(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.shepp...@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.k...@gmail.com> wrote: > > On Wed, Jun 24, 2020 at 3:31 PM Neal Becker <ndbeck...@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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion