4_LTC_Logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc28175d",
   "metadata": {},
   "source": [
    "# In this file LR is used for both balanced and imbalanced dataset.\n",
    "\n",
    "* resource :https://www.geeksforgeeks.org/logistic-regression-using-statsmodels/\n",
    "\n",
    "\n",
    "### The sensitivity with balanced class is improved to a great extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9c3dd0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Import the liberaries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# visualization \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm \n",
    "from sklearn.metrics import confusion_matrix , accuracy_score, classification_report , roc_auc_score, f1_score,precision_score, recall_score\n",
    "from plot_metric.functions import BinaryClassification\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.preprocessing import PowerTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ae8cc9",
   "metadata": {},
   "source": [
    "# Read the data after cleaning and pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e1e1ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final=pd.read_hdf(\"data_final.h5\", key='data_final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c24802a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# these are the features that we have finilized to give the model \n",
    "\n",
    "feature_list_for_training=[\n",
    "'spec_pat_num_age',\n",
    "# Gender-male\n",
    "'patient_gender_M',\n",
    "# Specimen Collection location\n",
    "'collected_in_ed', 'collected_in_hospital', 'collected_in_dsl',\n",
    "# Resident During Collection\n",
    "'dsl_resident_during_collection',\n",
    "# Symptomatic During Collection\n",
    "'symptomatic_during_collection_U', 'symptomatic_during_collection_Y',\n",
    "# Result of the covid test\n",
    "'interp_result_Positive',\n",
    "# Specimen year-month collection\n",
    "'y_m__2020-10',\n",
    "'y_m__2020-11', 'y_m__2020-12', 'y_m__2020-3', 'y_m__2020-4',\n",
    "'y_m__2020-5', 'y_m__2021-3', 'y_m__2020-7', 'y_m__2020-8',\n",
    "'y_m__2020-9', 'y_m__2021-1', 'y_m__2021-2', \n",
    "# Num of Elixhauser \n",
    "'chf_2_years_full_1', 'htn_unc_2_years_full_1',\n",
    "'cancer_mets_2_years_full_1', 'fluid_elec_dis_2_years_full_1',\n",
    "'card_arrh_2_years_full_1', 'valv_dis_2_years_full_1',\n",
    "'pcd_2_years_full_1', 'pvd_2_years_full_1', 'htn_c_2_years_full_1',\n",
    "'paral_2_years_full_1', 'oth_neur_dis_2_years_full_1',\n",
    "'cpd_2_years_full_1', 'diab_2_years_full_1',\n",
    "'hypothyroidism_2_years_full_1', 'ren_fail_2_years_full_1',\n",
    "'liver_dis_2_years_full_1', 'pep_ulc_exc_bld_2_years_full_1',\n",
    "'aids_hiv_2_years_full_1', 'lymph_2_years_full_1',\n",
    "'tumour_no_mets_2_years_full_1', 'rheum_col_vasc_dis_2_years_full_1',\n",
    "'coag_2_years_full_1', 'obes_2_years_full_1', 'wt_loss_2_years_full_1',\n",
    "'anemia_2_years_full_1', 'alc_abuse_2_years_full_1',\n",
    "'drug_abuse_2_years_full_1', 'psych_2_years_full_1',\n",
    "'depress_2_years_full_1',\n",
    "#Num of Admits for 1 year\n",
    "'num_admits_1_year', 'num_scu_admits_1_year',\n",
    "#Num Procedures for 1 year\n",
    "'num_procs_dad_1_year', 'num_procs_nacrs_1_year',\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496dd648",
   "metadata": {},
   "source": [
    "# trian the model by Imbalanced data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfc4e69d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are imbalanceness:\n",
      "0    89.857528\n",
      "1    10.142472\n",
      "Name: died_within_60_days, dtype: float64\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.273800\n",
      "         Iterations 8\n",
      "Confusion Matrix : \n",
      " [[2289   14]\n",
      " [ 240   15]]\n",
      "Test accuracy =  0.9007036747458952\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.99      0.95      2303\n",
      "           1       0.52      0.06      0.11       255\n",
      "\n",
      "    accuracy                           0.90      2558\n",
      "   macro avg       0.71      0.53      0.53      2558\n",
      "weighted avg       0.87      0.90      0.86      2558\n",
      "\n",
      "F1 Score: 0.1056338028169014\n",
      "AUC: 0.53%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAHQCAYAAAA77cqvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACbQElEQVR4nOzddXhTdxvG8W/qSgsUKO42GAwfDIYPBoMixV2GS3FnsOEy3N2tuA0ZjJcxho2NocOdQlvqmuS8f4QeCG2hgbYp7fO5Lq4tJ+ecPPk17Z2jj0ZRFAUhhBBCAGBh7gKEEEKIlESCUQghhHiDBKMQQgjxBglGIYQQ4g0SjEIIIcQbJBiFEEKIN0gwCiGEEG+QYBRCCCHeIMEoRCoi9+tIfOYeU3O/flokwZhGtWvXjsKFCxv9K1KkCGXKlKFZs2bs37/fLHU9evSIwoULs2PHDrO8fgx/f3+mTZtG3bp1KVGiBBUrVqRDhw4cOHDArHXFJyoqismTJ7N371512vDhw6lRo0ay1nH37l3GjRtHrVq1KFGiBNWqVWPAgAFcv37daD5z1PYhtm3bxtSpUxNlXfPmzaNw4cIJnv/Zs2d0796dx48fq9Nq1KjB8OHDE6UeET8rcxcgzOezzz7jhx9+UB/rdDqePXvG6tWrGThwIM7Oznz99dfJWlPmzJnZsmULuXLlStbXfdP169fp2rUrlpaWdOjQgWLFihEcHMyvv/7KoEGDOHToEDNmzMDa2tpsNb7t+fPnrF69msmTJ6vTevXqRfv27ZOthiNHjjBkyBAKFixIz549yZEjB8+ePWPdunU0a9aMBQsWJPvn6WMtWrSI8uXLJ8q6mjVrRpUqVRI8/x9//MFvv/3GmDFj1Gnz58/HyckpUeoR8ZNgTMOcnJz44osvYk2vWrUqFStWZPv27cn+h8zGxibOmpJLeHg4vXr1ImPGjKxZswZXV1f1uVq1alG9enX69u1L3rx58fLyMludCZGcXy4ePHjA0KFDqVKlCrNnz8bS0lJ9rk6dOrRu3Zrhw4dz7Ngx7Ozskq2ulMTd3R13d/ePWsdnn32WSNWId5FdqSIWGxubOLeGtm3bRv369SlevDjVqlVj3rx5aLVao3lOnTpFmzZtKFWqFJUrV2bs2LEEBgaqzz958oSBAwdSvnx5SpYsSYcOHbh69ar6/Ju7Up89e0bRokVZs2aN0WsEBQXx+eefs3z5cgD0ej1Lly6ldu3aFC9enDp16rBu3TqjZdq1a8fgwYPp168fpUuXplu3bnG+9x07dvD48WN++OEHo1CM8c0331CvXj1Wr15NaGgoYNgt2K5dO7y9valevTqlSpWiffv2Ru/LlPe+atUqvv32W8qXL6/uUj569CitW7emVKlSFC9enLp167J+/Xp1uZo1awIwYsQIdRfl27sra9Sowdy5c5k6dSqVKlWiRIkSdOnShbt37xrVuXPnTurVq8fnn39Ow4YNOX36NJ999tk7d2+vW7eOqKgoRo8ebRSKAHZ2dgwbNgxPT0+CgoJijXedOnXU1/rf//5n9Py5c+fo0qUL5cqVo3jx4tSoUYN58+ah1+s/asxi+Pn5MXLkSCpVqkSpUqVo06YNFy5cUMfr8ePH7Ny5k8KFC/Po0aOP+jm+vSv14cOH9OzZkwoVKlCyZElatGjBiRMn1HEZMWIEADVr1lR3n769KzU0NJTJkyfz9ddf88UXX9CkSROOHTsW789JJIwEYxqmKAparVb9FxkZyf379xk9ejShoaF4eHio8y5ZsoQxY8ZQsWJFFi9eTJs2bVi2bBljx45V5zlx4gRdu3bF1dWVWbNmMWTIEI4dO0a/fv0Aw3G7li1bcuXKFcaMGcPMmTPR6/W0adOG27dvx6rP3d2dChUqxDqud+jQIbRaLQ0aNABg3LhxzJ07l4YNG7J48WLq1q3LpEmTWLBggdFyBw8exNramgULFsS7i/HkyZOkT5+e0qVLxztu9evXJzw8nD/++EOddu3aNWbNmkWfPn2YPn06AQEBtGvXDh8fH5Pf+6xZs+jSpQsTJkzgyy+/5LfffqN3794UK1aMhQsXMm/ePLJnz85PP/3EX3/9RebMmZk/fz4APXv2VP8/LmvXruXOnTtMnjyZCRMmcPnyZaM/tLt27WL48OGULl2ahQsXUqdOHXr16oVOp4t3nTHj9tlnn5ElS5Y4n69QoQIDBw4kc+bM6rSnT5+ydOlS+vfvz9y5c1EUhb59++Ln5wcYdml37NhR/TwtWrSI0qVLM3/+/FjHwE0dM4CwsDBatmzJH3/8waBBg5g/fz6Ojo507dqV27dvM3/+fDJlykTVqlXZsmULmTNn/qif45v0ej3du3cnLCyMadOmsXDhQlxdXenVqxf379+nWrVq9OzZEzDsPu3Vq1esMdXr9XTt2pWdO3fSrVs3Fi1aRKFChejTpw9nzpx5589LvIci0qS2bdsqhQoVivWvcOHCSoMGDZSDBw+q8wYFBSklS5ZUxo4da7SOrVu3KoUKFVL+++8/RVEUpUmTJkqjRo2M5vnll1+Ub775Rnn27Jny888/K59//rny6NEj9fnIyEilZs2aSt++fRVFUZSHDx8qhQoVUrZv364oiqLs2LFDKVSokPLw4UN1mfbt2ysdO3ZUFEVR7ty5oxQuXFhZsmSJ0evOmjVL+fzzzxV/f3/1/RYvXlwJDQ1957jUr19fadq06TvnuXHjhlKoUCFl1apViqIoyrBhw5RChQopZ8+eVefx8fFRPv/8c2XKlCmKoigmvfdBgwYZvd6yZcuUoUOHGk17+fKlUqhQIWXx4sVGy8aMW0xd1atXVx9Xr15dqV69uqLVatVp8+bNUwoVKqSOU7Vq1ZTu3bsbvdaSJUtirfttX3zxheLl5RXv82+LGbNbt26p006dOqUUKlRIOXr0qKIoirJz506la9euik6nU+fR6XRKmTJllDFjxhi97w8Zs/Xr1yuFCxdWrl27ps4TERGh1K1bV9m0aZOiKIYxGzZsmPr8x/wc586dqxQqVEhRFEV5/vy5UqhQIWX37t3q80FBQcqkSZOUGzduKIqiKNu3b4/12X+znuPHjxuNl6Ioil6vV1q2bKnMnj071piLhJNjjGlYsWLFGD9+PAA+Pj7MmTOH6OhoZs2aRf78+dX5Ll68SHh4ODVq1DDadRqzm+7UqVPkzJmTK1eu0LdvX6PXqFOnDnXq1AHg9OnTFC1alCxZsqjrsbCw4Ouvv2bPnj1x1vjNN98wfvx4Dhw4QLdu3Xjx4gVnz55VTzL5888/URQlztoWLVrEhQsXqFWrFgA5cuTAwcHhnWOiKApWVu/+tYjZVai8cRp9tmzZKFeunPo4c+bMlCpVSt0tZ8p7L1SokNHjrl27AoYtnAcPHnD37l3+/fdfAKKjo99Z69s+//xzo12dMce8wsPDCQoK4smTJ/Tv399omfr16zNz5sx3rlej0bx3q/Jt6dOnN/qc5cyZE4Dg4GAAGjVqRKNGjYiMjOTBgwfcv3+fK1euoNPpYr3vDxmz8+fPkyNHDooUKaIuZ2try8GDB+Ot+WN+jm9yc3OjQIECjBkzhj/++IOvv/6aypUrq7tPE+L8+fNYW1tTvXp1dZpGo2HTpk0JXoeImwRjGubo6Mjnn38OGP5glipVCg8PDzp37szOnTvJkCEDAAEBAQDxHpd7/vw5gYGBKIpCxowZ4329gIAA7t+/T7FixeJ8Pjw8PM4aa9WqpQbj/v37sbW1pXbt2ka11a9fP851xuzKBMMfo/fJnj07165de+c8MceasmXLpk57cxdhjIwZM3LlyhW1zoS+97fr9Pf354cffuDo0aNoNBpy585NmTJlANOvcbO3tzd6bGFhOJqi1+vx9/dX635TpkyZ3rve7Nmz8+TJk3if12q1+Pv7G43T219SNBqNWgtAREQEP/30E7t370ar1ZIjRw5KlSqFlZVVrPf9IWMWEBDwzs9rXD7m5/j2e125ciWLFi3iyJEj7Ny5E2tra2rVqsW4cePiPL4dVy2urq7qz1AkHglGocqYMSNjx46lb9++TJw4Ud1KSJcuHQAzZswgT548sZZzc3PDyckJjUaj/nGNERUVxenTpylRogTOzs6UL1+eoUOHxvn6NjY2cU738PCga9eu3Lt3j/3791OrVi0cHR2NaluzZo067U1vhldC1KhRgxMnTnDx4kVKlSoV5zy//PILdnZ2fPXVV+q0mIB+k6+vr/qH90PfO8DgwYO5ffs2q1atonTp0tjY2BAeHs62bdtMeGfvF7P1GHOML8bbj+NSuXJl1qxZw4sXL+IM0pMnT9KjRw9+/vnneL/EvG3ixIkcOnSI2bNnU6lSJTVIK1as+N5lEzJmzs7O6pecN128eBEnJycKFiwY67mP+Tm+LUuWLIwbN44ffviB69ev88svv7Bs2TJcXFzUPTnv4uzsTEBAAHq93igcr127hlarVb/0CtPJVw1h5JtvvqFKlSrs27dPPYBfsmRJrK2t8fHx4fPPP1f/WVtbM3PmTB49eoSjoyNFixbl119/NVrf77//Trdu3Xj27Bnly5fn7t275M2b12g9e/bsYdu2bbHOZoxRqVIlMmXKxLp167h06ZLRSUExuy9fvnxptM6AgABmz54dZ2C9S8OGDcmdOzdjx46Nc9njx4+za9cu2rVrZ3Q92YMHD7h165b62MfHh7///lv9I/6h7x3gwoUL1KlThy+//FL9wxtz9mbM1tW7lk8od3d3cuXKxZEjR4ymHzp06L3LtmnTBmtrayZMmBBrl2p4eDhz587FxcXFaLff+1y4cIEKFSpQq1YtNRQvX76Mv7+/+r7ftez7xqxs2bI8fPiQGzduqMtFRUXRt29ftm7dChBra+xjfo5vunjxIpUqVeLSpUtoNBqKFi3KgAEDKFSoEM+ePYvztd9WtmxZoqOj1TNZwbA1PGrUKBYtWpSgOkTcZItRxDJy5EgaNmzIhAkT2LlzJ+nTp6dr167MmTOHkJAQKlSooB6T1Gg06jGafv360bNnT7y8vGjSpAn+/v7MnDmT6tWrU7RoUTJkyMDu3bvp2LEjnTt3Jn369Bw4cICtW7e+89iKpaUlDRo0YM2aNWTKlIlKlSqpzxUqVIiGDRsyZswYHj9+TPHixbl79y6zZs0iR44ccW7hvouDgwPz5s2je/fuNGrUiI4dO/LZZ58RHh7OsWPH8Pb2pmbNmrGOwymKQq9evfDy8sLS0pL58+eTLl062rVrB0DHjh0/6L0DlChRgr1791KsWDHc3d25ePEiS5YsQaPRqLvunJ2dAcMxsPz581OyZEmT3jcYdu/169ePwYMH88MPP1C7dm2uX7+unt37rj/UOXLkYNy4cYwaNYo2bdrQsmVLsmbNyoMHD1i9ejX3799n2bJl7z3G+/b7PnjwIJs2bSJ//vxcv36dRYsWGb3vdy37vjFr0qQJ69ato2fPnvTv358MGTKwYcMGIiIi1J9bunTpuHr1KmfPnqVEiRIf9XN802effYadnR1Dhw6lb9++uLm58ccff3Dt2jX1jOmYvSFHjhzh66+/NjoeC1CtWjVKlSrFiBEj6N+/P7lz52bv3r38999/RjcFEKaTYBSx5MuXj3bt2rFy5UrWr19Px44d8fLyIlOmTGzcuJHly5fj4uJCxYoV1TvkAFSvXp0lS5Ywb948evfuTfr06fn222/VEMmSJQubN29m5syZjBs3jsjISPLkycPEiRPx9PR8Z00eHh6sXLmS+vXrx/pWPnnyZJYsWcLmzZt59uwZGTNmpF69empImSrmOsr169fj7e3No0ePsLOzo0iRIkybNi3OXYHZsmWjU6dOTJo0ifDwcCpVqsSiRYvUY0Uf896nTJnCTz/9xE8//QRAnjx5GD9+PHv27OH8+fOA4WYNnTp1YsuWLfz222+cOnXK5PcN0KBBA8LCwlixYgXbt2+nYMGCjBo1ilGjRr031Bo3bkzu3LlZs2YNs2fPxs/Pj0yZMlGqVCnmzJlDgQIFTKpl+PDhREdHM3v2bKKiosiRIwc9e/bk1q1bHDt27J0n+yR0zNavX8+0adOYOHEiWq2WkiVLsm7dOvXmCJ07d2bSpEl06dKFVatWUbZs2Q/+Ob7J1taWlStXMnPmTCZOnEhQUBB58uThxx9/pEmTJoDhEpdKlSoxc+ZMTp8+zdKlS43WYWlpybJly5g5cybz5s0jLCyMIkWKsHz58ngPA4iE0SimHr0XQhgZPnw4Z8+eTRUXVu/bt4/PPvuMfPnyqdN+++03unfvzu7du43O4BQitZItRiGEas+ePcyaNQsvLy+yZs3KvXv3mDt3LuXLl5dQFGmGBKMQQjV16lRmzpzJ9OnT8ff3x83NjW+//Va9e5EQaYHsShVCCCHeIJdrCCGEEG+QYBRCCCHeIMEohBBCvEGCUQghhHiDBKMQQgjxhjRzuYafXzAfev6tRgMZMzpTqFBhnj17irt7Vv7443ziFviJihmbjxnf1EjGJX4yNnGTcYlfYoxNzDoSIs0Eo6Lw0R+2EiVKkj17DjJkyCgf3LckxvimRjIu8ZOxiZuMS/ySa2zSTDAmhsWLV8gHVgghUjk5xiiEEEK8QYJRCCGEeIMEoxBCCPEGOcZogr59e/LypT/p02dg7lzpkC2EEKmRBKMJTpw4ztOnT8iaNZu5SxFCCJFEZFeqEEII8QbZYjTBb7+dQqdTsLDQmLsUIYQQSUSC0QRyYb8QQqR+sitVCCGEeINZg9Hf35/atWtz5syZeOc5ceIEDRo04IsvvuDbb7/l+PHjyVihEEKItMZswXjhwgVatGjBgwcP4p3n3r179O3bl/79+3P+/Hn69u2Ll5cXPj4+yVjpa4cPH2TPnp0cPnzQLK8vhBAi6ZklGHfu3MngwYMZMGDAe+crW7YstWrVwsrKinr16lGuXDm2bNmSTJUaGzx4AF27dmDIkHfXLYQQIvG8fOmfrK9nlpNvKleuTIMGDbCysnpnON66dYtChQoZTStQoADXr183+TU1H3EiaVzLfsz6UpOYcZDxMCbjEj8Zm7jJuMQWERTElK4dWHD8V/r06cO4cZM+eF2mjKtZgjFTpkwJmi80NBR7e3ujaXZ2doSFhZn8mgntw/Uu48b9QEhICE5OTri5ffz6UpPEGN/USMYlfjI2cZNxMQj29eW/8iWZ6OfHWeDPP/9MtrFJ0Zdr2NvbExERYTQtIiICR0dHk9eVGA0umzZtpa7D1zf4w1aWykhz1bjJuMRPxiZuMi6vBfg85clX5fk6MBCA3RoNtydNkkbFAIUKFeLKlStG027dukXx4sVNXldiNLiUBqLxk7GJm4xL/GRs4pbWx+X5wwe8qPol1UJCAAgDHk2aRuXatfH1TZ4vDSn6OsaGDRty9uxZDhw4gFar5cCBA5w9exYPDw9zlyaEECKRPbz5Hz6VyxmF4n8/zyNv1+7JWkeKC8ZSpUqxZ88eAPLnz8+CBQtYsmQJ5cqVY+HChcybN4+8efOauUohhBCJ6dblf3lZ4ytqhIcDEKbRcG/BUrK37ZDstZh9V+qNGzeMHl+8eNHocZUqVahSpUpylhSvsmVL8PTpE9zds3H+/CVzlyOEEKnClQtn0TX8lprR0QCEajQ8WbaGTA0bmaWeFLfFmJJFRUURFRVFdHSUuUsRQohU4czpP4hqUFcNxTALC16s2YSrmUIRUsAW46ekSJGiuLllws3NzdylCCHEJ+/48V/p1KkNrbVavgFCLSx4uWErjjW/MWtdEowm2Lp1Z5o+W0wIIRLLvn176NGjM1FRUSwHChYqTLuJ07CtWt3cpUkwCiGESF6bN63Ha0Af9Ho9APXqNaD1kpVY2dqauTIDOcYohBAi2axdNI/C/XvR7FUoNm/eiuXL12CbQkIRZItRCCFEMlAUhQXTJlFt5lSqAzWBqjVq0WTuIiwsUtY2mgSjCcaNG01AQAAuLq6MGzfB3OUIIcQnQVEUpowejseyRVR9NS3KxoYWg0egS2GhCLIr1SQ7dnizYcNadu70NncpQgjxSdDpdIzq35tGb4RiuK0dEXt+QVe2nFlri49sMQohhEgS0dHRDO7eiZ779hBzm5YIe3sidh9E+0Vps9b2LhKMJtixYy/R0VqsrGTYhBDiXcLDw+nbsTVDjv9K5VfTIh0cCN99EG3JUmat7X3kL7wJChQoKNcxCiHEe4SEBNO9lSc/nDnNV6+mRTo6Erb