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pattern3 / pattern

Goal: make Pattern compatible with Python 3.
http://www.clips.ua.ac.be/pages/pattern
BSD 3-Clause "New" or "Revised" License
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Pattern

.. image:: https://travis-ci.org/pattern3/pattern.svg?branch=master :target: https://travis-ci.org/pattern3/pattern

Pattern is a web mining module for Python. It has tools for:

It is well documented and bundled with 50+ examples and 350+ unit tests. The source code is licensed under BSD and available from http://www.clips.ua.ac.be/pages/pattern.

.. figure:: http://www.clips.ua.ac.be/media/pattern_schema.gif :alt: Pattern example workflow

Pattern example workflow Version

2.6

License

BSD, see LICENSE.txt for further details.

Installation

Pattern is written for Python 2.5+ (no support for Python 3 yet). The module has no external dependencies except when using LSA in the pattern.vector module, which requires NumPy (installed by default on Mac OS X). To install Pattern so that it is available in all your scripts, unzip the download and from the command line do:

.. code:: bash

cd pattern-2.6
python setup.py install

If you have pip, you can automatically download and install from the PyPi repository:

.. code:: bash

pip install pattern

If none of the above works, you can make Python aware of the module in three ways: - Put the pattern folder in the same folder as your script.

.. code:: python

MODULE = '/users/tom/desktop/pattern'
import sys; if MODULE not in sys.path: sys.path.append(MODULE)
from pattern.en import parsetree

Example

This example trains a classifier on adjectives mined from Twitter. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector, a dictionary of adjective → count items, labeled WIN or FAIL. The classifier uses the vectors to learn which other tweets look more like WIN or more like FAIL.

.. code:: python

from pattern.web    import Twitter
from pattern.en     import tag
from pattern.vector import KNN, count

twitter, knn = Twitter(), KNN()

for i in range(1, 3):
    for tweet in twitter.search('#win OR #fail', start=i, count=100):
        s = tweet.text.lower()
        p = '#win' in s and 'WIN' or 'FAIL'
        v = tag(s)
        v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
        v = count(v) # {'sweet': 1}
        if v:
            knn.train(v, type=p)

print knn.classify('sweet potato burger')
print knn.classify('stupid autocorrect')

Documentation

http://www.clips.ua.ac.be/pages/pattern

Reference

De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2031–2035.

Contribute

The source code is hosted on GitHub and contributions or donations are welcomed, see the developer documentation <http://www.clips.ua.ac.be/pages/pattern#contribute>__. If you use Pattern in your work, please cite our reference paper.

Bundled dependencies

Pattern is bundled with the following data sets, algorithms and Python packages:

Acknowledgements

Authors:

Contributors (chronological):