def test_component_sign():
# We should have a heuristic that flips the sign of components in
# CanICA to have more positive values than negative values, for
# instance by making sure that the largest value is positive.
# make data (SVD)
rng = np.random.RandomState(0)
shape = (20, 10, 1)
affine = np.eye(4)
components = _make_canica_components(shape)
# make +ve
for mp in components:
mp[rng.randn(*mp.shape) > .8] *= -5.
assert_less_equal(mp.max(), -mp.min()) # goal met ?
# synthesize data with given components
data = _make_data_from_components(components, affine, shape, rng=rng,
n_subjects=2)
mask_img = nibabel.Nifti1Image(np.ones(shape, dtype=np.int8), affine)
# run CanICA many times (this is known to produce different results)
canica = CanICA(n_components=4, random_state=rng, mask=mask_img)
for _ in range(3):
canica.fit(data)
for mp in iter_img(canica.masker_.inverse_transform(
canica.components_)):
mp = mp.get_data()
assert_less_equal(-mp.min(), mp.max())
def _make_canica_test_data(rng=None, n_subjects=8, noisy=False):
if rng is None:
rng = np.random.RandomState(0)
shape = (30, 30, 5)
affine = np.eye(4)
components = _make_canica_components(shape)
if noisy: # Creating noisy non positive data
components[rng.randn(*components.shape) > .8] *= -5.
for mp in components:
assert_less_equal(mp.max(), -mp.min()) # Goal met ?
# Create a "multi-subject" dataset
data = _make_data_from_components(components, affine, shape, rng=rng,
n_subjects=n_subjects)
mask = np.ones(shape)
mask[:5] = 0
mask[-5:] = 0
mask[:, :5] = 0
mask[:, -5:] = 0
mask[..., -2:] = 0
mask[..., :2] = 0
mask_img = nibabel.Nifti1Image(mask, affine)
return data, mask_img, components, rng
def _make_canica_test_data(rng=None, n_subjects=8, noisy=False):
if rng is None:
rng = np.random.RandomState(0)
shape = (30, 30, 5)
affine = np.eye(4)
components = _make_canica_components(shape)
if noisy: # Creating noisy non positive data
components[rng.randn(*components.shape) > .8] *= -5.
for mp in components:
assert_less_equal(mp.max(), -mp.min()) # Goal met ?
# Create a "multi-subject" dataset
data = _make_data_from_components(components,
affine,
shape,
rng=rng,
n_subjects=n_subjects)
mask = np.ones(shape)
mask[:5] = 0
mask[-5:] = 0
mask[:, :5] = 0
mask[:, -5:] = 0
mask[..., -2:] = 0
mask[..., :2] = 0
mask_img = nibabel.Nifti1Image(mask, affine)
return data, mask_img, components, rng
def test_component_sign():
# We should have a heuristic that flips the sign of components in
# CanICA 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)
# run CanICA many times (this is known to produce different results)
canica = CanICA(n_components=4, random_state=rng, mask=mask_img)
for _ in range(3):
canica.fit(data)
for mp in iter_img(canica.components_img_):
mp = get_data(mp)
assert_less_equal(-mp.min(), mp.max())
def test_component_sign():
# We should have a heuristic that flips the sign of components in
# CanICA 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)
# run CanICA many times (this is known to produce different results)
canica = CanICA(n_components=4, random_state=rng, mask=mask_img)
for _ in range(3):
canica.fit(data)
for mp in iter_img(canica.components_img_):
mp = mp.get_data()
assert_less_equal(-mp.min(), mp.max())
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 _make_test_data(rng=None, n_subjects=8, noisy=False):
if rng is None:
rng = np.random.RandomState(0)
shape = (20, 20, 1)
affine = np.eye(4)
components = _make_components(shape)
if noisy: # Creating noisy non positive data
components[rng.randn(*components.shape) > 0.8] *= -5.0
for mp in components:
assert_less_equal(mp.max(), -mp.min()) # Goal met ?
# Create a "multi-subject" dataset
data = _make_data_from_components(components, affine, shape, rng=rng, n_subjects=n_subjects)
mask_img = nibabel.Nifti1Image(np.ones(shape, dtype=np.int8), affine)
return data, mask_img, components, rng
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, init = _make_test_data(n_subjects=2,
noisy=True)
dict_fact = fMRIDictFact(n_components=4,
random_state=0,
mask=mask_img,
smoothing_fwhm=0.)
dict_fact.fit(data)
for mp in iter_img(dict_fact.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_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_test_data(n_subjects=2, noisy=True)
for mp in components:
assert_less_equal(-mp.min(), mp.max())
sparse_pca = SpcaFmri(n_components=4,
random_state=rng,
mask=mask_img,
smoothing_fwhm=0.)
sparse_pca.fit(data)
for mp in iter_img(
sparse_pca.masker_.inverse_transform(sparse_pca.components_)):
mp = mp.get_data()
assert_less_equal(np.sum(mp[mp <= 0]), np.sum(mp[mp > 0]))
def _make_test_data(n_subjects=8, noisy=False):
rng = np.random.RandomState(0)
shape = (20, 20, 1)
components = _make_components(shape)
if noisy: # Creating noisy non positive data
components[rng.randn(*components.shape) > .8] *= -5.
for component in components:
assert_less_equal(component.max(), -component.min()) # Goal met ?
# Create a "multi-subject" dataset
data = _make_data_from_components(components, n_subjects=n_subjects)
affine = np.eye(4)
mask_img = nibabel.Nifti1Image(np.ones(shape, dtype=np.int8), affine)
masker = MultiNiftiMasker(mask_img).fit()
init = components + 1 * rng.randn(*components.shape)
components = masker.inverse_transform(components)
init = masker.inverse_transform(init)
data = masker.inverse_transform(data)
return data, mask_img, components, init
