def test_max_statistic_sequential():
data = Data()
data.generate_mute_data(104, 10)
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 21,
'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
}
setup = MultivariateTE()
setup._initialise(settings, data, sources=[0, 1], target=2)
setup.current_value = (0, 4)
setup.selected_vars_sources = [(1, 1), (1, 2)]
setup.selected_vars_full = [(0, 1), (1, 1), (1, 2)]
setup._selected_vars_realisations = np.random.rand(
data.n_realisations(setup.current_value),
len(setup.selected_vars_full))
setup._current_value_realisations = np.random.rand(
data.n_realisations(setup.current_value),
1)
[sign, p, te] = stats.max_statistic_sequential(analysis_setup=setup,
data=data)
def test_define_candidates():
"""Test candidate definition from a list of procs and a list of samples."""
target = 1
tau_target = 3
max_lag_target = 10
current_val = (target, 10)
procs = [target]
samples = np.arange(current_val[1] - 1, current_val[1] - max_lag_target,
-tau_target)
# Test if candidates that are added manually to the conditioning set are
# removed from the candidate set.
nw = MultivariateTE()
nw.current_value = current_val
settings = [
{'add_conditionals': None},
{'add_conditionals': (2, 3)},
{'add_conditionals': [(2, 3), (4, 1)]},
{'add_conditionals': [(1, 9)]},
{'add_conditionals': [(1, 9), (2, 3), (4, 1)]}]
for s in settings:
nw.settings = s
candidates = nw._define_candidates(procs, samples)
assert (1, 9) in candidates, 'Sample missing from candidates: (1, 9).'
assert (1, 6) in candidates, 'Sample missing from candidates: (1, 6).'
assert (1, 3) in candidates, 'Sample missing from candidates: (1, 3).'
if s['add_conditionals'] is not None:
if type(s['add_conditionals']) is tuple:
cond_ind = nw._lag_to_idx([s['add_conditionals']])
else:
cond_ind = nw._lag_to_idx(s['add_conditionals'])
for c in cond_ind:
assert c not in candidates, (
'Sample added erronously to candidates: {}.'.format(c))