Congestive Heart Failure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Varad\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(2)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv1D, MaxPool1D\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "\n",
    "\n",
    "sns.set(style='white', context='notebook', palette='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   label      cl1      cl2      cl3      cl4      cl5      cl6      cl7  \\\n",
      "0      0 -0.15256 -0.18260 -0.21046 -0.17843 -0.15295 -0.18675 -0.20970   \n",
      "1      0  0.81460  0.91668  0.99092  0.99092  0.99092  0.99092  0.99136   \n",
      "2      0  0.49070  0.45079  0.40279  0.29840  0.23562  0.23562  0.23688   \n",
      "3      0  0.21846  0.16398  0.11329  0.11329  0.11329  0.11329  0.11485   \n",
      "4      0  0.38019  0.34004  0.30267  0.30267  0.30267  0.30267  0.30267   \n",
      "\n",
      "       cl8      cl9    ...       cl491    cl492    cl493    cl494    cl495  \\\n",
      "0 -0.17432 -0.15406    ...     0.42617  0.42749  0.42749  0.47871  0.59030   \n",
      "1  1.01510  1.06120    ...    -3.34280 -4.15830 -4.57750 -5.00520 -5.29190   \n",
      "2  0.28875  0.32065    ...    -0.37281 -0.25422 -0.11002 -0.16828 -0.27202   \n",
      "3  0.17900  0.21661    ...    -0.47329  0.45015  0.93955  0.77506  0.44745   \n",
      "4  0.30267  0.30267    ...     0.29959  0.24987  0.15163  0.14762  0.14762   \n",
      "\n",
      "      cl496     cl497     cl498     cl499     cl500  \n",
      "0  0.659950  0.713680  0.743660  0.773480  0.849310  \n",
      "1 -4.891200 -3.965800 -3.050200 -2.104600 -1.283300  \n",
      "2 -0.204740 -0.105620 -0.104480 -0.104480 -0.073866  \n",
      "3  0.361440  0.323940  0.370540  0.428610  0.428790  \n",
      "4  0.095191 -0.001893 -0.046843 -0.083106 -0.119630  \n",
      "\n",
      "[5 rows x 501 columns]\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "data_taken = pd.read_csv(\"Datasets/dataset_Copy.csv\")\n",
    "print(data_taken.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    30000\n",
       "0    30000\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = data_taken.label\n",
    "X = data_taken.drop('label', axis=1)\n",
    "g = sns.countplot(y)\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        ..\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <td>-5.291900</td>\n",
       "      <td>-4.891200</td>\n",
       "      <td>-3.965800</td>\n",
       "      <td>-3.050200</td>\n",
       "      <td>-2.104600</td>\n",
       "      <td>-1.283300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.490700</td>\n",
       "      <td>0.450790</td>\n",
       "      <td>0.402790</td>\n",
       "      <td>0.298400</td>\n",
       "      <td>0.235620</td>\n",
       "      <td>0.235620</td>\n",
       "      <td>0.236880</td>\n",
       "      <td>0.288750</td>\n",
       "      <td>0.320650</td>\n",
       "      <td>0.320650</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.372810</td>\n",
       "      <td>-0.254220</td>\n",
       "      <td>-0.110020</td>\n",
       "      <td>-0.168280</td>\n",
       "      <td>-0.272020</td>\n",
       "      <td>-0.204740</td>\n",
       "      <td>-0.105620</td>\n",
       "      <td>-0.104480</td>\n",
       "      <td>-0.104480</td>\n",
       "      <td>-0.073866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.218460</td>\n",
       "      <td>0.163980</td>\n",
       "      <td>0.113290</td>\n",
       "      <td>0.113290</td>\n",
       "      <td>0.113290</td>\n",
       "      <td>0.113290</td>\n",
       "      <td>0.114850</td>\n",
       "      <td>0.179000</td>\n",
       "      <td>0.216610</td>\n",
       "      <td>0.164340</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.473290</td>\n",
       "      <td>0.450150</td>\n",
       "      <td>0.939550</td>\n",
       "      <td>0.775060</td>\n",
       "      <td>0.447450</td>\n",
       "      <td>0.361440</td>\n",
       "      <td>0.323940</td>\n",
       "      <td>0.370540</td>\n",
       "      <td>0.428610</td>\n",
       "      <td>0.428790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.380190</td>\n",
       "      <td>0.340040</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>0.302670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299590</td>\n",
       "      <td>0.249870</td>\n",
       "      <td>0.151630</td>\n",
       "      <td>0.147620</td>\n",
       "      <td>0.147620</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>-0.001893</td>\n",
       "      <td>-0.046843</td>\n",
       "      <td>-0.083106</td>\n",
       "      <td>-0.119630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.130700</td>\n",
       "      <td>-0.093764</td>\n",
       "      <td>-0.072983</td>\n",
       "      <td>-0.072983</td>\n",
       "      <td>-0.073755</td>\n",
       "      <td>-0.141040</td>\n",
       "      <td>-0.187930</td>\n",
       "      <td>-0.163270</td>\n",
       "      <td>-0.121670</td>\n",
       "      <td>-0.047385</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.175680</td>\n",
       "      <td>-0.220610</td>\n",
       "      <td>-0.245350</td>\n",
       "      <td>-0.246120</td>\n",
       "      <td>-0.246120</td>\n",
       "      <td>-0.246120</td>\n",
       "      <td>-0.246120</td>\n",
       "      <td>-0.297620</td>\n",
       "      <td>-0.361360</td>\n",
       "      <td>-0.340770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.358290</td>\n",
       "      <td>0.493760</td>\n",
       "      <td>0.599210</td>\n",
       "      <td>0.632980</td>\n",
       "      <td>0.644790</td>\n",
       "      <td>0.616930</td>\n",
       "      <td>0.599980</td>\n",
       "      <td>0.730010</td>\n",
       "      <td>0.919740</td>\n",
       "      <td>1.167000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.506420</td>\n",
       "      <td>-0.599840</td>\n",
       "      <td>-0.781570</td>\n",
       "      <td>-0.717250</td>\n",
       "      <td>-0.600610</td>\n",
       "      <td>-0.597780</td>\n",
       "      <td>-0.597780</td>\n",
       "      <td>-0.576460</td>\n",
       "      <td>-0.550060</td>\n",
       "      <td>-0.573020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.130370</td>\n",
       "      <td>0.115490</td>\n",
       "      <td>0.091294</td>\n",
       "      <td>0.066703</td>\n",
       "      <td>0.054090</td>\n",
       "      <td>0.054090</td>\n",
       "      <td>0.054090</td>\n",
       "      <td>0.054090</td>\n",
       "      <td>0.053421</td>\n",
       "      <td>0.034465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.400560</td>\n",
       "      <td>-0.377610</td>\n",
       "      <td>-0.329280</td>\n",
       "      <td>-0.337760</td>\n",
       "      <td>-0.362520</td>\n",
       "      <td>-0.386370</td>\n",
       "      <td>-0.403420</td>\n",
       "      <td>-0.390980</td>\n",
       "      <td>-0.366390</td>\n",
       "      <td>-0.342190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.670300</td>\n",
       "      <td>1.644200</td>\n",
       "      <td>1.619900</td>\n",
       "      <td>1.619900</td>\n",
       "      <td>1.618500</td>\n",
       "      <td>1.491000</td>\n",
       "      <td>1.357800</td>\n",
       "      <td>1.313300</td>\n",
       "      <td>1.245400</td>\n",
       "      <td>1.013400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.698110</td>\n",
       "      <td>-0.689650</td>\n",
       "      <td>-0.656910</td>\n",
       "      <td>-0.630640</td>\n",
       "      <td>-0.604800</td>\n",
       "      <td>-0.573480</td>\n",
       "      <td>-0.550910</td>\n",
       "      <td>-0.550680</td>\n",
       "      <td>-0.550680</td>\n",
       "      <td>-0.486930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.066110</td>\n",
       "      <td>-0.046800</td>\n",
       "      <td>-0.131120</td>\n",
       "      <td>-0.099990</td>\n",
       "      <td>-0.074840</td>\n",
       "      <td>-0.074840</td>\n",
       "      <td>-0.075685</td>\n",
       "      <td>-0.115680</td>\n",
       "      <td>-0.168230</td>\n",
       "      <td>-0.223080</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162770</td>\n",
       "      <td>-0.169010</td>\n",
       "      <td>-0.186870</td>\n",
       "      <td>-0.166450</td>\n",
       "      <td>-0.132050</td>\n",
       "      <td>-0.154360</td>\n",
       "      <td>-0.187220</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>-0.174010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.407130</td>\n",
       "      <td>-0.463190</td>\n",
       "      <td>-0.515360</td>\n",
       "      <td>-0.515360</td>\n",
       "      <td>-0.513920</td>\n",
       "      <td>-0.387740</td>\n",
       "      <td>-0.299970</td>\n",
       "      <td>-0.351160</td>\n",
       "      <td>-0.418420</td>\n",
       "      <td>-0.488620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125460</td>\n",
       "      <td>0.134060</td>\n",
       "      <td>0.134060</td>\n",
       "      <td>0.093455</td>\n",
       "      <td>0.027429</td>\n",
       "      <td>0.025827</td>\n",
       "      <td>0.025827</td>\n",
       "      <td>0.122400</td>\n",
       "      <td>0.241930</td>\n",
       "      <td>0.137960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.471290</td>\n",
       "      <td>0.435220</td>\n",
       "      <td>0.401780</td>\n",
