release: 1.3.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3fselect
 Title: Feature Selection for 'mlr3'
-Version: 1.2.1.9000
+Version: 1.3.0
 Authors@R: c(
     person("Marc", "Becker", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-8115-0400")),

NAMESPACE

@@ -69,3 +69,4 @@ importFrom(utils,bibentry)
 importFrom(utils,combn)
 importFrom(utils,head)
 importFrom(utils,packageVersion)
+importFrom(utils,tail)

NEWS.md

@@ -1,4 +1,4 @@
-# mlr3fselect (development version)
+# mlr3fselect 1.3.0
 
 * refactor: Use [fastVoteR](https://github.com/bblodfon/fastVoteR) for feature ranking in `EnsembleFSResult()` objects
 * feat: Add embedded ensemble feature selection `embedded_ensemble_fselect()`

R/ArchiveBatchFSelect.R

@@ -184,8 +184,7 @@ ArchiveBatchFSelect = R6Class("ArchiveBatchFSelect",
       tab = if (is.null(batch)) self$data else self$data[list(batch), , on = "batch_nr"]
 
       if (self$codomain$target_length == 1L) {
-        y = tab[[self$cols_y]] * -self$codomain$maximization_to_minimization
-
+        y = tab[[self$cols_y]] * -self$codomain$direction
         if (ties_method == "least_features") {
           ii = which(y == max(y))
           tab = tab[ii]
@@ -197,7 +196,7 @@ ArchiveBatchFSelect = R6Class("ArchiveBatchFSelect",
         }
       } else {
         ymat = t(as.matrix(tab[, self$cols_y, with = FALSE]))
-        ymat = self$codomain$maximization_to_minimization * ymat
+        ymat = self$codomain$direction * ymat
         tab[!is_dominated(ymat)]
       }
     }

R/EnsembleFSResult.R

@@ -83,7 +83,7 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
     #'  The column with the performance scores on the inner resampling of the train sets is not mandatory,
     #'  but note that it should be named as `{inner_measure$id}_inner` to distinguish from
     #'  the `{measure$id}`.
-    #' @param features ([character()])\cr
+    #' @param features (`character()`)\cr
     #'  The vector of features of the task that was used in the ensemble feature
     #'  selection.
     #' @param benchmark_result ([mlr3::BenchmarkResult])\cr

R/FSelectInstanceBatchSingleCrit.R

@@ -42,7 +42,6 @@
 #'
 #' * Utilize the built-in feature importance of models with [Recursive Feature Elimination](https://mlr-org.com/gallery/optimization/2023-02-07-recursive-feature-elimination/).
 #' * Run a feature selection with [Shadow Variable Search](https://mlr-org.com/gallery/optimization/2023-02-01-shadow-variable-search/).
-#' * [Feature Selection](https://mlr-org.com/gallery/optimization/2020-09-14-mlr3fselect-basic/) on the Titanic data set.
 #'
 #' @template param_task
 #' @template param_learner

R/ensemble_fselect.R

@@ -20,18 +20,15 @@
 #' The result object also includes the performance scores calculated during the inner resampling of the training sets, using models with the best feature subsets.
 #' These scores are stored in a column named `{measure_id}_inner`.
 #'
-#' @section Note:
-#'
+#' @note
 #' The **active measure** of performance is the one applied to the test sets.
 #' This is preferred, as inner resampling scores on the training sets are likely to be overestimated when using the final models.
 #' Users can change the active measure by using the `set_active_measure()` method of the [EnsembleFSResult].
 #'
 #' @param learners (list of [mlr3::Learner])\cr
 #'  The learners to be used for feature selection.
 #' @param init_resampling ([mlr3::Resampling])\cr
-#'  The initial resampling strategy of the data, from which each train set
-#'  will be passed on to the [auto_fselector] to optimize the learners and
-#'  perform feature selection.
+#'  The initial resampling strategy of the data, from which each train set will be passed on to the [auto_fselector] to optimize the learners and perform feature selection.
 #'  Each test set will be used for prediction on the final models returned by [auto_fselector].
 #'  Can only be [mlr3::ResamplingSubsampling] or [mlr3::ResamplingBootstrap].
 #' @param inner_resampling ([mlr3::Resampling])\cr

R/zzz.R

@@ -5,7 +5,7 @@
 #' @import mlr3
 #' @import bbotk
 #' @importFrom R6 R6Class
-#' @importFrom utils combn head packageVersion
+#' @importFrom utils combn head tail packageVersion
 #' @importFrom stats sd
 "_PACKAGE"
 

README.Rmd

@@ -46,7 +46,6 @@ The [gallery](https://mlr-org.com/gallery.html) features a collection of case st
 
 * Utilize the built-in feature importance of models with [Recursive Feature Elimination](https://mlr-org.com/gallery/optimization/2023-02-07-recursive-feature-elimination/).
 * Run a feature selection with [Shadow Variable Search](https://mlr-org.com/gallery/optimization/2023-02-01-shadow-variable-search/).
-* [Feature Selection](https://mlr-org.com/gallery/optimization/2020-09-14-mlr3fselect-basic/) on the Titanic data set.
 
 The [cheatsheet](https://cheatsheets.mlr-org.com/mlr3fselect.pdf) summarizes the most important functions of mlr3fselect.
 

README.md

@@ -47,9 +47,6 @@ case studies and demos about optimization.
     Elimination](https://mlr-org.com/gallery/optimization/2023-02-07-recursive-feature-elimination/).
   - Run a feature selection with [Shadow Variable
     Search](https://mlr-org.com/gallery/optimization/2023-02-01-shadow-variable-search/).
-  - [Feature
-    Selection](https://mlr-org.com/gallery/optimization/2020-09-14-mlr3fselect-basic/)
-    on the Titanic data set.
 
 The [cheatsheet](https://cheatsheets.mlr-org.com/mlr3fselect.pdf)
 summarizes the most important functions of mlr3fselect.