7INoSX5iztASRYBRCCJFo/P39aNWqKVYX/+KLV9OinJwJ230A7eclzVlagsnJN0IIIRKFj88zGjeuz8WLf3EOaOHoSHimzITsPvjJhCLIFqNJzp07Q2RkFDY2NpQrV8Hc5QghRIrx4MF9PD0bcu/eXQAyZ87C0G27CcmXH1LQxfsJIcFogi5dOvD06ROyZs3GP/9cN3c5QgiRIty8+R+dm3xHbZ9nLANy5szFtm27yZcvv7lL+yASjEIIIT7YpUt/830zDza/fEl5oESGDFTZe4hs2bKbu7QPJsFogs6dvyc4OBhnZ2dzlyKEEGb355+n6d3ak+0hwcRcqt8DDQHWNnzKJ/BLMJrAy2uQXK4hhBDAsWNHGdixNXsiIijzapo2oxtBO/ejZMpk1to+lpyVKoQQwiR79+6mX9vm7H0jFHVumQjadQBdkaJmrS0xSDAKIYRIsM2bNzCsa3t+0WqJuambLlMmAncdQFe4iFlrSywSjEIIIRJk2bJFjO/XkyOKQsxN3XSZsxC46yC6QoXNWltikmOMJqhVqyrPn/uQOXMWjhw5Ye5yhBAiWSiKws8/T2Pq1InsBfWONrrMWQjcuR9dwUJmrC7xSTCa4PlzH54+fWLuMoQQItkoisK4caNZtGgeAF5AZScnnB2dDKFYoKBZ60sKEowmyJw5i9F/hRAiNdPpdAwdOoB161ar09qNm4i+7rcEKgq6/KkvFEGC0SRHj56QyzWEEGlCdHQ0vXt/z/927cAK0Gk0zJgxh3btOqIzd3FJTIJRCCGEkfDwcLp2bc8/Rw7xP+C6RoP/giU08mxp7tKShZyVKoQQQhUcHESrVk3558ghjgHFgKaKQtsL581dWrKRLUYhhBCAoZdiy5ZNePr3RY4Dn72arsueg7BuvcxZWrKSYDTB7Nkz1Xul9u8/yNzlCCFEonn27CnNmnkQeOM6x4GY+9focuQkYMc+9HnymrO8ZCXBaIKVK5epbackGIUQqcX9+/fw9GxI5P17HAdi7l+jy5nLEIq585ixuuQnxxiFECINu3HjOg0a1CHq/j1+461Q3Lk/zYUiyBajSVasWENkZBQ2NjbmLkUIIT7aP/9cpEWLxtj6+/MbEHP/Gl2u3IZQzJnLjNWZjwSjCcqVqyDXMQohUoU///yDNm2aExwchAsQZe8A4WHocuUhYNd+9DlymrtEs5FdqUIIkcYcO3aEFi0aExwcBECRChVxPHWWCI8mBOw+kKZDEWSLUQgh0pS9e3fRo0cXoqOjAahRoxYrV67HwcGB4GWrzVtcCiHBaIJbt24SHa3FysqKAqnwxrlCiNRt06b1DBjQh+x6PT8DO+rUY/qKtXLexFskGE3QpEkD9XKNf/65bu5yhBAiwZYuXcjo0cPJBRwH8gEeTx4TGBaKIsFoRIJRCCFSMUVRmDFjCtOnTyY3hlCMuVRfExaKJiICOafQmASjCZo08SQgIAAXF1dzlyKEEO+lKApjx45kyZIF5AZ+A/K8ek5boCCBO/ahd89qtvpSKglGE4wbN0Eu1xBCfBJ0Oh2DB/dnw4a15MEQirlfPactWMgQilnczVZfSibBKIQQqUxUVBS9e3dj9+4d5MUQijGX6msLFiJgx36ULNJwPT4SjEIIkYqEhYXRpUs7fv31CPkwHFNUQ7FwEQK270PJnNmMFaZ8EoxCCJFKBAcH0bZtC06fPgXAAEtLcul0gISiKSQYTdC8eWN8fX1xc3Njy5ad5i5HCCFUfn5+tGrVhL//vgiAk5MzudZsJGLdKqyuXzOEYqZMZq7y0yDBaILr16+p1zEKIURKEdNL8cYNw/XV6dOnZ8uWnXzxRWmCK36FJjgIJX0GM1f56ZBgNIGNjQ02NjZYW8vFsEKIlOHevbt4enrw4ME9CgFuGd2YsnM/RYq8ajVsZSWhaCIJRhOcP39JLtcQQqQYN25cp1kzD549e0ph4ISlJek1GkKsrNCZu7hPmHTXEEKIT9Dff/+Fh0ddnj17ShHgf5aWZNHpsPF9geOY4eYu75MmwSiEEJ+Y06dP0aRJA/z9/SkKnLSyIvOrs0+ji5cgeMFS8xb4iZNgFEKIT8jRo4do0aIxISHBFAV+t7bGTasFIPrzkgRu34OSIaN5i/zEyTFGE6xdu4qQkFAcHR1p376TucsRQqQxu3fvoGfPrmi1WooBJ62tSf+qr2J0iS8I3LZLTrRJBBKMJpgxY6p6uYYEoxAiOW3YsJZBg/qh1+spBvxuY4NrVBQA0SVLGULRNb15i0wlZFeqEEKkcIsXz2fAgD7o9XrcgD9sbV+HYqnSBHrvllBMRLLFaIIZM2YRHh6BnZ2duUsRQqQBiqIwffpkZsyYok5r2r03Fq6uMHUi0aXLELhlJ4q0wktUEowm+Oabb+U6RiFEsjD0UhzBkiUL1WlDhoxg8ODhhGs0KFnciWzYCCWdixmrTJ0kGIUQIoXR6XQMGtSPjRvXAWALjP5pMt2791bniWjbwUzVpX4SjEIIkYJERUXRs+f37NljaFRQWqPht3Qu6Ip8RrSZa0sr5OQbE/j7++Hr64u/v5+5SxFCpEJhYWG0a9dSDcVyVlb8Ye+Ac2AALu1aYHXujJkrTBskGE1QrdpXfPZZPqpX/8rcpQghUpmgoEDq1q3LsWNHAahoY8PvtrbYhoUCoC3xBbqin5mzxDRDdqUKIYSZ+fn50aJFYy5d+huAKvYOHLWwwCY0BIDoChUJ3OSN4uRsxirTDrMEo5+fH2PGjOHs2bNYWlrSsGFDhg0bhpVV7HLWrFnDmjVrCAgIIHv27PTp04c6deqYoWqoWrU6L1/6k17uLCGESCRPnz6hWTMP/vvvBgA106XjoE6P9atQjPqyEoEbvcHJyZxlpilmCUYvLy+yZMnCyZMn8fX1pWfPnqxevZquXbsazXfixAmWLFnC+vXryZcvH4cOHcLLy4sjR46QI0eOZK973rxFcrmGECLRvNlLEaC+mxu7wsOxCjXsPo2qVJnA9VslFJNZsh9jvH//PmfPnmXIkCHY29uTM2dOevXqxYYNG2LNe+fOHRRFUf9ZWlpibW0d55alEEJ8Sq5fv0aDBnXUUGzk7s6eiIjXofhVFQI3bJNQNINkT5ibN2/i6upKlixZ1Gn58+fnyZMnBAUFkS5dOnV6/fr12bFjB/Xq1cPS0hKNRsP06dNxd3c3+XU1mg+vOWbZj1lHaiVjEzcZl/jJ2MDFixdo0aIJL1++BKBw4SLM6dEbi8FeAERVqUrQ+i1oHBzMWGXKkRifGVOWTfZgDA0Nxd7e3mhazOOwsDCjYIyOjqZIkSJMnDiRIkWKsHfvXkaNGkX+/PkpXLiwSa+bMePHH7ROjHWkVjI2cZNxiV9aHZsTJ07QtGlDgoODAShbtiwHDx7Ezc0NsmeBFSuw2bEDNwnFWJLrM5Pswejg4EB4eLjRtJjHjo6ORtN/+uknSpcuTYkSJQBo2rQp+/btY+fOnQwfblqHaj+/4A8+PqjRGH4gnp7N8fPzI2PGjCxevOLDVpbKxIzNx4xvaiTjEr+0PDaHD/9Cly7tiYiIAKBixa/YsGELYIufXzAZmzXDr3odlDAdhAWbt9gUJDE+MzHrSIhkD8aCBQsSEBCAr6+v4RsScPv2bdzd3XF2Ni76yZMnFC9e3GialZUV1tbWJr+uovDRv4R//HFKbTuV1n6h3ycxxjc1knGJX1obm127ttOr1/doXzUV7l+2HGMaNkLvlM5oHBQ0aWpcTJFcn5lkP/kmT548lClThkmTJhESEsLDhw9ZuHAhnp6eseatUaMG69ev58qVK+j1en755RfOnDlDvXr1krtsIYT4YOvWraZ7985qKI6o/DU/X71KxhFDsFste59SGrOc3jl37lx+/PFHatasiYWFBY0aNaJXr14AlCpVivHjx9OwYUP69OmDpaUlffv2JTAwkNy5c7NgwQKKFi1qjrL5449z6PUKmrR81oAQwiQLF85j3LhR6uOfatdh5KnfsXh1RxubQweIaN8JLOVGZCmFRlHSxka7r+/H7Zt2c3P+qHWkVjI2cZNxiV9aGRtFUZg6dSI//zxNnTazYWMGHDmEJjwMgMiatQlatQHs7NLMuHyIxBibmHUkhFwQKIQQiUyv1zNmzHCWLVusTlvSqi3f79rxOhRrfUPQyvUgjc9THAlGIYRIRFqtloED+7J58+ublqzr1JU2mzegeXUGfmTtOoZQtLU1V5niHSQYTbBv3x7CwsKwt3fgu+8amrscIUQKExkZSc+eXdm3bzcAFhYWbOvRh8arlr0OxW/qErRinYRiCibBaIKRI4eql2tIMAoh3hQWFkanTm04fvxXAKytrVk2bxEe48a8DsU63xK0fK2EYgonwSiEEB8pKCiQNm2ac+bMacBwN69Vq9ZTo0ZtgvIXxMXTg+iKXxG0fA3Y2Ji5WvE+EowmGDFiDKGhobHu0COESLt8fX1p0aIx//77DwDOzunYsGErX35ZCQBtyVIE7D+CLk9eCcVPhASjCVq1aiOnUQshVE+ePKZZMw9u3vwPgIwZM7Jvykzyl//SaD5dIdPu7SzMS64oFUKID3Dnzm0aNKijhqK7e1ZODB9DhT7dcRo+KG3d7y6VkWAUQggTXbt2lYYN6/Lw4QMAcufOw/+Gj+azUUPRREZiv3oFdpvWm7lK8aFkV6oQQpjgr7/O06pVU7WXYpEiRfmljxe5BvZFExUFQIRHEyKatTRnmeIjyBajCUqUKELmzOkoWbKIuUsRQpjBqVMnadq0oRqKpUqV5ki/geQa0Od1KDZuSvCi5fABXYBEyiDBKIQQCXD48EFatmxCaGgIAJUqVeZgz75k798LTXQ0ABFNPAlesAysZGfcp0x+eiYoUaIk2bJlV/tICiHShp07vendu5vaNqp27TpsaNYSt97d3gjFZgTPXyKhmArIT9AE69dvkRPNhEhj1q5dxZAhXsQ0ImrcuCnLmjQnQ6c2aF4FZYRnC4LnLQZLS3OWKhKJ7EoVQoh4LFgwl8GD+6uh2K5dRxYuXA4lv0CXKzcAEc1aSiimMrLFKIQQbzH0UpzAzz9PV6f16tWPH374CY1Gg949K4E792O/chmhw0dLKKYyEoxCCPEGvV7P6NHDWL58iTpt+PDRDPAajEajeT1f1myEjvrBHCWKJCbBaILhwwcTEBCAq6srkyfPMHc5QohEptVqGTCgD1u2bFSnTZw4lT6ZMmPXqimBqzeCvb0ZKxTJQY4xmuDAgX1s376VAwf2mbsUIUQii4yM5PvvO6qhaGFhwdy5i+jjlgnnnl2xOf4rLu1aQkSEmSsVSU22GIUQaV5oaCidOrXht9+OAYZeikuWrKJpZATOPbui0esB0OXKJR0y0gAJRhPs3fsLWq0OSznQLkSqERgYQJs2zTl79k/A0Etx9eqN1PXzxblPdzUUw9t1JGT6bLCQHW2pnQSjCXLnziPXMQqRirx48YIWLRpz+fIlwNBLceNGb75+cA/nvj1eh2L7zoRM+1lCMY2Qn7IQIk168uQxHh511VDMmDEju3bt5+t7d4y3FDt2kVBMY+QnLYRIc2J6Kd66dROArFmzsWfPIcpeuYxzv55oXu0aCu/UlZCpEoppjexKNcGpUyeJiIjE1taWr76qYu5yhBAf4OrVKzRv3ojnz30AyJMnL97ee8iVMxd2o4a+DsUu3QiZNB3euHZRpA0SjCbo2fN7nj59Qtas2fjnn+vmLkcIYaILF87RqlVTAgICACha9DO2bt1FlizuAASu3ohLuxZoCxchdOI0CcU0SoJRCJEm/P77/2jbtgVhYaEAlC5dhk2btpM+fYbXMzk4ELjR23BJhoRimiXBaIKePfsQFBSEs3M6c5cihDDBoUMH6dq1PZGRkQB89VUV1q3bTMYjh4iu+BV696yvZ7a1NVOVIqWQYDRBz5595HINIT4x27dvpU+f7uh0OgC++aYuy5atIf22zTgP7o82fwECdx1A/2p3qhByqpUQItVas2YlvXp9r4ZikyaerFq1gfRbN+E8uD8AVrdvYeu91ZxlihRGglEIkSrNmzfbqMFw+/adWbBgGc7r1+A8xEudL6yPF+G9+pqpSpESya5UIUSqoigKkyf/xOzZrzvg9OnjxZgx47FfuQznEYPV6WF9BxA6epycaCOMSDCaoGrVivj4PCNzZndOnDht7nKEEG/R6/WMGjWUFSuWqtNGjhxL//6DsF+5FOcRQ9TpYf0HETpyrISiiEWC0QQvX77Ez88PGxs5a02IlEar1eLl1ZutWzep0yZPnk6XLt2xX7YIp1HD1OmhXoMJGzFGQlHESYLRBDlz5sLW1pbMmbOYuxQhxBsiIyPp3r0zBw7sBQy9FOfMWUiLFq2x/v1/xqE4cAhhw0ZLKIp4STCaYP/+w3K5hhApTGhoKB07tubEieMA2NjYsGTJKurXbwBA9FdVCO/YBfvVKwgdOJSwYaMkFMU7STAKIT5ZgYEBtGrlyfnzZwFwcHBg9eqNVKtW4/VMGg0hU2YSVfMbor6pK6Eo3ksu1xBCfJJevHhBo0b11VBMl86FLVt2Ua1aDTQvXhjPbGFBVJ1vJRRFgkgwCiE+OY8fP6JhwzpcufIvAG5ubuzcuZ8KFb7Efv4cMlQqg9Wlv81bpPhkya5UE0yfPpnAwCDSpUvHkCEjzF2OEGnSnTu38PT04NGjhwBky5Ydb+89FChQEPu5s3Ca8AMALp4N8T95DiWLnCwnTCPBaIJ169aobackGIVIfleuXKZ580a8ePEcgLx58+HtvYecOXNhP/dnnCaMU+cN79lXQlF8ENmVKoT4JJw/f5ZGjeqpoVi0aDH27DlEzpy5cJg9wygUQ0b9QNiAIfGsSYh3ky1GE6xbt4nIyChsbGzMXYoQacrJkydo166l2kuxTJmybNzoTfr0GXD4eRqOUyao84aMHk94vwHmKlWkAhKMJihZspRcxyhEMvvllwN8/30HtZdilSpVWbNmI05OzjjMnIrj1InqvCFjfiS8r5eZKhWphcm7Uv39/Vm9ejUTJ04kJCSE48ePJ0VdQgjB9u1b6dSpjRqKdep8y4YN2wyhOGOKcSj+MEFCUSQKk4LxypUr1K1bl19++QVvb29evnxJ//792b59e1LVJ4RIo1atWv5WL8VmrFy5Hjs7OwD06TOo84aMn0R4735mqVOkPibtSp08eTLDhw+nSZMmlCtXjpw5c7JgwQImT55M06ZNk6rGFOPq1StER0djZWXNZ58VM3c5QqRac+f+zIQ3Tqbp0KELU6fOxMLi9Xf5iC7dANBERxHeo09ylyhSMZOC8b///sPDwwMAzas7SFSpUgUvL69ELywlatmyqXq5xj//XDd3OUKkOoqiMHHieObO/VmdFtNLURPHXWtiwlGIxGTSrtQMGTJw584do2l37tzBzc0tUYsSQqQ9er2e4cMHGYXi6NHjGDv2RzSAw5SfsNm7y2z1ibTDpC3G1q1b0717d3r06IFWq+XAgQMsWrSIFi1aJFV9KUqLFq0JDAzAxcXV3KUIkapotVr69euJt/cWddqUKTPp3Pl7UBQcJ47HYe7PKJaWBC3VENXAw4zVitTOpGBs3749lpaWrFmzBr1ez9y5c2nevDmdOnVKqvpSlFGjxsrlGkIksoiICLp168Qvv+wHwNLSkrlzF9GsWUtDKE4Yh8O8WQBodDosXl3gL0RSMSkY//nnH9q0aUObNm2Mpv/vf//j66+/TtTChBCpX0hICB06tObkyd8AQy/FpUtXU6/ed4ZQ/HEsDgvmqPMHT/2ZiE5dzVKrSDtMOsYY15ZhSEgI/fv3T7SChBBpQ0DAS5o3b6SGooODAxs2bHsdiuPHGIfi9NkSiiJZvHeL8f79+9SvXx+dToeiKBQtWjTWPKVLl06S4oQQqZOPjw+NGn2nto1ycXFl48ZtlCtXwRCKP4zCYfF8df7gGXOIaJ82DtkI83tvMObOnZtt27YRFBREt27dWLZsmdHztra2FCpUKMkKTEkaN/6OFy+ekylTZnbs2GfucoT4JD169JDmzRtx8+ZNANzcMrF16y6KF//cEIpjR+KwZIE6f/DP84ho28Fc5Yo0KEHHGGO2Evft20fOnDmTtKCU7PbtWzx9+oSgoCBzlyLEJ+n27Zt4enrw+PEjALJnz4G3927y5y8IgOXtW9ivWQGAotEQ8vM8Itq0N1u9Im0y6eQbJycn5s6di4+PD3q9HoDo6Gj+++8/9uzZkyQFpiSOjo44OTnj6Oho7lKE+ORcvvwvzZs3wtf3BQD58uXH23sPOXK8/rKtK1CQwLWbcenYmpBJ04lo3c5c5Yo0zKRgHDFiBPfu3SNDhgyEhoaSNWtWfv/991hnqaZWp09fkMs1hPgA586doXXrZgQGBgBQokQJNm3aQaZMmWPNG12tBn5n/pEmw8JsTArGc+fOceDAAXx8fFi6dCnz589n9+7d7Nsnx9uEEHE7ceI4HTq0Vnspli1bjsOHD6HTWaHoFWyOHSGq5jdGy0goCnMy6XINKysrsmTJQp48ebhx4wYA9evX5+rVq0lSnBDi03bw4H7atGmmhmKVKtXYtm036dOnB70ep2EDcWnlicPP08xcqRCvmRSM2bNn5/Lly6RLl47Q0FD8/f0JCwsjIiLCpBf18/OjV69elC1blgoVKjBx4kS0Wm2c8549e5ZmzZpRqlQpqlatypIlS0x6LSGEeWzbtpnOndsSFRUFQN269dmwYStOTk6g1+M4dCD2qw0n2jhMm4TlNfmCLVIGk4KxdevWtGvXjidPnvDdd9/RoUMHOnToQLly5Ux6US8vLxwcHDh58iTe3t6cPn2a1atXx5rv9u3bdOvWjdatW/PXX3+xZMkSVq5cyS+//GLS6yWW5cuXMGfOTFaskHAW4l1WrlxG797d1F6Knp4tWLFiraGXol4PPXpgv2YlAIqFBcHzl6Ar+pk5SxZCpVEU004nuXTpEkWKFEGj0bBq1SpCQ0Pp0qUL6dKlS9Dy9+/f55tvvuF///sfWV4dRzhw4ADTp0/n+PHjRvP+9NNPBAQEMHPmTHXa3bt3cXJyIlOmTKaUja9v8AefOKPRgJubM9myZZe2U2+JGZuPGd/UKC2Py9u9FDt27MKUKa96Ker1OA/uj936NcCrUFy4jMgmzcxUbcqRlj8z75MYYxOzjoQw6eQbMJxNFqNbN0MvtEOHDlGnTp0ELX/z5k1cXV3VUATInz8/T54Yrg98M2AvXbpEpUqVGDhwIKdOnSJDhgx07Njxg7p5xNHK7aOW/Zj1pSYx4yDjYSwtjouiKPz00zjmvbrhN0C/fgMZPfoHQy9FvR6ngX2x27jOML+FBcGLlxPV2JM0NEzxSoufmYRKjLExZdkEBWNQUBATJ07k6tWrVKtWjQEDBmBhYUFYWBgTJkxg586dXLt2LUEvGBoair29vdG0mMdhYWFGwRgYGMjatWuZNWsW06ZN4+LFi3Tv3h0XFxfq1q2b0PcIQMaMCfum8C6rV68iIiICOzu7BH/zSCsSY3xTo7QyLnq9nt69e7N48WJ12uTJkxk+fHjMDNC1K7wKRSwt0WzYQLo00rLOFGnlM/MhkmtsEhSMP/zwA5cvX6ZWrVrs37+fzJkzU6dOHTp37syLFy+YM2fO+1fyioODA+Hh4UbTYh6/feG8jY0NNWvWpFq1agCUK1cODw8PDh48aHIw+vl93CZ4xozOlClTUV2Hr2/wh60slYkZm48Z39QoLY1LdHQ0ffv2YPv2bQBoNBqmTp1Jp05d1d8Th3GjcVi1CgDF0hLNpk341aiLIr9HqrT0mTFVYoxNzDoSIkHB+Oeff7J+/Xry589P/fr1+fHHH9m0aRMZMmRgxYoVRrtF36dgwYIEBATg6+uLm5sbYDjJxt3dHWdn46Lz58+vntEWI+Zm5qZSFD76w5YY60itZGziltrHxdBLsSO//HIAMO6l+Ob7jmjTAdttW7Dw8yV46SrSNWuGIsfS4pTaPzMfI7nGJkFnpUZERJA/f34AihcvzuXLlylatCirV682KRQB8uTJQ5kyZZg0aRIhISE8fPiQhQsX4unpGWveli1b8uuvv7J7924UReHcuXPs3bsXDw/p3i2EuYWEhNCmTTM1FG1sbFi5cr2hwfBbdAUKErhzP0Er1xPVsFEyVyqEaRIUjJq3jlra2NgwZswYrKxMPncHgLlz56LVaqlZsybNmzenSpUq9OrVC4BSpUqp912tWLEiCxcuZO3atZQpU4YRI0YwbNgwatas+UGv+7GePXvGkyeP8fF5ZpbXFyKlePnSn2bNGnLy5AkAHBwc2bjRm2+/rW+YQaeDt65N1hUsRFTM80KkYB+UbDY2Nri6un7wi7q5uTF37tw4n7t48aLR46pVq1K1atUPfq3E9M031eRyDZHm+fj40Lx5I65duwIYeilu2uRN2bLlDTNotTj36Q6KnuAFy+ADv0ALYS4J+sQqisLTp0/VY3t6vd7oMUC2bNmSpkIhRIrx8OEDPD0bcvfuHQAyZcrM1q27KFasuGEGrRbnPt2w2+FteGxjS/C8xfGsTYiUKUHBGB4eTo0aNdTHiqKojxVFQaPRJPhyjU9Z7dp1ePnyJa6u6c1dihDJ7tatmzRr9rqXYo4cOfH23k2+fAUMM2i1OPfqit2uHQAo1tZEfifnA4hPT4KC8ddff03qOj4JM2fOkbPFRJr077+XaNGiEb6+vgDkz1+Abdt2v+6lqNXi3LMrdrtfhaKNDUEr1xH1zbfmKlmID5agYMyePXtS1yGESKHOnj1D69aeBAUFAlCs2Ods2bKTzJlf9VKMjjaE4p6dwKtQXLWeqNqmXWssREph0k3EhRBpy2+/HaN5cw81FMuWLc/OnfuMQjFd987Gobh6g4Si+KRJMAoh4rR//17atm1OWFgYAF9/XZ2tW3e9PsYeHU26bp2w3bcbAMXWlsC1m4iqlbD7JguRUsl51Cbo3Lk9fn6+ZMzoxooVa81djhBJZsuWjXh59VbbRn377XcsWbLS0DbqFU1YKBYP7gOvQnHNJqJr1DJLvUIkpg/aYgwMDOTy5cvo9fpYt2xLzc6fP8sff/zO+fNnzV2KEElmxYql9O3bQw3FZs1avu6l+AbFxZVA791El6tA4NrNEooi1TApGENDQxk0aBAVKlSgbdu23Lt3j9q1a3Pnzp2kqk8IkUwURWH27BmMGDFYnda58/fMm7c43rtcKekzELDvMNHVzXM3KiGSgknBOG3aNMLCwjh48CDW1tbkzJmT6tWrM3HixKSqL0U5d+4fHj58wdmz/5i7FCESlaGX4g9MmvSjOs3LazCTJ88wNBgGiIzE8acf0Lw6EUclDQRFKmPSMcbjx4+zd+9eXFxc0Gg0WFtbM3z4cL7++uukqi9FsbW1lesYRaqj0+kYNmwQa9euVKeNGfMjfft6vZ4pMpJ0ndtie+QQ1qf+R+DWXSjpXJK/WCGSgUlbjHq9HhsbGwCj28PFTBNCfFqio6Pp3bubGooajYZp02YZh2JEBOk6tcH2yCEArK5fw/K/G2aoVojkYVIwfvnll/z444+Eh4erHTdmz55N+fLlk6Q4IUTSiYiIoHPntuzYYWgwbGlpycKFy+jYscubMxlC8ehhABQHBwI3eqMtK7/zIvUyaVfqiBEj6NmzJ+XKlUOn01GqVCny5MnD4sVp4ybB27dvJSwsHHt7e5o2bW7ucoT4YCEhwbRv34rff/8fYDhMsHz5WurUeeMWbhERuHRsjc2xowAoDo4EbvImuuJX5ihZiGSjURTTjpopisK///7L48ePcXd3p0SJElhaWiZVfYnG9yO6hWs04ObmTLZs2aXt1FtixuZjxjc1Ssnj8vKlP61be3LhwnnA0Etx3brNVKnyRnu38HBcOrTC5rdjwKtQ3Lyd6C8rffTrp+SxMScZl/glxtjErCMhTNpi7NGjB56enlSvXp0SJUp8UHFCCPPx8Xn2qpfiVQBcXV3ZtGk7ZcqUez1TeDgu7Vtic+I4AHpHJwI3bUf7ZUVzlCxEsjMpGPPnz8+PP/7IuHHj8PDwwNPTk7x58yZVbSnODz/8qO5KFeJT8+DBfTw9G3Lv3l0gjl6Kr9gvXWgcipt3oK3wZbLXK4S5mLwrVa/Xc/LkSXbt2sWxY8coXrw4zZo1o1GjRklUYuJIjE1w2cURm4xN3FLauNy8+R/Nmnnw5MljII5eim96dQ9U6xPHDaFYvkKi1pLSxialkHGJX3LvSjU5GN904sQJxo8fz9OnT1N8o2IJxqQhYxO3lDQu//77Dy1aNFZ7KRYoUJBt23aTPXuO+BeKjsby9i10RYomej0paWxSEhmX+KXoY4wADx48YNeuXezZs4fw8HA8PDxo1qyZyUUKIZLemTN/0qZNM7VtVPHiJdiyZSeZMmV6PVNoKBZ+vuhz5X49zdo6SUJRiE+BScHYsmVL/v33X7788kuGDBlCzZo1472HYmoUGRmpfluxtbU1bzFCvMfx47/SqVMbtW1UuXIV2LhxGy4urq9nCg3FpU