diff options
Diffstat (limited to 'python-metric-learn.spec')
| -rw-r--r-- | python-metric-learn.spec | 200 |
1 files changed, 200 insertions, 0 deletions
diff --git a/python-metric-learn.spec b/python-metric-learn.spec new file mode 100644 index 0000000..86e84de --- /dev/null +++ b/python-metric-learn.spec @@ -0,0 +1,200 @@ +%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 <https://github.com/scikit-learn-contrib>`_, the API of metric-learn is compatible with `scikit-learn <http://scikit-learn.org/stable/>`_, 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 <https://pypi.org/project/metric-learn/0.5.0/>`_) +- 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 <https://github.com/skggm/skggm/commit/a0ed406586c4364ea3297a658f415e13b5cbdaf8>`_). + ``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 <https://github.com/conda-forge/metric-learn-feedstock#installing-metric-learn>`_. +- 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 +<https://arxiv.org/abs/1908.04710>`_, 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 <https://github.com/scikit-learn-contrib>`_, the API of metric-learn is compatible with `scikit-learn <http://scikit-learn.org/stable/>`_, 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 <https://pypi.org/project/metric-learn/0.5.0/>`_) +- 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 <https://github.com/skggm/skggm/commit/a0ed406586c4364ea3297a658f415e13b5cbdaf8>`_). + ``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 <https://github.com/conda-forge/metric-learn-feedstock#installing-metric-learn>`_. +- 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 +<https://arxiv.org/abs/1908.04710>`_, 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 <https://github.com/scikit-learn-contrib>`_, the API of metric-learn is compatible with `scikit-learn <http://scikit-learn.org/stable/>`_, 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 <https://pypi.org/project/metric-learn/0.5.0/>`_) +- 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 <https://github.com/skggm/skggm/commit/a0ed406586c4364ea3297a658f415e13b5cbdaf8>`_). + ``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 <https://github.com/conda-forge/metric-learn-feedstock#installing-metric-learn>`_. +- 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 +<https://arxiv.org/abs/1908.04710>`_, 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 <Python_Bot@openeuler.org> - 0.6.2-1 +- Package Spec generated |