CFAD: Achieving Counterfactual Fairness for Anomaly Detection

A Pytorch implementation of CFAD.

🧰 Configuration

• Ubuntu 20.04
• NVIDIA driver 470.74
• CUDA 11.1
• Python 3.9.7
• PyTorch 1.9.1

🖥️ Hardware

• Intel(R) Core(TM) i9-10900X CPU @ 3.70GHz
• 64 GB Memory
• NVIDIA GeForce RTX 2080 Ti

⚙️ Installation

This code requires the packages listed in requirements.txt. A virtual environment is recommended to run this code

On macOS and Linux:

python3 -m pip install --user virtualenv
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
deactivate

Reference: https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/

🚀 Instructions

Clone the template project, replacing my-project with the name of the project you are creating:

    git clone https://github.com/hanxiao0607/CFAD.git my-project
    cd my-project

Run and test:

    python3 CFAD_adult.py
    or
    python3 CFAD_compas.py
    or
    python3 CFAD_synthetic.py

📄 Citation

@inproceedings{han2023achieving,
  title={Achieving Counterfactual Fairness for Anomaly Detection},
  author={Han, Xiao and Zhang, Lu and Wu, Yongkai and Yuan, Shuhan},
  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  pages={55--66},
  year={2023},
  organization={Springer}
}