Metadata-Version: 1.1
Name: pyHSICLasso
Version: 1.2.0
Summary: supervised feature selection considering the dependency of        nonlinear input and output.
Home-page: http://www.makotoyamada-ml.com/hsiclasso.html
Author: Makoto Yamada
Author-email: makoto.yamada@riken.jp
License: MIT
Download-URL: https://github.com/suecharo/pyHSICLasso
Description: pyHSICLasso
        ===========
        
        |Build Status|
        
        pyHSICLasso is a supervised feature selection considering the dependency
        of nonlinear input and output.
        
        What can you do with this?
        --------------------------
        
        The goal of supervised feature selection is to find a subset of input
        features that are responsible for predicting output values. By using
        this, you can supplement the dependence of nonlinear input and output
        and you can calculate the optimal solution efficiently for high
        dimensional problem. The effectiveness are demonstrated through feature
        selection experiments for classification and regression with thousands
        of features. Finding a subset of features in high-dimensional supervised
        learning is an important problem with many real- world applications such
        as gene selection from microarray data, document categorization, and
        prosthesis control.
        
        Install
        -------
        
        .. code:: sh
        
            $ pip install -r requirements.txt
            $ python setup.py install
        
        or
        
        .. code:: sh
        
            $ pip install pyHSICLasso
        
        Usage
        -----
        
        First, pyHSICLasso provides the single entry point as class
        ``HSICLasso()``
        
        This class has the following methods.
        
        -  input
        -  regression
        -  classification
        -  dump
        -  plot
        -  get\_index
        
        The input format corresponds to the following formats.
        
        -  MATLAB file (.mat)
        -  .csv
        -  .tsv
        -  python's list
        -  numpy's ndarray
        
        When using .mat, .csv, .tsv, it is better to use pandas dataframe. The
        rows of the dataframe are sample number. The first column is
        classification value. The remaining columns are values of each features.
        
        When using python's list or numpy's ndarray, Let each index be sample
        number, let values of each features for X[ind] and classification value
        for Y[ind].
        
        .. code:: py
        
            >>> from pyHSICLasso import HSICLasso
            >>> hsic_lasso = HSICLasso()
        
            >>> hsic_lasso.input("data.mat")
        
            >>> hsic_lasso.input("data.csv")
        
            >>> hsic_lasso.input("data.tsv")
        
            >>> hsic_lasso.input([[1, 1, 1], [2, 2, 2]], [0, 1])
        
            >>> hsic_lasso.input(np.array([[1, 1, 1], [2, 2, 2]]), np.array([0, 1]))
        
        You can specify the number of subset of feature selections with
        arguments ``regression`` and\ ``classification``.
        
        .. code:: py
        
            >>> hsic_lasso.regression(5)
        
            >>> hsic_lasso.classification(10)
        
        About output method, it is possible to select plots on the graph,
        details of the analysis result, output of the feature index.
        
        .. code:: py
        
            >>> hsic_lasso.plot()
            # plot the graph
        
            >>> hsic_lasso.dump()
            ============================================== HSICLasso : Result ==================================================
            | Order | Feature     | Score | Top-5 Related Feature (Relatedness Score)                                          |
            | 1     | v1423       | 1.000 | v493    (0.413), v1674   (0.384), v245    (0.384), v267    (0.384), v415    (0.346)|
            | 2     | v513        | 0.765 | v365    (0.563), v1648   (0.487), v1139   (0.456), v1912   (0.450), v241    (0.446)|
            | 3     | v249        | 0.679 | v267    (0.544), v245    (0.544), v822    (0.381), v824    (0.374), v1897   (0.343)|
            | 4     | v1671       | 0.639 | v513    (0.231), v1263   (0.217), v1771   (0.202), v1912   (0.197), v187    (0.179)|
            | 5     | v780        | 0.116 | v513    (0.439), v26     (0.439), v571    (0.410), v127    (0.369), v91     (0.361)|
        
            >>> hsic_lasso.get_index()
            [1422, 512, 248, 1670, 779]
        
            >>> hsic_lasso.get_index_neighbors(feat_index=0,num_neighbors=5)
            [492, 1673, 244, 266, 414]
        
            >>> hsic_lasso.get_index_neighbors_score(feat_index=0,num_neighbors=5)
            array([ 0.412915 ,  0.38446  ,  0.38446  ,  0.38446  ,  0.3462652])
        
        .. figure:: https://www.fastpic.jp/images.php?file=6530104232.png
           :alt: graph
        
           graph
        
        Contributors
        ------------
        
        Auther
        ~~~~~~
        
        Name : Makoto Yamada
        
        E-mail : makoto.yamada@riken.jp
        
        -  `HSICLasso Page <http://www.makotoyamada-ml.com/hsiclasso.html>`__
        -  `HSICLasso Paper <https://arxiv.org/pdf/1202.0515.pdf>`__
        
        Distributor
        ~~~~~~~~~~~
        
        Name : Hirotaka Suetake
        
        E-mail : hirotaka.suetake@riken.jp
        
        .. |Build Status| image:: https://travis-ci.org/suecharo/pyHSICLasso.svg?branch=master
           :target: https://travis-ci.org/suecharo/pyHSICLasso
        
Keywords: HSIC Lasso HSICLasso feature-selection data-science
Platform: python2.7
Platform: python3.4
Platform: python3.5
Platform: python3.6
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
