fix: randomForest feature types

.github/workflows/rcmdcheck.yml

@@ -39,7 +39,7 @@ jobs:
 
       - name: Install pak and query dependencies
         run: |
-          install.packages("pak", repos = "https://r-lib.github.io/p/pak/dev/")
+          install.packages("pak")
           saveRDS(pak::pkg_deps("local::.", dependencies = TRUE), ".github/r-depends.rds")
         shell: Rscript {0}
 

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3extralearners
 Title: Extra Learners For mlr3
-Version: 0.5.48-9000
+Version: 0.5.49
 Authors@R: c(
     person("Raphael", "Sonabend", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0001-9225-4654")),

NEWS.md

@@ -1,3 +1,8 @@
+# mlr3extralearners 0.5.49
+
+* Added missing feature type `"integer"` to `classif.randomForest`
+* Added missing feature type `"logical"` to {classif, regr}.randomForest
+
 # mlr3extralearners 0.5.48
 
 * Add rsm learner

R/learner_obliqueRSF_surv_obliqueRSF.R

@@ -36,7 +36,7 @@ delayedAssign(
       #' Creates a new instance of this [R6][R6::R6Class] class.
       initialize = function() {
 
-        warning("obliqueRSF has been superseded by aorsf. We highly recommend you use aorsf to fit oblique random survival forests: see https://github.com/bcjaeger/aorsf or install from CRAN with install.packages('aorsf')",
+        messagef("obliqueRSF has been superseded by aorsf. We highly recommend you use aorsf to fit oblique random survival forests: see https://github.com/bcjaeger/aorsf or install from CRAN with install.packages('aorsf')",
                 call. = FALSE)
 
         ps = ps(

R/learner_randomForest_classif_randomForest.R

@@ -35,7 +35,7 @@ LearnerClassifRandomForest = R6Class("LearnerClassifRandomForest",
           tags = "train"),
         maxnodes = p_int(lower = 1L, tags = "train"),
         importance = p_fct(default = FALSE,
-          levels = c("accuracy", "gini", "none", FALSE),
+          levels = c("accuracy", "gini", "none"),
           special_vals = list(FALSE),
           tags = "train"),
         localImp = p_lgl(default = FALSE, tags = "train"),
@@ -52,7 +52,7 @@ LearnerClassifRandomForest = R6Class("LearnerClassifRandomForest",
       super$initialize(
         id = "classif.randomForest",
         packages = c("mlr3extralearners", "randomForest"),
-        feature_types = c("numeric", "factor", "ordered"),
+        feature_types = c("integer", "numeric", "factor", "ordered", "logical"),
         predict_types = c("response", "prob"),
         param_set = ps,
         properties = c("weights", "twoclass", "multiclass", "importance", "oob_error"),

R/learner_randomForest_regr_randomForest.R

@@ -34,7 +34,7 @@ LearnerRegrRandomForest = R6Class("LearnerRegrRandomForest",
           tags = "train"),
         maxnodes = p_int(lower = 1L, tags = "train"),
         importance = p_fct(default = FALSE,
-          levels = c("mse", "nudepurity", "none", FALSE),
+          levels = c("mse", "nudepurity", "none"),
           special_vals = list(FALSE),
           tags = "train"),
         localImp = p_lgl(default = FALSE, tags = "train"),
@@ -51,7 +51,7 @@ LearnerRegrRandomForest = R6Class("LearnerRegrRandomForest",
       super$initialize(
         id = "regr.randomForest",
         packages = c("mlr3extralearners", "randomForest"),
-        feature_types = c("integer", "numeric", "factor", "ordered"),
+        feature_types = c("integer", "numeric", "factor", "ordered", "logical"),
         predict_types = c("response"),
         param_set = ps,
         properties = c("weights", "importance", "oob_error"),