4.1.xgboostBasic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import xgboost as xgb \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, roc_curve\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "#https://www.datacamp.com/community/tutorials/xgboost-in-python \n",
    "#tutorial above to help create the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size brca, ovca 1080 579\n",
      "normal size 1659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danielraad/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "def get_datasets(std_size=False):\n",
    "    # My python only likes absolute paths \n",
    "    brca = pd.read_csv('/Users/danielraad/Desktop/data/Gistic2_CopyNumber_BRCA', sep='\\t')\n",
    "    ovca = pd.read_csv('/Users/danielraad/Desktop/data/Gistic2_CopyNumber_OVCA', sep='\\t')\n",
    "    brca.set_index('Gene Symbol', inplace=True)\n",
    "    ovca.set_index('Gene Symbol', inplace=True)\n",
    "    brca = brca.transpose()\n",
    "    ovca = ovca.transpose()\n",
    "    brca['BRCA'] = 1\n",
    "    ovca['BRCA'] = 0\n",
    "    print('size brca, ovca', len(brca), len(ovca))\n",
    "    if not std_size:\n",
    "        print('normal size', len(brca)+len(ovca))\n",
    "        df = pd.concat([brca, ovca[1:]])\n",
    "    elif std_size:\n",
    "        print('sampled size', len(ovca)+len(ovca))\n",
    "        df = pd.concat([brca[0:len(ovca)], ovca[1:]])\n",
    "    return df\n",
    "df = get_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate the target variable from the other variables\n",
    "from sklearn.model_selection import train_test_split\n",
    "X= df.drop(['BRCA'], axis = 1)\n",
    "y = df['BRCA']\n",
    "X_train, X_test, y_train, y_tests = train_test_split(X, y, test_size = 0.33)\n",
    "y_red = pd.Series(y)\n",
    "features = list(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the Classification model, running \n",
    "xg_class = xgb.XGBClassifier()\n",
    "xg_class.fit(X_train,y_train)\n",
    "\n",
    "train_xg_predictions = xg_class.predict(X_train)\n",
    "train_xg_probs       = xg_class.predict_proba(X_train)[:, 1]\n",
    "\n",
    "xg_predictions = xg_class.predict(X_test)\n",
    "xg_probs       = xg_class.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>C19orf77</td>\n",
       "      <td>0.102283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23182</th>\n",
       "      <td>Vault|ENSG00000252283.1</td>\n",
       "      <td>0.050414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14408</th>\n",
       "      <td>PIAS4</td>\n",
       "      <td>0.046257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18866</th>\n",
       "      <td>SI</td>\n",
       "      <td>0.035553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>ACTRT3</td>\n",
       "      <td>0.031480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5829</th>\n",
       "      <td>FAM227A</td>\n",
       "      <td>0.027019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6314</th>\n",
       "      <td>FNDC8</td>\n",
       "      <td>0.023322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4667</th>\n",
       "      <td>DIRAS1</td>\n",
       "      <td>0.020029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6189</th>\n",
       "      <td>FGFR1</td>\n",
       "      <td>0.019925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3700</th>\n",
       "      <td>COL10A1</td>\n",
       "      <td>0.015615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       feature  importance\n",
       "2012                  C19orf77    0.102283\n",
       "23182  Vault|ENSG00000252283.1    0.050414\n",
       "14408                    PIAS4    0.046257\n",
       "18866                       SI    0.035553\n",
       "252                     ACTRT3    0.031480\n",
       "5829                   FAM227A    0.027019\n",
       "6314                     FNDC8    0.023322\n",
       "4667                    DIRAS1    0.020029\n",
       "6189                     FGFR1    0.019925\n",
       "3700                   COL10A1    0.015615"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 features \n",
    "features = list(X_train.columns)\n",
    "fi_model = pd.DataFrame({'feature': features,\n",
    "                   'importance': xg_class.feature_importances_}).\\\n",
    "                    sort_values('importance', ascending = False)\n",
    "fi_model.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.71%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_tests, xg_predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Oranges):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    Source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.figure(figsize = (10, 10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, size = 24)\n",
    "    plt.colorbar(aspect=4)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, size = 14)\n",
    "    plt.yticks(tick_marks, classes, size = 14)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    \n",
    "    # Labeling the plot\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 20,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.grid(None)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', size = 18)\n",
    "    plt.xlabel('Predicted label', size = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[181  16]\n",
      " [ 13 338]]\n"
     ]
    },
    {
     "data": {
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+iy6WjO+kXx1eE7pYQKes8+c216O1XzOZvxR62wV0DmHB9zytzftJ4Ph2ztXZoi0fZsEl419hwSXjL6PzJePf8nPTUGcSC7egTeuxj+5Bmw4X0KGYGedhFvw5aF0Kfk55LTvsK/Pv8UiKGXcmldvm3fmMu7oW5c9p68q1Z7TTZhDz/+24gj5axMvNza1/bI54Sw0y8zyKOZC/RJH3/RTFf/qDKf4zPgfYm2L57Wsa2r0BvJ9iGexHKQLaORSjcR/OzB/1YZ+nUOQzf4YikGpN4ZhMEVQcQrHgSdt2R1DMqHAC8BDFqPnwst0lFEvJ99koYNmH6ylmkvkMRbD6eLlrSYrP/iKKVJLVM/PPbdrOAHaiSJm4lmJ0e1h5jBOB9TPzfKqxF8UI5wMUPwezKd7fZpn5jw7anE4xQ8dZFLm/s5l/HS+g+BJxYE86kcXI8foUN2dOovh8ZgDXUXyuH8iO545fLGTmyxTz2f+G+dMjzgTOA7bN9mflafQx4NfAYxSf92rl1mm6WQ/8FFibYlrHL7bdmUUa1D4UXzi3A77aS+eV1A9F5mI345YkSZK02HHEW5IkSWoCA29JkiSpCQy8JUmSpCYw8JYkSZKaYHDVHVBhzPAhudqortbP0OJs+tBlGf7Gy1V3Q30klp9QdRfUx6bNmMmIYUtV3Q31odvuuOvFzFyu6n5o4DLw7idWGzWUmw9ar+uKWmxdN/6zbDXp91V3Q31k0Jd7suaLFkdX33w32272rqq7oT7UMmLs413XkhaeqSaSJElSExh4S5IkSU1g4C1JkiQ1gYG3JEmS1AQG3pIkSVITGHhLkiRJTWDgLUmSJDWBgbckSZLUBAbekiRJUhMYeEuSJElNYOAtSZIkNYGBtyRJktQEBt6SJElSExh4S5IkSU1g4C1JkiQ1gYG3JEmS1AQG3pIkSVITGHhLkiRJTWDgLUmSJDWBgbckSZLUBAbekiRJUhMYeEuSJElNYOAtSZIkNYGBtyRJktQEBt6SJElSExh4S5IkSU1g4C1JkiQ1gYG3JEmS1AQG3pIkSVITGHhLkiRJTWDgLUmSJDWBgbckSZLUBAbekiRJUhMYeEuSJElNYOAtSZIkNYGBtyRJktQEBt6SJElSExh4S5IkSU1g4C1JkiQ1gYG3JEmS1AQG3pIkSVITGHhLkiRJTWDgLUmSJDWBgbckSZLUBAbekiRJUhMYeEuSJElNYOAtSZIkNYGBtyRJktQEBt6SJElSExh4S5IkSU1g4C1JkiQ1gYG3JEmS1AQG3pIkSVITGHhLkiRJTWDgLUmSJDWBgbckSZLUBAbekiRJUhMYeEuSJElNYOAtSZIkNcHgqjsgSZJUZ2sNb8kZc7PqbtTC5Nf5e2buWNX5DbwlSZIqNGNucsB4Q7Jm+MEDc8ZUeX6vsiRJUsWi6g6oKQy8JUmSKhRRbBr4DLwlSZIq5mwX9eB1liRJkprAEW9JkqSKmWpSDwbekiRJFTPurgdTTSRJkqQmcMRbkiSpQoGpJnVh4C1JklQxUxDqwcBbkiSpYo5414NfsCRJkqQmcMRbkiSpYg5414OBtyRJUoW8ubI+TDWRJEmSmsARb0mSpIo54F0PBt6SJElVCmgx8q4FU00kSZKkJnDEW5IkqWIOeNeDgbckSVKFnNWkPgy8JUmSKmbcXQ/meEuSJElN4Ii3JElSxVoiq+6CmsDAW5IkqWKmmtSDqSaSJElSEzjiLUmSVKHAEe+6MPCWJEmqmNMJ1oOBtyRJUsWMu+vBHG9JkiSpCRzxliRJqliLQ961YOAtSZJUIW+urA9TTSRJkqQmcMRbkiSpSuGsJnVh4C1JklQx4+56MNVEkiRJagJHvCVJkirmrCb1YOAtSZJUIWc1qQ8Db0mSpIp5c2U9mOMtSZKk2ouIJSPiloi4KyLuiYgflOWrR8TNEfFQRJwVEUPL8iXK1w+X+8d3dQ4Db0mSpIqFW1O2LrwObJ+ZGwAbAjtGxObAMcBxmTkBeAXYr6y/H/BKZq4FHFfW65SBtyRJUsUi3JqxdSYL08qXQ8otge2Bc8ryU4Bdy+e7lK8p978vovOzGHhLkiSpLsZExK0N2wGNOyNiUETcCTwPXAo8AkzJzDlllaeAlcvnKwNPApT7XwVGd3Zyb66UJEmqUOBIaBO9mJmbdLQzM+cCG0bESOAvwLrtVSsf2xvdznbK3mTgLUmSVDFnNelfMnNKRFwFbA6MjIjB5aj2OOCZstpTwCrAUxExGFgGeLmz4/oFS5IkqWJV33RYl63TaxCxXDnSTUQsBbwfuA+4Evh4We0zwPnl8wvK15T7r8hMR7wlSZKkLqwInBIRgygGp8/OzIsi4l7gzIg4ArgDOKmsfxJwWkQ8TDHS/YmuTmDgLXVDrLM9seq7ieXXhuUnEEsMZ96/L2HeRYe332DQEGKDXWhZb2cYuTIMHsp7Zr5Oy0OjmHfL6fDaswvWX3p5Yr2dibFrE2PXhpErE9HCnN/uBlOe6vP3J9XZOX+5kKuvu4G77r6Hu/59D1OnTuOTe+7GaSf9usM2mcmpp5/Nyaedyd333MvMmbNYYexyvGejd/Oj7x3G2hPWbOI70OIuwiXj+4PMvBt4dzvljwKbtlM+C9i9J+cw8Ja6oWWLzxJj1yZfnw5Tn4clVu+4cgxi0Cf+l1hlA/LFx8j7/gFzZjN79e0YvsmexHo7M/e0z8NLj81vssLbGbTtF8icB1OegdenwZJva8I7k3TkT47jrn/dw4gRwxm30krcP/WhTuvPmjWLPfbZn4suuZR1JqzFXrt/jKWXHs7kyc9x7Q038eDDjxh4q8eMu+vBwFvqhnmX/w859Xl45Uli1Y0YtPdvOqwba29LrLIB8ybdwrwzv0LrDc53PjaVbcYNpmWr/WnZ7JPMu/iIN9vks/cz5w8HwvMPwRvTGbT3r2HVjfv6bUkCfnb0Dxm38kqstebqXH3tDWy/88c6rX/oNw/noksu5bD/+gpHfO+btLQseLvU7Nmz+7K7khZjBt5SN+QTt3W/8shies985Hrazio076FraNlqfxg2csE2U58vNklNt922W3W77iOPTuL4k07hPRtvyJHf/xbtrZUxZMiQ3uyeasJUk3ow8JZ624uPAhBrbEH+8ywag+9Yq/gPPif9s4qeSVpEZ/zpXObNm8en996T116byoWX/IMnn3qa0cuOYvttt2atNTtJQ5M64Dze9WHgLfWyfOR65j1wJS3rbEfsdzr5+D9h7mw2GL8dLWOWZ96tZ5G3/anqbkpaCLfeficAr772GmutvxkvvTx/yt6I4D/335dfHHskgwYNqqqLWkw5j3c9+AVL6gPz/nIYc689AUavSssme9Ky2acYNXZl8sk7mHfvPyDnVd1FSQvh+RdeBOD7R/yETTbagLtvvorXnn2Uyy46hzXXGM9vfvd7fnT0zyrupaT+ysBb6m2DhtKyy5G0bPpJ5v3jWOb8cifm/Gx77r76r8TbVmTQJ39LTNim6l5KWghz584FYMUVxnLuGb9nvXeuy4gRw9l+4tb86Q8n0dLSwnG/+i1vvPFGxT3V4qbFrSlb1fpDH6QBpeW9n6Zl3fcz75rfknf+Baa/DG9M5+XJTzL3vG8Sg4bQ8v6vVt1NSQth1MjixugP/Md2LLXUUgvs22D9d7L6+FWZOnUa9z3Q+ZSEUlsRbs3YqlZZ4B0RJ0dERsR32pRPLMvHLMKxx5fHaN1ej4gHI+K/2tQ7vKHOvIh4JiL+GBGrtHPMNSPipIh4sjzepIg4JyK2aKfuLyJibkR8fmHfgxZfsWZ5A2V7M6E8/xA581VimZWcp1taDLXOzz1ymWXa3d8amM+cObNpfZK0+Kh6xHsW8PWIWK6Pjr8jxfKfawM/Bn4cEXu2qfNAWWccsCewPnB2Y4WI2AS4HXgn8EXgHcBHgNuAX7apuwTwSeBoYP/efTtaLAwqphKLtlMGtu4bOrx4Pm9OEzslqTe8b+LWANxz7/1v2ff666/z0CPFrEbjV1u1qf3S4i2Alki3JmxVqzrwvhKYBHy3s0oRsU1E3BwRsyLiuYg4LiKGduP4L2Xms5n5eGb+HrgL2KhNnTllnWcy81rgd8DmEfG28twBnAw8CmyZmRdm5iOZeXdm/hh4X5vjfax8T0cC60bEet3opwaQfKqY9SDeu++bQXirlq0+TwwaTD5zD7wxo4LeSVoUO+3wPtZYfTX+ftmVXHrF1Qvs+9HRP+PVV19j2622YIWxy1fUQy2uqs59rstWtaqnE5wHHAacFxE/z8xH2laIiJWBS4DTgH2BNYETy7aHduckZfC8BbAucFQn9VagCJznlhvAhhQj3Z/MzLlt22TmlDZF+wN/yMwZEXFu+fqQ7vRT/VdM2IZYe9vixfDRRdnK69PywfI744xXmXflLwCYd8PJxFpb0zJ+U+LzZ5OP3ghzXmejNd9Hy+ix5OxZzL3srbMevHksgGXHF2XbHQRvTC+Oe9cF8NRdffMGpRo778KLOf+ivwHw7HPFQlY33nIrnz3wKwCMHr0sxx51OABDhw7l5ON/yQd22ZOdP7oXH/3wzqy66jhuve1Orrn+RpYbM5rjf3lsJe9DUv9XdeBNZl4cEddTjBB/op0qXwQmA1/MzHnAfRFxGHB8RHw3MzsbNrwmIuYBQ4EhwP9k5rlt6qwbEdMovgi13inzi8ycXj6fUD7e19V7iYg1gK0pUk0ATgXOjohvZObr7dQ/ADgAYOzoUVw3/rNdnUIVGb/OJoxfb5MFymLUOGLUOABmTZ/KTY9NfXPfkMsvYdV1N2TZFVdjqQ12AYLBr89i8qP38+R9dzJj6Htg/HsWON7E9T/0lvO2rLPdm88fnDGcZwe3/YON+o2b7666B1pI519yBaf88awFyh597HEefexxAMaOHcuHd/kY06bP5Oqb74ZBS/HrX/2SU047jcuuupZp06YxatQoPvTBD/LpfT7J5JenM9mfB/VEP7nxT30vMqvJd4mIk4ExmfmhiHgPcBOwGTCCIgVlucx8sRw1np6Z+zS0XQt4CNggM9/yr1tEjAceAz4K/Jsi6F4f+AVwYmZ+p6x3OLA3sDOwBLALsBuwTWvgXeaEnwlslJl3dPGejgA2zsydytctFGknX8vMszpru/G44XnzQWalDGTXjf8sW036fdXdUB8Z9OULq+6C+tjVN9/Ntpu9q+puqA+1jBh7W2Zu0nXN3rX20pG/3LA/JEIMfDteN6+Sa9yq8hFvgMz8Z0T8GTgG+FGb3UHjmtttmnZx6Kcy8+Hy+X3liPSPIuKIzJxVlr/RUOeeiJgA/C9FWgvAg+XjukCHgXdEDCrbrBQRjXfNtVCkm3QaeEuSpPpyxLse+tPXq29RpGns2Kb8XuC95ehxq62AN4C35IR3YS7Fl43Obsz8EfDJiNi4fH1n2YevlcH1AiKideqKHYHRwCYUeeGt24eA95Wj8JIkSaqpfjHiDZCZD0fECcDBbXb9muLmxF9HxM+BNSim6vtVF/ndAKPLGyYHU6SaHAxcmZmvddKPRyPiAooAfOfMzIj4LHAZcH2ZTnIfMAzYCdiDItjeH7gkM29vc8h/R8QDwOeA73XRX0mSVEP9aSRUfaffBN6lHwKfaSzIzKcjYifgvylGn6cAp1OMkHflb+XjXIobNC8Gvt2Ndj+lCLK3yMwbMvOWcgT8W8BvgeWBZ4FbgIMiYizFyPZnOjjen4DPRsTh5Q2ikiRJQOs83lX3Qs1QWeCdmfu2U/Y8sHQ75ddQ3HjZ3WNPovg57qre4cDh7ZTf0LZ9Zj4EdDbtyJCOdmTm93C0W5Ikqdb624i3JElS7XhzZT0YeEuSJFXMHO96MPCWJEmqUOCId134BUuSJElqAke8JUmSKuZIaD0YeEuSJFUpnE6wLvyCJUmSJDWBI96SJEkVCrqx+IgGBANvSZKkiplqUg+mmkiSJElN4Ii3JElSxRzwrgcDb0mSpAoFpprUhYG3JElSxVoiq+6CmsAcb0mSJKkJHPGWJEmqmJkm9WDgLUmSVCFzvOvDVBNJkiSpCRzxliRJqpgD3vVg4C1JklSlMNWkLgy8JUmSKhSY+1sXXmdJkiSpCRzxliRJqliYalILBt6SJEkVM8e7Hkw1kSRJkkD64BUAACAASURBVJrAEW9JkqSKOeBdDwbekiRJFQogTPJukqz07AbekiRJFTPurgdzvCVJkqQmMPCWJEmqUpFr4taMrbPLELFKRFwZEfdFxD0RcXBZfnhEPB0Rd5bbzg1tvhkRD0fEAxHxga4utakmkiRJFTPVpF+YAxyambdHxNLAbRFxabnvuMw8trFyRLwD+ATwTmAl4LKIWDsz53Z0Ake8JUmSVHuZOTkzby+fTwXuA1bupMkuwJmZ+XpmPgY8DGza2Tkc8ZYkSaqYs5o0zZiIuLXh9QmZeULbShExHng3cDOwJXBQRHwauJViVPwViqD8poZmT9F5oG7gLUmSVK0w8G6eFzNzk84qRMQI4M/AIZn5WkT8BvgRxVyEPwJ+CnyO9qdf73S+QlNNJEmSJCAihlAE3X/MzHMBMvO5zJybmfOA3zE/neQpYJWG5uOAZzo7viPekiRJVQocCu0Hovizw0nAfZn5s4byFTNzcvnyo8C/y+cXAKdHxM8obq6cANzS2TkMvCVJkirkypX9xpbAPsC/IuLOsuxbwF4RsSFFGskk4ECAzLwnIs4G7qWYEeVLnc1oAgbekiRJlTPurl5mXkf7edsXd9LmSODI7p7DP2xIkiRJTeCItyRJUsVMNakHA29JkqQqBe0nOGjAMdVEkiRJagJHvCVJkipmqkk9GHhLkiRVzLi7Hgy8JUmSKuQ83vVhjrckSZLUBI54S5IkVSrMNakJA29JkqQqGXfXhqkmkiRJUhM44i1JklQxb66sBwNvSZKkihl314OpJpIkSVITOOItSZJUNYe8a8HAW5IkqWLG3fVg4C1JklShCG+urAtzvCVJkqQmcMRbkiSpYo5414OBtyRJUsWMu+vBVBNJkiSpCRzxliRJqlQ45F0TBt6SJEkVM+6uBwNvSZKkKjmdYG2Y4y1JkiQ1gSPekiRJFQpMNakLA29JkqSqGXnXgqkmkiRJUhM44i1JklQxb66sBwNvSZKkihl314OBtyRJUpWcTrA2zPGWJEmSmsARb0mSpKo54F0LBt6SJEkVCoJoMQmhDjoMvCNi+YU5YGY+v/DdkSRJkgamzka8nwVyIY45aCH7IkmSVE/eXFkLnQXeP2HhAm9JkiR1l2vG10aHgXdmHtbMjkiSJEkDmTdXSpIkVSqI8ObKOuhR4B3F7O67AzsAY4FvZ+bdETGyLLsmM5/t/W5KkiQNYKaa1EK3A++IWBK4GJgIvAEMAY4rd08Dfgn8Fvh+73ZRkiRpgDPwroWe/F3j+8CWwF7AajRM9Z6Zc4BzgR17tXeSJEnSANGTVJM9gBMz86yIGN3O/geB3XqnW5IkSfURjnjXQk8C73HAHZ3snw68bdG6I0mSVDMR4M2VtdCTq/wKsEIn+9cFJi9adyRJkqSBqScj3lcA+0bEsW13RMQ44HPAmb3VMUmSpLqIFlNN6qAnI94/BJYHbqIIsgG2j4jvU6SgzAN+3LvdkyRJqoEIt2ZsnV6CWCUiroyI+yLinog4uCxfNiIujYiHysdRZXlExC8i4uGIuDsiNurqMnd7xDsz74+IHYDfA8eUxd8qHx8EPpWZk7p7PEmSJJXM8e4P5gCHZubtEbE0cFtEXArsC1yemUdHxGHAYcA3gJ2ACeW2GfCb8rFDPVpAJzNvioh3ABtT5HQH8BBwc2bO68mxJEmSpP4iMydT3q+YmVMj4j5gZWAXinVsAE4BrqIIvHcBTs3MBG6KiJERsWJ5nHb1eMn48uC3lpskSZIWRYTTCTbPmIhojGFPyMwT2laKiPHAu4GbgbGtwXRmTo6I5ctqKwNPNjR7qizrvcA7IsYAHwTWKIseBS7OzBd6eixJkiThypXN82JmbtJZhYgYAfwZOCQzX+vkS1F7O7KzY/co8I6Ir1HcZDm0zclej4jDM/OY9ltKkiRJ/VtEDKEIuv+YmeeWxc+1ppBExIrA82X5U8AqDc3HAc90dvxuZ/JHxIEUN1XeD+wHbA68t3z+AHBUWUeSJEk9UfVsH3XZOr0EEcBJwH2Z+bOGXRcAnymffwY4v6H80+XsJpsDr3aW3w09G/E+BLgN2DIz32govzkiTgduAL4KHN+DY0qSJNVaAOGsJv3BlsA+wL8i4s6y7FvA0cDZEbEf8ASwe7nvYmBn4GFgBvDZrk7Qk8B7deCwNkE3AJn5ekT8ATiqB8eTJEmS+oXMvI7287YB3tdO/QS+1JNz9CTwfhIY3sn+YRS5LpIkSeq2rtMgNDD0JPD+DfCViDih7QwmETEWOAD4n97snCRJ0oAXuGR8TXQYeEfEHm2KngZeBB6IiN9T3GSZwDsoEs0fpYs7OSVJktQOc7xrobMR7zMpAuvWr2CNz7/aTv2NgdOBs3qtd5IkSdIA0VngvVPTeiFJklRn5njXQoeBd2b+vZkdkSRJqieXjK8LE4okSZKkJujRkvEAEbE+sCkwircG7pmZ/90bHZMkSaqFYgWdqnuhJuh24B0RS1DccPkRih+R9m68TMDAW5IkqSec1aQWejLi/R1gF+BY4DLgb8DngZeAb1CMfu/f2x2UJEka6MzxroeefL3aA/hzZn4duK0seywzzwO2BZYq60iSJElqoycj3qsBPy+fzysfhwJk5hsRcTrF6pXf7b3uSZIkDXQBrlxZCz0JvKcxf4R8KkXwvULD/peBFXupX5IkSfUQEOZ410JPrvKjwASAzJwD3Ad8rGH/LhTLykuSJElqoycj3pcBn46Ir2bmPOBE4LiIuJdiNpO3A4f3fhclSZIGOG+urIWeBN7HAGcBg4B5mfnziBgOfIoi7eSHwJG930VJkqQBzsC7FrodeGfmq8BdbcqOAo7q7U5JkiTVRbhkfG2YyS9JkiQ1QYcj3hGx6cIcMDNvWfjuSJIk1ZCzmtRCZ6kmN1HcNNldrUvGD1qkHkmSJNVJYI53TXQWeH+hab2QJEmSBrgOA+/MPL6ZHam7WH4Cg7/y16q7ob50050M/ojXeKA6fCPXDxvo1vnyMfzgiztV3Q0NUN5cWQ89mU5QkiRJvS6gxRzvOvAqS5IkSU3giLckSVLVTDWpBQNvSZKkKgVOJ1gTBt6SJEmVCke8a8KvV5IkSVITLNSId0S0AKOAVzNzTu92SZIkqWZMNamFHl3liFg/Ii4GpgPPAduU5ctHxF8jYmLvd1GSJGmAi3BrxlaxbgfeEbEecAOwIXAOxa0AAGTm88AYYN9e7p8kSZI0IPQk1eRHwAvARmW7T7bZfymwey/1S5IkqSbCVJOa6EngvQ3w35k5JSJGt7P/CWCl3umWJElSTQT9Ig1Cfa8ngfcw4OVO9o9YxL5IkiTVkyPetdCTq/wo8O5O9k8E7l+k3kiSJEkDVE9GvM8CDouIM4B7yrIEiIgvAR8EDu3d7kmSJNWAqSa10JPA+yfAB4DLgX9RBN3HRMQYYDXgauCXvd5DSZKkAa1/THWnvtftVJPMnAVsB3wPGArMo5jhZHZZtmNmzu2LTkqSJEmLux6tXJmZbwA/LjciIjIz+6JjkiRJteHNlbWwUEvGtzLoliRJWkROJ1gb3Q68I2KP7tTLzLMXvjuSJEl14wI6ddGTEe8zKW6obPuVrO2ot4G3JEmS1EZPAu+dOmi/JvCfwBTgh73RKUmSpFox1aQWuh14Z+bfO9oXEb8DbgXWBv7WC/2SJEmqD1NNaqFXrnJmzgROBb7cG8eTJEmSBppFmtWkjRnAKr14PEmSpBpwAZ266JXAu1y98gDg8d44niRJUm0EpprURE+mE7y4g13LAusDSwH790anJEmSpGaLiP8DPgQ8n5nrlWWHA58HXiirfSszLy73fRPYD5gLfKWzeyKhZyPeG/HWqQMTeBn4O/CrzLyiB8eTJEkSmGrSf5wM/Iri3sVGx2XmsY0FEfEO4BPAO4GVgMsiYu3MnNvRwXsyq8kK3a0rSZKk7nIBnf4iM6+JiPHdrL4LcGZmvg48FhEPA5sCN3bUoFuBd0QMAw4CbsvMy7vZGUmSJHWHI97NMiYibm14fUJmntCNdgdFxKcpps8+NDNfAVYGbmqo81RZ1qFufb3KzBnAj4A1ulNfkiRJ6odezMxNGrbuBN2/oVgwckNgMvDTsry9b0tt07IX0JMc70eB5XtQX5IkSV1xVpN+LTOfa31eLhp5UfnyKRacSnsc8Exnx+rJVf4t8LmIWKYHbSRJktSVCLdmbAt1aWLFhpcfBf5dPr8A+ERELBERqwMTgFs6O1ZPRryfBV4DHoiIk4CHKBbNWUBmnt2DY0qSJEn9QkScAUykyAV/Cvg+MDEiNqRII5kEHAiQmfdExNnAvcAc4EudzWgCPQu8z2h4/s0O6iRg4C1JktRtzmrSX2TmXu0Un9RJ/SOBI7t7/J4E3jv1oK4kSZK6y1lNaqHTwDsiVgVeyMyZXa3EI0mSpIXkiHctdHWVH6NIIpckSZK0CLpKNfHvHpIkSX1pEWbc0OKlJznekiRJ6gummtSCV1mSJElqgu6MeG8dEd0eGc/MUxehP5IkSfXTYqpJHXQnoD6g3LoSFPN4G3hLkiT1hDnetdCdwPsE4Ka+7ogkSZI0kHUn8L42M0/v855IkiTVUbhyZV04q4kkSVLVTDWpBQNvSZKkqjniXQteZUmSJKkJOh3xzkwDc0mSpD5ljnddmGoiSZJUNQPvWvAqS5IkSU3giLckSVKVAmc1qQkDb0mSpEqZ410XBt6SJElVM/CuBa+yJEmS1ASOeEuSJFXNHO9aMPCWJEmqlDnedeFVliRJkprAEW9JkqQqBY5414SBtyRJUqVMNakLA29JkqSqeXNlLfj1SpIkSWoCR7wlSZKqZqpJLRh4S5IkVcoc77rwKkuSJElN4Ii3JElSlQJocSy0Dgy8JUmSquasJrXg1ytJkiSpCRzxliRJqpQ3V9aFgbckSVLVDLxrwcBbkiSpSoE53jXh1ytJkiSpCRzxliRJqpQ53nVh4C1JklQ1A+9a8CpLkiRJTeCItyRJUtUc8a4FA29JkqRKhbOa1ISBtyRJUpUCR7xrwqssSZIkNYEj3pIkSVVzxLsWDLwlSZIqZY53Xfj1SpIkSWoCR7wlSZKqZqpJvxAR/wd8CHg+M9cry5YFzgLGA5OAPTLzlYgI4OfAzsAMYN/MvL2z43uVJUmSqhYtbs3YunYysGObssOAyzNzAnB5+RpgJ2BCuR0A/Kargxt4S5IkSUBmXgO83KZ4F+CU8vkpwK4N5adm4SZgZESs2NnxTTWRJEmqUoSpJs0zJiJubXh9Qmae0EWbsZk5GSAzJ0fE8mX5ysCTDfWeKssmd3QgA29JkqSqtTirSZO8mJmb9NKx2rto2VkDA29JkqSqOeLdnz0XESuWo90rAs+X5U8BqzTUGwc809mBvMqSJElSxy4APlM+/wxwfkP5p6OwOfBqa0pKRxzxliRJqpQ53v1FRJwBTKTIBX8K+D5wNHB2ROwHPAHsXla/mGIqwYcpphP8bFfHN/CWJEmqUmDg3U9k5l4d7HpfO3UT+FJPju9VliRJkprAEW9JkqRKRTGloAY8A29JkqTKGXjXgYG3JElS1czxrgWvsiRJktQEjnhLkiRVzRzvWjDwliRJqlRgEkI9eJUlSZKkJnDEW5IkqWqmmtSCgbckSVKVAgPvmjDwliRJqpQ53nXhVZYkSZKawBFvSZKkqplqUgsG3pIkSVUz8K4FU00kSZKkJnDEW5IkqXKOhdaBgbckSVKlwlSTmjDwlhbROX+5gKuvu4E77/43d/3rHqZOncYn9/w4f/i/37yl7pNPPc2Pj/05t91xF48/8SSvTHmV0cuOYs01xvO5ffbmU3vtzpAhQyp4F9LA9/5Dj2Kl9TZm9PgJDBs1hjmzZjLlmSe4//LzueWPv2bmlJffrPu2Fcax9QHfYMV3bsTIlVZlyWVG8cbs2byw2abcce7J3H3BH5k3Z85bzjF82eXY4nP/jwnb7MgyK6/G3NlvMOXpx/n3X8/i1rNO4I3p05r5liX1Mwbe0iI64pifcde/7mHEiOGMW3kl7n/goQ7rPvLoJP541jlstsnG7PrhnVl21EheevkVLvnH5XzuCwdz6hlnc+mF5zB4sL+aUm9772cOZvK9d/DoDZcz/aXnGTJsOOM22Iztvvx9Nt5jf07ccytee/YpAJZddQ3W//BePH33Ldx/+QXMfPVlVtxqZ5ZbaVV2PepENtjlU5z2uR2ZN3fum8cfufJq7H/W9YwYM5bHbr6Kh679O4OXWJI1t3w/O3z9GN71kU9y4p5bMuf1WVV9BOrPwlSTOvB/d2kRHXfMEYxbeUXWWnMNrr72BrbbadcO626x+Xt45emHaWlZ8B/Y2bNns8NHdueqa67n3PMvYo/dOj6GpIXz402WZc4br7+lfPtDfsg2//lNtj7gG/z1h18G4Mk7buSYTZcjM9+st86coTz0m2+zz0mXsPpmE1n3Pz7KPX875839W3zuUEaMGcuVv/wBV//vEW+WR0sL+5x0CWu8d3veuePHuev8P/Thu9Tiy1STOvDrlbSIttt2KyastSbRjfy8oUOHviXoBhgyZAi7fmgnAB565NFe76Mk2g26Ae65pAiel11trTfL5s6evUDQ3WrenDncf9kFb6kPMGqV1QF44IqLFijPefN46OpLABi27JiF7L0GtNYl4936fquYgbfUD8ydO5eL/34ZAO9a750V90aql3W2+yAAzz34ry7rRksLE7bdsd36Lzx8LwBrb7vTgm0iWGvrDzBv7lweu+mqXuixpMWVqSZSBV588SV+dfxJZCYvvPgSl15xFQ8/8hh777EbH9pph6q7Jw1oW3zuqwwdNoIlRizDSuttzGqbbMWz99/NdSf85C11h40czaaf+iJEsPI227PDB//N6PETuPvCM3jwyr8uUPf6E49l7Yk7s/0hP2T8ZhOZfO8dDBoylDW3fD8jxqzABd89kGfvu7NZb1OLlTDHuyYMvKUKvPjSy/zgqP9+83VE8F8Hf4mjfvDtbqWsSFp4W3z2/zFiuRXefP3QNX/jvG/ux4xXXnxL3WGjxjDxoO+9+TrnzeP6k37K5cd95y11p7/8AifuuRW7HPk71t3ho6zx3u3fbHPbn07i0Rsu74N3o4HCf/vrwcBbqsDb15lATn+BuXPn8vQzk/nLBX/le0ccw3U33sxf/3w6yy47quouSgPWsVuvAsDw0cuzyrvfy/sPPZL//Ms/Of0/d2XyvXcsUPfFxx7g8LcPIVpa2PAbv2LoU/ex3Ve+z6obb8npB36Ema++8mbdkSuvxl6//guDl1iSP3z+Qzx5+w0MWWoY67zvI3zg6z/h7dt/mBM/sTVTnp7UzLcrqR/x7xpShQYNGsSqq4zj4C8dyPG/+Ck33XIr3zvi6Kq7JdXC9Jee5/7Lzue0/XZmqZGj+ejR/9dh3Zw3jxnTpnLzab/kou9/kVU23JztvnL4AnV2/fFJjF1nfc7+yp48fO3feX36VKa9+By3nfU7Lv+f7zFiuRWYeNB3+/hdafHV4taUrVqOeEv9xE47vA+Aq665oeKeSPXy6jNP8MLD97HiOzZk2MjRzJjyUqf1H7rmbwCMf882b5YNHT6C8Ztuy4wpL7V7k+akm68CYMV3btR7HdcA0j9m3FDfqz707yMRcXJEZMP2YkRcFBFvb6jTuH9aRNwVEfu2c6yIiP0j4saImBoRr0XE7RHx9Yh4W5u6y0fErIh4IsI7JdR9Tz8zGYDBgwdV3BOpfpZefkUA5s2b20VNeNvYlYu6DYvnDBoyFIAlhr+NQe2sPts6jeDc2W8scl81QFU9zV5dtooN9MDwMmDFctsBWAr4S5s6ny/3bwCcBfw+Ij7Qps5pwC+Bi4H3Ae8CvgtsB3ysTd19gQuBWUDb46jmbv7nbcyYMeMt5dOmTePgr30bgA/u+B/N7pY04I1ZfR1GjBn7lvKIYPtDfsiIMWN54vYbmPXaFABWftemDFlyqbfUHzpsODt+62cAPHj1xW+Wz5zyMi88fC+Dhgxhmy98e4E2g4cuwTZf+BYAj914Ra+9J0mLn4GeavJ6Zj5bPn82Io4DLoyIpTJzZlk+paHOURFxKEWQ/neAiNgD+CSwW2ae23DsScBfI2Jkm3N+DvgasCGwH3BJb78p9S/nXXgx511Y/Af87HPPA3DjLbey7wEHATBm9GiO/fEPAPjxsT/nqmuvZ9uttmDVVVZm2FLDePKpp7nk0suZMuVVttj8PXzzvw6u5o1IA9haW3+A//ja0Tx+67W88uSjzJjyEiNGj2W192zNsquuydTnJ3Phd//zzfpbH/B1xm+6LZP+eQ2vTn6S2TNnMG7bndn1U59lqWVG8cTtN3DdCccscI5Ljvwqex9/Adt+8dusucX7efLOGxm8xFJM2OYDjFx5PC9NeojrTvzvtl2TSgN9LFQA0d7KXANBRJwMjMnMD5WvlwZ+DWyQme8qyxLYPTPPiYhBwG4Uo97HZOZhZZ3zgbdn5jrdOOfWwLnASsA44H5gXGa+0EH9A4ADAMaOHbvxmae7jPDi6ORTTuWUU0/rcP/YsWM58/Q/MG36TP71r7u5/IoreOD+B3hlyivMmvU6Sy+9NGussToTt92WnXfakUGDTDVZHE2+7+6qu6BOLLPsaNZabwOWW3Elhg1fmiFLLMGc2bOZOuUVnnn8MR68+w7eeH3Wm/VXWm11Vlv77Sy7/AosudQwBg8ezBuzZzPlhed44uEHefS+f7e7suXI0WN4+7s3YfmVxrHksOFkJtNfm8JTjz3CfbffyuwOVs9U/7D3QYfelpmbNPu8m7xrnfznBSc0+7S11LL6xEqucauBHnh/iiLlA2A48CSwc2b+u6yT5f65wJLAIOAlYPPMfLiscy/wUGbu0o1zngJMzcyDytfXAOdn5k+7arvJRhvmrddd1qP3qMXLVTfdycTNN6y6G+ojh2+0YtVdUB9b58vH8MAvv1F1N9SHfvDAHAPvAa7qwHug/13jGoqUjw2BzYArgH9ExCoNdVrTQv4DuBP4SmvQXepWJn55k+XHKfLBW51GkW4iSZLUgX5w02FdtooN9BzvGY1BdETcBrxKkd7ROpnqs2WdhyNid+D2iLg9M+8v9z8IrNuNc+0NDAOub7P61KCI2DIzr1/E9yJJkgasgT4WKqjfVU5gHkWA/NadRQB+LvCThuLTgQkR0Xb2EgAabq7cD/gV80fYW7e/4qi3JElS7Q30Ee8lImKF8vko4CBgBMV0fx35KXBXRGyambcAZwO7An+MiKOAvwHPU4yCfxn4U0TcDmwC7NeaP94qIk4DToqIgzNzai++N0mSNFD0gzQI9b2BHni/H5hcPp9KMcvI7pl5VUcNMvNfEXEZcASwQ2ZmROxNMd/3fsA3KEbNHwHOAP4MHElxA2Z70xpcRPGXhb0A75yQJEltBLjmXi0M2MA7M/elWMymszrtfr3MzB3avE6KoLmjwPkrnZxjOh2ktkiSJBUc8a4Dv15JkiRJTTBgR7wlSZIWG+Z414KBtyRJUpUCc7xrwqssSZIkNYEj3pIkSZXqH6sqqu8ZeEuSJFXOwLsODLwlSZKqZo53LXiVJUmSpCZwxFuSJKlyppr0BxExiWK187nAnMzcJCKWBc4CxgOTgD0y85WFOb6BtyRJUqW8ubKf2S4zX2x4fRhweWYeHRGHla+/sTAHNtVEkiRJ6tguwCnl81OAXRf2QI54S5IkVc4R7yYZExG3Nrw+ITNPaHidwD8iIoHjy31jM3MyQGZOjojlF/bkBt6SJElVM9WkWV7MzE062f//27vvaMvKMs/j3x9FsEFBbcTGAXHAREtoFVuCLEGRkWbEhEiLgUFaMWIHDEhmMKECJmgKhUHoRpzVKM5yWsQWHAndSkYyAhIkSZJQxGf+ePfV05dbVbfS3rfqfD9r1bp19t7nnHffWqfO7zzn2e+7RVXd0oXrHye5YnE+ua0mkiRJElBVt3Q/bwdOAf4SuC3JmgDdz9sX9vEN3pIkSRp7SVZJ8rSJvwPbApcCpwLv6Q57D/D9hX0OW00kSZKGFGw1mRmeDZyS9m+xPPBPVfWvSX4BnJzkvcBvgLct7BMYvCVJkgZn8B5aVf0a2HiK7b8DXrs4nsNWE0mSJKkHVrwlSZIG5QI648LgLUmSNDiD9ziw1USSJEnqgRVvSZKkodlqMhYM3pIkSYMzeI8Dg7ckSdLQrHiPBXu8JUmSpB5Y8ZYkSRpUsNVkPBi8JUmShmaryViw1USSJEnqgRVvSZKkwVnxHgdWvCVJkqQeWPGWJEkaUiD2eI8FK96SJElSD6x4S5IkDc6K9zgweEuSJA0qTic4Jmw1kSRJknpgxVuSJGlwVrzHgcFbkiRpaLaajAVbTSRJkqQeWPGWJEkanBXvcWDwliRJGpqtJmPB4C1JkjSoYMV7PNjjLUmSJPXAirckSdKQgq0mY8LgLUmSNDiD9ziw1USSJEnqgRVvSZKkoVnwHgsGb0mSpMGZvMeBwVuSJGloXlw5FuzxliRJknpgxVuSJGlQLqAzLgzekiRJQ7PVZCzYaiJJkiT1wIq3JEnS4Kx4jwODtyRJ0oDOu+CiH2WVZ60+9DjGxJ1DPrnBW5IkaUBV9fqhx6B+2OMtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgpas6NQAAEfxJREFULUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9cDgLUmSJPXA4C1JkiT1wOAtSZIk9SBVNfQYBCS5A7hh6HFoiVoduHPoQUhaaL6Gl33rVNWzhh6Ell0Gb6knSX5ZVZsMPQ5JC8fXsKRFZauJJEmS1AODtyRJktQDg7fUn6OHHoCkReJrWNIiscdbkiRJ6oEVb0mSJKkHBm9JkiSpBwZvSZIkqQcGb0mSJKkHBm9JkiSpBwZvaYZLsmKS5bq/+5qVZpgkGXoMkpYOvolLM1iS9YFTgIOTrFlVTww9JklPMmv0hh+QJc2N/zlIM1SSWcDuwPrAM4ELk+yfZLspjpM0gCQbAmcleXuSjQAmPiBbCZc0mQvoSDNYkv8GnABsAGwGbAu8jVYFP72qTh45NuULWupVkqOA9wFnAHOAW4CDgDuq6qGJ16WvT0lg8JZmpNE36STHADdW1YHd7bWA64G7aG/yBwNXVtWlAw1XGltJXkX7MPwvwKPAUbTX5ePAfsD1VfW7keMN4NIYs9VEmmFGKmQTLSQXAm8cOWRv4FZgJ+BS4FDgpCQr+9W21Lurga2Al1fV2VW1ES1wvxr4PnBkkl2TrApg6JbGmxVvaYZI8jxawN4I+HRV3Tqy7zzgO8CLge2A7avq/G7fZrSK+E19j1kSJNkROBB4Y1Vdk+R4WmvYYcALgD2BHwE7VtUDw41U0tCWH3oAkv5wgdbJwM9o1ezfd9uX6y7U+ibwOeAmutA9sa+qzhlq3NI4SfJc4A3AKsAPR9q7LgBuBtZKsjfwOmC7qrqwu9/XAQzdkgze0sCSvBD4KTAbOKiqHuq2Z2T6wB8CBwAnT1S6nVpQ6k/34fhfgHOBG4BrJ/ZV1bVJLgD+jdbf/dqquqy736yqumaAIUuagWw1kQaUZAXgH4GnAO+uqse67aMXVy5XVU8k2Qd4C/DWqrpusEFLYybJi4Cf0z4c719Vj47sm3h9PofWDnZaVR080FAlzXBeXCkN7+XARVX12MTFkSOhe7TqfQqtX3TTYYYpjZ8kKwGfovVoHzQRukdeqxOvz9uA39AutJSkKRm8pWGtBqwN3AtPnvGgm91k+SRfo72pn0ib5URSP2YBrwQurKo5ExtHv5Hqbj9Om3Fo6yTvH2KgkmY+g7c0kG66wEdp83FvlWTluRz64u7PalW1R1Vd3tcYpXHWVbXXAtYALu+2rTh6zMgqlX/bHTeb1ustSU9i8JYGUlWPV9W9wDdoC3DsAn/8CnuikgbsTFuM4/4hximNq66qfR1tpqH3ddseGXltApBkc+CvgGuAD1fV1X2PVdLSwVlNpJ4kWQ/YnVYV+1VVfbnbdSLwMuDr3SIbp9IW5Xhhkt2AvwG2rKp7Bhi2NFamWFkytNfkbkn2qqpDp5hR6PXAI8AToxdeStJkzmoi9SDJxsC/0ub7XQXYEjisqv6+2/984GPAB4HfAQ8Dd9Pe9N85MR+wpCWn+3C8M+26i58AZ1TVHUnWos1YsjZwArA/8ATwX2mv2fcAW1XVJYMMXNJSw+AtLWFJNgLOAY4APk2reB8NbA1sMfpmnWQbYIPumJ/TLui6pfdBS2Nm5MPx+bTZgyZepwdV1f3d4jnfALagLXB1P+2i6GcAO/vhWNJ0GLylJSjJGsAvgWurauuR7d8Btqe9id9cVXcONERp7HWL45wDHAp8pqoeTfITYD3gpVV1d3fcM4FNaB+aVwT+HTi7qm4aZuSSljYGb2kJ6hbe2JfWw/3Zqvp2kk8CB9MC+X3AmrQZE/4vcBZwzeRpBSUtGUn+C3AjbVXYnUe2Hw+8HXgFcAmwXDdloCQtNIO3tIQl2QD4MK1K9italXs32pRjawDPAfahfb29IrBhVf1+mNFK4yfJxcAKwB5VdWaSvYDPATfTqtobA/9BuxD61qq6oLvf5AsxJWmeDN7SYpbkabQLKOdMzETSfZX9IeAdwOyRiyozsUgOrVd0Jb+2lpa8btrOWVX1WHf7P4BVgTOAtwI7AZcBtwPvp31rtRtwHrBdVd01wLAlLeUM3tJilOQlwFeA59Lm/51dVd8d2fcR4LW0C7a+3W1ffuLNX9KSl+QFtNlIngWcV1WHddvPpM04tFdVfWmK+70MuN0Px5IWlgvoSItJNyvCObR2kgNoX12/t6tmU1W/Ao6kTVO2X5J3dtsN3VJPutfpWcDzgacBX+xWnaSqXg2cDXwoyVbd6rKjy8Kfb+iWtCgM3tJi0E0Z+DPgq1X10ao6kRa+nwlsmORVAFV1EW1KstOAI5K8faAhS2NnZGrP2VX1BuCvgeOBNZKsAlBVrwJuAP4XsEWSWVMsmCNJC8VWE2kRdW/YVwG/r6oXj2z/PG2Z6XuAlWkzJ2xRVQ8neSnwLuAbVXXNAMOWxkqStWn92WdW1dtGtn8XWIf2Gr2F9uH5B0l+CrwU2L6qzhpizJKWPVa8pUVUVQ8AewPrJDkEIMknaBdk/Q2wA7AHsC5wVHefC4BPGrql3swCrgdWSbIlQDe15/bAD4DDaStTfiXJOt28+78AbhtmuJKWRVa8pcUkybuA42jTj61HW+r9x92+PwG+T5vpZIfBBimNsSTPB74GPE6rbu8AvKuqTuv2P5cWzj9aVV8bapySll1WvKXFpJulZBfatGNnToTubt9DtJaTG5Ms101lJqlH3TdMH6Fd+LwLcGhVnZZmBVogvxi4Ff4w5aAkLTYGb2kxqqqTaHP9viXJZ5KsCJDkIOA1wFeq6gkX3ZCGUVVXAx8A/h+wbZJXV/MorT1sVdq3Vvg6lbS4LT/0AKRlTVX9UzcN2bHAg0lWAvaiXVh55bCjk1RV1yb5MPBVYN8k99Pm198L2Lyqbhx0gJKWWfZ4SwtodJnoeS0ZneQdwAndzU2q6vy+xihp/rqFdA4DXgmsBmxWVecNOypJyzKDtzRNI8u7P6Wq5nTz+z6eZLm5zfOb5L8D11TVFT0PV9I0JHkR8AVg726RK0laYgze0jSMhO7X0+bmfipwF7BfVV017OgkLYokK3Q93pK0RHlxpTQNXeh+A21KwF8DVwPPBn6ZZHv447LSkpYuhm5JfbHiLU1DkqcBPwR+WlX7ddtWpF2c9dfAxlV13YBDlCRJM5wVOmkKSVadtGklYC3gym7/clX1CLAncAnwKefnliRJ82LwliZJ8qfAtUk+MLGtqu6krWi3Q9fv/UQXvucAvwGe7vzckiRpXgze0pM9BBwPHJ7kf8AfVrA7FVgX+AeAkZlMHgPuS7K8FW9JkjQ3LqAjTVJVDybZH3gA+GZX4f5WkmOAFwI7J9kSOB34C+BNwKZV9dhwo5YkSTOdwVsCkjwVmFVV9wJU1f1Jvkj7VuiYbs7u2Uk+DrwT2AHYFbiFtiKl8/9KkqR5clYTjb1u9br/DTwIHAPcVlX/Z2T//wT2BvaoqqNHtj8VeLSqHu55yJIkaSlk8NZY6+bePhj4FPAwcDnwDOAe4CLgW8C9wDbAocDOVXXyMKOVJElLM1tNNNa62Um+CqxM69++EjgC2AnYCjgJeAS4FLgTOCnJnKo6dZgRS5KkpZUVbwlI8hxaO8lmwHFV9dVu+18Afwa8F3gu8Apgg6q6bKixSpKkpZPBW+ok+TPg08DmwPeq6uCRfbOq6vEkq3dzekuSJC0Qg7c0YiR8b0oL34d021eoqkcHHZwkSVqqGbylSUbC9ybAT6pqn4GHJEmSlgGuXClNUlW3AocAVwCbd0vIS5IkLRIr3tJcJHk2QFXdNvRYJEnS0s/gLUmSJPXAVhNJkiSpBwZvSZIkqQcGb0mSJKkHBm9JkiSpBwZvSZIkqQcGb0nLlCS7JqkkW81r20yS5PokZ0zjuOd153HAIjxXJTluYe8/j8fdqnvsXRf3Y0vSssLgLWmRjASu0T/3JzkvyZ5JZg09xkXRnd8BSZ4+9FgkSUs3g7ekxeWfgXcB7wYOBlYGDgeOHHJQnW8DfwL8bCHuuxWwP2DwliQtkuWHHoCkZcb5VXXCxI0kRwKXA7sn2XduK4AmWQGYVVVzltTAqupx4PEl9fiSJE2HFW9JS0RV3QecAwRYF6Br2agkL0ny5SQ3AXOATSful2SbJKcluSfJnCQXJ9ljqudIsnuSK5I8nOSaJHt2zzf5uCl7vJOsmOTjSS5M8mCSe5P8MsmHu/3H0ardANeNtNIcMPIYqyX5fPf8Dye5I8k/J1l3inGsneTk7nnuS/KDJOstwK91Skk+2P3Obk7ySJLfJjkhyfPmcZ9tkpzbnfetSY5IssoUx037/CRJ82bFW9ISkSTA87ubd07afSLwEPAloIDfdvd5H3AUcC5wCPAA8DrgyCTrVdVeI4//MeAw4CJgb1pry17A7dMc34rAj2itJKcBJ9A+BGwIvAX4GvCPwKrAm4G/HTmPi7vHWA04G3gu8C3gV8CawAeBf0+ySVXd0B37dFqry9rdOV4GvBr4Ka0NZlH8A+139hXgLmADYHfgNUk2rKrfTTr+ZcCOwGzgeGBr4KPABkleV1VPLOj5SZLmz+AtaXFZOcnqtIrzmsBHgI2Bc6vq6knH3gNsU1WPTWxIsiYtOJ5UVe8YOfYbSY4A/i7JUVV1bRdiD6G1smxeVQ92j3EscMU0x/sxWuj+bFXtPbojyXIAVXVOkotpwft7VXX9pMc4iFbN37SqLhq5/3HAJcCBwK7d5o8DzwN2q6pjR87tcGDPaY55bjasqgcmncOpwOnAe4EvTD4eeHNVfW9kHEfQwvdOwEkLcX6SpPmw1UTS4nIgcAet4nwRsBtwKvCmKY49fDR0d3YEVgK+mWT10T/AD2j/X722O3ZbWoX76xOhG6CqbqJV06djF+BuWrj8TyYqvvPSVfR3oVWxb5403gdoFehtR+7yJuA2WoV51OenOd65mgjdSZbrWkNWp/0b3Au8coq7XDkSuid8rvv55u6xFvT8JEnzYcVb0uJyNPBdWuvIA8BVVXXXXI69aopt63c/T5/Hczy7+znRXzxVdfuy+YxzwguACxfhos5nAX9KC593zOWY0QC/LvCL7kLPP6iq3ya5ZyHHAECS1wD70UL2UybtfsYUd7l88oaRcUz8bhf0/CRJ82HwlrS4XF1V8wrNox6cYtvERZHvpuv5nsKvJx1b83ic6Zjq/tM18TynM/2q9dyeb0HG/J/vmLyC1qN+DfBJ4Dpa/3zRWkam+mZzOuNYmPOTJM2DwVvSTDHRB37nNAL8td3P9YF/m7RvfabnKmD9JCtV1cPzOG5uIfUOWq/6qtP8wPFr4IVJZo1Wvbve9tWmOeapvAOYBWxXVdeNPO4qTF3tBvjzyRtGxjHx4WZBz0+SNB/2eEuaKU4GHgYOTPKkWT663uWVups/plV1P5Rk5ZFj1qIF0ek4kRZM95niuUYrv/d3P585ekzXB34i8JdJdpzqCZKsMXLz+7RWmXdPOuwT0xzv3EyE+MlV872Z+//xL0oyufd+Yhzfg4U6P0nSfFjxljQjVNVNST4AHANcnuTbwA20XuMNaRcn/jlwfVXdnWRf4IvA2UmOp11suQetcv7SaTzlEcAbgH1G2jXmAC8BXgRs0x13bvfz80lO7I65tKouBT4NbAGcnOTk7thHgHWAvwLO44+zfnyB9qFgdpKX06bm2wrYjCdPt7ggTqFNdfjDJEd3z/86YKN5PO4lwAlJZtN+X1vTLm49E/jOyHELcn6SpPkweEuaMarq2CRX0ealfj9tmfY7gSuBfYFbR479UpL7gb8DPgvcSAvi99LmnJ7fcz2SZFvg72mB+DO0UH01cOzIcWcl+QQt1M+m/b95IC1835tki+4xdgLeCDwG3AT8nPYhYuJx7k6yJfBlWtU7wBm00PuTBfk9TTqPs5K8lfb7OZj2TcDptDnCfzaXu51P+70d0p3XfbR5y/cendFlQc5PkjR/qVqUa4skSZIkTYc93pIkSVIPDN6SJElSDwzekiRJUg8M3pIkSVIPDN6SJElSDwzekiRJUg8M3pIkSVIPDN6SJElSDwzekiRJUg/+Pzcd63VzdNA7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_tests, xg_predictions)\n",
    "plot_confusion_matrix(cm, classes = ['No BRCA', 'BRCA'],\n",
    "                      title = 'Cancer Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(xg_class,df,y_red,cv=10,scoring='accuracy')\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(predictions, probs, train_predictions, train_probs):\n",
    "    \"\"\"Compare machine learning model to baseline performance.\n",
    "    Computes statistics and shows ROC curve.\"\"\"\n",
    "    \n",
    "    baseline = {}\n",
    "    \n",
    "    baseline['recall'] = recall_score(y_tests, [1 for _ in range(len(y_tests))])\n",
    "    baseline['precision'] = precision_score(y_tests, [1 for _ in range(len(y_tests))])\n",
    "    baseline['roc'] = 0.5\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    results['recall'] = recall_score(y_tests, predictions)\n",
    "    results['precision'] = precision_score(y_tests, predictions)\n",
    "    results['roc'] = roc_auc_score(y_tests, probs)\n",
    "    \n",
    "    train_results = {}\n",
    "    train_results['recall'] = recall_score(y_train, train_predictions)\n",
    "    train_results['precision'] = precision_score(y_train, train_predictions)\n",
    "    train_results['roc'] = roc_auc_score(y_train, train_probs)\n",
    "    \n",
    "    for metric in ['recall', 'precision', 'roc']:\n",
    "        print(f'{metric.capitalize()} Baseline: {round(baseline[metric], 2)} Test: {round(results[metric], 2)} Train: {round(train_results[metric], 2)}')\n",
    "    \n",
    "    # Calculate false positive rates and true positive rates\n",
    "    base_fpr, base_tpr, _ = roc_curve(y_tests, [1 for _ in range(len(y_tests))])\n",
    "    model_fpr, model_tpr, _ = roc_curve(y_tests, probs)\n",
    "\n",
    "    plt.figure(figsize = (8, 6))\n",
    "    plt.rcParams['font.size'] = 16\n",
    "    \n",
    "    # Plot both curves\n",
    "    plt.plot(base_fpr, base_tpr, 'b', label = 'baseline')\n",
    "    plt.plot(model_fpr, model_tpr, 'r', label = 'model')\n",
    "    plt.legend();\n",
    "    plt.xlabel('False Positive Rate'); plt.ylabel('True Positive Rate'); plt.title('ROC Curves');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Baseline: 1.0 Test: 0.96 Train: 1.0\n",
      "Precision Baseline: 0.64 Test: 0.95 Train: 1.0\n",
      "Roc Baseline: 0.5 Test: 0.98 Train: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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27YIOHeCGG+DEE/3afjX+h04JgIiIxK1ly6BePRg82G/bO3cuHHdc0FHlDxoCEBGRuOOcr5zeqZOf6Dd1Klx2WdBR5S/qARARkbiyeTPceKPv9j//fPj6azX+0aAEQERE4sb8+b5a+oQJ8PTT8PHHUKlS0FHlT0oAYmXIELjwwn9uBCQiIuzfD336+G/8hQrBvHnw0ENQQK1U1GgOQKyk7QKonf9ERA7y++/QsqWv4d+sGQwaBKVKBR1V/qcEIJa0C6CIyEEmTfK1/HfuhOHD/c+q6Bcb6lwREZGY27MH7rsPGjWCKlX8Vr633qrGP5bUAyAiIjH1ww++q3/JErjnHj/Zr1ixoKNKPkoAREQkZt54A+68E4oW9TP9r7km6IiSl4YAREQk6rZvh1atoHVrOPNMv7ZfjX+wlACIiEhULV4Mder4xVA9e8LMmfCvfwUdlcQ8ATCzqmb2jpltNbNtZvaemR0d5r1Hm9lIM1tjZjvN7Acz621m2gxSRCTOpKbCc8/BOefA7t1+EVS3blCwYNCRCcR4DoCZlQBmAnuA1oADegOzzOx059zf2dxbEvgEKAx0BdYA9YCeQHXgpuhGLyIi4dqwwc/qnzwZGjeG116DsmWDjkrSi/UkwLbAcUAN59xPAGb2DfAj0B54Lpt7/41v6Bs656aFjs0ys7LAg2ZWwjm3M3qhi4hIOGbM8IV9Nm+Gl1/2k/60vC/+xHoI4BpgYVrjD+CcWwnMA67N4d4ioedtGY5vwf8e8fnPSyWARSRJpKTA449DgwZQujR8/jncdZca/3gV6wTgFGBZJseXAyfncO8n+J6CfmZ2spkdZmYXAx2BQdkNHwRKJYBFJAmsWgUXXABPPglt2sCiRVCrVtBRSXZiPQRQFticyfFNQJnsbnTO7Taz84B38QlDmmHA3RGLMBpUAlhE8rG334a2bcE5GDcObtKMrIQQxDJAl8mxHDuIzKwY8BZQEWgFXAA8hJ/890o297Uzs0Vmtmjjxo15i1hERP5h505o3x6aNIGaNX1npxr/xBHrHoDN+F6AjMqQec9AercBFwInOOd+Dh371My2AkPMbJBz7uuMNznnhgBDAOrWrZtZ8iEiIrm0bBk0bQrLl8Mjj0CvXlC4cNBRSW7EugdgOX4eQEYnA9/mcO9pwOZ0jX+aL0LPJx1ibCIikgPn/Ha99erBn3/CtGnQt68a/0QU6wTgQ6C+mR2XdsDMquGX+H2Yw73rgDJmdkKG42eHntdGKMbI0Ox/EclnNm+GG2+EO+7wE/6+/trP+JfEFOsEYCiwCphgZtea2TXABOBXYHDaRWZ2jJntM7Nu6e4dAWwHJptZazO7yMweAp4BFuOXEsYPzf4XkXxk3jz/52zCBOjf3xf4qVQp6KjkUMR0DoBz7u/Q0r3ngVH4yX8zgE7OuR3pLjWgIOkSFOfcKjOrD/TAVw8sj08chgB9nHOpMfklckOz/0Ukwe3fD089BT16wDHHwPz5vvtfEl/MtwN2zq0Brs/hmlVksjLAOfct0CQ6kYmISHpr1/od/GbN8h2Zr74KpUoFHZVESswTABERiX8ffQS33AK7dsHrr/ttfFXRL3/RdsAiInLAnj3QqRNcfTVUrQpLlvhEQI1//qMEQEREAPjhB79174svwr33woIFUKNG0FFJtGgIQEREeOMNv2tfsWLw4Ye+B0DyN/UAiIgkse3b/US/1q2hbl2/tl+Nf3JQAiAikqQWLYIzzvBlS3r2hBkzoEqVoKOSWFECICKSZFJT4bnn4NxzYe9eX66kWzcoWDDoyCSWNAdARCSJbNjgZ/V//DE0bgyvvQZlM9uiTfI99QCIiCSJGTOgVi2YORNeeQXee0+NfzJTAiAiks+lpMBjj/mNe8qUgS++8DP+tbY/ueVqCMDMagD/AcoBI5xz682sKvCXc25nNAIUEZG8W7UKmjWDhQvh9tvhhRegZMmgo5J4EFYCYGaFgeFAc3yNfgdMB9YDLwPLgceiFKOIiOTB229D27bgHIwbBzfdFHREEk/CHQLoBVwDtAWO4eCNeiYDDSMcl4iI5NHOndCuHTRpAjVr+p3J1fhLRuEmAC2Ars654cDvGc79Ahwb0ahERCRP/vtfv13vsGHQuTN89hkcq7/QkolwE4AKwLJszheLQCwiIpJHzvntes86CzZtgqlT4amnoHDhoCOTeBVuArAaqJfFubrAj5EJR0REcmvTJrjhBj+z/8ILfTnfBg2CjkriXbgJwGjgcTO7HkirFeXM7BzgfmBEFGITEZEczJ0LtWv7DXyeeQYmTYKKFYOOShJBuMsAnwLqAG8DO0LHZgGHA+8DL0Q+NBERycr+/b6Lv3t3P8Y/f74f+xcJV1gJgHNuH3CdmTXAz/ivCPwFTHHOTY1ifCIiksHatdCypa/h37y5H/svVSroqCTRhFsHoCK+2M90/Pr/9OcKAOWdcxuiEJ+IiKTz0Ue+lv/u3TBiBNx8syr6Sd6EOwfgD+DMLM6dETovIiJRsmcPdOwIV18NVavC4sXQurUaf8m7cBOA7P6JFQJSIxCLiIhkYsUKqF8fXnoJ7r3Xl/WtUSPoqCTRZTkEYGaHAelHlcqbWeUMlxXHlwdeH4XYRESSmnPwxhtw111QrJif6X/11UFHJflFdnMAHgC6hX52wMQsrjOgTySDEhFJdtu2+XX9b74JF1zgn6tUCToqyU+ySwA+AtbhG/iBwNPAygzX7AG+dc59EZ3wRESSz6JF0LQprFwJTzzht/ItWDDn+0RyI8sEwDm3GFgMYGYOeNc592esAhMRSTapqfD88/Doo3DkkTBnDpx3XtBRSX4Vbh2AwdEOREQkmW3Y4Gf1T5kC113nN/MpWzboqCQ/C7cSIGZ2InArUIN/bv7jnHNXRTIwEZFk8ckn0KoVbN4MAwdChw5a3ifRF24hoDOBz/Cz/Y8GVgBl8RUBfwfWRCtAEZH8KiUFunWDfv2gZk2YNg1OOy3oqCRZhFsHoC8wCaiOnxTY0jl3JNAo9B6PRCe8BDRkiN+Oa+nSoCMRkTi2ciWcfz707Qu33+4n/qnxl1gKNwGohd/xL63gT0EA59xk4En8CgEBGDPGN/61a/si3SIiGYwf7/9EfPcdvPWW/95QokTQUUmyCXcOQFFgu3Mu1cw2AZXSnfsWOD3ikSWy2rX9Lh0iIuns3OnL+Q4b5iv7jR0L1aoFHZUkq3B7AH4B0qoALgduSXeuJaCNgEREsvHf/0LduvDaa36Z36efqvGXYIXbA/Ax0AAYBzwFTAz1BOwDygEPRic8EZHE5hwMGgT33QdlyviJfpdeGnRUIuHXAXgs3c9TzOw/wA1ACWCKc+7DKMUnIpKwNm3yE/zefx+uuMJv31uxYtBRiXhh1wFIzzm3EFgY4VhERPKNuXP9POB16+DZZ6FTJygQ7qCrSAwc8j9HMzvZzMZGIhgRkUS3f7+v33/BBVCkCMyfD/ffr8Zf4k+2PQBmZsBp+OI/Pzvnvkt37jT8boHXAbuiGaSISCL47Tdo2dLX8G/Rwlf1K1Uq5/tEgpBlTmpmRwLzgK+ACcAyMxtpZoXM7OXQ8Ub4nQJPiEWwIiLxauJEvwJ40SI/1j9qlBp/iW/Z9QD0BWoDfYAlwLHAw8Ac4BzgLeAh59xv0Q5SRCRe7d4NjzwCL70EZ5wB48bBiScGHZVIzrJLABoATzjn+qYdMLNlwFRgkHPuzmgHJyISz1asgKZNffHPjh19Tf+iRYOOSiQ82SUAFfFDAOmlvdakPxFJWs7ByJFw991QrJjv/m/UKOioRHInu3mpBYE9GY6lvf47OuEkMG0CJJIUtm3zE/1uvRXq1YOvv1bjL4kppzoAl5lZ+gl+BQAHXG5mNdNf6JwbE+ngEoo2ARLJ9778Epo1g1WroFcvX9K3YMGgoxLJm5wSgCeyON47w2sHJHcCANoESCSfSk2F557zDX7lyn6Z37//HXRUIocmuwTgpJhFkciGDDn427+I5Cvr10Pr1jB1Kvzf//md/MqUCToqkUOXZQLgnFsRy0ASlrr+RfKt6dOhVSvYuhVefRXatwezoKMSiYyYF6c0s6pm9o6ZbTWzbWb2npkdnYv7TzKzt83sTzPbZWYrzKxjNGPOVPpJf2ld/+3axTwMEYm8lBTo3BkaNoRy5fzYf4cOavwlf8nTZkB5ZWYlgJn41QSt8XMHegOzzOx051y2qwvMrG7o/tnA7cBWoDpwWBTDzpy++YvkSytX+ol+n3/uc/rnn4cSJYKOSiTyYpoAAG2B44AazrmfAMzsG+BHoD3wXFY3mlkBYCQwwzl3XbpTs6IXbg406U8kX3nrLd/om8H48XDjjUFHJBI9sR4CuAZYmNb4AzjnVuILDF2bw70XAieTTZIgIpIXf/8Nbdv6qn6nnOI799T4S34X6wTgFGBZJseX4xv37JwXei5mZgvNLMXMNpjZS2ZWPKJRikjS+OYbqFsXXnsNHnvML/GrVi3oqESiL9cJgJmdYGZnh8bzc6sssDmT45uAnBbWVA49vwVMw+9V8DR+LkCWNQjMrJ2ZLTKzRRs3bsx9xCKSLzkHr7wCZ50FW7b4Gf99+kDhwkFHJhIbYScAZnabmf0GrADmAzVDx98xsw65+EyX2duHcV9arKOdc92cc7Odc88APYHGZpZpD4Jzbohzrq5zrm6FChVyEaaI5FebNvk1/XffDRdf7Mv5XnJJ0FGJxFZYCYCZ3QIMwc/Ab83BDfbnwE1hft5mfC9ARmXIvGcgvb9Cz9MzHJ8WelYVHhHJ0Wef+fm7kybBs8/CRx9BxYpBRyUSe+H2ADwEvOicu5l/7gT4HaHegDAsx88DyOhk4Nsw7oV/9iCkJSOpYcYgIklo/3544glfvqNoUViwAO6/HwrEvBqKSHwI95/+8cCkLM5tJ+fx+zQfAvXN7Li0A2ZWDfh36Fx2PsbXD7g8w/GGoedFYcYgIknmt998F3/37r5sx5IlcOaZQUclEqxwE4BNQNUszp0I/BHm+wwFVgETzOxaM7sGmAD8CgxOu8jMjjGzfWbWLe2Yc+4v4Cmgg5k9aWaXmllnoBswMv3SQhGRNB9+CLVqwaJFMHIkjBoFhx8edFQiwQs3AZgEdDGz9EmAM7PSQCd8I56jUKW/i4EfgFHAm8BK4GLn3I50lxpQMJP4ngAeBpoAk4E7gP74AkMiIgfs3g333gvXXgvHHOO/9d98c9BRicSPcCsBPg4sxI/Tz8WPwz+D3zFwB34mflicc2uA63O4ZhWZrAxwzjl8ISAVAxKRLK1YATfd5Gf3d+oEffv6cX8R+Z+wegCccxuAOsBLQAVgLX42/0jgbOdcTjP4RUSizjl4/XWoUwfWrvUz/J9/Xo2/SGbC3gvAObcF3xPwePTCERHJm23b/I59Y8fCRRfB6NFQuXLO94kkq3DrADxpZuEu9RMRiakvv4QzzvAb+PTu7av6qfEXyV64kwDvAZaHSureY2YqqScigUtNhf794dxzYd8++PRTePxxKFgw6MhE4l+4CUBF4GZgI34C3loz+8jMbjQzja6JSMytXw9XXgkPP+xn+i9d6hMBEQlPuJMAdznn3nTOXQH8C+gMHIXfmGe9mQ2NYowiIgeZPt2v7Z8zBwYNgrffhjLhliMTESAPuwE659Y7555zzp0JXIKvBNgm4pGJiGSQkgKPPAKXXQblyvmx//btwcLZTkxEDhL2KoA0oS7/xkBL4DL8ev2sygSLiETEL79As2bwxRfQrp1f3lciL5uSiwiQiwTAzC4EWuGL+JTC195/EBjrnPszKtGJiADjxv3vm/748XDjjUFHJJL4wkoAzGwNUAVfs/8V4A3n3IpoBiYi8vffvpzv8OFwzjkwZgxUqxZ0VCL5Q7g9ANPxjf6caAYjIpLmm298Od8VK+Cxx6BHDyhcOOioRPKPsBIA59xt0Q5ERAR8Od+BA+GBB6BsWfjkE7j44qCjEsl/skwAzOwsYJlzbmfo52w5576IaGQiknQ2bYI2bWDCBL/Gf8QIqKCyYyJRkV0PwEKgPvBF6GeXxXUWOqfaWyKSZ599Bs2b+wI/zz0HHTtCgVwvVBaRcGWXAFwBfBf6+UqyTgBERPJs/35fv/+JJ+C442DBAjjzzKCjEtZrS8MAACAASURBVMn/skwAnHNT0/08JTbhiEgy+e03aNHC1/Bv1QpeeQUOPzzoqESSQ7i7AX5rZqdlce5kM/s2smGJSH43YYIv57t4Mbzxhn+o8ReJnXBH2GoCxbM4VwKoEZlwRCS/270b7rkHGjf2a/q/+sp/+xeR2MrNFJus5gCcDmyNQCwiks99/z3Urw8vvwz33Qfz50P16kFHJZKcslsGeA9wT+ilA94xsz0ZLisOVAbeiU54IpIfOAevv+6/+ZcoAZMm+WV+IhKc7FYB/A4sDv18ArAC+CvDNXuAb4FXIx+aiOQHW7dChw6+nv9FF8Ho0VC5ctBRiUh2qwDeBd4FML/X5uPOuV9iFJeI5ANffAFNm8KaNdCnj9/Kt6AqhojEhXBLATeLdiAikn+kpsIzz8Djj/tv+59+CueeG3RUIpJednMAHsZvALQu9HN2nHOuf2RDE5FEtH493HwzTJsG118PQ4dCmTJBRyUiGWXXA9AXmA2sC/2cHQcoARBJctOm+SV927bBoEHQrh34EUQRiTfZJQDFnXNps/6zqgEgIsLevdClC/TvD6ecAjNmwKmnBh2ViGQnu0mAezL7WUQkvV9+gWbN/IS/9u39Rj4lSgQdlYjkJNxSwMeZWe10r4uaWXcze9vMbo9eeCISz8aNgzPOgB9+gHfe8d3+avxFEkO4lQAHAjene90L6AKcDAwys/aRDkxE4tfff8Ntt/lv/qeeCkuX+gl/IpI4wk0AagOfApgvCnAL8Jhz7hT8BMEOUYlOROLO0qV+u97XX/fL/ObMgWOOCToqEcmtcBOA0sCfoZ9rA+WA8aHX04HjIxyXiMQZ53wN/7PP9rP8P/kEeveGQmFVExGReBNuArABOC70cwNgpXNudeh1SWB/pAMTkfjx119w3XW+lv+ll8LXX8PFFwcdlYgcinBz94+APmZ2ItAOGJ7u3CnAykgHJiLx4dNPoUULX+Dn+eehY0et7RfJD8JNADoDhwM3AZ8AvdOdawLMjHBcIhKwfft8F3+vXnD88bBwIdSpE3RUIhIp4e4FsA1olcW5ehGNSEQC9+uv/lv/Z5/5sr4vvwyHHx50VCISSbmavmNmhwNnAWXxWwN/6ZzbHo3ARCQYEyZAmza+ut+oUdCyZdARiUg0hDsJEDPrAvwBTAPewg8F/GFmj0cpNhGJod274e67oXFjqFYNlixR4y+Sn4XVA2BmdwFPAG8Co/EbBB0JtASeMLNNzrlXoxaliETVd99B06bwzTdw//3w5JNQtGjQUYlINIU7BHA3MNA5d3e6Y18DU81sK3APoARAJME4B8OHw733+hK+kybBlVcGHZWIxEK4QwDHAROyODeB/9UIEJEEsXWrL+V7++1Qv75f26/GXyR5hJsAbAJqZHGuRui8iCSIzz/3m/i88w706QPTpkHlykFHJSKxFG4C8AG+ENCNob0AADCz6/AbA30QjeBEJLJSU6FfPzjvPP/zp5/CY49BwYJBRyYisZabQkB18LP/95jZBqACUBT4MnReROLYunV+Tf/06XDDDTB0KJQuHXRUIhKUcAsBbTWzc4HrgP/g6wBsAuYAE5xz2gtAJI5Nneob/23bYPBgaNtW5XxFkl3YhYBCjfw7oYeIJIC9e6FLF+jfH045BWbO9M8iItnOATCzpma20Mz+NLOfzKyPmWnzT5EE8PPPfqy/f3/o0AG+/FKNv4j8T5YJgJndCIzBF/yZB+zEj/X3zuqecJhZVTN7x8y2mtk2M3vPzI7Ow/s8ambOzOYeSjwi+dHYsX6W/48/+pn+r74KxYsHHZWIxJPsegDuByYB1Z1z1zrnTgf6AfeYWdglhNMzsxL4nQNrAq3xGwxVB2aZWclcvM9xwOPAhrzEIZJf/f23r+PfvDmcdhosXQrXXx90VCISj7JryGsArzrnUtIdewkoDhyTx89riy8a1Ng594FzbgJwTej92ufifV7FlyX+Lo9xiOQ7S5fCmWfCiBF+3H/OHDgmr/9PFZF8L7sEoDTwZ4ZjG0PPZfL4edcAC51zP6UdcM6txA8xXBvOG5hZc/ySxEfzGINIvuIcDBgAZ58N27fDjBnQqxcU0mwdEclGTl35LpfHc3IKsCyT48uBk3O62czKAM8DDzvnVH1Qkt5ff/nd++69Fy67zJfzveiioKMSkUSQ03eEeZb5YuHPMxx3zrlw9g4rC2zO5PgmwutV6A/8AIwI41oAzKwd0A7g6KNzPddQJG7NmQMtWsDGjfDCCz4J0Np+EQlXdglAvyh9Zma9Bzn+2TKz/wA3A3Wcc2H3QDjnhgBDAOrWrZvXnguRuLFvn+/i790bjj8eFiyAOnWCjkpEEk2WCYBzLhpj7JvxvQAZlSHznoH0BgOvAb+ZWVoB00JAwdDrXc65PRGLVCQO/fqr/9b/2WfQurUf+z/88KCjEpFEFOtpQsvx8wAyOhn4Nod7Two9OmRybjNwH/DCIUUnEsc++MAv8UtJgVGjoGXLoCMSkUQW6wTgQ+AZMzvOOfcLgJlVA/5NzhsKZTa16QWgIHAP8FMm50US3u7d8MADMHCgX+Y3dixUrx50VCKS6GKdAAwF7gYmmFkX/HyAXsCv+C5+AMzsGOBn4Ann3BMAzrnZGd/MzLYAhTI7J5IffPcd3HQT/Pe/cP/98NRTUKRI0FGJSH6Qp4p+eeWc+xu4GD+TfxS+mM9K4GLn3I50lxr+m31M4xOJF87BsGH+G/+6dTB5Mjz7rBp/EYmcmJcKcc6tAbItTuqcW0UYKwOccxdGJiqR+LF1K7RvD2+9BZdc4sf7jzoq6KhEJL/RN2yROPL5534Tn3fegSefhGnT1PiLSHSEnQCYWSUze9LM5prZt2Z2cuj4nWZWN3ohiuR/qanQr5/fvjc11S/ze/RRKKAUXUSiJKwhADOrCXwKFAa+BM4BioVO1wDOBbQoSSQP1q2DVq3gk0/gxhthyBAoXTrn+0REDkW43y+ewU/WOxa4koPH5+fhEwIRyaUpU+D002HePN/wv/WWGn8RiY1wE4ALgCedc1v4ZynfdYBGKUVyYe9eeOghuOIKqFQJFi2Ctm1Vy19EYic3qwD2Z3G8HLArArGIJIWff4ZmzeDLL+GOO/zyvuLFg45KRJJNuD0Ai4BWWZy7HlgYmXBE8rcxY/ws/x9/hHff9dX91PiLSBDC7QHoA0wxs4n44j0OON/M2gNNyLxMr4iE7NgB99wDI0bAv//tEwHtTi0iQQqrB8A59wm+oa8FjMFPAnwOuApo4pybF7UIRRLc0qVQty6MHAldu8Ls2Wr8RSR4Yc8BcM69Z2bv43fzqwj8BfzXOZcareBEEplzfrvehx6C8uVhxgy4SH1lIhInclUK2DnngGVRikUk3/jzT79178SJ0KgRvP66TwJEROJFuIWAmuR0jXNu/KGHI5L4Zs+GFi18EvDii37sX8v7RCTehNsDMC6L4+lrAigBkKS2bx888QT07g0nnAAffeRn/IuIxKNwE4CTMjlWDmgE3AC0jlhEIglozRr/rX/uXGjdGl5+GQ47LOioRESyFlYC4JxbkcWp+Wa2H7gDWBCxqEQSyPvvw223QUoKjB7tEwERkXgXib3GZgHXROB9RBLKrl1w113wf/8Hxx0HX32lxl9EEkckEoC6wM4IvI9Iwvj2Wzj7bF/J74EHYP58P+4vIpIowl0F8HAmh4sApwLXAUMjGZRIvHIOXnsN7r3Xj/FPnuw39BERSTThTgLsm8mx/cBa4HmgZ8QiEolTW7ZA+/Ywfjxceim88QYcpX0wRSRBhZsAZLZdSYqqAEqyWLjQ7+D366/w1FPw8MNQIBIDaCIiAcnxT5iZFQF6AKc65/ake6jxl3wvNRX69oXzzvOv586Fzp3V+ItI4svxz5hzbi/QESgZ/XBE4scff0DDhvDoo3D99X6Wf/36QUclIhIZ4X6P+Ro4OZqBiMSTKVOgVi2YNw+GDoVx46B06aCjEhGJnHATgIeBR8zs0mgGIxK0vXvhwQf9zP5KlWDRIrj9dtXyF5H8J9xJgMOB0sBUM9sJrOPgfQCcc65GpIMTiaWffvIT/RYtgjvvhGeegeKZTX8VEckHwk0AFnNwgy+Sr7z5JnToAIUKwXvvwXXXBR2RiEh0hbsXQNNoByIShB074O67YeRIP9P/zTfh6KODjkpEJPqynANgZr+YWa1YBiMSS199BWee6Qv6dOsGs2ap8ReR5JHdJMBqQNEYxSESM87Biy/6JX07dsDMmdCzp+/+FxFJFvqTJ0nlzz/h1lvho4/g6qth+HAoXz7oqEREYi+nZYCa+Cf5xuzZfm3/tGm+B2DCBDX+IpK8cuoB6Glmf4bxPs451zoSAYlE2r598MQT0Ls3VK/uv/2fcUbQUYmIBCunBKA2sCeM91FPgcSlNWugeXNf0e+WW2DAAL+Nr4hIssspAWjsnPsiJpGIRNh778Ftt8H+/X55X/PmQUckIhI/tKeZ5Du7dvlKftdfDyec4Jf7qfEXETmYEgDJV5Yvh7POgldf9TX9582D448POioRkfijZYCSLzgHw4ZBx45+jP/jj+Hyy4OOSkQkfmWZADjn1DsgCWHLFmjXDt5+Gy69FEaNgiOPDDoqEZH4pkZeEtqCBVC7Nrz/PvTtC1OnqvEXEQmHEgBJSKmp8NRT8J//gBl89hk88ggU0L9oEZGwaA6AJJw//oBWrWDGDGjSBAYPhtKlg45KRCSxKAGQhPLxx9C6td/EZ9gwaNPG9wCIiEjuqMNUEsLevfDAA3DllXDUUbB4sS/yo8ZfRCRv1AMgce+nn6BpU9/o33UXPPMMFCsWdFQiIolNCYDEtdGj4Y47oHBhX9r3uuuCjkhEJH/QEIDEpR07/Fh/q1Z+md/SpWr8RUQiKeYJgJlVNbN3zGyrmW0zs/fM7Ogw7qtrZkPM7Hsz22lma8zsTTM7NhZxS+wsWQJ16vhv/926waxZcHSO/0JERCQ3YpoAmFkJYCZQE2gNtAKqA7PMrGQOtzcFTgFeAq4AOgN1gEVmVjVqQUvMOAcvvgjnnAM7d8LMmdCzJxTSQJWISMTF+k9rW+A4oIZz7icAM/sG+BFoDzyXzb39nHMb0x8ws3nAytD7dotKxBITGzfCrbfCpElw9dUwfDiULx90VCIi+VeshwCuARamNf4AzrmVwDzg2uxuzNj4h46tBjYCVSIcp8TQrFlQqxZMnw4vvQQTJqjxFxGJtlgnAKcAyzI5vhw4ObdvZmYnARWB7w4xLgnAvn3QtStccgmUKgWffw733KO1/SIisRDrIYCywOZMjm8CyuTmjcysEDAI3wPw2qGHJrG0ejU0bw7z5/uu/wEDoGROs0BERCRigphe5TI5lpfvfC8D5wJXOecySyr8G5u1A9oBHK2p5HHhvfd8Fb/9+2HMGGjWLOiIRESST6yHADbjewEyKkPmPQOZMrOn8I16G+fctOyudc4Ncc7Vdc7VrVChQq6ClcjatcsX9bn+eqheHb76So2/iEhQYp0ALMfPA8joZODbcN7AzB7HLwHs6JwbFcHYJIqWL4ezzoJBg+Chh2DuXDj++KCjEhFJXrFOAD4E6pvZcWkHzKwa8O/QuWyZ2b1Ab+Bx59yAKMUoEeQcDBkC9erBhg0wZQo8/TQUKRJ0ZCIiyS3WCcBQYBUwwcyuNbNrgAnAr8DgtIvM7Bgz22dm3dIdawq8AEwBZppZ/XSPXK8gkOjbsgVuugnat4fzzoOvv4aGDYOOSkREIMaTAJ1zf5vZxcDzwCj85L8ZQCfn3I50lxpQkIMTlMtDxy8PPdKbA1wYpbAlDxYs8OP7a9dCv37w4INQQDtPiIjEjZivAnDOrQGuz+GaVWRYGeCcuwW4JVpxSWTs3+8b/G7dfP3+uXPh7LODjkpERDJSlXWJmN9/97v3zZzpu/4HD4Yjjgg6KhERyYwSAImIyZP99r1//w3DhkGbNqroJyISzzQqK4dkzx64/3646iqoXBkWL/ZFftT4i4jEN/UASJ79+CM0bQpLlsBdd8Ezz0CxYkFHJSIi4VACIHkyahTceScULgzvvw+NGwcdkYiI5IaGACRXtm+Hm2/2jzPO8Gv71fiLiCQeJQAStiVL4Mwz4c03oXt3P9u/atWgoxIRkbxQAiA5cg5eeAHq14edO33D36MHFNIAkohIwtKfcMnWxo1wyy1+md8118Dw4VCuXNBRiUhWtm3bxoYNG0hJSQk6FImiwoULU7FiRUqVKpXn91ACIFmaNQtatIC//oIBA/xMfy3vE4lf27ZtY/369VSpUoXixYtj+j9svuScY9euXaxduxYgz0mAhgDkH/btgy5d4JJLoFQp+OILuPtuNf4i8W7Dhg1UqVKFEiVKqPHPx8yMEiVKUKVKFTZs2JDn91EPgBxk9Wpo3hzmz/fV/F56CUqWDDoqEQlHSkoKxYsXDzoMiZHixYsf0lCPEgA54N134fbb/YY+Y8b43fxEJLHom3/yONT/rTUEIOzaBR06wA03QPXq8NVXavxFRPI7JQBJbvlyqFfP79z38MN++97jjw86KhERiTYlAEnKOd/o163rl/pNmQL9+kGRIkFHJiLyPz169MDM2LdvX9ChHDB79mzMjNmzZx84duGFF3LhhRcGFlNeaA5AEtq8Gdq29WP+DRrAG2/AkUcGHZWISOIaOHBg0CHkmhKAJDN/vh/f//13/43/wQehgPqBREQOycknnxx0CLmmP/1JYv9+6NMHzj8fChb0Y/0PP6zGX0QSw3fffcdFF11EiRIlOOqoo+jWrRupqakA7N69m/vuu49TTz2Vww47jCOPPJKrr76a77///qD3WLduHa1bt6Zy5coULVqUo446ikaNGh20ln7nzp088sgjHHvssRQpUoRjjz2WPn36HPisrGQcAkgbJvjwww+5++67KV++PBUqVKBly5Zs2bLloHv37dvHU089Rc2aNSlatCiVK1fmgQceYPfu3Yf4Xy176gFIAr//Di1b+sp+TZvCoEFwxBFBRyUiEr7GjRvTpk0bHn30UaZOnUqvXr0oUKAAPXr0YM+ePWzfvp0uXbpw1FFHsWnTJgYOHEj9+vX5/vvvOTI0xtmqVStWr15N//79qVq1KuvXr2fGjBns3LkT8A1xw4YN+fbbb+natSunnXYaCxcupFevXmzatIlnn30213F37NiRRo0aMWbMGFasWMHDDz9MwYIFGTly5IFrWrZsycSJE3nkkUc499xz+e677+jatSurVq3i3Xffjcx/wEwoAcjnJk3ytfx37oTXXoNbb1VFP5Fk06kTLF0abAy1a/tNxfKqbdu2dO7cGYDLLruMbdu28eyzz9KpUydKly7NsGHDDly7f/9+GjZsSKVKlRg7diz33XcfAAsWLODJJ5+kRYsWB6698cYbD/w8duxY5s6dy5w5czj//PMBuOSSSwDo2bMnjzzyCBUrVsxV3Oeffz4DBgw4EPeKFSsYNmwYI0aMwMz47LPPeOuttxg5ciQ333wzAJdeeilly5alZcuWLF26lNq1a+f2P1dY1AGcT+3ZA/fdB40aQeXKsGiRr+ynxl9EElGTJk0Oet20aVN27NjBsmXLABg/fjxnn302pUuXplChQpQsWZIdO3awYsWKA/fUq1eP/v378+KLL/Lf//4X59xB7zllyhSOOeYYzj33XPbt23fgcdlll5GSksLChQtzHfdVV1110OvTTjuNPXv2sH79+gOfWaRIEa6//vp/fCbAp59+muvPDJd6APKhH3/0Xf1Llvga/v37Q7FiQUclIkE5lG/e8aJSpUqZvl67di0TJ07kpptuonXr1nTv3p3y5ctToEABrrzyyoPG0d966y169uzJ008/TadOnTjqqKPo0KEDXbp0oUCBAmzYsIHVq1dTuHDhTGP466+/ch132bJlD3pdtGhRgANxbdiwgb1793LYYYdF7DPDpQQgnxk1Cu6806/n/+ADuPbaoCMSETl069ev57jjjjvoNUCVKlV49dVXOeGEExgxYsSB8ykpKWzatOmg96hYsSKvvPIKr7zyCitWrGDkyJF0796dChUqcMcdd1CuXDmOPfZYxo8fn2kM1apVi/jvVa5cOYoVK8Znn32W6fnKlStH/DPTKAHIJ7Zv9w3/6NF+pv/o0VC1atBRiYhExvjx4w/MAQAYN24chx12GKeeeio7d+6kUKGDm7NRo0axf//+LN+vRo0aPPnkkwwaNOjAMMLll1/Ou+++y2GHHUbNmjWj84tkcPnll9OvXz+2bt16YL5BrCgByAcWL/Zd/r/8Aj16+K18CxYMOioRkcgZOnQoqamp1KtXj6lTpzJs2DB69OhB6dKlufzyy/nggw+47777aNSoEYsXL+all16idOnSB+7funUrl156KS1atKBmzZoULlyYCRMmsHnz5gPj7S1atOD111/nkksu4YEHHqBWrVrs3buXn3/+mQ8//JAPPviAEiVKRPT3uvDCC2nWrBk33HAD999/P2eddRYFChRg1apVTJ48mX79+nHiiSdG9DPTKAFIYM75sb1HHoFKlfwyv9DEVRGRfGXChAncc8899OrViyOOOIIuXbrQtWtXwK8Q+PXXXxk+fDiDBw+mXr16TJw4keuuu+7A/cWKFaNOnToMHTqU1atXU6BAAWrUqMGbb77JtaGx0sKFCzN16lT69u3LkCFDWLlyJSVLluT444/nqquuokiUaqWPHj2aAQMGMHz4cPr06UPRokWpVq3agZUM0WIZZ0HmZ3Xr1nWLFi2KzJulFXxIVws6ljZu9Mv7Jk/24/yvvQblygUSiojEie+++46TTjop6DAkhnL639zMFjvn6mZ2TssAE9DMmVCrFsyYAS+/DO+/r8ZfRERyRwlAAklJgccfh0sv9ZX8Pv8c7rpLa/tFRCT3NAcgQaxaBc2bw4IFcNtt8OKLULJk0FGJiEiiUgKQAN55B26/HVJTYexYP+NfRETkUGgIII7t3Ant28ONN0KNGr6Wtxp/ERGJBCUAcWrZMjjrLBgyxG/bO3cupCuCJSIickiUAMQZ5/x2vfXqwZ9/wtSp0K8fZFGaWkREJE+UAMSRzZt9d/8dd/iCPl9/DaECVSIiIhGlBCBOzJvn98ueMAGefho+/thX9xMREYkGJQAB278feveGCy6AQoV8IvDQQ1BA/8uIiEgUqZkJ0O+/Q4MG0LUrNGkCX33lJ/6JiEiwRowYgZmxatWqXN9rZvTo0SPiMUWa6gAE5KOPfC3/Xbtg+HD/syr6iYhIrKgHIMb27IFOneDqq+Ff//Jb+d56qxp/ERGJLSUAMfTDD3DOOb6M7z33wMKFULNm0FGJiMSvHj16YGZ8//33NGzYkJIlS3L00Ufz+uuvAzBq1Chq1qzJYYcdxkUXXcTPP/984N6UlBS6dOlCtWrVKFKkCNWqVaNLly6kpKQc9Bm//PILV111FSVKlKBChQp07NiRPXv2ZBrP0KFDqVWrFsWKFaN8+fLcdtttbNq0KXr/AaJIQwAx8sYbcOedULSon+l/zTVBRyQikjhuvPFG2rZty4MPPsjAgQNp06YNP/74I7Nnz6Zv376kpKTQsWNHmjdvzueffw5A69atGT9+PI899hjnnXceCxYsoHfv3vzyyy+MGTMGgL1799KgQQN27drFK6+8QsWKFRk8eDDvvffeP2Lo3Lkzzz77LPfeey/9+/dn7dq1dOnShWXLljF//nwKFiwY0/8mh0oJQJRt3+4b/tGj/dr+N9/0Xf8iIjHTqZOvJR6k2rXhhRfyfPtDDz3EzTffDEDdunWZOHEigwcPZuXKlZQqVQqAP/74g44dO7J69Wq2b9/O2LFj6d69+4EJeZdddhkFCxaka9eudO7cmdNPP52RI0fyyy+/sGDBAurXrw/AFVdcwWmnnXbQ569atYr+/fvTvXt3unXrduD4iSeeyHnnncfEiRNp3Lhxnn+/IGgIIIoWL4Y6dWDMGOjZE2bOVOMvIpIXV1xxxYGfy5QpQ8WKFalfv/6Bxh+gZmhM9ddff+XTTz8FoGXLlge9T9rrOXPmALBgwQKqVq16oPEHKFCgAE2aNDnovunTp5OamkqLFi3Yt2/fgcfZZ59NqVKlDnxeIlEPQBSkpvpEt3NnX8xn9mz4z3+CjkpEktYhfPOOF2XKlDnodZEiRTI9BrB