From 62a4a82d9293722fd90713be481adbf905736f8e Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Thu, 19 Sep 2019 10:27:58 -0400
Subject: [PATCH 1/6] deprecated notanarray

---
 sklearn/utils/estimator_checks.py            | 23 ++++++++++++++++----
 sklearn/utils/tests/test_deprecated_utils.py |  6 +++++
 sklearn/utils/tests/test_multiclass.py       | 22 +++++--------------
 sklearn/utils/tests/test_validation.py       |  4 ++--
 4 files changed, 33 insertions(+), 22 deletions(-)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index 5a96a4260ceb9..ee2a4c5b08ec1 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -520,6 +520,21 @@ def set_checking_parameters(estimator):
 
 
 class NotAnArray:
+    #TODO: remove in 0.24
+
+    def __init__(self, data):
+        warnings.warn(
+            "The NotAnArray classe is deprecated in 0.22 and will be "
+            "removed in 0.24.",
+            DeprecationWarning
+        )
+        self.data = data
+
+    def __array__(self, dtype=None):
+        return self.data
+
+
+class _NotAnArray:
     """An object that is convertible to an array
 
     Parameters
@@ -1107,8 +1122,8 @@ def check_transformer_data_not_an_array(name, transformer):
     # like NMF
     X -= X.min() - .1
     X = pairwise_estimator_convert_X(X, transformer)
-    this_X = NotAnArray(X)
-    this_y = NotAnArray(np.asarray(y))
+    this_X = _NotAnArray(X)
+    this_y = _NotAnArray(np.asarray(y))
     _check_transformer(name, transformer, this_X, this_y)
     # try the same with some list
     _check_transformer(name, transformer, X.tolist(), y.tolist())
@@ -2303,8 +2318,8 @@ def check_estimators_data_not_an_array(name, estimator_orig, X, y):
     set_random_state(estimator_1)
     set_random_state(estimator_2)
 
-    y_ = NotAnArray(np.asarray(y))
-    X_ = NotAnArray(np.asarray(X))
+    y_ = _NotAnArray(np.asarray(y))
+    X_ = _NotAnArray(np.asarray(X))
 
     # fit
     estimator_1.fit(X_, y_)
diff --git a/sklearn/utils/tests/test_deprecated_utils.py b/sklearn/utils/tests/test_deprecated_utils.py
index 83dc599e0e7be..806fc9ac153eb 100644
--- a/sklearn/utils/tests/test_deprecated_utils.py
+++ b/sklearn/utils/tests/test_deprecated_utils.py
@@ -3,6 +3,7 @@
 
 from sklearn.dummy import DummyClassifier
 from sklearn.utils.estimator_checks import choose_check_classifiers_labels
+from sklearn.utils.estimator_checks import NotAnArray
 from sklearn.utils.estimator_checks import enforce_estimator_tags_y
 
 
@@ -17,3 +18,8 @@ def test_choose_check_classifiers_labels_deprecated():
 def test_enforce_estimator_tags_y():
     with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
         enforce_estimator_tags_y(DummyClassifier(), np.array([0, 1]))
+
+
+def test_notanarray_deprecated():
+    with pytest.warns(DeprecationWarning, match="will be removed in 0.24"):
+        NotAnArray([1, 2])
\ No newline at end of file
diff --git a/sklearn/utils/tests/test_multiclass.py b/sklearn/utils/tests/test_multiclass.py
index d4c4a7ceef5fd..f0a57ffa664ae 100644
--- a/sklearn/utils/tests/test_multiclass.py
+++ b/sklearn/utils/tests/test_multiclass.py
@@ -17,6 +17,7 @@
 from sklearn.utils.testing import assert_raises_regex
 from sklearn.utils.testing import assert_allclose
 from sklearn.utils.testing import SkipTest
+from sklearn.utils.estimator_checks import _NotAnArray
 
 from sklearn.utils.multiclass import unique_labels
 from sklearn.utils.multiclass import is_multilabel
@@ -31,17 +32,6 @@
 from sklearn import datasets
 
