def test_class_srm_inverse_transform(input_format, low_ram, tempdir, atlas,
n_jobs, n_timeframes, aggregate):
with tempfile.TemporaryDirectory() as datadir:
X, W, S = generate_data(n_voxels, n_timeframes, n_subjects,
n_components, datadir, 0, input_format)
if tempdir:
temp_dir = datadir
else:
temp_dir = None
srm = FastSRM(atlas=atlas,
n_components=n_components,
n_iter=10,
temp_dir=temp_dir,
low_ram=low_ram,
verbose=True,
n_jobs=n_jobs,
aggregate=aggregate,
seed=0)
# Check that there is no difference between fit_transform
# and fit then transform
srm.fit(X)
shared_response_raw = srm.transform(X)
# Check inverse transform
if input_format == "list_of_array":
reconstructed_data = srm.inverse_transform(shared_response_raw,
subjects_indexes=[0, 2])
for i, ii in enumerate([0, 2]):
assert_array_almost_equal(reconstructed_data[i], X[ii])
reconstructed_data = srm.inverse_transform(shared_response_raw,
subjects_indexes=None)
for i in range(len(X)):
assert_array_almost_equal(reconstructed_data[i], X[i])
else:
reconstructed_data = srm.inverse_transform(shared_response_raw,
sessions_indexes=[1],
subjects_indexes=[0, 2])
for i, ii in enumerate([0, 2]):
for j, jj in enumerate([1]):
assert_array_almost_equal(reconstructed_data[i][j],
safe_load(X[ii][jj]))
reconstructed_data = srm.inverse_transform(shared_response_raw,
subjects_indexes=None,
sessions_indexes=None)
for i in range(len(X)):
for j in range(len(X[i])):
assert_array_almost_equal(reconstructed_data[i][j],
safe_load(X[i][j]))
def test_fastsrm_identity():
# In this function we test whether fastsrm and DetSRM have
# identical behavior when atlas=None
# We authorize different timeframes for different sessions
# but they should be the same across subject
n_voxels = 8
n_timeframes = [4, 5, 6]
n_subjects = 2
n_components = 3 # number of components used for SRM model
np.random.seed(0)
paths, W, S = generate_data(n_voxels,
n_timeframes,
n_subjects,
n_components,
None,
input_format="list_of_array")
# Test if generated data has the good shape
for subject in range(n_subjects):
assert paths[subject].shape == (n_voxels, np.sum([n_timeframes]))
srm = DetSRM(n_iter=11, features=3, rand_seed=0)
srm.fit(paths)
shared = srm.transform(paths)
fastsrm = FastSRM(atlas=None,
n_components=3,
verbose=True,
seed=0,
n_jobs=1,
n_iter=10)
fastsrm.fit(paths)
shared_fast = fastsrm.transform(paths)
assert_array_almost_equal(shared_fast, np.mean(shared, axis=0))
for i in range(n_subjects):
assert_array_almost_equal(safe_load(fastsrm.basis_list[i]),
srm.w_[i].T)
def test_fastsrm_class_correctness(input_format, low_ram, tempdir, atlas,
n_jobs, n_timeframes, aggregate):
with tempfile.TemporaryDirectory() as datadir:
np.random.seed(0)
X, W, S = generate_data(n_voxels, n_timeframes, n_subjects,
n_components, datadir, 0, input_format)
XX, n_sessions = apply_input_format(X, input_format)
if tempdir:
temp_dir = datadir
else:
temp_dir = None
srm = FastSRM(atlas=atlas,
n_components=n_components,
n_iter=10,
temp_dir=temp_dir,
low_ram=low_ram,
verbose=True,
n_jobs=n_jobs,
aggregate=aggregate,
seed=0)
# Check that there is no difference between fit_transform
# and fit then transform
srm.fit(X)
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)
shared_response_fittransform = apply_aggregate(srm.fit_transform(X),
aggregate, input_format)
for j in range(n_sessions):
assert_array_almost_equal(shared_response_fittransform[j],
shared_response[j])
# 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]))