From d86648584002fe81f2ae06add3268c557d5d5b40 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 18 Dec 2024 22:45:56 +0100 Subject: [PATCH] MAINT compatibility sklearn 1.6 (#1109) --- .coveragerc | 1 + .github/workflows/linters.yml | 2 +- .github/workflows/tests.yml | 8 +- imblearn/_config.py | 344 - imblearn/base.py | 52 +- imblearn/datasets/_imbalance.py | 2 +- imblearn/datasets/_zenodo.py | 2 +- imblearn/ensemble/_bagging.py | 21 +- imblearn/ensemble/_common.py | 3 +- imblearn/ensemble/_easy_ensemble.py | 34 +- imblearn/ensemble/_forest.py | 37 +- imblearn/ensemble/_weight_boosting.py | 34 +- imblearn/ensemble/tests/test_easy_ensemble.py | 18 +- imblearn/ensemble/tests/test_forest.py | 4 +- .../ensemble/tests/test_weight_boosting.py | 26 +- imblearn/metrics/_classification.py | 3 +- imblearn/metrics/pairwise.py | 20 +- imblearn/metrics/tests/test_classification.py | 5 +- imblearn/over_sampling/_adasyn.py | 6 +- .../over_sampling/_random_over_sampler.py | 13 +- imblearn/over_sampling/_smote/base.py | 41 +- imblearn/over_sampling/_smote/cluster.py | 2 +- imblearn/over_sampling/_smote/filter.py | 2 +- .../_smote/tests/test_smote_nc.py | 15 - imblearn/over_sampling/base.py | 3 +- imblearn/pipeline.py | 617 +- imblearn/tests/test_common.py | 42 +- imblearn/tests/test_docstring_parameters.py | 50 +- imblearn/tests/test_pipeline.py | 144 +- imblearn/tests/test_public_functions.py | 3 +- .../_cluster_centroids.py | 7 +- .../_condensed_nearest_neighbour.py | 7 +- .../_edited_nearest_neighbours.py | 21 +- .../_instance_hardness_threshold.py | 7 +- .../_prototype_selection/_nearmiss.py | 10 +- .../_neighbourhood_cleaning_rule.py | 7 +- .../_one_sided_selection.py | 7 +- .../_random_under_sampler.py | 11 +- .../_prototype_selection/_tomek_links.py | 5 + imblearn/under_sampling/base.py | 3 +- imblearn/utils/_metadata_requests.py | 1603 - imblearn/utils/_param_validation.py | 932 - imblearn/utils/_sklearn_compat.py | 830 + imblearn/utils/_tags.py | 1 + imblearn/utils/_test_common/__init__.py | 0 .../utils/_test_common/instance_generator.py | 244 + imblearn/utils/_validation.py | 6 +- imblearn/utils/estimator_checks.py | 274 +- imblearn/utils/fixes.py | 138 - imblearn/utils/tests/test_estimator_checks.py | 14 +- imblearn/utils/tests/test_param_validation.py | 785 - pixi.lock | 24631 +++++++--------- pyproject.toml | 29 +- 53 files changed, 13211 insertions(+), 17915 deletions(-) delete mode 100644 imblearn/_config.py delete mode 100644 imblearn/utils/_metadata_requests.py delete mode 100644 imblearn/utils/_param_validation.py create mode 100644 imblearn/utils/_sklearn_compat.py create mode 100644 imblearn/utils/_tags.py create mode 100644 imblearn/utils/_test_common/__init__.py create mode 100644 imblearn/utils/_test_common/instance_generator.py delete mode 100644 imblearn/utils/fixes.py delete mode 100644 imblearn/utils/tests/test_param_validation.py diff --git a/.coveragerc b/.coveragerc index 7eeecd171..a473c0c00 100644 --- a/.coveragerc +++ b/.coveragerc @@ -10,3 +10,4 @@ ignore_errors = True omit = */tests/* **/setup.py + **/_sklearn_compat.py diff --git a/.github/workflows/linters.yml b/.github/workflows/linters.yml index 4f0815a56..96cfb8606 100644 --- a/.github/workflows/linters.yml +++ b/.github/workflows/linters.yml @@ -17,7 +17,7 @@ jobs: - uses: actions/checkout@v4 - uses: prefix-dev/setup-pixi@v0.8.1 with: - pixi-version: v0.31.0 + pixi-version: v0.39.2 frozen: true - name: Run tests diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index e8c189fcb..05fc46429 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -18,12 +18,12 @@ jobs: ci-py310-min-optional-dependencies, ci-py310-min-keras, ci-py310-min-tensorflow, - ci-py311-sklearn-1-3, ci-py311-sklearn-1-4, + ci-py311-sklearn-1-5, ci-py311-latest-keras, ci-py311-latest-tensorflow, - ci-py312-latest-dependencies, - ci-py312-latest-optional-dependencies, + ci-py313-latest-dependencies, + ci-py313-latest-optional-dependencies, ] exclude: - os: windows-latest @@ -39,7 +39,7 @@ jobs: - uses: actions/checkout@v4 - uses: prefix-dev/setup-pixi@v0.8.1 with: - pixi-version: v0.31.0 + pixi-version: v0.39.2 environments: ${{ matrix.environment }} # we can freeze the environment and manually bump the dependencies to the # latest version time to time. diff --git a/imblearn/_config.py b/imblearn/_config.py deleted file mode 100644 index ef98e7305..000000000 --- a/imblearn/_config.py +++ /dev/null @@ -1,344 +0,0 @@ -"""This is copy of sklearn/_config.py -# TODO: remove this file when scikit-learn minimum version is 1.3 -We remove the array_api_dispatch for the moment. -""" -import os -import threading -from contextlib import contextmanager as contextmanager - -import sklearn -from sklearn.utils.fixes import parse_version - -sklearn_version = parse_version(sklearn.__version__) - -if sklearn_version < parse_version("1.3"): - _global_config = { - "assume_finite": bool(os.environ.get("SKLEARN_ASSUME_FINITE", False)), - "working_memory": int(os.environ.get("SKLEARN_WORKING_MEMORY", 1024)), - "print_changed_only": True, - "display": "diagram", - "pairwise_dist_chunk_size": int( - os.environ.get("SKLEARN_PAIRWISE_DIST_CHUNK_SIZE", 256) - ), - "enable_cython_pairwise_dist": True, - "transform_output": "default", - "enable_metadata_routing": False, - "skip_parameter_validation": False, - } - _threadlocal = threading.local() - - def _get_threadlocal_config(): - """Get a threadlocal **mutable** configuration. If the configuration - does not exist, copy the default global configuration.""" - if not hasattr(_threadlocal, "global_config"): - _threadlocal.global_config = _global_config.copy() - return _threadlocal.global_config - - def get_config(): - """Retrieve current values for configuration set by :func:`set_config`. - - Returns - ------- - config : dict - Keys are parameter names that can be passed to :func:`set_config`. - - See Also - -------- - config_context : Context manager for global scikit-learn configuration. - set_config : Set global scikit-learn configuration. - """ - # Return a copy of the threadlocal configuration so that users will - # not be able to modify the configuration with the returned dict. - return _get_threadlocal_config().copy() - - def set_config( - assume_finite=None, - working_memory=None, - print_changed_only=None, - display=None, - pairwise_dist_chunk_size=None, - enable_cython_pairwise_dist=None, - transform_output=None, - enable_metadata_routing=None, - skip_parameter_validation=None, - ): - """Set global scikit-learn configuration - - .. versionadded:: 0.19 - - Parameters - ---------- - assume_finite : bool, default=None - If True, validation for finiteness will be skipped, - saving time, but leading to potential crashes. If - False, validation for finiteness will be performed, - avoiding error. Global default: False. - - .. versionadded:: 0.19 - - working_memory : int, default=None - If set, scikit-learn will attempt to limit the size of temporary arrays - to this number of MiB (per job when parallelised), often saving both - computation time and memory on expensive operations that can be - performed in chunks. Global default: 1024. - - .. versionadded:: 0.20 - - print_changed_only : bool, default=None - If True, only the parameters that were set to non-default - values will be printed when printing an estimator. For example, - ``print(SVC())`` while True will only print 'SVC()' while the default - behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with - all the non-changed parameters. - - .. versionadded:: 0.21 - - display : {'text', 'diagram'}, default=None - If 'diagram', estimators will be displayed as a diagram in a Jupyter - lab or notebook context. If 'text', estimators will be displayed as - text. Default is 'diagram'. - - .. versionadded:: 0.23 - - pairwise_dist_chunk_size : int, default=None - The number of row vectors per chunk for the accelerated pairwise- - distances reduction backend. Default is 256 (suitable for most of - modern laptops' caches and architectures). - - Intended for easier benchmarking and testing of scikit-learn internals. - End users are not expected to benefit from customizing this configuration - setting. - - .. versionadded:: 1.1 - - enable_cython_pairwise_dist : bool, default=None - Use the accelerated pairwise-distances reduction backend when - possible. Global default: True. - - Intended for easier benchmarking and testing of scikit-learn internals. - End users are not expected to benefit from customizing this configuration - setting. - - .. versionadded:: 1.1 - - transform_output : str, default=None - Configure output of `transform` and `fit_transform`. - - See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` - for an example on how to use the API. - - - `"default"`: Default output format of a transformer - - `"pandas"`: DataFrame output - - `None`: Transform configuration is unchanged - - .. versionadded:: 1.2 - - enable_metadata_routing : bool, default=None - Enable metadata routing. By default this feature is disabled. - - Refer to :ref:`metadata routing user guide ` for more - details. - - - `True`: Metadata routing is enabled - - `False`: Metadata routing is disabled, use the old syntax. - - `None`: Configuration is unchanged - - .. versionadded:: 1.3 - - skip_parameter_validation : bool, default=None - If `True`, disable the validation of the hyper-parameters' types - and values in the fit method of estimators and for arguments passed - to public helper functions. It can save time in some situations but - can lead to low level crashes and exceptions with confusing error - messages. - - Note that for data parameters, such as `X` and `y`, only type validation is - skipped but validation with `check_array` will continue to run. - - .. versionadded:: 1.3 - - See Also - -------- - config_context : Context manager for global scikit-learn configuration. - get_config : Retrieve current values of the global configuration. - """ - local_config = _get_threadlocal_config() - - if assume_finite is not None: - local_config["assume_finite"] = assume_finite - if working_memory is not None: - local_config["working_memory"] = working_memory - if print_changed_only is not None: - local_config["print_changed_only"] = print_changed_only - if display is not None: - local_config["display"] = display - if pairwise_dist_chunk_size is not None: - local_config["pairwise_dist_chunk_size"] = pairwise_dist_chunk_size - if enable_cython_pairwise_dist is not None: - local_config["enable_cython_pairwise_dist"] = enable_cython_pairwise_dist - if transform_output is not None: - local_config["transform_output"] = transform_output - if enable_metadata_routing is not None: - local_config["enable_metadata_routing"] = enable_metadata_routing - if skip_parameter_validation is not None: - local_config["skip_parameter_validation"] = skip_parameter_validation - - @contextmanager - def config_context( - *, - assume_finite=None, - working_memory=None, - print_changed_only=None, - display=None, - pairwise_dist_chunk_size=None, - enable_cython_pairwise_dist=None, - transform_output=None, - enable_metadata_routing=None, - skip_parameter_validation=None, - ): - """Context manager for global scikit-learn configuration. - - Parameters - ---------- - assume_finite : bool, default=None - If True, validation for finiteness will be skipped, - saving time, but leading to potential crashes. If - False, validation for finiteness will be performed, - avoiding error. If None, the existing value won't change. - The default value is False. - - working_memory : int, default=None - If set, scikit-learn will attempt to limit the size of temporary arrays - to this number of MiB (per job when parallelised), often saving both - computation time and memory on expensive operations that can be - performed in chunks. If None, the existing value won't change. - The default value is 1024. - - print_changed_only : bool, default=None - If True, only the parameters that were set to non-default - values will be printed when printing an estimator. For example, - ``print(SVC())`` while True will only print 'SVC()', but would print - 'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters - when False. If None, the existing value won't change. - The default value is True. - - .. versionchanged:: 0.23 - Default changed from False to True. - - display : {'text', 'diagram'}, default=None - If 'diagram', estimators will be displayed as a diagram in a Jupyter - lab or notebook context. If 'text', estimators will be displayed as - text. If None, the existing value won't change. - The default value is 'diagram'. - - .. versionadded:: 0.23 - - pairwise_dist_chunk_size : int, default=None - The number of row vectors per chunk for the accelerated pairwise- - distances reduction backend. Default is 256 (suitable for most of - modern laptops' caches and architectures). - - Intended for easier benchmarking and testing of scikit-learn internals. - End users are not expected to benefit from customizing this configuration - setting. - - .. versionadded:: 1.1 - - enable_cython_pairwise_dist : bool, default=None - Use the accelerated pairwise-distances reduction backend when - possible. Global default: True. - - Intended for easier benchmarking and testing of scikit-learn internals. - End users are not expected to benefit from customizing this configuration - setting. - - .. versionadded:: 1.1 - - transform_output : str, default=None - Configure output of `transform` and `fit_transform`. - - See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` - for an example on how to use the API. - - - `"default"`: Default output format of a transformer - - `"pandas"`: DataFrame output - - `None`: Transform configuration is unchanged - - .. versionadded:: 1.2 - - enable_metadata_routing : bool, default=None - Enable metadata routing. By default this feature is disabled. - - Refer to :ref:`metadata routing user guide ` for more - details. - - - `True`: Metadata routing is enabled - - `False`: Metadata routing is disabled, use the old syntax. - - `None`: Configuration is unchanged - - .. versionadded:: 1.3 - - skip_parameter_validation : bool, default=None - If `True`, disable the validation of the hyper-parameters' types - and values in the fit method of estimators and for arguments passed - to public helper functions. It can save time in some situations but - can lead to low level crashes and exceptions with confusing error - messages. - - Note that for data parameters, such as `X` and `y`, only type validation is - skipped but validation with `check_array` will continue to run. - - .. versionadded:: 1.3 - - Yields - ------ - None. - - See Also - -------- - set_config : Set global scikit-learn configuration. - get_config : Retrieve current values of the global configuration. - - Notes - ----- - All settings, not just those presently modified, will be returned to - their previous values when the context manager is exited. - - Examples - -------- - >>> import sklearn - >>> from sklearn.utils.validation import assert_all_finite - >>> with sklearn.config_context(assume_finite=True): - ... assert_all_finite([float('nan')]) - >>> with sklearn.config_context(assume_finite=True): - ... with sklearn.config_context(assume_finite=False): - ... assert_all_finite([float('nan')]) - Traceback (most recent call last): - ... - ValueError: Input contains NaN... - """ - old_config = get_config() - set_config( - assume_finite=assume_finite, - working_memory=working_memory, - print_changed_only=print_changed_only, - display=display, - pairwise_dist_chunk_size=pairwise_dist_chunk_size, - enable_cython_pairwise_dist=enable_cython_pairwise_dist, - transform_output=transform_output, - enable_metadata_routing=enable_metadata_routing, - skip_parameter_validation=skip_parameter_validation, - ) - - try: - yield - finally: - set_config(**old_config) - -else: - from sklearn._config import ( # type: ignore[no-redef] - _get_threadlocal_config, - _global_config, - config_context, # noqa - get_config, - ) diff --git a/imblearn/base.py b/imblearn/base.py index 6e3954532..e837ee8da 100644 --- a/imblearn/base.py +++ b/imblearn/base.py @@ -12,30 +12,11 @@ from sklearn.utils.multiclass import check_classification_targets from .utils import check_sampling_strategy, check_target_type -from .utils._param_validation import validate_parameter_constraints +from .utils._sklearn_compat import _fit_context, get_tags, validate_data from .utils._validation import ArraysTransformer -class _ParamsValidationMixin: - """Mixin class to validate parameters.""" - - def _validate_params(self): - """Validate types and values of constructor parameters. - - The expected type and values must be defined in the `_parameter_constraints` - class attribute, which is a dictionary `param_name: list of constraints`. See - the docstring of `validate_parameter_constraints` for a description of the - accepted constraints. - """ - if hasattr(self, "_parameter_constraints"): - validate_parameter_constraints( - self._parameter_constraints, - self.get_params(deep=False), - caller_name=self.__class__.__name__, - ) - - -class SamplerMixin(_ParamsValidationMixin, BaseEstimator, metaclass=ABCMeta): +class SamplerMixin(metaclass=ABCMeta): """Mixin class for samplers with abstract method. Warning: This class should not be used directly. Use the derive classes @@ -44,6 +25,7 @@ class SamplerMixin(_ParamsValidationMixin, BaseEstimator, metaclass=ABCMeta): _estimator_type = "sampler" + @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y): """Check inputs and statistics of the sampler. @@ -133,7 +115,7 @@ def _fit_resample(self, X, y): pass -class BaseSampler(SamplerMixin, OneToOneFeatureMixin): +class BaseSampler(SamplerMixin, OneToOneFeatureMixin, BaseEstimator): """Base class for sampling algorithms. Warning: This class should not be used directly. Use the derive classes @@ -147,7 +129,7 @@ def _check_X_y(self, X, y, accept_sparse=None): if accept_sparse is None: accept_sparse = ["csr", "csc"] y, binarize_y = check_target_type(y, indicate_one_vs_all=True) - X, y = self._validate_data(X, y, reset=True, accept_sparse=accept_sparse) + X, y = validate_data(self, X=X, y=y, reset=True, accept_sparse=accept_sparse) return X, y, binarize_y def fit(self, X, y): @@ -199,6 +181,24 @@ def fit_resample(self, X, y): def _more_tags(self): return {"X_types": ["2darray", "sparse", "dataframe"]} + def __sklearn_tags__(self): + from .utils._sklearn_compat import TargetTags + from .utils._tags import InputTags, SamplerTags, Tags + + tags = Tags( + estimator_type="sampler", + target_tags=TargetTags(required=True), + transformer_tags=None, + regressor_tags=None, + classifier_tags=None, + sampler_tags=SamplerTags(), + ) + tags.input_tags = InputTags() + tags.input_tags.two_d_array = True + tags.input_tags.sparse = True + tags.input_tags.dataframe = True + return tags + def _identity(X, y): return X, y @@ -217,7 +217,11 @@ def is_sampler(estimator): is_sampler : bool True if estimator is a sampler, otherwise False. """ - if estimator._estimator_type == "sampler": + + if hasattr(estimator, "_estimator_type") and estimator._estimator_type == "sampler": + return True + tags = get_tags(estimator) + if hasattr(tags, "sampler_tags") and tags.sampler_tags is not None: return True return False diff --git a/imblearn/datasets/_imbalance.py b/imblearn/datasets/_imbalance.py index 9e6e51290..c216042cf 100644 --- a/imblearn/datasets/_imbalance.py +++ b/imblearn/datasets/_imbalance.py @@ -10,7 +10,7 @@ from ..under_sampling import RandomUnderSampler from ..utils import check_sampling_strategy -from ..utils._param_validation import validate_params +from ..utils._sklearn_compat import validate_params @validate_params( diff --git a/imblearn/datasets/_zenodo.py b/imblearn/datasets/_zenodo.py index 4498ebadb..e8d8138ac 100644 --- a/imblearn/datasets/_zenodo.py +++ b/imblearn/datasets/_zenodo.py @@ -55,7 +55,7 @@ from sklearn.datasets import get_data_home from sklearn.utils import Bunch, check_random_state -from ..utils._param_validation import validate_params +from ..utils._sklearn_compat import validate_params URL = "https://zenodo.org/record/61452/files/benchmark-imbalanced-learn.tar.gz" PRE_FILENAME = "x" diff --git a/imblearn/ensemble/_bagging.py b/imblearn/ensemble/_bagging.py index 79559cd2c..fd4c253aa 100644 --- a/imblearn/ensemble/_bagging.py +++ b/imblearn/ensemble/_bagging.py @@ -8,36 +8,32 @@ import numbers import numpy as np -import sklearn from sklearn.base import clone from sklearn.ensemble import BaggingClassifier from sklearn.ensemble._bagging import _parallel_decision_function from sklearn.ensemble._base import _partition_estimators from sklearn.tree import DecisionTreeClassifier +from sklearn.utils._param_validation import HasMethods, Interval, StrOptions from sklearn.utils.fixes import parse_version from sklearn.utils.metaestimators import available_if from sklearn.utils.parallel import Parallel, delayed from sklearn.utils.validation import check_is_fitted -from ..base import _ParamsValidationMixin from ..pipeline import Pipeline from ..under_sampling import RandomUnderSampler from ..under_sampling.base import BaseUnderSampler from ..utils import Substitution, check_sampling_strategy, check_target_type from ..utils._docstring import _n_jobs_docstring, _random_state_docstring -from ..utils._param_validation import HasMethods, Interval, StrOptions -from ..utils.fixes import _fit_context +from ..utils._sklearn_compat import _fit_context, sklearn_version, validate_data from ._common import _bagging_parameter_constraints, _estimator_has -sklearn_version = parse_version(sklearn.__version__) - @Substitution( sampling_strategy=BaseUnderSampler._sampling_strategy_docstring, n_jobs=_n_jobs_docstring, random_state=_random_state_docstring, ) -class BalancedBaggingClassifier(_ParamsValidationMixin, BaggingClassifier): +class BalancedBaggingClassifier(BaggingClassifier): """A Bagging classifier with additional balancing. This implementation of Bagging is similar to the scikit-learn @@ -382,11 +378,12 @@ def decision_function(self, X): check_is_fitted(self) # Check data - X = self._validate_data( - X, + X = validate_data( + self, + X=X, accept_sparse=["csr", "csc"], dtype=None, - force_all_finite=False, + ensure_all_finite=False, reset=False, ) @@ -425,3 +422,7 @@ def _more_tags(self): else: tags[tags_key] = {failing_test: reason} return tags + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + return tags diff --git a/imblearn/ensemble/_common.py b/imblearn/ensemble/_common.py index 588fa5e2c..7a78590a3 100644 --- a/imblearn/ensemble/_common.py +++ b/imblearn/ensemble/_common.py @@ -1,8 +1,7 @@ from numbers import Integral, Real from sklearn.tree._criterion import Criterion - -from ..utils._param_validation import ( +from sklearn.utils._param_validation import ( HasMethods, Hidden, Interval, diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py index aec7f6837..9d5f83ab9 100644 --- a/imblearn/ensemble/_easy_ensemble.py +++ b/imblearn/ensemble/_easy_ensemble.py @@ -9,29 +9,31 @@ import warnings import numpy as np -import sklearn from sklearn.base import clone from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier from sklearn.ensemble._bagging import _parallel_decision_function from sklearn.ensemble._base import _partition_estimators +from sklearn.utils._param_validation import Interval, StrOptions from sklearn.utils._tags import _safe_tags from sklearn.utils.fixes import parse_version from sklearn.utils.metaestimators import available_if from sklearn.utils.parallel import Parallel, delayed from sklearn.utils.validation import check_is_fitted -from ..base import _ParamsValidationMixin from ..pipeline import Pipeline from ..under_sampling import RandomUnderSampler from ..under_sampling.base import BaseUnderSampler from ..utils import Substitution, check_sampling_strategy, check_target_type from ..utils._docstring import _n_jobs_docstring, _random_state_docstring -from ..utils._param_validation import Interval, StrOptions -from ..utils.fixes import _fit_context +from ..utils._sklearn_compat import ( + _fit_context, + get_tags, + sklearn_version, + validate_data, +) from ._common import _bagging_parameter_constraints, _estimator_has MAX_INT = np.iinfo(np.int32).max -sklearn_version = parse_version(sklearn.__version__) @Substitution( @@ -39,7 +41,7 @@ n_jobs=_n_jobs_docstring, random_state=_random_state_docstring, ) -class EasyEnsembleClassifier(_ParamsValidationMixin, BaggingClassifier): +class EasyEnsembleClassifier(BaggingClassifier): """Bag of balanced boosted learners also known as EasyEnsemble. This algorithm is known as EasyEnsemble [1]_. The classifier is an @@ -311,11 +313,14 @@ def decision_function(self, X): check_is_fitted(self) # Check data - X = self._validate_data( - X, + X = validate_data( + self, + X=X, accept_sparse=["csr", "csc"], dtype=None, - force_all_finite=False, + ensure_all_finite=( + "allow_nan" if get_tags(self).input_tags.allow_nan else True + ), reset=False, ) @@ -346,9 +351,16 @@ def base_estimator_(self): def _get_estimator(self): if self.estimator is None: - return AdaBoostClassifier(algorithm="SAMME") + if parse_version("1.4") <= sklearn_version < parse_version("1.6"): + return AdaBoostClassifier(algorithm="SAMME") + else: + return AdaBoostClassifier() return self.estimator - # TODO: remove when minimum supported version of scikit-learn is 1.5 def _more_tags(self): return {"allow_nan": _safe_tags(self._get_estimator(), "allow_nan")} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.allow_nan = get_tags(self._get_estimator()).input_tags.allow_nan + return tags diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py index 587db01d8..6f4e8247b 100644 --- a/imblearn/ensemble/_forest.py +++ b/imblearn/ensemble/_forest.py @@ -8,7 +8,6 @@ from warnings import warn import numpy as np -import sklearn from numpy import float32 as DTYPE from numpy import float64 as DOUBLE from scipy.sparse import issparse @@ -23,23 +22,21 @@ from sklearn.exceptions import DataConversionWarning from sklearn.tree import DecisionTreeClassifier from sklearn.utils import _safe_indexing, check_random_state +from sklearn.utils._param_validation import Hidden, Interval, StrOptions from sklearn.utils.fixes import parse_version from sklearn.utils.multiclass import type_of_target from sklearn.utils.parallel import Parallel, delayed from sklearn.utils.validation import _check_sample_weight -from ..base import _ParamsValidationMixin from ..pipeline import make_pipeline from ..under_sampling import RandomUnderSampler from ..utils import Substitution from ..utils._docstring import _n_jobs_docstring, _random_state_docstring -from ..utils._param_validation import Hidden, Interval, StrOptions +from ..utils._sklearn_compat import _fit_context, sklearn_version, validate_data from ..utils._validation import check_sampling_strategy -from ..utils.fixes import _fit_context from ._common import _random_forest_classifier_parameter_constraints MAX_INT = np.iinfo(np.int32).max -sklearn_version = parse_version(sklearn.__version__) def _local_parallel_build_trees( @@ -77,7 +74,7 @@ def _local_parallel_build_trees( "bootstrap": bootstrap, } - if parse_version(sklearn_version.base_version) >= parse_version("1.4"): + if sklearn_version >= parse_version("1.4"): # TODO: remove when the minimum supported version of scikit-learn will be 1.4 # support for missing values params_parallel_build_trees["missing_values_in_feature_mask"] = ( @@ -93,7 +90,7 @@ def _local_parallel_build_trees( n_jobs=_n_jobs_docstring, random_state=_random_state_docstring, ) -class BalancedRandomForestClassifier(_ParamsValidationMixin, RandomForestClassifier): +class BalancedRandomForestClassifier(RandomForestClassifier): """A balanced random forest classifier. A balanced random forest differs from a classical random forest by the @@ -474,7 +471,7 @@ def __init__( "max_samples": max_samples, } # TODO: remove when the minimum supported version of scikit-learn will be 1.4 - if parse_version(sklearn_version.base_version) >= parse_version("1.4"): + if sklearn_version >= parse_version("1.4"): # use scikit-learn support for monotonic constraints params_random_forest["monotonic_cst"] = monotonic_cst else: @@ -596,22 +593,23 @@ def fit(self, X, y, sample_weight=None): # TODO: remove when the minimum supported version of scipy will be 1.4 # Support for missing values - if parse_version(sklearn_version.base_version) >= parse_version("1.4"): - force_all_finite = False + if sklearn_version >= parse_version("1.4"): + ensure_all_finite = False else: - force_all_finite = True + ensure_all_finite = True - X, y = self._validate_data( - X, - y, + X, y = validate_data( + self, + X=X, + y=y, multi_output=True, accept_sparse="csc", dtype=DTYPE, - force_all_finite=force_all_finite, + ensure_all_finite=ensure_all_finite, ) # TODO: remove when the minimum supported version of scikit-learn will be 1.4 - if parse_version(sklearn_version.base_version) >= parse_version("1.4"): + if sklearn_version >= parse_version("1.4"): # _compute_missing_values_in_feature_mask checks if X has missing values and # will raise an error if the underlying tree base estimator can't handle # missing values. Only the criterion is required to determine if the tree @@ -882,3 +880,10 @@ def _compute_oob_predictions(self, X, y): def _more_tags(self): return {"multioutput": False, "multilabel": False} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.target_tags.multi_output = False + tags.classifier_tags.multi_label = False + tags.input_tags.allow_nan = sklearn_version >= parse_version("1.4") + return tags diff --git a/imblearn/ensemble/_weight_boosting.py b/imblearn/ensemble/_weight_boosting.py index 9da02255e..df043eee7 100644 --- a/imblearn/ensemble/_weight_boosting.py +++ b/imblearn/ensemble/_weight_boosting.py @@ -1,35 +1,32 @@ import copy import numbers +import warnings from copy import deepcopy import numpy as np -import sklearn from sklearn.base import clone from sklearn.ensemble import AdaBoostClassifier from sklearn.ensemble._base import _set_random_states from sklearn.tree import DecisionTreeClassifier from sklearn.utils import _safe_indexing +from sklearn.utils._param_validation import Hidden, Interval, StrOptions from sklearn.utils.fixes import parse_version from sklearn.utils.validation import has_fit_parameter -from ..base import _ParamsValidationMixin from ..pipeline import make_pipeline from ..under_sampling import RandomUnderSampler from ..under_sampling.base import BaseUnderSampler from ..utils import Substitution, check_target_type from ..utils._docstring import _random_state_docstring -from ..utils._param_validation import Interval, StrOptions -from ..utils.fixes import _fit_context +from ..utils._sklearn_compat import _fit_context, sklearn_version from ._common import _adaboost_classifier_parameter_constraints -sklearn_version = parse_version(sklearn.__version__) - @Substitution( sampling_strategy=BaseUnderSampler._sampling_strategy_docstring, random_state=_random_state_docstring, ) -class RUSBoostClassifier(_ParamsValidationMixin, AdaBoostClassifier): +class RUSBoostClassifier(AdaBoostClassifier): """Random under-sampling integrated in the learning of AdaBoost. During learning, the problem of class balancing is alleviated by random @@ -167,6 +164,10 @@ class RUSBoostClassifier(_ParamsValidationMixin, AdaBoostClassifier): _parameter_constraints.update( { + "algorithm": [ + StrOptions({"SAMME", "SAMME.R"}), + Hidden(StrOptions({"deprecated"})), + ], "sampling_strategy": [ Interval(numbers.Real, 0, 1, closed="right"), StrOptions({"auto", "majority", "not minority", "not majority", "all"}), @@ -186,7 +187,7 @@ def __init__( *, n_estimators=50, learning_rate=1.0, - algorithm="SAMME.R", + algorithm="deprecated", sampling_strategy="auto", replacement=False, random_state=None, @@ -194,9 +195,9 @@ def __init__( super().