xgboost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-rmse:0.444096\ttrain-rmse:0.411716\n",
      "Multiple eval metrics have been passed: 'train-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until train-rmse hasn't improved in 10 rounds.\n",
      "[1]\teval-rmse:0.415364\ttrain-rmse:0.355559\n",
      "[2]\teval-rmse:0.401988\ttrain-rmse:0.315775\n",
      "[3]\teval-rmse:0.389962\ttrain-rmse:0.289015\n",
      "[4]\teval-rmse:0.384522\ttrain-rmse:0.267998\n",
      "[5]\teval-rmse:0.382291\ttrain-rmse:0.250928\n",
      "[6]\teval-rmse:0.381688\ttrain-rmse:0.238674\n",
      "[7]\teval-rmse:0.379367\ttrain-rmse:0.228976\n",
      "[8]\teval-rmse:0.378704\ttrain-rmse:0.217621\n",
      "[9]\teval-rmse:0.380197\ttrain-rmse:0.20861\n",
      "[10]\teval-rmse:0.382082\ttrain-rmse:0.199585\n",
      "[11]\teval-rmse:0.384251\ttrain-rmse:0.191445\n",
      "[12]\teval-rmse:0.382889\ttrain-rmse:0.184003\n",
      "[13]\teval-rmse:0.383883\ttrain-rmse:0.179041\n",
      "[14]\teval-rmse:0.384582\ttrain-rmse:0.176569\n",
      "[15]\teval-rmse:0.383514\ttrain-rmse:0.168435\n",
      "[16]\teval-rmse:0.384136\ttrain-rmse:0.159926\n",
      "[17]\teval-rmse:0.38502\ttrain-rmse:0.153724\n",
      "[18]\teval-rmse:0.383275\ttrain-rmse:0.147944\n",
      "[19]\teval-rmse:0.383252\ttrain-rmse:0.145118\n",
      "[20]\teval-rmse:0.38337\ttrain-rmse:0.140306\n",
      "[21]\teval-rmse:0.384714\ttrain-rmse:0.136156\n",
      "[22]\teval-rmse:0.388849\ttrain-rmse:0.132296\n",
      "[23]\teval-rmse:0.389499\ttrain-rmse:0.126763\n",
      "[24]\teval-rmse:0.388361\ttrain-rmse:0.125405\n",
      "[25]\teval-rmse:0.38937\ttrain-rmse:0.121749\n",
      "[26]\teval-rmse:0.388672\ttrain-rmse:0.114247\n",
      "[27]\teval-rmse:0.388139\ttrain-rmse:0.110197\n",
      "[28]\teval-rmse:0.388499\ttrain-rmse:0.105446\n",
      "[29]\teval-rmse:0.389235\ttrain-rmse:0.103687\n",
      "[30]\teval-rmse:0.387929\ttrain-rmse:0.099606\n",
      "[31]\teval-rmse:0.388117\ttrain-rmse:0.096634\n",
      "[32]\teval-rmse:0.387727\ttrain-rmse:0.094194\n",
      "[33]\teval-rmse:0.38876\ttrain-rmse:0.091093\n",
      "[34]\teval-rmse:0.389185\ttrain-rmse:0.087172\n",
      "[35]\teval-rmse:0.389681\ttrain-rmse:0.08417\n",
      "[36]\teval-rmse:0.389381\ttrain-rmse:0.080678\n",
      "[37]\teval-rmse:0.38941\ttrain-rmse:0.07809\n",
      "[38]\teval-rmse:0.389737\ttrain-rmse:0.074272\n",
      "[39]\teval-rmse:0.390105\ttrain-rmse:0.07278\n",
      "[40]\teval-rmse:0.390248\ttrain-rmse:0.070973\n",
      "[41]\teval-rmse:0.390705\ttrain-rmse:0.067204\n",
      "[42]\teval-rmse:0.391594\ttrain-rmse:0.065755\n",
      "[43]\teval-rmse:0.392592\ttrain-rmse:0.06516\n",
      "[44]\teval-rmse:0.391883\ttrain-rmse:0.064228\n",
      "[45]\teval-rmse:0.390827\ttrain-rmse:0.061507\n",
      "[46]\teval-rmse:0.390435\ttrain-rmse:0.059863\n",
      "[47]\teval-rmse:0.388099\ttrain-rmse:0.057171\n",
      "[48]\teval-rmse:0.387633\ttrain-rmse:0.055369\n",
      "[49]\teval-rmse:0.387626\ttrain-rmse:0.053246\n",
      "[50]\teval-rmse:0.387804\ttrain-rmse:0.050991\n",
      "[51]\teval-rmse:0.388053\ttrain-rmse:0.049446\n",
      "[52]\teval-rmse:0.388722\ttrain-rmse:0.047814\n",
      "[53]\teval-rmse:0.391221\ttrain-rmse:0.044873\n",
      "[54]\teval-rmse:0.391127\ttrain-rmse:0.043324\n",
      "[55]\teval-rmse:0.390996\ttrain-rmse:0.041767\n",
      "[56]\teval-rmse:0.390767\ttrain-rmse:0.041321\n",
      "[57]\teval-rmse:0.389833\ttrain-rmse:0.039533\n",
      "[58]\teval-rmse:0.390695\ttrain-rmse:0.037592\n",
      "[59]\teval-rmse:0.390065\ttrain-rmse:0.036579\n",
      "[60]\teval-rmse:0.390557\ttrain-rmse:0.036046\n",
      "[61]\teval-rmse:0.390995\ttrain-rmse:0.034344\n",
      "[62]\teval-rmse:0.390129\ttrain-rmse:0.033509\n",
      "[63]\teval-rmse:0.391058\ttrain-rmse:0.032581\n",
      "[64]\teval-rmse:0.390252\ttrain-rmse:0.031135\n",
      "[65]\teval-rmse:0.390427\ttrain-rmse:0.030655\n",
      "[66]\teval-rmse:0.390534\ttrain-rmse:0.030292\n",
      "[67]\teval-rmse:0.390307\ttrain-rmse:0.028946\n",
      "[68]\teval-rmse:0.391109\ttrain-rmse:0.028455\n",
      "[69]\teval-rmse:0.390355\ttrain-rmse:0.027799\n",
      "[70]\teval-rmse:0.390594\ttrain-rmse:0.026545\n",
      "[71]\teval-rmse:0.391236\ttrain-rmse:0.025902\n",
      "[72]\teval-rmse:0.392249\ttrain-rmse:0.025009\n",
      "[73]\teval-rmse:0.392584\ttrain-rmse:0.024215\n",
      "[74]\teval-rmse:0.393413\ttrain-rmse:0.023935\n",
      "[75]\teval-rmse:0.39329\ttrain-rmse:0.023621\n",
      "[76]\teval-rmse:0.393314\ttrain-rmse:0.023003\n",
      "[77]\teval-rmse:0.393153\ttrain-rmse:0.022189\n",
      "[78]\teval-rmse:0.393641\ttrain-rmse:0.021636\n",
      "[79]\teval-rmse:0.394231\ttrain-rmse:0.0214\n",
      "[80]\teval-rmse:0.394942\ttrain-rmse:0.020739\n",
      "[81]\teval-rmse:0.394614\ttrain-rmse:0.020212\n",
      "[82]\teval-rmse:0.394603\ttrain-rmse:0.019925\n",
      "[83]\teval-rmse:0.395046\ttrain-rmse:0.019397\n",
      "[84]\teval-rmse:0.396061\ttrain-rmse:0.018882\n",
      "[85]\teval-rmse:0.396026\ttrain-rmse:0.018627\n",
      "[86]\teval-rmse:0.396455\ttrain-rmse:0.018093\n",
      "[87]\teval-rmse:0.396252\ttrain-rmse:0.017633\n",
