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gen_test_case.py
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gen_test_case.py
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import json
import random
import threading
import alpha_pruning
import cal_alpha_pruning
import cal_paths_weights
import cal_all_paths
import cal_shortest_path
import time
import cal_scores
import openpyxl
from Metro import Metro
import make_dataset
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
def _gen_test(fm_trans_json, fm_line_json, fm_edges_fix_json, input_vertex, turn_count):
with open(fm_trans_json, 'r', encoding='UTF-8') as json_file:
trans_data = json.load(json_file)
with open(fm_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)
alpha_for_alpha_pruning = 1.2
alpha_for_straight_forward = 1.2
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])
# reresult.append(alpha_pruning2.get_result(metro, input_data[str(j)]['from'], input_data[str(j)]['to']))
# a = a + 1
# sett = []
# for i in range(50000):
# ran_start, ran_end = rand_start_end(data_set)
# if not [ran_start, ran_end] in sett and not [ran_end, ran_start] in sett:
# sett.append([ran_start, ran_end])
# # print([ran_start, ran_end])
# print(len(sett))
# if len(sett) >= 10000:
# break
# print(sett)
# print(len(sett))
# file_len = len(sett)
file_len = 1999
# make_dataset.make_data(len(sett), sett, input_vertex)
metro = Metro(fm_trans_json, fm_line_json, fm_edges_fix_json)
# fm_line_small_json = 'line_' + ','.join(input_vertex) + '_smalldata.json'
#
# with open(fm_line_small_json, 'r', encoding='UTF-8') as line_file:
# line_smalldata = json.load(line_file)
################ shortest_path구하는 곳
# wb = openpyxl.load_workbook('cls.xlsx')
# sheet = wb['Sheet1']
# for i in range(file_len):
# # cal_shortest_path.get_result(metro, line_smalldata[str(i)]['from'], line_smalldata[str(i)]['to'], trans_data, i, sheet)
# cal_shortest_path.get_result(metro, sheet.cell(i + 2, 2).value, sheet.cell(i + 2, 3).value, trans_data, i, sheet)
# print(i)
# wb.save('cls.xlsx')
# /////////////////////////////////////
# ############### cal_scores
# wb = openpyxl.load_workbook('cls.xlsx')
# sheet = wb['Sheet1']
# for i in range(file_len):
# cal_scores.get_result(metro, sheet.cell(i + 2, 2).value, sheet.cell(i + 2, 3).value, sheet.cell(i+2, 8).value, trans_data, i, sheet)
# print(i)
# wb.save('cls.xlsx')
# /////////////////////////////////////
# ############### cal_weights
wb = openpyxl.load_workbook('cls.xlsx')
sheet = wb['Sheet1']
for i in range(file_len):
cal_paths_weights.get_result(metro, sheet.cell(i + 2, 2).value, sheet.cell(i + 2, 3).value, sheet.cell(i+2, 15).value, trans_data, i, sheet)
print(i)
wb.save('cls.xlsx')
# /////////////////////////////////////
cal_time = 0
cal_time2 = 0
# 9734
# wb = openpyxl.load_workbook('cls.xlsx')
# sheet = wb['Sheet1']
#
# for i in range(file_len):
# # reresult.append(cal_all_paths.get_result(metro, line_smalldata[str(i)]['from'], line_smalldata[str(i)]['to'], alpha_for_straight_forward))
# # if sheet.cell(i+2, 5).value == 6 or sheet.cell(i+2, 5).value == 7 or sheet.cell(i+2, 5).value == 8 or sheet.cell(i+2, 5).value == 9 or sheet.cell(i+2, 5).value == 10:
# # pass
# # else:
# if sheet.cell(i+2, 5).value == 9:
# # if sheet.cell(i+2, 9).value == 0:
# start_time = time.time()
# reresult.append(cal_all_paths.get_result(metro, sheet.cell(i + 2, 2).value, sheet.cell(i + 2, 3).value,
# alpha_for_straight_forward))
# end_time = time.time()
# cal_time = end_time - start_time
# print(i, "th _all path finish", ":", sheet.cell(i + 2, 2).value, sheet.cell(i + 2, 3).value, "->",
# cal_time)
# sheet.cell(row=i + 2, column=9, value=cal_time)
# wb.save('cls.xlsx')
# else:
# start_time = time.time()
# reresult.append(cal_all_paths.get_result(metro, sheet.cell(i+2, 2).value, sheet.cell(i+2, 3).value, alpha_for_straight_forward))
# end_time = time.time()
# cal_time = end_time - start_time
# print(i, "th _all path finish", ":", sheet.cell(i+2, 2).value, sheet.cell(i+2, 3).value, "->", cal_time)
# sheet.cell(row=i + 2, column=9, value=cal_time)
# wb.save('cls.xlsx')
# if i % 2000 == 0:
# print("하는중 -> ", i , len(sett))
# ################ alpha
# wb = openpyxl.load_workbook('cls.xlsx')
# sheet = wb['Sheet1']
# # for kkkk in range(100):
# for j in range(file_len):
# # if sheet.cell(j+2, 5).value == 6 or sheet.cell(j+2, 5).value == 7 or sheet.cell(j+2, 5).value == 8 or sheet.cell(j+2, 5).value == 9 or sheet.cell(j+2, 5).value == 10:
# # pass
# # else:
# if sheet.cell(j + 2, 5).value == 9:
# # if sheet.cell(j+2, 5).value == 0 or sheet.cell(j+2, 5).value == 1 or sheet.cell(j+2, 5).value == 2 or sheet.cell(j+2, 5).value == 3 or sheet.cell(j + 2, 5).value == 4 or sheet.cell(j+2, 5).value == 5 or sheet.cell(j+2, 5).value == 6 or sheet.cell(j+2, 5).value == 7 or sheet.cell(j+2, 5).value == 8:
# # if sheet.cell(j+2, 10).value == 0:
# start_time2 = time.time()
# reresult.append(
# cal_alpha_pruning.get_result(metro, sheet.cell(j + 2, 2).value, sheet.cell(j + 2, 3).value,
# alpha_for_alpha_pruning))
# end_time2 = time.time()
# cal_time2 = end_time2 - start_time2
# print(j, "th _alpha path finish", ":", sheet.cell(j + 2, 2).value, sheet.cell(j + 2, 3).value, "->",
# cal_time2)
# sheet.cell(row=j + 2, column=10, value=cal_time2)
# sheet.cell(row=j + 2, column=15, value=str(reresult[-1][0]))
#
# wb.save('cls.xlsx')
# print("fin")
# alpha /////////////////////////
# reresult.append(cal_alpha_pruning.get_result(metro, line_smalldata[str(j)]['from'], line_smalldata[str(j)]['to'], alpha_for_alpha_pruning))
# else:
# start_time2 = time.time()
# reresult.append(cal_alpha_pruning.get_result(metro, sheet.cell(j + 2, 2).value, sheet.cell(j + 2, 3).value, alpha_for_alpha_pruning))
# end_time2 = time.time()
# cal_time2 = end_time2 - start_time2
# print(j, "th _alpha path finish", ":", sheet.cell(j+2, 2).value, sheet.cell(j+2, 3).value, "->", cal_time2)
# sheet.cell(row=j + 2, column=10, value=cal_time2)
# wb.save('cls.xlsx')
# # if i % 2000 == 0:
# # print("하는중 -> ", i , len(sett))
# print(cal_time / file_len)
# print(cal_time2 / file_len)
# print(len(trans_data['trans']))
# wb.save('cls.xlsx')
# wb = openpyxl.load_workbook('smalldata_result.xlsx')
# sheet = wb['Sheet4']
# sheet.cell(row=turn_count, column=3, value=len(trans_data['trans']))
# sheet.cell(row=turn_count, column=4, value=len(sett))
# sheet.cell(row=turn_count, column=5, value=cal_time/file_len)
# sheet.cell(row=turn_count, column=6, value=cal_time2/file_len)
# wb.save('smalldata_result.xlsx')
# for i in reresult[0]:
# print(i)
#
# print(len(reresult[0]))
# set_of_reresult = []
# for d in range(len(reresult)):
# set_of_reresult.append(reresult[0])
#
# index = 1
# for v in reresult:
# compare_paths_transdata = []
# 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)
#
# 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])
# print(ran_start, ran_end)
# else:
# i = i - 1
# # pass
# wb.save('result2.xlsx')