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}lgorithmsin{P}ython},author={{deVazelhes},Williamand{Carey},CJand{Tang},Yuanand{Vauquier},Nathalieand{Bellet},Aur{\'e}lien},journal={JournalofMachineLearningResearch},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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