def test_return_local_values():
"""Test estimation of local values."""
max_lag = 5
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'local_values': True, # request calculation of local values
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': max_lag,
'tau': 1
}
data = Data()
data.generate_mute_data(100, 3)
ais = ActiveInformationStorage()
processes = [1, 2]
results = ais.analyse_network(settings, data, processes)
for p in processes:
lais = results.get_single_process(p, fdr=False)['ais']
if lais is np.nan:
continue
assert type(lais) is np.ndarray, (
'LAIS estimation did not return an array of values: {0}'.format(
lais))
assert lais.shape[0] == data.n_replications, (
'Wrong dim (no. replications) in LAIS estimate: {0}'.format(
lais.shape))
assert lais.shape[1] == data.n_realisations_samples((0, max_lag)), (
'Wrong dim (no. samples) in LAIS estimate: {0}'.format(lais.shape))
def test_return_local_values():
"""Test estimation of local values."""
max_lag = 5
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'local_values': True, # request calculation of local values
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': max_lag,
'tau': 1}
data = Data()
data.generate_mute_data(100, 3)
ais = ActiveInformationStorage()
processes = [1, 2]
results = ais.analyse_network(settings, data, processes)
for p in processes:
lais = results.get_single_process(p, fdr=False)['ais']
if lais is np.nan:
continue
assert type(lais) is np.ndarray, (
'LAIS estimation did not return an array of values: {0}'.format(
lais))
assert lais.shape[0] == data.n_replications, (
'Wrong dim (no. replications) in LAIS estimate: {0}'.format(
lais.shape))
assert lais.shape[1] == data.n_realisations_samples((0, max_lag)), (
'Wrong dim (no. samples) in LAIS estimate: {0}'.format(lais.shape))
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_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)
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_compare_jidt_open_cl_estimator():
"""Compare results from OpenCl and JIDT estimators for AIS calculation."""
data = Data()
data.generate_mute_data(1000, 2)
settings = {
'cmi_estimator': 'OpenCLKraskovCMI',
'alpha_mi': 0.05,
'tail_mi': 'one_bigger',
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 5,
'tau': 1
}
processes = [2, 3]
network_analysis = ActiveInformationStorage()
res_opencl = network_analysis.analyse_network(settings, data, processes)
settings['cmi_estimator'] = 'JidtKraskovCMI'
res_jidt = network_analysis.analyse_network(settings, data, processes)
# Note that I require equality up to three digits. Results become more
# exact for bigger data sizes, but this takes too long for a unit test.
ais_opencl_2 = res_opencl._single_process[2].ais
ais_jidt_2 = res_jidt._single_process[2].ais
ais_opencl_3 = res_opencl._single_process[3].ais
ais_jidt_3 = res_jidt._single_process[3].ais
print('AIS for MUTE data proc 2 - opencl: {0} and jidt: {1}'.format(
ais_opencl_2, ais_jidt_2))
print('AIS for MUTE data proc 3 - opencl: {0} and jidt: {1}'.format(
ais_opencl_3, ais_jidt_3))
if not (ais_opencl_2 is np.nan or ais_jidt_2 is np.nan):
assert (ais_opencl_2 - ais_jidt_2) < 0.05, (
'AIS results differ between OpenCl and JIDT estimator.')
else:
assert ais_opencl_2 is ais_jidt_2, (
'AIS results differ between OpenCl and JIDT estimator.')
if not (ais_opencl_3 is np.nan or ais_jidt_3 is np.nan):
assert (ais_opencl_3 - ais_jidt_3) < 0.05, (
'AIS results differ between OpenCl and JIDT estimator.')
else:
assert ais_opencl_3 is ais_jidt_3, (
'AIS results differ between OpenCl and JIDT estimator.')
def test_compare_jidt_open_cl_estimator():
"""Compare results from OpenCl and JIDT estimators for AIS calculation."""
