From 3f69adbcc475a128162dcbfe9b0c855a55b0184c Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Fri, 23 Aug 2024 16:39:51 +0200
Subject: [PATCH] refactor: returned predictions with different sets (#1113)

* refactor: returned predictions with different sets

* docs: empty list
---
 R/ResampleResult.R             |  3 ++-
 R/as_prediction.R              |  2 +-
 man/ResampleResult.Rd          |  7 ++++---
 tests/testthat/test_resample.R | 32 ++++++++++++++++++++++++++++++++
 4 files changed, 39 insertions(+), 5 deletions(-)

diff --git a/R/ResampleResult.R b/R/ResampleResult.R
index 5097c523d..83b79ada3 100644
--- a/R/ResampleResult.R
+++ b/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)
diff --git a/R/as_prediction.R b/R/as_prediction.R
index cab59dc8d..a5ab76a90 100644
--- a/R/as_prediction.R
+++ b/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")
diff --git a/man/ResampleResult.Rd b/man/ResampleResult.Rd
index de18cdca3..8ea3c7d18 100644
--- a/man/ResampleResult.Rd
+++ b/man/ResampleResult.Rd
@@ -198,13 +198,13 @@ If you calculate the performance on this prediction object directly, this is cal
 \subsection{Arguments}{
 \if{html}{\out{<div class="arguments">}}
 \describe{
-\item{\code{predict_sets}}{(\code{character()})\cr}
+\item{\code{predict_sets}}{(\code{character()})\cr
+Subset of \verb{\{"train", "test"\}}.}
 }
 \if{html}{\out{</div>}}
 }
 \subsection{Returns}{
-\link{Prediction}.
-Subset of \verb{\{"train", "test"\}}.
+\link{Prediction} or empty \code{list()} if no predictions are available.
 }
 }
 \if{html}{\out{<hr>}}
@@ -231,6 +231,7 @@ Subset of \verb{\{"train", "test", "internal_valid"\}}.}
 }
 \subsection{Returns}{
 List of \link{Prediction} objects, one per element in \code{predict_sets}.
+Or list of empty \code{list()}s if no predictions are available.
 }
 }
 \if{html}{\out{<hr>}}
diff --git a/tests/testthat/test_resample.R b/tests/testthat/test_resample.R
index 1473c7cf6..1efcd09ef 100644
--- a/tests/testthat/test_resample.R
+++ b/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)
+})