R/mlr_learners.R

In mlr3: Machine Learning in R - Next Generation

#' @title Dictionary of Learners
#'
#' @usage NULL
#' @format [R6::R6Class] object inheriting from [mlr3misc::Dictionary].
#'
#' @description
#' A simple [mlr3misc::Dictionary] storing objects of class [Learner].
#' Each learner has an associated help page, see `mlr_learners_[id]`.
#'
#' This dictionary can get populated with additional learners by add-on packages.
#' For an opinionated set of solid classification and regression learners,
#' install and load the \CRANpkg{mlr3learners} package.
#' More learners are connected via \url{https://github.com/mlr-org/mlr3extralearners}.
#'
#' For a more convenient way to retrieve and construct learners, see [lrn()]/[lrns()].
#'
#' @section Methods:
#' See [mlr3misc::Dictionary].
#'
#' @section S3 methods:
#' * `as.data.table(dict, ..., objects = FALSE)`\cr
#'   [mlr3misc::Dictionary] -> [data.table::data.table()]\cr
#'   Returns a [data.table::data.table()] with fields "key", "label", "task_type", "feature_types", "packages",
#'   "properties", and "predict_types" as columns.
#'   If `objects` is set to `TRUE`, the constructed objects are returned in the list column named `object`.
#'
#'
#' @family Dictionary
#' @family Learner
#' @seealso
#' Sugar functions: [lrn()], [lrns()]
#'
#' Extension Packages: \CRANpkg{mlr3learners}
#' @export
#' @examples
#' as.data.table(mlr_learners)
#' mlr_learners$get("classif.featureless")
#' lrn("classif.rpart")
mlr_learners = R6Class("DictionaryLearner", inherit = Dictionary, cloneable = FALSE, )$new()

#' @export
as.data.table.DictionaryLearner = function(x, ..., objects = FALSE) {
  assert_flag(objects)

  setkeyv(
    map_dtr(
      x$keys(),
      function(key) {
        l = withCallingHandlers(x$get(key, .prototype = TRUE), packageNotFoundWarning = function(w) {
          invokeRestart("muffleWarning")
        })
        insert_named(
          list(
            key = key,
            label = l$label,
            task_type = l$task_type,
            feature_types = list(l$feature_types),
            packages = list(l$packages),
            properties = list(l$properties),
            predict_types = list(l$predict_types)
          ),
          if (objects) list(object = list(l))
        )
      },
      .fill = TRUE
    ),
    "key"
  )[]
}