data = Data()
data.generate_mute_data(1000, 2)
settings = {
'cmi_estimator': 'OpenCLKraskovCMI',
'alpha_mi': 0.05,
'tail_mi': 'one_bigger',
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 5,
'tau': 1
}
processes = [2, 3]
network_analysis = ActiveInformationStorage()
res_opencl = network_analysis.analyse_network(settings, data, processes)
settings['cmi_estimator'] = 'JidtKraskovCMI'
res_jidt = network_analysis.analyse_network(settings, data, processes)
# Note that I require equality up to three digits. Results become more
# exact for bigger data sizes, but this takes too long for a unit test.
ais_opencl_2 = res_opencl._single_process[2].ais
ais_jidt_2 = res_jidt._single_process[2].ais
ais_opencl_3 = res_opencl._single_process[3].ais
ais_jidt_3 = res_jidt._single_process[3].ais
print('AIS for MUTE data proc 2 - opencl: {0} and jidt: {1}'.format(
ais_opencl_2, ais_jidt_2))
print('AIS for MUTE data proc 3 - opencl: {0} and jidt: {1}'.format(
ais_opencl_3, ais_jidt_3))
if not (ais_opencl_2 is np.nan or ais_jidt_2 is np.nan):
assert (ais_opencl_2 - ais_jidt_2) < 0.05, (
'AIS results differ between OpenCl and JIDT estimator.')
else:
assert ais_opencl_2 is ais_jidt_2, (
'AIS results differ between OpenCl and JIDT estimator.')
if not (ais_opencl_3 is np.nan or ais_jidt_3 is np.nan):
assert (ais_opencl_3 - ais_jidt_3) < 0.05, (
'AIS results differ between OpenCl and JIDT estimator.')
else:
assert ais_opencl_3 is ais_jidt_3, (
'AIS results differ between OpenCl and JIDT estimator.')
def test_active_information_storage_opencl():
"""Test AIS estimation in MuTE example network."""
data = Data()
data.generate_mute_data(1000, 5)
settings = {
'cmi_estimator': 'OpenCLKraskovCMI',
'max_lag': 5,
'tau': 1,
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [1, 2, 3]
network_analysis = ActiveInformationStorage()
results = network_analysis.analyse_network(settings, data, processes)
print('AIS for MUTE data proc 1: {0}'.format(
results.get_single_process(1, fdr=False)['ais']))
print('AIS for MUTE data proc 2: {0}'.format(
results.get_single_process(2, fdr=False)['ais']))
print('AIS for MUTE data proc 3: {0}'.format(
results.get_single_process(3, fdr=False)['ais']))
def test_active_information_storage_opencl():
"""Test AIS estimation in MuTE example network."""
data = Data()
data.generate_mute_data(1000, 5)
settings = {
'cmi_estimator': 'OpenCLKraskovCMI',
'max_lag': 5,
'tau': 1,
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [1, 2, 3]
network_analysis = ActiveInformationStorage()
results = network_analysis.analyse_network(settings, data, processes)
print('AIS for MUTE data proc 1: {0}'.format(
results.get_single_process(1, fdr=False)['ais']))
print('AIS for MUTE data proc 2: {0}'.format(
results.get_single_process(2, fdr=False)['ais']))
print('AIS for MUTE data proc 3: {0}'.format(
results.get_single_process(3, fdr=False)['ais']))
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_single_source_storage_gaussian():
n = 1000
proc_1 = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
proc_2 = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
# Cast everything to numpy so the idtxl estimator understands it.
dat = Data(np.array([proc_1, proc_2]), dim_order='ps')
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_mi': 50,
'alpha_mi': 0.05,
'tail_mi': 'one_bigger',
'n_perm_max_stat': 21,
'max_lag': 5,
'tau': 1
}
processes = [1]
network_analysis = ActiveInformationStorage()
res = network_analysis.analyse_network(settings, dat, processes)
print('AIS for random normal data without memory (expected is NaN): '
'{0}'.format(res[1]['ais']))
assert res[1]['ais'] is np.nan, ('Estimator did not return nan for '
'memoryless data.')
def test_single_source_storage_gaussian():
n = 1000
proc_1 = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
proc_2 = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
# Cast everything to numpy so the idtxl estimator understands it.
data = Data(np.array([proc_1, proc_2]), dim_order='ps')
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'alpha_mi': 0.05,
'tail_mi': 'one_bigger',
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 5,
'tau': 1
}
processes = [1]
network_analysis = ActiveInformationStorage()
results = network_analysis.analyse_network(settings, data, processes)
print('AIS for random normal data without memory (expected is NaN): '
'{0}'.format(results._single_process[1].ais))
assert results._single_process[1].ais is np.nan, (
'Estimator did not return nan for memoryless data.')
