Case 1 - Heart Disease Classification.ipynb

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    "Case 1 - Heart Disease Classification\n",
    "=====================================\n",
    "31.1.2020  \n",
    "Neural Networks for Health Technology Applications  \n",
    "Helsinki Metropolia University of Applied Sciences\n",
    "\n",
    "Introduction\n",
    "------------\n",
    "\n",
    "In this exercise, the aim is to create and train a dense neural network to predict the presence of heart disease using the dataset at https://archive.ics.uci.edu/ml/datasets/Heart+Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import tensorflow as tf\n",
    "from scipy.signal import savgol_filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset\n",
    "-------\n",
    "\n",
    "Let's load the UCI Heart Disease dataset and look further into its features.\n",
    "\n",
    "The Cleveland database used here is a subset of this dataset. It has 14 attributes, contains 303 samples, and is commonly used in machine learning experiments. The presence of heart disease is distinguished by values 1, 2, 3 and 4. Value 0 denotes the absence of heart disease. It is not specified in the dataset documentation, which medical conditions are considered encompassed by \"heart disease\". The assumption is that coronary artery disease is the prevailing diagnosis in this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalac</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>54.438944</td>\n",
       "      <td>0.679868</td>\n",
       "      <td>3.158416</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>4.734219</td>\n",
       "      <td>0.937294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.467299</td>\n",
       "      <td>0.960126</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.994971</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>1.939706</td>\n",
       "      <td>1.228536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
       "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  241.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  275.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg      thalac       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  299.000000   \n",
       "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.672241   \n",
       "std      0.994971   22.875003    0.469794    1.161075    0.616226    0.937438   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal         num  \n",
       "count  301.000000  303.000000  \n",
       "mean     4.734219    0.937294  \n",
       "std      1.939706    1.228536  \n",
       "min      3.000000    0.000000  \n",
       "25%      3.000000    0.000000  \n",
       "50%      3.000000    0.000000  \n",
       "75%      7.000000    2.000000  \n",
       "max      7.000000    4.000000  "
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = r'http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data'\n",
    "col_names = ['age','sex','cp','trestbps','chol','fbs','restecg','thalac','exang','oldpeak','slope','ca','thal','num']\n",
    "df = pd.read_csv(url, \n",
    "                 sep = ',', \n",
    "                 header = None, \n",
    "                 index_col = None,\n",
    "                 names = col_names,\n",
    "                 na_values = '?')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above is a summary containing statistical key figures of the dataset in use. The data is loaded into a pandas data frame.\n",
    "\n",
    "Let's print a few key figures and make a quick histogram visualization of the data to get an idea of what we're dealing with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size: 303 rows, of which 6 (2.0 %) contain empty values.\n",
      "Healthy: 164 (54.1 %) Diseased: 139 (45.9 %)\n"
     ]
    },
    {
     "data": {
      "image/png": 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wF5JWkV1jc0UXnsOsLBYBs6vK5gPLI2I6sDxNAxwNTE+3ubjXQDMzM7OmdOSiiYgYAobS40eAQzqx3fGknXEGrLgi4lZJ06qK55CNIQBZD4FDZH8GzAGuSl1s3iZp4sgI0L2J1szMzKycOn8luJmNZmAkUYmI9ZL2SuVTgDUVy430HviypGYsPQe22otdUXqpK3Kvg80oe/zQH/tgZmb9z0mNWXE01XvgWHoOvPTqG1vqxa4ovdQVudfBZpQ9fuiPfTAzs/7XjWtqzKyxDZImA6T7jal8GJhasZx7DzQzMzNrgpMas95bStYzILy8h8ClwCnKHAZs8vU0ZmZmZqNz8zOzLpJ0DVmnAJMkDQPnAQuAJZLOBB4Djk+L30zWnfMqsi6dT+95wGZmZmYl5KTGrIsi4sQ6s46ssWwAZ3U3IjMzM7P+46SmhlrdK9cayMjMzMzMzPLnpMbMzEpF0mrgWWArsCUiZknaA7gOmAasBj4YEU/lFaOZmfWWOwowM7MyOiIiZkbErDQ9H1geEdOB5WnazMzGCSc1ZmbWD+YAi9PjxcBxOcZiZmY95uZnZmZWNgH8QFIAX08D0g6MdIEeEesl7VVrRUlzgbkAAwMDDA0NdTSweTO2NLXcwM7NL9tNmzdv7vhr4BjMLA9OaszMrGwOj4h1KXFZJunBZldMCdBCgFmzZsXg4GBHA2u2Q5l5M7Zw0Yr8D8GLZk+g06/BWA0NDTkGM2ubm5+ZmVmpRMS6dL8RuAE4BNggaTJAut+YX4RmZtZrTmrMzKw0JE2QtOvIY+DdwH3AUuDUtNipwI35RGhmZnnI/9y3mZlZ8waAGyRBdgz7VkR8X9JPgCWSzgQeA47PMUYzM+sxJzVmZlYaEfEI8LYa5b8Cjux9RGZmVgRufmZmZmZmZqXmpMbMzMzMzErNSY2ZmZmZmZWakxozMzMzMys1dxRgZmZmNg5IWg08C2wFtkTELEl7ANcB04DVwAcj4qm8YjRrlZMaMyuEaTVGYp83Y0tTI7SvXnBsN0IyM+tHR0TEExXT84HlEbFA0vw0fU4+oZm1zs3PzMzMzMavOcDi9HgxcFyOsZi1zGdqzMzMzMaHAH4gKYCvR8RCYCAi1gNExHpJe9VaUdJcYC7AwMAAQ0NDNZ9g8+bNdecV0Yq1m7YpG9gZLr36xobrzZjy6m6FVNe8GVuaWm5g522XzeM9aTbeaq1+hlpOaiRNBa4CXgu8BCyMiIvdNtPMzKwcVqzd1FQTz2pu8llah0fEupS4LJP0YLMrpgRoIcCsWbNicHCw5nJDQ0PUm1dEtT7/82Zs4aIVjX8irz5psEsR1ddsXa0Vf5HjrbZo9oSWPkPtND/bAsyLiDcDhwFnSTqA37bNnA4sT9NmZmZmlqOIWJfuNwI3AIcAGyRNBkj3G/OL0Kx1LSc1EbE+Iu5Oj58FVgJTcNtMMzMzs0KRNEHSriOPgXcD9wFLgVPTYqcCjdtdmRVUR66pkTQNOAi4nQ63zcxDrTaAtdon9rNu7W+R3ue8uWtNMzProQHgBkmQ/f77VkR8X9JPgCWSzgQeA47PMUazlrWd1EjaBfg28ImIeCZVllE12zYzD622r+wn3drfPNp0Fpy71jQzs66LiEeAt9Uo/xVwZO8jMuustrp0lrQDWUJzdUR8JxW7baZZ69x808zMzGyM2un9TMAVwMqI+ELFrJG2mQtw20yzRlrqWnMsTTdbbUZYlK4fm42/qM0ay9a1aS39sA9mZtb/2mlfdDhwMrBC0j2p7FNkyYzbZpqNrqWuNcfSdPPSq29sqRlhUbp+bLYZZFGbNZata9Na+mEfzMys/7Wc1ETEj4B6F9C4babZKCq71pT0sq4101kaN980q9JgjLTzgY8Aj6dFPxURN+cTpZmZ9Vpb19SYWWvctaZZy+qNkQbwxYiYmW5OaMzMxpHx052XWbG4a02zFqRrzkauO3tW0sgYaWZmNo45qTHLgbvWNGtf1RhphwNnSzoFuJPsbM42Yzx1e4y0ZjvmKMrYZ0XoTKQInVEUIQYza4+TGjMzK50aY6RdBlxA1qvgBcBFwBnV63V7jLRaHV7UUpSxz1qNo5OdcxShM4oixGBm7cn/G9XGlWlNHvBrWb3g2A5GYtaeZj7L82Zs2eZHrj/H7as1RlpEbKiYfzlwU07hmZlZDtxRgJmZlUa9MdJGBn1O3k/W8YaZmY0TPlNjZmZlUm+MtBMlzSRrfrYa+Gg+4ZmZWR6c1JiZWWk0GCPNXTibmY1jbn5mZmZmZmal5qTGzMzMzMxKzc3PzMzMzKwjVqzd1HTX5tXcO6S1o6+Tmna6DzYzMzMzs3Jw8zMzMzMzMys1JzVmZmZmZlZqTmrMzMzMzKzU+vqaGjMza+/6wkWzJ3QwkvLwNZlmZuXiMzVmZmZmZlZqTmrMzMzMzKzUnNSYmZmZmVmpOakxMzMzM7NSc1JjZmZmZmal5qTGzMzMzMxKzV06W2m02sXq6gXHdjgSMzMzMysSJzVmZmbWM9V/UM2bsYXTmvjTyn9QmVkjXUtqJM0GLga2A74REQu69Vxm/cR1x6w1rju948FJ+4vrjvWDriQ1krYDvgIcBQwDP5G0NCIeGOu2/MVp40kn647ZeOK6Y9Ya1x3rF906U3MIsCoiHgGQdC0wB3AFMWvMdcesNa471nHt/LFaouZyrjvWFxQRnd+o9AFgdkR8OE2fDBwaEWdXLDMXmJsm9wce6nggnTUJeCLvIHpoPO3vvhGxZ95BQFfqTtnfR8efv0b7UKq6k8qLcuwpymejCHGMxxj6ue4U4f1sV9n3oZ/jr1t3unWmRjXKXpY9RcRCYGGXnr/jJN0ZEbPyjqNXxtv+FkhH607Z30fHn78S7cOodQeKc+wpyutahDgcQ+46Wnf64bUs+z6M1/i7NU7NMDC1YnofYF2Xnsusn7jumLXGdcesNa471he6ldT8BJguaT9JOwInAEu79Fxm/cR1x6w1rjtmrXHdsb7QleZnEbFF0tnAv5B1D3hlRNzfjefqodybK/TYeNvfQuhC3Sn7++j481eKfSjhcacor2sR4nAMOfJxp6ay78O4jL8rHQWYmZmZmZn1Srean5mZmZmZmfWEkxozMzMzMys1JzV1SNpO0k8l3ZSm95N0u6SHJV2XLqbrC5ImSrpe0oOSVkp6h6Q9JC1L+7tM0u55x2nNkzRb0kOSVkman3c8YyXpSkkbJd2XdyytkDRV0i2pPt0v6eN5xzQWkl4p6Q5JP0vxfybvmMqm3meg3nerMpekOnuvpIM7EEPN97He8UzSTml6VZo/rd0YKmJp6pjarRgkrZa0QtI9ku5MZT17L8YDf2/nq1++t6u/K8bCSU19HwdWVkz/HfDFiJgOPAWcmUtU3XEx8P2I+B3gbWT7PR9YnvZ3eZq2EpC0HfAV4GjgAOBESQfkG9WYLQJm5x1EG7YA8yLizcBhwFklew9eAN4VEW8DZgKzJR2Wc0xlU+8zUO+79WhgerrNBS7rQAz13sd6x7Mzgaci4o3AF9NyndLsMbWbMRwRETMrxr/o5XsxHizC39t56pfv7erviqY5qalB0j7AscA30rSAdwHXp0UWA8flE11nSdoNeCdwBUBEvBgRTwNzyPYT+mh/x4lDgFUR8UhEvAhcS/Z+lkZE3Ao8mXccrYqI9RFxd3r8LNkX9JR8o2peZDanyR3Szb3KjEGDz0C979Y5wFXptb8NmChpcpsx1Hsf6x3PKmO7HjgyHf/aMsZjaldiqKNn78V44O/tfPXD93b1d8VYOamp7UvAJ4GX0vRrgKcjYkuaHqZEH/RRvB54HPhmOt33DUkTgIGIWA9ZRQf2yjNIG5MpwJqK6X76vJZOaj5zEHB7vpGMTWoCcA+wEVgWEaWKv0iqPgP1vlu7Um+r30fg59Q/nv0mhjR/E9nxr11jOaZ2K4YAfiDpLklzU1lP3wsrD39v56b6u2JMnNRUkfReYGNE3FVZXGPRUmW/DWwPHAxcFhEHAc/hpmZl18+f11KRtAvwbeATEfFM3vGMRURsjYiZZKOLHyLpLXnHVEZj+Ax0pd5Wv4/Amxs8T8djaOGY2q3vr8Mj4mCypmVnSXpng2X9HTqO+Xs7H3W+K8bESc22DgfeJ2k1WbOdd5FljhMljQxWug+wLp/wOm4YGK7I5q8nS3I2jJxuT/cbc4rPxm4YmFox3U+f19KQtAPZgfHqiPhO3vG0KjVHHaLcbeVzUeczUO+7tav1tuJ9PIz6x7PfxJDmv5r2mxON9ZjajRiIiHXpfiNwA1mCl8t7YcXl7+1cbfNdIekfx7IBJzVVIuLciNgnIqYBJwA/jIiTgFuAD6TFTgVuzCnEjoqIXwJrJO2fio4EHgCWku0n9NH+jhM/Aaan3oV2JPscL805pnElXQNwBbAyIr6QdzxjJWlPSRPT452BPwAezDeqcmnwGaj33boUOCX1vHUYsGmkaVQbMdR6H1dS/3hWGdsHyI5/bZ2haOGY2vEYJE2QtOvIY+DdwH308L2w4vP3dr7qfFd8aCzb2H70RSw5B7hW0v