feat: ties methods

NEWS.md

@@ -1,5 +1,7 @@
 # mlr3fselect (development version)
 
+* feat: Add `ties_method` options `"least_features"` and `"random"` to `ArchiveFSelect$best()`.
+* refactor: Optimize runtime of `ArchiveFSelect$best()` method.
 * feat: Add importance scores to result of `FSelectorRFE`.
 * feat: Add number of features to `as.data.table.ArchiveFSelect()`.
 * feat: Features can be always included with the `always_include` column role.

R/ArchiveFSelect.R

@@ -49,6 +49,8 @@
 #'     * `measures` (list of [mlr3::Measure])\cr
 #'       Score feature sets on additional measures.
 #'
+#' @template param_ties_method
+#'
 #' @export
 ArchiveFSelect = R6Class("ArchiveFSelect",
   inherit = Archive,
@@ -72,8 +74,14 @@ ArchiveFSelect = R6Class("ArchiveFSelect",
     #'
     #' @param check_values (`logical(1)`)\cr
     #'   If `TRUE` (default), hyperparameter configurations are check for validity.
-    initialize = function(search_space, codomain, check_values = TRUE) {
+    initialize = function(
+      search_space,
+      codomain,
+      check_values = TRUE,
+      ties_method = "least_features"
+      ) {
       super$initialize(search_space, codomain, check_values)
+      self$ties_method = ties_method
 
       # initialize empty benchmark result
       self$benchmark_result = BenchmarkResult$new()
@@ -141,7 +149,65 @@ ArchiveFSelect = R6Class("ArchiveFSelect",
     print = function() {
       catf(format(self))
       print(self$data[, setdiff(names(self$data), "uhash"), with = FALSE], digits=2)
+    },
+
+    #' @description
+    #' Returns the best scoring feature sets.
+    #'
+    #' @param batch (`integer()`)\cr
+    #' The batch number(s) to limit the best results to.
+    #' Default is all batches.
+    #' @param ties_method (`character(1)`)\cr
+    #' Method to handle ties.
+    #' If `NULL` (default), the global ties method set during initialization is used.
+    #' The default global ties method is `least_features` which selects the feature set with the least features.
+    #' If there are multiple best feature sets with the same number of features, one is selected randomly.
+    #' The `random` method returns a random feature set from the best feature sets.
+    #
+    #' @return [data.table::data.table()]
+    best = function(batch = NULL, ties_method = NULL) {
+      ties_method = assert_choice(ties_method, c("least_features", "random"), null.ok = TRUE) %??% self$ties_method
+      assert_subset(batch, seq_len(self$n_batch))
+      if (self$n_batch == 0L) return(data.table())
+
+      tab = if (is.null(batch)) self$data else self$data[list(batch), , on = "batch_nr"]
+
+      if (self$codomain$target_length == 1L) {
+        y = tab[[self$cols_y]] * -self$codomain$maximization_to_minimization
+
+        if (ties_method == "least_features") {
+          ii = which(y == max(y))
+          tab = tab[ii]
+          ii = which_min(rowSums(tab[, self$cols_x, with = FALSE]), ties_method = "random")
+          tab[ii]
+        } else {
+          ii = which_max(y, ties_method = "random")
+          tab[ii]
+        }
+      } else {
+        ymat = t(as.matrix(tab[, self$cols_y, with = FALSE]))
+        ymat = self$codomain$maximization_to_minimization * ymat
+        tab[!is_dominated(ymat)]
+      }
+    }
+  ),
+
+  active = list(
+
+    #' @field ties_method (`character(1)`)\cr
+    #' Method to handle ties.
+    ties_method = function(rhs) {
+      if (!missing(rhs)) {
+        assert_choice(rhs, c("least_features", "random"))
+        private$.ties_method = rhs
+      } else {
+        private$.ties_method
+      }
     }
+  ),
+
+  private = list(
+    .ties_method = NULL
   )
 )
 

R/AutoFSelector.R

@@ -36,6 +36,7 @@
 #' @template param_store_models
 #' @template param_check_values
 #' @template param_callbacks
+#' @template param_ties_method
 #'
 #' @export
 #' @examples
@@ -111,7 +112,19 @@ AutoFSelector = R6Class("AutoFSelector",
     #'
     #' @param fselector ([FSelector])\cr
     #'   Optimization algorithm.
-    initialize = function(fselector, learner, resampling, measure = NULL, terminator, store_fselect_instance = TRUE, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
+    initialize = function(
+      fselector,
+      learner,
+      resampling,
+      measure = NULL,
+      terminator,
+      store_fselect_instance = TRUE,
+      store_benchmark_result = TRUE,
+      store_models = FALSE,
+      check_values = FALSE,
+      callbacks = list(),
+      ties_method = "least_features"
+      ) {
       ia = list()
       self$fselector = assert_r6(fselector, "FSelector")$clone()
       ia$learner = assert_learner(as_learner(learner, clone = TRUE))
@@ -125,6 +138,7 @@ AutoFSelector = R6Class("AutoFSelector",
 
       ia$check_values = assert_flag(check_values)
       ia$callbacks = assert_callbacks(as_callbacks(callbacks))
+      ia$ties_method = assert_choice(ties_method, c("least_features", "random"))
       self$instance_args = ia
 
       super$initialize(

R/FSelectInstanceSingleCrit.R

@@ -54,6 +54,7 @@
 #' @template param_store_benchmark_result
 #' @template param_callbacks
 #' @template param_xdt
+#' @template param_ties_method
 #'
 #' @export
 #' @examples
@@ -93,12 +94,24 @@ FSelectInstanceSingleCrit = R6Class("FSelectInstanceSingleCrit",
 
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
-    initialize = function(task, learner, resampling, measure, terminator, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
+    initialize = function(
+      task,
+      learner,
+      resampling,
+      measure,
+      terminator,
+      store_benchmark_result = TRUE,
+      store_models = FALSE,
+      check_values = FALSE,
+      callbacks = list(),
+      ties_method = "least_features"
+      ) {
       # initialized specialized fselect archive and objective
       archive = ArchiveFSelect$new(
         search_space = task_to_domain(assert_task(task)),
         codomain = measures_to_codomain(assert_measure(measure)),
-        check_values = check_values)
+        check_values = check_values,
+        ties_method = ties_method)
 
       objective = ObjectiveFSelect$new(
         task = task,

R/auto_fselector.R

@@ -19,10 +19,25 @@
 #' @template param_store_models
 #' @template param_check_values
 #' @template param_callbacks
+#' @template param_ties_method
 #'
 #' @export
 #' @inherit AutoFSelector examples
-auto_fselector = function(fselector, learner, resampling, measure = NULL, term_evals = NULL, term_time = NULL, terminator = NULL, store_fselect_instance = TRUE, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
+auto_fselector = function(
+  fselector,
+  learner,
+  resampling,
+  measure = NULL,
+  term_evals = NULL,
+  term_time = NULL,
+  terminator = NULL,
+  store_fselect_instance = TRUE,
+  store_benchmark_result = TRUE,
+  store_models = FALSE,
+  check_values = FALSE,
+  callbacks = list(),
+  ties_method = "least_features"
+  ) {
   terminator = terminator %??% terminator_selection(term_evals, term_time)
 
   AutoFSelector$new(
@@ -35,5 +50,6 @@ auto_fselector = function(fselector, learner, resampling, measure = NULL, term_e
     store_benchmark_result = store_benchmark_result,
     store_models = store_models,
     check_values = check_values,
-    callbacks = callbacks)
+    callbacks = callbacks,
+    ties_method = ties_method)
 }

R/fselect.R

@@ -36,6 +36,7 @@
 #' @template param_store_models
 #' @template param_check_values
 #' @template param_callbacks
+#' @template param_ties_method
 #'
 #' @export
 #' @examples
@@ -60,21 +61,48 @@
 #'
 #' # Inspect all evaluated configurations
 #' as.data.table(instance$archive)
-fselect = function(fselector, task, learner, resampling, measures = NULL, term_evals = NULL, term_time = NULL, terminator = NULL, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
+fselect = function(
+  fselector,
+  task,
+  learner,
+  resampling,
+  measures = NULL,
+  term_evals = NULL,
+  term_time = NULL,
+  terminator = NULL,
+  store_benchmark_result = TRUE,
+  store_models = FALSE,
+  check_values = FALSE,
+  callbacks = list(),
+  ties_method = "least_features"
+  ) {
   assert_fselector(fselector)
   terminator = terminator %??% terminator_selection(term_evals, term_time)
 
