Ml M thru python.ipynb

{
    "nbformat": 4, 
    "metadata": {
        "language_info": {
            "version": "3.5.2", 
            "nbconvert_exporter": "python", 
            "name": "python", 
            "pygments_lexer": "ipython3", 
            "mimetype": "text/x-python", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }, 
            "file_extension": ".py"
        }, 
        "kernelspec": {
            "language": "python", 
            "name": "python3-spark21", 
            "display_name": "Python 3.5 (Experimental) with Spark 2.1"
        }
    }, 
    "cells": [
        {
            "execution_count": 1, 
            "metadata": {}, 
            "source": "# basic ML algos and no Deep learning stuff..", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "#### Steps\n    Define problem\n    Analyse data : descriptive stats\n    prep data: transforms.. normalizaiton and etc.. better expose the structure to the algo\n    Evaluate algorithms: use a test harness to eval several algorithms\n    improve results: Use algorithm tuning and ensemble methods\n    present results...", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "Python Ecosystem for Machine Learning.  \nPython and SciPy Crash Course.  \nLoad Datasets from CSV.  \nUnderstand Data With Descriptive Statistics. (Analyze Data)  \nUnderstand Data With Visualization. (Analyze Data)  \nPre-Process Data. (Prepare Data)  \nFeature Selection. (Prepare Data)  \nResampling Methods. (Evaluate Algorithms)  \nAlgorithm Evaluation Metrics. (Evaluate Algorithms)  \nSpot-Check Classi\fcation Algorithms. (Evaluate Algorithms)  \nSpot-Check Regression Algorithms. (Evaluate Algorithms)  \nModel Selection. (Evaluate Algorithms)  \nPipelines. (Evaluate Algorithms)  \nEnsemble Methods. (Improve Results)  \nAlgorithm Parameter Tuning. (Improve Results)  \nModel Finalization. (Present Results)  ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 2, 
            "metadata": {}, 
            "source": "# scipy\nimport scipy\nprint('scipy: {}'.format(scipy.__version__))\n# numpy\nimport numpy\nprint('numpy: {}'.format(numpy.__version__))\n# matplotlib\nimport matplotlib\nprint('matplotlib: {}'.format(matplotlib.__version__))\n# pandas\nimport pandas\nprint('pandas: {}'.format(pandas.__version__))\n# scikit-learn\nimport sklearn\nprint('sklearn: {}'.format(sklearn.__version__))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "scipy: 0.17.1\nnumpy: 1.14.0\nmatplotlib: 1.5.1\npandas: 0.18.1\nsklearn: 0.19.1\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 3, 
            "metadata": {}, 
            "source": "!pip install -U scikit-learn\n# upgrade.. becaus most of the docs refer to version .18", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Requirement already up-to-date: scikit-learn in /gpfs/global_fs01/sym_shared/YPProdSpark/user/sa0d-218294071c3031-cf228a6161b9/.local/lib/python3.5/site-packages\r\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 4, 
            "metadata": {}, 
            "source": "i = 0\nwhile i < 10:\n    print (i)\n    i += 1", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 5, 
            "metadata": {}, 
            "source": "a = (1, 2, 3)\nprint (a)\n# tuples are read only", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "(1, 2, 3)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 6, 
            "metadata": {}, 
            "source": "mylist = [1, 2, 3]\n#print(\"Zeroth Value: %d\") % mylist[0]\nprint(\"Zeroth Value:\", mylist[0])\nmylist.append(4)\nprint(\"List Length:\", len(mylist))\nfor value in mylist:\n    print (value)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Zeroth Value: 1\nList Length: 4\n1\n2\n3\n4\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 7, 
            "metadata": {}, 
            "source": "mydict = {'a': 1, 'b': 2, 'c': 3}\nprint(\"A value: \",mydict['a'])\nmydict['a'] = 11\nprint(\"A value: \", mydict['a'])\nprint(\"Keys: \", mydict.keys())\nprint(\"Values: \",mydict.values())\nfor key in mydict.keys():\n    print (mydict[key])\n    \nprint(\"asadasd\")", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "A value:  1\nA value:  11\nKeys:  dict_keys(['a', 'b', 'c'])\nValues:  dict_values([11, 2, 3])\n11\n2\n3\nasadasd\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 8, 
            "metadata": {}, 
            "source": "#matplotlib\n# basic line plot\nimport matplotlib.pyplot as plt\nimport numpy\nmyarray = numpy.array([1, 2, 3])\nplt.plot(myarray)\nplt.xlabel('some x axis')\nplt.ylabel('some y axis')\nplt.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7f1722bf4748>", 
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SyIuIPwKek5n3D7oWaVTYAZEkSX3nVzDSiIiIwyPixoh4NCIejIilEbFJ+VpE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\nQmCC\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 9, 
            "metadata": {}, 
            "source": "x = numpy.array([1,2,3,4,5,6,7,8,2])\ny = numpy.array([2,1,2,3,1,2,1,4,5])\n\nplt.scatter(x,y)\nplt.xlabel('fooo')\nplt.ylabel('baar')\nplt.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7f172043f048>", 
