6-XGB-CoreLib-Clean-Synthesis-All.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 matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "game_csv = pd.read_csv('./data/Training_dataset_Original.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "gameDf = pd.DataFrame(game_csv)\n",
    "\n",
    "dataDict = pd.read_csv('./data/Data_Dictionary.csv',na_values=\"NA\")\n",
    "\n",
    "#Leaderboard\n",
    "leader_csv = pd.read_csv('./data/Leaderboard_dataset.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "leaderDf = pd.DataFrame(leader_csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(gameDf.shape)\n",
    "print(dataDict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove ID column\n",
    "del gameDf[dic['Application ID (primary key)']]\n",
    "del dic['Application ID (primary key)']\n",
    "\n",
    "leaderAppID = leaderDf['application_key']\n",
    "del leaderDf['application_key']\n",
    "leaderX = leaderDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = gameDf.iloc[:,:-1]\n",
    "gameY = gameDf.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 80000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(gameDf[\"mvar47\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#one hot encoding C=0 L=1\n",
    "gameX['mvar47'] = pd.get_dummies(gameX['mvar47'])\n",
    "\n",
    "leaderX['mvar47'] = pd.get_dummies(leaderX['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesys begins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 47)\n",
      "(25000, 47)\n",
      "(80000, 48)\n",
      "(25000, 48)\n",
      "(80000, 49)\n",
      "(25000, 49)\n",
      "(80000, 50)\n",
      "(25000, 50)\n",
      "(80000, 51)\n",
      "(25000, 51)\n",
      "(80000, 52)\n",
      "(25000, 52)\n",
      "(80000, 53)\n",
      "(25000, 53)\n",
      "(80000, 54)\n",
      "(25000, 54)\n",
      "(80000, 55)\n",
      "(25000, 55)\n",
      "(80000, 56)\n",
      "(25000, 56)\n",
      "(80000, 57)\n",
      "(25000, 57)\n",
      "(80000, 58)\n",
      "(25000, 58)\n",
      "(80000, 59)\n",
      "(25000, 59)\n",
      "(80000, 60)\n",
      "(25000, 60)\n",
      "(80000, 61)\n",
      "(25000, 61)\n",
      "(80000, 62)\n",
      "(25000, 62)\n",
      "(80000, 63)\n",
      "(25000, 63)\n",
      "(80000, 64)\n",
      "(25000, 64)\n",
      "(80000, 65)\n",
      "(25000, 65)\n",
      "(80000, 66)\n",
      "(25000, 66)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var345 = (1+gameX['mvar3'])*(1+gameX['mvar4'])*(1+gameX['mvar5']))\n",
    "leaderX = leaderX.assign(var345 = (1+leaderX['mvar3'])*(1+leaderX['mvar4'])*(1+leaderX['mvar5']) )\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX  = gameX.assign(mvar678 = (gameX['mvar7'] + gameX['mvar8'])/(1+gameX['mvar6']))\n",
    "leaderX = leaderX.assign(mvar678 = (leaderX['mvar7'] + leaderX['mvar8'])/(1+leaderX['mvar6']))\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp16 = df['mvar16'].copy()\n",
    "temp16[temp16.isnull()] = -1\n",
    "temp17 = df['mvar17'].copy()\n",
    "temp17[temp17.isnull()] = -1\n",
    "temp18 = df['mvar18'].copy()\n",
    "temp18[temp18.isnull()] = -1\n",
    "\n",
    "\n",
    "temp16 = temp16 + temp17+ temp18\n",
    "temp16[temp16>0] = 0.5265\n",
    "temp16[temp16==0] = 0.1763\n",
    "temp16[temp16==-1] = 0.2594\n",
    "temp16[temp16==-2] = 0.2594\n",
    "temp16[temp16==-3] = 0.4423\n",
    "\n",
    "df = df.assign(var161718 = temp16)\n",
    "\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp16 = df['mvar16'].copy()\n",
    "temp16[temp16.isnull()] = -1\n",
    "temp17 = df['mvar17'].copy()\n",
    "temp17[temp17.isnull()] = -1\n",
    "temp18 = df['mvar18'].copy()\n",
    "temp18[temp18.isnull()] = -1\n",
    "\n",
    "\n",
    "temp16 = temp16 + temp17+ temp18\n",
    "temp16[temp16>0] = 0.5265\n",
    "temp16[temp16==0] = 0.1763\n",
    "temp16[temp16==-1] = 0.2594\n",
    "temp16[temp16==-2] = 0.2594\n",
    "temp16[temp16==-3] = 0.4423\n",
    "\n",
    "df = df.assign(var161718 = temp16)\n",
    "\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var2223 = gameX['mvar23'] - gameX['mvar22'])\n",
    "leaderX = leaderX.assign(var2223 = leaderX['mvar23'] - leaderX['mvar22'])\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp26 = df['mvar26'].copy()\n",
    "temp26[temp26.isnull()] = -365\n",
    "temp27 = df['mvar27'].copy()\n",
    "temp27[temp27.isnull()] = -365\n",
    "\n",
    "temp26 = (temp26+temp27)/2/365\n",
    "temp26 = temp26.astype('int64')\n",
    "\n",
    "temp26[(temp26>=0) & (temp26<=5)] = 0.3243\n",
    "temp26[temp26>5] = 0.1562\n",
    "temp26[temp26<0] = 0.4026\n",
    "\n",
    "df = df.assign(var2627 = temp26)\n",
    "gameX = df.copy()\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp26 = df['mvar26'].copy()\n",
    "temp26[temp26.isnull()] = -365\n",
    "temp27 = df['mvar27'].copy()\n",
    "temp27[temp27.isnull()] = -365\n",
    "\n",
    "temp26 = (temp26+temp27)/2/365\n",
    "temp26 = temp26.astype('int64')\n",
    "\n",
    "temp26[(temp26>=0) & (temp26<=5)] = 0.3243\n",
    "temp26[temp26>5] = 0.1562\n",
    "temp26[temp26<0] = 0.4026\n",
    "\n",
    "df = df.assign(var2627 = temp26)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp28 = df['mvar28'].copy()\n",
    "temp28[temp28.isnull()] = -365\n",
    "\n",
    "temp28 = temp28/365\n",
    "temp28 = temp28.astype('int64')\n",
    "\n",
    "temp28[temp28!=0] = 0.1231\n",
    "temp28[temp28==0] = 0.3605\n",
    "\n",
    "df = df.assign(var28 = temp28)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp28 = df['mvar28'].copy()\n",
    "temp28[temp28.isnull()] = -365\n",
    "\n",
    "temp28 = temp28/365\n",
    "temp28 = temp28.astype('int64')\n",
    "\n",
    "temp28[temp28!=0] = 0.1231\n",
    "temp28[temp28==0] = 0.3605\n",
    "\n",
    "df = df.assign(var28 = temp28)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "df = df.assign(var3031 = df[[\"mvar30\", \"mvar31\"]].max(axis=1))\n",
    "gameX = df.copy()\n",
    "\n",
    "df = leaderX.copy()\n",
    "df = df.assign(var3031 = df[[\"mvar30\", \"mvar31\"]].max(axis=1))\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp33 = df['mvar33'].copy()\n",
    "temp33[temp33.isnull()] = -5\n",
    "\n",
    "temp33 = temp33/5\n",
    "temp33 = temp33.astype('int64')\n",
    "\n",
    "temp33[temp33<0] = 0.3118\n",
    "temp33[temp33==1] = 0.2138\n",
    "temp33[temp33>1] = 0.1601\n",
    "temp33[temp33==0] = 0.2959\n",
    "\n",
    "df = df.assign(var33 = temp33)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp33 = df['mvar33'].copy()\n",
    "temp33[temp33.isnull()] = -5\n",
    "\n",
    "temp33 = temp33/5\n",
    "temp33 = temp33.astype('int64')\n",
    "\n",
    "temp33[temp33<0] = 0.3118\n",
    "temp33[temp33==1] = 0.2138\n",
    "temp33[temp33>1] = 0.1601\n",
    "temp33[temp33==0] = 0.2959\n",
    "\n",
    "df = df.assign(var33 = temp33)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp34 = df['mvar34'].copy()\n",
    "temp34[temp34.isnull()] = -1\n",
    "\n",
    "temp34[temp34==0] = 0.2151\n",
    "temp34[temp34==-1] = 0.2151\n",
    "temp34[temp34==1] = 0.3589\n",
    "temp34[temp34==2] = 0.4459\n",
    "temp34[temp34>2] = 0.5641\n",
    "\n",
    "df = df.assign(var34 = temp34)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp34 = df['mvar34'].copy()\n",
    "temp34[temp34.isnull()] = -1\n",
    "\n",
    "temp34[temp34==0] = 0.2151\n",
    "temp34[temp34==-1] = 0.2151\n",
    "temp34[temp34==1] = 0.3589\n",
    "temp34[temp34==2] = 0.4459\n",
    "temp34[temp34>2] = 0.5641\n",
    "\n",
    "df = df.assign(var34 = temp34)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp35 = df['mvar35'].copy()\n",
    "temp35[temp35.isnull()] = -1\n",
    "\n",
    "temp35[temp35>0] = 0.5137\n",
    "temp35[temp35==0] = 0.1878\n",
    "temp35[temp35==-1] = 0.2623\n",
    "\n",
    "df = df.assign(var35 = temp35)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp35 = df['mvar35'].copy()\n",
    "temp35[temp35.isnull()] = -1\n",
    "\n",
    "temp35[temp35>0] = 0.5137\n",
    "temp35[temp35==0] = 0.1878\n",
    "temp35[temp35==-1] = 0.2623\n",
    "\n",
    "df = df.assign(var35 = temp35)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "#mvar36, 37, 38\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp36 = df['mvar36'].copy()\n",
    "\n",
    "temp36[temp36.isnull()] = -1\n",
    "\n",
    "temp36[temp36==1] = 0.163769\n",
    "temp36[temp36==2] = 0.170170\n",
    "temp36[temp36==3] = 0.190171\n",
    "temp36[temp36==4] = 0.211005\n",
    "temp36[temp36==5] = 0.226932\n",
    "temp36[temp36==6] = 0.247148\n",
    "temp36[temp36==7] = 0.271086\n",
    "temp36[temp36==8] = 0.296529\n",
    "temp36[temp36==9] = 0.309190\n",
    "temp36[temp36==10] = 0.323132\n",
    "temp36[temp36==11] = 0.330296\n",
    "temp36[temp36==12] = 0.33815\n",
    "temp36[temp36==13] = 0.352103\n",
    "temp36[temp36==14] = 0.370433\n",
    "temp36[temp36==15] = 0.365985\n",
    "temp36[temp36==16] = 0.384926\n",
    "temp36[temp36==17] = 0.395310\n",
    "temp36[temp36==18] = 0.449219\n",
    "temp36[temp36==19] = 0.425926\n",
    "temp36[temp36<0] = 0.176674\n",
    "temp36[temp36>=20] = 0.4647\n",
    "\n",
    "df = df.assign(var36 = temp36)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp36 = df['mvar36'].copy()\n",
    "\n",
    "temp36[temp36.isnull()] = -1\n",
    "\n",
    "temp36[temp36==1] = 0.163769\n",
    "temp36[temp36==2] = 0.170170\n",
    "temp36[temp36==3] = 0.190171\n",
    "temp36[temp36==4] = 0.211005\n",
    "temp36[temp36==5] = 0.226932\n",
    "temp36[temp36==6] = 0.247148\n",
    "temp36[temp36==7] = 0.271086\n",
    "temp36[temp36==8] = 0.296529\n",
    "temp36[temp36==9] = 0.309190\n",
    "temp36[temp36==10] = 0.323132\n",
    "temp36[temp36==11] = 0.330296\n",
    "temp36[temp36==12] = 0.33815\n",
    "temp36[temp36==13] = 0.352103\n",
    "temp36[temp36==14] = 0.370433\n",
    "temp36[temp36==15] = 0.365985\n",
    "temp36[temp36==16] = 0.384926\n",
    "temp36[temp36==17] = 0.395310\n",
    "temp36[temp36==18] = 0.449219\n",
    "temp36[temp36==19] = 0.425926\n",
    "temp36[temp36<0] = 0.176674\n",
    "temp36[temp36>=20] = 0.4647\n",
    "\n",
    "df = df.assign(var36 = temp36)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp37 = df['mvar37'].copy()\n",
    "\n",
    "temp37[temp37.isnull()] = -1\n",
    "\n",
    "temp37[temp37<0] = 0.458344\n",
    "temp37[temp37==0] = 0.381028\n",
    "temp37[temp37==1] = 0.309995\n",
    "temp37[temp37==2] = 0.239528\n",
    "temp37[temp37==3] = 0.186923\n",
    "temp37[temp37==4] = 0.159852\n",
    "temp37[temp37==5] = 0.14485\n",
    "temp37[temp37==6] = 0.131449\n",
    "temp37[temp37==7] = 0.118592\n",
    "temp37[temp37==8] = 0.111612\n",
    "temp37[temp37==9] = 0.102641\n",
    "temp37[temp37>=10] = 0.0904\n",
    "\n",
    "df = df.assign(var37 = temp37)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp37 = df['mvar37'].copy()\n",
    "\n",
    "temp37[temp37.isnull()] = -1\n",
    "\n",
    "temp37[temp37<0] = 0.458344\n",
    "temp37[temp37==0] = 0.381028\n",
    "temp37[temp37==1] = 0.309995\n",
    "temp37[temp37==2] = 0.239528\n",
    "temp37[temp37==3] = 0.186923\n",
    "temp37[temp37==4] = 0.159852\n",
    "temp37[temp37==5] = 0.14485\n",
    "temp37[temp37==6] = 0.131449\n",
    "temp37[temp37==7] = 0.118592\n",
    "temp37[temp37==8] = 0.111612\n",
    "temp37[temp37==9] = 0.102641\n",
    "temp37[temp37>=10] = 0.0904\n",
    "\n",
    "df = df.assign(var37 = temp37)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp38 = df['mvar38'].copy()\n",
    "\n",
    "temp38[temp38.isnull()] = -1\n",
    "\n",
    "temp38[temp38<0] = 0.215139\n",
    "temp38[temp38==0] = 0.145566\n",
    "temp38[temp38==1] = 0.176224\n",
