vatsan/PyMADlib_Tutorial

## PyMADlib_Tutorial
{
 "metadata": {
  "name": "PyMADlib"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "PyMADlib"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "A Python wrapper for MADlib - an open source library for scalable in-database machine learning algorithms"
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Datasets Overview"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "- UCI Wine Dataset\n- UCI Auto-MPG Dataset\n- Obama-Romney Second Presidential Debate -2012 Transcripts "
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Create a Connection Object to the Database"
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "from pymadlib.pymadlib import *\nfrom pymadlib import example\nfrom pymadlib.example import *\nconn = DBConnect()\nconn.conn",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 1,
       "text": "<connection object at 0x1022ad0a0; dsn: 'host='localhost' port ='5432' dbname='vatsandb' user='gpadmin' password=xxxxxxxxxxx', closed: 0>"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Linear Regression"
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "#View Documentation\nmdl = LinearRegression(conn)\nprint(mdl.train.__doc__)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \n              Given train a linear regression model on the specified table \n              for the given set of independent and dependent variables \n              Inputs :\n              ========\n              table_name : (String) input table name\n              indep : (list of strings) the independent variables to be used to build the model on\n              dep : (string) the class label\n              \n              Output :\n              ========\n              The Model coefficients, r2, p_values and t_stats\n              The function also returns the model object.\n              \n            \n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Train Model and Score\nlreg = LinearRegression(conn)\nlreg.train('public.wine_training_set',['1','alcohol','proline','hue','color_intensity','flavanoids'],'quality')\ncursor = lreg.predict('public.wine_test_set','quality')\n#Print Prediction Results\nrowset = conn.printTable(cursor,['id','quality','prediction']) ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nstatement : \n                      select (madlib.linregr(quality,array[1,alcohol,proline,hue,color_intensity,flavanoids])).* \n                      from public.wine_training_set\n                   \n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n             Model Parameters\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\nstd_err           \t |  [176.45127024525, 34.9120997622056, 40.7792991586509, 7.79463508830726, 33.7169283855118, 42.0751829713439]\ncondition_no      \t |  6780.55233691\nr2                \t |  0.667006811765\ncoef              \t |  [1416.22464229863, -361.192816094753, -88.8425299895989, 58.4638933049179, -23.0330507155227, 1.19714448390368]\np_values          \t |  [7.08655356618237e-13, 0.0, 0.031282488716348, 1.13492548692307e-11, 0.495819848493887, 0.977347736470074]\nt_stats           \t |  [8.0261515846852, -10.3457774970546, -2.17861836330142, 7.50052986991265, -0.683130160973382, 0.0284525080905536]\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n--------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nid             \t | quality        \t | prediction     \n--------------------------------------------------------------------------------\n32             \t | 630.0          \t | 550.955896378  \n46             \t | 392.0          \t | 711.14959836   \n40             \t | 550.0          \t | 386.660785675  \n2              \t | 355.0          \t | 646.727206469  \n34             \t | 520.0          \t | 539.247125745  \n22             \t | 1035.0         \t | 968.434422731  \n16             \t | 840.0          \t | 700.782611847  \n48             \t | 680.0          \t | 498.484868526  \n10             \t | 880.0          \t | 593.444784403  \n4              \t | 428.0          \t | 701.318163157  \n30             \t | 1120.0         \t | 1098.35082555  \n24             \t | 1295.0         \t | 