# 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) %>%
      set_engine("torch", verbose = TRUE) %>%
      set_mode("classification")

We also define our `workflow` object:

    wf <- workflow() %>%
      add_model(mod) %>%
      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: 3 × 6
      .metric     .estimator   mean     n  std_err .config
      <chr>       <chr>       <dbl> <int>    <dbl> <chr>
    1 accuracy    binary     0.945      5 0.000869 Preprocessor1_Model1
    2 brier_class binary     0.0535     5 0.00122  Preprocessor1_Model1
    3 roc_auc     binary     0.611      5 0.0153   Preprocessor1_Model1

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

    model <- wf %>% fit(train)
    model %>%
      augment( test) %>%
      roc_auc(Class, .pred_good, event_level = "second")

    # A tibble: 1 x 3
      .metric .estimator .estimate
      <chr>   <chr>          <dbl>
    1 roc_auc binary         0.710