def test_add_conditional_manually():
"""Enforce the conditioning on additional variables."""
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag_sources': 5,
'min_lag_sources': 3
}
nw = MultivariateMI()
data = Data()
data.generate_mute_data()
# Add a conditional with a lag bigger than the max_lag requested above
settings['add_conditionals'] = (8, 0)
with pytest.raises(IndexError):
nw._initialise(settings, data, sources=[1, 2], target=0)
# Add valid conditionals and test if they were added
settings['add_conditionals'] = [(0, 1), (1, 3)]
nw._initialise(settings=settings, data=data, target=0, sources=[1, 2])
# Get list of conditionals after intialisation and convert absolute samples
# back to lags for comparison.
cond_list = nw._idx_to_lag(nw.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_multivariate_mi_one_realisation_per_replication():
"""Test boundary case of one realisation per replication."""
# Create a data set where one pattern fits into the time series exactly
# once, this way, we get one realisation per replication for each variable.
# This is easyer to assert/verify later. We also test data.get_realisations
# this way.
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 21,
'max_lag_sources': 5,
'min_lag_sources': 4
}
target = 0
data = Data(normalise=False)
n_repl = 10
n_procs = 2
n_points = n_procs * (settings['max_lag_sources'] + 1) * n_repl
data.set_data(
np.arange(n_points).reshape(n_procs, settings['max_lag_sources'] + 1,
n_repl), 'psr')
nw_0 = MultivariateMI()
nw_0._initialise(settings, data, 'all', target)
assert (not nw_0.selected_vars_full)
assert (not nw_0.selected_vars_sources)
assert (not nw_0.selected_vars_target)
assert ((nw_0._replication_index == np.arange(n_repl)).all())
assert (nw_0._current_value == (target, settings['max_lag_sources']))
assert (nw_0._current_value_realisations[:, 0] == data.data[target,
-1, :]).all()
def test_multivariate_mi_one_realisation_per_replication():
"""Test boundary case of one realisation per replication."""
# Create a data set where one pattern fits into the time series exactly
# once, this way, we get one realisation per replication for each variable.
# This is easyer to assert/verify later. We also test data.get_realisations
# this way.
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 21,
'max_lag_sources': 5,
'min_lag_sources': 4}
target = 0
data = Data(normalise=False)
n_repl = 10
n_procs = 2
n_points = n_procs * (settings['max_lag_sources'] + 1) * n_repl
data.set_data(np.arange(n_points).reshape(
n_procs,
settings['max_lag_sources'] + 1,
n_repl), 'psr')
nw_0 = MultivariateMI()
nw_0._initialise(settings, data, 'all', target)
assert (not nw_0.selected_vars_full)
assert (not nw_0.selected_vars_sources)
assert (not nw_0.selected_vars_target)
assert ((nw_0._replication_index == np.arange(n_repl)).all())
assert (nw_0._current_value == (target, settings['max_lag_sources']))
assert (nw_0._current_value_realisations[:, 0] ==
data.data[target, -1, :]).all()
def test_faes_method():
"""Check if the Faes method is working."""
settings = {'cmi_estimator': 'JidtKraskovCMI',
'add_conditionals': 'faes',
'max_lag_sources': 5,
'min_lag_sources': 3}
nw_1 = MultivariateMI()
data = Data()
data.generate_mute_data()
sources = [1, 2, 3]
target = 0
nw_1._initialise(settings, data, sources, target)
assert (nw_1._selected_vars_sources ==
[i for i in it.product(sources, [nw_1.current_value[1]])]), (
'Did not add correct additional conditioning vars.')
def test_faes_method():
"""Check if the Faes method is working."""
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'add_conditionals': 'faes',
'max_lag_sources': 5,
'min_lag_sources': 3
}
nw_1 = MultivariateMI()
data = Data()
data.generate_mute_data()
sources = [1, 2, 3]
target = 0
nw_1._initialise(settings, data, sources, target)
assert (nw_1._selected_vars_sources == [
i for i in it.product(sources, [nw_1.current_value[1]])
]), ('Did not add correct additional conditioning vars.')
def test_add_conditional_manually():
"""Enforce the conditioning on additional variables."""
settings = {'cmi_estimator': 'JidtKraskovCMI',
'max_lag_sources': 5,
'min_lag_sources': 3}
nw = MultivariateMI()
data = Data()
data.generate_mute_data()
# Add a conditional with a lag bigger than the max_lag requested above
settings['add_conditionals'] = (8, 0)
with pytest.raises(IndexError):
nw._initialise(settings, data, sources=[1, 2], target=0)
# Add valid conditionals and test if they were added
settings['add_conditionals'] = [(0, 1), (1, 3)]
nw._initialise(settings=settings, data=data, target=0, sources=[1, 2])
# Get list of conditionals after intialisation and convert absolute samples
# back to lags for comparison.
cond_list = nw._idx_to_lag(nw.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.')