    "temp38[temp38==2] = 0.200382\n",
    "temp38[temp38==3] = 0.232083\n",
    "temp38[temp38==4] = 0.252492\n",
    "temp38[temp38==5] = 0.275282\n",
    "temp38[temp38==6] = 0.297332\n",
    "temp38[temp38==7] = 0.298409\n",
    "temp38[temp38==8] = 0.328731\n",
    "temp38[temp38==9] = 0.342270\n",
    "temp38[temp38>=10] = 0.3907\n",
    "\n",
    "df = df.assign(var38 = temp38)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp38 = df['mvar38'].copy()\n",
    "\n",
    "temp38[temp38.isnull()] = -1\n",
    "\n",
    "temp38[temp38<0] = 0.215139\n",
    "temp38[temp38==0] = 0.145566\n",
    "temp38[temp38==1] = 0.176224\n",
    "temp38[temp38==2] = 0.200382\n",
    "temp38[temp38==3] = 0.232083\n",
    "temp38[temp38==4] = 0.252492\n",
    "temp38[temp38==5] = 0.275282\n",
    "temp38[temp38==6] = 0.297332\n",
    "temp38[temp38==7] = 0.298409\n",
    "temp38[temp38==8] = 0.328731\n",
    "temp38[temp38==9] = 0.342270\n",
    "temp38[temp38>=10] = 0.3907\n",
    "\n",
    "df = df.assign(var38 = temp38)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var363738 = (gameX['mvar36']*gameX['mvar37']/(1+gameX['mvar38'])) )\n",
    "leaderX = leaderX.assign(var363738 = (leaderX['mvar36']*leaderX['mvar37']/(1+leaderX['mvar38']) ) )\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "\n",
    "\n",
    "#mvar39\n",
    "df = gameX.copy()\n",
    "\n",
    "temp39 = df['mvar35'].copy()\n",
    "temp39[temp39.isnull()] = -1\n",
    "\n",
    "temp39[temp39>0] = 0.4925\n",
    "temp39[temp39==0] = 0.2109\n",
    "temp39[temp39==-1] = 0.4390\n",
    "\n",
    "df = df.assign(var39 = temp39)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp39 = df['mvar35'].copy()\n",
    "temp39[temp39.isnull()] = -1\n",
    "\n",
    "temp39[temp39>0] = 0.4925\n",
    "temp39[temp39==0] = 0.2109\n",
    "temp39[temp39==-1] = 0.4390\n",
    "\n",
    "df = df.assign(var39 = temp39)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp40 = df['mvar40'].copy()\n",
    "temp40[temp40.isnull()] = -50\n",
    "temp40 = temp40/50\n",
    "temp40 = temp40.round(0)\n",
    "\n",
    "temp40[temp40>=2] = 0.375\n",
    "temp40[temp40==1] = 0.1643\n",
    "temp40[temp40==0] = 0.1540\n",
    "temp40[temp40<0] = 0.2199\n",
    "\n",
    "df = df.assign(var40 = temp40)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp40 = df['mvar40'].copy()\n",
    "temp40[temp40.isnull()] = -50\n",
    "temp40 = temp40/50\n",
    "temp40 = temp40.round(0)\n",
    "\n",
    "temp40[temp40>=2] = 0.375\n",
    "temp40[temp40==1] = 0.1643\n",
    "temp40[temp40==0] = 0.1540\n",
    "temp40[temp40<0] = 0.2199\n",
    "\n",
    "df = df.assign(var40 = temp40)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp41 = df['mvar41'].copy()\n",
    "temp41[temp41.isnull()] = -25\n",
    "temp41 = temp41/25\n",
    "temp41 = temp41.round(0)\n",
    "\n",
    "temp41[temp41>=4] = 0.3000\n",
    "temp41[temp41==3] = 0.2231\n",
    "temp41[ (temp41==2) | (temp41==1) | (temp41==0)] = 0.1610\n",
    "temp41[temp41<0] = 0.2526\n",
    "\n",
    "df = df.assign(var41 = temp41)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp41 = df['mvar41'].copy()\n",
    "temp41[temp41.isnull()] = -25\n",
    "temp41 = temp41/25\n",
    "temp41 = temp41.round(0)\n",
    "\n",
    "temp41[temp41>=4] = 0.3000\n",
    "temp41[temp41==3] = 0.2231\n",
    "temp41[ (temp41==2) | (temp41==1) | (temp41==0)] = 0.1610\n",
    "temp41[temp41<0] = 0.2526\n",
    "\n",
    "df = df.assign(var41 = temp41)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp45 = df['mvar45'].copy()\n",
    "temp45[temp45.isnull()] = -1\n",
    "\n",
    "temp45[temp45>0] = 0.3481\n",
    "temp45[temp45==0] = 0.1268\n",
    "temp45[temp45<0] = 0.3281\n",
    "\n",
    "df = df.assign(var45 = temp45)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp45 = df['mvar45'].copy()\n",
    "temp45[temp45.isnull()] = -1\n",
    "\n",
    "temp45[temp45>0] = 0.3481\n",
    "temp45[temp45==0] = 0.1268\n",
    "temp45[temp45<0] = 0.3281\n",
    "\n",
    "df = df.assign(var45 = temp45)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp46 = df['mvar46'].copy()\n",
    "temp46[temp46.isnull()] = -1\n",
    "\n",
    "temp46[temp46>0] = 0.4979\n",
    "temp46[temp46==0] = 0.2010\n",
    "temp46[temp46<0] = 0.2968\n",
    "\n",
    "df = df.assign(var46 = temp46)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp46 = df['mvar46'].copy()\n",
    "temp46[temp46.isnull()] = -1\n",
    "\n",
    "temp46[temp46>0] = 0.4979\n",
    "temp46[temp46==0] = 0.2010\n",
    "temp46[temp46<0] = 0.2968\n",
    "\n",
    "df = df.assign(var46 = temp46)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#drop\n",
    "df = gameX.copy() \n",
    "leader_df = leaderX.copy()\n",
    "drop_target = ['mvar27']\n",
    "#number of misssing values\n",
    "# for i in df.columns:\n",
    "#     if (100*(80000-gameDf[i].describe()['count'])/80000) > "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in drop_target:\n",
    "    del df[i]\n",
    "    del leader_df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leader_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = df\n",
    "leaderX = leader_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 66)\n",
      "(25000, 66)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>var35</th>\n",
       "      <th>var36</th>\n",
       "      <th>var37</th>\n",
       "      <th>var38</th>\n",
       "      <th>var363738</th>\n",
       "      <th>var39</th>\n",
       "      <th>var40</th>\n",
       "      <th>var41</th>\n",
       "      <th>var45</th>\n",
       "      <th>var46</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>0.500100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>19179.000000</td>\n",
       "      <td>206.00000</td>\n",
       "      <td>14221.000000</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>1.970100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>19820.000000</td>\n",
       "      <td>19820.00000</td>\n",
       "      <td>176895.000000</td>\n",
       "      <td>66346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1774.0</td>\n",
       "      <td>0.171800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>2563.000000</td>\n",
       "      <td>877.00000</td>\n",
       "      <td>8869.000000</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>0.112300</td>\n",
       "      <td>5.299</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.163769</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1832.0</td>\n",
       "      <td>1.444200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>7577.000000</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>21059.000000</td>\n",
       "      <td>20306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.395310</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.054400</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>14039.000000</td>\n",
       "      <td>10976.00000</td>\n",
       "      <td>278941.000000</td>\n",
       "      <td>74030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.370433</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.075300</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>16141.000000</td>\n",
       "      <td>3568.00000</td>\n",
       "      <td>69482.000000</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.296529</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>10.285714</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1935.0</td>\n",
       "      <td>0.560100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>44919.000000</td>\n",
       "      <td>17115.00000</td>\n",
       "      <td>124814.000000</td>\n",
       "      <td>124167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.342270</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1885.0</td>\n",
       "      <td>0.081100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7369.000000</td>\n",
       "      <td>19264.000000</td>\n",
       "      <td>19264.00000</td>\n",
       "      <td>59956.000000</td>\n",
       "      <td>26633.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.186923</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>0.744400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2103.000000</td>\n",
       "      <td>582.00000</td>\n",
       "      <td>16429.000000</td>\n",
       "      <td>9197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.342270</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.509700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>8673.000000</td>\n",
       "      <td>8673.00000</td>\n",
       "      <td>6739.000000</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.163769</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1755.0</td>\n",
       "      <td>0.301300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>4051.000000</td>\n",
       "      <td>261.00000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>2.290900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "      <td>7620.00000</td>\n",
       "      <td>64983.000000</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.323132</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1562.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.611</td>\n",
       "      <td>3.7845</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.370433</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>5.082500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>3359.000000</td>\n",
       "      <td>2973.00000</td>\n",
       "      <td>22099.000000</td>\n",
       "      <td>13507.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1684.0</td>\n",
       "      <td>1.547900</td>\n",
       "      <td>1.338</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>13874.000000</td>\n",
       "      <td>13874.00000</td>\n",
       "      <td>98011.000000</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1862.0</td>\n",
       "      <td>0.240100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1487.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>3964.00000</td>\n",
       "      <td>7928.000000</td>\n",
       "      <td>26564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1767.0</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>1.514</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.00000</td>\n",
       "      <td>8790.000000</td>\n",
       "      <td>6065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.176674</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1802.0</td>\n",
       "      <td>0.636400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18759.000000</td>\n",
       "      <td>10905.00000</td>\n",
       "      <td>51037.000000</td>\n",
       "      <td>58544.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.131449</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>1.955900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>12525.000000</td>\n",
       "      <td>12525.00000</td>\n",
       "      <td>18932.000000</td>\n",
       "      <td>14602.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1714.0</td>\n",
       "      <td>2.266400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1671.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>53893.000000</td>\n",
       "      <td>24739.00000</td>\n",
       "      <td>128435.000000</td>\n",
       "      <td>125679.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.720800</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>3621.000000</td>\n",
       "      <td>2323.00000</td>\n",
       "      <td>9018.000000</td>\n",
       "      <td>9649.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.328731</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>1.660500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4524.000000</td>\n",
