On 01/15/2015 08:00 PM, Dario Strbenac wrote:
I don't think that is the problem. bpparam() should automatically choose
settings that work on Windows. BiocInstaller also doesn't pass the checking
process, according to the online report page. The last version that worked on
Windows was 1.11.9, so there must have been a change in 1.11.10 which caused
a problem, which remains in version 1.11.11.
I'm not sure about the archaeology, but as Dan mentioned this is related to the
different ways in which parallel evaluation works by default on mac / linux
(multicore) versus windows (SnowParam).
You can recreate the error on non-Windows with
register(registered("SnowParam"))
which sets the default to Snow. Running the problematic code chunk from the
vignette then reproduces the error.
I've refactored the relevant problem a bit, but you have
fun <- function(sampleFolds, sampleNumber) {
if (verbose >= 1 && sampleNumber%%10 == 0)
message("Processing sample set ", sampleNumber, ".")
if (bootMode == "fold") {
lapply(1:length(sampleFolds), function(foldIndex) {
runTest(expression, training = unlist(sampleFolds[-foldIndex]),
testing = sampleFolds[[foldIndex]], params = params,
verbose = verbose)
})
}
else {
runTest(expression, training = sampleFolds[[1]],
testing = sampleFolds[[2]],
params = params, verbose = verbose)
}
}
results <- bpmapply(fun, samplesFolds, as.list(1:length(samplesFolds)),
BPPARAM = parallelParams)
notice that runTest is not told explicitly about datasetName. When run on a
single thread or in multicore it's being found in the calling environment. But
on the new processes started by Snow it's nowhere to be found.
A solution is to write a more 'functional'-style version of 'fun()', one that
does not rely on implicit variables. I did this using '...'
fun <- function(sampleFolds, sampleNumber, ...) {
if (verbose >= 1 && sampleNumber%%10 == 0)
message("Processing sample set ", sampleNumber, ".")
if (bootMode == "fold") {
lapply(1:length(sampleFolds), function(foldIndex) {
runTest(expression, ...,
training = unlist(sampleFolds[-foldIndex]),
testing = sampleFolds[[foldIndex]], params = params,
verbose = verbose)
})
}
else {
runTest(expression, ...,
training = sampleFolds[[1]],
testing = sampleFolds[[2]],
params = params, verbose = verbose)
}
}
and then invoking it with enough information
results <- bpmapply(fun,
samplesFolds, as.list(1:length(samplesFolds)),
datasetName=datasetName,
classificationName=classificationName,
BPPARAM = parallelParams)
Martin
-------------------------------------- Dario Strbenac PhD Student University
of Sydney Camperdown NSW 2050 Australia
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