From 19d6742c7d863870aae6be9cc8d2c0eb5207f0c0 Mon Sep 17 00:00:00 2001
From: Sebastian Fischer <sebf.fischer@gmail.com>
Date: Tue, 31 Oct 2023 09:18:54 +0100
Subject: [PATCH] small improvements to technical solutions

---
 book/chapters/appendices/solutions.qmd | 23 +++++++----------------
 1 file changed, 7 insertions(+), 16 deletions(-)

diff --git a/book/chapters/appendices/solutions.qmd b/book/chapters/appendices/solutions.qmd
index 4cf41abb2..8b00d84b2 100644
--- a/book/chapters/appendices/solutions.qmd
+++ b/book/chapters/appendices/solutions.qmd
@@ -857,8 +857,8 @@ bmr$aggregate(msr("classif.bbrier"))[, c(4, 7)]
 
 ## Solutions to @sec-technical
 
-1. Consider the following example where you resample a learner (debug learner, sleeps for 3 seconds during train) on 4 workers using the multisession backend:
-```{r technical-050, eval = FALSE}
+1. Consider the following example where you resample a learner (debug learner, sleeps for 3 seconds during `$train()) on 4 workers using the multisession backend:
+```{r technical-050}
 task = tsk("penguins")
 learner = lrn("classif.debug", sleep_train = function() 3)
 resampling = rsmp("cv", folds = 6)
@@ -871,12 +871,12 @@ i. Assuming that the learner would actually calculate something and not just sle
 ii. Prove your point by measuring the elapsed time, e.g., using `r ref("system.time()")`.
 iii. What would you change in the setup and why?
 
-Not all CPUs would be utilized in the example.
-All 4 of them are occupied for the first 4 iterations of the cross validation.
+Not all CPUs would be utilized for the whole duration.
+All 4 of them are occupied for the first 4 iterations of the cross-validation.
 The 5th iteration, however, only runs in parallel to the 6th fold, leaving 2 cores idle.
 This is supported by the elapsed time of roughly 6 seconds for 6 jobs compared to also roughly 6 seconds for 8 jobs:
 
-```{r solutions-022, eval = FALSE}
+```{r solutions-022}
 task = tsk("penguins")
 learner = lrn("classif.debug", sleep_train = function() 3)
 
@@ -918,12 +918,12 @@ MeasureCustom = R6::R6Class("MeasureCustom",
   inherit = mlr3::MeasureClassif, # classification measure
   public = list(
     initialize = function() { # initialize class
-      super$initialize(
+      super$initialize( # initialize method of parent class
         id = "custom", # unique ID
         packages = character(), # no dependencies
         properties = character(), # no special properties
         predict_type = "response", # measures response prediction
-        range = c(0, Inf), # results in values between (0, 1)
+        range = c(0, Inf), # results in values between [0, Inf)
         minimize = TRUE # smaller values are better
       )
     }
@@ -931,15 +931,6 @@ MeasureCustom = R6::R6Class("MeasureCustom",
 
   private = list(
     .score = function(prediction, ...) { # define score as private method
-      # define loss
-      costsens = function(truth, prediction) {
-        score = numeric(length(truth))
-        score[truth == "A" & prediction == "B"] = 10
-        score[truth == "B" & prediction == "A"] = 1
-
-        mean(score)
-      }
-
       # call loss function
       costsens(prediction$truth, prediction$response)
     }