Welcome to MANILA’s documentation!
MANILA is a low-code web-application to benchmark the fairness and effectiveness of machine learning models and fairness enhancing methods.
Installation
To install MANILA, you can use the following command:
Using Docker
To run the application using Docker, execute the following command:
docker-compose up
The application will be available at http://localhost:3000.
Manually
To run the application manually, follow the steps below:
Install the dependencies of the backend:
cd app
pip install -r requirements.txt
Install and launch the Redis database (refer to the official documentation (https://redis.io/download) for more information).
Launch the backend:
cd app
python manila/app.py
Launch the Celery worker:
cd app/manila
celery -A run_celery.celery worker --loglevel=info
Install the dependencies of the frontend:
cd frontend
npm install
Launch the frontend:
cd frontend
npm start
The application will be available at http://localhost:3000.
Citation Request
Please cite our work if you use MANILA in your research:
d’Aloisio, G., Di Marco, A., Stilo, G. (2023). Democratizing Quality-Based Machine Learning Development through Extended Feature Models. In: Lambers, L., Uchitel, S. (eds) Fundamental Approaches to Software Engineering. FASE 2023. Lecture Notes in Computer Science, vol 13991. Springer, Cham. https://doi.org/10.1007/978-3-031-30826-0_5
@inproceedings{d2023democratizing,
title={Democratizing Quality-Based Machine Learning Development through Extended Feature Models},
author={d’Aloisio, Giordano and Di Marco, Antinisca and Stilo, Giovanni},
booktitle={International Conference on Fundamental Approaches to Software Engineering},
pages={88--110},
year={2023},
doi={10.1007/978-3-031-30826-0_5},
organization={Springer Nature Switzerland Cham}
}