refactor: returned predictions with different sets (#1113)

* refactor: returned predictions with different sets

* docs: empty list

R/ResampleResult.R

@@ -96,8 +96,8 @@ ResampleResult = R6Class("ResampleResult",
     #' If you calculate the performance on this prediction object directly, this is called micro averaging.
     #'
     #' @param predict_sets (`character()`)\cr
-    #' @return [Prediction].
     #'   Subset of `{"train", "test"}`.
+    #' @return [Prediction] or empty `list()` if no predictions are available.
     prediction = function(predict_sets = "test") {
       private$.data$prediction(private$.view, predict_sets)
     },
@@ -113,6 +113,7 @@ ResampleResult = R6Class("ResampleResult",
     #' @param predict_sets (`character()`)\cr
     #'   Subset of `{"train", "test", "internal_valid"}`.
     #' @return List of [Prediction] objects, one per element in `predict_sets`.
+    #' Or list of empty `list()`s if no predictions are available.
     predictions = function(predict_sets = "test") {
       assert_subset(predict_sets, mlr_reflections$predict_sets, empty.ok = FALSE)
       private$.data$predictions(private$.view, predict_sets)

R/as_prediction.R

@@ -42,7 +42,7 @@ as_predictions = function(x, predict_sets = "test", ...) {
 #' @rdname as_prediction
 #' @export
 as_predictions.list = function(x, predict_sets = "test", ...) { # nolint
-  result = vector("list", length(x))
+  result = replicate(length(x), list())
   ii = lengths(x) > 0L
   result[ii] = map(x[ii], function(li) {
     assert_list(li, "PredictionData")

tests/testthat/test_resample.R

@@ -450,3 +450,35 @@ test_that("multiple named measures", {
   expect_numeric(res[["classif.acc"]])
   expect_numeric(res[["classif.ce"]])
 })
+
+test_that("resample result works with not predicted predict set", {
+  learner = lrn("classif.debug", predict_sets = "train")
+  task = tsk("iris")
+  resampling = rsmp("holdout")
+
+  rr = resample(task, learner, resampling)
+
+  expect_list(rr$prediction(predict_sets = "test"), len = 0)
+  expect_list(rr$predictions(predict_sets = "test"), len = 1)
+  expect_list(rr$predictions(predict_sets = "test")[[1L]], len = 0)
+
+  tab = as.data.table(rr)
+  expect_list(tab$prediction, len = 1)
+  expect_list(tab$prediction[[1]], len = 0)
+})
+
+test_that("resample results works with no predicted predict set", {
+  learner = lrn("classif.debug", predict_sets = NULL)
+  task = tsk("iris")
+  resampling = rsmp("holdout")
+
+  rr = resample(task, learner, resampling)
+
+  expect_list(rr$prediction(predict_sets = "test"), len = 0)
+  expect_list(rr$predictions(predict_sets = "test"), len = 1)
+  expect_list(rr$predictions(predict_sets = "test")[[1L]], len = 0)
+
+  tab = as.data.table(rr)
+  expect_list(tab$prediction, len = 1)
+  expect_list(tab$prediction[[1]], len = 0)
+})