Tennis!.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import scipy as sp\n",
      "import matplotlib.pyplot as plt\n",
      "import pickle\n",
      "\n",
      "from scipy.stats import beta\n",
      "from pystan import stan, StanModel\n",
      "from hashlib import md5\n",
      "\n",
      "from rpy2.robjects.packages import importr\n",
      "import rpy2.robjects as ro\n",
      "import pandas.rpy.common as com\n",
      "\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "def stan_cache(model_code, model_name=None, **kwargs):\n",
      "    \"\"\"Use just as you would `stan`\"\"\"\n",
      "    code_hash = md5(model_code.encode('ascii')).hexdigest()\n",
      "    if model_name is None:\n",
      "        cache_fn = 'cached-model-{}.pkl'.format(code_hash)\n",
      "    else:\n",
      "        cache_fn = 'cached-{}-{}.pkl'.format(model_name, code_hash)\n",
      "    try:\n",
      "        sm = pickle.load(open(cache_fn, 'rb'))\n",
      "    except:\n",
      "        sm = StanModel(model_code=model_code)\n",
      "        with open(cache_fn, 'wb') as f:\n",
      "            pickle.dump(sm, f)\n",
      "    else:\n",
      "        print(\"Using cached StanModel\")\n",
      "    return sm.sampling(**kwargs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('Wimbledon_2013_Sample_Point_by_Point_data.csv')\n",
      "df = df[df.Event == 'MS']\n",
      "df = df.drop(['Tournament', 'Year', 'Court', 'Event', 'Round', '1st Serve Timestamp', '2nd Serve Timestamp', 'End of Point Timestamp'], axis=1)\n",
      "df.rename(columns={'Tiebreak?': 'Tiebreak',\n",
      "                   'Service (0=Double Fault|1=First Serve|2=Second Serve)': 'Service', \n",
      "                   'Speed MPH (where avail)': 'Speed', \n",
      "                   'Shot Main (1=FH|2=BH|3=ACE|4=Service Winner|5=Double Fault|6=Other)': 'Main_shot',\n",
      "                   'Shot Detail (0=None|1=Drive|2=Return|3=Passing|4=Volley|5=Smash|6=Stroke at Net|7=Passing-Volley|8=Drop-Shot|9=Lob|10=Approach|11=Approach-Return)': 'Shot_detail',\n",
      "                   'Shot Win/Err (0=Undefined|1=Winner|2=Unforced Error|3=Forced Error)': 'Shot_win/err'\n",
      "                   },inplace=True)\n",
      "df.Server = df.Server.apply(lambda x: np.int(x[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head(5)"
     ],
     "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>Match</th>\n",
        "      <th>Player 1</th>\n",
        "      <th>Player 2</th>\n",
        "      <th>Set</th>\n",
        "      <th>Game</th>\n",
        "      <th>Tiebreak</th>\n",
        "      <th>Point</th>\n",
        "      <th>Server</th>\n",
        "      <th>Winner</th>\n",
        "      <th>Score 1</th>\n",
        "      <th> Score 2</th>\n",
        "      <th>Service</th>\n",
        "      <th>Serve Direction</th>\n",
        "      <th>S&amp;V?</th>\n",
        "      <th>Return Hand</th>\n",
        "      <th>Speed</th>\n",
        "      <th>Main_shot</th>\n",
        "      <th>Shot_detail</th>\n",
        "      <th>Rally Count</th>\n",
        "      <th>Shot_win/err</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> MS101</td>\n",
        "      <td> N.Djokovic</td>\n",
        "      <td> F.Mayer</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> False</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> C</td>\n",
        "      <td> False</td>\n",
        "      <td> B</td>\n",
        "      <td> 117</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td>  5</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> MS101</td>\n",
        "      <td> N.Djokovic</td>\n",
        "      <td> F.Mayer</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> False</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 15</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> C</td>\n",
        "      <td> False</td>\n",
        "      <td> F</td>\n",
        "      <td> 105</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> MS101</td>\n",
        "      <td> N.Djokovic</td>\n",
        "      <td> F.Mayer</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> False</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 30</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> C</td>\n",
        "      <td> False</td>\n",
        "      <td> B</td>\n",
        "      <td> 124</td>\n",
        "      <td> 1</td>\n",
