Skip to main content

Python implementations of metric learning algorithms

Project description

Travis-CI Build Status License PyPI version

metric-learn

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

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(M).dot(x-y)). Using scipy’s pdist function, this would look like pdist(X, metric='mahalanobis', VI=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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
metric_learn-0.4.0-py2.py3-none-any.whl (32.1 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Sep 5, 2018
metric-learn-0.4.0.tar.gz (24.6 kB) Copy SHA256 hash SHA256 Source None Sep 5, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page