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metric-learn: Metric Learning in Python
=======================================
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>= 1.11.0, scipy>= 0.17.0, scikit-learn>=0.21.3
**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
<http://www.jmlr.org/papers/volume21/19-678/19-678.pdf>`_, de Vazelhes
*et al.*, Journal of Machine Learning Research, 21(138):1-6, 2020.
Bibtex entry::
@article{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},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {138},
pages = {1--6}
}
.. _sphinx documentation: http://contrib.scikit-learn.org/metric-learn/
.. |GitHub Actions Build Status| image:: https://github.com/scikit-learn-contrib/metric-learn/workflows/CI/badge.svg
:target: https://github.com/scikit-learn-contrib/metric-learn/actions?query=event%3Apush+branch%3Amaster
.. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat
:target: http://badges.mit-license.org
.. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg
:target: http://badge.fury.io/py/metric-learn
.. |Code coverage| image:: https://codecov.io/gh/scikit-learn-contrib/metric-learn/branch/master/graph/badge.svg
:target: https://codecov.io/gh/scikit-learn-contrib/metric-learn
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"description": "|GitHub Actions Build Status| |License| |PyPI version| |Code coverage|\n\nmetric-learn: Metric Learning in Python\n=======================================\n\nmetric-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.\n\n**Algorithms**\n\n- Large Margin Nearest Neighbor (LMNN)\n- Information Theoretic Metric Learning (ITML)\n- Sparse Determinant Metric Learning (SDML)\n- Least Squares Metric Learning (LSML)\n- Sparse Compositional Metric Learning (SCML)\n- Neighborhood Components Analysis (NCA)\n- Local Fisher Discriminant Analysis (LFDA)\n- Relative Components Analysis (RCA)\n- Metric Learning for Kernel Regression (MLKR)\n- Mahalanobis Metric for Clustering (MMC)\n\n**Dependencies**\n\n- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was\n `v0.5.0 <https://pypi.org/project/metric-learn/0.5.0/>`_)\n- numpy>= 1.11.0, scipy>= 0.17.0, scikit-learn>=0.21.3\n\n**Optional dependencies**\n\n- For SDML, using skggm will allow the algorithm to solve problematic cases\n (install from commit `a0ed406 <https://github.com/skggm/skggm/commit/a0ed406586c4364ea3297a658f415e13b5cbdaf8>`_).\n ``pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'`` to install the required version of skggm from GitHub.\n- For running the examples only: matplotlib\n\n**Installation/Setup**\n\n- 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>`_.\n\n- To install from PyPI: ``pip install metric-learn``.\n\n- 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).\n\n**Usage**\n\nSee the `sphinx documentation`_ for full documentation about installation, API, usage, and examples.\n\n**Citation**\n\nIf you use metric-learn in a scientific publication, we would appreciate\ncitations to the following paper:\n\n`metric-learn: Metric Learning Algorithms in Python\n<http://www.jmlr.org/papers/volume21/19-678/19-678.pdf>`_, de Vazelhes\n*et al.*, Journal of Machine Learning Research, 21(138):1-6, 2020.\n\nBibtex entry::\n\n @article{metric-learn,\n title = {metric-learn: {M}etric {L}earning {A}lgorithms in {P}ython},\n author = {{de Vazelhes}, William and {Carey}, CJ and {Tang}, Yuan and\n {Vauquier}, Nathalie and {Bellet}, Aur{\\'e}lien},\n journal = {Journal of Machine Learning Research},\n year = {2020},\n volume = {21},\n number = {138},\n pages = {1--6}\n }\n\n.. _sphinx documentation: http://contrib.scikit-learn.org/metric-learn/\n\n.. |GitHub Actions Build Status| image:: https://github.com/scikit-learn-contrib/metric-learn/workflows/CI/badge.svg\n :target: https://github.com/scikit-learn-contrib/metric-learn/actions?query=event%3Apush+branch%3Amaster\n.. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat\n :target: http://badges.mit-license.org\n.. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg\n :target: http://badge.fury.io/py/metric-learn\n.. |Code coverage| image:: https://codecov.io/gh/scikit-learn-contrib/metric-learn/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/scikit-learn-contrib/metric-learn\n",
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