8000 DOC Ensures that SelectFwe passes numpydoc validation by jmloyola · Pull Request #20986 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that SelectFwe passes numpydoc validation #20986

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Merged
merged 3 commits into from
Sep 9, 2021
Merged

DOC Ensures that SelectFwe passes numpydoc validation #20986

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9ce43dd
Remove SelectFwe from DOCSTRING_IGNORE_LIST
jmloyola Sep 8, 2021
ce04847
Fix numpydocs from SelectFwe
jmloyola Sep 8, 2021
14910bc
Merge branch 'main' into fix_numpydoc_selectfwe
glemaitre Sep 9, 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 @@ -61,7 +61,6 @@
"SGDOneClassSVM",
"SGDRegressor",
"SelectFromModel",
"SelectFwe",
"SelfTrainingClassifier",
"SequentialFeatureSelector",
"SimpleImputer",
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24 changes: 12 additions & 12 deletions sklearn/feature_selection/_univariate_selection.py
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Expand Up @@ -785,7 +785,7 @@ def _get_support_mask(self):


class SelectFwe(_BaseFilter):
"""Filter: Select the p-values corresponding to Family-wise error rate
"""Filter: Select the p-values corresponding to Family-wise error rate.

Read more in the :ref:`User Guide <univariate_feature_selection>`.

Expand All @@ -800,17 +800,6 @@ class SelectFwe(_BaseFilter):
alpha : float, default=5e-2
The highest uncorrected p-value for features to keep.

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import SelectFwe, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFwe(chi2, alpha=0.01).fit_transform(X, y)
>>> X_new.shape
(569, 15)

Attributes
----------
scores_ : array-like of shape (n_features,)
Expand Down Expand Up @@ -842,6 +831,17 @@ class SelectFwe(_BaseFilter):
SelectFdr : Select features based on an estimated false discovery rate.
GenericUnivariateSelect : Univariate feature selector with configurable
mode.

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import SelectFwe, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFwe(chi2, alpha=0.01).fit_transform(X, y)
>>> X_new.shape
(569, 15)
"""

def __init__(self, score_func=f_classif, *, alpha=5e-2):
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0