__init__( n_estimators=n_estimators, learning_rate=learning_rate, - algorithm=algorithm, random_state=random_state, ) + self.algorithm = algorithm self.estimator = estimator self.sampling_strategy = sampling_strategy self.replacement = replacement @@ -394,3 +395,18 @@ def _boost_discrete(self, iboost, X, y, sample_weight, random_state): sample_weight *= np.exp(estimator_weight * incorrect * (sample_weight > 0)) return sample_weight, estimator_weight, estimator_error + + def _boost(self, iboost, X, y, sample_weight, random_state): + if self.algorithm != "deprecated": + warnings.warn( + ( + "`algorithm` parameter is deprecated in 0.12 and will be removed in" + " 0.14. In the future, the SAMME algorithm will always be used." + ), + FutureWarning, + ) + if self.algorithm == "SAMME.R": + return self._boost_real(iboost, X, y, sample_weight, random_state) + + else: # elif self.algorithm == "SAMME": + return self._boost_discrete(iboost, X, y, sample_weight, random_state) diff --git a/imblearn/ensemble/tests/test_easy_ensemble.py b/imblearn/ensemble/tests/test_easy_ensemble.py index 6fe50500f..b31bb6891 100644 --- a/imblearn/ensemble/tests/test_easy_ensemble.py +++ b/imblearn/ensemble/tests/test_easy_ensemble.py @@ -6,7 +6,7 @@ import numpy as np import pytest from sklearn.datasets import load_iris, make_hastie_10_2 -from sklearn.ensemble import AdaBoostClassifier +from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier from sklearn.feature_selection import SelectKBest from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.utils._testing import assert_allclose, assert_array_equal @@ -41,8 +41,8 @@ @pytest.mark.parametrize( "estimator", [ - AdaBoostClassifier(algorithm="SAMME", n_estimators=5), - AdaBoostClassifier(algorithm="SAMME", n_estimators=10), + GradientBoostingClassifier(n_estimators=5), + GradientBoostingClassifier(n_estimators=10), ], ) def test_easy_ensemble_classifier(n_estimators, estimator): @@ -89,10 +89,10 @@ def test_estimator(): assert isinstance(ensemble.estimator_.steps[-1][1], AdaBoostClassifier) ensemble = EasyEnsembleClassifier( - 2, AdaBoostClassifier(algorithm="SAMME"), n_jobs=-1, random_state=0 + 2, GradientBoostingClassifier(), n_jobs=-1, random_state=0 ).fit(X_train, y_train) - assert isinstance(ensemble.estimator_.steps[-1][1], AdaBoostClassifier) + assert isinstance(ensemble.estimator_.steps[-1][1], GradientBoostingClassifier) def test_bagging_with_pipeline(): @@ -104,9 +104,7 @@ def test_bagging_with_pipeline(): ) estimator = EasyEnsembleClassifier( n_estimators=2, - estimator=make_pipeline( - SelectKBest(k=1), AdaBoostClassifier(algorithm="SAMME") - ), + estimator=make_pipeline(SelectKBest(k=1), GradientBoostingClassifier()), ) estimator.fit(X, y).predict(X) @@ -198,7 +196,7 @@ def test_easy_ensemble_classifier_single_estimator(): clf1 = EasyEnsembleClassifier(n_estimators=1, random_state=0).fit(X_train, y_train) clf2 = make_pipeline( RandomUnderSampler(random_state=0), - AdaBoostClassifier(algorithm="SAMME", random_state=0), + GradientBoostingClassifier(random_state=0), ).fit(X_train, y_train) assert_array_equal(clf1.predict(X_test), clf2.predict(X_test)) @@ -217,7 +215,7 @@ def test_easy_ensemble_classifier_grid_search(): "estimator__n_estimators": [3, 4], } grid_search = GridSearchCV( - EasyEnsembleClassifier(estimator=AdaBoostClassifier(algorithm="SAMME")), + EasyEnsembleClassifier(estimator=GradientBoostingClassifier()), parameters, cv=5, ) diff --git a/imblearn/ensemble/tests/test_forest.py b/imblearn/ensemble/tests/test_forest.py index 792824531..2653512c2 100644 --- a/imblearn/ensemble/tests/test_forest.py +++ b/imblearn/ensemble/tests/test_forest.py @@ -1,14 +1,12 @@ import numpy as np import pytest -import sklearn from sklearn.datasets import make_classification from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.utils._testing import assert_allclose, assert_array_equal from sklearn.utils.fixes import parse_version from imblearn.ensemble import BalancedRandomForestClassifier - -sklearn_version = parse_version(sklearn.__version__) +from imblearn.utils._sklearn_compat import sklearn_version @pytest.fixture diff --git a/imblearn/ensemble/tests/test_weight_boosting.py b/imblearn/ensemble/tests/test_weight_boosting.py index 8096a2b16..7756154e3 100644 --- a/imblearn/ensemble/tests/test_weight_boosting.py +++ b/imblearn/ensemble/tests/test_weight_boosting.py @@ -23,9 +23,7 @@ def imbalanced_dataset(): ) -@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"]) -@pytest.mark.filterwarnings("ignore:The SAMME.R algorithm (the default) is") -def test_rusboost(imbalanced_dataset, algorithm): +def test_rusboost(imbalanced_dataset): X, y = imbalanced_dataset X_train, X_test, y_train, y_test = train_test_split( X, y, stratify=y, random_state=1 @@ -33,9 +31,7 @@ def test_rusboost(imbalanced_dataset, algorithm): classes = np.unique(y) n_estimators = 500 - rusboost = RUSBoostClassifier( - n_estimators=n_estimators, algorithm=algorithm, random_state=0 - ) + rusboost = RUSBoostClassifier(n_estimators=n_estimators, random_state=0) rusboost.fit(X_train, y_train) assert_array_equal(classes, rusboost.classes_) @@ -63,18 +59,16 @@ def test_rusboost(imbalanced_dataset, algorithm): assert rusboost.decision_function(X_test).shape[1] == len(classes) score = rusboost.score(X_test, y_test) - assert score > 0.6, f"Failed with algorithm {algorithm} and score {score}" + assert score > 0.6, f"Failed with score {score}" y_pred = rusboost.predict(X_test) assert y_pred.shape == y_test.shape -@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"]) -@pytest.mark.filterwarnings("ignore:The SAMME.R algorithm (the default) is") -def test_rusboost_sample_weight(imbalanced_dataset, algorithm): +def test_rusboost_sample_weight(imbalanced_dataset): X, y = imbalanced_dataset sample_weight = np.ones_like(y) - rusboost = RUSBoostClassifier(algorithm=algorithm, random_state=0) + rusboost = RUSBoostClassifier(random_state=0) # Predictions should be the same when sample_weight are all ones y_pred_sample_weight = rusboost.fit(X, y, sample_weight).predict(X) @@ -88,3 +82,13 @@ def test_rusboost_sample_weight(imbalanced_dataset, algorithm): with pytest.raises(AssertionError): assert_array_equal(y_pred_no_sample_weight, y_pred_sample_weight) + + +@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"]) +def test_rusboost_algorithm(imbalanced_dataset, algorithm): + X, y = imbalanced_dataset + + rusboost = RUSBoostClassifier(algorithm=algorithm) + warn_msg = "`algorithm` parameter is deprecated in 0.12 and will be removed" + with pytest.warns(FutureWarning, match=warn_msg): + rusboost.fit(X, y) diff --git a/imblearn/metrics/_classification.py b/imblearn/metrics/_classification.py index 163f2d0da..17797a9a7 100644 --- a/imblearn/metrics/_classification.py +++ b/imblearn/metrics/_classification.py @@ -24,10 +24,11 @@ from sklearn.metrics import mean_absolute_error, precision_recall_fscore_support from sklearn.metrics._classification import _check_targets, _prf_divide from sklearn.preprocessing import LabelEncoder +from sklearn.utils._param_validation import Interval, StrOptions from sklearn.utils.multiclass import unique_labels from sklearn.utils.validation import check_consistent_length, column_or_1d -from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils._sklearn_compat import validate_params @validate_params( diff --git a/imblearn/metrics/pairwise.py b/imblearn/metrics/pairwise.py index 766a6d399..360ed812c 100644 --- a/imblearn/metrics/pairwise.py +++ b/imblearn/metrics/pairwise.py @@ -9,14 +9,14 @@ from scipy.spatial import distance_matrix from sklearn.base import BaseEstimator from sklearn.utils import check_consistent_length +from sklearn.utils._param_validation import StrOptions from sklearn.utils.multiclass import unique_labels from sklearn.utils.validation import check_is_fitted -from ..base import _ParamsValidationMixin -from ..utils._param_validation import StrOptions +from ..utils._sklearn_compat import _fit_context, check_array, validate_data -class ValueDifferenceMetric(_ParamsValidationMixin, BaseEstimator): +class ValueDifferenceMetric(BaseEstimator): r"""Class implementing the Value Difference Metric. This metric computes the distance between samples containing only @@ -118,6 +118,7 @@ class ValueDifferenceMetric(_ParamsValidationMixin, BaseEstimator): [0.04, 0. , 1.44], [1.96, 1.44, 0. ]]) """ + _parameter_constraints: dict = { "n_categories": [StrOptions({"auto"}), "array-like"], "k": [numbers.Integral], @@ -129,6 +130,7 @@ def __init__(self, *, n_categories="auto", k=1, r=2): self.k = k self.r = r + @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y): """Compute the necessary statistics from the training set. @@ -148,7 +150,8 @@ def fit(self, X, y): """ self._validate_params() check_consistent_length(X, y) - X, y = self._validate_data(X, y, reset=True, dtype=np.int32) + X, y = validate_data(self, X=X, y=y, reset=True, dtype=np.int32) + X = check_array(X, ensure_non_negative=True) if isinstance(self.n_categories, str) and self.n_categories == "auto": # categories are expected to be encoded from 0 to n_categories - 1 @@ -207,11 +210,11 @@ def pairwise(self, X, Y=None): The VDM pairwise distance. """ check_is_fitted(self) - X = self._validate_data(X, reset=False, dtype=np.int32) + X = check_array(X, ensure_non_negative=True, dtype=np.int32) n_samples_X = X.shape[0] if Y is not None: - Y = self._validate_data(Y, reset=False, dtype=np.int32) + Y = check_array(Y, ensure_non_negative=True, dtype=np.int32) n_samples_Y = Y.shape[0] else: n_samples_Y = n_samples_X @@ -232,3 +235,8 @@ def _more_tags(self): return { "requires_positive_X": True, # X should be encoded with OrdinalEncoder } + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.positive_only = True + return tags diff --git a/imblearn/metrics/tests/test_classification.py b/imblearn/metrics/tests/test_classification.py index 8169cee81..26d6be0ad 100644 --- a/imblearn/metrics/tests/test_classification.py +++ b/imblearn/metrics/tests/test_classification.py @@ -23,7 +23,6 @@ from sklearn.utils._testing import ( assert_allclose, assert_array_equal, - assert_no_warnings, ) from sklearn.utils.validation import check_random_state @@ -105,10 +104,10 @@ def test_sensitivity_specificity_score_binary(): # binary class case the score is the value of the measure for the positive # class (e.g. label == 1). This is deprecated for average != 'binary'. for kwargs in ({}, {"average": "binary"}): - sen = assert_no_warnings(sensitivity_score, y_true, y_pred, **kwargs) + sen = sensitivity_score(y_true, y_pred, **kwargs) assert sen == pytest.approx(0.68, rel=R_TOL) - spe = assert_no_warnings(specificity_score, y_true, y_pred, **kwargs) + spe = specificity_score(y_true, y_pred, **kwargs) assert spe == pytest.approx(0.88, rel=R_TOL) diff --git a/imblearn/over_sampling/_adasyn.py b/imblearn/over_sampling/_adasyn.py index 2dffed228..c94494443 100644 --- a/imblearn/over_sampling/_adasyn.py +++ b/imblearn/over_sampling/_adasyn.py @@ -10,10 +10,10 @@ import numpy as np from scipy import sparse from sklearn.utils import _safe_indexing, check_random_state +from sklearn.utils._param_validation import HasMethods, Interval from ..utils import Substitution, check_neighbors_object from ..utils._docstring import _n_jobs_docstring, _random_state_docstring -from ..utils._param_validation import HasMethods, Interval from .base import BaseOverSampler @@ -233,3 +233,7 @@ def _more_tags(self): return { "X_types": ["2darray"], } + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + return tags diff --git a/imblearn/over_sampling/_random_over_sampler.py b/imblearn/over_sampling/_random_over_sampler.py index 993788a42..6ac116305 100644 --- a/imblearn/over_sampling/_random_over_sampler.py +++ b/imblearn/over_sampling/_random_over_sampler.py @@ -10,11 +10,12 @@ import numpy as np from scipy import sparse from sklearn.utils import _safe_indexing, check_array, check_random_state +from sklearn.utils._param_validation import Interval from sklearn.utils.sparsefuncs import mean_variance_axis from ..utils import Substitution, check_target_type from ..utils._docstring import _random_state_docstring -from ..utils._param_validation import Interval +from ..utils._sklearn_compat import validate_data from ..utils._validation import _check_X from .base import BaseOverSampler @@ -156,8 +157,7 @@ def __init__( def _check_X_y(self, X, y): y, binarize_y = check_target_type(y, indicate_one_vs_all=True) X = _check_X(X) - self._check_n_features(X, reset=True) - self._check_feature_names(X, reset=True) + validate_data(self, X=X, y=y, reset=True, skip_check_array=True) return X, y, binarize_y def _fit_resample(self, X, y): @@ -258,3 +258,10 @@ def _more_tags(self): "check_complex_data": "Robust to this type of data.", }, } + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.allow_nan = True + tags.input_tags.string = True + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/over_sampling/_smote/base.py b/imblearn/over_sampling/_smote/base.py index 95e10b246..c0be9e47b 100644 --- a/imblearn/over_sampling/_smote/base.py +++ b/imblearn/over_sampling/_smote/base.py @@ -11,8 +11,8 @@ import warnings import numpy as np -import sklearn from scipy import sparse +from scipy.stats import mode from sklearn.base import clone from sklearn.exceptions import DataConversionWarning from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder @@ -21,7 +21,7 @@ check_array, check_random_state, ) -from sklearn.utils.fixes import parse_version +from sklearn.utils._param_validation import HasMethods, Interval, StrOptions from sklearn.utils.sparsefuncs_fast import ( csr_mean_variance_axis0, ) @@ -30,17 +30,10 @@ from ...metrics.pairwise import ValueDifferenceMetric from ...utils import Substitution, check_neighbors_object, check_target_type from ...utils._docstring import _n_jobs_docstring, _random_state_docstring -from ...utils._param_validation import HasMethods, Interval, StrOptions +from ...utils._sklearn_compat import _get_column_indices, _is_pandas_df, validate_data from ...utils._validation import _check_X -from ...utils.fixes import _is_pandas_df, _mode from ..base import BaseOverSampler -sklearn_version = parse_version(sklearn.__version__).base_version -if parse_version(sklearn_version) < parse_version("1.5"): - from sklearn.utils import _get_column_indices -else: - from sklearn.utils._indexing import _get_column_indices - class BaseSMOTE(BaseOverSampler): """Base class for the different SMOTE algorithms.""" @@ -601,8 +594,7 @@ def _check_X_y(self, X, y): """ y, binarize_y = check_target_type(y, indicate_one_vs_all=True) X = _check_X(X) - self._check_n_features(X, reset=True) - self._check_feature_names(X, reset=True) + validate_data(self, X=X, y=y, reset=True, skip_check_array=True) return X, y, binarize_y def _validate_column_types(self, X): @@ -821,6 +813,15 @@ def ohe_(self): ) return self.categorical_encoder_ + def _more_tags(self): + return {"X_types": ["2darray", "dataframe", "string"]} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.sparse = False + tags.input_tags.string = True + return tags + @Substitution( sampling_strategy=BaseOverSampler._sampling_strategy_docstring, @@ -963,9 +964,10 @@ def __init__( def _check_X_y(self, X, y): """Check should accept strings and not sparse matrices.""" y, binarize_y = check_target_type(y, indicate_one_vs_all=True) - X, y = self._validate_data( - X, - y, + X, y = validate_data( + self, + X=X, + y=y, reset=True, dtype=None, accept_sparse=["csr", "csc"], @@ -987,7 +989,8 @@ def _make_samples(self, X_class, klass, y_dtype, nn_indices, n_samples): # where for each feature individually, each category generated is the # most common category X_new = np.squeeze( - _mode(X_class[nn_indices[samples_indices]], axis=1).mode, axis=1 + mode(X_class[nn_indices[samples_indices]], axis=1, keepdims=True).mode, + axis=1, ) y_new = np.full(n_samples, fill_value=klass, dtype=y_dtype) return X_new, y_new @@ -1063,3 +1066,9 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"X_types": ["2darray", "dataframe", "string"]} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.sparse = False + tags.input_tags.string = True + return tags diff --git a/imblearn/over_sampling/_smote/cluster.py b/imblearn/over_sampling/_smote/cluster.py index 963b5f36a..a1959df50 100644 --- a/imblearn/over_sampling/_smote/cluster.py +++ b/imblearn/over_sampling/_smote/cluster.py @@ -14,10 +14,10 @@ from sklearn.cluster import MiniBatchKMeans from sklearn.metrics import pairwise_distances from sklearn.utils import _safe_indexing +from sklearn.utils._param_validation import HasMethods, Interval, StrOptions from ...utils import Substitution from ...utils._docstring import _n_jobs_docstring, _random_state_docstring -from ...utils._param_validation import HasMethods, Interval, StrOptions from ..base import BaseOverSampler from .base import BaseSMOTE diff --git a/imblearn/over_sampling/_smote/filter.py b/imblearn/over_sampling/_smote/filter.py index 7adcded2f..74782e67c 100644 --- a/imblearn/over_sampling/_smote/filter.py +++ b/imblearn/over_sampling/_smote/filter.py @@ -14,10 +14,10 @@ from sklearn.base import clone from sklearn.svm import SVC from sklearn.utils import _safe_indexing, check_random_state +from sklearn.utils._param_validation import HasMethods, Interval, StrOptions from ...utils import Substitution, check_neighbors_object from ...utils._docstring import _n_jobs_docstring, _random_state_docstring -from ...utils._param_validation import HasMethods, Interval, StrOptions from ..base import BaseOverSampler from .base import BaseSMOTE diff --git a/imblearn/over_sampling/_smote/tests/test_smote_nc.py b/imblearn/over_sampling/_smote/tests/test_smote_nc.py index 2d1e2b3f0..d17873e73 100644 --- a/imblearn/over_sampling/_smote/tests/test_smote_nc.py +++ b/imblearn/over_sampling/_smote/tests/test_smote_nc.py @@ -14,10 +14,6 @@ from sklearn.utils._testing import assert_allclose, assert_array_equal from imblearn.over_sampling import SMOTENC -from imblearn.utils.estimator_checks import ( - _set_checking_parameters, - check_param_validation, -) def data_heterogneous_ordered(): @@ -292,17 +288,6 @@ def test_smotenc_deprecation_ohe_(): smote.ohe_ -def test_smotenc_param_validation(): - """Check that we validate the parameters correctly since this estimator requires - a specific parameter. - """ - categorical_features = [0] - smote = SMOTENC(categorical_features=categorical_features, random_state=0) - name = smote.__class__.__name__ - _set_checking_parameters(smote) - check_param_validation(name, smote) - - def test_smotenc_bool_categorical(): """Check that we don't try to early convert the full input data to numeric when handling a pandas dataframe. diff --git a/imblearn/over_sampling/base.py b/imblearn/over_sampling/base.py index 1a5308931..95deeb48e 100644 --- a/imblearn/over_sampling/base.py +++ b/imblearn/over_sampling/base.py @@ -8,8 +8,9 @@ import numbers from collections.abc import Mapping +from sklearn.utils._param_validation import Interval, StrOptions + from ..base import BaseSampler -from ..utils._param_validation import Interval, StrOptions class BaseOverSampler(BaseSampler): diff --git a/imblearn/pipeline.py b/imblearn/pipeline.py index 7453446ad..7972b9b30 100644 --- a/imblearn/pipeline.py +++ b/imblearn/pipeline.py @@ -2,6 +2,7 @@ The :mod:`imblearn.pipeline` module implements utilities to build a composite estimator, as a chain of transforms, samples and estimators. """ + # Adapted from scikit-learn # Author: Edouard Duchesnay @@ -12,38 +13,111 @@ # Christos Aridas # Guillaume Lemaitre # License: BSD -import sklearn +import warnings +from contextlib import contextmanager +from copy import deepcopy + from sklearn import pipeline from sklearn.base import clone +from sklearn.exceptions import NotFittedError from sklearn.utils import Bunch -from sklearn.utils.fixes import parse_version -from sklearn.utils.metaestimators import available_if -from sklearn.utils.validation import check_memory - -from .base import _ParamsValidationMixin -from .utils._metadata_requests import ( +from sklearn.utils._metadata_requests import ( METHODS, MetadataRouter, MethodMapping, - _raise_for_params, _routing_enabled, + get_routing_for_object, +) +from sklearn.utils._param_validation import HasMethods +from sklearn.utils.fixes import parse_version +from sklearn.utils.metaestimators import available_if +from sklearn.utils.validation import check_is_fitted, check_memory + +from .utils._sklearn_compat import ( + _fit_context, + _print_elapsed_time, + _raise_for_params, + get_tags, process_routing, + sklearn_version, + validate_params, ) -from .utils._param_validation import HasMethods, validate_params -from .utils.fixes import _fit_context +if "fit_predict" not in METHODS: + METHODS.append("fit_predict") +if "fit_transform" not in METHODS: + METHODS.append("fit_transform") METHODS.append("fit_resample") __all__ = ["Pipeline", "make_pipeline"] -sklearn_version = parse_version(sklearn.__version__).base_version -if parse_version(sklearn_version) < parse_version("1.5"): - from sklearn.utils import _print_elapsed_time -else: - from sklearn.utils._user_interface import _print_elapsed_time + +@contextmanager +def _raise_or_warn_if_not_fitted(estimator): + """A context manager to make sure a NotFittedError is raised, if a sub-estimator + raises the error. + Otherwise, we raise a warning if the pipeline is not fitted, with the deprecation. + TODO(0.15): remove this context manager and replace with check_is_fitted. + """ + try: + yield + except NotFittedError as exc: + raise NotFittedError("Pipeline is not fitted yet.") from exc + + # we only get here if the above didn't raise + try: + check_is_fitted(estimator) + except NotFittedError: + warnings.warn( + ( + "This Pipeline instance is not fitted yet. Call 'fit' with " + "appropriate arguments before using other methods such as transform, " + "predict, etc. This will raise an error in 0.15 instead of the current " + "warning." + ), + FutureWarning, + ) + + +def _cached_transform( + sub_pipeline, *, cache, param_name, param_value, transform_params +): + """Transform a parameter value using a sub-pipeline and cache the result. + Parameters + ---------- + sub_pipeline : Pipeline + The sub-pipeline to be used for transformation. + cache : dict + The cache dictionary to store the transformed values. + param_name : str + The name of the parameter to be transformed. + param_value : object + The value of the parameter to be transformed. + transform_params : dict + The metadata to be used for transformation. This passed to the + `transform` method of the sub-pipeline. + Returns + ------- + transformed_value : object + The transformed value of the parameter. + """ + if param_name not in cache: + # If the parameter is a tuple, transform each element of the + # tuple. This is needed to support the pattern present in + # `lightgbm` and `xgboost` where users can pass multiple + # validation sets. + if isinstance(param_value, tuple): + cache[param_name] = tuple( + sub_pipeline.transform(element, **transform_params) + for element in param_value + ) + else: + cache[param_name] = sub_pipeline.transform(param_value, **transform_params) + + return cache[param_name] -class Pipeline(_ParamsValidationMixin, pipeline.Pipeline): +class Pipeline(pipeline.Pipeline): """Pipeline of transforms and resamples with a final estimator. Sequentially apply a list of transforms, sampling, and a final estimator. @@ -69,6 +143,20 @@ class Pipeline(_ParamsValidationMixin, pipeline.Pipeline): fit/transform/fit_resample) that are chained, in the order in which they are chained, with the last object an estimator. + transform_input : list of str, default=None + The names of the :term:`metadata` parameters that should be transformed by the + pipeline before passing it to the step consuming it. + + This enables transforming some input arguments to ``fit`` (other than ``X``) + to be transformed by the steps of the pipeline up to the step which requires + them. Requirement is defined via :ref:`metadata routing `. + For instance, this can be used to pass a validation set through the pipeline. + + You can only set this if metadata routing is enabled, which you + can enable using ``sklearn.set_config(enable_metadata_routing=True)``. + + .. versionadded:: 1.6 + memory : Instance of joblib.Memory or str, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to @@ -95,6 +183,10 @@ class Pipeline(_ParamsValidationMixin, pipeline.Pipeline): n_features_in_ : int Number of features seen during first step `fit` method. + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Only defined if the + underlying estimator exposes such an attribute when fit. + See Also -------- make_pipeline : Helper function to make pipeline. @@ -152,10 +244,17 @@ class Pipeline(_ParamsValidationMixin, pipeline.Pipeline): _parameter_constraints: dict = { "steps": "no_validation", # validated in `_validate_steps` + "transform_input": [list, None], "memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"], } + def __init__(self, steps, *, transform_input=None, memory=None, verbose=False): + self.steps = steps + self.transform_input = transform_input + self.memory = memory + self.verbose = verbose + # BaseEstimator interface def _validate_steps(self): @@ -222,10 +321,87 @@ def _iter(self, with_final=True, filter_passthrough=True, filter_resample=True): else: return it + def _get_metadata_for_step(self, *, step_idx, step_params, all_params): + """Get params (metadata) for step `name`. + + This transforms the metadata up to this step if required, which is + indicated by the `transform_input` parameter. + + If a param in `step_params` is included in the `transform_input` list, + it will be transformed. + + Parameters + ---------- + step_idx : int + Index of the step in the pipeline. + + step_params : dict + Parameters specific to the step. These are routed parameters, e.g. + `routed_params[name]`. If a parameter name here is included in the + `pipeline.transform_input`, then it will be transformed. Note that + these parameters are *after* routing, so the aliases are already + resolved. + + all_params : dict + All parameters passed by the user. Here this is used to call + `transform` on the slice of the pipeline itself. + + Returns + ------- + dict + Parameters to be passed to the step. The ones which should be + transformed are transformed. + """ + if ( + self.transform_input is None + or not all_params + or not step_params + or step_idx == 0 + ): + # we only need to process step_params if transform_input is set + # and metadata is given by the user. + return step_params + + sub_pipeline = self[:step_idx] + sub_metadata_routing = get_routing_for_object(sub_pipeline) + # here we get the metadata required by sub_pipeline.transform + transform_params = { + key: value + for key, value in all_params.items() + if key + in sub_metadata_routing.consumes( + method="transform", params=all_params.keys() + ) + } + transformed_params = dict() # this is to be returned + transformed_cache = dict() # used to transform each param once + # `step_params` is the output of `process_routing`, so it has a dict for each + # method (e.g. fit, transform, predict), which are the args to be passed to + # those methods. We need to transform the parameters which are in the + # `transform_input`, before returning these dicts. + for method, method_params in step_params.items(): + transformed_params[method] = Bunch() + for param_name, param_value in method_params.items(): + # An example of `(param_name, param_value)` is + # `('sample_weight', array([0.5, 0.5, ...]))