def test_masker_attributes_with_fit():
# Test base module at sub-class
data, mask_img, components, rng = _make_canica_test_data(n_subjects=3)
# Passing mask_img
dict_learning = DictLearning(n_components=3, mask=mask_img, random_state=0)
dict_learning.fit(data)
assert dict_learning.mask_img_ == mask_img
assert dict_learning.mask_img_ == dict_learning.masker_.mask_img_
# Passing masker
masker = NiftiMasker(mask_img=mask_img)
dict_learning = DictLearning(n_components=3, mask=masker, random_state=0)
dict_learning.fit(data)
assert dict_learning.mask_img_ == dict_learning.masker_.mask_img_
dict_learning = DictLearning(mask=mask_img, n_components=3)
with pytest.raises(ValueError,
match="Object has no components_ attribute. "
"This is probably because "
"fit has not been called"):
dict_learning.transform(data)
# Test if raises an error when empty list of provided.
with pytest.raises(ValueError,
match='Need one or more Niimg-like objects '
'as input, an empty list was given.'):
dict_learning.fit([])
# Test passing masker arguments to estimator
dict_learning = DictLearning(n_components=3,
target_affine=np.eye(4),
target_shape=(6, 8, 10),
mask_strategy='background')
dict_learning.fit(data)