def test_lag_to_idx():
n = NetworkAnalysis()
n.current_value = (0, 5)
lag_list = [(1, 5), (1, 4), (1, 3), (1, 2), (1, 1), (1, 0)]
reference_list = [(1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5)]
idx_list = n._lag_to_idx(lag_list)
results = [True for i, j in zip(idx_list, reference_list) if i == j]
assert np.array(results).all(), (
'Lags were not converted to indices correctly.')
with pytest.raises(IndexError):
idx_list = [(1, 7)]
n._idx_to_lag(idx_list)
def test_write_checkpoint():
# Test writing of checkpoints for network analysis
filename_ckp = 'test_checkpoint'
nw = NetworkAnalysis()
nw.current_value = (0, 0)
nw.settings = {'filename_ckp': filename_ckp}
with pytest.raises(FileNotFoundError):
nw._update_checkpoint(filename_ckp)
with pytest.raises(RuntimeError):
nw._write_checkpoint()
def test_lag_to_idx():
n = NetworkAnalysis()
n.current_value = (0, 5)
lag_list = [(1, 5), (1, 4), (1, 3), (1, 2), (1, 1), (1, 0)]
reference_list = [(1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5)]
idx_list = n._lag_to_idx(lag_list)
res = [True for i, j in zip(idx_list, reference_list) if i == j]
assert np.array(res).all(), 'Lags were not converted to indices correctly.'
with pytest.raises(IndexError):
idx_list = [(1, 7)]
n._idx_to_lag(idx_list)
def test_separate_realisations():
n = NetworkAnalysis()
r_1 = np.ones((10, 1))
r_2 = np.ones((10, 1)) * 2
r_3 = np.ones((10, 1)) * 3
idx = [(0, 1), (0, 2), (0, 3)]
n._append_selected_vars_idx(idx)
n._append_selected_vars_realisations(np.hstack((r_1, r_2, r_3)))
print(n._selected_vars_realisations)
[remain, single] = n._separate_realisations(idx, idx[0])
print(single.shape)
a = np.empty((10, 1))
b = np.empty((10, 2))
a[0:10, ] = single
b[0:10, ] = remain
print(a.shape)
print(b)
assert np.all(remain == np.hstack((r_2, r_3))), 'Remainder is incorrect.'
assert np.all(single == r_1), 'Single realisations are incorrect.'
[remain, single] = n._separate_realisations([idx[0]], idx[0])
assert remain is None, 'Remainder should be None.'
def test_separate_realisations():
n = NetworkAnalysis()
r_1 = np.ones((10, 1))
r_2 = np.ones((10, 1)) * 2
r_3 = np.ones((10, 1)) * 3
idx = [(0, 1), (0, 2), (0, 3)]
n._append_selected_vars_idx(idx)
n._append_selected_vars_realisations(np.hstack((r_1, r_2, r_3)))
print(n._selected_vars_realisations)
[remain, single] = n._separate_realisations(idx, idx[0])
print(single.shape)
a = np.empty((10, 1))
b = np.empty((10, 2))
a[0:10, ] = single
b[0:10, ] = remain
print(a.shape)
print(b)
assert np.all(remain == np.hstack((r_2, r_3))), 'Remainder is incorrect.'
assert np.all(single == r_1), 'Single realisations are incorrect.'
[remain, single] = n._separate_realisations([idx[0]], idx[0])
assert remain is None, 'Remainder should be None.'
def test_calculate_single_link():
"""Test calculation of single link (conditional) MI and TE."""
