feat: add boruta filter (#163)

* feat: add boruta filter

* docs: update documentation

* fix: donttest

* fix: donttest

* docs: citation

* docs: ordering

* fix: id

* fix: non ascii

* chore: michels improvements

DESCRIPTION

@@ -31,6 +31,7 @@ Imports:
  paradox,
  R6
 Suggests:
+ Boruta,
  care,
  caret,
  carSurv,
@@ -55,6 +56,7 @@ Collate:
  'mlr_filters.R'
  'FilterAUC.R'
  'FilterAnova.R'
+ 'FilterBoruta.R'
  'FilterCMIM.R'
  'FilterCarScore.R'
  'FilterCarSurvScore.R'

NAMESPACE

@@ -5,6 +5,7 @@ S3method(as.data.table,Filter)
 export(Filter)
 export(FilterAUC)
 export(FilterAnova)
+export(FilterBoruta)
 export(FilterCMIM)
 export(FilterCarScore)
 export(FilterCarSurvScore)

R/FilterBoruta.R

@@ -0,0 +1,84 @@
+#' @title Burota Filter
+#'
+#' @name mlr_filters_boruta
+#'
+#' @description
+#' Filter using the Boruta algorithm for feature selection.
+#' If `keep = "tentative"`, confirmed and tentative features are returned.
+#' Note that there is no ordering in the selected features.
+#' Selected features get a score of 1, deselected features get a score of 0.
+#' The order of selected features is random.
+#' In combination with \CRANpkg{mlr3pipelines}, only the filter criterion `cutoff` makes sense.
+#'
+#' @references
+#' `r format_bib("kursa_2010")`
+#'
+#' @family Filter
+#' @include Filter.R
+#' @template seealso_filter
+#' @export
+#' @examples
+#' \donttest{
+#' if (requireNamespace("Boruta")) {
+#' task = mlr3::tsk("sonar")
+#' filter = flt("boruta")
+#' filter$calculate(task)
+#' as.data.table(filter)
+#' }
+#' }
+
+FilterBoruta = R6Class("FilterBoruta",
+ inherit = Filter,
+
+ public = list(
+
+ #' @description
+ #' Creates a new instance of this [R6][R6::R6Class] class.
+ initialize = function() {
+
+ param_set = ps(
+ pValue = p_dbl(default = 0.01),
+ mcAdj = p_lgl(default = TRUE),
+ maxRuns = p_int(lower = 1, default = 100),
+ doTrace = p_int(lower = 0, upper = 4, default = 0),
+ holdHistory = p_lgl(default = TRUE),
+ getImp = p_uty(),
+ keep = p_fct(levels = c("confirmed", "tentative"), default = "confirmed")
+ )
+
+ param_set$set_values(keep = "confirmed")
+
+ super$initialize(
+ id = "boruta",
+ task_types = c("regr", "classif"),
+ param_set = param_set,
+ packages = "Boruta",
+ feature_types = c("integer", "numeric"),
+ label = "Burota",
+ man = "mlr3filters::mlr_filters_boruta"
+ )
+ }
+ ),
+
+ private = list(
+ .calculate = function(task, nfeat) {
+ pv = self$param_set$values
+ data = task$data()
+ target = task$target_names
+ features = task$feature_names
+ formula = formulate(target, features)
+ keep = pv$keep
+ pv$keep = NULL
+
+ res = invoke(Boruta::Boruta, formula = formula, data = data, .args = pv)
+
+ selected_features = Boruta::getSelectedAttributes(res, withTentative = (keep == "tentative"))
+
+ set_names(as.numeric(features %in% selected_features), features)
+ }
+ )
+)
+
+
+#' @include mlr_filters.R
+mlr_filters$add("boruta", FilterBoruta)

R/bibentries.R

@@ -34,5 +34,14 @@ bibentries = c(
  author = "Andrea Bommert and Thomas Welchowski and Matthias Schmid and J\u00f6rg Rahnenf\u00fchrer",
  title = "Benchmark of filter methods for feature selection in high-dimensional gene expression survival data",
  journal = "Briefings in Bioinformatics"
- )
+ ),
+
+ kursa_2010 = bibentry("article",
+ title = "Feature Selection with the Boruta Package",
+ volume = "36",
+ number = "11",
+ journal = "Journal of Statistical Software",
+ author = "Miron B. Kursa and Witold R. Rudnicki",
+ year = "2010",
+ pages = "1-13")
 )