` + if param_name in self.transform_input: + # This parameter now needs to be transformed by the sub_pipeline, to + # this step. We cache these computations to avoid repeating them. + transformed_params[method][param_name] = _cached_transform( + sub_pipeline, + cache=transformed_cache, + param_name=param_name, + param_value=param_value, + transform_params=transform_params, + ) + else: + transformed_params[method][param_name] = param_value + return transformed_params + # Estimator interface # def _fit(self, X, y=None, **fit_params_steps): - def _fit(self, X, y=None, routed_params=None): + def _fit(self, X, y=None, routed_params=None, raw_params=None): self.steps = list(self.steps) self._validate_steps() # Setup the memory @@ -249,6 +425,11 @@ def _fit(self, X, y=None, routed_params=None): cloned_transformer = clone(transformer) # Fit or load from cache the current transformer + step_params = self._get_metadata_for_step( + step_idx=step_idx, + step_params=routed_params[name], + all_params=raw_params, + ) if hasattr(cloned_transformer, "transform") or hasattr( cloned_transformer, "fit_transform" ): @@ -256,10 +437,10 @@ def _fit(self, X, y=None, routed_params=None): cloned_transformer, X, y, - None, + weight=None, message_clsname="Pipeline", message=self._log_message(step_idx), - params=routed_params[name], + params=step_params, ) elif hasattr(cloned_transformer, "fit_resample"): X, y, fitted_transformer = fit_resample_one_cached( @@ -325,11 +506,29 @@ def fit(self, X, y=None, **params): self : Pipeline This estimator. """ + if not _routing_enabled() and self.transform_input is not None: + raise ValueError( + "The `transform_input` parameter can only be set if metadata " + "routing is enabled. You can enable metadata routing using " + "`sklearn.set_config(enable_metadata_routing=True)`." + ) + + if sklearn_version < parse_version("1.4") and self.transform_input is not None: + raise ValueError( + "The `transform_input` parameter is not supported in scikit-learn " + "versions prior to 1.4. Please upgrade to scikit-learn 1.4 or " + "later." + ) + routed_params = self._check_method_params(method="fit", props=params) - Xt, yt = self._fit(X, y, routed_params) + Xt, yt = self._fit(X, y, routed_params, raw_params=params) with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if self._final_estimator != "passthrough": - last_step_params = routed_params[self.steps[-1][0]] + last_step_params = self._get_metadata_for_step( + step_idx=len(self) - 1, + step_params=routed_params[self.steps[-1][0]], + all_params=params, + ) self._final_estimator.fit(Xt, yt, **last_step_params["fit"]) return self @@ -395,7 +594,11 @@ def fit_transform(self, X, y=None, **params): with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if last_step == "passthrough": return Xt - last_step_params = routed_params[self.steps[-1][0]] + last_step_params = self._get_metadata_for_step( + step_idx=len(self) - 1, + step_params=routed_params[self.steps[-1][0]], + all_params=params, + ) if hasattr(last_step, "fit_transform"): return last_step.fit_transform( Xt, yt, **last_step_params["fit_transform"] @@ -452,18 +655,22 @@ def predict(self, X, **params): y_pred : ndarray Result of calling `predict` on the final estimator. """ - Xt = X + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + Xt = X - if not _routing_enabled(): - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt) - return self.steps[-1][1].predict(Xt, **params) + if not _routing_enabled(): + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform(Xt) + return self.steps[-1][1].predict(Xt, **params) - # metadata routing enabled - routed_params = process_routing(self, "predict", **params) - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt, **routed_params[name].transform) - return self.steps[-1][1].predict(Xt, **routed_params[self.steps[-1][0]].predict) + # metadata routing enabled + routed_params = process_routing(self, "predict", **params) + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform(Xt, **routed_params[name].transform) + return self.steps[-1][1].predict( + Xt, **routed_params[self.steps[-1][0]].predict + ) def _can_fit_resample(self): return self._final_estimator == "passthrough" or hasattr( @@ -642,20 +849,22 @@ def predict_proba(self, X, **params): y_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_proba` on the final estimator. """ - Xt = X + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + Xt = X - if not _routing_enabled(): + if not _routing_enabled(): + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform(Xt) + return self.steps[-1][1].predict_proba(Xt, **params) + + # metadata routing enabled + routed_params = process_routing(self, "predict_proba", **params) for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt) - return self.steps[-1][1].predict_proba(Xt, **params) - - # metadata routing enabled - routed_params = process_routing(self, "predict_proba", **params) - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt, **routed_params[name].transform) - return self.steps[-1][1].predict_proba( - Xt, **routed_params[self.steps[-1][0]].predict_proba - ) + Xt = transform.transform(Xt, **routed_params[name].transform) + return self.steps[-1][1].predict_proba( + Xt, **routed_params[self.steps[-1][0]].predict_proba + ) @available_if(pipeline._final_estimator_has("decision_function")) def decision_function(self, X, **params): @@ -687,20 +896,23 @@ def decision_function(self, X, **params): y_score : ndarray of shape (n_samples, n_classes) Result of calling `decision_function` on the final estimator. """ - _raise_for_params(params, self, "decision_function") + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + _raise_for_params(params, self, "decision_function") - # not branching here since params is only available if - # enable_metadata_routing=True - routed_params = process_routing(self, "decision_function", **params) + # not branching here since params is only available if + # enable_metadata_routing=True + routed_params = process_routing(self, "decision_function", **params) - Xt = X - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform( - Xt, **routed_params.get(name, {}).get("transform", {}) + Xt = X + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform( + Xt, **routed_params.get(name, {}).get("transform", {}) + ) + return self.steps[-1][1].decision_function( + Xt, + **routed_params.get(self.steps[-1][0], {}).get("decision_function", {}), ) - return self.steps[-1][1].decision_function( - Xt, **routed_params.get(self.steps[-1][0], {}).get("decision_function", {}) - ) @available_if(pipeline._final_estimator_has("score_samples")) def score_samples(self, X): @@ -722,10 +934,12 @@ def score_samples(self, X): y_score : ndarray of shape (n_samples,) Result of calling `score_samples` on the final estimator. """ - Xt = X - for _, _, transformer in self._iter(with_final=False): - Xt = transformer.transform(Xt) - return self.steps[-1][1].score_samples(Xt) + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + Xt = X + for _, _, transformer in self._iter(with_final=False): + Xt = transformer.transform(Xt) + return self.steps[-1][1].score_samples(Xt) @available_if(pipeline._final_estimator_has("predict_log_proba")) def predict_log_proba(self, X, **params): @@ -769,20 +983,22 @@ def predict_log_proba(self, X, **params): y_log_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_log_proba` on the final estimator. """ - Xt = X + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + Xt = X + + if not _routing_enabled(): + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform(Xt) + return self.steps[-1][1].predict_log_proba(Xt, **params) - if not _routing_enabled(): + # metadata routing enabled + routed_params = process_routing(self, "predict_log_proba", **params) for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt) - return self.steps[-1][1].predict_log_proba(Xt, **params) - - # metadata routing enabled - routed_params = process_routing(self, "predict_log_proba", **params) - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt, **routed_params[name].transform) - return self.steps[-1][1].predict_log_proba( - Xt, **routed_params[self.steps[-1][0]].predict_log_proba - ) + Xt = transform.transform(Xt, **routed_params[name].transform) + return self.steps[-1][1].predict_log_proba( + Xt, **routed_params[self.steps[-1][0]].predict_log_proba + ) def _can_transform(self): return self._final_estimator == "passthrough" or hasattr( @@ -822,15 +1038,17 @@ def transform(self, X, **params): Xt : ndarray of shape (n_samples, n_transformed_features) Transformed data. """ - _raise_for_params(params, self, "transform") - - # not branching here since params is only available if - # enable_metadata_routing=True - routed_params = process_routing(self, "transform", **params) - Xt = X - for _, name, transform in self._iter(): - Xt = transform.transform(Xt, **routed_params[name].transform) - return Xt + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + _raise_for_params(params, self, "transform") + + # not branching here since params is only available if + # enable_metadata_routing=True + routed_params = process_routing(self, "transform", **params) + Xt = X + for _, name, transform in self._iter(): + Xt = transform.transform(Xt, **routed_params[name].transform) + return Xt def _can_inverse_transform(self): return all(hasattr(t, "inverse_transform") for _, _, t in self._iter()) @@ -865,17 +1083,19 @@ def inverse_transform(self, Xt, **params): Inverse transformed data, that is, data in the original feature space. """ - _raise_for_params(params, self, "inverse_transform") - - # we don't have to branch here, since params is only non-empty if - # enable_metadata_routing=True. - routed_params = process_routing(self, "inverse_transform", **params) - reverse_iter = reversed(list(self._iter())) - for _, name, transform in reverse_iter: - Xt = transform.inverse_transform( - Xt, **routed_params[name].inverse_transform - ) - return Xt + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + _raise_for_params(params, self, "inverse_transform") + + # we don't have to branch here, since params is only non-empty if + # enable_metadata_routing=True. + routed_params = process_routing(self, "inverse_transform", **params) + reverse_iter = reversed(list(self._iter())) + for _, name, transform in reverse_iter: + Xt = transform.inverse_transform( + Xt, **routed_params[name].inverse_transform + ) + return Xt @available_if(pipeline._final_estimator_has("score")) def score(self, X, y=None, sample_weight=None, **params): @@ -914,24 +1134,28 @@ def score(self, X, y=None, sample_weight=None, **params): score : float Result of calling `score` on the final estimator. """ - Xt = X - if not _routing_enabled(): - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt) - score_params = {} - if sample_weight is not None: - score_params["sample_weight"] = sample_weight - return self.steps[-1][1].score(Xt, y, **score_params) - - # metadata routing is enabled. - routed_params = process_routing( - self, "score", sample_weight=sample_weight, **params - ) + # TODO(1.8): Remove the context manager and use check_is_fitted(self) + with _raise_or_warn_if_not_fitted(self): + Xt = X + if not _routing_enabled(): + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform(Xt) + score_params = {} + if sample_weight is not None: + score_params["sample_weight"] = sample_weight + return self.steps[-1][1].score(Xt, y, **score_params) + + # metadata routing is enabled. + routed_params = process_routing( + self, "score", sample_weight=sample_weight, **params + ) - Xt = X - for _, name, transform in self._iter(with_final=False): - Xt = transform.transform(Xt, **routed_params[name].transform) - return self.steps[-1][1].score(Xt, y, **routed_params[self.steps[-1][0]].score) + Xt = X + for _, name, transform in self._iter(with_final=False): + Xt = transform.transform(Xt, **routed_params[name].transform) + return self.steps[-1][1].score( + Xt, y, **routed_params[self.steps[-1][0]].score + ) # TODO: once scikit-learn >= 1.4, the following function should be simplified by # calling `super().get_metadata_routing()` @@ -955,35 +1179,29 @@ def get_metadata_routing(self): # fit, fit_predict, and fit_transform call fit_transform if it # exists, or else fit and transform if hasattr(trans, "fit_transform"): - ( - method_mapping.add(caller="fit", callee="fit_transform") - .add(caller="fit_transform", callee="fit_transform") - .add(caller="fit_predict", callee="fit_transform") - .add(caller="fit_resample", callee="fit_transform") - ) + method_mapping.add(caller="fit", callee="fit_transform") + method_mapping.add(caller="fit_transform", callee="fit_transform") + method_mapping.add(caller="fit_predict", callee="fit_transform") + method_mapping.add(caller="fit_resample", callee="fit_transform") else: - ( - method_mapping.add(caller="fit", callee="fit") - .add(caller="fit", callee="transform") - .add(caller="fit_transform", callee="fit") - .add(caller="fit_transform", callee="transform") - .add(caller="fit_predict", callee="fit") - .add(caller="fit_predict", callee="transform") - .add(caller="fit_resample", callee="fit") - .add(caller="fit_resample", callee="transform") - ) - - ( - method_mapping.add(caller="predict", callee="transform") - .add(caller="predict", callee="transform") - .add(caller="predict_proba", callee="transform") - .add(caller="decision_function", callee="transform") - .add(caller="predict_log_proba", callee="transform") - .add(caller="transform", callee="transform") - .add(caller="inverse_transform", callee="inverse_transform") - .add(caller="score", callee="transform") - .add(caller="fit_resample", callee="transform") - ) + method_mapping.add(caller="fit", callee="fit") + method_mapping.add(caller="fit", callee="transform") + method_mapping.add(caller="fit_transform", callee="fit") + method_mapping.add(caller="fit_transform", callee="transform") + method_mapping.add(caller="fit_predict", callee="fit") + method_mapping.add(caller="fit_predict", callee="transform") + method_mapping.add(caller="fit_resample", callee="fit") + method_mapping.add(caller="fit_resample", callee="transform") + + method_mapping.add(caller="predict", callee="transform") + method_mapping.add(caller="predict", callee="transform") + method_mapping.add(caller="predict_proba", callee="transform") + method_mapping.add(caller="decision_function", callee="transform") + method_mapping.add(caller="predict_log_proba", callee="transform") + method_mapping.add(caller="transform", callee="transform") + method_mapping.add(caller="inverse_transform", callee="inverse_transform") + method_mapping.add(caller="score", callee="transform") + method_mapping.add(caller="fit_resample", callee="transform") router.add(method_mapping=method_mapping, **{name: trans}) @@ -994,30 +1212,24 @@ def get_metadata_routing(self): # then we add the last step method_mapping = MethodMapping() if hasattr(final_est, "fit_transform"): - ( - method_mapping.add(caller="fit_transform", callee="fit_transform").add( - caller="fit_resample", callee="fit_transform" - ) - ) + method_mapping.add(caller="fit_transform", callee="fit_transform") + method_mapping.add(caller="fit_resample", callee="fit_transform") else: - ( - method_mapping.add(caller="fit", callee="fit") - .add(caller="fit", callee="transform") - .add(caller="fit_resample", callee="fit") - .add(caller="fit_resample", callee="transform") - ) - ( method_mapping.add(caller="fit", callee="fit") - .add(caller="predict", callee="predict") - .add(caller="fit_predict", callee="fit_predict") - .add(caller="predict_proba", callee="predict_proba") - .add(caller="decision_function", callee="decision_function") - .add(caller="predict_log_proba", callee="predict_log_proba") - .add(caller="transform", callee="transform") - .add(caller="inverse_transform", callee="inverse_transform") - .add(caller="score", callee="score") - .add(caller="fit_resample", callee="fit_resample") - ) + method_mapping.add(caller="fit", callee="transform") + method_mapping.add(caller="fit_resample", callee="fit") + method_mapping.add(caller="fit_resample", callee="transform") + + method_mapping.add(caller="fit", callee="fit") + method_mapping.add(caller="predict", callee="predict") + method_mapping.add(caller="fit_predict", callee="fit_predict") + method_mapping.add(caller="predict_proba", callee="predict_proba") + method_mapping.add(caller="decision_function", callee="decision_function") + method_mapping.add(caller="predict_log_proba", callee="predict_log_proba") + method_mapping.add(caller="transform", callee="transform") + method_mapping.add(caller="inverse_transform", callee="inverse_transform") + method_mapping.add(caller="score", callee="score") + method_mapping.add(caller="fit_resample", callee="fit_resample") router.add(method_mapping=method_mapping, **{final_name: final_est}) return router @@ -1051,6 +1263,67 @@ def _check_method_params(self, method, props, **kwargs): fit_params_steps[step]["fit_predict"][param] = pval return fit_params_steps + def __sklearn_is_fitted__(self): + """Indicate whether pipeline has been fit. + + This is done by checking whether the last non-`passthrough` step of the + pipeline is fitted. + + An empty pipeline is considered fitted. + """ + + # First find the last step that is not 'passthrough' + last_step = None + for _, estimator in reversed(self.steps): + if estimator != "passthrough": + last_step = estimator + break + + if last_step is None: + # All steps are 'passthrough', so the pipeline is considered fitted + return True + + try: + # check if the last step of the pipeline is fitted + # we only check the last step since if the last step is fit, it + # means the previous steps should also be fit. This is faster than + # checking if every step of the pipeline is fit. + check_is_fitted(last_step) + return True + except NotFittedError: + return False + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + + if not self.steps: + return tags + + try: + if self.steps[0][1] is not None and self.steps[0][1] != "passthrough": + tags.input_tags.pairwise = get_tags( + self.steps[0][1] + ).input_tags.pairwise + except (ValueError, AttributeError, TypeError): + # This happens when the `steps` is not a list of (name, estimator) + # tuples and `fit` is not called yet to validate the steps. + pass + + try: + if self.steps[-1][1] is not None and self.steps[-1][1] != "passthrough": + last_step_tags = get_tags(self.steps[-1][1]) + tags.estimator_type = last_step_tags.estimator_type + tags.target_tags.multi_output = last_step_tags.target_tags.multi_output + tags.classifier_tags = deepcopy(last_step_tags.classifier_tags) + tags.regressor_tags = deepcopy(last_step_tags.regressor_tags) + tags.transformer_tags = deepcopy(last_step_tags.transformer_tags) + except (ValueError, AttributeError, TypeError): + # This happens when the `steps` is not a list of (name, estimator) + # tuples and `fit` is not called yet to validate the steps. + pass + + return tags + def _fit_resample_one(sampler, X, y, message_clsname="", message=None, params=None): with _print_elapsed_time(message_clsname, message): @@ -1059,7 +1332,7 @@ def _fit_resample_one(sampler, X, y, message_clsname="", message=None, params=No return X_res, y_res, sampler -def _transform_one(transformer, X, y, weight, params): +def _transform_one(transformer, X, y, weight, params=None): """Call transform and apply weight to output. Parameters @@ -1113,10 +1386,14 @@ def _fit_transform_one( @validate_params( - {"memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"]}, + { + "memory": [None, str, HasMethods(["cache"])], + "transform_input": [None, list], + "verbose": ["boolean"], + }, prefer_skip_nested_validation=True, ) -def make_pipeline(*steps, memory=None, verbose=False): +def make_pipeline(*steps, memory=None, transform_input=None, verbose=False): """Construct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and @@ -1138,6 +1415,17 @@ def make_pipeline(*steps, memory=None, verbose=False): inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. + transform_input : list of str, default=None + This enables transforming some input arguments to ``fit`` (other than ``X``) + to be transformed by the steps of the pipeline up to the step which requires + them. Requirement is defined via :ref:`metadata routing `. + This can be used to pass a validation set through the pipeline for instance. + + You can only set this if metadata routing is enabled, which you + can enable using ``sklearn.set_config(enable_metadata_routing=True)``. + + .. versionadded:: 1.6 + verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. @@ -1160,4 +1448,9 @@ def make_pipeline(*steps, memory=None, verbose=False): Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) """ - return Pipeline(pipeline._name_estimators(steps), memory=memory, verbose=verbose) + return Pipeline( + pipeline._name_estimators(steps), + memory=memory, + transform_input=transform_input, + verbose=verbose, + ) diff --git a/imblearn/tests/test_common.py b/imblearn/tests/test_common.py index f04dd1d4c..fee54b8b8 100644 --- a/imblearn/tests/test_common.py +++ b/imblearn/tests/test_common.py @@ -1,4 +1,5 @@ """Common tests""" + # Authors: Guillaume Lemaitre # Christos Aridas # License: MIT @@ -8,16 +9,19 @@ import numpy as np import pytest -from sklearn.base import clone from sklearn.exceptions import ConvergenceWarning -from sklearn.utils._testing import SkipTest, ignore_warnings, set_random_state -from sklearn.utils.estimator_checks import _construct_instance, _get_check_estimator_ids -from sklearn.utils.estimator_checks import ( - parametrize_with_checks as parametrize_with_checks_sklearn, -) +from sklearn.utils._testing import ignore_warnings from imblearn.over_sampling import RandomOverSampler -from imblearn.under_sampling import NearMiss, RandomUnderSampler +from imblearn.under_sampling import RandomUnderSampler +from imblearn.utils._sklearn_compat import ( + parametrize_with_checks as parametrize_with_checks_sklearn, +) +from imblearn.utils._test_common.instance_generator import ( + _get_check_estimator_ids, + _get_expected_failed_checks, + _tested_estimators, +) from imblearn.utils.estimator_checks import ( _set_checking_parameters, check_dataframe_column_names_consistency, @@ -34,29 +38,17 @@ def test_all_estimator_no_base_class(name, Estimator): assert not name.lower().startswith("base"), msg -def _tested_estimators(): - for name, Estimator in all_estimators(): - try: - estimator = _construct_instance(Estimator) - set_random_state(estimator) - except SkipTest: - continue - - if isinstance(estimator, NearMiss): - # For NearMiss, let's check the three algorithms - for version in (1, 2, 3): - yield clone(estimator).set_params(version=version) - else: - yield estimator - - -@parametrize_with_checks_sklearn(list(_tested_estimators())) +@parametrize_with_checks_sklearn( + list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks +) def test_estimators_compatibility_sklearn(estimator, check, request): _set_checking_parameters(estimator) check(estimator) -@parametrize_with_checks(list(_tested_estimators())) +@parametrize_with_checks( + list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks +) def test_estimators_imblearn(estimator, check, request): # Common tests for estimator instances with ignore_warnings( diff --git a/imblearn/tests/test_docstring_parameters.py b/imblearn/tests/test_docstring_parameters.py index 03b66b3cb..364ca09f9 100644 --- a/imblearn/tests/test_docstring_parameters.py +++ b/imblearn/tests/test_docstring_parameters.py @@ -10,7 +10,6 @@ import pytest from sklearn.datasets import make_classification -from sklearn.linear_model import LogisticRegression from sklearn.utils._testing import ( _get_func_name, check_docstring_parameters, @@ -18,15 +17,15 @@ ) from sklearn.utils.deprecation import _is_deprecated from sklearn.utils.estimator_checks import ( - _construct_instance, _enforce_estimator_tags_X, _enforce_estimator_tags_y, ) import imblearn from imblearn.base import is_sampler +from imblearn.under_sampling import NearMiss +from imblearn.utils._test_common.instance_generator import _tested_estimators from imblearn.utils.estimator_checks import _set_checking_parameters -from imblearn.utils.testing import all_estimators # walk_packages() ignores DeprecationWarnings, now we need to ignore # FutureWarnings @@ -43,10 +42,10 @@ ) # functions to ignore args / docstring of -_DOCSTRING_IGNORES = [ - "RUSBoostClassifier", # TODO remove after releasing scikit-learn 1.0.1 - "ValueDifferenceMetric", -] +_DOCSTRING_IGNORES = ["ValueDifferenceMetric"] +_IGNORE_ATTRIBUTES = { + NearMiss: ["nn_ver3_"], +} # Methods where y param should be ignored if y=None by default _METHODS_IGNORE_NONE_Y = [ @@ -159,28 +158,19 @@ def test_tabs(): ) -def _construct_compose_pipeline_instance(Estimator): - # Minimal / degenerate instances: only useful to test the docstrings. - if Estimator.__name__ == "Pipeline": - return Estimator(steps=[("clf", LogisticRegression())]) - - -@pytest.mark.parametrize("name, Estimator", all_estimators()) -def test_fit_docstring_attributes(name, Estimator): +@pytest.mark.parametrize("estimator", list(_tested_estimators())) +def test_fit_docstring_attributes(estimator): pytest.importorskip("numpydoc") from numpydoc import docscrape + Estimator = estimator.__class__ if Estimator.__name__ in _DOCSTRING_IGNORES: return doc = docscrape.ClassDoc(Estimator) attributes = doc["Attributes"] - if Estimator.__name__ == "Pipeline": - est = _construct_compose_pipeline_instance(Estimator) - else: - est = _construct_instance(Estimator) - _set_checking_parameters(est) + _set_checking_parameters(estimator) X, y = make_classification( n_samples=20, @@ -190,16 +180,16 @@ def test_fit_docstring_attributes(name, Estimator): random_state=2, ) - y = _enforce_estimator_tags_y(est, y) - X = _enforce_estimator_tags_X(est, X) + y = _enforce_estimator_tags_y(estimator, y) + X = _enforce_estimator_tags_X(estimator, X) - if "oob_score" in est.get_params(): - est.set_params(bootstrap=True, oob_score=True) + if "oob_score" in estimator.get_params(): + estimator.set_params(bootstrap=True, oob_score=True) - if is_sampler(est): - est.fit_resample(X, y) + if is_sampler(estimator): + estimator.fit_resample(X, y) else: - est.fit(X, y) + estimator.fit(X, y) skipped_attributes = set( [ @@ -218,9 +208,11 @@ def test_fit_docstring_attributes(name, Estimator): continue # ignore deprecation warnings with ignore_warnings(category=FutureWarning): - assert hasattr(est, attr.name) + if attr.name in _IGNORE_ATTRIBUTES.get(Estimator, []): + continue + assert hasattr(estimator, attr.name) - fit_attr = _get_all_fitted_attributes(est) + fit_attr = _get_all_fitted_attributes(estimator) fit_attr_names = [attr.name for attr in attributes] undocumented_attrs = set(fit_attr).difference(fit_attr_names) undocumented_attrs = set(undocumented_attrs).difference(skipped_attributes) diff --git a/imblearn/tests/test_pipeline.py b/imblearn/tests/test_pipeline.py index ed90b263c..dfe74442a 100644 --- a/imblearn/tests/test_pipeline.py +++ b/imblearn/tests/test_pipeline.py @@ -1,6 +1,7 @@ """ Test the pipeline module. """ + # Authors: Guillaume Lemaitre # Christos Aridas # License: MIT @@ -15,7 +16,8 @@ import pytest from joblib import Memory from pytest import raises -from sklearn.base import BaseEstimator, clone +from sklearn import config_context +from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin, clone from sklearn.cluster import KMeans from sklearn.datasets import load_iris, make_classification from sklearn.decomposition import PCA @@ -30,11 +32,13 @@ assert_array_almost_equal, assert_array_equal, ) +from sklearn.utils.fixes import parse_version from imblearn.datasets import make_imbalance from imblearn.pipeline import Pipeline, make_pipeline from imblearn.under_sampling import EditedNearestNeighbours as ENN from imblearn.under_sampling import RandomUnderSampler +from imblearn.utils._sklearn_compat import sklearn_version from imblearn.utils.estimator_checks import check_param_validation JUNK_FOOD_DOCS = ( @@ -92,6 +96,9 @@ class Mult(BaseEstimator): def __init__(self, mult=1): self.mult = mult + def __sklearn_is_fitted__(self): + return True + def fit(self, X, y): return self @@ -117,7 +124,9 @@ def __init__(self): self.successful = False def fit(self, X, y, should_succeed=False): + self.fitted_ = True self.successful = should_succeed + return self def predict(self, X): return self.successful @@ -146,6 +155,9 @@ def fit(self, X, y): class DummyEstimatorParams(BaseEstimator): """Mock classifier that takes params on predict""" + def __sklearn_is_fitted__(self): + return True + def fit(self, X, y): return self @@ -168,6 +180,9 @@ def fit_resample(self, X, y): class FitTransformSample(NoTrans): """Estimator implementing both transform and sample""" + def __sklearn_is_fitted__(self): + return True + def fit(self, X, y, should_succeed=False): pass @@ -534,6 +549,7 @@ def make(): "m2__mult": 2, "last__mult": 5, "verbose": False, + "transform_input": None, } assert pipeline.get_params(deep=True) == expected_params @@ -1353,3 +1369,129 @@ def test_pipeline_with_set_output(): assert isinstance(X_res, pd.DataFrame) # transformer will not change `y` and sampler will always preserve the type of `y` assert isinstance(y_res, type(y)) + + +# TODO(0.15): change warning to checking for NotFittedError +@pytest.mark.parametrize( + "method", + [ + "predict", + "predict_proba", + "predict_log_proba", + "decision_function", + "score", + "score_samples", + "transform", + "inverse_transform", + ], +) +def test_pipeline_warns_not_fitted(method): + class StatelessEstimator(BaseEstimator): + """Stateless estimator that doesn't check if it's fitted. + Stateless estimators that don't require fit, should properly set the + `requires_fit` flag and implement a `__sklearn_check_is_fitted__` returning + `True`. + """ + + def fit(self, X, y): + return self # pragma: no cover + + def transform(self, X): + return X + + def predict(self, X): + return np.ones(len(X)) + + def predict_proba(self, X): + return np.ones(len(X)) + + def predict_log_proba(self, X): + return np.zeros(len(X)) + + def decision_function(self, X): + return np.ones(len(X)) + + def score(self, X, y): + return 1 + + def score_samples(self, X): + return np.ones(len(X)) + + def inverse_transform(self, X): + return X + + pipe = Pipeline([("estimator", StatelessEstimator())]) + with pytest.warns(FutureWarning, match="This Pipeline instance is not fitted yet."): + getattr(pipe, method)([[1]]) + + +# transform_input tests +# ===================== + + +@pytest.mark.skipif( + sklearn_version < parse_version("1.4"), + reason="scikit-learn < 1.4 does not support transform_input", +) +@config_context(enable_metadata_routing=True) +def test_transform_input_explicit_value_check(): + """Test that the right transformed values are passed to `fit`.""" + + class Transformer(TransformerMixin, BaseEstimator): + def fit(self, X, y): + self.fitted_ = True + return self + + def transform(self, X): + return X + 1 + + class Estimator(ClassifierMixin, BaseEstimator): + def fit(self, X, y, X_val=None, y_val=None): + assert_array_equal(X, np.array([[1, 2]])) + assert_array_equal(y, np.array([0, 1])) + assert_array_equal(X_val, np.array([[2, 3]])) + assert_array_equal(y_val, np.array([0, 1])) + return self + + X = np.array([[0, 1]]) + y = np.array([0, 1]) + X_val = np.array([[1, 2]]) + y_val = np.array([0, 1]) + pipe = Pipeline( + [ + ("transformer", Transformer()), + ("estimator", Estimator().set_fit_request(X_val=True, y_val=True)), + ], + transform_input=["X_val"], + ) + pipe.fit(X, y, X_val=X_val, y_val=y_val) + + +def test_transform_input_no_slep6(): + """Make sure the right error is raised if slep6 is not enabled.""" + X = np.array([[1, 2], [3, 4]]) + y = np.array([0, 1]) + msg = "The `transform_input` parameter can only be set if metadata" + with pytest.raises(ValueError, match=msg): + make_pipeline(DummyTransf(), transform_input=["blah"]).fit(X, y) + + +@pytest.mark.skipif( + sklearn_version >= parse_version("1.4"), + reason="scikit-learn >= 1.4 supports transform_input", +) +@config_context(enable_metadata_routing=True) +def test_transform_input_sklearn_version(): + """Test that transform_input raises error with sklearn < 1.4.""" + X = np.array([[1, 2], [3, 4]]) + y = np.array([0, 1]) + msg = ( + "The `transform_input` parameter is not supported in scikit-learn versions " + "prior to 1.4" + ) + with pytest.raises(ValueError, match=msg): + make_pipeline(DummyTransf(), transform_input=["blah"]).fit(X, y) + + +# end of transform_input tests +# ============================= diff --git a/imblearn/tests/test_public_functions.py b/imblearn/tests/test_public_functions.py index d84732007..067569ee6 100644 --- a/imblearn/tests/test_public_functions.py +++ b/imblearn/tests/test_public_functions.py @@ -5,8 +5,7 @@ from inspect import signature import pytest - -from imblearn.utils._param_validation import ( +from sklearn.utils._param_validation import ( generate_invalid_param_val, generate_valid_param, make_constraint, diff --git a/imblearn/under_sampling/_prototype_generation/_cluster_centroids.py b/imblearn/under_sampling/_prototype_generation/_cluster_centroids.py index 5e2ca3a82..9203527a1 100644 --- a/imblearn/under_sampling/_prototype_generation/_cluster_centroids.py +++ b/imblearn/under_sampling/_prototype_generation/_cluster_centroids.py @@ -12,10 +12,10 @@ from sklearn.cluster import KMeans from sklearn.neighbors import NearestNeighbors from sklearn.utils import _safe_indexing +from sklearn.utils._param_validation import HasMethods, StrOptions from ...utils import Substitution from ...utils._docstring import _random_state_docstring -from ...utils._param_validation import HasMethods, StrOptions from ..base import BaseUnderSampler VOTING_KIND = ("auto", "hard", "soft") @@ -203,3 +203,8 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": False} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = False + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py b/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py index fe49f1707..db79fee73 100644 --- a/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py +++ b/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py @@ -14,10 +14,10 @@ from sklearn.base import clone from sklearn.neighbors import KNeighborsClassifier from sklearn.utils import _safe_indexing, check_random_state +from sklearn.utils._param_validation import HasMethods, Interval from ...utils import Substitution from ...utils._docstring import _n_jobs_docstring, _random_state_docstring -from ...utils._param_validation import HasMethods, Interval from ..base import BaseCleaningSampler @@ -261,3 +261,8 @@ def estimator_(self): def _more_tags(self): return {"sample_indices": True} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py b/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py index 38abd4bed..87832d2c0 100644 --- a/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py +++ b/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py @@ -10,12 +10,12 @@ from collections import Counter import numpy as np +from scipy.stats import mode from sklearn.utils import _safe_indexing +from sklearn.utils._param_validation import HasMethods, Interval, StrOptions from ...utils import Substitution, check_neighbors_object from ...utils._docstring import _n_jobs_docstring -from ...utils._param_validation import HasMethods, Interval, StrOptions -from ...utils.fixes import _mode from ..base import BaseCleaningSampler SEL_KIND = ("all", "mode") @@ -168,7 +168,7 @@ def _fit_resample(self, X, y): nnhood_idx = self.nn_.kneighbors(X_class, return_distance=False)[:, 1:] nnhood_label = y[nnhood_idx] if self.kind_sel == "mode": - nnhood_label, _ = _mode(nnhood_label, axis=1) + nnhood_label, _ = mode(nnhood_label, axis=1, keepdims=False) nnhood_bool = np.ravel(nnhood_label) == y_class elif self.kind_sel == "all": nnhood_label = nnhood_label == target_class @@ -192,6 +192,11 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": True} + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags + @Substitution( sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring, @@ -413,6 +418,11 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": True} + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags + @Substitution( sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring, @@ -621,3 +631,8 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": True} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py b/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py index dac3f3c33..56c7b56b9 100644 --- a/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py +++ b/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py @@ -15,10 +15,10 @@ from sklearn.ensemble._base import _set_random_states from sklearn.model_selection import StratifiedKFold, cross_val_predict from sklearn.utils import _safe_indexing, check_random_state +from sklearn.utils._param_validation import HasMethods from ...utils import Substitution from ...utils._docstring import _n_jobs_docstring, _random_state_docstring -from ...utils._param_validation import HasMethods from ..base import BaseUnderSampler @@ -202,3 +202,8 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": True} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_nearmiss.py b/imblearn/under_sampling/_prototype_selection/_nearmiss.py index 70f647fa5..16c532d3b 100644 --- a/imblearn/under_sampling/_prototype_selection/_nearmiss.py +++ b/imblearn/under_sampling/_prototype_selection/_nearmiss.py @@ -10,10 +10,10 @@ import numpy as np from sklearn.utils import _safe_indexing +from sklearn.utils._param_validation import HasMethods, Interval from ...utils import Substitution, check_neighbors_object from ...utils._docstring import _n_jobs_docstring -from ...utils._param_validation import HasMethods, Interval from ..base import BaseUnderSampler @@ -62,6 +62,9 @@ class NearMiss(BaseUnderSampler): nn_ : estimator object Validated K-nearest Neighbours object created from `n_neighbors` parameter. + nn_ver3_ : estimator object + Validated K-nearest Neighbours object created from `n_neighbors_ver3` parameter. + sample_indices_ : ndarray of shape (n_new_samples,) Indices of the samples selected. @@ -312,3 +315,8 @@ def _more_tags(self): } } # fmt: on + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py b/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py index 7277a3c99..2c2b50c28 100644 --- a/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py +++ b/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py @@ -12,10 +12,10 @@ from sklearn.base import clone from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors from sklearn.utils import _safe_indexing +from sklearn.utils._param_validation import HasMethods, Hidden, Interval, StrOptions from ...utils import Substitution from ...utils._docstring import _n_jobs_docstring -from ...utils._param_validation import HasMethods, Hidden, Interval, StrOptions from ..base import BaseCleaningSampler from ._edited_nearest_neighbours import EditedNearestNeighbours @@ -258,3 +258,8 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": True} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py b/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py index bfd3449bd..b7e6a69e9 100644 --- a/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py +++ b/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py @@ -12,10 +12,10 @@ from sklearn.base import clone from sklearn.neighbors import KNeighborsClassifier from sklearn.utils import _safe_indexing, check_random_state +from sklearn.utils._param_validation import HasMethods, Interval from ...utils import Substitution from ...utils._docstring import _n_jobs_docstring, _random_state_docstring -from ...utils._param_validation import HasMethods, Interval from ..base import BaseCleaningSampler from ._tomek_links import TomekLinks @@ -227,3 +227,8 @@ def estimator_(self): def _more_tags(self): return {"sample_indices": True} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py b/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py index 876195a6d..6696069c3 100644 --- a/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py +++ b/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py @@ -9,6 +9,7 @@ from ...utils import Substitution, check_target_type from ...utils._docstring import _random_state_docstring +from ...utils._sklearn_compat import validate_data from ...utils._validation import _check_X from ..base import BaseUnderSampler @@ -99,8 +100,7 @@ def __init__( def _check_X_y(self, X, y): y, binarize_y = check_target_type(y, indicate_one_vs_all=True) X = _check_X(X) - self._check_n_features(X, reset=True) - self._check_feature_names(X, reset=True) + X, y = validate_data(self, X=X, y=y, reset=True, skip_check_array=True) return X, y, binarize_y def _fit_resample(self, X, y): @@ -140,3 +140,10 @@ def _more_tags(self): "check_complex_data": "Robust to this type of data.", }, } + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.allow_nan = True + tags.input_tags.string = True + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/_prototype_selection/_tomek_links.py b/imblearn/under_sampling/_prototype_selection/_tomek_links.py index b0f954959..e1e8a0a3c 100644 --- a/imblearn/under_sampling/_prototype_selection/_tomek_links.py +++ b/imblearn/under_sampling/_prototype_selection/_tomek_links.py @@ -158,3 +158,8 @@ def _fit_resample(self, X, y): def _more_tags(self): return {"sample_indices": True} + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.sampler_tags.sample_indices = True + return tags diff --git a/imblearn/under_sampling/base.py b/imblearn/under_sampling/base.py index c341f3d2c..72f9f6d4a 100644 --- a/imblearn/under_sampling/base.py +++ b/imblearn/under_sampling/base.py @@ -7,8 +7,9 @@ import numbers from collections.abc import Mapping +from sklearn.utils._param_validation import Interval, StrOptions + from ..base import BaseSampler -from ..utils._param_validation import Interval, StrOptions class BaseUnderSampler(BaseSampler): diff --git a/imblearn/utils/_metadata_requests.py b/imblearn/utils/_metadata_requests.py deleted file mode 100644 index c81aa4ff0..000000000 --- a/imblearn/utils/_metadata_requests.py +++ /dev/null @@ -1,1603 +0,0 @@ -""" -This is a copy of sklearn/utils/_metadata_requests.py. It can be removed once -we support scikit-learn >= 1.4. - -Metadata Routing Utility - -In order to better understand the components implemented in this file, one -needs to understand their relationship to one another. - -The only relevant public API for end users are the ``set_{method}_request``, -e.g. ``estimator.set_fit_request(sample_weight=True)``. However, third-party -developers and users who implement custom meta-estimators, need to deal with -the objects implemented in this file. - -All estimators (should) implement a ``get_metadata_routing`` method, returning -the routing requests set for the estimator. This method is automatically -implemented via ``BaseEstimator`` for all simple estimators, but needs a custom -implementation for meta-estimators. - -In non-routing consumers, i.e. the simplest case, e.g. ``SVM``, -``get_metadata_routing`` returns a ``MetadataRequest`` object. - -In routers, e.g. meta-estimators and a multi metric scorer, -``get_metadata_routing`` returns a ``MetadataRouter`` object. - -An object which is both a router and a consumer, e.g. a meta-estimator which -consumes ``sample_weight`` and routes ``sample_weight`` to its sub-estimators, -routing information includes both information about the object itself (added -via ``MetadataRouter.add_self_request``), as well as the routing information -for its sub-estimators. - -A ``MetadataRequest`` instance includes one ``MethodMetadataRequest`` per -method in ``METHODS``, which includes ``fit``, ``score``, etc. - -Request values are added to the routing mechanism by adding them to -``MethodMetadataRequest`` instances, e.g. -``metadatarequest.fit.add(param="sample_weight", alias="my_weights")``. This is -used in ``set_{method}_request`` which are automatically generated, so users -and developers almost never need to directly call methods on a -``MethodMetadataRequest``. - -The ``alias`` above in the ``add`` method has to be either a string (an alias), -or a {True (requested), False (unrequested), None (error if passed)}``. There -are some other special values such as ``UNUSED`` and ``WARN`` which are used -for purposes such as warning of removing a metadata in a child class, but not -used by the end users. - -``MetadataRouter`` includes information about sub-objects' routing and how -methods are mapped together. For instance, the information about which methods -of a sub-estimator are called in which methods of the meta-estimator are all -stored here. Conceptually, this information looks like: - -``` -{ - "sub_estimator1": ( - mapping=[(caller="fit", callee="transform"), ...], - router=MetadataRequest(...), # or another MetadataRouter - ), - ... -} -``` - -To give the above representation some structure, we use the following objects: - -- ``(caller, callee)`` is a namedtuple called ``MethodPair`` - -- The list of ``MethodPair`` stored in the ``mapping`` field is a - ``MethodMapping`` object - -- ``(mapping=..., router=...)`` is a namedtuple called ``RouterMappingPair`` - -The ``set_{method}_request`` methods are dynamically generated for estimators -which inherit from the ``BaseEstimator``. This is done by attaching instances -of the ``RequestMethod`` descriptor to classes, which is done in the -``_MetadataRequester`` class, and ``BaseEstimator`` inherits from this mixin. -This mixin also implements the ``get_metadata_routing``, which meta-estimators -need to override, but it works for simple consumers as is. -""" - -# Author: Adrin Jalali -# License: BSD 3 clause - -import inspect -from collections import namedtuple -from copy import deepcopy -from typing import TYPE_CHECKING, Optional, Union -from warnings import warn - -from sklearn import __version__, get_config -from sklearn.utils import Bunch -from sklearn.utils.fixes import parse_version - -sklearn_version = parse_version(__version__) - -if parse_version(sklearn_version.base_version) < parse_version("1.4"): - # Only the following methods are supported in the routing mechanism. Adding new - # methods at the moment involves monkeypatching this list. - # Note that if this list is changed or monkeypatched, the corresponding method - # needs to be added under a TYPE_CHECKING condition like the one done here in - # _MetadataRequester - SIMPLE_METHODS = [ - "fit", - "partial_fit", - "predict", - "predict_proba", - "predict_log_proba", - "decision_function", - "score", - "split", - "transform", - "inverse_transform", - ] - - # These methods are a composite of other methods and one cannot set their - # requests directly. Instead they should be set by setting the requests of the - # simple methods which make the composite ones. - COMPOSITE_METHODS = { - "fit_transform": ["fit", "transform"], - "fit_predict": ["fit", "predict"], - } - - METHODS = SIMPLE_METHODS + list(COMPOSITE_METHODS.keys()) - - def _routing_enabled(): - """Return whether metadata routing is enabled. - - .. versionadded:: 1.3 - - Returns - ------- - enabled : bool - Whether metadata routing is enabled. If the config is not set, it - defaults to False. - """ - return get_config().get("enable_metadata_routing", False) - - def _raise_for_params(params, owner, method): - """Raise an error if metadata routing is not enabled and params are passed. - - .. versionadded:: 1.4 - - Parameters - ---------- - params : dict - The metadata passed to a method. - - owner : object - The object to which the method belongs. - - method : str - The name of the method, e.g. "fit". - - Raises - ------ - ValueError - If metadata routing is not enabled and params are passed. - """ - caller = ( - f"{owner.__class__.__name__}.{method}" - if method - else owner.__class__.__name__ - ) - if not _routing_enabled() and params: - raise ValueError( - f"Passing extra keyword arguments to {caller} is only supported if" - " enable_metadata_routing=True, which you can set using" - " `sklearn.set_config`. See the User Guide" - " for more" - f" details. Extra parameters passed are: {set(params)}" - ) - - def _raise_for_unsupported_routing(obj, method, **kwargs): - """Raise when metadata routing is enabled and metadata is passed. - - This is used in meta-estimators which have not implemented metadata routing - to prevent silent bugs. There is no need to use this function if the - meta-estimator is not accepting any metadata, especially in `fit`, since - if a meta-estimator accepts any metadata, they would do that in `fit` as - well. - - Parameters - ---------- - obj : estimator - The estimator for which we're raising the error. - - method : str - The method where the error is raised. - - **kwargs : dict - The metadata passed to the method. - """ - kwargs = {key: value for key, value in kwargs.items() if value is not None} - if _routing_enabled() and kwargs: - cls_name = obj.__class__.__name__ - raise NotImplementedError( - f"{cls_name}.{method} cannot accept given metadata " - f"({set(kwargs.keys())}) since metadata routing is not yet implemented " - f"for {cls_name}." - ) - - class _RoutingNotSupportedMixin: - """A mixin to be used to remove the default `get_metadata_routing`. - - This is used in meta-estimators where metadata routing is not yet - implemented. - - This also makes it clear in our rendered documentation that this method - cannot be used. - """ - - def get_metadata_routing(self): - """Raise `NotImplementedError`. - - This estimator does not support metadata routing yet.""" - raise NotImplementedError( - f"{self.__class__.__name__} has not implemented metadata routing yet." - ) - - # Request values - # ============== - # Each request value needs to be one of the following values, or an alias. - - # this is used in `__metadata_request__*` attributes to indicate that a - # metadata is not present even though it may be present in the - # corresponding method's signature. - UNUSED = "$UNUSED$" - - # this is used whenever a default value is changed, and therefore the user - # should explicitly set the value, otherwise a warning is shown. An example - # is when a meta-estimator is only a router, but then becomes also a - # consumer in a new release. - WARN = "$WARN$" - - # this is the default used in `set_{method}_request` methods to indicate no - # change requested by the user. - UNCHANGED = "$UNCHANGED$" - - VALID_REQUEST_VALUES = [False, True, None, UNUSED, WARN] - - def request_is_alias(item): - """Check if an item is a valid alias. - - Values in ``VALID_REQUEST_VALUES`` are not considered aliases in this - context. Only a string which is a valid identifier is. - - Parameters - ---------- - item : object - The given item to be checked if it can be an alias. - - Returns - ------- - result : bool - Whether the given item is a valid alias. - """ - if item in VALID_REQUEST_VALUES: - return False - - # item is only an alias if it's a valid identifier - return isinstance(item, str) and item.isidentifier() - - def request_is_valid(item): - """Check if an item is a valid request value (and not an alias). - - Parameters - ---------- - item : object - The given item to be checked. - - Returns - ------- - result : bool - Whether the given item is valid. - """ - return item in VALID_REQUEST_VALUES - - # Metadata Request for Simple Consumers - # ===================================== - # This section includes MethodMetadataRequest and MetadataRequest which are - # used in simple consumers. - - class MethodMetadataRequest: - """A prescription of how metadata is to be passed to a single method. - - Refer to :class:`MetadataRequest` for how this class is used. - - .. versionadded:: 1.3 - - Parameters - ---------- - owner : str - A display name for the object owning these requests. - - method : str - The name of the method to which these requests belong. - - requests : dict of {str: bool, None or str}, default=None - The initial requests for this method. - """ - - def __init__(self, owner, method, requests=None): - self._requests = requests or dict() - self.owner = owner - self.method = method - - @property - def requests(self): - """Dictionary of the form: ``{key: alias}``.""" - return self._requests - - def add_request( - self, - *, - param, - alias, - ): - """Add request info for a metadata. - - Parameters - ---------- - param : str - The property for which a request is set. - - alias : str, or {True, False, None} - Specifies which metadata should be routed to `param` - - - str: the name (or alias) of metadata given to a meta-estimator that - should be routed to this parameter. - - - True: requested - - - False: not requested - - - None: error if passed - """ - if not request_is_alias(alias) and not request_is_valid(alias): - raise ValueError( - f"The alias you're setting for `{param}` should be either a " - "valid identifier or one of {None, True, False}, but given " - f"value is: `{alias}`" - ) - - if alias == param: - alias = True - - if alias == UNUSED: - if param in self._requests: - del self._requests[param] - else: - raise ValueError( - f"Trying to remove parameter {param} with UNUSED which doesn't" - " exist." - ) - else: - self._requests[param] = alias - - return self - - def _get_param_names(self, return_alias): - """Get names of all metadata that can be consumed or routed by this method. - - This method returns the names of all metadata, even the ``False`` - ones. - - Parameters - ---------- - return_alias : bool - Controls whether original or aliased names should be returned. If - ``False``, aliases are ignored and original names are returned. - - Returns - ------- - names : set of str - A set of strings with the names of all parameters. - """ - return set( - alias if return_alias and not request_is_valid(alias) else prop - for prop, alias in self._requests.items() - if not request_is_valid(alias) or alias is not False - ) - - def _check_warnings(self, *, params): - """Check whether metadata is passed which is marked as WARN. - - If any metadata is passed which is marked as WARN, a warning is raised. - - Parameters - ---------- - params : dict - The metadata passed to a method. - """ - params = {} if params is None else params - warn_params = { - prop - for prop, alias in self._requests.items() - if alias == WARN and prop in params - } - for param in warn_params: - warn( - f"Support for {param} has recently been added to this class. " - "To maintain backward compatibility, it is ignored now. " - "You can set the request value to False to silence this " - "warning, or to True to consume and use the metadata." - ) - - def _route_params(self, params): - """Prepare the given parameters to be passed to the method. - - The output of this method can be used directly as the input to the - corresponding method as extra props. - - Parameters - ---------- - params : dict - A dictionary of provided metadata. - - Returns - ------- - params : Bunch - A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to - the corresponding method. - """ - self._check_warnings(params=params) - unrequested = dict() - args = {arg: value for arg, value in params.items() if value is not None} - res = Bunch() - for prop, alias in self._requests.items(): - if alias is False or alias == WARN: - continue - elif alias is True and prop in args: - res[prop] = args[prop] - elif alias is None and prop in args: - unrequested[prop] = args[prop] - elif alias in args: - res[prop] = args[alias] - if unrequested: - raise UnsetMetadataPassedError( - message=( - f"[{', '.join([key for key in unrequested])}] are passed but " - "are not explicitly set as requested or not for" - f" {self.owner}.{self.method}" - ), - unrequested_params=unrequested, - routed_params=res, - ) - return res - - def _consumes(self, params): - """Check whether the given parameters are consumed by this method. - - Parameters - ---------- - params : iterable of str - An iterable of parameters to check. - - Returns - ------- - consumed : set of str - A set of parameters which are consumed by this method. - """ - params = set(params) - res = set() - for prop, alias in self._requests.items(): - if alias is True and prop in params: - res.add(prop) - elif isinstance(alias, str) and alias in params: - res.add(alias) - return res - - def _serialize(self): - """Serialize the object. - - Returns - ------- - obj : dict - A serialized version of the instance in the form of a dictionary. - """ - return self._requests - - def __repr__(self): - return str(self._serialize()) - - def __str__(self): - return str(repr(self)) - - class MetadataRequest: - """Contains the metadata request info of a consumer. - - Instances of `MethodMetadataRequest` are used in this class for each - available method under `metadatarequest.{method}`. - - Consumer-only classes such as simple estimators return a serialized - version of this class as the output of `get_metadata_routing()`. - - .. versionadded:: 1.3 - - Parameters - ---------- - owner : str - The name of the object to which these requests belong. - """ - - # this is here for us to use this attribute's value instead of doing - # `isinstance` in our checks, so that we avoid issues when people vendor - # this file instead of using it directly from scikit-learn. - _type = "metadata_request" - - def __init__(self, owner): - self.owner = owner - for method in SIMPLE_METHODS: - setattr( - self, - method, - MethodMetadataRequest(owner=owner, method=method), - ) - - def consumes(self, method, params): - """Check whether the given parameters are consumed by the given method. - - .. versionadded:: 1.4 - - Parameters - ---------- - method : str - The name of the method to check. - - params : iterable of str - An iterable of parameters to check. - - Returns - ------- - consumed : set of str - A set of parameters which are consumed by the given method. - """ - return getattr(self, method)._consumes(params=params) - - def __getattr__(self, name): - # Called when the default attribute access fails with an AttributeError - # (either __getattribute__() raises an AttributeError because name is - # not an instance attribute or an attribute in the class tree for self; - # or __get__() of a name property raises AttributeError). This method - # should either return the (computed) attribute value or raise an - # AttributeError exception. - # https://docs.python.org/3/reference/datamodel.html#object.__getattr__ - if name not in COMPOSITE_METHODS: - raise AttributeError( - f"'{self.__class__.__name__}' object has no attribute '{name}'" - ) - - requests = {} - for method in COMPOSITE_METHODS[name]: - mmr = getattr(self, method) - existing = set(requests.keys()) - upcoming = set(mmr.requests.keys()) - common = existing & upcoming - conflicts = [ - key for key in common if requests[key] != mmr._requests[key] - ] - if conflicts: - raise ValueError( - f"Conflicting metadata requests for {', '.join(conflicts)} " - f"while composing the requests for {name}. Metadata with the " - f"same name for methods {', '.join(COMPOSITE_METHODS[name])} " - "should have the same request value." - ) - requests.update(mmr._requests) - return MethodMetadataRequest( - owner=self.owner, method=name, requests=requests - ) - - def _get_param_names(self, method, return_alias, ignore_self_request=None): - """Get names of all metadata that can be consumed or routed by specified \ - method. - - This method returns the names of all metadata, even the ``False`` - ones. - - Parameters - ---------- - method : str - The name of the method for which metadata names are requested. - - return_alias : bool - Controls whether original or aliased names should be returned. If - ``False``, aliases are ignored and original names are returned. - - ignore_self_request : bool - Ignored. Present for API compatibility. - - Returns - ------- - names : set of str - A set of strings with the names of all parameters. - """ - return getattr(self, method)._get_param_names(return_alias=return_alias) - - def _route_params(self, *, method, params): - """Prepare the given parameters to be passed to the method. - - The output of this method can be used directly as the input to the - corresponding method as extra keyword arguments to pass metadata. - - Parameters - ---------- - method : str - The name of the method for which the parameters are requested and - routed. - - params : dict - A dictionary of provided metadata. - - Returns - ------- - params : Bunch - A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to - the corresponding method. - """ - return getattr(self, method)._route_params(params=params) - - def _check_warnings(self, *, method, params): - """Check whether metadata is passed which is marked as WARN. - - If any metadata is passed which is marked as WARN, a warning is raised. - - Parameters - ---------- - method : str - The name of the method for which the warnings should be checked. - - params : dict - The metadata passed to a method. - """ - getattr(self, method)._check_warnings(params=params) - - def _serialize(self): - """Serialize the object. - - Returns - ------- - obj : dict - A serialized version of the instance in the form of a dictionary. - """ - output = dict() - for method in SIMPLE_METHODS: - mmr = getattr(self, method) - if len(mmr.requests): - output[method] = mmr._serialize() - return output - - def __repr__(self): - return str(self._serialize()) - - def __str__(self): - return str(repr(self)) - - # Metadata Request for Routers - # ============================ - # This section includes all objects required for MetadataRouter which is used - # in routers, returned by their ``get_metadata_routing``. - - # This namedtuple is used to store a (mapping, routing) pair. Mapping is a - # MethodMapping object, and routing is the output of `get_metadata_routing`. - # MetadataRouter stores a collection of these namedtuples. - RouterMappingPair = namedtuple("RouterMappingPair", ["mapping", "router"]) - - # A namedtuple storing a single method route. A collection of these namedtuples - # is stored in a MetadataRouter. - MethodPair = namedtuple("MethodPair", ["callee", "caller"]) - - class MethodMapping: - """Stores the mapping between callee and caller methods for a router. - - This class is primarily used in a ``get_metadata_routing()`` of a router - object when defining the mapping between a sub-object (a sub-estimator or a - scorer) to the router's methods. It stores a collection of ``Route`` - namedtuples. - - Iterating through an instance of this class will yield named - ``MethodPair(callee, caller)`` tuples. - - .. versionadded:: 1.3 - """ - - def __init__(self): - self._routes = [] - - def __iter__(self): - return iter(self._routes) - - def add(self, *, callee, caller): - """Add a method mapping. - - Parameters - ---------- - callee : str - Child object's method name. This method is called in ``caller``. - - caller : str - Parent estimator's method name in which the ``callee`` is called. - - Returns - ------- - self : MethodMapping - Returns self. - """ - if callee not in METHODS: - raise ValueError( - f"Given callee:{callee} is not a valid method. Valid methods are:" - f" {METHODS}" - ) - if caller not in METHODS: - raise ValueError( - f"Given caller:{caller} is not a valid method. Valid methods are:" - f" {METHODS}" - ) - self._routes.append(MethodPair(callee=callee, caller=caller)) - return self - - def _serialize(self): - """Serialize the object. - - Returns - ------- - obj : list - A serialized version of the instance in the form of a list. - """ - result = list() - for route in self._routes: - result.append({"callee": route.callee, "caller": route.caller}) - return result - - @classmethod - def from_str(cls, route): - """Construct an instance from a string. - - Parameters - ---------- - route : str - A string representing the mapping, it can be: - - - `"one-to-one"`: a one to one mapping for all methods. - - `"method"`: the name of a single method, such as ``fit``, - ``transform``, ``score``, etc. - - Returns - ------- - obj : MethodMapping - A :class:`~sklearn.utils.metadata_routing.MethodMapping` instance - constructed from the given string. - """ - routing = cls() - if route == "one-to-one": - for method in METHODS: - routing.add(callee=method, caller=method) - elif route in METHODS: - routing.add(callee=route, caller=route) - else: - raise ValueError("route should be 'one-to-one' or a single method!") - return routing - - def __repr__(self): - return str(self._serialize()) - - def __str__(self): - return str(repr(self)) - - class MetadataRouter: - """Stores and handles metadata routing for a router object. - - This class is used by router objects to store and handle metadata routing. - Routing information is stored as a dictionary of the form ``{"object_name": - RouteMappingPair(method_mapping, routing_info)}``, where ``method_mapping`` - is an instance of :class:`~sklearn.utils.metadata_routing.MethodMapping` and - ``routing_info`` is either a - :class:`~sklearn.utils.metadata_routing.MetadataRequest` or a - :class:`~sklearn.utils.metadata_routing.MetadataRouter` instance. - - .. versionadded:: 1.3 - - Parameters - ---------- - owner : str - The name of the object to which these requests belong. - """ - - # this is here for us to use this attribute's value instead of doing - # `isinstance`` in our checks, so that we avoid issues when people vendor - # this file instead of using it directly from scikit-learn. - _type = "metadata_router" - - def __init__(self, owner): - self._route_mappings = dict() - # `_self_request` is used if the router is also a consumer. - # _self_request, (added using `add_self_request()`) is treated - # differently from the other objects which are stored in - # _route_mappings. - self._self_request = None - self.owner = owner - - def add_self_request(self, obj): - """Add `self` (as a consumer) to the routing. - - This method is used if the router is also a consumer, and hence the - router itself needs to be included in the routing. The passed object - can be an estimator or a - :class:`~sklearn.utils.metadata_routing.MetadataRequest`. - - A router should add itself using this method instead of `add` since it - should be treated differently than the other objects to which metadata - is routed by the router. - - Parameters - ---------- - obj : object - This is typically the router instance, i.e. `self` in a - ``get_metadata_routing()`` implementation. It can also be a - ``MetadataRequest`` instance. - - Returns - ------- - self : MetadataRouter - Returns `self`. - """ - if getattr(obj, "_type", None) == "metadata_request": - self._self_request = deepcopy(obj) - elif hasattr(obj, "_get_metadata_request"): - self._self_request = deepcopy(obj._get_metadata_request()) - else: - raise ValueError( - "Given `obj` is neither a `MetadataRequest` nor does it implement " - "the required API. Inheriting from `BaseEstimator` implements the " - "required API." - ) - return self - - def add(self, *, method_mapping, **objs): - """Add named objects with their corresponding method mapping. - - Parameters - ---------- - method_mapping : MethodMapping or str - The mapping between the child and the parent's methods. If str, the - output of :func:`~sklearn.utils.metadata_routing.MethodMapping.from_str` - is used. - - **objs : dict - A dictionary of objects from which metadata is extracted by calling - :func:`~sklearn.utils.metadata_routing.get_routing_for_object` on them. - - Returns - ------- - self : MetadataRouter - Returns `self`. - """ - if isinstance(method_mapping, str): - method_mapping = MethodMapping.from_str(method_mapping) - else: - method_mapping = deepcopy(method_mapping) - - for name, obj in objs.items(): - self._route_mappings[name] = RouterMappingPair( - mapping=method_mapping, router=get_routing_for_object(obj) - ) - return self - - def consumes(self, method, params): - """Check whether the given parameters are consumed by the given method. - - .. versionadded:: 1.4 - - Parameters - ---------- - method : str - The name of the method to check. - - params : iterable of str - An iterable of parameters to check. - - Returns - ------- - consumed : set of str - A set of parameters which are consumed by the given method. - """ - res = set() - if self._self_request: - res = res | self._self_request.consumes(method=method, params=params) - - for _, route_mapping in self._route_mappings.items(): - for callee, caller in route_mapping.mapping: - if caller == method: - res = res | route_mapping.router.consumes( - method=callee, params=params - ) - - return res - - def _get_param_names(self, *, method, return_alias, ignore_self_request): - """Get names of all metadata that can be consumed or routed by specified \ - method. - - This method returns the names of all metadata, even the ``False`` - ones. - - Parameters - ---------- - method : str - The name of the method for which metadata names are requested. - - return_alias : bool - Controls whether original or aliased names should be returned, - which only applies to the stored `self`. If no `self` routing - object is stored, this parameter has no effect. - - ignore_self_request : bool - If `self._self_request` should be ignored. This is used in - `_route_params`. If ``True``, ``return_alias`` has no effect. - - Returns - ------- - names : set of str - A set of strings with the names of all parameters. - """ - res = set() - if self._self_request and not ignore_self_request: - res = res.union( - self._self_request._get_param_names( - method=method, return_alias=return_alias - ) - ) - - for name, route_mapping in self._route_mappings.items(): - for callee, caller in route_mapping.mapping: - if caller == method: - res = res.union( - route_mapping.router._get_param_names( - method=callee, - return_alias=True, - ignore_self_request=False, - ) - ) - return res - - def _route_params(self, *, params, method): - """Prepare the given parameters to be passed to the method. - - This is used when a router is used as a child object of another router. - The parent router then passes all parameters understood by the child - object to it and delegates their validation to the child. - - The output of this method can be used directly as the input to the - corresponding method as extra props. - - Parameters - ---------- - method : str - The name of the method for which the parameters are requested and - routed. - - params : dict - A dictionary of provided metadata. - - Returns - ------- - params : Bunch - A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to - the corresponding method. - """ - res = Bunch() - if self._self_request: - res.update( - self._self_request._route_params(params=params, method=method) - ) - - param_names = self._get_param_names( - method=method, return_alias=True, ignore_self_request=True - ) - child_params = { - key: value for key, value in params.items() if key in param_names - } - for key in set(res.keys()).intersection(child_params.keys()): - # conflicts are okay if the passed objects are the same, but it's - # an issue if they're different objects. - if child_params[key] is not res[key]: - raise ValueError( - f"In {self.owner}, there is a conflict on {key} between what is" - " requested for this estimator and what is requested by its" - " children. You can resolve this conflict by using an alias for" - " the child estimator(s) requested metadata." - ) - - res.update(child_params) - return res - - def route_params(self, *, caller, params): - """Return the input parameters requested by child objects. - - The output of this method is a bunch, which includes the inputs for all - methods of each child object that are used in the router's `caller` - method. - - If the router is also a consumer, it also checks for warnings of - `self`'s/consumer's requested metadata. - - Parameters - ---------- - caller : str - The name of the method for which the parameters are requested and - routed. If called inside the :term:`fit` method of a router, it - would be `"fit"`. - - params : dict - A dictionary of provided metadata. - - Returns - ------- - params : Bunch - A :class:`~sklearn.utils.Bunch` of the form - ``{"object_name": {"method_name": {prop: value}}}`` which can be - used to pass the required metadata to corresponding methods or - corresponding child objects. - """ - if self._self_request: - self._self_request._check_warnings(params=params, method=caller) - - res = Bunch() - for name, route_mapping in self._route_mappings.items(): - router, mapping = route_mapping.router, route_mapping.mapping - - res[name] = Bunch() - for _callee, _caller in mapping: - if _caller == caller: - res[name][_callee] = router._route_params( - params=params, method=_callee - ) - return res - - def validate_metadata(self, *, method, params): - """Validate given metadata for a method. - - This raises a ``TypeError`` if some of the passed metadata are not - understood by child objects. - - Parameters - ---------- - method : str - The name of the method for which the parameters are requested and - routed. If called inside the :term:`fit` method of a router, it - would be `"fit"`. - - params : dict - A dictionary of provided metadata. - """ - param_names = self._get_param_names( - method=method, return_alias=False, ignore_self_request=False - ) - if self._self_request: - self_params = self._self_request._get_param_names( - method=method, return_alias=False - ) - else: - self_params = set() - extra_keys = set(params.keys()) - param_names - self_params - if extra_keys: - raise TypeError( - f"{self.owner}.{method} got unexpected argument(s) {extra_keys}, " - "which are not requested metadata in any object." - ) - - def _serialize(self): - """Serialize the object. - - Returns - ------- - obj : dict - A serialized version of the instance in the form of a dictionary. - """ - res = dict() - if self._self_request: - res["$self_request"] = self._self_request._serialize() - for name, route_mapping in self._route_mappings.items(): - res[name] = dict() - res[name]["mapping"] = route_mapping.mapping._serialize() - res[name]["router"] = route_mapping.router._serialize() - - return res - - def __iter__(self): - if self._self_request: - yield ( - "$self_request", - RouterMappingPair( - mapping=MethodMapping.from_str("one-to-one"), - router=self._self_request, - ), - ) - for name, route_mapping in self._route_mappings.items(): - yield (name, route_mapping) - - def __repr__(self): - return str(self._serialize()) - - def __str__(self): - return str(repr(self)) - - def get_routing_for_object(obj=None): - """Get a ``Metadata{Router, Request}`` instance from the given object. - - This function returns a - :class:`~sklearn.utils.metadata_routing.MetadataRouter` or a - :class:`~sklearn.utils.metadata_routing.MetadataRequest` from the given input. - - This function always returns a copy or an instance constructed from the - input, such that changing the output of this function will not change the - original object. - - .. versionadded:: 1.3 - - Parameters - ---------- - obj : object - - If the object is already a - :class:`~sklearn.utils.metadata_routing.MetadataRequest` or a - :class:`~sklearn.utils.metadata_routing.MetadataRouter`, return a copy - of that. - - If the object provides a `get_metadata_routing` method, return a copy - of the output of that method. - - Returns an empty :class:`~sklearn.utils.metadata_routing.MetadataRequest` - otherwise. - - Returns - ------- - obj : MetadataRequest or MetadataRouting - A ``MetadataRequest`` or a ``MetadataRouting`` taken or created from - the given object. - """ - # doing this instead of a try/except since an AttributeError could be raised - # for other reasons. - if hasattr(obj, "get_metadata_routing"): - return deepcopy(obj.get_metadata_routing()) - - elif getattr(obj, "_type", None) in ["metadata_request", "metadata_router"]: - return deepcopy(obj) - - return MetadataRequest(owner=None) - - # Request method - # ============== - # This section includes what's needed for the request method descriptor and - # their dynamic generation in a meta class. - - # These strings are used to dynamically generate the docstrings for - # set_{method}_request methods. - REQUESTER_DOC = """ Request metadata passed to the ``{method}`` method. - - Note that this method is only relevant if - ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`). - Please see :ref:`User Guide ` on how the routing - mechanism works. - - The options for each parameter are: - - - ``True``: metadata is requested, and \ - passed to ``{method}`` if provided. The request is ignored if \ - metadata is not provided. - - - ``False``: metadata is not requested and the meta-estimator \ - will not pass it to ``{method}``. - - - ``None``: metadata is not requested, and the meta-estimator \ - will raise an error if the user provides it. - - - ``str``: metadata should be passed to the meta-estimator with \ - this given alias instead of the original name. - - The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the - existing request. This allows you to change the request for some - parameters and not others. - - .. versionadded:: 1.3 - - .. note:: - This method is only relevant if this estimator is used as a - sub-estimator of a meta-estimator, e.g. used inside a - :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect. - - Parameters - ---------- - """ - REQUESTER_DOC_PARAM = """ {metadata} : str, True, False, or None, \ - default=sklearn.utils.metadata_routing.UNCHANGED - Metadata routing for ``{metadata}`` parameter in ``{method}``. - - """ - REQUESTER_DOC_RETURN = """ Returns - ------- - self : object - The updated object. - """ - - class RequestMethod: - """ - A descriptor for request methods. - - .. versionadded:: 1.3 - - Parameters - ---------- - name : str - The name of the method for which the request function should be - created, e.g. ``"fit"`` would create a ``set_fit_request`` function. - - keys : list of str - A list of strings which are accepted parameters by the created - function, e.g. ``["sample_weight"]`` if the corresponding method - accepts it as a metadata. - - validate_keys : bool, default=True - Whether to check if the requested parameters fit the actual parameters - of the method. - - Notes - ----- - This class is a descriptor [1]_ and uses PEP-362 to set the signature of - the returned function [2]_. - - References - ---------- - .. [1] https://docs.python.org/3/howto/descriptor.html - - .. [2] https://www.python.org/dev/peps/pep-0362/ - """ - - def __init__(self, name, keys, validate_keys=True): - self.name = name - self.keys = keys - self.validate_keys = validate_keys - - def __get__(self, instance, owner): - # we would want to have a method which accepts only the expected args - def func(*args, **kw): - """Updates the request for provided parameters - - This docstring is overwritten below. - See REQUESTER_DOC for expected functionality - """ - if not _routing_enabled(): - raise RuntimeError( - "This method is only available when metadata routing is " - "enabled. You can enable it using" - " sklearn.set_config(enable_metadata_routing=True)." - ) - - if self.validate_keys and (set(kw) - set(self.keys)): - raise TypeError( - f"Unexpected args: {set(kw) - set(self.keys)}. Accepted " - f"arguments are: {set(self.keys)}" - ) - - # This makes it possible to use the decorated method as an unbound - # method, for instance when monkeypatching. - # https://github.com/scikit-learn/scikit-learn/issues/28632 - if instance is None: - _instance = args[0] - args = args[1:] - else: - _instance = instance - - # Replicating python's behavior when positional args are given other - # than `self`, and `self` is only allowed if this method is unbound. - if args: - raise TypeError( - f"set_{self.name}_request() takes 0 positional argument but" - f" {len(args)} were given" - ) - - requests = _instance._get_metadata_request() - method_metadata_request = getattr(requests, self.name) - - for prop, alias in kw.items(): - if alias is not UNCHANGED: - method_metadata_request.add_request(param=prop, alias=alias) - _instance._metadata_request = requests - - return _instance - - # Now we set the relevant attributes of the function so that it seems - # like a normal method to the end user, with known expected arguments. - func.__name__ = f"set_{self.name}_request" - params = [ - inspect.Parameter( - name="self", - kind=inspect.Parameter.POSITIONAL_OR_KEYWORD, - annotation=owner, - ) - ] - params.extend( - [ - inspect.Parameter( - k, - inspect.Parameter.KEYWORD_ONLY, - default=UNCHANGED, - annotation=Optional[Union[bool, None, str]], - ) - for k in self.keys - ] - ) - func.__signature__ = inspect.Signature( - params, - return_annotation=owner, - ) - doc = REQUESTER_DOC.format(method=self.name) - for metadata in self.keys: - doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name) - doc += REQUESTER_DOC_RETURN - func.__doc__ = doc - return func - - class _MetadataRequester: - """Mixin class for adding metadata request functionality. - - ``BaseEstimator`` inherits from this Mixin. - - .. versionadded:: 1.3 - """ - - if TYPE_CHECKING: # pragma: no cover - # This code is never run in runtime, but it's here for type checking. - # Type checkers fail to understand that the `set_{method}_request` - # methods are dynamically generated, and they complain that they are - # not defined. We define them here to make type checkers happy. - # During type checking analyzers assume this to be True. - # The following list of defined methods mirrors the list of methods - # in SIMPLE_METHODS. - # fmt: off - def set_fit_request(self, **kwargs): pass - def set_partial_fit_request(self, **kwargs): pass - def set_predict_request(self, **kwargs): pass - def set_predict_proba_request(self, **kwargs): pass - def set_predict_log_proba_request(self, **kwargs): pass - def set_decision_function_request(self, **kwargs): pass - def set_score_request(self, **kwargs): pass - def set_split_request(self, **kwargs): pass - def set_transform_request(self, **kwargs): pass - def set_inverse_transform_request(self, **kwargs): pass - # fmt: on - - def __init_subclass__(cls, **kwargs): - """Set the ``set_{method}_request`` methods. - - This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It - looks for the information available in the set default values which are - set using ``__metadata_request__*`` class attributes, or inferred - from method signatures. - - The ``__metadata_request__*`` class attributes are used when a method - does not explicitly accept a metadata through its arguments or if the - developer would like to specify a request value for those metadata - which are different from the default ``None``. - - References - ---------- - .. [1] https://www.python.org/dev/peps/pep-0487 - """ - try: - requests = cls._get_default_requests() - except Exception: - # if there are any issues in the default values, it will be raised - # when ``get_metadata_routing`` is called. Here we are going to - # ignore all the issues such as bad defaults etc. - super().__init_subclass__(**kwargs) - return - - for method in SIMPLE_METHODS: - mmr = getattr(requests, method) - # set ``set_{method}_request``` methods - if not len(mmr.requests): - continue - setattr( - cls, - f"set_{method}_request", - RequestMethod(method, sorted(mmr.requests.keys())), - ) - super().__init_subclass__(**kwargs) - - @classmethod - def _build_request_for_signature(cls, router, method): - """Build the `MethodMetadataRequest` for a method using its signature. - - This method takes all arguments from the method signature and uses - ``None`` as their default request value, except ``X``, ``y``, ``Y``, - ``Xt``, ``yt``, ``*args``, and ``**kwargs``. - - Parameters - ---------- - router : MetadataRequest - The parent object for the created `MethodMetadataRequest`. - method : str - The name of the method. - - Returns - ------- - method_request : MethodMetadataRequest - The prepared request using the method's signature. - """ - mmr = MethodMetadataRequest(owner=cls.__name__, method=method) - # Here we use `isfunction` instead of `ismethod` because calling `getattr` - # on a class instead of an instance returns an unbound function. - if not hasattr(cls, method) or not inspect.isfunction(getattr(cls, method)): - return mmr - # ignore the first parameter of the method, which is usually "self" - params = list(inspect.signature(getattr(cls, method)).parameters.items())[ - 1: - ] - for pname, param in params: - if pname in {"X", "y", "Y", "Xt", "yt"}: - continue - if param.kind in {param.VAR_POSITIONAL, param.VAR_KEYWORD}: - continue - mmr.add_request( - param=pname, - alias=None, - ) - return mmr - - @classmethod - def _get_default_requests(cls): - """Collect default request values. - - This method combines the information present in ``__metadata_request__*`` - class attributes, as well as determining request keys from method - signatures. - """ - requests = MetadataRequest(owner=cls.__name__) - - for method in SIMPLE_METHODS: - setattr( - requests, - method, - cls._build_request_for_signature(router=requests, method=method), - ) - - # Then overwrite those defaults with the ones provided in - # __metadata_request__* attributes. Defaults set in - # __metadata_request__* attributes take precedence over signature - # sniffing. - - # need to go through the MRO since this is a class attribute and - # ``vars`` doesn't report the parent class attributes. We go through - # the reverse of the MRO so that child classes have precedence over - # their parents. - defaults = dict() - for base_class in reversed(inspect.getmro(cls)): - base_defaults = { - attr: value - for attr, value in vars(base_class).items() - if "__metadata_request__" in attr - } - defaults.update(base_defaults) - defaults = dict(sorted(defaults.items())) - - for attr, value in defaults.items(): - # we don't check for attr.startswith() since python prefixes attrs - # starting with __ with the `_ClassName`. - substr = "__metadata_request__" - method = attr[attr.index(substr) + len(substr) :] - for prop, alias in value.items(): - getattr(requests, method).add_request(param=prop, alias=alias) - - return requests - - def _get_metadata_request(self): - """Get requested data properties. - - Please check :ref:`User Guide ` on how the routing - mechanism works. - - Returns - ------- - request : MetadataRequest - A :class:`~sklearn.utils.metadata_routing.MetadataRequest` instance. - """ - if hasattr(self, "_metadata_request"): - requests = get_routing_for_object(self._metadata_request) - else: - requests = self._get_default_requests() - - return requests - - def get_metadata_routing(self): - """Get metadata routing of this object. - - Please check :ref:`User Guide ` on how the routing - mechanism works. - - Returns - ------- - routing : MetadataRequest - A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating - routing information. - """ - return self._get_metadata_request() - - # Process Routing in Routers - # ========================== - # This is almost always the only method used in routers to process and route - # given metadata. This is to minimize the boilerplate required in routers. - - # Here the first two arguments are positional only which makes everything - # passed as keyword argument a metadata. The first two args also have an `_` - # prefix to reduce the chances of name collisions with the passed metadata, and - # since they're positional only, users will never type those underscores. - def process_routing(_obj, _method, /, **kwargs): - """Validate and route input parameters. - - This function is used inside a router's method, e.g. :term:`fit`, - to validate the metadata and handle the routing. - - Assuming this signature: ``fit(self, X, y, sample_weight=None, **fit_params)``, - a call to this function would be: - ``process_routing(self, sample_weight=sample_weight, **fit_params)``. - - Note that if routing is not enabled and ``kwargs`` is empty, then it - returns an empty routing where ``process_routing(...).ANYTHING.ANY_METHOD`` - is always an empty dictionary. - - .. versionadded:: 1.3 - - Parameters - ---------- - _obj : object - An object implementing ``get_metadata_routing``. Typically a - meta-estimator. - - _method : str - The name of the router's method in which this function is called. - - **kwargs : dict - Metadata to be routed. - - Returns - ------- - routed_params : Bunch - A :class:`~sklearn.utils.Bunch` of the form ``{"object_name": - {"method_name": {prop: value}}}`` which can be used to pass the required - metadata to corresponding methods or corresponding child objects. The object - names are those defined in `obj.get_metadata_routing()`. - """ - if not kwargs: - # If routing is not enabled and kwargs are empty, then we don't have to - # try doing any routing, we can simply return a structure which returns - # an empty dict on routed_params.ANYTHING.ANY_METHOD. - class EmptyRequest: - def get(self, name, default=None): - return Bunch(**{method: dict() for method in METHODS}) - - def __getitem__(self, name): - return Bunch(**{method: dict() for method in METHODS}) - - def __getattr__(self, name): - return Bunch(**{method: dict() for method in METHODS}) - - return EmptyRequest() - - if not ( - hasattr(_obj, "get_metadata_routing") or isinstance(_obj, MetadataRouter) - ): - raise AttributeError( - f"The given object ({repr(_obj.__class__.__name__)}) needs to either" - " implement the routing method `get_metadata_routing` or be a" - " `MetadataRouter` instance." - ) - if _method not in METHODS: - raise TypeError( - f"Can only route and process input on these methods: {METHODS}, " - f"while the passed method is: {_method}." - ) - - request_routing = get_routing_for_object(_obj) - request_routing.validate_metadata(params=kwargs, method=_method) - routed_params = request_routing.route_params(params=kwargs, caller=_method) - - return routed_params - -else: - from sklearn.exceptions import UnsetMetadataPassedError - from sklearn.utils._metadata_requests import ( # type: ignore[no-redef] - COMPOSITE_METHODS, # noqa - METHODS, # noqa - SIMPLE_METHODS, # noqa - UNCHANGED, - UNUSED, - WARN, - MetadataRequest, - MetadataRouter, - MethodMapping, - _MetadataRequester, # noqa - _raise_for_params, # noqa - _raise_for_unsupported_routing, # noqa - _routing_enabled, - _RoutingNotSupportedMixin, # noqa - get_routing_for_object, - process_routing, # noqa - ) diff --git a/imblearn/utils/_param_validation.py b/imblearn/utils/_param_validation.py deleted file mode 100644 index c8941876e..000000000 --- a/imblearn/utils/_param_validation.py +++ /dev/null @@ -1,932 +0,0 @@ -"""This is a copy of sklearn/utils/_param_validation.py.""" -# mypy: ignore-errors -import functools -import math -import operator -import re -from abc import ABC, abstractmethod -from collections.abc import Iterable -from inspect import signature -from numbers import Integral, Real - -import numpy as np -import sklearn -from scipy.sparse import csr_matrix, issparse -from sklearn.utils.fixes import parse_version -from sklearn.utils.validation import _is_arraylike_not_scalar - -from .._config import config_context, get_config - -sklearn_version = parse_version(sklearn.__version__) - -if sklearn_version < parse_version("1.4"): - - class InvalidParameterError(ValueError, TypeError): - """Custom exception to be raised when the parameter of a class/method/function - does not have a valid type or value. - """ - - # Inherits from ValueError and TypeError to keep backward compatibility. - - def validate_parameter_constraints(parameter_constraints, params, caller_name): - """Validate types and values of given parameters. - - Parameters - ---------- - parameter_constraints : dict or {"no_validation"} - If "no_validation", validation is skipped for this parameter. - - If a dict, it must be a dictionary `param_name: list of constraints`. - A parameter is valid if it satisfies one of the constraints from the list. - Constraints can be: - - an Interval object, representing a continuous or discrete range of numbers - - the string "array-like" - - the string "sparse matrix" - - the string "random_state" - - callable - - None, meaning that None is a valid value for the parameter - - any type, meaning that any instance of this type is valid - - an Options object, representing a set of elements of a given type - - a StrOptions object, representing a set of strings - - the string "boolean" - - the string "verbose" - - the string "cv_object" - - the string "nan" - - a MissingValues object representing markers for missing values - - a HasMethods object, representing method(s) an object must have - - a Hidden object, representing a constraint not meant to be exposed to the - user - - params : dict - A dictionary `param_name: param_value`. The parameters to validate against - the constraints. - - caller_name : str - The name of the estimator or function or method that called this function. - """ - for param_name, param_val in params.items(): - # We allow parameters to not have a constraint so that third party - # estimators can inherit from sklearn estimators without having to - # necessarily use the validation tools. - if param_name not in parameter_constraints: - continue - - constraints = parameter_constraints[param_name] - - if constraints == "no_validation": - continue - - constraints = [make_constraint(constraint) for constraint in constraints] - - for constraint in constraints: - if constraint.is_satisfied_by(param_val): - # this constraint is satisfied, no need to check further. - break - else: - # No constraint is satisfied, raise with an informative message. - - # Ignore constraints that we don't want to expose in the error - # message, i.e. options that are for internal purpose or not - # officially supported. - constraints = [ - constraint for constraint in constraints if not constraint.hidden - ] - - if len(constraints) == 1: - constraints_str = f"{constraints[0]}" - else: - constraints_str = ( - f"{', '.join([str(c) for c in constraints[:-1]])} or" - f" {constraints[-1]}" - ) - - raise InvalidParameterError( - f"The {param_name!r} parameter of {caller_name} must be" - f" {constraints_str}. Got {param_val!r} instead." - ) - - def make_constraint(constraint): - """Convert the constraint into the appropriate Constraint object. - - Parameters - ---------- - constraint : object - The constraint to convert. - - Returns - ------- - constraint : instance of _Constraint - The converted constraint. - """ - if isinstance(constraint, str) and constraint == "array-like": - return _ArrayLikes() - if isinstance(constraint, str) and constraint == "sparse matrix": - return _SparseMatrices() - if isinstance(constraint, str) and constraint == "random_state": - return _RandomStates() - if constraint is callable: - return _Callables() - if constraint is None: - return _NoneConstraint() - if isinstance(constraint, type): - return _InstancesOf(constraint) - if isinstance( - constraint, (Interval, StrOptions, Options, HasMethods, MissingValues) - ): - return constraint - if isinstance(constraint, str) and constraint == "boolean": - return _Booleans() - if isinstance(constraint, str) and constraint == "verbose": - return _VerboseHelper() - if isinstance(constraint, str) and constraint == "cv_object": - return _CVObjects() - if isinstance(constraint, Hidden): - constraint = make_constraint(constraint.constraint) - constraint.hidden = True - return constraint - if isinstance(constraint, str) and constraint == "nan": - return _NanConstraint() - raise ValueError(f"Unknown constraint type: {constraint}") - - def validate_params(parameter_constraints, *, prefer_skip_nested_validation): - """Decorator to validate types and values of functions and methods. - - Parameters - ---------- - parameter_constraints : dict - A dictionary `param_name: list of constraints`. See the docstring of - `validate_parameter_constraints` for a description of the accepted - constraints. - - Note that the *args and **kwargs parameters are not validated and must not - be present in the parameter_constraints dictionary. - - prefer_skip_nested_validation : bool - If True, the validation of parameters of inner estimators or functions - called by the decorated function will be skipped. - - This is useful to avoid validating many times the parameters passed by the - user from the public facing API. It's also useful to avoid validating - parameters that we pass internally to inner functions that are guaranteed to - be valid by the test suite. - - It should be set to True for most functions, except for those that receive - non-validated objects as parameters or that are just wrappers around classes - because they only perform a partial validation. - - Returns - ------- - decorated_function : function or method - The decorated function. - """ - - def decorator(func): - # The dict of parameter constraints is set as an attribute of the function - # to make it possible to dynamically introspect the constraints for - # automatic testing. - setattr(func, "_skl_parameter_constraints", parameter_constraints) - - @functools.wraps(func) - def wrapper(*args, **kwargs): - global_skip_validation = get_config()["skip_parameter_validation"] - if global_skip_validation: - return func(*args, **kwargs) - - func_sig = signature(func) - - # Map *args/**kwargs to the function signature - params = func_sig.bind(*args, **kwargs) - params.apply_defaults() - - # ignore self/cls and positional/keyword markers - to_ignore = [ - p.name - for p in func_sig.parameters.values() - if p.kind in (p.VAR_POSITIONAL, p.VAR_KEYWORD) - ] - to_ignore += ["self", "cls"] - params = { - k: v for k, v in params.arguments.items() if k not in to_ignore - } - - validate_parameter_constraints( - parameter_constraints, params, caller_name=func.__qualname__ - ) - - try: - with config_context( - skip_parameter_validation=( - prefer_skip_nested_validation or global_skip_validation - ) - ): - return func(*args, **kwargs) - except InvalidParameterError as e: - # When the function is just a wrapper around an estimator, we allow - # the function to delegate validation to the estimator, but we - # replace the name of the estimator by the name of the function in - # the error message to avoid confusion. - msg = re.sub( - r"parameter of \w+ must be", - f"parameter of {func.__qualname__} must be", - str(e), - ) - raise InvalidParameterError(msg) from e - - return wrapper - - return decorator - - class RealNotInt(Real): - """A type that represents reals that are not instances of int. - - Behaves like float, but also works with values extracted from numpy arrays. - isintance(1, RealNotInt) -> False - isinstance(1.0, RealNotInt) -> True - """ - - RealNotInt.register(float) - - def _type_name(t): - """Convert type into human readable string.""" - module = t.