers skfda.ml.classification._parameterized_functional_qda) +LOG: Writing skfda.ml.classification /home/carlos/git/scikit-fda/skfda/ml/classification/__init__.py skfda/ml/classification/__init__.meta.json skfda/ml/classification/__init__.data.json +TRACE: Interface for skfda.ml.classification has changed +LOG: Cached module skfda.ml.classification has changed interface +TRACE: Priorities for skfda.exploratory.outliers: skfda.exploratory.outliers._boxplot:25 skfda.exploratory.outliers._directional_outlyingness:25 +TRACE: Priorities for skfda.exploratory.outliers._directional_outlyingness: skfda.exploratory.outliers:20 +TRACE: Priorities for skfda.exploratory.outliers._boxplot: skfda.exploratory.outliers:20 +LOG: Processing SCC of size 3 (skfda.exploratory.outliers skfda.exploratory.outliers._directional_outlyingness skfda.exploratory.outliers._boxplot) as inherently stale with stale deps (__future__ builtins dataclasses numpy numpy.linalg skfda._utils._sklearn_adapter skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers._directional_outlyingness_experiment_results skfda.exploratory.outliers._envelopes skfda.exploratory.outliers._outliergram skfda.exploratory.outliers.neighbors_outlier skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.outliers /home/carlos/git/scikit-fda/skfda/exploratory/outliers/__init__.py skfda/exploratory/outliers/__init__.meta.json skfda/exploratory/outliers/__init__.data.json +TRACE: Interface for skfda.exploratory.outliers has changed +LOG: Cached module skfda.exploratory.outliers has changed interface +LOG: Writing skfda.exploratory.outliers._directional_outlyingness /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py skfda/exploratory/outliers/_directional_outlyingness.meta.json skfda/exploratory/outliers/_directional_outlyingness.data.json +TRACE: Interface for skfda.exploratory.outliers._directional_outlyingness has changed +LOG: Cached module skfda.exploratory.outliers._directional_outlyingness has changed interface +LOG: Writing skfda.exploratory.outliers._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_boxplot.py skfda/exploratory/outliers/_boxplot.meta.json skfda/exploratory/outliers/_boxplot.data.json +TRACE: Interface for skfda.exploratory.outliers._boxplot has changed +LOG: Cached module skfda.exploratory.outliers._boxplot has changed interface +TRACE: Priorities for skfda.inference.anova._anova_oneway: +LOG: Processing SCC singleton (skfda.inference.anova._anova_oneway) as inherently stale with stale deps (__future__ builtins numpy skfda.datasets skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.inference.anova._anova_oneway /home/carlos/git/scikit-fda/skfda/inference/anova/_anova_oneway.py skfda/inference/anova/_anova_oneway.meta.json skfda/inference/anova/_anova_oneway.data.json +TRACE: Interface for skfda.inference.anova._anova_oneway has changed +LOG: Cached module skfda.inference.anova._anova_oneway has changed interface +TRACE: Priorities for skfda.ml: +LOG: Processing SCC singleton (skfda.ml) as inherently stale with stale deps (builtins skfda.ml.classification skfda.ml.clustering skfda.ml.regression) +LOG: Writing skfda.ml /home/carlos/git/scikit-fda/skfda/ml/__init__.py skfda/ml/__init__.meta.json skfda/ml/__init__.data.json +TRACE: Interface for skfda.ml has changed +LOG: Cached module skfda.ml has changed interface +TRACE: Priorities for skfda.exploratory.visualization._outliergram: +LOG: Processing SCC singleton (skfda.exploratory.visualization._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation) +LOG: Writing skfda.exploratory.visualization._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_outliergram.py skfda/exploratory/visualization/_outliergram.meta.json skfda/exploratory/visualization/_outliergram.data.json +TRACE: Interface for skfda.exploratory.visualization._outliergram has changed +LOG: Cached module skfda.exploratory.visualization._outliergram has changed interface +TRACE: Priorities for skfda.exploratory.visualization._magnitude_shape_plot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._magnitude_shape_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.depth skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.visualization._magnitude_shape_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_magnitude_shape_plot.py skfda/exploratory/visualization/_magnitude_shape_plot.meta.json skfda/exploratory/visualization/_magnitude_shape_plot.data.json +TRACE: Interface for skfda.exploratory.visualization._magnitude_shape_plot has changed +LOG: Cached module skfda.exploratory.visualization._magnitude_shape_plot has changed interface +TRACE: Priorities for skfda.exploratory.visualization._boxplot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._boxplot) as inherently stale with stale deps (__future__ abc builtins math numpy skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._envelopes skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.visualization._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_boxplot.py skfda/exploratory/visualization/_boxplot.meta.json skfda/exploratory/visualization/_boxplot.data.json +TRACE: Interface for skfda.exploratory.visualization._boxplot has changed +LOG: Cached module skfda.exploratory.visualization._boxplot has changed interface +TRACE: Priorities for skfda.inference.anova: +LOG: Processing SCC singleton (skfda.inference.anova) as inherently stale with stale deps (builtins skfda.inference.anova._anova_oneway) +LOG: Writing skfda.inference.anova /home/carlos/git/scikit-fda/skfda/inference/anova/__init__.py skfda/inference/anova/__init__.meta.json skfda/inference/anova/__init__.data.json +TRACE: Interface for skfda.inference.anova has changed +LOG: Cached module skfda.inference.anova has changed interface +TRACE: Priorities for skfda.exploratory.visualization: +LOG: Processing SCC singleton (skfda.exploratory.visualization) as inherently stale with stale deps (builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.exploratory.visualization._ddplot skfda.exploratory.visualization._magnitude_shape_plot skfda.exploratory.visualization._multiple_display skfda.exploratory.visualization._outliergram skfda.exploratory.visualization._parametric_plot skfda.exploratory.visualization.fpca typing) +LOG: Writing skfda.exploratory.visualization /home/carlos/git/scikit-fda/skfda/exploratory/visualization/__init__.py skfda/exploratory/visualization/__init__.meta.json skfda/exploratory/visualization/__init__.data.json +TRACE: Interface for skfda.exploratory.visualization has changed +LOG: Cached module skfda.exploratory.visualization has changed interface +LOG: No fresh SCCs left in queue +LOG: Build finished in 56.565 seconds with 440 modules, and 0 errors diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index ec6e5db09..2e1ed8ccf 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -9,7 +9,6 @@ ], submod_attrs={ "_utils": [ - "RandomStateLike", "_cartesian_product", "_check_array_key", "_check_estimator", @@ -36,7 +35,6 @@ if TYPE_CHECKING: from ._utils import ( - RandomStateLike as RandomStateLike, _cartesian_product as _cartesian_product, _check_array_key as _check_array_key, _check_estimator as _check_estimator, diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 9d7c2bd89..003f743ab 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -31,8 +31,6 @@ from ..typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt, NDArrayStr from ._sklearn_adapter import BaseEstimator -RandomStateLike = Optional[Union[int, np.random.RandomState]] - ArrayDTypeT = TypeVar("ArrayDTypeT", bound="np.generic") if TYPE_CHECKING: diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index 01ffe04ee..fda6c47cb 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -3,19 +3,14 @@ import numpy as np import scipy.integrate -import sklearn.utils from scipy.stats import multivariate_normal -from .._utils import ( - RandomStateLike, - _cartesian_product, - _to_grid_points, - normalize_warping, -) +from .._utils import _cartesian_product, _to_grid_points, normalize_warping from ..misc import covariances +from ..misc.validation import validate_random_state from ..representation import FDataGrid from ..representation.interpolation import SplineInterpolation -from ..typing._base import DomainRangeLike, GridPointsLike +from ..typing._base import DomainRangeLike, GridPointsLike, RandomStateLike from ..typing._numpy import NDArrayFloat MeanCallable = Callable[[np.ndarray], np.ndarray] @@ -62,7 +57,7 @@ def make_gaussian( Gaussian processes. """ - random_state = sklearn.utils.check_random_state(random_state) + random_state = validate_random_state(random_state) if cov is None: cov = covariances.Brownian() @@ -193,7 +188,7 @@ def make_sinusoidal_process( :class:`FDataGrid` object comprising all the samples. """ - random_state = sklearn.utils.check_random_state(random_state) + random_state = validate_random_state(random_state) t = np.linspace(start, stop, n_features) @@ -256,7 +251,7 @@ def make_multimodal_landmarks( sample i. """ - random_state = sklearn.utils.check_random_state(random_state) + random_state = validate_random_state(random_state) modes_location = np.linspace(start, stop, n_modes + 2)[1:-1] modes_location = np.repeat( @@ -329,7 +324,7 @@ def make_multimodal_samples( :class:`FDataGrid` object comprising all the samples. """ - random_state = sklearn.utils.check_random_state(random_state) + random_state = validate_random_state(random_state) if modes_location is None: @@ -446,7 +441,7 @@ def make_random_warping( # Based on the original implementation of J. D. Tucker in the # package python_fdasrsf . - random_state = sklearn.utils.check_random_state(random_state) + random_state = validate_random_state(random_state) freq = shape_parameter + 1 diff --git a/skfda/exploratory/outliers/_directional_outlyingness.py b/skfda/exploratory/outliers/_directional_outlyingness.py index eb7ffa7bb..30e8338d3 100644 --- a/skfda/exploratory/outliers/_directional_outlyingness.py +++ b/skfda/exploratory/outliers/_directional_outlyingness.py @@ -8,11 +8,11 @@ import scipy.stats from numpy import linalg as la from sklearn.covariance import MinCovDet -from sklearn.utils.validation import check_random_state -from ..._utils import RandomStateLike from ..._utils._sklearn_adapter import BaseEstimator, OutlierMixin +from ...misc.validation import validate_random_state from ...representation import FDataGrid +from ...typing._base import RandomStateLike from ...typing._numpy import NDArrayFloat, NDArrayInt from ..depth.multivariate import Depth, ProjectionDepth from . import _directional_outlyingness_experiment_results as experiments @@ -495,7 +495,7 @@ def fit_predict( # noqa: D102 y: object = None, ) -> NDArrayInt: - self.random_state_ = check_random_state(self.random_state) + self.random_state_ = validate_random_state(self.random_state) self.points_ = self._compute_points(X) # The square mahalanobis distances of the samples are diff --git a/skfda/inference/__init__.py b/skfda/inference/__init__.py index 73a2e789d..f536bddd9 100644 --- a/skfda/inference/__init__.py +++ b/skfda/inference/__init__.py @@ -1 +1,9 @@ -from . import anova, hotelling +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "anova", + "hotelling", + ], +) diff --git a/skfda/inference/anova/_anova_oneway.py b/skfda/inference/anova/_anova_oneway.py index 54c74a93e..be30d3417 100644 --- a/skfda/inference/anova/_anova_oneway.py +++ b/skfda/inference/anova/_anova_oneway.py @@ -3,13 +3,13 @@ from typing import Sequence, Tuple, TypeVar, overload import numpy as np -from sklearn.utils import check_random_state from typing_extensions import Literal -from ..._utils import RandomStateLike from ...datasets import make_gaussian from ...misc.metrics import lp_distance +from ...misc.validation import validate_random_state from ...representation import FData, FDataGrid, concatenate +from ...typing._base import RandomStateLike from ...typing._numpy import ArrayLike, NDArrayFloat @@ -95,7 +95,7 @@ def _v_asymptotic_stat_with_reps( *fds: FData, weights: ArrayLike, p: int = 2, -) -> float: +) -> NDArrayFloat: """Vectorized version of v_asymptotic_stat for repetitions.""" weights = np.asarray(weights) if len(weights) != len(fds): @@ -112,7 +112,7 @@ def _v_asymptotic_stat_with_reps( right_fd = fds[pair[0]] * coef results[i] = lp_distance(left_fd, right_fd, p=p) ** p - return np.sum(results, axis=0) + return np.sum(results, axis=0) # type: ignore[no-any-return] def v_asymptotic_stat( @@ -206,7 +206,7 @@ def _anova_bootstrap( sizes = [fd.n_samples for fd in fd_grouped] # Instance a random state object in case random_state is an int - random_state = check_random_state(random_state) + random_state = validate_random_state(random_state) if equal_var: k_est = concatenate(fd_grouped).cov().data_matrix[0, ..., 0] @@ -233,7 +233,6 @@ def _anova_bootstrap( for i in range(n_groups) ] - v_samples = np.empty(n_reps) return _v_asymptotic_stat_with_reps(*sim, weights=sizes, p=p) @@ -385,7 +384,7 @@ def oneway_anova( equal_var=equal_var, ) - p_value = np.sum(simulation > vn) / len(simulation) + p_value = float(np.sum(simulation > vn) / len(simulation)) if return_dist: return vn, p_value, simulation diff --git a/skfda/inference/hotelling/_hotelling.py b/skfda/inference/hotelling/_hotelling.py index bae282e1a..43b8834ab 100644 --- a/skfda/inference/hotelling/_hotelling.py +++ b/skfda/inference/hotelling/_hotelling.py @@ -5,11 +5,11 @@ import numpy as np import scipy.special -from sklearn.utils import check_random_state from typing_extensions import Literal -from ..._utils import RandomStateLike +from ...misc.validation import validate_random_state from ...representation import FData, FDataBasis +from ...typing._base import RandomStateLike from ...typing._numpy import NDArrayFloat @@ -188,8 +188,8 @@ def hotelling_test_ind( TypeError: In case of bad arguments. Examples: - >>> from skfda.inference.hotelling import hotelling_t2 - >>> from skfda.representation import FDataGrid, basis + >>> from skfda.inference.hotelling import hotelling_test_ind + >>> from skfda.representation import FDataGrid >>> from numpy import printoptions >>> fd1 = FDataGrid([[1, 1, 1], [3, 3, 3]]) @@ -223,7 +223,7 @@ def hotelling_test_ind( indices = np.arange(n) if n_reps is not None: # Computing n_reps random permutations - random_state = check_random_state(random_state) + random_state = validate_random_state(random_state) dist = np.empty(n_reps) for i in range(n_reps): random_state.shuffle(indices) @@ -238,7 +238,7 @@ def hotelling_test_ind( sample1, sample2 = sample[sample1_i], sample[sample2_i] dist[i] = hotelling_t2(sample1, sample2) - p_value = np.sum(dist > t2_0) / len(dist) + p_value = float(np.sum(dist > t2_0) / len(dist)) if return_dist: return t2_0, p_value, dist diff --git a/skfda/misc/validation.py b/skfda/misc/validation.py index d37f476e1..52285c34f 100644 --- a/skfda/misc/validation.py +++ b/skfda/misc/validation.py @@ -7,9 +7,16 @@ from typing import Container, Sequence, Tuple, cast import numpy as np +from sklearn.utils import check_random_state as _check_random_state from ..representation import FData, FDataBasis, FDataGrid -from ..typing._base import DomainRange, DomainRangeLike, EvaluationPoints +from ..typing._base import ( + DomainRange, + DomainRangeLike, + EvaluationPoints, + RandomState, + RandomStateLike, +) from ..typing._numpy import ArrayLike @@ -255,3 +262,11 @@ def validate_domain_range(domain_range: DomainRangeLike) -> DomainRange: domain_range = cast(Sequence[Sequence[float]], domain_range) return tuple(_validate_domain_range_limits(s) for s in domain_range) + + +def validate_random_state(random_state: RandomStateLike) -> RandomState: + """Validate random state or seed.""" + if isinstance(random_state, np.random.Generator): + return random_state + + return _check_random_state(random_state) # type: ignore[no-any-return] diff --git a/skfda/ml/clustering/_kmeans.py b/skfda/ml/clustering/_kmeans.py index 649e4d56b..fa56c4ed7 100644 --- a/skfda/ml/clustering/_kmeans.py +++ b/skfda/ml/clustering/_kmeans.py @@ -8,13 +8,15 @@ import numpy as np from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin -from sklearn.utils import check_random_state from sklearn.utils.validation import check_is_fitted -from ..._utils import RandomStateLike from ...misc.metrics import PairwiseMetric, l2_distance -from ...misc.validation import check_fdata_same_dimensions +from ...misc.validation import ( + check_fdata_same_dimensions, + validate_random_state, +) from ...representation import FDataGrid +from ...typing._base import RandomStateLike from ...typing._metric import Metric from ...typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt @@ -309,7 +311,7 @@ def fit( """ fdata = self._check_clustering(X) - random_state = check_random_state(self.random_state) + random_state = validate_random_state(self.random_state) self._check_params() diff --git a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py index 1b91c50ac..1f8b1d179 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py @@ -21,12 +21,13 @@ ) from typing_extensions import Final, Literal -from ...._utils import RandomStateLike, _compute_dependence, _DependenceMeasure +from ...._utils import _compute_dependence, _DependenceMeasure from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, ) from ....representation.grid import FDataGrid +from ....typing._base import RandomStateLike from ....typing._numpy import NDArrayFloat, NDArrayInt, NDArrayReal _Criterion = Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat] diff --git a/skfda/typing/_base.py b/skfda/typing/_base.py index 0bf16b404..b7badd7c5 100644 --- a/skfda/typing/_base.py +++ b/skfda/typing/_base.py @@ -1,6 +1,7 @@ """Common types.""" from typing import Optional, Sequence, Tuple, TypeVar, Union +import numpy as np from typing_extensions import Protocol from ._numpy import ArrayLike, NDArrayFloat @@ -23,6 +24,10 @@ EvaluationPoints = NDArrayFloat +RandomStateLike = Union[int, np.random.RandomState, np.random.Generator, None] +RandomState = Union[np.random.RandomState, np.random.Generator] + + class Vector(Protocol): """ Protocol representing a generic vector. 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true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi", "plugin_data": null, "size": 2278, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.synchronize: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.synchronize -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi (multiprocessing.synchronize) -TRACE: Looking for multiprocessing.managers at multiprocessing/managers.meta.json -TRACE: Meta multiprocessing.managers {"data_mtime": 1662126100, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 1, 1], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["queue", "sys", "threading", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", 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false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi", "plugin_data": null, "size": 8556, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.managers: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.managers -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi (multiprocessing.managers) -TRACE: Looking for multiprocessing.process at multiprocessing/process.meta.json -TRACE: Meta multiprocessing.process {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "builtins", "_typeshed", "abc"], "hash": "fa47cbb61f64a2309b6d7043a2c42c4c096b85f17dbd51181d9c83455a89e5c3", "id": "multiprocessing.process", "ignore_all": true, "interface_hash": "77c612e6cb8b00cd8763eb96f60de3ddd7c40a47a2ea78f5710fb059dea117d2", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, 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(multiprocessing.process) -TRACE: Looking for multiprocessing.queues at multiprocessing/queues.meta.json -TRACE: Meta multiprocessing.queues {"data_mtime": 1662126100, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30], "dependencies": ["queue", "sys", "typing", "builtins", "_typeshed", "abc"], "hash": "daf7a7147647126f56cf864be94a85c23c7801d703f87280168714c3eda8a44c", "id": "multiprocessing.queues", "ignore_all": true, "interface_hash": "38370111a855e180ba5a53c74c70d2ab11961893618ccd17ecc4b397487fd0ae", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi", "plugin_data": null, "size": 1398, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.queues: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.queues -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi (multiprocessing.queues) -TRACE: Looking for multiprocessing.spawn at multiprocessing/spawn.meta.json -TRACE: Meta multiprocessing.spawn {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 1, 1], "dep_prios": [5, 5, 5, 5, 30], "dependencies": ["collections.abc", "types", "typing", "builtins", "abc"], "hash": "b49c0c45edfea0381227f3a4dd3408fe75dd8c69e824da53e7271f0005371a6a", "id": "multiprocessing.spawn", "ignore_all": true, "interface_hash": "864e86b58a2b7fe05e14bfb866bac09cbeff6f212bcefb318fedda6f40e77af4", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, 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normal -LOG: Metadata not found for multiprocessing.spawn -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi (multiprocessing.spawn) -TRACE: Looking for _weakrefset at _weakrefset.meta.json -TRACE: Meta _weakrefset {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 4, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "typing", "builtins", "abc"], "hash": "b782eb1547d8f9158e20866c201b6753d853647d67d993ccd2e266b1ae292828", "id": "_weakrefset", "ignore_all": true, "interface_hash": "454315f7522e84d316cc85d5404e0d35f6174236661489ccb7cebf12f69b03ea", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": 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follow_imports: silent != normal -LOG: Metadata not found for _weakrefset -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi (_weakrefset) -TRACE: Looking for _weakref at _weakref.meta.json -TRACE: Meta _weakref {"data_mtime": 1662126099, "dep_lines": [1, 2, 3, 4, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "2eb8b487360e720252edcc10dc29f1d3ecb8948a81c02e9a6693df62b92e592c", "id": "_weakref", "ignore_all": true, "interface_hash": "60fad058d2e4209acdc3f2e2722c4eea7a895c8f8586a9f8724b37cab26f2e8a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, 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1658259053, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, 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/home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py (skfda.preprocessing.feature_construction._function_transformers) -LOG: Loaded graph with 440 nodes (2.813 sec) -LOG: Found 176 SCCs; largest has 72 nodes -TRACE: Priorities for typing_extensions: abc:10 collections:10 sys:10 _typeshed:5 collections.abc:5 typing:5 -TRACE: Priorities for typing: collections:10 sys:10 _typeshed:5 abc:5 types:5 typing_extensions:5 -TRACE: Priorities for types: sys:10 _typeshed:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 typing:5 typing_extensions:5 -TRACE: Priorities for sys: _typeshed:5 builtins:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 io:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for subprocess: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for posixpath: sys:10 _typeshed:5 collections.abc:5 genericpath:5 os:5 typing:5 typing_extensions:5 -TRACE: Priorities for pickle: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for pathlib: sys:10 _typeshed:5 collections.abc:5 io:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for os.path: sys:10 posixpath:5 -TRACE: Priorities for os: sys:10 os.path:10 _typeshed:5 abc:5 builtins:5 collections.abc:5 contextlib:5 io:5 subprocess:5 typing:5 typing_extensions:5 -TRACE: Priorities for mmap: sys:10 _typeshed:5 collections.abc:5 typing:5 -TRACE: Priorities for io: builtins:10 codecs:10 sys:10 _typeshed:5 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for importlib.metadata: abc:10 pathlib:5 sys:10 _typeshed:5 collections.abc:5 email.message:5 importlib.abc:5 os:5 typing:5 -TRACE: Priorities for importlib.machinery: importlib.abc:10 importlib:20 sys:10 types:10 collections.abc:5 typing:5 importlib.metadata:5 -TRACE: Priorities for importlib.abc: sys:10 types:10 _typeshed:5 abc:5 collections.abc:5 importlib.machinery:5 io:5 typing:5 typing_extensions:5 -TRACE: Priorities for importlib: collections.abc:5 importlib.abc:5 types:5 -TRACE: Priorities for genericpath: os:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for email.policy: abc:5 collections.abc:5 email.contentmanager:5 email.errors:5 email.header:5 email.message:5 typing:5 -TRACE: Priorities for email.message: collections.abc:5 email:5 email.charset:5 email.contentmanager:5 email.errors:5 email.policy:5 typing:5 typing_extensions:5 -TRACE: Priorities for email.header: email.charset:5 -TRACE: Priorities for email.errors: sys:10 -TRACE: Priorities for email.contentmanager: collections.abc:5 email.message:5 typing:5 -TRACE: Priorities for email.charset: collections.abc:5 -TRACE: Priorities for email: collections.abc:5 email.message:5 email.policy:5 typing:5 typing_extensions:5 -TRACE: Priorities for ctypes: sys:10 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for contextlib: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for collections.abc: _collections_abc:5 -TRACE: Priorities for collections: sys:10 _collections_abc:5 _typeshed:5 typing:5 typing_extensions:5 -TRACE: Priorities for codecs: types:10 _codecs:5 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for array: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for abc: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for _typeshed: array:10 ctypes:10 mmap:10 pickle:10 sys:10 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for _collections_abc: sys:10 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for _codecs: codecs:10 sys:10 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for _ast: sys:10 typing:5 typing_extensions:5 -TRACE: Priorities for builtins: sys:10 types:5 _ast:5 _collections_abc:5 _typeshed:5 collections.abc:5 io:5 typing:5 typing_extensions:5 -LOG: Processing SCC of size 36 (typing_extensions typing types sys subprocess posixpath pickle pathlib os.path os mmap io importlib.metadata importlib.machinery importlib.abc importlib genericpath email.policy email.message email.header email.errors email.contentmanager email.charset email ctypes contextlib collections.abc collections codecs array abc _typeshed _collections_abc _codecs _ast builtins) as inherently stale -LOG: Writing typing_extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing_extensions.pyi typing_extensions.meta.json typing_extensions.data.json -TRACE: Interface for typing_extensions has changed -LOG: Cached module typing_extensions has changed interface -LOG: Writing typing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing.pyi typing.meta.json typing.data.json -TRACE: Interface for typing has changed -LOG: Cached module typing has changed interface -LOG: Writing types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/types.pyi types.meta.json types.data.json -TRACE: Interface for types has changed -LOG: Cached module types has changed interface -LOG: Writing sys /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sys.pyi sys.meta.json sys.data.json -TRACE: Interface for sys has changed -LOG: Cached module sys has changed interface -LOG: Writing subprocess /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/subprocess.pyi subprocess.meta.json subprocess.data.json -TRACE: Interface for subprocess has changed -LOG: Cached module subprocess has changed interface -LOG: Writing posixpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/posixpath.pyi posixpath.meta.json posixpath.data.json -TRACE: Interface for posixpath has changed -LOG: Cached module posixpath has changed interface -LOG: Writing pickle /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pickle.pyi pickle.meta.json pickle.data.json -TRACE: Interface for pickle has changed -LOG: Cached module pickle has changed interface -LOG: Writing pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pathlib.pyi pathlib.meta.json pathlib.data.json -TRACE: Interface for pathlib has changed -LOG: Cached module pathlib has changed interface -LOG: Writing os.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/path.pyi os/path.meta.json os/path.data.json -TRACE: Interface for os.path has changed -LOG: Cached module os.path has changed interface -LOG: Writing os /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/__init__.pyi os/__init__.meta.json os/__init__.data.json -TRACE: Interface for os has changed -LOG: Cached module os has changed interface -LOG: Writing mmap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/mmap.pyi mmap.meta.json mmap.data.json -TRACE: Interface for mmap has changed -LOG: Cached module mmap has changed interface -LOG: Writing io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/io.pyi io.meta.json io.data.json -TRACE: Interface for io has changed -LOG: Cached module io has changed interface -LOG: Writing importlib.metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi importlib/metadata/__init__.meta.json importlib/metadata/__init__.data.json -TRACE: Interface for importlib.metadata has changed -LOG: Cached module importlib.metadata has changed interface -LOG: Writing importlib.machinery /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi importlib/machinery.meta.json importlib/machinery.data.json -TRACE: Interface for importlib.machinery has changed -LOG: Cached module importlib.machinery has changed interface -LOG: Writing importlib.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/abc.pyi importlib/abc.meta.json importlib/abc.data.json -TRACE: Interface for importlib.abc has changed -LOG: Cached module importlib.abc has changed interface -LOG: Writing importlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi importlib/__init__.meta.json importlib/__init__.data.json -TRACE: Interface for importlib has changed -LOG: Cached module importlib has changed interface -LOG: Writing genericpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/genericpath.pyi genericpath.meta.json genericpath.data.json -TRACE: Interface for genericpath has changed -LOG: Cached module genericpath has changed interface -LOG: Writing email.policy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi email/policy.meta.json email/policy.data.json -TRACE: Interface for email.policy has changed -LOG: Cached module email.policy has changed interface -LOG: Writing email.message /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/message.pyi email/message.meta.json email/message.data.json -TRACE: Interface for email.message has changed -LOG: Cached module email.message has changed interface -LOG: Writing email.header /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/header.pyi email/header.meta.json email/header.data.json -TRACE: Interface for email.header has changed -LOG: Cached module email.header has changed interface -LOG: Writing email.errors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/errors.pyi email/errors.meta.json email/errors.data.json -TRACE: Interface for email.errors has changed -LOG: Cached module email.errors has changed interface -LOG: Writing email.contentmanager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi email/contentmanager.meta.json email/contentmanager.data.json -TRACE: Interface for email.contentmanager has changed -LOG: Cached module email.contentmanager has changed interface -LOG: Writing email.charset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi email/charset.meta.json email/charset.data.json -TRACE: Interface for email.charset has changed -LOG: Cached module email.charset has changed interface -LOG: Writing email /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/__init__.pyi email/__init__.meta.json email/__init__.data.json -TRACE: Interface for email has changed -LOG: Cached module email has changed interface -LOG: Writing ctypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ctypes/__init__.pyi ctypes/__init__.meta.json ctypes/__init__.data.json -TRACE: Interface for ctypes has changed -LOG: Cached module ctypes has changed interface -LOG: Writing contextlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextlib.pyi contextlib.meta.json contextlib.data.json -TRACE: Interface for contextlib has changed -LOG: Cached module contextlib has changed interface -LOG: Writing collections.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/abc.pyi collections/abc.meta.json collections/abc.data.json -TRACE: Interface for collections.abc has changed -LOG: Cached module collections.abc has changed interface -LOG: Writing collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/__init__.pyi collections/__init__.meta.json collections/__init__.data.json -TRACE: Interface for collections has changed -LOG: Cached module collections has changed interface -LOG: Writing codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/codecs.pyi codecs.meta.json codecs.data.json -TRACE: Interface for codecs has changed -LOG: Cached module codecs has changed interface -LOG: Writing array /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/array.pyi array.meta.json array.data.json -TRACE: Interface for array has changed -LOG: Cached module array has changed interface -LOG: Writing abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/abc.pyi abc.meta.json abc.data.json -TRACE: Interface for abc has changed -LOG: Cached module abc has changed interface -LOG: Writing _typeshed /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_typeshed/__init__.pyi _typeshed/__init__.meta.json _typeshed/__init__.data.json -TRACE: Interface for _typeshed has changed -LOG: Cached module _typeshed has changed interface -LOG: Writing _collections_abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_collections_abc.pyi _collections_abc.meta.json _collections_abc.data.json -TRACE: Interface for _collections_abc has changed -LOG: Cached module _collections_abc has changed interface -LOG: Writing _codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_codecs.pyi _codecs.meta.json _codecs.data.json -TRACE: Interface for _codecs has changed -LOG: Cached module _codecs has changed interface -LOG: Writing _ast /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_ast.pyi _ast.meta.json _ast.data.json -TRACE: Interface for _ast has changed -LOG: Cached module _ast has changed interface -LOG: Writing builtins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/builtins.pyi builtins.meta.json builtins.data.json -TRACE: Interface for builtins has changed -LOG: Cached module builtins has changed interface -TRACE: Priorities for _socket: -LOG: Processing SCC singleton (_socket) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing typing_extensions) -LOG: Writing _socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi _socket.meta.json _socket.data.json -TRACE: Interface for _socket has changed -LOG: Cached module _socket has changed interface -TRACE: Priorities for multiprocessing.shared_memory: -LOG: Processing SCC singleton (multiprocessing.shared_memory) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing) -LOG: Writing multiprocessing.shared_memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi multiprocessing/shared_memory.meta.json multiprocessing/shared_memory.data.json -TRACE: Interface for multiprocessing.shared_memory has changed -LOG: Cached module multiprocessing.shared_memory has changed interface -TRACE: Priorities for copyreg: -LOG: Processing SCC singleton (copyreg) as inherently stale with stale deps (builtins collections.abc typing typing_extensions) -LOG: Writing copyreg /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi copyreg.meta.json copyreg.data.json -TRACE: Interface for 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numpy._typing._callable numpy._typing numpy.core._internal numpy.matrixlib numpy.ma numpy.lib numpy.ctypeslib numpy numpy.testing._private.utils numpy.random.bit_generator numpy.polynomial.polynomial numpy.polynomial.legendre numpy.polynomial.laguerre numpy.polynomial.hermite_e numpy.polynomial.hermite numpy.polynomial.chebyshev numpy.lib.scimath numpy.lib.mixins numpy.fft.helper numpy.fft._pocketfft numpy.core.records numpy.core.defchararray numpy.linalg.linalg numpy.linalg numpy.random.mtrand numpy.random._sfc64 numpy.random._philox numpy.random._pcg64 numpy.random._mt19937 numpy.testing numpy.polynomial numpy.fft numpy.random._generator numpy.random numpy._typing._add_docstring numpy.typing numpy._typing._ufunc) as inherently stale with stale deps (__future__ abc array ast builtins collections.abc contextlib ctypes datetime enum math mmap numpy._pytesttester numpy._typing._char_codes numpy._typing._nbit numpy._typing._nested_sequence numpy._typing._shape numpy.core 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xarray.core.common:5 xarray.core.computation:5 xarray.core.concat:5 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.extensions:5 xarray.core.merge:5 xarray.core.options:5 xarray.core.parallel:5 xarray.core.variable:5 -TRACE: Priorities for xarray.backends: xarray.backends.cfgrib_:5 xarray.backends.common:5 xarray.backends.file_manager:5 xarray.backends.h5netcdf_:5 xarray.backends.memory:5 xarray.backends.netCDF4_:5 xarray.backends.plugins:5 xarray.backends.pseudonetcdf_:5 xarray.backends.pydap_:5 xarray.backends.pynio_:5 xarray.backends.scipy_:5 xarray.backends.zarr:5 -TRACE: Priorities for xarray.convert: xarray.core.duck_array_ops:10 xarray.coding.times:5 xarray.conventions:5 xarray.core.dataarray:5 xarray.core.dtypes:5 xarray.core.pycompat:5 -TRACE: Priorities for xarray.core.resample_cftime: xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 -LOG: Processing SCC of size 72 (xarray.core.types xarray.core.utils xarray.core.dtypes xarray.core.pycompat xarray.core.options xarray.core.dask_array_compat xarray.core.nputils xarray.plot.utils xarray.core.rolling_exp xarray.backends.file_manager xarray.core.dask_array_ops xarray.core.duck_array_ops xarray.core._reductions xarray.core.nanops xarray.core.ops xarray.core.indexing xarray.core.formatting xarray.plot.facetgrid xarray.core.formatting_html xarray.core.indexes xarray.core.common xarray.core.accessor_dt xarray.core._typed_ops xarray.plot.dataset_plot xarray.core.arithmetic xarray.core.accessor_str xarray.plot.plot xarray.core.missing xarray.core.coordinates xarray.coding.variables xarray.coding.times xarray.core.groupby xarray.core.variable xarray.core.merge xarray.core.dataset xarray.core.dataarray xarray.core.concat xarray.core.computation xarray.core.alignment xarray.coding.cftimeindex xarray.backends.netcdf3 xarray.core.rolling xarray.core.resample xarray.core.weighted xarray.coding.strings xarray.core.parallel xarray.core.extensions xarray.core.combine xarray.conventions xarray.coding.cftime_offsets xarray.ufuncs xarray.testing xarray.backends.common xarray.coding.frequencies xarray.backends.memory xarray.backends.store xarray.backends.plugins xarray.backends.rasterio_ xarray.backends.api xarray.backends.scipy_ xarray.backends.pynio_ xarray.backends.pydap_ xarray.backends.pseudonetcdf_ xarray.backends.netCDF4_ xarray.backends.cfgrib_ xarray.backends.zarr xarray.tutorial xarray.backends.h5netcdf_ xarray xarray.backends xarray.convert xarray.core.resample_cftime) as inherently stale with stale deps (__future__ builtins codecs collections collections.abc contextlib copy datetime distutils.version enum functools glob gzip html importlib importlib.metadata importlib.resources importlib_metadata inspect io itertools logging numbers numpy numpy.core.multiarray numpy.core.numeric operator os pathlib re sys textwrap threading time traceback typing typing_extensions unicodedata uuid warnings xarray.backends.locks xarray.backends.lru_cache xarray.coding 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dcor._dcor_internals dcor._partial_dcor dcor._dcor dcor.homogeneity dcor._rowwise dcor.independence dcor) as inherently stale with stale deps (__future__ builtins dataclasses dcor._fast_dcov_avl dcor._fast_dcov_mergesort dcor._hypothesis dcor._utils dcor.distances enum errno numpy os pathlib typing typing_extensions warnings) -LOG: Writing dcor._energy /home/carlos/git/dcor/dcor/_energy.py dcor/_energy.meta.json dcor/_energy.data.json -TRACE: Interface for dcor._energy has changed -LOG: Cached module dcor._energy has changed interface -LOG: Writing dcor._dcor_internals /home/carlos/git/dcor/dcor/_dcor_internals.py dcor/_dcor_internals.meta.json dcor/_dcor_internals.data.json -TRACE: Interface for dcor._dcor_internals has changed -LOG: Cached module dcor._dcor_internals has changed interface -LOG: Writing dcor._partial_dcor /home/carlos/git/dcor/dcor/_partial_dcor.py dcor/_partial_dcor.meta.json dcor/_partial_dcor.data.json -TRACE: Interface for dcor._partial_dcor has changed -LOG: Cached module dcor._partial_dcor has changed interface -LOG: Writing dcor._dcor /home/carlos/git/dcor/dcor/_dcor.py dcor/_dcor.meta.json dcor/_dcor.data.json -TRACE: Interface for dcor._dcor has changed -LOG: Cached module dcor._dcor has changed interface -LOG: Writing dcor.homogeneity /home/carlos/git/dcor/dcor/homogeneity.py dcor/homogeneity.meta.json dcor/homogeneity.data.json -TRACE: Interface for dcor.homogeneity has changed -LOG: Cached module dcor.homogeneity has changed interface -LOG: Writing dcor._rowwise /home/carlos/git/dcor/dcor/_rowwise.py dcor/_rowwise.meta.json dcor/_rowwise.data.json -TRACE: Interface for dcor._rowwise has changed -LOG: Cached module dcor._rowwise has changed interface -LOG: Writing dcor.independence /home/carlos/git/dcor/dcor/independence.py dcor/independence.meta.json dcor/independence.data.json -TRACE: Interface for dcor.independence has changed -LOG: Cached module dcor.independence has changed interface -LOG: Writing dcor 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-TRACE: Priorities for skfda.misc.metrics._angular: skfda.representation:5 skfda.misc._math:5 skfda.misc.metrics._utils:5 -TRACE: Priorities for skfda.exploratory.stats._fisher_rao: skfda._utils:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.interpolation:5 -TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 -TRACE: Priorities for skfda.preprocessing.registration._landmark_registration: skfda.representation:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 -TRACE: Priorities for skfda.representation._functional_data: skfda._utils:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.grid:25 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 -TRACE: Priorities for skfda.exploratory.visualization._utils: skfda.representation._functional_data:5 -TRACE: Priorities for skfda.exploratory.visualization._baseplot: skfda.representation:5 skfda.exploratory.visualization._utils:5 -TRACE: Priorities for skfda.exploratory.visualization.representation: skfda._utils:5 skfda.misc.validation:5 skfda.representation._functional_data:5 skfda.exploratory.visualization._baseplot:5 skfda.exploratory.visualization._utils:5 -TRACE: Priorities for skfda.misc.hat_matrix: skfda.misc:20 skfda.representation._functional_data:5 skfda.representation.basis:5 -TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 -TRACE: Priorities for skfda.preprocessing.smoothing._kernel_smoothers: skfda._utils._utils:5 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.preprocessing.smoothing: skfda.preprocessing.smoothing.kernel_smoothers:20 skfda.preprocessing.smoothing._basis:25 skfda.preprocessing.smoothing._kernel_smoothers:25 -TRACE: Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 -TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 -TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 -LOG: Processing SCC of size 62 (skfda.exploratory.depth skfda.exploratory.stats skfda.misc.regularization skfda.preprocessing.dim_reduction skfda.preprocessing.registration skfda.misc.operators skfda._utils skfda.representation.basis skfda.misc skfda.representation skfda.misc.metrics skfda skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda.misc.operators._operators skfda._utils._utils skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda.misc.validation skfda.exploratory.depth._depth skfda.exploratory.stats._functional_transformers skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.preprocessing.smoothing._basis skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.operators._identity skfda._utils._warping skfda.representation.basis._basis skfda.representation.evaluator skfda.misc.regularization._regularization skfda.misc._math skfda.misc.metrics._lp_distances skfda.exploratory.stats._stats skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.representation.extrapolation skfda.representation.interpolation skfda.misc.metrics._mahalanobis skfda.misc.metrics._fisher_rao skfda.misc.metrics._angular skfda.exploratory.stats._fisher_rao skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.misc.hat_matrix skfda.preprocessing.registration._fisher_rao skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.representation.grid skfda.preprocessing.dim_reduction._fpca skfda.representation.basis._fdatabasis) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses dcor errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._parse skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) -LOG: Writing skfda.exploratory.depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/__init__.py skfda/exploratory/depth/__init__.meta.json skfda/exploratory/depth/__init__.data.json -TRACE: Interface for skfda.exploratory.depth has changed -LOG: Cached module skfda.exploratory.depth has changed interface -LOG: Writing skfda.exploratory.stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/__init__.py skfda/exploratory/stats/__init__.meta.json skfda/exploratory/stats/__init__.data.json -TRACE: Interface for skfda.exploratory.stats has changed -LOG: Cached module skfda.exploratory.stats has changed interface -LOG: Writing skfda.misc.regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/__init__.py skfda/misc/regularization/__init__.meta.json skfda/misc/regularization/__init__.data.json -TRACE: Interface for skfda.misc.regularization has changed -LOG: Cached module skfda.misc.regularization has changed interface -LOG: Writing skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json skfda/preprocessing/dim_reduction/__init__.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction has changed -LOG: Cached module skfda.preprocessing.dim_reduction has changed interface -LOG: Writing skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json -TRACE: Interface for skfda.preprocessing.registration has changed -LOG: Cached module skfda.preprocessing.registration has changed interface -LOG: Writing skfda.misc.operators /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py skfda/misc/operators/__init__.meta.json skfda/misc/operators/__init__.data.json -TRACE: Interface for skfda.misc.operators has changed -LOG: Cached module skfda.misc.operators has changed interface -LOG: Writing skfda._utils /home/carlos/git/scikit-fda/skfda/_utils/__init__.py skfda/_utils/__init__.meta.json skfda/_utils/__init__.data.json 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/home/carlos/git/scikit-fda/skfda/misc/metrics/__init__.py skfda/misc/metrics/__init__.meta.json skfda/misc/metrics/__init__.data.json -TRACE: Interface for skfda.misc.metrics has changed -LOG: Cached module skfda.misc.metrics has changed interface -LOG: Writing skfda /home/carlos/git/scikit-fda/skfda/__init__.py skfda/__init__.meta.json skfda/__init__.data.json -TRACE: Interface for skfda has changed -LOG: Cached module skfda has changed interface -LOG: Writing skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json skfda/preprocessing/smoothing/_linear.data.json -TRACE: Interface for skfda.preprocessing.smoothing._linear has changed -LOG: Cached module skfda.preprocessing.smoothing._linear has changed interface -LOG: Writing skfda.preprocessing.smoothing.validation /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json -TRACE: Interface for skfda.preprocessing.smoothing.validation has changed -LOG: Cached module skfda.preprocessing.smoothing.validation has changed interface -LOG: Writing skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json -TRACE: Interface for skfda.misc.operators._operators has changed -LOG: Cached module skfda.misc.operators._operators has changed interface -LOG: Writing skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json -TRACE: Interface for skfda._utils._utils has changed -LOG: Cached module skfda._utils._utils has changed interface -LOG: Writing skfda.misc.metrics._utils /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py skfda/misc/metrics/_utils.meta.json skfda/misc/metrics/_utils.data.json -TRACE: Interface for skfda.misc.metrics._utils has changed -LOG: Cached module skfda.misc.metrics._utils has changed interface -LOG: Writing skfda.misc.metrics._lp_norms /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py skfda/misc/metrics/_lp_norms.meta.json skfda/misc/metrics/_lp_norms.data.json -TRACE: Interface for skfda.misc.metrics._lp_norms has changed -LOG: Cached module skfda.misc.metrics._lp_norms has changed interface -LOG: Writing skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json -TRACE: Interface for skfda.misc.validation has changed -LOG: Cached module skfda.misc.validation has changed interface -LOG: Writing skfda.exploratory.depth._depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py skfda/exploratory/depth/_depth.meta.json skfda/exploratory/depth/_depth.data.json -TRACE: Interface for skfda.exploratory.depth._depth has changed -LOG: Cached module skfda.exploratory.depth._depth has changed interface -LOG: Writing skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json -TRACE: Interface for skfda.exploratory.stats._functional_transformers has changed -LOG: Cached module skfda.exploratory.stats._functional_transformers has changed interface -LOG: Writing skfda.preprocessing.registration.base /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py skfda/preprocessing/registration/base.meta.json skfda/preprocessing/registration/base.data.json -TRACE: Interface for skfda.preprocessing.registration.base has changed -LOG: Cached module skfda.preprocessing.registration.base has changed interface -LOG: Writing skfda.preprocessing.registration.validation /home/carlos/git/scikit-fda/skfda/preprocessing/registration/validation.py skfda/preprocessing/registration/validation.meta.json skfda/preprocessing/registration/validation.data.json -TRACE: Interface for skfda.preprocessing.registration.validation has changed -LOG: Cached module skfda.preprocessing.registration.validation has changed interface -LOG: Writing skfda.preprocessing.smoothing._basis /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py skfda/preprocessing/smoothing/_basis.meta.json skfda/preprocessing/smoothing/_basis.data.json -TRACE: Interface for skfda.preprocessing.smoothing._basis has changed -LOG: Cached module skfda.preprocessing.smoothing._basis has changed interface -LOG: Writing skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json -TRACE: Interface for skfda.misc.operators._srvf has changed -LOG: Cached module skfda.misc.operators._srvf has changed interface -LOG: Writing skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json -TRACE: Interface for skfda.misc.operators._linear_differential_operator has changed -LOG: Cached module skfda.misc.operators._linear_differential_operator has changed interface -LOG: Writing skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json -TRACE: Interface for skfda.misc.operators._integral_transform has changed -LOG: Cached module skfda.misc.operators._integral_transform has changed interface -LOG: Writing skfda.misc.operators._identity /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json -TRACE: Interface for skfda.misc.operators._identity has changed -LOG: Cached module skfda.misc.operators._identity has changed interface -LOG: Writing skfda._utils._warping /home/carlos/git/scikit-fda/skfda/_utils/_warping.py skfda/_utils/_warping.meta.json skfda/_utils/_warping.data.json -TRACE: Interface for skfda._utils._warping has changed -LOG: Cached module skfda._utils._warping has changed interface -LOG: Writing skfda.representation.basis._basis /home/carlos/git/scikit-fda/skfda/representation/basis/_basis.py skfda/representation/basis/_basis.meta.json skfda/representation/basis/_basis.data.json -TRACE: Interface for skfda.representation.basis._basis has changed -LOG: Cached module skfda.representation.basis._basis has changed interface -LOG: Writing skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json -TRACE: Interface for skfda.representation.evaluator has changed -LOG: Cached module skfda.representation.evaluator has changed interface -LOG: Writing skfda.misc.regularization._regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py skfda/misc/regularization/_regularization.meta.json skfda/misc/regularization/_regularization.data.json -TRACE: Interface for skfda.misc.regularization._regularization has changed -LOG: Cached module skfda.misc.regularization._regularization has changed interface -LOG: Writing skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json -TRACE: Interface for skfda.misc._math has changed -LOG: Cached module skfda.misc._math has changed interface -LOG: Writing skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json -TRACE: Interface for skfda.misc.metrics._lp_distances has changed -LOG: Cached module skfda.misc.metrics._lp_distances has changed interface -LOG: Writing skfda.exploratory.stats._stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/_stats.py skfda/exploratory/stats/_stats.meta.json skfda/exploratory/stats/_stats.data.json -TRACE: Interface for skfda.exploratory.stats._stats has changed -LOG: Cached module skfda.exploratory.stats._stats has changed interface -LOG: Writing skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json -TRACE: Interface for skfda.representation.basis._vector_basis has changed -LOG: Cached module skfda.representation.basis._vector_basis has changed interface -LOG: Writing skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json -TRACE: Interface for skfda.representation.basis._tensor_basis has changed -LOG: Cached module skfda.representation.basis._tensor_basis has changed interface -LOG: Writing skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json -TRACE: Interface for skfda.representation.basis._monomial has changed -LOG: Cached module skfda.representation.basis._monomial has changed interface -LOG: Writing skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json -TRACE: Interface for skfda.representation.basis._fourier has changed -LOG: Cached module skfda.representation.basis._fourier has changed interface -LOG: Writing skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json -TRACE: Interface for skfda.representation.basis._finite_element has changed -LOG: Cached module skfda.representation.basis._finite_element has changed interface -LOG: Writing skfda.representation.basis._constant /home/carlos/git/scikit-fda/skfda/representation/basis/_constant.py skfda/representation/basis/_constant.meta.json skfda/representation/basis/_constant.data.json -TRACE: Interface for skfda.representation.basis._constant has changed -LOG: Cached module skfda.representation.basis._constant has changed interface -LOG: Writing skfda.representation.basis._bspline /home/carlos/git/scikit-fda/skfda/representation/basis/_bspline.py skfda/representation/basis/_bspline.meta.json skfda/representation/basis/_bspline.data.json -TRACE: Interface for skfda.representation.basis._bspline has changed -LOG: Cached module skfda.representation.basis._bspline has changed interface -LOG: Writing skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json -TRACE: Interface for skfda.representation.extrapolation has changed -LOG: Cached module skfda.representation.extrapolation has changed interface -LOG: Writing skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json -TRACE: Interface for skfda.representation.interpolation has changed -LOG: Cached module skfda.representation.interpolation has changed interface -LOG: Writing skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json -TRACE: Interface for skfda.misc.metrics._mahalanobis has changed -LOG: Cached module skfda.misc.metrics._mahalanobis has changed interface -LOG: Writing skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json -TRACE: Interface for skfda.misc.metrics._fisher_rao has changed -LOG: Cached module skfda.misc.metrics._fisher_rao has changed interface -LOG: Writing skfda.misc.metrics._angular /home/carlos/git/scikit-fda/skfda/misc/metrics/_angular.py skfda/misc/metrics/_angular.meta.json skfda/misc/metrics/_angular.data.json -TRACE: Interface for skfda.misc.metrics._angular has changed -LOG: Cached module skfda.misc.metrics._angular has changed interface -LOG: Writing skfda.exploratory.stats._fisher_rao /home/carlos/git/scikit-fda/skfda/exploratory/stats/_fisher_rao.py skfda/exploratory/stats/_fisher_rao.meta.json skfda/exploratory/stats/_fisher_rao.data.json -TRACE: Interface for skfda.exploratory.stats._fisher_rao has changed -LOG: Cached module skfda.exploratory.stats._fisher_rao has changed interface -LOG: Writing skfda.preprocessing.registration._lstsq_shift_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py skfda/preprocessing/registration/_lstsq_shift_registration.meta.json skfda/preprocessing/registration/_lstsq_shift_registration.data.json -TRACE: Interface for skfda.preprocessing.registration._lstsq_shift_registration has changed -LOG: Cached module skfda.preprocessing.registration._lstsq_shift_registration has changed interface -LOG: Writing skfda.preprocessing.registration._landmark_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py skfda/preprocessing/registration/_landmark_registration.meta.json skfda/preprocessing/registration/_landmark_registration.data.json -TRACE: Interface for skfda.preprocessing.registration._landmark_registration has changed -LOG: Cached module skfda.preprocessing.registration._landmark_registration has changed interface -LOG: Writing skfda.representation._functional_data /home/carlos/git/scikit-fda/skfda/representation/_functional_data.py skfda/representation/_functional_data.meta.json skfda/representation/_functional_data.data.json -TRACE: Interface for skfda.representation._functional_data has changed -LOG: Cached module skfda.representation._functional_data has changed interface -LOG: Writing skfda.exploratory.visualization._utils /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_utils.py skfda/exploratory/visualization/_utils.meta.json skfda/exploratory/visualization/_utils.data.json -TRACE: Interface for skfda.exploratory.visualization._utils has changed -LOG: Cached module skfda.exploratory.visualization._utils has changed interface -LOG: Writing skfda.exploratory.visualization._baseplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py skfda/exploratory/visualization/_baseplot.meta.json skfda/exploratory/visualization/_baseplot.data.json -TRACE: Interface for skfda.exploratory.visualization._baseplot has changed -LOG: Cached module skfda.exploratory.visualization._baseplot has changed interface -LOG: Writing skfda.exploratory.visualization.representation /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py skfda/exploratory/visualization/representation.meta.json skfda/exploratory/visualization/representation.data.json -TRACE: Interface for skfda.exploratory.visualization.representation has changed -LOG: Cached module skfda.exploratory.visualization.representation has changed interface -LOG: Writing skfda.misc.hat_matrix /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py skfda/misc/hat_matrix.meta.json skfda/misc/hat_matrix.data.json -TRACE: Interface for skfda.misc.hat_matrix has changed -LOG: Cached module skfda.misc.hat_matrix has changed interface -LOG: Writing skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json -TRACE: Interface for skfda.preprocessing.registration._fisher_rao has changed -LOG: Cached module skfda.preprocessing.registration._fisher_rao has changed interface -LOG: Writing skfda.preprocessing.smoothing._kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py skfda/preprocessing/smoothing/_kernel_smoothers.meta.json skfda/preprocessing/smoothing/_kernel_smoothers.data.json -TRACE: Interface for skfda.preprocessing.smoothing._kernel_smoothers has changed -LOG: Cached module skfda.preprocessing.smoothing._kernel_smoothers has changed interface -LOG: Writing skfda.preprocessing.smoothing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py skfda/preprocessing/smoothing/__init__.meta.json skfda/preprocessing/smoothing/__init__.data.json -TRACE: Interface for skfda.preprocessing.smoothing has changed -LOG: Cached module skfda.preprocessing.smoothing has changed interface -LOG: Writing skfda.preprocessing.smoothing.kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/kernel_smoothers.py skfda/preprocessing/smoothing/kernel_smoothers.meta.json skfda/preprocessing/smoothing/kernel_smoothers.data.json -TRACE: Interface for skfda.preprocessing.smoothing.kernel_smoothers has changed -LOG: Cached module skfda.preprocessing.smoothing.kernel_smoothers has changed interface -LOG: Writing skfda.representation.grid /home/carlos/git/scikit-fda/skfda/representation/grid.py skfda/representation/grid.meta.json skfda/representation/grid.data.json -TRACE: Interface for skfda.representation.grid has changed -LOG: Cached module skfda.representation.grid has changed interface -LOG: Writing skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction._fpca has changed -LOG: Cached module skfda.preprocessing.dim_reduction._fpca has changed interface -LOG: Writing skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json -TRACE: Interface for skfda.representation.basis._fdatabasis has changed -LOG: Cached module skfda.representation.basis._fdatabasis has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) -LOG: Writing skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._function_transformers has changed -LOG: Cached module skfda.preprocessing.feature_construction._function_transformers has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._fda_feature_union has changed -LOG: Cached module skfda.preprocessing.feature_construction._fda_feature_union has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._evaluation_trasformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._evaluation_trasformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation._functional_data skfda.representation.extrapolation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._evaluation_trasformer has changed -LOG: Cached module skfda.preprocessing.feature_construction._evaluation_trasformer has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as inherently stale with stale deps (__future__ builtins skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._coefficients_transformer has changed -LOG: Cached module skfda.preprocessing.feature_construction._coefficients_transformer has changed interface -TRACE: Priorities for skfda.ml.regression._coefficients: -LOG: Processing SCC singleton (skfda.ml.regression._coefficients) as inherently stale with stale deps (__future__ abc builtins functools numpy skfda.misc._math skfda.representation.basis typing) -LOG: Writing skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json -TRACE: Interface for skfda.ml.regression._coefficients has changed -LOG: Cached module skfda.ml.regression._coefficients has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction._per_class_transformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._per_class_transformer) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation.basis skfda.representation.grid skfda.typing._numpy typing warnings) -LOG: Writing skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._per_class_transformer has changed -LOG: Cached module skfda.preprocessing.feature_construction._per_class_transformer has changed interface -TRACE: Priorities for skfda.ml.regression._kernel_regression: -LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as inherently stale with stale deps (__future__ builtins numpy skfda.misc.hat_matrix skfda.misc.metrics skfda.representation._functional_data skfda.typing._metric typing) -LOG: Writing skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json -TRACE: Interface for skfda.ml.regression._kernel_regression has changed -LOG: Cached module skfda.ml.regression._kernel_regression has changed interface -TRACE: Priorities for skfda.ml.regression._historical_linear_model: -LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as inherently stale with stale deps (__future__ builtins math numpy skfda._utils skfda.representation skfda.representation.basis typing) -LOG: Writing skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json -TRACE: Interface for skfda.ml.regression._historical_linear_model has changed -LOG: Cached module skfda.ml.regression._historical_linear_model has changed interface -TRACE: Priorities for skfda.ml.clustering._kmeans: -LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as inherently stale with stale deps (__future__ abc builtins numpy skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._metric skfda.typing._numpy typing warnings) -LOG: Writing skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json -TRACE: Interface for skfda.ml.clustering._kmeans has changed -LOG: Cached module skfda.ml.clustering._kmeans has changed interface -TRACE: Priorities for skfda.ml.clustering._hierarchical: -LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as inherently stale with stale deps (__future__ builtins enum numpy skfda.misc.metrics skfda.misc.metrics._parse skfda.representation skfda.typing._metric typing typing_extensions) -LOG: Writing skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json -TRACE: Interface for skfda.ml.clustering._hierarchical has changed -LOG: Cached module skfda.ml.clustering._hierarchical has changed interface -TRACE: Priorities for skfda.ml.classification._parameterized_functional_qda: -LOG: Processing SCC singleton (skfda.ml.classification._parameterized_functional_qda) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json -TRACE: Interface for skfda.ml.classification._parameterized_functional_qda has changed -LOG: Cached module skfda.ml.classification._parameterized_functional_qda has changed interface -TRACE: Priorities for skfda.ml.classification._logistic_regression: -LOG: Processing SCC singleton (skfda.ml.classification._logistic_regression) as inherently stale with stale deps (__future__ builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json 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has changed interface -TRACE: Priorities for skfda.ml._neighbors_base: -LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml._neighbors_base /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py skfda/ml/_neighbors_base.meta.json skfda/ml/_neighbors_base.data.json -TRACE: Interface for skfda.ml._neighbors_base has changed -LOG: Cached module skfda.ml._neighbors_base has changed interface -TRACE: Priorities for skfda.exploratory.outliers._outliergram: -LOG: Processing SCC singleton (skfda.exploratory.outliers._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda._utils._sklearn_adapter skfda.exploratory.depth._depth skfda.exploratory.stats skfda.representation skfda.typing._numpy) -LOG: Writing skfda.exploratory.outliers._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py skfda/exploratory/outliers/_outliergram.meta.json skfda/exploratory/outliers/_outliergram.data.json -TRACE: Interface for skfda.exploratory.outliers._outliergram has changed -LOG: Cached module skfda.exploratory.outliers._outliergram has changed interface -TRACE: Priorities for skfda.exploratory.outliers._envelopes: -LOG: Processing SCC singleton (skfda.exploratory.outliers._envelopes) as inherently stale with stale deps (__future__ builtins math numpy skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.outliers._envelopes /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py skfda/exploratory/outliers/_envelopes.meta.json skfda/exploratory/outliers/_envelopes.data.json -TRACE: Interface for skfda.exploratory.outliers._envelopes has changed -LOG: Cached module skfda.exploratory.outliers._envelopes has changed interface -TRACE: Priorities for skfda.exploratory.visualization.fpca: -LOG: Processing SCC singleton (skfda.exploratory.visualization.fpca) as inherently stale with stale deps (__future__ builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.representation typing warnings) -LOG: Writing skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json -TRACE: Interface for skfda.exploratory.visualization.fpca has changed -LOG: Cached module skfda.exploratory.visualization.fpca has changed interface -TRACE: Priorities for skfda.exploratory.visualization._parametric_plot: -LOG: Processing SCC singleton (skfda.exploratory.visualization._parametric_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils 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skfda/exploratory/visualization/_multiple_display.meta.json skfda/exploratory/visualization/_multiple_display.data.json -TRACE: Interface for skfda.exploratory.visualization._multiple_display has changed -LOG: Cached module skfda.exploratory.visualization._multiple_display has changed interface -TRACE: Priorities for skfda.exploratory.visualization._ddplot: -LOG: Processing SCC singleton (skfda.exploratory.visualization._ddplot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.depth.multivariate skfda.exploratory.visualization._baseplot skfda.representation._functional_data skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json -TRACE: Interface for skfda.exploratory.visualization._ddplot has changed -LOG: Cached module skfda.exploratory.visualization._ddplot has changed interface -TRACE: Priorities for skfda.datasets._real_datasets: -LOG: Processing SCC singleton (skfda.datasets._real_datasets) as inherently stale with stale deps (builtins numpy rdata skfda.representation typing typing_extensions warnings) -LOG: Writing skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json -TRACE: Interface for skfda.datasets._real_datasets has changed -LOG: Cached module skfda.datasets._real_datasets has changed interface -TRACE: Priorities for skfda.inference.hotelling._hotelling: -LOG: Processing SCC singleton (skfda.inference.hotelling._hotelling) as inherently stale with stale deps (__future__ builtins itertools numpy skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.inference.hotelling._hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/_hotelling.py skfda/inference/hotelling/_hotelling.meta.json skfda/inference/hotelling/_hotelling.data.json -TRACE: Interface for skfda.inference.hotelling._hotelling has changed -LOG: Cached module skfda.inference.hotelling._hotelling has changed interface -TRACE: Priorities for skfda.preprocessing.feature_construction: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction) as inherently stale with stale deps (builtins skfda.preprocessing.feature_construction._coefficients_transformer skfda.preprocessing.feature_construction._evaluation_trasformer skfda.preprocessing.feature_construction._fda_feature_union skfda.preprocessing.feature_construction._function_transformers skfda.preprocessing.feature_construction._per_class_transformer typing) -LOG: Writing skfda.preprocessing.feature_construction /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py skfda/preprocessing/feature_construction/__init__.meta.json skfda/preprocessing/feature_construction/__init__.data.json -TRACE: Interface for skfda.preprocessing.feature_construction has changed -LOG: Cached module skfda.preprocessing.feature_construction has changed interface -TRACE: Priorities for skfda.ml.regression._neighbors_regression: -LOG: Processing SCC singleton (skfda.ml.regression._neighbors_regression) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json -TRACE: Interface for skfda.ml.regression._neighbors_regression has changed -LOG: Cached module skfda.ml.regression._neighbors_regression has changed interface -TRACE: Priorities for skfda.ml.regression._linear_regression: -LOG: Processing SCC singleton (skfda.ml.regression._linear_regression) as inherently stale with stale deps (__future__ builtins itertools numpy skfda.misc.lstsq skfda.misc.regularization skfda.ml.regression._coefficients skfda.representation skfda.representation.basis typing warnings) -LOG: Writing skfda.ml.regression._linear_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py skfda/ml/regression/_linear_regression.meta.json skfda/ml/regression/_linear_regression.data.json -TRACE: Interface for skfda.ml.regression._linear_regression has changed -LOG: Cached module skfda.ml.regression._linear_regression has changed interface -TRACE: Priorities for skfda.ml.clustering._neighbors_clustering: -LOG: Processing SCC singleton (skfda.ml.clustering._neighbors_clustering) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml.clustering._neighbors_clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py skfda/ml/clustering/_neighbors_clustering.meta.json skfda/ml/clustering/_neighbors_clustering.data.json -TRACE: Interface for skfda.ml.clustering._neighbors_clustering has changed -LOG: Cached module skfda.ml.clustering._neighbors_clustering has changed interface -TRACE: Priorities for skfda.ml.classification._neighbors_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json -TRACE: Interface for skfda.ml.classification._neighbors_classifiers has changed -LOG: Cached module skfda.ml.classification._neighbors_classifiers has changed interface -TRACE: Priorities for skfda.ml.classification._depth_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._depth_classifiers) as inherently stale with stale deps (__future__ builtins collections contextlib itertools numpy skfda._utils skfda.exploratory.depth skfda.preprocessing.feature_construction._per_class_transformer skfda.representation.grid skfda.typing._numpy typing) -LOG: Writing skfda.ml.classification._depth_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py skfda/ml/classification/_depth_classifiers.meta.json skfda/ml/classification/_depth_classifiers.data.json -TRACE: Interface for skfda.ml.classification._depth_classifiers has changed -LOG: Cached module skfda.ml.classification._depth_classifiers has changed interface -TRACE: Priorities for skfda.exploratory.outliers.neighbors_outlier: -LOG: Processing SCC singleton (skfda.exploratory.outliers.neighbors_outlier) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json -TRACE: Interface for skfda.exploratory.outliers.neighbors_outlier has changed -LOG: Cached module skfda.exploratory.outliers.neighbors_outlier has changed interface -TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:25 -TRACE: Priorities for skfda.misc.covariances: skfda.datasets:20 -TRACE: Priorities for skfda.datasets._samples_generators: skfda.misc.covariances:10 -LOG: Processing SCC of size 3 (skfda.datasets skfda.misc.covariances skfda.datasets._samples_generators) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc skfda.misc.validation skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) -LOG: Writing skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json -TRACE: Interface for skfda.datasets has changed -LOG: Cached module skfda.datasets has changed interface -LOG: Writing skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json -TRACE: Interface for skfda.misc.covariances has changed -LOG: Cached module skfda.misc.covariances has changed interface -LOG: Writing skfda.datasets._samples_generators /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py skfda/datasets/_samples_generators.meta.json skfda/datasets/_samples_generators.data.json -TRACE: Interface for skfda.datasets._samples_generators has changed -LOG: Cached module skfda.datasets._samples_generators has changed interface -TRACE: Priorities for skfda.inference.hotelling: -LOG: Processing SCC singleton (skfda.inference.hotelling) as inherently stale with stale deps (builtins skfda.inference.hotelling._hotelling) -LOG: Writing skfda.inference.hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/__init__.py skfda/inference/hotelling/__init__.meta.json skfda/inference/hotelling/__init__.data.json -TRACE: Interface for skfda.inference.hotelling has changed -LOG: Cached module skfda.inference.hotelling has changed interface -TRACE: Priorities for skfda.ml.regression: -LOG: Processing SCC singleton (skfda.ml.regression) as inherently stale with stale deps (builtins skfda.ml.regression._historical_linear_model skfda.ml.regression._kernel_regression skfda.ml.regression._linear_regression skfda.ml.regression._neighbors_regression) -LOG: Writing skfda.ml.regression /home/carlos/git/scikit-fda/skfda/ml/regression/__init__.py skfda/ml/regression/__init__.meta.json skfda/ml/regression/__init__.data.json -TRACE: Interface for skfda.ml.regression has changed -LOG: Cached module skfda.ml.regression has changed interface -TRACE: Priorities for skfda.ml.clustering: -LOG: Processing SCC singleton (skfda.ml.clustering) as inherently stale with stale deps (builtins skfda.ml.clustering._hierarchical skfda.ml.clustering._kmeans skfda.ml.clustering._neighbors_clustering) -LOG: Writing skfda.ml.clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py skfda/ml/clustering/__init__.meta.json skfda/ml/clustering/__init__.data.json -TRACE: Interface for skfda.ml.clustering has changed -LOG: Cached module skfda.ml.clustering has changed interface -TRACE: Priorities for skfda.ml.classification: -LOG: Processing SCC singleton (skfda.ml.classification) as inherently stale with stale deps (builtins skfda.ml.classification._centroid_classifiers skfda.ml.classification._depth_classifiers skfda.ml.classification._logistic_regression skfda.ml.classification._neighbors_classifiers skfda.ml.classification._parameterized_functional_qda) -LOG: Writing skfda.ml.classification /home/carlos/git/scikit-fda/skfda/ml/classification/__init__.py skfda/ml/classification/__init__.meta.json skfda/ml/classification/__init__.data.json -TRACE: Interface for skfda.ml.classification has changed -LOG: Cached module skfda.ml.classification has changed interface -TRACE: Priorities for skfda.exploratory.outliers: skfda.exploratory.outliers._boxplot:25 skfda.exploratory.outliers._directional_outlyingness:25 -TRACE: Priorities for skfda.exploratory.outliers._directional_outlyingness: skfda.exploratory.outliers:20 -TRACE: Priorities for skfda.exploratory.outliers._boxplot: skfda.exploratory.outliers:20 -LOG: Processing SCC of size 3 (skfda.exploratory.outliers skfda.exploratory.outliers._directional_outlyingness skfda.exploratory.outliers._boxplot) as inherently stale with stale deps (__future__ builtins dataclasses numpy numpy.linalg skfda._utils._sklearn_adapter skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers._directional_outlyingness_experiment_results skfda.exploratory.outliers._envelopes skfda.exploratory.outliers._outliergram skfda.exploratory.outliers.neighbors_outlier skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.outliers /home/carlos/git/scikit-fda/skfda/exploratory/outliers/__init__.py skfda/exploratory/outliers/__init__.meta.json skfda/exploratory/outliers/__init__.data.json -TRACE: Interface for skfda.exploratory.outliers has changed -LOG: Cached module skfda.exploratory.outliers has changed interface -LOG: Writing skfda.exploratory.outliers._directional_outlyingness /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py skfda/exploratory/outliers/_directional_outlyingness.meta.json skfda/exploratory/outliers/_directional_outlyingness.data.json -TRACE: Interface for skfda.exploratory.outliers._directional_outlyingness has changed -LOG: Cached module skfda.exploratory.outliers._directional_outlyingness has changed interface -LOG: Writing skfda.exploratory.outliers._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_boxplot.py skfda/exploratory/outliers/_boxplot.meta.json skfda/exploratory/outliers/_boxplot.data.json -TRACE: Interface for skfda.exploratory.outliers._boxplot has changed -LOG: Cached module skfda.exploratory.outliers._boxplot has changed interface -TRACE: Priorities for skfda.inference.anova._anova_oneway: -LOG: Processing SCC singleton (skfda.inference.anova._anova_oneway) as inherently stale with stale deps (__future__ builtins numpy skfda.datasets skfda.misc.metrics skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.inference.anova._anova_oneway /home/carlos/git/scikit-fda/skfda/inference/anova/_anova_oneway.py skfda/inference/anova/_anova_oneway.meta.json skfda/inference/anova/_anova_oneway.data.json -TRACE: Interface for skfda.inference.anova._anova_oneway has changed -LOG: Cached module skfda.inference.anova._anova_oneway has changed interface -TRACE: Priorities for skfda.ml: -LOG: Processing SCC singleton (skfda.ml) as inherently stale with stale deps (builtins skfda.ml.classification skfda.ml.clustering skfda.ml.regression) -LOG: Writing skfda.ml /home/carlos/git/scikit-fda/skfda/ml/__init__.py skfda/ml/__init__.meta.json skfda/ml/__init__.data.json -TRACE: Interface for skfda.ml has changed -LOG: Cached module skfda.ml has changed interface -TRACE: Priorities for 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skfda.exploratory.visualization._baseplot skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.visualization._magnitude_shape_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_magnitude_shape_plot.py skfda/exploratory/visualization/_magnitude_shape_plot.meta.json skfda/exploratory/visualization/_magnitude_shape_plot.data.json -TRACE: Interface for skfda.exploratory.visualization._magnitude_shape_plot has changed -LOG: Cached module skfda.exploratory.visualization._magnitude_shape_plot has changed interface -TRACE: Priorities for skfda.exploratory.visualization._boxplot: -LOG: Processing SCC singleton (skfda.exploratory.visualization._boxplot) as inherently stale with stale deps (__future__ abc builtins math numpy skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._envelopes skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.visualization._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_boxplot.py skfda/exploratory/visualization/_boxplot.meta.json skfda/exploratory/visualization/_boxplot.data.json -TRACE: Interface for skfda.exploratory.visualization._boxplot has changed -LOG: Cached module skfda.exploratory.visualization._boxplot has changed interface -TRACE: Priorities for skfda.inference.anova: -LOG: Processing SCC singleton (skfda.inference.anova) as inherently stale with stale deps (builtins skfda.inference.anova._anova_oneway) -LOG: Writing skfda.inference.anova /home/carlos/git/scikit-fda/skfda/inference/anova/__init__.py skfda/inference/anova/__init__.meta.json skfda/inference/anova/__init__.data.json -TRACE: Interface for skfda.inference.anova has changed -LOG: Cached module skfda.inference.anova has changed interface -TRACE: Priorities for skfda.exploratory.visualization: -LOG: Processing SCC singleton (skfda.exploratory.visualization) as inherently stale with stale deps (builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.exploratory.visualization._ddplot skfda.exploratory.visualization._magnitude_shape_plot skfda.exploratory.visualization._multiple_display skfda.exploratory.visualization._outliergram skfda.exploratory.visualization._parametric_plot skfda.exploratory.visualization.fpca typing) -LOG: Writing skfda.exploratory.visualization /home/carlos/git/scikit-fda/skfda/exploratory/visualization/__init__.py skfda/exploratory/visualization/__init__.meta.json skfda/exploratory/visualization/__init__.data.json -TRACE: Interface for skfda.exploratory.visualization has changed -LOG: Cached module skfda.exploratory.visualization has changed interface -LOG: No fresh SCCs left in queue -LOG: Build finished in 56.565 seconds with 440 modules, and 0 errors diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index a85986e95..2ff3046a3 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1,9 +1,10 @@ +from __future__ import annotations + import warnings -from typing import Any, Mapping, Optional, Tuple, Union, overload +from typing import Any, Mapping, Tuple, overload import numpy as np import pandas as pd -from numpy import ndarray from pandas import DataFrame, Series from sklearn.utils import Bunch from typing_extensions import Literal @@ -11,6 +12,7 @@ import rdata from ..representation import FDataGrid +from ..typing._numpy import NDArrayFloat, NDArrayInt def _get_skdatasets_repositories() -> Any: @@ -25,7 +27,7 @@ def _get_skdatasets_repositories() -> Any: def fdata_constructor( obj: Any, - attrs: Mapping[Union[str, bytes], Any], + attrs: Mapping[str | bytes, Any], ) -> FDataGrid: """ Construct a :func:`FDataGrid` objet from a R `fdata` object. @@ -49,8 +51,8 @@ def fdata_constructor( def functional_constructor( obj: Any, - attrs: Mapping[Union[str, bytes], Any], -) -> FDataGrid: + attrs: Mapping[str | bytes, Any], +) -> Tuple[FDataGrid, NDArrayInt]: """ Construct a :func:`FDataGrid` objet from a R `functional` object. @@ -95,7 +97,7 @@ def fetch_cran( name: str, package_name: str, *, - converter: Optional[rdata.conversion.Converter] = None, + converter: rdata.conversion.Converter | None = None, **kwargs: Any, ) -> Any: """ @@ -131,7 +133,7 @@ def fetch_cran( ) -def _ucr_to_fdatagrid(name: str, data: np.ndarray) -> FDataGrid: +def _ucr_to_fdatagrid(name: str, data: NDArrayFloat) -> FDataGrid: if data.dtype == np.object_: data = np.array(data.tolist()) @@ -162,7 +164,7 @@ def fetch_ucr( *, return_X_y: Literal[True], **kwargs: Any, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -171,7 +173,7 @@ def fetch_ucr( *, return_X_y: bool = False, **kwargs: Any, -) -> Union[Bunch, Tuple[FDataGrid, ndarray]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt]: """ Fetch a dataset from the UCR. @@ -307,7 +309,7 @@ def fetch_phoneme( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -324,7 +326,7 @@ def fetch_phoneme( *, return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, Series]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, Series]: """ Load the phoneme dataset. @@ -417,7 +419,7 @@ def fetch_growth( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -433,7 +435,7 @@ def fetch_growth( def fetch_growth( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, Series]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, Series]: """ Load the Berkeley Growth Study dataset. @@ -543,7 +545,7 @@ def fetch_tecator( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayFloat]: pass @@ -559,7 +561,7 @@ def fetch_tecator( def fetch_tecator( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, DataFrame]]: +) -> Bunch | Tuple[FDataGrid, NDArrayFloat] | Tuple[DataFrame, DataFrame]: """ Load the Tecator dataset. @@ -655,7 +657,7 @@ def fetch_medflies( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -671,7 +673,7 @@ def fetch_medflies( def fetch_medflies( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, Series]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, Series]: """ Load the Medflies dataset. @@ -751,7 +753,7 @@ def fetch_weather( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -767,7 +769,7 @@ def fetch_weather( def fetch_weather( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, Series]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, Series]: """ Load the Canadian Weather dataset. @@ -923,7 +925,7 @@ def fetch_aemet( def fetch_aemet( # noqa: WPS210 return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, None], Tuple[DataFrame, None]]: +) -> Bunch | Tuple[FDataGrid, None] | Tuple[DataFrame, None]: """ Load the Spanish Weather dataset. @@ -1048,7 +1050,7 @@ def fetch_octane( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -1064,7 +1066,7 @@ def fetch_octane( def fetch_octane( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, Series]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, Series]: """Load near infrared spectra of gasoline samples. This function fetchs the octane dataset from the R package 'mrfDepth' @@ -1180,7 +1182,7 @@ def fetch_gait( def fetch_gait( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, None], Tuple[DataFrame, None]]: +) -> Bunch | Tuple[FDataGrid, None] | Tuple[DataFrame, None]: """ Load the GAIT dataset. @@ -1195,8 +1197,8 @@ def fetch_gait( data_matrix = np.asarray(data) data_matrix = np.transpose(data_matrix, axes=(1, 0, 2)) - grid_points = np.asarray(data.coords.get('dim_0'), np.float64) - sample_names = np.asarray(data.coords.get('dim_1')) + grid_points = np.asarray(data.coords.get('dim_0'), dtype=np.float64) + sample_names = data.coords.get('dim_1') feature_name = 'gait' curves = FDataGrid( @@ -1276,7 +1278,7 @@ def fetch_handwriting( def fetch_handwriting( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, None], Tuple[DataFrame, None]]: +) -> Bunch | Tuple[FDataGrid, None] | Tuple[DataFrame, None]: """ Load the HANDWRIT dataset. @@ -1291,8 +1293,8 @@ def fetch_handwriting( data_matrix = np.asarray(data) data_matrix = np.transpose(data_matrix, axes=(1, 0, 2)) - grid_points = np.asarray(data.coords.get('dim_0'), np.float64) - sample_names = np.asarray(data.coords.get('dim_1')) + grid_points = np.asarray(data.coords.get('dim_0'), dtype=np.float64) + sample_names = data.coords.get('dim_1') feature_name = 'handwrit' curves = FDataGrid( @@ -1362,7 +1364,7 @@ def fetch_nox( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -1378,7 +1380,7 @@ def fetch_nox( def fetch_nox( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, DataFrame]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, DataFrame]: """ Load the NOx dataset. @@ -1471,7 +1473,7 @@ def fetch_mco( *, return_X_y: Literal[True], as_frame: Literal[False] = False, -) -> Tuple[FDataGrid, ndarray]: +) -> Tuple[FDataGrid, NDArrayInt]: pass @@ -1487,7 +1489,7 @@ def fetch_mco( def fetch_mco( return_X_y: bool = False, as_frame: bool = False, -) -> Union[Bunch, Tuple[FDataGrid, ndarray], Tuple[DataFrame, DataFrame]]: +) -> Bunch | Tuple[FDataGrid, NDArrayInt] | Tuple[DataFrame, DataFrame]: """ Load the mithochondiral calcium overload (MCO) dataset. diff --git a/skfda/datasets/_samples_generators.py b/skfda/datasets/_samples_generators.py index fda6c47cb..47b8fbee7 100644 --- a/skfda/datasets/_samples_generators.py +++ b/skfda/datasets/_samples_generators.py @@ -1,12 +1,14 @@ +from __future__ import annotations + import itertools -from typing import Callable, Optional, Sequence, Union +from typing import Callable, Sequence, Union import numpy as np import scipy.integrate from scipy.stats import multivariate_normal from .._utils import _cartesian_product, _to_grid_points, normalize_warping -from ..misc import covariances +from ..misc.covariances import Brownian, CovarianceLike, _execute_covariance from ..misc.validation import validate_random_state from ..representation import FDataGrid from ..representation.interpolation import SplineInterpolation @@ -16,17 +18,16 @@ MeanCallable = Callable[[np.ndarray], np.ndarray] CovarianceCallable = Callable[[np.ndarray, np.ndarray], np.ndarray] -MeanLike = Union[float, np.ndarray, MeanCallable] -CovarianceLike = Union[None, np.ndarray, CovarianceCallable] +MeanLike = Union[float, NDArrayFloat, MeanCallable] def make_gaussian( n_samples: int = 100, *, grid_points: GridPointsLike, - domain_range: Optional[DomainRangeLike] = None, + domain_range: DomainRangeLike | None = None, mean: MeanLike = 0, - cov: CovarianceLike = None, + cov: CovarianceLike | None = None, noise: float = 0, random_state: RandomStateLike = None, ) -> FDataGrid: @@ -60,14 +61,17 @@ def make_gaussian( random_state = validate_random_state(random_state) if cov is None: - cov = covariances.Brownian() + cov = Brownian() grid_points = _to_grid_points(grid_points) input_points = _cartesian_product(grid_points) - covariance = covariances._execute_covariance( - cov, input_points, input_points) + covariance = _execute_covariance( + cov, + input_points, + input_points, + ) if noise: covariance += np.eye(len(covariance)) * noise ** 2 @@ -102,7 +106,7 @@ def make_gaussian_process( start: float = 0, stop: float = 1, mean: MeanLike = 0, - cov: CovarianceLike = None, + cov: CovarianceLike | None = None, noise: float = 0, random_state: RandomStateLike = None, ) -> FDataGrid: @@ -220,7 +224,7 @@ def make_multimodal_landmarks( stop: float = 1, std: float = 0.05, random_state: RandomStateLike = None, -) -> np.ndarray: +) -> NDArrayFloat: """Generate landmarks points. Used by :func:`make_multimodal_samples` to generate the location of the @@ -281,7 +285,7 @@ def make_multimodal_samples( std: float = 0.05, mode_std: float = 0.02, noise: float = 0, - modes_location: Optional[Union[Sequence[float], NDArrayFloat]] = None, + modes_location: Sequence[float] | NDArrayFloat | None = None, random_state: RandomStateLike = None, ) -> FDataGrid: r""" From c51ece29eb7c3f88bd2881e44e8702a5dfae35e9 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 2 Sep 2022 19:09:47 +0200 Subject: [PATCH 276/400] Type classification module. --- skfda/datasets/_real_datasets.py | 8 +- skfda/ml/__init__.py | 12 ++- skfda/ml/classification/__init__.py | 54 ++++++++--- .../classification/_centroid_classifiers.py | 39 ++++---- skfda/ml/classification/_depth_classifiers.py | 97 +++++++++++-------- .../_parameterized_functional_qda.py | 14 +-- 6 files changed, 144 insertions(+), 80 deletions(-) diff --git a/skfda/datasets/_real_datasets.py b/skfda/datasets/_real_datasets.py index 2ff3046a3..df5f8c34e 100644 --- a/skfda/datasets/_real_datasets.py +++ b/skfda/datasets/_real_datasets.py @@ -1198,7 +1198,9 @@ def fetch_gait( data_matrix = np.asarray(data) data_matrix = np.transpose(data_matrix, axes=(1, 0, 2)) grid_points = np.asarray(data.coords.get('dim_0'), dtype=np.float64) - sample_names = data.coords.get('dim_1') + sample_names = list( + np.asarray(data.coords.get('dim_1'), dtype=np.str_), + ) feature_name = 'gait' curves = FDataGrid( @@ -1294,7 +1296,9 @@ def fetch_handwriting( data_matrix = np.asarray(data) data_matrix = np.transpose(data_matrix, axes=(1, 0, 2)) grid_points = np.asarray(data.coords.get('dim_0'), dtype=np.float64) - sample_names = data.coords.get('dim_1') + sample_names = list( + np.asarray(data.coords.get('dim_1'), dtype=np.str_), + ) feature_name = 'handwrit' curves = FDataGrid( diff --git a/skfda/ml/__init__.py b/skfda/ml/__init__.py index e0cea54ed..a58cb4115 100644 --- a/skfda/ml/__init__.py +++ b/skfda/ml/__init__.py @@ -1 +1,11 @@ -from . import classification, clustering, regression +"""Machine learning methods for functional data.""" +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submodules=[ + "classification", + "clustering", + "regression", + ], +) diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index ac822f9c9..28ab4d7e7 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -1,13 +1,45 @@ """Classification.""" -from ._centroid_classifiers import DTMClassifier, NearestCentroid -from ._depth_classifiers import ( - DDClassifier, - DDGClassifier, - MaximumDepthClassifier, + +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_centroid_classifiers": [ + "DTMClassifier", + "NearestCentroid", + ], + "_depth_classifiers": [ + "DDClassifier", + "DDGClassifier", + "MaximumDepthClassifier", + ], + "_logistic_regression": ["LogisticRegression"], + "_neighbors_classifiers": [ + "KNeighborsClassifier", + "RadiusNeighborsClassifier", + ], + "_parameterized_functional_qda": ["ParameterizedFunctionalQDA"], + }, ) -from ._logistic_regression import LogisticRegression -from ._neighbors_classifiers import ( - KNeighborsClassifier, - RadiusNeighborsClassifier, -) -from ._parameterized_functional_qda import ParameterizedFunctionalQDA + +if TYPE_CHECKING: + from ._centroid_classifiers import ( + DTMClassifier as DTMClassifier, + NearestCentroid as NearestCentroid, + ) + from ._depth_classifiers import ( + DDClassifier as DDClassifier, + DDGClassifier as DDGClassifier, + MaximumDepthClassifier as MaximumDepthClassifier, + ) + from ._logistic_regression import LogisticRegression as LogisticRegression + from ._neighbors_classifiers import ( + KNeighborsClassifier as KNeighborsClassifier, + RadiusNeighborsClassifier as RadiusNeighborsClassifier, + ) + from ._parameterized_functional_qda import ( + ParameterizedFunctionalQDA as ParameterizedFunctionalQDA, + ) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 79ec3ecaf..9f07c93ac 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -1,12 +1,12 @@ """Centroid-based models for supervised classification.""" from __future__ import annotations -from typing import Callable, Generic, Optional, TypeVar +from typing import Callable, TypeVar -from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes +from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin from ...exploratory.depth import Depth, ModifiedBandDepth from ...exploratory.stats import mean, trim_mean from ...misc.metrics import PairwiseMetric, l2_distance @@ -15,15 +15,15 @@ from ...typing._metric import Metric from ...typing._numpy import NDArrayInt -T = TypeVar("T", bound=FData) +Input = TypeVar("Input", bound=FData) class NearestCentroid( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore - Generic[T], + BaseEstimator, + ClassifierMixin[Input, NDArrayInt], ): - """Nearest centroid classifier for functional data. + """ + Nearest centroid classifier for functional data. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid. @@ -70,13 +70,13 @@ class and return a :class:`FData` object with only one sample def __init__( self, - metric: Metric[T] = l2_distance, - centroid: Callable[[T], T] = mean, + metric: Metric[Input] = l2_distance, + centroid: Callable[[Input], Input] = mean, ): self.metric = metric self.centroid = centroid - def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: + def fit(self, X: Input, y: NDArrayInt) -> NearestCentroid[Input]: """Fit the model using X as training data and y as target values. Args: @@ -101,7 +101,7 @@ def fit(self, X: T, y: NDArrayInt) -> NearestCentroid[T]: return self - def predict(self, X: T) -> NDArrayInt: + def predict(self, X: Input) -> NDArrayInt: """Predict the class labels for the provided data. Args: @@ -113,14 +113,15 @@ def predict(self, X: T) -> NDArrayInt: """ check_is_fitted(self) - return self._classes[PairwiseMetric(self.metric)( - X, - self.centroids_, - ).argmin(axis=1) + return self._classes[ # type: ignore[no-any-return] + PairwiseMetric(self.metric)( + X, + self.centroids_, + ).argmin(axis=1) ] -class DTMClassifier(NearestCentroid[T]): +class DTMClassifier(NearestCentroid[Input]): """Distance to trimmed means (DTM) classification. Test samples are classified to the class that minimizes the distance of @@ -181,8 +182,8 @@ class DTMClassifier(NearestCentroid[T]): def __init__( self, proportiontocut: float, - depth_method: Optional[Depth[T]] = None, - metric: Metric[T] = l2_distance, + depth_method: Depth[Input] | None = None, + metric: Metric[Input] = l2_distance, ) -> None: self.proportiontocut = proportiontocut self.depth_method = depth_method @@ -192,7 +193,7 @@ def __init__( centroid=self._centroid, ) - def _centroid(self, fdatagrid: T) -> T: + def _centroid(self, fdatagrid: Input) -> Input: if self.depth_method is None: self.depth_method = ModifiedBandDepth() diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index 63747c728..419c73b98 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -5,8 +5,8 @@ from contextlib import suppress from itertools import combinations from typing import ( - Any, - Generic, + DefaultDict, + Dict, Mapping, Optional, Sequence, @@ -18,40 +18,41 @@ import numpy as np import pandas as pd from scipy.interpolate import lagrange -from sklearn.base import BaseEstimator, ClassifierMixin, clone +from sklearn.base import clone from sklearn.metrics import accuracy_score from sklearn.pipeline import FeatureUnion, make_pipeline from sklearn.utils.validation import check_is_fitted as sklearn_check_is_fitted from ..._utils import _classifier_get_classes +from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin from ...exploratory.depth import Depth, ModifiedBandDepth from ...preprocessing.feature_construction._per_class_transformer import ( PerClassTransformer, ) -from ...representation.grid import FData -from ...typing._numpy import NDArrayFloat, NDArrayInt +from ...representation import FData +from ...typing._numpy import NDArrayFloat, NDArrayInt, NDArrayStr -T = TypeVar("T", bound=FData) +Input = TypeVar("Input", bound=FData) def _classifier_get_depth_methods( - classes: NDArrayInt, - X: T, + classes: NDArrayInt | NDArrayStr, + X: Input, y_ind: NDArrayInt, - depth_methods: Sequence[Depth[T]], -) -> Sequence[Depth[T]]: + depth_methods: Sequence[Depth[Input]], +) -> Sequence[Depth[Input]]: return [ clone(depth_method).fit(X[y_ind == cur_class]) - for cur_class in range(classes.size) + for cur_class in range(len(classes)) for depth_method in depth_methods ] def _classifier_fit_depth_methods( - X: T, + X: Input, y: NDArrayInt, - depth_methods: Sequence[Depth[T]], -) -> Tuple[NDArrayInt, Sequence[Depth[T]]]: + depth_methods: Sequence[Depth[Input]], +) -> Tuple[NDArrayStr | NDArrayInt, Sequence[Depth[Input]]]: classes, y_ind = _classifier_get_classes(y) class_depth_methods_ = _classifier_get_depth_methods( @@ -62,9 +63,8 @@ def _classifier_fit_depth_methods( class DDClassifier( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore - Generic[T], + BaseEstimator, + ClassifierMixin[Input, NDArrayInt], ): """Depth-versus-depth (DD) classifer for functional data. @@ -125,12 +125,12 @@ class DDClassifier( def __init__( self, degree: int, - depth_method: Optional[Depth[T]] = None, + depth_method: Optional[Depth[Input]] = None, ) -> None: self.depth_method = depth_method self.degree = degree - def fit(self, X: T, y: NDArrayInt) -> DDClassifier[T]: + def fit(self, X: Input, y: NDArrayInt) -> DDClassifier[Input]: """Fit the model using X as training data and y as target values. Args: @@ -188,7 +188,7 @@ def fit(self, X: T, y: NDArrayInt) -> DDClassifier[T]: return self - def predict(self, X: T) -> NDArrayInt: + def predict(self, X: Input) -> NDArrayInt: """Predict the class labels for the provided data. Args: @@ -207,16 +207,15 @@ def predict(self, X: T) -> NDArrayInt: predicted_values = np.polyval(self.poly_, dd_coordinates[0]) - return self._classes[( + return self._classes[( # type: ignore[no-any-return] dd_coordinates[1] > predicted_values ).astype(int) ] class DDGClassifier( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore - Generic[T], + BaseEstimator, + ClassifierMixin[Input, NDArrayInt], ): r"""Generalized depth-versus-depth (DD) classifer for functional data. @@ -321,13 +320,18 @@ class DDGClassifier( def __init__( # noqa: WPS234 self, *, - multivariate_classifier: ClassifierMixin = None, - depth_method: Union[Depth[T], Sequence[Tuple[str, Depth[T]]], None] = None, + multivariate_classifier: ClassifierMixin[ + NDArrayFloat, + NDArrayInt, + ] | None = None, + depth_method: Depth[Input] | Sequence[ + Tuple[str, Depth[Input]] + ] | None = None, ) -> None: self.multivariate_classifier = multivariate_classifier self.depth_method = depth_method - def get_params(self, deep: bool = True) -> Mapping[str, Any]: + def get_params(self, deep: bool = True) -> Mapping[str, object]: params = BaseEstimator.get_params(self, deep=deep) if deep and isinstance(self.depth_method, Sequence): for name, depth in self.depth_method: @@ -336,10 +340,10 @@ def get_params(self, deep: bool = True) -> Mapping[str, Any]: for key, value in depth_params.items(): params[f"depth_method__{name}__{key}"] = value - return params + return params # type: ignore[no-any-return] # Copied from scikit-learn's _BaseComposition with minor modifications - def _set_params(self, attr, **params): + def _set_params(self, attr: str, **params: object) -> DDGClassifier[Input]: # Ensure strict ordering of parameter setting: # 1. All steps if attr in params: @@ -352,7 +356,10 @@ def _set_params(self, attr, **params): # elements of length 2 with suppress(TypeError): item_names, _ = zip(*items) - item_params = defaultdict(dict) + item_params: DefaultDict[ + str, + Dict[str, object], + ] = defaultdict(dict) for name in list(params.keys()): if name.startswith(f"{attr}__"): key, delim, sub_key = name.partition("__") @@ -374,7 +381,12 @@ def _set_params(self, attr, **params): return self # Copied from scikit-learn's _BaseComposition - def _replace_estimator(self, attr, name, new_val): + def _replace_estimator( + self, + attr: str, + name: str, + new_val: object, + ) -> None: # assumes `name` is a valid estimator name new_estimators = list(getattr(self, attr)) for i, (estimator_name, _) in enumerate(new_estimators): @@ -383,11 +395,14 @@ def _replace_estimator(self, attr, name, new_val): break setattr(self, attr, new_estimators) - def set_params(self, **params: Mapping[str, Any]): + def set_params( + self, + **params: object, + ) -> DDGClassifier[Input]: return self._set_params("depth_method", **params) - def fit(self, X: T, y: NDArrayInt) -> DDGClassifier[T]: + def fit(self, X: Input, y: NDArrayInt) -> DDGClassifier[Input]: """Fit the model using X as training data and y as target values. Args: @@ -420,7 +435,7 @@ def fit(self, X: T, y: NDArrayInt) -> DDGClassifier[T]: return self - def predict(self, X: T) -> NDArrayInt: + def predict(self, X: Input) -> NDArrayInt: """Predict the class labels for the provided data. Args: @@ -430,12 +445,12 @@ def predict(self, X: T) -> NDArrayInt: Array of shape (n_samples) with class labels for each data sample. """ - return self._pipeline.predict(X) + return self._pipeline.predict(X) # type: ignore[no-any-return] class _ArgMaxClassifier( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore + BaseEstimator, + ClassifierMixin[NDArrayFloat, NDArrayInt], ): r"""Arg max classifier for multivariate data. @@ -487,10 +502,12 @@ def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> NDArrayInt: """ if isinstance(X, pd.DataFrame): X = X.to_numpy() - return self._classes[np.argmax(X, axis=1)] + return self._classes[ # type: ignore[no-any-return] + np.argmax(X, axis=1) + ] -class MaximumDepthClassifier(DDGClassifier[T]): +class MaximumDepthClassifier(DDGClassifier[Input]): """Maximum depth classifier for functional data. Test samples are classified to the class where they are deeper. @@ -538,7 +555,7 @@ class MaximumDepthClassifier(DDGClassifier[T]): related classifiers. Scandinavian Journal of Statistics, 32, 327–350. """ - def __init__(self, depth_method: Optional[Depth[T]] = None) -> None: + def __init__(self, depth_method: Depth[Input] | None = None) -> None: super().__init__( multivariate_classifier=_ArgMaxClassifier(), depth_method=depth_method, diff --git a/skfda/ml/classification/_parameterized_functional_qda.py b/skfda/ml/classification/_parameterized_functional_qda.py index e5442f262..580860488 100644 --- a/skfda/ml/classification/_parameterized_functional_qda.py +++ b/skfda/ml/classification/_parameterized_functional_qda.py @@ -6,17 +6,17 @@ from GPy.kern import Kern from GPy.models import GPRegression from scipy.linalg import logm -from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes +from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin from ...representation import FDataGrid from ...typing._numpy import NDArrayFloat, NDArrayInt class ParameterizedFunctionalQDA( - BaseEstimator, # type: ignore - ClassifierMixin, # type: ignore + BaseEstimator, + ClassifierMixin[FDataGrid, NDArrayInt], ): """Parameterized functional quadratic discriminant analysis. @@ -100,7 +100,7 @@ def __init__(self, kernel: Kern, regularizer: float) -> None: self.kernel = kernel self.regularizer = regularizer - def fit(self, X: FDataGrid, y: np.ndarray) -> ParameterizedFunctionalQDA: + def fit(self, X: FDataGrid, y: NDArrayInt) -> ParameterizedFunctionalQDA: """ Fit the model using X as training data and y as target values. @@ -147,12 +147,12 @@ def predict(self, X: FDataGrid) -> NDArrayInt: """ check_is_fitted(self) - return np.argmax( + return np.argmax( # type: ignore[no-any-return] self._calculate_log_likelihood(X.data_matrix), axis=1, ) - def _calculate_priors(self, y: np.ndarray) -> NDArrayFloat: + def _calculate_priors(self, y: NDArrayInt) -> NDArrayFloat: """ Calculate the prior probability of each class. @@ -208,7 +208,7 @@ def _fit_gaussian_process( return np.asarray(kernels), np.asarray(means) - def _calculate_log_likelihood(self, X: np.ndarray) -> NDArrayFloat: + def _calculate_log_likelihood(self, X: NDArrayFloat) -> NDArrayFloat: """ Calculate the log likelihood quadratic discriminant analysis. From 9f7ac3c58d4a56d09015ef8a93847617fd166997 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 2 Sep 2022 20:38:30 +0200 Subject: [PATCH 277/400] Typed clustering. --- skfda/_utils/_sklearn_adapter.py | 13 ++++ skfda/ml/clustering/__init__.py | 23 +++++- skfda/ml/clustering/_hierarchical.py | 28 +++---- skfda/ml/clustering/_kmeans.py | 111 ++++++++++++++------------- 4 files changed, 106 insertions(+), 69 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index ce0347ba9..50e91d4d1 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -146,6 +146,19 @@ def score( # noqa: D102 ) +class ClusterMixin( # noqa: D101 + ABC, + Generic[Input], + sklearn.base.ClusterMixin, # type: ignore[misc] +): + def fit_predict( # noqa: D102 + self, + X: Input, + y: object = None, + ) -> NDArrayInt: + pass + + class RegressorMixin( # noqa: D101 ABC, Generic[Input, TargetPrediction], diff --git a/skfda/ml/clustering/__init__.py b/skfda/ml/clustering/__init__.py index dc414ebf9..ffb431ec4 100644 --- a/skfda/ml/clustering/__init__.py +++ b/skfda/ml/clustering/__init__.py @@ -1,4 +1,21 @@ """Clustering.""" -from ._hierarchical import AgglomerativeClustering -from ._kmeans import BaseKMeans, FuzzyCMeans, KMeans -from ._neighbors_clustering import NearestNeighbors + +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_hierarchical": ["AgglomerativeClustering"], + "_kmeans": ["FuzzyCMeans", "KMeans"], + "_neighbors_clustering": ["NearestNeighbors"], + }, +) + +if TYPE_CHECKING: + from ._hierarchical import ( + AgglomerativeClustering as AgglomerativeClustering, + ) + from ._kmeans import FuzzyCMeans as FuzzyCMeans, KMeans as KMeans + from ._neighbors_clustering import NearestNeighbors as NearestNeighbors diff --git a/skfda/ml/clustering/_hierarchical.py b/skfda/ml/clustering/_hierarchical.py index d5057d6fe..c8f63cf81 100644 --- a/skfda/ml/clustering/_hierarchical.py +++ b/skfda/ml/clustering/_hierarchical.py @@ -1,18 +1,19 @@ from __future__ import annotations import enum -from typing import Callable, Generic, Optional, TypeVar, Union +from typing import Callable, TypeVar, Union import joblib import numpy as np import sklearn.cluster -from sklearn.base import BaseEstimator, ClusterMixin from typing_extensions import Literal +from ..._utils._sklearn_adapter import BaseEstimator, ClusterMixin from ...misc.metrics import PRECOMPUTED, PairwiseMetric, l2_distance from ...misc.metrics._parse import _parse_metric, _PrecomputedTypes from ...representation import FData from ...typing._metric import Metric +from ...typing._numpy import NDArrayInt kk = ["ward", "average", "complete"] @@ -47,9 +48,8 @@ class LinkageCriterion(enum.Enum): class AgglomerativeClustering( # noqa: WPS230 - ClusterMixin, # type: ignore - BaseEstimator, # type: ignore - Generic[MetricElementType], + ClusterMixin[MetricElementType], + BaseEstimator, ): r""" Agglomerative Clustering. @@ -151,14 +151,14 @@ class AgglomerativeClustering( # noqa: WPS230 def __init__( self, - n_clusters: Optional[int] = 2, + n_clusters: int | None = 2, *, metric: MetricOrPrecomputed[MetricElementType] = l2_distance, - memory: Union[str, joblib.Memory, None] = None, + memory: str | joblib.Memory | None = None, connectivity: Connectivity[MetricElementType] = None, - compute_full_tree: Union[Literal['auto'], bool] = 'auto', + compute_full_tree: Literal['auto'] | bool = 'auto', linkage: LinkageCriterionLike, - distance_threshold: Optional[float] = None, + distance_threshold: float | None = None, ) -> None: self.n_clusters = n_clusters self.metric = metric @@ -183,12 +183,12 @@ def _init_estimator(self) -> None: def _copy_attrs(self) -> None: self.n_clusters_: int = self._estimator.n_clusters_ - self.labels_: np.ndarray = self._estimator.labels_ + self.labels_: NDArrayInt = self._estimator.labels_ self.n_leaves_: int = self._estimator.n_leaves_ self.n_connected_components_: int = ( self._estimator.n_connected_components_ ) - self.children_: np.ndarray = self._estimator.children_ + self.children_: NDArrayInt = self._estimator.children_ def fit( # noqa: D102 self, @@ -212,8 +212,8 @@ def fit( # noqa: D102 def fit_predict( # noqa: D102 self, X: MetricElementType, - y: None = None, - ) -> np.ndarray: + y: object = None, + ) -> NDArrayInt: self._init_estimator() @@ -226,4 +226,4 @@ def fit_predict( # noqa: D102 self._copy_attrs() - return predicted + return predicted # type: ignore[no-any-return] diff --git a/skfda/ml/clustering/_kmeans.py b/skfda/ml/clustering/_kmeans.py index fa56c4ed7..166feffc6 100644 --- a/skfda/ml/clustering/_kmeans.py +++ b/skfda/ml/clustering/_kmeans.py @@ -4,31 +4,38 @@ import warnings from abc import abstractmethod -from typing import Any, Generic, Optional, Tuple, TypeVar +from typing import Any, Generic, Tuple, TypeVar import numpy as np -from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin from sklearn.utils.validation import check_is_fitted +from ..._utils._sklearn_adapter import ( + BaseEstimator, + ClusterMixin, + TransformerMixin, +) from ...misc.metrics import PairwiseMetric, l2_distance from ...misc.validation import ( check_fdata_same_dimensions, validate_random_state, ) from ...representation import FDataGrid -from ...typing._base import RandomStateLike +from ...typing._base import RandomState, RandomStateLike from ...typing._metric import Metric from ...typing._numpy import NDArrayAny, NDArrayFloat, NDArrayInt -SelfType = TypeVar("SelfType", bound="BaseKMeans[Any]") +SelfType = TypeVar("SelfType", bound="BaseKMeans[Any, Any]") MembershipType = TypeVar("MembershipType", bound=NDArrayAny) +# TODO: Generalize to FData and NDArray, without losing performance +Input = TypeVar("Input", bound=FDataGrid) + class BaseKMeans( - BaseEstimator, # type: ignore - ClusterMixin, # type: ignore - TransformerMixin, # type: ignore - Generic[MembershipType], + BaseEstimator, + ClusterMixin[Input], + TransformerMixin[Input, NDArrayFloat, object], + Generic[Input, MembershipType], ): """Base class to implement K-Means clustering algorithms. @@ -42,8 +49,8 @@ def __init__( self, *, n_clusters: int = 2, - init: Optional[FDataGrid] = None, - metric: Metric[FDataGrid] = l2_distance, + init: Input | None = None, + metric: Metric[Input] = l2_distance, n_init: int = 1, max_iter: int = 100, tol: float = 1e-4, @@ -84,7 +91,7 @@ def __init__( self.tol = tol self.random_state = random_state - def _check_clustering(self, fdata: FDataGrid) -> FDataGrid: + def _check_clustering(self, fdata: Input) -> Input: """Check the arguments used in fit. Args: @@ -95,12 +102,7 @@ def _check_clustering(self, fdata: FDataGrid) -> FDataGrid: Validated input. """ - if fdata.dim_domain > 1: - raise NotImplementedError( - "Only support 1 dimension on the domain.", - ) - - if fdata.n_samples < 2: + if len(fdata) < 2: raise ValueError( "The number of observations must be greater than 1.", ) @@ -144,17 +146,17 @@ def _check_clustering(self, fdata: FDataGrid) -> FDataGrid: return fdata - def _tolerance(self, fdata: FDataGrid) -> float: + def _tolerance(self, fdata: Input) -> float: variance = fdata.var() mean_variance = np.mean(variance[0].data_matrix) - return mean_variance * self.tol + return float(mean_variance * self.tol) def _init_centroids( self, - fdatagrid: FDataGrid, - random_state: np.random.RandomState, - ) -> FDataGrid: + fdatagrid: Input, + random_state: RandomState, + ) -> Input: """ Compute the initial centroids. @@ -201,18 +203,18 @@ def _create_membership(self, n_samples: int) -> MembershipType: @abstractmethod def _update( self, - fdata: FDataGrid, + fdata: Input, membership_matrix: MembershipType, distances_to_centroids: NDArrayFloat, - centroids: FDataGrid, + centroids: Input, ) -> None: pass def _algorithm( self, - fdata: FDataGrid, - random_state: np.random.RandomState, - ) -> Tuple[NDArrayFloat, FDataGrid, NDArrayFloat, int]: + fdata: Input, + random_state: RandomState, + ) -> Tuple[NDArrayFloat, Input, NDArrayFloat, int]: """ Fuzzy K-Means algorithm. @@ -285,15 +287,15 @@ def _algorithm( def _compute_inertia( self, membership: MembershipType, - centroids: FDataGrid, + centroids: Input, distances_to_centroids: NDArrayFloat, ) -> float: pass def fit( self: SelfType, - X: FDataGrid, - y: None = None, + X: Input, + y: object = None, sample_weight: None = None, ) -> SelfType: """ @@ -354,7 +356,7 @@ def fit( def _predict_membership( self, - X: FDataGrid, + X: Input, sample_weight: None = None, ) -> MembershipType: """Predict the closest cluster each sample in X belongs to. @@ -395,7 +397,7 @@ def _prediction_from_membership( def predict( self, - X: FDataGrid, + X: Input, sample_weight: None = None, ) -> NDArrayInt: """Predict the closest cluster each sample in X belongs to. @@ -412,7 +414,7 @@ def predict( self._predict_membership(X, sample_weight), ) - def transform(self, X: FDataGrid) -> NDArrayFloat: + def transform(self, X: Input) -> NDArrayFloat: """Transform X to a cluster-distance space. Args: @@ -430,8 +432,8 @@ def transform(self, X: FDataGrid) -> NDArrayFloat: def fit_transform( self, - X: FDataGrid, - y: None = None, + X: Input, + y: object = None, sample_weight: None = None, ) -> NDArrayFloat: """Compute clustering and transform X to cluster-distance space. @@ -450,8 +452,8 @@ def fit_transform( def score( self, - X: FDataGrid, - y: None = None, + X: Input, + y: object = None, sample_weight: None = None, ) -> float: """Opposite of the value of X on the K-means objective. @@ -472,7 +474,7 @@ def score( return -self.inertia_ -class KMeans(BaseKMeans[NDArrayInt]): +class KMeans(BaseKMeans[Input, NDArrayInt]): r"""K-Means algorithm for functional data. Let :math:`\mathbf{X = \left\{ x_{1}, x_{2}, ..., x_{n}\right\}}` be a @@ -588,7 +590,7 @@ class KMeans(BaseKMeans[NDArrayInt]): def _compute_inertia( self, membership: NDArrayInt, - centroids: FDataGrid, + centroids: Input, distances_to_centroids: NDArrayFloat, ) -> float: distances_to_their_center = np.choose( @@ -596,7 +598,7 @@ def _compute_inertia( distances_to_centroids.T, ) - return np.sum(distances_to_their_center**2) + return float(np.sum(distances_to_their_center**2)) def _create_membership(self, n_samples: int) -> NDArrayInt: return np.empty(n_samples, dtype=int) @@ -609,10 +611,10 @@ def _prediction_from_membership( def _update( self, - fdata: FDataGrid, + fdata: Input, membership_matrix: NDArrayInt, distances_to_centroids: NDArrayFloat, - centroids: FDataGrid, + centroids: Input, ) -> None: membership_matrix[:] = np.argmin(distances_to_centroids, axis=1) @@ -628,7 +630,7 @@ def _update( ) -class FuzzyCMeans(BaseKMeans[NDArrayFloat]): +class FuzzyCMeans(BaseKMeans[Input, NDArrayFloat]): r""" Fuzzy c-Means clustering for functional data. @@ -756,8 +758,8 @@ def __init__( self, *, n_clusters: int = 2, - init: Optional[FDataGrid] = None, - metric: Metric[FDataGrid] = l2_distance, + init: Input | None = None, + metric: Metric[Input] = l2_distance, n_init: int = 1, max_iter: int = 100, tol: float = 1e-4, @@ -787,11 +789,13 @@ def _check_params(self) -> None: def _compute_inertia( self, membership: NDArrayFloat, - centroids: FDataGrid, + centroids: Input, distances_to_centroids: NDArrayFloat, ) -> float: - return np.sum( - membership**self.fuzzifier * distances_to_centroids**2, + return float( + np.sum( + membership**self.fuzzifier * distances_to_centroids**2, + ) ) def _create_membership(self, n_samples: int) -> NDArrayFloat: @@ -801,14 +805,17 @@ def _prediction_from_membership( self, membership_matrix: NDArrayFloat, ) -> NDArrayInt: - return np.argmax(membership_matrix, axis=1) + return np.argmax( # type: ignore[no-any-return] + membership_matrix, + axis=1, + ) def _update( self, - fdata: FDataGrid, + fdata: Input, membership_matrix: NDArrayFloat, distances_to_centroids: NDArrayFloat, - centroids: FDataGrid, + centroids: Input, ) -> None: # Divisions by zero allowed with np.errstate(divide='ignore'): @@ -849,7 +856,7 @@ def _update( def predict_proba( self, - X: FDataGrid, + X: Input, sample_weight: None = None, ) -> NDArrayFloat: """Predict the probability of belonging to each cluster. From f35ca838a29a88509f18291f42bac49048f340bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carlos=20Ramos=20Carre=C3=B1o?= Date: Fri, 2 Sep 2022 21:00:17 +0200 Subject: [PATCH 278/400] Fix style --- skfda/preprocessing/dim_reduction/_fpca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index d58fca29f..bf99e907e 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -21,7 +21,7 @@ WeightsCallable = Callable[[np.ndarray], np.ndarray] -class FPCA( # noqa: WPS230 (too many public attributes) +class FPCA( # noqa: WPS230 (too many public attributes) BaseEstimator, InductiveTransformerMixin[FData, NDArrayFloat, object], ): From f16462a7bf3f86880f2ad71fec9248ab57a205d5 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sat, 3 Sep 2022 18:41:58 +0200 Subject: [PATCH 279/400] Typing machine learning module. --- skfda/misc/hat_matrix.py | 74 ++++++++++++++++--- skfda/ml/_neighbors_base.py | 9 +-- skfda/ml/regression/__init__.py | 33 +++++++-- skfda/ml/regression/_coefficients.py | 47 ++++++------ .../ml/regression/_historical_linear_model.py | 50 +++++++------ skfda/ml/regression/_kernel_regression.py | 35 +++++---- skfda/ml/regression/_linear_regression.py | 47 ++++++------ skfda/preprocessing/registration/__init__.py | 5 ++ 8 files changed, 199 insertions(+), 101 deletions(-) diff --git a/skfda/misc/hat_matrix.py b/skfda/misc/hat_matrix.py index e1645673a..c9185d95a 100644 --- a/skfda/misc/hat_matrix.py +++ b/skfda/misc/hat_matrix.py @@ -11,7 +11,7 @@ import abc import math -from typing import Callable +from typing import Callable, TypeVar, Union, overload import numpy as np @@ -22,6 +22,9 @@ from ..typing._numpy import NDArrayFloat from . import kernels +Input = TypeVar("Input", bound=Union[FData, GridPointsLike]) +Prediction = TypeVar("Prediction", bound=Union[NDArrayFloat, FData]) + class HatMatrix( BaseEstimator, @@ -44,16 +47,42 @@ def __init__( ): self.kernel = kernel + @overload def __call__( self, *, delta_x: NDArrayFloat, - X_train: FData | GridPointsLike | None = None, - X: FData | GridPointsLike | None = None, - y_train: NDArrayFloat | None = None, + X_train: Input | None = None, + X: Input | None = None, + y_train: None = None, weights: NDArrayFloat | None = None, _cv: bool = False, ) -> NDArrayFloat: + pass + + @overload + def __call__( + self, + *, + delta_x: NDArrayFloat, + X_train: Input | None = None, + X: Input | None = None, + y_train: Prediction | None = None, + weights: NDArrayFloat | None = None, + _cv: bool = False, + ) -> Prediction: + pass + + def __call__( + self, + *, + delta_x: NDArrayFloat, + X_train: Input | None = None, + X: Input | None = None, + y_train: NDArrayFloat | FData | None = None, + weights: NDArrayFloat | None = None, + _cv: bool = False, + ) -> NDArrayFloat | FData: r""" Calculate the hat matrix or the prediction. @@ -244,16 +273,42 @@ def __init__( super().__init__(kernel=kernel) self.bandwidth = bandwidth - def __call__( # noqa: D102 + @overload + def __call__( self, *, delta_x: NDArrayFloat, X_train: FData | GridPointsLike | None = None, X: FData | GridPointsLike | None = None, - y_train: NDArrayFloat | None = None, + y_train: None = None, weights: NDArrayFloat | None = None, _cv: bool = False, ) -> NDArrayFloat: + pass + + @overload + def __call__( + self, + *, + delta_x: NDArrayFloat, + X_train: FData | GridPointsLike | None = None, + X: FData | GridPointsLike | None = None, + y_train: Prediction | None = None, + weights: NDArrayFloat | None = None, + _cv: bool = False, + ) -> Prediction: + pass + + def __call__( # noqa: D102 + self, + *, + delta_x: NDArrayFloat, + X_train: FData | GridPointsLike | None = None, + X: FData | GridPointsLike | None = None, + y_train: NDArrayFloat | FData | None = None, + weights: NDArrayFloat | None = None, + _cv: bool = False, + ) -> NDArrayFloat | FData: bandwidth = ( np.percentile(np.abs(delta_x), 15) @@ -262,7 +317,8 @@ def __call__( # noqa: D102 ) # Regression - if isinstance(X_train, FData) and isinstance(X, FData): + if isinstance(X_train, FData): + assert isinstance(X, FData) if not ( isinstance(X_train, FDataBasis) @@ -300,11 +356,11 @@ def __call__( # noqa: D102 # Smoothing else: - return super().__call__( # noqa: WPS503 + return super().