Find Indexes that Maps a Numpy Array to Another

Question

If we have an numpy array a that needs to be sampled with replacement to create a second numpy array b,

import numpy as np

a = np.arange(10, 200*1000)
b = np.random.choice(a, len(a), replace=True)

What is the most efficient way to find an array of indexes named mapping that will transform a to b? It is OK to change np.random.choice to a more suitable function.

The following code is too slow and takes 7-8 seconds on a Macbook Pro to creating the mapping array. With an array size of 1 million, it will take much longer.

mapping = np.array([], dtype=np.int)
for n in b:
    m = np.searchsorted(a, n)
    mapping = np.append(mapping, m)

Just tried it with the software package numba, I was able to minimize the runtime 3 to 4 seconds on average. Unfortunately, the numpy append is very inefficient, whereas the append of lists is much faster. — Higs
– Higs, Commented Oct 14, 2020 at 22:31
Both np.searchsorted() and np.append() are substitutes for some looping actions. It should, indeed, to be a pain in performance if they are performed on every iteration instead of that. — mathfux
– mathfux, Commented Oct 14, 2020 at 23:56

Andy L. · Accepted Answer · 2020-10-14 21:16:40Z

1

Perhaps, run the choice on index of a and slice a using this random index mapping:

mapping = np.random.choice(np.arange(len(a)), len(a), replace=True)
b = a[mapping]

answered Oct 14, 2020 at 21:16

Andy L.

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1 Comment

Athena Wisdom Over a year ago

Wow, your code took 0.004 secs to run on my laptop!

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