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        "author": "Makoto Yamada",
        "author_email": "makoto.yamada@riken.jp",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": "pyHSICLasso\n===========\n\n`pypi <https://pypi.python.org/pypi/pyHSICLasso>`__ `MIT\nLicense <LICENSE>`__ `Build\nStatus <https://travis-ci.org/riken-aip/pyHSICLasso>`__\n\npyHSICLasso is a package of the Hilbert Schmidt Independence Criterion\nLasso (HSIC Lasso), which is a nonlinear feature selection method\nconsidering the nonlinear input and output relationship.\n\nAdvantage of HSIC Lasso\n-----------------------\n\n-  Can find nonlinearly related features efficiently.\n-  Can obtain a globally optimal solution.\n-  Can deal with both regression and classification problems through\n   kernels.\n\nFeature Selection\n-----------------\n\nThe goal of supervised feature selection is to find a subset of input\nfeatures that are responsible for predicting output values. By using\nthis, you can supplement the dependence of nonlinear input and output\nand you can calculate the optimal solution efficiently for high\ndimensional problem. The effectiveness are demonstrated through feature\nselection experiments for classification and regression with thousands\nof features. Finding a subset of features in high-dimensional supervised\nlearning is an important problem with many real- world applications such\nas gene selection from microarray data, document categorization, and\nprosthesis control.\n\nInstall\n-------\n\n.. code:: sh\n\n   $ pip install -r requirements.txt\n   $ python setup.py install\n\nor\n\n.. code:: sh\n\n   $ pip install pyHSICLasso\n\nUsage\n-----\n\nFirst, pyHSICLasso provides the single entry point as class\n``HSICLasso()``\n\nThis class has the following methods.\n\n-  input\n-  regression\n-  classification\n-  dump\n-  plot_path\n-  plot_dendrogram\n-  plot_heatmap\n-  get_features\n-  get_features_neighbors\n-  get_index\n-  get_index_score\n-  get_index_neighbors\n-  get_index_neighbors_score\n\nThe input format corresponds to the following formats.\n\n-  MATLAB file (.mat)\n-  .csv\n-  .tsv\n-  python\u2019s list\n-  numpy\u2019s ndarray\n\nInput file\n----------\n\nWhen using .mat, .csv, .tsv, we support pandas dataframe. The rows of\nthe dataframe are sample number. The output variable should have\n``class`` tag. If you wish to use your own tag, you need to specify the\noutput variables by list (``output_list=['tag']``) The remaining columns\nare values of each features. The following is a sample data (csv\nformat).\n\n::\n\n   class,v1,v2,v3,v4,v5,v6,v7,v8,v9,v10\n   -1,2,0,0,0,-2,0,-2,0,2,0\n   1,2,2,0,0,-2,0,0,0,2,0\n   ...\n\nWhen using python\u2019s list or numpy\u2019s ndarray, Let each index be sample\nnumber, let values of each features for X[ind] and classification value\nfor Y[ind].\n\nExample\n-------\n\n.. code:: py\n\n   >>> from pyHSICLasso import HSICLasso\n   >>> hsic_lasso = HSICLasso()\n\n   >>> hsic_lasso.input(\"data.mat\")\n\n   >>> hsic_lasso.input(\"data.csv\")\n\n   >>> hsic_lasso.input(\"data.tsv\")\n\n   >>> hsic_lasso.input([[1, 1, 1], [2, 2, 2]], [0, 1])\n\n   >>> hsic_lasso.input(np.array([[1, 1, 1], [2, 2, 2]]), np.array([0, 1]))\n\nYou can specify the number of subset of feature selections with\narguments ``regression`` and\\ ``classification``.\n\n.. code:: py\n\n   >>> hsic_lasso.regression(5)\n\n   >>> hsic_lasso.classification(10)\n\nAbout output method, it is possible to select plots on the graph,\ndetails of the analysis result, output of the feature index.\n\n.. code:: py\n\n   >>> hsic_lasso.plot()\n   # plot the graph\n\n   >>> hsic_lasso.dump()\n   ============================================== HSICLasso : Result ==================================================\n   | Order | Feature      | Score | Top-5 Related Feature (Relatedness Score)                                          |\n   | 1     | 1100         | 1.000 | 100          (0.979), 385          (0.104), 1762         (0.098), 762          (0.098), 1385         (0.097)|\n   | 2     | 100          | 0.537 | 1100         (0.979), 385          (0.100), 1762         (0.095), 762          (0.094), 1385         (0.092)|\n   | 3     | 200          | 0.336 | 1200         (0.979), 264          (0.094), 1482         (0.094), 1264         (0.093), 482          (0.091)|\n   | 4     | 1300         | 0.140 | 300          (0.984), 1041         (0.107), 1450         (0.104), 1869         (0.102), 41           (0.101)|\n   | 5     | 300          | 0.033 | 1300         (0.984), 1041         (0.110), 41           (0.106), 1450         (0.100), 1869         (0.099)|\n   >>> hsic_lasso.get_index()\n   [1099, 99, 199, 1299, 299]\n\n   >>> hsic_lasso.get_index_score()\n   array([0.09723658, 0.05218047, 0.03264885, 0.01360242, 0.00319763])\n\n   >>> hsic_lasso.get_features()\n   ['1100', '100', '200', '1300', '300']\n\n   >>> hsic_lasso.get_index_neighbors(feat_index=0,num_neighbors=5)\n   [99, 384, 1761, 761, 1384]\n\n   >>> hsic_lasso.get_features_neighbors(feat_index=0,num_neighbors=5)\n   ['100', '385', '1762', '762', '1385']\n\n   >>> hsic_lasso.get_index_neighbors_score(feat_index=0,num_neighbors=5)\n   array([0.9789888 , 0.10350618, 0.09757666, 0.09751763, 0.09678892])\n\n\n.. figure:: https://www.fastpic.jp/images.php?file=6530104232.png\n   :alt: graph\n\n   graph\n\nContributors\n------------\n\nDevelopers\n~~~~~~~~~~\n\nName : Makoto Yamada, H\u00e9ctor Climente-Gonz\u00e1lez\n\nE-mail : makoto.yamada@riken.jp\n\n-  `HSICLasso Page <http://www.makotoyamada-ml.com/hsiclasso.html>`__\n-  `HSICLasso Paper <https://arxiv.org/pdf/1202.0515.pdf>`__\n\nDistributor\n~~~~~~~~~~~\n\nName : Hirotaka Suetake\n\nE-mail : hirotaka.suetake@riken.jp\n\n\n",
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        "keywords": "HSIC Lasso HSICLasso feature-selection feature-extraction machine-learning nonlinear data-science",
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