ilt-editdistance 0.8.2

Fast implementation of the edit distance (Levenshtein distance)

NRC-ILT fork: ilt-editdistance

This fork of editdistance aims to support the needs of the National Research Council Canada's Indigenous Language Technologies (NRC ILT) projects, applying minimal patches to editdistance for our projects to work on all OSes and Python versions we support.

Our fork can be installed via pip install ilt-editdistance but it is a drop-in replacement the regular editdistance package.

You should continue to cite and give credit to the original author if you use our fork.

The only change we plan to make is to keep publishing new patch increments with pre-built wheels as new versions of Python are published.

editdistance

Fast implementation of the edit distance (Levenshtein distance).

This library simply implements Levenshtein distance with C++ and Cython.

The algorithm used in this library is proposed by Heikki Hyyrö, "Explaining and extending the bit-parallel approximate string matching algorithm of Myers", (2001)

Binary wheels

Thanks to pypa/cibuildwheel There are binary wheels on Linux, Mac OS, and Windows.

Install

You can install via pip:

pip install editdistance

Usage

It's quite simple:

import editdistance
editdistance.eval('banana', 'bahama')
# 2L

Simple Benchmark

With IPython, I tried several libraries:

• pyxDamerauLevenshtein
• pylev
• python-Levenshtein

On Python 2.7.5:

a = 'fsffvfdsbbdfvvdavavavavavava'
b = 'fvdaabavvvvvadvdvavavadfsfsdafvvav'
import pylev
timeit pylev.levenshtein(a, b)
# 100 loops, best of 3: 7.48 ms per loop

from pyxdameraulevenshtein import damerau_levenshtein_distance
timeit damerau_levenshtein_distance(a, b)
# 100000 loops, best of 3: 11.4 µs per loop

timeit editdistance.eval(a, b)  # my library
# 100000 loops, best of 3: 3.5 µs per loop

import Levenshtein

timeit Levenshtein.distance(a, b)
# 100000 loops, best of 3: 3.21 µs per loop

Distance with Any Object

Above libraries only support strings. But Sometimes other type of objects such as list of strings(words). I support any iterable, only requires hashable object of it:

Levenshtein.distance(['spam', 'egg'], ['spam', 'ham'])
# ---------------------------------------------------------------------------
# TypeError                                 Traceback (most recent call last)
# <ipython-input-22-3e0b30d145ac> in <module>()
# ----> 1 Levenshtein.distance(['spam', 'egg'], ['spam', 'ham'])
#
# TypeError: distance expected two Strings or two Unicodes

editdistance.eval(['spam', 'egg'], ['spam', 'ham'])
# 1L

So if object's hash is same, it's same. You can provide __hash__ method to your object instances.

Enjoy!

License

It is released under the MIT license.

Copyright (c) 2013 Hiroyuki Tanaka

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.