__module__ - qualname = t.__qualname__ - if module == "builtins": - return qualname - elif t == Real: - return "float" - elif t == Integral: - return "int" - return f"{module}.{qualname}" - - class _Constraint(ABC): - """Base class for the constraint objects.""" - - def __init__(self): - self.hidden = False - - @abstractmethod - def is_satisfied_by(self, val): - """Whether or not a value satisfies the constraint. - - Parameters - ---------- - val : object - The value to check. - - Returns - ------- - is_satisfied : bool - Whether or not the constraint is satisfied by this value. - """ - - @abstractmethod - def __str__(self): - """A human readable representational string of the constraint.""" - - class _InstancesOf(_Constraint): - """Constraint representing instances of a given type. - - Parameters - ---------- - type : type - The valid type. - """ - - def __init__(self, type): - super().__init__() - self.type = type - - def is_satisfied_by(self, val): - return isinstance(val, self.type) - - def __str__(self): - return f"an instance of {_type_name(self.type)!r}" - - class _NoneConstraint(_Constraint): - """Constraint representing the None singleton.""" - - def is_satisfied_by(self, val): - return val is None - - def __str__(self): - return "None" - - class _NanConstraint(_Constraint): - """Constraint representing the indicator `np.nan`.""" - - def is_satisfied_by(self, val): - return ( - not isinstance(val, Integral) - and isinstance(val, Real) - and math.isnan(val) - ) - - def __str__(self): - return "numpy.nan" - - class _PandasNAConstraint(_Constraint): - """Constraint representing the indicator `pd.NA`.""" - - def is_satisfied_by(self, val): - try: - import pandas as pd - - return isinstance(val, type(pd.NA)) and pd.isna(val) - except ImportError: - return False - - def __str__(self): - return "pandas.NA" - - class Options(_Constraint): - """Constraint representing a finite set of instances of a given type. - - Parameters - ---------- - type : type - - options : set - The set of valid scalars. - - deprecated : set or None, default=None - A subset of the `options` to mark as deprecated in the string - representation of the constraint. - """ - - def __init__(self, type, options, *, deprecated=None): - super().__init__() - self.type = type - self.options = options - self.deprecated = deprecated or set() - - if self.deprecated - self.options: - raise ValueError( - "The deprecated options must be a subset of the options." - ) - - def is_satisfied_by(self, val): - return isinstance(val, self.type) and val in self.options - - def _mark_if_deprecated(self, option): - """Add a deprecated mark to an option if needed.""" - option_str = f"{option!r}" - if option in self.deprecated: - option_str = f"{option_str} (deprecated)" - return option_str - - def __str__(self): - options_str = ( - f"{', '.join([self._mark_if_deprecated(o) for o in self.options])}" - ) - return f"a {_type_name(self.type)} among {{{options_str}}}" - - class StrOptions(Options): - """Constraint representing a finite set of strings. - - Parameters - ---------- - options : set of str - The set of valid strings. - - deprecated : set of str or None, default=None - A subset of the `options` to mark as deprecated in the string - representation of the constraint. - """ - - def __init__(self, options, *, deprecated=None): - super().__init__(type=str, options=options, deprecated=deprecated) - - class Interval(_Constraint): - """Constraint representing a typed interval. - - Parameters - ---------- - type : {numbers.Integral, numbers.Real, RealNotInt} - The set of numbers in which to set the interval. - - If RealNotInt, only reals that don't have the integer type - are allowed. For example 1.0 is allowed but 1 is not. - - left : float or int or None - The left bound of the interval. None means left bound is -∞. - - right : float, int or None - The right bound of the interval. None means right bound is +∞. - - closed : {"left", "right", "both", "neither"} - Whether the interval is open or closed. Possible choices are: - - - `"left"`: the interval is closed on the left and open on the right. - It is equivalent to the interval `[ left, right )`. - - `"right"`: the interval is closed on the right and open on the left. - It is equivalent to the interval `( left, right ]`. - - `"both"`: the interval is closed. - It is equivalent to the interval `[ left, right ]`. - - `"neither"`: the interval is open. - It is equivalent to the interval `( left, right )`. - - Notes - ----- - Setting a bound to `None` and setting the interval closed is valid. For - instance, strictly speaking, `Interval(Real, 0, None, closed="both")` - corresponds to `[0, +∞) U {+∞}`. - """ - - def __init__(self, type, left, right, *, closed): - super().__init__() - self.type = type - self.left = left - self.right = right - self.closed = closed - - self._check_params() - - def _check_params(self): - if self.type not in (Integral, Real, RealNotInt): - raise ValueError( - "type must be either numbers.Integral, numbers.Real or RealNotInt." - f" Got {self.type} instead." - ) - - if self.closed not in ("left", "right", "both", "neither"): - raise ValueError( - "closed must be either 'left', 'right', 'both' or 'neither'. " - f"Got {self.closed} instead." - ) - - if self.type is Integral: - suffix = "for an interval over the integers." - if self.left is not None and not isinstance(self.left, Integral): - raise TypeError(f"Expecting left to be an int {suffix}") - if self.right is not None and not isinstance(self.right, Integral): - raise TypeError(f"Expecting right to be an int {suffix}") - if self.left is None and self.closed in ("left", "both"): - raise ValueError( - f"left can't be None when closed == {self.closed} {suffix}" - ) - if self.right is None and self.closed in ("right", "both"): - raise ValueError( - f"right can't be None when closed == {self.closed} {suffix}" - ) - else: - if self.left is not None and not isinstance(self.left, Real): - raise TypeError("Expecting left to be a real number.") - if self.right is not None and not isinstance(self.right, Real): - raise TypeError("Expecting right to be a real number.") - - if ( - self.right is not None - and self.left is not None - and self.right <= self.left - ): - raise ValueError( - f"right can't be less than left. Got left={self.left} and " - f"right={self.right}" - ) - - def __contains__(self, val): - if not isinstance(val, Integral) and np.isnan(val): - return False - - left_cmp = operator.lt if self.closed in ("left", "both") else operator.le - right_cmp = operator.gt if self.closed in ("right", "both") else operator.ge - - left = -np.inf if self.left is None else self.left - right = np.inf if self.right is None else self.right - - if left_cmp(val, left): - return False - if right_cmp(val, right): - return False - return True - - def is_satisfied_by(self, val): - if not isinstance(val, self.type): - return False - - return val in self - - def __str__(self): - type_str = "an int" if self.type is Integral else "a float" - left_bracket = "[" if self.closed in ("left", "both") else "(" - left_bound = "-inf" if self.left is None else self.left - right_bound = "inf" if self.right is None else self.right - right_bracket = "]" if self.closed in ("right", "both") else ")" - - # better repr if the bounds were given as integers - if not self.type == Integral and isinstance(self.left, Real): - left_bound = float(left_bound) - if not self.type == Integral and isinstance(self.right, Real): - right_bound = float(right_bound) - - return ( - f"{type_str} in the range " - f"{left_bracket}{left_bound}, {right_bound}{right_bracket}" - ) - - class _ArrayLikes(_Constraint): - """Constraint representing array-likes""" - - def is_satisfied_by(self, val): - return _is_arraylike_not_scalar(val) - - def __str__(self): - return "an array-like" - - class _SparseMatrices(_Constraint): - """Constraint representing sparse matrices.""" - - def is_satisfied_by(self, val): - return issparse(val) - - def __str__(self): - return "a sparse matrix" - - class _Callables(_Constraint): - """Constraint representing callables.""" - - def is_satisfied_by(self, val): - return callable(val) - - def __str__(self): - return "a callable" - - class _RandomStates(_Constraint): - """Constraint representing random states. - - Convenience class for - [Interval(Integral, 0, 2**32 - 1, closed="both"), np.random.RandomState, None] - """ - - def __init__(self): - super().__init__() - self._constraints = [ - Interval(Integral, 0, 2**32 - 1, closed="both"), - _InstancesOf(np.random.RandomState), - _NoneConstraint(), - ] - - def is_satisfied_by(self, val): - return any(c.is_satisfied_by(val) for c in self._constraints) - - def __str__(self): - return ( - f"{', '.join([str(c) for c in self._constraints[:-1]])} or" - f" {self._constraints[-1]}" - ) - - class _Booleans(_Constraint): - """Constraint representing boolean likes. - - Convenience class for - [bool, np.bool_, Integral (deprecated)] - """ - - def __init__(self): - super().__init__() - self._constraints = [ - _InstancesOf(bool), - _InstancesOf(np.bool_), - ] - - def is_satisfied_by(self, val): - return any(c.is_satisfied_by(val) for c in self._constraints) - - def __str__(self): - return ( - f"{', '.join([str(c) for c in self._constraints[:-1]])} or" - f" {self._constraints[-1]}" - ) - - class _VerboseHelper(_Constraint): - """Helper constraint for the verbose parameter. - - Convenience class for - [Interval(Integral, 0, None, closed="left"), bool, numpy.bool_] - """ - - def __init__(self): - super().__init__() - self._constraints = [ - Interval(Integral, 0, None, closed="left"), - _InstancesOf(bool), - _InstancesOf(np.bool_), - ] - - def is_satisfied_by(self, val): - return any(c.is_satisfied_by(val) for c in self._constraints) - - def __str__(self): - return ( - f"{', '.join([str(c) for c in self._constraints[:-1]])} or" - f" {self._constraints[-1]}" - ) - - class MissingValues(_Constraint): - """Helper constraint for the `missing_values` parameters. - - Convenience for - [ - Integral, - Interval(Real, None, None, closed="both"), - str, # when numeric_only is False - None, # when numeric_only is False - _NanConstraint(), - _PandasNAConstraint(), - ] - - Parameters - ---------- - numeric_only : bool, default=False - Whether to consider only numeric missing value markers. - - """ - - def __init__(self, numeric_only=False): - super().__init__() - - self.numeric_only = numeric_only - - self._constraints = [ - _InstancesOf(Integral), - # we use an interval of Real to ignore np.nan that has its own - # constraint - Interval(Real, None, None, closed="both"), - _NanConstraint(), - _PandasNAConstraint(), - ] - if not self.numeric_only: - self._constraints.extend([_InstancesOf(str), _NoneConstraint()]) - - def is_satisfied_by(self, val): - return any(c.is_satisfied_by(val) for c in self._constraints) - - def __str__(self): - return ( - f"{', '.join([str(c) for c in self._constraints[:-1]])} or" - f" {self._constraints[-1]}" - ) - - class HasMethods(_Constraint): - """Constraint representing objects that expose specific methods. - - It is useful for parameters following a protocol and where we don't want to - impose an affiliation to a specific module or class. - - Parameters - ---------- - methods : str or list of str - The method(s) that the object is expected to expose. - """ - - @validate_params( - {"methods": [str, list]}, - prefer_skip_nested_validation=True, - ) - def __init__(self, methods): - super().__init__() - if isinstance(methods, str): - methods = [methods] - self.methods = methods - - def is_satisfied_by(self, val): - return all(callable(getattr(val, method, None)) for method in self.methods) - - def __str__(self): - if len(self.methods) == 1: - methods = f"{self.methods[0]!r}" - else: - methods = ( - f"{', '.join([repr(m) for m in self.methods[:-1]])} and" - f" {self.methods[-1]!r}" - ) - return f"an object implementing {methods}" - - class _IterablesNotString(_Constraint): - """Constraint representing iterables that are not strings.""" - - def is_satisfied_by(self, val): - return isinstance(val, Iterable) and not isinstance(val, str) - - def __str__(self): - return "an iterable" - - class _CVObjects(_Constraint): - """Constraint representing cv objects. - - Convenient class for - [ - Interval(Integral, 2, None, closed="left"), - HasMethods(["split", "get_n_splits"]), - _IterablesNotString(), - None, - ] - """ - - def __init__(self): - super().__init__() - self._constraints = [ - Interval(Integral, 2, None, closed="left"), - HasMethods(["split", "get_n_splits"]), - _IterablesNotString(), - _NoneConstraint(), - ] - - def is_satisfied_by(self, val): - return any(c.is_satisfied_by(val) for c in self._constraints) - - def __str__(self): - return ( - f"{', '.join([str(c) for c in self._constraints[:-1]])} or" - f" {self._constraints[-1]}" - ) - - class Hidden: - """Class encapsulating a constraint not meant to be exposed to the user. - - Parameters - ---------- - constraint : str or _Constraint instance - The constraint to be used internally. - """ - - def __init__(self, constraint): - self.constraint = constraint - - def generate_invalid_param_val(constraint): - """Return a value that does not satisfy the constraint. - - Raises a NotImplementedError if there exists no invalid value for this - constraint. - - This is only useful for testing purpose. - - Parameters - ---------- - constraint : _Constraint instance - The constraint to generate a value for. - - Returns - ------- - val : object - A value that does not satisfy the constraint. - """ - if isinstance(constraint, StrOptions): - return f"not {' or '.join(constraint.options)}" - - if isinstance(constraint, MissingValues): - return np.array([1, 2, 3]) - - if isinstance(constraint, _VerboseHelper): - return -1 - - if isinstance(constraint, HasMethods): - return type("HasNotMethods", (), {})() - - if isinstance(constraint, _IterablesNotString): - return "a string" - - if isinstance(constraint, _CVObjects): - return "not a cv object" - - if isinstance(constraint, Interval) and constraint.type is Integral: - if constraint.left is not None: - return constraint.left - 1 - if constraint.right is not None: - return constraint.right + 1 - - # There's no integer outside (-inf, +inf) - raise NotImplementedError - - if isinstance(constraint, Interval) and constraint.type in (Real, RealNotInt): - if constraint.left is not None: - return constraint.left - 1e-6 - if constraint.right is not None: - return constraint.right + 1e-6 - - # bounds are -inf, +inf - if constraint.closed in ("right", "neither"): - return -np.inf - if constraint.closed in ("left", "neither"): - return np.inf - - # interval is [-inf, +inf] - return np.nan - - raise NotImplementedError - - def generate_valid_param(constraint): - """Return a value that does satisfy a constraint. - - This is only useful for testing purpose. - - Parameters - ---------- - constraint : Constraint instance - The constraint to generate a value for. - - Returns - ------- - val : object - A value that does satisfy the constraint. - """ - if isinstance(constraint, _ArrayLikes): - return np.array([1, 2, 3]) - - if isinstance(constraint, _SparseMatrices): - return csr_matrix([[0, 1], [1, 0]]) - - if isinstance(constraint, _RandomStates): - return np.random.RandomState(42) - - if isinstance(constraint, _Callables): - return lambda x: x - - if isinstance(constraint, _NoneConstraint): - return None - - if isinstance(constraint, _InstancesOf): - if constraint.type is np.ndarray: - # special case for ndarray since it can't be instantiated without - # arguments - return np.array([1, 2, 3]) - - if constraint.type in (Integral, Real): - # special case for Integral and Real since they are abstract classes - return 1 - - return constraint.type() - - if isinstance(constraint, _Booleans): - return True - - if isinstance(constraint, _VerboseHelper): - return 1 - - if isinstance(constraint, MissingValues) and constraint.numeric_only: - return np.nan - - if isinstance(constraint, MissingValues) and not constraint.numeric_only: - return "missing" - - if isinstance(constraint, HasMethods): - return type( - "ValidHasMethods", - (), - {m: lambda self: None for m in constraint.methods}, - )() - - if isinstance(constraint, _IterablesNotString): - return [1, 2, 3] - - if isinstance(constraint, _CVObjects): - return 5 - - if isinstance(constraint, Options): # includes StrOptions - for option in constraint.options: - return option - - if isinstance(constraint, Interval): - interval = constraint - if interval.left is None and interval.right is None: - return 0 - elif interval.left is None: - return interval.right - 1 - elif interval.right is None: - return interval.left + 1 - else: - if interval.type is Real: - return (interval.left + interval.right) / 2 - else: - return interval.left + 1 - - raise ValueError(f"Unknown constraint type: {constraint}") - -else: - from sklearn.utils._param_validation import generate_invalid_param_val # noqa - from sklearn.utils._param_validation import generate_valid_param # noqa - from sklearn.utils._param_validation import validate_parameter_constraints # noqa - from sklearn.utils._param_validation import ( - HasMethods, - Hidden, - Interval, - InvalidParameterError, - MissingValues, - Options, - RealNotInt, - StrOptions, - _ArrayLikes, - _Booleans, - _Callables, - _CVObjects, - _InstancesOf, - _IterablesNotString, - _NanConstraint, - _NoneConstraint, - _PandasNAConstraint, - _RandomStates, - _SparseMatrices, - _VerboseHelper, - make_constraint, - validate_params, - ) diff --git a/imblearn/utils/_sklearn_compat.py b/imblearn/utils/_sklearn_compat.py new file mode 100644 index 000000000..6914ee0fa --- /dev/null +++ b/imblearn/utils/_sklearn_compat.py @@ -0,0 +1,830 @@ +"""Ease developer experience to support multiple versions of scikit-learn. + +This file is intended to be vendored in your project if you do not want to depend on +`sklearn-compat` as a package. Then, you can import directly from this file. + +Be aware that depending on `sklearn-compat` does not add any additional dependencies: +we are only depending on `scikit-learn`. + +Version: 0.1.1 +""" + +from __future__ import annotations + +import functools +import inspect +import platform +import sys +import types +from dataclasses import dataclass, field +from typing import Callable, Literal + +import sklearn +from sklearn.utils.fixes import parse_version + +sklearn_version = parse_version(parse_version(sklearn.__version__).base_version) + + +######################################################################################## +# The following code does not depend on the sklearn version +######################################################################################## + + +# tags infrastructure +def _dataclass_args(): + if sys.version_info < (3, 10): + return {} + return {"slots": True} + + +def get_tags(estimator): + """Get estimator tags in a consistent format across different sklearn versions. + + This function provides compatibility between sklearn versions before and after 1.6. + It returns either a Tags object (sklearn >= 1.6) or a converted Tags object from + the dictionary format (sklearn < 1.6) containing metadata about the estimator's + requirements and capabilities. + + Parameters + ---------- + estimator : estimator object + A scikit-learn estimator instance. + + Returns + ------- + tags : Tags + An object containing metadata about the estimator's requirements and + capabilities (e.g., input types, fitting requirements, classifier/regressor + specific tags). + """ + try: + from sklearn.utils._tags import get_tags + + return get_tags(estimator) + except ImportError: + from sklearn.utils._tags import _safe_tags + + return _to_new_tags(_safe_tags(estimator), estimator) + + +def _to_new_tags(old_tags, estimator=None): + """Utility function convert old tags (dictionary) to new tags (dataclass).""" + input_tags = InputTags( + one_d_array="1darray" in old_tags["X_types"], + two_d_array="2darray" in old_tags["X_types"], + three_d_array="3darray" in old_tags["X_types"], + sparse="sparse" in old_tags["X_types"], + categorical="categorical" in old_tags["X_types"], + string="string" in old_tags["X_types"], + dict="dict" in old_tags["X_types"], + positive_only=old_tags["requires_positive_X"], + allow_nan=old_tags["allow_nan"], + pairwise=old_tags["pairwise"], + dataframe="dataframe" in old_tags["X_types"], + ) + target_tags = TargetTags( + required=old_tags["requires_y"], + one_d_labels="1dlabels" in old_tags["X_types"], + two_d_labels="2dlabels" in old_tags["X_types"], + positive_only=old_tags["requires_positive_y"], + multi_output=old_tags["multioutput"] or old_tags["multioutput_only"], + single_output=not old_tags["multioutput_only"], + ) + if estimator is not None and ( + hasattr(estimator, "transform") or hasattr(estimator, "fit_transform") + ): + transformer_tags = TransformerTags( + preserves_dtype=old_tags["preserves_dtype"], + ) + else: + transformer_tags = None + estimator_type = getattr(estimator, "_estimator_type", None) + if estimator_type == "classifier": + classifier_tags = ClassifierTags( + poor_score=old_tags["poor_score"], + multi_class=not old_tags["binary_only"], + multi_label=old_tags["multilabel"], + ) + else: + classifier_tags = None + if estimator_type == "regressor": + regressor_tags = RegressorTags( + poor_score=old_tags["poor_score"], + multi_label=old_tags["multilabel"], + ) + else: + regressor_tags = None + + if estimator_type == "sampler": + sampler_tags = SamplerTags( + sample_indices=old_tags.get("sample_indices", False), + ) + else: + sampler_tags = None + + return Tags( + estimator_type=estimator_type, + target_tags=target_tags, + transformer_tags=transformer_tags, + classifier_tags=classifier_tags, + regressor_tags=regressor_tags, + sampler_tags=sampler_tags, + input_tags=input_tags, + # Array-API was introduced in 1.3, we need to default to False if not inside + # the old-tags. + array_api_support=old_tags.get("array_api_support", False), + no_validation=old_tags["no_validation"], + non_deterministic=old_tags["non_deterministic"], + requires_fit=old_tags["requires_fit"], + _skip_test=old_tags["_skip_test"], + ) + + +######################################################################################## +# Upgrading for scikit-learn 1.3 +######################################################################################## + +if sklearn_version < parse_version("1.3"): + # parameter validation + def _fit_context(*, prefer_skip_nested_validation): + """Decorator to run the fit methods of estimators within context managers.""" + + def decorator(fit_method): + @functools.wraps(fit_method) + def wrapper(estimator, *args, **kwargs): + estimator._validate_params() + return fit_method(estimator, *args, **kwargs) + + return wrapper + + return decorator + + def validate_params(parameter_constraints, *, prefer_skip_nested_validation): + """Validate the parameters of an estimator.""" + from sklearn.utils._param_validation import validate_params + + return validate_params(parameter_constraints) + +else: + # parameter validation + + from sklearn.base import _fit_context # noqa: F401 + from sklearn.utils._param_validation import validate_params # noqa: F401 + + +######################################################################################## +# Upgrading for scikit-learn 1.4 +######################################################################################## + + +if sklearn_version < parse_version("1.4"): + + def _is_fitted(estimator, attributes=None, all_or_any=all): + """Determine if an estimator is fitted + + Parameters + ---------- + estimator : estimator instance + Estimator instance for which the check is performed. + + attributes : str, list or tuple of str, default=None + Attribute name(s) given as string or a list/tuple of strings + Eg.: ``["coef_", "estimator_", ...], "coef_"`` + + If `None`, `estimator` is considered fitted if there exist an + attribute that ends with a underscore and does not start with double + underscore. + + all_or_any : callable, {all, any}, default=all + Specify whether all or any of the given attributes must exist. + + Returns + ------- + fitted : bool + Whether the estimator is fitted. + """ + if attributes is not None: + if not isinstance(attributes, (list, tuple)): + attributes = [attributes] + return all_or_any([hasattr(estimator, attr) for attr in attributes]) + + if hasattr(estimator, "__sklearn_is_fitted__"): + return estimator.__sklearn_is_fitted__() + + fitted_attrs = [ + v for v in vars(estimator) if v.endswith("_") and not v.startswith("__") + ] + return len(fitted_attrs) > 0 + + def process_routing(_obj, _method, /, **kwargs): + """Validate and route input parameters.""" + from sklearn.utils._metadata_requests import process_routing + + return process_routing(_obj, _method, other_params=None, **kwargs) + + def _raise_for_params(params, owner, method): + """Raise an error if metadata routing is not enabled and params are passed.""" + from sklearn.utils._metadata_requests import _routing_enabled + + caller = ( + f"{owner.__class__.__name__}.{method}" + if method + else owner.__class__.__name__ + ) + if not _routing_enabled() and params: + raise ValueError( + f"Passing extra keyword arguments to {caller} is only supported if" + " enable_metadata_routing=True, which you can set using" + " `sklearn.set_config`. See the User Guide" + " for more" + f" details. Extra parameters passed are: {set(params)}" + ) + + def _is_pandas_df(X): + """Return True if the X is a pandas dataframe.""" + try: + pd = sys.modules["pandas"] + except KeyError: + return False + return isinstance(X, pd.DataFrame) + +else: + from sklearn.utils._metadata_requests import ( + _raise_for_params, # noqa: F401 + process_routing, # noqa: F401 + ) + from sklearn.utils.validation import ( + _is_fitted, # noqa: F401 + _is_pandas_df, # noqa: F401 + ) + + +######################################################################################## +# Upgrading for scikit-learn 1.5 +######################################################################################## + + +if sklearn_version < parse_version("1.5"): + # chunking + # extmath + # fixes + from sklearn.utils import ( + _IS_32BIT, # noqa: F401 + _approximate_mode, # noqa: F401 + _in_unstable_openblas_configuration, # noqa: F401 + gen_batches, # noqa: F401 + gen_even_slices, # noqa: F401 + get_chunk_n_rows, # noqa: F401 + safe_sqr, # noqa: F401 + ) + from sklearn.utils import _chunk_generator as chunk_generator # noqa: F401 + + _IS_WASM = platform.machine() in ["wasm32", "wasm64"] + # indexing + # mask + # missing + # optional dependencies + # user interface + # validation + from sklearn.utils import ( + _determine_key_type, # noqa: F401 + _get_column_indices, # noqa: F401 + _print_elapsed_time, # noqa: F401 + _safe_assign, # noqa: F401 + _safe_indexing, # noqa: F401 + _to_object_array, # noqa: F401 + axis0_safe_slice, # noqa: F401 + check_matplotlib_support, # noqa: F401 + check_pandas_support, # noqa: F401 + indices_to_mask, # noqa: F401 + is_scalar_nan, # noqa: F401 + resample, # noqa: F401 + safe_mask, # noqa: F401 + shuffle, # noqa: F401 + ) + from sklearn.utils import _is_pandas_na as is_pandas_na # noqa: F401 +else: + # chunking + from sklearn.utils._chunking import ( + chunk_generator, # noqa: F401 + gen_batches, # noqa: F401 + gen_even_slices, # noqa: F401 + get_chunk_n_rows, # noqa: F401 + ) + + # indexing + from sklearn.utils._indexing import ( + _determine_key_type, # noqa: F401 + _get_column_indices, # noqa: F401 + _safe_assign, # noqa: F401 + _safe_indexing, # noqa: F401 + resample, # noqa: F401 + shuffle, # noqa: F401 + ) + + # mask + from sklearn.utils._mask import ( + axis0_safe_slice, # noqa: F401 + indices_to_mask, # noqa: F401 + safe_mask, # noqa: F401 + ) + + # missing + from sklearn.utils._missing import ( + is_pandas_na, # noqa: F401 + is_scalar_nan, # noqa: F401 + ) + + # optional dependencies + from sklearn.utils._optional_dependencies import ( # noqa: F401 + check_matplotlib_support, + check_pandas_support, # noqa: F401 + ) + + # user interface + from sklearn.utils._user_interface import _print_elapsed_time # noqa: F401 + + # extmath + from sklearn.utils.extmath import ( + _approximate_mode, # noqa: F401 + safe_sqr, # noqa: F401 + ) + + # fixes + from sklearn.utils.fixes import ( + _IS_32BIT, # noqa: F401 + _IS_WASM, # noqa: F401 + _in_unstable_openblas_configuration, # noqa: F401 + ) + + # validation + from sklearn.utils.validation import _to_object_array # noqa: F401 + +######################################################################################## +# Upgrading for scikit-learn 1.6 +######################################################################################## + + +if sklearn_version < parse_version("1.6"): + # test_common + from sklearn.utils.estimator_checks import _construct_instance + + def type_of_target(y, input_name="", *, raise_unknown=False): + # fix for raise_unknown which is introduced in scikit-learn 1.6 + from sklearn.utils.multiclass import type_of_target + + def _raise_or_return(target_type): + """Depending on the value of raise_unknown, either raise an error or + return 'unknown'. + """ + if raise_unknown and target_type == "unknown": + input = input_name if input_name else "data" + raise ValueError(f"Unknown label type for {input}: {y!r}") + else: + return target_type + + target_type = type_of_target(y, input_name=input_name) + return _raise_or_return(target_type) + + def _construct_instances(Estimator): + yield _construct_instance(Estimator) + + # validation + def validate_data( + _estimator, + /, + X="no_validation", + y="no_validation", + reset=True, + validate_separately=False, + skip_check_array=False, + **kwargs, + ): + """Validate input data and set or check feature names and counts of the input. + + See the original scikit-learn documentation: + https://scikit-learn.org/stable/modules/generated/sklearn.utils.validation.validate_data.html#sklearn.utils.validation.validate_data + """ + if skip_check_array: + _check_n_features(_estimator, X, reset=reset) + _check_feature_names(_estimator, X, reset=reset) + + no_val_X = isinstance(X, str) and X == "no_validation" + no_val_y = y is None or isinstance(y, str) and y == "no_validation" + if not no_val_X and no_val_y: + out = X + elif no_val_X and not no_val_y: + out = y + else: + out = X, y + return out + else: + if "ensure_all_finite" in kwargs: + force_all_finite = kwargs.pop("ensure_all_finite") + else: + force_all_finite = True + return _estimator._validate_data( + X=X, + y=y, + reset=reset, + validate_separately=validate_separately, + force_all_finite=force_all_finite, + **kwargs, + ) + + def _check_n_features(estimator, X, *, reset): + """Set the `n_features_in_` attribute, or check against it on an estimator.""" + return estimator._check_n_features(X, reset=reset) + + def _check_feature_names(estimator, X, *, reset): + """Check `input_features` and generate names if needed.""" + return estimator._check_feature_names(X, reset=reset) + + def check_array( + array, + accept_sparse=False, + *, + accept_large_sparse=True, + dtype="numeric", + order=None, + copy=False, + force_writeable=False, + ensure_all_finite=None, + ensure_non_negative=False, + ensure_2d=True, + allow_nd=False, + ensure_min_samples=1, + ensure_min_features=1, + estimator=None, + input_name="", + ): + """Input validation on an array, list, sparse matrix or similar. + + Check