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tuneIrace = function(learner, task, resampling, measures, par.set, control, opt.path, show.info, resample.fun) {
requirePackages("irace", why = "tuneIrace", default.method = "load")
targetRunnerParallel = function(experiment, exec.target.runner, scenario, target.runner) {
# get our param settings that irace should try
cands = extractSubList(experiment, "configuration", simplify = FALSE)
# some conversion code
cands = lapply(cands, as.data.frame)
cands = lapply(cands, dfRowToList, i = 1, par.set = par.set,
enforce.col.types = TRUE)
# the instance is always the same for all different param setting
rin = experiment[[1L]]$instance
ys = tunerFitnFunVectorized(cands, learner = learner, task = task, resampling = rin, measures = measures,
par.set = par.set, ctrl = control, opt.path = opt.path, show.info = show.info,
convertx = convertXVectorizedBooleanStringsToLogical, remove.nas = TRUE, resample.fun)
# FIXME: irace can also use time now, we should add it
res = lapply(ys, function(y) list(cost = y, time = NA_real_))
return(res)
}
n.instances = control$extra.args$n.instances
control$extra.args$n.instances = NULL
show.irace.output = control$extra.args$show.irace.output
control$extra.args$show.irace.output = NULL
instances = lapply(seq_len(n.instances), function(i) makeResampleInstance(resampling, task = task))
if (is.null(control$extra.args$digits)) {
control$extra.args$digits = 15
} else {
control$extra.args$digits = asInt(control$extra.args$digits)
}
parameters = convertParamSetToIrace(par.set)
log.file = tempfile()
tuner.config = c(list(targetRunnerParallel = targetRunnerParallel,
instances = instances, logFile = log.file), control$extra.args)
g = if (show.irace.output) identity else capture.output
g({
or = irace::irace(scenario = tuner.config, parameters = parameters)
})
unlink(log.file)
if (nrow(or) == 0L) {
stop("irace produced no result, possibly the budget was set too low?")
}
# get best configuarion
x1 = as.list(irace::removeConfigurationsMetaData(or[1L, ]))
# we need chars, not factors / logicals, so we can match 'x'
d = convertDfCols(as.data.frame(opt.path), logicals.as.factor = TRUE)
d = convertDfCols(d, factors.as.char = TRUE)
par.names = names(x1)
# get all lines in opt.path which correspond to x and average their perf values
j = vlapply(seq_row(d), function(i) {
isTRUE(all.equal(removeMissingValues(as.list(d[i, par.names, drop = FALSE])),
removeMissingValues(x1)))
})
if (!any(j)) {
stop("No matching rows for final elite configuarion found in opt.path! This cannot be!")
}
y = colMeans(d[j, opt.path$y.names, drop = FALSE])
# take first index of mating lines to get recommended x
e = getOptPathEl(opt.path, which.first(j))
x = trafoValue(par.set, e$x)
x = removeMissingValues(x)
if (control$tune.threshold) {
# now get thresholds and average them
threshold = getThresholdFromOptPath(opt.path, which(j))
} else {
threshold = NULL
}
makeTuneResult(learner, control, x, y, resampling, threshold, opt.path)
}
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