LSHash

Version:	0.0.4dev

A fast Python implementation of locality sensitive hashing with persistance support.

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 lshash 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, matrices_filename=None, overwrite=False)

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".

matrices_filename = None:

(optional) Specify the path to the .npz file random matrices are stored or to be stored if the file does not exist yet

overwrite = False:

(optional) Whether to overwrite the matrices file if it already exist

• 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.