R/learner_plugdensity_dens_plug.R

In mlr-org/mlr3extralearners: Extra Learners For mlr3

#' @title Density Plug-In Kernel Learner
#' @author RaphaelS1
#' @name mlr_learners_dens.plug
#'
#' @description
#' Kernel density estimation with global bandwidth selection via "plug-in".
#' Calls [plugdensity::plugin.density()] from \CRANpkg{plugdensity}.
#'
#' @template learner
#' @templateVar id dens.plug
#'
#' @references
#' `r format_bib("engel1994iterative")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerDensPlugin = R6Class("LearnerDensPlugin",
  inherit = mlr3proba::LearnerDens,

  public = list(
    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function() {
      ps = ps(
        na.rm = p_lgl(default = FALSE, tags = "train")
      )

      super$initialize(
        id = "dens.plug",
        packages = c("mlr3extralearners", "plugdensity"),
        feature_types = "numeric",
        predict_types = "pdf",
        param_set = ps,
        properties = "missings",
        man = "mlr3extralearners::mlr_learners_dens.plug",
        label = "Kernel Density Estimator"
      )
    }
  ),

  private = list(
    .train = function(task) {
      pdf = function(x) {} # nolint
      body(pdf) = substitute({
        invoke(plugdensity::plugin.density, x = data, xout = x, na.rm = TRUE)$y
      }, list(data = task$data()[[1]]))

      distr6::Distribution$new(
        name = "Plugin KDE",
        short_name = "PluginKDE",
        pdf = pdf,
        type = set6::Reals$new())
    },

    .predict = function(task) {
      pars = self$param_set$get_values(tags = "predict")
      invoke(list, pdf = self$model$pdf(task$data()[[1]]), .args = pars)
    }
  )
)

.extralrns_dict$add("dens.plug", LearnerDensPlugin)