__call__( # type: ignore[misc, type-var] # noqa: WPS503 delta_x=delta_x, X_train=X_train, X=X, - y_train=y_train, + y_train=y_train, # type: ignore[arg-type] weights=weights, _cv=_cv, ) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index e2646c2af..3beb3b44c 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -777,12 +777,11 @@ def _functional_score( if len(sample_weight) != len(y): raise ValueError("Must be a weight for each sample.") - sample_weight = np.asarray(sample_weight) - sample_weight = sample_weight / sample_weight.sum() + normalized_sample_weight = sample_weight / sample_weight.sum() data_u_t = data_u.T - data_u_t *= sample_weight + data_u_t *= normalized_sample_weight data_v_t = data_v.T - data_v_t *= sample_weight + data_v_t *= normalized_sample_weight # Sum and integrate sum_u = np.sum(data_u, axis=0) @@ -791,4 +790,4 @@ def _functional_score( int_u = simps(sum_u, x=u.grid_points[0]) int_v = simps(sum_v, x=v.grid_points[0]) - return 1 - int_u / int_v + return float(1 - int_u / int_v) diff --git a/skfda/ml/regression/__init__.py b/skfda/ml/regression/__init__.py index 87964e07c..b5c6d1d2a 100644 --- a/skfda/ml/regression/__init__.py +++ b/skfda/ml/regression/__init__.py @@ -1,8 +1,29 @@ """Regression.""" -from ._historical_linear_model import HistoricalLinearRegression -from ._kernel_regression import KernelRegression -from ._linear_regression import LinearRegression -from ._neighbors_regression import ( - KNeighborsRegressor, - RadiusNeighborsRegressor, + +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_historical_linear_model": ["HistoricalLinearRegression"], + "_kernel_regression": ["KernelRegression"], + "_linear_regression": ["LinearRegression"], + "_neighbors_regression": [ + "KNeighborsRegressor", + "RadiusNeighborsRegressor", + ], + }, ) + +if TYPE_CHECKING: + from ._historical_linear_model import ( + HistoricalLinearRegression as HistoricalLinearRegression, + ) + from ._kernel_regression import KernelRegression as KernelRegression + from ._linear_regression import LinearRegression as LinearRegression + from ._neighbors_regression import ( + KNeighborsRegressor as KNeighborsRegressor, + RadiusNeighborsRegressor as RadiusNeighborsRegressor, + ) diff --git a/skfda/ml/regression/_coefficients.py b/skfda/ml/regression/_coefficients.py index 3a2d8af07..e9cd9f72d 100644 --- a/skfda/ml/regression/_coefficients.py +++ b/skfda/ml/regression/_coefficients.py @@ -8,6 +8,7 @@ from ...misc._math import inner_product from ...representation.basis import Basis, FDataBasis +from ...typing._numpy import NDArrayFloat CovariateType = TypeVar("CovariateType") @@ -31,8 +32,8 @@ def __init__( def regression_matrix( self, X: CovariateType, - y: np.ndarray, - ) -> np.ndarray: + y: NDArrayFloat, + ) -> NDArrayFloat: """ Return the constant coefficients matrix for regression. @@ -49,7 +50,7 @@ def regression_matrix( @abc.abstractmethod def convert_from_constant_coefs( self, - coefs: np.ndarray, + coefs: NDArrayFloat, ) -> CovariateType: """ Return the coefficients object from the constant coefs. @@ -68,7 +69,7 @@ def inner_product( self, coefs: CovariateType, X: CovariateType, - ) -> np.ndarray: + ) -> NDArrayFloat: """ Inner product. @@ -79,28 +80,28 @@ def inner_product( pass -class CoefficientInfoNdarray(CoefficientInfo[np.ndarray]): +class CoefficientInfoNdarray(CoefficientInfo[NDArrayFloat]): def regression_matrix( # noqa: D102 self, - X: np.ndarray, - y: np.ndarray, - ) -> np.ndarray: + X: NDArrayFloat, + y: NDArrayFloat, + ) -> NDArrayFloat: return np.atleast_2d(X) def convert_from_constant_coefs( # noqa: D102 self, - coefs: np.ndarray, - ) -> np.ndarray: + coefs: NDArrayFloat, + ) -> NDArrayFloat: return coefs def inner_product( # noqa: D102 self, - coefs: np.ndarray, - X: np.ndarray, - ) -> np.ndarray: + coefs: NDArrayFloat, + X: NDArrayFloat, + ) -> NDArrayFloat: return inner_product(coefs, X) @@ -117,8 +118,8 @@ class CoefficientInfoFDataBasis(CoefficientInfo[FDataBasis]): def regression_matrix( # noqa: D102 self, X: FDataBasis, - y: np.ndarray, - ) -> np.ndarray: + y: NDArrayFloat, + ) -> NDArrayFloat: # The matrix is the matrix of coefficients multiplied by # the matrix of inner products. @@ -128,7 +129,7 @@ def regression_matrix( # noqa: D102 def convert_from_constant_coefs( # noqa: D102 self, - coefs: np.ndarray, + coefs: NDArrayFloat, ) -> FDataBasis: return FDataBasis(self.basis.basis, coefs.T) @@ -136,7 +137,7 @@ def inner_product( # noqa: D102 self, coefs: FDataBasis, X: FDataBasis, - ) -> np.ndarray: + ) -> NDArrayFloat: # Efficient implementation of the inner product using the # inner product matrix previously computed return inner_product(coefs, X, inner_product_matrix=self.inner_basis.T) @@ -145,7 +146,7 @@ def inner_product( # noqa: D102 @singledispatch def coefficient_info_from_covariate( X: CovariateType, - y: np.ndarray, + y: NDArrayFloat, **_: Any, ) -> CoefficientInfo[CovariateType]: """Make a coefficient info object from a covariate.""" @@ -153,18 +154,18 @@ def coefficient_info_from_covariate( @coefficient_info_from_covariate.register(np.ndarray) -def _coefficient_info_from_covariate_ndarray( - X: np.ndarray, - y: np.ndarray, +def _coefficient_info_from_covariate_ndarray( # type: ignore[misc] + X: NDArrayFloat, + y: NDArrayFloat, **_: Any, -) -> CoefficientInfo[np.ndarray]: +) -> CoefficientInfo[NDArrayFloat]: return CoefficientInfoNdarray(basis=np.identity(X.shape[1], dtype=X.dtype)) @coefficient_info_from_covariate.register(FDataBasis) def _coefficient_info_from_covariate_fdatabasis( X: FDataBasis, - y: np.ndarray, + y: NDArrayFloat, *, basis: Basis, **_: Any, diff --git a/skfda/ml/regression/_historical_linear_model.py b/skfda/ml/regression/_historical_linear_model.py index b6d81d2e8..5f2e033cc 100644 --- a/skfda/ml/regression/_historical_linear_model.py +++ b/skfda/ml/regression/_historical_linear_model.py @@ -5,22 +5,23 @@ import numpy as np import scipy.integrate -from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted from ..._utils import _cartesian_product, _pairwise_symmetric -from ...representation import FDataBasis, FDataGrid +from ..._utils._sklearn_adapter import BaseEstimator, RegressorMixin +from ...representation import FData, FDataBasis, FDataGrid from ...representation.basis import Basis, FiniteElement, VectorValued +from ...typing._numpy import NDArrayFloat _MeanType = Union[FDataGrid, float] def _pairwise_fem_inner_product( - basis_fd: FDataBasis, - fd: FDataGrid, + basis_fd: FData, + fd: FData, y_val: float, - grid: np.ndarray, -) -> np.ndarray: + grid: NDArrayFloat, +) -> NDArrayFloat: eval_grid_fem = np.concatenate( ( @@ -38,15 +39,15 @@ def _pairwise_fem_inner_product( prod = eval_fem * eval_fd integral = scipy.integrate.simps(prod, grid, axis=1) - return np.sum(integral, axis=-1) + return np.sum(integral, axis=-1) # type: ignore[no-any-return] def _inner_product_matrix( basis: Basis, - fd: FDataGrid, + fd: FData, limits: Tuple[float, float], y_val: float, -) -> np.ndarray: +) -> NDArrayFloat: """ Compute inner products with the FEM basis. @@ -67,16 +68,16 @@ def _inner_product_matrix( Matrix of inner products. """ - basis_fd = basis.to_basis() + basis_fd: FData = basis.to_basis() grid = fd.grid_points[0] grid_index = (grid >= limits[0]) & (grid <= limits[1]) grid = grid[grid_index] return _pairwise_symmetric( - _pairwise_fem_inner_product, + _pairwise_fem_inner_product, # type: ignore[arg-type] basis_fd, fd, - y_val=y_val, + y_val=y_val, # type: ignore[arg-type] grid=grid, ) @@ -84,8 +85,8 @@ def _inner_product_matrix( def _design_matrix( basis: Basis, fd: FDataGrid, - pred_points: np.ndarray, -) -> np.ndarray: + pred_points: NDArrayFloat, +) -> NDArrayFloat: """ Compute the indefinite integrals of the curves over s up to each t-value. @@ -112,7 +113,7 @@ def _get_valid_points( interval_len: float, n_intervals: int, lag: float, -) -> np.ndarray: +) -> NDArrayFloat: """Return the valid points as integer tuples.""" interval_points = np.arange(n_intervals + 1) full_grid_points = _cartesian_product((interval_points, interval_points)) @@ -123,15 +124,15 @@ def _get_valid_points( discrete_lag = np.inf if lag == np.inf else math.ceil(lag / interval_len) - return past_points[ + return past_points[ # type: ignore[no-any-return] past_points[:, 1] - past_points[:, 0] <= discrete_lag ] def _get_triangles( n_intervals: int, - valid_points: np.ndarray, -) -> np.ndarray: + valid_points: NDArrayFloat, +) -> NDArrayFloat: """Construct the triangle grid given the valid points.""" # A matrix where the (integer) coords of a point match # to its index or to -1 if it does not exist. @@ -176,7 +177,7 @@ def _get_triangles( triangles = np.concatenate((down_triangles, up_triangles)) has_wrong_index = np.any(triangles < 0, axis=1) - return triangles[~has_wrong_index] + return triangles[~has_wrong_index] # type: ignore[no-any-return] def _create_fem_basis( @@ -209,8 +210,8 @@ def _create_fem_basis( class HistoricalLinearRegression( - BaseEstimator, # type: ignore - RegressorMixin, # type: ignore + BaseEstimator, + RegressorMixin[FDataGrid, FDataGrid], ): r"""Historical functional linear regression. @@ -339,7 +340,7 @@ def _fit_and_return_centered_matrix( self, X: FDataGrid, y: FDataGrid, - ) -> Tuple[np.ndarray, _MeanType]: + ) -> Tuple[NDArrayFloat, _MeanType]: X_centered, y_centered, X_mean, y_mean = self._center_X_y(X, y) @@ -391,7 +392,10 @@ def _fit_and_return_centered_matrix( return design_matrix, y_mean - def _prediction_from_matrix(self, design_matrix: np.ndarray) -> FDataGrid: + def _prediction_from_matrix( + self, + design_matrix: NDArrayFloat, + ) -> FDataGrid: points = (design_matrix @ self._coef_coefs).reshape( -1, diff --git a/skfda/ml/regression/_kernel_regression.py b/skfda/ml/regression/_kernel_regression.py index 4ed08985f..db0494dfc 100644 --- a/skfda/ml/regression/_kernel_regression.py +++ b/skfda/ml/regression/_kernel_regression.py @@ -1,20 +1,23 @@ from __future__ import annotations -from typing import Optional +from typing import TypeVar, Union -import numpy as np -from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted +from ..._utils._sklearn_adapter import BaseEstimator, RegressorMixin from ...misc.hat_matrix import HatMatrix, NadarayaWatsonHatMatrix from ...misc.metrics import PairwiseMetric, l2_distance from ...representation._functional_data import FData from ...typing._metric import Metric +from ...typing._numpy import NDArrayFloat + +Input = TypeVar("Input", bound=Union[NDArrayFloat, FData], contravariant=True) +Prediction = TypeVar("Prediction", bound=Union[NDArrayFloat, FData]) class KernelRegression( BaseEstimator, - RegressorMixin, + RegressorMixin[Input, Prediction], ): r"""Kernel regression with scalar response. @@ -38,6 +41,7 @@ class KernelRegression( (default = :func:`L2 distance `). Examples: + >>> import numpy as np >>> from skfda import FDataGrid >>> from skfda.misc.hat_matrix import NadarayaWatsonHatMatrix >>> from skfda.misc.hat_matrix import KNeighborsHatMatrix @@ -67,8 +71,8 @@ class KernelRegression( def __init__( self, *, - kernel_estimator: Optional[HatMatrix] = None, - metric: Metric[FData] = l2_distance, + kernel_estimator: HatMatrix | None = None, + metric: Metric[Input] = l2_distance, ): self.kernel_estimator = kernel_estimator @@ -76,29 +80,32 @@ def __init__( def fit( # noqa: D102 self, - X: FData, - y: np.ndarray, - weight: Optional[np.ndarray] = None, - ) -> KernelRegression: + X: Input, + y: Prediction, + weight: NDArrayFloat | None = None, + ) -> KernelRegression[Input, Prediction]: self.X_train_ = X self.y_train_ = y self.weights_ = weight + self._kernel_estimator: HatMatrix if self.kernel_estimator is None: - self.kernel_estimator = NadarayaWatsonHatMatrix() + self._kernel_estimator = NadarayaWatsonHatMatrix() + else: + self._kernel_estimator = self.kernel_estimator return self def predict( # noqa: D102 self, - X: FData, - ) -> np.ndarray: + X: Input, + ) -> Prediction: check_is_fitted(self) delta_x = PairwiseMetric(self.metric)(X, self.X_train_) - return self.kernel_estimator( + return self._kernel_estimator( delta_x=delta_x, X_train=self.X_train_, y_train=self.y_train_, diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index cabe023f1..9a168a05b 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -5,13 +5,14 @@ from typing import Any, Iterable, List, Optional, Sequence, Tuple, Union import numpy as np -from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_is_fitted +from ..._utils._sklearn_adapter import BaseEstimator, RegressorMixin from ...misc.lstsq import solve_regularized_weighted_lstsq from ...misc.regularization import L2Regularization, compute_penalty_matrix from ...representation import FData from ...representation.basis import Basis +from ...typing._numpy import NDArrayFloat from ._coefficients import CoefficientInfo, coefficient_info_from_covariate RegularizationType = Union[ @@ -28,27 +29,28 @@ AcceptedDataType = Union[ FData, - np.ndarray, + NDArrayFloat, ] AcceptedDataCoefsType = Union[ CoefficientInfo[FData], - CoefficientInfo[np.ndarray], + CoefficientInfo[NDArrayFloat], ] BasisCoefsType = Sequence[Optional[Basis]] + ArgcheckResultType = Tuple[ - List[AcceptedDataType], - np.ndarray, - Optional[np.ndarray], - List[AcceptedDataCoefsType], + Sequence[AcceptedDataType], + NDArrayFloat, + Optional[NDArrayFloat], + Sequence[AcceptedDataCoefsType], ] class LinearRegression( - BaseEstimator, # type: ignore - RegressorMixin, # type: ignore + BaseEstimator, + RegressorMixin[AcceptedDataType, NDArrayFloat], ): r"""Linear regression with multivariate response. @@ -165,9 +167,9 @@ def __init__( def fit( # noqa: D102 self, - X: Union[AcceptedDataType, Sequence[AcceptedDataType]], - y: np.ndarray, - sample_weight: Optional[np.ndarray] = None, + X: AcceptedDataType, + y: NDArrayFloat, + sample_weight: Optional[NDArrayFloat] = None, ) -> LinearRegression: X_new, y, sample_weight, coef_info = self._argcheck_X_y( @@ -181,8 +183,11 @@ def fit( # noqa: D102 if self.fit_intercept: new_x = np.ones((len(y), 1)) - X_new = [new_x] + X_new - coef_info = [coefficient_info_from_covariate(new_x, y)] + coef_info + X_new = [new_x] + list(X_new) + new_coef_info_list: List[AcceptedDataCoefsType] = [ + coefficient_info_from_covariate(new_x, y), + ] + coef_info = new_coef_info_list + list(coef_info) if isinstance(regularization, Iterable): regularization = itertools.chain([None], regularization) @@ -190,7 +195,7 @@ def fit( # noqa: D102 regularization = (None, regularization) inner_products_list = [ - c.regression_matrix(x, y) + c.regression_matrix(x, y) # type: ignore[arg-type] for x, c in zip(X_new, coef_info) ] @@ -242,14 +247,14 @@ def fit( # noqa: D102 def predict( # noqa: D102 self, X: Union[AcceptedDataType, Sequence[AcceptedDataType]], - ) -> np.ndarray: + ) -> NDArrayFloat: check_is_fitted(self) X = self._argcheck_X(X) result = np.sum( [ - coef_info.inner_product(coef, x) + coef_info.inner_product(coef, x) # type: ignore[arg-type] for coef, x, coef_info in zip(self.coef_, X, self._coef_info) ], @@ -261,7 +266,7 @@ def predict( # noqa: D102 if self._target_ndim == 1: result = result.ravel() - return result + return result # type: ignore[no-any-return] def _argcheck_X( self, @@ -279,9 +284,9 @@ def _argcheck_X( def _argcheck_X_y( self, - X: Union[AcceptedDataType, Sequence[AcceptedDataType]], - y: np.ndarray, - sample_weight: Optional[np.ndarray] = None, + X: AcceptedDataType, + y: NDArrayFloat, + sample_weight: Optional[NDArrayFloat] = None, coef_basis: Optional[BasisCoefsType] = None, ) -> ArgcheckResultType: """Do some checks to types and shapes.""" diff --git a/skfda/preprocessing/registration/__init__.py b/skfda/preprocessing/registration/__init__.py index c9ab29e42..c57b5209e 100644 --- a/skfda/preprocessing/registration/__init__.py +++ b/skfda/preprocessing/registration/__init__.py @@ -7,6 +7,11 @@ import lazy_loader as lazy +from ..._utils import ( + invert_warping as invert_warping, + normalize_warping as normalize_warping, +) + __getattr__, __dir__, __all__ = lazy.attach( __name__, submodules=[ From 269bc520b7ae90ad106f7abf931fac0a7de300c4 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Mon, 5 Sep 2022 14:43:17 +0200 Subject: [PATCH 280/400] Remaining typing. --- mypy.out | 6747 +++++++++++++++++ setup.cfg | 3 + skfda/_utils/_utils.py | 3 +- .../stats/_functional_transformers.py | 10 +- skfda/inference/anova/__init__.py | 6 +- skfda/inference/hotelling/__init__.py | 5 +- .../_linear_differential_operator.py | 9 +- skfda/ml/regression/_linear_regression.py | 9 +- .../_fda_feature_union.py | 6 +- .../_per_class_transformer.py | 21 +- skfda/tests/test_basis.py | 7 +- skfda/tests/test_clustering.py | 2 +- skfda/tests/test_covariances.py | 2 +- skfda/tests/test_extrapolation.py | 22 +- skfda/tests/test_fpca.py | 4 +- skfda/tests/test_functional_transformers.py | 5 +- skfda/tests/test_grid.py | 4 +- skfda/tests/test_interpolation.py | 2 +- skfda/tests/test_kernel_regression.py | 18 +- skfda/tests/test_math.py | 10 +- skfda/tests/test_pandas_fdatabasis.py | 160 +- skfda/tests/test_pandas_fdatagrid.py | 38 +- skfda/tests/test_per_class_transformer.py | 9 +- skfda/tests/test_recursive_maxima_hunting.py | 2 +- skfda/tests/test_registration.py | 35 +- skfda/tests/test_regression.py | 351 +- skfda/tests/test_smoothing.py | 20 +- skfda/tests/test_stats.py | 8 +- skfda/tests/test_ufunc_numpy.py | 19 +- 29 files changed, 7232 insertions(+), 305 deletions(-) create mode 100644 mypy.out diff --git a/mypy.out b/mypy.out new file mode 100644 index 000000000..301e9b2a5 --- /dev/null +++ b/mypy.out @@ -0,0 +1,6747 @@ +TRACE: Plugins 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(collections) +TRACE: Looking for contextlib at contextlib.meta.json +TRACE: Meta contextlib {"data_mtime": 1662379575, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", "builtins", "abc"], "hash": "efccb4c04ef824457bb97ad1cadd489b4791c578764a52f04975beff1da99742", "id": "contextlib", "ignore_all": true, "interface_hash": "a6bea22a771f186f4deb3a600c6c7e1fc9af14472655c60f193142f45f6ba84d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextlib.pyi (contextlib) +TRACE: Looking for importlib at importlib/__init__.meta.json +TRACE: Meta importlib {"data_mtime": 1662379575, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [5, 5, 5, 5, 30, 30], "dependencies": ["collections.abc", "importlib.abc", "types", "builtins", "abc", "typing"], "hash": "5d991f55cfdc58d18e38d479666e5c73bc0eea2f9cc01f07cfbe9452aba97a48", "id": "importlib", "ignore_all": true, "interface_hash": "0c40f65f30f4d30ad6d6e30eecd5e6953d8534c5c37cd74b977a8a08840b8812", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, 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numpy.ma +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/ma/__init__.pyi (numpy.ma) +TRACE: Looking for unittest at unittest/__init__.meta.json +TRACE: Meta unittest {"data_mtime": 1662379577, "dep_lines": [1, 31, 3, 12, 19, 20, 21, 22, 28, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["sys", "unittest.async_case", "unittest.case", "unittest.loader", "unittest.main", "unittest.result", "unittest.runner", "unittest.signals", "unittest.suite", "builtins", "_typeshed", "abc", "typing"], "hash": "41e26ce057f9a9fad2cd64dccf2f37bfe190ea152e19711cc0f517db696ebcd4", "id": "unittest", "ignore_all": true, "interface_hash": "d8d695bf94c985ff258e90a252e5dd9889a7c283c5ca65f1de9ce3cd306c0011", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/polynomial/hermite.pyi (numpy.polynomial.hermite) +TRACE: Looking for numpy.polynomial.hermite_e at numpy/polynomial/hermite_e.meta.json +TRACE: Meta numpy.polynomial.hermite_e {"data_mtime": 1662379578, "dep_lines": [1, 3, 4, 5, 1, 1], "dep_prios": [5, 5, 5, 5, 5, 30], "dependencies": ["typing", "numpy", "numpy.polynomial._polybase", "numpy.polynomial.polyutils", "builtins", "abc"], "hash": "cd5ee6b1bf6ff6b5748affeb9c3dd28cff931b273aa5dde6682aa23c28f73db0", "id": "numpy.polynomial.hermite_e", "ignore_all": true, "interface_hash": "bbb67121836088939860abf524dbedc5de428889aa36f500c7bb0e5825f87cb6", "mtime": 1657484228, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi (importlib.metadata) +TRACE: Looking for genericpath at genericpath.meta.json +TRACE: Meta genericpath {"data_mtime": 1662379575, "dep_lines": [1, 2, 3, 4, 5, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 30], "dependencies": ["os", "_typeshed", "collections.abc", "typing", "typing_extensions", "builtins", "abc"], "hash": "2fe32f00cbf3bc2b362290b7e79d52bd1ba5f83805b7d728a43b3521cfe41262", "id": "genericpath", "ignore_all": true, "interface_hash": "8442fd45c778474f4d956371feee1b93b41f6154c11c1480bffe6b0cff44b335", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, 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py.io at py/io.meta.json +TRACE: Meta py.io {"data_mtime": 1662373496, "dep_lines": [5, 1, 3, 4, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 30], "dependencies": ["sys", "io", "typing", "typing_extensions", "builtins", "abc"], "hash": "9ee0b744854c5cea7ec6c69705b3d83591f1cdc0841e00dd2294bdc9198947e8", "id": "py.io", "ignore_all": true, "interface_hash": "c9a20b78334296e13f0be5b15f683c160478bba4423510bb1a880da196d1606d", "mtime": 1641858822, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/core.py +TRACE: Looking for pyparsing.helpers at pyparsing/helpers.meta.json +TRACE: Meta pyparsing.helpers {"data_mtime": 1662373517, "dep_lines": [2, 2, 3, 4, 7, 6, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 20, 10, 10, 5, 5, 5, 5, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30], "dependencies": ["html.entities", "html", "re", "typing", "pyparsing.core", "pyparsing", "pyparsing.util", "builtins", "_collections_abc", "_typeshed", "abc", "array", "ctypes", "enum", "mmap", "numpy", "numpy.ma", "numpy.ma.core", "pickle", "pyparsing.actions", "pyparsing.exceptions", "pyparsing.results", "sre_constants", "typing_extensions"], "hash": "42950e8d6d3ea6cbee78cc166fd6d0a54da7a2a282bfdf3fc27c35552cd2755a", "id": "pyparsing.helpers", "ignore_all": true, "interface_hash": "127e6e2959a58014b7941b7a25453ec2c330e9732a5df6f93f5a1cf756942ad3", "mtime": 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"logging", "multiprocessing.pool", "multiprocessing.process", "multiprocessing.sharedctypes", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "129aa906ca0c9bd1cd4217b4cc68d85fa7ed194c3362d57e68b3e74224aba37f", "id": "multiprocessing.context", "ignore_all": true, "interface_hash": "ab55938d9dded14c79a43c0eecc049076d9ec7c4739e8a9d4a29a7dbbfc0cd25", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/context.pyi", "plugin_data": null, "size": 7459, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.context: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.context +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/context.pyi (multiprocessing.context) +TRACE: Looking for multiprocessing.pool at multiprocessing/pool.meta.json +TRACE: Meta multiprocessing.pool {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", "builtins", "abc"], "hash": "54b1838925da66641a952e7215adc67812bdd526c7aa65933551f499586af913", "id": "multiprocessing.pool", "ignore_all": true, "interface_hash": "ad41aa31511113a03fb9f87c03a86605a0b3923d7f7a37ab755df4b15c423d8d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi", "plugin_data": null, "size": 4755, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.pool: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.pool +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi (multiprocessing.pool) +TRACE: Looking for multiprocessing.reduction at multiprocessing/reduction.meta.json +TRACE: Meta multiprocessing.reduction {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["pickle", "sys", "abc", "copyreg", "typing", "typing_extensions", "builtins", "array", "ctypes", "mmap"], "hash": "30ab326562cf82254f55d7c9f59e11159fbac4f767058f24cb69a2fe013a48e9", "id": "multiprocessing.reduction", "ignore_all": true, "interface_hash": "bbd4eacb98803ecd8041a117ca2b4e3abe2c467a2392ff056525e356dd449d0d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi", "plugin_data": null, "size": 2619, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.reduction: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.reduction +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi (multiprocessing.reduction) +TRACE: Looking for multiprocessing.synchronize at multiprocessing/synchronize.meta.json +TRACE: Meta multiprocessing.synchronize {"data_mtime": 1662379577, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "threading", "collections.abc", "contextlib", "multiprocessing.context", "types", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "f28836537a5963d10f30e158b6f5fa9e513b13c44cfe7ebce1a5eaad723e3e0d", "id": "multiprocessing.synchronize", "ignore_all": true, "interface_hash": "f2d28555b342d19d8f5975080246ffa4f55c04c8591f35788df98fb011c89862", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi", "plugin_data": null, "size": 2278, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.synchronize: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.synchronize +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi (multiprocessing.synchronize) +TRACE: Looking for multiprocessing.managers at multiprocessing/managers.meta.json +TRACE: Meta multiprocessing.managers {"data_mtime": 1662379577, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 1, 1], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["queue", "sys", "threading", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", 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false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi", "plugin_data": null, "size": 8556, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.managers: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.managers +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi (multiprocessing.managers) +TRACE: Looking for multiprocessing.process at multiprocessing/process.meta.json +TRACE: Meta multiprocessing.process {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "builtins", "_typeshed", "abc"], "hash": "fa47cbb61f64a2309b6d7043a2c42c4c096b85f17dbd51181d9c83455a89e5c3", "id": "multiprocessing.process", "ignore_all": true, "interface_hash": "77c612e6cb8b00cd8763eb96f60de3ddd7c40a47a2ea78f5710fb059dea117d2", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi", "plugin_data": null, "size": 1371, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.process: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.process +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi (multiprocessing.process) +TRACE: Looking for multiprocessing.queues at multiprocessing/queues.meta.json +TRACE: Meta multiprocessing.queues {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30], "dependencies": ["queue", "sys", "typing", "builtins", "_typeshed", "abc"], "hash": "daf7a7147647126f56cf864be94a85c23c7801d703f87280168714c3eda8a44c", "id": "multiprocessing.queues", "ignore_all": true, "interface_hash": "38370111a855e180ba5a53c74c70d2ab11961893618ccd17ecc4b397487fd0ae", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi", "plugin_data": null, "size": 1398, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.queues: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.queues +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi (multiprocessing.queues) +TRACE: Looking for multiprocessing.spawn at multiprocessing/spawn.meta.json +TRACE: Meta multiprocessing.spawn {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 1, 1], "dep_prios": [5, 5, 5, 5, 30], "dependencies": ["collections.abc", "types", "typing", "builtins", "abc"], "hash": "b49c0c45edfea0381227f3a4dd3408fe75dd8c69e824da53e7271f0005371a6a", "id": "multiprocessing.spawn", "ignore_all": true, "interface_hash": "864e86b58a2b7fe05e14bfb866bac09cbeff6f212bcefb318fedda6f40e77af4", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi", "plugin_data": null, "size": 859, "suppressed": [], "version_id": "0.971"} +LOG: Metadata abandoned for multiprocessing.spawn: options differ +TRACE: follow_imports: silent != normal +LOG: Metadata not found for multiprocessing.spawn +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi (multiprocessing.spawn) +TRACE: Looking for jinja2.bccache at jinja2/bccache.meta.json +TRACE: Meta jinja2.bccache {"data_mtime": 1662373513, "dep_lines": [8, 9, 10, 11, 12, 13, 14, 15, 16, 22, 17, 18, 19, 23, 1, 1, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 10, 10, 10, 10, 10, 10, 10, 10, 25, 5, 5, 5, 25, 5, 30, 30, 30, 30, 30, 30, 30], "dependencies": ["errno", "fnmatch", "marshal", "os", "pickle", "stat", "sys", "tempfile", "typing", "typing_extensions", "hashlib", "io", "types", "jinja2.environment", "builtins", "_stat", "_typeshed", "abc", "array", "ctypes", "mmap", "posixpath"], "hash": "9a1cf9c6d2f109c1d101ae7a6b33a1a612007b5f6ed707b4a2389f34c0a50e2a", "id": "jinja2.bccache", "ignore_all": true, "interface_hash": 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"dep_lines": [1, 2, 3, 4, 5, 6, 8, 16, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "concurrent.futures._base", "typing", "typing_extensions", "asyncio.events", "contextvars", "builtins", "abc"], "hash": "786a47524849086ce2a161b7e91452b3bf05ceb4f082a4c98cdba1273dd6876b", "id": "asyncio.futures", "ignore_all": true, "interface_hash": "61e0c19e45b3c787eda5d4dc162e5a8ffc0910c68622bd7171c2e4d928cad23f", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/futures.pyi", "plugin_data": null, "size": 3382, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for asyncio.futures: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/futures.pyi +TRACE: Looking for asyncio.locks at asyncio/locks.meta.json +TRACE: Meta asyncio.locks {"data_mtime": 1662373500, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["enum", "sys", "_typeshed", "collections", "collections.abc", "types", "typing", "typing_extensions", "asyncio.events", "asyncio.futures", "builtins", "abc"], "hash": "8cfa7eec73859baa1bce2d0203d318967a10b5f934e10dc0c07861e67363dcd0", "id": "asyncio.locks", "ignore_all": true, "interface_hash": "e3d424bcd1f36edf5530a34227a47e9a1d08f21acfe1529bf935e85d4a9b12d6", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/locks.pyi", "plugin_data": null, "size": 4380, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for asyncio.locks: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/locks.pyi +TRACE: Looking for asyncio.protocols at asyncio/protocols.meta.json +TRACE: Meta asyncio.protocols {"data_mtime": 1662373500, "dep_lines": [1, 3, 3, 2, 4, 1, 1], "dep_prios": [10, 10, 20, 5, 5, 5, 30], "dependencies": ["sys", "asyncio.transports", "asyncio", "_typeshed", "typing", "builtins", "abc"], "hash": "294dd709444397ca7ef43f34734f4437a1f1b1b18b4e7fc85f7e29609699ab21", "id": "asyncio.protocols", "ignore_all": true, "interface_hash": "7b08345ff41e38aba20f5bed689c19a81b18451dda996e1e3b2162d5c3feff06", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/protocols.pyi", "plugin_data": null, "size": 1815, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for asyncio.protocols: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/protocols.pyi +TRACE: Looking for asyncio.queues at asyncio/queues.meta.json +TRACE: Meta asyncio.queues {"data_mtime": 1662373500, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "asyncio.events", "typing", "builtins", "_typeshed", "abc"], "hash": "05a94ecd5fe3f04f5af8bb5fb96bde7a99c460ececc1936af21875abecdcc425", "id": "asyncio.queues", "ignore_all": true, "interface_hash": "b3fa451f14ba14672a49e13ff08accce0de8e6255b3ea5945347b07cc2cf192b", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/queues.pyi", "plugin_data": null, "size": 1395, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for asyncio.queues: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/queues.pyi +TRACE: Looking for asyncio.streams at asyncio/streams.meta.json +TRACE: Meta asyncio.streams {"data_mtime": 1662373500, "dep_lines": [1, 2, 8, 8, 8, 8, 3, 4, 5, 6, 9, 1, 1], "dep_prios": [10, 10, 10, 10, 10, 20, 5, 5, 5, 5, 5, 5, 30], 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"follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/subprocess.pyi", "plugin_data": null, "size": 6097, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for asyncio.subprocess: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/subprocess.pyi +TRACE: Looking for asyncio.tasks at asyncio/tasks.meta.json +TRACE: Meta asyncio.tasks {"data_mtime": 1662373500, "dep_lines": [1, 1, 2, 3, 4, 5, 6, 8, 9, 1, 1, 1], "dep_prios": [10, 20, 10, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["concurrent.futures", "concurrent", "sys", "collections.abc", "types", "typing", "typing_extensions", "asyncio.events", "asyncio.futures", "builtins", "_typeshed", "abc"], "hash": "dfb8c973acbf9dde115fe580aee591b67c9c5cd4c93e64af7477c8e91cc92d02", "id": "asyncio.tasks", "ignore_all": true, "interface_hash": "6a51c4916d09a5eff74005c05c58890cdf9337d6be01c3727cf7c4f5b681f26e", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, 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asyncio.transports at asyncio/transports.meta.json +TRACE: Meta asyncio.transports {"data_mtime": 1662373500, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "asyncio.events", "asyncio.protocols", "collections.abc", "socket", "typing", "builtins", "_typeshed", "abc"], "hash": "a96d95c93d150e40cbe35528dd969a5385277e1c6463bed02013173a8b9a1d3c", "id": "asyncio.transports", "ignore_all": true, "interface_hash": "5ab3dd81a6b6319a46036207260562954dbcd50ee6b35144a21614d3d1ca8ac9", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, 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Looking for asyncio.runners at asyncio/runners.meta.json +TRACE: Meta asyncio.runners {"data_mtime": 1662373500, "dep_lines": [1, 2, 3, 4, 5, 7, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "contextvars", "typing", "asyncio.events", "builtins", "abc"], "hash": "e039237c5c59b6d02b06833c56288aacc4ab468d1e6324069507611357b099de", "id": "asyncio.runners", "ignore_all": true, "interface_hash": "e857ac3e44f2be2caa33e85ed7120db7a25ef85358b7ed171ab7b5d606028592", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, 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"show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/sandbox.py", "plugin_data": null, "size": 14584, "suppressed": ["_string"], "version_id": "0.971"} +LOG: Metadata fresh for jinja2.sandbox: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/sandbox.py +TRACE: Looking for concurrent.futures.process at concurrent/futures/process.meta.json +TRACE: Meta concurrent.futures.process {"data_mtime": 1662373498, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30], "dependencies": ["sys", "collections.abc", "multiprocessing.connection", "multiprocessing.context", "multiprocessing.queues", "threading", "types", "typing", "weakref", "concurrent.futures._base", "builtins", "_typeshed", "abc", "multiprocessing", "multiprocessing.process", "queue"], "hash": "294fe1d7e893ef87801d2f57bbc19d8136bba9db5fe12d97659d558c33e55c2a", "id": "concurrent.futures.process", "ignore_all": true, "interface_hash": "bb20f8f5417ec90397e691f8dea278b49fcf7a3dea920cbc317393bdfa18e1b5", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/process.pyi", "plugin_data": null, "size": 7135, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for concurrent.futures.process: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/process.pyi +TRACE: Looking for concurrent.futures.thread at concurrent/futures/thread.meta.json +TRACE: Meta concurrent.futures.thread {"data_mtime": 1662373498, "dep_lines": [1, 2, 3, 4, 5, 6, 8, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["queue", "sys", "collections.abc", "threading", "typing", "weakref", "concurrent.futures._base", "builtins", "_typeshed", "abc"], "hash": "207c990861869400494768f09d2d94978f1554174ce0af6191c6831456c6095b", "id": "concurrent.futures.thread", "ignore_all": true, "interface_hash": "64db2b3bbb18bb06c2c83da1aa6b029c3ccb2273b3a3d1828814f82fa25f8c45", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/thread.pyi", "plugin_data": null, "size": 2285, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for concurrent.futures.thread: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/thread.pyi +TRACE: Looking for selectors at selectors.meta.json +TRACE: Meta selectors {"data_mtime": 1662373495, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30], "dependencies": ["sys", "_typeshed", "abc", "collections.abc", "typing", "typing_extensions", "builtins", "array", "ctypes", "mmap", "pickle"], "hash": "08361c33128299fb193a665ba156df246565f1cbecfe05fcd014912d713d5695", "id": "selectors", "ignore_all": true, "interface_hash": "23ce586de751cd9dee0056cf9bd435730ecc29e1d22c4dea59f37592dcbc314a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/selectors.pyi", "plugin_data": null, "size": 3714, "suppressed": [], "version_id": "0.971"} +LOG: Metadata fresh for selectors: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/selectors.pyi +LOG: Loaded graph with 662 nodes (2.294 sec) +LOG: Found 309 SCCs; largest has 72 nodes +TRACE: Priorities for typing_extensions: abc:10 collections:10 sys:10 _typeshed:5 collections.abc:5 typing:5 +TRACE: Priorities for typing: collections:10 sys:10 _typeshed:5 abc:5 types:5 typing_extensions:5 +TRACE: Priorities for types: sys:10 _typeshed:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 typing:5 typing_extensions:5 +TRACE: Priorities for sys: _typeshed:5 builtins:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 io:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for subprocess: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for posixpath: sys:10 _typeshed:5 collections.abc:5 genericpath:5 os:5 typing:5 typing_extensions:5 +TRACE: Priorities for pickle: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for pathlib: sys:10 _typeshed:5 collections.abc:5 io:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for os.path: sys:10 posixpath:5 +TRACE: Priorities for os: sys:10 os.path:10 _typeshed:5 abc:5 builtins:5 collections.abc:5 contextlib:5 io:5 subprocess:5 typing:5 typing_extensions:5 +TRACE: Priorities for mmap: sys:10 _typeshed:5 collections.abc:5 typing:5 +TRACE: Priorities for io: builtins:10 codecs:10 sys:10 _typeshed:5 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for importlib.metadata: abc:10 pathlib:5 sys:10 _typeshed:5 collections.abc:5 email.message:5 importlib.abc:5 os:5 typing:5 +TRACE: Priorities for importlib.machinery: importlib.abc:10 importlib:20 sys:10 types:10 collections.abc:5 typing:5 importlib.metadata:5 +TRACE: Priorities for importlib.abc: sys:10 types:10 _typeshed:5 abc:5 collections.abc:5 importlib.machinery:5 io:5 typing:5 typing_extensions:5 +TRACE: Priorities for importlib: collections.abc:5 importlib.abc:5 types:5 +TRACE: Priorities for genericpath: os:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for email.policy: abc:5 collections.abc:5 email.contentmanager:5 email.errors:5 email.header:5 email.message:5 typing:5 +TRACE: Priorities for email.message: collections.abc:5 email:5 email.charset:5 email.contentmanager:5 email.errors:5 email.policy:5 typing:5 typing_extensions:5 +TRACE: Priorities for email.header: email.charset:5 +TRACE: Priorities for email.errors: sys:10 +TRACE: Priorities for email.contentmanager: collections.abc:5 email.message:5 typing:5 +TRACE: Priorities for email.charset: collections.abc:5 +TRACE: Priorities for email: collections.abc:5 email.message:5 email.policy:5 typing:5 typing_extensions:5 +TRACE: Priorities for ctypes: sys:10 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for contextlib: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for collections.abc: _collections_abc:5 +TRACE: Priorities for collections: sys:10 _collections_abc:5 _typeshed:5 typing:5 typing_extensions:5 +TRACE: Priorities for codecs: types:10 _codecs:5 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for array: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for abc: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for _typeshed: array:10 ctypes:10 mmap:10 pickle:10 sys:10 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for _collections_abc: sys:10 types:5 typing:5 typing_extensions:5 +TRACE: Priorities for _codecs: codecs:10 sys:10 collections.abc:5 typing:5 typing_extensions:5 +TRACE: Priorities for _ast: sys:10 typing:5 typing_extensions:5 +TRACE: Priorities for builtins: sys:10 types:5 _ast:5 _collections_abc:5 _typeshed:5 collections.abc:5 io:5 typing:5 typing_extensions:5 +LOG: Processing SCC of size 36 (typing_extensions typing types sys subprocess posixpath pickle pathlib os.path os mmap io importlib.metadata importlib.machinery importlib.abc importlib genericpath email.policy email.message email.header email.errors email.contentmanager email.charset email ctypes contextlib collections.abc collections codecs array abc _typeshed _collections_abc _codecs _ast builtins) as inherently stale +LOG: Writing typing_extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing_extensions.pyi typing_extensions.meta.json typing_extensions.data.json +TRACE: Interface for typing_extensions has changed +LOG: Cached module typing_extensions has changed interface +LOG: Writing typing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing.pyi typing.meta.json typing.data.json +TRACE: Interface for typing has changed +LOG: Cached module typing has changed interface +LOG: Writing types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/types.pyi types.meta.json types.data.json +TRACE: Interface for types has changed +LOG: Cached module types has changed interface +LOG: Writing sys /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sys.pyi sys.meta.json sys.data.json +TRACE: Interface for sys has changed +LOG: Cached module sys has changed interface +LOG: Writing subprocess /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/subprocess.pyi subprocess.meta.json subprocess.data.json +TRACE: Interface for subprocess has changed +LOG: Cached module subprocess has changed interface +LOG: Writing posixpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/posixpath.pyi posixpath.meta.json posixpath.data.json +TRACE: Interface for posixpath has changed +LOG: Cached module posixpath has changed interface +LOG: Writing pickle /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pickle.pyi pickle.meta.json pickle.data.json +TRACE: Interface for pickle has changed +LOG: Cached module pickle has changed interface +LOG: Writing pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pathlib.pyi pathlib.meta.json pathlib.data.json +TRACE: Interface for pathlib has changed +LOG: Cached module pathlib has changed interface +LOG: Writing os.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/path.pyi os/path.meta.json os/path.data.json +TRACE: Interface for os.path has changed +LOG: Cached module os.path has changed interface +LOG: Writing os /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/__init__.pyi os/__init__.meta.json os/__init__.data.json +TRACE: Interface for os has changed +LOG: Cached module os has changed interface +LOG: Writing mmap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/mmap.pyi mmap.meta.json mmap.data.json +TRACE: Interface for mmap has changed +LOG: Cached module mmap has changed interface +LOG: Writing io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/io.pyi io.meta.json io.data.json +TRACE: Interface for io has changed +LOG: Cached module io has changed interface +LOG: Writing importlib.metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi importlib/metadata/__init__.meta.json importlib/metadata/__init__.data.json +TRACE: Interface for importlib.metadata has changed +LOG: Cached module importlib.metadata has changed interface +LOG: Writing importlib.machinery /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi importlib/machinery.meta.json importlib/machinery.data.json +TRACE: Interface for importlib.machinery has changed +LOG: Cached module importlib.machinery has changed interface +LOG: Writing importlib.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/abc.pyi importlib/abc.meta.json importlib/abc.data.json +TRACE: Interface for importlib.abc has changed +LOG: Cached module importlib.abc has changed interface +LOG: Writing importlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi importlib/__init__.meta.json importlib/__init__.data.json +TRACE: Interface for importlib has changed +LOG: Cached module importlib has changed interface +LOG: Writing genericpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/genericpath.pyi genericpath.meta.json genericpath.data.json +TRACE: Interface for genericpath has changed +LOG: Cached module genericpath has changed interface +LOG: Writing email.policy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi email/policy.meta.json email/policy.data.json +TRACE: Interface for email.policy has changed +LOG: Cached module email.policy has changed interface +LOG: Writing email.message /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/message.pyi email/message.meta.json email/message.data.json +TRACE: Interface for email.message has changed +LOG: Cached module email.message has changed interface +LOG: Writing email.header /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/header.pyi email/header.meta.json email/header.data.json +TRACE: Interface for email.header has changed +LOG: Cached module email.header has changed interface +LOG: Writing email.errors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/errors.pyi email/errors.meta.json email/errors.data.json +TRACE: Interface for email.errors has changed +LOG: Cached module email.errors has changed interface +LOG: Writing email.contentmanager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi email/contentmanager.meta.json email/contentmanager.data.json +TRACE: Interface for email.contentmanager has changed +LOG: Cached module email.contentmanager has changed interface +LOG: Writing email.charset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/charset.pyi email/charset.meta.json email/charset.data.json +TRACE: Interface for email.charset has changed +LOG: Cached module email.charset has changed interface +LOG: Writing email /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/__init__.pyi email/__init__.meta.json email/__init__.data.json +TRACE: Interface for email has changed +LOG: Cached module email has changed interface +LOG: Writing ctypes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ctypes/__init__.pyi ctypes/__init__.meta.json ctypes/__init__.data.json +TRACE: Interface for ctypes has changed +LOG: Cached module ctypes has changed interface +LOG: Writing contextlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextlib.pyi contextlib.meta.json contextlib.data.json +TRACE: Interface for contextlib has changed +LOG: Cached module contextlib has changed interface +LOG: Writing collections.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/abc.pyi collections/abc.meta.json collections/abc.data.json +TRACE: Interface for collections.abc has changed +LOG: Cached module collections.abc has changed interface +LOG: Writing collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/collections/__init__.pyi collections/__init__.meta.json collections/__init__.data.json +TRACE: Interface for collections has changed +LOG: Cached module collections has changed interface +LOG: Writing codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/codecs.pyi codecs.meta.json codecs.data.json +TRACE: Interface for codecs has changed +LOG: Cached module codecs has changed interface +LOG: Writing array /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/array.pyi array.meta.json array.data.json +TRACE: Interface for array has changed +LOG: Cached module array has changed interface +LOG: Writing abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/abc.pyi abc.meta.json abc.data.json +TRACE: Interface for abc has changed +LOG: Cached module abc has changed interface +LOG: Writing _typeshed /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_typeshed/__init__.pyi _typeshed/__init__.meta.json _typeshed/__init__.data.json +TRACE: Interface for _typeshed has changed +LOG: Cached module _typeshed has changed interface +LOG: Writing _collections_abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_collections_abc.pyi _collections_abc.meta.json _collections_abc.data.json +TRACE: Interface for _collections_abc has changed +LOG: Cached module _collections_abc has changed interface +LOG: Writing _codecs /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_codecs.pyi _codecs.meta.json _codecs.data.json +TRACE: Interface for _codecs has changed +LOG: Cached module _codecs has changed interface +LOG: Writing _ast /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_ast.pyi _ast.meta.json _ast.data.json +TRACE: Interface for _ast has changed +LOG: Cached module _ast has changed interface +LOG: Writing builtins /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/builtins.pyi builtins.meta.json builtins.data.json +TRACE: Interface for builtins has changed +LOG: Cached module builtins has changed interface +TRACE: Priorities for selectors: +LOG: Processing SCC singleton (selectors) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/selectors.pyi (selectors) +LOG: Writing selectors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/selectors.pyi selectors.meta.json selectors.data.json +TRACE: Interface for selectors is unchanged +LOG: Cached module selectors has same interface +TRACE: Priorities for concurrent: +LOG: Processing SCC singleton (concurrent) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/__init__.pyi (concurrent) +LOG: Writing concurrent /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/__init__.pyi concurrent/__init__.meta.json concurrent/__init__.data.json +TRACE: Interface for concurrent is unchanged +LOG: Cached module concurrent has same interface +TRACE: Priorities for contextvars: +LOG: Processing SCC singleton (contextvars) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextvars.pyi (contextvars) +LOG: Writing contextvars /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/contextvars.pyi contextvars.meta.json contextvars.data.json +TRACE: Interface for contextvars is unchanged +LOG: Cached module contextvars has same interface +TRACE: Priorities for _random: +LOG: Processing SCC singleton (_random) as stale due to deps (abc builtins typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_random.pyi (_random) +LOG: Writing _random /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_random.pyi _random.meta.json _random.data.json +TRACE: Interface for _random is unchanged +LOG: Cached module _random has same interface +TRACE: Priorities for keyword: +LOG: Processing SCC singleton (keyword) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/keyword.pyi (keyword) +LOG: Writing keyword /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/keyword.pyi keyword.meta.json keyword.data.json +TRACE: Interface for keyword is unchanged +LOG: Cached module keyword has same interface +TRACE: Priorities for asyncio.exceptions: +LOG: Processing SCC singleton (asyncio.exceptions) as stale due to deps (_typeshed abc builtins sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/exceptions.pyi (asyncio.exceptions) +LOG: Writing asyncio.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/exceptions.pyi asyncio/exceptions.meta.json asyncio/exceptions.data.json +TRACE: Interface for asyncio.exceptions is unchanged +LOG: Cached module asyncio.exceptions has same interface +TRACE: Priorities for asyncio.coroutines: +LOG: Processing SCC singleton (asyncio.coroutines) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/coroutines.pyi (asyncio.coroutines) +LOG: Writing asyncio.coroutines /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/asyncio/coroutines.pyi asyncio/coroutines.meta.json asyncio/coroutines.data.json +TRACE: Interface for asyncio.coroutines is unchanged +LOG: Cached module asyncio.coroutines has same interface +TRACE: Priorities for _socket: +LOG: Processing SCC singleton (_socket) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing typing_extensions) +LOG: Writing _socket /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_socket.pyi _socket.meta.json _socket.data.json +TRACE: Interface for _socket has changed +LOG: Cached module _socket has changed interface +TRACE: Priorities for jinja2.constants: +LOG: Processing SCC singleton (jinja2.constants) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/constants.py (jinja2.constants) +LOG: Writing jinja2.constants /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/constants.py jinja2/constants.meta.json jinja2/constants.data.json +TRACE: Interface for jinja2.constants is unchanged +LOG: Cached module jinja2.constants has same interface +TRACE: Priorities for zipimport: +LOG: Processing SCC singleton (zipimport) as stale due to deps (_typeshed abc builtins importlib.abc importlib.machinery os sys types typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/zipimport.pyi (zipimport) +LOG: Writing zipimport /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/zipimport.pyi zipimport.meta.json zipimport.data.json +TRACE: Interface for zipimport is unchanged +LOG: Cached module zipimport has same interface +TRACE: Priorities for hashlib: +LOG: Processing SCC singleton (hashlib) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/hashlib.pyi (hashlib) +LOG: Writing hashlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/hashlib.pyi hashlib.meta.json hashlib.data.json +TRACE: Interface for hashlib is unchanged +LOG: Cached module hashlib has same interface +TRACE: Priorities for multiprocessing.shared_memory: +LOG: Processing SCC singleton (multiprocessing.shared_memory) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing) +LOG: Writing multiprocessing.shared_memory /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/shared_memory.pyi multiprocessing/shared_memory.meta.json multiprocessing/shared_memory.data.json +TRACE: Interface for multiprocessing.shared_memory has changed +LOG: Cached module multiprocessing.shared_memory has changed interface +TRACE: Priorities for copyreg: +LOG: Processing SCC singleton (copyreg) as inherently stale with stale deps (builtins collections.abc typing typing_extensions) +LOG: Writing copyreg /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copyreg.pyi copyreg.meta.json copyreg.data.json +TRACE: Interface for copyreg has changed +LOG: Cached module copyreg has changed interface +TRACE: Priorities for multiprocessing.spawn: +LOG: Processing SCC singleton (multiprocessing.spawn) as inherently stale with stale deps (builtins collections.abc types typing) +LOG: Writing multiprocessing.spawn /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/spawn.pyi multiprocessing/spawn.meta.json multiprocessing/spawn.data.json +TRACE: Interface for multiprocessing.spawn has changed +LOG: Cached module multiprocessing.spawn has changed interface +TRACE: Priorities for multiprocessing.process: +LOG: Processing SCC singleton (multiprocessing.process) as inherently stale with stale deps (builtins collections.abc sys typing) +LOG: Writing multiprocessing.process /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi multiprocessing/process.meta.json multiprocessing/process.data.json +TRACE: Interface for multiprocessing.process has changed +LOG: Cached module multiprocessing.process has changed interface +TRACE: Priorities for multiprocessing.pool: +LOG: Processing SCC singleton (multiprocessing.pool) as inherently stale with stale deps (_typeshed builtins collections.abc sys types typing typing_extensions) +LOG: Writing multiprocessing.pool /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi multiprocessing/pool.meta.json multiprocessing/pool.data.json +TRACE: Interface for multiprocessing.pool has changed +LOG: Cached module multiprocessing.pool has changed interface +TRACE: Priorities for sysconfig: +LOG: Processing SCC singleton (sysconfig) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sysconfig.pyi (sysconfig) +LOG: Writing sysconfig /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sysconfig.pyi sysconfig.meta.json sysconfig.data.json +TRACE: Interface for sysconfig is unchanged +LOG: Cached module sysconfig has same interface +TRACE: Priorities for _csv: +LOG: Processing SCC singleton (_csv) as inherently stale with stale deps (_typeshed builtins collections.abc typing typing_extensions) +LOG: Writing _csv /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_csv.pyi _csv.meta.json _csv.data.json +TRACE: Interface for _csv has changed +LOG: Cached module _csv has changed interface +TRACE: Priorities for importlib.resources: +LOG: Processing SCC singleton (importlib.resources) as inherently stale with stale deps (builtins collections.abc contextlib os pathlib sys types typing typing_extensions) +LOG: Writing importlib.resources /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/resources.pyi importlib/resources.meta.json importlib/resources.data.json +TRACE: Interface for importlib.resources has changed +LOG: Cached module importlib.resources has changed interface +TRACE: Priorities for distutils: +LOG: Processing SCC singleton (distutils) as inherently stale with stale deps (builtins) +LOG: Writing distutils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/__init__.pyi distutils/__init__.meta.json distutils/__init__.data.json +TRACE: Interface for distutils has changed +LOG: Cached module distutils has changed interface +TRACE: Priorities for html.entities: +LOG: Processing SCC singleton (html.entities) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/html/entities.pyi (html.entities) +LOG: Writing html.entities /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/html/entities.pyi html/entities.meta.json html/entities.data.json +TRACE: Interface for html.entities is unchanged +LOG: Cached module html.entities has same interface +TRACE: Priorities for tomli._types: +LOG: Processing SCC singleton (tomli._types) as stale due to deps (_typeshed abc array builtins ctypes mmap pickle typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/tomli/_types.py (tomli._types) +LOG: Writing tomli._types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/tomli/_types.py tomli/_types.meta.json tomli/_types.data.json +TRACE: Interface for tomli._types is unchanged +LOG: Cached module tomli._types has same interface +TRACE: Priorities for importlib_metadata._collections: +LOG: Processing SCC singleton (importlib_metadata._collections) as inherently stale with stale deps (builtins collections) +LOG: Writing importlib_metadata._collections /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_collections.py importlib_metadata/_collections.meta.json importlib_metadata/_collections.data.json +TRACE: Interface for importlib_metadata._collections has changed +LOG: Cached module importlib_metadata._collections has changed interface +TRACE: Priorities for xarray.util: +LOG: Processing SCC singleton (xarray.util) as inherently stale with stale deps (builtins) +LOG: Writing xarray.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/util/__init__.py xarray/util/__init__.meta.json xarray/util/__init__.data.json +TRACE: Interface for xarray.util has changed +LOG: Cached module xarray.util has changed interface +TRACE: Priorities for html: +LOG: Processing SCC singleton (html) as inherently stale with stale deps (builtins typing) +LOG: Writing html /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/html/__init__.pyi html/__init__.meta.json html/__init__.data.json +TRACE: Interface for html has changed +LOG: Cached module html has changed interface +TRACE: Priorities for distutils.version: +LOG: Processing SCC singleton (distutils.version) as inherently stale with stale deps (_typeshed abc builtins typing) +LOG: Writing distutils.version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/distutils/version.pyi distutils/version.meta.json distutils/version.data.json +TRACE: Interface for distutils.version has changed +LOG: Cached module distutils.version has changed interface +TRACE: Priorities for xarray.coding: +LOG: Processing SCC singleton (xarray.coding) as inherently stale with stale deps (builtins) +LOG: Writing xarray.coding /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/coding/__init__.py xarray/coding/__init__.meta.json xarray/coding/__init__.data.json +TRACE: Interface for xarray.coding has changed +LOG: Cached module xarray.coding has changed interface +TRACE: Priorities for xarray.core: +LOG: Processing SCC singleton (xarray.core) as inherently stale with stale deps (builtins) +LOG: Writing xarray.core /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/__init__.py xarray/core/__init__.meta.json xarray/core/__init__.data.json +TRACE: Interface for xarray.core has changed +LOG: Cached module xarray.core has changed interface +TRACE: Priorities for numpy.compat.py3k: +LOG: Processing SCC singleton (numpy.compat.py3k) as inherently stale with stale deps (builtins importlib.machinery io os pathlib pickle sys) +LOG: Writing numpy.compat.py3k /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/compat/py3k.py numpy/compat/py3k.meta.json numpy/compat/py3k.data.json +TRACE: Interface for numpy.compat.py3k has changed +LOG: Cached module numpy.compat.py3k has changed interface +TRACE: Priorities for packaging.__about__: +LOG: Processing SCC singleton (packaging.__about__) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/__about__.py (packaging.__about__) +LOG: Writing packaging.__about__ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/__about__.py packaging/__about__.meta.json packaging/__about__.data.json +TRACE: Interface for packaging.__about__ is unchanged +LOG: Cached module packaging.__about__ has same interface +TRACE: Priorities for unicodedata: +LOG: Processing SCC singleton (unicodedata) as inherently stale with stale deps (builtins sys typing typing_extensions) +LOG: Writing unicodedata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/unicodedata.pyi unicodedata.meta.json unicodedata.data.json +TRACE: Interface for unicodedata has changed +LOG: Cached module unicodedata has changed interface +TRACE: Priorities for py.xml: +LOG: Processing SCC singleton (py.xml) as stale due to deps (abc builtins typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/xml.pyi (py.xml) +LOG: Writing py.xml /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/xml.pyi py/xml.meta.json py/xml.data.json +TRACE: Interface for py.xml is unchanged +LOG: Cached module py.xml has same interface +TRACE: Priorities for py.io: +LOG: Processing SCC singleton (py.io) as stale due to deps (abc builtins io sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/io.pyi (py.io) +LOG: Writing py.io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/io.pyi py/io.meta.json py/io.data.json +TRACE: Interface for py.io is unchanged +LOG: Cached module py.io has same interface +TRACE: Priorities for py.path: +LOG: Processing SCC singleton (py.path) as stale due to deps (_typeshed abc builtins os sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/path.pyi (py.path) +LOG: Writing py.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/path.pyi py/path.meta.json py/path.data.json +TRACE: Interface for py.path is unchanged +LOG: Cached module py.path has same interface +TRACE: Priorities for py.iniconfig: +LOG: Processing SCC singleton (py.iniconfig) as stale due to deps (abc builtins typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/iniconfig.pyi (py.iniconfig) +LOG: Writing py.iniconfig /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/iniconfig.pyi py/iniconfig.meta.json py/iniconfig.data.json +TRACE: Interface for py.iniconfig is unchanged +LOG: Cached module py.iniconfig has same interface +TRACE: Priorities for py.error: +LOG: Processing SCC singleton (py.error) as stale due to deps (builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/error.pyi (py.error) +LOG: Writing py.error /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/py/error.pyi py/error.meta.json py/error.data.json +TRACE: Interface for py.error is unchanged +LOG: Cached module py.error has same interface +TRACE: Priorities for _stat: +LOG: Processing SCC singleton (_stat) as stale due to deps (abc builtins sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_stat.pyi (_stat) +LOG: Writing _stat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_stat.pyi _stat.meta.json _stat.data.json +TRACE: Interface for _stat is unchanged +LOG: Cached module _stat has same interface +TRACE: Priorities for _bisect: +LOG: Processing SCC singleton (_bisect) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_bisect.pyi (_bisect) +LOG: Writing _bisect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_bisect.pyi _bisect.meta.json _bisect.data.json +TRACE: Interface for _bisect is unchanged +LOG: Cached module _bisect has same interface +TRACE: Priorities for token: +LOG: Processing SCC singleton (token) as stale due to deps (_typeshed abc builtins sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/token.pyi (token) +LOG: Writing token /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/token.pyi token.meta.json token.data.json +TRACE: Interface for token is unchanged +LOG: Cached module token has same interface +TRACE: Priorities for zlib: +LOG: Processing SCC singleton (zlib) as inherently stale with stale deps (array builtins sys typing typing_extensions) +LOG: Writing zlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/zlib.pyi zlib.meta.json zlib.data.json +TRACE: Interface for zlib has changed +LOG: Cached module zlib has changed interface +TRACE: Priorities for _compression: +LOG: Processing SCC singleton (_compression) as inherently stale with stale deps (_typeshed builtins collections.abc io typing) +LOG: Writing _compression /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_compression.pyi _compression.meta.json _compression.data.json +TRACE: Interface for _compression has changed +LOG: Cached module _compression has changed interface +TRACE: Priorities for reprlib: +LOG: Processing SCC singleton (reprlib) as stale due to deps (abc array builtins collections collections.abc typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/reprlib.pyi (reprlib) +LOG: Writing reprlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/reprlib.pyi reprlib.meta.json reprlib.data.json +TRACE: Interface for reprlib is unchanged +LOG: Cached module reprlib has same interface +TRACE: Priorities for _weakref: +LOG: Processing SCC singleton (_weakref) as inherently stale with stale deps (builtins collections.abc sys typing typing_extensions) +LOG: Writing _weakref /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakref.pyi _weakref.meta.json _weakref.data.json +TRACE: Interface for _weakref has changed +LOG: Cached module _weakref has changed interface +TRACE: Priorities for _weakrefset: +LOG: Processing SCC singleton (_weakrefset) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing) +LOG: Writing _weakrefset /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_weakrefset.pyi _weakrefset.meta.json _weakrefset.data.json +TRACE: Interface for _weakrefset has changed +LOG: Cached module _weakrefset has changed interface +TRACE: Priorities for cmd: +LOG: Processing SCC singleton (cmd) as stale due to deps (abc builtins collections.abc typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/cmd.pyi (cmd) +LOG: Writing cmd /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/cmd.pyi cmd.meta.json cmd.data.json +TRACE: Interface for cmd is unchanged +LOG: Cached module cmd has same interface +TRACE: Priorities for glob: +LOG: Processing SCC singleton (glob) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing) +LOG: Writing glob /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/glob.pyi glob.meta.json glob.data.json +TRACE: Interface for glob has changed +LOG: Cached module glob has changed interface +TRACE: Priorities for urllib: +LOG: Processing SCC singleton (urllib) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/urllib/__init__.pyi (urllib) +LOG: Writing urllib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/urllib/__init__.pyi urllib/__init__.meta.json urllib/__init__.data.json +TRACE: Interface for urllib is unchanged +LOG: Cached module urllib has same interface +TRACE: Priorities for urllib.parse: +LOG: Processing SCC singleton (urllib.parse) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/urllib/parse.pyi (urllib.parse) +LOG: Writing urllib.parse /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/urllib/parse.pyi urllib/parse.meta.json urllib/parse.data.json +TRACE: Interface for urllib.parse is unchanged +LOG: Cached module urllib.parse has same interface +TRACE: Priorities for packaging._structures: +LOG: Processing SCC singleton (packaging._structures) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_structures.py (packaging._structures) +LOG: Writing packaging._structures /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_structures.py packaging/_structures.meta.json packaging/_structures.data.json +TRACE: Interface for packaging._structures is unchanged +LOG: Cached module packaging._structures has same interface +TRACE: Priorities for atexit: +LOG: Processing SCC singleton (atexit) as stale due to deps (builtins collections.abc typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/atexit.pyi (atexit) +LOG: Writing atexit /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/atexit.pyi atexit.meta.json atexit.data.json +TRACE: Interface for atexit is unchanged +LOG: Cached module atexit has same interface +TRACE: Priorities for attr._version_info: +LOG: Processing SCC singleton (attr._version_info) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/_version_info.pyi (attr._version_info) +LOG: Writing attr._version_info /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/_version_info.pyi attr/_version_info.meta.json attr/_version_info.data.json +TRACE: Interface for attr._version_info is unchanged +LOG: Cached module attr._version_info has same interface +TRACE: Priorities for attr.exceptions: +LOG: Processing SCC singleton (attr.exceptions) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/exceptions.pyi (attr.exceptions) +LOG: Writing attr.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/exceptions.pyi attr/exceptions.meta.json attr/exceptions.data.json +TRACE: Interface for attr.exceptions is unchanged +LOG: Cached module attr.exceptions has same interface +TRACE: Priorities for json.encoder: +LOG: Processing SCC singleton (json.encoder) as inherently stale with stale deps (builtins collections.abc typing) +LOG: Writing json.encoder /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/json/encoder.pyi json/encoder.meta.json json/encoder.data.json +TRACE: Interface for json.encoder has changed +LOG: Cached module json.encoder has changed interface +TRACE: Priorities for json.decoder: +LOG: Processing SCC singleton (json.decoder) as inherently stale with stale deps (builtins collections.abc typing) +LOG: Writing json.decoder /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/json/decoder.pyi json/decoder.meta.json json/decoder.data.json +TRACE: Interface for json.decoder has changed +LOG: Cached module json.decoder has changed interface +TRACE: Priorities for difflib: +LOG: Processing SCC singleton (difflib) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/difflib.pyi (difflib) +LOG: Writing difflib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/difflib.pyi difflib.meta.json difflib.data.json +TRACE: Interface for difflib is unchanged +LOG: Cached module difflib has same interface +TRACE: Priorities for struct: +LOG: Processing SCC singleton (struct) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing) +LOG: Writing struct /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/struct.pyi struct.meta.json struct.data.json +TRACE: Interface for struct has changed +LOG: Cached module struct has changed interface +TRACE: Priorities for marshal: +LOG: Processing SCC singleton (marshal) as stale due to deps (builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/marshal.pyi (marshal) +LOG: Writing marshal /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/marshal.pyi marshal.meta.json marshal.data.json +TRACE: Interface for marshal is unchanged +LOG: Cached module marshal has same interface +TRACE: Priorities for importlib.util: +LOG: Processing SCC singleton (importlib.util) as stale due to deps (_typeshed abc builtins collections.abc importlib importlib.abc importlib.machinery sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/util.pyi (importlib.util) +LOG: Writing importlib.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/util.pyi importlib/util.meta.json importlib/util.data.json +TRACE: Interface for importlib.util is unchanged +LOG: Cached module importlib.util has same interface +TRACE: Priorities for opcode: +LOG: Processing SCC singleton (opcode) as inherently stale with stale deps (builtins sys typing_extensions) +LOG: Writing opcode /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/opcode.pyi opcode.meta.json opcode.data.json +TRACE: Interface for opcode has changed +LOG: Cached module opcode has changed interface +TRACE: Priorities for xdrlib: +LOG: Processing SCC singleton (xdrlib) as inherently stale with stale deps (builtins collections.abc typing) +LOG: Writing xdrlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/xdrlib.pyi xdrlib.meta.json xdrlib.data.json +TRACE: Interface for xdrlib has changed +LOG: Cached module xdrlib has changed interface +TRACE: Priorities for lzma: +LOG: Processing SCC singleton (lzma) as inherently stale with stale deps (_typeshed builtins collections.abc io typing typing_extensions) +LOG: Writing lzma /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/lzma.pyi lzma.meta.json lzma.data.json +TRACE: Interface for lzma has changed +LOG: Cached module lzma has changed interface +TRACE: Priorities for numpy.compat._inspect: +LOG: Processing SCC singleton (numpy.compat._inspect) as inherently stale with stale deps (builtins types) +LOG: Writing numpy.compat._inspect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/compat/_inspect.py numpy/compat/_inspect.meta.json numpy/compat/_inspect.data.json +TRACE: Interface for numpy.compat._inspect has changed +LOG: Cached module numpy.compat._inspect has changed interface +TRACE: Priorities for numpy.testing._private: +LOG: Processing SCC singleton (numpy.testing._private) as inherently stale with stale deps (builtins) +LOG: Writing numpy.testing._private /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/testing/_private/__init__.py numpy/testing/_private/__init__.meta.json numpy/testing/_private/__init__.data.json +TRACE: Interface for numpy.testing._private has changed +LOG: Cached module numpy.testing._private has changed interface +TRACE: Priorities for _thread: threading:5 +TRACE: Priorities for threading: _thread:5 +LOG: Processing SCC of size 2 (_thread threading) as inherently stale with stale deps (_typeshed builtins collections.abc sys types typing typing_extensions) +LOG: Writing _thread /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_thread.pyi _thread.meta.json _thread.data.json +TRACE: Interface for _thread has changed +LOG: Cached module _thread has changed interface +LOG: Writing threading /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/threading.pyi threading.meta.json threading.data.json +TRACE: Interface for threading has changed +LOG: Cached module threading has changed interface +TRACE: Priorities for numpy.polynomial.polyutils: +LOG: Processing SCC singleton (numpy.polynomial.polyutils) as inherently stale with stale deps (builtins) +LOG: Writing numpy.polynomial.polyutils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/polynomial/polyutils.pyi numpy/polynomial/polyutils.meta.json numpy/polynomial/polyutils.data.json +TRACE: Interface for numpy.polynomial.polyutils has changed +LOG: Cached module numpy.polynomial.polyutils has changed interface +TRACE: Priorities for numpy.polynomial._polybase: +LOG: Processing SCC singleton (numpy.polynomial._polybase) as inherently stale with stale deps (abc builtins typing) +LOG: Writing numpy.polynomial._polybase /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/polynomial/_polybase.pyi numpy/polynomial/_polybase.meta.json numpy/polynomial/_polybase.data.json +TRACE: Interface for numpy.polynomial._polybase has changed +LOG: Cached module numpy.polynomial._polybase has changed interface +TRACE: Priorities for getpass: +LOG: Processing SCC singleton (getpass) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/getpass.pyi (getpass) +LOG: Writing getpass /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/getpass.pyi getpass.meta.json getpass.data.json +TRACE: Interface for getpass is unchanged +LOG: Cached module getpass has same interface +TRACE: Priorities for bdb: +LOG: Processing SCC singleton (bdb) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bdb.pyi (bdb) +LOG: Writing bdb /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bdb.pyi bdb.meta.json bdb.data.json +TRACE: Interface for bdb is unchanged +LOG: Cached module bdb has same interface +TRACE: Priorities for traceback: +LOG: Processing SCC singleton (traceback) as inherently stale with stale deps (_typeshed builtins collections.abc sys types typing typing_extensions) +LOG: Writing traceback /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/traceback.pyi traceback.meta.json traceback.data.json +TRACE: Interface for traceback has changed +LOG: Cached module traceback has changed interface +TRACE: Priorities for shutil: +LOG: Processing SCC singleton (shutil) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap os pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/shutil.pyi (shutil) +LOG: Writing shutil /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/shutil.pyi shutil.meta.json shutil.data.json +TRACE: Interface for shutil is unchanged +LOG: Cached module shutil has same interface +TRACE: Priorities for platform: +LOG: Processing SCC singleton (platform) as inherently stale with stale deps (builtins sys typing) +LOG: Writing platform /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/platform.pyi platform.meta.json platform.data.json +TRACE: Interface for platform has changed +LOG: Cached module platform has changed interface +TRACE: Priorities for gc: +LOG: Processing SCC singleton (gc) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/gc.pyi (gc) +LOG: Writing gc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/gc.pyi gc.meta.json gc.data.json +TRACE: Interface for gc is unchanged +LOG: Cached module gc has same interface +TRACE: Priorities for fnmatch: +LOG: Processing SCC singleton (fnmatch) as stale due to deps (abc builtins collections.abc typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/fnmatch.pyi (fnmatch) +LOG: Writing fnmatch /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/fnmatch.pyi fnmatch.meta.json fnmatch.data.json +TRACE: Interface for fnmatch is unchanged +LOG: Cached module fnmatch has same interface +TRACE: Priorities for iniconfig: +LOG: Processing SCC singleton (iniconfig) as stale due to deps (abc builtins typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/iniconfig/__init__.pyi (iniconfig) +LOG: Writing iniconfig /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/iniconfig/__init__.pyi iniconfig/__init__.meta.json iniconfig/__init__.data.json +TRACE: Interface for iniconfig is unchanged +LOG: Cached module iniconfig has same interface +TRACE: Priorities for pkgutil: +LOG: Processing SCC singleton (pkgutil) as stale due to deps (_typeshed abc builtins collections.abc importlib importlib.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pkgutil.pyi (pkgutil) +LOG: Writing pkgutil /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pkgutil.pyi pkgutil.meta.json pkgutil.data.json +TRACE: Interface for pkgutil is unchanged +LOG: Cached module pkgutil has same interface +TRACE: Priorities for gettext: +LOG: Processing SCC singleton (gettext) as stale due to deps (_typeshed abc builtins collections.abc io os sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/gettext.pyi (gettext) +LOG: Writing gettext /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/gettext.pyi gettext.meta.json gettext.data.json +TRACE: Interface for gettext is unchanged +LOG: Cached module gettext has same interface +TRACE: Priorities for textwrap: +LOG: Processing SCC singleton (textwrap) as inherently stale with stale deps (builtins collections.abc typing) +LOG: Writing textwrap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/textwrap.pyi textwrap.meta.json textwrap.data.json +TRACE: Interface for textwrap has changed +LOG: Cached module textwrap has changed interface +TRACE: Priorities for shlex: +LOG: Processing SCC singleton (shlex) as stale due to deps (_typeshed abc builtins collections.abc sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/shlex.pyi (shlex) +LOG: Writing shlex /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/shlex.pyi shlex.meta.json shlex.data.json +TRACE: Interface for shlex is unchanged +LOG: Cached module shlex has same interface +TRACE: Priorities for argparse: +LOG: Processing SCC singleton (argparse) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/argparse.pyi (argparse) +LOG: Writing argparse /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/argparse.pyi argparse.meta.json argparse.data.json +TRACE: Interface for argparse is unchanged +LOG: Cached module argparse has same interface +TRACE: Priorities for tempfile: +LOG: Processing SCC singleton (tempfile) as stale due to deps (_typeshed abc builtins collections.abc io sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/tempfile.pyi (tempfile) +LOG: Writing tempfile /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/tempfile.pyi tempfile.meta.json tempfile.data.json +TRACE: Interface for tempfile is unchanged +LOG: Cached module tempfile has same interface +TRACE: Priorities for pprint: +LOG: Processing SCC singleton (pprint) as stale due to deps (_typeshed abc builtins sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pprint.pyi (pprint) +LOG: Writing pprint /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pprint.pyi pprint.meta.json pprint.data.json +TRACE: Interface for pprint is unchanged +LOG: Cached module pprint has same interface +TRACE: Priorities for _pytest._version: +LOG: Processing SCC singleton (_pytest._version) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_version.py (_pytest._version) +LOG: Writing _pytest._version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_version.py _pytest/_version.meta.json _pytest/_version.data.json +TRACE: Interface for _pytest._version is unchanged +LOG: Cached module _pytest._version has same interface +TRACE: Priorities for ast: +LOG: Processing SCC singleton (ast) as inherently stale with stale deps (_ast builtins collections.abc sys typing typing_extensions) +LOG: Writing ast /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ast.pyi ast.meta.json ast.data.json +TRACE: Interface for ast has changed +LOG: Cached module ast has changed interface +TRACE: Priorities for zipfile: +LOG: Processing SCC singleton (zipfile) as inherently stale with stale deps (_typeshed builtins collections.abc io os sys types typing typing_extensions) +LOG: Writing zipfile 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colorsys.meta.json colorsys.data.json +TRACE: Interface for colorsys has changed +LOG: Cached module colorsys has changed interface +TRACE: Priorities for copy: +LOG: Processing SCC singleton (copy) as inherently stale with stale deps (builtins typing) +LOG: Writing copy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/copy.pyi copy.meta.json copy.data.json +TRACE: Interface for copy has changed +LOG: Cached module copy has changed interface +TRACE: Priorities for math: +LOG: Processing SCC singleton (math) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing typing_extensions) +LOG: Writing math /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/math.pyi math.meta.json math.data.json +TRACE: Interface for math has changed +LOG: Cached module math has changed interface +TRACE: Priorities for itertools: +LOG: Processing SCC singleton (itertools) as inherently stale with stale deps (_typeshed builtins collections.abc sys typing typing_extensions) +LOG: Writing itertools /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/itertools.pyi itertools.meta.json itertools.data.json +TRACE: Interface for itertools has changed +LOG: Cached module itertools has changed interface +TRACE: Priorities for numbers: +LOG: Processing SCC singleton (numbers) as inherently stale with stale deps (abc builtins typing) +LOG: Writing numbers /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/numbers.pyi numbers.meta.json numbers.data.json +TRACE: Interface for numbers has changed +LOG: Cached module numbers has changed interface +TRACE: Priorities for functools: +LOG: Processing SCC singleton (functools) as inherently stale with stale deps (_typeshed builtins collections.abc sys types typing typing_extensions) +LOG: Writing functools /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/functools.pyi functools.meta.json functools.data.json +TRACE: Interface for functools has changed +LOG: Cached module functools has changed interface +TRACE: Priorities for __future__: +LOG: Processing SCC singleton (__future__) as inherently stale with stale deps (builtins sys) +LOG: Writing __future__ /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/__future__.pyi __future__.meta.json __future__.data.json +TRACE: Interface for __future__ has changed +LOG: Cached module __future__ has changed interface +TRACE: Priorities for errno: +LOG: Processing SCC singleton (errno) as inherently stale with stale deps (builtins collections.abc) +LOG: Writing errno /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/errno.pyi errno.meta.json errno.data.json +TRACE: Interface for errno has changed +LOG: Cached module errno has changed interface +TRACE: Priorities for skfda.typing: +LOG: Processing SCC singleton (skfda.typing) as inherently stale with stale deps (builtins) +LOG: Writing skfda.typing /home/carlos/git/scikit-fda/skfda/typing/__init__.py skfda/typing/__init__.meta.json skfda/typing/__init__.data.json +TRACE: Interface for skfda.typing has changed +LOG: Cached module skfda.typing has changed interface +TRACE: Priorities for skfda.tests: +LOG: Processing SCC singleton (skfda.tests) as inherently stale with stale deps (builtins) +LOG: Writing skfda.tests /home/carlos/git/scikit-fda/skfda/tests/__init__.py skfda/tests/__init__.meta.json skfda/tests/__init__.data.json +TRACE: Interface for skfda.tests has changed +LOG: Cached module skfda.tests has changed interface +TRACE: 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Writing skfda.inference /home/carlos/git/scikit-fda/skfda/inference/__init__.py skfda/inference/__init__.meta.json skfda/inference/__init__.data.json +TRACE: Interface for skfda.inference is unchanged +LOG: Cached module skfda.inference has same interface +TRACE: Priorities for skfda.exploratory.outliers._directional_outlyingness_experiment_results: +LOG: Processing SCC singleton (skfda.exploratory.outliers._directional_outlyingness_experiment_results) as inherently stale with stale deps (builtins) +LOG: Writing skfda.exploratory.outliers._directional_outlyingness_experiment_results /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness_experiment_results.py skfda/exploratory/outliers/_directional_outlyingness_experiment_results.meta.json skfda/exploratory/outliers/_directional_outlyingness_experiment_results.data.json +TRACE: Interface for skfda.exploratory.outliers._directional_outlyingness_experiment_results has changed +LOG: Cached module 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typing_extensions) +LOG: Writing multiprocessing.reduction /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi multiprocessing/reduction.meta.json multiprocessing/reduction.data.json +TRACE: Interface for multiprocessing.reduction has changed +LOG: Cached module multiprocessing.reduction has changed interface +TRACE: Priorities for xarray.backends.lru_cache: +LOG: Processing SCC singleton (xarray.backends.lru_cache) as inherently stale with stale deps (builtins collections threading typing) +LOG: Writing xarray.backends.lru_cache /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/lru_cache.py xarray/backends/lru_cache.meta.json xarray/backends/lru_cache.data.json +TRACE: Interface for xarray.backends.lru_cache has changed +LOG: Cached module xarray.backends.lru_cache has changed interface +TRACE: Priorities for 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csv.meta.json csv.data.json +TRACE: Interface for csv has changed +LOG: Cached module csv has changed interface +TRACE: Priorities for xarray.core.pdcompat: +LOG: Processing SCC singleton (xarray.core.pdcompat) as inherently stale with stale deps (builtins distutils.version) +LOG: Writing xarray.core.pdcompat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/pdcompat.py xarray/core/pdcompat.meta.json xarray/core/pdcompat.data.json +TRACE: Interface for xarray.core.pdcompat has changed +LOG: Cached module xarray.core.pdcompat has changed interface +TRACE: Priorities for pyparsing.unicode: +LOG: Processing SCC singleton (pyparsing.unicode) as stale due to deps (_typeshed abc array builtins ctypes itertools mmap pickle sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/unicode.py (pyparsing.unicode) +LOG: Writing pyparsing.unicode 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py/__init__.data.json +TRACE: Interface for py is unchanged +LOG: Cached module py has same interface +TRACE: Priorities for stat: +LOG: Processing SCC singleton (stat) as stale due to deps (builtins) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/stat.pyi (stat) +LOG: Writing stat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/stat.pyi stat.meta.json stat.data.json +TRACE: Interface for stat is unchanged +LOG: Cached module stat has same interface +TRACE: Priorities for uuid: +LOG: Processing SCC singleton (uuid) as inherently stale with stale deps (builtins enum sys typing_extensions) +LOG: Writing uuid /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/uuid.pyi uuid.meta.json uuid.data.json +TRACE: Interface for uuid has changed +LOG: Cached module uuid has changed interface +TRACE: Priorities for bisect: +LOG: Processing SCC singleton (bisect) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bisect.pyi (bisect) +LOG: Writing bisect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bisect.pyi bisect.meta.json bisect.data.json +TRACE: Interface for bisect is unchanged +LOG: Cached module bisect has same interface +TRACE: Priorities for tokenize: +LOG: Processing SCC singleton (tokenize) as stale due to deps (_typeshed abc builtins collections.abc sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/tokenize.pyi (tokenize) +LOG: Writing tokenize 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bz2.pyi bz2.meta.json bz2.data.json +TRACE: Interface for bz2 has changed +LOG: Cached module bz2 has changed interface +TRACE: Priorities for _pytest._io.saferepr: +LOG: Processing SCC singleton (_pytest._io.saferepr) as stale due to deps (_typeshed abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/saferepr.py (_pytest._io.saferepr) +LOG: Writing _pytest._io.saferepr /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/saferepr.py _pytest/_io/saferepr.meta.json _pytest/_io/saferepr.data.json +TRACE: Interface for _pytest._io.saferepr is unchanged +LOG: Cached module _pytest._io.saferepr has same interface +TRACE: Priorities for weakref: +LOG: Processing SCC singleton (weakref) as inherently stale with stale deps (_typeshed _weakref _weakrefset builtins collections.abc sys typing typing_extensions) +LOG: Writing weakref /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/weakref.pyi weakref.meta.json weakref.data.json +TRACE: Interface for weakref has changed +LOG: Cached module weakref has changed interface +TRACE: Priorities for _pytest.timing: +LOG: Processing SCC singleton (_pytest.timing) as stale due to deps (abc builtins time typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/timing.py (_pytest.timing) +LOG: Writing _pytest.timing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/timing.py _pytest/timing.meta.json _pytest/timing.data.json +TRACE: Interface for _pytest.timing is unchanged +LOG: Cached module _pytest.timing has same interface +TRACE: Priorities for _pytest._argcomplete: +LOG: Processing SCC singleton (_pytest._argcomplete) as stale due to deps (_typeshed abc builtins genericpath glob os posixpath sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_argcomplete.py (_pytest._argcomplete) +LOG: Writing _pytest._argcomplete /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_argcomplete.py _pytest/_argcomplete.meta.json _pytest/_argcomplete.data.json +TRACE: Interface for _pytest._argcomplete is unchanged +LOG: Cached module _pytest._argcomplete has same interface +TRACE: Priorities for attr: attr.converters:10 attr.filters:10 attr.setters:10 attr.validators:10 +TRACE: Priorities for attr.validators: attr:5 +TRACE: Priorities for attr.setters: attr:5 +TRACE: Priorities for attr.filters: attr:5 +TRACE: Priorities for attr.converters: attr:5 +LOG: Processing SCC of size 5 (attr attr.validators attr.setters attr.filters attr.converters) as stale due to deps (_typeshed abc builtins sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/__init__.pyi (attr) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/validators.pyi (attr.validators) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/setters.pyi (attr.setters) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/filters.pyi (attr.filters) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/converters.pyi (attr.converters) +LOG: Writing attr /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/__init__.pyi attr/__init__.meta.json attr/__init__.data.json +TRACE: Interface for attr is unchanged +LOG: Cached module attr has same interface +LOG: Writing attr.validators /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/validators.pyi attr/validators.meta.json attr/validators.data.json +TRACE: Interface for attr.validators is unchanged +LOG: Cached module attr.validators has same interface +LOG: Writing attr.setters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/setters.pyi attr/setters.meta.json attr/setters.data.json +TRACE: Interface for attr.setters is unchanged +LOG: Cached module attr.setters has same interface +LOG: Writing attr.filters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/filters.pyi attr/filters.meta.json attr/filters.data.json +TRACE: Interface for attr.filters is unchanged +LOG: Cached module attr.filters has same interface +LOG: Writing attr.converters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/converters.pyi attr/converters.meta.json attr/converters.data.json +TRACE: Interface for attr.converters is unchanged +LOG: Cached module attr.converters has same interface +TRACE: Priorities for json: +LOG: Processing SCC singleton (json) as inherently stale with stale deps (_typeshed builtins collections.abc json.decoder json.encoder typing) +LOG: Writing json /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/json/__init__.pyi json/__init__.meta.json json/__init__.data.json +TRACE: Interface for json has changed +LOG: Cached module json has changed interface +TRACE: Priorities for dis: +LOG: Processing SCC singleton (dis) as inherently stale with stale deps (_typeshed builtins collections.abc opcode sys types typing typing_extensions) +LOG: Writing dis /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/dis.pyi dis.meta.json dis.data.json +TRACE: Interface for dis has changed +LOG: Cached module dis has changed interface +TRACE: Priorities for sre_parse: +LOG: Processing SCC singleton (sre_parse) as inherently stale with stale deps (builtins collections.abc sre_constants sys typing typing_extensions) +LOG: Writing sre_parse /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sre_parse.pyi sre_parse.meta.json sre_parse.data.json +TRACE: Interface for sre_parse has changed +LOG: Cached module sre_parse has changed interface +TRACE: Priorities for numpy.core.umath: +LOG: Processing SCC singleton (numpy.core.umath) as inherently stale with stale deps (builtins numpy.core) +LOG: Writing numpy.core.umath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/umath.py numpy/core/umath.meta.json numpy/core/umath.data.json +TRACE: Interface for numpy.core.umath has changed +LOG: Cached module numpy.core.umath has changed interface +TRACE: Priorities for numpy._typing._nested_sequence: +LOG: Processing SCC singleton (numpy._typing._nested_sequence) as inherently stale with stale deps (__future__ builtins typing) +LOG: Writing numpy._typing._nested_sequence /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/_typing/_nested_sequence.py numpy/_typing/_nested_sequence.meta.json numpy/_typing/_nested_sequence.data.json +TRACE: Interface for numpy._typing._nested_sequence has changed +LOG: Cached module numpy._typing._nested_sequence has changed interface +TRACE: Priorities for numpy.core.overrides: +LOG: Processing SCC singleton (numpy.core.overrides) as inherently stale with stale deps (builtins collections functools numpy.compat._inspect os) +LOG: Writing numpy.core.overrides /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/core/overrides.py numpy/core/overrides.meta.json numpy/core/overrides.data.json +TRACE: Interface for numpy.core.overrides has changed +LOG: Cached module numpy.core.overrides has changed interface +TRACE: Priorities for _pytest: +LOG: Processing SCC singleton (_pytest) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/__init__.py (_pytest) +LOG: Writing _pytest /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/__init__.py _pytest/__init__.meta.json _pytest/__init__.data.json +TRACE: Interface for _pytest is unchanged +LOG: Cached module _pytest has same interface +TRACE: Priorities for datetime: +LOG: Processing SCC singleton (datetime) as inherently stale with stale deps (_typeshed builtins sys time typing typing_extensions) +LOG: Writing datetime /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/datetime.pyi datetime.meta.json datetime.data.json +TRACE: Interface for datetime has changed +LOG: Cached module datetime has changed interface +TRACE: Priorities for operator: +LOG: Processing SCC singleton (operator) as inherently stale with stale deps (_operator builtins sys) +LOG: Writing operator /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/operator.pyi operator.meta.json operator.data.json +TRACE: Interface for operator has changed +LOG: Cached module operator has changed interface +TRACE: Priorities for dataclasses: +LOG: Processing SCC singleton (dataclasses) as inherently stale with stale deps (builtins collections.abc enum sys types typing typing_extensions) +LOG: Writing dataclasses /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/dataclasses.pyi dataclasses.meta.json dataclasses.data.json +TRACE: Interface for dataclasses has changed +LOG: Cached module dataclasses has changed interface +TRACE: Priorities for warnings: +LOG: Processing SCC singleton (warnings) as inherently stale with stale deps (_warnings builtins collections.abc sys types typing typing_extensions) +LOG: Writing warnings /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/warnings.pyi warnings.meta.json warnings.data.json +TRACE: Interface for warnings has changed +LOG: Cached module warnings has changed interface +TRACE: Priorities for ssl: +LOG: Processing SCC singleton (ssl) as stale due to deps (_socket _typeshed abc builtins collections.abc enum socket sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ssl.pyi (ssl) +LOG: Writing ssl /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/ssl.pyi ssl.meta.json ssl.data.json +TRACE: Interface for ssl is unchanged +LOG: Cached module ssl has same interface +TRACE: Priorities for multiprocessing.queues: +LOG: Processing SCC singleton (multiprocessing.queues) as inherently stale with stale deps (builtins queue sys typing) +LOG: Writing multiprocessing.queues /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi multiprocessing/queues.meta.json multiprocessing/queues.data.json +TRACE: Interface for multiprocessing.queues has changed +LOG: Cached module multiprocessing.queues has changed interface +TRACE: Priorities for multiprocessing.connection: +LOG: Processing SCC singleton (multiprocessing.connection) as inherently stale with stale deps (_typeshed builtins collections.abc socket sys types typing typing_extensions) +LOG: Writing multiprocessing.connection /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/connection.pyi multiprocessing/connection.meta.json multiprocessing/connection.data.json +TRACE: Interface for multiprocessing.connection has changed +LOG: Cached module multiprocessing.connection has changed interface +TRACE: Priorities for importlib_metadata._meta: +LOG: Processing SCC singleton (importlib_metadata._meta) as inherently stale with stale deps (builtins importlib_metadata._compat typing) +LOG: Writing importlib_metadata._meta /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_meta.py importlib_metadata/_meta.meta.json importlib_metadata/_meta.data.json +TRACE: Interface for importlib_metadata._meta has changed +LOG: Cached module importlib_metadata._meta has changed interface +TRACE: Priorities for pyparsing.results: +LOG: Processing SCC singleton (pyparsing.results) as stale due to deps (_collections_abc _typeshed _weakref abc array builtins collections.abc ctypes mmap pickle typing typing_extensions weakref) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/results.py (pyparsing.results) +LOG: Writing pyparsing.results /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/results.py pyparsing/results.meta.json pyparsing/results.data.json +TRACE: Interface for pyparsing.results is unchanged +LOG: Cached module pyparsing.results has same interface +TRACE: Priorities for pyparsing.util: +LOG: Processing SCC singleton (pyparsing.util) as stale due to deps (_typeshed _warnings abc array builtins collections ctypes functools itertools mmap pickle types typing typing_extensions warnings) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/util.py (pyparsing.util) +LOG: Writing pyparsing.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/util.py pyparsing/util.meta.json pyparsing/util.data.json +TRACE: Interface for pyparsing.util is unchanged +LOG: Cached module pyparsing.util has same interface +TRACE: Priorities for decimal: +LOG: Processing SCC singleton (decimal) as inherently stale with stale deps (_decimal builtins) +LOG: Writing decimal /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/decimal.pyi decimal.meta.json decimal.data.json +TRACE: Interface for decimal has changed +LOG: Cached module decimal has changed interface +TRACE: Priorities for numpy._version: +LOG: Processing SCC singleton (numpy._version) as inherently stale with stale deps (builtins json) +LOG: Writing numpy._version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/_version.py numpy/_version.meta.json numpy/_version.data.json +TRACE: Interface for numpy._version has changed +LOG: Cached module numpy._version has changed interface +TRACE: Priorities for _pytest.freeze_support: +LOG: Processing SCC singleton (_pytest.freeze_support) as stale due to deps (abc array builtins ctypes importlib importlib.abc mmap os pickle posixpath types typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/freeze_support.py (_pytest.freeze_support) +LOG: Writing _pytest.freeze_support /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/freeze_support.py _pytest/freeze_support.meta.json _pytest/freeze_support.data.json +TRACE: Interface for _pytest.freeze_support is unchanged +LOG: Cached module _pytest.freeze_support has same interface +TRACE: Priorities for inspect: +LOG: Processing SCC singleton (inspect) as inherently stale with stale deps (_typeshed builtins collections collections.abc dis enum sys types typing typing_extensions) +LOG: Writing inspect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/inspect.pyi inspect.meta.json inspect.data.json +TRACE: Interface for inspect has changed +LOG: Cached module inspect has changed interface +TRACE: Priorities for sre_compile: +LOG: Processing SCC singleton (sre_compile) as inherently stale with stale deps (builtins sre_constants sre_parse typing) +LOG: Writing sre_compile /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sre_compile.pyi sre_compile.meta.json sre_compile.data.json +TRACE: Interface for sre_compile has changed +LOG: Cached module sre_compile has changed interface +TRACE: Priorities for locale: +LOG: Processing SCC singleton (locale) as inherently stale with stale deps (_typeshed builtins collections.abc decimal sys typing) +LOG: Writing locale /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/locale.pyi locale.meta.json locale.data.json +TRACE: Interface for locale has changed +LOG: Cached module locale has changed interface +TRACE: Priorities for fractions: +LOG: Processing SCC singleton (fractions) as inherently stale with stale deps (_typeshed builtins collections.abc decimal numbers sys typing typing_extensions) +LOG: Writing fractions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/fractions.pyi fractions.meta.json fractions.data.json +TRACE: Interface for fractions has changed +LOG: Cached module fractions has changed interface +TRACE: Priorities for pdb: +LOG: Processing SCC singleton (pdb) as stale due to deps (_typeshed abc builtins collections.abc inspect sys types typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pdb.pyi (pdb) +LOG: Writing pdb /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pdb.pyi pdb.meta.json pdb.data.json +TRACE: Interface for pdb is unchanged +LOG: Cached module pdb has same interface +TRACE: Priorities for _pytest._code.source: +LOG: Processing SCC singleton (_pytest._code.source) as stale due to deps (_ast _typeshed abc array ast builtins ctypes inspect mmap pickle textwrap types typing typing_extensions warnings) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/source.py (_pytest._code.source) +LOG: Writing _pytest._code.source /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/source.py _pytest/_code/source.meta.json _pytest/_code/source.data.json +TRACE: Interface for _pytest._code.source is unchanged +LOG: Cached module _pytest._code.source has same interface +TRACE: Priorities for numpy.version: +LOG: Processing SCC singleton (numpy.version) as inherently stale with stale deps (__future__ builtins numpy._version) +LOG: Writing numpy.version /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/version.py numpy/version.meta.json numpy/version.data.json +TRACE: Interface for numpy.version has changed +LOG: Cached module numpy.version has changed interface +TRACE: Priorities for multimethod: +LOG: Processing SCC singleton (multimethod) as inherently stale with stale deps (abc builtins collections contextlib functools inspect itertools types typing) +LOG: Writing multimethod /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/multimethod/__init__.py multimethod/__init__.meta.json multimethod/__init__.data.json +TRACE: Interface for multimethod has changed +LOG: Cached module multimethod has changed interface +TRACE: Priorities for re: +LOG: Processing SCC singleton (re) as inherently stale with stale deps (_typeshed builtins collections.abc enum sre_compile sre_constants sys typing typing_extensions) +LOG: Writing re /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/re.pyi re.meta.json re.data.json +TRACE: Interface for re has changed +LOG: Cached module re has changed interface +TRACE: Priorities for jinja2._identifier: +LOG: Processing SCC singleton (jinja2._identifier) as stale due to deps (abc builtins enum re typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/_identifier.py (jinja2._identifier) +LOG: Writing jinja2._identifier /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/_identifier.py jinja2/_identifier.meta.json jinja2/_identifier.data.json +TRACE: Interface for jinja2._identifier is unchanged +LOG: Cached module jinja2._identifier has same interface +TRACE: Priorities for random: +LOG: Processing SCC singleton (random) as stale due to deps (_typeshed abc builtins collections.abc fractions numbers sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/random.pyi (random) +LOG: Writing random /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/random.pyi random.meta.json random.data.json +TRACE: Interface for random is unchanged +LOG: Cached module random has same interface +TRACE: Priorities for packaging._musllinux: +LOG: Processing SCC singleton (packaging._musllinux) as stale due to deps (_operator _typeshed abc array builtins contextlib ctypes enum functools io mmap operator os pickle re struct subprocess sys typing typing_extensions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_musllinux.py (packaging._musllinux) +LOG: Writing packaging._musllinux /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_musllinux.py packaging/_musllinux.meta.json packaging/_musllinux.data.json +TRACE: Interface for packaging._musllinux is unchanged +LOG: Cached module packaging._musllinux has same interface +TRACE: Priorities for packaging._manylinux: +LOG: Processing SCC singleton (packaging._manylinux) as stale due to deps (_typeshed _warnings abc array builtins collections ctypes enum functools io mmap os pickle re struct sys typing typing_extensions warnings) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_manylinux.py (packaging._manylinux) +LOG: Writing packaging._manylinux /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_manylinux.py packaging/_manylinux.meta.json packaging/_manylinux.data.json +TRACE: Interface for packaging._manylinux is unchanged +LOG: Cached module packaging._manylinux has same interface +TRACE: Priorities for importlib_metadata._text: +LOG: Processing SCC singleton (importlib_metadata._text) as inherently stale with stale deps (builtins importlib_metadata._functools re) +LOG: Writing importlib_metadata._text /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/importlib_metadata/_text.py importlib_metadata/_text.meta.json importlib_metadata/_text.data.json +TRACE: Interface for importlib_metadata._text has changed +LOG: Cached module importlib_metadata._text has 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multiprocessing.queues queue sys threading types typing weakref) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/process.pyi (concurrent.futures.process) +LOG: Writing concurrent.futures.process /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/process.pyi concurrent/futures/process.meta.json concurrent/futures/process.data.json +TRACE: Interface for concurrent.futures.process is unchanged +LOG: Cached module concurrent.futures.process has same interface +TRACE: Priorities for xarray.backends.locks: +LOG: Processing SCC singleton (xarray.backends.locks) as inherently stale with stale deps (builtins multiprocessing threading typing weakref) +LOG: Writing xarray.backends.locks /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/locks.py 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xarray.core.utils:5 xarray.core.dataarray:20 xarray.core.dataset:20 xarray.core.types:25 xarray.core.variable:20 xarray.core.weighted:25 xarray.core.computation:20 xarray.coding.cftimeindex:20 xarray.core.resample:20 xarray.core.resample_cftime:20 xarray.core.alignment:20 +TRACE: Priorities for xarray.core.accessor_dt: xarray.core.common:5 xarray.core.pycompat:5 xarray.coding.cftimeindex:20 xarray.core.dataset:20 +TRACE: Priorities for xarray.core._typed_ops: xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.groupby:5 xarray.core.types:5 xarray.core.variable:5 +TRACE: Priorities for xarray.plot.dataset_plot: xarray.core.alignment:5 xarray.plot.facetgrid:5 xarray.plot.utils:5 +TRACE: Priorities for xarray.core.arithmetic: xarray.core._typed_ops:5 xarray.core.common:5 xarray.core.ops:5 xarray.core.options:5 xarray.core.pycompat:5 xarray.core.computation:20 +TRACE: Priorities for xarray.core.accessor_str: xarray.core.computation:5 +TRACE: Priorities for xarray.plot.plot: 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xarray.coding.cftimeindex:5 xarray.coding.times:5 +TRACE: Priorities for xarray.ufuncs: xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.groupby:5 xarray.core.pycompat:5 xarray.core.variable:5 +TRACE: Priorities for xarray.testing: xarray.core.duck_array_ops:10 xarray.core.formatting:10 xarray.core.utils:10 xarray.core.dataarray:5 xarray.core.dataset:5 xarray.core.indexes:5 xarray.core.variable:5 +TRACE: Priorities for xarray.backends.common: xarray.core.indexing:10 xarray.conventions:5 xarray.core.pycompat:5 xarray.core.utils:5 +TRACE: Priorities for xarray.coding.frequencies: xarray.core.common:5 xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 xarray.core.dataarray:20 +TRACE: Priorities for xarray.backends.memory: xarray.core.variable:5 xarray.backends.common:5 +TRACE: Priorities for xarray.backends.store: xarray.conventions:10 xarray:20 xarray.core.dataset:5 xarray.backends.common:5 +TRACE: Priorities for xarray.backends.plugins: xarray.backends.common:5 +TRACE: 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xarray.backends.plugins:5 xarray.backends.pseudonetcdf_:5 xarray.backends.pydap_:5 xarray.backends.pynio_:5 xarray.backends.scipy_:5 xarray.backends.zarr:5 +TRACE: Priorities for xarray.convert: xarray.core.duck_array_ops:10 xarray.coding.times:5 xarray.conventions:5 xarray.core.dataarray:5 xarray.core.dtypes:5 xarray.core.pycompat:5 +TRACE: Priorities for xarray.core.resample_cftime: xarray.coding.cftime_offsets:5 xarray.coding.cftimeindex:5 +LOG: Processing SCC of size 72 (xarray.core.types xarray.core.utils xarray.core.dtypes xarray.core.pycompat xarray.core.options xarray.core.dask_array_compat xarray.core.nputils xarray.plot.utils xarray.core.rolling_exp xarray.backends.file_manager xarray.core.dask_array_ops xarray.core.duck_array_ops xarray.core._reductions xarray.core.nanops xarray.core.ops xarray.core.indexing xarray.core.formatting xarray.plot.facetgrid xarray.core.formatting_html xarray.core.indexes xarray.core.common xarray.core.accessor_dt xarray.core._typed_ops xarray.plot.dataset_plot xarray.core.arithmetic xarray.core.accessor_str xarray.plot.plot xarray.core.missing xarray.core.coordinates xarray.coding.variables xarray.coding.times xarray.core.groupby xarray.core.variable xarray.core.merge xarray.core.dataset xarray.core.dataarray xarray.core.concat xarray.core.computation xarray.core.alignment xarray.coding.cftimeindex xarray.backends.netcdf3 xarray.core.rolling xarray.core.resample xarray.core.weighted xarray.coding.strings xarray.core.parallel xarray.core.extensions xarray.core.combine xarray.conventions xarray.coding.cftime_offsets xarray.ufuncs xarray.testing xarray.backends.common xarray.coding.frequencies xarray.backends.memory xarray.backends.store xarray.backends.plugins xarray.backends.rasterio_ xarray.backends.api xarray.backends.scipy_ xarray.backends.pynio_ xarray.backends.pydap_ xarray.backends.pseudonetcdf_ xarray.backends.netCDF4_ xarray.backends.cfgrib_ xarray.backends.zarr xarray.tutorial xarray.backends.h5netcdf_ xarray xarray.backends xarray.convert xarray.core.resample_cftime) as inherently stale with stale deps (__future__ builtins codecs collections collections.abc contextlib copy datetime distutils.version enum functools glob gzip html importlib importlib.metadata importlib.resources importlib_metadata inspect io itertools logging numbers numpy numpy.core.multiarray numpy.core.numeric operator os pathlib re sys textwrap threading time traceback typing typing_extensions unicodedata uuid warnings xarray.backends.locks xarray.backends.lru_cache xarray.coding xarray.core xarray.core.npcompat xarray.core.pdcompat xarray.util.print_versions) +LOG: Writing xarray.core.types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/types.py xarray/core/types.meta.json xarray/core/types.data.json +TRACE: Interface for xarray.core.types has changed +LOG: Cached module xarray.core.types has changed interface +LOG: Writing xarray.core.utils 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interface +LOG: Writing xarray.core.resample_cftime /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/resample_cftime.py xarray/core/resample_cftime.meta.json xarray/core/resample_cftime.data.json +TRACE: Interface for xarray.core.resample_cftime has changed +LOG: Cached module xarray.core.resample_cftime has changed interface +TRACE: Priorities for dcor._fast_dcov_mergesort: +LOG: Processing SCC singleton (dcor._fast_dcov_mergesort) as inherently stale with stale deps (__future__ builtins dcor._utils numpy typing warnings) +LOG: Writing dcor._fast_dcov_mergesort /home/carlos/git/dcor/dcor/_fast_dcov_mergesort.py dcor/_fast_dcov_mergesort.meta.json dcor/_fast_dcov_mergesort.data.json +TRACE: Interface for dcor._fast_dcov_mergesort has changed +LOG: Cached module dcor._fast_dcov_mergesort has changed interface +TRACE: Priorities for dcor._fast_dcov_avl: +LOG: Processing SCC singleton (dcor._fast_dcov_avl) as inherently stale with stale deps (__future__ builtins dcor._utils math numpy typing warnings) +LOG: Writing dcor._fast_dcov_avl /home/carlos/git/dcor/dcor/_fast_dcov_avl.py dcor/_fast_dcov_avl.meta.json dcor/_fast_dcov_avl.data.json +TRACE: Interface for dcor._fast_dcov_avl has changed +LOG: Cached module dcor._fast_dcov_avl has changed interface +TRACE: Priorities for dcor._hypothesis: +LOG: Processing SCC singleton (dcor._hypothesis) as inherently stale with stale deps (__future__ builtins dataclasses dcor._utils numpy typing warnings) +LOG: Writing dcor._hypothesis /home/carlos/git/dcor/dcor/_hypothesis.py dcor/_hypothesis.meta.json dcor/_hypothesis.data.json +TRACE: Interface for dcor._hypothesis has changed +LOG: Cached module dcor._hypothesis has changed interface +TRACE: Priorities for rdata.parser: +LOG: Processing SCC singleton (rdata.parser) as inherently stale with stale deps (builtins rdata.parser._parser) +LOG: Writing rdata.parser /home/carlos/git/rdata/rdata/parser/__init__.py rdata/parser/__init__.meta.json rdata/parser/__init__.data.json +TRACE: Interface for rdata.parser has changed +LOG: Cached module rdata.parser has changed interface +TRACE: Priorities for dcor.distances: +LOG: Processing SCC singleton (dcor.distances) as inherently stale with stale deps (__future__ builtins dcor._utils numpy typing) +LOG: Writing dcor.distances /home/carlos/git/dcor/dcor/distances.py dcor/distances.meta.json dcor/distances.data.json +TRACE: Interface for dcor.distances has changed +LOG: Cached module dcor.distances has changed interface +TRACE: Priorities for skfda.typing._base: +LOG: Processing SCC singleton (skfda.typing._base) as inherently stale with stale deps (builtins numpy skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.typing._base /home/carlos/git/scikit-fda/skfda/typing/_base.py skfda/typing/_base.meta.json skfda/typing/_base.data.json +TRACE: Interface for skfda.typing._base has changed +LOG: Cached module skfda.typing._base has changed interface +TRACE: Priorities for skfda.misc.lstsq: +LOG: Processing SCC singleton (skfda.misc.lstsq) as inherently stale with stale deps (__future__ builtins numpy skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.misc.lstsq /home/carlos/git/scikit-fda/skfda/misc/lstsq.py skfda/misc/lstsq.meta.json skfda/misc/lstsq.data.json +TRACE: Interface for skfda.misc.lstsq has changed +LOG: Cached module skfda.misc.lstsq has changed interface +TRACE: Priorities for skfda.misc.kernels: +LOG: Processing SCC singleton (skfda.misc.kernels) as inherently stale with stale deps (builtins math numpy skfda.typing._numpy) +LOG: Writing skfda.misc.kernels /home/carlos/git/scikit-fda/skfda/misc/kernels.py skfda/misc/kernels.meta.json skfda/misc/kernels.data.json +TRACE: Interface for skfda.misc.kernels has changed +LOG: Cached module skfda.misc.kernels has changed interface +TRACE: Priorities for skfda._utils._sklearn_adapter: +LOG: Processing SCC singleton (skfda._utils._sklearn_adapter) as inherently stale with stale deps (__future__ abc builtins skfda.typing._numpy typing) +LOG: Writing skfda._utils._sklearn_adapter /home/carlos/git/scikit-fda/skfda/_utils/_sklearn_adapter.py skfda/_utils/_sklearn_adapter.meta.json skfda/_utils/_sklearn_adapter.data.json +TRACE: Interface for skfda._utils._sklearn_adapter has changed +LOG: Cached module skfda._utils._sklearn_adapter has changed interface +TRACE: Priorities for jinja2.bccache: jinja2.environment:25 +TRACE: Priorities for jinja2.utils: jinja2.runtime:20 jinja2.environment:20 jinja2.lexer:20 jinja2.exceptions:30 +TRACE: Priorities for jinja2.exceptions: jinja2.runtime:20 +TRACE: Priorities for jinja2.async_utils: jinja2.utils:5 +TRACE: Priorities for jinja2.debug: jinja2.exceptions:5 jinja2.utils:5 jinja2.runtime:25 +TRACE: Priorities for jinja2.lexer: jinja2.exceptions:5 jinja2.utils:5 jinja2.environment:25 +TRACE: Priorities for jinja2.nodes: jinja2.utils:5 jinja2.environment:25 jinja2.compiler:20 jinja2.runtime:30 +TRACE: Priorities for jinja2.loaders: jinja2.exceptions:5 jinja2.utils:5 jinja2.environment:25 jinja2.bccache:30 jinja2.nodes:30 jinja2.runtime:30 +TRACE: Priorities for jinja2.visitor: jinja2.nodes:5 +TRACE: Priorities for jinja2.parser: jinja2.nodes:10 jinja2:20 jinja2.exceptions:5 jinja2.lexer:5 jinja2.environment:25 jinja2.ext:30 +TRACE: Priorities for jinja2.runtime: jinja2.async_utils:5 jinja2.exceptions:5 jinja2.nodes:5 jinja2.utils:5 jinja2.environment:25 +TRACE: Priorities for jinja2.tests: jinja2.runtime:5 jinja2.utils:5 jinja2.environment:25 jinja2.exceptions:30 +TRACE: Priorities for jinja2.filters: jinja2.async_utils:5 jinja2.exceptions:5 jinja2.runtime:5 jinja2.utils:5 jinja2.environment:25 jinja2.nodes:25 jinja2.sandbox:25 +TRACE: Priorities for jinja2.optimizer: jinja2.nodes:10 jinja2:20 jinja2.visitor:5 jinja2.environment:25 +TRACE: Priorities for jinja2.idtracking: jinja2.nodes:10 jinja2:20 jinja2.visitor:5 +TRACE: Priorities for jinja2.defaults: jinja2.filters:5 jinja2.tests:5 jinja2.utils:5 +TRACE: Priorities for jinja2.compiler: jinja2.nodes:5 jinja2:20 jinja2.exceptions:5 jinja2.idtracking:5 jinja2.optimizer:5 jinja2.utils:5 jinja2.visitor:5 jinja2.environment:25 jinja2.runtime:20 +TRACE: Priorities for jinja2.environment: jinja2.nodes:5 jinja2:20 jinja2.compiler:5 jinja2.defaults:5 jinja2.exceptions:5 jinja2.lexer:5 jinja2.parser:5 jinja2.runtime:5 jinja2.utils:5 jinja2.bccache:25 jinja2.ext:25 jinja2.loaders:20 jinja2.debug:20 jinja2.visitor:30 +TRACE: Priorities for jinja2: jinja2.bccache:5 jinja2.environment:5 jinja2.exceptions:5 jinja2.loaders:5 jinja2.runtime:5 jinja2.utils:5 +TRACE: Priorities for jinja2.sandbox: jinja2.environment:5 jinja2.exceptions:5 jinja2.runtime:5 jinja2.bccache:30 jinja2.ext:30 jinja2.loaders:30 +TRACE: Priorities for jinja2.ext: jinja2.defaults:10 jinja2:20 jinja2.nodes:10 jinja2.environment:5 jinja2.exceptions:5 jinja2.runtime:5 jinja2.utils:5 jinja2.lexer:25 jinja2.parser:25 jinja2.bccache:30 jinja2.loaders:30 +LOG: Processing SCC of size 21 (jinja2.bccache jinja2.utils jinja2.exceptions jinja2.async_utils jinja2.debug jinja2.lexer jinja2.nodes jinja2.loaders jinja2.visitor jinja2.parser jinja2.runtime jinja2.tests jinja2.filters jinja2.optimizer jinja2.idtracking jinja2.defaults jinja2.compiler jinja2.environment jinja2 jinja2.sandbox jinja2.ext) as stale due to deps (_ast _collections_abc _operator _typeshed _weakref abc array ast builtins collections collections.abc contextlib ctypes enum errno functools genericpath importlib importlib.abc importlib.machinery inspect io itertools json json.encoder logging math mmap numbers operator os pickle posixpath re string sys textwrap threading types typing typing_extensions weakref zipfile) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/bccache.py (jinja2.bccache) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/utils.py (jinja2.utils) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/exceptions.py (jinja2.exceptions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/async_utils.py (jinja2.async_utils) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/debug.py (jinja2.debug) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/lexer.py (jinja2.lexer) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/nodes.py (jinja2.nodes) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/loaders.py (jinja2.loaders) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/visitor.py (jinja2.visitor) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/parser.py (jinja2.parser) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/runtime.py (jinja2.runtime) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/tests.py (jinja2.tests) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/filters.py (jinja2.filters) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/optimizer.py (jinja2.optimizer) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/idtracking.py (jinja2.idtracking) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/defaults.py (jinja2.defaults) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/compiler.py (jinja2.compiler) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/environment.py (jinja2.environment) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/__init__.py (jinja2) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/sandbox.py (jinja2.sandbox) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/ext.py (jinja2.ext) +LOG: Writing jinja2.bccache /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/bccache.py jinja2/bccache.meta.json jinja2/bccache.data.json +TRACE: Interface for jinja2.bccache is unchanged +LOG: Cached module jinja2.bccache has same interface +LOG: Writing jinja2.utils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/utils.py jinja2/utils.meta.json jinja2/utils.data.json +TRACE: Interface for jinja2.utils is unchanged +LOG: Cached module jinja2.utils has same interface +LOG: Writing jinja2.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/exceptions.py jinja2/exceptions.meta.json jinja2/exceptions.data.json +TRACE: Interface for jinja2.exceptions is unchanged +LOG: Cached module jinja2.exceptions has same interface +LOG: Writing jinja2.async_utils /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/async_utils.py jinja2/async_utils.meta.json jinja2/async_utils.data.json +TRACE: Interface for jinja2.async_utils is unchanged +LOG: Cached module jinja2.async_utils has same interface +LOG: Writing jinja2.debug /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/debug.py jinja2/debug.meta.json jinja2/debug.data.json +TRACE: Interface for jinja2.debug is unchanged +LOG: Cached module jinja2.debug has same interface +LOG: Writing jinja2.lexer /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/lexer.py jinja2/lexer.meta.json jinja2/lexer.data.json +TRACE: Interface for jinja2.lexer is unchanged +LOG: Cached module jinja2.lexer has same interface +LOG: Writing jinja2.nodes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/nodes.py jinja2/nodes.meta.json jinja2/nodes.data.json +TRACE: Interface for jinja2.nodes is unchanged +LOG: Cached module jinja2.nodes has same interface +LOG: Writing jinja2.loaders /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/loaders.py jinja2/loaders.meta.json jinja2/loaders.data.json +TRACE: Interface for jinja2.loaders is unchanged +LOG: Cached module jinja2.loaders has same interface +LOG: Writing jinja2.visitor /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/visitor.py jinja2/visitor.meta.json jinja2/visitor.data.json +TRACE: Interface for jinja2.visitor is unchanged +LOG: Cached module jinja2.visitor has same interface +LOG: Writing jinja2.parser /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/parser.py jinja2/parser.meta.json jinja2/parser.data.json +TRACE: Interface for jinja2.parser is unchanged +LOG: Cached module jinja2.parser has same interface +LOG: Writing jinja2.runtime /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/runtime.py jinja2/runtime.meta.json jinja2/runtime.data.json +TRACE: Interface for jinja2.runtime is unchanged +LOG: Cached module jinja2.runtime has same interface +LOG: Writing jinja2.tests /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/tests.py jinja2/tests.meta.json jinja2/tests.data.json +TRACE: Interface for jinja2.tests is unchanged +LOG: Cached module jinja2.tests has same interface +LOG: Writing jinja2.filters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/filters.py jinja2/filters.meta.json jinja2/filters.data.json +TRACE: Interface for jinja2.filters is unchanged +LOG: Cached module jinja2.filters has same interface +LOG: Writing jinja2.optimizer /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/optimizer.py jinja2/optimizer.meta.json jinja2/optimizer.data.json +TRACE: Interface for jinja2.optimizer is unchanged +LOG: Cached module jinja2.optimizer has same interface +LOG: Writing jinja2.idtracking /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/idtracking.py jinja2/idtracking.meta.json jinja2/idtracking.data.json +TRACE: Interface for jinja2.idtracking is unchanged +LOG: Cached module jinja2.idtracking has same interface +LOG: Writing jinja2.defaults /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/defaults.py jinja2/defaults.meta.json jinja2/defaults.data.json +TRACE: Interface for jinja2.defaults is unchanged +LOG: Cached module jinja2.defaults has same interface +LOG: Writing jinja2.compiler /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/compiler.py jinja2/compiler.meta.json jinja2/compiler.data.json +TRACE: Interface for jinja2.compiler is unchanged +LOG: Cached module jinja2.compiler has same interface +LOG: Writing jinja2.environment /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/environment.py jinja2/environment.meta.json jinja2/environment.data.json +TRACE: Interface for jinja2.environment is unchanged +LOG: Cached module jinja2.environment has same interface +LOG: Writing jinja2 /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/__init__.py jinja2/__init__.meta.json jinja2/__init__.data.json +TRACE: Interface for jinja2 is unchanged +LOG: Cached module jinja2 has same interface +LOG: Writing jinja2.sandbox /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/sandbox.py jinja2/sandbox.meta.json jinja2/sandbox.data.json +TRACE: Interface for jinja2.sandbox is unchanged +LOG: Cached module jinja2.sandbox has same interface +LOG: Writing jinja2.ext /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/ext.py jinja2/ext.meta.json jinja2/ext.data.json +TRACE: Interface for jinja2.ext is unchanged +LOG: Cached module jinja2.ext has same interface +TRACE: Priorities for xarray.plot: +LOG: Processing SCC singleton (xarray.plot) as inherently stale with stale deps (builtins xarray.plot.dataset_plot xarray.plot.facetgrid xarray.plot.plot) +LOG: Writing xarray.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py xarray/plot/__init__.meta.json xarray/plot/__init__.data.json +TRACE: Interface for xarray.plot has changed +LOG: Cached module xarray.plot has changed interface +TRACE: Priorities for rdata.conversion._conversion: rdata:20 +TRACE: Priorities for rdata.conversion: rdata.conversion._conversion:5 +TRACE: Priorities for rdata: rdata.conversion:10 +LOG: Processing SCC of size 3 (rdata.conversion._conversion rdata.conversion rdata) as inherently stale with stale deps (__future__ abc builtins dataclasses errno fractions numpy os pathlib rdata.parser types typing warnings xarray) +LOG: Writing rdata.conversion._conversion /home/carlos/git/rdata/rdata/conversion/_conversion.py rdata/conversion/_conversion.meta.json rdata/conversion/_conversion.data.json +TRACE: Interface for rdata.conversion._conversion has changed +LOG: Cached module rdata.conversion._conversion has changed interface +LOG: Writing rdata.conversion /home/carlos/git/rdata/rdata/conversion/__init__.py rdata/conversion/__init__.meta.json rdata/conversion/__init__.data.json +TRACE: Interface for rdata.conversion has changed +LOG: Cached module rdata.conversion has changed interface +LOG: Writing rdata /home/carlos/git/rdata/rdata/__init__.py rdata/__init__.meta.json rdata/__init__.data.json +TRACE: Interface for rdata has changed +LOG: Cached module rdata has changed interface +TRACE: Priorities for dcor._energy: dcor:20 +TRACE: Priorities for dcor._dcor_internals: dcor:20 +TRACE: Priorities for dcor._partial_dcor: dcor._dcor_internals:5 +TRACE: Priorities for dcor._dcor: dcor._dcor_internals:5 +TRACE: Priorities for dcor.homogeneity: dcor:20 dcor._energy:5 +TRACE: Priorities for dcor._rowwise: dcor._dcor:10 dcor:20 +TRACE: Priorities for dcor.independence: dcor._dcor:5 dcor._dcor_internals:5 +TRACE: Priorities for dcor: dcor.homogeneity:10 dcor.independence:10 dcor._dcor:5 dcor._dcor_internals:5 dcor._energy:5 dcor._partial_dcor:5 dcor._rowwise:5 +LOG: Processing SCC of size 8 (dcor._energy dcor._dcor_internals dcor._partial_dcor dcor._dcor dcor.homogeneity dcor._rowwise dcor.independence dcor) as inherently stale with stale deps (__future__ builtins dataclasses dcor._fast_dcov_avl dcor._fast_dcov_mergesort dcor._hypothesis dcor._utils dcor.distances enum errno numpy os pathlib typing typing_extensions warnings) +LOG: Writing dcor._energy /home/carlos/git/dcor/dcor/_energy.py dcor/_energy.meta.json dcor/_energy.data.json +TRACE: Interface for dcor._energy has changed +LOG: Cached module dcor._energy has changed interface +LOG: Writing dcor._dcor_internals /home/carlos/git/dcor/dcor/_dcor_internals.py dcor/_dcor_internals.meta.json dcor/_dcor_internals.data.json +TRACE: Interface for dcor._dcor_internals has changed +LOG: Cached module dcor._dcor_internals has changed interface +LOG: Writing dcor._partial_dcor /home/carlos/git/dcor/dcor/_partial_dcor.py dcor/_partial_dcor.meta.json dcor/_partial_dcor.data.json +TRACE: Interface for dcor._partial_dcor has changed +LOG: Cached module dcor._partial_dcor has changed interface +LOG: Writing dcor._dcor /home/carlos/git/dcor/dcor/_dcor.py dcor/_dcor.meta.json dcor/_dcor.data.json +TRACE: Interface for dcor._dcor has changed +LOG: Cached module dcor._dcor has changed interface +LOG: Writing dcor.homogeneity /home/carlos/git/dcor/dcor/homogeneity.py dcor/homogeneity.meta.json dcor/homogeneity.data.json +TRACE: Interface for dcor.homogeneity has changed +LOG: Cached module dcor.homogeneity has changed interface +LOG: Writing dcor._rowwise /home/carlos/git/dcor/dcor/_rowwise.py dcor/_rowwise.meta.json dcor/_rowwise.data.json +TRACE: Interface for dcor._rowwise has changed +LOG: Cached module dcor._rowwise has changed interface +LOG: Writing dcor.independence /home/carlos/git/dcor/dcor/independence.py dcor/independence.meta.json dcor/independence.data.json +TRACE: Interface for dcor.independence has changed +LOG: Cached module dcor.independence has changed interface +LOG: Writing dcor /home/carlos/git/dcor/dcor/__init__.py dcor/__init__.meta.json dcor/__init__.data.json +TRACE: Interface for dcor has changed +LOG: Cached module dcor has changed interface +TRACE: Priorities for skfda.typing._metric: +LOG: Processing SCC singleton (skfda.typing._metric) as inherently stale with stale deps (abc builtins skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.typing._metric /home/carlos/git/scikit-fda/skfda/typing/_metric.py skfda/typing/_metric.meta.json skfda/typing/_metric.data.json +TRACE: Interface for skfda.typing._metric has changed +LOG: Cached module skfda.typing._metric has changed interface +TRACE: Priorities for skfda.exploratory.depth.multivariate: +LOG: Processing SCC singleton (skfda.exploratory.depth.multivariate) as inherently stale with stale deps (__future__ abc builtins math numpy skfda._utils._sklearn_adapter skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.exploratory.depth.multivariate /home/carlos/git/scikit-fda/skfda/exploratory/depth/multivariate.py skfda/exploratory/depth/multivariate.meta.json skfda/exploratory/depth/multivariate.data.json +TRACE: Interface for skfda.exploratory.depth.multivariate has changed +LOG: Cached module skfda.exploratory.depth.multivariate has changed interface +TRACE: Priorities for pyparsing.exceptions: pyparsing.core:20 +TRACE: Priorities for pyparsing.actions: pyparsing.exceptions:5 pyparsing.core:20 +TRACE: Priorities for pyparsing.core: pyparsing.exceptions:5 pyparsing.actions:5 pyparsing.testing:20 pyparsing.diagram:20 +TRACE: Priorities for pyparsing.testing: pyparsing.core:5 pyparsing.exceptions:30 +TRACE: Priorities for pyparsing.common: pyparsing.core:5 pyparsing.helpers:5 pyparsing.exceptions:30 +TRACE: Priorities for pyparsing.helpers: pyparsing.core:5 pyparsing:5 pyparsing.actions:30 pyparsing.exceptions:30 +TRACE: Priorities for pyparsing: pyparsing.exceptions:5 pyparsing.actions:5 pyparsing.core:5 pyparsing.helpers:5 pyparsing.testing:5 pyparsing.common:5 +TRACE: Priorities for pyparsing.diagram: pyparsing:10 pyparsing.core:30 +LOG: Processing SCC of size 8 (pyparsing.exceptions pyparsing.actions pyparsing.core pyparsing.testing pyparsing.common pyparsing.helpers pyparsing pyparsing.diagram) as stale due to deps (_collections_abc _operator _typeshed _warnings abc array builtins collections.abc contextlib copy ctypes datetime enum functools html inspect io mmap numpy numpy.ma numpy.ma.core operator os pathlib pickle re sre_constants string sys threading traceback types typing typing_extensions warnings) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/exceptions.py (pyparsing.exceptions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/actions.py (pyparsing.actions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/core.py (pyparsing.core) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/testing.py (pyparsing.testing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/common.py (pyparsing.common) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/helpers.py (pyparsing.helpers) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/__init__.py (pyparsing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/diagram/__init__.py (pyparsing.diagram) +LOG: Writing pyparsing.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/exceptions.py pyparsing/exceptions.meta.json pyparsing/exceptions.data.json +TRACE: Interface for pyparsing.exceptions is unchanged +LOG: Cached module pyparsing.exceptions has same interface +LOG: Writing pyparsing.actions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/actions.py pyparsing/actions.meta.json pyparsing/actions.data.json +TRACE: Interface for pyparsing.actions is unchanged +LOG: Cached module pyparsing.actions has same interface +LOG: Writing pyparsing.core /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/core.py pyparsing/core.meta.json pyparsing/core.data.json +TRACE: Interface for pyparsing.core is unchanged +LOG: Cached module pyparsing.core has same interface +LOG: Writing pyparsing.testing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/testing.py pyparsing/testing.meta.json pyparsing/testing.data.json +TRACE: Interface for pyparsing.testing is unchanged +LOG: Cached module pyparsing.testing has same interface +LOG: Writing pyparsing.common /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/common.py pyparsing/common.meta.json pyparsing/common.data.json +TRACE: Interface for pyparsing.common is unchanged +LOG: Cached module pyparsing.common has same interface +LOG: Writing pyparsing.helpers /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/helpers.py pyparsing/helpers.meta.json pyparsing/helpers.data.json +TRACE: Interface for pyparsing.helpers is unchanged +LOG: Cached module pyparsing.helpers has same interface +LOG: Writing pyparsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/__init__.py pyparsing/__init__.meta.json pyparsing/__init__.data.json +TRACE: Interface for pyparsing is unchanged +LOG: Cached module pyparsing has same interface +LOG: Writing pyparsing.diagram /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/diagram/__init__.py pyparsing/diagram/__init__.meta.json pyparsing/diagram/__init__.data.json +TRACE: Interface for pyparsing.diagram is unchanged +LOG: Cached module pyparsing.diagram has same interface +TRACE: Priorities for skfda.misc.metrics._parse: +LOG: Processing SCC singleton (skfda.misc.metrics._parse) as inherently stale with stale deps (builtins enum skfda.typing._metric typing typing_extensions) +LOG: Writing skfda.misc.metrics._parse /home/carlos/git/scikit-fda/skfda/misc/metrics/_parse.py skfda/misc/metrics/_parse.meta.json skfda/misc/metrics/_parse.data.json +TRACE: Interface for skfda.misc.metrics._parse has changed +LOG: Cached module skfda.misc.metrics._parse has changed interface +TRACE: Priorities for packaging.markers: +LOG: Processing SCC singleton (packaging.markers) as stale due to deps (_operator _typeshed abc array builtins ctypes mmap operator os pickle platform sys typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/markers.py (packaging.markers) +LOG: Writing packaging.markers /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/markers.py packaging/markers.meta.json packaging/markers.data.json +TRACE: Interface for packaging.markers is unchanged +LOG: Cached module packaging.markers has same interface +TRACE: Priorities for skfda.representation.basis: skfda.representation.basis._basis:25 skfda.representation.basis._bspline:25 skfda.representation.basis._constant:25 skfda.representation.basis._fdatabasis:25 skfda.representation.basis._finite_element:25 skfda.representation.basis._fourier:25 skfda.representation.basis._monomial:25 skfda.representation.basis._tensor_basis:25 skfda.representation.basis._vector_basis:25 +TRACE: Priorities for skfda.representation: skfda.representation._functional_data:25 skfda.representation.basis:25 skfda.representation.grid:25 +TRACE: Priorities for skfda.preprocessing.dim_reduction: skfda.preprocessing.dim_reduction._fpca:25 +TRACE: Priorities for skfda.misc.regularization: skfda.misc.regularization._regularization:25 +TRACE: Priorities for skfda.misc.operators: skfda.misc.operators._identity:25 skfda.misc.operators._integral_transform:25 skfda.misc.operators._linear_differential_operator:25 skfda.misc.operators._operators:25 skfda.misc.operators._srvf:25 +TRACE: Priorities for skfda.misc.metrics: skfda.misc.metrics._angular:25 skfda.misc.metrics._fisher_rao:25 skfda.misc.metrics._lp_distances:25 skfda.misc.metrics._lp_norms:25 skfda.misc.metrics._mahalanobis:25 skfda.misc.metrics._utils:25 +TRACE: Priorities for skfda.misc: skfda.misc._math:25 +TRACE: Priorities for skfda.exploratory.stats: skfda.exploratory.stats._fisher_rao:25 skfda.exploratory.stats._functional_transformers:25 skfda.exploratory.stats._stats:25 +TRACE: Priorities for skfda.exploratory.depth: skfda.exploratory.depth._depth:25 +TRACE: Priorities for skfda._utils: skfda._utils._utils:25 skfda._utils._warping:25 +TRACE: Priorities for skfda: skfda.representation:25 +TRACE: Priorities for skfda.preprocessing.smoothing._linear: skfda._utils:5 skfda.representation:5 skfda.preprocessing.smoothing.validation:20 +TRACE: Priorities for skfda.preprocessing.smoothing.validation: skfda.representation:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.registration: skfda._utils:5 skfda.preprocessing.registration._fisher_rao:25 skfda.preprocessing.registration._landmark_registration:25 skfda.preprocessing.registration._lstsq_shift_registration:25 +TRACE: Priorities for skfda.misc.validation: skfda.representation:5 +TRACE: Priorities for skfda.misc.operators._operators: skfda.representation:5 skfda.representation.basis:5 skfda.misc:20 +TRACE: Priorities for skfda.misc.metrics._utils: skfda._utils:5 skfda.representation:5 +TRACE: Priorities for skfda.misc.metrics._lp_norms: skfda.representation:5 skfda.misc:20 +TRACE: Priorities for skfda._utils._utils: skfda.representation:25 skfda.representation.basis:25 skfda.representation.extrapolation:25 skfda:20 +TRACE: Priorities for skfda.representation.evaluator: skfda.representation._functional_data:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.basis._basis: skfda.representation.basis._fdatabasis:25 skfda.misc.validation:20 skfda.representation.basis:20 skfda.misc:20 skfda._utils:20 +TRACE: Priorities for skfda.preprocessing.smoothing._basis: skfda._utils:5 skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.registration.base: skfda.representation:5 skfda.preprocessing.registration.validation:20 +TRACE: Priorities for skfda.preprocessing.registration.validation: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 skfda.preprocessing.registration.base:5 skfda.misc.metrics:20 +TRACE: Priorities for skfda.misc.regularization._regularization: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._srvf: skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._linear_differential_operator: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._integral_transform: skfda.representation:5 skfda.misc.operators._operators:5 +TRACE: Priorities for skfda.misc.operators._identity: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 skfda.misc.metrics:20 +TRACE: Priorities for skfda.misc.metrics._lp_distances: skfda.representation:5 skfda.misc.metrics._lp_norms:5 skfda.misc.metrics._utils:5 skfda.misc:20 +TRACE: Priorities for skfda.misc._math: skfda._utils:5 skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 +TRACE: Priorities for skfda.exploratory.stats._functional_transformers: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 +TRACE: Priorities for skfda.exploratory.depth._depth: skfda.misc.metrics:5 skfda.misc.metrics._utils:5 skfda.representation:5 +TRACE: Priorities for skfda._utils._warping: skfda.representation:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.interpolation: skfda.representation.evaluator:5 skfda.representation.grid:25 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.extrapolation: skfda.representation.evaluator:5 skfda.representation._functional_data:25 +TRACE: Priorities for skfda.representation.basis._vector_basis: skfda.representation.basis._basis:5 skfda._utils:20 +TRACE: Priorities for skfda.representation.basis._tensor_basis: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._monomial: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._fourier: skfda.representation.basis._basis:5 skfda.misc.validation:20 +TRACE: Priorities for skfda.representation.basis._finite_element: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._constant: skfda.representation.basis._basis:5 +TRACE: Priorities for skfda.representation.basis._bspline: skfda.representation.basis._basis:5 skfda.misc.validation:20 +TRACE: Priorities for skfda.misc.metrics._mahalanobis: skfda.representation:5 skfda.representation.basis:5 skfda.misc._math:5 skfda.misc.regularization._regularization:5 skfda.preprocessing.dim_reduction:20 +TRACE: Priorities for skfda.misc.metrics._fisher_rao: skfda._utils:5 skfda.representation:5 skfda.misc.operators:5 skfda.misc.metrics._lp_distances:5 skfda.misc.metrics._utils:5 skfda.preprocessing.registration:20 +TRACE: Priorities for skfda.misc.metrics._angular: skfda.representation:5 skfda.misc._math:5 skfda.misc.metrics._utils:5 +TRACE: Priorities for skfda.exploratory.stats._stats: skfda.misc.metrics._lp_distances:5 skfda.representation:5 skfda.exploratory.depth:5 +TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 +TRACE: Priorities for skfda.preprocessing.registration._landmark_registration: skfda.representation:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 +TRACE: Priorities for skfda.representation._functional_data: skfda._utils:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.grid:25 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 +TRACE: Priorities for skfda.exploratory.visualization._utils: skfda.representation._functional_data:5 +TRACE: Priorities for skfda.exploratory.visualization._baseplot: skfda.representation:5 skfda.exploratory.visualization._utils:5 +TRACE: Priorities for skfda.exploratory.visualization.representation: skfda._utils:5 skfda.misc.validation:5 skfda.representation._functional_data:5 skfda.exploratory.visualization._baseplot:5 skfda.exploratory.visualization._utils:5 +TRACE: Priorities for skfda.exploratory.stats._fisher_rao: skfda._utils:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.interpolation:5 +TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 +TRACE: Priorities for skfda.misc.hat_matrix: skfda.misc:20 skfda.representation._functional_data:5 skfda.representation.basis:5 +TRACE: Priorities for skfda.preprocessing.smoothing._kernel_smoothers: skfda._utils._utils:5 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.preprocessing.smoothing: skfda.preprocessing.smoothing.kernel_smoothers:20 skfda.preprocessing.smoothing._basis:25 skfda.preprocessing.smoothing._kernel_smoothers:25 +TRACE: Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 +TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 +TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 +TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 +LOG: Processing SCC of size 62 (skfda.representation.basis skfda.representation skfda.preprocessing.dim_reduction skfda.misc.regularization skfda.misc.operators skfda.misc.metrics skfda.misc skfda.exploratory.stats skfda.exploratory.depth skfda._utils skfda skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda.preprocessing.registration skfda.misc.validation skfda.misc.operators._operators skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda._utils._utils skfda.representation.evaluator skfda.representation.basis._basis skfda.preprocessing.smoothing._basis skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.misc.regularization._regularization skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.operators._identity skfda.misc.metrics._lp_distances skfda.misc._math skfda.exploratory.stats._functional_transformers skfda.exploratory.depth._depth skfda._utils._warping skfda.representation.interpolation skfda.representation.extrapolation skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.misc.metrics._mahalanobis skfda.misc.metrics._fisher_rao skfda.misc.metrics._angular skfda.exploratory.stats._stats skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.exploratory.stats._fisher_rao skfda.preprocessing.registration._fisher_rao skfda.misc.hat_matrix skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.representation.grid skfda.representation.basis._fdatabasis skfda.preprocessing.dim_reduction._fpca) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses dcor errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._parse skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) +LOG: Writing skfda.representation.basis /home/carlos/git/scikit-fda/skfda/representation/basis/__init__.py skfda/representation/basis/__init__.meta.json skfda/representation/basis/__init__.data.json +TRACE: Interface for skfda.representation.basis has changed +LOG: Cached module skfda.representation.basis has changed interface +LOG: Writing skfda.representation /home/carlos/git/scikit-fda/skfda/representation/__init__.py skfda/representation/__init__.meta.json skfda/representation/__init__.data.json +TRACE: Interface for skfda.representation has changed +LOG: Cached module skfda.representation has changed interface +LOG: Writing skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json skfda/preprocessing/dim_reduction/__init__.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction has changed +LOG: Cached module skfda.preprocessing.dim_reduction has changed interface +LOG: Writing skfda.misc.regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/__init__.py skfda/misc/regularization/__init__.meta.json skfda/misc/regularization/__init__.data.json +TRACE: Interface for skfda.misc.regularization has changed +LOG: Cached module skfda.misc.regularization has changed interface +LOG: Writing skfda.misc.operators /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py skfda/misc/operators/__init__.meta.json skfda/misc/operators/__init__.data.json +TRACE: Interface for skfda.misc.operators has changed +LOG: Cached module skfda.misc.operators has changed interface +LOG: Writing skfda.misc.metrics /home/carlos/git/scikit-fda/skfda/misc/metrics/__init__.py skfda/misc/metrics/__init__.meta.json skfda/misc/metrics/__init__.data.json +TRACE: Interface for skfda.misc.metrics has changed +LOG: Cached module skfda.misc.metrics has changed interface +LOG: Writing skfda.misc /home/carlos/git/scikit-fda/skfda/misc/__init__.py skfda/misc/__init__.meta.json skfda/misc/__init__.data.json +TRACE: Interface for skfda.misc has changed +LOG: Cached module skfda.misc has changed interface +LOG: Writing skfda.exploratory.stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/__init__.py skfda/exploratory/stats/__init__.meta.json skfda/exploratory/stats/__init__.data.json +TRACE: Interface for skfda.exploratory.stats has changed +LOG: Cached module skfda.exploratory.stats has changed interface +LOG: Writing skfda.exploratory.depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/__init__.py skfda/exploratory/depth/__init__.meta.json skfda/exploratory/depth/__init__.data.json +TRACE: Interface for skfda.exploratory.depth has changed +LOG: Cached module skfda.exploratory.depth has changed interface +LOG: Writing skfda._utils /home/carlos/git/scikit-fda/skfda/_utils/__init__.py skfda/_utils/__init__.meta.json skfda/_utils/__init__.data.json +TRACE: Interface for skfda._utils has changed +LOG: Cached module skfda._utils has changed interface +LOG: Writing skfda /home/carlos/git/scikit-fda/skfda/__init__.py skfda/__init__.meta.json skfda/__init__.data.json +TRACE: Interface for skfda has changed +LOG: Cached module skfda has changed interface +LOG: Writing skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json skfda/preprocessing/smoothing/_linear.data.json +TRACE: Interface for skfda.preprocessing.smoothing._linear has changed +LOG: Cached module skfda.preprocessing.smoothing._linear has changed interface +LOG: Writing skfda.preprocessing.smoothing.validation /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json +TRACE: Interface for skfda.preprocessing.smoothing.validation has changed +LOG: Cached module skfda.preprocessing.smoothing.validation has changed interface +LOG: Writing skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json +TRACE: Interface for skfda.preprocessing.registration has changed +LOG: Cached module skfda.preprocessing.registration has changed interface +LOG: Writing skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json +TRACE: Interface for skfda.misc.validation has changed +LOG: Cached module skfda.misc.validation has changed interface +LOG: Writing skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json +TRACE: Interface for skfda.misc.operators._operators has changed +LOG: Cached module skfda.misc.operators._operators has changed interface +LOG: Writing skfda.misc.metrics._utils /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py skfda/misc/metrics/_utils.meta.json skfda/misc/metrics/_utils.data.json +TRACE: Interface for skfda.misc.metrics._utils has changed +LOG: Cached module skfda.misc.metrics._utils has changed interface +LOG: Writing skfda.misc.metrics._lp_norms /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py skfda/misc/metrics/_lp_norms.meta.json skfda/misc/metrics/_lp_norms.data.json +TRACE: Interface for skfda.misc.metrics._lp_norms has changed +LOG: Cached module skfda.misc.metrics._lp_norms has changed interface +LOG: Writing skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json +TRACE: Interface for skfda._utils._utils has changed +LOG: Cached module skfda._utils._utils has changed interface +LOG: Writing skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json +TRACE: Interface for skfda.representation.evaluator has changed +LOG: Cached module skfda.representation.evaluator has changed interface +LOG: Writing skfda.representation.basis._basis /home/carlos/git/scikit-fda/skfda/representation/basis/_basis.py skfda/representation/basis/_basis.meta.json skfda/representation/basis/_basis.data.json +TRACE: Interface for skfda.representation.basis._basis has changed +LOG: Cached module skfda.representation.basis._basis has changed interface +LOG: Writing skfda.preprocessing.smoothing._basis /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py skfda/preprocessing/smoothing/_basis.meta.json skfda/preprocessing/smoothing/_basis.data.json +TRACE: Interface for skfda.preprocessing.smoothing._basis has changed +LOG: Cached module skfda.preprocessing.smoothing._basis has changed interface +LOG: Writing skfda.preprocessing.registration.base /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py skfda/preprocessing/registration/base.meta.json skfda/preprocessing/registration/base.data.json +TRACE: Interface for skfda.preprocessing.registration.base has changed +LOG: Cached module skfda.preprocessing.registration.base has changed interface +LOG: Writing skfda.preprocessing.registration.validation /home/carlos/git/scikit-fda/skfda/preprocessing/registration/validation.py skfda/preprocessing/registration/validation.meta.json skfda/preprocessing/registration/validation.data.json +TRACE: Interface for skfda.preprocessing.registration.validation has changed +LOG: Cached module skfda.preprocessing.registration.validation has changed interface +LOG: Writing skfda.misc.regularization._regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py skfda/misc/regularization/_regularization.meta.json skfda/misc/regularization/_regularization.data.json +TRACE: Interface for skfda.misc.regularization._regularization has changed +LOG: Cached module skfda.misc.regularization._regularization has changed interface +LOG: Writing skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json +TRACE: Interface for skfda.misc.operators._srvf has changed +LOG: Cached module skfda.misc.operators._srvf has changed interface +LOG: Writing skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json +TRACE: Interface for skfda.misc.operators._linear_differential_operator has changed +LOG: Cached module skfda.misc.operators._linear_differential_operator has changed interface +LOG: Writing skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json +TRACE: Interface for skfda.misc.operators._integral_transform has changed +LOG: Cached module skfda.misc.operators._integral_transform has changed interface +LOG: Writing skfda.misc.operators._identity /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json +TRACE: Interface for skfda.misc.operators._identity has changed +LOG: Cached module skfda.misc.operators._identity has changed interface +LOG: Writing skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json +TRACE: Interface for skfda.misc.metrics._lp_distances has changed +LOG: Cached module skfda.misc.metrics._lp_distances has changed interface +LOG: Writing skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json +TRACE: Interface for skfda.misc._math has changed +LOG: Cached module skfda.misc._math has changed interface +LOG: Writing skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json +TRACE: Interface for skfda.exploratory.stats._functional_transformers has changed +LOG: Cached module skfda.exploratory.stats._functional_transformers has changed interface +LOG: Writing skfda.exploratory.depth._depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py skfda/exploratory/depth/_depth.meta.json skfda/exploratory/depth/_depth.data.json +TRACE: Interface for skfda.exploratory.depth._depth has changed +LOG: Cached module skfda.exploratory.depth._depth has changed interface +LOG: Writing skfda._utils._warping /home/carlos/git/scikit-fda/skfda/_utils/_warping.py skfda/_utils/_warping.meta.json skfda/_utils/_warping.data.json +TRACE: Interface for skfda._utils._warping has changed +LOG: Cached module skfda._utils._warping has changed interface +LOG: Writing skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json +TRACE: Interface for skfda.representation.interpolation has changed +LOG: Cached module skfda.representation.interpolation has changed interface +LOG: Writing skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json +TRACE: Interface for skfda.representation.extrapolation has changed +LOG: Cached module skfda.representation.extrapolation has changed interface +LOG: Writing skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json +TRACE: Interface for skfda.representation.basis._vector_basis has changed +LOG: Cached module skfda.representation.basis._vector_basis has changed interface +LOG: Writing skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json +TRACE: Interface for skfda.representation.basis._tensor_basis has changed +LOG: Cached module skfda.representation.basis._tensor_basis has changed interface +LOG: Writing skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json +TRACE: Interface for skfda.representation.basis._monomial has changed +LOG: Cached module skfda.representation.basis._monomial has changed interface +LOG: Writing skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json +TRACE: Interface for skfda.representation.basis._fourier has changed +LOG: Cached module skfda.representation.basis._fourier has changed interface +LOG: Writing skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json +TRACE: Interface for skfda.representation.basis._finite_element has changed +LOG: Cached module skfda.representation.basis._finite_element has changed interface +LOG: Writing skfda.representation.basis._constant /home/carlos/git/scikit-fda/skfda/representation/basis/_constant.py skfda/representation/basis/_constant.meta.json skfda/representation/basis/_constant.data.json +TRACE: Interface for skfda.representation.basis._constant has changed +LOG: Cached module skfda.representation.basis._constant has changed interface +LOG: Writing skfda.representation.basis._bspline /home/carlos/git/scikit-fda/skfda/representation/basis/_bspline.py skfda/representation/basis/_bspline.meta.json skfda/representation/basis/_bspline.data.json +TRACE: Interface for skfda.representation.basis._bspline has changed +LOG: Cached module skfda.representation.basis._bspline has changed interface +LOG: Writing skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json +TRACE: Interface for skfda.misc.metrics._mahalanobis has changed +LOG: Cached module skfda.misc.metrics._mahalanobis has changed interface +LOG: Writing skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json +TRACE: Interface for skfda.misc.metrics._fisher_rao has changed +LOG: Cached module skfda.misc.metrics._fisher_rao has changed interface +LOG: Writing skfda.misc.metrics._angular /home/carlos/git/scikit-fda/skfda/misc/metrics/_angular.py skfda/misc/metrics/_angular.meta.json skfda/misc/metrics/_angular.data.json +TRACE: Interface for skfda.misc.metrics._angular has changed +LOG: Cached module skfda.misc.metrics._angular has changed interface +LOG: Writing skfda.exploratory.stats._stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/_stats.py skfda/exploratory/stats/_stats.meta.json skfda/exploratory/stats/_stats.data.json +TRACE: Interface for skfda.exploratory.stats._stats has changed +LOG: Cached module skfda.exploratory.stats._stats has changed interface +LOG: Writing skfda.preprocessing.registration._lstsq_shift_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py skfda/preprocessing/registration/_lstsq_shift_registration.meta.json skfda/preprocessing/registration/_lstsq_shift_registration.data.json +TRACE: Interface for skfda.preprocessing.registration._lstsq_shift_registration has changed +LOG: Cached module skfda.preprocessing.registration._lstsq_shift_registration has changed interface +LOG: Writing skfda.preprocessing.registration._landmark_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py skfda/preprocessing/registration/_landmark_registration.meta.json skfda/preprocessing/registration/_landmark_registration.data.json +TRACE: Interface for skfda.preprocessing.registration._landmark_registration has changed +LOG: Cached module skfda.preprocessing.registration._landmark_registration has changed interface +LOG: Writing skfda.representation._functional_data /home/carlos/git/scikit-fda/skfda/representation/_functional_data.py skfda/representation/_functional_data.meta.json skfda/representation/_functional_data.data.json +TRACE: Interface for skfda.representation._functional_data has changed +LOG: Cached module skfda.representation._functional_data has changed interface +LOG: Writing skfda.exploratory.visualization._utils /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_utils.py skfda/exploratory/visualization/_utils.meta.json skfda/exploratory/visualization/_utils.data.json +TRACE: Interface for skfda.exploratory.visualization._utils has changed +LOG: Cached module skfda.exploratory.visualization._utils has changed interface +LOG: Writing skfda.exploratory.visualization._baseplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py skfda/exploratory/visualization/_baseplot.meta.json skfda/exploratory/visualization/_baseplot.data.json +TRACE: Interface for skfda.exploratory.visualization._baseplot has changed +LOG: Cached module skfda.exploratory.visualization._baseplot has changed interface +LOG: Writing skfda.exploratory.visualization.representation /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py skfda/exploratory/visualization/representation.meta.json skfda/exploratory/visualization/representation.data.json +TRACE: Interface for skfda.exploratory.visualization.representation has changed +LOG: Cached module skfda.exploratory.visualization.representation has changed interface +LOG: Writing skfda.exploratory.stats._fisher_rao /home/carlos/git/scikit-fda/skfda/exploratory/stats/_fisher_rao.py skfda/exploratory/stats/_fisher_rao.meta.json skfda/exploratory/stats/_fisher_rao.data.json +TRACE: Interface for skfda.exploratory.stats._fisher_rao has changed +LOG: Cached module skfda.exploratory.stats._fisher_rao has changed interface +LOG: Writing skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json +TRACE: Interface for skfda.preprocessing.registration._fisher_rao has changed +LOG: Cached module skfda.preprocessing.registration._fisher_rao has changed interface +LOG: Writing skfda.misc.hat_matrix /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py skfda/misc/hat_matrix.meta.json skfda/misc/hat_matrix.data.json +TRACE: Interface for skfda.misc.hat_matrix has changed +LOG: Cached module skfda.misc.hat_matrix has changed interface +LOG: Writing skfda.preprocessing.smoothing._kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py skfda/preprocessing/smoothing/_kernel_smoothers.meta.json skfda/preprocessing/smoothing/_kernel_smoothers.data.json +TRACE: Interface for skfda.preprocessing.smoothing._kernel_smoothers has changed +LOG: Cached module skfda.preprocessing.smoothing._kernel_smoothers has changed interface +LOG: Writing skfda.preprocessing.smoothing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py skfda/preprocessing/smoothing/__init__.meta.json skfda/preprocessing/smoothing/__init__.data.json +TRACE: Interface for skfda.preprocessing.smoothing has changed +LOG: Cached module skfda.preprocessing.smoothing has changed interface +LOG: Writing skfda.preprocessing.smoothing.kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/kernel_smoothers.py skfda/preprocessing/smoothing/kernel_smoothers.meta.json skfda/preprocessing/smoothing/kernel_smoothers.data.json +TRACE: Interface for skfda.preprocessing.smoothing.kernel_smoothers has changed +LOG: Cached module skfda.preprocessing.smoothing.kernel_smoothers has changed interface +LOG: Writing skfda.representation.grid /home/carlos/git/scikit-fda/skfda/representation/grid.py skfda/representation/grid.meta.json skfda/representation/grid.data.json +TRACE: Interface for skfda.representation.grid has changed +LOG: Cached module skfda.representation.grid has changed interface +LOG: Writing skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json +TRACE: Interface for skfda.representation.basis._fdatabasis has changed +LOG: Cached module skfda.representation.basis._fdatabasis has changed interface +LOG: Writing skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction._fpca has changed +LOG: Cached module skfda.preprocessing.dim_reduction._fpca has changed interface +TRACE: Priorities for packaging.requirements: +LOG: Processing SCC singleton (packaging.requirements) as stale due to deps (_typeshed abc array builtins ctypes enum mmap pickle re string typing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/requirements.py (packaging.requirements) +LOG: Writing packaging.requirements /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/requirements.py packaging/requirements.meta.json packaging/requirements.data.json +TRACE: Interface for packaging.requirements is unchanged +LOG: Cached module packaging.requirements has same interface +TRACE: Priorities for skfda.tests.test_pandas: +LOG: Processing SCC singleton (skfda.tests.test_pandas) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.representation.grid typing unittest unittest.case) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_pandas.py (skfda.tests.test_pandas) +LOG: Writing skfda.tests.test_pandas /home/carlos/git/scikit-fda/skfda/tests/test_pandas.py skfda/tests/test_pandas.meta.json skfda/tests/test_pandas.data.json +TRACE: Interface for skfda.tests.test_pandas is unchanged +LOG: Cached module skfda.tests.test_pandas has same interface +TRACE: Priorities for skfda.tests.test_linear_differential_operator: +LOG: Processing SCC singleton (skfda.tests.test_linear_differential_operator) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda.misc skfda.misc.operators skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._constant skfda.representation.basis._fdatabasis skfda.representation.basis._monomial skfda.representation.evaluator types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_linear_differential_operator.py (skfda.tests.test_linear_differential_operator) +LOG: Writing skfda.tests.test_linear_differential_operator /home/carlos/git/scikit-fda/skfda/tests/test_linear_differential_operator.py skfda/tests/test_linear_differential_operator.meta.json skfda/tests/test_linear_differential_operator.data.json +TRACE: Interface for skfda.tests.test_linear_differential_operator is unchanged +LOG: Cached module skfda.tests.test_linear_differential_operator has same interface +TRACE: Priorities for skfda.tests.test_interpolation: +LOG: Processing SCC singleton (skfda.tests.test_interpolation) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.representation.interpolation types typing typing_extensions unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_interpolation.py (skfda.tests.test_interpolation) +LOG: Writing skfda.tests.test_interpolation /home/carlos/git/scikit-fda/skfda/tests/test_interpolation.py skfda/tests/test_interpolation.meta.json skfda/tests/test_interpolation.data.json +TRACE: Interface for skfda.tests.test_interpolation is unchanged +LOG: Cached module skfda.tests.test_interpolation has same interface +TRACE: Priorities for skfda.tests.test_grid: +LOG: Processing SCC singleton (skfda.tests.test_grid) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._stats skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_grid.py (skfda.tests.test_grid) +LOG: Writing skfda.tests.test_grid /home/carlos/git/scikit-fda/skfda/tests/test_grid.py skfda/tests/test_grid.meta.json skfda/tests/test_grid.data.json +TRACE: Interface for skfda.tests.test_grid is unchanged +LOG: Cached module skfda.tests.test_grid has same interface +TRACE: Priorities for skfda.tests.test_fdatabasis_evaluation: +LOG: Processing SCC singleton (skfda.tests.test_fdatabasis_evaluation) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.function_base numpy.core.multiarray numpy.core.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._constant skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.basis._tensor_basis skfda.representation.basis._vector_basis skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fdatabasis_evaluation.py (skfda.tests.test_fdatabasis_evaluation) +LOG: Writing skfda.tests.test_fdatabasis_evaluation /home/carlos/git/scikit-fda/skfda/tests/test_fdatabasis_evaluation.py skfda/tests/test_fdatabasis_evaluation.meta.json skfda/tests/test_fdatabasis_evaluation.data.json +TRACE: Interface for skfda.tests.test_fdatabasis_evaluation is unchanged +LOG: Cached module skfda.tests.test_fdatabasis_evaluation has same interface +TRACE: Priorities for skfda.tests.test_depth: +LOG: Processing SCC singleton (skfda.tests.test_depth) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.lib numpy.lib.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid typing unittest unittest.case) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_depth.py (skfda.tests.test_depth) +LOG: Writing skfda.tests.test_depth /home/carlos/git/scikit-fda/skfda/tests/test_depth.py skfda/tests/test_depth.meta.json skfda/tests/test_depth.data.json +TRACE: Interface for skfda.tests.test_depth is unchanged +LOG: Cached module skfda.tests.test_depth has same interface +TRACE: Priorities for skfda.preprocessing.feature_construction._per_class_transformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._per_class_transformer) as stale due to deps (__future__ _warnings abc array builtins ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.core numpy.core.shape_base pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing warnings) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py (skfda.preprocessing.feature_construction._per_class_transformer) +LOG: Writing skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._per_class_transformer is unchanged +LOG: Cached module skfda.preprocessing.feature_construction._per_class_transformer has same interface +TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py (skfda.preprocessing.feature_construction._function_transformers) +LOG: Writing skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._function_transformers is unchanged +LOG: Cached module skfda.preprocessing.feature_construction._function_transformers has same interface +TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py (skfda.preprocessing.feature_construction._fda_feature_union) +LOG: Writing skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._fda_feature_union is unchanged +LOG: Cached module skfda.preprocessing.feature_construction._fda_feature_union has same interface +TRACE: Priorities for skfda.preprocessing.feature_construction._evaluation_trasformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._evaluation_trasformer) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.extrapolation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py (skfda.preprocessing.feature_construction._evaluation_trasformer) +LOG: Writing skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._evaluation_trasformer is unchanged +LOG: Cached module skfda.preprocessing.feature_construction._evaluation_trasformer has same interface +TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py (skfda.preprocessing.feature_construction._coefficients_transformer) +LOG: Writing skfda.preprocessing.feature_construction._coefficients_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_coefficients_transformer.py skfda/preprocessing/feature_construction/_coefficients_transformer.meta.json skfda/preprocessing/feature_construction/_coefficients_transformer.data.json +TRACE: Interface for skfda.preprocessing.feature_construction._coefficients_transformer is unchanged +LOG: Cached module skfda.preprocessing.feature_construction._coefficients_transformer has same interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection.mrmr: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection.mrmr) as stale due to deps (__future__ _operator _typeshed abc array builtins ctypes dataclasses mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.index_tricks numpy.random numpy.random._generator numpy.random.mtrand operator pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py (skfda.preprocessing.dim_reduction.variable_selection.mrmr) +LOG: Writing skfda.preprocessing.dim_reduction.variable_selection.mrmr /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py skfda/preprocessing/dim_reduction/variable_selection/mrmr.meta.json skfda/preprocessing/dim_reduction/variable_selection/mrmr.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection.mrmr is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection.mrmr has same interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting) as stale due to deps (__future__ abc builtins dcor dcor._dcor dcor._utils enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py (skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting) +LOG: Writing skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.meta.json skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting has same interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection._rkvs: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection._rkvs) as stale due to deps (__future__ _typeshed abc array builtins ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.core.shape_base numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.lib.twodim_base numpy.linalg numpy.linalg.linalg pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py (skfda.preprocessing.dim_reduction.variable_selection._rkvs) +LOG: Writing skfda.preprocessing.dim_reduction.variable_selection._rkvs /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py skfda/preprocessing/dim_reduction/variable_selection/_rkvs.meta.json skfda/preprocessing/dim_reduction/variable_selection/_rkvs.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection._rkvs is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection._rkvs has same interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.projection: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.projection) as stale due to deps (_warnings abc builtins skfda.preprocessing.dim_reduction typing warnings) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/projection/__init__.py (skfda.preprocessing.dim_reduction.projection) +LOG: Writing skfda.preprocessing.dim_reduction.projection /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/projection/__init__.py skfda/preprocessing/dim_reduction/projection/__init__.meta.json skfda/preprocessing/dim_reduction/projection/__init__.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.projection is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.projection has same interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.feature_extraction: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.feature_extraction) as stale due to deps (_warnings abc builtins skfda.preprocessing.dim_reduction typing warnings) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py (skfda.preprocessing.dim_reduction.feature_extraction) +LOG: Writing skfda.preprocessing.dim_reduction.feature_extraction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/feature_extraction/__init__.py skfda/preprocessing/dim_reduction/feature_extraction/__init__.meta.json skfda/preprocessing/dim_reduction/feature_extraction/__init__.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.feature_extraction is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.feature_extraction has same interface +TRACE: Priorities for skfda.ml.regression._kernel_regression: +LOG: Processing SCC singleton (skfda.ml.regression._kernel_regression) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.hat_matrix skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py (skfda.ml.regression._kernel_regression) +LOG: Writing skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json +TRACE: Interface for skfda.ml.regression._kernel_regression is unchanged +LOG: Cached module skfda.ml.regression._kernel_regression has same interface +TRACE: Priorities for skfda.ml.regression._historical_linear_model: +LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as stale due to deps (__future__ abc builtins enum math numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.core.shape_base numpy.linalg numpy.linalg.linalg skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._finite_element skfda.representation.basis._vector_basis skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py (skfda.ml.regression._historical_linear_model) +LOG: Writing skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json +TRACE: Interface for skfda.ml.regression._historical_linear_model is unchanged +LOG: Cached module skfda.ml.regression._historical_linear_model has same interface +TRACE: Priorities for skfda.ml.regression._coefficients: +LOG: Processing SCC singleton (skfda.ml.regression._coefficients) as stale due to deps (__future__ abc array builtins functools mmap multimethod numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.numeric numpy.core.shape_base skfda.misc._math skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py (skfda.ml.regression._coefficients) +LOG: Writing skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json +TRACE: Interface for skfda.ml.regression._coefficients is unchanged +LOG: Cached module skfda.ml.regression._coefficients has same interface +TRACE: Priorities for skfda.ml.clustering._kmeans: +LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as stale due to deps (__future__ _typeshed _warnings abc array builtins contextlib ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.einsumfunc numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.random numpy.random._generator numpy.random.mtrand pickle skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py (skfda.ml.clustering._kmeans) +LOG: Writing skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json +TRACE: Interface for skfda.ml.clustering._kmeans is unchanged +LOG: Cached module skfda.ml.clustering._kmeans has same interface +TRACE: Priorities for skfda.ml.clustering._hierarchical: +LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as stale due to deps (__future__ abc builtins enum numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._parse skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py (skfda.ml.clustering._hierarchical) +LOG: Writing skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json +TRACE: Interface for skfda.ml.clustering._hierarchical is unchanged +LOG: Cached module skfda.ml.clustering._hierarchical has same interface +TRACE: Priorities for skfda.ml.classification._parameterized_functional_qda: +LOG: Processing SCC singleton (skfda.ml.classification._parameterized_functional_qda) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.arraysetops numpy.lib.twodim_base numpy.linalg numpy.linalg.linalg skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._numpy typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py (skfda.ml.classification._parameterized_functional_qda) +LOG: Writing skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json +TRACE: Interface for skfda.ml.classification._parameterized_functional_qda is unchanged +LOG: Cached module skfda.ml.classification._parameterized_functional_qda has same interface +TRACE: Priorities for skfda.ml.classification._logistic_regression: +LOG: Processing SCC singleton (skfda.ml.classification._logistic_regression) as stale due to deps (__future__ _typeshed abc builtins contextlib numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py (skfda.ml.classification._logistic_regression) +LOG: Writing skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json skfda/ml/classification/_logistic_regression.data.json +TRACE: Interface for skfda.ml.classification._logistic_regression is unchanged +LOG: Cached module skfda.ml.classification._logistic_regression has same interface +TRACE: Priorities for skfda.ml.classification._centroid_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._centroid_classifiers) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.exploratory.stats skfda.exploratory.stats._stats skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py (skfda.ml.classification._centroid_classifiers) +LOG: Writing skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json +TRACE: Interface for skfda.ml.classification._centroid_classifiers is unchanged +LOG: Cached module skfda.ml.classification._centroid_classifiers has same interface +TRACE: Priorities for skfda.ml._neighbors_base: +LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.ml._neighbors_base /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py skfda/ml/_neighbors_base.meta.json skfda/ml/_neighbors_base.data.json +TRACE: Interface for skfda.ml._neighbors_base has changed +LOG: Cached module skfda.ml._neighbors_base has changed interface +TRACE: Priorities for skfda.inference.hotelling._hotelling: +LOG: Processing SCC singleton (skfda.inference.hotelling._hotelling) as stale due to deps (__future__ _typeshed abc array builtins ctypes itertools mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.linalg numpy.linalg.linalg numpy.random numpy.random._generator numpy.random.mtrand pickle skfda.misc skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.typing._base skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/hotelling/_hotelling.py (skfda.inference.hotelling._hotelling) +LOG: Writing skfda.inference.hotelling._hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/_hotelling.py skfda/inference/hotelling/_hotelling.meta.json skfda/inference/hotelling/_hotelling.data.json +TRACE: Interface for skfda.inference.hotelling._hotelling is unchanged +LOG: Cached module skfda.inference.hotelling._hotelling has same interface +TRACE: Priorities for skfda.exploratory.visualization.fpca: +LOG: Processing SCC singleton (skfda.exploratory.visualization.fpca) as inherently stale with stale deps (__future__ builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.representation typing warnings) +LOG: Writing skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json +TRACE: Interface for skfda.exploratory.visualization.fpca has changed +LOG: Cached module skfda.exploratory.visualization.fpca has changed interface +TRACE: Priorities for skfda.exploratory.visualization.clustering: +LOG: Processing SCC singleton (skfda.exploratory.visualization.clustering) as stale due to deps (__future__ abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.function_base skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.misc skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py (skfda.exploratory.visualization.clustering) +LOG: Writing skfda.exploratory.visualization.clustering /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py skfda/exploratory/visualization/clustering.meta.json skfda/exploratory/visualization/clustering.data.json +TRACE: Interface for skfda.exploratory.visualization.clustering is unchanged +LOG: Cached module skfda.exploratory.visualization.clustering has same interface +TRACE: Priorities for skfda.exploratory.visualization._parametric_plot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._parametric_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.exploratory.visualization.representation skfda.representation typing) +LOG: Writing skfda.exploratory.visualization._parametric_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py skfda/exploratory/visualization/_parametric_plot.meta.json skfda/exploratory/visualization/_parametric_plot.data.json +TRACE: Interface for skfda.exploratory.visualization._parametric_plot has changed +LOG: Cached module skfda.exploratory.visualization._parametric_plot has changed interface +TRACE: Priorities for skfda.exploratory.visualization._multiple_display: +LOG: Processing SCC singleton 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skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json +TRACE: Interface for skfda.exploratory.visualization._ddplot has changed +LOG: Cached module skfda.exploratory.visualization._ddplot has changed interface +TRACE: Priorities for skfda.exploratory.outliers._outliergram: +LOG: Processing SCC singleton (skfda.exploratory.outliers._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda._utils._sklearn_adapter skfda.exploratory.depth._depth skfda.exploratory.stats skfda.representation skfda.typing._numpy) +LOG: Writing skfda.exploratory.outliers._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py skfda/exploratory/outliers/_outliergram.meta.json skfda/exploratory/outliers/_outliergram.data.json +TRACE: Interface for skfda.exploratory.outliers._outliergram has changed +LOG: Cached module skfda.exploratory.outliers._outliergram has changed interface +TRACE: Priorities for skfda.exploratory.outliers._envelopes: +LOG: Processing SCC singleton (skfda.exploratory.outliers._envelopes) as inherently stale with stale deps (__future__ builtins math numpy skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.outliers._envelopes /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_envelopes.py skfda/exploratory/outliers/_envelopes.meta.json skfda/exploratory/outliers/_envelopes.data.json +TRACE: Interface for skfda.exploratory.outliers._envelopes has changed +LOG: Cached module skfda.exploratory.outliers._envelopes has changed interface +TRACE: Priorities for skfda.datasets._real_datasets: +LOG: Processing SCC singleton (skfda.datasets._real_datasets) as inherently stale with stale deps (__future__ builtins numpy rdata skfda.representation skfda.typing._numpy typing typing_extensions warnings) +LOG: Writing skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json +TRACE: Interface for skfda.datasets._real_datasets has changed +LOG: Cached module skfda.datasets._real_datasets has changed interface +TRACE: Priorities for _pytest.scope: _pytest.outcomes:20 +TRACE: Priorities for _pytest.compat: _pytest.outcomes:20 _pytest._io:30 +TRACE: Priorities for _pytest._io.terminalwriter: _pytest.compat:5 _pytest.config.exceptions:20 _pytest.config:30 +TRACE: Priorities for _pytest.config.exceptions: _pytest.compat:5 +TRACE: Priorities for _pytest.warning_types: _pytest.compat:5 +TRACE: Priorities for _pytest._io: _pytest._io.terminalwriter:5 +TRACE: Priorities for _pytest.deprecated: _pytest.warning_types:5 +TRACE: Priorities for _pytest.hookspec: _pytest.deprecated:5 _pytest._code.code:25 _pytest.config:25 _pytest.config.argparsing:25 _pytest.fixtures:25 _pytest.main:25 _pytest.nodes:25 _pytest.outcomes:25 _pytest.python:25 _pytest.reports:25 _pytest.runner:25 _pytest.terminal:25 _pytest.compat:25 _pytest.warning_types:30 +TRACE: Priorities for _pytest.outcomes: _pytest.deprecated:5 _pytest.config:20 pytest:20 _pytest.config.exceptions:30 _pytest.warning_types:30 +TRACE: Priorities for _pytest.config.argparsing: _pytest._io:10 _pytest.compat:5 _pytest.config.exceptions:5 _pytest.deprecated:5 _pytest._io.terminalwriter:30 _pytest.warning_types:30 +TRACE: Priorities for _pytest.pathlib: _pytest.compat:5 _pytest.outcomes:5 _pytest.warning_types:5 +TRACE: Priorities for _pytest.config.findpaths: _pytest.config.exceptions:5 _pytest.outcomes:5 _pytest.pathlib:5 _pytest.config:25 +TRACE: Priorities for _pytest._code.code: _pytest._io:5 _pytest.compat:5 _pytest.deprecated:5 _pytest.pathlib:5 _pytest._io.terminalwriter:30 +TRACE: Priorities for _pytest._code: _pytest._code.code:5 +TRACE: Priorities for _pytest.python_api: _pytest._code:10 _pytest.compat:5 _pytest.outcomes:5 _pytest._code.code:30 +TRACE: Priorities for _pytest.config: _pytest._code:5 _pytest.deprecated:10 _pytest.hookspec:10 _pytest.assertion:20 pytest:20 _pytest.config.exceptions:5 _pytest.config.findpaths:5 _pytest._io:5 _pytest.compat:5 _pytest.outcomes:5 _pytest.pathlib:5 _pytest.warning_types:5 _pytest._code.code:25 _pytest.terminal:25 _pytest.config.argparsing:20 _pytest.config.compat:20 _pytest.cacheprovider:20 _pytest.helpconfig:20 _pytest._io.terminalwriter:30 _pytest.assertion.rewrite:30 +TRACE: Priorities for _pytest.mark.structures: _pytest._code:5 _pytest.compat:5 _pytest.config:5 _pytest.deprecated:5 _pytest.outcomes:5 _pytest.warning_types:5 _pytest.nodes:20 _pytest.scope:25 _pytest._code.code:30 +TRACE: Priorities for _pytest.assertion.util: _pytest._code:10 _pytest.outcomes:10 _pytest.config:5 _pytest.python_api:20 _pytest._code.code:30 _pytest._io:30 +TRACE: Priorities for _pytest.nodes: _pytest._code:5 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_pytest._code:30 _pytest._io.terminalwriter:30 _pytest.config.compat:30 +TRACE: Priorities for _pytest.fixtures: _pytest.nodes:10 pytest:20 _pytest.python:20 _pytest._code:5 _pytest._code.code:5 _pytest._io:5 _pytest.compat:5 _pytest.config:5 _pytest.config.argparsing:5 _pytest.deprecated:5 _pytest.mark:5 _pytest.mark.structures:5 _pytest.outcomes:5 _pytest.pathlib:5 _pytest.scope:5 _pytest.main:25 _pytest._io.terminalwriter:30 _pytest.runner:30 _pytest.warning_types:30 +TRACE: Priorities for _pytest.pytester_assertions: _pytest.reports:5 +TRACE: Priorities for _pytest.terminal: _pytest.nodes:5 _pytest.config:5 _pytest._code:5 _pytest._code.code:5 _pytest.compat:5 _pytest.config.argparsing:5 _pytest.pathlib:5 _pytest.reports:5 _pytest.main:25 _pytest.warnings:20 _pytest._io:30 _pytest._io.terminalwriter:30 _pytest.config.compat:30 +TRACE: Priorities for _pytest.runner: _pytest.reports:5 _pytest._code.code:5 _pytest.compat:5 _pytest.config.argparsing:5 _pytest.deprecated:5 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_pytest.reports _pytest.fixtures _pytest.pytester_assertions _pytest.terminal _pytest.runner _pytest.recwarn _pytest.monkeypatch _pytest.debugging _pytest.capture _pytest.tmpdir _pytest.main _pytest.assertion.rewrite _pytest.python _pytest.pytester _pytest.logging _pytest.cacheprovider _pytest.assertion _pytest.legacypath pytest.collect pytest _pytest.warnings _pytest.helpconfig) as stale due to deps (_ast _collections_abc _decimal _typeshed _warnings _weakref abc array ast builtins collections collections.abc contextlib copy ctypes datetime decimal enum errno functools genericpath importlib importlib.abc importlib.machinery importlib.metadata inspect io itertools json json.decoder json.encoder logging math mmap numbers numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.numeric os os.path pathlib pickle platform posixpath re struct subprocess sys textwrap time traceback types typing typing_extensions uuid warnings weakref) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/scope.py (_pytest.scope) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/compat.py (_pytest.compat) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/terminalwriter.py (_pytest._io.terminalwriter) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/exceptions.py (_pytest.config.exceptions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warning_types.py (_pytest.warning_types) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/__init__.py (_pytest._io) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/deprecated.py (_pytest.deprecated) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/hookspec.py (_pytest.hookspec) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/outcomes.py (_pytest.outcomes) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/argparsing.py (_pytest.config.argparsing) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pathlib.py (_pytest.pathlib) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/findpaths.py (_pytest.config.findpaths) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/code.py (_pytest._code.code) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/__init__.py (_pytest._code) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python_api.py (_pytest.python_api) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/__init__.py (_pytest.config) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/structures.py (_pytest.mark.structures) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/util.py (_pytest.assertion.util) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/nodes.py (_pytest.nodes) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/__init__.py (_pytest.mark) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/compat.py (_pytest.config.compat) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/truncate.py (_pytest.assertion.truncate) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/reports.py (_pytest.reports) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/fixtures.py (_pytest.fixtures) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester_assertions.py (_pytest.pytester_assertions) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/terminal.py (_pytest.terminal) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/runner.py (_pytest.runner) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/recwarn.py (_pytest.recwarn) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/monkeypatch.py (_pytest.monkeypatch) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/debugging.py (_pytest.debugging) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/capture.py (_pytest.capture) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/tmpdir.py (_pytest.tmpdir) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/main.py (_pytest.main) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/rewrite.py (_pytest.assertion.rewrite) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python.py (_pytest.python) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester.py (_pytest.pytester) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/logging.py (_pytest.logging) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/cacheprovider.py (_pytest.cacheprovider) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/__init__.py (_pytest.assertion) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/legacypath.py (_pytest.legacypath) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/collect.py (pytest.collect) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/__init__.py (pytest) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warnings.py (_pytest.warnings) +LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/helpconfig.py (_pytest.helpconfig) +LOG: Writing _pytest.scope /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/scope.py _pytest/scope.meta.json _pytest/scope.data.json +TRACE: Interface for _pytest.scope is unchanged +LOG: Cached module _pytest.scope has same interface +LOG: Writing _pytest.compat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/compat.py _pytest/compat.meta.json _pytest/compat.data.json +TRACE: Interface for _pytest.compat is unchanged +LOG: Cached module _pytest.compat has same interface +LOG: Writing _pytest._io.terminalwriter /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/terminalwriter.py _pytest/_io/terminalwriter.meta.json _pytest/_io/terminalwriter.data.json +TRACE: Interface for _pytest._io.terminalwriter is unchanged +LOG: Cached module _pytest._io.terminalwriter has same interface +LOG: Writing _pytest.config.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/exceptions.py _pytest/config/exceptions.meta.json _pytest/config/exceptions.data.json +TRACE: Interface for _pytest.config.exceptions is unchanged +LOG: Cached module _pytest.config.exceptions has same interface +LOG: Writing _pytest.warning_types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warning_types.py _pytest/warning_types.meta.json _pytest/warning_types.data.json +TRACE: Interface for _pytest.warning_types is unchanged +LOG: Cached module _pytest.warning_types has same interface +LOG: Writing _pytest._io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/__init__.py _pytest/_io/__init__.meta.json _pytest/_io/__init__.data.json +TRACE: Interface for _pytest._io is unchanged +LOG: Cached module _pytest._io has same interface +LOG: Writing _pytest.deprecated /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/deprecated.py _pytest/deprecated.meta.json _pytest/deprecated.data.json +TRACE: Interface for _pytest.deprecated is unchanged +LOG: Cached module _pytest.deprecated has same interface +LOG: Writing _pytest.hookspec /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/hookspec.py _pytest/hookspec.meta.json _pytest/hookspec.data.json +TRACE: Interface for _pytest.hookspec is unchanged +LOG: Cached module _pytest.hookspec has same interface +LOG: Writing _pytest.outcomes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/outcomes.py _pytest/outcomes.meta.json _pytest/outcomes.data.json +TRACE: Interface for _pytest.outcomes is unchanged +LOG: Cached module _pytest.outcomes has same interface +LOG: Writing _pytest.config.argparsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/argparsing.py _pytest/config/argparsing.meta.json _pytest/config/argparsing.data.json +TRACE: Interface for _pytest.config.argparsing is unchanged +LOG: Cached module _pytest.config.argparsing has same interface +LOG: Writing _pytest.pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pathlib.py _pytest/pathlib.meta.json _pytest/pathlib.data.json +TRACE: Interface for _pytest.pathlib is unchanged +LOG: Cached module _pytest.pathlib has same interface +LOG: Writing _pytest.config.findpaths /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/findpaths.py _pytest/config/findpaths.meta.json _pytest/config/findpaths.data.json +TRACE: Interface for _pytest.config.findpaths is unchanged +LOG: Cached module _pytest.config.findpaths has same interface +LOG: Writing _pytest._code.code /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/code.py _pytest/_code/code.meta.json _pytest/_code/code.data.json +TRACE: Interface for _pytest._code.code is unchanged +LOG: Cached module _pytest._code.code has same interface +LOG: Writing _pytest._code /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/__init__.py _pytest/_code/__init__.meta.json _pytest/_code/__init__.data.json +TRACE: Interface for _pytest._code is unchanged +LOG: Cached module _pytest._code has same interface +LOG: Writing _pytest.python_api /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python_api.py _pytest/python_api.meta.json _pytest/python_api.data.json +TRACE: Interface for _pytest.python_api is unchanged +LOG: Cached module _pytest.python_api has same interface +LOG: Writing _pytest.config /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/__init__.py _pytest/config/__init__.meta.json _pytest/config/__init__.data.json +TRACE: Interface for _pytest.config is unchanged +LOG: Cached module _pytest.config has same interface +LOG: Writing _pytest.mark.structures /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/structures.py _pytest/mark/structures.meta.json _pytest/mark/structures.data.json +TRACE: Interface for _pytest.mark.structures is unchanged +LOG: Cached module _pytest.mark.structures has same interface +LOG: Writing _pytest.assertion.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/util.py _pytest/assertion/util.meta.json _pytest/assertion/util.data.json +TRACE: Interface for _pytest.assertion.util is unchanged +LOG: Cached module _pytest.assertion.util has same interface +LOG: Writing _pytest.nodes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/nodes.py _pytest/nodes.meta.json _pytest/nodes.data.json +TRACE: Interface for _pytest.nodes is unchanged +LOG: Cached module _pytest.nodes has same interface +LOG: Writing _pytest.mark /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/__init__.py _pytest/mark/__init__.meta.json _pytest/mark/__init__.data.json +TRACE: Interface for _pytest.mark is unchanged +LOG: Cached module _pytest.mark has same interface +LOG: Writing _pytest.config.compat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/compat.py _pytest/config/compat.meta.json _pytest/config/compat.data.json +TRACE: Interface for _pytest.config.compat is unchanged +LOG: Cached module _pytest.config.compat has same interface +LOG: Writing _pytest.assertion.truncate /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/truncate.py _pytest/assertion/truncate.meta.json _pytest/assertion/truncate.data.json +TRACE: Interface for _pytest.assertion.truncate is unchanged +LOG: Cached module _pytest.assertion.truncate has same interface +LOG: Writing _pytest.reports /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/reports.py _pytest/reports.meta.json _pytest/reports.data.json +TRACE: Interface for _pytest.reports is unchanged +LOG: Cached module _pytest.reports has same interface +LOG: Writing _pytest.fixtures /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/fixtures.py _pytest/fixtures.meta.json _pytest/fixtures.data.json +TRACE: Interface for _pytest.fixtures is unchanged +LOG: Cached module _pytest.fixtures has same interface +LOG: Writing _pytest.pytester_assertions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester_assertions.py _pytest/pytester_assertions.meta.json _pytest/pytester_assertions.data.json +TRACE: Interface for _pytest.pytester_assertions is unchanged +LOG: Cached module _pytest.pytester_assertions has same interface +LOG: Writing _pytest.terminal /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/terminal.py _pytest/terminal.meta.json _pytest/terminal.data.json +TRACE: Interface for _pytest.terminal is unchanged +LOG: Cached module _pytest.terminal has same interface +LOG: Writing _pytest.runner /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/runner.py _pytest/runner.meta.json _pytest/runner.data.json +TRACE: Interface for _pytest.runner is unchanged +LOG: Cached module _pytest.runner has same interface +LOG: Writing _pytest.recwarn /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/recwarn.py _pytest/recwarn.meta.json _pytest/recwarn.data.json +TRACE: Interface for _pytest.recwarn is unchanged +LOG: Cached module _pytest.recwarn has same interface +LOG: Writing _pytest.monkeypatch /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/monkeypatch.py _pytest/monkeypatch.meta.json _pytest/monkeypatch.data.json +TRACE: Interface for _pytest.monkeypatch is unchanged +LOG: Cached module _pytest.monkeypatch has same interface +LOG: Writing _pytest.debugging /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/debugging.py _pytest/debugging.meta.json _pytest/debugging.data.json +TRACE: Interface for _pytest.debugging is unchanged +LOG: Cached module _pytest.debugging has same interface +LOG: Writing _pytest.capture /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/capture.py _pytest/capture.meta.json _pytest/capture.data.json +TRACE: Interface for _pytest.capture is unchanged +LOG: Cached module _pytest.capture has same interface +LOG: Writing _pytest.tmpdir /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/tmpdir.py _pytest/tmpdir.meta.json _pytest/tmpdir.data.json +TRACE: Interface for _pytest.tmpdir is unchanged +LOG: Cached module _pytest.tmpdir has same interface +LOG: Writing _pytest.main /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/main.py _pytest/main.meta.json _pytest/main.data.json +TRACE: Interface for _pytest.main is unchanged +LOG: Cached module _pytest.main has same interface +LOG: Writing _pytest.assertion.rewrite /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/rewrite.py _pytest/assertion/rewrite.meta.json _pytest/assertion/rewrite.data.json +TRACE: Interface for _pytest.assertion.rewrite is unchanged +LOG: Cached module _pytest.assertion.rewrite has same interface +LOG: Writing _pytest.python /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python.py _pytest/python.meta.json _pytest/python.data.json +TRACE: Interface for _pytest.python is unchanged +LOG: Cached module _pytest.python has same interface +LOG: Writing _pytest.pytester /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester.py _pytest/pytester.meta.json _pytest/pytester.data.json +TRACE: Interface for _pytest.pytester is unchanged +LOG: Cached module _pytest.pytester has same interface +LOG: Writing _pytest.logging /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/logging.py _pytest/logging.meta.json _pytest/logging.data.json +TRACE: Interface for _pytest.logging is unchanged +LOG: Cached module _pytest.logging has same interface +LOG: Writing _pytest.cacheprovider /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/cacheprovider.py _pytest/cacheprovider.meta.json _pytest/cacheprovider.data.json +TRACE: Interface for _pytest.cacheprovider is unchanged +LOG: Cached module _pytest.cacheprovider has same interface +LOG: Writing _pytest.assertion /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/__init__.py _pytest/assertion/__init__.meta.json _pytest/assertion/__init__.data.json +TRACE: Interface for _pytest.assertion is unchanged +LOG: Cached module _pytest.assertion has same interface +LOG: Writing _pytest.legacypath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/legacypath.py _pytest/legacypath.meta.json _pytest/legacypath.data.json +TRACE: Interface for _pytest.legacypath is unchanged +LOG: Cached module _pytest.legacypath has same interface +LOG: Writing pytest.collect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/collect.py pytest/collect.meta.json pytest/collect.data.json +TRACE: Interface for pytest.collect is unchanged +LOG: Cached module pytest.collect has same interface +LOG: Writing pytest /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/__init__.py pytest/__init__.meta.json pytest/__init__.data.json +TRACE: Interface for pytest is unchanged +LOG: Cached module pytest has same interface +LOG: Writing _pytest.warnings /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warnings.py _pytest/warnings.meta.json _pytest/warnings.data.json +TRACE: Interface for _pytest.warnings is unchanged +LOG: Cached module _pytest.warnings has same interface +LOG: Writing _pytest.helpconfig /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/helpconfig.py _pytest/helpconfig.meta.json _pytest/helpconfig.data.json +TRACE: Interface for _pytest.helpconfig is unchanged +LOG: Cached module _pytest.helpconfig has same interface +TRACE: Priorities for skfda.preprocessing.feature_construction: +LOG: Processing SCC singleton (skfda.preprocessing.feature_construction) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py (skfda.preprocessing.feature_construction) +LOG: Writing skfda.preprocessing.feature_construction /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py skfda/preprocessing/feature_construction/__init__.meta.json skfda/preprocessing/feature_construction/__init__.data.json +TRACE: Interface for skfda.preprocessing.feature_construction is unchanged +LOG: Cached module skfda.preprocessing.feature_construction has same interface +TRACE: Priorities for skfda.ml.regression._neighbors_regression: +LOG: Processing SCC singleton (skfda.ml.regression._neighbors_regression) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py (skfda.ml.regression._neighbors_regression) +LOG: Writing skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json +TRACE: Interface for skfda.ml.regression._neighbors_regression is unchanged +LOG: Cached module skfda.ml.regression._neighbors_regression has same interface +TRACE: Priorities for skfda.ml.regression._linear_regression: +LOG: Processing SCC singleton (skfda.ml.regression._linear_regression) as stale due to deps (__future__ _warnings abc array builtins enum functools itertools mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.shape_base skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.lstsq skfda.misc.regularization skfda.misc.regularization._regularization skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.evaluator skfda.typing._numpy typing warnings) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py (skfda.ml.regression._linear_regression) +LOG: Writing skfda.ml.regression._linear_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py skfda/ml/regression/_linear_regression.meta.json skfda/ml/regression/_linear_regression.data.json +TRACE: Interface for skfda.ml.regression._linear_regression is unchanged +LOG: Cached module skfda.ml.regression._linear_regression has same interface +TRACE: Priorities for skfda.ml.clustering._neighbors_clustering: +LOG: Processing SCC singleton (skfda.ml.clustering._neighbors_clustering) as stale due to deps (__future__ abc builtins numpy skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py (skfda.ml.clustering._neighbors_clustering) +LOG: Writing skfda.ml.clustering._neighbors_clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py skfda/ml/clustering/_neighbors_clustering.meta.json skfda/ml/clustering/_neighbors_clustering.data.json +TRACE: Interface for skfda.ml.clustering._neighbors_clustering is unchanged +LOG: Cached module skfda.ml.clustering._neighbors_clustering has same interface +TRACE: Priorities for skfda.ml.classification._neighbors_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py (skfda.ml.classification._neighbors_classifiers) +LOG: Writing skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json +TRACE: Interface for skfda.ml.classification._neighbors_classifiers is unchanged +LOG: Cached module skfda.ml.classification._neighbors_classifiers has same interface +TRACE: Priorities for skfda.inference.hotelling: +LOG: Processing SCC singleton (skfda.inference.hotelling) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/hotelling/__init__.py (skfda.inference.hotelling) +LOG: Writing skfda.inference.hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/__init__.py skfda/inference/hotelling/__init__.meta.json skfda/inference/hotelling/__init__.data.json +TRACE: Interface for skfda.inference.hotelling is unchanged +LOG: Cached module skfda.inference.hotelling has same interface +TRACE: Priorities for skfda.exploratory.outliers.neighbors_outlier: +LOG: Processing SCC singleton (skfda.exploratory.outliers.neighbors_outlier) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Writing skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json +TRACE: Interface for skfda.exploratory.outliers.neighbors_outlier has changed +LOG: Cached module skfda.exploratory.outliers.neighbors_outlier has changed interface +TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:25 +TRACE: Priorities for skfda.misc.covariances: skfda.datasets:20 +TRACE: Priorities for skfda.datasets._samples_generators: skfda.misc.covariances:5 +LOG: Processing SCC of size 3 (skfda.datasets skfda.misc.covariances skfda.datasets._samples_generators) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc.validation skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) +LOG: Writing skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json +TRACE: Interface for skfda.datasets has changed +LOG: Cached module skfda.datasets has changed interface +LOG: Writing skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json +TRACE: Interface for skfda.misc.covariances has changed +LOG: Cached module skfda.misc.covariances has changed interface +LOG: Writing skfda.datasets._samples_generators /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py skfda/datasets/_samples_generators.meta.json skfda/datasets/_samples_generators.data.json +TRACE: Interface for skfda.datasets._samples_generators has changed +LOG: Cached module skfda.datasets._samples_generators has changed interface +TRACE: Priorities for skfda.tests.test_ufunc_numpy: +LOG: Processing SCC singleton (skfda.tests.test_ufunc_numpy) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.function_base skfda skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid typing unittest unittest.case) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_ufunc_numpy.py (skfda.tests.test_ufunc_numpy) +LOG: Writing skfda.tests.test_ufunc_numpy /home/carlos/git/scikit-fda/skfda/tests/test_ufunc_numpy.py skfda/tests/test_ufunc_numpy.meta.json skfda/tests/test_ufunc_numpy.data.json +TRACE: Interface for skfda.tests.test_ufunc_numpy is unchanged +LOG: Cached module skfda.tests.test_ufunc_numpy has same interface +TRACE: Priorities for skfda.tests.test_stats: +LOG: Processing SCC singleton (skfda.tests.test_stats) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda.datasets skfda.datasets._real_datasets skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._stats skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing skfda.typing._metric typing unittest) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_stats.py (skfda.tests.test_stats) +LOG: Writing skfda.tests.test_stats /home/carlos/git/scikit-fda/skfda/tests/test_stats.py skfda/tests/test_stats.meta.json skfda/tests/test_stats.data.json +TRACE: Interface for skfda.tests.test_stats is unchanged +LOG: Cached module skfda.tests.test_stats has same interface +TRACE: Priorities for skfda.tests.test_smoothing: +LOG: Processing SCC singleton (skfda.tests.test_smoothing) as inherently stale with stale deps (builtins numpy skfda skfda._utils skfda.datasets skfda.misc.hat_matrix skfda.misc.operators skfda.misc.regularization skfda.preprocessing skfda.preprocessing.smoothing skfda.preprocessing.smoothing.validation skfda.representation.basis skfda.representation.grid typing typing_extensions unittest) +LOG: Writing skfda.tests.test_smoothing /home/carlos/git/scikit-fda/skfda/tests/test_smoothing.py skfda/tests/test_smoothing.meta.json skfda/tests/test_smoothing.data.json +TRACE: Interface for skfda.tests.test_smoothing has changed +LOG: Cached module skfda.tests.test_smoothing has changed interface +TRACE: Priorities for skfda.tests.test_registration: +LOG: Processing SCC singleton (skfda.tests.test_registration) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda._utils._warping skfda.datasets skfda.datasets._samples_generators skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._stats skfda.preprocessing skfda.preprocessing.registration skfda.preprocessing.registration._landmark_registration skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid skfda.representation.interpolation types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_registration.py (skfda.tests.test_registration) +LOG: Writing skfda.tests.test_registration /home/carlos/git/scikit-fda/skfda/tests/test_registration.py skfda/tests/test_registration.meta.json skfda/tests/test_registration.data.json +TRACE: Interface for skfda.tests.test_registration is unchanged +LOG: Cached module skfda.tests.test_registration has same interface +TRACE: Priorities for skfda.tests.test_pandas_fdatagrid: +LOG: Processing SCC singleton (skfda.tests.test_pandas_fdatagrid) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.numeric skfda skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatagrid.py (skfda.tests.test_pandas_fdatagrid) +LOG: Writing skfda.tests.test_pandas_fdatagrid /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatagrid.py skfda/tests/test_pandas_fdatagrid.meta.json skfda/tests/test_pandas_fdatagrid.data.json +TRACE: Interface for skfda.tests.test_pandas_fdatagrid is unchanged +LOG: Cached module skfda.tests.test_pandas_fdatagrid has same interface +TRACE: Priorities for skfda.tests.test_metrics: +LOG: Processing SCC singleton (skfda.tests.test_metrics) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._lp_norms skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_metrics.py (skfda.tests.test_metrics) +LOG: Writing skfda.tests.test_metrics /home/carlos/git/scikit-fda/skfda/tests/test_metrics.py skfda/tests/test_metrics.meta.json skfda/tests/test_metrics.data.json +TRACE: Interface for skfda.tests.test_metrics is unchanged +LOG: Cached module skfda.tests.test_metrics has same interface +TRACE: Priorities for skfda.tests.test_math: +LOG: Processing SCC singleton (skfda.tests.test_math) as stale due to deps (abc builtins enum multimethod numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._utils skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc._math skfda.misc.covariances skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._monomial skfda.representation.basis._tensor_basis skfda.representation.basis._vector_basis skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_math.py (skfda.tests.test_math) +LOG: Writing skfda.tests.test_math /home/carlos/git/scikit-fda/skfda/tests/test_math.py skfda/tests/test_math.meta.json skfda/tests/test_math.data.json +TRACE: Interface for skfda.tests.test_math is unchanged +LOG: Cached module skfda.tests.test_math has same interface +TRACE: Priorities for skfda.tests.test_hotelling: +LOG: Processing SCC singleton (skfda.tests.test_hotelling) as stale due to deps (_typeshed abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.random numpy.random._generator numpy.random.mtrand skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_hotelling.py (skfda.tests.test_hotelling) +LOG: Writing skfda.tests.test_hotelling /home/carlos/git/scikit-fda/skfda/tests/test_hotelling.py skfda/tests/test_hotelling.meta.json skfda/tests/test_hotelling.data.json +TRACE: Interface for skfda.tests.test_hotelling is unchanged +LOG: Cached module skfda.tests.test_hotelling has same interface +TRACE: Priorities for skfda.tests.test_functional_transformers: +LOG: Processing SCC singleton (skfda.tests.test_functional_transformers) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.datasets skfda.datasets._real_datasets skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.grid types typing unittest unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_functional_transformers.py (skfda.tests.test_functional_transformers) +LOG: Writing skfda.tests.test_functional_transformers /home/carlos/git/scikit-fda/skfda/tests/test_functional_transformers.py skfda/tests/test_functional_transformers.meta.json skfda/tests/test_functional_transformers.data.json +TRACE: Interface for skfda.tests.test_functional_transformers is unchanged +LOG: Cached module skfda.tests.test_functional_transformers has same interface +TRACE: Priorities for skfda.tests.test_fpca: +LOG: Processing SCC singleton (skfda.tests.test_fpca) as stale due to deps (_typeshed abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.random numpy.random.bit_generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._real_datasets skfda.misc skfda.misc.operators skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.misc.regularization skfda.misc.regularization._regularization skfda.preprocessing skfda.preprocessing.dim_reduction skfda.preprocessing.dim_reduction._fpca skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fpca.py (skfda.tests.test_fpca) +LOG: Writing skfda.tests.test_fpca /home/carlos/git/scikit-fda/skfda/tests/test_fpca.py skfda/tests/test_fpca.meta.json skfda/tests/test_fpca.data.json +TRACE: Interface for skfda.tests.test_fpca is unchanged +LOG: Cached module skfda.tests.test_fpca has same interface +TRACE: Priorities for skfda.tests.test_fda_feature_union: +LOG: Processing SCC singleton (skfda.tests.test_fda_feature_union) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._real_datasets skfda.misc skfda.misc.hat_matrix skfda.misc.operators skfda.misc.operators._operators skfda.misc.operators._srvf skfda.preprocessing skfda.preprocessing.smoothing skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing._linear skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fda_feature_union.py (skfda.tests.test_fda_feature_union) +LOG: Writing skfda.tests.test_fda_feature_union /home/carlos/git/scikit-fda/skfda/tests/test_fda_feature_union.py skfda/tests/test_fda_feature_union.meta.json skfda/tests/test_fda_feature_union.data.json +TRACE: Interface for skfda.tests.test_fda_feature_union is unchanged +LOG: Cached module skfda.tests.test_fda_feature_union has same interface +TRACE: Priorities for skfda.tests.test_extrapolation: +LOG: Processing SCC singleton (skfda.tests.test_extrapolation) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.datasets skfda.datasets._samples_generators skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.extrapolation skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_extrapolation.py (skfda.tests.test_extrapolation) +LOG: Writing skfda.tests.test_extrapolation /home/carlos/git/scikit-fda/skfda/tests/test_extrapolation.py skfda/tests/test_extrapolation.meta.json skfda/tests/test_extrapolation.data.json +TRACE: Interface for skfda.tests.test_extrapolation is unchanged +LOG: Cached module skfda.tests.test_extrapolation has same interface +TRACE: Priorities for skfda.tests.test_elastic: +LOG: Processing SCC singleton (skfda.tests.test_elastic) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.lib numpy.lib.twodim_base numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda._utils._warping skfda.datasets skfda.datasets._samples_generators skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._fisher_rao skfda.misc skfda.misc.metrics skfda.misc.metrics._fisher_rao skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.misc.operators skfda.misc.operators._operators skfda.misc.operators._srvf skfda.preprocessing skfda.preprocessing.registration skfda.preprocessing.registration._fisher_rao skfda.preprocessing.registration.base skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric types typing typing_extensions unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_elastic.py (skfda.tests.test_elastic) +LOG: Writing skfda.tests.test_elastic /home/carlos/git/scikit-fda/skfda/tests/test_elastic.py skfda/tests/test_elastic.meta.json skfda/tests/test_elastic.data.json +TRACE: Interface for skfda.tests.test_elastic is unchanged +LOG: Cached module skfda.tests.test_elastic has same interface +TRACE: Priorities for skfda.tests.test_covariances: +LOG: Processing SCC singleton (skfda.tests.test_covariances) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.function_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.misc skfda.misc.covariances typing unittest unittest.case) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_covariances.py (skfda.tests.test_covariances) +LOG: Writing skfda.tests.test_covariances /home/carlos/git/scikit-fda/skfda/tests/test_covariances.py skfda/tests/test_covariances.meta.json skfda/tests/test_covariances.data.json +TRACE: Interface for skfda.tests.test_covariances is unchanged +LOG: Cached module skfda.tests.test_covariances has same interface +TRACE: Priorities for skfda.tests.test_basis: +LOG: Processing SCC singleton (skfda.tests.test_basis) as inherently stale with stale deps (builtins itertools numpy skfda skfda.datasets skfda.misc skfda.representation.basis skfda.representation.grid unittest) +LOG: Writing skfda.tests.test_basis /home/carlos/git/scikit-fda/skfda/tests/test_basis.py skfda/tests/test_basis.meta.json skfda/tests/test_basis.data.json +TRACE: Interface for skfda.tests.test_basis has changed +LOG: Cached module skfda.tests.test_basis has changed interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting) as stale due to deps (__future__ _typeshed abc array builtins copy ctypes dcor dcor._dcor dcor._utils dcor.distances enum mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.core.shape_base numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.lib.index_tricks numpy.lib.type_check numpy.linalg numpy.linalg.linalg numpy.ma numpy.ma.core pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.misc skfda.misc.covariances skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py (skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting) +LOG: Writing skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.meta.json skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting has same interface +TRACE: Priorities for skfda.ml.regression: +LOG: Processing SCC singleton (skfda.ml.regression) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/__init__.py (skfda.ml.regression) +LOG: Writing skfda.ml.regression /home/carlos/git/scikit-fda/skfda/ml/regression/__init__.py skfda/ml/regression/__init__.meta.json skfda/ml/regression/__init__.data.json +TRACE: Interface for skfda.ml.regression is unchanged +LOG: Cached module skfda.ml.regression has same interface +TRACE: Priorities for skfda.ml.clustering: +LOG: Processing SCC singleton (skfda.ml.clustering) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py (skfda.ml.clustering) +LOG: Writing skfda.ml.clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py skfda/ml/clustering/__init__.meta.json skfda/ml/clustering/__init__.data.json +TRACE: Interface for skfda.ml.clustering is unchanged +LOG: Cached module skfda.ml.clustering has same interface +TRACE: Priorities for skfda.ml.classification._depth_classifiers: +LOG: Processing SCC singleton (skfda.ml.classification._depth_classifiers) as stale due to deps (__future__ _collections_abc _typeshed abc array builtins collections contextlib ctypes enum itertools mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.lib numpy.lib.function_base numpy.lib.polynomial pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.preprocessing skfda.representation skfda.representation._functional_data skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py (skfda.ml.classification._depth_classifiers) +LOG: Writing skfda.ml.classification._depth_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py skfda/ml/classification/_depth_classifiers.meta.json skfda/ml/classification/_depth_classifiers.data.json +TRACE: Interface for skfda.ml.classification._depth_classifiers is unchanged +LOG: Cached module skfda.ml.classification._depth_classifiers has same interface +TRACE: Priorities for skfda.inference.anova._anova_oneway: +LOG: Processing SCC singleton (skfda.inference.anova._anova_oneway) as stale due to deps (__future__ abc array builtins ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.twodim_base numpy.random numpy.random._generator numpy.random.mtrand pickle skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/anova/_anova_oneway.py (skfda.inference.anova._anova_oneway) +LOG: Writing skfda.inference.anova._anova_oneway /home/carlos/git/scikit-fda/skfda/inference/anova/_anova_oneway.py skfda/inference/anova/_anova_oneway.meta.json skfda/inference/anova/_anova_oneway.data.json +TRACE: Interface for skfda.inference.anova._anova_oneway is unchanged +LOG: Cached module skfda.inference.anova._anova_oneway has same interface +TRACE: Priorities for skfda.exploratory.outliers: skfda.exploratory.outliers._boxplot:25 skfda.exploratory.outliers._directional_outlyingness:25 +TRACE: Priorities for skfda.exploratory.outliers._directional_outlyingness: skfda.exploratory.outliers:20 +TRACE: Priorities for skfda.exploratory.outliers._boxplot: skfda.exploratory.outliers:20 +LOG: Processing SCC of size 3 (skfda.exploratory.outliers skfda.exploratory.outliers._directional_outlyingness skfda.exploratory.outliers._boxplot) as inherently stale with stale deps (__future__ builtins dataclasses numpy numpy.linalg skfda._utils._sklearn_adapter skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers._directional_outlyingness_experiment_results skfda.exploratory.outliers._envelopes skfda.exploratory.outliers._outliergram skfda.exploratory.outliers.neighbors_outlier skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.outliers /home/carlos/git/scikit-fda/skfda/exploratory/outliers/__init__.py skfda/exploratory/outliers/__init__.meta.json skfda/exploratory/outliers/__init__.data.json +TRACE: Interface for skfda.exploratory.outliers has changed +LOG: Cached module skfda.exploratory.outliers has changed interface +LOG: Writing skfda.exploratory.outliers._directional_outlyingness /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py skfda/exploratory/outliers/_directional_outlyingness.meta.json skfda/exploratory/outliers/_directional_outlyingness.data.json +TRACE: Interface for skfda.exploratory.outliers._directional_outlyingness has changed +LOG: Cached module skfda.exploratory.outliers._directional_outlyingness has changed interface +LOG: Writing skfda.exploratory.outliers._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_boxplot.py skfda/exploratory/outliers/_boxplot.meta.json skfda/exploratory/outliers/_boxplot.data.json +TRACE: Interface for skfda.exploratory.outliers._boxplot has changed +LOG: Cached module skfda.exploratory.outliers._boxplot has changed interface +TRACE: Priorities for skfda.tests.test_regularization: +LOG: Processing SCC singleton (skfda.tests.test_regularization) as stale due to deps (__future__ abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.function_base numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.operators skfda.misc.operators._identity skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.misc.regularization skfda.misc.regularization._regularization skfda.ml skfda.preprocessing skfda.preprocessing.smoothing skfda.preprocessing.smoothing._basis skfda.preprocessing.smoothing._linear skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._constant skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case warnings) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_regularization.py (skfda.tests.test_regularization) +LOG: Writing skfda.tests.test_regularization /home/carlos/git/scikit-fda/skfda/tests/test_regularization.py skfda/tests/test_regularization.meta.json skfda/tests/test_regularization.data.json +TRACE: Interface for skfda.tests.test_regularization is unchanged +LOG: Cached module skfda.tests.test_regularization has same interface +TRACE: Priorities for skfda.tests.test_regression: +LOG: Processing SCC singleton (skfda.tests.test_regression) as stale due to deps (__future__ abc builtins contextlib enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.twodim_base numpy.random numpy.random._generator numpy.random.bit_generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc.covariances skfda.misc.operators skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.misc.regularization skfda.misc.regularization._regularization skfda.ml skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid types typing typing_extensions unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_regression.py (skfda.tests.test_regression) +LOG: Writing skfda.tests.test_regression /home/carlos/git/scikit-fda/skfda/tests/test_regression.py skfda/tests/test_regression.meta.json skfda/tests/test_regression.data.json +TRACE: Interface for skfda.tests.test_regression is unchanged +LOG: Cached module skfda.tests.test_regression has same interface +TRACE: Priorities for skfda.tests.test_pandas_fdatabasis: +LOG: Processing SCC singleton (skfda.tests.test_pandas_fdatabasis) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator typing typing_extensions) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatabasis.py (skfda.tests.test_pandas_fdatabasis) +LOG: Writing skfda.tests.test_pandas_fdatabasis /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatabasis.py skfda/tests/test_pandas_fdatabasis.meta.json skfda/tests/test_pandas_fdatabasis.data.json +TRACE: Interface for skfda.tests.test_pandas_fdatabasis is unchanged +LOG: Cached module skfda.tests.test_pandas_fdatabasis has same interface +TRACE: Priorities for skfda.tests.test_outliers: +LOG: Processing SCC singleton (skfda.tests.test_outliers) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.lib numpy.lib.shape_base numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._directional_outlyingness skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_outliers.py (skfda.tests.test_outliers) +LOG: Writing skfda.tests.test_outliers /home/carlos/git/scikit-fda/skfda/tests/test_outliers.py skfda/tests/test_outliers.meta.json skfda/tests/test_outliers.data.json +TRACE: Interface for skfda.tests.test_outliers is unchanged +LOG: Cached module skfda.tests.test_outliers has same interface +TRACE: Priorities for skfda.tests.test_kernel_regression: +LOG: Processing SCC singleton (skfda.tests.test_kernel_regression) as stale due to deps (_typeshed abc array builtins enum mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.twodim_base numpy.linalg numpy.linalg.linalg numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._real_datasets skfda.misc skfda.misc.hat_matrix skfda.misc.kernels skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric typing typing_extensions unittest) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_kernel_regression.py (skfda.tests.test_kernel_regression) +LOG: Writing skfda.tests.test_kernel_regression /home/carlos/git/scikit-fda/skfda/tests/test_kernel_regression.py skfda/tests/test_kernel_regression.meta.json skfda/tests/test_kernel_regression.data.json +TRACE: Interface for skfda.tests.test_kernel_regression is unchanged +LOG: Cached module skfda.tests.test_kernel_regression has same interface +TRACE: Priorities for skfda.tests.test_clustering: +LOG: Processing SCC singleton (skfda.tests.test_clustering) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda._utils skfda._utils._sklearn_adapter skfda.ml skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_clustering.py (skfda.tests.test_clustering) +LOG: Writing skfda.tests.test_clustering /home/carlos/git/scikit-fda/skfda/tests/test_clustering.py skfda/tests/test_clustering.meta.json skfda/tests/test_clustering.data.json +TRACE: Interface for skfda.tests.test_clustering is unchanged +LOG: Cached module skfda.tests.test_clustering has same interface +TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection: +LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/__init__.py (skfda.preprocessing.dim_reduction.variable_selection) +LOG: Writing skfda.preprocessing.dim_reduction.variable_selection /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/__init__.py skfda/preprocessing/dim_reduction/variable_selection/__init__.meta.json skfda/preprocessing/dim_reduction/variable_selection/__init__.data.json +TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection is unchanged +LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection has same interface +TRACE: Priorities for skfda.ml.classification: +LOG: Processing SCC singleton (skfda.ml.classification) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/__init__.py (skfda.ml.classification) +LOG: Writing skfda.ml.classification /home/carlos/git/scikit-fda/skfda/ml/classification/__init__.py skfda/ml/classification/__init__.meta.json skfda/ml/classification/__init__.data.json +TRACE: Interface for skfda.ml.classification is unchanged +LOG: Cached module skfda.ml.classification has same interface +TRACE: Priorities for skfda.inference.anova: +LOG: Processing SCC singleton (skfda.inference.anova) as stale due to deps (abc builtins typing) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/anova/__init__.py (skfda.inference.anova) +LOG: Writing skfda.inference.anova /home/carlos/git/scikit-fda/skfda/inference/anova/__init__.py skfda/inference/anova/__init__.meta.json skfda/inference/anova/__init__.data.json +TRACE: Interface for skfda.inference.anova is unchanged +LOG: Cached module skfda.inference.anova has same interface +TRACE: Priorities for skfda.exploratory.visualization._outliergram: +LOG: Processing SCC singleton (skfda.exploratory.visualization._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation) +LOG: Writing skfda.exploratory.visualization._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_outliergram.py skfda/exploratory/visualization/_outliergram.meta.json skfda/exploratory/visualization/_outliergram.data.json +TRACE: Interface for skfda.exploratory.visualization._outliergram has changed +LOG: Cached module skfda.exploratory.visualization._outliergram has changed interface +TRACE: Priorities for skfda.exploratory.visualization._magnitude_shape_plot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._magnitude_shape_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.depth skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.visualization._magnitude_shape_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_magnitude_shape_plot.py skfda/exploratory/visualization/_magnitude_shape_plot.meta.json skfda/exploratory/visualization/_magnitude_shape_plot.data.json +TRACE: Interface for skfda.exploratory.visualization._magnitude_shape_plot has changed +LOG: Cached module skfda.exploratory.visualization._magnitude_shape_plot has changed interface +TRACE: Priorities for skfda.exploratory.visualization._boxplot: +LOG: Processing SCC singleton (skfda.exploratory.visualization._boxplot) as inherently stale with stale deps (__future__ abc builtins math numpy skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._envelopes skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.representation skfda.typing._numpy typing) +LOG: Writing skfda.exploratory.visualization._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_boxplot.py skfda/exploratory/visualization/_boxplot.meta.json skfda/exploratory/visualization/_boxplot.data.json +TRACE: Interface for skfda.exploratory.visualization._boxplot has changed +LOG: Cached module skfda.exploratory.visualization._boxplot has changed interface +TRACE: Priorities for skfda.tests.test_recursive_maxima_hunting: +LOG: Processing SCC singleton (skfda.tests.test_recursive_maxima_hunting) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._samples_generators skfda.preprocessing skfda.preprocessing.dim_reduction skfda.representation skfda.representation._functional_data skfda.representation.grid types typing unittest unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_recursive_maxima_hunting.py (skfda.tests.test_recursive_maxima_hunting) +LOG: Writing skfda.tests.test_recursive_maxima_hunting /home/carlos/git/scikit-fda/skfda/tests/test_recursive_maxima_hunting.py skfda/tests/test_recursive_maxima_hunting.meta.json skfda/tests/test_recursive_maxima_hunting.data.json +TRACE: Interface for skfda.tests.test_recursive_maxima_hunting is unchanged +LOG: Cached module skfda.tests.test_recursive_maxima_hunting has same interface +TRACE: Priorities for skfda.tests.test_per_class_transformer: +LOG: Processing SCC singleton (skfda.tests.test_per_class_transformer) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.datasets skfda.datasets._real_datasets skfda.ml skfda.ml._neighbors_base skfda.preprocessing skfda.preprocessing.dim_reduction skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing skfda.typing._metric types typing typing_extensions unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_per_class_transformer.py (skfda.tests.test_per_class_transformer) +LOG: Writing skfda.tests.test_per_class_transformer /home/carlos/git/scikit-fda/skfda/tests/test_per_class_transformer.py skfda/tests/test_per_class_transformer.meta.json skfda/tests/test_per_class_transformer.data.json +TRACE: Interface for 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skfda.datasets._samples_generators skfda.exploratory skfda.exploratory.outliers skfda.exploratory.outliers.neighbors_outlier skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.grid skfda.typing skfda.typing._metric types typing typing_extensions unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_neighbors.py (skfda.tests.test_neighbors) +LOG: Writing skfda.tests.test_neighbors /home/carlos/git/scikit-fda/skfda/tests/test_neighbors.py skfda/tests/test_neighbors.meta.json skfda/tests/test_neighbors.data.json +TRACE: Interface for skfda.tests.test_neighbors is unchanged +LOG: Cached module skfda.tests.test_neighbors has same interface +TRACE: 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skfda/tests/test_classification.meta.json skfda/tests/test_classification.data.json +TRACE: Interface for skfda.tests.test_classification is unchanged +LOG: Cached module skfda.tests.test_classification has same interface +TRACE: Priorities for skfda.exploratory.visualization: +LOG: Processing SCC singleton (skfda.exploratory.visualization) as inherently stale with stale deps (builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.exploratory.visualization._ddplot skfda.exploratory.visualization._magnitude_shape_plot skfda.exploratory.visualization._multiple_display skfda.exploratory.visualization._outliergram skfda.exploratory.visualization._parametric_plot skfda.exploratory.visualization.fpca typing) +LOG: Writing skfda.exploratory.visualization /home/carlos/git/scikit-fda/skfda/exploratory/visualization/__init__.py skfda/exploratory/visualization/__init__.meta.json skfda/exploratory/visualization/__init__.data.json +TRACE: Interface for 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(skfda.tests.test_magnitude_shape) +LOG: Writing skfda.tests.test_magnitude_shape /home/carlos/git/scikit-fda/skfda/tests/test_magnitude_shape.py skfda/tests/test_magnitude_shape.meta.json skfda/tests/test_magnitude_shape.data.json +TRACE: Interface for skfda.tests.test_magnitude_shape is unchanged +LOG: Cached module skfda.tests.test_magnitude_shape has same interface +TRACE: Priorities for skfda.tests.test_fdata_boxplot: +LOG: Processing SCC singleton (skfda.tests.test_fdata_boxplot) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.exploratory.visualization skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) +LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fdata_boxplot.py (skfda.tests.test_fdata_boxplot) +LOG: Writing skfda.tests.test_fdata_boxplot /home/carlos/git/scikit-fda/skfda/tests/test_fdata_boxplot.py skfda/tests/test_fdata_boxplot.meta.json skfda/tests/test_fdata_boxplot.data.json +TRACE: Interface for skfda.tests.test_fdata_boxplot is unchanged +LOG: Cached module skfda.tests.test_fdata_boxplot has same interface +LOG: No fresh SCCs left in queue +LOG: Build finished in 78.418 seconds with 662 modules, and 0 errors diff --git a/setup.cfg b/setup.cfg index 1ce2af7e8..401c13788 100644 --- a/setup.cfg +++ b/setup.cfg @@ -179,6 +179,9 @@ ignore_missing_imports = True [mypy-matplotlib.*] ignore_missing_imports = True +[mypy-mpl_toolkits.*] +ignore_missing_imports = True + [mypy-multimethod.*] ignore_missing_imports = True diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 003f743ab..1cfb98707 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -14,6 +14,7 @@ Sequence, Sized, Tuple, + Type, TypeVar, Union, cast, @@ -574,7 +575,7 @@ def _check_array_key(array: NDArrayAny, key: Any) -> Any: return key -def _check_estimator(estimator: BaseEstimator) -> None: +def _check_estimator(estimator: Type[BaseEstimator]) -> None: from sklearn.utils.estimator_checks import ( check_get_params_invariance, check_set_params, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index fc2a1771d..6444a1470 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -3,7 +3,7 @@ from __future__ import annotations import itertools -from typing import Optional, Sequence, Tuple, TypeVar, Union, cast +from typing import Optional, Sequence, Tuple, TypeVar, Union, cast, overload import numpy as np from typing_extensions import Protocol, TypeGuard @@ -19,6 +19,14 @@ class _BasicUfuncProtocol(Protocol): + @overload + def __call__(self, __arg: FDataGrid) -> FDataGrid: # noqa: WPS112 + pass + + @overload + def __call__(self, __arg: NDArrayFloat) -> NDArrayFloat: # noqa: WPS112 + pass + def __call__(self, __arg: T) -> T: # noqa: WPS112 pass diff --git a/skfda/inference/anova/__init__.py b/skfda/inference/anova/__init__.py index 044f4adb0..3850ac336 100644 --- a/skfda/inference/anova/__init__.py +++ b/skfda/inference/anova/__init__.py @@ -1,2 +1,6 @@ """Implementation of ANOVA for functional data.""" -from ._anova_oneway import oneway_anova, v_asymptotic_stat, v_sample_stat +from ._anova_oneway import ( + oneway_anova as oneway_anova, + v_asymptotic_stat as v_asymptotic_stat, + v_sample_stat as v_sample_stat, +) diff --git a/skfda/inference/hotelling/__init__.py b/skfda/inference/hotelling/__init__.py index d637b2634..46ea191d3 100644 --- a/skfda/inference/hotelling/__init__.py +++ b/skfda/inference/hotelling/__init__.py @@ -1,4 +1,7 @@ """ Hotelling statistic and test. """ -from ._hotelling import hotelling_t2, hotelling_test_ind +from ._hotelling import ( + hotelling_t2 as hotelling_t2, + hotelling_test_ind as hotelling_test_ind, +) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 16ae88e71..a76e22ddd 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -9,7 +9,7 @@ from scipy.interpolate import PPoly from ...representation import FData, FDataGrid -from ...representation.basis import BSpline, Constant, Fourier, Monomial +from ...representation.basis import Basis, BSpline, Constant, Fourier, Monomial from ...typing._base import DomainRangeLike from ...typing._numpy import NDArrayFloat from ._operators import Operator, gramian_matrix_optimization @@ -25,7 +25,7 @@ class LinearDifferentialOperator( - Operator[FData, Callable[[NDArrayFloat], NDArrayFloat]], + Operator[Union[FData, Basis], Callable[[NDArrayFloat], NDArrayFloat]], ): r""" Defines the structure of a linear differential operator function system. @@ -186,7 +186,10 @@ def constant_weights(self) -> NDArrayFloat | None: return None if weights.dtype == np.object_ else weights - def __call__(self, f: FData) -> Callable[[NDArrayFloat], NDArrayFloat]: + def __call__( + self, + f: FData | Basis, + ) -> Callable[[NDArrayFloat], NDArrayFloat]: """Return the function that results of applying the operator.""" function_derivatives = [ f.derivative(order=i) for i, _ in enumerate(self.weights) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 9a168a05b..343ac3b8d 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -50,7 +50,10 @@ class LinearRegression( BaseEstimator, - RegressorMixin[AcceptedDataType, NDArrayFloat], + RegressorMixin[ + Union[AcceptedDataType, Sequence[AcceptedDataType]], + NDArrayFloat, + ], ): r"""Linear regression with multivariate response. @@ -167,7 +170,7 @@ def __init__( def fit( # noqa: D102 self, - X: AcceptedDataType, + X: AcceptedDataType | Sequence[AcceptedDataType], y: NDArrayFloat, sample_weight: Optional[NDArrayFloat] = None, ) -> LinearRegression: @@ -284,7 +287,7 @@ def _argcheck_X( def _argcheck_X_y( self, - X: AcceptedDataType, + X: AcceptedDataType | Sequence[AcceptedDataType], y: NDArrayFloat, sample_weight: Optional[NDArrayFloat] = None, coef_basis: Optional[BasisCoefsType] = None, diff --git a/skfda/preprocessing/feature_construction/_fda_feature_union.py b/skfda/preprocessing/feature_construction/_fda_feature_union.py index f9cb56bfd..aca1c7b46 100644 --- a/skfda/preprocessing/feature_construction/_fda_feature_union.py +++ b/skfda/preprocessing/feature_construction/_fda_feature_union.py @@ -1,7 +1,7 @@ """Feature extraction union for dimensionality reduction.""" from __future__ import annotations -from typing import Any, Mapping, Sequence, Union +from typing import Any, Mapping, Sequence, Tuple, Union import pandas as pd from sklearn.pipeline import FeatureUnion @@ -91,7 +91,9 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore[misc] def __init__( self, - transformer_list: Sequence[TransformerMixin[Any, Any, Any]], + transformer_list: Sequence[ + Tuple[str, TransformerMixin[Any, Any, Any]], + ], *, n_jobs: int = 1, transformer_weights: Mapping[str, float] | None = None, diff --git a/skfda/preprocessing/feature_construction/_per_class_transformer.py b/skfda/preprocessing/feature_construction/_per_class_transformer.py index da687f56f..2a08ae5b4 100644 --- a/skfda/preprocessing/feature_construction/_per_class_transformer.py +++ b/skfda/preprocessing/feature_construction/_per_class_transformer.py @@ -18,18 +18,17 @@ Input = TypeVar("Input", bound=Union[FData, NDArrayFloat]) Output = TypeVar("Output", bound=Union[pd.DataFrame, NDArrayFloat]) -Target = TypeVar("Target", bound=NDArrayInt) TransformerOutput = Union[FData, NDArrayFloat] def _fit_feature_transformer( # noqa: WPS320 WPS234 X: Input, - y: Target, - transformer: TransformerMixin[Input, Output, Target], + y: NDArrayInt, + transformer: TransformerMixin[Input, Output, object], ) -> Tuple[ Union[NDArrayAny, NDArrayFloat], - Sequence[TransformerMixin[Input, Output, Target]], + Sequence[TransformerMixin[Input, Output, object]], ]: classes, y_ind = _classifier_get_classes(y) @@ -42,7 +41,7 @@ def _fit_feature_transformer( # noqa: WPS320 WPS234 return classes, class_feature_transformers -class PerClassTransformer(TransformerMixin[Input, Output, Target]): +class PerClassTransformer(TransformerMixin[Input, Output, NDArrayInt]): r"""Per class feature transformer for functional data. This class takes a transformer and performs the following map: @@ -156,7 +155,7 @@ class PerClassTransformer(TransformerMixin[Input, Output, Target]): def __init__( self, - transformer: TransformerMixin[Input, TransformerOutput, Target], + transformer: TransformerMixin[Input, TransformerOutput, object], *, array_output: bool = False, ) -> None: @@ -224,8 +223,8 @@ def _validate_transformer( def fit( # type: ignore[override] self, X: Input, - y: Target, - ) -> PerClassTransformer[Input, Output, Target]: + y: NDArrayInt, + ) -> PerClassTransformer[Input, Output]: """ Fit the model on each class. @@ -250,7 +249,7 @@ def fit( # type: ignore[override] return self - def transform(self, X: Input) -> Output: + def transform(self, X: Input, y: object = None) -> Output: """ Transform the provided data using the already fitted transformer. @@ -290,7 +289,7 @@ def transform(self, X: Input) -> Output: def fit_transform( # type: ignore[override] self, X: Input, - y: Target, + y: NDArrayInt, ) -> Output: """ Fits and transforms the provided data. @@ -305,4 +304,4 @@ def fit_transform( # type: ignore[override] Eiter array of shape (n_samples, G) or a Data Frame \ including the transformed data. """ - return self.fit(X, y).transform(X) + return self.fit(X, y).transform(X, y) diff --git a/skfda/tests/test_basis.py b/skfda/tests/test_basis.py index faff95c2f..1a79f5139 100644 --- a/skfda/tests/test_basis.py +++ b/skfda/tests/test_basis.py @@ -7,6 +7,7 @@ import skfda from skfda import concatenate +from skfda.datasets import fetch_weather from skfda.misc import inner_product_matrix from skfda.representation.basis import ( BSpline, @@ -323,7 +324,7 @@ def test_fdatabasis_mul(self) -> None: ) self.assertTrue( - (monomial2 * [1, 2]).equals( + (monomial2 * np.array([1, 2])).equals( FDataBasis( basis, [[1, 2, 3], [6, 8, 10]], @@ -332,7 +333,7 @@ def test_fdatabasis_mul(self) -> None: ) self.assertTrue( - ([1, 2] * monomial2).equals( + (np.array([1, 2]) * monomial2).equals( FDataBasis( basis, [[1, 2, 3], [6, 8, 10]], @@ -620,7 +621,7 @@ class TestVectorValuedBasis(unittest.TestCase): def test_vector_valued(self) -> None: """Test vector valued basis.""" - X, _ = skfda.datasets.fetch_weather(return_X_y=True) + X, _ = fetch_weather(return_X_y=True) basis_dim = skfda.representation.basis.Fourier( n_basis=7, diff --git a/skfda/tests/test_clustering.py b/skfda/tests/test_clustering.py index 91b4b26aa..1934139ce 100644 --- a/skfda/tests/test_clustering.py +++ b/skfda/tests/test_clustering.py @@ -77,7 +77,7 @@ def test_univariate(self) -> None: ] grid_points = [0, 2, 4, 6, 8, 10] fd = FDataGrid(data_matrix, grid_points) - fuzzy_kmeans = FuzzyCMeans() + fuzzy_kmeans = FuzzyCMeans[FDataGrid]() fuzzy_kmeans.fit(fd) np.testing.assert_array_equal( fuzzy_kmeans.predict_proba(fd).round(3), diff --git a/skfda/tests/test_covariances.py b/skfda/tests/test_covariances.py index 7dd0da9e9..1005da49d 100644 --- a/skfda/tests/test_covariances.py +++ b/skfda/tests/test_covariances.py @@ -2,7 +2,7 @@ import numpy as np -import skfda +import skfda.misc.covariances class TestsSklearn(unittest.TestCase): diff --git a/skfda/tests/test_extrapolation.py b/skfda/tests/test_extrapolation.py index d3ccf54d4..1ffeaae55 100644 --- a/skfda/tests/test_extrapolation.py +++ b/skfda/tests/test_extrapolation.py @@ -77,13 +77,13 @@ def test_setting(self) -> None: a.extrapolation = PeriodicExtrapolation() self.assertEqual(a.extrapolation, PeriodicExtrapolation()) - a.extrapolation = "bounds" + a.extrapolation = "bounds" # type: ignore[assignment] self.assertEqual(a.extrapolation, BoundaryExtrapolation()) a.extrapolation = ExceptionExtrapolation() self.assertEqual(a.extrapolation, ExceptionExtrapolation()) - a.extrapolation = "zeros" + a.extrapolation = "zeros" # type: ignore[assignment] self.assertEqual(a.extrapolation, FillExtrapolation(0)) self.assertNotEqual(a.extrapolation, FillExtrapolation(1)) @@ -102,7 +102,7 @@ def test_periodic(self) -> None: rtol=1e-6, ) - self.basis.extrapolation = "periodic" + self.basis.extrapolation = "periodic" # type: ignore[assignment] data = self.basis([-0.5, 0, 1.5]) np.testing.assert_allclose( @@ -116,7 +116,7 @@ def test_periodic(self) -> None: def test_boundary(self) -> None: """Test boundary-copying extrapolation.""" - self.grid.extrapolation = "bounds" + self.grid.extrapolation = "bounds" # type: ignore[assignment] data = self.grid([-0.5, 0, 1.5]) np.testing.assert_allclose( @@ -128,7 +128,7 @@ def test_boundary(self) -> None: rtol=1e-6, ) - self.basis.extrapolation = "bounds" + self.basis.extrapolation = "bounds" # type: ignore[assignment] data = self.basis([-0.5, 0, 1.5]) np.testing.assert_allclose( @@ -142,19 +142,19 @@ def test_boundary(self) -> None: def test_exception(self) -> None: """Test no extrapolation (exception).""" - self.grid.extrapolation = "exception" + self.grid.extrapolation = "exception" # type: ignore[assignment] with np.testing.assert_raises(ValueError): self.grid([-0.5, 0, 1.5]) - self.basis.extrapolation = "exception" + self.basis.extrapolation = "exception" # type: ignore[assignment] with np.testing.assert_raises(ValueError): self.basis([-0.5, 0, 1.5]) def test_zeros(self) -> None: """Test zeros extrapolation.""" - self.grid.extrapolation = "zeros" + self.grid.extrapolation = "zeros" # type: ignore[assignment] data = self.grid([-0.5, 0, 1.5]) np.testing.assert_allclose( @@ -166,7 +166,7 @@ def test_zeros(self) -> None: rtol=1e-6, ) - self.basis.extrapolation = "zeros" + self.basis.extrapolation = "zeros" # type: ignore[assignment] data = self.basis([-0.5, 0, 1.5]) np.testing.assert_allclose( @@ -180,7 +180,7 @@ def test_zeros(self) -> None: def test_nan(self) -> None: """Test nan extrapolation.""" - self.grid.extrapolation = "nan" + self.grid.extrapolation = "nan" # type: ignore[assignment] data = self.grid([-0.5, 0, 1.5]) np.testing.assert_allclose( @@ -192,7 +192,7 @@ def test_nan(self) -> None: rtol=1e-6, ) - self.basis.extrapolation = "nan" + self.basis.extrapolation = "nan" # type: ignore[assignment] data = self.basis([-0.5, 0, 1.5]) np.testing.assert_allclose( diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index d93484c86..6fcbad348 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -19,7 +19,7 @@ def test_basis_fpca_fit_exceptions(self) -> None: """Check that invalid arguments in fit raise exception for basis.""" fpca = FPCA() with self.assertRaises(AttributeError): - fpca.fit(None) # type: ignore + fpca.fit(None) # type: ignore[arg-type] basis = Fourier(n_basis=1) # Check that if n_components is bigger than the number of samples then @@ -38,7 +38,7 @@ def test_discretized_fpca_fit_exceptions(self) -> None: """Check that invalid arguments in fit raise exception for grid.""" fpca = FPCA() with self.assertRaises(AttributeError): - fpca.fit(None) # type: ignore + fpca.fit(None) # type: ignore[arg-type] # Check that if n_components is bigger than the number of samples then # an exception should be thrown diff --git a/skfda/tests/test_functional_transformers.py b/skfda/tests/test_functional_transformers.py index bf9e32434..1d51353ee 100644 --- a/skfda/tests/test_functional_transformers.py +++ b/skfda/tests/test_functional_transformers.py @@ -16,7 +16,10 @@ def test_transform(self) -> None: """Compare results with grid and basis representations.""" X = fetch_growth(return_X_y=True)[0] - data_grid = unconditional_expected_value(X[:5], np.log) + data_grid = unconditional_expected_value( + X[:5], + np.log, + ) data_basis = unconditional_expected_value( X[:5].to_basis(basis.BSpline(n_basis=7)), np.log, diff --git a/skfda/tests/test_grid.py b/skfda/tests/test_grid.py index e1c0c899a..629755108 100644 --- a/skfda/tests/test_grid.py +++ b/skfda/tests/test_grid.py @@ -134,10 +134,10 @@ def test_concatenate_coordinates(self) -> None: # Testing labels self.assertEqual(("y", "t"), fd.coordinate_names) - fd2.coordinate_names = None + fd2.coordinate_names = None # type: ignore[assignment] fd = fd1.concatenate(fd2, as_coordinates=True) self.assertEqual(("y", None), fd.coordinate_names) - fd1.coordinate_names = None + fd1.coordinate_names = None # type: ignore[assignment] fd = fd1.concatenate(fd2, as_coordinates=True) self.assertEqual((None, None), fd.coordinate_names) diff --git a/skfda/tests/test_interpolation.py b/skfda/tests/test_interpolation.py index 43032d276..5bac5d1a3 100644 --- a/skfda/tests/test_interpolation.py +++ b/skfda/tests/test_interpolation.py @@ -403,7 +403,7 @@ def _coordinate_function( *args: float, ) -> np.typing.NDArray[np.float_]: _, *domain_indexes, _ = args - return np.sum(domain_indexes) + return np.sum(domain_indexes) # type: ignore[no-any-return] class TestEvaluationSplineArbitraryDim(unittest.TestCase): diff --git a/skfda/tests/test_kernel_regression.py b/skfda/tests/test_kernel_regression.py index f7b8d7cbf..e8e87c6d7 100644 --- a/skfda/tests/test_kernel_regression.py +++ b/skfda/tests/test_kernel_regression.py @@ -151,7 +151,7 @@ def test_nadaraya_watson(self) -> None: fd_train_grid, fd_test_grid, y_train_grid = _create_data_grid() # Test NW method with basis representation and bandwidth=1 - nw_basis = KernelRegression( + nw_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]]( kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), ) nw_basis.fit(fd_train_basis, y_train_basis) @@ -168,7 +168,7 @@ def test_nadaraya_watson(self) -> None: ) # Test NW method with grid representation and bandwidth=1 - nw_grid = KernelRegression( + nw_grid = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]]( kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), ) nw_grid.fit(fd_train_grid, y_train_grid) @@ -192,7 +192,7 @@ def test_knn(self) -> None: # Test KNN method with basis representation, n_neighbours=3 and # uniform kernel - knn_basis = KernelRegression( + knn_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]]( kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), ) knn_basis.fit(fd_train_basis, y_train_basis) @@ -210,7 +210,7 @@ def test_knn(self) -> None: # Test KNN method with grid representation, n_neighbours=3 and # uniform kernel - knn_grid = KernelRegression( + knn_grid = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]]( kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), ) knn_grid.fit(fd_train_grid, y_train_grid) @@ -253,7 +253,7 @@ def test_llr(self) -> None: # Creating data fd_train_basis, fd_test_basis, y_train_basis = _create_data_basis() - llr_basis = KernelRegression( + llr_basis = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]]( kernel_estimator=LocalLinearRegressionHatMatrix(bandwidth=1), ) llr_basis.fit(fd_train_basis, y_train_basis) @@ -273,7 +273,7 @@ def test_nw_r(self) -> None: """Comparison of NW's results with results from fda.usc.""" X_train, X_test, y_train = _create_data_r() - nw = KernelRegression( + nw = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]]( kernel_estimator=NadarayaWatsonHatMatrix(bandwidth=1), ) nw.fit(X_train, y_train) @@ -298,7 +298,7 @@ def test_knn_r(self) -> None: """Comparison of NW's results with results from fda.usc.""" X_train, X_test, y_train = _create_data_r() - knn = KernelRegression( + knn = KernelRegression[FDataGrid, np.typing.NDArray[np.float_]]( kernel_estimator=KNeighborsHatMatrix(n_neighbors=3), ) knn.fit(X_train, y_train) @@ -337,6 +337,8 @@ def test_llr_non_orthonormal(self) -> None: bandwidth=100, kernel=uniform, ) - kr = KernelRegression(kernel_estimator=llr) + kr = KernelRegression[FDataBasis, np.typing.NDArray[np.float_]]( + kernel_estimator=llr, + ) kr.fit(X_train, y_train) np.testing.assert_almost_equal(kr.predict(X), 4.35735166) diff --git a/skfda/tests/test_math.py b/skfda/tests/test_math.py index 7e3781f12..d4abac05b 100644 --- a/skfda/tests/test_math.py +++ b/skfda/tests/test_math.py @@ -6,6 +6,8 @@ import skfda from skfda._utils import _pairwise_symmetric +from skfda.datasets import make_gaussian_process +from skfda.misc.covariances import Gaussian from skfda.representation.basis import Monomial, Tensor, VectorValued @@ -111,16 +113,16 @@ def test_matrix(self) -> None: """Test inner_product_matrix function.""" basis = skfda.representation.basis.BSpline(n_basis=12) - X = skfda.datasets.make_gaussian_process( + X = make_gaussian_process( n_samples=10, n_features=20, - cov=skfda.misc.covariances.Gaussian(), + cov=Gaussian(), random_state=0, ) - Y = skfda.datasets.make_gaussian_process( + Y = make_gaussian_process( n_samples=10, n_features=20, - cov=skfda.misc.covariances.Gaussian(), + cov=Gaussian(), random_state=1, ) diff --git a/skfda/tests/test_pandas_fdatabasis.py b/skfda/tests/test_pandas_fdatabasis.py index ea578939a..537bb21e5 100644 --- a/skfda/tests/test_pandas_fdatabasis.py +++ b/skfda/tests/test_pandas_fdatabasis.py @@ -1,4 +1,6 @@ -import operator +from __future__ import annotations + +from typing import Any, Callable, Iterable, NoReturn import numpy as np import pandas @@ -6,30 +8,38 @@ from pandas import Series from pandas.tests.extension import base -import skfda -from skfda.representation.basis import BSpline, Fourier, Monomial +from skfda.representation.basis import ( + Basis, + BSpline, + FDataBasis, + FDataBasisDType, + Fourier, + Monomial, +) ############################################################################## # Fixtures ############################################################################## -@pytest.fixture(params=[Monomial(n_basis=5), Fourier(n_basis=5), - BSpline(n_basis=5)]) -def basis(request): +@pytest.fixture(params=[ + Monomial(n_basis=5), + Fourier(n_basis=5), + BSpline(n_basis=5), +]) +def basis(request: Any) -> Any: """A fixture providing the ExtensionDtype to validate.""" - return request.param @pytest.fixture -def dtype(basis): +def dtype(basis: Basis) -> FDataBasisDType: """A fixture providing the ExtensionDtype to validate.""" - return skfda.representation.basis.FDataBasisDType(basis=basis) + return FDataBasisDType(basis=basis) @pytest.fixture -def data(basis): +def data(basis: Basis) -> FDataBasis: """ Length-100 array for this type. * data[0] and data[1] should both be non missing @@ -38,28 +48,29 @@ def data(basis): coef_matrix = np.arange(100 * basis.n_basis).reshape(100, basis.n_basis) - return skfda.FDataBasis(basis=basis, coefficients=coef_matrix) + return FDataBasis(basis=basis, coefficients=coef_matrix) @pytest.fixture -def data_for_twos(): +def data_for_twos() -> NoReturn: """Length-100 array in which all the elements are two.""" raise NotImplementedError @pytest.fixture -def data_missing(basis): - """Length-2 array with [NA, Valid]""" - +def data_missing(basis: Basis) -> FDataBasis: + """Length-2 array with [NA, Valid].""" coef_matrix = np.arange( - 2 * basis.n_basis, dtype=np.float_).reshape(2, basis.n_basis) + 2 * basis.n_basis, + dtype=np.float_, + ).reshape(2, basis.n_basis) coef_matrix[0, :] = np.NaN - return skfda.FDataBasis(basis=basis, coefficients=coef_matrix) + return FDataBasis(basis=basis, coefficients=coef_matrix) @pytest.fixture(params=["data", "data_missing"]) -def all_data(request, data, data_missing): +def all_data(request: Any, data: Any, data_missing: Any) -> Any: """Parametrized fixture giving 'data' and 'data_missing'""" if request.param == "data": return data @@ -68,7 +79,7 @@ def all_data(request, data, data_missing): @pytest.fixture -def data_repeated(data): +def data_repeated(data: FDataBasis) -> Callable[[int], Iterable[FDataBasis]]: """ Generate many datasets. Parameters @@ -81,7 +92,7 @@ def data_repeated(data): returns a generator yielding `count` datasets. """ - def gen(count): + def gen(count: int) -> Iterable[FDataBasis]: for _ in range(count): yield data @@ -89,7 +100,7 @@ def gen(count): @pytest.fixture -def data_for_sorting(): +def data_for_sorting() -> NoReturn: """ Length-3 array with a known sort order. This should be three items [B, C, A] with @@ -99,7 +110,7 @@ def data_for_sorting(): @pytest.fixture -def data_missing_for_sorting(): +def data_missing_for_sorting() -> NoReturn: """ Length-3 array with a known sort order. This should be three items [B, NA, A] with @@ -109,28 +120,30 @@ def data_missing_for_sorting(): @pytest.fixture -def na_cmp(): +def na_cmp() -> Callable[[Any, Any], bool]: """ Binary operator for comparing NA values. Should return a function of two arguments that returns True if both arguments are (scalar) NA for your type. By default, uses ``operator.is_`` """ - def isna(x, y): - return ((x is pandas.NA or all(x.isna())) - and (y is pandas.NA or all(y.isna()))) + def isna(x: Any, y: Any) -> bool: + return ( + (x is pandas.NA or all(x.isna())) + and (y is pandas.NA or all(y.isna())) + ) return isna @pytest.fixture -def na_value(): +def na_value() -> pandas.NA: """The scalar missing value for this type. Default 'None'""" return pandas.NA @pytest.fixture -def data_for_grouping(): +def data_for_grouping() -> NoReturn: """ Data for factorization, grouping, and unique tests. Expected to be like [B, B, NA, NA, A, A, B, C] @@ -140,7 +153,7 @@ def data_for_grouping(): @pytest.fixture(params=[True, False]) -def box_in_series(request): +def box_in_series(request: Any) -> Any: """Whether to box the data in a Series""" return request.param @@ -154,7 +167,7 @@ def box_in_series(request): ], ids=["scalar", "list", "series", "object"], ) -def groupby_apply_op(request): +def groupby_apply_op(request: Any) -> Any: """ Functions to test groupby.apply(). """ @@ -162,7 +175,7 @@ def groupby_apply_op(request): @pytest.fixture(params=[True, False]) -def as_frame(request): +def as_frame(request: Any) -> Any: """ Boolean fixture to support Series and Series.to_frame() comparison testing. """ @@ -170,7 +183,7 @@ def as_frame(request): @pytest.fixture(params=[True, False]) -def as_series(request): +def as_series(request: Any) -> Any: """ Boolean fixture to support arr and Series(arr) comparison testing. """ @@ -178,7 +191,7 @@ def as_series(request): @pytest.fixture(params=[True, False]) -def use_numpy(request): +def use_numpy(request: Any) -> Any: """ Boolean fixture to support comparison testing of ExtensionDtype array and numpy array. @@ -187,7 +200,7 @@ def use_numpy(request): @pytest.fixture(params=["ffill", "bfill"]) -def fillna_method(request): +def fillna_method(request: Any) -> Any: """ Parametrized fixture giving method parameters 'ffill' and 'bfill' for Series.fillna(method=) testing. @@ -196,7 +209,7 @@ def fillna_method(request): @pytest.fixture(params=[True, False]) -def as_array(request): +def as_array(request: Any) -> Any: """ Boolean fixture to support ExtensionDtype _from_sequence method testing. """ @@ -222,7 +235,7 @@ def as_array(request): @pytest.fixture(params=_all_arithmetic_operators) -def all_arithmetic_operators(request): +def all_arithmetic_operators(request: Any) -> Any: """ Fixture for dunder names for common arithmetic operations. """ @@ -232,7 +245,7 @@ def all_arithmetic_operators(request): @pytest.fixture(params=["__eq__", "__ne__", # "__le__", "__lt__", "__ge__", "__gt__" ]) -def all_compare_operators(request): +def all_compare_operators(request: Any) -> Any: """ Fixture for dunder names for common compare operations """ @@ -254,7 +267,7 @@ def all_compare_operators(request): @pytest.fixture(params=_all_numeric_reductions) -def all_numeric_reductions(request): +def all_numeric_reductions(request: Any) -> Any: """ Fixture for numeric reduction names. """ @@ -265,122 +278,139 @@ def all_numeric_reductions(request): ############################################################################## -class TestCasting(base.BaseCastingTests): +class TestCasting(base.BaseCastingTests): # type: ignore[misc] # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_astype_str(self): + def test_astype_str(self) -> None: pass # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_astype_string(self): + def test_astype_string(self) -> None: pass -class TestConstructors(base.BaseConstructorsTests): +class TestConstructors(base.BaseConstructorsTests): # type: ignore[misc] # Does not support scalars which are also ExtensionArrays @pytest.mark.skip(reason="Unsupported") - def test_series_constructor_scalar_with_index(self): + def test_series_constructor_scalar_with_index(self) -> None: pass # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_from_dtype(self): + def test_from_dtype(self) -> None: pass -class TestDtype(base.BaseDtypeTests): +class TestDtype(base.BaseDtypeTests): # type: ignore[misc] # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_construct_from_string_own_name(self): + def test_construct_from_string_own_name(self) -> None: pass # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_is_dtype_from_name(self): + def test_is_dtype_from_name(self) -> None: pass # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_eq_with_str(self): + def test_eq_with_str(self) -> None: pass # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") - def test_construct_from_string(self, dtype): + def test_construct_from_string(self, dtype: Any) -> None: pass -class TestGetitem(base.BaseGetitemTests): +class TestGetitem(base.BaseGetitemTests): # type: ignore[misc] pass -class TestInterface(base.BaseInterfaceTests): +class TestInterface(base.BaseInterfaceTests): # type: ignore[misc] # Does not support scalars which are also array_like @pytest.mark.skip(reason="Unsupported") - def test_array_interface(self): + def test_array_interface(self) -> None: pass # We do not implement setitem @pytest.mark.skip(reason="Unsupported") - def test_copy(self, dtype): + def test_copy(self, dtype: Any) -> None: pass # We do not implement setitem @pytest.mark.skip(reason="Unsupported") - def test_view(self, dtype): + def test_view(self, dtype: Any) -> None: pass # Pending https://github.com/pandas-dev/pandas/issues/38812 resolution @pytest.mark.skip(reason="Bugged") - def test_contains(self, data, data_missing): + def test_contains(self, data: Any, data_missing: Any) -> None: pass -class TestArithmeticOps(base.BaseArithmeticOpsTests): +class TestArithmeticOps(base.BaseArithmeticOpsTests): # type: ignore[misc] series_scalar_exc = None # Bug introduced by https://github.com/pandas-dev/pandas/pull/37132 @pytest.mark.skip(reason="Unsupported") - def test_arith_frame_with_scalar(self, data, all_arithmetic_operators): + def test_arith_frame_with_scalar( + self, + data: Any, + all_arithmetic_operators: Any, + ) -> None: pass # FDatabasis does not implement division by non constant @pytest.mark.skip(reason="Unsupported") - def test_divmod_series_array(self, dtype): + def test_divmod_series_array(self, dtype: Any) -> None: pass # Does not convert properly a list of FData to a FData @pytest.mark.skip(reason="Unsupported") - def test_arith_series_with_array(self, dtype): + def test_arith_series_with_array(self, dtype: Any) -> None: pass # Does not error on operations @pytest.mark.skip(reason="Unsupported") - def test_error(self, dtype): + def test_error(self, dtype: Any) -> None: pass -class TestComparisonOps(base.BaseComparisonOpsTests): +class TestComparisonOps(base.BaseComparisonOpsTests): # type: ignore[misc] # Cannot be compared with 0 @pytest.mark.skip(reason="Unsupported") - def test_compare_scalar(self, data, all_compare_operators): + def test_compare_scalar( + self, + data: Any, + all_compare_operators: Any, + ) -> None: pass # Not sure how to pass it. Should it be reimplemented? @pytest.mark.skip(reason="Unsupported") - def test_compare_array(self, data, all_compare_operators): + def test_compare_array( + self, + data: Any, + all_compare_operators: Any, + ) -> None: pass -class TestNumericReduce(base.BaseNumericReduceTests): +class TestNumericReduce(base.BaseNumericReduceTests): # type: ignore[misc] - def check_reduce(self, s, op_name, skipna): + def check_reduce( + self, + s: Any, + op_name: str, + skipna: bool, + ) -> None: result = getattr(s, op_name)(skipna=skipna) assert result.n_samples == 1 diff --git a/skfda/tests/test_pandas_fdatagrid.py b/skfda/tests/test_pandas_fdatagrid.py index b7b9ad27d..47db6c383 100644 --- a/skfda/tests/test_pandas_fdatagrid.py +++ b/skfda/tests/test_pandas_fdatagrid.py @@ -79,7 +79,7 @@ def data_missing() -> ExtensionArray: @pytest.fixture(params=["data", "data_missing"]) def all_data( - request, + request: Any, data: ExtensionArray, data_missing: ExtensionArray, ) -> ExtensionArray: @@ -174,7 +174,7 @@ def data_for_grouping() -> NoReturn: @pytest.fixture(params=[True, False]) -def box_in_series(request) -> bool: +def box_in_series(request: Any) -> Any: """Whether to box the data in a Series.""" return request.param @@ -188,25 +188,25 @@ def box_in_series(request) -> bool: ], ids=["scalar", "list", "series", "object"], ) -def groupby_apply_op(request) -> Callable[[FDataGrid], Any]: +def groupby_apply_op(request: Any) -> Any: """Functions to test groupby.apply().""" return request.param @pytest.fixture(params=[True, False]) -def as_frame(request) -> bool: +def as_frame(request: Any) -> Any: """Whether to support Series and Series.to_frame() comparison testing.""" return request.param @pytest.fixture(params=[True, False]) -def as_series(request) -> bool: +def as_series(request: Any) -> Any: """Boolean fixture to support arr and Series(arr) comparison testing.""" return request.param @pytest.fixture(params=[True, False]) -def use_numpy(request) -> bool: +def use_numpy(request: Any) -> Any: """ Compare ExtensionDtype and numpy. @@ -217,7 +217,7 @@ def use_numpy(request) -> bool: @pytest.fixture(params=["ffill", "bfill"]) -def fillna_method(request) -> str: +def fillna_method(request: Any) -> Any: """ Series.fillna parameter fixture. @@ -228,7 +228,7 @@ def fillna_method(request) -> str: @pytest.fixture(params=[True, False]) -def as_array(request) -> bool: +def as_array(request: Any) -> Any: """Whether to support ExtensionDtype _from_sequence method testing.""" return request.param @@ -252,7 +252,7 @@ def as_array(request) -> bool: @pytest.fixture(params=_all_arithmetic_operators) -def all_arithmetic_operators(request) -> Callable[..., Any]: +def all_arithmetic_operators(request: Any) -> Any: """ Fixture for dunder names for common arithmetic operations. """ @@ -262,7 +262,7 @@ def all_arithmetic_operators(request) -> Callable[..., Any]: @pytest.fixture(params=["__eq__", "__ne__", # "__le__", "__lt__", "__ge__", "__gt__" ]) -def all_compare_operators(request) -> Callable[..., Any]: +def all_compare_operators(request: Any) -> Any: """ Fixture for dunder names for common compare operations """ @@ -284,7 +284,7 @@ def all_compare_operators(request) -> Callable[..., Any]: @pytest.fixture(params=_all_numeric_reductions) -def all_numeric_reductions(request): +def all_numeric_reductions(request: Any) -> Any: """ Fixture for numeric reduction names. """ @@ -295,7 +295,7 @@ def all_numeric_reductions(request): ############################################################################## -class TestCasting(base.BaseCastingTests): +class TestCasting(base.BaseCastingTests): # type: ignore[misc] # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") @@ -308,7 +308,7 @@ def test_astype_string(self) -> None: pass -class TestConstructors(base.BaseConstructorsTests): +class TestConstructors(base.BaseConstructorsTests): # type: ignore[misc] # Does not support scalars which are also ExtensionArrays @pytest.mark.skip(reason="Unsupported") @@ -321,7 +321,7 @@ def test_from_dtype(self) -> None: pass -class TestDtype(base.BaseDtypeTests): +class TestDtype(base.BaseDtypeTests): # type: ignore[misc] # Tries to construct dtype from string @pytest.mark.skip(reason="Unsupported") @@ -347,11 +347,11 @@ def test_construct_from_string( pass -class TestGetitem(base.BaseGetitemTests): +class TestGetitem(base.BaseGetitemTests): # type: ignore[misc] pass -class TestInterface(base.BaseInterfaceTests): +class TestInterface(base.BaseInterfaceTests): # type: ignore[misc] # Does not support scalars which are also array_like @pytest.mark.skip(reason="Unsupported") @@ -384,7 +384,7 @@ def test_contains( pass -class TestArithmeticOps(base.BaseArithmeticOpsTests): +class TestArithmeticOps(base.BaseArithmeticOpsTests): # type: ignore[misc] series_scalar_exc = None @@ -414,7 +414,7 @@ def test_error( pass -class TestComparisonOps(base.BaseComparisonOpsTests): +class TestComparisonOps(base.BaseComparisonOpsTests): # type: ignore[misc] # Cannot be compared with 0 @pytest.mark.skip(reason="Unsupported") @@ -435,7 +435,7 @@ def test_compare_array( pass -class TestNumericReduce(base.BaseNumericReduceTests): +class TestNumericReduce(base.BaseNumericReduceTests): # type: ignore[misc] def check_reduce( self, diff --git a/skfda/tests/test_per_class_transformer.py b/skfda/tests/test_per_class_transformer.py index 6df4ace7b..4e3cd73d0 100644 --- a/skfda/tests/test_per_class_transformer.py +++ b/skfda/tests/test_per_class_transformer.py @@ -11,6 +11,7 @@ RecursiveMaximaHunting, ) from skfda.preprocessing.feature_construction import PerClassTransformer +from skfda.representation import FDataGrid class TestPerClassTransformer(unittest.TestCase): @@ -24,8 +25,8 @@ def setUp(self) -> None: def test_transform(self) -> None: """Check the data transformation is done correctly.""" - t = PerClassTransformer( - RecursiveMaximaHunting(), # type: ignore + t = PerClassTransformer[FDataGrid, np.typing.NDArray[np.float_]]( + RecursiveMaximaHunting(), array_output=True, ) t.fit_transform(self.X, self.y) @@ -45,7 +46,9 @@ def test_transform(self) -> None: def test_not_transformer_argument(self) -> None: """Check that invalid arguments in fit raise exception.""" - t = PerClassTransformer(KNeighborsClassifier()) # type: ignore + t = PerClassTransformer[FDataGrid, np.typing.NDArray[np.float_]]( + KNeighborsClassifier(), + ) self.assertRaises( TypeError, t.fit, diff --git a/skfda/tests/test_recursive_maxima_hunting.py b/skfda/tests/test_recursive_maxima_hunting.py index 6628aea45..4ad5ce821 100644 --- a/skfda/tests/test_recursive_maxima_hunting.py +++ b/skfda/tests/test_recursive_maxima_hunting.py @@ -29,7 +29,7 @@ def mean_1( # noqa: WPS430 t: np.typing.NDArray[np.float_], ) -> np.typing.NDArray[np.float_]: - return ( + return ( # type: ignore[no-any-return] np.abs(t - 0.25) - 2 * np.abs(t - 0.5) + np.abs(t - 0.75) diff --git a/skfda/tests/test_registration.py b/skfda/tests/test_registration.py index 66cc3377d..eb1efe114 100644 --- a/skfda/tests/test_registration.py +++ b/skfda/tests/test_registration.py @@ -249,7 +249,7 @@ def setUp(self) -> None: error_std=0, random_state=1, ) - self.fd.extrapolation = "periodic" + self.fd.extrapolation = "periodic" # type: ignore[assignment] def test_fit_transform(self) -> None: @@ -281,7 +281,7 @@ def test_fit_and_transform(self) -> None: