It can be anything, but “good practice” is to use a number that would have 2 properties:

1. When expressed as binary number, it would have a large number of both 0s and 1s
2. The total number of digits in the binary representation is somewhere between 32 and 128.

The binary representation of the one I chose (by mashing numbers) is:

'0b10011110000100100101100111001110010001101111111001100111100011000101001111111100100010000'

This has 43 0s and 46 1s.

Many people just use 0, which is fine in the sense that the stream should have the same properties as if any of 2**N number were chosen. Simple choices so, however, have a slight consequence in the sense that these generate strange dependence across researchers if everyone uses a small number of seeds (e.g., 0 or 1234).

Kevin

From: Evgeni Burovski
Sent: Monday, June 29, 2020 4:01 PM
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] reseed random generator (1.19)

Thanks Kevin!

A possibly dumb follow-up question: in your example,

> entropy = 382193877439745928479635728

is it relevant that `entropy` is a long integer? I.e., what are the

constraints on its value, can one use

entropy = 1234 or

entropy = 0 or

entropy = 1

On Mon, Jun 29, 2020 at 5:37 PM Kevin Sheppard

<kevin.k.sheppard@gmail.com> wrote:

>

> The best practice is to use a SeedSequence to spawn child SeedSequences, and then to use these children to initialize your generators or bit generators.

>

>

>

>

>

> from numpy.random import SeedSequence, Generator, PCG64, default_rng

>

>

>

> entropy = 382193877439745928479635728

>

>

>

> seed_seq = SeedSequence(entropy)

>

> NUM_STREAMS = 2**15

>

> children = seed_seq.spawn(NUM_STREAMS)

>

> # if you want the current best bit generator, which may change

>

> rngs = [default_rng(child) for child in children]

>

> # If you want the most control across version, set the bit generator

>

> # this uses PCG64, which is the current default. Each bit generator needs to be wrapped in a generator

>

> rngs = [Generator(PCG64(child)) for child in children]

>

>

>

> Kevin

>

>

>

>

>

> From: Evgeni Burovski

> Sent: Monday, June 29, 2020 2:21 PM

> To: Discussion of Numerical Python

> Subject: Re: [Numpy-discussion] reseed random generator (1.19)

>

>

>

> (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@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@gmail.com> wrote:

>

> >

>

> > On Wed, Jun 24, 2020 at 3:31 PM Neal Becker <ndbecker2@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

>

> >

>

> > _______________________________________________

>

> > NumPy-Discussion mailing list

>

> > NumPy-Discussion@python.org

>

> > https://mail.python.org/mailman/listinfo/numpy-discussion

>

> >

>

> >

>

> >

>

> >

>

> > --

>

> >

>

> > Those who don't understand recursion are doomed to repeat it

>

> >

>

> >

>

> >

>

> > _______________________________________________

>

> > NumPy-Discussion mailing list

>

> > NumPy-Discussion@python.org

>

> > https://mail.python.org/mailman/listinfo/numpy-discussion

>

> _______________________________________________

>

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>

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>

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