                        "image/png": 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PYd++fXVHO+K5brRe3NbUTWovDIdwLJDA3rqDHE5btmxjcnI3MA7cCowzObmbsbHzak4m\n143Wi9uauslRdQdYLCICuBS4LjNvrjvP4TIzM8PExDVUbxJb21O3sn9/MjGxjdnZWfr7+2tMeORy\n3Wi9uK2p2zSqMACXA88Anl964vbt29m4ceNB08bGxhgbGztM0dbO/Px8+9aZSx45C4C5uTnfKGri\nutF6cVvT4dZqtWi1WgdNW1hY6Hh+jSkMEfFXwChwRmbeXnr+jh07GBoaOvzBDoOTTjqpfesLHPhm\nAfB5APr6+tY7ktpcN1ovbms63A71JXp6eprh4c4OEWzEMQztsvAS4AWZeWvdeQ63gYEBNm0aZcOG\nC6mGI28Dxtmw4SI2bRr1W0WNXDdaL25r6ja1F4aIuJyqXm8B7o6IJ7T/jq452mHVao0zMnI6sI3q\nRyHbGBk5nVZrvOZkct1ovbitqZtEZtYbIOJ+ql9FLPXqzLziEM8fAqampqa6dpfEYrOzs8zNzdHX\n1+c3ioZx3Wi9uK1pvSzaJTGcmdMreW3txzBkZu2jHHXq7+/3DaKhXDdaL25r6gZH9Ie1JElaHguD\nJEkqsjBIkqQiC4MkSSqyMEiSpCILgyRJKrIwSJKkIguDJEkqsjBIkqQiC4MkSSqyMEiSpCILgyRJ\nKrIwSJKkIguDJEkqsjBIkqQiC4MkSSqyMEiSpCILgyRJKrIwSJKkIguDJEkqsjBIkqQiC4MkSSqy\nMEiSpCILgyRJKrIwSJKkIguDJEkqsjBIkqQiC4MkSSqyMEiSpCILgyRJKrIwSJKkIguDJEkqsjBI\nkqQiC4MkSSqyMEiSpCILgyRJKrIwSJKkIguDJEkqsjBIkqQiC4MkSSpqRGGIiDMi4qqI+EFE3B8R\nL647kyRJOqARhQE4Bvgq8AYga84iSZKWOKruAACZuQvYBRARUXMcrcLMzAzz8/P09fXR399fdxz1\nMLe15nLd9KamjDCoy+3du5fNm89hcHCQ0dFRBgYG2Lz5HPbt21d3NPUYt7Xmct30NguD1sSWLduY\nnNwNjAO3AuNMTu5mbOy8mpOp17itNZfrprc1YpeEutvMzAwTE9dQvUlsbU/dyv79ycTENmZnZx2W\n1JpwW2su103v69rCsH37djZu3HjQtLGxMcbGxmpKdOSan59v3zpzySNnATA3N+cbhdaE21pzuW6a\np9Vq0Wq1Dpq2sLDQ8fy6tjDs2LGDoaGhumMIOOmkk9q3vsCBbxYAnwegr69vvSOpR7mtNZfrpnkO\n9SV6enqa4eHhjubXiGMYIuKYiDglIp7VnnRi+/7xtQbTsgwMDLBp0ygbNlxINRx5GzDOhg0XsWnT\nqN8qtGbc1prLddP7GlEYgFOBrwBTVOdheD8wDbyjzlBavlZrnJGR04FtwAnANkZGTqfVGq85mXqN\n21pzuW56W2R213mSImIImJqamnKXRAPNzs4yNzfn76912LmtNZfrprkW7ZIYzszplby2a49hUDP1\n9/f7BqF14bbWXK6b3tSUXRKSJKnBLAySJKnIwiBJkoosDJIkqcjCIEmSiiwMkiSpyMIgSZKKLAyS\nJKnIwiBJkoosDJIkqcjCIEmSiiwMkiSpyMIgSZKKLAySJKnIwiBJkoosDJIkqcjCIEmSiiwMkiSp\nyMIgSZKKLAySJKnIwiBJkoosDJIkqcjCIEmSiiwMkiSpyMIgSZKKLAySJKnIwiBJkoosDJIkqcjC\nIEmSiiwMkiSpyMIgSZKKLAySJKnIwiBJkoosDJIkqcjCIEmSiiwMkiSpyMIgSZKKLAySJKnIwiBJ\nkoosDJIkqagxhSEi/lNEfCcifh4RuyPiOXVnWi+tVqvuCGuql5anl5YFXJ4m66VlAZenFzWiMETE\nHwDvB94GPBu4CZiIiN+oNdg66bUNsZeWp5eWBVyeJuulZQGXpxc1ojAA24G/zswrMvMW4ALgHuA1\n9caSJEnQgMIQEQ8HhoHPPTAtMxOYBJ5XVy5JknRA7YUB+A1gA3DHkul3AE9c/ziSJGmpo+oO0IGj\nAfbs2VN3jjWzsLDA9PR03THWTC8tTy8tC7g8TdZLywIuT1Mt+uw8eqWvjWr0vz7tXRL3AP8hM69a\nNP1jwMbMPHfJ87cAH1/XkJIk9ZatmfmJlbyg9hGGzLw3IqaAFwJXAUREtO9fdoiXTABbge8Cv1in\nmJIk9YKjgadRfZauSO0jDAAR8QrgY1S/jriB6lcTLweenpk/qTGaJEmiASMMAJn5qfY5F94JPAH4\nKrDJsiBJUjM0YoRBkiQ1WxN+VilJkhrOwiBJkoq6rjD0ykWqIuKMiLgqIn4QEfdHxIvrztSpiLg4\nIm6IiJ9GxB0R8emIGKg7V6ci4oKIuCkiFtp/X4yIzXXnWgsR8eb29vaBurN0IiLe1s6/+O/munOt\nRkQ8OSKujIg7I+Ke9rY3VHeuTrTfm5eun/sj4kN1Z1upiHhYRPxFRHy7vV7mIuKSunOtRkQ8OiIu\njYjvtpfpuog4dbmv76rC0GMXqTqG6uDONwDdfiDJGcCHgNOAEeDhwD9FxCNrTdW524A3AUNUpy2/\nFvjHiDi51lSr1C7Xr6P6f9PNvkF1cPQT23+/W2+czkXEscD1wC+BTcDJwJ8C++rMtQqncmC9PBH4\nfar3t0/VGapDbwb+mOo9+unAnwF/FhFvrDXV6vx3qlMWbAWeCXwWmIyIJy3nxV110GNE7Aa+nJkX\nte8H1Zv7ZZn53lrDrUJE3A+8dPGJq7pZu8D9GDgzM6+rO89aiIi7gP+SmR+tO0snIuLRwBTweuAt\nwFcy80/qTbVyEfE24CWZ2ZXfwJeKiPcAz8vMs+rOcjhExKXAaGZ23YhjRHwG+FFmvnbRtH8A7snM\nP6wvWWci4mjgZ8CLMnPXouk3Atdk5ltL8+iaEQYvUtVVjqX6VrG37iCr1R6WfCXwKOBLdedZhQ8D\nn8nMa+sOsgb627vy5iNiPCKOrzvQKrwIuDEiPtXenTcdEefXHWottN+zt1J9q+1GXwReGBH9ABFx\nCvB84JpaU3XuKKrrNv1yyfSfs8xRukach2GZHuoiVYPrH0eH0h71uRS4LjO7dt9yRDyTqiA80MrP\nbV96veu0C8+zqIaLu91u4FXAt4AnAW8HvhARz8zMu2vM1akTqUZ93g+8C3gucFlE/DIzr6w12eqd\nC2wE/q7uIB16D/DrwC0RsZ/qC/afZ+Yn643Vmcz814j4EvCWiLiF6rNzC9UX7tnlzKObCoO6w+XA\nM6iaeDe7BTiF6g3v5cAVEXFmt5WGiHgKVYEbycx7686zWpm5+HS234iIG4DvAa8AunF30cOAGzLz\nLe37N7XL6gVAtxeG1wA7M/NHdQfp0B9QfaC+EriZqnR/MCJ+2MVl7jzgb4EfAPcB08AnqEbvi7qp\nMNwJ7Kc62GmxJwDdukH2lIj4K2AUOCMzb687z2pk5n3At9t3vxIRzwUuovo22E2GgccB0+3RH6hG\n6s5sH7z1iOymA5mWyMyFiJgB+urO0qHbgaWX3t0DvKyGLGsmIk6gOgD6pXVnWYX3Au/OzP/Rvv/N\niHgacDFdWuYy8zvAC9oHpP96Zt4REZ/kwHvdQ+qaYxja344euEgVcNBFqr5YVy5V2mXhJcALMvPW\nuvMcBg8DHlF3iA5MAv+O6tvRKe2/G4Fx4JRuLgvwq4M5+6g+eLvR9fz/u1QHqUZNutlrqIa8u3V/\nP1THLe1fMu1+uuhz88Fk5s/bZeE4ql/n/K/lvK6bRhgAPgB8rH11ywcuUvUoqgtXdZWIOIbqje6B\nb30ntg+q2ZuZt9WXbOUi4nJgDHgxcHdEPDAKtJCZXXdF0Yj4S2AncCvwGKoDt84Czq4zVyfa+/UP\nOpYkIu4G7srMpd9sGy8i3gd8huoD9TeBdwD3Aq06c63CDuD6iLiY6qeHpwHnA699yFc1WPuL3KuA\nj2Xm/TXHWY3PAJdExPeBb1L9zHo78De1plqFiDib6jPnW0A/1SjKzSzzM7SrCkOPXaTqVOB/U/2a\nIKkOeoLqAKHX1BWqQxdQLcM/L5n+auCKdU+zeo+nWg9PAhaArwFn98gvDKC7z/vxFKp9ro8FfgJc\nB5yemXfVmqpDmXljRJxLdYDdW4DvABd164F1bSPA8XTnMSWLvRH4C6pfGD0e+CHwkfa0brUReDdV\n2d4L/ANwSWYuHUk5pK46D4MkSapH1++LkSRJh5+FQZIkFVkYJElSkYVBkiQVWRgkSVKRhUGSJBVZ\nGCRJUpGFQZIkFVkYJD2kiPivEXFXROyPiN+uO4+kenimR0kPKiI2U12Y5iyq0xbf2eXXB5DUoa66\nloSkddcH3J6ZX647iKR6uUtC0iFFxEeBy4ATIuL+iPh2RPxaRFwWEXdExM8j4v9ExKlLXndWRHw5\nIn4RET+MiHdHxMMWPV6ch6TmsTBIejAXAm8Fvk91ddjnAO8DzgW2Ac8G5oCJiDgWICKeDFwNfBn4\nbaormf4RcMmi+T7kPCQ1k8cwSHpQEXER1eWWT4yIRwH7gD/MzL9vP34U8F1gR2a+PyLeBZybmc9Y\nNI/XA+/JzI3Lmcc6Lp6kFfAYBknLdRLVe8YXH5iQmfdFxA3Aye1JTwe+tOR11wOPjoinAMctYx6S\nGshdEpIkqcjCIGm55oF7gec/MKG9O+E5wDfbk/YAz1vyut8FfpaZ31/mPCQ1kIVB0rJk5j3AR4D3\nRcSmiHgG8DfAI4G/bT/tcuD4iPhQRAxGxEuAtwPvX8E8JDWQxzBIWok3AwFcATwGuBE4OzMXADLz\nhxExSvVLiK8Ce4H/BrxrufOQ1Ez+SkKSJBW5S0KSJBVZGCRJUpGFQZIkFVkYJElSkYVBkiQVWRgk\nSVKRhUGSJBVZGCRJUpGFQZIkFVkYJElSkYVBkiQVWRgkSVLR/wMBGrAMmaUt2wAAAABJRU5ErkJg\ngg==\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 10, 
            "metadata": {}, 
            "source": "#Pandas\n#series is a 1D array where rows and columns can be labeled\nimport pandas\nmyarray = numpy.array([1, 2, 3])\nrownames = ['a', 'b', 'c']\nmyseries = pandas.Series(myarray, index=rownames)\nprint(myseries)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "a    1\nb    2\nc    3\ndtype: int64\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 11, 
            "metadata": {}, 
            "source": "print(myseries[\"a\"])", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "1\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 12, 
            "metadata": {}, 
