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_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."""
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."""
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_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."""
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.')