 
-class NotAnArray:
-    """An object that is convertable to an array. This is useful to
-    simulate a Pandas timeseries."""
-
-    def __init__(self, data):
-        self.data = data
-
-    def __array__(self, dtype=None):
-        return self.data
-
-
 EXAMPLES = {
     'multilabel-indicator': [
         # valid when the data is formatted as sparse or dense, identified
@@ -58,7 +48,7 @@ def __array__(self, dtype=None):
         # Only valid when data is dense
         np.array([[-1, 1], [1, -1]]),
         np.array([[-3, 3], [3, -3]]),
-        NotAnArray(np.array([[-3, 3], [3, -3]])),
+        _NotAnArray(np.array([[-3, 3], [3, -3]])),
     ],
     'multiclass': [
         [1, 0, 2, 2, 1, 4, 2, 4, 4, 4],
@@ -68,7 +58,7 @@ def __array__(self, dtype=None):
         np.array([1, 0, 2], dtype=np.float),
         np.array([1, 0, 2], dtype=np.float32),
         np.array([[1], [0], [2]]),
-        NotAnArray(np.array([1, 0, 2])),
+        _NotAnArray(np.array([1, 0, 2])),
         [0, 1, 2],
         ['a', 'b', 'c'],
         np.array(['a', 'b', 'c']),
@@ -85,7 +75,7 @@ def __array__(self, dtype=None):
         np.array([['a', 'b'], ['c', 'd']]),
         np.array([['a', 'b'], ['c', 'd']], dtype=object),
         np.array([[1, 0, 2]]),
-        NotAnArray(np.array([[1, 0, 2]])),
+        _NotAnArray(np.array([[1, 0, 2]])),
     ],
     'binary': [
         [0, 1],
@@ -99,7 +89,7 @@ def __array__(self, dtype=None):
         np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.float),
         np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.float32),
         np.array([[0], [1]]),
-        NotAnArray(np.array([[0], [1]])),
+        _NotAnArray(np.array([[0], [1]])),
         [1, -1],
         [3, 5],
         ['a'],
@@ -153,7 +143,7 @@ def __array__(self, dtype=None):
     [[1], [2], [0, 1]],
     [(), (2), (0, 1)],
     np.array([[], [1, 2]], dtype='object'),
-    NotAnArray(np.array([[], [1, 2]], dtype='object'))
+    _NotAnArray(np.array([[], [1, 2]], dtype='object'))
 ]
 
 
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 0f7ffe9a3e4f0..d1fb44e2d1710 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -25,7 +25,7 @@
 from sklearn.utils import check_X_y
 from sklearn.utils import deprecated
 from sklearn.utils.mocking import MockDataFrame
-from sklearn.utils.estimator_checks import NotAnArray
+from sklearn.utils.estimator_checks import _NotAnArray
 from sklearn.random_projection import sparse_random_matrix
 from sklearn.linear_model import ARDRegression
 from sklearn.neighbors import KNeighborsClassifier
@@ -301,7 +301,7 @@ def test_check_array():
     assert_raises(ValueError, check_array, X_ndim.tolist())
     check_array(X_ndim.tolist(), allow_nd=True)  # doesn't raise
     # convert weird stuff to arrays
-    X_no_array = NotAnArray(X_dense)
+    X_no_array = _NotAnArray(X_dense)
     result = check_array(X_no_array)
     assert isinstance(result, np.ndarray)
 

From 47c1bc3e0a20fb23a49197ac4579bb93586174a5 Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Thu, 19 Sep 2019 10:31:18 -0400
Subject: [PATCH 2/6] deprecated is_public_parameter

---
 sklearn/utils/estimator_checks.py            |  8 +++++++-
 sklearn/utils/tests/test_deprecated_utils.py | 10 ++++++++--
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index ee2a4c5b08ec1..516bdc2e7848a 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -859,7 +859,13 @@ def check_dict_unchanged(name, estimator_orig):
                 'Estimator changes __dict__ during %s' % method)
 
