https://mail.python.org/pipermail/numpy-discussion/2018-June/078126.html
Hi, sklearners!
I have a NEP out for discussion that proposes a change in numpy.random's
stream-compatibility policy. As scikit-learn is a well-disciplined consumer of
reproducible streams, I would appreciate your input on the numpy-discussion
thread linked above.
The very short form is that there is a new PRNG subsystem being developed with
better core PRNGs (among other things, providing nice features like independent
streams for parallel computations), and we would like to relax our strict
stream-compatibility policy for the non-uniform distributions in this new
subsystem so that we can improve our algorithms. The core uniform numbers would
still be strictly stream-compatible across numpy versions. But we would like to
be able to upgrade our non-uniform algorithms, for example, to make normal
variates faster to generate.
RandomState would be frozen and subject to a long deprecation cycle for a period
of strict backwards compatibility. There would be some non-deprecated provision
to get strictly-compatible streams for a subset of distributions for the limited
purpose of generating test data for unit tests.
Please read the NEP and the thread through. I do propose at least one
alternative in the thread and would like some feedback on it. I would also
appreciate it if we could consolidate the discussion on the numpy-discussion
thread and not have a split-off conversation here too.
Thank you very much! I appreciate your attention.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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