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https://github.com/axiak/fuzzyset

A simple fuzzy matching set for python strings
https://github.com/axiak/fuzzyset

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A simple fuzzy matching set for python strings

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README 

        
===========================================

fuzzyset - A fuzzy string set for python.

===========================================



fuzzyset is a data structure that performs something akin to fulltext search

against data to determine likely misspellings and approximate string matching.



Usage

-----



The usage is simple. Just add a string to the set, and ask for it later

by using either ``.get`` or ``[]``::



   >>> a = fuzzyset.FuzzySet()

   >>> a.add("michael axiak")

   >>> a.get("micael asiak")

   [(0.8461538461538461, u'michael axiak')]



The result will be a list of ``(score, mached_value)`` tuples.

The score is between 0 and 1, with 1 being a perfect match.



For roughly 15% performance increase, there is also a Cython-implemented

version called ``cfuzzyset``. So you can write the following, akin to

``cStringIO`` and ``cPickle``::



    try:

        from cfuzzyset import cFuzzySet as FuzzySet

    except ImportError:

        from fuzzyset import FuzzySet



Construction Arguments

----------------------



 - iterable: An iterable that yields strings to initialize the data structure with

 - gram_size_lower: The lower bound of gram sizes to use, inclusive (see Theory of operation). Default: 2

 - gram_size_upper: The upper bound of gram sizes to use, inclusive (see Theory of operation). Default: 3

 - use_levenshtein: Whether or not to use the levenshtein distance to determine the match scoring. Default: True



Theory of operation

-------------------



Adding to the data structure

~~~~~~~~~~~~~~~~~~~~~~~~~~~~



First let's look at adding a string, 'michaelich' to an empty set. We first break apart the string into n-grams (strings of length

n). So trigrams of 'michaelich' would look like::



    '-mi'

    'mic'

    'ich'

    'cha'

    'hae'

    'ael'

    'eli'

    'lic'

    'ich'

    'ch-'



Note that fuzzyset will first normalize the string by removing non word characters except for spaces and commas and force

everything to be lowercase.



Next the fuzzyset essentially creates a reverse index on those grams. Maintaining a dictionary that says::



     'mic' -> (1, 0)

     'ich' -> (2, 0)

     ...



And there's a list that looks like::



    [(3.31, 'michaelich')]



Note that we maintain this reverse index for *all* grams from ``gram_size_lower`` to ``gram_size_upper`` in the constructor.

This becomes important in a second.



Retrieving

~~~~~~~~~~



To search the data structure, we take the n-grams of the query string and perform a reverse index look up. To illustrate,

let's consider looking up ``'michael'`` in our fictitious set containing ``'michaelich'`` where the ``gram_size_upper``

and ``gram_size_lower`` parameters are default (3 and 2 respectively).



We begin by considering first all trigrams (the value of ``gram_size_upper``). Those grams are::



   '-mi'

   'mic'

   'ich'

   'cha'

   'el-'



Then we create a list of any element in the set that has *at least one* occurrence of a trigram listed above. Note that

this is just a dictionary lookup 5 times. For each of these matched elements, we compute the `cosine similarity`_ between

each element and the query string. We then sort to get the most similar matched elements.



If ``use_levenshtein`` is false, then we return all top matched elements with the same cosine similarity.



If ``use_levenshtein`` is true, then we truncate the possible search space to 50, compute a score based on the levenshtein

distance (so that we handle transpositions), and return based on that.



In the event that none of the trigrams matched, we try the whole thing again with bigrams (note though that if there are no matches,

the failure to match will be quick). Bigram searching will always be slower because there will be a much larger set to order.



.. _cosine similarity: http://en.wikipedia.org/wiki/Cosine_similarity



Install

--------



``pip install fuzzyset2``



Afterwards, you can import the package simply with::



    try:

        from cfuzzyset import cFuzzySet as FuzzySet

    except ImportError:

        from fuzzyset import FuzzySet



License

-------



BSD



Author

--------



-  Mike Axiak 

-  Adrian Altenhoff