Question

我运行了一个python脚本，它在多个线程中启动相同的功能。这些函数创建并处理2个计数器（c1和c2）。来自分叉进程的所有c1计数器的结果应该合并在一起。与所有c2计数器的结果相同，由不同的叉子返回。

我的（伪）代码看起来像这样：

def countIt(cfg)
   c1 = Counter
   c2 = Counter
   #do some things and fill the counters by counting words in an text, like
   #c1= Counter({'apple': 3, 'banana': 0})
   #c2= Counter({'blue': 3, 'green': 0})    

   return c1, c2

if __name__ == '__main__':
        cP1 = Counter()
        cP2 = Counter()
        cfg = "myConfig"
        p = multiprocessing.Pool(4)  #creating 4 forks
        c1, c2 = p.map(countIt,cfg)[:2]
        # 1.) This will only work with [:2] which seams to be no good idea
        # 2.) at this point c1 and c2 are lists, not a counter anymore,
        # so the following will not work:
        cP1 + c1
        cP2 + c2

按照上面的例子，我需要一个结果，如： cP1 =计数器（{'apple'：25，'banana'：247，'orange'：24}） cP2 =计数器（{'red'：11，'blue'：56，'green'：3}）

所以我的问题是：为了聚合父进程中的每个计数器（所有c1和所有c2），我如何计算分解进程的东西？

Answer 1

你需要＆＃34;解压缩＆＃34;通过使用例如for-each循环来获得结果。您将收到一个元组列表，其中每个元组是一对计数器：(c1, c2) 使用当前的解决方案，您实际上将它们混合起您已将[(c1a, c2a), (c1b, c2b)]分配给c1, c2，表示c1包含(c1a, c2a)且c2包含(c1b, c2b)。

试试这个：

if __name__ == '__main__':
        from contextlib import closing

        cP1 = Counter()
        cP2 = Counter()

        # I hope you have an actual list of configs here, otherwise map will
        # will call `countIt` with the single characters of the string 'myConfig'
        cfg = "myConfig"

        # `contextlib.closing` makes sure the pool is closed after we're done.
        # In python3, Pool is itself a contextmanager and you don't need to
        # surround it with `closing` in order to be able to use it in the `with`
        # construct.
        # This approach, however, is compatible with both python2 and python3.
        with closing(multiprocessing.Pool(4)) as p:
            # Just counting, no need to order the results.
            # This might actually be a bit faster.
            for c1, c2 in p.imap_unordered(countIt, cfg):
                cP1 += c1
                cP2 += c2

多处理/映射函数

1 个答案: