numpy - Weighted random sample without replacement in python

Question

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

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])) 

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

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

Answer 1

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

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

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

For example:

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)