def generate_gauss_data(n_replications=1, discrete=False):
settings = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
covariance_1 = 0.4
covariance_2 = 0.3
n = 10000
delay = 1
if discrete:
d = np.zeros((3, n - 2*delay, n_replications), dtype=int)
else:
d = np.zeros((3, n - 2*delay, n_replications))
for r in range(n_replications):
proc_1 = np.random.normal(0, 1, size=n)
proc_2 = (covariance_1 * proc_1 + (1 - covariance_1) *
np.random.normal(0, 1, size=n))
proc_3 = (covariance_2 * proc_2 + (1 - covariance_2) *
np.random.normal(0, 1, size=n))
proc_1 = proc_1[(2*delay):]
proc_2 = proc_2[delay:-delay]
proc_3 = proc_3[:-(2*delay)]
if discrete: # discretise data
proc_1_dis, proc_2_dis = est._discretise_vars(
var1=proc_1, var2=proc_2)
proc_1_dis, proc_3_dis = est._discretise_vars(
var1=proc_1, var2=proc_3)
d[0, :, r] = proc_1_dis
d[1, :, r] = proc_2_dis
d[2, :, r] = proc_3_dis
else:
d[0, :, r] = proc_1
d[1, :, r] = proc_2
d[2, :, r] = proc_3
return d
def generate_gauss_data(n_replications=1, discrete=False):
settings = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
covariance_1 = 0.4
covariance_2 = 0.3
n = 10000
delay = 1
if discrete:
d = np.zeros((3, n - 2 * delay, n_replications), dtype=int)
else:
d = np.zeros((3, n - 2 * delay, n_replications))
for r in range(n_replications):
proc_1 = np.random.normal(0, 1, size=n)
proc_2 = (covariance_1 * proc_1 +
(1 - covariance_1) * np.random.normal(0, 1, size=n))
proc_3 = (covariance_2 * proc_2 +
(1 - covariance_2) * np.random.normal(0, 1, size=n))
proc_1 = proc_1[(2 * delay):]
proc_2 = proc_2[delay:-delay]
proc_3 = proc_3[:-(2 * delay)]
if discrete: # discretise data
proc_1_dis, proc_2_dis = est._discretise_vars(var1=proc_1,
var2=proc_2)
proc_1_dis, proc_3_dis = est._discretise_vars(var1=proc_1,
var2=proc_3)
d[0, :, r] = proc_1_dis
d[1, :, r] = proc_2_dis
d[2, :, r] = proc_3_dis
else:
d[0, :, r] = proc_1
d[1, :, r] = proc_2
d[2, :, r] = proc_3
return d
def test_analytical_surrogates():
# Generate discrete test data.
covariance = 0.4
n = 10000
delay = 1
source = np.random.normal(0, 1, size=n)
target = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
source = source[delay:]
target = target[:-delay]
settings = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
source_dis, target_dis = est._discretise_vars(var1=source, var2=target)
data = Data(np.vstack((source_dis, target_dis)),
dim_order='ps', normalise=False)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 100,
'n_perm_min_stat': 21,
'n_perm_omnibus': 21,
'n_perm_max_seq': 21,
'max_lag_sources': 5,
'min_lag_sources': 1,
'max_lag_target': 5
}
nw = MultivariateTE()
res = nw.analyse_single_target(settings, data, target=1)
assert res.settings.analytical_surrogates, (
'Surrogates were not created analytically.')
def test_analytical_surrogates():
# Test generation of analytical surrogates.
# Generate data and discretise it such that we can use analytical
# surrogates.
expected_mi, source1, source2, target = _get_gauss_data(covariance=0.4)
settings = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
source_dis, target_dis = est._discretise_vars(var1=source1, var2=target)
data = Data(np.hstack((source_dis, target_dis)),
dim_order='sp',
normalise=False)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 100,
'n_perm_min_stat': 21,
'n_perm_omnibus': 21,
'n_perm_max_seq': 21,
'max_lag_sources': 5,
'min_lag_sources': 1,
'max_lag_target': 5
}
nw = MultivariateTE()
res = nw.analyse_single_target(settings, data, target=1)
# Check if generation of analytical surrogates is documented in the
# settings.
assert res.settings.analytical_surrogates, (
'Surrogates were not created analytically.')
def test_discrete_input():
"""Test AIS estimation from discrete data."""
