scikit-learn · amueller · Oct 11, 2018 · Oct 1, 2018 · Oct 1, 2018 · Oct 1, 2018
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
@@ -1512,23 +1512,4 @@ To be removed in 0.22
    :template: deprecated_function.rst
 
    covariance.graph_lasso
-   datasets.fetch_mldata
-
-
-To be removed in 0.21
----------------------
-
-.. autosummary::
-   :toctree: generated/
-   :template: deprecated_class.rst
-
-   linear_model.RandomizedLasso
-   linear_model.RandomizedLogisticRegression
-   neighbors.LSHForest
-
-.. autosummary::
-   :toctree: generated/
-   :template: deprecated_function.rst
-
-   datasets.load_mlcomp
-   linear_model.lasso_stability_path
+   datasets.fetch_mldata
diff --git a/sklearn/cluster/hierarchical.py b/sklearn/cluster/hierarchical.py
@@ -339,9 +339,8 @@ def ward_tree(X, connectivity=None, n_clusters=None, return_distance=False):
 
 
 # single average and complete linkage
-def linkage_tree(X, connectivity=None, n_components='deprecated',
-                 n_clusters=None, linkage='complete', affinity="euclidean",
-                 return_distance=False):
+def linkage_tree(X, connectivity=None, n_clusters=None, linkage='complete',
+                 affinity="euclidean", return_distance=False):
     """Linkage agglomerative clustering based on a Feature matrix.
 
     The inertia matrix uses a Heapq-based representation.
@@ -362,9 +361,6 @@ def linkage_tree(X, connectivity=None, n_components='deprecated',
         be symmetric and only the upper triangular half is used.
         Default is None, i.e, the Ward algorithm is unstructured.
 
-    n_components : int (optional)
-        The number of connected components in the graph.
-
     n_clusters : int (optional)
         Stop early the construction of the tree at n_clusters. This is
         useful to decrease computation time if the number of clusters is
@@ -420,10 +416,6 @@ def linkage_tree(X, connectivity=None, n_components='deprecated',
     --------
     ward_tree : hierarchical clustering with ward linkage
     """
-    if n_components != 'deprecated':
-        warnings.warn("n_components was deprecated in 0.19"
-                      "will be removed in 0.21", DeprecationWarning)
-
     X = np.asarray(X)
     if X.ndim == 1:
         X = np.reshape(X, (-1, 1))

diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py
@@ -38,21 +38,6 @@
 from sklearn.datasets import make_moons, make_circles
 
 
-def test_deprecation_of_n_components_in_linkage_tree():
-    rng = np.random.RandomState(0)
-    X = rng.randn(50, 100)
-    # Test for warning of deprecation of n_components in linkage_tree
-    children, n_nodes, n_leaves, parent = assert_warns(DeprecationWarning,
-                                                       linkage_tree,
-                                                       X.T,
-                                                       n_components=10)
-    children_t, n_nodes_t, n_leaves_t, parent_t = linkage_tree(X.T)
-    assert_array_equal(children, children_t)
-    assert_equal(n_nodes, n_nodes_t)
-    assert_equal(n_leaves, n_leaves_t)
-    assert_equal(parent, parent_t)
-
-
 def test_linkage_misc():
     # Misc tests on linkage
     rng = np.random.RandomState(42)

diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
@@ -586,12 +586,6 @@ def __init__(self, alphas=4, n_refinements=4, cv='warn', tol=1e-4,
         self.cv = cv
         self.n_jobs = n_jobs
 
-    @property
-    @deprecated("Attribute grid_scores was deprecated in version 0.19 and "
-                "will be removed in 0.21. Use ``grid_scores_`` instead")
-    def grid_scores(self):
-        return self.grid_scores_
-
     def fit(self, X, y=None):
         """Fits the GraphicalLasso covariance model to X.
 

diff --git a/sklearn/covariance/tests/test_graph_lasso.py b/sklearn/covariance/tests/test_graph_lasso.py
@@ -9,7 +9,6 @@
 
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_array_less
-from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import ignore_warnings
 
 from sklearn.covariance import (graph_lasso, GraphLasso, GraphLassoCV,
@@ -19,8 +18,6 @@
 from sklearn.utils import check_random_state
 from sklearn import datasets
 
-from numpy.testing import assert_equal
-
 
 @ignore_warnings(category=DeprecationWarning)
 def test_graph_lasso(random_state=0):
@@ -141,25 +138,3 @@ def test_graph_lasso_cv(random_state=1):
 
