def test_whitening_inverse_transform(dim_red, rndstate, get_covmats):
"""Test Whitening inverse transform"""
n_trials, n_channels = 20, 6
cov = get_covmats(n_trials, n_channels)
whit = Whitening(dim_red=dim_red).fit(cov)
cov_iw = whit.inverse_transform(whit.transform(cov))
assert cov_iw.shape == (n_trials, n_channels, n_channels)
if dim_red is None:
assert_array_almost_equal(cov, cov_iw)
def test_whitening_transform(dim_red, rndstate, get_covmats):
"""Test Whitening"""
n_trials, n_channels = 20, 6
cov = get_covmats(n_trials, n_channels)
# Test transform
whit = Whitening().fit(cov)
cov_w = whit.transform(cov)
if dim_red is None:
n_comp = n_channels
else:
n_comp = whit.n_components_
assert cov_w.shape == (n_trials, n_comp, n_comp)
# after whitening, mean = identity
assert_array_almost_equal(cov_w.mean(axis=0), np.eye(n_comp))
if dim_red is not None and "max_cond" in dim_red.keys():
assert np.linalg.cond(cov_w.mean(axis=0)) <= max_cond
def test_whitening_error(rndstate, get_covmats):
"""Test Whitening"""
n_trials, n_channels = 20, 6
cov = get_covmats(n_trials, n_channels)
# Test Fit
with pytest.raises(ValueError): # len dim_red not equal to 1
Whitening(dim_red={"n_components": 2, "expl_var": 0.5}).fit(cov)
with pytest.raises(ValueError): # n_components not superior to 1
Whitening(dim_red={"n_components": 0}).fit(cov)
with pytest.raises(ValueError): # n_components not a int
Whitening(dim_red={"n_components": 2.5}).fit(cov)
with pytest.raises(ValueError): # expl_var out of bound
Whitening(dim_red={"expl_var": 0}).fit(cov)
with pytest.raises(ValueError): # expl_var out of bound
Whitening(dim_red={"expl_var": 1.1}).fit(cov)
with pytest.raises(ValueError): # max_cond not strictly superior to 1
Whitening(dim_red={"max_cond": 1}).fit(cov)
with pytest.raises(ValueError): # unknown key
Whitening(dim_red={"abc": 42}).fit(cov)
with pytest.raises(ValueError): # unknown type
Whitening(dim_red="max_cond").fit(cov)
with pytest.raises(ValueError): # unknown type
Whitening(dim_red=20).fit(cov)
def test_whitening_dimred(dim_red, rndstate, get_covmats):
"""Test Whitening"""
n_trials, n_channels = 20, 6
cov = get_covmats(n_trials, n_channels)
w = rndstate.rand(n_trials)
whit = Whitening(dim_red=dim_red).fit(cov, sample_weight=w)
if dim_red is None:
n_comp = n_channels
else:
n_comp = whit.n_components_
if dim_red and list(dim_red.keys())[0] in ["expl_var", "max_cond"]:
assert whit.n_components_ <= n_channels
else:
assert whit.n_components_ == n_comp
assert whit.filters_.shape == (n_channels, n_comp)
assert whit.inv_filters_.shape == (n_comp, n_channels)
def test_whitening_init():
whit = Whitening()
assert whit.metric == "euclid"
assert whit.dim_red is None
assert not whit.verbose