def test_missing_indicator_new(missing_values, arr_type, dtype, param_features,
n_features, features_indices):
X_fit = np.array([[missing_values, missing_values, 1],
[4, 2, missing_values]])
X_trans = np.array([[missing_values, missing_values, 1], [4, 12, 10]])
X_fit_expected = np.array([[1, 1, 0], [0, 0, 1]])
X_trans_expected = np.array([[1, 1, 0], [0, 0, 0]])
# convert the input to the right array format and right dtype
X_fit = arr_type(X_fit).astype(dtype)
X_trans = arr_type(X_trans).astype(dtype)
X_fit_expected = X_fit_expected.astype(dtype)
X_trans_expected = X_trans_expected.astype(dtype)
indicator = MissingIndicator(missing_values=missing_values,
features=param_features,
sparse=False)
X_fit_mask = indicator.fit_transform(X_fit)
X_trans_mask = indicator.transform(X_trans)
assert X_fit_mask.shape[1] == n_features
assert X_trans_mask.shape[1] == n_features
assert_array_equal(indicator.features_, features_indices)
assert_allclose(X_fit_mask, X_fit_expected[:, features_indices])
assert_allclose(X_trans_mask, X_trans_expected[:, features_indices])
assert X_fit_mask.dtype == bool
assert X_trans_mask.dtype == bool
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
indicator.set_params(sparse=True)
X_fit_mask_sparse = indicator.fit_transform(X_fit)
X_trans_mask_sparse = indicator.transform(X_trans)
assert X_fit_mask_sparse.dtype == bool
assert X_trans_mask_sparse.dtype == bool
assert X_fit_mask_sparse.format == 'csc'
assert X_trans_mask_sparse.format == 'csc'
assert_allclose(X_fit_mask_sparse.toarray(), X_fit_mask)
assert_allclose(X_trans_mask_sparse.toarray(), X_trans_mask)
def test_missing_indicator_error(X_fit, X_trans, params, msg_err):
indicator = MissingIndicator(missing_values=-1)
indicator.set_params(**params)
with pytest.raises(ValueError, match=msg_err):
indicator.fit(X_fit).transform(X_trans)