        "      <td> 6</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> MS101</td>\n",
        "      <td> N.Djokovic</td>\n",
        "      <td> F.Mayer</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> False</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 40</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> C</td>\n",
        "      <td> False</td>\n",
        "      <td> F</td>\n",
        "      <td> 102</td>\n",
        "      <td> 6</td>\n",
        "      <td> 2</td>\n",
        "      <td>  1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> MS101</td>\n",
        "      <td> N.Djokovic</td>\n",
        "      <td> F.Mayer</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> False</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td> B</td>\n",
        "      <td> False</td>\n",
        "      <td> B</td>\n",
        "      <td>  90</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   Match    Player 1 Player 2  Set  Game Tiebreak  Point  Server  Winner  \\\n",
        "0  MS101  N.Djokovic  F.Mayer    1     1    False      1       1       1   \n",
        "1  MS101  N.Djokovic  F.Mayer    1     1    False      2       1       1   \n",
        "2  MS101  N.Djokovic  F.Mayer    1     1    False      3       1       1   \n",
        "3  MS101  N.Djokovic  F.Mayer    1     1    False      4       1       1   \n",
        "4  MS101  N.Djokovic  F.Mayer    1     2    False      1       2       2   \n",
        "\n",
        "  Score 1  Score 2  Service Serve Direction   S&V? Return Hand  Speed  \\\n",
        "0       0        0        1               C  False           B    117   \n",
        "1      15        0        2               C  False           F    105   \n",
        "2      30        0        1               C  False           B    124   \n",
        "3      40        0        2               C  False           F    102   \n",
        "4       0        0        2               B  False           B     90   \n",
        "\n",
        "   Main_shot  Shot_detail Rally Count  Shot_win/err  \n",
        "0          2            1           5             3  \n",
        "1          2            1           3             1  \n",
        "2          1            6           3             1  \n",
        "3          6            2           1             3  \n",
        "4          1            5          17             1  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(m):\n",
      "    s_0, s_1, s_2 = sum(m.Service == 0), sum(m.Service == 1), sum(m.Service == 2)\n",
      "    \n",
      "    # Times served 1st serve:\n",
      "    n_try1 = s_0 + s_1 + s_2\n",
      "    \n",
      "    # Times won 1st serve:\n",
      "    n_win1 = sum((m.Service == 1) & (m.Server == m.Winner))\n",
      "    \n",
      "    # MLE probability to win 1st serve:\n",
      "    p_mle_win1 = n_win1/n_try1\n",
      "    \n",
      "    # Times served 2nd serve:\n",
      "    n_try2 = s_0 + s_2\n",
      "    \n",
      "    # Times won 2nd serve:\n",
      "    n_win2 = sum((m.Service == 2) & (m.Server == m.Winner))\n",
      "    \n",
      "    # MLE probability to win 2nd serve\n",
      "    p_mle_win2 = n_win2/n_try2\n",
      "        \n",
      "    return pd.Series({'n_try1': n_try1, \n",
      "                      'n_try2': n_try2,\n",
      "                      'n_win1': n_win1, \n",
      "                      'n_win2': n_win2,\n",
      "                      'p_mle_win1': p_mle_win1,\n",
      "                      'p_mle_win2': p_mle_win2,\n",
      "                      })\n",
      "\n",
      "df = df.groupby(['Match', 'Player 1', 'Player 2', 'Server']).apply(f)\n",
      "df['p_mle_delta'] = df.p_mle_win2 - df.p_mle_win1\n",
      "df = df.sort('p_mle_delta')\n",
      "df.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></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>n_try1</th>\n",
        "      <th>n_try2</th>\n",
        "      <th>n_win1</th>\n",
        "      <th>n_win2</th>\n",
        "      <th>p_mle_win1</th>\n",
        "      <th>p_mle_win2</th>\n",
        "      <th>p_mle_delta</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Match</th>\n",
        "      <th>Player 1</th>\n",
        "      <th>Player 2</th>\n",
        "      <th>Server</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>MS219</th>\n",
        "      <th>J.Isner</th>\n",
