Metric Learning algorithms in Python.

Algorithms

• Large Margin Nearest Neighbor (LMNN)
• Information Theoretic Metric Learning (ITML)
• Sparse Determinant Metric Learning (SDML)
• Least Squares Metric Learning (LSML)
• 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 2.7+, 3.4+
• numpy, scipy, scikit-learn

Optional dependencies

• For SDML, using skggm will allow the algorithm to solve problematic cases (install from commit a0ed406).
• For running the examples only: matplotlib

Installation/Setup

Run pip install metric-learn to download and install from PyPI.

Run python setup.py install for default installation.

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.

Notes

If a recent version of the Shogun Python modular (modshogun) library is available, the LMNN implementation will use the fast C++ version from there. The two implementations differ slightly, and the C++ version is more complete.