Utilities for computing similarities between sequences
This package provides helpers for computing similarities between arbitrary sequences. Included metrics are:
- Levenshtein distance
- Hamming distance
- Jaccard distance
- Sorensen distance
All distance computations are implemented in pure Python. Levenshtein and Hamming distances are also implemented in C.
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.
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
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, you can pass in tuples of chars:
>>> 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
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 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.
A corresponding iterator ifast_comp is provided, which comes handy for filtering from a long list of strings the one that resemble a given one, e.g.:
>>> g = ifast_comp("foo", ["fo", "bar", "foob", "foo", "foobaz"]) >>> sorted(g) [(0, 'foo'), (1, 'fo'), (1, 'foob')]
ifast_comp can handle 1 million tokens without a problem.
fast_comp and ifast_comp take an optional keyword argument transpositions; if its value evaluates to True (this is not the default), transpositions will be taken into account for the computation of the edit distance.
See the functions documentation (help(funcname)) for more 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.
- 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.
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