我正在使用多处理运行以下代码。一切都工作正常，除了输出似乎小于应有的输出。我在下面提出了一个自包含的例子。
import pandas as pd
import multiprocessing
from multiprocessing import Pool, cpu_count
from functools import partial
import timeit
import numpy as np
prng = 1234
cpu_cores = cpu_count()-1
temp_df1 = pd.DataFrame({'trip_id':[22186702,22186703,22186704,26777219,26777220,26777221,26777222,26777223],
'tour_id':[13525325,13525325,13525325,13525328,13525328,13525328,13525328,13525328],
'start_time':[8,0,0,10.92,0,0,0,0],
'ttime_mins':[3.810553,4.649286,2.917499,5.415158,3.800613,1.829472,1.829472,8.643289],
'arrival_time':[8.063509,0,0,11.010253,0,0,0,0],
'weight_column':['HBO_outbound','HBO_outbound','HBO_inbound','HBO_outbound','HBO_outbound','NHB_outbound','NHB_inbound','HBM_inbound']})
二、时间采样数据在多处理中运行
time_dist = pd.DataFrame({'Time':[8,9,10,11,12,13,14],
'HBO_outbound':[1573,419,339,544,600,453,100],
'HBO_inbound':[1573,419,339,544,100,953,800],
'HBM_outbound':[1573,419,339,544,640,463,90],
'HBM_inbound':[1573,419,339,544,320,453,100],
'WBO_outbound':[1573,419,339,544,600,453,100],
'WBO_inbound':[1573,419,339,544,450,803,190],
'NHB_outbound':[1573,419,339,544,901,543,290],
'NHB_inbound':[1573,419,339,544,863,453,330]})
results_frow = []
result_list_final = []
def func(df, time_dist_df):
"""
"""
for i in range(0, df.shape[0]):
if i == 0:
start_time = df['start_time'].iloc[i]
arrival_time = df['arrival_time'].iloc[i]
tour_id = df['tour_id'].iloc[i]
results_frow.append(start_time)
results_frow.append(arrival_time)
results_frow.append(tour_id)
else:
tour_id = df['tour_id'].iloc[i]
arrival_time_prev = results_frow[-2]
time_dist1 = time_dist.loc[time_dist['Time'] >= arrival_time_prev]
weight_column = df['weight_column'].iloc[i]
# sample a time and calculate a new arrival time as a result
if len(time_dist1) > 0:
start_time = time_dist1.sample(n=1, weights=time_dist1[weight_column], replace=True, random_state=prng)
start_time = start_time[['Time']].values ###
start_time = start_time[0][0]
else:
start_time = results_frow[-2]
newarrival_time = start_time + df['ttime_mins'].iloc[i] / 60
results_frow.append(start_time)
results_frow.append(newarrival_time)
results_frow.append(tour_id)
return results_frow
现在运行多处理并收集结果
def collect_results(result_list):
return pd.DataFrame({'start_time': result_list[0::3],
'arrival_time': result_list[1::3],
'tour_id': result_list[2::3]})
# create list of grouped dataframes
grplist = []
for name, group in temp_df1.groupby('tour_id'):
grplist.append(group)
# use partial to fix the second argument in the function so that multiprocessing does not have an issue
func_partial = partial(func, time_dist_df = time_dist)
if __name__ == '__main__':
start = timeit.default_timer()
pool = multiprocessing.Pool(processes=cpu_cores)
result_list = pool.map(func_partial, grplist)
result_list_final = result_list[1]
results_df = collect_results(result_list_final) #### Here lies the problem. Instead of getting back 8 rows, I am only getting back 5 i.e. the last group in the grplist
stop = timeit.default_timer()
total_time = stop - start
print("It took a total of %sec" %total_time)
results_df.to_csv(r"c:/stimes_parallelized.csv", index=False)
pool.close()
pool.join()
问题出在多处理代码块results_df。它只返回最后一组(5 行(的结果，而不是两组或 8 行的结果。如果我在 Pycharm 中进入调试模式，我会在results_df中看到所有 8 行，但当我将文件另存为 csv 时并非如此。
您在这里有多个问题：
1( 为什么在从池导入池时导入多进程：
from multiprocessing import Pool, cpu_count
这意味着您导入了 2 次多处理和池：
import multiprocessing
所以替换：
pool = multiprocessing.Pool(processes=cpu_cores)
由：
pool = Pool(processes=cpu_cores)
2(
你没有在描述中指定你的 Python 版本，你想我们如何回答，而我们不知道使用哪个版本
3(
我认为要解决这个问题，您必须从多处理中导入freeze_support
from multiprocessing import freeze_support
像这样使用它：
if __name__ == '__main__':
freeze_support()
我说(我认为(是因为我过去有这个问题，而你没有指定使用Python版本。