def test_pcmci(self):
# Setting up strict test level
pc_alpha = 0.05 #[0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
tau_max = 2
alpha_level = 0.01
dataframe = pp.DataFrame(self.data)
cond_ind_test = ParCorr(verbosity=verbosity)
pcmci = PCMCI(dataframe=dataframe,
cond_ind_test=cond_ind_test,
verbosity=verbosity)
results = pcmci.run_pcmci(
tau_max=tau_max,
pc_alpha=pc_alpha,
)
parents = pcmci._return_significant_parents(
pq_matrix=results['p_matrix'],
val_matrix=results['val_matrix'],
alpha_level=alpha_level)['parents']
# print parents
# print self.true_parents
assert_graphs_equal(parents, self.true_parents)
class PCMCIPlugin:
def input(self, inputfile):
self.links_coeffs = {}
infile = open(inputfile, 'r')
for line in infile:
contents = line.split('\t')
var = int(contents[0])
driver = int(contents[1])
lag = int(contents[2])
coeff = float(contents[3])
if (not var in self.links_coeffs):
self.links_coeffs[var] = []
self.links_coeffs[var].append(((driver, lag), coeff))
def run(self):
data, _ = pp.var_process(self.links_coeffs, T=1000)
dataframe = pp.DataFrame(data)
cond_ind_test = ParCorr()
self.pcmciobj = PCMCI(dataframe=dataframe, cond_ind_test=cond_ind_test)
self.results = self.pcmciobj.run_pcmci(tau_max=2, pc_alpha=None)
def output(self, outputfile):
self.pcmciobj.print_significant_links(
p_matrix=self.results['p_matrix'],
val_matrix=self.results['val_matrix'],
alpha_level=0.05)
def issue38():
dpath = os.path.dirname(os.path.abspath(__file__))
fname = 'tigramite_issue_38_input_example.csv'
fpath = os.path.join(dpath, fname)
df = pd.read_csv(fpath, index_col=0)
print(df)
data = df.values
tdf = DataFrame(
data=data,
mask=None,
missing_flag=None,
var_names=df.columns,
datatime=None,
)
indp_test = CMIknn(
# knn=None,
# shuffle_neighbors=None,
# transform=None,
# significance=None,
)
selected_variables = [col_lbl for col_lbl in df.columns if 'i' in col_lbl]
selected_variables_ix = [
df.columns.get_loc(lbl) for lbl in selected_variables
]
print(("Init PCMCI with:"
f"dataframe={tdf},"
f"cond_ind_test={indp_test},"
f"selected_variables={selected_variables_ix},"
f"verbosity=10,"))
pcmci = PCMCI(
dataframe=tdf,
cond_ind_test=indp_test,
selected_variables=selected_variables_ix,
verbosity=10,
)
max_lag = 24
alpha = 0.1
print("Running PCMCI...")
pcmci.run_pcmci(tau_max=max_lag, pc_alpha=alpha)
print("Done successfully! No errors!")
correlations = pcmci.get_lagged_dependencies(tau_max=3)
lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations,
setup_args={
'var_names': headers,
'x_base': 5,
'y_base': .5
})
if verbose > 1:
if display_images:
lag_func_matrix.savefig()
if save_images:
lag_func_matrix.savefig("lag_func.png")
pcmci.verbosity = 1
results = pcmci.run_pcmci(tau_max=tau_max, pc_alpha=None)
#Print results
print("p-values")
print(results['p_matrix'].round(3))
print("MCI partial correlations")
print(results['val_matrix'].round(2))
#Save results to file
p_matrix = results['p_matrix']
with open("p-values.csv", "w") as csv_file:
writer = csv.writer(csv_file,
delimiter=",",
quotechar="|",
quoting=csv.QUOTE_MINIMAL)
#[[[1 2 3]]] Three brackets to get through.
