feat: default_fallback function

DESCRIPTION

@@ -181,6 +181,7 @@ Collate:
     'benchmark.R'
     'benchmark_grid.R'
     'bibentries.R'
+    'default_fallback.R'
     'default_measures.R'
     'fix_factor_levels.R'
     'helper.R'

NAMESPACE

@@ -74,6 +74,9 @@ S3method(col_info,DataBackend)
 S3method(col_info,data.table)
 S3method(create_empty_prediction_data,TaskClassif)
 S3method(create_empty_prediction_data,TaskRegr)
+S3method(default_fallback,Learner)
+S3method(default_fallback,LearnerClassif)
+S3method(default_fallback,LearnerRegr)
 S3method(default_values,Learner)
 S3method(default_values,LearnerClassifRpart)
 S3method(default_values,LearnerRegrRpart)

NEWS.md

@@ -20,6 +20,7 @@
 * 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
+* feat: Create default fallback learner with `default_fallback()`.
 * feat: Check column roles when using `$set_col_roles()` and `$col_roles`.
 
 # mlr3 0.20.2

R/Learner.R

@@ -567,11 +567,11 @@ Learner = R6Class("Learner",
       assert_names(names(rhs), subset.of = c("train", "predict"))
       private$.encapsulate = insert_named(default, rhs)
 
+      # if there is no fallback, we get a default one
       if (is.null(private$.fallback)) {
-        # if there is no fallback, we get a default one from the reflections table
-        fallback_id = mlr_reflections$learner_fallback[[self$task_type]]
-        if (!is.null(fallback_id)) {
-          self$fallback = lrn(fallback_id, predict_type = self$predict_type)
+        fallback = default_fallback(self)
+        if (!is.null(fallback)) {
+          self$fallback = fallback
         }
       }
     },

R/default_fallback.R

@@ -0,0 +1,64 @@
+#' @title Create a Fallback Learner
+#'
+#' @description
+#' Create a fallback learner for a given learner.
+#' The function searches for a suitable fallback learner based on the task type.
+#' Additional checks are performed to ensure that the fallback learner supports the predict type.
+#'
+#' @param learner [Learner]\cr
+#'  The learner for which a fallback learner should be created.
+#' @param ... `any`\cr
+#' ignored.
+#'
+#' @return [Learner]
+default_fallback = function(learner, ...) {
+  UseMethod("default_fallback")
+}
+
+#' @rdname default_fallback
+#' @export
+default_fallback.Learner = function(learner, ...) {
+  # FIXME: remove when new encapsulate/fallback system is in place
+  return(NULL)
+}
+
+#' @rdname default_fallback
+#' @export
+default_fallback.LearnerClassif = function(learner, ...) {
+  fallback = lrn("classif.featureless")
+
+  # set predict type
+  if (learner$predict_type %nin% fallback$predict_types) {
+    stopf("Fallback learner '%s' does not support predict type '%s'.", fallback$id, learner$predict_type)
+  }
+
+  fallback$predict_type = learner$predict_type
+
+  return(fallback)
+}
+
+#' @rdname default_fallback
+#' @export
+default_fallback.LearnerRegr = function(learner, ...) {
+  fallback = lrn("regr.featureless")
+
+  # set predict type
+  if (learner$predict_type %nin% fallback$predict_types) {
+    stopf("Fallback learner '%s' does not support predict type '%s'.", fallback$id, learner$predict_type)
+  }
+
+  fallback$predict_type = learner$predict_type
+
+  # set quantiles
+  if (learner$predict_type == "quantiles") {
+
+    if (is.null(learner$quantiles) || is.null(learner$quantile_response)) {
+      stopf("Cannot set quantiles for fallback learner. Set `$quantiles` and `$quantile_response` in %s.", learner$id)
+    }
+
+    fallback$quantiles = learner$quantiles
+    fallback$quantile_response = learner$quantile_response
+  }
+
+  return(fallback)
+}

R/mlr_reflections.R

@@ -127,11 +127,6 @@ local({
     regr = list(response = "response", se = c("response", "se"), quantiles = c("response", "quantiles"), distr = c("response", "se", "distr"))
   )
 
-  mlr_reflections$learner_fallback = list(
-    classif = "classif.featureless",
-    regr = "regr.featureless"
-  )
-
   # Allowed tags for parameters
   mlr_reflections$learner_param_tags = c("train", "predict", "hotstart", "importance", "threads", "required", "internal_tuning")
 

pkgdown/_pkgdown.yml

@@ -78,6 +78,7 @@ reference:
       - starts_with("mlr_learners")
       - as_learner
       - HotstartStack
+      - default_fallback
   - title: Measures
     contents:
       - starts_with("mlr_measures")

tests/testthat/test_set_fallback.R

@@ -0,0 +1,25 @@
+test_that("fallback = default_fallback() works", {
+  learner = lrn("classif.rpart")
+  fallback = default_fallback(learner)
+
+  expect_class(fallback, "LearnerClassifFeatureless")
+  expect_equal(fallback$predict_type, "response")
+
+  learner = lrn("classif.rpart", predict_type = "prob")
+  fallback = default_fallback(learner)
+
+  expect_class(fallback, "LearnerClassifFeatureless")
+  expect_equal(fallback$predict_type, "prob")
+
+  learner = lrn("regr.rpart")
+  fallback = default_fallback(learner)
+
+  expect_class(fallback, "LearnerRegrFeatureless")
+  expect_equal(fallback$predict_type, "response")
+
+  learner = lrn("regr.debug", predict_type = "se")
+  fallback = default_fallback(learner)
+
+  expect_class(fallback, "LearnerRegrFeatureless")
+  expect_equal(fallback$predict_type, "se")
+})