SyncRNG 1.3.2

Generate the same random numbers in R and Python

SyncRNG

Generate the same random numbers in R and Python.

Why?

This program was created because I wanted to have the same random numbers in both R and Python programs. Although both languages implement a Mersenne-Twister random number generator (RNG), the implementations are so different that it is not possible to get the same random numbers, even with the same seed.

SyncRNG is a "Tausworthe" RNG implemented in C and linked to both R and Python. Since both use the same underlying C code, the random numbers will be the same in both languages when the same seed is used.

You can read more about my motivations for creating this here.

Installation

Installing the R package can be done through CRAN:

> install.packages('SyncRNG')

The Python package can be installed using pip:

$ pip install syncrng

Usage

After installing the package, you can use the basic SyncRNG random number generator. In Python you can do:

>>> from SyncRNG import SyncRNG
>>> s = SyncRNG(seed=123456)
>>> for i in range(10):
>>>     print(s.randi())

And in R you can use:

> library(SyncRNG)
> s <- SyncRNG(seed=123456)
> for (i in 1:10) {
>    cat(s$randi(), '\n')
> }

You'll notice that the random numbers are indeed the same.

R: User defined RNG

R allows the user to define a custom random number generator, which is then used for the common runif and rnorm functions in R. This has also been implemented in SyncRNG as of version 1.3.0. To enable this, run:

> library(SyncRNG)
> set.seed(123456, 'user', 'user')
> runif(10)

These numbers are between [0, 1) and multiplying by 2**32 - 1 gives the same results as above.

Functionality

In both R and Python the following methods are available for the SyncRNG class:

1. randi(): generate a random integer on the interval [0, 2^32).
2. rand(): generate a random floating point number on the interval [0.0, 1.0)
3. randbelow(n): generate a random integer below a given integer n.
4. shuffle(x): generate a permutation of a given list of numbers x.

Functionality is deliberately kept minimal to make maintaining this library easier. It is straightforward to build more advanced applications on the existing methods, as the following example shows.

Creating the same train/test splits

A common use case for this package is to create the same train and test splits in R and Python. Below are some code examples that illustrate how to do this. Both assume you have a matrix X with 100 rows.

In R:

# This function creates a list with train and test indices for each fold
k.fold <- function(n, K, shuffle=TRUE, seed=0)
{
	idxs <- c(1:n)
	if (shuffle) {
		rng <- SyncRNG(seed=seed)
		idxs <- rng$shuffle(idxs)
	}

	# Determine fold sizes
        fsizes <- c(1:K)*0 + floor(n / K)
        mod <- n %% K
        if (mod > 0)
		fsizes[1:mod] <- fsizes[1:mod] + 1

        out <- list(n=n, num.folds=K)
	current <- 1
        for (f in 1:K) {
		fs <- fsizes[f]
		startidx <- current
		stopidx <- current + fs - 1
		test.idx <- idxs[startidx:stopidx]
		train.idx <- idxs[!(idxs %in% test.idx)]
		out$testidxs[[f]] <- test.idx
		out$trainidxs[[f]] <- train.idx
		current <- stopidx
	}
	return(out)
}

# Which you can use as follows
folds <- k.fold(nrow(X), K=10, shuffle=T, seed=123)
for (f in 1:folds$num.folds) {
        X.train <- X[folds$trainidx[[f]], ]
        X.test <- X[folds$testidx[[f]], ]

        # continue using X.train and X.test here
}

And in Python:

def k_fold(n, K, shuffle=True, seed=0):
    """Generator for train and test indices"""
    idxs = list(range(n))
    if shuffle:
        rng = SyncRNG(seed=seed)
        idxs = rng.shuffle(idxs)

    fsizes = [n // K]*K
    mod = n % K
    if mod > 0:
        fsizes[:mod] = [x+1 for x in fsizes[:mod]]

    current = 0
    for fs in fsizes:
        startidx = current
        stopidx = current + fs
        test_idx = idxs[startidx:stopidx]
        train_idx = [x for x in idxs if not x in test_idx]
        yield train_idx, test_idx
        current = stopidx

# Which you can use as follows
kf = k_fold(X.shape[0], K=3, shuffle=True, seed=123)
for trainidx, testidx in kf:
    X_train = X[trainidx, :]
    X_test = X[testidx, :]

    # continue using X_train and X_test here

Notes

The random numbers are uniformly distributed on [0, 2^32 - 1]. No attention has been paid to thread-safety and you shouldn't use this random number generator for cryptographic applications.

Questions and Issues

If you have questions, comments, or suggestions about SyncRNG or you encounter a problem, please open an issue on GitHub. Please don't hesitate to contact me, you're helping to make this project better for everyone! If you prefer not to use Github you can email me at gertjanvandenburg at gmail dot com.