1006.53949356  \n18             \t | 407.0          \t | 506.590887785  \n42             \t | 550.0          \t | 739.476576567  \n36             \t | 1510.0         \t | 1058.28127122  \n50             \t | 1105.0         \t | 1050.27399993  \n12             \t | 515.0          \t | 760.553958893  \n44             \t | 1095.0         \t | 1012.94448829  \n6              \t | 920.0          \t | 1005.30491066  \n38             \t | 352.0          \t | 634.38564086   \n26             \t | 725.0          \t | 629.40588069   \n20             \t | 990.0          \t | 982.077078581  \n14             \t | 480.0          \t | 806.207940698  \n8              \t | 466.0          \t | 614.466643095  \n28             \t | 750.0          \t | 590.516592503  \n31             \t | 495.0          \t | 479.127750397  \n25             \t | 1260.0         \t | 1255.48760939  \n19             \t | 735.0          \t | 1008.71905088  \n13             \t | 315.0          \t | 527.815455437  \n37             \t | 1515.0         \t | 1161.07556272  \n45             \t | 1285.0         \t | 1011.64890575  \n7              \t | 520.0          \t | 547.024941921  \n39             \t | 675.0          \t | 772.617235661  \n1              \t | 1195.0         \t | 1019.59951043  \n33             \t | 472.0          \t | 644.038324594  \n27             \t | 1480.0         \t | 1158.94038153  \n21             \t | 885.0          \t | 1008.9016943   \n15             \t | 345.0          \t | 593.393530846  \n9              \t | 385.0          \t | 559.086925745  \n3              \t | 937.0          \t | 539.633692265  \n29             \t | 1680.0         \t | 1274.24084854  \n23             \t | 420.0          \t | 660.143455029  \n47             \t | 1065.0         \t | 987.11175108   \n41             \t | 495.0          \t | 653.476777911  \n35             \t | 714.0          \t | 604.747849941  \n17             \t | 1065.0         \t | 989.334903002  \n49             \t | 1045.0         \t | 1063.58939695  \n11             \t | 870.0          \t | 678.149069548  \n43             \t | 770.0          \t | 962.67329216   \n5              \t | 438.0          \t | 530.141533136  \n--------------------------------------------------------------------------------\n\n\n\n\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": "#Generate Scatter Plot\ncols = conn.fetchColumns(rowset,['quality','prediction'])\nactual = cols['quality']\npredicted = cols['prediction'] \nscatterPlot(actual,predicted, 'wine_test_set') ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Ev7cGwNDQEDdv3hRd06qu5+7OurWxksJOWFpgRwKGKQNcNwN3XgCH+wK+ecTg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\nIQwgCVgIIYQwwP8HlOACtBd87ecAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Categorical Variables"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Train Linear Regression Model on a mixture of Numeric and Categorical Variables\nlreg.train('public.auto_mpg_train',['1','height','width','length','highway_mpg','engine_size','make','fuel_type','fuel_system'],'price')\ncursor = lreg.predict('public.auto_mpg_test','price')\n\n#Show prediction results\nrowset = conn.printTable(cursor,['id','price','prediction']) ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nstatement : \n                      select (madlib.linregr(price,array[1,height,width,length,highway_mpg,engine_size,make_val_0,make_val_1,make_val_2,make_val_3,make_val_4,make_val_5,make_val_6,make_val_7,make_val_8,make_val_9,make_val_10,make_val_11,make_val_12,make_val_13,fuel_type_val_0,fuel_system_val_0,fuel_system_val_1,fuel_system_val_2,fuel_system_val_3,fuel_system_val_4])).