Two_Sigma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: h5py in /Users/chelimin/miniconda2/lib/python2.7/site-packages\r\n",
      "Requirement already satisfied: six in /Users/chelimin/miniconda2/lib/python2.7/site-packages (from h5py)\r\n",
      "Requirement already satisfied: numpy>=1.7 in /Users/chelimin/miniconda2/lib/python2.7/site-packages (from h5py)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'h5py'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-5834382587f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'h5py'"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File train.h5 does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-0b98de2820a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_hdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/chelimin/miniconda2/envs/ipykernel_py3/lib/python3.6/site-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mread_hdf\u001b[0;34m(path_or_buf, key, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mexists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m             raise compat.FileNotFoundError(\n\u001b[0;32m--> 326\u001b[0;31m                 'File %s does not exist' % path_or_buf)\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0;31m# can't auto open/close if we are using an iterator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: File train.h5 does not exist"
     ]
    }
   ],
   "source": [
    "df = pd.read_hdf('train.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['fundamental_38']\n",
    "del df['fundamental_5']\n",
    "del df['fundamental_1']\n",
    "del df['fundamental_6']\n",
    "del df['fundamental_61']\n",
    "del df['fundamental_28']\n",
    "del df['fundamental_26']\n",
    "del df['fundamental_57']\n",
    "del df['fundamental_24']\n",
    "del df['fundamental_9']\n",
    "del df['fundamental_22']\n",
    "del df['fundamental_49']\n",
    "del df['timestamp']\n",
    "del df['derived_0']\n",
    "del df['derived_2']\n",
    "del df['derived_1']\n",
    "del df['derived_3']\n",
    "del df['derived_4']\n",
    "del df['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "y = df['y']\n",
    "X = df.drop(['y'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01493607833981514"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.abs().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for name in X:\n",
    "    if pd.isnull(X[name]).sum() > 0:\n",
    "        median = X[name].median()\n",
    "        X[name] = X[name].apply(lambda y: median if pd.isnull(y) else y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for name in X.ix[:,:]:\n",
    "    x_min=X[name].min()\n",
    "    x_max=X[name].max()\n",
    "    X[name] = X[name].apply(lambda y: (y-x_min)/(x_max-x_min) if (y-x_min)>0 else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X.to_csv(\"financial.csv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_min=y.min()\n",
    "y_max=y.max()\n",
    "y = y.apply(lambda x: (x-y_min)/(y_max-y_min) if (x-y_min)>0 else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=X*100\n",
    "y=y*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.093497805"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1710756, 93)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>89</th>\n",
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       "      <td>0.000091</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.166820</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.039779</td>\n",
       "      <td>4.698345e-07</td>\n",
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       "      <td>0.865410</td>\n",
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       "      <td>3.111706e-06</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>6.112324e-07</td>\n",
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       "      <td>9.134729e-08</td>\n",
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       "      <td>6.103421e-06</td>\n",
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       "      <td>7.293257e-07</td>\n",
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       "      <td>1.189155e-06</td>\n",
       "      <td>0.000134</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
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       "      <td>0.468546</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>0.933612</td>\n",
       "      <td>0.050083</td>\n",
       "      <td>4.109632e-07</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.865657</td>\n",
       "      <td>2.865305e-07</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>1.320870e-06</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.037652</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>0.933257</td>\n",
       "      <td>0.071756</td>\n",
       "      <td>2.502371e-07</td>\n",
       "      <td>0.003485</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.865068</td>\n",
       "      <td>4.800210e-08</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>1.306589e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089966</td>\n",
       "      <td>0.036550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107073</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.566954</td>\n",
       "      <td>0.928507</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>5.158115e-07</td>\n",
       "      <td>0.004863</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.858622</td>\n",
       "      <td>5.688922e-08</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>2.854937e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108521</td>\n",
       "      <td>0.032490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066135</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.663506</td>\n",
       "      <td>0.933720</td>\n",
       "      <td>0.053250</td>\n",
       "      <td>6.997622e-07</td>\n",
       "      <td>0.002631</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.865652</td>\n",
       "      <td>3.518537e-08</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>2.558769e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100781</td>\n",
       "      <td>0.039668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.553179</td>\n",
       "      <td>0.933299</td>\n",
       "      <td>0.101109</td>\n",
       "      <td>5.799871e-07</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.865096</td>\n",
       "      <td>5.084750e-08</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>1.835241e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072414</td>\n",
       "      <td>0.032135</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299037</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.683227</td>\n",
       "      <td>0.933327</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>2.029772e-07</td>\n",
       "      <td>0.002620</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.865191</td>\n",
       "      <td>1.141890e-07</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>2.569574e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080041</td>\n",
       "      <td>0.029404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226463</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.736112</td>\n",
       "      <td>0.932790</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>1.424828e-06</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.863837</td>\n",
       "      <td>3.754922e-07</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>8.089167e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020885</td>\n",
       "      <td>0.008718</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.665890</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.718943</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>9.248346e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>5.085501e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089008</td>\n",
       "      <td>0.032172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266938</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.558696</td>\n",
       "      <td>0.933788</td>\n",
       "      <td>0.028656</td>\n",
       "      <td>1.478938e-06</td>\n",
       "      <td>0.004218</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.865733</td>\n",
       "      <td>1.268928e-07</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>1.010736e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022200</td>\n",
       "      <td>0.006900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.405892</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.709102</td>\n",
       "      <td>0.933219</td>\n",
       "      <td>0.172469</td>\n",
       "      <td>9.561667e-08</td>\n",
       "      <td>0.006736</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.865019</td>\n",
       "      <td>8.259395e-06</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>7.340897e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083875</td>\n",
       "      <td>0.036712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309545</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.664707</td>\n",
       "      <td>0.933248</td>\n",
       "      <td>0.111609</td>\n",
       "      <td>2.647956e-07</td>\n",
       "      <td>0.005485</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.865069</td>\n",
       "      <td>1.191160e-06</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>2.620121e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174677</td>\n",
       "      <td>0.053493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120706</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.535284</td>\n",
       "      <td>0.933308</td>\n",
       "      <td>0.040341</td>\n",
       "      <td>1.097178e-06</td>\n",
       "      <td>0.003162</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.864965</td>\n",
       "      <td>4.558973e-08</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>3.885553e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052244</td>\n",
       "      <td>0.027105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290515</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.612880</td>\n",
       "      <td>0.933325</td>\n",
       "      <td>0.038533</td>\n",
       "      <td>5.080649e-07</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0.865211</td>\n",
       "      <td>9.927668e-07</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>9.568215e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041864</td>\n",
       "      <td>0.030220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402072</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.662689</td>\n",
       "      <td>0.933373</td>\n",
       "      <td>0.031727</td>\n",
       "      <td>8.020420e-07</td>\n",
       "      <td>0.006347</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.865087</td>\n",
       "      <td>3.421274e-08</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>6.879347e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055251</td>\n",
       "      <td>0.025294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041412</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.611186</td>\n",
       "      <td>0.933730</td>\n",
       "      <td>0.032379</td>\n",
       "      <td>5.015112e-07</td>\n",
       "      <td>0.011114</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.865744</td>\n",
       "      <td>7.742297e-08</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>1.424010e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121466</td>\n",
       "      <td>0.040440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142771</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.701802</td>\n",
       "      <td>0.933536</td>\n",
       "      <td>0.066199</td>\n",
       "      <td>7.936032e-07</td>\n",
       "      <td>0.008030</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.865250</td>\n",
       "      <td>5.475734e-08</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>1.144430e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129191</td>\n",
       "      <td>0.043792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.249783</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.562969</td>\n",
       "      <td>0.933330</td>\n",
       "      <td>0.091559</td>\n",
       "      <td>7.164813e-07</td>\n",
       "      <td>0.004117</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.865316</td>\n",
       "      <td>3.754922e-07</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>3.016906e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068436</td>\n",
       "      <td>0.023489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.328075</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.623284</td>\n",
       "      <td>0.933174</td>\n",
       "      <td>0.022165</td>\n",
       "      <td>2.192930e-07</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.864933</td>\n",
       "      <td>3.260582e-05</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>5.682544e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105996</td>\n",
       "      <td>0.051350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.369505</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.649146</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>5.608629e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>7.097805e-08</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105212</td>\n",
       "      <td>0.042305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180685</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.637472</td>\n",
       "      <td>0.933505</td>\n",
       "      <td>0.066783</td>\n",
       "      <td>2.528095e-07</td>\n",
       "      <td>0.002223</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.865482</td>\n",
       "      <td>4.474445e-07</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>1.836717e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070834</td>\n",
       "      <td>0.029239</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188629</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.734726</td>\n",
       "      <td>0.933322</td>\n",
       "      <td>0.122310</td>\n",
       "      <td>2.654102e-07</td>\n",
       "      <td>0.003271</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>0.865211</td>\n",
       "      <td>3.458693e-06</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>2.449602e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112021</td>\n",
       "      <td>0.045233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265283</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.620038</td>\n",
       "      <td>0.933261</td>\n",
       "      <td>0.095465</td>\n",
       "      <td>3.584959e-07</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.865136</td>\n",
       "      <td>3.099575e-05</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>3.394943e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196292</td>\n",
       "      <td>0.048662</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994991</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.616659</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>7.326262e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>2.224120e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093620</td>\n",
       "      <td>0.030466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173502</td>\n",
       "      <td>0.394047</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468546</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>1710726</th>\n",
       "      <td>0.688308</td>\n",
       "      <td>0.933577</td>\n",
       "      <td>0.036684</td>\n",
       "      <td>5.088770e-07</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.865708</td>\n",
       "      <td>1.697974e-06</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>1.691974e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089405</td>\n",
       "      <td>0.033394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044145</td>\n",
       "      <td>0.231598</td>\n",
       "      <td>0.502392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.280130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710727</th>\n",
       "      <td>0.638185</td>\n",
       "      <td>0.933289</td>\n",
       "      <td>0.005404</td>\n",
       "      <td>1.900334e-06</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.865211</td>\n",
       "      <td>4.180485e-06</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>1.253509e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123213</td>\n",
       "      <td>0.033840</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.274659</td>\n",
       "      <td>0.402159</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.005154</td>\n",
       "      <td>0.464656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710728</th>\n",
       "      <td>0.628626</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>4.033186e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>1.858643e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087395</td>\n",
       "      <td>0.040894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.418633</td>\n",
       "      <td>0.382780</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.488901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710729</th>\n",
       "      <td>0.538463</td>\n",
       "      <td>0.933469</td>\n",
       "      <td>0.048545</td>\n",
       "      <td>6.918600e-07</td>\n",
       "      <td>0.003796</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.865364</td>\n",
       "      <td>2.742129e-07</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>6.267503e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116300</td>\n",
       "      <td>0.039737</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.162256</td>\n",
       "      <td>0.683379</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.543995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710730</th>\n",
       "      <td>0.517535</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>8.108448e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>6.965063e-08</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101286</td>\n",
       "      <td>0.049623</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.549569</td>\n",
       "      <td>0.304209</td>\n",
       "      <td>0.054409</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.662151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710731</th>\n",
       "      <td>0.683030</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>7.508602e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>8.644568e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092908</td>\n",
       "      <td>0.040911</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.316537</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.500177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.396581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710732</th>\n",
       "      <td>0.613949</td>\n",
       "      <td>0.933714</td>\n",
       "      <td>0.060939</td>\n",
       "      <td>6.785553e-07</td>\n",
       "      <td>0.004886</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.865287</td>\n",
       "      <td>7.757028e-08</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>7.209726e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122244</td>\n",
       "      <td>0.061097</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.372513</td>\n",
       "      <td>0.342801</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.225307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710733</th>\n",
       "      <td>0.676117</td>\n",
       "      <td>0.933466</td>\n",
       "      <td>0.027603</td>\n",
       "      <td>1.016409e-06</td>\n",
       "      <td>0.003053</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.865335</td>\n",
       "      <td>2.571548e-07</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>1.193602e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164692</td>\n",
       "      <td>0.079218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.360014</td>\n",
       "      <td>0.333934</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.405864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710734</th>\n",
       "      <td>0.557208</td>\n",
       "      <td>0.933209</td>\n",
       "      <td>0.008585</td>\n",
       "      <td>7.894721e-07</td>\n",
       "      <td>0.004491</td>\n",
       "      <td>0.005171</td>\n",
       "      <td>0.864906</td>\n",
       "      <td>9.967471e-08</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>9.504918e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265918</td>\n",
       "      <td>0.107243</td>\n",
       "      <td>0.999645</td>\n",
       "      <td>0.999645</td>\n",
       "      <td>0.999645</td>\n",
       "      <td>0.370781</td>\n",
       "      <td>0.494048</td>\n",
       "      <td>0.499993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710735</th>\n",
       "      <td>0.684817</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>1.689787e-06</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>2.146089e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076021</td>\n",
       "      <td>0.044544</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448823</td>\n",
       "      <td>0.414891</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.005154</td>\n",
       "      <td>0.551712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710736</th>\n",
       "      <td>0.569986</td>\n",
       "      <td>0.932986</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>6.204361e-07</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.004683</td>\n",
       "      <td>0.864766</td>\n",
       "      <td>3.253012e-08</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>2.822831e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044579</td>\n",
       "      <td>0.018272</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120790</td>\n",
       "      <td>0.440399</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.348299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710737</th>\n",
       "      <td>0.516083</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>6.715056e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>7.920221e-08</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124466</td>\n",
       "      <td>0.047501</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.896335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.419750</td>\n",
       "      <td>0.449561</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.629858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710738</th>\n",
       "      <td>0.590775</td>\n",
       "      <td>0.933604</td>\n",
       "      <td>0.051812</td>\n",
       "      <td>1.016876e-06</td>\n",
       "      <td>0.001546</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.865678</td>\n",
       "      <td>7.995383e-07</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>1.941321e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.033010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.210726</td>\n",
       "      <td>0.338122</td>\n",
       "      <td>0.448167</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.430557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710739</th>\n",
       "      <td>0.640655</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>9.588030e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>7.920262e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065866</td>\n",
       "      <td>0.029006</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142690</td>\n",
       "      <td>0.416293</td>\n",
       "      <td>0.500177</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>0.595937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710740</th>\n",
       "      <td>0.570985</td>\n",
       "      <td>0.932200</td>\n",
       "      <td>0.136646</td>\n",
       "      <td>3.887381e-07</td>\n",
       "      <td>0.005742</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.863452</td>\n",
       "      <td>4.702603e-07</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>9.339235e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254818</td>\n",
       "      <td>0.065972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.439718</td>\n",
       "      <td>0.409636</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.543366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710741</th>\n",
       "      <td>0.559277</td>\n",
       "      <td>0.933483</td>\n",
       "      <td>0.052330</td>\n",
       "      <td>1.116899e-06</td>\n",
       "      <td>0.023080</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.865270</td>\n",
       "      <td>2.809291e-07</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>2.060131e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113533</td>\n",
       "      <td>0.056898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.390162</td>\n",
       "      <td>0.265939</td>\n",
       "      <td>0.448168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710742</th>\n",
       "      <td>0.706983</td>\n",
       "      <td>0.933468</td>\n",
       "      <td>0.028444</td>\n",
       "      <td>6.527512e-07</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.865405</td>\n",
       "      <td>5.394176e-07</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>7.815158e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054695</td>\n",
       "      <td>0.024309</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.233712</td>\n",
       "      <td>0.331290</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.376249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710743</th>\n",
       "      <td>0.665901</td>\n",
       "      <td>0.933523</td>\n",
       "      <td>0.115191</td>\n",
       "      <td>4.690618e-07</td>\n",
       "      <td>0.003069</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.865640</td>\n",
       "      <td>5.552271e-06</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.283832e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072519</td>\n",
       "      <td>0.029924</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.999986</td>\n",
       "      <td>0.078061</td>\n",
       "      <td>0.563227</td>\n",
       "      <td>0.500170</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.508395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710744</th>\n",
       "      <td>0.762552</td>\n",
       "      <td>0.933180</td>\n",
       "      <td>0.034429</td>\n",
       "      <td>1.553669e-07</td>\n",
       "      <td>0.006451</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.864947</td>\n",
       "      <td>3.905350e-05</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>2.895006e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176882</td>\n",
       "      <td>0.068307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108804</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.294215</td>\n",
       "      <td>0.437304</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710745</th>\n",
       "      <td>0.505202</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>7.395597e-07</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>5.795954e-08</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.178162</td>\n",
       "      <td>0.176813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108804</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.739751</td>\n",
       "      <td>0.421524</td>\n",
       "      <td>0.551825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.626731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710746</th>\n",
       "      <td>0.586594</td>\n",
       "      <td>0.933769</td>\n",
       "      <td>0.067400</td>\n",
       "      <td>9.925368e-07</td>\n",
       "      <td>0.011946</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.865444</td>\n",
       "      <td>9.289639e-08</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>1.073884e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075775</td>\n",
       "      <td>0.037243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159710</td>\n",
       "      <td>0.463490</td>\n",
       "      <td>0.499993</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.551382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710747</th>\n",
       "      <td>0.568623</td>\n",
       "      <td>0.933474</td>\n",
       "      <td>0.078709</td>\n",
       "      <td>7.466404e-07</td>\n",
       "      <td>0.003553</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.865400</td>\n",
       "      <td>3.196952e-07</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>8.180688e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093162</td>\n",
       "      <td>0.037652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.282102</td>\n",
       "      <td>0.389103</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.348358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710748</th>\n",
       "      <td>0.648449</td>\n",
       "      <td>0.932996</td>\n",
       "      <td>0.030797</td>\n",
       "      <td>4.059724e-07</td>\n",
       "      <td>0.003421</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>0.864565</td>\n",
       "      <td>1.403899e-05</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>9.650305e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044346</td>\n",
       "      <td>0.033058</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.530250</td>\n",
       "      <td>0.433697</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.450660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710749</th>\n",
       "      <td>0.734156</td>\n",
       "      <td>0.933618</td>\n",
       "      <td>0.024951</td>\n",
       "      <td>6.077643e-07</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.865820</td>\n",
       "      <td>2.352603e-06</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>1.274866e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084162</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030129</td>\n",
       "      <td>0.744358</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.384086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710750</th>\n",
       "      <td>0.695488</td>\n",
       "      <td>0.933353</td>\n",
       "      <td>0.094760</td>\n",
       "      <td>3.922390e-07</td>\n",
       "      <td>0.006721</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.865273</td>\n",
