RWeka will not work with caret or possibly %dopar% - r

Question

I am completing the exercises from Applied Predictive Modeling, the R textbook for the caret package, by the authors. I cannot get the train function to work with methods M5P or M5Rules.
The code will run fine manually:
data("permeability")
trainIndex <- createDataPartition(permeability[, 1], p = 0.75,
list = FALSE)
fingerNZV <- nearZeroVar(fingerprints, saveMetrics = TRUE)
trainY <- permeability[trainIndex, 1]
testY <- permeability[-trainIndex, 1]
trainX <- fingerprints[trainIndex, !fingerNZV$nzv]
testX <- fingerprints[-trainIndex, !fingerNZV$nzv]
indx <- createFolds(trainY, k = 10, returnTrain = TRUE)
ctrl <- trainControl('cv', index = indx)
m5Tuner <- t(as.matrix(expand.grid(
N = c(1, 0),
U = c(1, 0),
M = floor(seq(4, 15, length.out = 3))
)))
startTime <- Sys.time()
m5Tune <- foreach(tuner = m5Tuner) %do% {
m5ctrl <- Weka_control(M = tuner[3],
N = tuner[1] == 1,
U = tuner[2] == 1)
mods <- lapply(ctrl$index,function(fold) {
d <- cbind(data.frame(permeability = trainY[fold]),
trainX[fold, ])
mod <- M5P(permeability ~ ., d, control = m5ctrl)
rmse <- RMSE(predict(mod, as.data.frame(trainX[-fold, ])),
trainY[-fold])
list(model = mod, rmse = rmse)
})
mean_rmse <- mean(sapply(mods, '[[', 'rmse'))
list(models = mods, mean_rmse = mean_rmse)
}
endTime <- Sys.time()
endTime - startTime
# Time difference of 59.17742 secs
The same data and controls (swapping 'rules' for 'M' -why can't I specify M as a tuning parameter?) will not finish:
m5Tuner <- expand.grid(
pruned = c("Yes", "No"),
smoothed = c("Yes", "No"),
rules = c("Yes", "No")
)
m5Tune <- train(trainX, trainY,
method = 'M5',
trControl = ctrl,
tuneGrid = m5Tuner,
control = Weka_control(M = 10))
The example from the book will not finish, either:
library(caret)
data(solubility)
set.seed(100)
indx <- createFolds(solTrainY, returnTrain = TRUE)
ctrl <- trainControl(method = "cv", index = indx)
set.seed(100)
m5Tune <- train(x = solTrainXtrans, y = solTrainY,
method = "M5",
trControl = ctrl,
control = Weka_control(M = 10))
This may be a problem with the use of a parallel backend with RWeka, for me, at least. My example from above will not finish with %dopar%.
I have run sudo R CMD javareconf before each example and restarted Rstudio.
sessionInfo()
R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Arch Linux
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
[9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] APMBook_0.0.0.9000 RWeka_0.4-27
[3] caret_6.0-68 ggplot2_2.1.0
[5] lattice_0.20-30 AppliedPredictiveModeling_1.1-6
# dozens others loaded via namespace.

Answer 1

When using parallel processing with train and RWeka models, you should have gotten the error:
In train.default(trainX, trainY, method = "M5", trControl = ctrl, :
Models using Weka will not work with parallel processing with multicore/doMC
The java interface to Weka does not work with multiple workers.
It takes a while but the train call will complete if you don't have workers registered with foreach
Max