def test_calculate_mean():
"""Test if mean over CMI estimates is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
res_0 = pickle.load(
open(os.path.join(os.path.dirname(__file__), 'data/mute_results_0.p'),
'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
cmi = comp._calculate_cmi_all_links(data)
cmi_mean = comp._calculate_mean([cmi, cmi])
for t in comp.union.targets_analysed:
assert (cmi_mean[t] == cmi[t]).all(), ('Error in mean of CMI for '
'target {0}'.format(t))
if len(cmi[t]) == 0: # skip if no links in results
continue
assert (cmi_mean[t] == cmi[t][0]).all(), (
'Error in mean of CMI for target {0} - actual: ({1}), expected: '
'({2})'.format(t, cmi_mean[t], cmi[t][0]))
def test_calculate_mean():
"""Test if mean over CMI estimates is calculated correctly."""
dat = Data()
dat.generate_mute_data(100, 5)
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_0.pkl'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
cmi = comp._calculate_cmi_all_links(dat)
cmi_mean = comp._calculate_mean([cmi, cmi])
for t in comp.union['targets']:
assert (cmi_mean[t] == cmi[t]).all(), ('Error in mean of CMI for '
'target {0}'.format(t))
def test_calculate_cmi_all_links():
"""Test if the CMI is estimated correctly."""
expected_mi, source, source_uncorr, target = _get_gauss_data()
source = source[1:]
source_uncorr = source_uncorr[1:]
target = target[:-1]
data = Data(np.hstack((source, target)), dim_order='sp', normalise=False)
res_0 = pickle.load(
open(os.path.join(os.path.dirname(__file__), 'data/mute_results_0.p'),
'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
# Set selected variable to the source, one sample in the past of the
# current_value (1, 5).
comp.union._single_target[1]['selected_vars_sources'] = [(0, 4)]
cmi = comp._calculate_cmi_all_links(data)
print('correlated Gaussians: TE result {0:.4f} bits; expected to be '
'{1:0.4f} bit for the copy'.format(cmi[1][0], expected_mi))
assert np.isclose(cmi[1][0], expected_mi, atol=0.05), (
'Estimated TE {0:0.6f} differs from expected TE {1:0.6f}.'.format(
cmi[1][0], expected_mi))
def test_calculate_mean():
"""Test if mean over CMI estimates is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_results_0.p'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
cmi = comp._calculate_cmi_all_links(data)
cmi_mean = comp._calculate_mean([cmi, cmi])
for t in comp.union.targets_analysed:
assert (cmi_mean[t] == cmi[t]).all(), ('Error in mean of CMI for '
'target {0}'.format(t))
def test_calculate_cmi_all_links():
"""Test if the CMI is estimated correctly."""
dat = Data()
n = 1000
cov = 0.4
source = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
target = [0] + [sum(pair) for pair in zip(
[cov * y for y in source[0:n - 1]],
[(1 - cov) * y for y in
[rn.normalvariate(0, 1) for r in range(n - 1)]])]
dat.set_data(np.vstack((source, target)), 'ps')
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_0.pkl'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
comp.union[1]['selected_vars_sources'] = [(0, 4)]
cmi = comp._calculate_cmi_all_links(dat)
cmi_expected = np.log(1 / (1 - cov ** 2))
print('correlated Gaussians: TE result {0:.4f} bits; expected to be '
'{1:0.4f} bit for the copy'.format(cmi[1][0], cmi_expected))
np.testing.assert_almost_equal(
cmi[1][0], cmi_expected, decimal=1,
err_msg='when calculating cmi for correlated Gaussians.')
def test_get_permuted_replications():
"""Test if permutation of replications works."""
# Load previously generated example data
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
# Check permutation for dependent samples test: Replace realisations by
# zeros and ones, check if realisations get swapped correctly.
dat1 = Data()
dat1.normalise = False
dat1.set_data(np.zeros((5, 100, 5)), 'psr')
dat2 = Data()
dat2.normalise = False
dat2.set_data(np.ones((5, 100, 5)), 'psr')
[cond_a_perm, cv_a_perm, cond_b_perm,
cv_b_perm] = comp._get_permuted_replications(data_a=dat1,
data_b=dat2,
target=1)
n_vars = cond_a_perm.shape[1]
assert (np.sum(cond_a_perm + cond_b_perm, axis=1) == n_vars).all(), (
'Dependent samples permutation did not work correctly.')
assert np.logical_xor(cond_a_perm, cond_b_perm).all(), (
'Dependent samples permutation did not work correctly.')
