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DOC Ensures that GenericUnivariateSelect passes numpydoc validation #20495

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Merged
merged 4 commits into from
Jul 11, 2021
Merged

DOC Ensures that GenericUnivariateSelect passes numpydoc validation #20495

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eb5db94
Remove GenericUnivariateSelect from docstring_ignore_list
embandera Jul 2, 2021
48ea84f
Ensure GenericUnivariateSelect passes numpydoc validation in _base.py
embandera Jul 10, 2021
f1151b3
Ensure GenericUnivariateSelect passes numpydoc validation in _univari…
embandera Jul 10, 2021
5ee498d
Merge branch 'main' into GenericUnivariateSelect_numpydoc
ogrisel Jul 11, 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 @@ -42,7 +42,6 @@
"GaussianNB",
"GaussianProcessRegressor",
"GaussianRandomProjection",
"GenericUnivariateSelect",
"GradientBoostingClassifier",
"GradientBoostingRegressor",
"GraphicalLasso",
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25 changes: 13 additions & 12 deletions sklearn/feature_selection/_univariate_selection.py
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Expand Up @@ -392,6 +392,7 @@ def fit(self, X, y):
Returns
-------
self : object
Returns the instance itself.
"""
X, y = self._validate_data(
X, y, accept_sparse=["csr", "csc"], multi_output=True
Expand Down Expand Up @@ -860,18 +861,6 @@ class GenericUnivariateSelect(_BaseFilter):

.. versionadded:: 0.24

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import GenericUnivariateSelect, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> transformer = GenericUnivariateSelect(chi2, mode='k_best', param=20)
>>> X_new = transformer.fit_transform(X, y)
>>> X_new.shape
(569, 20)

See Also
--------
f_classif : ANOVA F-value between label/feature for classification tasks.
Expand All @@ -885,6 +874,18 @@ class GenericUnivariateSelect(_BaseFilter):
SelectFpr : Select features based on a false positive rate test.
SelectFdr : Select features based on an estimated false discovery rate.
SelectFwe : Select features based on family-wise error rate.

Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import GenericUnivariateSelect, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> transformer = GenericUnivariateSelect(chi2, mode='k_best', param=20)
>>> X_new = transformer.fit_transform(X, y)
>>> X_new.shape
(569, 20)
"""

_selection_modes: dict = {
Expand Down