            "source": "print(myseries[0])", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "1\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 13, 
            "metadata": {}, 
            "source": "myarray = numpy.array([[1,2,3],[3,4,5]])\nrownames = ['a','b']\ncolnames = ['one','two','three']\nmydataframe = pandas.DataFrame(myarray, rownames, colnames)\nprint (mydataframe)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "   one  two  three\na    1    2      3\nb    3    4      5\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 14, 
            "metadata": {}, 
            "source": "print(mydataframe.one)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "a    1\nb    3\nName: one, dtype: int64\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 15, 
            "metadata": {}, 
            "source": "print(mydataframe['one'])", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "a    1\nb    3\nName: one, dtype: int64\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 16, 
            "metadata": {}, 
            "source": "!mkdir pimaIndians\n", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "mkdir: cannot create directory \u2018pimaIndians\u2019: File exists\r\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 17, 
            "metadata": {}, 
            "source": "!cd pimaIndians/\n", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 18, 
            "metadata": {}, 
            "source": "!pwd", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "/gpfs/fs01/user/sa0d-218294071c3031-cf228a6161b9/notebook/work\r\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 19, 
            "metadata": {}, 
            "source": "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data -O pimaIndians.csv", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "--2018-01-27 06:15:22--  https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\nResolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249\nConnecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 23279 (23K) [text/plain]\nSaving to: \u2018pimaIndians.csv\u2019\n\n100%[======================================>] 23,279      --.-K/s   in 0.04s   \n\n2018-01-27 06:15:22 (622 KB/s) - \u2018pimaIndians.csv\u2019 saved [23279/23279]\n\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 20, 
            "metadata": {}, 
            "source": "import csv\nimport numpy\nfilename = 'pimaIndians.csv'\nraw_data = open(filename, 'r') # remove the b .. because it indicates binary\nreader = csv.reader(raw_data, delimiter=',', quoting=csv.QUOTE_NONE)\nx = list(reader)\ndata = numpy.array(x).astype('float')\nprint(data.shape)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "(768, 9)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 21, 
            "metadata": {}, 
            "source": "# the above was using basic python\n#now using numPy\n# Load CSV using NumPy\nfrom numpy import loadtxt\nfilename = 'pimaIndians.csv'\nraw_data = open(filename, 'rb')\ndata = loadtxt(raw_data, delimiter=\",\")\nprint(data.shape)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "(768, 9)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 22, 
            "metadata": {}, 
            "source": "# Load CSV from URL using NumPy\nfrom numpy import loadtxt\n# from urllib import urlopen # deprecated in python 3\nfrom urllib.request import urlopen\nurl = 'https://goo.gl/vhm1eU'\nraw_data = urlopen(url)\ndataset = loadtxt(raw_data, delimiter=\",\")\nprint(dataset.shape)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "(768, 9)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 23, 
            "metadata": {}, 
            "source": "# Load CSV using Pandas\nfrom pandas import read_csv\nfilename = 'pimaIndians.csv'\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndata = read_csv(filename, names=names)\nprint(data.shape)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "(768, 9)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 24, 
            "metadata": {}, 
            "source": "#URL\nfrom pandas import read_csv\nurl = 'https://goo.gl/vhm1eU'\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndata = read_csv(url, names=names)\nprint(data.shape)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "(768, 9)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 25, 
            "metadata": {}, 
            "source": "data.head() # by default 5 \n#data is the name of the df we used in pandas\n# pandas by default stores data in DF", 
            "outputs": [
                {
                    "execution_count": 25, 
                    "data": {
                        "text/plain": "   preg  plas  pres  skin  test  mass   pedi  age  class\n0     6   148    72    35     0  33.6  0.627   50      1\n1     1    85    66    29     0  26.6  0.351   31      0\n2     8   183    64     0     0  23.3  0.672   32      1\n3     1    89    66    23    94  28.1  0.167   21      0\n4     0   137    40    35   168  43.1  2.288   33      1", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>preg</th>\n      <th>plas</th>\n      <th>pres</th>\n      <th>skin</th>\n      <th>test</th>\n      <th>mass</th>\n      <th>pedi</th>\n      <th>age</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 26, 
            "metadata": {}, 
            "source": "data.shape", 
            "outputs": [
                {
                    "execution_count": 26, 
                    "data": {
                        "text/plain": "(768, 9)"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 27, 
