-
Notifications
You must be signed in to change notification settings - Fork 1
/
main_PCMCI.py
53 lines (42 loc) · 1.76 KB
/
main_PCMCI.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
from tigramite.independence_tests.gpdc import GPDC
from causalflow.CPrinter import CPLevel
from causalflow.causal_discovery.baseline import PCMCI
from causalflow.preprocessing.data import Data
from causalflow.preprocessing.subsampling_methods.Static import Static
from causalflow.preprocessing.subsampling_methods.SubsamplingMethod import SubsamplingMethod
from causalflow.preprocessing.subsampling_methods.WSDynamic import WSDynamic
from causalflow.preprocessing.subsampling_methods.WSFFTStatic import WSFFTStatic
from causalflow.preprocessing.subsampling_methods.WSStatic import WSStatic
from causalflow.selection_methods.TE import TE, TEestimator
from causalflow.basics.constants import LabelType
import numpy as np
from time import time
from datetime import timedelta
if __name__ == '__main__':
alpha = 0.05
min_lag = 1
max_lag = 1
np.random.seed(1)
nsample = 500
nfeature = 6
d = np.random.random(size = (nsample, nfeature))
for t in range(max_lag, nsample):
d[t, 0] += 2 * d[t-1, 1] + 3 * d[t-1, 3]
d[t, 2] += 1.1 * d[t-1, 1]**2
d[t, 3] += d[t-1, 3] * d[t-1, 2]
d[t, 4] += d[t-1, 4] + d[t-1, 5] * d[t-1, 0]
df = Data(d)
start = time()
pcmci = PCMCI(df,
alpha = alpha,
pc_alpha = alpha,
min_lag = min_lag,
max_lag = max_lag,
val_condtest = GPDC(significance = 'analytic', gp_params = None),
verbosity = CPLevel.DEBUG,
neglect_only_autodep = False,
resfolder = 'ex_PCMCI')
selector_res = pcmci.run()
elapsed_PCMCI = time() - start
print(str(timedelta(seconds = elapsed_PCMCI)))
pcmci.dag(label_type = LabelType.NoLabels, node_layout = 'circular')