        "      <th>A.Mannarino</th>\n",
        "      <th>2</th>\n",
        "      <td>   5</td>\n",
        "      <td>  1</td>\n",
        "      <td>  4</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS311</th>\n",
        "      <th>N.Almagro</th>\n",
        "      <th>J.Janowicz</th>\n",
        "      <th>1</th>\n",
        "      <td> 107</td>\n",
        "      <td> 30</td>\n",
        "      <td> 58</td>\n",
        "      <td>  8</td>\n",
        "      <td> 0.542056</td>\n",
        "      <td> 0.266667</td>\n",
        "      <td>-0.275389</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS159</th>\n",
        "      <th>M.Gicquel</th>\n",
        "      <th>V.Pospisil</th>\n",
        "      <th>2</th>\n",
        "      <td>  88</td>\n",
        "      <td> 26</td>\n",
        "      <td> 56</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0.636364</td>\n",
        "      <td> 0.423077</td>\n",
        "      <td>-0.213287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS308</th>\n",
        "      <th>G.Zemlja</th>\n",
        "      <th>J.Del Potro</th>\n",
        "      <th>1</th>\n",
        "      <td> 101</td>\n",
        "      <td> 33</td>\n",
        "      <td> 53</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0.524752</td>\n",
        "      <td> 0.333333</td>\n",
        "      <td>-0.191419</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS214</th>\n",
        "      <th>M.Llodra</th>\n",
        "      <th>A.Seppi</th>\n",
        "      <th>1</th>\n",
        "      <td>  36</td>\n",
        "      <td> 13</td>\n",
        "      <td> 17</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.472222</td>\n",
        "      <td> 0.307692</td>\n",
        "      <td>-0.164530</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                                    n_try1  n_try2  n_win1  n_win2  \\\n",
        "Match Player 1  Player 2    Server                                   \n",
        "MS219 J.Isner   A.Mannarino 2            5       1       4       0   \n",
        "MS311 N.Almagro J.Janowicz  1          107      30      58       8   \n",
        "MS159 M.Gicquel V.Pospisil  2           88      26      56      11   \n",
        "MS308 G.Zemlja  J.Del Potro 1          101      33      53      11   \n",
        "MS214 M.Llodra  A.Seppi     1           36      13      17       4   \n",
        "\n",
        "                                    p_mle_win1  p_mle_win2  p_mle_delta  \n",
        "Match Player 1  Player 2    Server                                       \n",
        "MS219 J.Isner   A.Mannarino 2         0.800000    0.000000    -0.800000  \n",
        "MS311 N.Almagro J.Janowicz  1         0.542056    0.266667    -0.275389  \n",
        "MS159 M.Gicquel V.Pospisil  2         0.636364    0.423077    -0.213287  \n",
        "MS308 G.Zemlja  J.Del Potro 1         0.524752    0.333333    -0.191419  \n",
        "MS214 M.Llodra  A.Seppi     1         0.472222    0.307692    -0.164530  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ro.r('library(PropCIs)')\n",
      "rdf = com.convert_to_r_dataframe(df[['n_win1', 'n_try1', 'n_win2', 'n_try2']])\n",
      "ro.globalenv['data'] = rdf\n",
      "cis = ro.r('mapply(diffscoreci, data$n_win2, data$n_try2, data$n_win1, data$n_try1, .95)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['2.5%_freq'] = [i[0] for i in cis]\n",
      "df['97.5%_freq'] = [i[1] for i in cis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>n_try1</th>\n",
        "      <th>n_try2</th>\n",
        "      <th>n_win1</th>\n",
        "      <th>n_win2</th>\n",
        "      <th>p_mle_win1</th>\n",
        "      <th>p_mle_win2</th>\n",
        "      <th>p_mle_delta</th>\n",
        "      <th>2.5%_freq</th>\n",
        "      <th>97.5%_freq</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Match</th>\n",
        "      <th>Player 1</th>\n",
        "      <th>Player 2</th>\n",
        "      <th>Server</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>MS219</th>\n",
        "      <th>J.Isner</th>\n",
        "      <th>A.Mannarino</th>\n",
        "      <th>2</th>\n",
        "      <td>   5</td>\n",
        "      <td>  1</td>\n",
        "      <td>  4</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.800000</td>\n",
        "      <td>-0.968279</td>\n",
        "      <td> 0.205700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS311</th>\n",