            "metadata": {}, 
            "source": "data.dtypes # data type", 
            "outputs": [
                {
                    "execution_count": 27, 
                    "data": {
                        "text/plain": "preg       int64\nplas       int64\npres       int64\nskin       int64\ntest       int64\nmass     float64\npedi     float64\nage        int64\nclass      int64\ndtype: object"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 28, 
            "metadata": {}, 
            "source": "data.describe() # descriptive stats", 
            "outputs": [
                {
                    "execution_count": 28, 
                    "data": {
                        "text/plain": "             preg        plas        pres        skin        test        mass  \\\ncount  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \nmean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \nstd      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \nmin      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \nmax     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n\n             pedi         age       class  \ncount  768.000000  768.000000  768.000000  \nmean     0.471876   33.240885    0.348958  \nstd      0.331329   11.760232    0.476951  \nmin      0.078000   21.000000    0.000000  \n25%      0.243750   24.000000    0.000000  \n50%      0.372500   29.000000    0.000000  \n75%      0.626250   41.000000    1.000000  \nmax      2.420000   81.000000    1.000000  ", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>preg</th>\n      <th>plas</th>\n      <th>pres</th>\n      <th>skin</th>\n      <th>test</th>\n      <th>mass</th>\n      <th>pedi</th>\n      <th>age</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.845052</td>\n      <td>120.894531</td>\n      <td>69.105469</td>\n      <td>20.536458</td>\n      <td>79.799479</td>\n      <td>31.992578</td>\n      <td>0.471876</td>\n      <td>33.240885</td>\n      <td>0.348958</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.369578</td>\n      <td>31.972618</td>\n      <td>19.355807</td>\n      <td>15.952218</td>\n      <td>115.244002</td>\n      <td>7.884160</td>\n      <td>0.331329</td>\n      <td>11.760232</td>\n      <td>0.476951</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.078000</td>\n      <td>21.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>99.000000</td>\n      <td>62.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>27.300000</td>\n      <td>0.243750</td>\n      <td>24.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.000000</td>\n      <td>117.000000</td>\n      <td>72.000000</td>\n      <td>23.000000</td>\n      <td>30.500000</td>\n      <td>32.000000</td>\n      <td>0.372500</td>\n      <td>29.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.000000</td>\n      <td>140.250000</td>\n      <td>80.000000</td>\n      <td>32.000000</td>\n      <td>127.250000</td>\n      <td>36.600000</td>\n      <td>0.626250</td>\n      <td>41.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>17.000000</td>\n      <td>199.000000</td>\n      <td>122.000000</td>\n      <td>99.000000</td>\n      <td>846.000000</td>\n      <td>67.100000</td>\n      <td>2.420000</td>\n      <td>81.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "        1. Number of times pregnant\n       2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n       3. Diastolic blood pressure (mm Hg)\n       4. Triceps skin fold thickness (mm)\n       5. 2-Hour serum insulin (mu U/ml)\n       6. Body mass index (weight in kg/(height in m)^2)\n       7. Diabetes pedigree function\n       8. Age (years)\n       9. Class variable (0 or 1)", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 29, 
            "metadata": {}, 
            "source": "data.groupby('class').size()\n# kind of a check for uneven representation\n# highly imbalances data sets.. with more representaitons of one kind.. than the other.. \n# would reqire special handling at this stage...", 
            "outputs": [
                {
                    "execution_count": 29, 
                    "data": {
                        "text/plain": "class\n0    500\n1    268\ndtype: int64"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 30, 
            "metadata": {}, 
            "source": "# zeros are nearly double .. the ones in the class column", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 31, 
            "metadata": {}, 
            "source": "#pearson correlaiton assumes normal distribution of attributes\n# range -1 to 1\nfrom pandas import set_option\n\nset_option('display.width', 100)\nset_option('precision', 3)\ncorrelations = data.corr(method='pearson')\nprint(correlations)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "        preg   plas   pres   skin   test   mass   pedi    age  class\npreg   1.000  0.129  0.141 -0.082 -0.074  0.018 -0.034  0.544  0.222\nplas   0.129  1.000  0.153  0.057  0.331  0.221  0.137  0.264  0.467\npres   0.141  0.153  1.000  0.207  0.089  0.282  0.041  0.240  0.065\nskin  -0.082  0.057  0.207  1.000  0.437  0.393  0.184 -0.114  0.075\ntest  -0.074  0.331  0.089  0.437  1.000  0.198  0.185 -0.042  0.131\nmass   0.018  0.221  0.282  0.393  0.198  1.000  0.141  0.036  0.293\npedi  -0.034  0.137  0.041  0.184  0.185  0.141  1.000  0.034  0.174\nage    0.544  0.264  0.240 -0.114 -0.042  0.036  0.034  1.000  0.238\nclass  0.222  0.467  0.065  0.075  0.131  0.293  0.174  0.238  1.000\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 32, 