       "      <td>1.496515e-06</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>1.632559e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120925</td>\n",
       "      <td>0.040695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.171763</td>\n",
       "      <td>0.396841</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.365673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710751</th>\n",
       "      <td>0.561145</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>1.116261e-06</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>3.433649e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091409</td>\n",
       "      <td>0.042935</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.374310</td>\n",
       "      <td>0.320522</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.472221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710752</th>\n",
       "      <td>0.550985</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>1.323783e-06</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>2.629766e-06</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087359</td>\n",
       "      <td>0.036531</td>\n",
       "      <td>0.896335</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.319798</td>\n",
       "      <td>0.493728</td>\n",
       "      <td>0.500170</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.633585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710753</th>\n",
       "      <td>0.597724</td>\n",
       "      <td>0.933353</td>\n",
       "      <td>0.065665</td>\n",
       "      <td>3.204201e-07</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.865284</td>\n",
       "      <td>3.754922e-07</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>1.715733e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092253</td>\n",
       "      <td>0.039400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108819</td>\n",
       "      <td>0.108818</td>\n",
       "      <td>0.272830</td>\n",
       "      <td>0.405081</td>\n",
       "      <td>0.056802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.582573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710754</th>\n",
       "      <td>0.687333</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>1.102351e-06</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000952</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>1.162546e-07</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077489</td>\n",
       "      <td>0.035180</td>\n",
       "      <td>0.896336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896336</td>\n",
       "      <td>0.434575</td>\n",
       "      <td>0.519902</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.425173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710755</th>\n",
       "      <td>0.618914</td>\n",
       "      <td>0.933440</td>\n",
       "      <td>0.048113</td>\n",
       "      <td>2.518183e-06</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>6.942554e-08</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>3.052112e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054753</td>\n",
       "      <td>0.063001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.379563</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1710756 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1         2             3         4         5   \\\n",
       "0        0.539684  0.934871  0.028011  1.126453e-06  0.005020  0.000288   \n",
       "1        0.631016  0.933284  0.085144  2.195188e-07  0.002245  0.000064   \n",
       "2        0.587948  0.932804  0.048113  1.545077e-07  0.001320  0.000076   \n",
       "3        0.677773  0.933460  0.039779  4.698345e-07  0.003723  0.000100   \n",
       "4        0.667285  0.933450  0.082272  6.112324e-07  0.003503  0.000546   \n",
       "5        0.722713  0.932960  0.048113  7.293257e-07  0.007497  0.015412   \n",
       "6        0.577938  0.933440  0.048113  4.967631e-07  0.003676  0.000042   \n",
       "7        0.636913  0.933612  0.050083  4.109632e-07  0.002402  0.000172   \n",
       "8        0.631930  0.933257  0.071756  2.502371e-07  0.003485  0.000157   \n",
       "9        0.566954  0.928507  0.048113  5.158115e-07  0.004863  0.000176   \n",
       "10       0.663506  0.933720  0.053250  6.997622e-07  0.002631  0.000151   \n",
       "11       0.553179  0.933299  0.101109  5.799871e-07  0.002097  0.000535   \n",
       "12       0.683227  0.933327  0.024380  2.029772e-07  0.002620  0.000139   \n",
       "13       0.736112  0.932790  0.048113  1.424828e-06  0.001814  0.000826   \n",
       "14       0.718943  0.933440  0.048113  9.248346e-07  0.003676  0.000357   \n",
       "15       0.558696  0.933788  0.028656  1.478938e-06  0.004218  0.000288   \n",
       "16       0.709102  0.933219  0.172469  9.561667e-08  0.006736  0.000117   \n",
       "17       0.664707  0.933248  0.111609  2.647956e-07  0.005485  0.000151   \n",
       "18       0.535284  0.933308  0.040341  1.097178e-06  0.003162  0.000288   \n",
       "19       0.612880  0.933325  0.038533  5.080649e-07  0.001833  0.000782   \n",
       "20       0.662689  0.933373  0.031727  8.020420e-07  0.006347  0.000193   \n",
       "21       0.611186  0.933730  0.032379  5.015112e-07  0.011114  0.000153   \n",
       "22       0.701802  0.933536  0.066199  7.936032e-07  0.008030  0.000106   \n",
       "23       0.562969  0.933330  0.091559  7.164813e-07  0.004117  0.000382   \n",
       "24       0.623284  0.933174  0.022165  2.192930e-07  0.003068  0.000309   \n",
       "25       0.649146  0.933440  0.048113  5.608629e-07  0.003676  0.000076   \n",
       "26       0.637472  0.933505  0.066783  2.528095e-07  0.002223  0.000065   \n",
       "27       0.734726  0.933322  0.122310  2.654102e-07  0.003271  0.001413   \n",
       "28       0.620038  0.933261  0.095465  3.584959e-07  0.002206  0.000269   \n",
       "29       0.616659  0.933440  0.048113  7.326262e-07  0.003676  0.000078   \n",
       "...           ...       ...       ...           ...       ...       ...   \n",
       "1710726  0.688308  0.933577  0.036684  5.088770e-07  0.001608  0.000091   \n",
       "1710727  0.638185  0.933289  0.005404  1.900334e-06  0.001478  0.000781   \n",
       "1710728  0.628626  0.933440  0.048113  4.033186e-07  0.003676  0.000138   \n",
       "1710729  0.538463  0.933469  0.048545  6.918600e-07  0.003796  0.000127   \n",
       "1710730  0.517535  0.933440  0.048113  8.108448e-07  0.003676  0.000288   \n",
       "1710731  0.683030  0.933440  0.048113  7.508602e-07  0.003676  0.000132   \n",
       "1710732  0.613949  0.933714  0.060939  6.785553e-07  0.004886  0.000735   \n",
       "1710733  0.676117  0.933466  0.027603  1.016409e-06  0.003053  0.000234   \n",
       "1710734  0.557208  0.933209  0.008585  7.894721e-07  0.004491  0.005171   \n",
       "1710735  0.684817  0.933440  0.048113  1.689787e-06  0.003676  0.000397   \n",
       "1710736  0.569986  0.932986  0.048113  6.204361e-07  0.002755  0.004683   \n",
       "1710737  0.516083  0.933440  0.048113  6.715056e-07  0.003676  0.000288   \n",
       "1710738  0.590775  0.933604  0.051812  1.016876e-06  0.001546  0.000281   \n",
       "1710739  0.640655  0.933440  0.048113  9.588030e-07  0.003676  0.000336   \n",
       "1710740  0.570985  0.932200  0.136646  3.887381e-07  0.005742  0.000735   \n",
       "1710741  0.559277  0.933483  0.052330  1.116899e-06  0.023080  0.000301   \n",
       "1710742  0.706983  0.933468  0.028444  6.527512e-07  0.000649  0.000172   \n",
       "1710743  0.665901  0.933523  0.115191  4.690618e-07  0.003069  0.000058   \n",
       "1710744  0.762552  0.933180  0.034429  1.553669e-07  0.006451  0.000157   \n",
       "1710745  0.505202  0.933440  0.048113  7.395597e-07  0.003676  0.000288   \n",
       "1710746  0.586594  0.933769  0.067400  9.925368e-07  0.011946  0.000339   \n",
       "1710747  0.568623  0.933474  0.078709  7.466404e-07  0.003553  0.000039   \n",
       "1710748  0.648449  0.932996  0.030797  4.059724e-07  0.003421  0.001404   \n",
       "1710749  0.734156  0.933618  0.024951  6.077643e-07  0.002278  0.000213   \n",
       "1710750  0.695488  0.933353  0.094760  3.922390e-07  0.006721  0.000036   \n",
       "1710751  0.561145  0.933440  0.048113  1.116261e-06  0.003676  0.000165   \n",
       "1710752  0.550985  0.933440  0.048113  1.323783e-06  0.003676  0.000408   \n",
       "1710753  0.597724  0.933353  0.065665  3.204201e-07  0.001986  0.000087   \n",
       "1710754  0.687333  0.933440  0.048113  1.102351e-06  0.003676  0.000952   \n",
       "1710755  0.618914  0.933440  0.048113  2.518183e-06  0.003676  0.000288   \n",
       "\n",
       "               6             7         8             9     ...           81  \\\n",
       "0        0.867016  5.270701e-08  0.001096  1.290783e-06    ...     0.144625   \n",
       "1        0.865154  1.921332e-05  0.000091  1.810216e-06    ...     0.100578   \n",
       "2        0.864350  1.330090e-07  0.000166  1.897644e-06    ...     0.109349   \n",
       "3        0.865410  4.726706e-07  0.000380  3.111706e-06    ...     0.132962   \n",
       "4        0.865194  9.134729e-08  0.000063  6.103421e-06    ...     0.123237   \n",
       "5        0.864535  1.189155e-06  0.000134  8.238115e-06    ...     0.061139   \n",
       "6        0.865360  3.751552e-07  0.000227  3.052112e-06    ...     0.175145   \n",
       "7        0.865657  2.865305e-07  0.000421  1.320870e-06    ...     0.113354   \n",
       "8        0.865068  4.800210e-08  0.000244  1.306589e-06    ...     0.089966   \n",
       "9        0.858622  5.688922e-08  0.000269  2.854937e-06    ...     0.108521   \n",
       "10       0.865652  3.518537e-08  0.000271  2.558769e-06    ...     0.100781   \n",
       "11       0.865096  5.084750e-08  0.000065  1.835241e-06    ...     0.072414   \n",
       "12       0.865191  1.141890e-07  0.000697  2.569574e-06    ...     0.080041   \n",
       "13       0.863837  3.754922e-07  0.000058  8.089167e-06    ...     0.020885   \n",
       "14       0.865360  5.085501e-07  0.000227  3.052112e-06    ...     0.089008   \n",
       "15       0.865733  1.268928e-07  0.000356  1.010736e-06    ...     0.022200   \n",
       "16       0.865019  8.259395e-06  0.000194  7.340897e-06    ...     0.083875   \n",
       "17       0.865069  1.191160e-06  0.000098  2.620121e-06    ...     0.174677   \n",
       "18       0.864965  4.558973e-08  0.000042  3.885553e-05    ...     0.052244   \n",
       "19       0.865211  9.927668e-07  0.000258  9.568215e-07    ...     0.041864   \n",
       "20       0.865087  3.421274e-08  0.000099  6.879347e-06    ...     0.055251   \n",
       "21       0.865744  7.742297e-08  0.000436  1.424010e-06    ...     0.121466   \n",
       "22       0.865250  5.475734e-08  0.000091  1.144430e-05    ...     0.129191   \n",
       "23       0.865316  3.754922e-07  0.000115  3.016906e-06    ...     0.068436   \n",
       "24       0.864933  3.260582e-05  0.000250  5.682544e-06    ...     0.105996   \n",
       "25       0.865360  7.097805e-08  0.000227  3.052112e-06    ...     0.105212   \n",
       "26       0.865482  4.474445e-07  0.000202  1.836717e-06    ...     0.070834   \n",
       "27       0.865211  3.458693e-06  0.000213  2.449602e-06    ...     0.112021   \n",
       "28       0.865136  3.099575e-05  0.000154  3.394943e-06    ...     0.196292   \n",
       "29       0.865360  2.224120e-07  0.000227  3.052112e-06    ...     0.093620   \n",
       "...           ...           ...       ...           ...    ...          ...   \n",
       "1710726  0.865708  1.697974e-06  0.000670  1.691974e-06    ...     0.089405   \n",
       "1710727  0.865211  4.180485e-06  0.000472  1.253509e-06    ...     0.123213   \n",
       "1710728  0.865360  1.858643e-07  0.000227  3.052112e-06    ...     0.087395   \n",
       "1710729  0.865364  2.742129e-07  0.000188  6.267503e-06    ...     0.116300   \n",
       "1710730  0.865360  6.965063e-08  0.000227  3.052112e-06    ...     0.101286   \n",
       "1710731  0.865360  8.644568e-07  0.000227  3.052112e-06    ...     0.092908   \n",
       "1710732  0.865287  7.757028e-08  0.000207  7.209726e-06    ...     0.122244   \n",
       "1710733  0.865335  2.571548e-07  0.000194  1.193602e-06    ...     0.164692   \n",
       "1710734  0.864906  9.967471e-08  0.000094  9.504918e-06    ...     0.265918   \n",
       "1710735  0.865360  2.146089e-07  0.000227  3.052112e-06    ...     0.076021   \n",
       "1710736  0.864766  3.253012e-08  0.000057  2.822831e-05    ...     0.044579   \n",
       "1710737  0.865360  7.920221e-08  0.000227  3.052112e-06    ...     0.124466   \n",
       "1710738  0.865678  7.995383e-07  0.000267  1.941321e-06    ...     0.089000   \n",
       "1710739  0.865360  7.920262e-07  0.000227  3.052112e-06    ...     0.065866   \n",
       "1710740  0.863452  4.702603e-07  0.000140  9.339235e-06    ...     0.254818   \n",
       "1710741  0.865270  2.809291e-07  0.000093  2.060131e-05    ...     0.113533   \n",
       "1710742  0.865405  5.394176e-07  0.000376  7.815158e-06    ...     0.054695   \n",
       "1710743  0.865640  5.552271e-06  0.000256  1.283832e-06    ...     0.072519   \n",
       "1710744  0.864947  3.905350e-05  0.000218  2.895006e-06    ...     0.176882   \n",
       "1710745  0.865360  5.795954e-08  0.000227  3.052112e-06    ...     0.178162   \n",
       "1710746  0.865444  9.289639e-08  0.000108  1.073884e-05    ...     0.075775   \n",
       "1710747  0.865400  3.196952e-07  0.000150  8.180688e-07    ...     0.093162   \n",
       "1710748  0.864565  1.403899e-05  0.000178  9.650305e-06    ...     0.044346   \n",
       "1710749  0.865820  2.352603e-06  0.000752  1.274866e-06    ...     0.084162   \n",
       "1710750  0.865273  1.496515e-06  0.000172  1.632559e-06    ...     0.120925   \n",
       "1710751  0.865360  3.433649e-07  0.000227  3.052112e-06    ...     0.091409   \n",
       "1710752  0.865360  2.629766e-06  0.000227  3.052112e-06    ...     0.087359   \n",
       "1710753  0.865284  3.754922e-07  0.000432  1.715733e-06    ...     0.092253   \n",
       "1710754  0.865360  1.162546e-07  0.000227  3.052112e-06    ...     0.077489   \n",
       "1710755  0.865360  6.942554e-08  0.000227  3.052112e-06    ...     0.054753   \n",
       "\n",
       "               82        83        84        85        86        87        88  \\\n",
       "0        0.048040  1.000000  1.000000  1.000000  0.052672  0.394047  0.500000   \n",
       "1        0.033418  1.000000  1.000000  1.000000  0.120077  0.394047  0.500000   \n",
       "2        0.035881  1.000000  1.000000  1.000000  0.166820  0.394047  0.500000   \n",
       "3        0.053126  1.000000  1.000000  1.000000  0.149728  0.394047  0.500000   \n",
       "4        0.045212  1.000000  1.000000  1.000000  0.249783  0.394047  0.500000   \n",
       "5        0.025518  1.000000  1.000000  1.000000  0.249783  0.394047  0.500000   \n",
       "6        0.056961  1.000000  1.000000  1.000000  0.418160  0.394047  0.500000   \n",
       "7        0.037652  1.000000  1.000000  1.000000  0.170464  0.394047  0.500000   \n",
       "8        0.036550  1.000000  1.000000  1.000000  0.107073  0.394047  0.500000   \n",
       "9        0.032490  1.000000  1.000000  1.000000  0.066135  0.394047  0.500000   \n",
       "10       0.039668  1.000000  1.000000  1.000000  0.106796  0.394047  0.500000   \n",