pc_alpha = pcA_set2
pc_alpha_name = 'set2'
elif p == 4:
pc_alpha = pcA_set3
pc_alpha_name = 'set3'
elif p == 5:
pc_alpha = pcA_set4
pc_alpha_name = 'set4'
elif p == 6:
pc_alpha = pcA_none
pc_alpha_name = 'none'
# ======================================================================================================================
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(
a = nc_file.variables["NAOIN"][:]
b = nc_file.variables["NAOIS"][:]
data[:, 4] = a - b
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'],
# In[7]:
correlations = pcmci.get_lagged_dependencies(tau_max=20,
val_only=True)['val_matrix']
lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations,
setup_args={
'figsize': (6, 6),
'var_names': var_names,
'x_base': 10,
'y_base': .5
})
# In[8]:
pcmci.verbosity = 0
results = pcmci.run_pcmci(tau_max=8, pc_alpha=None)
# In[9]:
print("p-values")
print(results['p_matrix'].round(3))
print("MCI partial correlations")
print(results['val_matrix'].round(2))
# In[10]:
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,
val_matrix=results['val_matrix'],
def run_PCMCI(ex, outdic_actors, s, df_splits, map_proj):
#=====================================================================================
#
# 4) PCMCI-algorithm
#
#=====================================================================================
# save output
if ex['SaveTF'] == True:
# from contextlib import redirect_stdout
orig_stdout = sys.stdout
# buffer print statement output to f
if sys.version[:1] == '3':
sys.stdout = f = io.StringIO()
elif sys.version[:1] == '2':
sys.stdout = f = open(os.path.join(ex['fig_subpath'], 'old.txt'),
'w+')
#%%
# amount of text printed:
verbosity = 3
# alpha level for independence test within the pc procedure (finding parents)
pc_alpha = ex['pcA_sets'][ex['pcA_set']]
# alpha level for multiple linear regression model while conditining on parents of
# parents
alpha_level = ex['alpha_level_tig']
print('run tigramite 4, run.pcmci')
print(('alpha level(s) for independence tests within the pc procedure'
'(finding parents): {}'.format(pc_alpha)))
print((
'alpha level for multiple linear regression model while conditining on parents of '
'parents: {}'.format(ex['alpha_level_tig'])))
# Retrieve traintest info
traintest = df_splits
# load Response Variable class
RV = ex[ex['RV_name']]
# create list with all actors, these will be merged into the fulldata array
allvar = ex['vars'][0]
var_names_corr = []
actorlist = []
cols = [[RV.name]]
for var in allvar[:]:
print(var)
actor = outdic_actors[var]
if actor.ts_corr[s].size != 0:
ts_train = actor.ts_corr[s].values
actorlist.append(ts_train)
# create array which numbers the regions
var_idx = allvar.index(var)
n_regions = actor.ts_corr[s].shape[1]
actor.var_info = [[i + 1, actor.ts_corr[s].columns[i], var_idx]
for i in range(n_regions)]
# Array of corresponing regions with var_names_corr (first entry is RV)
var_names_corr = var_names_corr + actor.var_info
cols.append(list(actor.ts_corr[s].columns))
index_dates = actor.ts_corr[s].index
var_names_corr.insert(0, RV.name)
# stack actor time-series together:
fulldata = np.concatenate(tuple(actorlist), axis=1)
print(('There are {} regions in total'.format(fulldata.shape[1])))
# add the full 1D time series of interest as first entry:
fulldata = np.column_stack((RV.RVfullts, fulldata))
df_data = pd.DataFrame(fulldata, columns=flatten(cols), index=index_dates)
if ex['import_prec_ts'] == True:
var_names_full = var_names_corr.copy()
for d in ex['precursor_ts']:
path_data = d[1]
if len(path_data) > 1:
path_data = ''.join(list(path_data))
# skip first col because it is the RV ts
df_data_ext = func_fc.load_hdf5(
path_data)['df_data'].iloc[:, 1:].loc[s]
cols_ts = np.logical_or(df_data_ext.dtypes == 'float64',
df_data_ext.dtypes == 'float32')
cols_ext = list(df_data_ext.columns[cols_ts])
# cols_ext must be of format '{}_{int}_{}'
lab_int = 100
for i, c in enumerate(cols_ext):
char = c.split('_')[1]
if char.isdigit():
pass
else:
cols_ext[i] = c.replace(char, str(lab_int)) + char
lab_int += 1
df_data_ext = df_data_ext[cols_ext]
to_freq = ex['tfreq']
if to_freq != 1:
start_end_date = (ex['sstartdate'], ex['senddate'])
start_end_year = (ex['startyear'], ex['endyear'])
df_data_ext = functions_pp.time_mean_bins(df_data_ext,
to_freq,
start_end_date,
start_end_year,
seldays='part')[0]
