scikit-learn · thomasjpfan · Jun 7, 2021 · Jun 2, 2021 · Jun 2, 2021 · Jun 3, 2021
diff --git a/sklearn/cluster/_bicluster.py b/sklearn/cluster/_bicluster.py
@@ -255,6 +255,9 @@ class SpectralCoclustering(BaseSpectral):
     column_labels_ : array-like of shape (n_cols,)
         The bicluster label of each column.
 
+    biclusters_ : tuple of two ndarrays
+        The tuple contains the `rows_` and `columns_` arrays.
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralCoclustering

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -1177,6 +1177,10 @@ class GridSearchCV(BaseSearchCV):
     multimetric_ : bool
         Whether or not the scorers compute several metrics.
 
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. This is present only if ``refit`` is specified and
+        the underlying estimator is a classifier.
+
     Notes
     -----
     The parameters selected are those that maximize the score of the left out
@@ -1499,6 +1503,10 @@ class RandomizedSearchCV(BaseSearchCV):
     multimetric_ : bool
         Whether or not the scorers compute several metrics.
 
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. This is present only if ``refit`` is specified and
+        the underlying estimator is a classifier.
+
     Notes
     -----
     The parameters selected are those that maximize the score of the held-out

diff --git a/sklearn/tests/test_docstring_parameters.py b/sklearn/tests/test_docstring_parameters.py
@@ -224,6 +224,7 @@ def test_fit_docstring_attributes(name, Estimator):
                'StackingRegressor', 'TfidfVectorizer', 'VotingClassifier',
                'VotingRegressor', 'SequentialFeatureSelector',
                'HalvingGridSearchCV', 'HalvingRandomSearchCV'}
+
     if Estimator.__name__ in IGNORED or Estimator.__name__.startswith('_'):
         pytest.skip("Estimator cannot be fit easily to test fit attributes")
 
@@ -284,10 +285,34 @@ def test_fit_docstring_attributes(name, Estimator):
         with ignore_warnings(category=FutureWarning):
             assert hasattr(est, attr.name)
 
-    fit_attr = [k for k in est.__dict__.keys() if k.endswith('_')
-                and not k.startswith('_')]
+    fit_attr = _get_all_fitted_attributes(est)
     fit_attr_names = [attr.name for attr in attributes]
     undocumented_attrs = set(fit_attr).difference(fit_attr_names)
     undocumented_attrs = set(undocumented_attrs).difference(skipped_attributes)
     assert not undocumented_attrs,\
         "Undocumented attributes: {}".format(undocumented_attrs)
+
+
+def _get_all_fitted_attributes(estimator):
+    "Get all the fitted attributes of an estimator including properties"
+    # attributes
+    fit_attr = list(estimator.__dict__.keys())
+
+    # properties
+    with warnings.catch_warnings():
+        warnings.filterwarnings("error", category=FutureWarning)
+
+        for name in dir(estimator.__class__):
+            obj = getattr(estimator.__class__, name)
+            if not isinstance(obj, property):
+                continue
+
+            # ignore properties that raises an AttributeError and deprecated
+            # properties
+            try:
+                getattr(estimator, name)
+            except (AttributeError, FutureWarning):
+                continue
+            fit_attr.append(name)
+
+    return [k for k in fit_attr if k.endswith('_') and not k.startswith('_')]