[英]Data frame creation inside Parlapply in R
I am trying something pretty simple, want to run a bunch of regressions parallelly.我正在尝试一些非常简单的事情,想要并行运行一堆回归。 When I use the following data generator (PART 1), The parallel part does not work and give the error listed below当我使用以下数据生成器(第 1 部分)时,并行部分不起作用并给出下面列出的错误
#PART 1
p <- 20; rho<-0.7;
cdc<- diag(p)
for( i in 1:(p-1) ){ for( j in (i+1):p ){
cdc[i,j] <- cdc[j,i] <- rho^abs(i-j)
}}
my.data <- mvrnorm(n=100, mu = rep(0, p), Sigma = cdc)
The following Parallel Part does work but if I generate the data as PART 2以下并行部分确实有效,但如果我将数据生成为第 2 部分
# PART 2
my.data<-matrix(rnorm(1000,0,1),nrow=100,ncol=10)
I configured the function that I want to run parallelly... as我将要并行运行的 function 配置为...
parallel_fun<-function(obj,my.data){
p1 <- nrow(cov(my.data));store.beta<-matrix(0,p1,length(obj))
count<-1
for (itration in obj) {
my_df<-data.frame(my.data)
colnames(my_df)[itration] <- "y"
my.model<-bas.lm(y ~ ., data= my_df, alpha=3,
prior="ZS-null", force.heredity = FALSE, pivot = TRUE)
cf<-coef(my.model, estimator="MPM")
betas<-cf$postmean[-1]
store.beta[ -itration, count]<- betas
count<-count+1
}
result<-list('Beta'=store.beta)
}
So I write the following way of running parlapply所以我写了以下运行parlapply的方式
{
no_cores <- detectCores(logical = TRUE)
myclusternumber<-(no_cores-1)
cl <- makeCluster(myclusternumber)
registerDoParallel(cl)
p1 <- ncol(my.data)
obj<-splitIndices(p1, myclusternumber)
clusterExport(cl,list('parallel_fun','my.data','obj'),envir=environment())
clusterEvalQ(cl, {
library(MASS)
library(Matrix)
library(BAS)
})
newresult<-parallel::parLapply(cl,obj,fun = parallel_fun,my.data)
stopCluster(cl)
}
But whenever am doing PART 1 I get the following error但是每当我在做第 1 部分时,我都会收到以下错误
Error in checkForRemoteErrors(val): 7 nodes produced errors; checkForRemoteErrors(val) 中的错误:7 个节点产生错误; first error: object 'my_df' not found第一个错误:未找到 object 'my_df'
But this should not happen, the data frame should be created, I have no idea why this is happening.但这不应该发生,应该创建数据框,我不知道为什么会这样。 Any help is appreciated.任何帮助表示赞赏。
Posting this as one possible workaround, see if it works:将此作为一种可能的解决方法发布,看看它是否有效:
parallel_fun<-function(obj,my.data){
p1 <- nrow(cov(my.data));store.beta<-matrix(0,p1,length(obj))
count<-1
for (itration in obj) {
my_df<-data.frame(my.data)
colnames(my_df)[itration] <- "y"
my_df <<- my_df
my.model<-bas.lm(y ~ ., data= my_df, alpha=3,
prior="ZS-null", force.heredity = FALSE, pivot = TRUE)
cf<-BAS:::coef.bas(my.model, estimator="MPM")
betas<-cf$postmean[-1]
store.beta[ -itration, count]<- betas
count<-count+1
}
result<-list('Beta'=store.beta)
}
The issue seems to be with BAS:::coef.bas
function, that calls eval
in order to get my_df
and fails to do that when called in parallel.问题似乎出在BAS:::coef.bas
function 上,它调用eval
以获取my_df
并且在并行调用时无法做到这一点。 The "hack" here is to force my_df
out to the parent environment by calling my_df <<- my_df
.这里的“hack”是通过调用my_df <<- my_df
来强制my_df
进入父环境。
There should be a better way to do this, but <<-
might be the fastest one.应该有更好的方法来做到这一点,但<<-
可能是最快的方法。 In general, <<-
may cause unwanted behaviour, especially when used in loops.通常, <<-
可能会导致不需要的行为,尤其是在循环中使用时。 Assigning unique variable name before exporting (and don't forgetting to remove after use) is one way to tackle them.在导出之前分配唯一的变量名(并且不要忘记在使用后删除)是解决它们的一种方法。
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