0zLO/eIWDnfvT58punWCFSEJNSrVKlSvz8889ky5YtqepJ0cqVKymXa4hPwr59e+jRo7Pa/aZq1eqsXr0RR0fH1zOFhODSphk2p08B4NKpLS+PnwILadMq0rYEBeOzZ89wd3fH09MTgCdPnsSaJ62GpRApzebNG/Dy6o1erwegXr0GLFmy0mgvhyYkGJdWnlifOQ2A3sWV4DkLJBSFIIHBWK9ePf766y9q1KiBRqNBURT1lnAx/5/ST75JDGXLllcbFQuREq1YsYQRI4aoj5s3b8Xs2QuMDnloQoJxadkU67N/AqB3dSVw2260JUsle71CpEQJCsb9+/cD8OuvvyZpMSndypVr5WwxkSLF9FKcPPkndVqXLt2YOHHa67ZRgCY4yBCK584Ar0LRew/aEl8kd8lCpFgJ2m+SNWtWACZMmED27Nlj/Rs2bFiSFimEiJ+iKIwfP8YoFAcMGMykSdNjh2KLJq9DMX16ArfvlVAU4i3v3WJ89OgRu3btAuD3339n/vz5Rs+HhIRw44a0oBHCHHQ6HUOHDmTdulXqtLFjf6JPn/7GM0ZE4NK8MdYXzgGGUAzw3ovuc7m1oxBve28wZsuWjZs3b+Lv749Op+PMmTNGz9va2vLDDz8kWYFCiLhFR0fTp083du7cDhh6KU6fPpv27TvFntnWlqiq1bC+cA59hgyGUCz+eTJXLMSn4b3BaGFhwZw5cwAYPXo0EyZMSPKiUqpBg/rz8uVLXF3TM3PmHHOXI9Kw8PBwunZtz5GY5sFWVsyfv4QmTeK52YZGQ9iw0WBnT2StOhKKQryDSZdr9OrVK85LNSBtXK5x5Mgh9TpGIcwlJCSYdu1acurUScCw12bFirV88823xjMqiuE+WDE0GsK8BidjpUJ8mj74co03pZXLNYQwN39/P1q1asrFi38B4OjoxLp1m6lc+Wuj+TQBL0nXoTVhQ0cS/VUVc5QqxCdLLtcwweHDv6HT6T6Jxswi9Ymrl+LmzTsoXbqs0Xyal/64NGuE9aW/sW7TjMBN24mu+JU5Shbik5SgYIy5XCN79uz4+vri5uZGVFQU3t7eZMiQgbp16yZpkSmFu7u7XMcozOLtXoqZM2dh27bdFC36mdF8mpf+uHh6YP3vPwAoDo7oM2RM9nqF+JSZdK/Ubdu2MXHiRP7++2+mT5/OgQMH0Gg03Llzh169eiVVjUKkaTdv/oenZ0OePjUc38+ZMxfbtu0m31s3/Nb4+xlC8fIlAPSZMhOwYx+6wkWSvWYhPmUm3Rhx/fr1LFiwAJ1Ox44dO5g3bx6bNm1i69atSVWfEGnapUt/07BhHTUUCxYsxN69h2KHop8frk0bqqGoy5yFgF0HJBSF+AAmbTE+ffqUr776ir/++gsrKytKly4NQFBQUJIUl9IcP/4rERGR2NraUr16TXOXI1K5P/88TZs2zQgONvx+ff55SbZs2Ymbm/G9eg2h2ACrq5cBQygG7tyPrmChZK9ZiNTApGB0cXHh/v37HDp0iPLlywPw559/Gjc9TcX69+8tbadEsjh27CidOrUhPDwcgPLlv2TDhq3GvRQBja+vIRSvXQFAl8XdEIoFCiZ3yUKkGiYFY6dOnWjQoAEA69at48KFC3Tv3l3ufCNEItq7dzc9enQmOjoagOrVa7Jq1QYcHBxizWv91zksbxguldK5ZyVw5z50+SUUhfgYJgVj69atqVKlClZWVmTNmhV/f382bNhA8eLFk6q+FKVfvwGEhITg5ORk7lJEKvV2L8XvvvNg0aLlRr0U3xT1zbcEL1yG44RxBHrvRpevQHKWK0SqZFIwAlhbW7N3714eP35MpkyZ+O6775KirhSpa9fucrmGSDLLli1i1KjXnWpatmzDzz/PM+qlGJfIJs2I/PY7sLdP6hKFSBNMOiv133//pX79+hw+fJjAwECOHTuGh4cHFy5cSKr6hEj1FEVh5sypRqH4/fc9YjUYBtA8f47tru2xVyKhKESiMWmLcfr06fTv35/27dur09asWcOMGTPYtGlTohcnRGqnKArjxo1m0aJ56rSBA4cybNgoNG/e5xTQ+Pjg2qQ+Vjf/IzgkhIi2HZK7XCHSBJO2GG/cuEHr1q2NprVu3Zr//vsvUYsSIi3Q6XQMHtzfKBTHjZvI8OGjY4Wihc8zNRQBHGbPgLCwZK1XiLTCpGC0t7fn6dOnRtOePn2Ki4tLohaVUlWsWIZ8+bJTqVIZc5ciPnHR0dH07NmFdetWA4Yb8c+cOZdevfrGmtfi2VNcGtVTQ1GXMxcB2/dCHGepCiE+nknBWK9ePfr27cvJkye5e/cuJ06coF+/ftSrVy+p6ktRQkNDCQkJJjQ01NyliE9YeHg4HTu2ZteuHYChl+LixSto165jrHktnj3FpXF9rG7fAl6F4s796HPnScaKhUhbTDrG2L9/f/z9/enVqxfR0dHY2trStGlT+vaN/S03NcqfvwDp0qUjU6bM5i5FfKKCg4No164lf/zxOwB2dnasWLGW2rVj34jf4ukTQyjeuQ2ALlduQyjmzJWsNQuR1miUt5srJkBUVBSBgYG4ubnFOhaSUvn6Bn/wpRYaDbi5OX/UOlIrGZu4xTUu/v5+tGzZhL//vggYeimuX7+Fr+Lol2jx5LEhFO/eAUCXKw8Bu/ajz5Ez2d5DUpHPTNxkXOKXGGMTs46ESPAW4/z587ly5QqVK1emTZs2aeY2cEIkhmfPntKsmQc3bhhuJZg+fXo2b95BqVJxHK9WFNJ1aP06FHPnMWwppoJQFOJTkKBjjNOmTWPjxo1YW1szd+5cli5dmtR1CZFq3L9/jwYN6qihmCWLO7t2HYw7FAE0GkKmzEDvnA5dnrwE7DogoShEMkrQFuO+fftYs2YNBQsW5MyZM0yYMIFu3boldW1CfPJu3LiOp6cHz54ZzubOlSs327btJm/efO9cTlumHIHbdqF3z4o+W/bkKFUI8UqCgjE4OJiCBQ03Ji5Tpgw+Pj5JWlRKNXHijwQGBuDi4srIkWPNXY5I4S5cuEDDhnXx9/cHoFChwmzbtpusWbPFmlfj54eSIYPhQMgr2tJlk61WIcRrCdqVamHxerb33bcxNduyZSOrVi1ny5aN5i5FpHCnT/9BjRo11FAsUeILdu06GGcoWjy4T/o61XAcMxw560II80tQMH7AiatCpFnHjh2hRYvGagPvChUqsmPH3lgNhsEQiq6N62P54D4OSxdhP292MlcrhHhbgjb/tFotu3btUh9HR0cbPQZo1KhRIpaVMm3evJ3o6GisrKzNXYpIofbs2UnPnl3VXoo1atRi5cr1cfZStLh/zxCKjx4CoC1QkMgWrZK1XiFEbAm6jrFGjRrvXolGw6+//ppoRSUFuY4xacjYvLZx4zoGDuyr9lL09PRkzpzFWFvbxJrX4t5dQyg+fgSAtmAhAnbsR8mSJVlrNgf5zMRNxiV+KfI6xmPHjn1YJUKkEUuWLGDMmBHq41at2rJ27SoCAsJj/SJb3L1jCMUnjwHQFipsCMXMckclIVICk+6VKoQwpigK06dPNgrF7t17MXv2/DhPVLO4c9s4FAsXkVAUIoVJu6eYfoB//rlIZGQUNjY2lCxZytzlCDNTFIWxY0eyZMkCddrgwcMZMmQEFhaxb5WohuLTJwBoixQlYPs+FLmLlBApigSjCdq1a8XTp0/ImjUb//xz3dzlCDOK6aW4YcNaddqPP06iR48+8S9kaws2huON2qLFCNi+FyWOM1WFEOYlu1KFMFFUVBQ9enRRQ1Gj0TBr1vx3hyKgz56DgJ37iaxbX0JRiBTM5GCMioriyJEjrF69mvDwcK5fN33Lyc/Pj169elG2bFkqVKjAxIkT0Wq171zmv//+o2TJkpw5c8bk10ss7dp1oHv33rRt28FsNQjzCgsLo0OHVuze/bqX4tKlq2jTpn2CltfnyEnQ2k0SikKkYCbtSn3w4AGdO3cmOjqaoKAgqlatStOmTZk/fz7Vq1dP8Hq8vLzIkiULJ0+exNfXl549e7J69Wq6du0a5/zh4eEMGjSIiIgIU8pNdEOGjJDTqNOw4OAg2rZtwenTpwBDL8WVK9dRq1aduBe4dg2ncT8RPGMO2NklY6VCiI9h0hbjxIkTadKkCb/99htWVlbkzZuXCRMmMHfu3ASv4/79+5w9e5YhQ4Zgb29Pzpw56dWrFxs2bIh3mfHjx1OrVi1TShUiUfn5+dG0aQM1FJ2cnNm8eUe8oWh54zpUr47d1k24dGwNZv5SJ4RIOJO2GP/++2/mzZuHRqNRGxR7eHgwceLEBK/j5s2buLq6kuWNC5nz58/PkydPCAoKIl26dEbz79q1i/v37zNx4kQWLlxoSrlGPqafcsyyn0hP5mSVFsbm6dMnNGvWyKiX4tatO/nii9Jxzm954zoujevDixcAWLx4gUVkOIq9bDVC2vjMfAgZl/glxtiYsqxJwejs7Iyvry/Zsr2+EfKLFy9wcXFJ8DpCQ0Oxt7c3mhbzOCwszCgYb9++zaxZs9i0aROWlpamlBpLxowJu+NBUq8jtUqtY3Pnzh08PL7l7t27AGTNmpUjR45QrFixuBe4cgXeCEXKlMHq8GEyZsiQTBV/OlLrZ+ZjybjEL7nGxqRgbNCgAX369GHQoEHo9XouXbrE9OnTqV+/foLX4eDgQHh4uNG0mMeOjo7qtMjISAYMGMDIkSONgvhD+fl93K2EMmZ05ssvK+Lj40PmzFnYv//wR9eUGsSMzceMb0p1/fo1PD098PF5BkDu3Hnw9t5Nliy58PUNjjW/5bWruDT5DgtfX8OEsmXx37wdvd4a4pg/rUrNn5mPIeMSv8QYm5h1JIRJwdirVy8iIiLo06cP4eHhtGvXDk9PT/r0efdp6m8qWLAgAQEB+Pr6qt0Gbt++jbu7O87Or4v+999/uXfvHqNGjWLUqFHq9B49euDh4cG4ceNMKR1F+fiOPg8ePODp0ydERkbKB/ctiTG+Kcnff/9Fy5ZN1LZRhQsXYevWXWTNmi3O92l59QouTb/Dws8PgOhSpbE+cgS91jJVjUtiSm2fmcQi4xK/5BqbBN1EPC7+/v6kT59ePdZoitatW+Pu7s6PP/7Iy5cv6dmzJ3Xq1KFv377vXK5w4cKsXbuWChUqmPyaiXHz2WLFiuPj84zMmd05ceL0h60slUmNNz4+ffoUbdo0JyTEsJVXsmQpNm/eQcaMGeOc3/LKZVw9G7wOxdJlCNq6k4z5c6aqcUksqfEzkxhkXOKXIm8iHuPtVlNvMqXt1Ny5c/nxxx+pWbMmFhYWNGrUiF69egFQqlQpxo8fT8OGDU0pLVmcOHFaPrCp3NGjh+jcuZ16aVDFil+xfv0WnJ3TxbuM48ypr0OxTFkCt+wEE467CyFSFpO2GN9uPxUYGEh4eDhlypRh3bp1iV5cYpK2U0kjNY3N7t076Nmzq3qziZo1a7Nixbo4eykaCQ3FpbUnmqgoArfsQEnnkqrGJbHJ2MRNxiV+KXqL8e32U4qisGzZMgICAkxZjRApzoYNaxk0qJ/aS7Fhw8YsXLgMG5vYvRRjcXQkcMM2NIoe5R1blkKIT8NH3StVo9HQpUsXdu/enVj1CJHsFi+ez4ABfdRQbNOmPUuWrIw3FC3/vYTm+XPjiU5OEopCpBIf3V3j7t27H3QCzqdo0aL5BAUF4eycjp49E34mrkiZYnopzpgxRZ3WvXtvfvxxUryfaat/LuLi6YE+SxbpoyhEKmVSMLZr187oD0Z0dDQ3btxIkSfKJIVFi+arbackGD9ter2eH34YyZIlr++mNGTICAYPHh5/KF68gEvzxlgEBmARGIDj5B8JmTU/uUoWQiQTk4Lx7cskLCws6Nixo9zHVHxSdDodAwf2ZdOm9eq0n36aTPfuveNdxuqv84ZQDAoEIOrLSoT8NCXe+YUQny6TgvHly5cMGDAAJyenpKonRVu0aBkREZHY2tqauxTxgaKioujZsyt79+4CDF/ufv55Hq1bt4t3GasL5wyhGBxkWEelygSu3wpp9PdAiNTOpGDcu3cvI0eOTKpaUryvvqoip1F/wsLCwujcuS3Hjh0FwNramkWLltOwYeN4l7E6fxaXFk1eh+JXVQyh+MbtC4UQqYtJwdi0aVPGjx9PkyZNyJQpk9GxmMS4n6kQSSUoKJC2bVvw559/AIYb169atZ4aNWrHu4zVuTOGUHx1B5yoyl8TuG6LhKIQqZxJwbhq1SoAtm7dqoaioihoNBquXbuW+NUJkQj8