Auto-Tabnet

Auto-TabNet is the implementation of Google's TabNet model pytorch implementation by dreamquark-ai along with hyperparameter search with Optuna.

Overview

Google's TabNet was proposed in 2019 with the idea of effectively using deep neural networks for tabular data.

TabNet is a complex model composed of a feature transformer, attentive transformer, and feature masking, that soft feature selection with controllable sparsity in end-to-end learning. The reason for the high performance of TabNet is that it focuses on the most important features that have been considered by the Attentive Transformer. The Attentive Transformer performs feature selection to select which model features to reason from at each step in the model, and a Feature Transformer processes feature into more useful representations and learn complex data patterns, which improve interpretability and help it learn more accurate models.

Motivation

For Tabular data, models like TabNet are able to capture perform boost leveraged by gradient descent based training without explicitly deploying heavy deep learning neural network. Without the requirement of any feature engineering, the model can pick the best features and bring along interpretability too.

This package facilitates the use of TabNet for training a classifier (regressor and other classes would be added on in later versions) with auto-hyperparamenter optimization implemented with the help of Optuna.

Pre-Requisites

• python 3.7

• pip (python package manager)

Installation

From PyPI

pip install auto-tabnet

From Source Code

If you want to use it locally within a pip virtualenv:

• Clone the repository

git clone https://github.com/Femme-js/auto-tabnet.git

• Create a pip virtual environment.

virtualenv env

• Install the dependencies from requirements.txt file.

pip install -r requirements.txt

Example Usage

from auto_tabnet import AutoTabnetClassifier

clf = AutoTabnetClassifier(X, y, X_test)

To get the prediction on test data.

results = clf.predict()

To get the auc_roc_score:

results = clf.get_roc_auc_score()

To get the best hyperparamters tuned by optuna:

results = clf.get_best_params()

Note: The target column in training data should contain a unique type (e.g. they must all be strings or integers).

Scope of Functionalities

This package is still under development. The next goal is to add the class for AutoTabnetRegressor.

License and author info

This package is licensed under a GPL-3.0 license.