#' @title Hierarchical DBSCAN (HDBSCAN) Clustering Learner
#'
#' @name mlr_learners_clust.hdbscan
#'
#' @description
#' HDBSCAN (Hierarchical DBSCAN) clustering.
#' Calls [dbscan::hdbscan()] from \CRANpkg{dbscan}.
#'
#' @templateVar id clust.hdbscan
#' @template learner
#'
#' @references
#' `r format_bib("hahsler2019dbscan", "campello2013density")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClustHDBSCAN = R6Class("LearnerClustHDBSCAN",
inherit = LearnerClust,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(
minPts = p_int(0L, tags = c("required", "train")),
gen_hdbscan_tree = p_lgl(default = FALSE, tags = "train"),
gen_simplified_tree = p_lgl(default = FALSE, tags = "train"),
verbose = p_lgl(default = FALSE, tags = "train")
)
super$initialize(
id = "clust.hdbscan",
feature_types = c("logical", "integer", "numeric"),
predict_types = "partition",
param_set = param_set,
properties = c("density", "exclusive", "complete"),
packages = "dbscan",
man = "mlr3cluster::mlr_learners_clust.hdbscan",
label = "HDBSCAN Clustering"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
data = task$data()
m = invoke(dbscan::hdbscan, x = data, .args = pv)
m = insert_named(m, list(data = data))
if (self$save_assignments) {
self$assignments = m$cluster
}
m
},
.predict = function(task) {
partition = as.integer(invoke(predict, self$model, newdata = task$data(), data = self$model$data))
PredictionClust$new(task = task, partition = partition)
}
)
)
#' @include zzz.R
register_learner("clust.hdbscan", LearnerClustHDBSCAN)
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