Helpers 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 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
Fist import the module:
>>> import distance
All functions provided take two arguments, which are the objects to compare. Arguments provided to
levenshtein should support the sequence protocol: unicode strings, byte strings, lists, and tuples are ok.
sorensen use sets, so you can pass 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
To compare the results returned by the available metrics, a
normalize keyword parameter can be supplied to
levenshtein. If it evaluates to True, the return value will be a float between 0 and 1 inclusive. 0 means identic, and 1 totally different. Jaccard and sorensen return a normalized value per default.
>>> distance.levenshtein("decide", "resize", normalized=True) 0.5 >>> distance.hamming("decide", "resize", normalized=True) 0.5 >>> distance.sorensen("decide", "resize") 0.5555555555555556 >>> distance.jaccard("decide", "resize") 0.7142857142857143
In the C implementation, unicode strings are handled separately from the other sequence objects, in an efficient manner. Computing similarities between lists, tuples, and byte strings is likely to be slower, in particular for byte objects, which are internally converted to tuples.
05/11/13: Added Sorensen and Jaccard metrics, fixed memory issue in Levenshtein.