# Generate AR data
order = 1
n = 10000 - order
self_coupling = 0.5
process = np.zeros(n + order)
process[0:order] = np.random.normal(size=(order))
for n in range(order, n + order):
process[n] = self_coupling * process[n - 1] + np.random.normal()
# Discretise data
settings = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
process_dis, temp = est._discretise_vars(var1=process, var2=process)
data = Data(process_dis, dim_order='s', normalise=False)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 2
}
nw = ActiveInformationStorage()
nw.analyse_single_process(settings=settings, data=data, process=0)
def test_discrete_input():
"""Test AIS estimation from discrete data."""
# Generate AR data
order = 1
n = 10000 - order
self_coupling = 0.5
process = np.zeros(n + order)
process[0:order] = np.random.normal(size=(order))
for n in range(order, n + order):
process[n] = self_coupling * process[n - 1] + np.random.normal()
# Discretise data
settings = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
process_dis, temp = est._discretise_vars(var1=process, var2=process)
data = Data(process_dis, dim_order='s', normalise=False)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 2}
nw = ActiveInformationStorage()
nw.analyse_single_process(settings=settings, data=data, process=0)
def test_plot_network():
"""Test results class for multivariate TE network inference."""
covariance = 0.4
n = 10000
delay = 1
normalisation = False
source = np.random.normal(0, 1, size=n)
target_1 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
target_2 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
source = source[delay:]
target_1 = target_1[:-delay]
target_2 = target_2[:-delay]
# Discretise data for speed
settings_dis = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings_dis)
source_dis, target_1_dis = est._discretise_vars(var1=source, var2=target_1)
source_dis, target_2_dis = est._discretise_vars(var1=source, var2=target_2)
data = Data(np.vstack((source_dis, target_1_dis, target_2_dis)),
dim_order='ps',
normalise=normalisation)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 21,
'n_perm_omnibus': 30,
'n_perm_max_seq': 30,
'min_lag_sources': 1,
'max_lag_sources': 2,
'max_lag_target': 1,
'alpha_fdr': 0.5
}
nw = MultivariateTE()
# Analyse a single target and the whole network
res_single = nw.analyse_single_target(settings=settings,
data=data,
target=1)
res_network = nw.analyse_network(settings=settings, data=data)
graph, fig = plot_network(res_single, 'max_te_lag', fdr=False)
plt.close(fig)
graph, fig = plot_network(res_network, 'max_te_lag', fdr=False)
plt.close(fig)
for sign_sources in [True, False]:
graph, fig = plot_selected_vars(res_network,
target=1,
sign_sources=True,
fdr=False)
plt.close(fig)
def _generate_gauss_data(covariance=0.4, n=10000, delay=1, normalise=False):
# Generate two coupled Gaussian time series
source = np.random.normal(0, 1, size=n)
target = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
source = source[delay:]
target = target[:-delay]
# Discretise data for speed
settings = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
source_dis, target_dis = est._discretise_vars(var1=source, var2=target)
return Data(np.vstack((source_dis, target_dis)),
dim_order='ps', normalise=normalise)
def _generate_gauss_data(covariance=0.4, n=10000, delay=1, normalise=False):
# Generate two coupled Gaussian time series
source = np.random.normal(0, 1, size=n)
target = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
source = source[delay:]
target = target[:-delay]
# Discretise data for speed
settings = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
source_dis, target_dis = est._discretise_vars(var1=source, var2=target)
return Data(np.vstack((source_dis, target_dis)),
dim_order='ps',
normalise=normalise)
def test_discrete_input():
"""Test bivariate TE estimation from discrete data."""
# Generate Gaussian test data
covariance = 0.4
n = 10000
delay = 1
source = np.random.normal(0, 1, size=n)
target = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
corr_expected = covariance / (1 * np.sqrt(covariance**2 +
(1 - covariance)**2))
expected_mi = calculate_mi(corr_expected)
source = source[delay:]
target = target[:-delay]
# Discretise data
settings = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
source_dis, target_dis = est._discretise_vars(var1=source, var2=target)
data = Data(np.vstack((source_dis, target_dis)),
dim_order='ps',
normalise=False)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 21,
'n_perm_omnibus': 30,
'n_perm_max_seq': 30,
'min_lag_sources': 1,
'max_lag_sources': 2,
'max_lag_target': 1
}
nw = BivariateTE()
res = nw.analyse_single_target(settings=settings, data=data, target=1)
assert np.isclose(
res._single_target[1].omnibus_te, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: '
'{0}, Actual results: {1}.'.format(
expected_mi, res._single_target[1].omnibus_te))
def test_discrete_input():
"""Test bivariate TE estimation from discrete data."""