       "      <td>15530.000000</td>\n",
       "      <td>14819.00000</td>\n",
       "      <td>116154.000000</td>\n",
       "      <td>100684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.118592</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>6201.000000</td>\n",
       "      <td>6201.00000</td>\n",
       "      <td>66477.000000</td>\n",
       "      <td>34053.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.425926</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>7.307692</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1811.0</td>\n",
       "      <td>0.911700</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>27717.000000</td>\n",
       "      <td>9815.00000</td>\n",
       "      <td>110001.000000</td>\n",
       "      <td>69683.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.296529</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1658.0</td>\n",
       "      <td>2.382900</td>\n",
       "      <td>37.127</td>\n",
       "      <td>10.3678</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6566.000000</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.186923</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1827.0</td>\n",
       "      <td>0.280100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "      <td>1189.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.163769</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>1734.0</td>\n",
       "      <td>0.927900</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>11643.000000</td>\n",
       "      <td>1524.00000</td>\n",
       "      <td>16748.000000</td>\n",
       "      <td>25359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>1849.0</td>\n",
       "      <td>1.830500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2251.000000</td>\n",
       "      <td>33275.000000</td>\n",
       "      <td>7505.00000</td>\n",
       "      <td>44843.000000</td>\n",
       "      <td>47453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>1.525100</td>\n",
       "      <td>2.727</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.727</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>720.00000</td>\n",
       "      <td>5896.000000</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>1642.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.353</td>\n",
       "      <td>1.2114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>6290.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.458344</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>1709.0</td>\n",
       "      <td>3.680100</td>\n",
       "      <td>7.583</td>\n",
       "      <td>3.1371</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>8132.000000</td>\n",
       "      <td>297.00000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.464700</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>1890.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>15162.000000</td>\n",
       "      <td>15162.00000</td>\n",
       "      <td>68181.000000</td>\n",
       "      <td>43334.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>1727.0</td>\n",
       "      <td>1.844800</td>\n",
       "      <td>15.181</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2234.000000</td>\n",
       "      <td>2754.000000</td>\n",
       "      <td>2234.00000</td>\n",
       "      <td>2676.000000</td>\n",
       "      <td>6539.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.186923</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>1.299400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>17838.000000</td>\n",
       "      <td>17838.00000</td>\n",
       "      <td>8424.000000</td>\n",
       "      <td>22307.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>1706.0</td>\n",
       "      <td>0.499700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>546.000000</td>\n",
       "      <td>643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>1638.0</td>\n",
       "      <td>4.680200</td>\n",
       "      <td>16.751</td>\n",
       "      <td>2.6642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>7937.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3032.000000</td>\n",
       "      <td>10489.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>1855.0</td>\n",
       "      <td>1.996500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>30203.000000</td>\n",
       "      <td>22656.00000</td>\n",
       "      <td>168999.000000</td>\n",
       "      <td>166720.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.338150</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>18018.000000</td>\n",
       "      <td>13297.00000</td>\n",
       "      <td>3914.000000</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>1546.0</td>\n",
       "      <td>0.349100</td>\n",
       "      <td>50.803</td>\n",
       "      <td>4.2695</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3330.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.395310</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.328731</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>3.962900</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23707.000000</td>\n",
       "      <td>23707.00000</td>\n",
       "      <td>33432.000000</td>\n",
       "      <td>19640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.352103</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24775.000000</td>\n",
       "      <td>24775.00000</td>\n",
       "      <td>80772.000000</td>\n",
       "      <td>69483.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.145566</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.166300</td>\n",
       "      <td>1.979</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9591.000000</td>\n",
       "      <td>2627.00000</td>\n",
       "      <td>46903.000000</td>\n",
       "      <td>30402.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.352103</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>28.166667</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>1681.0</td>\n",
       "      <td>0.173900</td>\n",
       "      <td>9.006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3004.000000</td>\n",
       "      <td>2251.00000</td>\n",
       "      <td>6818.000000</td>\n",
       "      <td>7783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.296529</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>1723.0</td>\n",
       "      <td>1.057000</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>28479.000000</td>\n",
       "      <td>28479.00000</td>\n",
       "      <td>16352.000000</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>1693.0</td>\n",
       "      <td>1.214500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>1592.0</td>\n",
       "      <td>6.480500</td>\n",
       "      <td>5.250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.204</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.458344</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>1641.0</td>\n",
       "      <td>0.660100</td>\n",
       "      <td>8.614</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.458344</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.390100</td>\n",
       "      <td>4.322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>29460.000000</td>\n",
       "      <td>9449.00000</td>\n",
       "      <td>42047.000000</td>\n",
       "      <td>31583.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>1651.0</td>\n",
       "      <td>2.490900</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.9108</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3163.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1.360100</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5675.000000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>15071.000000</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>1.670100</td>\n",
       "      <td>5.554</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>5692.000000</td>\n",
       "      <td>496.00000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>8125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>12.000200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>8751.000000</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.176674</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.055100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>14867.000000</td>\n",
       "      <td>9613.00000</td>\n",
       "      <td>46490.000000</td>\n",
       "      <td>44722.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>1686.0</td>\n",
       "      <td>1.330100</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.025</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>4582.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.145566</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>1826.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>26277.000000</td>\n",
       "      <td>9058.00000</td>\n",
       "      <td>72739.000000</td>\n",
       "      <td>86797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>0.139400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.00000</td>\n",
       "      <td>28343.000000</td>\n",
       "      <td>51174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.145566</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mvar1      mvar2   mvar3    mvar4  mvar5         mvar6         mvar7  \\\n",
       "0      1753.0   0.500100   0.000   0.0000  0.000    206.000000  19179.000000   \n",
       "1      1894.0   1.970100   0.000   0.0000  0.000    297.000000  19820.000000   \n",
       "2      1774.0   0.171800   0.000   0.0000  0.000    197.000000   2563.000000   \n",
       "3      1586.0   0.112300   5.299   0.0000  0.000   1606.702372  17595.807537   \n",
       "4      1832.0   1.444200   0.000   0.0000  0.000    179.000000   7577.000000   \n",
       "5      1838.0   0.054400   0.241   0.0000  0.000    882.000000  14039.000000   \n",
       "6      1743.0   1.075300   0.272   0.0000  0.000    397.000000  16141.000000   \n",
       "7      1935.0   0.560100   0.000   0.0000  0.000    496.000000  44919.000000   \n",
       "8      1885.0   0.081100   0.000   0.0000  0.000   7369.000000  19264.000000   \n",
       "9      1730.0   0.744400   0.000   0.0000  0.000      0.000000   2103.000000   \n",
       "10     1731.0   0.509700   0.000   0.0000  0.000   2433.000000   8673.000000   \n",
       "11     1755.0   0.301300   0.000   0.0000  0.000    261.000000   4051.000000   \n",
       "12     1739.0   2.290900   0.000   0.0000  0.000      0.000000   7620.000000   \n",
       "13     1562.0   0.000000  34.611   3.7845  0.000   1606.702372     18.000000   \n",
       "14     1798.0   5.082500   0.000   0.0000  0.000    259.000000   3359.000000   \n",
       "15     1684.0   1.547900   1.338   0.0000  0.000   1606.702372  17595.807537   \n",
       "16     1789.0   0.000000   1.428   0.0000  0.000   2478.000000  13874.000000   \n",
       "17     1862.0   0.240100   0.000   0.0000  0.000   1487.000000  12310.000000   \n",
       "18     1767.0   0.300100   1.514   0.0000  0.000    991.000000   2478.000000   \n",
       "19     1802.0   0.636400   0.000   0.0000  0.000     96.000000  18759.000000   \n",
       "20     1780.0   1.955900   0.000   0.0000  0.000    191.000000  12525.000000   \n",
       "21     1714.0   2.266400   0.000   0.0000  0.000     31.000000   1671.000000   \n",
       "22     1923.0   0.088000   0.000   0.0000  0.000   5946.000000  53893.000000   \n",
       "23     1743.0   1.720800   2.295   0.0000  0.000    159.000000   3621.000000   \n",
       "24     1700.0   1.660500   0.000   0.0000  0.000      0.000000   1037.000000   \n",
