加权随机样本，无需在python中进行替换 [英] Weighted random sample without replacement in python

问题描述

我需要获取一个 k大小的样本，而无需从种群中进行替换，种群中的每个成员都有相关的权重(W).

I need to obtain a k-sized sample without replacement from a population, where each member of the population has a associated weight (W).

Numpy的 random.choices 未经替换将无法执行此任务，并且 random.sample 不会接受加权输入.

Numpy's random.choices will not perform this task without replacement, and random.sample won't take a weighted input.

当前，这是我正在使用的:

Currently, this is what I am using:

P = np.zeros((1,Parent_number))
n=0
while n < Parent_number:
    draw = random.choices(population,weights=W,k=1)
    if draw not in P:
        P[0,n] = draw[0]
        n=n+1
P=np.asarray(sorted(P[0])) 

虽然可行，但需要从数组来回切换到列表再回到数组，因此不理想.

While this works, it reqires switching back and forth from arrays, to lists and back to arrays and is, therefore, less than ideal.

我正在寻找最简单易懂的解决方案，因为此代码将与他人共享.

I am looking for the simplest and easiest to understand solution as this code will be shared with others.

推荐答案

您可以将np.random.choice与replace=False结合使用，如下所示:

You can use np.random.choice with replace=False as follows:

np.random.choice(vec,size,replace=False, p=P)

其中vec是人口，P是权重向量.

where vec is your population and P is the weight vector.

例如:

import numpy as np
vec=[1,2,3]
P=[0.5,0.2,0.3]
np.random.choice(vec,size=2,replace=False, p=P)

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