python循环中的多处理

我在正对的帮助下生成负对。我想通过使用CPU的所有核心来加快进程。在一个CPU内核上，它几乎需要五天的时间，包括白天和晚上

我倾向于在多处理中更改以下代码。同时，我也没有“肯定和否定.csv”的列表

修改代码

def multi_func(iden, negatives):
    for combo in tqdm(itertools.combinations(iden.values(), 2), desc="Negatives"):
        for cross_sample in itertools.product(combo[0], combo[1]):
            negatives = negatives.append(pd.Series({"file_x": cross_sample[0], "file_y": cross_sample[1]}).T,
                                         ignore_index=True)

if Path("positives_negatives.csv").exists():
    df = pd.read_csv("positives_negatives.csv")
else:
    with ProcessPoolExecutor() as pool:
        # take cpu_count combinations from identities.values
        for combos in tqdm(more_itertools.ichunked(itertools.combinations(identities.values(), 2), cpu_count())):
            # for each combination iterator that comes out, calculate the cross
            for cross_samples in pool.map(compute_cross_samples, combos):
                # for each product iterator "cross_samples", iterate over its values and append them to negatives
                negatives = negatives.append(cross_samples)

    negatives["decision"] = "No"

    negatives = negatives.sample(positives.shape[0])
    df = pd.concat([positives, negatives]).reset_index(drop=True)
    df.to_csv("positives_negatives.csv", index=False)

已使用

if Path("positives_negatives.csv").exists():
    df = pd.read_csv("positives_negatives.csv")
else:
    with concurrent.futures.ProcessPoolExecutor() as executor:
        secs = [5, 4, 3, 2, 1]
        results = executor.map(multi_func(identities, negatives), secs)

    negatives["decision"] = "No"
    negatives = negatives.sample(positives.shape[0])
    df = pd.concat([positives, negatives]).reset_index(drop=True)
    df.to_csv("positives_negatives.csv", index=False)

---

最好的方法是实现进程池执行器类并创建一个单独的函数。就像你可以通过这种方式实现一样

库

from concurrent.futures.process import ProcessPoolExecutor
import more_itertools
from os import cpu_count

def compute_cross_samples(x):
    return pd.DataFrame(itertools.product(*x), columns=["file_x", "file_y"])

修改代码

def multi_func(iden, negatives):
    for combo in tqdm(itertools.combinations(iden.values(), 2), desc="Negatives"):
        for cross_sample in itertools.product(combo[0], combo[1]):
            negatives = negatives.append(pd.Series({"file_x": cross_sample[0], "file_y": cross_sample[1]}).T,
                                         ignore_index=True)

if Path("positives_negatives.csv").exists():
    df = pd.read_csv("positives_negatives.csv")
else:
    with ProcessPoolExecutor() as pool:
        # take cpu_count combinations from identities.values
        for combos in tqdm(more_itertools.ichunked(itertools.combinations(identities.values(), 2), cpu_count())):
            # for each combination iterator that comes out, calculate the cross
            for cross_samples in pool.map(compute_cross_samples, combos):
                # for each product iterator "cross_samples", iterate over its values and append them to negatives
                negatives = negatives.append(cross_samples)

    negatives["decision"] = "No"

    negatives = negatives.sample(positives.shape[0])
    df = pd.concat([positives, negatives]).reset_index(drop=True)
    df.to_csv("positives_negatives.csv", index=False)

---

你最好的办法是将工作分解成子组，然后从那里使用多重处理。如果可能的话，请给我一个与“else”子句相关的例子。其实。。。从也许开始？事实上，我已经做了两次，但都不管用。你能再补充一些吗？你看到了什么样的加速？另外，你应该能够在2天之后将你自己的答案标记为答案，并将支票放在左边@是的。稍后，我将添加更多细节