def test_multi_pca():
# Smoke test the MultiPCA
# XXX: this is mostly a smoke test
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_data[2:4, 2:4, 2:4, :] += 10
data.append(nibabel.Nifti1Image(this_data, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
multi_pca = MultiPCA(mask=mask_img, n_components=3,
random_state=0)
# Test that the components are the same if we put twice the same data, and
# that fit output is deterministic
components1 = multi_pca.fit(data).components_
components2 = multi_pca.fit(data).components_
components3 = multi_pca.fit(2 * data).components_
np.testing.assert_array_equal(components1, components2)
np.testing.assert_array_almost_equal(components1, components3)
# Smoke test fit with 'confounds' argument
confounds = [np.arange(10).reshape(5, 2)] * 8
multi_pca.fit(data, confounds=confounds)
# Smoke test that multi_pca also works with single subject data
multi_pca.fit(data[0])
# Check that asking for too little components raises a ValueError
multi_pca = MultiPCA()
assert_raises(ValueError, multi_pca.fit, data[:2])
# Smoke test the use of a masker and without CCA
multi_pca = MultiPCA(mask=MultiNiftiMasker(mask_args=dict(opening=0)),
do_cca=False, n_components=3)
multi_pca.fit(data[:2])
# Smoke test the transform and inverse_transform
multi_pca.inverse_transform(multi_pca.transform(data[-2:]))
# Smoke test to fit with no img
assert_raises(TypeError, multi_pca.fit)
multi_pca = MultiPCA(mask=mask_img, n_components=3)
assert_raises_regex(ValueError,
"Object has no components_ attribute. "
"This is probably because fit has not been called",
multi_pca.transform, data)
def test_multi_pca():
# Smoke test the MultiPCA
# XXX: this is mostly a smoke test
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_data[2:4, 2:4, 2:4, :] += 10
data.append(nibabel.Nifti1Image(this_data, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
multi_pca = MultiPCA(mask=mask_img, n_components=3, random_state=0)
# Test that the components are the same if we put twice the same data, and
# that fit output is deterministic
components1 = multi_pca.fit(data).components_
components2 = multi_pca.fit(data).components_
components3 = multi_pca.fit(2 * data).components_
np.testing.assert_array_equal(components1, components2)
np.testing.assert_array_almost_equal(components1, components3)
# Smoke test fit with 'confounds' argument
confounds = [np.arange(10).reshape(5, 2)] * 8
multi_pca.fit(data, confounds=confounds)
# Smoke test that multi_pca also works with single subject data
multi_pca.fit(data[0])
# Check that asking for too little components raises a ValueError
multi_pca = MultiPCA()
assert_raises(ValueError, multi_pca.fit, data[:2])
# Smoke test the use of a masker and without CCA
multi_pca = MultiPCA(mask=MultiNiftiMasker(mask_args=dict(opening=0)),
do_cca=False,
n_components=3)
multi_pca.fit(data[:2])
# Smoke test the transform and inverse_transform
multi_pca.inverse_transform(multi_pca.transform(data[-2:]))
# Smoke test to fit with no img
assert_raises(TypeError, multi_pca.fit)
multi_pca = MultiPCA(mask=mask_img, n_components=3)
assert_raises_regex(
ValueError, "Object has no components_ attribute. "
"This is probably because fit has not been called",
multi_pca.transform, data)
def test_multi_pca():
# Smoke test the MultiPCA
# XXX: this is mostly a smoke test
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_data[2:4, 2:4, 2:4, :] += 10
data.append(nibabel.Nifti1Image(this_data, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
multi_pca = MultiPCA(mask=mask_img, n_components=3)
# Test that the components are the same if we put twice the same data
components1 = multi_pca.fit(data).components_
components2 = multi_pca.fit(2 * data).components_
np.testing.assert_array_almost_equal(components1, components2)
# Smoke test that multi_pca also works with single subject data
multi_pca.fit(data[0])
