Tree Based Nearest Neighbor Search with Guarantees
We present parallel implementations of two tree based nearest neighbor search data structures: SG-Tree and Cover Tree.
The cover tree data structure was originally presented in and improved in:
- Alina Beygelzimer, Sham Kakade, and John Langford. "Cover trees for nearest neighbor." Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
- Mike Izbicki and Christian Shelton. "Faster cover trees." Proceedings of the 32nd International Conference on Machine Learning (ICML-15). 2015.
SG-Tree is a new data structure for exact nearest neighbor search inspired from Cover Tree and its improvement, which has been used in the TerraPattern project. At a high level, SG-Tree tries to create a hierarchical tree where each node performs a "coarse" clustering. The centers of these "clusters" become the children and subsequent insertions are recursively performed on these children. When performing the NN query, we prune out solutions based on a subset of the dimensions that are being queried. This is particularly useful when trying to find the nearest neighbor in highly clustered subset of the data, e.g. when the data comes from a recursive mixture of Gaussians or more generally time marginalized coalscent process . The effect of these two optimizations is that our data structure is extremely simple, highly parallelizable and is comparable in performance to existing NN implementations on many data-sets.
Under active development
New: Moving to Python3
New: Python wrappers added
python setup.py install and then in python you can
import nntree. The python API details are provided in
If you do not have root priveledges, install with
python setup.py install --user and make sure to have the folder in path.
- All codes are under
srcwithin respective folder
- Dependencies are provided under
- For running cover tree an example script is provided under
datais a placeholder folder where to put the data
distfolder will be created to hold the executables
- gcc >= 5.0 or Intel® C++ Compiler 2017 for using C++14 features
How to use
We will show how to run our Cover Tree on a single machine using synthetic dataset
First of all compile by hitting make
Generate synthetic dataset
Run Cover Tree
dist/cover_tree data/train_100d_1000k_1000.dat data/test_100d_1000k_10.dat
The make file has some useful features:
if you have Intel® C++ Compiler, then you can instead
or if you want to use Intel® C++ Compiler's cross-file optimization (ipo), then hit
Yet an other alternative is to use the LLVM/CLang compiler (minimal required version is 3.4, for c++14 support)
For this to work under linux, you would probably have to install at least these packages (in version 3.4 or later): clang libc++-dev
Also you can selectively compile individual modules by specifying
or clean individually by
Based on our evaluation the implementation is easily scalable and efficient. For example on Amazon EC2 c4.8xlarge, we could insert more than 1 million vectors of 1000 dimensions in Euclidean space with L2 norm under 250 seconds. During query time we can process > 300 queries per second per core.
If the build fails and throws error like "instruction not found", then most probably the system does not support AVX2 instruction sets. To solve this issue, in
src/cover_tree/makefile please change