-  FSelectInstance = if (!is.list(measures)) FSelectInstanceSingleCrit else FSelectInstanceMultiCrit
-  instance = FSelectInstance$new(
-    task = task,
-    learner = learner,
-    resampling = resampling,
-    measures,
-    terminator = terminator,
-    store_benchmark_result = store_benchmark_result,
-    store_models = store_models,
-    check_values = check_values,
-    callbacks = callbacks)
+  instance = if (!is.list(measures)) {
+    FSelectInstanceSingleCrit$new(
+      task = task,
+      learner = learner,
+      resampling = resampling,
+      measure = measures,
+      terminator = terminator,
+      store_benchmark_result = store_benchmark_result,
+      store_models = store_models,
+      check_values = check_values,
+      callbacks = callbacks,
+      ties_method = ties_method)
+  } else {
+    FSelectInstanceMultiCrit$new(
+      task = task,
+      learner = learner,
+      resampling = resampling,
+      measures = measures,
+      terminator = terminator,
+      store_benchmark_result = store_benchmark_result,
+      store_models = store_models,
+      check_values = check_values,
+      callbacks = callbacks)
+  }
 
   fselector$optimize(instance)
   instance

R/fselect_nested.R

@@ -22,6 +22,7 @@
 #' @template param_store_models
 #' @template param_check_values
 #' @template param_callbacks
+#' @template param_ties_method
 #'
 #' @export
 #' @examples
@@ -40,7 +41,23 @@
 #'
 #' # Unbiased performance of the final model trained on the full data set
 #' rr$aggregate()
-fselect_nested = function(fselector, task, learner, inner_resampling, outer_resampling, measure = NULL, term_evals = NULL, term_time = NULL, terminator = NULL, store_fselect_instance = TRUE, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
+fselect_nested = function(
+  fselector,
+  task,
+  learner,
+  inner_resampling,
+  outer_resampling,
+  measure = NULL,
+  term_evals = NULL,
+  term_time = NULL,
+  terminator = NULL,
+  store_fselect_instance = TRUE,
+  store_benchmark_result = TRUE,
+  store_models = FALSE,
+  check_values = FALSE,
+  callbacks = list(),
+  ties_method = "least_features"
+  ) {
   assert_task(task)
   assert_resampling(inner_resampling)
   assert_resampling(outer_resampling)
@@ -56,7 +73,8 @@ fselect_nested = function(fselector, task, learner, inner_resampling, outer_resa
     store_benchmark_result = store_benchmark_result,
     store_models = store_models,
     check_values = check_values,
-    callbacks = callbacks)
+    callbacks = callbacks,
+    ties_method = ties_method)
 
   resample(task, afs, outer_resampling, store_models = TRUE)
 }

R/mlr_callbacks.R

@@ -116,6 +116,7 @@ load_callback_svm_rfe = function() {
 #'
 #' @description
 #' Selects the smallest feature set within one standard error of the best as the result.
+#' If there are multiple feature sets with the same performance and number of features, the first one is selected.
 #'
 #' @source
 #' `r format_bib("kuhn2013")`

R/sugar.R

@@ -46,13 +46,47 @@ fss = function(.keys, ...) {
 #' @template param_store_models
 #' @template param_check_values
 #' @template param_callbacks
+#' @template param_ties_method
 #'
 #' @inheritSection FSelectInstanceSingleCrit Resources
 #' @inheritSection FSelectInstanceSingleCrit Default Measures
 #'
 #' @export
 #' @inherit FSelectInstanceSingleCrit examples
-fsi = function(task, learner, resampling, measures = NULL, terminator, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, callbacks = list()) {
-  FSelectInstance = if (!is.list(measures)) FSelectInstanceSingleCrit else FSelectInstanceMultiCrit
-  FSelectInstance$new(task, learner, resampling, measures, terminator, store_benchmark_result, store_models, check_values, callbacks)
+fsi = function(
+  task,
+  learner,
+  resampling,
+  measures = NULL,
+  terminator,
+  store_benchmark_result = TRUE,
+  store_models = FALSE,
+  check_values = FALSE,
+  callbacks = list(),
+  ties_method = "least_features"
+  ) {
+  if (!is.list(measures)) {
+    FSelectInstanceSingleCrit$new(
+      task = task,
+      learner = learner,
+      resampling = resampling,
+      measure = measures,
+      terminator = terminator,
+      store_benchmark_result = store_benchmark_result,
+      store_models = store_models,
+      check_values = check_values,
+      callbacks = callbacks,
+      ties_method = ties_method)
+  } else {
+    FSelectInstanceMultiCrit$new(
+      task = task,
+      learner = learner,
+      resampling = resampling,
+      measures = measures,
+      terminator = terminator,
+      store_benchmark_result = store_benchmark_result,
+      store_models = store_models,
+      check_values = check_values,
+      callbacks = callbacks)
+  }
 }

inst/testthat/helper_expectations.R

@@ -17,3 +17,7 @@ expect_max_features = function(features, n) {
   res = max(rowSums(features))
   expect_set_equal(res, n)
 }
+
+expect_features = function(res, identical_to = NULL, must_include = NULL) {
+  expect_names(names(res)[as.logical(res)], must.include = must_include, identical.to = identical_to)
+}