8Cfkq6sL5P/BlwdfoB/AhwOlnCu0TSmcBjwPE5xmdjEBFbJJ0N/AuwHXBlRNyfc1hjIukaYBCYJGkYOC8iylTnDgdOBlak9s0An4qIm3OMaSwmA4tTT3qvAJZExJi71xznan4GgAXU/m69GTgGWAU8T/Y93K6a76OkB6h9PLsC+AdJq8jOjpzQgRjqqXdM7UYMA8ANqb+B7YFvRcT3Jf2E3r0Xfc/f27kb99/bavMPEDMzMzMzs1y5+ZmZmZmZmZWakxoz61uS9k9dlT8r6cnU1MXMzKwlkhY1OpZICklv7HIM09Lz+DKSCk5qzKyffRIYiohdcWcJZmZmfctJjZn1s32BUnWSYGZmZmPnpCZnkvaW9G1Jj0t6VNKfp/KbJV1Usdx1kq5Mj98g6YeSfiXpCUlXj3Tjl+avlvSXku6VtCmt+8qK+Z+UtF7SOkkf7sWpUrNek/RD4Ajgy5I2AzuS9cqzLDVH+z+S9k3LStIXJW1MdeZelWjQMrN21TtuSDpN0o+qlv3NMSM1xfmqpH+WtFnSv0t6raQvSXpK0oOSDspnr8xaJ+nNkoYkPS3pfknvq7PcX1X8pjqjat4iSV+rddxJ838nzXtS0kOSPlgx79jUfPoZSWsknd8g1j9KdXhcH7ec1ORI0iuA7wI/A6aQjRHzCUnvAc4ATpb0LkknAb8LfHxkVeBzwN5ko0NPBc6v2vwHyQZd2g94K3Baes7ZwF+Q9V/+RuD3u7N3ZvmKiHcB/wacHRG7AC8CJwEXAJOAe4Cr0+LvBt4JvAmYCPwx8Ktex2yWs5rHjSbX+2uyevUC8GPg7jR9PVC6MT9sfFM2COd3gR8Ae/HboS/2r1puNvCXwFHAdLLfVtVqHneUjZm0DPhWeo4Tga9KOjCt9xxwCtkx6VjgTyUdVyPW04G/A/4gIu5rfa/Lz0lNvn4X2DMiPhsRL0bEI8DlwAlpUMw/ARYDFwOnRMSzABGxKiKWRcQLEfE42QGjOjm5JCLWRcSTZBVzZir/IPDNiLg/Ip4HPtP1vTQrju9FxK0R8QLwaeAdkqYC/w3sCvwOWVf3Kz3Yno1D9Y4bo7khIu6KiF8DNwC/joirImIrcB3gMzVWNocBuwAL0u+zHwI3kSUelUZ+U90XEc+x7R/MUP+4815gdUR8MyK2RMTdwLdJg9JGxFBErIiIlyLiXuAatv2t9wngr4DBiFjViR0vMyc1+doX2Dud2nxa0tNkg7MNpPk3kQ2e+FBE/Ob0v6S9JF0raa2kZ4B/JPsHoNIvKx4/T1Y5ITu7s6ZiXuVjs373m897RGwmG9xv73TA+jLwFWCDpIWSdsspRrO81DtujGZDxeP/qjHd7HbMimJvYE1EvFRR9guyVjXbLFe1TLWaxx2y34CHVv0GPAl4LYCkQyXdki5P2ET2R3f1b72/Ar4SEcNj3sM+5KQmX2uARyNiYsVt14g4Js2/EFgJTJZU+e/A54AA3hoRuwEfImuS1oz1wD4V01Pb2wWzUvnN513SLsAewDqAiLgkIt4OHEjWDO2vconQrFieA141MiHptTnGYtYr64Cp6TKBEa8D1lYttwApUFMAACAASURBVJ6X/456XY1t1TvurAH+T9VvwF0i4k/T4t8i67VzakS8Gvga2/7Wezfw15L+aGy715+c1OTrDuAZSedI2lnSdpLeIul3Jb0TOJ2sPeUpwKWSRv4h2BXYDDydysby42sJcHq6AO5VwN90bnfMCu8YSb8naUeyNs63R8SaVOcOTe2onwN+DWzNNVKzYvgZcKCkmanDmfNzjsesF24nOxZ8UtIOkgaBPwSurVpuCXCapAPSb6rzamyr5nGHrDXOmySdnJ5jh3QsenNab1fgyYj4taRDgP9RY9v3k10H95V6HRmMJ05qcpTaG/8hWbvlR4EngG8Ak4GryC5wXpuanl0BfFOSyK6DORjYBHwP+M4YnvOfgUuAW4BVZBd0QnZxp1m/+xbZQedJ4O1kp/oBdiO7nu0psuYDvwI+n0eAZkUSEf8JfBb4V+Bh4EeN1zArv4h4EXgfcDTZb7Ovkl3b/GDVcv8MfAn4Idlvqh/W2FzN4066TvrdwAlkZ25+SXbB/05pvY8Bn5X0LNkf0EvqxPozsutzLpd0dGt73B8UEXnHYDlK/wjcB+wUEVvyjsfMzMysH0haBAxHxF/nHct44DM145Ck90vaUdLuZP8KfNcJjZmZmZmVlZOa8emjwOPAz8muG/jTxoubmZmZmRWXm5+ZmZmZmVmp+UyNmZmZmZmV2vZ5BwAwadKkmDZtWs15zz33HBMmTOhtQKMoWkyOp7HR4rnrrrueiIg9exhSxzSqO1C+96LXHM/oGsVU5roD5Tr2OJ7RFS0m153ycey9N9a4G9adiMj99va3vz3queWWW+rOy0vRYnI8jY0WD3BnFKAetHJrVHea2fdeczyNFS2eiMYxlbnuRMmOPY5ndEWLyXWnfBx774017kZ1x83PzMzMzPqEpCslbZR0X0XZ+ZLWSron3Y6pmHeupFWSHpL0nnyiNmufkxozMzOz/rGIbJT5al+MiJnpdjOApAPIBn88MK3zVUnb9SxSsw5yUmNmZmbWJyLiVrLR65sxB7g2Il6IiEeBVcAhXQvOrItG7ShA0lTgKuC1wEvAwoi4WNIewHXANGA18MGIeEqSgIuBY4DngdMi4u7uhG9mZmZmTThb0inAncC8iHgKmALcVrHMcCrbhqS5wFyAgYEBhoaGaj7J5s2b684rOsfee52Mu5nez7aQffjvlrQrcJekZcBpwPKIWCBpPjAfOAc4GpiebocCl6X7lqxYu4nT5n+vpXVXLzi21ac16wut1h/XHRvvXHesz1wGXABEur8IOANQjWVrDmAYEQuBhQCzZs2KwcHBmk906dU3ctGPnmspyLzrz9DQEPX2q+jKGnsn4x61+VlErB850xIRzwIrybL4OcDitNhi4Lj0eA5wVeqk4DZgoqTJHYnWzMzMzMYkIjZExNaIeAm4nN82MRsGplYsug+wrtfxmXXCmMapkTQNOAi4HRiIiPWQJT6S9kqLTQHWVKw2cipzfdW2mjqNObAzzJuxZSxh/ka3TsMV7RSf42msaPGYmZn1kqTJI7/ZgPcDIz2jLQW+JekLwN5krWzuyCFEs7Y1ndRI2gX4NvCJiHgmu3Sm9qI1yrY5lTmm05grWhsjdPVJtbfZrqKd4nM8jRUtHjMzs26RdA0wCEySNAycBwxKmkn2e2w18FGAiLhf0hLgAbLLDc6KiK15xG3WrqayBUk7kCU0V0fEd1LxhpHMPzUv25jKfSrTzMzMLAcRcWKN4isaLH8hcGH3IjLrjVGvqUm9mV0BrIyIL1TMWgqcmh6fCtxYUX6KMocBmypOeZqZmZmZmXVUM+PUHA6cDLyraiTaBcBRkh4GjkrTADcDj5D1dX458LHOh21mZv3Mo6KbmdlYjNr8LCJ+RO3rZACOrLF8AGe1GZeZmY1vi4Avk42TVumLEfH5yoKqUdH3Bv5V0pt8bYCZ2fjRzJkaMzOznvKo6GZmNhatdStmZmaWj56Mit7qcAIeSiA/RYupaPGY9TsnNWZmVha9HRW9heEEPJRAfooWU9HiMet3bn5mZmal4FHRzcysHic1ZmZWCmlMtBHVo6KfIGknSfvhUdHNzMYdNz8zM7PC8ajoZmY2Fk5qzMyscDwqupmZjYWbn5mZmZmZWak5qTEzMzMzs1JzUmNmZmZmZqXmpMbMzMzMzErNSY2ZmZmZmZWakxqzLpE0VdItklZKul/Sx1P5HpKWSXo43e+eyiXpEkmrJN0r6eB898DMzMysHJzUmHXPFmBeRLwZOAw4S9IBwHxgeURMB5anaYCjyQYNnA7MBS7rfchmZmZm5eOkxqxLImJ9RNydHj8LrASmAHOAxWmxxcBx6fEc4KrI3AZMrBpB3czMzMxq8OCbZj0gaRpwEHA7MBAR6yFLfCTtlRabAqypWG04la2v2tZcsjM5DAwMMDQ0VPd5B3aGeTO2jDneRttsx+bNm7u27VY4ntEVMSYzM7NqTmrMukzSLsC3gU9ExDOS6i5aoyy2KYhYCCwEmDVrVgwODtZ97kuvvpGLVoy9mq8+qf422zE0NESjeHvN8YyuiDGZmZlVG7X5maQrJW2UdF9F2fmS1kq6J92OqZh3brrQ+SFJ7+lW4GZlIGkHsoTm6oj4TireMNKsLN1vTOXDwNSK1fcB1vUqVjMzM7OyauaamkXA7BrlX4yImel2M0C6CPoE4MC0zlclbdepYM3KRNkpmSuAlRHxhYpZS4FT0+NTgRsryk9JvaAdBmwaaaZmZmZmZvWN2i4lIm5N1wM0Yw5wbUS8ADwqaRVwCPDjliM0K6/DgZOBFZLuSWWfAhYASySdCTwGHJ/m3QwcA6wCngdO7224ZmZmZuXUzjU1Z0s6BbiTrNvap8guar6tYpmRC5230ezFzq1e6Ay+2DkvjicTET+i9nUyAEfWWD6As7oalJmZmVkfajWpuQy4gOwi5guAi4AzaPJCZ2j+YudWL3QGX+ycF8djZmZmZr3U0jg1EbEhIrZGxEvA5WRNzMAXOpuZmZmZWY+1lNRUDQj4fmCkZ7SlwAmSdpK0H9nI6He0F6KZmZmZNaNOr7V7SFom6eF0v3sql6RLUq+190o6OL/IzdrTTJfO15Bd6L+/pOF0cfPfS1oh6V7gCOB/AkTE/cAS4AHg+8BZEbG1a9GbmZmZWaVFbNtr7XxgeURMB5anaYCjyf6Ank52nfNlPYrRrOOa6f3sxBrFVzRY/kLgwnaCMjMzM7Oxq9Nr7RxgMD1eDAwB56Tyq1JHNbdJmihpsocTsDJqp/czMzMzMyu+gZFEJSLWS9orlU8B1lQsN9Jr7TZJTZl7rW1W0XpvHYuyxt7JuJ3UmJmZmY1P46LX2maVubfUssbeybhb6ijAzMzMzEpjw0gnT+l+Yyp3r7XWN5zUmJmZmfW3pcCp6fGpwI0V5aekXtAOAzb5ehorKyc1ZmZWOO6W1qw1dXqtXQAcJelh4Kg0DXAz8AiwimzcwY/lELJZR/iaGjMzK6JFwJeBqyrKRrqlXSBpfpo+h5d3S3soWbe0h/Y0WrOCqNNrLcCRNZYN4KzuRmTWGz5TY2ZmhRMRtwJPVhXPIeuOlnR/XEX5VZG5DZhYNUi0mZn1OZ+pMTOzsih8t7Td6lK1aN21Fi0eKF5MRYvHrN85qTEzs7IrTLe03eqStmjdtRYtHiheTEWLx6zfufmZmZmVhbulNTOzmpzUmJlZWbhbWjMzq8nNz8zMrHBSt7SDwCRJw8B5ZN3QLkld1D4GHJ8Wvxk4hqxb2ueB03sesJmZ5cpJjZmZFY67pTUzs7Fw8zMzMzMzMys1JzVmZmZmZlZqTmrMzMzMzKzUnNSYmZmZmVmpjZrUSLpS0kZJ91WU7SFpmaSH0/3uqVySLpG0StK9kg7uZvBmZmZmZmbNnKlZBMyuKpsPLI+I6cDyNA1wNDA93eYCl3UmTDMzMzMzs9pGTWoi4lbgyariOcDi9HgxcFxF+VWRuQ2YODL6s5mZmZmZWTe0Ok7NwMhozRGxXtJeqXwKsKZiueFUts3IzpLmkp3NYWBggKGhodpPtDPMm7GlpSDrbbNdmzdv7tq2WzFe4lmxdlNL6+336u0K9fqYmZmZWWd1evBN1SiLWgtGxEJgIcCsWbNicHCw5gYvvfpGLlrRWpirT6q9zXYNDQ1RL948jJd4Tpv/vZbWWzR7QqFeHzMzMzPrrFZ7P9sw0qws3W9M5cPA1Irl9gHWtR6emZmZmZlZY60mNUuBU9PjU4EbK8pPSb2gHQZsGmmmZmZmZmZm1g2jtuuSdA0wCEySNAycBywAlkg6E3gMOD4tfjNwDLAKeB44vQsxm5mZmZmZ/caoSU1EnFhn1pE1lg3grHaDMjMzMzMza1arzc/MzMzMzMwKwUmNWRdJulLSRkn3VZTtIWmZpIfT/e6pXJIukbRK0r2SDs4vcjMzM7PycFJj1l2LgNlVZfOB5RExHViepgGOBqan21zgsh7FaGZmZlZqTmrMuigibgWerCqeAyxOjxcDx1WUXxWZ24CJI12nm5mZmVl9nR5808xGNzDS1XlErJe0VyqfAqypWG44lb2sW3RJc8nO5DAwMMDQ0FD9J9oZ5s3YMuYAG22zHZs3b+7atlvheEZXxJjMzMyqOakxKw7VKIttCiIWAgsBZs2aFYODg3U3eOnVN3LRirFX89Un1d9mO4aGhmgUb685ntEVMSYzM7Nqbn5m1nsbRpqVpfuNqXwYmFqx3D7Auh7HZmZmZlY6TmrMem8pcGp6fCpwY0X5KakXtMOATSPN1MzMzNolabWkFZLukXRnKqvZI6dZ2TipMesiSdcAPwb2lzQs6UxgAXCUpIeBo9I0wM3AI8Aq4HLgYzmEbGZm/e2IiJgZEbPSdL0eOc1KxdfUmHVRRJxYZ9aRNZYN4KzuRmRmZvYyc4DB9HgxMASck1cwZq1yUmNmZqUiaTXwLLAV2BIRsyTtAVwHTANWAx+MiKfyirEV0+Z/r+68eTO2cFqd+asXHNutkKz/BPADSQF8PXU8U69HzpdptufNVnvdhKxzm1bMmPLqltarVubeHssaeyfjdlJjZmZldEREPFExPdKEZoGk+Wna/zabvdzhEbEuJS7LJD3Y7IrN9rzZaq+b7ehUj51l7u2xrLF3Mm5fU2NmZv2g3qC2ZpZExLp0vxG4ATiE+j1ympWKz9SYmVnZFLYJTTvNKBo9X6N48mhyUsSmLkWLqWjxSJoAvCIink2P3w18lt/2yLmAl/fIaVYqTmrMzKxsCtuEpp1mMPWumYEsoakXT7cGy22kiE1dihZT0eIBBoAbJEH2++9bEfF9ST8BlqTeOR8Djs8xRrOWOakxM7NSqWxCI+llTWjSWRo3oTGrEhGPAG+rUf4ravTIaVY2bV1T40GczMyslyRNkLTryGOyJjT3UX9QWzMzGwc6cabGPdCYmVmvuAmNmb1Mo+7QR+Mu0ftHN5qfeRAnMzPrCjehMTOzWtpNagrbAw10r0eYovVoMl7iafVzULTXx8zMzMw6q92kprA90ED3eoQpWo8m4yWeRj0DNbJo9oRCvT5mZmZm1lltdRTgQZzMzMzMzCxvLZ+p8SBOZmZm49OKtZtaPnvuC7PNrBvaaX7mHmjMzMysJ0br4WrejC11Ey0nUmb9r+Wkxj3QmJmZmZlZEbR1TY2ZmZmZmVnenNSYmZmZmVmpdWPwTTMzK5B2RtteNHtCByMxMzPrDic1ZmZmZjYuVf7p06iziWrufKJ43PzMzMzMzMxKzUmNmZmZmZmVmpufmZmZmZmNQTvXKrrpWnf4TI2ZmZmZmZWakxozMzMzMys1JzVmZmZmZlZqTmrMzMzMzKzUnNSYmZmZmVmpufczMzMzsxra6eFq0ewJHYzEzEbjpMbMzMzMrEdaTZbdFXRjbn5mZmZmZmal5qTGzMzMzMxKzc3PzMzMzMwKrlGztXkztnBag/njoela187USJot6SFJqyTN79bzmPUb1x2z1rjumLXGdcf6QVeSGknbAV8BjgYOAE6UdEA3nsusn7jumLXGdcesNa471i+61fzsEGBVRDwCIOlaYA7wQJeez6xfuO6YtcZ1x6w1rjvWFc308lar2VyrTeUUES2t2HCj0geA2RHx4TR9MnBoRJxdscxcYG6a3B94qM7mJgFPdDzI9hQtJsfT2Gjx7BsRe/YqmEY6XHegfO9Frzme0TWKqVR1J5WX9djjeEZXtJhcd8rHsffeWOOuW3e6daZGNcpelj1FxEJg4agbku6MiFmdCqwTihaT42msaPGMomN1B4q3746nsaLFA8WMqY5R6w6U99jjeEZXtJiKFk8DfV13xsKx914n4+5WRwHDwNSK6X2AdV16LrN+4rpj1hrXHbPWuO5YX+hWUvMTYLqk/STtCJwALO3Sc5n1E9cds9a47pi1xnXH+kJXmp9FxBZJZwP/AmwHXBkR97e4uaaa2fRY0WJyPI0VLZ66Olx3oHj77ngaK1o8UMyYtuG603NFiweKF1PR4qlpHNSdsXDsvdexuLvSUYCZmZmZmVmvdG3wTTMzMzMzs15wUmNmZmZmZqWWa1IjabakhyStkjS/xvydJF2X5t8uaVrFvHNT+UOS3tOjeP5C0gOS7pW0XNK+FfO2Sron3TpygV0T8Zwm6fGK5/1wxbxTJT2cbqd2Ip4mY/piRTz/KenpinkdfY0kXSlpo6T76syXpEtSrPdKOrhiXlden15x3elITD2tP0WqO2mb47b+NDLa+5RDPA3fp16TNFXSLZJWSrpf0sdzjueVku6Q9LMUz2fyjGeEpO0k/VTSTXnH0ktFqz/NKtrneqzK+nmTNFHS9ZIeTK/9O9raYETkciO7GO3nwOuBHYGfAQdULfMx4Gvp8QnAdenxAWn5nYD90na260E8RwCvSo//dCSeNL05h9fnNODLNdbdA3gk3e+eHu/ei5iqlv8zsgsOu/UavRM4GLivzvxjgH8m64P/MOD2br4+vbq57pSv/hSt7qRtjsv608n3qUcxNXyfcohnMnBwerwr8J95vkbp87lLerwDcDtwWAFep78AvgXclHcsPdznwtWfMcReqM91C/GX8vMGLAY+nB7vCExsZ3t5nqk5BFgVEY9ExIvAtcCcqmXmkO0wwPXAkZKUyq+NiBci4lFgVdpeV+OJiFsi4vk0eRtZX+7d0szrU897gGUR8WREPAUsA2bnENOJwDUdeN6aIuJW4MkGi8wBrorMbcBESZPp3uvTK647HYipgW58PgpVd2Bc159G2vncdEUT71NPRcT6iLg7PX4WWAlMyTGeiIjNaXKHdMu1ByRJ+wDHAt/IM44cFK7+NKton+uxKOvnTdJuZH/aXAEQES9GxNON12osz6RmCrCmYnqYbT9Av1kmIrYAm4DXNLluN+KpdCbZv5gjXinpTkm3STquzVjGEs8fpaYh10saGTyrG6/PmLabmhftB/yworjTr9Fo6sXbrdenV1x3OhdTr+pP2eoO9G/9aaSf963jUrPWg8jOjuQZx3aS7gE2kiXcucYDfAn4JPBSznH0Wl/Un6J8rsegrJ+31wOPA99MTee+IWlCOxvMM6lRjbLqf1fqLdPMut2IJ1tQ+hAwC/jfFcWvi4hZwP8AviTpDT2I57vAtIh4K/Cv/Paf+W68PmPd7gnA9RGxtaKs06/RaHr5+ekl153OxNTL+lO2ugP9W38a6ed96yhJuwDfBj4REc/kGUtEbI2ImWRngA+R9Ja8YpH0XmBjRNyVVww5Kn39KdLnuhkl/7xtT9a09rKIOAh4DmjrOqw8k5phYGrF9D7AunrLSNoeeDXZafhm1u1GPEj6A+DTwPsi4oWR8ohYl+4fAYbIsvyuxhMRv6qI4XLg7c2u262YKpxAVfOZLrxGo6kXb7den15x3elATD2uP2WrO9C/9aeRft63jpG0A9kPv6sj4jt5xzMiNV0ZIt/mkIcD75O0mqz51bsk/WOO8fRSqetPUT/Xoyjz520YGK44s3o9WZLTunYuyGnnRpahPULWzGLkgrIDq5Y5i5df7LwkPT6Ql1/s/AjtX+zcTDwHkV0EN72qfHdgp/R4EvAwbV5g1mQ8kysevx+4LT3eA3g0xbV7erxHL96ztNz+wGrS4K7deo3StqZR/0LnY3n5hc53dPP16dXNdad89aeIdSdtb9zVn068TznEVfd9yiEWAVcBX8o7lhTPnqSLi4GdgX8D3pt3XCmeQUp24Xab+1vI+tNk7IX6XLe4D6X7vKX6un96fD7wv9vaXs47cwxZDxM/Bz6dyj5L9k8uwCuBfyK7mPkO4PUV6346rfcQcHSP4vlXYANwT7otTeX/D7AiVeAVwJk9iudzwP3peW8Bfqdi3TPS67YKOL1X71nFB3NB1Xodf43I/s1eD/w3WcZ/JvAnwJ+k+QK+kmJdAczq9uvTq5vrTvnqT5HqTtruuK0/Y32fco5nm/cp53h+j6xJ0b0V9fmYHON5K/DTFM99wN/k/Z5VxDZIyX5kdmCfC1V/xhB3oT7XLe5D6T5vwEzgzvS6//+02ZOm0kbNzMzMzMxKKdfBN83MzMzMzNrlpMbMzMzMzErNSY2ZmZn9hqTzS9SDklnhSJomKVLvo62sH5Le2Om4+p2TmhJqt7KYmZmZWedIWp2GLrCcOKnJkZMSMzMzM7P2OanpsZTJnyPpXuA5Sa+T9G1Jj0t6VNKfVyx7iKQ7JT0jaYOkL6RZt6b7pyVtlvSOtPwZklZKekrSv0jat2JbB0paJunJtK1PpfKdJS1O66yU9ElJwz16Ocy6JtWztZKelfSQpCMlvULSfEk/l/QrSUsk7ZGW/2NJj0jaLU0fLemXkvbMd0/MuqdWPamxzPsk3S/paUlDkt5cMW+1pHMlPZCOI9+U9MqK+e+VdE9a9z8kvbVX+2bWK5L+AXgd8F1Jm4EPplknSXpM0hOSPl2x/CGSfpzqxXpJX5a0Yx6x9xMnNfk4kWxguz2AG8jGn5gCHAl8QtJ70nIXAxdHxG7AG4Alqfyd6X5iROwSET+WdBzwKeD/IxuM7N9Io5JL2pVsnJDvA3sDbwSWp22cRzaw2+uBo4APdWF/zXpK0v7A2cDvRsSuwHvIBrX8c+A44PfJ6sJTZGOxEBHXAT8GLpH0GuAK4MMR8XjPd8CsBxrUk8pl3kR2LPkE2bHlZrIfbpU/wE5K674BeBPw12ndg4ErgY8CrwG+DiyVtFP39sqs9yLiZOAx4A8jYhd++3vt98gGVT4S+JuKPwS2Av+TbEDld6T5H+tp0H3ISU0+LomINcBbgD0j4rMR8WJEPAJcTjYCPGSDrb1R0qSI2BwRtzXY5keBz0XEyojYAvwtMDOdrXkv8MuIuCgifh0Rz0bE7Wm9DwJ/GxFPRcQwcEkX9tes17YCOwEHSNohIlZHxM/J6smnI2I4Il4gG+zyAxVNQc8C3gUMAd+NiJt6H7pZz9SrJ5X+GPheRCyLiP8GPg/sTDYo7IgvR8SaiHgSuJDsjzuAjwBfj4jbI2JrRCwGXgAO6+ZOmRXIZyLivyLiZ2R/YL8NICLuiojbImJLRKwmS/h/P8c4+4KTmnysSff7Anun049PS3qa7GzLQJp/Jtm/Xg9K+omk9zbY5r7AxRXbeZJsRPApwFSy0X1r2bsiHqoem5VSRKwi+2f5fGCjpGsl7U1WT26oqCcryX7YDaT1ngb+iewPh4vyiN2sVxrUk0p7A7+oWOclsuPElIplKo8bv0jrQFbf5lUd46ZWzDfrd7+sePw8sAtkZ0Al3ZSaOD9D9kf0pDwC7CdOavIR6X4N8GhETKy47RoRxwBExMMRcSKwF/B3wPWSJlSsX2kN8NGqbe0cEf+R5r2hTizrgX0qpqd2YP/MchcR34qI3yP7YRVkdWgNcHRVPXllRKwFkDQTOIOsuY3PWlrfq1NPKq1L8wCQJLLjxNqKZSqPG69L60BW3y6sqm+viohrOr0fZgVQ67dZPZcBDwLT0yUGnyL7I9ra4KQmX3cAz6QLNXeWtJ2kt0j6XQBJH5K0Z/pn7Om0zlbgceAlsutgRnwNOFfSgWndV0s6Ps27CXitpE9I2knSrpIOTfOWpPV2lzSFrH21WalJ2l/Su1Lb/V8D/0VWd74GXDjSiYakPSXNSY//b3t3Hy7ZWdb5/vszAcSAJjHQE5JIRyeiaDRgnxAHRzdgJCRK4znABCOkAU/7EhS1HWl0LmFAx/Y4gKhjsCFMggaSiHCSIVGJgT1cnjHBBAMhBEwMbdKkSUAgpIEBO9znj7W2VDq1X7pqV9Vatb+f66qrqlatl7ueXU/tutfzsr4e+FOafy4vBI5LYh9nza0V6smgy4Cz2ok2HgLsoOlC9r8G1jkvyfHtpBu/BlzaLn8j8DNJnpTGEUnOasd5SvPmbh74u2wljwQ+D+xP8h3Az04sqg3EpGaGqup+4MeAU4CPA58G3gR8U7vKGcDN7UwarwfObsfEfJGm3/L/1zbpn1ZV76Q5w3ZJ25T5YeAZ7XHuo5kE4MdomkJvBZ7SHuNVwN72+H8NvJ3mH5bUZw8DdtHUqU/StHb+Gk09ugJ4d5L7gGuBpQT/t4G9VXV+O97mJ4HfTHLStIOXpmS5evKvqupjNHXhD9r1foxmMPRXBlZ7K/Bu4Pb29pvtttfTjKv5Q5pJOW4Dtk3s3Uiz9dvAf2q7WT57lXV/BfgJ4D6a5P/SlVfXWqTqUFrLNO+S/CxN8uSANUnSipLsoZkl8K9nHYukjc2Wmg0uybFJnpzm+h2Po+la8M5ZxyVJkiStlVe010NpphI8kWbcziXAH800IkmSJOkQ2P1MkiRJUq/Z/UySJElSr3Wi+9kxxxxTmzdvHvraF77wBY444ojpBrSKrsVkPCtbLZ4bbrjh01X1qCmGtG5WqjvQv7/FtBnP6laKqc91B/r1v8d4Vte1mKw73dC1eKB7MfUpnhXrTlXN/PZ93/d9tZz3vve9y742K12LyXhW6BcqLgAAIABJREFUtlo8wPXVgXowym2lurOW9z5txrOyrsVTtXJMfa471bP/Pcazuq7FZN3phq7FU9W9mPoUz0p1x+5nkiRJknpt1aQmyZuT3JPkwwPLXpnkE0lubG9nDrz28iS3JflYkqdPKnBJkiRJgrVNFHAhzZXtD/a6qjqlvV0FkOTxwNnAd7Xb/FGSw9YrWEmSJEk62KoTBVTV+5JsXuP+tgKXVNWXgY8nuQ04FfjbUQO86RP3sm3nlSNtu2fXWaMeVhpbkhOAtwD/BvgqsLuqXp/kaOBSYDOwB3huVX02SYDXA2cCXwS2VdUHZhG75svmEb9DAS48ozuDR6dp1P89/t/RRufvNs3KOLOfvSTJC4DrgR1V9VngOODagXX2tsseJMl2YDvApk2bWFxcHHqQTQ+HHScfGCnA5fY5rv37909s36MwnpXNMJ4DNHXjA0keCdyQ5GpgG3BNVe1KshPYCbwMeAZwUnt7EnB+ey9JneIPV0ldM2pScz7waqDa+9cALwIyZN2hV/esqt3AboAtW7bUwsLC0AP9wcWX85qbRgtzzznD9zmuxcVFlot3FjZKPKOebb7wjEfMpHyqah+wr318X5JbaJL8rcBSQBcBizRJzVbgLe3sHtcmOTLJse1+JEmStIyRsoWqunvpcZI3Au9qn+4FThhY9XjgrpGjk+ZE24XzCcB1wKalRKWq9iV5dLvaccCdA5sttXQ+IKlZaysn2Gq2mo0Sz6it3dC9MpIkaZiRkpqDzh7/OLA0M9oVwFuTvBZ4DE03mvePHaXUY0keAfw58ItV9flm6MzwVYcse1BL51pbOWHjtOKNaqPEM2o3IWjG1HSpjCRJGmbVpCbJ22i6yhyTZC/wCmAhySk0P7j2AD8NUFU3J7kM+AjNeILzqur+yYQudV+Sh9AkNBdX1TvaxXcvnRhIcixwT7vclk5JkqQRrGX2s+cNWXzBCuv/FvBb4wQlzYN2NrMLgFuq6rUDL10BnAvsau8vH1j+kiSX0EwQcK/jaSRJkla3luvUSBrNk4HnA0896EK1u4DTk9wKnN4+B7gKuB24DXgj8HMziFmS1GNeNF0b1ThTOktaQVX9DcPHyQA8bcj6BZw30aAkSfPuQuAPaa6TNuh1VfVfBxccdNH0xwB/neTbHTqgPrKlRpIkaU5U1fuAz6xx9X+9aHpVfZymp8CpEwtOmiBbaiRJkubfhrxoehenpe9aTPMSj0mNJEnSfNuwF03v2tT90L2Y5iUeu59JkiTNsaq6u6rur6qv0kxEs9TFzEsJaG6Y1EiSJM2x9ppoSw6+aPrZSR6W5ES8aLp6zO5nkiRJc8KLpmujMqmRJEmaE140XRuV3c8kSZIk9ZpJjSRJkqReM6mRJEmS1GsmNZIkSZJ6zaRGkiRJUq+Z1EiSJEnqNZMaSZIkSb1mUiNJkiSp10xqJEmSJPWaSY0kSZKkXjOpkSYoyZuT3JPkwwPLXpnkE0lubG9nDrz28iS3JflYkqfPJmpJkqR+MamRJutC4Iwhy19XVae0t6sAkjweOBv4rnabP0py2NQilSRJ6imTGmmCqup9wGfWuPpW4JKq+nJVfRy4DTh1YsFJHbZMK+fRSa5Ocmt7f1S7PEl+v23l/FCSJ84ucknSLBw+6wCkDeolSV4AXA/sqKrPAscB1w6ss7dd9gBJtgPbATZt2sTi4uKyB9m/f/+Kr0+b8axsUvHsOPnAyNvOsIwuBP4QeMvAsp3ANVW1K8nO9vnLgGcAJ7W3JwHnt/eSpA3CpEaavvOBVwPV3r8GeBGQIevWgxZU7QZ2A2zZsqUWFhaWPdDi4iIrvT5txrOyScWzbeeVI2974RlHzKSMqup9STYftHgrsBTMRcAiTVKzFXhLVRVwbZIjkxxbVfumE60kadZMaqQpq6q7lx4neSPwrvbpXuCEgVWPB+6aYmhS121aSlSqal+SR7fLjwPuHFhvqZXzQUnNWls6Nz18tBauSbVqda1VcdTygY1TRl2LR5q2zSOeULvwjCNG2s6kRpqyg84g/ziwNGbgCuCtSV4LPIamK837ZxCi1DdrauWEtbd0/sHFl/Oamw79X+Sec4bvb1xda1UctXxg45RR1+KR5t2qEwU4WFMaXZK3AX8LPC7J3iQvBv6fJDcl+RDwFOCXAKrqZuAy4CPAXwLnVdX9Mwpd6qK7kxwLzckB4J52ua2ckrTBrWX2swt58JS0S4M1TwKuaZ/DAwdrbqcZOyBtWFX1vKo6tqoeUlXHV9UFVfX8qjq5qr6nqp452O+/qn6rqr6tqh5XVX8xy9ilDroCOLd9fC5w+cDyF7Qn1k4D7nU8jSRtLKsmNctMSbuVZpAm7f2zBpa/pRrXAkcunVWTJGmtlmnl3AWcnuRW4PT2OcBVwO0006C/Efi5GYQsSZqhUcfUdH6wJjgYcVa6Ni1t18pH0uqq6nnLvPS0IesWcN5kI5Ikddl6TxTQmcGa4GDEWenatLSzmpJWkiRJ07GWMTXDOFhTkiRJUieMmtQ4WFOSJKljnLVWG9VapnR2sKYkSVI/XIiz1moDWnWwioM1JUmS+qGq3pdk80GLtwIL7eOLgEXgZQzMWgtcm+TIgy4QLfXGek8UIEmSpG7ZsLPWdnEG1K7FNC+z1prUSJIkbUxzP2tt12aIhe7FNC+z1o46UYAkSZL6wVlrNfdMaiRJkuabs9Zq7tn9TJIkaU60s9YuAMck2Qu8gmaW2svaGWzvAJ7Trn4VcCbNrLVfBF449YCldWJSI0mSNCectVYbld3PJEmSJPWaSY0kSZKkXjOpkSRJktRrJjXSBCV5c5J7knx4YNnRSa5Ocmt7f1S7PEl+P8ltST6U5Imzi1ySJKk/TGqkyboQOOOgZTuBa6rqJOCa9jnAM4CT2tt24PwpxShJktRrJjXSBFXV+4DPHLR4K3BR+/gi4FkDy99SjWuBI5culiZJkqTlOaWzNH2bli5uVlX7kjy6XX4ccOfAenvbZQ+4EFqS7TQtOWzatInFxcVlD7R///4VX58241nZpOLZcfKBkbftWhlJkjSMSY3UHRmyrB60oGo3sBtgy5YttbCwsOwOFxcXWen1aTOelU0qnm07rxx52wvPOKJTZSRJ0jAmNdL03Z3k2LaV5ljgnnb5XuCEgfWOB+4a50A3feLekX7Q7tl11jiHlSRJmirH1EjTdwVwbvv4XODygeUvaGdBOw24d6mbmiRJkpZnS400QUneBiwAxyTZC7wC2AVcluTFwB3Ac9rVrwLOBG4Dvgi8cOoBS5Ik9ZBJjTRBVfW8ZV562pB1CzhvshFJkiTNH7ufSZIkSeo1kxpJkiRJvWZSI0mSJKnXTGokSZIk9ZpJjSRJkqRec/YzSVKvJNkD3AfcDxyoqi1JjgYuBTYDe4DnVtVnZxWjJGm6bKmRJPXRU6rqlKra0j7fCVxTVScB17TPJUkbhEmNJGkebAUuah9fBDxrhrFIkqZsrO5ndgGQJM1AAe9OUsAfV9VuYFNV7QOoqn1JHj1swyTbge0AmzZtYnFxcegBNj0cdpx84JADW25/49q/f//E9j2KUcsHNk4ZdS0ead6tx5iap1TVpweeL3UB2JVkZ/v8ZetwHEmSAJ5cVXe1icvVST661g3bBGg3wJYtW2phYWHoen9w8eW85qZD/xe555zh+xvX4uIiy8U6C6OWD2ycMupaPNK8m0T3M7sASJImpqruau/vAd4JnArcneRYgPb+ntlFKEmatnFbajrbBQBs4p6VScUz6uega+UjaXRJjgC+rqruax//CPAq4ArgXGBXe3/57KKUuslhA5pn4yY1ne0CADZxz8qk4tm288qRtrvwjCM6VT6SxrIJeGcSaP6HvbWq/jLJ3wGXJXkxcAfwnBnGKHWZwwY0l8ZKaga7ACR5QBeAtpXGLgCSpHVTVbcD3ztk+T8DT5t+RFLvbQUW2scXAYuY1KiHRk5q7AIgSZLUKxtu2EAXu6B3LaZ5GTYwTkuNXQAkSZL6Y8MNG+haF33oXkzzMmxg5KTGLgCSJEn94bABzbNJTOksaQ2S7ElyU5Ibk1zfLjs6ydVJbm3vj5p1nJKk/ktyRJJHLj2mGTbwYb42bAAcNqAeM6mRZuspVXVKVW1pny/NQnMScE37XJKkcW0C/ibJB4H3A1dW1V/SjIE+PcmtwOntc6l3xp3SWdL6chYaSdK6c9iA5p1JjTQ7I81Cs9YZaGD0WWi8cO1sdG0GGuheGUmSNIxJjTQ7I81Cs9YZaGD0WWi8cO1sdG0GGvDitZKkfnBMjTQjg7PQAA+YhQbAWWgkSZLWxqRGmgFnoZEkSVo/dj+TZsOL10qSJK0TkxppBpyFRpIkaf3Y/UySJElSr5nUSJIkSeo1kxpJkiRJvWZSI0mSJKnXnChAkiRpiM1jXrhW0vTYUiNJkiSp10xqJEmSJPWaSY0kSZKkXnNMjSRJkjTHVhoftuPkA2xb5vU9u86aVEjrzpYaSZIkSb1mUiNJkiSp10xqJEmSJPWaSY0kSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKvmdRIkiRJ6rWJXXwzyRnA64HDgDdV1a5JHWuju+kT9y570aSV9OmCShuJdUcajXVHGo11R/NgIi01SQ4D/hvwDODxwPOSPH4Sx5LmiXVHGo11RxqNdUfzYlLdz04Fbquq26vqK8AlwNYJHUuaJ9YdaTTWHWk01h3NhUl1PzsOuHPg+V7gSYMrJNkObG+f7k/ysWX2dQzw6VGCyO+MstWajBzThIwUz0Ypn6f8zqrxPHZasazBetYd8LOxGuNZxSr1p1d1Byb/v8e6s7qNUkbWnUM3oc9Gpz4XrU7F9AsrxDPB+rqsUevOpJKaDFlWD3hStRvYveqOkuurast6BbYeuhaT8aysa/GsYt3qDnTvvRvPyroWD3QzpmWsWnegv/97jGd1XYupa/GswLozZV2LaV7imVT3s73ACQPPjwfumtCxpHli3ZFGY92RRmPd0VyYVFLzd8BJSU5M8lDgbOCKCR1LmifWHWk01h1pNNYdzYWJdD+rqgNJXgL8Fc30gG+uqptH3N2autlMWddiMp6VdS2eZa1z3YHuvXfjWVnX4oFuxvQg1p2p61o80L2YuhbPUNadmehaTHMRT6oe1G1SkiRJknpjUt3PJEmSJGkqTGokSZIk9VpnkpokZyT5WJLbkuwc8vrDklzavn5dks0zjmdbkk8lubG9/dSE43lzknuSfHiZ15Pk99t4P5TkiTOOZyHJvQPl8xsTjueEJO9NckuSm5O8dMg6Uy2jSRqnviR5ebv8Y0mePqV4fjnJR9pyvybJYwdeu3/gc7Jug1PHqcNJzk1ya3s7d0rxvG4gln9I8rmB19a9jMb5TplE+cyC36urxtOp79U1xjPtMvr6JO9P8sE2pv88ZJ2p/n6ZlbWUxSwkOSzJ3yd5Vwdi2ZPkpvazeX0H4jkyyduTfLStV98/43geN1B3b0zy+SS/uOYdVNXMbzQD0/4R+FbgocAHgccftM7PAW9oH58NXDrjeLYBfzjFMvpB4InAh5d5/UzgL2jmmz8NuG7G8SwA75pi+RwLPLF9/EjgH4b8zaZaRhN8ryPXF+Dx7foPA05s93PYFOJ5CvAN7eOfHay/wP4ZldHQOgwcDdze3h/VPj5q0vEctP7P0wzWnWQZjfSdMonymdXN79VV4+nU9+oa45l2GQV4RPv4IcB1wGkHrTO13y+zvK2lLGYU1y8Db53m52KFWPYAx8w6joF4LgJ+qn38UODIWcc0ENthwCeBx651m6601JwK3FZVt1fVV4BLgK0HrbOVpvAB3g48LcmwC0ZNK56pqqr3AZ9ZYZWtwFuqcS1wZJJjZxjPVFXVvqr6QPv4PuAWmqskD5pqGU3QOPVlK3BJVX25qj4O3Nbub6LxVNV7q+qL7dNraa6DMEnj1OGnA1dX1Weq6rPA1cAZU47necDbxjzmisb4TplE+cyE36sr69r36hrjmar2fe9vnz6kvR08A9M0f7/MzBrLYqqSHA+cBbxplnF0UZJvpDmRcgFAVX2lqj638lZT9TTgH6vqn9a6QVeSmuOAOwee7+XBX1T/uk5VHQDuBb55hvEA/F9tc/vbk5ww5PVpWmvM0/T9bTP0XyT5rmkdtG3afwLNWaJBXSyjUYxTXyZRBoe6zxfTnNld8vVJrk9ybZJnjRnLocY0rA7PtIzSdM07EXjPwOJJlNFqlot5XurRWnTxvfq9unI8MOUyars33QjcQ5PwL1tGU/j9MlNrKItp+z3gV4GvzjiOJQW8O8kNSbbPOJZvBT4F/Pe2e96bkhwx45gGnc0hntzrSlIz7IzFwdn9WtZZL2s51v8ANlfV9wB/zdfOwszKNMtnLT5A02T4vcAfAP/vNA6a5BHAnwO/WFWfP/jlIZv0cU7zcerLJMpgzftM8pPAFuB3BxZ/S1VtAX4C+L0k3zZmPGuNabk6PNMyovkif3tV3T+wbBJltJppfoa6qmvv1e/V1eOZehlV1f1VdQpNC/SpSb774JCHbTbpuGZhDWUxNUl+FLinqm6YVQxDPLmqngg8AzgvyQ/OMJbDabq7nl9VTwC+ADxovOcspLkI7DOBPzuU7bqS1OwFBls6jgfuWm6dJIcD38TkmulXjaeq/rmqvtw+fSPwfROKZa3WUoZTU1WfX2qGrqqrgIckOWaSx0zyEJp/dBdX1TuGrNKpMhrDOPVlEmWwpn0m+WHg14FnDtQdququ9v52YJHm7Ou4xqnDMyuj1oPOTk2ojFazXMzzUo/WolPv1e/V1eOZRRkNHPtzNPXz4O6Y0/z90gkrlMU0PRl4ZpI9NF1+n5rkT2cYz+B3+T3AOxm/+/c49gJ7B1rT3k6T5HTBM4APVNXdh7JRV5KavwNOSnJim52dDRw8w88VwNIsO88G3lNVkzrTsWo8B/UZfiZN395ZugJ4QTsTzWnAvVW1b1bBJPk3S32Gk5xK81n75wkeLzT9Qm+pqtcus1qnymgM49SXK4Cz29l4TgROAt4/6XiSPAH4Y5qE5p6B5UcleVj7+Biaf0IfGTOetca0XB3+K+BH2tiOAn6kXTbReNqYHkcz+P5vB5ZNqoxWs1x9mUT5dFWnvjM2+vfqWuKZQRk9KsmR7eOHAz8MfPSg1ab5+2Vm1lgWU1NVL6+q46tqM8137nuq6idnFU+SI5I8cukxzXfn0JkOp6GqPgnc2f7fgWYMyzT+t6zFSONKD59AIIesqg4keQnNP8bDaGb9uTnJq4Drq+oKmi+yP0lyG80ZjrNnHM8vJHkmcKCNZ9uk4gFI8jaaWV2OSbIXeAXNIDyq6g3AVTSz0NwGfBF44YzjeTbws0kOAF8Czp7wl/iTgecDN7X9eQF+DfiWgZimWkaTMk59ade7jOaL6wBw3kHdnCYVz+8CjwD+rP29cUdVPRP4TuCPk3yV5sfHrqoa+0t1nDpcVZ9J8mqaRATgVVU11lnVNcYDzRf5JQfVlYmU0ajfKZMon1nxe3VVXfteXUs80y6jY4GLkhxGUz8vq6p3zer3y4wNLYsZx9Qlm4B3tv8DDwfeWlV/OduQ+Hng4vZk2+104HdRkm8ATgd++pC3ncOTBZIkSZI2kK50P5MkSZKkkZjUdEiSzUkqzUDCUbavJP92veOSNookFyb5zVnHIUmSDo1JzYwl2ZNmVihJQ0yqjiTZluRv1nu/kiRp+kxqJPXWqK2akiRpvpjUzFCSP6GZteV/JNkPPLd96ZwkdyT5dJJfH1j/1CR/m+RzSfYl+cN2xoph+z4rzRViP5/kziSvPOj1H0jyv9p93Zlk22TepTS6g+tIkl9tu1m+OMkdwHva9U4b+Dx/MMnCwD62Jbk9yX1JPp7knCTfCbyB5srj+5N8buCwxyS5ul3/fyZ57MC+KskvtPv7dJLfTfJ17Wv/tl3/3va1S6dQRJIkCZOamaqq5wN3AD9WVY8ALmtf+gHgcTRzhv9G+wMM4H7gl4BjgO9vX/+5ZXb/BeAFwJHAWTRTXD4LIMm3AH9Bc7XlRwGnADcusx9pZlaoIz9EM9Xx05McB1wJ/CZwNPArwJ+nuWbCEcDvA8+oqkcC/w64sapuAX4G+NuqekRVHTlw2HOAV9PUsxuBiw8K68eBLTQXKdsKvKhd/mrg3TTXmTmepn5JkqQpMKnppv9cVV+qqg8CHwS+F6Cqbqiqa6vqQFXtobmY4Q8N20FVLVbVTVX11ar6EM1FjJbWPQf466p6W1X9S3tldZMa9ckrq+oLVfUl4CeBq6rqqvbzfjVwPc21MwC+Cnx3kodX1b6qunmVfV9ZVe+rqi8Dv07TmjN4xfTfqarPVNUdwO/RXFsG4F+AxwKPqar/XVWO15EkaUpMarrpkwOPv0hz0UKSfHuSdyX5ZJLPA/+F5mzygyR5UpL3JvlUkntpzkovrXsC8I+TC1+auDsHHj8WeE7b9exzbVeyHwCOraovAP+B5vO/L8mVSb5jrfuuqv00F8t7zDLH/qeB134VCPD+JDcneRGSJGkqTGpm71Cufno+8FHgpKr6RporKWeZdd8KXAGcUFXfRDN+YGndO4FvGy1caeqG1ZHBZXcCf1JVRw7cjqiqXQBV9VdVdTrN1a4/Crxxhf1Ck/QDkOQRNF3a7hr2Os14n7va43yyqv7vqnoMzZWQ/8gp1iVJmg6Tmtm7G/jWNa77SODzwP72bPPPrrLuZ6rqfyc5FfiJgdcuBn44yXOTHJ7km5OcMkrw0hSsVkf+FPixJE9PcliSr0+ykOT4JJuSPLMdW/NlYD/N2LSl/R4/ZLKNM9uJNB5KM07muqoabJ35j0mOarukvRS4FCDJc5Ic367zWZqk6X4kSdLEmdTM3m8D/6ntMvPsVdb9FZrk5D6as80rza70c8CrktwH/AZfG2BNOxbgTGAHTdeaG2nH7UgdtGIdaROOrTQtl5+iabn5jzTfb19H8zm/i+az/kN8bXKN9wA3A59M8umBXb4VeEW7/vfRjEEbdDlwA029uRK4oF3+fwDXtTMZXgG8tKo+PvK7liRJa5aqQ+n9JEkbV5Ki6f5526xjkSRJX2NLjSRJkqReM6mRJEmS1Gt2P5MkSZLUa2O11CT5pfZ6DB9O8rZ21qETk1yX5NYklw6ZWUiSJEmS1s3ILTVJjgP+Bnh8VX0pyWXAVTSzar2jqi5J8gbgg1V1/kr7OuaYY2rz5s0jxTENX/jCFzjiiCNmHcZE+N7ghhtu+HRVPWoKIa27cetOV/7+xtHNOGDlWPpcdyRJ8+Xwddj+4Un+BfgGYB/wVL52TZSLgFfSXDRyWZs3b+b6668fM5TJWVxcZGFhYdZhTITvDZL80+SjmYxx605X/v7G0c04YOVY+lx3JEnzZeSkpqo+keS/AncAXwLeTXPths9V1YF2tb3AccO2T7Id2A6wadMmFhcXRw1l4vbv39/p+Mbhe5MkSVLfjZzUJDmK5oJ3JwKfA/4MeMaQVYf2b6uq3cBugC1btlRXzkoO06WzpuvN9yZJkqS+G2eigB8GPl5Vn6qqfwHeAfw74MgkS8nS8TRX8pYkSZKkiRhnTM0dwGlJvoGm+9nTgOuB9wLPBi4BzgUuHzdITcbmnVey4+QDbNt55SFvu2fXWROISBvZ5hE+h0v8PEqStLGN3FJTVdcBbwc+ANzU7ms38DLgl5PcBnwzcME6xClJkiRJQ401+1lVvQJ4xUGLbwdOHWe/kiRJkrRWY118U5IkSZJmzaRGkiRJUq+Z1EiSJEnqNZMaSZIkSb1mUiNJkiSp10xqJEmSJPWaSY0kSZKkXhvrOjWSdLDNO6885G12nHwAv44kSdKobKmRJEmS1GsmNZIkSZJ6zaRGkiRJUq+Z1EiSJEnqNZMaSZIkSb1mUiNJkiSp10xqpBlJcmSStyf5aJJbknx/kqOTXJ3k1vb+qFnHKUmS1HUmNdLsvB74y6r6DuB7gVuAncA1VXUScE37XJIkSSswqZFmIMk3Aj8IXABQVV+pqs8BW4GL2tUuAp41mwglSZL6w0t4S7PxrcCngP+e5HuBG4CXApuqah9AVe1L8uiDN0yyHdgOsGnTJhYXF0cOYv/+/WNtP8yOkw8c8jabHj7adkvW6z1Mojz6HAd0KxZJkpZjUiPNxuHAE4Gfr6rrkryeNXY1q6rdwG6ALVu21MLCwshBLC4uMs72w2zbeeUhb7Pj5AO85qbRv472nLMw8raDJlEefY4DuhWLJEnLGSupSXIk8Cbgu4ECXgR8DLgU2AzsAZ5bVZ8dK0p1zuYRfrgC7Nl11jpH0lt7gb1VdV37/O00Sc3dSY5tW2mOBe6ZWYSSJEk9Me6YGgc6SyOoqk8CdyZ5XLvoacBHgCuAc9tl5wKXzyA8SZKkXhm5pWZgoPM2aAY6A19JshVYaFe7CFgEXjZOkNKc+nng4iQPBW4HXkhzouGyJC8G7gCeM8P4JEmSemGc7mcjD3SG9R3sPGnzOlB2x8kHxh6gfaimWY5d/7tV1Y3AliEvPW3asUiSJPXZOEnNyAOdYX0HO0/avA6U3bbzyrEHaB+q9RrQvRbz+nfTgznGS5KkjW2cMTXDBjo/kXagM4ADnSVJkiRN2shJjQOdJUmSJHXBuP2OHOgsSZIkaabGSmoc6CxJkiRp1sa9To0kSZIkzZRJjSRJkqReM6mRJEmS1GvTu0CJJmbUa3RIkiRJ88CWGkmSJEm9ZlIjSZIkqddMaiRJkiT1mkmNJEmSpF4zqZEkSZLUayY1kiRJknrNpEaSJElSr5nUSJIkSeo1kxpJkiRJvWZSI0mSJKnXTGokSZIk9drhsw5A2qiSHAZcD3yiqn40yYnAJcDRwAeA51fVV2YR2+adV87isJIkSSOxpUaanZcCtww8/x3gdVV1EvBZ4MUziUqSJKlnTGqkGUhyPHAW8Kb2eYCnAm9vV7kIeNZsopMkSeqXsbufdbkLjdTXloiyAAAKZklEQVRhvwf8KvDI9vk3A5+rqgPt873AccM2TLId2A6wadMmFhcXRw5i//79Q7ffcfKBB688QZsePv1jAg9678uVx7R1JQ7oViySJC1nPcbULHWh+cb2+VIXmkuSvIGmC83563AcaS4k+VHgnqq6IcnC0uIhq9aw7atqN7AbYMuWLbWwsDBstTVZXFxk2PbbpjymZsfJB3jNTdMf4rfnnIUHPF+uPKatK3FAt2KRJGk5Y3U/swuNNJInA89MsoemVfOpNC03RyZZ+mV/PHDXbMKTJEnql3FPjXaiC82kdb37xTjddqbd7Wea5djVv1tVvRx4OUDbUvMrVXVOkj8Dnk2T6JwLXD6zICVJknpk5KSmS11oJq3r3S/G6So07W4/B3f3maSu/92GeBlwSZLfBP4euGDG8UiSJPXCOL9ml7rQnAl8Pc2Ymn/tQtO21tiFRlpBVS0Ci+3j24FTZxmPJElSH408pqaqXl5Vx1fVZuBs4D1VdQ7wXpouNGAXGkmSJEkTNonr1LwM+OUkt9GMsbELjSRJkqSJWZfBFHahkSRJkjQr078whIbaPOXrgkiSJEnzYhLdzyRJkiRpakxqJEmSJPWaSY0kSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKvmdRIkiRJ6jWTGkmSJEm95sU3JW1YB1/0dsfJB9i2xgvh7tl11iRCkiRJI7ClRpIkSVKvmdRIkiRJ6jWTGkmSJEm95piadXZwH3090Djl4xgGSZIkDWNLjSRJkqReM6mRJEmS1GsmNdIMJDkhyXuT3JLk5iQvbZcfneTqJLe290fNOlZJkqSuM6mRZuMAsKOqvhM4DTgvyeOBncA1VXUScE37XJIkSSsYOanxTLM0uqraV1UfaB/fB9wCHAdsBS5qV7sIeNZsIpQkSeqPcWY/WzrT/IEkjwRuSHI1sI3mTPOuJDtpzjS/bPxQpfmUZDPwBOA6YFNV7YMm8Uny6CHrbwe2A2zatInFxcWRj71///6h2+84+cDI+xzFpodP/5jjxjFOua9mub/LLHQpFkmSljNyUtP+8Fr68XVfksEzzQvtahcBi5jUSEMleQTw58AvVtXnk6y6TVXtBnYDbNmypRYWFkY+/uLiIsO23zblqcl3nHyA19w0+xnmDyWOPecsTCyO5f4us9ClWCRJWs66/Io41DPN7TbrdrZ50g7lTGUXzjYfiq6cIV+LQ/2MdP0Mc5KH0CQ0F1fVO9rFdyc5tq07xwL3zC5CSZKkfhg7qRnlTDOs79nmSTuUM5XTPsM9rq6cIV+LQz0z3uUzzGkqygXALVX12oGXrgDOBXa195fPIDxJkqReGWv2s5XONLeve6ZZGu7JwPOBpya5sb2dSZPMnJ7kVuD09rkkSZJWMPIpes80S6Orqr8BlmvWfNo0Y5EkSeq7cfodLZ1pvinJje2yX6NJZi5L8mLgDuA544UoSZIkScsbZ/YzzzRLkiRJmrmxxtRIkiRJ0qz1Y9orSeqYzSPOdLhn11nrHIkkSbKlRpIkSVKvmdRIkiRJ6jWTGkmSJEm9ZlIjSZIkqdecKGCIgwcA7zj5ANtGHBQsSZIkabJMaqQ5ttoMXSbskiRpHtj9TJIkSVKv2VIjSVO0luvbLNeC5jVuJEkazpYaSZIkSb1mUiNJkiSp10xqJEmSJPXaXI+pWUvfdUmSJEn9NtdJjebLoSapS4OtHVwtSZI03+x+JkmSJKnXTGokSZIk9ZrdzySpJ0YdJ2gXTEnSvJtYUpPkDOD1wGHAm6pq16SOJc0T647W2ziTplx4xhHrGIkkSZMxkaQmyWHAfwNOB/YCf5fkiqr6yKHuyxnMtJGsZ92RJEnaKCY1puZU4Laqur2qvgJcAmyd0LGkeWLdkSRJOkSpqvXfafJs4Iyq+qn2+fOBJ1XVSwbW2Q5sb58+DvjYugeyfo4BPj3rICbE9waPrapHTTqYtZhB3enK3984HqgrccDKsXSm7kiSNrZJjanJkGUPyJ6qajewe0LHX1dJrq+qLbOOYxJ8b50z1brTlTIyjm7GAd2KRZKk5Uyq+9le4ISB58cDd03oWNI8se5IkiQdokklNX8HnJTkxCQPBc4GrpjQsaR5Yt2RJEk6RBPpflZVB5K8BPgrmmlp31xVN0/iWFPSi25yI/K9dcgM6k5Xysg4HqgrcUC3YpEkaaiJTBQgSZIkSdMyqe5nkiRJkjQVJjWSJEmSem3DJzVJ3pzkniQfHlh2dJKrk9za3h/VLk+S309yW5IPJXni7CJf3TLv7ZVJPpHkxvZ25sBrL2/f28eSPH02Ua9NkhOSvDfJLUluTvLSdvlc/O3WS1c+38vE8btJPtoe651JjmyXb07ypYHP6BsmHMfU68QycVw6EMOeJDe2yydZHtYjSdJc2PBJDXAhcMZBy3YC11TVScA17XOAZwAntbftwPlTinFUF/Lg9wbwuqo6pb1dBZDk8TQzbX1Xu80fJTlsapEeugPAjqr6TuA04Lz2PczL3269XEg3Pt/D4rga+O6q+h7gH4CXD7z2jwOf0Z+ZcBww/TrxoDiq6j8sxQD8OfCOgZcnVR7WI0nSXNjwSU1VvQ/4zEGLtwIXtY8vAp41sPwt1bgWODLJsdOJ9NAt896WsxW4pKq+XFUfB24DTp1YcGOqqn1V9YH28X3ALcBxzMnfbr105fM9LI6qendVHWifXktzTZ6J6kqdWCmOJAGeC7xtPY61ShzWI0nSXNjwSc0yNlXVPmj+6QOPbpcfB9w5sN7edlnfvKTtOvLmpW4l9Pi9JdkMPAG4jvn/262HLpbRi4C/GHh+YpK/T/I/k/z7KRy/S3Xi3wN3V9WtA8smXh7WI0lSn5nUHJoMWda3ObHPB74NOAXYB7ymXd7L95bkETRddX6xqj6/0qpDlnX+/U3ZTMooya/TdIO6uF20D/iWqnoC8MvAW5N84wRD6FqdeB4PbKWZeHlYjyRJfWdSM9zdS10q2vt72uV7gRMG1jseuGvKsY2lqu6uqvur6qvAG/lad5revbckD6H5IXZxVS2NP5jbv9066kwZJTkX+FHgnGovmtV29/rn9vENwD8C3z6pGLpUJ5IcDvyfwKUD8U20PKxHkqR5YFIz3BXAue3jc4HLB5a/oJ0B6DTg3qUuGn1xUP/3HweWZl+6Ajg7ycOSnEgzEPj9045vrdpxBxcAt1TVawdemtu/3TrqRBklOQN4GfDMqvriwPJHLQ3IT/KtNJ/F2ycYR5fqxA8DH62qvQPxTaw8rEeSpHlx+KwDmLUkbwMWgGOS7AVeAewCLkvyYuAO4Dnt6lcBZ9IMGP4i8MKpB3wIlnlvC0lOoekysgf4aYCqujnJZcBHaLoCnVdV988i7jV6MvB84KalqW+BX2NO/nbrpSuf72XieDnwMODq5rc117Yze/0g8KokB4D7gZ+pqrUO7h8ljqnXiWFxVNUFNLOtHTxBwMTKA+uRJGlOpO3xIUmSJEm9ZPczSZIkSb1mUiNJkiSp10xqJEmSJPWaSY0kSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKv/f9fLOiAETjJLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_size = df['num'].count()\n",
    "\n",
    "empty = df.isna().sum().sum()\n",
    "empty_p = (empty / data_size) * 100 # percentage\n",
    "\n",
    "diseased = df['num'][df['num'] > 0].count()\n",
    "diseased_p = (diseased / data_size) * 100 # percentage\n",
    "\n",
    "healthy = data_size - diseased\n",
    "healthy_p = (healthy / data_size) * 100 # percentage\n",
    "\n",
    "print(\"Data size: %d rows, of which %d (%3.1f %%) contain empty values.\" %(data_size, empty, empty_p))\n",
    "print(\"Healthy: %d (%3.1f %%) Diseased: %d (%3.1f %%)\" %(healthy, healthy_p, diseased, diseased_p))\n",
    "\n",
    "fig = df.hist(figsize=(14,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a helper function to visualize each column.\n",
    "def plot_attribute(name):\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8,3))\n",
    "    fig.suptitle(name, fontsize=16)\n",
    "    # histogram left\n",
    "    df[name].hist(ax = axes[0], color='green')\n",
    "    # box plot right - this may help spot outliers.\n",
    "    df[name].plot(ax = axes[1], kind='box', color=\"magenta\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attribute Description\n",
    "---------------------\n",
    "\n",
    "Now we will describe each attribute in more detail and consider their significance. We will also decide for each column, if they should be excluded from the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min: 29.0 max: 77.0\n"
     ]
    }
   ],
   "source": [
    "plot_attribute('age')\n",
    "print(\"min:\", df['age'].min(), \"max:\", df['age'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Patient age__ in years. Increasing age is recognized as a major risk factor for coronary disease by American Heart Association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Males: 206 Females: 97\n"
     ]
    }
   ],
   "source": [
    "plot_attribute('sex')\n",
    "print(\"Males:\", len(df['sex'][df['sex']==1]), \"Females:\", len(df['sex'][df['sex']==0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Value 1 denotes male __sex__. Male sex is recognized as a major risk factor for coronary disease by American Heart Association. A binary categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('cp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Chest pain type.__ This is a categorical variable. Individuals without pain are in category 4. Categories 1, 2 and 3 denote \"typical\", \"atypical\" and \"non-anginal\" pain, respectfully:\n",
    "\n",
    "- 1 Typical\n",
    "- 2 Atypical\n",
    "- 3 Non-anginal\n",
    "- 4 Asymptomatic\n",
    "\n",
    "The severities of categories 1 to 4 perhaps bear an ordinal relationship, where value 1 is the most alarming and value 4 is the least alarming. An ordinal scale variable can probably be scaled safely. Then again, one-hot encoding it into four separate columns would probably be best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('trestbps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Resting blood pressure__ on admission. Judging by the values, this is the systolic blood pressure. High blood pressure is recognized as a major risk factor for coronary disease by American Heart Association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('chol')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Serum cholesterol__ in mg/dl. High blood cholesterol is recognized as a major risk factor for coronary disease by American Heart Association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('fbs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High __fasting blood glucose__. A categorical variable, where value 1 denotes glucose reading above 120 mg/dl, otherwise 0.\n",
    "\n",
    "Diabetes, uncontrolled or not, is recognized as a major risk factor for coronary disease by American Heart Association.\n",
    "\n",
    "As we later examine the correlation table, we will see that __fbs__ has almost no correlation to heart disease, so we choose to exclude this feature for the sake of model simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('restecg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ECG findings__, patient at rest.\n",
    "\n",
    "- 0 denotes no findings\n",
    "- 1 denotes abnormality in ST-T waveform\n",
    "- 2 denotes a finding characteristic to a condition called left ventricular hypertrophy.\n",
    "\n",
    "This categorical variable is of a nominal scale, i.e. there is no order between the values, or at least interpreting such order would be highly subjective. This is because both findings have unique diagnostic value. One-hot encoding it into three separate columns should be done. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('thalac')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Maximum heart rate achieved__ during thallium-201 scintigraphy stress test. This variable should have a negative correlation to heart disease, as a young and healthy heart can beat faster than a sick and old one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('exang')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Exercise-induced angina.__ Chest pain provoked by physical exertion _may_ be a sign of coronary artery disease.\n",
    "\n",
    "This is a categorical variable:\n",
    "\n",
    "- 1 Yes\n",
    "- 2 No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('oldpeak')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Exercise-induced ST-level depression__ in ECG. Assumedly the unit of this variable is millimeters.\n",
    "\n",
