R/LearnerClustKMeans.R

In mlr3cluster: Cluster Extension for 'mlr3'

#' @title K-Means Clustering Learner
#'
#' @name mlr_learners_clust.kmeans
#' @include LearnerClust.R
#'
#' @description
#' K-means clustering.
#' Calls [stats::kmeans()] from package \pkg{stats}.
#'
#' The `centers` parameter is set to 2 by default since [stats::kmeans()] doesn't have a default value for the number of
#' clusters. The predict method uses [clue::cl_predict()] to compute the cluster memberships for new data.
#'
#' @templateVar id clust.kmeans
#' @template learner
#'
#' @references
#' `r format_bib("forgy1965cluster", "hartigan1979algorithm", "lloyd1982least", "macqueen1967some")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClustKMeans = R6Class(
  "LearnerClustKMeans",
  inherit = LearnerClust,
  public = list(
    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function() {
      param_set = ps(
        centers = p_uty(tags = c("train", "required"), custom_check = check_centers),
        iter.max = p_int(1L, default = 10L, tags = "train"),
        algorithm = p_fct(
          c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
          default = "Hartigan-Wong",
          tags = "train"
        ),
        nstart = p_int(1L, default = 1L, tags = "train"),
        trace = p_lgl(default = FALSE, tags = "train")
      )

      param_set$set_values(centers = 2L)

      super$initialize(
        id = "clust.kmeans",
        feature_types = c("logical", "integer", "numeric"),
        predict_types = "partition",
        param_set = param_set,
        properties = c("partitional", "exclusive", "complete"),
        packages = c("stats", "clue"),
        man = "mlr3cluster::mlr_learners_clust.kmeans",
        label = "K-Means"
      )
    }
  ),

  private = list(
    .train = function(task) {
      pv = self$param_set$get_values(tags = "train")
      if (!is.null(pv$nstart) && !test_int(pv$centers)) {
        warning_config("`nstart` parameter is only relevant when `centers` is integer.")
      }

      assert_centers_param(pv$centers, task, "centers")

      m = invoke(stats::kmeans, x = task$data(), .args = pv)
      if (self$save_assignments) {
        self$assignments = m$cluster
      }
      m
    },

    .predict = function(task) {
      partition = unclass(invoke(
        clue::cl_predict,
        self$model,
        newdata = ordered_features(task, self),
        type = "class_ids"
      ))
      list(partition = partition)
    }
  )
)

#' @include zzz.R
register_learner("clust.kmeans", LearnerClustKMeans)