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$mbo.control
# put all required info into the function env
force(learner)
force(task)
force(resampling)
force(measures)
force(par.set)
force(control)
force(opt.path)
force(show.info)
tff = tunerSmoofFun(learner = learner, task = task, resampling = resampling, measures = measures,
par.set = par.set, ctrl = control, opt.path = opt.path, show.info = show.info,
convertx = convertXIdentity, remove.nas = TRUE, resample.fun = resample.fun)
state = mbo.control$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$learner, control = mbo.control, show.info = FALSE)
}
x = trafoValue(par.set, or$x)
y = setNames(or$y, opt.path$y.names[1L])
# we take the point that mbo proposes and its estimated y
# FIXME: threshold
if (!control$mbo.keep.result)
or = NULL
res = makeTuneResult(learner, control, removeMissingValues(x), y, NULL, opt.path, mbo.result = or)
res
}
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