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()'