baseline_data_ensemble_pcmci_linear.py

import csv
import itertools
import sys
from datetime import datetime

import numpy as np
import pcmci_linear_baseline_ensemble
from load_data import load_data

startTime = datetime.now()
print("starting time: ", startTime)

if len(sys.argv) < 3:
    print("arguments: maxlag, data file name, number of partitions")

maxlag = int(sys.argv[1])
data_file_name = sys.argv[2]
num_partitions = int(sys.argv[3])

alpha = 0.05

data_ori, header = load_data(data_file_name)
split_arr = np.array_split(data_ori, num_partitions)
print(len(split_arr[0]))

dt = np.arange(len(split_arr[0]))
t, n = split_arr[0].shape

result_arr = []
for local_pcmci in range(0, num_partitions):
    res_pcmci = pcmci_linear_baseline_ensemble.pcmci_causality(split_arr[local_pcmci], dt, local_pcmci, header, t, n, maxlag)
    result_arr.append(res_pcmci)
print(result_arr)

# flatten the result with partition index 
merged = list(itertools.chain.from_iterable(result_arr))
res_pcmci = merged
print(res_pcmci)

for iter_num_partition in range(0, num_partitions):
    dic_name = 'dic_partition_' + str(iter_num_partition)
    #     ensembled_dic_name_partition = 'en_partition_' + str(iter_num_partition)
    locals()[dic_name] = {}
# locals()[ensembled_dic_name_partition] = {}

for item_pcmci in res_pcmci:
    # print(item_gc)
    for iter_partition in range(0, num_partitions):
        # print(iter_partition)
        if item_pcmci[2] == iter_partition:
            exec('get_dic_name = dic_partition_{}'.format(iter_partition))
            # get_dic_name
            if str(item_pcmci[0]) + str(item_pcmci[1]) not in get_dic_name:
                get_dic_name[str(item_pcmci[0]) + str(item_pcmci[1])] = 1
            else:
                get_dic_name[str(item_pcmci[0]) + str(item_pcmci[1])] += 1

print("partition 0 ")
print(dic_partition_0)
print("partition 1 ")
print(dic_partition_1)

ensemble_result = {}
ensembled_partition_dic = {}

for iter_num in range(0, num_partitions):
    # exec('print(dic_partition_{})'.format(iter_num))
    exec('current_dic = dic_partition_{}'.format(iter_num))
    print(current_dic)
    for item_en_partition in current_dic:
        # if that edge exists in partition dictionary, the value of key x1x2 is 1
        if current_dic[item_en_partition] == 1:
            print("partition{} ensemble results: effect, cause".format(iter_num))
            print(item_en_partition)
            print("this pair appear {} times".format(current_dic[item_en_partition]))
            if item_en_partition not in ensembled_partition_dic:
                ensembled_partition_dic[item_en_partition] = 1
            else:
                ensembled_partition_dic[item_en_partition] += 1

print(ensembled_partition_dic)

final_res_arr = []
for ensembled_partition_dic_iter in ensembled_partition_dic:
    if ensembled_partition_dic[ensembled_partition_dic_iter] >= num_partitions / 2:
        print("data ensemble results: {}".format(ensembled_partition_dic_iter))
        final_res_arr.append(ensembled_partition_dic_iter)

with open("baseline_data_ensemble_pcmci_linear.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(final_res_arr)

print("total time")
print(datetime.now() - startTime)