    "Exertion testing can reveal coronary disease through demand ischemia and a subsequent ECG finding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zs4Q4MZuZmSXEidnMzCwhTsxmZmYJaSox50+auVvSE/kzXc+RdKykTZKezH++vahgzczMxrtmR8yfA74ZEb8GnA7sAj4OPBARc4EH8vdmZmZWh4YTs6S3Ae8mu9cuEfGziDhI9vzWNXm1NcAlzQZpZmbWKZoZMf974Dng/0j6vqQvSjoG6Bq6yX3+8/gC4jQzM+sIym6F28CGUg+wBTg3IrZK+hxwGPhQREwtq/dCRLzmOHP581u7urrmrV+/fsT29h3Yx8CrAw3FWmnejHmF7Kdeg4ODTJkypa1tFqGoPnd/16eeuBcsWNAfET1tCqkhPT09sW3bttEOY/wRo/MYxtFqtwNIqvp9buYhFgPAQERszd/fTXY8eZ+kGRGxV9IMYH+1jSOiD+iD7Ivc29s7YmOr161m5e6VTYRb1vbS9v4vK5VK1Pp8KSqqz93f9RmrcZtZsRqeyo6IHwPPSDo5L7oAeJzs+a3L8rJlwH1NRWhmZtZBmn3s44eAOyS9EXiK7FmtbwDukrQceBq4tMk2zMzMOkZTiTkidgDVjndd0Mx+zczMOpXv/GVmZpYQJ2YzM7OEODGbmZklxInZzMwsIU7MZmZmCXFiNjMzS4gTs5mZWUKcmM3MzBLixGxmZpYQJ2YzM7OEODGbmZklxInZzMwsIU7MZh1K0m2S9kt6dJj1vZIOSdqRvz7V7hjNOlHTiVnSBEnfl/T1/P2JkrZKelLSnfkjIc0sPbcDC2vU+U5EnJG/Pt2GmMw6XhEj5quAXWXvbwA+GxFzgReA5QW0YWYFi4gHgQOjHYeZHa2pxCypG/jPwBfz9wLOB+7Oq6wBLmmmDTMbVedIeljS/ZJOHe1gzDpBsyPmvwY+Cvwif38ccDAijuTvB4CZTbZhZqNjO3BCRJwO/A3wteEqSlohaZukbc8991zbAjQbjyY2uqGki4H9EdEvqXeouErVGGb7FcAKgK6uLkql0ojtdU/uZtVJqxoN9yi12ira4OBg29ssQlF97v6uT2pxR8ThsuWNkj4vaVpE/KRK3T6gD6Cnp6fqd97M6tNwYgbOBd4r6SLgTcDbyEbQUyVNzEfN3cCz1Tau/CL39vaO2NjqdatZuXtlE+GWtb20vb83SqUStT5fiorqc/d3fVKLW9I7gH0REZLOJpthe36UwzIb9xqeyo6IqyOiOyLmAEuAb0XE7wObgcV5tWXAfU1HaWaFk7QO+C5wsqQBScslXS7p8rzKYuBRSQ8DNwFLIsKjYbMWa2bEPJyPAesl/SXwfeDWFrRhZk2KiKU11t8M3NymcMwsV0hijogSUMqXnwLOLmK/ZmZmncZ3/jIzM0uIE7OZmVlCnJjNzMwS4sRsZmaWECdmMzOzhDgxm5mZJcSJ2czMLCFOzGZmZglxYjYzM0uIE7OZmVlCnJjNzMwS4sRsZmaWECdmMzOzhDScmCXNkrRZ0i5Jj0m6Ki8/VtImSU/mP99eXLhmZmbjWzMj5iPARyLiXcB84EpJpwAfBx6IiLnAA/l7MzMzq0PDiTki9kbE9nz5RWAXMBNYBKzJq60BLmk2SDMzs04xsYidSJoDnAlsBboiYi9kyVvS8UW0YZYiXavC9rX5vM2F7cvMxi5FRHM7kKYA3waui4h7JR2MiKll61+IiNccZ5a0AlgB0NXVNW/9+vUjtrPvwD4GXh1oKtYh82bMK2Q/9RocHGTKlCltbbMIRfX5eO7v/r39he3r5LeeXDPuBQsW9EdET2GNtkBPT09s27ZttMMYfwQ09+t6bLXbASRV/T43NWKWNAm4B7gjIu7Ni/dJmpGPlmcA+6ttGxF9QB9kX+Te3t4R21q9bjUrd69sJtx/a3tpe/+XlUolan2+FBXV5+O5vxdcu6CwfW0+b/OY/H9iZsVq5qxsAbcCuyLixrJVG4Bl+fIy4L7GwzMzM+sszYyYzwXeD+yUtCMv+wRwPXCXpOXA08ClzYVoZmbWORpOzBHxENnRh2ouaHS/ZmZmncx3/jIzM0uIE7OZmVlCnJjNzMwS4sRsZmaWECdmMzOzhDgxm3UoSbdJ2i/p0WHWS9JNkvZIekTSWe2O0awTOTGbda7bgYUjrL8QmJu/VgC3tCEms47nxGzWoSLiQeDACFUWAWsjswWYmt9m18xayInZzIYzE3im7P1AXmZmLVTIYx/NbFyqdme/qk8kKX9a3OzZs1sZU2cr7imj9XvNswGt1TxiNrPhDACzyt53A89WqxgRfRHRExE906dPb0twHSdG6TXSwQ5rCSdmMxvOBuCy/Ozs+cChiNg72kGZjXeeyjbrUJLWAb3ANEkDwDXAJICI+AKwEbgI2AO8DHxwdCI16ywtS8ySFgKfAyYAX4yI61vVlpm9fhGxtMb6AK5sUzhmlmvJVLakCcDfkl0HeQqwVNIprWjLzMxsPGnVMeazgT0R8VRE/AxYT3ZNpJmZmY2gVYnZ1z+amZk1oFXHmGte/1h+3SMwKOkHNfY5DfhJAbGh/9n2iwELi73NConb/V2fBSyoJ+4T2hFLM/r7+38i6V9GOw47ypj8TnSAqt/nViXmmtc/RkQf0FfvDiVti4ieYsJrr7Eau+Nur7Ead6WI8IXMiRkv/7c6Raumsv8JmCvpRElvBJaQXRNpZmZmI2jJiDkijkj6E+D/kV0udVtEPNaKtszMzMaTll3HHBEbyW5QUJS6p70TNFZjd9ztNVbjtvT5/9YYouweAmZmZpYC3yvbzMwsIcklZkm3Sdov6dFh1kvSTZL2SHpE0lntjrGaOuLulXRI0o789al2x1glplmSNkvaJekxSVdVqZNqf9cTe4p9/iZJ35P0cB73tVXqTJZ0Z97nWyXNaX+kNpZIKknyWdfjRIoPsbgduBlYO8z6C4G5+eu3gFvyn6PtdkaOG+A7EXFxe8KpyxHgIxGxXdJbgX5JmyLi8bI6qfZ3PbFDen3+KnB+RAxKmgQ8JOn+iNhSVmc58EJEvFPSEuAG4H2jEayZtV9yI+aIeJCRnwC6CFgbmS3AVEkz2hPd8OqIOzkRsTcitufLLwK7eO0d2lLt73piT07ej4P520n5q/JEj0XAmnz5buACSW2/S4ulSdIxkr6Rz7o8Kul9FeuXStqZr7uhrHxQ0mpJ2yU9IGl6Xv6rkr4pqV/SdyT9Wrs/kx0tucRch7F8u89z8i/T/ZJOHe1gyuXTpWcCWytWJd/fI8QOCfa5pAmSdgD7gU0RMWyfR8QR4BBwXHujtIQtBJ6NiNMj4jTgm0MrJP07shmW84EzgN+UdEm++hhge0ScBXyb7DGfkJ2x/aGImAesBD7fno9hwxmLibnm7T4TtR04ISJOB/4G+Noox/NLkqYA9wAfjojDlaurbJJMf9eIPck+j4ifR8QZZHfEO1vSaRVVku5zG3U7gfdIukHSf4yIQ2XrfhMoRcRz+R91dwDvztf9ArgzX/4y8B/y789vA1/J/1j8O2DUZ8Q63VhMzDVv95miiDg8NIWZX+M9SdK0UQ6L/DjnPcAdEXFvlSrJ9net2FPt8yERcRAokY2Ayv2yzyVNBH6FMXaYxFonInYD88gS9F9VnNT4eg55BFkOOBgRZ5S93lVguNaAsZiYNwCX5WcLzwcORcTe0Q6qFknvGDpOKOlssr5/fpRjEnArsCsibhymWpL9XU/sifb5dElT8+U3A+8BnqiotgFYli8vBr4VvuGA5fLp6pcj4svAKqD8SomtwHmSpkmaACwlm7aG7P//4nz594CH8lmmH0q6NN+3JJ3ejs9hw0vurGxJ64BeYJqkAbLjIJMAIuILZHcTuwjYA7wMfHB0Ij1aHXEvBq6QdAR4BViSwC/bc4H3AzvzaSyATwCzIe3+pr7YU+zzGcCa/JfmG4C7IuLrkj4NbIuIDWR/cHxJ0h6ykfKS0QvXEvTrwGck/QL4V+AKsgRNROyVdDWwmWz0vDEi7su3ewk4VVI/2XkLQyeN/T5wi6S/IPudtR54uF0fxl7Ld/4yM+sAkgYjYspox2G1jcWpbDMzs3HLI2YzM7OEeMRsZmaWECdmMzOzhDgxm5mZJcSJ2czMLCFOzGZmZglxYjYzM0vI/wegM+86230xFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('slope')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Slope of the peak exercise ST segment.__ A categorical variable:\n",
    "\n",
    "- 1 upsloping\n",
    "- 2 flat\n",
    "- 3 downsloping\n",
    "\n",
    "ST slope is a marker of transient myocardial ischemia. An ordinal variable ranging from the least alarming 1 to the most alarming 3. Should be safely scalable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('ca')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Number of major vessels__ (fluoroscopy). Assumedly, this is coronary calcification detected in fluoroscopic angiography in any three of the main vessels.\n",
    "\n",
    "- 0 No findings\n",
    "- 1 One major coronary vessel impacted\n",
    "- 2 Two major coronary vessels impacted\n",
    "- 3 Three major coronary vessels impacted\n",
    "\n",
    "This column contains a few empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attribute('thal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Thallium-201 stress scintigraphy results.__\n",
    "\n",
    "- 3 Normal\n",
    "- 6 Fixed defect\n",
    "- 7 Reversable defect\n",
    "\n",
    "Reversible defect during exertion test is a sign of poor perfusion. A fixed (permanent) defect implies old myocardial infarction.\n",
    "\n",
    "This column contains a few empty values. This categorical variable should be one-hot encoded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing\n",
    "-------------\n",
    "\n",
    "scale or normalize the variables  \n",
    "replace missing values with zeros, means/medians or random values\n",
    "\n",
    "__Imputation (handling of empty values)__\n",
    "\n",
    "There are six samples in the dataset containing empty values. This represents only 2 % of all the records, as mentioned earlier. The empty values occur in columns `ca` and `thal`, which are both problematic when it comes to inventing meaningful substitute data. Both are categorical variables containing actual clinical results, so all kinds of mean/interpolation methods will probably just create distortion and error. Therefore we choose to do listwise deletion, eliminating incomplete samples entirely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eliminate incomplete data\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Splitting the Data__\n",
    "\n",
    "We now split the data into two sets, setting aside 20 percent of it in a test set. The __test set__ will only be used for final model performance evaluation, and its contents shall not be exposed to the model development process in any way.\n",
    "\n",
    "The rest 80 % of the data will be used for training and validation. The data split is done following the 60-20-20 rule introduced in class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num'] = (df['num'] > 0) * 1.0  # convert output labels to zeros and ones\n",
    "train_data, test_data = np.split(df, [int(.8 * len(df)),])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that data has been split, let's examine if these subsets are actually representative of the entire data. We want both training and testing sets to contain both sick and healthy individuals in roughly the same proportion as the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_counts = train_data['num'].value_counts()\n",
    "test_counts = test_data['num'].value_counts()\n",
    "total_counts = train_counts + test_counts\n",
    "categories = train_data['num'].unique()\n",
    "\n",
    "plt.bar(categories+0.125, total_counts / df['num'].count(), width=0.5, alpha=0.5)\n",
    "plt.bar(categories, train_counts / train_data['num'].count(), width=0.1)\n",
    "plt.bar(categories+0.25, test_counts / test_data['num'].count(), width=0.1)\n",
    "plt.legend(['total', 'train', 'test'], loc=3)\n",
    "plt.xticks([0.5, 1.5], labels=['healthy', 'sick'])\n",
    "plt.title('Representation of target classes in split data sets')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target class distribution seems fine in both subsets. Total sick-healthy-distribution is close to 50:50, which suggests, that _accuracy_ will be a useful metric when evaluating the model.\n",
    "\n",
    "__Scaling__\n",
    "\n",
    "There are many ways to scale the data to even out each attribute's effect on the model. We could normalize it by subtracting mean and dividing by standard deviation, or standardize it by scaling its range down to -1...1. Both of these methods behave badly with outliers, compressing the inliers into a narrow range. Using sklearn's `RobustScaler` mitigates this problem.\n",
    "\n",
    "The scaling parameters are fitted to the training/validation set. The training set and test set are both then scaled using these parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set aside output labels, while scaling input data\n",
    "train_num = train_data['num'].values\n",
    "test_num = test_data['num'].values\n",
    "\n",
    "# using the RobustScaler that was advertised on Wednesday's lecture\n",
    "scaler = RobustScaler()\n",
    "# fit scaler parameters to the training/validation set and scale it\n",
    "train_data = pd.DataFrame(scaler.fit_transform(train_data))\n",
    "train_data.columns = col_names\n",
    "# scale the test set using the same parameters\n",
    "test_data = pd.DataFrame(scaler.transform(test_data))\n",
