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#' @import data.table
#' @import mlr3misc
#' @import paradox
#' @import mlr3
#' @import checkmate
#' @importFrom R6 R6Class
#' @importFrom clue cl_predict
#' @importFrom fpc cluster.stats
#' @importFrom cluster silhouette
#' @importFrom stats model.frame terms predict runif dist
"_PACKAGE"
register_mlr3 = function() {
# reflections
x = utils::getFromNamespace("mlr_reflections", ns = "mlr3")
# task
x$task_types = x$task_types[!"clust"]
x$task_types = setkeyv(rbind(x$task_types, rowwise_table(
~type, ~package, ~task, ~learner, ~prediction, ~prediction_data, ~measure,
"clust", "mlr3cluster", "TaskClust", "LearnerClust", "PredictionClust", "PredictionDataClust", "MeasureClust"
), fill = TRUE), "type")
x$task_col_roles$clust = x$task_col_roles$regr
x$task_properties$clust = x$task_properties$regr
x$learner_properties$clust = c(
"missings", "partitional", "hierarchical", "exclusive", "overlapping", "fuzzy", "complete", "partial"
)
# measure
x$measure_properties$clust = x$measure_properties$regr
# learner
x$learner_predict_types$clust = list(partition = "partition", prob = c("partition", "prob"))
x$default_measures$clust = "clust.dunn"
# tasks
x = utils::getFromNamespace("mlr_tasks", ns = "mlr3")
x$add("usarrests", load_task_usarrests)
x$add("ruspini", load_task_ruspini)
# learners
x = utils::getFromNamespace("mlr_learners", ns = "mlr3")
iwalk(learners, function(obj, nm) x$add(nm, obj))
# measures
x = utils::getFromNamespace("mlr_measures", ns = "mlr3")
x$add("clust.silhouette", MeasureClustSil, name = "silhouette", label = "Silhouette")
x$add("clust.dunn", MeasureClustFPC, name = "dunn", label = "Dunn")
x$add("clust.ch", MeasureClustFPC, name = "ch", label = "Calinski Harabasz")
x$add("clust.wss", MeasureClustFPC, name = "wss", label = "Within Sum of Squares")
}
.onLoad = function(libname, pkgname) {
backports::import(pkgname)
register_mlr3()
}
.onUnload = function(libpaths) { # nolint
mlr_learners = mlr3::mlr_learners
mlr_measures = mlr3::mlr_measures
mlr_tasks = mlr3::mlr_tasks
walk(names(learners), function(id) mlr_learners$remove(id))
walk(names(measures), function(id) mlr_measures$remove(paste("clust", id, sep = ".")))
walk(names(tasks), function(id) mlr_tasks$remove(id))
}
leanify_package()
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