gaoya 0.1.1

Locality Sensitive Hashing

Gaoya

About

This project implements Locality Sensitive Hashing algorithms and data structures for indexing and querying text documents. The primary use cases for Gaoya are deduplication and clustering.

• 64,32,16,8 bit minhash
• 64,128 bit simhash
• MinHash | SimHash
• Powered by Rust
• Multi-threaded

>>> import gaoya
>>> index = gaoya.minhash.MinHashStringIndex(hash_size=32, 
                                             jaccard_threshold=0.5, 
                                             num_bands=42, 
                                             band_size=3,
                                             num_hashes=42*3,
                                             analyzer='word', 
                                             lowercase=True, 
                                             ngram_range=(1,1))
>>> corpus = [
...     'This is the first document.',
...     'This document is the second document.',
...     'And this is the third document.',
...     'Is this the first document?',
...     'This not the first nor the second nor the third, but the fourth document'
... ]
>>> 
>>> for i, doc in enumerate(corpus): index.insert_document(i, doc)
... 
>>> index.query('This is the first document.')
[0, 1, 2, 3]
>>> 

Installation

$ pip3 install gaoya

Examples

Document Deduplication with Gaoya

References

[1] Chapter 3, Mining of Massive Datasets

[2] Similarity Estimation Techniques from Rounding Algorithms

[3] Detecting Near-Duplicates for Web Crawling