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)