       "      <td>0.440230</td>\n",
       "      <td>0.471290</td>\n",
       "      <td>0.471290</td>\n",
       "      <td>0.472320</td>\n",
       "      <td>0.514800</td>\n",
       "      <td>0.539700</td>\n",
       "      <td>0.505090</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225000</td>\n",
       "      <td>-0.201340</td>\n",
       "      <td>-0.109300</td>\n",
       "      <td>0.410270</td>\n",
       "      <td>1.301600</td>\n",
       "      <td>1.754700</td>\n",
       "      <td>2.000900</td>\n",
       "      <td>1.068700</td>\n",
       "      <td>-0.843000</td>\n",
       "      <td>-2.808200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.260590</td>\n",
       "      <td>0.260590</td>\n",
       "      <td>0.260410</td>\n",
       "      <td>0.199500</td>\n",
       "      <td>0.150290</td>\n",
       "      <td>0.150290</td>\n",
       "      <td>0.149480</td>\n",
       "      <td>0.115840</td>\n",
       "      <td>0.095145</td>\n",
       "      <td>0.095145</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.454160</td>\n",
       "      <td>-0.432530</td>\n",
       "      <td>-0.360440</td>\n",
       "      <td>-0.281260</td>\n",
       "      <td>-0.151840</td>\n",
       "      <td>0.464920</td>\n",
       "      <td>1.324900</td>\n",
       "      <td>1.651100</td>\n",
       "      <td>1.804400</td>\n",
       "      <td>0.956870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.038984</td>\n",
       "      <td>-0.076784</td>\n",
       "      <td>-0.111890</td>\n",
       "      <td>-0.084507</td>\n",
       "      <td>-0.033775</td>\n",
       "      <td>0.057609</td>\n",
       "      <td>0.106960</td>\n",
       "      <td>0.106960</td>\n",
       "      <td>0.105880</td>\n",
       "      <td>0.072936</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.398050</td>\n",
       "      <td>-0.403850</td>\n",
       "      <td>-0.403850</td>\n",
       "      <td>-0.448770</td>\n",
       "      <td>-0.541600</td>\n",
       "      <td>-0.593170</td>\n",
       "      <td>-0.622490</td>\n",
       "      <td>-0.557660</td>\n",
       "      <td>-0.477070</td>\n",
       "      <td>-0.476820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.092442</td>\n",
       "      <td>0.134450</td>\n",
       "      <td>0.173540</td>\n",
       "      <td>0.173540</td>\n",
       "      <td>0.172460</td>\n",
       "      <td>0.077914</td>\n",
       "      <td>0.012151</td>\n",
       "      <td>0.050507</td>\n",
       "      <td>0.100900</td>\n",
       "      <td>0.153500</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228740</td>\n",
       "      <td>-0.211930</td>\n",
       "      <td>-0.159320</td>\n",
       "      <td>-0.108930</td>\n",
       "      <td>-0.070572</td>\n",
       "      <td>-0.069762</td>\n",
       "      <td>-0.069762</td>\n",
       "      <td>-0.042942</td>\n",
       "      <td>0.009348</td>\n",
       "      <td>0.045973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.233300</td>\n",
       "      <td>-0.216180</td>\n",
       "      <td>-0.182360</td>\n",
       "      <td>-0.121860</td>\n",
       "      <td>-0.086213</td>\n",
       "      <td>-0.156310</td>\n",
       "      <td>-0.242090</td>\n",
       "      <td>-0.306820</td>\n",
       "      <td>-0.331840</td>\n",
       "      <td>-0.331840</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037545</td>\n",
       "      <td>-0.203990</td>\n",
       "      <td>-0.516180</td>\n",
       "      <td>-0.528930</td>\n",
       "      <td>-0.528930</td>\n",
       "      <td>-0.448040</td>\n",
       "      <td>-0.333160</td>\n",
       "      <td>-0.331840</td>\n",
       "      <td>-0.331840</td>\n",
       "      <td>-0.364930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.623330</td>\n",
       "      <td>0.657350</td>\n",
       "      <td>0.689010</td>\n",
       "      <td>0.689010</td>\n",
       "      <td>0.687830</td>\n",
       "      <td>0.570360</td>\n",
       "      <td>0.406090</td>\n",
       "      <td>0.235550</td>\n",
       "      <td>0.164680</td>\n",
       "      <td>0.197320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.497180</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>0.523610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.400630</td>\n",
       "      <td>-0.274470</td>\n",
       "      <td>-0.183090</td>\n",
       "      <td>-0.303420</td>\n",
       "      <td>-0.400140</td>\n",
       "      <td>-0.357800</td>\n",
       "      <td>-0.327990</td>\n",
       "      <td>-0.327990</td>\n",
       "      <td>-0.327510</td>\n",
       "      <td>-0.311930</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.251000</td>\n",
       "      <td>-0.272990</td>\n",
       "      <td>-0.291040</td>\n",
       "      <td>-0.291670</td>\n",
       "      <td>-0.291670</td>\n",
       "      <td>-0.336390</td>\n",
       "      <td>-0.399900</td>\n",
       "      <td>-0.400630</td>\n",
       "      <td>-0.400630</td>\n",
       "      <td>-0.387550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.114200</td>\n",
       "      <td>1.182700</td>\n",
       "      <td>1.205300</td>\n",
       "      <td>0.832970</td>\n",
       "      <td>0.117530</td>\n",
       "      <td>-1.314500</td>\n",
       "      <td>-2.089600</td>\n",
       "      <td>-1.321200</td>\n",
       "      <td>-0.404710</td>\n",
       "      <td>0.373480</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.244040</td>\n",
       "      <td>-0.140650</td>\n",
       "      <td>0.092540</td>\n",
       "      <td>0.102060</td>\n",
       "      <td>0.102060</td>\n",
       "      <td>0.026536</td>\n",
       "      <td>-0.080727</td>\n",
       "      <td>-0.028182</td>\n",
       "      <td>0.096142</td>\n",
       "      <td>0.310850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.108560</td>\n",
       "      <td>-0.126170</td>\n",
       "      <td>-0.154820</td>\n",
       "      <td>-0.183940</td>\n",
       "      <td>-0.198870</td>\n",
       "      <td>-0.198870</td>\n",
       "      <td>-0.198490</td>\n",
       "      <td>-0.179200</td>\n",
       "      <td>-0.146650</td>\n",
       "      <td>-0.088531</td>\n",
       "      <td>...</td>\n",
       "      <td>4.547300</td>\n",
       "      <td>3.776900</td>\n",
       "      <td>1.545100</td>\n",
       "      <td>-0.765640</td>\n",
       "      <td>-2.980900</td>\n",
       "      <td>-4.222600</td>\n",
       "      <td>-4.888900</td>\n",
       "      <td>-4.589100</td>\n",
       "      <td>-3.889400</td>\n",
       "      <td>-2.880800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.011685</td>\n",
       "      <td>0.043346</td>\n",
       "      <td>0.094372</td>\n",
       "      <td>0.035707</td>\n",
       "      <td>-0.010974</td>\n",
       "      <td>0.050949</td>\n",
       "      <td>0.093491</td>\n",
       "      <td>0.043247</td>\n",
       "      <td>-0.022769</td>\n",
       "      <td>-0.091673</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.122150</td>\n",
       "      <td>-0.144170</td>\n",
       "      <td>-0.213080</td>\n",
       "      <td>-0.279090</td>\n",
       "      <td>-0.329340</td>\n",
       "      <td>-0.330400</td>\n",
       "      <td>-0.330400</td>\n",
       "      <td>-0.283010</td>\n",
       "      <td>-0.224340</td>\n",
       "      <td>-0.224160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.290050</td>\n",
       "      <td>-0.454180</td>\n",
       "      <td>-0.606630</td>\n",
       "      <td>-0.519150</td>\n",
       "      <td>-0.448470</td>\n",
       "      <td>-0.448470</td>\n",
       "      <td>-0.447300</td>\n",
       "      <td>-0.398980</td>\n",
       "      <td>-0.371310</td>\n",
       "      <td>-0.421500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.343670</td>\n",
       "      <td>0.326560</td>\n",
       "      <td>0.274790</td>\n",
       "      <td>0.200700</td>\n",
       "      <td>0.114410</td>\n",
       "      <td>0.058940</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>0.062149</td>\n",
       "      <td>0.105890</td>\n",
       "      <td>0.077503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.469690</td>\n",
       "      <td>-0.407830</td>\n",
       "      <td>-0.320680</td>\n",
       "      <td>-0.181840</td>\n",
       "      <td>-0.022908</td>\n",
       "      <td>0.130430</td>\n",
       "      <td>0.269420</td>\n",
       "      <td>0.387370</td>\n",
       "      <td>0.485450</td>\n",
       "      <td>0.579460</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105080</td>\n",
       "      <td>-0.096219</td>\n",
       "      <td>-0.048621</td>\n",
       "      <td>0.170520</td>\n",
       "      <td>0.461260</td>\n",
       "      <td>0.387240</td>\n",
       "      <td>0.196560</td>\n",
       "      <td>-0.026377</td>\n",
       "      <td>-0.246130</td>\n",
       "      <td>-0.394500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.261480</td>\n",
       "      <td>0.223520</td>\n",
       "      <td>0.154200</td>\n",
       "      <td>0.056349</td>\n",
       "      <td>0.002307</td>\n",
       "      <td>0.122660</td>\n",
       "      <td>0.278920</td>\n",
       "      <td>0.415040</td>\n",
       "      <td>0.469810</td>\n",
       "      <td>0.469810</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.521820</td>\n",
       "      <td>-0.519750</td>\n",