       "25     1922.0   1.105629   0.000   0.0000  0.000   4524.000000  15530.000000   \n",
       "26     1777.0   0.412000   0.000   0.0000  0.000    369.000000   6201.000000   \n",
       "27     1811.0   0.911700   0.410   0.0000  0.410    297.000000  27717.000000   \n",
       "28     1658.0   2.382900  37.127  10.3678  0.000      0.000000    396.000000   \n",
       "29     1827.0   0.280100   0.000   0.0000  0.000    678.000000   1189.000000   \n",
       "...       ...        ...     ...      ...    ...           ...           ...   \n",
       "24970  1734.0   0.927900   0.520   0.0000  0.000    256.000000  11643.000000   \n",
       "24971  1849.0   1.830500   0.000   0.0000  0.000   2251.000000  33275.000000   \n",
       "24972  1739.0   1.525100   2.727   0.0000  2.727      8.000000   2129.000000   \n",
       "24973  1642.0   0.000000   3.353   1.2114  0.000   1606.702372   6290.000000   \n",
       "24974  1709.0   3.680100   7.583   3.1371  0.000    297.000000   8132.000000   \n",
       "24975  1890.0   0.000000   0.000   0.0000  0.000   1982.000000  15162.000000   \n",
       "24976  1727.0   1.844800  15.181   0.0000  0.000   2234.000000   2754.000000   \n",
       "24977  1789.0   1.299400   0.000   0.0000  0.000    364.000000  17838.000000   \n",
       "24978  1706.0   0.499700   0.000   0.0000  0.000     98.000000    297.000000   \n",
       "24979  1638.0   4.680200  16.751   2.6642  0.000   1606.702372   7937.000000   \n",
       "24980  1855.0   1.996500   0.000   0.0000  0.000    350.000000  30203.000000   \n",
       "24981  1790.0   0.205000   0.000   0.0000  0.000    237.000000  18018.000000   \n",
       "24982  1546.0   0.349100  50.803   4.2695  0.000   1606.702372  17595.807537   \n",
       "24983  1777.0   3.962900   0.968   0.0000  0.000     14.000000  23707.000000   \n",
       "24984  1943.0   1.105629   0.000   0.0000  0.000      0.000000  24775.000000   \n",
       "24985  1691.0   0.166300   1.979   0.0000  0.000      0.000000   9591.000000   \n",
       "24986  1681.0   0.173900   9.006   0.0000  6.568      0.000000   3004.000000   \n",
       "24987  1723.0   1.057000   0.285   0.0000  0.000    248.000000  28479.000000   \n",
       "24988  1693.0   1.214500   0.000   0.0000  0.000     18.000000    100.000000   \n",
       "24989  1592.0   6.480500   5.250   0.0000  1.204   1606.702372      0.000000   \n",
       "24990  1641.0   0.660100   8.614   0.0000  0.000   1606.702372      0.000000   \n",
       "24991  1740.0   0.390100   4.322   0.0000  0.000    490.000000  29460.000000   \n",
       "24992  1651.0   2.490900   1.335   0.9108  0.000      4.000000   3163.000000   \n",
       "24993  1729.0   1.360100   0.218   0.0000  0.000      0.000000   5675.000000   \n",
       "24994  1730.0   1.670100   5.554   0.0000  0.000    104.000000   5692.000000   \n",
       "24995  1715.0  12.000200   0.000   0.0000  0.000   1606.702372    991.000000   \n",
       "24996  1800.0   0.055100   0.000   0.0000  0.000   5946.000000  14867.000000   \n",
       "24997  1686.0   1.330100   2.025   0.0000  2.025   1606.702372      0.000000   \n",
       "24998  1826.0   1.105629   0.428   0.0000  0.000   3568.000000  26277.000000   \n",
       "24999  1916.0   0.139400   0.000   0.0000  0.000  18889.000000  18889.000000   \n",
       "\n",
       "             mvar8          mvar9    mvar10   ...     var35     var36  \\\n",
       "0        206.00000   14221.000000   19438.0   ...    0.1878  0.190171   \n",
       "1      19820.00000  176895.000000   66346.0   ...    0.1878  0.190171   \n",
       "2        877.00000    8869.000000    3637.0   ...    0.2623  0.190171   \n",
       "3       6773.68666   34097.436013       0.0   ...    0.2623  0.163769   \n",
       "4        179.00000   21059.000000   20306.0   ...    0.2623  0.395310   \n",
       "5      10976.00000  278941.000000   74030.0   ...    0.2623  0.370433   \n",
       "6       3568.00000   69482.000000   56755.0   ...    0.1878  0.296529   \n",
       "7      17115.00000  124814.000000  124167.0   ...    0.1878  0.271086   \n",
       "8      19264.00000   59956.000000   26633.0   ...    0.1878  0.170170   \n",
       "9        582.00000   16429.000000    9197.0   ...    0.1878  0.247148   \n",
       "10      8673.00000    6739.000000    7388.0   ...    0.1878  0.163769   \n",
       "11       261.00000     297.000000     261.0   ...    0.1878  0.190171   \n",
       "12      7620.00000   64983.000000   14067.0   ...    0.1878  0.323132   \n",
       "13      6773.68666     198.000000      18.0   ...    0.2623  0.370433   \n",
       "14      2973.00000   22099.000000   13507.0   ...    0.1878  0.309190   \n",
       "15      6773.68666   34097.436013       0.0   ...    0.1878  0.190171   \n",
       "16     13874.00000   98011.000000   56200.0   ...    0.1878  0.190171   \n",
       "17      3964.00000    7928.000000   26564.0   ...    0.1878  0.190171   \n",
       "18      2478.00000    8790.000000    6065.0   ...    0.5137  0.176674   \n",
       "19     10905.00000   51037.000000   58544.0   ...    0.2623  0.271086   \n",
       "20     12525.00000   18932.000000   14602.0   ...    0.1878  0.247148   \n",
       "21        57.00000     396.000000    2582.0   ...    0.1878  0.190171   \n",
       "22     24739.00000  128435.000000  125679.0   ...    0.1878  0.226932   \n",
       "23      2323.00000    9018.000000    9649.0   ...    0.1878  0.226932   \n",
       "24        46.00000     892.000000    1235.0   ...    0.1878  0.226932   \n",
       "25     14819.00000  116154.000000  100684.0   ...    0.1878  0.309190   \n",
       "26      6201.00000   66477.000000   34053.0   ...    0.1878  0.425926   \n",
       "27      9815.00000  110001.000000   69683.0   ...    0.2623  0.296529   \n",
       "28         0.00000    6566.000000    4068.0   ...    0.5137  0.271086   \n",
       "29      1189.00000     892.000000    2065.0   ...    0.1878  0.163769   \n",
       "...            ...            ...       ...   ...       ...       ...   \n",
       "24970   1524.00000   16748.000000   25359.0   ...    0.1878  0.309190   \n",
       "24971   7505.00000   44843.000000   47453.0   ...    0.1878  0.211005   \n",
       "24972    720.00000    5896.000000    5206.0   ...    0.1878  0.309190   \n",
       "24973   6773.68666   34097.436013    6290.0   ...    0.2623  0.211005   \n",
       "24974    297.00000    1041.000000    6960.0   ...    0.1878  0.464700   \n",
       "24975  15162.00000   68181.000000   43334.0   ...    0.1878  0.271086   \n",
       "24976   2234.00000    2676.000000    6539.0   ...    0.1878  0.271086   \n",
       "24977  17838.00000    8424.000000   22307.0   ...    0.1878  0.247148   \n",
       "24978     98.00000     546.000000     643.0   ...    0.1878  0.211005   \n",
       "24979   6773.68666    3032.000000   10489.0   ...    0.2623  0.211005   \n",
       "24980  22656.00000  168999.000000  166720.0   ...    0.1878  0.338150   \n",
       "24981  13297.00000    3914.000000    1305.0   ...    0.1878  0.170170   \n",
       "24982   6773.68666    3330.000000       0.0   ...    0.2623  0.395310   \n",
       "24983  23707.00000   33432.000000   19640.0   ...    0.1878  0.352103   \n",
       "24984  24775.00000   80772.000000   69483.0   ...    0.2623  0.211005   \n",
       "24985   2627.00000   46903.000000   30402.0   ...    0.5137  0.352103   \n",
       "24986   2251.00000    6818.000000    7783.0   ...    0.2623  0.296529   \n",
       "24987  28479.00000   16352.000000    3117.0   ...    0.5137  0.211005   \n",
       "24988    100.00000     991.000000     118.0   ...    0.1878  0.190171   \n",
       "24989   6773.68666   34097.436013       0.0   ...    0.2623  0.226932   \n",
       "24990   6773.68666   34097.436013       0.0   ...    0.2623  0.211005   \n",
       "24991   9449.00000   42047.000000   31583.0   ...    0.1878  0.190171   \n",
       "24992      4.00000    1982.000000    3167.0   ...    0.1878  0.309190   \n",
       "24993    167.00000   15071.000000   10305.0   ...    0.5137  0.247148   \n",
       "24994    496.00000    4410.000000    8125.0   ...    0.5137  0.211005   \n",
       "24995   6773.68666    8751.000000    1814.0   ...    0.2623  0.176674   \n",
       "24996   9613.00000   46490.000000   44722.0   ...    0.1878  0.170170   \n",
       "24997   6773.68666    4582.000000       0.0   ...    0.2623  0.211005   \n",
       "24998   9058.00000   72739.000000   86797.0   ...    0.2623  0.271086   \n",
       "24999  18889.00000   28343.000000   51174.0   ...    0.2623  0.170170   \n",
       "\n",
       "          var37     var38  var363738   var39   var40   var41   var45   var46  \n",
       "0      0.090400  0.176224  16.500000  0.2109  0.2199  0.1610  0.3281  0.2010  \n",
       "1      0.090400  0.232083   9.000000  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "2      0.159852  0.176224   6.000000  0.4390  0.2199  0.2231  0.1268  0.2010  \n",
       "3      0.381028  0.176224   0.000000  0.4390  0.2199  0.2526  0.3281  0.2968  \n",
       "4      0.381028  0.232083   0.000000  0.4390  0.3750  0.2231  0.1268  0.2010  \n",
       "5      0.090400  0.252492  53.200000  0.4390  0.2199  0.2231  0.1268  0.2010  \n",
       "6      0.102641  0.297332  10.285714  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "7      0.090400  0.342270   7.000000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "8      0.186923  0.176224   3.000000  0.2109  0.2199  0.2526  0.1268  0.2968  \n",
       "9      0.309995  0.342270   0.600000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "10     0.239528  0.200382   0.666667  0.2109  0.2199  0.2526  0.1268  0.2968  \n",
       "11     0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "12     0.090400  0.390700  15.000000  0.2109  0.2199  0.3000  0.3281  0.2010  \n",
       "13     0.381028  0.298409   0.000000  0.4390  0.3750  0.3000  0.3481  0.4979  \n",
       "14     0.239528  0.275282   3.000000  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "15     0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "16     0.090400  0.252492   6.600000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "17     0.309995  0.297332   0.428571  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "18     0.381028  0.176224   7.256161  0.4925  0.2199  0.2526  0.3281  0.2968  \n",
       "19     0.131449  0.232083  10.500000  0.4390  0.2199  0.1610  0.1268  0.2010  \n",
       "20     0.381028  0.298409   0.000000  0.2109  0.2199  0.3000  0.1268  0.2010  \n",
       "21     0.309995  0.297332   0.428571  0.2109  0.2199  0.3000  0.3281  0.2010  \n",
       "22     0.090400  0.200382  16.666667  0.2109  0.2199  0.3000  0.1268  0.2010  \n",
       "23     0.309995  0.328731   0.555556  0.2109  0.2199  0.2231  0.3281  0.2010  \n",
       "24     0.381028  0.275282   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "25     0.118592  0.232083  15.750000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "26     0.144850  0.390700   7.307692  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "27     0.102641  0.232083  18.000000  0.4390  0.2199  0.2231  0.1268  0.2010  \n",
       "28     0.186923  0.390700   1.400000  0.4925  0.2199  0.2526  0.3281  0.4979  \n",
       "29     0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "...         ...       ...        ...     ...     ...     ...     ...     ...  \n",
       "24970  0.381028  0.298409   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "24971  0.159852  0.200382   5.333333  0.2109  0.2199  0.3000  0.1268  0.2010  \n",
       "24972  0.309995  0.297332   1.285714  0.2109  0.3750  0.3000  0.3281  0.2010  \n",
       "24973  0.458344  0.176224   7.256161  0.4390  0.2199  0.1610  0.3281  0.2010  \n",
