Modify makeBaseWrapper to unlock wrapped feature selection and tuning and to prevent errors in wrapped feature selection and imputation

R/BaseWrapper.R

@@ -1,8 +1,7 @@
 makeBaseWrapper = function(id, type, next.learner, package = character(0L), par.set = makeParamSet(),
   par.vals = list(), learner.subclass, model.subclass) {
-
-  if (inherits(next.learner, "OptWrapper"))
-    stop("Cannot wrap an optimization wrapper with something else!")
+  if (inherits(next.learner, "OptWrapper") && is.element("TuneWrapper", learner.subclass))
+    stop("Cannot wrap a tuning wrapper around another optimization wrapper!")
   ns = intersect(names(par.set$pars), names(next.learner$par.set$pars))
   if (length(ns) > 0L)
     stopf("Hyperparameter names in wrapper clash with base learner names: %s", collapse(ns))

tests/testthat/test_base_BaseWrapper.R

@@ -22,3 +22,50 @@ test_that("BaseWrapper", {
   lrn2.rm = removeHyperPars(lrn2, names(getHyperPars(lrn2)))
   expect_equal(length(getHyperPars(lrn2.rm)), 0)
 })
+
+test_that("Joint model performance estimation, tuning, and model performance", {
+  lrn = makeLearner("classif.ksvm", predict.type = "prob")
+  lrn2 = makeTuneWrapper(
+    learner = lrn,
+    par.set = makeParamSet(
+      makeDiscreteParam("C", values = 2 ^ (-2:2)),
+      makeDiscreteParam("sigma", values = 2 ^ (-2:2))
+    ),
+    measures = list(auc, acc),
+    control = makeTuneControlRandom(maxit = 3L),
+    resampling = makeResampleDesc(method = "Holdout")
+  )
+  lrn3 = makeFeatSelWrapper(
+    learner = lrn2,
+    measures = list(auc, acc),
+    control = makeFeatSelControlRandom(maxit = 3L),
+    resampling = makeResampleDesc(method = "Holdout")
+  )
+  bmrk = benchmark(lrn3, pid.task, makeResampleDesc(method = "Holdout"))
+  expect_is(bmrk, "BenchmarkResult")
+})
+
+test_that("Error when wrapping tune wrapper around another optimization wrapper", {
+  expect_error({
+    lrn = makeLearner("classif.ksvm", predict.type = "prob")
+    lrn2 = makeFeatSelWrapper(
+      learner = lrn,
+      measures = list(auc, acc),
+      control = makeFeatSelControlRandom(maxit = 3L),
+      resampling = makeResampleDesc(method = "Holdout")
+    )
+    lrn3 = makeTuneWrapper(
+      learner = lrn2,
+      par.set = makeParamSet(
+        makeDiscreteParam("C", values = 2 ^ (-2:2)),
+        makeDiscreteParam("sigma", values = 2 ^ (-2:2))
+      ),
+      measures = list(auc, acc),
+      control = makeTuneControlRandom(maxit = 3L),
+      resampling = makeResampleDesc(method = "Holdout")
+    )
+    bmrk = benchmark(lrn3, pid.task)
+  }, "Cannot wrap a tuning wrapper around another optimization wrapper!")
+})
+
+