GSDT

This project is a PyTorch implementation of Gaussian Soft Decision Trees for Interpretable Feature-Based Classification, published as a conference proceeding at PAKDD 2021. This paper proposes Gaussian Soft Decision Trees (GSDT), a novel tree-based classifier with multi-branched structures, Gaussian mixtured-based decisions, and a hinge loss with path regularization.

Prerequisites

• Python 3.6+
• PyTorch 1.4.0
• NumPy
• scikit-learn
• pandas

Datasets

The paper use six datasets for feature-based classification. The raw datasets are included in data/raw and also available at the follwing websites:

• brain-tumor: https://www.kaggle.com/pranavraikokte/braintumorfeaturesextracted
• breast-cancer: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer
• breast-cancer-wisconsin: https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)
• diabetes: https://www.kaggle.com/uciml/pima-indians-diabetes-database
• heart-disease: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
• hepatitis: https://archive.ics.uci.edu/ml/datasets/Hepatitis

Usage

You can reproduce the main experimental results of the paper by running the following command in the bin directory:

bash main.sh

It first preprocesses the datasets and saves the results at data/preprocessed. Then, it runs a training script for each dataset using multiple GPUs at the same time. Each experiment is run eight times, and the average and standard deviation of accuracy are reported. You may need to change main.sh, as it currently uses 4 GPUs (from 0 to 3) for parallel experiments with different random seeds. You can also change other hyperparameters by modifying the script.

For instance, you can run the following command in src to change the tree depth and the number of children at each branch as command line arguments. The --device option is needed to use a GPU environment.

python main.py --device 0 --data brain-tumor --depth 8 --branch 2 

Reference

Please cite our paper if you use the implementation.

@inproceedings{YooS21,
  author    = {Jaemin Yoo and Lee Sael},
  title     = {Gaussian Soft Decision Trees for Interpretable Feature-Based Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2021}
}