A simple fix for occasional errors when using sffs (#2486)

* This fixes #2485

* add a test for the #2485 fix

* conform coding style and reduce test time

* Update test_featsel_selectFeaturesSequential.R

* NEWS for #2486

Author: bmihaljevic

NEWS.md

@@ -60,6 +60,7 @@ In this case, the package name is omitted.
  * The FeatSelResult object now contains an additional slot `x.bit.names` that stores the optimal bits
  * The slot `x` now always contains the real feature names and not the bit.names
  * This fixes a bug and makes `makeFeatSelWrapper` usable with custom `bit.names`.
+ * Fixed a bug due to which `sffs` crashed in some cases (@bmihaljevic, #2486)
 
 # mlr 2.13:
 

R/selectFeaturesSequential.R

@@ -93,6 +93,7 @@ selectFeaturesSequential = function(learner, task, resampling, measures, bit.nam
       state2 = seq.step(!forward, state, gns, compare)
       if (!is.null(state2)) {
         state = state2
+        state$x = unlist(state$x)
         fail = 0
       } else {
         fail = fail + 1

tests/testthat/test_featsel_selectFeaturesSequential.R

@@ -0,0 +1,18 @@
+context("selectFeaturesSequential")
+
+# This used to cause an error. Now ensure it does not.
+test_that("no crash with sffs", {
+  p = mlbench::mlbench.waveform(1000)
+  dataset = as.data.frame(p)
+  dataset  = droplevels(subset(dataset, classes != 3))
+
+  mCT = makeClassifTask(data = dataset, target = "classes")
+  ctrl = makeFeatSelControlSequential(method = "sffs", maxit = NA,alpha = 0.001)
+  mL = makeLearner("classif.logreg", predict.type = "prob")
+  inner = makeResampleDesc("Holdout",stratify = TRUE)
+  lrn = makeFeatSelWrapper(mL, resampling = inner, control = ctrl)
+  outer = makeResampleDesc("CV", iters = 2, stratify = TRUE)
+  # No error occurs
+  expect_error(resample(lrn, mCT, outer, extract = getFeatSelResult, measures = list(mlr::auc, mlr::acc, mlr::brier), models=TRUE),
+                NA)
+})