       "      <td>-0.519750</td>\n",
       "      <td>-0.480670</td>\n",
       "      <td>-0.417130</td>\n",
       "      <td>-0.415580</td>\n",
       "      <td>-0.415580</td>\n",
       "      <td>-0.438820</td>\n",
       "      <td>-0.467580</td>\n",
       "      <td>-0.407600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.532240</td>\n",
       "      <td>0.437720</td>\n",
       "      <td>0.318350</td>\n",
       "      <td>0.225570</td>\n",
       "      <td>0.183250</td>\n",
       "      <td>0.244400</td>\n",
       "      <td>0.286760</td>\n",
       "      <td>0.253680</td>\n",
       "      <td>0.210610</td>\n",
       "      <td>0.171610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.269220</td>\n",
       "      <td>-0.256090</td>\n",
       "      <td>-0.210520</td>\n",
       "      <td>-0.156160</td>\n",
       "      <td>-0.101620</td>\n",
       "      <td>-0.079414</td>\n",
       "      <td>-0.063884</td>\n",
       "      <td>-0.046017</td>\n",
       "      <td>-0.028113</td>\n",
       "      <td>-0.012767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.461840</td>\n",
       "      <td>0.408910</td>\n",
       "      <td>0.359650</td>\n",
       "      <td>0.359650</td>\n",
       "      <td>0.360340</td>\n",
       "      <td>0.419900</td>\n",
       "      <td>0.461840</td>\n",
       "      <td>0.461840</td>\n",
       "      <td>0.467130</td>\n",
       "      <td>0.596620</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049100</td>\n",
       "      <td>0.020494</td>\n",
       "      <td>0.149990</td>\n",
       "      <td>0.116940</td>\n",
       "      <td>0.054601</td>\n",
       "      <td>0.080258</td>\n",
       "      <td>0.148670</td>\n",
       "      <td>0.264610</td>\n",
       "      <td>0.359380</td>\n",
       "      <td>0.359650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.335780</td>\n",
       "      <td>0.335780</td>\n",
       "      <td>0.335780</td>\n",
       "      <td>0.335780</td>\n",
       "      <td>0.334500</td>\n",
       "      <td>0.223200</td>\n",
       "      <td>0.147040</td>\n",
       "      <td>0.246070</td>\n",
       "      <td>0.348230</td>\n",
       "      <td>0.410630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.207790</td>\n",
       "      <td>0.147300</td>\n",
       "      <td>0.180650</td>\n",
       "      <td>0.238890</td>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.240300</td>\n",
       "      <td>0.197710</td>\n",
       "      <td>0.144990</td>\n",
       "      <td>0.190850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.253290</td>\n",
       "      <td>-0.231040</td>\n",
       "      <td>-0.194840</td>\n",
       "      <td>-0.158060</td>\n",
       "      <td>-0.140330</td>\n",
       "      <td>-0.240100</td>\n",
       "      <td>-0.309200</td>\n",
       "      <td>-0.255240</td>\n",
       "      <td>-0.184340</td>\n",
       "      <td>-0.110330</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.249200</td>\n",
       "      <td>-4.921400</td>\n",
       "      <td>-2.354900</td>\n",
       "      <td>-2.571200</td>\n",
       "      <td>-3.093200</td>\n",
       "      <td>-2.898500</td>\n",
       "      <td>-2.360800</td>\n",
       "      <td>-1.472800</td>\n",
       "      <td>-0.509380</td>\n",
       "      <td>0.045648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.847530</td>\n",
       "      <td>0.919380</td>\n",
       "      <td>0.959150</td>\n",
       "      <td>0.734990</td>\n",
       "      <td>0.502130</td>\n",
       "      <td>0.425030</td>\n",
       "      <td>0.401750</td>\n",
       "      <td>0.538730</td>\n",
       "      <td>0.622980</td>\n",
       "      <td>0.622980</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.064740</td>\n",
       "      <td>-0.050685</td>\n",
       "      <td>-0.050685</td>\n",
       "      <td>-0.112240</td>\n",
       "      <td>-0.257990</td>\n",
       "      <td>-0.398390</td>\n",
       "      <td>-0.498780</td>\n",
       "      <td>-0.469140</td>\n",
       "      <td>-0.391560</td>\n",
       "      <td>-0.203620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.851800</td>\n",
       "      <td>1.743900</td>\n",
       "      <td>1.635900</td>\n",
       "      <td>1.513900</td>\n",
       "      <td>1.425300</td>\n",
       "      <td>1.393400</td>\n",
       "      <td>1.349200</td>\n",
       "      <td>1.182800</td>\n",
       "      <td>0.969170</td>\n",
       "      <td>0.725030</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.189230</td>\n",
       "      <td>-0.186440</td>\n",
       "      <td>-0.186440</td>\n",
       "      <td>-0.160070</td>\n",
       "      <td>-0.117200</td>\n",
       "      <td>-0.173850</td>\n",
       "      <td>-0.255790</td>\n",
       "      <td>-0.225370</td>\n",
       "      <td>-0.186560</td>\n",
       "      <td>-0.186440</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>59970</th>\n",
       "      <td>1.197800</td>\n",
       "      <td>1.219300</td>\n",
       "      <td>1.233600</td>\n",
       "      <td>1.269400</td>\n",
       "      <td>1.255100</td>\n",
       "      <td>1.176400</td>\n",
       "      <td>1.147700</td>\n",
       "      <td>1.047500</td>\n",
       "      <td>1.004600</td>\n",
       "      <td>0.933030</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.154820</td>\n",
       "      <td>-0.212070</td>\n",
       "      <td>-0.212070</td>\n",
       "      <td>-0.183450</td>\n",
       "      <td>-0.097563</td>\n",
       "      <td>-0.119030</td>\n",
       "      <td>-0.068935</td>\n",
       "      <td>-0.054622</td>\n",
       "      <td>-0.068935</td>\n",
       "      <td>-0.018837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59971</th>\n",
       "      <td>0.063810</td>\n",
       "      <td>0.050872</td>\n",
       "      <td>-0.020287</td>\n",
       "      <td>-0.052632</td>\n",
       "      <td>-0.065570</td>\n",
       "      <td>-0.078508</td>\n",
       "      <td>-0.117320</td>\n",
       "      <td>-0.136730</td>\n",
       "      <td>-0.136730</td>\n",
       "      <td>-0.162600</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.188480</td>\n",
       "      <td>-0.046163</td>\n",
       "      <td>0.070279</td>\n",
       "      <td>0.141440</td>\n",
       "      <td>0.193190</td>\n",
       "      <td>0.251410</td>\n",
       "      <td>0.264350</td>\n",
       "      <td>0.296690</td>\n",
       "      <td>0.322570</td>\n",
       "      <td>0.348450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59972</th>\n",
       "      <td>0.167240</td>\n",
       "      <td>0.194920</td>\n",
       "      <td>0.236440</td>\n",
       "      <td>0.243350</td>\n",
       "      <td>0.250270</td>\n",
       "      <td>0.312550</td>\n",
       "      <td>0.326390</td>\n",
       "      <td>0.360980</td>\n",
       "      <td>0.381740</td>\n",
       "      <td>0.444020</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.109530</td>\n",
       "      <td>-0.116450</td>\n",
       "      <td>-0.095695</td>\n",
       "      <td>-0.081856</td>\n",
       "      <td>-0.109530</td>\n",
       "      <td>-0.123370</td>\n",
       "      <td>-0.095695</td>\n",
       "      <td>-0.102610</td>\n",
       "      <td>-0.109530</td>\n",
       "      <td>-0.130290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59973</th>\n",
       "      <td>-0.052431</td>\n",
       "      <td>-0.038648</td>\n",
       "      <td>-0.024865</td>\n",
       "      <td>-0.052431</td>\n",
       "      <td>-0.052431</td>\n",
       "      <td>-0.052431</td>\n",
       "      <td>-0.052431</td>\n",
       "      <td>-0.038648</td>\n",
       "      <td>-0.059323</td>\n",
       "      <td>-0.031757</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.355660</td>\n",
       "      <td>-0.410800</td>\n",
       "      <td>-0.369450</td>\n",
       "      <td>-0.252290</td>\n",
       "      <td>-0.197160</td>\n",
       "      <td>-0.507280</td>\n",
       "      <td>-1.210200</td>\n",
       "      <td>-2.092400</td>\n",
       "      <td>-2.974500</td>\n",
       "      <td>-3.856600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59974</th>\n",
       "      <td>-4.888600</td>\n",
       "      <td>-5.174500</td>\n",
       "      <td>-4.693000</td>\n",
       "      <td>-3.737700</td>\n",
       "      <td>-2.789900</td>\n",
       "      <td>-2.105300</td>\n",
       "      <td>-1.691600</td>\n",
       "      <td>-1.315500</td>\n",
       "      <td>-1.022100</td>\n",
       "      <td>-0.834040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008470</td>\n",
       "      <td>0.023515</td>\n",
       "      <td>0.023515</td>\n",
       "      <td>0.031037</td>\n",
       "      <td>0.053605</td>\n",
       "      <td>0.015993</td>\n",
       "      <td>-0.006575</td>\n",
       "      <td>-0.006575</td>\n",
       "      <td>-0.006575</td>\n",
       "      <td>-0.006575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59975</th>\n",
       "      <td>0.035906</td>\n",
       "      <td>0.028158</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>-0.018333</td>\n",
       "      <td>-0.041578</td>\n",
       "      <td>-0.033830</td>\n",
       "      <td>-0.026081</td>\n",
       "      <td>-0.026081</td>\n",
       "      <td>-0.026081</td>\n",
       "      <td>-0.049327</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074649</td>\n",