 
+@deprecated("is_public_parameter is deprecated in version "
+            "0.22 and will be removed in version 0.24.")
 def is_public_parameter(attr):
+    return _is_public_parameter(attr)
+
+
+def _is_public_parameter(attr):
     return not (attr.startswith('_') or attr.endswith('_'))
 
 
@@ -890,7 +896,7 @@ def check_dont_overwrite_parameters(name, estimator_orig):
     dict_after_fit = estimator.__dict__
 
     public_keys_after_fit = [key for key in dict_after_fit.keys()
-                             if is_public_parameter(key)]
+                             if _is_public_parameter(key)]
 
     attrs_added_by_fit = [key for key in public_keys_after_fit
                           if key not in dict_before_fit.keys()]
diff --git a/sklearn/utils/tests/test_deprecated_utils.py b/sklearn/utils/tests/test_deprecated_utils.py
index 806fc9ac153eb..c92cc1d4d62b4 100644
--- a/sklearn/utils/tests/test_deprecated_utils.py
+++ b/sklearn/utils/tests/test_deprecated_utils.py
@@ -5,6 +5,7 @@
 from sklearn.utils.estimator_checks import choose_check_classifiers_labels
 from sklearn.utils.estimator_checks import NotAnArray
 from sklearn.utils.estimator_checks import enforce_estimator_tags_y
+from sklearn.utils.estimator_checks import is_public_parameter
 
 
 # This file tests the utils that are deprecated
@@ -20,6 +21,11 @@ def test_enforce_estimator_tags_y():
         enforce_estimator_tags_y(DummyClassifier(), np.array([0, 1]))
 
 
-def test_notanarray_deprecated():
+def test_notanarray():
     with pytest.warns(DeprecationWarning, match="will be removed in 0.24"):
-        NotAnArray([1, 2])
\ No newline at end of file
+        NotAnArray([1, 2])
+
+
+def test_is_public_parameter():
+    with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
+        is_public_parameter('hello')

From 217f32c253ea82270f110e70f27d11cbb62b5d1f Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Thu, 19 Sep 2019 10:34:19 -0400
Subject: [PATCH 3/6] deprecated pairwise_estimator_convert_X

---
 sklearn/utils/estimator_checks.py            | 64 +++++++++++---------
 sklearn/utils/tests/test_deprecated_utils.py |  6 ++
 2 files changed, 41 insertions(+), 29 deletions(-)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index 516bdc2e7848a..80a8b7057e719 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -584,7 +584,13 @@ def _is_pairwise_metric(estimator):
     return bool(metric == 'precomputed')
 
 
+@deprecated("pairwise_estimator_convert_X is deprecated in version "
+            "0.22 and will be removed in version 0.24.")
 def pairwise_estimator_convert_X(X, estimator, kernel=linear_kernel):
+    return _pairwise_estimator_convert_X(X, estimator, kernel)
+
+
+def _pairwise_estimator_convert_X(X, estimator, kernel=linear_kernel):
 
