R/hyperopt.R

In jakob-r/mlrHyperopt: Easy Hyperparameteroptimization with mlr and mlrMBO

#' @title
#' Tune Hyperparameters for a machine learning task
#'
#' @description
#' Tunes the Hyperparameters for a given task and learner.
#' Tries to find the best parameter set to tune for the given learner.
#'
#' @template arg_task
#' @param learner [\code{Learner}]
#'  The learner that is subject to the Hyperparameter Tuning.
#'  If no learner is given the learner referenced in the \code{par.config} will be used, if available.
#' @template arg_parconfig
#' @template arg_hypercontrol
#' @template arg_showinfo
#' @return [\code{\link[mlr]{TuneResult}}]
#' @import mlr
#' @examples
#' # the shortest way of hyperparameter optimization
#' hyperopt(iris.task, "classif.svm")
#'
#' # manually defining the paramer space configuration
#' par.config = makeParConfig(
#'   par.set = makeParamSet(
#'     makeIntegerParam("mtry", lower = 1, upper = 4),
#'     makeDiscreteParam("ntree", values = c(10, 25, 50))
#'   ),
#'   par.vals = list(replace = FALSE),
#'   learner.name = "randomForest"
#' )
#' hyperopt(bh.task, par.config = par.config)
#' @export

hyperopt = function(task, learner = NULL, par.config = NULL, hyper.control = NULL, show.info = getMlrOptions()$show.info) {
  assertClass(task, classes = "Task")

  par.config = par.config %??% generateParConfig(learner = learner, task = task)
  assertClass(par.config, "ParConfig")

  learner = learner %??% generateLearner(task = task, par.config = par.config) 
  learner = checkLearner(learner)

  hyper.control = hyper.control %??% generateHyperControl(task = task, par.config = par.config)
  assertList(hyper.control)

  if (!is.null(getParConfigParVals(par.config))) {
    learner = setHyperPars(learner, par.vals = getParConfigParVals(par.config))
  }

  measures = getHyperControlMeasures(hyper.control)
  # for some measures the learner has to change it's type
  if ("req.prob" %in% measures[[1]]$properties) {
    learner = setPredictType(learner, "prob")
  }
  learner$config = insert(learner$config, list(on.learner.error = "warn"))


  tune.res = tuneParams(
    learner = learner,
    task = task,
    resampling = getHyperControlResampling(hyper.control),
    measures = measures,
    par.set = getParConfigParSet(par.config, task = task),
    control = getHyperControlMlrControl(hyper.control),
    show.info = show.info)

  tune.res$learner = setHyperPars(learner, par.vals = tune.res$x)
  return(tune.res)
}