Make explain_tidymodels avoid covariates with certain roles (defined by recipes) #92
Description
Hello!
Thanks for the package, it's very useful. I'm using explain_tidymodels with model_parts for a classification task. I would like model_parts not to consider certain variables declared with a specific role with recipes. In my case, it's specially important not to consider that variable with role as a predictor, since it's a numerical version of the target variable. My concern is that the importance scores have been calculated with this numerical target variable as a predictor of the binary target variable, with would obviously make the rest of predictors "no important".
I have tried these two solutions proposed in #65, but none works...
# Problem with ROLES in DALEXtra
## Option A: recipes --------
library(DALEXtra)
#> Warning: package 'DALEXtra' was built under R version 4.4.3
#> Cargando paquete requerido: DALEX
#> Warning: package 'DALEX' was built under R version 4.4.3
#> Welcome to DALEX (version: 2.4.3).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
library(tidymodels)
dataset <- titanic_imputed
dataset$survived <- as.factor(dataset$survived)
rec <- recipe(survived ~ ., data = dataset) %>%
update_role(parch, new_role = "test_role") %>%
step_normalize(fare) %>%
step_dummy(all_nominal_predictors())
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 = dataset)
explainer <- explain_tidymodels(
model_fitted,
data = rec %>% prep() %>% bake(new_data = NULL, all_predictors(), all_outcomes()),
y = titanic_imputed$survived
)
#> Preparation of a new explainer is initiated
#> -> model label : workflow ( default )
#> -> data : 2207 rows 14 cols
#> -> data : tibble converted into a data.frame
#> -> target variable : 2207 values
#> -> predict function : yhat.workflow will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package tidymodels , ver. 1.2.0 , task classification ( default )
#> -> predicted values : the predict_function returns an error when executed ( WARNING )
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : the residual_function returns an error when executed ( WARNING )
#> A new explainer has been created!
model_parts(explainer)
#> Error in `validate_column_names()`:
#> ! The following required columns are missing: 'gender', 'class', 'embarked'.Here my suspiction is that bake() creates the design matrix (with one-hot encoding as mandated by step_dummy()), but the function requires the original variable.
An alternative is removing these variables manually, which does not work either.
## Option B: remove parch manually --------
explainer2 <- explain_tidymodels(
model_fitted,
data = dataset %>% select(-parch),
y = titanic_imputed$survived
)
#> Preparation of a new explainer is initiated
#> -> model label : workflow ( default )
#> -> data : 2207 rows 7 cols
#> -> target variable : 2207 values
#> -> predict function : yhat.workflow will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package tidymodels , ver. 1.2.0 , task classification ( default )
#> -> predicted values : the predict_function returns an error when executed ( WARNING )
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : the residual_function returns an error when executed ( WARNING )
#> A new explainer has been created!
model_parts(explainer2)
#> Error in `validate_column_names()`:
#> ! The following required columns are missing: 'parch'.Created on 2025-07-17 with reprex v2.1.1
My use case is slightly different because I would like to estimate the importance scores with the test set. I have tried bake(new_data = testdata, all_predictors(), all_outcomes()) and testdata %>% select(-parch) and did not work either...
Thanks in advance!