Overall Item Impact Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import sklearn.linear_model as sklm\n",
    "import sklearn.cross_validation as skcv\n",
    "import sklearn.feature_selection as skfs\n",
    "import sklearn.ensemble as skes\n",
    "import sys\n",
    "import json\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Preprocessing\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119720\n",
      "119720\n"
     ]
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"parser_silver.csv\")\n",
    "print(len(df_raw))\n",
    "df = df_raw[df_raw['gold10'].isnull() == False]\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def list_str(str):\n",
    "    return json.loads(str)\n",
    "df['allyChampList'] = df['allyChampions'].apply(list_str)\n",
    "df['opponentChampList'] = df['opponentChampions'].apply(list_str)\n",
    "df['item10List'] = df['items10'].apply(list_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,48,50,51,53,54,55,56,57,58,59,60,61,62,63,64,67,68,69,72,74,75,76,77,78,79,80,81,82,83,84,85,86,89,90,91,92,96,98,99,101,102,103,104,105,106,107,110,111,112,113,114,115,117,119,120,121,122,126,127,131,133,134,143,150,154,157,161,201,203,222,223,236,238,245,254,266,267,268,412,420,421,429,432,128\n"
     ]
    }
   ],
   "source": [
    "champ_ids = df['championId'].unique()\n",
    "champ_ids.sort()\n",
    "base_cols = []\n",
    "\n",
    "for c_id in champ_ids:\n",
    "    sys.stdout.write(\"{0},\".format(c_id))\n",
    "    allyc_col = \"allyc_{0}\".format(c_id)\n",
    "    oppc_col = \"opponentc_{0}\".format(c_id)\n",
    "    df[allyc_col] = df['allyChampList'].map(lambda x: c_id in x)\n",
    "    df[oppc_col] = df['opponentChampList'].map(lambda x: c_id in x)\n",
    "    base_cols.append(allyc_col)\n",
    "    base_cols.append(oppc_col)\n",
    "print(len(champ_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n"
     ]
    }
   ],
   "source": [
    "print(len(champ_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['lanerole'] = df['lane'] + df['role']\n",
    "lroles = df['lanerole'].unique()\n",
    "for lrole in lroles:\n",
    "    lrole_col = \"lr_{0}\".format(lrole)\n",
    "    df[lrole_col] = (df['lanerole'] == lrole)\n",
    "    base_cols.append(lrole_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "205\n",
      "1001,1004,1006,1011,1018,1026,1027,1028,1029,1031,1033,1036,1037,1038,1039,1041,1042,1043,1051,1052,1053,1054,1055,1056,1057,1058,1082,1083,1300,1301,1303,1304,1305,1306,1307,1308,1309,1310,1311,1313,1314,1315,1316,1317,1318,1319,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1333,1334,1400,1401,1402,1403,1408,1409,1410,1411,1412,1413,1414,1415,2003,2004,2010,2015,2031,2032,2033,2041,2043,2044,2045,2049,2053,2138,2139,2301,2302,2303,3001,3004,3006,3009,3010,3020,3022,3024,3025,3027,3028,3031,3033,3034,3035,3036,3041,3044,3046,3047,3050,3052,3053,3057,3065,3067,3068,3069,3070,3071,3072,3074,3075,3077,3078,3082,3083,3085,3086,3087,3089,3092,3093,3094,3096,3097,3098,3100,3101,3105,3108,3110,3111,3113,3114,3115,3116,3117,3123,3124,3133,3134,3135,3136,3140,3141,3142,3144,3145,3146,3151,3152,3153,3155,3156,3157,3158,3165,3174,3191,3196,3197,3211,3285,3301,3302,3303,3340,3341,3342,3363,3401,3508,3512,3599,3706,3707,3708,3709,3710,3711,3713,3714,3715,3717,3718,3722,3723,3724,3725,3726,3742,3748,3751,3800,3801,"
     ]
    }
   ],
   "source": [
    "itemSet = set()\n",
    "for itemList in df['item10List']:\n",
    "    itemSet.update(itemList)\n",
    "item_id_list = list(itemSet)\n",
    "item_id_list.sort()\n",
    "print(len(item_id_list))\n",
    "\n",
    "item_cols = []\n",
    "\n",
    "for item_id in item_id_list:\n",
    "    sys.stdout.write(\"{0},\".format(item_id))\n",
    "    i_col = \"i_{0}\".format(item_id)\n",
    "    df[i_col] = df['item10List'].map(lambda x: item_id in x)\n",
    "    item_cols.append(i_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "state_cols = []\n",
    "cstate_cols = []\n",
    "for col in ['gold5', 'gold10', 'xp5', 'xp10']:\n",
    "    zcol = col + 'z'\n",
    "    ccol = col + 'c'\n",
    "    \n",
    "    df[zcol] = (df[col] - df[col].mean())/df[col].std(ddof=0)\n",
    "    df[ccol] = pd.cut(df[zcol], bins=[-20,-1,0,1,20], labels=False)\n",
    "    \n",
    "    state_cols.append(zcol)\n",
