components.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from model.Season import Season\n",
    "from model.util import remove_mr_ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017\n"
     ]
    }
   ],
   "source": [
    "with open('seasons.pickle') as f:\n",
    "    seasons = pickle.load(f)\n",
    "for year, season in seasons.iteritems():\n",
    "    print year,\n",
    "    season.load_scores()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (9,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "men = pd.read_csv('pd_data/components_men.csv')\n",
    "ladies = pd.read_csv('pd_data/components_ladies.csv')\n",
    "pairs = pd.read_csv('pd_data/components_pairs.csv')\n",
    "dance = pd.read_csv('pd_data/components_dance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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iiI9XwdChULly/BoSEYkIJRGYWSWCJJDr7i8WtY+7T3X3LHfPqlmzZnIDjCI7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\nnBKBiEiG+3/r3XcyRLVPcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c297610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(all_comps['Score'], line='s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iQlJjqVSHiNhAJcDo7u6Onp6ehucql8sUGd8qZ2Nft61+rLnNVOlfOMrdA0X+e7eG+6pP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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf89490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_comps['Final Points'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.1966623753697823"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(men[men['Component Name'] == 'Transitions'].Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4598731784814287"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(men.Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_comps = pd.concat([men, ladies, pairs, dance])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E90vpUNZFSSvTeu5saTOyrtuAF9J5DzMzq8Ck34ZExGFJ+4BXgQbwPZrv9j8O7JW0EXgT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\n5tAwM7NsDg0zM8vm0DAzs2z/F417yYTX/jB4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c1f0290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_comps.Score.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6131237938037315"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comps[all_comps.Season == 2017].Score.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7164260915588629"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comps[all_comps.Season == 2016].Score.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4344274081092998"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comps[all_comps.Season == 2015].Score.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006 1.56322431881 0\n",
      "2007 1.58016026138 0\n",
      "2008 1.88735155161 0\n",
      "2009 1.78972986192 0\n",
      "2010 1.98632056307 30\n",
      "2011 1.55749546633 28\n",
      "2012 1.79393199527 81\n",
      "2013 1.70844704011 106\n",
      "2014 1.74174435842 273\n",
      "2015 1.43442740811 20\n",
      "2016 1.71642609156 231\n",
      "2017 1.6131237938 164\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "y = []\n",
    "for year in xrange(2006, 2018):\n",
    "    x.append(year)\n",
    "    this_season = all_comps[all_comps.Season == year]\n",
    "    y.append(len(this_season[this_season.Score == 10.00]))\n",
    "    print year, this_season.Score.var(), len(this_season[this_season.Score == 10.00])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIQva5yH5E4XvOdzNQDCkHbE2muiVyhENzR2UFOaxal5mJ7hpJYU4y4uTVnkz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e11690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.title('Number of Perfect 10s over Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "perfect = all_comps[all_comps.Score == 10.00]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "has_judges = all_comps[all_comps['Judge Nation'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gpusa2016', 'gpcan2016', 'gprus2016', 'gpfra2016', 'gpchn2016',\n",
       "       'gpjpn2016', 'gpf1617'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_judges[has_judges['Judge Nation'] != 'ISU'].Event.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'Base Value', u'Component Name', u'Dedutions',\n",
       "       u'Discipline', u'Event', u'Factor', u'Final Points', u'Judge',\n",
       "       u'Judge Nation', u'PCS', u'Score', u'Season', u'Segment',\n",
       "       u'Segment Rank', u'Skater', u'Skater Nation', u'Start', u'TES',\n",
       "       u'Judge Number', u'Nation Match', u'Point Difference', u'Intercept'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_judges.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1182d79d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "has_judges.Score.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hOEiSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpA7DQZLUYThIkjoMB0lSh+EgSeow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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176b9790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "has_judges['Point Difference'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.151364221794 1.6131237938\n"
     ]
    }
   ],
   "source": [
    "print has_judges['Point Difference'].var(), has_judges.Score.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'Base Value', u'Component Name', u'Dedutions',\n",
       "       u'Discipline', u'Event', u'Factor', u'Final Points', u'Judge',\n",
       "       u'Judge Nation', u'PCS', u'Score', u'Season', u'Segment',\n",
       "       u'Segment Rank', u'Skater', u'Skater Nation', u'Start', u'TES',\n",
       "       u'Judge Number', u'Nation Match', u'Point Difference'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_judges.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def same_nation(row):\n",
    "    return 1 if row['Judge Nation'] == row['Skater Nation'] else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "has_judges['Nation Match'] = has_judges.apply(same_nation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "has_judges['Point Difference'] = has_judges.apply(lambda row: row['Final Points'] - row['Score'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years = []\n",
    "avg = []\n",
    "var = []\n",
    "for year in xrange(2006, 2018):\n",
    "    years.append(year)\n",
    "    avg.append(np.median(all_comps[all_comps.Season == year].Score))\n",
    "    var.append(np.var(all_comps[all_comps.Season == year].Score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ACTMX0al1E+bmZNK+ReOgSxKRCAp+ianl2w4yYeYiMlo2piAniw4tmwRdkohU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bca7850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PIZ1Wg2Vp8ZLXugshcLCyDVcvTUZCLKdkZlKYm4TRcS9ON4dX3v2IvwRyLquO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\nhfokCi7ZwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bd7ccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(years, avg, label='median')\n",
    "plt.show()\n",
    "plt.plot(years, var, label='variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.23236904761904759"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(has_judges[has_judges['Nation Match'] == 1]['Point Difference'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('pd_data/name_fixes_judge.json') as f:\n",
    "    judge_name_fixes = f.read()\n",
    "judge_name_fixes = json.loads(judge_name_fixes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:477: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "for wrong_name, right_name in judge_name_fixes.iteritems():\n",
    "    has_judges.loc[has_judges.Judge == wrong_name, 'Judge'] = right_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(has_judges.Judge.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = has_judges[['Nation Match']]\n",
    "X = sm.add_constant(X)\n",
    "y = has_judges['Point Difference']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Point Difference   R-squared:                       0.020\n",
      "Model:                            OLS   Adj. R-squared:                  0.020\n",
      "Method:                 Least Squares   F-statistic:                     1028.\n",
      "Date:                Tue, 30 Jan 2018   Prob (F-statistic):          3.07e-223\n",
      "Time:                        20:09:09   Log-Likelihood:                -22916.\n",
      "No. Observations:               49325   AIC:                         4.584e+04\n",
      "Df Residuals:                   49323   BIC:                         4.585e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "--------------------------------------------------------------------------------\n",
      "const            0.0201      0.002     11.291      0.000         0.017     0.024\n",
      "Nation Match    -0.2525      0.008    -32.061      0.000        -0.268    -0.237\n",
      "==============================================================================\n",
      "Omnibus:                     7419.161   Durbin-Watson:                   2.194\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            60987.721\n",
      "Skew:                           0.482   Prob(JB):                         0.00\n",
      "Kurtosis:                       8.362   Cond. No.                         4.55\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "mod = sm.OLS(y, X)\n",
    "res = mod.fit()\n",
    "print res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.908104798546\n",
      "[ 0.2545985  0.9950425]\n",
      "0.0164792332928\n"
     ]
    }
   ],
   "source": [
    "print lr.score(X, y)\n",
    "print lr.coef_\n",
    "print lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}