R/tuneParamsMultiCrit.R

In mlr: Machine Learning in R

#' @title Hyperparameter tuning for multiple measures at once.
#'
#' @description
#' Optimizes the hyperparameters of a learner in a multi-criteria fashion.
#' Allows for different optimization methods, such as grid search, evolutionary strategies, etc.
#' You can select such an algorithm (and its settings)
#' by passing a corresponding control object. For a complete list of implemented algorithms look at
#' [TuneMultiCritControl].
#'
#' @template arg_learner
#' @template arg_task
#' @param resampling ([ResampleInstance] | [ResampleDesc])\cr
#'   Resampling strategy to evaluate points in hyperparameter space. If you pass a description,
#'   it is instantiated once at the beginning by default, so all points are
#'   evaluated on the same training/test sets.
#'   If you want to change that behavior, look at [TuneMultiCritControl].
#' @param par.set ([ParamHelpers::ParamSet])\cr
#'   Collection of parameters and their constraints for optimization.
#'   Dependent parameters with a `requires` field must use `quote` and not
#'   `expression` to define it.
#' @param measures [list of [Measure])\cr
#'   Performance measures to optimize simultaneously.
#' @param control ([TuneMultiCritControl])\cr
#'   Control object for search method. Also selects the optimization algorithm for tuning.
#' @template arg_showinfo
#' @param resample.fun ([closure])\cr
#'   The function to use for resampling. Defaults to [resample] and should take the
#'   same arguments as, and return the same result type as, [resample].
#' @return ([TuneMultiCritResult]).
#' @family tune_multicrit
#' @export
#' @examples
#' \donttest{
#' # multi-criteria optimization of (tpr, fpr) with NGSA-II
#' lrn = makeLearner("classif.ksvm")
#' rdesc = makeResampleDesc("Holdout")
#' ps = makeParamSet(
#'   makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
#'   makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
#' )
#' ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
#' res = tuneParamsMultiCrit(lrn, sonar.task, rdesc, par.set = ps,
#'   measures = list(tpr, fpr), control = ctrl)
#' plotTuneMultiCritResult(res, path = TRUE)
#' }
tuneParamsMultiCrit = function(learner, task, resampling, measures, par.set, control, show.info = getMlrOption("show.info"), resample.fun = resample) {

  learner = checkLearner(learner)
  assertClass(task, classes = "Task")
  assertList(measures, types = "Measure", min.len = 2L)
  assertClass(par.set, classes = "ParamSet")
  assertClass(control, classes = "TuneMultiCritControl")
  if (!inherits(resampling, "ResampleDesc") && !inherits(resampling, "ResampleInstance")) {
    stop("Argument resampling must be of class ResampleDesc or ResampleInstance!")
  }
  if (inherits(resampling, "ResampleDesc") && control$same.resampling.instance) {
    resampling = makeResampleInstance(resampling, task = task)
  }
  assertFlag(show.info)
  control = setDefaultImputeVal(control, measures)
  checkTunerParset(learner, par.set, measures, control)
  requirePackages("emoa", why = "tuneParamsMultiCrit", default.method = "load")

  cl = getClass1(control)
  sel.func = switch(cl,
    TuneMultiCritControlRandom = tuneMultiCritRandom,
    TuneMultiCritControlGrid = tuneMultiCritGrid,
    TuneMultiCritControlNSGA2 = tuneMultiCritNSGA2,
    TuneMultiCritControlMBO = tuneMBO,
    stopf("Tuning algorithm for '%s' does not exist!", cl)
  )
  opt.path = makeOptPathDFFromMeasures(par.set, measures, include.extra = getMlrOption("on.error.dump"))
  if (show.info) {
    messagef("[Tune] Started tuning learner %s for parameter set:", learner$id)
    messagef(printToChar(par.set))
    messagef("With control class: %s", cl)
    messagef("Imputation value: %g", control$impute.val)
  }
  or = sel.func(learner, task, resampling, measures, par.set, control, opt.path, show.info, resample.fun)
  if (show.info) {
    messagef("[Tune] Result: Points on front : %i", length(or$x))
  }
  return(or)
}