def test_poly_features(
clf_dataset,
degree, # noqa: F811
interaction_only,
include_bias,
order):
X_np, X = clf_dataset
polyfeatures = cuPolynomialFeatures(degree=degree,
order=order,
interaction_only=interaction_only,
include_bias=include_bias)
t_X = polyfeatures.fit_transform(X)
assert type(X) == type(t_X)
if isinstance(t_X, np.ndarray):
if order == 'C':
assert t_X.flags['C_CONTIGUOUS']
elif order == 'F':
assert t_X.flags['F_CONTIGUOUS']
polyfeatures = skPolynomialFeatures(degree=degree,
order=order,
interaction_only=interaction_only,
include_bias=include_bias)
sk_t_X = polyfeatures.fit_transform(X_np)
assert_allclose(t_X, sk_t_X, rtol=0.1, atol=0.1)
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_poly_features_sparse(sparse_clf_dataset, degree, # noqa: F811
interaction_only, include_bias):
X_np, X = sparse_clf_dataset
polyfeatures = cuPolynomialFeatures(degree=degree,
interaction_only=interaction_only,
include_bias=include_bias)
t_X = polyfeatures.fit_transform(X)
assert type(t_X) == type(X)
polyfeatures = skPolynomialFeatures(degree=degree,
interaction_only=interaction_only,
include_bias=include_bias)
sk_t_X = polyfeatures.fit_transform(X_np)
assert_allclose(t_X, sk_t_X, rtol=0.1, atol=0.1)
