def a_run_pcmciplus(a_pcmciplus, a_pcmciplus_params):
# Unpack the pcmci and the true parents, and common parameters
dataframe, true_graph, links_coeffs, tau_min, tau_max = a_pcmciplus
# Unpack the parameters
(
pc_alpha,
contemp_collider_rule,
conflict_resolution,
reset_lagged_links,
cond_ind_test_class,
) = a_pcmciplus_params
if cond_ind_test_class == 'oracle_ci':
cond_ind_test = OracleCI(links_coeffs)
elif cond_ind_test_class == 'par_corr':
cond_ind_test = ParCorr()
# Run the PCMCI algorithm with the given parameters
pcmci = PCMCI(dataframe=dataframe,
cond_ind_test=cond_ind_test,
verbosity=2)
results = pcmci.run_pcmciplus(
selected_links=None,
tau_min=tau_min,
tau_max=tau_max,
pc_alpha=pc_alpha,
contemp_collider_rule=contemp_collider_rule,
conflict_resolution=conflict_resolution,
reset_lagged_links=reset_lagged_links,
max_conds_dim=None,
max_conds_py=None,
max_conds_px=None,
)
# Print true links
print("************************")
print("\nTrue Graph")
for lag in range(tau_max):
print("Lag %d = ", lag)
print(true_graph[:, :, lag])
# pcmci.print_significant_links(
# p_matrix=(true_graph != ""),
# val_matrix=true_graph,
# conf_matrix=None,
# q_matrix=None,
# graph=true_graph,
# ambiguous_triples=None,
# alpha_level=0.05)
# Return the results and the expected result
return results['graph'], true_graph
def test_order_independence_pcmciplus(a_pcmciplus_order_independence,
a_pcmciplus_params_order_independence):
# Unpack the pcmci and the true parents, and common parameters
dataframe, true_graph, links_coeffs, tau_min, tau_max = \
a_pcmciplus_order_independence
data = dataframe.values
T, N = data.shape
# Unpack the parameters
(
pc_alpha,
contemp_collider_rule,
conflict_resolution,
reset_lagged_links,
cond_ind_test_class,
) = a_pcmciplus_params_order_independence
if cond_ind_test_class == 'oracle_ci':
cond_ind_test = OracleCI(links_coeffs)
elif cond_ind_test_class == 'par_corr':
cond_ind_test = ParCorr()
# Run the PCMCI algorithm with the given parameters
pcmci = PCMCI(dataframe=dataframe,
cond_ind_test=cond_ind_test,
verbosity=1)
print("************************")
print("\nTrue Graph")
pcmci.print_significant_links(p_matrix=(true_graph == 0),
val_matrix=true_graph,
conf_matrix=None,
q_matrix=None,
graph=true_graph,
ambiguous_triples=None,
alpha_level=0.05)
results = pcmci.run_pcmciplus(
selected_links=None,
tau_min=tau_min,
tau_max=tau_max,
pc_alpha=pc_alpha,
contemp_collider_rule=contemp_collider_rule,
conflict_resolution=conflict_resolution,
reset_lagged_links=reset_lagged_links,
max_conds_dim=None,
max_conds_py=None,
max_conds_px=None,
)
correct_results = results['graph']
for perm in itertools.permutations(range(N)):
print(perm)
data_new = np.copy(data[:, perm])
dataframe = pp.DataFrame(data_new, var_names=list(perm))
pcmci = PCMCI(dataframe=dataframe,
cond_ind_test=cond_ind_test,
verbosity=1)
results = pcmci.run_pcmciplus(
selected_links=None,
tau_min=tau_min,
tau_max=tau_max,
pc_alpha=pc_alpha,
contemp_collider_rule=contemp_collider_rule,
conflict_resolution=conflict_resolution,
reset_lagged_links=reset_lagged_links,
max_conds_dim=None,
max_conds_py=None,
max_conds_px=None,
)
tmp = np.take(correct_results, perm, axis=0)
back_converted_result = np.take(tmp, perm, axis=1)
for tau in range(tau_max + 1):
if not np.allclose(results['graph'][:, :, tau],
back_converted_result[:, :, tau]):
print(tau)
print(results['graph'][:, :, tau])
