feat: add result object

DESCRIPTION

@@ -31,7 +31,8 @@ Imports:
     lgr,
     mlr3misc (>= 0.15.0.9000),
     paradox (>= 1.0.0),
-    R6
+    R6,
+    stabm
 Suggests:
     e1071,
     genalg,
@@ -55,6 +56,7 @@ Collate:
     'AutoFSelector.R'
     'CallbackBatchFSelect.R'
     'ContextBatchFSelect.R'
+    'EnsembleFSResult.R'
     'FSelectInstanceBatchSingleCrit.R'
     'FSelectInstanceBatchMultiCrit.R'
     'mlr_fselectors.R'

R/EnsembleFSResult.R

@@ -0,0 +1,75 @@
+#' @title Ensemble Feature Selection Result
+#'
+#' @description
+#' The `EnsembleFSResult` stores the results of the ensemble feature selection.
+#' The function [ensemble_fselect()] returns an object of this class.
+#'
+#' @examples
+#' \donttest{
+#' efsr = ensemble_fselect(
+#'   fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
+#'   task = tsk("sonar"),
+#'   learners = lrns(c("classif.rpart", "classif.featureless")),
+#'   init_resampling = rsmp("subsampling", repeats = 2),
+#'   inner_resampling = rsmp("cv", folds = 3),
+#'   measure = msr("classif.ce"),
+#'   terminator = trm("none")
+#' )
+#'
+#' # contains the benchmark result
+#' efsr$benchmark_result
+#'
+#' # contains the selected features for each iteration
+#' efsr$grid
+#'
+#' # returns the stability of the selected features
+#' efsr$stability(stability_measure = "jaccard")
+#' }
+EnsembleFSResult = R6Class("EnsembleFSResult",
+  public = list(
+
+    #' @field benchmark_result (`BenchmarkResult`)\cr
+    #' The benchmark result object.
+    benchmark_result = NULL,
+
+    #' @field grid (`data.table`)\cr
+    #' The grid of feature selection results.
+    grid = NULL,
+
+    #' @description
+    #' Creates a new instance of this [R6][R6::R6Class] class.
+    #'
+    #' @param benchmark_result (`BenchmarkResult`)\cr
+    #'  The benchmark result object.
+    #' @param grid (`data.table`)\cr
+    #'  The grid of feature selection results.
+    initialize = function(benchmark_result, grid) {
+      self$benchmark_result = assert_benchmark_result(benchmark_result)
+      self$grid = assert_data_table(grid)
+    },
+
+    #' @description
+    #' Returns the feature ranking.
+    feature_ranking = function() {
+
+    },
+
+    #' @description
+    #' Calculates the stability of the selected features with the `stabm` package.
+    #'
+    #' @param stability_measure (`character(1)`)\cr
+    #'  The stability measure to be used.
+    #'  One of the measures returned by [stabm::listStabilityMeasures()] in lower case.
+    #'  Default is `"jaccard"`.
+    #' @param ... (`any`)\cr
+    #'  Additional arguments passed to the stability measure function.
+    stability = function(stability_measure = "jaccard", ...) {
+      funs = stabm::listStabilityMeasures()$Name
+      keys =  tolower(gsub("stability", "", funs))
+      assert_choice(stability_measure, choices = keys)
+
+      fun = get(funs[which(stability_measure == keys)], envir = asNamespace("stabm"))
+      fun(self$grid$features, ...)
+    }
+  )
+)

R/ensemble_fselect.R

@@ -134,5 +134,5 @@ ensemble_fselect = function(
     set(grid, j = "importance", value = imp_scores)
   }
 
-  grid
+  EnsembleFSResult$new(bmr, grid)
 }

tests/testthat/test_ensemble_fselect.R

@@ -1,5 +1,5 @@
 test_that("ensemble feature selection works", {
-  res = ensemble_fselect(
+  efsr = ensemble_fselect(
     fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
     task = tsk("sonar"),
     learners = lrns(c("classif.rpart", "classif.featureless")),
@@ -9,11 +9,26 @@ test_that("ensemble feature selection works", {
     terminator = trm("none")
   )
 
-  expect_data_table(res, nrows = 4)
-  expect_list(res$features, any.missing = FALSE, len = 4)
-  expect_vector(res$n_features, size = 4)
-  expect_vector(res$classif.ce, size = 4)
-  expect_list(res$importance, any.missing = FALSE, len = 4)
+  expect_data_table(efsr$grid, nrows = 4)
+  expect_list(efsr$grid$features, any.missing = FALSE, len = 4)
+  expect_vector(efsr$grid$n_features, size = 4)
+  expect_vector(efsr$grid$classif.ce, size = 4)
+  expect_list(efsr$grid$importance, any.missing = FALSE, len = 4)
+  expect_benchmark_result(efsr$benchmark_result)
+})
+
+test_that("stability method works", {
+  efsr = ensemble_fselect(
+    fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
+    task = tsk("sonar"),
+    learners = lrns(c("classif.rpart", "classif.featureless")),
+    init_resampling = rsmp("subsampling", repeats = 2),
+    inner_resampling = rsmp("cv", folds = 3),
+    measure = msr("classif.ce"),
+    terminator = trm("none")
+  )
+
+  expect_number(efsr$stability(stability_measure = "jaccard"))
 })