      "[88]\teval-rmse:0.397333\ttrain-rmse:0.017165\n",
      "[89]\teval-rmse:0.397291\ttrain-rmse:0.016872\n",
      "[90]\teval-rmse:0.397131\ttrain-rmse:0.016555\n",
      "[91]\teval-rmse:0.396081\ttrain-rmse:0.016182\n",
      "[92]\teval-rmse:0.396285\ttrain-rmse:0.015933\n",
      "[93]\teval-rmse:0.396684\ttrain-rmse:0.015679\n",
      "[94]\teval-rmse:0.396402\ttrain-rmse:0.015347\n",
      "[95]\teval-rmse:0.39598\ttrain-rmse:0.015021\n",
      "[96]\teval-rmse:0.396222\ttrain-rmse:0.014594\n",
      "[97]\teval-rmse:0.396614\ttrain-rmse:0.01422\n",
      "[98]\teval-rmse:0.396777\ttrain-rmse:0.013971\n",
      "[99]\teval-rmse:0.396804\ttrain-rmse:0.013771\n",
      "[100]\teval-rmse:0.396749\ttrain-rmse:0.013585\n",
      "[101]\teval-rmse:0.397237\ttrain-rmse:0.013342\n",
      "[102]\teval-rmse:0.396468\ttrain-rmse:0.013071\n",
      "[103]\teval-rmse:0.395901\ttrain-rmse:0.012791\n",
      "[104]\teval-rmse:0.395842\ttrain-rmse:0.012516\n",
      "[105]\teval-rmse:0.396483\ttrain-rmse:0.012193\n",
      "[106]\teval-rmse:0.396616\ttrain-rmse:0.011975\n",
      "[107]\teval-rmse:0.396449\ttrain-rmse:0.011773\n",
      "[108]\teval-rmse:0.396839\ttrain-rmse:0.011635\n",
      "[109]\teval-rmse:0.397306\ttrain-rmse:0.011334\n",
      "[110]\teval-rmse:0.397079\ttrain-rmse:0.011091\n",
      "[111]\teval-rmse:0.397076\ttrain-rmse:0.010974\n",
      "[112]\teval-rmse:0.397432\ttrain-rmse:0.010806\n",
      "[113]\teval-rmse:0.397954\ttrain-rmse:0.010702\n",
      "[114]\teval-rmse:0.397776\ttrain-rmse:0.010576\n",
      "[115]\teval-rmse:0.397639\ttrain-rmse:0.010368\n",
      "[116]\teval-rmse:0.397942\ttrain-rmse:0.01018\n",
      "[117]\teval-rmse:0.397997\ttrain-rmse:0.010051\n",
      "[118]\teval-rmse:0.399113\ttrain-rmse:0.009838\n",
      "[119]\teval-rmse:0.398625\ttrain-rmse:0.009743\n",
      "[120]\teval-rmse:0.398845\ttrain-rmse:0.009587\n",
      "[121]\teval-rmse:0.399502\ttrain-rmse:0.009426\n",
      "[122]\teval-rmse:0.399441\ttrain-rmse:0.009243\n",
      "[123]\teval-rmse:0.399538\ttrain-rmse:0.009159\n",
      "[124]\teval-rmse:0.39885\ttrain-rmse:0.009088\n",
      "[125]\teval-rmse:0.398747\ttrain-rmse:0.009004\n",
      "[126]\teval-rmse:0.399664\ttrain-rmse:0.008859\n",
      "[127]\teval-rmse:0.400044\ttrain-rmse:0.008778\n",
      "[128]\teval-rmse:0.399694\ttrain-rmse:0.008603\n",
      "[129]\teval-rmse:0.399174\ttrain-rmse:0.008456\n",
      "[130]\teval-rmse:0.398882\ttrain-rmse:0.008322\n",
      "[131]\teval-rmse:0.39928\ttrain-rmse:0.008224\n",
      "[132]\teval-rmse:0.399692\ttrain-rmse:0.008096\n",
      "[133]\teval-rmse:0.399929\ttrain-rmse:0.00802\n",
      "[134]\teval-rmse:0.399828\ttrain-rmse:0.007918\n",
      "[135]\teval-rmse:0.40008\ttrain-rmse:0.007826\n",
      "[136]\teval-rmse:0.400077\ttrain-rmse:0.007814\n",
      "[137]\teval-rmse:0.399963\ttrain-rmse:0.007716\n",
      "[138]\teval-rmse:0.399315\ttrain-rmse:0.007623\n",
      "[139]\teval-rmse:0.398783\ttrain-rmse:0.007487\n",
      "[140]\teval-rmse:0.398649\ttrain-rmse:0.007372\n",
      "[141]\teval-rmse:0.39861\ttrain-rmse:0.007334\n",
      "[142]\teval-rmse:0.399715\ttrain-rmse:0.007217\n",
      "[143]\teval-rmse:0.399771\ttrain-rmse:0.007119\n",
      "[144]\teval-rmse:0.399773\ttrain-rmse:0.007026\n",
      "[145]\teval-rmse:0.399643\ttrain-rmse:0.006935\n",
      "[146]\teval-rmse:0.399916\ttrain-rmse:0.006831\n",
      "[147]\teval-rmse:0.400324\ttrain-rmse:0.006731\n",
      "[148]\teval-rmse:0.400078\ttrain-rmse:0.006675\n",
      "[149]\teval-rmse:0.400089\ttrain-rmse:0.006576\n",
      "[150]\teval-rmse:0.399919\ttrain-rmse:0.006518\n",
      "[151]\teval-rmse:0.400196\ttrain-rmse:0.006429\n",
      "[152]\teval-rmse:0.400581\ttrain-rmse:0.006382\n",
      "[153]\teval-rmse:0.400111\ttrain-rmse:0.006321\n",
      "[154]\teval-rmse:0.400359\ttrain-rmse:0.006245\n",
      "[155]\teval-rmse:0.399921\ttrain-rmse:0.006165\n",
      "[156]\teval-rmse:0.400153\ttrain-rmse:0.006098\n",
      "[157]\teval-rmse:0.399741\ttrain-rmse:0.006039\n",
      "[158]\teval-rmse:0.399541\ttrain-rmse:0.006009\n",
      "[159]\teval-rmse:0.399918\ttrain-rmse:0.005926\n",
      "[160]\teval-rmse:0.400095\ttrain-rmse:0.005863\n",
      "[161]\teval-rmse:0.400128\ttrain-rmse:0.005802\n",
      "[162]\teval-rmse:0.400693\ttrain-rmse:0.005744\n",
      "[163]\teval-rmse:0.400399\ttrain-rmse:0.005686\n",
      "[164]\teval-rmse:0.400505\ttrain-rmse:0.005635\n",
      "[165]\teval-rmse:0.40072\ttrain-rmse:0.005567\n",
      "[166]\teval-rmse:0.401092\ttrain-rmse:0.005523\n",
      "[167]\teval-rmse:0.401118\ttrain-rmse:0.005496\n",
      "[168]\teval-rmse:0.401615\ttrain-rmse:0.005446\n",
      "[169]\teval-rmse:0.401494\ttrain-rmse:0.005391\n",
      "[170]\teval-rmse:0.401868\ttrain-rmse:0.005333\n",
      "[171]\teval-rmse:0.402365\ttrain-rmse:0.005275\n",
      "[172]\teval-rmse:0.402117\ttrain-rmse:0.00523\n",
      "[173]\teval-rmse:0.402038\ttrain-rmse:0.005187\n",
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      "[1827]\teval-rmse:0.40996\ttrain-rmse:0.000464\n",
      "[1828]\teval-rmse:0.409888\ttrain-rmse:0.000464\n",
      "[1829]\teval-rmse:0.4099\ttrain-rmse:0.000463\n",
