def test_tfa():
from brainiak.factoranalysis.tfa import TFA
import numpy as np
n_voxel = 100
n_tr = 20
K = 5
max_iter = 5
max_num_voxel = n_voxel
max_num_tr = n_tr
tfa = TFA(K=K,
max_iter=max_iter,
verbose=True,
max_num_voxel=max_num_voxel,
max_num_tr=max_num_tr)
assert tfa, "Invalid TFA instance!"
R = np.random.randint(2, high=102, size=(n_voxel, 3))
X = [1, 2, 3]
# Check that does NOT run with wrong data type
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "Input data should be an array" in str(excinfo.value)
X = np.random.rand(n_voxel)
# Check that does NOT run with wrong array dimension
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "Input data should be 2D array" in str(excinfo.value)
X = np.random.rand(n_voxel, n_tr)
R = [1, 2, 3]
# Check that does NOT run with wrong data type
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "coordinate matrix should be an array" in str(excinfo.value)
R = np.random.rand(n_voxel)
# Check that does NOT run with wrong array dimension
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "coordinate matrix should be 2D array" in str(excinfo.value)
R = np.random.randint(2, high=102, size=(n_voxel - 1, 3))
# Check that does NOT run if n_voxel in X and R does not match
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "The number of voxels should be the same in X and R" in str(
excinfo.value)
R = np.random.randint(2, high=102, size=(n_voxel, 3))
tfa.fit(X, R=R)
assert True, "Success running TFA with one subject!"
posterior_size = K * (tfa.n_dim + 1)
assert tfa.local_posterior_.shape[
0] == posterior_size,\
"Invalid result of TFA! (wrong # element in local_posterior)"
weight_method = 'ols'
tfa = TFA(weight_method=weight_method,
K=K,
max_iter=max_iter,
verbose=True,
max_num_voxel=max_num_voxel,
max_num_tr=max_num_tr)
assert tfa, "Invalid TFA instance!"
X = np.random.rand(n_voxel, n_tr)
tfa.fit(X, R=R)
assert True, "Success running TFA with one subject!"
template_prior, _, _ = tfa.get_template(R)
tfa.set_K(K)
tfa.set_seed(200)
tfa.fit(X, R=R, template_prior=template_prior)
assert True, "Success running TFA with one subject and template prior!"
assert tfa.local_posterior_.shape[
0] == posterior_size,\
"Invalid result of TFA! (wrong # element in local_posterior)"
weight_method = 'odd'
tfa = TFA(weight_method=weight_method,
K=K,
max_iter=max_iter,
verbose=True,
max_num_voxel=max_num_voxel,
max_num_tr=max_num_tr)
with pytest.raises(ValueError) as excinfo:
tfa.fit(X, R=R)
assert "'rr' and 'ols' are accepted as weight_method!" in str(
excinfo.value)
def test_tfa():
from brainiak.factoranalysis.tfa import TFA
import numpy as np
n_voxel = 100
n_tr = 20
K = 5
max_iter = 5
max_num_voxel = n_voxel
max_num_tr = n_tr
tfa = TFA(
K=K,
max_iter=max_iter,
verbose=True,
max_num_voxel=max_num_voxel,
max_num_tr=max_num_tr)
assert tfa, "Invalid TFA instance!"
R = np.random.randint(2, high=102, size=(n_voxel, 3))
X = [1, 2, 3]
# Check that does NOT run with wrong data type
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "Input data should be an array" in str(excinfo.value)
X = np.random.rand(n_voxel)
# Check that does NOT run with wrong array dimension
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "Input data should be 2D array" in str(excinfo.value)
X = np.random.rand(n_voxel, n_tr)
R = [1, 2, 3]
# Check that does NOT run with wrong data type
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "coordinate matrix should be an array" in str(excinfo.value)
R = np.random.rand(n_voxel)
# Check that does NOT run with wrong array dimension
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "coordinate matrix should be 2D array" in str(excinfo.value)
R = np.random.randint(2, high=102, size=(n_voxel - 1, 3))
# Check that does NOT run if n_voxel in X and R does not match
with pytest.raises(TypeError) as excinfo:
tfa.fit(X, R=R)
assert "The number of voxels should be the same in X and R" in str(
excinfo.value)
R = np.random.randint(2, high=102, size=(n_voxel, 3))
tfa.fit(X, R=R)
assert True, "Success running TFA with one subject!"
posterior_size = K * (tfa.n_dim + 1)
assert tfa.local_posterior_.shape[
0] == posterior_size,\
"Invalid result of TFA! (wrong # element in local_posterior)"
weight_method = 'ols'
tfa = TFA(
weight_method=weight_method,
K=K,
max_iter=max_iter,
verbose=True,
max_num_voxel=max_num_voxel,
max_num_tr=max_num_tr)
assert tfa, "Invalid TFA instance!"
X = np.random.rand(n_voxel, n_tr)
tfa.fit(X, R=R)
assert True, "Success running TFA with one subject!"
template_prior, _, _ = tfa.get_template(R)
tfa.set_K(K)
tfa.set_seed(200)
tfa.fit(X, R=R, template_prior=template_prior)
assert True, "Success running TFA with one subject and template prior!"
assert tfa.local_posterior_.shape[
0] == posterior_size,\
"Invalid result of TFA! (wrong # element in local_posterior)"
weight_method = 'odd'
tfa = TFA(
weight_method=weight_method,
K=K,
max_iter=max_iter,
verbose=True,
max_num_voxel=max_num_voxel,
max_num_tr=max_num_tr)
with pytest.raises(ValueError) as excinfo:
tfa.fit(X, R=R)
assert "'rr' and 'ols' are accepted as weight_method!" in str(
excinfo.value)