     if _is_pairwise_metric(estimator):
         return pairwise_distances(X, metric='euclidean')
@@ -631,7 +637,7 @@ def check_estimator_sparse_data(name, estimator_orig):
     rng = np.random.RandomState(0)
     X = rng.rand(40, 10)
     X[X < .8] = 0
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     X_csr = sparse.csr_matrix(X)
     tags = _safe_tags(estimator_orig)
     if tags['binary_only']:
@@ -696,7 +702,7 @@ def check_sample_weights_pandas_series(name, estimator_orig):
             X = np.array([[1, 1], [1, 2], [1, 3], [1, 4],
                           [2, 1], [2, 2], [2, 3], [2, 4],
                           [3, 1], [3, 2], [3, 3], [3, 4]])
-            X = pd.DataFrame(pairwise_estimator_convert_X(X, estimator_orig))
+            X = pd.DataFrame(_pairwise_estimator_convert_X(X, estimator_orig))
             y = pd.Series([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2])
             weights = pd.Series([1] * 12)
             if _safe_tags(estimator, "multioutput_only"):
@@ -720,7 +726,7 @@ def check_sample_weights_list(name, estimator_orig):
         estimator = clone(estimator_orig)
         rnd = np.random.RandomState(0)
         n_samples = 30
-        X = pairwise_estimator_convert_X(rnd.uniform(size=(n_samples, 3)),
+        X = _pairwise_estimator_convert_X(rnd.uniform(size=(n_samples, 3)),
                                          estimator_orig)
         if _safe_tags(estimator, 'binary_only'):
             y = np.arange(n_samples) % 2
@@ -774,7 +780,7 @@ def check_sample_weights_invariance(name, estimator_orig):
 def check_dtype_object(name, estimator_orig):
     # check that estimators treat dtype object as numeric if possible
     rng = np.random.RandomState(0)
-    X = pairwise_estimator_convert_X(rng.rand(40, 10), estimator_orig)
+    X = _pairwise_estimator_convert_X(rng.rand(40, 10), estimator_orig)
     X = X.astype(object)
     tags = _safe_tags(estimator_orig)
     if tags['binary_only']:
@@ -833,7 +839,7 @@ def check_dict_unchanged(name, estimator_orig):
     else:
         X = 2 * rnd.uniform(size=(20, 3))
 
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
 
     y = X[:, 0].astype(np.int)
     estimator = clone(estimator_orig)
@@ -878,7 +884,7 @@ def check_dont_overwrite_parameters(name, estimator_orig):
     estimator = clone(estimator_orig)
     rnd = np.random.RandomState(0)
     X = 3 * rnd.uniform(size=(20, 3))
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     y = X[:, 0].astype(np.int)
     if _safe_tags(estimator, 'binary_only'):
         y[y == 2] = 1
@@ -929,7 +935,7 @@ def check_fit2d_predict1d(name, estimator_orig):
     # check by fitting a 2d array and predicting with a 1d array
     rnd = np.random.RandomState(0)
     X = 3 * rnd.uniform(size=(20, 3))
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     y = X[:, 0].astype(np.int)
     tags = _safe_tags(estimator_orig)
     if tags['binary_only']:
@@ -980,7 +986,7 @@ def check_methods_subset_invariance(name, estimator_orig):
     # on mini batches or the whole set
     rnd = np.random.RandomState(0)
     X = 3 * rnd.uniform(size=(20, 3))
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     y = X[:, 0].astype(np.int)
     if _safe_tags(estimator_orig, 'binary_only'):
         y[y == 2] = 1
@@ -1022,7 +1028,7 @@ def check_fit2d_1sample(name, estimator_orig):
     # the number of samples or the number of classes.
     rnd = np.random.RandomState(0)
     X = 3 * rnd.uniform(size=(1, 10))
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
 
     y = X[:, 0].astype(np.int)
     estimator = clone(estimator_orig)
@@ -1055,7 +1061,7 @@ def check_fit2d_1feature(name, estimator_orig):
     # informative message
     rnd = np.random.RandomState(0)
     X = 3 * rnd.uniform(size=(10, 1))
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     y = X[:, 0].astype(np.int)
     estimator = clone(estimator_orig)
     y = _enforce_estimator_tags_y(estimator, y)
@@ -1111,7 +1117,7 @@ def check_transformer_general(name, transformer, readonly_memmap=False):
                       random_state=0, n_features=2, cluster_std=0.1)
     X = StandardScaler().fit_transform(X)
     X -= X.min()
-    X = pairwise_estimator_convert_X(X, transformer)
+    X = _pairwise_estimator_convert_X(X, transformer)
 
