def test_permutation_distance():
"""Test one way permutation test"""
covset = generate_cov(10, 5)
labels = np.array([0, 1]).repeat(5)
groups = np.array([0] * 5 + [1] * 5)
with pytest.raises(ValueError):
PermutationDistance(mode='badmode')
# pairwise
p = PermutationDistance(100, mode='pairwise')
p.test(covset, labels)
# with group
p.test(covset, labels, groups=groups)
# t-test
p = PermutationDistance(100, mode='ttest')
p.test(covset, labels)
# f-test
p = PermutationDistance(100, mode='ftest')
p.test(covset, labels)
# with custom estimator
p = PermutationDistance(10, mode='pairwise', estimator=CSP(2, log=False))
p.test(covset, labels)
# unique perms
p = PermutationDistance(1000)
p.test(covset, labels)
p.plot(nbins=2)
def test_permutation_pairwise_plot(get_covmats, get_labels):
"""Test one way permutation with estimator"""
n_matrices, n_channels, n_classes = 6, 3, 2
covmats = get_covmats(n_matrices, n_channels)
labels = get_labels(n_matrices, n_classes)
p = PermutationDistance(100, mode="pairwise")
p.test(covmats, labels)
p.plot(nbins=2)
def test_permutation_distance():
"""Test one way permutation test"""
covset = generate_cov(10, 5)
labels = np.array([0, 1]).repeat(5)
assert_raises(ValueError, PermutationDistance, mode='badmode')
# pairwise
p = PermutationDistance(100, mode='pairwise')
p.test(covset, labels)
# t-test
p = PermutationDistance(100, mode='ttest')
p.test(covset, labels)
# f-test
p = PermutationDistance(100, mode='ftest')
p.test(covset, labels)
# with custom estimator
p = PermutationDistance(10, mode='pairwise', estimator=CSP(2, log=False))
p.test(covset, labels)
# unique perms
p = PermutationDistance(1000)
p.test(covset, labels)
p.plot(nbins=2)
# compute covariance matrices
covmats = Covariances().fit_transform(epochs_data)
n_perms = 500
###############################################################################
# Pairwise distance based permutation test
###############################################################################
t_init = time()
p_test = PermutationDistance(n_perms, metric='riemann', mode='pairwise')
p, F = p_test.test(covmats, labels)
duration = time() - t_init
fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
p_test.plot(nbins=10, axes=axes)
plt.title('Pairwise distance - %.2f sec.' % duration)
print('p-value: %.3f' % p)
sns.despine()
plt.tight_layout()
plt.show()
###############################################################################
# t-test distance based permutation test
###############################################################################
t_init = time()
p_test = PermutationDistance(n_perms, metric='riemann', mode='ttest')
p, F = p_test.test(covmats, labels)
duration = time() - t_init
# compute covariance matrices
covmats = Covariances().fit_transform(epochs_data)
n_perms = 500
###############################################################################
# Pairwise distance based permutation test
###############################################################################
t_init = time()
p_test = PermutationDistance(n_perms, metric='riemann', mode='pairwise')
p, F = p_test.test(covmats, labels)
duration = time() - t_init
fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
p_test.plot(nbins=10, axes=axes)
plt.title('Pairwise distance - %.2f sec.' % duration)
print('p-value: %.3f' % p)
sns.despine()
plt.tight_layout()
plt.show()
###############################################################################
# t-test distance based permutation test
###############################################################################
t_init = time()
p_test = PermutationDistance(n_perms, metric='riemann', mode='ttest')
p, F = p_test.test(covmats, labels)
duration = time() - t_init