/
gen_test_case.py
169 lines (138 loc) · 5.83 KB
/
gen_test_case.py
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import json
import random
import threading
import alpha_pruning
import alpha_pruning2
import straight_forward
import straight_forward_for_datasetting
import dijkstra_distributer
import time
import openpyxl
from Metro import Metro
with open('trans.json', 'r', encoding='UTF-8') as json_file:
trans_data = json.load(json_file)
with open('line.json', 'r', encoding='UTF-8') as line_file:
line_data = json.load(line_file)
# with open('trans_classification_experiment_data.json', 'r', encoding='UTF-8') as data_file:
with open('trans_classification_list.json', 'r', encoding='UTF-8') as data_file:
input_data = json.load(data_file)
data_set = []
for i in line_data:
for j in line_data[i]:
data_set.append(j + i)
def rand_start_end(_data_set):
while True:
_start = random.choice(_data_set)
_end = random.choice(_data_set)
if _start is not _end:
break
return _start, _end
alpha_for_alpha_pruning = 1.2
alpha_for_straight_forward = 1.0
metro = Metro()
alpha_pruning_result = []
alpha_pruning_result_avg = 0
straight_forward_result = []
straight_forward_result_avg = 0
wb = openpyxl.load_workbook('result for cal.xlsx')
sheet = wb['Sheet1']
# for k in range(1, 11):
# start_time = time.time()
reresult = []
path_start_end_pairs = {}
compare_paths_transdata = []
a = 0
for j in range(0, len(input_data)):
print(a)
# reresult.extend(dijkstra_distributer.cal_dists(input_data[str(j)]['from'], input_data[str(j)]['to']))
# reresult.extend(alpha_pruning.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to'], alpha_for_alpha_pruning))
# reresult.append(alpha_pruning2.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to'], alpha_for_alpha_pruning)[0])
reresult.append(alpha_pruning2.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to'], alpha_for_alpha_pruning)[0])
a = a + 1
# set_of_reresult = []
# for d in range(len(reresult)):
# set_of_reresult.append(reresult[0])
index = 1
for v in reresult:
compare_paths_transdata = []
# compare_paths_transdata = list(set(v).intersection(trans_data["trans"]))
for v2 in trans_data["trans"]:
if v2 in v[0]:
compare_paths_transdata.append(v2)
print(compare_paths_transdata)
len_compare_paths_transdata = len(compare_paths_transdata)
# if v[0] in compare_paths_transdata and v[0] in trans_data["trans"]:
# len_compare_paths_transdata = len_compare_paths_transdata - 1
# if v[-1] in compare_paths_transdata and v[-1] in trans_data["trans"]:
# len_compare_paths_transdata = len_compare_paths_transdata - 1
path_start_end_pairs[len_compare_paths_transdata] = (v[0], v[-1])ㅔ
print(len_compare_paths_transdata, ",", v[0], ",", v[-1])
print(v[0][0], v[0][-1])
print(v)
# 지나가는 경유역 개수 (동일역 포함
sheet.cell(row=index, column=1, value=len_compare_paths_transdata)
sheet.cell(row=index, column=2, value=v[0][0])
sheet.cell(row=index, column=3, value=v[0][-1])
sheet.cell(row=index, column=4, value=v[-1])
sheet.cell(row=index, column=5, value=str(v[0]))
# sheet.cell(row=index, column=4, value=v)
# sheet.cell(row=index, column=4).value = v
# for r in range(0, len(v)):
# sheet.cell(row=index, column=4).value=v[r]
index = index + 1
# alpha_pruning_result.append(alpha_pruning.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to'], alpha_for_alpha_pruning))
# print(alpha_pruning.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to'], alpha_for_alpha_pruning))
# alpha_pruning_result_avg = alpha_pruning_result_avg + alpha_pruning_result[j][2]
wb.save('result for cal.xlsx')
# end_time = time.time()
# print(alpha_pruning_result_avg / len(input_data))
# print("AP_WorkingTime: {} sec".format(end_time-start_time))
#
# sheet.cell(row=k, column=1, value="알파 프루닝 결과 평균치")
# sheet.cell(row=k, column=2, value=alpha_pruning_result_avg / len(input_data))
# sheet.cell(row=k, column=3, value="알파 프루닝 결과 시간")
# sheet.cell(row=k, column=4, value=format(end_time-start_time))
#
# wb.save('result.xlsx')
# start_time = time.time()
# for j in range(len(input_data)):
# straight_forward_result.append(straight_forward.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to'], alpha_for_straight_forward))
# straight_forward_result_avg = straight_forward_result_avg + straight_forward_result[j][2]
#
# end_time = time.time()
# print(straight_forward_result_avg / len(input_data))
# print("SF_WorkingTime: {} sec".format(end_time - start_time))
# # 1.2 내 포함되는 데이터셋 만드는 코드
# time_out = 3
# deny = []
# for i in range(0, 10000):
# inlist = []
# ran_start, ran_end = rand_start_end(data_set)
#
# done_counting = threading.Event()
# # t = threading.Thread(target=alpha_pruning.get_result, args=(metro, ran_start, ran_end, alpha))
# # t = threading.Thread(target=straight_forward.get_result, args=(metro, ran_start, ran_end, alpha_for_alpha_pruning))
# t = threading.Thread(target=straight_forward_for_datasetting.get_result, args=(metro, ran_start, ran_end, alpha_for_alpha_pruning))
# t.setDaemon(True)
#
# t.start()
#
# t.join(time_out)
# done_counting.wait(time_out)
# # if runtime out (difficult problem)
# if t.is_alive():
# # print(ran_start, ran_end)
# pass
# # if runtime out (easy problem)
# else:
# inlist = [ran_start, ran_end]
# if not list in deny:
# deny.append([ran_start, ran_end])
# # sheet.cell(row=i+1, column=1, value=ran_start)
# # sheet.cell(row=i+1, column=2, value=ran_end)
# print(ran_start, ran_end)
# # wb.save('result2.xlsx')
# else:
# i = i - 1
# # pass
# wb.save('result2.xlsx')