     if readonly_memmap:
         X, y = create_memmap_backed_data([X, y])
@@ -1127,7 +1133,7 @@ def check_transformer_data_not_an_array(name, transformer):
     # We need to make sure that we have non negative data, for things
     # like NMF
     X -= X.min() - .1
-    X = pairwise_estimator_convert_X(X, transformer)
+    X = _pairwise_estimator_convert_X(X, transformer)
     this_X = _NotAnArray(X)
     this_y = _NotAnArray(np.asarray(y))
     _check_transformer(name, transformer, this_X, this_y)
@@ -1233,7 +1239,7 @@ def check_pipeline_consistency(name, estimator_orig):
     X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                       random_state=0, n_features=2, cluster_std=0.1)
     X -= X.min()
-    X = pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
+    X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
     estimator = clone(estimator_orig)
     y = _enforce_estimator_tags_y(estimator, y)
     set_random_state(estimator)
@@ -1259,7 +1265,7 @@ def check_fit_score_takes_y(name, estimator_orig):
     rnd = np.random.RandomState(0)
     n_samples = 30
     X = rnd.uniform(size=(n_samples, 3))
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     if _safe_tags(estimator_orig, 'binary_only'):
         y = np.arange(n_samples) % 2
     else:
@@ -1288,7 +1294,7 @@ def check_fit_score_takes_y(name, estimator_orig):
 def check_estimators_dtypes(name, estimator_orig):
     rnd = np.random.RandomState(0)
     X_train_32 = 3 * rnd.uniform(size=(20, 5)).astype(np.float32)
-    X_train_32 = pairwise_estimator_convert_X(X_train_32, estimator_orig)
+    X_train_32 = _pairwise_estimator_convert_X(X_train_32, estimator_orig)
     X_train_64 = X_train_32.astype(np.float64)
     X_train_int_64 = X_train_32.astype(np.int64)
     X_train_int_32 = X_train_32.astype(np.int32)
@@ -1336,7 +1342,7 @@ def check_estimators_empty_data_messages(name, estimator_orig):
 def check_estimators_nan_inf(name, estimator_orig):
     # Checks that Estimator X's do not contain NaN or inf.
     rnd = np.random.RandomState(0)
-    X_train_finite = pairwise_estimator_convert_X(rnd.uniform(size=(10, 3)),
+    X_train_finite = _pairwise_estimator_convert_X(rnd.uniform(size=(10, 3)),
                                                   estimator_orig)
     X_train_nan = rnd.uniform(size=(10, 3))
     X_train_nan[0, 0] = np.nan
@@ -1427,7 +1433,7 @@ def check_estimators_pickle(name, estimator_orig):
 
     # some estimators can't do features less than 0
     X -= X.min()
-    X = pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
+    X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
 
     tags = _safe_tags(estimator_orig)
     # include NaN values when the estimator should deal with them
@@ -1625,7 +1631,7 @@ def check_classifiers_train(name, classifier_orig, readonly_memmap=False):
         n_classes = len(classes)
         n_samples, n_features = X.shape
         classifier = clone(classifier_orig)
-        X = pairwise_estimator_convert_X(X, classifier)
+        X = _pairwise_estimator_convert_X(X, classifier)
         y = _enforce_estimator_tags_y(classifier, y)
 
         set_random_state(classifier)
@@ -1828,7 +1834,7 @@ def check_estimators_fit_returns_self(name, estimator_orig,
     X, y = make_blobs(random_state=0, n_samples=21, centers=n_centers)
     # some want non-negative input
     X -= X.min()
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
 
     estimator = clone(estimator_orig)
     y = _enforce_estimator_tags_y(estimator, y)
@@ -1864,7 +1870,7 @@ def check_supervised_y_2d(name, estimator_orig):
         return
     rnd = np.random.RandomState(0)
     n_samples = 30
-    X = pairwise_estimator_convert_X(
+    X = _pairwise_estimator_convert_X(
         rnd.uniform(size=(n_samples, 3)), estimator_orig
     )
     if tags['binary_only']:
@@ -1964,8 +1970,8 @@ def check_classifiers_classes(name, classifier_orig):
     X_binary = X_multiclass[y_multiclass != 2]
     y_binary = y_multiclass[y_multiclass != 2]
 
-    X_multiclass = pairwise_estimator_convert_X(X_multiclass, classifier_orig)
-    X_binary = pairwise_estimator_convert_X(X_binary, classifier_orig)
+    X_multiclass = _pairwise_estimator_convert_X(X_multiclass, classifier_orig)
+    X_binary = _pairwise_estimator_convert_X(X_binary, classifier_orig)
 
     labels_multiclass = ["one", "two", "three"]
     labels_binary = ["one", "two"]
@@ -1991,7 +1997,7 @@ def check_classifiers_classes(name, classifier_orig):
 @ignore_warnings(category=(DeprecationWarning, FutureWarning))
 def check_regressors_int(name, regressor_orig):
     X, _ = _boston_subset()
-    X = pairwise_estimator_convert_X(X[:50], regressor_orig)
+    X = _pairwise_estimator_convert_X(X[:50], regressor_orig)
     rnd = np.random.RandomState(0)
     y = rnd.randint(3, size=X.shape[0])
     y = _enforce_estimator_tags_y(regressor_orig, y)
@@ -2019,7 +2025,7 @@ def check_regressors_int(name, regressor_orig):
 @ignore_warnings(category=(DeprecationWarning, FutureWarning))
 def check_regressors_train(name, regressor_orig, readonly_memmap=False):
     X, y = _boston_subset()
-    X = pairwise_estimator_convert_X(X, regressor_orig)
+    X = _pairwise_estimator_convert_X(X, regressor_orig)
     y = StandardScaler().fit_transform(y.reshape(-1, 1))  # X is already scaled
     y = y.ravel()
     regressor = clone(regressor_orig)
@@ -2068,7 +2074,7 @@ def check_regressors_no_decision_function(name, regressor_orig):
     regressor = clone(regressor_orig)
 
     X = rng.normal(size=(10, 4))
-    X = pairwise_estimator_convert_X(X, regressor_orig)
+    X = _pairwise_estimator_convert_X(X, regressor_orig)
     y = _enforce_estimator_tags_y(regressor, X[:, 0])
 
     if hasattr(regressor, "n_components"):
@@ -2207,7 +2213,7 @@ def check_estimators_overwrite_params(name, estimator_orig):
     X, y = make_blobs(random_state=0, n_samples=21, centers=n_centers)
     # some want non-negative input
     X -= X.min()
-    X = pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
+    X = _pairwise_estimator_convert_X(X, estimator_orig, kernel=rbf_kernel)
     estimator = clone(estimator_orig)
     y = _enforce_estimator_tags_y(estimator, y)
 
@@ -2298,7 +2304,7 @@ def check_sparsify_coefficients(name, estimator_orig):
 def check_classifier_data_not_an_array(name, estimator_orig):
     X = np.array([[3, 0], [0, 1], [0, 2], [1, 1], [1, 2], [2, 1],
                   [0, 3], [1, 0], [2, 0], [4, 4], [2, 3], [3, 2]])
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     y = [1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2]
     y = _enforce_estimator_tags_y(estimator_orig, y)
     check_estimators_data_not_an_array(name, estimator_orig, X, y)
@@ -2307,7 +2313,7 @@ def check_classifier_data_not_an_array(name, estimator_orig):
 @ignore_warnings(category=DeprecationWarning)
 def check_regressor_data_not_an_array(name, estimator_orig):
     X, y = _boston_subset(n_samples=50)
-    X = pairwise_estimator_convert_X(X, estimator_orig)
+    X = _pairwise_estimator_convert_X(X, estimator_orig)
     y = _enforce_estimator_tags_y(estimator_orig, y)
     check_estimators_data_not_an_array(name, estimator_orig, X, y)
 
