def test_kbinsdiscretizer( blobs_dataset, n_bins, # noqa: F811 encode, strategy): X_np, X = blobs_dataset transformer = cuKBinsDiscretizer(n_bins=n_bins, encode=encode, strategy=strategy) t_X = transformer.fit_transform(X) r_X = transformer.inverse_transform(t_X) if encode != 'onehot': assert type(t_X) == type(X) assert type(r_X) == type(t_X) transformer = skKBinsDiscretizer(n_bins=n_bins, encode=encode, strategy=strategy) sk_t_X = transformer.fit_transform(X_np) sk_r_X = transformer.inverse_transform(sk_t_X) if strategy == 'kmeans': assert_allclose(t_X, sk_t_X, ratio_tol=0.2) else: assert_allclose(t_X, sk_t_X) assert_allclose(r_X, sk_r_X)
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()'