def test_single_source_storage_gaussian():
n = 1000
np.random.seed(SEED)
proc_1 = np.random.normal(0, 1, size=n)
proc_2 = np.random.normal(0, 1, size=n)
# Cast everything to numpy so the idtxl estimator understands it.
data = Data(np.array([proc_1, proc_2]), dim_order='ps')
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'alpha_mi': 0.05,
'tail_mi': 'one_bigger',
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 5,
'tau': 1
}
processes = [1]
network_analysis = ActiveInformationStorage()
results = network_analysis.analyse_network(settings, data, processes)
print('AIS for random normal data without memory (expected is NaN): '
'{0}'.format(results._single_process[1].ais))
assert results._single_process[1].ais is np.nan, (
'Estimator did not return nan for memoryless data.')
def test_analyse_network():
"""Test AIS estimation for the whole network."""
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'max_lag': 5,
'tau': 1}
data = Data()
data.generate_mute_data(10, 3)
ais = ActiveInformationStorage()
# Test analysis of 'all' processes
r = ais.analyse_network(settings, data)
k = list(r.keys())
assert all(np.array(k) == np.arange(data.n_processes)), (
'Network analysis did not run on all targets.')
# Test check for correct definition of processes
with pytest.raises(ValueError): # no list
ais.analyse_network(settings, data=data, processes={})
with pytest.raises(ValueError): # no list of ints
ais.analyse_network(settings, data=data, processes=[1.5, 0.7])
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 = ActiveInformationStorage()
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))
def test_analyse_network():
"""Test AIS estimation for the whole network."""
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 21,
'n_perm_min_stat': 21,
'n_perm_mi': 21,
'max_lag': 5,
'tau': 1}
data = Data()
data.generate_mute_data(10, 3)
ais = ActiveInformationStorage()
# Test analysis of 'all' processes
results = ais.analyse_network(settings, data)
k = results.processes_analysed
assert all(np.array(k) == np.arange(data.n_processes)), (
'Network analysis did not run on all targets.')
# Test check for correct definition of processes
with pytest.raises(ValueError): # no list
ais.analyse_network(settings, data=data, processes={})
with pytest.raises(ValueError): # no list of ints
ais.analyse_network(settings, data=data, processes=[1.5, 0.7])
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 = ActiveInformationStorage()
settings = [{
'add_conditionals': None
}, {
'add_conditionals': (2, 3)
}, {
'add_conditionals': [(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).'
settings = [{
'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) not 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).'
def active_info_storage(self,
res_states=None,
compute_res_states=False,
inputs=None,
init_state=None):
"""
Computes Active Information Storage of the ESN using 'IDTxl' library
Args:
res_states: the reservoir states to be used in computing AIS, shape [<timeserieslength> x self.res_units]
compute_res_states: if True, then res_states are computed for the given 'inputs' and 'init_state'
inputs: shape [<timeserieslength> x self.in_units]
init_state: shape [1 x self.res_units]
Returns:
A tuple of (AIS_results, AIS value for the network)
"""
total_AIS = 0
targets_list = []
for n in range(self.res_units):
sources = []
for i in range(self.res_units):
if self.sparse_mask[i][n] == 1.0:
if i != n:
sources.append(i)
if len(sources) != 0:
targets_list.append(n)
if compute_res_states:
res_states = self.res_states(inputs=inputs,
init_state=init_state)[0]
# length of discarded initial transient
init_transient = 999
# Dataset for IDTxl MultivariateTE()
d = Data(res_states[init_transient:, :], dim_order='sp')
# Settings for ActiveInformationStorage() computation
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag': 3,
'n_perm_max_stat': 200,
'n_perm_min_stat': 200,
'n_perm_mi': 400,
'verbose': False
}
AIS_results = ActiveInformationStorage().analyse_network(
settings=settings, data=d, processes=targets_list)
for i in targets_list:
if AIS_results.get_single_process(process=i,
fdr=False).ais != None:
total_AIS += AIS_results.get_single_process(process=i,
fdr=False).ais
avg_AIS = total_AIS / len(targets_list)
return AIS_results, avg_AIS
def test_ActiveInformationStorage_init():
"""Test instance creation for ActiveInformationStorage class."""