       "      <td>0.059152</td>\n",
       "      <td>0.051403</td>\n",
       "      <td>0.020409</td>\n",
       "      <td>-0.002836</td>\n",
       "      <td>-0.002836</td>\n",
       "      <td>0.028158</td>\n",
       "      <td>0.051403</td>\n",
       "      <td>0.082397</td>\n",
       "      <td>0.059152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59976</th>\n",
       "      <td>0.044358</td>\n",
       "      <td>0.057201</td>\n",
       "      <td>0.025094</td>\n",
       "      <td>0.012252</td>\n",
       "      <td>-0.000591</td>\n",
       "      <td>0.005830</td>\n",
       "      <td>0.025094</td>\n",
       "      <td>-0.000591</td>\n",
       "      <td>-0.000591</td>\n",
       "      <td>-0.000591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.545220</td>\n",
       "      <td>0.538800</td>\n",
       "      <td>0.564480</td>\n",
       "      <td>0.590170</td>\n",
       "      <td>0.686490</td>\n",
       "      <td>0.705750</td>\n",
       "      <td>0.718590</td>\n",
       "      <td>0.782810</td>\n",
       "      <td>0.821340</td>\n",
       "      <td>0.885550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59977</th>\n",
       "      <td>1.117000</td>\n",
       "      <td>1.177800</td>\n",
       "      <td>1.238500</td>\n",
       "      <td>1.276500</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>1.428300</td>\n",
       "      <td>1.511800</td>\n",
       "      <td>1.519400</td>\n",
       "      <td>1.618100</td>\n",
       "      <td>1.640800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016276</td>\n",
       "      <td>0.039051</td>\n",
       "      <td>0.039051</td>\n",
       "      <td>0.039051</td>\n",
       "      <td>0.039051</td>\n",
       "      <td>0.016276</td>\n",
       "      <td>0.077008</td>\n",
       "      <td>0.077008</td>\n",
       "      <td>0.077008</td>\n",
       "      <td>0.001093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59978</th>\n",
       "      <td>0.003536</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.074003</td>\n",
       "      <td>0.093221</td>\n",
       "      <td>0.093221</td>\n",
       "      <td>0.150880</td>\n",
       "      <td>0.157280</td>\n",
       "      <td>0.170090</td>\n",
       "      <td>0.118840</td>\n",
       "      <td>0.138060</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.994500</td>\n",
       "      <td>-2.180900</td>\n",
       "      <td>-1.367400</td>\n",
       "      <td>-0.899720</td>\n",
       "      <td>-0.630660</td>\n",
       "      <td>-0.476920</td>\n",
       "      <td>-0.316770</td>\n",
       "      <td>-0.227080</td>\n",
       "      <td>-0.156610</td>\n",
       "      <td>-0.073336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59979</th>\n",
       "      <td>-0.299810</td>\n",
       "      <td>-0.195370</td>\n",
       "      <td>-0.083959</td>\n",
       "      <td>0.013522</td>\n",
       "      <td>0.117970</td>\n",
       "      <td>0.152780</td>\n",
       "      <td>0.166710</td>\n",
       "      <td>0.264190</td>\n",
       "      <td>0.285080</td>\n",
       "      <td>0.292040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605370</td>\n",
       "      <td>0.487000</td>\n",
       "      <td>0.431300</td>\n",
       "      <td>0.305960</td>\n",
       "      <td>0.236340</td>\n",
       "      <td>0.166710</td>\n",
       "      <td>0.124930</td>\n",
       "      <td>0.076188</td>\n",
       "      <td>0.083151</td>\n",
       "      <td>0.013522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59980</th>\n",
       "      <td>0.181740</td>\n",
       "      <td>0.174600</td>\n",
       "      <td>0.181740</td>\n",
       "      <td>0.124630</td>\n",
       "      <td>0.160320</td>\n",
       "      <td>0.110350</td>\n",
       "      <td>0.103210</td>\n",
       "      <td>0.117490</td>\n",
       "      <td>0.096069</td>\n",
       "      <td>0.110350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053232</td>\n",
       "      <td>0.003256</td>\n",
       "      <td>-0.011023</td>\n",
       "      <td>-0.053861</td>\n",
       "      <td>-0.118120</td>\n",
       "      <td>-0.082419</td>\n",
       "      <td>-0.118120</td>\n",
       "      <td>-0.153810</td>\n",
       "      <td>-0.168090</td>\n",
       "      <td>-0.168090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59981</th>\n",
       "      <td>0.026981</td>\n",
       "      <td>0.039916</td>\n",
       "      <td>0.072252</td>\n",
       "      <td>0.046383</td>\n",
       "      <td>0.046383</td>\n",
       "      <td>0.065784</td>\n",
       "      <td>0.065784</td>\n",
       "      <td>0.059317</td>\n",
       "      <td>0.052850</td>\n",
       "      <td>0.039916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.402080</td>\n",
       "      <td>0.434410</td>\n",
       "      <td>0.473220</td>\n",
       "      <td>0.499090</td>\n",
       "      <td>0.492620</td>\n",
       "      <td>0.524960</td>\n",
       "      <td>0.531420</td>\n",
       "      <td>0.563760</td>\n",
       "      <td>0.563760</td>\n",
       "      <td>0.589630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59982</th>\n",
       "      <td>0.476210</td>\n",
       "      <td>0.525580</td>\n",
       "      <td>0.560840</td>\n",
       "      <td>0.610210</td>\n",
       "      <td>0.652530</td>\n",
       "      <td>0.673690</td>\n",
       "      <td>0.716000</td>\n",
       "      <td>0.751260</td>\n",
       "      <td>0.835890</td>\n",
       "      <td>0.920520</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116200</td>\n",
       "      <td>-0.123250</td>\n",
       "      <td>-0.116200</td>\n",
       "      <td>-0.123250</td>\n",
       "      <td>-0.151460</td>\n",
       "      <td>-0.109140</td>\n",
       "      <td>-0.151460</td>\n",
       "      <td>-0.144410</td>\n",
       "      <td>-0.123250</td>\n",
       "      <td>-0.116200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59983</th>\n",
       "      <td>-0.060835</td>\n",
       "      <td>-0.039313</td>\n",
       "      <td>-0.060835</td>\n",
       "      <td>-0.039313</td>\n",
       "      <td>-0.024965</td>\n",
       "      <td>-0.017791</td>\n",
       "      <td>-0.024965</td>\n",
       "      <td>-0.032139</td>\n",
       "      <td>-0.053661</td>\n",
       "      <td>-0.039313</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.089531</td>\n",
       "      <td>-0.068009</td>\n",
       "      <td>-0.082357</td>\n",
       "      <td>-0.118230</td>\n",
       "      <td>-0.089531</td>\n",
       "      <td>-0.075183</td>\n",
       "      <td>-0.103880</td>\n",
       "      <td>-0.139750</td>\n",
       "      <td>-0.161270</td>\n",
       "      <td>-0.182790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59984</th>\n",
       "      <td>-0.115960</td>\n",
       "      <td>0.009565</td>\n",
       "      <td>-0.059474</td>\n",
       "      <td>-0.429770</td>\n",
       "      <td>-1.157800</td>\n",
       "      <td>-1.961200</td>\n",
       "      <td>-2.764500</td>\n",
       "      <td>-3.567900</td>\n",
       "      <td>-4.283400</td>\n",
       "      <td>-4.484200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.762720</td>\n",
       "      <td>0.781550</td>\n",
       "      <td>0.806650</td>\n",
       "      <td>0.844310</td>\n",
       "      <td>0.907070</td>\n",
       "      <td>0.976110</td>\n",
       "      <td>1.001200</td>\n",
       "      <td>1.057700</td>\n",
       "      <td>1.101600</td>\n",
       "      <td>1.145600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59985</th>\n",
       "      <td>1.206400</td>\n",
       "      <td>1.227300</td>\n",
       "      <td>1.282900</td>\n",
       "      <td>1.310800</td>\n",
       "      <td>1.366500</td>\n",
       "      <td>1.352500</td>\n",
       "      <td>1.352500</td>\n",
       "      <td>1.359500</td>\n",
       "      <td>1.296900</td>\n",
       "      <td>1.262100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023368</td>\n",
       "      <td>0.037286</td>\n",
       "      <td>0.023368</td>\n",
       "      <td>0.030327</td>\n",
       "      <td>-0.011427</td>\n",
       "      <td>0.030327</td>\n",
       "      <td>0.030327</td>\n",
       "      <td>0.016409</td>\n",
       "      <td>0.002491</td>\n",
       "      <td>-0.011427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59986</th>\n",
       "      <td>0.121390</td>\n",
       "      <td>0.121390</td>\n",
       "      <td>0.128560</td>\n",
       "      <td>0.121390</td>\n",
       "      <td>0.121390</td>\n",
       "      <td>0.135720</td>\n",
       "      <td>0.092743</td>\n",
       "      <td>0.121390</td>\n",
       "      <td>0.099906</td>\n",
       "      <td>0.107070</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.107810</td>\n",
       "      <td>-0.107810</td>\n",
       "      <td>-0.150790</td>\n",
       "      <td>-0.165120</td>\n",
       "      <td>-0.157950</td>\n",
       "      <td>-0.157950</td>\n",
       "      <td>-0.136460</td>\n",
       "      <td>-0.186600</td>\n",
       "      <td>-0.236740</td>\n",
       "      <td>-0.258230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59987</th>\n",
       "      <td>-0.105420</td>\n",
       "      <td>-0.005330</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>-0.230530</td>\n",
