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),
      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")[]
}