def plot_results(pcmci, results, cond_ind_test, pc_alpha, tau_min, tau_max,
var_names):
"""Generates a network and a timeseries plot for the given results of the PCMCI algorithm and saves them as files.
Args:
pcmci: The PCMCI object used.
results: The results of the PCMCI algorithm.
cond_ind_test: The conditional independence test used.
pc_alpha: Alpha threshold for significance of links.
tau_min: The minimum lag.
tau_max: The maximum lag.
var_names: The names of the variables in the data.
"""
base_path = "experiments/causal_discovery/results/"
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'],
fdr_method='fdr_bh')
link_matrix = pcmci.return_significant_parents(
pq_matrix=q_matrix, val_matrix=results['val_matrix'],
alpha_level=0.01)['link_matrix']
class_prefix = ""
if isinstance(cond_ind_test, RCOT):
class_prefix = f"{cond_ind_test.measure}_num_f_{cond_ind_test.num_f}"
else:
class_prefix = cond_ind_test.measure
file_name_prefix = f"{class_prefix}_alpha_{pc_alpha:.5f}_tau_{tau_min}to{tau_max}"
tp.plot_graph(val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names,
link_colorbar_label='cross-MCI',
node_colorbar_label='auto-MCI',
figsize=(20, 20),
label_fontsize=24,
node_label_size=24,
link_label_fontsize=14,
save_name=base_path + file_name_prefix + "_graph.png")
tp.plot_time_series_graph(val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names,
link_colorbar_label='MCI',
figsize=(20, 20),
label_fontsize=24,
node_label_size=24,
save_name=base_path + file_name_prefix +
"_time_series_graph.png")
pq_matrix=q_matrix, val_matrix=results['val_matrix'],
alpha_level=0.01)['link_matrix']
graph = tp.plot_graph(
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=headers,
link_colorbar_label='cross-MCI',
node_colorbar_label='auto-MCI',
)
if verbose > 1:
if display_images:
graph[0].show()
if save_images:
graph[0].savefig("causal.png")
if verbose > 0:
# Plot time series graph
if display_images:
plot = tp.plot_time_series_graph(val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=headers,
link_colorbar_label='MCI')
if save_images:
plot = tp.plot_time_series_graph(val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=headers,
link_colorbar_label='MCI',
save_name="causal_time_series.png")
data_mask = np.zeros(data.shape)
for t in range(1, T + 1):
if (t % 73) >= 12 and (t % 73) <= 30:
data_mask[t - 1, :] = True
# Initialize dataframe object, specify time axis and variable names
var_names = ['WPSH', 'IO', 'WNP', 'ENSO', 'NAO']
dataframe = pp.DataFrame(data, mask=data_mask)
parcorr = ParCorr(significance='analytic', mask_type='xyz')
pcmci = PCMCI(dataframe=dataframe, cond_ind_test=parcorr)
results = pcmci.run_pcmci(tau_max=12, pc_alpha=0.03)
# Correct p-values
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'],
fdr_method='fdr_bh')
# Plotting
link_matrix = pcmci.return_significant_parents(
pq_matrix=q_matrix, val_matrix=results['val_matrix'],
alpha_level=0.03)['link_matrix']
tp.plot_graph(val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names)
"""
tp.plot_time_series_graph(val_matrix=results['val_matrix'],
link_matrix=link_matrix, var_names=var_names)
"""
plt.savefig('./NWPAC.svg')
tp.plot_graph(
figsize=(5, 5),
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names,
link_colorbar_label='cross-MCI',
node_colorbar_label='auto-MCI',
)
plt.show()
# In[14]:
# Plot time series graph
tp.plot_time_series_graph(
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=var_names,
link_colorbar_label='MCI',
)
plt.show()
# ## 非线性测试
# In[15]:
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
## use `%matplotlib notebook` for interactive figures
# plt.style.use('ggplot')
import sklearn
print('\n\n#### Target Value Predictors: #### \n')
print(*[
str(data_filled.columns[tup[0]]) + ', lag = ' + str(tup[1])
for tup in predictors[target]
],
sep="\n")
link_matrix = np.zeros((N, N, tau_max + 1), dtype='bool')
for j in [target]:
for p in predictors[j]:
link_matrix[p[0], j, abs(p[1])] = 1
# Plot time series graph
tp.plot_time_series_graph(
figsize=(6, 3),
# node_aspect=2.,
val_matrix=np.ones(link_matrix.shape),
link_matrix=link_matrix,
var_names=None,
link_colorbar_label='',
)
plt.show()
#------
pred.fit(target_predictors=predictors,
selected_targets=[target],
tau_max=tau_max,
return_data=True)
new_data_vals = data_filled[study_vars].copy(
) if use_study_vars == True else data_filled.copy()
new_data_vals = new_data_vals.replace(mssng, np.nan)
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