def test_add_conditional_manually():
"""Enforce the conditioning on additional variables."""
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag': 5,
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21
}
data = Data()
data.generate_mute_data(10, 3)
ais = ActiveInformationStorage()
# Add a conditional with a lag bigger than the max_lag requested above
settings['add_conditionals'] = (8, 0)
with pytest.raises(IndexError):
ais.analyse_single_process(settings=settings, data=data, process=0)
# Add valid conditionals and test if they were added
settings['add_conditionals'] = [(0, 1), (1, 3)]
ais._initialise(settings, data, 0)
# Get list of conditionals after intialisation and convert absolute samples
# back to lags for comparison.
cond_list = ais._idx_to_lag(ais.selected_vars_full)
assert settings['add_conditionals'][0] in cond_list, (
'First enforced conditional is missing from results.')
assert settings['add_conditionals'][1] in cond_list, (
'Second enforced conditional is missing from results.')
def test_add_conditional_manually():
"""Enforce the conditioning on additional variables."""
settings = {'cmi_estimator': 'JidtKraskovCMI',
'max_lag': 5,
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21}
data = Data()
data.generate_mute_data(10, 3)
ais = ActiveInformationStorage()
# Add a conditional with a lag bigger than the max_lag requested above
settings['add_conditionals'] = (8, 0)
with pytest.raises(IndexError):
ais.analyse_single_process(settings=settings, data=data, process=0)
# Add valid conditionals and test if they were added
settings['add_conditionals'] = [(0, 1), (1, 3)]
ais._initialise(settings, data, 0)
# Get list of conditionals after intialisation and convert absolute samples
# back to lags for comparison.
cond_list = ais._idx_to_lag(ais.selected_vars_full)
assert settings['add_conditionals'][0] in cond_list, (
'First enforced conditional is missing from results.')
assert settings['add_conditionals'][1] in cond_list, (
'Second enforced conditional is missing from results.')
def test_ais_fdr():
settings = {'n_perm_max_seq': 1000, 'n_perm_mi': 1000}
process_0 = {
'selected_vars': [(0, 1), (0, 2), (0, 3)],
'ais_pval': 0.0001,
'ais_sign': True}
process_1 = {
'selected_vars': [(1, 0), (1, 1), (1, 2)],
'ais_pval': 0.031,
'ais_sign': True}
process_2 = {
'selected_vars': [],
'ais_pval': 0.41,
'ais_sign': False}
res_1 = ResultsSingleProcessAnalysis(
n_nodes=3, n_realisations=1000, normalised=True)
res_1._add_single_result(process=0, settings=settings, results=process_0)
res_1._add_single_result(process=1, settings=settings, results=process_1)
res_2 = ResultsSingleProcessAnalysis(
n_nodes=3, n_realisations=1000, normalised=True)
res_2._add_single_result(process=2, settings=settings, results=process_2)
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'alpha_fdr': 0.05,
'max_lag': 3}
data = Data()
data.generate_mute_data(n_samples=100, n_replications=3)
analysis_setup = ActiveInformationStorage()
analysis_setup._initialise(settings=settings, data=data, process=1)
res_pruned = stats.ais_fdr(settings, res_1, res_2)
assert (not res_pruned._single_process[2].selected_vars_sources), (
'Process 2 has not been pruned from results.')
alpha_fdr = res_pruned.settings.alpha_fdr
for k in res_pruned.processes_analysed:
if not res_pruned._single_process[k]['ais_sign']:
assert (res_pruned._single_process[k]['ais_pval'] > alpha_fdr), (
'P-value of non-sign. AIS is not 1.')
assert (not res_pruned._single_process[k]['selected_vars']), (
'List of significant past variables is not empty')
else:
assert (res_pruned._single_process[k]['ais_pval'] < 1), (
'P-value of sign. AIS is not smaller 1.')
assert (res_pruned._single_process[k]['selected_vars']), (
'List of significant past variables is empty')
# Test function call for single result
res_pruned = stats.ais_fdr(settings, res_1)
print('successful call on single result dict.')
# Test None result for insufficient no. permutations, no FDR-corrected
# results (the results class throws an error if no FDR-corrected results
# exist).
res_1.settings['n_perm_mi'] = 2
res_2.settings['n_perm_mi'] = 2
res_pruned = stats.ais_fdr(settings, res_1, res_2)
with pytest.raises(RuntimeError):
res_pruned.get_significant_processes(fdr=True)