        "      <th>N.Almagro</th>\n",
        "      <th>J.Janowicz</th>\n",
        "      <th>1</th>\n",
        "      <td> 107</td>\n",
        "      <td> 30</td>\n",
        "      <td> 58</td>\n",
        "      <td>  8</td>\n",
        "      <td> 0.542056</td>\n",
        "      <td> 0.266667</td>\n",
        "      <td>-0.275389</td>\n",
        "      <td>-0.436619</td>\n",
        "      <td>-0.074081</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS159</th>\n",
        "      <th>M.Gicquel</th>\n",
        "      <th>V.Pospisil</th>\n",
        "      <th>2</th>\n",
        "      <td>  88</td>\n",
        "      <td> 26</td>\n",
        "      <td> 56</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0.636364</td>\n",
        "      <td> 0.423077</td>\n",
        "      <td>-0.213287</td>\n",
        "      <td>-0.411075</td>\n",
        "      <td> 0.002900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS308</th>\n",
        "      <th>G.Zemlja</th>\n",
        "      <th>J.Del Potro</th>\n",
        "      <th>1</th>\n",
        "      <td> 101</td>\n",
        "      <td> 33</td>\n",
        "      <td> 53</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0.524752</td>\n",
        "      <td> 0.333333</td>\n",
        "      <td>-0.191419</td>\n",
        "      <td>-0.362470</td>\n",
        "      <td> 0.005662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MS214</th>\n",
        "      <th>M.Llodra</th>\n",
        "      <th>A.Seppi</th>\n",
        "      <th>1</th>\n",
        "      <td>  36</td>\n",
        "      <td> 13</td>\n",
        "      <td> 17</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0.472222</td>\n",
        "      <td> 0.307692</td>\n",
        "      <td>-0.164530</td>\n",
        "      <td>-0.421411</td>\n",
        "      <td> 0.152915</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                                    n_try1  n_try2  n_win1  n_win2  \\\n",
        "Match Player 1  Player 2    Server                                   \n",
        "MS219 J.Isner   A.Mannarino 2            5       1       4       0   \n",
        "MS311 N.Almagro J.Janowicz  1          107      30      58       8   \n",
        "MS159 M.Gicquel V.Pospisil  2           88      26      56      11   \n",
        "MS308 G.Zemlja  J.Del Potro 1          101      33      53      11   \n",
        "MS214 M.Llodra  A.Seppi     1           36      13      17       4   \n",
        "\n",
        "                                    p_mle_win1  p_mle_win2  p_mle_delta  \\\n",
        "Match Player 1  Player 2    Server                                        \n",
        "MS219 J.Isner   A.Mannarino 2         0.800000    0.000000    -0.800000   \n",
        "MS311 N.Almagro J.Janowicz  1         0.542056    0.266667    -0.275389   \n",
        "MS159 M.Gicquel V.Pospisil  2         0.636364    0.423077    -0.213287   \n",
        "MS308 G.Zemlja  J.Del Potro 1         0.524752    0.333333    -0.191419   \n",
        "MS214 M.Llodra  A.Seppi     1         0.472222    0.307692    -0.164530   \n",
        "\n",
        "                                    2.5%_freq  97.5%_freq  \n",
        "Match Player 1  Player 2    Server                         \n",
        "MS219 J.Isner   A.Mannarino 2       -0.968279    0.205700  \n",
        "MS311 N.Almagro J.Janowicz  1       -0.436619   -0.074081  \n",
        "MS159 M.Gicquel V.Pospisil  2       -0.411075    0.002900  \n",
        "MS308 G.Zemlja  J.Del Potro 1       -0.362470    0.005662  \n",
        "MS214 M.Llodra  A.Seppi     1       -0.421411    0.152915  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    return list(x.values.astype(np.int))\n",
      "\n",
      "stan_data = {'N': len(df), \n",
      "             'try1': f(df.n_try1), 'try2': f(df.n_try2),\n",
      "             'win1': f(df.n_win1), 'win2': f(df.n_win2)}\n",
      "\n",
      "stan_code = '''\n",
      "data {\n",
      "    int<lower=1> N;\n",
      "    int try1[N];\n",
      "    int try2[N];\n",
      "    int win1[N];\n",
      "    int win2[N];\n",
      "}\n",
      "parameters {\n",
      "    real<lower=0,upper=1> phi1;\n",
      "    real<lower=0,upper=1> phi2;\n",
      "    real<lower=0.1> lambda1;\n",
      "    real<lower=0.1> lambda2;\n",
      "    vector<lower=0,upper=1>[N] theta1;\n",
      "    vector<lower=0,upper=1>[N] theta2;\n",