# Generate Gaussian test data
covariance = 0.4
n = 10000
delay = 1
source = np.random.normal(0, 1, size=n)
target = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
corr_expected = covariance / (
1 * np.sqrt(covariance**2 + (1-covariance)**2))
expected_mi = calculate_mi(corr_expected)
source = source[delay:]
target = target[:-delay]
# Discretise data
settings = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings)
source_dis, target_dis = est._discretise_vars(var1=source, var2=target)
data = Data(np.vstack((source_dis, target_dis)),
dim_order='ps', normalise=False)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 21,
'n_perm_omnibus': 30,
'n_perm_max_seq': 30,
'min_lag_sources': 1,
'max_lag_sources': 2,
'max_lag_target': 1}
nw = BivariateTE()
res = nw.analyse_single_target(settings=settings, data=data, target=1)
assert np.isclose(
res._single_target[1].omnibus_te, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: '
'{0}, Actual results: {1}.'.format(
expected_mi, res._single_target[1].omnibus_te))
def test_results_network_inference():
"""Test results class for multivariate TE network inference."""
covariance = 0.4
n = 10000
delay = 1
normalisation = False
source = np.random.normal(0, 1, size=n)
target_1 = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
target_2 = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
corr_expected = covariance / (
1 * np.sqrt(covariance**2 + (1-covariance)**2))
expected_mi = calculate_mi(corr_expected)
source = source[delay:]
target_1 = target_1[:-delay]
target_2 = target_2[:-delay]
# Discretise data for speed
settings_dis = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings_dis)
source_dis, target_1_dis = est._discretise_vars(var1=source, var2=target_1)
source_dis, target_2_dis = est._discretise_vars(var1=source, var2=target_2)
data = Data(np.vstack((source_dis, target_1_dis, target_2_dis)),
dim_order='ps', normalise=normalisation)
nw = MultivariateTE()
# TE - single target
res_single_multi_te = nw.analyse_single_target(
settings=settings, data=data, target=1)
# TE whole network
res_network_multi_te = nw.analyse_network(settings=settings, data=data)
nw = BivariateTE()
# TE - single target
res_single_biv_te = nw.analyse_single_target(
settings=settings, data=data, target=1)
# TE whole network
res_network_biv_te = nw.analyse_network(settings=settings, data=data)
nw = MultivariateMI()
# TE - single target
res_single_multi_mi = nw.analyse_single_target(
settings=settings, data=data, target=1)
# TE whole network
res_network_multi_mi = nw.analyse_network(settings=settings, data=data)
nw = BivariateMI()
# TE - single target
res_single_biv_mi = nw.analyse_single_target(
settings=settings, data=data, target=1)
# TE whole network
res_network_biv_mi = nw.analyse_network(settings=settings, data=data)
res_te = [res_single_multi_te, res_network_multi_te, res_single_biv_te,
res_network_biv_te]
res_mi = [res_single_multi_mi, res_network_multi_mi, res_single_biv_mi,
res_network_biv_mi]
res_all = res_te + res_mi
# Check estimated values
for res in res_te:
est_te = res._single_target[1].omnibus_te
assert np.isclose(est_te, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: '
'{0}, Actual results: {1}.'.format(expected_mi, est_te))
for res in res_mi:
est_mi = res._single_target[1].omnibus_mi
assert np.isclose(est_mi, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: '
'{0}, Actual results: {1}.'.format(expected_mi, est_mi))
est_te = res_network_multi_te._single_target[2].omnibus_te
assert np.isclose(est_te, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: {0}, '
'Actual results: {1}.'.format(expected_mi, est_te))
est_mi = res_network_multi_mi._single_target[2].omnibus_mi
assert np.isclose(est_mi, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: {0}, '
'Actual results: {1}.'.format(expected_mi, est_mi))
# Check data parameters in results objects
n_nodes = 3
n_realisations = n - delay - max(
settings['max_lag_sources'], settings['max_lag_target'])
for res in res_all:
assert res.data_properties.n_nodes == n_nodes, 'Incorrect no. nodes.'
assert res.data_properties.n_nodes == n_nodes, 'Incorrect no. nodes.'
assert res.data_properties.n_realisations == n_realisations, (
'Incorrect no. realisations.')
assert res.data_properties.n_realisations == n_realisations, (
'Incorrect no. realisations.')
assert res.data_properties.normalised == normalisation, (
'Incorrect value for data normalisation.')
assert res.data_properties.normalised == normalisation, (
'Incorrect value for data normalisation.')
adj_matrix = res.get_adjacency_matrix('binary', fdr=False)
assert adj_matrix.shape[0] == n_nodes, (
'Incorrect number of rows in adjacency matrix.')
assert adj_matrix.shape[1] == n_nodes, (
'Incorrect number of columns in adjacency matrix.')