       "11       0.032135  1.000000  1.000000  1.000000  0.299037  0.394047  0.500000   \n",
       "12       0.029404  1.000000  1.000000  1.000000  0.226463  0.394047  0.500000   \n",
       "13       0.008718  1.000000  1.000000  1.000000  0.665890  0.394047  0.500000   \n",
       "14       0.032172  1.000000  1.000000  1.000000  0.266938  0.394047  0.500000   \n",
       "15       0.006900  1.000000  1.000000  1.000000  0.405892  0.394047  0.500000   \n",
       "16       0.036712  1.000000  1.000000  1.000000  0.309545  0.394047  0.500000   \n",
       "17       0.053493  1.000000  1.000000  1.000000  0.120706  0.394047  0.500000   \n",
       "18       0.027105  1.000000  1.000000  1.000000  0.290515  0.394047  0.500000   \n",
       "19       0.030220  1.000000  1.000000  1.000000  0.402072  0.394047  0.500000   \n",
       "20       0.025294  1.000000  1.000000  1.000000  0.041412  0.394047  0.500000   \n",
       "21       0.040440  1.000000  1.000000  1.000000  0.142771  0.394047  0.500000   \n",
       "22       0.043792  1.000000  1.000000  1.000000  0.249783  0.394047  0.500000   \n",
       "23       0.023489  1.000000  1.000000  1.000000  0.328075  0.394047  0.500000   \n",
       "24       0.051350  1.000000  1.000000  1.000000  0.369505  0.394047  0.500000   \n",
       "25       0.042305  1.000000  1.000000  1.000000  0.180685  0.394047  0.500000   \n",
       "26       0.029239  1.000000  1.000000  1.000000  0.188629  0.394047  0.500000   \n",
       "27       0.045233  1.000000  1.000000  1.000000  0.265283  0.394047  0.500000   \n",
       "28       0.048662  1.000000  1.000000  1.000000  0.994991  0.394047  0.500000   \n",
       "29       0.030466  1.000000  1.000000  1.000000  0.173502  0.394047  0.500000   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "1710726  0.033394  1.000000  0.999986  1.000000  0.044145  0.231598  0.502392   \n",
       "1710727  0.033840  1.000000  1.000000  0.108819  0.274659  0.402159  0.500000   \n",
       "1710728  0.040894  1.000000  1.000000  1.000000  0.418633  0.382780  0.500000   \n",
       "1710729  0.039737  0.999999  0.999999  0.999999  0.162256  0.683379  0.500000   \n",
       "1710730  0.049623  0.999999  0.999999  0.999999  0.549569  0.304209  0.054409   \n",
       "1710731  0.040911  1.000000  0.108819  0.108819  0.316537  0.358321  0.500177   \n",
       "1710732  0.061097  0.999985  0.999985  1.000000  0.372513  0.342801  0.500000   \n",
       "1710733  0.079218  1.000000  0.999999  0.999999  0.360014  0.333934  0.500000   \n",
       "1710734  0.107243  0.999645  0.999645  0.999645  0.370781  0.494048  0.499993   \n",
       "1710735  0.044544  1.000000  1.000000  1.000000  0.448823  0.414891  0.500000   \n",
       "1710736  0.018272  1.000000  1.000000  1.000000  0.120790  0.440399  0.500000   \n",
       "1710737  0.047501  0.108819  0.896335  1.000000  0.419750  0.449561  0.500000   \n",
       "1710738  0.033010  1.000000  1.000000  0.999999  0.210726  0.338122  0.448167   \n",
       "1710739  0.029006  0.999999  0.999999  1.000000  0.142690  0.416293  0.500177   \n",
       "1710740  0.065972  1.000000  1.000000  0.999999  0.439718  0.409636  0.500000   \n",
       "1710741  0.056898  1.000000  1.000000  1.000000  0.390162  0.265939  0.448168   \n",
       "1710742  0.024309  1.000000  1.000000  1.000000  0.233712  0.331290  1.000000   \n",
       "1710743  0.029924  0.999986  0.999986  0.999986  0.078061  0.563227  0.500170   \n",
       "1710744  0.068307  1.000000  0.108804  0.108819  0.294215  0.437304  0.002577   \n",
       "1710745  0.176813  1.000000  0.108804  0.108819  0.739751  0.421524  0.551825   \n",
       "1710746  0.037243  1.000000  1.000000  1.000000  0.159710  0.463490  0.499993   \n",
       "1710747  0.037652  1.000000  1.000000  1.000000  0.282102  0.389103  0.500000   \n",
       "1710748  0.033058  1.000000  1.000000  1.000000  0.530250  0.433697  0.500000   \n",
       "1710749  0.026527  1.000000  1.000000  1.000000  0.030129  0.744358  0.500000   \n",
       "1710750  0.040695  1.000000  0.999999  0.999999  0.171763  0.396841  0.002577   \n",
       "1710751  0.042935  0.999999  1.000000  0.999999  0.374310  0.320522  0.500000   \n",
       "1710752  0.036531  0.896335  0.999999  0.999999  0.319798  0.493728  0.500170   \n",
       "1710753  0.039400  1.000000  0.108819  0.108818  0.272830  0.405081  0.056802   \n",
       "1710754  0.035180  0.896336  1.000000  0.896336  0.434575  0.519902  0.500000   \n",
       "1710755  0.063001  1.000000  1.000000  1.000000  0.001132  0.379563  0.500000   \n",
       "\n",
       "               89        90  \n",
       "0        0.000000  0.468546  \n",
       "1        0.000000  0.468546  \n",
       "2        0.000000  0.468546  \n",
       "3        0.000000  0.468546  \n",
       "4        1.000000  0.468546  \n",
       "5        0.000000  0.468546  \n",
       "6        0.000000  0.468546  \n",
       "7        0.000000  0.468546  \n",
       "8        0.000000  0.468546  \n",
       "9        0.000000  0.468546  \n",
       "10       0.000000  0.468546  \n",
       "11       0.000000  0.468546  \n",
       "12       0.000000  0.468546  \n",
       "13       1.000000  0.468546  \n",
       "14       0.000000  0.468546  \n",
       "15       0.000000  0.468546  \n",
       "16       1.000000  0.468546  \n",
       "17       0.000000  0.468546  \n",
       "18       1.000000  0.468546  \n",
       "19       0.000000  0.468546  \n",
       "20       1.000000  0.468546  \n",
       "21       0.000000  0.468546  \n",
       "22       0.000000  0.468546  \n",
       "23       1.000000  0.468546  \n",
       "24       1.000000  0.468546  \n",
       "25       0.000000  0.468546  \n",
       "26       1.000000  0.468546  \n",
       "27       1.000000  0.468546  \n",
       "28       1.000000  0.468546  \n",
       "29       0.000000  0.468546  \n",
       "...           ...       ...  \n",
       "1710726  1.000000  0.280130  \n",
       "1710727  0.005154  0.464656  \n",
       "1710728  1.000000  0.488901  \n",
       "1710729  1.000000  0.543995  \n",
       "1710730  0.999999  0.662151  \n",
       "1710731  1.000000  0.396581  \n",
       "1710732  0.999999  0.225307  \n",
       "1710733  1.000000  0.405864  \n",
       "1710734  1.000000  0.257457  \n",
       "1710735  0.005154  0.551712  \n",
       "1710736  0.000000  0.348299  \n",
       "1710737  1.000000  0.629858  \n",
       "1710738  0.108819  0.430557  \n",
       "1710739  0.999985  0.595937  \n",
       "1710740  1.000000  0.543366  \n",
       "1710741  1.000000  0.478529  \n",
       "1710742  0.000000  0.376249  \n",
       "1710743  0.999999  0.508395  \n",
       "1710744  1.000000  0.572039  \n",
       "1710745  1.000000  0.626731  \n",
       "1710746  0.999999  0.551382  \n",
       "1710747  0.999999  0.348358  \n",
       "1710748  1.000000  0.450660  \n",
       "1710749  0.000015  0.384086  \n",
       "1710750  1.000000  0.365673  \n",
       "1710751  1.000000  0.472221  \n",
       "1710752  0.999999  0.633585  \n",
       "1710753  1.000000  0.582573  \n",
       "1710754  0.000370  0.425173  \n",
       "1710755  1.000000  0.468546  \n",
       "\n",
       "[1710756 rows x 91 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import pandas as pd\n",
    "#from sklearn import preprocessing\n",
    "\n",
    "#min_max_scaler = preprocessing.MinMaxScaler()\n",
    "#np_scaled = min_max_scaler.fit_transform(X)\n",
    "#df_normalized = pd.DataFrame(np_scaled)\n",
    "#df_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(df.dropna())\n",
    "y = df['y']\n",
    "X = df.drop(['y'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#scaler = preprocessing.StandardScaler().fit(df)\n",
    "#df_new=scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(50,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "reg = MLPRegressor(hidden_layer_sizes=(50,))\n",
    "reg.fit(X_train,y_train)                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict=reg.predict(X_test)\n",
    "cd=reg.score(X_test, y_test, sample_weight=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00016670927473561026"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32321.775383683904"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_predict-y_test).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16444949.92577968"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16477271.701163732"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'timestamp',\n",
       " 'derived_0',\n",
       " 'derived_1',\n",
       " 'derived_2',\n",
       " 'derived_3',\n",
       " 'derived_4',\n",
       " 'fundamental_0',\n",
       " 'fundamental_1',\n",
       " 'fundamental_2',\n",
       " 'fundamental_3',\n",
       " 'fundamental_5',\n",
       " 'fundamental_6',\n",
       " 'fundamental_7',\n",
       " 'fundamental_8',\n",
       " 'fundamental_9',\n",
       " 'fundamental_10',\n",
       " 'fundamental_11',\n",
       " 'fundamental_12',\n",
       " 'fundamental_13',\n",
       " 'fundamental_14',\n",
       " 'fundamental_15',\n",
       " 'fundamental_16',\n",
       " 'fundamental_17',\n",
       " 'fundamental_18',\n",
       " 'fundamental_19',\n",
       " 'fundamental_20',\n",
       " 'fundamental_21',\n",
       " 'fundamental_22',\n",
       " 'fundamental_23',\n",
       " 'fundamental_24',\n",
       " 'fundamental_25',\n",
       " 'fundamental_26',\n",
       " 'fundamental_27',\n",
       " 'fundamental_28',\n",
       " 'fundamental_29',\n",
       " 'fundamental_30',\n",
       " 'fundamental_31',\n",
       " 'fundamental_32',\n",
       " 'fundamental_33',\n",
       " 'fundamental_34',\n",
       " 'fundamental_35',\n",
       " 'fundamental_36',\n",
       " 'fundamental_37',\n",
       " 'fundamental_38',\n",
       " 'fundamental_39',\n",
       " 'fundamental_40',\n",
       " 'fundamental_41',\n",
       " 'fundamental_42',\n",
       " 'fundamental_43',\n",
       " 'fundamental_44',\n",
       " 'fundamental_45',\n",
       " 'fundamental_46',\n",
       " 'fundamental_47',\n",
       " 'fundamental_48',\n",
       " 'fundamental_49',\n",
       " 'fundamental_50',\n",
       " 'fundamental_51',\n",
       " 'fundamental_52',\n",
       " 'fundamental_53',\n",
       " 'fundamental_54',\n",
       " 'fundamental_55',\n",
       " 'fundamental_56',\n",
       " 'fundamental_57',\n",
       " 'fundamental_58',\n",
       " 'fundamental_59',\n",
       " 'fundamental_60',\n",
       " 'fundamental_61',\n",
       " 'fundamental_62',\n",
       " 'fundamental_63',\n",
       " 'technical_0',\n",
       " 'technical_1',\n",
       " 'technical_2',\n",
       " 'technical_3',\n",
       " 'technical_5',\n",
       " 'technical_6',\n",
       " 'technical_7',\n",
       " 'technical_9',\n",
       " 'technical_10',\n",
       " 'technical_11',\n",
       " 'technical_12',\n",
       " 'technical_13',\n",
       " 'technical_14',\n",
       " 'technical_16',\n",
       " 'technical_17',\n",
       " 'technical_18',\n",
       " 'technical_19',\n",
       " 'technical_20',\n",
       " 'technical_21',\n",
       " 'technical_22',\n",
       " 'technical_24',\n",
       " 'technical_25',\n",
       " 'technical_27',\n",
       " 'technical_28',\n",
       " 'technical_29',\n",
       " 'technical_30',\n",
       " 'technical_31',\n",
       " 'technical_32',\n",
       " 'technical_33',\n",
       " 'technical_34',\n",
       " 'technical_35',\n",
       " 'technical_36',\n",
       " 'technical_37',\n",
       " 'technical_38',\n",
       " 'technical_39',\n",
       " 'technical_40',\n",
       " 'technical_41',\n",
       " 'technical_42',\n",
       " 'technical_43',\n",
       " 'technical_44',\n",
       " 'y']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d1=df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uRFIehgJJue3bBz/+8bXs21d2JZLyMBRIKsj8sguQlJOhQJIkAYYCSZKUMRRIKsi6sguQ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\nSJKkjKFAkiQBhgJJkpT5/22+q263vDFzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127e7d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.boxplot(column='fundamental_0', return_type='axes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MvH8W1jPRjHJFxJkR8YWIeLBVfj48yzmkFwQLg6QyUXUFKaUnU0o/mo0wz0MX\n8ADNm87trSmD1PEsDNICERGfBs4DPtL6l/SzrUuUz46Iv4uIJyNiX0R8duITYKNpY0TcHxEHIuKh\niPj3k1b/CxHxDxHx44i4OyLeOGH597UeYb8sIu5rfc5/i4hTJmabeEii7DMj4uMR8a3W5z0QEZe1\nbv52xFoPwLs4pXQT8PTzWYckC4O0kHwE+ApwHc1ntpwKPAXcDgwBvTTvivpy4KYJy30c2AhcSvPW\n7iuBfbT7M+AKmrdt/zZwY0RM/PujC/gj4D3AvwAWA1dOk7XsM39E8xLrHpqHMz4IrJ9+8yXllPM+\nDJLmUErpRxHxNDCWUnoUICL+BGiklD42Pi8iPgjsiYhfpPlL+sPAH6aUtrWmPMihd2T9y5TS37eW\n3wR8E/hFmuUBmn+X/F5K6aHWnGuAj3EYEfGiss9MKf3FhEX2RMRVNEvFdCVEUkYWBmlhOwf4VxHx\n5KTxBPwC8BLgZ2g+9G06/2vCz3tpntfwcv5/YRgbLwsT5rx8inX1lH1mRKwE1rUyvojm31U/LMko\nKSMLg7SwvQi4hebu/8knL+6l+Qt5Jp6Z8PP41QlHTfH++JypTpbcP90Htc6P2EZzD8UumkVhFfDR\nGWaVlIGFQVpYngYmnhzYAP4N8HBK6bnJkyPifppPeT0f+Osp1jnbt4Mt+8w3AQ+llD4+Iefps5xB\n0hGyMEgLy0PAGyLiVTRPeNxM84TB7RFxBfA4zWe4rAQ+kFL6SUT0A1dExDPAl4GXAWellMZ/mVe+\nrHKiGXzm/cDi1mGJrwO/Afzm8/28iDgWOJPmdvwMcFpEnAM8lVJ6oNrWSC8cXiUhLSxX0nyQ233A\no8CxwJtp/r++E7gHuBp4IrUeJJNSugy4iuYVC/cB22n+Ah93uD0MlfY6TPeZKaVbgU/QfMLtXcAb\ngcsqfNwrWusZonn1yB/T3PNyXYV1Si84PnxKkiSVcg+DJEkq5TkMkjpa65LRw12VkYBfTyl9ee5T\nSQuPhUFSpztnmve+N2cppAXOcxgkSVIpz2GQJEmlLAySJKmUhUGSJJWyMEiSpFIWBkmSVMrCIEmS\nSlkYJElSqf8HdVeHNuP5uZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15018e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='technical_1', return_type='axes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Q1 = df.dropna().quantile(0.25)\n",
    "Q3 = df.dropna().quantile(0.75)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d2=((df.dropna() < (Q1 - 1.5 * IQR)) | (df.dropna() > (Q3 + 1.5 * IQR))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    0\n",
       "timestamp             0\n",
       "derived_0         26027\n",
       "derived_1         21137\n",
       "derived_2         43020\n",
       "derived_3         32283\n",
       "derived_4         28672\n",
       "fundamental_0      3344\n",
       "fundamental_1      9506\n",
       "fundamental_2     25812\n",
       "fundamental_3      7520\n",
       "fundamental_5     20063\n",
       "fundamental_6     33217\n",
       "fundamental_7      9847\n",
       "fundamental_8     18748\n",
       "fundamental_9     13588\n",
       "fundamental_10    21429\n",
       "fundamental_11    26615\n",
       "fundamental_12    24204\n",
       "fundamental_13     9569\n",
       "fundamental_14    21554\n",
       "fundamental_15    17115\n",
       "fundamental_16     4712\n",
       "fundamental_17    38977\n",
       "fundamental_18    25110\n",
       "fundamental_19    21606\n",
       "fundamental_20    19472\n",
       "fundamental_21    11260\n",
       "fundamental_22     9572\n",
       "fundamental_23    14515\n",
       "                  ...  \n",
       "technical_13      20803\n",
       "technical_14          0\n",
       "technical_16      74464\n",
       "technical_17          0\n",
       "technical_18      54484\n",
       "technical_19      13013\n",
       "technical_20      17877\n",
       "technical_21      14899\n",
       "technical_22          0\n",
       "technical_24       1440\n",
       "technical_25       1646\n",
       "technical_27      15153\n",
