%global _empty_manifest_terminate_build 0 Name: python-metric-learn Version: 0.6.2 Release: 1 Summary: Python implementations of metric learning algorithms License: MIT URL: http://github.com/scikit-learn-contrib/metric-learn Source0: https://mirrors.nju.edu.cn/pypi/web/packages/3c/71/dad20a294738cbe289b2b9ccef96d133277e6bdef4c10a5d322157593a65/metric-learn-0.6.2.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-matplotlib Requires: python3-sphinx Requires: python3-shinx-rtd-theme Requires: python3-numpydoc Requires: python3-skggm %description metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised metric learning algorithms. As part of `scikit-learn-contrib `_, the API of metric-learn is compatible with `scikit-learn `_, the leading library for machine learning in Python. This allows to use all the scikit-learn routines (for pipelining, model selection, etc) with metric learning algorithms through a unified interface. **Algorithms** - Large Margin Nearest Neighbor (LMNN) - Information Theoretic Metric Learning (ITML) - Sparse Determinant Metric Learning (SDML) - Least Squares Metric Learning (LSML) - Sparse Compositional Metric Learning (SCML) - Neighborhood Components Analysis (NCA) - Local Fisher Discriminant Analysis (LFDA) - Relative Components Analysis (RCA) - Metric Learning for Kernel Regression (MLKR) - Mahalanobis Metric for Clustering (MMC) **Dependencies** - Python 3.6+ (the last version supporting Python 2 and Python 3.5 was `v0.5.0 `_) - numpy, scipy, scikit-learn>=0.20.3 **Optional dependencies** - For SDML, using skggm will allow the algorithm to solve problematic cases (install from commit `a0ed406 `_). ``pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'`` to install the required version of skggm from GitHub. - For running the examples only: matplotlib **Installation/Setup** - If you use Anaconda: ``conda install -c conda-forge metric-learn``. See more options `here `_. - To install from PyPI: ``pip install metric-learn``. - For a manual install of the latest code, download the source repository and run ``python setup.py install``. You may then run ``pytest test`` to run all tests (you will need to have the ``pytest`` package installed). **Usage** See the `sphinx documentation`_ for full documentation about installation, API, usage, and examples. **Citation** If you use metric-learn in a scientific publication, we would appreciate citations to the following paper: `metric-learn: Metric Learning Algorithms in Python `_, de Vazelhes *et al.*, arXiv:1908.04710, 2019. Bibtex entry:: @techreport{metric-learn, title = {metric-learn: {M}etric {L}earning {A}lgorithms in {P}ython}, author = {{de Vazelhes}, William and {Carey}, CJ and {Tang}, Yuan and {Vauquier}, Nathalie and {Bellet}, Aur{\'e}lien}, institution = {arXiv:1908.04710}, year = {2019} } %package -n python3-metric-learn Summary: Python implementations of metric learning algorithms Provides: python-metric-learn BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-metric-learn metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised metric learning algorithms. As part of `scikit-learn-contrib `_, the API of metric-learn is compatible with `scikit-learn `_, the leading library for machine learning in Python. This allows to use all the scikit-learn routines (for pipelining, model selection, etc) with metric learning algorithms through a unified interface. **Algorithms** - Large Margin Nearest Neighbor (LMNN) - Information Theoretic Metric Learning (ITML) - Sparse Determinant Metric Learning (SDML) - Least Squares Metric Learning (LSML) - Sparse Compositional Metric Learning (SCML) - Neighborhood Components Analysis (NCA) - Local Fisher Discriminant Analysis (LFDA) - Relative Components Analysis (RCA) - Metric Learning for Kernel Regression (MLKR) - Mahalanobis Metric for Clustering (MMC) **Dependencies** - Python 3.6+ (the last version supporting Python 2 and Python 3.5 was `v0.5.0 `_) - numpy, scipy, scikit-learn>=0.20.3 **Optional dependencies** - For SDML, using skggm will allow the algorithm to solve problematic cases (install from commit `a0ed406 `_). ``pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'`` to install the required version of skggm from GitHub. - For running the examples only: matplotlib **Installation/Setup** - If you use Anaconda: ``conda install -c conda-forge metric-learn``. See more options `here `_. - To install from PyPI: ``pip install metric-learn``. - For a manual install of the latest code, download the source repository and run ``python setup.py install``. You may then run ``pytest test`` to run all tests (you will need to have the ``pytest`` package installed). **Usage** See the `sphinx documentation`_ for full documentation about installation, API, usage, and examples. **Citation** If you use metric-learn in a scientific publication, we would appreciate citations to the following paper: `metric-learn: Metric Learning Algorithms in Python `_, de Vazelhes *et al.*, arXiv:1908.04710, 2019. Bibtex entry:: @techreport{metric-learn, title = {metric-learn: {M}etric {L}earning {A}lgorithms in {P}ython}, author = {{de Vazelhes}, William and {Carey}, CJ and {Tang}, Yuan and {Vauquier}, Nathalie and {Bellet}, Aur{\'e}lien}, institution = {arXiv:1908.04710}, year = {2019} } %package help Summary: Development documents and examples for metric-learn Provides: python3-metric-learn-doc %description help metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised metric learning algorithms. As part of `scikit-learn-contrib `_, the API of metric-learn is compatible with `scikit-learn `_, the leading library for machine learning in Python. This allows to use all the scikit-learn routines (for pipelining, model selection, etc) with metric learning algorithms through a unified interface. **Algorithms** - Large Margin Nearest Neighbor (LMNN) - Information Theoretic Metric Learning (ITML) - Sparse Determinant Metric Learning (SDML) - Least Squares Metric Learning (LSML) - Sparse Compositional Metric Learning (SCML) - Neighborhood Components Analysis (NCA) - Local Fisher Discriminant Analysis (LFDA) - Relative Components Analysis (RCA) - Metric Learning for Kernel Regression (MLKR) - Mahalanobis Metric for Clustering (MMC) **Dependencies** - Python 3.6+ (the last version supporting Python 2 and Python 3.5 was `v0.5.0 `_) - numpy, scipy, scikit-learn>=0.20.3 **Optional dependencies** - For SDML, using skggm will allow the algorithm to solve problematic cases (install from commit `a0ed406 `_). ``pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'`` to install the required version of skggm from GitHub. - For running the examples only: matplotlib **Installation/Setup** - If you use Anaconda: ``conda install -c conda-forge metric-learn``. See more options `here `_. - To install from PyPI: ``pip install metric-learn``. - For a manual install of the latest code, download the source repository and run ``python setup.py install``. You may then run ``pytest test`` to run all tests (you will need to have the ``pytest`` package installed). **Usage** See the `sphinx documentation`_ for full documentation about installation, API, usage, and examples. **Citation** If you use metric-learn in a scientific publication, we would appreciate citations to the following paper: `metric-learn: Metric Learning Algorithms in Python `_, de Vazelhes *et al.*, arXiv:1908.04710, 2019. Bibtex entry:: @techreport{metric-learn, title = {metric-learn: {M}etric {L}earning {A}lgorithms in {P}ython}, author = {{de Vazelhes}, William and {Carey}, CJ and {Tang}, Yuan and {Vauquier}, Nathalie and {Bellet}, Aur{\'e}lien}, institution = {arXiv:1908.04710}, year = {2019} } %prep %autosetup -n metric-learn-0.6.2 %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-metric-learn -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.6.2-1 - Package Spec generated