Reproduction instructions

This repository contains the code required to reproduce the results presented in the paper "Distribution and volume based scoring for Isolation Forests".

Setup

The benchmarks are run using the ADBench: Anomaly Detection Benchmark. Hence users are required to set up that benchmark. However, since that benchmark is quite large and we don't require all the packages required in that repository, we ask users to follow the following custom setup instructions

1. In a new folder, check out the ADBench repository at the NeurIPS2022 commit. Assuming you're using git > 2.5:

   • git init
   • git remote add origin https://github.com/Minqi824/ADBench
   • git fetch origin 6345a6b35d66b460bd5a590f6db9774e59e71487 (downloads ~2GB)
   • git reset --hard FETCH_HEAD

2. From this directory, clone our repo and replace one of the ADBench repo files:

   • git clone https://github.com/porscheofficial/distribution_and_volume_based_isolation_forest.git
   • cd distribution_and_volume_based_isolation_forest
   • mv data_generator.py ../

3. Create a virtual environment (we used Python version 3.9.6) and install the requirements from the requirements.txt contained in this folder:

   • pip3.9 install virtualenv
   • python3.9 -m virtualenv venv
   • source venv/bin/activate
   • pip install -r requirements.txt

You can then execute the two notebooks required for generating results and plots from the paper as usual.

How to cite

Please consider citing our paper if you use our code in your project.

@misc{dhouib2023distribution,
      title={Distribution and volume based scoring for Isolation Forests}, 
      author={Hichem Dhouib and Alissa Wilms and Paul Boes},
      year={2023},
      eprint={2309.11450},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

Contributing

This repository is openly developed in the wild and contributions (both internal and external) are highly appreciated. See CONTRIBUTING.md on how to get started.

If you have feedback or want to propose a new feature, please open an issue. Thank you! 😊

Acknowledgements

This project is part of the AI research of Porsche Digital. ✨

License

Copyright © 2023 Porsche Digital GmbH

Porsche Digital GmbH publishes this open source software and accompanied documentation (if any) subject to the terms of the MIT license. All rights not explicitly granted to you under the MIT license remain the sole and exclusive property of Porsche Digital GmbH.

Apart from the software and documentation described above, the texts, images, graphics, animations, video and audio files as well as all other contents on this website are subject to the legal provisions of copyright law and, where applicable, other intellectual property rights. The aforementioned proprietary content of this website may not be duplicated, distributed, reproduced, made publicly accessible or otherwise used without the prior consent of the right holder.