def srm_reduce_data(X, n_components):
"""
Reduce the number of features in X via FastSRM
Parameters
----------
X : np array of shape (n_groups, n_features, n_samples)
Training vector, where n_groups is the number of groups,
n_samples is the number of samples and
n_components is the number of components.
n_components : int, optional
Number of components to extract.
If None, no dimension reduction is performed
Returns
-------
projection: np array of shape (n_groups, n_components, n_features)
The projection matrix that projects data in reduced space
reduced: np array of shape (n_groups, n_components, n_samples)
Reduced data
"""
srm = IdentifiableFastSRM(
n_components=n_components,
tol=1e-10,
aggregate=None,
identifiability="decorr",
)
S = np.array(srm.fit_transform([x for x in X]))
W = np.array(srm.basis_list)
return W, S
def test_random_state():
n_voxels = 500
n_timeframes = 10
n_subjects = 4
X_train = [
np.random.rand(n_voxels, n_timeframes) for _ in range(n_subjects)
]
srm = IdentifiableFastSRM(
identifiability=None,
n_components=5,
n_iter=1,
n_iter_reduced=3,
random_state=None,
)
srm2 = IdentifiableFastSRM(
identifiability=None,
n_components=5,
n_iter=1,
n_iter_reduced=3,
random_state=0,
)
srm3 = IdentifiableFastSRM(
identifiability=None,
n_components=5,
n_iter=1,
n_iter_reduced=3,
random_state=0,
)
S1 = srm.fit_transform(X_train)
S2 = srm2.fit_transform(X_train)
S3 = srm3.fit_transform(X_train)
print(S1)
print(S2)
print(S3)
np.testing.assert_allclose(S2, S3)
for S in [S2, S3]:
with pytest.raises(AssertionError, ):
np.testing.assert_allclose(S, S1)
def test_use_pca():
for i in tqdm(range(20)):
X_train = [np.random.rand(100, 10) for _ in range(3)]
srm = IdentifiableFastSRM(
n_components=5,
use_pca=False,
tol=1e-18,
identifiability="decorr",
n_iter=10000,
)
A = srm.fit_transform(X_train)
srm2 = IdentifiableFastSRM(
n_components=5,
use_pca=True,
n_iter=1,
identifiability="decorr",
tol=1e-18,
n_iter_reduced=10000,
)
B = srm2.fit_transform(X_train)
np.testing.assert_array_almost_equal(A, B, 4)
def test_atlas():
n_voxels = 500
n_timeframes = 100
n_subjects = 4
atlas = np.repeat(np.arange(10), 50)
X_train = [
np.random.rand(n_voxels, n_timeframes) for _ in range(n_subjects)
]
srm = IdentifiableFastSRM(identifiability=None,
atlas=atlas,
n_components=5,
n_iter=1)
S = srm.fit_transform(X_train)
X_pred = srm.inverse_transform(S)
def test_recover_decorr_basis():
n_voxels = 50
n_timeframes = [100, 101]
n_subjects = 4
X, W, S = generate_decorr_friendly_data(n_voxels, n_timeframes, n_subjects,
None, 0, "list_of_list")
srm = IdentifiableFastSRM(n_components=3,
identifiability="decorr",
n_iter=10,
aggregate="mean")
S_pred = srm.fit_transform(X)
S_pred_full = np.concatenate(S_pred, axis=1)
S_full = np.concatenate(S, axis=1)
srm.basis_list = align_basis(srm.basis_list, W)
for i in range(len(srm.basis_list)):
assert_array_almost_equal(srm.basis_list[i], W[i])
def test_fastsrm_class_correctness(
input_format,
low_ram,
tempdir,
atlas,
n_jobs,
n_timeframes,
aggregate,
identifiability,
):
with tempfile.TemporaryDirectory() as datadir:
np.random.seed(0)
X, W, S = generate_ica_friendly_data(n_voxels, n_timeframes,
n_subjects, datadir, 0,
input_format)
XX, n_sessions = apply_input_format(X, input_format)
if tempdir:
temp_dir = datadir
else:
temp_dir = None
srm = IdentifiableFastSRM(
identifiability=identifiability,
n_subjects_ica=n_subjects,
atlas=atlas,
n_components=n_components,
n_iter=1000,
tol=1e-7,
temp_dir=temp_dir,
low_ram=low_ram,
n_jobs=n_jobs,
aggregate=aggregate,
)
# Check that there is no difference between fit_transform
# and fit then transform
shared_response_fittransform = apply_aggregate(srm.fit_transform(X),
aggregate, input_format)
prev_basis = srm.basis_list
# we need to align both basis though...
srm.basis_list = align_basis(srm.basis_list, prev_basis)
basis = [safe_load(b) for b in srm.basis_list]
shared_response_raw = srm.transform(X)
shared_response = apply_aggregate(shared_response_raw, aggregate,
input_format)
for j in range(n_sessions):
assert_array_almost_equal(shared_response_fittransform[j],
shared_response[j], 1)
# Check that the decomposition works
for i in range(n_subjects):
for j in range(n_sessions):
assert_array_almost_equal(shared_response[j].T.dot(basis[i]),
XX[i][j].T)
# Check that if we use all subjects but one if gives almost the
# same shared response
shared_response_partial_raw = srm.transform(X[1:5],
subjects_indexes=list(
range(1, 5)))
shared_response_partial = apply_aggregate(shared_response_partial_raw,
aggregate, input_format)
for j in range(n_sessions):
assert_array_almost_equal(shared_response_partial[j],
shared_response[j])
# Check that if we perform add 2 times the same subject we
# obtain the same decomposition
srm.add_subjects(X[:1], shared_response_raw)
assert_array_almost_equal(safe_load(srm.basis_list[0]),
safe_load(srm.basis_list[-1]))