      "[1830]\teval-rmse:0.409848\ttrain-rmse:0.000463\n",
      "[1831]\teval-rmse:0.40988\ttrain-rmse:0.000463\n",
      "[1832]\teval-rmse:0.40988\ttrain-rmse:0.000463\n",
      "[1833]\teval-rmse:0.409919\ttrain-rmse:0.000463\n",
      "[1834]\teval-rmse:0.409922\ttrain-rmse:0.000463\n",
      "[1835]\teval-rmse:0.409866\ttrain-rmse:0.000463\n",
      "[1836]\teval-rmse:0.409865\ttrain-rmse:0.000463\n",
      "[1837]\teval-rmse:0.4099\ttrain-rmse:0.000462\n",
      "[1838]\teval-rmse:0.409825\ttrain-rmse:0.000462\n",
      "[1839]\teval-rmse:0.409828\ttrain-rmse:0.000462\n",
      "[1840]\teval-rmse:0.409904\ttrain-rmse:0.000462\n",
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      "[1842]\teval-rmse:0.409978\ttrain-rmse:0.000461\n",
      "[1843]\teval-rmse:0.409999\ttrain-rmse:0.000461\n",
      "[1844]\teval-rmse:0.409978\ttrain-rmse:0.000461\n",
      "[1845]\teval-rmse:0.410038\ttrain-rmse:0.000461\n",
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      "[1847]\teval-rmse:0.410029\ttrain-rmse:0.000461\n",
      "[1848]\teval-rmse:0.410081\ttrain-rmse:0.00046\n",
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      "[1850]\teval-rmse:0.410027\ttrain-rmse:0.00046\n",
      "[1851]\teval-rmse:0.410009\ttrain-rmse:0.00046\n",
      "[1852]\teval-rmse:0.410025\ttrain-rmse:0.00046\n",
      "[1853]\teval-rmse:0.409924\ttrain-rmse:0.00046\n",
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      "[1855]\teval-rmse:0.409897\ttrain-rmse:0.00046\n",
      "[1856]\teval-rmse:0.409909\ttrain-rmse:0.000459\n",
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      "[1858]\teval-rmse:0.409941\ttrain-rmse:0.000459\n",
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      "[1860]\teval-rmse:0.409922\ttrain-rmse:0.000459\n",
      "[1861]\teval-rmse:0.409926\ttrain-rmse:0.000458\n",
      "[1862]\teval-rmse:0.409892\ttrain-rmse:0.000458\n",
      "[1863]\teval-rmse:0.409881\ttrain-rmse:0.000458\n",
      "[1864]\teval-rmse:0.409803\ttrain-rmse:0.000458\n",
      "[1865]\teval-rmse:0.409839\ttrain-rmse:0.000458\n",
      "[1866]\teval-rmse:0.409876\ttrain-rmse:0.000457\n",
      "[1867]\teval-rmse:0.409854\ttrain-rmse:0.000457\n",
      "[1868]\teval-rmse:0.409827\ttrain-rmse:0.000457\n",
      "[1869]\teval-rmse:0.409844\ttrain-rmse:0.000457\n",
      "[1870]\teval-rmse:0.409809\ttrain-rmse:0.000457\n",
      "[1871]\teval-rmse:0.409817\ttrain-rmse:0.000456\n",
      "[1872]\teval-rmse:0.409825\ttrain-rmse:0.000456\n",
      "[1873]\teval-rmse:0.409843\ttrain-rmse:0.000456\n",
      "[1874]\teval-rmse:0.409866\ttrain-rmse:0.000456\n",
      "[1875]\teval-rmse:0.409772\ttrain-rmse:0.000456\n",
      "[1876]\teval-rmse:0.409791\ttrain-rmse:0.000455\n",
      "[1877]\teval-rmse:0.409823\ttrain-rmse:0.000455\n",
      "[1878]\teval-rmse:0.409876\ttrain-rmse:0.000455\n",
      "[1879]\teval-rmse:0.409821\ttrain-rmse:0.000455\n",
      "[1880]\teval-rmse:0.409823\ttrain-rmse:0.000455\n",
      "[1881]\teval-rmse:0.409805\ttrain-rmse:0.000454\n",
      "[1882]\teval-rmse:0.409809\ttrain-rmse:0.000454\n",
      "[1883]\teval-rmse:0.409783\ttrain-rmse:0.000454\n",
      "[1884]\teval-rmse:0.409816\ttrain-rmse:0.000454\n",
      "[1885]\teval-rmse:0.409808\ttrain-rmse:0.000454\n",
      "[1886]\teval-rmse:0.409755\ttrain-rmse:0.000454\n",
      "[1887]\teval-rmse:0.409747\ttrain-rmse:0.000453\n",
      "[1888]\teval-rmse:0.409769\ttrain-rmse:0.000453\n",
      "[1889]\teval-rmse:0.409792\ttrain-rmse:0.000453\n",
      "[1890]\teval-rmse:0.409706\ttrain-rmse:0.000453\n",
      "[1891]\teval-rmse:0.409752\ttrain-rmse:0.000453\n",
      "[1892]\teval-rmse:0.409737\ttrain-rmse:0.000453\n",
      "[1893]\teval-rmse:0.409744\ttrain-rmse:0.000453\n",
      "[1894]\teval-rmse:0.409801\ttrain-rmse:0.000452\n",
      "[1895]\teval-rmse:0.40978\ttrain-rmse:0.000452\n",
      "[1896]\teval-rmse:0.409808\ttrain-rmse:0.000452\n",
      "[1897]\teval-rmse:0.409778\ttrain-rmse:0.000452\n",
      "[1898]\teval-rmse:0.409734\ttrain-rmse:0.000452\n",
      "[1899]\teval-rmse:0.409728\ttrain-rmse:0.000451\n",
      "[1900]\teval-rmse:0.409766\ttrain-rmse:0.000451\n",
      "[1901]\teval-rmse:0.409782\ttrain-rmse:0.000451\n",
      "[1902]\teval-rmse:0.409757\ttrain-rmse:0.000451\n",
      "[1903]\teval-rmse:0.409755\ttrain-rmse:0.000451\n",
      "[1904]\teval-rmse:0.409732\ttrain-rmse:0.000451\n",
      "[1905]\teval-rmse:0.409706\ttrain-rmse:0.00045\n",
      "[1906]\teval-rmse:0.409752\ttrain-rmse:0.00045\n",
      "[1907]\teval-rmse:0.409717\ttrain-rmse:0.00045\n",
      "[1908]\teval-rmse:0.409764\ttrain-rmse:0.00045\n",
      "[1909]\teval-rmse:0.409852\ttrain-rmse:0.00045\n",
      "[1910]\teval-rmse:0.409826\ttrain-rmse:0.00045\n",
      "[1911]\teval-rmse:0.409834\ttrain-rmse:0.000449\n",
      "[1912]\teval-rmse:0.409803\ttrain-rmse:0.000449\n",
      "[1913]\teval-rmse:0.409787\ttrain-rmse:0.000449\n",
      "[1914]\teval-rmse:0.409748\ttrain-rmse:0.000449\n",
      "[1915]\teval-rmse:0.409661\ttrain-rmse:0.000449\n",
      "[1916]\teval-rmse:0.409646\ttrain-rmse:0.000448\n",
      "[1917]\teval-rmse:0.409691\ttrain-rmse:0.000448\n",
      "[1918]\teval-rmse:0.409685\ttrain-rmse:0.000448\n",
