import time import multiprocessing from proxypool.processors.server import app from proxypool.processors.getter import Getter from proxypool.processors.tester import Tester from proxypool.setting import CYCLE_GETTER, CYCLE_TESTER, API_HOST, API_THREADED, API_PORT, ENABLE_SERVER, \ ENABLE_GETTER, ENABLE_TESTER, IS_WINDOWS from loguru import logger if IS_WINDOWS: multiprocessing.freeze_support() tester_process, getter_process, server_process = None, None, None class Scheduler(): def run_tester(self, cycle=CYCLE_TESTER): if not ENABLE_TESTER: logger.info('tester not enabled, exit') return tester = Tester() loop = 0 while True: logger.debug(f'tester loop {loop} start...') tester.run() loop += 1 time.sleep(cycle) # CYCLE_GETTER=100 def run_getter(self, cycle=CYCLE_GETTER): if not ENABLE_GETTER: logger.info('getter not enabled, exit') return getter = Getter() loop = 0 while True: logger.debug(f'getter loop {loop} start...') getter.run() loop += 1 time.sleep(cycle) def run_server(self): if not ENABLE_SERVER: logger.info('server not enabled, exit') return app.run(host=API_HOST, port=API_PORT, threaded=API_THREADED) def run(self): global tester_process, getter_process, server_process try: logger.info('starting proxypool...') if ENABLE_TESTER: tester_process = multiprocessing.Process(target=self.run_tester) logger.info(f'starting tester, pid {tester_process.pid}...') tester_process.start() if ENABLE_GETTER: getter_process = multiprocessing.Process(target=self.run_getter) logger.info(f'starting getter, pid{getter_process.pid}...') getter_process.start() if ENABLE_SERVER: server_process = multiprocessing.Process(target=self.run_server) logger.info(f'starting server, pid{server_process.pid}...') server_process.start() tester_process.join() getter_process.join() server_process.join() except KeyboardInterrupt: logger.info('received keyboard interrupt signal') tester_process.terminate() getter_process.terminate() server_process.terminate() finally: # must call join method before calling is_alive tester_process.join() getter_process.join() server_process.join() logger.info(f'tester is {"alive" if tester_process.is_alive() else "dead"}') logger.info(f'getter is {"alive" if getter_process.is_alive() else "dead"}') logger.info(f'server is {"alive" if server_process.is_alive() else "dead"}') logger.info('proxy terminated') if name == 'main': scheduler = Scheduler() scheduler.run()给这段代码加注释

浅谈python3.x pool.map()方法的实质

from multiprocessing import Pool from time import sleep from datetime import datetime class ForMap: def __init__(self): self.name = '没啥用的初始化' def forTest(self, i): sleep(i) print(i) ...

优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold from sklearn.decomposition import PCA from sklearn....

解释代码importjson importos importtime frommultiprocessingimportPool,cpu_count fromdatetimeimporttimedelta frompydubimportAudioSegment

- time：提供了处理时间的函数。 - multiprocessing：提供了多进程编程的支持。 - Pool 和 cpu_count：用于创建进程池和获取CPU核心数量。 - timedelta：用于处理时间差。 - AudioSegment：用于处理音频...

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random 请确保在运行这些代码之前，...

运行下面代码需要安装哪些包from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

运行上述代码需要安装以下Python包： - scikit-learn：提供了机器学习算法和工具，包括模型选择、特征提取、预处理等。可以使用pip install scikit-learn进行安装。 - pandas：提供了高性能、易于使用的数据结构...

import os.path import random import time from multiprocessing import Pool import requests from lxml import etree headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36' } def get_img_src(page): ''' 抓取页面的图片的src :return: ''' for i in range(1, page+1): url = f'https://www.pkdoutu.com/photo/list/?page={i}' print(f'抓取{url}页数据') res = requests.get(url, headers=headers) res.encoding = res.apparent_encoding tree = etree.HTML(res.text) # 抓取图片 img_list = tree.xpath('//img[@referrerpolicy="no-referrer"]/@data-original') yield img_list def download_img(url): ''' 下载图片 :return: ''' time.sleep(random.uniform(0, 1)) img_res = requests.get(url, headers=headers) img_name = url.split('_')[-1] # 通过图片url进行拆分，拿到图片名称和后缀 path = 'img' # 判断路径是否存在，不存在则创建 if not os.path.exists(path): os.mkdir(path) # 写入到文件中 with open(os.path.join(path, img_name), 'wb') as f: f.write(img_res.content) if name == 'main': pool = Pool() # 通过生成器get_img_src返回图片列表 for url_list in get_img_src(2): for url in url_list: # 循环加入进程池 pool.apply_async(download_img, args=(url, )) pool.close() pool.join() print('over')

1. 导入所需的库，包括 os.path、random、time、multiprocessing 和 requests。 2. 定义抓取图片链接的函数 get_img_src，通过循环抓取指定页数的页面，并使用 lxml 库解析页面内容，抓取图片链接并返回。 3. 定义...

为什么python中from multiprocessing.dummy import Pool 是线程池

在Python中，multiprocessing模块提供了一种使用进程池来并行执行任务的方式。...因此，当我们在Python中使用from multiprocessing.dummy import Pool时，我们实际上是在导入一个线程池，而不是进程池。

防止multiprocessing.Pool().apply_async进程提前结束

在使用multiprocessing.Pool()时，apply_async()方法会在进程池中异步地执行一个函数，但是由于进程池是异步的，可能会在执行过程中出现提前结束的情况。为了解决这个问题，你可以使用Pool().close()和Pool()...

python 中 multiprocessing.dummy import Pool as ThreadPool 如何使用

from multiprocessing.dummy import Pool as ThreadPool # 定义一个任务函数 def task(number): print(f"Executing task {number}") # 在这里编写具体的任务逻辑 return number * 2 # 创建一个线程池，指定线程...

import multiprocessing import queue def worker(num, q): """子进程要执行的任务""" result = num * 2 q.put(result) # 将结果放入队列中 if name == 'main': # 创建队列和进程池 q = multiprocessing.Queue() pool = multiprocessing.Pool() # 提交任务到进程池中

如果您在这里提交任务到进程池中，您需要调用 pool.apply_async(worker, args=(num, q)) 方法，其中 num 是传入进程的参数，q 是进程完成任务后将结果放入的队列。使用 apply_async() 方法可以异步提交任务...

class 'multiprocessing.managers.ArrayProxy怎样添加元素

multiprocessing.managers.ArrayProxy是一个代理对象，它允许将一个multiprocessing.Manager().Array对象暴露给其他进程。ArrayProxy对象本身没有append()或extend()方法，因为这些方法是在远程Array...

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