       "      <td>-0.874840</td>\n",
       "      <td>-1.675500</td>\n",
       "      <td>-2.476200</td>\n",
       "      <td>-3.276900</td>\n",
       "      <td>-4.077600</td>\n",
       "      <td>-4.553000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.595190</td>\n",
       "      <td>0.626470</td>\n",
       "      <td>0.632730</td>\n",
       "      <td>0.670260</td>\n",
       "      <td>0.682770</td>\n",
       "      <td>0.720300</td>\n",
       "      <td>0.745330</td>\n",
       "      <td>0.795370</td>\n",
       "      <td>0.851670</td>\n",
       "      <td>0.857920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59988</th>\n",
       "      <td>0.926690</td>\n",
       "      <td>1.014900</td>\n",
       "      <td>1.089500</td>\n",
       "      <td>1.137000</td>\n",
       "      <td>1.191200</td>\n",
       "      <td>1.218300</td>\n",
       "      <td>1.286200</td>\n",
       "      <td>1.313300</td>\n",
       "      <td>1.354000</td>\n",
       "      <td>1.394700</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009347</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>-0.029695</td>\n",
       "      <td>-0.036478</td>\n",
       "      <td>-0.009347</td>\n",
       "      <td>-0.016130</td>\n",
       "      <td>-0.016130</td>\n",
       "      <td>0.011002</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>-0.016130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59989</th>\n",
       "      <td>0.048005</td>\n",
       "      <td>0.075948</td>\n",
       "      <td>0.068962</td>\n",
       "      <td>0.068962</td>\n",
       "      <td>0.027048</td>\n",
       "      <td>0.041020</td>\n",
       "      <td>0.020063</td>\n",
       "      <td>0.034034</td>\n",
       "      <td>0.054991</td>\n",
       "      <td>0.061976</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042808</td>\n",
       "      <td>-0.042808</td>\n",
       "      <td>-0.070750</td>\n",
       "      <td>-0.112660</td>\n",
       "      <td>-0.119650</td>\n",
       "      <td>-0.070750</td>\n",
       "      <td>-0.063765</td>\n",
       "      <td>-0.035822</td>\n",
       "      <td>-0.056779</td>\n",
       "      <td>-0.056779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59990</th>\n",
       "      <td>-0.036539</td>\n",
       "      <td>-0.042922</td>\n",
       "      <td>-0.036539</td>\n",
       "      <td>-0.068456</td>\n",
       "      <td>-0.087606</td>\n",
       "      <td>-0.074839</td>\n",
       "      <td>-0.049306</td>\n",
       "      <td>-0.055689</td>\n",
       "      <td>-0.081223</td>\n",
       "      <td>-0.068456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257100</td>\n",
       "      <td>0.359230</td>\n",
       "      <td>0.448600</td>\n",
       "      <td>0.454990</td>\n",
       "      <td>0.512440</td>\n",
       "      <td>0.544350</td>\n",
       "      <td>0.576270</td>\n",
       "      <td>0.595420</td>\n",
       "      <td>0.608190</td>\n",
       "      <td>0.633720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59991</th>\n",
       "      <td>0.554540</td>\n",
       "      <td>0.575910</td>\n",
       "      <td>0.590150</td>\n",
       "      <td>0.640010</td>\n",
       "      <td>0.668490</td>\n",
       "      <td>0.689860</td>\n",
       "      <td>0.711220</td>\n",
       "      <td>0.753950</td>\n",
       "      <td>0.775320</td>\n",
       "      <td>0.810930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412110</td>\n",
       "      <td>0.326650</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.191330</td>\n",
       "      <td>0.134360</td>\n",
       "      <td>0.091629</td>\n",
       "      <td>0.070263</td>\n",
       "      <td>0.041776</td>\n",
       "      <td>0.020411</td>\n",
       "      <td>0.006167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59992</th>\n",
       "      <td>0.141590</td>\n",
       "      <td>0.148760</td>\n",
       "      <td>0.134410</td>\n",
       "      <td>0.120060</td>\n",
       "      <td>0.141590</td>\n",
       "      <td>0.148760</td>\n",
       "      <td>0.120060</td>\n",
       "      <td>0.127240</td>\n",
       "      <td>0.134410</td>\n",
       "      <td>0.105710</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026791</td>\n",
       "      <td>0.033966</td>\n",
       "      <td>0.062665</td>\n",
       "      <td>0.134410</td>\n",
       "      <td>0.112890</td>\n",
       "      <td>0.127240</td>\n",
       "      <td>0.141590</td>\n",
       "      <td>0.098539</td>\n",
       "      <td>0.055490</td>\n",
       "      <td>0.026791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59993</th>\n",
       "      <td>0.123920</td>\n",
       "      <td>0.086204</td>\n",
       "      <td>0.042196</td>\n",
       "      <td>0.029623</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>-0.008097</td>\n",
       "      <td>-0.026957</td>\n",
       "      <td>0.004476</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>0.010763</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.239500</td>\n",
       "      <td>-4.044200</td>\n",
       "      <td>-4.528300</td>\n",
       "      <td>-4.213900</td>\n",
       "      <td>-3.415500</td>\n",
       "      <td>-2.617100</td>\n",
       "      <td>-1.818700</td>\n",
       "      <td>-1.095700</td>\n",
       "      <td>-0.781360</td>\n",
       "      <td>-0.573900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59994</th>\n",
       "      <td>-0.586800</td>\n",
       "      <td>-0.477570</td>\n",
       "      <td>-0.361500</td>\n",
       "      <td>-0.300050</td>\n",
       "      <td>-0.224950</td>\n",
       "      <td>-0.136190</td>\n",
       "      <td>-0.040610</td>\n",
       "      <td>0.048147</td>\n",
       "      <td>0.171040</td>\n",
       "      <td>0.225660</td>\n",
       "      <td>...</td>\n",
       "      <td>1.167800</td>\n",
       "      <td>1.202000</td>\n",
       "      <td>1.242900</td>\n",
       "      <td>1.304400</td>\n",
       "      <td>1.304400</td>\n",
       "      <td>1.331700</td>\n",
       "      <td>1.338500</td>\n",
       "      <td>1.331700</td>\n",
       "      <td>1.297600</td>\n",
       "      <td>1.277100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>1.240900</td>\n",
       "      <td>1.157600</td>\n",
       "      <td>1.053500</td>\n",
       "      <td>0.935580</td>\n",
       "      <td>0.831500</td>\n",
       "      <td>0.748240</td>\n",
       "      <td>0.623340</td>\n",
       "      <td>0.540070</td>\n",
       "      <td>0.435990</td>\n",
       "      <td>0.352730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075175</td>\n",
       "      <td>0.082114</td>\n",
       "      <td>0.089052</td>\n",
       "      <td>0.109870</td>\n",
       "      <td>0.068236</td>\n",
       "      <td>0.116810</td>\n",
       "      <td>0.082114</td>\n",
       "      <td>0.075175</td>\n",
       "      <td>0.095991</td>\n",
       "      <td>0.061297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>-0.055874</td>\n",
       "      <td>-0.042006</td>\n",
       "      <td>-0.083611</td>\n",
       "      <td>-0.083611</td>\n",
       "      <td>-0.062808</td>\n",
       "      <td>-0.083611</td>\n",
       "      <td>-0.076677</td>\n",
       "      <td>-0.083611</td>\n",
       "      <td>-0.062808</td>\n",
       "      <td>-0.083611</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.104410</td>\n",
       "      <td>-0.083611</td>\n",
       "      <td>-0.062808</td>\n",
       "      <td>-0.069742</td>\n",
       "      <td>-0.062808</td>\n",
       "      <td>-0.055874</td>\n",
       "      <td>-0.062808</td>\n",
       "      <td>-0.111350</td>\n",
       "      <td>-0.125210</td>\n",
       "      <td>-0.083611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>-0.140850</td>\n",
       "      <td>-0.121840</td>\n",
       "      <td>-0.134520</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.223220</td>\n",
       "      <td>-0.229560</td>\n",
       "      <td>-0.210550</td>\n",
       "      <td>-0.128180</td>\n",
       "      <td>-0.007794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.340700</td>\n",
       "      <td>0.391380</td>\n",
       "      <td>0.423070</td>\n",
       "      <td>0.461080</td>\n",
       "      <td>0.473750</td>\n",
       "      <td>0.480090</td>\n",
       "      <td>0.499100</td>\n",
       "      <td>0.505440</td>\n",
       "      <td>0.524440</td>\n",
       "      <td>0.549790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>0.451730</td>\n",
       "      <td>0.492850</td>\n",
       "      <td>0.506550</td>\n",
       "      <td>0.513410</td>\n",
       "      <td>0.554530</td>\n",
       "      <td>0.609350</td>\n",
       "      <td>0.616210</td>\n",
       "      <td>0.698450</td>\n",
       "      <td>0.753280</td>\n",
       "      <td>0.780690</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198150</td>\n",
       "      <td>0.150170</td>\n",
       "      <td>0.095345</td>\n",
       "      <td>0.047371</td>\n",
       "      <td>0.026811</td>\n",
       "      <td>-0.007457</td>\n",
       "      <td>-0.062284</td>\n",
       "      <td>-0.055431</td>\n",
       "      <td>-0.096552</td>\n",
       "      <td>-0.089698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>0.064428</td>\n",
       "      <td>0.050262</td>\n",
       "      <td>0.036096</td>\n",
