fix #36, update vignette

vignettes/introduction.Rmd

@@ -40,13 +40,14 @@ credit application is rejected.
 
 ### Data: German Credit Dataset
 
-As training data, we use the German Credit Data from the `rchallenge`` package.
+As training data, we use the German Credit Data from the `rchallenge` package.
 The data set contains 1000 observations with 21 features and the binary target variable `credit_risk`.
 For illustrative purposes, we only consider 8 of the 21 features in the following: 
 
 ```{r}
 data(german, package = "rchallenge")  
-credit = german[, c("duration", "amount", "purpose", "age", "employment_duration", "housing", "number_credits", "credit_risk")]
+credit = german[, c("duration", "amount", "purpose", "age", 
+  "employment_duration", "housing", "number_credits", "credit_risk")]
 ```
 
 ```{r, echo=FALSE}
@@ -64,7 +65,8 @@ column_descr = data.frame(
   )
 )
 names(column_descr) <- c("Variable", "Description")
-knitr::kable(column_descr, escape = FALSE, format = "html", table.attr = "style='width:100%;'")
+knitr::kable(column_descr, escape = FALSE, format = "html", 
+  table.attr = "style='width:100%;'")
 ```
 
 ### Fitting a model
@@ -120,7 +122,8 @@ if (!file.exists("introduction-res/cfactuals_credit.RDS")) {
   predictor, epsilon = 0, fixed_features = c("age", "employment_duration"), 
     termination_crit = "genstag", n_generations = 10L, quiet = TRUE
 )
-  cfactuals = moc_classif$find_counterfactuals(x_interest, desired_class = "good", desired_prob = c(0.6, 1))
+  cfactuals = moc_classif$find_counterfactuals(x_interest, 
+    desired_class = "good", desired_prob = c(0.6, 1))
   dir.create("introduction-res")
   saveRDS(moc_classif, file = "introduction-res/moc_classif_credit.RDS")
   saveRDS(cfactuals, file = "introduction-res/cfactuals_credit.RDS")
@@ -134,11 +137,6 @@ cfactuals = readRDS("introduction-res/cfactuals_credit.RDS")
 
 The resulting `Counterfactuals` object holds the counterfactuals in the `data` field and possesses several methods for their 
 evaluation and visualization.
-
-```{r}
-class(cfactuals)
-```
-
 Printing a `Counterfactuals` object, gives an overview of the results.
 ```{r}
 print(cfactuals)
@@ -158,15 +156,17 @@ and negative values indicate a decrease; for factors, the counterfactual feature
 differs from `x_interest.`; `NA` means "no difference" in both cases.
 
 ```{r}
-head(cfactuals$evaluate(show_diff = TRUE, measures = c("dist_x_interest", "dist_target", "no_changed", "dist_train")), 3L)
+head(cfactuals$evaluate(show_diff = TRUE, 
+  measures = c("dist_x_interest", "dist_target", "no_changed", "dist_train")), 3L)
 ```
 
 By design, not all counterfactuals generated with MOC have a prediction equal to the desired 
 prediction. We can use `subset_to_valid()` to omit all counterfactuals that do not achieve 
-the desired predicition. 
+the desired prediction. This step can be reverted with `revert_subset_to_valid()`.
+
 ```{r}
 cfactuals$subset_to_valid()
- nrow(cfactuals$data)
+nrow(cfactuals$data)
 ```
 
 The `plot_freq_of_feature_changes()` method plots the frequency of feature changes across all
@@ -182,7 +182,7 @@ We specify `feature_names` to order the features according to their frequency of
 
 ```{r, message=FALSE, fig.height=2.5}
 cfactuals$plot_parallel(feature_names = names(
-  cfactuals$get_freq_of_feature_changes()),  digits_min_max = 2L)
+  cfactuals$get_freq_of_feature_changes()), digits_min_max = 2L)
 
 ```
 
@@ -269,7 +269,8 @@ First, we train a model to predict `plasma_retinol`, again omitting `x_interest`
 This time we use a regression tree trained with the `mlr3` and `rpart` package. 
 
 ```{r}
-tsk = mlr3::TaskRegr$new(id = "plasma", backend = plasma[-100L,], target = "retplasma")
+tsk = TaskRegr$new(id = "plasma", backend = plasma[-100L,], 
+  target = "retplasma")
 tree = lrn("regr.rpart")
 model = tree$train(tsk)
 ```
@@ -300,10 +301,11 @@ nice_regr = NICERegr$new(predictor, optimization = "proximity",
 ```
 
 Then, we use the `find_counterfactuals()` method to find counterfactuals for `x_interest` with a predicted
-plasma concentration in the interval [500, Inf].
+plasma concentration in the interval [500, Inf).
 
 ```{r}
-cfactuals = nice_regr$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
+cfactuals = nice_regr$find_counterfactuals(x_interest, 
+  desired_outcome = c(500, Inf))
 ```
 
 ### The counterfactuals object
@@ -317,7 +319,7 @@ cfactuals
 To inspect the counterfactual, we can use the same tools as before.
 For example, in the surface plot, we see that increasing betaplasma helps while changing the age alone has no impact on the prediction.
 
-```{r, fig.height=3, fig.width=9}
+```{r, fig.height=3}
 cfactuals$plot_surface(feature_names = c("betaplasma", "age"), grid_size = 200)
 ```
 
@@ -353,7 +355,8 @@ Replacing the distance function is fairly easy:
 nice_regr = NICERegr$new(predictor, optimization = "proximity", 
   margin_correct = 0.5, return_multiple = FALSE, 
   distance_function = l0_norm)
-cfactuals = nice_regr$find_counterfactuals(x_interest, desired_outcome = c(500, 1000))
+cfactuals = nice_regr$find_counterfactuals(x_interest, 
+  desired_outcome = c(500, 1000))
 cfactuals
 ```