    "    cstate_cols.append(ccol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "509"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11972 entries, 1 to 119719\n",
      "Columns: 509 entries, matchId to xp10c\n",
      "dtypes: bool(477), float64(9), int64(13), object(10)\n",
      "memory usage: 8.5+ MB\n"
     ]
    }
   ],
   "source": [
    "idxs = []\n",
    "# want to select 1 sample per match\n",
    "for i in range(0,len(df),10):\n",
    "    idxs.append(random.randint(i, i+9))\n",
    "df_m = df.iloc[idxs]\n",
    "df_m.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_s, df_test, y_s, y_test = skcv.train_test_split(\n",
    "    df_m, df_m['winner'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9577 entries, 96407 to 64875\n",
      "Columns: 509 entries, matchId to xp10c\n",
      "dtypes: bool(477), float64(9), int64(13), object(10)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_s.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['allyc_1', 'opponentc_1', 'allyc_2', 'opponentc_2', 'allyc_3']\n",
      "['gold5z', 'gold10z', 'xp5z', 'xp10z']\n",
      "['i_1001', 'i_1004', 'i_1006', 'i_1011', 'i_1018']\n"
     ]
    }
   ],
   "source": [
    "print(base_cols[:5])\n",
    "print(state_cols)\n",
    "print(item_cols[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c: 0.0001, score: 0.506003968886\n",
      "c: 0.001, score: 0.506003968886\n",
      "c: 0.01, score: 0.506003968886\n",
      "c: 0.1, score: 0.55163493873\n",
      "c: 1, score: 0.55727375373\n",
      "c: 10, score: 0.559153431371\n",
      "c: 100, score: 0.559362090734\n",
      "c: 1000, score: 0.559257652092\n"
     ]
    }
   ],
   "source": [
    "for i in range(-4, 4, 1):\n",
    "    c = 10**i\n",
    "    champ_scores = skcv.cross_val_score(\n",
    "        sklm.LogisticRegression(C=c, penalty='l1'), \n",
    "        df_s[base_cols], \n",
    "        df_s['winner'], \n",
    "        scoring='accuracy', \n",
    "        cv=5)\n",
    "    print(\"c: {}, score: {}\".format(c, champ_scores.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55727375373019472"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(penalty='l1'), \n",
    "    df_s[base_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59005953329492478"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_gold_scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(penalty='l1'), \n",
    "    df_s[base_cols+state_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_gold_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58724012579490092"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_gold_item_scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(penalty='l1'), \n",
    "    df_s[base_cols+state_cols+item_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_gold_item_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6256656573039574"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = base_cols+state_cols+item_cols\n",
    "m = sklm.LogisticRegression().fit(\n",
    "    df_s[cols], \n",
    "    df_s['winner'], \n",
    ")\n",
    "m.score(df_s[cols], df_s['winner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52051767135060634"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_item_scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(penalty='l1'), \n",
    "    df_s[item_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "only_item_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52734864300626305"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = item_cols\n",
    "m = sklm.LogisticRegression(penalty='l2').fit(\n",
    "    df_s[cols], \n",
    "    df_s['winner'], \n",
    ")\n",
    "m.score(df_test[cols], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
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       " (0.11499505527237436, 'i_3706'),\n",
       " (0.11883850714072113, 'i_3108'),\n",
       " (-0.12116805138520589, 'i_1027'),\n",
       " (0.12197207564136668, 'i_3024'),\n",
       " (-0.12374827265427571, 'i_3285'),\n",
       " (-0.12766145889547531, 'i_1006'),\n",
       " (0.13406024119038018, 'i_3401'),\n",
       " (0.1369493164248303, 'i_3057'),\n",
       " (0.14097669911686966, 'i_1011'),\n",
       " (0.14809629862942897, 'i_2302'),\n",
       " (0.15249315308173386, 'i_3134'),\n",
