摘要

本文主要介绍了利用python的 threading和queue库实现多线程编程，并封装为一个类，方便读者嵌入自己的业务逻辑。最后以机器学习的一个超参数选择为例进行演示。

多线程实现逻辑封装

实例化该类后，在.object_func函数中加入自己的业务逻辑，再调用.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: 线程数
    :param logger: 日志对象
    """
    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


        """
        此处加入自己的业务逻辑代码
        """


  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()

模型参数选择实例

# -*- 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: 线程数
    :param logger: 日志对象
    """
    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
      # 业务代码
      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()

# 创建日志对象
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)

# 创建需要调整参数的集合
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])

# 创建多线程工具
threading_tool = CyrusThread(num_thread=20,logger=logger)
threading_tool.run(args)

运行结果

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

到此这篇关于python中threading和queue库实现多线程编程的文章就介绍到这了,更多相关python 多线程编程内容请搜索python博客以前的文章或继续浏览下面的相关文章希望大家以后多多支持python博客！