def test_components_img():
data, mask_img, _, _ = _make_canica_test_data(n_subjects=3)
n_components = 3
dict_learning = DictLearning(n_components=n_components, mask=mask_img)
dict_learning.fit(data)
components_img = dict_learning.components_img_
assert_true(isinstance(components_img, nibabel.Nifti1Image))
check_shape = data[0].shape + (n_components,)
assert_true(components_img.shape, check_shape)
def test_with_globbing_patterns_with_single_subject():
# single subject
data, mask_img, _, _ = _make_canica_test_data(n_subjects=1)
n_components = 3
dictlearn = DictLearning(n_components=n_components, mask=mask_img)
with write_tmp_imgs(data[0], create_files=True, use_wildcards=True) as img:
input_image = _tmp_dir() + img
dictlearn.fit(input_image)
components_img = dictlearn.components_img_
assert_true(isinstance(components_img, nibabel.Nifti1Image))
# n_components = 3
check_shape = data[0].shape[:3] + (3,)
assert_true(components_img.shape, check_shape)
def test_component_sign():
# Regression test
# We should have a heuristic that flips the sign of components in
# DictLearning to have more positive values than negative values, for
# instance by making sure that the largest value is positive.
data, mask_img, components, rng = _make_canica_test_data(n_subjects=2, noisy=True)
for mp in components:
assert_less_equal(-mp.min(), mp.max())
dict_learning = DictLearning(n_components=4, random_state=rng, mask=mask_img, smoothing_fwhm=0.0, alpha=1)
dict_learning.fit(data)
for mp in iter_img(dict_learning.masker_.inverse_transform(dict_learning.components_)):
mp = mp.get_data()
assert_less_equal(np.sum(mp[mp <= 0]), np.sum(mp[mp > 0]))
def test_component_sign():
# Regression test
# We should have a heuristic that flips the sign of components in
# DictLearning to have more positive values than negative values, for
# instance by making sure that the largest value is positive.
data, mask_img, components, rng = _make_canica_test_data(n_subjects=2,
noisy=True)
for mp in components:
assert_less_equal(-mp.min(), mp.max())
dict_learning = DictLearning(n_components=4, random_state=rng,
mask=mask_img,
smoothing_fwhm=0., alpha=1)
dict_learning.fit(data)
for mp in iter_img(dict_learning.components_img_):
mp = get_data(mp)
assert_less_equal(np.sum(mp[mp <= 0]), np.sum(mp[mp > 0]))
def test_dict_learning():
data, mask_img, components, rng = _make_canica_test_data(n_subjects=8)
mask = NiftiMasker(mask_img=mask_img).fit()
dict_init = mask.inverse_transform(components)
dict_learning = DictLearning(n_components=4, random_state=0,
dict_init=dict_init,
mask=mask_img,
smoothing_fwhm=0., alpha=1)
dict_learning_auto_init = DictLearning(n_components=4, random_state=0,
mask=mask_img,
smoothing_fwhm=0., n_epochs=10,
alpha=1)
maps = {}
for estimator in [dict_learning,
dict_learning_auto_init]:
estimator.fit(data)
maps[estimator] = estimator.masker_. \
inverse_transform(estimator.components_).get_data()
maps[estimator] = np.reshape(np.rollaxis(maps[estimator], 3, 0),
(4, 400))
for this_dict_learning in [dict_learning]:
these_maps = maps[this_dict_learning]
S = np.sqrt(np.sum(components ** 2, axis=1))
S[S == 0] = 1
components /= S[:, np.newaxis]
S = np.sqrt(np.sum(these_maps ** 2, axis=1))
S[S == 0] = 1
these_maps /= S[:, np.newaxis]
K = np.abs(components.dot(these_maps.T))
recovered_maps = np.sum(K > 0.9)
assert(recovered_maps >= 2)
# Smoke test n_epochs > 1
dict_learning = DictLearning(n_components=4, random_state=0,
dict_init=dict_init,
mask=mask_img,
smoothing_fwhm=0., n_epochs=2, alpha=1)
dict_learning.fit(data)
def test_dict_learning():
data, mask_img, components, rng = _make_canica_test_data(n_subjects=8)
masker = NiftiMasker(mask_img=mask_img).fit()
mask = mask_img.get_data() != 0
flat_mask = mask.ravel()
dict_init = masker.inverse_transform(components[:, flat_mask])
dict_learning = DictLearning(n_components=4,
random_state=0,
dict_init=dict_init,
mask=mask_img,
smoothing_fwhm=0.,
alpha=1)
dict_learning_auto_init = DictLearning(n_components=4,
random_state=0,
mask=mask_img,
smoothing_fwhm=0.,
n_epochs=10,
alpha=1)
maps = {}
for estimator in [dict_learning, dict_learning_auto_init]:
estimator.fit(data)
maps[estimator] = estimator.masker_. \
inverse_transform(estimator.components_).get_data()
maps[estimator] = np.reshape(
np.rollaxis(maps[estimator], 3, 0)[:, mask], (4, flat_mask.sum()))
masked_components = components[:, flat_mask]
for this_dict_learning in [dict_learning]:
these_maps = maps[this_dict_learning]
S = np.sqrt(np.sum(masked_components**2, axis=1))
S[S == 0] = 1
masked_components /= S[:, np.newaxis]
S = np.sqrt(np.sum(these_maps**2, axis=1))
S[S == 0] = 1
these_maps /= S[:, np.newaxis]
K = np.abs(masked_components.dot(these_maps.T))
recovered_maps = np.sum(K > 0.9)
assert (recovered_maps >= 2)
# Smoke test n_epochs > 1
dict_learning = DictLearning(n_components=4,
random_state=0,
dict_init=dict_init,
mask=mask_img,
smoothing_fwhm=0.,
n_epochs=2,
alpha=1)
dict_learning.fit(data)
def test_dictlearning_score():
# Multi subjects
imgs, mask_img, _, _ = _make_canica_test_data(n_subjects=3)
n_components = 10
dictlearn = DictLearning(n_components=10, mask=mask_img, random_state=0)
# Test error before object fit
with pytest.raises(ValueError,
match="Object has no components_ attribute. "
"This is probably because fit has not been called"):
dictlearn.score(imgs, per_component=False)
dictlearn.fit(imgs)
# One score for all components
scores = dictlearn.score(imgs, per_component=False)
assert scores <= 1
assert 0 <= scores
# Per component score
scores = dictlearn.score(imgs, per_component=True)
assert scores.shape, (n_components, )
assert np.all(scores <= 1)
assert np.all(0 <= scores)
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_true(dict_learning.mask_img_ == mask_img)
assert_true(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_true(dict_learning.mask_img_ == dict_learning.masker_.mask_img_)
dict_learning = DictLearning(mask=mask_img, n_components=3)
assert_raises_regex(ValueError,
"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.
assert_raises_regex(ValueError,
'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)