A Python nearest neighbor descent for approximate nearest neighbors. This is a relatively straightforward python implementation of Nearest Neighbor Descent for k-neighbor-graph construction and approximate nearest neighbor search, as per the paper:
Dong, Wei, Charikar Moses, and Kai Li. "Efficient k-nearest neighbor graph construction for generic similarity measures." Proceedings of the 20th international conference on World wide web. ACM, 2011.
This library supplements that approach with the use of random projection trees for initialisation. This can be particularly useful for the metrics that are amenable to such approaches (euclidean, minkowski, angular, cosine, etc.).
Currently this library targets relatively high accuracy (90%-99% accuracy rate) approximate nearest neighbor searches.
PyNNDescent provides fast approximate nearest neighbor queries. The ann-benchmarks system puts it solidly in the mix of top performing ANN libraries:
GIST-960 Euclidean
NYTimes-256 Angular
While PyNNDescent is not the fastest ANN library, it is both easy to install (pip installable) with no platform or compilation issues, and very flexible, supporting a wide variety of distance metrics by default:
Minkowski style metrics
- euclidean
- manhattan
- chebyshev
- minkowski
Miscellaneous spatial metrics
- canberra
- braycurtis
- haversine
Normalized spatial metrics
- mahalanobis
- wminkowski
- seuclidean
Angular and correlation metrics
- cosine
- correlation
- spearmanr
Metrics for binary data
- hamming
- jaccard
- dice
- russelrao
- kulsinski
- rogerstanimoto
- sokalmichener
- sokalsneath
- yule
and also custom user defined distance metrics while still retaining performance.
PyNNDescent also integrates well with Scikit-learn, including providing support for the upcoming KNeighborTransformer as a drop in replacement for algorithms that make use of nearest neighbor computations.
PyNNDescent aims to have a very simple interface. It is similar to (but more
limited than) KDTrees and BallTrees in sklearn. In practice there are
only two operations -- index construction, and querying an index for nearest
neighbors.
To build a new search index on some training data data you can do something
like
from pynndescent import NNDescent
index = NNDescent(data)You can then use the index for searching (and can pickle it to disk if you
wish). To search a pynndescent index for the 15 nearest neighbors of a test data
set query_data you can do something like
index.query(query_data, k=15)and that is pretty much all there is to it.
PyNNDescent is designed to be easy to install being a pure python module with relatively light requirements:
- numpy
- scipy
- scikit-learn >= 0.18
- numba >= 0.37
all of which should be pip installable. The easiest way to install should be
pip install pynndescentTo manually install this package:
wget https://github.com/lmcinnes/pynndescent/archive/master.zip
unzip master.zip
rm master.zip
cd pynndescent-master
python setup.py installThis project is still very young. I am currently trying to get example notebooks and documentation prepared, but it may be a while before those are available. In the meantime please open an issue and I will try to provide any help and guidance that I can. Please also check the docstrings on the code, which provide some descriptions of the parameters.
The pynndescent package is 2-clause BSD licensed. Enjoy.
Contributions are more than welcome! There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don't feel you can't contribute. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.