@@ -2659,7 +2665,7 @@ def check_fit_idempotent(name, estimator_orig):
 
     n_samples = 100
     X = rng.normal(loc=100, size=(n_samples, 2))
-    X = pairwise_estimator_convert_X(X, estimator)
+    X = _pairwise_estimator_convert_X(X, estimator)
     if is_regressor(estimator_orig):
         y = rng.normal(size=n_samples)
     else:
diff --git a/sklearn/utils/tests/test_deprecated_utils.py b/sklearn/utils/tests/test_deprecated_utils.py
index c92cc1d4d62b4..65a8a92d1cc32 100644
--- a/sklearn/utils/tests/test_deprecated_utils.py
+++ b/sklearn/utils/tests/test_deprecated_utils.py
@@ -6,6 +6,7 @@
 from sklearn.utils.estimator_checks import NotAnArray
 from sklearn.utils.estimator_checks import enforce_estimator_tags_y
 from sklearn.utils.estimator_checks import is_public_parameter
+from sklearn.utils.estimator_checks import pairwise_estimator_convert_X
 
 
 # This file tests the utils that are deprecated
@@ -29,3 +30,8 @@ def test_notanarray():
 def test_is_public_parameter():
     with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
         is_public_parameter('hello')
+
+
+def test_pairwise_estimator_convert_X():
+    with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
+        pairwise_estimator_convert_X([[1, 2]], DummyClassifier())

From aaa439bed2e41268650ef6cbde0b92d1df7c0578 Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Thu, 19 Sep 2019 10:37:12 -0400
Subject: [PATCH 4/6] deprecated checking_parameters

---
 sklearn/tests/test_common.py                 | 4 ++--
 sklearn/utils/estimator_checks.py            | 6 ++++++
 sklearn/utils/tests/test_deprecated_utils.py | 6 ++++++
 sklearn/utils/tests/test_estimator_checks.py | 6 +++---
 4 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index 9114a8e5f7631..3045478d628e6 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -32,7 +32,7 @@
 from sklearn.utils.testing import SkipTest
 from sklearn.utils.estimator_checks import (
     _construct_instance,
-    set_checking_parameters,
+    _set_checking_parameters,
     _set_check_estimator_ids,
     check_parameters_default_constructible,
     check_class_weight_balanced_linear_classifier,
@@ -93,7 +93,7 @@ def test_estimators(estimator, check):
     # Common tests for estimator instances
     with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
                                    UserWarning, FutureWarning)):
-        set_checking_parameters(estimator)
+        _set_checking_parameters(estimator)
         check(estimator)
 
 
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index 80a8b7057e719..61cc028e8c786 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -438,7 +438,13 @@ def _boston_subset(n_samples=200):
     return BOSTON
 
 
+@deprecated("set_checking_parameters is deprecated in version "
+            "0.22 and will be removed in version 0.24.")
 def set_checking_parameters(estimator):
+    _set_checking_parameters(estimator)
+
+
+def _set_checking_parameters(estimator):
     # set parameters to speed up some estimators and
     # avoid deprecated behaviour
     params = estimator.get_params()
diff --git a/sklearn/utils/tests/test_deprecated_utils.py b/sklearn/utils/tests/test_deprecated_utils.py
index 65a8a92d1cc32..e45e7fee2351a 100644
--- a/sklearn/utils/tests/test_deprecated_utils.py
+++ b/sklearn/utils/tests/test_deprecated_utils.py
@@ -7,6 +7,7 @@
 from sklearn.utils.estimator_checks import enforce_estimator_tags_y
 from sklearn.utils.estimator_checks import is_public_parameter
 from sklearn.utils.estimator_checks import pairwise_estimator_convert_X
+from sklearn.utils.estimator_checks import set_checking_parameters
 