the original documentation for more details: + https://scikit-learn.org/stable/modules/generated/sklearn.utils.check_array.html + """ + from sklearn.utils.validation import check_array as _check_array + + if ensure_all_finite is not None: + force_all_finite = ensure_all_finite + else: + force_all_finite = True + + check_array_params = inspect.signature(_check_array).parameters + kwargs = {} + if "force_writeable" in check_array_params: + kwargs["force_writeable"] = force_writeable + if "ensure_non_negative" in check_array_params: + kwargs["ensure_non_negative"] = ensure_non_negative + + return _check_array( + array, + accept_sparse=accept_sparse, + accept_large_sparse=accept_large_sparse, + dtype=dtype, + order=order, + copy=copy, + force_all_finite=force_all_finite, + ensure_2d=ensure_2d, + allow_nd=allow_nd, + ensure_min_samples=ensure_min_samples, + ensure_min_features=ensure_min_features, + estimator=estimator, + input_name=input_name, + **kwargs, + ) + + # tags infrastructure + @dataclass(**_dataclass_args()) + class InputTags: + """Tags for the input data. + + Parameters + ---------- + one_d_array : bool, default=False + Whether the input can be a 1D array. + + two_d_array : bool, default=True + Whether the input can be a 2D array. Note that most common + tests currently run only if this flag is set to ``True``. + + three_d_array : bool, default=False + Whether the input can be a 3D array. + + sparse : bool, default=False + Whether the input can be a sparse matrix. + + categorical : bool, default=False + Whether the input can be categorical. + + string : bool, default=False + Whether the input can be an array-like of strings. + + dict : bool, default=False + Whether the input can be a dictionary. + + positive_only : bool, default=False + Whether the estimator requires positive X. + + allow_nan : bool, default=False + Whether the estimator supports data with missing values encoded as `np.nan`. + + pairwise : bool, default=False + This boolean attribute indicates whether the data (`X`), + :term:`fit` and similar methods consists of pairwise measures + over samples rather than a feature representation for each + sample. It is usually `True` where an estimator has a + `metric` or `affinity` or `kernel` parameter with value + 'precomputed'. Its primary purpose is to support a + :term:`meta-estimator` or a cross validation procedure that + extracts a sub-sample of data intended for a pairwise + estimator, where the data needs to be indexed on both axes. + Specifically, this tag is used by + `sklearn.utils.metaestimators._safe_split` to slice rows and + columns. + """ + + one_d_array: bool = False + two_d_array: bool = True + three_d_array: bool = False + sparse: bool = False + categorical: bool = False + string: bool = False + dict: bool = False + positive_only: bool = False + allow_nan: bool = False + pairwise: bool = False + dataframe: bool = False + + @dataclass(**_dataclass_args()) + class TargetTags: + """Tags for the target data. + + Parameters + ---------- + required : bool + Whether the estimator requires y to be passed to `fit`, + `fit_predict` or `fit_transform` methods. The tag is ``True`` + for estimators inheriting from `~sklearn.base.RegressorMixin` + and `~sklearn.base.ClassifierMixin`. + + one_d_labels : bool, default=False + Whether the input is a 1D labels (y). + + two_d_labels : bool, default=False + Whether the input is a 2D labels (y). + + positive_only : bool, default=False + Whether the estimator requires a positive y (only applicable + for regression). + + multi_output : bool, default=False + Whether a regressor supports multi-target outputs or a classifier supports + multi-class multi-output. + + single_output : bool, default=True + Whether the target can be single-output. This can be ``False`` if the + estimator supports only multi-output cases. + """ + + required: bool + one_d_labels: bool = False + two_d_labels: bool = False + positive_only: bool = False + multi_output: bool = False + single_output: bool = True + + @dataclass(**_dataclass_args()) + class TransformerTags: + """Tags for the transformer. + + Parameters + ---------- + preserves_dtype : list[str], default=["float64"] + Applies only on transformers. It corresponds to the data types + which will be preserved such that `X_trans.dtype` is the same + as `X.dtype` after calling `transformer.transform(X)`. If this + list is empty, then the transformer is not expected to + preserve the data type. The first value in the list is + considered as the default data type, corresponding to the data + type of the output when the input data type is not going to be + preserved. + """ + + preserves_dtype: list[str] = field(default_factory=lambda: ["float64"]) + + @dataclass(**_dataclass_args()) + class ClassifierTags: + """Tags for the classifier. + + Parameters + ---------- + poor_score : bool, default=False + Whether the estimator fails to provide a "reasonable" test-set + score, which currently for classification is an accuracy of + 0.83 on ``make_blobs(n_samples=300, random_state=0)``. The + datasets and values are based on current estimators in scikit-learn + and might be replaced by something more systematic. + + multi_class : bool, default=True + Whether the classifier can handle multi-class + classification. Note that all classifiers support binary + classification. Therefore this flag indicates whether the + classifier is a binary-classifier-only or not. + + multi_label : bool, default=False + Whether the classifier supports multi-label output. + """ + + poor_score: bool = False + multi_class: bool = True + multi_label: bool = False + + @dataclass(**_dataclass_args()) + class RegressorTags: + """Tags for the regressor. + + Parameters + ---------- + poor_score : bool, default=False + Whether the estimator fails to provide a "reasonable" test-set + score, which currently for regression is an R2 of 0.5 on + ``make_regression(n_samples=200, n_features=10, + n_informative=1, bias=5.0, noise=20, random_state=42)``. The + dataset and values are based on current estimators in scikit-learn + and might be replaced by something more systematic. + + multi_label : bool, default=False + Whether the regressor supports multilabel output. + """ + + poor_score: bool = False + multi_label: bool = False + + @dataclass(**_dataclass_args()) + class SamplerTags: + """Tags for the sampler. + + Parameters + ---------- + sample_indices : bool, default=False + Whether the sampler returns the indices of the samples that were + selected. + """ + + sample_indices: bool = False + + @dataclass(**_dataclass_args()) + class Tags: + """Tags for the estimator. + + See :ref:`estimator_tags` for more information. + + Parameters + ---------- + estimator_type : str or None + The type of the estimator. Can be one of: + - "classifier" + - "regressor" + - "transformer" + - "clusterer" + - "outlier_detector" + - "density_estimator" + + target_tags : :class:`TargetTags` + The target(y) tags. + + transformer_tags : :class:`TransformerTags` or None + The transformer tags. + + classifier_tags : :class:`ClassifierTags` or None + The classifier tags. + + regressor_tags : :class:`RegressorTags` or None + The regressor tags. + + array_api_support : bool, default=False + Whether the estimator supports Array API compatible inputs. + + no_validation : bool, default=False + Whether the estimator skips input-validation. This is only meant for + stateless and dummy transformers! + + non_deterministic : bool, default=False + Whether the estimator is not deterministic given a fixed ``random_state``. + + requires_fit : bool, default=True + Whether the estimator requires to be fitted before calling one of + `transform`, `predict`, `predict_proba`, or `decision_function`. + + _skip_test : bool, default=False + Whether to skip common tests entirely. Don't use this unless + you have a *very good* reason. + + input_tags : :class:`InputTags` + The input data(X) tags. + """ + + estimator_type: str | None + target_tags: TargetTags + transformer_tags: TransformerTags | None = None + classifier_tags: ClassifierTags | None = None + regressor_tags: RegressorTags | None = None + array_api_support: bool = False + no_validation: bool = False + non_deterministic: bool = False + requires_fit: bool = True + _skip_test: bool = False + input_tags: InputTags = field(default_factory=InputTags) + sampler_tags: SamplerTags | None = None + + def _patched_more_tags(estimator, expected_failed_checks): + original_class_more_tags = estimator.__class__._more_tags + + def patched_instance_more_tags(self): + """Instance-level _more_tags that combines class tags with _xfail_checks""" + # Get tags from class-level _more_tags + tags = original_class_more_tags(self) + # Update with the xfail checks + tags.update({"_xfail_checks": expected_failed_checks}) + return tags + + # Patch both class and instance level + estimator.__class__._more_tags = patched_instance_more_tags + estimator._more_tags = types.MethodType(patched_instance_more_tags, estimator) + return estimator + + def check_estimator( + estimator=None, + generate_only=False, + *, + legacy: bool = True, + expected_failed_checks: dict[str, str] | None = None, + on_skip: Literal["warn"] | None = "warn", + on_fail: Literal["raise", "warn"] | None = "raise", + callback: Callable | None = None, + ): + # legacy, on_skip, on_fail, and callback are not supported and ignored + from sklearn.utils.estimator_checks import check_estimator + + return check_estimator( + _patched_more_tags(estimator, expected_failed_checks), + generate_only=generate_only, + ) + + def parametrize_with_checks( + estimators, + *, + legacy: bool = True, + expected_failed_checks: Callable | None = None, + ): + # legacy is not supported and ignored + from sklearn.utils.estimator_checks import parametrize_with_checks + + estimators = [ + _patched_more_tags(estimator, expected_failed_checks(estimator)) + for estimator in estimators + ] + + return parametrize_with_checks(estimators) + +else: + # test_common + # tags infrastructure + from sklearn.utils import ( + ClassifierTags, + InputTags, + RegressorTags, + Tags, + TargetTags, + TransformerTags, + ) + + @dataclass(**_dataclass_args()) + class InputTags(InputTags): + dataframe: bool = True + + @dataclass(**_dataclass_args()) + class SamplerTags: + sample_indices: bool = False + + @dataclass(**_dataclass_args()) + class Tags(Tags): + sampler_tags: SamplerTags | None = None + + from sklearn.utils._test_common.instance_generator import ( + _construct_instances, # noqa: F401 + ) + from sklearn.utils.estimator_checks import ( + check_estimator, # noqa: F401 + parametrize_with_checks, # noqa: F401 + ) + from sklearn.utils.multiclass import type_of_target # noqa: F401 + + # validation + from sklearn.utils.validation import ( + _check_feature_names, # noqa: F401 + _check_n_features, # noqa: F401 + check_array, # noqa: F401 + validate_data, # noqa: F401 + ) diff --git a/imblearn/utils/_tags.py b/imblearn/utils/_tags.py new file mode 100644 index 000000000..a981f7b40 --- /dev/null +++ b/imblearn/utils/_tags.py @@ -0,0 +1 @@ +from ._sklearn_compat import InputTags, SamplerTags, Tags # noqa: F401 diff --git a/imblearn/utils/_test_common/__init__.py b/imblearn/utils/_test_common/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/imblearn/utils/_test_common/instance_generator.py b/imblearn/utils/_test_common/instance_generator.py new file mode 100644 index 000000000..e18fc8018 --- /dev/null +++ b/imblearn/utils/_test_common/instance_generator.py @@ -0,0 +1,244 @@ +# Authors: Guillaume Lemaitre +# Christos Aridas +# License: MIT + +import re +import warnings +from contextlib import suppress +from functools import partial +from inspect import isfunction + +from sklearn import clone, config_context +from sklearn.exceptions import SkipTestWarning +from sklearn.linear_model import LogisticRegression +from sklearn.tree import DecisionTreeClassifier +from sklearn.utils._testing import SkipTest +from sklearn.utils.fixes import parse_version + +from imblearn.combine import SMOTEENN, SMOTETomek +from imblearn.ensemble import ( + BalancedBaggingClassifier, + BalancedRandomForestClassifier, + EasyEnsembleClassifier, + RUSBoostClassifier, +) +from imblearn.over_sampling import ( + ADASYN, + SMOTE, + SMOTEN, + SMOTENC, + SVMSMOTE, + BorderlineSMOTE, + KMeansSMOTE, + RandomOverSampler, +) +from imblearn.pipeline import Pipeline +from imblearn.under_sampling import ( + ClusterCentroids, + CondensedNearestNeighbour, + InstanceHardnessThreshold, + NearMiss, + OneSidedSelection, + RandomUnderSampler, +) +from imblearn.utils._sklearn_compat import sklearn_version +from imblearn.utils.testing import all_estimators + +# The following dictionary is to indicate constructor arguments suitable for the test +# suite, which uses very small datasets, and is intended to run rather quickly. +INIT_PARAMS = { + # estimator + BalancedBaggingClassifier: dict(random_state=42), + BalancedRandomForestClassifier: dict(random_state=42), + EasyEnsembleClassifier: [ + # AdaBoostClassifier does not allow nan values + dict(random_state=42), + # DecisionTreeClassifier allows nan values + dict(estimator=DecisionTreeClassifier(random_state=42), random_state=42), + ], + Pipeline: dict( + steps=[ + ("sampler", RandomUnderSampler(random_state=0)), + ("logistic", LogisticRegression()), + ] + ), + # over-sampling + ADASYN: dict(random_state=42), + BorderlineSMOTE: dict(random_state=42), + KMeansSMOTE: dict(random_state=0), + RandomOverSampler: dict(random_state=42), + SMOTE: dict(random_state=42), + SMOTEN: dict(random_state=42), + SMOTENC: dict(categorical_features=[0], random_state=42), + SVMSMOTE: dict(random_state=42), + # under-sampling + ClusterCentroids: dict(random_state=42), + CondensedNearestNeighbour: dict(random_state=42), + InstanceHardnessThreshold: dict(random_state=42), + NearMiss: [dict(version=1), dict(version=2), dict(version=3)], + OneSidedSelection: dict(random_state=42), + RandomUnderSampler: dict(random_state=42), + # combination + SMOTEENN: dict(random_state=42), + SMOTETomek: dict(random_state=42), +} + +# This dictionary stores parameters for specific checks. It also enables running the +# same check with multiple instances of the same estimator with different parameters. +# The special key "*" allows to apply the parameters to all checks. +# TODO(devtools): allow third-party developers to pass test specific params to checks +PER_ESTIMATOR_CHECK_PARAMS: dict = { + Pipeline: { + "check_classifiers_with_encoded_labels": dict( + sampler__sampling_strategy={"setosa": 20, "virginica": 20} + ) + } +} + +SKIPPED_ESTIMATORS = [SMOTENC] + + +def _tested_estimators(type_filter=None): + for _, Estimator in all_estimators(type_filter=type_filter): + with suppress(SkipTest): + for estimator in _construct_instances(Estimator): + yield estimator + + +def _construct_instances(Estimator): + """Construct Estimator instances if possible. + + If parameter sets in INIT_PARAMS are provided, use them. If there are a list + of parameter sets, return one instance for each set. + """ + if Estimator in SKIPPED_ESTIMATORS: + msg = f"Can't instantiate estimator {Estimator.__name__}" + # raise additional warning to be shown by pytest + warnings.warn(msg, SkipTestWarning) + raise SkipTest(msg) + + if Estimator in INIT_PARAMS: + param_sets = INIT_PARAMS[Estimator] + if not isinstance(param_sets, list): + param_sets = [param_sets] + for params in param_sets: + est = Estimator(**params) + yield est + else: + yield Estimator() + + +def _get_check_estimator_ids(obj): + """Create pytest ids for checks. + + When `obj` is an estimator, this returns the pprint version of the + estimator (with `print_changed_only=True`). When `obj` is a function, the + name of the function is returned with its keyword arguments. + + `_get_check_estimator_ids` is designed to be used as the `id` in + `pytest.mark.parametrize` where `check_estimator(..., generate_only=True)` + is yielding estimators and checks. + + Parameters + ---------- + obj : estimator or function + Items generated by `check_estimator`. + + Returns + ------- + id : str or None + + See Also + -------- + check_estimator + """ + if isfunction(obj): + return obj.__name__ + if isinstance(obj, partial): + if not obj.keywords: + return obj.func.__name__ + kwstring = ",".join(["{}={}".format(k, v) for k, v in obj.keywords.items()]) + return "{}({})".format(obj.func.__name__, kwstring) + if hasattr(obj, "get_params"): + with config_context(print_changed_only=True): + return re.sub(r"\s", "", str(obj)) + + +def _yield_instances_for_check(check, estimator_orig): + """Yield instances for a check. + + For most estimators, this is a no-op. + + For estimators which have an entry in PER_ESTIMATOR_CHECK_PARAMS, this will yield + an estimator for each parameter set in PER_ESTIMATOR_CHECK_PARAMS[estimator]. + """ + # TODO(devtools): enable this behavior for third party estimators as well + if type(estimator_orig) not in PER_ESTIMATOR_CHECK_PARAMS: + yield estimator_orig + return + + check_params = PER_ESTIMATOR_CHECK_PARAMS[type(estimator_orig)] + + try: + check_name = check.__name__ + except AttributeError: + # partial tests + check_name = check.func.__name__ + + if check_name not in check_params: + yield estimator_orig + return + + param_set = check_params[check_name] + if isinstance(param_set, dict): + param_set = [param_set] + + for params in param_set: + estimator = clone(estimator_orig) + estimator.set_params(**params) + yield estimator + + +PER_ESTIMATOR_XFAIL_CHECKS = { + BalancedRandomForestClassifier: { + "check_sample_weight_equivalence": "FIXME", + "check_sample_weight_equivalence_on_sparse_data": "FIXME", + "check_sample_weight_equivalence_on_dense_data": "FIXME", + }, + NearMiss: { + "check_samplers_fit_resample": "FIXME", + }, + Pipeline: { + "check_classifiers_train": "FIXME", + "check_supervised_y_2d": "FIXME", + "check_dont_overwrite_parameters": ( + "Pipeline changes the `steps` parameter, which it shouldn't. " + "Therefore this test is x-fail until we fix this." + ), + "check_estimators_overwrite_params": ( + "Pipeline changes the `steps` parameter, which it shouldn't. " + "Therefore this test is x-fail until we fix this." + ), + }, + RUSBoostClassifier: { + "check_sample_weight_equivalence": "FIXME", + "check_sample_weight_equivalence_on_sparse_data": "FIXME", + "check_sample_weight_equivalence_on_dense_data": "FIXME", + "check_estimator_sparse_data": "FIXME", + "check_estimator_sparse_matrix": "FIXME", + "check_estimator_sparse_array": "FIXME", + }, +} + +if sklearn_version < parse_version("1.4"): + for _, Estimator in all_estimators(): + if Estimator in PER_ESTIMATOR_XFAIL_CHECKS: + PER_ESTIMATOR_XFAIL_CHECKS[Estimator]["check_estimators_pickle"] = "FIXME" + else: + PER_ESTIMATOR_XFAIL_CHECKS[Estimator] = {"check_estimators_pickle": "FIXME"} + + +def _get_expected_failed_checks(estimator): + """Get the expected failed checks for all estimators in scikit-learn.""" + failed_checks = PER_ESTIMATOR_XFAIL_CHECKS.get(type(estimator), {}) + return failed_checks diff --git a/imblearn/utils/_validation.py b/imblearn/utils/_validation.py index bb17cf015..d7e614a4f 100644 --- a/imblearn/utils/_validation.py +++ b/imblearn/utils/_validation.py @@ -13,11 +13,11 @@ from scipy.sparse import issparse from sklearn.base import clone from sklearn.neighbors import NearestNeighbors -from sklearn.utils import check_array, column_or_1d +from sklearn.utils import column_or_1d from sklearn.utils.multiclass import type_of_target from sklearn.utils.validation import _num_samples -from .fixes import _is_pandas_df +from ..utils._sklearn_compat import _is_pandas_df, check_array SAMPLING_KIND = ( "over-sampling", @@ -644,5 +644,5 @@ def _check_X(X): if _is_pandas_df(X): return X return check_array( - X, dtype=None, accept_sparse=["csr", "csc"], force_all_finite=False + X, dtype=None, accept_sparse=["csr", "csc"], ensure_all_finite=False ) diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py index fc58c321c..01d0139ad 100644 --- a/imblearn/utils/estimator_checks.py +++ b/imblearn/utils/estimator_checks.py @@ -9,10 +9,9 @@ import traceback import warnings from collections import Counter -from functools import partial +from functools import partial, wraps import numpy as np -import sklearn from scipy import sparse from sklearn.base import clone, is_classifier, is_regressor from sklearn.cluster import KMeans @@ -24,7 +23,7 @@ ) from sklearn.exceptions import SkipTestWarning from sklearn.preprocessing import StandardScaler, label_binarize -from sklearn.utils._tags import _safe_tags +from sklearn.utils._param_validation import generate_invalid_param_val, make_constraint from sklearn.utils._testing import ( SkipTest, assert_allclose, @@ -35,18 +34,17 @@ from sklearn.utils.estimator_checks import ( _enforce_estimator_tags_X, _enforce_estimator_tags_y, - _get_check_estimator_ids, - _maybe_mark_xfail, ) -from sklearn.utils.fixes import parse_version from sklearn.utils.multiclass import type_of_target from imblearn.datasets import make_imbalance from imblearn.over_sampling.base import BaseOverSampler from imblearn.under_sampling.base import BaseCleaningSampler, BaseUnderSampler -from imblearn.utils._param_validation import generate_invalid_param_val, make_constraint - -sklearn_version = parse_version(sklearn.__version__) +from imblearn.utils._sklearn_compat import get_tags +from imblearn.utils._test_common.instance_generator import ( + _get_check_estimator_ids, + _yield_instances_for_check, +) def sample_dataset_generator(): @@ -80,20 +78,25 @@ def _set_checking_parameters(estimator): def _yield_sampler_checks(sampler): - tags = sampler._get_tags() + tags = get_tags(sampler) + accept_sparse = tags.input_tags.sparse + accept_dataframe = tags.input_tags.dataframe + accept_string = tags.input_tags.string + allow_nan = tags.input_tags.allow_nan + yield check_target_type yield check_samplers_one_label yield check_samplers_fit yield check_samplers_fit_resample yield check_samplers_sampling_strategy_fit_resample - if "sparse" in tags["X_types"]: + if accept_sparse: yield check_samplers_sparse - if "dataframe" in tags["X_types"]: + if accept_dataframe: yield check_samplers_pandas yield check_samplers_pandas_sparse - if "string" in tags["X_types"]: + if accept_string: yield check_samplers_string - if tags["allow_nan"]: + if allow_nan: yield check_samplers_nan yield check_samplers_list yield check_samplers_multiclass_ova @@ -112,10 +115,12 @@ def _yield_classifier_checks(classifier): yield check_classifiers_with_encoded_labels -def _yield_all_checks(estimator): +def _yield_all_checks(estimator, legacy=True): name = estimator.__class__.__name__ - tags = estimator._get_tags() - if tags["_skip_test"]: + tags = get_tags(estimator) + + skip_test = tags._skip_test + if skip_test: warnings.warn( f"Explicit SKIP via _skip_test tag for estimator {name}.", SkipTestWarning, @@ -130,9 +135,139 @@ def _yield_all_checks(estimator): yield check -def parametrize_with_checks(estimators): +def _check_name(check): + if hasattr(check, "__wrapped__"): + return _check_name(check.__wrapped__) + return check.func.__name__ if isinstance(check, partial) else check.__name__ + + +def _maybe_mark(estimator, check, expected_failed_checks=None, mark=None, pytest=None): + """Mark the test as xfail or skip if needed. + + Parameters + ---------- + estimator : estimator object + Estimator instance for which to generate checks. + check : partial or callable + Check to be marked. + expected_failed_checks : dict[str, str], default=None + Dictionary of the form {check_name: reason} for checks that are expected to + fail. + mark : "xfail" or "skip" or None + Whether to mark the check as xfail or skip. + pytest : pytest module, default=None + Pytest module to use to mark the check. This is only needed if ``mark`` is + `"xfail"`. Note that one can run `check_estimator` without having `pytest` + installed. This is used in combination with `parametrize_with_checks` only. + """ + should_be_marked, reason = _should_be_skipped_or_marked( + estimator, check, expected_failed_checks + ) + if not should_be_marked or mark is None: + return estimator, check + + estimator_name = estimator.__class__.__name__ + if mark == "xfail": + return pytest.param(estimator, check, marks=pytest.mark.xfail(reason=reason)) + else: + + @wraps(check) + def wrapped(*args, **kwargs): + raise SkipTest( + f"Skipping {_check_name(check)} for {estimator_name}: {reason}" + ) + + return estimator, wrapped + + +def _should_be_skipped_or_marked( + estimator, check, expected_failed_checks: dict[str, str] | None = None +) -> tuple[bool, str]: + """Check whether a check should be skipped or marked as xfail. + + Parameters + ---------- + estimator : estimator object + Estimator instance for which to generate checks. + check : partial or callable + Check to be marked. + expected_failed_checks : dict[str, str], default=None + Dictionary of the form {check_name: reason} for checks that are expected to + fail. + + Returns + ------- + should_be_marked : bool + Whether the check should be marked as xfail or skipped. + reason : str + Reason for skipping the check. + """ + + expected_failed_checks = expected_failed_checks or {} + + check_name = _check_name(check) + if check_name in expected_failed_checks: + return True, expected_failed_checks[check_name] + + return False, "Check is not expected to fail" + + +def estimator_checks_generator( + estimator, *, legacy=True, expected_failed_checks=None, mark=None +): + """Iteratively yield all check callables for an estimator. + + .. versionadded:: 1.6 + + Parameters + ---------- + estimator : estimator object + Estimator instance for which to generate checks. + legacy : bool, default=True + Whether to include legacy checks. Over time we remove checks from this category + and move them into their specific category. + expected_failed_checks : dict[str, str], default=None + Dictionary of the form {check_name: reason} for checks that are expected to + fail. + mark : {"xfail", "skip"} or None, default=None + Whether to mark the checks that are expected to fail as + xfail(`pytest.mark.xfail`) or skip. Marking a test as "skip" is done via + wrapping the check in a function that raises a + :class:`~sklearn.exceptions.SkipTest` exception. + + Returns + ------- + estimator_checks_generator : generator + Generator that yields (estimator, check) tuples. + """ + if mark == "xfail": + import pytest + else: + pytest = None # type: ignore + + name = type(estimator).__name__ + for check in _yield_all_checks(estimator, legacy=legacy): + check_with_name = partial(check, name) + for check_instance in _yield_instances_for_check(check, estimator): + yield _maybe_mark( + check_instance, + check_with_name, + expected_failed_checks=expected_failed_checks, + mark=mark, + pytest=pytest, + ) + + +def parametrize_with_checks(estimators, *, legacy=True, expected_failed_checks=None): """Pytest specific decorator for parametrizing estimator checks. + Checks are categorised into the following groups: + + - API checks: a set of checks to ensure API compatibility with scikit-learn. + Refer to https://scikit-learn.org/dev/developers/develop.html a requirement of + scikit-learn estimators. + - legacy: a set of checks which gradually will be grouped into other categories. + The `id` of each check is set to be a pprint version of the estimator and the name of the check with its keyword arguments. This allows to use `pytest -k` to specify which tests to run:: @@ -144,10 +279,41 @@ def parametrize_with_checks(estimators): estimators : list of estimators instances Estimators to generated checks for. + .. versionchanged:: 0.24 + Passing a class was deprecated in version 0.23, and support for + classes was removed in 0.24. Pass an instance instead. + + .. versionadded:: 0.24 + + + legacy : bool, default=True + Whether to include legacy checks. Over time we remove checks from this category + and move them into their specific category. + + .. versionadded:: 1.6 + + expected_failed_checks : callable, default=None + A callable that takes an estimator as input and returns a dictionary of the + form:: + + { + "check_name": "my reason", + } + + Where `"check_name"` is the name of the check, and `"my reason"` is why + the check fails. These tests will be marked as xfail if the check fails. + + + .. versionadded:: 1.6 + Returns ------- decorator : `pytest.mark.parametrize` + See Also + -------- + check_estimator : Check if estimator adheres to scikit-learn conventions. + Examples -------- >>> from sklearn.utils.estimator_checks import parametrize_with_checks @@ -158,18 +324,29 @@ def parametrize_with_checks(estimators): ... DecisionTreeRegressor()]) ... def test_sklearn_compatible_estimator(estimator, check): ... check(estimator) + """ import pytest - def checks_generator(): + if any(isinstance(est, type) for est in estimators): + msg = ( + "Passing a class was deprecated in version 0.23 " + "and isn't supported anymore from 0.24." + "Please pass an instance instead." + ) + raise TypeError(msg) + + def _checks_generator(estimators, legacy, expected_failed_checks): for estimator in estimators: - name = type(estimator).__name__ - for check in _yield_all_checks(estimator): - check = partial(check, name) - yield _maybe_mark_xfail(estimator, check, pytest) + args = {"estimator": estimator, "legacy": legacy, "mark": "xfail"} + if callable(expected_failed_checks): + args["expected_failed_checks"] = expected_failed_checks(estimator) + yield from estimator_checks_generator(**args) return pytest.mark.parametrize( - "estimator, check", checks_generator(), ids=_get_check_estimator_ids + "estimator, check", + _checks_generator(estimators, legacy, expected_failed_checks), + ids=_get_check_estimator_ids, ) @@ -404,9 +581,9 @@ def check_samplers_sample_indices(name, sampler_orig): sampler = clone(sampler_orig) X, y = sample_dataset_generator() sampler.fit_resample(X, y) - sample_indices = sampler._get_tags().get("sample_indices", None) - if sample_indices: - assert hasattr(sampler, "sample_indices_") is sample_indices + tags = get_tags(sampler) + if tags.sampler_tags.sample_indices: + assert hasattr(sampler, "sample_indices_") is tags.sampler_tags.sample_indices else: assert not hasattr(sampler, "sample_indices_") @@ -471,7 +648,6 @@ def check_classifiers_with_encoded_labels(name, classifier_orig): "virginica": 50, }, ) - classifier.set_params(sampling_strategy={"setosa": 20, "virginica": 20}) classifier.fit(df, y) assert set(classifier.classes_) == set(y.cat.categories.tolist()) y_pred = classifier.predict(df) @@ -529,14 +705,7 @@ def check_param_validation(name, estimator_orig): continue with raises(ValueError, match=match, err_msg=err_msg): - if any( - isinstance(X_type, str) and X_type.endswith("labels") - for X_type in _safe_tags(estimator, key="X_types") - ): - # The estimator is a label transformer and take only `y` - getattr(estimator, method)(y) # pragma: no cover - else: - getattr(estimator, method)(X, y) + getattr(estimator, method)(X, y) # Then, for constraints that are more than a type constraint, check that the # error is raised if param does match a valid type but does not match any valid @@ -557,14 +726,7 @@ def check_param_validation(name, estimator_orig): continue with raises(ValueError, match=match, err_msg=err_msg): - if any( - X_type.endswith("labels") - for X_type in _safe_tags(estimator, key="X_types") - ): - # The estimator is a label transformer and take only `y` - getattr(estimator, method)(y) # pragma: no cover - else: - getattr(estimator, method)(X, y) + getattr(estimator, method)(X, y) def check_dataframe_column_names_consistency(name, estimator_orig): @@ -575,12 +737,11 @@ def check_dataframe_column_names_consistency(name, estimator_orig): "pandas is not installed: not checking column name consistency for pandas" ) - tags = _safe_tags(estimator_orig) - is_supported_X_types = ( - "2darray" in tags["X_types"] or "categorical" in tags["X_types"] - ) + tags = get_tags(estimator_orig) + is_supported_X_types = tags.input_tags.two_d_array or tags.input_tags.categorical + no_validation = tags.no_validation - if not is_supported_X_types or tags["no_validation"]: + if not is_supported_X_types or no_validation: return rng = np.random.RandomState(0) @@ -711,8 +872,12 @@ def check_dataframe_column_names_consistency(name, estimator_orig): def check_sampler_get_feature_names_out(name, sampler_orig): - tags = sampler_orig._get_tags() - if "2darray" not in tags["X_types"] or tags["no_validation"]: + tags = get_tags(sampler_orig) + + two_d_array = tags.input_tags.two_d_array + no_validation = tags.no_validation + + if not two_d_array or no_validation: return X, y = make_blobs( @@ -759,8 +924,11 @@ def check_sampler_get_feature_names_out_pandas(name, sampler_orig): "pandas is not installed: not checking column name consistency for pandas" ) - tags = sampler_orig._get_tags() - if "2darray" not in tags["X_types"] or tags["no_validation"]: + tags = get_tags(sampler_orig) + two_d_array = tags.input_tags.two_d_array + no_validation = tags.no_validation + + if not two_d_array or no_validation: return X, y = make_blobs( diff --git a/imblearn/utils/fixes.py b/imblearn/utils/fixes.py deleted file mode 100644 index 9e4852566..000000000 --- a/imblearn/utils/fixes.