clips/pattern

 clips / pattern

clips / pattern

 
 

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization. http://www.clips.ua.ac.be/pages/pattern

 README

Pattern

Build
Status Coverage PyPi
version License

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

  • Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser
  • Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
  • Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron)
  • Network Analysis: graph centrality and visualization.

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

Example
workflow

Example

This example trains a classifier on adjectives mined from Twitter using Python 3. 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'))

Installation

Pattern supports Python 2.7 and Python 3.6+. The Python 3 version is currently only available on the development branch. 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

Documentation

For documentation and examples see the user documentation. If you are a developer, go check out the developer documentation.

Version

2.6

License

BSD , see LICENSE.txt for further details.

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. Please have look at 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:

  • 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
  • spelling corrector , Peter Norvig

Acknowledgements

Authors:

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
  • Dan Fu
  • Salvatore Di Dio
  • Vincent Van Asch
  • Frederik Elwert