R/fselect.R

In mlr3fselect: Feature Selection for 'mlr3'

#' @title Function for Feature Selection
#'
#' @include FSelectInstanceSingleCrit.R ArchiveFSelect.R
#'
#' @description
#' Function to optimize the features of a [mlr3::Learner].
#' The function internally creates a [FSelectInstanceSingleCrit] or [FSelectInstanceMultiCrit] which describes the feature selection problem.
#' It executes the feature selection with the [FSelector] (`method`) and returns the result with the fselect instance (`$result`).
#' The [ArchiveFSelect] (`$archive`) stores all evaluated hyperparameter configurations and performance scores.
#'
#' @details
#' The [mlr3::Task], [mlr3::Learner], [mlr3::Resampling], [mlr3::Measure] and [Terminator] are used to construct a [FSelectInstanceSingleCrit].
#' If multiple performance [Measures][Measure] are supplied, a [FSelectInstanceMultiCrit] is created.
#' The parameter `term_evals` and `term_time` are shortcuts to create a [Terminator].
#' If both parameters are passed, a [TerminatorCombo] is constructed.
#' For other [Terminators][Terminator], pass one with `terminator`.
#' If no termination criterion is needed, set `term_evals`, `term_time` and `terminator` to `NULL`.
#'
#' @inheritSection FSelectInstanceSingleCrit Resources
#' @inheritSection ArchiveFSelect Analysis
#'
#' @param measures ([mlr3::Measure] or list of [mlr3::Measure])\cr
#'   A single measure creates a [FSelectInstanceSingleCrit] and multiple measures a [FSelectInstanceMultiCrit].
#'   If `NULL`, default measure is used.
#'
#' @return [FSelectInstanceSingleCrit] | [FSelectInstanceMultiCrit]
#'
#' @template param_fselector
#' @template param_task
#' @template param_learner
#' @template param_resampling
#' @template param_term_evals
#' @template param_term_time
#' @template param_terminator
#' @template param_store_benchmark_result
#' @template param_store_models
#' @template param_check_values
#' @template param_callbacks
#'
#' @export
#' @examples
#' # Feature selection on the Palmer Penguins data set
#' task = tsk("pima")
#' learner = lrn("classif.rpart")
#'
#' # Run feature selection
#' instance = fselect(
#'   fselector = fs("random_search"),
#'   task = task,
#'   learner = learner,
#'   resampling = rsmp ("holdout"),
#'   measures = msr("classif.ce"),
#'   term_evals = 4)
#'
#' # Subset task to optimized feature set
#' task$select(instance$result_feature_set)
#'
#' # Train the learner with optimal feature set on the full data set
#' learner$train(task)
#'
#' # Inspect all evaluated configurations
#' as.data.table(instance$archive)
fselect = function(fselector, task, learner, resampling, measures = NULL, term_evals = NULL, term_time = NULL, terminator = NULL, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
  assert_fselector(fselector)
  terminator = terminator %??% terminator_selection(term_evals, term_time)

  FSelectInstance = if (!is.list(measures)) FSelectInstanceSingleCrit else FSelectInstanceMultiCrit
  instance = FSelectInstance$new(
    task = task,
    learner = learner,
    resampling = resampling,
    measures,
    terminator = terminator,
    store_benchmark_result = store_benchmark_result,
    store_models = store_models,
    check_values = check_values,
    callbacks = callbacks)

  fselector$optimize(instance)
  instance
}

terminator_selection = function(term_evals, term_time) {
  assert_int(term_evals, null.ok = TRUE)
  assert_int(term_time, null.ok = TRUE)

  if (is.null(term_evals) && is.null(term_time)) {
    trm("none")
  } else if (!is.null(term_evals) && !is.null(term_time)) {
    trm("combo", list(trm("evals", n_evals = term_evals), trm("run_time", secs = term_time)))
  } else if (!is.null(term_evals)) {
    trm("evals", n_evals = term_evals)
  } else if (!is.null(term_time)) {
    trm("run_time", secs = term_time)
  }
}