random_state=10, ) - reg = LeastSquaresShiftRegistration() + reg = LeastSquaresShiftRegistration[FDataGrid]() response = reg.fit(self.fd) # Check attributes and returned value @@ -294,7 +294,7 @@ def test_fit_and_transform(self) -> None: def test_inverse_transform(self) -> None: - reg = LeastSquaresShiftRegistration() + reg = LeastSquaresShiftRegistration[FDataGrid]() fd = reg.fit_transform(self.fd) fd = reg.inverse_transform(fd) @@ -306,7 +306,7 @@ def test_inverse_transform(self) -> None: def test_raises(self) -> None: - reg = LeastSquaresShiftRegistration() + reg = LeastSquaresShiftRegistration[FDataGrid]() # Test not fitted with np.testing.assert_raises(NotFittedError): @@ -342,16 +342,18 @@ def test_raises(self) -> None: def test_template(self) -> None: - reg = LeastSquaresShiftRegistration() + reg = LeastSquaresShiftRegistration[FDataGrid]() fd_registered_1 = reg.fit_transform(self.fd) - reg_2 = LeastSquaresShiftRegistration(template=reg.template_) + reg_2 = LeastSquaresShiftRegistration[FDataGrid]( + template=reg.template_) fd_registered_2 = reg_2.fit_transform(self.fd) - reg_3 = LeastSquaresShiftRegistration(template=mean) + reg_3 = LeastSquaresShiftRegistration[FDataGrid](template=mean) fd_registered_3 = reg_3.fit_transform(self.fd) - reg_4 = LeastSquaresShiftRegistration(template=reg.template_) + reg_4 = LeastSquaresShiftRegistration[FDataGrid]( + template=reg.template_) fd_registered_4 = reg_4.fit(self.fd).transform(self.fd) np.testing.assert_array_almost_equal( @@ -372,7 +374,7 @@ def test_template(self) -> None: ) def test_restrict_domain(self) -> None: - reg = LeastSquaresShiftRegistration(restrict_domain=True) + reg = LeastSquaresShiftRegistration[FDataGrid](restrict_domain=True) fd_registered_1 = reg.fit_transform(self.fd) np.testing.assert_array_almost_equal( @@ -380,7 +382,7 @@ def test_restrict_domain(self) -> None: [[0.022, 0.969]], ) - reg2 = LeastSquaresShiftRegistration( + reg2 = LeastSquaresShiftRegistration[FDataGrid]( restrict_domain=True, template=reg.template_.copy(domain_range=self.fd.domain_range), ) @@ -392,7 +394,7 @@ def test_restrict_domain(self) -> None: decimal=3, ) - reg3 = LeastSquaresShiftRegistration( + reg3 = LeastSquaresShiftRegistration[FDataGrid]( restrict_domain=True, template=mean, ) @@ -404,14 +406,18 @@ def test_restrict_domain(self) -> None: ) def test_initial_estimation(self) -> None: - reg = LeastSquaresShiftRegistration(initial=[-0.02161235, 0.03032652]) + reg = LeastSquaresShiftRegistration[FDataGrid]( + initial=[-0.02161235, 0.03032652], + ) reg.fit_transform(self.fd) # Only needed 1 iteration until convergence self.assertEqual(reg.n_iter_, 1) def test_custom_grid_points(self) -> None: - reg = LeastSquaresShiftRegistration(grid_points=np.linspace(0, 1, 50)) + reg = LeastSquaresShiftRegistration[FDataGrid]( + grid_points=np.linspace(0, 1, 50), + ) reg.fit_transform(self.fd) @@ -421,7 +427,8 @@ class TestRegistrationValidation(unittest.TestCase): def setUp(self) -> None: """Initialize the samples.""" self.X = make_sinusoidal_process(error_std=0, random_state=0) - self.shift_registration = LeastSquaresShiftRegistration().fit(self.X) + self.shift_registration = LeastSquaresShiftRegistration[FDataGrid]() + self.shift_registration.fit(self.X) def test_amplitude_phase_score(self) -> None: """Test basic usage of AmplitudePhaseDecomposition.""" diff --git a/skfda/tests/test_regression.py b/skfda/tests/test_regression.py index 4680b9d0b..d14df5c9e 100644 --- a/skfda/tests/test_regression.py +++ b/skfda/tests/test_regression.py @@ -1,4 +1,7 @@ +from __future__ import annotations + import unittest +from typing import Sequence import numpy as np from scipy.integrate import cumtrapz @@ -14,44 +17,58 @@ class TestScalarLinearRegression(unittest.TestCase): - def test_regression_single_explanatory(self): + def test_regression_single_explanatory(self) -> None: x_basis = Monomial(n_basis=7) x_fd = FDataBasis(x_basis, np.identity(7)) beta_basis = Fourier(n_basis=5) beta_fd = FDataBasis(beta_basis, [1, 1, 1, 1, 1]) - y = [0.9999999999999993, - 0.162381381441085, - 0.08527083481359901, - 0.08519946930844623, - 0.09532291032042489, - 0.10550022969639987, - 0.11382675064746171] + y = np.array([ + 0.9999999999999993, + 0.162381381441085, + 0.08527083481359901, + 0.08519946930844623, + 0.09532291032042489, + 0.10550022969639987, + 0.11382675064746171, + ]) scalar = LinearRegression(coef_basis=[beta_basis]) scalar.fit(x_fd, y) - np.testing.assert_allclose(scalar.coef_[0].coefficients, - beta_fd.coefficients) - np.testing.assert_allclose(scalar.intercept_, - 0.0, atol=1e-6) + assert isinstance(scalar.coef_[0], FDataBasis) + np.testing.assert_allclose( + scalar.coef_[0].coefficients, + beta_fd.coefficients, + ) + np.testing.assert_allclose( + scalar.intercept_, + 0.0, atol=1e-6, + ) y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) - scalar = LinearRegression(coef_basis=[beta_basis], - fit_intercept=False) + scalar = LinearRegression( + coef_basis=[beta_basis], + fit_intercept=False, + ) scalar.fit(x_fd, y) - np.testing.assert_allclose(scalar.coef_[0].coefficients, - beta_fd.coefficients) - np.testing.assert_equal(scalar.intercept_, - 0.0) + assert isinstance(scalar.coef_[0], FDataBasis) + np.testing.assert_allclose( + scalar.coef_[0].coefficients, + beta_fd.coefficients, + ) + np.testing.assert_equal( + scalar.intercept_, + 0.0, + ) y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) - def test_regression_multiple_explanatory(self): - y = [1, 2, 3, 4, 5, 6, 7] + def test_regression_multiple_explanatory(self) -> None: + y = np.array([1, 2, 3, 4, 5, 6, 7]) X = FDataBasis(Monomial(n_basis=7), np.identity(7)) @@ -61,35 +78,55 @@ def test_regression_multiple_explanatory(self): scalar.fit(X, y) - np.testing.assert_allclose(scalar.intercept_.round(4), - np.array([32.65]), rtol=1e-3) + np.testing.assert_allclose( + scalar.intercept_.round(4), + np.array([32.65]), + rtol=1e-3, + ) + assert isinstance(scalar.coef_[0], FDataBasis) np.testing.assert_allclose( scalar.coef_[0].coefficients.round(4), - np.array([[-28.6443, - 80.3996, - -188.587, - 236.5832, - -481.3449]]), rtol=1e-3) + np.array([[ + -28.6443, + 80.3996, + -188.587, + 236.5832, + -481.3449, + ]]), + rtol=1e-3, + ) y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) - def test_regression_mixed(self): + def test_regression_mixed(self) -> None: + + multivariate = np.array([ + [0, 0], [2, 7], [1, 7], [3, 9], + [4, 16], [2, 14], [3, 5], + ]) + + X: Sequence[ + np.typing.NDArray[np.float_] | FDataBasis, + ] = [ + multivariate, + FDataBasis( + Monomial(n_basis=3), + [ + [1, 0, 0], [0, 1, 0], [0, 0, 1], + [1, 0, 1], [1, 0, 0], [0, 1, 0], + [0, 0, 1], + ], + ), + ] - multivariate = np.array([[0, 0], [2, 7], [1, 7], [3, 9], - [4, 16], [2, 14], [3, 5]]) - - X = [multivariate, - FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], [0, 0, 1], - [1, 0, 1], [1, 0, 0], [0, 1, 0], - [0, 0, 1]])] - - # y = 2 + sum([3, 1] * array) + int(3 * function) intercept = 2 coefs_multivariate = np.array([3, 1]) coefs_functions = FDataBasis( - Monomial(n_basis=3), [[3, 0, 0]]) + Monomial(n_basis=3), + [[3, 0, 0]], + ) y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) y_sum = multivariate @ coefs_multivariate y = 2 + y_sum + y_integral @@ -97,31 +134,48 @@ def test_regression_mixed(self): scalar = LinearRegression() scalar.fit(X, y) - np.testing.assert_allclose(scalar.intercept_, - intercept, atol=0.01) + np.testing.assert_allclose( + scalar.intercept_, + intercept, + atol=0.01, + ) np.testing.assert_allclose( scalar.coef_[0], - coefs_multivariate, atol=0.01) + coefs_multivariate, + atol=0.01, + ) + assert isinstance(scalar.coef_[1], FDataBasis) np.testing.assert_allclose( scalar.coef_[1].coefficients, - coefs_functions.coefficients, atol=0.01) + coefs_functions.coefficients, + atol=0.01, + ) y_pred = scalar.predict(X) np.testing.assert_allclose(y_pred, y, atol=0.01) - def test_regression_mixed_regularization(self): - - multivariate = np.array([[0, 0], [2, 7], [1, 7], [3, 9], - [4, 16], [2, 14], [3, 5]]) - - X = [multivariate, - FDataBasis(Monomial(n_basis=3), [[1, 0, 0], [0, 1, 0], [0, 0, 1], - [1, 0, 1], [1, 0, 0], [0, 1, 0], - [0, 0, 1]])] + def test_regression_mixed_regularization(self) -> None: + + multivariate = np.array([ + [0, 0], [2, 7], [1, 7], [3, 9], + [4, 16], [2, 14], [3, 5], + ]) + + X: Sequence[ + np.typing.NDArray[np.float_] | FDataBasis, + ] = [ + multivariate, + FDataBasis( + Monomial(n_basis=3), + [ + [1, 0, 0], [0, 1, 0], [0, 0, 1], + [1, 0, 1], [1, 0, 0], [0, 1, 0], + [0, 0, 1], + ]), + ] - # y = 2 + sum([3, 1] * array) + int(3 * function) intercept = 2 coefs_multivariate = np.array([3, 1]) y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) @@ -129,81 +183,118 @@ def test_regression_mixed_regularization(self): y = 2 + y_sum + y_integral scalar = LinearRegression( - regularization=[L2Regularization(lambda x: x), - L2Regularization( - LinearDifferentialOperator(2))]) + regularization=[ + L2Regularization(lambda x: x), + L2Regularization( + LinearDifferentialOperator(2), + ), + ], + ) scalar.fit(X, y) - np.testing.assert_allclose(scalar.intercept_, - intercept, atol=0.01) + np.testing.assert_allclose( + scalar.intercept_, + intercept, + atol=0.01, + ) np.testing.assert_allclose( scalar.coef_[0], - [2.536739, 1.072186], atol=0.01) + [2.536739, 1.072186], + atol=0.01, + ) + assert isinstance(scalar.coef_[1], FDataBasis) np.testing.assert_allclose( scalar.coef_[1].coefficients, - [[2.125676, 2.450782, 5.808745e-4]], atol=0.01) + [[2.125676, 2.450782, 5.808745e-4]], + atol=0.01, + ) y_pred = scalar.predict(X) np.testing.assert_allclose( y_pred, - [5.349035, 16.456464, 13.361185, 23.930295, - 32.650965, 23.961766, 16.29029], - atol=0.01) + [ + 5.349035, 16.456464, 13.361185, 23.930295, + 32.650965, 23.961766, 16.29029, + ], + atol=0.01, + ) - def test_regression_regularization(self): + def test_regression_regularization(self) -> None: x_basis = Monomial(n_basis=7) x_fd = FDataBasis(x_basis, np.identity(7)) beta_basis = Fourier(n_basis=5) - beta_fd = FDataBasis(beta_basis, [1.0403, 0, 0, 0, 0]) - y = [1.0000684777229512, - 0.1623672257830915, - 0.08521053851548224, - 0.08514200869281137, - 0.09529138749665378, - 0.10549625973303875, - 0.11384314859153018] - - y_pred_compare = [0.890341, - 0.370162, - 0.196773, - 0.110079, - 0.058063, - 0.023385, - -0.001384] + beta_fd = FDataBasis( + beta_basis, + [1.0403, 0, 0, 0, 0], + ) + y = np.array([ + 1.0000684777229512, + 0.1623672257830915, + 0.08521053851548224, + 0.08514200869281137, + 0.09529138749665378, + 0.10549625973303875, + 0.11384314859153018, + ]) + + y_pred_compare = [ + 0.890341, + 0.370162, + 0.196773, + 0.110079, + 0.058063, + 0.023385, + -0.001384, + ] scalar = LinearRegression( coef_basis=[beta_basis], regularization=L2Regularization( - LinearDifferentialOperator(2))) + LinearDifferentialOperator(2), + ), + ) scalar.fit(x_fd, y) - np.testing.assert_allclose(scalar.coef_[0].coefficients, - beta_fd.coefficients, atol=1e-3) - np.testing.assert_allclose(scalar.intercept_, - -0.15, atol=1e-4) + assert isinstance(scalar.coef_[0], FDataBasis) + np.testing.assert_allclose( + scalar.coef_[0].coefficients, + beta_fd.coefficients, + atol=1e-3, + ) + np.testing.assert_allclose( + scalar.intercept_, + -0.15, + atol=1e-4, + ) y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y_pred_compare, atol=1e-4) x_basis = Monomial(n_basis=3) - x_fd = FDataBasis(x_basis, [[1, 0, 0], - [0, 1, 0], - [0, 0, 1], - [2, 0, 1]]) + x_fd = FDataBasis( + x_basis, + [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [2, 0, 1], + ]) beta_fd = FDataBasis(x_basis, [3, 2, 1]) - y = [1 + 13 / 3, 1 + 29 / 12, 1 + 17 / 10, 1 + 311 / 30] + y = np.array([1 + 13 / 3, 1 + 29 / 12, 1 + 17 / 10, 1 + 311 / 30]) # Non regularized scalar = LinearRegression() scalar.fit(x_fd, y) - np.testing.assert_allclose(scalar.coef_[0].coefficients, - beta_fd.coefficients) - np.testing.assert_allclose(scalar.intercept_, - 1) + assert isinstance(scalar.coef_[0], FDataBasis) + np.testing.assert_allclose( + scalar.coef_[0].coefficients, + beta_fd.coefficients, + ) + np.testing.assert_allclose(scalar.intercept_, 1) y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y) @@ -214,20 +305,27 @@ def test_regression_regularization(self): scalar_reg = LinearRegression( regularization=L2Regularization( - LinearDifferentialOperator(2))) + LinearDifferentialOperator(2), + ), + ) scalar_reg.fit(x_fd, y) - np.testing.assert_allclose(scalar_reg.coef_[0].coefficients, - beta_fd_reg.coefficients, atol=0.001) - np.testing.assert_allclose(scalar_reg.intercept_, - 0.998, atol=0.001) + assert isinstance(scalar_reg.coef_[0], FDataBasis) + np.testing.assert_allclose( + scalar_reg.coef_[0].coefficients, + beta_fd_reg.coefficients, + atol=0.001, + ) + np.testing.assert_allclose( + scalar_reg.intercept_, + 0.998, + atol=0.001, + ) y_pred = scalar_reg.predict(x_fd) np.testing.assert_allclose(y_pred, y_reg, atol=0.001) - def test_error_X_not_FData(self): - """Tests that at least one of the explanatory variables - is an FData object. """ - + def test_error_X_not_FData(self) -> None: + """Tests that at least one variable is an FData object.""" x_fd = np.identity(7) y = np.zeros(7) @@ -236,23 +334,21 @@ def test_error_X_not_FData(self): with np.testing.assert_warns(UserWarning): scalar.fit([x_fd], y) - def test_error_y_is_FData(self): - """Tests that none of the explained variables is an FData object - """ + def test_error_y_is_FData(self) -> None: + """Tests that none of the explained variables is an FData object.""" x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) y = list(FDataBasis(Monomial(n_basis=7), np.identity(7))) scalar = LinearRegression(coef_basis=[Fourier(n_basis=5)]) with np.testing.assert_raises(ValueError): - scalar.fit([x_fd], y) + scalar.fit([x_fd], y) # type: ignore[arg-type] - def test_error_X_beta_len_distinct(self): - """ Test that the number of beta bases and explanatory variables + def test_error_X_beta_len_distinct(self) -> None: + """Test that the number of beta bases and explanatory variables are not different """ - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) - y = [1 for _ in range(7)] + y = np.array([1 for _ in range(7)]) beta = Fourier(n_basis=5) scalar = LinearRegression(coef_basis=[beta]) @@ -263,12 +359,12 @@ def test_error_X_beta_len_distinct(self): with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) - def test_error_y_X_samples_different(self): - """ Test that the number of response samples and explanatory samples + def test_error_y_X_samples_different(self) -> None: + """Test that the number of response samples and explanatory samples are not different """ x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) - y = [1 for _ in range(8)] + y = np.array([1 for _ in range(8)]) beta = Fourier(n_basis=5) scalar = LinearRegression(coef_basis=[beta]) @@ -276,43 +372,39 @@ def test_error_y_X_samples_different(self): scalar.fit([x_fd], y) x_fd = FDataBasis(Monomial(n_basis=8), np.identity(8)) - y = [1 for _ in range(7)] + y = np.array([1 for _ in range(7)]) beta = Fourier(n_basis=5) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) - def test_error_beta_not_basis(self): - """ Test that all beta are Basis objects. """ - + def test_error_beta_not_basis(self) -> None: + """Test that all beta are Basis objects. """ x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) - y = [1 for _ in range(7)] + y = np.array([1 for _ in range(7)]) beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(TypeError): scalar.fit([x_fd], y) - def test_error_weights_lenght(self): - """ Test that the number of weights is equal to the - number of samples """ - + def test_error_weights_lenght(self) -> None: + """Test that the number of weights is equal to n_samples.""" x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) - y = [1 for _ in range(7)] - weights = [1 for _ in range(8)] + y = np.array([1 for _ in range(7)]) + weights = np.array([1 for _ in range(8)]) beta = Monomial(n_basis=7) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y, weights) - def test_error_weights_negative(self): - """ Test that none of the weights are negative. """ - + def test_error_weights_negative(self) -> None: + """Test that none of the weights are negative.""" x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) - y = [1 for _ in range(7)] - weights = [-1 for _ in range(7)] + y = np.array([1 for _ in range(7)]) + weights = np.array([-1 for _ in range(7)]) beta = Monomial(n_basis=7) scalar = LinearRegression(coef_basis=[beta]) @@ -467,5 +559,4 @@ def test_historical_vectorial(self) -> None: if __name__ == '__main__': - print() unittest.main() diff --git a/skfda/tests/test_smoothing.py b/skfda/tests/test_smoothing.py index 368c44abd..7c6f68932 100644 --- a/skfda/tests/test_smoothing.py +++ b/skfda/tests/test_smoothing.py @@ -1,13 +1,16 @@ """Test smoothing methods.""" import unittest +from typing import Tuple import numpy as np import sklearn +from typing_extensions import Literal import skfda import skfda.preprocessing.smoothing as smoothing import skfda.preprocessing.smoothing.validation as validation from skfda._utils import _check_estimator +from skfda.datasets import fetch_weather from skfda.misc.hat_matrix import ( HatMatrix, KNeighborsHatMatrix, @@ -37,14 +40,16 @@ def __call__( estimator: KernelSmoother, X: FDataGrid, y: FDataGrid, - ) -> None: + ) -> float: """Calculate Leave-One-Out score.""" estimator_clone = sklearn.base.clone(estimator) estimator_clone._cv = True # noqa: WPS437 y_est = estimator_clone.fit_transform(X) - return -np.mean((y.data_matrix[..., 0] - y_est.data_matrix[..., 0])**2) + return float( + -np.mean((y.data_matrix[..., 0] - y_est.data_matrix[..., 0])**2), + ) class TestLeaveOneOut(unittest.TestCase): @@ -116,7 +121,7 @@ class TestKernelSmoother(unittest.TestCase): def _test_hat_matrix( self, kernel_estimator: HatMatrix, - ) -> np.ndarray: + ) -> np.typing.NDArray[np.float_]: return KernelSmoother( # noqa: WPS437 kernel_estimator=kernel_estimator, )._hat_matrix( @@ -250,7 +255,7 @@ def test_monomial_smoothing(self) -> None: def test_vector_valued_smoothing(self) -> None: """Test Basis Smoother for vector values functions.""" - X, _ = skfda.datasets.fetch_weather(return_X_y=True) + X, _ = fetch_weather(return_X_y=True) basis_dim = skfda.representation.basis.Fourier( n_basis=7, domain_range=X.domain_range, @@ -260,7 +265,12 @@ def test_vector_valued_smoothing(self) -> None: [basis_dim] * 2, ) - for method in ('cholesky', 'qr', 'svd'): + method_set: Tuple[Literal['cholesky', 'qr', 'svd'], ...] = ( + 'cholesky', + 'qr', + 'svd', + ) + for method in method_set: with self.subTest(method=method): basis_smoother = smoothing.BasisSmoother( diff --git a/skfda/tests/test_stats.py b/skfda/tests/test_stats.py index 7c0d8e465..22b74a437 100644 --- a/skfda/tests/test_stats.py +++ b/skfda/tests/test_stats.py @@ -2,7 +2,7 @@ import numpy as np -import skfda +from skfda.datasets import fetch_phoneme, fetch_tecator, fetch_weather from skfda.exploratory.stats import geometric_median, modified_epigraph_index @@ -16,7 +16,7 @@ def test_R_comparison(self) -> None: The R package used is the Gmedian package. """ - X, _ = skfda.datasets.fetch_tecator(return_X_y=True) + X, _ = fetch_tecator(return_X_y=True) r_res = [ # noqa: WPS317 2.74083, 2.742715, 2.744627, 2.74659, 2.748656, @@ -54,7 +54,7 @@ def test_R_comparison(self) -> None: def test_big(self) -> None: """Test a bigger dataset.""" - X, _ = skfda.datasets.fetch_phoneme(return_X_y=True) + X, _ = fetch_phoneme(return_X_y=True) res = np.array([ # noqa: WPS317 10.87814495, 12.10539654, 15.19841961, 16.29929599, 15.52206033, @@ -128,7 +128,7 @@ class TestMEI(unittest.TestCase): def test_mei(self) -> None: """Test modified epigraph index.""" - fd, _ = skfda.datasets.fetch_weather(return_X_y=True) + fd, _ = fetch_weather(return_X_y=True) fd_temperatures = fd.coordinates[0] mei = modified_epigraph_index(fd_temperatures) np.testing.assert_allclose( diff --git a/skfda/tests/test_ufunc_numpy.py b/skfda/tests/test_ufunc_numpy.py index 4c80f1d37..27c4a6f8d 100644 --- a/skfda/tests/test_ufunc_numpy.py +++ b/skfda/tests/test_ufunc_numpy.py @@ -1,13 +1,21 @@ """Tests of compatibility between numpy ufuncs and FDataGrid.""" import unittest -from typing import Any, Callable, TypeVar +from typing import Any, Callable, Protocol, TypeVar import numpy as np import pytest -import skfda from skfda import FDataGrid +from skfda.representation.basis import Fourier + +T = TypeVar("T", "np.typing.NDArray[Any]", FDataGrid) + + +class _MonaryUfuncProtocol(Protocol): + + def __call__(self, __arg: T) -> T: # noqa: WPS112 + pass @pytest.fixture(params=[ @@ -29,10 +37,7 @@ def monary(request: Any) -> Any: return request.param -T = TypeVar("T", np.ndarray, FDataGrid) - - -def test_monary_ufuncs(monary: Callable[[T], T]) -> None: +def test_monary_ufuncs(monary: _MonaryUfuncProtocol) -> None: """Test that unary ufuncs can be applied to FDataGrid.""" data_matrix = np.arange(15).reshape(3, 5) + 1 @@ -88,7 +93,7 @@ def test_commutativity_basis(self) -> None: """Test that operations with numpy arrays for basis commute.""" X = FDataGrid([[1, 2, 3], [4, 5, 6]]) arr = np.array([1, 2]) - basis = skfda.representation.basis.Fourier(n_basis=5) + basis = Fourier(n_basis=5) X_basis = X.to_basis(basis) From bac9920e5c8f230a017b88f25ed24b5ac22692aa Mon Sep 17 00:00:00 2001 From: VNMabus Date: Mon, 5 Sep 2022 15:50:30 +0200 Subject: [PATCH 281/400] Improve number of crossings function. --- skfda/exploratory/stats/__init__.py | 4 +- .../stats/_functional_transformers.py | 46 +++++--- .../feature_construction/__init__.py | 4 +- .../_function_transformers.py | 105 ++++++++++-------- 4 files changed, 98 insertions(+), 61 deletions(-) diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 1a051f72c..a356c82c2 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -13,7 +13,7 @@ ], "_functional_transformers": [ "local_averages", - "number_up_crossings", + "number_crossings", "occupation_measure", "unconditional_central_moment", "unconditional_expected_value", @@ -39,7 +39,7 @@ ) from ._functional_transformers import ( local_averages as local_averages, - number_up_crossings as number_up_crossings, + number_crossings as number_crossings, occupation_measure as occupation_measure, unconditional_central_moment as unconditional_central_moment, unconditional_expected_value as unconditional_expected_value, diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/exploratory/stats/_functional_transformers.py index 6444a1470..fcaee767b 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/exploratory/stats/_functional_transformers.py @@ -6,7 +6,7 @@ from typing import Optional, Sequence, Tuple, TypeVar, Union, cast, overload import numpy as np -from typing_extensions import Protocol, TypeGuard +from typing_extensions import Literal, Protocol, TypeGuard from ..._utils import nquad_vec from ...misc.validation import check_fdata_dimensions, validate_domain_range @@ -289,12 +289,14 @@ def occupation_measure( ) -def number_up_crossings( - data: FDataGrid, - levels: ArrayLike, +def number_crossings( + fd: FDataGrid, + *, + levels: ArrayLike = 0, + direction: Literal["up", "down", "all"] = "all", ) -> NDArrayInt: r""" - Calculate the number of up crossings to a level of a FDataGrid. + Calculate the number of crossings to a level of a FDataGrid. Let f_1(X) = N_i, where N_i is the number of up crossings of X to a level c_i \in \mathbb{R}, i = 1,\dots,p. @@ -308,10 +310,13 @@ def number_up_crossings( :math:`N_i = card\{t \in[a,b]: X(t) = c_i, X' (t) > 0\}.` Args: - data: FDataGrid where we want to calculate + fd: FDataGrid where we want to calculate the number of up crossings. - levels: sequence of numbers including the levels - we want to consider for the crossings. + levels: Sequence of numbers including the levels + we want to consider for the crossings. By + default it calculates zero-crossings. + direction: Whether to consider only up-crossings, + down-crossings or both. Returns: ndarray of shape (n_samples, len(levels))\ @@ -325,7 +330,7 @@ def number_up_crossings( First of all we import the Bessel Function and create the X axis data grid. Then we create the FdataGrid. - >>> from skfda.exploratory.stats import number_up_crossings + >>> from skfda.exploratory.stats import number_crossings >>> from scipy.special import jv >>> import numpy as np >>> x_grid = np.linspace(0, 14, 14) @@ -352,11 +357,14 @@ def number_up_crossings( Finally we evaluate the number of up crossings method with the FDataGrid created. - >>> number_up_crossings(fd_grid, np.asarray([0])) + >>> number_crossings(fd_grid, levels=0, direction="up") array([[2]]) """ - levels = np.asarray(levels) - curves = data.data_matrix[:, :, 0] + # This is only defined for univariate functions + check_fdata_dimensions(fd, dim_domain=1, dim_codomain=1) + + levels = np.atleast_1d(levels) + curves = fd.data_matrix[:, :, 0] distances = np.subtract.outer(levels, curves) @@ -364,12 +372,24 @@ def number_up_crossings( points_smaller = distances <= 0 growing = distances[:, :, :-1] < points_greater[:, :, 1:] + lowering = distances[:, :, :-1] > points_greater[:, :, 1:] + upcrossing_positions: NDArrayBool = ( points_smaller[:, :, :-1] & points_greater[:, :, 1:] & growing ) + downcrossing_positions: NDArrayBool = ( + points_greater[:, :, :-1] & points_smaller[:, :, 1:] & lowering + ) + + positions = { + "all": upcrossing_positions | downcrossing_positions, + "up": upcrossing_positions, + "down": downcrossing_positions, + } + return np.sum( # type: ignore[no-any-return] - upcrossing_positions, + positions[direction], axis=2, ).T diff --git a/skfda/preprocessing/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py index 25f190847..47b9d1cc2 100644 --- a/skfda/preprocessing/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -11,7 +11,7 @@ "_fda_feature_union": ["FDAFeatureUnion"], "_function_transformers": [ "LocalAveragesTransformer", - "NumberUpCrossingsTransformer", + "NumberCrossingsTransformer", "OccupationMeasureTransformer", ], "_per_class_transformer": ["PerClassTransformer"], @@ -28,7 +28,7 @@ from ._fda_feature_union import FDAFeatureUnion as FDAFeatureUnion from ._function_transformers import ( LocalAveragesTransformer as LocalAveragesTransformer, - NumberUpCrossingsTransformer as NumberUpCrossingsTransformer, + NumberCrossingsTransformer as NumberCrossingsTransformer, OccupationMeasureTransformer as OccupationMeasureTransformer, ) from ._per_class_transformer import ( diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index 49a08050b..a2ccd9d0f 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -3,16 +3,18 @@ from typing import Optional, Sequence, Tuple +from typing_extensions import Literal + from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin from ...exploratory.stats._functional_transformers import ( local_averages, - number_up_crossings, + number_crossings, occupation_measure, ) from ...representation import FData from ...representation.grid import FDataGrid from ...typing._base import DomainRangeLike -from ...typing._numpy import NDArrayFloat, NDArrayInt +from ...typing._numpy import ArrayLike, NDArrayFloat, NDArrayInt class LocalAveragesTransformer( @@ -176,7 +178,7 @@ def transform(self, X: FData, y: object = None) -> NDArrayFloat: return occupation_measure(X, self.intervals, n_points=self.n_points) -class NumberUpCrossingsTransformer( +class NumberCrossingsTransformer( BaseEstimator, TransformerMixin[FDataGrid, NDArrayInt, object], ): @@ -184,50 +186,61 @@ class NumberUpCrossingsTransformer( Transformer that works as an adapter for the number_up_crossings function. Args: - levels: sequence of numbers including the levels - we want to consider for the crossings. + levels: Sequence of numbers including the levels + we want to consider for the crossings. By + default it calculates zero-crossings. + direction: Whether to consider only up-crossings, + down-crossings or both. + Example: - For this example we will use a well known function so the correct - functioning of this method can be checked. - We will create and use a DataFrame with a sample extracted from - the Bessel Function of first type and order 0. - First of all we import the Bessel Function and create the X axis - data grid. Then we create the FdataGrid. - >>> from skfda.preprocessing.feature_construction import ( - ... NumberUpCrossingsTransformer, - ... ) - >>> from scipy.special import jv - >>> from skfda.representation import FDataGrid - >>> import numpy as np - >>> x_grid = np.linspace(0, 14, 14) - >>> fd_grid = FDataGrid( - ... data_matrix=[jv([0], x_grid)], - ... grid_points=x_grid, - ... ) - >>> fd_grid.data_matrix - array([[[ 1. ], - [ 0.73041066], - [ 0.13616752], - [-0.32803875], - [-0.35967936], - [-0.04652559], - [ 0.25396879], - [ 0.26095573], - [ 0.01042895], - [-0.22089135], - [-0.2074856 ], - [ 0.0126612 ], - [ 0.20089319], - [ 0.17107348]]]) - - Finally we evaluate the number of up crossings method with the FDataGrid - created. - >>> NumberUpCrossingsTransformer(np.asarray([0])).fit_transform(fd_grid) - array([[2]]) + For this example we will use a well known function so the correct + functioning of this method can be checked. + We will create and use a DataFrame with a sample extracted from + the Bessel Function of first type and order 0. + First of all we import the Bessel Function and create the X axis + data grid. Then we create the FdataGrid. + >>> from skfda.preprocessing.feature_construction import ( + ... NumberCrossingsTransformer, + ... ) + >>> from scipy.special import jv + >>> from skfda.representation import FDataGrid + >>> import numpy as np + >>> x_grid = np.linspace(0, 14, 14) + >>> fd_grid = FDataGrid( + ... data_matrix=[jv([0], x_grid)], + ... grid_points=x_grid, + ... ) + >>> fd_grid.data_matrix + array([[[ 1. ], + [ 0.73041066], + [ 0.13616752], + [-0.32803875], + [-0.35967936], + [-0.04652559], + [ 0.25396879], + [ 0.26095573], + [ 0.01042895], + [-0.22089135], + [-0.2074856 ], + [ 0.0126612 ], + [ 0.20089319], + [ 0.17107348]]]) + + Finally we evaluate the number of zero-upcrossings method with the + FDataGrid created. + >>> tf = NumberCrossingsTransformer(levels=0, direction="up") + >>> tf.fit_transform(fd_grid) + array([[2]]) """ - def __init__(self, levels: NDArrayFloat): + def __init__( + self, + *, + levels: ArrayLike = 0, + direction: Literal["up", "down", "all"] = "all", + ): self.levels = levels + self.direction = direction def transform(self, X: FDataGrid, y: object = None) -> NDArrayInt: """ @@ -240,4 +253,8 @@ def transform(self, X: FDataGrid, y: object = None) -> NDArrayInt: Array of shape (n_samples, len(levels)) including the transformed data. """ - return number_up_crossings(X, self.levels) + return number_crossings( + X, + levels=self.levels, + direction=self.direction, + ) From f76c462f030a68df5397d859a18c6c3ed845c06d Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 7 Sep 2022 12:31:23 +0200 Subject: [PATCH 282/400] Functional QDA now can choose the covariance estimator. --- docs/modules/ml/classification.rst | 6 +- examples/plot_classification_methods.py | 45 +++++++------ skfda/exploratory/stats/__init__.py | 1 + .../exploratory/stats/covariance/__init__.py | 19 ++++++ .../stats/covariance/_empirical.py | 38 +++++++++++ .../stats/covariance/_parametric_gaussian.py | 43 +++++++++++++ skfda/ml/classification/__init__.py | 6 +- ...arameterized_functional_qda.py => _qda.py} | 64 ++++++++++--------- 8 files changed, 166 insertions(+), 56 deletions(-) create mode 100644 skfda/exploratory/stats/covariance/__init__.py create mode 100644 skfda/exploratory/stats/covariance/_empirical.py create mode 100644 skfda/exploratory/stats/covariance/_parametric_gaussian.py rename skfda/ml/classification/{_parameterized_functional_qda.py => _qda.py} (84%) diff --git a/docs/modules/ml/classification.rst b/docs/modules/ml/classification.rst index 56082355a..7bfaf3d90 100644 --- a/docs/modules/ml/classification.rst +++ b/docs/modules/ml/classification.rst @@ -56,11 +56,11 @@ Classifier based on logistic regression. skfda.ml.classification.LogisticRegression -Parameterized functional quadratic discriminant analysis --------------------------------------------------------- +Functional quadratic discriminant analysis +------------------------------------------ Classifier based on the quadratic discriminant analysis. .. autosummary:: :toctree: autosummary - skfda.ml.classification.ParameterizedFunctionalQDA + skfda.ml.classification.QuadraticDiscriminantAnalysis diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index a55931c83..bf7e8ca04 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -24,11 +24,12 @@ from skfda.datasets import fetch_growth from skfda.exploratory.depth import ModifiedBandDepth +from skfda.exploratory.stats.covariance import ParametricGaussianCovariance from skfda.ml.classification import ( KNeighborsClassifier, MaximumDepthClassifier, NearestCentroid, - ParameterizedFunctionalQDA, + QuadraticDiscriminantAnalysis, ) ############################################################################## @@ -69,7 +70,7 @@ # :class:`~skfda.ml.classification.MaximumDepthClassifier`, # :class:`~skfda.ml.classification.KNeighborsClassifier`, # :class:`~skfda.ml.classification.NearestCentroid` and -# :class:`~skfda.ml.classification.ParameterizedFunctionalQDA` +# :class:`~skfda.ml.classification.QuadraticDiscriminantAnalysis` ############################################################################## @@ -109,35 +110,39 @@ ############################################################################## -# The fourth method considered is a Parameterized functional quadratic -# discriminant. -# We have selected a gaussian kernel with initial parameters: variance=6 and -# mean=1. The selection of the initial parameters does not really affect the -# results as the algorithm will automatically optimize them. +# The fourth method considered is a functional quadratic discriminant, where +# the covariance is assumed as having a parametric form, specified with a +# kernel, or covariance function. +# +# We have selected a Gaussian kernel with initial hyperparameters: variance=6 +# and lengthscale=1. The selection of the initial parameters does not really +# affect the results as the algorithm will automatically optimize them. # As regularizer a small value 0.05 has been chosen. -pfqda = ParameterizedFunctionalQDA( - kernel=RBF(input_dim=1, variance=6, lengthscale=1), +qda = QuadraticDiscriminantAnalysis( + ParametricGaussianCovariance( + RBF(input_dim=1, variance=6, lengthscale=1), + ), regularizer=0.05, ) -pfqda.fit(X_train, y_train) -pfqda_pred = pfqda.predict(X_test) -print(pfqda_pred) -print('The score of Parameterized Functional QDA is {0:2.2%}'.format( - pfqda.score(X_test, y_test), +qda.fit(X_train, y_train) +qda_pred = qda.predict(X_test) +print(qda_pred) +print('The score of functional QDA is {0:2.2%}'.format( + qda.score(X_test, y_test), )) ############################################################################## # As it can be seen, the classifier with the lowest score is the Maximum # Depth Classifier. It obtains a 82.14% accuracy for the test set. -# KNN and Parameterized Functional QDA can be seen as the best classifiers +# KNN and the functional QDA can be seen as the best classifiers # for this problem, with an accuracy of 96.43%. # Instead, the Nearest Centroid Classifier is not as good as the others. # However, it obtains an accuracy of 85.71% for the test set. # It can be concluded that all classifiers work well for this problem, as they # achieve more than an 80% of score, but the most robust ones are KNN and -# Parameterized Functional QDA. +# functional QDA. accuracies = pd.DataFrame({ 'Classification methods': @@ -145,7 +150,7 @@ 'Maximum Depth Classifier', 'K-Nearest-Neighbors', 'Nearest Centroid Classifier', - 'Parameterized Functional QDA', + 'Functional QDA', ], 'Accuracy': [ @@ -159,7 +164,7 @@ centroid.score(X_test, y_test), ), '{0:2.2%}'.format( - pfqda.score(X_test, y_test), + qda.score(X_test, y_test), ), ], }) @@ -185,7 +190,7 @@ X_test.plot(group=knn_pred, group_names=categories, axes=axs[1][0]) axs[1][0].set_title('KNN', loc='left') -X_test.plot(group=pfqda_pred, group_names=categories, axes=axs[1][1]) -axs[1][1].set_title('Parameterized Functional QDA', loc='left') +X_test.plot(group=qda_pred, group_names=categories, axes=axs[1][1]) +axs[1][1].set_title('Functional QDA', loc='left') plt.show() diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index a356c82c2..51d6b3a5c 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -6,6 +6,7 @@ __getattr__, __dir__, __all__ = lazy.attach( __name__, + submodules=["covariance"], submod_attrs={ "_fisher_rao": [ "_fisher_rao_warping_mean", diff --git a/skfda/exploratory/stats/covariance/__init__.py b/skfda/exploratory/stats/covariance/__init__.py new file mode 100644 index 000000000..c4342c4ea --- /dev/null +++ b/skfda/exploratory/stats/covariance/__init__.py @@ -0,0 +1,19 @@ +"""Covariance estimation.""" + +from typing import TYPE_CHECKING + +import lazy_loader as lazy + +__getattr__, __dir__, __all__ = lazy.attach( + __name__, + submod_attrs={ + "_empirical": ["EmpiricalCovariance"], + "_parametric_gaussian": ["ParametricGaussianCovariance"], + }, +) + +if TYPE_CHECKING: + from ._empirical import EmpiricalCovariance as EmpiricalCovariance + from ._parametric_gaussian import ( + ParametricGaussianCovariance as ParametricGaussianCovariance, + ) diff --git a/skfda/exploratory/stats/covariance/_empirical.py b/skfda/exploratory/stats/covariance/_empirical.py new file mode 100644 index 000000000..7be83a39f --- /dev/null +++ b/skfda/exploratory/stats/covariance/_empirical.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +from typing import Generic, TypeVar + +from ...._utils._sklearn_adapter import BaseEstimator +from ....representation import FData + +Input = TypeVar("Input", bound=FData) + + +class EmpiricalCovariance( + BaseEstimator, + Generic[Input], +): + + def __init__( + self, + *, + assume_centered: bool = False, + ) -> None: + self.assume_centered = assume_centered + + def _fit_mean(self, X: Input): + self.location_ = X.mean() + if self.assume_centered: + self.location_ *= 0 + + def fit(self, X: Input, y: object = None) -> EmpiricalCovariance[Input]: + + self._fit_mean(X) + + self.covariance_ = X.cov() + + return self + + def score(self, X_test: Input, y: object = None) -> float: + + pass diff --git a/skfda/exploratory/stats/covariance/_parametric_gaussian.py b/skfda/exploratory/stats/covariance/_parametric_gaussian.py new file mode 100644 index 000000000..fd27e53a9 --- /dev/null +++ b/skfda/exploratory/stats/covariance/_parametric_gaussian.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import numpy as np +from GPy.models import GPRegression + +from ....representation import FDataGrid +from ._empirical import EmpiricalCovariance + + +class ParametricGaussianCovariance(EmpiricalCovariance[FDataGrid]): + + def __init__( + self, + cov, + *, + assume_centered: bool = False, + ) -> None: + super().__init__(assume_centered=assume_centered) + self.cov = cov + + def fit( + self, + X: FDataGrid, + y: object = None, + ) -> ParametricGaussianCovariance: + + self._fit_mean(X) + + X_centered = X - self.location_ + + data_matrix = X_centered.data_matrix[:, :, 0] + + grid_points = X_centered.grid_points[0][:, np.newaxis] + + regressor = GPRegression(grid_points, data_matrix.T, kernel=self.cov) + regressor.optimize() + + # TODO: Skip cov computation? + self.covariance_ = X.cov().copy( + data_matrix=regressor.kern.K(grid_points)[np.newaxis, ...], + ) + + return self diff --git a/skfda/ml/classification/__init__.py b/skfda/ml/classification/__init__.py index 28ab4d7e7..cf3c008b1 100644 --- a/skfda/ml/classification/__init__.py +++ b/skfda/ml/classification/__init__.py @@ -21,7 +21,7 @@ "KNeighborsClassifier", "RadiusNeighborsClassifier", ], - "_parameterized_functional_qda": ["ParameterizedFunctionalQDA"], + "_qda": ["QuadraticDiscriminantAnalysis"], }, ) @@ -40,6 +40,6 @@ KNeighborsClassifier as KNeighborsClassifier, RadiusNeighborsClassifier as RadiusNeighborsClassifier, ) - from ._parameterized_functional_qda import ( - ParameterizedFunctionalQDA as ParameterizedFunctionalQDA, + from ._qda import ( + QuadraticDiscriminantAnalysis as QuadraticDiscriminantAnalysis, ) diff --git a/skfda/ml/classification/_parameterized_functional_qda.py b/skfda/ml/classification/_qda.py similarity index 84% rename from skfda/ml/classification/_parameterized_functional_qda.py rename to skfda/ml/classification/_qda.py index 580860488..1bd88589b 100644 --- a/skfda/ml/classification/_parameterized_functional_qda.py +++ b/skfda/ml/classification/_qda.py @@ -3,9 +3,8 @@ from typing import Tuple import numpy as np -from GPy.kern import Kern -from GPy.models import GPRegression from scipy.linalg import logm +from sklearn.base import clone from sklearn.utils.validation import check_is_fitted from ..._utils import _classifier_get_classes @@ -14,11 +13,12 @@ from ...typing._numpy import NDArrayFloat, NDArrayInt -class ParameterizedFunctionalQDA( +class QuadraticDiscriminantAnalysis( BaseEstimator, ClassifierMixin[FDataGrid, NDArrayInt], ): - """Parameterized functional quadratic discriminant analysis. + """ + Functional quadratic discriminant analysis. It is based on the assumption that the data is part of a Gaussian process and depending on the output label, the covariance and mean parameters are @@ -73,34 +73,50 @@ class ParameterizedFunctionalQDA( We will use random values such as 1 for the mean and 6 for the variance. + >>> from skfda.exploratory.stats.covariance import ( + ... ParametricGaussianCovariance + ... ) + >>> from skfda.ml.classification import QuadraticDiscriminantAnalysis >>> from GPy.kern import RBF >>> rbf = RBF(input_dim=1, variance=6, lengthscale=1) We will fit the ParameterizedFunctionalQDA with training data. We use as regularizer parameter a low value such as 0.05. - >>> pfqda = ParameterizedFunctionalQDA(rbf, 0.05) - >>> pfqda = pfqda.fit(X_train, y_train) + >>> qda = QuadraticDiscriminantAnalysis( + ... ParametricGaussianCovariance(rbf), + ... regularizer=0.05, + ... ) + >>> qda = qda.fit(X_train, y_train) We can predict the class of new samples. - >>> list(pfqda.predict(X_test)) + >>> list(qda.predict(X_test)) [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1] Finally, we calculate the mean accuracy for the test data. - >>> round(pfqda.score(X_test, y_test), 2) + >>> round(qda.score(X_test, y_test), 2) 0.96 """ - def __init__(self, kernel: Kern, regularizer: float) -> None: - self.kernel = kernel + def __init__( + self, + cov_estimator, + *, + regularizer: float = 0, + ) -> None: + self.cov_estimator = cov_estimator self.regularizer = regularizer - def fit(self, X: FDataGrid, y: NDArrayInt) -> ParameterizedFunctionalQDA: + def fit( + self, + X: FDataGrid, + y: NDArrayInt, + ) -> QuadraticDiscriminantAnalysis: """ Fit the model using X as training data and y as target values. @@ -115,8 +131,7 @@ def fit(self, X: FDataGrid, y: NDArrayInt) -> ParameterizedFunctionalQDA: self.classes = classes self.y_ind = y_ind - cov_kernels, means = self._fit_gaussian_process(X) - self.cov_kernels_ = cov_kernels + _, means = self._fit_gaussian_process(X) self.means_ = means self.priors_ = self._calculate_priors(y) @@ -182,31 +197,20 @@ def _fit_gaussian_process( Tuple containing a ndarray of fitted kernels and another ndarray with the means of each class. """ - grid = X.grid_points[0][:, np.newaxis] - kernels = [] + cov_estimators = [] means = [] covariance = [] for class_index, _ in enumerate(self.classes): X_class = X[self.y_ind == class_index] - X_class_mean = X_class.mean() - X_class_centered = X_class - X_class_mean - - data_matrix = X_class_centered.data_matrix[:, :, 0] - - grid_points = X_class.grid_points[0][:, np.newaxis] - - regressor = GPRegression(grid, data_matrix.T, kernel=self.kernel) - regressor.optimize() + cov_estimator = clone(self.cov_estimator).fit(X_class) - kernels += [regressor.kern] - means += [X_class_mean.data_matrix[0]] - covariance += [ - regressor.kern.K(grid_points), - ] + cov_estimators.append(cov_estimator) + means.append(cov_estimator.location_.data_matrix[0]) + covariance.append(cov_estimator.covariance_.data_matrix[0, ..., 0]) self._covariances = np.asarray(covariance) - return np.asarray(kernels), np.asarray(means) + return np.asarray(cov_estimators), np.asarray(means) def _calculate_log_likelihood(self, X: NDArrayFloat) -> NDArrayFloat: """ From 36f80ea7efe333e6c1331604d5e0291607db69da Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 7 Sep 2022 12:48:29 +0200 Subject: [PATCH 283/400] Fix bug in clusterers. --- skfda/_utils/_sklearn_adapter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 50e91d4d1..3c23aa330 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -156,7 +156,7 @@ def fit_predict( # noqa: D102 X: Input, y: object = None, ) -> NDArrayInt: - pass + return super().fit_predict(X, y) # type: ignore[no-any-return] class RegressorMixin( # noqa: D101 From e71acb27d0d77d18ea93397d29bd4f67e0fecebe Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 7 Sep 2022 13:52:17 +0200 Subject: [PATCH 284/400] Fix autosummary errors. --- docs/modules/exploratory/stats.rst | 2 +- docs/modules/preprocessing/feature_construction.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/modules/exploratory/stats.rst b/docs/modules/exploratory/stats.rst index 1988e77d1..8217c686b 100644 --- a/docs/modules/exploratory/stats.rst +++ b/docs/modules/exploratory/stats.rst @@ -43,5 +43,5 @@ The following statistics can be used to estimate additional properties of the da skfda.exploratory.stats.modified_epigraph_index skfda.exploratory.stats.local_averages skfda.exploratory.stats.occupation_measure - skfda.exploratory.stats.number_up_crossings + skfda.exploratory.stats.number_crossings diff --git a/docs/modules/preprocessing/feature_construction.rst b/docs/modules/preprocessing/feature_construction.rst index e8795144d..6d05ab780 100644 --- a/docs/modules/preprocessing/feature_construction.rst +++ b/docs/modules/preprocessing/feature_construction.rst @@ -44,6 +44,6 @@ for each functional datum, which can then be used for prediction. skfda.preprocessing.feature_construction.LocalAveragesTransformer 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file changed, 6747 deletions(-) delete mode 100644 mypy.out diff --git a/mypy.out b/mypy.out deleted file mode 100644 index 301e9b2a5..000000000 --- a/mypy.out +++ /dev/null @@ -1,6747 +0,0 @@ -TRACE: Plugins snapshot {} -TRACE: Options({'allow_redefinition': False, - 'allow_untyped_globals': False, - 'always_false': [], - 'always_true': [], - 'bazel': False, - 'build_type': 1, - 'cache_dir': '.mypy_cache', - 'cache_fine_grained': False, - 'cache_map': {}, - 'check_untyped_defs': True, - 'color_output': True, - 'config_file': 'setup.cfg', - 'custom_typeshed_dir': None, - 'custom_typing_module': None, - 'debug_cache': False, - 'disable_error_code': [], - 'disabled_error_codes': set(), - 'disallow_any_decorated': False, - 'disallow_any_explicit': False, - 'disallow_any_expr': False, - 'disallow_any_generics': True, - 'disallow_any_unimported': False, - 'disallow_incomplete_defs': True, - 'disallow_subclassing_any': True, - 'disallow_untyped_calls': True, - 'disallow_untyped_decorators': 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"plugin_data": null, "size": 4118, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for xarray.backends.netcdf3: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for xarray.backends.netcdf3 -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/backends/netcdf3.py (xarray.backends.netcdf3) -TRACE: Looking for multiprocessing.connection at multiprocessing/connection.meta.json -TRACE: Meta multiprocessing.connection {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 1, 1], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 30], "dependencies": ["socket", "sys", "types", "_typeshed", "collections.abc", "typing", "typing_extensions", "builtins", "abc"], "hash": "909a2aa1540eb909faaad14302d9096aaeca6fcb3c72d7427899217908eb1145", "id": "multiprocessing.connection", "ignore_all": true, "interface_hash": "e970d3da98f8e7fa9c9204a4c5a1a8590975c93cd86181fa81f6c080751df7ff", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/connection.pyi", "plugin_data": null, "size": 2467, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.connection: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.connection -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/connection.pyi (multiprocessing.connection) -TRACE: Looking for multiprocessing.context at multiprocessing/context.meta.json -TRACE: Meta multiprocessing.context {"data_mtime": 1662379577, "dep_lines": [1, 2, 3, 7, 7, 4, 6, 8, 9, 10, 11, 12, 1, 1, 1], "dep_prios": [5, 10, 10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["ctypes", "multiprocessing", "sys", "multiprocessing.queues", "multiprocessing.synchronize", "collections.abc", 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"implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/context.pyi", "plugin_data": null, "size": 7459, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.context: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.context -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/context.pyi (multiprocessing.context) -TRACE: Looking for multiprocessing.pool at multiprocessing/pool.meta.json -TRACE: Meta multiprocessing.pool {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["sys", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", "builtins", "abc"], "hash": "54b1838925da66641a952e7215adc67812bdd526c7aa65933551f499586af913", "id": "multiprocessing.pool", "ignore_all": true, "interface_hash": "ad41aa31511113a03fb9f87c03a86605a0b3923d7f7a37ab755df4b15c423d8d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi", "plugin_data": null, "size": 4755, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.pool: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.pool -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/pool.pyi (multiprocessing.pool) -TRACE: Looking for multiprocessing.reduction at multiprocessing/reduction.meta.json -TRACE: Meta multiprocessing.reduction {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 30, 30, 30], "dependencies": ["pickle", "sys", "abc", "copyreg", "typing", "typing_extensions", "builtins", "array", "ctypes", "mmap"], "hash": "30ab326562cf82254f55d7c9f59e11159fbac4f767058f24cb69a2fe013a48e9", "id": "multiprocessing.reduction", "ignore_all": true, "interface_hash": "bbd4eacb98803ecd8041a117ca2b4e3abe2c467a2392ff056525e356dd449d0d", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, 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-LOG: Metadata abandoned for multiprocessing.reduction: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.reduction -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/reduction.pyi (multiprocessing.reduction) -TRACE: Looking for multiprocessing.synchronize at multiprocessing/synchronize.meta.json -TRACE: Meta multiprocessing.synchronize {"data_mtime": 1662379577, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["sys", "threading", "collections.abc", "contextlib", "multiprocessing.context", "types", "typing", "typing_extensions", "builtins", "_typeshed", "abc"], "hash": "f28836537a5963d10f30e158b6f5fa9e513b13c44cfe7ebce1a5eaad723e3e0d", "id": "multiprocessing.synchronize", "ignore_all": true, "interface_hash": "f2d28555b342d19d8f5975080246ffa4f55c04c8591f35788df98fb011c89862", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi", "plugin_data": null, "size": 2278, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.synchronize: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.synchronize -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/synchronize.pyi (multiprocessing.synchronize) -TRACE: Looking for multiprocessing.managers at multiprocessing/managers.meta.json -TRACE: Meta multiprocessing.managers {"data_mtime": 1662379577, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 1, 1], "dep_prios": [10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30], "dependencies": ["queue", "sys", "threading", "_typeshed", "collections.abc", "types", "typing", "typing_extensions", 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false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi", "plugin_data": null, "size": 8556, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.managers: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.managers -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/managers.pyi (multiprocessing.managers) -TRACE: Looking for multiprocessing.process at multiprocessing/process.meta.json -TRACE: Meta multiprocessing.process {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 30, 30], "dependencies": ["sys", "collections.abc", "typing", "builtins", "_typeshed", "abc"], "hash": "fa47cbb61f64a2309b6d7043a2c42c4c096b85f17dbd51181d9c83455a89e5c3", "id": "multiprocessing.process", "ignore_all": true, "interface_hash": "77c612e6cb8b00cd8763eb96f60de3ddd7c40a47a2ea78f5710fb059dea117d2", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi", "plugin_data": null, "size": 1371, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.process: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.process -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/process.pyi (multiprocessing.process) -TRACE: Looking for multiprocessing.queues at multiprocessing/queues.meta.json -TRACE: Meta multiprocessing.queues {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 30, 30], "dependencies": ["queue", "sys", "typing", "builtins", "_typeshed", "abc"], "hash": "daf7a7147647126f56cf864be94a85c23c7801d703f87280168714c3eda8a44c", "id": "multiprocessing.queues", "ignore_all": true, "interface_hash": "38370111a855e180ba5a53c74c70d2ab11961893618ccd17ecc4b397487fd0ae", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "silent", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi", "plugin_data": null, "size": 1398, "suppressed": [], "version_id": "0.971"} -LOG: Metadata abandoned for multiprocessing.queues: options differ -TRACE: follow_imports: silent != normal -LOG: Metadata not found for multiprocessing.queues -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/multiprocessing/queues.pyi (multiprocessing.queues) -TRACE: Looking for multiprocessing.spawn at multiprocessing/spawn.meta.json -TRACE: Meta multiprocessing.spawn {"data_mtime": 1662379576, "dep_lines": [1, 2, 3, 1, 1], "dep_prios": [5, 5, 5, 5, 30], "dependencies": ["collections.abc", "types", "typing", "builtins", "abc"], "hash": "b49c0c45edfea0381227f3a4dd3408fe75dd8c69e824da53e7271f0005371a6a", "id": "multiprocessing.spawn", "ignore_all": true, "interface_hash": "864e86b58a2b7fe05e14bfb866bac09cbeff6f212bcefb318fedda6f40e77af4", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, 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"show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/sandbox.py", "plugin_data": null, "size": 14584, "suppressed": ["_string"], "version_id": "0.971"} -LOG: Metadata fresh for jinja2.sandbox: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/sandbox.py -TRACE: Looking for concurrent.futures.process at concurrent/futures/process.meta.json -TRACE: Meta concurrent.futures.process {"data_mtime": 1662373498, "dep_lines": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 1, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30, 30], "dependencies": ["sys", "collections.abc", "multiprocessing.connection", "multiprocessing.context", 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"ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/process.pyi", "plugin_data": null, "size": 7135, "suppressed": [], "version_id": "0.971"} -LOG: Metadata fresh for concurrent.futures.process: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/process.pyi -TRACE: Looking for concurrent.futures.thread at concurrent/futures/thread.meta.json -TRACE: Meta concurrent.futures.thread {"data_mtime": 1662373498, "dep_lines": [1, 2, 3, 4, 5, 6, 8, 1, 1, 1], "dep_prios": [10, 10, 5, 5, 5, 5, 5, 5, 30, 30], "dependencies": ["queue", "sys", "collections.abc", "threading", "typing", "weakref", "concurrent.futures._base", "builtins", "_typeshed", "abc"], "hash": "207c990861869400494768f09d2d94978f1554174ce0af6191c6831456c6095b", "id": "concurrent.futures.thread", "ignore_all": true, "interface_hash": "64db2b3bbb18bb06c2c83da1aa6b029c3ccb2273b3a3d1828814f82fa25f8c45", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/thread.pyi", "plugin_data": null, "size": 2285, "suppressed": [], "version_id": "0.971"} -LOG: Metadata fresh for concurrent.futures.thread: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/concurrent/futures/thread.pyi -TRACE: Looking for selectors at selectors.meta.json -TRACE: Meta selectors {"data_mtime": 1662373495, "dep_lines": [1, 2, 3, 4, 5, 6, 1, 1, 1, 1, 1], "dep_prios": [10, 5, 5, 5, 5, 5, 5, 30, 30, 30, 30], "dependencies": ["sys", "_typeshed", "abc", "collections.abc", "typing", "typing_extensions", "builtins", "array", "ctypes", "mmap", "pickle"], "hash": "08361c33128299fb193a665ba156df246565f1cbecfe05fcd014912d713d5695", "id": "selectors", "ignore_all": true, "interface_hash": "23ce586de751cd9dee0056cf9bd435730ecc29e1d22c4dea59f37592dcbc314a", "mtime": 1658479475, "options": {"allow_redefinition": false, "allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": true, "disallow_any_decorated": false, "disallow_any_explicit": false, "disallow_any_expr": false, "disallow_any_generics": true, "disallow_any_unimported": false, "disallow_incomplete_defs": true, "disallow_subclassing_any": true, "disallow_untyped_calls": true, "disallow_untyped_decorators": true, "disallow_untyped_defs": true, "follow_imports": "normal", "follow_imports_for_stubs": false, "ignore_errors": false, "ignore_missing_imports": false, "implicit_reexport": false, "local_partial_types": false, "mypyc": false, "no_implicit_optional": true, "platform": "linux", "plugins": [], "show_none_errors": true, "strict_concatenate": true, "strict_equality": true, "strict_optional": true, "strict_optional_whitelist": null, "warn_no_return": true, "warn_return_any": true, "warn_unreachable": false, "warn_unused_ignores": true}, "path": "/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/selectors.pyi", "plugin_data": null, "size": 3714, "suppressed": [], "version_id": "0.971"} -LOG: Metadata fresh for selectors: file /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/selectors.pyi -LOG: Loaded graph with 662 nodes (2.294 sec) -LOG: Found 309 SCCs; largest has 72 nodes -TRACE: Priorities for typing_extensions: abc:10 collections:10 sys:10 _typeshed:5 collections.abc:5 typing:5 -TRACE: Priorities for typing: collections:10 sys:10 _typeshed:5 abc:5 types:5 typing_extensions:5 -TRACE: Priorities for types: sys:10 _typeshed:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 typing:5 typing_extensions:5 -TRACE: Priorities for sys: _typeshed:5 builtins:5 collections.abc:5 importlib.abc:5 importlib.machinery:5 io:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for subprocess: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for posixpath: sys:10 _typeshed:5 collections.abc:5 genericpath:5 os:5 typing:5 typing_extensions:5 -TRACE: Priorities for pickle: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for pathlib: sys:10 _typeshed:5 collections.abc:5 io:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for os.path: sys:10 posixpath:5 -TRACE: Priorities for os: sys:10 os.path:10 _typeshed:5 abc:5 builtins:5 collections.abc:5 contextlib:5 io:5 subprocess:5 typing:5 typing_extensions:5 -TRACE: Priorities for mmap: sys:10 _typeshed:5 collections.abc:5 typing:5 -TRACE: Priorities for io: builtins:10 codecs:10 sys:10 _typeshed:5 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for importlib.metadata: abc:10 pathlib:5 sys:10 _typeshed:5 collections.abc:5 email.message:5 importlib.abc:5 os:5 typing:5 -TRACE: Priorities for importlib.machinery: importlib.abc:10 importlib:20 sys:10 types:10 collections.abc:5 typing:5 importlib.metadata:5 -TRACE: Priorities for importlib.abc: sys:10 types:10 _typeshed:5 abc:5 collections.abc:5 importlib.machinery:5 io:5 typing:5 typing_extensions:5 -TRACE: Priorities for importlib: collections.abc:5 importlib.abc:5 types:5 -TRACE: Priorities for genericpath: os:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for email.policy: abc:5 collections.abc:5 email.contentmanager:5 email.errors:5 email.header:5 email.message:5 typing:5 -TRACE: Priorities for email.message: collections.abc:5 email:5 email.charset:5 email.contentmanager:5 email.errors:5 email.policy:5 typing:5 typing_extensions:5 -TRACE: Priorities for email.header: email.charset:5 -TRACE: Priorities for email.errors: sys:10 -TRACE: Priorities for email.contentmanager: collections.abc:5 email.message:5 typing:5 -TRACE: Priorities for email.charset: collections.abc:5 -TRACE: Priorities for email: collections.abc:5 email.message:5 email.policy:5 typing:5 typing_extensions:5 -TRACE: Priorities for ctypes: sys:10 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for contextlib: sys:10 _typeshed:5 collections.abc:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for collections.abc: _collections_abc:5 -TRACE: Priorities for collections: sys:10 _collections_abc:5 _typeshed:5 typing:5 typing_extensions:5 -TRACE: Priorities for codecs: types:10 _codecs:5 _typeshed:5 abc:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for array: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for abc: sys:10 _typeshed:5 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for _typeshed: array:10 ctypes:10 mmap:10 pickle:10 sys:10 collections.abc:5 os:5 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for _collections_abc: sys:10 types:5 typing:5 typing_extensions:5 -TRACE: Priorities for _codecs: codecs:10 sys:10 collections.abc:5 typing:5 typing_extensions:5 -TRACE: Priorities for _ast: sys:10 typing:5 typing_extensions:5 -TRACE: Priorities for builtins: sys:10 types:5 _ast:5 _collections_abc:5 _typeshed:5 collections.abc:5 io:5 typing:5 typing_extensions:5 -LOG: Processing SCC of size 36 (typing_extensions typing types sys subprocess posixpath pickle pathlib os.path os mmap io importlib.metadata importlib.machinery importlib.abc importlib genericpath email.policy email.message email.header email.errors email.contentmanager email.charset email ctypes contextlib collections.abc collections codecs array abc _typeshed _collections_abc _codecs _ast builtins) as inherently stale -LOG: Writing typing_extensions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing_extensions.pyi typing_extensions.meta.json typing_extensions.data.json -TRACE: Interface for typing_extensions has changed -LOG: Cached module typing_extensions has changed interface -LOG: Writing typing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/typing.pyi typing.meta.json typing.data.json -TRACE: Interface for typing has changed -LOG: Cached module typing has changed interface -LOG: Writing types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/types.pyi types.meta.json types.data.json -TRACE: Interface for types has changed -LOG: Cached module types has changed interface -LOG: Writing sys /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/sys.pyi sys.meta.json sys.data.json -TRACE: Interface for sys has changed -LOG: Cached module sys has changed interface -LOG: Writing subprocess /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/subprocess.pyi subprocess.meta.json subprocess.data.json -TRACE: Interface for subprocess has changed -LOG: Cached module subprocess has changed interface -LOG: Writing posixpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/posixpath.pyi posixpath.meta.json posixpath.data.json -TRACE: Interface for posixpath has changed -LOG: Cached module posixpath has changed interface -LOG: Writing pickle /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pickle.pyi pickle.meta.json pickle.data.json -TRACE: Interface for pickle has changed -LOG: Cached module pickle has changed interface -LOG: Writing pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pathlib.pyi pathlib.meta.json pathlib.data.json -TRACE: Interface for pathlib has changed -LOG: Cached module pathlib has changed interface -LOG: Writing os.path /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/path.pyi os/path.meta.json os/path.data.json -TRACE: Interface for os.path has changed -LOG: Cached module os.path has changed interface -LOG: Writing os /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/os/__init__.pyi os/__init__.meta.json os/__init__.data.json -TRACE: Interface for os has changed -LOG: Cached module os has changed interface -LOG: Writing mmap /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/mmap.pyi mmap.meta.json mmap.data.json -TRACE: Interface for mmap has changed -LOG: Cached module mmap has changed interface -LOG: Writing io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/io.pyi io.meta.json io.data.json -TRACE: Interface for io has changed -LOG: Cached module io has changed interface -LOG: Writing importlib.metadata /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/metadata/__init__.pyi importlib/metadata/__init__.meta.json importlib/metadata/__init__.data.json -TRACE: Interface for importlib.metadata has changed -LOG: Cached module importlib.metadata has changed interface -LOG: Writing importlib.machinery /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/machinery.pyi importlib/machinery.meta.json importlib/machinery.data.json -TRACE: Interface for importlib.machinery has changed -LOG: Cached module importlib.machinery has changed interface -LOG: Writing importlib.abc /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/abc.pyi importlib/abc.meta.json importlib/abc.data.json -TRACE: Interface for importlib.abc has changed -LOG: Cached module importlib.abc has changed interface -LOG: Writing importlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/__init__.pyi importlib/__init__.meta.json importlib/__init__.data.json -TRACE: Interface for importlib has changed -LOG: Cached module importlib has changed interface -LOG: Writing genericpath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/genericpath.pyi genericpath.meta.json genericpath.data.json -TRACE: Interface for genericpath has changed -LOG: Cached module genericpath has changed interface -LOG: Writing email.policy /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/policy.pyi email/policy.meta.json email/policy.data.json -TRACE: Interface for email.policy has changed -LOG: Cached module email.policy has changed interface -LOG: Writing email.message /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/message.pyi email/message.meta.json email/message.data.json -TRACE: Interface for email.message has changed -LOG: Cached module email.message has changed interface -LOG: Writing email.header /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/header.pyi email/header.meta.json email/header.data.json -TRACE: Interface for email.header has changed -LOG: Cached module email.header has changed interface -LOG: Writing email.errors /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/errors.pyi email/errors.meta.json email/errors.data.json -TRACE: Interface for email.errors has changed -LOG: Cached module email.errors has changed interface -LOG: Writing email.contentmanager /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/email/contentmanager.pyi email/contentmanager.meta.json email/contentmanager.data.json -TRACE: Interface for email.contentmanager has changed -LOG: Cached module email.contentmanager has changed interface -LOG: Writing email.charset 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py/error.meta.json py/error.data.json -TRACE: Interface for py.error is unchanged -LOG: Cached module py.error has same interface -TRACE: Priorities for _stat: -LOG: Processing SCC singleton (_stat) as stale due to deps (abc builtins sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_stat.pyi (_stat) -LOG: Writing _stat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_stat.pyi _stat.meta.json _stat.data.json -TRACE: Interface for _stat is unchanged -LOG: Cached module _stat has same interface -TRACE: Priorities for _bisect: -LOG: Processing SCC singleton (_bisect) as stale due to deps (_typeshed abc builtins collections.abc sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_bisect.pyi (_bisect) -LOG: Writing _bisect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_bisect.pyi _bisect.meta.json _bisect.data.json -TRACE: Interface for _bisect is unchanged -LOG: Cached module _bisect has same interface -TRACE: Priorities for token: -LOG: Processing SCC singleton (token) as stale due to deps (_typeshed abc builtins sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/token.pyi (token) -LOG: Writing token /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/token.pyi token.meta.json token.data.json -TRACE: Interface for token is unchanged -LOG: Cached module token has same interface -TRACE: Priorities for zlib: -LOG: Processing SCC singleton (zlib) as inherently stale with stale deps (array builtins sys typing typing_extensions) -LOG: Writing zlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/zlib.pyi zlib.meta.json zlib.data.json -TRACE: Interface for zlib has changed -LOG: Cached module zlib has changed interface -TRACE: Priorities for _compression: -LOG: Processing SCC singleton (_compression) as inherently stale with stale deps (_typeshed builtins collections.abc io typing) -LOG: Writing _compression /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/_compression.pyi _compression.meta.json _compression.data.json -TRACE: Interface for _compression has changed -LOG: Cached module _compression has changed interface -TRACE: Priorities for reprlib: -LOG: Processing SCC singleton (reprlib) as stale due to deps (abc array builtins collections collections.abc typing typing_extensions) -LOG: Parsing 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mmap pickle sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/urllib/parse.pyi (urllib.parse) -LOG: Writing urllib.parse /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/urllib/parse.pyi urllib/parse.meta.json urllib/parse.data.json -TRACE: Interface for urllib.parse is unchanged -LOG: Cached module urllib.parse has same interface -TRACE: Priorities for packaging._structures: -LOG: Processing SCC singleton (packaging._structures) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_structures.py (packaging._structures) -LOG: Writing packaging._structures /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/_structures.py packaging/_structures.meta.json packaging/_structures.data.json -TRACE: Interface for packaging._structures is unchanged -LOG: Cached module packaging._structures has same interface -TRACE: Priorities for atexit: -LOG: Processing SCC singleton (atexit) as stale due to deps (builtins collections.abc typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/atexit.pyi (atexit) -LOG: Writing atexit /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/atexit.pyi atexit.meta.json atexit.data.json -TRACE: Interface for atexit is unchanged -LOG: Cached module atexit has same interface -TRACE: Priorities for attr._version_info: -LOG: Processing SCC singleton (attr._version_info) as stale due to deps (builtins) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/_version_info.pyi (attr._version_info) -LOG: Writing attr._version_info /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/_version_info.pyi attr/_version_info.meta.json attr/_version_info.data.json -TRACE: Interface for attr._version_info is unchanged -LOG: Cached module attr._version_info has same interface -TRACE: Priorities for attr.exceptions: -LOG: Processing SCC singleton (attr.exceptions) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/exceptions.pyi (attr.exceptions) -LOG: Writing attr.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/attr/exceptions.pyi attr/exceptions.meta.json attr/exceptions.data.json -TRACE: Interface for attr.exceptions is unchanged -LOG: Cached module attr.exceptions has same interface -TRACE: Priorities for json.encoder: -LOG: Processing SCC singleton (json.encoder) as inherently stale 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/home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/marshal.pyi (marshal) -LOG: Writing marshal /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/marshal.pyi marshal.meta.json marshal.data.json -TRACE: Interface for marshal is unchanged -LOG: Cached module marshal has same interface -TRACE: Priorities for importlib.util: -LOG: Processing SCC singleton (importlib.util) as stale due to deps (_typeshed abc builtins collections.abc importlib importlib.abc importlib.machinery sys types typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/util.pyi (importlib.util) -LOG: Writing importlib.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/importlib/util.pyi importlib/util.meta.json importlib/util.data.json -TRACE: Interface for importlib.util is unchanged -LOG: Cached module importlib.util has same interface -TRACE: Priorities for opcode: -LOG: Processing SCC singleton (opcode) as inherently stale with stale deps (builtins sys typing_extensions) -LOG: Writing opcode /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/opcode.pyi opcode.meta.json opcode.data.json -TRACE: Interface for opcode has changed -LOG: Cached module opcode has changed interface -TRACE: Priorities for xdrlib: -LOG: Processing SCC singleton (xdrlib) as inherently stale with stale deps (builtins collections.abc typing) -LOG: Writing xdrlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/xdrlib.pyi xdrlib.meta.json xdrlib.data.json -TRACE: Interface for xdrlib has changed -LOG: Cached module xdrlib has changed interface -TRACE: Priorities for lzma: -LOG: Processing 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numpy.polynomial._polybase /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/numpy/polynomial/_polybase.pyi numpy/polynomial/_polybase.meta.json numpy/polynomial/_polybase.data.json -TRACE: Interface for numpy.polynomial._polybase has changed -LOG: Cached module numpy.polynomial._polybase has changed interface -TRACE: Priorities for getpass: -LOG: Processing SCC singleton (getpass) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/getpass.pyi (getpass) -LOG: Writing getpass /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/getpass.pyi getpass.meta.json getpass.data.json -TRACE: Interface for getpass is unchanged -LOG: Cached module getpass has same interface -TRACE: Priorities for bdb: -LOG: Processing SCC singleton (bdb) as stale due to deps (_typeshed abc builtins collections.abc sys types typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bdb.pyi (bdb) -LOG: Writing bdb /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/bdb.pyi bdb.meta.json bdb.data.json -TRACE: Interface for bdb is unchanged -LOG: Cached module bdb has same interface -TRACE: Priorities for traceback: -LOG: Processing SCC singleton (traceback) as inherently stale with stale deps (_typeshed builtins collections.abc sys types typing typing_extensions) -LOG: Writing traceback /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/traceback.pyi traceback.meta.json traceback.data.json -TRACE: Interface for traceback has changed -LOG: Cached module traceback has changed interface -TRACE: Priorities for shutil: -LOG: Processing SCC singleton (shutil) as stale due to deps (_typeshed abc array builtins collections.abc ctypes mmap os pickle sys typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/shutil.pyi (shutil) -LOG: Writing shutil /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/shutil.pyi shutil.meta.json shutil.data.json -TRACE: Interface for shutil is unchanged -LOG: Cached module shutil has same interface -TRACE: Priorities for platform: -LOG: Processing SCC singleton (platform) as inherently stale with stale deps (builtins sys typing) -LOG: Writing platform /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/platform.pyi platform.meta.json platform.data.json -TRACE: Interface for platform has changed -LOG: Cached module platform has changed interface -TRACE: Priorities for gc: -LOG: Processing SCC 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unchanged -LOG: Cached module fnmatch has same interface -TRACE: Priorities for iniconfig: -LOG: Processing SCC singleton (iniconfig) as stale due to deps (abc builtins typing typing_extensions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/iniconfig/__init__.pyi (iniconfig) -LOG: Writing iniconfig /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/iniconfig/__init__.pyi iniconfig/__init__.meta.json iniconfig/__init__.data.json -TRACE: Interface for iniconfig is unchanged -LOG: Cached module iniconfig has same interface -TRACE: Priorities for pkgutil: -LOG: Processing SCC singleton (pkgutil) as stale due to deps (_typeshed abc builtins collections.abc importlib importlib.abc sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/mypy/typeshed/stdlib/pkgutil.pyi (pkgutil) -LOG: Writing pkgutil 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xarray.plot.dataset_plot xarray.core.arithmetic xarray.core.accessor_str xarray.plot.plot xarray.core.missing xarray.core.coordinates xarray.coding.variables xarray.coding.times xarray.core.groupby xarray.core.variable xarray.core.merge xarray.core.dataset xarray.core.dataarray xarray.core.concat xarray.core.computation xarray.core.alignment xarray.coding.cftimeindex xarray.backends.netcdf3 xarray.core.rolling xarray.core.resample xarray.core.weighted xarray.coding.strings xarray.core.parallel xarray.core.extensions xarray.core.combine xarray.conventions xarray.coding.cftime_offsets xarray.ufuncs xarray.testing xarray.backends.common xarray.coding.frequencies xarray.backends.memory xarray.backends.store xarray.backends.plugins xarray.backends.rasterio_ xarray.backends.api xarray.backends.scipy_ xarray.backends.pynio_ xarray.backends.pydap_ xarray.backends.pseudonetcdf_ xarray.backends.netCDF4_ xarray.backends.cfgrib_ xarray.backends.zarr xarray.tutorial xarray.backends.h5netcdf_ xarray xarray.backends xarray.convert xarray.core.resample_cftime) as inherently stale with stale deps (__future__ builtins codecs collections collections.abc contextlib copy datetime distutils.version enum functools glob gzip html importlib importlib.metadata importlib.resources importlib_metadata inspect io itertools logging numbers numpy numpy.core.multiarray numpy.core.numeric operator os pathlib re sys textwrap threading time traceback typing typing_extensions unicodedata uuid warnings xarray.backends.locks xarray.backends.lru_cache xarray.coding xarray.core xarray.core.npcompat xarray.core.pdcompat xarray.util.print_versions) -LOG: Writing xarray.core.types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/core/types.py xarray/core/types.meta.json xarray/core/types.data.json -TRACE: Interface for xarray.core.types has changed -LOG: Cached module xarray.core.types has changed interface -LOG: Writing xarray.core.utils 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stale deps (__future__ builtins dcor._utils math numpy typing warnings) -LOG: Writing dcor._fast_dcov_avl /home/carlos/git/dcor/dcor/_fast_dcov_avl.py dcor/_fast_dcov_avl.meta.json dcor/_fast_dcov_avl.data.json -TRACE: Interface for dcor._fast_dcov_avl has changed -LOG: Cached module dcor._fast_dcov_avl has changed interface -TRACE: Priorities for dcor._hypothesis: -LOG: Processing SCC singleton (dcor._hypothesis) as inherently stale with stale deps (__future__ builtins dataclasses dcor._utils numpy typing warnings) -LOG: Writing dcor._hypothesis /home/carlos/git/dcor/dcor/_hypothesis.py dcor/_hypothesis.meta.json dcor/_hypothesis.data.json -TRACE: Interface for dcor._hypothesis has changed -LOG: Cached module dcor._hypothesis has changed interface -TRACE: Priorities for rdata.parser: -LOG: Processing SCC singleton (rdata.parser) as inherently stale with stale deps (builtins rdata.parser._parser) -LOG: Writing rdata.parser /home/carlos/git/rdata/rdata/parser/__init__.py 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jinja2.tests:5 jinja2.utils:5 -TRACE: Priorities for jinja2.compiler: jinja2.nodes:5 jinja2:20 jinja2.exceptions:5 jinja2.idtracking:5 jinja2.optimizer:5 jinja2.utils:5 jinja2.visitor:5 jinja2.environment:25 jinja2.runtime:20 -TRACE: Priorities for jinja2.environment: jinja2.nodes:5 jinja2:20 jinja2.compiler:5 jinja2.defaults:5 jinja2.exceptions:5 jinja2.lexer:5 jinja2.parser:5 jinja2.runtime:5 jinja2.utils:5 jinja2.bccache:25 jinja2.ext:25 jinja2.loaders:20 jinja2.debug:20 jinja2.visitor:30 -TRACE: Priorities for jinja2: jinja2.bccache:5 jinja2.environment:5 jinja2.exceptions:5 jinja2.loaders:5 jinja2.runtime:5 jinja2.utils:5 -TRACE: Priorities for jinja2.sandbox: jinja2.environment:5 jinja2.exceptions:5 jinja2.runtime:5 jinja2.bccache:30 jinja2.ext:30 jinja2.loaders:30 -TRACE: Priorities for jinja2.ext: jinja2.defaults:10 jinja2:20 jinja2.nodes:10 jinja2.environment:5 jinja2.exceptions:5 jinja2.runtime:5 jinja2.utils:5 jinja2.lexer:25 jinja2.parser:25 jinja2.bccache:30 jinja2.loaders:30 -LOG: Processing SCC of size 21 (jinja2.bccache jinja2.utils jinja2.exceptions jinja2.async_utils jinja2.debug jinja2.lexer jinja2.nodes jinja2.loaders jinja2.visitor jinja2.parser jinja2.runtime jinja2.tests jinja2.filters jinja2.optimizer jinja2.idtracking jinja2.defaults jinja2.compiler jinja2.environment jinja2 jinja2.sandbox jinja2.ext) as stale due to deps (_ast _collections_abc _operator _typeshed _weakref abc array ast builtins collections collections.abc contextlib ctypes enum errno functools genericpath importlib importlib.abc importlib.machinery inspect io itertools json json.encoder logging math mmap numbers operator os pickle posixpath re string sys textwrap threading types typing typing_extensions weakref zipfile) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/bccache.py (jinja2.bccache) -LOG: Parsing 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unchanged -LOG: Cached module jinja2.async_utils has same interface -LOG: Writing jinja2.debug /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/debug.py jinja2/debug.meta.json jinja2/debug.data.json -TRACE: Interface for jinja2.debug is unchanged -LOG: Cached module jinja2.debug has same interface -LOG: Writing jinja2.lexer /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/lexer.py jinja2/lexer.meta.json jinja2/lexer.data.json -TRACE: Interface for jinja2.lexer is unchanged -LOG: Cached module jinja2.lexer has same interface -LOG: Writing jinja2.nodes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/nodes.py jinja2/nodes.meta.json jinja2/nodes.data.json -TRACE: Interface for jinja2.nodes is unchanged -LOG: Cached module jinja2.nodes has same interface -LOG: Writing jinja2.loaders 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jinja2/runtime.meta.json jinja2/runtime.data.json -TRACE: Interface for jinja2.runtime is unchanged -LOG: Cached module jinja2.runtime has same interface -LOG: Writing jinja2.tests /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/tests.py jinja2/tests.meta.json jinja2/tests.data.json -TRACE: Interface for jinja2.tests is unchanged -LOG: Cached module jinja2.tests has same interface -LOG: Writing jinja2.filters /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/filters.py jinja2/filters.meta.json jinja2/filters.data.json -TRACE: Interface for jinja2.filters is unchanged -LOG: Cached module jinja2.filters has same interface -LOG: Writing jinja2.optimizer /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/jinja2/optimizer.py jinja2/optimizer.meta.json jinja2/optimizer.data.json -TRACE: Interface for jinja2.optimizer is unchanged -LOG: Cached 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jinja2/ext.meta.json jinja2/ext.data.json -TRACE: Interface for jinja2.ext is unchanged -LOG: Cached module jinja2.ext has same interface -TRACE: Priorities for xarray.plot: -LOG: Processing SCC singleton (xarray.plot) as inherently stale with stale deps (builtins xarray.plot.dataset_plot xarray.plot.facetgrid xarray.plot.plot) -LOG: Writing xarray.plot /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/xarray/plot/__init__.py xarray/plot/__init__.meta.json xarray/plot/__init__.data.json -TRACE: Interface for xarray.plot has changed -LOG: Cached module xarray.plot has changed interface -TRACE: Priorities for rdata.conversion._conversion: rdata:20 -TRACE: Priorities for rdata.conversion: rdata.conversion._conversion:5 -TRACE: Priorities for rdata: rdata.conversion:10 -LOG: Processing SCC of size 3 (rdata.conversion._conversion rdata.conversion rdata) as inherently stale with stale deps (__future__ abc builtins dataclasses errno fractions numpy os pathlib rdata.parser types typing warnings xarray) -LOG: Writing rdata.conversion._conversion /home/carlos/git/rdata/rdata/conversion/_conversion.py rdata/conversion/_conversion.meta.json rdata/conversion/_conversion.data.json -TRACE: Interface for rdata.conversion._conversion has changed -LOG: Cached module rdata.conversion._conversion has changed interface -LOG: Writing rdata.conversion /home/carlos/git/rdata/rdata/conversion/__init__.py rdata/conversion/__init__.meta.json rdata/conversion/__init__.data.json -TRACE: Interface for rdata.conversion has changed -LOG: Cached module rdata.conversion has changed interface -LOG: Writing rdata /home/carlos/git/rdata/rdata/__init__.py rdata/__init__.meta.json rdata/__init__.data.json -TRACE: Interface for rdata has changed -LOG: Cached module rdata has changed interface -TRACE: Priorities for dcor._energy: dcor:20 -TRACE: Priorities for dcor._dcor_internals: dcor:20 -TRACE: Priorities for dcor._partial_dcor: dcor._dcor_internals:5 -TRACE: Priorities for dcor._dcor: dcor._dcor_internals:5 -TRACE: Priorities for dcor.homogeneity: dcor:20 dcor._energy:5 -TRACE: Priorities for dcor._rowwise: dcor._dcor:10 dcor:20 -TRACE: Priorities for dcor.independence: dcor._dcor:5 dcor._dcor_internals:5 -TRACE: Priorities for dcor: dcor.homogeneity:10 dcor.independence:10 dcor._dcor:5 dcor._dcor_internals:5 dcor._energy:5 dcor._partial_dcor:5 dcor._rowwise:5 -LOG: Processing SCC of size 8 (dcor._energy dcor._dcor_internals dcor._partial_dcor dcor._dcor dcor.homogeneity dcor._rowwise dcor.independence dcor) as inherently stale with stale deps (__future__ builtins dataclasses dcor._fast_dcov_avl dcor._fast_dcov_mergesort dcor._hypothesis dcor._utils dcor.distances enum errno numpy os pathlib typing typing_extensions warnings) -LOG: Writing dcor._energy /home/carlos/git/dcor/dcor/_energy.py dcor/_energy.meta.json dcor/_energy.data.json -TRACE: Interface for dcor._energy has changed -LOG: Cached module dcor._energy has changed interface -LOG: Writing dcor._dcor_internals /home/carlos/git/dcor/dcor/_dcor_internals.py dcor/_dcor_internals.meta.json dcor/_dcor_internals.data.json -TRACE: Interface for dcor._dcor_internals has changed -LOG: Cached module dcor._dcor_internals has changed interface -LOG: Writing dcor._partial_dcor /home/carlos/git/dcor/dcor/_partial_dcor.py dcor/_partial_dcor.meta.json dcor/_partial_dcor.data.json -TRACE: Interface for dcor._partial_dcor has changed -LOG: Cached module dcor._partial_dcor has changed interface -LOG: Writing dcor._dcor /home/carlos/git/dcor/dcor/_dcor.py dcor/_dcor.meta.json dcor/_dcor.data.json -TRACE: Interface for dcor._dcor has changed -LOG: Cached module dcor._dcor has changed interface -LOG: Writing dcor.homogeneity /home/carlos/git/dcor/dcor/homogeneity.py dcor/homogeneity.meta.json dcor/homogeneity.data.json -TRACE: Interface for dcor.homogeneity has changed -LOG: Cached module dcor.homogeneity has changed interface -LOG: Writing dcor._rowwise /home/carlos/git/dcor/dcor/_rowwise.py dcor/_rowwise.meta.json dcor/_rowwise.data.json -TRACE: Interface for dcor._rowwise has changed -LOG: Cached module dcor._rowwise has changed interface -LOG: Writing dcor.independence /home/carlos/git/dcor/dcor/independence.py dcor/independence.meta.json dcor/independence.data.json -TRACE: Interface for dcor.independence has changed -LOG: Cached module dcor.independence has changed interface -LOG: Writing dcor /home/carlos/git/dcor/dcor/__init__.py dcor/__init__.meta.json dcor/__init__.data.json -TRACE: Interface for dcor has changed -LOG: Cached module dcor has changed interface -TRACE: Priorities for skfda.typing._metric: -LOG: Processing SCC singleton (skfda.typing._metric) as inherently stale with stale deps (abc builtins skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.typing._metric /home/carlos/git/scikit-fda/skfda/typing/_metric.py skfda/typing/_metric.meta.json skfda/typing/_metric.data.json -TRACE: Interface for skfda.typing._metric has changed -LOG: Cached module skfda.typing._metric has changed interface -TRACE: Priorities for skfda.exploratory.depth.multivariate: -LOG: Processing SCC singleton (skfda.exploratory.depth.multivariate) as inherently stale with stale deps (__future__ abc builtins math numpy skfda._utils._sklearn_adapter skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.exploratory.depth.multivariate /home/carlos/git/scikit-fda/skfda/exploratory/depth/multivariate.py skfda/exploratory/depth/multivariate.meta.json skfda/exploratory/depth/multivariate.data.json -TRACE: Interface for skfda.exploratory.depth.multivariate has changed -LOG: Cached module skfda.exploratory.depth.multivariate has changed interface -TRACE: Priorities for pyparsing.exceptions: pyparsing.core:20 -TRACE: Priorities for pyparsing.actions: pyparsing.exceptions:5 pyparsing.core:20 -TRACE: Priorities for pyparsing.core: pyparsing.exceptions:5 pyparsing.actions:5 pyparsing.testing:20 pyparsing.diagram:20 -TRACE: Priorities for pyparsing.testing: pyparsing.core:5 pyparsing.exceptions:30 -TRACE: Priorities for pyparsing.common: pyparsing.core:5 pyparsing.helpers:5 pyparsing.exceptions:30 -TRACE: Priorities for pyparsing.helpers: pyparsing.core:5 pyparsing:5 pyparsing.actions:30 pyparsing.exceptions:30 -TRACE: Priorities for pyparsing: pyparsing.exceptions:5 pyparsing.actions:5 pyparsing.core:5 pyparsing.helpers:5 pyparsing.testing:5 pyparsing.common:5 -TRACE: Priorities for pyparsing.diagram: pyparsing:10 pyparsing.core:30 -LOG: Processing SCC of size 8 (pyparsing.exceptions pyparsing.actions pyparsing.core pyparsing.testing pyparsing.common pyparsing.helpers pyparsing pyparsing.diagram) as stale due to deps (_collections_abc _operator _typeshed _warnings abc array builtins collections.abc contextlib copy ctypes datetime enum functools html inspect io mmap numpy numpy.ma numpy.ma.core operator os pathlib pickle re sre_constants string sys threading traceback types typing typing_extensions warnings) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/exceptions.py (pyparsing.exceptions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/actions.py (pyparsing.actions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/core.py (pyparsing.core) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/testing.py (pyparsing.testing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/common.py (pyparsing.common) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/helpers.py (pyparsing.helpers) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/__init__.py (pyparsing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/diagram/__init__.py (pyparsing.diagram) -LOG: Writing pyparsing.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/exceptions.py pyparsing/exceptions.meta.json pyparsing/exceptions.data.json -TRACE: Interface for pyparsing.exceptions is unchanged -LOG: Cached module pyparsing.exceptions has same interface -LOG: Writing pyparsing.actions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/actions.py pyparsing/actions.meta.json pyparsing/actions.data.json -TRACE: Interface for pyparsing.actions is unchanged -LOG: Cached module pyparsing.actions has same interface -LOG: Writing pyparsing.core /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/core.py pyparsing/core.meta.json pyparsing/core.data.json -TRACE: Interface for pyparsing.core is unchanged -LOG: Cached module pyparsing.core has same interface -LOG: Writing pyparsing.testing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/testing.py pyparsing/testing.meta.json pyparsing/testing.data.json -TRACE: Interface for pyparsing.testing is unchanged -LOG: Cached module pyparsing.testing has same interface -LOG: Writing pyparsing.common /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/common.py pyparsing/common.meta.json pyparsing/common.data.json -TRACE: Interface for pyparsing.common is unchanged -LOG: Cached module pyparsing.common has same interface -LOG: Writing pyparsing.helpers /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/helpers.py pyparsing/helpers.meta.json pyparsing/helpers.data.json -TRACE: Interface for pyparsing.helpers is unchanged -LOG: Cached module pyparsing.helpers has same interface -LOG: Writing pyparsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/__init__.py pyparsing/__init__.meta.json pyparsing/__init__.data.json -TRACE: Interface for pyparsing is unchanged -LOG: Cached module pyparsing has same interface -LOG: Writing pyparsing.diagram /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pyparsing/diagram/__init__.py pyparsing/diagram/__init__.meta.json pyparsing/diagram/__init__.data.json -TRACE: Interface for pyparsing.diagram is unchanged -LOG: Cached module pyparsing.diagram has same interface -TRACE: Priorities for skfda.misc.metrics._parse: -LOG: Processing SCC singleton (skfda.misc.metrics._parse) as inherently stale with stale deps (builtins enum skfda.typing._metric typing typing_extensions) -LOG: Writing skfda.misc.metrics._parse /home/carlos/git/scikit-fda/skfda/misc/metrics/_parse.py skfda/misc/metrics/_parse.meta.json skfda/misc/metrics/_parse.data.json -TRACE: Interface for skfda.misc.metrics._parse has changed -LOG: Cached module skfda.misc.metrics._parse has changed interface -TRACE: Priorities for packaging.markers: -LOG: Processing SCC singleton (packaging.markers) as stale due to deps (_operator _typeshed abc array builtins ctypes mmap operator os pickle platform sys typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/markers.py (packaging.markers) -LOG: Writing packaging.markers /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/markers.py packaging/markers.meta.json packaging/markers.data.json -TRACE: Interface for packaging.markers is unchanged -LOG: Cached module packaging.markers has same interface -TRACE: Priorities for skfda.representation.basis: skfda.representation.basis._basis:25 skfda.representation.basis._bspline:25 skfda.representation.basis._constant:25 skfda.representation.basis._fdatabasis:25 skfda.representation.basis._finite_element:25 skfda.representation.basis._fourier:25 skfda.representation.basis._monomial:25 skfda.representation.basis._tensor_basis:25 skfda.representation.basis._vector_basis:25 -TRACE: Priorities for skfda.representation: skfda.representation._functional_data:25 skfda.representation.basis:25 skfda.representation.grid:25 -TRACE: Priorities for skfda.preprocessing.dim_reduction: skfda.preprocessing.dim_reduction._fpca:25 -TRACE: Priorities for skfda.misc.regularization: skfda.misc.regularization._regularization:25 -TRACE: Priorities for skfda.misc.operators: skfda.misc.operators._identity:25 skfda.misc.operators._integral_transform:25 skfda.misc.operators._linear_differential_operator:25 skfda.misc.operators._operators:25 skfda.misc.operators._srvf:25 -TRACE: Priorities for skfda.misc.metrics: skfda.misc.metrics._angular:25 skfda.misc.metrics._fisher_rao:25 skfda.misc.metrics._lp_distances:25 skfda.misc.metrics._lp_norms:25 skfda.misc.metrics._mahalanobis:25 skfda.misc.metrics._utils:25 -TRACE: Priorities for skfda.misc: skfda.misc._math:25 -TRACE: Priorities for skfda.exploratory.stats: skfda.exploratory.stats._fisher_rao:25 skfda.exploratory.stats._functional_transformers:25 skfda.exploratory.stats._stats:25 -TRACE: Priorities for skfda.exploratory.depth: skfda.exploratory.depth._depth:25 -TRACE: Priorities for skfda._utils: skfda._utils._utils:25 skfda._utils._warping:25 -TRACE: Priorities for skfda: skfda.representation:25 -TRACE: Priorities for skfda.preprocessing.smoothing._linear: skfda._utils:5 skfda.representation:5 skfda.preprocessing.smoothing.validation:20 -TRACE: Priorities for skfda.preprocessing.smoothing.validation: skfda.representation:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.preprocessing.registration: skfda._utils:5 skfda.preprocessing.registration._fisher_rao:25 skfda.preprocessing.registration._landmark_registration:25 skfda.preprocessing.registration._lstsq_shift_registration:25 -TRACE: Priorities for skfda.misc.validation: skfda.representation:5 -TRACE: Priorities for skfda.misc.operators._operators: skfda.representation:5 skfda.representation.basis:5 skfda.misc:20 -TRACE: Priorities for skfda.misc.metrics._utils: skfda._utils:5 skfda.representation:5 -TRACE: Priorities for skfda.misc.metrics._lp_norms: skfda.representation:5 skfda.misc:20 -TRACE: Priorities for skfda._utils._utils: skfda.representation:25 skfda.representation.basis:25 skfda.representation.extrapolation:25 skfda:20 -TRACE: Priorities for skfda.representation.evaluator: skfda.representation._functional_data:25 skfda.misc.validation:20 -TRACE: Priorities for skfda.representation.basis._basis: skfda.representation.basis._fdatabasis:25 skfda.misc.validation:20 skfda.representation.basis:20 skfda.misc:20 skfda._utils:20 -TRACE: Priorities for skfda.preprocessing.smoothing._basis: skfda._utils:5 skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.preprocessing.registration.base: skfda.representation:5 skfda.preprocessing.registration.validation:20 -TRACE: Priorities for skfda.preprocessing.registration.validation: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 skfda.preprocessing.registration.base:5 skfda.misc.metrics:20 -TRACE: Priorities for skfda.misc.regularization._regularization: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators:5 skfda.misc.operators._operators:5 -TRACE: Priorities for skfda.misc.operators._srvf: skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 skfda.misc.operators._operators:5 -TRACE: Priorities for skfda.misc.operators._linear_differential_operator: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 -TRACE: Priorities for skfda.misc.operators._integral_transform: skfda.representation:5 skfda.misc.operators._operators:5 -TRACE: Priorities for skfda.misc.operators._identity: skfda.representation:5 skfda.representation.basis:5 skfda.misc.operators._operators:5 skfda.misc.metrics:20 -TRACE: Priorities for skfda.misc.metrics._lp_distances: skfda.representation:5 skfda.misc.metrics._lp_norms:5 skfda.misc.metrics._utils:5 skfda.misc:20 -TRACE: Priorities for skfda.misc._math: skfda._utils:5 skfda.representation:5 skfda.representation.basis:5 skfda.misc.validation:5 -TRACE: Priorities for skfda.exploratory.stats._functional_transformers: skfda._utils:5 skfda.misc.validation:5 skfda.representation:5 -TRACE: Priorities for skfda.exploratory.depth._depth: skfda.misc.metrics:5 skfda.misc.metrics._utils:5 skfda.representation:5 -TRACE: Priorities for skfda._utils._warping: skfda.representation:25 skfda.misc.validation:20 -TRACE: Priorities for skfda.representation.interpolation: skfda.representation.evaluator:5 skfda.representation.grid:25 skfda.misc.validation:20 -TRACE: Priorities for skfda.representation.extrapolation: skfda.representation.evaluator:5 skfda.representation._functional_data:25 -TRACE: Priorities for skfda.representation.basis._vector_basis: skfda.representation.basis._basis:5 skfda._utils:20 -TRACE: Priorities for skfda.representation.basis._tensor_basis: skfda.representation.basis._basis:5 -TRACE: Priorities for skfda.representation.basis._monomial: skfda.representation.basis._basis:5 -TRACE: Priorities for skfda.representation.basis._fourier: skfda.representation.basis._basis:5 skfda.misc.validation:20 -TRACE: Priorities for skfda.representation.basis._finite_element: skfda.representation.basis._basis:5 -TRACE: Priorities for skfda.representation.basis._constant: skfda.representation.basis._basis:5 -TRACE: Priorities for skfda.representation.basis._bspline: skfda.representation.basis._basis:5 skfda.misc.validation:20 -TRACE: Priorities for skfda.misc.metrics._mahalanobis: skfda.representation:5 skfda.representation.basis:5 skfda.misc._math:5 skfda.misc.regularization._regularization:5 skfda.preprocessing.dim_reduction:20 -TRACE: Priorities for skfda.misc.metrics._fisher_rao: skfda._utils:5 skfda.representation:5 skfda.misc.operators:5 skfda.misc.metrics._lp_distances:5 skfda.misc.metrics._utils:5 skfda.preprocessing.registration:20 -TRACE: Priorities for skfda.misc.metrics._angular: skfda.representation:5 skfda.misc._math:5 skfda.misc.metrics._utils:5 -TRACE: Priorities for skfda.exploratory.stats._stats: skfda.misc.metrics._lp_distances:5 skfda.representation:5 skfda.exploratory.depth:5 -TRACE: Priorities for skfda.preprocessing.registration._lstsq_shift_registration: skfda.misc._math:5 skfda.misc.metrics._lp_norms:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.extrapolation:5 skfda.preprocessing.registration.base:5 -TRACE: Priorities for skfda.preprocessing.registration._landmark_registration: skfda.representation:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 -TRACE: Priorities for skfda.representation._functional_data: skfda._utils:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.grid:25 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 -TRACE: Priorities for skfda.exploratory.visualization._utils: skfda.representation._functional_data:5 -TRACE: Priorities for skfda.exploratory.visualization._baseplot: skfda.representation:5 skfda.exploratory.visualization._utils:5 -TRACE: Priorities for skfda.exploratory.visualization.representation: skfda._utils:5 skfda.misc.validation:5 skfda.representation._functional_data:5 skfda.exploratory.visualization._baseplot:5 skfda.exploratory.visualization._utils:5 -TRACE: Priorities for skfda.exploratory.stats._fisher_rao: skfda._utils:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.interpolation:5 -TRACE: Priorities for skfda.preprocessing.registration._fisher_rao: skfda._utils:5 skfda.exploratory.stats:5 skfda.exploratory.stats._fisher_rao:5 skfda.misc.operators:5 skfda.misc.validation:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.interpolation:5 skfda.preprocessing.registration.base:5 -TRACE: Priorities for skfda.misc.hat_matrix: skfda.misc:20 skfda.representation._functional_data:5 skfda.representation.basis:5 -TRACE: Priorities for skfda.preprocessing.smoothing._kernel_smoothers: skfda._utils._utils:5 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.preprocessing.smoothing: skfda.preprocessing.smoothing.kernel_smoothers:20 skfda.preprocessing.smoothing._basis:25 skfda.preprocessing.smoothing._kernel_smoothers:25 -TRACE: Priorities for skfda.preprocessing.smoothing.kernel_smoothers: skfda.misc:20 skfda.misc.hat_matrix:5 skfda.preprocessing.smoothing:5 skfda.preprocessing.smoothing._linear:5 -TRACE: Priorities for skfda.representation.grid: skfda._utils:5 skfda.representation._functional_data:5 skfda.representation.evaluator:5 skfda.representation.extrapolation:5 skfda.representation.interpolation:5 skfda.representation.basis:25 skfda.misc.validation:20 skfda.exploratory.visualization.representation:20 skfda.preprocessing.smoothing:20 -TRACE: Priorities for skfda.representation.basis._fdatabasis: skfda._utils:5 skfda.representation.grid:10 skfda.representation:20 skfda.representation._functional_data:5 skfda.representation.extrapolation:5 skfda.representation.basis:25 -TRACE: Priorities for skfda.preprocessing.dim_reduction._fpca: skfda.misc.regularization:5 skfda.representation:5 skfda.representation.basis:5 skfda.representation.grid:5 -LOG: Processing SCC of size 62 (skfda.representation.basis skfda.representation skfda.preprocessing.dim_reduction skfda.misc.regularization skfda.misc.operators skfda.misc.metrics skfda.misc skfda.exploratory.stats skfda.exploratory.depth skfda._utils skfda skfda.preprocessing.smoothing._linear skfda.preprocessing.smoothing.validation skfda.preprocessing.registration skfda.misc.validation skfda.misc.operators._operators skfda.misc.metrics._utils skfda.misc.metrics._lp_norms skfda._utils._utils skfda.representation.evaluator skfda.representation.basis._basis skfda.preprocessing.smoothing._basis skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.misc.regularization._regularization skfda.misc.operators._srvf skfda.misc.operators._linear_differential_operator skfda.misc.operators._integral_transform skfda.misc.operators._identity skfda.misc.metrics._lp_distances skfda.misc._math skfda.exploratory.stats._functional_transformers skfda.exploratory.depth._depth skfda._utils._warping skfda.representation.interpolation skfda.representation.extrapolation skfda.representation.basis._vector_basis skfda.representation.basis._tensor_basis skfda.representation.basis._monomial skfda.representation.basis._fourier skfda.representation.basis._finite_element skfda.representation.basis._constant skfda.representation.basis._bspline skfda.misc.metrics._mahalanobis skfda.misc.metrics._fisher_rao skfda.misc.metrics._angular skfda.exploratory.stats._stats skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration._landmark_registration skfda.representation._functional_data skfda.exploratory.visualization._utils skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.exploratory.stats._fisher_rao skfda.preprocessing.registration._fisher_rao skfda.misc.hat_matrix skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing skfda.preprocessing.smoothing.kernel_smoothers skfda.representation.grid skfda.representation.basis._fdatabasis skfda.preprocessing.dim_reduction._fpca) as inherently stale with stale deps (__future__ abc builtins colorsys copy dataclasses dcor errno functools importlib io itertools math multimethod numbers numpy os re skfda._utils._sklearn_adapter skfda._utils.constants skfda.exploratory.depth.multivariate skfda.misc.kernels skfda.misc.lstsq skfda.misc.metrics._parse skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) -LOG: Writing skfda.representation.basis /home/carlos/git/scikit-fda/skfda/representation/basis/__init__.py skfda/representation/basis/__init__.meta.json skfda/representation/basis/__init__.data.json -TRACE: Interface for skfda.representation.basis has changed -LOG: Cached module skfda.representation.basis has changed interface -LOG: Writing skfda.representation /home/carlos/git/scikit-fda/skfda/representation/__init__.py skfda/representation/__init__.meta.json skfda/representation/__init__.data.json -TRACE: Interface for skfda.representation has changed -LOG: Cached module skfda.representation has changed interface -LOG: Writing skfda.preprocessing.dim_reduction /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/__init__.py skfda/preprocessing/dim_reduction/__init__.meta.json skfda/preprocessing/dim_reduction/__init__.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction has changed -LOG: Cached module skfda.preprocessing.dim_reduction has changed interface -LOG: Writing skfda.misc.regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/__init__.py skfda/misc/regularization/__init__.meta.json skfda/misc/regularization/__init__.data.json -TRACE: Interface for skfda.misc.regularization has changed -LOG: Cached module skfda.misc.regularization has changed interface -LOG: Writing skfda.misc.operators /home/carlos/git/scikit-fda/skfda/misc/operators/__init__.py skfda/misc/operators/__init__.meta.json skfda/misc/operators/__init__.data.json -TRACE: Interface for skfda.misc.operators has changed -LOG: Cached module skfda.misc.operators has changed interface -LOG: Writing skfda.misc.metrics /home/carlos/git/scikit-fda/skfda/misc/metrics/__init__.py skfda/misc/metrics/__init__.meta.json skfda/misc/metrics/__init__.data.json -TRACE: Interface for skfda.misc.metrics has changed -LOG: Cached module skfda.misc.metrics has changed interface -LOG: Writing skfda.misc /home/carlos/git/scikit-fda/skfda/misc/__init__.py skfda/misc/__init__.meta.json skfda/misc/__init__.data.json -TRACE: Interface for skfda.misc has changed -LOG: Cached module skfda.misc has changed interface -LOG: Writing skfda.exploratory.stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/__init__.py skfda/exploratory/stats/__init__.meta.json skfda/exploratory/stats/__init__.data.json -TRACE: Interface for skfda.exploratory.stats has changed -LOG: Cached module skfda.exploratory.stats has changed interface -LOG: Writing skfda.exploratory.depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/__init__.py skfda/exploratory/depth/__init__.meta.json skfda/exploratory/depth/__init__.data.json -TRACE: Interface for skfda.exploratory.depth has changed -LOG: Cached module skfda.exploratory.depth has changed interface -LOG: Writing skfda._utils /home/carlos/git/scikit-fda/skfda/_utils/__init__.py skfda/_utils/__init__.meta.json skfda/_utils/__init__.data.json -TRACE: Interface for skfda._utils has changed -LOG: Cached module skfda._utils has changed interface -LOG: Writing skfda /home/carlos/git/scikit-fda/skfda/__init__.py skfda/__init__.meta.json skfda/__init__.data.json -TRACE: Interface for skfda has changed -LOG: Cached module skfda has changed interface -LOG: Writing skfda.preprocessing.smoothing._linear /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_linear.py skfda/preprocessing/smoothing/_linear.meta.json skfda/preprocessing/smoothing/_linear.data.json -TRACE: Interface for skfda.preprocessing.smoothing._linear has changed -LOG: Cached module skfda.preprocessing.smoothing._linear has changed interface -LOG: Writing skfda.preprocessing.smoothing.validation /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/validation.py skfda/preprocessing/smoothing/validation.meta.json skfda/preprocessing/smoothing/validation.data.json -TRACE: Interface for skfda.preprocessing.smoothing.validation has changed -LOG: Cached module skfda.preprocessing.smoothing.validation has changed interface -LOG: Writing skfda.preprocessing.registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/__init__.py skfda/preprocessing/registration/__init__.meta.json skfda/preprocessing/registration/__init__.data.json -TRACE: Interface for skfda.preprocessing.registration has changed -LOG: Cached module skfda.preprocessing.registration has changed interface -LOG: Writing skfda.misc.validation /home/carlos/git/scikit-fda/skfda/misc/validation.py skfda/misc/validation.meta.json skfda/misc/validation.data.json -TRACE: Interface for skfda.misc.validation has changed -LOG: Cached module skfda.misc.validation has changed interface -LOG: Writing skfda.misc.operators._operators /home/carlos/git/scikit-fda/skfda/misc/operators/_operators.py skfda/misc/operators/_operators.meta.json skfda/misc/operators/_operators.data.json -TRACE: Interface for skfda.misc.operators._operators has changed -LOG: Cached module skfda.misc.operators._operators has changed interface -LOG: Writing skfda.misc.metrics._utils /home/carlos/git/scikit-fda/skfda/misc/metrics/_utils.py skfda/misc/metrics/_utils.meta.json skfda/misc/metrics/_utils.data.json -TRACE: Interface for skfda.misc.metrics._utils has changed -LOG: Cached module skfda.misc.metrics._utils has changed interface -LOG: Writing skfda.misc.metrics._lp_norms /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_norms.py skfda/misc/metrics/_lp_norms.meta.json skfda/misc/metrics/_lp_norms.data.json -TRACE: Interface for skfda.misc.metrics._lp_norms has changed -LOG: Cached module skfda.misc.metrics._lp_norms has changed interface -LOG: Writing skfda._utils._utils /home/carlos/git/scikit-fda/skfda/_utils/_utils.py skfda/_utils/_utils.meta.json skfda/_utils/_utils.data.json -TRACE: Interface for skfda._utils._utils has changed -LOG: Cached module skfda._utils._utils has changed interface -LOG: Writing skfda.representation.evaluator /home/carlos/git/scikit-fda/skfda/representation/evaluator.py skfda/representation/evaluator.meta.json skfda/representation/evaluator.data.json -TRACE: Interface for skfda.representation.evaluator has changed -LOG: Cached module skfda.representation.evaluator has changed interface -LOG: Writing skfda.representation.basis._basis /home/carlos/git/scikit-fda/skfda/representation/basis/_basis.py skfda/representation/basis/_basis.meta.json skfda/representation/basis/_basis.data.json -TRACE: Interface for skfda.representation.basis._basis has changed -LOG: Cached module skfda.representation.basis._basis has changed interface -LOG: Writing skfda.preprocessing.smoothing._basis /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_basis.py skfda/preprocessing/smoothing/_basis.meta.json skfda/preprocessing/smoothing/_basis.data.json -TRACE: Interface for skfda.preprocessing.smoothing._basis has changed -LOG: Cached module skfda.preprocessing.smoothing._basis has changed interface -LOG: Writing skfda.preprocessing.registration.base /home/carlos/git/scikit-fda/skfda/preprocessing/registration/base.py skfda/preprocessing/registration/base.meta.json skfda/preprocessing/registration/base.data.json -TRACE: Interface for skfda.preprocessing.registration.base has changed -LOG: Cached module skfda.preprocessing.registration.base has changed interface -LOG: Writing skfda.preprocessing.registration.validation /home/carlos/git/scikit-fda/skfda/preprocessing/registration/validation.py skfda/preprocessing/registration/validation.meta.json skfda/preprocessing/registration/validation.data.json -TRACE: Interface for skfda.preprocessing.registration.validation has changed -LOG: Cached module skfda.preprocessing.registration.validation has changed interface -LOG: Writing skfda.misc.regularization._regularization /home/carlos/git/scikit-fda/skfda/misc/regularization/_regularization.py skfda/misc/regularization/_regularization.meta.json skfda/misc/regularization/_regularization.data.json -TRACE: Interface for skfda.misc.regularization._regularization has changed -LOG: Cached module skfda.misc.regularization._regularization has changed interface -LOG: Writing skfda.misc.operators._srvf /home/carlos/git/scikit-fda/skfda/misc/operators/_srvf.py skfda/misc/operators/_srvf.meta.json skfda/misc/operators/_srvf.data.json -TRACE: Interface for skfda.misc.operators._srvf has changed -LOG: Cached module skfda.misc.operators._srvf has changed interface -LOG: Writing skfda.misc.operators._linear_differential_operator /home/carlos/git/scikit-fda/skfda/misc/operators/_linear_differential_operator.py skfda/misc/operators/_linear_differential_operator.meta.json skfda/misc/operators/_linear_differential_operator.data.json -TRACE: Interface for skfda.misc.operators._linear_differential_operator has changed -LOG: Cached module skfda.misc.operators._linear_differential_operator has changed interface -LOG: Writing skfda.misc.operators._integral_transform /home/carlos/git/scikit-fda/skfda/misc/operators/_integral_transform.py skfda/misc/operators/_integral_transform.meta.json skfda/misc/operators/_integral_transform.data.json -TRACE: Interface for skfda.misc.operators._integral_transform has changed -LOG: Cached module skfda.misc.operators._integral_transform has changed interface -LOG: Writing skfda.misc.operators._identity /home/carlos/git/scikit-fda/skfda/misc/operators/_identity.py skfda/misc/operators/_identity.meta.json skfda/misc/operators/_identity.data.json -TRACE: Interface for skfda.misc.operators._identity has changed -LOG: Cached module skfda.misc.operators._identity has changed interface -LOG: Writing skfda.misc.metrics._lp_distances /home/carlos/git/scikit-fda/skfda/misc/metrics/_lp_distances.py skfda/misc/metrics/_lp_distances.meta.json skfda/misc/metrics/_lp_distances.data.json -TRACE: Interface for skfda.misc.metrics._lp_distances has changed -LOG: Cached module skfda.misc.metrics._lp_distances has changed interface -LOG: Writing skfda.misc._math /home/carlos/git/scikit-fda/skfda/misc/_math.py skfda/misc/_math.meta.json skfda/misc/_math.data.json -TRACE: Interface for skfda.misc._math has changed -LOG: Cached module skfda.misc._math has changed interface -LOG: Writing skfda.exploratory.stats._functional_transformers /home/carlos/git/scikit-fda/skfda/exploratory/stats/_functional_transformers.py skfda/exploratory/stats/_functional_transformers.meta.json skfda/exploratory/stats/_functional_transformers.data.json -TRACE: Interface for skfda.exploratory.stats._functional_transformers has changed -LOG: Cached module skfda.exploratory.stats._functional_transformers has changed interface -LOG: Writing skfda.exploratory.depth._depth /home/carlos/git/scikit-fda/skfda/exploratory/depth/_depth.py skfda/exploratory/depth/_depth.meta.json skfda/exploratory/depth/_depth.data.json -TRACE: Interface for skfda.exploratory.depth._depth has changed -LOG: Cached module skfda.exploratory.depth._depth has changed interface -LOG: Writing skfda._utils._warping /home/carlos/git/scikit-fda/skfda/_utils/_warping.py skfda/_utils/_warping.meta.json skfda/_utils/_warping.data.json -TRACE: Interface for skfda._utils._warping has changed -LOG: Cached module skfda._utils._warping has changed interface -LOG: Writing skfda.representation.interpolation /home/carlos/git/scikit-fda/skfda/representation/interpolation.py skfda/representation/interpolation.meta.json skfda/representation/interpolation.data.json -TRACE: Interface for skfda.representation.interpolation has changed -LOG: Cached module skfda.representation.interpolation has changed interface -LOG: Writing skfda.representation.extrapolation /home/carlos/git/scikit-fda/skfda/representation/extrapolation.py skfda/representation/extrapolation.meta.json skfda/representation/extrapolation.data.json -TRACE: Interface for skfda.representation.extrapolation has changed -LOG: Cached module skfda.representation.extrapolation has changed interface -LOG: Writing skfda.representation.basis._vector_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_vector_basis.py skfda/representation/basis/_vector_basis.meta.json skfda/representation/basis/_vector_basis.data.json -TRACE: Interface for skfda.representation.basis._vector_basis has changed -LOG: Cached module skfda.representation.basis._vector_basis has changed interface -LOG: Writing skfda.representation.basis._tensor_basis /home/carlos/git/scikit-fda/skfda/representation/basis/_tensor_basis.py skfda/representation/basis/_tensor_basis.meta.json skfda/representation/basis/_tensor_basis.data.json -TRACE: Interface for skfda.representation.basis._tensor_basis has changed -LOG: Cached module skfda.representation.basis._tensor_basis has changed interface -LOG: Writing skfda.representation.basis._monomial /home/carlos/git/scikit-fda/skfda/representation/basis/_monomial.py skfda/representation/basis/_monomial.meta.json skfda/representation/basis/_monomial.data.json -TRACE: Interface for skfda.representation.basis._monomial has changed -LOG: Cached module skfda.representation.basis._monomial has changed interface -LOG: Writing skfda.representation.basis._fourier /home/carlos/git/scikit-fda/skfda/representation/basis/_fourier.py skfda/representation/basis/_fourier.meta.json skfda/representation/basis/_fourier.data.json -TRACE: Interface for skfda.representation.basis._fourier has changed -LOG: Cached module skfda.representation.basis._fourier has changed interface -LOG: Writing skfda.representation.basis._finite_element /home/carlos/git/scikit-fda/skfda/representation/basis/_finite_element.py skfda/representation/basis/_finite_element.meta.json skfda/representation/basis/_finite_element.data.json -TRACE: Interface for skfda.representation.basis._finite_element has changed -LOG: Cached module skfda.representation.basis._finite_element has changed interface -LOG: Writing skfda.representation.basis._constant /home/carlos/git/scikit-fda/skfda/representation/basis/_constant.py skfda/representation/basis/_constant.meta.json skfda/representation/basis/_constant.data.json -TRACE: Interface for skfda.representation.basis._constant has changed -LOG: Cached module skfda.representation.basis._constant has changed interface -LOG: Writing skfda.representation.basis._bspline /home/carlos/git/scikit-fda/skfda/representation/basis/_bspline.py skfda/representation/basis/_bspline.meta.json skfda/representation/basis/_bspline.data.json -TRACE: Interface for skfda.representation.basis._bspline has changed -LOG: Cached module skfda.representation.basis._bspline has changed interface -LOG: Writing skfda.misc.metrics._mahalanobis /home/carlos/git/scikit-fda/skfda/misc/metrics/_mahalanobis.py skfda/misc/metrics/_mahalanobis.meta.json skfda/misc/metrics/_mahalanobis.data.json -TRACE: Interface for skfda.misc.metrics._mahalanobis has changed -LOG: Cached module skfda.misc.metrics._mahalanobis has changed interface -LOG: Writing skfda.misc.metrics._fisher_rao /home/carlos/git/scikit-fda/skfda/misc/metrics/_fisher_rao.py skfda/misc/metrics/_fisher_rao.meta.json skfda/misc/metrics/_fisher_rao.data.json -TRACE: Interface for skfda.misc.metrics._fisher_rao has changed -LOG: Cached module skfda.misc.metrics._fisher_rao has changed interface -LOG: Writing skfda.misc.metrics._angular /home/carlos/git/scikit-fda/skfda/misc/metrics/_angular.py skfda/misc/metrics/_angular.meta.json skfda/misc/metrics/_angular.data.json -TRACE: Interface for skfda.misc.metrics._angular has changed -LOG: Cached module skfda.misc.metrics._angular has changed interface -LOG: Writing skfda.exploratory.stats._stats /home/carlos/git/scikit-fda/skfda/exploratory/stats/_stats.py skfda/exploratory/stats/_stats.meta.json skfda/exploratory/stats/_stats.data.json -TRACE: Interface for skfda.exploratory.stats._stats has changed -LOG: Cached module skfda.exploratory.stats._stats has changed interface -LOG: Writing skfda.preprocessing.registration._lstsq_shift_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_lstsq_shift_registration.py skfda/preprocessing/registration/_lstsq_shift_registration.meta.json skfda/preprocessing/registration/_lstsq_shift_registration.data.json -TRACE: Interface for skfda.preprocessing.registration._lstsq_shift_registration has changed -LOG: Cached module skfda.preprocessing.registration._lstsq_shift_registration has changed interface -LOG: Writing skfda.preprocessing.registration._landmark_registration /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_landmark_registration.py skfda/preprocessing/registration/_landmark_registration.meta.json skfda/preprocessing/registration/_landmark_registration.data.json -TRACE: Interface for skfda.preprocessing.registration._landmark_registration has changed -LOG: Cached module skfda.preprocessing.registration._landmark_registration has changed interface -LOG: Writing skfda.representation._functional_data /home/carlos/git/scikit-fda/skfda/representation/_functional_data.py skfda/representation/_functional_data.meta.json skfda/representation/_functional_data.data.json -TRACE: Interface for skfda.representation._functional_data has changed -LOG: Cached module skfda.representation._functional_data has changed interface -LOG: Writing skfda.exploratory.visualization._utils /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_utils.py skfda/exploratory/visualization/_utils.meta.json skfda/exploratory/visualization/_utils.data.json -TRACE: Interface for skfda.exploratory.visualization._utils has changed -LOG: Cached module skfda.exploratory.visualization._utils has changed interface -LOG: Writing skfda.exploratory.visualization._baseplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_baseplot.py skfda/exploratory/visualization/_baseplot.meta.json skfda/exploratory/visualization/_baseplot.data.json -TRACE: Interface for skfda.exploratory.visualization._baseplot has changed -LOG: Cached module skfda.exploratory.visualization._baseplot has changed interface -LOG: Writing skfda.exploratory.visualization.representation /home/carlos/git/scikit-fda/skfda/exploratory/visualization/representation.py skfda/exploratory/visualization/representation.meta.json skfda/exploratory/visualization/representation.data.json -TRACE: Interface for skfda.exploratory.visualization.representation has changed -LOG: Cached module skfda.exploratory.visualization.representation has changed interface -LOG: Writing skfda.exploratory.stats._fisher_rao /home/carlos/git/scikit-fda/skfda/exploratory/stats/_fisher_rao.py skfda/exploratory/stats/_fisher_rao.meta.json skfda/exploratory/stats/_fisher_rao.data.json -TRACE: Interface for skfda.exploratory.stats._fisher_rao has changed -LOG: Cached module skfda.exploratory.stats._fisher_rao has changed interface -LOG: Writing skfda.preprocessing.registration._fisher_rao /home/carlos/git/scikit-fda/skfda/preprocessing/registration/_fisher_rao.py skfda/preprocessing/registration/_fisher_rao.meta.json skfda/preprocessing/registration/_fisher_rao.data.json -TRACE: Interface for skfda.preprocessing.registration._fisher_rao has changed -LOG: Cached module skfda.preprocessing.registration._fisher_rao has changed interface -LOG: Writing skfda.misc.hat_matrix /home/carlos/git/scikit-fda/skfda/misc/hat_matrix.py skfda/misc/hat_matrix.meta.json skfda/misc/hat_matrix.data.json -TRACE: Interface for skfda.misc.hat_matrix has changed -LOG: Cached module skfda.misc.hat_matrix has changed interface -LOG: Writing skfda.preprocessing.smoothing._kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/_kernel_smoothers.py skfda/preprocessing/smoothing/_kernel_smoothers.meta.json skfda/preprocessing/smoothing/_kernel_smoothers.data.json -TRACE: Interface for skfda.preprocessing.smoothing._kernel_smoothers has changed -LOG: Cached module skfda.preprocessing.smoothing._kernel_smoothers has changed interface -LOG: Writing skfda.preprocessing.smoothing /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/__init__.py skfda/preprocessing/smoothing/__init__.meta.json skfda/preprocessing/smoothing/__init__.data.json -TRACE: Interface for skfda.preprocessing.smoothing has changed -LOG: Cached module skfda.preprocessing.smoothing has changed interface -LOG: Writing skfda.preprocessing.smoothing.kernel_smoothers /home/carlos/git/scikit-fda/skfda/preprocessing/smoothing/kernel_smoothers.py skfda/preprocessing/smoothing/kernel_smoothers.meta.json skfda/preprocessing/smoothing/kernel_smoothers.data.json -TRACE: Interface for skfda.preprocessing.smoothing.kernel_smoothers has changed -LOG: Cached module skfda.preprocessing.smoothing.kernel_smoothers has changed interface -LOG: Writing skfda.representation.grid /home/carlos/git/scikit-fda/skfda/representation/grid.py skfda/representation/grid.meta.json skfda/representation/grid.data.json -TRACE: Interface for skfda.representation.grid has changed -LOG: Cached module skfda.representation.grid has changed interface -LOG: Writing skfda.representation.basis._fdatabasis /home/carlos/git/scikit-fda/skfda/representation/basis/_fdatabasis.py skfda/representation/basis/_fdatabasis.meta.json skfda/representation/basis/_fdatabasis.data.json -TRACE: Interface for skfda.representation.basis._fdatabasis has changed -LOG: Cached module skfda.representation.basis._fdatabasis has changed interface -LOG: Writing skfda.preprocessing.dim_reduction._fpca /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/_fpca.py skfda/preprocessing/dim_reduction/_fpca.meta.json skfda/preprocessing/dim_reduction/_fpca.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction._fpca has changed -LOG: Cached module skfda.preprocessing.dim_reduction._fpca has changed interface -TRACE: Priorities for packaging.requirements: -LOG: Processing SCC singleton (packaging.requirements) as stale due to deps (_typeshed abc array builtins ctypes enum mmap pickle re string typing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/requirements.py (packaging.requirements) -LOG: Writing packaging.requirements /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/packaging/requirements.py packaging/requirements.meta.json packaging/requirements.data.json -TRACE: Interface for packaging.requirements is unchanged -LOG: Cached module packaging.requirements has same interface -TRACE: Priorities for skfda.tests.test_pandas: -LOG: Processing SCC singleton (skfda.tests.test_pandas) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.representation.grid typing unittest unittest.case) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_pandas.py (skfda.tests.test_pandas) -LOG: Writing skfda.tests.test_pandas /home/carlos/git/scikit-fda/skfda/tests/test_pandas.py skfda/tests/test_pandas.meta.json skfda/tests/test_pandas.data.json -TRACE: Interface for skfda.tests.test_pandas is unchanged -LOG: Cached module skfda.tests.test_pandas has same interface -TRACE: Priorities for skfda.tests.test_linear_differential_operator: -LOG: Processing SCC singleton (skfda.tests.test_linear_differential_operator) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda.misc skfda.misc.operators skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._constant skfda.representation.basis._fdatabasis skfda.representation.basis._monomial skfda.representation.evaluator types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_linear_differential_operator.py (skfda.tests.test_linear_differential_operator) -LOG: Writing skfda.tests.test_linear_differential_operator /home/carlos/git/scikit-fda/skfda/tests/test_linear_differential_operator.py skfda/tests/test_linear_differential_operator.meta.json skfda/tests/test_linear_differential_operator.data.json -TRACE: Interface for skfda.tests.test_linear_differential_operator is unchanged -LOG: Cached module skfda.tests.test_linear_differential_operator has same interface -TRACE: Priorities for skfda.tests.test_interpolation: -LOG: Processing SCC singleton (skfda.tests.test_interpolation) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.representation.interpolation types typing typing_extensions unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_interpolation.py (skfda.tests.test_interpolation) -LOG: Writing skfda.tests.test_interpolation /home/carlos/git/scikit-fda/skfda/tests/test_interpolation.py skfda/tests/test_interpolation.meta.json skfda/tests/test_interpolation.data.json -TRACE: Interface for skfda.tests.test_interpolation is unchanged -LOG: Cached module skfda.tests.test_interpolation has same interface -TRACE: Priorities for skfda.tests.test_grid: -LOG: Processing SCC singleton (skfda.tests.test_grid) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._stats skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_grid.py (skfda.tests.test_grid) -LOG: Writing skfda.tests.test_grid /home/carlos/git/scikit-fda/skfda/tests/test_grid.py skfda/tests/test_grid.meta.json skfda/tests/test_grid.data.json -TRACE: Interface for skfda.tests.test_grid is unchanged -LOG: Cached module skfda.tests.test_grid has same interface -TRACE: Priorities for skfda.tests.test_fdatabasis_evaluation: -LOG: Processing SCC singleton (skfda.tests.test_fdatabasis_evaluation) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.function_base numpy.core.multiarray numpy.core.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._constant skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.basis._tensor_basis skfda.representation.basis._vector_basis skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fdatabasis_evaluation.py (skfda.tests.test_fdatabasis_evaluation) -LOG: Writing skfda.tests.test_fdatabasis_evaluation /home/carlos/git/scikit-fda/skfda/tests/test_fdatabasis_evaluation.py skfda/tests/test_fdatabasis_evaluation.meta.json skfda/tests/test_fdatabasis_evaluation.data.json -TRACE: Interface for skfda.tests.test_fdatabasis_evaluation is unchanged -LOG: Cached module skfda.tests.test_fdatabasis_evaluation has same interface -TRACE: Priorities for skfda.tests.test_depth: -LOG: Processing SCC singleton (skfda.tests.test_depth) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.lib numpy.lib.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid typing unittest unittest.case) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_depth.py (skfda.tests.test_depth) -LOG: Writing skfda.tests.test_depth /home/carlos/git/scikit-fda/skfda/tests/test_depth.py skfda/tests/test_depth.meta.json skfda/tests/test_depth.data.json -TRACE: Interface for skfda.tests.test_depth is unchanged -LOG: Cached module skfda.tests.test_depth has same interface -TRACE: Priorities for skfda.preprocessing.feature_construction._per_class_transformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._per_class_transformer) as stale due to deps (__future__ _warnings abc array builtins ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.core numpy.core.shape_base pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing warnings) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py (skfda.preprocessing.feature_construction._per_class_transformer) -LOG: Writing skfda.preprocessing.feature_construction._per_class_transformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_per_class_transformer.py skfda/preprocessing/feature_construction/_per_class_transformer.meta.json skfda/preprocessing/feature_construction/_per_class_transformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._per_class_transformer is unchanged -LOG: Cached module skfda.preprocessing.feature_construction._per_class_transformer has same interface -TRACE: Priorities for skfda.preprocessing.feature_construction._function_transformers: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._function_transformers) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._base skfda.typing._numpy typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py (skfda.preprocessing.feature_construction._function_transformers) -LOG: Writing skfda.preprocessing.feature_construction._function_transformers /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_function_transformers.py skfda/preprocessing/feature_construction/_function_transformers.meta.json skfda/preprocessing/feature_construction/_function_transformers.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._function_transformers is unchanged -LOG: Cached module skfda.preprocessing.feature_construction._function_transformers has same interface -TRACE: Priorities for skfda.preprocessing.feature_construction._fda_feature_union: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._fda_feature_union) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.typing._numpy typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py (skfda.preprocessing.feature_construction._fda_feature_union) -LOG: Writing skfda.preprocessing.feature_construction._fda_feature_union /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_fda_feature_union.py skfda/preprocessing/feature_construction/_fda_feature_union.meta.json skfda/preprocessing/feature_construction/_fda_feature_union.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._fda_feature_union is unchanged -LOG: Cached module skfda.preprocessing.feature_construction._fda_feature_union has same interface -TRACE: Priorities for skfda.preprocessing.feature_construction._evaluation_trasformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._evaluation_trasformer) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.extrapolation skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py (skfda.preprocessing.feature_construction._evaluation_trasformer) -LOG: Writing skfda.preprocessing.feature_construction._evaluation_trasformer /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/_evaluation_trasformer.py skfda/preprocessing/feature_construction/_evaluation_trasformer.meta.json skfda/preprocessing/feature_construction/_evaluation_trasformer.data.json -TRACE: Interface for skfda.preprocessing.feature_construction._evaluation_trasformer is unchanged -LOG: Cached module skfda.preprocessing.feature_construction._evaluation_trasformer has same interface -TRACE: Priorities for skfda.preprocessing.feature_construction._coefficients_transformer: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction._coefficients_transformer) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.typing._numpy typing) -LOG: Parsing 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numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.index_tricks numpy.random numpy.random._generator numpy.random.mtrand operator pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py (skfda.preprocessing.dim_reduction.variable_selection.mrmr) -LOG: Writing skfda.preprocessing.dim_reduction.variable_selection.mrmr /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py skfda/preprocessing/dim_reduction/variable_selection/mrmr.meta.json skfda/preprocessing/dim_reduction/variable_selection/mrmr.