tabnet
An R implementation of: TabNet: Attentive Interpretable Tabular Learning. The code in this repository is an R port of dreamquark-ai/tabnet PyTorch’s implementation using the torch package.
Installation
You can install the released version from CRAN with:
install.packages("tabnet")
The development version can be installed from GitHub with:
# install.packages("remotes")
remotes::install_github("mlverse/tabnet")
Basic Binary Classification Example
Here we show a binary classification example of the attrition
dataset, using a recipe for dataset input specification.
library(tabnet)
suppressPackageStartupMessages(library(recipes))
library(yardstick)
library(ggplot2)
set.seed(1)
data("attrition", package = "modeldata")
test_idx <- sample.int(nrow(attrition), size = 0.2 * nrow(attrition))
train <- attrition[-test_idx,]
test <- attrition[test_idx,]
rec <- recipe(Attrition ~ ., data = train) %>%
step_normalize(all_numeric(), -all_outcomes())
fit <- tabnet_fit(rec, train, epochs = 30, valid_split=0.1, learn_rate = 5e-3)
autoplot(fit)
The plots gives you an immediate insight about model overfitting, and if any, the available model checkpoints available before the overfitting
Keep in mind that regression as well as multi-class classification are also available, and that you can specify dataset through data.frame and formula as well. You will find them in the package vignettes.
Model performance results
As the standard method predict()
is used, you can rely on your usual
metric functions for model performance results. Here we use {yardstick}
:
metrics <- metric_set(accuracy, precision, recall)
cbind(test, predict(fit, test)) %>%
metrics(Attrition, estimate = .pred_class)
#> # A tibble: 3 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 accuracy binary 0.837
#> 2 precision binary 0.837
#> 3 recall binary 1
cbind(test, predict(fit, test, type = "prob")) %>%
roc_auc(Attrition, .pred_No)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.554
Explain model on test-set with attention map
TabNet has intrinsic explainability feature through the visualization of attention map, either aggregated:
explain <- tabnet_explain(fit, test)
autoplot(explain)
or at each layer through the type = "steps"
option:
autoplot(explain, type = "steps")
Self-supervised pretraining
For cases when a consistent part of your dataset has no outcome, TabNet offers a self-supervised training step allowing to model to capture predictors intrinsic features and predictors interactions, upfront the supervised task.
pretrain <- tabnet_pretrain(rec, train, epochs = 50, valid_split=0.1, learn_rate = 1e-2)
autoplot(pretrain)
The exemple here is a toy example as the train
dataset does actually
contain outcomes. The vignette on Unsupervised training and
fine-tuning
will gives you the complete correct workflow step-by-step.
Missing data in predictors
{tabnet} leverage the masking mechanism to deal with missing data, so you don’t have to remove the entries in your dataset with some missing values in the predictors variables.