Metadata-Version: 2.0
Name: ahocorapy
Version: 1.4.2
Summary: ahocorapy - Pure python ahocorasick implementation
Home-page: https://github.com/abusix/ahocorapy
Author: abusix
Author-email: fp@abusix.com
License: MIT
Project-URL: Source, https://github.com/abusix/ahocorapy
Project-URL: Company, https://www.abusix.com/
Description-Content-Type: UNKNOWN
Keywords: keyword,search,purepython,aho-corasick,ahocorasick,abusix
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=2.7
Requires-Dist: future

|Build Status| |Test Coverage| |PyPi Version| |PyPi License| |PyPi
Versions| |PyPi Wheel|

ahocorapy - Fast Many-Keyword Search in Pure Python
===================================================

ahocorapy is a pure python implementation of the Aho-Corasick Algorithm.
Given a list of keywords one can check if at least one of the keywords
exist in a given text in linear time.

Comparison:
-----------

Why another Aho-Corasick implementation?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We started working on this in the beginning of 2016. Our requirements
included unicode support combined with python2.7. That was impossible
with C-extension based libraries (like
`pyahocorasick <https://github.com/WojciechMula/pyahocorasick/>`__).
Pure python libraries were very slow or unusable due to memory
explosion. Since then another pure python library was released
`py-aho-corasick <https://github.com/JanFan/py-aho-corasick>`__. The
repository also contains some discussion about different
implementations. There is also
`acora <https://github.com/scoder/acora>`__, but it includes the note
('current construction algorithm is not suitable for really large sets
of keywords') which really was the case the last time I tested, because
RAM ran out quickly.

Differences
~~~~~~~~~~~

-  Compared to
   `pyahocorasick <https://github.com/WojciechMula/pyahocorasick/>`__
   our library supports unicode in python 2.7 just like
   `py-aho-corasick <https://github.com/JanFan/py-aho-corasick>`__. We
   don't use any C-Extension so the library is not platform dependant.

-  On top of the standard Aho-Corasick longest suffix search, we also
   perform a shortcutting routine in the end, so that our lookup is fast
   while, the setup takes longer. During set up we go through the states
   and directly add transitions that are "offered" by the longest suffix
   or their longest suffixes. This leads to faster lookup times, because
   in the end we only have to follow simple transitions and don't have
   to perform any additional suffix lookup. It also leads to a bigger
   memory footprint, because the number of transitions is higher,
   because they are all included explicitely and not implicitely hidden
   by suffix pointers.

-  We added a small tool that helps you visualize the resulting graph.
   This may help understanding the algorithm, if you'd like. See below.

Performance
~~~~~~~~~~~

I compared the two libraries mentioned above with ahocorapy. We used
50,000 keywords long list and an input text of 34,199 characters. In the
text only one keyword of the list is contained. The setup process was
run once per library and the search process was run 100 times. The
following results are in seconds (not averaged for the lookup).

You can perform this test yourself using
``python tests/ahocorapy_performance_test.py``. (Except for the
pyahocorasick\_py results. These were taken by importing the pure python
version of the code of
`pyahocorasick <https://github.com/WojciechMula/pyahocorasick/>`__. It's
not available through pypi as stated in the code.)

I also added measurements for the pure python libraries with run with
pypy.

These are the results:

+------------------------------------------------------+--------------+-----------------+
| Library (Variant)                                    | Setup (1x)   | Search (100x)   |
+======================================================+==============+=================+
| ahocorapy                                            | 1.2s         | 1.76s           |
+------------------------------------------------------+--------------+-----------------+
| ahocorapy (run with pypy)                            | 0.9s         | 0.09s           |
+------------------------------------------------------+--------------+-----------------+
| pyahocorasick                                        | 0.1s         | 0.06s           |
+------------------------------------------------------+--------------+-----------------+
| pyahocorasick (pure python variant in github repo)   | 0.5s         | 1.68s           |
+------------------------------------------------------+--------------+-----------------+
| py\_aho\_corasick                                    | 1.9s         | 13.2s           |
+------------------------------------------------------+--------------+-----------------+
| py\_aho\_corasick (run with pypy)                    | 1.3s         | 3.71s           |
+------------------------------------------------------+--------------+-----------------+

As expected the C-Extension shatters the pure python implementations.
Even though there is probably still room for optimization in ahocorapy
we are not going to get to the mark that pyahocorasick sets. ahocorapy's
lookups are faster than py\_aho\_corasick. When run with pypy [PyPy
5.1.2 with GCC 5.3.1 20160413] ahocorapy is almost as fast as
pyahocorasick, at least when it comes to searching. The setup overhead
is higher due to the suffix shortcutting mechanism used.

Basic Usage:
------------

Installation
~~~~~~~~~~~~

::

    pip install ahocorapy

Creation of the Search Tree
~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    from ahocorapy.keywordtree import KeywordTree
    kwtree = KeywordTree(case_insensitive=True)
    kwtree.add('malaga')
    kwtree.add('lacrosse')
    kwtree.add('mallorca')
    kwtree.add('mallorca bella')
    kwtree.add('orca')
    kwtree.finalize()

Searching
~~~~~~~~~

.. code:: python

    result = kwtree.search('My favorite islands are malaga and sylt.')
    print(result)

Prints :

.. code:: python

    ('malaga', 24)

The search\_all method returns a generator for all keywords found, or
None if there is none.

.. code:: python

    results = kwtree.search_all('malheur on mallorca bellacrosse')
    for result in results:
        print(result)

Prints :

.. code:: python

    ('mallorca', 11)
    ('orca', 15)
    ('mallorca bella', 11)
    ('lacrosse', 23)

Drawing Graph
-------------

You can print the underlying graph with the Visualizer class. This
feature requires a working pygraphviz library installed.

.. code:: python

    from ahocorapy_visualizer.visualizer import Visualizer
    visualizer = Visualizer()
    visualizer.draw('readme_example.png', kwtree)

The resulting .png of the graph looks like this:

.. figure:: https://raw.githubusercontent.com/abusix/ahocorapy/master/img/readme_example.png
   :alt: Keyword Tree

   graph for kwtree

.. |Build Status| image:: https://img.shields.io/travis/abusix/ahocorapy/master.svg
   :target: https://travis-ci.org/abusix/ahocorapy
.. |Test Coverage| image:: https://img.shields.io/coveralls/github/abusix/ahocorapy/master.svg
   :target: https://coveralls.io/github/abusix/ahocorapy
.. |PyPi Version| image:: https://img.shields.io/pypi/v/ahocorapy.svg
   :target: https://pypi.python.org/pypi/ahocorapy
.. |PyPi License| image:: https://img.shields.io/pypi/l/ahocorapy.svg
   :target: https://pypi.python.org/pypi/ahocorapy
.. |PyPi Versions| image:: https://img.shields.io/pypi/pyversions/ahocorapy.svg
   :target: https://pypi.python.org/pypi/ahocorapy
.. |PyPi Wheel| image:: https://img.shields.io/pypi/wheel/ahocorapy.svg
   :target: https://pypi.python.org/pypi/ahocorapy