print(back_converted_result[:, :, tau])
print(back_converted_result[:, :, tau] -
results['graph'][:, :, tau])
print(perm)
# np.allclose(results['graph'], back_converted_result)
np.testing.assert_equal(results['graph'], back_converted_result)
def build_link_pcmci_noself(p_data_values, p_agent_names, p_var_sou,
p_var_tar):
"""
build links by n column data
"""
[times_num, agent_num] = p_data_values.shape
# set the data for PCMCI
data_frame = pp.DataFrame(p_data_values,
var_names=p_agent_names,
missing_flag=BaseConfig.BACKGROUND_VALUE)
# new PCMCI
pcmci = PCMCI(dataframe=data_frame, cond_ind_test=ParCorr())
# run PCMCI
alpha_level = 0.01
results_pcmci = pcmci.run_pcmciplus(tau_min=0,
tau_max=2,
pc_alpha=alpha_level)
# get the result
graph_pcmci = results_pcmci['graph']
q_matrix = results_pcmci['q_matrix']
p_matrix = results_pcmci['p_matrix']
val_matrix = results_pcmci['val_matrix']
conf_matrix = results_pcmci['conf_matrix']
ambiguous_triples = results_pcmci['ambiguous_triples']
# filter these links
links_df = pd.DataFrame(columns=('VarSou', 'VarTar', 'Source', 'Target',
'TimeLag', 'Strength', 'Unoriented'))
if graph_pcmci is not None:
sig_links = (graph_pcmci != "") * (graph_pcmci != "<--")
elif q_matrix is not None:
sig_links = (q_matrix <= alpha_level)
else:
sig_links = (p_matrix <= alpha_level)
for j in range(agent_num):
links = {(p[0], -p[1]): np.abs(val_matrix[p[0], j, abs(p[1])])
for p in zip(*np.where(sig_links[:, j, :]))}
# Sort by value
sorted_links = sorted(links, key=links.get, reverse=True)
for p in sorted_links:
VarSou = p_var_sou
VarTar = p_var_tar
Source = p_agent_names[j]
Target = p_agent_names[p[0]]
TimeLag = p[1]
Strength = val_matrix[p[0], j, abs(p[1])]
Unoriented = None
if graph_pcmci is not None:
if p[1] == 0 and graph_pcmci[j, p[0], 0] == "o-o":
Unoriented = 1
# "unoriented link"
elif graph_pcmci[p[0], j, abs(p[1])] == "x-x":
Unoriented = 1
# "unclear orientation due to conflict"
else:
Unoriented = 0
links_df = links_df.append(pd.DataFrame({
'VarSou': [VarSou],
'VarTar': [VarTar],
'Source': [Source],
'Target': [Target],
'TimeLag': [TimeLag],
'Strength': [Strength],
'Unoriented': [Unoriented]
}),
ignore_index=True)
# remove the self correlation edges
links_df = links_df.loc[links_df['Source'] != links_df['Target']]
return links_df
# set the data for PCMCI
data_frame = pp.DataFrame(p_data_values,
var_names=p_agent_names,
missing_flag=BaseConfig.BACKGROUND_VALUE)
# new PCMCI
pcmci = PCMCI(dataframe=data_frame,
cond_ind_test=ParCorr(significance='analytic'))
# correlations = pcmci.run_bivci(tau_max=10, val_only=True)['val_matrix']
# lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations,
# setup_args={'var_names':p_agent_names, 'figsize':(15, 10),
# 'x_base':5, 'y_base':.5})
# run PCMCI
alpha_level = None
results_pcmci = pcmci.run_pcmciplus(tau_min=0, tau_max=2, pc_alpha=alpha_level)
# get the result
graph_pcmci = results_pcmci['graph']
q_matrix = results_pcmci['q_matrix']
p_matrix = results_pcmci['p_matrix']
val_matrix = results_pcmci['val_matrix']
conf_matrix = results_pcmci['conf_matrix']
ambiguous_triples = results_pcmci['ambiguous_triples']
pcmci.print_significant_links(p_matrix=results_pcmci['p_matrix'],
q_matrix=q_matrix,
val_matrix=results_pcmci['val_matrix'],
alpha_level=0.01)
# filter these links
links_df = pd.DataFrame(columns=('VarSou', 'VarTar', 'Source', 'Target',