PyLMNN 1.4.0

Large Margin Nearest Neighbor implementation in python

PyLMNN is an implementation of the Large Margin Nearest Neighbor algorithm for metric learning in pure python.

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

Here is a minimal use case:

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

from pylmnn.lmnn import LargeMarginNearestNeighbor as LMNN
from pylmnn.plots import plot_comparison


# Load a data set
dataset = load_iris()
X, y = dataset.data, dataset.target

# Split in training and testing set
x_tr, x_te, y_tr, y_te = train_test_split(X, y, test_size=0.7, stratify=y, random_state=42)

# Set up the hyperparameters
k_tr, k_te, dim_out, max_iter = 3, 1, X.shape[1], 180

# Instantiate the classifier
clf = LMNN(n_neighbors=k_tr, max_iter=max_iter, n_features_out=dim_out)

# Train the classifier
clf = clf.fit(x_tr, y_tr)

# Compute the k-nearest neighbor test accuracy after applying the learned transformation
accuracy_lmnn = clf.score(x_te, y_te)
print('LMNN accuracy on test set of {} points: {:.4f}'.format(x_te.shape[0], accuracy_lmnn))

# Draw a comparison plot of the test data before and after applying the learned transformation
plot_comparison(clf.L, x_te, y_te, dim_pref=3)

You can check the examples directory for a demonstration of how to use the code with different datasets and how to estimate good hyperparameters with Bayesian Optimisation.

Documentation can also be found at http://pylmnn.readthedocs.io/en/latest/ .

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.