ENH: check_random_state: Accept np.random.Generator
#654
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There is incompatibility between Use |
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As demonstrated by #655, some calls can be replaced independent of this PR, without any breaking of the code (except for |
oyamad
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oyamad
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Nov 24, 2022
quantecon/util/random.py
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| return scipy_rng_integers(gen, low, high=high, size=size, | ||
| dtype=dtype, endpoint=endpoint) |
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I would say this is redundant. Isn't just importing scipy._lib._util.rng_integers directly enough?
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I propose to update the docstrings as follows (to keep them short): def some_function(main_arguments, random_state=None):
"""
Parameters
----------
...
random_state : int or np.random.RandomState/Generator, optional
Random seed (integer) or np.random.RandomState or Generator
instance to set the initial state of the random number generator
for reproducibility. If None, a randomly initialized RandomState
is used.
"""
...class SomeClass:
def some_method(main_arguments, random_state=None):
"""
Parameters
----------
...
random_state : int or np.random.RandomState/Generator, optional
Random seed (integer) or np.random.RandomState or Generator
instance to set the initial state of the random number
generator for reproducibility. If None, a randomly
initialized RandomState is used.
"""
... |
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oyamad
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np.random.Generatornp.random.Generator
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Let me merge this. Thanks for your help @Smit-create. |
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Numpy's recommended procedure to generate random numbers now seems to be to use
np.random.Generatorinstead ofnp.random.RandomState: https://numpy.org/doc/stable/reference/random/index.htmlThis PR is to update
check_random_stateto acceptnp.random.Generator, following the corresponding code in scipy:https://github.com/scipy/scipy/blob/b4a970bf4eba1065ea31f8d0988cd4ec523a07d4/scipy/_lib/_util.py#L174-L203
There are discussions on this at scipy and scikit-learn:
The issue is that some methods are present for
np.random.RandomStatebut not fornp.random.Generator: see the table in here. Thus, for example,random_sample()andrand()have to be replaced withrandom(), andrandn()withstandard_normal()and so on.These can be done as commits added to this PR or as independent PRs.
TODOs:
np.random.RandomStatespecific methods tonp.random.Generatorcompatible methodsrandom_sample()->random()rand()->random(), butrand(d0, d1, ..., dn)->random((d0, d1, ..., dn))(orrandom(size=(d0, d1, ..., dn)))randn()->standard_normal(), butrandn(d0, d1, ..., dn)->standard_normal((d0, d1, ..., dn))(orstandard_normal(size=(d0, d1, ..., dn)))randint()->util.rng_integers()