# Check that asking for too little components raises a ValueError
multi_pca = MultiPCA()
nose.tools.assert_raises(ValueError, multi_pca.fit, data[:2])
# Smoke test the use of a masker and without CCA
multi_pca = MultiPCA(mask=MultiNiftiMasker(mask_opening=0),
do_cca=False,
n_components=3)
multi_pca.fit(data[:2])
# Smoke test the transform and inverse_transform
multi_pca.inverse_transform(multi_pca.transform(data[-2:]))
def test_multi_pca():
# Smoke test the MultiPCA
# XXX: this is mostly a smoke test
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_data[2:4, 2:4, 2:4, :] += 10
data.append(nibabel.Nifti1Image(this_data, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
multi_pca = MultiPCA(mask=mask_img, n_components=3)
# Test that the components are the same if we put twice the same data
components1 = multi_pca.fit(data).components_
components2 = multi_pca.fit(2 * data).components_
np.testing.assert_array_almost_equal(components1, components2)
# Smoke test that multi_pca also works with single subject data
multi_pca.fit(data[0])
# Check that asking for too little components raises a ValueError
multi_pca = MultiPCA()
nose.tools.assert_raises(ValueError, multi_pca.fit, data[:2])
# Smoke test the use of a masker and without CCA
multi_pca = MultiPCA(mask=MultiNiftiMasker(mask_args=dict(opening=0)),
do_cca=False, n_components=3)
multi_pca.fit(data[:2])
# Smoke test the transform and inverse_transform
multi_pca.inverse_transform(multi_pca.transform(data[-2:]))
def test_multi_pca():
# Smoke test the MultiPCA
# XXX: this is mostly a smoke test
shape = (6, 8, 10, 5)
affine = np.eye(4)
rng = np.random.RandomState(0)
# Create a "multi-subject" dataset
data = []
for i in range(8):
this_data = rng.normal(size=shape)
# Create fake activation to get non empty mask
this_data[2:4, 2:4, 2:4, :] += 10
data.append(nibabel.Nifti1Image(this_data, affine))
mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)
multi_pca = MultiPCA(mask=mask_img, n_components=3,
random_state=0)
# fit to the data and test for masker attributes
multi_pca.fit(data)
assert multi_pca.mask_img_ == mask_img
assert multi_pca.mask_img_ == multi_pca.masker_.mask_img_
# Test that the components are the same if we put twice the same data, and
# that fit output is deterministic
components1 = multi_pca.components_
components2 = multi_pca.fit(data).components_
components3 = multi_pca.fit(2 * data).components_
np.testing.assert_array_equal(components1, components2)
np.testing.assert_array_almost_equal(components1, components3)
# Smoke test fit with 'confounds' argument
confounds = [np.arange(10).reshape(5, 2)] * 8
multi_pca.fit(data, confounds=confounds)
# Smoke test that multi_pca also works with single subject data
multi_pca.fit(data[0])
# Check that asking for too little components raises a ValueError
multi_pca = MultiPCA()
pytest.raises(ValueError, multi_pca.fit, data[:2])
# Test fit on data with the use of a masker
masker = MultiNiftiMasker()
multi_pca = MultiPCA(mask=masker, n_components=3)
multi_pca.fit(data)
assert multi_pca.mask_img_ == multi_pca.masker_.mask_img_
# Smoke test the use of a masker and without CCA
multi_pca = MultiPCA(mask=MultiNiftiMasker(mask_args=dict(opening=0)),
do_cca=False, n_components=3)
multi_pca.fit(data[:2])
# Smoke test the transform and inverse_transform
multi_pca.inverse_transform(multi_pca.transform(data[-2:]))
# Smoke test to fit with no img
pytest.raises(TypeError, multi_pca.fit)
multi_pca = MultiPCA(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"):
multi_pca.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.'):
multi_pca.fit([])
# Test passing masker arguments to estimator
multi_pca = MultiPCA(target_affine=affine,
target_shape=shape[:3],
n_components=3,
mask_strategy='background')
multi_pca.fit(data)
