feat: classif.debug, regr.debug: add methods "importance" and "selected_features"

NEWS.md

@@ -23,6 +23,7 @@
 * feat: Added new measure `mu_auc`.
 * feat: Add option to calculate the mean of the true values on the train set in `msr("regr.rsq")`.
 * feat: Default fallback learner is set when encapsulation is activated.
+* feat: Learners classif.debug and regr.debug have new methods `$importance()` and `$selected_features()` for testing, also in downstream packages
 
 # mlr3 0.20.2
 

R/LearnerClassifDebug.R

@@ -105,6 +105,27 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
     #'   Additional arguments passed to [`unmarshal_model()`].
     unmarshal = function(...) {
       learner_unmarshal(.learner = self, ...)
+    },
+
+    #' @description
+    #' Returns 0 for each feature seen in training.
+    #' @return Named `numeric()`.
+    importance = function() {
+      if (is.null(self$model)) {
+        stopf("No model stored")
+      }
+      fns = self$state$feature_names
+      set_names(rep(0, length(fns)), fns)
+    },
+
+    #' @description
+    #' Always returns character(0).
+    #' @return `character()`.
+    selected_features = function() {
+      if (is.null(self$model)) {
+        stopf("No model stored")
+      }
+      character(0)
     }
   ),
   active = list(
@@ -169,8 +190,15 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
         stopf("Early stopping is only possible when a validation task is present.")
       }
 
-      model = list(response = as.character(sample(task$truth(), 1L)), pid = Sys.getpid(), id = UUIDgenerate(),
-        random_number = sample(100000, 1), iter = if (isTRUE(pv$early_stopping)) sample(pv$iter %??% 1L, 1L) else pv$iter %??% 1L
+      model = list(
+          response = as.character(sample(task$truth(), 1L)),
+          pid = Sys.getpid(),
+          id = UUIDgenerate(),
+          random_number = sample(100000, 1),
+          iter = if (isTRUE(pv$early_stopping))
+            sample(pv$iter %??% 1L, 1L)
+          else
+            pv$iter %??% 1L
       )
 
       if (!is.null(valid_truth)) {

R/LearnerRegrDebug.R

@@ -48,6 +48,27 @@ LearnerRegrDebug = R6Class("LearnerRegrDebug", inherit = LearnerRegr,
         man = "mlr3::mlr_learners_regr.debug",
         label = "Debug Learner for Regression"
       )
+    },
+
+    #' @description
+    #' Returns 0 for each feature seen in training.
+    #' @return Named `numeric()`.
+    importance = function() {
+      if (is.null(self$model)) {
+        stopf("No model stored")
+      }
+      fns = self$state$feature_names
+      set_names(rep(0, length(fns)), fns)
+    },
+
+    #' @description
+    #' Always returns character(0).
+    #' @return `character()`.
+    selected_features = function() {
+      if (is.null(self$model)) {
+        stopf("No model stored")
+      }
+      character(0)
     }
   ),
   private = list(

tests/testthat/test_mlr_learners_classif_debug.R

@@ -81,3 +81,13 @@ test_that("marshaling", {
   p2 = l$marshal()$unmarshal()$predict(task)
   expect_equal(p1, p2)
 })
+
+test_that("importance and selected features", {
+  l = lrn("classif.debug")
+  task = tsk("iris")
+  l$train(task)
+  expect_equal(l$selected_features(), character(0))
+  expect_equal(l$importance(), set_names(rep(0, task$n_features), task$feature_names))
+})
+
+

tests/testthat/test_mlr_learners_regr_debug.R

@@ -0,0 +1,25 @@
+# this test / files was missing, only classif.debug was unit-tested
+# I added at least a few basic tests when i added methods "importance" and "selected_features"
+
+test_that("Simple training/predict", {
+  task = tsk("mtcars")
+  learner = lrn("regr.debug")
+  expect_learner(learner, task)
+
+  prediction = learner$train(task)$predict(task)
+  expect_class(learner$model, "regr.debug_model")
+  expect_numeric(learner$model$response, len = 1L, any.missing = FALSE)
+  expect_numeric(prediction$response, any.missing = FALSE)
+})
+
+
+test_that("importance and selected features", {
+  l = lrn("regr.debug")
+  task = tsk("mtcars")
+  l$train(task)
+  expect_equal(l$selected_features(), character(0))
+  expect_equal(l$importance(), set_names(rep(0, task$n_features), task$feature_names))
+})
+
+
+