reference1.lrn.par.set = makeLrnPsSets(learner = makeLearner("classif.ranger", predict.type = "prob"),
param.set = makeParamSet(
makeIntegerParam("num.trees", lower = 2000, upper = 2000, default = 2000)
)
)
reference2.lrn.par.set = makeLrnPsSets(learner = makeLearner("classif.featureless", predict.type = "prob"),
param.set = makeParamSet(
makeDiscreteParam("method", values = "majority", default = "majority")
)
)
tasks = listOMLTasks(number.of.classes = 2L, number.of.missing.values = 0,
data.tag = "study_14", estimation.procedure = "10-fold Crossvalidation")
for (i in 1:nrow(tasks)) {
fixed.task = function() list(id = tasks$task.id[i], name = tasks$name[i])
runBot(10, sample.learner.fun = sampleRandomLearner,
sample.task.fun = fixed.task, sample.configuration.fun = sampleRandomConfiguration,
lrn.ps.sets = reference1.lrn.par.set, upload = TRUE,
path = "reference", extra.tag = "referenceV1")
unlink("reference", recursive = TRUE)
runBot(10, sample.learner.fun = sampleRandomLearner,
sample.task.fun = fixed.task, sample.configuration.fun = sampleRandomConfiguration,
lrn.ps.sets = reference2.lrn.par.set, upload = TRUE,
path = "reference", extra.tag = "referenceV1")
unlink("reference", recursive = TRUE)
}
overview = getMlrRandomBotOverview("referenceV1")
print(overview)
a = listOMLRuns(tag = "referenceV1")
getRunDf("referenceV1")
tbl.results = getRunTable("referenceV1")
print(tbl.results)
for(i in 1:nrow(a)){
deleteOMLObject(a$run.id[i], object = "run")
}
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