/Pxo0aIxly79DRh6KW7cuI0vv6wU7zKWt28ah2KVqoZQfN/F/kKIT16CgvHChQuUKVOGX3/9NanrSdHu37+HVqvD0tKS3LnzmLsckQCGXooe/PffDQAyZMjAli07KVmy1DuX0+XNT6RHY+w3rCXq6+oErt0koShEGpGgYPz+++/566+/yJ49e1LXk6I1aFBXvVzjn3+um7sc8R737t3F09ODBw/uAeDunpVt23ZTuHCR9y9sYUHIzLnoihQlvH1neKuHqBAi9UpQMH5gAw4hzOb69Ws0a/Z2L8U9797Sj4qCN+92Y2FB+Dsu4RBCpE4JCsa0cmeb96lX7zsCAgJwdXU1dyniHS5evEDLlk14+fIlYOiluG3bbtzds8a7jPXpUzj37kbg2s3oin+eXKUKIVKgBAVjeHg4NWvWfOc8aeH445QpM+RyjRTujz9+p23bFmovxS++KMWmTfH3UgSw/uN3Q3eMsDBcPRsQsP8IuvwFk6tkIUQKk6BgtLa2pk8fuQWaSNmOHPmFLl3aq70UK1WqzLp1m9/ZS9H69//h0rY5mrAwAKJLl0WXPWey1CuESJkSFIxWVlY0bhz/RdBCmNuuXdvp1et7tZdi7dp1WL58LfbvOGnG+uQJQyiGhwMQWbsOQSvXg9zZSIg0LUFtp+TkG5GSrVu3mu7dO6uh2KhRE1at2vDuUPzfb8ah+E1dCUUhBJDALca00j3jfdq2bYGvry9ubm6sW7fF3OUIYOHCeYwbN0p93LZtB6ZPn42lpWW8y1ifOI5LuxZoXu1yjazzLUHL10ooCiGABAbj+PHjk7qOT8KlS/+o1zEK81IUhalTJ/Lzz9PUaT179mXcuAnvPIva+vivuHRo9ToU69YnaPka48s0hBBp2kc3KhYiuen1esaMGc6yZYvVacOGjWLgwKHvvbTI8vbN16H47XcELVstoSiEMCLBaIJLl67L5RpmptVqGTiwL5s3b1CnTZgwhW7deiVo+YiuPdDodFj/eZqgpavA2jqpShVCfKIkGMUnIzIykp49u7Jv327A0Etx1qz5tGrV1qT1hHfvTfj3PcEiQeeeCSHSGAlG8UkICwujU6c2HD9uuJGEtbU1ixevoEGDRu9czuboIYjWEvVtfeMnJBSFEPGQYBQpXlBQIG3aNOfMmdNAwnopAtgcPki6zu1AUQhauZ6oOt8mR7lCiE+cBKMJNm3aQGhoKI6OjrRs2cbc5aQJvr6+tGjRmH///QcAZ+d0bNiwjS+/rPjO5WwOHSRd57ZooqMBsN2zU4JRCJEgEowmmDz5J/VyDQnGpPfkyWOaNfPg5s3/AMiYMSNbtuykRIkv3rmczS8HSNelnRqKEU08CZ6zMKnLFUKkEhKMIkW6c+c2zZp58PDhAwCyZs3Gtm27KVSo8DuXszm4n3Rd278Ris0Inr8ErOSjLoRIGPlrYYJJk6YRFhaGvb10ck9K165dpVkzD54/9wEgT568eHvvIVeu3O9czmb/XtJ93wHNq1vDRXi2IHjeYnjHXXCEEOJtEowm+O67hnIdYxL766/ztGrVVO2lWKRIUbZt202WLO7vXM5m3x7Sdev4OhSbtSR47iIJRSGEyeScdZFinDp1kqZNG6qhWKpUaXbtOvDeUNT4+eHct8frUGzeSkJRCPHBJBhFinD48EFatmxCaGgIYOiluH37XjJkiL/BcAwlY0aCl69GsbEhomUbw4k2EopCiA8ku1JNEBISjF6voNFocHJyNnc5qcbOnd707t3NpF6Kb4uq+Q0vDx5DV6y4XLwvhPgo8hfEBJUqlSN//hx89VU5c5eSaqxdu4oePbqoodi4cVNWr9743lC0fHUJx5t0n5eQUBRCfDT5KyLMZsGCuQwe3F9thN2uXScWLlyO9Xtu7G27Yxvpv66A/dxZyVGmECKNkV2pJqhU6Sv8/PwSdNxLxE9RFKZM+YlZs2ao03r37s/YsT++t22U7fatOPfuhkavx2nCD2iLf050jVpJXbIQIg2RYDTB4sUr5HKNj6TX6xk9ehjLly9Rp40YMQYvr8HvD8Vtmw1nn+r1AIS370x0tRpJWq8QIu2RYBTJRqvVMmBAH7Zs2ahOmzRpGl279njvsrZbNxlC8dU3k/COXQiZMlOOKQohEp0Eo0gWkZGR9OjRhf379wCGXoqzZy9I0D1nbTdvwLl/r9eh2KmrIRTfs4UphBAfQoJRJLnQ0FA6dWrDb78dAwy9FJcsWcV33zV877KxQrFLN0ImTZdQFEIkGQlGE/Tt25OXL/1Jnz4Dc+cuMnc5n4TAwADatGnO2bN/AoZeiqtXb6R69ZrvXdZ222ajUAzr2p3QidMkFIUQSUqC0QQnThxX206J93vx4gUtWjTm8uVLgKGX4saN3lSo8GWClteW+AIlY0Y0vr6EdetJ6E9TJBSFEElOglEkiSdPHuPp2ZBbt24Chl6KW7fu4vPPSyZ4HbrCRQjYsR/b3TsIGzpSQlEIkSwkGE3w22+n0OkULCzkD/S7xNVL0dt7DwULFnr/wopiFIC6IkUJKzIqqUoVQohY5Fx3E2TIkBE3Nze5wP8drl69QoMGddRQzJs3H3v3HkpQKNqtXoFz3x6g0yV1mUIIES/ZYhSJ5sKFc7Rq1ZSAgAAAihb9jK1bd5MlS5b3Lmu3ajnOwwaqj4PnLpJrFIUQZiF/eUSi+P33/9G0aUM1FEuXLvOql2ICQnHFUqNQ1Gdxl+OJQgizkS1GExw+fJDw8Ajs7Oz45ptvzV1OirF3715atmxGZGQkAJUrf83atZsS1JrLbsUSnEcMUR+H9RtI6KgfJBiFEGYjwWiCwYMHqJdrSDAabN++ld69u6N7dVywTp1vWbZsDXZ2du9d1m75YpxHDlUfh3oNJmzEGAlFIYRZya5U8cHWrFlJz57fq6HYpIknK1euT1Ao2i9daByKA4dIKAohUgTZYjTB4MHDCAkJxdHR0dylmN28ebP56aex6uMOHTozZcpMLC0t37us/ZIFOI0ZoT4OHTiUsGGjJBSFECmCBKMJ2rfvlObbTimKwuTJPzF79uteikOHDmXw4FFAAoItMhK7za+7a4QOHm64eF8IIVII2ZUqEkyv1zNy5BCjUBw5cixTpkx5by9Fla0tAd570BYtRuiQERKKQogUR7YYRYJotVr69+/Ftm2b1WmTJ0+na9fuCQ/FV5SMGXl58FdwcEjsMoUQ4qPJFqN4r8jISLp0aa+GoqWlJfPmLaZLl+4JWt526yY0wUHGEyUUhRAplASjCcqWLUGOHG6ULVvC3KUkm9DQUNq0ac7Bg/sAsLGxYfnytbRo0TpBy9vPmUm6Pt1xadkUTUhwUpYqhBCJQoLRBFFRUURFRREdHWXuUpJFYGAAzZp58L//HQfAwcGB9eu3Ur9+gwQt7zB7Bk4TxwNgfe4MNgf2JVmtQgiRWOQYowmKFCmKm1sm3NzczF1Kknvx4gXNmzfiypV/AUiXzoWNG70pX75CgpZ3+HkajlMmqI9DRo8nsnmrJKlVCCESkwSjCbZu3ZkmLtd4/PgRnp4NuX37FgBubm5s2bKLzz9P2C5khxlTcJw2SX0cMuZHwvt6JUWpQgiR6CQYhZE7d27h6enBo0cPAciWLTve3nsoUKBggpZ3mD4Zx+mT1cchP0wgvHe/JKlVCCGSggSjUF25cpnmzRvx4sVzwNBL0dt7Dzlz5nr/woqCw9RJOM6cqk4KGT+J8J59kqpcIYRIEmY5+cbPz49evXpRtmxZKlSowMSJE9FqtXHOu2nTJurUqUOpUqWoU6cOGzZsSOZq04bz58/SqFE9NRSLFi3Gnj2HEhaKgM1Ob+NQ/FFCUQjxaTLLFqOXlxdZsmTh5MmT+Pr60rNnT1avXk3Xrl2N5jt69Cg///wzy5Yto2TJkvz9999069YNNzc36tSpk+x1jxs3moCAAFxcXBk3bsL7F/hEnDx5gnbtWhIWFgpAmTJl2bjRm/TpMyR4HVHfeRBZxxvbQwcJmTCF8G69kqpcIYRIUsm+xXj//n3Onj3LkCFDsLe3J2fOnPTq1SvOLUEfHx++//57vvjiCzQaDaVKlaJChQqcO3cuucsGYMcObzZsWMvOnd5mef2kcPDgflq39lRDsUqVqmzbtsekUATAxoag5WsJXLtZQlEI8UlL9i3Gmzdv4urqatTZPX/+/Dx58oSgoCDSpUunTm/Tpo3Rsn5+fpw7d44RI0Zgqo9p3BDXsqmhEYS39xb69Omhto2qW7cey5atTlDbKBQFTcBLyGAIUI0GsLMl+tt6CbmVeKoX8/lIDZ+TxCZjEzcZl/glxtiYsmyyB2NoaCj29vZG02Ieh4WFGQXjm168eEH37t0pXrw43333ncmvmzHj+7vJv8/x48fQarVYWVnh5vbx6zOnRYsW0bt3b5RX15+0adOGVatWYW1t/f6FFQVGjIBNm+C33yCjc6KMb2ok4xI/GZu4ybjEL7nGJtmD0cHBgfDwcKNpMY/j63P4999/079/f8qWLcvkyZOxsjK9bD+/4A++BlGjMfxA3Nyyqevw9f10b282Z87PTJgwTn3csWMXpk6dSWBgBBDx7oUVBYcfx+Iwfw4AumrVsbx6Bb8IfZq4xjOhYj4zH/O5S61kbOIm4xK/xBibmHUkRLIHY8GCBQkICMDX11e9g8zt27dxd3fH2Tl20d7e3kyYMIF+/frRuXPnD35dReGjP2yJsQ5zUhSFiRPHM3fuz+q0vn0HMHr0ODQazfvfm6LgOG40DovmqZPC+w3AydERJVx+mePyqX9mkpKMTdxkXOKXXGOT7Cff5MmThzJlyjBp0iRCQkJ4+PAhCxcuxNPTM9a8hw4dYty4ccybN++jQlEYeikOHz7IKBRHjx7HmDHjE9Y2SlFw/GGUUSgGz5hDRAf5uQghUhezXMc4d+5ctFotNWvWpHnz5lSpUoVevQxnMpYqVYo9e/YAMH/+fHQ6Hf369aNUqVLqv7Fjx5qjbM6dO8OpUyc5d+6MWV7/Q2m1Wvr06c6qVcvVaVOmzKRfv4EJW4Gi4Dh2BA6L56uTgn+eR0T7ToldqhBCmJ1GUdLGRruv78ftm3ZzcyZbtuw8ffqErFmz8c8/1xO3wCQSERFBt26d+OWX/YChl+LcuYto1qxlwlagKDiOHobDssWGhxoNIT/PI6JNe+D12HzM+KZGMi7xk7GJm4xL/BJjbGLWkRByS7hULCQkhA4dWnPy5G+AoZfi0qWrqVcvgWf1KgqOo4bisHyJ4aFGQ8is+US0bpck9QohREogwWiCzp2/Jzg4OM6ThFKagICXtG7djPPnzwKGs4HXrNlE1arVTVuRneFSGkWjIXjOQiJbtnnPAkII8WmTYDSBl9egT2IXx/Pnz2nevBFXr14GwMXFlY0bt1GuXMJ6Kao0GkLHjAeNBm3BQhKKQog0QYIxlXn06CGeng25c+c2AG5umdi6dRfFi3/+YSuMCUchhEgjzHJWqkgat2/fpEGDOmooZs+eg717f0l4KOr1OI4ZjtWfp5OwSiGESNkkGFOJy5f/pUGDujx+/AiAfPnys3fvIfLnT1iDYfR6nIZ44bBkIS6tmmJ15s8krFYIIVIuCUYT1KpVlZIli1C7dlVzl2Lk3LkzNG5cH1/fFwB89llx9uw5RI4cORO2Ar0ep8H9sV+3GgBNeBiWTx8nUbVCCJGyyTFGEzx/7sPTp0/MXYaREyeO06FD6zd6KZZj0yZvXF3TJ2wFej1Og/phv2EtAIqlJcGLlhPZqGlSlSyEECmaBKMJMmfOYvRfczt4cD/ff9+BqKgoAKpUqcaaNRtxcnJK2Ar0epwG9MF+03rgVSguXkGkR5OkKlkIIVI8CUYTHD16IsVcrrFt22b69ev5Ri/F+ixduiphvRQBdDqcB/TBbrOhQbRiaUnQkpVENWycVCULIcQnQY4xfoJWrlxG797d1FD09GzBihVrTQtFr96vQ9HKiqClqyUUhRACCcZPzty5PzN8+CD1cceOXZg/f0nCGgy/YnXuLLZbNwFvhGIDj0SvVQghPkUSjJ8IRVH46acfjBoM9+8/iKlTf8bCwrQfo/bLigTPXYRia0vQsjVEfdcwkasVQohPlxxjNMHs2TPVe6X27z/o/QskEr1ez7Bhg1izZoU6bfTo8fTrN+CD1xnZojXRX1dDnzVbYpQohBCphgSjCVauXKa2nUquYIyOjqZv3x7s2LENAI1Gw5QpM+nUqWvCV6LVYn3mNNFfVTGaLKEohBCxya7UFCwiIoIuXdqpoWhpacmCBUtNDkXn3t/j0uQ7bF+dbCOEECJ+ssVoghUr1hAZGYWNjU2Sv5ahl2IrTp48AYCtrS3Llq2hbt16CV+JVotzz67Y7d4BgPPQAURXr4k+i3tSlCyEEKmCBKMJypWrkCzXMb586U/r1p5cuHAeAAcHR9au3cTXX1dL+Eqiow2huGcnAIqNDUEr1kooCiHEe0gwpjA+Pj40b96Ia9euAIZeips2eVO2bPmEryQ6mnTdO2O7bzeA4ezT1RuIqvlNUpSsmj59EocPHwRAp9MRHR1tdG3ljBlz2feqplGjxiVpLQATJ4774Nd6+vQJzZo1ZNu2PWSN41jsgQN7WblyKd7eez+otoMH97F69XL8/HzJnTsvAwYMoXjxEnHOe+XKZXr06GQ0loUKFWHBgmUAPHhwn5kzp3D16hUcHBxo2rQ57dt3/qC6hBASjCnKw4cP8PRsyN27dwDIlCkzW7fuolix4glfSXQ06bp1wnb/HsAQioFrNhJdo3ZSlGxkyJCRDBkyEog/OGKCMS3766/zzJo1nRkz5vDZZ8XZvn0Lw4cPxNt7X5w3abh+/QpffFGaefOWxHpOq9UydOgAqlatzowZc7l79zZDhw4gR45c1KhRKznejhCpjpx8Y4Jbt25y/fo1bt26mSTrbtiwrhqKOXLkZO/eX0wLxago0n3f8a1Q3JQsoWiKly/9GT16GPXr18TDoy7bt29Rn/P0bMD06ZNo2LAOnTq1Rq/Xc+PGdfr27U7dutVp2bIxW7ZsQHm1T9vX9wWDBvXj229r0LhxPUaOHIKvr2+CXiswMICpUyfi4VGH+vVrMnSoFw8fPoiz5vv379GnTzdq165C+/Yt+O+/6+98j7VqVWHPnj1xPrdv325q1vyGEiW+wMrKihYt2uDi4sqvvx6Oc/5r165SpMhncT538eIF/Px86dq1B9bW1hQqVARPzxbs2LH1nfUJIeInwWiCJk0a8PXXFWjatEGirvfffy/RsGEdtZdi/vwF2LPnF/LlK2DSepy9emN7wLCFptjZEbh2M9EpcKvhr7/O4+HRhH37jtKjRx9mz56htswCuHr1Mhs2eDN37hL8/f3o378H1arVZN++I0yePJOdO73Z/eqEosWL55M5c2b27j3Mhg3bCA8PY/361Ql6rVGjhvLkySNWrtzAjh37yZUrD/379yQ0NMSoXq1Wy5Ah/cmXLz/79h1l3LhJ/O9/v73zPR49epKGDeO+ccLdu7fJnz+/0bQ8efLG+4Xr+vWr3LhxjZYtG9OgwTeMHTuC5899Xq3rDjlz5jK681GePPm4deu/d9YnhIifBKOZnT0b00vRsJVTvHgJ03opviG8Qxf0jk6GUFy3hejqNRO73ERRtmwFypWrgEajoVatOiiKwuPHr/s/VqtWE2dnZ5ydnTl06AC5c+eladPmWFlZkTdvPlq1aqduEdna2nLp0t8cPXqIsLAwZs6ch5fX4Pe+1uPHj/j777/w8hpCxoxu2Nra0bNnX3Q6LadPnzKq999//8HH5xm9e/fH1taWfPny07Jl2w9+/2FhYdjZ2RtNs7OzIzw8LNa8Op2OjBkzUb78lyxfvo5167ai0cCQIV7odDrCwkKxt49rXeEfXJ8QaZ0cYzRBkyaeBAQE4OLimijr++23Y3Ts2JqwMMMfxHLlKrBx47YPXr+2wpcEbtqOJjKC6KrVE6XGpODi4qL+f8yWjk6nVae5uWVS///p06fcuHGNunWrqdP0ekW9DZ6X1xDWrl3Jpk3rmDhxHAUKFMTLawglS5Z652u9fOkPQLZs2dXnLS0tyZzZnadPn1Ks2Ofq9BcvXuDq6oqt7evjf9mz5/jg929nZ09kZITRtIiIiDh/7paWlsyZs9BompfXUBo0qM39+3ext7cnIiL2uuztHT64PiHSOglGE4wbNyHRLtfYv38v3bt3UnspVq1andWrN+Lo6JjwlURHg5UVaDTqJO2XFROnQDPSvPF+MmfOTOnS5fj553nqtMDAAPXLxI0b1/HwaEqXLt15+fIlq1cvY9SoIezbd/Sdr+HubjjT9PHjR+TLZ9itqdPp8PF5hpubm9G8WbJkISDA8JoODobAefHC54PfX758+dVjyTHu3btLxYpfxZrXx+cZW7dupEuXHuprR0cbPjO2tnbky5efhw8foNVqsbKyerWuO+p7EkKYTnalmsGWLRvp2rW9Gor16jVg/fqtpoViRATpOrTCcfwYUkyTyCTwzTffcuXKJQ4fPohWq8XX15ehQwcwb94sANauXcmsWVMJDQ3B2dkZOzv7BG1xu7m5UbHiV8yZMwM/P18iIyNYtGgeer2Or966dV7x4iXImTM3s2dPJyIigkePHrLpVXPnD1G/fkMOH/6Fv/46j1arZevWjfj7+/P117G38l1dXTl69BBLly4kMjKSgIAAfv55KmXKlCd79hyUKlUWFxdXFi+eT2RkJDdv/oe39xa++066pQjxoSQYk9mKFUvp27eH2kuxWbOWLF++Bltb24SvJCKCdJ3aYHv0MA4L5+IwdWISVWt+7u5ZmTlzHrt376BBg2/o2LEVuXPnYdSoHwAYOnQUer1Cs2YefPttDa5evcxPP01J0LrHjPmRbNmy07lzWxo0qMPt2zeZM2cx6dK5GM1naWnJjBlz8PX1pUGD2gwa1JfKlau+c93vOiu1bNnyDBo0jBkzJvPtt9U5cuQQM2bMVV937dqVtG3bHDBsFc6cOZ979+7g4VGXli0b4+joyE8/TQbAysqKWbPmc+fOLTw86jB0qBeeni2oVy9xTxATIi3RKEoq3tx4g69v8AdvWGk04Obm/FHrUBSFOXNmMmnSj+q0zp2/Z9Kk6aa1jYqIwKVDK2yO/2pYr4MDgRu9ia5U+cMK+0iJMTapkYxL/GRs4ibjEr/EGJuYdSSEHGM0QfPmjfH19cXNzY0tW3YmeLmYXorz589Wp3l5DWbEiDFGx9PeKzzcEIq/HTOs18GRwE3eRMdxbEoIIcSHkWA0wfXr19S2Uwml0+kYNmwQa9euVKeNGfMjfft6mfbi4eG4tG+JzYnjAOgdnQjctD1VnGwjhBApiQSjCWxsbLCxscHaOmHdNeLqpTh16s907NjFtBcOC8OlfSts/vdGKG7egbbCl6atR3zy9Hq90aUtnyKNxnBJSXR0lOwyfIOMS/wSMjaWllamHZZ6BwlGE5w/fynBH9iIiAi+/74Dhw4ZbqptaWnJ/PlLaNq0uWkvGhaGS7sW2LxqP6V3cjaEYvkKpq1HfNIURSEoyJ/w8JD3z/wJ8Pe3QK/Xm7uMFEfGJX4JGRt7eyfSpctg2iGqOEgwJoGQkGDat2/F77//DzDcnWX58rXUqfOtyeuyCHiJ5b27wKtQ3LIDbTkJxbQmJhSdnNJjY2P70b/45mZpqUGnk82it8m4xO9dY6MoClFRkYSEvATAxSXjR72WBGMie/nSn1atmvLXXxcAcHR0Yt26zVSu/PUHrU+fLTsBO/fj0r4VwTNmozWl/ZRIFfR6nRqKTk7pzF1OorCyskCrlS2jt8m4xO99Y2NjY7jkLSTkJc7O6T9qt6oEYyLy8Xn2qpfiVcBwcfamTdspU6bcR61Xnys3L4/9Dom0/1x8WmKueY35xRdCxC3md0Sn02JhkbBzQeIiwWiCtWtXERISiqOjI+3bdzJ67sGD+3h6NuTeq92emTJlZtu23Xz2WTHTXiQkBIf5swgbMBTevOhfQjHN+9R3nwqR1BLrd0SC0QQzZkxVL9d4Mxhv3vyPZs08ePLE0CEiZ85cbNu22+T7VWpCgnFp5Yn1mdNYXblM0PK1xuEohBAiyclmyEf6999/8PCoq4ZigQIFX/VS/IBQbNkU6zOnAbD+8zSW9+8ldrlCCCHeQ7YYTTBjxizCwyOwszO0Hzpz5k/atGlGUFAgYLjZ9JYtO8mUKdO7VhOLJjjIEIrnzgCgd3Ul0HsPukKFE/cNCJHMPD0b4O/vh6WlJWA4e9DCwpJChQrRr98gChUqos776NFDVq9ezrlzZwgNDSFdOhe+/LIS7dt3wd3d3Wi9V69eZuPGtfzzz99ERkaSKVMm6tatT6tW7dQuI5+SEyeOcebMaUaOHGPuUkwWHh7OrFnT+P33/6HTaalcuSqDBg1Xu8G8bcaMyezfv8fo59SnzwA8PJoQGRnJwoVzOH78V8LDw8mbNx/du/dWz9OYOnUiX35ZkapVayTpe5ItxoTS6ajv6EQzrZb6jk789uthWrRopIZiuXIV2Llz34eFYosmr0MxfXoCt+9FW+KLxH4HQpjF4MEjOHLkJEeOnOTo0d/ZvHkHTk7OjBw5RL0u7fr1q3Tu3BYbGxsWLVrBkSMnWbhwORqNho4dW3H79i11fcePH6Vfvx588UVpNm/ewaFDv/HDDxM4fPgg48ePNtfb/GAvX75k/vw5dOvW29ylfJBZs6bh4+PD5s072Lx5Jz4+z1i0aF6881+7dpWhQ0epn4kjR07i4dEEgKVLF3L16mVWrdrAoUO/UbdufYYPH6i2mevRozcLFszh5cuXSfqePr2vVmZgs28PjBmOy6NH6rSKQB1gJx/YSxHQBAUaQvHCOcAQigHee9F9XiLxihcihcmQISONGjVh8GAvgoKCcHV1ZerUCdSoUYuhQ0ep87m7Z2XIkJGEhIQwZcpPLFu2hsjISGbMmEynTt/j6dlSnbdQoSL88MNEVq9eRlBQYKwOKQDnzv3J0qULuXfvLq6u6WnZsg1Nm7bgwIG9rFy5FG/vveq8ffp0o1SpMnTp0p2JE8cRHh7O3bu3CQwMoEKFijx+/JjFi1/f5nHhwrncvXub6dPn4O/vx/z5szl//iwajYbKlb+md+/+ODjE/fdh48a1VKjwJa6urgD4+r5g7tyfuXbtCv7+fmTI4EaHDp3VVmKVK5fF07MFR478QrFiJZg2bRbnzp1h6dIFPHz4ADe3zLRv34lvvjFcNx0aGsL8+bO5ePECvr4vcHJypkmTZrRv3zlWLc+ePaNdu2Zx1tmuXadYy0RERHD48EHmzVuijnnPnv3o1687vXv3V/euxYiKiuLOnVsULlw0ztfo1asf0dHR2NnZER4eTlBQIE5OzurWpYuLK+XLf8mmTWvp1at/nOtIDBKM72Gzbw/OXdrF6nmYDfAGJpcuS4f1W01rG0VMKDbG+sJ5APQZMhhCsfjn71lSCIM9e3YydepEQkKS7244Tk5ODB8+mgYNGn3wOnx8nrFt22aKFv0MV1dXnj59ws2b/9G//+A452/YsDH9+/fk2bNnPHr0gMDAQGrVqhNrvgIFCjJhwrQ41/HgwX2GDRvIwIHDqFu3Prdu3aRfvx7kyJErQTWfOXOaJUtWkilTFkJDQ2je3IOHDx+QM2cudDodhw8fZMAAwxbw8OGDyJkzF5s37yA6OpqJE8czdepExo+fFGu9Wq2WvXt3MnXqLHXalCk/4eLiwrp1W7G2tmbbtk3MmjWNGjVqq7snHz9+xPbt+4mOjubmzf8YPnwgY8f+ROXKVbl69TIjRgzCxcWVChUqsmjRfJ48ecKyZWtxcnLixIljjB49jBo1apMjR06jetzd3Tly5GSCxgRQm2Tnz19AnZY3b14iIyN5+PA+BQsaHw66des/tFotK1Ys5tKlf3B0dOK77xrSunV7LCwssLS0xNLSkt27dzBjxmSsrKwYO/YnbGxeX3pRq1Ydhg8fSLduvZNst7kE47vodDiNHhpnI2ALQA8Mf/YU/w/44ThO+vF1KGbMaAjFYsU/smCRlixYMIebN/8zy+uaEowzZ05h7tyZaLVaoqOjyZzZnWrVqtO2bUfAsIUEhi3JuLi5ZVLnCwgw7ELLmNHNpJqPHj1EoUJF1K2uIkWKsnDhcjJmdOP06d/fu3yxYsXJl8/wx9/Z2ZmyZcvzyy/7+f77npw9+yc6nY6vvvqa69evcuPGNWbPXqiGWJ8+XrRu3ZSBA4fGaqJ948Z1wsPDKVr09WVdw4aNxtHRESsrK3x8nuHg4EhkZCRBQUHqOmvXroudnR12dnYsWbKAypWrqsfdPv+8JA0aNGb79q1UqFCRLl26YWlpiaOjI8+f+6jX+vn6vogVjKaK2cVpZ2evTrO1tXv1XHis+UNDQyhVqgyeni0ZP34y//13nZEjh2BhYUHr1u3V+erWrU/9+g357bdf+fHHMWTM6Ebp0qUBKFq0GOHh4dy4cZ1iSfQ3M80EY6VKZQkJCWHhwmVGHdrv379Hw4Z1AahX7zsmT56