[Numpy-discussion] reseed random generator (1.19)
Evgeni Burovski
evgeny.burovskiy at gmail.com
Mon Jun 29 09:20:18 EDT 2020
(apologies for jumping into a conversation)
So what is the recommendation for instantiating a number of generators
with manually controlled seeds?
The use case is running a series of MC simulations with reproducible
streams. The runs are independent and are run in parallel in separate
OS processes, where I do not control the time each process starts
(jobs are submitted to the batch queue), so default seeding seems
dubious?
Previously, I would just do roughly
seeds = [1234, 1235, 1236, ...]
rngs = [np.random.RandomState(seed) for seed in seeds]
...
and each process operates with its own `rng`.
What would be the recommended way with the new `Generator` framework?
A human-friendly way would be preferable if possible.
Thanks,
Evgeni
On Mon, Jun 29, 2020 at 3:20 PM Kevin Sheppard
<kevin.k.sheppard at gmail.com> wrote:
>
> If you want to use the same entropy-initialized generator for temporarily-reproducible experiments, then you can use
>
>
>
> gen = np.random.default_rng()
>
> state = gen.bit_generator.state
>
> gen.standard_normal()
>
> # 0.5644742559549797, will vary across runs
>
> gen.bit_generator.state = state
>
> gen.standard_normal()
>
> # Always the same as before 0.5644742559549797
>
>
>
> The equivalent to the old way of calling seed to reseed is:
>
>
>
> SEED = 918273645
>
> gen = np.random.default_rng(SEED)
>
> gen.standard_normal()
>
> # 0.12345677
>
> gen = np.random.default_rng(SEED)
>
> gen.standard_normal()
>
> # Identical value
>
>
>
> Rather than reseeding the same object, you just create a new object. At some point in the development of Generator both methods were timed and there was no performance to reusing the same object by reseeding.
>
>
>
> Kevin
>
>
>
>
>
>
>
> From: Neal Becker
> Sent: Monday, June 29, 2020 1:01 PM
> To: Discussion of Numerical Python
> Subject: Re: [Numpy-discussion] reseed random generator (1.19)
>
>
>
> I was using this to reset the generator, in order to repeat the same sequence again for testing purposes.
>
>
>
> On Wed, Jun 24, 2020 at 6:40 PM Robert Kern <robert.kern at gmail.com> wrote:
>
> On Wed, Jun 24, 2020 at 3:31 PM Neal Becker <ndbecker2 at gmail.com> wrote:
>
> Consider the following:
>
>
>
> from numpy.random import default_rng
> rs = default_rng()
>
>
>
> Now how do I re-seed the generator?
>
> I thought perhaps rs.bit_generator.seed(), but there is no such attribute.
>
>
>
> In general, reseeding an existing generator instance is not a good practice. What effect are you trying to accomplish? I assume that you are asking this because you are currently using `RandomState.seed()`. In what circumstances?
>
>
>
> The raw `bit_generator.state` property *can* be assigned to, in order to support some advanced use cases (mostly involving de/serialization and similar kinds of meta-programming tasks). It's also been helpful for me to construct worst-case scenarios for testing parallel streams. But it quite deliberately bypasses the notion of deriving the state from a human-friendly seed number.
>
>
>
> --
>
> Robert Kern
>
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>
> Those who don't understand recursion are doomed to repeat it
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