BREAKING CHANGE: remove loglik method from all learnres

NEWS.md

@@ -1,9 +1,9 @@
 # mlr3learners (development version)
 
-* feat: update hyperparameter set of `lrn("classif.ranger")` and `lrn("regr.ranger")` for 0.17.0, adding `na.action` parameter and `"missings"` property, and `poisson` splitrule for regression with a new `poisson.tau` parameter.
+* BREACKING CHANGE: Remove ``$loglik()`` method from all learners.
+* feat: Update hyperparameter set of `lrn("classif.ranger")` and `lrn("regr.ranger")` for 0.17.0, adding `na.action` parameter and `"missings"` property, and `poisson` splitrule for regression with a new `poisson.tau` parameter.
 * compatibility: mlr3 0.22.0.
 
-
 # mlr3learners 0.8.0
 
 * fix: Hyperparameter set of `lrn("classif.ranger")` and `lrn("regr.ranger")`.

R/LearnerClassifLogReg.R

@@ -53,17 +53,11 @@ LearnerClassifLogReg = R6Class("LearnerClassifLogReg",
         param_set = ps,
         predict_types = c("response", "prob"),
         feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered"),
-        properties = c("weights", "twoclass", "loglik"),
+        properties = c("weights", "twoclass"),
         packages = c("mlr3learners", "stats"),
         label = "Logistic Regression",
         man = "mlr3learners::mlr_learners_classif.log_reg"
       )
-    },
-
-    #' @description
-    #' Extract the log-likelihood (e.g., via [stats::logLik()] from the fitted model.
-    loglik = function() {
-      extract_loglik(self)
     }
   ),
 

R/LearnerClassifMultinom.R

@@ -46,17 +46,11 @@ LearnerClassifMultinom = R6Class("LearnerClassifMultinom",
         param_set = ps,
         predict_types = c("response", "prob"),
         feature_types = c("logical", "integer", "numeric", "factor"),
-        properties = c("weights", "twoclass", "multiclass", "loglik"),
+        properties = c("weights", "twoclass", "multiclass"),
         packages = c("mlr3learners", "nnet"),
         label = "Multinomial Log-Linear Model",
         man = "mlr3learners::mlr_learners_classif.multinom"
       )
-    },
-
-    #' @description
-    #' Extract the log-likelihood (e.g., via [stats::logLik()] from the fitted model.
-    loglik = function() {
-      extract_loglik(self)
     }
   ),
 

R/LearnerRegrLM.R

@@ -44,17 +44,11 @@ LearnerRegrLM = R6Class("LearnerRegrLM",
         param_set = ps,
         predict_types = c("response", "se"),
         feature_types = c("logical", "integer", "numeric", "factor", "character"),
-        properties = c("weights", "loglik"),
+        properties = "weights",
         packages = c("mlr3learners", "stats"),
         label = "Linear Model",
         man = "mlr3learners::mlr_learners_regr.lm"
       )
-    },
-
-    #' @description
-    #' Extract the log-likelihood (e.g., via [stats::logLik()] from the fitted model.
-    loglik = function() {
-      extract_loglik(self)
     }
   ),
 

R/helpers.R

@@ -35,14 +35,4 @@ rename = function(x, old, new) {
   x
 }
 
-
-extract_loglik = function(self) {
-  require_namespaces(self$packages)
-  if (is.null(self$model)) {
-    stopf("Learner '%s' has no model stored", self$id)
-  }
-  stats::logLik(self$model)
-}
-
-
 opts_default_contrasts = list(contrasts = c("contr.treatment", "contr.poly"))