def test_robust_scaler_sparse(
sparse_clf_dataset, # noqa: F811
with_scaling,
quantile_range):
X_np, X = sparse_clf_dataset
if X.format != 'csc':
X = X.tocsc()
scaler = cuRobustScaler(with_centering=False,
with_scaling=with_scaling,
quantile_range=quantile_range,
copy=True)
t_X = scaler.fit_transform(X)
r_X = scaler.inverse_transform(t_X)
assert type(t_X) == type(X)
assert type(r_X) == type(t_X)
scaler = skRobustScaler(with_centering=False,
with_scaling=with_scaling,
quantile_range=quantile_range,
copy=True)
sk_t_X = scaler.fit_transform(X_np)
sk_r_X = scaler.inverse_transform(sk_t_X)
assert_allclose(t_X, sk_t_X)
assert_allclose(r_X, sk_r_X)
def test_robust_scaler(
clf_dataset,
with_centering, # noqa: F811
with_scaling,
quantile_range):
X_np, X = clf_dataset
scaler = cuRobustScaler(with_centering=with_centering,
with_scaling=with_scaling,
quantile_range=quantile_range,
copy=True)
t_X = scaler.fit_transform(X)
r_X = scaler.inverse_transform(t_X)
assert type(t_X) == type(X)
assert type(r_X) == type(t_X)
scaler = skRobustScaler(with_centering=with_centering,
with_scaling=with_scaling,
quantile_range=quantile_range,
copy=True)
sk_t_X = scaler.fit_transform(X_np)
sk_r_X = scaler.inverse_transform(sk_t_X)
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()'