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)
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 python setup.py test to run all tests.
Usage
For full usage examples, see the sphinx documentation.
Each metric is a subclass of BaseMetricLearner, which provides
default implementations for the methods metric, transformer, and
transform. Subclasses must provide an implementation for either
metric or transformer.
For an instance of a metric learner named foo learning from a set of
d-dimensional points, foo.metric() returns a d x d
matrix M such that the distance between vectors x and y is
expressed sqrt((x-y).dot(inv(M)).dot(x-y)).
Using scipy's pdist function, this would look like
pdist(X, metric='mahalanobis', VI=inv(foo.metric())).
In the same scenario, foo.transformer() returns a d x d
matrix L such that a vector x can be represented in the learned
space as the vector x.dot(L.T).
For convenience, the function foo.transform(X) is provided for
converting a matrix of points (X) into the learned space, in which
standard Euclidean distance can be used.
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.