# df_data_ext = functions_pp.time_mean_bins(df_data_ext,
# ex, ex['tfreq'],
# seldays='part')[0]
# Expand var_names_corr
n = var_names_full[-1][0] + 1
add_n = n + len(cols_ext)
n_var_idx = var_names_full[-1][-1] + 1
for i in range(n, add_n):
var_names_full.append([i, cols_ext[i - n], n_var_idx])
df_data = df_data.merge(df_data_ext,
left_index=True,
right_index=True)
else:
var_names_full = var_names_corr
bool_train = traintest.loc[s]['TrainIsTrue']
bool_RV_train = np.logical_and(bool_train, traintest.loc[s]['RV_mask'])
dates_train = traintest.loc[s]['TrainIsTrue'][bool_train].index
dates_RV_train = traintest.loc[s]['TrainIsTrue'][bool_RV_train].index
RVfull_train = RV.RVfullts.sel(time=dates_train)
datesfull_train = pd.to_datetime(RVfull_train.time.values)
data = df_data.loc[datesfull_train].values
print((data.shape))
# get RV datamask (same shape als data)
data_mask = [
True if d in dates_RV_train else False for d in datesfull_train
]
data_mask = np.repeat(data_mask, data.shape[1]).reshape(data.shape)
# add traintest mask to fulldata
# dates_all = pd.to_datetime(RV.RVfullts.index)
# dates_RV = pd.to_datetime(RV.RV_ts.index)
dates_all = pd.to_datetime(RV.RVfullts.time.values)
dates_RV = pd.to_datetime(RV.RV_ts.time.values)
df_data['TrainIsTrue'] = [
True if d in datesfull_train else False for d in dates_all
]
df_data['RV_mask'] = [True if d in dates_RV else False for d in dates_all]
# ======================================================================================================================
# tigramite 3
# ======================================================================================================================
T, N = data.shape # Time, Regions
# ======================================================================================================================
# Initialize dataframe object (needed for tigramite functions)
# ======================================================================================================================
dataframe = pp.DataFrame(data=data,
mask=data_mask,
var_names=var_names_full)
# ======================================================================================================================
# pc algorithm: only parents for selected_variables are calculated
# ======================================================================================================================
parcorr = ParCorr(significance='analytic',
mask_type='y',
verbosity=verbosity)
#==========================================================================
# multiple testing problem:
#==========================================================================
pcmci = PCMCI(dataframe=dataframe,
cond_ind_test=parcorr,
selected_variables=None,
verbosity=4)
# selected_variables : list of integers, optional (default: range(N))
# Specify to estimate parents only for selected variables. If None is
# passed, parents are estimated for all variables.
# ======================================================================================================================
#selected_links = dictionary/None
results = pcmci.run_pcmci(tau_max=ex['tigr_tau_max'],
pc_alpha=pc_alpha,
tau_min=0,
max_combinations=ex['max_comb_actors'])
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,
val_matrix=results['val_matrix'],
alpha_level=alpha_level)
# returns all parents, not just causal precursors (of lag>0)
sig = rgcpd.return_sign_parents(pcmci,
pq_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=alpha_level)
all_parents = sig['parents']
# link_matrix = sig['link_matrix']
links_RV = all_parents[0]
df = rgcpd.bookkeeping_precursors(links_RV, var_names_full)
#%%
rgcpd.print_particular_region_new(links_RV, var_names_corr, s,
outdic_actors, map_proj, ex)
#%%
if ex['SaveTF'] == True:
if sys.version[:1] == '3':
fname = f's{s}_' + ex['params'] + '.txt'
file = io.open(os.path.join(ex['fig_subpath'], fname), mode='w+')
file.write(f.getvalue())
file.close()
f.close()
elif sys.version[:1] == '2':
f.close()
sys.stdout = orig_stdout
return df, df_data
def pcmci_causality(data, dt, index, headers, T_data, N_data, maxlag):
T = T_data
N = N_data
tau_max = maxlag
# Verbosity:
# 0 - nothing
# 1 - final graph only
# 2 - everything
verbose_max = 2
verbose = 2
print("======")
# print(list(data)) # got 100 records as itertools.chain object, not numpy df
data = np.array(list(data))
print("data len is ")
print(len(data))
