{
 "metadata": {
  "name": "predicting_with_regression"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pandas.rpy.common as com"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "faithful = com.load_data('faithful')\n",
      "faithful.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(272, 2)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -o trainSamples\n",
      "set.seed(333)\n",
      "trainSamples <- sample(1:272,size=(272/2),replace=F)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainFaith = faithful.ix[trainSamples,:]\n",
      "testFaith = faithful.ix[faithful.index - trainSamples,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainFaith.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>eruptions</th>\n",
        "      <th>waiting</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>128</strong></td>\n",
        "      <td> 4.500</td>\n",
        "      <td> 82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>23 </strong></td>\n",
        "      <td> 3.450</td>\n",
        "      <td> 78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>263</strong></td>\n",
        "      <td> 1.850</td>\n",
        "      <td> 58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>154</strong></td>\n",
        "      <td> 4.600</td>\n",
        "      <td> 81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6  </strong></td>\n",
        "      <td> 2.883</td>\n",
        "      <td> 55</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "     eruptions  waiting\n",
        "128      4.500       82\n",
        "23       3.450       78\n",
        "263      1.850       58\n",
        "154      4.600       81\n",
        "6        2.883       55"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.formula.api import ols\n",
      "\n",
      "lm1 = ols('eruptions ~ waiting', trainFaith).fit()\n",
      "lm1.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>        <td>eruptions</td>    <th>  R-squared:         </th> <td>   0.814</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.812</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   584.8</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Thu, 28 Feb 2013</td> <th>  Prob (F-statistic):</th> <td>1.02e-50</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>22:56:59</td>     <th>  Log-Likelihood:    </th> <td> -95.927</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   136</td>      <th>  AIC:               </th> <td>   195.9</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td>   134</td>      <th>  BIC:               </th> <td>   201.7</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Intercept</th> <td>   -1.9249</td> <td>    0.229</td> <td>   -8.397</td> <td> 0.000</td> <td>   -2.378    -1.472</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>waiting</th>   <td>    0.0764</td> <td>    0.003</td> <td>   24.182</td> <td> 0.000</td> <td>    0.070     0.083</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td> 2.318</td> <th>  Durbin-Watson:     </th> <td>   1.995</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th> <td> 0.314</td> <th>  Jarque-Bera (JB):  </th> <td>   2.305</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>          <td>-0.310</td> <th>  Prob(JB):          </th> <td>   0.316</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 2.850</td> <th>  Cond. No.          </th> <td>    393.</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:              eruptions   R-squared:                       0.814\n",
        "Model:                            OLS   Adj. R-squared:                  0.812\n",
        "Method:                 Least Squares   F-statistic:                     584.8\n",
        "Date:                Thu, 28 Feb 2013   Prob (F-statistic):           1.02e-50\n",
        "Time:                        22:56:59   Log-Likelihood:                -95.927\n",
        "No. Observations:                 136   AIC:                             195.9\n",
        "Df Residuals:                     134   BIC:                             201.7\n",
        "Df Model:                           1                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept     -1.9249      0.229     -8.397      0.000        -2.378    -1.472\n",
        "waiting        0.0764      0.003     24.182      0.000         0.070     0.083\n",
        "==============================================================================\n",
        "Omnibus:                        2.318   Durbin-Watson:                   1.995\n",
        "Prob(Omnibus):                  0.314   Jarque-Bera (JB):                2.305\n",
        "Skew:                          -0.310   Prob(JB):                        0.316\n",
        "Kurtosis:                       2.850   Cond. No.                         393.\n",
        "==============================================================================\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(trainFaith['waiting'], trainFaith['eruptions'], 'o')\n",
      "plot(trainFaith['waiting'], lm1.fittedvalues, 'k', linewidth=3)\n",
      "xlabel('Waiting')\n",
      "ylabel('Duration');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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IiNBueqFSqbTH16xZgzVr1gh9jRBihHtDUu/Nak5MbBsKykcmq+c93rNnE+/xzgXvhAlD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\nEEKIFgUFQgghWhQUCCGEaP1/Zck/Geqxi8MAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print lm1.params['Intercept'] + lm1.params['waiting'] * 80"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4.18615649492\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print lm1.predict({'waiting' : [80]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 4.18615649]\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, (ax1, ax2) = subplots(ncols=2)\n",
