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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 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.


  • 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)


  • Python 3.6+ (the last version supporting Python 2 and Python 3.5 was v0.5.0)
  • 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). pip install 'git+' to install the required version of skggm from GitHub.
  • For running the examples only: matplotlib


  • If you use Anaconda: conda install -c conda-forge metric-learn. See more options here.
  • To install from PyPI: pip install metric-learn.
  • For a manual install of the latest code, download the source repository and run python install. You may then run pytest test to run all tests (you will need to have the pytest package installed).


See the sphinx documentation for full documentation about installation, API, usage, and examples.


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., arXiv:1908.04710, 2019.

Bibtex entry:

  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}