[Rd] Seeding non-R RNG with numbers from R's RNG stream

[Duncan Murdoch]

On 30/07/2020 4:30 p.m., Tommy Jones wrote:
> Thank you for this. I'd like to be sure I understand the 
> intuition correctly. Is the following true from what you said?
> 
> I can just fix the seed at the C++ level and the results will still be 
> (pseudo) random because the initialization at the R level is (pseudo) 
> random.

No, that's not quite right.  Let me try again:

You can fix the seed at the C++ level and the results will be 
pseudo-random because you have chosen to use a good pseudo-random 
generator.

  - R has nothing to do with it.
  - If you haven't actually chosen a good generator, then seeding from R 
won't necessarily help.
  - If you re-seed too frequently, you might break even a good generator.

For an example of the latter:  consider re-seeding with the current time 
(to the nearest second) with every draw.  If you draw more than once per 
second, you'll get exact repeats.

The scheme you chose won't be so obviously wrong, but there could still 
be interactions between the R generator and the C++ generator.  For 
example, maybe the C++ generator is based on a similar algorithm to the 
R generator.  If you re-seed it every tenth draw, and only draw one 
value from R, it might happen that you effectively take 9 steps back 
with each re-seeding, so again you'll get exact repeats.

The real effect, if there is one, is likely to be much more subtle and 
hard to detect.  In fact, it might be so hard to detect that there 
really isn't a problem!  The practical issue is that by effectively 
inventing your own algorithm, you can't rely on the accumulated 
experience of everyone else to know whether the generator is good.

Duncan Murdoch


> 
> On Thu, Jul 30, 2020 at 3:36 PM Duncan Murdoch <murdoch.duncan using gmail.com 
> <mailto:murdoch.duncan using gmail.com>> wrote:
> 
>     I wouldn't trust the C++ generator to be as good if you seed it this
>     way
>     as if you just seeded it once with your phone number (or any other
>     fixed
>     value) and let it run, because it's probably never been tested to be
>     good when run this way.  Is it good enough for the way you plan to use
>     it?  Maybe.
> 
>     Duncan Murdoch
> 
>     On 30/07/2020 3:05 p.m., Tommy Jones wrote:
>      > Hi,
>      >
>      > I am constructing a function that does sampling in C++ using a
>     non-R RNG
>      > stream for thread safety reasons. This C++ function is wrapped by
>     an R
>      > function, which is user facing. The R wrapper does some sampling
>     itself to
>      > initialize some variables before passing them off to C++. So that
>     my users
>      > do not have to manage two mechanisms to set random seeds, I've
>     constructed
>      > a solution (shown below) that allows both RNGs to be seeded with
>     set.seed
>      > and respond to the state of R's RNG stream.
>      >
>      > I believe the below works. However, I am hoping to get feedback
>     from more
>      > experienced useRs as to whether or not the below approach is
>     unsafe in ways
>      > that may affect reproducibility, modify global variables in bad
>     ways, or
>      > have other unintended consequences I have not anticipated.
>      >
>      > Could I trouble one or more folks on this list to weigh in on the
>     safety
>      > (or perceived wisdom) of using R's internal RNG stream to seed an RNG
>      > external to R? Many thanks in advance.
>      >
>      > This relates to a Stackoverflow question here:
>      >
>     https://stackoverflow.com/questions/63165955/is-there-a-best-practice-for-using-non-r-rngs-in-rcpp-code
>      >
>      > Pseudocode of a trivial facsimile of my current approach is below.
>      >
>      > --Tommy
>      >
>      > sample_wrapper <- function() {
>      >    # initialize a variable to pass to C++
>      >    init_var <- runif(1)
>      >
>      >    # get current state of RNG stream
>      >    # first entry of .Random.seed is an integer representing the
>     algorithm used
>      >    # second entry is current position in RNG stream
>      >    # subsequent entries are pseudorandom numbers
>      >    seed_pos <- .Random.seed[2]
>      >
>      >    seed <- .Random.seed[seed_pos + 2]
>      >
>      >    out <- sample_cpp(init_var = init_var, seed = seed)
>      >
>      >    # move R's position in the RNG stream forward by 1 with a
>     throw away sample
>      >    runif(1)
>      >
>      >    # return the output
>      >    out}
>      >
>      >       [[alternative HTML version deleted]]
>      >
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