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection.mrmr is unchanged -LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection.mrmr has same interface -TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting: -LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting) as stale due to deps (__future__ abc builtins dcor dcor._dcor dcor._utils enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py (skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting) -LOG: Writing skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.meta.json skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting is unchanged -LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection.maxima_hunting has same interface -TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection._rkvs: -LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection._rkvs) as stale due to deps (__future__ _typeshed abc array builtins ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.core.shape_base numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.lib.twodim_base numpy.linalg numpy.linalg.linalg pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py (skfda.preprocessing.dim_reduction.variable_selection._rkvs) -LOG: Writing skfda.preprocessing.dim_reduction.variable_selection._rkvs /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/_rkvs.py skfda/preprocessing/dim_reduction/variable_selection/_rkvs.meta.json skfda/preprocessing/dim_reduction/variable_selection/_rkvs.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection._rkvs is unchanged -LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection._rkvs has same interface -TRACE: 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skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py (skfda.ml.regression._kernel_regression) -LOG: Writing skfda.ml.regression._kernel_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_kernel_regression.py skfda/ml/regression/_kernel_regression.meta.json skfda/ml/regression/_kernel_regression.data.json -TRACE: Interface for skfda.ml.regression._kernel_regression is unchanged -LOG: Cached module skfda.ml.regression._kernel_regression has same interface -TRACE: Priorities for skfda.ml.regression._historical_linear_model: -LOG: Processing SCC singleton (skfda.ml.regression._historical_linear_model) as stale due to deps (__future__ abc builtins enum math numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.core.shape_base numpy.linalg numpy.linalg.linalg skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._finite_element skfda.representation.basis._vector_basis skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py (skfda.ml.regression._historical_linear_model) -LOG: Writing skfda.ml.regression._historical_linear_model /home/carlos/git/scikit-fda/skfda/ml/regression/_historical_linear_model.py skfda/ml/regression/_historical_linear_model.meta.json skfda/ml/regression/_historical_linear_model.data.json -TRACE: Interface for skfda.ml.regression._historical_linear_model is unchanged -LOG: Cached module skfda.ml.regression._historical_linear_model has same interface -TRACE: Priorities for skfda.ml.regression._coefficients: -LOG: Processing SCC singleton (skfda.ml.regression._coefficients) as stale due to deps (__future__ abc array builtins functools mmap multimethod numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.numeric numpy.core.shape_base skfda.misc._math skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.typing._numpy typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py (skfda.ml.regression._coefficients) -LOG: Writing skfda.ml.regression._coefficients /home/carlos/git/scikit-fda/skfda/ml/regression/_coefficients.py skfda/ml/regression/_coefficients.meta.json skfda/ml/regression/_coefficients.data.json -TRACE: Interface for skfda.ml.regression._coefficients is unchanged -LOG: Cached module skfda.ml.regression._coefficients has same interface -TRACE: Priorities for skfda.ml.clustering._kmeans: -LOG: Processing SCC singleton (skfda.ml.clustering._kmeans) as stale due to deps (__future__ _typeshed _warnings abc array builtins contextlib ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.einsumfunc numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.random numpy.random._generator numpy.random.mtrand pickle skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions warnings) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py (skfda.ml.clustering._kmeans) -LOG: Writing skfda.ml.clustering._kmeans /home/carlos/git/scikit-fda/skfda/ml/clustering/_kmeans.py skfda/ml/clustering/_kmeans.meta.json skfda/ml/clustering/_kmeans.data.json -TRACE: Interface for skfda.ml.clustering._kmeans is unchanged -LOG: Cached module skfda.ml.clustering._kmeans has same interface -TRACE: Priorities for skfda.ml.clustering._hierarchical: -LOG: Processing SCC singleton (skfda.ml.clustering._hierarchical) as stale due to deps (__future__ abc builtins enum numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._parse skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py (skfda.ml.clustering._hierarchical) -LOG: Writing skfda.ml.clustering._hierarchical /home/carlos/git/scikit-fda/skfda/ml/clustering/_hierarchical.py skfda/ml/clustering/_hierarchical.meta.json skfda/ml/clustering/_hierarchical.data.json -TRACE: Interface for skfda.ml.clustering._hierarchical is unchanged -LOG: Cached module skfda.ml.clustering._hierarchical has same interface -TRACE: Priorities for skfda.ml.classification._parameterized_functional_qda: -LOG: Processing SCC singleton (skfda.ml.classification._parameterized_functional_qda) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.arraysetops numpy.lib.twodim_base numpy.linalg numpy.linalg.linalg skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._numpy typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py (skfda.ml.classification._parameterized_functional_qda) -LOG: Writing skfda.ml.classification._parameterized_functional_qda /home/carlos/git/scikit-fda/skfda/ml/classification/_parameterized_functional_qda.py skfda/ml/classification/_parameterized_functional_qda.meta.json skfda/ml/classification/_parameterized_functional_qda.data.json -TRACE: Interface for skfda.ml.classification._parameterized_functional_qda is unchanged -LOG: Cached module skfda.ml.classification._parameterized_functional_qda has same interface -TRACE: Priorities for skfda.ml.classification._logistic_regression: -LOG: Processing SCC singleton (skfda.ml.classification._logistic_regression) as stale due to deps (__future__ _typeshed abc builtins contextlib numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py (skfda.ml.classification._logistic_regression) -LOG: Writing skfda.ml.classification._logistic_regression /home/carlos/git/scikit-fda/skfda/ml/classification/_logistic_regression.py skfda/ml/classification/_logistic_regression.meta.json skfda/ml/classification/_logistic_regression.data.json -TRACE: Interface for skfda.ml.classification._logistic_regression is unchanged -LOG: Cached module skfda.ml.classification._logistic_regression has same interface -TRACE: Priorities for skfda.ml.classification._centroid_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._centroid_classifiers) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.exploratory.stats skfda.exploratory.stats._stats skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py (skfda.ml.classification._centroid_classifiers) -LOG: Writing skfda.ml.classification._centroid_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_centroid_classifiers.py skfda/ml/classification/_centroid_classifiers.meta.json skfda/ml/classification/_centroid_classifiers.data.json -TRACE: Interface for skfda.ml.classification._centroid_classifiers is unchanged -LOG: Cached module skfda.ml.classification._centroid_classifiers has same interface -TRACE: Priorities for skfda.ml._neighbors_base: -LOG: Processing SCC singleton (skfda.ml._neighbors_base) as inherently stale with stale deps (__future__ builtins copy numpy skfda._utils._sklearn_adapter skfda.misc.metrics skfda.misc.metrics._utils skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.ml._neighbors_base /home/carlos/git/scikit-fda/skfda/ml/_neighbors_base.py skfda/ml/_neighbors_base.meta.json skfda/ml/_neighbors_base.data.json -TRACE: Interface for skfda.ml._neighbors_base has changed -LOG: Cached module skfda.ml._neighbors_base has changed interface -TRACE: Priorities for skfda.inference.hotelling._hotelling: -LOG: Processing SCC singleton (skfda.inference.hotelling._hotelling) as stale due to deps (__future__ _typeshed abc array builtins ctypes itertools mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.linalg numpy.linalg.linalg numpy.random numpy.random._generator numpy.random.mtrand pickle skfda.misc skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.evaluator skfda.typing._base skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/hotelling/_hotelling.py (skfda.inference.hotelling._hotelling) -LOG: Writing skfda.inference.hotelling._hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/_hotelling.py skfda/inference/hotelling/_hotelling.meta.json skfda/inference/hotelling/_hotelling.data.json -TRACE: Interface for skfda.inference.hotelling._hotelling is unchanged -LOG: Cached module skfda.inference.hotelling._hotelling has same interface -TRACE: Priorities for skfda.exploratory.visualization.fpca: -LOG: Processing SCC singleton (skfda.exploratory.visualization.fpca) as inherently stale with stale deps (__future__ builtins skfda.exploratory.visualization._baseplot skfda.exploratory.visualization.representation skfda.representation typing warnings) -LOG: Writing skfda.exploratory.visualization.fpca /home/carlos/git/scikit-fda/skfda/exploratory/visualization/fpca.py skfda/exploratory/visualization/fpca.meta.json skfda/exploratory/visualization/fpca.data.json -TRACE: Interface for skfda.exploratory.visualization.fpca has changed -LOG: Cached module skfda.exploratory.visualization.fpca has changed interface -TRACE: Priorities for skfda.exploratory.visualization.clustering: -LOG: Processing SCC singleton (skfda.exploratory.visualization.clustering) as stale due to deps (__future__ abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.function_base skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.misc skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py (skfda.exploratory.visualization.clustering) -LOG: Writing skfda.exploratory.visualization.clustering /home/carlos/git/scikit-fda/skfda/exploratory/visualization/clustering.py skfda/exploratory/visualization/clustering.meta.json skfda/exploratory/visualization/clustering.data.json -TRACE: Interface for skfda.exploratory.visualization.clustering is unchanged -LOG: Cached module skfda.exploratory.visualization.clustering has same interface -TRACE: Priorities for skfda.exploratory.visualization._parametric_plot: -LOG: Processing SCC singleton (skfda.exploratory.visualization._parametric_plot) as inherently stale with stale deps (__future__ builtins numpy skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._utils skfda.exploratory.visualization.representation skfda.representation typing) -LOG: Writing skfda.exploratory.visualization._parametric_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_parametric_plot.py skfda/exploratory/visualization/_parametric_plot.meta.json skfda/exploratory/visualization/_parametric_plot.data.json -TRACE: Interface for skfda.exploratory.visualization._parametric_plot has changed -LOG: Cached module skfda.exploratory.visualization._parametric_plot has changed interface -TRACE: Priorities for skfda.exploratory.visualization._multiple_display: -LOG: Processing SCC singleton 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skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.visualization._ddplot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_ddplot.py skfda/exploratory/visualization/_ddplot.meta.json skfda/exploratory/visualization/_ddplot.data.json -TRACE: Interface for skfda.exploratory.visualization._ddplot has changed -LOG: Cached module skfda.exploratory.visualization._ddplot has changed interface -TRACE: Priorities for skfda.exploratory.outliers._outliergram: -LOG: Processing SCC singleton (skfda.exploratory.outliers._outliergram) as inherently stale with stale deps (__future__ builtins numpy skfda._utils._sklearn_adapter skfda.exploratory.depth._depth skfda.exploratory.stats skfda.representation skfda.typing._numpy) -LOG: Writing skfda.exploratory.outliers._outliergram /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_outliergram.py skfda/exploratory/outliers/_outliergram.meta.json skfda/exploratory/outliers/_outliergram.data.json -TRACE: Interface for 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typing_extensions warnings) -LOG: Writing skfda.datasets._real_datasets /home/carlos/git/scikit-fda/skfda/datasets/_real_datasets.py skfda/datasets/_real_datasets.meta.json skfda/datasets/_real_datasets.data.json -TRACE: Interface for skfda.datasets._real_datasets has changed -LOG: Cached module skfda.datasets._real_datasets has changed interface -TRACE: Priorities for _pytest.scope: _pytest.outcomes:20 -TRACE: Priorities for _pytest.compat: _pytest.outcomes:20 _pytest._io:30 -TRACE: Priorities for _pytest._io.terminalwriter: _pytest.compat:5 _pytest.config.exceptions:20 _pytest.config:30 -TRACE: Priorities for _pytest.config.exceptions: _pytest.compat:5 -TRACE: Priorities for _pytest.warning_types: _pytest.compat:5 -TRACE: Priorities for _pytest._io: _pytest._io.terminalwriter:5 -TRACE: Priorities for _pytest.deprecated: _pytest.warning_types:5 -TRACE: Priorities for _pytest.hookspec: _pytest.deprecated:5 _pytest._code.code:25 _pytest.config:25 _pytest.config.argparsing:25 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_pytest.outcomes:5 _pytest.pytester:5 _pytest.python:5 _pytest.python_api:5 _pytest.recwarn:5 _pytest.reports:5 _pytest.runner:5 _pytest.tmpdir:5 _pytest.warning_types:5 -TRACE: Priorities for _pytest.warnings: pytest:10 _pytest.config:5 _pytest.main:5 _pytest.nodes:5 _pytest.terminal:5 _pytest.assertion:30 _pytest.config.compat:30 _pytest.mark:30 _pytest.mark.structures:30 _pytest.warning_types:30 -TRACE: Priorities for _pytest.helpconfig: pytest:10 _pytest.config:5 _pytest.config.argparsing:5 _pytest.config.exceptions:30 -LOG: Processing SCC of size 44 (_pytest.scope _pytest.compat _pytest._io.terminalwriter _pytest.config.exceptions _pytest.warning_types _pytest._io _pytest.deprecated _pytest.hookspec _pytest.outcomes _pytest.config.argparsing _pytest.pathlib _pytest.config.findpaths _pytest._code.code _pytest._code _pytest.python_api _pytest.config _pytest.mark.structures _pytest.assertion.util _pytest.nodes _pytest.mark _pytest.config.compat _pytest.assertion.truncate _pytest.reports _pytest.fixtures _pytest.pytester_assertions _pytest.terminal _pytest.runner _pytest.recwarn _pytest.monkeypatch _pytest.debugging _pytest.capture _pytest.tmpdir _pytest.main _pytest.assertion.rewrite _pytest.python _pytest.pytester _pytest.logging _pytest.cacheprovider _pytest.assertion _pytest.legacypath pytest.collect pytest _pytest.warnings _pytest.helpconfig) as stale due to deps (_ast _collections_abc _decimal _typeshed _warnings _weakref abc array ast builtins collections collections.abc contextlib copy ctypes datetime decimal enum errno functools genericpath importlib importlib.abc importlib.machinery importlib.metadata inspect io itertools json json.decoder json.encoder logging math mmap numbers numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.numeric os os.path pathlib pickle platform posixpath re struct subprocess sys textwrap time traceback types typing typing_extensions uuid warnings weakref) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/scope.py (_pytest.scope) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/compat.py (_pytest.compat) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/terminalwriter.py (_pytest._io.terminalwriter) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/exceptions.py (_pytest.config.exceptions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warning_types.py (_pytest.warning_types) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/__init__.py (_pytest._io) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/deprecated.py (_pytest.deprecated) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/hookspec.py (_pytest.hookspec) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/outcomes.py (_pytest.outcomes) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/argparsing.py (_pytest.config.argparsing) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pathlib.py (_pytest.pathlib) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/findpaths.py (_pytest.config.findpaths) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/code.py (_pytest._code.code) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/__init__.py (_pytest._code) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python_api.py (_pytest.python_api) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/__init__.py (_pytest.config) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/structures.py (_pytest.mark.structures) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/util.py (_pytest.assertion.util) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/nodes.py (_pytest.nodes) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/__init__.py (_pytest.mark) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/compat.py (_pytest.config.compat) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/truncate.py (_pytest.assertion.truncate) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/reports.py (_pytest.reports) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/fixtures.py (_pytest.fixtures) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester_assertions.py (_pytest.pytester_assertions) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/terminal.py (_pytest.terminal) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/runner.py (_pytest.runner) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/recwarn.py (_pytest.recwarn) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/monkeypatch.py (_pytest.monkeypatch) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/debugging.py (_pytest.debugging) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/capture.py (_pytest.capture) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/tmpdir.py (_pytest.tmpdir) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/main.py (_pytest.main) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/rewrite.py (_pytest.assertion.rewrite) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python.py (_pytest.python) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester.py (_pytest.pytester) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/logging.py (_pytest.logging) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/cacheprovider.py (_pytest.cacheprovider) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/__init__.py (_pytest.assertion) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/legacypath.py (_pytest.legacypath) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/collect.py (pytest.collect) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/__init__.py (pytest) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warnings.py (_pytest.warnings) -LOG: Parsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/helpconfig.py (_pytest.helpconfig) -LOG: Writing _pytest.scope /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/scope.py _pytest/scope.meta.json _pytest/scope.data.json -TRACE: Interface for _pytest.scope is unchanged -LOG: Cached module _pytest.scope has same interface -LOG: Writing _pytest.compat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/compat.py _pytest/compat.meta.json _pytest/compat.data.json -TRACE: Interface for _pytest.compat is unchanged -LOG: Cached module _pytest.compat has same interface -LOG: Writing _pytest._io.terminalwriter /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/terminalwriter.py _pytest/_io/terminalwriter.meta.json _pytest/_io/terminalwriter.data.json -TRACE: Interface for _pytest._io.terminalwriter is unchanged -LOG: Cached module _pytest._io.terminalwriter has same interface -LOG: Writing _pytest.config.exceptions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/exceptions.py _pytest/config/exceptions.meta.json _pytest/config/exceptions.data.json -TRACE: Interface for _pytest.config.exceptions is unchanged -LOG: Cached module _pytest.config.exceptions has same interface -LOG: Writing _pytest.warning_types /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warning_types.py _pytest/warning_types.meta.json _pytest/warning_types.data.json -TRACE: Interface for _pytest.warning_types is unchanged -LOG: Cached module _pytest.warning_types has same interface -LOG: Writing _pytest._io /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_io/__init__.py _pytest/_io/__init__.meta.json _pytest/_io/__init__.data.json -TRACE: Interface for _pytest._io is unchanged -LOG: Cached module _pytest._io has same interface -LOG: Writing _pytest.deprecated /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/deprecated.py _pytest/deprecated.meta.json _pytest/deprecated.data.json -TRACE: Interface for _pytest.deprecated is unchanged -LOG: Cached module _pytest.deprecated has same interface -LOG: Writing _pytest.hookspec /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/hookspec.py _pytest/hookspec.meta.json _pytest/hookspec.data.json -TRACE: Interface for _pytest.hookspec is unchanged -LOG: Cached module _pytest.hookspec has same interface -LOG: Writing _pytest.outcomes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/outcomes.py _pytest/outcomes.meta.json _pytest/outcomes.data.json -TRACE: Interface for _pytest.outcomes is unchanged -LOG: Cached module _pytest.outcomes has same interface -LOG: Writing _pytest.config.argparsing /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/argparsing.py _pytest/config/argparsing.meta.json _pytest/config/argparsing.data.json -TRACE: Interface for _pytest.config.argparsing is unchanged -LOG: Cached module _pytest.config.argparsing has same interface -LOG: Writing _pytest.pathlib /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pathlib.py _pytest/pathlib.meta.json _pytest/pathlib.data.json -TRACE: Interface for _pytest.pathlib is unchanged -LOG: Cached module _pytest.pathlib has same interface -LOG: Writing _pytest.config.findpaths /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/findpaths.py _pytest/config/findpaths.meta.json _pytest/config/findpaths.data.json -TRACE: Interface for _pytest.config.findpaths is unchanged -LOG: Cached module _pytest.config.findpaths has same interface -LOG: Writing _pytest._code.code /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/code.py _pytest/_code/code.meta.json _pytest/_code/code.data.json -TRACE: Interface for _pytest._code.code is unchanged -LOG: Cached module _pytest._code.code has same interface -LOG: Writing _pytest._code /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/_code/__init__.py _pytest/_code/__init__.meta.json _pytest/_code/__init__.data.json -TRACE: Interface for _pytest._code is unchanged -LOG: Cached module _pytest._code has same interface -LOG: Writing _pytest.python_api /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python_api.py _pytest/python_api.meta.json _pytest/python_api.data.json -TRACE: Interface for _pytest.python_api is unchanged -LOG: Cached module _pytest.python_api has same interface -LOG: Writing _pytest.config /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/__init__.py _pytest/config/__init__.meta.json _pytest/config/__init__.data.json -TRACE: Interface for _pytest.config is unchanged -LOG: Cached module _pytest.config has same interface -LOG: Writing _pytest.mark.structures /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/structures.py _pytest/mark/structures.meta.json _pytest/mark/structures.data.json -TRACE: Interface for _pytest.mark.structures is unchanged -LOG: Cached module _pytest.mark.structures has same interface -LOG: Writing _pytest.assertion.util /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/util.py _pytest/assertion/util.meta.json _pytest/assertion/util.data.json -TRACE: Interface for _pytest.assertion.util is unchanged -LOG: Cached module _pytest.assertion.util has same interface -LOG: Writing _pytest.nodes /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/nodes.py _pytest/nodes.meta.json _pytest/nodes.data.json -TRACE: Interface for _pytest.nodes is unchanged -LOG: Cached module _pytest.nodes has same interface -LOG: Writing _pytest.mark /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/mark/__init__.py _pytest/mark/__init__.meta.json _pytest/mark/__init__.data.json -TRACE: Interface for _pytest.mark is unchanged -LOG: Cached module _pytest.mark has same interface -LOG: Writing _pytest.config.compat /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/config/compat.py _pytest/config/compat.meta.json _pytest/config/compat.data.json -TRACE: Interface for _pytest.config.compat is unchanged -LOG: Cached module _pytest.config.compat has same interface -LOG: Writing _pytest.assertion.truncate /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/truncate.py _pytest/assertion/truncate.meta.json _pytest/assertion/truncate.data.json -TRACE: Interface for _pytest.assertion.truncate is unchanged -LOG: Cached module _pytest.assertion.truncate has same interface -LOG: Writing _pytest.reports /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/reports.py _pytest/reports.meta.json _pytest/reports.data.json -TRACE: Interface for _pytest.reports is unchanged -LOG: Cached module _pytest.reports has same interface -LOG: Writing _pytest.fixtures /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/fixtures.py _pytest/fixtures.meta.json _pytest/fixtures.data.json -TRACE: Interface for _pytest.fixtures is unchanged -LOG: Cached module _pytest.fixtures has same interface -LOG: Writing _pytest.pytester_assertions /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester_assertions.py _pytest/pytester_assertions.meta.json _pytest/pytester_assertions.data.json -TRACE: Interface for _pytest.pytester_assertions is unchanged -LOG: Cached module _pytest.pytester_assertions has same interface -LOG: Writing _pytest.terminal /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/terminal.py _pytest/terminal.meta.json _pytest/terminal.data.json -TRACE: Interface for _pytest.terminal is unchanged -LOG: Cached module _pytest.terminal has same interface -LOG: Writing _pytest.runner /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/runner.py _pytest/runner.meta.json _pytest/runner.data.json -TRACE: Interface for _pytest.runner is unchanged -LOG: Cached module _pytest.runner has same interface -LOG: Writing _pytest.recwarn /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/recwarn.py _pytest/recwarn.meta.json _pytest/recwarn.data.json -TRACE: Interface for _pytest.recwarn is unchanged -LOG: Cached module _pytest.recwarn has same interface -LOG: Writing _pytest.monkeypatch /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/monkeypatch.py _pytest/monkeypatch.meta.json _pytest/monkeypatch.data.json -TRACE: Interface for _pytest.monkeypatch is unchanged -LOG: Cached module _pytest.monkeypatch has same interface -LOG: Writing _pytest.debugging /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/debugging.py _pytest/debugging.meta.json _pytest/debugging.data.json -TRACE: Interface for _pytest.debugging is unchanged -LOG: Cached module _pytest.debugging has same interface -LOG: Writing _pytest.capture /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/capture.py _pytest/capture.meta.json _pytest/capture.data.json -TRACE: Interface for _pytest.capture is unchanged -LOG: Cached module _pytest.capture has same interface -LOG: Writing _pytest.tmpdir /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/tmpdir.py _pytest/tmpdir.meta.json _pytest/tmpdir.data.json -TRACE: Interface for _pytest.tmpdir is unchanged -LOG: Cached module _pytest.tmpdir has same interface -LOG: Writing _pytest.main /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/main.py _pytest/main.meta.json _pytest/main.data.json -TRACE: Interface for _pytest.main is unchanged -LOG: Cached module _pytest.main has same interface -LOG: Writing _pytest.assertion.rewrite /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/rewrite.py _pytest/assertion/rewrite.meta.json _pytest/assertion/rewrite.data.json -TRACE: Interface for _pytest.assertion.rewrite is unchanged -LOG: Cached module _pytest.assertion.rewrite has same interface -LOG: Writing _pytest.python /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/python.py _pytest/python.meta.json _pytest/python.data.json -TRACE: Interface for _pytest.python is unchanged -LOG: Cached module _pytest.python has same interface -LOG: Writing _pytest.pytester /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/pytester.py _pytest/pytester.meta.json _pytest/pytester.data.json -TRACE: Interface for _pytest.pytester is unchanged -LOG: Cached module _pytest.pytester has same interface -LOG: Writing _pytest.logging /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/logging.py _pytest/logging.meta.json _pytest/logging.data.json -TRACE: Interface for _pytest.logging is unchanged -LOG: Cached module _pytest.logging has same interface -LOG: Writing _pytest.cacheprovider /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/cacheprovider.py _pytest/cacheprovider.meta.json _pytest/cacheprovider.data.json -TRACE: Interface for _pytest.cacheprovider is unchanged -LOG: Cached module _pytest.cacheprovider has same interface -LOG: Writing _pytest.assertion /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/assertion/__init__.py _pytest/assertion/__init__.meta.json _pytest/assertion/__init__.data.json -TRACE: Interface for _pytest.assertion is unchanged -LOG: Cached module _pytest.assertion has same interface -LOG: Writing _pytest.legacypath /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/legacypath.py _pytest/legacypath.meta.json _pytest/legacypath.data.json -TRACE: Interface for _pytest.legacypath is unchanged -LOG: Cached module _pytest.legacypath has same interface -LOG: Writing pytest.collect /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/collect.py pytest/collect.meta.json pytest/collect.data.json -TRACE: Interface for pytest.collect is unchanged -LOG: Cached module pytest.collect has same interface -LOG: Writing pytest /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/pytest/__init__.py pytest/__init__.meta.json pytest/__init__.data.json -TRACE: Interface for pytest is unchanged -LOG: Cached module pytest has same interface -LOG: Writing _pytest.warnings /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/warnings.py _pytest/warnings.meta.json _pytest/warnings.data.json -TRACE: Interface for _pytest.warnings is unchanged -LOG: Cached module _pytest.warnings has same interface -LOG: Writing _pytest.helpconfig /home/carlos/Programas/Utilidades/Lenguajes/miniconda3/envs/fda38/lib/python3.8/site-packages/_pytest/helpconfig.py _pytest/helpconfig.meta.json _pytest/helpconfig.data.json -TRACE: Interface for _pytest.helpconfig is unchanged -LOG: Cached module _pytest.helpconfig has same interface -TRACE: Priorities for skfda.preprocessing.feature_construction: -LOG: Processing SCC singleton (skfda.preprocessing.feature_construction) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py (skfda.preprocessing.feature_construction) -LOG: Writing skfda.preprocessing.feature_construction /home/carlos/git/scikit-fda/skfda/preprocessing/feature_construction/__init__.py skfda/preprocessing/feature_construction/__init__.meta.json skfda/preprocessing/feature_construction/__init__.data.json -TRACE: Interface for skfda.preprocessing.feature_construction is unchanged -LOG: Cached module skfda.preprocessing.feature_construction has same interface -TRACE: Priorities for skfda.ml.regression._neighbors_regression: -LOG: Processing SCC singleton (skfda.ml.regression._neighbors_regression) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py (skfda.ml.regression._neighbors_regression) -LOG: Writing skfda.ml.regression._neighbors_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_neighbors_regression.py skfda/ml/regression/_neighbors_regression.meta.json skfda/ml/regression/_neighbors_regression.data.json -TRACE: Interface for skfda.ml.regression._neighbors_regression is unchanged -LOG: Cached module skfda.ml.regression._neighbors_regression has same interface -TRACE: Priorities for skfda.ml.regression._linear_regression: -LOG: Processing SCC singleton (skfda.ml.regression._linear_regression) as stale due to deps (__future__ _warnings abc array builtins enum functools itertools mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.shape_base skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.lstsq skfda.misc.regularization skfda.misc.regularization._regularization skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.evaluator skfda.typing._numpy typing warnings) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py (skfda.ml.regression._linear_regression) -LOG: Writing skfda.ml.regression._linear_regression /home/carlos/git/scikit-fda/skfda/ml/regression/_linear_regression.py skfda/ml/regression/_linear_regression.meta.json skfda/ml/regression/_linear_regression.data.json -TRACE: Interface for skfda.ml.regression._linear_regression is unchanged -LOG: Cached module skfda.ml.regression._linear_regression has same interface -TRACE: Priorities for skfda.ml.clustering._neighbors_clustering: -LOG: Processing SCC singleton (skfda.ml.clustering._neighbors_clustering) as stale due to deps (__future__ abc builtins numpy skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py (skfda.ml.clustering._neighbors_clustering) -LOG: Writing skfda.ml.clustering._neighbors_clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/_neighbors_clustering.py skfda/ml/clustering/_neighbors_clustering.meta.json skfda/ml/clustering/_neighbors_clustering.data.json -TRACE: Interface for skfda.ml.clustering._neighbors_clustering is unchanged -LOG: Cached module skfda.ml.clustering._neighbors_clustering has same interface -TRACE: Priorities for skfda.ml.classification._neighbors_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._neighbors_classifiers) as stale due to deps (__future__ abc builtins numpy skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.typing skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py (skfda.ml.classification._neighbors_classifiers) -LOG: Writing skfda.ml.classification._neighbors_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_neighbors_classifiers.py skfda/ml/classification/_neighbors_classifiers.meta.json skfda/ml/classification/_neighbors_classifiers.data.json -TRACE: Interface for skfda.ml.classification._neighbors_classifiers is unchanged -LOG: Cached module skfda.ml.classification._neighbors_classifiers has same interface -TRACE: Priorities for skfda.inference.hotelling: -LOG: Processing SCC singleton (skfda.inference.hotelling) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/hotelling/__init__.py (skfda.inference.hotelling) -LOG: Writing skfda.inference.hotelling /home/carlos/git/scikit-fda/skfda/inference/hotelling/__init__.py skfda/inference/hotelling/__init__.meta.json skfda/inference/hotelling/__init__.data.json -TRACE: Interface for skfda.inference.hotelling is unchanged -LOG: Cached module skfda.inference.hotelling has same interface -TRACE: Priorities for skfda.exploratory.outliers.neighbors_outlier: -LOG: Processing SCC singleton (skfda.exploratory.outliers.neighbors_outlier) as inherently stale with stale deps (__future__ builtins skfda.misc.metrics skfda.ml._neighbors_base skfda.representation skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Writing skfda.exploratory.outliers.neighbors_outlier /home/carlos/git/scikit-fda/skfda/exploratory/outliers/neighbors_outlier.py skfda/exploratory/outliers/neighbors_outlier.meta.json skfda/exploratory/outliers/neighbors_outlier.data.json -TRACE: Interface for skfda.exploratory.outliers.neighbors_outlier has changed -LOG: Cached module skfda.exploratory.outliers.neighbors_outlier has changed interface -TRACE: Priorities for skfda.datasets: skfda.datasets._samples_generators:25 -TRACE: Priorities for skfda.misc.covariances: skfda.datasets:20 -TRACE: Priorities for skfda.datasets._samples_generators: skfda.misc.covariances:5 -LOG: Processing SCC of size 3 (skfda.datasets skfda.misc.covariances skfda.datasets._samples_generators) as inherently stale with stale deps (__future__ abc builtins itertools numpy skfda._utils skfda.datasets._real_datasets skfda.exploratory.visualization._utils skfda.misc.validation skfda.representation skfda.representation.interpolation skfda.typing._base skfda.typing._numpy typing) -LOG: Writing skfda.datasets /home/carlos/git/scikit-fda/skfda/datasets/__init__.py skfda/datasets/__init__.meta.json skfda/datasets/__init__.data.json -TRACE: Interface for skfda.datasets has changed -LOG: Cached module skfda.datasets has changed interface -LOG: Writing skfda.misc.covariances /home/carlos/git/scikit-fda/skfda/misc/covariances.py skfda/misc/covariances.meta.json skfda/misc/covariances.data.json -TRACE: Interface for skfda.misc.covariances has changed -LOG: Cached module skfda.misc.covariances has changed interface -LOG: Writing skfda.datasets._samples_generators /home/carlos/git/scikit-fda/skfda/datasets/_samples_generators.py skfda/datasets/_samples_generators.meta.json skfda/datasets/_samples_generators.data.json -TRACE: Interface for skfda.datasets._samples_generators has changed -LOG: Cached module skfda.datasets._samples_generators has changed interface -TRACE: Priorities for skfda.tests.test_ufunc_numpy: -LOG: Processing SCC singleton (skfda.tests.test_ufunc_numpy) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.lib numpy.lib.function_base skfda skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid typing unittest unittest.case) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_ufunc_numpy.py (skfda.tests.test_ufunc_numpy) -LOG: Writing skfda.tests.test_ufunc_numpy /home/carlos/git/scikit-fda/skfda/tests/test_ufunc_numpy.py skfda/tests/test_ufunc_numpy.meta.json skfda/tests/test_ufunc_numpy.data.json -TRACE: Interface for skfda.tests.test_ufunc_numpy is unchanged -LOG: Cached module skfda.tests.test_ufunc_numpy has same interface -TRACE: Priorities for skfda.tests.test_stats: -LOG: Processing SCC singleton (skfda.tests.test_stats) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda.datasets skfda.datasets._real_datasets skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._stats skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing skfda.typing._metric typing unittest) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_stats.py (skfda.tests.test_stats) -LOG: Writing skfda.tests.test_stats /home/carlos/git/scikit-fda/skfda/tests/test_stats.py skfda/tests/test_stats.meta.json skfda/tests/test_stats.data.json -TRACE: Interface for skfda.tests.test_stats is unchanged -LOG: Cached module skfda.tests.test_stats has same interface -TRACE: Priorities for skfda.tests.test_smoothing: -LOG: Processing SCC singleton (skfda.tests.test_smoothing) as inherently stale with stale deps (builtins numpy skfda skfda._utils skfda.datasets skfda.misc.hat_matrix skfda.misc.operators skfda.misc.regularization skfda.preprocessing skfda.preprocessing.smoothing skfda.preprocessing.smoothing.validation skfda.representation.basis skfda.representation.grid typing typing_extensions unittest) -LOG: Writing skfda.tests.test_smoothing /home/carlos/git/scikit-fda/skfda/tests/test_smoothing.py skfda/tests/test_smoothing.meta.json skfda/tests/test_smoothing.data.json -TRACE: Interface for skfda.tests.test_smoothing has changed -LOG: Cached module skfda.tests.test_smoothing has changed interface -TRACE: Priorities for skfda.tests.test_registration: -LOG: Processing SCC singleton (skfda.tests.test_registration) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda._utils._warping skfda.datasets skfda.datasets._samples_generators skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._stats skfda.preprocessing skfda.preprocessing.registration skfda.preprocessing.registration._landmark_registration skfda.preprocessing.registration._lstsq_shift_registration skfda.preprocessing.registration.base skfda.preprocessing.registration.validation skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid skfda.representation.interpolation types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_registration.py (skfda.tests.test_registration) -LOG: Writing skfda.tests.test_registration /home/carlos/git/scikit-fda/skfda/tests/test_registration.py skfda/tests/test_registration.meta.json skfda/tests/test_registration.data.json -TRACE: Interface for skfda.tests.test_registration is unchanged -LOG: Cached module skfda.tests.test_registration has same interface -TRACE: Priorities for skfda.tests.test_pandas_fdatagrid: -LOG: Processing SCC singleton (skfda.tests.test_pandas_fdatagrid) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.numeric skfda skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatagrid.py (skfda.tests.test_pandas_fdatagrid) -LOG: Writing skfda.tests.test_pandas_fdatagrid /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatagrid.py skfda/tests/test_pandas_fdatagrid.meta.json skfda/tests/test_pandas_fdatagrid.data.json -TRACE: Interface for skfda.tests.test_pandas_fdatagrid is unchanged -LOG: Cached module skfda.tests.test_pandas_fdatagrid has same interface -TRACE: Priorities for skfda.tests.test_metrics: -LOG: Processing SCC singleton (skfda.tests.test_metrics) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._lp_norms skfda.misc.metrics._utils skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_metrics.py (skfda.tests.test_metrics) -LOG: Writing skfda.tests.test_metrics /home/carlos/git/scikit-fda/skfda/tests/test_metrics.py skfda/tests/test_metrics.meta.json skfda/tests/test_metrics.data.json -TRACE: Interface for skfda.tests.test_metrics is unchanged -LOG: Cached module skfda.tests.test_metrics has same interface -TRACE: Priorities for skfda.tests.test_math: -LOG: Processing SCC singleton (skfda.tests.test_math) as stale due to deps (abc builtins enum multimethod numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._utils skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc._math skfda.misc.covariances skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._monomial skfda.representation.basis._tensor_basis skfda.representation.basis._vector_basis skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_math.py (skfda.tests.test_math) -LOG: Writing skfda.tests.test_math /home/carlos/git/scikit-fda/skfda/tests/test_math.py skfda/tests/test_math.meta.json skfda/tests/test_math.data.json -TRACE: Interface for skfda.tests.test_math is unchanged -LOG: Cached module skfda.tests.test_math has same interface -TRACE: Priorities for skfda.tests.test_hotelling: -LOG: Processing SCC singleton (skfda.tests.test_hotelling) as stale due to deps (_typeshed abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.random numpy.random._generator numpy.random.mtrand skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_hotelling.py (skfda.tests.test_hotelling) -LOG: Writing skfda.tests.test_hotelling /home/carlos/git/scikit-fda/skfda/tests/test_hotelling.py skfda/tests/test_hotelling.meta.json skfda/tests/test_hotelling.data.json -TRACE: Interface for skfda.tests.test_hotelling is unchanged -LOG: Cached module skfda.tests.test_hotelling has same interface -TRACE: Priorities for skfda.tests.test_functional_transformers: -LOG: Processing SCC singleton (skfda.tests.test_functional_transformers) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.datasets skfda.datasets._real_datasets skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._functional_transformers skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.grid types typing unittest unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_functional_transformers.py (skfda.tests.test_functional_transformers) -LOG: Writing skfda.tests.test_functional_transformers /home/carlos/git/scikit-fda/skfda/tests/test_functional_transformers.py skfda/tests/test_functional_transformers.meta.json skfda/tests/test_functional_transformers.data.json -TRACE: Interface for skfda.tests.test_functional_transformers is unchanged -LOG: Cached module skfda.tests.test_functional_transformers has same interface -TRACE: Priorities for skfda.tests.test_fpca: -LOG: Processing SCC singleton (skfda.tests.test_fpca) as stale due to deps (_typeshed abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.random numpy.random.bit_generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._real_datasets skfda.misc skfda.misc.operators skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.misc.regularization skfda.misc.regularization._regularization skfda.preprocessing skfda.preprocessing.dim_reduction skfda.preprocessing.dim_reduction._fpca skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fpca.py (skfda.tests.test_fpca) -LOG: Writing skfda.tests.test_fpca /home/carlos/git/scikit-fda/skfda/tests/test_fpca.py skfda/tests/test_fpca.meta.json skfda/tests/test_fpca.data.json -TRACE: Interface for skfda.tests.test_fpca is unchanged -LOG: Cached module skfda.tests.test_fpca has same interface -TRACE: Priorities for skfda.tests.test_fda_feature_union: -LOG: Processing SCC singleton (skfda.tests.test_fda_feature_union) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._real_datasets skfda.misc skfda.misc.hat_matrix skfda.misc.operators skfda.misc.operators._operators skfda.misc.operators._srvf skfda.preprocessing skfda.preprocessing.smoothing skfda.preprocessing.smoothing._kernel_smoothers skfda.preprocessing.smoothing._linear skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fda_feature_union.py (skfda.tests.test_fda_feature_union) -LOG: Writing skfda.tests.test_fda_feature_union /home/carlos/git/scikit-fda/skfda/tests/test_fda_feature_union.py skfda/tests/test_fda_feature_union.meta.json skfda/tests/test_fda_feature_union.data.json -TRACE: Interface for skfda.tests.test_fda_feature_union is unchanged -LOG: Cached module skfda.tests.test_fda_feature_union has same interface -TRACE: Priorities for skfda.tests.test_extrapolation: -LOG: Processing SCC singleton (skfda.tests.test_extrapolation) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._nested_sequence numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.datasets skfda.datasets._samples_generators skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.evaluator skfda.representation.extrapolation skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_extrapolation.py (skfda.tests.test_extrapolation) -LOG: Writing skfda.tests.test_extrapolation /home/carlos/git/scikit-fda/skfda/tests/test_extrapolation.py skfda/tests/test_extrapolation.meta.json skfda/tests/test_extrapolation.data.json -TRACE: Interface for skfda.tests.test_extrapolation is unchanged -LOG: Cached module skfda.tests.test_extrapolation has same interface -TRACE: Priorities for skfda.tests.test_elastic: -LOG: Processing SCC singleton (skfda.tests.test_elastic) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.function_base numpy.core.multiarray numpy.lib numpy.lib.twodim_base numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda._utils._warping skfda.datasets skfda.datasets._samples_generators skfda.exploratory skfda.exploratory.stats skfda.exploratory.stats._fisher_rao skfda.misc skfda.misc.metrics skfda.misc.metrics._fisher_rao skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.misc.operators skfda.misc.operators._operators skfda.misc.operators._srvf skfda.preprocessing skfda.preprocessing.registration skfda.preprocessing.registration._fisher_rao skfda.preprocessing.registration.base skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric types typing typing_extensions unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_elastic.py (skfda.tests.test_elastic) -LOG: Writing skfda.tests.test_elastic /home/carlos/git/scikit-fda/skfda/tests/test_elastic.py skfda/tests/test_elastic.meta.json skfda/tests/test_elastic.data.json -TRACE: Interface for skfda.tests.test_elastic is unchanged -LOG: Cached module skfda.tests.test_elastic has same interface -TRACE: Priorities for skfda.tests.test_covariances: -LOG: Processing SCC singleton (skfda.tests.test_covariances) as stale due to deps (abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.function_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda.misc skfda.misc.covariances typing unittest unittest.case) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_covariances.py (skfda.tests.test_covariances) -LOG: Writing skfda.tests.test_covariances /home/carlos/git/scikit-fda/skfda/tests/test_covariances.py skfda/tests/test_covariances.meta.json skfda/tests/test_covariances.data.json -TRACE: Interface for skfda.tests.test_covariances is unchanged -LOG: Cached module skfda.tests.test_covariances has same interface -TRACE: Priorities for skfda.tests.test_basis: -LOG: Processing SCC singleton (skfda.tests.test_basis) as inherently stale with stale deps (builtins itertools numpy skfda skfda.datasets skfda.misc skfda.representation.basis skfda.representation.grid unittest) -LOG: Writing skfda.tests.test_basis /home/carlos/git/scikit-fda/skfda/tests/test_basis.py skfda/tests/test_basis.meta.json skfda/tests/test_basis.data.json -TRACE: Interface for skfda.tests.test_basis has changed -LOG: Cached module skfda.tests.test_basis has changed interface -TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting: -LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting) as stale due to deps (__future__ _typeshed abc array builtins copy ctypes dcor dcor._dcor dcor._utils dcor.distances enum mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.multiarray numpy.core.numeric numpy.core.shape_base numpy.lib numpy.lib.arraysetops numpy.lib.function_base numpy.lib.index_tricks numpy.lib.type_check numpy.linalg numpy.linalg.linalg numpy.ma numpy.ma.core pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.misc skfda.misc.covariances skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py (skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting) -LOG: Writing skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.meta.json skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting is unchanged -LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting has same interface -TRACE: Priorities for skfda.ml.regression: -LOG: Processing SCC singleton (skfda.ml.regression) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/regression/__init__.py (skfda.ml.regression) -LOG: Writing skfda.ml.regression /home/carlos/git/scikit-fda/skfda/ml/regression/__init__.py skfda/ml/regression/__init__.meta.json skfda/ml/regression/__init__.data.json -TRACE: Interface for skfda.ml.regression is unchanged -LOG: Cached module skfda.ml.regression has same interface -TRACE: Priorities for skfda.ml.clustering: -LOG: Processing SCC singleton (skfda.ml.clustering) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py (skfda.ml.clustering) -LOG: Writing skfda.ml.clustering /home/carlos/git/scikit-fda/skfda/ml/clustering/__init__.py skfda/ml/clustering/__init__.meta.json skfda/ml/clustering/__init__.data.json -TRACE: Interface for skfda.ml.clustering is unchanged -LOG: Cached module skfda.ml.clustering has same interface -TRACE: Priorities for skfda.ml.classification._depth_classifiers: -LOG: Processing SCC singleton (skfda.ml.classification._depth_classifiers) as stale due to deps (__future__ _collections_abc _typeshed abc array builtins collections contextlib ctypes enum itertools mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.lib numpy.lib.function_base numpy.lib.polynomial pickle skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.preprocessing skfda.representation skfda.representation._functional_data skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py (skfda.ml.classification._depth_classifiers) -LOG: Writing skfda.ml.classification._depth_classifiers /home/carlos/git/scikit-fda/skfda/ml/classification/_depth_classifiers.py skfda/ml/classification/_depth_classifiers.meta.json skfda/ml/classification/_depth_classifiers.data.json -TRACE: Interface for skfda.ml.classification._depth_classifiers is unchanged -LOG: Cached module skfda.ml.classification._depth_classifiers has same interface -TRACE: Priorities for skfda.inference.anova._anova_oneway: -LOG: Processing SCC singleton (skfda.inference.anova._anova_oneway) as stale due to deps (__future__ abc array builtins ctypes mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy._typing._ufunc numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.twodim_base numpy.random numpy.random._generator numpy.random.mtrand pickle skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.validation skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._base skfda.typing._metric skfda.typing._numpy typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/inference/anova/_anova_oneway.py (skfda.inference.anova._anova_oneway) -LOG: Writing skfda.inference.anova._anova_oneway /home/carlos/git/scikit-fda/skfda/inference/anova/_anova_oneway.py skfda/inference/anova/_anova_oneway.meta.json skfda/inference/anova/_anova_oneway.data.json -TRACE: Interface for skfda.inference.anova._anova_oneway is unchanged -LOG: Cached module skfda.inference.anova._anova_oneway has same interface -TRACE: Priorities for skfda.exploratory.outliers: skfda.exploratory.outliers._boxplot:25 skfda.exploratory.outliers._directional_outlyingness:25 -TRACE: Priorities for skfda.exploratory.outliers._directional_outlyingness: skfda.exploratory.outliers:20 -TRACE: Priorities for skfda.exploratory.outliers._boxplot: skfda.exploratory.outliers:20 -LOG: Processing SCC of size 3 (skfda.exploratory.outliers skfda.exploratory.outliers._directional_outlyingness skfda.exploratory.outliers._boxplot) as inherently stale with stale deps (__future__ builtins dataclasses numpy numpy.linalg skfda._utils._sklearn_adapter skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers._directional_outlyingness_experiment_results skfda.exploratory.outliers._envelopes skfda.exploratory.outliers._outliergram skfda.exploratory.outliers.neighbors_outlier skfda.misc.validation skfda.representation skfda.typing._base skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.outliers /home/carlos/git/scikit-fda/skfda/exploratory/outliers/__init__.py skfda/exploratory/outliers/__init__.meta.json skfda/exploratory/outliers/__init__.data.json -TRACE: Interface for skfda.exploratory.outliers has changed -LOG: Cached module skfda.exploratory.outliers has changed interface -LOG: Writing skfda.exploratory.outliers._directional_outlyingness /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_directional_outlyingness.py skfda/exploratory/outliers/_directional_outlyingness.meta.json skfda/exploratory/outliers/_directional_outlyingness.data.json -TRACE: Interface for skfda.exploratory.outliers._directional_outlyingness has changed -LOG: Cached module skfda.exploratory.outliers._directional_outlyingness has changed interface -LOG: Writing skfda.exploratory.outliers._boxplot /home/carlos/git/scikit-fda/skfda/exploratory/outliers/_boxplot.py skfda/exploratory/outliers/_boxplot.meta.json skfda/exploratory/outliers/_boxplot.data.json -TRACE: Interface for skfda.exploratory.outliers._boxplot has changed -LOG: Cached module skfda.exploratory.outliers._boxplot has changed interface -TRACE: Priorities for skfda.tests.test_regularization: -LOG: Processing SCC singleton (skfda.tests.test_regularization) as stale due to deps (__future__ abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.function_base numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.misc skfda.misc.operators skfda.misc.operators._identity skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.misc.regularization skfda.misc.regularization._regularization skfda.ml skfda.preprocessing skfda.preprocessing.smoothing skfda.preprocessing.smoothing._basis skfda.preprocessing.smoothing._linear skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._constant skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case warnings) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_regularization.py (skfda.tests.test_regularization) -LOG: Writing skfda.tests.test_regularization /home/carlos/git/scikit-fda/skfda/tests/test_regularization.py skfda/tests/test_regularization.meta.json skfda/tests/test_regularization.data.json -TRACE: Interface for skfda.tests.test_regularization is unchanged -LOG: Cached module skfda.tests.test_regularization has same interface -TRACE: Priorities for skfda.tests.test_regression: -LOG: Processing SCC singleton (skfda.tests.test_regression) as stale due to deps (__future__ abc builtins contextlib enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.fromnumeric numpy.core.function_base numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.twodim_base numpy.random numpy.random._generator numpy.random.bit_generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._samples_generators skfda.misc skfda.misc.covariances skfda.misc.operators skfda.misc.operators._linear_differential_operator skfda.misc.operators._operators skfda.misc.regularization skfda.misc.regularization._regularization skfda.ml skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid types typing typing_extensions unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_regression.py (skfda.tests.test_regression) -LOG: Writing skfda.tests.test_regression /home/carlos/git/scikit-fda/skfda/tests/test_regression.py skfda/tests/test_regression.meta.json skfda/tests/test_regression.data.json -TRACE: Interface for skfda.tests.test_regression is unchanged -LOG: Cached module skfda.tests.test_regression has same interface -TRACE: Priorities for skfda.tests.test_pandas_fdatabasis: -LOG: Processing SCC singleton (skfda.tests.test_pandas_fdatabasis) as stale due to deps (__future__ abc builtins numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._bspline skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator typing typing_extensions) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatabasis.py (skfda.tests.test_pandas_fdatabasis) -LOG: Writing skfda.tests.test_pandas_fdatabasis /home/carlos/git/scikit-fda/skfda/tests/test_pandas_fdatabasis.py skfda/tests/test_pandas_fdatabasis.meta.json skfda/tests/test_pandas_fdatabasis.data.json -TRACE: Interface for skfda.tests.test_pandas_fdatabasis is unchanged -LOG: Cached module skfda.tests.test_pandas_fdatabasis has same interface -TRACE: Priorities for skfda.tests.test_outliers: -LOG: Processing SCC singleton (skfda.tests.test_outliers) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.lib numpy.lib.shape_base numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._directional_outlyingness skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_outliers.py (skfda.tests.test_outliers) -LOG: Writing skfda.tests.test_outliers /home/carlos/git/scikit-fda/skfda/tests/test_outliers.py skfda/tests/test_outliers.meta.json skfda/tests/test_outliers.data.json -TRACE: Interface for skfda.tests.test_outliers is unchanged -LOG: Cached module skfda.tests.test_outliers has same interface -TRACE: Priorities for skfda.tests.test_kernel_regression: -LOG: Processing SCC singleton (skfda.tests.test_kernel_regression) as stale due to deps (_typeshed abc array builtins enum mmap numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.numeric numpy.lib numpy.lib.twodim_base numpy.linalg numpy.linalg.linalg numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._real_datasets skfda.misc skfda.misc.hat_matrix skfda.misc.kernels skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.basis._monomial skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric typing typing_extensions unittest) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_kernel_regression.py (skfda.tests.test_kernel_regression) -LOG: Writing skfda.tests.test_kernel_regression /home/carlos/git/scikit-fda/skfda/tests/test_kernel_regression.py skfda/tests/test_kernel_regression.meta.json skfda/tests/test_kernel_regression.data.json -TRACE: Interface for skfda.tests.test_kernel_regression is unchanged -LOG: Cached module skfda.tests.test_kernel_regression has same interface -TRACE: Priorities for skfda.tests.test_clustering: -LOG: Processing SCC singleton (skfda.tests.test_clustering) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.random numpy.random._generator numpy.random.mtrand numpy.testing numpy.testing._private numpy.testing._private.utils skfda._utils skfda._utils._sklearn_adapter skfda.ml skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid skfda.typing skfda.typing._metric types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_clustering.py (skfda.tests.test_clustering) -LOG: Writing skfda.tests.test_clustering /home/carlos/git/scikit-fda/skfda/tests/test_clustering.py skfda/tests/test_clustering.meta.json skfda/tests/test_clustering.data.json -TRACE: Interface for skfda.tests.test_clustering is unchanged -LOG: Cached module skfda.tests.test_clustering has same interface -TRACE: Priorities for skfda.preprocessing.dim_reduction.variable_selection: -LOG: Processing SCC singleton (skfda.preprocessing.dim_reduction.variable_selection) as stale due to deps (abc builtins typing) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/__init__.py (skfda.preprocessing.dim_reduction.variable_selection) -LOG: Writing skfda.preprocessing.dim_reduction.variable_selection /home/carlos/git/scikit-fda/skfda/preprocessing/dim_reduction/variable_selection/__init__.py skfda/preprocessing/dim_reduction/variable_selection/__init__.meta.json skfda/preprocessing/dim_reduction/variable_selection/__init__.data.json -TRACE: Interface for skfda.preprocessing.dim_reduction.variable_selection is unchanged -LOG: Cached module skfda.preprocessing.dim_reduction.variable_selection has same interface -TRACE: Priorities for 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stale deps (__future__ builtins numpy skfda.exploratory.depth skfda.exploratory.outliers skfda.exploratory.visualization._baseplot skfda.representation skfda.typing._numpy typing) -LOG: Writing skfda.exploratory.visualization._magnitude_shape_plot /home/carlos/git/scikit-fda/skfda/exploratory/visualization/_magnitude_shape_plot.py skfda/exploratory/visualization/_magnitude_shape_plot.meta.json skfda/exploratory/visualization/_magnitude_shape_plot.data.json -TRACE: Interface for skfda.exploratory.visualization._magnitude_shape_plot has changed -LOG: Cached module skfda.exploratory.visualization._magnitude_shape_plot has changed interface -TRACE: Priorities for skfda.exploratory.visualization._boxplot: -LOG: Processing SCC singleton (skfda.exploratory.visualization._boxplot) as inherently stale with stale deps (__future__ abc builtins math numpy skfda.exploratory.depth skfda.exploratory.depth.multivariate skfda.exploratory.outliers skfda.exploratory.outliers._envelopes 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skfda._utils._sklearn_adapter skfda.datasets skfda.datasets._samples_generators skfda.preprocessing skfda.preprocessing.dim_reduction skfda.representation skfda.representation._functional_data skfda.representation.grid types typing unittest unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_recursive_maxima_hunting.py (skfda.tests.test_recursive_maxima_hunting) -LOG: Writing skfda.tests.test_recursive_maxima_hunting /home/carlos/git/scikit-fda/skfda/tests/test_recursive_maxima_hunting.py skfda/tests/test_recursive_maxima_hunting.meta.json skfda/tests/test_recursive_maxima_hunting.data.json -TRACE: Interface for skfda.tests.test_recursive_maxima_hunting is unchanged -LOG: Cached module skfda.tests.test_recursive_maxima_hunting has same interface -TRACE: Priorities for skfda.tests.test_per_class_transformer: -LOG: Processing SCC singleton (skfda.tests.test_per_class_transformer) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.core.shape_base numpy.testing numpy.testing._private numpy.testing._private.utils skfda._utils skfda._utils._sklearn_adapter skfda._utils._utils skfda.datasets skfda.datasets._real_datasets skfda.ml skfda.ml._neighbors_base skfda.preprocessing skfda.preprocessing.dim_reduction skfda.representation skfda.representation._functional_data skfda.representation.grid skfda.typing skfda.typing._metric types typing typing_extensions unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_per_class_transformer.py (skfda.tests.test_per_class_transformer) -LOG: Writing skfda.tests.test_per_class_transformer /home/carlos/git/scikit-fda/skfda/tests/test_per_class_transformer.py skfda/tests/test_per_class_transformer.meta.json skfda/tests/test_per_class_transformer.data.json -TRACE: Interface for 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skfda.datasets._samples_generators skfda.exploratory skfda.exploratory.outliers skfda.exploratory.outliers.neighbors_outlier skfda.misc skfda.misc.metrics skfda.misc.metrics._lp_distances skfda.misc.metrics._utils skfda.ml skfda.ml._neighbors_base skfda.representation skfda.representation._functional_data skfda.representation.basis skfda.representation.basis._basis skfda.representation.basis._fdatabasis skfda.representation.basis._fourier skfda.representation.grid skfda.typing skfda.typing._metric types typing typing_extensions unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_neighbors.py (skfda.tests.test_neighbors) -LOG: Writing skfda.tests.test_neighbors /home/carlos/git/scikit-fda/skfda/tests/test_neighbors.py skfda/tests/test_neighbors.meta.json skfda/tests/test_neighbors.data.json -TRACE: Interface for skfda.tests.test_neighbors is unchanged -LOG: Cached module skfda.tests.test_neighbors has same interface -TRACE: 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(skfda.tests.test_magnitude_shape) -LOG: Writing skfda.tests.test_magnitude_shape /home/carlos/git/scikit-fda/skfda/tests/test_magnitude_shape.py skfda/tests/test_magnitude_shape.meta.json skfda/tests/test_magnitude_shape.data.json -TRACE: Interface for skfda.tests.test_magnitude_shape is unchanged -LOG: Cached module skfda.tests.test_magnitude_shape has same interface -TRACE: Priorities for skfda.tests.test_fdata_boxplot: -LOG: Processing SCC singleton (skfda.tests.test_fdata_boxplot) as stale due to deps (abc builtins enum numpy numpy._typing numpy._typing._array_like numpy._typing._dtype_like numpy._typing._nested_sequence numpy.core numpy.core.multiarray numpy.testing numpy.testing._private numpy.testing._private.utils skfda skfda._utils skfda._utils._sklearn_adapter skfda.exploratory skfda.exploratory.depth skfda.exploratory.depth._depth skfda.exploratory.depth.multivariate skfda.exploratory.visualization skfda.exploratory.visualization._baseplot skfda.exploratory.visualization._boxplot skfda.representation skfda.representation._functional_data skfda.representation.evaluator skfda.representation.grid types typing unittest unittest.case unittest.loader unittest.main) -LOG: Parsing /home/carlos/git/scikit-fda/skfda/tests/test_fdata_boxplot.py (skfda.tests.test_fdata_boxplot) -LOG: Writing skfda.tests.test_fdata_boxplot /home/carlos/git/scikit-fda/skfda/tests/test_fdata_boxplot.py skfda/tests/test_fdata_boxplot.meta.json skfda/tests/test_fdata_boxplot.data.json -TRACE: Interface for skfda.tests.test_fdata_boxplot is unchanged -LOG: Cached module skfda.tests.test_fdata_boxplot has same interface -LOG: No fresh SCCs left in queue -LOG: Build finished in 78.418 seconds with 662 modules, and 0 errors From 15302882eafe5f192fcabafa9825367bd5068a47 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 8 Sep 2022 12:53:02 +0200 Subject: [PATCH 287/400] Replace GPy implementation of QDA with scikit-learn. --- examples/plot_classification_methods.py | 4 +- .../exploratory/stats/covariance/__init__.py | 2 + skfda/exploratory/stats/covariance/_base.py | 41 +++++++++++++++++++ .../stats/covariance/_empirical.py | 23 ++--------- .../stats/covariance/_parametric_gaussian.py | 22 +++++++--- skfda/ml/classification/_qda.py | 30 +++++++------- 6 files changed, 81 insertions(+), 41 deletions(-) create mode 100644 skfda/exploratory/stats/covariance/_base.py diff --git a/examples/plot_classification_methods.py b/examples/plot_classification_methods.py index bf7e8ca04..0b760cf05 100644 --- a/examples/plot_classification_methods.py +++ b/examples/plot_classification_methods.py @@ -19,12 +19,12 @@ import matplotlib.pyplot as plt import pandas as pd -from GPy.kern import RBF from sklearn.model_selection import train_test_split from skfda.datasets import fetch_growth from skfda.exploratory.depth import ModifiedBandDepth from skfda.exploratory.stats.covariance import ParametricGaussianCovariance +from skfda.misc.covariances import Gaussian from skfda.ml.classification import ( KNeighborsClassifier, MaximumDepthClassifier, @@ -121,7 +121,7 @@ qda = QuadraticDiscriminantAnalysis( ParametricGaussianCovariance( - RBF(input_dim=1, variance=6, lengthscale=1), + Gaussian(variance=6, length_scale=1), ), regularizer=0.05, ) diff --git a/skfda/exploratory/stats/covariance/__init__.py b/skfda/exploratory/stats/covariance/__init__.py index c4342c4ea..33742cc8c 100644 --- a/skfda/exploratory/stats/covariance/__init__.py +++ b/skfda/exploratory/stats/covariance/__init__.py @@ -7,12 +7,14 @@ __getattr__, __dir__, __all__ = lazy.attach( __name__, submod_attrs={ + "_base": ["CovarianceEstimator"], "_empirical": ["EmpiricalCovariance"], "_parametric_gaussian": ["ParametricGaussianCovariance"], }, ) if TYPE_CHECKING: + from ._base import CovarianceEstimator as CovarianceEstimator from ._empirical import EmpiricalCovariance as EmpiricalCovariance from ._parametric_gaussian import ( ParametricGaussianCovariance as ParametricGaussianCovariance, diff --git a/skfda/exploratory/stats/covariance/_base.py b/skfda/exploratory/stats/covariance/_base.py new file mode 100644 index 000000000..cf79343e0 --- /dev/null +++ b/skfda/exploratory/stats/covariance/_base.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +from abc import abstractmethod +from typing import Generic, TypeVar + +from ...._utils._sklearn_adapter import BaseEstimator +from ....representation import FData + +Input = TypeVar("Input", bound=FData) + + +class CovarianceEstimator( + BaseEstimator, + Generic[Input], +): + + location_: Input + covariance_: Input + + def __init__( + self, + *, + assume_centered: bool = False, + ) -> None: + self.assume_centered = assume_centered + + def _fit_mean(self, X: Input) -> None: + self.location_ = X.mean() + if self.assume_centered: + self.location_ *= 0 + + @abstractmethod + def fit(self, X: Input, y: object = None) -> CovarianceEstimator[Input]: + + self._fit_mean(X) + + return self + + def score(self, X_test: Input, y: object = None) -> float: + + pass diff --git a/skfda/exploratory/stats/covariance/_empirical.py b/skfda/exploratory/stats/covariance/_empirical.py index 7be83a39f..ba1efcf03 100644 --- a/skfda/exploratory/stats/covariance/_empirical.py +++ b/skfda/exploratory/stats/covariance/_empirical.py @@ -2,37 +2,20 @@ from typing import Generic, TypeVar -from ...._utils._sklearn_adapter import BaseEstimator from ....representation import FData +from ._base import CovarianceEstimator Input = TypeVar("Input", bound=FData) class EmpiricalCovariance( - BaseEstimator, - Generic[Input], + CovarianceEstimator[Input], ): - def __init__( - self, - *, - assume_centered: bool = False, - ) -> None: - self.assume_centered = assume_centered - - def _fit_mean(self, X: Input): - self.location_ = X.mean() - if self.assume_centered: - self.location_ *= 0 - def fit(self, X: Input, y: object = None) -> EmpiricalCovariance[Input]: - self._fit_mean(X) + super(EmpiricalCovariance, self).fit(X, y) self.covariance_ = X.cov() return self - - def score(self, X_test: Input, y: object = None) -> float: - - pass diff --git a/skfda/exploratory/stats/covariance/_parametric_gaussian.py b/skfda/exploratory/stats/covariance/_parametric_gaussian.py index fd27e53a9..df22180f4 100644 --- a/skfda/exploratory/stats/covariance/_parametric_gaussian.py +++ b/skfda/exploratory/stats/covariance/_parametric_gaussian.py @@ -1,8 +1,10 @@ from __future__ import annotations import numpy as np -from GPy.models import GPRegression +from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.gaussian_process.kernels import Kernel, WhiteKernel +from ....misc.covariances import Covariance from ....representation import FDataGrid from ._empirical import EmpiricalCovariance @@ -11,12 +13,14 @@ class ParametricGaussianCovariance(EmpiricalCovariance[FDataGrid]): def __init__( self, - cov, + cov: Kernel | Covariance, *, assume_centered: bool = False, + fit_noise: bool = True, ) -> None: super().__init__(assume_centered=assume_centered) self.cov = cov + self.fit_noise = fit_noise def fit( self, @@ -32,12 +36,20 @@ def fit( grid_points = X_centered.grid_points[0][:, np.newaxis] - regressor = GPRegression(grid_points, data_matrix.T, kernel=self.cov) - regressor.optimize() + cov = self.cov + to_sklearn = getattr(cov, "to_sklearn", None) + if to_sklearn: + cov = to_sklearn() + + if self.fit_noise: + cov += WhiteKernel() + + regressor = GaussianProcessRegressor(kernel=cov) + regressor.fit(grid_points, data_matrix.T) # TODO: Skip cov computation? self.covariance_ = X.cov().copy( - data_matrix=regressor.kern.K(grid_points)[np.newaxis, ...], + data_matrix=regressor.kernel_(grid_points)[np.newaxis, ...], ) return self diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py index 1bd88589b..a34348de5 100644 --- a/skfda/ml/classification/_qda.py +++ b/skfda/ml/classification/_qda.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Tuple +from typing import Sequence import numpy as np from scipy.linalg import logm @@ -9,6 +9,7 @@ from ..._utils import _classifier_get_classes from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin +from ...exploratory.stats.covariance import CovarianceEstimator from ...representation import FDataGrid from ...typing._numpy import NDArrayFloat, NDArrayInt @@ -77,8 +78,8 @@ class QuadraticDiscriminantAnalysis( ... ParametricGaussianCovariance ... ) >>> from skfda.ml.classification import QuadraticDiscriminantAnalysis - >>> from GPy.kern import RBF - >>> rbf = RBF(input_dim=1, variance=6, lengthscale=1) + >>> from skfda.misc.covariances import Gaussian + >>> rbf = Gaussian(variance=6, length_scale=1) We will fit the ParameterizedFunctionalQDA with training data. We use as regularizer parameter a low value such as 0.05. @@ -102,10 +103,11 @@ class QuadraticDiscriminantAnalysis( 0.96 """ + means_: Sequence[FDataGrid] def __init__( self, - cov_estimator, + cov_estimator: CovarianceEstimator[FDataGrid], *, regularizer: float = 0, ) -> None: @@ -131,8 +133,7 @@ def fit( self.classes = classes self.y_ind = y_ind - _, means = self._fit_gaussian_process(X) - self.means_ = means + self._fit_gaussian_process(X) self.priors_ = self._calculate_priors(y) self._log_priors = np.log(self.priors_) @@ -183,7 +184,7 @@ def _calculate_priors(self, y: NDArrayInt) -> NDArrayFloat: def _fit_gaussian_process( self, X: FDataGrid, - ) -> Tuple[NDArrayFloat, NDArrayFloat]: + ) -> None: """ Fit the kernel to the data in each class. @@ -193,9 +194,6 @@ def _fit_gaussian_process( Args: X: FDataGrid with the training data. - Returns: - Tuple containing a ndarray of fitted kernels and - another ndarray with the means of each class. """ cov_estimators = [] means = [] @@ -205,13 +203,12 @@ def _fit_gaussian_process( cov_estimator = clone(self.cov_estimator).fit(X_class) cov_estimators.append(cov_estimator) - means.append(cov_estimator.location_.data_matrix[0]) + means.append(cov_estimator.location_) covariance.append(cov_estimator.covariance_.data_matrix[0, ..., 0]) + self.means_ = means self._covariances = np.asarray(covariance) - return np.asarray(cov_estimators), np.asarray(means) - def _calculate_log_likelihood(self, X: NDArrayFloat) -> NDArrayFloat: """ Calculate the log likelihood quadratic discriminant analysis. @@ -224,7 +221,12 @@ def _calculate_log_likelihood(self, X: NDArrayFloat) -> NDArrayFloat: output classes. """ # Calculates difference wrt. the mean (x - un) - X_centered = X[:, np.newaxis, :, :] - self.means_[np.newaxis, :, :, :] + mean_values = np.array([m.data_matrix[0] for m in self.means_]) + + X_centered = ( + X[:, np.newaxis, :, :] + - mean_values[np.newaxis, :, :, :] + ) # Calculates mahalanobis distance (-1/2*(x - un).T*inv(sum)*(x - un)) mahalanobis_distances = np.reshape( From 09f7765a1bb7d35cf904313956acbb6ec8a1878a Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 8 Sep 2022 20:56:09 +0200 Subject: [PATCH 288/400] Update plot_kernel_smoothing.py --- examples/plot_kernel_smoothing.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index fdb708e8b..3d1c77134 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -37,7 +37,8 @@ dataset = skfda.datasets.fetch_phoneme() fd = dataset['data'][:300] -out = fd[:5].plot() +fd[:5].plot() +plt.show() ############################################################################# # To better illustrate the smoothing effects and the influence of different @@ -49,7 +50,8 @@ # We will take the curve found at index 10 (a random choice). # Below the original (without any smoothing) curve is plotted. -out = fd[10].plot() +fd[10].plot() +plt.show() ############################################################################# # The library currently has three smoothing methods available: @@ -77,13 +79,15 @@ # Over-smoothed fig = fd[10].plot() -out = fd_os[10].plot(fig=fig) +fd_os[10].plot(fig=fig) +plt.show() ############################################################################## # Under-smoothed fig = fd[10].plot() -out = fd_us[10].plot(fig=fig) +fd_us[10].plot(fig=fig) +plt.show() ############################################################################# # The same could be done, for example, with different kernel. For example, @@ -94,7 +98,8 @@ ).fit_transform(fd) fig = fd[10].plot() -out = fd_os[10].plot(fig=fig) +fd_os[10].plot(fig=fig) +plt.show() ############################################################################## # The values for which the undersmoothing and oversmoothing occur are different @@ -207,7 +212,7 @@ knn_fd[10].plot(fig=fig) llr_fd[10].plot(fig=fig) nw_fd[10].plot(fig=fig) -out = ax.legend( +ax.legend( [ 'original data', 'k-nearest neighbors', @@ -216,6 +221,7 @@ ], title='Smoothing method', ) +plt.show() ############################################################################## @@ -228,4 +234,5 @@ nw_fd[:5].plot(ax[1]) # Disable xticks and xlabel of first image ax[0].set_xticks([]) -out = ax[0].set_xlabel('') +ax[0].set_xlabel('') +plt.show() From 5aae5ee9e0c0ecd6800dcdca3f52e573d0577a34 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 8 Sep 2022 21:04:58 +0200 Subject: [PATCH 289/400] Update plot_kernel_smoothing.py * All out removed * Small changes in the text --- examples/plot_kernel_smoothing.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index 3d1c77134..c569c5da6 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -90,7 +90,7 @@ plt.show() ############################################################################# -# The same could be done, for example, with different kernel. For example, +# The same could be done with different kernel. For example, # over-smoothed case with uniform kernel: fd_os = KernelSmoother( @@ -177,7 +177,8 @@ # The plot of the mean test scores for all smoothers is shown below. # As the X axis we will use the neighbors for all the smoothers in order # to compare k-NN with the others, but remember that the bandwidth for the -# other two estimators is related to the distance to the k-th neighbor. +# other two estimators, in this case, is related to the distance to the k-th +# neighbor. fig = plt.figure() ax = fig.add_subplot(1, 1, 1) @@ -196,7 +197,8 @@ nw.cv_results_['mean_test_score'], label='Nadaraya-Watson', ) -out = ax.legend() +ax.legend() +plt.show() ############################################################################## # We can plot the smoothed curves corresponding to the 11th element of the From 4a5efb511f64573668cdaadef6152a481ff72892 Mon Sep 17 00:00:00 2001 From: ElenaPetrunina Date: Thu, 8 Sep 2022 21:48:16 +0200 Subject: [PATCH 290/400] Changes in the imports --- examples/plot_kernel_smoothing.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/plot_kernel_smoothing.py b/examples/plot_kernel_smoothing.py index c569c5da6..780d95a32 100644 --- a/examples/plot_kernel_smoothing.py +++ b/examples/plot_kernel_smoothing.py @@ -17,7 +17,6 @@ import numpy as np import skfda -import skfda.preprocessing.smoothing.validation as val from skfda.misc.hat_matrix import ( KNeighborsHatMatrix, LocalLinearRegressionHatMatrix, @@ -25,6 +24,7 @@ ) from skfda.misc.kernels import uniform from skfda.preprocessing.smoothing import KernelSmoother +from skfda.preprocessing.smoothing.validation import SmoothingParameterSearch ############################################################################## # This dataset contains the log-periodograms of several phoneme pronunciations. @@ -142,7 +142,7 @@ # K-nearest neighbours kernel smoothing. -knn = val.SmoothingParameterSearch( +knn = SmoothingParameterSearch( KernelSmoother(kernel_estimator=KNeighborsHatMatrix()), n_neighbors, param_name='kernel_estimator__n_neighbors', @@ -151,7 +151,7 @@ knn_fd = knn.transform(fd) # Local linear regression kernel smoothing. -llr = val.SmoothingParameterSearch( +llr = SmoothingParameterSearch( KernelSmoother(kernel_estimator=LocalLinearRegressionHatMatrix()), bandwidth, param_name='kernel_estimator__bandwidth', @@ -160,7 +160,7 @@ llr_fd = llr.transform(fd) # Nadaraya-Watson kernel smoothing. -nw = val.SmoothingParameterSearch( +nw = SmoothingParameterSearch( KernelSmoother(kernel_estimator=NadarayaWatsonHatMatrix()), bandwidth, param_name='kernel_estimator__bandwidth', From b7f0cf7a08069541cba414b2d2db58604be20e36 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Wed, 21 Sep 2022 00:02:36 +0200 Subject: [PATCH 291/400] doctest failure --- skfda/ml/regression/_linear_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 9285c3e93..028812070 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -179,7 +179,7 @@ class LinearRegression( >>> _ = linear.fit(df, y) >>> linear.coef_[0] FDataBasis( - basis=Monomial(domain_range=((0, 1),), n_basis=3), + basis=Monomial(domain_range=((0.0, 1.0),), n_basis=3), coefficients=[[-15. 96. -90.]], ...) >>> linear.intercept_ From 117727528fc697635ace46106076cf8902b68a9a Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Wed, 21 Sep 2022 14:07:27 +0200 Subject: [PATCH 292/400] doctest: constant remaining in coef basis --- skfda/ml/regression/_linear_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 028812070..42bb6d957 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -202,7 +202,7 @@ class LinearRegression( >>> df = pd.DataFrame(cov_dict) >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( - ... coef_basis=[None, Constant()]) + ... coef_basis=[None, Constant(), Constant()]) >>> _ = linear.fit(df, y) >>> linear.coef_[0] array([2.]) From 627aa872b15d4f4f1ef8a51ccfd1e2a55df9318a Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Sat, 24 Sep 2022 11:07:07 +0200 Subject: [PATCH 293/400] fix doctest --- skfda/ml/regression/_linear_regression.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 42bb6d957..e7ea7f4c8 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -198,21 +198,21 @@ class LinearRegression( ... [2, 2]]) >>> mult1 = np.asarray([1, 2, 4, 1, 3, 2]) >>> mult2 = np.asarray([7, 3, 2, 1, 1, 5]) - >>> cov_dict = {"fd": x_fd, "m1": mult1, "m2": mult2} + >>> cov_dict = {"m1": mult1, "m2": mult2, "fd": x_fd} >>> df = pd.DataFrame(cov_dict) >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( ... coef_basis=[None, Constant(), Constant()]) >>> _ = linear.fit(df, y) >>> linear.coef_[0] - array([2.]) + array([ 2.]) >>> linear.coef_[1] - array([1.]) + array([ 1.]) >>> linear.coef_[2] FDataBasis( - basis=Constant(domain_range=((0, 1),), n_basis=1), - coefficients=[[1.]], - ...) + basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), + coefficients=[[ 1.]], + ...) >>> linear.intercept_ array([ 1.]) >>> linear.predict(df) From a8f202e1cfdfd93de598ea7696f3d692a85145ec Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Sat, 24 Sep 2022 11:43:57 +0200 Subject: [PATCH 294/400] isort applied. Style. --- skfda/ml/regression/_linear_regression.py | 6 ++---- skfda/tests/test_regression.py | 8 +++++++- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index e7ea7f4c8..69c384fd4 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -6,7 +6,6 @@ import numpy as np import pandas as pd - from sklearn.utils.validation import check_is_fitted from ..._utils._sklearn_adapter import BaseEstimator, RegressorMixin @@ -334,7 +333,7 @@ def predict( # noqa: D102 return result # type: ignore[no-any-return] - def _argcheck_X( + def _argcheck_X( # noqa: N802 self, X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], ) -> Sequence[AcceptedDataType]: @@ -380,7 +379,7 @@ def _check_and_convert( new_X = new_X[:, np.newaxis] return new_X - def _argcheck_X_y( + def _argcheck_X_y( # noqa: N802 self, X: Union[AcceptedDataType, Sequence[AcceptedDataType], pd.DataFrame], y: NDArrayFloat, @@ -388,7 +387,6 @@ def _argcheck_X_y( coef_basis: Optional[BasisCoefsType] = None, ) -> ArgcheckResultType: """Do some checks to types and shapes.""" - new_X = self._argcheck_X(X) if any(isinstance(i, FData) for i in y): diff --git a/skfda/tests/test_regression.py b/skfda/tests/test_regression.py index 8a7c1b88e..4c4405f3f 100644 --- a/skfda/tests/test_regression.py +++ b/skfda/tests/test_regression.py @@ -12,7 +12,13 @@ from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.ml.regression import HistoricalLinearRegression, LinearRegression -from skfda.representation.basis import BSpline, FDataBasis, Fourier, Monomial, Constant +from skfda.representation.basis import ( + BSpline, + Constant, + FDataBasis, + Fourier, + Monomial, +) from skfda.representation.grid import FDataGrid From 4f6fb38d12ab3aa6d096bf4109007d73c4dbc7e8 Mon Sep 17 00:00:00 2001 From: Rafael Hidalgo Date: Sat, 24 Sep 2022 12:06:54 +0200 Subject: [PATCH 295/400] refactoring: added _sample_weight_check function. --- skfda/ml/regression/_linear_regression.py | 26 ++++++++++++++--------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 69c384fd4..2dc865ba0 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -417,19 +417,25 @@ def _argcheck_X_y( # noqa: N802 ] if sample_weight is not None: + self._sample_weight_check(sample_weight, y) - if len(sample_weight) != len(y): - raise ValueError( - "The number of sample weights should be " - "equal to the number of samples.", - ) + return new_X, y, sample_weight, coef_info - if np.any(np.array(sample_weight) < 0): - raise ValueError( - "The sample weights should be non negative values", - ) + def _sample_weight_check( + self, + sample_weight: Optional[NDArrayFloat], + y: NDArrayFloat, + ): + if len(sample_weight) != len(y): + raise ValueError( + "The number of sample weights should be " + "equal to the number of samples.", + ) - return new_X, y, sample_weight, coef_info + if np.any(np.array(sample_weight) < 0): + raise ValueError( + "The sample weights should be non negative values", + ) def _dataframe_conversion( self, From a7d725778ec1efb1674d562565900bde92111ac6 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Mon, 26 Sep 2022 19:32:16 +0200 Subject: [PATCH 296/400] Fix example. --- full_examples/plot_aemet_unsupervised.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index d509cff93..2e4d7edc6 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -31,8 +31,7 @@ ############################################################################## # We will first load the AEMET dataset and plot it. dataset = fetch_aemet() -X = dataset["data"] -X = X.coordinates[0] +X, _ = fetch_aemet(return_X_y=True) X.plot() plt.show() From d8ce11e9f130a242d5bdb8a340b03ba693ab391e Mon Sep 17 00:00:00 2001 From: pedrog99 Date: Mon, 26 Sep 2022 23:51:58 +0200 Subject: [PATCH 297/400] Solucionado error de Sphinx en Windows 10 --- conftest.py | 13 ++++++++++ .../_function_transformers.py | 26 +++++++++---------- skfda/representation/_functional_data.py | 21 ++++++++------- 3 files changed, 38 insertions(+), 22 deletions(-) diff --git a/conftest.py b/conftest.py index 0bf55fc27..64b79a681 100644 --- a/conftest.py +++ b/conftest.py @@ -1,4 +1,6 @@ import pytest +import sys +import asyncio # https://github.com/scikit-learn/scikit-learn/issues/8959 import numpy as np @@ -7,6 +9,17 @@ except TypeError: pass +''' +Cambio introducido para adaptarse bien al entorno del sistema +operativo Windows 10. +Más información del problema en el siguiente issue: +https://github.com/jupyter/jupyter-sphinx/issues/171#issuecomment-766953182 +''' + +if sys.version_info[0] == 3 and sys.version_info[1] >= 8\ + and sys.platform.startswith('win'): + asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) + collect_ignore = ['setup.py', 'docs/conf.py'] pytest.register_assert_rewrite("skfda") diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index a2ccd9d0f..b45c5567c 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -212,19 +212,19 @@ class NumberCrossingsTransformer( ... ) >>> fd_grid.data_matrix array([[[ 1. ], - [ 0.73041066], - [ 0.13616752], - [-0.32803875], - [-0.35967936], - [-0.04652559], - [ 0.25396879], - [ 0.26095573], - [ 0.01042895], - [-0.22089135], - [-0.2074856 ], - [ 0.0126612 ], - [ 0.20089319], - [ 0.17107348]]]) + [ 0.73041066], + [ 0.13616752], + [-0.32803875], + [-0.35967936], + [-0.04652559], + [ 0.25396879], + [ 0.26095573], + [ 0.01042895], + [-0.22089135], + [-0.2074856 ], + [ 0.0126612 ], + [ 0.20089319], + [ 0.17107348]]]) Finally we evaluate the number of zero-upcrossings method with the FDataGrid created. diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index f975393c4..4d13af9b8 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -1163,22 +1163,25 @@ def take( # noqa: WPS238 fill_value: Optional[T] = None, axis: int = 0, ) -> T: - """Take elements from an array. + """ + Take elements from an array. Parameters: - indices: - Indices to be taken. + indices: Indices to be taken. allow_fill: How to handle negative values in `indices`. - * False: negative values in `indices` indicate positional - indices from the right (the default). This is similar to - :func:`numpy.take`. - * True: negative values in `indices` indicate - missing values. These values are set to `fill_value`. Any - other negative values raise a ``ValueError``. + * False: negative values in `indices` indicate positional + indices from the right (the default). This is similar to + :func:`numpy.take`. + + * True: negative values in `indices` indicate + missing values. These values are set to `fill_value`. Any + other negative values raise a ``ValueError``. fill_value: Fill value to use for NA-indices when `allow_fill` is True. + This may be ``None``, in which case the default NA value for the type, ``self.dtype.na_value``, is used. + For many ExtensionArrays, there will be two representations of `fill_value`: a user-facing "boxed" scalar, and a low-level physical NA value. `fill_value` should be the user-facing From f0fe15818e6b29721405b158201e1be6798979b8 Mon Sep 17 00:00:00 2001 From: pedrog99 Date: Tue, 27 Sep 2022 00:04:28 +0200 Subject: [PATCH 298/400] Checked pull request test errors --- conftest.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/conftest.py b/conftest.py index 64b79a681..b1261669b 100644 --- a/conftest.py +++ b/conftest.py @@ -1,9 +1,10 @@ import pytest import sys import asyncio +import numpy as np # https://github.com/scikit-learn/scikit-learn/issues/8959 -import numpy as np + try: np.set_printoptions(sign=' ') except TypeError: From 43c91e3cbc47dcdb15a717faf65dd38f5af8da8f Mon Sep 17 00:00:00 2001 From: VNMabus Date: Tue, 27 Sep 2022 16:28:18 +0200 Subject: [PATCH 299/400] Fix example. --- full_examples/plot_aemet_unsupervised.py | 2 +- skfda/exploratory/visualization/_utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/full_examples/plot_aemet_unsupervised.py b/full_examples/plot_aemet_unsupervised.py index 2e4d7edc6..8f8886c30 100644 --- a/full_examples/plot_aemet_unsupervised.py +++ b/full_examples/plot_aemet_unsupervised.py @@ -30,8 +30,8 @@ ############################################################################## # We will first load the AEMET dataset and plot it. -dataset = fetch_aemet() X, _ = fetch_aemet(return_X_y=True) +X = X.coordinates[0] X.plot() plt.show() diff --git a/skfda/exploratory/visualization/_utils.py b/skfda/exploratory/visualization/_utils.py index 3d288270a..dd1efc178 100644 --- a/skfda/exploratory/visualization/_utils.py +++ b/skfda/exploratory/visualization/_utils.py @@ -269,7 +269,7 @@ def _set_labels( elif patches is not None: axes[0].legend(handles=patches) - assert len(axes) == fdata.dim_codomain + assert len(axes) >= fdata.dim_codomain # Axis labels if axes[0].name == '3d': From 02170d17b56507c649018860f2f0cb3271493c1e Mon Sep 17 00:00:00 2001 From: pedrog99 Date: Tue, 27 Sep 2022 18:47:54 +0200 Subject: [PATCH 300/400] Pull request issues corrected --- conftest.py | 14 +++++++------- skfda/representation/_functional_data.py | 12 ++++++------ 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/conftest.py b/conftest.py index b1261669b..11f769acc 100644 --- a/conftest.py +++ b/conftest.py @@ -10,15 +10,15 @@ except TypeError: pass -''' -Cambio introducido para adaptarse bien al entorno del sistema -operativo Windows 10. -Más información del problema en el siguiente issue: +""" +I introduced this change in order to adapt it to Windows 10 +operating system. +More information about this problem in this GitHub issue: https://github.com/jupyter/jupyter-sphinx/issues/171#issuecomment-766953182 -''' +""" -if sys.version_info[0] == 3 and sys.version_info[1] >= 8\ - and sys.platform.startswith('win'): +if (sys.version_info[0] == 3 and sys.version_info[1] >= 8 + and sys.platform.startswith('win')): asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) collect_ignore = ['setup.py', 'docs/conf.py'] diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 4d13af9b8..076897bee 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -1169,13 +1169,13 @@ def take( # noqa: WPS238 Parameters: indices: Indices to be taken. allow_fill: How to handle negative values in `indices`. - * False: negative values in `indices` indicate positional - indices from the right (the default). This is similar to - :func:`numpy.take`. - * True: negative values in `indices` indicate - missing values. These values are set to `fill_value`. Any - other negative values raise a ``ValueError``. + * False: negative values in `indices` indicate positional + indices from the right (the default). This is similar to + :func:`numpy.take`. + * True: negative values in `indices` indicate + missing values. These values are set to `fill_value`. Any + other negative values raise a ``ValueError``. fill_value: Fill value to use for NA-indices when `allow_fill` is True. From 1e51985c1c22ad9dafd425649e6faa4f273901af Mon Sep 17 00:00:00 2001 From: pedrog99 Date: Tue, 27 Sep 2022 18:53:06 +0200 Subject: [PATCH 301/400] Trying to solve pull requests error v2 --- conftest.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/conftest.py b/conftest.py index 11f769acc..2b87037e3 100644 --- a/conftest.py +++ b/conftest.py @@ -11,14 +11,14 @@ pass """ -I introduced this change in order to adapt it to Windows 10 +I introduced this change in order to adapt it to Windows 10 operating system. More information about this problem in this GitHub issue: https://github.com/jupyter/jupyter-sphinx/issues/171#issuecomment-766953182 """ - if (sys.version_info[0] == 3 and sys.version_info[1] >= 8 - and sys.platform.startswith('win')): + and sys.platform.startswith('win') + ): asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) collect_ignore = ['setup.py', 'docs/conf.py'] From 5f4df7500e6a4937169c1b023720f6b3555fece1 Mon Sep 17 00:00:00 2001 From: pedrog99 Date: Tue, 27 Sep 2022 18:58:06 +0200 Subject: [PATCH 302/400] Trying to solve pull request issues v3 --- conftest.py | 3 +-- skfda/representation/_functional_data.py | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/conftest.py b/conftest.py index 2b87037e3..ba732b7fa 100644 --- a/conftest.py +++ b/conftest.py @@ -17,8 +17,7 @@ https://github.com/jupyter/jupyter-sphinx/issues/171#issuecomment-766953182 """ if (sys.version_info[0] == 3 and sys.version_info[1] >= 8 - and sys.platform.startswith('win') - ): + and sys.platform.startswith('win')): asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) collect_ignore = ['setup.py', 'docs/conf.py'] diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 076897bee..61b98aeb2 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -1181,7 +1181,7 @@ def take( # noqa: WPS238 This may be ``None``, in which case the default NA value for the type, ``self.dtype.na_value``, is used. - + For many ExtensionArrays, there will be two representations of `fill_value`: a user-facing "boxed" scalar, and a low-level physical NA value. `fill_value` should be the user-facing From afd319799d0f84797c28edd086f00d23a02e5a42 Mon Sep 17 00:00:00 2001 From: pedrog99 Date: Wed, 28 Sep 2022 21:38:41 +0200 Subject: [PATCH 303/400] Solving pull request style issues --- conftest.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/conftest.py b/conftest.py index ba732b7fa..f0d210b5d 100644 --- a/conftest.py +++ b/conftest.py @@ -10,14 +10,16 @@ except TypeError: pass -""" -I introduced this change in order to adapt it to Windows 10 -operating system. -More information about this problem in this GitHub issue: -https://github.com/jupyter/jupyter-sphinx/issues/171#issuecomment-766953182 -""" -if (sys.version_info[0] == 3 and sys.version_info[1] >= 8 - and sys.platform.startswith('win')): + +# I introduced this change in order to adapt it to Windows 10 +# operating system. +# More information about this problem in this GitHub issue: +# https://github.com/jupyter/jupyter-sphinx/issues/171#issuecomment-766953182 + +if ( + sys.version_info[0] == 3 and sys.version_info[1] >= 8 + and sys.platform.startswith('win') +): asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) collect_ignore = ['setup.py', 'docs/conf.py'] From 4f07cdbbb90d2c27c1faf524004c0591246b3728 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sun, 2 Oct 2022 09:27:21 +0200 Subject: [PATCH 304/400] Fix wrong variable name in fpca --- skfda/preprocessing/dim_reduction/_fpca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index bf99e907e..8a0b4e667 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -392,7 +392,7 @@ def _fit_grid( sample_names=(None,) * self.n_components, ) - self.explained_variance_ratio = ( + self.explained_variance_ratio_ = ( pca.explained_variance_ratio_ ) self.explained_variance_ = pca.explained_variance_ From 72fb792ded223c4256711144a96ddeca45829db3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Thu, 6 Oct 2022 12:29:48 +0200 Subject: [PATCH 305/400] Test for classes_ attribute in all classifiers --- skfda/tests/test_classifier_classes.py | 98 ++++++++++++++++++++++++++ 1 file changed, 98 insertions(+) create mode 100644 skfda/tests/test_classifier_classes.py diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py new file mode 100644 index 000000000..501a8f618 --- /dev/null +++ b/skfda/tests/test_classifier_classes.py @@ -0,0 +1,98 @@ +"""Tests classes attribute of classifiers.""" + +import unittest + +import numpy as np +from sklearn.model_selection import train_test_split + +from skfda._utils._utils import _classifier_get_classes +from skfda.datasets import fetch_growth +from skfda.misc.metrics import l2_distance +from skfda.ml.classification import ( + DDClassifier, + DDGClassifier, + DTMClassifier, + KNeighborsClassifier, + MaximumDepthClassifier, + NearestCentroid, + RadiusNeighborsClassifier, +) +from skfda.ml.classification._depth_classifiers import _ArgMaxClassifier +from skfda.representation import FData + + +class TestClassifierClasses(unittest.TestCase): + """Test for classifiers classes.""" + + def setUp(self) -> None: + """Establish train and test data sets.""" + X, y = fetch_growth(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=0.25, + stratify=y, + random_state=0, + ) + self._X_train = X_train + self._X_test = X_test + self._y_train = y_train + self._y_test = y_test + self.classes = _classifier_get_classes(self._y_train)[0] + + def test_classes_kneighbors(self) -> None: + """Check classes attribute of KNeighborsClassifier.""" + clf: KNeighborsClassifier[FData] = KNeighborsClassifier() + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_radiusneighbors(self) -> None: + """Check classes attribute of RadiusNeighborsClassifier.""" + clf: RadiusNeighborsClassifier[FData] = RadiusNeighborsClassifier() + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_nearestcentroid(self) -> None: + """Check classes attribute of NearestCentroid.""" + clf: NearestCentroid[FData] = NearestCentroid() + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_ddg(self) -> None: + """Check classes attribute of DDGClassifier.""" + clf: DDGClassifier[FData] = DDGClassifier( + multivariate_classifier=KNeighborsClassifier(), + ) + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_dd(self) -> None: + """Check classes attribute of DDClassifier.""" + clf: DDClassifier[FData] = DDClassifier(degree=2) + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_dtm(self) -> None: + """Check classes attribute of DTMClassifier.""" + clf: DTMClassifier[FData] = DTMClassifier( + proportiontocut=0, + metric=l2_distance, + ) + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_maximumdepth(self) -> None: + """Check classes attribute of MaximumDepthClassifier.""" + clf: MaximumDepthClassifier[FData] = MaximumDepthClassifier() + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_argmax(self) -> None: + """Check classes attribute of _ArgMaxClassifier.""" + clf: _ArgMaxClassifier = _ArgMaxClassifier() + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + +if __name__ == "__main__": + unittest.main() From bb3e55aefa32464256e8e856316866845b09a121 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Thu, 6 Oct 2022 12:47:34 +0200 Subject: [PATCH 306/400] Tests for regression classifiers --- skfda/tests/test_classifier_classes.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index 501a8f618..73769146a 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -7,14 +7,18 @@ from skfda._utils._utils import _classifier_get_classes from skfda.datasets import fetch_growth +from skfda.exploratory.stats.covariance import ParametricGaussianCovariance +from skfda.misc.covariances import Gaussian from skfda.misc.metrics import l2_distance from skfda.ml.classification import ( DDClassifier, DDGClassifier, DTMClassifier, KNeighborsClassifier, + LogisticRegression, MaximumDepthClassifier, NearestCentroid, + QuadraticDiscriminantAnalysis, RadiusNeighborsClassifier, ) from skfda.ml.classification._depth_classifiers import _ArgMaxClassifier @@ -93,6 +97,21 @@ def test_classes_argmax(self) -> None: clf.fit(self._X_train, self._y_train) np.testing.assert_array_equal(clf.classes_, self.classes) + def test_classes_logistic_regression(self) -> None: + """Check classes attribute of LogisticRegression.""" + clf: LogisticRegression = LogisticRegression(max_iter=1000) + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + + def test_classes_quadratic_discriminant_analysis(self) -> None: + """Check classes attribute of QuadraticDiscriminantAnalysis.""" + rbf = Gaussian(variance=6, length_scale=1) + clf: QuadraticDiscriminantAnalysis = QuadraticDiscriminantAnalysis( + cov_estimator=ParametricGaussianCovariance(rbf), + ) + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) + if __name__ == "__main__": unittest.main() From 7fa41e3a51035f9dba9db80b8f292c324d8b9bcd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Thu, 6 Oct 2022 15:47:33 +0200 Subject: [PATCH 307/400] Convert tests into subtests using a classifier list --- skfda/tests/test_classifier_classes.py | 99 ++++++++------------------ 1 file changed, 29 insertions(+), 70 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index 73769146a..d1a405638 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -5,6 +5,7 @@ import numpy as np from sklearn.model_selection import train_test_split +from skfda._utils._sklearn_adapter import ClassifierMixin from skfda._utils._utils import _classifier_get_classes from skfda.datasets import fetch_growth from skfda.exploratory.stats.covariance import ParametricGaussianCovariance @@ -24,6 +25,8 @@ from skfda.ml.classification._depth_classifiers import _ArgMaxClassifier from skfda.representation import FData +from ..typing._numpy import NDArrayAny + class TestClassifierClasses(unittest.TestCase): """Test for classifiers classes.""" @@ -31,7 +34,7 @@ class TestClassifierClasses(unittest.TestCase): def setUp(self) -> None: """Establish train and test data sets.""" X, y = fetch_growth(return_X_y=True) - X_train, X_test, y_train, y_test = train_test_split( + X_train, X_test, y_train, _ = train_test_split( X, y, test_size=0.25, @@ -41,76 +44,32 @@ def setUp(self) -> None: self._X_train = X_train self._X_test = X_test self._y_train = y_train - self._y_test = y_test - self.classes = _classifier_get_classes(self._y_train)[0] - - def test_classes_kneighbors(self) -> None: - """Check classes attribute of KNeighborsClassifier.""" - clf: KNeighborsClassifier[FData] = KNeighborsClassifier() - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_radiusneighbors(self) -> None: - """Check classes attribute of RadiusNeighborsClassifier.""" - clf: RadiusNeighborsClassifier[FData] = RadiusNeighborsClassifier() - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_nearestcentroid(self) -> None: - """Check classes attribute of NearestCentroid.""" - clf: NearestCentroid[FData] = NearestCentroid() - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_ddg(self) -> None: - """Check classes attribute of DDGClassifier.""" - clf: DDGClassifier[FData] = DDGClassifier( - multivariate_classifier=KNeighborsClassifier(), - ) - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_dd(self) -> None: - """Check classes attribute of DDClassifier.""" - clf: DDClassifier[FData] = DDClassifier(degree=2) - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - def test_classes_dtm(self) -> None: - """Check classes attribute of DTMClassifier.""" - clf: DTMClassifier[FData] = DTMClassifier( - proportiontocut=0, - metric=l2_distance, - ) - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_maximumdepth(self) -> None: - """Check classes attribute of MaximumDepthClassifier.""" - clf: MaximumDepthClassifier[FData] = MaximumDepthClassifier() - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_argmax(self) -> None: - """Check classes attribute of _ArgMaxClassifier.""" - clf: _ArgMaxClassifier = _ArgMaxClassifier() - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_logistic_regression(self) -> None: - """Check classes attribute of LogisticRegression.""" - clf: LogisticRegression = LogisticRegression(max_iter=1000) - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) - - def test_classes_quadratic_discriminant_analysis(self) -> None: - """Check classes attribute of QuadraticDiscriminantAnalysis.""" - rbf = Gaussian(variance=6, length_scale=1) - clf: QuadraticDiscriminantAnalysis = QuadraticDiscriminantAnalysis( - cov_estimator=ParametricGaussianCovariance(rbf), - ) - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) + self.classes = _classifier_get_classes(self._y_train)[0] + self.tested_classifiers: list[ClassifierMixin[FData, NDArrayAny]] = [ + KNeighborsClassifier(), + RadiusNeighborsClassifier(), + NearestCentroid(), + DDGClassifier(multivariate_classifier=KNeighborsClassifier()), + DDClassifier(degree=2), + DTMClassifier(proportiontocut=0, metric=l2_distance), + MaximumDepthClassifier(), + _ArgMaxClassifier(), + LogisticRegression(max_iter=1000), + QuadraticDiscriminantAnalysis( + cov_estimator=ParametricGaussianCovariance( + Gaussian(variance=6, length_scale=1), + ), + ), + ] + + def test_classes(self) -> None: + """Check classes attribute.""" + # Iterate over all classifiers with index + for i, clf in enumerate(self.tested_classifiers): + with self.subTest(i=i): + clf.fit(self._X_train, self._y_train) + np.testing.assert_array_equal(clf.classes_, self.classes) if __name__ == "__main__": From 90f278dcb040cc5328cd866b7d5db6603ccd62b1 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 7 Oct 2022 08:59:33 +0200 Subject: [PATCH 308/400] Fix Mypy errors in scoring module. --- skfda/exploratory/stats/_stats.py | 2 +- skfda/misc/scoring.py | 274 +++++++++++++------------ skfda/representation/interpolation.py | 2 + {tests => skfda/tests}/test_scoring.py | 2 +- 4 files changed, 143 insertions(+), 137 deletions(-) rename {tests => skfda/tests}/test_scoring.py (99%) diff --git a/skfda/exploratory/stats/_stats.py b/skfda/exploratory/stats/_stats.py index abc9c2216..ac89a992b 100644 --- a/skfda/exploratory/stats/_stats.py +++ b/skfda/exploratory/stats/_stats.py @@ -20,7 +20,7 @@ def mean( X: F, - weights: Optional[NDArrayFloat] = None, + weights: NDArrayFloat | None = None, ) -> F: """ Compute the mean of all the samples in a FData object. diff --git a/skfda/misc/scoring.py b/skfda/misc/scoring.py index 26042a30a..a29a0c41a 100644 --- a/skfda/misc/scoring.py +++ b/skfda/misc/scoring.py @@ -1,22 +1,28 @@ """Scoring methods for FData.""" +import math import warnings from functools import singledispatch from typing import Optional, TypeVar, Union, overload import numpy as np -import scipy.integrate -import sklearn +import sklearn.metrics from typing_extensions import Literal, Protocol -from .. import FData -from ..exploratory.stats import mean, var -from ..representation import FDataBasis, FDataGrid +from .._utils import nquad_vec +from ..exploratory.stats._stats import mean, var +from ..representation import FData, FDataBasis, FDataGrid from ..representation._functional_data import EvalPointsType -from ..representation._typing import NDArrayFloat +from ..typing._numpy import NDArrayFloat DataType = TypeVar('DataType', FDataGrid, FDataBasis, NDArrayFloat) DataTypeRawValues = TypeVar('DataTypeRawValues', FDataGrid, NDArrayFloat) +MultiOutputType = Literal['uniform_average', 'raw_values'] + + +class InfiniteErrorException(Exception): + pass + class ScoreFunction(Protocol): """Type definition for score functions.""" @@ -26,10 +32,9 @@ def __call__( # noqa: D102 y_true: Union[FData, NDArrayFloat], y_pred: Union[FData, NDArrayFloat], sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] - = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[NDArrayFloat, FDataGrid, float]: - ... # noqa: WPS428 + pass # noqa: WPS428 def _domain_measure(fd: FData) -> float: @@ -46,7 +51,7 @@ def _var( if weights is None: return var(x) - return mean( + return mean( # type: ignore[no-any-return] np.power(x - mean(x, weights=weights), 2), weights=weights, ) @@ -60,7 +65,7 @@ def explained_variance_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... # noqa: WPS428 + pass # noqa: WPS428 @overload @@ -71,7 +76,7 @@ def explained_variance_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... # noqa: WPS428 + pass # noqa: WPS428 @singledispatch @@ -150,15 +155,17 @@ def explained_variance_score( multioutput = 'raw_values', ndarray. """ - return sklearn.metrics.explained_variance_score( - y_true, - y_pred, - sample_weight=sample_weight, - multioutput=multioutput, + return ( # type: ignore [no-any-return] + sklearn.metrics.explained_variance_score( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + ) ) -@explained_variance_score.register +@explained_variance_score.register # type: ignore[attr-defined, misc] def _explained_variance_score_fdatagrid( y_true: FDataGrid, y_pred: FDataGrid, @@ -183,10 +190,10 @@ def _explained_variance_score_fdatagrid( # Score only contains 1 function # If the dimension of the codomain is > 1, # the mean of the scores is taken - return np.mean(score.integrate()[0]) / _domain_measure(score) + return float(np.mean(score.integrate()[0]) / _domain_measure(score)) -@explained_variance_score.register +@explained_variance_score.register # type: ignore[attr-defined, misc] def _explained_variance_score_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, @@ -194,8 +201,6 @@ def _explained_variance_score_fdatabasis( sample_weight: Optional[NDArrayFloat] = None, ) -> float: - start, end = y_true.domain_range[0] - def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 num = np.average( ( @@ -219,31 +224,32 @@ def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 axis=0, ) + # Divisions by zero allowed + with np.errstate(divide='ignore', invalid='ignore'): + score = 1 - num / den + # 0/0 case, the score is 1. - if num == 0 and den == 0: - return 1 + score[np.isnan(score)] = 1 # r/0 case, r!= 0. Return -inf outside this function - if num != 0 and den == 0: - raise ValueError + if np.any(np.isinf(score)): + raise InfiniteErrorException - score = 1 - num / den - - # Score only contains 1 function - return score[0] + # Score only contains 1 input point + assert score.shape[0] == 1 + return score[0] # type: ignore [no-any-return] try: - integral = scipy.integrate.quad_vec( + integral = nquad_vec( _ev_func, - start, - end, + y_true.domain_range, ) - except ValueError: - return float('-inf') + except InfiniteErrorException: + return -math.inf # If the dimension of the codomain is > 1, # the mean of the scores is taken - return np.mean(integral[0]) / (end - start) + return float(np.mean(integral[0]) / _domain_measure(y_true)) @overload @@ -254,7 +260,7 @@ def mean_absolute_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... # noqa: WPS428 + pass # noqa: WPS428 @overload @@ -265,7 +271,7 @@ def mean_absolute_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... # noqa: WPS428 + pass # noqa: WPS428 @singledispatch @@ -331,7 +337,7 @@ def mean_absolute_error( multioutput = 'raw_values', ndarray. """ - return sklearn.metrics.mean_absolute_error( + return sklearn.metrics.mean_absolute_error( # type: ignore [no-any-return] y_true, y_pred, sample_weight=sample_weight, @@ -339,7 +345,7 @@ def mean_absolute_error( ) -@mean_absolute_error.register +@mean_absolute_error.register # type: ignore[attr-defined, misc] def _mean_absolute_error_fdatagrid( y_true: FDataGrid, y_pred: FDataGrid, @@ -348,7 +354,7 @@ def _mean_absolute_error_fdatagrid( multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', ) -> Union[float, FDataGrid]: - error = mean(np.abs(y_true - y_pred), weights=sample_weight) + error: FDataGrid = mean(np.abs(y_true - y_pred), weights=sample_weight) if multioutput == 'raw_values': return error @@ -356,10 +362,10 @@ def _mean_absolute_error_fdatagrid( # Error only contains 1 function # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(error.integrate()[0]) / _domain_measure(error) + return float(np.mean(error.integrate()[0]) / _domain_measure(error)) -@mean_absolute_error.register +@mean_absolute_error.register # type: ignore[attr-defined, misc] def _mean_absolute_error_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, @@ -367,8 +373,6 @@ def _mean_absolute_error_fdatabasis( sample_weight: Optional[NDArrayFloat] = None, ) -> float: - start, end = y_true.domain_range[0] - def _mae_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 error = np.average( np.abs(y_true(x) - y_pred(x)), @@ -376,18 +380,18 @@ def _mae_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 axis=0, ) - # Error only contains 1 function - return error[0] + # Error only contains 1 input point + assert error.shape[0] == 1 + return error[0] # type: ignore [no-any-return] - integral = scipy.integrate.quad_vec( + integral = nquad_vec( _mae_func, - start, - end, + y_true.domain_range, ) # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(integral[0]) / (end - start) + return float(np.mean(integral[0]) / _domain_measure(y_true)) @overload @@ -398,7 +402,7 @@ def mean_absolute_percentage_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... # noqa: WPS428 + pass # noqa: WPS428 @overload @@ -409,7 +413,7 @@ def mean_absolute_percentage_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... # noqa: WPS428 + pass # noqa: WPS428 @singledispatch @@ -419,7 +423,7 @@ def mean_absolute_percentage_error( *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', -) -> Union[float, FDataGrid]: +) -> Union[float, NDArrayFloat, FDataGrid]: r"""Mean Absolute Percentage Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -478,15 +482,17 @@ def mean_absolute_percentage_error( multioutput = 'raw_values', ndarray. """ - return sklearn.metrics.mean_absolute_percentage_error( - y_true, - y_pred, - sample_weight=sample_weight, - multioutput=multioutput, + return ( # type: ignore [no-any-return] + sklearn.metrics.mean_absolute_percentage_error( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + ) ) -@mean_absolute_percentage_error.register +@mean_absolute_percentage_error.register # type: ignore[attr-defined, misc] def _mean_absolute_percentage_error_fdatagrid( y_true: FDataGrid, y_pred: FDataGrid, @@ -502,7 +508,7 @@ def _mean_absolute_percentage_error_fdatagrid( mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon) - error = mean(mape, weights=sample_weight) + error: FDataGrid = mean(mape, weights=sample_weight) if multioutput == 'raw_values': return error @@ -510,10 +516,10 @@ def _mean_absolute_percentage_error_fdatagrid( # Error only contains 1 function # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(error.integrate()[0]) / _domain_measure(error) + return float(np.mean(error.integrate()[0]) / _domain_measure(error)) -@mean_absolute_percentage_error.register +@mean_absolute_percentage_error.register # type: ignore[attr-defined, misc] def _mean_absolute_percentage_error_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, @@ -536,19 +542,18 @@ def _mape_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 axis=0, ) - # Error only contains 1 function - return error[0] + # Error only contains 1 input point + assert error.shape[0] == 1 + return error[0] # type: ignore [no-any-return] - start, end = y_true.domain_range[0] - integral = scipy.integrate.quad_vec( + integral = nquad_vec( _mape_func, - start, - end, + y_true.domain_range, ) # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(integral[0]) / (end - start) + return float(np.mean(integral[0]) / _domain_measure(y_true)) @overload @@ -560,7 +565,7 @@ def mean_squared_error( multioutput: Literal['uniform_average'] = 'uniform_average', squared: bool = True, ) -> float: - ... # noqa: WPS428 + pass # noqa: WPS428 @overload @@ -572,7 +577,7 @@ def mean_squared_error( multioutput: Literal['raw_values'], squared: bool = True, ) -> DataTypeRawValues: - ... # noqa: WPS428 + pass # noqa: WPS428 @singledispatch @@ -583,7 +588,7 @@ def mean_squared_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', squared: bool = True, -) -> Union[float, FDataGrid]: +) -> Union[float, NDArrayFloat, FDataGrid]: r"""Mean Squared Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -640,7 +645,7 @@ def mean_squared_error( multioutput = 'raw_values', ndarray. """ - return mean_squared_error( + return sklearn.metrics.mean_squared_error( # type: ignore [no-any-return] y_true, y_pred, sample_weight=sample_weight, @@ -649,7 +654,7 @@ def mean_squared_error( ) -@mean_squared_error.register +@mean_squared_error.register # type: ignore[attr-defined, misc] def _mean_squared_error_fdatagrid( y_true: FDataGrid, y_pred: FDataGrid, @@ -659,7 +664,7 @@ def _mean_squared_error_fdatagrid( squared: bool = True, ) -> Union[float, FDataGrid]: - error = mean( + error: FDataGrid = mean( np.power(y_true - y_pred, 2), weights=sample_weight, ) @@ -673,10 +678,10 @@ def _mean_squared_error_fdatagrid( # Error only contains 1 function # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(error.integrate()[0]) / _domain_measure(error) + return float(np.mean(error.integrate()[0]) / _domain_measure(error)) -@mean_squared_error.register +@mean_squared_error.register # type: ignore[attr-defined, misc] def _mean_squared_error_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, @@ -685,11 +690,9 @@ def _mean_squared_error_fdatabasis( squared: bool = True, ) -> float: - start, end = y_true.domain_range[0] - def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 - error = np.average( + error: NDArrayFloat = np.average( (y_true(x) - y_pred(x)) ** 2, weights=sample_weight, axis=0, @@ -698,18 +701,18 @@ def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 if not squared: return np.sqrt(error) - # Error only contains 1 function - return error[0] + # Error only contains 1 input point + assert error.shape[0] == 1 + return error[0] # type: ignore [no-any-return] - integral = scipy.integrate.quad_vec( + integral = nquad_vec( _mse_func, - start, - end, + y_true.domain_range, ) # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(integral[0]) / (end - start) + return float(np.mean(integral[0]) / _domain_measure(y_true)) @overload @@ -721,7 +724,7 @@ def mean_squared_log_error( multioutput: Literal['uniform_average'] = 'uniform_average', squared: bool = True, ) -> float: - ... # noqa: WPS428 + pass # noqa: WPS428 @overload @@ -733,7 +736,7 @@ def mean_squared_log_error( multioutput: Literal['raw_values'], squared: bool = True, ) -> DataTypeRawValues: - ... # noqa: WPS428 + pass # noqa: WPS428 @singledispatch @@ -744,7 +747,7 @@ def mean_squared_log_error( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', squared: bool = True, -) -> Union[float, FDataGrid]: +) -> Union[float, NDArrayFloat, FDataGrid]: r"""Mean Squared Log Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -806,16 +809,18 @@ def mean_squared_log_error( multioutput = 'raw_values', ndarray. """ - return sklearn.metrics.mean_squared_log_error( - y_true, - y_pred, - sample_weight=sample_weight, - multioutput=multioutput, - squared=squared, + return ( # type: ignore [no-any-return] + sklearn.metrics.mean_squared_log_error( + y_true, + y_pred, + sample_weight=sample_weight, + multioutput=multioutput, + squared=squared, + ) ) -@mean_squared_log_error.register +@mean_squared_log_error.register # type: ignore[attr-defined, misc] def _mean_squared_log_error_fdatagrid( y_true: FDataGrid, y_pred: FDataGrid, @@ -840,7 +845,7 @@ def _mean_squared_log_error_fdatagrid( ) -@mean_squared_log_error.register +@mean_squared_log_error.register # type: ignore[attr-defined, misc] def _mean_squared_log_error_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, @@ -849,18 +854,19 @@ def _mean_squared_log_error_fdatabasis( squared: bool = True, ) -> float: - start, end = y_true.domain_range[0] - def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 - if np.any(y_true(x) < 0) or np.any(y_pred(x) < 0): + y_true_eval = y_true(x) + y_pred_eval = y_pred(x) + + if np.any(y_true_eval < 0) or np.any(y_pred_eval < 0): raise ValueError( "Mean Squared Logarithmic Error cannot be used when " "targets functions have negative values.", ) - error = np.average( - (np.log1p(y_true(x)) - np.log1p(y_pred(x))) ** 2, + error: NDArrayFloat = np.average( + (np.log1p(y_true_eval) - np.log1p(y_pred_eval)) ** 2, weights=sample_weight, axis=0, ) @@ -868,18 +874,18 @@ def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 if not squared: return np.sqrt(error) - # Error only contains 1 function - return error[0] + # Error only contains 1 input point + assert error.shape[0] == 1 + return error[0] # type: ignore [no-any-return] - integral = scipy.integrate.quad_vec( + integral = nquad_vec( _msle_func, - start, - end, + y_true.domain_range, ) # If the dimension of the codomain is > 1, # the mean of the errors is taken - return np.mean(integral[0]) / (end - start) + return float(np.mean(integral) / _domain_measure(y_true)) @overload @@ -890,7 +896,7 @@ def r2_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: - ... # noqa: WPS428 + pass # noqa: WPS428 @overload @@ -901,7 +907,7 @@ def r2_score( sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['raw_values'], ) -> DataTypeRawValues: - ... # noqa: WPS428 + pass # noqa: WPS428 @singledispatch @@ -911,7 +917,7 @@ def r2_score( *, sample_weight: Optional[NDArrayFloat] = None, multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', -) -> Union[float, FDataGrid]: +) -> Union[float, NDArrayFloat, FDataGrid]: r"""R^2 score for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -970,7 +976,7 @@ def r2_score( multioutput = 'raw_values', ndarray. """ - return sklearn.metrics.r2_score( + return sklearn.metrics.r2_score( # type: ignore [no-any-return] y_true, y_pred, sample_weight=sample_weight, @@ -978,7 +984,7 @@ def r2_score( ) -@r2_score.register +@r2_score.register # type: ignore[attr-defined, misc] def _r2_score_fdatagrid( y_true: FDataGrid, y_pred: FDataGrid, @@ -1001,7 +1007,7 @@ def _r2_score_fdatagrid( # Divisions by zero allowed with np.errstate(divide='ignore', invalid='ignore'): - score = 1 - ss_res / ss_tot + score: FDataGrid = 1 - ss_res / ss_tot # 0 / 0 divisions should be 0 in this context and the score, 1 score.data_matrix[np.isnan(score.data_matrix)] = 1 @@ -1012,10 +1018,10 @@ def _r2_score_fdatagrid( # Score only contains 1 function # If the dimension of the codomain is > 1, # the mean of the scores is taken - return np.mean(score.integrate()[0]) / _domain_measure(score) + return float(np.mean(score.integrate()[0]) / _domain_measure(score)) -@r2_score.register +@r2_score.register # type: ignore[attr-defined, misc] def _r2_score_fdatabasis( y_true: FDataBasis, y_pred: FDataBasis, @@ -1023,14 +1029,12 @@ def _r2_score_fdatabasis( sample_weight: Optional[NDArrayFloat] = None, ) -> float: - start, end = y_true.domain_range[0] - if y_pred.n_samples < 2: raise ValueError( 'R^2 score is not well-defined with less than two samples.', ) - def _r2_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 + def _r2_func(x: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 ss_res = np.average( (y_true(x) - y_pred(x)) ** 2, weights=sample_weight, @@ -1046,29 +1050,29 @@ def _r2_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 axis=0, ) + # Divisions by zero allowed + with np.errstate(divide='ignore', invalid='ignore'): + score = 1 - ss_res / ss_tot + # 0/0 case, the score is 1. - if ss_res == 0 and ss_tot == 0: - return 1 + score[np.isnan(score)] = 1 # r/0 case, r!= 0. Return -inf outside this function - if ss_res != 0 and ss_tot == 0: - raise ValueError + if np.any(np.isinf(score)): + raise InfiniteErrorException - score = 1 - ss_res / ss_tot - - # Score only contains 1 function - return score[0] + # Score only had 1 input point + assert score.shape[0] == 1 + return score[0] # type: ignore [no-any-return] try: - integral = scipy.integrate.quad_vec( + integral = nquad_vec( _r2_func, - start, - end, + y_true.domain_range, ) - - except ValueError: - return float('-inf') + except InfiniteErrorException: + return -math.inf # If the dimension of the codomain is > 1, # the mean of the scores is taken - return np.mean(integral[0]) / (end - start) + return float(np.mean(integral) / _domain_measure(y_true)) diff --git a/skfda/representation/interpolation.py b/skfda/representation/interpolation.py index b546766d4..1c107564b 100644 --- a/skfda/representation/interpolation.py +++ b/skfda/representation/interpolation.py @@ -183,6 +183,8 @@ def _get_interpolator_1d( fdatagrid.data_matrix, k=self.interpolation_order, axis=1, + # Orders 0 and 1 behave well + check_finite=self.interpolation_order > 1, ) def _get_interpolator_nd( diff --git a/tests/test_scoring.py b/skfda/tests/test_scoring.py similarity index 99% rename from tests/test_scoring.py rename to skfda/tests/test_scoring.py index 972ebb6d9..8f33cf21e 100644 --- a/tests/test_scoring.py +++ b/skfda/tests/test_scoring.py @@ -16,8 +16,8 @@ mean_squared_log_error, r2_score, ) -from skfda.representation._typing import NDArrayFloat from skfda.representation.basis import BSpline, Fourier, Monomial +from skfda.typing._numpy import NDArrayFloat def _create_data_grid(n: int) -> Tuple[FDataGrid, FDataGrid]: From d991532fff62bc1e88c6b546caf0640f9c83ff8a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Fri, 7 Oct 2022 15:18:19 +0200 Subject: [PATCH 309/400] Simplified data, classes and subtest messages --- skfda/tests/test_classifier_classes.py | 76 ++++++++++---------------- 1 file changed, 28 insertions(+), 48 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index d1a405638..df2f443d8 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -1,28 +1,12 @@ """Tests classes attribute of classifiers.""" import unittest +from typing import List import numpy as np -from sklearn.model_selection import train_test_split from skfda._utils._sklearn_adapter import ClassifierMixin -from skfda._utils._utils import _classifier_get_classes -from skfda.datasets import fetch_growth -from skfda.exploratory.stats.covariance import ParametricGaussianCovariance -from skfda.misc.covariances import Gaussian -from skfda.misc.metrics import l2_distance -from skfda.ml.classification import ( - DDClassifier, - DDGClassifier, - DTMClassifier, - KNeighborsClassifier, - LogisticRegression, - MaximumDepthClassifier, - NearestCentroid, - QuadraticDiscriminantAnalysis, - RadiusNeighborsClassifier, -) -from skfda.ml.classification._depth_classifiers import _ArgMaxClassifier +from skfda.datasets import make_multimodal_samples from skfda.representation import FData from ..typing._numpy import NDArrayAny @@ -33,43 +17,39 @@ class TestClassifierClasses(unittest.TestCase): def setUp(self) -> None: """Establish train and test data sets.""" - X, y = fetch_growth(return_X_y=True) - X_train, X_test, y_train, _ = train_test_split( - X, - y, - test_size=0.25, - stratify=y, - random_state=0, + # List of classes to test + # Adding new classes to this list will test the classifiers with them + self.classes_list: List[NDArrayAny] = [ + np.array([0, 1, 2]), + np.array(["class_a", "class_b", "class_c"]), + ] + + # Create one target y data of length n_samples from each class set + n_samples = 30 + self.y_list = [ + np.resize(classes, n_samples) + for classes in self.classes_list + ] + self.X = make_multimodal_samples( + n_samples=n_samples, ) - self._X_train = X_train - self._X_test = X_test - self._y_train = y_train - self.classes = _classifier_get_classes(self._y_train)[0] - self.tested_classifiers: list[ClassifierMixin[FData, NDArrayAny]] = [ - KNeighborsClassifier(), - RadiusNeighborsClassifier(), - NearestCentroid(), - DDGClassifier(multivariate_classifier=KNeighborsClassifier()), - DDClassifier(degree=2), - DTMClassifier(proportiontocut=0, metric=l2_distance), - MaximumDepthClassifier(), - _ArgMaxClassifier(), - LogisticRegression(max_iter=1000), - QuadraticDiscriminantAnalysis( - cov_estimator=ParametricGaussianCovariance( - Gaussian(variance=6, length_scale=1), - ), - ), + self.tested_classifiers: List[ClassifierMixin[FData, NDArrayAny]] = [ ] def test_classes(self) -> None: """Check classes attribute.""" # Iterate over all classifiers with index - for i, clf in enumerate(self.tested_classifiers): - with self.subTest(i=i): - clf.fit(self._X_train, self._y_train) - np.testing.assert_array_equal(clf.classes_, self.classes) + for clf in self.tested_classifiers: + # Iterate over all class sets to test different types of classes + for classes, y in zip(self.classes_list, self.y_list): + message = ( + f'Testing classifier {clf.__class__.__name__} ' + f'with classes {classes}' + ) + with self.subTest(msg=message): + clf.fit(self.X, y) + np.testing.assert_array_equal(clf.classes_, classes) if __name__ == "__main__": From 83aeb2daf7afd388c32d749a6e1662cf34f104fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Fri, 7 Oct 2022 15:41:37 +0200 Subject: [PATCH 310/400] Added already working LogisticRegression --- skfda/tests/test_classifier_classes.