       "      <td>0.064428</td>\n",
       "      <td>0.029013</td>\n",
       "      <td>0.029013</td>\n",
       "      <td>0.029013</td>\n",
       "      <td>0.007763</td>\n",
       "      <td>0.021929</td>\n",
       "      <td>0.021929</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006403</td>\n",
       "      <td>0.029013</td>\n",
       "      <td>0.036096</td>\n",
       "      <td>0.014846</td>\n",
       "      <td>-0.041819</td>\n",
       "      <td>-0.063068</td>\n",
       "      <td>-0.091401</td>\n",
       "      <td>-0.133900</td>\n",
       "      <td>-0.148070</td>\n",
       "      <td>-0.148070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cl1       cl2       cl3       cl4       cl5       cl6       cl7  \\\n",
       "0     -0.152560 -0.182600 -0.210460 -0.178430 -0.152950 -0.186750 -0.209700   \n",
       "1      0.814600  0.916680  0.990920  0.990920  0.990920  0.990920  0.991360   \n",
       "2      0.490700  0.450790  0.402790  0.298400  0.235620  0.235620  0.236880   \n",
       "3      0.218460  0.163980  0.113290  0.113290  0.113290  0.113290  0.114850   \n",
       "4      0.380190  0.340040  0.302670  0.302670  0.302670  0.302670  0.302670   \n",
       "5     -0.130700 -0.093764 -0.072983 -0.072983 -0.073755 -0.141040 -0.187930   \n",
       "6      0.358290  0.493760  0.599210  0.632980  0.644790  0.616930  0.599980   \n",
       "7      0.130370  0.115490  0.091294  0.066703  0.054090  0.054090  0.054090   \n",
       "8      1.670300  1.644200  1.619900  1.619900  1.618500  1.491000  1.357800   \n",
       "9      0.066110 -0.046800 -0.131120 -0.099990 -0.074840 -0.074840 -0.075685   \n",
       "10    -0.407130 -0.463190 -0.515360 -0.515360 -0.513920 -0.387740 -0.299970   \n",
       "11     0.471290  0.435220  0.401780  0.440230  0.471290  0.471290  0.472320   \n",
       "12     0.260590  0.260590  0.260410  0.199500  0.150290  0.150290  0.149480   \n",
       "13    -0.038984 -0.076784 -0.111890 -0.084507 -0.033775  0.057609  0.106960   \n",
       "14     0.092442  0.134450  0.173540  0.173540  0.172460  0.077914  0.012151   \n",
       "15    -0.233300 -0.216180 -0.182360 -0.121860 -0.086213 -0.156310 -0.242090   \n",
       "16     0.623330  0.657350  0.689010  0.689010  0.687830  0.570360  0.406090   \n",
       "17    -0.400630 -0.274470 -0.183090 -0.303420 -0.400140 -0.357800 -0.327990   \n",
       "18     1.114200  1.182700  1.205300  0.832970  0.117530 -1.314500 -2.089600   \n",
       "19    -0.108560 -0.126170 -0.154820 -0.183940 -0.198870 -0.198870 -0.198490   \n",
       "20    -0.011685  0.043346  0.094372  0.035707 -0.010974  0.050949  0.093491   \n",
       "21    -0.290050 -0.454180 -0.606630 -0.519150 -0.448470 -0.448470 -0.447300   \n",
       "22    -0.469690 -0.407830 -0.320680 -0.181840 -0.022908  0.130430  0.269420   \n",
       "23     0.261480  0.223520  0.154200  0.056349  0.002307  0.122660  0.278920   \n",
       "24     0.532240  0.437720  0.318350  0.225570  0.183250  0.244400  0.286760   \n",
       "25     0.461840  0.408910  0.359650  0.359650  0.360340  0.419900  0.461840   \n",
       "26     0.335780  0.335780  0.335780  0.335780  0.334500  0.223200  0.147040   \n",
       "27    -0.253290 -0.231040 -0.194840 -0.158060 -0.140330 -0.240100 -0.309200   \n",
       "28     0.847530  0.919380  0.959150  0.734990  0.502130  0.425030  0.401750   \n",
       "29     1.851800  1.743900  1.635900  1.513900  1.425300  1.393400  1.349200   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "59970  1.197800  1.219300  1.233600  1.269400  1.255100  1.176400  1.147700   \n",
       "59971  0.063810  0.050872 -0.020287 -0.052632 -0.065570 -0.078508 -0.117320   \n",
       "59972  0.167240  0.194920  0.236440  0.243350  0.250270  0.312550  0.326390   \n",
       "59973 -0.052431 -0.038648 -0.024865 -0.052431 -0.052431 -0.052431 -0.052431   \n",
       "59974 -4.888600 -5.174500 -4.693000 -3.737700 -2.789900 -2.105300 -1.691600   \n",
       "59975  0.035906  0.028158  0.004913 -0.018333 -0.041578 -0.033830 -0.026081   \n",
       "59976  0.044358  0.057201  0.025094  0.012252 -0.000591  0.005830  0.025094   \n",
       "59977  1.117000  1.177800  1.238500  1.276500  1.360000  1.428300  1.511800   \n",
       "59978  0.003536  0.029160  0.074003  0.093221  0.093221  0.150880  0.157280   \n",
       "59979 -0.299810 -0.195370 -0.083959  0.013522  0.117970  0.152780  0.166710   \n",
       "59980  0.181740  0.174600  0.181740  0.124630  0.160320  0.110350  0.103210   \n",
       "59981  0.026981  0.039916  0.072252  0.046383  0.046383  0.065784  0.065784   \n",
       "59982  0.476210  0.525580  0.560840  0.610210  0.652530  0.673690  0.716000   \n",
       "59983 -0.060835 -0.039313 -0.060835 -0.039313 -0.024965 -0.017791 -0.024965   \n",
       "59984 -0.115960  0.009565 -0.059474 -0.429770 -1.157800 -1.961200 -2.764500   \n",
       "59985  1.206400  1.227300  1.282900  1.310800  1.366500  1.352500  1.352500   \n",
       "59986  0.121390  0.121390  0.128560  0.121390  0.121390  0.135720  0.092743   \n",
       "59987 -0.105420 -0.005330  0.000926 -0.230530 -0.874840 -1.675500 -2.476200   \n",
       "59988  0.926690  1.014900  1.089500  1.137000  1.191200  1.218300  1.286200   \n",
       "59989  0.048005  0.075948  0.068962  0.068962  0.027048  0.041020  0.020063   \n",
       "59990 -0.036539 -0.042922 -0.036539 -0.068456 -0.087606 -0.074839 -0.049306   \n",
       "59991  0.554540  0.575910  0.590150  0.640010  0.668490  0.689860  0.711220   \n",
       "59992  0.141590  0.148760  0.134410  0.120060  0.141590  0.148760  0.120060   \n",
       "59993  0.123920  0.086204  0.042196  0.029623  0.017050 -0.008097 -0.026957   \n",
       "59994 -0.586800 -0.477570 -0.361500 -0.300050 -0.224950 -0.136190 -0.040610   \n",
       "59995  1.240900  1.157600  1.053500  0.935580  0.831500  0.748240  0.623340   \n",
       "59996 -0.055874 -0.042006 -0.083611 -0.083611 -0.062808 -0.083611 -0.076677   \n",
       "59997 -0.140850 -0.121840 -0.134520 -0.166200 -0.166200 -0.223220 -0.229560   \n",
       "59998  0.451730  0.492850  0.506550  0.513410  0.554530  0.609350  0.616210   \n",
       "59999  0.064428  0.050262  0.036096  0.064428  0.029013  0.029013  0.029013   \n",
       "\n",
       "            cl8       cl9      cl10    ...        cl491     cl492     cl493  \\\n",
       "0     -0.174320 -0.154060 -0.190810    ...     0.426170  0.427490  0.427490   \n",
       "1      1.015100  1.061200  1.174200    ...    -3.342800 -4.158300 -4.577500   \n",
       "2      0.288750  0.320650  0.320650    ...    -0.372810 -0.254220 -0.110020   \n",
       "3      0.179000  0.216610  0.164340    ...    -0.473290  0.450150  0.939550   \n",
       "4      0.302670  0.302670  0.302670    ...     0.299590  0.249870  0.151630   \n",
       "5     -0.163270 -0.121670 -0.047385    ...    -0.175680 -0.220610 -0.245350   \n",
       "6      0.730010  0.919740  1.167000    ...    -0.506420 -0.599840 -0.781570   \n",
       "7      0.054090  0.053421  0.034465    ...    -0.400560 -0.377610 -0.329280   \n",
       "8      1.313300  1.245400  1.013400    ...    -0.698110 -0.689650 -0.656910   \n",
       "9     -0.115680 -0.168230 -0.223080    ...    -0.162770 -0.169010 -0.186870   \n",
       "10    -0.351160 -0.418420 -0.488620    ...     0.125460  0.134060  0.134060   \n",
       "11     0.514800  0.539700  0.505090    ...    -0.225000 -0.201340 -0.109300   \n",
       "12     0.115840  0.095145  0.095145    ...    -0.454160 -0.432530 -0.360440   \n",
       "13     0.106960  0.105880  0.072936    ...    -0.398050 -0.403850 -0.403850   \n",
       "14     0.050507  0.100900  0.153500    ...    -0.228740 -0.211930 -0.159320   \n",
       "15    -0.306820 -0.331840 -0.331840    ...    -0.037545 -0.203990 -0.516180   \n",
       "16     0.235550  0.164680  0.197320    ...     0.497180  0.491950  0.491950   \n",
       "17    -0.327990 -0.327510 -0.311930    ...    -0.251000 -0.272990 -0.291040   \n",
       "18    -1.321200 -0.404710  0.373480    ...    -0.244040 -0.140650  0.092540   \n",
       "19    -0.179200 -0.146650 -0.088531    ...     4.547300  3.776900  1.545100   \n",
       "20     0.043247 -0.022769 -0.091673    ...    -0.122150 -0.144170 -0.213080   \n",
       "21    -0.398980 -0.371310 -0.421500    ...     0.343670  0.326560  0.274790   \n",
       "22     0.387370  0.485450  0.579460    ...    -0.105080 -0.096219 -0.048621   \n",
       "23     0.415040  0.469810  0.469810    ...    -0.521820 -0.519750 -0.519750   \n",
       "24     0.253680  0.210610  0.171610    ...    -0.269220 -0.256090 -0.210520   \n",
       "25     0.461840  0.467130  0.596620    ...    -0.049100  0.020494  0.149990   \n",
       "26     0.246070  0.348230  0.410630    ...     0.240300  0.207790  0.147300   \n",
       "27    -0.255240 -0.184340 -0.110330    ...    -6.249200 -4.921400 -2.354900   \n",
       "28     0.538730  0.622980  0.622980    ...    -0.064740 -0.050685 -0.050685   \n",
       "29     1.182800  0.969170  0.725030    ...    -0.189230 -0.186440 -0.186440   \n",
       "...         ...       ...       ...    ...          ...       ...       ...   \n",
       "59970  1.047500  1.004600  0.933030    ...    -0.154820 -0.212070 -0.212070   \n",
       "59971 -0.136730 -0.136730 -0.162600    ...    -0.188480 -0.046163  0.070279   \n",
       "59972  0.360980  0.381740  0.444020    ...    -0.109530 -0.116450 -0.095695   \n",