      "\n",
      "ax1.plot(trainFaith['waiting'], trainFaith['eruptions'], 'o')\n",
      "ax1.plot(trainFaith['waiting'], lm1.predict({'waiting' : trainFaith['waiting'].values}), 'k')\n",
      "ax1.set_xlabel('Waiting')\n",
      "ax1.set_ylabel('Duration')\n",
      "\n",
      "ax2.plot(testFaith['waiting'], testFaith['eruptions'], 'o')\n",
      "ax2.plot(testFaith['waiting'], lm1.predict({'waiting' : testFaith['waiting'].values}), 'k')\n",
      "ax2.set_xlabel('Waiting')\n",
      "ax2.set_ylabel('Duration')\n",
      "\n",
      "f.set_size_inches(8,3)\n",
      "f.tight_layout();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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mwfjVbgP/K34LWq11OM2IMwaaSREZY+PvYceOxWhtDUJgYAuvYac/InXTPIJQ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\nN8v1AwIAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calculate RMSE on training\n",
      "print sqrt(sum((lm1.fittedvalues - trainFaith['eruptions']) ** 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5.71292527997\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calculate RMSE on test\n",
      "print sqrt(sum((lm1.predict({'waiting' : testFaith['waiting'].values}) - testFaith['eruptions']) ** 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5.82715154245\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# no prediction interval"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ravensData = pd.read_csv('../data/ravensData.csv')\n",
      "ravensData.head(6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ravenWinNum</th>\n",
        "      <th>ravenWin</th>\n",
        "      <th>ravenScore</th>\n",
        "      <th>opponentScore</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> W</td>\n",
        "      <td> 24</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> W</td>\n",
        "      <td> 38</td>\n",
        "      <td> 35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> W</td>\n",
        "      <td> 28</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> W</td>\n",
        "      <td> 34</td>\n",
        "      <td> 31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> W</td>\n",
        "      <td> 44</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> L</td>\n",
        "      <td> 23</td>\n",
        "      <td> 24</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "   ravenWinNum ravenWin  ravenScore  opponentScore\n",
        "0            1        W          24              9\n",
        "1            1        W          38             35\n",
        "2            1        W          28             13\n",
        "3            1        W          34             31\n",
        "4            1        W          44             13\n",
        "5            0        L          23             24"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.formula.api import glm\n",
      "from statsmodels.api import families as f\n",
      "\n",
      "glm1 = glm('ravenWinNum ~ ravenScore', ravensData, family=f.Binomial()).fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'pred' : glm1.predict(), 'ravenWinNum' : ravensData['ravenWinNum'].values})\n",
      "\n",
      "df.boxplot(by='ravenWinNum');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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5sEavYDwnmZSGY9Z8WKMnIrJhTPSKppM7AKJG0skdgE1ioicisnKs0SsY652k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\n+dO7aEytAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xx = np.linspace(0, 1, num=10)\n",
      "\n",
      "err = [sum( (df['pred'] > x) != df['ravenWinNum'] ) for x in xx]\n",
      "\n",
      "plot(xx, err, 'ok')\n",
      "xlabel('Cutoff')\n",
      "ylabel('Error');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cost(win, pred=0):\n",
      "    return np.mean(np.abs(win - pred) > 0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ???? is this correct?\n",
      "\n",
      "from sklearn.cross_validation import KFold\n",
      "\n",
      "kf = KFold(len(ravensData), n_folds=3, indices=False)\n",
      "\n",
      "cv1train = []\n",
      "cv1test = []\n",
      "cv2train = []\n",
      "cv2test = []\n",
      "\n",
      "for train, test in kf:\n",
      "    glm1 = glm('ravenWinNum ~ ravenScore', ravensData[train], family=f.Binomial()).fit()\n",
      "    glm2 = glm('ravenWinNum ~ ravenScore', ravensData[train], family=f.Gaussian()).fit()\n",
      "    \n",
      "    cv1train.append( cost(ravensData[train]['ravenWinNum'], glm1.predict()) )\n",
      "    cv2train.append( cost(ravensData[train]['ravenWinNum'], glm2.predict()) )\n",
      "    \n",
      "    cv1test.append( cost(ravensData[test]['ravenWinNum'], glm1.predict(ravensData[test])) )\n",
      "    cv2test.append( cost(ravensData[test]['ravenWinNum'], glm2.predict(ravensData[test])) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zip( cv1train, cv1test )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "[(0.42857142857142855, 0.16666666666666666),\n",
        " (0.2857142857142857, 0.16666666666666666),\n",
        " (0.25, 0.5)]"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.mean(cv2train), np.mean(cv2test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 163,
       "text": [
        "(0.29365079365079361, 0.27777777777777773)"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}