q_matrix = results_pcmci['q_matrix'] KeyError: 'q_matrix'

[gaonan0902]

q_matrix = results_pcmci['q_matrix']

KeyError: 'q_matrix'

I am very confused about this problem, here is the complete function defined

def build_link_pcmci_noself(p_data_values, p_agent_names, p_var_sou,
p_var_tar):
"""
build links by n column data
"""
p_data_values = p_data_values.astype(float)
# print(p_data_values)
[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(significance='analytic'))
# run PCMCI
alpha_level = None
results_pcmci = pcmci.run_pcmciplus(tau_min=0,
tau_max=3,
pc_alpha=alpha_level)

# get the result
graph_pcmci = results_pcmci['graph']
print(graph_pcmci)
q_matrix = results_pcmci['q_matrix']
p_matrix = results_pcmci['p_matrix']
print(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 != "<--")
# q_matrix is the corrected p_matrix
# 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[p[0]]
        Target = p_agent_names[j]
        TimeLag = p[1]
        Strength = val_matrix[p[0], j, abs(p[1])]
        Unoriented = None
        if graph_pcmci is not None:
            if 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

[jakobrunge]

q_matrix provides corrected p-values using false-discovery-rate control. It is not returned from run_pcmciplus. But you can get those using the function:

pcmci.get_corrected_pvalues(self, p_matrix, fdr_method='fdr_bh', exclude_contemporaneous=True, tau_min=0, tau_max=1, link_assumptions=None, )