       "24974  0.381028  0.390700   0.000000  0.2109  0.3750  0.2526  0.3281  0.4979  \n",
       "24975  0.102641  0.200382  21.000000  0.2109  0.3750  0.1610  0.3281  0.2010  \n",
       "24976  0.186923  0.252492   4.200000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "24977  0.144850  0.275282   5.000000  0.2109  0.3750  0.2231  0.1268  0.2010  \n",
       "24978  0.381028  0.298409   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "24979  0.381028  0.232083   0.000000  0.4390  0.3750  0.1610  0.3281  0.2010  \n",
       "24980  0.090400  0.200382  40.000000  0.2109  0.2199  0.2526  0.1268  0.2968  \n",
       "24981  0.239528  0.275282   0.666667  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "24982  0.309995  0.328731   1.888889  0.4390  0.2199  0.2526  0.3481  0.4979  \n",
       "24983  0.090400  0.390700  11.000000  0.2109  0.2199  0.2231  0.3281  0.2010  \n",
       "24984  0.159852  0.145566  16.000000  0.4390  0.2199  0.2526  0.1268  0.2968  \n",
       "24985  0.090400  0.275282  28.166667  0.4925  0.3750  0.2231  0.3281  0.2010  \n",
       "24986  0.159852  0.176224  16.000000  0.4390  0.3750  0.2526  0.1268  0.2010  \n",
       "24987  0.309995  0.298409   0.500000  0.4925  0.2199  0.2526  0.3281  0.2010  \n",
       "24988  0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "24989  0.458344  0.232083   7.256161  0.4390  0.3750  0.2526  0.3281  0.2968  \n",
       "24990  0.458344  0.232083   7.256161  0.4390  0.3750  0.2526  0.3281  0.2968  \n",
       "24991  0.102641  0.232083   6.750000  0.2109  0.2199  0.2526  0.3481  0.2010  \n",
       "24992  0.381028  0.390700   0.000000  0.2109  0.2199  0.2231  0.3281  0.2010  \n",
       "24993  0.309995  0.275282   1.000000  0.4925  0.2199  0.2526  0.3281  0.2010  \n",
       "24994  0.239528  0.275282   1.333333  0.4925  0.2199  0.2231  0.3281  0.2010  \n",
       "24995  0.309995  0.176224   7.256161  0.4390  0.2199  0.2526  0.3281  0.2010  \n",
       "24996  0.309995  0.252492   0.400000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "24997  0.144850  0.145566  20.000000  0.4390  0.3750  0.2526  0.3281  0.2968  \n",
       "24998  0.102641  0.200382  21.000000  0.4390  0.2199  0.1610  0.3281  0.2010  \n",
       "24999  0.159852  0.145566   8.000000  0.4390  0.2199  0.2526  0.1268  0.2010  \n",
       "\n",
       "[25000 rows x 66 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values median\n",
    "gameX.fillna(gameX.mean(),inplace = True)\n",
    "leaderX.fillna(gameX.mean(),inplace = True)\n",
    "\n",
    "# gameX.fillna(0,inplace = True)\n",
    "# leaderX.fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#BREAK OPEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Method 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running cross validation\n",
      "running cross validation, with preprocessing function\n",
      "running cross validation, with cutomsized loss function\n",
      "[0]\ttrain-error:0.753787\ttest-error:0.753787\n",
      "Multiple eval metrics have been passed: 'test-error' will be used for early stopping.\n",
      "\n",
      "Will train until test-error hasn't improved in 50 rounds.\n",
      "[1]\ttrain-error:0.753787\ttest-error:0.753787\n",
      "[2]\ttrain-error:0.36955\ttest-error:0.371713\n",
      "[3]\ttrain-error:0.284816\ttest-error:0.286575\n",
      "[4]\ttrain-error:0.245163\ttest-error:0.247437\n",
      "[5]\ttrain-error:0.212703\ttest-error:0.215738\n",
      "[6]\ttrain-error:0.207472\ttest-error:0.21025\n",
      "[7]\ttrain-error:0.204878\ttest-error:0.207387\n",
      "[8]\ttrain-error:0.203591\ttest-error:0.205487\n",
      "[9]\ttrain-error:0.202441\ttest-error:0.204887\n",
      "[10]\ttrain-error:0.201412\ttest-error:0.203575\n",
      "[11]\ttrain-error:0.200553\ttest-error:0.2029\n",
      "[12]\ttrain-error:0.200172\ttest-error:0.202337\n",
      "[13]\ttrain-error:0.200022\ttest-error:0.201687\n",
      "[14]\ttrain-error:0.199413\ttest-error:0.201137\n",
      "[15]\ttrain-error:0.199253\ttest-error:0.201187\n",
      "[16]\ttrain-error:0.198778\ttest-error:0.200388\n",
      "[17]\ttrain-error:0.198515\ttest-error:0.200462\n",
      "[18]\ttrain-error:0.198319\ttest-error:0.2005\n",
      "[19]\ttrain-error:0.197688\ttest-error:0.200187\n",
      "[20]\ttrain-error:0.197438\ttest-error:0.199825\n",
      "[21]\ttrain-error:0.197147\ttest-error:0.199775\n",
      "[22]\ttrain-error:0.196863\ttest-error:0.199413\n",
      "[23]\ttrain-error:0.196853\ttest-error:0.199225\n",
      "[24]\ttrain-error:0.196344\ttest-error:0.198788\n",
      "[25]\ttrain-error:0.195819\ttest-error:0.198837\n",
      "[26]\ttrain-error:0.195425\ttest-error:0.1983\n",
      "[27]\ttrain-error:0.195206\ttest-error:0.198525\n",
      "[28]\ttrain-error:0.194906\ttest-error:0.198187\n",
      "[29]\ttrain-error:0.194647\ttest-error:0.198175\n",
      "[30]\ttrain-error:0.194316\ttest-error:0.19785\n",
      "[31]\ttrain-error:0.194041\ttest-error:0.1978\n",
      "[32]\ttrain-error:0.193688\ttest-error:0.197612\n",
      "[33]\ttrain-error:0.193378\ttest-error:0.197475\n",
      "[34]\ttrain-error:0.193112\ttest-error:0.197288\n",
      "[35]\ttrain-error:0.19285\ttest-error:0.197012\n",
      "[36]\ttrain-error:0.192606\ttest-error:0.196662\n",
      "[37]\ttrain-error:0.192322\ttest-error:0.196538\n",
      "[38]\ttrain-error:0.192019\ttest-error:0.196388\n",
      "[39]\ttrain-error:0.191709\ttest-error:0.195988\n",
      "[40]\ttrain-error:0.191391\ttest-error:0.195862\n",
      "[41]\ttrain-error:0.191275\ttest-error:0.19595\n",
      "[42]\ttrain-error:0.190988\ttest-error:0.195825\n",
      "[43]\ttrain-error:0.190691\ttest-error:0.195863\n",
      "[44]\ttrain-error:0.190369\ttest-error:0.195663\n",
      "[45]\ttrain-error:0.190266\ttest-error:0.19505\n",
      "[46]\ttrain-error:0.189866\ttest-error:0.194925\n",
      "[47]\ttrain-error:0.189625\ttest-error:0.1947\n",
      "[48]\ttrain-error:0.189463\ttest-error:0.194938\n",
      "[49]\ttrain-error:0.1892\ttest-error:0.19465\n",
      "[50]\ttrain-error:0.188944\ttest-error:0.194512\n",
      "[51]\ttrain-error:0.188787\ttest-error:0.19455\n",
      "[52]\ttrain-error:0.188581\ttest-error:0.194538\n",
      "[53]\ttrain-error:0.188428\ttest-error:0.194462\n",
      "[54]\ttrain-error:0.188128\ttest-error:0.194188\n",
      "[55]\ttrain-error:0.187922\ttest-error:0.1942\n",
      "[56]\ttrain-error:0.1878\ttest-error:0.194\n",
      "[57]\ttrain-error:0.187612\ttest-error:0.193962\n",
      "[58]\ttrain-error:0.187447\ttest-error:0.193912\n",
      "[59]\ttrain-error:0.187297\ttest-error:0.193925\n",
      "[60]\ttrain-error:0.187066\ttest-error:0.194037\n",
      "[61]\ttrain-error:0.186881\ttest-error:0.193737\n",
      "[62]\ttrain-error:0.186863\ttest-error:0.19375\n",
      "[63]\ttrain-error:0.18671\ttest-error:0.193587\n",
      "[64]\ttrain-error:0.186362\ttest-error:0.1935\n",
      "[65]\ttrain-error:0.18614\ttest-error:0.19325\n",
      "[66]\ttrain-error:0.185922\ttest-error:0.19335\n",
      "[67]\ttrain-error:0.185734\ttest-error:0.193125\n",
      "[68]\ttrain-error:0.185575\ttest-error:0.193125\n",
      "[69]\ttrain-error:0.185372\ttest-error:0.19315\n",
      "[70]\ttrain-error:0.185284\ttest-error:0.193213\n",
      "[71]\ttrain-error:0.1852\ttest-error:0.19315\n",
      "[72]\ttrain-error:0.185016\ttest-error:0.193125\n",
      "[73]\ttrain-error:0.184897\ttest-error:0.193038\n",
      "[74]\ttrain-error:0.184619\ttest-error:0.192988\n",
      "[75]\ttrain-error:0.184572\ttest-error:0.192813\n",
      "[76]\ttrain-error:0.184466\ttest-error:0.192588\n",
      "[77]\ttrain-error:0.184359\ttest-error:0.192513\n",
      "[78]\ttrain-error:0.184128\ttest-error:0.19265\n",
      "[79]\ttrain-error:0.184078\ttest-error:0.192675\n",
      "[80]\ttrain-error:0.183966\ttest-error:0.192775\n",
      "[81]\ttrain-error:0.183878\ttest-error:0.1928\n",
      "[82]\ttrain-error:0.183659\ttest-error:0.19265\n",
      "[83]\ttrain-error:0.183588\ttest-error:0.1925\n",
      "[84]\ttrain-error:0.183472\ttest-error:0.192487\n",
      "[85]\ttrain-error:0.183409\ttest-error:0.192225\n",
      "[86]\ttrain-error:0.183397\ttest-error:0.1922\n",
      "[87]\ttrain-error:0.183262\ttest-error:0.192287\n",
      "[88]\ttrain-error:0.183122\ttest-error:0.19225\n",
      "[89]\ttrain-error:0.1829\ttest-error:0.191988\n",
      "[90]\ttrain-error:0.182722\ttest-error:0.191837\n",
      "[91]\ttrain-error:0.182594\ttest-error:0.191887\n",
      "[92]\ttrain-error:0.182612\ttest-error:0.191725\n",
      "[93]\ttrain-error:0.182456\ttest-error:0.191725\n",
      "[94]\ttrain-error:0.182266\ttest-error:0.191725\n",
      "[95]\ttrain-error:0.182228\ttest-error:0.191687\n",
      "[96]\ttrain-error:0.182222\ttest-error:0.191775\n",
      "[97]\ttrain-error:0.182106\ttest-error:0.19175\n",
      "[98]\ttrain-error:0.181978\ttest-error:0.191787\n",
      "[99]\ttrain-error:0.181934\ttest-error:0.1919\n",
      "[100]\ttrain-error:0.181894\ttest-error:0.191862\n",
      "[101]\ttrain-error:0.181734\ttest-error:0.191962\n",
      "[102]\ttrain-error:0.1816\ttest-error:0.191937\n",
      "[103]\ttrain-error:0.181522\ttest-error:0.191938\n",
      "[104]\ttrain-error:0.181269\ttest-error:0.191888\n",
      "[105]\ttrain-error:0.181159\ttest-error:0.191475\n",
      "[106]\ttrain-error:0.181125\ttest-error:0.191563\n",
      "[107]\ttrain-error:0.181078\ttest-error:0.191738\n",
      "[108]\ttrain-error:0.180972\ttest-error:0.191512\n",
      "[109]\ttrain-error:0.180788\ttest-error:0.191438\n",
      "[110]\ttrain-error:0.180784\ttest-error:0.191313\n",
      "[111]\ttrain-error:0.180606\ttest-error:0.191275\n",
      "[112]\ttrain-error:0.180581\ttest-error:0.19125\n",
      "[113]\ttrain-error:0.180444\ttest-error:0.191325\n",
      "[114]\ttrain-error:0.180397\ttest-error:0.191362\n",
      "[115]\ttrain-error:0.180259\ttest-error:0.191325\n",
      "[116]\ttrain-error:0.180163\ttest-error:0.191175\n",
      "[117]\ttrain-error:0.180053\ttest-error:0.19115\n",
      "[118]\ttrain-error:0.179981\ttest-error:0.191112\n",
      "[119]\ttrain-error:0.179956\ttest-error:0.1913\n",
      "[120]\ttrain-error:0.179912\ttest-error:0.191137\n",
      "[121]\ttrain-error:0.179865\ttest-error:0.191187\n",
      "[122]\ttrain-error:0.179788\ttest-error:0.191262\n",
      "[123]\ttrain-error:0.179719\ttest-error:0.191313\n",
      "[124]\ttrain-error:0.179653\ttest-error:0.191388\n",
      "[125]\ttrain-error:0.179584\ttest-error:0.191288\n",
      "[126]\ttrain-error:0.179547\ttest-error:0.191238\n",
      "[127]\ttrain-error:0.179406\ttest-error:0.191338\n",
      "[128]\ttrain-error:0.179312\ttest-error:0.191325\n",
      "[129]\ttrain-error:0.179169\ttest-error:0.191312\n",
      "[130]\ttrain-error:0.179013\ttest-error:0.191225\n",
      "[131]\ttrain-error:0.178928\ttest-error:0.191175\n",
      "[132]\ttrain-error:0.178897\ttest-error:0.191062\n",
      "[133]\ttrain-error:0.178881\ttest-error:0.191012\n",
      "[134]\ttrain-error:0.178756\ttest-error:0.190962\n",
      "[135]\ttrain-error:0.178769\ttest-error:0.191025\n",
      "[136]\ttrain-error:0.178647\ttest-error:0.191075\n",
      "[137]\ttrain-error:0.178478\ttest-error:0.191113\n",
      "[138]\ttrain-error:0.178428\ttest-error:0.190987\n",
      "[139]\ttrain-error:0.178409\ttest-error:0.190988\n",
      "[140]\ttrain-error:0.1784\ttest-error:0.190975\n",
      "[141]\ttrain-error:0.178256\ttest-error:0.19105\n",
      "[142]\ttrain-error:0.178181\ttest-error:0.190962\n",
      "[143]\ttrain-error:0.178125\ttest-error:0.190887\n",
      "[144]\ttrain-error:0.178047\ttest-error:0.191063\n",
      "[145]\ttrain-error:0.177925\ttest-error:0.191138\n",
      "[146]\ttrain-error:0.177844\ttest-error:0.191038\n",
