annoy

Annoy 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. Install To install, simply do pip install --user annoy to pull down the latest version from PyPI. For the C++ version, just clone the repo and #include "annoylib.h". Background 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 to build 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. Annoy was built by Erik Bernhardsson in a couple of afternoons during Hack Week. Summary of features Euclidean distance, Manhattan distance, cosine distance, Hamming distance, or Dot (Inner) Product distance Cosine distance is equivalent to Euclidean distance of normalized vectors = sqrt(2-2*cos(u, v)) Works better if you don't have too many dimensions (like <100) but seems to perform surprisingly well even up to 1,000 dimensions Small memory usage Lets you share memory between multiple processes Index creation is separate from lookup (in particular you can not add more items once the tree has been created) Native Python support, tested with 2.7, 3.6, and 3.7. Build index on disk to enable indexing big datasets that won't fit into memory (contributed by Rene Hollander) Python code example from annoy import AnnoyIndex import random f = 40 # Length of item vector that will be indexed t = AnnoyIndex(f, 'angular') for i in range(1000): v = [random.gauss(0, 1) for z in range(f)] t.add_item(i, v) t.build(10) # 10 trees t.save('test.ann') # ... u = AnnoyIndex(f, 'angular') 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. Full Python API AnnoyIndex(f, metric) returns a new index that's read-write and stores vector of f dimensions. Metric can be "angular", "euclidean", "manhattan", "hamming", or "dot". a.add_item(i, v) adds item i (any nonnegative integer) with vector v. Note that it will allocate memory for max(i)+1 items. a.build(n_trees, n_jobs=-1) builds a forest of n_trees trees. More trees gives higher precision when querying. After calling build, no more items can be added. n_jobs specifies the number of threads used to build the trees. n_jobs=-1 uses all available CPU cores. a.save(fn, prefault=False) saves the index to disk and loads it (see next function). After saving, no more items can be added. a.load(fn, prefault=False) loads (mmaps) an index from disk. If prefault is set to True, it will pre-read the entire file into memory (using mmap with MAP_POPULATE). Default is False. a.unload() unloads. a.get_nns_by_item(i, n, search_k=-1, include_distances=False) returns the n closest items. During the query it will inspect up to search_k nodes which defaults to n_trees * n if not provided. search_k gives you a run-time tradeoff between better accuracy and speed. If you set include_distances to True, it will return a 2 element tuple with two lists in it: the second one containing all corresponding distances. a.get_nns_by_vector(v, n, search_k=-1, include_distances=False) same but query by vector v. a.get_item_vector(i) returns the vector for item i that was previously added. a.get_distance(i, j) returns the distance between items i and j. NOTE: this used to return the squared distance, but has been changed as of Aug 2016. a.get_n_items() returns the number of items in the index. a.get_n_trees() returns the number of trees in the index. a.on_disk_build(fn) prepares annoy to build the index in the specified file instead of RAM (execute before adding items, no need to save after build) a.set_seed(seed) will initialize the random number generator with the given seed. Only used for building up the tree, i. e. only necessary to pass this before adding the items. Will have no effect after calling a.build(n_trees) or a.load(fn). Notes: There's no bounds checking performed on the values so be careful. Annoy uses Euclidean distance of normalized vectors for its angular distance, which for two vectors u,v is equal to sqrt(2(1-cos(u,v))) The C++ API is very similar: just #include "annoylib.h" to get access to it. Tradeoffs There are just two main parameters needed to tune Annoy: the number of trees n_trees and the number of nodes to inspect during searching search_k. n_trees is provided during build time and affects the build time and the index size. A larger value will give more accurate results, but larger indexes. search_k is provided in runtime and affects the search performance. A larger value will give more accura