Metadata-Version: 2.1
Name: dirichletcal
Version: 0.3.dev2
Summary: Python code for Dirichlet calibration
Home-page: https://github.com/dirichletcal/dirichlet_python
Author: Miquel Perello Nieto and Hao Song
Author-email: perello.nieto@gmail.com
License: UNKNOWN
Download-URL: https://github.com/dirichletcal/dirichlet_python/archive/0.3.dev2.tar.gz
Description: [![CI][ci:b]][ci]
        [![License BSD3][license:b]][license]
        ![Python3.8][python:b]
        [![pypi][pypi:b]][pypi]
        [![codecov][codecov:b]][codecov]
        
        [ci]: https://github.com/dirichletcal/dirichlet_python/actions/workflows/ci.yml
        [ci:b]: https://github.com/dirichletcal/dirichlet_python/workflows/CI/badge.svg
        [license]: https://github.com/dirichletcal/dirichlet_python/blob/master/LICENSE.txt
        [license:b]: https://img.shields.io/github/license/dirichletcal/dirichlet_python.svg
        [python:b]: https://img.shields.io/badge/python-3.8-blue
        [pypi]: https://badge.fury.io/py/dirichletcal
        [pypi:b]: https://badge.fury.io/py/dirichletcal.svg
        [codecov]: https://codecov.io/gh/dirichletcal/dirichlet_python
        [codecov:b]: https://codecov.io/gh/dirichletcal/dirichlet_python/branch/master/graph/badge.svg
        
        # 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](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
        ```
        
Keywords: classifier,calibration,dirichlet,multiclass,probability
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