      "}\n",
      "transformed parameters {\n",
      "    real<lower=0> alpha1;\n",
      "    real<lower=0> alpha2;\n",
      "    real<lower=0> beta1;\n",
      "    real<lower=0> beta2;\n",
      "    alpha1 <- lambda1*phi1;\n",
      "    alpha2 <- lambda2*phi2;\n",
      "    beta1 <- lambda1*(1-phi1);\n",
      "    beta2 <- lambda2*(1-phi2);\n",
      "}\n",
      "model {\n",
      "    phi1 ~ beta(1,1);\n",
      "    phi2 ~ beta(1,1);\n",
      "    lambda1 ~ pareto(0.1, 1.5);\n",
      "    lambda2 ~ pareto(0.1, 1.5);\n",
      "    \n",
      "    for (i in 1:N) {\n",
      "        theta1[i] ~ beta(alpha1, beta1);\n",
      "        theta2[i] ~ beta(alpha2, beta2);\n",
      "        win1[i] ~ binomial(try1[i], theta1[i]);\n",
      "        win2[i] ~ binomial(try2[i], theta2[i]);\n",
      "    }\n",
      "}\n",
      "generated quantities {\n",
      "    vector[N] delta;\n",
      "    for (i in 1:N)\n",
      "        delta[i] <- theta2[i] - theta1[i];\n",
      "}\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fit = stan_cache(model_code=stan_code, data=stan_data)\n",
      "summary = fit.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached StanModel\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def split(namesids):\n",
      "    names = [i.split('[')[0] for i in namesids]\n",
      "    ids = [int(i.split('[')[1][:-1]) if '[' in i else 0 for i in namesids]\n",
      "    return names, ids\n",
      "\n",
      "res = pd.DataFrame(data=summary['summary'], columns=summary['summary_colnames'])\n",
      "names, ids = split(summary['summary_rownames'])\n",
      "res['name'] = names\n",
      "res['id'] = ids\n",
      "res = res.set_index(['name', 'id'])\n",
      "res.tail(10)"
     ],
     "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></th>\n",
        "      <th>mean</th>\n",
        "      <th>se_mean</th>\n",
        "      <th>sd</th>\n",
        "      <th>2.5%</th>\n",
        "      <th>25%</th>\n",
        "      <th>50%</th>\n",
        "      <th>75%</th>\n",
        "      <th>97.5%</th>\n",
        "      <th>n_eff</th>\n",
        "      <th>Rhat</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th>id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"9\" valign=\"top\">delta</th>\n",
        "      <th>211</th>\n",
        "      <td>     0.124364</td>\n",
        "      <td> 0.001711</td>\n",
        "      <td>  0.062484</td>\n",
        "      <td>     0.000060</td>\n",
        "      <td>     0.081460</td>\n",
        "      <td>     0.124050</td>\n",
        "      <td>     0.168498</td>\n",
        "      <td>     0.243954</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 1.000423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>212</th>\n",
        "      <td>     0.122032</td>\n",
        "      <td> 0.001921</td>\n",
        "      <td>  0.070155</td>\n",
        "      <td>    -0.014103</td>\n",
        "      <td>     0.074661</td>\n",
        "      <td>     0.121516</td>\n",
        "      <td>     0.169691</td>\n",
        "      <td>     0.258000</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 0.999262</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>213</th>\n",
        "      <td>     0.106834</td>\n",
        "      <td> 0.002004</td>\n",
        "      <td>  0.073188</td>\n",
        "      <td>    -0.037159</td>\n",
        "      <td>     0.057288</td>\n",
        "      <td>     0.106548</td>\n",
        "      <td>     0.156558</td>\n",
        "      <td>     0.251426</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 1.000459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>214</th>\n",
        "      <td>     0.138768</td>\n",
        "      <td> 0.001838</td>\n",
        "      <td>  0.067139</td>\n",
        "      <td>     0.004362</td>\n",
        "      <td>     0.094102</td>\n",
        "      <td>     0.139670</td>\n",
        "      <td>     0.183241</td>\n",
        "      <td>     0.272580</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 1.000365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>215</th>\n",
        "      <td>     0.140091</td>\n",
        "      <td> 0.001852</td>\n",
        "      <td>  0.067625</td>\n",
        "      <td>     0.003471</td>\n",
        "      <td>     0.096614</td>\n",
        "      <td>     0.140814</td>\n",
        "      <td>     0.184581</td>\n",
