Add missing exports required for some foreach backend

[lepennec]

I've stumbled upon a bug for some foreach backend (doFuture with multisession for instance...). There are some missing exports in workflows.R. It seems that completing the list as proposed below fix this issue.

[codecov-io]

Codecov Report

Merging #748 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #748   +/-   ##
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  Coverage   16.97%   16.97%           
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  Files          90       90           
  Lines       13187    13187           
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  Hits         2238     2238           
  Misses      10949    10949

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[freezby]

I had the same problem using doMPI on a cluster of two nodes (localhost and one remote computer)
But to get things working I had to complete the export variable in a different way :
export <- c("optimism_xy", "requireNamespaceQuietStop","subset_x","pred_failed",
"model_failed", "fail_warning", "fill_failed_pred", "fill_failed_prob",
"trim_values", "outcome_conversion", "flatTable", "ppOpts", "createModel",
"predictionFunction","probFunction"
)

[lepennec]

OK. It looks like my list should be enlarged...

[brnleehng]

I had a similar issue with doAzureParallel, it seems that all of the internal functions will need to be
in the export list since it depends on what caret function you are using

#743

[lepennec]

Experimenting with foreach, it seems that forcing to load caret with the .packages option helps. Can you help me checking this is indeed the case by sending a reproducible example or testing the proposed modification?

[brnleehng]

I ran one of our samples codes. Installed your branch, It works for me.

library(DAAG)
library(caret)

set.seed(998)
inTraining <- createDataPartition(spam7$yesno, p = .75, list = FALSE)
training <- spam7[ inTraining,]
testing <- spam7[-inTraining,]

fitControl <- trainControl(## 10-fold cross validation
method = "repeatedcv",
number = 10,
## repeat 10 times
repeats = 10,
classProbs = TRUE,
summaryFunction = twoClassSummary,
search = "random",
## run on the parallel backend
allowParallel = TRUE)

rf_fit <- train(
yesno ~ .,
data = training,
method = "rf",
metric = "ROC",
tuneLength = 30,
trControl = fitControl)

rf_fit

[topepo]

Thanks for fixing this. I run a bunch of tests using doMC. I'll add some more for doParallel. The issue is somewhat OS dependent (and cluster type dependent) too.

[topepo]

@brnleehng have you tested these updates on doAzureParallel? I'm getting a release together and it would be good to have confirmation.

@lepennec Anything else that you think needs to happen?

[lepennec]

I expect that if we cover sufficient different backends, which seems to be the case, we should not have more surprise...