       " (0.15680738781893794, 'i_3142'),\n",
       " (0.15806753195222312, 'i_3144'),\n",
       " (0.16142375772747944, 'i_1026'),\n",
       " (0.16496120513901938, 'i_1082'),\n",
       " (0.16606890997709103, 'i_1029'),\n",
       " (0.17212603766429327, 'i_2049'),\n",
       " (0.17946758803678201, 'i_3071'),\n",
       " (0.18478799813099145, 'i_1410'),\n",
       " (-0.1870287509685617, 'i_1042'),\n",
       " (-0.18865386637848627, 'i_3711'),\n",
       " (-0.19977795829240921, 'i_1018'),\n",
       " (0.20123516841011888, 'i_2033'),\n",
       " (0.20689710913378226, 'i_3044'),\n",
       " (0.21032386798853778, 'i_3006'),\n",
       " (-0.2152247512414398, 'i_3724'),\n",
       " (0.21826088036513755, 'i_3041'),\n",
       " (-0.21842027957900814, 'i_3123'),\n",
       " (-0.22024697592986789, 'i_3718'),\n",
       " (0.22215071793458435, 'i_1306'),\n",
       " (0.23362136236564393, 'i_1037'),\n",
       " (0.24057357529557122, 'i_1053'),\n",
       " (0.24152379902585733, 'i_1402'),\n",
       " (0.24206785495813637, 'i_2041'),\n",
       " (0.25236832846841128, 'i_3010'),\n",
       " (0.25528310727859405, 'i_1415'),\n",
       " (0.25932135985768656, 'i_3152'),\n",
       " (0.26239000893941888, 'i_3067'),\n",
       " (0.26398227231150401, 'i_3709'),\n",
       " (0.26440787839786961, 'i_3133'),\n",
       " (-0.27315087180619491, 'i_2004'),\n",
       " (0.27320966100601091, 'i_3748'),\n",
       " (-0.27363742441543421, 'i_1033'),\n",
       " (0.27455060830932942, 'i_3113'),\n",
       " (-0.2747599194772718, 'i_1408'),\n",
       " (0.27744679118980559, 'i_3340'),\n",
       " (0.28364689678045579, 'i_3035'),\n",
       " (-0.2916735273875094, 'i_3020'),\n",
       " (0.29441823356063207, 'i_3715'),\n",
       " (0.30110839539392698, 'i_3001'),\n",
       " (-0.30648921314828609, 'i_3197'),\n",
       " (0.31131735635052549, 'i_1329'),\n",
       " (0.31324119069639023, 'i_3717'),\n",
       " (0.32226288827453853, 'i_1323'),\n",
       " (0.32233814643917652, 'i_1327'),\n",
       " (0.32685980970204964, 'i_3077'),\n",
       " (0.32888380425723424, 'i_1038'),\n",
       " (-0.33100348343774905, 'i_1311'),\n",
       " (0.33217864998229429, 'i_2053'),\n",
       " (0.34235233684269711, 'i_1400'),\n",
       " (-0.3457972554405207, 'i_1412'),\n",
       " (-0.35200060251253668, 'i_3153'),\n",
       " (-0.3695442558254935, 'i_3065'),\n",
       " (-0.37172236958938121, 'i_3707'),\n",
       " (0.37203478857113681, 'i_3140'),\n",
       " (0.37969746512896513, 'i_3072'),\n",
       " (-0.37994747997259631, 'i_1319'),\n",
       " (0.39538839962631805, 'i_3086'),\n",
       " (0.40197715800042794, 'i_3031'),\n",
       " (0.40288350091889386, 'i_3046'),\n",
       " (0.42041165828702326, 'i_3742'),\n",
       " (0.42731027179468473, 'i_3110'),\n",
       " (0.43227250393637939, 'i_3751'),\n",
       " (0.44339051321240253, 'i_3034'),\n",
       " (0.4440421980675483, 'i_1058'),\n",
       " (0.4459228652130528, 'i_3052'),\n",
       " (0.44800209672596353, 'i_3136'),\n",
       " (0.45977333439896767, 'i_3111'),\n",
       " (0.46597424250312552, 'i_1300'),\n",
       " (0.47288559471804209, 'i_1305'),\n",
       " (0.49104065872124381, 'i_3082'),\n",
       " (0.49170661829396001, 'i_3093'),\n",
       " (-0.50221987732717988, 'i_3094'),\n",
       " (-0.51401174556155438, 'i_3115'),\n",
       " (0.53128195108810683, 'i_3068'),\n",
       " (0.53368575283746389, 'i_3092'),\n",
       " (0.53472951776016753, 'i_3726'),\n",
       " (-0.56666546491309877, 'i_3713'),\n",
       " (0.56746653287600723, 'i_3710'),\n",
       " (-0.57053736663102617, 'i_3725'),\n",
       " (0.58799015283034806, 'i_3087'),\n",
       " (0.60227450853761411, 'i_2303'),\n",
       " (0.60763042174151527, 'i_1403'),\n",
       " (0.62236418387503845, 'i_3117'),\n",
       " (0.72714138393727701, 'i_3124'),\n",
       " (0.74455421785460174, 'i_3027'),\n",
       " (0.74727084739770555, 'i_3156'),\n",
       " (0.75247706113656987, 'i_3101'),\n",
       " (-0.81020535308320507, 'i_1411'),\n",
       " (0.83749930329099498, 'i_3085'),\n",
       " (0.91256464764866729, 'i_2301'),\n",
       " (0.96028414210756852, 'i_3508')]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_coeffs = zip(m.coef_[0], cols)\n",
    "i_coeffs.sort(key=lambda x: abs(x[0]))\n",
