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
- (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.