# Fitting tabnet with tidymodels

library(tabnet)
library(tidymodels)
library(modeldata)

In this vignette we show how to create a TabNet model using the tidymodels interface.

We are going to use the lending_club dataset available in the modeldata package.

First let’s split our dataset into training and testing so we can later access performance of our model:

set.seed(123)
data("lending_club", package = "modeldata")
split <- initial_split(lending_club, strata = Class)
train <- training(split)
test  <- testing(split)

We now define our pre-processing steps. Note that tabnet handles categorical variables, so we don’t need to do any kind of transformation to them. Normalizing the numeric variables is a good idea though.

rec <- recipe(Class ~ ., train) %>%
step_normalize(all_numeric())

Next, we define our model. We are going to train for 50 epochs with a batch size of 128. There are other hyperparameters but, we are going to use the defaults.

mod <- tabnet(epochs = 50, batch_size = 128) %>%
set_engine("torch", verbose = TRUE) %>%
set_mode("classification")

We also define our workflow object:

wf <- workflow() %>%
add_recipe(rec)

We can now define our cross-validation strategy:

folds <- vfold_cv(train, v = 5)

And finally, fit the model:

fit_rs <- wf %>%
fit_resamples(folds)

After a few minutes we can get the results:

collect_metrics(fit_rs)
# A tibble: 2 x 5
.metric  .estimator  mean     n  std_err
<chr>    <chr>      <dbl> <int>    <dbl>
1 accuracy binary     0.946     5 0.000713
2 roc_auc  binary     0.732     5 0.00539 

And finally, we can verify the results in our test set:

model <- wf %>% fit(train)
test %>%
bind_cols(
predict(model, test, type = "prob")
) %>%
roc_auc(Class, .pred_bad)
# A tibble: 1 x 3
.metric .estimator .estimate
<chr>   <chr>          <dbl>
1 roc_auc binary         0.710