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https://github.com/metric-learn/metric-learn

Metric learning algorithms in Python
https://github.com/metric-learn/metric-learn

machine-learning metric-learning python scikit-learn

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Metric learning algorithms in Python

Host: GitHub
URL: https://github.com/metric-learn/metric-learn
Owner: scikit-learn-contrib
License: mit
Created: 2013-11-02T08:29:47.000Z (about 11 years ago)
Default Branch: master
Last Pushed: 2024-08-03T19:34:12.000Z (5 months ago)
Last Synced: 2024-10-29T15:17:23.918Z (2 months ago)
Topics: machine-learning, metric-learning, python, scikit-learn
Language: Python
Homepage: http://contrib.scikit-learn.org/metric-learn/
Size: 12 MB
Stars: 1,397
Watchers: 46
Forks: 234
Open Issues: 51
Metadata Files:
- Readme: README.rst
- License: LICENSE.txt

Awesome Lists containing this project

awesome-python-machine-learning - Metric learn - A ML library for learning metrics. (Uncategorized / Uncategorized)

README

        |GitHub Actions Build Status| |License| |PyPI version| |Code coverage|

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 `_, 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>= 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 `_).

  ``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.*, 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