        "      <td>     0.274584</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 1.000380</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>216</th>\n",
        "      <td>     0.160218</td>\n",
        "      <td> 0.001727</td>\n",
        "      <td>  0.063073</td>\n",
        "      <td>     0.034879</td>\n",
        "      <td>     0.117735</td>\n",
        "      <td>     0.160637</td>\n",
        "      <td>     0.202576</td>\n",
        "      <td>     0.286397</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 0.999609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>217</th>\n",
        "      <td>     0.164800</td>\n",
        "      <td> 0.001867</td>\n",
        "      <td>  0.068173</td>\n",
        "      <td>     0.031682</td>\n",
        "      <td>     0.119945</td>\n",
        "      <td>     0.165414</td>\n",
        "      <td>     0.210673</td>\n",
        "      <td>     0.297063</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 0.999521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>218</th>\n",
        "      <td>     0.137521</td>\n",
        "      <td> 0.002103</td>\n",
        "      <td>  0.076797</td>\n",
        "      <td>    -0.015100</td>\n",
        "      <td>     0.086375</td>\n",
        "      <td>     0.137531</td>\n",
        "      <td>     0.191198</td>\n",
        "      <td>     0.288271</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 0.999829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>219</th>\n",
        "      <td>     0.193570</td>\n",
        "      <td> 0.002031</td>\n",
        "      <td>  0.074167</td>\n",
        "      <td>     0.048554</td>\n",
        "      <td>     0.143070</td>\n",
        "      <td>     0.193464</td>\n",
        "      <td>     0.244258</td>\n",
        "      <td>     0.344669</td>\n",
        "      <td> 1334</td>\n",
        "      <td> 1.000155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lp__</th>\n",
        "      <th>0  </th>\n",
        "      <td>-21812.686193</td>\n",
        "      <td> 1.230215</td>\n",
        "      <td> 24.201179</td>\n",
        "      <td>-21857.849709</td>\n",
        "      <td>-21828.761804</td>\n",
        "      <td>-21813.260551</td>\n",
        "      <td>-21797.055006</td>\n",
        "      <td>-21761.745709</td>\n",
        "      <td>  387</td>\n",
        "      <td> 1.007729</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                   mean   se_mean         sd          2.5%           25%  \\\n",
        "name  id                                                                   \n",
        "delta 211      0.124364  0.001711   0.062484      0.000060      0.081460   \n",
        "      212      0.122032  0.001921   0.070155     -0.014103      0.074661   \n",
        "      213      0.106834  0.002004   0.073188     -0.037159      0.057288   \n",
        "      214      0.138768  0.001838   0.067139      0.004362      0.094102   \n",
        "      215      0.140091  0.001852   0.067625      0.003471      0.096614   \n",
        "      216      0.160218  0.001727   0.063073      0.034879      0.117735   \n",
        "      217      0.164800  0.001867   0.068173      0.031682      0.119945   \n",
        "      218      0.137521  0.002103   0.076797     -0.015100      0.086375   \n",
        "      219      0.193570  0.002031   0.074167      0.048554      0.143070   \n",
        "lp__  0   -21812.686193  1.230215  24.201179 -21857.849709 -21828.761804   \n",
        "\n",
        "                    50%           75%         97.5%  n_eff      Rhat  \n",
        "name  id                                                              \n",
        "delta 211      0.124050      0.168498      0.243954   1334  1.000423  \n",
        "      212      0.121516      0.169691      0.258000   1334  0.999262  \n",
        "      213      0.106548      0.156558      0.251426   1334  1.000459  \n",
        "      214      0.139670      0.183241      0.272580   1334  1.000365  \n",
        "      215      0.140814      0.184581      0.274584   1334  1.000380  \n",
        "      216      0.160637      0.202576      0.286397   1334  0.999609  \n",
        "      217      0.165414      0.210673      0.297063   1334  0.999521  \n",
        "      218      0.137531      0.191198      0.288271   1334  0.999829  \n",
        "      219      0.193464      0.244258      0.344669   1334  1.000155  \n",
