使用插入符号完全可重现的并行模型

当我在插入符中运行 2 个随机森林时，如果设置随机种子，我会得到完全相同的结果：

library(caret)
library(doParallel)

set.seed(42)
myControl <- trainControl(method='cv', index=createFolds(iris$Species))

set.seed(42)
model1 <- train(Species~., iris, method='rf', trControl=myControl)

set.seed(42)
model2 <- train(Species~., iris, method='rf', trControl=myControl)

> all.equal(predict(model1, type='prob'), predict(model2, type='prob'))
[1] TRUE

但是，如果我注册一个并行后端来加速建模，则每次运行模型时都会得到不同的结果：

cl <- makeCluster(detectCores())
registerDoParallel(cl)

set.seed(42)
myControl <- trainControl(method='cv', index=createFolds(iris$Species))

set.seed(42)
model1 <- train(Species~., iris, method='rf', trControl=myControl)

set.seed(42)
model2 <- train(Species~., iris, method='rf', trControl=myControl)

stopCluster(cl)

> all.equal(predict(model1, type='prob'), predict(model2, type='prob'))
[1] "Component 2: Mean relative difference: 0.01813729"
[2] "Component 3: Mean relative difference: 0.02271638"

有什么办法可以解决这个问题吗？一个建议是使用doRNG http://cran.r-project.org/web/packages/doRNG/index.html包，但是train使用当前不支持的嵌套循环：

library(doRNG)
cl <- makeCluster(detectCores())
registerDoParallel(cl)
registerDoRNG()

set.seed(42)
myControl <- trainControl(method='cv', index=createFolds(iris$Species))

set.seed(42)
> model1 <- train(Species~., iris, method='rf', trControl=myControl)
Error in list(e1 = list(args = seq(along = resampleIndex)(), argnames = "iter",  : 
  nested/conditional foreach loops are not supported yet.
See the package's vignette for a work around.

更新： 我认为这个问题可以通过使用来解决doSNOW and clusterSetupRNG，但我无法完全到达那里。

set.seed(42)
library(caret)
library(doSNOW)
cl <- makeCluster(8, type = "SOCK")
registerDoSNOW(cl)

myControl <- trainControl(method='cv', index=createFolds(iris$Species))

clusterSetupRNG(cl, seed=rep(12345,6))
a <- clusterCall(cl, runif, 10000)
model1 <- train(Species~., iris, method='rf', trControl=myControl)

clusterSetupRNG(cl, seed=rep(12345,6))
b <- clusterCall(cl, runif, 10000)
model2 <- train(Species~., iris, method='rf', trControl=myControl)

all.equal(a, b)
[1] TRUE
all.equal(predict(model1, type='prob'), predict(model2, type='prob'))
[1] "Component 2: Mean relative difference: 0.01890339"
[2] "Component 3: Mean relative difference: 0.01656751"

stopCluster(cl)

foreach 有什么特别之处，为什么它不使用我在集群上启动的种子？物体a and b是相同的，所以为什么不呢model1 and model2?

一种使用并行模式运行完全可重现模型的简单方法caret包是通过在调用列车控制时使用种子参数来实现的。到这里上面的问题就解决了，查看trainControl帮助页面以获取更多信息。

library(doParallel); library(caret)

#create a list of seed, here change the seed for each resampling
set.seed(123)

#length is = (n_repeats*nresampling)+1
seeds <- vector(mode = "list", length = 11)

#(3 is the number of tuning parameter, mtry for rf, here equal to ncol(iris)-2)
for(i in 1:10) seeds[[i]]<- sample.int(n=1000, 3)

#for the last model
seeds[[11]]<-sample.int(1000, 1)

 #control list
 myControl <- trainControl(method='cv', seeds=seeds, index=createFolds(iris$Species))

 #run model in parallel
 cl <- makeCluster(detectCores())
 registerDoParallel(cl)
 model1 <- train(Species~., iris, method='rf', trControl=myControl)

 model2 <- train(Species~., iris, method='rf', trControl=myControl)
 stopCluster(cl)

 #compare
 all.equal(predict(model1, type='prob'), predict(model2, type='prob'))
[1] TRUE