       "technical_28       1742\n",
       "technical_29          0\n",
       "technical_30      29127\n",
       "technical_31       1350\n",
       "technical_32      51350\n",
       "technical_33       2923\n",
       "technical_34          0\n",
       "technical_35       7958\n",
       "technical_36      12279\n",
       "technical_37      50658\n",
       "technical_38      51175\n",
       "technical_39      52195\n",
       "technical_40       6259\n",
       "technical_41       2025\n",
       "technical_42      88971\n",
       "technical_43          0\n",
       "technical_44       1858\n",
       "y                 14772\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d1.to_csv(\"Missing1\", sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d2.to_csv(\"Outliers1\", sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Importing Kagglegym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "def r_score(y_true, y_pred, sample_weight=None, multioutput=None):\n",
    "    r2 = r2_score(y_true, y_pred, sample_weight=sample_weight,\n",
    "                  multioutput=multioutput)\n",
    "    r = (np.sign(r2)*np.sqrt(np.abs(r2)))\n",
    "    if r <= -1:\n",
    "        return -1\n",
    "    else:\n",
    "        return r\n",
    "\n",
    "\n",
    "class Observation(object):\n",
    "    def __init__(self, train, target, features):\n",
    "        self.train = train\n",
    "        self.target = target\n",
    "        self.features = features\n",
    "\n",
    "\n",
    "class Environment(object):\n",
    "    def __init__(self):\n",
    "        with pd.HDFStore(\"train.h5\", \"r\") as hfdata:\n",
    "            self.timestamp = 0\n",
    "            fullset = hfdata.get(\"train\")\n",
    "            self.unique_timestamp = fullset[\"timestamp\"].unique()\n",
    "            # Get a list of unique timestamps\n",
    "            # use the first half for training and\n",
    "            # the second half for the test set\n",
    "            n = len(self.unique_timestamp)\n",
    "            i = int(n/2)\n",
    "            timesplit = self.unique_timestamp[i]\n",
    "            self.n = n\n",
    "            self.unique_idx = i\n",
    "            self.train = fullset[fullset.timestamp < timesplit]\n",
    "            self.test = fullset[fullset.timestamp >= timesplit]\n",
    "            # Needed to compute final score\n",
    "            self.full = self.test.loc[:, ['timestamp', 'y']]\n",
    "            self.full['y_hat'] = 0.0\n",
    "            self.temp_test_y = None\n",
    "    def reset(self):\n",
    "        timesplit = self.unique_timestamp[self.unique_idx]\n",
    "        self.unique_idx = int(self.n / 2)\n",
    "        self.unique_idx += 1\n",
    "        subset = self.test[self.test.timestamp == timesplit]\n",
    "        # reset index to conform to how kagglegym works\n",
    "        target = subset.loc[:, ['id', 'y']].reset_index(drop=True)\n",
    "        self.temp_test_y = target['y']\n",
    "        target.loc[:, 'y'] = 0.0  # set the prediction column to zero\n",
    "        # changed bounds to 0:110 from 1:111 to mimic the behavior\n",
    "        # of api for feature\n",
    "        features = subset.iloc[:, :110].reset_index(drop=True)\n",
    "        observation = Observation(self.train, target, features)\n",
    "        return observation\n",
    "    def step(self, target):\n",
    "        timesplit = self.unique_timestamp[self.unique_idx-1]\n",
    "        # Since full and target have a different index we need\n",
    "        # to do a _values trick here to get the assignment working\n",
    "        y_hat = target.loc[:, ['y']]\n",
    "        self.full.loc[self.full.timestamp == timesplit, ['y_hat']] = y_hat._values\n",
    "        if self.unique_idx == self.n:\n",
    "            done = True\n",
    "            observation = None\n",
    "            reward = r_score(self.temp_test_y, target.loc[:, 'y'])\n",
    "            score = r_score(self.full['y'], self.full['y_hat'])\n",
    "            info = {'public_score': score}\n",
    "        else:\n",
    "            reward = r_score(self.temp_test_y, target.loc[:, 'y'])\n",
    "            done = False\n",
    "            info = {}\n",
    "            timesplit = self.unique_timestamp[self.unique_idx]\n",
    "            self.unique_idx += 1\n",
    "            subset = self.test[self.test.timestamp == timesplit]\n",
    "            # reset index to conform to how kagglegym works\n",
    "            target = subset.loc[:, ['id', 'y']].reset_index(drop=True)\n",
    "            self.temp_test_y = target['y']\n",
    "            # set the prediction column to zero\n",
    "            target.loc[:, 'y'] = 0\n",
    "            # column bound change on the subset\n",
    "            # reset index to conform to how kagglegym works\n",
    "            features = subset.iloc[:, 0:110].reset_index(drop=True)\n",
    "            observation = Observation(self.train, target, features)\n",
    "        return observation, reward, done, info\n",
    "    def __str__(self):\n",
    "        return \"Environment()\"\n",
    "\n",
    "def make():\n",
    "    return Environment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env =make()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "observation = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_36</th>\n",
       "      <th>technical_37</th>\n",
       "      <th>technical_38</th>\n",
       "      <th>technical_39</th>\n",
       "      <th>technical_40</th>\n",
       "      <th>technical_41</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370326</td>\n",
       "      <td>-0.006316</td>\n",
       "      <td>0.222831</td>\n",
       "      <td>-0.213030</td>\n",
       "      <td>0.729277</td>\n",
       "      <td>-0.335633</td>\n",
       "      <td>0.113292</td>\n",
       "      <td>1.621238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.414776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>-0.038064</td>\n",
       "      <td>-0.017425</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.114285</td>\n",
       "      <td>-0.210185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.273607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.010622</td>\n",
       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
       "      <td>-0.764516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.175710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.211506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.176693</td>\n",
       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>-0.125687</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "0  10          0   0.370326  -0.006316   0.222831  -0.213030   0.729277   \n",
       "1  11          0   0.014765  -0.038064  -0.017425   0.320652  -0.034134   \n",
       "2  12          0  -0.010622  -0.050577   3.379575  -0.157525  -0.068550   \n",
       "3  25          0        NaN        NaN        NaN        NaN        NaN   \n",
       "4  26          0   0.176693  -0.025284  -0.057680   0.015100   0.180894   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2    ...     technical_36  \\\n",
       "0      -0.335633       0.113292       1.621238    ...         0.775208   \n",
       "1       0.004413       0.114285      -0.210185    ...         0.025590   \n",
       "2      -0.155937       1.219439      -0.764516    ...         0.151881   \n",
       "3       0.178495            NaN      -0.007262    ...         1.035936   \n",
       "4       0.139445      -0.125687      -0.018707    ...         0.630232   \n",
       "\n",
       "   technical_37  technical_38  technical_39  technical_40  technical_41  \\\n",
       "0           NaN           NaN           NaN     -0.414776           NaN   \n",
       "1           NaN           NaN           NaN     -0.273607           NaN   \n",
       "2           NaN           NaN           NaN     -0.175710           NaN   \n",
       "3           NaN           NaN           NaN     -0.211506           NaN   \n",
       "4           NaN           NaN           NaN     -0.001957           NaN   \n",
       "\n",
       "   technical_42  technical_43  technical_44         y  \n",
       "0           NaN          -2.0           NaN -0.011753  \n",
       "1           NaN          -2.0           NaN -0.001240  \n",
       "2           NaN          -2.0           NaN -0.020940  \n",
       "3           NaN          -2.0           NaN -0.015959  \n",
       "4           NaN           0.0           NaN -0.007338  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observation.train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
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       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_35</th>\n",
       "      <th>technical_36</th>\n",
       "      <th>technical_37</th>\n",
       "      <th>technical_38</th>\n",
       "      <th>technical_39</th>\n",
       "      <th>technical_40</th>\n",
       "      <th>technical_41</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>906</td>\n",
       "      <td>0.246848</td>\n",
       "      <td>0.102251</td>\n",
       "      <td>0.002781</td>\n",
       "      <td>-0.029337</td>\n",
       "      <td>0.400748</td>\n",
       "      <td>-0.273942</td>\n",
       "      <td>0.535335</td>\n",
       "      <td>0.243197</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083835</td>\n",
       "      <td>-0.193842</td>\n",
       "      <td>-4.642828e-10</td>\n",
       "      <td>-4.642828e-10</td>\n",
       "      <td>-4.436047e-10</td>\n",
       "      <td>-0.057845</td>\n",
       "      <td>-0.054224</td>\n",
       "      <td>4.406503e-05</td>\n",
       "      <td>-1.999907e+00</td>\n",
       "      <td>-0.052079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>906</td>\n",
       "      <td>0.217796</td>\n",
       "      <td>1.922894</td>\n",
       "      <td>0.752644</td>\n",
       "      <td>-0.237133</td>\n",
       "      <td>0.149876</td>\n",
       "      <td>-0.324906</td>\n",
       "      <td>-0.110032</td>\n",
       "      <td>-0.493290</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003478</td>\n",
       "      <td>-0.139345</td>\n",
       "      <td>-1.562500e-02</td>\n",
       "      <td>-1.562500e-02</td>\n",
       "      <td>-1.562500e-02</td>\n",
       "      <td>-0.017113</td>\n",
       "      <td>-0.067621</td>\n",
       "      <td>-1.562500e-02</td>\n",
       "      <td>-3.330669e-16</td>\n",
       "      <td>0.010135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>906</td>\n",
       "      <td>-0.074121</td>\n",
       "      <td>-0.057722</td>\n",
       "      <td>1.595148</td>\n",
       "      <td>0.311800</td>\n",
       "      <td>-0.003560</td>\n",
       "      <td>0.274813</td>\n",
       "      <td>0.088182</td>\n",
       "      <td>-0.170704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073679</td>\n",
       "      <td>-0.083068</td>\n",
       "      <td>-3.359413e-15</td>\n",
       "      <td>-7.448059e-23</td>\n",
       "      <td>-7.128695e-01</td>\n",
       "      <td>-0.045347</td>\n",
       "      <td>-0.094896</td>\n",
       "      <td>-7.277103e-01</td>\n",
       "      <td>-1.968750e+00</td>\n",
       "      <td>0.025098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>906</td>\n",
       "      <td>0.160421</td>\n",
       "      <td>-0.037247</td>\n",
       "      <td>0.455026</td>\n",
       "      <td>0.022488</td>\n",
       "      <td>0.154796</td>\n",
       "      <td>-0.079147</td>\n",
       "      <td>0.865164</td>\n",
       "      <td>0.016182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471345</td>\n",
       "      <td>0.467192</td>\n",
       "      <td>-5.463366e-24</td>\n",
       "      <td>-6.938400e-04</td>\n",
       "      <td>-6.144731e-33</td>\n",
       "      <td>-0.030753</td>\n",
       "      <td>-0.240190</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.301181e-13</td>\n",
       "      <td>-0.001249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>906</td>\n",
       "      <td>-0.025554</td>\n",
       "      <td>-0.046826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.026069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049894</td>\n",
       "      <td>0.046646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115415</td>\n",
       "      <td>0.090820</td>\n",
       "      <td>-3.105529e-20</td>\n",
       "      <td>-1.614121e-21</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.133357</td>\n",
       "      <td>-0.068176</td>\n",
       "      <td>-5.743766e-24</td>\n",
       "      <td>-3.330669e-16</td>\n",
       "      <td>0.019396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "0   0        906   0.246848   0.102251   0.002781  -0.029337   0.400748   \n",
       "1   7        906   0.217796   1.922894   0.752644  -0.237133   0.149876   \n",
       "2  11        906  -0.074121  -0.057722   1.595148   0.311800  -0.003560   \n",
       "3  12        906   0.160421  -0.037247   0.455026   0.022488   0.154796   \n",
       "4  13        906  -0.025554  -0.046826        NaN  -0.026069        NaN   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2      ...       technical_35  \\\n",
       "0      -0.273942       0.535335       0.243197      ...          -0.083835   \n",
       "1      -0.324906      -0.110032      -0.493290      ...          -0.003478   \n",
       "2       0.274813       0.088182      -0.170704      ...          -0.073679   \n",
       "3      -0.079147       0.865164       0.016182      ...           0.471345   \n",
       "4      -0.049894       0.046646            NaN      ...           0.115415   \n",
       "\n",
       "   technical_36  technical_37  technical_38  technical_39  technical_40  \\\n",
       "0     -0.193842 -4.642828e-10 -4.642828e-10 -4.436047e-10     -0.057845   \n",
       "1     -0.139345 -1.562500e-02 -1.562500e-02 -1.562500e-02     -0.017113   \n",
       "2     -0.083068 -3.359413e-15 -7.448059e-23 -7.128695e-01     -0.045347   \n",
       "3      0.467192 -5.463366e-24 -6.938400e-04 -6.144731e-33     -0.030753   \n",
       "4      0.090820 -3.105529e-20 -1.614121e-21  0.000000e+00     -0.133357   \n",
       "\n",
       "   technical_41  technical_42  technical_43  technical_44  \n",
       "0     -0.054224  4.406503e-05 -1.999907e+00     -0.052079  \n",
       "1     -0.067621 -1.562500e-02 -3.330669e-16      0.010135  \n",
       "2     -0.094896 -7.277103e-01 -1.968750e+00      0.025098  \n",
       "3     -0.240190  0.000000e+00 -1.301181e-13     -0.001249  \n",
       "4     -0.068176 -5.743766e-24 -3.330669e-16      0.019396  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observation.features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "968"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observation.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import ElasticNetCV, LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting a model\n",
      "predict the same data\n",
      "fitting a model\n",
      "predict the same data\n",
      "fitting a model\n",
