如何使用 foreach 和 doMC 包为随机模拟设置种子? [英] How to set seed for random simulations with foreach and doMC packages?

问题描述

我需要进行一些模拟，出于调试目的，我想使用 set.seed 来获得相同的结果.这是我正在尝试做的示例:

I need to do some simulations and for debugging purposes I want to use set.seed to get the same result. Here is the example of what I am trying to do:

library(foreach)
library(doMC)
registerDoMC(2)

set.seed(123)
a <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}
set.seed(123)
b <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}

对象 a 和 b 应该相同，即 sum(abs(ab)) 应该为零，但事实并非如此.我做错了什么，还是我偶然发现了某些功能?

Objects a and b should be identical, i.e. sum(abs(a-b)) should be zero, but this is not the case. I am doing something wrong, or have I stumbled on to some feature?

我可以使用 R 2.13 和 R 2.14 在两个不同的系统上重现这个

I am able to reproduce this on two different systems with R 2.13 and R 2.14

推荐答案

我的默认答案曾经是那么不要那样做"(使用 foreach) 作为 snow 包为你做这件事(可靠！).

My default answer used to be "well then don't do that" (using foreach) as the snow package does this (reliably!) for you.

但正如@Spacedman 指出的那样，如果您想留下来，Renaud 的新 doRNG 正是您要寻找的与 doFoo/foreach 家族一起.

But as @Spacedman points out, Renaud's new doRNG is what you are looking for if you want to remain with the doFoo / foreach family.

不过，真正的关键是一个 clusterApply 风格的调用，用于在所有节点上设置种子.并以跨流协调的方式.哦，我有没有提到 Tierney、Rossini、Li 和 Sevcikova 的 snow 几乎已经为你做这件事了十年?

The real key though is a clusterApply-style call to get the seeds set on all nodes. And in a fashion that coordinated across streams. Oh, and did I mention that snow by Tierney, Rossini, Li and Sevcikova has been doing this for you for almost a decade?

虽然您没有询问 snow，但为了完整性这是命令行中的示例:

And while you didn't ask about snow, for completeness here is an example from the command-line:

edd@max:~$ r -lsnow -e'cl <- makeSOCKcluster(c("localhost","localhost"));
         clusterSetupRNG(cl);
         print(do.call("rbind", clusterApply(cl, 1:4, 
                                             function(x) { stats::rnorm(1) } )))'
Loading required package: utils
Loading required package: utils
Loading required package: rlecuyer
           [,1]
[1,] -1.1406340
[2,]  0.7049582
[3,] -0.4981589
[4,]  0.4821092
edd@max:~$ r -lsnow -e'cl <- makeSOCKcluster(c("localhost","localhost"));
         clusterSetupRNG(cl);
         print(do.call("rbind", clusterApply(cl, 1:4, 
                                             function(x) { stats::rnorm(1) } )))'
Loading required package: utils
Loading required package: utils
Loading required package: rlecuyer
           [,1]
[1,] -1.1406340
[2,]  0.7049582
[3,] -0.4981589
[4,]  0.4821092
edd@max:~$ 

为了完整起见，这里是您的示例与 文档中的内容相结合做

And for completeness, here is your example combined with what is in the docs for doRNG

> library(foreach)
R> library(doMC)
Loading required package: multicore

Attaching package: ‘multicore’

The following object(s) are masked from ‘package:parallel’:

    mclapply, mcparallel, pvec

R> registerDoMC(2)
R> library(doRNG)
R> set.seed(123)
R> a <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}
R> set.seed(123)
R> b <- foreach(i=1:2,.combine=cbind) %dopar% {rnorm(5)}
R> identical(a,b)
[1] FALSE                     ## ie standard approach not reproducible
R>
R> seed <- doRNGseed()
R> a <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> b <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> doRNGseed(seed)
R> a1 <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> b1 <- foreach(i=1:2,combine=cbind) %dorng% { rnorm(5) }
R> identical(a,a1) && identical(b,b1)
[1] TRUE                      ## all is well now with doRNGseed()
R> 

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