R/LearnerClustKMeans.R

In mlr3cluster: Cluster Extension for 'mlr3'

#' @title K-Means Clustering Learner
#'
#' @name mlr_learners_clust.kmeans
#' @include LearnerClust.R
#' @include aaa.R
#'
#' @description
#' A [LearnerClust] for k-means clustering implemented in [stats::kmeans()].
#' [stats::kmeans()] doesn't have a default value for the number of clusters.
#' Therefore, the `centers` parameter here is set to 2 by default.
#' The predict method uses [clue::cl_predict()] to compute the
#' cluster memberships for new data.
#'
#' @templateVar id clust.kmeans
#' @template learner
#' @template example
#'
#' @export
LearnerClustKMeans = R6Class("LearnerClustKMeans",
  inherit = LearnerClust,
  public = list(
    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function() {
      ps = ps(
        centers = p_uty(tags = c("required", "train"), default = 2L,
          custom_check = function(x) {
            if (test_data_frame(x)) {
              return(TRUE)
            } else if (test_int(x)) {
              assert_true(x >= 1L)
            } else {
              return("`centers` must be integer or data.frame with initial cluster centers")
            }
          }
        ),
        iter.max = p_int(lower = 1L, default = 10L, tags = c("train")),
        algorithm = p_fct(levels = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), default = "Hartigan-Wong", tags = c("train")),
        nstart = p_int(lower = 1L, default = 1L, tags = c("train")),
        trace = p_int(lower = 0L, default = 0L, tags = c("train"))
      )
      ps$values = list(centers = 2L)

      super$initialize(
        id = "clust.kmeans",
        feature_types = c("logical", "integer", "numeric"),
        predict_types = "partition",
        param_set = ps,
        properties = c("partitional", "exclusive", "complete"),
        packages = c("stats", "clue"),
        man = "mlr3cluster::mlr_learners_clust.kmeans",
        label = "K-Means"
      )
    }
  ),
  private = list(
    .train = function(task) {
      if ("nstart" %in% names(self$param_set$values)) {
        if (!test_int(self$param_set$values$centers)) {
          warning("`nstart` parameter is only relevant when `centers` is integer.")
        }
      }

      check_centers_param(self$param_set$values$centers, task, test_data_frame, "centers")

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

      return(m)
    },
    .predict = function(task) {
      partition = unclass(cl_predict(self$model, newdata = task$data(), type = "class_ids"))
      PredictionClust$new(task = task, partition = partition)
    }
  )
)

learners[["clust.kmeans"]] = LearnerClustKMeans