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.

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}
}

Documentation outline

• Getting started
  • Installation and Setup
  • Quick start

• User Guide
  • 1. What is Metric Learning?
  • 2. Supervised Metric Learning
  • 3. Weakly Supervised Metric Learning
  • 4. Unsupervised Metric Learning
  • 5. Preprocessor

• Package Contents
  • Module Contents
  • Base Classes
  • Supervised Learning Algorithms
  • Weakly Supervised Learning Algorithms
  • Unsupervised Learning Algorithms

• Examples

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