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Compatibility fixes for older version of python, numpy, scipy, and -scikit-learn. - -If you add content to this file, please give the version of the package at -which the fix is no longer needed. -""" -import functools -import sys - -import scipy -import scipy.stats -import sklearn -from sklearn.utils.fixes import parse_version - -from .._config import config_context, get_config - -sp_version = parse_version(scipy.__version__) -sklearn_version = parse_version(sklearn.__version__) - - -# TODO: Remove when SciPy 1.9 is the minimum supported version -def _mode(a, axis=0): - if sp_version >= parse_version("1.9.0"): - return scipy.stats.mode(a, axis=axis, keepdims=True) - return scipy.stats.mode(a, axis=axis) - - -# TODO: remove when scikit-learn minimum version is 1.3 -if sklearn_version < parse_version("1.3"): - - def _fit_context(*, prefer_skip_nested_validation): - """Decorator to run the fit methods of estimators within context managers. - - Parameters - ---------- - prefer_skip_nested_validation : bool - If True, the validation of parameters of inner estimators or functions - called during fit will be skipped. - - This is useful to avoid validating many times the parameters passed by the - user from the public facing API. It's also useful to avoid validating - parameters that we pass internally to inner functions that are guaranteed to - be valid by the test suite. - - It should be set to True for most estimators, except for those that receive - non-validated objects as parameters, such as meta-estimators that are given - estimator objects. - - Returns - ------- - decorated_fit : method - The decorated fit method. - """ - - def decorator(fit_method): - @functools.wraps(fit_method) - def wrapper(estimator, *args, **kwargs): - global_skip_validation = get_config()["skip_parameter_validation"] - - # we don't want to validate again for each call to partial_fit - partial_fit_and_fitted = ( - fit_method.__name__ == "partial_fit" and _is_fitted(estimator) - ) - - if not global_skip_validation and not partial_fit_and_fitted: - estimator._validate_params() - - with config_context( - skip_parameter_validation=( - prefer_skip_nested_validation or global_skip_validation - ) - ): - return fit_method(estimator, *args, **kwargs) - - return wrapper - - return decorator - -else: - from sklearn.base import _fit_context # type: ignore[no-redef] # noqa - -# TODO: remove when scikit-learn minimum version is 1.3 -if sklearn_version < parse_version("1.3"): - - def _is_fitted(estimator, attributes=None, all_or_any=all): - """Determine if an estimator is fitted - - Parameters - ---------- - estimator : estimator instance - Estimator instance for which the check is performed. - - attributes : str, list or tuple of str, default=None - Attribute name(s) given as string or a list/tuple of strings - Eg.: ``["coef_", "estimator_", ...], "coef_"`` - - If `None`, `estimator` is considered fitted if there exist an - attribute that ends with a underscore and does not start with double - underscore. - - all_or_any : callable, {all, any}, default=all - Specify whether all or any of the given attributes must exist. - - Returns - ------- - fitted : bool - Whether the estimator is fitted. - """ - if attributes is not None: - if not isinstance(attributes, (list, tuple)): - attributes = [attributes] - return all_or_any([hasattr(estimator, attr) for attr in attributes]) - - if hasattr(estimator, "__sklearn_is_fitted__"): - return estimator.__sklearn_is_fitted__() - - fitted_attrs = [ - v for v in vars(estimator) if v.endswith("_") and not v.startswith("__") - ] - return len(fitted_attrs) > 0 - -else: - from sklearn.utils.validation import _is_fitted # type: ignore[no-redef] - -try: - from sklearn.utils.validation import _is_pandas_df -except ImportError: - - def _is_pandas_df(X): - """Return True if the X is a pandas dataframe.""" - if hasattr(X, "columns") and hasattr(X, "iloc"): - # Likely a pandas DataFrame, we explicitly check the type to confirm. - try: - pd = sys.modules["pandas"] - except KeyError: - return False - return isinstance(X, pd.DataFrame) - return False diff --git a/imblearn/utils/tests/test_estimator_checks.py b/imblearn/utils/tests/test_estimator_checks.py index 32e9c6723..0044c6db5 100644 --- a/imblearn/utils/tests/test_estimator_checks.py +++ b/imblearn/utils/tests/test_estimator_checks.py @@ -6,6 +6,7 @@ from imblearn.base import BaseSampler from imblearn.over_sampling.base import BaseOverSampler from imblearn.utils import check_target_type as target_check +from imblearn.utils._sklearn_compat import validate_data from imblearn.utils.estimator_checks import ( check_samplers_fit, check_samplers_nan, @@ -47,7 +48,7 @@ class NotFittedSampler(BaseBadSampler): """Sampler without target checking.""" def fit(self, X, y): - X, y = self._validate_data(X, y) + X, y = validate_data(self, X=X, y=y) return self @@ -55,7 +56,7 @@ class NoAcceptingSparseSampler(BaseBadSampler): """Sampler which does not accept sparse matrix.""" def fit(self, X, y): - X, y = self._validate_data(X, y) + X, y = validate_data(self, X=X, y=y) self.sampling_strategy_ = "sampling_strategy_" return self @@ -72,12 +73,13 @@ def _fit_resample(self, X, y): class IndicesSampler(BaseOverSampler): def _check_X_y(self, X, y): y, binarize_y = target_check(y, indicate_one_vs_all=True) - X, y = self._validate_data( - X, - y, + X, y = validate_data( + self, + X=X, + y=y, reset=True, dtype=None, - force_all_finite=False, + ensure_all_finite=False, ) return X, y, binarize_y diff --git a/imblearn/utils/tests/test_param_validation.py b/imblearn/utils/tests/test_param_validation.py deleted file mode 100644 index 38af6642d..000000000 --- a/imblearn/utils/tests/test_param_validation.py +++ /dev/null @@ -1,785 +0,0 @@ -"""This is a copy of sklearn/utils/tests/test_param_validation.py. It can be -removed when we support scikit-learn >= 1.2. -""" -from numbers import Integral, Real - -import numpy as np -import pytest -from scipy.sparse import csr_matrix -from sklearn.base import BaseEstimator -from sklearn.model_selection import LeaveOneOut -from sklearn.utils import deprecated - -from imblearn._config import config_context, get_config -from imblearn.base import _ParamsValidationMixin -from imblearn.utils._param_validation import ( - HasMethods, - Hidden, - Interval, - InvalidParameterError, - MissingValues, - Options, - RealNotInt, - StrOptions, - _ArrayLikes, - _Booleans, - _Callables, - _CVObjects, - _InstancesOf, - _IterablesNotString, - _NanConstraint, - _NoneConstraint, - _PandasNAConstraint, - _RandomStates, - _SparseMatrices, - _VerboseHelper, - generate_invalid_param_val, - generate_valid_param, - make_constraint, - validate_params, -) -from imblearn.utils.fixes import _fit_context - - -# Some helpers for the tests -@validate_params( - {"a": [Real], "b": [Real], "c": [Real], "d": [Real]}, - prefer_skip_nested_validation=True, -) -def _func(a, b=0, *args, c, d=0, **kwargs): - """A function to test the validation of functions.""" - - -class _Class: - """A class to test the _InstancesOf constraint and the validation of methods.""" - - @validate_params({"a": [Real]}, prefer_skip_nested_validation=True) - def _method(self, a): - """A validated method""" - - @deprecated() - @validate_params({"a": [Real]}, prefer_skip_nested_validation=True) - def _deprecated_method(self, a): - """A deprecated validated method""" - - -class _Estimator(_ParamsValidationMixin, BaseEstimator): - """An estimator to test the validation of estimator parameters.""" - - _parameter_constraints: dict = {"a": [Real]} - - def __init__(self, a): - self.a = a - - @_fit_context(prefer_skip_nested_validation=True) - def fit(self, X=None, y=None): - pass - - -@pytest.mark.parametrize("interval_type", [Integral, Real]) -def test_interval_range(interval_type): - """Check the range of values depending on closed.""" - interval = Interval(interval_type, -2, 2, closed="left") - assert -2 in interval - assert 2 not in interval - - interval = Interval(interval_type, -2, 2, closed="right") - assert -2 not in interval - assert 2 in interval - - interval = Interval(interval_type, -2, 2, closed="both") - assert -2 in interval - assert 2 in interval - - interval = Interval(interval_type, -2, 2, closed="neither") - assert -2 not in interval - assert 2 not in interval - - -@pytest.mark.parametrize("interval_type", [Integral, Real]) -def test_interval_large_integers(interval_type): - """Check that Interval constraint work with large integers. - - non-regression test for #26648. - """ - interval = Interval(interval_type, 0, 2, closed="neither") - assert 2**65 not in interval - assert 2**128 not in interval - assert float(2**65) not in interval - assert float(2**128) not in interval - - interval = Interval(interval_type, 0, 2**128, closed="neither") - assert 2**65 in interval - assert 2**128 not in interval - assert float(2**65) in interval - assert float(2**128) not in interval - - assert 2**1024 not in interval - - -def test_interval_inf_in_bounds(): - """Check that inf is included iff a bound is closed and set to None. - - Only valid for real intervals. - """ - interval = Interval(Real, 0, None, closed="right") - assert np.inf in interval - - interval = Interval(Real, None, 0, closed="left") - assert -np.inf in interval - - interval = Interval(Real, None, None, closed="neither") - assert np.inf not in interval - assert -np.inf not in interval - - -@pytest.mark.parametrize( - "interval", - [Interval(Real, 0, 1, closed="left"), Interval(Real, None, None, closed="both")], -) -def test_nan_not_in_interval(interval): - """Check that np.nan is not in any interval.""" - assert np.nan not in interval - - -@pytest.mark.parametrize( - "params, error, match", - [ - ( - {"type": Integral, "left": 1.0, "right": 2, "closed": "both"}, - TypeError, - r"Expecting left to be an int for an interval over the integers", - ), - ( - {"type": Integral, "left": 1, "right": 2.0, "closed": "neither"}, - TypeError, - "Expecting right to be an int for an interval over the integers", - ), - ( - {"type": Integral, "left": None, "right": 0, "closed": "left"}, - ValueError, - r"left can't be None when closed == left", - ), - ( - {"type": Integral, "left": 0, "right": None, "closed": "right"}, - ValueError, - r"right can't be None when closed == right", - ), - ( - {"type": Integral, "left": 1, "right": -1, "closed": "both"}, - ValueError, - r"right can't be less than left", - ), - ], -) -def test_interval_errors(params, error, match): - """Check that informative errors are raised for invalid combination of parameters""" - with pytest.raises(error, match=match): - Interval(**params) - - -def test_stroptions(): - """Sanity check for the StrOptions constraint""" - options = StrOptions({"a", "b", "c"}, deprecated={"c"}) - assert options.is_satisfied_by("a") - assert options.is_satisfied_by("c") - assert not options.is_satisfied_by("d") - - assert "'c' (deprecated)" in str(options) - - -def test_options(): - """Sanity check for the Options constraint""" - options = Options(Real, {-0.5, 0.5, np.inf}, deprecated={-0.5}) - assert options.is_satisfied_by(-0.5) - assert options.is_satisfied_by(np.inf) - assert not options.is_satisfied_by(1.23) - - assert "-0.5 (deprecated)" in str(options) - - -@pytest.mark.parametrize( - "type, expected_type_name", - [ - (int, "int"), - (Integral, "int"), - (Real, "float"), - (np.ndarray, "numpy.ndarray"), - ], -) -def test_instances_of_type_human_readable(type, expected_type_name): - """Check the string representation of the _InstancesOf constraint.""" - constraint = _InstancesOf(type) - assert str(constraint) == f"an instance of '{expected_type_name}'" - - -def test_hasmethods(): - """Check the HasMethods constraint.""" - constraint = HasMethods(["a", "b"]) - - class _Good: - def a(self): - pass # pragma: no cover - - def b(self): - pass # pragma: no cover - - class _Bad: - def a(self): - pass # pragma: no cover - - assert constraint.is_satisfied_by(_Good()) - assert not constraint.is_satisfied_by(_Bad()) - assert str(constraint) == "an object implementing 'a' and 'b'" - - -@pytest.mark.parametrize( - "constraint", - [ - Interval(Real, None, 0, closed="left"), - Interval(Real, 0, None, closed="left"), - Interval(Real, None, None, closed="neither"), - StrOptions({"a", "b", "c"}), - MissingValues(), - MissingValues(numeric_only=True), - _VerboseHelper(), - HasMethods("fit"), - _IterablesNotString(), - _CVObjects(), - ], -) -def test_generate_invalid_param_val(constraint): - """Check that the value generated does not satisfy the constraint""" - bad_value = generate_invalid_param_val(constraint) - assert not constraint.is_satisfied_by(bad_value) - - -@pytest.mark.parametrize( - "integer_interval, real_interval", - [ - ( - Interval(Integral, None, 3, closed="right"), - Interval(RealNotInt, -5, 5, closed="both"), - ), - ( - Interval(Integral, None, 3, closed="right"), - Interval(RealNotInt, -5, 5, closed="neither"), - ), - ( - Interval(Integral, None, 3, closed="right"), - Interval(RealNotInt, 4, 5, closed="both"), - ), - ( - Interval(Integral, None, 3, closed="right"), - Interval(RealNotInt, 5, None, closed="left"), - ), - ( - Interval(Integral, None, 3, closed="right"), - Interval(RealNotInt, 4, None, closed="neither"), - ), - ( - Interval(Integral, 3, None, closed="left"), - Interval(RealNotInt, -5, 5, closed="both"), - ), - ( - Interval(Integral, 3, None, closed="left"), - Interval(RealNotInt, -5, 5, closed="neither"), - ), - ( - Interval(Integral, 3, None, closed="left"), - Interval(RealNotInt, 1, 2, closed="both"), - ), - ( - Interval(Integral, 3, None, closed="left"), - Interval(RealNotInt, None, -5, closed="left"), - ), - ( - Interval(Integral, 3, None, closed="left"), - Interval(RealNotInt, None, -4, closed="neither"), - ), - ( - Interval(Integral, -5, 5, closed="both"), - Interval(RealNotInt, None, 1, closed="right"), - ), - ( - Interval(Integral, -5, 5, closed="both"), - Interval(RealNotInt, 1, None, closed="left"), - ), - ( - Interval(Integral, -5, 5, closed="both"), - Interval(RealNotInt, -10, -4, closed="neither"), - ), - ( - Interval(Integral, -5, 5, closed="both"), - Interval(RealNotInt, -10, -4, closed="right"), - ), - ( - Interval(Integral, -5, 5, closed="neither"), - Interval(RealNotInt, 6, 10, closed="neither"), - ), - ( - Interval(Integral, -5, 5, closed="neither"), - Interval(RealNotInt, 6, 10, closed="left"), - ), - ( - Interval(Integral, 2, None, closed="left"), - Interval(RealNotInt, 0, 1, closed="both"), - ), - ( - Interval(Integral, 1, None, closed="left"), - Interval(RealNotInt, 0, 1, closed="both"), - ), - ], -) -def test_generate_invalid_param_val_2_intervals(integer_interval, real_interval): - """Check that the value generated for an interval constraint does not satisfy any of - the interval constraints. - """ - bad_value = generate_invalid_param_val(constraint=real_interval) - assert not real_interval.is_satisfied_by(bad_value) - assert not integer_interval.is_satisfied_by(bad_value) - - bad_value = generate_invalid_param_val(constraint=integer_interval) - assert not real_interval.is_satisfied_by(bad_value) - assert not integer_interval.is_satisfied_by(bad_value) - - -@pytest.mark.parametrize( - "constraint", - [ - _ArrayLikes(), - _InstancesOf(list), - _Callables(), - _NoneConstraint(), - _RandomStates(), - _SparseMatrices(), - _Booleans(), - Interval(Integral, None, None, closed="neither"), - ], -) -def test_generate_invalid_param_val_all_valid(constraint): - """Check that the function raises NotImplementedError when there's no invalid value - for the constraint. - """ - with pytest.raises(NotImplementedError): - generate_invalid_param_val(constraint) - - -@pytest.mark.parametrize( - "constraint", - [ - _ArrayLikes(), - _Callables(), - _InstancesOf(list), - _NoneConstraint(), - _RandomStates(), - _SparseMatrices(), - _Booleans(), - _VerboseHelper(), - MissingValues(), - MissingValues(numeric_only=True), - StrOptions({"a", "b", "c"}), - Options(Integral, {1, 2, 3}), - Interval(Integral, None, None, closed="neither"), - Interval(Integral, 0, 10, closed="neither"), - Interval(Integral, 0, None, closed="neither"), - Interval(Integral, None, 0, closed="neither"), - Interval(Real, 0, 1, closed="neither"), - Interval(Real, 0, None, closed="both"), - Interval(Real, None, 0, closed="right"), - HasMethods("fit"), - _IterablesNotString(), - _CVObjects(), - ], -) -def test_generate_valid_param(constraint): - """Check that the value generated does satisfy the constraint.""" - value = generate_valid_param(constraint) - assert constraint.is_satisfied_by(value) - - -@pytest.mark.parametrize( - "constraint_declaration, value", - [ - (Interval(Real, 0, 1, closed="both"), 0.42), - (Interval(Integral, 0, None, closed="neither"), 42), - (StrOptions({"a", "b", "c"}), "b"), - (Options(type, {np.float32, np.float64}), np.float64), - (callable, lambda x: x + 1), - (None, None), - ("array-like", [[1, 2], [3, 4]]), - ("array-like", np.array([[1, 2], [3, 4]])), - ("sparse matrix", csr_matrix([[1, 2], [3, 4]])), - ("random_state", 0), - ("random_state", np.random.RandomState(0)), - ("random_state", None), - (_Class, _Class()), - (int, 1), - (Real, 0.5), - ("boolean", False), - ("verbose", 1), - ("nan", np.nan), - (MissingValues(), -1), - (MissingValues(), -1.0), - (MissingValues(), 2**1028), - (MissingValues(), None), - (MissingValues(), float("nan")), - (MissingValues(), np.nan), - (MissingValues(), "missing"), - (HasMethods("fit"), _Estimator(a=0)), - ("cv_object", 5), - ], -) -def test_is_satisfied_by(constraint_declaration, value): - """Sanity check for the is_satisfied_by method""" - constraint = make_constraint(constraint_declaration) - assert constraint.is_satisfied_by(value) - - -@pytest.mark.parametrize( - "constraint_declaration, expected_constraint_class", - [ - (Interval(Real, 0, 1, closed="both"), Interval), - (StrOptions({"option1", "option2"}), StrOptions), - (Options(Real, {0.42, 1.23}), Options), - ("array-like", _ArrayLikes), - ("sparse matrix", _SparseMatrices), - ("random_state", _RandomStates), - (None, _NoneConstraint), - (callable, _Callables), - (int, _InstancesOf), - ("boolean", _Booleans), - ("verbose", _VerboseHelper), - (MissingValues(numeric_only=True), MissingValues), - (HasMethods("fit"), HasMethods), - ("cv_object", _CVObjects), - ("nan", _NanConstraint), - ], -) -def test_make_constraint(constraint_declaration, expected_constraint_class): - """Check that make_constraint dispatches to the appropriate constraint class""" - constraint = make_constraint(constraint_declaration) - assert constraint.__class__ is expected_constraint_class - - -def test_make_constraint_unknown(): - """Check that an informative error is raised when an unknown constraint is passed""" - with pytest.raises(ValueError, match="Unknown constraint"): - make_constraint("not a valid constraint") - - -def test_validate_params(): - """Check that validate_params works no matter how the arguments are passed""" - with pytest.raises( - InvalidParameterError, match="The 'a' parameter of _func must be" - ): - _func("wrong", c=1) - - with pytest.raises( - InvalidParameterError, match="The 'b' parameter of _func must be" - ): - _func(*[1, "wrong"], c=1) - - with pytest.raises( - InvalidParameterError, match="The 'c' parameter of _func must be" - ): - _func(1, **{"c": "wrong"}) - - with pytest.raises( - InvalidParameterError, match="The 'd' parameter of _func must be" - ): - _func(1, c=1, d="wrong") - - # check in the presence of extra positional and keyword args - with pytest.raises( - InvalidParameterError, match="The 'b' parameter of _func must be" - ): - _func(0, *["wrong", 2, 3], c=4, **{"e": 5}) - - with pytest.raises( - InvalidParameterError, match="The 'c' parameter of _func must be" - ): - _func(0, *[1, 2, 3], c="four", **{"e": 5}) - - -def test_validate_params_missing_params(): - """Check that no error is raised when there are parameters without - constraints - """ - - @validate_params({"a": [int]}, prefer_skip_nested_validation=True) - def func(a, b): - pass - - func(1, 2) - - -def test_decorate_validated_function(): - """Check that validate_params functions can be decorated""" - decorated_function = deprecated()(_func) - - with pytest.warns(FutureWarning, match="Function _func is deprecated"): - decorated_function(1, 2, c=3) - - # outer decorator does not interfere with validation - with pytest.warns(FutureWarning, match="Function _func is deprecated"): - with pytest.raises( - InvalidParameterError, match=r"The 'c' parameter of _func must be" - ): - decorated_function(1, 2, c="wrong") - - -def test_validate_params_method(): - """Check that validate_params works with methods""" - with pytest.raises( - InvalidParameterError, match="The 'a' parameter of _Class._method must be" - ): - _Class()._method("wrong") - - # validated method can be decorated - with pytest.warns(FutureWarning, match="Function _deprecated_method is deprecated"): - with pytest.raises( - InvalidParameterError, - match="The 'a' parameter of _Class._deprecated_method must be", - ): - _Class()._deprecated_method("wrong") - - -def test_validate_params_estimator(): - """Check that validate_params works with Estimator instances""" - # no validation in init - est = _Estimator("wrong") - - with pytest.raises( - InvalidParameterError, match="The 'a' parameter of _Estimator must be" - ): - est.fit() - - -def test_stroptions_deprecated_subset(): - """Check that the deprecated parameter must be a subset of options.""" - with pytest.raises(ValueError, match="deprecated options must be a subset"): - StrOptions({"a", "b", "c"}, deprecated={"a", "d"}) - - -def test_hidden_constraint(): - """Check that internal constraints are not exposed in the error message.""" - - @validate_params( - {"param": [Hidden(list), dict]}, prefer_skip_nested_validation=True - ) - def f(param): - pass - - # list and dict are valid params - f({"a": 1, "b": 2, "c": 3}) - f([1, 2, 3]) - - with pytest.raises( - InvalidParameterError, match="The 'param' parameter" - ) as exc_info: - f(param="bad") - - # the list option is not exposed in the error message - err_msg = str(exc_info.value) - assert "an instance of 'dict'" in err_msg - assert "an instance of 'list'" not in err_msg - - -def test_hidden_stroptions(): - """Check that we can have 2 StrOptions constraints, one being hidden.""" - - @validate_params( - {"param": [StrOptions({"auto"}), Hidden(StrOptions({"warn"}))]}, - prefer_skip_nested_validation=True, - ) - def f(param): - pass - - # "auto" and "warn" are valid params - f("auto") - f("warn") - - with pytest.raises( - InvalidParameterError, match="The 'param' parameter" - ) as exc_info: - f(param="bad") - - # the "warn" option is not exposed in the error message - err_msg = str(exc_info.value) - assert "auto" in err_msg - assert "warn" not in err_msg - - -def test_validate_params_set_param_constraints_attribute(): - """Check that the validate_params decorator properly sets the parameter constraints - as attribute of the decorated function/method. - """ - assert hasattr(_func, "_skl_parameter_constraints") - assert hasattr(_Class()._method, "_skl_parameter_constraints") - - -def test_boolean_constraint_deprecated_int(): - """Check that validate_params raise a deprecation message but still passes - validation when using an int for a parameter accepting a boolean. - """ - - @validate_params({"param": ["boolean"]}, prefer_skip_nested_validation=True) - def f(param): - pass - - # True/False and np.bool_(True/False) are valid params - f(True) - f(np.bool_(False)) - - -def test_no_validation(): - """Check that validation can be skipped for a parameter.""" - - @validate_params( - {"param1": [int, None], "param2": "no_validation"}, - prefer_skip_nested_validation=True, - ) - def f(param1=None, param2=None): - pass - - # param1 is validated - with pytest.raises(InvalidParameterError, match="The 'param1' parameter"): - f(param1="wrong") - - # param2 is not validated: any type is valid. - class SomeType: - pass - - f(param2=SomeType) - f(param2=SomeType()) - - -def test_pandas_na_constraint_with_pd_na(): - """Add a specific test for checking support for `pandas.NA`.""" - pd = pytest.importorskip("pandas") - - na_constraint = _PandasNAConstraint() - assert na_constraint.is_satisfied_by(pd.NA) - assert not na_constraint.is_satisfied_by(np.array([1, 2, 3])) - - -def test_iterable_not_string(): - """Check that a string does not satisfy the _IterableNotString constraint.""" - constraint = _IterablesNotString() - assert constraint.is_satisfied_by([1, 2, 3]) - assert constraint.is_satisfied_by(range(10)) - assert not constraint.is_satisfied_by("some string") - - -def test_cv_objects(): - """Check that the _CVObjects constraint accepts all current ways - to pass cv objects.""" - constraint = _CVObjects() - assert constraint.is_satisfied_by(5) - assert constraint.is_satisfied_by(LeaveOneOut()) - assert constraint.is_satisfied_by([([1, 2], [3, 4]), ([3, 4], [1, 2])]) - assert constraint.is_satisfied_by(None) - assert not constraint.is_satisfied_by("not a CV object") - - -def test_third_party_estimator(): - """Check that the validation from a scikit-learn estimator inherited by a third - party estimator does not impose a match between the dict of constraints and the - parameters of the estimator. - """ - - class ThirdPartyEstimator(_Estimator): - def __init__(self, b): - self.b = b - super().__init__(a=0) - - def fit(self, X=None, y=None): - super().fit(X, y) - - # does not raise, even though "b" is not in the constraints dict and "a" is not - # a parameter of the estimator. - ThirdPartyEstimator(b=0).fit() - - -def test_interval_real_not_int(): - """Check for the type RealNotInt in the Interval constraint.""" - constraint = Interval(RealNotInt, 0, 1, closed="both") - assert constraint.is_satisfied_by(1.0) - assert not constraint.is_satisfied_by(1) - - -def test_real_not_int(): - """Check for the RealNotInt type.""" - assert isinstance(1.0, RealNotInt) - assert not isinstance(1, RealNotInt) - assert isinstance(np.float64(1), RealNotInt) - assert not isinstance(np.int64(1), RealNotInt) - - -def test_skip_param_validation(): - """Check that param validation can be skipped using config_context.""" - - @validate_params({"a": [int]}, prefer_skip_nested_validation=True) - def f(a): - pass - - with pytest.raises(InvalidParameterError, match="The 'a' parameter"): - f(a="1") - - # does not raise - with config_context(skip_parameter_validation=True): - f(a="1") - - -@pytest.mark.parametrize("prefer_skip_nested_validation", [True, False]) -def test_skip_nested_validation(prefer_skip_nested_validation): - """Check that nested validation can be skipped.""" - - @validate_params({"a": [int]}, prefer_skip_nested_validation=True) - def f(a): - pass - - @validate_params( - {"b": [int]}, - prefer_skip_nested_validation=prefer_skip_nested_validation, - ) - def g(b): - # calls f with a bad parameter type - return f(a="invalid_param_value") - - # Validation for g is never skipped. - with pytest.raises(InvalidParameterError, match="The 'b' parameter"): - g(b="invalid_param_value") - - if prefer_skip_nested_validation: - g(b=1) # does not raise because inner f is not validated - else: - with pytest.raises(InvalidParameterError, match="The 'a' parameter"): - g(b=1) - - -@pytest.mark.parametrize( - "skip_parameter_validation, prefer_skip_nested_validation, expected_skipped", - [ - (True, True, True), - (True, False, True), - (False, True, True), - (False, False, False), - ], -) -def test_skip_nested_validation_and_config_context( - skip_parameter_validation, prefer_skip_nested_validation, expected_skipped -): - """Check interaction between global skip and local skip.""" - - @validate_params( - {"a": [int]}, prefer_skip_nested_validation=prefer_skip_nested_validation - ) - def g(a): - return get_config()["skip_parameter_validation"] - - with config_context(skip_parameter_validation=skip_parameter_validation): - actual_skipped = g(1) - - assert actual_skipped == expected_skipped diff --git a/pixi.lock b/pixi.lock index 7a151ce5f..864c5b2fd 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,4 +1,4 @@ -version: 5 +version: 6 environments: ci-py310-min-dependencies: channels: @@ -11,247 +11,251 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - 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[tool.pixi.feature.sklearn-1-4.dependencies] scikit-learn = "~=1.4.0" +[tool.pixi.feature.sklearn-1-5.dependencies] +scikit-learn = "~=1.5.0" + [tool.pixi.feature.py310.dependencies] python = "~=3.10.0" @@ -169,6 +172,9 @@ python = "~=3.11.0" [tool.pixi.feature.py312.dependencies] python = "~=3.12.0" +[tool.pixi.feature.py313.dependencies] +python = "~=3.13.0" + [tool.pixi.feature.tests.dependencies] packaging = ">=23.2,<25" pytest = ">=7.2.2,<9" @@ -191,7 +197,8 @@ tests = { cmd = "pytest -vsl --cov=imblearn --cov-report=xml imblearn" } [tool.pixi.environments] linters = ["linters"] docs = ["optional", "docs", "tensorflow"] -tests = ["optional", "tests", "tensorflow"] +optional = ["optional"] +tests = ["tests", "tensorflow"] dev = ["dev", "optional", "docs", "linters", "tests", "tensorflow"] ci-py310-min-dependencies = ["py310", "min-dependencies", "tests"] @@ -199,13 +206,13 @@ ci-py310-min-optional-dependencies = ["py310", "min-dependencies", "min-optional ci-py310-min-keras = ["py310", "min-keras", "tests"] ci-py310-min-tensorflow = ["py310", "min-tensorflow", "tests"] -ci-py311-sklearn-1-3 = ["py311", "sklearn-1-3", "tests"] ci-py311-sklearn-1-4 = ["py311", "sklearn-1-4", "tests"] +ci-py311-sklearn-1-5 = ["py311", "sklearn-1-5", "tests"] ci-py311-latest-tensorflow = ["py311", "tensorflow", "tests"] ci-py311-latest-keras = ["py311", "keras", "tests"] -ci-py312-latest-dependencies = ["py312", "tests"] -ci-py312-latest-optional-dependencies = ["py312", "optional", "tests"] +ci-py313-latest-dependencies = ["py313", "tests"] +ci-py313-latest-optional-dependencies = ["py313", "optional", "tests"] [tool.black] line-length = 88