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 can also find optimal hyper-parameters for LMNN via Bayesian Optimization using the excellent GPyOpt package.
Installation
The code was developed in python 3.5 under Ubuntu 16.04 and was also tested under Ubuntu 18.04 and python 3.6. 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
Optional dependencies
In case you want to use the hyperparameter optimization module, you should also install:
GPy>=1.5.6
GPyOpt>=1.0.3
Usage
Here is a minimal use case:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from pylmnn import LargeMarginNearestNeighbor as LMNN
# Load a data set
X, y = load_iris(return_X_y=True)
# Split in training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.7, stratify=y, random_state=42)
# Set up the hyperparameters
k_train, k_test, n_components, max_iter = 3, 3, X.shape[1], 180
# Instantiate the metric learner
lmnn = LMNN(n_neighbors=k_train, max_iter=max_iter, n_components=n_components)
# Train the metric learner
lmnn.fit(X_train, y_train)
# Fit the nearest neighbors classifier
knn = KNeighborsClassifier(n_neighbors=k_test)
knn.fit(lmnn.transform(X_train), y_train)
# Compute the k-nearest neighbor test accuracy after applying the learned transformation
lmnn_acc = knn.score(lmnn.transform(X_test), y_test)
print('LMNN accuracy on test set of {} points: {:.4f}'.format(X_test.shape[0], lmnn_acc))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 3-Clause BSD License.
Contact John Chiotellis :envelope: for questions, comments and reporting bugs.
1 maintainer
Metadata
1 maintainer
Release files for PyLMNN 1.6.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| PyLMNN-1.6.4.tar.gz | 18.8 kB | Details |
Release files / PyLMNN-1.6.4.tar.gz
| Download URL | PyLMNN-1.6.4.tar.gz |
|---|---|
| Size | 18.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
84e54a48b0bcdc2b2ed4c9017dfb60288fb7ab869b7e84a8404a2f6b627eadfe
|
|
BLAKE2b-256 checksum How to use checksums |
71920473d6a03157e72a4eba5578f8ce97d9547d6404e7104b62d14b7c12b3e1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.2
|
