10000 DOC Ensures that BaggingClassifier passes numpydoc validation #20377 by jbsilva · Pull Request #20407 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that BaggingClassifier passes numpydoc validation #20377 #20407

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Merged
merged 7 commits into from
Jun 29, 2021
Merged

DOC Ensures that BaggingClassifier passes numpydoc validation #20377 #20407

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57224dc
Remove BaggingClassifier from the docstring ignore list.
jbsilva Jun 26, 2021
f6dbfcb
Make summary fit in a single line.
jbsilva Jun 26, 2021
31184c8
Fix sections order.
jbsilva Jun 26, 2021
6894a1c
Remove double line break.
jbsilva Jun 26, 2021
5ba6a8e
Add See Also section.
jbsilva Jun 26, 2021
871ebf9
Add descriptions to the Return sections.
jbsilva Jun 26, 2021
4eb55d6
Merge branch 'main' into Issue_20308_BaggingClassifier
ogrisel Jun 28, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -12,7 +12,6 @@
"AdditiveChi2Sampler",
"AffinityPropagation",
"AgglomerativeClustering",
"BaggingClassifier",
"BaggingRegressor",
"BernoulliNB",
"BernoulliRBM",
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32 changes: 18 additions & 14 deletions sklearn/ensemble/_bagging.py
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Expand Up @@ -235,8 +235,7 @@ def __init__(
self.verbose = verbose

def fit(self, X, y, sample_weight=None):
"""Build a Bagging ensemble of estimators from the training
set (X, y).
"""Build a Bagging ensemble of estimators from the training set (X, y).

Parameters
----------
Expand All @@ -256,6 +255,7 @@ def fit(self, X, y, sample_weight=None):
Returns
-------
self : object
Fitted estimator.
"""
return self._fit(X, y, self.max_samples, sample_weight=sample_weight)

Expand Down Expand Up @@ -291,6 +291,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
Returns
-------
self : object
Fitted estimator.
"""
random_state = check_random_state(self.random_state)

Expand Down Expand Up @@ -605,18 +606,9 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):
`oob_decision_function_` might contain NaN. This attribute exists
only when ``oob_score`` is True.

Examples
See Also
--------
>>> from sklearn.svm import SVC
>>> from sklearn.ensemble import BaggingClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, n_features=4,
... n_informative=2, n_redundant=0,
... random_state=0, shuffle=False)
>>> clf = BaggingClassifier(base_estimator=SVC(),
... n_estimators=10, random_state=0).fit(X, y)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
BaggingRegressor : A Bagging regressor.

References
----------
Expand All @@ -633,6 +625,19 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):

.. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine
Learning and Knowledge Discovery in Databases, 346-361, 2012.

Examples
--------
>>> from sklearn.svm import SVC
>>> from sklearn.ensemble import BaggingClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, n_features=4,
... n_informative=2, n_redundant=0,
... random_state=0, shuffle=False)
>>> clf = BaggingClassifier(base_estimator=SVC(),
... n_estimators=10, random_state=0).fit(X, y)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
"""

def __init__(
Expand Down Expand Up @@ -866,7 +871,6 @@ def decision_function(self, X):
to the classes in sorted order, as they appear in the attribute
``classes_``. Regression and binary classification are special
cases with ``k == 1``, otherwise ``k==n_classes``.

"""
check_is_fitted(self)

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0