[Numpy-discussion] NEP: Random Number Generator Policy (original) (raw)

Robert Kern robert.kern at gmail.com
Mon Jun 4 18🔞25 EDT 2018

On Sun, Jun 3, 2018 at 8:22 PM Ralf Gommers <ralf.gommers at gmail.com> wrote:

It may be worth having a look at test suites for scipy, statsmodels, scikit-learn, etc. and estimate how much work this NEP causes those projects. If the devs of those packages are forced to do large scale migrations from RandomState to StableState, then why not instead keep RandomState and just add a new API next to it?

The problem is that we can't really have an ecosystem with two different general purpose systems. To properly use pseudorandom numbers, I need to instantiate a PRNG and thread it through all of the code in my program: both the parts that I write and the third party libraries that I don't write.

Generating test data for unit tests is separable, though. That's why I propose having a StableRandom built on the new architecture. Its purpose would be well-documented, and in my proposal is limited in features such that it will be less likely to be abused outside of that purpose. If you make it fully-featured, it is more likely to be abused by building library code around it. But even if it is so abused, because it is built on the new architecture, at least I can thread the same core PRNG state through the StableRandom distributions from the abusing library and use the better distributions class elsewhere (randomgen names it "Generator"). Just keeping RandomState around can't work like that because it doesn't have a replaceable core PRNG.

But that does suggest another alternative that we should explore:

The new architecture separates the core uniform PRNG from the wide variety of non-uniform probability distributions. That is, the core PRNG state is encapsulated in a discrete object that can be shared between instances of different distribution-providing classes. numpy.random should provide two such distribution-providing classes. The main one (let us call it Generator, as it is called in the prototype) will follow the new policy: distribution methods can break the stream in feature releases. There will also be a secondary distributions class (let us call it LegacyGenerator) which contains distribution methods exactly as they exist in the current RandomState implementation. When one combines LegacyGenerator with the MT19937 core PRNG, it should reproduce the exact same stream as RandomState for all distribution methods. The LegacyGenerator methods will be forever frozen. numpy.random.RandomState() will instantiate a LegacyGenerator with the MT19937 core PRNG, and whatever tricks needed to make isinstance(prng, RandomState) and unpickling work should be done. This way of creating the LegacyGenerator by way of RandomState will be deprecated, becoming progressively noisier over a number of release cycles, in favor of explicitly instantiating LegacyGenerator.

LegacyGenerator CAN be used during this deprecation period in library and application code until libraries and applications can migrate to the new Generator. Libraries and applications SHOULD migrate but MUST NOT be forced to. LegacyGenerator CAN be used to generate test data for unit tests where cross-release stability of the streams is important. Test writers SHOULD consider ways to mitigate their reliance on such stability and SHOULD limit their usage to distribution methods that have fewer cross-platform stability risks.

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