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* Network Analysis: graph centrality and visualization.
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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>.
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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>.
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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`.
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License
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-------
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```python
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from pattern.web import Twitter
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from pattern.en import tag
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from pattern.vector importKNN, count
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**BSD**, see `LICENSE.txt` for further details.
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twitter, knn = Twitter(), KNN()
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for i inrange(1, 3):
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for tweet in twitter.search('#win OR #fail', start=i, count=100):
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s = tweet.text.lower()
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p ='#win'in s and'WIN'or'FAIL'
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v = tag(s)
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v = [word for word, pos in v if pos =='JJ'] # JJ = adjective
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v = count(v) # {'sweet': 1}
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if v:
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knn.train(v, type=p)
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print(knn.classify('sweet potato burger'))
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print(knn.classify('stupid autocorrect'))
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```
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Installation
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------------
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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:
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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:
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```bash
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cd pattern-2.6
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python setup.py install
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```
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If you have pip, you can automatically download and install from the PyPi repository:
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If you have pip, you can automatically download and install from the [PyPI repository](https://pypi.python.org/pypi/Pattern):
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```bash
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pip install pattern
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```
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from pattern.en import parsetree
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```
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Example
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-------
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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`.
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```python
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from pattern.web import Twitter
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from pattern.en import tag
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from pattern.vector importKNN, count
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Documentation
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-------------
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twitter, knn = Twitter(), KNN()
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For documentation and examples see the [user documentation](http://www.clips.ua.ac.be/pages/pattern). If you are a developer, go check out the [developer documentation](http://www.clips.ua.ac.be/pages/pattern-dev).
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for i inrange(1, 3):
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for tweet in twitter.search('#win OR #fail', start=i, count=100):
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s = tweet.text.lower()
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p ='#win'in s and'WIN'or'FAIL'
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v = tag(s)
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v = [word for word, pos in v if pos =='JJ'] # JJ = adjective
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v = count(v) # {'sweet': 1}
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if v:
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knn.train(v, type=p)
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Version
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-------
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print knn.classify('sweet potato burger')
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print knn.classify('stupid autocorrect')
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```
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2.6
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Documentation
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-------------
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License
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-------
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<http://www.clips.ua.ac.be/pages/pattern>
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**BSD**, see `LICENSE.txt` for further details.
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Reference
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---------
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Contribute
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----------
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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.
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The source code is hosted on GitHub and contributions or donations are welcomed. Please have look at the [developer documentation](http://www.clips.ua.ac.be/pages/pattern-dev). If you use Pattern in your work, please cite our reference paper.
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Bundled dependencies
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--------------------
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Pattern is bundled with the following data sets, algorithms and Python packages:
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-**Beautiful Soup**, Leonard Richardson
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-**Brill tagger**, Eric Brill
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-**Brill tagger for Dutch**, Jeroen Geertzen
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-**Brill tagger for German**, Gerold Schneider & Martin Volk
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-**LIBSVM**, Chih-Chung Chang & Chih-Jen Lin
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-**LIBLINEAR**, Rong-En Fan et al.
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-**NetworkX centrality**, Aric Hagberg, Dan Schult & Pieter Swart
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