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, source_uncorr, target)), dim_order='sp')
n = NetworkAnalysis()
n._cmi_estimator = JidtKraskovCMI(settings={})
n.settings = {'local_values': False}
current_value = (2, 1)
# Test single link estimation for a single and multiple sources for
# cases: no target vars, source vars/no source vars (tests if the
# conditioning set is built correctly for conditioning='full').
source_realisations = data.get_realisations(current_value, [(0, 0)])[0]
current_value_realisations = data.get_realisations(current_value,
[current_value])[0]
expected_mi = n._cmi_estimator.estimate(current_value_realisations,
source_realisations)
# cond. on second source
cond_realisations = data.get_realisations(current_value, [(1, 0)])[0]
expected_mi_cond1 = n._cmi_estimator.estimate(current_value_realisations,
source_realisations,
cond_realisations)
for sources in ['all', [0]]:
for conditioning in ['full', 'target', 'none']:
for source_vars in [[(0, 0)], [(0, 0), (1, 0)]]:
mi = n._calculate_single_link(data,
current_value,
source_vars,
target_vars=None,
sources=sources,
conditioning=conditioning)
if mi.shape[0] > 1: # array for source='all'
mi = mi[0]
if source_vars == [(0, 0)]: # no conditioning
assert np.isclose(mi, expected_mi, rtol=0.05), (
'Estimated single-link MI ({0}) differs from expected '
'MI ({1}).'.format(mi, expected_mi))
else:
if conditioning == 'full': # cond. on second source
assert np.isclose(mi, expected_mi_cond1, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi_cond1))
else: # no conditioning
assert np.isclose(mi, expected_mi, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi))
# Test single link estimation for a single and multiple sources for
# cases: target vars/no target vars, source vars (tests if the
# conditioning set is built correctly for conditioning='full').
cond_realisations = np.hstack(( # cond. on second source and target
data.get_realisations(current_value, [(1, 0)])[0],
data.get_realisations(current_value, [(2, 0)])[0]))
expected_mi_cond2 = n._cmi_estimator.estimate(
current_value_realisations, source_realisations, cond_realisations)
# cond. on target
cond_realisations = data.get_realisations(current_value, [(2, 0)])[0]
expected_mi_cond3 = n._cmi_estimator.estimate(
current_value_realisations, source_realisations, cond_realisations)
for target_vars in [None, [(2, 0)]]:
for conditioning in ['full', 'target', 'none']:
mi = n._calculate_single_link(data,
current_value,
source_vars=[(0, 0), (1, 0)],
target_vars=target_vars,
sources=sources,
conditioning=conditioning)
if mi.shape[0] > 1: # array for source='all'
mi = mi[0]
if conditioning == 'none': # no conditioning
assert np.isclose(mi, expected_mi, rtol=0.05), (
'Estimated single-link MI ({0}) differs from expected '
'MI ({1}).'.format(mi, expected_mi))
else:
# target only
if target_vars is not None and conditioning == 'target':
assert np.isclose(mi, expected_mi_cond3, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi_cond3))
# target and 2nd source
if target_vars is not None and conditioning == 'full':
assert np.isclose(mi, expected_mi_cond2, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi_cond2))
# target is None, condition on second target
else:
if conditioning == 'full':
assert np.isclose(
mi, expected_mi_cond1, rtol=0.05
), ('Estimated single-link MI ({0}) differs from expected '
'MI ({1}).'.format(mi, expected_mi_cond1))
# Test requested sources not in source vars
with pytest.raises(RuntimeError):
mi = n._calculate_single_link(data,
current_value,
source_vars=[(0, 0), (3, 0)],
target_vars=None,
sources=4,
conditioning='full')
# Test source vars not in data/processes
with pytest.raises(IndexError):
mi = n._calculate_single_link(data,
current_value,
source_vars=[(0, 0), (10, 0)],
target_vars=None,
sources='all',
conditioning='full')
# Test unknown conditioning
with pytest.raises(RuntimeError):
mi = n._calculate_single_link(data,
current_value,
source_vars=[(0, 0)],
conditioning='test')
def test_calculate_single_link():
"""Test calculation of single link (conditional) MI and TE."""