assert adj_matrix.shape[0] == n_nodes, (
'Incorrect number of rows in adjacency matrix.')
assert adj_matrix.shape[1] == n_nodes, (
'Incorrect number of columns in adjacency matrix.')
def test_delay_reconstruction():
"""Test the reconstruction of information transfer delays from results."""
covariance = 0.4
corr_expected = covariance / (
1 * np.sqrt(covariance**2 + (1-covariance)**2))
expected_mi = calculate_mi(corr_expected)
n = 10000
delay_1 = 1
delay_2 = 3
delay_3 = 5
normalisation = False
source = np.random.normal(0, 1, size=n)
target_1 = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
target_2 = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
target_3 = (covariance * source + (1 - covariance) *
np.random.normal(0, 1, size=n))
source = source[delay_3:]
target_1 = target_1[(delay_3-delay_1):-delay_1]
target_2 = target_2[(delay_3-delay_2):-delay_2]
target_3 = target_3[:-delay_3]
# Discretise data for speed
settings_dis = {'discretise_method': 'equal',
'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings_dis)
source_dis, target_1_dis = est._discretise_vars(var1=source, var2=target_1)
source_dis, target_2_dis = est._discretise_vars(var1=source, var2=target_2)
source_dis, target_3_dis = est._discretise_vars(var1=source, var2=target_3)
data = Data(
np.vstack((source_dis, target_1_dis, target_2_dis, target_3_dis)),
dim_order='ps', normalise=normalisation)
nw = MultivariateTE()
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 21,
'n_perm_omnibus': 30,
'n_perm_max_seq': 30,
'min_lag_sources': 1,
'max_lag_sources': delay_3 + 1,
'max_lag_target': 1}
res_network = nw.analyse_single_target(
settings=settings, data=data, target=1)
res_network.combine_results(nw.analyse_single_target(
settings=settings, data=data, target=2))
res_network.combine_results(nw.analyse_single_target(
settings=settings, data=data, target=3))
adj_mat = res_network.get_adjacency_matrix('max_te_lag', fdr=False)
print(adj_mat)
assert adj_mat[0, 1] == delay_1, ('Estimate for delay 1 is not correct.')
assert adj_mat[0, 2] == delay_2, ('Estimate for delay 2 is not correct.')
assert adj_mat[0, 3] == delay_3, ('Estimate for delay 3 is not correct.')
for target in range(1, 4):
est_mi = res_network._single_target[target].omnibus_te
assert np.isclose(est_mi, expected_mi, atol=0.05), (
'Estimated TE for target {0} is not correct. Expected: {1}, '
'Actual results: {2}.'.format(target, expected_mi, est_mi))
def test_results_network_inference():
"""Test results class for multivariate TE network inference."""
covariance = 0.4
n = 10000
delay = 1
normalisation = False
source = np.random.normal(0, 1, size=n)
target_1 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
target_2 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
corr_expected = covariance / (1 * np.sqrt(covariance**2 +
(1 - covariance)**2))
expected_mi = calculate_mi(corr_expected)
source = source[delay:]
target_1 = target_1[:-delay]
target_2 = target_2[:-delay]
# Discretise data for speed
settings_dis = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings_dis)
source_dis, target_1_dis = est._discretise_vars(var1=source, var2=target_1)
source_dis, target_2_dis = est._discretise_vars(var1=source, var2=target_2)
data = Data(np.vstack((source_dis, target_1_dis, target_2_dis)),
dim_order='ps',
normalise=normalisation)
nw = MultivariateTE()
# TE - single target
res_single_multi_te = nw.analyse_single_target(settings=settings,
data=data,
target=1)
# TE whole network
res_network_multi_te = nw.analyse_network(settings=settings, data=data)
nw = BivariateTE()
# TE - single target
res_single_biv_te = nw.analyse_single_target(settings=settings,
data=data,
target=1)
# TE whole network
res_network_biv_te = nw.analyse_network(settings=settings, data=data)
nw = MultivariateMI()
# TE - single target
res_single_multi_mi = nw.analyse_single_target(settings=settings,
data=data,
target=1)
# TE whole network
res_network_multi_mi = nw.analyse_network(settings=settings, data=data)
nw = BivariateMI()
# TE - single target
res_single_biv_mi = nw.analyse_single_target(settings=settings,
data=data,
target=1)
# TE whole network
res_network_biv_mi = nw.analyse_network(settings=settings, data=data)
res_te = [
res_single_multi_te, res_network_multi_te, res_single_biv_te,
res_network_biv_te
]
res_mi = [
res_single_multi_mi, res_network_multi_mi, res_single_biv_mi,
res_network_biv_mi
]
res_all = res_te + res_mi
# Check estimated values
for res in res_te:
est_te = res._single_target[1].omnibus_te
assert np.isclose(est_te, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: '
'{0}, Actual results: {1}.'.format(expected_mi, est_te))
for res in res_mi:
est_mi = res._single_target[1].omnibus_mi
assert np.isclose(est_mi, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: '
'{0}, Actual results: {1}.'.format(expected_mi, est_mi))
est_te = res_network_multi_te._single_target[2].omnibus_te
assert np.isclose(est_te, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: {0}, '
'Actual results: {1}.'.format(expected_mi, est_te))
est_mi = res_network_multi_mi._single_target[2].omnibus_mi
assert np.isclose(est_mi, expected_mi, atol=0.05), (
'Estimated TE for discrete variables is not correct. Expected: {0}, '
'Actual results: {1}.'.format(expected_mi, est_mi))
# Check data parameters in results objects
n_nodes = 3
n_realisations = n - delay - max(settings['max_lag_sources'],
settings['max_lag_target'])
for res in res_all:
assert res.data_properties.n_nodes == n_nodes, 'Incorrect no. nodes.'
