Fast hyperboloid decision tree algorithms
This repository contains code for the paper Fast Hyperboloid Decision Tree Algorithms (ICLR 2024), which you can find at one of these links:
To install, run this from your repo directory:
git clone https://github.com/pchlenski/hyperdt
cd hyperdt
pip install -e .If you are installing with e.g. a conda environment or virtualenv, you can find exact dependencies in requirements.txt.
These are installable in the usual way:
pip install -r requirements.txtAdditionally, hyperDT is available on PyPI. It can be pip installed as follows:
pip install hyperdtA basic tutorial demonstrating key HyperDT functionality is available in notebooks/tutorial.ipynb.
All figures and tables in the paper were generated using a combination of Python scripts and Jupyter notebooks. The notebooks used in development were filtered down to only those that remained relevant to the final paper and moved to the notebooks/archive directory. The notebooks directory contains a tutorial and symbolic links to notebooks of particular relevance to a figure, table, or section of a paper, named according to the section they reproduce.
hororf_benchmarks.py runs the benchmarks contributing to Tables 1, 5, and 6, and scaling_benchmarks.py runs the benchmarks contributing to Figures 6 and 7.
All relevant datasets, plus benchmarking code outputs, can be found on Google Drive.
To cite HyperDT, please use the following:
@inproceedings{
chlenski2024fast,
title={Fast Hyperboloid Decision Tree Algorithms},
author={Philippe Chlenski and Ethan Turok and Antonio Khalil Moretti and Itsik Pe'er},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=TTonmgTT9X}
}