    "i_coeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59916492693110646"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.score(df_test[item_cols+state_cols+base_cols], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c: 0.0001, score: 0.506003968886\n",
      "c: 0.001, score: 0.506003968886\n",
      "c: 0.01, score: 0.542237313252\n",
      "c: 0.1, score: 0.556752105321\n",
      "c: 1, score: 0.558526581596\n",
      "c: 10, score: 0.559675515622\n",
      "c: 100, score: 0.559884283946\n",
      "c: 1000, score: 0.559779845303\n"
     ]
    }
   ],
   "source": [
    "for i in range(-4, 4, 1):\n",
    "    c = 10**i\n",
    "    champ_scores = skcv.cross_val_score(\n",
    "        svm.LinearSVC(C=c, dual=False, penalty='l1'), \n",
    "        df_s[base_cols], \n",
    "        df_s['winner'], \n",
    "        scoring='accuracy', \n",
    "        cv=5)\n",
    "    print(\"c: {}, score: {}\".format(c, champ_scores.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55842236087446251"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_scores = skcv.cross_val_score(\n",
    "    svm.LinearSVC(), \n",
    "    df_s[base_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60269568548485919"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_gold_scores = skcv.cross_val_score(\n",
    "    svm.LinearSVC(), \n",
    "    df_s[base_cols+state_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_gold_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5960123196806959"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_gold_item_scores = skcv.cross_val_score(\n",
    "    svm.LinearSVC(), \n",
    "    df_s[base_cols+state_cols+item_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_gold_item_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_ss = df_s.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5535000000000001"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_scores = skcv.cross_val_score(\n",
    "    svm.SVC(kernel='linear'), \n",
    "    df_ss[base_cols], \n",
    "    df_ss['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bLB1VTz19bIHUA9gvIq4FiIiVEbE8x1jNzKyAGnsy7zuS3m7g9VYF264GFmTm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\ns8JykjIzs8JykjIzs8L6/wbkBMYOybCgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc6c810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic = [0.56761094672024281, 0.60217316505038165, 0.59851841114026771]\n",
    "linearsvm = [0.56698436926122708, 0.60269568548485919, 0.5960123196806959]\n",
    "n_groups = 3\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "\n",
    "opacity = 0.4\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "rects1 = plt.bar(index, logistic, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='Logistic')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, linearsvm, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='r',\n",
    "                 label='SVM')\n",
    "\n",
    "ax.set_ylim([.5,.65])\n",
    "\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('5-fold CV Accuracy')\n",
    "plt.title('Model Accuracy for Linear clasifiers')\n",
    "plt.xticks(index + bar_width, (1, 2, 3))\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('linear.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols = base_cols+state_cols+item_cols\n",
    "m = svm.LinearSVC().fit(\n",
    "    df_s[cols], \n",
    "    df_s['winner'], \n",
    ")\n",
    "predicted = m.predict(df_test[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[743, 463],\n",
       "       [504, 685]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.confusion_matrix(y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[750, 456],\n",
       "       [500, 689]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = base_cols+state_cols+item_cols\n",
    "m = sklm.LogisticRegression().fit(\n",
    "    df_s[cols], \n",
    "    df_s['winner'], \n",
    ")\n",
    "predicted = m.predict(df_test[cols])\n",
    "sklearn.metrics.confusion_matrix(y_test, predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Poster Computations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5827500269\n",
      "0.564582824283\n",
      "0.544845773218\n",
      "0.535969033425\n",
      "0.526991777568\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "for d in [1, 2, 3, 4, 5]:\n",
