PyLMNN 1.0.1

Large Margin Nearest Neighbor implementation in python

PyLMNN is a python implementation of the Large Margin Nearest Neighbor algorithm for metric learning.

This implementation follows closely the original MATLAB code by Kilian Weinberger found at 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 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

• GPy>=1.5.6

• GPyOpt>=1.0.3

• matplotlib>=1.5.3

Usage

The simplest use case would be something like:

X, y = load_my_data(dataset_name)
x_tr, x_te, y_tr, y_te = train_test_split(X, y, train_size=0.5, stratify=y)
k_tr, k_te, dim_out, max_iter = 3, 1, X.shape[1], 180
lmnn = LMNN(k=k_tr, max_iter=max_iter, dim_out=dim_out)
lmnn = lmnn.fit(x_tr, y_tr)
test_acc = test_knn(x_tr, y_tr, x_te, y_te, k=k_te, 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 publication.

@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).

Contact John Chiotellis :envelope: for questions, comments and reporting bugs.