Applied pre-commit, committed style changes

src/obnb/label/split/__init__.py

@@ -1,4 +1,5 @@
 """Genearting data splits from the labelset collection."""
+from obnb.label.split.explicit import ByTermSplit
 from obnb.label.split.holdout import (
     AllHoldout,
     RandomRatioHoldout,
@@ -10,9 +11,6 @@
     RatioPartition,
     ThresholdPartition,
 )
-from obnb.label.split.explicit import (
-    ByTermSplit
-)
 
 __all__ = classes = [
     "AllHoldout",

src/obnb/label/split/base.py

@@ -41,10 +41,9 @@ def __call__(
         ids: List[str],
         y: np.ndarray,
     ) -> Iterator[Tuple[np.ndarray, ...]]:
-        """
-        Split the input ids into multiple splits, e.g. a test, train, validation
-        split. The means by which this splitting occurs should be defined by
-        classes that inherit from this base class.
+        """Split the input ids into multiple splits, e.g. a test, train, validation
+        split. The means by which this splitting occurs should be defined by classes
+        that inherit from this base class.
 
         Note:
             Inheriting classes should yield the value instead of returning it,
@@ -58,8 +57,9 @@ def __call__(
         Yields:
             Iterator of splits. Each split is a tuple of numpy arrays, where
             each array contains the IDs of the entities in the split.
+
         """
-        
+
         raise NotImplementedError
 
 

src/obnb/label/split/explicit.py

@@ -1,36 +1,34 @@
 from typing import Iterable, Iterator, List, Tuple
 
 import numpy
+from numpy import ndarray
 
 from obnb.label.collection import LabelsetCollection
 from obnb.label.split.base import BaseSplit
-from numpy import ndarray
 
 
 class ByTermSplit(BaseSplit):
-    """
-    Produces splits based on an explicit list of terms. Genes
-    which match each term will be placed in the split corresponding
-    to that term.
-
-    A split with a single term '*' will act as a catch-all for any
-    genes that weren't matched by any of the other splits. This would
-    allow you to, e.g., only retain a specific set of genes in the
-    training set, and place all others in the test set.
-
-    Note that if the '*' split is not provided, any genes that don't
-    match any of the other splits will not be present in the returned
-    splits at all.
+    """Produces splits based on an explicit list of terms. Genes which match each term
+    will be placed in the split corresponding to that term.
+
+    A split with a single term '*' will act as a catch-all for any genes that
+    weren't matched by any of the other splits. This would allow you to, e.g.,
+    only retain a specific set of genes in the training set, and place all
+    others in the test set.
+
+    Note that if the '*' split is not provided, any genes that don't match any
+    of the other splits will not be present in the returned splits at all.
+
     """
 
     def __init__(
-            self, labelset: LabelsetCollection,
-            split_terms: Iterable[Iterable[str]],
-            exclusive: bool = False
-        ) -> None:
-        """
-        Initialize ByTermSplit object with reference labels and terms into
-        which to create splits.
+        self,
+        labelset: LabelsetCollection,
+        split_terms: Iterable[Iterable[str]],
+        exclusive: bool = False,
+    ) -> None:
+        """Initialize ByTermSplit object with reference labels and terms into which to
+        create splits.
 
         Args:
             labelset: LabelsetCollection object containing terms for each
@@ -40,6 +38,7 @@ def __init__(
                 terms that should be matched to place a gene in that split.
             exclusive: if True, a gene can occur only once across all the
                 splits; it will belong to the first split in which it occurs.
+
         """
 
         self.labelset = labelset
@@ -57,7 +56,7 @@ def __init__(
         self.long_df = df.melt(
             id_vars=["Name"],
             value_vars=df.columns.difference(["Info", "Size", "Name"]),
-            value_name="Value"
+            value_name="Value",
         ).dropna(subset=["Value"])
 
         # group gene id and aggregate terms into a set, which makes
@@ -72,12 +71,11 @@ def __init__(
         super().__init__()
 
     def __call__(self, ids: List[str], y: ndarray) -> Iterator[Tuple[ndarray, ...]]:
-        """
-        For each gene ID, look up the term it's associated with
-        in the labelset, and place it in the corresponding split.
+        """For each gene ID, look up the term it's associated with in the labelset, and
+        place it in the corresponding split.
+
+        Returns as many splits as there are elements in the split_terms tuple.
 
-        Returns as many splits as there are elements in the split_terms
-        tuple.
         """
 
         # alias field to shorten the code below
@@ -87,10 +85,13 @@ def __call__(self, ids: List[str], y: ndarray) -> Iterator[Tuple[ndarray, ...]]:
         # term in the split
         result = [
             (
-                set([
-                    id for id in ids
+                {
+                    id
+                    for id in ids
                     if gdf[gdf["GeneID"] == str(id)]["Terms"].values[0] & terms
-                ]) if terms != {"*"} else None
+                }
+                if terms != {"*"}
+                else None
             )
             for terms in self.split_terms
         ]
@@ -100,13 +101,14 @@ def __call__(self, ids: List[str], y: ndarray) -> Iterator[Tuple[ndarray, ...]]:
         # the other splits
         for idx in range(len(result)):
             if result[idx] is None:
-                result[idx] = set([
-                    id for id in ids
+                result[idx] = {
+                    id
+                    for id in ids
                     if not any(
                         gdf[gdf["GeneID"] == str(id)]["Terms"].values[0] & terms
                         for terms in self.split_terms
                     )
-                ])
+                }
 
         if self.exclusive:
             # if exclusive, remove genes in the current split that occurred
@@ -120,4 +122,4 @@ def __call__(self, ids: List[str], y: ndarray) -> Iterator[Tuple[ndarray, ...]]:
         # numpy arrays. we cast to list because leaving it as a set would cause
         # numpy.asarray() to create an array with a single element, the set,
         # rather than an array with the elements of the list
-        yield tuple([ numpy.asarray(list(x)) for x in result ])
+        yield tuple([numpy.asarray(list(x)) for x in result])