      "[1919]\teval-rmse:0.409641\ttrain-rmse:0.000448\n",
      "[1920]\teval-rmse:0.409628\ttrain-rmse:0.000448\n",
      "[1921]\teval-rmse:0.409655\ttrain-rmse:0.000448\n",
      "[1922]\teval-rmse:0.409704\ttrain-rmse:0.000448\n",
      "[1923]\teval-rmse:0.409748\ttrain-rmse:0.000447\n",
      "[1924]\teval-rmse:0.409736\ttrain-rmse:0.000447\n",
      "[1925]\teval-rmse:0.409753\ttrain-rmse:0.000447\n",
      "[1926]\teval-rmse:0.409717\ttrain-rmse:0.000447\n",
      "[1927]\teval-rmse:0.409709\ttrain-rmse:0.000447\n",
      "[1928]\teval-rmse:0.409711\ttrain-rmse:0.000446\n",
      "[1929]\teval-rmse:0.409727\ttrain-rmse:0.000446\n",
      "[1930]\teval-rmse:0.40972\ttrain-rmse:0.000446\n",
      "[1931]\teval-rmse:0.409709\ttrain-rmse:0.000446\n",
      "[1932]\teval-rmse:0.409752\ttrain-rmse:0.000446\n",
      "[1933]\teval-rmse:0.409791\ttrain-rmse:0.000446\n",
      "[1934]\teval-rmse:0.409736\ttrain-rmse:0.000446\n",
      "[1935]\teval-rmse:0.409725\ttrain-rmse:0.000445\n",
      "[1936]\teval-rmse:0.409686\ttrain-rmse:0.000445\n",
      "[1937]\teval-rmse:0.409659\ttrain-rmse:0.000445\n",
      "[1938]\teval-rmse:0.40971\ttrain-rmse:0.000445\n",
      "[1939]\teval-rmse:0.409738\ttrain-rmse:0.000445\n",
      "[1940]\teval-rmse:0.409696\ttrain-rmse:0.000445\n",
      "[1941]\teval-rmse:0.409702\ttrain-rmse:0.000445\n",
      "[1942]\teval-rmse:0.409798\ttrain-rmse:0.000444\n",
      "[1943]\teval-rmse:0.409804\ttrain-rmse:0.000444\n",
      "[1944]\teval-rmse:0.409762\ttrain-rmse:0.000444\n",
      "[1945]\teval-rmse:0.409752\ttrain-rmse:0.000444\n",
      "[1946]\teval-rmse:0.409797\ttrain-rmse:0.000444\n",
      "[1947]\teval-rmse:0.409776\ttrain-rmse:0.000444\n",
      "[1948]\teval-rmse:0.409804\ttrain-rmse:0.000444\n",
      "[1949]\teval-rmse:0.409771\ttrain-rmse:0.000444\n",
      "[1950]\teval-rmse:0.409818\ttrain-rmse:0.000444\n",
      "[1951]\teval-rmse:0.409842\ttrain-rmse:0.000443\n",
      "[1952]\teval-rmse:0.409846\ttrain-rmse:0.000443\n",
      "[1953]\teval-rmse:0.409853\ttrain-rmse:0.000443\n",
      "[1954]\teval-rmse:0.409866\ttrain-rmse:0.000443\n",
      "[1955]\teval-rmse:0.409848\ttrain-rmse:0.000443\n",
      "[1956]\teval-rmse:0.409855\ttrain-rmse:0.000443\n",
      "[1957]\teval-rmse:0.409865\ttrain-rmse:0.000442\n",
      "[1958]\teval-rmse:0.409839\ttrain-rmse:0.000442\n",
      "[1959]\teval-rmse:0.409782\ttrain-rmse:0.000442\n",
      "[1960]\teval-rmse:0.40977\ttrain-rmse:0.000442\n",
      "[1961]\teval-rmse:0.409796\ttrain-rmse:0.000442\n",
      "[1962]\teval-rmse:0.409842\ttrain-rmse:0.000442\n",
      "[1963]\teval-rmse:0.409881\ttrain-rmse:0.000442\n",
      "[1964]\teval-rmse:0.409869\ttrain-rmse:0.000441\n",
      "[1965]\teval-rmse:0.409894\ttrain-rmse:0.000441\n",
      "[1966]\teval-rmse:0.409936\ttrain-rmse:0.000441\n",
      "[1967]\teval-rmse:0.409935\ttrain-rmse:0.000441\n",
      "[1968]\teval-rmse:0.409926\ttrain-rmse:0.000441\n",
      "[1969]\teval-rmse:0.40982\ttrain-rmse:0.000441\n",
      "[1970]\teval-rmse:0.409821\ttrain-rmse:0.000441\n",
      "[1971]\teval-rmse:0.40983\ttrain-rmse:0.000441\n",
      "[1972]\teval-rmse:0.409758\ttrain-rmse:0.000441\n",
      "[1973]\teval-rmse:0.409762\ttrain-rmse:0.00044\n",
      "[1974]\teval-rmse:0.40976\ttrain-rmse:0.00044\n",
      "[1975]\teval-rmse:0.409731\ttrain-rmse:0.00044\n",
      "[1976]\teval-rmse:0.40981\ttrain-rmse:0.00044\n",
      "[1977]\teval-rmse:0.409796\ttrain-rmse:0.000439\n",
      "[1978]\teval-rmse:0.409802\ttrain-rmse:0.000439\n",
      "[1979]\teval-rmse:0.409784\ttrain-rmse:0.000439\n",
      "[1980]\teval-rmse:0.409729\ttrain-rmse:0.000439\n",
      "[1981]\teval-rmse:0.409734\ttrain-rmse:0.000439\n",
      "[1982]\teval-rmse:0.409718\ttrain-rmse:0.000439\n",
      "[1983]\teval-rmse:0.409713\ttrain-rmse:0.000438\n",
      "[1984]\teval-rmse:0.409658\ttrain-rmse:0.000438\n",
      "[1985]\teval-rmse:0.40966\ttrain-rmse:0.000438\n",
      "[1986]\teval-rmse:0.409687\ttrain-rmse:0.000438\n",
      "[1987]\teval-rmse:0.409665\ttrain-rmse:0.000438\n",
      "[1988]\teval-rmse:0.40967\ttrain-rmse:0.000438\n",
      "[1989]\teval-rmse:0.409677\ttrain-rmse:0.000438\n",
      "[1990]\teval-rmse:0.409682\ttrain-rmse:0.000438\n",
      "[1991]\teval-rmse:0.409686\ttrain-rmse:0.000437\n",
      "[1992]\teval-rmse:0.409651\ttrain-rmse:0.000437\n",
      "[1993]\teval-rmse:0.409652\ttrain-rmse:0.000437\n",
      "[1994]\teval-rmse:0.40972\ttrain-rmse:0.000437\n",
      "[1995]\teval-rmse:0.409735\ttrain-rmse:0.000437\n",
      "[1996]\teval-rmse:0.409734\ttrain-rmse:0.000437\n",
      "[1997]\teval-rmse:0.409769\ttrain-rmse:0.000437\n",
      "[1998]\teval-rmse:0.409725\ttrain-rmse:0.000437\n",
      "[1999]\teval-rmse:0.409665\ttrain-rmse:0.000436\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, matthews_corrcoef\n",
    "\n",
    "poslabels = [\"-%02d\" % (i) for i in range(10, 0, -1)] + [\"+%02d\" % (i) for i in range(1,11)]\n",
    "# 18 H  17 S  15 E\n",
    "proplables = [\"H%02d\" % (i) for i in range(18)] + [\"S%02d\" % (i) for i in range(17)] + [\"E%02d\" % (i) for i in range(15)]\n",
    "\n",
    "cols = [\"target\"]\n",
    "for poslabel in poslabels:\n",
