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_class_srm_inverse_transform(
input_format,
low_ram,
tempdir,
atlas,
n_jobs,
n_timeframes,
aggregate,
identifiability,
):
with tempfile.TemporaryDirectory() as datadir:
X, W, S = generate_ica_friendly_data(n_voxels, n_timeframes,
n_subjects, datadir, 0,
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=10,
temp_dir=temp_dir,
low_ram=low_ram,
n_jobs=n_jobs,
aggregate=aggregate,
)
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_memory():
n_voxels = 500
n_timeframes = [100, 101]
n_subjects = 4
with tempfile.TemporaryDirectory() as datadir:
X, W, S = generate_ica_friendly_data(n_voxels, n_timeframes,
n_subjects, datadir, 0)
dts = []
for (low_ram, tempdir, n_jobs, aggregate, identifiability) in [
(True, True, 1, "mean", "decorr"),
(False, False, 2, None, "ica"),
(True, True, 1, "mean", None),
(False, False, 1, None, None),
]:
if tempdir:
temp_dir = datadir
else:
temp_dir = None
srm = IdentifiableFastSRM(
identifiability=identifiability,
n_subjects_ica=n_subjects,
atlas=np.arange(n_voxels),
n_components=n_components,
n_iter=100,
n_iter_reduced=100,
temp_dir=temp_dir,
low_ram=low_ram,
tol=0,
n_jobs=n_jobs,
aggregate=aggregate,
memory=datadir + "/memory",
)
t0 = time()
srm.fit(X)
t1 = time()
dts.append(t1 - t0)
shared_response_raw = srm.transform(X)
# Check inverse transform
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]))
srm.clean()
for i in range(len(dts) - 1):
assert dts[0] > dts[i + 1]
def test_fastsrm_class(identifiability):
n_jobs = 1
with tempfile.TemporaryDirectory() as datadir:
np.random.seed(0)
np.random.seed(0)
paths, W, S = generate_ica_friendly_data(n_voxels, n_timeframes,
n_subjects, datadir, 0)
atlas = np.arange(1, n_voxels + 1)
srm = IdentifiableFastSRM(
identifiability=identifiability,
n_subjects_ica=n_subjects,
atlas=atlas,
n_components=n_components,
n_iter=10,
temp_dir=datadir,
low_ram=True,
n_jobs=n_jobs,
)
# Raises an error because model is not fitted yet
with pytest.raises(NotFittedError):
srm.transform(paths)
srm.fit(paths)
# An error can occur if temporary directory already exists
with pytest.raises(
ValueError,
match=("Path %s already exists. When a model "
"is used, filesystem should be "
r"cleaned by using the .clean\(\) "
"method" % srm.temp_dir),
):
# Error can occur if the filesystem is uncleaned
create_temp_dir(srm.temp_dir)
create_temp_dir(srm.temp_dir)
shared_response = srm.transform(paths)
# Raise error when wrong index
with pytest.raises(
ValueError,
match=("subjects_indexes should be either "
"a list, an array or None but "
"received type <class 'int'>"),
):
srm.transform(paths, subjects_indexes=1000)
with pytest.raises(
ValueError,
match=("subjects_indexes should be either "
"a list, an array or None but "
"received type <class 'int'>"),
):
srm.inverse_transform(shared_response, subjects_indexes=1000)
with pytest.raises(
ValueError,
match=("sessions_indexes should be either "
"a list, an array or None but "
"received type <class 'int'>"),
):
srm.inverse_transform(shared_response, sessions_indexes=1000)
with pytest.raises(
ValueError,
match=("Input data imgs has len 5 whereas "
"subject_indexes has len 1. "
"The number of basis used to compute "
"the shared response should be equal to "
"the number of subjects in imgs"),
):
srm.transform(paths, subjects_indexes=[0])
with pytest.raises(
ValueError,
match=("Index 1 of subjects_indexes has value 8 "
"whereas value should be between 0 and 4"),
):
srm.transform(paths[:2], subjects_indexes=[0, 8])
with pytest.raises(
ValueError,
match=("Index 1 of sessions_indexes has value 8 "
"whereas value should be between 0 and 1"),
):
srm.inverse_transform(shared_response, sessions_indexes=[0, 8])
# Check behavior of .clean
assert os.path.exists(srm.temp_dir_)
srm.clean()
assert not os.path.exists(srm.temp_dir_)