using `random.choice` to sample integers in a large range

So I wanted to generate some unique integers with the following code:

import numpy as np
max_int = np.iinfo(np.uint32).max
numbers = np.random.choice(max_int, 10, replace=False)

I wanted these numbers as deterministic seeds for some simulations. When I do this almost 16 GB of memory are filled.

I looked into the code for choice and in this case it essentially generates a permutation, similar to shuffling a np.arange(max_int), in order to then take a small slice from that array. This seems like a bad strategy since providing only an int to random.choice makes it clear that numbers in that range should be sampled. At least when the integer argument a is much larger than the argument size.

So for now I will just generate a set of numbers but I was wondering if there is a more general way.

[bashtage]

@mattip Fixed in Generator, can’t be fixed in RandomState

In [1]: import numpy as np
   ...: max_int = np.iinfo(np.uint32).max
In [2]: g = np.random.Generator()
In [3]: %timeit numbers = g.choice(max_int, 10, replace=False)
19.2 µs ± 1.72 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

[mattip]

Ok, closing. For performance use the new APIs.