    "    for proplable in proplables:\n",
    "        cols.append(\"%s%s\" % (poslabel, proplable))\n",
    "       \n",
    "# read in data\n",
    "data_dir=\"/workspace/chipper_data/chipper-0.3.0-data/\"\n",
    "dtrain = xgb.DMatrix(data_dir + \"training_data.ll\", feature_names=cols)\n",
    "dtest = xgb.DMatrix(data_dir + \"testing_data.ll\", feature_names=cols)\n",
    "\n",
    "watchlist  = [(dtest,'eval'), (dtrain,'train')]\n",
    "# specify parameters via map\n",
    "# 'binary:logistic'\n",
    "param = {'n_estimators': 10000, 'max_depth':10, 'objective':'reg:logistic'} #, \"eval_metric\": \"auc\"}\n",
    "num_round = 2000\n",
    "bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=10)\n",
    "# make prediction\n",
    "preds = bst.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#bst.save_model(\"/tmp/model.txt\")\n",
    "#bst.dump_model(\"/tmp/dump.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Logistic Regression Report, cutoff= 0.21 (MCC=0.631) **\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.83      0.79      0.81       208\n",
      "        1.0       0.80      0.84      0.82       208\n",
      "\n",
      "avg / total       0.82      0.81      0.81       416\n",
      "\n",
      "LR: sensitivity(recall)=84.1, specificity=78.8, precision=79.9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110c11590>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DylWxW6OWEpB1AAMGDGDgwIFxx0K3bt3KIo5ypeOTmY5N83R8mqfjk5mOTVYKNoRBg1BF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110cd2650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_classification_vector = lambda cutoff: [1 if pred >= cutoff else 0 for pred in preds]\n",
    "\n",
    "def find_best_mcc():\n",
    "    best_mcc = 0.0\n",
    "    best_cutoff = 0.0\n",
    "    for i in range(1, 100):\n",
    "        pred_cutoff = i/100.0\n",
    "        mcc = matthews_corrcoef(dtest.get_label(), LR_classification_vector(pred_cutoff))\n",
    "        if (mcc > best_mcc):\n",
    "            best_mcc = mcc\n",
    "            best_cutoff = pred_cutoff\n",
    "    return (best_cutoff, best_mcc)\n",
    "\n",
    "(best_cutoff, best_mcc) = find_best_mcc()\n",
    "print \"** Logistic Regression Report, cutoff= %.2f (MCC=%.3f) **\" % (best_cutoff, best_mcc)\n",
    "print classification_report(dtest.get_label(), LR_classification_vector(best_cutoff))\n",
    "\n",
    "\n",
    "def print_metrics(name, actual, predicted):\n",
    "    ((tn, fp), (fn, tp)) = confusion_matrix(actual, predicted)\n",
    "    sensitivity = 100.0 * tp / (tp + fn)\n",
    "    specificity = 100.0 * tn / (tn + fp)\n",
    "    precision = 100.0 * tp / (tp + fp)\n",
    "    print \"%s: sensitivity(recall)=%.1f, specificity=%.1f, precision=%.1f\" % (name, sensitivity, specificity, precision)\n",
    "\n",
    "print_metrics(\"LR\", dtest.get_label(), LR_classification_vector(best_cutoff))\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, metrics\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(dtest.get_label(), preds, pos_label=1)\n",
    "roc_auc = metrics.roc_auc_score(dtest.get_label(), preds, average='macro', sample_weight=None)\n",
    "plt.title('Logistic Regression ROC Curve')\n",
    "plt.plot(fpr, tpr, 'b', label='AUC = %0.2f'% (roc_auc))\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.2])\n",
    "plt.ylim([-0.1,1.2])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "#LR: sensitivity(recall)=77.6, specificity=84.8, precision=86.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "-07H10 (60) ******************************\n",
      "+06E10 (60) ******************************\n",
      "-04H10 (60) ******************************\n",
      "-10H09 (60) ******************************\n",
      "-10H11 (59) *****************************\n",
      "-10S14 (59) *****************************\n",
      "-09S01 (59) *****************************\n",
      "-07H09 (58) *****************************\n",
      "+09S00 (58) *****************************\n",
      "+08H07 (58) *****************************\n",
      "+08S02 (58) *****************************\n",
      "-06E05 (58) *****************************\n",
      "-01E00 (58) *****************************\n",
      "-10H07 (58) *****************************\n",
      "-03H10 (57) ****************************\n",
      "-03H14 (57) ****************************\n",
      "-08H11 (57) ****************************\n",
      "+05S01 (57) ****************************\n",
      "-10S04 (57) ****************************\n",
      "+02H14 (57) ****************************\n",
      "-04S08 (56) ****************************\n",
      "+06E05 (56) ****************************\n",
      "+01S07 (56) ****************************\n",
      "+07S04 (56) ****************************\n",
      "+09H10 (55) ***************************\n",
      "-05H10 (55) ***************************\n",
      "+08H14 (55) ***************************\n",
      "+03S07 (55) ***************************\n",
      "+10E05 (55) ***************************\n",
      "+03H07 (55) ***************************\n",
      "+09H09 (55) ***************************\n",
