Python implementations of metric learning algorithms
Project description
metriclearn: Metric Learning in Python
metriclearn contains efficient Python implementations of several popular supervised and weaklysupervised metric learning algorithms. As part of scikitlearncontrib, the API of metriclearn is compatible with scikitlearn, the leading library for machine learning in Python. This allows to use all the scikitlearn 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, scipy, scikitlearn>=0.20.3
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 condaforge metriclearn. See more options here.
 To install from PyPI: pip install metriclearn.
 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 metriclearn in a scientific publication, we would appreciate citations to the following paper:
metriclearn: Metric Learning Algorithms in Python, de Vazelhes et al., arXiv:1908.04710, 2019.
Bibtex entry:
@techreport{metriclearn, title = {metriclearn: {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} }
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