def test__repr__():
assert cuStandardScaler().__repr__() == 'StandardScaler()'
assert cuMinMaxScaler().__repr__() == 'MinMaxScaler()'
assert cuMaxAbsScaler().__repr__() == 'MaxAbsScaler()'
assert cuNormalizer().__repr__() == 'Normalizer()'
assert cuBinarizer().__repr__() == 'Binarizer()'
assert cuPolynomialFeatures().__repr__() == 'PolynomialFeatures()'
assert cuSimpleImputer().__repr__() == 'SimpleImputer()'
assert cuRobustScaler().__repr__() == 'RobustScaler()'
assert cuKBinsDiscretizer().__repr__() == 'KBinsDiscretizer()'
def test_normalizer(clf_dataset, norm): # noqa: F811
X_np, X = clf_dataset
normalizer = cuNormalizer(norm=norm, copy=True)
t_X = normalizer.fit_transform(X)
assert type(t_X) == type(X)
normalizer = skNormalizer(norm=norm, copy=True)
sk_t_X = normalizer.fit_transform(X_np)
assert_allclose(t_X, sk_t_X)
def test_normalizer_sparse(sparse_clf_dataset, norm): # noqa: F811
X_np, X = sparse_clf_dataset
if X.format == 'csc':
pytest.skip("Skipping CSC matrices")
normalizer = cuNormalizer(norm=norm, copy=True)
t_X = normalizer.fit_transform(X)
assert type(t_X) == type(X)
normalizer = skNormalizer(norm=norm, copy=True)
sk_t_X = normalizer.fit_transform(X_np)
assert_allclose(t_X, sk_t_X)