library(devtools)
load_all()
# On LRZ
# Serial cluster
setOMLConfig(apikey = "34ebc7dff3057d8e224e5beac53aea0e")
max.resources = list(walltime = 3600*5, memory = 2000)
lrn.par.set = getMultipleLearners()
simple.lrn.par.set = getSimpleLearners()
for(i in 1:10000) {
try(runBot(30, path = paste0("/naslx/projects/ua341/di49ruw/test", i),
sample.learner.fun = sampleRandomLearner, sample.task.fun = sampleRandomTask,
sample.configuration.fun = sampleRandomConfiguration, max.resources = max.resources,
lrn.ps.sets = lrn.par.set, upload = TRUE, extra.tag = "botV1"))
}
# second account
for(i in 1:10000) {
try(runBot(20, path = paste0("/home/hpc/pr74di/di49ruw2/files/test", i),
sample.learner.fun = sampleRandomLearner, sample.task.fun = sampleRandomTask,
sample.configuration.fun = sampleRandomConfiguration, max.resources = max.resources,
lrn.ps.sets = lrn.par.set, upload = TRUE, extra.tag = "botV1"))
}
# Locally
for(i in 1:1000){
try(runBot(100, path = paste0("test", i),
sample.learner.fun = sampleRandomLearner, sample.task.fun = sampleRandomTask,
sample.configuration.fun = sampleRandomConfiguration,
lrn.ps.sets = lrn.par.set, upload = TRUE, extra.tag = "botV1"))
}
# Geht nicht bei zu großen Ergebnissen; stattdessen mit limit und listOMLRuns
tag = "mlrRandomBot"
numRuns = 160000
results = do.call("rbind",
lapply(0:floor(numRuns/10000), function(i) {
return(listOMLRuns(tag = tag, limit = 10000, offset = (10000 * i) + 1))
})
)
table(results$flow.id, results$task.id)
table(results$uploader)
?listOMLSetup
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