      "predict the same data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119d2a978>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y5Mkmx0E3CgoKzOngnRw9s7dB5iukjIEXbi52+nSkcX/oag/RBa1a7cJ0Tdjc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10486e4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QJUH24VnQc3wmb366z7uujx6YwoT+/OeFW4j7Zgx1tr0Mu+awfKuP/60HyDPZ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\nMp577rl8V9Fs2LCBrKwsXnjhBZvjI0eOLHC9AgMDLUEJGNMG2rdvT2J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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10486e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lRYSJIAjVjysFNj5haerZ6ph1TPwgkwHV3sYCnDhbRAlAREQEn3zy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\nMFFKKaVUyPh/SEKLQ94hLE8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104228e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class fitModel():\n",
    "    '''\n",
    "        This class is going to take train values\n",
    "        and a particular type of model and take care of\n",
    "        the prediction step and wil contain a fit\n",
    "        step. \n",
    "        \n",
    "        Remember to send in a copy of train because\n",
    "        there is a high chance that it will be modified\n",
    "        \n",
    "        the model is a sklearn model like ElasticNetCV\n",
    "        \n",
    "        all other parameters are passed onto the model\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, model, train, columns):\n",
    "\n",
    "        # first save the model ...\n",
    "        self.model   = model\n",
    "        self.columns = columns\n",
    "        \n",
    "        # Get the X, and y values, \n",
    "        y = np.array(train.y)\n",
    "        \n",
    "        X = train[columns]\n",
    "        self.xMeans = X.mean(axis=0) # Remember to save this value\n",
    "        self.xStd   = X.std(axis=0)  # Remember to save this value\n",
    "\n",
    "        X = np.array(X.fillna( self.xMeans ))\n",
    "        X = (X - np.array(self.xMeans))/np.array(self.xStd)\n",
    "        \n",
    "        # fit the model\n",
    "        self.model.fit(X, y)\n",
    "        \n",
    "        return\n",
    "    \n",
    "    def predict(self, features):\n",
    "        '''\n",
    "            This function is going to return the predicted\n",
    "            value of the function that we are trying to \n",
    "            predict, given the observations. \n",
    "        '''\n",
    "        X = features[self.columns]\n",
    "        X = np.array(X.fillna( self.xMeans ))\n",
    "        X = (X - np.array(self.xMeans))/np.array(self.xStd)\n",
    "\n",
    "        return self.model.predict(X)\n",
    "def checkModel(modelToUse, columns):\n",
    "    '''\n",
    "        This  function checks and makes sure that the \n",
    "        model provided is doing what it is supposed to\n",
    "        do. This is a sanity check ...\n",
    "    '''\n",
    "    \n",
    "    rewards = []\n",
    "    env = make()\n",
    "    observation = env.reset()\n",
    "    \n",
    "    train = observation.train\n",
    "    \n",
    "    # Just to make things easier to visualize\n",
    "    # and also to speed things up ...\n",
    "    # -----------------------------------------\n",
    "    train   = train[['timestamp', 'y'] + columns]\n",
    "    train   = train.groupby('timestamp').aggregate(np.mean)\n",
    "    train.y = np.cumsum(train.y) # easier to visualize\n",
    "    \n",
    "    print('fitting a model')\n",
    "    model = fitModel(modelToUse, train, columns)\n",
    "    \n",
    "    print('predict the same data')\n",
    "    yHat = model.predict(train) # We already select required columns\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(yHat, color='black', lw=2, label='predicted')\n",
    "    plt.plot(train.y, '.', mec='None', mfc='orange', label='original')\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "    return\n",
    "    \n",
    "columns = ['technical_30', 'technical_20', 'fundamental_11', 'technical_19']\n",
    "checkModel(LinearRegression(), columns)\n",
    "plt.title('four columns')\n",
    "    \n",
    "# Get all columns here \n",
    "env     = make()\n",
    "allCols = env.reset().train.columns\n",
    "\n",
    "checkModel(LinearRegression(), [c for c in allCols if 'fundamental' in c])\n",
    "plt.title('fundamentals')\n",
    "\n",
    "checkModel(LinearRegression(), [c for c in allCols if 'technical' in c])\n",
    "plt.title('technicals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class fitModel():\n",
    "    '''\n",
    "        This class is going to take train values\n",
    "        and a particular type of model and take care of\n",
    "        the prediction step and wil contain a fit\n",
    "        step. \n",
    "        \n",
    "        Remember to send in a copy of train because\n",
    "        there is a high chance that it will be modified\n",
    "        \n",
    "        the model is a sklearn model like ElasticNetCV\n",
    "        \n",
    "        all other parameters are passed onto the model\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, model, train, columns):\n",
    "\n",
    "        # first save the model ...\n",
    "        self.model   = model\n",
    "        self.columns = columns\n",
    "        \n",
    "        # Get the X, and y values, \n",
    "        y = np.array(train.y)\n",
    "        \n",
    "        X = train[columns]\n",
    "        self.xMeans = X.mean(axis=0) # Remember to save this value\n",
    "        self.xStd   = X.std(axis=0)  # Remember to save this value\n",
    "\n",
    "        X = np.array(X.fillna( self.xMeans ))\n",
    "        X = (X - np.array(self.xMeans))/np.array(self.xStd)\n",
    "        \n",
    "        # fit the model\n",
    "        self.model.fit(X, y)\n",
    "        \n",
    "        return\n",
    "    \n",
    "    def predict(self, features):\n",
    "        '''\n",
    "            This function is going to return the predicted\n",
    "            value of the function that we are trying to \n",
    "            predict, given the observations. \n",
    "        '''\n",
    "        X = features[self.columns]\n",
    "        X = np.array(X.fillna( self.xMeans ))\n",
    "        X = (X - np.array(self.xMeans))/np.array(self.xStd)\n",
    "\n",
    "        return self.model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting a model\n",
      "predict the same data\n",
      "fitting a model\n",
      "predict the same data\n",
      "fitting a model\n",
      "predict the same data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11656add8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WjYLC3OJModZtgUD9HCo7sS3tdWUA2o/jy1+mAbB6TwPoOYP/zXmB/dvXcsflDuM0HqgJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115030b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QJUH24VnQc3wmb366z7uujx6YwoT+/OeFW4j7Zgx1tr0Mu+awfKuP/60HyDPZ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\nMp577rl8V9Fs2LCBrKwsXnjhBZvjI0eOLHC9AgMDLUEJGNMG2rdvT2J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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11567ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lRYSJIAjVjysFNj5haerZ6ph1TPwgkwHV3sYCnDhbRAlAREQEn3zy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\nMFFKKaVUyPh/SEKLQ94hLE8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1042f4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def checkModel(modelToUse, columns):\n",
    "    '''\n",
    "        This  function checks and makes sure that the \n",
    "        model provided is doing what it is supposed to\n",
    "        do. This is a sanity check ...\n",
    "    '''\n",
    "    \n",
    "    rewards = []\n",
    "    env = make()\n",
    "    observation = env.reset()\n",
    "    \n",
    "    train = observation.train\n",
    "    \n",
    "    # Just to make things easier to visualize\n",
    "    # and also to speed things up ...\n",
    "    # -----------------------------------------\n",
    "    train   = train[['timestamp', 'y'] + columns]\n",
    "    train   = train.groupby('timestamp').aggregate(np.mean)\n",
    "    train.y = np.cumsum(train.y) # easier to visualize\n",
    "    \n",
    "    print('fitting a model')\n",
    "    model = fitModel(modelToUse, train, columns)\n",
    "    \n",
    "    print('predict the same data')\n",
    "    yHat = model.predict(train) # We already select required columns\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(yHat, color='black', lw=2, label='predicted')\n",
    "    plt.plot(train.y, '.', mec='None', mfc='orange', label='original')\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "    return\n",
    "    \n",
    "columns = ['technical_30', 'technical_20', 'fundamental_11', 'technical_19']\n",
    "checkModel(LinearRegression(), columns)\n",
    "plt.title('four columns')\n",
    "    \n",
    "# Get all columns here \n",
    "env     = make()\n",
    "allCols = env.reset().train.columns\n",
    "\n",
    "checkModel(LinearRegression(), [c for c in allCols if 'fundamental' in c])\n",
    "plt.title('fundamentals')\n",
    "\n",
    "checkModel(LinearRegression(), [c for c in allCols if 'technical' in c])\n",
    "plt.title('technicals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getScore(modelToUse, columns):\n",
    "    \n",
    "    print('Starting a new calculation for score')\n",
    "    rewards = []\n",
    "    env = make()\n",
    "    observation = env.reset()\n",
    "    \n",
    "    print('fitting a model')\n",
    "    model = fitModel(modelToUse, observation.train.copy(), columns)\n",
    "\n",
    "    print('Starting to fit a model')\n",
    "    while True:\n",
    "        \n",
    "        prediction  = model.predict(observation.features.copy())\n",
    "        target      = observation.target\n",
    "        target['y'] = prediction\n",
    "        \n",
    "        timestamp = observation.features[\"timestamp\"][0]\n",
    "        if timestamp % 100 == 0:\n",
    "            print(timestamp)\n",
    "\n",
    "        observation, reward, done, info = env.step(target)\n",
    "        rewards.append(reward)\n",
    "        if done: break\n",
    "            \n",
    "    return info['public_score'], rewards\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting a new calculation for score\n",
      "fitting a model\n",
      "Starting to fit a model\n",
      "1000\n",
      "1100\n",
      "1200\n",
      "1300\n",
      "1400\n",
      "1500\n",
      "1600\n",
      "1700\n",
      "1800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.019201715082680988"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['technical_30', 'technical_20', 'fundamental_11', 'technical_43']\n",
    "getScore(ElasticNetCV(), columns)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training completed...\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import RobustScaler, Imputer, PolynomialFeatures\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.pipeline import Pipeline\n",
    "import math\n",
    "\n",
    "def _reward(y_true, y_fit): \n",
    "    R2 = 1 - np.sum((y_true - y_fit)**2) / np.sum((y_true - np.mean(y_true))**2)\n",
    "    R = np.sign(R2) * math.sqrt(abs(R2))\n",
    "    return(R)\n",
    "\n",
    "env = make()\n",
    "o = env.reset()\n",
    "excl = ['ID','y', 'timestamp']\n",
    "col = [c for c in o.train.columns if c not in excl]\n",
    "\n",
    "im = Imputer(strategy='mean')\n",
    "rs = RobustScaler(with_centering=False, quantile_range=(25.0, 75.0))\n",
    "rfr = RandomForestRegressor(n_estimators=10, max_depth=5, n_jobs=-1, random_state=123)\n",
    "sfm = SelectFromModel(rfr, threshold='mean')\n",
    "reg = Pipeline([('im', im), ('rs', rs), ('sfm', sfm), ('rfr', rfr)])\n",
    "mod1 = reg.fit(o.train[col], o.train.y)\n",
    "print('training completed...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0796681775885\n",
      "-0.169707488693\n",
      "-0.164118164993\n",
      "0.0835129707447\n",
      "-0.310454774892\n",
      "-0.141395260967\n",
      "-0.00465283100069\n",
      "0.0738992761139\n",
      "-0.146870700594\n",
      "-0.139766973134\n",
      "el fin ... 0.00404089695784\n"
     ]
    }
   ],
   "source": [
    "o = env.reset()\n",
    "i = 0; reward_ = []\n",
    "while True:\n",
    "    o.target.y = mod1.predict(o.features[col])\n",
    "    o, reward, done, info = env.step(o.target)\n",
    "    reward_.append([i,reward])\n",
    "    if i % 100 == 0:\n",
    "        print(reward)\n",
    "    i += 1\n",
    "    if done:\n",
    "        print(\"el fin ...\", info[\"public_score\"])\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8nQIAAGRJ2rvEEfw7LAtPpwA8o6OjTo2JAYC4uVIpSvDvuLQ/nQJAnOg+BLjLlUpRgn8A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\niIhUhIJ/EREREZGK+D8tzoPzM6az9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11371de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "_ = pd.DataFrame(reward_, columns=['timestamp','Reward']).plot(kind='bar', x='timestamp', y='Reward', figsize=(9,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##XGBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processed medians\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashokvardhan/.local/lib/python3.5/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "788557 [  4.7967096   10.07235529  -4.38579377  -9.20176369   8.77051007\n",
      "  -9.62992077] -6.13848550407e-05\n",
      "train proc\n",
      "33.25048112869263\n",
      "0.822220728131\n",
      "prepping xgb now\n",
      "xtrain+valid\n",
      "[0]\ttrain-rmse:0.460114\tvalid-rmse:0.46026\ttrain-error:-50.8505\tvalid-error:-21.0952\n",
      "Multiple eval metrics have been passed: 'valid-error' will be used for early stopping.\n",
      "\n",
      "Will train until valid-error hasn't improved in 20 rounds.\n",
      "[10]\ttrain-rmse:0.200035\tvalid-rmse:0.200736\ttrain-error:-22.089\tvalid-error:-9.15628\n",
      "[20]\ttrain-rmse:0.087273\tvalid-rmse:0.089216\ttrain-error:-9.59508\tvalid-error:-3.96964\n",
      "[30]\ttrain-rmse:0.038776\tvalid-rmse:0.04329\ttrain-error:-4.16795\tvalid-error:-1.71626\n",
      "[40]\ttrain-rmse:0.018706\tvalid-rmse:0.027074\ttrain-error:-1.80986\tvalid-error:-0.737048\n",
      "[50]\ttrain-rmse:0.011496\tvalid-rmse:0.022823\ttrain-error:-0.784104\tvalid-error:-0.310888\n",
      "[60]\ttrain-rmse:0.00954\tvalid-rmse:0.02196\ttrain-error:-0.33479\tvalid-error:-0.123644\n",
      "[70]\ttrain-rmse:0.009122\tvalid-rmse:0.021806\ttrain-error:-0.128976\tvalid-error:-0.033446\n",
      "[80]\ttrain-rmse:0.009038\tvalid-rmse:0.021781\ttrain-error:0.0431\tvalid-error:0.033825\n",
      "[89]\ttrain-rmse:0.009021\tvalid-rmse:0.021778\ttrain-error:0.075328\tvalid-error:0.037801\n",
      "[0]\ttrain-rmse:0.460113\tvalid-rmse:0.46026\ttrain-error:-50.8504\tvalid-error:-21.0952\n",
      "Multiple eval metrics have been passed: 'valid-error' will be used for early stopping.\n",
      "\n",
      "Will train until valid-error hasn't improved in 20 rounds.\n",
      "[10]\ttrain-rmse:0.200035\tvalid-rmse:0.200736\ttrain-error:-22.0889\tvalid-error:-9.15625\n",
      "[20]\ttrain-rmse:0.087275\tvalid-rmse:0.089217\ttrain-error:-9.59524\tvalid-error:-3.96967\n",
      "[30]\ttrain-rmse:0.038775\tvalid-rmse:0.043289\ttrain-error:-4.16781\tvalid-error:-1.71621\n",
      "[40]\ttrain-rmse:0.018705\tvalid-rmse:0.027072\ttrain-error:-1.80966\tvalid-error:-0.736938\n",