    "    tree_champ_scores = skcv.cross_val_score(\n",
    "        skes.AdaBoostClassifier(\n",
    "            n_estimators = 200,\n",
    "            base_estimator=sk.tree.DecisionTreeClassifier(max_depth=d)\n",
    "        ), \n",
    "        df_s[base_cols + state_cols + item_cols],\n",
    "        df_s['winner'], \n",
    "        scoring='accuracy', \n",
    "        cv=5,\n",
    "        n_jobs=2)\n",
    "    m = tree_champ_scores.mean()\n",
    "    print(m)\n",
    "    scores.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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252GzknokqXkR8YpeR1YjTlJWdC+8UJp5ePlydx42y6tHkpoSEW3dH9k3nKSs\nP9m6NY12sWRJSlx77VVKWJ11Hr755lmccsrxfRmmWZ+pJkkN6cHFXg3MkPRgRPyh19GZDTL5/lbb\ntpU6D8+bV+o8PH16GvkCYOHCZdxxBxx4YBsHHjilscGbNUhXTdDviYgZ2ev3AR8CfgW8HvhtRHy5\nz6LshEtSNhDkOw8vXpwaWhx4IMyaNYthw45n2LBZnH++S1M28PS2n9SORhKS5gCnRcRKSaOAuyPi\n5TWPuIecpGygiUidh2+/fRnXXitgCrCM171OvPSlUxgzBvbcMy2jRrkzsfVvva3ua8rG7RPQFBEr\nASJio6StNYzTzDIS7LMPrF3bxkknHc/27dDePpWVK2dx1FFTWLUqdSZeuxY2b07PsjqSVnkCMxsI\nukpSY0ijnwOEpEkRsSKbodfM6mThwmWsXj2VPfZIo1iMGgXr109h9Og2jjyy9Gxq69aUrNatS/8+\n+yw8/nhaf+GFnZNWftl99wa+ObMe6lHrPgBJI4GJEbG4imNnApcCTcD3I+IrZfubgV8Di7JN10TE\nF3L7m4A5QFtEvKXC9V3dZwPOTTfdwzPP7DpkxcSJ2zj11BlVXWPLlpS4OpaORLZ2bWq0kU9a+WQ2\nYkSt341Z52reBL2HN28CHiUNUrscmA2cExELcsc0Ax+LiNM7ucbHgKOB0ZWOcZIy67nNm3dOWvkk\nFtF5AnNHZKu1mjZBfxFmAE9ExJIsmKuAM4AFZcdVDFDSFOCNwBeBj9UvTLPBZfjw1Kl4woRd9/3l\nLzsnraVLS+u77bZrFWLH+rBhff8+bHCoZ5KaDCzLrbcBx5YdE8AJku4nlbY+ERHzs32XAJ8kPRsz\nsz4wYkRaJk7cdV97+84lr8WLS6+HDNm15NWx7rELrTfqmaSqqYe7D5gaEZsknQZcCxwq6c3AsxEx\nN6sSNLMG2333tOy77677Nm3aufrwiSdS8lq3LpWyKpW+xoxJyc2sK/X8FVkOTM2tTyWVpnaIiPW5\n19dL+o6kvYETgNMlvREYAYyRdEVEnFd+k5aWlh2vm5ubaW5uruV7MLMqjByZlkmTdt4ekUaIz1ch\nPv10+nf9+pT0KpW+xozxdCcDUWtrK62trT06p54NJ4aQGk6cTJo08R52bTgxkVRiCkkzgF9ExLSy\n67yWVA3o1n1mA0gEbNhQuQXihg0p6VWqQhw92hNMDhQNbTgREVslXQjcSGqCfnlELJB0Qbb/MuBM\n4ANZ5+AmhBE0AAAKLElEQVRNwNmdXa5ecZpZY0gp4Ywenebfytu+fdcE1taWXm/cmPqOVWqBOHq0\nR+EYaOpWkuoLLkmZDT7bt6eqwvwzsI6lvR322GPXDsxjxqTt3SUwjzrftxrdBN3MrOZ2262UfMpt\n27ZzteFzz1UeRqq8CnHkSFi0yKPOF5FLUmY2KGzdunMCy79+4QWYMyeNOj969CzOPfd4DyPVB1yS\nMjPLDBkC48alpdyjjy5j+fKp7LYbPPfcFO69t41Ro6awdm2qXuxsHESPwlF/LkmZ2aD3wx/OYsuW\n0rOooUNLc3ht3rzrEFJr1pRG4aiUvNyJuTouSZmZdSM/6nyH55+fwqJF6dnU8OFp+pR99tn13Pwo\nHGvXwsKFpURW3ok5n8DcB6x6TlJmNqgtWrSCffZpAp7ZsW3kSFi4cFu3DSg6G4Uj34m5I2mtWJH+\nXb++1AesI2mNHZv+dR+wXbm6z8ysD0V03oR+06bKTegH6kzMDZ2qoy84SZnZQFLehD7/evPmykNI\njR3bf1sgOkmZmQ0QW7bsOg9YZxNZ9pcWiE5SZmaDQMdElh2tDvMJrLwFYv4ZWKNbIDpJmZkNcuUt\nEPNViY1ugegkZWZmFUWkhhqVSl+VWiDWYxR6JykzM+uxvmqB6CRlZmY1tW1b5wmsowVi/rlXfhDf\nvIULl3Hwwft7xAkzM6udpqaUgMaO3XVfeQvEp5+Gxx5LVYrlLRBvvrlt1wtU4CRlZmY1MXQo7L13\nWsp1tEBcuxYefngZTz45tapregAOMzOru+HDYcIEOPhgeP75No46qro5u5ykzMysz3QM6FstV/eZ\nmVmfqTSgb1fcus/MzBqimiboru4zM7PCcpIyM7PCqnuSkjRT0iOSHpd0cYX9zZLWSpqbLZ/Ltk+V\ndIukhyU9JOmiesdqZmbFUteGE5KagG8BpwDLgdmSrouIBWWH3hoRp5dt2wL8U0TMk7QHcK+kmyqc\na2ZmA1S9S1IzgCciYklEbAGuAs6ocNwuD84i4umImJe93gAsAParZ7BmZlYs9U5Sk4FlufW2bFte\nACdIul/S7yUdVn4RSdOAo4C76xSnmZkVUL37SVXTPvw+YGpEbJJ0GnAtcGjHzqyq72rgI1mJaict\nLS07Xjc3N9Pc3NzLkM3MrB5aW1tpbW3t0Tl17Scl6TigJSJmZuufBrZHxFe6OGcxcHREPC9pKPBb\n4PqIuLTCse4nZWbWTxWhn9Qc4BBJ0yQNA84CrssfIGmilGYgkTSDlDifz7ZdDsyvlKDMzGzgq2t1\nX0RslXQhcCPQBFweEQskXZDtvww4E/iApK3AJuDs7PQTgXOBByTNzbZ9OiJuqGfMZmZWHB4WyczM\nGqII1X1mZmYvmpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkV\nlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOUmZkVlpOU\nmZkVlpOUmZkVlpOUmZkVVl2TlKSZkh6R9Likiyvsb5a0VtLcbPlctedaY7S2tjY6hEHFn3ff8udd\nPHVLUpKagG8BM4HDgHMkvazCobdGxFHZ8oUenmt9zP+J+5Y/777lz7t46lmSmgE8ERFLImILcBVw\nRoXj1ItzzcxsAKtnkpoMLMutt2Xb8gI4QdL9kn4v6bAenGtmZgOcIqI+F5b+FpgZEe/L1s8Fjo2I\nD+eOGQ1si4hNkk4Dvh4Rh0o6E3hDV+dm2+sTvJmZ9YmIqFSbtsOQOt57OTA1tz6VVCLaISLW515f\nL+k7ksZlx3V5bnZOl2/OzMz6t3pW980BDpE0TdIw4CzguvwBkiZKUvZ6Bqlk93w155qZ2cBXt5JU\nRGyVdCFwI9AEXB4RCyRdkO2/DDgT+ICkrcAm4Oyuzq1XrGZmVkx1eyZlZmbWW/1yxAlJP5D0jKQH\nGx3LYCBpqqRbJD0s6SFJFzU6poFM0ghJd0uaJ2m+pC83OqbBQFJTNqjAbxody2AgaYmkB7LP/J5O\nj+uPJSlJrwY2AFdExOGNjmegk7QvsG9EzJO0B3Av8FZXwdaPpJFZq9chwO3AJyLi9kbHNZBJ+hhw\nNDA6Ik5vdDwDnaTFwNFZO4RO9cuSVETcBqxudByDRUQ8HRHzstcbgAXAfo2NamCLiE3Zy2Gk57Jd\n/ke23pE0BXgj8H0qDzBg9dHtZ90vk5Q1jqRpwFHA3Y2NZGCTtJukecAzwC0RMb/RMQ1wlwCfBLY3\nOpBBJICbJc2R9L7ODnKSsqplVX1XAx/JSlRWJxGxPSJeAUwBXiOpucEhDViS3gw8GxFzcSmqL50Y\nEUcBpwEfyh7j7MJJyqoiaShwDXBlRFzb6HgGi4hYC/wOOKbRsQxgJwCnZ89Ifg68TtIVDY5pwIuI\nFdm/K4FfkcZs3YWTlHUr63B9OTA/Ii5tdDwDnaTxksZmr3cHTgXmNjaqgSsiPhMRUyNiOqmv5p8i\n4rxGxzWQSRqZDYuHpFHA64GKrbX7ZZKS9HPgTuBQScsknd/omAa4E4Fzgb/Ozf01s9FBDWCTgD9l\nz6TuBn4TEX9scEyDSf9r8tz/TARuy/2O/zYi/lDpwH7ZBN3MzAaHflmSMjOzwcFJyszMCstJyszM\nCstJyszMCstJyszMCstJyszMCstJyqyHJO2d6y+2QlJb9vq+bNTy3l6/WdLa7HqPSLpV0pt6cb0D\nJJ2TW3+3pG/2Nk6zvlC3mXnNBqqIeI40yC6SPg+sj4ivdeyX1BQR23p5mz9HxFuy6x0JXCupPSL+\n9CKuNR14O2nIH3BnVetHXJIy6z1J+pGk70q6C/iKpIMkXZ+N8PxnSS/JDpwg6WpJ92TLCd1dPCLu\nB/4NuLCra0hqkfQTSXdKekzSe7NL/Afw6qy099Fs235ZfI9J+kqtPxCzWnFJyqw2gjTH1vEREZL+\nCFwQEU9IOhb4DnAy8HXgkoi4Q9L+wA3AYVVcfy5pKgm6ucbLgeOAPYC5kn4HXEyaNLGjZPZu4BXZ\n8gLwqKRvRMTy3n0EZrXnJGVWO7/MEtQewPHAL9PYvECavBDgFOBlue2jO2bh7eba+SkkKl1jFClR\n/joiNgObJd1CGll6Tdm1AvhjRKwHkDQfmAY4SVnhOEmZ1U5HotkNWJPNlVNOwLER8UIPr30U0DHx\nYcVr5JJWXmeT+G3Ovd5Gmv3XrHD8TMqsxiJiHbBY0pmQHlhJOiLb/Qfgoo5jJb2iu+tl534O+HY3\n1xBwhqThkvYGmoHZwHpgdP6SlW7T/Tsz63tOUma1k2819w7gPdlUBA8Bp2fbLwKOkXS/pIeB93dy\nnVd3NEEHvgV8OCJu6eYaATwA3ALMAv4tIp7Otm2TNC9rOBHs2sLPLf6skDxVh9kAkTWH3xARX210\nLGa14pKU2cDib502oLgkZWZmheWSlJmZFZaTlJmZFZaTlJmZFZaTlJmZFZaTlJmZFdb/B1kbGRS9\ncPDCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c1823d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = [0.5827500269,\n",
    "0.564582824283,\n",
    "0.544845773218,\n",
    "0.535969033425,\n",
    "0.526991777568]\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "opacity = 0.4\n",
    "\n",
    "rects1 = plt.plot([1, 2, 3, 4, 5], scores,\n",
    "                  marker='^',\n",
    "                  alpha=opacity,\n",
    "                 label='Adaboost')\n",
    "\n",
    "ax.set_ylim([.5,.65])\n",
    "\n",
    "plt.xlabel('Tree Depth')\n",
    "plt.ylabel('5-fold CV Accuracy')\n",
    "plt.title('Model 3 Accuracy wrt Tree Depth')\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('treedepth.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5CHB5RCwBiIin8t/HIuL2PL0CWADsUHG8ZmZWI1UnqfHA4sL8krysaFdgXD60N0fSca0r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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c17cd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adaboost = [0.5692803836185758, 0.59183406378105197, 0.58807487253867152]\n",
    "n_groups = 3\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "\n",
    "opacity = 0.4\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "rects1 = plt.bar(index, adaboost, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='Adaboost')\n",
    "\n",
    "ax.set_ylim([.5,.65])\n",
    "\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('5-fold CV Accuracy')\n",
    "plt.title('Model Accuracy for Depth-1 Adaboost Trees')\n",
    "plt.xticks(index + bar_width/2, (1, 2, 3))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('adaboost.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generalization Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.809439281612\n",
      "0.523173277662\n"
     ]
    }
   ],
   "source": [
    "cols = base_cols\n",
    "m = skes.AdaBoostClassifier(\n",
    "        n_estimators = 200,\n",
    "        base_estimator = sk.tree.DecisionTreeClassifier(max_depth=3)\n",
    "    ).fit(\n",
    "    df_s[cols],\n",
    "    df_s['winner']\n",
    ")\n",
    "print(m.score(df_s[cols], df_s['winner']))\n",
    "print(m.score(df_test[cols], y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.841077581706\n",
      "0.54739039666\n"
     ]
    }
   ],
   "source": [
    "cols = base_cols + state_cols\n",
    "m = skes.AdaBoostClassifier(\n",
    "        n_estimators = 200,\n",
    "        base_estimator = sk.tree.DecisionTreeClassifier(max_depth=3)\n",
    "    ).fit(\n",
    "    df_s[cols],\n",
    "    df_s['winner']\n",
    ")\n",
    "print(m.score(df_s[cols], df_s['winner']))\n",
    "print(m.score(df_test[cols], y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.844836587658\n",
      "0.560751565762\n"
     ]
    }
   ],
   "source": [
    "cols = base_cols + state_cols + item_cols\n",
    "m = skes.AdaBoostClassifier(\n",
    "        n_estimators = 200,\n",
    "        base_estimator = sk.tree.DecisionTreeClassifier(max_depth=3)\n",
    "    ).fit(\n",
    "    df_s[cols],\n",
    "    df_s['winner']\n",
    ")\n",