      "[147]\ttrain-error:0.177644\ttest-error:0.19105\n",
      "[148]\ttrain-error:0.177641\ttest-error:0.190963\n",
      "[149]\ttrain-error:0.177537\ttest-error:0.190963\n",
      "[150]\ttrain-error:0.177535\ttest-error:0.1911\n",
      "[151]\ttrain-error:0.17755\ttest-error:0.191175\n",
      "[152]\ttrain-error:0.177475\ttest-error:0.191112\n",
      "[153]\ttrain-error:0.177372\ttest-error:0.191037\n",
      "[154]\ttrain-error:0.177262\ttest-error:0.190875\n",
      "[155]\ttrain-error:0.177187\ttest-error:0.190925\n",
      "[156]\ttrain-error:0.177147\ttest-error:0.190813\n",
      "[157]\ttrain-error:0.177009\ttest-error:0.19065\n",
      "[158]\ttrain-error:0.176916\ttest-error:0.190638\n",
      "[159]\ttrain-error:0.176828\ttest-error:0.190738\n",
      "[160]\ttrain-error:0.176778\ttest-error:0.190812\n",
      "[161]\ttrain-error:0.176687\ttest-error:0.190837\n",
      "[162]\ttrain-error:0.176528\ttest-error:0.190725\n",
      "[163]\ttrain-error:0.17655\ttest-error:0.1907\n",
      "[164]\ttrain-error:0.176472\ttest-error:0.190663\n",
      "[165]\ttrain-error:0.176325\ttest-error:0.19055\n",
      "[166]\ttrain-error:0.176206\ttest-error:0.190688\n",
      "[167]\ttrain-error:0.176162\ttest-error:0.190812\n",
      "[168]\ttrain-error:0.175988\ttest-error:0.190713\n",
      "[169]\ttrain-error:0.175953\ttest-error:0.190763\n",
      "[170]\ttrain-error:0.175866\ttest-error:0.190775\n",
      "[171]\ttrain-error:0.175831\ttest-error:0.190625\n",
      "[172]\ttrain-error:0.175813\ttest-error:0.1907\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[173]\ttrain-error:0.175697\ttest-error:0.190688\n",
      "[174]\ttrain-error:0.175644\ttest-error:0.190588\n",
      "[175]\ttrain-error:0.175528\ttest-error:0.190725\n",
      "[176]\ttrain-error:0.175387\ttest-error:0.190775\n",
      "[177]\ttrain-error:0.175337\ttest-error:0.190787\n",
      "[178]\ttrain-error:0.175275\ttest-error:0.19095\n",
      "[179]\ttrain-error:0.175141\ttest-error:0.1908\n",
      "[180]\ttrain-error:0.175025\ttest-error:0.190787\n",
      "[181]\ttrain-error:0.174984\ttest-error:0.1909\n",
      "[182]\ttrain-error:0.175041\ttest-error:0.190862\n",
      "[183]\ttrain-error:0.174965\ttest-error:0.19085\n",
      "[184]\ttrain-error:0.174937\ttest-error:0.190712\n",
      "[185]\ttrain-error:0.174797\ttest-error:0.190825\n",
      "[186]\ttrain-error:0.174741\ttest-error:0.190938\n",
      "[187]\ttrain-error:0.174666\ttest-error:0.191\n",
      "[188]\ttrain-error:0.174609\ttest-error:0.190937\n",
      "[189]\ttrain-error:0.174613\ttest-error:0.190887\n",
      "[190]\ttrain-error:0.174525\ttest-error:0.190937\n",
      "[191]\ttrain-error:0.174428\ttest-error:0.191025\n",
      "[192]\ttrain-error:0.174303\ttest-error:0.1911\n",
      "[193]\ttrain-error:0.174294\ttest-error:0.19125\n",
      "[194]\ttrain-error:0.174194\ttest-error:0.191262\n",
      "[195]\ttrain-error:0.174088\ttest-error:0.191088\n",
      "[196]\ttrain-error:0.17395\ttest-error:0.191075\n",
      "[197]\ttrain-error:0.173934\ttest-error:0.191025\n",
      "[198]\ttrain-error:0.173869\ttest-error:0.1911\n",
      "[199]\ttrain-error:0.173747\ttest-error:0.191188\n",
      "[200]\ttrain-error:0.173697\ttest-error:0.190975\n",
      "[201]\ttrain-error:0.173569\ttest-error:0.191087\n",
      "[202]\ttrain-error:0.173425\ttest-error:0.19105\n",
      "[203]\ttrain-error:0.173381\ttest-error:0.191037\n",
      "[204]\ttrain-error:0.173272\ttest-error:0.190963\n",
      "[205]\ttrain-error:0.173244\ttest-error:0.190975\n",
      "[206]\ttrain-error:0.173156\ttest-error:0.190962\n",
      "[207]\ttrain-error:0.173159\ttest-error:0.191013\n",
      "[208]\ttrain-error:0.173025\ttest-error:0.19095\n",
      "[209]\ttrain-error:0.172928\ttest-error:0.190962\n",
      "[210]\ttrain-error:0.172878\ttest-error:0.191125\n",
      "[211]\ttrain-error:0.172819\ttest-error:0.191162\n",
      "[212]\ttrain-error:0.17265\ttest-error:0.191137\n",
      "[213]\ttrain-error:0.172644\ttest-error:0.191063\n",
      "[214]\ttrain-error:0.172625\ttest-error:0.191225\n",
      "[215]\ttrain-error:0.172635\ttest-error:0.19115\n",
      "Stopping. Best iteration:\n",
      "[165]\ttrain-error:0.176325+0.000166989\ttest-error:0.19055+0.00149102\n",
      "\n",
      "[0]\teval-error:0.753788\ttrain-error:0.753788\n",
      "[1]\teval-error:0.753788\ttrain-error:0.753788\n",
      "[2]\teval-error:0.371225\ttrain-error:0.371225\n",
      "[3]\teval-error:0.297088\ttrain-error:0.297088\n",
      "[4]\teval-error:0.224587\ttrain-error:0.224587\n",
      "[5]\teval-error:0.212413\ttrain-error:0.212413\n",
      "[6]\teval-error:0.210037\ttrain-error:0.210037\n",
      "[7]\teval-error:0.206838\ttrain-error:0.206838\n",
      "[8]\teval-error:0.202725\ttrain-error:0.202725\n",
      "[9]\teval-error:0.202487\ttrain-error:0.202487\n",
      "[10]\teval-error:0.201063\ttrain-error:0.201063\n",
      "[11]\teval-error:0.200412\ttrain-error:0.200412\n",
      "[12]\teval-error:0.199687\ttrain-error:0.199687\n",
      "[13]\teval-error:0.199738\ttrain-error:0.199738\n",
      "[14]\teval-error:0.1992\ttrain-error:0.1992\n",
      "[15]\teval-error:0.1979\ttrain-error:0.1979\n",
      "[16]\teval-error:0.198763\ttrain-error:0.198763\n",
      "[17]\teval-error:0.198662\ttrain-error:0.198662\n",
      "[18]\teval-error:0.198362\ttrain-error:0.198362\n",
      "[19]\teval-error:0.197963\ttrain-error:0.197963\n",
      "[20]\teval-error:0.197275\ttrain-error:0.197275\n",
      "[21]\teval-error:0.197162\ttrain-error:0.197162\n",
      "[22]\teval-error:0.196337\ttrain-error:0.196337\n",
      "[23]\teval-error:0.1969\ttrain-error:0.1969\n",
      "[24]\teval-error:0.196637\ttrain-error:0.196637\n",
      "[25]\teval-error:0.19595\ttrain-error:0.19595\n",
      "[26]\teval-error:0.195725\ttrain-error:0.195725\n",
      "[27]\teval-error:0.195775\ttrain-error:0.195775\n",
      "[28]\teval-error:0.195312\ttrain-error:0.195312\n",
      "[29]\teval-error:0.195175\ttrain-error:0.195175\n",
      "[30]\teval-error:0.194637\ttrain-error:0.194637\n",
      "[31]\teval-error:0.194125\ttrain-error:0.194125\n",
      "[32]\teval-error:0.1941\ttrain-error:0.1941\n",
      "[33]\teval-error:0.193862\ttrain-error:0.193862\n",
      "[34]\teval-error:0.193775\ttrain-error:0.193775\n",
      "[35]\teval-error:0.19335\ttrain-error:0.19335\n",
      "[36]\teval-error:0.193425\ttrain-error:0.193425\n",
      "[37]\teval-error:0.192925\ttrain-error:0.192925\n",
      "[38]\teval-error:0.192525\ttrain-error:0.192525\n",
      "[39]\teval-error:0.192225\ttrain-error:0.192225\n",
      "[40]\teval-error:0.191725\ttrain-error:0.191725\n",
      "[41]\teval-error:0.191337\ttrain-error:0.191337\n",
      "[42]\teval-error:0.191075\ttrain-error:0.191075\n",
      "[43]\teval-error:0.191\ttrain-error:0.191\n",
      "[44]\teval-error:0.19085\ttrain-error:0.19085\n",
      "[45]\teval-error:0.191025\ttrain-error:0.191025\n",
      "[46]\teval-error:0.190837\ttrain-error:0.190837\n",
      "[47]\teval-error:0.19015\ttrain-error:0.19015\n",
      "[48]\teval-error:0.1899\ttrain-error:0.1899\n",
      "[49]\teval-error:0.189525\ttrain-error:0.189525\n",
      "[50]\teval-error:0.189438\ttrain-error:0.189438\n",
      "[51]\teval-error:0.189125\ttrain-error:0.189125\n",
      "[52]\teval-error:0.188925\ttrain-error:0.188925\n",
      "[53]\teval-error:0.18885\ttrain-error:0.18885\n",
      "[54]\teval-error:0.188512\ttrain-error:0.188512\n",
      "[55]\teval-error:0.188112\ttrain-error:0.188112\n",
      "[56]\teval-error:0.187837\ttrain-error:0.187837\n",
      "[57]\teval-error:0.187663\ttrain-error:0.187663\n",
      "[58]\teval-error:0.187575\ttrain-error:0.187575\n",
      "[59]\teval-error:0.18735\ttrain-error:0.18735\n",
      "[60]\teval-error:0.1872\ttrain-error:0.1872\n",
      "[61]\teval-error:0.1869\ttrain-error:0.1869\n",
      "[62]\teval-error:0.186675\ttrain-error:0.186675\n",
      "[63]\teval-error:0.18665\ttrain-error:0.18665\n",
      "[64]\teval-error:0.186837\ttrain-error:0.186837\n",
      "[65]\teval-error:0.186738\ttrain-error:0.186738\n",
      "[66]\teval-error:0.186413\ttrain-error:0.186413\n",
      "[67]\teval-error:0.186238\ttrain-error:0.186238\n",
      "[68]\teval-error:0.1862\ttrain-error:0.1862\n",
      "[69]\teval-error:0.18615\ttrain-error:0.18615\n",
      "[70]\teval-error:0.186112\ttrain-error:0.186112\n",
      "[71]\teval-error:0.186125\ttrain-error:0.186125\n",
      "[72]\teval-error:0.186087\ttrain-error:0.186087\n",
      "[73]\teval-error:0.18585\ttrain-error:0.18585\n",
      "[74]\teval-error:0.185725\ttrain-error:0.185725\n",
      "[75]\teval-error:0.18565\ttrain-error:0.18565\n",
      "[76]\teval-error:0.185537\ttrain-error:0.185537\n",
      "[77]\teval-error:0.185425\ttrain-error:0.185425\n",
      "[78]\teval-error:0.1853\ttrain-error:0.1853\n",
      "[79]\teval-error:0.185375\ttrain-error:0.185375\n",
      "[80]\teval-error:0.185025\ttrain-error:0.185025\n",
      "[81]\teval-error:0.185087\ttrain-error:0.185087\n",
      "[82]\teval-error:0.184888\ttrain-error:0.184888\n",
      "[83]\teval-error:0.184762\ttrain-error:0.184762\n",
      "[84]\teval-error:0.184537\ttrain-error:0.184537\n",
      "[85]\teval-error:0.184337\ttrain-error:0.184337\n",
      "[86]\teval-error:0.1841\ttrain-error:0.1841\n",
      "[87]\teval-error:0.184138\ttrain-error:0.184138\n",
      "[88]\teval-error:0.183987\ttrain-error:0.183987\n",
      "[89]\teval-error:0.184062\ttrain-error:0.184062\n",
      "[90]\teval-error:0.184\ttrain-error:0.184\n",
      "[91]\teval-error:0.18385\ttrain-error:0.18385\n",
      "[92]\teval-error:0.1839\ttrain-error:0.1839\n",
      "[93]\teval-error:0.184062\ttrain-error:0.184062\n",
      "[94]\teval-error:0.1839\ttrain-error:0.1839\n",
      "[95]\teval-error:0.183562\ttrain-error:0.183562\n",
      "[96]\teval-error:0.183375\ttrain-error:0.183375\n",
      "[97]\teval-error:0.18335\ttrain-error:0.18335\n",
      "[98]\teval-error:0.183388\ttrain-error:0.183388\n",
      "[99]\teval-error:0.183025\ttrain-error:0.183025\n",
      "[100]\teval-error:0.183163\ttrain-error:0.183163\n",
      "[101]\teval-error:0.182875\ttrain-error:0.182875\n",
      "[102]\teval-error:0.182938\ttrain-error:0.182938\n",
      "[103]\teval-error:0.1829\ttrain-error:0.1829\n",
      "[104]\teval-error:0.182863\ttrain-error:0.182863\n",
      "[105]\teval-error:0.182825\ttrain-error:0.182825\n",
      "[106]\teval-error:0.182762\ttrain-error:0.182762\n",
      "[107]\teval-error:0.1828\ttrain-error:0.1828\n",
      "[108]\teval-error:0.182875\ttrain-error:0.182875\n",
      "[109]\teval-error:0.1828\ttrain-error:0.1828\n",
      "[110]\teval-error:0.182525\ttrain-error:0.182525\n",
      "[111]\teval-error:0.182588\ttrain-error:0.182588\n",
      "[112]\teval-error:0.18235\ttrain-error:0.18235\n",
      "[113]\teval-error:0.182312\ttrain-error:0.182312\n",
      "[114]\teval-error:0.1822\ttrain-error:0.1822\n",
      "[115]\teval-error:0.182188\ttrain-error:0.182188\n",
      "[116]\teval-error:0.182125\ttrain-error:0.182125\n",
      "[117]\teval-error:0.182225\ttrain-error:0.182225\n",
      "[118]\teval-error:0.182287\ttrain-error:0.182287\n",
      "[119]\teval-error:0.1822\ttrain-error:0.1822\n",
      "[120]\teval-error:0.181987\ttrain-error:0.181987\n",
      "[121]\teval-error:0.181775\ttrain-error:0.181775\n",
      "[122]\teval-error:0.181775\ttrain-error:0.181775\n",
      "[123]\teval-error:0.181838\ttrain-error:0.181838\n",
      "[124]\teval-error:0.181725\ttrain-error:0.181725\n",
      "[125]\teval-error:0.1815\ttrain-error:0.1815\n",
      "[126]\teval-error:0.181388\ttrain-error:0.181388\n",
      "[127]\teval-error:0.1813\ttrain-error:0.1813\n",
