Metadata-Version: 1.0
Name: DocumentFeatureSelection
Version: 1.3.4
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`
        
        ### Note
        
        You might see error message during running this command, such as
        
        ```
        We failed to install numpy automatically. Try installing numpy manually or Try anaconda distribution.
        ```
        
        This is because `setup.py` tries to instal numpy and scipy with `pip`, however it fails. 
        We need numpy and scipy before we install `scikit-learn`.
        
        In this case, you take following choice
        
        * You install `numpy` and `scipy` manually
        * You use `anaconda` python distribution. Please visit [their site](https://www.continuum.io/downloads).
        
        # 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/basic_example.py`
        * Performance
            * Cython PMI takes 11.87 sec.
            * Python multiprocessing PMI takes 513.541 sec. (8.55 min.)
            
        ## 1.3.2 2016/11/29
        
        * You can put persisted-dict-object on disk-drive instead of dict-object on memory.
            * You can put huge dict object as data-source of `interface.run_feature_selection()`
            * See example `examples/huge_data_example.py`
            
            
        ## 1.3.3 2016/11/30
        
        * It introduced file-cache for keeping huge objects during computation.
        
        
        ## 1.3.4 2017/1/27
        
        * It introduced cython computation for BNS.
        * I cleaned up misery dependencies between modules.
Platform: UNKNOWN