# data = np.fromiter(data, float)
# print(data)
# Initialize dataframe object, specify time axis and variable names
dataframe = pp.DataFrame(data, datatime=dt, var_names=headers)
print(dataframe.var_names)
rcot = RCOT(significance='analytic')
pcmci_rcot = PCMCI(dataframe=dataframe, cond_ind_test=rcot, verbosity=0)
pcmci_rcot.verbosity = 1
results = pcmci_rcot.run_pcmci(tau_max=tau_max, pc_alpha=0.05)
# Print results
print("p-values")
print(results['p_matrix'].round(3))
print("MCI partial correlations")
print(results['val_matrix'].round(2))
# print("inside def pcmci_causality")
# output edges
result_arr = []
# result_arr.append(["effect","cause"])
for index_cause, item in enumerate(results['p_matrix']):
print("index is")
print(index)
print("item is")
print(item)
print("cause is")
cause = headers[index_cause]
print(headers[index_cause])
for index_effect, arr in enumerate(item):
print("effect arr is ")
print(arr)
print("effect name is")
effect = headers[index_effect]
print(headers[index_effect])
for arrItem in arr:
if arrItem < 0.05 and cause != effect:
result_arr.append([effect, cause, index])
print("{} caused by {}".format(effect, cause))
break
with open("pcmci_para_out{}.csv".format(index), "w", newline='') as f:
for row in result_arr:
f.write("%s\n" % ','.join(str(col) for col in row))
# print(pcmci)
return result_arr
parcorr = ParCorr(significance='analytic')
gpdc = GPDC(significance='analytic', gp_params=None)
pcmci_gpdc = PCMCI(
dataframe=dataframe,
cond_ind_test=gpdc,
verbosity=0)
pcmci = PCMCI(
dataframe=dataframe,
cond_ind_test=parcorr,
verbosity=1)
#
min_lag, max_lag = 1,6
results = pcmci.run_pcmci(tau_min = min_lag, tau_max=max_lag, pc_alpha=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,
val_matrix = results['val_matrix'],
alpha_level = 0.05)
link_matrix = pcmci.return_significant_links(pq_matrix = results['p_matrix'],
val_matrix=results['val_matrix'], alpha_level=0.05)['link_matrix']
tp.plot_graph(
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=study_data.columns,
def test(dataframes,max_lags=[4],alpha=[None],tests=['ParCorr'],limit=1):
''' This function performs the PCMCI algorithm for all the dataframes received as parameters, given the hyper-parameters of the conditional
independence test
Args:
dataframes: A list of TIGRAMITE dataframes
max_lags: Maximum number of lags to consider for the laggd time series
alpha: Significance level to perform the parent test
tests: A list of conditional independence test to be performed
limit: A limit for the instances to be considered
Returns:
'''
test_results = []
random.shuffle(dataframes)
total = limit*len(max_lags)*len(alpha)*len(tests)
data_frame_iter = iter(dataframes)
tests_to_evaluate=[]
if 'RCOT' in tests:
rcot = RCOT()
tests_to_evaluate.append(['RCOT',rcot])
if 'GPDC' in tests:
gpdc = GPDC()
tests_to_evaluate.append(['GPDC', gpdc])
if 'ParCorr' in tests:
parcorr = ParCorr(significance='analytic')
tests_to_evaluate.append(['ParCorr',parcorr])
if 'CMIknn' in tests:
cmiknn = CMIknn()
tests_to_evaluate.append(['CMIknn',cmiknn])
unique_complexities = list(set(l[1] for l in dataframes))
counts = {}
for i in unique_complexities:
counts[i] = 0
for test in tests_to_evaluate:
stop = False
for l in max_lags:
for a in alpha:
while not stop:
try:
i = random.sample(dataframes,1)[0]
if counts[i[1]] < limit:
print('evaluating: ' + str(i[3]))
start = time.time()
pcmci = PCMCI(
dataframe=i[2],
cond_ind_test=test[1],
verbosity=0)
# correlations = pcmci.get_lagged_dependencies(tau_max=20)
pcmci.verbosity = 1
results = pcmci.run_pcmci(tau_max=l, pc_alpha=a)
time_lapse = round(time.time() - start, 2)
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'], fdr_method='fdr_bh')
valid_parents = list(pcmci.return_significant_parents(pq_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=a)['parents'].values())
flat_list = []
for sublist in valid_parents:
for item in sublist:
flat_list.append(item)
valid_links = len(flat_list)
test_results.append([i[3], i[0], i[1], l,test[0],a,valid_links,time_lapse])
results_df = pd.DataFrame(test_results,
columns=['representation', 'complexity', 'sample_size', 'max_lag','test','alpha','valid_links_at_alpha',
'learning_time'])
print('results ready to be saved')
results_df.to_csv(
'results/performance_sample_sizes.csv',
index=False)
counts[i[1]] += 1
if all(value == limit for value in counts.values()):
stop = True
except:
print('Hoopla!')
