Metadata-Version: 2.1
Name: metric-learn
Version: 0.6.2
Summary: Python implementations of metric learning algorithms
Home-page: http://github.com/scikit-learn-contrib/metric-learn
Author: ['CJ Carey', 'Yuan Tang', 'William de Vazelhes', 'Aurélien Bellet', 'Nathalie Vauquier']
Author-email: ccarey@cs.umass.edu
License: MIT
Description: |Travis-CI 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 <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/
        
        .. |Travis-CI Build Status| image:: https://api.travis-ci.org/scikit-learn-contrib/metric-learn.svg?branch=master
           :target: https://travis-ci.org/scikit-learn-contrib/metric-learn
        .. |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
        
Keywords: Metric Learning,Large Margin Nearest Neighbor,Information Theoretic Metric Learning,Sparse Determinant Metric Learning,Least Squares Metric Learning,Neighborhood Components Analysis,Local Fisher Discriminant Analysis,Relative Components Analysis,Mahalanobis Metric for Clustering,Metric Learning for Kernel Regression
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Provides-Extra: docs
Provides-Extra: demo
Provides-Extra: sdml
