8000 DOC Ensures that OneVsRestClassifier passes numpydoc validation by bharatr21 · Pull Request #21014 · scikit-learn/scikit-learn · GitHub
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Sep 13, 2021
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DOC Ensures that OneVsRestClassifier passes numpydoc validation #21014

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DOC Ensures that OneVsRestClassifier passes numpydoc validation
bharatr21 Sep 11, 2021
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Apply suggestions from code review
glemaitre Sep 13, 2021
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Merge branch 'main' into ovr
glemaitre Sep 13, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,6 @@
"NearestCentroid",
"NeighborhoodComponentsAnalysis",
"Normalizer",
"OneVsRestClassifier",
"OrdinalEncoder",
"OrthogonalMatchingPursuit",
"OrthogonalMatchingPursuitCV",
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36 changes: 22 additions & 14 deletions sklearn/multiclass.py
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Expand Up @@ -275,6 +275,13 @@ class OneVsRestClassifier(

.. versionadded:: 1.0

See Also
--------
MultiOutputClassifier : Alternate way of extending an estimator for
multilabel classification.
sklearn.preprocessing.MultiLabelBinarizer : Transform iterable of iterables
to binary indicator matrix.

Examples
--------
>>> import numpy as np
Expand All @@ -292,13 +299,6 @@ class OneVsRestClassifier(
>>> clf = OneVsRestClassifier(SVC()).fit(X, y)
>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])
array([2, 0, 1])

See Also
--------
sklearn.multioutput.MultiOutputClassifier : Alternate way of extending an
estimator for multilabel classification.
sklearn.preprocessing.MultiLabelBinarizer : Transform iterable of iterables
to binary indicator matrix.
"""

def __init__(self, estimator, *, n_jobs=None):
Expand All @@ -319,7 +319,8 @@ def fit(self, X, y):

Returns
-------
self
self : object
Instance of fitted estimator.
"""
# A sparse LabelBinarizer, with sparse_output=True, has been shown to
# outperform or match a dense label binarizer in all cases and has also
Expand Down Expand Up @@ -355,7 +356,7 @@ def fit(self, X, y):

@available_if(_estimators_has("partial_fit"))
def partial_fit(self, X, y, classes=None):
"""Partially fit underlying estimators
"""Partially fit underlying estimators.

Should be used when memory is inefficient to train all data.
Chunks of data can be passed in several iteration.
Expand All @@ -378,7 +379,8 @@ def partial_fit(self, X, y, classes=None):

Returns
-------
self
self : object
Instance of partially fitted estimator.
"""
if _check_partial_fit_first_call(self, classes):
if not hasattr(self.estimator, "partial_fit"):
Expand Down Expand Up @@ -477,6 +479,7 @@ def predict_proba(self, X):
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.

Returns
-------
Expand All @@ -501,18 +504,22 @@ def predict_proba(self, X):

@available_if(_estimators_has("decision_function"))
def decision_function(self, X):
"""Returns the distance of each sample from the decision boundary for
each class. This can only be used with estimators which implement the
decision_function method.
"""Decision function for the OneVsRestClassifier.

Return the distance of each sample from the decision boundary for each
class. This can only be used with estimators which implement the
`decision_function` method.

Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data.

Returns
-------
T : array-like of shape (n_samples, n_classes) or (n_samples,) for \
binary classification.
Result of calling `decision_function` on the final estimator.

.. versionchanged:: 0.19
output shape changed to ``(n_samples,)`` to conform to
Expand All @@ -527,11 +534,12 @@ def decision_function(self, X):

@property
def multilabel_(self):
"""Whether this is a multilabel classifier"""
"""Whether this is a multilabel classifier."""
return self.label_binarizer_.y_type_.startswith("multilabel")

@property
def n_classes_(self):
"""Number of classes."""
return len(self.classes_)

# TODO: Remove coef_ attribute in 1.1
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0