pass
for i in unique_complexities:
counts[i] = 0
def pcmci_causality(data, dt, index, headers, T_data, N_data, maxlag):
T = T_data
N = N_data
# Run settings
# there is another tau_max in lagged dependencies that might be much longer!
tau_max = maxlag
# Verbosity:
# 0 - nothing
# 1 - final graph only
# 2 - everything
verbose_max = 2
verbose = 2
print("======")
# print(list(data)) # got 100 records as itertools.chain object, not numpy df
# Initialize dataframe object, specify time axis and variable names
dataframe = pp.DataFrame(data, datatime=dt, var_names=headers)
print(dataframe.var_names)
rcot = RCOT(significance='analytic')
pcmci_rcot = PCMCI(dataframe=dataframe, cond_ind_test=rcot, verbosity=0)
pcmci_rcot.verbosity = 1
results = pcmci_rcot.run_pcmci(tau_max=tau_max, pc_alpha=0.05)
# Print results
print("p-values")
print(results['p_matrix'].round(3))
print("MCI partial correlations")
print(results['val_matrix'].round(2))
# Save results to file
# p_matrix = results['p_matrix']
# with open("p-values_baseline.csv", "w") as csv_file:
# writer = csv.writer(csv_file, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL)
# # [[[1 2 3]]] Three brackets to get through.
# for sector in p_matrix:
# print("sector: ", sector)
# for row in sector:
# print("row: ", row)
# writer.writerow(row)
# writer.writerow([])
#
# print("inside def pcmci_causality")
# output edges
result_arr = []
for index_cause, item in enumerate(results['p_matrix']):
# print("index is")
# print(index)
# print("item is")
# print(item)
# print("cause is")
cause = headers[index_cause]
# print(headers[index_cause])
for index_effect, arr in enumerate(item):
# print("effect arr is ")
# print(arr)
# print("effect name is")
effect = headers[index_effect]
# print(headers[index_effect])
for arrItem in arr:
if arrItem < 0.05 and cause != effect:
result_arr.append([effect, cause, index])
print("{} caused by {}".format(effect, cause))
break
with open("pcmci_baseline_out.csv", "w", newline='') as f:
for row in result_arr:
f.write("%s\n" % ','.join(str(col) for col in row))
# print(pcmci)
print(result_arr)
return result_arr
def run_pcmci(data, data_mask, var_names, path_outsub2, s, tau_min=0, tau_max=1,
pc_alpha=None, alpha_level=0.05, max_conds_dim=4, max_combinations=1,
max_conds_py=None, max_conds_px=None, verbosity=4):
#%%
if path_outsub2 is not False:
txt_fname = os.path.join(path_outsub2, f'split_{s}_PCMCI_out.txt')
# from contextlib import redirect_stdout
orig_stdout = sys.stdout
# buffer print statement output to f
sys.stdout = f = io.StringIO()
#%%
# ======================================================================================================================
# tigramite 4
# ======================================================================================================================
T, N = data.shape # Time, Regions
# ======================================================================================================================
# Initialize dataframe object (needed for tigramite functions)
# ======================================================================================================================
dataframe = pp.DataFrame(data=data, mask=data_mask, var_names=var_names)
# ======================================================================================================================
# pc algorithm: only parents for selected_variables are calculated
# ======================================================================================================================
parcorr = ParCorr(significance='analytic',
mask_type='y',
verbosity=verbosity)
#==========================================================================
# multiple testing problem:
#==========================================================================
pcmci = PCMCI(dataframe=dataframe,
cond_ind_test=parcorr,
selected_variables=None,
verbosity=verbosity)
# selected_variables : list of integers, optional (default: range(N))
# Specify to estimate parents only for selected variables. If None is
# passed, parents are estimated for all variables.
# ======================================================================================================================
#selected_links = dictionary/None
results = pcmci.run_pcmci(tau_max=tau_max, pc_alpha=pc_alpha, tau_min=tau_min,
max_conds_dim=max_conds_dim,
max_combinations=max_combinations,
max_conds_px=max_conds_px,
max_conds_py=max_conds_py)
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,
val_matrix=results['val_matrix'],
alpha_level=alpha_level)
#%%
if path_outsub2 is not False:
file = io.open(txt_fname, mode='w+')
file.write(f.getvalue())
file.close()
f.close()
sys.stdout = orig_stdout
return pcmci, q_matrix, results
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
