DOC Ensures that SelectFwe passes numpydoc validation (#20986)

jmloyola · glemaitre · adrinjalali · commit ad127a53fd05 · 2021-09-23T10:40:03.000+02:00
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -61,7 +61,6 @@
     "SGDOneClassSVM",
     "SGDRegressor",
     "SelectFromModel",
-    "SelectFwe",
     "SelfTrainingClassifier",
     "SequentialFeatureSelector",
     "SimpleImputer",
diff --git a/sklearn/feature_selection/_univariate_selection.py b/sklearn/feature_selection/_univariate_selection.py
@@ -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>`.
 
@@ -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,)
@@ -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):
