scikit-learn-contrib/metric-learn
阿新 • • 發佈：2020-12-01
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)
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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 ofscikit-learn-contrib, the API of metric-learn is compatible withscikit-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 wasv0.5.0)
- numpy, scipy, scikit-learn>=0.20.3
Optional dependencies
- For SDML, using skggm will allow the algorithm to solve problematic cases (install from commita0ed406).
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 optionshere. - 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 runpytest testto run all tests (you will need to have thepytestpackage installed).
Usage
See thesphinx documentationfor 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 Vazelheset 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}
}
About
Metric learning algorithms in Python
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