79+4D4/JHHXXUQdcceeSRAAfO//HHH1TKpOpaxmMbNmwA4IQTTsg0vr/++ius3yOeKAGIsA0b/JK+jz+Gxo3htdegbNmgoxIRSS5lQ394161bx/HHH3/g+Lp16wAoV64c4BOE5cuX/+P+9evXH/Q67fpp06b9I/FIfz6RxHwIwMyqmtk7ZrbVzLaZ2XtmdnSY9xYzs/5m9oeZ7TKzBWZ2frRjDteMGVCrlu/qf/lleO89Nf4iIkG44IILABg3btxBx998800Azj/fNx3nnHMOv/76KwsXLjxwTWpqKuPHjz/ovgYNGlCgQAHWrFlD3bp1//E49thjo/nrREVMewDMrAQwE9gDtAYc/9/emUdLVV15+PuFQRRFQX26iCAgRoGILhvTzojaaiStLWorRKNRNEbp1taVgAsVVJpgxFkiYhwQjFOiLcssETRCMA0akiANKgYFJaBBBkV8yGB2/3FO6X2XegPvVr16Re1vrbNu1ZnuPrtu3bPPDKOAVyT1NrPP68niQaA/8BPgPeAK4EVJR5hZyWa4bN4MI0bAmDFwwAEwdWowBBzHcZzS0KtXLwYOHMjIkSPZsmULRx55JLNnz+bmm29m4MCB9O7dGwgrBcaMGcOAAQMYPXo0VVVVjB8/nnXr1tXIb7/99mPo0KEMGTKERYsW0bdvX9q0acOyZcuYPn06gwcPpl+/fqUoaqNp6iGAS4BuwAFmthhA0nzgr8CPgNtrSyjpYGAQcJGZPRz9ZgILgZuAkiysW7oUBg4Ma/ovvjis8W/bthSSOI7jOEkmTpxIE1pe/QAAEM1JREFUt27deOihhxg1ahQdO3Zk6NChjBgx4qs4rVu3Zvr06QwZMoTLL7+ctm3bMmjQIPr3789ll11WI7/Ro0fTo0cPxo0bx7hx45BEp06dOOGEE9h///2buniZkZk13c2kl4E2ZnZUyn8mgJn1rSPt9cD1wG5mVp3wvxEYBrQzs/w7N0T69Oljc+fOzVCCBMcdx8qP4VvLZ2AGEybAOecUJmvHcZzG8NZbb9GjR49Si+E0IfX95pL+ZGZ98oU19RyAXsCCPP4LgZ4NSLskWfkn0rYG8k/NLAJmYVe/N98MO/nNm+eVv+M4jlNeNPUQQAdgbR7/NcDW0yobnjYXvhWSLgUuBejcuUFzDetFgo/2PoSWHWHWLGjVqiDZOo7jOE6TUYplgPnGHBpyFI4ak9bMJgATIAwBNOA+DeKYP93pB/g4juM4ZUtTDwGsJX9LvT35W/dJ1tSRNhfeZHjl7ziO45QzTW0ALCSM5afpCbzZgLRd41LCdNpNwOLs4jmO4zhOZdDUBsAU4HBJ3XIekroAR8Ww+tK2As5OpG0JnANMq28FgOM4TiXQlCu7nNKS9bduagPgAWAp8Jyk0yWdBjwHLAPuz0WStK+kLZK+OnIpbvTzJHCnpMGSTgCeALoCI3Acx6lwWrVqxYYNG0othtNEbNiwgVYZZqE3qQEQd/o7HngHmAQ8BiwBjjez9YmoAlrkke+HwMOE3QN/C3QCTjGzPxdZdMdxnGZPVVUVy5cvp7q62nsCtmPMjOrqapYvX05VVVWj82nyVQBm9gFwZj1xlpJndr+ZbQCujs5xHMdJkDsad8WKFWzevLnE0jjFpFWrVuy11141jkPeVvw0QMdxnO2Idu3aZaoUnMqhyU8DdBzHcRyn9LgB4DiO4zgViBsAjuM4jlOBuAHgOI7jOBWIGwCO4ziOU4G4AeA4juM4FYgqabMISR8D7xcwyz2AVQXMrxJxHWbHdZgd12FhcD1mp9A63NfM9swXUFEGQKGRNNfM+pRajnLGdZgd12F2XIeFwfWYnabUoQ8BOI7jOE4F4gaA4ziO41QgbgBkY0KpBdgOcB1mx3WYHddhYXA9ZqfJdOhzABzHcRynAvEeAMdxHMepQNwASCGpk6RfS/pU0jpJz0jq3MC0bSTdKulDSRskzZZ0bLFlbm40VoeS+kiaIOltSdWSPpD0mKSuTSF3cyPLs5jK51pJJunVYsjZnMmqQ0k9JD0taVX8Ty+SdGUxZW5uZHwndpY0Mf6XqyW9I2mUpLbFlrs5IWkfSffEOqE6/h+7NDDtN+J/eKmkLyS9IenMQsjlBkACSTsBvwMOBC4Azgf2B15p4AP7IHAJcAPwPeBD4EVJhxRH4uZHRh2eC/QC7ga+CwwDDgXmSupUNKGbIQV4FnP5dAOGAyuLIWdzJqsOJfUBXgN2AAYDpwK3AS2KJXNzI4sOY/hLwLHA9UB/4JfANcBDRRS7OdId+HdgLTBrG9PeDIwE7iW8F+cAT0s6NbNUZuYuOuBK4Euge8KvK7AFuLqetAcDBvww4dcSWARMKXXZykSHe+bx2xf4B3BTqctWLnpM5fMicD8wA3i11OUqFx0SGkcLgWdLXY4y1uFJ8Z14Usp/TEy/U6nL14R6/Ebi8+Coly4NSFcFbARuTPm/DMzPKpf3ANTkNGCOmS3OeZjZEuAPwOkNSLsZeDKRdgvwBHCypB0KL26zpNE6NLOP8/i9D3wMfLPAcjZ3sjyLAEgaROhBubYoEjZ/sujwOKAncHvRpCsPsuiwdbyuS/l/QjCwVCghmztm9o9GJj2ZoMfJKf/JwEFZh0fdAKhJL2BBHv+FhJdBfWmXmFl1nrStCV1AlUAWHW6FpB4EK/itjHKVG5n0KKk9cAfwUzNbU2DZyoUsOjw6XttImiNps6SVku6WtGNBpWzeZNHhS8BfgVsk9ZS0s6TjCb0K483s88KKul3Si9ADsDjlvzBet/mdmsQNgJp0IIzRpFkDtM+QNhdeCWTRYQ0ktQTGE3oAHswuWlmRVY+3Au8AjxRQpnIjiw47xuuTwDTgX4CfE7pvf1UoAcuARuvQzL4gGFK54ZTPCF3XzwNDCivmdksH4BOL/f4JClKvtMySeDsl38YIDemqUoa02xuF0sO9wJFAfzPL9xLa3mmUHiUdA/wAODTPi6PSaOyzmGscTTazG+LnGZJaAGMk9TSzNwsiYfOnsc9hG4IBVUWYPPgB8B3CJOktwI8LKOP2SlHrFTcAarKW/BZVe/JbwUnWAPmWxrRPhFcCWXT4FZJ+BlwKXGBm0wokWzmRRY/3E3pM/iZpt+jXEmgRv28ws40Fk7T5kkWHq+N1esp/GmES2yFAJRgAWXR4MWEuRXczezf6/V7Sp8AESePN7I2CSbp9sgZoL0kpY74g9YoPAdRkIWHMJU1P6v+zLwS6xmUz6bSb2HoMZ3sliw4BkDScsATwSjObVEDZyokseuwBXEZ4QefcUcDh8XOltLyy/p9h69ZXruXV2Eld5UYWHR4ErE1U/jlej9ceGWWrBBYSlqHul/LPjf1nMkLdAKjJFODwuHYagLhZw1ExrL60rYCzE2lbAucA0yqkxQXZdIik/wRGAcPN7J4iyVgOZNFjvzzuDcJkrn7ArwsvbrMkiw5fIEy+OiXlf3K8zi2MiM2eLDr8iNB6TU+A/ud4XV4gGbdnphIakN9P+Z8HLIgrMhpPqddHNicHtCW01P+PsMTlNMKL8z1g50S8fQljWDek0j9BaGENBk4gvGi/IIzFlrx8zV2HhI2A/kF4+R6ecj1LXbZy0WMt+c2g8vYByPp/HhH9RwMnEnqlNgCPlLps5aBDoAthCeA7hE2E+gE/iX5zSayNrwQHnBXdfYSepR/H730TcbYAD6bSjYn1yNWEIZX74nvyXzPLVGqlNDdHGMf/TXxIPwP+h9SGDfHBNmBkyn9Hwrrhj+IP9hpwXKnLVC46JMxYt1rcjFKXq1z0WEteFWcAZNUhobv/6lgBbgLeB24CWpW6XGWkw57AU8AygvH0DjAWaF/qcpVAj/W+2+L3R1LpWgDXxedvIzAfOKsQMvlpgI7jOI5TgfgcAMdxHMepQNwAcBzHcZwKxA0Ax3Ecx6lA3ABwHMdxnArEDQDHcRzHqUDcAHAcx3GcCsQNAMdpJJIulGS1uBO3Ma/BMd0+xZI3db9RKXnXSnpN0rlFuFfLeI/rEn4DJF2VJ+6JMe7R6bBiIal7ShdfSvpQ0iRJ32xknodKGpk4i8Fxmh1+GJDjZOds4G8pv3I5KOaIeN0d+BHwuKTWZvZooW5gZlskHUHYDCbHAMJRsXemor8eZVpI0zMK+C1h7/UjCKfWHSjpCDPbso15HUrYSfAR4JNCCuk4hcINAMfJzjwzK8vDnsxsTu6zpGnAIuAqoGAGQPo+9cRbBzQobhF4NyHnTEk7ACMJJ/9Vyt7/TgXhQwCOU0Qk7SjpLkkLJX0eu5anSDqgAWnPlzQvpvtU0nxJg1Nx+kn6naT10b0gqWdtedaFmW0G5gFfHd4iaVdJv4hyb5K0SNKVKRnaSbpX0jJJGyX9XdJ0Sd+K4TWGACRNJhxusm+i231xDKsxBCBpgqQVklqk7tkm6mRswq9K0v0x/iZJb0m6uDG6iPw5Xmsc8x2HT/4iaZ2kVZJelvSdRPhg4IH4dUmijPsk9DE86nKjpOWSbo0Gh+M0Gd4D4DjZaRFPfsxhZvZl/LxjdDcRzojYHbgCmC3pQDNbmS9DSX2BiYQu8msI+4H35OtzwJF0OmGP9inAIIJBPwyYJam3mTXmtLWuxC7rWOm+APQGrid0y58G3ClpdzO7Iaa5i3Bq3nDCvvl7ELr3d63lHiNinIOBM6LfF7XEfRS4hHC41rSE/+lAO2BSlHU34A+EEzlvAJYCpwIPxCGN+xpU+pp0idf0cbYdgdsIwz47Ew66mSXpUDNbCDwHdAOuJQx1fBjT5X7rx4HvEg55mUM4bvcmgqFxTiPkdJzGUeoDEty5K1cHXEj+wz1qPXSHUJG3BaqB/0j4D45p94nfhwEr68hHhEruxZT/bsAaYGw9so+K92sZ3V7AzdFvbIzzb/H7eam0jxAq7A7x+9vAz+u4V8uYz3UJv8nA0jxxT4xxj06U8z1gUire88D8xPcbCYfN7JeK9zDwd6BFHfJ1j/e8KMralmBwrACeqEePLQhGx7vAbXl+zy6p+P2i/6CU/wXR/6BSP9fuKsf5EIDjZOcM4LCEq9HtLOlcSa9L+pRw3Od6Qq9AXcMAfwT2lPSopP6S0q3pAwlHsD4Wu5Rbxl6I9YRTKI9toOybo/uIcFTr7YSWPDGPLYRjrpNMJkyUy53r/kfgYknDJP2TpIK9V8zM4v3OkNQWQNKewMnUnKdwCvC/wPspfbwIVFG3rnM8SNDFeuAlQgv/gnQkSSdJmiFpNUE/mwgt/obc4xSC8fRsSs5c78YxDcjDcQqCGwCOk50FZjY34RblAiSdQejyXQAMJFSahxFa6W1qy9DMXiZ0B3chHL+6StI0Sd+OUaridSJfV+I5dwphqKEh5IyW7sAuZnaNmW2MYR2AVbb1DPiPEuEAlxPGvC8hTJZbKek2STs2UIb6eJTQKh8Qvw8kvLt+lYhTBRzP1rp4PIY3RB83EnRxHOHM9cOAe5IRJB1GWCnwKaHH4PAYbwF1/J4pOdsQeoCScq7YBjkdpyD4HADHKS7nAm+b2UU5D0ltCF31dWJmTwFPSdqZULndArwgqTOwOkb7KfBKnuQb8/jlu0dds9vXAHtIapkyAvaO19Uxj88IQxbDJHUhLIv8GaGlO5yMmNliSXOA8whj/ucBL5vZikS01YRlhlfXks2iWvyTLE3oY6akdsBgSePNLDch8CxCuc5M6kRSB8JQQ32sJlT+fWsJX1GLv+MUHDcAHKe47EToJk7yA7ah983M1gNTJHUnTD5rT9hnYBnQ08xuLZCsaWYC/wWcCTyZ8P8+oRJ8LY+sS4FbJZ0PfDsdnmAjYRikoUwC7pbUj9DiPj8VPpWwj8FSM1u1DfnWxVBC2UcQJh3C17+n5SJJOokwMfCtRNqcAZYu41TCpM62ZjazQHI6TqNwA8BxistU4N64XO0FQuV1BbCurkSS/pvQHfwKYRZ5Z2AIMNfM1sQ4Q4BnYo/C04TW5d7AkcB7ZnZXRtmfB2YTZtLvTajgvkeY/Hizma2NcrwGPEPoBv+cMNGtF3B/HXm/CVwk6VLgL8AGM1tQR/wngDsIhsDnwLOp8LGEnodZku4A3gF2IcyVONLMzmAbMbPlksYDV0k6xMzmEX7PIcDDkibG/K9j65Z7biOoIXHZ42bgDTN7SdLThDkAtxM2PoIw1HMqcI2ZpVcdOE5xKPUsRHfuytXx9SqA7nXEaQGMJlQQ1YQK/WDCBLNfJuKlVwGcRpgY9iGhNbmMMM6+dyr/owhj0msJrfIlhHHvw+uRfRRxjl098XYFfhHl2EToSr8yFWcsoRL/lDCBbj4wJBGebxXALoRehbUxbHH0r7EKIHWfZ2PYo7XI2oGwJHFplHUl8HsSqy1qSZdbBXBhnrCqWKbfJPyuivfYQKjA+wGvAi+l0t4Uf/cvU79tC0LPyvz4m31C2H/hFqBdqZ9rd5XjZPZVT5bjOI7jOBWCrwJwHMdxnArEDQDHcRzHqUDcAHAcx3GcCsQNAMdxHMepQNwAcBzHcZwKxA0Ax3Ecx6lA3ABwHMdxnArEDQDHcRzHqUDcAHAcx3GcCuT/AVhB1xRUkrryAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(xg_predictions, xg_probs, train_xg_predictions, train_xg_probs)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}