      "[50]\ttrain-rmse:0.011496\tvalid-rmse:0.022822\ttrain-error:-0.784023\tvalid-error:-0.310828\n",
      "[60]\ttrain-rmse:0.009539\tvalid-rmse:0.021959\ttrain-error:-0.334459\tvalid-error:-0.123192\n",
      "[70]\ttrain-rmse:0.00912\tvalid-rmse:0.021804\ttrain-error:-0.127919\tvalid-error:-0.030936\n",
      "[80]\ttrain-rmse:0.009037\tvalid-rmse:0.02178\ttrain-error:0.045564\tvalid-error:0.035716\n",
      "[89]\ttrain-rmse:0.00902\tvalid-rmse:0.021777\ttrain-error:0.077128\tvalid-error:0.039742\n",
      "xgb done, linear now\n",
      "linear done\n",
      "[0]\txvalid-rmse:0.480239\ttrain-rmse:0.480111\txvalid-error:-22.0129\ttrain-error:-53.0613\n",
      "Multiple eval metrics have been passed: 'train-error' will be used for early stopping.\n",
      "\n",
      "Will train until train-error hasn't improved in 20 rounds.\n",
      "[10]\txvalid-rmse:0.3196\ttrain-rmse:0.319265\txvalid-error:-14.6306\ttrain-error:-35.277\n",
      "[20]\txvalid-rmse:0.21301\ttrain-rmse:0.212366\txvalid-error:-9.72262\ttrain-error:-23.4533\n",
      "[30]\txvalid-rmse:0.142456\ttrain-rmse:0.14135\txvalid-error:-6.45962\ttrain-error:-15.5926\n",
      "[40]\txvalid-rmse:0.096006\ttrain-rmse:0.094216\txvalid-error:-4.2902\ttrain-error:-10.3664\n",
      "[50]\txvalid-rmse:0.065782\ttrain-rmse:0.063002\txvalid-error:-2.84791\ttrain-error:-6.89198\n",
      "[60]\txvalid-rmse:0.046581\ttrain-rmse:0.042427\txvalid-error:-1.88898\ttrain-error:-4.58197\n",
      "[70]\txvalid-rmse:0.034912\ttrain-rmse:0.029005\txvalid-error:-1.25147\ttrain-error:-3.04625\n",
      "[80]\txvalid-rmse:0.028289\ttrain-rmse:0.020433\txvalid-error:-0.827552\ttrain-error:-2.02519\n",
      "[90]\txvalid-rmse:0.024827\ttrain-rmse:0.015171\txvalid-error:-0.545663\ttrain-error:-1.34625\n",
      "[100]\txvalid-rmse:0.023149\ttrain-rmse:0.012139\txvalid-error:-0.358162\ttrain-error:-0.89468\n",
      "[110]\txvalid-rmse:0.02238\ttrain-rmse:0.010524\txvalid-error:-0.23348\ttrain-error:-0.594455\n",
      "[120]\txvalid-rmse:0.022039\ttrain-rmse:0.009725\txvalid-error:-0.150428\ttrain-error:-0.394585\n",
      "[130]\txvalid-rmse:0.021892\ttrain-rmse:0.00935\txvalid-error:-0.094955\ttrain-error:-0.261343\n",
      "[140]\txvalid-rmse:0.02183\ttrain-rmse:0.00918\txvalid-error:-0.057616\ttrain-error:-0.172128\n",
      "[150]\txvalid-rmse:0.021805\ttrain-rmse:0.009103\txvalid-error:-0.03189\ttrain-error:-0.111962\n",
      "[160]\txvalid-rmse:0.021795\ttrain-rmse:0.009069\txvalid-error:-0.011764\ttrain-error:-0.070392\n",
      "[170]\txvalid-rmse:0.021792\ttrain-rmse:0.009054\txvalid-error:0.012321\ttrain-error:-0.039736\n",
      "[180]\txvalid-rmse:0.021791\ttrain-rmse:0.009047\txvalid-error:0.015018\ttrain-error:-0.006983\n",
      "[187]\txvalid-rmse:0.021791\ttrain-rmse:0.009044\txvalid-error:0.014994\ttrain-error:0.021744\n",
      "Timestamp #1000 7.250293016433716\n",
      "Timestamp #1100 7.576431035995483\n",
      "Timestamp #1200 7.644201993942261\n",
      "Timestamp #1300 7.222301006317139\n",
      "Timestamp #1400 7.699357032775879\n",
      "Timestamp #1500 7.484405994415283\n",
      "Timestamp #1600 7.45297908782959\n",
      "Timestamp #1700 8.12748098373413\n",
      "Timestamp #1800 9.376013994216919\n",
      "Public score: 0.02341814181358075\n"
     ]
    }
   ],
   "source": [
    "##XGBoosting\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "import xgboost as xgb\n",
    "import math\n",
    "\n",
    "import time\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "from operator import itemgetter\n",
    "\n",
    "localrun = False\n",
    "usepublic = False\n",
    "vmode = False\n",
    "all_features = False # use w/vmode for feature selection.  run twice, cutting # of rounds to peak round\n",
    "\n",
    "# This is taken from Frans Slothoubers post on the contest discussion forum.\n",
    "# https://www.kaggle.com/slothouber/two-sigma-financial-modeling/kagglegym-emulation\n",
    "\n",
    "def r_score(y_true, y_pred, sample_weight=None, multioutput=None):\n",
    "    r2 = r2_score(y_true, y_pred, sample_weight=sample_weight,\n",
    "                  multioutput=multioutput)\n",
    "    r = (np.sign(r2)*np.sqrt(np.abs(r2)))\n",
    "    if r <= -1:\n",
    "        return -1\n",
    "    else:\n",
    "        return r\n",
    "\n",
    "# From the xgboost script (along with the param settings)\n",
    "    \n",
    "# Function XGBOOST ########################################################\n",
    "def xgb_obj_custom_r(y_pred, dtrain):\n",
    "    y_true = dtrain.get_label()\n",
    "    y_mean = np.mean(y_true)\n",
    "    y_median = np.median(y_true)\n",
    "    c1 = y_true\n",
    "    #c1 = y_true - y_mean\n",
    "    #c1 = y_true - y_median\n",
    "    grad = 2*(y_pred-y_true)/(c1**2)\n",
    "    hess = 2/(c1**2)\n",
    "    return grad, hess\n",
    "\n",
    "def xgb_eval_custom_r(y_pred, dtrain):\n",
    "    #y_pred = np.clip(y_pred, -0.075, .075)\n",
    "#    y_pred[y_pred > .075] = .075\n",
    "#    y_pred[y_pred < -.075] = -.075\n",
    "    y_true = dtrain.get_label()\n",
    "    ybar = np.sum(y_true)/len(y_true)\n",
    "    ssres = np.sum((y_true - y_pred) ** 2)\n",
    "    sstot = np.sum((y_true - ybar)**2)\n",
    "    r2 = 1 - ssres/sstot\n",
    "    error = np.sign(r2) * np.absolute(r2)**0.5\n",
    "    return 'error', error\n",
    "\n",
    "env = make()\n",
    "o = env.reset()\n",
    "\n",
    "#excl = [env.ID_COL_NAME, env.SAMPLE_COL_NAME, env.TARGET_COL_NAME, env.TIME_COL_NAME]\n",
    "excl = ['id', 'sample', 'y', 'timestamp']\n",
    "basecols = [c for c in o.train.columns if c not in excl]\n",
    "\n",
    "\n",
    "rcol_orig = ['Dtechnical_20', 'y_prev_pred_avg_diff', 'Dtechnical_21', 'technical_43_prev', 'technical_20', 'y_prev_pred_avgT0', 'y_prev_pred_mavg5', 'y_prev_pred_avgT1', 'fundamental_8_prev', 'Dtechnical_40', 'technical_7_prev', 'technical_7', 'fundamental_5', 'Dtechnical_30', 'technical_32_prev', 'technical_14_prev', 'fundamental_1', 'fundamental_43_prev', 'Dfundamental_22', 'Dtechnical_35', 'Dtechnical_6', 'Dtechnical_17', 'Dtechnical_27', 'Dfundamental_42', 'fundamental_1_prev', 'Dtechnical_0', 'technical_40', 'technical_40_prev', 'fundamental_36', 'Dfundamental_33', 'Dfundamental_48', 'technical_27_prev', 'fundamental_62_prev', 'fundamental_41_prev', 'Dfundamental_50', 'fundamental_48', 'derived_2_prev', 'Dtechnical_18', 'fundamental_35', 'Dfundamental_49', 'fundamental_26_prev', 'technical_28_prev', 'Dfundamental_63', 'fundamental_10_prev', 'fundamental_36_prev', 'fundamental_16', 'Dfundamental_8', 'fundamental_32', 'fundamental_40_prev', 'derived_0', 'Dfundamental_32', 'fundamental_17', 'Dtechnical_7', 'fundamental_25', 'technical_35', 'Dtechnical_19', 'technical_35_prev', 'fundamental_8', 'Dtechnical_32', 'Dfundamental_18', 'Dtechnical_37', 'fundamental_33_prev', 'Dtechnical_28', 'fundamental_46', 'Dfundamental_1', 'Dfundamental_45', 'fundamental_18', 'technical_12', 'technical_44', 'fundamental_22', 'Dtechnical_5', 'technical_17_prev', 'Dfundamental_25']\n",
    "rcol = rcol_orig.copy()\n",
    "\n",
    "if all_features:\n",
    "    rcol = []\n",
    "    for c in basecols:\n",
    "        rcol.append(c)\n",
    "        rcol.append(c + '_prev')\n",
    "        rcol.append('D' + c)\n",
    "\n",
    "backy_fset = ['technical_13', 'technical_20', 'technical_13_prev', 'technical_20_prev', 'technical_30_prev', 'technical_30']\n",
    "for f in backy_fset:\n",
    "    if f not in rcol:\n",
    "        rcol.append(f)\n",
    "\n",
    "def get_basecols(rcol):\n",
    "    duse = {}\n",
    "\n",
    "    for r in rcol:\n",
    "        if 'y' in r:\n",
    "            continue\n",
    "\n",
    "        if 'D' in r:\n",
    "            duse[r[1:]] = True\n",
    "        elif '_prev' in r:\n",
    "            duse[r[:-5]] = True\n",
    "        elif r in basecols:\n",
    "            duse[r] = True\n",
    "\n",
    "    return [k for k in duse.keys()]\n",
    "\n",
    "basecols_touse = get_basecols(rcol)\n",
    "\n",
    "if vmode:\n",
    "    train = pd.read_hdf('../input/train.h5')\n",
    "else:\n",
    "    train = o.train.copy()\n",
    "\n",
    "d_mean = o.train[basecols_touse].median(axis=0)\n",
    "for c in basecols_touse:\n",
    "    d_mean[c + '_prev'] = d_mean[c]\n",
    "    d_mean['D' + c] = 0\n",
    "\n",
    "median = {t[0]:t[1] for t in zip(d_mean.index, d_mean.values)}\n",
    "median['y'] = 0\n",
    "\n",
    "print('processed medians')\n",
    "\n",
    "class DataPrep:\n",
    "    \n",
    "    def __init__(self, yprev_model = None, keepinput = True, cols = rcol):\n",
    "        self.previnput = None\n",
    "        self.prevavg = 0\n",
    "        self.cols = cols.copy()\n",
    "        \n",
    "        self.basecols = get_basecols(self.cols)\n",
    "        self.keepcols = ['y', 'id', 'timestamp'] + self.basecols\n",
    "        \n",
    "        self.allinput = [] if keepinput else None\n",
    "        \n",
    "        self.dayavg = []\n",
    "        \n",
    "        self.yprev_model = yprev_model\n",
    "        \n",
    "    def procday(self, day_in):\n",
    "        \n",
    "        if 'y' not in day_in and 'y' in self.keepcols:\n",
    "            self.keepcols.remove('y')\n",
    "        \n",
    "        day = day_in[self.keepcols].copy()\n",
    "        \n",
    "        notinnew = []\n",
    "        \n",
    "        if self.previnput is not None:\n",
    "            olen = len(day)\n",
    "            day = pd.merge(day, self.previnput, on='id', how = 'left', suffixes=['', '_prev'])\n",
    "            notinnew = self.previnput[~self.previnput.id.isin(day_in.id)].copy()\n",
    "            #print(day.iloc[0].timestamp, len(notinnew))\n",
    "        else:\n",
    "            for c in self.basecols:\n",
    "                day[c + '_prev'] = np.full_like(day[c], 0, dtype=np.float32)\n",
    "                #day[c + '_prev'] = np.zeros_like(day[c], dtype=np.float32)\n",
    "        \n",
    "        for c in self.cols:\n",
    "            if c == 'y_prev_pred':\n",
    "                continue\n",
    "\n",
    "            if c[0] == 'D':\n",
    "                day[c] = day[c[1:]] - day[c[1:] + '_prev']\n",
    "                \n",
    "        self.previnput = day_in[self.keepcols].copy()\n",
    "        if len(notinnew) > 0:\n",
    "            self.previnput = self.previnput.append(notinnew[self.keepcols])\n",
    "        \n",
    "        if self.yprev_model:\n",
    "            day['y_prev_pred'] = self.yprev_model.predict(day[backy_fset].fillna(d_mean).values.reshape(-1,len(backy_fset)))\n",
    "\n",
    "            avg = day.y_prev_pred.mean()\n",
    "\n",
    "            self.dayavg.append(avg)\n",
    "            day['y_prev_pred_mavg5'] = np.ma.average(np.array(self.dayavg[-5:]))#, weights=range(1, len(self.dayavg[-10:]) + 1))\n",
    "            day['y_prev_pred_min5'] = day.y_prev_pred - day.y_prev_pred_mavg5\n",
    "            day['y_prev_pred_mavg5d'] = avg - np.ma.average(np.array(self.dayavg[-5:]))\n",
    "            \n",
    "            day['y_prev_pred_mavg10'] = np.ma.average(np.array(self.dayavg[-10:]))#, weights=range(1, len(self.dayavg[-10:]) + 1))\n",
    "            day['y_prev_pred_mavg20'] = np.ma.average(np.array(self.dayavg[-20:]))\n",
    "            day['y_prev_pred_mavg40'] = np.ma.average(np.array(self.dayavg[-40:]))\n",
    "            \n",
    "            day['y_prev_pred_avgT1'] = self.prevavg\n",
    "            day['y_prev_pred_avgT0'] = avg\n",
    "            day['y_prev_pred_avg_diff'] = avg - self.prevavg\n",
    "\n",
    "            self.prevavg = avg\n",
    "            \n",
    "        if self.allinput is not None:\n",
    "            self.allinput.append(day.copy())\n",
    "\n",
    "        return day\n",
    "    \n",
    "    def run(self, df):\n",
    "        assert self.allinput is not None\n",
    "        \n",
    "        for g in df.groupby('timestamp'):\n",
    "            self.procday(g[1])\n",
    "            \n",
    "        return pd.concat(self.allinput)\n",
    "\n",
    "yptrain = DataPrep(keepinput=True, cols=backy_fset).run(train)\n",
    "\n",
    "#yptrain_prep = tmp.run(yptrain)\n",
    "\n",
    "yptrain.sort_values(['id', 'timestamp'], inplace=True)\n",
    "\n",
    "ypmodel = LinearRegression(n_jobs=-1)\n",
    "low_y_cut = -0.0725\n",
    "high_y_cut = 0.075\n",
    "\n",
    "mask = np.logical_and(yptrain.y > low_y_cut, yptrain.y < high_y_cut)\n",
    "for f in backy_fset:\n",
    "    mask = np.logical_and(mask, ~yptrain[f].isnull())\n",
    "    \n",
    "yptraina = yptrain[mask]\n",
    "ypmodel.fit(yptraina[backy_fset].values.reshape(-1,len(backy_fset)), yptraina.y_prev.fillna(0))\n",
    "\n",
    "print(len(yptraina), ypmodel.coef_, ypmodel.intercept_)\n",
    "\n",
    "#630210 [  4.94327753  10.22880098  -4.53049511  -9.34886941   8.94329596\n",
    "#  -9.83007277] -2.68988841901e-05\n",
    "\n",
    "\n",
    "d_mean['y'] = 0\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "train = DataPrep(keepinput = True, yprev_model = ypmodel).run(train)\n",
    "\n",
    "print('train proc')\n",
    "\n",
    "endt = time.time()\n",
    "print(endt - start)\n",
    "\n",
    "dcol = [c for c in train.columns if c not in excl]\n",
    "\n",
    "if usepublic:\n",
    "    data_all = pd.read_hdf('train.h5')\n",
    "\n",
    "    #public = data_all[data_all.timestamp > 905]\n",
    "    allpublic = DataPrep(yprev_model = ypmodel, keepinput=True).run(data_all)\n",
    "    public = DataPrep(yprev_model = ypmodel, keepinput=True).run(data_all[data_all.timestamp > 905])\n",
    "\n",
    "print(r_score(train.y_prev.fillna(0), train.y_prev_pred))\n",
    "\n",
    "if usepublic:\n",
    "    print(r_score(public.y_prev.fillna(0), public.y_prev_pred))\n",
    "\n",
    "train.sort_values(['id', 'timestamp'], inplace=True)\n",
    "\n",
    "print('prepping xgb now')\n",
    "xtrain, xvalid = train_test_split(train, test_size = 0.2, random_state = 2017)\n",
    "\n",
    "cols_to_use = [c for c in rcol if c in xtrain.columns and c in rcol_orig] \n",
    "\n",
    "                                                      \n",
    "# Convert to XGB format\n",
    "to_drop = ['timestamp', 'y']\n",
    "xtrain = xtrain[np.abs(xtrain['y']) < 0.018976588919758796]\n",
    "#xtrain = xtrain[np.abs(xtrain['y']) < 0.015]\n",
    "train_xgb = xgb.DMatrix(data=xtrain[cols_to_use],\n",
    "                        label=xtrain['y'])\n",
    "\n",
    "#del xtrain\n",
    "\n",
    "# determined by using testing w/public set that this matches public_xgb better.\n",
    "# higher values make it end sooner.\n",
    "#xvalid = xvalid[np.abs(xvalid['y']) > 0.009]\n",
    "valid_xgb = xgb.DMatrix(data=xvalid[cols_to_use],\n",
    "                        label=xvalid['y'])\n",
    "\n",
    "#del xvalid\n",
    "\n",
    "evallist = [(train_xgb, 'train'), (valid_xgb, 'valid')]\n",
    "\n",
    "if usepublic:\n",
    "    public_xgb = xgb.DMatrix(data=public[cols_to_use], label=public['y'])\n",
    "\n",
    "    evallist = [(train_xgb, 'train'), (valid_xgb, 'xvalid'), (public_xgb, 'public')]\n",
    "\n",
    "print('xtrain+valid')\n",
    "\n",
    "params = {\n",
    "    'objective': 'reg:linear'\n",
    "    ,'eta': 0.08\n",
    "    ,'max_depth': 3\n",
    "    , 'subsample': 0.9\n",
    "    #, 'colsample_bytree': 1\n",
    "    ,'min_child_weight': 2**11\n",
    "    #,'gamma': 100\n",
