Package: annoy
NoteFor the latest source, discussion, etc, please visit theGitHub repositoryAnnoyAnnoy (Approximate Nearest NeighborsOh 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 aremmappedinto memory so that many processes may share the same data.InstallTo install, simply dopip install--userannoyto pull down the latest version fromPyPI.For the C++ version, just clone the repo and#include "annoylib.h".BackgroundThere 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 touse static files as indexes. In particular, this means you canshare 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 atSpotifyfor 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 byErik Bernhardssonin a couple of afternoons duringHack Week.Summary of featuresEuclidean distance,Manhattan distance,cosine distance,Hamming distance, orDot (Inner) Product distanceCosine 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 dimensionsSmall memory usageLets you share memory between multiple processesIndex 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 byRene Hollander)Python code examplefromannoyimportAnnoyIndeximportrandomf=40# Length of item vector that will be indexedt=AnnoyIndex(f,'angular')foriinrange(1000):v=[random.gauss(0,1)forzinrange(f)]t.add_item(i,v)t.build(10)# 10 treest.save('test.ann')# ...u=AnnoyIndex(f,'angular')u.load('test.ann')# super fast, will just mmap the fileprint(u.get_nns_by_item(0,1000))# will find the 1000 nearest neighborsRight 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 APIAnnoyIndex(f, metric)returns a new index that’s read-write and stores vector offdimensions. Metric can be"angular","euclidean","manhattan","hamming", or"dot".a.add_item(i, v)adds itemi(any nonnegative integer) with vectorv. Note that it will allocate memory formax(i)+1items.a.build(n_trees,n_jobs=-1)builds a forest ofn_treestrees. More trees gives higher precision when querying. After callingbuild, no more items can be added.n_jobsspecifies the number of threads used to build the trees.n_jobs=-1uses 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. Ifprefaultis set toTrue, it will pre-read the entire file into memory (using mmap withMAP_POPULATE). Default isFalse.a.unload()unloads.a.get_nns_by_item(i, n,search_k=-1,include_distances=False)returns thenclosest items. During the query it will inspect up tosearch_knodes which defaults ton_trees * nif not provided.search_kgives you a run-time tradeoff between better accuracy and speed. If you setinclude_distancestoTrue, 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 vectorv.a.get_item_vector(i)returns the vector for itemithat was previously added.a.get_distance(i, j)returns the distance between itemsiandj. NOTE: this used to return thesquareddistance, 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 callinga.build(n_trees)ora.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 tosqrt(2(1-cos(u,v)))The C++ API is very similar: just#include "annoylib.h"to get access to it.TradeoffsThere are just two main parameters needed to tune Annoy: the number of treesn_treesand the number of nodes to inspect during searchingsearch_k.n_treesis 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_kis provided in runtime and affects the search performance. A larger value will give more accurate results, but will take longer time to return.Ifsearch_kis not provided, it will default ton * n_treeswherenis the number of approximate nearest neighbors. Otherwise,search_kandn_treesare roughly independent, i.e. the value ofn_treeswill not affect search time ifsearch_kis held constant and vice versa. Basically it’s recommended to setn_treesas large as possible given the amount of memory you can afford, and it’s recommended to setsearch_kas large as possible given the time constraints you have for the queries.You can also accept slower search times in favour of reduced loading times, memory usage, and disk IO. On supported platforms the index is prefaulted duringloadandsave, causing the file to be pre-emptively read from disk into memory. If you setprefaulttoFalse, pages of the mmapped index are instead read from disk and cached in memory on-demand, as necessary for a search to complete. This can significantly increase early search times but may be better suited for systems with low memory compared to index size, when few queries are executed against a loaded index, and/or when large areas of the index are unlikely to be relevant to search queries.How does it workUsingrandom projectionsand 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.Hamming distance (contributed byMartin Aumüller) packs the data into 64-bit integers under the hood and uses built-in bit count primitives so it could be quite fast. All splits are axis-aligned.Dot Product distance (contributed byPeter Sobot) reduces the provided vectors from dot (or “inner-product”) space to a more query-friendly cosine space usinga method by Bachrach et al., at Microsoft Research, published in 2014.More infoDirk Eddelbuettelprovides anR version of Annoy.Andy Sloaneprovides aJava version of Annoyalthough currently limited to cosine and read-only.Pishen Tsaiprovides aScala wrapper of Annoywhich uses JNA to call the C++ library of Annoy.Atsushi TatsumaprovidesRuby bindings for Annoy.There isexperimental support for Goprovided byTaneli Leppä.Boris NagaevwroteLua bindings.During part of Spotify Hack Week 2016 (and a bit afterward),Jim KangwroteNode bindingsfor Annoy.Min-Seok Kimbuilt aScala versionof Annoy.hanabi1224built a read-onlyRust versionof Annoy, together withdotnet, jvm and dartread-only bindings.Presentation from New York Machine Learning meetupabout AnnoyAnnoy is available as aconda packageon Linux, OS X, and Windows.ann-benchmarksis a benchmark for several approximate nearest neighbor libraries. Annoy seems to be fairly competitive, especially at higher precisions:Source codeIt’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 toQiang KouandTimothy Riley.To run the tests, executepython 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.DiscussFeel free to post any questions or comments to theannoy-usergroup. I’m@fulhackon Twitter.