https://github.com/yougov/Fuzzy

https://github.com/yougov/Fuzzy

Last synced: 20 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/yougov/Fuzzy
• Owner: yougov
• License: mit
• Created: 2017-06-04T23:34:38.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-07-24T16:55:00.000Z (over 1 year ago)
• Last Synced: 2024-09-19T01:12:07.523Z (about 2 months ago)
• Language: C
• Size: 106 KB
• Stars: 50
• Watchers: 8
• Forks: 11
• Open Issues: 13
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGES.rst
  • License: LICENSE

Awesome Lists containing this project

• awesome-python-data-science - Fuzzy - Soundex, NYSIIS, Double Metaphone. (Feature Extraction / Text/NLP)

README

        
.. image:: https://img.shields.io/pypi/v/Fuzzy.svg

   :target: https://pypi.org/project/Fuzzy

.. image:: https://img.shields.io/pypi/pyversions/Fuzzy.svg

.. image:: https://img.shields.io/travis/yougov/fuzzy/master.svg

   :target: http://travis-ci.org/yougov/fuzzy

Fuzzy is a python library implementing common phonetic algorithms quickly.

Typically this is in string similarity exercises, but they're pretty versatile.

It uses C Extensions (via Cython) for speed.

The algorithms are:

* `Soundex `_

* `NYSIIS `_

* `Double Metaphone `_ Based on Maurice

  Aubrey's C code from his perl implementation.

Usage

=====

The functions are quite easy to use!

>>> import fuzzy

>>> soundex = fuzzy.Soundex(4)

>>> soundex('fuzzy')

'F200'

>>> dmeta = fuzzy.DMetaphone()

>>> dmeta('fuzzy')

['FS', None]

>>> fuzzy.nysiis('fuzzy')

'FASY'

Performance

===========

Fuzzy's Double Metaphone was ~10 times faster than the pure python

implementation by  `Andrew Collins `_

in some recent `testing `_.

Soundex and NYSIIS should be similarly faster. Using iPython's timeit::

  In [3]: timeit soundex('fuzzy')

  1000000 loops, best of 3: 326 ns per loop

  In [4]: timeit dmeta('fuzzy')

  100000 loops, best of 3: 2.18 us per loop

  In [5]: timeit fuzzy.nysiis('fuzzy')

  100000 loops, best of 3: 13.7 us per loop

Distance Metrics

================

We recommend the `Python-Levenshtein `_

module for fast, C based string distance/similarity metrics. Among others

functions it includes:

 * `Levenshtein `_ edit distance

 * `Jaro `_ distance

 * `Jaro-Winkler `_ distance

 * `Hamming distance `_

In testing it's been several times faster than comparable pure python

implementations of those algorithms.