# Check permutations for independent samples test: Check the sum over
# realisations.
comp_settings['stats_type'] = 'independent'
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
[cond_a_perm, cv_a_perm, cond_b_perm,
cv_b_perm] = comp._get_permuted_replications(data_a=dat1,
data_b=dat2,
target=1)
n_samples = n_vars * dat1.n_realisations((0, comp.union['max_lag']))
assert np.sum(cond_a_perm + cond_b_perm, axis=None) == n_samples, (
'Independent samples permutation did not work correctly.')
# test unequal number of replications
dat2.generate_mute_data(100, 7)
with pytest.raises(AssertionError):
comp._get_permuted_replications(data_a=dat1, data_b=dat2, target=1)
def test_create_union_network():
"""Test creation of union of multiple networks."""
dat1 = Data()
dat1.generate_mute_data(100, 5)
dat2 = Data()
dat2.generate_mute_data(100, 5)
# Load previously generated example data
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_0.pkl'))
res_1 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_1.pkl'))
# comparison settings
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'independent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
src_1 = [(0, 2), (0, 1)]
src_2 = [(0, 4), (0, 5)]
res_0[1]['selected_vars_sources'] = src_1
res_1[1]['selected_vars_sources'] = src_2
comp._create_union(res_0, res_1)
ref_targets = [0, 1, 2]
assert (comp.union['targets'] == ref_targets).all(), (
'Union does not include all targets.')
assert np.array([True for i in ref_targets if
i in comp.union.keys()]).all(), (
'Not all targets contained in union network.')
assert comp.union['max_lag'] == res_0[0]['current_value'][1], (
'The max. lag was not defined correctly.')
src_union = comp._idx_to_lag(comp.union[1]['selected_vars_sources'],
comp.union['max_lag'])
assert src_union == (src_1 + src_2), ('Sources for target 1 were not '
'combined correctly.')
# unequal current values in single networks
res_0[1]['current_value'] = (1, 7) # the original is (1, 5)
with pytest.raises(ValueError):
comp._create_union(res_0, res_1)
def test_create_union_network():
"""Test creation of union of multiple networks."""
dat1 = Data()
dat1.generate_mute_data(100, 5)
dat2 = Data()
dat2.generate_mute_data(100, 5)
# Load previously generated example data
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
# comparison settings
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'independent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
src_1 = [(0, 2), (0, 1)]
src_2 = [(0, 4), (0, 5)]
res_0._single_target[1].selected_vars_sources = src_1
res_1._single_target[1].selected_vars_sources = src_2
comp._create_union(res_0, res_1)
ref_targets = np.array([0, 1, 2])
assert (comp.union.targets_analysed == ref_targets).all(), (
'Union does not include all targets.')
assert np.array([
True for i in ref_targets if i in comp.union.keys()]).all(), (
'Not all targets contained in union network.')
assert comp.union['max_lag'] == res_0._single_target[1].current_value[1], (
'The max. lag was not defined correctly.')
src_union = comp._idx_to_lag(
comp.union._single_target[1]['selected_vars_sources'],
comp.union['max_lag'])
assert src_union == (src_1 + src_2), (
'Sources for target 1 were not combined correctly.')
# unequal current values in single networks
res_0._single_target[1].current_value = (1, 7) # the original is (1, 5)
with pytest.raises(ValueError):
comp._create_union(res_0, res_1)
def test_p_value_union():
"""Test if the p-value is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'n_perm_comp': 6,
'alpha_comp': 0.2,
'tail_comp': 'one_bigger',
'stats_type': 'independent'
}
comp = NetworkComparison()
comp.compare_within(comp_settings, res_0, res_1, data, data)
# Replace the surrogate CMI by all zeros for source 0 and all ones for
# source 1. Set the CMI difference to 0.5 for both sources. Check if this
# results in one significant and one non-significant result with the
# correct p-values.
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
comp._calculate_cmi_diff_within(data, data)
comp._create_surrogate_distribution_within(data, data)
target = 1
source = 0
comp.cmi_surr[target] = np.zeros((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1 / comp_settings['n_perm_comp'], (
'The p-value was not calculated correctly: {0}'.format(p[target]))
comp.cmi_surr[target] = np.ones((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert not s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1.0, (
'The p-value was not calculated correctly: {0}'.format(p[target]))
def test_p_value_union():
"""Test if the p-value is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'n_perm_comp': 6,
'alpha_comp': 0.2,
'tail_comp': 'one_bigger',
'stats_type': 'independent'
}
comp = NetworkComparison()
comp.compare_within(comp_settings, res_0, res_1, data, data)
# Replace the surrogate CMI by all zeros for source 0 and all ones for
# source 1. Set the CMI difference to 0.5 for both sources. Check if this
# results in one significant and one non-significant result with the
# correct p-values.
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
comp._calculate_cmi_diff_within(data, data)
comp._create_surrogate_distribution_within(data, data)
target = 1
source = 0
comp.cmi_surr[target] = np.zeros((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1 / comp_settings['n_perm_comp'], (
'The p-value was not calculated correctly: {0}'.format(p[target]))
comp.cmi_surr[target] = np.ones((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert not s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1.0, (
'The p-value was not calculated correctly: {0}'.format(p[target]))
def test_p_value_union():
"""Test if the p-value is calculated correctly."""
