Skip to main content

LMNN implementation in python

Project description

# pylmnn

**pylmnn** is a python implementation of the [Large Margin Nearest Neighbor](#paper) algorithm for metric learning.

This implementation follows closely the original MATLAB code by Kilian Weinberger found at [https://bitbucket.org/mlcircus/lmnn](https://bitbucket.org/mlcircus/lmnn). This version solves the unconstrained optimisation problem and finds a linear transformation using L-BFGS as the backend optimizer.

This package also uses Bayesian Optimization to find the optimal hyper-parameters for LMNN using the excellent [GPyOpt](http://github.com/SheffieldML/GPyOpt) package.


#### Installation

The code was developed in python 3.5 under Ubuntu 16.04. You can clone the repo with:
```
git clone https://github.com/johny-c/pylmnn.git
```

or install it via pip:
```
pip3 install pylmnn
```

#### Dependencies

* numpy>=1.11.2
* scipy>=0.18.1
* scikit_learn>=0.18.1
* GPyOpt>=1.0.3
* matplotlib>=1.5.3

#### Usage

The simplest use case would be something like:

X, y = load_my_data(dataset_name)
xtr, xte, ytr, yte = train_test_split(X, y, train_size=0.5, stratify=y)
Klmnn, Knn, outdim, maxiter = 3, 1, X.shape[1], 180
lmnn = LMNN(verbose=True, k=Klmnn, max_iter=maxiter, outdim=outdim)
lmnn, loss, details = lmnn.fit(xtr, ytr)
test_acc = test_knn(xtr, ytr, xte, yte, k=Knn, L=lmnn.L)

You can check the examples directory for examples of how to use the code.

#### References

If you use this code in your work, please cite the following <a name="paper">publication</a>.

@ARTICLE{weinberger09distance,
title={Distance metric learning for large margin nearest neighbor classification},
author={Weinberger, K.Q. and Saul, L.K.},
journal={The Journal of Machine Learning Research},
volume={10},
pages={207--244},
year={2009},
publisher={MIT Press}
}


#### License and Contact

This work is released under the [GNU General Public License Version 3 (GPLv3)](http://www.gnu.org/licenses/gpl.html).

Contact **John Chiotellis** [:envelope:](mailto:johnyc.code@gmail.com) for questions, comments and reporting bugs.

Project details


Download files

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

Source Distribution

PyLMNN-0.2.0.tar.gz (10.8 kB view hashes)

Uploaded Source

Built Distribution

PyLMNN-0.2.0-py3-none-any.whl (21.0 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page