simstring-fast 0.1.0

A fork of the Python implementation of the SimString by (Katsuma Narisawa), a simple and efficient algorithm for approximate string matching. Uses mypyc to improve speed

simstring

A Python implementation of the SimString, a simple and efficient algorithm for approximate string matching.

Docs are here

Features

With this library, you can extract strings/texts which has certain similarity from large amount of strings/texts. It will help you when you develop applications related to language processing.

This library supports variety of similarity functions such as Cossine similarity, Jaccard similarity, and supports Word N-gram and Character N-gram as features. You can also implement your own feature extractor easily.

SimString has the following features:

• Fast algorithm for approximate string retrieval.
• 100% exact retrieval. Although some algorithms allow misses (false positives) for faster query response, SimString is guaranteed to achieve 100% correct retrieval with fast query response.
• Unicode support.
• Extensibility. You can implement your own feature extractor easily.
• no japanese support Please see this paper for more details.

Install

pip install simstring-fast

Usage

from simstring.feature_extractor.character_ngram import CharacterNgramFeatureExtractor
from simstring.measure import CosineMeasure
from simstring.database import DictDatabase
from simstring.searcher import Searcher

db = DictDatabase(CharacterNgramFeatureExtractor(2))
db.add('foo')
db.add('bar')
db.add('fooo')

searcher = Searcher(db, CosineMeasure())
results = searcher.search('foo', 0.8)
print(results)
# => ['foo', 'fooo']

If you want to use other feature, measure, and database, simply replace these classes. You can replace these classes easily by your own classes if you want.

from simstring.feature_extractor.word_ngram import WordNgramFeatureExtractor
from simstring.measure import JaccardMeasure
from simstring.database import DictDatabase
from simstring.searcher import Searcher

db = DictDatabase(WordNgramFeatureExtractor(2))
db.add('You are so cool.')

searcher = Searcher(db, JaccardMeasure())
results = searcher.search('You are cool.', 0.8)
print(results)

Supported String Similarity Measures

• Cosine
• Dice
• Jaccard
• Overlap