add learner weights

book/chapters/appendices/solutions.qmd

@@ -2162,10 +2162,12 @@ rows = seq_len(nrow(df))[df$race %in% c("Black", "White") & df$sex %in% c("Femal
 adult_subset$filter(rows)
 adult_subset$set_col_roles("race", add_to = "pta")
 ```
+
 And evaluate our measure again:
 
 ```{r solutions-122}
-prediction$score(msr_3, adult_subset)
+#| eval: false
+prediction$score(msr_3, task = adult_subset)
 ```
 
 We can see, that between women there is an even bigger discrepancy compared to men.

book/chapters/chapter1/introduction_and_overview.qmd

@@ -3,6 +3,18 @@ aliases:
   - "/introduction_and_overview.html"
 ---
 
+
+```{r}
+# extra packages that must be installed in the docker image
+remotes::install_github("mlr-org/mlr3")
+remotes::install_github("mlr-org/mlr3pipelines")
+remotes::install_github("mlr-org/mlr3fairness@weights")
+remotes::install_github("mlr-org/mlr3learners")
+remotes::install_github("mlr-org/mlr3batchmark")
+remotes::install_cran("iml")
+remotes::install_github("mlr-org/mlr3spatiotempcv@task_row_hash")
+```
+
 # Introduction and Overview {#sec-introduction}
 
 {{< include ../../common/_setup.qmd >}}

book/chapters/chapter2/data_and_basic_modeling.qmd

@@ -1027,7 +1027,8 @@ There are seven column roles:
 4. `"order"`: Variable(s) used to order data returned by `$data()`; must be sortable with `order()`.
 5. `"group"`: Variable used to keep observations together during resampling.
 6. `"stratum"`: Variable(s) to stratify during resampling.
-7. `"weight"`: Observation weights. Only one numeric column may have this role.
+7. `"weights_learner"`: Weights used during training by the learner. Only one numeric column may have this role.
+8. `"weights_measure"`: Weights used during scoring by the measure. Only one numeric column may have this role.
 
 We have already seen how features and targets work in @sec-tasks, which are the only column roles that each task must have.
 In @sec-strat-group we will have a look at the `stratum` and `group` column roles.
@@ -1051,7 +1052,7 @@ tsk_mtcars_order$data(ordered = TRUE)
 In this example we can see that by setting `"idx"` to have the `"order"` column role, it is no longer used as a feature when we run `$data()` but instead is used to order the observations according to its value.
 This metadata is not passed to a learner.
 
-The `weights` column role is used to weight data points differently.
+The `weights_learner` column role is used to weight data points differently.
 One example of why we would do this is in classification tasks with severe class imbalance, where weighting the minority class more heavily may improve the model's predictive performance for that class.
 For example in the `breast_cancer` dataset, there are more instances of benign tumors than malignant tumors, so if we want to better predict malignant tumors we could weight the data in favor of this class:
 
@@ -1065,7 +1066,7 @@ df$weights = ifelse(df$class == "malignant", 2, 1)
 
 # create new task and role
 cancer_weighted = as_task_classif(df, target = "class")
-cancer_weighted$set_col_roles("weights", roles = "weight")
+cancer_weighted$set_col_roles("weights", roles = "weights_learner")
 
 # compare weighted and unweighted predictions
 split = partition(cancer_unweighted)

book/chapters/chapter9/preprocessing.qmd

@@ -239,6 +239,7 @@ magick::image_trim(fig)
 Using this pipeline we can now run experiments with `lrn("regr.ranger")`, which cannot handle missing data; we also compare a simpler pipeline that only uses OOR imputation to demonstrate performance differences resulting from different strategies.
 
 ```{r preprocessing-015}
+#| eval: false
 glrn_rf_impute_hist = as_learner(impute_hist %>>% lrn("regr.ranger"))
 glrn_rf_impute_hist$id = "RF_imp_Hist"
 
@@ -446,6 +447,7 @@ These outputs look sensible compared to @fig-energy so we can now run our final
 We do not need to add the `PipeOp` to each learner as we can apply it once (as above) before any model training by applying it to all available data.
 
 ```{r preprocessing-026, warning=FALSE, R.options = list(datatable.print.nrows = 13, datatable.print.class = FALSE, datatable.print.keys = FALSE, datatable.print.trunc.cols = TRUE)}
+#| eval: false
 learners = list(lrn_baseline, lrn("regr.rpart"), glrn_xgb_impact,
     glrn_rf_impute_oor, glrn_lm_robust, glrn_log_lm_robust)