Text Classification in Python – using Pandas, scikit-learn, IPython Notebook and matplotlib

Text Classification in Python – using Pandas, scikit-learn, IPython Notebook and matplotlib

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  Text Classification inPython – using Pandas, scikit-learn, IPython Notebook and matplotlib Jimmy Lai r97922028 [at] ntu.edu.tw http://tw.linkedin.com/pub/jimmy-lai/27/4a/536 2013/02/17
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  Critical Technologies forBig Data Analysis User Generated Machine Content Generated Data • Please refer http://www.slideshare.net/jimmy Collecting _lai/when-big-data-meet-python for more detail. Storage Infrastructure C/JAVA Computing Python/R Analysis Javascript Visualization 2
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  Fast prototyping -IPython Notebook • Write python code in browser: – Exploit the remote server resources – View the graphical results in web page – Sketch code pieces as blocks – Refer http://www.slideshare.net/jimmy_lai/fast-data-mining-flow- prototyping-using-ipython-notebook for more introduction. Text Classification in Python 3
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  Demo Code • DemoCode: ipython_demo/text_classification_demo.ipynb in https://bitbucket.org/noahsark/slideshare • Ipython Notebook: – Install $ pip install ipython – Execution (under ipython_demo dir) $ ipython notebook --pylab=inline – Open notebook with browser, e.g. http://127.0.0.1:8888 Text Classification in Python 4
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  Machine learning classification • 𝑋 𝑖 = [𝑥1 , 𝑥2 , … , 𝑥 𝑛 ], 𝑥 𝑛 ∈ 𝑅 • 𝑦𝑖 ∈ 𝑁 • 𝑑𝑎𝑡𝑎𝑠𝑒𝑡 = 𝑋, 𝑌 • 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑟 𝑓: 𝑦 𝑖 = 𝑓(𝑋 𝑖 ) Text Classification in Python 5
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  Text classification Feature Generation Model Feature Parameter Selection Tuning Classification Model Training Text Classification in Python 6
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  From: zyeh@caspian.usc.edu (zhenghaoyeh) Subject: Re: Newsgroup Split Organization: University of Southern California, Los Angeles, CA Lines: 18 Dataset: Distribution: world 20 newsgroups NNTP-Posting-Host: caspian.usc.edu Structured Data dataset In article <1quvdoINN3e7@srvr1.engin.umich.edu>, tdawson@engin.umich.edu (Chris Herringshaw) writes: |> Concerning the proposed newsgroup split, I personally am not in favor of |> doing this. I learn an awful lot about all aspects of graphics by reading |> this group, from code to hardware to algorithms. I just think making 5 |> different groups out of this is a wate, and will only result in a few posts |> a week per group. I kind of like the convenience of having one big forum |> for discussing all aspects of graphics. Anyone else feel this way? |> Just curious. |> |> |> Daemon |> Text I agree with you. Of cause I'll try to be a daemon :-) Yeh Text Classification in Python 7 USC
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  Dataset in sklearn •sklearn.datasets – Toy datasets – Download data from http://mldata.org repository • Data format of classification problem – Dataset • data: [raw_data or numerical] • target: [int] • target_names: [str] Text Classification in Python 8
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  Feature extraction fromstructured data (1/2) • Count the frequency of Keyword Count keyword and select the Distribution 2549 keywords as features: Summary 397 ['From', 'Subject', Disclaimer 125 File 257 'Organization', Expires 116 'Distribution', 'Lines'] Subject 11612 • E.g. From 11398 Keywords 943 From: lerxst@wam.umd.edu (where's my thing) Subject: WHAT car is this!? Originator 291 Organization: University of Maryland, College Organization 10872 Park Lines 11317 Distribution: None Internet 140 Lines: 15 To 106 Text Classification in Python 9
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  Feature extraction fromstructured data (2/2) • Separate structured • Transform token matrix data and text data as numerical matrix by – Text data start from sklearn.feature_extract “Line:” ionDictVectorizer • E.g. [{‘a’: 1, ‘b’: 1}, {‘c’: 1}] => [[1, 1, 0], [0, 0, 1]] Text Classification in Python 10
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  Text Feature extractionin sklearn • sklearn.feature_extraction.text • CountVectorizer – Transform articles into token-count matrix • TfidfVectorizer – Transform articles into token-TFIDF matrix • Usage: – fit(): construct token dictionary given dataset – transform(): generate numerical matrix Text Classification in Python 11
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  Text Feature extraction •Analyzer – Preprocessor: str -> str • Default: lowercase • Extra: strip_accents – handle unicode chars – Tokenizer: str -> [str] • Default: re.findall(ur"(?u)bww+b“, string) – Analyzer: str -> [str] 1. Call preprocessor and tokenizer 2. Filter stopwords 3. Generate n-gram tokens Text Classification in Python 12
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  Text Classification inPython 13
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  Feature Selection • Decreasethe number of features: – Reduce the resource usage for faster learning – Remove the most common tokens and the most rare tokens (words with less information): • Parameter for Vectorizer: – max_df – min_df – max_features Text Classification in Python 14
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  Classification Model Training •Common classifiers in sklearn: – sklearn.linear_model – sklearn.svm • Usage: – fit(X, Y): train the model – predict(X): get predicted Y Text Classification in Python 15
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  Cross Validation • Whentuning the parameters of model, let each article as training and testing data alternately to ensure the parameters are not dedicated to some specific articles. – from sklearn.cross_validation import KFold – for train_index, test_index in KFold(10, 2): • train_index = [5 6 7 8 9] • test_index = [0 1 2 3 4] Text Classification in Python 16
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  Performance Evaluation 𝑡𝑝 • sklearn.metrics • 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝+𝑓𝑝 – precision_score 𝑡𝑝 • 𝑟𝑒𝑐𝑎𝑙𝑙 = – recall_score 𝑡𝑝+𝑓𝑛 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑟𝑒𝑐𝑎𝑙𝑙 – f1_score • 𝑓1𝑠𝑐𝑜𝑟𝑒 = 2 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙 Text Classification in Python 17 Source: http://en.wikipedia.org/wiki/Precision_and_recall
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  Visualization 1. Matplotlib 2. plot()function of Series, DataFrame Text Classification in Python 18
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  Experiment Result • Futureworks: – Feature selection by statistics or dimension reduction – Parameter tuning – Ensemble models Text Classification in Python 19