    "print(m.score(df_s[cols], df_s['winner']))\n",
    "print(m.score(df_test[cols], y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ArwJ/yTIwMzOzWs9JSWoj6UpJP5V0iaRtJPUDpgJnN1yIZmbWVG2sJzUGWAG8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\nZpZbTlJmZpZbTlJmZpZb/x9PB6st+zbCIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107a9e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = [0.809439281612, 0.841077581706, 0.844836587658]\n",
    "train = [0.523173277662, 0.54739039666, 0.560751565762]\n",
    "n_groups = 3\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "\n",
    "opacity = .4\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "rects1 = plt.bar(index, test, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='g',\n",
    "                 label='Test Set')\n",
    "rects1 = plt.bar(index+bar_width, train, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='b',\n",
    "                 label='Training Set')\n",
    "\n",
    "ax.set_ylim([.5,1])\n",
    "\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Raw Accuracy')\n",
    "plt.title('Overfitting for Depth-3 Adaboost')\n",
    "plt.xticks(index + bar_width, (1, 2, 3))\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('adaboosttraintest.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Champ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5692803836185758"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_champ_scores = skcv.cross_val_score(\n",
    "    skes.AdaBoostClassifier(\n",
    "        n_estimators = 200,\n",
    "    ), \n",
    "    df_s[base_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=2)\n",
    "tree_champ_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5463111299747998"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_champ_scores = skcv.cross_val_score(\n",
    "    skes.GradientBoostingClassifier(\n",
    "        max_depth = 2,\n",
    "        n_estimators = 100,\n",
    "    ), \n",
    "    df_s[base_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=2)\n",
    "tree_champ_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55581264969348199"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_champ_scores = skcv.cross_val_score(\n",
    "    skes.RandomForestClassifier(\n",
    "        max_depth=3,\n",
    "        n_estimators=200,\n",
    "        n_jobs=2,\n",
    "    ), \n",
    "    df_s[base_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=2)\n",
    "tree_champ_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57815587124989132"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_state_scores = skcv.cross_val_score(\n",
    "    skes.RandomForestClassifier(\n",
    "        max_depth=3,\n",
    "        n_estimators=100,\n",
    "        n_jobs=2,\n",
    "    ), \n",
    "    df_s[base_cols + state_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=-1)\n",
    "tree_state_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59183406378105197"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_state_scores = skcv.cross_val_score(\n",
    "    skes.AdaBoostClassifier(\n",
    "        n_estimators=200,\n",
    "    ), \n",
    "    df_s[base_cols + state_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=-1)\n",
    "tree_state_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55988237691977161"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_state_scores = skcv.cross_val_score(\n",
    "    skes.AdaBoostClassifier(\n",
    "        n_estimators=200,\n",
    "        base_estimator=sk.tree.DecisionTreeClassifier(max_depth=2)\n",
    "    ), \n",
    "    df_s[base_cols + state_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=-1)\n",
    "tree_state_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57116071684424186"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_state_scores = skcv.cross_val_score(\n",
    "    skes.AdaBoostClassifier(\n",
    "        n_estimators=3,\n",
    "        base_estimator=sk.tree.DecisionTreeClassifier(max_depth=2)\n",
    "    ), \n",
    "    df_s[state_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=-1)\n",
    "tree_state_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimensionality Reduction\n",
    "====="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_counts = df[item_cols].sum()\n",