      "[128]\teval-error:0.1812\ttrain-error:0.1812\n",
      "[129]\teval-error:0.181287\ttrain-error:0.181287\n",
      "[130]\teval-error:0.181312\ttrain-error:0.181312\n",
      "[131]\teval-error:0.181262\ttrain-error:0.181262\n",
      "[132]\teval-error:0.18095\ttrain-error:0.18095\n",
      "[133]\teval-error:0.1808\ttrain-error:0.1808\n",
      "[134]\teval-error:0.180875\ttrain-error:0.180875\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[135]\teval-error:0.18075\ttrain-error:0.18075\n",
      "[136]\teval-error:0.180613\ttrain-error:0.180613\n",
      "[137]\teval-error:0.180537\ttrain-error:0.180537\n",
      "[138]\teval-error:0.180663\ttrain-error:0.180663\n",
      "[139]\teval-error:0.180537\ttrain-error:0.180537\n",
      "[140]\teval-error:0.18045\ttrain-error:0.18045\n",
      "[141]\teval-error:0.180537\ttrain-error:0.180537\n",
      "[142]\teval-error:0.180512\ttrain-error:0.180512\n",
      "[143]\teval-error:0.18045\ttrain-error:0.18045\n",
      "[144]\teval-error:0.180537\ttrain-error:0.180537\n",
      "[145]\teval-error:0.1804\ttrain-error:0.1804\n",
      "[146]\teval-error:0.18035\ttrain-error:0.18035\n",
      "[147]\teval-error:0.180388\ttrain-error:0.180388\n",
      "[148]\teval-error:0.1804\ttrain-error:0.1804\n",
      "[149]\teval-error:0.18025\ttrain-error:0.18025\n",
      "[150]\teval-error:0.18015\ttrain-error:0.18015\n",
      "[151]\teval-error:0.180025\ttrain-error:0.180025\n",
      "[152]\teval-error:0.179838\ttrain-error:0.179838\n",
      "[153]\teval-error:0.179762\ttrain-error:0.179762\n",
      "[154]\teval-error:0.179613\ttrain-error:0.179613\n",
      "[155]\teval-error:0.17965\ttrain-error:0.17965\n",
      "[156]\teval-error:0.179675\ttrain-error:0.179675\n",
      "[157]\teval-error:0.179512\ttrain-error:0.179512\n",
      "[158]\teval-error:0.1794\ttrain-error:0.1794\n",
      "[159]\teval-error:0.179338\ttrain-error:0.179338\n",
      "[160]\teval-error:0.179237\ttrain-error:0.179237\n",
      "[161]\teval-error:0.179163\ttrain-error:0.179163\n",
      "[162]\teval-error:0.178975\ttrain-error:0.178975\n",
      "[163]\teval-error:0.178925\ttrain-error:0.178925\n",
      "[164]\teval-error:0.178875\ttrain-error:0.178875\n",
      "[165]\teval-error:0.178875\ttrain-error:0.178875\n",
      "time taken: 405.32858872413635\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "import time\n",
    "\n",
    "\n",
    "dtrain = xgb.DMatrix(gameX, gameY)\n",
    "num_round = 1000\n",
    "early_stopping_rounds = 50\n",
    "n_fold = 5\n",
    "watchlist = [(dtrain, 'eval'), (dtrain, 'train')]\n",
    "\n",
    "print('running cross validation')\n",
    "\n",
    "print('running cross validation, with preprocessing function')\n",
    "# define the preprocessing function\n",
    "# used to return the preprocessed training, test data, and parameter\n",
    "# we can use this to do weight rescale, etc.\n",
    "# as a example, we try to set scale_pos_weight\n",
    "def fpreproc(dtrain, dtest, param):\n",
    "    label = dtrain.get_label()\n",
    "    ratio = float(np.sum(label == 0)) / np.sum(label == 1)\n",
    "    param['scale_pos_weight'] = ratio\n",
    "    return (dtrain, dtest, param)\n",
    "\n",
    "# do cross validation, for each fold\n",
    "# the dtrain, dtest, param will be passed into fpreproc\n",
    "# then the return value of fpreproc will be used to generate\n",
    "# results of that fold\n",
    "\n",
    "###\n",
    "# you can also do cross validation with customized loss function\n",
    "# See custom_objective.py\n",
    "##\n",
    "print('running cross validation, with cutomsized loss function')\n",
    "#Custom\n",
    "# wt = 0.5\n",
    "# def logregobj(preds, dtrain):\n",
    "#     labels = dtrain.get_label()\n",
    "#     preds = 1.0 / (1.0 + np.exp(-preds))\n",
    "#     grad = wt*(1.0-labels)*preds - (1-wt)*labels*(1-preds)\n",
    "#     hess = preds * (1.0 - preds) * (wt*(1.0-labels)+(1.0-wt)*labels)\n",
    "#     return grad, hess\n",
    "\n",
    "def logregobj(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    preds = 1.0 / (1.0 + np.exp(-preds))\n",
    "    grad = preds - labels\n",
    "    hess = preds * (1.0 - preds)\n",
    "    return grad, hess\n",
    "def evalerror(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)\n",
    "\n",
    "param = {\n",
    "#     'max_depth': 3,#3 \n",
    "    'eta': 0.1, #0.1\n",
    "    'silent': 1, \n",
    "    'gamma': 0, \n",
    "    'max_depth': 4, #4\n",
    "    'min_child_weight': 1, #1\n",
    "    'subsample': 0.8, #0.8\n",
    "    'colsample_bytree': 0.8, #0,8\n",
    "    'reg_lambda': 1, #L2 reg default:1\n",
    "    'reg_alpha': 0.05, #L1 reg default:0\n",
    "    'scale_pos_weight': 1, \n",
    "#     'missing': -999\n",
    "#     'objective': 'binary:logistic', #for custom loss define it in xgb.train(obj=custom_loss)\n",
    "#     'eval_metric': 'error'\n",
    "}\n",
    "\n",
    "t = time.time()\n",
    "# CV to find optimum number of boosted trees (num_round)\n",
    "cvresult = xgb.cv(\n",
    "            param, #param set by user\n",
    "            dtrain, #train set\n",
    "            num_round, #max number of boosted tree, >early_stopping_round\n",
    "            nfold=n_fold, #n fold CV\n",
    "            seed=0, \n",
    "            obj=logregobj, #custom loss func\n",
    "            feval=evalerror, #custom eval metric\n",
    "            callbacks=[xgb.callback.print_evaluation(show_stdv=False),\n",
    "                       xgb.callback.early_stop(early_stopping_rounds)], #decides optimum number of boosted trees, <num_round\n",
    "#             fpreproc=fpreproc, #function for preprocessing\n",
    "            )\n",
    "\n",
    "num_round = cvresult.shape[0] #optimum number of boosted trees\n",
    "# bst = xgb.train(param, dtrain, num_round, watchlist, obj=logregobj, feval=evalerror, fpreproc=fpreproc)\n",
    "bst = xgb.train(param, dtrain, num_round, watchlist, obj=logregobj, feval=evalerror)\n",
    "\n",
    "print(\"time taken:\", time.time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#910k\n",
    "import xgboost as xgb\n",
    "import time\n",
    "\n",
    "\n",
    "dtrain = xgb.DMatrix(gameX, gameY)\n",
    "num_round = 1000\n",
    "early_stopping_rounds = 50\n",
    "n_fold = 5\n",
    "watchlist = [(dtrain, 'eval'), (dtrain, 'train')]\n",
    "\n",
    "\n",
    "def logregobj(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    preds = 1.0 / (1.0 + np.exp(-preds))\n",
    "    grad = preds - labels\n",
    "    hess = preds * (1.0 - preds)\n",
    "    return grad, hess\n",
    "def evalerror(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)\n",
    "\n",
    "\n",
    "param = {\n",
    "#     'max_depth': 3,#3 \n",
    "    'eta': 0.1, #0.1\n",
    "    'silent': 1, \n",
    "    'gamma': 0, \n",
    "    'max_depth': 4, #4\n",
    "    'min_child_weight': 1.5, #1\n",
    "    'subsample': 0.8, #0.8\n",
    "    'colsample_bytree': 0.8, #0,8\n",
    "    'reg_lambda': 1, #L2 reg default:1\n",
    "    'reg_alpha': 0.05, #L1 reg default:0\n",
    "    'scale_pos_weight': 0.25, \n",
    "#     'missing': -999\n",
    "#     'objective': 'binary:logistic', #for custom loss define it in xgb.train(obj=custom_loss)\n",
    "#     'eval_metric': 'error'\n",
    "}\n",
    "\n",
    "num_round = 231\n",
    "\n",
    "bst = xgb.train(param, dtrain, num_round, obj=logregobj, feval=evalerror)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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9dIqLi5HEj3/8Y7Zs2cK3v/3tgx5bU1MTvXr1OujnyWfOgXMAzgHkbw5Gjhy5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\nKheLZmZmZpbq/wNyRNmG9NiluAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ae0016940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(bst, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iUi/cfvvtdO7cmYKCAiZNCu9sXX/99fz4xz+msLCQvn37smrVqixXKSJS+3Lh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\nF0lEQVRjGuyJiIiI5DEN9kRERETy2P8HZzvFaJhMld0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f30864a2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(bst, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvresult.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.80279839, -2.42240548, -1.00476098, ...,  1.03145206,\n",
       "       -1.72268009, -0.39294541], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bst.predict(dtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.30942723,  0.08148005,  0.26800638, ...,  0.73719734,\n",
       "        0.15152627,  0.40300846], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/(1+np.exp(-bst.predict(dtrain)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(leaderX)\n",
    "# pred = bst.predict(leaderXxgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = 1/(1+np.exp(-bst.predict(dtest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = pd.DataFrame(pred)\n",
    "pred['application_key'] = leaderAppID\n",
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>application_key</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.046387</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.056530</td>\n",
       "      <td>350054</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.163575</td>\n",
       "      <td>350055</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.636362</td>\n",
       "      <td>350056</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.163096</td>\n",
       "      <td>350057</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.013001</td>\n",
       "      <td>350058</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.066192</td>\n",
       "      <td>350059</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004022</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.005259</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.270663</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.076810</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.203587</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.388400</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.847309</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.318125</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.561309</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.015315</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.087622</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.171770</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.105407</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.240207</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.356124</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.015477</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.225674</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.350232</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.003914</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.103175</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.079744</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.867938</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.110399</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.142269</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.033684</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.482596</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.659822</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.698215</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.020151</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.218426</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.037009</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.381439</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.592445</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.008740</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.079155</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.745980</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.241216</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.018844</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.380456</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.199170</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.294925</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.473878</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.894031</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.465668</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.042409</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.558851</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.357524</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.235350</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.544646</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.022200</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.176715</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.038090</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.009513</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0  application_key\n",
