def test_missing_indicator(failure_logger, int_dataset, # noqa: F811
missing_values, features):
zero_filled, one_filled, nan_filled = int_dataset
if missing_values == 0:
X_np, X = zero_filled
elif missing_values == 1:
X_np, X = one_filled
else:
X_np, X = nan_filled
indicator = cuMissingIndicator(missing_values=missing_values,
features=features)
ft_X = indicator.fit_transform(X)
assert type(ft_X) == type(X)
indicator.fit(X)
t_X = indicator.transform(X)
assert type(t_X) == type(X)
indicator = skMissingIndicator(missing_values=missing_values,
features=features)
sk_ft_X = indicator.fit_transform(X_np)
indicator.fit(X_np)
sk_t_X = indicator.transform(X_np)
assert_allclose(ft_X, sk_ft_X)
assert_allclose(t_X, sk_t_X)
def test_missing_indicator_sparse(sparse_int_dataset, # noqa: F811
features):
X_np, X = sparse_int_dataset
indicator = cuMissingIndicator(features=features,
missing_values=1)
ft_X = indicator.fit_transform(X)
# assert type(ft_X) == type(X)
assert cp.sparse.issparse(ft_X) or scipy.sparse.issparse(ft_X)
indicator.fit(X)
t_X = indicator.transform(X)
# assert type(t_X) == type(X)
assert cp.sparse.issparse(t_X) or scipy.sparse.issparse(t_X)
indicator = skMissingIndicator(features=features,
missing_values=1)
sk_ft_X = indicator.fit_transform(X_np)
indicator.fit(X_np)
sk_t_X = indicator.transform(X_np)
assert_allclose(ft_X, sk_ft_X)
assert_allclose(t_X, sk_t_X)