            "metadata": {}, 
            "source": "# many ML assume gaussian distribution\n# no skew\n# check for skew\ndata.skew()", 
            "outputs": [
                {
                    "execution_count": 32, 
                    "data": {
                        "text/plain": "preg     0.902\nplas     0.174\npres    -1.844\nskin     0.109\ntest     2.272\nmass    -0.429\npedi     1.920\nage      1.130\nclass    0.635\ndtype: float64"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 33, 
            "metadata": {}, 
            "source": "# + skew... more data on the left side.. right tailed distribution\n# - skew mode data on the right side.. left tailed distribution", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "### Visualizing the data\n", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 34, 
            "metadata": {}, 
            "source": "# univariate histograms\nfrom matplotlib import pyplot\ndata.hist()\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7f17163fe3c8>", 
                        "image/png": 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XwXUinge2qnC9kn0eEvhd2MWWaT6X/qInro68HFI1ou3UgH2m8QcyreeVbkci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2+FTedNB/ZsU9bG4ToVQRb9bEVOutuOPHW7FXnpfTsKuS9akfaeKUM/0+pmmzZtgenlRPo/B4ZH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\nYII=\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 35, 
            "metadata": {}, 
            "source": "# desity plots\n%matplotlib inline\nfrom matplotlib import pyplot\nfrom pandas import read_csv\nfilename = 'pimaIndians.csv'\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndata = read_csv(filename, names=names)\n\"\"\"data.plot(kind='density', subplots=True, layout=(3,3), sharex=False)\n\"\"\"\n#Does this cause the kernet to restart...?  https://github.com/ipython/ipython/issues/5903", 
            "outputs": [
                {
                    "execution_count": 35, 
                    "data": {
                        "text/plain": "\"data.plot(kind='density', subplots=True, layout=(3,3), sharex=False)\\n\""
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 36, 
            "metadata": {}, 
            "source": "data.plot(kind='box', subplots=True, layout=(3,3), sharex=False, sharey=False)\npyplot.show() # plenty of outliers", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7f170c2fcba8>", 
                        "image/png": 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/6cRaqoVVaeebA7OBLxKMP3X0A+4o7tIS50aCJvxIQhjwAeBBSX3A7bEYRPu0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\nBsD/A48gscdOgZgPAAAAAElFTkSuQmCC\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 37, 
            "metadata": {}, 
            "source": "correlations = data.corr()\n# plot correlation matrix\nfig = pyplot.figure()\nax = fig.add_subplot(111) #\"111\" means \"1x1 grid, first subplot\" and \"234\" means \"2x3 grid, 4th subplot\".\ncax = ax.matshow(correlations, vmin=-1, vmax=1)\nfig.colorbar(cax)\n\nticks = numpy.arange(0,9,1)\nax.set_xticks(ticks)\nax.set_yticks(ticks)\nax.set_xticklabels(names)\nax.set_yticklabels(names)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7f16b77f8518>", 
                        "image/png": 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2WlhWxVZN4uxv+tPAmNOBic0OwszM8qPfdIdGxGJgcbPjMDPLmw5aaK8iHXQ0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\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 38, 
            "metadata": {}, 
            "source": "#some machine learning algorithms like linear\n#and logistic regression can have poor performance if there are highly correlated input variables in the data", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 39, 
            "metadata": {}, 
            "source": "from pandas.tools.plotting import scatter_matrix\nscatter_matrix(data)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7f16b77d6a90>", 
                        "image/png": 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ImclAGhS/itDNekQZz0CEYD/QQVNTjLNnO4E8YrFDxGJ1pKQ4qK9ffI2trJuR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KXUjUiK8PMmVMjNVSQm2rn++pgzao4MxMDAIat1Bxs2bMDl8g6K9g9g4EK129v7lBerdQdHjhwh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XGO0tLSUZdo7J2hGBLbM55+XstCgJrT+k4UAerLkHX8C0Sd+eiXx6lg/pQ3zYXkv4FUoLdS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\ni6Wq6gCvvlqFw9FOVdWS4SPIrwQcOnSIw4dHn8TX2toKwMDAk9hsP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aa1wPc0TfvWJNr7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                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "### preparing the data translates to one or more of the foll\n1. Rescale data.  \n2. Standardize data.\n3. Normalize data.\n4. Binarize data.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 40, 
            "metadata": {}, 
            "source": "# load data\n#split data to input and output[if avl with the data] variables\n# apply pre processing transform to input variables\n# summarize the change", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "Fit and Multiple Transform.  \nCombined Fit-And-Transform.  \nAre preprocessing methods available from scikit learn\nhttp://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 41, 
            "metadata": {}, 
            "source": "# Rescale data (between 0 and 1)\nfrom pandas import read_csv\nfrom numpy import set_printoptions\nfrom sklearn.preprocessing import MinMaxScaler\n\nfilename = 'pimaIndians.csv'\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndataframe = read_csv(filename, names=names)\n\narray = dataframe.values\n# separate array into input and output components\nX = array[:,0:8] # all rows.. o to 8 columns\nY = array[:,8]\nscaler = MinMaxScaler(feature_range=(0, 1))\nrescaledX = scaler.fit_transform(X)\n# summarize transformed data\nset_printoptions(precision=3)", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 42, 
            "metadata": {}, 
            "source": "print(rescaledX[0:5,:]) # resclared is a pandas DF.. rows 0 to 5 all cols.. all values in the range 0 to 1", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[[0.353 0.744 0.59  0.354 0.    0.501 0.234 0.483]\n [0.059 0.427 0.541 0.293 0.    0.396 0.117 0.167]\n [0.471 0.92  0.525 0.    0.    0.347 0.254 0.183]\n [0.059 0.447 0.541 0.232 0.111 0.419 0.038 0.   ]\n [0.    0.688 0.328 0.354 0.199 0.642 0.944 0.2  ]]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 43, 
            "metadata": {}, 
            "source": "from sklearn.preprocessing import StandardScaler\n# standaridiser : get starndard normal distribution..\n# of what were already normal distributions.. for uniformity\n\n# You call the fit() function to prepare the parameters of the data into the preprocessing class.. in this case standardiser\n# transform.. is used to transform all the data .. basedon the parameters that were obtained in the fit phase..\n\nscaler = StandardScaler().fit(X)\nrescaledX = scaler.transform(X)\n# summarize transformed data\nset_printoptions(precision=3)\nprint(rescaledX[0:5,:]) # op is stand normal distri mu = 0 sd = 1", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[[ 0.64   0.848  0.15   0.907 -0.693  0.204  0.468  1.426]\n [-0.845 -1.123 -0.161  0.531 -0.693 -0.684 -0.365 -0.191]\n [ 1.234  1.944 -0.264 -1.288 -0.693 -1.103  0.604 -0.106]\n [-0.845 -0.998 -0.161  0.155  0.123 -0.494 -0.921 -1.042]\n [-1.142  0.504 -1.505  0.907  0.766  1.41   5.485 -0.02 ]]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 44, 
            "metadata": {}, 
            "source": "from sklearn.preprocessing import Normalizer\narray = dataframe.values\n# separate array into input and output components\nX = array[:,0:8]\nY = array[:,8]\n\n#Normalizing transform each row to have a length of 1\n# use ful can be useful for sparse datasets (lots of zeros) with attributes of varying scales when using\n#algorithms that weight input values such as neural networks and algorithms that use distance measures\n\n\nscaler = Normalizer().fit(X)\nnormalizedX = scaler.transform(X)\n# summarize transformed data\nset_printoptions(precision=3)\nprint(normalizedX[0:5,:])\n", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[[0.034 0.828 0.403 0.196 0.    0.188 0.004 0.28 ]\n [0.008 0.716 0.556 0.244 0.    0.224 0.003 0.261]\n [0.04  0.924 0.323 0.    0.    0.118 0.003 0.162]\n [0.007 0.588 0.436 0.152 0.622 0.186 0.001 0.139]\n [0.    0.596 0.174 0.152 0.731 0.188 0.01  0.144]]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 45, 
            "metadata": {}, 
            "source": "# binarize.. make the op binary based on some threshold...\nfrom sklearn.preprocessing import Binarizer\nX = array[:,0:8]\nY = array[:,8]\nbinarizer = Binarizer(threshold=0.0).fit(X)\nbinaryX = binarizer.transform(X)\n# summarize transformed data\nset_printoptions(precision=3)\nprint(binaryX[0:5,:])", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[[1. 1. 1. 1. 0. 1. 1. 1.]\n [1. 1. 1. 1. 0. 1. 1. 1.]\n [1. 1. 1. 0. 0. 1. 1. 1.]\n [1. 1. 1. 1. 1. 1. 1. 1.]\n [0. 1. 1. 1. 1. 1. 1. 1.]]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "### Feature selection for machine learning...\n1. Univariate Selection.\n2. Recursive Feature Elimination.\n3. Principle Component Analysis.\n4. Feature Importance.\n\nFeature seleciton reduces overfitting, improves accuracy, reduces training time, ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "the SelectKBest class2 that can be used with a suite of different statistical tests to select a specifc number of features.   \nwe use the chi-squared (chi2) statistical test for non-negative features to select 4 of the best  \nfeatures from the Pima Indians onset of diabetes dataset.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 46, 
            "metadata": {}, 
            "source": "from sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\narray = dataframe.values\nX = array[:,0:8]\nY = array[:,8]\n\n# feature extraction\n\ntest = SelectKBest(score_func=chi2, k=4)\nfit = test.fit(X, Y)\n# summarize scores\nset_printoptions(precision=3)\nprint(fit.scores_)\nfeatures = fit.transform(X)\n# summarize selected features\nprint(features[0:5,:])", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[ 111.52  1411.887   17.605   53.108 2175.565  127.669    5.393  181.304]\n[[148.    0.   33.6  50. ]\n [ 85.    0.   26.6  31. ]\n [183.    0.   23.3  32. ]\n [ 89.   94.   28.1  21. ]\n [137.  168.   43.1  33. ]]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 47, 
            "metadata": {}, 
            "source": "# Feature Extraction with RFE\nfrom pandas import read_csv\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import LogisticRegression\n\n# Recursive Feature Elimination (or RFE) works by recursively removing attributes and building a model on those attributes that remain.\n# feature extraction\nfilename = 'pimaIndians.csv'\nnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\ndataframe = read_csv(filename, names=names)\narray = dataframe.values\nX = array[:,0:8]\nY = array[:,8]\n\nmodel = LogisticRegression()\nrfe = RFE(model, 3)\n#fit = rfe.fit(X, Y)\n## The above line causes the kernel to crash\n#print(\"Num Features:\", fit.n_features_)\n#print(\"Selected Features:\", fit.support_)\n#print(\"Feature Ranking: \", fit.ranking_)\n", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 48, 
            "metadata": {}, 
            "source": "#Trying the  code from  http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html\n\nfrom sklearn.datasets import make_friedman1\nfrom sklearn.feature_selection import RFE\nfrom sklearn.svm import SVR\n\nX, y = make_friedman1(n_samples=50, n_features=10, random_state=0)\nestimator = SVR(kernel=\"linear\")\nselector = RFE(estimator, 5, step=1)\nselector = selector.fit(X, y)\nselector.support_ \n\nselector.ranking_\n", 
            "outputs": [
                {
                    "execution_count": 48, 
                    "data": {
                        "text/plain": "array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 49, 
            "metadata": {}, 
            "source": "from pandas import read_csv\nfrom sklearn.decomposition import PCA\n\n# feature extraction\npca = PCA(n_components=3)\n#fit = pca.fit(X)\n\n#the above lie causes kernel to crash\n\n\n# summarize components\n#print(\"Explained Variance:\", fit.explained_variance_ratio_)\n#print(fit.components_)", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 50, 
            "metadata": {}, 
            "source": "# Feature Importance with Extra Trees Classifier\nfrom pandas import read_csv\nfrom sklearn.ensemble import ExtraTreesClassifier\n# load data\n\narray = dataframe.values\nX = array[:,0:8]\nY = array[:,8]\n# feature extraction\nmodel = ExtraTreesClassifier()\nmodel.fit(X, Y)\nprint(model.feature_importances_)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[0.114 0.222 0.106 0.082 0.073 0.144 0.119 0.14 ]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "### Evaluating performance of machine learning algorithms...\n    1: compare how accurate it could predict against the existing data..\n    2) resampling methods", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 51, 
            "metadata": {}, 
            "source": "\n# split data to training and test\nfrom sklearn.cross_validation  import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n\nX = array[:,0:8]\nY = array[:,8]\ntest_size = 0.33\nseed = 7\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed)\nmodel = LogisticRegression()\n#model.fit(X_train, Y_train)\n#result = model.score(X_test, Y_test)\n#print(\"Accuracy: %.3f%%\") % (result*100.0)", 
            "outputs": [
                {
                    "name": "stderr", 
                    "output_type": "stream", 
                    "text": "/gpfs/fs01/user/sa0d-218294071c3031-cf228a6161b9/.local/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 52, 
            "metadata": {}, 
            "source": "#!pip install -U scikit-learn", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 53, 
            "metadata": {}, 
            "source": "# K fold cross validation\n\n# from sklearn.cross_validation import KFold\n# from sklearn.cross_validation import cross_val_score\n\nfrom sklearn.model_selection  import KFold\nfrom sklearn.model_selection  import cross_val_score\n# change in the module which contains the above ...\n\nfrom sklearn.linear_model import LogisticRegression\n\nnum_folds = 10\nseed = 7\nkfold = KFold(n_splits = num_folds, random_state=seed)\nmodel = LogisticRegression()\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(\"Accuracy: \",(results.mean()*100.0, results.std()*100.0))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Accuracy:  (76.95146958304852, 4.841051924567195)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 54, 
            "metadata": {}, 
            "source": "# leave oneout validaiton\n# similar to k fold.. except k here is the number of observatrions.. and 1 is left out\n# computationally expensive.\n\nfrom sklearn.model_selection import LeaveOneOut\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LogisticRegression\n\narray = dataframe.values\nX = array[:,0:8]\nY = array[:,8]\nnum_folds = 10\nloocv = LeaveOneOut()\nmodel = LogisticRegression()\nresults = cross_val_score(model, X, Y, cv=loocv)\nprint(\"Accuracy:\",results.mean()*100.0, results.std()*100.0)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Accuracy: 76.82291666666666 42.1963403803346\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 55, 
            "metadata": {}, 
            "source": "# much more variaion in the results... ? :(", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 56, 
            "metadata": {}, 
            "source": "# Evaluate using Shuffle Split Cross Validation\n\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LogisticRegression\n \narray = dataframe.values\nX = array[:,0:8]\nY = array[:,8]\nn_splits = 10\ntest_size = 0.33\nseed = 7\nkfold = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=seed)\nmodel = LogisticRegression()\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))\n", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Accuracy: (76.53543307086613, 1.6721858725665606)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 57, 
            "metadata": {}, 