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index df2f443d8..fd542e334 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -7,6 +7,7 @@ from skfda._utils._sklearn_adapter import ClassifierMixin from skfda.datasets import make_multimodal_samples +from skfda.ml.classification import LogisticRegression from skfda.representation import FData from ..typing._numpy import NDArrayAny @@ -20,8 +21,8 @@ def setUp(self) -> None: # List of classes to test # Adding new classes to this list will test the classifiers with them self.classes_list: List[NDArrayAny] = [ - np.array([0, 1, 2]), - np.array(["class_a", "class_b", "class_c"]), + np.array([0, 1]), + np.array(["class_a", "class_b"]), ] # Create one target y data of length n_samples from each class set @@ -32,9 +33,11 @@ def setUp(self) -> None: ] self.X = make_multimodal_samples( n_samples=n_samples, + noise=0.05, ) self.tested_classifiers: List[ClassifierMixin[FData, NDArrayAny]] = [ + LogisticRegression(), ] def test_classes(self) -> None: From e384f80f579feabb084dff6b0899ca2c58bb9ed4 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 7 Oct 2022 19:10:22 +0200 Subject: [PATCH 311/400] Fix scoring tests: - Fix Mypy errors - Fix non-converging integral - Eliminate nondeterminism from tests --- skfda/misc/scoring.py | 32 ++++++- skfda/tests/test_scoring.py | 177 ++++++++++++++---------------------- 2 files changed, 97 insertions(+), 112 deletions(-) diff --git a/skfda/misc/scoring.py b/skfda/misc/scoring.py index a29a0c41a..809cc73de 100644 --- a/skfda/misc/scoring.py +++ b/skfda/misc/scoring.py @@ -14,23 +14,49 @@ from ..representation._functional_data import EvalPointsType from ..typing._numpy import NDArrayFloat -DataType = TypeVar('DataType', FDataGrid, FDataBasis, NDArrayFloat) -DataTypeRawValues = TypeVar('DataTypeRawValues', FDataGrid, NDArrayFloat) +DataType = TypeVar('DataType', bound=Union[FData, NDArrayFloat]) +DataTypeRawValues = TypeVar( + 'DataTypeRawValues', + bound=Union[FDataGrid, NDArrayFloat], +) MultiOutputType = Literal['uniform_average', 'raw_values'] class InfiniteErrorException(Exception): - pass + pass # noqa: WPS428 class ScoreFunction(Protocol): """Type definition for score functions.""" + @overload + def __call__( + self, + y_true: DataType, + y_pred: DataType, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['uniform_average'] = 'uniform_average', + ) -> float: + pass # noqa: WPS428 + + @overload + def __call__( # noqa: D102 + self, + y_true: DataTypeRawValues, + y_pred: DataTypeRawValues, + *, + sample_weight: Optional[NDArrayFloat] = None, + multioutput: Literal['raw_values'], + ) -> DataTypeRawValues: + pass # noqa: WPS428 + def __call__( # noqa: D102 self, y_true: Union[FData, NDArrayFloat], y_pred: Union[FData, NDArrayFloat], + *, sample_weight: Optional[NDArrayFloat] = None, multioutput: MultiOutputType = 'uniform_average', ) -> Union[NDArrayFloat, FDataGrid, float]: diff --git a/skfda/tests/test_scoring.py b/skfda/tests/test_scoring.py index 8f33cf21e..d11328487 100644 --- a/skfda/tests/test_scoring.py +++ b/skfda/tests/test_scoring.py @@ -1,12 +1,12 @@ """Test for scoring module.""" +import math import unittest -from typing import Optional, Tuple +from typing import Any, Optional, Tuple import numpy as np -import sklearn +import sklearn.metrics from skfda import FDataBasis, FDataGrid -from skfda.datasets import fetch_tecator from skfda.misc.scoring import ( ScoreFunction, explained_variance_score, @@ -20,16 +20,6 @@ from skfda.typing._numpy import NDArrayFloat -def _create_data_grid(n: int) -> Tuple[FDataGrid, FDataGrid]: - X, y = fetch_tecator(return_X_y=True, as_frame=True) - fd = X.iloc[:, 0].values - - y_true = fd[:n] - y_pred = fd[n:2 * n] - - return y_true, y_pred - - def _create_data_basis() -> Tuple[FDataBasis, FDataBasis]: coef_true = [[1, 2, 3], [4, 5, 6]] coef_pred = [[1, 2, 3], [4, 6, 5]] @@ -41,7 +31,7 @@ def _create_data_basis() -> Tuple[FDataBasis, FDataBasis]: coefficients=coef_true, ) - # y_true: 1) 1 + 2x + 3x^2 + # y_pred: 1) 1 + 2x + 3x^2 # 2) 4 + 6x + 5x^2 y_pred = FDataBasis( basis=Monomial(domain_range=((0, 3),), n_basis=3), @@ -53,50 +43,41 @@ def _create_data_basis() -> Tuple[FDataBasis, FDataBasis]: return y_true, y_pred +def _create_data_grid() -> Tuple[FDataGrid, FDataGrid]: + + y_true, y_pred = _create_data_basis() + grid = np.linspace(*y_true.domain_range[0], 100) + + return y_true.to_grid(grid), y_pred.to_grid(grid) + + class TestScoreFunctionsGrid(unittest.TestCase): """Tests for score functions with FDataGrid representation.""" - n = 10 - def _test_generic_grid( self, function: ScoreFunction, sklearn_function: ScoreFunction, weight: Optional[NDArrayFloat] = None, - squared: bool = True, + **kwargs: Any, ) -> None: - y_true, y_pred = _create_data_grid(self.n) + y_true, y_pred = _create_data_grid() - if squared: - score = function( - y_true, - y_pred, - multioutput='raw_values', - sample_weight=weight, - ) - - score_sklearn = sklearn_function( - y_true.data_matrix.squeeze(), - y_pred.data_matrix.squeeze(), - multioutput='raw_values', - sample_weight=weight, - ) - else: - score = function( - y_true, - y_pred, - multioutput='raw_values', - sample_weight=weight, - squared=False, - ) + score = function( + y_true, + y_pred, + multioutput='raw_values', + sample_weight=weight, + **kwargs, + ) - score_sklearn = sklearn_function( - y_true.data_matrix.squeeze(), - y_pred.data_matrix.squeeze(), - multioutput='raw_values', - sample_weight=weight, - squared=False, - ) + score_sklearn = sklearn_function( + y_true.data_matrix.squeeze(), + y_pred.data_matrix.squeeze(), + multioutput='raw_values', + sample_weight=weight, + **kwargs, + ) np.testing.assert_allclose( score.data_matrix.squeeze(), @@ -113,7 +94,7 @@ def test_explained_variance_score_grid(self) -> None: self._test_generic_grid( explained_variance_score, sklearn.metrics.explained_variance_score, - np.random.random_sample(self.n), + weight=np.array([3, 1]), ) def test_mean_absolute_error_grid(self) -> None: @@ -126,7 +107,7 @@ def test_mean_absolute_error_grid(self) -> None: self._test_generic_grid( mean_absolute_error, sklearn.metrics.mean_absolute_error, - np.random.random_sample(self.n), + weight=np.array([3, 1]), ) def test_mean_absolute_percentage_error_grid(self) -> None: @@ -139,7 +120,7 @@ def test_mean_absolute_percentage_error_grid(self) -> None: self._test_generic_grid( mean_absolute_percentage_error, sklearn.metrics.mean_absolute_percentage_error, - np.random.random_sample(self.n), + weight=np.array([3, 1]), ) def test_mean_squared_error_grid(self) -> None: @@ -158,7 +139,7 @@ def test_mean_squared_error_grid(self) -> None: self._test_generic_grid( mean_squared_error, sklearn.metrics.mean_squared_error, - np.random.random_sample(self.n), + weight=np.array([3, 1]), ) def test_mean_squared_log_error_grid(self) -> None: @@ -177,7 +158,7 @@ def test_mean_squared_log_error_grid(self) -> None: self._test_generic_grid( mean_squared_log_error, sklearn.metrics.mean_squared_log_error, - np.random.random_sample(self.n), + weight=np.array([3, 1]), ) def test_r2_score_grid(self) -> None: @@ -190,22 +171,20 @@ def test_r2_score_grid(self) -> None: self._test_generic_grid( r2_score, sklearn.metrics.r2_score, - np.random.random_sample(self.n), + weight=np.array([3, 1]), ) class TestScoreFunctionGridBasis(unittest.TestCase): """Compare the results obtained for FDataGrid and FDataBasis.""" - n = 10 - def _test_grid_basis_generic( self, score_function: ScoreFunction, - sample_weight: Optional[NDArrayFloat] = None, - squared: bool = True, + weight: Optional[NDArrayFloat] = None, + **kwargs: Any, ) -> None: - y_true_grid, y_pred_grid = _create_data_grid(self.n) + y_true_grid, y_pred_grid = _create_data_grid() y_true_basis = y_true_grid.to_basis(basis=BSpline(n_basis=10)) y_pred_basis = y_pred_grid.to_basis(basis=BSpline(n_basis=10)) @@ -214,30 +193,18 @@ def _test_grid_basis_generic( # do not give same functions. precision = 2 - if squared: - score_grid = score_function( - y_true_grid, - y_pred_grid, - sample_weight=sample_weight, - ) - score_basis = score_function( - y_true_basis, - y_pred_basis, - sample_weight=sample_weight, - ) - else: - score_grid = score_function( - y_true_grid, - y_pred_grid, - sample_weight=sample_weight, - squared=False, - ) - score_basis = score_function( - y_true_basis, - y_pred_basis, - sample_weight=sample_weight, - squared=False, - ) + score_grid = score_function( + y_true_grid, + y_pred_grid, + sample_weight=weight, + **kwargs, + ) + score_basis = score_function( + y_true_basis, + y_pred_basis, + sample_weight=weight, + **kwargs, + ) self.assertAlmostEqual(score_basis, score_grid, places=precision) @@ -246,7 +213,7 @@ def test_explained_variance_score(self) -> None: self._test_grid_basis_generic(explained_variance_score) self._test_grid_basis_generic( explained_variance_score, - np.random.random_sample((self.n,)), + weight=np.array([3, 1]), ) def test_mean_absolute_error(self) -> None: @@ -254,7 +221,7 @@ def test_mean_absolute_error(self) -> None: self._test_grid_basis_generic(mean_absolute_error) self._test_grid_basis_generic( mean_absolute_error, - np.random.random_sample((self.n,)), + weight=np.array([3, 1]), ) def test_mean_absolute_percentage_error(self) -> None: @@ -262,7 +229,7 @@ def test_mean_absolute_percentage_error(self) -> None: self._test_grid_basis_generic(mean_absolute_percentage_error) self._test_grid_basis_generic( mean_absolute_percentage_error, - np.random.random_sample((self.n,)), + weight=np.array([3, 1]), ) def test_mean_squared_error(self) -> None: @@ -270,7 +237,7 @@ def test_mean_squared_error(self) -> None: self._test_grid_basis_generic(mean_squared_error) self._test_grid_basis_generic( mean_squared_error, - np.random.random_sample((self.n,)), + weight=np.array([3, 1]), ) self._test_grid_basis_generic(mean_squared_error, squared=False) @@ -279,7 +246,7 @@ def test_mean_squared_log_error(self) -> None: self._test_grid_basis_generic(mean_squared_log_error) self._test_grid_basis_generic( mean_squared_log_error, - np.random.random_sample((self.n,)), + weight=np.array([3, 1]), ) self._test_grid_basis_generic(mean_squared_log_error, squared=False) @@ -288,7 +255,7 @@ def test_r2_score(self) -> None: self._test_grid_basis_generic(r2_score) self._test_grid_basis_generic( r2_score, - np.random.random_sample((self.n,)), + weight=np.array([3, 1]), ) @@ -418,7 +385,7 @@ def test_zero_r2(self) -> None: y_pred_grid, multioutput='raw_values', ).evaluate(1), - [[[float('-inf')]]], + [[[-math.inf]]], ) # r/0 for FDataBasis (r != 0) @@ -427,7 +394,7 @@ def test_zero_r2(self) -> None: y_true_basis, y_pred_basis, ), - float('-inf'), + -math.inf, ) def test_zero_ev(self) -> None: @@ -489,7 +456,7 @@ def test_zero_ev(self) -> None: y_pred_grid, multioutput='raw_values', ).evaluate(1), - [[[float('-inf')]]], + [[[-math.inf]]], ) # r/0 for FDataBasis (r != 0) @@ -498,13 +465,13 @@ def test_zero_ev(self) -> None: y_true_basis, y_pred_basis, ), - float('-inf'), + -math.inf, ) def test_zero_mape(self) -> None: """Test Mean Absolute Percentage Error when y_true can be zero.""" - basis_coef_true = [[3, 0, 0], [0, 0, 1]] - basis_coef_pred = [[1, 0, 0], [1, 0, 1]] + basis_coef_true = [[3, 0, 0, 0, 0], [0, 0, 1, 0, 0]] + basis_coef_pred = [[1, 0, 0, 0, 0], [0, 0, 1, 1, 0]] # Fourier basis defined in (0, 2) with 3 elements # The functions are @@ -512,25 +479,21 @@ def test_zero_mape(self) -> None: # 2) 1/sqrt(2) * cos(pi t) # # y_pred(t) = 1) 1/sqrt(2) - # 2) 1/sqrt(2) + 1/sqrt(2) * cos(pi t) + # 2) 1/sqrt(2) * cos(pi t) + 1/sqrt(2) * sin(2 pi t) # The second function in y_true should be zero at t = 0.5 and t = 1.5 y_true_basis = FDataBasis( - basis=Fourier(domain_range=((0, 2),), n_basis=3), + basis=Fourier(domain_range=((0, 2),), n_basis=5), coefficients=basis_coef_true, ) y_pred_basis = FDataBasis( - basis=Fourier(domain_range=((0, 2),), n_basis=3), + basis=Fourier(domain_range=((0, 2),), n_basis=5), coefficients=basis_coef_pred, ) - self.assertWarns( - RuntimeWarning, - mean_absolute_percentage_error, - y_true_basis, - y_pred_basis, - ) + with self.assertWarns(RuntimeWarning): + mean_absolute_percentage_error(y_true_basis, y_pred_basis) grid_points = np.linspace(0, 2, 9) @@ -539,12 +502,8 @@ def test_zero_mape(self) -> None: y_true_grid = y_true_basis.to_grid(grid_points=grid_points) y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) - self.assertWarns( - RuntimeWarning, - mean_absolute_percentage_error, - y_true_grid, - y_pred_grid, - ) + with self.assertWarns(RuntimeWarning): + mean_absolute_percentage_error(y_true_grid, y_pred_grid) def test_negative_msle(self) -> None: """Test Mean Squared Log Error when there are negative data.""" From 6686e2c4cdeb010e2d1866f04c980ded033348ec Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 7 Oct 2022 20:19:27 +0200 Subject: [PATCH 312/400] Remove code repetition. --- skfda/misc/scoring.py | 177 +++++++++++++--------------------- skfda/tests/test_scoring.py | 184 +++++++++--------------------------- 2 files changed, 111 insertions(+), 250 deletions(-) diff --git a/skfda/misc/scoring.py b/skfda/misc/scoring.py index 809cc73de..b3aac6879 100644 --- a/skfda/misc/scoring.py +++ b/skfda/misc/scoring.py @@ -2,7 +2,7 @@ import math import warnings from functools import singledispatch -from typing import Optional, TypeVar, Union, overload +from typing import Callable, Optional, TypeVar, Union, overload import numpy as np import sklearn.metrics @@ -23,7 +23,7 @@ MultiOutputType = Literal['uniform_average', 'raw_values'] -class InfiniteErrorException(Exception): +class InfiniteScoreError(Exception): pass # noqa: WPS428 @@ -83,6 +83,42 @@ def _var( ) +def _uniform_average_basis( + y_true: FDataBasis, + integrand: Callable[[NDArrayFloat], NDArrayFloat], +) -> float: + + try: + integral = nquad_vec( + integrand, + y_true.domain_range, + ) + except InfiniteScoreError: + return -math.inf + + # If the dimension of the codomain is > 1, + # the mean of the scores is taken + return float(np.mean(integral) / _domain_measure(y_true)) + + +def _multioutput_score_grid( + score: FDataGrid, + multioutput: MultiOutputType, + squared: bool = True, +) -> Union[float, FDataGrid]: + + if not squared: + score = np.sqrt(score) + + if multioutput == 'raw_values': + return score + + # Score only contains 1 function + # If the dimension of the codomain is > 1, + # the mean of the scores is taken + return float(np.mean(score.integrate()[0]) / _domain_measure(score)) + + @overload def explained_variance_score( y_true: DataType, @@ -111,7 +147,7 @@ def explained_variance_score( y_pred: Union[FData, NDArrayFloat], *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid, NDArrayFloat]: r"""Explained variance score for :class:`~skfda.representation.FData`. @@ -197,7 +233,7 @@ def _explained_variance_score_fdatagrid( y_pred: FDataGrid, *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: num = _var(y_true - y_pred, weights=sample_weight) @@ -210,13 +246,7 @@ def _explained_variance_score_fdatagrid( # 0 / 0 divisions should be 0 in this context, and the score, 1 score.data_matrix[np.isnan(score.data_matrix)] = 1 - if multioutput == 'raw_values': - return score - - # Score only contains 1 function - # If the dimension of the codomain is > 1, - # the mean of the scores is taken - return float(np.mean(score.integrate()[0]) / _domain_measure(score)) + return _multioutput_score_grid(score, multioutput) @explained_variance_score.register # type: ignore[attr-defined, misc] @@ -259,23 +289,13 @@ def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 # r/0 case, r!= 0. Return -inf outside this function if np.any(np.isinf(score)): - raise InfiniteErrorException + raise InfiniteScoreError # Score only contains 1 input point assert score.shape[0] == 1 return score[0] # type: ignore [no-any-return] - try: - integral = nquad_vec( - _ev_func, - y_true.domain_range, - ) - except InfiniteErrorException: - return -math.inf - - # If the dimension of the codomain is > 1, - # the mean of the scores is taken - return float(np.mean(integral[0]) / _domain_measure(y_true)) + return _uniform_average_basis(y_true, _ev_func) @overload @@ -306,7 +326,7 @@ def mean_absolute_error( y_pred: Union[FData, NDArrayFloat], *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid, NDArrayFloat]: r"""Mean Absolute Error for :class:`~skfda.representation.FData`. @@ -377,18 +397,11 @@ def _mean_absolute_error_fdatagrid( y_pred: FDataGrid, *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: - error: FDataGrid = mean(np.abs(y_true - y_pred), weights=sample_weight) - - if multioutput == 'raw_values': - return error - - # Error only contains 1 function - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(error.integrate()[0]) / _domain_measure(error)) + error = mean(np.abs(y_true - y_pred), weights=sample_weight) + return _multioutput_score_grid(error, multioutput) @mean_absolute_error.register # type: ignore[attr-defined, misc] @@ -410,14 +423,7 @@ def _mae_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - integral = nquad_vec( - _mae_func, - y_true.domain_range, - ) - - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(integral[0]) / _domain_measure(y_true)) + return _uniform_average_basis(y_true, _mae_func) @overload @@ -448,7 +454,7 @@ def mean_absolute_percentage_error( y_pred: Union[FData, NDArrayFloat], *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, NDArrayFloat, FDataGrid]: r"""Mean Absolute Percentage Error for :class:`~skfda.representation.FData`. @@ -524,7 +530,7 @@ def _mean_absolute_percentage_error_fdatagrid( y_pred: FDataGrid, *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: epsilon = np.finfo(np.float64).eps @@ -534,15 +540,8 @@ def _mean_absolute_percentage_error_fdatagrid( mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon) - error: FDataGrid = mean(mape, weights=sample_weight) - - if multioutput == 'raw_values': - return error - - # Error only contains 1 function - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(error.integrate()[0]) / _domain_measure(error)) + error = mean(mape, weights=sample_weight) + return _multioutput_score_grid(error, multioutput) @mean_absolute_percentage_error.register # type: ignore[attr-defined, misc] @@ -572,14 +571,7 @@ def _mape_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - integral = nquad_vec( - _mape_func, - y_true.domain_range, - ) - - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(integral[0]) / _domain_measure(y_true)) + return _uniform_average_basis(y_true, _mape_func) @overload @@ -612,7 +604,7 @@ def mean_squared_error( y_pred: Union[FData, NDArrayFloat], *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', squared: bool = True, ) -> Union[float, NDArrayFloat, FDataGrid]: r"""Mean Squared Error for :class:`~skfda.representation.FData`. @@ -686,7 +678,7 @@ def _mean_squared_error_fdatagrid( y_pred: FDataGrid, *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', squared: bool = True, ) -> Union[float, FDataGrid]: @@ -695,16 +687,7 @@ def _mean_squared_error_fdatagrid( weights=sample_weight, ) - if not squared: - error = np.sqrt(error) - - if multioutput == 'raw_values': - return error - - # Error only contains 1 function - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(error.integrate()[0]) / _domain_measure(error)) + return _multioutput_score_grid(error, multioutput, squared=squared) @mean_squared_error.register # type: ignore[attr-defined, misc] @@ -731,14 +714,7 @@ def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - integral = nquad_vec( - _mse_func, - y_true.domain_range, - ) - - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(integral[0]) / _domain_measure(y_true)) + return _uniform_average_basis(y_true, _mse_func) @overload @@ -771,7 +747,7 @@ def mean_squared_log_error( y_pred: Union[FData, NDArrayFloat], *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', squared: bool = True, ) -> Union[float, NDArrayFloat, FDataGrid]: r"""Mean Squared Log Error for :class:`~skfda.representation.FData`. @@ -852,7 +828,7 @@ def _mean_squared_log_error_fdatagrid( y_pred: FDataGrid, *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', squared: bool = True, ) -> Union[float, FDataGrid]: @@ -904,14 +880,7 @@ def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - integral = nquad_vec( - _msle_func, - y_true.domain_range, - ) - - # If the dimension of the codomain is > 1, - # the mean of the errors is taken - return float(np.mean(integral) / _domain_measure(y_true)) + return _uniform_average_basis(y_true, _msle_func) @overload @@ -942,7 +911,7 @@ def r2_score( y_pred: Union[FData, NDArrayFloat], *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, NDArrayFloat, FDataGrid]: r"""R^2 score for :class:`~skfda.representation.FData`. @@ -1016,7 +985,7 @@ def _r2_score_fdatagrid( y_pred: FDataGrid, *, sample_weight: Optional[NDArrayFloat] = None, - multioutput: Literal['uniform_average', 'raw_values'] = 'uniform_average', + multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: if y_pred.n_samples < 2: @@ -1038,13 +1007,7 @@ def _r2_score_fdatagrid( # 0 / 0 divisions should be 0 in this context and the score, 1 score.data_matrix[np.isnan(score.data_matrix)] = 1 - if multioutput == 'raw_values': - return score - - # Score only contains 1 function - # If the dimension of the codomain is > 1, - # the mean of the scores is taken - return float(np.mean(score.integrate()[0]) / _domain_measure(score)) + return _multioutput_score_grid(score, multioutput) @r2_score.register # type: ignore[attr-defined, misc] @@ -1085,20 +1048,10 @@ def _r2_func(x: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 # r/0 case, r!= 0. Return -inf outside this function if np.any(np.isinf(score)): - raise InfiniteErrorException + raise InfiniteScoreError # Score only had 1 input point assert score.shape[0] == 1 return score[0] # type: ignore [no-any-return] - try: - integral = nquad_vec( - _r2_func, - y_true.domain_range, - ) - except InfiniteErrorException: - return -math.inf - - # If the dimension of the codomain is > 1, - # the mean of the scores is taken - return float(np.mean(integral) / _domain_measure(y_true)) + return _uniform_average_basis(y_true, _r2_func) diff --git a/skfda/tests/test_scoring.py b/skfda/tests/test_scoring.py index d11328487..7a5b4233e 100644 --- a/skfda/tests/test_scoring.py +++ b/skfda/tests/test_scoring.py @@ -1,7 +1,7 @@ """Test for scoring module.""" import math import unittest -from typing import Any, Optional, Tuple +from typing import Any, Optional, Sequence, Tuple import numpy as np import sklearn.metrics @@ -19,6 +19,15 @@ from skfda.representation.basis import BSpline, Fourier, Monomial from skfda.typing._numpy import NDArrayFloat +score_functions: Sequence[ScoreFunction] = ( + explained_variance_score, + mean_absolute_error, + mean_absolute_percentage_error, + mean_squared_error, + mean_squared_log_error, + r2_score, +) + def _create_data_basis() -> Tuple[FDataBasis, FDataBasis]: coef_true = [[1, 2, 3], [4, 5, 6]] @@ -57,7 +66,6 @@ class TestScoreFunctionsGrid(unittest.TestCase): def _test_generic_grid( self, function: ScoreFunction, - sklearn_function: ScoreFunction, weight: Optional[NDArrayFloat] = None, **kwargs: Any, ) -> None: @@ -71,7 +79,7 @@ def _test_generic_grid( **kwargs, ) - score_sklearn = sklearn_function( + score_sklearn = function( y_true.data_matrix.squeeze(), y_pred.data_matrix.squeeze(), multioutput='raw_values', @@ -84,95 +92,25 @@ def _test_generic_grid( score_sklearn, ) - def test_explained_variance_score_grid(self) -> None: - """Test Explained Variance Score for FDataGrid.""" - self._test_generic_grid( - explained_variance_score, - sklearn.metrics.explained_variance_score, - ) - - self._test_generic_grid( - explained_variance_score, - sklearn.metrics.explained_variance_score, - weight=np.array([3, 1]), - ) - - def test_mean_absolute_error_grid(self) -> None: - """Test Mean Absolute Error for FDataGrid.""" - self._test_generic_grid( - mean_absolute_error, - sklearn.metrics.mean_absolute_error, - ) - - self._test_generic_grid( - mean_absolute_error, - sklearn.metrics.mean_absolute_error, - weight=np.array([3, 1]), - ) - - def test_mean_absolute_percentage_error_grid(self) -> None: - """Test Mean Absolute Percentage Error for FDataGrid.""" - self._test_generic_grid( - mean_absolute_percentage_error, - sklearn.metrics.mean_absolute_percentage_error, - ) - - self._test_generic_grid( - mean_absolute_percentage_error, - sklearn.metrics.mean_absolute_percentage_error, - weight=np.array([3, 1]), - ) - - def test_mean_squared_error_grid(self) -> None: - """Test Mean Squared Error for FDataGrid.""" - self._test_generic_grid( - mean_squared_error, - sklearn.metrics.mean_squared_error, - ) - - self._test_generic_grid( - mean_squared_error, - sklearn.metrics.mean_squared_error, - squared=False, - ) - - self._test_generic_grid( - mean_squared_error, - sklearn.metrics.mean_squared_error, - weight=np.array([3, 1]), - ) - - def test_mean_squared_log_error_grid(self) -> None: - """Test Mean Squared Log Error for FDataGrid.""" - self._test_generic_grid( - mean_squared_log_error, - sklearn.metrics.mean_squared_log_error, - ) - - self._test_generic_grid( - mean_squared_log_error, - sklearn.metrics.mean_squared_log_error, - squared=False, - ) + def test_all(self) -> None: + """Test all score functions.""" + for score_function in score_functions: + with self.subTest(function=score_function): - self._test_generic_grid( - mean_squared_log_error, - sklearn.metrics.mean_squared_log_error, - weight=np.array([3, 1]), - ) + self._test_generic_grid(score_function) - def test_r2_score_grid(self) -> None: - """Test R2 Score for FDataGrid.""" - self._test_generic_grid( - r2_score, - sklearn.metrics.r2_score, - ) + try: + self._test_generic_grid( + score_function, + squared=False, + ) + except TypeError: + pass - self._test_generic_grid( - r2_score, - sklearn.metrics.r2_score, - weight=np.array([3, 1]), - ) + self._test_generic_grid( + score_function, + weight=np.array([3, 1]), + ) class TestScoreFunctionGridBasis(unittest.TestCase): @@ -208,55 +146,25 @@ def _test_grid_basis_generic( self.assertAlmostEqual(score_basis, score_grid, places=precision) - def test_explained_variance_score(self) -> None: - """Explained variance score for FDataGrid and FDataBasis.""" - self._test_grid_basis_generic(explained_variance_score) - self._test_grid_basis_generic( - explained_variance_score, - weight=np.array([3, 1]), - ) - - def test_mean_absolute_error(self) -> None: - """Mean Absolute Error for FDataGrid and FDataBasis.""" - self._test_grid_basis_generic(mean_absolute_error) - self._test_grid_basis_generic( - mean_absolute_error, - weight=np.array([3, 1]), - ) - - def test_mean_absolute_percentage_error(self) -> None: - """Mean Absolute Percentage Error for FDataGrid and FDataBasis.""" - self._test_grid_basis_generic(mean_absolute_percentage_error) - self._test_grid_basis_generic( - mean_absolute_percentage_error, - weight=np.array([3, 1]), - ) - - def test_mean_squared_error(self) -> None: - """Mean Squared Error for FDataGrid and FDataBasis.""" - self._test_grid_basis_generic(mean_squared_error) - self._test_grid_basis_generic( - mean_squared_error, - weight=np.array([3, 1]), - ) - self._test_grid_basis_generic(mean_squared_error, squared=False) - - def test_mean_squared_log_error(self) -> None: - """Mean Squared Log Error for FDataGrid and FDataBasis.""" - self._test_grid_basis_generic(mean_squared_log_error) - self._test_grid_basis_generic( - mean_squared_log_error, - weight=np.array([3, 1]), - ) - self._test_grid_basis_generic(mean_squared_log_error, squared=False) - - def test_r2_score(self) -> None: - """R2 Score for FDataGrid and FDataBasis.""" - self._test_grid_basis_generic(r2_score) - self._test_grid_basis_generic( - r2_score, - weight=np.array([3, 1]), - ) + def test_all(self) -> None: + """Test all score functions.""" + for score_function in score_functions: + with self.subTest(function=score_function): + + self._test_grid_basis_generic(score_function) + + try: + self._test_grid_basis_generic( + score_function, + squared=False, + ) + except TypeError: + pass + + self._test_grid_basis_generic( + score_function, + weight=np.array([3, 1]), + ) class TestScoreFunctionsBasis(unittest.TestCase): From 75f3c1bb56d7ff626a1e45199e7ea6785ea84320 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sun, 9 Oct 2022 20:53:04 +0200 Subject: [PATCH 313/400] Use r2 scoring for functional regressors - Replace old scoring in neighbors regression. - Fix test which wrongly assumed that the integral was computed before the quotient for r2 score. - Remove unnecessary test. - Fix circular import. - Raise ValueError for operation between FDataGrid and invalid Numpy array. --- skfda/_utils/_sklearn_adapter.py | 6 +- skfda/misc/scoring.py | 182 +++++++++++++++++-------------- skfda/ml/_neighbors_base.py | 129 ---------------------- skfda/representation/grid.py | 5 + skfda/tests/test_neighbors.py | 17 +-- 5 files changed, 110 insertions(+), 229 deletions(-) diff --git a/skfda/_utils/_sklearn_adapter.py b/skfda/_utils/_sklearn_adapter.py index 3c23aa330..f3eaecb0b 100644 --- a/skfda/_utils/_sklearn_adapter.py +++ b/skfda/_utils/_sklearn_adapter.py @@ -184,8 +184,10 @@ def score( # noqa: D102 y: TargetPrediction, sample_weight: NDArrayFloat | None = None, ) -> float: - return super().score( # type: ignore[no-any-return] - X, + from ..misc.scoring import r2_score + y_pred = self.predict(X) + return r2_score( y, + y_pred, sample_weight=sample_weight, ) diff --git a/skfda/misc/scoring.py b/skfda/misc/scoring.py index b3aac6879..c57902668 100644 --- a/skfda/misc/scoring.py +++ b/skfda/misc/scoring.py @@ -1,4 +1,6 @@ """Scoring methods for FData.""" +from __future__ import annotations + import math import warnings from functools import singledispatch @@ -9,22 +11,17 @@ from typing_extensions import Literal, Protocol from .._utils import nquad_vec -from ..exploratory.stats._stats import mean, var from ..representation import FData, FDataBasis, FDataGrid from ..representation._functional_data import EvalPointsType from ..typing._numpy import NDArrayFloat -DataType = TypeVar('DataType', bound=Union[FData, NDArrayFloat]) -DataTypeRawValues = TypeVar( - 'DataTypeRawValues', - bound=Union[FDataGrid, NDArrayFloat], -) +DataType = TypeVar('DataType') MultiOutputType = Literal['uniform_average', 'raw_values'] -class InfiniteScoreError(Exception): - pass # noqa: WPS428 +class _InfiniteScoreError(Exception): + """Exception for skipping integral on infinite value.""" class ScoreFunction(Protocol): @@ -36,7 +33,7 @@ def __call__( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: pass # noqa: WPS428 @@ -44,22 +41,22 @@ def __call__( @overload def __call__( # noqa: D102 self, - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], - ) -> DataTypeRawValues: + ) -> DataType: pass # noqa: WPS428 def __call__( # noqa: D102 self, - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', - ) -> Union[NDArrayFloat, FDataGrid, float]: + ) -> float | DataType: pass # noqa: WPS428 @@ -72,8 +69,10 @@ def _domain_measure(fd: FData) -> float: def _var( x: FDataGrid, - weights: Optional[NDArrayFloat] = None, + weights: NDArrayFloat | None = None, ) -> FDataGrid: + from ..exploratory.stats import mean, var + if weights is None: return var(x) @@ -83,17 +82,24 @@ def _var( ) -def _uniform_average_basis( +def _multioutput_score_basis( y_true: FDataBasis, + multioutput: MultiOutputType, integrand: Callable[[NDArrayFloat], NDArrayFloat], ) -> float: + if multioutput != "uniform_average": + raise ValueError( + f"Only \"uniform_average\" is supported for \"multioutput\" when " + f"the input is a FDatabasis: received {multioutput} instead", + ) + try: integral = nquad_vec( integrand, y_true.domain_range, ) - except InfiniteScoreError: + except _InfiniteScoreError: return -math.inf # If the dimension of the codomain is > 1, @@ -105,7 +111,7 @@ def _multioutput_score_grid( score: FDataGrid, multioutput: MultiOutputType, squared: bool = True, -) -> Union[float, FDataGrid]: +) -> float | FDataGrid: if not squared: score = np.sqrt(score) @@ -124,7 +130,7 @@ def explained_variance_score( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: pass # noqa: WPS428 @@ -132,23 +138,23 @@ def explained_variance_score( @overload def explained_variance_score( - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], -) -> DataTypeRawValues: +) -> DataType: pass # noqa: WPS428 @singledispatch def explained_variance_score( - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', -) -> Union[float, FDataGrid, NDArrayFloat]: +) -> float | DataType: r"""Explained variance score for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -255,6 +261,7 @@ def _explained_variance_score_fdatabasis( y_pred: FDataBasis, *, sample_weight: Optional[NDArrayFloat] = None, + multioutput: MultiOutputType = 'uniform_average', ) -> float: def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 @@ -289,13 +296,13 @@ def _ev_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 # r/0 case, r!= 0. Return -inf outside this function if np.any(np.isinf(score)): - raise InfiniteScoreError + raise _InfiniteScoreError # Score only contains 1 input point assert score.shape[0] == 1 return score[0] # type: ignore [no-any-return] - return _uniform_average_basis(y_true, _ev_func) + return _multioutput_score_basis(y_true, multioutput, _ev_func) @overload @@ -303,7 +310,7 @@ def mean_absolute_error( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: pass # noqa: WPS428 @@ -311,23 +318,23 @@ def mean_absolute_error( @overload def mean_absolute_error( - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], -) -> DataTypeRawValues: +) -> DataType: pass # noqa: WPS428 @singledispatch def mean_absolute_error( - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', -) -> Union[float, FDataGrid, NDArrayFloat]: +) -> float | DataType: r"""Mean Absolute Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -399,6 +406,7 @@ def _mean_absolute_error_fdatagrid( sample_weight: Optional[NDArrayFloat] = None, multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: + from ..exploratory.stats import mean error = mean(np.abs(y_true - y_pred), weights=sample_weight) return _multioutput_score_grid(error, multioutput) @@ -410,6 +418,7 @@ def _mean_absolute_error_fdatabasis( y_pred: FDataBasis, *, sample_weight: Optional[NDArrayFloat] = None, + multioutput: MultiOutputType = 'uniform_average', ) -> float: def _mae_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 @@ -423,7 +432,7 @@ def _mae_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - return _uniform_average_basis(y_true, _mae_func) + return _multioutput_score_basis(y_true, multioutput, _mae_func) @overload @@ -431,7 +440,7 @@ def mean_absolute_percentage_error( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: pass # noqa: WPS428 @@ -439,23 +448,23 @@ def mean_absolute_percentage_error( @overload def mean_absolute_percentage_error( - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], -) -> DataTypeRawValues: +) -> DataType: pass # noqa: WPS428 @singledispatch def mean_absolute_percentage_error( - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', -) -> Union[float, NDArrayFloat, FDataGrid]: +) -> float | DataType: r"""Mean Absolute Percentage Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -532,6 +541,7 @@ def _mean_absolute_percentage_error_fdatagrid( sample_weight: Optional[NDArrayFloat] = None, multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: + from ..exploratory.stats import mean epsilon = np.finfo(np.float64).eps @@ -550,6 +560,7 @@ def _mean_absolute_percentage_error_fdatabasis( y_pred: FDataBasis, *, sample_weight: Optional[NDArrayFloat] = None, + multioutput: MultiOutputType = 'uniform_average', ) -> float: def _mape_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 @@ -571,7 +582,7 @@ def _mape_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - return _uniform_average_basis(y_true, _mape_func) + return _multioutput_score_basis(y_true, multioutput, _mape_func) @overload @@ -579,7 +590,7 @@ def mean_squared_error( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', squared: bool = True, ) -> float: @@ -588,25 +599,25 @@ def mean_squared_error( @overload def mean_squared_error( - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], squared: bool = True, -) -> DataTypeRawValues: +) -> DataType: pass # noqa: WPS428 @singledispatch def mean_squared_error( - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', squared: bool = True, -) -> Union[float, NDArrayFloat, FDataGrid]: +) -> float | DataType: r"""Mean Squared Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -681,6 +692,7 @@ def _mean_squared_error_fdatagrid( multioutput: MultiOutputType = 'uniform_average', squared: bool = True, ) -> Union[float, FDataGrid]: + from ..exploratory.stats import mean error: FDataGrid = mean( np.power(y_true - y_pred, 2), @@ -697,6 +709,7 @@ def _mean_squared_error_fdatabasis( *, sample_weight: Optional[NDArrayFloat] = None, squared: bool = True, + multioutput: MultiOutputType = 'uniform_average', ) -> float: def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 @@ -714,7 +727,7 @@ def _mse_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - return _uniform_average_basis(y_true, _mse_func) + return _multioutput_score_basis(y_true, multioutput, _mse_func) @overload @@ -722,7 +735,7 @@ def mean_squared_log_error( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', squared: bool = True, ) -> float: @@ -731,25 +744,25 @@ def mean_squared_log_error( @overload def mean_squared_log_error( - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], squared: bool = True, -) -> DataTypeRawValues: +) -> DataType: pass # noqa: WPS428 @singledispatch def mean_squared_log_error( - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', squared: bool = True, -) -> Union[float, NDArrayFloat, FDataGrid]: +) -> float | DataType: r"""Mean Squared Log Error for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -854,6 +867,7 @@ def _mean_squared_log_error_fdatabasis( *, sample_weight: Optional[NDArrayFloat] = None, squared: bool = True, + multioutput: MultiOutputType = 'uniform_average', ) -> float: def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 @@ -880,7 +894,7 @@ def _msle_func(x: EvalPointsType) -> NDArrayFloat: # noqa: WPS430 assert error.shape[0] == 1 return error[0] # type: ignore [no-any-return] - return _uniform_average_basis(y_true, _msle_func) + return _multioutput_score_basis(y_true, multioutput, _msle_func) @overload @@ -888,7 +902,7 @@ def r2_score( y_true: DataType, y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average', ) -> float: pass # noqa: WPS428 @@ -896,23 +910,23 @@ def r2_score( @overload def r2_score( - y_true: DataTypeRawValues, - y_pred: DataTypeRawValues, + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values'], -) -> DataTypeRawValues: +) -> DataType: pass # noqa: WPS428 @singledispatch def r2_score( - y_true: Union[FData, NDArrayFloat], - y_pred: Union[FData, NDArrayFloat], + y_true: DataType, + y_pred: DataType, *, - sample_weight: Optional[NDArrayFloat] = None, + sample_weight: NDArrayFloat | None = None, multioutput: MultiOutputType = 'uniform_average', -) -> Union[float, NDArrayFloat, FDataGrid]: +) -> float | DataType: r"""R^2 score for :class:`~skfda.representation.FData`. With :math:`y\_true = (X_1, X_2, ..., X_n)` being the real values, @@ -987,6 +1001,7 @@ def _r2_score_fdatagrid( sample_weight: Optional[NDArrayFloat] = None, multioutput: MultiOutputType = 'uniform_average', ) -> Union[float, FDataGrid]: + from ..exploratory.stats import mean if y_pred.n_samples < 2: raise ValueError( @@ -1016,6 +1031,7 @@ def _r2_score_fdatabasis( y_pred: FDataBasis, *, sample_weight: Optional[NDArrayFloat] = None, + multioutput: MultiOutputType = 'uniform_average', ) -> float: if y_pred.n_samples < 2: @@ -1048,10 +1064,10 @@ def _r2_func(x: NDArrayFloat) -> NDArrayFloat: # noqa: WPS430 # r/0 case, r!= 0. Return -inf outside this function if np.any(np.isinf(score)): - raise InfiniteScoreError + raise _InfiniteScoreError # Score only had 1 input point assert score.shape[0] == 1 return score[0] # type: ignore [no-any-return] - return _uniform_average_basis(y_true, _r2_func) + return _multioutput_score_basis(y_true, multioutput, _r2_func) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index 3beb3b44c..f655ff7d8 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -662,132 +662,3 @@ def _functional_predict( ) return concatenate(iterable) # type: ignore[no-any-return] - - def score( - self, - X: Input, - y: TargetRegression, - sample_weight: NDArrayFloat | None = None, - ) -> float: - r"""Return the coefficient of determination R^2 of the prediction. - - In the multivariate response case, the coefficient :math:`R^2` is - defined as - - .. math:: - 1 - \frac{\sum_{i=1}^{n} (y_i - \hat y_i)^2} - {\sum_{i=1}^{n} (y_i - \frac{1}{n}\sum_{i=1}^{n}y_i)^2} - - where :math:`\hat{y}_i` is the prediction associated to the test sample - :math:`X_i`, and :math:`{y}_i` is the true response. See - :func:`sklearn.metrics.r2_score ` for more - information. - - - In the functional case it is returned an extension of the coefficient - of determination :math:`R^2`, defined as - - .. math:: - 1 - \frac{\sum_{i=1}^{n}\int (y_i(t) - \hat{y}_i(t))^2dt} - {\sum_{i=1}^{n} \int (y_i(t)- \frac{1}{n}\sum_{i=1}^{n}y_i(t))^2dt} - - - The best possible score is 1.0 and it can be negative - (because the model can be arbitrarily worse). A constant model that - always predicts the expected value of y, disregarding the input - features, would get a R^2 score of 0.0. - - Args: - X: Test samples to be predicted. - y: True responses of the test samples. - sample_weight: Sample weights. - - Returns: - Coefficient of determination. - - """ - if self._functional: - return self._functional_score(X, y, sample_weight=sample_weight) - - # Default sklearn multivariate score - return super().score(X, y, sample_weight=sample_weight) - - def _functional_score( - self: NeighborsRegressorMixin[Input, TargetRegressionFData], - X: Input, - y: TargetRegressionFData, - sample_weight: NDArrayFloat | None = None, - ) -> float: - r""" - Return an extension of the coefficient of determination R^2. - - The coefficient is defined as - - .. math:: - 1 - \frac{\sum_{i=1}^{n}\int (y_i(t) - \hat{y}_i(t))^2dt} - {\sum_{i=1}^{n} \int (y_i(t)- \frac{1}{n}\sum_{i=1}^{n}y_i(t))^2dt} - - where :math:`\hat{y}_i` is the prediction associated to the test sample - :math:`X_i`, and :math:`{y}_i` is the true response. - - The best possible score is 1.0 and it can be negative - (because the model can be arbitrarily worse). A constant model that - always predicts the expected value of y, disregarding the input - features, would get a R^2 score of 0.0. - - Args: - X: Test samples to be predicted. - y: True responses of the test samples. - sample_weight (array_like, shape = [n_samples], optional): Sample - weights. - - Returns: - Coefficient of determination. - - """ - # TODO: If it is created a module in ml.regression with other - # score metrics, move it. - from scipy.integrate import simps - - if y.dim_codomain != 1 or y.dim_domain != 1: - raise ValueError( - "Score not implemented for multivariate " - "functional data.", - ) - - # Make prediction - pred = self.predict(X) - - u = y - pred - v = y - y.mean() - - # Discretize to integrate and make squares if needed - if not isinstance(u, FDataGrid): - u = u.to_grid() - v = v.to_grid() - - data_u = u.data_matrix[..., 0] - data_v = v.data_matrix[..., 0] - - # Square without allocate more memory - np.square(data_u, out=data_u) - np.square(data_v, out=data_v) - - if sample_weight is not None: - if len(sample_weight) != len(y): - raise ValueError("Must be a weight for each sample.") - - normalized_sample_weight = sample_weight / sample_weight.sum() - data_u_t = data_u.T - data_u_t *= normalized_sample_weight - data_v_t = data_v.T - data_v_t *= normalized_sample_weight - - # Sum and integrate - sum_u = np.sum(data_u, axis=0) - sum_v = np.sum(data_v, axis=0) - - int_u = simps(sum_u, x=u.grid_points[0]) - int_v = simps(sum_v, x=v.grid_points[0]) - - return float(1 - int_u / int_v) diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 4df8e6cfe..855923b1b 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -707,6 +707,11 @@ def _get_op_matrix( return other[other_index] + raise ValueError( + f"Invalid dimensions in operator between FDataGrid and Numpy " + f"array: {other.shape}" + ) + elif isinstance(other, FDataGrid): self._check_same_dimensions(other) return other.data_matrix diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index 2ffb38e5b..f8091b564 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -357,7 +357,7 @@ def test_score_functional_response(self) -> None: y = 5 * self.X + 1 neigh.fit(self.X, y) r = neigh.score(self.X, y) - np.testing.assert_almost_equal(r, 0.962651178452408) + np.testing.assert_almost_equal(r, 0.65599399478951) # Weighted case and basis form y = y.to_basis(Fourier(domain_range=y.domain_range[0], n_basis=5)) @@ -368,7 +368,7 @@ def test_score_functional_response(self) -> None: y[:7], sample_weight=np.array(4 * [1.0 / 5] + 3 * [1.0 / 15]), ) - np.testing.assert_almost_equal(r, 0.9982527586114364) + np.testing.assert_almost_equal(r, 0.9802105817331564) def test_score_functional_response_exceptions(self) -> None: """Test weights with invalid length.""" @@ -381,19 +381,6 @@ def test_score_functional_response_exceptions(self) -> None: with np.testing.assert_raises(ValueError): neigh.score(self.X, self.X, sample_weight=np.array([1, 2, 3])) - def test_multivariate_response_score(self) -> None: - """Test multivariate score.""" - neigh = RadiusNeighborsRegressor[ - FDataGrid, - np.typing.NDArray[np.float_], - ]() - y = make_multimodal_samples(n_samples=5, dim_domain=2, random_state=0) - neigh.fit(self.X[:5], y) - - # It is not supported the multivariate score by the moment - with np.testing.assert_raises(ValueError): - neigh.score(self.X[:5], y) - def test_lof_fit_predict(self) -> None: """Test same results with different forms to call fit_predict.""" # Outliers From 769cb7f04e2b3a149d26e355e3079685ade42f17 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sun, 9 Oct 2022 21:54:01 +0200 Subject: [PATCH 314/400] Fix scoring docs. --- docs/modules/misc.rst | 2 +- .../misc/{score_functions.rst => scoring.rst} | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) rename docs/modules/misc/{score_functions.rst => scoring.rst} (56%) diff --git a/docs/modules/misc.rst b/docs/modules/misc.rst index c1a53d05a..6d1669f9c 100644 --- a/docs/modules/misc.rst +++ b/docs/modules/misc.rst @@ -57,4 +57,4 @@ functional data: misc/operators misc/regularization misc/hat_matrix - misc/score_functions + misc/scoring diff --git a/docs/modules/misc/score_functions.rst b/docs/modules/misc/scoring.rst similarity index 56% rename from docs/modules/misc/score_functions.rst rename to docs/modules/misc/scoring.rst index c6ba92ab2..b1b5e2fd2 100644 --- a/docs/modules/misc/score_functions.rst +++ b/docs/modules/misc/scoring.rst @@ -10,9 +10,9 @@ Only scores that support multioutput are included. .. autosummary:: :toctree: autosummary - skfda.misc.score_functions.explained_variance_score - skfda.misc.score_functions.mean_absolute_error - skfda.misc.score_functions.mean_absolute_percentage_error - skfda.misc.score_functions.mean_squared_error - skfda.misc.score_functions.mean_squared_log_error - skfda.misc.score_functions.r2_score + skfda.misc.scoring.explained_variance_score + skfda.misc.scoring.mean_absolute_error + skfda.misc.scoring.mean_absolute_percentage_error + skfda.misc.scoring.mean_squared_error + skfda.misc.scoring.mean_squared_log_error + skfda.misc.scoring.r2_score From 520acd6c83a97e9ccf4d9029098c791968ab693b Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 13:46:21 +0200 Subject: [PATCH 315/400] Make weights parameter private --- skfda/preprocessing/dim_reduction/_fpca.py | 21 ++++----------------- 1 file changed, 4 insertions(+), 17 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index 8a0b4e667..8e84f81b2 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -29,8 +29,8 @@ class FPCA( # noqa: WPS230 (too many public attributes) Principal component analysis. Class that implements functional principal component analysis for both - basis and grid representations of the data. Most parameters are shared - when fitting a FDataBasis or FDataGrid, except weights and + basis and grid representations of the data. The parameters are shared + when fitting a FDataBasis or FDataGrid, except for ``components_basis``. Parameters: @@ -45,14 +45,6 @@ class FPCA( # noqa: WPS230 (too many public attributes) components. We can use a different basis than the basis contained in the passed FDataBasis object. This parameter is only used when fitting a FDataBasis. - weights: the weights vector used for - discrete integration. If none then Simpson's rule is used for - computing the weights. If a callable object is passed, then the - weight vector will be obtained by evaluating the object at the - sample points of the passed FDataGrid object in the fit method. - This parameter is only used when fitting a FDataGrid. - - .. deprecated:: 0.7.2 Attributes: components\_: this contains the principal components. @@ -98,19 +90,14 @@ def __init__( n_components: int = 3, centering: bool = True, regularization: Optional[L2Regularization[FData]] = None, - weights: Optional[Union[ArrayLike, WeightsCallable]] = None, components_basis: Optional[Basis] = None, + _weights: Optional[Union[ArrayLike, WeightsCallable]] = None, ) -> None: self.n_components = n_components self.centering = centering self.regularization = regularization - self.weights = weights + self.weights = _weights self.components_basis = components_basis - if weights is not None: - warnings.warn( - "The 'weights' parameter is deprecated and will be removed.", - DeprecationWarning, - ) def _center_if_necessary( self, From 0096d749f15f9cf09336c6f54db20de4d40153a1 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 13:54:58 +0200 Subject: [PATCH 316/400] Remove unused import --- skfda/preprocessing/dim_reduction/_fpca.py | 1 - 1 file changed, 1 deletion(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index 8e84f81b2..551d9cf18 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -2,7 +2,6 @@ from __future__ import annotations -import warnings from typing import Callable, Optional, TypeVar, Union import numpy as np From 0569862d803fda5d22634bb52a57d296c54424e4 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 13:55:27 +0200 Subject: [PATCH 317/400] Rename the usages of the weights parameter --- skfda/tests/test_fpca.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index 6fcbad348..f814cd321 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -251,7 +251,7 @@ def test_grid_fpca_fit_sklearn(self) -> None: fd_data = fetch_weather()['data'].coordinates[0] - fpca = FPCA(n_components=n_components, weights=[1] * 365) + fpca = FPCA(n_components=n_components, _weights=[1] * 365) fpca.fit(fd_data) pca = PCA(n_components=n_components) @@ -297,7 +297,7 @@ def test_grid_fpca_fit_result(self) -> None: fd_data = fetch_weather()['data'].coordinates[0] - fpca = FPCA(n_components=n_components, weights=[1] * 365) + fpca = FPCA(n_components=n_components, _weights=[1] * 365) fpca.fit(fd_data) # Results obtained using fda.usc for the first component @@ -410,7 +410,7 @@ def test_grid_fpca_transform_result(self) -> None: fd_data = fetch_weather()['data'].coordinates[0] - fpca = FPCA(n_components=n_components, weights=[1] * 365) + fpca = FPCA(n_components=n_components, _weights=[1] * 365) fpca.fit(fd_data) scores = fpca.transform(fd_data) @@ -437,7 +437,7 @@ def test_grid_fpca_regularization_fit_result(self) -> None: fpca = FPCA( n_components=n_components, - weights=[1] * 365, + _weights=[1] * 365, regularization=L2Regularization( LinearDifferentialOperator(2), ), From e834ae9734a12a6c0a325bdf726024f25ead16ed Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 16:59:00 +0200 Subject: [PATCH 318/400] Removed missed "weights_" --- skfda/preprocessing/dim_reduction/_fpca.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index 551d9cf18..bb6daaabf 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -331,17 +331,17 @@ def _fit_grid( if self.weights is None: # grid_points is a list with one array in the 1D case identity = np.eye(len(X.grid_points[0])) - self.weights_ = scipy.integrate.simps(identity, X.grid_points[0]) + self.weights = scipy.integrate.simps(identity, X.grid_points[0]) elif callable(self.weights): - self.weights_ = self.weights(X.grid_points[0]) + self.weights = self.weights(X.grid_points[0]) # if its a FDataGrid then we need to reduce the dimension to 1-D # array if isinstance(self.weights, FDataGrid): - self.weights_ = np.squeeze(self.weights.data_matrix) + self.weights = np.squeeze(self.weights.data_matrix) else: - self.weights_ = self.weights + self.weights = self.weights - weights_matrix = np.diag(self.weights_) + weights_matrix = np.diag(self.weights) basis = FDataGrid( data_matrix=np.identity(n_points_discretization), @@ -407,7 +407,7 @@ def _transform_grid( return ( # type: ignore[no-any-return] X.data_matrix.reshape(X.data_matrix.shape[:-1]) - * self.weights_ + * self.weights @ np.transpose( self.components_.data_matrix.reshape( self.components_.data_matrix.shape[:-1], From 8cf2aaed68bcafc93b1e69e7df16ffad1b8a93fa Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 17:05:20 +0200 Subject: [PATCH 319/400] Decrease comparison tolerance in fpca tests By comparing with the reconstructed function, instead of the scores of the function, the tolerance can be considerably lower. --- skfda/tests/test_fpca.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index 6fcbad348..b9869146f 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -2,10 +2,12 @@ import unittest import numpy as np +import scipy from sklearn.decomposition import PCA from skfda import FDataBasis, FDataGrid from skfda.datasets import fetch_weather +from skfda.misc.metrics import l2_norm from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.preprocessing.dim_reduction import FPCA @@ -412,6 +414,14 @@ def test_grid_fpca_transform_result(self) -> None: fpca = FPCA(n_components=n_components, weights=[1] * 365) fpca.fit(fd_data) + + # Ramsay uses a different inner product to calculate the scores + # so we need to use the same inner product to compare the results + fpca.weights = scipy.integrate.simps( + np.eye(len(fd_data.grid_points[0])), + fd_data.grid_points[0], + ) + scores = fpca.transform(fd_data) # results obtained @@ -427,7 +437,20 @@ def test_grid_fpca_transform_result(self) -> None: 216.43002289, 233.53770292, 344.18479151, ]) - np.testing.assert_allclose(scores.ravel(), results, rtol=0.25) + untransformed_scores = fpca.inverse_transform(scores) + untransformed_results = fpca.inverse_transform(results.reshape(-1, 1)) + differences = l2_norm(untransformed_scores - untransformed_results) + + # Take into account the length of the interval: + differences /= ( + fd_data.domain_range[0][1] - fd_data.domain_range[0][0] + ) + + np.testing.assert_allclose( + differences, + [0] * results.shape[0], + atol=0.001, + ) def test_grid_fpca_regularization_fit_result(self) -> None: """Compare the components in grid against the fda.usc package.""" From c66baf544ebeebd9dd7a63464359ade93882728f Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 18:51:55 +0200 Subject: [PATCH 320/400] Check differences at each point instead of norm --- skfda/tests/test_fpca.py | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index b9869146f..f2dc924f0 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -7,7 +7,6 @@ from skfda import FDataBasis, FDataGrid from skfda.datasets import fetch_weather -from skfda.misc.metrics import l2_norm from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.preprocessing.dim_reduction import FPCA @@ -439,17 +438,10 @@ def test_grid_fpca_transform_result(self) -> None: untransformed_scores = fpca.inverse_transform(scores) untransformed_results = fpca.inverse_transform(results.reshape(-1, 1)) - differences = l2_norm(untransformed_scores - untransformed_results) - - # Take into account the length of the interval: - differences /= ( - fd_data.domain_range[0][1] - fd_data.domain_range[0][0] - ) - np.testing.assert_allclose( - differences, - [0] * results.shape[0], - atol=0.001, + untransformed_scores.data_matrix.ravel(), + untransformed_results.data_matrix.ravel(), + atol=0.1, ) def test_grid_fpca_regularization_fit_result(self) -> None: From 62157053a60369cd526f5de7ab7be64e0411d43b Mon Sep 17 00:00:00 2001 From: Ddelval Date: Wed, 12 Oct 2022 18:56:08 +0200 Subject: [PATCH 321/400] Rename internal "weights" attribute in fpca to "_weights" --- skfda/preprocessing/dim_reduction/_fpca.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index bb6daaabf..ce520637c 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -95,7 +95,7 @@ def __init__( self.n_components = n_components self.centering = centering self.regularization = regularization - self.weights = _weights + self._weights = _weights self.components_basis = components_basis def _center_if_necessary( @@ -328,20 +328,20 @@ def _fit_grid( X = self._center_if_necessary(X) # establish weights for each point of discretization - if self.weights is None: + if self._weights is None: # grid_points is a list with one array in the 1D case identity = np.eye(len(X.grid_points[0])) - self.weights = scipy.integrate.simps(identity, X.grid_points[0]) - elif callable(self.weights): - self.weights = self.weights(X.grid_points[0]) + self._weights = scipy.integrate.simps(identity, X.grid_points[0]) + elif callable(self._weights): + self._weights = self._weights(X.grid_points[0]) # if its a FDataGrid then we need to reduce the dimension to 1-D # array - if isinstance(self.weights, FDataGrid): - self.weights = np.squeeze(self.weights.data_matrix) + if isinstance(self._weights, FDataGrid): + self._weights = np.squeeze(self._weights.data_matrix) else: - self.weights = self.weights + self._weights = self._weights - weights_matrix = np.diag(self.weights) + weights_matrix = np.diag(self._weights) basis = FDataGrid( data_matrix=np.identity(n_points_discretization), @@ -407,7 +407,7 @@ def _transform_grid( return ( # type: ignore[no-any-return] X.data_matrix.reshape(X.data_matrix.shape[:-1]) - * self.weights + * self._weights @ np.transpose( self.components_.data_matrix.reshape( self.components_.data_matrix.shape[:-1], From 168dac6cd412362c9cfcfe039ec410191a8ec282 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Fri, 14 Oct 2022 15:36:10 +0200 Subject: [PATCH 322/400] Apply the name change of weights to the test --- skfda/tests/test_fpca.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index b01f00456..e31672018 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -416,7 +416,9 @@ def test_grid_fpca_transform_result(self) -> None: # Ramsay uses a different inner product to calculate the scores # so we need to use the same inner product to compare the results - fpca.weights = scipy.integrate.simps( + # flake8 ignore comment required since a protected member is being + # accessed + fpca._weights = scipy.integrate.simps( # noqa: WPS437 np.eye(len(fd_data.grid_points[0])), fd_data.grid_points[0], ) From 0196e7fba9065d8e5d1609ff75c8fda8abd28cb4 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Mon, 17 Oct 2022 19:29:17 +0200 Subject: [PATCH 323/400] Basis from fdata first draft --- .../representation/basis/_basis_from_fdata.py | 98 +++++++++++++++++++ 1 file changed, 98 insertions(+) create mode 100644 skfda/representation/basis/_basis_from_fdata.py diff --git a/skfda/representation/basis/_basis_from_fdata.py b/skfda/representation/basis/_basis_from_fdata.py new file mode 100644 index 000000000..97e172548 --- /dev/null +++ b/skfda/representation/basis/_basis_from_fdata.py @@ -0,0 +1,98 @@ +"""Abstract base class for basis.""" + +from __future__ import annotations + +from typing import Any, Tuple, TypeVar + +from basis import Basis + +from ...typing._numpy import NDArrayFloat +from .._functional_data import FData +from _fdatabasis import FDataBasis + +T = TypeVar("T", bound='BasisFromFdata') + + +class BasisFromFdata(Basis): + """Defines the structure of a basis of functions. + + Parameters: + domain_range: The :term:`domain range` over which the basis can be + evaluated. + n_basis: number of functions in the basis. + + """ + + def __init__( + self, + *, + fdata: FData, + ) -> None: + """Basis constructor.""" + super().__init__( + domain_range=fdata.domain_range, + n_basis=fdata.n_samples, + ) + + def _evaluate( + self, + eval_points: NDArrayFloat, + ) -> NDArrayFloat: + """Evaluate Basis object.""" + return self.fdata(eval_points) + + def __len__(self) -> int: + return self.n_basis + + def _derivative_basis_and_coefs( + self: T, + coefs: NDArrayFloat, + order: int = 1, + ) -> Tuple[T, NDArrayFloat]: + """ + Return basis and coefficients of the derivative. + + Args: + coefs: Coefficients of a vector expressed in this basis. + order: Order of the derivative. + + Returns: + Tuple with the basis of the derivative and its coefficients. + + Subclasses can override this to provide derivative construction. + + """ + derivated_basis = BasisFromFdata(fdata=self.fdata.derivative(order)) + + return derivated_basis, coefs + + raise NotImplementedError( + f"{type(self)} basis does not support the construction of a " + "basis of the derivatives.", + ) + + def _coordinate_nonfull( + self, + coefs: NDArrayFloat, + key: int | slice, + ) -> Tuple[Basis, NDArrayFloat]: + """ + Return a basis and coefficients for the indexed coordinate functions. + + Subclasses can override this to provide coordinate indexing. + + """ + raise NotImplementedError("Coordinate indexing not implemented") + + def __eq__(self, other: Any) -> bool: + """Test equality of Basis.""" + from ..._utils import _same_domain + return ( + isinstance(other, type(self)) + and _same_domain(self, other) + and self.fdata == other.fdata + ) + + def __hash__(self) -> int: + """Hash a Basis.""" + return hash(self.fdata) From 4e0acd5cdbad0eb054915d853856ee156cb46678 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 24 Oct 2022 22:39:02 +0200 Subject: [PATCH 324/400] Corrections using gaussian --- skfda/tests/test_classifier_classes.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index fd542e334..2b7ed870f 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -6,7 +6,7 @@ import numpy as np from skfda._utils._sklearn_adapter import ClassifierMixin -from skfda.datasets import make_multimodal_samples +from skfda.datasets import make_gaussian_process from skfda.ml.classification import LogisticRegression from skfda.representation import FData @@ -31,7 +31,7 @@ def setUp(self) -> None: np.resize(classes, n_samples) for classes in self.classes_list ] - self.X = make_multimodal_samples( + self.X = make_gaussian_process( n_samples=n_samples, noise=0.05, ) @@ -46,11 +46,7 @@ def test_classes(self) -> None: for clf in self.tested_classifiers: # Iterate over all class sets to test different types of classes for classes, y in zip(self.classes_list, self.y_list): - message = ( - f'Testing classifier {clf.__class__.__name__} ' - f'with classes {classes}' - ) - with self.subTest(msg=message): + with self.subTest(classifier=clf, classes=classes): clf.fit(self.X, y) np.testing.assert_array_equal(clf.classes_, classes) From 8b054d622a1aec71b46aaab70a2028f41a8336d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 24 Oct 2022 23:18:36 +0200 Subject: [PATCH 325/400] Pytest instead of unittest --- skfda/tests/test_classifier_classes.py | 74 +++++++++++--------------- 1 file changed, 32 insertions(+), 42 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index 2b7ed870f..3f74fafb0 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -1,9 +1,11 @@ """Tests classes attribute of classifiers.""" -import unittest -from typing import List +from __future__ import annotations + +from typing import Any import numpy as np +import pytest from skfda._utils._sklearn_adapter import ClassifierMixin from skfda.datasets import make_gaussian_process @@ -13,43 +15,31 @@ from ..typing._numpy import NDArrayAny -class TestClassifierClasses(unittest.TestCase): - """Test for classifiers classes.""" - - def setUp(self) -> None: - """Establish train and test data sets.""" - # List of classes to test - # Adding new classes to this list will test the classifiers with them - self.classes_list: List[NDArrayAny] = [ - np.array([0, 1]), - np.array(["class_a", "class_b"]), - ] - - # Create one target y data of length n_samples from each class set - n_samples = 30 - self.y_list = [ - np.resize(classes, n_samples) - for classes in self.classes_list - ] - self.X = make_gaussian_process( - n_samples=n_samples, - noise=0.05, - ) - - self.tested_classifiers: List[ClassifierMixin[FData, NDArrayAny]] = [ - LogisticRegression(), - ] - - def test_classes(self) -> None: - """Check classes attribute.""" - # Iterate over all classifiers with index - for clf in self.tested_classifiers: - # Iterate over all class sets to test different types of classes - for classes, y in zip(self.classes_list, self.y_list): - with self.subTest(classifier=clf, classes=classes): - clf.fit(self.X, y) - np.testing.assert_array_equal(clf.classes_, classes) - - -if __name__ == "__main__": - unittest.main() +@pytest.fixture(params=[ + LogisticRegression(), +]) +def classifier(request: Any) -> Any: + """Fixture for classifiers to test.""" + return request.param + + +@pytest.fixture(params=[ + np.array([0, 1]), + np.array(["class_a", "class_b"]), +]) +def classes(request: Any) -> Any: + """Fixture for classes to test.""" + return request.param + + +def test_classes( + classifier: ClassifierMixin[FData, NDArrayAny], + classes: NDArrayAny, +) -> None: + """Test classes attribute of classifiers.""" + n_samples = 30 + y = np.resize(classes, n_samples) + X = make_gaussian_process(n_samples=n_samples) + classifier.fit(X, y) + + np.testing.assert_array_equal(classifier.classes_, classes) From 68ad2660f35ffac10e778069d9cde462ff252975 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 24 Oct 2022 23:27:49 +0200 Subject: [PATCH 326/400] ids for better visualization during collect --- skfda/tests/test_classifier_classes.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index 3f74fafb0..d0afdabb5 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -15,18 +15,23 @@ from ..typing._numpy import NDArrayAny -@pytest.fixture(params=[ - LogisticRegression(), -]) +@pytest.fixture( + params=[ + LogisticRegression(), + ], + ids=lambda clf: type(clf).__name__, +) def classifier(request: Any) -> Any: """Fixture for classifiers to test.""" return request.param -@pytest.fixture(params=[ - np.array([0, 1]), - np.array(["class_a", "class_b"]), -]) +@pytest.fixture( + params=[ + np.array([0, 1]), + np.array(["class_a", "class_b"]), + ], ids=["int", "str"], +) def classes(request: Any) -> Any: """Fixture for classes to test.""" return request.param From 32a2558fcaf2ca4b60d9dc17e4da44713ee7350a Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 27 Oct 2022 23:53:00 +0200 Subject: [PATCH 327/400] Initial basis_from_fdata --- skfda/representation/basis/__init__.py | 2 + skfda/representation/basis/_basis_of_fdata.py | 134 +++++++++++ skfda/tests/test_basis_of_fdata.py | 225 ++++++++++++++++++ 3 files changed, 361 insertions(+) create mode 100644 skfda/representation/basis/_basis_of_fdata.py create mode 100644 skfda/tests/test_basis_of_fdata.py diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 33e344783..dca696083 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -7,6 +7,7 @@ __name__, submod_attrs={ '_basis': ["Basis"], + '_basis_of_fdata': ["BasisOfFData"], "_bspline": ["BSpline"], "_constant": ["Constant"], "_fdatabasis": ["FDataBasis", "FDataBasisDType"], @@ -20,6 +21,7 @@ if TYPE_CHECKING: from ._basis import Basis as Basis + from ._basis_of_fdata import BasisOfFData as BasisOfFData from ._bspline import BSpline as BSpline from ._constant import Constant as Constant from ._fdatabasis import ( diff --git a/skfda/representation/basis/_basis_of_fdata.py b/skfda/representation/basis/_basis_of_fdata.py new file mode 100644 index 000000000..60b8a4e3b --- /dev/null +++ b/skfda/representation/basis/_basis_of_fdata.py @@ -0,0 +1,134 @@ +"""Abstract base class for basis.""" + +from __future__ import annotations + +from typing import Any, Tuple, TypeVar + +import numpy as np + +from ...representation.grid import FDataGrid +from ...typing._numpy import NDArrayFloat +from .._functional_data import FData +from ._basis import Basis +from ._fdatabasis import FDataBasis + +T = TypeVar("T", bound='BasisOfFData') + + +class BasisOfFData(Basis): + """Defines the structure of a basis of functions. + + Parameters: + domain_range: The :term:`domain range` over which the basis can be + evaluated. + n_basis: number of functions in the basis. + + """ + + def __init__( + self, + *, + fdata: FData, + ) -> None: + """Basis constructor.""" + super().__init__( + domain_range=fdata.domain_range, + n_basis=fdata.n_samples, + ) + if isinstance(fdata, FDataGrid): + self._check_linearly_independent_grid(fdata) + else: + self._check_linearly_independent_basis(fdata) + + self.fdata = fdata + + def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: + """Check if the observations in the FDataGrid linearly independent.""" + if fdata.n_samples > fdata.data_matrix.shape[1]: + raise ValueError( + "Too many samples in the basis. " + "The number of samples must be less than or equal to the " + "number of sampling points.", + ) + if np.linalg.matrix_rank(fdata.data_matrix[..., 0]) < fdata.n_samples: + raise ValueError( + "There are only {rank} linearly independent functions".format( + rank=np.linalg.matrix_rank(fdata.data_matrix), + ), + ) + + def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: + """Check if the observations in the FDataBasis linearly independent.""" + if fdata.n_samples > fdata.basis.n_basis: + raise ValueError( + "Too many samples in the basis. " + "The number of samples must be less than or equal to the " + "number of basis functions.", + ) + if np.linalg.matrix_rank(fdata.coefficients) < fdata.n_samples: + raise ValueError( + "There are only {rank} linearly independent functions".format( + rank=np.linalg.matrix_rank(fdata.coefficients), + ), + ) + pass + + def _evaluate( + self, + eval_points: NDArrayFloat, + ) -> NDArrayFloat: + """Evaluate Basis object.""" + return self.fdata(eval_points) + + def __len__(self) -> int: + return self.n_basis + + def _derivative_basis_and_coefs( + self: T, + coefs: NDArrayFloat, + order: int = 1, + ) -> Tuple[T, NDArrayFloat]: + """ + Return basis and coefficients of the derivative. + + Args: + coefs: Coefficients of a vector expressed in this basis. + order: Order of the derivative. + + Returns: + Tuple with the basis of the derivative and its coefficients. + + Subclasses can override this to provide derivative construction. + + """ + derivated_basis = BasisOfFData( + fdata=self.fdata.derivative(order=order), + ) + + return derivated_basis, coefs + + def _coordinate_nonfull( + self, + coefs: NDArrayFloat, + key: int | slice, + ) -> Tuple[Basis, NDArrayFloat]: + """ + Return a basis and coefficients for the indexed coordinate functions. + + Subclasses can override this to provide coordinate indexing. + + """ + return BasisOfFData(fdata=self.fdata.coordinates[key]), coefs + + def __eq__(self, other: Any) -> bool: + """Test equality of Basis.""" + from ..._utils import _same_domain + return ( + isinstance(other, type(self)) + and _same_domain(self, other) + and self.fdata == other.fdata + ) + + def __hash__(self) -> int: + """Hash a Basis.""" + return hash(self.fdata) diff --git a/skfda/tests/test_basis_of_fdata.py b/skfda/tests/test_basis_of_fdata.py new file mode 100644 index 000000000..5e26da03e --- /dev/null +++ b/skfda/tests/test_basis_of_fdata.py @@ -0,0 +1,225 @@ +"""Tests of BasisOfFData.""" + +import unittest + +import numpy as np + +from skfda.representation.basis import BasisOfFData, FDataBasis, Fourier +from skfda.representation.grid import FDataGrid + + +class TestBasis(unittest.TestCase): + """Tests for BasisOfFData.""" + + def test_grid(self): + """Test a datagrid toy example.""" + grid_points = np.array([0, 1, 2]) + sample = FDataGrid( + data_matrix=np.array([[1, 2, 3], [4, 5, 6]]), + grid_points=np.array([[0, 1, 2]]), + ) + + data_basis = FDataBasis( + basis=BasisOfFData(fdata=sample), + coefficients=np.array([[1, 0], [0, 1], [1, 1]]), + ) + + evaluated = [ + np.array([1, 2, 3]), + np.array([4, 5, 6]), + np.array([5, 7, 9]), + ] + + np.testing.assert_equal(data_basis(grid_points)[..., 0], evaluated) + + def test_basis(self): + """Test a databasis toy example.""" + basis = Fourier(n_basis=3) + coeficients = np.array( + [ + [2, 1, 0], + [3, 1, 0], + [1, 2, 9], + ], + ) + + data_basis = FDataBasis( + basis=BasisOfFData( + fdata=FDataBasis(basis=basis, coefficients=coeficients), + ), + coefficients=coeficients, + ) + + combined_basis = FDataBasis( + basis=basis, + coefficients=coeficients @ coeficients, + ) + + eval_points = np.linspace(0, 1, 100) + np.testing.assert_almost_equal( + data_basis(eval_points), + combined_basis(eval_points), + ) + + def test_not_linearly_independent_too_many(self): + """ + Test that a non linearly independent basis raises an error. + + In this case, the number of samples is greater than the number + of sampling points or base functions in the underlying base. + """ + sample = FDataGrid( + data_matrix=np.array( + [[1, 2, 3], [2, 4, 6], [15, 4, -2], [1, 28, 0]], + ), + grid_points=np.array([[0, 1, 2]]), + ) + + with self.assertRaises(ValueError): + BasisOfFData(fdata=sample) + + sample = FDataBasis( + basis=Fourier(n_basis=3), + coefficients=np.array( + [ + [0, 1, 0], + [1, 0, 0], + [0, 0, 1], + [1, 1, 1], + ], + ), + ) + + with self.assertRaises(ValueError): + BasisOfFData(fdata=sample) + + def test_not_linearly_independent_range(self): + """ + Test that a non linearly independent basis raises an error. + + In this case, the number of samples is valid but the given + samples are not linearly independent. + """ + sample = FDataGrid( + data_matrix=np.array([[1, 2, 3], [2, 4, 6]]), + grid_points=np.array([[0, 1, 2]]), + ) + + with self.assertRaises(ValueError): + BasisOfFData(fdata=sample) + + sample = FDataBasis( + basis=Fourier(n_basis=3), + coefficients=np.array( + [ + [2, 1, 0], + [3, 1, 0], + [1, 2, 0], + ], + ), + ) + + with self.assertRaises(ValueError): + BasisOfFData(fdata=sample) + + def test_derivative_grid(self): + """Test the derivative of a basis constructed from a FDataGrid.""" + base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 1, 5]]), + grid_points=np.array([[0, 1, 2]]), + ) + # The derivative of the first function is always 1 + # The derivative of the second function is 0 and then 4 + + basis = BasisOfFData(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 1]]) + derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( + coefs=coefs, + order=1, + ) + + derivative = FDataBasis( + basis=derivate_basis, + coefficients=derivative_coefs, + ) + + eval_points = np.array([0.5, 1.5]) + + # Derivative of the original functions sampled in the given points + # and multiplied by the coefs + derivative_evaluated = np.array([[1, 1], [0, 4], [1, 5]]) + np.testing.assert_equal( + derivative(eval_points)[..., 0], + derivative_evaluated, + ) + + def test_derivative_basis(self): + """Test the derivative of a basis constructed from a FDataBasis.""" + basis_coef = np.array( + [ + [2, 1, 0, 10, 0], + [3, 1, 99, 15, 99], + [0, 2, 9, 22, 0], + ], + ) + + base_functions = FDataBasis( + basis=Fourier(n_basis=5), + coefficients=basis_coef, + ) + + basis = BasisOfFData(fdata=base_functions) + + coefs = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [2, 1, 5]]) + derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( + coefs=coefs, + order=1, + ) + + derivative = FDataBasis( + basis=derivate_basis, + coefficients=derivative_coefs, + ) + + eval_points = np.linspace(0, 1, 100) + + # Derivative of the original functions + basis_derivative, coefs_derivative = Fourier( + n_basis=5, + ).derivative_basis_and_coefs(coefs=coefs @ basis_coef) + + derivative_on_the_basis = FDataBasis( + basis=basis_derivative, + coefficients=coefs_derivative, + ) + + np.testing.assert_almost_equal( + derivative(eval_points), + derivative_on_the_basis(eval_points), + ) + + def test_linearly_dependent_derivatives(self): + """ + Construct a basis whose derivative is not a basis. + + An error should be raised + """ + base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), + grid_points=np.array([[0, 1, 2]]), + ) + # The derivative of the first function is 1 + # The derivative of the second function is 2 + # Therefore, the derivative of the basis is not a basis + + # The basis is linearly independent + base = BasisOfFData(fdata=base_functions) + + # Its derivative is not linearly independent + with self.assertRaises(ValueError): + base.derivative_basis_and_coefs(np.ndarray([1, 1])) + + +if __name__ == "__main__": + unittest.main() From 4248f5f5ce90575538ebb0daaa5b596854d4084b Mon Sep 17 00:00:00 2001 From: Ddelval Date: Fri, 28 Oct 2022 00:48:57 +0200 Subject: [PATCH 328/400] Change quadrature in fpca test --- skfda/tests/test_fpca.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index f814cd321..45a738655 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -412,6 +412,13 @@ def test_grid_fpca_transform_result(self) -> None: fpca = FPCA(n_components=n_components, _weights=[1] * 365) fpca.fit(fd_data) + + # The fda.usc uses the trapezoid rule to compute the integral + # with the following weights + weights = np.ones(len(fd_data.grid_points[0])) + weights[0] = 0.5 + weights[-1] = 0.5 + fpca._weights = weights # noqa: WPS437 (protected access) scores = fpca.transform(fd_data) # results obtained @@ -427,7 +434,7 @@ def test_grid_fpca_transform_result(self) -> None: 216.43002289, 233.53770292, 344.18479151, ]) - np.testing.assert_allclose(scores.ravel(), results, rtol=0.25) + np.testing.assert_allclose(scores.ravel(), results) def test_grid_fpca_regularization_fit_result(self) -> None: """Compare the components in grid against the fda.usc package.""" From 08a6ca51440e228cb66f72117646599ab1cc94cb Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sat, 29 Oct 2022 12:19:52 +0200 Subject: [PATCH 329/400] Rename to CustomBasis --- skfda/representation/basis/__init__.py | 4 +- .../representation/basis/_basis_from_fdata.py | 98 ------------------- .../{_basis_of_fdata.py => _custom_basis.py} | 64 ++++++------ ...basis_of_fdata.py => test_custom_basis.py} | 68 +++++++++++-- 4 files changed, 88 insertions(+), 146 deletions(-) delete mode 100644 skfda/representation/basis/_basis_from_fdata.py rename skfda/representation/basis/{_basis_of_fdata.py => _custom_basis.py} (73%) rename skfda/tests/{test_basis_of_fdata.py => test_custom_basis.py} (75%) diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index dca696083..d11d04797 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -7,7 +7,7 @@ __name__, submod_attrs={ '_basis': ["Basis"], - '_basis_of_fdata': ["BasisOfFData"], + '_custom_basis': ["CustomBasis"], "_bspline": ["BSpline"], "_constant": ["Constant"], "_fdatabasis": ["FDataBasis", "FDataBasisDType"], @@ -21,9 +21,9 @@ if TYPE_CHECKING: from ._basis import Basis as Basis - from ._basis_of_fdata import BasisOfFData as BasisOfFData from ._bspline import BSpline as BSpline from ._constant import Constant as Constant + from ._custom_basis import CustomBasis as CustomBasis from ._fdatabasis import ( FDataBasis as FDataBasis, FDataBasisDType as FDataBasisDType, diff --git a/skfda/representation/basis/_basis_from_fdata.py b/skfda/representation/basis/_basis_from_fdata.py deleted file mode 100644 index 97e172548..000000000 --- a/skfda/representation/basis/_basis_from_fdata.py +++ /dev/null @@ -1,98 +0,0 @@ -"""Abstract base class for basis.""" - -from __future__ import annotations - -from typing import Any, Tuple, TypeVar - -from basis import Basis - -from ...typing._numpy import NDArrayFloat -from .._functional_data import FData -from _fdatabasis import FDataBasis - -T = TypeVar("T", bound='BasisFromFdata') - - -class BasisFromFdata(Basis): - """Defines the structure of a basis of functions. - - Parameters: - domain_range: The :term:`domain range` over which the basis can be - evaluated. - n_basis: number of functions in the basis. - - """ - - def __init__( - self, - *, - fdata: FData, - ) -> None: - """Basis constructor.""" - super().__init__( - domain_range=fdata.domain_range, - n_basis=fdata.n_samples, - ) - - def _evaluate( - self, - eval_points: NDArrayFloat, - ) -> NDArrayFloat: - """Evaluate Basis object.""" - return self.fdata(eval_points) - - def __len__(self) -> int: - return self.n_basis - - def _derivative_basis_and_coefs( - self: T, - coefs: NDArrayFloat, - order: int = 1, - ) -> Tuple[T, NDArrayFloat]: - """ - Return basis and coefficients of the derivative. - - Args: - coefs: Coefficients of a vector expressed in this basis. - order: Order of the derivative. - - Returns: - Tuple with the basis of the derivative and its coefficients. - - Subclasses can override this to provide derivative construction. - - """ - derivated_basis = BasisFromFdata(fdata=self.fdata.derivative(order)) - - return derivated_basis, coefs - - raise NotImplementedError( - f"{type(self)} basis does not support the construction of a " - "basis of the derivatives.", - ) - - def _coordinate_nonfull( - self, - coefs: NDArrayFloat, - key: int | slice, - ) -> Tuple[Basis, NDArrayFloat]: - """ - Return a basis and coefficients for the indexed coordinate functions. - - Subclasses can override this to provide coordinate indexing. - - """ - raise NotImplementedError("Coordinate indexing not implemented") - - def __eq__(self, other: Any) -> bool: - """Test equality of Basis.""" - from ..._utils import _same_domain - return ( - isinstance(other, type(self)) - and _same_domain(self, other) - and self.fdata == other.fdata - ) - - def __hash__(self) -> int: - """Hash a Basis.""" - return hash(self.fdata) diff --git a/skfda/representation/basis/_basis_of_fdata.py b/skfda/representation/basis/_custom_basis.py similarity index 73% rename from skfda/representation/basis/_basis_of_fdata.py rename to skfda/representation/basis/_custom_basis.py index 60b8a4e3b..d5ad33e49 100644 --- a/skfda/representation/basis/_basis_of_fdata.py +++ b/skfda/representation/basis/_custom_basis.py @@ -6,16 +6,16 @@ import numpy as np -from ...representation.grid import FDataGrid from ...typing._numpy import NDArrayFloat from .._functional_data import FData +from ..grid import FDataGrid from ._basis import Basis from ._fdatabasis import FDataBasis -T = TypeVar("T", bound='BasisOfFData') +T = TypeVar("T", bound="CustomBasis") -class BasisOfFData(Basis): +class CustomBasis(Basis): """Defines the structure of a basis of functions. Parameters: @@ -50,10 +50,18 @@ def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: "The number of samples must be less than or equal to the " "number of sampling points.", ) - if np.linalg.matrix_rank(fdata.data_matrix[..., 0]) < fdata.n_samples: + + new_shape = ( + fdata.data_matrix.shape[0], + fdata.data_matrix.shape[1] * fdata.data_matrix.shape[2], + ) + rank = np.linalg.matrix_rank(fdata.data_matrix.reshape(new_shape)) + + if rank != fdata.n_samples: raise ValueError( - "There are only {rank} linearly independent functions".format( - rank=np.linalg.matrix_rank(fdata.data_matrix), + "There are only {rank} linearly independent " + "functions".format( + rank=rank, ), ) @@ -73,35 +81,13 @@ def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: ) pass - def _evaluate( - self, - eval_points: NDArrayFloat, - ) -> NDArrayFloat: - """Evaluate Basis object.""" - return self.fdata(eval_points) - - def __len__(self) -> int: - return self.n_basis - def _derivative_basis_and_coefs( self: T, coefs: NDArrayFloat, order: int = 1, ) -> Tuple[T, NDArrayFloat]: - """ - Return basis and coefficients of the derivative. - Args: - coefs: Coefficients of a vector expressed in this basis. - order: Order of the derivative. - - Returns: - Tuple with the basis of the derivative and its coefficients. - - Subclasses can override this to provide derivative construction. - - """ - derivated_basis = BasisOfFData( + derivated_basis = CustomBasis( fdata=self.fdata.derivative(order=order), ) @@ -112,17 +98,24 @@ def _coordinate_nonfull( coefs: NDArrayFloat, key: int | slice, ) -> Tuple[Basis, NDArrayFloat]: - """ - Return a basis and coefficients for the indexed coordinate functions. + return CustomBasis(fdata=self.fdata.coordinates[key]), coefs + + def _evaluate( + self, + eval_points: NDArrayFloat, + ) -> NDArrayFloat: + return self.fdata(eval_points) - Subclasses can override this to provide coordinate indexing. + def __len__(self) -> int: + return self.n_basis - """ - return BasisOfFData(fdata=self.fdata.coordinates[key]), coefs + @property + def dim_codomain(self) -> int: + return self.fdata.dim_codomain def __eq__(self, other: Any) -> bool: - """Test equality of Basis.""" from ..._utils import _same_domain + return ( isinstance(other, type(self)) and _same_domain(self, other) @@ -130,5 +123,4 @@ def __eq__(self, other: Any) -> bool: ) def __hash__(self) -> int: - """Hash a Basis.""" return hash(self.fdata) diff --git a/skfda/tests/test_basis_of_fdata.py b/skfda/tests/test_custom_basis.py similarity index 75% rename from skfda/tests/test_basis_of_fdata.py rename to skfda/tests/test_custom_basis.py index 5e26da03e..cba62172f 100644 --- a/skfda/tests/test_basis_of_fdata.py +++ b/skfda/tests/test_custom_basis.py @@ -4,7 +4,7 @@ import numpy as np -from skfda.representation.basis import BasisOfFData, FDataBasis, Fourier +from skfda.representation.basis import CustomBasis, FDataBasis, Fourier from skfda.representation.grid import FDataGrid @@ -20,7 +20,7 @@ def test_grid(self): ) data_basis = FDataBasis( - basis=BasisOfFData(fdata=sample), + basis=CustomBasis(fdata=sample), coefficients=np.array([[1, 0], [0, 1], [1, 1]]), ) @@ -44,7 +44,7 @@ def test_basis(self): ) data_basis = FDataBasis( - basis=BasisOfFData( + basis=CustomBasis( fdata=FDataBasis(basis=basis, coefficients=coeficients), ), coefficients=coeficients, @@ -76,7 +76,7 @@ def test_not_linearly_independent_too_many(self): ) with self.assertRaises(ValueError): - BasisOfFData(fdata=sample) + CustomBasis(fdata=sample) sample = FDataBasis( basis=Fourier(n_basis=3), @@ -91,7 +91,7 @@ def test_not_linearly_independent_too_many(self): ) with self.assertRaises(ValueError): - BasisOfFData(fdata=sample) + CustomBasis(fdata=sample) def test_not_linearly_independent_range(self): """ @@ -106,7 +106,7 @@ def test_not_linearly_independent_range(self): ) with self.assertRaises(ValueError): - BasisOfFData(fdata=sample) + CustomBasis(fdata=sample) sample = FDataBasis( basis=Fourier(n_basis=3), @@ -120,7 +120,7 @@ def test_not_linearly_independent_range(self): ) with self.assertRaises(ValueError): - BasisOfFData(fdata=sample) + CustomBasis(fdata=sample) def test_derivative_grid(self): """Test the derivative of a basis constructed from a FDataGrid.""" @@ -131,7 +131,7 @@ def test_derivative_grid(self): # The derivative of the first function is always 1 # The derivative of the second function is 0 and then 4 - basis = BasisOfFData(fdata=base_functions) + basis = CustomBasis(fdata=base_functions) coefs = np.array([[1, 0], [0, 1], [1, 1]]) derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( @@ -169,7 +169,7 @@ def test_derivative_basis(self): coefficients=basis_coef, ) - basis = BasisOfFData(fdata=base_functions) + basis = CustomBasis(fdata=base_functions) coefs = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [2, 1, 5]]) derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( @@ -214,12 +214,60 @@ def test_linearly_dependent_derivatives(self): # Therefore, the derivative of the basis is not a basis # The basis is linearly independent - base = BasisOfFData(fdata=base_functions) + base = CustomBasis(fdata=base_functions) # Its derivative is not linearly independent with self.assertRaises(ValueError): base.derivative_basis_and_coefs(np.ndarray([1, 1])) + def test_multivariate_codomain(self): + """Test basis from a multivariate function.""" + points = np.array([0, 1, 2]) + base_functions = FDataGrid( + data_matrix=np.array( + [ + [[0, 0, 1], [0, 0, 2], [0, 0, 3]], + [[1, 0, 0], [2, 0, 0], [3, 0, 0]], + ], + ), + grid_points=points, + ) + base = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 1]]) + + functions = FDataBasis( + basis=base, + coefficients=coefs, + ) + + expected_data = np.array( + [ + [[0, 0, 1], [0, 0, 2], [0, 0, 3]], + [[1, 0, 0], [2, 0, 0], [3, 0, 0]], + [[1, 0, 1], [2, 0, 2], [3, 0, 3]], + ], + ) + np.testing.assert_equal(functions(points), expected_data) + + def test_multivariate_codomain_linearly_dependent(self): + """Test basis from multivariate linearly dependent functions.""" + points = np.array([0, 1, 2]) + base_functions = FDataGrid( + data_matrix=np.array( + [ + [[0, 0, 1], [0, 0, 2], [0, 0, 3]], + [[1, 0, 0], [2, 0, 0], [3, 0, 0]], + [[1, 0, 1], [2, 0, 2], [3, 0, 3]], + ], + ), + grid_points=points, + ) + # The third function is the sum of the first two + + with self.assertRaises(ValueError): + CustomBasis(fdata=base_functions) + if __name__ == "__main__": unittest.main() From 8de7cab9b7fffad7b0cf77dfd7647fd9b299ab69 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sat, 29 Oct 2022 13:28:41 +0200 Subject: [PATCH 330/400] Use mutimethods --- skfda/representation/basis/_custom_basis.py | 44 +++++++++++++-------- 1 file changed, 27 insertions(+), 17 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index d5ad33e49..da4f965ef 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -5,6 +5,7 @@ from typing import Any, Tuple, TypeVar import numpy as np +import multimethod from ...typing._numpy import NDArrayFloat from .._functional_data import FData @@ -35,29 +36,38 @@ def __init__( domain_range=fdata.domain_range, n_basis=fdata.n_samples, ) - if isinstance(fdata, FDataGrid): - self._check_linearly_independent_grid(fdata) - else: - self._check_linearly_independent_basis(fdata) + self._check_linearly_independent(fdata) self.fdata = fdata - def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: - """Check if the observations in the FDataGrid linearly independent.""" - if fdata.n_samples > fdata.data_matrix.shape[1]: - raise ValueError( - "Too many samples in the basis. " - "The number of samples must be less than or equal to the " - "number of sampling points.", - ) + @multimethod.multidispatch + def _check_linearly_independent(self, fdata) -> None: + """Check if the functions are linearly independent.""" + raise ValueError( + "The basis creation functionality is not available for the " + "type of FData object provided", + ) - new_shape = ( + @_check_linearly_independent.register + def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: + """Ensure the functions in the FDataGrid are linearly independent.""" + # Flatten the last dimension of the data matrix + flattened_shape = ( fdata.data_matrix.shape[0], fdata.data_matrix.shape[1] * fdata.data_matrix.shape[2], ) - rank = np.linalg.matrix_rank(fdata.data_matrix.reshape(new_shape)) - if rank != fdata.n_samples: + if fdata.n_samples > flattened_shape[1]: + raise ValueError( + "Too many samples in the basis. The number of samples " + "must be less than or equal to the number of sampling points " + "times the dimension of the codomain.", + ) + rank = np.linalg.matrix_rank( + fdata.data_matrix.reshape(flattened_shape), + ) + + if rank < fdata.n_samples: raise ValueError( "There are only {rank} linearly independent " "functions".format( @@ -65,8 +75,9 @@ def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: ), ) + @_check_linearly_independent.register def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: - """Check if the observations in the FDataBasis linearly independent.""" + """Ensure the functions in the FDataBasis are linearly independent.""" if fdata.n_samples > fdata.basis.n_basis: raise ValueError( "Too many samples in the basis. " @@ -79,7 +90,6 @@ def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: rank=np.linalg.matrix_rank(fdata.coefficients), ), ) - pass def _derivative_basis_and_coefs( self: T, From c671efa2501b2704c530e32a1ffa295da8c6cfeb Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sat, 29 Oct 2022 17:31:37 +0200 Subject: [PATCH 331/400] First version of the derivative --- skfda/representation/basis/_custom_basis.py | 50 ++++++++++++++--- skfda/tests/test_custom_basis.py | 62 ++++++++++++++------- 2 files changed, 83 insertions(+), 29 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index da4f965ef..0f3c0e8f6 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -4,8 +4,8 @@ from typing import Any, Tuple, TypeVar -import numpy as np import multimethod +import numpy as np from ...typing._numpy import NDArrayFloat from .._functional_data import FData @@ -17,12 +17,15 @@ class CustomBasis(Basis): - """Defines the structure of a basis of functions. + """Basis composed of custom functions. + + Defines a basis composed of the functions in the :class: `FData` object + passed as argument. + The functions must be linearly independent, otherwise + an exception is raised. Parameters: - domain_range: The :term:`domain range` over which the basis can be - evaluated. - n_basis: number of functions in the basis. + fdata: Functions that define the basis. """ @@ -96,12 +99,41 @@ def _derivative_basis_and_coefs( coefs: NDArrayFloat, order: int = 1, ) -> Tuple[T, NDArrayFloat]: + deriv_fdata = self.fdata.derivative(order=order) + new_basis = None + + if isinstance(deriv_fdata, FDataBasis): + q, r = np.linalg.qr(deriv_fdata.coefficients.T) + new_basis = CustomBasis(fdata=deriv_fdata.copy(coefficients=q.T)) + coefs = coefs @ deriv_fdata.coefficients @ q + + else: + flattened_shape = ( + deriv_fdata.data_matrix.shape[0], + ( + deriv_fdata.data_matrix.shape[1] + * deriv_fdata.data_matrix.shape[2] + ), + ) - derivated_basis = CustomBasis( - fdata=self.fdata.derivative(order=order), - ) + mat = deriv_fdata.data_matrix.reshape(flattened_shape) + + q, r = np.linalg.qr(mat.T) + new_data = q.T.reshape( + ( + -1, + deriv_fdata.data_matrix.shape[1], + deriv_fdata.data_matrix.shape[2], + ), + ) + new_basis = CustomBasis( + fdata=deriv_fdata.copy( + data_matrix=new_data, + ), + ) + coefs = coefs @ mat @ q - return derivated_basis, coefs + return new_basis, coefs def _coordinate_nonfull( self, diff --git a/skfda/tests/test_custom_basis.py b/skfda/tests/test_custom_basis.py index cba62172f..397d5dc5e 100644 --- a/skfda/tests/test_custom_basis.py +++ b/skfda/tests/test_custom_basis.py @@ -149,9 +149,10 @@ def test_derivative_grid(self): # Derivative of the original functions sampled in the given points # and multiplied by the coefs derivative_evaluated = np.array([[1, 1], [0, 4], [1, 5]]) - np.testing.assert_equal( + np.testing.assert_allclose( derivative(eval_points)[..., 0], derivative_evaluated, + atol=1e-15, ) def test_derivative_basis(self): @@ -182,7 +183,7 @@ def test_derivative_basis(self): coefficients=derivative_coefs, ) - eval_points = np.linspace(0, 1, 100) + eval_points = np.linspace(0, 1, 10) # Derivative of the original functions basis_derivative, coefs_derivative = Fourier( @@ -193,32 +194,31 @@ def test_derivative_basis(self): basis=basis_derivative, coefficients=coefs_derivative, ) - np.testing.assert_almost_equal( derivative(eval_points), derivative_on_the_basis(eval_points), ) - def test_linearly_dependent_derivatives(self): - """ - Construct a basis whose derivative is not a basis. + # def test_linearly_dependent_derivatives(self): + # """ + # Construct a basis whose derivative is not a basis. - An error should be raised - """ - base_functions = FDataGrid( - data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), - grid_points=np.array([[0, 1, 2]]), - ) - # The derivative of the first function is 1 - # The derivative of the second function is 2 - # Therefore, the derivative of the basis is not a basis + # An error should be raised + # """ + # base_functions = FDataGrid( + # data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), + # grid_points=np.array([[0, 1, 2]]), + # ) + # # The derivative of the first function is 1 + # # The derivative of the second function is 2 + # # Therefore, the derivative of the basis is not a basis - # The basis is linearly independent - base = CustomBasis(fdata=base_functions) + # # The basis is linearly independent + # base = CustomBasis(fdata=base_functions) - # Its derivative is not linearly independent - with self.assertRaises(ValueError): - base.derivative_basis_and_coefs(np.ndarray([1, 1])) + # # Its derivative is not linearly independent + # with self.assertRaises(ValueError): + # base.derivative_basis_and_coefs(np.ndarray([1, 1])) def test_multivariate_codomain(self): """Test basis from a multivariate function.""" @@ -268,6 +268,28 @@ def test_multivariate_codomain_linearly_dependent(self): with self.assertRaises(ValueError): CustomBasis(fdata=base_functions) + def test_evaluate_derivative(self): + grid_points = np.array([[0, 1, 2]]) + base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), + grid_points=grid_points, + ) + basis = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 2]]) + + functions = FDataBasis( + basis=basis, + coefficients=coefs, + ) + deriv = functions.derivative(order=1) + derivate_evalated = deriv(np.array([0.5, 1.5])) + np.testing.assert_allclose( + derivate_evalated[..., 0], + np.array([[1, 1], [2, 2], [5, 5]]), + rtol=1e-15, + ) + if __name__ == "__main__": unittest.main() From 070cac39ab10b71d984f22bc59787427ad6b6aa9 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sat, 29 Oct 2022 18:37:16 +0200 Subject: [PATCH 332/400] Rework implementation --- skfda/representation/basis/_custom_basis.py | 112 +++++++++----------- skfda/tests/test_custom_basis.py | 22 +--- 2 files changed, 50 insertions(+), 84 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index 0f3c0e8f6..ae8c16ed3 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -44,33 +44,48 @@ def __init__( self.fdata = fdata @multimethod.multidispatch - def _check_linearly_independent(self, fdata) -> None: - """Check if the functions are linearly independent.""" + def _get_coordinates_matrix(self, fdata) -> NDArrayFloat: raise ValueError( - "The basis creation functionality is not available for the " - "type of FData object provided", + "Unexpected type of functional data object.", ) - @_check_linearly_independent.register - def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: - """Ensure the functions in the FDataGrid are linearly independent.""" - # Flatten the last dimension of the data matrix - flattened_shape = ( - fdata.data_matrix.shape[0], - fdata.data_matrix.shape[1] * fdata.data_matrix.shape[2], + @multimethod.multidispatch + def _set_coordinates_matrix(self, fdata, matrix: np.ndarray): + raise ValueError( + "Unexpected type of functional data object.", ) - if fdata.n_samples > flattened_shape[1]: + @_get_coordinates_matrix.register + def _get_coordinates_matrix_grid(self, fdata: FDataGrid) -> NDArrayFloat: + return fdata.data_matrix + + @_set_coordinates_matrix.register + def _set_coordinates_matrix_grid( + self, fdata: FDataGrid, matrix: np.ndarray, + ): + fdata.data_matrix = matrix + + @_get_coordinates_matrix.register + def _get_coordinates_matrix_basis(self, fdata: FDataBasis) -> NDArrayFloat: + return fdata.coefficients + + @_set_coordinates_matrix.register + def _set_coordinates_matrix_basis( + self, fdata: FDataBasis, matrix: np.ndarray, + ): + fdata.coefficients = matrix + + def _check_linearly_independent(self, fdata) -> None: + """Check if the functions are linearly independent.""" + coord_matrix = self._get_coordinates_matrix(fdata) + coord_matrix = coord_matrix.reshape(coord_matrix.shape[0], -1) + if coord_matrix.shape[0] > coord_matrix.shape[1]: raise ValueError( - "Too many samples in the basis. The number of samples " - "must be less than or equal to the number of sampling points " - "times the dimension of the codomain.", + "Too many samples in the basis", ) - rank = np.linalg.matrix_rank( - fdata.data_matrix.reshape(flattened_shape), - ) - if rank < fdata.n_samples: + rank = np.linalg.matrix_rank(coord_matrix) + if rank != coord_matrix.shape[0]: raise ValueError( "There are only {rank} linearly independent " "functions".format( @@ -78,22 +93,6 @@ def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: ), ) - @_check_linearly_independent.register - def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: - """Ensure the functions in the FDataBasis are linearly independent.""" - if fdata.n_samples > fdata.basis.n_basis: - raise ValueError( - "Too many samples in the basis. " - "The number of samples must be less than or equal to the " - "number of basis functions.", - ) - if np.linalg.matrix_rank(fdata.coefficients) < fdata.n_samples: - raise ValueError( - "There are only {rank} linearly independent functions".format( - rank=np.linalg.matrix_rank(fdata.coefficients), - ), - ) - def _derivative_basis_and_coefs( self: T, coefs: NDArrayFloat, @@ -102,36 +101,23 @@ def _derivative_basis_and_coefs( deriv_fdata = self.fdata.derivative(order=order) new_basis = None - if isinstance(deriv_fdata, FDataBasis): - q, r = np.linalg.qr(deriv_fdata.coefficients.T) - new_basis = CustomBasis(fdata=deriv_fdata.copy(coefficients=q.T)) - coefs = coefs @ deriv_fdata.coefficients @ q - - else: - flattened_shape = ( - deriv_fdata.data_matrix.shape[0], - ( - deriv_fdata.data_matrix.shape[1] - * deriv_fdata.data_matrix.shape[2] - ), - ) + coord_matrix = self._get_coordinates_matrix(deriv_fdata) + coord_matrix_reshaped = coord_matrix.reshape( + coord_matrix.shape[0], + -1, + ) - mat = deriv_fdata.data_matrix.reshape(flattened_shape) + q, r = np.linalg.qr(coord_matrix_reshaped.T) - q, r = np.linalg.qr(mat.T) - new_data = q.T.reshape( - ( - -1, - deriv_fdata.data_matrix.shape[1], - deriv_fdata.data_matrix.shape[2], - ), - ) - new_basis = CustomBasis( - fdata=deriv_fdata.copy( - data_matrix=new_data, - ), - ) - coefs = coefs @ mat @ q + new_data = q.T.reshape( + -1, + *coord_matrix.shape[1:], + ) + + self._set_coordinates_matrix(deriv_fdata, new_data) + + new_basis = CustomBasis(fdata=deriv_fdata) + coefs = coefs @ coord_matrix_reshaped @ q return new_basis, coefs diff --git a/skfda/tests/test_custom_basis.py b/skfda/tests/test_custom_basis.py index 397d5dc5e..2eefcd632 100644 --- a/skfda/tests/test_custom_basis.py +++ b/skfda/tests/test_custom_basis.py @@ -199,27 +199,6 @@ def test_derivative_basis(self): derivative_on_the_basis(eval_points), ) - # def test_linearly_dependent_derivatives(self): - # """ - # Construct a basis whose derivative is not a basis. - - # An error should be raised - # """ - # base_functions = FDataGrid( - # data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), - # grid_points=np.array([[0, 1, 2]]), - # ) - # # The derivative of the first function is 1 - # # The derivative of the second function is 2 - # # Therefore, the derivative of the basis is not a basis - - # # The basis is linearly independent - # base = CustomBasis(fdata=base_functions) - - # # Its derivative is not linearly independent - # with self.assertRaises(ValueError): - # base.derivative_basis_and_coefs(np.ndarray([1, 1])) - def test_multivariate_codomain(self): """Test basis from a multivariate function.""" points = np.array([0, 1, 2]) @@ -269,6 +248,7 @@ def test_multivariate_codomain_linearly_dependent(self): CustomBasis(fdata=base_functions) def test_evaluate_derivative(self): + """Test the evaluation of the derivative of a DataBasis.""" grid_points = np.array([[0, 1, 2]]) base_functions = FDataGrid( data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), From 906d178366a5846b24a33848f08527318aee56aa Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sat, 29 Oct 2022 23:45:51 +0200 Subject: [PATCH 333/400] Renaming and polishing --- skfda/representation/basis/_custom_basis.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index ae8c16ed3..b84693488 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -78,10 +78,15 @@ def _set_coordinates_matrix_basis( def _check_linearly_independent(self, fdata) -> None: """Check if the functions are linearly independent.""" coord_matrix = self._get_coordinates_matrix(fdata) + + # Reshape to a bidimensional matrix. This only affects FDataGrids + # whose codomain is not 1-dimensional. coord_matrix = coord_matrix.reshape(coord_matrix.shape[0], -1) + if coord_matrix.shape[0] > coord_matrix.shape[1]: raise ValueError( - "Too many samples in the basis", + "There are more functions than the maximum dimension of the " + "space that they could generate.", ) rank = np.linalg.matrix_rank(coord_matrix) @@ -102,6 +107,12 @@ def _derivative_basis_and_coefs( new_basis = None coord_matrix = self._get_coordinates_matrix(deriv_fdata) + + # If the basis formed by the derivatives has maximum rank, + # we can just return that + if np.linalg.matrix_rank(coord_matrix) == coord_matrix.shape[0]: + return CustomBasis(fdata=deriv_fdata), coefs + coord_matrix_reshaped = coord_matrix.reshape( coord_matrix.shape[0], -1, @@ -109,12 +120,12 @@ def _derivative_basis_and_coefs( q, r = np.linalg.qr(coord_matrix_reshaped.T) - new_data = q.T.reshape( + new_coordinates = q.T.reshape( -1, *coord_matrix.shape[1:], ) - self._set_coordinates_matrix(deriv_fdata, new_data) + self._set_coordinates_matrix(deriv_fdata, new_coordinates) new_basis = CustomBasis(fdata=deriv_fdata) coefs = coefs @ coord_matrix_reshaped @ q From edcf3d121990ab4a32d20f46272adb49d2c803c7 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sun, 30 Oct 2022 00:30:05 +0200 Subject: [PATCH 334/400] Batch renaming --- examples/plot_extrapolation.py | 8 +- examples/plot_fpca.py | 12 +- examples/plot_kernel_regression.py | 2 +- .../plot_neighbors_functional_regression.py | 4 +- examples/plot_oneway.py | 4 +- examples/plot_representation.py | 6 +- examples/plot_shift_registration.py | 4 +- full_examples/plot_tecator_regression.py | 4 +- skfda/inference/hotelling/_hotelling.py | 4 +- skfda/misc/_math.py | 12 +- .../_linear_differential_operator.py | 27 ++-- .../ml/regression/_historical_linear_model.py | 9 +- skfda/ml/regression/_linear_regression.py | 24 ++-- skfda/preprocessing/dim_reduction/_fpca.py | 2 +- .../_coefficients_transformer.py | 4 +- .../_evaluation_trasformer.py | 4 +- .../registration/_landmark_registration.py | 4 +- .../registration/_lstsq_shift_registration.py | 4 +- skfda/preprocessing/smoothing/_basis.py | 10 +- skfda/representation/basis/__init__.py | 28 ++--- .../basis/{_bspline.py => _bspline_basis.py} | 12 +- .../{_constant.py => _constant_basis.py} | 6 +- skfda/representation/basis/_fdatabasis.py | 24 ++-- ...te_element.py => _finite_element_basis.py} | 10 +- .../basis/{_fourier.py => _fourier_basis.py} | 8 +- .../{_monomial.py => _monomial_basis.py} | 6 +- skfda/representation/basis/_tensor_basis.py | 10 +- skfda/representation/basis/_vector_basis.py | 15 +-- skfda/representation/grid.py | 2 +- skfda/tests/test_basis.py | 115 +++++++++--------- skfda/tests/test_extrapolation.py | 6 +- skfda/tests/test_fdatabasis_evaluation.py | 48 ++++---- skfda/tests/test_fpca.py | 18 +-- skfda/tests/test_functional_transformers.py | 2 +- skfda/tests/test_hotelling.py | 14 +-- skfda/tests/test_kernel_regression.py | 6 +- .../test_linear_differential_operator.py | 8 +- skfda/tests/test_math.py | 18 +-- skfda/tests/test_metrics.py | 4 +- skfda/tests/test_neighbors.py | 10 +- skfda/tests/test_oneway_anova.py | 8 +- skfda/tests/test_pandas.py | 2 +- skfda/tests/test_pandas_fdatabasis.py | 12 +- skfda/tests/test_registration.py | 6 +- skfda/tests/test_regression.py | 70 +++++------ skfda/tests/test_regularization.py | 34 +++--- skfda/tests/test_scoring.py | 30 ++--- skfda/tests/test_smoothing.py | 12 +- skfda/tests/test_ufunc_numpy.py | 4 +- tutorial/plot_basis_representation.py | 24 ++-- 50 files changed, 366 insertions(+), 354 deletions(-) rename skfda/representation/basis/{_bspline.py => _bspline_basis.py} (97%) rename skfda/representation/basis/{_constant.py => _constant_basis.py} (92%) rename skfda/representation/basis/{_finite_element.py => _finite_element_basis.py} (96%) rename skfda/representation/basis/{_fourier.py => _fourier_basis.py} (97%) rename skfda/representation/basis/{_monomial.py => _monomial_basis.py} (96%) diff --git a/examples/plot_extrapolation.py b/examples/plot_extrapolation.py index afab1caf4..83afeea05 100644 --- a/examples/plot_extrapolation.py +++ b/examples/plot_extrapolation.py @@ -44,13 +44,15 @@ n_samples=2, error_std=0, random_state=0) fdgrid.dataset_name = "Grid" -fd_fourier = fdgrid.to_basis(skfda.representation.basis.Fourier()) +fd_fourier = fdgrid.to_basis(skfda.representation.basis.FourierBasis()) fd_fourier.dataset_name = "Fourier Basis" -fd_monomial = fdgrid.to_basis(skfda.representation.basis.Monomial(n_basis=5)) +fd_monomial = fdgrid.to_basis( + skfda.representation.basis.MonomialBasis(n_basis=5)) fd_monomial.dataset_name = "Monomial Basis" -fd_bspline = fdgrid.to_basis(skfda.representation.basis.BSpline(n_basis=5)) +fd_bspline = fdgrid.to_basis( + skfda.representation.basis.BSplineBasis(n_basis=5)) fd_bspline.dataset_name = "BSpline Basis" diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index f20d99ea3..ea077286f 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -14,7 +14,7 @@ from skfda.datasets import fetch_growth from skfda.exploratory.visualization import FPCAPlot from skfda.preprocessing.dim_reduction import FPCA -from skfda.representation.basis import BSpline, Fourier, Monomial +from skfda.representation.basis import BSplineBasis, FourierBasis, MonomialBasis ############################################################################## # In this example we are going to use functional principal component analysis @@ -50,7 +50,7 @@ # We also plot the data for better visual representation. dataset = fetch_growth() fd = dataset['data'] -basis = skfda.representation.basis.BSpline(n_basis=7) +basis = skfda.representation.basis.BSplineBasis(n_basis=7) basis_fd = fd.to_basis(basis) basis_fd.plot() @@ -90,8 +90,8 @@ # basis dataset = fetch_growth() fd = dataset['data'] -basis_fd = fd.to_basis(BSpline(n_basis=7)) -fpca = FPCA(n_components=2, components_basis=Fourier(n_basis=7)) +basis_fd = fd.to_basis(BSplineBasis(n_basis=7)) +fpca = FPCA(n_components=2, components_basis=FourierBasis(n_basis=7)) fpca.fit(basis_fd) fpca.components_.plot() @@ -103,7 +103,7 @@ # principal components dataset = fetch_growth() fd = dataset['data'] -basis_fd = fd.to_basis(BSpline(n_basis=7)) -fpca = FPCA(n_components=2, components_basis=Monomial(n_basis=4)) +basis_fd = fd.to_basis(BSplineBasis(n_basis=7)) +fpca = FPCA(n_components=2, components_basis=MonomialBasis(n_basis=4)) fpca.fit(basis_fd) fpca.components_.plot() diff --git a/examples/plot_kernel_regression.py b/examples/plot_kernel_regression.py index d3cb895bb..d1f9a693a 100644 --- a/examples/plot_kernel_regression.py +++ b/examples/plot_kernel_regression.py @@ -117,7 +117,7 @@ # number of functions in the basis affects the estimation result and should # ideally also be chosen using cross-validation. -fourier = skfda.representation.basis.Fourier(n_basis=10) +fourier = skfda.representation.basis.FourierBasis(n_basis=10) X_basis = X.to_basis(basis=fourier) X_basis_train, X_basis_test, y_train, y_test = train_test_split( diff --git a/examples/plot_neighbors_functional_regression.py b/examples/plot_neighbors_functional_regression.py index aa70fa65d..6c0805503 100644 --- a/examples/plot_neighbors_functional_regression.py +++ b/examples/plot_neighbors_functional_regression.py @@ -14,7 +14,7 @@ import skfda from skfda.ml.regression import KNeighborsRegressor -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis ############################################################################## # @@ -54,7 +54,7 @@ # to make a smoothing of the precipitation curves using a basis # representation, employing for it a fourier basis with 5 elements. -y = y.to_basis(Fourier(n_basis=5)) +y = y.to_basis(FourierBasis(n_basis=5)) y.plot() diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 06d8b68b1..26d9b50eb 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -14,7 +14,7 @@ import skfda from skfda.inference.anova import oneway_anova from skfda.representation import FDataGrid, FDataBasis -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis ################################################################################ # *One-way ANOVA* (analysis of variance) is a test that can be used to @@ -31,7 +31,7 @@ # of hips and knees from 39 different boys in a 20 point movement cycle. dataset = skfda.datasets.fetch_gait() fd_hip = dataset['data'].coordinates[0] -fd_knee = dataset['data'].coordinates[1].to_basis(Fourier(n_basis=10)) +fd_knee = dataset['data'].coordinates[1].to_basis(FourierBasis(n_basis=10)) ################################################################################ # Let's start with the first feature, the angle of the hip. The sample diff --git a/examples/plot_representation.py b/examples/plot_representation.py index fb40be899..058fc1630 100644 --- a/examples/plot_representation.py +++ b/examples/plot_representation.py @@ -80,14 +80,14 @@ ############################################################################## # We will represent it using a basis of B-splines. -fd_basis = fd.to_basis(basis.BSpline(n_basis=4)) +fd_basis = fd.to_basis(basis.BSplineBasis(n_basis=4)) fd_basis.plot() ############################################################################## # We can increase the number of elements in the basis to try to reproduce the # original data with more fidelity. -fd_basis_big = fd.to_basis(basis.BSpline(n_basis=7)) +fd_basis_big = fd.to_basis(basis.BSplineBasis(n_basis=7)) fd_basis_big.plot() @@ -105,7 +105,7 @@ # For example, in the Fourier basis the functions start and end at the same # points if the period is equal to the domain range, so this basis is clearly # non suitable for the Growth dataset. -fd_basis = fd.to_basis(basis.Fourier(n_basis=7)) +fd_basis = fd.to_basis(basis.FourierBasis(n_basis=7)) fd_basis.plot() diff --git a/examples/plot_shift_registration.py b/examples/plot_shift_registration.py index b87ec0339..e5e9db7b8 100644 --- a/examples/plot_shift_registration.py +++ b/examples/plot_shift_registration.py @@ -15,7 +15,7 @@ from skfda.datasets import make_sinusoidal_process from skfda.preprocessing.registration import LeastSquaresShiftRegistration -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis ############################################################################## # In this example we will use a @@ -36,7 +36,7 @@ # is essential due to the use of derivatives in the optimization process. # Because of their sinusoidal nature we will use a Fourier basis. -fd_basis = fd.to_basis(Fourier(n_basis=11)) +fd_basis = fd.to_basis(FourierBasis(n_basis=11)) fd_basis.plot() ############################################################################## diff --git a/full_examples/plot_tecator_regression.py b/full_examples/plot_tecator_regression.py index e7d58ee8f..96333ce4a 100644 --- a/full_examples/plot_tecator_regression.py +++ b/full_examples/plot_tecator_regression.py @@ -23,7 +23,7 @@ MaximaHunting, RelativeLocalMaximaSelector, ) -from skfda.representation.basis import BSpline +from skfda.representation.basis import BSplineBasis ############################################################################## # We will first load the Tecator data, keeping only the fat content target, @@ -43,7 +43,7 @@ ############################################################################## # In order to compute functional linear regression we first convert the data # to a basis expansion. -basis = BSpline( +basis = BSplineBasis( n_basis=10, ) X_der_basis = X_der.to_basis(basis) diff --git a/skfda/inference/hotelling/_hotelling.py b/skfda/inference/hotelling/_hotelling.py index 43b8834ab..3580d0b42 100644 --- a/skfda/inference/hotelling/_hotelling.py +++ b/skfda/inference/hotelling/_hotelling.py @@ -66,8 +66,8 @@ def hotelling_t2( >>> fd2 = FDataGrid([[3, 3, 3], [5, 5, 5]]) >>> '%.2f' % hotelling_t2(fd1, fd2) '2.00' - >>> fd1 = fd1.to_basis(basis.Fourier(n_basis=3)) - >>> fd2 = fd2.to_basis(basis.Fourier(n_basis=3)) + >>> fd1 = fd1.to_basis(basis.FourierBasis(n_basis=3)) + >>> fd2 = fd2.to_basis(basis.FourierBasis(n_basis=3)) >>> '%.2f' % hotelling_t2(fd1, fd2) '2.00' diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 68751d10f..0cb71737b 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -296,21 +296,21 @@ def inner_product( It also work with basis objects - >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> basis = skfda.representation.basis.MonomialBasis(n_basis=3) >>> >>> fd1 = skfda.FDataBasis(basis, [0, 1, 0]) >>> fd2 = skfda.FDataBasis(basis, [1, 0, 0]) >>> inner_product(fd1, fd2) array([ 0.5]) - >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> basis = skfda.representation.basis.MonomialBasis(n_basis=3) >>> >>> fd1 = skfda.FDataBasis(basis, [[0, 1, 0], [0, 0, 1]]) >>> fd2 = skfda.FDataBasis(basis, [1, 0, 0]) >>> inner_product(fd1, fd2) array([ 0.5 , 0.33333333]) - >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> basis = skfda.representation.basis.MonomialBasis(n_basis=3) >>> >>> fd1 = skfda.FDataBasis(basis, [[0, 1, 0], [0, 0, 1]]) >>> fd2 = skfda.FDataBasis(basis, [[1, 0, 0], [0, 1, 0]]) @@ -615,21 +615,21 @@ def cosine_similarity( It also work with basis objects - >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> basis = skfda.representation.basis.MonomialBasis(n_basis=3) >>> >>> fd1 = skfda.FDataBasis(basis, [0, 1, 0]) >>> fd2 = skfda.FDataBasis(basis, [1, 0, 0]) >>> cosine_similarity(fd1, fd2) array([ 0.8660254]) - >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> basis = skfda.representation.basis.MonomialBasis(n_basis=3) >>> >>> fd1 = skfda.FDataBasis(basis, [[0, 1, 0], [0, 0, 1]]) >>> fd2 = skfda.FDataBasis(basis, [1, 0, 0]) >>> cosine_similarity(fd1, fd2) array([ 0.8660254 , 0.74535599]) - >>> basis = skfda.representation.basis.Monomial(n_basis=3) + >>> basis = skfda.representation.basis.MonomialBasis(n_basis=3) >>> >>> fd1 = skfda.FDataBasis(basis, [[0, 1, 0], [0, 0, 1]]) >>> fd2 = skfda.FDataBasis(basis, [[1, 0, 0], [0, 1, 0]]) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index a76e22ddd..36f28cd7e 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -9,7 +9,7 @@ from scipy.interpolate import PPoly from ...representation import FData, FDataGrid -from ...representation.basis import Basis, BSpline, Constant, Fourier, Monomial +from ...representation.basis import Basis, BSplineBasis, ConstantBasis, FourierBasis, MonomialBasis from ...typing._base import DomainRangeLike from ...typing._numpy import NDArrayFloat from ._operators import Operator, gramian_matrix_optimization @@ -60,7 +60,8 @@ class LinearDifferentialOperator( >>> from skfda.misc.operators import LinearDifferentialOperator >>> from skfda.representation.basis import (FDataBasis, - ... Monomial, Constant) + ... MonomialBasis, + ... ConstantBasis) >>> >>> LinearDifferentialOperator(2) LinearDifferentialOperator( @@ -77,23 +78,23 @@ class LinearDifferentialOperator( Create a linear differential operator with non-constant weights. - >>> constant = Constant() - >>> monomial = Monomial(domain_range=(0, 1), n_basis=3) + >>> constant = ConstantBasis() + >>> monomial = MonomialBasis(domain_range=(0, 1), n_basis=3) >>> fdlist = [FDataBasis(constant, [0.]), ... FDataBasis(constant, [0.]), ... FDataBasis(monomial, [1., 2., 3.])] >>> LinearDifferentialOperator(weights=fdlist) LinearDifferentialOperator( weights=(FDataBasis( - basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), + basis=ConstantBasis(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 0.]], ...), FDataBasis( - basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), + basis=ConstantBasis(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 0.]], ...), FDataBasis( - basis=Monomial(domain_range=((0.0, 1.0),), n_basis=3), + basis=MonomialBasis(domain_range=((0.0, 1.0),), n_basis=3), coefficients=[[ 1. 2. 3.]], ...)), ) @@ -219,7 +220,7 @@ def applied_linear_diff_op( @gramian_matrix_optimization.register def constant_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, - basis: Constant, + basis: ConstantBasis, ) -> NDArrayFloat: """Optimized version for Constant basis.""" coefs = linear_operator.constant_weights() @@ -233,7 +234,7 @@ def constant_penalty_matrix_optimized( def _monomial_evaluate_constant_linear_diff_op( - basis: Monomial, + basis: MonomialBasis, weights: NDArrayFloat, ) -> NDArrayFloat: """Evaluate constant weights over the monomial basis.""" @@ -295,7 +296,7 @@ def _monomial_evaluate_constant_linear_diff_op( @gramian_matrix_optimization.register def monomial_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, - basis: Monomial, + basis: MonomialBasis, ) -> NDArrayFloat: """Optimized version for Monomial basis.""" weights = linear_operator.constant_weights() @@ -358,7 +359,7 @@ def monomial_penalty_matrix_optimized( def _fourier_penalty_matrix_optimized_orthonormal( - basis: Fourier, + basis: FourierBasis, weights: NDArrayFloat, ) -> NDArrayFloat: """Return the penalty when the basis is orthonormal.""" @@ -423,7 +424,7 @@ def _fourier_penalty_matrix_optimized_orthonormal( @gramian_matrix_optimization.register def fourier_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, - basis: Fourier, + basis: FourierBasis, ) -> NDArrayFloat: """Optimized version for Fourier basis.""" weights = linear_operator.constant_weights() @@ -441,7 +442,7 @@ def fourier_penalty_matrix_optimized( @gramian_matrix_optimization.register def bspline_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, - basis: BSpline, + basis: BSplineBasis, ) -> NDArrayFloat: """Optimized version for BSpline basis.""" coefs = linear_operator.constant_weights() diff --git a/skfda/ml/regression/_historical_linear_model.py b/skfda/ml/regression/_historical_linear_model.py index 5f2e033cc..be6aac812 100644 --- a/skfda/ml/regression/_historical_linear_model.py +++ b/skfda/ml/regression/_historical_linear_model.py @@ -10,7 +10,8 @@ from ..._utils import _cartesian_product, _pairwise_symmetric from ..._utils._sklearn_adapter import BaseEstimator, RegressorMixin from ...representation import FData, FDataBasis, FDataGrid -from ...representation.basis import Basis, FiniteElement, VectorValued +from ...representation.basis import Basis, FiniteElementBasis +from ...representation.basis import VectorValuedBasis from ...typing._numpy import NDArrayFloat _MeanType = Union[FDataGrid, float] @@ -185,7 +186,7 @@ def _create_fem_basis( stop: float, n_intervals: int, lag: float, -) -> FiniteElement: +) -> FiniteElementBasis: interval_len = (stop - start) / n_intervals @@ -202,7 +203,7 @@ def _create_fem_basis( valid_points=valid_points, ) - return FiniteElement( + return FiniteElementBasis( vertices=final_points, cells=triangles, domain_range=((start, stop),) * 2, @@ -354,7 +355,7 @@ def _fit_and_return_centered_matrix( lag=self.lag, ) - self._basis = VectorValued( + self._basis = VectorValuedBasis( [fem_basis] * X_centered.dim_codomain ) diff --git a/skfda/ml/regression/_linear_regression.py b/skfda/ml/regression/_linear_regression.py index 2dc865ba0..8b1ba5fee 100644 --- a/skfda/ml/regression/_linear_regression.py +++ b/skfda/ml/regression/_linear_regression.py @@ -111,14 +111,14 @@ class LinearRegression( Examples: >>> from skfda.ml.regression import LinearRegression - >>> from skfda.representation.basis import (FDataBasis, Monomial, - ... Constant) + >>> from skfda.representation.basis import (FDataBasis, MonomialBasis, + ... ConstantBasis) >>> import pandas as pd Multivariate linear regression can be used with functions expressed in a basis. Also, a functional basis for the weights can be specified: - >>> x_basis = Monomial(n_basis=3) + >>> x_basis = MonomialBasis(n_basis=3) >>> x_fd = FDataBasis(x_basis, [[0, 0, 1], ... [0, 1, 0], ... [0, 1, 1], @@ -128,7 +128,7 @@ class LinearRegression( >>> _ = linear.fit(x_fd, y) >>> linear.coef_[0] FDataBasis( - basis=Monomial(domain_range=((0.0, 1.0),), n_basis=3), + basis=MonomialBasis(domain_range=((0.0, 1.0),), n_basis=3), coefficients=[[-15. 96. -90.]], ...) >>> linear.intercept_ @@ -138,7 +138,7 @@ class LinearRegression( Covariates can include also multivariate data: - >>> x_basis = Monomial(n_basis=2) + >>> x_basis = MonomialBasis(n_basis=2) >>> x_fd = FDataBasis(x_basis, [[0, 2], ... [0, 4], ... [1, 0], @@ -148,13 +148,13 @@ class LinearRegression( >>> x = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( - ... coef_basis=[None, Constant()]) + ... coef_basis=[None, ConstantBasis()]) >>> _ = linear.fit([x, x_fd], y) >>> linear.coef_[0] array([ 2., 1.]) >>> linear.coef_[1] FDataBasis( - basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), + basis=ConstantBasis(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 1.]], ...) >>> linear.intercept_ @@ -166,7 +166,7 @@ class LinearRegression( First example: - >>> x_basis = Monomial(n_basis=3) + >>> x_basis = MonomialBasis(n_basis=3) >>> x_fd = FDataBasis(x_basis, [[0, 0, 1], ... [0, 1, 0], ... [0, 1, 1], @@ -178,7 +178,7 @@ class LinearRegression( >>> _ = linear.fit(df, y) >>> linear.coef_[0] FDataBasis( - basis=Monomial(domain_range=((0.0, 1.0),), n_basis=3), + basis=MonomialBasis(domain_range=((0.0, 1.0),), n_basis=3), coefficients=[[-15. 96. -90.]], ...) >>> linear.intercept_ @@ -188,7 +188,7 @@ class LinearRegression( Second example: - >>> x_basis = Monomial(n_basis=2) + >>> x_basis = MonomialBasis(n_basis=2) >>> x_fd = FDataBasis(x_basis, [[0, 2], ... [0, 4], ... [1, 0], @@ -201,7 +201,7 @@ class LinearRegression( >>> df = pd.DataFrame(cov_dict) >>> y = [11, 10, 12, 6, 10, 13] >>> linear = LinearRegression( - ... coef_basis=[None, Constant(), Constant()]) + ... coef_basis=[None, ConstantBasis(), ConstantBasis()]) >>> _ = linear.fit(df, y) >>> linear.coef_[0] array([ 2.]) @@ -209,7 +209,7 @@ class LinearRegression( array([ 1.]) >>> linear.coef_[2] FDataBasis( - basis=Constant(domain_range=((0.0, 1.0),), n_basis=1), + basis=ConstantBasis(domain_range=((0.0, 1.0),), n_basis=1), coefficients=[[ 1.]], ...) >>> linear.intercept_ diff --git a/skfda/preprocessing/dim_reduction/_fpca.py b/skfda/preprocessing/dim_reduction/_fpca.py index ce520637c..89e468bc3 100644 --- a/skfda/preprocessing/dim_reduction/_fpca.py +++ b/skfda/preprocessing/dim_reduction/_fpca.py @@ -64,7 +64,7 @@ class FPCA( # noqa: WPS230 (too many public attributes) >>> data_matrix = np.array([[1.0, 0.0], [0.0, 2.0]]) >>> grid_points = [0, 1] >>> fd = FDataGrid(data_matrix, grid_points) - >>> basis = skfda.representation.basis.Monomial( + >>> basis = skfda.representation.basis.MonomialBasis( ... domain_range=(0,1), n_basis=2 ... ) >>> basis_fd = fd.to_basis(basis) diff --git a/skfda/preprocessing/feature_construction/_coefficients_transformer.py b/skfda/preprocessing/feature_construction/_coefficients_transformer.py index cb838bef3..8ac3f6041 100644 --- a/skfda/preprocessing/feature_construction/_coefficients_transformer.py +++ b/skfda/preprocessing/feature_construction/_coefficients_transformer.py @@ -25,9 +25,9 @@ class CoefficientsTransformer( ... ) >>> from skfda.representation.basis import ( ... FDataBasis, - ... Monomial, + ... MonomialBasis, ... ) - >>> basis = Monomial(n_basis=4) + >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] >>> fd = FDataBasis(basis, coefficients) >>> diff --git a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py index d41e34294..43991208d 100644 --- a/skfda/preprocessing/feature_construction/_evaluation_trasformer.py +++ b/skfda/preprocessing/feature_construction/_evaluation_trasformer.py @@ -51,7 +51,7 @@ class EvaluationTransformer( >>> from skfda.preprocessing.feature_construction import ( ... EvaluationTransformer, ... ) - >>> from skfda.representation.basis import Monomial + >>> from skfda.representation.basis import MonomialBasis Functional data object with 2 samples representing a function :math:`f : \mathbb{R}\longmapsto\mathbb{R}`. @@ -91,7 +91,7 @@ class EvaluationTransformer( Evaluation of a functional data object at several points. - >>> basis = Monomial(n_basis=4) + >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] >>> fd = FDataBasis(basis, coefficients) >>> diff --git a/skfda/preprocessing/registration/_landmark_registration.py b/skfda/preprocessing/registration/_landmark_registration.py index 1f44a962e..3f9275c33 100644 --- a/skfda/preprocessing/registration/_landmark_registration.py +++ b/skfda/preprocessing/registration/_landmark_registration.py @@ -371,7 +371,7 @@ def landmark_elastic_registration( >>> from skfda.preprocessing.registration import ( ... landmark_elastic_registration, ... ) - >>> from skfda.representation.basis import BSpline + >>> from skfda.representation.basis import BSplineBasis We will create a data with landmarks as example @@ -388,7 +388,7 @@ def landmark_elastic_registration( This method will work for FDataBasis as for FDataGrids - >>> fd = fd.to_basis(BSpline(n_basis=12)) + >>> fd = fd.to_basis(BSplineBasis(n_basis=12)) >>> landmark_elastic_registration(fd, landmarks) FDataGrid(...) diff --git a/skfda/preprocessing/registration/_lstsq_shift_registration.py b/skfda/preprocessing/registration/_lstsq_shift_registration.py index 5587debcf..6c73f0b47 100644 --- a/skfda/preprocessing/registration/_lstsq_shift_registration.py +++ b/skfda/preprocessing/registration/_lstsq_shift_registration.py @@ -101,7 +101,7 @@ class LeastSquaresShiftRegistration( ... LeastSquaresShiftRegistration, ... ) >>> from skfda.datasets import make_sinusoidal_process - >>> from skfda.representation.basis import Fourier + >>> from skfda.representation.basis import FourierBasis Registration and creation of dataset in discretized form: @@ -126,7 +126,7 @@ class LeastSquaresShiftRegistration( >>> fd = make_sinusoidal_process(n_samples=2, error_std=0, ... random_state=2) - >>> fd_basis = fd.to_basis(Fourier()) + >>> fd_basis = fd.to_basis(FourierBasis()) >>> reg.transform(fd_basis) FDataGrid(...) diff --git a/skfda/preprocessing/smoothing/_basis.py b/skfda/preprocessing/smoothing/_basis.py index c5eab609b..687ab16b1 100644 --- a/skfda/preprocessing/smoothing/_basis.py +++ b/skfda/preprocessing/smoothing/_basis.py @@ -95,7 +95,7 @@ class BasisSmoother(_LinearSmoother): array([ 3., 3., 1., 1., 3.]) >>> fd = skfda.FDataGrid(data_matrix=x, grid_points=t) - >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) + >>> basis = skfda.representation.basis.FourierBasis((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother(basis) >>> fd_smooth = smoother.fit_transform(fd) >>> fd_smooth.data_matrix.round(2) @@ -109,7 +109,7 @@ class BasisSmoother(_LinearSmoother): in basis form, by default, without extra smoothing: >>> fd = skfda.FDataGrid(data_matrix=x, grid_points=t) - >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) + >>> basis = skfda.representation.basis.FourierBasis((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, ... method='cholesky', @@ -150,7 +150,7 @@ class BasisSmoother(_LinearSmoother): >>> from skfda.misc.operators import LinearDifferentialOperator >>> >>> fd = skfda.FDataGrid(data_matrix=x, grid_points=t) - >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) + >>> basis = skfda.representation.basis.FourierBasis((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, ... method='cholesky', @@ -164,7 +164,7 @@ class BasisSmoother(_LinearSmoother): array([[ 2.04, 0.51, 0.55]]) >>> fd = skfda.FDataGrid(data_matrix=x, grid_points=t) - >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) + >>> basis = skfda.representation.basis.FourierBasis((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, ... method='qr', @@ -178,7 +178,7 @@ class BasisSmoother(_LinearSmoother): array([[ 2.04, 0.51, 0.55]]) >>> fd = skfda.FDataGrid(data_matrix=x, grid_points=t) - >>> basis = skfda.representation.basis.Fourier((0, 1), n_basis=3) + >>> basis = skfda.representation.basis.FourierBasis((0, 1), n_basis=3) >>> smoother = skfda.preprocessing.smoothing.BasisSmoother( ... basis, ... method='svd', diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 33e344783..9be35c620 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -7,27 +7,27 @@ __name__, submod_attrs={ '_basis': ["Basis"], - "_bspline": ["BSpline"], - "_constant": ["Constant"], + "_bspline_basis": ["BSplineBasis"], + "_constant_basis": ["ConstantBasis"], "_fdatabasis": ["FDataBasis", "FDataBasisDType"], - "_finite_element": ["FiniteElement"], - "_fourier": ["Fourier"], - "_monomial": ["Monomial"], - "_tensor_basis": ["Tensor"], - "_vector_basis": ["VectorValued"], + "_finite_element_basis": ["FiniteElementBasis"], + "_fourier_basis": ["FourierBasis"], + "_monomial_basis": ["MonomialBasis"], + "_tensor_basis": ["TensorBasis"], + "_vector_basis": ["VectorValuedBasis"], }, ) if TYPE_CHECKING: from ._basis import Basis as Basis - from ._bspline import BSpline as BSpline - from ._constant import Constant as Constant + from ._bspline_basis import BSplineBasis as BSplineBasis + from ._constant_basis import ConstantBasis as ConstantBasis from ._fdatabasis import ( FDataBasis as FDataBasis, FDataBasisDType as FDataBasisDType, ) - from ._finite_element import FiniteElement as FiniteElement - from ._fourier import Fourier as Fourier - from ._monomial import Monomial as Monomial - from ._tensor_basis import Tensor as Tensor - from ._vector_basis import VectorValued as VectorValued + from ._finite_element_basis import FiniteElementBasis as FiniteElementBasis + from ._fourier_basis import FourierBasis as FourierBasis + from ._monomial_basis import MonomialBasis as MonomialBasis + from ._tensor_basis import TensorBasis as TensorBasis + from ._vector_basis import VectorValuedBasis as VectorValuedBasis diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline_basis.py similarity index 97% rename from skfda/representation/basis/_bspline.py rename to skfda/representation/basis/_bspline_basis.py index 8b41d301c..31c74d64b 100644 --- a/skfda/representation/basis/_bspline.py +++ b/skfda/representation/basis/_bspline_basis.py @@ -10,10 +10,10 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='BSpline') +T = TypeVar("T", bound='BSplineBasis') -class BSpline(Basis): +class BSplineBasis(Basis): r"""BSpline basis. BSpline basis elements are defined recursively as: @@ -43,21 +43,21 @@ class BSpline(Basis): Examples: Constructs specifying number of basis and order. - >>> bss = BSpline(n_basis=8, order=4) + >>> bss = BSplineBasis(n_basis=8, order=4) If no order is specified defaults to 4 because cubic splines are the most used. So the previous example is the same as: - >>> bss = BSpline(n_basis=8) + >>> bss = BSplineBasis(n_basis=8) It is also possible to create a BSpline basis specifying the knots. - >>> bss = BSpline(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) + >>> bss = BSplineBasis(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) Once we create a basis we can evaluate each of its functions at a set of points. - >>> bss = BSpline(n_basis=3, order=3) + >>> bss = BSplineBasis(n_basis=3, order=3) >>> bss([0, 0.5, 1]) array([[[ 1. ], [ 0.25], diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant_basis.py similarity index 92% rename from skfda/representation/basis/_constant.py rename to skfda/representation/basis/_constant_basis.py index b5e8f9cda..9b07550f1 100644 --- a/skfda/representation/basis/_constant.py +++ b/skfda/representation/basis/_constant_basis.py @@ -6,10 +6,10 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='Constant') +T = TypeVar("T", bound='ConstantBasis') -class Constant(Basis): +class ConstantBasis(Basis): """Constant basis. Basis for constant functions @@ -22,7 +22,7 @@ class Constant(Basis): Defines a contant base over the interval :math:`[0, 5]` consisting on the constant function 1 on :math:`[0, 5]`. - >>> bs_cons = Constant((0,5)) + >>> bs_cons = ConstantBasis((0,5)) """ diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index fb5af17fb..86a7e2b66 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -65,13 +65,13 @@ class FDataBasis(FData): # noqa: WPS214 types of extrapolation. Examples: - >>> from skfda.representation.basis import FDataBasis, Monomial + >>> from skfda.representation.basis import FDataBasis, MonomialBasis >>> - >>> basis = Monomial(n_basis=4) + >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [1, 1, 3, .5] >>> FDataBasis(basis, coefficients) FDataBasis( - basis=Monomial(domain_range=((0.0, 1.0),), n_basis=4), + basis=MonomialBasis(domain_range=((0.0, 1.0),), n_basis=4), coefficients=[[ 1. 1. 3. 0.5]], ...) @@ -176,8 +176,8 @@ def from_data( >>> x array([ 3., 3., 1., 1., 3.]) - >>> from skfda.representation.basis import FDataBasis, Fourier - >>> basis = Fourier((0, 1), n_basis=3) + >>> from skfda.representation.basis import FDataBasis, FourierBasis + >>> basis = FourierBasis((0, 1), n_basis=3) >>> fd = FDataBasis.from_data(x, grid_points=t, basis=basis) >>> fd.coefficients.round(2) array([[ 2. , 0.71, 0.71]]) @@ -359,8 +359,8 @@ def integrate( Examples: We first create the data basis. - >>> from skfda.representation.basis import FDataBasis, Monomial - >>> basis = Monomial(n_basis=4) + >>> from skfda.representation.basis import FDataBasis, MonomialBasis + >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [1, 1, 3, .5] >>> fdata = FDataBasis(basis, coefficients) @@ -409,12 +409,12 @@ def sum( # noqa: WPS125 FDataBasis object. Examples: - >>> from skfda.representation.basis import FDataBasis, Monomial - >>> basis = Monomial(n_basis=4) + >>> from skfda.representation.basis import FDataBasis, MonomialBasis + >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] >>> FDataBasis(basis, coefficients).sum() FDataBasis( - basis=Monomial(domain_range=((0.0, 1.0),), n_basis=4), + basis=MonomialBasis(domain_range=((0.0, 1.0),), n_basis=4), coefficients=[[ 2. 2. 6. 