* \n                      from public.gp_pymdlib_auto_mpg_train\n                   \n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n             Model Parameters\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\nstd_err           \t |  [16613.1097084184, 124.479943337153, 242.264831832431, 37.1403629738067, 65.2804356109868, 16.0849662777181, 1166.34436370139, 1680.81795753747, 1315.51488977053, 1849.54130588577, 990.837833073393, 1294.15928209115, 1240.69628282232, 961.591952063424, 1225.18184104948, 1373.47489698111, 918.984647877449, 1018.3496214136, 832.622520380731, 1006.3833436486, 732.083068237177, 2063.18998614013, 1118.7258211047, 732.083068236985, 2126.45109180399, 1255.53183959977]\ncondition_no      \t |  inf\nr2                \t |  0.95028645716\ncoef              \t |  [-14135.0920725423, -218.887424625356, 717.968847393939, -11.139007393805, -243.07773813379, 33.0294768017193, -732.787478989165, -2017.41165462347, -5769.49444754779, -5695.78114864106, -5768.32713194712, 6735.28199665331, -7112.12879006049, -4482.12569945376, -685.62036397255, -6080.09602732393, -1658.14370847461, -6197.85392583956, -4920.1509052387, -4553.60737000622, 584.594863615988, 371.30276372662, -261.038398801291, 584.594863615843, -1134.05798918608, -337.19065615657]\np_values          \t |  [0.397366696196921, 0.0824540440447666, 0.00399174004260638, 0.765009172098666, 0.000361599170050519, 0.0432569143071544, 0.5315899680881, 0.23353858685713, 3.44696344571904e-05, 0.00283038879547226, 1.12632038917759e-07, 1.44037678795339e-06, 1.63958805532438e-07, 1.22041508766602e-05, 0.577291726445808, 2.95877567791925e-05, 0.0748979930970457, 3.65996161155735e-08, 7.778490374033e-08, 2.04950519373526e-05, 0.426894516148574, 0.857629216185837, 0.81608959533545, 0.426894516148567, 0.595279238124451, 0.788948040814391]\nt_stats           \t |  [-0.85083962729624, -1.75841520133489, 2.96357024650834, -0.29991649251411, -3.72359246470592, 2.05343774002646, -0.628277121058541, -1.2002558906374, -4.3857310110372, -3.07956417654229, -5.82166620955001, 5.20436865064261, -5.73236890327578, -4.66115142689769, -0.559607024035919, -4.42679807303939, -1.80432144574381, -6.08617492019698, -5.90922150771142, -4.52472449861633, 0.798536244013492, 0.179965377023404, -0.233335455280299, 0.798536244013503, -0.533310168081035, -0.268564002537806]\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n--------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nid             \t | price          \t | prediction     \n--------------------------------------------------------------------------------\n31             \t | 20970.0        \t | 16807.7980042  \n25             \t | 6479.0         \t | 3600.97329712  \n19             \t | 7999.0         \t | 7531.80499268  \n13             \t | 22018.0        \t | 20119.8197327  \n37             \t | 21105.0        \t | 16807.7980042  \n57             \t | 6229.0         \t | 6548.61895752  \n51             \t | 11549.0        \t | 11891.4570618  \n45             \t | 9279.0         \t | 8833.89758301  \n7              \t | 6377.0         \t | 6504.84161377  \n39             \t | 15985.0        \t | 17221.686554   \n1              \t | 7995.0         \t | 7425.13757324  \n33             \t | 9258.0         \t | 8483.48651123  \n27             \t | 10245.0        \t | 9702.81317139  \n21             \t | 9988.0         \t | 10849.5627747  \n53             \t | 9995.0         \t | 9718.19937134  \n15             \t | 8845.0         \t | 10096.8102417  \n9              \t | 7975.0         \t | 9232.04360962  \n3              \t | 7129.0         \t | 8080.17727661  \n29             \t | 5572.0         \t | 5465.00665283  \n23             \t | 10898.0        \t | 10630.6750793  \n47             \t | 10698.0        \t | 11497.6240234  \n41             \t | 16630.0        \t | 17590.2693176  \n35             \t | 6575.0         \t | 8618.56866455  \n55             \t | 16558.0        \t | 15602.0802307  \n17             \t | 7689.0         \t | 7632.10314941  \n49             \t | 8845.0         \t | 9168.08169556  \n11             \t | 7775.0         \t | 7425.13757324  \n43             \t | 32250.0        \t | 25620.8760071  \n5              \t | 7295.0         \t | 6681.63418579  \n32             \t | 15690.0        \t | 14232.9558411  \n52             \t | 7895.0         \t | 9431.19009399  \n46             \t | 14399.0        \t | 15085.3013     \n40             \t | 22625.0        \t | 19324.6885071  \n2              \t | 8948.0         \t | 10144.5193176  \n34             \t | 16515.0        \t | 16937.1338196  \n22             \t | 31600.0        \t | 30635.0737305  \n54             \t | 8238.0         \t | 8213.34381104  \n16             \t | 7395.0         \t | 5998.0725708   \n48             \t | 9980.0         \t | 10378.5294495  \n10             \t | 13499.0        \t | 14574.9560852  \n4              \t | 6989.0         \t | 8483.05767822  \n30             \t | 17199.0        \t | 16916.9359436  \n24             \t | 9960.0         \t | 8666.22402954  \n18             \t | 13415.0        \t | 16937.1338196  \n42             \t | 8449.0         \t | 11891.4570618  \n36             \t | 18280.0        \t | 11421.3580627  \n56             \t | 18150.0        \t | 18134.1106262  \n50             \t | 8013.0         \t | 8928.45709229  \n12             \t | 22470.0        \t | 20477.0307617  \n44             \t | 10595.0        \t | 10096.8102417  \n6              \t | 6855.0         \t | 7490.21743774  \n38             \t | 5389.0         \t | 4929.81427002  \n26             \t | 7957.0         \t | 8664.13586426  \n20             \t | 8921.0         \t | 7607.48773193  \n14             \t | 9538.0         \t | 9352.58071899  \n8              \t | 28248.0        \t | 29234.9177246  \n28             \t | 16430.0        \t | 14715.0697327  \n--------------------------------------------------------------------------------\n\n\n\n\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Display Scatter Plot of Actual Vs Predicted Values\ncols = conn.fetchColumns(rowset,['price','prediction'])\nactual = cols['price']\npredicted = cols['prediction'] \nscatterPlot(actual,predicted, 'auto_mpg_test')  ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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AsRI4OgzwaF5v2eGys/q4Wpsxxp41kQlSYLbUBw4PAe4FAimTgNGOUpB+41dt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\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Logistic Regression"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#1) Logistic Regression with Numeric Variables Alone\nlog_reg = LogisticRegression(conn)\n#Train Model\nlog_reg.train('public.wine_bool_training_set','indep','quality_label')\n#Scoring\ncursor = log_reg.predict('wine_bool_test_set','',None)\n\n#Display ROC Curve\ncols = conn.fetchColumns(cursor,['quality_label','prediction'])\nactual = cols['quality_label']\npredicted = cols['prediction']\nROCPlot('ROC curve Logistic Reg. on Continuous Features ',['Logistic Regression'],actual,predicted) ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nstatement : \n                      select * \n                      from madlib.logregr('public.wine_bool_training_set','quality_label','indep',100, 'irls', 0.001) \n                   \n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n             Model Parameters\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\nstd_err             \t |  [2.62370220266303, 0.645203905535534, 0.583811938681368, 0.126975428265817, 0.48793731244426, 0.567356075421763]\nodds_ratios         \t |  [39.3036672395389, 0.0948315596232434, 0.693224535777532, 1.76962044606699, 0.864158253007184, 1.07564201713425]\ncondition_no        \t |  6198.90723196\nlog_likelihood      \t |  -59.6640513625\ncoef                \t |  [3.67131782850492, -2.35565301770436, -0.366401326819681, 0.570765086340494, -0.145999363747654, 0.0729177085527688]\np_values            \t |  [0.161726314395655, 0.000261199864726505, 0.530264937852516, 6.95428226814735e-06, 0.764774127325118, 0.897735929525397]\nnum_iterations      \t |  6\nz_stats             \t |  [1.39928907510104, -3.65102101443282, -0.62760163426472, 4.49508297893372, -0.299217460981389, 0.128521948934032]\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n\nstatement: \n                          select *,\n                                (1.0/(1.0 + exp(-1.0*madlib.array_dot(indep, array[3.67131782850492, -2.35565301770436, -0.366401326819681, 0.570765086340494, -0.145999363747654, 0.0729177085527688]::real[]))))  as prediction \n                         from wine_bool_test_set\n                       \n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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yQUFBtGnTpnLreVViZZ7+CZyrRSS+vg7WB6aqK5R5ia0uxGBgep1WrYS8vDwc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\nJneepfueioqKaOHCheTk5ER2dnYUHBz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      