[Numpy-discussion] Proposal: numpy.random.random_seed

[Stephan Hoyer]

I have recently encountered several use cases for randomly generate random
number seeds:

1. When writing a library of stochastic functions that take a seed as an
input argument, and some of these functions call multiple other such
stochastic functions. Dask is one such example [1].

2. When a library needs to produce results that are reproducible after
calling numpy.random.seed, but that do not want to use the functions in
numpy.random directly. This came up recently in a pandas pull request [2],
because we want to allow using RandomState objects as an alternative to
global state in numpy.random. A major advantage of this approach is that it
provides an obvious alternative to reusing the private numpy.random._mtrand
[3].

The implementation of this function (and the corresponding method on
RandomState) is almost trivial, and I've already written such a utility for
my code:

def random_seed():
    # numpy.random uses uint32 seeds
    np.random.randint(2 ** 32)

The advantage of adding a new method is that it avoids the need for
explanation by making the intent of code using this pattern obvious. So I
think it is a good candidate for inclusion in numpy.random.

Any opinions?

[1]
https://github.com/dask/dask/blob/e0b246221957c4bd618e57246f3a7ccc8863c494/dask/utils.py#L336
[2] https://github.com/pydata/pandas/pull/13161
[3] On a side note, if there's no longer a good reason to keep this object
private, perhaps we should expose it in our public API. It would certainly
be useful -- scikit-learn is already using it (see links in the pandas PR
above).
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