feat: add to fsi

R/fselect.R

@@ -91,7 +91,7 @@ fselect = function(
       check_values = check_values,
       callbacks = callbacks,
       ties_method = ties_method)
-  }  else {
+  } else {
     FSelectInstanceMultiCrit$new(
       task = task,
       learner = learner,

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.
 #'
 #' @examples
 #' clbk("mlr3fselect.one_se_rule")

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$new(task, learner, resampling, measures, terminator, store_benchmark_result, store_models, check_values, callbacks)
+fsi = function(
+  task,
+  learner,
+  resampling,
+  measures = NULL,
+  terminator,
+  store_benchmark_result = TRUE,
+  store_models = FALSE,
+  check_values = FALSE,
+  callbacks = list(),
+  ties_method = "n_features"
+  ) {
+  if (!is.list(measures)) {
+    FSelectInstanceSingleCrit$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)
+  }
 }

tests/testthat/test_ArchiveFSelect.R

@@ -140,7 +140,61 @@ test_that("ArchiveFSelect as.data.table function works", {
   expect_equal(tab$batch_nr, 1:10)
 })
 
-test_that("best method works with ties and maximization", {
+test_that("global ties method works", {
+  design = mlr3misc::rowwise_table(
+    ~x1,   ~x2,   ~x3,    ~x4,
+    FALSE, TRUE,  FALSE,  TRUE,
+    TRUE,  FALSE, FALSE,  TRUE,
+    TRUE,  FALSE, FALSE,  FALSE,
+    FALSE, TRUE,  FALSE,  FALSE
+  )
+
+  score_design = data.table(
+    score = c(0.1, 0.2, 0.2, 0.1),
+    features = list(c("x2", "x4"), c("x1", "x4"), "x1", c("x1", "x2"))
+  )
+  measure = msr("dummy", score_design = score_design, minimize = FALSE)
+
+  # n_features
+  instance = fselect(
+    fselector = fs("design_points", design = design),
+    task = TEST_MAKE_TSK(),
+    learner = lrn("regr.rpart"),
+    resampling = rsmp("cv", folds = 3),
+    measures = measure,
+    ties_method = "n_features"
+  )
+
+  expect_equal(instance$result_feature_set, "x1")
+
+  # first
+  instance$clear()
+  instance = fselect(
+    fselector = fs("design_points", design = design),
+    task = TEST_MAKE_TSK(),
+    learner = lrn("regr.rpart"),
+    resampling = rsmp("cv", folds = 3),
+    measures = measure,
+    ties_method = "first"
+  )
+
+  expect_equal(instance$result_feature_set, c("x1", "x4"))
+
+  # random
+  instance$clear()
+  instance = fselect(
+    fselector = fs("design_points", design = design),
+    task = TEST_MAKE_TSK(),
+    learner = lrn("regr.rpart"),
+    resampling = rsmp("cv", folds = 3),
+    measures = measure,
+    ties_method = "random"
+  )
+
+  expect_names(instance$result_feature_set, must.include = "x1")
+})
+
+test_that("local ties method works when maximize measure", {
 
   design = mlr3misc::rowwise_table(
     ~x1,   ~x2,   ~x3,    ~x4,
@@ -169,7 +223,7 @@ test_that("best method works with ties and maximization", {
   expect_features(instance$archive$best(ties_method = "n_features")[, list(x1, x2, x3, x4)], identical_to = "x1")
 })
 
-test_that("best method works with ties and minimization", {
+test_that("local ties method works when minimize measure", {
 
   design = mlr3misc::rowwise_table(
     ~x1,   ~x2,   ~x3,    ~x4,
@@ -198,7 +252,7 @@ test_that("best method works with ties and minimization", {
   expect_features(instance$archive$best(ties_method = "n_features")[, list(x1, x2, x3, x4)], identical_to = "x2")
 })
 
-test_that("best method works with batches and ties", {
+test_that("local ties method works with batches", {
 
   design = mlr3misc::rowwise_table(
     ~x1,   ~x2,   ~x3,    ~x4,

tests/testthat/test_fselect.R

@@ -38,7 +38,7 @@ test_that("fselect interface is equal to FSelectInstanceSingleCrit", {
 
 test_that("fselect interface is equal to FSelectInstanceMultiCrit", {
   fselect_args = formalArgs(fselect)
-  fselect_args = fselect_args[fselect_args != "fselector"]
+  fselect_args = fselect_args[fselect_args %nin% c("fselector", "ties_method")]
 
   instance_args = formalArgs(FSelectInstanceMultiCrit$public_methods$initialize)
   instance_args = c(instance_args, "term_evals", "term_time")