rcot = RCOT2(significance=parameters['cond_ind_test.significance'],
num_f=parameters['cond_ind_test.num_f'])
pcmci = PCMCI(dataframe,
cond_ind_test=rcot,
selected_variables=parameters['selected_variables'],
var_names=parameters['var_names'],
verbosity=10)
q_matrix = pcmci.get_corrected_pvalues(p_matrix=p_matrix, fdr_method='fdr_bh')
q_matrix_tsbh = pcmci.get_corrected_pvalues(p_matrix=p_matrix, fdr_method='fdr_tsbh')
#%% print results
pcmci._print_significant_links(
p_matrix = p_matrix,
q_matrix = q_matrix,
val_matrix = val_matrix,
alpha_level = 0.1)
#%% get selected parents and fit linear model
q_0 = 0.05
parameters['q_0'] = q_0
parameters['q_matrix'] = q_matrix
parent_dict = pcmci._return_significant_parents(q_matrix, val_matrix, q_0)
parameters['parent_dict'] = parent_dict
med = LinearMediation(dataframe=dataframe) #, data_transform=False)
med.fit_model(all_parents=parent_dict['parents'])
# ======================================================================================================================
results = pcmci.run_pcmci(
tau_max=tau_max,
pc_alpha=pc_alpha,
tau_min=tau_min,
max_combinations=1) #selected_links = dictionary/None
#results = pcmci.run_pcmci(selected_links =None, tau_max=tau_max, pc_alpha = pc_alpha, tau_min = tau_min,save_iterations=False, max_conds_dim=None, max_combinations=1, max_conds_py=None, max_conds_px=None) #selected_links = dictionary/None
#results = pcmci.run_pcmci(selected_links = dictionary, tau_max=tau_max, pc_alpha = pc_alpha, tau_min = tau_min,save_iterations=False, max_conds_dim=None, max_combinations=1, max_conds_py=None, max_conds_px=None) #selected_links = dictionary/None
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'],
fdr_method='fdr_bh')
pcmci._print_significant_links(
p_matrix=results['p_matrix'],
q_matrix=q_matrix, #results['p_matrix']
val_matrix=results['val_matrix'],
alpha_level=alpha_level)
sig = pcmci._return_significant_parents(
pq_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=alpha_level)
all_parents = sig['parents']
link_matrix = sig['link_matrix']
# parents of index of interest:
# parents_neighbors = all_parents, estimates, iterations
parents_MT = all_parents[0]
def caus_gpdc(data, var_names):
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
import sklearn
import tigramite
from tigramite import data_processing as pp
from tigramite import plotting as tp
from tigramite.pcmci import PCMCI
from tigramite.independence_tests import ParCorr, GPDC, CMIknn, CMIsymb
from tigramite.models import LinearMediation, Prediction
data_mask_row = np.zeros(len(data))
for i in range(68904):
if (i % 72) < 30 or (i % 72) > 47:
data_mask_row[i] = True
data_mask = np.zeros(data.shape)
data_mask[:, 0] = data_mask_row
data_mask[:, 1] = data_mask_row
data_mask[:, 2] = data_mask_row
data_mask[:, 9] = data_mask_row
data_mask[:, 10] = data_mask_row
data_mask[:, 11] = data_mask_row
dataframe = pp.DataFrame(data, mask=data_mask)
datatime = np.arange(len(data))
# tp.plot_timeseries(data, datatime, var_names, use_mask=True,
# mask=data_mask, grey_masked_samples='data')
gpdc = GPDC(significance='analytic',
gp_params=None,
use_mask=True,
mask_type='y')
gpdc.generate_and_save_nulldists(sample_sizes=range(495, 501),
null_dist_filename='dc_nulldists.npz')
gpdc.null_dist_filename = 'dc_nulldists.npz'
pcmci_gpdc = PCMCI(dataframe=dataframe,
cond_ind_test=gpdc,
var_names=var_names,
verbosity=1)
# correlations = pcmci.get_lagged_dependencies(tau_max=20)
# lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations,
# setup_args={'var_names':var_names,
# 'x_base':5, 'y_base':.5})
results = pcmci_gpdc.run_pcmci(tau_max=6, tau_min=1, pc_alpha=0.01)
# print("p-values")
# print (results['p_matrix'].round(3))
# print("MCI partial correlations")
# print (results['val_matrix'].round(2))
q_matrix = pcmci_gpdc.get_corrected_pvalues(p_matrix=results['p_matrix'],
fdr_method='fdr_bh')
pcmci_gpdc._print_significant_links(p_matrix=results['p_matrix'],
q_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=0.01)
link_matrix = pcmci_gpdc._return_significant_parents(
pq_matrix=q_matrix, val_matrix=results['val_matrix'],
alpha_level=0.01)['link_matrix']
tp.plot_time_series_graph(
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names,
link_colorbar_label='MCI',
)
return results, link_matrix