Question

我正在尝试将this answer应用于我的代码，以显示scipy.optimize.differential_evolution方法的进度条。

我认为differential_evolution将对func（称为最小化的函数）进行popsize * maxiter次评估，但显然并非如此。

下面的代码应显示一个进度条，最多增加到100%：

[####################] 100%

但是实际上，随着DEdist()函数的求值次数比popsize * maxiter（我用作total函数的updt()参数）的次数要多得多

如何计算differential_evolution执行的功能评估的总数？可以做到吗？

from scipy.optimize import differential_evolution as DE
import sys


popsize, maxiter = 10, 50


def updt(total, progress, extra=""):
    """
    Displays or updates a console progress bar.

    Original source: https://stackoverflow.com/a/15860757/1391441
    """
    barLength, status = 20, ""
    progress = float(progress) / float(total)
    if progress >= 1.:
        progress, status = 1, "\r\n"
    block = int(round(barLength * progress))
    text = "\r[{}] {:.0f}% {}{}".format(
        "#" * block + "-" * (barLength - block),
        round(progress * 100, 0), extra, status)
    sys.stdout.write(text)
    sys.stdout.flush()


def DEdist(model, info):
    updt(popsize * maxiter, info['Nfeval'] + 1)
    info['Nfeval'] += 1

    res = (1. - model[0])**2 + 100.0 * (model[1] - model[0]**2)**2 + \
        (1. - model[1])**2 + 100.0 * (model[2] - model[1]**2)**2

    return res


bounds = [[0., 10.], [0., 10.], [0., 10.], [0., 10.]]
result = DE(
    DEdist, bounds, popsize=popsize, maxiter=maxiter,
    args=({'Nfeval': 0},))

Answer 1

来自help(scipy.optimize.differential_evolution)：

maxiter : int, optional
    The maximum number of generations over which the entire population is
    evolved. The maximum number of function evaluations (with no polishing)
    is: ``(maxiter + 1) * popsize * len(x)``

默认情况下也为polish=True：

polish : bool, optional
    If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
    method is used to polish the best population member at the end, which
    can improve the minimization slightly.

所以您需要更改两件事：

1在此处使用正确的薄膜：

updt(popsize * (maxiter + 1) * len(model), info['Nfeval'] + 1)

2通过polish=False参数：

result = DE(
    DEdist, bounds, popsize=popsize, maxiter=maxiter, polish=False,
    args=({'Nfeval': 0},))

此后，您将看到进度条在达到100％时完全停止。

scipy.optimize.differential_evolution评估要最小化的功能多少次？

1 个答案: