Distance 0.1.3

Installation

If you don’t want or need to use the C extension, just unpack the archive and run, as root:

# python setup.py install

For the C extension to work, you need the Python source files, and a C compiler (typically Microsoft Visual C++ 2010 on Windows, and GCC on Mac and Linux). On a Debian-like system, you can get all of these with:

# apt-get install gcc pythonX.X-dev

where X.X is the number of your Python version.

Then you should type:

# python setup.py install –with-c

Note the use of the –with-c switch.

Usage

A common use case for this module is to compare single words for similarity:

>>> distance.levenshtein("lenvestein", "levenshtein")
3
>>> distance.hamming("hamming", "hamning")
1

If there is not a one-to-one mapping between sounds and glyphs in your language, or if you want to compare not glyphs, but syllables or phonems, you can pass in tuples of characters:

>>> t1 = ("de", "ci", "si", "ve")
>>> t2 = ("de", "ri", "si", "ve")
>>> distance.levenshtein(t1, t2)
1

Comparing lists of strings can also be useful for computing similarities between sentences, paragraphs, etc.:

>>> sent1 = ['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']
>>> sent2 = ['the', 'lazy', 'fox', 'jumps', 'over', 'the', 'crazy', 'dog']
>>> distance.levenshtein(sent1, sent2)
3

Hamming and Levenshtein distance can be normalized, so that the results of several distance measures can be meaningfully compared. Two strategies are available for Levenshtein: either the length of the shortest alignment between the sequences is taken as factor, or the length of the longer one. Example uses:

>>> distance.hamming("fat", "cat", normalized=True)
0.3333333333333333
>>> distance.nlevenshtein("abc", "acd", method=1)  # shortest alignment
0.6666666666666666
>>> distance.nlevenshtein("abc", "acd", method=2)  # longest alignment
0.5

jaccard and sorensen return a normalized value per default:

>>> distance.sorensen("decide", "resize")
0.5555555555555556
>>> distance.jaccard("decide", "resize")
0.7142857142857143

As for the bonuses, there is a fast_comp function, which computes the distance between two strings up to a value of 2 included. If the distance between the strings is higher than that, -1 is returned. This function is of limited use, but on the other hand it is quite faster than levenshtein. There is also a lcsubstrings function which can be used to find the longest common substrings in two sequences.

Finally, two convenience iterators ilevenshtein and ifast_comp are provided, which are intended to be used for filtering from a long list of sequences the ones that are close to a reference one. They both return a series of tuples (distance, sequence). Example:

>>> tokens = ["fo", "bar", "foob", "foo", "fooba", "foobar"]
>>> sorted(distance.ifast_comp("foo", tokens))
[(0, 'foo'), (1, 'fo'), (1, 'foob'), (2, 'fooba')]
>>> sorted(distance.ilevenshtein("foo", tokens, max_dist=1))
[(0, 'foo'), (1, 'fo'), (1, 'foob')]

ifast_comp is particularly efficient, and can handle 1 million tokens without a problem.

For more informations, see the functions documentation (help(funcname)).

Have fun!

Changelog

20/11/13: * Switched back to using the to-be-deprecated Python unicode api. Good news is that this makes the C extension compatible with Python 2.7+, and that distance computations on unicode strings is now much faster. * Added a C version of lcsubstrings. * Added a new method for computing normalized Levenshtein distance. * Added some tests.

12/11/13: Expanded fast_comp (formerly quick_levenshtein) so that it can handle transpositions. Fixed variable interversions in (C) levenshtein which produced sometimes strange results.

10/11/13: Added quick_levenshtein and iquick_levenshtein.

05/11/13: Added Sorensen and Jaccard metrics, fixed memory issue in Levenshtein.