scikit-learn · glemaitre · Sep 13, 2021 · Sep 9, 2021 · Sep 10, 2021 · Sep 10, 2021
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -9,7 +9,6 @@
 
 # List of modules ignored when checking for numpydoc validation.
 DOCSTRING_IGNORE_LIST = [
-    "GridSearchCV",
     "HalvingGridSearchCV",
     "HalvingRandomSearchCV",
     "HashingVectorizer",

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -432,7 +432,7 @@ def _pairwise(self):
         return getattr(self.estimator, "_pairwise", False)
 
     def score(self, X, y=None):
-        """Returns the score on the given data, if the estimator has been refit.
+        """Return the score on the given data, if the estimator has been refit.
 
         This uses the score defined by ``scoring`` where provided, and the
         ``best_estimator_.score`` method otherwise.
@@ -451,6 +451,8 @@ def score(self, X, y=None):
         Returns
         -------
         score : float
+            The score defined by ``scoring`` if provided, and the
+            ``best_estimator_.score`` method otherwise.
         """
         _check_refit(self, "score")
         check_is_fitted(self)
@@ -491,6 +493,7 @@ def score_samples(self, X):
         Returns
         -------
         y_score : ndarray of shape (n_samples,)
+            The ``best_estimator_.score_samples`` method.
         """
         check_is_fitted(self)
         return self.best_estimator_.score_samples(X)
@@ -508,6 +511,11 @@ def predict(self, X):
             Must fulfill the input assumptions of the
             underlying estimator.
 
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted labels or values for `X` based on the estimator with
+            the best found parameters.
         """
         check_is_fitted(self)
         return self.best_estimator_.predict(X)
@@ -525,6 +533,12 @@ def predict_proba(self, X):
             Must fulfill the input assumptions of the
             underlying estimator.
 
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes)
+            Predicted class probabilities for `X` based on the estimator with
+            the best found parameters. The order of the classes corresponds
+            to that in the fitted attribute :term:`classes_`.
         """
         check_is_fitted(self)
         return self.best_estimator_.predict_proba(X)
@@ -542,6 +556,12 @@ def predict_log_proba(self, X):
             Must fulfill the input assumptions of the
             underlying estimator.
 
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes)
+            Predicted class log-probabilities for `X` based on the estimator
+            with the best found parameters. The order of the classes
+            corresponds to that in the fitted attribute :term:`classes_`.
         """
         check_is_fitted(self)
         return self.best_estimator_.predict_log_proba(X)
@@ -559,6 +579,12 @@ def decision_function(self, X):
             Must fulfill the input assumptions of the
             underlying estimator.
 
+        Returns
+        -------
+        y_score : ndarray of shape (n_samples,) or (n_samples, n_classes) \
+                or (n_samples, n_classes * (n_classes-1) / 2)
+            Result of the decision function for `X` based on the estimator with
+            the best found parameters.
         """
         check_is_fitted(self)
         return self.best_estimator_.decision_function(X)
@@ -576,6 +602,11 @@ def transform(self, X):
             Must fulfill the input assumptions of the
             underlying estimator.
 
+        Returns
+        -------
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            `X` transformed in the new space based on the estimator with
+            the best found parameters.
         """
         check_is_fitted(self)
         return self.best_estimator_.transform(X)
@@ -593,12 +624,21 @@ def inverse_transform(self, Xt):
             Must fulfill the input assumptions of the
             underlying estimator.
 
+        Returns
+        -------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Result of the `inverse_transform` function for `Xt` based on the
+            estimator with the best found parameters.
         """
         check_is_fitted(self)
         return self.best_estimator_.inverse_transform(Xt)
 
     @property
     def n_features_in_(self):
+        """Number of features seen during :term:`fit`.
+
+        Only available when `refit=True`.
+        """
         # For consistency with other estimators we raise a AttributeError so
         # that hasattr() fails if the search estimator isn't fitted.
         try:
@@ -614,6 +654,10 @@ def n_features_in_(self):
 
     @property
     def classes_(self):
+        """Class labels.
+
+        Only available when `refit=True` and the estimator is a classifier.
+        """
         _estimator_has("classes_")(self)
         return self.best_estimator_.classes_
 
@@ -733,7 +777,12 @@ def fit(self, X, y=None, *, groups=None, **fit_params):
             instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).
 
         **fit_params : dict of str -> object
-            Parameters passed to the ``fit`` method of the estimator
+            Parameters passed to the ``fit`` method of the estimator.
+
+        Returns
+        -------
+        self : object
+            Instance of fitted estimator.
         """
         estimator = self.estimator
         refit_metric = "score"
@@ -1002,7 +1051,7 @@ class GridSearchCV(BaseSearchCV):
 
     Parameters
     ----------
-    estimator : estimator object.
+    estimator : estimator object
         This is assumed to implement the scikit-learn estimator interface.
         Either estimator needs to provide a ``score`` function,
         or ``scoring`` must be passed.
@@ -1137,25 +1186,6 @@ class GridSearchCV(BaseSearchCV):
         .. versionchanged:: 0.21
             Default value was changed from ``True`` to ``False``
 
-
-    Examples
-    --------
-    >>> from sklearn import svm, datasets
-    >>> from sklearn.model_selection import GridSearchCV
-    >>> iris = datasets.load_iris()
-    >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
-    >>> svc = svm.SVC()
-    >>> clf = GridSearchCV(svc, parameters)
-    >>> clf.fit(iris.data, iris.target)
-    GridSearchCV(estimator=SVC(),
-                 param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})
-    >>> sorted(clf.cv_results_.keys())
-    ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
-     'param_C', 'param_kernel', 'params',...
-     'rank_test_score', 'split0_test_score',...
-     'split2_test_score', ...
-     'std_fit_time', 'std_score_time', 'std_test_score']
-
     Attributes
     ----------
     cv_results_ : dict of numpy (masked) ndarrays
@@ -1308,6 +1338,23 @@ class GridSearchCV(BaseSearchCV):
     sklearn.metrics.make_scorer : Make a scorer from a performance metric or
         loss function.
 
+    Examples
+    --------
+    >>> from sklearn import svm, datasets
+    >>> from sklearn.model_selection import GridSearchCV
+    >>> iris = datasets.load_iris()
+    >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
+    >>> svc = svm.SVC()
+    >>> clf = GridSearchCV(svc, parameters)
+    >>> clf.fit(iris.data, iris.target)
+    GridSearchCV(estimator=SVC(),
+                 param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})
+    >>> sorted(clf.cv_results_.keys())
+    ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
+     'param_C', 'param_kernel', 'params',...
+     'rank_test_score', 'split0_test_score',...
+     'split2_test_score', ...
+     'std_fit_time', 'std_score_time', 'std_test_score']
     """
 
     _required_parameters = ["estimator", "param_grid"]