1.]], ...) @@ -501,9 +501,9 @@ def to_grid( object. Examples: - >>> from skfda.representation.basis import FDataBasis, Monomial + >>> from skfda.representation.basis import FDataBasis, MonomialBasis >>> fd = FDataBasis(coefficients=[[1, 1, 1], [1, 0, 1]], - ... basis=Monomial(domain_range=(0,5), n_basis=3)) + ... basis=MonomialBasis(domain_range=(0,5), n_basis=3)) >>> fd.to_grid([0, 1, 2]) FDataGrid( array([[[ 1.], diff --git a/skfda/representation/basis/_finite_element.py b/skfda/representation/basis/_finite_element_basis.py similarity index 96% rename from skfda/representation/basis/_finite_element.py rename to skfda/representation/basis/_finite_element_basis.py index ea5129161..ebd25c765 100644 --- a/skfda/representation/basis/_finite_element.py +++ b/skfda/representation/basis/_finite_element_basis.py @@ -6,10 +6,10 @@ from ...typing._numpy import ArrayLike, NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='FiniteElement') +T = TypeVar("T", bound='FiniteElementBasis') -class FiniteElement(Basis): +class FiniteElementBasis(Basis): """Finite element basis. Given a n-dimensional grid made of simplices, each element of the basis @@ -22,8 +22,8 @@ class FiniteElement(Basis): :math:`n+1` vertices for an n-dimensional domain space. Examples: - >>> from skfda.representation.basis import FiniteElement - >>> basis = FiniteElement( + >>> from skfda.representation.basis import FiniteElementBasis + >>> basis = FiniteElementBasis( ... vertices=[[0, 0], [0, 1], [1, 0], [1, 1]], ... cells=[[0, 1, 2], [1, 2, 3]], ... domain_range=[(0, 1), (0, 1)], @@ -57,7 +57,7 @@ class FiniteElement(Basis): >>> n_points = 10 >>> points = np.random.uniform(size=(n_points, 2)) >>> delaunay = Delaunay(points) - >>> basis = FiniteElement( + >>> basis = FiniteElementBasis( ... vertices=delaunay.points, ... cells=delaunay.simplices, ... ) diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier_basis.py similarity index 97% rename from skfda/representation/basis/_fourier.py rename to skfda/representation/basis/_fourier_basis.py index 6021ee8fe..0372dc45a 100644 --- a/skfda/representation/basis/_fourier.py +++ b/skfda/representation/basis/_fourier_basis.py @@ -7,7 +7,7 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='Fourier') +T = TypeVar("T", bound='FourierBasis') class _SinCos(Protocol): @@ -20,7 +20,7 @@ def __call__( pass -class Fourier(Basis): +class FourierBasis(Basis): r"""Fourier basis. Defines a functional basis for representing functions on a fourier @@ -52,7 +52,7 @@ class Fourier(Basis): Examples: Constructs specifying number of basis, definition interval and period. - >>> fb = Fourier((0, np.pi), n_basis=3, period=1) + >>> fb = FourierBasis((0, np.pi), n_basis=3, period=1) >>> fb([0, np.pi / 4, np.pi / 2, np.pi]).round(2) array([[[ 1. ], [ 1. ], @@ -93,7 +93,7 @@ def __init__( period: Optional[float] = None, ) -> None: """ - Construct a Fourier object. + Construct a FourierBasis object. It forces the object to have an odd number of basis. If n_basis is even, it is incremented by one. diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial_basis.py similarity index 96% rename from skfda/representation/basis/_monomial.py rename to skfda/representation/basis/_monomial_basis.py index 00188b759..6e9a6c292 100644 --- a/skfda/representation/basis/_monomial.py +++ b/skfda/representation/basis/_monomial_basis.py @@ -6,10 +6,10 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='Monomial') +T = TypeVar("T", bound='MonomialBasis') -class Monomial(Basis): +class MonomialBasis(Basis): """Monomial basis. Basis formed by powers of the argument :math:`t`: @@ -26,7 +26,7 @@ class Monomial(Basis): Defines a monomial base over the interval :math:`[0, 5]` consisting on the first 3 powers of :math:`t`: :math:`1, t, t^2`. - >>> bs_mon = Monomial(domain_range=(0,5), n_basis=3) + >>> bs_mon = MonomialBasis(domain_range=(0,5), n_basis=3) And evaluates all the functions in the basis in a list of descrete values. diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index 84d7e393e..69f42de46 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -8,7 +8,7 @@ from ._basis import Basis -class Tensor(Basis): +class TensorBasis(Basis): r"""Tensor basis. Basis for multivariate functions constructed as a tensor product of @@ -28,12 +28,12 @@ class Tensor(Basis): 1, v, u, uv, u^2, u^2v - >>> from skfda.representation.basis import Tensor, Monomial + >>> from skfda.representation.basis import TensorBasis, MonomialBasis >>> - >>> basis_x = Monomial(domain_range=(0,5), n_basis=3) - >>> basis_y = Monomial(domain_range=(0,3), n_basis=2) + >>> basis_x = MonomialBasis(domain_range=(0,5), n_basis=3) + >>> basis_y = MonomialBasis(domain_range=(0,3), n_basis=2) >>> - >>> basis = Tensor([basis_x, basis_y]) + >>> basis = TensorBasis([basis_x, basis_y]) And evaluates all the functions in the basis in a list of descrete diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index 4b00cc975..c48220ffe 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -8,10 +8,10 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='VectorValued') +T = TypeVar("T", bound='VectorValuedBasis') -class VectorValued(Basis): +class VectorValuedBasis(Basis): r"""Vector-valued basis. Basis for :term:`vector-valued functions ` @@ -33,12 +33,13 @@ class VectorValued(Basis): 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} - >>> from skfda.representation.basis import VectorValued, Monomial + >>> from skfda.representation.basis import VectorValuedBasis + >>> from skfda.representation.basis import MonomialBasis >>> - >>> basis_x = Monomial(domain_range=(0,5), n_basis=3) - >>> basis_y = Monomial(domain_range=(0,5), n_basis=2) + >>> basis_x = MonomialBasis(domain_range=(0,5), n_basis=3) + >>> basis_y = MonomialBasis(domain_range=(0,5), n_basis=2) >>> - >>> basis = VectorValued([basis_x, basis_y]) + >>> basis = VectorValuedBasis([basis_x, basis_y]) And evaluates all the functions in the basis in a list of descrete @@ -159,7 +160,7 @@ def _coordinate_nonfull( new_basis = self.basis_list[key] if not isinstance(new_basis, Basis): - new_basis = VectorValued(new_basis) + new_basis = VectorValuedBasis(new_basis) new_coefs = coef_splits[key] if not isinstance(new_coefs, np.ndarray): diff --git a/skfda/representation/grid.py b/skfda/representation/grid.py index 855923b1b..b54a6f19c 100644 --- a/skfda/representation/grid.py +++ b/skfda/representation/grid.py @@ -918,7 +918,7 @@ def to_basis(self, basis: Basis, **kwargs: Any) -> FDataBasis: array([ 3., 3., 1., 1., 3.]) >>> fd = FDataGrid(x, t) - >>> basis = skfda.representation.basis.Fourier(n_basis=3) + >>> basis = skfda.representation.basis.FourierBasis(n_basis=3) >>> fd_b = fd.to_basis(basis) >>> fd_b.coefficients.round(2) array([[ 2. , 0.71, 0.71]]) diff --git a/skfda/tests/test_basis.py b/skfda/tests/test_basis.py index 1a79f5139..ced980b58 100644 --- a/skfda/tests/test_basis.py +++ b/skfda/tests/test_basis.py @@ -10,11 +10,11 @@ from skfda.datasets import fetch_weather from skfda.misc import inner_product_matrix from skfda.representation.basis import ( - BSpline, - Constant, + BSplineBasis, + ConstantBasis, FDataBasis, - Fourier, - Monomial, + FourierBasis, + MonomialBasis, ) from skfda.representation.grid import FDataGrid @@ -28,7 +28,7 @@ def test_from_data_cholesky(self) -> None: """Test basis conversion using Cholesky method.""" t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) - basis = BSpline((0, 1), n_basis=5) + basis = BSplineBasis((0, 1), n_basis=5) np.testing.assert_array_almost_equal( FDataBasis.from_data( x, @@ -43,7 +43,7 @@ def test_from_data_qr(self) -> None: """Test basis conversion using QR method.""" t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) - basis = BSpline((0, 1), n_basis=5) + basis = BSplineBasis((0, 1), n_basis=5) np.testing.assert_array_almost_equal( FDataBasis.from_data( x, @@ -56,7 +56,7 @@ def test_from_data_qr(self) -> None: def test_basis_inner_matrix(self) -> None: """Test the inner product matrix of FDataBasis objects.""" - basis = Monomial(n_basis=3) + basis = MonomialBasis(n_basis=3) np.testing.assert_array_almost_equal( basis.inner_product_matrix(), @@ -77,7 +77,7 @@ def test_basis_inner_matrix(self) -> None: ) np.testing.assert_array_almost_equal( - basis.inner_product_matrix(Monomial(n_basis=4)), + basis.inner_product_matrix(MonomialBasis(n_basis=4)), [ [1, 1 / 2, 1 / 3, 1 / 4], [1 / 2, 1 / 3, 1 / 4, 1 / 5], @@ -89,7 +89,7 @@ def test_basis_inner_matrix(self) -> None: def test_basis_gram_matrix_monomial(self) -> None: """Test the Gram matrix with monomial basis.""" - basis = Monomial(n_basis=3) + basis = MonomialBasis(n_basis=3) gram_matrix = basis.gram_matrix() gram_matrix_numerical = basis._gram_matrix_numerical() # noqa: WPS437 gram_matrix_res = np.array([ @@ -109,7 +109,7 @@ def test_basis_gram_matrix_monomial(self) -> None: def test_basis_gram_matrix_fourier(self) -> None: """Test the Gram matrix with fourier basis.""" - basis = Fourier(n_basis=3) + basis = FourierBasis(n_basis=3) gram_matrix = basis.gram_matrix() gram_matrix_numerical = basis._gram_matrix_numerical() # noqa: WPS437 gram_matrix_res = np.identity(3) @@ -127,7 +127,7 @@ def test_basis_gram_matrix_fourier(self) -> None: def test_basis_gram_matrix_bspline(self) -> None: """Test the Gram matrix with B-spline basis.""" - basis = BSpline(n_basis=6) + basis = BSplineBasis(n_basis=6) gram_matrix = basis.gram_matrix() gram_matrix_numerical = basis._gram_matrix_numerical() # noqa: WPS437 gram_matrix_res = np.array([ @@ -158,8 +158,8 @@ def test_basis_gram_matrix_bspline(self) -> None: def test_basis_basis_inprod(self) -> None: """Test inner product between different basis.""" - monomial = Monomial(n_basis=4) - bspline = BSpline(n_basis=5, order=4) + monomial = MonomialBasis(n_basis=4) + bspline = BSplineBasis(n_basis=5, order=4) np.testing.assert_allclose( monomial.inner_product_matrix(bspline), np.array([ @@ -177,8 +177,8 @@ def test_basis_basis_inprod(self) -> None: def test_basis_fdatabasis_inprod(self) -> None: """Test inner product between different basis expansions.""" - monomial = Monomial(n_basis=4) - bspline = BSpline(n_basis=5, order=3) + monomial = MonomialBasis(n_basis=4) + bspline = BSplineBasis(n_basis=5, order=3) bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( @@ -194,7 +194,7 @@ def test_basis_fdatabasis_inprod(self) -> None: def test_fdatabasis_fdatabasis_inprod(self) -> None: """Test inner product between FDataBasis objects.""" - monomial = Monomial(n_basis=4) + monomial = MonomialBasis(n_basis=4) monomialfd = FDataBasis( monomial, [ @@ -205,7 +205,7 @@ def test_fdatabasis_fdatabasis_inprod(self) -> None: [5, 6, 2, 0], ], ) - bspline = BSpline(n_basis=5, order=3) + bspline = BSplineBasis(n_basis=5, order=3) bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( @@ -222,8 +222,8 @@ def test_fdatabasis_fdatabasis_inprod(self) -> None: def test_comutativity_inprod(self) -> None: """Test commutativity of the inner product.""" - monomial = Monomial(n_basis=4) - bspline = BSpline(n_basis=5, order=3) + monomial = MonomialBasis(n_basis=4) + bspline = BSplineBasis(n_basis=5, order=3) bsplinefd = FDataBasis(bspline, np.arange(0, 15).reshape(3, 5)) np.testing.assert_allclose( @@ -235,8 +235,8 @@ def test_concatenate(self) -> None: """Test concatenation of two FDataBasis.""" sample1 = np.arange(0, 10) sample2 = np.arange(10, 20) - fd1 = FDataGrid([sample1]).to_basis(Fourier(n_basis=5)) - fd2 = FDataGrid([sample2]).to_basis(Fourier(n_basis=5)) + fd1 = FDataGrid([sample1]).to_basis(FourierBasis(n_basis=5)) + fd2 = FDataGrid([sample2]).to_basis(FourierBasis(n_basis=5)) fd = concatenate([fd1, fd2]) @@ -254,13 +254,14 @@ class TestFDataBasisOperations(unittest.TestCase): def test_fdatabasis_add(self) -> None: """Test addition of FDataBasis.""" - monomial1 = FDataBasis(Monomial(n_basis=3), [1, 2, 3]) - monomial2 = FDataBasis(Monomial(n_basis=3), [[1, 2, 3], [3, 4, 5]]) + monomial1 = FDataBasis(MonomialBasis(n_basis=3), [1, 2, 3]) + monomial2 = FDataBasis(MonomialBasis( + n_basis=3), [[1, 2, 3], [3, 4, 5]]) self.assertTrue( (monomial1 + monomial2).equals( FDataBasis( - Monomial(n_basis=3), + MonomialBasis(n_basis=3), [[2, 4, 6], [4, 6, 8]], ), ), @@ -268,19 +269,20 @@ def test_fdatabasis_add(self) -> None: with np.testing.assert_raises(TypeError): monomial2 + FDataBasis( # noqa: WPS428 - Fourier(n_basis=3), + FourierBasis(n_basis=3), [[2, 2, 3], [5, 4, 5]], ) def test_fdatabasis_sub(self) -> None: """Test subtraction of FDataBasis.""" - monomial1 = FDataBasis(Monomial(n_basis=3), [1, 2, 3]) - monomial2 = FDataBasis(Monomial(n_basis=3), [[1, 2, 3], [3, 4, 5]]) + monomial1 = FDataBasis(MonomialBasis(n_basis=3), [1, 2, 3]) + monomial2 = FDataBasis(MonomialBasis( + n_basis=3), [[1, 2, 3], [3, 4, 5]]) self.assertTrue( (monomial1 - monomial2).equals( FDataBasis( - Monomial(n_basis=3), + MonomialBasis(n_basis=3), [[0, 0, 0], [-2, -2, -2]], ), ), @@ -288,13 +290,13 @@ def test_fdatabasis_sub(self) -> None: with np.testing.assert_raises(TypeError): monomial2 - FDataBasis( # noqa: WPS428 - Fourier(n_basis=3), + FourierBasis(n_basis=3), [[2, 2, 3], [5, 4, 5]], ) def test_fdatabasis_mul(self) -> None: """Test multiplication of FDataBasis.""" - basis = Monomial(n_basis=3) + basis = MonomialBasis(n_basis=3) monomial1 = FDataBasis(basis, [1, 2, 3]) monomial2 = FDataBasis(basis, [[1, 2, 3], [3, 4, 5]]) @@ -343,7 +345,7 @@ def test_fdatabasis_mul(self) -> None: with np.testing.assert_raises(TypeError): monomial2 * FDataBasis( # noqa: WPS428 - Fourier(n_basis=3), + FourierBasis(n_basis=3), [[2, 2, 3], [5, 4, 5]], ) @@ -352,7 +354,7 @@ def test_fdatabasis_mul(self) -> None: def test_fdatabasis_div(self) -> None: """Test division of FDataBasis.""" - basis = Monomial(n_basis=3) + basis = MonomialBasis(n_basis=3) monomial1 = FDataBasis(basis, [1, 2, 3]) monomial2 = FDataBasis(basis, [[1, 2, 3], [3, 4, 5]]) @@ -389,14 +391,14 @@ class TestFDataBasisDerivatives(unittest.TestCase): def test_fdatabasis_derivative_constant(self) -> None: """Test derivatives with a constant basis.""" constant = FDataBasis( - Constant(), + ConstantBasis(), [[1], [2], [3], [4]], ) self.assertTrue( constant.derivative().equals( FDataBasis( - Constant(), + ConstantBasis(), [[0], [0], [0], [0]], ), ), @@ -405,7 +407,7 @@ def test_fdatabasis_derivative_constant(self) -> None: self.assertTrue( constant.derivative(order=0).equals( FDataBasis( - Constant(), + ConstantBasis(), [[1], [2], [3], [4]], ), ), @@ -414,12 +416,12 @@ def test_fdatabasis_derivative_constant(self) -> None: def test_fdatabasis_derivative_monomial(self) -> None: """Test derivatives with a monomial basis.""" monomial = FDataBasis( - Monomial(n_basis=8), + MonomialBasis(n_basis=8), [1, 5, 8, 9, 7, 8, 4, 5], ) monomial2 = FDataBasis( - Monomial(n_basis=5), + MonomialBasis(n_basis=5), [ [4, 9, 7, 4, 3], [1, 7, 9, 8, 5], @@ -430,7 +432,7 @@ def test_fdatabasis_derivative_monomial(self) -> None: self.assertTrue( monomial.derivative().equals( FDataBasis( - Monomial(n_basis=7), + MonomialBasis(n_basis=7), [5, 16, 27, 28, 40, 24, 35], ), ), @@ -443,7 +445,7 @@ def test_fdatabasis_derivative_monomial(self) -> None: self.assertTrue( monomial.derivative(order=6).equals( FDataBasis( - Monomial(n_basis=2), + MonomialBasis(n_basis=2), [2880, 25200], ), ), @@ -452,7 +454,7 @@ def test_fdatabasis_derivative_monomial(self) -> None: self.assertTrue( monomial2.derivative().equals( FDataBasis( - Monomial(n_basis=4), + MonomialBasis(n_basis=4), [ [9, 14, 12, 12], [7, 18, 24, 20], @@ -469,7 +471,7 @@ def test_fdatabasis_derivative_monomial(self) -> None: self.assertTrue( monomial2.derivative(order=3).equals( FDataBasis( - Monomial(n_basis=2), + MonomialBasis(n_basis=2), [ [24, 72], [48, 120], @@ -482,12 +484,12 @@ def test_fdatabasis_derivative_monomial(self) -> None: def test_fdatabasis_derivative_fourier(self) -> None: """Test derivatives with a fourier basis.""" fourier = FDataBasis( - Fourier(n_basis=7), + FourierBasis(n_basis=7), [1, 5, 8, 9, 8, 4, 5], ) fourier2 = FDataBasis( - Fourier(n_basis=5), + FourierBasis(n_basis=5), [ [4, 9, 7, 4, 3], [1, 7, 9, 8, 5], @@ -550,11 +552,11 @@ def test_fdatabasis_derivative_fourier(self) -> None: def test_fdatabasis_derivative_bspline(self) -> None: """Test derivatives with a B-spline basis.""" bspline = FDataBasis( - BSpline(n_basis=8), + BSplineBasis(n_basis=8), [1, 5, 8, 9, 7, 8, 4, 5], ) bspline2 = FDataBasis( - BSpline(n_basis=5), + BSplineBasis(n_basis=5), [ [4, 9, 7, 4, 3], [1, 7, 9, 8, 5], @@ -565,7 +567,7 @@ def test_fdatabasis_derivative_bspline(self) -> None: bs0 = bspline.derivative(order=0) bs1 = bspline.derivative() bs2 = bspline.derivative(order=2) - np.testing.assert_equal(bs1.basis, BSpline(n_basis=7, order=3)) + np.testing.assert_equal(bs1.basis, BSplineBasis(n_basis=7, order=3)) np.testing.assert_almost_equal( bs1.coefficients, @@ -576,7 +578,7 @@ def test_fdatabasis_derivative_bspline(self) -> None: np.testing.assert_equal( bs2.basis, - BSpline(n_basis=6, order=2), + BSplineBasis(n_basis=6, order=2), ) np.testing.assert_almost_equal( @@ -588,7 +590,7 @@ def test_fdatabasis_derivative_bspline(self) -> None: bs1 = bspline2.derivative() bs2 = bspline2.derivative(order=2) - np.testing.assert_equal(bs1.basis, BSpline(n_basis=4, order=3)) + np.testing.assert_equal(bs1.basis, BSplineBasis(n_basis=4, order=3)) np.testing.assert_almost_equal( bs1.coefficients, @@ -603,7 +605,7 @@ def test_fdatabasis_derivative_bspline(self) -> None: np.testing.assert_equal( bs2.basis, - BSpline(n_basis=3, order=2), + BSplineBasis(n_basis=3, order=2), ) np.testing.assert_almost_equal( @@ -623,11 +625,11 @@ def test_vector_valued(self) -> None: """Test vector valued basis.""" X, _ = fetch_weather(return_X_y=True) - basis_dim = skfda.representation.basis.Fourier( + basis_dim = skfda.representation.basis.FourierBasis( n_basis=7, domain_range=X.domain_range, ) - basis = skfda.representation.basis.VectorValued( + basis = skfda.representation.basis.VectorValuedBasis( [basis_dim] * 2, ) @@ -661,11 +663,14 @@ def setUp(self) -> None: self.dims = (self.n_x, self.n_y, self.n_z) - self.basis_x = skfda.representation.basis.Monomial(n_basis=self.n_x) - self.basis_y = skfda.representation.basis.Fourier(n_basis=self.n_y) - self.basis_z = skfda.representation.basis.BSpline(n_basis=self.n_z) + self.basis_x = skfda.representation.basis.MonomialBasis( + n_basis=self.n_x) + self.basis_y = skfda.representation.basis.FourierBasis( + n_basis=self.n_y) + self.basis_z = skfda.representation.basis.BSplineBasis( + n_basis=self.n_z) - self.basis = skfda.representation.basis.Tensor([ + self.basis = skfda.representation.basis.TensorBasis([ self.basis_x, self.basis_y, self.basis_z, diff --git a/skfda/tests/test_extrapolation.py b/skfda/tests/test_extrapolation.py index 1ffeaae55..59951162f 100644 --- a/skfda/tests/test_extrapolation.py +++ b/skfda/tests/test_extrapolation.py @@ -6,7 +6,7 @@ from skfda import FDataBasis, FDataGrid from skfda.datasets import make_sinusoidal_process -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis from skfda.representation.extrapolation import ( BoundaryExtrapolation, ExceptionExtrapolation, @@ -21,13 +21,13 @@ class TestExtrapolation(unittest.TestCase): def setUp(self) -> None: """Create example data.""" self.grid = make_sinusoidal_process(n_samples=2, random_state=0) - self.basis = self.grid.to_basis(Fourier()) + self.basis = self.grid.to_basis(FourierBasis()) self.dummy_data = [[1, 2, 3], [2, 3, 4]] def test_constructor_fdatabasis_setting(self) -> None: """Check argument normalization in constructor for FDataBasis.""" coeff = self.dummy_data - basis = Fourier(n_basis=3) + basis = FourierBasis(n_basis=3) a = FDataBasis(basis, coeff) self.assertEqual(a.extrapolation, None) diff --git a/skfda/tests/test_fdatabasis_evaluation.py b/skfda/tests/test_fdatabasis_evaluation.py index 889caeda0..d39171359 100644 --- a/skfda/tests/test_fdatabasis_evaluation.py +++ b/skfda/tests/test_fdatabasis_evaluation.py @@ -4,13 +4,13 @@ import numpy as np from skfda.representation.basis import ( - BSpline, - Constant, + BSplineBasis, + ConstantBasis, FDataBasis, - Fourier, - Monomial, - Tensor, - VectorValued, + FourierBasis, + MonomialBasis, + TensorBasis, + VectorValuedBasis, ) @@ -24,7 +24,7 @@ class TestFDataBasisEvaluation(unittest.TestCase): def test_evaluation_single_point(self) -> None: """Test the evaluation of a single point FDataBasis.""" - fourier = Fourier( + fourier = FourierBasis( domain_range=(0, 1), n_basis=3, ) @@ -54,7 +54,7 @@ def test_evaluation_grid_1d(self) -> None: Nothing should change as the domain dimension is 1. """ - fourier = Fourier( + fourier = FourierBasis( domain_range=(0, 1), n_basis=3, ) @@ -93,7 +93,7 @@ def test_evaluation_grid_1d(self) -> None: def test_evaluation_unaligned(self) -> None: """Test the unaligned evaluation.""" - fourier = Fourier( + fourier = FourierBasis( domain_range=(0, 1), n_basis=3, ) @@ -134,7 +134,7 @@ class TestBasisEvaluationFourier(unittest.TestCase): def test_simple_evaluation(self) -> None: """Compare Fourier evaluation with R package fda.""" - fourier = Fourier( + fourier = FourierBasis( domain_range=(0, 2), n_basis=5, ) @@ -164,7 +164,7 @@ def test_simple_evaluation(self) -> None: def test_evaluation_derivative(self) -> None: """Test the evaluation of the derivative of Fourier.""" - fourier = Fourier(domain_range=(0, 1), n_basis=3) + fourier = FourierBasis(domain_range=(0, 1), n_basis=3) coefficients = np.array([ [0.00078238, 0.48857741, 0.63971985], @@ -193,7 +193,7 @@ class TestBasisEvaluationBSpline(unittest.TestCase): def test_simple_evaluation(self) -> None: """Compare BSpline evaluation with R package fda.""" - bspline = BSpline( + bspline = BSplineBasis( domain_range=(0, 2), n_basis=5, ) @@ -223,7 +223,7 @@ def test_simple_evaluation(self) -> None: def test_evaluation_derivative(self) -> None: """Test the evaluation of the derivative of BSpline.""" - bspline = BSpline( + bspline = BSplineBasis( domain_range=(0, 1), n_basis=5, order=3, @@ -253,7 +253,7 @@ class TestBasisEvaluationMonomial(unittest.TestCase): def test_evaluation_simple_monomial(self) -> None: """Compare Monomial evaluation with R package fda.""" - monomial = Monomial( + monomial = MonomialBasis( domain_range=(0, 2), n_basis=5, ) @@ -283,7 +283,7 @@ def test_evaluation_simple_monomial(self) -> None: def test_evaluation_derivative(self) -> None: """Test the evaluation of the derivative of Monomial.""" - monomial = Monomial( + monomial = MonomialBasis( domain_range=(0, 1), n_basis=3, ) @@ -312,10 +312,10 @@ class TestBasisEvaluationVectorValued(unittest.TestCase): def test_constant(self) -> None: """Test vector-valued constant basis.""" - basis_first = Constant() - basis_second = Constant() + basis_first = ConstantBasis() + basis_second = ConstantBasis() - basis = VectorValued([basis_first, basis_second]) + basis = VectorValuedBasis([basis_first, basis_second]) fd = FDataBasis(basis=basis, coefficients=[[1, 2], [3, 4]]) @@ -327,13 +327,13 @@ def test_constant(self) -> None: def test_monomial(self) -> None: """Test vector-valued monomial basis.""" - basis_first = Constant(domain_range=(0, 5)) - basis_second = Monomial( + basis_first = ConstantBasis(domain_range=(0, 5)) + basis_second = MonomialBasis( n_basis=3, domain_range=(0, 5), ) - basis = VectorValued([basis_first, basis_second]) + basis = VectorValuedBasis([basis_first, basis_second]) fd = FDataBasis( basis=basis, @@ -360,9 +360,9 @@ class TestBasisEvaluationTensor(unittest.TestCase): def test_tensor_monomial_constant(self) -> None: """Test monomial ⊗ constant basis.""" - basis = Tensor([ - Monomial(n_basis=2), - Constant(), + basis = TensorBasis([ + MonomialBasis(n_basis=2), + ConstantBasis(), ]) fd = FDataBasis( diff --git a/skfda/tests/test_fpca.py b/skfda/tests/test_fpca.py index f814cd321..4bf406e18 100644 --- a/skfda/tests/test_fpca.py +++ b/skfda/tests/test_fpca.py @@ -9,7 +9,7 @@ from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.preprocessing.dim_reduction import FPCA -from skfda.representation.basis import Basis, BSpline, Fourier +from skfda.representation.basis import Basis, BSplineBasis, FourierBasis class FPCATestCase(unittest.TestCase): @@ -21,7 +21,7 @@ def test_basis_fpca_fit_exceptions(self) -> None: with self.assertRaises(AttributeError): fpca.fit(None) # type: ignore[arg-type] - basis = Fourier(n_basis=1) + basis = FourierBasis(n_basis=1) # Check that if n_components is bigger than the number of samples then # an exception should be thrown fd = FDataBasis(basis, [[0.9]]) @@ -60,7 +60,7 @@ def test_basis_fpca_fit_result(self) -> None: fd_data = fetch_weather()['data'].coordinates[0] # Initialize basis data - basis = Fourier(n_basis=n_basis, domain_range=(0, 365)) + basis = FourierBasis(n_basis=n_basis, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) fpca = FPCA( @@ -114,7 +114,7 @@ def test_basis_fpca_transform_result(self) -> None: fd_data = fetch_weather()['data'].coordinates[0] # Initialize basis data - basis = Fourier(n_basis=n_basis, domain_range=(0, 365)) + basis = FourierBasis(n_basis=n_basis, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) fpca = FPCA( @@ -198,7 +198,7 @@ def test_basis_fpca_noregularization_fit_result(self) -> None: fd_data = fetch_weather()['data'].coordinates[0] # Initialize basis data - basis = Fourier(n_basis=n_basis, domain_range=(0, 365)) + basis = FourierBasis(n_basis=n_basis, domain_range=(0, 365)) fd_basis = fd_data.to_basis(basis) fpca = FPCA(n_components=n_components) @@ -594,7 +594,7 @@ def test_grid_fpca_inverse_transform(self) -> None: # Low dimensional case (n_samples>n_grid) n_samples = 1000 n_grid = 100 - bsp = BSpline( + bsp = BSplineBasis( domain_range=(0, 50), n_basis=100, order=3, @@ -610,7 +610,7 @@ def test_grid_fpca_inverse_transform(self) -> None: # (almost) High dimensional case (n_samples None: # Low dimensional case: n_basis< None: # Case n_samples None: np.log, ) data_basis = unconditional_expected_value( - X[:5].to_basis(basis.BSpline(n_basis=7)), + X[:5].to_basis(basis.BSplineBasis(n_basis=7)), np.log, ) np.testing.assert_allclose(data_basis, data_grid, rtol=1e-3) diff --git a/skfda/tests/test_hotelling.py b/skfda/tests/test_hotelling.py index 6953890a7..299499eeb 100644 --- a/skfda/tests/test_hotelling.py +++ b/skfda/tests/test_hotelling.py @@ -3,7 +3,7 @@ from skfda.inference.hotelling import hotelling_t2, hotelling_test_ind from skfda.representation import FDataGrid -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis class HotellingTests(unittest.TestCase): @@ -14,9 +14,9 @@ def test_hotelling_test_ind_args(self) -> None: fd1 = FDataGrid([[1, 1, 1]]) with self.assertRaises(TypeError): - hotelling_test_ind(fd1.to_basis(Fourier(n_basis=3)), fd1) + hotelling_test_ind(fd1.to_basis(FourierBasis(n_basis=3)), fd1) with self.assertRaises(TypeError): - hotelling_test_ind(fd1, fd1.to_basis(Fourier(n_basis=3))) + hotelling_test_ind(fd1, fd1.to_basis(FourierBasis(n_basis=3))) with self.assertRaises(ValueError): hotelling_test_ind(fd1, fd1, n_reps=0) @@ -25,9 +25,9 @@ def test_hotelling_t2_args(self) -> None: fd1 = FDataGrid([[1, 1, 1]]) with self.assertRaises(TypeError): - hotelling_t2(fd1.to_basis(Fourier(n_basis=3)), fd1) + hotelling_t2(fd1.to_basis(FourierBasis(n_basis=3)), fd1) with self.assertRaises(TypeError): - hotelling_t2(fd1, fd1.to_basis(Fourier(n_basis=3))) + hotelling_t2(fd1, fd1.to_basis(FourierBasis(n_basis=3))) def test_hotelling_t2(self) -> None: """Trivial checks for the statistic.""" @@ -36,8 +36,8 @@ def test_hotelling_t2(self) -> None: self.assertAlmostEqual(hotelling_t2(fd1, fd1), 0) self.assertAlmostEqual(hotelling_t2(fd1, fd2), 1) - fd1 = fd1.to_basis(Fourier(n_basis=3)) - fd2 = fd2.to_basis(Fourier(n_basis=3)) + fd1 = fd1.to_basis(FourierBasis(n_basis=3)) + fd2 = fd2.to_basis(FourierBasis(n_basis=3)) self.assertAlmostEqual(hotelling_t2(fd1, fd1), 0) self.assertAlmostEqual(hotelling_t2(fd1, fd2), 1) diff --git a/skfda/tests/test_kernel_regression.py b/skfda/tests/test_kernel_regression.py index e8e87c6d7..dd3fbee2b 100644 --- a/skfda/tests/test_kernel_regression.py +++ b/skfda/tests/test_kernel_regression.py @@ -15,7 +15,7 @@ from skfda.misc.kernels import normal, uniform from skfda.misc.metrics import l2_distance from skfda.ml.regression import KernelRegression -from skfda.representation.basis import FDataBasis, Fourier, Monomial +from skfda.representation.basis import FDataBasis, FourierBasis, MonomialBasis from skfda.representation.grid import FDataGrid FloatArray = np.typing.NDArray[np.float_] @@ -100,7 +100,7 @@ def _create_data_basis( fd = X.iloc[:, 0].values fat = y['fat'].values - basis = Fourier( + basis = FourierBasis( n_basis=10, domain_range=fd.domain_range, ) @@ -327,7 +327,7 @@ def test_llr_non_orthonormal(self) -> None: """Test LocalLinearRegression with monomial basis.""" coef1 = [[1, 5, 8], [4, 6, 6], [9, 4, 1]] coef2 = [[6, 3, 5]] - basis = Monomial(n_basis=3, domain_range=(0, 3)) + basis = MonomialBasis(n_basis=3, domain_range=(0, 3)) X_train = FDataBasis(coefficients=coef1, basis=basis) X = FDataBasis(coefficients=coef2, basis=basis) diff --git a/skfda/tests/test_linear_differential_operator.py b/skfda/tests/test_linear_differential_operator.py index e676de8b7..2a77eafe9 100644 --- a/skfda/tests/test_linear_differential_operator.py +++ b/skfda/tests/test_linear_differential_operator.py @@ -6,7 +6,7 @@ import numpy as np from skfda.misc.operators import LinearDifferentialOperator -from skfda.representation.basis import Constant, FDataBasis, Monomial +from skfda.representation.basis import ConstantBasis, FDataBasis, MonomialBasis WeightCallable = Callable[[np.ndarray], np.ndarray] @@ -85,7 +85,7 @@ def test_init_list_fdatabasis(self) -> None: n_basis = 4 n_weights = 6 - monomial = Monomial(domain_range=(0, 1), n_basis=n_basis) + monomial = MonomialBasis(domain_range=(0, 1), n_basis=n_basis) weights = np.arange(n_basis * n_weights).reshape((n_weights, n_basis)) @@ -101,7 +101,7 @@ def test_init_list_fdatabasis(self) -> None: ) # Check failure if intervals do not match - constant = Constant(domain_range=(0, 2)) + constant = ConstantBasis(domain_range=(0, 2)) fdlist.append(FDataBasis(constant, 1)) with np.testing.assert_raises(ValueError): LinearDifferentialOperator(weights=fdlist) @@ -113,7 +113,7 @@ def test_init_wrong_params(self) -> None: LinearDifferentialOperator(1, weights=[1, 1]) # Check invalid domain range - monomial = Monomial(domain_range=(0, 1), n_basis=3) + monomial = MonomialBasis(domain_range=(0, 1), n_basis=3) fdlist = [FDataBasis(monomial, [1, 2, 3])] with np.testing.assert_raises(ValueError): diff --git a/skfda/tests/test_math.py b/skfda/tests/test_math.py index d4abac05b..3acbfc1db 100644 --- a/skfda/tests/test_math.py +++ b/skfda/tests/test_math.py @@ -8,7 +8,7 @@ from skfda._utils import _pairwise_symmetric from skfda.datasets import make_gaussian_process from skfda.misc.covariances import Gaussian -from skfda.representation.basis import Monomial, Tensor, VectorValued +from skfda.representation.basis import MonomialBasis, TensorBasis, VectorValuedBasis def _ndm( @@ -45,10 +45,10 @@ def f( # noqa: WPS430 grid_points=grid_points, ) - basis = Tensor([ - Monomial(n_basis=5, domain_range=(0, 1)), - Monomial(n_basis=5, domain_range=(0, 2)), - Monomial(n_basis=5, domain_range=(0, 3)), + basis = TensorBasis([ + MonomialBasis(n_basis=5, domain_range=(0, 1)), + MonomialBasis(n_basis=5, domain_range=(0, 2)), + MonomialBasis(n_basis=5, domain_range=(0, 3)), ]) fd_basis = fd.to_basis(basis) @@ -89,9 +89,9 @@ def g( # noqa: WPS430 grid_points=grid_points, ) - basis = VectorValued([ - Monomial(n_basis=5), - Monomial(n_basis=5), + basis = VectorValuedBasis([ + MonomialBasis(n_basis=5), + MonomialBasis(n_basis=5), ]) fd_basis = fd.to_basis(basis) @@ -111,7 +111,7 @@ def g( # noqa: WPS430 def test_matrix(self) -> None: """Test inner_product_matrix function.""" - basis = skfda.representation.basis.BSpline(n_basis=12) + basis = skfda.representation.basis.BSplineBasis(n_basis=12) X = make_gaussian_process( n_samples=10, diff --git a/skfda/tests/test_metrics.py b/skfda/tests/test_metrics.py index 7593bc109..4e4746901 100644 --- a/skfda/tests/test_metrics.py +++ b/skfda/tests/test_metrics.py @@ -13,7 +13,7 @@ linf_norm, lp_norm, ) -from skfda.representation.basis import Monomial +from skfda.representation.basis import MonomialBasis class TestLp(unittest.TestCase): @@ -29,7 +29,7 @@ def setUp(self) -> None: ], grid_points=grid_points, ) - basis = Monomial(n_basis=3, domain_range=(1, 5)) + basis = MonomialBasis(n_basis=3, domain_range=(1, 5)) self.fd_basis = FDataBasis(basis, [[1, 1, 0], [0, 0, 1]]) self.fd_vector_valued = self.fd.concatenate( self.fd, diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index f8091b564..9c5a8ddfa 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -17,7 +17,7 @@ from skfda.ml.clustering import NearestNeighbors from skfda.ml.regression import KNeighborsRegressor, RadiusNeighborsRegressor from skfda.representation import FDataBasis, FDataGrid -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis class TestNeighbors(unittest.TestCase): @@ -250,7 +250,8 @@ def test_functional_response_custom_weights(self) -> None: FDataGrid, FDataGrid, ](weights=self._weights, n_neighbors=5) - response = self.X.to_basis(Fourier(domain_range=(-1, 1), n_basis=10)) + response = self.X.to_basis(FourierBasis( + domain_range=(-1, 1), n_basis=10)) knnr.fit(self.X, response) res = knnr.predict(self.X) @@ -287,7 +288,8 @@ def test_functional_response_basis(self) -> None: FDataGrid, FDataBasis, ](weights='distance', n_neighbors=5) - response = self.X.to_basis(Fourier(domain_range=(-1, 1), n_basis=10)) + response = self.X.to_basis(FourierBasis( + domain_range=(-1, 1), n_basis=10)) knnr.fit(self.X, response) res = knnr.predict(self.X) @@ -360,7 +362,7 @@ def test_score_functional_response(self) -> None: np.testing.assert_almost_equal(r, 0.65599399478951) # Weighted case and basis form - y = y.to_basis(Fourier(domain_range=y.domain_range[0], n_basis=5)) + y = y.to_basis(FourierBasis(domain_range=y.domain_range[0], n_basis=5)) neigh.fit(self.X, y) r = neigh.score( diff --git a/skfda/tests/test_oneway_anova.py b/skfda/tests/test_oneway_anova.py index 20e6e9dc6..24d7ad54d 100644 --- a/skfda/tests/test_oneway_anova.py +++ b/skfda/tests/test_oneway_anova.py @@ -10,7 +10,7 @@ v_sample_stat, ) from skfda.representation import FDataGrid -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis class OnewayAnovaTests(unittest.TestCase): @@ -43,7 +43,7 @@ def test_v_stats(self) -> None: self.assertEqual(v_sample_stat(fd, weights), 7.0) self.assertAlmostEqual( v_sample_stat( - fd.to_basis(Fourier(n_basis=5)), + fd.to_basis(FourierBasis(n_basis=5)), weights, ), 7.0, @@ -56,7 +56,7 @@ def test_v_stats(self) -> None: self.assertAlmostEqual(v_asymptotic_stat(fd, weights=weights), res) self.assertAlmostEqual( v_asymptotic_stat( - fd.to_basis(Fourier(n_basis=5)), + fd.to_basis(FourierBasis(n_basis=5)), weights=weights, ), res, @@ -86,7 +86,7 @@ def test_asymptotic_behaviour(self) -> None: big_sim = oneway_anova(fd1, fd2, fd3, n_reps=2000, random_state=100)[1] self.assertAlmostEqual(little_sim, big_sim, delta=0.05) - fd = fd.to_basis(Fourier(n_basis=5)) + fd = fd.to_basis(FourierBasis(n_basis=5)) fd1 = fd[:5] fd2 = fd[5:10] diff --git a/skfda/tests/test_pandas.py b/skfda/tests/test_pandas.py index 1d79a1be4..b5039f40a 100644 --- a/skfda/tests/test_pandas.py +++ b/skfda/tests/test_pandas.py @@ -18,7 +18,7 @@ def setUp(self) -> None: ], ) self.fd_basis = self.fd.to_basis( - skfda.representation.basis.BSpline( + skfda.representation.basis.BSplineBasis( n_basis=5, ), ) diff --git a/skfda/tests/test_pandas_fdatabasis.py b/skfda/tests/test_pandas_fdatabasis.py index 537bb21e5..e44f207ab 100644 --- a/skfda/tests/test_pandas_fdatabasis.py +++ b/skfda/tests/test_pandas_fdatabasis.py @@ -10,11 +10,11 @@ from skfda.representation.basis import ( Basis, - BSpline, + BSplineBasis, FDataBasis, FDataBasisDType, - Fourier, - Monomial, + FourierBasis, + MonomialBasis, ) @@ -22,9 +22,9 @@ # Fixtures ############################################################################## @pytest.fixture(params=[ - Monomial(n_basis=5), - Fourier(n_basis=5), - BSpline(n_basis=5), + MonomialBasis(n_basis=5), + FourierBasis(n_basis=5), + BSplineBasis(n_basis=5), ]) def basis(request: Any) -> Any: """A fixture providing the ExtensionDtype to validate.""" diff --git a/skfda/tests/test_registration.py b/skfda/tests/test_registration.py index eb1efe114..682a55333 100644 --- a/skfda/tests/test_registration.py +++ b/skfda/tests/test_registration.py @@ -25,7 +25,7 @@ PairwiseCorrelation, SobolevLeastSquares, ) -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis from skfda.representation.interpolation import SplineInterpolation @@ -268,7 +268,7 @@ def test_fit_transform(self) -> None: np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) # Test with Basis - fd = self.fd.to_basis(Fourier()) + fd = self.fd.to_basis(FourierBasis()) reg.fit_transform(fd) deltas = reg.deltas_.round(3) np.testing.assert_array_almost_equal(deltas, [-0.022, 0.03]) @@ -439,7 +439,7 @@ def test_amplitude_phase_score(self) -> None: def test_amplitude_phase_score_with_basis(self) -> None: """Test the AmplitudePhaseDecomposition with FDataBasis.""" scorer = AmplitudePhaseDecomposition() - X = self.X.to_basis(Fourier()) + X = self.X.to_basis(FourierBasis()) score = scorer(self.shift_registration, X) np.testing.assert_allclose(score, 0.995086, rtol=1e-6) diff --git a/skfda/tests/test_regression.py b/skfda/tests/test_regression.py index 4c4405f3f..06ff0c367 100644 --- a/skfda/tests/test_regression.py +++ b/skfda/tests/test_regression.py @@ -13,11 +13,11 @@ from skfda.misc.regularization import L2Regularization from skfda.ml.regression import HistoricalLinearRegression, LinearRegression from skfda.representation.basis import ( - BSpline, - Constant, + BSplineBasis, + ConstantBasis, FDataBasis, - Fourier, - Monomial, + FourierBasis, + MonomialBasis, ) from skfda.representation.grid import FDataGrid @@ -26,10 +26,10 @@ class TestScalarLinearRegression(unittest.TestCase): def test_regression_single_explanatory(self) -> None: - x_basis = Monomial(n_basis=7) + x_basis = MonomialBasis(n_basis=7) x_fd = FDataBasis(x_basis, np.identity(7)) - beta_basis = Fourier(n_basis=5) + beta_basis = FourierBasis(n_basis=5) beta_fd = FDataBasis(beta_basis, [1, 1, 1, 1, 1]) y = np.array([ 0.9999999999999993, @@ -77,9 +77,9 @@ def test_regression_single_explanatory(self) -> None: def test_regression_multiple_explanatory(self) -> None: y = np.array([1, 2, 3, 4, 5, 6, 7]) - X = FDataBasis(Monomial(n_basis=7), np.identity(7)) + X = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) - beta1 = BSpline(domain_range=(0, 1), n_basis=5) + beta1 = BSplineBasis(domain_range=(0, 1), n_basis=5) scalar = LinearRegression(coef_basis=[beta1]) @@ -119,7 +119,7 @@ def test_regression_mixed(self) -> None: ] = [ multivariate, FDataBasis( - Monomial(n_basis=3), + MonomialBasis(n_basis=3), [ [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0], [0, 1, 0], @@ -131,7 +131,7 @@ def test_regression_mixed(self) -> None: intercept = 2 coefs_multivariate = np.array([3, 1]) coefs_functions = FDataBasis( - Monomial(n_basis=3), + MonomialBasis(n_basis=3), [[3, 0, 0]], ) y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) @@ -173,7 +173,7 @@ def test_same_result_1d_2d_multivariate_arrays(self) -> None: multivariate2 = np.asarray([7, 3, 2, 1, 1, 5]) multivariate = [[1, 7], [2, 3], [4, 2], [1, 1], [3, 1], [2, 5]] - x_basis = Monomial(n_basis=2) + x_basis = MonomialBasis(n_basis=2) x_fd = FDataBasis( x_basis, [[0, 2], [0, 4], [1, 0], [2, 0], [1, 2], [2, 2]], @@ -181,10 +181,10 @@ def test_same_result_1d_2d_multivariate_arrays(self) -> None: y = [11, 10, 12, 6, 10, 13] - linear = LinearRegression(coef_basis=[None, Constant()]) + linear = LinearRegression(coef_basis=[None, ConstantBasis()]) linear.fit([multivariate, x_fd], y) - linear2 = LinearRegression(coef_basis=[None, None, Constant()]) + linear2 = LinearRegression(coef_basis=[None, None, ConstantBasis()]) linear2.fit([multivariate1, multivariate2, x_fd], y) np.testing.assert_equal( @@ -208,7 +208,7 @@ def test_regression_df_multivariate(self) -> None: # noqa: D102 multivariate2 = [0, 7, 7, 9, 16, 14, 5] multivariate = [list(obs) for obs in zip(multivariate1, multivariate2)] - x_basis = Monomial(n_basis=3) + x_basis = MonomialBasis(n_basis=3) x_fd = FDataBasis(x_basis, [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]) @@ -221,7 +221,7 @@ def test_regression_df_multivariate(self) -> None: # noqa: D102 intercept = 2 coefs_multivariate = np.array([3, 1]) coefs_functions = FDataBasis( - Monomial(n_basis=3), [[3, 0, 0]], + MonomialBasis(n_basis=3), [[3, 0, 0]], ) y_integral = np.array([3, 3 / 2, 1, 4, 3, 3 / 2, 1]) y_sum = multivariate @ coefs_multivariate @@ -263,7 +263,7 @@ def test_regression_mixed_regularization(self) -> None: ] = [ multivariate, FDataBasis( - Monomial(n_basis=3), + MonomialBasis(n_basis=3), [ [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0], [0, 1, 0], @@ -318,10 +318,10 @@ def test_regression_mixed_regularization(self) -> None: def test_regression_regularization(self) -> None: - x_basis = Monomial(n_basis=7) + x_basis = MonomialBasis(n_basis=7) x_fd = FDataBasis(x_basis, np.identity(7)) - beta_basis = Fourier(n_basis=5) + beta_basis = FourierBasis(n_basis=5) beta_fd = FDataBasis( beta_basis, [1.0403, 0, 0, 0, 0], @@ -368,7 +368,7 @@ def test_regression_regularization(self) -> None: y_pred = scalar.predict(x_fd) np.testing.assert_allclose(y_pred, y_pred_compare, atol=1e-4) - x_basis = Monomial(n_basis=3) + x_basis = MonomialBasis(n_basis=3) x_fd = FDataBasis( x_basis, [ @@ -424,17 +424,17 @@ def test_error_X_not_FData(self) -> None: x_fd = np.identity(7) y = np.zeros(7) - scalar = LinearRegression(coef_basis=[Fourier(n_basis=5)]) + scalar = LinearRegression(coef_basis=[FourierBasis(n_basis=5)]) with np.testing.assert_warns(UserWarning): scalar.fit([x_fd], y) def test_error_y_is_FData(self) -> None: """Tests that none of the explained variables is an FData object.""" - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) - y = list(FDataBasis(Monomial(n_basis=7), np.identity(7))) + x_fd = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) + y = list(FDataBasis(MonomialBasis(n_basis=7), np.identity(7))) - scalar = LinearRegression(coef_basis=[Fourier(n_basis=5)]) + scalar = LinearRegression(coef_basis=[FourierBasis(n_basis=5)]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) # type: ignore[arg-type] @@ -442,9 +442,9 @@ def test_error_y_is_FData(self) -> None: def test_error_X_beta_len_distinct(self) -> None: """Test that the number of beta bases and explanatory variables are not different """ - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) + x_fd = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) y = np.array([1 for _ in range(7)]) - beta = Fourier(n_basis=5) + beta = FourierBasis(n_basis=5) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): @@ -458,17 +458,17 @@ def test_error_y_X_samples_different(self) -> None: """Test that the number of response samples and explanatory samples are not different """ - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) + x_fd = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) y = np.array([1 for _ in range(8)]) - beta = Fourier(n_basis=5) + beta = FourierBasis(n_basis=5) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): scalar.fit([x_fd], y) - x_fd = FDataBasis(Monomial(n_basis=8), np.identity(8)) + x_fd = FDataBasis(MonomialBasis(n_basis=8), np.identity(8)) y = np.array([1 for _ in range(7)]) - beta = Fourier(n_basis=5) + beta = FourierBasis(n_basis=5) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): @@ -476,9 +476,9 @@ def test_error_y_X_samples_different(self) -> None: def test_error_beta_not_basis(self) -> None: """Test that all beta are Basis objects. """ - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) + x_fd = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) y = np.array([1 for _ in range(7)]) - beta = FDataBasis(Monomial(n_basis=7), np.identity(7)) + beta = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(TypeError): @@ -486,10 +486,10 @@ def test_error_beta_not_basis(self) -> None: def test_error_weights_lenght(self) -> None: """Test that the number of weights is equal to n_samples.""" - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) + x_fd = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) y = np.array([1 for _ in range(7)]) weights = np.array([1 for _ in range(8)]) - beta = Monomial(n_basis=7) + beta = MonomialBasis(n_basis=7) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): @@ -497,10 +497,10 @@ def test_error_weights_lenght(self) -> None: def test_error_weights_negative(self) -> None: """Test that none of the weights are negative.""" - x_fd = FDataBasis(Monomial(n_basis=7), np.identity(7)) + x_fd = FDataBasis(MonomialBasis(n_basis=7), np.identity(7)) y = np.array([1 for _ in range(7)]) weights = np.array([-1 for _ in range(7)]) - beta = Monomial(n_basis=7) + beta = MonomialBasis(n_basis=7) scalar = LinearRegression(coef_basis=[beta]) with np.testing.assert_raises(ValueError): diff --git a/skfda/tests/test_regularization.py b/skfda/tests/test_regularization.py index 092f2c322..8b7033dea 100644 --- a/skfda/tests/test_regularization.py +++ b/skfda/tests/test_regularization.py @@ -24,10 +24,10 @@ from skfda.ml.regression import LinearRegression from skfda.representation.basis import ( Basis, - BSpline, - Constant, - Fourier, - Monomial, + BSplineBasis, + ConstantBasis, + FourierBasis, + MonomialBasis, ) LinearDifferentialOperatorInput = Union[ @@ -76,7 +76,7 @@ def _test_penalty( def test_constant_penalty(self) -> None: """Test penalty for Constant basis.""" - basis = Constant(domain_range=(0, 3)) + basis = ConstantBasis(domain_range=(0, 3)) res = np.array([[12]]) @@ -86,7 +86,7 @@ def test_monomial_linear_diff_op(self) -> None: """Test directly the penalty for Monomial basis.""" n_basis = 5 - basis = Monomial(n_basis=n_basis) + basis = MonomialBasis(n_basis=n_basis) linear_diff_op = [3] res = np.array([ @@ -141,7 +141,7 @@ def test_monomial_linear_diff_op(self) -> None: def test_monomial_penalty(self) -> None: """Test penalty for Monomial basis.""" - basis = Monomial(n_basis=5, domain_range=(0, 3)) + basis = MonomialBasis(n_basis=5, domain_range=(0, 3)) # Theorethical result res = np.array([ @@ -154,7 +154,7 @@ def test_monomial_penalty(self) -> None: self._test_penalty(basis, linear_diff_op=2, result=res) - basis = Monomial(n_basis=8, domain_range=(1, 5)) + basis = MonomialBasis(n_basis=8, domain_range=(1, 5)) self._test_penalty(basis, linear_diff_op=[1, 2, 3]) self._test_penalty(basis, linear_diff_op=7) @@ -164,7 +164,7 @@ def test_monomial_penalty(self) -> None: def test_fourier_penalty(self) -> None: """Test penalty for Fourier basis.""" - basis = Fourier(n_basis=5) + basis = FourierBasis(n_basis=5) res = np.array([ [0, 0, 0, 0, 0], @@ -177,7 +177,7 @@ def test_fourier_penalty(self) -> None: # Those comparisons require atol as there are zeros involved self._test_penalty(basis, linear_diff_op=2, atol=0.01, result=res) - basis = Fourier(n_basis=9, domain_range=(1, 5)) + basis = FourierBasis(n_basis=9, domain_range=(1, 5)) self._test_penalty(basis, linear_diff_op=[1, 2, 3], atol=1e-7) self._test_penalty(basis, linear_diff_op=[2, 3, 0.1, 1], atol=1e-7) self._test_penalty(basis, linear_diff_op=0, atol=1e-7) @@ -186,7 +186,7 @@ def test_fourier_penalty(self) -> None: def test_bspline_penalty(self) -> None: """Test penalty for BSpline basis.""" - basis = BSpline(n_basis=5) + basis = BSplineBasis(n_basis=5) res = np.array([ [96, -132, 24, 12, 0], @@ -198,7 +198,7 @@ def test_bspline_penalty(self) -> None: self._test_penalty(basis, linear_diff_op=2, result=res) - basis = BSpline(n_basis=9, domain_range=(1, 5)) + basis = BSplineBasis(n_basis=9, domain_range=(1, 5)) self._test_penalty(basis, linear_diff_op=[1, 2, 3]) self._test_penalty(basis, linear_diff_op=[2, 3, 0.1, 1]) self._test_penalty(basis, linear_diff_op=0) @@ -206,12 +206,12 @@ def test_bspline_penalty(self) -> None: self._test_penalty(basis, linear_diff_op=3) self._test_penalty(basis, linear_diff_op=4) - basis = BSpline(n_basis=16, order=8) + basis = BSplineBasis(n_basis=16, order=8) self._test_penalty(basis, linear_diff_op=0, atol=1e-7) def test_bspline_penalty_special_case(self) -> None: """Test for behavior like in issue #185.""" - basis = BSpline(n_basis=5) + basis = BSplineBasis(n_basis=5) res = np.array([ [1152, -2016, 1152, -288, 0], @@ -246,7 +246,7 @@ def test_basis_default(self) -> None: fd = skfda.FDataGrid(data_matrix.T) smoother = skfda.preprocessing.smoothing.BasisSmoother( - basis=skfda.representation.basis.BSpline( + basis=skfda.representation.basis.BSplineBasis( n_basis=10, domain_range=fd.domain_range, ), @@ -254,7 +254,7 @@ def test_basis_default(self) -> None: ) smoother2 = skfda.preprocessing.smoothing.BasisSmoother( - basis=skfda.representation.basis.BSpline( + basis=skfda.representation.basis.BSplineBasis( n_basis=10, domain_range=fd.domain_range, ), @@ -282,7 +282,7 @@ def test_basis_conversion(self) -> None: fd = skfda.FDataGrid(data_matrix.T) smoother = skfda.preprocessing.smoothing.BasisSmoother( - basis=skfda.representation.basis.BSpline( + basis=skfda.representation.basis.BSplineBasis( n_basis=10, domain_range=fd.domain_range, ), diff --git a/skfda/tests/test_scoring.py b/skfda/tests/test_scoring.py index 7a5b4233e..1a9a0dbe3 100644 --- a/skfda/tests/test_scoring.py +++ b/skfda/tests/test_scoring.py @@ -16,7 +16,7 @@ mean_squared_log_error, r2_score, ) -from skfda.representation.basis import BSpline, Fourier, Monomial +from skfda.representation.basis import BSplineBasis, FourierBasis, MonomialBasis from skfda.typing._numpy import NDArrayFloat score_functions: Sequence[ScoreFunction] = ( @@ -36,14 +36,14 @@ def _create_data_basis() -> Tuple[FDataBasis, FDataBasis]: # y_true: 1) 1 + 2x + 3x^2 # 2) 4 + 5x + 6x^2 y_true = FDataBasis( - basis=Monomial(domain_range=((0, 3),), n_basis=3), + basis=MonomialBasis(domain_range=((0, 3),), n_basis=3), coefficients=coef_true, ) # y_pred: 1) 1 + 2x + 3x^2 # 2) 4 + 6x + 5x^2 y_pred = FDataBasis( - basis=Monomial(domain_range=((0, 3),), n_basis=3), + basis=MonomialBasis(domain_range=((0, 3),), n_basis=3), coefficients=coef_pred, ) @@ -124,8 +124,8 @@ def _test_grid_basis_generic( ) -> None: y_true_grid, y_pred_grid = _create_data_grid() - y_true_basis = y_true_grid.to_basis(basis=BSpline(n_basis=10)) - y_pred_basis = y_pred_grid.to_basis(basis=BSpline(n_basis=10)) + y_true_basis = y_true_grid.to_basis(basis=BSplineBasis(n_basis=10)) + y_pred_basis = y_pred_grid.to_basis(basis=BSplineBasis(n_basis=10)) # The results should be close but not equal as the two representations # do not give same functions. @@ -241,12 +241,12 @@ def test_zero_r2(self) -> None: # y_true and y_pred are 2 sets of 3 straight lines # for all f, f(1) = 1 y_true_basis = FDataBasis( - basis=Monomial(domain_range=((0, 2),), n_basis=2), + basis=MonomialBasis(domain_range=((0, 2),), n_basis=2), coefficients=basis_coef_true, ) y_pred_basis = FDataBasis( - basis=Monomial(domain_range=((0, 2),), n_basis=2), + basis=MonomialBasis(domain_range=((0, 2),), n_basis=2), coefficients=basis_coef_pred, ) @@ -280,7 +280,7 @@ def test_zero_r2(self) -> None: # for all f in y_pred, f(1) = 2 basis_coef_pred = [[2, 0], [3, -1], [4, -2]] y_pred_basis = FDataBasis( - basis=Monomial(domain_range=((0, 2),), n_basis=2), + basis=MonomialBasis(domain_range=((0, 2),), n_basis=2), coefficients=basis_coef_pred, ) @@ -315,12 +315,12 @@ def test_zero_ev(self) -> None: # for all f, f(1) = 1 # var(y_true(1)) = 0 and var(y_true(1) - y_pred(1)) = 0 y_true_basis = FDataBasis( - basis=Monomial(domain_range=((0, 2),), n_basis=2), + basis=MonomialBasis(domain_range=((0, 2),), n_basis=2), coefficients=basis_coef_true, ) y_pred_basis = FDataBasis( - basis=Monomial(domain_range=((0, 2),), n_basis=2), + basis=MonomialBasis(domain_range=((0, 2),), n_basis=2), coefficients=basis_coef_pred, ) @@ -352,7 +352,7 @@ def test_zero_ev(self) -> None: # Case when numerator is non-zero and denominator is zero (in t = 1) basis_coef_pred = [[2, 0], [2, -1], [2, -2]] y_pred_basis = FDataBasis( - basis=Monomial(domain_range=((0, 2),), n_basis=2), + basis=MonomialBasis(domain_range=((0, 2),), n_basis=2), coefficients=basis_coef_pred, ) y_pred_grid = y_pred_basis.to_grid(grid_points=grid_points) @@ -391,12 +391,12 @@ def test_zero_mape(self) -> None: # The second function in y_true should be zero at t = 0.5 and t = 1.5 y_true_basis = FDataBasis( - basis=Fourier(domain_range=((0, 2),), n_basis=5), + basis=FourierBasis(domain_range=((0, 2),), n_basis=5), coefficients=basis_coef_true, ) y_pred_basis = FDataBasis( - basis=Fourier(domain_range=((0, 2),), n_basis=5), + basis=FourierBasis(domain_range=((0, 2),), n_basis=5), coefficients=basis_coef_pred, ) @@ -430,12 +430,12 @@ def test_negative_msle(self) -> None: # All functions in y_pred should be always positive y_true_basis = FDataBasis( - basis=Fourier(domain_range=((0, 2),), n_basis=3), + basis=FourierBasis(domain_range=((0, 2),), n_basis=3), coefficients=basis_coef_true, ) y_pred_basis = FDataBasis( - basis=Fourier(domain_range=((0, 2),), n_basis=3), + basis=FourierBasis(domain_range=((0, 2),), n_basis=3), coefficients=basis_coef_pred, ) diff --git a/skfda/tests/test_smoothing.py b/skfda/tests/test_smoothing.py index 7c6f68932..a76825640 100644 --- a/skfda/tests/test_smoothing.py +++ b/skfda/tests/test_smoothing.py @@ -20,7 +20,7 @@ from skfda.misc.operators import LinearDifferentialOperator from skfda.misc.regularization import L2Regularization from skfda.preprocessing.smoothing import KernelSmoother -from skfda.representation.basis import BSpline, Monomial +from skfda.representation.basis import BSplineBasis, MonomialBasis from skfda.representation.grid import FDataGrid @@ -180,7 +180,7 @@ def test_cholesky(self) -> None: """Test Basis Smoother using BSpline basis and Cholesky method.""" t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) - basis = BSpline((0, 1), n_basis=5) + basis = BSplineBasis((0, 1), n_basis=5) fd = FDataGrid(data_matrix=x, grid_points=t) @@ -205,7 +205,7 @@ def test_qr(self) -> None: """Test Basis Smoother using BSpline basis and QR method.""" t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) - basis = BSpline((0, 1), n_basis=5) + basis = BSplineBasis((0, 1), n_basis=5) fd = FDataGrid(data_matrix=x, grid_points=t) @@ -232,7 +232,7 @@ def test_monomial_smoothing(self) -> None: # where the fit is very good but its just for testing purposes t = np.linspace(0, 1, 5) x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t) - basis = Monomial(n_basis=4) + basis = MonomialBasis(n_basis=4) fd = FDataGrid(data_matrix=x, grid_points=t) @@ -257,11 +257,11 @@ def test_vector_valued_smoothing(self) -> None: """Test Basis Smoother for vector values functions.""" X, _ = fetch_weather(return_X_y=True) - basis_dim = skfda.representation.basis.Fourier( + basis_dim = skfda.representation.basis.FourierBasis( n_basis=7, domain_range=X.domain_range, ) - basis = skfda.representation.basis.VectorValued( + basis = skfda.representation.basis.VectorValuedBasis( [basis_dim] * 2, ) diff --git a/skfda/tests/test_ufunc_numpy.py b/skfda/tests/test_ufunc_numpy.py index 27c4a6f8d..209aad36b 100644 --- a/skfda/tests/test_ufunc_numpy.py +++ b/skfda/tests/test_ufunc_numpy.py @@ -7,7 +7,7 @@ import pytest from skfda import FDataGrid -from skfda.representation.basis import Fourier +from skfda.representation.basis import FourierBasis T = TypeVar("T", "np.typing.NDArray[Any]", FDataGrid) @@ -93,7 +93,7 @@ def test_commutativity_basis(self) -> None: """Test that operations with numpy arrays for basis commute.""" X = FDataGrid([[1, 2, 3], [4, 5, 6]]) arr = np.array([1, 2]) - basis = Fourier(n_basis=5) + basis = FourierBasis(n_basis=5) X_basis = X.to_basis(basis) diff --git a/tutorial/plot_basis_representation.py b/tutorial/plot_basis_representation.py index 641659c21..9485481d6 100644 --- a/tutorial/plot_basis_representation.py +++ b/tutorial/plot_basis_representation.py @@ -132,7 +132,7 @@ # .. math:: # x(t) = 3 + 2t - 4t^2 + t^3 -basis = skfda.representation.basis.Monomial( +basis = skfda.representation.basis.MonomialBasis( n_basis=4, domain_range=(-10, 10), ) @@ -182,7 +182,7 @@ fig, axes = plt.subplots(nrows=3, ncols=3) for n_basis in range(1, max_basis + 1): - basis = skfda.representation.basis.Monomial(n_basis=n_basis) + basis = skfda.representation.basis.MonomialBasis(n_basis=n_basis) X_basis = X.to_basis(basis) ax = axes.ravel()[n_basis - 1] @@ -243,7 +243,7 @@ ############################################################################## # Here we show the first five elements of the monomial basis. -basis = skfda.representation.basis.Monomial(n_basis=5) +basis = skfda.representation.basis.MonomialBasis(n_basis=5) basis.plot() plt.show() @@ -278,7 +278,7 @@ ############################################################################## # Here we show the first five elements of a Fourier basis. -basis = skfda.representation.basis.Fourier(n_basis=5) +basis = skfda.representation.basis.FourierBasis(n_basis=5) basis.plot() plt.show() @@ -314,7 +314,7 @@ ############################################################################## # Here we show the first five elements of a B-spline basis. -basis = skfda.representation.basis.BSpline(n_basis=5) +basis = skfda.representation.basis.BSplineBasis(n_basis=5) basis.plot() plt.show() @@ -365,12 +365,12 @@ fd = skfda.FDataGrid(X) -basis = skfda.representation.basis.Tensor([ - skfda.representation.basis.Fourier( # X axis +basis = skfda.representation.basis.TensorBasis([ + skfda.representation.basis.FourierBasis( # X axis n_basis=5, domain_range=fd.domain_range[0], ), - skfda.representation.basis.BSpline( # Y axis + skfda.representation.basis.BSplineBasis( # Y axis n_basis=6, domain_range=fd.domain_range[1], ), @@ -435,7 +435,7 @@ ############################################################################## # We now represent the digits dataset in this basis. -basis = skfda.representation.basis.FiniteElement( +basis = skfda.representation.basis.FiniteElementBasis( vertices=vertices, cells=cells, ) @@ -475,12 +475,12 @@ # are now expressed in a Fourier basis, while we express precipitations as # B-splines. -basis = skfda.representation.basis.VectorValued([ - skfda.representation.basis.Fourier( # First coordinate function +basis = skfda.representation.basis.VectorValuedBasis([ + skfda.representation.basis.FourierBasis( # First coordinate function n_basis=5, domain_range=X.domain_range, ), - skfda.representation.basis.BSpline( # Second coordinate function + skfda.representation.basis.BSplineBasis( # Second coordinate function n_basis=10, domain_range=X.domain_range, ), From 1f2d9ea35ee79b780d67dfa98efc07d1591d52c8 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sun, 30 Oct 2022 00:38:57 +0200 Subject: [PATCH 335/400] Fix some style issues --- skfda/misc/operators/_linear_differential_operator.py | 8 +++++++- skfda/ml/regression/_historical_linear_model.py | 7 +++++-- skfda/representation/basis/_fdatabasis.py | 3 ++- skfda/tests/test_math.py | 6 +++++- skfda/tests/test_scoring.py | 6 +++++- 5 files changed, 24 insertions(+), 6 deletions(-) diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 36f28cd7e..02b6f190e 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -9,7 +9,13 @@ from scipy.interpolate import PPoly from ...representation import FData, FDataGrid -from ...representation.basis import Basis, BSplineBasis, ConstantBasis, FourierBasis, MonomialBasis +from ...representation.basis import ( + Basis, + BSplineBasis, + ConstantBasis, + FourierBasis, + MonomialBasis, +) from ...typing._base import DomainRangeLike from ...typing._numpy import NDArrayFloat from ._operators import Operator, gramian_matrix_optimization diff --git a/skfda/ml/regression/_historical_linear_model.py b/skfda/ml/regression/_historical_linear_model.py index be6aac812..5c1e85f34 100644 --- a/skfda/ml/regression/_historical_linear_model.py +++ b/skfda/ml/regression/_historical_linear_model.py @@ -10,8 +10,11 @@ from ..._utils import _cartesian_product, _pairwise_symmetric from ..._utils._sklearn_adapter import BaseEstimator, RegressorMixin from ...representation import FData, FDataBasis, FDataGrid -from ...representation.basis import Basis, FiniteElementBasis -from ...representation.basis import VectorValuedBasis +from ...representation.basis import ( + Basis, + FiniteElementBasis, + VectorValuedBasis, +) from ...typing._numpy import NDArrayFloat _MeanType = Union[FDataGrid, float] diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 86a7e2b66..fb5004dae 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -359,7 +359,8 @@ def integrate( Examples: We first create the data basis. - >>> from skfda.representation.basis import FDataBasis, MonomialBasis + >>> from skfda.representation.basis import FDataBasis + >>> from skfda.representation.basis import MonomialBasis >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [1, 1, 3, .5] >>> fdata = FDataBasis(basis, coefficients) diff --git a/skfda/tests/test_math.py b/skfda/tests/test_math.py index 3acbfc1db..3a325cfe5 100644 --- a/skfda/tests/test_math.py +++ b/skfda/tests/test_math.py @@ -8,7 +8,11 @@ from skfda._utils import _pairwise_symmetric from skfda.datasets import make_gaussian_process from skfda.misc.covariances import Gaussian -from skfda.representation.basis import MonomialBasis, TensorBasis, VectorValuedBasis +from skfda.representation.basis import ( + MonomialBasis, + TensorBasis, + VectorValuedBasis, +) def _ndm( diff --git a/skfda/tests/test_scoring.py b/skfda/tests/test_scoring.py index 1a9a0dbe3..89421389c 100644 --- a/skfda/tests/test_scoring.py +++ b/skfda/tests/test_scoring.py @@ -16,7 +16,11 @@ mean_squared_log_error, r2_score, ) -from skfda.representation.basis import BSplineBasis, FourierBasis, MonomialBasis +from skfda.representation.basis import ( + BSplineBasis, + FourierBasis, + MonomialBasis, +) from skfda.typing._numpy import NDArrayFloat score_functions: Sequence[ScoreFunction] = ( From 3712aa586bd5958145da51196e521a10afdf047f Mon Sep 17 00:00:00 2001 From: Ddelval Date: Sun, 30 Oct 2022 02:38:04 +0200 Subject: [PATCH 336/400] Add old deprecated classes --- docs/modules/representation.rst | 29 +++-- skfda/representation/basis/__init__.py | 14 +++ skfda/representation/basis/_bspline.py | 107 ++++++++++++++++++ skfda/representation/basis/_constant.py | 35 ++++++ skfda/representation/basis/_finite_element.py | 84 ++++++++++++++ skfda/representation/basis/_fourier.py | 93 +++++++++++++++ skfda/representation/basis/_monomial.py | 84 ++++++++++++++ skfda/representation/basis/_tensor.py | 73 ++++++++++++ skfda/representation/basis/_vector.py | 75 ++++++++++++ 9 files changed, 587 insertions(+), 7 deletions(-) create mode 100644 skfda/representation/basis/_bspline.py create mode 100644 skfda/representation/basis/_constant.py create mode 100644 skfda/representation/basis/_finite_element.py create mode 100644 skfda/representation/basis/_fourier.py create mode 100644 skfda/representation/basis/_monomial.py create mode 100644 skfda/representation/basis/_tensor.py create mode 100644 skfda/representation/basis/_vector.py diff --git a/docs/modules/representation.rst b/docs/modules/representation.rst index 16e0a75d1..099109b7a 100644 --- a/docs/modules/representation.rst +++ b/docs/modules/representation.rst @@ -51,10 +51,10 @@ The following classes are used to define different basis for .. autosummary:: :toctree: autosummary - skfda.representation.basis.BSpline - skfda.representation.basis.Fourier - skfda.representation.basis.Monomial - skfda.representation.basis.Constant + skfda.representation.basis.BSplineBasis + skfda.representation.basis.FourierBasis + skfda.representation.basis.MonomialBasis + skfda.representation.basis.ConstantBasis The following classes, allow the construction of a basis for :math:`\mathbb{R}^n \to \mathbb{R}` functions. @@ -62,8 +62,8 @@ The following classes, allow the construction of a basis for .. autosummary:: :toctree: autosummary - skfda.representation.basis.Tensor - skfda.representation.basis.FiniteElement + skfda.representation.basis.TensorBasis + skfda.representation.basis.FiniteElementBasis The following class, allows the construction of a basis for :math:`\mathbb{R}^n \to \mathbb{R}^m` functions from @@ -72,7 +72,7 @@ several :math:`\mathbb{R}^n \to \mathbb{R}` bases. .. autosummary:: :toctree: autosummary - skfda.representation.basis.VectorValued + skfda.representation.basis.VectorValuedBasis All the aforementioned basis inherit the basics from an abstract base class :class:`Basis`. Users can create their own @@ -107,3 +107,18 @@ interval using extrapolation methods. :maxdepth: 4 representation/extrapolation + +Deprecated Classes +---------------------- + +.. autosummary:: + :toctree: autosummary + + skfda.representation.basis.BSpline + skfda.representation.basis.Fourier + skfda.representation.basis.Monomial + skfda.representation.basis.Constant + skfda.representation.basis.Tensor + skfda.representation.basis.FiniteElement + skfda.representation.basis.VectorValued + diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 9be35c620..9d939ea48 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -8,26 +8,40 @@ submod_attrs={ '_basis': ["Basis"], "_bspline_basis": ["BSplineBasis"], + "_bspline": ["BSpline"], "_constant_basis": ["ConstantBasis"], + "_constant": ["Constant"], "_fdatabasis": ["FDataBasis", "FDataBasisDType"], "_finite_element_basis": ["FiniteElementBasis"], + "_finite_element": ["FiniteElement"], "_fourier_basis": ["FourierBasis"], + "_fourier": ["Fourier"], "_monomial_basis": ["MonomialBasis"], + "_monomial": ["Monomial"], "_tensor_basis": ["TensorBasis"], + "_tensor": ["Tensor"], "_vector_basis": ["VectorValuedBasis"], + "_vector": ["VectorValued"], }, ) if TYPE_CHECKING: from ._basis import Basis as Basis + from ._bspline import BSpline as BSpline from ._bspline_basis import BSplineBasis as BSplineBasis + from ._constant import Constant as Constant from ._constant_basis import ConstantBasis as ConstantBasis from ._fdatabasis import ( FDataBasis as FDataBasis, FDataBasisDType as FDataBasisDType, ) + from ._finite_element import FiniteElement as FiniteElement from ._finite_element_basis import FiniteElementBasis as FiniteElementBasis + from ._fourier import Fourier as Fourier from ._fourier_basis import FourierBasis as FourierBasis + from ._monomial import Monomial as Monomial from ._monomial_basis import MonomialBasis as MonomialBasis + from ._tensor import Tensor as Tensor from ._tensor_basis import TensorBasis as TensorBasis + from ._vector import VectorValued as VectorValued from ._vector_basis import VectorValuedBasis as VectorValuedBasis diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py new file mode 100644 index 000000000..77dfc6a86 --- /dev/null +++ b/skfda/representation/basis/_bspline.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import warnings +from typing import Sequence + +from ...typing._base import DomainRangeLike +from ._bspline_basis import BSplineBasis + + +class BSpline(BSplineBasis): + r"""BSpline basis. + + BSpline basis elements are defined recursively as: + + .. math:: + B_{i, 1}(x) = 1 \quad \text{if } t_i \le x < t_{i+1}, + \quad 0 \text{ otherwise} + + .. math:: + B_{i, k}(x) = \frac{x - t_i}{t_{i+k} - t_i} B_{i, k-1}(x) + + \frac{t_{i+k+1} - x}{t_{i+k+1} - t_{i+1}} B_{i+1, k-1}(x) + + Where k indicates the order of the spline. + + .. deprecated:: 0.8 + Use BSplineBasis instead. + + Implementation details: In order to allow a discontinuous behaviour at + the boundaries of the domain it is necessary to placing m knots at the + boundaries [RS05]_. This is automatically done so that the user only has to + specify a single knot at the boundaries. + + + Parameters: + domain_range: A tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis: Number of functions in the basis. + order: Order of the splines. One greather than their degree. + knots: List of knots of the spline functions. + + Examples: + Constructs specifying number of basis and order. + + >>> bss = BSpline(n_basis=8, order=4) + + If no order is specified defaults to 4 because cubic splines are + the most used. So the previous example is the same as: + + >>> bss = BSplineBasis(n_basis=8) + + It is also possible to create a BSpline basis specifying the knots. + + >>> bss = BSplineBasis(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) + + Once we create a basis we can evaluate each of its functions at a + set of points. + + >>> bss = BSplineBasis(n_basis=3, order=3) + >>> bss([0, 0.5, 1]) + array([[[ 1. ], + [ 0.25], + [ 0. ]], + [[ 0. ], + [ 0.5 ], + [ 0. ]], + [[ 0. ], + [ 0.25], + [ 1. ]]]) + + And evaluates first derivative + + >>> deriv = bss.derivative() + >>> deriv([0, 0.5, 1]) + array([[[-2.], + [-1.], + [ 0.]], + [[ 2.], + [ 0.], + [-2.]], + [[ 0.], + [ 1.], + [ 2.]]]) + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + + """ + + def __init__( # noqa: WPS238 + self, + domain_range: DomainRangeLike | None = None, + n_basis: int | None = None, + order: int = 4, + knots: Sequence[float] | None = None, + ) -> None: + super().__init__( + domain_range=domain_range, + n_basis=n_basis, + order=order, + knots=knots, + ) + warnings.warn( + "The BSplines class is deprecated. Use " + "BSplineBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py new file mode 100644 index 000000000..6ce210913 --- /dev/null +++ b/skfda/representation/basis/_constant.py @@ -0,0 +1,35 @@ +import warnings +from typing import Optional + +from ...typing._base import DomainRangeLike +from ._constant_basis import ConstantBasis + + +class Constant(ConstantBasis): + """Constant basis. + + Basis for constant functions + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.ConstantBasis` instead. + + Parameters: + domain_range: The :term:`domain range` over which the basis can be + evaluated. + + Examples: + Defines a contant base over the interval :math:`[0, 5]` consisting + on the constant function 1 on :math:`[0, 5]`. + + >>> bs_cons = ConstantBasis((0,5)) + + """ + + def __init__(self, domain_range: Optional[DomainRangeLike] = None) -> None: + """Constant basis constructor.""" + warnings.warn( + "The Constant class is deprecated. Use " + "ConstantBasis instead.", + DeprecationWarning, + ) + super().__init__(domain_range=domain_range) diff --git a/skfda/representation/basis/_finite_element.py b/skfda/representation/basis/_finite_element.py new file mode 100644 index 000000000..03f7abb3f --- /dev/null +++ b/skfda/representation/basis/_finite_element.py @@ -0,0 +1,84 @@ +import warnings +from typing import Optional + +from ...typing._base import DomainRangeLike +from ...typing._numpy import ArrayLike +from ._finite_element_basis import FiniteElementBasis + + +class FiniteElement(FiniteElementBasis): + """Finite element basis. + + Given a n-dimensional grid made of simplices, each element of the basis + is a piecewise linear function that takes the value 1 at exactly one + vertex and 0 in the other vertices. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.FiniteElementBasis` instead. + + Parameters: + vertices: The vertices of the grid. + cells: A list of individual cells, consisting in the indexes of + :math:`n+1` vertices for an n-dimensional domain space. + + Examples: + >>> from skfda.representation.basis import FiniteElementBasis + >>> basis = FiniteElementBasis( + ... vertices=[[0, 0], [0, 1], [1, 0], [1, 1]], + ... cells=[[0, 1, 2], [1, 2, 3]], + ... domain_range=[(0, 1), (0, 1)], + ... ) + + Evaluates all the functions in the basis in a list of discrete + values. + + >>> basis([[0.1, 0.1], [0.6, 0.6], [0.1, 0.2], [0.8, 0.9]]) + array([[[ 0.8], + [ 0. ], + [ 0.7], + [ 0. ]], + [[ 0.1], + [ 0.4], + [ 0.2], + [ 0.2]], + [[ 0.1], + [ 0.4], + [ 0.1], + [ 0.1]], + [[ 0. ], + [ 0.2], + [ 0. ], + [ 0.7]]]) + + + >>> from scipy.spatial import Delaunay + >>> import numpy as np + >>> + >>> n_points = 10 + >>> points = np.random.uniform(size=(n_points, 2)) + >>> delaunay = Delaunay(points) + >>> basis = FiniteElementBasis( + ... vertices=delaunay.points, + ... cells=delaunay.simplices, + ... ) + >>> basis.n_basis + 10 + + """ + + def __init__( + self, + vertices: ArrayLike, + cells: ArrayLike, + domain_range: Optional[DomainRangeLike] = None, + ) -> None: + super().__init__( + vertices=vertices, + cells=cells, + domain_range=domain_range, + ) + warnings.warn( + "The FiniteElement class is deprecated. Use " + "FiniteElementBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py new file mode 100644 index 000000000..eb11af755 --- /dev/null +++ b/skfda/representation/basis/_fourier.py @@ -0,0 +1,93 @@ +import warnings +from typing import Optional + +import numpy as np + +from ...typing._base import DomainRangeLike +from ._fourier_basis import FourierBasis + + +class Fourier(FourierBasis): + r"""Fourier basis. + + Defines a functional basis for representing functions on a fourier + series expansion of period :math:`T`. The number of basis is always odd. + If instantiated with an even number of basis, they will be incremented + automatically by one. + + .. math:: + \phi_0(t) = \frac{1}{\sqrt{2}} + + .. math:: + \phi_{2n -1}(t) = \frac{sin\left(\frac{2 \pi n}{T} t\right)} + {\sqrt{\frac{T}{2}}} + + .. math:: + \phi_{2n}(t) = \frac{cos\left(\frac{2 \pi n}{T} t\right)} + {\sqrt{\frac{T}{2}}} + + + This basis will be orthonormal if the period coincides with the length + of the interval in which it is defined. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.FourierBasis` instead. + + Parameters: + domain_range: A tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis: Number of functions in the basis. + period: Period (:math:`T`). + + Examples: + Constructs specifying number of basis, definition interval and period. + + >>> fb = FourierBasis((0, np.pi), n_basis=3, period=1) + >>> fb([0, np.pi / 4, np.pi / 2, np.pi]).round(2) + array([[[ 1. ], + [ 1. ], + [ 1. ], + [ 1. ]], + [[ 0. ], + [-1.38], + [-0.61], + [ 1.1 ]], + [[ 1.41], + [ 0.31], + [-1.28], + [ 0.89]]]) + + And evaluate second derivative + + >>> deriv2 = fb.derivative(order=2) + >>> deriv2([0, np.pi / 4, np.pi / 2, np.pi]).round(2) + array([[[ 0. ], + [ 0. ], + [ 0. ], + [ 0. ]], + [[ 0. ], + [ 54.46], + [ 24.02], + [-43.37]], + [[-55.83], + [-12.32], + [ 50.4 ], + [-35.16]]]) + + """ + + def __init__( + self, + domain_range: Optional[DomainRangeLike] = None, + n_basis: int = 3, + period: Optional[float] = None, + ) -> None: + warnings.warn( + "The Fourier class is deprecated. Use FourierBasis instead.", + DeprecationWarning, + ) + super().__init__( + domain_range=domain_range, + n_basis=n_basis, + period=period, + ) diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py new file mode 100644 index 000000000..00caf0f9c --- /dev/null +++ b/skfda/representation/basis/_monomial.py @@ -0,0 +1,84 @@ +import warnings +from typing import Optional + +from ...typing._base import DomainRangeLike +from ._monomial_basis import MonomialBasis + + +class Monomial(MonomialBasis): + """Monomial basis. + + Basis formed by powers of the argument :math:`t`: + + .. math:: + 1, t, t^2, t^3... + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.MonomialBasis` instead. + + Attributes: + domain_range: a tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis: number of functions in the basis. + + Examples: + Defines a monomial base over the interval :math:`[0, 5]` consisting + on the first 3 powers of :math:`t`: :math:`1, t, t^2`. + + >>> bs_mon = MonomialBasis(domain_range=(0,5), n_basis=3) + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> bs_mon([0., 1., 2.]) + array([[[ 1.], + [ 1.], + [ 1.]], + [[ 0.], + [ 1.], + [ 2.]], + [[ 0.], + [ 1.], + [ 4.]]]) + + And also evaluates its derivatives + + >>> deriv = bs_mon.derivative() + >>> deriv([0, 1, 2]) + array([[[ 0.], + [ 0.], + [ 0.]], + [[ 1.], + [ 1.], + [ 1.]], + [[ 0.], + [ 2.], + [ 4.]]]) + >>> deriv2 = bs_mon.derivative(order=2) + >>> deriv2([0, 1, 2]) + array([[[ 0.], + [ 0.], + [ 0.]], + [[ 0.], + [ 0.], + [ 0.]], + [[ 2.], + [ 2.], + [ 2.]]]) + """ + + def __init__( + self, + *, + domain_range: Optional[DomainRangeLike] = None, + n_basis: int = 1, + ): + super().__init__( + domain_range=domain_range, + n_basis=n_basis, + ) + warnings.warn( + "The BSplines class is deprecated. Use " + "BSplineBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_tensor.py b/skfda/representation/basis/_tensor.py new file mode 100644 index 000000000..db618c99e --- /dev/null +++ b/skfda/representation/basis/_tensor.py @@ -0,0 +1,73 @@ + +import warnings +from typing import Iterable + +from ._basis import Basis +from ._tensor_basis import TensorBasis + + +class Tensor(TensorBasis): + r"""Tensor basis. + + Basis for multivariate functions constructed as a tensor product of + :math:`\mathbb{R} \to \mathbb{R}` bases. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.TensorBasis` instead. + + Attributes: + domain_range (tuple): a tuple of length ``dim_domain`` containing + the range of input values for each dimension. + n_basis (int): number of functions in the basis. + + Examples: + Defines a tensor basis over the interval :math:`[0, 5] \times [0, 3]` + consisting on the functions + + .. math:: + + 1, v, u, uv, u^2, u^2v + + >>> from skfda.representation.basis import TensorBasis, MonomialBasis + >>> + >>> basis_x = MonomialBasis(domain_range=(0,5), n_basis=3) + >>> basis_y = MonomialBasis(domain_range=(0,3), n_basis=2) + >>> + >>> basis = TensorBasis([basis_x, basis_y]) + + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> basis([(0., 2.), (3., 0), (2., 3.)]) + array([[[ 1.], + [ 1.], + [ 1.]], + [[ 2.], + [ 0.], + [ 3.]], + [[ 0.], + [ 3.], + [ 2.]], + [[ 0.], + [ 0.], + [ 6.]], + [[ 0.], + [ 9.], + [ 4.]], + [[ 0.], + [ 0.], + [ 12.]]]) + + """ + + def __init__(self, basis_list: Iterable[Basis]): + + super().__init__( + basis_list=basis_list, + ) + warnings.warn( + "The Tensor class is deprecated. Use " + "TensorBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_vector.py b/skfda/representation/basis/_vector.py new file mode 100644 index 000000000..9102c992a --- /dev/null +++ b/skfda/representation/basis/_vector.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import warnings +from typing import Iterable + +from ._basis import Basis +from ._vector_basis import VectorValuedBasis + + +class VectorValued(VectorValuedBasis): + r"""Vector-valued basis. + + Basis for :term:`vector-valued functions ` + constructed from scalar-valued bases. + + For each dimension in the :term:`codomain`, it uses a scalar-valued basis + multiplying each basis by the corresponding unitary vector. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.VectorValuedBasis` instead. + + Attributes: + domain_range (tuple): a tuple of length ``dim_domain`` containing + the range of input values for each dimension. + n_basis (int): number of functions in the basis. + + Examples: + Defines a vector-valued base over the interval :math:`[0, 5]` + consisting on the functions + + .. math:: + + 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} + + >>> from skfda.representation.basis import VectorValuedBasis + >>> from skfda.representation.basis import MonomialBasis + >>> + >>> basis_x = MonomialBasis(domain_range=(0,5), n_basis=3) + >>> basis_y = MonomialBasis(domain_range=(0,5), n_basis=2) + >>> + >>> basis = VectorValuedBasis([basis_x, basis_y]) + + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> basis([0., 1., 2.]) + array([[[ 1., 0.], + [ 1., 0.], + [ 1., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 2., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 4., 0.]], + [[ 0., 1.], + [ 0., 1.], + [ 0., 1.]], + [[ 0., 0.], + [ 0., 1.], + [ 0., 2.]]]) + + """ + + def __init__(self, basis_list: Iterable[Basis]) -> None: + super().__init__( + basis_list=basis_list, + ) + + warnings.warn( + "The VectorValued class is deprecated. Use " + "VectorValuedBasis instead.", + DeprecationWarning, + ) From e7419920266c9f9525f0591a486483d8dc474502 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 31 Oct 2022 12:41:06 +0100 Subject: [PATCH 337/400] Style quickfix --- skfda/tests/test_classifier_classes.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index d0afdabb5..e22ac85c4 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -30,7 +30,8 @@ def classifier(request: Any) -> Any: params=[ np.array([0, 1]), np.array(["class_a", "class_b"]), - ], ids=["int", "str"], + ], + ids=["int", "str"], ) def classes(request: Any) -> Any: """Fixture for classes to test.""" From 6db17ca6c06bd0deea99a890d9ce825ff79ec90d Mon Sep 17 00:00:00 2001 From: VNMabus Date: Tue, 1 Nov 2022 11:43:43 +0100 Subject: [PATCH 338/400] Expose ICTAI examples. --- docs/index.rst | 7 +++++++ full_examples/README.txt | 7 ++++--- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 48bcb3d6c..9d6d41182 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -26,6 +26,13 @@ Github you can find more information related to the development of the package. :hidden: auto_examples/index + +.. toctree:: + :maxdepth: 1 + :titlesonly: + :hidden: + + auto_full_examples/index .. toctree:: :maxdepth: 2 diff --git a/full_examples/README.txt b/full_examples/README.txt index 97afa5eb9..658318787 100644 --- a/full_examples/README.txt +++ b/full_examples/README.txt @@ -1,4 +1,5 @@ -Full examples -============= +ICTAI examples +============== -Examples of complete analyses showcasing the main functionalities of the scikit-fda package. +Examples of complete analyses showcasing the main functionalities of the scikit-fda package, +presented at the 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). From e401bf2926156792da227d550f683a54d3ab3df3 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 2 Nov 2022 16:27:50 +0100 Subject: [PATCH 339/400] Remove warnings. --- skfda/tests/test_scoring.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/tests/test_scoring.py b/skfda/tests/test_scoring.py index 7a5b4233e..98f76fcae 100644 --- a/skfda/tests/test_scoring.py +++ b/skfda/tests/test_scoring.py @@ -292,7 +292,7 @@ def test_zero_r2(self) -> None: y_true_grid, y_pred_grid, multioutput='raw_values', - ).evaluate(1), + )(1), [[[-math.inf]]], ) @@ -363,7 +363,7 @@ def test_zero_ev(self) -> None: y_true_grid, y_pred_grid, multioutput='raw_values', - ).evaluate(1), + )(1), [[[-math.inf]]], ) From b5c504d387e88c7b3c59d5f3a0f45f7443ddc5bb Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 3 Nov 2022 12:17:14 +0100 Subject: [PATCH 340/400] Move functional feature constructors out of the stats module. --- docs/modules/exploratory/stats.rst | 13 ---------- .../preprocessing/feature_construction.rst | 15 +++++++++--- skfda/exploratory/stats/__init__.py | 16 ------------- .../feature_construction/__init__.py | 16 +++++++++++++ .../_function_transformers.py | 6 +---- .../feature_construction/_functions.py} | 24 ++++++++++++------- skfda/tests/test_functional_transformers.py | 4 +++- 7 files changed, 48 insertions(+), 46 deletions(-) rename skfda/{exploratory/stats/_functional_transformers.py => preprocessing/feature_construction/_functions.py} (96%) diff --git a/docs/modules/exploratory/stats.rst b/docs/modules/exploratory/stats.rst index 8217c686b..17642a732 100644 --- a/docs/modules/exploratory/stats.rst +++ b/docs/modules/exploratory/stats.rst @@ -32,16 +32,3 @@ statistics can be used. skfda.exploratory.stats.cov skfda.exploratory.stats.var -Additional statistics ---------------------- - -The following statistics can be used to estimate additional properties of the data. - -.. autosummary:: - :toctree: autosummary - - skfda.exploratory.stats.modified_epigraph_index - skfda.exploratory.stats.local_averages - skfda.exploratory.stats.occupation_measure - skfda.exploratory.stats.number_crossings - diff --git a/docs/modules/preprocessing/feature_construction.rst b/docs/modules/preprocessing/feature_construction.rst index 6d05ab780..8db82bb56 100644 --- a/docs/modules/preprocessing/feature_construction.rst +++ b/docs/modules/preprocessing/feature_construction.rst @@ -36,8 +36,18 @@ data of a particular class. Functional features ------------------- -The following transformers can be used to extract specific functional features -for each functional datum, which can then be used for prediction. +The following functions can be used to create new features from functional data. + +.. autosummary:: + :toctree: autosummary + + skfda.exploratory.stats.modified_epigraph_index + skfda.preprocessing.feature_construction.local_averages + skfda.preprocessing.feature_construction.occupation_measure + skfda.preprocessing.feature_construction.number_crossings + +Some of them are also available as transformers that can be directly used in a +pipeline: .. autosummary:: :toctree: autosummary @@ -45,5 +55,4 @@ for each functional datum, which can then be used for prediction. skfda.preprocessing.feature_construction.LocalAveragesTransformer skfda.preprocessing.feature_construction.OccupationMeasureTransformer skfda.preprocessing.feature_construction.NumberCrossingsTransformer - \ No newline at end of file diff --git a/skfda/exploratory/stats/__init__.py b/skfda/exploratory/stats/__init__.py index 51d6b3a5c..0a1f3a6de 100644 --- a/skfda/exploratory/stats/__init__.py +++ b/skfda/exploratory/stats/__init__.py @@ -12,14 +12,6 @@ "_fisher_rao_warping_mean", "fisher_rao_karcher_mean", ], - "_functional_transformers": [ - "local_averages", - "number_crossings", - "occupation_measure", - "unconditional_central_moment", - "unconditional_expected_value", - "unconditional_moment", - ], "_stats": [ "cov", "depth_based_median", @@ -38,14 +30,6 @@ _fisher_rao_warping_mean as _fisher_rao_warping_mean, fisher_rao_karcher_mean as fisher_rao_karcher_mean, ) - from ._functional_transformers import ( - local_averages as local_averages, - number_crossings as number_crossings, - occupation_measure as occupation_measure, - unconditional_central_moment as unconditional_central_moment, - unconditional_expected_value as unconditional_expected_value, - unconditional_moment as unconditional_moment, - ) from ._stats import ( cov as cov, depth_based_median as depth_based_median, diff --git a/skfda/preprocessing/feature_construction/__init__.py b/skfda/preprocessing/feature_construction/__init__.py index 47b9d1cc2..ff0e400bd 100644 --- a/skfda/preprocessing/feature_construction/__init__.py +++ b/skfda/preprocessing/feature_construction/__init__.py @@ -14,6 +14,14 @@ "NumberCrossingsTransformer", "OccupationMeasureTransformer", ], + "_functions": [ + "local_averages", + "number_crossings", + "occupation_measure", + "unconditional_central_moment", + "unconditional_expected_value", + "unconditional_moment", + ], "_per_class_transformer": ["PerClassTransformer"], }, ) @@ -31,6 +39,14 @@ NumberCrossingsTransformer as NumberCrossingsTransformer, OccupationMeasureTransformer as OccupationMeasureTransformer, ) + from ._functions import ( + local_averages as local_averages, + number_crossings as number_crossings, + occupation_measure as occupation_measure, + unconditional_central_moment as unconditional_central_moment, + unconditional_expected_value as unconditional_expected_value, + unconditional_moment as unconditional_moment, + ) from ._per_class_transformer import ( PerClassTransformer as PerClassTransformer, ) diff --git a/skfda/preprocessing/feature_construction/_function_transformers.py b/skfda/preprocessing/feature_construction/_function_transformers.py index b45c5567c..a23fc3da1 100644 --- a/skfda/preprocessing/feature_construction/_function_transformers.py +++ b/skfda/preprocessing/feature_construction/_function_transformers.py @@ -6,15 +6,11 @@ from typing_extensions import Literal from ..._utils._sklearn_adapter import BaseEstimator, TransformerMixin -from ...exploratory.stats._functional_transformers import ( - local_averages, - number_crossings, - occupation_measure, -) from ...representation import FData from ...representation.grid import FDataGrid from ...typing._base import DomainRangeLike from ...typing._numpy import ArrayLike, NDArrayFloat, NDArrayInt +from ._functions import local_averages, number_crossings, occupation_measure class LocalAveragesTransformer( diff --git a/skfda/exploratory/stats/_functional_transformers.py b/skfda/preprocessing/feature_construction/_functions.py similarity index 96% rename from skfda/exploratory/stats/_functional_transformers.py rename to skfda/preprocessing/feature_construction/_functions.py index fcaee767b..3359a77af 100644 --- a/skfda/exploratory/stats/_functional_transformers.py +++ b/skfda/preprocessing/feature_construction/_functions.py @@ -83,7 +83,7 @@ def local_averages( 10 to 18: >>> import numpy as np - >>> from skfda.exploratory.stats import local_averages + >>> from skfda.preprocessing.feature_construction import local_averages >>> averages = local_averages( ... X, ... domains=[(1, 3), (3, 10), (10, 18)], @@ -103,8 +103,6 @@ def local_averages( consider. For example, we could want to split the domain in 2 intervals of the same length. - >>> import numpy as np - >>> from skfda.exploratory.stats import local_averages >>> np.around(local_averages(X, domains=2), decimals=2) array([[[ 116.94], [ 177.26]], @@ -245,7 +243,9 @@ def occupation_measure( we want that the function takes into account to interpolate. We are going to use 501 points. - >>> from skfda.exploratory.stats import occupation_measure + >>> from skfda.preprocessing.feature_construction import ( + ... occupation_measure, + ... ) >>> np.around( ... occupation_measure( ... fd_grid, @@ -330,7 +330,9 @@ def number_crossings( First of all we import the Bessel Function and create the X axis data grid. Then we create the FdataGrid. - >>> from skfda.exploratory.stats import number_crossings + >>> from skfda.preprocessing.feature_construction import ( + ... number_crossings, + ... ) >>> from scipy.special import jv >>> import numpy as np >>> x_grid = np.linspace(0, 14, 14) @@ -437,7 +439,9 @@ def unconditional_central_moment( the specified moment order. >>> import numpy as np - >>> from skfda.exploratory.stats import unconditional_central_moment + >>> from skfda.preprocessing.feature_construction import ( + ... unconditional_central_moment, + ... ) >>> np.around(unconditional_central_moment(X[:5], 1), decimals=2) array([[ 0.01, 0.01], [ 0.02, 0.01], @@ -497,7 +501,9 @@ def unconditional_moment( the specified moment order. >>> import numpy as np - >>> from skfda.exploratory.stats import unconditional_moment + >>> from skfda.preprocessing.feature_construction import ( + ... unconditional_moment, + ... ) >>> np.around(unconditional_moment(X[:5], 1), decimals=2) array([[ 4.7 , 4.03], [ 6.16, 3.96], @@ -557,7 +563,9 @@ def unconditional_expected_value( Then we call the function with the dataset and the function. - >>> from skfda.exploratory.stats import unconditional_expected_value + >>> from skfda.preprocessing.feature_construction import ( + ... unconditional_expected_value, + ... ) >>> np.around(unconditional_expected_value(X[:5], f), decimals=2) array([[ 4.96], [ 4.88], diff --git a/skfda/tests/test_functional_transformers.py b/skfda/tests/test_functional_transformers.py index 1d51353ee..adfa7d9a6 100644 --- a/skfda/tests/test_functional_transformers.py +++ b/skfda/tests/test_functional_transformers.py @@ -6,7 +6,9 @@ import skfda.representation.basis as basis from skfda.datasets import fetch_growth -from skfda.exploratory.stats import unconditional_expected_value +from skfda.preprocessing.feature_construction import ( + unconditional_expected_value, +) class TestUnconditionalExpectedValues(unittest.TestCase): From 566e3e3f8e87e4ccf51cf71c538da57feefe4d74 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 3 Nov 2022 12:28:07 +0100 Subject: [PATCH 341/400] Small RMH changes. --- .../recursive_maxima_hunting.py | 52 +++++++++---------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py index 86a38e2f5..ec75f671e 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py @@ -3,6 +3,7 @@ import abc import copy +from dataclasses import dataclass from typing import ( TYPE_CHECKING, Any, @@ -16,14 +17,14 @@ overload, ) +from typing_extensions import Literal + +import dcor import numpy as np import numpy.linalg as linalg import numpy.ma as ma import scipy.stats import sklearn.utils -from typing_extensions import Literal - -import dcor from ...._utils import _compute_dependence, _DependenceMeasure as _DepMeasure from ...._utils._sklearn_adapter import ( @@ -788,20 +789,14 @@ def __call__( ) +@dataclass class RMHResult(object): - - def __init__(self, index: Tuple[int, ...], score: float) -> None: - self.index = index - self.score = score - self.matrix_after_correction: Optional[NDArrayFloat] = None - self.original_dependence: Optional[NDArrayFloat] = None - self.influence_mask: Optional[NDArrayBool] = None - self.current_mask: Optional[NDArrayBool] = None - - def __repr__(self) -> str: - return ( - f"{type(self).__name__}(index={self.index}, score={self.score})" - ) + index: Tuple[int, ...] + score: float + matrix_after_correction: NDArrayFloat + original_dependence: NDArrayFloat + influence_mask: NDArrayBool + current_mask: NDArrayBool def _get_influence_mask( @@ -884,7 +879,6 @@ def _rec_maxima_hunting_gen_no_copy( redundancy_condition: Optional[RedundancyCondition] = None, stopping_condition: Optional[StoppingCondition] = None, mask: Optional[NDArrayBool] = None, - get_intermediate_results: bool = False, ) -> Iterable[RMHResult]: """ Recursive maxima hunting algorithm. @@ -902,7 +896,6 @@ def _rec_maxima_hunting_gen_no_copy( consideration as a maximum. stopping_condition: Condition to stop the algorithm. mask: Masked values. - get_intermediate_results: Return additional debug info. """ y = np.asfarray(y) @@ -968,14 +961,15 @@ def _rec_maxima_hunting_gen_no_copy( X=X, selected_index=t_max_index, ) - result = RMHResult(index=t_max_index, score=score) - - # Additional info, useful for debugging - if get_intermediate_results: - result.matrix_after_correction = np.copy(X.data_matrix) - result.original_dependence = dependences - result.influence_mask = influence_mask - result.current_mask = mask + result = RMHResult( + index=t_max_index, + score=score, + # Additional info, useful for debugging + matrix_after_correction=np.copy(X.data_matrix), + original_dependence=dependences, + influence_mask=influence_mask, + current_mask=mask, + ) new_X = yield result # Accept modifications to the matrix if new_X is not None: @@ -1131,6 +1125,7 @@ def fit( # type: ignore[override] # noqa: D102 self.features_shape_ = X.data_matrix.shape[1:] indexes = [] + relevances = [] for i, result in enumerate( _rec_maxima_hunting_gen( X=X.copy(), @@ -1143,11 +1138,16 @@ def fit( # type: ignore[override] # noqa: D102 ): indexes.append(result.index) + relevances.append(result.original_dependence) if self.max_features is not None and i + 1 >= self.max_features: break self.indexes_ = tuple(np.transpose(indexes).tolist()) + self._relevances = FDataGrid( + data_matrix=np.vstack(relevances), + grid_points=X.grid_points, + ) return self From d4122e3ca20dc5b06dcc2816d2e0c7bcbbace7bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Sat, 5 Nov 2022 01:06:30 +0100 Subject: [PATCH 342/400] SelfTypeClassifier usage and classes_ attr in fit --- skfda/ml/_neighbors_base.py | 45 ++++++++++++++++++++++++++++++++----- 1 file changed, 39 insertions(+), 6 deletions(-) diff --git a/skfda/ml/_neighbors_base.py b/skfda/ml/_neighbors_base.py index f655ff7d8..fa1180855 100644 --- a/skfda/ml/_neighbors_base.py +++ b/skfda/ml/_neighbors_base.py @@ -17,21 +17,29 @@ ClassifierMixin, RegressorMixin, ) +from .._utils._utils import _classifier_get_classes from ..misc.metrics import l2_distance from ..misc.metrics._utils import _fit_metric -from ..representation import FData, FDataGrid, concatenate +from ..representation import FData, concatenate from ..typing._metric import Metric -from ..typing._numpy import NDArrayFloat, NDArrayInt +from ..typing._numpy import NDArrayFloat, NDArrayInt, NDArrayStr FDataType = TypeVar("FDataType", bound="FData") SelfType = TypeVar("SelfType", bound="NeighborsBase[Any, Any]") +SelfTypeClassifier = TypeVar( + "SelfTypeClassifier", + bound="NeighborsClassifierMixin[Any, Any]", +) SelfTypeRegressor = TypeVar( "SelfTypeRegressor", bound="NeighborsRegressorMixin[Any, Any]", ) Input = TypeVar("Input", contravariant=True, bound=Union[NDArrayFloat, FData]) Target = TypeVar("Target") -TargetClassification = TypeVar("TargetClassification", bound=NDArrayInt) +TargetClassification = TypeVar( + "TargetClassification", + bound=Union[NDArrayInt, NDArrayStr], +) TargetRegression = TypeVar( "TargetRegression", bound=Union[NDArrayFloat, FData], @@ -482,15 +490,15 @@ def radius_neighbors_graph( class NeighborsClassifierMixin( - NeighborsBase[Input, Target], - ClassifierMixin[Input, Target], + NeighborsBase[Input, TargetClassification], + ClassifierMixin[Input, TargetClassification], ): """Mixin class for classifiers based in nearest neighbors.""" def predict( self, X: Input, - ) -> Target: + ) -> TargetClassification: """ Predict the class labels for the provided data. @@ -537,6 +545,31 @@ def predict_proba( self._estimator.predict_proba(X_dist) ) + def fit( + self: SelfTypeClassifier, + X: Input, + y: TargetClassification, + ) -> SelfTypeClassifier: + """ + Fit the model using X as training data and y as responses. + + Args: + X: Training data. FDataGrid + with the training data or array matrix with shape + [n_samples, n_samples] if metric='precomputed'. + y: Training data. FData with the training respones (functional + response case) or array matrix with length `n_samples` in + the multivariate response case. + + Returns: + Self. + + Note: + This method adds the attribute `classes_` to the classifier. + """ + self.classes_ = _classifier_get_classes(y)[0] + return super().fit(X, y) + class NeighborsRegressorMixin( NeighborsBase[Input, TargetRegression], From 51bf170eac008ee16c3fd045b75f0bdbe221d164 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Sat, 5 Nov 2022 01:06:55 +0100 Subject: [PATCH 343/400] Test neighbours classifiers --- skfda/tests/test_classifier_classes.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index e22ac85c4..e6372a711 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -9,7 +9,11 @@ from skfda._utils._sklearn_adapter import ClassifierMixin from skfda.datasets import make_gaussian_process -from skfda.ml.classification import LogisticRegression +from skfda.ml.classification import ( + KNeighborsClassifier, + LogisticRegression, + RadiusNeighborsClassifier, +) from skfda.representation import FData from ..typing._numpy import NDArrayAny @@ -18,6 +22,8 @@ @pytest.fixture( params=[ LogisticRegression(), + KNeighborsClassifier(), + RadiusNeighborsClassifier(), ], ids=lambda clf: type(clf).__name__, ) From c5515928ae9ff6d598f2786b8c7d3ffa29bd53e2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Sat, 5 Nov 2022 01:19:17 +0100 Subject: [PATCH 344/400] Rename _classes to classes_ in DDC and _ArgMax --- skfda/ml/classification/_depth_classifiers.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index 419c73b98..d959b492f 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -147,10 +147,10 @@ def fit(self, X: Input, y: NDArrayInt) -> DDClassifier[Input]: X, y, [self.depth_method], ) - self._classes = classes + self.classes_ = classes self.class_depth_methods_ = class_depth_methods - if (len(self._classes) != 2): + if (len(self.classes_) != 2): raise ValueError("DDClassifier only accepts two classes.") dd_coordinates = [ @@ -175,7 +175,7 @@ def fit(self, X: Input, y: NDArrayInt) -> DDClassifier[Input]: predicted_values = np.polyval(poly, dd_coordinates[0]) - y_pred = self._classes[( + y_pred = self.classes_[( dd_coordinates[1] > predicted_values ).astype(int) ] @@ -207,7 +207,7 @@ def predict(self, X: Input) -> NDArrayInt: predicted_values = np.polyval(self.poly_, dd_coordinates[0]) - return self._classes[( # type: ignore[no-any-return] + return self.classes_[( # type: ignore[no-any-return] dd_coordinates[1] > predicted_values ).astype(int) ] @@ -487,7 +487,7 @@ def fit(self, X: NDArrayFloat, y: NDArrayInt) -> _ArgMaxClassifier: self """ classes, _ = _classifier_get_classes(y) - self._classes = classes + self.classes_ = classes return self def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> NDArrayInt: @@ -502,7 +502,7 @@ def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> NDArrayInt: """ if isinstance(X, pd.DataFrame): X = X.to_numpy() - return self._classes[ # type: ignore[no-any-return] + return self.classes_[ # type: ignore[no-any-return] np.argmax(X, axis=1) ] From 878c920cd3618e27d8179cce63869f0a462b0a33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Sat, 5 Nov 2022 01:55:00 +0100 Subject: [PATCH 345/400] DDG classes_ after fit --- skfda/ml/classification/_depth_classifiers.py | 1 + 1 file changed, 1 insertion(+) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index d959b492f..73872a888 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -432,6 +432,7 @@ def fit(self, X: Input, y: NDArrayInt) -> DDGClassifier[Input]: ) self._pipeline.fit(X, y) + self.classes_ = _classifier_get_classes(y)[0] return self From b71328e1584928b9411082b0e3296b7ca2f5db3e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Sat, 5 Nov 2022 02:17:36 +0100 Subject: [PATCH 346/400] Rename _classes to classes_ --- skfda/ml/classification/_centroid_classifiers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 9f07c93ac..f86d3402d 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -92,10 +92,10 @@ def fit(self, X: Input, y: NDArrayInt) -> NearestCentroid[Input]: classes, y_ind = _classifier_get_classes(y) - self._classes = classes + self.classes_ = classes self.centroids_ = self.centroid(X[y_ind == 0]) - for cur_class in range(1, self._classes.size): + for cur_class in range(1, self.classes_.size): centroid = self.centroid(X[y_ind == cur_class]) self.centroids_ = self.centroids_.concatenate(centroid) From 481a08645dcee08d8a4002efb40eb38e7fc878ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Sat, 5 Nov 2022 02:20:18 +0100 Subject: [PATCH 347/400] Rename classes to classes_ --- skfda/ml/classification/_qda.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py index a34348de5..8c416827c 100644 --- a/skfda/ml/classification/_qda.py +++ b/skfda/ml/classification/_qda.py @@ -130,7 +130,7 @@ def fit( self """ classes, y_ind = _classifier_get_classes(y) - self.classes = classes + self.classes_ = classes self.y_ind = y_ind self._fit_gaussian_process(X) @@ -198,7 +198,7 @@ def _fit_gaussian_process( cov_estimators = [] means = [] covariance = [] - for class_index, _ in enumerate(self.classes): + for class_index, _ in enumerate(self.classes_): X_class = X[self.y_ind == class_index] cov_estimator = clone(self.cov_estimator).fit(X_class) @@ -235,7 +235,7 @@ def _calculate_log_likelihood(self, X: NDArrayFloat) -> NDArrayFloat: self._regularized_covariances, X_centered, ), - (-1, self.classes.size), + (-1, self.classes_.size), ) return np.asarray( From a29efec138b39b7459c2fafdc8b26d1c81031f9d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 13:21:33 +0100 Subject: [PATCH 348/400] Compare classes_ and predicted result classes --- skfda/tests/test_classifier_classes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index e6372a711..6dacfb399 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -11,7 +11,6 @@ from skfda.datasets import make_gaussian_process from skfda.ml.classification import ( KNeighborsClassifier, - LogisticRegression, RadiusNeighborsClassifier, ) from skfda.representation import FData @@ -21,7 +20,6 @@ @pytest.fixture( params=[ - LogisticRegression(), KNeighborsClassifier(), RadiusNeighborsClassifier(), ], @@ -53,5 +51,7 @@ def test_classes( y = np.resize(classes, n_samples) X = make_gaussian_process(n_samples=n_samples) classifier.fit(X, y) + resulting_classes = np.unique(classifier.predict(X)) np.testing.assert_array_equal(classifier.classes_, classes) + np.testing.assert_array_equal(classifier.classes_, resulting_classes) From a32c4b356c31febb2a7c7b53e277eccd0288c35c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 18:35:31 +0100 Subject: [PATCH 349/400] classes_ rename in predict --- skfda/ml/classification/_centroid_classifiers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index f86d3402d..86fb7972d 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -113,7 +113,7 @@ def predict(self, X: Input) -> NDArrayInt: """ check_is_fitted(self) - return self._classes[ # type: ignore[no-any-return] + return self.classes_[ # type: ignore[no-any-return] PairwiseMetric(self.metric)( X, self.centroids_, From 553cc5593e7f636f56a56c7cd8398c90867fc90e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 19:16:46 +0100 Subject: [PATCH 350/400] Target generic bound to int and str NDArrays --- skfda/ml/classification/_qda.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py index 8c416827c..cb67a581e 100644 --- a/skfda/ml/classification/_qda.py +++ b/skfda/ml/classification/_qda.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Sequence +from typing import Sequence, TypeVar import numpy as np from scipy.linalg import logm @@ -11,12 +11,14 @@ from ..._utils._sklearn_adapter import BaseEstimator, ClassifierMixin from ...exploratory.stats.covariance import CovarianceEstimator from ...representation import FDataGrid -from ...typing._numpy import NDArrayFloat, NDArrayInt +from ...typing._numpy import NDArrayFloat, NDArrayInt, NDArrayStr + +Target = TypeVar("Target", bound=NDArrayInt | NDArrayStr) class QuadraticDiscriminantAnalysis( BaseEstimator, - ClassifierMixin[FDataGrid, NDArrayInt], + ClassifierMixin[FDataGrid, Target], ): """ Functional quadratic discriminant analysis. @@ -117,8 +119,8 @@ def __init__( def fit( self, X: FDataGrid, - y: NDArrayInt, - ) -> QuadraticDiscriminantAnalysis: + y: Target, + ) -> QuadraticDiscriminantAnalysis[Target]: """ Fit the model using X as training data and y as target values. @@ -150,7 +152,7 @@ def fit( return self - def predict(self, X: FDataGrid) -> NDArrayInt: + def predict(self, X: FDataGrid) -> Target: """ Predict the class labels for the provided data. @@ -163,10 +165,11 @@ def predict(self, X: FDataGrid) -> NDArrayInt: """ check_is_fitted(self) - return np.argmax( # type: ignore[no-any-return] - self._calculate_log_likelihood(X.data_matrix), - axis=1, - ) + return self.classes_[ # type: ignore[no-any-return] + np.argmax( + self._calculate_log_likelihood(X.data_matrix), + axis=1, + )] def _calculate_priors(self, y: NDArrayInt) -> NDArrayFloat: """ From cef0b76be6759cea4bfe8ecc1343b0ac9eab8a5f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 19:20:41 +0100 Subject: [PATCH 351/400] Target generic to consider str classes --- skfda/ml/classification/_centroid_classifiers.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 86fb7972d..5c1f20d43 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -13,14 +13,15 @@ from ...misc.metrics._utils import _fit_metric from ...representation import FData from ...typing._metric import Metric -from ...typing._numpy import NDArrayInt +from ...typing._numpy import NDArrayInt, NDArrayStr Input = TypeVar("Input", bound=FData) +Target = TypeVar("Target", bound=NDArrayInt | NDArrayStr) class NearestCentroid( BaseEstimator, - ClassifierMixin[Input, NDArrayInt], + ClassifierMixin[Input, Target], ): """ Nearest centroid classifier for functional data. @@ -76,7 +77,7 @@ def __init__( self.metric = metric self.centroid = centroid - def fit(self, X: Input, y: NDArrayInt) -> NearestCentroid[Input]: + def fit(self, X: Input, y: Target) -> NearestCentroid[Input, Target]: """Fit the model using X as training data and y as target values. Args: @@ -101,7 +102,7 @@ def fit(self, X: Input, y: NDArrayInt) -> NearestCentroid[Input]: return self - def predict(self, X: Input) -> NDArrayInt: + def predict(self, X: Input) -> Target: """Predict the class labels for the provided data. Args: @@ -121,7 +122,7 @@ def predict(self, X: Input) -> NDArrayInt: ] -class DTMClassifier(NearestCentroid[Input]): +class DTMClassifier(NearestCentroid[Input, Target]): """Distance to trimmed means (DTM) classification. Test samples are classified to the class that minimizes the distance of From 420f7e453d7f26745fc7054250d89528ac3026ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 19:26:20 +0100 Subject: [PATCH 352/400] Hotfix: Target type in priors --- skfda/ml/classification/_qda.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py index cb67a581e..ddcdccdb6 100644 --- a/skfda/ml/classification/_qda.py +++ b/skfda/ml/classification/_qda.py @@ -171,7 +171,7 @@ def predict(self, X: FDataGrid) -> Target: axis=1, )] - def _calculate_priors(self, y: NDArrayInt) -> NDArrayFloat: + def _calculate_priors(self, y: Target) -> NDArrayFloat: """ Calculate the prior probability of each class. From e81fb933a261777ef0cc3a7e0f7b5acd621fce33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 19:30:14 +0100 Subject: [PATCH 353/400] Union type fix --- skfda/ml/classification/_centroid_classifiers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/ml/classification/_centroid_classifiers.py b/skfda/ml/classification/_centroid_classifiers.py index 5c1f20d43..dd19c2f1a 100644 --- a/skfda/ml/classification/_centroid_classifiers.py +++ b/skfda/ml/classification/_centroid_classifiers.py @@ -1,7 +1,7 @@ """Centroid-based models for supervised classification.""" from __future__ import annotations -from typing import Callable, TypeVar +from typing import Callable, TypeVar, Union from sklearn.utils.validation import check_is_fitted @@ -16,7 +16,7 @@ from ...typing._numpy import NDArrayInt, NDArrayStr Input = TypeVar("Input", bound=FData) -Target = TypeVar("Target", bound=NDArrayInt | NDArrayStr) +Target = TypeVar("Target", bound=Union[NDArrayInt, NDArrayStr]) class NearestCentroid( From 28f5a24326292ae2730c9e223ed70c31a9eefb3b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 19:34:33 +0100 Subject: [PATCH 354/400] Union type fix --- skfda/ml/classification/_qda.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py index ddcdccdb6..4d4f7ea3c 100644 --- a/skfda/ml/classification/_qda.py +++ b/skfda/ml/classification/_qda.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Sequence, TypeVar +from typing import Sequence, TypeVar, Union import numpy as np from scipy.linalg import logm @@ -13,7 +13,7 @@ from ...representation import FDataGrid from ...typing._numpy import NDArrayFloat, NDArrayInt, NDArrayStr -Target = TypeVar("Target", bound=NDArrayInt | NDArrayStr) +Target = TypeVar("Target", bound=Union[NDArrayInt, NDArrayStr]) class QuadraticDiscriminantAnalysis( From d6229d0ddb7e8f5ce97cfed8a48057405358e6f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 7 Nov 2022 19:50:20 +0100 Subject: [PATCH 355/400] Target type var as union --- skfda/ml/classification/_depth_classifiers.py | 31 +++++++++++-------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/skfda/ml/classification/_depth_classifiers.py b/skfda/ml/classification/_depth_classifiers.py index 73872a888..49179e4c9 100644 --- a/skfda/ml/classification/_depth_classifiers.py +++ b/skfda/ml/classification/_depth_classifiers.py @@ -33,6 +33,7 @@ from ...typing._numpy import NDArrayFloat, NDArrayInt, NDArrayStr Input = TypeVar("Input", bound=FData) +Target = TypeVar("Target", bound=Union[NDArrayInt, NDArrayStr]) def _classifier_get_depth_methods( @@ -50,7 +51,7 @@ def _classifier_get_depth_methods( def _classifier_fit_depth_methods( X: Input, - y: NDArrayInt, + y: NDArrayInt | NDArrayStr, depth_methods: Sequence[Depth[Input]], ) -> Tuple[NDArrayStr | NDArrayInt, Sequence[Depth[Input]]]: classes, y_ind = _classifier_get_classes(y) @@ -64,7 +65,7 @@ def _classifier_fit_depth_methods( class DDClassifier( BaseEstimator, - ClassifierMixin[Input, NDArrayInt], + ClassifierMixin[Input, Target], ): """Depth-versus-depth (DD) classifer for functional data. @@ -130,7 +131,7 @@ def __init__( self.depth_method = depth_method self.degree = degree - def fit(self, X: Input, y: NDArrayInt) -> DDClassifier[Input]: + def fit(self, X: Input, y: Target) -> DDClassifier[Input, Target]: """Fit the model using X as training data and y as target values. Args: @@ -188,7 +189,7 @@ def fit(self, X: Input, y: NDArrayInt) -> DDClassifier[Input]: return self - def predict(self, X: Input) -> NDArrayInt: + def predict(self, X: Input) -> Target: """Predict the class labels for the provided data. Args: @@ -215,7 +216,7 @@ def predict(self, X: Input) -> NDArrayInt: class DDGClassifier( BaseEstimator, - ClassifierMixin[Input, NDArrayInt], + ClassifierMixin[Input, Target], ): r"""Generalized depth-versus-depth (DD) classifer for functional data. @@ -343,7 +344,11 @@ def get_params(self, deep: bool = True) -> Mapping[str, object]: return params # type: ignore[no-any-return] # Copied from scikit-learn's _BaseComposition with minor modifications - def _set_params(self, attr: str, **params: object) -> DDGClassifier[Input]: + def _set_params( + self, + attr: str, + **params: object, + ) -> DDGClassifier[Input, Target]: # Ensure strict ordering of parameter setting: # 1. All steps if attr in params: @@ -398,11 +403,11 @@ def _replace_estimator( def set_params( self, **params: object, - ) -> DDGClassifier[Input]: + ) -> DDGClassifier[Input, Target]: return self._set_params("depth_method", **params) - def fit(self, X: Input, y: NDArrayInt) -> DDGClassifier[Input]: + def fit(self, X: Input, y: Target) -> DDGClassifier[Input, Target]: """Fit the model using X as training data and y as target values. Args: @@ -436,7 +441,7 @@ def fit(self, X: Input, y: NDArrayInt) -> DDGClassifier[Input]: return self - def predict(self, X: Input) -> NDArrayInt: + def predict(self, X: Input) -> Target: """Predict the class labels for the provided data. Args: @@ -451,7 +456,7 @@ def predict(self, X: Input) -> NDArrayInt: class _ArgMaxClassifier( BaseEstimator, - ClassifierMixin[NDArrayFloat, NDArrayInt], + ClassifierMixin[NDArrayFloat, Target], ): r"""Arg max classifier for multivariate data. @@ -477,7 +482,7 @@ class _ArgMaxClassifier( array([1, 0, 0]) """ - def fit(self, X: NDArrayFloat, y: NDArrayInt) -> _ArgMaxClassifier: + def fit(self, X: NDArrayFloat, y: Target) -> _ArgMaxClassifier[Target]: """Fit the model using X as training data and y as target values. Args: @@ -491,7 +496,7 @@ def fit(self, X: NDArrayFloat, y: NDArrayInt) -> _ArgMaxClassifier: self.classes_ = classes return self - def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> NDArrayInt: + def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> Target: """Predict the class labels for the provided data. Args: @@ -508,7 +513,7 @@ def predict(self, X: Union[NDArrayFloat, pd.DataFrame]) -> NDArrayInt: ] -class MaximumDepthClassifier(DDGClassifier[Input]): +class MaximumDepthClassifier(DDGClassifier[Input, Target]): """Maximum depth classifier for functional data. Test samples are classified to the class where they are deeper. From ef6cc33dcad725b7e51895ef09479fc279121a01 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 9 Nov 2022 21:05:32 +0100 Subject: [PATCH 356/400] Simplify RMH. --- skfda/_utils/__init__.py | 4 - skfda/_utils/_utils.py | 56 +-- .../dim_reduction/variable_selection/_base.py | 90 +++++ .../variable_selection/maxima_hunting.py | 14 +- .../dim_reduction/variable_selection/mrmr.py | 2 +- .../recursive_maxima_hunting.py | 361 +++++++----------- 6 files changed, 239 insertions(+), 288 deletions(-) create mode 100644 skfda/preprocessing/dim_reduction/variable_selection/_base.py diff --git a/skfda/_utils/__init__.py b/skfda/_utils/__init__.py index 2e1ed8ccf..387f22b58 100644 --- a/skfda/_utils/__init__.py +++ b/skfda/_utils/__init__.py @@ -13,8 +13,6 @@ "_check_array_key", "_check_estimator", "_classifier_get_classes", - "_compute_dependence", - "_DependenceMeasure", "_evaluate_grid", "_int_to_real", "_MapAcceptable", @@ -39,8 +37,6 @@ _check_array_key as _check_array_key, _check_estimator as _check_estimator, _classifier_get_classes as _classifier_get_classes, - _compute_dependence as _compute_dependence, - _DependenceMeasure as _DependenceMeasure, _evaluate_grid as _evaluate_grid, _int_to_real as _int_to_real, _MapAcceptable as _MapAcceptable, diff --git a/skfda/_utils/_utils.py b/skfda/_utils/_utils.py index 1cfb98707..866de7957 100644 --- a/skfda/_utils/_utils.py +++ b/skfda/_utils/_utils.py @@ -4,6 +4,7 @@ import functools import numbers +from functools import singledispatch from typing import ( TYPE_CHECKING, Any, @@ -604,58 +605,3 @@ def _classifier_get_classes( f'one; got {classes.size} class', ) return classes, y_ind - - -dtype_bound = np.number -dtype_X_T = TypeVar("dtype_X_T", bound="dtype_bound[Any]") -dtype_y_T = TypeVar("dtype_y_T", bound="dtype_bound[Any]") - -depX_T = TypeVar("depX_T", bound=NDArrayAny) -depy_T = TypeVar("depy_T", bound=NDArrayAny) - -_DependenceMeasure = Callable[ - [depX_T, depy_T], - NDArrayFloat, -] - - -def _compute_dependence( - X: np.typing.NDArray[dtype_X_T], - y: np.typing.NDArray[dtype_y_T], - *, - dependence_measure: _DependenceMeasure[ - np.typing.NDArray[dtype_X_T], - np.typing.NDArray[dtype_y_T], - ], -) -> NDArrayFloat: - """ - Compute dependence between points and target. - - Computes the dependence of each point in each trajectory in X with the - corresponding class label in Y. - - """ - from dcor import rowwise - - # Move n_samples to the end - # The shape is now input_shape + n_samples + n_output - X = np.moveaxis(X, 0, -2) - - input_shape = X.shape[:-2] - - # Join input in a list for rowwise - X = X.reshape(-1, X.shape[-2], X.shape[-1]) - - if y.ndim == 1: - y = np.atleast_2d(y).T - Y = np.array([y] * len(X)) - - dependence_results = rowwise( # type: ignore[no-untyped-call] - dependence_measure, - X, - Y, - ) - - return dependence_results.reshape( # type: ignore[no-any-return] - input_shape, - ) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/_base.py b/skfda/preprocessing/dim_reduction/variable_selection/_base.py new file mode 100644 index 000000000..6c4753199 --- /dev/null +++ b/skfda/preprocessing/dim_reduction/variable_selection/_base.py @@ -0,0 +1,90 @@ +from functools import singledispatch +from typing import Any, Callable, TypeVar + +import numpy as np + +from ....representation import FDataGrid +from ....typing._numpy import NDArrayAny, NDArrayFloat + +dtype_bound = np.number +X_T = TypeVar("X_T") +dtype_X_T = TypeVar("dtype_X_T", bound="dtype_bound[Any]") +dtype_y_T = TypeVar("dtype_y_T", bound="dtype_bound[Any]") + +depX_T = TypeVar("depX_T", bound=NDArrayAny) +depy_T = TypeVar("depy_T", bound=NDArrayAny) + +_DependenceMeasure = Callable[ + [depX_T, depy_T], + NDArrayFloat, +] + + +@singledispatch +def _compute_dependence( + X: X_T, + y: np.typing.NDArray[dtype_y_T], + *, + dependence_measure: _DependenceMeasure[ + np.typing.NDArray[dtype_X_T], + np.typing.NDArray[dtype_y_T], + ], +) -> X_T: + """ + Compute dependence between points and target. + + Computes the dependence of each point in each trajectory in X with the + corresponding class label in Y. + + """ + from dcor import rowwise + + assert isinstance(X, np.ndarray) + X_ndarray = X + + # Shape without number of samples and codomain dimension + input_shape = X_ndarray.shape[1:-1] + + # Move n_samples to the end + # The shape is now input_shape + n_samples + n_output + X_ndarray = np.moveaxis(X_ndarray, 0, -2) + + # Join input in a list for rowwise + X_ndarray = X_ndarray.reshape(-1, X_ndarray.shape[-2], X_ndarray.shape[-1]) + + if y.ndim == 1: + y = np.atleast_2d(y).T + + Y = np.array([y] * len(X_ndarray)) + + dependence_results = rowwise( # type: ignore[no-untyped-call] + dependence_measure, + X_ndarray, + Y, + ) + + return dependence_results.reshape( # type: ignore[no-any-return] + input_shape, + ) + + +@_compute_dependence.register +def _compute_dependence_fdatagrid( + X: FDataGrid, + y: np.typing.NDArray[dtype_y_T], + *, + dependence_measure: _DependenceMeasure[ + np.typing.NDArray[dtype_X_T], + np.typing.NDArray[dtype_y_T], + ], +) -> FDataGrid: + + return X.copy( + data_matrix=_compute_dependence( + X.data_matrix, + y, + dependence_measure=dependence_measure, + ), + coordinate_names=("relevance",), + sample_names=("relevance function",), + ) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py index 66d1b4ce4..d7592d7bc 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/maxima_hunting.py @@ -10,13 +10,13 @@ from dcor import u_distance_correlation_sqr -from ...._utils import _compute_dependence, _DependenceMeasure from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, ) from ....representation import FDataGrid from ....typing._numpy import NDArrayFloat, NDArrayInt, NDArrayReal +from ._base import _compute_dependence, _DependenceMeasure _LocalMaximaSelector = Callable[[FDataGrid], NDArrayInt] @@ -230,14 +230,10 @@ def fit( # type: ignore[override] # noqa: D102 ) self.features_shape_ = X.data_matrix.shape[1:] - self.dependence_ = FDataGrid( - data_matrix=_compute_dependence( - X.data_matrix, - y.astype(np.float_), - dependence_measure=self.dependence_measure, - ), - grid_points=X.grid_points, - domain_range=X.domain_range, + self.dependence_ = _compute_dependence( + X, + y.astype(np.float_), + dependence_measure=self.dependence_measure, ) self.indexes_ = self._maxima_selector( diff --git a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py index 1f8b1d179..dc9265133 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/mrmr.py @@ -21,7 +21,6 @@ ) from typing_extensions import Final, Literal -from ...._utils import _compute_dependence, _DependenceMeasure from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, @@ -29,6 +28,7 @@ from ....representation.grid import FDataGrid from ....typing._base import RandomStateLike from ....typing._numpy import NDArrayFloat, NDArrayInt, NDArrayReal +from ._base import _compute_dependence, _DependenceMeasure _Criterion = Callable[[NDArrayFloat, NDArrayFloat], NDArrayFloat] _CriterionLike = Union[ diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py index ec75f671e..717e56e68 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py @@ -3,12 +3,14 @@ import abc import copy +import math from dataclasses import dataclass from typing import ( TYPE_CHECKING, Any, Callable, Iterable, + List, Mapping, Optional, Sequence, @@ -17,22 +19,22 @@ overload, ) -from typing_extensions import Literal - -import dcor import numpy as np import numpy.linalg as linalg import numpy.ma as ma import scipy.stats import sklearn.utils +from typing_extensions import Literal + +import dcor -from ...._utils import _compute_dependence, _DependenceMeasure as _DepMeasure from ...._utils._sklearn_adapter import ( BaseEstimator, InductiveTransformerMixin, ) -from ....representation import FDataGrid +from ....representation import FDataGrid, concatenate from ....typing._numpy import ArrayLike, NDArrayBool, NDArrayFloat, NDArrayInt +from ._base import _compute_dependence, _DependenceMeasure as _DepMeasure if TYPE_CHECKING: from ....misc.covariances import CovarianceLike @@ -51,16 +53,6 @@ def _transform_to_2d(t: ArrayLike) -> NDArrayFloat: return t -def _execute_kernel( - kernel: CovarianceLike, - t_0: ArrayLike, - t_1: ArrayLike, -) -> NDArrayFloat: - from ....misc.covariances import _execute_covariance - - return _execute_covariance(kernel, t_0, t_1) - - class _PicklableKernel(): """Class used to pickle GPy kernels.""" @@ -99,12 +91,12 @@ def make_kernel(k: CovarianceLike) -> CovarianceLike: if isinstance(k, GPy.kern.Kern): return _PicklableKernel(k) - else: - return k + + return k def _absolute_argmax( - function: NDArrayFloat, + function: FDataGrid, *, mask: NDArrayBool, ) -> Tuple[int, ...]: @@ -123,13 +115,13 @@ def _absolute_argmax( """ masked_function = ma.array( # type: ignore[no-untyped-call] - function, + function.data_matrix, mask=mask, ) t_max = ma.argmax(masked_function) - return np.unravel_index(t_max, function.shape) + return np.unravel_index(t_max, function.data_matrix.shape[1:-1]) class Correction(BaseEstimator): @@ -141,7 +133,7 @@ class Correction(BaseEstimator): """ - def begin(self, X: FDataGrid, Y: NDArrayFloat) -> None: + def begin(self, X: FDataGrid, y: NDArrayFloat) -> None: """ Initialize the correction for a run. @@ -171,7 +163,7 @@ def correct( self, X: FDataGrid, selected_index: Tuple[int, ...], - ) -> None: + ) -> FDataGrid: """ Correct the trajectories. @@ -186,8 +178,8 @@ def correct( """ pass - def __call__(self, *args: Any, **kwargs: Any) -> None: - self.correct(*args, **kwargs) + def __call__(self, *args: Any, **kwargs: Any) -> FDataGrid: + return self.correct(*args, **kwargs) class ConditionalMeanCorrection(Correction): @@ -206,7 +198,7 @@ def conditional_mean( self, X: FDataGrid, selected_index: Tuple[int, ...], - ) -> NDArrayFloat: + ) -> FDataGrid: """ Mean of the process conditioned to the value observed. @@ -222,14 +214,12 @@ def correct( self, X: FDataGrid, selected_index: Tuple[int, ...], - ) -> None: + ) -> FDataGrid: - X.data_matrix[...] -= self.conditional_mean( + return X - self.conditional_mean( X, selected_index, - ).T - - X.data_matrix[:, selected_index] = 0 + ) class GaussianCorrection(ConditionalMeanCorrection): @@ -311,9 +301,11 @@ def _evaluate_cov( t_0: NDArrayFloat, t_1: NDArrayFloat, ) -> NDArrayFloat: + from ....misc.covariances import _execute_covariance + cov = getattr(self, "cov_", self.cov) - return _execute_kernel(cov, t_0, t_1) + return _execute_covariance(cov, t_0, t_1) def conditioned( self, @@ -331,7 +323,8 @@ def conditioned( correction = GaussianConditionedCorrection( mean=self.mean, cov=cov, - conditioning_points=np.asarray(t_0)) + conditioning_points=np.asarray(t_0), + ) correction._covariance_matrix_inv() @@ -345,7 +338,7 @@ def conditional_mean( self, X: FDataGrid, selected_index: Tuple[int, ...], - ) -> NDArrayFloat: + ) -> FDataGrid: T = X.grid_points[0] @@ -372,7 +365,10 @@ def conditional_mean( * (x_0.T - t_0_expectation) ) if var else expectation + np.zeros_like(x_0.T) - return cond_expectation # type: ignore[no-any-return] + return X.copy( + data_matrix=cond_expectation.T, + sample_names=None, + ) class GaussianConditionedCorrection(GaussianCorrection): @@ -508,16 +504,16 @@ class GaussianSampleCorrection(ConditionalMeanCorrection): """ - def begin(self, X: FDataGrid, Y: NDArrayFloat) -> None: + def begin(self, X: FDataGrid, y: NDArrayFloat) -> None: X_copy = np.copy(X.data_matrix[..., 0]) - Y = np.ravel(Y) - for class_label in np.unique(Y): - trajectories = X_copy[Y == class_label, :] + y = np.ravel(y) + for class_label in np.unique(y): + trajectories = X_copy[y == class_label, :] mean = np.mean(trajectories, axis=0) - X_copy[Y == class_label, :] -= mean + X_copy[y == class_label, :] -= mean self.cov_matrix_ = np.cov(X_copy, rowvar=False) self.t_ = np.ravel(X.grid_points) @@ -558,7 +554,7 @@ def conditional_mean( self, X: FDataGrid, selected_index: Tuple[int, ...], - ) -> NDArrayFloat: + ) -> FDataGrid: return self.gaussian_correction_.conditional_mean( X, @@ -594,14 +590,11 @@ def correct( self, X: FDataGrid, selected_index: Tuple[int, ...], - ) -> None: - x_index = (slice(None),) + tuple(selected_index) + (np.newaxis,) - - # Have to copy it because otherwise is a view and shouldn't be - # subtracted from the original matrix - x_0 = np.copy(X.data_matrix[x_index]) + ) -> FDataGrid: + x_index = (slice(None),) + selected_index + x_0 = X.data_matrix[x_index] - X.data_matrix[...] -= x_0 + return X - x_0 class StoppingCondition(BaseEstimator): @@ -618,7 +611,7 @@ def __call__( self, *, selected_index: Tuple[int, ...], - dependences: NDArrayFloat, + dependences: FDataGrid, selected_variable: NDArrayFloat, X: FDataGrid, y: NDArrayFloat, @@ -654,13 +647,13 @@ def __call__( self, *, selected_index: Tuple[int, ...], - dependences: NDArrayFloat, + dependences: FDataGrid, **kwargs: Any, ) -> bool: - score = dependences[selected_index] + score = float(dependences.data_matrix[0, selected_index, 0]) - return score < self.threshold # type: ignore[no-any-return] + return score < self.threshold class AsymptoticIndependenceTestStop(StoppingCondition): @@ -789,27 +782,13 @@ def __call__( ) -@dataclass -class RMHResult(object): - index: Tuple[int, ...] - score: float - matrix_after_correction: NDArrayFloat - original_dependence: NDArrayFloat - influence_mask: NDArrayBool - current_mask: NDArrayBool - - def _get_influence_mask( X: NDArrayFloat, t_max_index: Tuple[int, ...], redundancy_condition: RedundancyCondition, old_mask: NDArrayBool, ) -> NDArrayBool: - """ - Get the mask of the points that have a large dependence with the - selected point. - - """ + """Get the mask of points that have a large dependence with another.""" sl = slice(None) def get_index( @@ -867,135 +846,6 @@ def update_mask( return new_mask -def _rec_maxima_hunting_gen_no_copy( - X: FDataGrid, - y: Union[NDArrayInt, NDArrayFloat], - *, - dependence_measure: _DepMeasure[ - NDArrayFloat, - NDArrayFloat, - ] = dcor.u_distance_correlation_sqr, - correction: Optional[Correction] = None, - redundancy_condition: Optional[RedundancyCondition] = None, - stopping_condition: Optional[StoppingCondition] = None, - mask: Optional[NDArrayBool] = None, -) -> Iterable[RMHResult]: - """ - Recursive maxima hunting algorithm. - - Find the most relevant features of a function using recursive maxima - hunting. It changes the original matrix. - - Parameters: - dependence_measure: Dependence measure to use. By default, - it uses the bias corrected squared distance correlation. - correction: Correction used to subtract the information - of each selected point in each iteration. - redundancy_condition: Condition to consider a point - redundant with the selected maxima and discard it from future - consideration as a maximum. - stopping_condition: Condition to stop the algorithm. - mask: Masked values. - - """ - y = np.asfarray(y) - - if correction is None: - correction = UniformCorrection() - - if mask is None: - mask = np.zeros([len(t) for t in X.grid_points], dtype=bool) - - if redundancy_condition is None: - redundancy_condition = DependenceThresholdRedundancy() - - if stopping_condition is None: - stopping_condition = AsymptoticIndependenceTestStop() - - first_pass = True - - correction.begin(X, y) - - while True: - dependences = _compute_dependence( - X=X.data_matrix, - y=y, - dependence_measure=dependence_measure, - ) - - t_max_index = _absolute_argmax( - dependences, - mask=mask, - ) - score = dependences[t_max_index] - - repeated_point = mask[t_max_index] - - stopping_condition_reached = stopping_condition( - selected_index=t_max_index, - dependences=dependences, - selected_variable=X.data_matrix[ - (slice(None),) + tuple(t_max_index) - ], - X=X, - y=y, - ) - - if ( - (repeated_point or stopping_condition_reached) - and not first_pass - ): - return - - influence_mask = _get_influence_mask( - X=X.data_matrix, - t_max_index=t_max_index, - redundancy_condition=redundancy_condition, - old_mask=mask, - ) - - mask |= influence_mask - - # Correct the influence of t_max - correction( - X=X, - selected_index=t_max_index, - ) - result = RMHResult( - index=t_max_index, - score=score, - # Additional info, useful for debugging - matrix_after_correction=np.copy(X.data_matrix), - original_dependence=dependences, - influence_mask=influence_mask, - current_mask=mask, - ) - - new_X = yield result # Accept modifications to the matrix - if new_X is not None: - X.data_matrix = new_X - - correction = correction.conditioned( - X=X.data_matrix, - T=X.grid_points[0], - t_0=X.grid_points[0][t_max_index], - ) - - first_pass = False - - -def _rec_maxima_hunting_gen( - X: FDataGrid, - *args: Any, - **kwargs: Any, -) -> Iterable[RMHResult]: - yield from _rec_maxima_hunting_gen_no_copy( - copy.copy(X), - *args, - **kwargs, - ) - - class RecursiveMaximaHunting( BaseEstimator, InductiveTransformerMixin[ @@ -1109,47 +959,120 @@ def __init__( correction: Optional[Correction] = None, redundancy_condition: Optional[RedundancyCondition] = None, stopping_condition: Optional[StoppingCondition] = None, + _get_intermediate_results: bool = False, ) -> None: self.dependence_measure = dependence_measure self.max_features = max_features self.correction = correction self.redundancy_condition = redundancy_condition self.stopping_condition = stopping_condition + self._get_intermediate_results = _get_intermediate_results def fit( # type: ignore[override] # noqa: D102 self, X: FDataGrid, y: Union[NDArrayInt, NDArrayFloat], ) -> RecursiveMaximaHunting: - + """Recursive maxima hunting algorithm.""" self.features_shape_ = X.data_matrix.shape[1:] - indexes = [] + y = np.asfarray(y) + + correction = ( + self.correction + if self.correction + else UniformCorrection() + ) + + redundancy_condition = ( + self.redundancy_condition + if self.redundancy_condition + else DependenceThresholdRedundancy() + ) + + stopping_condition = ( + self.stopping_condition + if self.stopping_condition + else AsymptoticIndependenceTestStop() + ) + + max_features = ( + self.max_features + if self.max_features + else math.inf + ) + + mask = np.zeros([len(t) for t in X.grid_points], dtype=bool) + indexes: List[Tuple[int, ...]] = [] + corrected_functions = [] relevances = [] - for i, result in enumerate( - _rec_maxima_hunting_gen( - X=X.copy(), - y=y, + first_pass = True + + correction.begin(X, y) + + while True: + dependences = _compute_dependence( + X, + y, dependence_measure=self.dependence_measure, - correction=self.correction, - redundancy_condition=self.redundancy_condition, - stopping_condition=self.stopping_condition, - ), - ): - - indexes.append(result.index) - relevances.append(result.original_dependence) - - if self.max_features is not None and i + 1 >= self.max_features: - break - - self.indexes_ = tuple(np.transpose(indexes).tolist()) - self._relevances = FDataGrid( - data_matrix=np.vstack(relevances), - grid_points=X.grid_points, - ) + ) + corrected_functions.append(X) + relevances.append(dependences) - return self + t_max_index = _absolute_argmax( + dependences, + mask=mask, + ) + + repeated_point = mask[t_max_index] + + stopping_condition_reached = stopping_condition( + selected_index=t_max_index, + dependences=dependences, + selected_variable=X.data_matrix[ + (slice(None),) + tuple(t_max_index) + ], + X=X, + y=y, + ) + + if ( + ( + len(indexes) >= max_features + or repeated_point + or stopping_condition_reached + ) + and not first_pass + ): + self.indexes_ = tuple(np.transpose(indexes).tolist()) + self._relevances = concatenate(relevances) + self._corrected_functions = corrected_functions + return self + + indexes.append(t_max_index) + + influence_mask = _get_influence_mask( + X=X.data_matrix, + t_max_index=t_max_index, + redundancy_condition=redundancy_condition, + old_mask=mask, + ) + + mask |= influence_mask + + # Correct the influence of t_max + X = correction( + X=X, + selected_index=t_max_index, + ) + + correction = correction.conditioned( + X=X.data_matrix, + T=X.grid_points[0], + t_0=X.grid_points[0][t_max_index], + ) + + first_pass = False def transform(self, X: FDataGrid) -> NDArrayFloat: From 3589f88f949146419869763d8b1340e87a2d210d Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 10 Nov 2022 09:46:23 +0100 Subject: [PATCH 357/400] Small changes in RMH. --- .../variable_selection/recursive_maxima_hunting.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py index 717e56e68..7f1be322d 100644 --- a/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py +++ b/skfda/preprocessing/dim_reduction/variable_selection/recursive_maxima_hunting.py @@ -4,7 +4,6 @@ import abc import copy import math -from dataclasses import dataclass from typing import ( TYPE_CHECKING, Any, @@ -1114,7 +1113,7 @@ def get_support( if indices: return self.indexes_ - else: - mask = np.zeros(self.features_shape_[0], dtype=bool) - mask[self.indexes_] = True - return mask + + mask = np.zeros(self.features_shape_[0], dtype=bool) + mask[self.indexes_] = True + return mask From 026a23954a9dace5fca98b437520c1718c131068 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 14 Nov 2022 11:46:37 +0100 Subject: [PATCH 358/400] Blank line after docstring fix --- skfda/ml/classification/_qda.