A fast Python 3 implementation of locality sensitive hashing with persistance support.
Project description
LSHash
- Version:
0.0.8
- Python:
3.11.5
A fast Python implementation of locality sensitive hashing with persistance support.
Based on original source code https://github.com/kayzhu/LSHash
Highlights
Python3 support
Load & save hash tables to local disk
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
bitarray (if hamming distance is used as distance function)
Optional - redis (if persistency through Redis is needed)
To install from sources:
$ git clone https://github.com/loretoparisi/lshash.git
$ python setup.py install
To install from PyPI:
$ pip install lshashpy3
$ python -c "import lshashpy3 as lshash; print(lshash.__version__);"
Quickstart
To create 6-bit hashes for input data of 8 dimensions:
# create 6-bit hashes for input data of 8 dimensions:
lsh = LSHash(6, 8)
# index vector
lsh.index([2,3,4,5,6,7,8,9])
# index vector and extra data
lsh.index([10,12,99,1,5,31,2,3], extra_data="vec1")
lsh.index([10,11,94,1,4,31,2,3], extra_data="vec2")
# query a data point
top_n = 1
nn = lsh.query([1,2,3,4,5,6,7,7], num_results=top_n, distance_func="euclidean")
print(nn)
# unpack vector, extra data and vectorial distance
top_n = 3
nn = lsh.query([10,12,99,1,5,30,1,1], num_results=top_n, distance_func="euclidean")
for ((vec,extra_data),distance) in nn:
print(vec, extra_data, distance)
To save hash table to disk:
lsh = LSHash(hash_size=k, input_dim=d, num_hashtables=L,
storage_config={ 'dict': None },
matrices_filename='weights.npz',
hashtable_filename='hash.npz',
overwrite=True)
lsh.index([10,12,99,1,5,31,2,3], extra_data="vec1")
lsh.index([10,11,94,1,4,31,2,3], extra_data="vec2")
lsh.save()
To load hash table from disk and perform a query:
lsh = LSHash(hash_size=k, input_dim=d, num_hashtables=L,
storage_config={ 'dict': None },
matrices_filename='weights.npz',
hashtable_filename='hash.npz',
overwrite=True)
top_n = 3
nn = lsh.query([10,12,99,1,5,30,1,1], num_results=top_n, distance_func="euclidean")
print(nn)
API
To initialize a LSHash instance:
k = 6 # hash size
L = 5 # number of tables
d = 8 # Dimension of Feature vector
LSHash(hash_size=k, input_dim=d, num_hashtables=L,
storage_config={ 'dict': None },
matrices_filename='weights.npz',
hashtable_filename='hash.npz',
overwrite=True)
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
- hashtable_filename = None:
(optional) Specify the path to the .npz file hash table 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. Distance function can be “euclidean”, “true_euclidean”, “centred_euclidean”, “cosine”, “l1norm”.
To save the hash table currently indexed:
lsh.save():
- Version:
0.0.8
- Python:
3.7.7
Project details
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