            "source": "# shuffle split like the k fold .. but makes random splits.. repeatedly.. can take a split proportion...", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 58, 
            "metadata": {}, 
            "source": "# K fold.. the gold standard..\n# use test split.. for faster o/p.. slow algos...\n# leave one out.. or repeated random splits.. for intermediate stuff..", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "### ML algorithm evaluaiton metrics...", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "    Evaluaiton:\n    classificaiotn pima indians\n    Regression Boston housing...\n    A 10-fold cross validation test harness is used to demonstrate each metric, because this is the most likely scenario you will use when employing different algorithm evaluation metrics.\n    ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "    Evaluaiton metrics...:\n    \n    Classiffcation Accuracy.\n    Logarithmic Loss.\n    Area Under ROC Curve.\n    Confusion Matrix.\n    Classiffcation Report.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 59, 
            "metadata": {}, 
            "source": "model = LogisticRegression()\nscoring = 'accuracy'\nresults = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\nprint(\"Accuracy: \", (results.mean(), results.std()))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Accuracy:  (0.7653543307086613, 0.016721858725665607)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 60, 
            "metadata": {}, 
            "source": "# same model as bove\n# log loss\nscoring = 'neg_log_loss'\nresults = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\nprint(\"Logloss: \",(results.mean(), results.std()))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Logloss:  (-0.5031080303529742, 0.015732268151751543)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "### a smaller log loss is better.. with 0 being perfect..\n### the measure is inverted to be ascending when using the cross val score() function.\n    cross validation.cross val score function does allow the use of different scoring metrics\n     all scores are reported so that they can be sorted in **ascending** order (largest score is best). \n     Some evaluation metrics (like mean squared error) are naturally **descending** scores (the smallest score is best) and as such are reported as negative by the cross validation.cross val score() function.", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 61, 
            "metadata": {}, 
            "source": "# area under ROC curve\nscoring = 'roc_auc'\nresults = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\nprint(\"AUC: \",(results.mean(), results.std()))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "AUC:  (0.8195797322793215, 0.018524613794451666)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 62, 
            "metadata": {}, 
            "source": "#confusion matrix\n\nfrom sklearn.metrics import confusion_matrix\n\ntest_size = 0.33\nseed = 7\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed)\nmodel = LogisticRegression()\nmodel.fit(X_train, Y_train)\npredicted = model.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nprint(matrix)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "[[141  21]\n [ 41  51]]\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 1, 
            "metadata": {}, 
            "source": "from sklearn.metrics import classification_report\ntest_size = 0.33\nseed = 7\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed)\nmodel = LogisticRegression()\nmodel.fit(X_train, Y_train)\npredicted = model.predict(X_test)\nreport = classification_report(Y_test, predicted)\n# 0's and 1's are the classification outputs\nprint(report)", 
            "outputs": [
                {
                    "traceback": [
                        "\u001b[1;31m\u001b[0m", 
                        "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", 
                        "\u001b[1;32m<ipython-input-1-92905d86e965>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.33\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n\u001b[0m\u001b[0;32m      5\u001b[0m random_state=seed)\n\u001b[0;32m      6\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 
                        "\u001b[1;31mNameError\u001b[0m: name 'train_test_split' is not defined"
                    ], 
                    "evalue": "name 'train_test_split' is not defined", 
                    "ename": "NameError", 
                    "output_type": "error"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "source": "## Regression metrics\n    Mean Absolute Error.\n    Mean Squared Error.\n    R2", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "# using boston housing prices data set\nimport pandas as pd # conventional alias\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LinearRegression\n\ndataset = load_boston()\ndataframe = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n\ntype(dataset)\ndataframe['target'] = dataset.target\n\narray = dataframe.values\nX = array[:,0:13]\nY = array[:,13]\nkfold = KFold(n_splits=10, random_state=7)\nmodel = LinearRegression()\nscoring = 'neg_mean_absolute_error'\nresults = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\nprint(\"MAE:\",(results.mean(), results.std()))\n", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "", 
            "outputs": [], 
            "cell_type": "code"
        }
    ], 
    "nbformat_minor": 1
}