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#' @title Control object for tuning
#'
#' @description General tune control object.
#' @param same.resampling.instance (`logical(1)`)\cr
#' Should the same resampling instance be used for all evaluations to reduce variance?
#' Default is `TRUE`.
#' @template arg_imputey
#' @param start ([list])\cr
#' Named list of initial parameter values.
#' @param tune.threshold (`logical(1)`)\cr
#' Should the threshold be tuned for the measure at hand, after each hyperparameter evaluation,
#' via [tuneThreshold]?
#' Only works for classification if the predict type is \dQuote{prob}.
#' Default is `FALSE`.
#' @param tune.threshold.args ([list])\cr
#' Further arguments for threshold tuning that are passed down to [tuneThreshold].
#' Default is none.
#' @template arg_log_fun
#' @param final.dw.perc (`boolean`)\cr
#' If a Learner wrapped by a [makeDownsampleWrapper] is used, you can define the value of `dw.perc` which is used to train the Learner with the final parameter setting found by the tuning.
#' Default is `NULL` which will not change anything.
#' @param ... (any)\cr
#' Further control parameters passed to the `control` arguments of
#' [cmaes::cma_es] or [GenSA::GenSA], as well as
#' towards the `tunerConfig` argument of [irace::irace].
#' @name TuneControl
#' @rdname TuneControl
#' @family tune
NULL
makeTuneControl = function(same.resampling.instance, impute.val = NULL,
start = NULL, tune.threshold = FALSE, tune.threshold.args = list(),
log.fun = "default", final.dw.perc = NULL, budget = NULL, ..., cl) {
if (!is.null(start)) {
assertList(start, min.len = 1L, names = "unique")
}
if (identical(log.fun, "default")) {
log.fun = logFunTune
} else if (identical(log.fun, "memory")) {
log.fun = logFunTuneMemory
}
if (!is.null(budget)) {
budget = asCount(budget)
}
if (!is.null(final.dw.perc)) {
assertNumeric(final.dw.perc, lower = 0, upper = 1)
}
x = makeOptControl(same.resampling.instance, impute.val, tune.threshold, tune.threshold.args, log.fun, final.dw.perc, ...)
x$start = start
x$budget = budget
addClasses(x, c(cl, "TuneControl"))
}
#' @export
print.TuneControl = function(x, ...) {
catf("Tune control: %s", class(x)[1])
catf("Same resampling instance: %s", x$same.resampling.instance)
catf("Imputation value: %s", ifelse(is.null(x$impute.val), "<worst>", sprintf("%g", x$impute.val)))
catf("Start: %s", convertToShortString(x$start))
catf("Budget: %i", x$budget)
catf("Tune threshold: %s", x$tune.threshold)
catf("Further arguments: %s", convertToShortString(x$extra.args))
}
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