    "major_item_cols = item_counts[item_counts > 4000].axes[0].tolist()\n",
    "len(major_item_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_counts = df[base_cols].sum()\n",
    "major_base_cols = base_counts[base_counts > 6000].axes[0].tolist()\n",
    "len(major_base_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58744965684251527"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "champ_scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(), \n",
    "    df_s[major_base_cols+state_cols+major_item_cols], \n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5)\n",
    "champ_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5857797847720263"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_state_scores = skcv.cross_val_score(\n",
    "    skes.RandomForestClassifier(\n",
    "        max_depth=8,\n",
    "        n_estimators=200,\n",
    "        n_jobs=2,\n",
    "    ), \n",
    "    df_s[base_cols + state_cols + item_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=-1)\n",
    "tree_state_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58807487253867152"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_state_scores = skcv.cross_val_score(\n",
    "    skes.AdaBoostClassifier(\n",
    "        n_estimators=200,\n",
    "    ), \n",
    "    df_s[base_cols + state_cols + item_cols],\n",
    "    df_s['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=5,\n",
    "    n_jobs=-1)\n",
    "tree_state_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.564926931106\n",
      "0.85068393025\n"
     ]
    }
   ],
   "source": [
    "c = skes.AdaBoostClassifier(\n",
    "        n_estimators=200,\n",
    "        base_estimator=sk.tree.DecisionTreeClassifier(max_depth=3)\n",
    "    ).fit(df_s[base_cols + state_cols + item_cols],y_s)\n",
    "print(c.score(df_test[base_cols + state_cols + item_cols], y_test))\n",
    "print(c.score(df_s[base_cols + state_cols + item_cols], y_s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.588308977035\n",
      "0.650308029654\n"
     ]
    }
   ],
   "source": [
    "c = skes.AdaBoostClassifier(\n",
    "        n_estimators=200,\n",
    "    ).fit(df_s[base_cols + state_cols + item_cols],y_s)\n",
    "print(c.score(df_test[base_cols + state_cols + item_cols], y_test))\n",
    "print(c.score(df_s[base_cols + state_cols + item_cols], y_s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.533993115318\n"
     ]
    }
   ],
   "source": [
    "c = skes.RandomForestClassifier(\n",
    "        n_estimators=200,\n",
    "    ).fit(df[base_cols].iloc[1:50000],df['winner'].iloc[1:50000])\n",
    "print(c.score(df[base_cols].iloc[1:50000],df['winner'].iloc[1:50000]))\n",
    "print(c.score(df[base_cols].iloc[50000:],df['winner'].iloc[50000:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(c.feature_importances_,base_cols + state_cols + item_cols))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scratch\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "svmodel = sk.svm.SVC(kernel='poly')\n",
    "svmodel.fit(\n",
    "    df_gold[champ_cols+['gold5','gold10']+item_cols],\n",
    "    df_gold['winner'], \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "champ_gold_item_scores = skcv.cross_val_score(\n",
    "    sk.svm.SVC(kernel='poly'), \n",
    "    df_gold[champ_cols+['gold5','gold10']+item_cols], \n",
    "    df_gold['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "champ_gold_item_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(df_gold[df_gold['championId']==4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "champ_no = 7\n",
    "champ_specific_data = df_s[df_s['championId']==champ_no]\n",
    "len(df_s[df_s['championId']==champ_no])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(), \n",
    "    champ_specific_data[champ_cols+state_cols], \n",
    "    champ_specific_data['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=10)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scores = skcv.cross_val_score(\n",
    "    sklm.LogisticRegression(), \n",
    "    champ_specific_data[champ_cols+state_cols+item_cols], \n",
    "    champ_specific_data['winner'], \n",
    "    scoring='accuracy', \n",
    "    cv=10)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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