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FilterDISR.R
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FilterDISR.R
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#' @title Double Input Symmetrical Relevance Filter
#'
#' @name mlr_filters_disr
#'
#' @description Double input symmetrical relevance filter calling
#' [praznik::DISR()] from package \CRANpkg{praznik}.
#'
#' This filter supports partial scoring (see [Filter]).
#'
#' @family Filter
#' @template seealso_filter
#' @export
#' @examples
#' task = mlr3::tsk("iris")
#' filter = flt("disr")
#' filter$calculate(task, nfeat = 2)
#' as.data.table(filter)
FilterDISR = R6Class("FilterDISR", inherit = Filter,
public = list(
#' @description Create a FilterDISR object.
#' @param id (`character(1)`)\cr
#' Identifier for the filter.
#' @param task_type (`character()`)\cr
#' Types of the task the filter can operator on. E.g., `"classif"` or
#' `"regr"`.
#' @param param_set ([paradox::ParamSet])\cr
#' Set of hyperparameters.
#' @param feature_types (`character()`)\cr
#' Feature types the filter operates on.
#' Must be a subset of
#' [`mlr_reflections$task_feature_types`][mlr3::mlr_reflections].
#' @param packages (`character()`)\cr
#' Set of required packages.
#' Note that these packages will be loaded via [requireNamespace()], and
#' are not attached.
initialize = function(id = "disr",
task_type = "classif",
param_set = ParamSet$new(list(
ParamInt$new("threads", lower = 0L, default = 0L)
)),
packages = "praznik",
feature_types = c("integer", "numeric", "factor", "ordered")) {
super$initialize(
id = id,
task_type = task_type,
param_set = param_set,
feature_types = feature_types,
packages = packages,
man = "mlr3filters::mlr_filters_disr"
)
}
),
private = list(
.calculate = function(task, nfeat) {
threads = self$param_set$values$threads %??% 0L
X = task$data(cols = task$feature_names)
Y = task$truth()
praznik::DISR(X = X, Y = Y, k = nfeat, threads = threads)$score
}
)
)
#' @include mlr_filters.R
mlr_filters$add("disr", FilterDISR)