def test_addsubs_wo_fit(tempdir, identifiability):
with tempfile.TemporaryDirectory() as datadir:
X, W, S = generate_data(
n_voxels,
[24, 25],
n_subjects,
n_components,
datadir,
0,
"list_of_list",
)
if tempdir:
temp_dir = datadir
else:
temp_dir = None
srm = IdentifiableFastSRM(
identifiability=identifiability,
n_subjects_ica=n_subjects,
n_components=n_components,
n_iter=10,
temp_dir=temp_dir,
)
srm.add_subjects(X, S)
for i in range(len(W)):
assert_array_almost_equal(safe_load(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]))