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, source_uncorr, target)), dim_order='sp')
n = NetworkAnalysis()
n._cmi_estimator = JidtKraskovCMI(settings={})
n.settings = {
'local_values': False
}
current_value = (2, 1)
# Test single link estimation for a single and multiple sources for
# cases: no target vars, source vars/no source vars (tests if the
# conditioning set is built correctly for conditioning='full').
source_realisations = data.get_realisations(current_value, [(0, 0)])[0]
current_value_realisations = data.get_realisations(
current_value, [current_value])[0]
expected_mi = n._cmi_estimator.estimate(
current_value_realisations, source_realisations)
# cond. on second source
cond_realisations = data.get_realisations(current_value, [(1, 0)])[0]
expected_mi_cond1 = n._cmi_estimator.estimate(
current_value_realisations, source_realisations, cond_realisations)
for sources in ['all', [0]]:
for conditioning in ['full', 'target', 'none']:
for source_vars in [[(0, 0)], [(0, 0), (1, 0)]]:
mi = n._calculate_single_link(
data, current_value, source_vars, target_vars=None,
sources=sources, conditioning=conditioning)
if mi.shape[0] > 1: # array for source='all'
mi = mi[0]
if source_vars == [(0, 0)]: # no conditioning
assert np.isclose(mi, expected_mi, rtol=0.05), (
'Estimated single-link MI ({0}) differs from expected '
'MI ({1}).'.format(mi, expected_mi))
else:
if conditioning == 'full': # cond. on second source
assert np.isclose(mi, expected_mi_cond1, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi_cond1))
else: # no conditioning
assert np.isclose(mi, expected_mi, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi))
# Test single link estimation for a single and multiple sources for
# cases: target vars/no target vars, source vars (tests if the
# conditioning set is built correctly for conditioning='full').
cond_realisations = np.hstack(( # cond. on second source and target
data.get_realisations(current_value, [(1, 0)])[0],
data.get_realisations(current_value, [(2, 0)])[0]
))
expected_mi_cond2 = n._cmi_estimator.estimate(
current_value_realisations, source_realisations, cond_realisations)
# cond. on target
cond_realisations = data.get_realisations(current_value, [(2, 0)])[0]
expected_mi_cond3 = n._cmi_estimator.estimate(
current_value_realisations, source_realisations, cond_realisations)
for target_vars in [None, [(2, 0)]]:
for conditioning in ['full', 'target', 'none']:
mi = n._calculate_single_link(
data, current_value, source_vars=[(0, 0), (1, 0)],
target_vars=target_vars, sources=sources,
conditioning=conditioning)
if mi.shape[0] > 1: # array for source='all'
mi = mi[0]
if conditioning == 'none': # no conditioning
assert np.isclose(mi, expected_mi, rtol=0.05), (
'Estimated single-link MI ({0}) differs from expected '
'MI ({1}).'.format(mi, expected_mi))
else:
# target only
if target_vars is not None and conditioning == 'target':
assert np.isclose(mi, expected_mi_cond3, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi_cond3))
# target and 2nd source
if target_vars is not None and conditioning == 'full':
assert np.isclose(mi, expected_mi_cond2, rtol=0.05), (
'Estimated single-link MI ({0}) differs from '
'expected MI ({1}).'.format(mi, expected_mi_cond2))
# target is None, condition on second target
else:
if conditioning == 'full':
assert np.isclose(mi, expected_mi_cond1, rtol=0.05), (
'Estimated single-link MI ({0}) differs from expected '
'MI ({1}).'.format(mi, expected_mi_cond1))
# Test requested sources not in source vars
with pytest.raises(RuntimeError):
mi = n._calculate_single_link(
data, current_value, source_vars=[(0, 0), (3, 0)],
target_vars=None, sources=4, conditioning='full')
# Test source vars not in data/processes
with pytest.raises(IndexError):
mi = n._calculate_single_link(
data, current_value, source_vars=[(0, 0), (10, 0)],
target_vars=None, sources='all', conditioning='full')
# Test unknown conditioning
with pytest.raises(RuntimeError):
mi = n._calculate_single_link(
data, current_value, source_vars=[(0, 0)], conditioning='test')