[Numpy-discussion] NEP: Random Number Generator Policy

[Robert Kern]

On Sun, Jun 10, 2018 at 8:04 PM Ralf Gommers <ralf.gommers at gmail.com> wrote:
>
> On Sun, Jun 10, 2018 at 6:08 PM, Robert Kern <robert.kern at gmail.com>
wrote:
>>
>> On Sun, Jun 10, 2018 at 5:27 PM Ralf Gommers <ralf.gommers at gmail.com>
wrote:
>> >
>> > On Mon, Jun 4, 2018 at 3:18 PM, Robert Kern <robert.kern at gmail.com>
wrote:
>> >>
>> >> 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.
>> >
>> > Can't = prefer not to.
>>
>> I meant what I wrote. :-)
>>
>> > But yes, that's true. That's not what I was saying though. We want one
generic one, and one meant for unit testing only. You can achieve that in
two ways:
>> > 1. Change the current np.random API to new generic, and add a new
RandomStable for unit tests.
>> > 2. Add a new generic API, and document the current np.random API as
being meant for unit tests only, for other usage <new API> should be
preferred.
>> >
>> > (2) has a couple of pros:
>> > - you're not forcing almost every library and end user out there to
migrate their unit tests.
>>
>> But it has the cons that I talked about. RandomState *is* a fully
functional general purpose PRNG system. After all, that's its current use.
Documenting it as intended to be something else will not change that fact.
Documentation alone provides no real impetus to move to the new system
outside of the unit tests. And the community does need to move together to
the new system in their library code, or else we won't be able to combine
libraries together; these PRNG objects need to thread all the way through
between code from different authors if we are to write programs with a
controlled seed. The failure mode when people don't pay attention to the
documentation is that I can no longer write programs that compose these
libraries together. That's why I wrote "can't". It's not a mere preference
for not having two systems to maintain. It has binary Go/No Go implications
for building reproducible programs.
>
> I strongly suspect you are right, but only because you're asserting
"can't" so heavily. I have trouble formulating what would go wrong in case
there's two PRNGs used in a single program. It's not described in the NEP,
nor in the numpy.random docs (those don't even have any recommendations for
best practices listed as far as I can tell - that needs fixing). All you
explain in the NEP is that reproducible research isn't helped by the
current stream-compat guarantee. So a bit of (probably incorrect) devil's
advocate reasoning:
> - If there's no stream-compat guarantee, all a user can rely on is the
properties of drawing from a seeded PRNG.
> - Any use of a PRNG in library code can also only rely on properties
> - So now whether in a user's program libraries draw from one or two
seeded PRNGs doesn't matter for reproducibility, because those properties
don't change.

Correctly making a stochastic program reproducible while retaining good
statistical properties is difficult. People don't do it well in the best of
circumstances. The best way that we've found to manage that difficulty is
to instantiate a single stream and use it all throughout your code. Every
new stream requires the management of more seeds (unless if we use the
fancy new algorithms that have settable stream IDs, but by stipulation, we
don't have these in this case). And now I have to thread both of these
objects through my code, and pass the right object to each third-party
library. These third-party libraries don't know anything about this weird
2-stream workaround that you are doing, so we now have libraries that can't
build on each other unless if they are using the same compatible API, even
if I can make workarounds to build a program that combines two libraries
side-to-side.

So yeah, people "can" do this. "It's just a matter of code" as my boss
likes to say. But it's making an already-difficult task more difficult.

> Also, if there is to be a multi-year transitioning to the new API, would
there be two PRNG systems anyway during those years?

Sure, but with a deadline and not-just-documentation to motivate
transitioning.

But if we follow my alternative proposal, there'll be no need for
deprecation! You've convinced me to not deprecate RandomState. I just want
to change some of its internal implementation details, add a less-stable
set of distributions on the side, and a framework of core uniform PRNGs
that can be shared by both.

>> > - more design freedom for the new generic API. The current one is
clearly sub-optimal; in a new one you wouldn't have to expose all the
global state/functions that np.random exposes now. You could even restrict
it to a single class and put that in the main numpy namespace.
>>
>> I'm not sure why you are talking about the global state and np.random.*
convenience functions. What we do with those functions is out of scope for
this NEP and would be talked about it another NEP fully introducing the new
system.
>
> To quote you from one of the first emails in this thread: "
> I deliberately left it out of this one as it may, depending on our
choices, impinge upon the design of the new PRNG subsystem, which I
declared out of scope for this NEP. I have ideas (besides the glib "Let
them eat AttributeErrors!"), and now that I think more about it, that does
seem like it might be in scope just like the discussion of freezing
RandomState and StableRandom are. But I think I'd like to hold that thought
a little bit and get a little more screaming^Wfeedback on the core proposal
first. I'll return to this in a few days if not sooner.
> "
>
> So consider this some screaming^Wfeedback:)

Ahem. Yes, I just remembered I said that. :-) But still, there will be lots
of options about what to do with np.random.*, whatever proposal we go with.
It doesn't really impose constraints on the core proposals.

>> >> 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.
>>
>> I would appreciate your consideration of this proposal. Does it address
your concerns? It addresses my concerns with keeping around a
fully-functional RandomState implementation.
>
> My concerns are:
> 1. The amount of work caused by making libraries and end users migrate.
> 2. That this is a backwards compatibility break, which will cause
problems for users who relied on the old guarantees (the arguments in the
NEP that the old guarantees weren't 100% watertight don't mean that
backcompat doesn't matter at all).
>
> As far as I can tell, this new proposal doesn't deal with those concerns
directly. What it does seem to do is making transitioning a bit easier for
users that were already using RandomState instances.

Let me drop the deprecation of the name RandomState. RandomState(int_seed)
will forever and always create a backwards- and stream-compatible object.
No one will have to migrate.

How does that strike you?

--
Robert Kern
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