Uploaded Test files

venv/Lib/site-packages/sklearn/utils/tests/test_deprecated_utils.py

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+import pytest
+import types
+import numpy as np
+import warnings
+
+from sklearn.dummy import DummyClassifier
+from sklearn.utils import all_estimators
+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
+from sklearn.utils.estimator_checks import pairwise_estimator_convert_X
+from sklearn.utils.estimator_checks import set_checking_parameters
+from sklearn.utils.optimize import newton_cg
+from sklearn.utils.random import random_choice_csc
+from sklearn.utils import safe_indexing
+
+
+# This file tests the utils that are deprecated
+
+
+# TODO: remove in 0.24
+def test_choose_check_classifiers_labels_deprecated():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        choose_check_classifiers_labels(None, None, None)
+
+
+# TODO: remove in 0.24
+def test_enforce_estimator_tags_y():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        enforce_estimator_tags_y(DummyClassifier(), np.array([0, 1]))
+
+
+# TODO: remove in 0.24
+def test_notanarray():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        NotAnArray([1, 2])
+
+
+# TODO: remove in 0.24
+def test_is_public_parameter():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        is_public_parameter('hello')
+
+
+# TODO: remove in 0.24
+def test_pairwise_estimator_convert_X():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        pairwise_estimator_convert_X([[1, 2]], DummyClassifier())
+
+
+# TODO: remove in 0.24
+def test_set_checking_parameters():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        set_checking_parameters(DummyClassifier())
+
+
+# TODO: remove in 0.24
+def test_newton_cg():
+    rng = np.random.RandomState(0)
+    A = rng.normal(size=(10, 10))
+    x0 = np.ones(10)
+
+    def func(x):
+        Ax = A.dot(x)
+        return .5 * (Ax).dot(Ax)
+
+    def grad(x):
+        return A.T.dot(A.dot(x))
+
+    def grad_hess(x):
+        return grad(x), lambda x: A.T.dot(A.dot(x))
+
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        newton_cg(grad_hess, func, grad, x0)
+
+
+# TODO: remove in 0.24
+def test_random_choice_csc():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        random_choice_csc(10, [[2]])
+
+
+# TODO: remove in 0.24
+def test_safe_indexing():
+    with pytest.warns(FutureWarning,
+                      match="removed in version 0.24"):
+        safe_indexing([1, 2], 0)
+
+
+# TODO: remove in 0.24
+def test_partial_dependence_no_shadowing():
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/15842
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)
+        from sklearn.inspection.partial_dependence import partial_dependence as _  # noqa
+
+        # Calling all_estimators() also triggers a recursive import of all
+        # submodules, including deprecated ones.
+        all_estimators()
+
+    from sklearn.inspection import partial_dependence
+    assert isinstance(partial_dependence, types.FunctionType)
+
+
+# TODO: remove in 0.24
+def test_dict_learning_no_shadowing():
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/15842
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)
+        from sklearn.decomposition.dict_learning import dict_learning as _  # noqa
+
+        # Calling all_estimators() also triggers a recursive import of all
+        # submodules, including deprecated ones.
+        all_estimators()
+
+    from sklearn.decomposition import dict_learning
+    assert isinstance(dict_learning, types.FunctionType)