Pattern

Pattern is a web mining module for the Python programming language. It bundles tools for:

Data Mining: Google + Twitter + Wikipedia API, web crawler, HTML DOM parser
Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
Machine Learning: vector space model, k-means clustering, Naive Bayes + k-NN + SVM classiﬁers
Network Analysis: graph centrality and visualization.

It is well documented and bundled with 30+ examples and 350+ unit tests. The source code is licensed under BSD and available from http://www.clips.ua.ac.be/pages/pattern.

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:

cd pattern-2.6
python setup.py install

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

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.
Put the pattern folder in the standard location for modules so it is available to all scripts:
- c:\python26\Lib\site-packages\ (Windows),
- /Library/Python/2.6/site-packages/ (Mac OS X),
- /usr/lib/python2.6/site-packages/ (Unix).
Add the location of the module to sys.path in your script, before importing it:

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.

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. 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:

Beautiful Soup, Leonard Richardson
Brill tagger, Eric Brill
Brill tagger for Dutch, Jeroen Geertzen
Brill tagger for German, Gerold Schneider & Martin Volk
Brill tagger for Spanish, trained on Wikicorpus (Samuel Reese & Gemma Boleda et al.)
Brill tagger for French, trained on Lefff (Benoît Sagot & Lionel Clément et al.)
Brill tagger for Italian, mined from Wiktionary
English pluralization, Damian Conway
Spanish verb inflection, Fred Jehle
French verb inflection, Bob Salita
Graph JavaScript framework, Aslak Hellesoy & Dave Hoover
LIBSVM, Chih-Chung Chang & Chih-Jen Lin
LIBLINEAR, Rong-En Fan et al.
NetworkX centrality, Aric Hagberg, Dan Schult & Pieter Swart
PDFMiner, Yusuke Shinyama
PyWordNet, Oliver Steele
simplejson, Bob Ippolito
spelling corrector, Peter Norvig
Universal Feed Parser, Mark Pilgrim
WordNet, Christiane Fellbaum et al.

Acknowledgements

Authors:

Tom De Smedt (tom@organisms.be)
Walter Daelemans (walter.daelemans@ua.ac.be)

Contributors (chronological):

Frederik De Bleser
Jason Wiener
Daniel Friesen
Jeroen Geertzen
Thomas Crombez
Ken Williams
Peteris Erins
Rajesh Nair
F. De Smedt
Radim Řehůřek
Tom Loredo
John DeBovis
Thomas Sileo
Gerold Schneider
Martin Volk
Samuel Joseph
Shubhanshu Mishra
Robert Elwell
Fred Jehle
Antoine Mazières + fabelier.org
Rémi de Zoeten + closealert.nl
Kenneth Koch
Jens Grivolla
Fabio Marfia
Steven Loria
Colin Molter + tevizz.com
Peter Bull
Maurizio Sambati

NaturalLanguage/pattern