       "59973 -0.038648 -0.059323 -0.031757    ...    -0.355660 -0.410800 -0.369450   \n",
       "59974 -1.315500 -1.022100 -0.834040    ...     0.008470  0.023515  0.023515   \n",
       "59975 -0.026081 -0.026081 -0.049327    ...     0.074649  0.059152  0.051403   \n",
       "59976 -0.000591 -0.000591 -0.000591    ...     0.545220  0.538800  0.564480   \n",
       "59977  1.519400  1.618100  1.640800    ...     0.016276  0.039051  0.039051   \n",
       "59978  0.170090  0.118840  0.138060    ...    -2.994500 -2.180900 -1.367400   \n",
       "59979  0.264190  0.285080  0.292040    ...     0.605370  0.487000  0.431300   \n",
       "59980  0.117490  0.096069  0.110350    ...     0.053232  0.003256 -0.011023   \n",
       "59981  0.059317  0.052850  0.039916    ...     0.402080  0.434410  0.473220   \n",
       "59982  0.751260  0.835890  0.920520    ...    -0.116200 -0.123250 -0.116200   \n",
       "59983 -0.032139 -0.053661 -0.039313    ...    -0.089531 -0.068009 -0.082357   \n",
       "59984 -3.567900 -4.283400 -4.484200    ...     0.762720  0.781550  0.806650   \n",
       "59985  1.359500  1.296900  1.262100    ...     0.023368  0.037286  0.023368   \n",
       "59986  0.121390  0.099906  0.107070    ...    -0.107810 -0.107810 -0.150790   \n",
       "59987 -3.276900 -4.077600 -4.553000    ...     0.595190  0.626470  0.632730   \n",
       "59988  1.313300  1.354000  1.394700    ...    -0.009347  0.004219 -0.029695   \n",
       "59989  0.034034  0.054991  0.061976    ...    -0.042808 -0.042808 -0.070750   \n",
       "59990 -0.055689 -0.081223 -0.068456    ...     0.257100  0.359230  0.448600   \n",
       "59991  0.753950  0.775320  0.810930    ...     0.412110  0.326650  0.269670   \n",
       "59992  0.127240  0.134410  0.105710    ...     0.026791  0.033966  0.062665   \n",
       "59993  0.004476  0.010763  0.010763    ...    -3.239500 -4.044200 -4.528300   \n",
       "59994  0.048147  0.171040  0.225660    ...     1.167800  1.202000  1.242900   \n",
       "59995  0.540070  0.435990  0.352730    ...     0.075175  0.082114  0.089052   \n",
       "59996 -0.083611 -0.062808 -0.083611    ...    -0.104410 -0.083611 -0.062808   \n",
       "59997 -0.210550 -0.128180 -0.007794    ...     0.340700  0.391380  0.423070   \n",
       "59998  0.698450  0.753280  0.780690    ...     0.198150  0.150170  0.095345   \n",
       "59999  0.007763  0.021929  0.021929    ...    -0.006403  0.029013  0.036096   \n",
       "\n",
       "          cl494     cl495     cl496     cl497     cl498     cl499     cl500  \n",
       "0      0.478710  0.590300  0.659950  0.713680  0.743660  0.773480  0.849310  \n",
       "1     -5.005200 -5.291900 -4.891200 -3.965800 -3.050200 -2.104600 -1.283300  \n",
       "2     -0.168280 -0.272020 -0.204740 -0.105620 -0.104480 -0.104480 -0.073866  \n",
       "3      0.775060  0.447450  0.361440  0.323940  0.370540  0.428610  0.428790  \n",
       "4      0.147620  0.147620  0.095191 -0.001893 -0.046843 -0.083106 -0.119630  \n",
       "5     -0.246120 -0.246120 -0.246120 -0.246120 -0.297620 -0.361360 -0.340770  \n",
       "6     -0.717250 -0.600610 -0.597780 -0.597780 -0.576460 -0.550060 -0.573020  \n",
       "7     -0.337760 -0.362520 -0.386370 -0.403420 -0.390980 -0.366390 -0.342190  \n",
       "8     -0.630640 -0.604800 -0.573480 -0.550910 -0.550680 -0.550680 -0.486930  \n",
       "9     -0.166450 -0.132050 -0.154360 -0.187220 -0.187600 -0.187600 -0.174010  \n",
       "10     0.093455  0.027429  0.025827  0.025827  0.122400  0.241930  0.137960  \n",
       "11     0.410270  1.301600  1.754700  2.000900  1.068700 -0.843000 -2.808200  \n",
       "12    -0.281260 -0.151840  0.464920  1.324900  1.651100  1.804400  0.956870  \n",
       "13    -0.448770 -0.541600 -0.593170 -0.622490 -0.557660 -0.477070 -0.476820  \n",
       "14    -0.108930 -0.070572 -0.069762 -0.069762 -0.042942  0.009348  0.045973  \n",
       "15    -0.528930 -0.528930 -0.448040 -0.333160 -0.331840 -0.331840 -0.364930  \n",
       "16     0.491950  0.491950  0.491950  0.491950  0.491950  0.491950  0.523610  \n",
       "17    -0.291670 -0.291670 -0.336390 -0.399900 -0.400630 -0.400630 -0.387550  \n",
       "18     0.102060  0.102060  0.026536 -0.080727 -0.028182  0.096142  0.310850  \n",
       "19    -0.765640 -2.980900 -4.222600 -4.888900 -4.589100 -3.889400 -2.880800  \n",
       "20    -0.279090 -0.329340 -0.330400 -0.330400 -0.283010 -0.224340 -0.224160  \n",
       "21     0.200700  0.114410  0.058940  0.027110  0.062149  0.105890  0.077503  \n",
       "22     0.170520  0.461260  0.387240  0.196560 -0.026377 -0.246130 -0.394500  \n",
       "23    -0.480670 -0.417130 -0.415580 -0.415580 -0.438820 -0.467580 -0.407600  \n",
       "24    -0.156160 -0.101620 -0.079414 -0.063884 -0.046017 -0.028113 -0.012767  \n",
       "25     0.116940  0.054601  0.080258  0.148670  0.264610  0.359380  0.359650  \n",
       "26     0.180650  0.238890  0.240300  0.240300  0.197710  0.144990  0.190850  \n",
       "27    -2.571200 -3.093200 -2.898500 -2.360800 -1.472800 -0.509380  0.045648  \n",
       "28    -0.112240 -0.257990 -0.398390 -0.498780 -0.469140 -0.391560 -0.203620  \n",
       "29    -0.160070 -0.117200 -0.173850 -0.255790 -0.225370 -0.186560 -0.186440  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "59970 -0.183450 -0.097563 -0.119030 -0.068935 -0.054622 -0.068935 -0.018837  \n",
       "59971  0.141440  0.193190  0.251410  0.264350  0.296690  0.322570  0.348450  \n",
       "59972 -0.081856 -0.109530 -0.123370 -0.095695 -0.102610 -0.109530 -0.130290  \n",
       "59973 -0.252290 -0.197160 -0.507280 -1.210200 -2.092400 -2.974500 -3.856600  \n",
       "59974  0.031037  0.053605  0.015993 -0.006575 -0.006575 -0.006575 -0.006575  \n",
       "59975  0.020409 -0.002836 -0.002836  0.028158  0.051403  0.082397  0.059152  \n",
       "59976  0.590170  0.686490  0.705750  0.718590  0.782810  0.821340  0.885550  \n",
       "59977  0.039051  0.039051  0.016276  0.077008  0.077008  0.077008  0.001093  \n",
       "59978 -0.899720 -0.630660 -0.476920 -0.316770 -0.227080 -0.156610 -0.073336  \n",
       "59979  0.305960  0.236340  0.166710  0.124930  0.076188  0.083151  0.013522  \n",
       "59980 -0.053861 -0.118120 -0.082419 -0.118120 -0.153810 -0.168090 -0.168090  \n",
       "59981  0.499090  0.492620  0.524960  0.531420  0.563760  0.563760  0.589630  \n",
       "59982 -0.123250 -0.151460 -0.109140 -0.151460 -0.144410 -0.123250 -0.116200  \n",
       "59983 -0.118230 -0.089531 -0.075183 -0.103880 -0.139750 -0.161270 -0.182790  \n",
       "59984  0.844310  0.907070  0.976110  1.001200  1.057700  1.101600  1.145600  \n",
       "59985  0.030327 -0.011427  0.030327  0.030327  0.016409  0.002491 -0.011427  \n",
       "59986 -0.165120 -0.157950 -0.157950 -0.136460 -0.186600 -0.236740 -0.258230  \n",
       "59987  0.670260  0.682770  0.720300  0.745330  0.795370  0.851670  0.857920  \n",
       "59988 -0.036478 -0.009347 -0.016130 -0.016130  0.011002  0.004219 -0.016130  \n",
       "59989 -0.112660 -0.119650 -0.070750 -0.063765 -0.035822 -0.056779 -0.056779  \n",
       "59990  0.454990  0.512440  0.544350  0.576270  0.595420  0.608190  0.633720  \n",
       "59991  0.191330  0.134360  0.091629  0.070263  0.041776  0.020411  0.006167  \n",
       "59992  0.134410  0.112890  0.127240  0.141590  0.098539  0.055490  0.026791  \n",
       "59993 -4.213900 -3.415500 -2.617100 -1.818700 -1.095700 -0.781360 -0.573900  \n",
       "59994  1.304400  1.304400  1.331700  1.338500  1.331700  1.297600  1.277100  \n",
       "59995  0.109870  0.068236  0.116810  0.082114  0.075175  0.095991  0.061297  \n",
       "59996 -0.069742 -0.062808 -0.055874 -0.062808 -0.111350 -0.125210 -0.083611  \n",
       "59997  0.461080  0.473750  0.480090  0.499100  0.505440  0.524440  0.549790  \n",
       "59998  0.047371  0.026811 -0.007457 -0.062284 -0.055431 -0.096552 -0.089698  \n",
       "59999  0.014846 -0.041819 -0.063068 -0.091401 -0.133900 -0.148070 -0.148070  \n",
       "\n",
       "[60000 rows x 500 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of cl1      False\n",
       "cl2      False\n",
       "cl3      False\n",
       "cl4      False\n",
       "cl5      False\n",
       "cl6      False\n",
       "cl7      False\n",
       "cl8      False\n",
       "cl9      False\n",
       "cl10     False\n",
       "cl11     False\n",
       "cl12     False\n",
       "cl13     False\n",
       "cl14     False\n",
       "cl15     False\n",
       "cl16     False\n",
       "cl17     False\n",
       "cl18     False\n",
       "cl19     False\n",
       "cl20     False\n",
       "cl21     False\n",
       "cl22     False\n",
       "cl23     False\n",
       "cl24     False\n",
       "cl25     False\n",
       "cl26     False\n",
       "cl27     False\n",
       "cl28     False\n",
       "cl29     False\n",
       "cl30     False\n",
       "         ...  \n",
       "cl471    False\n",
       "cl472    False\n",
       "cl473    False\n",
       "cl474    False\n",
       "cl475    False\n",
       "cl476    False\n",
       "cl477    False\n",
       "cl478    False\n",
       "cl479    False\n",
       "cl480    False\n",
       "cl481    False\n",
       "cl482    False\n",
       "cl483    False\n",
       "cl484    False\n",
       "cl485    False\n",
       "cl486    False\n",
       "cl487    False\n",
       "cl488    False\n",