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#2) Logistic Regression with Mixture of Numeric and Categorical Variables\n\n#Train Model\nlog_reg.train('public.auto_mpg_bool_train',['1','height','width','length','highway_mpg','engine_size','make','fuel_type','fuel_system'],'is_expensive')\n#Scoring\ncursor = log_reg.predict('auto_mpg_bool_test','is_expensive',None)\n\n#Display ROC Curve\ncols = conn.fetchColumns(cursor,['is_expensive','prediction'])\nactual = cols['is_expensive']\npredicted = cols['prediction']  \nROCPlot('ROC curve Logistic Reg. including categorical data',['Logistic Regression'],actual,predicted) ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nstatement : \n                      select * \n                      from madlib.logregr('public.gp_pymdlib_arr_auto_mpg_bool_train','is_expensive','indep',100, 'irls', 0.001) \n                   \n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n             Model Parameters\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\nstd_err             \t |  [8260.75052933305, 1.03652077514348, 93.3156998618529, 32.3727321255478, 35.1587803852847, 11.7764652602903, 1048.72207606614, 1817.9721617305, 1142.9462294548, 2140.62154979112, 627.658988616015, 1212.25303818268, 1148.5925747908, 905.51831911587, 10179.997776188, 1175.40540655678, 971.023939939771, 589.594381627054, 576.867235921955, 884.128262263986, 5102.22372651156, 2366.61383087985, 1219.955835445, 5102.22372651264, 2482.2561061022, 1255.91741854684]\nodds_ratios         \t |  [0.0, 1.42137730071846, 819.356599462108, 204.275499512008, 0.00194264365415406, 0.368185820031342, 5092576381042.13, 1.09433309076233e+106, 1.47610865163205e+58, 3.04496896416429e+58, 1.82050722710836e+37, 1.04409484236975e+24, 9.65451082068503e+45, 7.72475633489319e+49, 3.47861111653924, 1.5045391289412e+66, 0.225937808714701, 7.9639065247808e+30, 5.26718987130233e+29, 2.8151350501707e+29, 3.29595046033607e+21, 2730575249.71417, 36523614262.71, 3.29595046071977e+21, 42.9493516072627, 3.48935985369781e+23]\ncondition_no        \t |  inf\nlog_likelihood      \t |  -4.62431873772\ncoef                \t |  [-1199.53601757484, 0.351626331618349, 6.70851939746899, 5.3194695705597, -6.24370552522441, -0.999167522518064, 29.2588049836668, 244.164164985559, 133.939344729341, 134.663426102372, 85.7947635992532, 55.3051925623731, 105.883754433424, 114.871099836586, 1.24663310968026, 152.379102762294, -1.48749550025786, 71.1524724387345, 68.4364646859103, 67.8099779324621, 49.5469815345714, 21.7277781381272, 24.3212248544442, 49.5469815346878, 3.76002155186143, 54.2091754352441]\np_values            \t |  [0.884545793953474, 0.734431101157513, 0.942688990038906, 0.869479670122989, 0.859048156976056, 0.932385068122934, 0.977742319839157, 0.893160784707686, 0.906711241024046, 0.949839315887018, 0.89127576309419, 0.963611675683649, 0.926550538206978, 0.899053788285458, 0.999902291795089, 0.896851560553204, 0.998777734326712, 0.903944202018277, 0.905564782173481, 0.938864597823002, 0.992251976314663, 0.992674759629806, 0.984094305819195, 0.992251976314647, 0.998791397072257, 0.965571591755914]\nnum_iterations      \t |  15\nz_stats             \t |  [-0.145209084007005, 0.339237128720044, 0.0718905758345108, 0.164319451009873, -0.177585953119058, -0.0848444334045813, 0.0278994841926276, 0.134305777682064, 0.117187791759225, 0.0629085632233835, 0.136690089929932, 0.0456218221942199, 0.0921856511676549, 0.126856737640321, 0.000122459074853263, 0.129639613628009, -0.0015318834470241, 0.120680377316997, 0.118634688233827, 0.0766969916319847, 0.0097108602425882, 0.00918095629063795, 0.0199361519063291, 0.00971086024260898, 0.00151475971501009, 0.0431630094739564]\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n\nstatement:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \n                          select *,\n                                (1.0/(1.0 + exp(-1.0*madlib.array_dot(indep, array[-1199.53601757484, 0.351626331618349, 6.70851939746899, 5.3194695705597, -6.24370552522441, -0.999167522518064, 29.2588049836668, 244.164164985559, 133.939344729341, 134.663426102372, 85.7947635992532, 55.3051925623731, 