Variable importance plot lists only original variables, not one-hot-encoded variables, when permutation importance calculated with `model_parts()` and `explain_tidymodels()` functions?

[kransom14]

I am using explain_tidymodels() to compute variable importance. I have a workflow which includes a recipe with a step_dummy() step. I'm trying to understand why the associated variable importance calculated with model_parts() is given for the original variables rather than the one-hot-encoded variables when this step is included. Is the permutation importance aggregated at some point for the group of one-hot-encoded variables that go together? I didn't see this explained in the documentation. Reprex below. Please advise, Thank you

library("DALEXtra")
library("tidymodels")
library("recipes")

# example with no dummy variables
data <- titanic_imputed

data$survived <- as.factor(data$survived)

rec <- recipe(survived ~ ., data = data) %>%
  step_normalize(fare)

model <- decision_tree(tree_depth = 25) %>%
  set_engine("rpart") %>%
  set_mode("classification")

wflow <- workflow() %>%
  add_recipe(rec) %>%
  add_model(model)

model_fitted <- wflow %>%
  fit(data = data)

explainTest <- explain_tidymodels(model_fitted, data = data, y = as.numeric(data$survived))
explainModelParts <- model_parts(explainTest, type="variable_importance")
plot(explainModelParts)


# example with dummy variables
data <- titanic_imputed

data$survived <- as.factor(data$survived)

rec <- recipe(survived ~ ., data = data) %>%
  step_dummy(gender, class, embarked, one_hot = TRUE) %>% # one hot encode the categorical variables
  step_normalize(fare)

model <- decision_tree(tree_depth = 25) %>%
  set_engine("rpart") %>%
  set_mode("classification")

wflow <- workflow() %>%
  add_recipe(rec) %>%
  add_model(model)

model_fitted <- wflow %>%
  fit(data = data)

explainModel <- explain_tidymodels(model_fitted, data = data, y = as.numeric(data$survived))

vipData <- model_parts(explainModel, type = "variable_importance")
plot(vipData) # this plot shows original variable names and does not include the one hot encoded variables

[mayer79]

It seems obvious that the importance is calculated on the features before preprocessing. Especially with permutation importance, permuting dummy columns is non-sensical as suddenly two dummies would be 1 within observation.