      "+05H11 (54) ***************************\n",
      "-01E10 (54) ***************************\n",
      "-09H11 (54) ***************************\n",
      "+07H14 (54) ***************************\n",
      "-01H12 (54) ***************************\n",
      "-08H09 (54) ***************************\n",
      "+03H10 (53) **************************\n",
      "-03H02 (53) **************************\n",
      "+02H09 (53) **************************\n",
      "+01H02 (53) **************************\n",
      "-10H10 (53) **************************\n",
      "+10H02 (53) **************************\n",
      "-05H07 (53) **************************\n",
      "-09H02 (53) **************************\n",
      "+07H02 (52) **************************\n",
      "-09H14 (52) **************************\n",
      "+03H14 (51) *************************\n",
      "-05H12 (51) *************************\n",
      "-04S01 (51) *************************\n",
      "+03H09 (51) *************************\n",
      "-02S04 (51) *************************\n",
      "+09S07 (51) *************************\n",
      "-09S07 (51) *************************\n",
      "+09H07 (51) *************************\n",
      "-06H10 (51) *************************\n",
      "-03H09 (50) *************************\n",
      "+04H10 (50) *************************\n",
      "+10S01 (50) *************************\n",
      "-09H09 (50) *************************\n",
      "+07S07 (50) *************************\n",
      "-07S07 (50) *************************\n",
      "+10H11 (50) *************************\n",
      "+10H10 (50) *************************\n",
      "-02H02 (49) ************************\n",
      "-07H11 (49) ************************\n",
      "-09H10 (49) ************************\n",
      "-04H17 (49) ************************\n",
      "-02S14 (49) ************************\n",
      "+01H09 (48) ************************\n",
      "+05H14 (48) ************************\n",
      "+09S01 (48) ************************\n",
      "-04H12 (48) ************************\n",
      "+02H17 (48) ************************\n",
      "+02H11 (48) ************************\n",
      "-03S01 (48) ************************\n",
      "+06S01 (48) ************************\n",
      "+08S14 (48) ************************\n",
      "+06H10 (48) ************************\n",
      "-05S14 (47) ***********************\n",
      "+05E10 (47) ***********************\n",
      "+04H12 (47) ***********************\n",
      "+06H02 (47) ***********************\n",
      "-01H07 (47) ***********************\n",
      "+01H11 (47) ***********************\n",
      "+10S07 (47) ***********************\n",
      "+08H02 (47) ***********************\n",
      "+02H12 (47) ***********************\n",
      "-01S04 (47) ***********************\n",
      "-09S04 (47) ***********************\n",
      "+03H11 (46) ***********************\n",
      "-08S01 (46) ***********************\n",
      "-01H02 (46) ***********************\n",
      "+05H09 (46) ***********************\n",
      "+02S01 (46) ***********************\n",
      "-07S01 (46) ***********************\n",
      "-04H05 (46) ***********************\n",
      "+02H02 (45) **********************\n",
      "+01H17 (45) **********************\n",
      "+01H10 (45) **********************\n",
      "+07H11 (45) **********************\n",
      "-08H17 (45) **********************\n",
      "-02H11 (45) **********************\n",
      "-10H02 (45) **********************\n",
      "-09H17 (45) **********************\n",
      "+03H12 (44) **********************\n",
      "+09H11 (44) **********************\n",
      "-06H02 (44) **********************\n",
      "+09H14 (44) **********************\n",
      "+04H14 (44) **********************\n",
      "+07H10 (44) **********************\n",
      "+06S14 (44) **********************\n",
      "+04S01 (44) **********************\n",
      "-03S07 (44) **********************\n",
      "+04E10 (44) **********************\n",
      "+06H11 (44) **********************\n",
      "-02H09 (43) *********************\n",
      "-06S07 (43) *********************\n",
      "+06H09 (43) *********************\n",
      "-02H10 (43) *********************\n",
      "+03H17 (42) *********************\n",
      "+10S14 (42) *********************\n",
      "+07H09 (42) *********************\n",
      "+08H17 (42) *********************\n",
      "-08H10 (42) *********************\n",
      "-10S01 (42) *********************\n",
      "+04S13 (42) *********************\n",
      "+06H17 (42) *********************\n",
      "+01H12 (41) ********************\n",
      "-08S08 (41) ********************\n",
      "+09H17 (41) ********************\n",
      "-05H14 (40) ********************\n",
      "+08H12 (40) ********************\n",
      "+04H17 (40) ********************\n",
      "+03H05 (40) ********************\n",
      "+08S01 (40) ********************\n",
      "+07S01 (40) ********************\n",
      "+02H10 (40) ********************\n",
      "-08H02 (40) ********************\n",
      "-02E02 (39) *******************\n",
      "-03S08 (39) *******************\n",
