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|
%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
|