       "0      0.046387           350053\n",
       "1      0.056530           350054\n",
       "2      0.163575           350055\n",
       "3      0.636362           350056\n",
       "4      0.163096           350057\n",
       "5      0.013001           350058\n",
       "6      0.066192           350059\n",
       "7      0.004022           350060\n",
       "8      0.005259           350061\n",
       "9      0.270663           350062\n",
       "10     0.076810           350063\n",
       "11     0.203587           350064\n",
       "12     0.388400           350065\n",
       "13     0.847309           350066\n",
       "14     0.318125           350067\n",
       "15     0.561309           350068\n",
       "16     0.015315           350069\n",
       "17     0.087622           350070\n",
       "18     0.171770           350071\n",
       "19     0.105407           350072\n",
       "20     0.240207           350073\n",
       "21     0.356124           350074\n",
       "22     0.015477           350075\n",
       "23     0.225674           350076\n",
       "24     0.350232           350077\n",
       "25     0.003914           350078\n",
       "26     0.103175           350079\n",
       "27     0.079744           350080\n",
       "28     0.867938           350081\n",
       "29     0.110399           350082\n",
       "...         ...              ...\n",
       "24970  0.142269           375023\n",
       "24971  0.033684           375024\n",
       "24972  0.482596           375025\n",
       "24973  0.659822           375026\n",
       "24974  0.698215           375027\n",
       "24975  0.020151           375028\n",
       "24976  0.218426           375029\n",
       "24977  0.037009           375030\n",
       "24978  0.381439           375031\n",
       "24979  0.592445           375032\n",
       "24980  0.008740           375033\n",
       "24981  0.079155           375034\n",
       "24982  0.745980           375035\n",
       "24983  0.241216           375036\n",
       "24984  0.018844           375037\n",
       "24985  0.380456           375038\n",
       "24986  0.199170           375039\n",
       "24987  0.294925           375040\n",
       "24988  0.473878           375041\n",
       "24989  0.894031           375042\n",
       "24990  0.465668           375043\n",
       "24991  0.042409           375044\n",
       "24992  0.558851           375045\n",
       "24993  0.357524           375046\n",
       "24994  0.235350           375047\n",
       "24995  0.544646           375048\n",
       "24996  0.022200           375049\n",
       "24997  0.176715           375050\n",
       "24998  0.038090           375051\n",
       "24999  0.009513           375052\n",
       "\n",
       "[25000 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pred.sort_values(by = 0,kind = 'mergesort',ascending = True) #true for xgb method3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = pred.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred = pred[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = pred.loc[:,'application_key':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit = pred['application_key'][:10000]\n",
    "submit = pd.DataFrame(submit)\n",
    "submit[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# submit.iloc[-400:, 1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>20330</th>\n",
       "      <td>370383</td>\n",
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       "    <tr>\n",
       "      <th>22865</th>\n",
       "      <td>372918</td>\n",
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       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>350246</td>\n",
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       "      <th>15480</th>\n",
       "      <td>365533</td>\n",
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       "    <tr>\n",
       "      <th>21270</th>\n",
       "      <td>371323</td>\n",
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       "      <th>23075</th>\n",
       "      <td>373128</td>\n",
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       "    <tr>\n",
       "      <th>12148</th>\n",
       "      <td>362201</td>\n",
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       "      <th>18842</th>\n",
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       "      <td>358575</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>353356</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17548</th>\n",
       "      <td>367601</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17277</th>\n",
       "      <td>367330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>353376</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>365666</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24172</th>\n",
       "      <td>374225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5119</th>\n",
       "      <td>355172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11074</th>\n",
       "      <td>361127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>350279</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23797</th>\n",
       "      <td>373850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>351965</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>352139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7061</th>\n",
       "      <td>357114</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24191</th>\n",
       "      <td>374244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9097</th>\n",
       "      <td>359150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24560</th>\n",
       "      <td>374613</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>351745</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6609</th>\n",
       "      <td>356662</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>350451</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19551</th>\n",
       "      <td>369604</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18568</th>\n",
       "      <td>368621</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7895</th>\n",
       "      <td>357948</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>363709</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13965</th>\n",
       "      <td>364018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>350591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>352143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24167</th>\n",
       "      <td>374220</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6599</th>\n",
       "      <td>356652</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19628</th>\n",
       "      <td>369681</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23590</th>\n",
       "      <td>373643</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24275</th>\n",
       "      <td>374328</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000</th>\n",
       "      <td>357053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>370689</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>350931</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20571</th>\n",
       "      <td>370624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12371</th>\n",
       "      <td>362424</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6882</th>\n",
       "      <td>356935</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9171</th>\n",
       "      <td>359224</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13511</th>\n",
       "      <td>363564</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10175</th>\n",
       "      <td>360228</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17746</th>\n",
       "      <td>367799</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11324</th>\n",
       "      <td>361377</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "22292           372345  0\n",
       "11416           361469  0\n",
       "3708            353761  0\n",
       "2760            352813  0\n",
       "10881           360934  0\n",
       "8922            358975  0\n",
       "8084            358137  0\n",
       "20330           370383  0\n",
       "22865           372918  0\n",
       "193             350246  0\n",
       "15480           365533  0\n",
       "21270           371323  0\n",
       "23075           373128  0\n",
       "12148           362201  0\n",
       "18842           368895  0\n",
       "17162           367215  0\n",
       "11521           361574  0\n",
       "8522            358575  0\n",
       "3303            353356  0\n",
       "17548           367601  0\n",
       "17277           367330  0\n",
       "3323            353376  0\n",
       "15613           365666  0\n",
       "24172           374225  0\n",
       "5119            355172  0\n",
       "11074           361127  0\n",
       "226             350279  0\n",
       "23797           373850  0\n",
       "1912            351965  0\n",
       "2086            352139  0\n",
       "...                ... ..\n",
       "7061            357114  0\n",
       "24191           374244  0\n",
       "9097            359150  0\n",
       "24560           374613  0\n",
       "1692            351745  0\n",
       "6609            356662  0\n",
       "398             350451  0\n",
       "19551           369604  0\n",
       "18568           368621  0\n",
       "7895            357948  0\n",
       "13656           363709  0\n",
       "13965           364018  0\n",
       "538             350591  0\n",
       "2090            352143  0\n",
       "24167           374220  0\n",
       "6599            356652  0\n",
       "19628           369681  0\n",
       "23590           373643  0\n",
       "24275           374328  0\n",
       "7000            357053  0\n",
       "20636           370689  0\n",
       "878             350931  0\n",
       "20571           370624  0\n",
       "12371           362424  0\n",
       "6882            356935  0\n",
       "9171            359224  0\n",
       "13511           363564  0\n",
       "10175           360228  0\n",
       "17746           367799  0\n",
       "11324           361377  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit.to_csv('flabbergasted_IITGuwahati_282.csv',header = False, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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