目前正在尝试使用并行处理重现SVM递归特征消除算法，但遇到了并行化后端的一些问题 .

当RFE SVM算法并行成功运行时，这需要大约250秒 . 但是，大多数情况下它从未完成计算，需要在30分钟后手动关闭 . 当后者发生时，对活动监视器的检查表明，尽管Rstudio已将其关闭，但核仍在运行 . 需要使用终端的 killall R 终止这些内核 .

包 AppliedPredictiveModeling 中的代码段如下所示，删除了冗余代码 .

library(AppliedPredictiveModeling)
data(AlzheimerDisease)

## The baseline set of predictors
bl <- c("Genotype", "age", "tau", "p_tau", "Ab_42", "male")

## The set of new assays
newAssays <- colnames(predictors)
newAssays <- newAssays[!(newAssays %in% c("Class", bl))]

## Decompose the genotype factor into binary dummy variables

predictors$E2 <- predictors$E3 <- predictors$E4 <- 0
predictors$E2[grepl("2", predictors$Genotype)] <- 1
predictors$E3[grepl("3", predictors$Genotype)] <- 1
predictors$E4[grepl("4", predictors$Genotype)] <- 1
genotype <-  predictors$Genotype

## Partition the data
library(caret)
set.seed(730)
split <- createDataPartition(diagnosis, p = .8, list = FALSE)

adData <- predictors
adData$Class <- diagnosis

training <- adData[ split, ]
testing  <- adData[-split, ]

predVars <- names(adData)[!(names(adData) %in% c("Class",  "Genotype"))]

## This summary function is used to evaluate the models.
fiveStats <- function(...) c(twoClassSummary(...), defaultSummary(...))

## We create the cross-validation files as a list to use with different 
## functions

set.seed(104)
index <- createMultiFolds(training$Class, times = 5)

## The candidate set of the number of predictors to evaluate
varSeq <- seq(1, length(predVars)-1, by = 2)

# Beginning parallelization 
library(doParallel)
cl <- makeCluster(7)
registerDoParallel(cl) 
getDoParWorkers() 

# Rfe and train control objects created
ctrl <- rfeControl(method = "repeatedcv", repeats = 5,
                   saveDetails = TRUE,
                   index = index,
                   returnResamp = "final")

fullCtrl <- trainControl(method = "repeatedcv",
                         repeats = 5,
                         summaryFunction = fiveStats,
                         classProbs = TRUE,
                         index = index)


## Here, the caretFuncs list allows for a model to be tuned at each iteration 
## of feature seleciton.

ctrl$functions <- caretFuncs
ctrl$functions$summary <- fiveStats

## This options tells train() to run it's model tuning
## sequentially. Otherwise, there would be parallel processing at two
## levels, which is possible but requires W^2 workers. On our machine,
## it was more efficient to only run the RFE process in parallel. 

cvCtrl <- trainControl(method = "cv",
                       verboseIter = FALSE,
                       classProbs = TRUE,
                       allowParallel = FALSE)

set.seed(721)
svmRFE <- rfe(training[, predVars],
              training$Class,
              sizes = varSeq,
              rfeControl = ctrl,
              metric = "ROC",
              ## Now arguments to train() are used.
              method = "svmRadial",
              tuneLength = 12,
              preProc = c("center", "scale"),
              trControl = cvCtrl)

这不是导致我出现问题的唯一模型 . 有时带有RFE的随机森林也会导致同样的问题 . 原始代码使用包 doMQ ，但是，对活动监视器的检查显示多个 rsession 用作并行化，并且我猜测使用GUI运行，因为当计算不停止时关闭它需要中止整个R通信和重新启动会话，而不是简单地放弃计算 . 前者当然有擦拭我的环境清洁的不幸后果 .

我正在使用2013年中期配备8核的MacBook Pro .

知道可能导致这个问题的原因吗？有没有办法解决它，如果有的话，什么？有没有办法确保并行化在没有GUI的情况下运行而不从终端运行脚本（我想控制执行哪些模型以及何时执行） .

编辑：似乎在退出失败的执行后，R在通过Caret并行化的所有后续任务上失败，即使是之前运行的那些任务 . 这意味着集群不再运营 .

r parallel-processing r-caret rfe