8000 DOC Ensures that SequentialFeatureSelector passes numpydoc validation by genvalen · Pull Request #21035 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that SequentialFeatureSelector passes numpydoc validation #21035

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Merged
merged 3 commits into from
Sep 14, 2021
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DOC Ensures that SequentialFeatureSelector passes numpydoc validation #21035

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a09b50e
Remove SequentialFeatureSelector from DOCSTRING_IGNORE_LIST
genvalen Sep 14, 2021
4e8889d
Ensure SequentialFeatureSelector passes numpydoc validation
genvalen Sep 14, 2021
eacef8f
Add option to See Also section
genvalen Sep 14, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -51,7 +51,6 @@
"SGDOneClassSVM",
"SGDRegressor",
"SelfTrainingClassifier",
"SequentialFeatureSelector",
"SimpleImputer",
"SparseRandomProjection",
"SpectralBiclustering",
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9 changes: 7 additions & 2 deletions sklearn/feature_selection/_sequential.py
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Expand Up @@ -97,6 +97,8 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator

See Also
--------
GenericUnivariateSelect : Univariate feature selector with configurable
strategy.
RFE : Recursive feature elimination based on importance weights.
RFECV : Recursive feature elimination based on importance weights, with
automatic selection of the number of features.
Expand Down Expand Up @@ -139,19 +141,22 @@ def __init__(
self.n_jobs = n_jobs

def fit(self, X, y=None):
"""Learn the features to select.
"""Learn the features to select from X.

Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vectors.
Training vectors, where `n_samples` is the number of samples and
`n_features` is the number of predictors.

y : array-like of shape (n_samples,), default=None
Target values. This parameter may be ignored for
unsupervised learning.

Returns
-------
self : object
Returns the instance itself.
"""
tags = self._get_tags()
X = self._validate_data(
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0