      "+01S01 (39) *******************\n",
      "+10H12 (39) *******************\n",
      "-03H17 (38) *******************\n",
      "-03H16 (38) *******************\n",
      "-07H14 (38) *******************\n",
      "+02S08 (38) *******************\n",
      "-02S08 (38) *******************\n",
      "+09H02 (38) *******************\n",
      "-06H12 (38) *******************\n",
      "-03H05 (37) ******************\n",
      "+07H12 (37) ******************\n",
      "-05S13 (37) ******************\n",
      "+05S08 (37) ******************\n",
      "-06S01 (37) ******************\n",
      "+08H10 (37) ******************\n",
      "+08H11 (37) ******************\n",
      "-07H17 (37) ******************\n",
      "+10H09 (37) ******************\n",
      "-02S07 (37) ******************\n",
      "-04S14 (37) ******************\n",
      "+08S07 (37) ******************\n",
      "+05H10 (37) ******************\n",
      "+06S08 (37) ******************\n",
      "+06S07 (37) ******************\n",
      "+06H14 (37) ******************\n",
      "+09H12 (36) ******************\n",
      "-10H12 (36) ******************\n",
      "+05H12 (36) ******************\n",
      "-09S13 (36) ******************\n",
      "-03H11 (36) ******************\n",
      "-02H14 (36) ******************\n",
      "+06H12 (36) ******************\n",
      "-05H17 (35) *****************\n",
      "+10H07 (35) *****************\n",
      "-07H12 (35) *****************\n",
      "+08H09 (35) *****************\n",
      "-10S07 (35) *****************\n",
      "-08H05 (35) *****************\n",
      "-05H05 (35) *****************\n",
      "-10H05 (35) *****************\n",
      "+07H17 (34) *****************\n",
      "+03S08 (34) *****************\n",
      "-02S01 (34) *****************\n",
      "-06H14 (34) *****************\n",
      "-10H17 (33) ****************\n",
      "+04S14 (33) ****************\n",
      "+04H11 (33) ****************\n",
      "+05H17 (33) ****************\n",
      "-03H12 (32) ****************\n",
      "-05S08 (32) ****************\n",
      "+04S08 (32) ****************\n",
      "+07S08 (32) ****************\n",
      "-01S01 (32) ****************\n",
      "+05S13 (31) ***************\n",
      "+03H16 (30) ***************\n",
      "-04S12 (30) ***************\n",
      "-09H16 (30) ***************\n",
      "-08H16 (30) ***************\n",
      "+05S07 (30) ***************\n",
      "-02H12 (30) ***************\n",
      "+10H14 (30) ***************\n",
      "-09S08 (29) **************\n",
      "-02H05 (29) **************\n",
      "+10S08 (29) **************\n",
      "+04S07 (29) **************\n",
      "+10H17 (29) **************\n",
      "+01H16 (28) **************\n",
      "-06H09 (28) **************\n",
      "-06H05 (28) **************\n",
      "+05H16 (28) **************\n",
      "-01S13 (28) **************\n",
      "-01H11 (28) **************\n",
      "-09H05 (28) **************\n",
      "+09H05 (28) **************\n",
      "+08S08 (28) **************\n",
      "+04H09 (27) *************\n",
      "+09S08 (27) *************\n",
      "-04E10 (27) *************\n",
      "-02H16 (27) *************\n",
      "-06H17 (27) *************\n",
      "-08E10 (26) *************\n",
      "+06H05 (26) *************\n",
      "+01S08 (26) *************\n",
      "-07E08 (26) *************\n",
      "-07H05 (25) ************\n",
      "-07S08 (25) ************\n",
      "+05H05 (25) ************\n",
      "-03S12 (24) ************\n",
      "-04H16 (24) ************\n",
      "-02H17 (24) ************\n",
      "+06H16 (24) ************\n",
      "-10H16 (23) ***********\n",
      "+07E10 (23) ***********\n",
      "+07S12 (23) ***********\n",
      "+07S13 (23) ***********\n",
      "-04S13 (23) ***********\n",
      "+06S13 (23) ***********\n",
      "-01H14 (23) ***********\n",
      "-09E10 (23) ***********\n",
      "+02H16 (23) ***********\n",
      "-09S12 (23) ***********\n",
      "-01S07 (23) ***********\n",
      "-10S08 (23) ***********\n",
      "-07H16 (22) ***********\n",
      "+08E10 (22) ***********\n",
      "+09S13 (22) ***********\n",
      "+02E10 (22) ***********\n",
      "-06E08 (22) ***********\n",
      "-09E08 (22) ***********\n",
      "-05H16 (21) **********\n",
      "+08H16 (21) **********\n",
      "+02H05 (21) **********\n",
      "-03S13 (21) **********\n",
      "-03E10 (21) **********\n",
      "+10H16 (21) **********\n",
      "-04E03 (20) **********\n",
      "+10E10 (20) **********\n",
      "+01E02 (20) **********\n",
      "-02S13 (20) **********\n",
      "+04S11 (20) **********\n",
      "-06S08 (20) **********\n",
      "+01H05 (19) *********\n",
      "-07S13 (19) *********\n",
      "+07H05 (19) *********\n",
      "+09S12 (19) *********\n",
      "+01S13 (19) *********\n",
      "-04H04 (19) *********\n",
      "+09E10 (19) *********\n",
      "+03S13 (18) *********\n",
      "+04H16 (18) *********\n",
      "-10S12 (18) *********\n",
      "-10S13 (18) *********\n",
      "-01S11 (18) *********\n",
      "+10H05 (18) *********\n",
      "-01H17 (18) *********\n",
      "+10S13 (18) *********\n",
      "+08S13 (18) *********\n",
