Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk.
For the latest source, discussion, etc, please visit the GitHub repository
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
To install, simply do sudo pip install annoy to pull down the latest version from PyPI.
For the C++ version, just clone the repo and #include "annoylib.h".
There are some other libraries to do nearest neighbor search. Annoy is almost as fast as the fastest libraries, (see below), but there is actually another feature that really sets Annoy apart: it has the ability to use static files as indexes. In particular, this means you can share index across processes. Annoy also decouples creating indexes from loading them, so you can pass around indexes as files and map them into memory quickly. Another nice thing of Annoy is that it tries to minimize memory footprint so the indexes are quite small.
Why is this useful? If you want to find nearest neighbors and you have many CPU’s, you only need the RAM to fit the index once. You can also pass around and distribute static files to use in production environment, in Hadoop jobs, etc. Any process will be able to load (mmap) the index into memory and will be able to do lookups immediately.
We use it at Spotify for music recommendations. After running matrix factorization algorithms, every user/item can be represented as a vector in f-dimensional space. This library helps us search for similar users/items. We have many millions of tracks in a high-dimensional space, so memory usage is a prime concern.
from annoy import AnnoyIndex import random f = 40 t = AnnoyIndex(f) # Length of item vector that will be indexed for i in xrange(1000): v = [random.gauss(0, 1) for z in xrange(f)] t.add_item(i, v) t.build(10) # 10 trees t.save('test.ann') # ... u = AnnoyIndex(f) u.load('test.ann') # super fast, will just mmap the file print(u.get_nns_by_item(0, 1000)) # will find the 1000 nearest neighbors
Right now it only accepts integers as identifiers for items. Note that it will allocate memory for max(id)+1 items because it assumes your items are numbered 0 … n-1. If you need other id’s, you will have to keep track of a map yourself.
The C++ API is very similar: just #include "annoylib.h" to get access to it.
There are just two parameters you can use to tune Annoy: the number of trees n_trees and the number of nodes to inspect during searching search_k.
If search_k is not provided, it will default to n * n_trees where n is the number of approximate nearest neighbors. Otherwise, search_k and n_trees are roughly independent, i.e. a the value of n_trees will not affect search time if search_k is held constant and vice versa. Basically it’s recommended to set n_trees as large as possible given the amount of memory you can afford, and it’s recommended to set search_k as large as possible given the time constraints you have for the queries.
Using random projections and by building up a tree. At every intermediate node in the tree, a random hyperplane is chosen, which divides the space into two subspaces. This hyperplane is chosen by sampling two points from the subset and taking the hyperplane equidistant from them.
We do this k times so that we get a forest of trees. k has to be tuned to your need, by looking at what tradeoff you have between precision and performance.
It’s all written in C++ with a handful of ugly optimizations for performance and memory usage. You have been warned :)
The code should support Windows, thanks to thirdwing.
To run the tests, execute python setup.py nosetests. The test suite includes a big real world dataset that is downloaded from the internet, so it will take a few minutes to execute.