How to compute FIRM importance measure using VIP package and tidymodels (including recipe)

Question

I want to compute the FIRM importance scores for a model made from a tidymodels workflow. For regex, I will use the iris dataset and try to predict whether an observation is setosa or not.

library(tidymodels)
library(readr)
library(vip)

#clean data
iris <- iris %>%
  mutate(class  = case_when(Species == 'setosa' ~ 'setosa',
                            TRUE ~ 'other'))
iris$class = as.factor(iris$class)
iris <- subset(iris, select = -c(Species))

#split data into training and testing
iris_split = initial_split(iris, prop = 0.8)
cv_splits = vfold_cv(training(iris_split), v = 5)

#preprocessing
iris_recipe = recipe(class ~., data = iris) %>%
  step_center(Sepal.Length) %>%
  prep()

#specify MARS model
model = rand_forest(
  mode = "classification",
  mtry = tune(),
  trees = 50
) %>% 
  set_engine("ranger", importance = "impurity")

#tuning parameters
tuning_grid = grid_regular(mtry(range=c(1,4)), levels = 4)

iris_wkfl = workflow() %>%
  add_recipe(iris_recipe) %>%
  add_model(model) 
  
iris_tune = tune_grid(iris_wkfl,
            resamples = cv_splits,
            grid = tuning_grid,
            metrics = metric_set(accuracy))

best_params = iris_tune %>%
  select_best(metric = "accuracy")

best_model = finalize_workflow(iris_wkfl, best_params) %>%
  parsnip::fit(data = training(iris_split)) %>%
  pull_workflow_fit()

vip(best_model, method = "firm")

The last line produces an error from the pdp package.

Error in get_training_data.default(object) : The training data could not be extracted from object. Please supply the raw training data using the train argument in the call to partial.

Is the following line correct? Or do I need to supply for transformed training data using my recipe first? I want to make sure that vip is applying my recipe when computing the importance scores. I know the error says "raw training data" but I am unsure if pdp knows about my workflow.

vip(best_model, method = "firm", train = training(iris_split))

Answer 1

You'll want to take the same approach that I outlined in this answer.

Start by tuning and then training your model on the training data:

library(tidymodels)

#clean data
iris = iris %>%
  mutate(class  = case_when(Species == 'setosa' ~ 'setosa',
                            TRUE ~ 'other'),
         class = factor(class)) %>%
  select(-Species)

#split data into training and testing
iris_split = initial_split(iris, prop = 0.8)
iris_train = training(iris_split)
iris_test = testing(iris_split)
cv_splits = vfold_cv(iris_train, v = 5)

#preprocessing
iris_recipe = recipe(class ~., data = iris_train) %>%
  step_center(Sepal.Length)

#specify ranger model
rf_spec = rand_forest(
  mode = "classification",
  mtry = tune(),
  trees = 50
) %>% 
  set_engine("ranger", importance = "impurity") 
## don't need any importance here if you will do it another way; probably remove

#tuning parameters
tuning_grid = grid_regular(mtry(range=c(1,4)), levels = 4)

iris_wkfl = workflow() %>%
  add_recipe(iris_recipe) %>%
  add_model(rf_spec) 

iris_tune = tune_grid(iris_wkfl,
                      resamples = cv_splits,
                      grid = tuning_grid,
                      metrics = metric_set(accuracy))
#> 
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#> 
#>     %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
#>     flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
#>     splice
#> 
#> Attaching package: 'vctrs'
#> The following object is masked from 'package:tibble':
#> 
#>     data_frame
#> The following object is masked from 'package:dplyr':
#> 
#>     data_frame

best_params = iris_tune %>%
  select_best(metric = "accuracy")

rf_fit = finalize_workflow(iris_wkfl, best_params) %>%
  fit(data = iris_train)

Your model is now trained and you can compute model-independent variable importance scores like FIRM. There are a couple of steps:

• You pull() the fitted model out of the workflow.
• You must specify the target/outcome variable, class.
• In this case, we need to pass both the original training data (you must use preprocessed training data here, which you get out of the recipe) and the right underlying function for predicting from ranger (for most models this is predict() but unfortunately for ranger it is predictions()).

library(vip)
#> 
#> Attaching package: 'vip'
#> The following object is masked from 'package:utils':
#> 
#>     vi
rf_fit %>%
  pull_workflow_fit() %>%
  vip(method = "firm", 
      target = "class", metric = "accuracy",
      pred_wrapper = ranger::predictions, 
      train = bake(prep(iris_recipe), new_data = NULL))

^{Created on 2020-12-10 by the reprex package (v0.3.0.9001)}