dat = Data()
dat.generate_mute_data(100, 5)
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_0.pkl'))
res_1 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_1.pkl'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'n_perm_comp': 6,
'alpha_comp': 0.2,
'tail_comp': 'one_bigger',
'stats_type': 'independent'
}
comp = NetworkComparison()
res_comp = comp.compare_within(comp_settings, res_0, res_1, dat, dat)
# Replace the surrogate CMI by all zeros for source 0 and all ones for
# source 1. Set the CMI difference to 0.5 for both sources. Check if this
# results in one significant and one non-significant result with the
# correct p-values.
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
comp._calculate_cmi_diff_within(dat, dat)
comp._create_surrogate_distribution_within(dat, dat)
target = 1
for p in range(comp_settings['n_perm_comp']):
comp.cmi_surr[p][target] = np.array([0, 1])
comp.cmi_diff[target] = np.array([0.5, 0.5])
[p, s] = comp._p_value_union()
assert (s[target] == np.array([True, False])).all(), (
'The significance was not determined '
'correctly: {0}'.format(s[target]))
p_1 = 1 / comp_settings['n_perm_comp']
p_2 = 1.0
print(p[target])
assert (p[target] == np.array([p_1, p_2])).all(), (
'The p-value was not calculated correctly: {0}'
.format(p[target]))
def test_calculate_cmi_all_links():
"""Test if the CMI is estimated correctly."""
data = Data()
n = 1000
cov = 0.4
source = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
target = [0] + [sum(pair) for pair in zip(
[cov * y for y in source[0:n - 1]],
[(1 - cov) * y for y in
[rn.normalvariate(0, 1) for r in range(n - 1)]])]
data.set_data(np.vstack((source, target)), 'ps')
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_results_0.p'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
comp.union._single_target[1]['selected_vars_sources'] = [(0, 4)]
cmi = comp._calculate_cmi_all_links(data)
corr_expected = cov / (1 * np.sqrt(cov**2 + (1-cov)**2))
expected_cmi = calculate_mi(corr_expected)
print('correlated Gaussians: TE result {0:.4f} bits; expected to be '
'{1:0.4f} bit for the copy'.format(cmi[1][0], expected_cmi))
np.testing.assert_almost_equal(
cmi[1][0], expected_cmi, decimal=1,
err_msg='when calculating cmi for correlated Gaussians.')
def test_get_permuted_replications():
"""Test if permutation of replications works."""
# Load previously generated example data
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
# Check permutation for dependent samples test: Replace realisations by
# zeros and ones, check if realisations get swapped correctly.
dat1 = Data()
dat1.normalise = False
dat1.set_data(np.zeros((5, 100, 5)), 'psr')
dat2 = Data()
dat2.normalise = False
dat2.set_data(np.ones((5, 100, 5)), 'psr')
[cond_a_perm,
cv_a_perm,
cond_b_perm,
cv_b_perm] = comp._get_permuted_replications(data_a=dat1,
data_b=dat2,
target=1)
n_vars = cond_a_perm.shape[1]
assert (np.sum(cond_a_perm + cond_b_perm, axis=1) == n_vars).all(), (
'Dependent samples permutation did not work correctly.')
assert np.logical_xor(cond_a_perm, cond_b_perm).all(), (
'Dependent samples permutation did not work correctly.')
# Check permutations for independent samples test: Check the sum over
# realisations.
comp_settings['stats_type'] = 'independent'
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
[cond_a_perm,
cv_a_perm,
cond_b_perm,
cv_b_perm] = comp._get_permuted_replications(data_a=dat1,
data_b=dat2,
target=1)
n_samples = n_vars * dat1.n_realisations((0, comp.union['max_lag']))
assert np.sum(cond_a_perm + cond_b_perm, axis=None) == n_samples, (
'Independent samples permutation did not work correctly.')
# test unequal number of replications
dat2.generate_mute_data(100, 7)
with pytest.raises(AssertionError):
comp._get_permuted_replications(data_a=dat1, data_b=dat2, target=1)
