scikit-learn · qinhanmin2014 · Dec 9, 2019 · Dec 5, 2019 · Dec 5, 2019 · Dec 5, 2019
diff --git a/sklearn/base.py b/sklearn/base.py
@@ -406,34 +406,17 @@ def score(self, X, y, sample_weight=None):
 
         Notes
         -----
-        The R2 score used when calling ``score`` on a regressor will use
+        The R2 score used when calling ``score`` on a regressor uses
         ``multioutput='uniform_average'`` from version 0.23 to keep consistent
-        with :func:`~sklearn.metrics.r2_score`. This will influence the
-        ``score`` method of all the multioutput regressors (except for
-        :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the
-        default value manually and avoid the warning, please either call
-        :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with
-        :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses
-        ``multioutput='uniform_average'``).
+        with default value of :func:`~sklearn.metrics.r2_score`.
+        This influences the ``score`` method of all the multioutput
+        regressors (except for
+        :class:`~sklearn.multioutput.MultiOutputRegressor`).
         """
 
         from .metrics import r2_score
-        from .metrics._regression import _check_reg_targets
         y_pred = self.predict(X)
-        # XXX: Remove the check in 0.23
-        y_type, _, _, _ = _check_reg_targets(y, y_pred, None)
-        if y_type == 'continuous-multioutput':
-            warnings.warn("The default value of multioutput (not exposed in "
-                          "score method) will change from 'variance_weighted' "
-                          "to 'uniform_average' in 0.23 to keep consistent "
-                          "with 'metrics.r2_score'. To specify the default "
-                          "value manually and avoid the warning, please "
-                          "either call 'metrics.r2_score' directly or make a "
-                          "custom scorer with 'metrics.make_scorer' (the "
-                          "built-in scorer 'r2' uses "
-                          "multioutput='uniform_average').", FutureWarning)
-        return r2_score(y, y_pred, sample_weight=sample_weight,
-                        multioutput='variance_weighted')
+        return r2_score(y, y_pred, sample_weight=sample_weight)
 
 
 class ClusterMixin:

diff --git a/sklearn/cluster/_hierarchical.py b/sklearn/cluster/_hierarchical.py
@@ -787,13 +787,6 @@ def __init__(self, n_clusters=2, affinity="euclidean",
         self.linkage = linkage
         self.affinity = affinity
 
-    @deprecated("The ``n_components_`` attribute was deprecated "
-                "in favor of ``n_connected_components_`` in 0.21 "
-                "and will be removed in 0.23.")
-    @property
-    def n_components_(self):
-        return self.n_connected_components_
-
     def fit(self, X, y=None):
         """Fit the hierarchical clustering from features, or distance matrix.
 

diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py
@@ -750,17 +750,3 @@ def test_dist_threshold_invalid_parameters():
         AgglomerativeClustering(n_clusters=None,
                                 distance_threshold=1,
                                 compute_full_tree=False).fit(X)
-
-
-def test_n_components_deprecation():
-    # Test that a Deprecation warning is thrown when n_components_
-    # attribute is accessed
-
-    X = np.array([[1, 2], [1, 4], [1, 0], [4, 2]])
-    agc = AgglomerativeClustering().fit(X)
-
-    match = ("``n_components_`` attribute was deprecated "
-             "in favor of ``n_connected_components_``")
-    with pytest.warns(FutureWarning, match=match):
-        n = agc.n_components_
-    assert n == agc.n_connected_components_
diff --git a/sklearn/cross_decomposition/tests/test_pls.py b/sklearn/cross_decomposition/tests/test_pls.py
@@ -426,7 +426,6 @@ def test_pls_errors():
                              clf.fit, X, Y)
 
 
-@pytest.mark.filterwarnings('ignore: The default value of multioutput')  # 0.23
 def test_pls_scaling():
     # sanity check for scale=True
     n_samples = 1000

diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py
@@ -215,8 +215,6 @@ def test_kernel_pca_invalid_kernel():
         kpca.fit(X_fit)
 
 
-# 0.23. warning about tol not having its correct default value.
-@pytest.mark.filterwarnings('ignore:max_iter and tol parameters have been')
 def test_gridsearch_pipeline():
     # Test if we can do a grid-search to find parameters to separate
     # circles with a perceptron model.
@@ -231,8 +229,6 @@ def test_gridsearch_pipeline():
     assert grid_search.best_score_ == 1
 
 
-# 0.23. warning about tol not having its correct default value.
-@pytest.mark.filterwarnings('ignore:max_iter and tol parameters have been')
 def test_gridsearch_pipeline_precomputed():
     # Test if we can do a grid-search to find parameters to separate
     # circles with a perceptron model using a precomputed kernel.
@@ -248,8 +244,6 @@ def test_gridsearch_pipeline_precomputed():
     assert grid_search.best_score_ == 1
 
 
-# 0.23. warning about tol not having its correct default value.
-@pytest.mark.filterwarnings('ignore:max_iter and tol parameters have been')
 def test_nested_circles():
     # Test the linear separability of the first 2D KPCA transform
     X, y = make_circles(n_samples=400, factor=.3, noise=.05,