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tuneMBO = function(learner, task, resampling, measures, par.set, control,
opt.path, show.info, resample.fun) {
requirePackages("mlrMBO", why = "tuneMBO", default.method = "load")
mbo.control = control$mbo.control
multicrit = mbo.control$n.objectives > 1L
if (multicrit) {
assertList(measures, len = mbo.control$n.objectives)
}
tff = tunerSmoofFun(learner = learner, task = task, resampling = resampling, measures = measures,
par.set = par.set, ctrl = control, opt.path = opt.path, show.info = show.info,
convertx = convertXIdentity, remove.nas = TRUE, resample.fun = resample.fun)
state = mbo.control$save.file.path
if (control$continue && file.exists(state)) {
messagef("Resuming previous MBO run using state in '%s'...", state)
or = mlrMBO::mboContinue(state)
} else {
or = mlrMBO::mbo(tff, design = control$mbo.design, learner = control$learner, control = mbo.control, show.info = FALSE)
}
if (multicrit) {
x = lapply(or$pareto.set, function(z) trafoValue(par.set, z))
y = or$pareto.front
colnames(y) = opt.path$y.names
ind = or$pareto.inds
} else {
# we take the point that mbo proposes and its estimated y
x = trafoValue(par.set, or$x)
y = setNames(or$y, opt.path$y.names[1L])
}
# if threshold tuning is on, we extract the threshold from extras
if (control$tune.threshold) {
el = getOptPathEl(opt.path, or$best.ind)
th = el$extra$threshold
} else {
th = NULL
}
if (multicrit) {
res = makeTuneMultiCritResult(learner, ind, removeMissingValues(x), y, resampling, control,
opt.path, measures, mbo.result = or)
} else {
res = makeTuneResult(learner, control, removeMissingValues(x), y, resampling, th, opt.path,
mbo.result = or)
}
return(res)
}
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