Merge branch 'main' into rfe_importance

NAMESPACE

@@ -51,6 +51,7 @@ importFrom(bbotk,trms)
 importFrom(mlr3misc,clbk)
 importFrom(mlr3misc,clbks)
 importFrom(mlr3misc,mlr_callbacks)
+importFrom(stats,sd)
 importFrom(utils,bibentry)
 importFrom(utils,combn)
 importFrom(utils,head)

R/bibentries.R

@@ -55,5 +55,19 @@ bibentries = c(
     journal     = "Machine Learning",
     author      = "Isabelle Guyon and Jason Weston and Stephen Barnhill and Vladimir Vapnik",
     year        = "2002"
+  ),
+
+  kuhn2013 = bibentry("Inbook",
+    author    = "Kuhn, Max and Johnson, Kjell",
+    chapter   = "Over-Fitting and Model Tuning",
+    title     = "Applied Predictive Modeling",
+    year      = "2013",
+    publisher = "Springer New York",
+    address   = "New York, NY",
+    pages     = "61--92",
+    isbn      = "978-1-4614-6849-3",
+    doi       = "10.1007/978-1-4614-6849-3_4",
+    url       = "https://doi.org/10.1007/978-1-4614-6849-3_4"
   )
 )
+

R/mlr_callbacks.R

@@ -117,6 +117,9 @@ load_callback_svm_rfe = function() {
 #' @description
 #' Selects the smallest feature set within one standard error of the best as the result.
 #'
+#' @source
+#' `r format_bib("kuhn2013")`
+#'
 #' @examples
 #' clbk("mlr3fselect.one_se_rule")
 #'
@@ -142,7 +145,7 @@ load_callback_one_se_rule = function() {
     on_result = function(callback, context) {
       archive = context$instance$archive
       data = as.data.table(archive)
-      data[, n_features := map(features, length)]
+      data[, "n_features" := map(get("features"), length)]
 
       # standard error
       y = data[[archive$cols_y]]

R/zzz.R

@@ -6,11 +6,12 @@
 #' @import bbotk
 #' @importFrom R6 R6Class
 #' @importFrom utils combn head
+#' @importFrom stats sd
 "_PACKAGE"
 
 .onLoad = function(libname, pkgname) {
   # nocov start
-    utils::globalVariables(c("super", "self"))
+    utils::globalVariables(c("super", "self", "n_features"))
 
   # reflections
   x = utils::getFromNamespace("bbotk_reflections", ns = "bbotk")

tests/testthat/test_FSelectInstanceSingleCrit.R

@@ -79,7 +79,7 @@ test_that("always include variable works", {
     task = task,
     learner = learner,
     resampling = resampling,
-    measure = msr("classif.ce"),
+    measures = msr("classif.ce"),
     terminator = trm("evals", n_evals = 100),
     store_models = TRUE
   )
@@ -105,7 +105,7 @@ test_that("always include variables works", {
     task = task,
     learner = learner,
     resampling = resampling,
-    measure = msr("classif.ce"),
+    measures = msr("classif.ce"),
     terminator = trm("evals", n_evals = 100),
     store_models = TRUE
   )