 
 # This file tests the utils that are deprecated
@@ -35,3 +36,8 @@ def test_is_public_parameter():
 def test_pairwise_estimator_convert_X():
     with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
         pairwise_estimator_convert_X([[1, 2]], DummyClassifier())
+
+
+def test_set_checking_parameters():
+    with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
+        set_checking_parameters(DummyClassifier())
diff --git a/sklearn/utils/tests/test_estimator_checks.py b/sklearn/utils/tests/test_estimator_checks.py
index e26a508566871..5891e722e0b71 100644
--- a/sklearn/utils/tests/test_estimator_checks.py
+++ b/sklearn/utils/tests/test_estimator_checks.py
@@ -17,7 +17,7 @@
 from sklearn.utils.estimator_checks \
     import check_class_weight_balanced_linear_classifier
 from sklearn.utils.estimator_checks import set_random_state
-from sklearn.utils.estimator_checks import set_checking_parameters
+from sklearn.utils.estimator_checks import _set_checking_parameters
 from sklearn.utils.estimator_checks import check_estimators_unfitted
 from sklearn.utils.estimator_checks import check_fit_score_takes_y
 from sklearn.utils.estimator_checks import check_no_attributes_set_in_init
@@ -452,7 +452,7 @@ def test_check_estimator_clones():
         with ignore_warnings(category=(FutureWarning, DeprecationWarning)):
             # when 'est = SGDClassifier()'
             est = Estimator()
-            set_checking_parameters(est)
+            _set_checking_parameters(est)
             set_random_state(est)
             # without fitting
             old_hash = joblib.hash(est)
@@ -462,7 +462,7 @@ def test_check_estimator_clones():
         with ignore_warnings(category=(FutureWarning, DeprecationWarning)):
             # when 'est = SGDClassifier()'
             est = Estimator()
-            set_checking_parameters(est)
+            _set_checking_parameters(est)
             set_random_state(est)
             # with fitting
             est.fit(iris.data + 10, iris.target)

From 41b2b2b55763b36ee1fc5013c77f8755911adb4a Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Thu, 19 Sep 2019 10:38:50 -0400
Subject: [PATCH 5/6] pep8

---
 sklearn/utils/estimator_checks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index 61cc028e8c786..a29c9da563be4 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -526,7 +526,7 @@ def _set_checking_parameters(estimator):
 
 
 class NotAnArray:
-    #TODO: remove in 0.24
+    # TODO: remove in 0.24
 
     def __init__(self, data):
         warnings.warn(

From 44d33fb2c1963b8598cbb0b44027db9748195000 Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Thu, 19 Sep 2019 12:56:43 -0400
Subject: [PATCH 6/6] use decorator for class

---
 sklearn/utils/estimator_checks.py            | 22 +++++++-------------
 sklearn/utils/tests/test_deprecated_utils.py |  2 +-
 2 files changed, 8 insertions(+), 16 deletions(-)

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index a29c9da563be4..7df833b6b6d96 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -525,21 +525,6 @@ def _set_checking_parameters(estimator):
         estimator.set_params(handle_unknown='ignore')
 
 
-class NotAnArray:
-    # TODO: remove in 0.24
-
-    def __init__(self, data):
-        warnings.warn(
-            "The NotAnArray classe is deprecated in 0.22 and will be "
-            "removed in 0.24.",
-            DeprecationWarning
-        )
-        self.data = data
-
-    def __array__(self, dtype=None):
-        return self.data
-
-
 class _NotAnArray:
     """An object that is convertible to an array
 
@@ -556,6 +541,13 @@ def __array__(self, dtype=None):
         return self.data
 
 
+@deprecated("NotAnArray is deprecated in version "
+            "0.22 and will be removed in version 0.24.")
+class NotAnArray(_NotAnArray):
+    # TODO: remove in 0.24
+    pass
+
+
 def _is_pairwise(estimator):
     """Returns True if estimator has a _pairwise attribute set to True.
 
diff --git a/sklearn/utils/tests/test_deprecated_utils.py b/sklearn/utils/tests/test_deprecated_utils.py
index e45e7fee2351a..18d97d67ef6d5 100644
--- a/sklearn/utils/tests/test_deprecated_utils.py
+++ b/sklearn/utils/tests/test_deprecated_utils.py
@@ -24,7 +24,7 @@ def test_enforce_estimator_tags_y():
 
 
 def test_notanarray():
-    with pytest.warns(DeprecationWarning, match="will be removed in 0.24"):
+    with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
         NotAnArray([1, 2])