# Test error on missing estimator
settings = {'max_lag': 5}
data = Data()
data.generate_mute_data(10, 3)
ais = ActiveInformationStorage()
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
# Test tau larger than maximum lag
settings['cmi_estimator'] = 'JidtKraskovCMI'
settings['tau'] = 10
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
# Test negative tau and maximum lag
settings['tau'] = -10
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
settings['tau'] = 1
settings['max_lag'] = -5
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
# Invalid: process is not an int
settings['max_lag'] = 5
with pytest.raises(RuntimeError): # no int
ais.analyse_single_process(settings, data, process=1.5)
with pytest.raises(RuntimeError): # negative
ais.analyse_single_process(settings, data, process=-1)
with pytest.raises(RuntimeError): # not in data
ais.analyse_single_process(settings, data, process=10)
with pytest.raises(RuntimeError): # wrong type
ais.analyse_single_process(settings, data, process={})
# 这个例子其实就是对于单个的随机过程来进行分析的，就是看看$X_n^{(k)}$对$X_{n+1}$的贡献，需要提前设置所要考察的最大的k值。
# In[30]:
# %load ./IDTxl/demos/demo_active_information_storage.py
# Import classes
from idtxl.active_information_storage import ActiveInformationStorage
from idtxl.data import Data
# a) Generate test data
data = Data()
data.generate_mute_data(n_samples=1000, n_replications=5)
# b) Initialise analysis object and define settings
network_analysis = ActiveInformationStorage()
settings = {'cmi_estimator': 'JidtGaussianCMI', 'max_lag': 5}
# c) Run analysis
results = network_analysis.analyse_network(settings=settings, data=data)
# d) Plot list of processes with significant AIS to console
print(results.get_significant_processes(fdr=False))
# ### Partial Information Decomposition
# 下面这个是一个异或问题的例子，输入是两个变量，输出是一个变量
# In[32]:
# # %load ./IDTxl/demos/demo_partial_information_decomposition.py
def test_ActiveInformationStorage_init():
"""Test instance creation for ActiveInformationStorage class."""
# Test error on missing estimator
settings = {'max_lag': 5}
data = Data()
data.generate_mute_data(10, 3)
ais = ActiveInformationStorage()
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
# Test tau larger than maximum lag
settings['cmi_estimator'] = 'JidtKraskovCMI'
settings['tau'] = 10
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
# Test negative tau and maximum lag
settings['tau'] = -10
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
settings['tau'] = 1
settings['max_lag'] = -5
with pytest.raises(RuntimeError):
ais.analyse_single_process(settings, data, process=0)
# Invalid: process is not an int
settings['max_lag'] = 5
with pytest.raises(RuntimeError): # no int
ais.analyse_single_process(settings, data, process=1.5)
with pytest.raises(RuntimeError): # negative
ais.analyse_single_process(settings, data, process=-1)
with pytest.raises(RuntimeError): # not in data
ais.analyse_single_process(settings, data, process=10)
with pytest.raises(RuntimeError): # wrong type
ais.analyse_single_process(settings, data, process={})
# Force conditionals
settings['add_conditionals'] = [(0, 1), (1, 3)]
settings['n_perm_max_stat'] = 21
settings['n_perm_min_stat'] = 21
res = ais.analyse_single_process(settings, data, process=0)
# Import classes
from idtxl.active_information_storage import ActiveInformationStorage
from idtxl.data import Data
# a) Generate test data
data = Data()
data.generate_mute_data(n_samples=1000, n_replications=5)
# b) Initialise analysis object and define settings
network_analysis = ActiveInformationStorage()
settings = {'cmi_estimator': 'JidtGaussianCMI',
'max_lag': 5}
# c) Run analysis
results = network_analysis.analyse_network(settings=settings, data=data)
# d) Plot list of processes with significant AIS to console
print(results.get_significant_processes(fdr=False))
# Import classes
from idtxl.active_information_storage import ActiveInformationStorage
from idtxl.data import Data
# a) Generate test data
data = Data()
data.generate_mute_data(n_samples=1000, n_replications=5)
# b) Initialise analysis object and define settings
network_analysis = ActiveInformationStorage()
settings = {'cmi_estimator': 'JidtGaussianCMI', 'max_lag': 5}
# c) Run analysis
results = network_analysis.analyse_network(settings=settings, data=data)
# d) Plot list of processes with significant AIS to console
print(results.get_significant_processes(fdr=False))