%global _empty_manifest_terminate_build 0 Name: python-dirichletcal Version: 0.3.dev4 Release: 1 Summary: Python code for Dirichlet calibration License: MIT License URL: https://github.com/dirichletcal/dirichlet_python Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fe/11/8d51ecdb233cbdfbe2d66454fa35da8b66f83ba2b5ef928e40c6893b0625/dirichletcal-0.3.dev4.tar.gz BuildArch: noarch %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 ``` %package -n python3-dirichletcal Summary: Python code for Dirichlet calibration Provides: python-dirichletcal BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dirichletcal [![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 ``` %package help Summary: Development documents and examples for dirichletcal Provides: python3-dirichletcal-doc %description help [![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 ``` %prep %autosetup -n dirichletcal-0.3.dev4 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-dirichletcal -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.3.dev4-1 - Package Spec generated