textdistance 2.0.1

TextDistance
============

**TextDistance** -- python library for compare distance between two or
more sequences by many algorithms.

Features:

- 30+ algorithms
- Pure python implementation
- Simple usage
- More than two sequences comparing
- Some algorithms have more than one implementation in one class.
- Optional numpy usage for maximum speed.

Algorithms
----------

Edit based
~~~~~~~~~~

+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| Algorithm | Class | Functions |
+================================================================================================+==========================+==============================+
| `Hamming <https://en.wikipedia.org/wiki/Hamming_distance>`__ | ``Hamming`` | ``hamming`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `MLIPNS <http://www.sial.iias.spb.su/files/386-386-1-PB.pdf>`__ | ``Mlipns`` | ``mlipns`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Levenshtein <https://en.wikipedia.org/wiki/Levenshtein_distance>`__ | ``Levenshtein`` | ``levenshtein`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Damerau-Levenshtein <https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance>`__ | ``DamerauLevenshtein`` | ``damerau_levenshtein`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Jaro-Winkler <https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance>`__ | ``JaroWinkler`` | ``jaro_winkler``, ``jaro`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Strcmp95 <http://cpansearch.perl.org/src/SCW/Text-JaroWinkler-0.1/strcmp95.c>`__ | ``StrCmp95`` | ``strcmp95`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Needleman-Wunsch <https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm>`__ | ``NeedlemanWunsch`` | ``needleman_wunsch`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Gotoh <https://www.cs.umd.edu/class/spring2003/cmsc838t/papers/gotoh1982.pdf>`__ | ``Gotoh`` | ``gotoh`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+
| `Smith-Waterman <https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm>`__ | ``SmithWaterman`` | ``smith_waterman`` |
+------------------------------------------------------------------------------------------------+--------------------------+------------------------------+

Token based
~~~~~~~~~~~

+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| Algorithm | Class | Functions |
+===========================================================================================================================+==================+=============================================+
| `Jaccard index <https://en.wikipedia.org/wiki/Jaccard_index>`__ | ``Jaccard`` | ``jaccard`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Sørensen–Dice coefficient <https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient>`__ | ``Sorensen`` | ``sorensen``, ``sorensen_dice``, ``dice`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Tversky index <https://en.wikipedia.org/wiki/Tversky_index>`__ | ``Tversky`` | ``tversky`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Overlap coefficient <https://en.wikipedia.org/wiki/Overlap_coefficient>`__ | ``Overlap`` | ``overlap`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Tanimoto distance <https://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance>`__ | ``Tanimoto`` | ``tanimoto`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Cosine similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`__ | ``Cosine`` | ``cosine`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Monge-Elkan <https://www.academia.edu/200314/Generalized_Monge-Elkan_Method_for_Approximate_Text_String_Comparison>`__ | ``MongeElkan`` | ``monge_elkan`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+
| `Bag distance <https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/bag.js>`__ | ``Bag`` | ``bag`` |
+---------------------------------------------------------------------------------------------------------------------------+------------------+---------------------------------------------+

Sequence based
~~~~~~~~~~~~~~

+--------------+----------+--------------+
| Algorithm | Class | Functions |
+==============+==========+==============+
| `longest | ``LCSSeq | ``lcsseq`` |
| common | `` | |
| subsequence | | |
| similarity < | | |
| https://en.w | | |
| ikipedia.org | | |
| /wiki/Longes | | |
| t_common_sub | | |
| sequence_pro | | |
| blem>`__ | | |
+--------------+----------+--------------+
| `longest | ``LCSStr | ``lcsstr`` |
| common | `` | |
| substring | | |
| similarity < | | |
| https://docs | | |
| .python.org/ | | |
| 2/library/di | | |
| fflib.html#d | | |
| ifflib.Seque | | |
| nceMatcher>` | | |
| __ | | |
+--------------+----------+--------------+
| `Ratcliff-Ob | ``Ratcli | ``ratcliff_o |
| ershelp | ffObersh | bershelp`` |
| similarity | elp`` | |
| similarity < | | |
| http://colla | | |
| boration.cmc | | |
| .ec.gc.ca/sc | | |
| ience/rpn/bi | | |
| blio/ddj/Web | | |
| site/article | | |
| s/DDJ/1988/8 | | |
| 807/8807c/88 | | |
| 07c.htm>`__ | | |
+--------------+----------+--------------+

Compression based
~~~~~~~~~~~~~~~~~

Work in progress. Now all algorithms compare two strings as array of
bits, not by chars.

``NCD`` - normalized compression distance.

Functions:

1. ``bz2_ncd``
2. ``lzma_ncd``
3. ``arith_ncd``
4. ``rle_ncd``
5. ``bwtrle_ncd``
6. ``zlib_ncd``

Phonetic
~~~~~~~~

+-----------------------------------------------------------------------------------+--------------+--------------+
| Algorithm | Class | Functions |
+===================================================================================+==============+==============+
| `MRA <https://en.wikipedia.org/wiki/Match_rating_approach>`__ | ``MRA`` | ``mra`` |
+-----------------------------------------------------------------------------------+--------------+--------------+
| `Editex <https://anhaidgroup.github.io/py_stringmatching/v0.3.x/Editex.html>`__ | ``Editex`` | ``editex`` |
+-----------------------------------------------------------------------------------+--------------+--------------+

Simple
~~~~~~

+-----------------------+----------------+----------------+
| Algorithm | Class | Functions |
+=======================+================+================+
| Prefix similarity | ``Prefix`` | ``prefix`` |
+-----------------------+----------------+----------------+
| Postfix similarity | ``Postfix`` | ``postfix`` |
+-----------------------+----------------+----------------+
| Length distance | ``Length`` | ``length`` |
+-----------------------+----------------+----------------+
| Identity similarity | ``Identity`` | ``identity`` |
+-----------------------+----------------+----------------+
| Matrix similarity | ``Matrix`` | ``matrix`` |
+-----------------------+----------------+----------------+

Usage
-----

All algorithms have 2 interfaces:

1. Class which can get some algorithm-specific params by init.
2. Class instance with default init params for quick and simple usage.

All algorithms have some common methods:

1. ``.distance(*sequences)`` -- calculate distance between sequences.
2. ``.similarity(*sequences)`` -- calculate similarity for sequences.
3. ``.maximum(*sequences)`` -- maximum possible value for distance and
similarity. ``distance + similarity == maximum``.
4. ``.normalized_distance(*sequences)`` -- normalized distance between
sequences. The return value is a float between 0 and 1, where 0 means
equal, and 1 totally different.
5. ``.normalized_distance(*sequences)`` -- normalized similarity for
sequences. The return value is a float between 0 and 1, where 0 means
totally different, and 1 equal.

Most common init arguments:

1. ``qval`` -- q-value for split sequences into q-grams. Possible
values:

- 1 (default) -- compare sequences by chars.
- 2 or more -- transform sequences to q-grams.
- None -- split sequences by words.

2. ``as_set`` -- for token-based algorithms:

- True -- ``t`` and ``ttt`` is equal.
- False (default) -- ``t`` and ``ttt`` is different.

Example
-------

.. code:: python

import textdistance

textdistance.hamming('test', 'text')
# 1

textdistance.hamming.distance('test', 'text')
# 1

textdistance.hamming.similarity('test', 'text')
# 3

textdistance.hamming.normalized_distance('test', 'text')
# 0.25

textdistance.hamming.normalized_similarity('test', 'text')
# 0.75

textdistance.Hamming(qval=2).distance('test', 'text')
# 2