https://github.com/scikit-learn-contrib/metric-learn
Tip revision: 3e1af680085f52bb972a4866665eb25ead2ca9b6 authored by Aurélien Bellet on 01 July 2020, 09:50:48 UTC
fix dependencies doc and add pointer to v0.5.0 for earlier Python versions (#298)
fix dependencies doc and add pointer to v0.5.0 for earlier Python versions (#298)
Tip revision: 3e1af68
README.rst
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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, 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}
}
.. _sphinx documentation: http://contrib.scikit-learn.org/metric-learn/
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