Dirichlet Calibration Python implementation
This is a Python implementation of the Dirichlet Calibration presented in Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration at NeurIPS 2019.
Installation
# Clone the repository
git clone git@github.com:dirichletcal/dirichlet_python.git
# Go into the folder
cd dirichlet_python
# Create a new virtual environment with Python3
python3.8 -m venv venv
# Load the generated virtual environment
source venv/bin/activate
# Upgrade pip
pip install --upgrade pip
# Install all the dependencies
pip install -r requirements.txt
pip install --upgrade jaxlib
Unittest
python -m unittest discover dirichletcal
Cite
If you use this code in a publication please cite the following paper
@inproceedings{kull2019dircal,
title={Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration},
author={Kull, Meelis and Nieto, Miquel Perello and K{\"a}ngsepp, Markus and Silva Filho, Telmo and Song, Hao and Flach, Peter},
booktitle={Advances in Neural Information Processing Systems},
pages={12295--12305},
year={2019}
}
Examples
You can find some examples on how to use this package in the folder examples
Pypi
To push a new version to Pypi first build the package
python3.8 setup.py sdist
And then upload to Pypi with twine
twine upload dist/*
It may require user and password if these are not set in your home directory a file .pypirc
[pypi]
username = __token__
password = pypi-yourtoken