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_qda.py b/skfda/ml/classification/_qda.py index 4d4f7ea3c..628ebdbc1 100644 --- a/skfda/ml/classification/_qda.py +++ b/skfda/ml/classification/_qda.py @@ -103,8 +103,8 @@ class QuadraticDiscriminantAnalysis( >>> round(qda.score(X_test, y_test), 2) 0.96 - """ + means_: Sequence[FDataGrid] def __init__( From edf9436d0af09e6db5af835c5b11b3a99b3f3111 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 14 Nov 2022 12:06:32 +0100 Subject: [PATCH 359/400] Remove randomness from test --- skfda/tests/test_classifier_classes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index 6dacfb399..0acf422a9 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -49,7 +49,7 @@ def test_classes( """Test classes attribute of classifiers.""" n_samples = 30 y = np.resize(classes, n_samples) - X = make_gaussian_process(n_samples=n_samples) + X = make_gaussian_process(n_samples=n_samples, random_state=0) classifier.fit(X, y) resulting_classes = np.unique(classifier.predict(X)) From 6c4b7f6992e9c94c3130a9905f847d843481cea4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Mon, 14 Nov 2022 12:33:26 +0100 Subject: [PATCH 360/400] Final fit using y instead of y_ind --- skfda/ml/classification/_logistic_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 34fe3296c..b99588bf7 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -171,7 +171,7 @@ def fit( # noqa: D102 selected_values[:, n_selected] = X.data_matrix[:, tmax, 0] # fit for the complete set of points - mvlr.fit(selected_values, y_ind) + mvlr.fit(selected_values, y) self.coef_ = mvlr.coef_ self.intercept_ = mvlr.intercept_ From 490821898a1856351fb171a48594796c80f7529b Mon Sep 17 00:00:00 2001 From: Ddelval Date: Mon, 14 Nov 2022 23:38:39 +0100 Subject: [PATCH 361/400] Move old classes to the same files as the new ones --- skfda/representation/basis/__init__.py | 60 +++++----- skfda/representation/basis/_bspline.py | 107 ------------------ skfda/representation/basis/_bspline_basis.py | 101 +++++++++++++++++ skfda/representation/basis/_constant.py | 35 ------ skfda/representation/basis/_constant_basis.py | 31 +++++ skfda/representation/basis/_finite_element.py | 84 -------------- .../basis/_finite_element_basis.py | 79 +++++++++++++ skfda/representation/basis/_fourier.py | 93 --------------- skfda/representation/basis/_fourier_basis.py | 87 ++++++++++++++ skfda/representation/basis/_monomial.py | 84 -------------- skfda/representation/basis/_monomial_basis.py | 83 +++++++++++++- skfda/representation/basis/_tensor.py | 73 ------------ skfda/representation/basis/_tensor_basis.py | 68 +++++++++++ skfda/representation/basis/_vector.py | 75 ------------ skfda/representation/basis/_vector_basis.py | 69 +++++++++++ 15 files changed, 549 insertions(+), 580 deletions(-) delete mode 100644 skfda/representation/basis/_bspline.py delete mode 100644 skfda/representation/basis/_constant.py delete mode 100644 skfda/representation/basis/_finite_element.py delete mode 100644 skfda/representation/basis/_fourier.py delete mode 100644 skfda/representation/basis/_monomial.py delete mode 100644 skfda/representation/basis/_tensor.py delete mode 100644 skfda/representation/basis/_vector.py diff --git a/skfda/representation/basis/__init__.py b/skfda/representation/basis/__init__.py index 9d939ea48..b0ae295a5 100644 --- a/skfda/representation/basis/__init__.py +++ b/skfda/representation/basis/__init__.py @@ -7,41 +7,45 @@ __name__, submod_attrs={ '_basis': ["Basis"], - "_bspline_basis": ["BSplineBasis"], - "_bspline": ["BSpline"], - "_constant_basis": ["ConstantBasis"], - "_constant": ["Constant"], + "_bspline_basis": ["BSplineBasis", "BSpline"], + "_constant_basis": ["ConstantBasis", "Constant"], "_fdatabasis": ["FDataBasis", "FDataBasisDType"], - "_finite_element_basis": ["FiniteElementBasis"], - "_finite_element": ["FiniteElement"], - "_fourier_basis": ["FourierBasis"], - "_fourier": ["Fourier"], - "_monomial_basis": ["MonomialBasis"], - "_monomial": ["Monomial"], - "_tensor_basis": ["TensorBasis"], - "_tensor": ["Tensor"], - "_vector_basis": ["VectorValuedBasis"], - "_vector": ["VectorValued"], + "_finite_element_basis": ["FiniteElementBasis", "FiniteElement"], + "_fourier_basis": ["FourierBasis", "Fourier"], + "_monomial_basis": ["MonomialBasis", "Monomial"], + "_tensor_basis": ["TensorBasis", "Tensor"], + "_vector_basis": ["VectorValuedBasis", "VectorValued"], }, ) if TYPE_CHECKING: from ._basis import Basis as Basis - from ._bspline import BSpline as BSpline - from ._bspline_basis import BSplineBasis as BSplineBasis - from ._constant import Constant as Constant - from ._constant_basis import ConstantBasis as ConstantBasis + from ._bspline_basis import ( + BSpline as BSpline, + BSplineBasis as BSplineBasis, + ) + from ._constant_basis import ( + Constant as Constant, + ConstantBasis as ConstantBasis, + ) from ._fdatabasis import ( FDataBasis as FDataBasis, FDataBasisDType as FDataBasisDType, ) - from ._finite_element import FiniteElement as FiniteElement - from ._finite_element_basis import FiniteElementBasis as FiniteElementBasis - from ._fourier import Fourier as Fourier - from ._fourier_basis import FourierBasis as FourierBasis - from ._monomial import Monomial as Monomial - from ._monomial_basis import MonomialBasis as MonomialBasis - from ._tensor import Tensor as Tensor - from ._tensor_basis import TensorBasis as TensorBasis - from ._vector import VectorValued as VectorValued - from ._vector_basis import VectorValuedBasis as VectorValuedBasis + from ._finite_element_basis import ( + FiniteElement as FiniteElement, + FiniteElementBasis as FiniteElementBasis, + ) + from ._fourier_basis import ( + Fourier as Fourier, + FourierBasis as FourierBasis, + ) + from ._monomial_basis import ( + Monomial as Monomial, + MonomialBasis as MonomialBasis, + ) + from ._tensor_basis import Tensor as Tensor, TensorBasis as TensorBasis + from ._vector_basis import ( + VectorValued as VectorValued, + VectorValuedBasis as VectorValuedBasis, + ) diff --git a/skfda/representation/basis/_bspline.py b/skfda/representation/basis/_bspline.py deleted file mode 100644 index 77dfc6a86..000000000 --- a/skfda/representation/basis/_bspline.py +++ /dev/null @@ -1,107 +0,0 @@ -from __future__ import annotations - -import warnings -from typing import Sequence - -from ...typing._base import DomainRangeLike -from ._bspline_basis import BSplineBasis - - -class BSpline(BSplineBasis): - r"""BSpline basis. - - BSpline basis elements are defined recursively as: - - .. math:: - B_{i, 1}(x) = 1 \quad \text{if } t_i \le x < t_{i+1}, - \quad 0 \text{ otherwise} - - .. math:: - B_{i, k}(x) = \frac{x - t_i}{t_{i+k} - t_i} B_{i, k-1}(x) - + \frac{t_{i+k+1} - x}{t_{i+k+1} - t_{i+1}} B_{i+1, k-1}(x) - - Where k indicates the order of the spline. - - .. deprecated:: 0.8 - Use BSplineBasis instead. - - Implementation details: In order to allow a discontinuous behaviour at - the boundaries of the domain it is necessary to placing m knots at the - boundaries [RS05]_. This is automatically done so that the user only has to - specify a single knot at the boundaries. - - - Parameters: - domain_range: A tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis: Number of functions in the basis. - order: Order of the splines. One greather than their degree. - knots: List of knots of the spline functions. - - Examples: - Constructs specifying number of basis and order. - - >>> bss = BSpline(n_basis=8, order=4) - - If no order is specified defaults to 4 because cubic splines are - the most used. So the previous example is the same as: - - >>> bss = BSplineBasis(n_basis=8) - - It is also possible to create a BSpline basis specifying the knots. - - >>> bss = BSplineBasis(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) - - Once we create a basis we can evaluate each of its functions at a - set of points. - - >>> bss = BSplineBasis(n_basis=3, order=3) - >>> bss([0, 0.5, 1]) - array([[[ 1. ], - [ 0.25], - [ 0. ]], - [[ 0. ], - [ 0.5 ], - [ 0. ]], - [[ 0. ], - [ 0.25], - [ 1. ]]]) - - And evaluates first derivative - - >>> deriv = bss.derivative() - >>> deriv([0, 0.5, 1]) - array([[[-2.], - [-1.], - [ 0.]], - [[ 2.], - [ 0.], - [-2.]], - [[ 0.], - [ 1.], - [ 2.]]]) - - References: - .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data - Analysis*. Springer. 50-51. - - """ - - def __init__( # noqa: WPS238 - self, - domain_range: DomainRangeLike | None = None, - n_basis: int | None = None, - order: int = 4, - knots: Sequence[float] | None = None, - ) -> None: - super().__init__( - domain_range=domain_range, - n_basis=n_basis, - order=order, - knots=knots, - ) - warnings.warn( - "The BSplines class is deprecated. Use " - "BSplineBasis instead.", - DeprecationWarning, - ) diff --git a/skfda/representation/basis/_bspline_basis.py b/skfda/representation/basis/_bspline_basis.py index 31c74d64b..6196083fc 100644 --- a/skfda/representation/basis/_bspline_basis.py +++ b/skfda/representation/basis/_bspline_basis.py @@ -1,5 +1,6 @@ from __future__ import annotations +import warnings from typing import Any, Sequence, Tuple, Type, TypeVar import numpy as np @@ -363,3 +364,103 @@ def __eq__(self, other: Any) -> bool: def __hash__(self) -> int: return hash((super().__hash__(), self.order, self.knots)) + + +class BSpline(BSplineBasis): + r"""BSpline basis. + + BSpline basis elements are defined recursively as: + + .. math:: + B_{i, 1}(x) = 1 \quad \text{if } t_i \le x < t_{i+1}, + \quad 0 \text{ otherwise} + + .. math:: + B_{i, k}(x) = \frac{x - t_i}{t_{i+k} - t_i} B_{i, k-1}(x) + + \frac{t_{i+k+1} - x}{t_{i+k+1} - t_{i+1}} B_{i+1, k-1}(x) + + Where k indicates the order of the spline. + + .. deprecated:: 0.8 + Use BSplineBasis instead. + + Implementation details: In order to allow a discontinuous behaviour at + the boundaries of the domain it is necessary to placing m knots at the + boundaries [RS05]_. This is automatically done so that the user only has to + specify a single knot at the boundaries. + + + Parameters: + domain_range: A tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis: Number of functions in the basis. + order: Order of the splines. One greather than their degree. + knots: List of knots of the spline functions. + + Examples: + Constructs specifying number of basis and order. + + >>> bss = BSpline(n_basis=8, order=4) + + If no order is specified defaults to 4 because cubic splines are + the most used. So the previous example is the same as: + + >>> bss = BSpline(n_basis=8) + + It is also possible to create a BSpline basis specifying the knots. + + >>> bss = BSpline(knots=[0, 0.2, 0.4, 0.6, 0.8, 1]) + + Once we create a basis we can evaluate each of its functions at a + set of points. + + >>> bss = BSpline(n_basis=3, order=3) + >>> bss([0, 0.5, 1]) + array([[[ 1. ], + [ 0.25], + [ 0. ]], + [[ 0. ], + [ 0.5 ], + [ 0. ]], + [[ 0. ], + [ 0.25], + [ 1. ]]]) + + And evaluates first derivative + + >>> deriv = bss.derivative() + >>> deriv([0, 0.5, 1]) + array([[[-2.], + [-1.], + [ 0.]], + [[ 2.], + [ 0.], + [-2.]], + [[ 0.], + [ 1.], + [ 2.]]]) + + References: + .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data + Analysis*. Springer. 50-51. + + """ + + def __init__( # noqa: WPS238 + self, + domain_range: DomainRangeLike | None = None, + n_basis: int | None = None, + order: int = 4, + knots: Sequence[float] | None = None, + ) -> None: + super().__init__( + domain_range=domain_range, + n_basis=n_basis, + order=order, + knots=knots, + ) + warnings.warn( + "The BSplines class is deprecated. Use " + "BSplineBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_constant.py b/skfda/representation/basis/_constant.py deleted file mode 100644 index 6ce210913..000000000 --- a/skfda/representation/basis/_constant.py +++ /dev/null @@ -1,35 +0,0 @@ -import warnings -from typing import Optional - -from ...typing._base import DomainRangeLike -from ._constant_basis import ConstantBasis - - -class Constant(ConstantBasis): - """Constant basis. - - Basis for constant functions - - .. deprecated:: 0.8 - Use :class:`~skfda.representation.basis.ConstantBasis` instead. - - Parameters: - domain_range: The :term:`domain range` over which the basis can be - evaluated. - - Examples: - Defines a contant base over the interval :math:`[0, 5]` consisting - on the constant function 1 on :math:`[0, 5]`. - - >>> bs_cons = ConstantBasis((0,5)) - - """ - - def __init__(self, domain_range: Optional[DomainRangeLike] = None) -> None: - """Constant basis constructor.""" - warnings.warn( - "The Constant class is deprecated. Use " - "ConstantBasis instead.", - DeprecationWarning, - ) - super().__init__(domain_range=domain_range) diff --git a/skfda/representation/basis/_constant_basis.py b/skfda/representation/basis/_constant_basis.py index 9b07550f1..c4914e598 100644 --- a/skfda/representation/basis/_constant_basis.py +++ b/skfda/representation/basis/_constant_basis.py @@ -1,3 +1,4 @@ +import warnings from typing import Optional, Tuple, TypeVar import numpy as np @@ -52,3 +53,33 @@ def _to_R(self) -> str: # noqa: N802 drange = self.domain_range[0] drange_str = f"c({str(drange[0])}, {str(drange[1])})" return f"create.constant.basis(rangeval = {drange_str})" + + +class Constant(ConstantBasis): + """Constant basis. + + Basis for constant functions + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.ConstantBasis` instead. + + Parameters: + domain_range: The :term:`domain range` over which the basis can be + evaluated. + + Examples: + Defines a contant base over the interval :math:`[0, 5]` consisting + on the constant function 1 on :math:`[0, 5]`. + + >>> bs_cons = Constant((0,5)) + + """ + + def __init__(self, domain_range: Optional[DomainRangeLike] = None) -> None: + """Constant basis constructor.""" + warnings.warn( + "The Constant class is deprecated. Use " + "ConstantBasis instead.", + DeprecationWarning, + ) + super().__init__(domain_range=domain_range) diff --git a/skfda/representation/basis/_finite_element.py b/skfda/representation/basis/_finite_element.py deleted file mode 100644 index 03f7abb3f..000000000 --- a/skfda/representation/basis/_finite_element.py +++ /dev/null @@ -1,84 +0,0 @@ -import warnings -from typing import Optional - -from ...typing._base import DomainRangeLike -from ...typing._numpy import ArrayLike -from ._finite_element_basis import FiniteElementBasis - - -class FiniteElement(FiniteElementBasis): - """Finite element basis. - - Given a n-dimensional grid made of simplices, each element of the basis - is a piecewise linear function that takes the value 1 at exactly one - vertex and 0 in the other vertices. - - .. deprecated:: 0.8 - Use :class:`~skfda.representation.basis.FiniteElementBasis` instead. - - Parameters: - vertices: The vertices of the grid. - cells: A list of individual cells, consisting in the indexes of - :math:`n+1` vertices for an n-dimensional domain space. - - Examples: - >>> from skfda.representation.basis import FiniteElementBasis - >>> basis = FiniteElementBasis( - ... vertices=[[0, 0], [0, 1], [1, 0], [1, 1]], - ... cells=[[0, 1, 2], [1, 2, 3]], - ... domain_range=[(0, 1), (0, 1)], - ... ) - - Evaluates all the functions in the basis in a list of discrete - values. - - >>> basis([[0.1, 0.1], [0.6, 0.6], [0.1, 0.2], [0.8, 0.9]]) - array([[[ 0.8], - [ 0. ], - [ 0.7], - [ 0. ]], - [[ 0.1], - [ 0.4], - [ 0.2], - [ 0.2]], - [[ 0.1], - [ 0.4], - [ 0.1], - [ 0.1]], - [[ 0. ], - [ 0.2], - [ 0. ], - [ 0.7]]]) - - - >>> from scipy.spatial import Delaunay - >>> import numpy as np - >>> - >>> n_points = 10 - >>> points = np.random.uniform(size=(n_points, 2)) - >>> delaunay = Delaunay(points) - >>> basis = FiniteElementBasis( - ... vertices=delaunay.points, - ... cells=delaunay.simplices, - ... ) - >>> basis.n_basis - 10 - - """ - - def __init__( - self, - vertices: ArrayLike, - cells: ArrayLike, - domain_range: Optional[DomainRangeLike] = None, - ) -> None: - super().__init__( - vertices=vertices, - cells=cells, - domain_range=domain_range, - ) - warnings.warn( - "The FiniteElement class is deprecated. Use " - "FiniteElementBasis instead.", - DeprecationWarning, - ) diff --git a/skfda/representation/basis/_finite_element_basis.py b/skfda/representation/basis/_finite_element_basis.py index ebd25c765..a48eb0505 100644 --- a/skfda/representation/basis/_finite_element_basis.py +++ b/skfda/representation/basis/_finite_element_basis.py @@ -1,3 +1,4 @@ +import warnings from typing import Optional, TypeVar import numpy as np @@ -151,3 +152,81 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: np.add.at(eval_matrix, indexes, cell_points_values) return eval_matrix + + +class FiniteElement(FiniteElementBasis): + """Finite element basis. + + Given a n-dimensional grid made of simplices, each element of the basis + is a piecewise linear function that takes the value 1 at exactly one + vertex and 0 in the other vertices. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.FiniteElementBasis` instead. + + Parameters: + vertices: The vertices of the grid. + cells: A list of individual cells, consisting in the indexes of + :math:`n+1` vertices for an n-dimensional domain space. + + Examples: + >>> from skfda.representation.basis import FiniteElement + >>> basis = FiniteElement( + ... vertices=[[0, 0], [0, 1], [1, 0], [1, 1]], + ... cells=[[0, 1, 2], [1, 2, 3]], + ... domain_range=[(0, 1), (0, 1)], + ... ) + + Evaluates all the functions in the basis in a list of discrete + values. + + >>> basis([[0.1, 0.1], [0.6, 0.6], [0.1, 0.2], [0.8, 0.9]]) + array([[[ 0.8], + [ 0. ], + [ 0.7], + [ 0. ]], + [[ 0.1], + [ 0.4], + [ 0.2], + [ 0.2]], + [[ 0.1], + [ 0.4], + [ 0.1], + [ 0.1]], + [[ 0. ], + [ 0.2], + [ 0. ], + [ 0.7]]]) + + + >>> from scipy.spatial import Delaunay + >>> import numpy as np + >>> + >>> n_points = 10 + >>> points = np.random.uniform(size=(n_points, 2)) + >>> delaunay = Delaunay(points) + >>> basis = FiniteElement( + ... vertices=delaunay.points, + ... cells=delaunay.simplices, + ... ) + >>> basis.n_basis + 10 + + """ + + def __init__( + self, + vertices: ArrayLike, + cells: ArrayLike, + domain_range: Optional[DomainRangeLike] = None, + ) -> None: + super().__init__( + vertices=vertices, + cells=cells, + domain_range=domain_range, + ) + warnings.warn( + "The FiniteElement class is deprecated. Use " + "FiniteElementBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_fourier.py b/skfda/representation/basis/_fourier.py deleted file mode 100644 index eb11af755..000000000 --- a/skfda/representation/basis/_fourier.py +++ /dev/null @@ -1,93 +0,0 @@ -import warnings -from typing import Optional - -import numpy as np - -from ...typing._base import DomainRangeLike -from ._fourier_basis import FourierBasis - - -class Fourier(FourierBasis): - r"""Fourier basis. - - Defines a functional basis for representing functions on a fourier - series expansion of period :math:`T`. The number of basis is always odd. - If instantiated with an even number of basis, they will be incremented - automatically by one. - - .. math:: - \phi_0(t) = \frac{1}{\sqrt{2}} - - .. math:: - \phi_{2n -1}(t) = \frac{sin\left(\frac{2 \pi n}{T} t\right)} - {\sqrt{\frac{T}{2}}} - - .. math:: - \phi_{2n}(t) = \frac{cos\left(\frac{2 \pi n}{T} t\right)} - {\sqrt{\frac{T}{2}}} - - - This basis will be orthonormal if the period coincides with the length - of the interval in which it is defined. - - .. deprecated:: 0.8 - Use :class:`~skfda.representation.basis.FourierBasis` instead. - - Parameters: - domain_range: A tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis: Number of functions in the basis. - period: Period (:math:`T`). - - Examples: - Constructs specifying number of basis, definition interval and period. - - >>> fb = FourierBasis((0, np.pi), n_basis=3, period=1) - >>> fb([0, np.pi / 4, np.pi / 2, np.pi]).round(2) - array([[[ 1. ], - [ 1. ], - [ 1. ], - [ 1. ]], - [[ 0. ], - [-1.38], - [-0.61], - [ 1.1 ]], - [[ 1.41], - [ 0.31], - [-1.28], - [ 0.89]]]) - - And evaluate second derivative - - >>> deriv2 = fb.derivative(order=2) - >>> deriv2([0, np.pi / 4, np.pi / 2, np.pi]).round(2) - array([[[ 0. ], - [ 0. ], - [ 0. ], - [ 0. ]], - [[ 0. ], - [ 54.46], - [ 24.02], - [-43.37]], - [[-55.83], - [-12.32], - [ 50.4 ], - [-35.16]]]) - - """ - - def __init__( - self, - domain_range: Optional[DomainRangeLike] = None, - n_basis: int = 3, - period: Optional[float] = None, - ) -> None: - warnings.warn( - "The Fourier class is deprecated. Use FourierBasis instead.", - DeprecationWarning, - ) - super().__init__( - domain_range=domain_range, - n_basis=n_basis, - period=period, - ) diff --git a/skfda/representation/basis/_fourier_basis.py b/skfda/representation/basis/_fourier_basis.py index 0372dc45a..798f88326 100644 --- a/skfda/representation/basis/_fourier_basis.py +++ b/skfda/representation/basis/_fourier_basis.py @@ -1,3 +1,4 @@ +import warnings from typing import Any, Optional, Sequence, Tuple, TypeVar import numpy as np @@ -239,3 +240,89 @@ def __eq__(self, other: Any) -> bool: def __hash__(self) -> int: return hash((super().__hash__(), self.period)) + + +class Fourier(FourierBasis): + r"""Fourier basis. + + Defines a functional basis for representing functions on a fourier + series expansion of period :math:`T`. The number of basis is always odd. + If instantiated with an even number of basis, they will be incremented + automatically by one. + + .. math:: + \phi_0(t) = \frac{1}{\sqrt{2}} + + .. math:: + \phi_{2n -1}(t) = \frac{sin\left(\frac{2 \pi n}{T} t\right)} + {\sqrt{\frac{T}{2}}} + + .. math:: + \phi_{2n}(t) = \frac{cos\left(\frac{2 \pi n}{T} t\right)} + {\sqrt{\frac{T}{2}}} + + + This basis will be orthonormal if the period coincides with the length + of the interval in which it is defined. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.FourierBasis` instead. + + Parameters: + domain_range: A tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis: Number of functions in the basis. + period: Period (:math:`T`). + + Examples: + Constructs specifying number of basis, definition interval and period. + + >>> fb = Fourier((0, np.pi), n_basis=3, period=1) + >>> fb([0, np.pi / 4, np.pi / 2, np.pi]).round(2) + array([[[ 1. ], + [ 1. ], + [ 1. ], + [ 1. ]], + [[ 0. ], + [-1.38], + [-0.61], + [ 1.1 ]], + [[ 1.41], + [ 0.31], + [-1.28], + [ 0.89]]]) + + And evaluate second derivative + + >>> deriv2 = fb.derivative(order=2) + >>> deriv2([0, np.pi / 4, np.pi / 2, np.pi]).round(2) + array([[[ 0. ], + [ 0. ], + [ 0. ], + [ 0. ]], + [[ 0. ], + [ 54.46], + [ 24.02], + [-43.37]], + [[-55.83], + [-12.32], + [ 50.4 ], + [-35.16]]]) + + """ + + def __init__( + self, + domain_range: Optional[DomainRangeLike] = None, + n_basis: int = 3, + period: Optional[float] = None, + ) -> None: + warnings.warn( + "The Fourier class is deprecated. Use FourierBasis instead.", + DeprecationWarning, + ) + super().__init__( + domain_range=domain_range, + n_basis=n_basis, + period=period, + ) diff --git a/skfda/representation/basis/_monomial.py b/skfda/representation/basis/_monomial.py deleted file mode 100644 index 00caf0f9c..000000000 --- a/skfda/representation/basis/_monomial.py +++ /dev/null @@ -1,84 +0,0 @@ -import warnings -from typing import Optional - -from ...typing._base import DomainRangeLike -from ._monomial_basis import MonomialBasis - - -class Monomial(MonomialBasis): - """Monomial basis. - - Basis formed by powers of the argument :math:`t`: - - .. math:: - 1, t, t^2, t^3... - - .. deprecated:: 0.8 - Use :class:`~skfda.representation.basis.MonomialBasis` instead. - - Attributes: - domain_range: a tuple of length 2 containing the initial and - end values of the interval over which the basis can be evaluated. - n_basis: number of functions in the basis. - - Examples: - Defines a monomial base over the interval :math:`[0, 5]` consisting - on the first 3 powers of :math:`t`: :math:`1, t, t^2`. - - >>> bs_mon = MonomialBasis(domain_range=(0,5), n_basis=3) - - And evaluates all the functions in the basis in a list of descrete - values. - - >>> bs_mon([0., 1., 2.]) - array([[[ 1.], - [ 1.], - [ 1.]], - [[ 0.], - [ 1.], - [ 2.]], - [[ 0.], - [ 1.], - [ 4.]]]) - - And also evaluates its derivatives - - >>> deriv = bs_mon.derivative() - >>> deriv([0, 1, 2]) - array([[[ 0.], - [ 0.], - [ 0.]], - [[ 1.], - [ 1.], - [ 1.]], - [[ 0.], - [ 2.], - [ 4.]]]) - >>> deriv2 = bs_mon.derivative(order=2) - >>> deriv2([0, 1, 2]) - array([[[ 0.], - [ 0.], - [ 0.]], - [[ 0.], - [ 0.], - [ 0.]], - [[ 2.], - [ 2.], - [ 2.]]]) - """ - - def __init__( - self, - *, - domain_range: Optional[DomainRangeLike] = None, - n_basis: int = 1, - ): - super().__init__( - domain_range=domain_range, - n_basis=n_basis, - ) - warnings.warn( - "The BSplines class is deprecated. Use " - "BSplineBasis instead.", - DeprecationWarning, - ) diff --git a/skfda/representation/basis/_monomial_basis.py b/skfda/representation/basis/_monomial_basis.py index 6e9a6c292..2e2ae9aa1 100644 --- a/skfda/representation/basis/_monomial_basis.py +++ b/skfda/representation/basis/_monomial_basis.py @@ -1,8 +1,10 @@ -from typing import Tuple, TypeVar +import warnings +from typing import Optional, Tuple, TypeVar import numpy as np import scipy.linalg +from ...typing._base import DomainRangeLike from ...typing._numpy import NDArrayFloat from ._basis import Basis @@ -131,3 +133,82 @@ def _to_R(self) -> str: # noqa: N802 f"rangeval = {rangeval}, " f"nbasis = {self.n_basis})" ) + + +class Monomial(MonomialBasis): + """Monomial basis. + + Basis formed by powers of the argument :math:`t`: + + .. math:: + 1, t, t^2, t^3... + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.MonomialBasis` instead. + + Attributes: + domain_range: a tuple of length 2 containing the initial and + end values of the interval over which the basis can be evaluated. + n_basis: number of functions in the basis. + + Examples: + Defines a monomial base over the interval :math:`[0, 5]` consisting + on the first 3 powers of :math:`t`: :math:`1, t, t^2`. + + >>> bs_mon = Monomial(domain_range=(0,5), n_basis=3) + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> bs_mon([0., 1., 2.]) + array([[[ 1.], + [ 1.], + [ 1.]], + [[ 0.], + [ 1.], + [ 2.]], + [[ 0.], + [ 1.], + [ 4.]]]) + + And also evaluates its derivatives + + >>> deriv = bs_mon.derivative() + >>> deriv([0, 1, 2]) + array([[[ 0.], + [ 0.], + [ 0.]], + [[ 1.], + [ 1.], + [ 1.]], + [[ 0.], + [ 2.], + [ 4.]]]) + >>> deriv2 = bs_mon.derivative(order=2) + >>> deriv2([0, 1, 2]) + array([[[ 0.], + [ 0.], + [ 0.]], + [[ 0.], + [ 0.], + [ 0.]], + [[ 2.], + [ 2.], + [ 2.]]]) + """ + + def __init__( + self, + *, + domain_range: Optional[DomainRangeLike] = None, + n_basis: int = 1, + ): + super().__init__( + domain_range=domain_range, + n_basis=n_basis, + ) + warnings.warn( + "The BSplines class is deprecated. Use " + "BSplineBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_tensor.py b/skfda/representation/basis/_tensor.py deleted file mode 100644 index db618c99e..000000000 --- a/skfda/representation/basis/_tensor.py +++ /dev/null @@ -1,73 +0,0 @@ - -import warnings -from typing import Iterable - -from ._basis import Basis -from ._tensor_basis import TensorBasis - - -class Tensor(TensorBasis): - r"""Tensor basis. - - Basis for multivariate functions constructed as a tensor product of - :math:`\mathbb{R} \to \mathbb{R}` bases. - - .. deprecated:: 0.8 - Use :class:`~skfda.representation.basis.TensorBasis` instead. - - Attributes: - domain_range (tuple): a tuple of length ``dim_domain`` containing - the range of input values for each dimension. - n_basis (int): number of functions in the basis. - - Examples: - Defines a tensor basis over the interval :math:`[0, 5] \times [0, 3]` - consisting on the functions - - .. math:: - - 1, v, u, uv, u^2, u^2v - - >>> from skfda.representation.basis import TensorBasis, MonomialBasis - >>> - >>> basis_x = MonomialBasis(domain_range=(0,5), n_basis=3) - >>> basis_y = MonomialBasis(domain_range=(0,3), n_basis=2) - >>> - >>> basis = TensorBasis([basis_x, basis_y]) - - - And evaluates all the functions in the basis in a list of descrete - values. - - >>> basis([(0., 2.), (3., 0), (2., 3.)]) - array([[[ 1.], - [ 1.], - [ 1.]], - [[ 2.], - [ 0.], - [ 3.]], - [[ 0.], - [ 3.], - [ 2.]], - [[ 0.], - [ 0.], - [ 6.]], - [[ 0.], - [ 9.], - [ 4.]], - [[ 0.], - [ 0.], - [ 12.]]]) - - """ - - def __init__(self, basis_list: Iterable[Basis]): - - super().__init__( - basis_list=basis_list, - ) - warnings.warn( - "The Tensor class is deprecated. Use " - "TensorBasis instead.", - DeprecationWarning, - ) diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index 69f42de46..f93193388 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -1,5 +1,6 @@ import itertools import math +import warnings from typing import Any, Iterable, Tuple import numpy as np @@ -116,3 +117,70 @@ def __eq__(self, other: Any) -> bool: def __hash__(self) -> int: return hash((super().__hash__(), self.basis_list)) + + +class Tensor(TensorBasis): + r"""Tensor basis. + + Basis for multivariate functions constructed as a tensor product of + :math:`\mathbb{R} \to \mathbb{R}` bases. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.TensorBasis` instead. + + Attributes: + domain_range (tuple): a tuple of length ``dim_domain`` containing + the range of input values for each dimension. + n_basis (int): number of functions in the basis. + + Examples: + Defines a tensor basis over the interval :math:`[0, 5] \times [0, 3]` + consisting on the functions + + .. math:: + + 1, v, u, uv, u^2, u^2v + + >>> from skfda.representation.basis import Tensor, Monomial + >>> + >>> basis_x = Monomial(domain_range=(0,5), n_basis=3) + >>> basis_y = Monomial(domain_range=(0,3), n_basis=2) + >>> + >>> basis = Tensor([basis_x, basis_y]) + + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> basis([(0., 2.), (3., 0), (2., 3.)]) + array([[[ 1.], + [ 1.], + [ 1.]], + [[ 2.], + [ 0.], + [ 3.]], + [[ 0.], + [ 3.], + [ 2.]], + [[ 0.], + [ 0.], + [ 6.]], + [[ 0.], + [ 9.], + [ 4.]], + [[ 0.], + [ 0.], + [ 12.]]]) + + """ + + def __init__(self, basis_list: Iterable[Basis]): + + super().__init__( + basis_list=basis_list, + ) + warnings.warn( + "The Tensor class is deprecated. Use " + "TensorBasis instead.", + DeprecationWarning, + ) diff --git a/skfda/representation/basis/_vector.py b/skfda/representation/basis/_vector.py deleted file mode 100644 index 9102c992a..000000000 --- a/skfda/representation/basis/_vector.py +++ /dev/null @@ -1,75 +0,0 @@ -from __future__ import annotations - -import warnings -from typing import Iterable - -from ._basis import Basis -from ._vector_basis import VectorValuedBasis - - -class VectorValued(VectorValuedBasis): - r"""Vector-valued basis. - - Basis for :term:`vector-valued functions ` - constructed from scalar-valued bases. - - For each dimension in the :term:`codomain`, it uses a scalar-valued basis - multiplying each basis by the corresponding unitary vector. - - .. deprecated:: 0.8 - Use :class:`~skfda.representation.basis.VectorValuedBasis` instead. - - Attributes: - domain_range (tuple): a tuple of length ``dim_domain`` containing - the range of input values for each dimension. - n_basis (int): number of functions in the basis. - - Examples: - Defines a vector-valued base over the interval :math:`[0, 5]` - consisting on the functions - - .. math:: - - 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} - - >>> from skfda.representation.basis import VectorValuedBasis - >>> from skfda.representation.basis import MonomialBasis - >>> - >>> basis_x = MonomialBasis(domain_range=(0,5), n_basis=3) - >>> basis_y = MonomialBasis(domain_range=(0,5), n_basis=2) - >>> - >>> basis = VectorValuedBasis([basis_x, basis_y]) - - - And evaluates all the functions in the basis in a list of descrete - values. - - >>> basis([0., 1., 2.]) - array([[[ 1., 0.], - [ 1., 0.], - [ 1., 0.]], - [[ 0., 0.], - [ 1., 0.], - [ 2., 0.]], - [[ 0., 0.], - [ 1., 0.], - [ 4., 0.]], - [[ 0., 1.], - [ 0., 1.], - [ 0., 1.]], - [[ 0., 0.], - [ 0., 1.], - [ 0., 2.]]]) - - """ - - def __init__(self, basis_list: Iterable[Basis]) -> None: - super().__init__( - basis_list=basis_list, - ) - - warnings.warn( - "The VectorValued class is deprecated. Use " - "VectorValuedBasis instead.", - DeprecationWarning, - ) diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index c48220ffe..41117dfbb 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -1,5 +1,6 @@ from __future__ import annotations +import warnings from typing import Any, Iterable, Tuple, TypeVar, Union import numpy as np @@ -180,3 +181,71 @@ def __eq__(self, other: Any) -> bool: def __hash__(self) -> int: return hash((super().__hash__(), self.basis_list)) + + +class VectorValued(VectorValuedBasis): + r"""Vector-valued basis. + + Basis for :term:`vector-valued functions ` + constructed from scalar-valued bases. + + For each dimension in the :term:`codomain`, it uses a scalar-valued basis + multiplying each basis by the corresponding unitary vector. + + .. deprecated:: 0.8 + Use :class:`~skfda.representation.basis.VectorValuedBasis` instead. + + Attributes: + domain_range (tuple): a tuple of length ``dim_domain`` containing + the range of input values for each dimension. + n_basis (int): number of functions in the basis. + + Examples: + Defines a vector-valued base over the interval :math:`[0, 5]` + consisting on the functions + + .. math:: + + 1 \vec{i}, t \vec{i}, t^2 \vec{i}, 1 \vec{j}, t \vec{j} + + >>> from skfda.representation.basis import VectorValued + >>> from skfda.representation.basis import Monomial + >>> + >>> basis_x = Monomial(domain_range=(0,5), n_basis=3) + >>> basis_y = Monomial(domain_range=(0,5), n_basis=2) + >>> + >>> basis = VectorValued([basis_x, basis_y]) + + + And evaluates all the functions in the basis in a list of descrete + values. + + >>> basis([0., 1., 2.]) + array([[[ 1., 0.], + [ 1., 0.], + [ 1., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 2., 0.]], + [[ 0., 0.], + [ 1., 0.], + [ 4., 0.]], + [[ 0., 1.], + [ 0., 1.], + [ 0., 1.]], + [[ 0., 0.], + [ 0., 1.], + [ 0., 2.]]]) + + """ + + def __init__(self, basis_list: Iterable[Basis]) -> None: + super().__init__( + basis_list=basis_list, + ) + + warnings.warn( + "The VectorValued class is deprecated. Use " + "VectorValuedBasis instead.", + DeprecationWarning, + ) From 8d5d30292eae86292454a0bf7310583298cd4908 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:03:58 +0100 Subject: [PATCH 362/400] Style fixes in modified files --- examples/plot_extrapolation.py | 6 ++-- examples/plot_fpca.py | 6 +++- examples/plot_oneway.py | 29 +++++++++---------- .../ml/regression/_historical_linear_model.py | 2 +- skfda/tests/test_basis.py | 21 +++++++++----- skfda/tests/test_neighbors.py | 10 ++++--- skfda/tests/test_oneway_anova.py | 4 ++- skfda/tests/test_pandas_fdatabasis.py | 2 -- skfda/tests/test_registration.py | 6 ++-- skfda/tests/test_regression.py | 3 +- 10 files changed, 53 insertions(+), 36 deletions(-) diff --git a/examples/plot_extrapolation.py b/examples/plot_extrapolation.py index 83afeea05..b5b5beed0 100644 --- a/examples/plot_extrapolation.py +++ b/examples/plot_extrapolation.py @@ -48,11 +48,13 @@ fd_fourier.dataset_name = "Fourier Basis" fd_monomial = fdgrid.to_basis( - skfda.representation.basis.MonomialBasis(n_basis=5)) + skfda.representation.basis.MonomialBasis(n_basis=5), +) fd_monomial.dataset_name = "Monomial Basis" fd_bspline = fdgrid.to_basis( - skfda.representation.basis.BSplineBasis(n_basis=5)) + skfda.representation.basis.BSplineBasis(n_basis=5), +) fd_bspline.dataset_name = "BSpline Basis" diff --git a/examples/plot_fpca.py b/examples/plot_fpca.py index ea077286f..100bac3e8 100644 --- a/examples/plot_fpca.py +++ b/examples/plot_fpca.py @@ -14,7 +14,11 @@ from skfda.datasets import fetch_growth from skfda.exploratory.visualization import FPCAPlot from skfda.preprocessing.dim_reduction import FPCA -from skfda.representation.basis import BSplineBasis, FourierBasis, MonomialBasis +from skfda.representation.basis import ( + BSplineBasis, + FourierBasis, + MonomialBasis, +) ############################################################################## # In this example we are going to use functional principal component analysis diff --git a/examples/plot_oneway.py b/examples/plot_oneway.py index 26d9b50eb..697387317 100644 --- a/examples/plot_oneway.py +++ b/examples/plot_oneway.py @@ -13,15 +13,14 @@ import skfda from skfda.inference.anova import oneway_anova -from skfda.representation import FDataGrid, FDataBasis from skfda.representation.basis import FourierBasis -################################################################################ +############################################################################### # *One-way ANOVA* (analysis of variance) is a test that can be used to # compare the means of different samples of data. # Let :math:`X_{ij}(t), j=1, \dots, n_i` be trajectories corresponding to -# :math:`k` independent samples :math:`(i=1,\dots,k)` and let :math:`E(X_i(t)) = -# m_i(t)`. Thus, the null hypothesis in the statistical test is: +# :math:`k` independent samples :math:`(i=1,\dots,k)` and let :math:`E(X_i(t)) +# = m_i(t)`. Thus, the null hypothesis in the statistical test is: # # .. math:: # H_0: m_1(t) = \dots = m_k(t) @@ -33,7 +32,7 @@ fd_hip = dataset['data'].coordinates[0] fd_knee = dataset['data'].coordinates[1].to_basis(FourierBasis(n_basis=10)) -################################################################################ +############################################################################### # Let's start with the first feature, the angle of the hip. The sample # consists in 39 different trajectories, each representing the movement of the # hip of each of the boys studied. @@ -41,8 +40,8 @@ ############################################################################### # The example is going to be divided in three different groups. Then we are -# going to apply the ANOVA procedure to this groups to test if the means of this -# three groups are equal or not. +# going to apply the ANOVA procedure to this groups to test if the means of +# this three groups are equal or not. fd_hip1 = fd_hip[0:13] fd_hip2 = fd_hip[13:26] @@ -53,7 +52,7 @@ fd_means = skfda.concatenate(means) fig = fd_means.plot() -############################################################################### +############################################################################## # At this point is time to perform the *ANOVA* test. This functionality is # implemented in the function :func:`~skfda.inference.anova.oneway_anova`. As # it consists in an asymptotic method it is possible to set the number of @@ -63,7 +62,7 @@ v_n, p_val = oneway_anova(fd_hip1, fd_hip2, fd_hip3) -################################################################################ +############################################################################### # The function returns first the statistic :func:`~skfda.inference.anova # .v_sample_stat` used to measure the variability between groups, # second the *p-value* of the test . For further information visit @@ -72,12 +71,12 @@ print('Statistic: ', v_n) print('p-value: ', p_val) -################################################################################ +############################################################################### # This was the simplest way to call this function. Let's see another example, # this time using knee angles, this time with data in basis representation. fig = fd_knee.plot() -################################################################################ +############################################################################### # The same procedure as before is followed to prepare the data. fd_knee1 = fd_knee[0:13] @@ -89,7 +88,7 @@ fd_means = skfda.concatenate(means) fig = fd_means.plot() -################################################################################ +############################################################################## # In this case the optional arguments of the function are going to be set. # First, there is a `n_reps` parameter, which allows the user to select the # number of simulations to perform in the asymptotic procedure of the test ( @@ -110,9 +109,9 @@ print('p-value: ', p_val) print('Distribution: ', dist) -################################################################################ +############################################################################### # **References:** # -# [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An anova test -# for functional data". *Computational Statistics Data Analysis*, +# [1] Antonio Cuevas, Manuel Febrero-Bande, and Ricardo Fraiman. "An anova +# test for functional data". *Computational Statistics Data Analysis*, # 47:111-112, 02 2004 diff --git a/skfda/ml/regression/_historical_linear_model.py b/skfda/ml/regression/_historical_linear_model.py index 5c1e85f34..2f38a697a 100644 --- a/skfda/ml/regression/_historical_linear_model.py +++ b/skfda/ml/regression/_historical_linear_model.py @@ -359,7 +359,7 @@ def _fit_and_return_centered_matrix( ) self._basis = VectorValuedBasis( - [fem_basis] * X_centered.dim_codomain + [fem_basis] * X_centered.dim_codomain, ) design_matrix = _design_matrix( diff --git a/skfda/tests/test_basis.py b/skfda/tests/test_basis.py index ced980b58..6703495b4 100644 --- a/skfda/tests/test_basis.py +++ b/skfda/tests/test_basis.py @@ -255,8 +255,10 @@ class TestFDataBasisOperations(unittest.TestCase): def test_fdatabasis_add(self) -> None: """Test addition of FDataBasis.""" monomial1 = FDataBasis(MonomialBasis(n_basis=3), [1, 2, 3]) - monomial2 = FDataBasis(MonomialBasis( - n_basis=3), [[1, 2, 3], [3, 4, 5]]) + monomial2 = FDataBasis( + MonomialBasis(n_basis=3), + [[1, 2, 3], [3, 4, 5]], + ) self.assertTrue( (monomial1 + monomial2).equals( @@ -276,8 +278,10 @@ def test_fdatabasis_add(self) -> None: def test_fdatabasis_sub(self) -> None: """Test subtraction of FDataBasis.""" monomial1 = FDataBasis(MonomialBasis(n_basis=3), [1, 2, 3]) - monomial2 = FDataBasis(MonomialBasis( - n_basis=3), [[1, 2, 3], [3, 4, 5]]) + monomial2 = FDataBasis( + MonomialBasis(n_basis=3), + [[1, 2, 3], [3, 4, 5]], + ) self.assertTrue( (monomial1 - monomial2).equals( @@ -664,11 +668,14 @@ def setUp(self) -> None: self.dims = (self.n_x, self.n_y, self.n_z) self.basis_x = skfda.representation.basis.MonomialBasis( - n_basis=self.n_x) + n_basis=self.n_x, + ) self.basis_y = skfda.representation.basis.FourierBasis( - n_basis=self.n_y) + n_basis=self.n_y, + ) self.basis_z = skfda.representation.basis.BSplineBasis( - n_basis=self.n_z) + n_basis=self.n_z, + ) self.basis = skfda.representation.basis.TensorBasis([ self.basis_x, diff --git a/skfda/tests/test_neighbors.py b/skfda/tests/test_neighbors.py index 9c5a8ddfa..9463d6d09 100644 --- a/skfda/tests/test_neighbors.py +++ b/skfda/tests/test_neighbors.py @@ -250,8 +250,9 @@ def test_functional_response_custom_weights(self) -> None: FDataGrid, FDataGrid, ](weights=self._weights, n_neighbors=5) - response = self.X.to_basis(FourierBasis( - domain_range=(-1, 1), n_basis=10)) + response = self.X.to_basis( + FourierBasis(domain_range=(-1, 1), n_basis=10), + ) knnr.fit(self.X, response) res = knnr.predict(self.X) @@ -288,8 +289,9 @@ def test_functional_response_basis(self) -> None: FDataGrid, FDataBasis, ](weights='distance', n_neighbors=5) - response = self.X.to_basis(FourierBasis( - domain_range=(-1, 1), n_basis=10)) + response = self.X.to_basis( + FourierBasis(domain_range=(-1, 1), n_basis=10), + ) knnr.fit(self.X, response) res = knnr.predict(self.X) diff --git a/skfda/tests/test_oneway_anova.py b/skfda/tests/test_oneway_anova.py index 24d7ad54d..4ffe4c1d2 100644 --- a/skfda/tests/test_oneway_anova.py +++ b/skfda/tests/test_oneway_anova.py @@ -29,7 +29,9 @@ def test_v_stats_args(self) -> None: v_asymptotic_stat(FDataGrid([0]), weights=[0, 1]) with self.assertRaises(ValueError): v_asymptotic_stat( - FDataGrid([[1, 1, 1], [1, 1, 1]]), weights=[0, 0]) + FDataGrid([[1, 1, 1], [1, 1, 1]]), + weights=[0, 0], + ) def test_v_stats(self) -> None: """Test statistic behaviour.""" diff --git a/skfda/tests/test_pandas_fdatabasis.py b/skfda/tests/test_pandas_fdatabasis.py index e44f207ab..6192ee7b7 100644 --- a/skfda/tests/test_pandas_fdatabasis.py +++ b/skfda/tests/test_pandas_fdatabasis.py @@ -34,7 +34,6 @@ def basis(request: Any) -> Any: @pytest.fixture def dtype(basis: Basis) -> FDataBasisDType: """A fixture providing the ExtensionDtype to validate.""" - return FDataBasisDType(basis=basis) @@ -45,7 +44,6 @@ def data(basis: Basis) -> FDataBasis: * data[0] and data[1] should both be non missing * data[0] and data[1] should not be equal """ - coef_matrix = np.arange(100 * basis.n_basis).reshape(100, basis.n_basis) return FDataBasis(basis=basis, coefficients=coef_matrix) diff --git a/skfda/tests/test_registration.py b/skfda/tests/test_registration.py index 682a55333..a8526246d 100644 --- a/skfda/tests/test_registration.py +++ b/skfda/tests/test_registration.py @@ -346,14 +346,16 @@ def test_template(self) -> None: fd_registered_1 = reg.fit_transform(self.fd) reg_2 = LeastSquaresShiftRegistration[FDataGrid]( - template=reg.template_) + template=reg.template_, + ) fd_registered_2 = reg_2.fit_transform(self.fd) reg_3 = LeastSquaresShiftRegistration[FDataGrid](template=mean) fd_registered_3 = reg_3.fit_transform(self.fd) reg_4 = LeastSquaresShiftRegistration[FDataGrid]( - template=reg.template_) + template=reg.template_, + ) fd_registered_4 = reg_4.fit(self.fd).transform(self.fd) np.testing.assert_array_almost_equal( diff --git a/skfda/tests/test_regression.py b/skfda/tests/test_regression.py index 06ff0c367..6bab0950f 100644 --- a/skfda/tests/test_regression.py +++ b/skfda/tests/test_regression.py @@ -50,7 +50,8 @@ def test_regression_single_explanatory(self) -> None: ) np.testing.assert_allclose( scalar.intercept_, - 0.0, atol=1e-6, + 0.0, + atol=1e-6, ) y_pred = scalar.predict(x_fd) From 77261f6519d63833e5bfd2cc1972d9d5cb2174a2 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:11:10 +0100 Subject: [PATCH 363/400] Reformat basis files consistently --- skfda/representation/basis/_bspline_basis.py | 54 ++++++++++--------- skfda/representation/basis/_constant_basis.py | 8 +-- .../basis/_finite_element_basis.py | 7 ++- skfda/representation/basis/_fourier_basis.py | 5 +- skfda/representation/basis/_monomial_basis.py | 16 +++--- skfda/representation/basis/_tensor_basis.py | 11 ++-- skfda/representation/basis/_vector_basis.py | 15 ++---- 7 files changed, 52 insertions(+), 64 deletions(-) diff --git a/skfda/representation/basis/_bspline_basis.py b/skfda/representation/basis/_bspline_basis.py index 6196083fc..dae5f3bb5 100644 --- a/skfda/representation/basis/_bspline_basis.py +++ b/skfda/representation/basis/_bspline_basis.py @@ -11,7 +11,7 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='BSplineBasis') +T = TypeVar("T", bound="BSplineBasis") class BSplineBasis(Basis): @@ -116,16 +116,14 @@ def __init__( # noqa: WPS238 domain_range = (knots[0], knots[-1]) elif domain_range[0] != knots[0] or domain_range[1] != knots[-1]: raise ValueError( - "The ends of the knots must be the same " - "as the domain_range.", + "The ends of the knots must be the same " "as the domain_range.", ) # n_basis default to number of knots + order of the splines - 2 if n_basis is None: if knots is None: raise ValueError( - "Must provide either a list of knots or the" - "number of basis.", + "Must provide either a list of knots or the" "number of basis.", ) n_basis = len(knots) + order - 2 @@ -151,10 +149,12 @@ def __init__( # noqa: WPS238 @property def knots(self) -> Tuple[float, ...]: if self._knots is None: - return tuple(np.linspace( - *self.domain_range[0], - self.n_basis - self.order + 2, - )) + return tuple( + np.linspace( + *self.domain_range[0], + self.n_basis - self.order + 2, + ) + ) return self._knots @@ -174,7 +174,8 @@ def _evaluation_knots(self) -> Tuple[float, ...]: Analysis*. Springer. 50-51. """ return tuple( - (self.knots[0],) * (self.order - 1) + self.knots + (self.knots[0],) * (self.order - 1) + + self.knots + (self.knots[-1],) * (self.order - 1), ) @@ -208,14 +209,13 @@ def rescale( # noqa: D102 ) -> T: from ...misc.validation import validate_domain_range - knots = np.array(self.knots, dtype=np.dtype('float')) + knots = np.array(self.knots, dtype=np.dtype("float")) if domain_range is not None: # Rescales the knots domain_range = validate_domain_range(domain_range)[0] knots -= knots[0] - knots *= ( - (domain_range[1] - domain_range[0]) - / (self.knots[-1] - self.knots[0]) + knots *= (domain_range[1] - domain_range[0]) / ( + self.knots[-1] - self.knots[0] ) knots += domain_range[0] @@ -280,7 +280,7 @@ def _gram_matrix(self) -> NDArrayFloat: for i in range(self.n_basis): poly_i = np.trim_zeros( ppoly_lst[i][:, interval], - 'f', + "f", ) # Indefinite integral square = polymul(poly_i, poly_i) @@ -289,14 +289,15 @@ def _gram_matrix(self) -> NDArrayFloat: # Definite integral matrix[i, i] += np.diff( polyval( - integral, np.array(self.knots[interval: interval + 2]) + integral, + np.array(self.knots[interval : interval + 2]) - self.knots[interval], ), )[0] # The Gram matrix is banded, so not all intervals are used for j in range(i + 1, min(i + self.order, self.n_basis)): - poly_j = np.trim_zeros(ppoly_lst[j][:, interval], 'f') + poly_j = np.trim_zeros(ppoly_lst[j][:, interval], "f") # Indefinite integral integral = polyint(polymul(poly_i, poly_j)) @@ -305,7 +306,7 @@ def _gram_matrix(self) -> NDArrayFloat: matrix[i, j] += np.diff( polyval( integral, - np.array(self.knots[interval: interval + 2]) + np.array(self.knots[interval : interval + 2]) - self.knots[interval], ), )[0] @@ -327,11 +328,13 @@ def _to_scipy_bspline(self, coefs: NDArrayFloat) -> SciBSpline: self.order - 1, ) - knots: NDArrayFloat = np.concatenate(( - repeated_initial, - knots_array, - repeated_final, - )) + knots: NDArrayFloat = np.concatenate( + ( + repeated_initial, + knots_array, + repeated_final, + ) + ) return SciBSpline(knots, coefs.T, self.order - 1) @@ -345,7 +348,7 @@ def _from_scipy_bspline( # Remove additional knots at the borders if order != 0: - knots = knots[order: -order] + knots = knots[order:-order] coefs = bspline.c domain_range = [knots[0], knots[-1]] @@ -460,7 +463,6 @@ def __init__( # noqa: WPS238 knots=knots, ) warnings.warn( - "The BSplines class is deprecated. Use " - "BSplineBasis instead.", + "The BSplines class is deprecated. Use " "BSplineBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_constant_basis.py b/skfda/representation/basis/_constant_basis.py index c4914e598..934f518d5 100644 --- a/skfda/representation/basis/_constant_basis.py +++ b/skfda/representation/basis/_constant_basis.py @@ -7,7 +7,7 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='ConstantBasis') +T = TypeVar("T", bound="ConstantBasis") class ConstantBasis(Basis): @@ -40,7 +40,8 @@ def _derivative_basis_and_coefs( order: int = 1, ) -> Tuple[T, NDArrayFloat]: return ( - (self.copy(), coefs.copy()) if order == 0 + (self.copy(), coefs.copy()) + if order == 0 else (self.copy(), np.zeros(coefs.shape)) ) @@ -78,8 +79,7 @@ class Constant(ConstantBasis): def __init__(self, domain_range: Optional[DomainRangeLike] = None) -> None: """Constant basis constructor.""" warnings.warn( - "The Constant class is deprecated. Use " - "ConstantBasis instead.", + "The Constant class is deprecated. Use " "ConstantBasis instead.", DeprecationWarning, ) super().__init__(domain_range=domain_range) diff --git a/skfda/representation/basis/_finite_element_basis.py b/skfda/representation/basis/_finite_element_basis.py index a48eb0505..2769d4f38 100644 --- a/skfda/representation/basis/_finite_element_basis.py +++ b/skfda/representation/basis/_finite_element_basis.py @@ -7,7 +7,7 @@ from ...typing._numpy import ArrayLike, NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='FiniteElementBasis') +T = TypeVar("T", bound="FiniteElementBasis") class FiniteElementBasis(Basis): @@ -212,7 +212,7 @@ class FiniteElement(FiniteElementBasis): >>> basis.n_basis 10 - """ + """ def __init__( self, @@ -226,7 +226,6 @@ def __init__( domain_range=domain_range, ) warnings.warn( - "The FiniteElement class is deprecated. Use " - "FiniteElementBasis instead.", + "The FiniteElement class is deprecated. Use " "FiniteElementBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_fourier_basis.py b/skfda/representation/basis/_fourier_basis.py index 798f88326..c8d324330 100644 --- a/skfda/representation/basis/_fourier_basis.py +++ b/skfda/representation/basis/_fourier_basis.py @@ -8,11 +8,10 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='FourierBasis') +T = TypeVar("T", bound="FourierBasis") class _SinCos(Protocol): - def __call__( self, __array: NDArrayFloat, # noqa: WPS112 @@ -144,7 +143,7 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: phase_coefs = omega * seq_pairs # Multiply the phase coefficients elementwise - res = np.einsum('ij,k->ijk', phase_coefs, eval_points) + res = np.einsum("ij,k->ijk", phase_coefs, eval_points) # Apply odd and even functions for i in (0, 1): diff --git a/skfda/representation/basis/_monomial_basis.py b/skfda/representation/basis/_monomial_basis.py index 2e2ae9aa1..4c6738295 100644 --- a/skfda/representation/basis/_monomial_basis.py +++ b/skfda/representation/basis/_monomial_basis.py @@ -8,7 +8,7 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='MonomialBasis') +T = TypeVar("T", bound="MonomialBasis") class MonomialBasis(Basis): @@ -104,13 +104,12 @@ def _gram_matrix(self) -> NDArrayFloat: # We obtain the powers of both extremes in the domain range power_domain_limits = np.vander( - self.domain_range[0], 2 * self.n_basis, + self.domain_range[0], + 2 * self.n_basis, ) # Subtract the powers (Barrow's rule) - power_domain_limits_diff = ( - power_domain_limits[1] - power_domain_limits[0] - ) + power_domain_limits_diff = power_domain_limits[1] - power_domain_limits[0] # Multiply the constants that appear in the integration evaluated_points = integral_coefs * power_domain_limits_diff @@ -121,8 +120,8 @@ def _gram_matrix(self) -> NDArrayFloat: # Build the matrix return scipy.linalg.hankel( # type: ignore[no-any-return] - ordered_evaluated_points[:self.n_basis], - ordered_evaluated_points[self.n_basis - 1:], + ordered_evaluated_points[: self.n_basis], + ordered_evaluated_points[self.n_basis - 1 :], ) def _to_R(self) -> str: # noqa: N802 @@ -208,7 +207,6 @@ def __init__( n_basis=n_basis, ) warnings.warn( - "The BSplines class is deprecated. Use " - "BSplineBasis instead.", + "The BSplines class is deprecated. Use " "BSplineBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index f93193388..859e1b22a 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -66,10 +66,7 @@ def __init__(self, basis_list: Iterable[Basis]): self._basis_list = tuple(basis_list) - if not all( - b.dim_domain == 1 and b.dim_codomain == 1 - for b in self._basis_list - ): + if not all(b.dim_domain == 1 and b.dim_codomain == 1 for b in self._basis_list): raise ValueError( "The basis functions must be univariate and scalar valued", ) @@ -88,8 +85,7 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: matrix = np.zeros((self.n_basis, len(eval_points), self.dim_codomain)) basis_evaluations = [ - b(eval_points[:, i:i + 1]) - for i, b in enumerate(self.basis_list) + b(eval_points[:, i : i + 1]) for i, b in enumerate(self.basis_list) ] for i, ev in enumerate(itertools.product(*basis_evaluations)): @@ -180,7 +176,6 @@ def __init__(self, basis_list: Iterable[Basis]): basis_list=basis_list, ) warnings.warn( - "The Tensor class is deprecated. Use " - "TensorBasis instead.", + "The Tensor class is deprecated. Use " "TensorBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index 41117dfbb..788eed39b 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -9,7 +9,7 @@ from ...typing._numpy import NDArrayFloat from ._basis import Basis -T = TypeVar("T", bound='VectorValuedBasis') +T = TypeVar("T", bound="VectorValuedBasis") class VectorValuedBasis(Basis): @@ -81,8 +81,7 @@ def __init__(self, basis_list: Iterable[Basis]) -> None: for b in basis_list ): raise ValueError( - "The basis must all have the same domain " - "dimension and range", + "The basis must all have the same domain " "dimension and range", ) self._basis_list = basis_list @@ -113,7 +112,7 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: for i, ev in enumerate(basis_evaluations): - matrix[n_basis_eval:n_basis_eval + len(ev), :, i] = ev[..., 0] + matrix[n_basis_eval : n_basis_eval + len(ev), :, i] = ev[..., 0] n_basis_eval += len(ev) return matrix @@ -171,10 +170,7 @@ def _coordinate_nonfull( def __repr__(self) -> str: """Representation of a Basis object.""" - return ( - f"{self.__class__.__name__}(" - f"basis_list={self.basis_list})" - ) + return f"{self.__class__.__name__}(" f"basis_list={self.basis_list})" def __eq__(self, other: Any) -> bool: return super().__eq__(other) and self.basis_list == other.basis_list @@ -245,7 +241,6 @@ def __init__(self, basis_list: Iterable[Basis]) -> None: ) warnings.warn( - "The VectorValued class is deprecated. Use " - "VectorValuedBasis instead.", + "The VectorValued class is deprecated. Use " "VectorValuedBasis instead.", DeprecationWarning, ) From bf101015a0b6f05249b22c25c38a85c0ac176d40 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:19:58 +0100 Subject: [PATCH 364/400] Fix more syntax issues in basis --- skfda/representation/basis/_bspline_basis.py | 16 +++++++++------- skfda/representation/basis/_constant_basis.py | 2 +- skfda/representation/basis/_fdatabasis.py | 11 ++++++++--- .../basis/_finite_element_basis.py | 3 ++- skfda/representation/basis/_monomial_basis.py | 6 ++++-- skfda/representation/basis/_tensor_basis.py | 8 +++++--- skfda/representation/basis/_vector_basis.py | 8 +++++--- 7 files changed, 34 insertions(+), 20 deletions(-) diff --git a/skfda/representation/basis/_bspline_basis.py b/skfda/representation/basis/_bspline_basis.py index dae5f3bb5..85c029aa4 100644 --- a/skfda/representation/basis/_bspline_basis.py +++ b/skfda/representation/basis/_bspline_basis.py @@ -116,14 +116,16 @@ def __init__( # noqa: WPS238 domain_range = (knots[0], knots[-1]) elif domain_range[0] != knots[0] or domain_range[1] != knots[-1]: raise ValueError( - "The ends of the knots must be the same " "as the domain_range.", + "The ends of the knots must be the same " + "as the domain_range.", ) # n_basis default to number of knots + order of the splines - 2 if n_basis is None: if knots is None: raise ValueError( - "Must provide either a list of knots or the" "number of basis.", + "Must provide either a list of knots or the" + "number of basis.", ) n_basis = len(knots) + order - 2 @@ -153,7 +155,7 @@ def knots(self) -> Tuple[float, ...]: np.linspace( *self.domain_range[0], self.n_basis - self.order + 2, - ) + ), ) return self._knots @@ -290,7 +292,7 @@ def _gram_matrix(self) -> NDArrayFloat: matrix[i, i] += np.diff( polyval( integral, - np.array(self.knots[interval : interval + 2]) + np.array(self.knots[interval:interval + 2]) - self.knots[interval], ), )[0] @@ -306,7 +308,7 @@ def _gram_matrix(self) -> NDArrayFloat: matrix[i, j] += np.diff( polyval( integral, - np.array(self.knots[interval : interval + 2]) + np.array(self.knots[interval:interval + 2]) - self.knots[interval], ), )[0] @@ -333,7 +335,7 @@ def _to_scipy_bspline(self, coefs: NDArrayFloat) -> SciBSpline: repeated_initial, knots_array, repeated_final, - ) + ), ) return SciBSpline(knots, coefs.T, self.order - 1) @@ -463,6 +465,6 @@ def __init__( # noqa: WPS238 knots=knots, ) warnings.warn( - "The BSplines class is deprecated. Use " "BSplineBasis instead.", + "The BSplines class is deprecated. Use BSplineBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_constant_basis.py b/skfda/representation/basis/_constant_basis.py index 934f518d5..6d7d6e24b 100644 --- a/skfda/representation/basis/_constant_basis.py +++ b/skfda/representation/basis/_constant_basis.py @@ -79,7 +79,7 @@ class Constant(ConstantBasis): def __init__(self, domain_range: Optional[DomainRangeLike] = None) -> None: """Constant basis constructor.""" warnings.warn( - "The Constant class is deprecated. Use " "ConstantBasis instead.", + "The Constant class is deprecated. Use ConstantBasis instead.", DeprecationWarning, ) super().__init__(domain_range=domain_range) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index fb5004dae..685c8b08c 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -410,7 +410,10 @@ def sum( # noqa: WPS125 FDataBasis object. Examples: - >>> from skfda.representation.basis import FDataBasis, MonomialBasis + >>> from skfda.representation.basis import ( + ... FDataBasis, + ... MonomialBasis, + ... ) >>> basis = MonomialBasis(n_basis=4) >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]] >>> FDataBasis(basis, coefficients).sum() @@ -503,8 +506,10 @@ def to_grid( Examples: >>> from skfda.representation.basis import FDataBasis, MonomialBasis - >>> fd = FDataBasis(coefficients=[[1, 1, 1], [1, 0, 1]], - ... basis=MonomialBasis(domain_range=(0,5), n_basis=3)) + >>> fd = FDataBasis( + ... coefficients=[[1, 1, 1], [1, 0, 1]], + ... basis=MonomialBasis(domain_range=(0,5), n_basis=3), + ... ) >>> fd.to_grid([0, 1, 2]) FDataGrid( array([[[ 1.], diff --git a/skfda/representation/basis/_finite_element_basis.py b/skfda/representation/basis/_finite_element_basis.py index 2769d4f38..a2a64b0d1 100644 --- a/skfda/representation/basis/_finite_element_basis.py +++ b/skfda/representation/basis/_finite_element_basis.py @@ -226,6 +226,7 @@ def __init__( domain_range=domain_range, ) warnings.warn( - "The FiniteElement class is deprecated. Use " "FiniteElementBasis instead.", + "The FiniteElement class is deprecated. Use " + "FiniteElementBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_monomial_basis.py b/skfda/representation/basis/_monomial_basis.py index 4c6738295..ea4df7e50 100644 --- a/skfda/representation/basis/_monomial_basis.py +++ b/skfda/representation/basis/_monomial_basis.py @@ -109,7 +109,9 @@ def _gram_matrix(self) -> NDArrayFloat: ) # Subtract the powers (Barrow's rule) - power_domain_limits_diff = power_domain_limits[1] - power_domain_limits[0] + power_domain_limits_diff = ( + power_domain_limits[1] - power_domain_limits[0] + ) # Multiply the constants that appear in the integration evaluated_points = integral_coefs * power_domain_limits_diff @@ -207,6 +209,6 @@ def __init__( n_basis=n_basis, ) warnings.warn( - "The BSplines class is deprecated. Use " "BSplineBasis instead.", + "The Monomial class is deprecated. Use MonomialBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_tensor_basis.py b/skfda/representation/basis/_tensor_basis.py index 859e1b22a..f4f83dd2d 100644 --- a/skfda/representation/basis/_tensor_basis.py +++ b/skfda/representation/basis/_tensor_basis.py @@ -66,7 +66,9 @@ def __init__(self, basis_list: Iterable[Basis]): self._basis_list = tuple(basis_list) - if not all(b.dim_domain == 1 and b.dim_codomain == 1 for b in self._basis_list): + if not all( + b.dim_domain == 1 and b.dim_codomain == 1 for b in self._basis_list + ): raise ValueError( "The basis functions must be univariate and scalar valued", ) @@ -85,7 +87,7 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: matrix = np.zeros((self.n_basis, len(eval_points), self.dim_codomain)) basis_evaluations = [ - b(eval_points[:, i : i + 1]) for i, b in enumerate(self.basis_list) + b(eval_points[:, i:i + 1]) for i, b in enumerate(self.basis_list) ] for i, ev in enumerate(itertools.product(*basis_evaluations)): @@ -176,6 +178,6 @@ def __init__(self, basis_list: Iterable[Basis]): basis_list=basis_list, ) warnings.warn( - "The Tensor class is deprecated. Use " "TensorBasis instead.", + "The Tensor class is deprecated. Use TensorBasis instead.", DeprecationWarning, ) diff --git a/skfda/representation/basis/_vector_basis.py b/skfda/representation/basis/_vector_basis.py index 788eed39b..8c02af61b 100644 --- a/skfda/representation/basis/_vector_basis.py +++ b/skfda/representation/basis/_vector_basis.py @@ -81,7 +81,8 @@ def __init__(self, basis_list: Iterable[Basis]) -> None: for b in basis_list ): raise ValueError( - "The basis must all have the same domain " "dimension and range", + "The basis must all have the same domain " + "dimension and range", ) self._basis_list = basis_list @@ -112,7 +113,7 @@ def _evaluate(self, eval_points: NDArrayFloat) -> NDArrayFloat: for i, ev in enumerate(basis_evaluations): - matrix[n_basis_eval : n_basis_eval + len(ev), :, i] = ev[..., 0] + matrix[n_basis_eval:n_basis_eval + len(ev), :, i] = ev[..., 0] n_basis_eval += len(ev) return matrix @@ -241,6 +242,7 @@ def __init__(self, basis_list: Iterable[Basis]) -> None: ) warnings.warn( - "The VectorValued class is deprecated. Use " "VectorValuedBasis instead.", + "The VectorValued class is deprecated. " + "Use VectorValuedBasis instead.", DeprecationWarning, ) From 7371e4eab183e318140194161e9c3997f122b5bf Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:26:07 +0100 Subject: [PATCH 365/400] One more line too long --- skfda/representation/basis/_fdatabasis.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 685c8b08c..43ad3a6a2 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -505,7 +505,10 @@ def to_grid( object. Examples: - >>> from skfda.representation.basis import FDataBasis, MonomialBasis + >>> from skfda.representation.basis import( + ... FDataBasis, + ... MonomialBasis, + ... ) >>> fd = FDataBasis( ... coefficients=[[1, 1, 1], [1, 0, 1]], ... basis=MonomialBasis(domain_range=(0,5), n_basis=3), From 2b1af636c0de4f7572df8e2dfe9eae3b3d7faf6b Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:30:59 +0100 Subject: [PATCH 366/400] Fix style in file that causes flake8 to fail --- examples/plot_extrapolation.py | 54 +++++++++++++++++----------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/examples/plot_extrapolation.py b/examples/plot_extrapolation.py index b5b5beed0..b4020132c 100644 --- a/examples/plot_extrapolation.py +++ b/examples/plot_extrapolation.py @@ -10,13 +10,10 @@ # sphinx_gallery_thumbnail_number = 2 -import skfda - -import mpl_toolkits.mplot3d - import matplotlib.pyplot as plt import numpy as np +import skfda ############################################################################## # @@ -39,9 +36,12 @@ # To show how it works we will create a dataset with two unidimensional curves # defined in (0,1), and we will represent it using a grid and different types # of basis. -# + fdgrid = skfda.datasets.make_sinusoidal_process( - n_samples=2, error_std=0, random_state=0) + n_samples=2, + error_std=0, + random_state=0, +) fdgrid.dataset_name = "Grid" fd_fourier = fdgrid.to_basis(skfda.representation.basis.FourierBasis()) @@ -83,7 +83,7 @@ # For this reason the behavior outside the domain will change depending on the # representation, obtaining a periodic behavior in the case of the Fourier # basis and polynomial behaviors in the rest of the cases. -# + domain_extended = (-0.2, 1.2) @@ -91,10 +91,10 @@ # Plot objects in the domain range extended -fdgrid.plot(ax[0][0], domain_range=domain_extended, linestyle='--') -fd_fourier.plot(ax[0][1], domain_range=domain_extended, linestyle='--') -fd_monomial.plot(ax[1][0], domain_range=domain_extended, linestyle='--') -fd_bspline.plot(ax[1][1], domain_range=domain_extended, linestyle='--') +fdgrid.plot(ax[0][0], domain_range=domain_extended, linestyle="--") +fd_fourier.plot(ax[0][1], domain_range=domain_extended, linestyle="--") +fd_monomial.plot(ax[1][0], domain_range=domain_extended, linestyle="--") +fd_bspline.plot(ax[1][1], domain_range=domain_extended, linestyle="--") # Plot configuration for axes in fig.axes: @@ -121,7 +121,7 @@ # It should be noted that the Fourier basis is periodic in itself, but the # period does not have to coincide with the domain range, obtaining different # results applying or not extrapolation in case of not coinciding. -# + t = np.linspace(*domain_extended) fig = plt.figure() @@ -131,7 +131,7 @@ # Extrapolation supplied in the evaluation values = fdgrid(t, extrapolation="periodic")[..., 0] -plt.plot(t, values.T, linestyle='--') +plt.plot(t, values.T, linestyle="--") plt.gca().set_prop_cycle(None) # Reset color cycle @@ -142,7 +142,7 @@ # # Another possible extrapolation, ``"bounds"``, will use the values of the # interval bounds for points outside the domain range. -# + fig = plt.figure() fdgrid.dataset_name = "Boundary extrapolation" @@ -152,7 +152,7 @@ # Evaluation of the grid values = fdgrid(t)[..., 0] -plt.plot(t, values.T, linestyle='--') +plt.plot(t, values.T, linestyle="--") plt.gca().set_prop_cycle(None) # Reset color cycle @@ -165,7 +165,7 @@ # the points extrapolated with the same value. The case of filling with zeros # could be specified with the string ``"zeros"``, which is equivalent to # ``extrapolation=FillExtrapolation(0)``. -# + fdgrid.dataset_name = "Fill with zeros" @@ -173,7 +173,7 @@ fdgrid.extrapolation = "zeros" # Plot in domain extended -fig = fdgrid.plot(domain_range=domain_extended, linestyle='--') +fig = fdgrid.plot(domain_range=domain_extended, linestyle="--") plt.gca().set_prop_cycle(None) # Reset color cycle @@ -183,7 +183,7 @@ ############################################################################## # # The string ``"nan"`` is equivalent to ``FillExtrapolation(np.nan)``. -# + values = fdgrid([-1, 0, 0.5, 1, 2], extrapolation="nan") print(values) @@ -192,7 +192,7 @@ # # It is possible to configure the extrapolation to raise an exception in case # of evaluating a point outside the domain. -# + try: res = fd_fourier(t, extrapolation="exception") @@ -206,7 +206,7 @@ # using periodic extrapolation. fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') +ax = fig.add_subplot(111, projection="3d") # Make data. t = np.arange(-2.5, 2.75, 0.25) @@ -223,20 +223,20 @@ T, S = np.meshgrid(t, t) -ax.plot_wireframe(T, S, values[0, ..., 0], alpha=.3, color="C0") +ax.plot_wireframe(T, S, values[0, ..., 0], alpha=0.3, color="C0") ax.plot_surface(X, Y, Z, color="C0") ############################################################################### # -# The previous extension can be compared with the extrapolation using the values -# of the bounds. +# The previous extension can be compared with the extrapolation using the +# values of the bounds. values = fd_surface((t, t), grid=True, extrapolation="bounds") fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.plot_wireframe(T, S, values[0, ..., 0], alpha=.3, color="C0") +ax = fig.add_subplot(111, projection="3d") +ax.plot_wireframe(T, S, values[0, ..., 0], alpha=0.3, color="C0") ax.plot_surface(X, Y, Z, color="C0") ############################################################################### @@ -247,6 +247,6 @@ values = fd_surface((t, t), grid=True, extrapolation="zeros") fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.plot_wireframe(T, S, values[0, ..., 0], alpha=.3, color="C0") +ax = fig.add_subplot(111, projection="3d") +ax.plot_wireframe(T, S, values[0, ..., 0], alpha=0.3, color="C0") ax.plot_surface(X, Y, Z, color="C0") From 550bceefcb8729842c44f6d1ddf071a085d4df68 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:42:20 +0100 Subject: [PATCH 367/400] Remove tailing whitespace --- skfda/representation/basis/_fdatabasis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/representation/basis/_fdatabasis.py b/skfda/representation/basis/_fdatabasis.py index 43ad3a6a2..8b44310be 100644 --- a/skfda/representation/basis/_fdatabasis.py +++ b/skfda/representation/basis/_fdatabasis.py @@ -506,7 +506,7 @@ def to_grid( Examples: >>> from skfda.representation.basis import( - ... FDataBasis, + ... FDataBasis, ... MonomialBasis, ... ) >>> fd = FDataBasis( From 6eaa6b22114dc60df82d5c6a1e6c0101b8a94a13 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 00:49:05 +0100 Subject: [PATCH 368/400] Fix another whitespace --- skfda/representation/basis/_monomial_basis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/representation/basis/_monomial_basis.py b/skfda/representation/basis/_monomial_basis.py index ea4df7e50..be6370398 100644 --- a/skfda/representation/basis/_monomial_basis.py +++ b/skfda/representation/basis/_monomial_basis.py @@ -123,7 +123,7 @@ def _gram_matrix(self) -> NDArrayFloat: # Build the matrix return scipy.linalg.hankel( # type: ignore[no-any-return] ordered_evaluated_points[: self.n_basis], - ordered_evaluated_points[self.n_basis - 1 :], + ordered_evaluated_points[self.n_basis - 1:], ) def _to_R(self) -> str: # noqa: N802 From 230016314e4ac3e51db8796f5f60db08d130b009 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 17:24:13 +0100 Subject: [PATCH 369/400] Use new basis names --- skfda/tests/test_custom_basis.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/tests/test_custom_basis.py b/skfda/tests/test_custom_basis.py index 2eefcd632..184327a08 100644 --- a/skfda/tests/test_custom_basis.py +++ b/skfda/tests/test_custom_basis.py @@ -4,7 +4,7 @@ import numpy as np -from skfda.representation.basis import CustomBasis, FDataBasis, Fourier +from skfda.representation.basis import CustomBasis, FDataBasis, FourierBasis from skfda.representation.grid import FDataGrid @@ -34,7 +34,7 @@ def test_grid(self): def test_basis(self): """Test a databasis toy example.""" - basis = Fourier(n_basis=3) + basis = FourierBasis(n_basis=3) coeficients = np.array( [ [2, 1, 0], @@ -79,7 +79,7 @@ def test_not_linearly_independent_too_many(self): CustomBasis(fdata=sample) sample = FDataBasis( - basis=Fourier(n_basis=3), + basis=FourierBasis(n_basis=3), coefficients=np.array( [ [0, 1, 0], @@ -109,7 +109,7 @@ def test_not_linearly_independent_range(self): CustomBasis(fdata=sample) sample = FDataBasis( - basis=Fourier(n_basis=3), + basis=FourierBasis(n_basis=3), coefficients=np.array( [ [2, 1, 0], @@ -166,7 +166,7 @@ def test_derivative_basis(self): ) base_functions = FDataBasis( - basis=Fourier(n_basis=5), + basis=FourierBasis(n_basis=5), coefficients=basis_coef, ) @@ -186,7 +186,7 @@ def test_derivative_basis(self): eval_points = np.linspace(0, 1, 10) # Derivative of the original functions - basis_derivative, coefs_derivative = Fourier( + basis_derivative, coefs_derivative = FourierBasis( n_basis=5, ).derivative_basis_and_coefs(coefs=coefs @ basis_coef) From c6d9ea5bae80dc16d940ec422c023d4dd538cf4e Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 17:30:52 +0100 Subject: [PATCH 370/400] Refactor custom basis to separate grid and basis --- skfda/representation/basis/_custom_basis.py | 152 +++++++++++++------- 1 file changed, 101 insertions(+), 51 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index b84693488..c11d8bc8d 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -44,53 +44,35 @@ def __init__( self.fdata = fdata @multimethod.multidispatch - def _get_coordinates_matrix(self, fdata) -> NDArrayFloat: - raise ValueError( - "Unexpected type of functional data object.", - ) + def _check_linearly_independent(self, fdata: FData) -> None: + raise ValueError("Unexpected type of functional data object.") - @multimethod.multidispatch - def _set_coordinates_matrix(self, fdata, matrix: np.ndarray): - raise ValueError( - "Unexpected type of functional data object.", + @_check_linearly_independent.register + def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: + # Reshape to a bidimensional matrix. This only affects FDataGrids + # whose codomain is not 1-dimensional and it can be done because + # checking linear independence in (R^n)^k is equivalent to doing + # it in R^(nk). + coord_matrix = fdata.data_matrix.reshape( + fdata.data_matrix.shape[0], + -1, ) + return self._check_linearly_independent_matrix(coord_matrix) - @_get_coordinates_matrix.register - def _get_coordinates_matrix_grid(self, fdata: FDataGrid) -> NDArrayFloat: - return fdata.data_matrix - - @_set_coordinates_matrix.register - def _set_coordinates_matrix_grid( - self, fdata: FDataGrid, matrix: np.ndarray, - ): - fdata.data_matrix = matrix - - @_get_coordinates_matrix.register - def _get_coordinates_matrix_basis(self, fdata: FDataBasis) -> NDArrayFloat: - return fdata.coefficients + @_check_linearly_independent.register + def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: + return self._check_linearly_independent_matrix(fdata.coefficients) - @_set_coordinates_matrix.register - def _set_coordinates_matrix_basis( - self, fdata: FDataBasis, matrix: np.ndarray, - ): - fdata.coefficients = matrix - - def _check_linearly_independent(self, fdata) -> None: + def _check_linearly_independent_matrix(self, matrix: NDArrayFloat) -> None: """Check if the functions are linearly independent.""" - coord_matrix = self._get_coordinates_matrix(fdata) - - # Reshape to a bidimensional matrix. This only affects FDataGrids - # whose codomain is not 1-dimensional. - coord_matrix = coord_matrix.reshape(coord_matrix.shape[0], -1) - - if coord_matrix.shape[0] > coord_matrix.shape[1]: + if matrix.shape[0] > matrix.shape[1]: raise ValueError( "There are more functions than the maximum dimension of the " "space that they could generate.", ) - rank = np.linalg.matrix_rank(coord_matrix) - if rank != coord_matrix.shape[0]: + rank = np.linalg.matrix_rank(matrix) + if rank != matrix.shape[0]: raise ValueError( "There are only {rank} linearly independent " "functions".format( @@ -104,31 +86,99 @@ def _derivative_basis_and_coefs( order: int = 1, ) -> Tuple[T, NDArrayFloat]: deriv_fdata = self.fdata.derivative(order=order) - new_basis = None - coord_matrix = self._get_coordinates_matrix(deriv_fdata) + return self._create_subspace_basis_coef(deriv_fdata, coefs) - # If the basis formed by the derivatives has maximum rank, - # we can just return that - if np.linalg.matrix_rank(coord_matrix) == coord_matrix.shape[0]: - return CustomBasis(fdata=deriv_fdata), coefs + @multimethod.multidispatch + def _create_subspace_basis_coef( + self: T, + fdata: FData, + coefs: np.ndarray, + ) -> Tuple[T, NDArrayFloat]: + """ + Create a basis of the subspace generated by the given functions. + + Args: + fdata: The resulting basis will span the subspace generated + by these functions. + coefs: Coefficients of some functions in the given fdata. + These coefficients will be transformed into the coefficients + of the same functions in the resulting basis. + """ + raise ValueError( + "Unexpected type of functional data object: {type}.".format( + type=type(fdata), + ), + ) - coord_matrix_reshaped = coord_matrix.reshape( - coord_matrix.shape[0], + @_create_subspace_basis_coef.register + def _create_subspace_basis_coef_grid( + self: T, + fdata: FDataGrid, + coefs: np.ndarray, + ) -> Tuple[T, NDArrayFloat]: + + # Reshape to a bidimensional matrix. This can be done because + # working in (R^n)^k is equivalent to working in R^(nk) when + # it comes to linear independence and basis. + data_matrix_reshaped = fdata.data_matrix.reshape( + fdata.data_matrix.shape[0], -1, ) + # If the basis formed by the derivatives has maximum rank, + # we can just return that + rank = np.linalg.matrix_rank(data_matrix_reshaped) + if rank == fdata.n_samples: + return CustomBasis(fdata=fdata), coefs - q, r = np.linalg.qr(coord_matrix_reshaped.T) + # Otherwise, we need to find the basis of the subspace generated + # by the functions + q, r = np.linalg.qr(data_matrix_reshaped.T) - new_coordinates = q.T.reshape( + # Go back from R^(nk) to (R^n)^k + fdata.data_matrix = q.T.reshape( -1, - *coord_matrix.shape[1:], + *fdata.data_matrix.shape[1:], ) - self._set_coordinates_matrix(deriv_fdata, new_coordinates) + new_basis = CustomBasis(fdata=fdata) + + # Since the QR decomponsition yields an orthonormal basis, + # the coefficients are just the projections of values of + # the functions in every point (coefs @ data_matrix_reshaped) + # in the new basis (q). + # Note that to simply the calculations, we use both the data_matrix + # and the basis matrix in R^(nk) instead of the original space + values_in_eval_points = coefs @ data_matrix_reshaped + coefs = values_in_eval_points @ q + + return new_basis, coefs + + @_create_subspace_basis_coef.register + def _create_subspace_basis_coef_basis( + self: T, + fdata: FDataBasis, + coefs: np.ndarray, + ) -> Tuple[T, NDArrayFloat]: + + # If the basis formed by the derivatives has maximum rank, + # we can just return that + rank = np.linalg.matrix_rank(fdata.coefficients) + if rank == fdata.n_samples: + return CustomBasis(fdata=fdata), coefs + + q, r = np.linalg.qr(fdata.coefficients.T) + + fdata.coefficients = q.T + + new_basis = CustomBasis(fdata=fdata) - new_basis = CustomBasis(fdata=deriv_fdata) - coefs = coefs @ coord_matrix_reshaped @ q + # Since the QR decomponsition yields an orthonormal basis, + # the coefficients are just the result of projecting the + # coefficients in the underlying basis of the FDataBasis onto + # the new basis (q) + coefs_wrt_underlying_fdata_basis = coefs @ fdata.coefficients + coefs = coefs_wrt_underlying_fdata_basis @ q return new_basis, coefs From b8ab958d5bd0bb658ebf8367d72e9b18b7eb8b29 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 15 Nov 2022 17:37:09 +0100 Subject: [PATCH 371/400] Minor refactoring --- skfda/tests/test_custom_basis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/skfda/tests/test_custom_basis.py b/skfda/tests/test_custom_basis.py index 184327a08..25c6d4adf 100644 --- a/skfda/tests/test_custom_basis.py +++ b/skfda/tests/test_custom_basis.py @@ -9,7 +9,7 @@ class TestBasis(unittest.TestCase): - """Tests for BasisOfFData.""" + """Tests for CustomBasis.""" def test_grid(self): """Test a datagrid toy example.""" @@ -230,7 +230,7 @@ def test_multivariate_codomain(self): np.testing.assert_equal(functions(points), expected_data) def test_multivariate_codomain_linearly_dependent(self): - """Test basis from multivariate linearly dependent functions.""" + """Test basis from multivariate linearly dependent functions.""" points = np.array([0, 1, 2]) base_functions = FDataGrid( data_matrix=np.array( From 033c4fb6551f507817621a5c14de15cc4c250c77 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 15:39:10 +0100 Subject: [PATCH 372/400] Fix typing issues --- skfda/representation/_functional_data.py | 25 ++++++++++++++++++++- skfda/representation/basis/_custom_basis.py | 16 +++++-------- 2 files changed, 29 insertions(+), 12 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 61b98aeb2..8506e2d7b 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -22,6 +22,8 @@ overload, ) +from typing_extensions import Protocol + import numpy as np import pandas.api.extensions from matplotlib.figure import Figure @@ -188,7 +190,7 @@ def dim_codomain(self) -> int: @property @abstractmethod - def coordinates(self: T) -> Sequence[T]: + def coordinates(self: T) -> _FDataCoordinateSequence: r"""Return a component of the FDataGrid. If the functional object contains multivariate samples @@ -1317,3 +1319,24 @@ def concatenate(functions: Iterable[T], as_coordinates: bool = False) -> T: ) return first.concatenate(*functions, as_coordinates=as_coordinates) + + +class _FDataCoordinateSequence(Protocol): + """ + Sequence of FData coordinates. + + Note that this represents a sequence of coordinates, not a sequence of + FData objects. + """ + + def __init__(self, fdata: FData) -> None: + pass + + def __getitem__( + self, + key: Union[int, slice], + ) -> FData: + pass + + def __len__(self) -> int: + pass diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index c11d8bc8d..7bacb0e0e 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -129,7 +129,7 @@ def _create_subspace_basis_coef_grid( # we can just return that rank = np.linalg.matrix_rank(data_matrix_reshaped) if rank == fdata.n_samples: - return CustomBasis(fdata=fdata), coefs + return type(self)(fdata=fdata), coefs # Otherwise, we need to find the basis of the subspace generated # by the functions @@ -141,7 +141,7 @@ def _create_subspace_basis_coef_grid( *fdata.data_matrix.shape[1:], ) - new_basis = CustomBasis(fdata=fdata) + new_basis = type(self)(fdata=fdata) # Since the QR decomponsition yields an orthonormal basis, # the coefficients are just the projections of values of @@ -165,13 +165,13 @@ def _create_subspace_basis_coef_basis( # we can just return that rank = np.linalg.matrix_rank(fdata.coefficients) if rank == fdata.n_samples: - return CustomBasis(fdata=fdata), coefs + return type(self)(fdata=fdata), coefs q, r = np.linalg.qr(fdata.coefficients.T) fdata.coefficients = q.T - new_basis = CustomBasis(fdata=fdata) + new_basis = type(self)(fdata=fdata) # Since the QR decomponsition yields an orthonormal basis, # the coefficients are just the result of projecting the @@ -203,13 +203,7 @@ def dim_codomain(self) -> int: return self.fdata.dim_codomain def __eq__(self, other: Any) -> bool: - from ..._utils import _same_domain - - return ( - isinstance(other, type(self)) - and _same_domain(self, other) - and self.fdata == other.fdata - ) + return super().__eq__(other) and self.fdata == other.fdata def __hash__(self) -> int: return hash(self.fdata) From de9a87938e9e7b5efd60805abb995d356c9d4b52 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 15:48:38 +0100 Subject: [PATCH 373/400] Fix style --- skfda/representation/_functional_data.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 8506e2d7b..5050e6ede 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -22,12 +22,10 @@ overload, ) -from typing_extensions import Protocol - import numpy as np import pandas.api.extensions from matplotlib.figure import Figure -from typing_extensions import Literal +from typing_extensions import Literal, Protocol from .._utils import _evaluate_grid, _to_grid_points from ..typing._base import ( @@ -47,8 +45,8 @@ from .extrapolation import ExtrapolationLike, _parse_extrapolation if TYPE_CHECKING: - from .grid import FDataGrid from .basis import Basis, FDataBasis + from .grid import FDataGrid T = TypeVar('T', bound='FData') From b644a9ac483b01c62d818dd770056c4e7af5f62c Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 15:48:46 +0100 Subject: [PATCH 374/400] Typing in decorators --- skfda/representation/basis/_custom_basis.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index 7bacb0e0e..a5e0602f8 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -43,11 +43,11 @@ def __init__( self.fdata = fdata - @multimethod.multidispatch + @multimethod.multidispatch(FData) def _check_linearly_independent(self, fdata: FData) -> None: raise ValueError("Unexpected type of functional data object.") - @_check_linearly_independent.register + @_check_linearly_independent.register(FDataGrid) def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: # Reshape to a bidimensional matrix. This only affects FDataGrids # whose codomain is not 1-dimensional and it can be done because @@ -59,7 +59,7 @@ def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: ) return self._check_linearly_independent_matrix(coord_matrix) - @_check_linearly_independent.register + @_check_linearly_independent.register(FDataBasis) def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: return self._check_linearly_independent_matrix(fdata.coefficients) @@ -89,7 +89,7 @@ def _derivative_basis_and_coefs( return self._create_subspace_basis_coef(deriv_fdata, coefs) - @multimethod.multidispatch + @multimethod.multidispatch(FData) def _create_subspace_basis_coef( self: T, fdata: FData, @@ -111,7 +111,7 @@ def _create_subspace_basis_coef( ), ) - @_create_subspace_basis_coef.register + @_create_subspace_basis_coef.register(FDataGrid, np.ndarray) def _create_subspace_basis_coef_grid( self: T, fdata: FDataGrid, @@ -154,7 +154,7 @@ def _create_subspace_basis_coef_grid( return new_basis, coefs - @_create_subspace_basis_coef.register + @_create_subspace_basis_coef.register(FDataBasis, np.ndarray) def _create_subspace_basis_coef_basis( self: T, fdata: FDataBasis, From d6c3a18b11b22d6424db3ef1c63d941db45b3f84 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 15:58:34 +0100 Subject: [PATCH 375/400] Fix more style issues --- skfda/representation/_functional_data.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 5050e6ede..246c3c9bb 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -75,6 +75,7 @@ class FData( # noqa: WPS214 coordinate functions. """ + dataset_name: Optional[str] def __init__( @@ -1169,7 +1170,6 @@ def take( # noqa: WPS238 Parameters: indices: Indices to be taken. allow_fill: How to handle negative values in `indices`. - * False: negative values in `indices` indicate positional indices from the right (the default). This is similar to :func:`numpy.take`. @@ -1178,10 +1178,8 @@ def take( # noqa: WPS238 other negative values raise a ``ValueError``. fill_value: Fill value to use for NA-indices when `allow_fill` is True. - This may be ``None``, in which case the default NA value for the type, ``self.dtype.na_value``, is used. - For many ExtensionArrays, there will be two representations of `fill_value`: a user-facing "boxed" scalar, and a low-level physical NA value. `fill_value` should be the user-facing From 5e7af3252c874968ca36e33941f930f248d97d55 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 19:41:11 +0100 Subject: [PATCH 376/400] Remove typing from decorators --- skfda/representation/basis/_custom_basis.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index a5e0602f8..7bacb0e0e 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -43,11 +43,11 @@ def __init__( self.fdata = fdata - @multimethod.multidispatch(FData) + @multimethod.multidispatch def _check_linearly_independent(self, fdata: FData) -> None: raise ValueError("Unexpected type of functional data object.") - @_check_linearly_independent.register(FDataGrid) + @_check_linearly_independent.register def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: # Reshape to a bidimensional matrix. This only affects FDataGrids # whose codomain is not 1-dimensional and it can be done because @@ -59,7 +59,7 @@ def _check_linearly_independent_grid(self, fdata: FDataGrid) -> None: ) return self._check_linearly_independent_matrix(coord_matrix) - @_check_linearly_independent.register(FDataBasis) + @_check_linearly_independent.register def _check_linearly_independent_basis(self, fdata: FDataBasis) -> None: return self._check_linearly_independent_matrix(fdata.coefficients) @@ -89,7 +89,7 @@ def _derivative_basis_and_coefs( return self._create_subspace_basis_coef(deriv_fdata, coefs) - @multimethod.multidispatch(FData) + @multimethod.multidispatch def _create_subspace_basis_coef( self: T, fdata: FData, @@ -111,7 +111,7 @@ def _create_subspace_basis_coef( ), ) - @_create_subspace_basis_coef.register(FDataGrid, np.ndarray) + @_create_subspace_basis_coef.register def _create_subspace_basis_coef_grid( self: T, fdata: FDataGrid, @@ -154,7 +154,7 @@ def _create_subspace_basis_coef_grid( return new_basis, coefs - @_create_subspace_basis_coef.register(FDataBasis, np.ndarray) + @_create_subspace_basis_coef.register def _create_subspace_basis_coef_basis( self: T, fdata: FDataBasis, From 8897e16fae67746910b0740467e878eeb3920d38 Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 21:01:08 +0100 Subject: [PATCH 377/400] Migrate test to pytest --- skfda/tests/test_custom_basis.py | 502 +++++++++++++++---------------- 1 file changed, 251 insertions(+), 251 deletions(-) diff --git a/skfda/tests/test_custom_basis.py b/skfda/tests/test_custom_basis.py index 25c6d4adf..cf60ce048 100644 --- a/skfda/tests/test_custom_basis.py +++ b/skfda/tests/test_custom_basis.py @@ -1,275 +1,275 @@ """Tests of BasisOfFData.""" -import unittest - import numpy as np +import pytest from skfda.representation.basis import CustomBasis, FDataBasis, FourierBasis from skfda.representation.grid import FDataGrid -class TestBasis(unittest.TestCase): - """Tests for CustomBasis.""" +def test_grid(): + """Test a datagrid toy example.""" + grid_points = np.array([0, 1, 2]) + sample = FDataGrid( + data_matrix=np.array([[1, 2, 3], [4, 5, 6]]), + grid_points=np.array([[0, 1, 2]]), + ) + + data_basis = FDataBasis( + basis=CustomBasis(fdata=sample), + coefficients=np.array([[1, 0], [0, 1], [1, 1]]), + ) + + evaluated = [ + np.array([1, 2, 3]), + np.array([4, 5, 6]), + np.array([5, 7, 9]), + ] + + np.testing.assert_equal(data_basis(grid_points)[..., 0], evaluated) + + +def test_basis(): + """Test a databasis toy example.""" + basis = FourierBasis(n_basis=3) + coeficients = np.array( + [ + [2, 1, 0], + [3, 1, 0], + [1, 2, 9], + ], + ) + + data_basis = FDataBasis( + basis=CustomBasis( + fdata=FDataBasis(basis=basis, coefficients=coeficients), + ), + coefficients=coeficients, + ) + + combined_basis = FDataBasis( + basis=basis, + coefficients=coeficients @ coeficients, + ) + + eval_points = np.linspace(0, 1, 100) + np.testing.assert_almost_equal( + data_basis(eval_points), + combined_basis(eval_points), + ) + + +def test_not_linearly_independent_too_many(): + """ + Test that a non linearly independent basis raises an error. + + In this case, the number of samples is greater than the number + of sampling points or base functions in the underlying base. + """ + sample = FDataGrid( + data_matrix=np.array( + [[1, 2, 3], [2, 4, 6], [15, 4, -2], [1, 28, 0]], + ), + grid_points=np.array([[0, 1, 2]]), + ) + + with pytest.raises(ValueError): + CustomBasis(fdata=sample) + + sample = FDataBasis( + basis=FourierBasis(n_basis=3), + coefficients=np.array( + [ + [0, 1, 0], + [1, 0, 0], + [0, 0, 1], + [1, 1, 1], + ], + ), + ) + + with pytest.raises(ValueError): + CustomBasis(fdata=sample) - def test_grid(self): - """Test a datagrid toy example.""" - grid_points = np.array([0, 1, 2]) - sample = FDataGrid( - data_matrix=np.array([[1, 2, 3], [4, 5, 6]]), - grid_points=np.array([[0, 1, 2]]), - ) - data_basis = FDataBasis( - basis=CustomBasis(fdata=sample), - coefficients=np.array([[1, 0], [0, 1], [1, 1]]), - ) +def test_not_linearly_independent_range(): + """ + Test that a non linearly independent basis raises an error. - evaluated = [ - np.array([1, 2, 3]), - np.array([4, 5, 6]), - np.array([5, 7, 9]), - ] + In this case, the number of samples is valid but the given + samples are not linearly independent. + """ + sample = FDataGrid( + data_matrix=np.array([[1, 2, 3], [2, 4, 6]]), + grid_points=np.array([[0, 1, 2]]), + ) - np.testing.assert_equal(data_basis(grid_points)[..., 0], evaluated) + with pytest.raises(ValueError): + CustomBasis(fdata=sample) - def test_basis(self): - """Test a databasis toy example.""" - basis = FourierBasis(n_basis=3) - coeficients = np.array( + sample = FDataBasis( + basis=FourierBasis(n_basis=3), + coefficients=np.array( [ [2, 1, 0], [3, 1, 0], - [1, 2, 9], + [1, 2, 0], ], - ) - - data_basis = FDataBasis( - basis=CustomBasis( - fdata=FDataBasis(basis=basis, coefficients=coeficients), - ), - coefficients=coeficients, - ) - - combined_basis = FDataBasis( - basis=basis, - coefficients=coeficients @ coeficients, - ) - - eval_points = np.linspace(0, 1, 100) - np.testing.assert_almost_equal( - data_basis(eval_points), - combined_basis(eval_points), - ) - - def test_not_linearly_independent_too_many(self): - """ - Test that a non linearly independent basis raises an error. - - In this case, the number of samples is greater than the number - of sampling points or base functions in the underlying base. - """ - sample = FDataGrid( - data_matrix=np.array( - [[1, 2, 3], [2, 4, 6], [15, 4, -2], [1, 28, 0]], - ), - grid_points=np.array([[0, 1, 2]]), - ) - - with self.assertRaises(ValueError): - CustomBasis(fdata=sample) - - sample = FDataBasis( - basis=FourierBasis(n_basis=3), - coefficients=np.array( - [ - [0, 1, 0], - [1, 0, 0], - [0, 0, 1], - [1, 1, 1], - ], - ), - ) - - with self.assertRaises(ValueError): - CustomBasis(fdata=sample) - - def test_not_linearly_independent_range(self): - """ - Test that a non linearly independent basis raises an error. - - In this case, the number of samples is valid but the given - samples are not linearly independent. - """ - sample = FDataGrid( - data_matrix=np.array([[1, 2, 3], [2, 4, 6]]), - grid_points=np.array([[0, 1, 2]]), - ) - - with self.assertRaises(ValueError): - CustomBasis(fdata=sample) - - sample = FDataBasis( - basis=FourierBasis(n_basis=3), - coefficients=np.array( - [ - [2, 1, 0], - [3, 1, 0], - [1, 2, 0], - ], - ), - ) - - with self.assertRaises(ValueError): - CustomBasis(fdata=sample) - - def test_derivative_grid(self): - """Test the derivative of a basis constructed from a FDataGrid.""" - base_functions = FDataGrid( - data_matrix=np.array([[1, 2, 3], [1, 1, 5]]), - grid_points=np.array([[0, 1, 2]]), - ) - # The derivative of the first function is always 1 - # The derivative of the second function is 0 and then 4 - - basis = CustomBasis(fdata=base_functions) - - coefs = np.array([[1, 0], [0, 1], [1, 1]]) - derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( - coefs=coefs, - order=1, - ) - - derivative = FDataBasis( - basis=derivate_basis, - coefficients=derivative_coefs, - ) - - eval_points = np.array([0.5, 1.5]) - - # Derivative of the original functions sampled in the given points - # and multiplied by the coefs - derivative_evaluated = np.array([[1, 1], [0, 4], [1, 5]]) - np.testing.assert_allclose( - derivative(eval_points)[..., 0], - derivative_evaluated, - atol=1e-15, - ) - - def test_derivative_basis(self): - """Test the derivative of a basis constructed from a FDataBasis.""" - basis_coef = np.array( + ), + ) + + with pytest.raises(ValueError): + CustomBasis(fdata=sample) + + +def test_derivative_grid(): + """Test the derivative of a basis constructed from a FDataGrid.""" + base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 1, 5]]), + grid_points=np.array([[0, 1, 2]]), + ) + # The derivative of the first function is always 1 + # The derivative of the second function is 0 and then 4 + + basis = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 1]]) + derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( + coefs=coefs, + order=1, + ) + + derivative = FDataBasis( + basis=derivate_basis, + coefficients=derivative_coefs, + ) + + eval_points = np.array([0.5, 1.5]) + + # Derivative of the original functions sampled in the given points + # and multiplied by the coefs + derivative_evaluated = np.array([[1, 1], [0, 4], [1, 5]]) + np.testing.assert_allclose( + derivative(eval_points)[..., 0], + derivative_evaluated, + atol=1e-15, + ) + + +def test_derivative_basis(): + """Test the derivative of a basis constructed from a FDataBasis.""" + basis_coef = np.array( + [ + [2, 1, 0, 10, 0], + [3, 1, 99, 15, 99], + [0, 2, 9, 22, 0], + ], + ) + + base_functions = FDataBasis( + basis=FourierBasis(n_basis=5), + coefficients=basis_coef, + ) + + basis = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [2, 1, 5]]) + derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( + coefs=coefs, + order=1, + ) + + derivative = FDataBasis( + basis=derivate_basis, + coefficients=derivative_coefs, + ) + + eval_points = np.linspace(0, 1, 10) + + # Derivative of the original functions + basis_derivative, coefs_derivative = FourierBasis( + n_basis=5, + ).derivative_basis_and_coefs(coefs=coefs @ basis_coef) + + derivative_on_the_basis = FDataBasis( + basis=basis_derivative, + coefficients=coefs_derivative, + ) + np.testing.assert_almost_equal( + derivative(eval_points), + derivative_on_the_basis(eval_points), + ) + + +def test_multivariate_codomain(): + """Test basis from a multivariate function.""" + points = np.array([0, 1, 2]) + base_functions = FDataGrid( + data_matrix=np.array( [ - [2, 1, 0, 10, 0], - [3, 1, 99, 15, 99], - [0, 2, 9, 22, 0], + [[0, 0, 1], [0, 0, 2], [0, 0, 3]], + [[1, 0, 0], [2, 0, 0], [3, 0, 0]], ], - ) - - base_functions = FDataBasis( - basis=FourierBasis(n_basis=5), - coefficients=basis_coef, - ) - - basis = CustomBasis(fdata=base_functions) - - coefs = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [2, 1, 5]]) - derivate_basis, derivative_coefs = basis.derivative_basis_and_coefs( - coefs=coefs, - order=1, - ) - - derivative = FDataBasis( - basis=derivate_basis, - coefficients=derivative_coefs, - ) - - eval_points = np.linspace(0, 1, 10) - - # Derivative of the original functions - basis_derivative, coefs_derivative = FourierBasis( - n_basis=5, - ).derivative_basis_and_coefs(coefs=coefs @ basis_coef) - - derivative_on_the_basis = FDataBasis( - basis=basis_derivative, - coefficients=coefs_derivative, - ) - np.testing.assert_almost_equal( - derivative(eval_points), - derivative_on_the_basis(eval_points), - ) - - def test_multivariate_codomain(self): - """Test basis from a multivariate function.""" - points = np.array([0, 1, 2]) - base_functions = FDataGrid( - data_matrix=np.array( - [ - [[0, 0, 1], [0, 0, 2], [0, 0, 3]], - [[1, 0, 0], [2, 0, 0], [3, 0, 0]], - ], - ), - grid_points=points, - ) - base = CustomBasis(fdata=base_functions) - - coefs = np.array([[1, 0], [0, 1], [1, 1]]) - - functions = FDataBasis( - basis=base, - coefficients=coefs, - ) - - expected_data = np.array( + ), + grid_points=points, + ) + base = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 1]]) + + functions = FDataBasis( + basis=base, + coefficients=coefs, + ) + + expected_data = np.array( + [ + [[0, 0, 1], [0, 0, 2], [0, 0, 3]], + [[1, 0, 0], [2, 0, 0], [3, 0, 0]], + [[1, 0, 1], [2, 0, 2], [3, 0, 3]], + ], + ) + np.testing.assert_equal(functions(points), expected_data) + + +def test_multivariate_codomain_linearly_dependent(): + """Test basis from multivariate linearly dependent functions.""" + points = np.array([0, 1, 2]) + base_functions = FDataGrid( + data_matrix=np.array( [ [[0, 0, 1], [0, 0, 2], [0, 0, 3]], [[1, 0, 0], [2, 0, 0], [3, 0, 0]], [[1, 0, 1], [2, 0, 2], [3, 0, 3]], ], - ) - np.testing.assert_equal(functions(points), expected_data) - - def test_multivariate_codomain_linearly_dependent(self): - """Test basis from multivariate linearly dependent functions.""" - points = np.array([0, 1, 2]) - base_functions = FDataGrid( - data_matrix=np.array( - [ - [[0, 0, 1], [0, 0, 2], [0, 0, 3]], - [[1, 0, 0], [2, 0, 0], [3, 0, 0]], - [[1, 0, 1], [2, 0, 2], [3, 0, 3]], - ], - ), - grid_points=points, - ) - # The third function is the sum of the first two - - with self.assertRaises(ValueError): - CustomBasis(fdata=base_functions) - - def test_evaluate_derivative(self): - """Test the evaluation of the derivative of a DataBasis.""" - grid_points = np.array([[0, 1, 2]]) - base_functions = FDataGrid( - data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), - grid_points=grid_points, - ) - basis = CustomBasis(fdata=base_functions) - - coefs = np.array([[1, 0], [0, 1], [1, 2]]) - - functions = FDataBasis( - basis=basis, - coefficients=coefs, - ) - deriv = functions.derivative(order=1) - derivate_evalated = deriv(np.array([0.5, 1.5])) - np.testing.assert_allclose( - derivate_evalated[..., 0], - np.array([[1, 1], [2, 2], [5, 5]]), - rtol=1e-15, - ) - - -if __name__ == "__main__": - unittest.main() + ), + grid_points=points, + ) + # The third function is the sum of the first two + + with pytest.raises(ValueError): + CustomBasis(fdata=base_functions) + + +def test_evaluate_derivative(): + """Test the evaluation of the derivative of a DataBasis.""" + grid_points = np.array([[0, 1, 2]]) + base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), + grid_points=grid_points, + ) + basis = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 2]]) + + functions = FDataBasis( + basis=basis, + coefficients=coefs, + ) + deriv = functions.derivative(order=1) + derivate_evalated = deriv(np.array([0.5, 1.5])) + np.testing.assert_allclose( + derivate_evalated[..., 0], + np.array([[1, 1], [2, 2], [5, 5]]), + rtol=1e-15, + ) From fad90b2a82732d716ae4500f05fa165aad129d5e Mon Sep 17 00:00:00 2001 From: Ddelval Date: Thu, 17 Nov 2022 21:27:28 +0100 Subject: [PATCH 378/400] Test and fix equality implementation --- skfda/representation/basis/_custom_basis.py | 12 ++++- skfda/tests/test_custom_basis.py | 60 +++++++++++++++++++++ 2 files changed, 71 insertions(+), 1 deletion(-) diff --git a/skfda/representation/basis/_custom_basis.py b/skfda/representation/basis/_custom_basis.py index 7bacb0e0e..184aa40b6 100644 --- a/skfda/representation/basis/_custom_basis.py +++ b/skfda/representation/basis/_custom_basis.py @@ -203,7 +203,17 @@ def dim_codomain(self) -> int: return self.fdata.dim_codomain def __eq__(self, other: Any) -> bool: - return super().__eq__(other) and self.fdata == other.fdata + return super().__eq__(other) and all(self.fdata == other.fdata) + + def __ne__(self, other: Any) -> bool: + return not self.__eq__(other) + + def __repr__(self) -> str: + """Representation of a CustomBasis object.""" + return "{super}, fdata={fdata}".format( + super=super().__repr__(), + fdata=self.fdata, + ) def __hash__(self) -> int: return hash(self.fdata) diff --git a/skfda/tests/test_custom_basis.py b/skfda/tests/test_custom_basis.py index cf60ce048..23b5fe498 100644 --- a/skfda/tests/test_custom_basis.py +++ b/skfda/tests/test_custom_basis.py @@ -273,3 +273,63 @@ def test_evaluate_derivative(): np.array([[1, 1], [2, 2], [5, 5]]), rtol=1e-15, ) + + +def test_coordinates(): + """Test the coordinates of a basis.""" + grid_points = np.array([[0, 1]]) + base_functions = FDataGrid( + data_matrix=np.array([ + [[1, 2, 3], [1, 3, 5]], + [[4, 5, 6], [7, 8, 9]], + ]), + grid_points=grid_points, + ) + basis = CustomBasis(fdata=base_functions) + + coefs = np.array([[1, 0], [0, 1], [1, 2]]) + + functions = FDataBasis( + basis=basis, + coefficients=coefs, + ) + + # First coordinate at 0 + np.testing.assert_allclose( + functions.coordinates[0](0), + np.array([[[1]], [[4]], [[9]]]), + ) + + # Second two coordinates at 1 + np.testing.assert_allclose( + functions.coordinates[1:3](1), + np.array([[[3, 5]], [[8, 9]], [[16 + 3, 18 + 5]]]), + ) + + +def test_equality(): + """Test the equality of two basis.""" + grid_points = np.array([[0, 1, 2]]) + base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), + grid_points=grid_points, + ) + basis = CustomBasis(fdata=base_functions) + + other_base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 3, 5]]), + grid_points=grid_points, + ) + + other_basis = CustomBasis(fdata=other_base_functions) + + assert basis == other_basis + + different_base_functions = FDataGrid( + data_matrix=np.array([[1, 2, 3], [1, 8, 5]]), + grid_points=grid_points, + ) + + different_basis = CustomBasis(fdata=different_base_functions) + + assert basis != different_basis From 90e6f72799655c511ef0378051c2f78c5ffa06d8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Fri, 18 Nov 2022 16:29:36 +0100 Subject: [PATCH 379/400] Test all classifiers --- skfda/tests/test_classifier_classes.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py index 0acf422a9..cd9e73be5 100644 --- a/skfda/tests/test_classifier_classes.py +++ b/skfda/tests/test_classifier_classes.py @@ -9,8 +9,18 @@ from skfda._utils._sklearn_adapter import ClassifierMixin from skfda.datasets import make_gaussian_process +from skfda.exploratory.depth import ModifiedBandDepth +from skfda.exploratory.stats.covariance import ParametricGaussianCovariance +from skfda.misc.covariances import Gaussian from skfda.ml.classification import ( + DDClassifier, + DDGClassifier, + DTMClassifier, KNeighborsClassifier, + LogisticRegression, + MaximumDepthClassifier, + NearestCentroid, + QuadraticDiscriminantAnalysis, RadiusNeighborsClassifier, ) from skfda.representation import FData @@ -20,7 +30,21 @@ @pytest.fixture( params=[ + DDClassifier(degree=2), + DDGClassifier( + depth_method=[("mbd", ModifiedBandDepth())], + multivariate_classifier=KNeighborsClassifier(), + ), + DTMClassifier(proportiontocut=0.25), KNeighborsClassifier(), + LogisticRegression(), + MaximumDepthClassifier(), + NearestCentroid(), + QuadraticDiscriminantAnalysis( + cov_estimator=ParametricGaussianCovariance( + Gaussian(), + ), + ), RadiusNeighborsClassifier(), ], ids=lambda clf: type(clf).__name__, From 1d42f0400ebc1e8b436a26cc9da5adcdcf52e307 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Wed, 23 Nov 2022 12:48:09 +0100 Subject: [PATCH 380/400] Add external:class to setup.cfg, and use Target typevar with str --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 401c13788..7a8ce1504 100644 --- a/setup.cfg +++ b/setup.cfg @@ -114,7 +114,7 @@ rst-directives = versionadded,versionchanged, rst-roles = - attr,class,doc,footcite,footcite:ts,func,meth,mod,obj,ref,term, + attr,class,doc,footcite,footcite:ts,func,meth,mod,obj,ref,term,external:class allowed-domain-names = data, obj, result, results, val, value, values, var From 9d43fdd50119cc8b9a08730a230f8a0bc071d49b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Wed, 23 Nov 2022 12:55:00 +0100 Subject: [PATCH 381/400] Fix style of internal loop --- skfda/ml/classification/_logistic_regression.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index b99588bf7..8cf98c795 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -162,7 +162,10 @@ def fit( # noqa: D102 # This does not improve selected_indexes = selected_indexes[:n_selected] selected_values = selected_values[:, :n_selected] - likelihood_curves_data = likelihood_curves_data[:n_selected, t] + likelihood_curves_data = likelihood_curves_data[ + :n_selected, + n_selected - 1, + ] break last_max_likelihood = max_likelihood From 864855b8bc5a6d6d8410ac4dbbf07e540cb34d46 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Wed, 23 Nov 2022 12:56:08 +0100 Subject: [PATCH 382/400] Quickfix index variable --- skfda/ml/classification/_logistic_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 8cf98c795..59525c9d4 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -164,7 +164,7 @@ def fit( # noqa: D102 selected_values = selected_values[:, :n_selected] likelihood_curves_data = likelihood_curves_data[ :n_selected, - n_selected - 1, + n_features - 1, ] break From a9e9e518347903eb44a9498eb91a2cbf54e59199 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mart=C3=ADn=20S=C3=A1nchez=20Signorini?= Date: Wed, 23 Nov 2022 13:00:55 +0100 Subject: [PATCH 383/400] NOQA: Too many local variables --- skfda/ml/classification/_logistic_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py index 59525c9d4..5a29975ef 100644 --- a/skfda/ml/classification/_logistic_regression.py +++ b/skfda/ml/classification/_logistic_regression.py @@ -105,7 +105,7 @@ def __init__( self.solver = solver self.max_iter = max_iter - def fit( # noqa: D102 + def fit( # noqa: D102, WPS210 self, X: FDataGrid, y: NDArrayAny, From f1d3887708a19d224a4263eb810c03b427999d31 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Mon, 28 Nov 2022 13:04:41 +0100 Subject: [PATCH 384/400] Fix covariances. --- skfda/misc/covariances.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/covariances.py b/skfda/misc/covariances.py index 8c7c8fa78..127643d73 100644 --- a/skfda/misc/covariances.py +++ b/skfda/misc/covariances.py @@ -14,7 +14,7 @@ def _squared_norms(x: NDArrayFloat, y: NDArrayFloat) -> NDArrayFloat: return ( # type: ignore[no-any-return] - (x[np.newaxis, :, :] - y[:, np.newaxis, :]) ** 2 + (x[:, np.newaxis, :] - y[np.newaxis, :, :]) ** 2 ).sum(2) From 0c9cc5cd83f8fae598ef13657845ed5ac31993dd Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 29 Nov 2022 17:39:41 +0100 Subject: [PATCH 385/400] Implement requested changes --- skfda/representation/_functional_data.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 246c3c9bb..274976254 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -189,7 +189,7 @@ def dim_codomain(self) -> int: @property @abstractmethod - def coordinates(self: T) -> _FDataCoordinateSequence: + def coordinates(self: T) -> _CoordinateSequence: r"""Return a component of the FDataGrid. If the functional object contains multivariate samples @@ -1317,21 +1317,21 @@ def concatenate(functions: Iterable[T], as_coordinates: bool = False) -> T: return first.concatenate(*functions, as_coordinates=as_coordinates) -class _FDataCoordinateSequence(Protocol): +F = TypeVar("F") + + +class _CoordinateSequence(Protocol[F]): """ - Sequence of FData coordinates. + Sequence of coordinates. Note that this represents a sequence of coordinates, not a sequence of FData objects. """ - def __init__(self, fdata: FData) -> None: - pass - def __getitem__( self, key: Union[int, slice], - ) -> FData: + ) -> F: pass def __len__(self) -> int: From 0f2909949890b01a62839acd39d213e3d838edda Mon Sep 17 00:00:00 2001 From: Ddelval Date: Tue, 29 Nov 2022 17:49:24 +0100 Subject: [PATCH 386/400] typing fixes --- skfda/representation/_functional_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/representation/_functional_data.py b/skfda/representation/_functional_data.py index 274976254..9317d0400 100644 --- a/skfda/representation/_functional_data.py +++ b/skfda/representation/_functional_data.py @@ -1317,7 +1317,7 @@ def concatenate(functions: Iterable[T], as_coordinates: bool = False) -> T: return first.concatenate(*functions, as_coordinates=as_coordinates) -F = TypeVar("F") +F = TypeVar("F", covariant=True) class _CoordinateSequence(Protocol[F]): From b5269990781839f73462e4763faff530c2181ae9 Mon Sep 17 00:00:00 2001 From: Saumyaranjan Date: Sat, 24 Dec 2022 16:35:10 +0530 Subject: [PATCH 387/400] changed gramian to gram --- skfda/misc/_math.py | 2 +- skfda/misc/operators/__init__.py | 8 +++---- skfda/misc/operators/_identity.py | 6 ++--- .../_linear_differential_operator.py | 14 ++++++------ skfda/misc/operators/_operators.py | 22 +++++++++---------- skfda/tests/test_regularization.py | 12 +++++----- 6 files changed, 32 insertions(+), 32 deletions(-) diff --git a/skfda/misc/_math.py b/skfda/misc/_math.py index 0cb71737b..9b5f0b5a8 100644 --- a/skfda/misc/_math.py +++ b/skfda/misc/_math.py @@ -468,7 +468,7 @@ def _inner_product_integrate( len_arg2 = len(arg2) else: # If the arguments are callables, we need to pass the domain range - # explicitly. This is used internally for computing the gramian + # explicitly. This is used internally for computing the gram # matrix of operators. assert _domain_range is not None domain_range = _domain_range diff --git a/skfda/misc/operators/__init__.py b/skfda/misc/operators/__init__.py index 605b3a933..482a3a5cb 100644 --- a/skfda/misc/operators/__init__.py +++ b/skfda/misc/operators/__init__.py @@ -12,8 +12,8 @@ "_operators": [ "MatrixOperator", "Operator", - "gramian_matrix", - "gramian_matrix_optimization", + "gram_matrix", + "gram_matrix_optimization", ], "_srvf": ["SRSF"], }, @@ -28,7 +28,7 @@ from ._operators import ( MatrixOperator as MatrixOperator, Operator as Operator, - gramian_matrix as gramian_matrix, - gramian_matrix_optimization as gramian_matrix_optimization, + gram_matrix as gram_matrix, + gram_matrix_optimization as gram_matrix_optimization, ) from ._srvf import SRSF as SRSF diff --git a/skfda/misc/operators/_identity.py b/skfda/misc/operators/_identity.py index 11f2084a2..43be259cf 100644 --- a/skfda/misc/operators/_identity.py +++ b/skfda/misc/operators/_identity.py @@ -7,7 +7,7 @@ from ...representation import FDataGrid from ...representation.basis import Basis from ...typing._numpy import NDArrayFloat -from ._operators import InputType, Operator, gramian_matrix_optimization +from ._operators import InputType, Operator, gram_matrix_optimization T = TypeVar("T", bound=InputType) @@ -28,7 +28,7 @@ def __call__(self, f: T) -> T: # noqa: D102 return f -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def basis_penalty_matrix_optimized( linear_operator: Identity[Any], basis: Basis, @@ -37,7 +37,7 @@ def basis_penalty_matrix_optimized( return basis.gram_matrix() -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def fdatagrid_penalty_matrix_optimized( linear_operator: Identity[Any], basis: FDataGrid, diff --git a/skfda/misc/operators/_linear_differential_operator.py b/skfda/misc/operators/_linear_differential_operator.py index 02b6f190e..776b4aa72 100644 --- a/skfda/misc/operators/_linear_differential_operator.py +++ b/skfda/misc/operators/_linear_differential_operator.py @@ -18,7 +18,7 @@ ) from ...typing._base import DomainRangeLike from ...typing._numpy import NDArrayFloat -from ._operators import Operator, gramian_matrix_optimization +from ._operators import Operator, gram_matrix_optimization Order = int @@ -218,12 +218,12 @@ def applied_linear_diff_op( ############################################################# # -# Optimized implementations of gramian matrix for each basis. +# Optimized implementations of gram matrix for each basis. # ############################################################# -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def constant_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: ConstantBasis, @@ -299,7 +299,7 @@ def _monomial_evaluate_constant_linear_diff_op( return polynomials # type: ignore[no-any-return] -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def monomial_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: MonomialBasis, @@ -427,7 +427,7 @@ def _fourier_penalty_matrix_optimized_orthonormal( return penalty_matrix -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def fourier_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: FourierBasis, @@ -445,7 +445,7 @@ def fourier_penalty_matrix_optimized( return _fourier_penalty_matrix_optimized_orthonormal(basis, weights) -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def bspline_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: BSplineBasis, @@ -576,7 +576,7 @@ def bspline_penalty_matrix_optimized( return penalty_matrix -@gramian_matrix_optimization.register +@gram_matrix_optimization.register def fdatagrid_penalty_matrix_optimized( linear_operator: LinearDifferentialOperator, basis: FDataGrid, diff --git a/skfda/misc/operators/_operators.py b/skfda/misc/operators/_operators.py index 770401d7a..221c4ac89 100644 --- a/skfda/misc/operators/_operators.py +++ b/skfda/misc/operators/_operators.py @@ -37,26 +37,26 @@ def __call__(self, vector: OperatorInput) -> OperatorOutput: @multimethod.multidispatch -def gramian_matrix_optimization( +def gram_matrix_optimization( linear_operator: Any, basis: OperatorInput, ) -> NDArrayFloat: """ - Efficient implementation of gramian_matrix. + Efficient implementation of gram_matrix. Generic function that can be subclassed for different combinations of operator and basis in order to provide a more efficient implementation - for the gramian matrix. + for the gram matrix. """ return NotImplemented -def gramian_matrix_numerical( +def gram_matrix_numerical( linear_operator: Operator[OperatorInput, OutputType], basis: OperatorInput, ) -> NDArrayFloat: """ - Return the gramian matrix given a basis, computed numerically. + Return the gram matrix given a basis, computed numerically. This method should work for every linear operator. @@ -70,19 +70,19 @@ def gramian_matrix_numerical( return inner_product_matrix(evaluated_basis, _domain_range=domain_range) -def gramian_matrix( +def gram_matrix( linear_operator: Operator[OperatorInput, OutputType], basis: OperatorInput, ) -> NDArrayFloat: r""" - Return the gramian matrix given a basis. + Return the gram matrix given a basis. - The gramian operator of a linear operator :math:`\Gamma` is + The gram operator of a linear operator :math:`\Gamma` is .. math:: G = \Gamma*\Gamma - This method evaluates that gramian operator in a given basis, + This method evaluates that gram operator in a given basis, which is necessary for performing Tikhonov regularization, among other things. @@ -91,11 +91,11 @@ def gramian_matrix( """ # Try to use a more efficient implementation - matrix = gramian_matrix_optimization(linear_operator, basis) + matrix = gram_matrix_optimization(linear_operator, basis) if matrix is not NotImplemented: return matrix - return gramian_matrix_numerical(linear_operator, basis) + return gram_matrix_numerical(linear_operator, basis) class MatrixOperator(Operator[NDArrayFloat, NDArrayFloat]): diff --git a/skfda/tests/test_regularization.py b/skfda/tests/test_regularization.py index 8b7033dea..fa3d4d717 100644 --- a/skfda/tests/test_regularization.py +++ b/skfda/tests/test_regularization.py @@ -14,12 +14,12 @@ from skfda.misc.operators import ( Identity, LinearDifferentialOperator, - gramian_matrix, + gram_matrix, ) from skfda.misc.operators._linear_differential_operator import ( _monomial_evaluate_constant_linear_diff_op, ) -from skfda.misc.operators._operators import gramian_matrix_numerical +from skfda.misc.operators._operators import gram_matrix_numerical from skfda.misc.regularization import L2Regularization from skfda.ml.regression import LinearRegression from skfda.representation.basis import ( @@ -58,8 +58,8 @@ def _test_penalty( operator = LinearDifferentialOperator(linear_diff_op) - penalty = gramian_matrix(operator, basis) - numerical_penalty = gramian_matrix_numerical(operator, basis) + penalty = gram_matrix(operator, basis) + numerical_penalty = gram_matrix_numerical(operator, basis) np.testing.assert_allclose( penalty, @@ -222,8 +222,8 @@ def test_bspline_penalty_special_case(self) -> None: ]) operator = LinearDifferentialOperator(basis.order - 1) - penalty = gramian_matrix(operator, basis) - numerical_penalty = gramian_matrix_numerical(operator, basis) + penalty = gram_matrix(operator, basis) + numerical_penalty = gram_matrix_numerical(operator, basis) np.testing.assert_allclose( penalty, From cc1189a41a735a9e78cfa1bb39126d1b84fffb0d Mon Sep 17 00:00:00 2001 From: Saumyaranjan Date: Sat, 24 Dec 2022 21:35:56 +0530 Subject: [PATCH 388/400] changed gramian to gram for better consistency --- skfda/misc/regularization/_regularization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 47c3a5432..28f789788 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -11,7 +11,7 @@ from ...representation import FData from ...representation.basis import Basis from ...typing._numpy import NDArrayFloat -from ..operators import Identity, Operator, gramian_matrix +from ..operators import Identity, Operator, gram_matrix from ..operators._operators import OperatorInput From a20fe9beebd780f997957e7e86d37d0bec9fc36c Mon Sep 17 00:00:00 2001 From: Saumyaranjan Date: Sat, 24 Dec 2022 21:41:15 +0530 Subject: [PATCH 389/400] gramian to gram --- skfda/misc/regularization/_regularization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skfda/misc/regularization/_regularization.py b/skfda/misc/regularization/_regularization.py index 28f789788..d8ca23788 100644 --- a/skfda/misc/regularization/_regularization.py +++ b/skfda/misc/regularization/_regularization.py @@ -101,7 +101,7 @@ def penalty_matrix( else self.linear_operator ) - return self.regularization_parameter * gramian_matrix( + return self.regularization_parameter * gram_matrix( linear_operator, basis, ) From 0b578efd1dc78a56124a7e310a35d30116398429 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Wed, 28 Dec 2022 14:23:15 +0100 Subject: [PATCH 390/400] Remove pinned Numba version in CI. --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 2d910db96..8ffb3eae9 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -28,7 +28,7 @@ jobs: - name: Run tests run: | pip3 debug --verbose . - pip3 install numba==0.53 + pip3 install numba pip3 install --upgrade-strategy eager -v . coverage run --source=skfda/ setup.py test; From 864f8305c7b38a1c4beea441063e6e931d31db67 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Thu, 29 Dec 2022 13:09:59 +0100 Subject: [PATCH 391/400] Fix scikit-learn package name in requirements. --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 78c981489..0a9ce64ed 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,6 @@ numpy scipy setuptools Cython -sklearn +scikit-learn multimethod>=1.2 findiff From cea03369902b4f728b4979a990357635cc437f47 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 30 Dec 2022 00:10:08 +0100 Subject: [PATCH 392/400] Change to Pydata Sphinx Theme. --- docs/conf.py | 33 ++++++++++++++++++--------------- readthedocs-requirements.txt | 2 +- 2 files changed, 19 insertions(+), 16 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 29a88f602..1b9324422 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -115,7 +115,7 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = "sphinx_rtd_theme" +html_theme = "pydata_sphinx_theme" html_logo = "logos/notitle_logo/notitle_logo.png" @@ -124,8 +124,23 @@ # documentation. # html_theme_options = { - 'logo_only': True, - 'style_nav_header_background': 'Gainsboro', + "use_edit_page_button": True, + "github_url": "https://github.com/GAA-UAM/scikit-fda", + "icon_links": [ + { + "name": "PyPI", + "url": "https://pypi.org/project/scikit-fda", + "icon": "https://avatars.githubusercontent.com/u/2964877", + "type": "url", + }, + ], +} + +html_context = { + "github_user": "GAA-UAM", + "github_repo": "scikit-fda", + "github_version": "develop", + "doc_path": "docs", } # Add any paths that contain custom static files (such as style sheets) here, @@ -135,18 +150,6 @@ # Custom sidebar templates, must be a dictionary that maps document names # to template names. -# -# This is required for the alabaster theme -# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars -html_sidebars = { - '**': [ - 'about.html', - 'navigation.html', - 'relations.html', # needs 'show_related': True theme option to display - 'searchbox.html', - 'donate.html', - ] -} # -- Options for HTMLHelp output ------------------------------------------ diff --git a/readthedocs-requirements.txt b/readthedocs-requirements.txt index 4328bdbec..966023f33 100644 --- a/readthedocs-requirements.txt +++ b/readthedocs-requirements.txt @@ -3,7 +3,7 @@ basemap basemap-data basemap-data-hires Sphinx>=3 -sphinx_rtd_theme +pydata-sphinx-theme sphinx-gallery pillow setuptools>=41.2 From 604ec8095507b2ec7a98934ca5eaae2eca327c08 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 30 Dec 2022 00:29:35 +0100 Subject: [PATCH 393/400] Fix logo. --- docs/_static/logo.png | Bin 0 -> 519286 bytes docs/conf.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 docs/_static/logo.png diff --git a/docs/_static/logo.png 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zKv-(f8XWmiI1KDR^85G0zY_RY0{=?jf0+bg`V;o3sLly1%0AHkUpDlAhx@Mt{*}PL r68KjF|4QIr3H&R8|6e7rGqH2QM0pWsp8O-~_`!;DYO;9`pT77%(}E@I literal 0 HcmV?d00001 diff --git a/docs/conf.py b/docs/conf.py index 1b9324422..ec64cd935 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -117,7 +117,7 @@ # html_theme = "pydata_sphinx_theme" -html_logo = "logos/notitle_logo/notitle_logo.png" +html_logo = "logo.png" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the From cc466faa7e4699835560d3dde6b1f2667353f985 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 30 Dec 2022 00:47:57 +0100 Subject: [PATCH 394/400] Try to fix logo again. --- docs/conf.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index ec64cd935..7419b55fb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -134,6 +134,10 @@ "type": "url", }, ], + "logo": { + "image_light": "logo.png", + "image_dark": "logo.png", + } } html_context = { From d65222b910328d42480b566352dec576e80a3430 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Fri, 30 Dec 2022 20:20:49 +0100 Subject: [PATCH 395/400] Move ICTAI examples to normal page of examples. --- docs/.gitignore | 1 - docs/conf.py | 6 +++--- docs/index.rst | 7 ------- {full_examples => examples/full_examples}/README.txt | 0 .../full_examples}/plot_aemet_unsupervised.py | 0 .../full_examples}/plot_phonemes_classification.py | 0 .../full_examples}/plot_tecator_regression.py | 0 setup.cfg | 2 +- 8 files changed, 4 insertions(+), 12 deletions(-) rename {full_examples => examples/full_examples}/README.txt (100%) rename {full_examples => examples/full_examples}/plot_aemet_unsupervised.py (100%) rename {full_examples => examples/full_examples}/plot_phonemes_classification.py (100%) rename {full_examples => examples/full_examples}/plot_tecator_regression.py (100%) diff --git a/docs/.gitignore b/docs/.gitignore index ff2ab8c70..eef2fd5cb 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -2,4 +2,3 @@ /auto_tutorial/ /backreferences/ **/autosummary/ -/auto_full_examples/ diff --git a/docs/conf.py b/docs/conf.py index 7419b55fb..f10f375aa 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -137,7 +137,7 @@ "logo": { "image_light": "logo.png", "image_dark": "logo.png", - } + }, } html_context = { @@ -276,9 +276,9 @@ def __call__(self, filename: str) -> str: sphinx_gallery_conf = { # path to your examples scripts - 'examples_dirs': ['../examples', '../tutorial', '../full_examples'], + 'examples_dirs': ['../examples', '../tutorial'], # path where to save gallery generated examples - 'gallery_dirs': ['auto_examples', 'auto_tutorial', 'auto_full_examples'], + 'gallery_dirs': ['auto_examples', 'auto_tutorial'], 'reference_url': { # The module you locally document uses None 'skfda': None, diff --git a/docs/index.rst b/docs/index.rst index 9d6d41182..48bcb3d6c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -26,13 +26,6 @@ Github you can find more information related to the development of the package. :hidden: auto_examples/index - -.. toctree:: - :maxdepth: 1 - :titlesonly: - :hidden: - - auto_full_examples/index .. toctree:: :maxdepth: 2 diff --git a/full_examples/README.txt b/examples/full_examples/README.txt similarity index 100% rename from full_examples/README.txt rename to examples/full_examples/README.txt diff --git a/full_examples/plot_aemet_unsupervised.py b/examples/full_examples/plot_aemet_unsupervised.py similarity index 100% rename from full_examples/plot_aemet_unsupervised.py rename to examples/full_examples/plot_aemet_unsupervised.py diff --git a/full_examples/plot_phonemes_classification.py b/examples/full_examples/plot_phonemes_classification.py similarity index 100% rename from full_examples/plot_phonemes_classification.py rename to examples/full_examples/plot_phonemes_classification.py diff --git a/full_examples/plot_tecator_regression.py b/examples/full_examples/plot_tecator_regression.py similarity index 100% rename from full_examples/plot_tecator_regression.py rename to examples/full_examples/plot_tecator_regression.py diff --git a/setup.cfg b/setup.cfg index 7a8ce1504..8adc2a9a4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -6,7 +6,7 @@ env = EAGER_IMPORT=1 addopts = --doctest-modules doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS -norecursedirs = .* build dist *.egg venv .svn _build docs/auto_examples examples docs/auto_tutorial tutorial docs/auto_full_examples full_examples +norecursedirs = .* build dist *.egg venv .svn _build docs/auto_examples examples docs/auto_tutorial tutorial [flake8] ignore = From a54c78d49c65ea697665990eec967b27a1692fe9 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sat, 31 Dec 2022 00:19:29 +0100 Subject: [PATCH 396/400] Move metadata to pyproject.toml --- .zenodo.json | 56 ------------------------------------------- pyproject.toml | 59 ++++++++++++++++++++++++++++++++++++++++++++++ setup.py | 64 ++------------------------------------------------ 3 files changed, 61 insertions(+), 118 deletions(-) delete mode 100644 .zenodo.json diff --git a/.zenodo.json b/.zenodo.json deleted file mode 100644 index 745629812..000000000 --- a/.zenodo.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "creators": [ - { - "affiliation": "Universidad Autónoma de Madrid", - "name": "Ramos-Carreño, Carlos", - "orcid": "0000-0003-2566-7058" - }, - { - "affiliation": "Universidad Autónoma de Madrid", - "name": "Suárez, Alberto", - "orcid": "0000-0003-4534-0909" - }, - { - "affiliation": "Universidad Autónoma de Madrid", - "name": "Torrecilla, José Luis", - "orcid": "0000-0003-3719-5190" - }, - { - "name": "Carbajo Berrocal, Miguel" - }, - { - "name": "Marcos Manchón, Pablo" - }, - { - "name": "Pérez Manso, Pablo" - }, - { - "name": "Hernando Bernabé, Amanda" - }, - { - "name": "García Fernández, David" - }, - { - "name": "Hong, Yujian" - }, - { - "name": "Rodríguez-Ponga Eyriès, Pedro Martín" - }, - { - "name": "Sánchez Romero, Álvaro" - }, - { - "name": "Petrunina, Elena" - }, - { - "name": "Castillo, Álvaro" - }, - { - "name": "Serna, Diego" - }, - { - "name": "Hidalgo, Rafael" - } - ], - "license": "BSD-3-Clause" -} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 486c41f3c..0515a4a03 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,62 @@ +[project] +name = "scikit-fda" +description = "Functional Data Analysis Python package." +readme = "README.rst" +requires-python = ">=3.8" +license = {file = "LICENSE.txt"} +keywords = [ + "functional data", + "statistics", + "machine learning", +] +maintainers = [ + {name = "Carlos Ramos Carreño", email = "vnmabus@gmail.com"}, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: BSD License", + "Natural Language :: English", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Software Development :: Libraries :: Python Modules", + "Typing :: Typed", +] + +dynamic = ["version"] + +dependencies = [ + "cython", + "dcor", + "fdasrsf>=2.2.0", + "findiff", + "lazy_loader", + "matplotlib", + "multimethod>=1.5", + "numpy>=1.16", + "pandas>=1.0", + "rdata", + "scikit-datasets[cran]>=0.1.24", + "scikit-learn>=0.20", + "scipy>=1.3.0", + "typing-extensions", +] + +[project.optional-dependencies] +test = [ + "pytest", + "pytest-env", + "pytest-subtests", +] + +[project.urls] +homepage = "https://github.com/GAA-UAM/scikit-fda" +documentation = "https://fda.readthedocs.io" +repository = "https://github.com/GAA-UAM/scikit-fda" + [build-system] # Minimum requirements for the build system to execute. requires = ["setuptools", "wheel"] \ No newline at end of file diff --git a/setup.py b/setup.py index dc2a2568b..c0330c273 100644 --- a/setup.py +++ b/setup.py @@ -20,67 +20,7 @@ the package, along with several examples showing different funcionalities. """ -import os -import sys -from setuptools import find_packages, setup +from setuptools import setup -needs_pytest = {'pytest', 'test', 'ptr'}.intersection(sys.argv) -pytest_runner = ['pytest-runner'] if needs_pytest else [] - -DOCLINES = (__doc__ or '').split("\n") - -with open(os.path.join(os.path.dirname(__file__), - 'VERSION'), 'r') as version_file: - version = version_file.read().strip() - -setup(name='scikit-fda', - version=version, - description=DOCLINES[1], - long_description="\n".join(DOCLINES[3:]), - url='https://fda.readthedocs.io', - project_urls={ - "Bug Tracker": "https://github.com/GAA-UAM/scikit-fda/issues", - "Documentation": "https://fda.readthedocs.io", - "Source Code": "https://github.com/GAA-UAM/scikit-fda", - }, - maintainer='Carlos Ramos Carreño', - maintainer_email='vnmabus@gmail.com', - include_package_data=True, - platforms=['any'], - license='BSD', - packages=find_packages(), - python_requires='>=3.7, <4', - classifiers=[ - 'Development Status :: 4 - Beta', - 'Intended Audience :: Developers', - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: BSD License', - 'Natural Language :: English', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Topic :: Scientific/Engineering :: Mathematics', - 'Topic :: Software Development :: Libraries :: Python Modules', - 'Typing :: Typed', - ], - install_requires=[ - 'cython', - 'dcor', - 'fdasrsf>=2.2.0', - 'findiff', - 'lazy_loader', - 'matplotlib', - 'multimethod>=1.5', - 'numpy>=1.16', - 'pandas>=1.0', - 'rdata', - 'scikit-datasets[cran]>=0.1.24', - 'scikit-learn>=0.20', - 'scipy>=1.3.0', - 'typing-extensions', - ], - setup_requires=pytest_runner, - tests_require=['pytest', 'pytest-env'], - test_suite='skfda.tests', - zip_safe=False) +setup(name="scikit-fda") From 0cd447e721d6f99d938333481ee6a78033167e57 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sat, 31 Dec 2022 00:26:33 +0100 Subject: [PATCH 397/400] Fix setup.py. --- setup.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index c0330c273..63d4dd7c2 100644 --- a/setup.py +++ b/setup.py @@ -20,7 +20,19 @@ the package, along with several examples showing different funcionalities. """ +import os -from setuptools import setup +from setuptools import find_packages, setup -setup(name="scikit-fda") +with open( + os.path.join(os.path.dirname(__file__), "VERSION"), + "r", +) as version_file: + version = version_file.read().strip() + +setup( + name="scikit-fda", + version=version, + include_package_data=True, + packages=find_packages(), +) From f4235c7c32207c93670778c81b0c862e539325eb Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sat, 31 Dec 2022 00:38:05 +0100 Subject: [PATCH 398/400] Update workflow. --- .github/workflows/tests.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 8ffb3eae9..0e64042ee 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -29,8 +29,8 @@ jobs: run: | pip3 debug --verbose . pip3 install numba - pip3 install --upgrade-strategy eager -v . - coverage run --source=skfda/ setup.py test; + pip3 install ".[test]" + coverage run --source=skfda/ pytest; - name: Upload coverage to Codecov uses: codecov/codecov-action@v1 From 3b4c703bedcd6c599580b18cfce37cfc379dc2b5 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sat, 31 Dec 2022 00:43:28 +0100 Subject: [PATCH 399/400] Fix workflow again. --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 0e64042ee..39d6d668f 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -30,7 +30,7 @@ jobs: pip3 debug --verbose . pip3 install numba pip3 install ".[test]" - coverage run --source=skfda/ pytest; + coverage run --source=skfda/ -m pytest; - name: Upload coverage to Codecov uses: codecov/codecov-action@v1 From d0d7a90e23cf3e1b760e672ee58c96b942827945 Mon Sep 17 00:00:00 2001 From: VNMabus Date: Sat, 31 Dec 2022 14:48:46 +0100 Subject: [PATCH 400/400] Bump version. --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 39e898a4f..aec258df7 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -0.7.1 +0.8

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