    "test_data.columns = col_names\n",
    "\n",
    "# replace original target labels\n",
    "train_data['num'] = (train_num > 0) * 1.0\n",
    "test_data['num'] = (test_num > 0) * 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection\n",
    "-----------------\n",
    "\n",
    "Let's see how our input variables correlate to being affected by a heart disease. Correlation may suggest whether or not an input variable is useful or not. There are also other methods of gauging feature importance. Plain correlation is by no means a perfect measure of feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0.250104\n",
       "sex         0.272576\n",
       "cp          0.448295\n",
       "trestbps    0.156313\n",
       "chol        0.112987\n",
       "fbs        -0.026038\n",
       "restecg     0.197816\n",
       "thalac     -0.457597\n",
       "exang       0.433948\n",
       "oldpeak     0.415809\n",
       "slope       0.353332\n",
       "ca          0.449147\n",
       "thal        0.525390\n",
       "num         1.000000\n",
       "Name: num, dtype: float64"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.corr()['num']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's filter the desired columns from the dataframe and separate the input data from output labels.\n",
    "\n",
    "We eliminate from the analysis `fbs`, because it seems to have no correlation to heart disease. Also eliminating `restecg` and `thal`, because they would require one-hot encoding to be meaningful. This leaves us with ten input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = ['age', 'sex', 'cp', 'trestbps', 'chol', 'thalac', 'exang', 'oldpeak', 'slope', 'ca']\n",
    "# input\n",
    "train_X = train_data.filter(selected_features)\n",
    "test_X = test_data.filter(selected_features)\n",
    "# output\n",
    "train_y = train_data.filter(['num'])\n",
    "test_y = test_data.filter(['num'])"
   ]
  },
  {
   "attachments": {
    "accuracies.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model\n",
    "-----\n",
    "\n",
    "Next, we will construct a feed-forward neural network to predict heart disease. We experiment by adjusting hyperparameters like number of layers, number of neurons, choice of optimizer and its learning rate, activation function, dropout, regularization, batch size and number of epochs.\n",
    "\n",
    "We will inspect the learning curves to detect overfitting, maximize accuracy and minimize loss. Based on the learning curve, we choose a good number of epochs for early stopping.\n",
    "\n",
    "While iteratively improving our model, we employ 20 % of the data set (25 % of the training set) for validation. This allows us to detect overfitting. Diverging learning curves for training and validation is a sign of overfitting.\n",
    "\n",
    "We use [this material from Stanford cs231n course](http://cs231n.github.io/neural-networks-3/#baby) as a guideline for adjusting hyperparameters like learning rate and model complexity.\n",
    "\n",
    "![accuracies.jpg](attachment:accuracies.jpg)\n",
    "\n",
    "_\"The gap between the training and validation accuracy indicates the amount of overfitting.\" Source: Stanford University_\n",
    "\n",
    "In this case we can, for example, add regularization, decrease model complexity or add dropout.\n",
    "\n",
    "![learningrates.jpg](attachment:learningrates.jpg)\n",
    "\n",
    "_\"With low learning rates the improvements will be linear. With high learning rates they will start to look more exponential. Higher learning rates will decay the loss faster, but they get stuck at worse values of loss (green line).\" Source: Stanford University_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    reg = tf.keras.regularizers.l2(0.002)\n",
    "    model = tf.keras.models.Sequential([\n",
    "        tf.keras.layers.Dense(13, activation='sigmoid', kernel_regularizer=reg, input_shape=(train_X.shape[1],)),\n",
    "        tf.keras.layers.Dropout(0.2),\n",
    "        tf.keras.layers.Dense(6, activation='sigmoid', kernel_regularizer=reg),\n",
    "        tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "\n",
    "    opt = tf.keras.optimizers.RMSprop(lr=0.001)\n",
    "\n",
    "    model.compile(optimizer=opt,\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "batch_size = 30\n",
    "epochs = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_model()\n",
    "#print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(np.asarray(train_X),\n",
    "                    np.asarray(train_y),\n",
    "                    verbose=0,\n",
    "                    batch_size=batch_size,\n",
    "                    validation_split=0.25,\n",
    "                    epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237/1 - 1s - loss: 0.3514 - accuracy: 0.8692\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.42446552210719274, 0.8691983]"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(np.asarray(train_X), np.asarray(train_y), verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss and accuracy learning curves for training and validation sets\n",
    "\n",
    "print(history.history.keys())\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "time = range(1,len(loss)+1)\n",
    "\n",
    "plt.plot(time, loss, 'b-')\n",
    "plt.plot(time, val_loss, 'r-')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlim((5,epochs))\n",
    "plt.show()\n",
    "\n",
    "plt.plot(time, acc, 'b-')\n",
    "plt.plot(time, savgol_filter(val_acc, 5, 3), 'r-')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlim((10,epochs))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">The code from this point on should only be used in final evaluation</span>\n",
    "--------------------------------------------------------------------------------------------------\n",
    "If we run this code __and__ make modifications to the model based on its results, information will leak from the test set into our model. We have tucked away the test set to do final testing with data previously unseen to our model, and taken measures to prevent data leakage at any point in development.\n",
    "\n",
    "A steady stream of fresh data would eliminate the need to guard this test set so closely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Evaluation\n",
    "----------------\n",
    "Next, we create a fresh model and retrain it from scratch, now using the entire training set without reserving data for validation. Then we evaluate the model against the previously unseen test set to see how well it actually performs.\n",
    "\n",
    "If we see much poorer performance (accuracy) here, compared to training and validation, our model is probably overfitted, i.e. it has become an expert in reciting the training/validation set, but gets confused when encountering unseen samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a fresh model for final evaluation\n",
    "model = create_model()\n",
    "# train model with early stopping based on learning curve, to avoid overfitting\n",
    "# use all data: no validation set\n",
    "_ = model.fit(np.asarray(train_X), np.asarray(train_y), verbose=0, batch_size=batch_size, epochs=170)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.5868688106536866 accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate(np.asarray(test_X), np.asarray(test_y), verbose=0)\n",
    "print(\"loss:\", loss, \"accuracy:\", acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results\n",
    "-------\n",
    "Let's use various __metrics__ to gain insight into our model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Healthy       0.69      0.86      0.77        29\n",
      "       Sicko       0.83      0.65      0.73        31\n",
      "\n",
      "    accuracy                           0.75        60\n",
      "   macro avg       0.76      0.75      0.75        60\n",
      "weighted avg       0.77      0.75      0.75        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.array(model.predict(np.asarray(test_X))).flatten()\n",
    "target_names = ['Healthy', 'Sicko']\n",
    "print(classification_report(test_y, y_pred.round(), target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __classification report__ shows accuracy, precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25, 11],\n",
       "       [ 4, 20]], dtype=int64)"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_y, y_pred.round()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This __confusion matrix__ contains the following values:\n",
