From 7c3c74b614c4c943f61e2e03992ee26b1e177605 Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:59:36 +0100
Subject: [PATCH] BREAKING CHANGE: remove $loglik method from all learners
 (#1207)

---
 NEWS.md             |  1 +
 R/Learner.R         |  3 ---
 R/MeasureAIC.R      | 12 +++++++-----
 R/MeasureBIC.R      | 10 ++++++----
 R/mlr_reflections.R |  2 +-
 man/Learner.Rd      |  2 --
 6 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 7b3909adb..8c9054232 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,7 @@
 
 * fix: Quantiles must not ascend with probabilities.
 * refactor: Replace `tsk("boston_housing")` with `tsk("california_housing")`.
+* BREAKING CHANGE: Remove ``$loglik()`` method from all learners.
 
 # mlr3 0.21.1
 
diff --git a/R/Learner.R b/R/Learner.R
index 213c7855a..72014c8f5 100644
--- a/R/Learner.R
+++ b/R/Learner.R
@@ -59,9 +59,6 @@
 #' * `oob_error(...)`: Returns the out-of-bag error of the model as `numeric(1)`.
 #'   The learner must be tagged with property `"oob_error"`.
 #'
-#' * `loglik(...)`: Extracts the log-likelihood (c.f. [stats::logLik()]).
-#'   This can be used in measures like [mlr_measures_aic] or [mlr_measures_bic].
-#'
 #' * `internal_valid_scores`: Returns the internal validation score(s) of the model as a named `list()`.
 #'   Only available for [`Learner`]s with the `"validation"` property.
 #'   If the learner is not trained yet, this returns `NULL`.
diff --git a/R/MeasureAIC.R b/R/MeasureAIC.R
index 5a0203d1d..e0a988ebc 100644
--- a/R/MeasureAIC.R
+++ b/R/MeasureAIC.R
@@ -40,12 +40,14 @@ MeasureAIC = R6Class("MeasureAIC",
   private = list(
     .score = function(prediction, learner, ...) {
       learner = learner$base_learner()
-      if ("loglik" %nin% learner$properties) {
-        return(NA_real_)
-      }
-
       k = self$param_set$values$k %??% 2
-      return(stats::AIC(learner$loglik(), k = k))
+
+      tryCatch({
+        return(stats::AIC(stats::logLik(learner$model), k = k))
+      }, error = function(e) {
+        warningf("Learner '%s' does not support AIC calculation", learner$id)
+        return(NA_real_)
+      })
     }
   )
 )
diff --git a/R/MeasureBIC.R b/R/MeasureBIC.R
index 94d67d515..b3d48f205 100644
--- a/R/MeasureBIC.R
+++ b/R/MeasureBIC.R
@@ -38,11 +38,13 @@ MeasureBIC = R6Class("MeasureBIC",
   private = list(
     .score = function(prediction, learner, ...) {
       learner = learner$base_learner()
-      if ("loglik" %nin% learner$properties) {
-        return(NA_real_)
-      }
 
-      return(stats::BIC(learner$loglik()))
+      tryCatch({
+        return(stats::BIC(stats::logLik(learner$model)))
+      }, error = function(e) {
+        warningf("Learner '%s' does not support BIC calculation", learner$id)
+        return(NA_real_)
+      })
     }
   )
 )
diff --git a/R/mlr_reflections.R b/R/mlr_reflections.R
index e1111dc16..283562658 100644
--- a/R/mlr_reflections.R
+++ b/R/mlr_reflections.R
@@ -118,7 +118,7 @@ local({
   )
 
   ### Learner
-  tmp = c("featureless", "missings", "weights", "importance", "selected_features", "oob_error", "loglik", "hotstart_forward", "hotstart_backward", "validation", "internal_tuning", "marshal")
+  tmp = c("featureless", "missings", "weights", "importance", "selected_features", "oob_error", "hotstart_forward", "hotstart_backward", "validation", "internal_tuning", "marshal")
   mlr_reflections$learner_properties = list(
     classif = c(tmp, "twoclass", "multiclass"),
     regr = tmp
diff --git a/man/Learner.Rd b/man/Learner.Rd
index 8f3aacaeb..fee67c15a 100644
--- a/man/Learner.Rd
+++ b/man/Learner.Rd
@@ -46,8 +46,6 @@ To filter variables using the importance scores, see package \CRANpkg{mlr3filter
 The learner must be tagged with property \code{"selected_features"}.
 \item \code{oob_error(...)}: Returns the out-of-bag error of the model as \code{numeric(1)}.
 The learner must be tagged with property \code{"oob_error"}.
-\item \code{loglik(...)}: Extracts the log-likelihood (c.f. \code{\link[stats:logLik]{stats::logLik()}}).
-This can be used in measures like \link{mlr_measures_aic} or \link{mlr_measures_bic}.
 \item \code{internal_valid_scores}: Returns the internal validation score(s) of the model as a named \code{list()}.
 Only available for \code{\link{Learner}}s with the \code{"validation"} property.
 If the learner is not trained yet, this returns \code{NULL}.