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Utilities for computing similarities between sequences

Project description

This package provides helpers for computing similarities between arbitrary sequences. Included metrics are:

  • Levenshtein distance (classic and faster version)
  • Hamming distance
  • Jaccard distance
  • Sorensen distance

All distance computations are implemented in pure Python. Levenshtein (classic and faster version) and Hamming distances are also implemented in C.

Installation

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

# python setup.py install

For the C extension to work, you need Python 3.3+, its headers 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 python3.3-dev

Then you should type:

# python3.3 setup.py install –with-c

Note the use of the –with-c switch.

Usage

Fist import the module:

>>> import distance

All functions provided take two arguments, which are the objects to compare. Arguments provided to hamming and levenshtein can be unicode strings, byte strings, lists, or tuples. jaccard and sorensen use sets, so you can pass in any iterable, at the condition that it is hashable.

Typical use case is to compare single words for similarity:

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

If a normalized keyword parameter is supplied to hamming or levenshtein and evaluates to True, the return value of these functions will be a float between 0 and 1 inclusive, where 0 means identic, and 1 totally different:

>>> distance.levenshtein("decide", "resize", normalized=True)
0.5
>>> distance.hamming("decide", "resize", normalized=True)
0.5

jaccard and sorensen return a normalized value per default:

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

Finally, there is a quick_levenshtein function, which computes the Levenshtein distance between two strings up to a value of 2 included, and is quite faster than the classic Levenshtein implementation. The python version comes from [here](http://writingarchives.sakura.ne.jp/fastcomp), and has been rewritten in C.

Also, an iterator type iquick_levenshtein is provided, which comes handy to filter from a long list of strings the ones that resemble a given one:

>>> g = iquick_levenshtein("foo", ["fo", "bar", "foob", "foo", "foobaz"])
>>> sorted(g)
[(0, 'foo'), (1, 'fo'), (1, 'foob')]

See the functions documentation (help(funcname)) for more details.

Have fun!

Implementation details

In the C implementation, unicode strings are handled separately from the other sequence objects. Computing similarities between lists, tuples, and byte strings is likely to be slower.

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

10/11/13: Added quick_levenshtein and iquick_levenshtein.

Project details


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