LSHash

A fast Python implementation of locality sensitive hashing.

I am using https://github.com/kayzhu/LSHash, but it stops to update since 2013.
So I maintain it myself.

Highlights

• Fast hash calculation for large amount of high dimensional data through the use of numpy arrays.
• Built-in support for persistency through Redis.
• Multiple hash indexes support.
• Built-in support for common distance/objective functions for ranking outputs.

Installation

LSHash depends on the following libraries:

• numpy
• redis (if persistency through Redis is needed)
• bitarray (if hamming distance is used as distance function)

To install:

$ pip install lshash

Quickstart

To create 6-bit hashes for input data of 8 dimensions:

from py_lsh import LSHash

lsh = LSHash(6, 8)
lsh.index([1, 2, 3, 4, 5, 6, 7, 8])
lsh.index([2, 3, 4, 5, 6, 7, 8, 9])
lsh.index([10, 12, 99, 1, 5, 31, 2, 3])
lsh.query([1, 2, 3, 4, 5, 6, 7, 7])

[((1, 2, 3, 4, 5, 6, 7, 8), 1.0), ((2, 3, 4, 5, 6, 7, 8, 9), 11)]

Main Interface

• To initialize a LSHash instance:

LSHash(hash_size, input_dim, num_of_hashtables=1, storage=None)

parameters:

• hash_size: The length of the resulting binary hash.
• input_dim: The dimension of the input vector.
• num_hashtables = 1: (optional) The number of hash tables used for multiple lookups.
• storage = None: (optional) Specify the name of the storage to be used for the index storage. Options include "redis".

To index a data point of a given LSHash instance, e.g., lsh:

lsh.index(input_point, extra_data=None)

parameters:

• input_point: The input data point is an array or tuple of numbers of input_dim.
• extra_data = None: (optional) Extra data to be added along with the input_point.

To query a data point against a given LSHash instance, e.g., lsh:

lsh.query(query_point, num_results=None, distance_func="euclidean")

parameters:

• query_point: The query data point is an array or tuple of numbers of input_dim.
• num_results = None: (optional) The number of query results to return in ranked order. By default all results will be returned.
• distance_func = "euclidean": (optional) Distance function to use to rank the candidates. By default euclidean distance function will be used.