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我想使用多处理加速 DEAP，但总是得到 OSError。这是我的代码的缩写版本：

import operator
import math
import random
import numpy as np
import pandas as pd    
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
from deap import gp
import multiprocessing

# protectedDiv
def protectedDiv(left, right):
    try:
        return left / right
    except ZeroDivisionError:
        return 1

# omitting some other functions

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax)

# here is DEAP strong typed GP setting
pset = gp.PrimitiveSetTyped("MAIN", [np.ndarray] * 12, np.ndarray)
pset.addPrimitive(operator.add, [np.ndarray, np.ndarray], np.ndarray)
pset.addPrimitive(operator.sub, [np.ndarray, np.ndarray], np.ndarray)
pset.renameArguments(ARG0='close')
pset.renameArguments(ARG1='open')

# here is fitness function. My goal is maximum stock return's ICIR.
def evalSymbReg(individual):
    # omitting code
    return icir,

toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=3)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("compile", gp.compile, pset=pset)

toolbox.register("evaluate", evalSymbReg)
toolbox.register("select", tools.selTournament, tournsize=10)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
toolbox.register("mutUniform", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=10))
toolbox.decorate("mutUniform", gp.staticLimit(key=operator.attrgetter("height"), max_value=10))

def main():
    n_sample = 5000
    n_gen = 40
    cxpb = 0.6
    mutUniformpb = 0.4

    pop = toolbox.population(n=n_sample)
    hof = tools.HallOfFame(10)

    stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
    stats_size = tools.Statistics(len)
    mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
    mstats.register("avg", np.nanmean)
    mstats.register("min", np.nanmin)
    mstats.register("max", np.nanmax)

    pop, log = algorithms.my_eaSimple(pop, toolbox, cxpb, mutUniformpb, mutNodeReplacementpb, mutEphemeralpb, mutShrinkpb,
                                      n_gen, stats=mstats, halloffame=hof, verbose=True)

    # print log
    return pop, log, hof, info, top10

# here is my data file.
df = pd.read_csv(r'C:\Users\xxyao\research\国债期货\data\data_summary.csv')
df['pct-1'] = df['close'].pct_change().shift(-1)
df['month'] = [x[0:7] for x in df['date']]

if __name__ == "__main__":
    pool = multiprocessing.Pool(processes=6)
    toolbox.register('map', pool.map)
    pop, log, hof, info, top10 = main()

当我运行代码时，我收到如下错误消息：

在此处输入图像描述

此消息在窗口中快速重复显示。我不知道哪里错了。正如 DEAP 文件所说，我保护Pool()in 。__name__ == __main__但它仍然无法工作。有人可以帮我吗。

4

1 回答 1

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将此代码放入 中main，该功能将起作用。

pool = multiprocessing.Pool(processes=6)
toolbox.register('map', pool.map)
于 2019-08-01T01:57:20.410 回答