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statistic.py
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statistic.py
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import pickle
from functools import *
from tqdm import tqdm
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from config import *
# records = pickle.load(open("./output/multi_thread_order.pickle",'rb'))
# order = pickle.load(open("input/dataset.pickle",'rb'))
# data = pd.read_csv("input/multi_thread.csv")
# records = pickle.load(open("toy_records_price_limitation.pickle",'rb'))
# for item in tqdm(records):
# for record in item.values():
# if isinstance(record[0],list):
# print(record[0][-1])
# calculate matching rate
def get_matching_rate(records,order):
total_num = 0
matched_num = 0
# calculate the number of orders
for time in range(env_params['t_initial'],env_params['t_end']):
if time in order.keys():
total_num += len(order[time])
# calculate the number of orders that are matched
for time_item in records:
for k,v in time_item.items():
if isinstance(v[0],list):
matched_num += 1
return matched_num * 1.0 / total_num
# print("匹配率:",get_matching_rate(records,order))
# 计算乘客平均等待时间（pre_matching time)
def get_avg_prematching_waiting_time(records,order):
result_list = []
new_order = []
for time_item in order:
for item in order[time_item]:
new_order.append(item)
column_name = ['order_id', 'origin_id', 'origin_lat', 'origin_lng', 'dest_id', 'dest_lat', 'dest_lng',
'trip_distance', 'start_time', 'origin_grid_id', 'dest_grid_id', 'itinerary_node_list',
'itinerary_segment_dis_list', 'trip_time', 'designed_reward', 'cancel_prob']
new_order = pd.DataFrame(new_order, columns=column_name)
order = new_order
for time_item in records:
for k,v in time_item.items():
if isinstance(v[0],list):
matching_time = v[0][-1]
start_time = order[order['order_id'] == int(v[0][2])].start_time.values[0]
result_list.append(matching_time - start_time)
if len(result_list) != 0:
return sum(result_list) / len(result_list)
else:return 0
# print("乘客平均等待时间",get_avg_prematching_waiting_time(records,data))
# 计算乘客接单时间(可选择平均时间）
def get_postmatching_pickup_time(records,avg = True):
result = []
for j,time_item in enumerate(records):
for k,v in time_item.items():
if isinstance(v[0],list):
# start_time = i*env_params['delta_t'] + env_params['t_initial']
start_time = v[0][-1]
if v[0][-1] != j*env_params['delta_t'] + env_params['t_initial']:
print(j*env_params['delta_t'] + env_params['t_initial'])
print(v[0][-1])
end_time = v[0][-1]
for i in range(1,len(v)):
# print(v[i])
if v[i][-2] == 2.0:
end_time = v[i][-1]
elif v[i][-2] == 1.0:
end_time = v[i][-1]
break
# sys.exit()
# print(end_time-start_time)
end_time = min(end_time,env_params['t_end'])
if end_time > start_time:
result.append(end_time - start_time)
if len(result) != 0:
if avg is True:
return sum(result) / len(result)
else: return sum(result)
else: return 0
# records = pickle.load(open('./output2/ma_ma_cruise=True/records_driver_num_2500.pickle', 'rb'))
# print(get_postmatching_pickup_time(records))
# print("乘客平均接单时间",get_postmatching_pickup_time(records))
# delivery利用time（包括delivery part）
def get_driver_delivery_time(records,start_time,end_time,driver_num,avg = True):
occupied_time = []
for i,time_item in enumerate(records):
for k,v in time_item.items():
if isinstance(v[0],list):
start_time_ = float("inf")
for i in range(len(v)):
if v[i][-2] == 1.0:
start_time_ = v[i][-1]
break
end_time_ = min(end_time,v[-1][-1])
if start_time_ < end_time_:
occupied_time.append(end_time_ - start_time_)
if avg == True:
return sum(occupied_time) / len(occupied_time)
return sum(occupied_time)
# delivery usage rate
def get_driver_delivery_ratio(records,start_time,end_time,driver_num):
return get_driver_delivery_time(records,start_time,end_time,driver_num,False) / (end_time - start_time) / driver_num
# print("司机利用率",get_driver_usage_rate(records,36000,79200,500))
# 司机接单率（pickup time / total time)
def get_driver_pickup_ratio(records,start_time,end_time,driver_num):
return get_postmatching_pickup_time(records,False) / (end_time - start_time) / driver_num
# print("司机接单率",get_driver_pickup_ratio(records,36000,79200,500))
def plot_figure():
start_time = env_params['t_initial']
end_time = env_params['t_end']
driver_num = [500,1000,1500]
matching_rate = []
pre_matching_waiting_time = []
post_matching_pickup_time = []
driver_usage_rate = []
driver_pickup_rate = []
for driver in driver_num:
records = pickle.load(open("statistic/toy_records_no_price_" +str(driver) +".pickle", 'rb'))
matching_rate.append(get_matching_rate(records,order))
pre_matching_waiting_time.append((get_avg_prematching_waiting_time(records,data)))
post_matching_pickup_time.append(get_postmatching_pickup_time(records))
driver_usage_rate.append(get_driver_usage_rate(records,start_time,end_time,driver))
driver_pickup_rate.append(get_driver_pickup_ratio(records,start_time,end_time,driver))
plt.xlabel("The number of driver")
plt.ylabel("Matching rate")
plt.plot(driver_num,matching_rate)
plt.show()
plt.xlabel("The number of driver")
plt.ylabel("pre_matching_waiting_time")
plt.plot(driver_num, pre_matching_waiting_time)
plt.show()
plt.xlabel("The number of driver")
plt.ylabel("post_matching_pickup_time")
plt.plot(driver_num, post_matching_pickup_time)
plt.show()
plt.xlabel("The number of driver")
plt.ylabel("driver_usage_rate")
plt.plot(driver_num, driver_usage_rate)
plt.show()
plt.xlabel("The number of driver")
plt.ylabel("driver_pickup_rate")
plt.plot(driver_num, driver_pickup_rate)
plt.show()
# plot_figure()