    "\n",
    "    true positives    false positives\n",
    "    false negatives   true negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the fpr and tpr for all thresholds of the classification\n",
    "probs = model.predict_proba(np.asarray(test_X))\n",
    "preds = probs[:,0]\n",
    "fpr, tpr, threshold = roc_curve(test_y, preds)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', color='green', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __ROC curve__ plots true positive rate against false positive rate at different discrimination thresholds. A high AUC (Area Under Curve) is a measure of good model performance.\n",
    "\n",
    "The ROC curve should travel as close to upper left corner as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare our model's predictions to actual truth. It is interesting to see the \"certainty\" of false and successful predictions. This certainty value is calculated using the network's output, which ranges from 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Sigmoid Output</th>\n",
       "      <th>Certainty (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.077</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.455</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.807</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.405</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.846</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.707</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.749</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.866</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.858</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.366</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.095</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.511</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.262</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.123</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.201</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.167</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.115</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.826</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.213</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.137</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.826</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.685</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.899</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.377</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.150</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.255</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.149</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.316</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.872</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.116</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.788</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.911</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.881</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.092</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.322</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.826</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.893</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.079</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.892</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.545</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.108</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.850</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.101</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Prediction  Sigmoid Output  Certainty (%)\n",
       "0        0           0           0.090             82\n",
       "1        0           0           0.077             84\n",
       "2        0           0           0.139             72\n",
       "3        1           0           0.455              9\n",
       "4        0           0           0.144             71\n",
       "5        1           1           0.807             61\n",
       "6        1           0           0.405             19\n",
       "7        1           1           0.846             69\n",
       "8        1           1           0.707             41\n",
       "9        0           0           0.105             79\n",
       "10       0           1           0.749             49\n",
       "11       1           1           0.866             73\n",
       "12       0           1           0.858             71\n",
       "13       0           0           0.104             79\n",
       "14       0           0           0.366             26\n",
       "15       0           0           0.095             80\n",
       "16       0           1           0.511              2\n",
       "17       0           0           0.262             47\n",
       "18       0           0           0.123             75\n",
       "19       1           0           0.111             77\n",
       "20       0           0           0.201             59\n",
       "21       1           0           0.167             66\n",
       "22       0           0           0.072             85\n",
       "23       0           0           0.115             76\n",
       "24       1           1           0.888             77\n",
       "25       1           1           0.826             65\n",
       "26       1           1           0.692             38\n",
       "27       1           0           0.213             57\n",
       "28       0           0           0.137             72\n",
       "29       1           1           0.826             65\n",
       "30       0           1           0.685             36\n",
       "31       1           1           0.899             79\n",
       "32       0           0           0.377             24\n",
       "33       1           0           0.150             69\n",
       "34       0           0           0.109             78\n",
       "35       0           0           0.255             49\n",
       "36       0           0           0.120             76\n",
       "37       1           0           0.149             70\n",
       "38       0           0           0.316             36\n",
       "39       1           1           0.872             74\n",
       "40       0           0           0.116             76\n",
       "41       1           1           0.788             57\n",
       "42       0           0           0.082             83\n",
       "43       1           1           0.506              1\n",
       "44       1           1           0.911             82\n",
       "45       1           1           0.881             76\n",
       "46       0           0           0.092             81\n",
       "47       0           0           0.125             74\n",
       "48       1           0           0.322             35\n",
       "49       0           0           0.088             82\n",
       "50       1           1           0.826             65\n",
       "51       1           1           0.893             78\n",
       "52       1           0           0.379             24\n",
       "53       0           0           0.079             84\n",
       "54       1           1           0.892             78\n",
       "55       1           1           0.545              9\n",
       "56       1           0           0.108             78\n",
       "57       1           1           0.888             77\n",
       "58       1           1           0.850             70\n",
       "59       1           0           0.101             79"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_labels = np.array(y_pred.round()).flatten()\n",
    "y_percent = np.abs((y_pred - 0.5)) * 2 * 100\n",
    "pred = pd.DataFrame()\n",
    "pred['Actual'] = np.asarray(test_y).flatten().astype(int)\n",
    "pred['Prediction'] = y_pred_labels.astype(int)\n",
    "pred['Sigmoid Output'] = y_pred.round(3)\n",
    "pred['Certainty (%)'] = y_percent.astype(int)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusions\n",
    "-----------\n",
    "Designing a viable ANN classifier is tedious work for an inexperienced novice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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