       "cl489    False\n",
       "cl490    False\n",
       "cl491    False\n",
       "cl492    False\n",
       "cl493    False\n",
       "cl494    False\n",
       "cl495    False\n",
       "cl496    False\n",
       "cl497    False\n",
       "cl498    False\n",
       "cl499    False\n",
       "cl500    False\n",
       "Length: 500, dtype: bool>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data\n",
    "X.isnull().any().describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "X_train:\n",
      "\n",
      "            cl1      cl2       cl3       cl4       cl5       cl6      cl7  \\\n",
      "9996  -0.283840 -0.31571 -0.345320 -0.322230 -0.279620 -0.213700 -0.15523   \n",
      "57432  0.849660  0.91628  0.997720  1.005100  1.042100  1.034700  1.03470   \n",
      "6744   0.145620  0.17939  0.195160  0.167760  0.146360  0.216950  0.30302   \n",
      "55126  0.071549  0.13258  0.132580  0.181400  0.218010  0.291250  0.34007   \n",
      "41565  0.139720  0.00014  0.035035  0.035035 -0.034756  0.035035 -0.20923   \n",
      "\n",
      "           cl8       cl9      cl10    ...        cl491     cl492     cl493  \\\n",
      "9996  -0.12799 -0.085125  0.072314    ...    -0.224740 -0.243250 -0.282240   \n",
      "57432  1.04210  1.027300  1.056900    ...     0.227820  0.235220  0.235220   \n",
      "6744   0.35914  0.399790  0.425440    ...    -0.549080 -0.549080 -0.549080   \n",
      "55126  0.44992  0.449920  0.474330    ...    -0.001684  0.071549 -0.001684   \n",
      "41565  0.10483  0.000140 -0.034756    ...     0.523570  0.558470  0.523570   \n",
      "\n",
      "          cl494     cl495     cl496     cl497     cl498     cl499     cl500  \n",
      "9996  -0.237660 -0.162580 -0.211270 -0.283010 -0.238400 -0.162840 -0.120600  \n",
      "57432  0.220410  0.190800  0.213010  0.176000  0.213010  0.190800  0.205610  \n",
      "6744  -0.562690 -0.594890 -0.625920 -0.648100 -0.648330 -0.648330 -0.630460  \n",
      "55126 -0.001684 -0.001684 -0.026095  0.010521 -0.062712 -0.087123 -0.074917  \n",
      "41565  0.523570  0.523570  0.488680  0.418890  0.383990  0.314200  0.349090  \n",
      "\n",
      "[5 rows x 500 columns]\n",
      "(30000, 500)\n",
      "\n",
      "X_test:\n",
      "\n",
      "           cl1       cl2       cl3       cl4       cl5       cl6       cl7  \\\n",
      "10327 -0.45956 -0.415210 -0.375500 -0.348750 -0.318130 -0.249250 -0.212870   \n",
      "11901  0.13053  0.352850  0.637110  0.773920  0.826120  0.476590  0.231950   \n",
      "13305  0.39119  0.408580  0.440310  0.480380  0.512120  0.524030  0.536780   \n",
      "27477  0.57629  0.719900  0.853530  0.853530  0.853530  0.853530  0.855580   \n",
      "55627 -0.16571 -0.079071 -0.050192 -0.035752 -0.079071 -0.079071 -0.079071   \n",
      "\n",
      "           cl8      cl9      cl10   ...        cl491     cl492     cl493  \\\n",
      "10327 -0.24642 -0.26705 -0.267050   ...     0.935900  0.954430  0.966530   \n",
      "11901  0.29292  0.32007  0.066769   ...     0.230470  0.332570  0.522540   \n",
      "13305  0.56059  0.58649  0.608750   ...     1.427600  1.415700  1.385000   \n",
      "27477  0.94014  0.99035  0.946440   ...    -0.047481 -0.047481 -0.047481   \n",
      "55627 -0.12239 -0.09351 -0.021313   ...     0.253040  0.224160  0.166400   \n",
      "\n",
      "          cl494     cl495     cl496     cl497     cl498    cl499    cl500  \n",
      "10327  1.014700  1.097600  1.168500  1.238900  1.307800  1.37940  1.46340  \n",
      "11901  0.448110  0.253500  0.066037 -0.068014 -0.003261  0.12562  0.25244  \n",
      "13305  1.370200  1.359100  1.348900  1.335500  1.264800  1.14730  1.04950  \n",
      "27477 -0.047481 -0.047481 -0.019034  0.021364 -0.040008 -0.11655 -0.11679  \n",
      "55627  0.151960  0.209720  0.224160  0.166400  0.195280  0.10864  0.15196  \n",
      "\n",
      "[5 rows x 500 columns]\n",
      "(30000, 500)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y,test_size=0.5,random_state=2)\n",
    "print(\"\\nX_train:\\n\")\n",
    "print(X_train.head())\n",
    "print(X_train.shape)\n",
    "print(\"\\nX_test:\\n\")\n",
    "print(X_test.head())\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data\n",
    "X_train = X_train / 255.0\n",
    "X_test = X_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 500)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
    "X_train = X_train.values.reshape(-1,500,1)\n",
    "X_test = X_test.values.reshape(-1,500,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 500, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the random seed\n",
    "random_seed = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the train and the validation set for the fitting\n",
    "#X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 500, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\n",
    "Y_train = to_categorical(Y_train, num_classes = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\n",
    "Y_test = to_categorical(Y_test, num_classes = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 496, 5)            30        \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 496, 5)            0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 248, 5)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 244, 5)            130       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 244, 5)            0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 122, 5)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 120, 10)           160       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 120, 10)           0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 60, 10)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 58, 10)            310       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 58, 10)            0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 29, 10)            0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 290)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 40)                11640     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                820       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 42        \n",
      "=================================================================\n",
      "Total params: 13,132\n",
      "Trainable params: 13,132\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import LeakyReLU\n",
    "\n",
    "# Set the CNN model \n",
    "# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(filters = 5, kernel_size = 5, strides = 1, input_shape = (500,1)))\n",
    "model.add(LeakyReLU(alpha=0.01))\n",
    "model.add(MaxPool1D(pool_size=2, strides=2))\n",
    "\n",
    "model.add(Conv1D(filters = 5, kernel_size = 5, strides = 1))\n",
    "model.add(LeakyReLU(alpha=0.01))\n",
    "model.add(MaxPool1D(pool_size=2, strides=2))\n",
    "\n",
    "model.add(Conv1D(filters = 10, kernel_size = 3, strides = 1))\n",
    "model.add(LeakyReLU(alpha=0.01))\n",
    "model.add(MaxPool1D(pool_size=2, strides=2))\n",
    "\n",
    "model.add(Conv1D(filters = 10, kernel_size = 3, strides = 1))\n",
    "model.add(LeakyReLU(alpha=0.01))\n",
    "model.add(MaxPool1D(pool_size=2, strides=2))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(40))\n",
    "model.add(LeakyReLU(alpha=0.01))\n",
    "model.add(Dense(20))\n",
    "model.add(LeakyReLU(alpha=0.01))\n",
    "model.add(Dense(2, activation = \"softmax\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 500, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the optimizer\n",
    "optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a learning rate annealer\n",
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=3, verbose=0, factor=0.5, min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 30 # Turn epochs to 30 to get 0.9967 accuracy\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 500, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Without data augmentation i obtained an accuracy of 0.98114\n",
    "model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs, \n",
    "         validation_data = (X_test, Y_test), verbose = 0)\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at confusion matrix \n",
    "\n",
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Predict the values from the validation dataset\n",
    "Y_pred = model.predict(X_test)\n",
    "# Convert predictions classes to one hot vectors \n",
    "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
    "# Convert validation observations to one hot vectors\n",
    "Y_true = np.argmax(Y_test,axis = 1) \n",
    "# compute the confusion matrix\n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "# plot the confusion matrix\n",
    "plot_confusion_matrix(confusion_mtx, classes = range(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and accuracy curves for training and validation \n",
    "fig, ax = plt.subplots(2,1)\n",
    "ax[0].plot(history.history['loss'], color='b', label=\"Training loss\")\n",
    "ax[0].plot(history.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\n",
    "legend = ax[0].legend(loc='best', shadow=True)\n",
    "\n",
    "ax[1].plot(history.history['acc'], color='b', label=\"Training accuracy\")\n",
    "ax[1].plot(history.history['val_acc'], color='r',label=\"Validation accuracy\")\n",
    "legend = ax[1].legend(loc='best', shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}