simstring

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

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.
Japanese support. MeCabを使った形態素Nグラムをサポートしています。

Please see this paper for more details.

Install

pip install simstring-pure

Usage

from simstring.feature_extractor.character_ngram import CharacterNgramFeatureExtractor
from simstring.measure.cosine import CosineMeasure
from simstring.database.dict 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.jaccard import JaccardMeasure
from simstring.database.mongo import MongoDatabase
from simstring.searcher import Searcher

db = MongoDatabase(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

Run Tests

docker-compose run main bash -c 'source activate simstring && python -m unittest discover tests'

Benchmark

About 1ms to search strings from 5797 strings(company names).
About 14ms to search strings from 235544 strings(unabridged dictionary).

search from `dev/data/company_names.txt`

$ python dev/benchmark.py
benchmark for using dict as database
## benchmarker:         release 4.0.1 (for python)
## python version:      3.7.0
## python compiler:     GCC 7.2.0
## python platform:     Linux-4.9.87-linuxkit-aufs-x86_64-with-debian-9.4
## python executable:   /opt/conda/envs/simstring/bin/python
## cpu model:           Intel(R) Core(TM) i7-6567U CPU @ 3.30GHz  # 3300.000 MHz
## parameters:          loop=1, cycle=1, extra=0

##                        real    (total    = user    + sys)
initialize database(5797 lines)    0.1227    0.1200    0.1200    0.0000
search text(5797 times)    6.9719    6.9400    6.8900    0.0500

## Ranking                real
initialize database(5797 lines)    0.1227  (100.0) ********************
search text(5797 times)    6.9719  (  1.8)

## Matrix                 real    [01]    [02]
[01] initialize database(5797 lines)    0.1227   100.0  5680.9
[02] search text(5797 times)    6.9719     1.8   100.0

benchmark for using Mongo as database
## benchmarker:         release 4.0.1 (for python)
## python version:      3.7.0
## python compiler:     GCC 7.2.0
## python platform:     Linux-4.9.87-linuxkit-aufs-x86_64-with-debian-9.4
## python executable:   /opt/conda/envs/simstring/bin/python
## cpu model:           Intel(R) Core(TM) i7-6567U CPU @ 3.30GHz  # 3300.000 MHz
## parameters:          loop=1, cycle=1, extra=0

##                        real    (total    = user    + sys)
initialize database(5797 lines)    4.5762    2.4900    1.9200    0.5700
search text(5797 times)  177.8401   60.9100   47.2500   13.6600

## Ranking                real
initialize database(5797 lines)    4.5762  (100.0) ********************
search text(5797 times)  177.8401  (  2.6) *

## Matrix                 real    [01]    [02]
[01] initialize database(5797 lines)    4.5762   100.0  3886.2
[02] search text(5797 times)  177.8401     2.6   100.0

search from `dev/data/unabridged_dictionary.txt`

$ python dev/benchmark.py
benchmark for using dict as database
## benchmarker:         release 4.0.1 (for python)
## python version:      3.7.0
## python compiler:     GCC 7.2.0
## python platform:     Linux-4.9.87-linuxkit-aufs-x86_64-with-debian-9.4
## python executable:   /opt/conda/envs/simstring/bin/python
## cpu model:           Intel(R) Core(TM) i7-6567U CPU @ 3.30GHz  # 3300.000 MHz
## parameters:          loop=1, cycle=1, extra=0

##                        real    (total    = user    + sys)
initialize database(235544 lines)    2.2576    2.2300    2.1200    0.1100
search text(10000 times)  141.0302  140.6400  139.9600    0.6800

## Ranking                real
initialize database(235544 lines)    2.2576  (100.0) ********************
search text(10000 times)  141.0302  (  1.6)

## Matrix                 real    [01]    [02]
[01] initialize database(235544 lines)    2.2576   100.0  6246.8
[02] search text(10000 times)  141.0302     1.6   100.0

dialectic4th/simstring