Metadata-Version: 1.0
Name: DocumentFeatureSelection
Version: 1.3
Summary: Various methods of feature selection from Text Data
Home-page: https://github.com/Kensuke-Mitsuzawa/DocumentFeatureSelection
Author: kensuke-mi
Author-email: kensuke.mit@gmail.com
License: CeCILL-B
Description: DocumentFeatureSelection
        ========================
        
        what's this?
        ============
        
        This is set of feature selection codes from text data. (About feature
        selection, see
        `here <http://nlp.stanford.edu/IR-book/html/htmledition/feature-selection-1.html>`__
        or
        `here <http://stackoverflow.com/questions/13603882/feature-selection-and-reduction-for-text-classification>`__)
        
        The Feature selection is really important when you use machine learning
        metrics on natural language data. The natural language data usually
        contains a lot of noise information, thus machine learning metrics are
        weak if you don't process any feature selection. (There is some
        exceptions of algorithms like *Decision Tree* or *Random forest* . They
        have feature selection metric inside the algorithm itself)
        
        The feature selection is also useful when you observe your text data.
        With the feature selection, you can get to know which features really
        contribute to specific labels.
        
        Please visit `project page on
        github <https://github.com/Kensuke-Mitsuzawa/DocumentFeatureSelection>`__.
        
        If you find any bugs and you report it to github issue, I'm glad.
        
        Any pull-requests are welcomed.
        
        Supporting methods
        ------------------
        
        This package provides you some feature selection metrics. Currently,
        this package supports following feature selection methods
        
        -  TF-IDF
        -  Pointwise mutual information (PMI)
        -  Strength of Association (SOA)
        -  Bi-Normal Separation (BNS)
        
        Contribution of this package
        ----------------------------
        
        -  Easy interface for pre-processing
        -  Easy interface for accessing feature selection methods
        -  Fast speed computation thanks to sparse matrix and multi-processing
        
        Overview of methods
        ===================
        
        TF-IDF
        ------
        
        This method, in fact, just calls ``TfidfTransformer`` of the
        scikit-learn.
        
        See `scikit-learn
        document <http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html>`__
        about detailed information.
        
        PMI
        ---
        
        PMI is calculated by correlation between *feature* (i.e. token) and
        *category* (i.e. label). Concretely, it makes *cross-table* (or called
        *contingency table*) and calculates joint probability and marginal
        probability on it.
        
        To know more, see
        `reference <https://www.eecis.udel.edu/~trnka/CISC889-11S/lectures/philip-pmi.pdf>`__
        
        In python world, `NLTK <http://www.nltk.org/howto/collocations.html>`__
        and `Other package <https://github.com/Bollegala/svdmi>`__ also provide
        PMI. Check them and choose based on your preference and usage.
        
        SOA
        ---
        
        SOA is improved feature-selection method from PMI. PMI is weak when
        feature has low word frequency. SOA is based on PMI computing, however,
        it is feasible on such low frequency features. Moreover, you can get
        anti-correlation between features and categories.
        
        In this package, SOA formula is from following paper,
        
        ``Saif Mohammad and Svetlana Kiritchenko, "Using Hashtags to Capture Fine Emotion Categories from Tweets", Computational Intelligence, 01/2014; 31(2).``
        
        ::
        
            SOA(w, e)\ =\ log_2\frac{freq(w, e) * freq(\neg{e})}{freq(e) * freq(w, \neg{e})}
        
        Where
        
        -  freq(w, e) is the number of times *w* occurs in an unit(sentence or
           document) with label *e*
        -  freq(w,¬e) is the number of times *w* occurs in units that does not
           have the label *e*
        -  freq(e) is the number of units having the label *e*
        -  freq(¬e) is the number of units having NOT the label *e*
        
        BNS
        ---
        
        BNS is a feature selection method for binary class data. There is
        several methods available for binary class data, such as *information
        gain (IG)*, *chi-squared (CHI)*, *odds ratio (Odds)*.
        
        The problem is when you execute your feature selection on skewed data.
        These methods are weak for such skewed data, however, *BNS* is feasible
        only for skewed data. The following paper shows how BNS is feasible for
        skewed data.
        
        ``Lei Tang and Huan Liu, "Bias Analysis in Text Classification for Highly Skewed Data", 2005``
        
        or
        
        ``George Forman, "An Extensive Empirical Study of Feature Selection Metrics for Text Classification",Journal of Machine Learning Research 3 (2003) 1289-1305``
        
        Requirement
        ===========
        
        -  Python 3.x(checked under Python 3.5)
        
        Setting up
        ==========
        
        install
        -------
        
        ``python setup.py install``
        
        Examples
        ========
        
        See scripts in ``examples/``
        
        Change log
        ==========
        
        For your reference I checked performance under following environment,
        
        -  MacBookPro (late 2015) 3.1 GHz Intel Core i7, 16 GB 1867 MHz DDR3
        -  input data has 98,600 feature dimensions.
        
        0.6 2016/04/02
        --------------
        
        supports PMI and TF-IDF under Python3.x
        
        0.7 2016/04/03
        --------------
        
        Added SOA under Python3.x
        
        0.8 2016/04/03
        --------------
        
        Added BNS under Python3.x
        
        0.9 2016/04/10
        --------------
        
        Removed a bug when calling n\_gram method of DataConverter
        
        1.0 2016/08/22
        --------------
        
        -  Refactored some modules. (I changed some module names. Sorry if you
           have problems...)
        -  Added interface script
        
        1.1 2016/9/16
        -------------
        
        -  Resolved bottleneck point in pre-processing
        -  Fixed a bug which n\_jobs parameter does not work in interface
        -  PMI takes around 6 minutes (with both of multiprocessing and
           multithreading)
        
        1.2 2016/9/16
        -------------
        
        -  A bug in calculating TF-IDF score, this bug was resolved.
        
        1.3 2016/9/28
        -------------
        
        -  Resolved bottleneck poins in pre-processing
        
           -  Introduced dict-vectorising in ScikitLearn
           -  Introduced Cython in calculating PMI & SOA. You can call them with
              ``use_cython=True`` flag. See ``examples/example_python3.py``
        
        -  Performance
        
           -  Cython PMI takes 11.87 sec.
           -  Python multiprocessing PMI takes 513.541 sec. (8.55 min.)
        
Platform: UNKNOWN
