fuzzysearch

Find parts of long text or data, allowing for some changes/typos.

https://github.com/taleinat/fuzzysearch

Keywords

Repository

Find parts of long text or data, allowing for some changes/typos.

README.rst

===========
fuzzysearch
===========

.. image:: https://img.shields.io/pypi/v/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Latest Version

.. image:: https://img.shields.io/coveralls/taleinat/fuzzysearch.svg?branch=master
    :target: https://coveralls.io/r/taleinat/fuzzysearch?branch=master
    :alt: Test Coverage

.. image:: https://img.shields.io/pypi/wheel/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Wheels

.. image:: https://img.shields.io/pypi/pyversions/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Supported Python versions

.. image:: https://img.shields.io/pypi/implementation/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch
    :alt: Supported Python implementations

.. image:: https://img.shields.io/pypi/l/fuzzysearch.svg?style=flat
    :target: https://pypi.python.org/pypi/fuzzysearch/
    :alt: License

Fuzzy search: Find parts of long text or data, allowing for some
changes/typos.

Highly optimized, simple to use, does one thing well.

.. code:: python

    >>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1)
    [Match(start=3, end=9, dist=1, matched="PATERN")]

* Two simple functions to use: one for in-memory data and one for files

  * Fastest search algorithm is chosen automatically

* Levenshtein Distance metric with configurable parameters

  * Separately configure the max. allowed distance, substitutions, deletions
    and/or insertions

* Advanced algorithms with optional C and Cython optimizations

* Properly handles Unicode; special optimizations for binary data

* Simple installation:
   * ``pip install fuzzysearch`` just works
   * pure-Python fallbacks for compiled modules
   * only one dependency (``attrs``)

* Extensively tested

* Free software: `MIT license `_

For more info, see the `documentation `_.


How is this different than FuzzyWuzzy or RapidFuzz?
---------------------------------------------------

The main difference is that fuzzysearch searches for fuzzy matches through
long texts or data. FuzzyWuzzy and RapidFuzz, on the other hand, are intended
for fuzzy comparison of pairs of strings, identifying how closely they match
according to some metric such as the Levenshtein distance.

These are very different use-cases, and the solutions are very different as
well.


How is this different than ElasticSearch and Lucene?
----------------------------------------------------

The main difference is that fuzzysearch does no indexing or other
preparations; it directly searches through the given text or data for a given
sub-string. Therefore, it is much simpler to use compared to systems based on
text indexing.


Installation
------------

``fuzzysearch`` supports Python versions 3.8+, as well as PyPy 3.9 and 3.10.

.. code::

    $ pip install fuzzysearch

This will work even if installing the C and Cython extensions fails, using
pure-Python fallbacks.


Usage
-----
Just call ``find_near_matches()`` with the sub-sequence you're looking for,
the sequence to search, and the matching parameters:

.. code:: python

    >>> from fuzzysearch import find_near_matches
    # search for 'PATTERN' with a maximum Levenshtein Distance of 1
    >>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1)
    [Match(start=3, end=9, dist=1, matched="PATERN")]

To search in a file, use ``find_near_matches_in_file()``:

.. code:: python

    >>> from fuzzysearch import find_near_matches_in_file
    >>> with open('data_file', 'rb') as f:
    ...     find_near_matches_in_file(b'PATTERN', f, max_l_dist=1)
    [Match(start=3, end=9, dist=1, matched="PATERN")]


Examples
--------

*fuzzysearch* is great for ad-hoc searches of genetic data, such as DNA or
protein sequences, before reaching for more complex tools:

.. code:: python

    >>> sequence = '''\
    GACTAGCACTGTAGGGATAACAATTTCACACAGGTGGACAATTACATTGAAAATCACAGATTGGTCACACACACA
    TTGGACATACATAGAAACACACACACATACATTAGATACGAACATAGAAACACACATTAGACGCGTACATAGACA
    CAAACACATTGACAGGCAGTTCAGATGATGACGCCCGACTGATACTCGCGTAGTCGTGGGAGGCAAGGCACACAG
    GGGATAGG'''
    >>> subsequence = 'TGCACTGTAGGGATAACAAT' # distance = 1
    >>> find_near_matches(subsequence, sequence, max_l_dist=2)
    [Match(start=3, end=24, dist=1, matched="TAGCACTGTAGGGATAACAAT")]

BioPython sequences are also supported:

.. code:: python

    >>> from Bio.Seq import Seq
    >>> from Bio.Alphabet import IUPAC
    >>> sequence = Seq('''\
    GACTAGCACTGTAGGGATAACAATTTCACACAGGTGGACAATTACATTGAAAATCACAGATTGGTCACACACACA
    TTGGACATACATAGAAACACACACACATACATTAGATACGAACATAGAAACACACATTAGACGCGTACATAGACA
    CAAACACATTGACAGGCAGTTCAGATGATGACGCCCGACTGATACTCGCGTAGTCGTGGGAGGCAAGGCACACAG
    GGGATAGG''', IUPAC.unambiguous_dna)
    >>> subsequence = Seq('TGCACTGTAGGGATAACAAT', IUPAC.unambiguous_dna)
    >>> find_near_matches(subsequence, sequence, max_l_dist=2)
    [Match(start=3, end=24, dist=1, matched="TAGCACTGTAGGGATAACAAT")]


Matching Criteria
-----------------
The search function supports four possible match criteria, which may be
supplied in any combination:

* maximum Levenshtein distance (``max_l_dist``)

* maximum # of subsitutions

* maximum # of deletions ("delete" = skip a character in the sub-sequence)

* maximum # of insertions ("insert" = skip a character in the sequence)

Not supplying a criterion means that there is no limit for it. For this reason,
one must always supply ``max_l_dist`` and/or all other criteria.

.. code:: python

    >>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1)
    [Match(start=3, end=9, dist=1, matched="PATERN")]

    # this will not match since max-deletions is set to zero
    >>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1, max_deletions=0)
    []

    # note that a deletion + insertion may be combined to match a substution
    >>> find_near_matches('PATTERN', '---PAT-ERN---', max_deletions=1, max_insertions=1, max_substitutions=0)
    [Match(start=3, end=10, dist=1, matched="PAT-ERN")] # the Levenshtein distance is still 1

    # ... but deletion + insertion may also match other, non-substitution differences
    >>> find_near_matches('PATTERN', '---PATERRN---', max_deletions=1, max_insertions=1, max_substitutions=0)
    [Match(start=3, end=10, dist=2, matched="PATERRN")]

Owner

GitHub Events

Committers

Last synced: over 2 years ago

Issues and Pull Requests

Last synced: 7 months ago

Packages