     # Smoke test with specified alphas
     GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1).fit(X)
-
-
-@ignore_warnings(category=DeprecationWarning)
-@pytest.mark.filterwarnings('ignore: You should specify a value')  # 0.22
-def test_deprecated_grid_scores(random_state=1):
-    dim = 5
-    n_samples = 6
-    random_state = check_random_state(random_state)
-    prec = make_sparse_spd_matrix(dim, alpha=.96,
-                                  random_state=random_state)
-    cov = linalg.inv(prec)
-    X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
-    graph_lasso = GraphLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1)
-    graph_lasso.fit(X)
-
-    depr_message = ("Attribute grid_scores was deprecated in version "
-                    "0.19 and will be removed in 0.21. Use "
-                    "``grid_scores_`` instead")
-
-    assert_warns_message(DeprecationWarning, depr_message,
-                         lambda: graph_lasso.grid_scores)
-    assert_equal(graph_lasso.grid_scores, graph_lasso.grid_scores_)
diff --git a/sklearn/covariance/tests/test_graphical_lasso.py b/sklearn/covariance/tests/test_graphical_lasso.py
@@ -8,17 +8,13 @@
 
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_array_less
-from sklearn.utils.testing import assert_warns_message
 
 from sklearn.covariance import (graphical_lasso, GraphicalLasso,
                                 GraphicalLassoCV, empirical_covariance)
 from sklearn.datasets.samples_generator import make_sparse_spd_matrix
 from sklearn.externals.six.moves import StringIO
 from sklearn.utils import check_random_state
 from sklearn import datasets
-from sklearn.utils.fixes import PY3_OR_LATER
-
-from numpy.testing import assert_equal
 
 
 def test_graphical_lasso(random_state=0):
@@ -137,25 +133,3 @@ def test_graphical_lasso_cv(random_state=1):
 
     # Smoke test with specified alphas
     GraphicalLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1).fit(X)
-
-
-@pytest.mark.filterwarnings('ignore: You should specify a value')  # 0.22
-@pytest.mark.skipif(not PY3_OR_LATER,
-                    reason='On Python 2 DeprecationWarning is not issued for some unkown reason.')
-def test_deprecated_grid_scores(random_state=1):
-    dim = 5
-    n_samples = 6
-    random_state = check_random_state(random_state)
-    prec = make_sparse_spd_matrix(dim, alpha=.96,
-                                  random_state=random_state)
-    cov = linalg.inv(prec)
-    X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
-    graphical_lasso = GraphicalLassoCV(alphas=[0.8, 0.5], tol=1e-1, n_jobs=1)
-    graphical_lasso.fit(X)
-
-    depr_message = ("Attribute grid_scores was deprecated in version "
-                    "0.19 and will be removed in 0.21. Use "
-                    "``grid_scores_`` instead")
-
-    with pytest.warns(DeprecationWarning, match=depr_message):
-        assert_equal(graphical_lasso.grid_scores, graphical_lasso.grid_scores_)
diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py
@@ -17,7 +17,6 @@
 from .base import clear_data_home
 from .covtype import fetch_covtype
 from .kddcup99 import fetch_kddcup99
-from .mlcomp import load_mlcomp
 from .lfw import fetch_lfw_pairs
 from .lfw import fetch_lfw_people
 from .twenty_newsgroups import fetch_20newsgroups
@@ -75,7 +74,6 @@
            'load_iris',
            'load_breast_cancer',
            'load_linnerud',
-           'load_mlcomp',
            'load_sample_image',
            'load_sample_images',
            'load_svmlight_file',

diff --git a/sklearn/datasets/mlcomp.py b/sklearn/datasets/mlcomp.py
diff --git a/sklearn/decomposition/fastica_.py b/sklearn/decomposition/fastica_.py
@@ -18,7 +18,6 @@
 from ..exceptions import ConvergenceWarning
 from ..externals import six
 from ..externals.six import moves
-from ..externals.six import string_types
 from ..utils import check_array, as_float_array, check_random_state
 from ..utils.validation import check_is_fitted
 from ..utils.validation import FLOAT_DTYPES
@@ -553,29 +552,22 @@ def fit(self, X, y=None):
         self._fit(X, compute_sources=False)
         return self
 
-    def transform(self, X, y='deprecated', copy=True):
+    def transform(self, X, copy=True):
         """Recover the sources from X (apply the unmixing matrix).
 
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
             Data to transform, where n_samples is the number of samples
             and n_features is the number of features.
-        y : (ignored)
-            .. deprecated:: 0.19
-               This parameter will be removed in 0.21.
+
         copy : bool (optional)
             If False, data passed to fit are overwritten. Defaults to True.
 
         Returns
         -------
         X_new : array-like, shape (n_samples, n_components)
         """
-        if not isinstance(y, string_types) or y != 'deprecated':
-            warnings.warn("The parameter y on transform() is "
-                          "deprecated since 0.19 and will be removed in 0.21",
-                          DeprecationWarning)
-
         check_is_fitted(self, 'mixing_')
 
         X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)