    "    , 'seed': 10\n",
    "    #, 'base_score': 0\n",
    "}\n",
    "\n",
    "\n",
    "model = []\n",
    "for seed in [5041976, 31338]:\n",
    "    params['seed'] = seed\n",
    "    model.append(xgb.train(params.items()\n",
    "                  , dtrain=train_xgb\n",
    "                  , num_boost_round=90\n",
    "                  , evals=evallist\n",
    "                  , early_stopping_rounds=20\n",
    "                  , maximize=True\n",
    "                  , verbose_eval=10\n",
    "                  , feval=xgb_eval_custom_r\n",
    "                  ))\n",
    "\n",
    "if not localrun:\n",
    "    del train_xgb\n",
    "    del valid_xgb\n",
    "    if usepublic:\n",
    "        del public_xgb\n",
    "\n",
    "print('xgb done, linear now')\n",
    "\n",
    "lin_features = ['Dtechnical_20', 'technical_20', 'Dtechnical_21']\n",
    "\n",
    "def prep_linear(df, c = lin_features):\n",
    "    df_tmp = df.fillna(d_mean)\n",
    "    m2mat = np.zeros((len(df), len(c)))\n",
    "    for i in range(len(c)):\n",
    "        m2mat[:,i] = df_tmp[c[i]].values\n",
    "    \n",
    "    return m2mat\n",
    "\n",
    "# Observed with histograns:\n",
    "#https://www.kaggle.com/bguberfain/two-sigma-financial-modeling/univariate-model-with-clip/run/482189\n",
    "low_y_cut = -0.075\n",
    "high_y_cut = 0.075\n",
    "traincut = train[np.logical_and(train.y > low_y_cut, train.y < high_y_cut)][['y'] + lin_features].copy().fillna(d_mean)\n",
    "\n",
    "model2 = LinearRegression(n_jobs=-1)\n",
    "model2.fit(prep_linear(traincut), traincut.y)\n",
    "\n",
    "print('linear done')\n",
    "\n",
    "if vmode:\n",
    "    preds_xgb = model[0].predict(valid_xgb, ntree_limit=model[0].best_ntree_limit)\n",
    "    preds_linear = model2.predict(prep_linear(xvalid))\n",
    "    \n",
    "    preds = (preds_xgb * 0.7) + (preds_linear * 0.3)\n",
    "    #preds = preds_xgb\n",
    "    \n",
    "    rs = kagglegym.r_score(xvalid.y, preds)\n",
    "    \n",
    "    ID = 'expv-{0}.pkl'.format(int(rs * 10000000))\n",
    "    print(rs, ID)\n",
    "    \n",
    "    #ID = 'subv-203172.pkl' # if actual submission\n",
    "    \n",
    "    output = xvalid[['id', 'timestamp', 'y']].copy()\n",
    "    output['y_hat'] = preds\n",
    "    output['y_hat_xgb'] = preds_xgb\n",
    "    output['y_hat_linear'] = preds_linear\n",
    "    \n",
    "    output.to_pickle(ID)\n",
    "\n",
    "if all_features:\n",
    "    m = model[0]\n",
    "\n",
    "    fs = m.get_fscore()\n",
    "    fsl = [(f,fs[f]) for f in fs.keys()]\n",
    "    fsl = sorted(fsl, key=itemgetter(1), reverse=True)\n",
    "\n",
    "    len(fsl)\n",
    "\n",
    "    print('rcol =', [f[0] for f in fsl])\n",
    "\n",
    "razz_params = {\n",
    "    'objective': 'reg:linear'\n",
    "    ,'eta': 0.04\n",
    "    ,'max_depth': 3\n",
    "    , 'subsample': 0.9\n",
    "    #, 'colsample_bytree': 1\n",
    "    ,'min_child_weight': 2**11\n",
    "    #,'gamma': 100\n",
    "    , 'seed': 10\n",
    "    #, 'base_score': 0\n",
    "}\n",
    "\n",
    "frazz = ['Dtechnical_20', 'technical_43_prev']\n",
    "dtrain_razz = xgb.DMatrix(xtrain[frazz], label = xtrain.y)\n",
    "dvalid_razz = xgb.DMatrix(xvalid[frazz], label = xvalid.y)\n",
    "razz_evallist = [(dvalid_razz, 'xvalid'), (dtrain_razz, 'train')]\n",
    "if usepublic:\n",
    "    dpublic_razz = xgb.DMatrix(public[frazz], label = public.y)\n",
    "    razz_evallist = [(dtrain_razz, 'train'), (dvalid_razz, 'xvalid'), (dpublic_razz, 'public')]\n",
    "    \n",
    "model_razz = xgb.train(razz_params.items()\n",
    "                  , dtrain=dtrain_razz\n",
    "                  , num_boost_round=400 if usepublic else 188\n",
    "                  , evals=razz_evallist\n",
    "                  , early_stopping_rounds=20\n",
    "                  , maximize=True\n",
    "                  , verbose_eval=10\n",
    "                  , feval=xgb_eval_custom_r)\n",
    "\n",
    "start = time.time()        \n",
    "\n",
    "dprep = DataPrep(yprev_model = ypmodel, keepinput=localrun)\n",
    "        \n",
    "\n",
    "if localrun:\n",
    "    env = kagglegym.make()\n",
    "    o = env.reset()\n",
    "\n",
    "while True:\n",
    "    test_preproc = o.features.copy()\n",
    "    \n",
    "    #if c in basecols:\n",
    "        #test_preproc.fillna(d_mean, inplace=True)\n",
    "    \n",
    "    test = dprep.procday(test_preproc)\n",
    "    \n",
    "    #test.fillna(0, inplace=True)\n",
    "    \n",
    "    test_xgb = xgb.DMatrix(data=test.drop(['id', 'timestamp'], axis=1)[cols_to_use])\n",
    "    test_xgb_razz = xgb.DMatrix(data=test.drop(['id', 'timestamp'], axis=1)[frazz])\n",
    "\n",
    "    xgbpreds = np.zeros(len(test), dtype=np.float64)\n",
    "    for m in model:\n",
    "        xgbpreds += m.predict(test_xgb, ntree_limit=m.best_ntree_limit)\n",
    "    xgbpreds /= len(model)\n",
    "    \n",
    "    preds_razz = model_razz.predict(test_xgb_razz, ntree_limit=model_razz.best_ntree_limit)\n",
    "    \n",
    "    preds_linear = model2.predict(prep_linear(test)).clip(low_y_cut, high_y_cut)\n",
    "    \n",
    "    test_y = (preds_razz * 0.25) + (xgbpreds * 0.55) + (preds_linear * 0.2)\n",
    "    \n",
    "    o.target['y'] = test_y\n",
    "    target = o.target\n",
    "    \n",
    "    timestamp = o.features[\"timestamp\"][0]\n",
    "    if timestamp % 100 == 0:\n",
    "        print(\"Timestamp #{0} {1}\".format(timestamp, time.time() - start))\n",
    "        start = time.time()\n",
    "\n",
    "    # We perform a \"step\" by making our prediction and getting back an updated \"observation\":\n",
    "    o, reward, done, info = env.step(target)\n",
    "    if done:\n",
    "        print(\"Public score: {}\".format(info[\"public_score\"]))\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import math\n",
    "FEATURE_SIZE=108   #total number of features also is the number of input to the network\n",
    "batch_size=32 \n",
    "LEARNING_RATE=0.01\n",
    "hidden1_units=FEATURE_SIZE\n",
    "hidden2_units=32\n",
    "d = tf.placeholder(tf.float32, shape=(batch_size,FEATURE_SIZE))                                              \n",
    "y = tf.placeholder(tf.float32, shape=(batch_size,1))\n",
    "#hidden layer 1 that interface with the input\n",
    "with tf.name_scope('hidden1'):\n",
    "    weights = tf.Variable(tf.truncated_normal([FEATURE_SIZE, hidden1_units],\n",
    "                        stddev=1.0 / math.sqrt(float(FEATURE_SIZE))),name='weights')\n",
    "    biases = tf.Variable(tf.zeros([hidden1_units]),name='biases')\n",
    "    hidden1 = tf.nn.relu(tf.matmul(d, weights) + biases)\n",
    "#hidden layer 2 \n",
    "with tf.name_scope('hidden2'):\n",
    "    weights = tf.Variable(tf.truncated_normal([FEATURE_SIZE, hidden2_units],\n",
    "                        stddev=1.0 / math.sqrt(float(FEATURE_SIZE))),name='weights')\n",
    "    biases = tf.Variable(tf.zeros([hidden2_units]),name='biases')\n",
    "    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)\n",
    "#softmax output\n",
    "with tf.name_scope('softmax_linear'):\n",
    "    weights = tf.Variable(tf.truncated_normal([hidden2_units, 1],\n",
    "                            stddev=1.0 / math.sqrt(float(hidden2_units))),name='weights')\n",
    "    biases = tf.Variable(tf.zeros([1]),name='biases')\n",
    "    logits = tf.matmul(hidden2, weights) + biases    \n",
    "\n",
    "#y = tf.to_int64(y)\n",
    "#cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, y, name='xentropy')\n",
    "#loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##xgboosting Ridge regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train has 806298 rows\n",
      "Target column names: \"id\", \"y\"\n",
      "training xgb model...\n",
      "[0]\ttrain-rmse:0.350494\n",
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      "[397]\ttrain-rmse:0.019466\n",
      "[398]\ttrain-rmse:0.019462\n",
      "[399]\ttrain-rmse:0.019456\n",
      "training ridge regression...\n",
      "Timestamp #1000\n",
      "Timestamp #1100\n",
      "Timestamp #1200\n",
      "Timestamp #1300\n",
      "Timestamp #1400\n",
      "Timestamp #1500\n",
      "Timestamp #1600\n",
      "Timestamp #1700\n",
      "Timestamp #1800\n",
      "Public score: -0.17925078536842223\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# The \"environment\" is our interface for code competitions\n",
    "env = make()\n",
    "\n",
    "# We get our initial observation by calling \"reset\"\n",
    "observation = env.reset()\n",
    "\n",
    "# Note that the first observation we get has a \"train\" dataframe\n",
    "print(\"Train has {} rows\".format(len(observation.train)))\n",
    "\n",
    "# The \"target\" dataframe is a template for what we need to predict:\n",
    "print(\"Target column names: {}\".format(\", \".join(['\"{}\"'.format(col) for col in list(observation.target.columns)])))\n",
    "\n",
    "train = observation.train\n",
    "median_values = train.median(axis=0)\n",
    "train.fillna(median_values, inplace=True)\n",
    "\n",
    "#feat = [name for name in observation.train.columns if name not in ['id', 'timestamp', 'y']]\n",
    "feat = ['technical_20', 'fundamental_53', 'technical_30', 'technical_27', 'derived_0',\\\n",
    "        'fundamental_42', 'fundamental_48', 'technical_21', 'technical_24', 'fundamental_11',\\\n",
    "        'fundamental_44', 'technical_19', 'technical_13', 'technical_17', 'fundamental_9']\n",
    "\n",
    "dtrain = xgb.DMatrix(train[feat].values, label = train['y'].values)\n",
    "\n",
    "params = {}\n",
    "params['booster']  = 'gbtree'\n",
    "params['objective'] = 'reg:linear'\n",
    "params['max_depth'] = 8\n",
    "params['subsample'] = 0.8\n",
    "params['colsample_bytree'] = 0.8\n",
    "params['silent'] = 1\n",
    "params['eval_metric'] = 'rmse'\n",
    "num_round = 400\n",
    "eval_list  = [(dtrain,'train')]\n",
    "\n",
    "print('training xgb model...')\n",
    "bst = xgb.train(params, dtrain, num_round, eval_list)\n",
    "\n",
    "print('training ridge regression...')\n",
    "lr = Ridge()\n",
    "lr.fit(train[feat].values, train['y'].values)\n",
    "\n",
    "while True:\n",
    "    observation.features.fillna(median_values, inplace=True)\n",
    "    dtest = xgb.DMatrix(observation.features[feat].values)\n",
    "    xgb_pred = bst.predict(dtest)\n",
    "    lr_pred = lr.predict(observation.features[feat].values)  \n",
    "    observation.target.y = 0.5*xgb_pred+0.5*lr_pred\n",
    "\n",
    "    target = observation.target\n",
    "    timestamp = observation.features[\"timestamp\"][0]\n",
    "    if timestamp % 100 == 0:\n",
    "        print(\"Timestamp #{}\".format(timestamp))\n",
    "\n",
    "    # We perform a \"step\" by making our prediction and getting back an updated \"observation\":\n",
    "    observation, reward, done, info = env.step(target)\n",
    "    if done:\n",
    "        print(\"Public score: {}\".format(info[\"public_score\"]))\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow.contrib.layers as layers\n",
    "import tensorflow.contrib.losses as losses\n",
    "\n",
    "N_FEATURES=108\n",
    "LEARNING_RATE = 0.001\n",
    "\n",
    "x = tf.placeholder(tf.float32, shape=(None, N_FEATURES))\n",
    "y = tf.placeholder(tf.float32, shape=(None,1))\n",
    "p = tf.placeholder(tf.float32)\n",
    "logits = layers.fully_connected(x, 54, activation_fn=tf.nn.relu)\n",
    "logits = layers.dropout(logits, keep_prob=p)\n",
    "\n",
    "logits = layers.fully_connected(x, 54, activation_fn=tf.nn.relu)\n",
    "logits = layers.dropout(logits, keep_prob=p)\n",
    "logits = layers.fully_connected(x, 54, activation_fn=tf.nn.relu)\n",
    "logits = layers.dropout(logits, keep_prob=p)\n",
    "y_ = layers.fully_connected(logits, 1)\n",
    "\n",
    "loss = tf.losses.mean_squared_error(y, y_)\n",
    "\n",
    "# loss = tf.reduce_mean(tf.reduce_sum(tf.square(y-y_)) / tf.square(y_ - tf.reduce_mean(y_))) # Equivalent to minimize R2\n",
    "\n",
    "train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env = make()\n",
    "# We get our initial observation by calling \"reset\"\n",
    "observation = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "traindf, testdf = train_test_split(observation.train.drop(axis=1, labels=[\"id\", \"timestamp\"]).dropna(),\n",
    "                                  train_size=0.8,\n",
    "                                  test_size=0.2)\n",
    "\n",
    "Y_train = traindf[\"y\"]\n",
    "X_train = traindf.drop(axis=1, labels=[\"y\"])\n",
    "\n",
    "Y_test = testdf[\"y\"]\n",
    "X_test = testdf.drop(axis=1, labels=[\"y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feeding 2178 batches per epoch\n",
      "Loss on batch 0: 1.0046087503433228\n",
      "Loss on batch 1000: 0.0003389652702026069\n",
      "Loss on batch 2000: 0.00027543900068849325\n",
      "Loss on batch 0: 0.00021009094780310988\n",
      "Loss on batch 1000: 0.0003389652702026069\n",
      "Loss on batch 2000: 0.00027543900068849325\n"
     ]
    }
   ],
   "source": [
    "num_examples = X_train.shape[0]\n",
    "batch_size = 32\n",
    "n_epoch = 2\n",
    "n_batch = int(num_examples / batch_size)\n",
    "print(\"Feeding {} batches per epoch\".format(n_batch))\n",
    "start = 0\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for _ in range(n_epoch):\n",
    "    start = 0\n",
    "    for batch_idx in range(n_batch-1):\n",
    "    #for batch_idx in range(15):\n",
    "        feeding_dict = { x: X_train.iloc[start:(start+batch_size)].values,\n",
    "                        y: Y_train.iloc[start:(start+batch_size)].values.reshape(-1, 1),\n",
    "                       p:0.5}\n",
    "        start+=batch_size\n",
    "\n",
    "        _, l  = sess.run([train_op, loss], feed_dict=feeding_dict)\n",
    "\n",
    "        if not(batch_idx%1000):\n",
    "            print(\"Loss on batch {}: {}\".format(batch_idx, l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feeding 544 batches per epoch\n",
      "Total loss: 0.00023403196246363223\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.contrib.metrics as metrics\n",
    "\n",
    "smse, smse_update_op = metrics.streaming_mean_squared_error(y, y_)\n",
    "\n",
    "num_examples = X_test.shape[0]\n",
    "batch_size = 32\n",
    "n_batch = int(num_examples / batch_size)\n",
    "print(\"Feeding {} batches per epoch\".format(n_batch))\n",
    "start = 0\n",
    "\n",
    "sess.run(tf.local_variables_initializer())\n",
    "\n",
    "for batch_idx in range(n_batch-1):\n",
    "    feeding_dict = { x: X_test.iloc[start:(start+batch_size)].values,\n",
    "    y: Y_test.iloc[start:(start+batch_size)].values.reshape(-1, 1),\n",
    "                   p:1.}\n",
    "    start+=batch_size\n",
    "    sess.run(smse_update_op, feed_dict=feeding_dict)\n",
    "print(\"Total loss: {}\".format(sess.run(smse)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asset = df[ df['id'] == 12 ] # some random asset\n",
    "\n",
    "y1 = asset['fundamental_1'] # some fundamental indicators\n",
    "y2 = asset['fundamental_3'] \n",
    "y3 = asset['fundamental_6']\n",
    "y4 = asset['fundamental_19']\n",
    "\n",
    "py.iplot({'data': [{'y': y1}, {'y': y2}, {'y': y3}, {'y': y4}]})"
   ]
  }
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