105.883754433424, 114.871099836586, 1.24663310968026, 152.379102762294, -1.48749550025786, 71.1524724387345, 68.4364646859103, 67.8099779324621, 49.5469815345714, 21.7277781381272, 24.3212248544442, 49.5469815346878, 3.76002155186143, 54.2091754352441]::real[]))))  as prediction \n                         from public.gp_pymdlib_arr_auto_mpg_bool_test\n                       \n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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I39+f6tSpQ6IK05jryHT+Ib3iWfv2RCdPyqx4xlAevs+HV1v4Xvf0cglIaQ4e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\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "K-Means"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Demonstrate K-Means\nexample.kmeansDemo(conn)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n\nKMeans with random cluster initialization\n\nstatement : \n                          select * from madlib.kmeans_random(\n                                                             'public.wine_bool_training_set',\n                                                             'indep',\n                                                             3,\n                                                             'madlib.squared_dist_norm2',\n                                                             'madlib.avg',\n                                                             20,\n                                                             0.001\n                                                             );                                                       \n                       \n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n             Model Parameters\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\nnum_iterations       \t |  10\nobjective_fn         \t |  285.251101444\ncentroids            \t |  [[1.0, 2.0, 2.77145454233343, 2.96581817106767, 1.5658181802793, 2.2052727244117], [1.0, 3.0, 1.67380951699756, 9.06999999000913, 1.32095237289156, 1.79333332039061], [1.0, 1.57692307692308, 2.75711538699957, 5.62903844393217, 1.65884615251651, 2.56576922994394]]\nfrac_reassigned      \t |  0.0\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n\n\nKMeans Plus Plus \n\nstatement : \n                          select * from madlib.kmeans_random(\n                                                             'public.wine_bool_training_set',\n                                                             'indep',\n                                                             3,\n                                                             'madlib.squared_dist_norm2',\n                                                             'madlib.avg',\n                                                             20,\n                                                             0.001\n                                                             );                                                       \n                       \n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n             Model Parameters\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\nnum_iterations       \t |  5\nobjective_fn         \t |  292.842264034\ncentroids            \t |  [[1.0, 2.15714285714286, 2.5991428562573, 3.43871427263532, 1.44585713957037, 2.09742856706892], [1.0, 1.10810810810811, 3.07729729768392, 5.81405403807357, 1.92351351557551, 2.91594594878119], [1.0, 3.0, 1.67380951699756, 9.06999999000913, 1.32095237289156, 1.79333332039061]]\nfrac_reassigned      \t |  0.0\n----------------------------------------------------------------------------------------------------------------------------------------------------------------\n\nCouldn't import dot_parser, loading of dot files will not be possible."
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      },
      {
       "output_type": "display_data",
       "png": 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0M8/C3KehVgyMeAxO7IE5T8LwOGjZG4xujps+Dm7BbfHL9O/WmYiICAICAqhZ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\nWwghaqH/BzhF2TyEFMVyAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "networkVizDemo()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}