def test_permutation_test_H0(numba_conditional):
"""Test that H0 is populated properly during testing."""
rng = np.random.RandomState(0)
data = rng.rand(7, 10, 1) - 0.5
with pytest.warns(RuntimeWarning, match='No clusters found'):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=100, n_permutations=1024, seed=rng)
assert_equal(len(h0), 0)
for n_permutations in (1024, 65, 64, 63):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=0.1, n_permutations=n_permutations, seed=rng)
assert_equal(len(h0), min(n_permutations, 64))
assert isinstance(clust[0], tuple) # sets of indices
for tail, thresh in zip((-1, 0, 1), (-0.1, 0.1, 0.1)):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=thresh, seed=rng, tail=tail, out_type='mask')
assert isinstance(clust[0], np.ndarray) # bool mask
# same as "128 if tail else 64"
assert_equal(len(h0), 2 ** (7 - (tail == 0))) # exact test
def test_permutation_test_H0():
"""Test that H0 is populated properly during testing."""
rng = np.random.RandomState(0)
data = rng.rand(7, 10, 1) - 0.5
with pytest.warns(RuntimeWarning, match='No clusters found'):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=100, n_permutations=1024, seed=rng)
assert_equal(len(h0), 0)
for n_permutations in (1024, 65, 64, 63):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=0.1, n_permutations=n_permutations, seed=rng)
assert_equal(len(h0), min(n_permutations, 64))
assert isinstance(clust[0], tuple) # sets of indices
for tail, thresh in zip((-1, 0, 1), (-0.1, 0.1, 0.1)):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=thresh, seed=rng, tail=tail, out_type='mask')
assert isinstance(clust[0], np.ndarray) # bool mask
# same as "128 if tail else 64"
assert_equal(len(h0), 2 ** (7 - (tail == 0))) # exact test
def test_permutation_test_H0():
"""Test that H0 is populated properly during testing."""
rng = np.random.RandomState(0)
data = rng.rand(7, 10, 1) - 0.5
with warnings.catch_warnings(record=True) as w:
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=100, n_permutations=1024, seed=rng)
assert_equal(len(w), 1)
assert_true('No clusters found' in str(w[0].message))
assert_equal(len(h0), 0)
for n_permutations in (1024, 65, 64, 63):
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=0.1, n_permutations=n_permutations, seed=rng)
assert_equal(len(h0), min(n_permutations, 64))
assert_true(isinstance(clust[0], tuple)) # sets of indices
for tail, thresh in zip((-1, 0, 1), (-0.1, 0.1, 0.1)):
with warnings.catch_warnings(record=True) as w:
t, clust, p, h0 = spatio_temporal_cluster_1samp_test(
data, threshold=thresh, seed=rng, tail=tail, out_type='mask')
assert_equal(len(w), 0)
assert_true(isinstance(clust[0], np.ndarray)) # bool mask
# same as "128 if tail else 64"
assert_equal(len(h0), 2**(7 - (tail == 0))) # exact test
def test_cluster_permutation_t_test_with_connectivity():
"""Test cluster level permutations T-test with connectivity matrix."""
try:
try:
from sklearn.feature_extraction.image import grid_to_graph
except ImportError:
from scikits.learn.feature_extraction.image import grid_to_graph
except ImportError:
return
# we don't care about p-values in any of these, so do fewer permutations
out = permutation_cluster_1samp_test(condition1_1d, n_permutations=50)
connectivity = grid_to_graph(1, condition1_1d.shape[1])
out_connectivity = permutation_cluster_1samp_test(condition1_1d,
n_permutations=50, connectivity=connectivity)
assert_array_equal(out[0], out_connectivity[0])
for a, b in zip(out_connectivity[1], out[1]):
assert_true(np.sum(out[0][a]) == np.sum(out[0][b]))
assert_true(np.all(a[b]))
# test spatio-temporal with no time connectivity (repeat spatial pattern)
connectivity_2 = sparse.coo_matrix(
linalg.block_diag(connectivity.asfptype().todense(),
connectivity.asfptype().todense()))
condition1_2 = np.concatenate((condition1_1d,
condition1_1d), axis=1)
out_connectivity_2 = permutation_cluster_1samp_test(condition1_2,
n_permutations=50, connectivity=connectivity_2)
# make sure we were operating on the same values
split = len(out[0])
assert_array_equal(out[0], out_connectivity_2[0][:split])
assert_array_equal(out[0], out_connectivity_2[0][split:])
# make sure we really got 2x the number of original clusters
n_clust_orig = len(out[1])
assert_true(len(out_connectivity_2[1]) == 2 * n_clust_orig)
# Make sure that we got the old ones back
n_pts = condition1_1d.shape[1]
data_1 = set([np.sum(out[0][b[:n_pts]]) for b in out[1]])
data_2 = set([np.sum(out_connectivity_2[0][a[:n_pts]]) for a in
out_connectivity_2[1][:]])
assert_true(len(data_1.intersection(data_2)) == len(data_1))
# now use the other algorithm
condition1_3 = np.reshape(condition1_2, (40, 2, 350))
out_connectivity_3 = spatio_temporal_cluster_1samp_test(
condition1_3, n_permutations=50,
connectivity=connectivity, max_step=0,
threshold=1.67, check_disjoint=True)
# make sure we were operating on the same values
split = len(out[0])
assert_array_equal(out[0], out_connectivity_3[0][0])
assert_array_equal(out[0], out_connectivity_3[0][1])
# make sure we really got 2x the number of original clusters
assert_true(len(out_connectivity_3[1]) == 2 * n_clust_orig)
# Make sure that we got the old ones back
data_1 = set([np.sum(out[0][b[:n_pts]]) for b in out[1]])
data_2 = set([np.sum(out_connectivity_3[0][a[0], a[1]]) for a in
out_connectivity_3[1]])
assert_true(len(data_1.intersection(data_2)) == len(data_1))
# test new versus old method old method
out_connectivity_4 = spatio_temporal_cluster_1samp_test(
condition1_3, n_permutations=50,
connectivity=connectivity, max_step=2,
threshold=1.67)
out_connectivity_5 = spatio_temporal_cluster_1samp_test(
condition1_3, n_permutations=50,
connectivity=connectivity, max_step=1,
threshold=1.67)
# clutsers could be in a different order
sums_4 = [np.sum(out_connectivity_4[0][a]) for a in out_connectivity_4[1]]
sums_5 = [np.sum(out_connectivity_4[0][a]) for a in out_connectivity_5[1]]
sums_4 = np.sort(sums_4)
sums_5 = np.sort(sums_5)
assert_array_almost_equal(sums_4, sums_5)
