Metadata-Version: 2.4
Name: pyspellchecker
Version: 0.9.0
Summary: Pure python spell checker based on work by Peter Norvig
Author-email: Tyler Barrus <barrust@gmail.com>
License-Expression: MIT
Project-URL: homepage, https://github.com/barrust/pyspellchecker
Project-URL: bug-tracker, https://github.com/barrust/pyspellchecker/issues
Project-URL: documentation, https://pyspellchecker.readthedocs.io/
Keywords: python,spelling,natural language processing,nlp,typo,checker
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Utilities
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: >=3.10
Description-Content-Type: text/x-rst
License-File: LICENSE
Dynamic: license-file

pyspellchecker
===============================================================================

.. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :target: https://opensource.org/licenses/MIT/
    :alt: License
.. image:: https://img.shields.io/github/release/barrust/pyspellchecker.svg
    :target: https://github.com/barrust/pyspellchecker/releases
    :alt: GitHub release
.. image:: https://github.com/barrust/pyspellchecker/workflows/Python%20package/badge.svg
    :target: https://github.com/barrust/pyspellchecker/actions?query=workflow%3A%22Python+package%22
    :alt: Build Status
.. image:: https://codecov.io/gh/barrust/pyspellchecker/branch/master/graph/badge.svg?token=OdETiNgz9k
    :target: https://codecov.io/gh/barrust/pyspellchecker
    :alt: Test Coverage
.. image:: https://badge.fury.io/py/pyspellchecker.svg
    :target: https://badge.fury.io/py/pyspellchecker
    :alt: PyPi Package
.. image:: http://pepy.tech/badge/pyspellchecker
    :target: https://pepy.tech/project/pyspellchecker
    :alt: Downloads


Pure Python Spell Checking based on `Peter
Norvig's <https://norvig.com/spell-correct.html>`__ blog post on setting
up a simple spell checking algorithm.

It uses a `Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance>`__
algorithm to find permutations within an edit distance of 2 from the
original word. It then compares all permutations (insertions, deletions,
replacements, and transpositions) to known words in a word frequency
list. Those words that are found more often in the frequency list are
**more likely** the correct results.

``pyspellchecker`` supports multiple languages including English, Spanish,
German, French, Portuguese, Arabic and Basque. For information on how the dictionaries were
created and how they can be updated and improved, please see the
**Dictionary Creation and Updating** section of the readme!

``pyspellchecker`` supports **Python 3**

``pyspellchecker`` allows for the setting of the Levenshtein Distance (up to two) to check.
For longer words, it is highly recommended to use a distance of 1 and not the
default 2. See the quickstart to find how one can change the distance parameter.


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

The easiest method to install is using pip:

.. code:: bash

    pip install pyspellchecker

To build from source:

.. code:: bash

    git clone https://github.com/barrust/pyspellchecker.git
    cd pyspellchecker
    python -m build

For *python 2.7* support, install `release 0.5.6 <https://github.com/barrust/pyspellchecker/releases/tag/v0.5.6>`__
but note that no future updates will support *python 2*.

.. code:: bash

    pip install pyspellchecker==0.5.6


Quickstart
-------------------------------------------------------------------------------

After installation, using ``pyspellchecker`` should be fairly straight
forward:

.. code:: python

    from spellchecker import SpellChecker

    spell = SpellChecker()

    # find those words that may be misspelled
    misspelled = spell.unknown(['something', 'is', 'hapenning', 'here'])

    for word in misspelled:
        # Get the one `most likely` answer
        print(spell.correction(word))

        # Get a list of `likely` options
        print(spell.candidates(word))


If the Word Frequency list is not to your liking, you can add additional
text to generate a more appropriate list for your use case.

.. code:: python

    from spellchecker import SpellChecker

    spell = SpellChecker()  # loads default word frequency list
    spell.word_frequency.load_text_file('./my_free_text_doc.txt')

    # if I just want to make sure some words are not flagged as misspelled
    spell.word_frequency.load_words(['microsoft', 'apple', 'google'])
    spell.known(['microsoft', 'google'])  # will return both now!


If the words that you wish to check are long, it is recommended to reduce the
`distance` to 1. This can be accomplished either when initializing the spell
check class or after the fact.

.. code:: python

    from spellchecker import SpellChecker

    spell = SpellChecker(distance=1)  # set at initialization

    # do some work on longer words

    spell.distance = 2  # set the distance parameter back to the default


Non-English Dictionaries
-------------------------------------------------------------------------------

``pyspellchecker`` supports several default dictionaries as part of the default
package. Each is simple to use when initializing the dictionary:

.. code:: python

    from spellchecker import SpellChecker

    english = SpellChecker()  # the default is English (language='en')
    spanish = SpellChecker(language='es')  # use the Spanish Dictionary
    russian = SpellChecker(language='ru')  # use the Russian Dictionary
    arabic = SpellChecker(language='ar')   # use the Arabic Dictionary


The currently supported dictionaries are:

* English       - 'en'
* Spanish       - 'es'
* French        - 'fr'
* Portuguese    - 'pt'
* German        - 'de'
* Italian       - 'it'
* Russian       - 'ru'
* Arabic        - 'ar'
* Basque        - 'eu'
* Latvian       - 'lv'
* Dutch         - 'nl'
* Persian       - 'fa'

Dictionary Creation and Updating
-------------------------------------------------------------------------------

The creation of the dictionaries is, unfortunately, not an exact science. I have provided a script that, given a text file of sentences (in this case from
`OpenSubtitles <http://opus.nlpl.eu/OpenSubtitles2018.php>`__) it will generate a word frequency list based on the words found within the text. The script then attempts to ***clean up*** the word frequency by, for example, removing words with invalid characters (usually from other languages), removing low count terms (misspellings?) and attempts to enforce rules as available (no more than one accent per word in Spanish). Then it removes words from a list of known words that are to be removed. It then adds words into the dictionary that are known to be missing or were removed for being too low frequency.

The script can be found here: ``scripts/build_dictionary.py```. The original word frequency list parsed from OpenSubtitles can be found in the ```scripts/data/``` folder along with each language's *include* and *exclude* text files.

Any help in updating and maintaining the dictionaries would be greatly desired. To do this, a
`discussion <https://github.com/barrust/pyspellchecker/discussions>`__ could be started on GitHub or pull requests to update the include and exclude files could be added.


Additional Methods
-------------------------------------------------------------------------------

`On-line documentation <http://pyspellchecker.readthedocs.io/en/latest/>`__ is available; below contains the cliff-notes version of some of the available functions:


``correction(word)``: Returns the most probable result for the
misspelled word

``candidates(word)``: Returns a set of possible candidates for the
misspelled word

``known([words])``: Returns those words that are in the word frequency
list

``unknown([words])``: Returns those words that are not in the frequency
list

``word_probability(word)``: The frequency of the given word out of all
words in the frequency list

The following are less likely to be needed by the user but are available:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``edit_distance_1(word)``: Returns a set of all strings at a Levenshtein
Distance of one based on the alphabet of the selected language

``edit_distance_2(word)``: Returns a set of all strings at a Levenshtein
Distance of two based on the alphabet of the selected language


Credits
-------------------------------------------------------------------------------

* `Peter Norvig <https://norvig.com/spell-correct.html>`__ blog post on setting up a simple spell checking algorithm
* P Lison and J Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