        "lp__  0   -21813.260551 -21797.055006 -21761.745709    387  1.007729  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = res.loc['delta', :][['2.5%', 'mean', '97.5%']]\n",
      "z.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>2.5%</th>\n",
        "      <th>mean</th>\n",
        "      <th>97.5%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>-0.156306</td>\n",
        "      <td> 0.018688</td>\n",
        "      <td> 0.191296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.223398</td>\n",
        "      <td>-0.086274</td>\n",
        "      <td> 0.049111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-0.203402</td>\n",
        "      <td>-0.066314</td>\n",
        "      <td> 0.066707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-0.181577</td>\n",
        "      <td>-0.053893</td>\n",
        "      <td> 0.075193</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.148787</td>\n",
        "      <td> 0.004266</td>\n",
        "      <td> 0.152567</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "        2.5%      mean     97.5%\n",
        "id                              \n",
        "0  -0.156306  0.018688  0.191296\n",
        "1  -0.223398 -0.086274  0.049111\n",
        "2  -0.203402 -0.066314  0.066707\n",
        "3  -0.181577 -0.053893  0.075193\n",
        "4  -0.148787  0.004266  0.152567"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ntop = 20\n",
      "nbot = 20\n",
      "top = list(range(nbot))\n",
      "bot = list(range(len(z)-ntop, len(z)))\n",
      "\n",
      "ids = top + bot\n",
      "\n",
      "fig,(ax1,ax2) = plt.subplots(2,1,sharex=True, figsize=(8, 12))\n",
      "\n",
      "ax1.hlines(ids[::-1], z['2.5%'].iloc[ids], z['97.5%'].iloc[ids], color = 'red', lw=2, alpha= .5)\n",
      "ax1.plot(z['mean'].values[ids], ids[::-1], 'r^', alpha=.5)\n",
      "ax1.hlines(ids[::-1], df['2.5%_freq'].iloc[ids], df['97.5%_freq'].iloc[ids], color = 'green', lw=2, alpha=.5)\n",
      "ax1.plot(df['p_mle_delta'].values[ids], ids[::-1], 'g^', alpha=.5)\n",
      "\n",
      "ax2.hlines(ids[::-1], z['2.5%'].iloc[ids], z['97.5%'].iloc[ids], color = 'red', lw=2, alpha= .5)\n",
      "ax2.plot(z['mean'].values[ids], ids[::-1], 'r^', alpha=.5)\n",
      "ax2.hlines(ids[::-1], df['2.5%_freq'].iloc[ids], df['97.5%_freq'].iloc[ids], color = 'green', lw=2, alpha=.5)\n",
      "ax2.plot(df['p_mle_delta'].values[ids], ids[::-1], 'g^', alpha=.5)\n",
      "\n",
      "ax1.set_ylim(len(z)-ntop - 2, len(z))\n",
      "ax1.vlines(0, len(z)-ntop - 2, len(z), linestyles='--')\n",
      "ax2.set_ylim(-1, nbot + 1)\n",
      "ax2.vlines(0, -1, nbot + 1, linestyles='--')\n",
      "\n",
      "\n",
      "plt.xlim(-1, 1)\n",
      "\n",
      "ax1.spines['bottom'].set_visible(False)\n",
      "ax2.spines['top'].set_visible(False)\n",
      "ax1.xaxis.tick_top()\n",
      "ax1.tick_params(labeltop='off') # don't put tick labels at the top\n",
      "ax2.xaxis.tick_bottom()\n",
      "\n",
      "d = .05 # how big to make the diagonal lines in axes coordinates\n",
      "# arguments to pass plot, just so we don't keep repeating them\n",
      "kwargs = dict(transform=ax1.transAxes, color='k', clip_on=False)\n",
      "ax1.plot((-d,+d),(-d,+d), **kwargs)      # top-left diagonal\n",
      "ax1.plot((1/2-d,1/2+d),(-d,+d), **kwargs)\n",
      "ax1.plot((1-d,1+d),(-d,+d), **kwargs)    # top-right diagonal\n",
      "\n",
      "kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes\n",
      "ax2.plot((-d,+d),(1-d,1+d), **kwargs)   # bottom-left diagonal\n",
      "ax2.plot((1/2-d,1/2+d),(1-d,1+d), **kwargs)\n",
      "ax2.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-right diagonal\n",
      "\n",
      "pairs = [(quat[quat[3]], quat[3 - quat[3]]) for quat in df.index]\n",
      "labels = [\"%s (vs. %s)\" % pair for pair in pairs][::-1]\n",
      "tlab = [labels[i] for i in top]\n",
      "blab = [labels[i] for i in bot]\n",
      "\n",
      "ax1.set_yticks(bot)\n",
      "ax1.set_yticklabels(blab)\n",
      "\n",
      "ax2.set_yticks(top)\n",
      "ax2.set_yticklabels(tlab)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EkiVLmDBhwiP3o6I+ld2u1WpJSUkhISGB33//nbfffvuh66tbty46nY7U1FSs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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fcdf6a089b0>"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}