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|
%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 <Python_Bot@openeuler.org> - 0.3.dev4-1
- Package Spec generated
|