assert res.data_properties.n_nodes == n_nodes, 'Incorrect no. nodes.'
assert res.data_properties.n_realisations == n_realisations, (
'Incorrect no. realisations.')
assert res.data_properties.n_realisations == n_realisations, (
'Incorrect no. realisations.')
assert res.data_properties.normalised == normalisation, (
'Incorrect value for data normalisation.')
assert res.data_properties.normalised == normalisation, (
'Incorrect value for data normalisation.')
adj_matrix = res.get_adjacency_matrix('binary', fdr=False)
assert adj_matrix._edge_matrix.shape[0] == n_nodes, (
'Incorrect number of rows in adjacency matrix.')
assert adj_matrix._edge_matrix.shape[1] == n_nodes, (
'Incorrect number of columns in adjacency matrix.')
assert adj_matrix._edge_matrix.shape[0] == n_nodes, (
'Incorrect number of rows in adjacency matrix.')
assert adj_matrix._edge_matrix.shape[1] == n_nodes, (
'Incorrect number of columns in adjacency matrix.')
def test_delay_reconstruction():
"""Test the reconstruction of information transfer delays from results."""
covariance = 0.4
corr_expected = covariance / (1 * np.sqrt(covariance**2 +
(1 - covariance)**2))
expected_mi = calculate_mi(corr_expected)
n = 10000
delay_1 = 1
delay_2 = 3
delay_3 = 5
normalisation = False
source = np.random.normal(0, 1, size=n)
target_1 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
target_2 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
target_3 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
source = source[delay_3:]
target_1 = target_1[(delay_3 - delay_1):-delay_1]
target_2 = target_2[(delay_3 - delay_2):-delay_2]
target_3 = target_3[:-delay_3]
# Discretise data for speed
settings_dis = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings_dis)
source_dis, target_1_dis = est._discretise_vars(var1=source, var2=target_1)
source_dis, target_2_dis = est._discretise_vars(var1=source, var2=target_2)
source_dis, target_3_dis = est._discretise_vars(var1=source, var2=target_3)
data = Data(np.vstack(
(source_dis, target_1_dis, target_2_dis, target_3_dis)),
dim_order='ps',
normalise=normalisation)
nw = MultivariateTE()
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 21,
'n_perm_omnibus': 30,
'n_perm_max_seq': 30,
'min_lag_sources': 1,
'max_lag_sources': delay_3 + 1,
'max_lag_target': 1
}
res_network = nw.analyse_single_target(settings=settings,
data=data,
target=1)
res_network.combine_results(
nw.analyse_single_target(settings=settings, data=data, target=2))
res_network.combine_results(
nw.analyse_single_target(settings=settings, data=data, target=3))
adj_mat = res_network.get_adjacency_matrix('max_te_lag', fdr=False)
adj_mat.print_matrix()
assert adj_mat._weight_matrix[0, 1] == delay_1, (
'Estimate for delay 1 is not correct.')
assert adj_mat._weight_matrix[0, 2] == delay_2, (
'Estimate for delay 2 is not correct.')
assert adj_mat._weight_matrix[0, 3] == delay_3, (
'Estimate for delay 3 is not correct.')
for target in range(1, 4):
est_mi = res_network._single_target[target].omnibus_te
assert np.isclose(est_mi, expected_mi, atol=0.05), (
'Estimated TE for target {0} is not correct. Expected: {1}, '
'Actual results: {2}.'.format(target, expected_mi, est_mi))