python中threading和queue庫實現多線程編程

摘要

本文主要介紹瞭利用python的 threading和queue庫實現多線程編程,並封裝為一個類,方便讀者嵌入自己的業務邏輯。最後以機器學習的一個超參數選擇為例進行演示。

多線程實現邏輯封裝

實例化該類後,在.object_func函數中加入自己的業務邏輯,再調用.run方法即可。

# -*- coding: utf-8 -*-
# @Time : 2021/2/4 14:36
# @Author : CyrusMay WJ
# @FileName: run.py
# @Software: PyCharm
# @Blog :https://blog.csdn.net/Cyrus_May
import queue
import threading

class CyrusThread(object):
  def __init__(self,num_thread = 10,logger=None):
    """
    
    :param num_thread: 線程數
    :param logger: 日志對象
    """
    self.num_thread = num_thread
    self.logger = logger

  def object_func(self,args_queue,max_q):
    while 1:
      try:
        arg = args_queue.get_nowait()
        step = args_queue.qsize()
        self.logger.info("progress:{}\{}".format(max_q,step))
      except:
        self.logger.info("no more arg for args_queue!")
        break
        
        
        """
        此處加入自己的業務邏輯代碼
        """
        
        
  def run(self,args):
    args_queue = queue.Queue()
    for value in args:
      args_queue.put(value)
    threads = []
    for i in range(self.num_thread):
      threads.append(threading.Thread(target=self.object_func,args = args_queue))
    for t in threads:
      t.start()
    for t in threads:
      t.join()

模型參數選擇實例

# -*- coding: utf-8 -*-
# @Time : 2021/2/4 14:36
# @Author : CyrusMay WJ
# @FileName: run.py
# @Software: PyCharm
# @Blog :https://blog.csdn.net/Cyrus_May
import queue
import threading
import numpy as np
from sklearn.datasets import load_boston
from sklearn.svm import SVR
import logging
import sys


class CyrusThread(object):
  def __init__(self,num_thread = 10,logger=None):
    """

    :param num_thread: 線程數
    :param logger: 日志對象
    """
    self.num_thread = num_thread
    self.logger = logger

  def object_func(self,args_queue,max_q):
    while 1:
      try:
        arg = args_queue.get_nowait()
        step = args_queue.qsize()
        self.logger.info("progress:{}\{}".format(max_q,max_q-step))
      except:
        self.logger.info("no more arg for args_queue!")
        break
      # 業務代碼
      C, epsilon, gamma = arg[0], arg[1], arg[2]
      svr_model = SVR(C=C, epsilon=epsilon, gamma=gamma)
      x, y = load_boston()["data"], load_boston()["target"]
      svr_model.fit(x, y)
      self.logger.info("score:{}".format(svr_model.score(x,y)))


  def run(self,args):
    args_queue = queue.Queue()
    max_q = 0
    for value in args:
      args_queue.put(value)
      max_q += 1
    threads = []
    for i in range(self.num_thread):
      threads.append(threading.Thread(target=self.object_func,args = (args_queue,max_q)))
    for t in threads:
      t.start()
    for t in threads:
      t.join()

# 創建日志對象
logger = logging.getLogger()
logger.setLevel(logging.INFO)
screen_handler = logging.StreamHandler(sys.stdout)
screen_handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(module)s.%(funcName)s:%(lineno)d - %(levelname)s - %(message)s')
screen_handler.setFormatter(formatter)
logger.addHandler(screen_handler)

# 創建需要調整參數的集合
args = []
for C in [i for i in np.arange(0.01,1,0.01)]:
  for epsilon in [i for i in np.arange(0.001,1,0.01)] + [i for i in range(1,10,1)]:
    for gamma in [i for i in np.arange(0.001,1,0.01)] + [i for i in range(1,10,1)]:
      args.append([C,epsilon,gamma])

# 創建多線程工具
threading_tool = CyrusThread(num_thread=20,logger=logger)
threading_tool.run(args)

運行結果

2021-02-04 20:52:22,824 – run.object_func:31 – INFO – progress:1176219\1
2021-02-04 20:52:22,824 – run.object_func:31 – INFO – progress:1176219\2
2021-02-04 20:52:22,826 – run.object_func:31 – INFO – progress:1176219\3
2021-02-04 20:52:22,833 – run.object_func:31 – INFO – progress:1176219\4
2021-02-04 20:52:22,837 – run.object_func:31 – INFO – progress:1176219\5
2021-02-04 20:52:22,838 – run.object_func:31 – INFO – progress:1176219\6
2021-02-04 20:52:22,841 – run.object_func:31 – INFO – progress:1176219\7
2021-02-04 20:52:22,862 – run.object_func:31 – INFO – progress:1176219\8
2021-02-04 20:52:22,873 – run.object_func:31 – INFO – progress:1176219\9
2021-02-04 20:52:22,884 – run.object_func:31 – INFO – progress:1176219\10
2021-02-04 20:52:22,885 – run.object_func:31 – INFO – progress:1176219\11
2021-02-04 20:52:22,897 – run.object_func:31 – INFO – progress:1176219\12
2021-02-04 20:52:22,900 – run.object_func:31 – INFO – progress:1176219\13
2021-02-04 20:52:22,904 – run.object_func:31 – INFO – progress:1176219\14
2021-02-04 20:52:22,912 – run.object_func:31 – INFO – progress:1176219\15
2021-02-04 20:52:22,920 – run.object_func:31 – INFO – progress:1176219\16
2021-02-04 20:52:22,920 – run.object_func:39 – INFO – score:-0.01674283914287855
2021-02-04 20:52:22,929 – run.object_func:31 – INFO – progress:1176219\17
2021-02-04 20:52:22,932 – run.object_func:39 – INFO – score:-0.007992354170952565
2021-02-04 20:52:22,932 – run.object_func:31 – INFO – progress:1176219\18
2021-02-04 20:52:22,945 – run.object_func:31 – INFO – progress:1176219\19
2021-02-04 20:52:22,954 – run.object_func:31 – INFO – progress:1176219\20
2021-02-04 20:52:22,978 – run.object_func:31 – INFO – progress:1176219\21
2021-02-04 20:52:22,984 – run.object_func:39 – INFO – score:-0.018769934807246536
2021-02-04 20:52:22,985 – run.object_func:31 – INFO – progress:1176219\22

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