      "+09H16 (17) ********\n",
      "+01E10 (17) ********\n",
      "+01E03 (17) ********\n",
      "+07H16 (17) ********\n",
      "-06S13 (17) ********\n",
      "+08H05 (17) ********\n",
      "+04H05 (17) ********\n",
      "-05E10 (17) ********\n",
      "+02S13 (17) ********\n",
      "+08S12 (17) ********\n",
      "-08S12 (16) ********\n",
      "+03S12 (16) ********\n",
      "+09E08 (16) ********\n",
      "-08S13 (16) ********\n",
      "-07S12 (16) ********\n",
      "+04S12 (16) ********\n",
      "-08E03 (16) ********\n",
      "-05E02 (16) ********\n",
      "+07H04 (15) *******\n",
      "-06E10 (15) *******\n",
      "-07E10 (15) *******\n",
      "-07E02 (15) *******\n",
      "-03E02 (15) *******\n",
      "+07E03 (15) *******\n",
      "-01H16 (15) *******\n",
      "-10S05 (15) *******\n",
      "-06E02 (15) *******\n",
      "+10E08 (15) *******\n",
      "+05S11 (15) *******\n",
      "-01S08 (15) *******\n",
      "+06E08 (15) *******\n",
      "-08E08 (15) *******\n",
      "-10E10 (14) *******\n",
      "-06S12 (14) *******\n",
      "-05S12 (14) *******\n",
      "+05S12 (14) *******\n",
      "-06H16 (14) *******\n",
      "-08S05 (13) ******\n",
      "-01H04 (13) ******\n",
      "-01H05 (13) ******\n",
      "+05E02 (13) ******\n",
      "-07E03 (13) ******\n",
      "-03E03 (13) ******\n",
      "+10S12 (13) ******\n",
      "+01S12 (13) ******\n",
      "-10H04 (13) ******\n",
      "-03E08 (13) ******\n",
      "+08E08 (12) ******\n",
      "-02E08 (12) ******\n",
      "+06H04 (12) ******\n",
      "+03S05 (12) ******\n",
      "-04E08 (12) ******\n",
      "+04H04 (12) ******\n",
      "+03E08 (12) ******\n",
      "-01H09 (12) ******\n",
      "-05E08 (11) *****\n",
      "-08H04 (11) *****\n",
      "+09E02 (11) *****\n",
      "+05E03 (11) *****\n",
      "+06E02 (11) *****\n",
      "+01E08 (11) *****\n",
      "+02E08 (11) *****\n",
      "-02E10 (11) *****\n",
      "-05E03 (11) *****\n",
      "-06H04 (10) *****\n",
      "+07E08 (10) *****\n",
      "+05E08 (10) *****\n",
      "-02S12 (10) *****\n",
      "+07E02 (10) *****\n",
      "-01E02 (10) *****\n",
      "+02S12 (10) *****\n",
      "-09E02 (10) *****\n",
      "-03H04 (9) ****\n",
      "+04E02 (9) ****\n",
      "-09S11 (9) ****\n",
      "+06S12 (8) ****\n",
      "+08E02 (8) ****\n",
      "-10E08 (8) ****\n",
      "+05S03 (8) ****\n",
      "-05S03 (8) ****\n",
      "+06S11 (8) ****\n",
      "+01S11 (8) ****\n",
      "+04E08 (8) ****\n",
      "-07H04 (8) ****\n",
      "-09E03 (8) ****\n",
      "+10E03 (7) ***\n",
      "-02H04 (7) ***\n",
      "+02H04 (7) ***\n",
      "+06E03 (7) ***\n",
      "-10E02 (7) ***\n",
      "-02E03 (7) ***\n",
      "+01S05 (7) ***\n",
      "+02E03 (7) ***\n",
      "+08H04 (7) ***\n",
      "+03E10 (7) ***\n",
      "-09H04 (7) ***\n",
      "+04E03 (7) ***\n",
      "+03E03 (6) ***\n",
      "+03E02 (6) ***\n",
      "+09E03 (6) ***\n",
      "+03E06 (6) ***\n",
      "-04S05 (6) ***\n",
      "+08E03 (6) ***\n",
      "+10S05 (6) ***\n",
      "+02S05 (6) ***\n",
      "+10E02 (6) ***\n",
      "-03S05 (6) ***\n",
      "+10H04 (6) ***\n",
      "+05H04 (5) **\n",
      "+01S03 (5) **\n",
      "-05H04 (5) **\n",
      "-06S11 (5) **\n",
      "+02E02 (5) **\n",
      "-08E02 (5) **\n",
      "-04E02 (5) **\n",
      "+09H04 (5) **\n",
      "-03E06 (5) **\n",
      "+08S11 (5) **\n",
      "+01H04 (4) **\n",
      "-05S11 (4) **\n",
      "+06S03 (4) **\n",
      "-10S11 (4) **\n",
      "+03H04 (4) **\n",
      "-07S11 (4) **\n",
      "-01S12 (4) **\n",
      "+10S11 (4) **\n",
      "-07S05 (4) **\n",
      "-09S05 (4) **\n",
      "-10E06 (3) *\n",
      "-10E03 (3) *\n",
      "-05S05 (3) *\n",
      "+01E06 (3) *\n",
      "-02S05 (3) *\n",
      "+05S05 (3) *\n",
      "-08S11 (3) *\n",
      "-06E06 (3) *\n",
      "-01E03 (3) *\n",
      "+02S11 (3) *\n",
      "-05E06 (3) *\n",
      "-09E06 (3) *\n",
      "-06S05 (2) *\n",
      "+04S05 (2) *\n",
      "+09E06 (2) *\n",
      "-01E08 (2) *\n",
      "+06E06 (2) *\n",
      "-04E06 (2) *\n",
      "-02S03 (2) *\n",
      "+02E06 (2) *\n",
      "+07S11 (2) *\n",
      "-03S11 (2) *\n",
      "-07E06 (2) *\n",
      "+08S03 (2) *\n",
      "-10S03 (2) *\n",
      "+02S03 (2) *\n",
      "-01E06 (2) *\n",
      "+07S03 (2) *\n",
      "-03S03 (2) *\n",
      "+06S05 (2) *\n",
      "-01S03 (2) *\n",
      "+08E06 (1) \n",
      "+07E06 (1) \n",
      "+03S11 (1) \n",
      "-08S03 (1) \n",
      "-04S03 (1) \n",
      "+10S03 (1) \n",
      "+03S03 (1) \n",
      "+09S05 (1) \n",
      "+04E06 (1) \n",
      "+08S05 (1) \n",
      "+10E06 (1) \n",
      "-08E06 (1) \n",
      "-02S11 (1) \n",
      "+09S11 (1) \n",
      "+05E06 (1) \n",
      "+09S03 (1) \n",
      "-07S03 (1) \n",
      "-06E03 (1) \n"
     ]
    }
   ],
   "source": [
    "importance = bst.get_score(importance_type=\"weight\")\n",
    "tuples = [(k, importance[k]) for k in importance]\n",
    "tuples = sorted(tuples, reverse=True, key=lambda x: x[1])\n",
    "for (key, value) in tuples:\n",
    "    print \"%5s (%d) %s\" % (key, value, \"*\" * (value/2))"
   ]
  }
 ],
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