hPmf95x9YPnkS7zotAJ48pkexAnw3Yw7ffddQfS4kJJivvjIcMK5Y8SsWL15htGznwEBOWluDTsdvKzfg8sYP+PDhgwwZMgCAQYOGxbpmsmzZEkRHR1GkSNFY7a/GjRvNzp3eAGzfvpcCBQqqz507d4auXTsYXr/z9/TvP8ho2dq1q/L8uQ+ZM2fhyJETRs/NmTOTlSuXAbB8+RrKvXFbuhs3blC9uuGXsnFjT8aNm2C0bIsWjbl+/RrW1jacP3/J6Lm1a1cxc+ZUAKZPn6Xu/gHw9/ejenVDS62qVaszd+4io2V79OjC6dOnADh16hxOTq97re3bt4dRo4YCMGLEGFq2bGO0bMmShpM+SpQoybp1W4yeGzFiMAcO7ANgz55fyJ07j/rcqVMn6dXr+1ev34eePfsYLVu1akUCAl6SM2cuzrw6wzjG9OmTWb9+zav3vYmSJUupz129eoVWrZq+Gq/WjBw51mjZ58+f8/JlMGBB0aKfAYY/uFOmTCAoKEi9CYClpaXRHT8URUGrNdx0POYb+Zt0Oi16veGLn7W1tdFzhhuWG6/XycmJ3r37o9fr1RtZODo6kidPXqNlHz58QFBQENHRUXh5DaFBg0ZERUXh7b2ZNWtW4O6ejUePHhEVFa2GnI/PU3Llys21a1fR6/XY2tpSoEBB9YxvNzc3nj17CsDp06coW7Y89vav/yCHhYXx999/4eLiSsaMGXF3z6o+5+fni6OjI1euXMbS0pIiRYoa/W7odDquXLkMQO7cud8aIx1WVtZcuXIZFxcXcuTISYMGjVi4cC5du/Zg27bNlC1bnhs3rvPs2VP0ej1NmtRTx1+v12NlZcWNG9cpX/5Lo/VeuHAWR0dHnj59on7Onjx5zIIFc7h//x6ZM2cmc+Ysr9b1+q4vdnb2ar0PHtzn8uV/qFu3mvp8dLSWTJkycfv2LTQaw+/vjRvXyZYtGzlzGt7fnTu3KVLkM6PdnXfv3qZbt44AaDQWWFi8vi6wTZuOVKjwJdHRWqytrShUqAj29oZlIyIiCA4OwtfXl4gIQyC+efJNREQEt2/fwsHBieHDx5AtW3YAPvusOM2bt+LAgX2ULFkajUbDZ58VU/fA1apVhz17duHtvQUrKyty5MhJunQuuLi48vz5MwoXLsKNG9cBwxeWrFlf/8zB+G/EH3+cl36Mb3v27CnBwcFERkYaTdfpdDx9agi/gIAAo+csfJ4laN02/n6Eh4cZTVMURV2vv79frGVehgTzODoagOiChYyei4iIUJcNDQ2N4708ISoqSv0m/abAwAB12Zg/iDGioqLU54KDg2Mt+/y5j/r824KDg9XnoqKijJ7TarXqc4GBAbGW9fX15enTJ0a7Q2KEhoaqy0ZERBg9p9e/HsOXL/1jLevv76c+/3a/7fDwsHeOYcxzMb+gbwoIeD2GMcEQIzIy8o0xDIq17PPnz/Dz84tz13pQUNA7xjD6nWOo1+uIjo5Go3kdeg0aNKJBg0b4+DxTl82bNz8uLq+Pr0VFRXH1quEPqKtr+lgBduvWTUJCDJ+FEiW+MApVPz9fHj58ABiuzX1zK02v1xMdHfWq9tjvVafTqZ0QYn42NjY2tG7dnhcvXrBw4RwGDBiKm5sb2bJlp3Dhouzbt5ty5b5Eq41Gp9OpIb5v3y4KFy6Ku3tWIiLCcXR05MyZP2LdUer27ZsMHerFwIHDqFDBuB1b5sxZuHz50quaDH/29u/fQ/r0GbCwsHi1NRv16r0psX4GimJ4vzGfhypVqjFz5lT+/PMUFy+eZ9iw0URHR5E5c2ZsbW3Zv/9XLC0tefHiBffu3cHPz5e8eWNfwqXT6dDrX3950WqjGTrUi27devPFF6UJDg7iwYP7sb5kAWq9GTNm5Ntvv2PIkJHqc//732/o9Xq0Wi0//jiar776mpkz52FlZcXVq1f49dfDaLXaWL83mTJlZvr0Oa/GzJ1s2Yxb7F25cll9XYBcufJgZWXF3bt3SJ8+A9HRUTx8eB9ra2ty5Xq9m1pRFKKjo/jnn4vo9Xo6dfpefS4qKgprayuio6NYuXIpX31VhRYtXn+RjY6Ows7OjqioKPVkLcOdbSzV9RrGLnbXmXf9jXiXNHNWqrt7VrJmzRbrD5alpSVZs2Yja9Zs6sHvGPosxqeIxycqQ0bs7Y1PTdZoNOp6M2TIiCbgJRpfX/X59OkzqM+/+a0MwM7OTn0urhN63N0Nz7m5xd6d5OLiqi779v53Gxsb9Tln59jfnDJnzkLWrNnUb6hvMnwbMyz7dsBZWVmpz729qwgM3/SzZs2Gu3vsPpaOjo7qsm8fqLeweD2G6dNniLVshgwZ1effvuOFvb3DO8cw5rm4xtDV9fUYvr2FZWtr+8YYxr5vaebM7vGOYbp06d4xhtbvHEMLC0usra2xsrKO5zlDO7S3x0Gj0ajPvf1eDK9rpT4fe70W6nNx3Xcy5rm4jvNYWlq+qif23Ug6dOhMjhw5WbNmBdHRhj9mw4eP4cyZ00yfPomAgAAsLa14+dKfqVMncO7cWYYPN5xtamtrR8uWbTlwYC979+4iLCwMnU7HP//8zYQJP1C6dFkKFy4a673WqlWHBw/uc/78OSwsLLl+/Rrz5s3CysqKPHnyEhDwkjt3bmNlZc3x40e5/9Y1xDFjEbNeKysrvv32O2bOnEqePPnImTM31tY2FC1ajBw5cjF//mzCwsLQaqPZtWs78+fPivMPc+bM7oSGhqh/8KOjtUREGC4Js7a2Jjg4mN27d7x6Llpd7s2fa+3adTly5BBnz/6JXq/n4cMHzJ49nePHf8XKyoqQkBBsbW2xtLTk5cuXrFmzQl3Huz8vcf3MYz4vhp+5nZ0dNWvWZvHieYSEBBMREcGePbuoVq2Gukv1zfVaWlqxbt0qzp8/i6IoXL58CW/vzVSvXhtraxsKFizMhg1ruX371qvjr7u4desmlSpVwcbG8DmMjIwkODiYLFncjeqN63P4rr8R76JRTInRT5ivb7DpPc50OjKUKYbF06do4lhY0WjQZ82G/4XLEMcfnRial/64NGuEJjqagO17UeL4Y/yp0mjAzc35w8Y3FUvMcYmOjsLP7ykZM2ZNcC/QlMLTswGdO3ejXr0GRtOfP39Ku3Yt+eabegwaNAww7EJcs2YF586dITAwQD15pH37zrGag589+yebNq3j5s0bREZGkSVLFurWrU/Llm3jPSHjr7/Os2jRPB48uEf69Blo164T9esbDn8sX76YAwf2EhYWxtdfVyMiIoLcufOoZ6UCjBo1zmh9Dx7co3VrT0aMGKuux/DefFiwYDYXL/5FVFQkRYsWo1+/QeTNmy9WTTqdDg+Puvz442RKly6LlZUF+/fvY/nyxQQEBJA+fXoaNGjMtm2bGDhwKNWq1aRy5bLMnbuY0qXLquv544/fWbFiCY8ePcDOzp5aterQo0cfrK2tOXPmNHPnzuT58+c4OztTq9Y3nD9/ltq1v6VVq7YJ+0G+Q1hYKPPmzebUqf8RHR1NlSpVGTBgqLqbu23b5nzzTV31jNZdu7azZcsGXrx4ToYMGWnZsi1NmhjOhFUUhfXr17B793ZCQkIoUKAgvXv3p2jRYupNxC9cOMf48aPZtetgrC9s7/pdifmdTAgJxvew2beHdK/OSn3z+4by6ttH0Ip1RL1xbPFtGn8/XDw9sL5sOLYWVa0GgVt3mV5ICiXBGDcJxneTLhKvzZ8/m4iIcAYPHiHj8g4xYzNt2kScndPRs2ffWPMkVjCmmV2pHyrqu4YEr1xHUDrj3Wb6rNneH4p+frg2baiGoi5zFkImxn06uRAibWrbtiOnTp2MdY6DiO3ly5f8+ecftGnT/v0zfwQJxgSI+q4hQ5s1oxrQCvhr5lz8L1xOQCg2wOrKv4AhFAN37kf31ok2Qoi0zdXVlT59BrBkyXxzl5LiLVkynz59BsR5A4fElGbOSv1Yj589I+YChuBjR1nVrmO882p8fQ2heO0KALos7oZQfOP0cCGEiFGzZm1q1qxt7jJSvOHDk+desrLFmECPHz9W//+vv87HO5/mxQtcm373OhTdsxK4S0JRCCE+FbLFmEBvBmN8NAEvDaF4/RoAuqzZCNy5D12+Au9ZUgghREohW4wJEBUVxYsXhttWffFFaU6dOhfnfIpzOrUrhi5bdgJ27pdQFEKIT4xsMSaAzxt3wMmRI6fRrceMWFoSPGch+oxuhHfsgj6O65aEEEKkbBKMCRBzf0YgVsNUFMVwgUwMS0tCx09MpsqEEEIkNtmVmgBPn74ZjK/vwGHh8wyXRvWwfHVMUQghxKdPgjEB3ryx9sOH99m3bw8Wz57i0qgeNqdP4drkOyxf3eFdCCHEp012pSbAm8cY16xZyeGD+2nn7IzV7VsAKA4OKG+0vxFCGHh6NsDf30+9+baiKFhYWFKoUCH69RtEoUJFkux147pHqxAJIcGYAG+3YrLw88XquQ8Auly5Cdi5H33OhHUCFyKtGTx4hFFA+fv7MW3aREaOHMLWrbvj7NwhhDmZJRj9/PwYM2YMZ8+exdLSkoYNGzJs2LA474p/4sQJZsyYwcOHD8maNStDhw6levXqyVpvzDFGV2B8hgyk8zf0BtTlykPArv3oP7ILthAfyn7RfOwXv/9WYtoSJQl6qyFzunYtsLr0z3uXDe/Rh/C3GjJ/jAwZMtKoURMGD/YiKCgIV1dX/v33H5YtW8T9+/cIDg4ib978DBgwlOLFP+evv84zadJ4GjRoxM6d3kRGRlK6dBlGjvwBR0cnFEVh3bpVbN++lcjICL77rpHaxgkgMjKCFSuWcPToYcLCwow6NgBUrlyWIUNGsmnTel688KFMmfJ07tyNOXOmc+vWTXLnzsu4cRPj7Hav0+lYuXIpe/fuQlEUqlatzu3bN2nQoDH16jWIteX611/n6devB7//brhJyOPHj5gzZyZXrlzCzs6eunXr0bHj91hbWxMWFsrUqRM4f/4slpZWFChQkH79BpEnT158fV8wefJPXL16GTs7O4oWLcbAgcPibKMmTGeWr2peXl44ODhw8uRJvL29OX36NKtXr44137179+jbty/9+/fn/Pnz9O3bFy8vL3x8fJK13mfPnpIDOK/R0M/fn46ALnceAnYfkFAUZqUJDsLy6ZP3/rN4oxdoDAtf3wQtq4mjIfPH8PF5xrZtmyla9DNcXV2JjIxg2LCBVK1ag507D7B//69kz56DhQvnqMs8e/aUFy9esGXLTpYtW8Ply/+yY4c3YGg4vHXrJqZNm82ePYextrbm+fPXfyNmzJjCmTN/MnfuYvbuPczXX1enf/9ePHv2+hDJkSO/sHTpKrZs2c2lS38zYsQghg8fq65v7dqVcb6XjRvXcfjwQebMWcT27ftwcnLm338vJWgcwsPD6d+/J/ny5WfHjgMsXLicc+fOsmLFEgA2bVpPaGgoO3bsx9t7LxkzurF48TwAFi+eT+bMmdm79zAbNmwjPDyM9etXm/RzEPFL9i3G+/fvc/bsWf73v/9hb29Pzpw56dWrF9OnT6dr165G8+7cuZOyZctSq1YtAOrVq8eOHTvYsmUL/fr1S5Z6FUXB4vFjfgPyv+ofpMuT17D7NHuOZKlBiPgozunQZY3dAPpt+ji2JPRubglaVomjIbMpZs6cwty5M9FqtURHR5M5szvVqlWnbduOgKFJ85Ilq8iRIydRUZE8ffqEdOlcuHbtqtF6OnXqiq2tHTly5KR06bI8fHgfgEOHDtCgQSMKFzYcr+zatQd79+4EIDIykqNHDzFp0nR1i69581YcOXKQI0d+od2rex43bdpcvTF1vnz5KVSoCLlz5wGgTJlyXLr0d5zvbf/+3bRp00Httfj99z05eHBvgsbljz9+Jzo6mu7de6PRaMiSxZ3u3XsyYsRQevTog42NLbdu3eTgwf2UL/8lI0aMVXc729ra8vfff3H06CHKli3PzJnzZJd0Ikr2YLx58yaurq5kyfK6w3n+/Pl58uQJQUFBpHujvdOtW7coVMi4G0WBAgW4ft30M0BNaN5sJCQkmGoR4eR/9ViXNx+Bu/ajZMvOB64yVYkZ1w8d39QqMcflXesI7/nhuznf3rWaVAYNGk69eg2IiorC23sza9eupFKlr3BxcQXA0tKSv/46z+DB/QgPDydv3nxYWlqhKMZ9CTNmfB3uVlZWxLSSffHiBVmyvL6+2NLSkixZsgIQHBxMdHQ02bIZf4nNmjU7z569PncgphYACwsLnJ1f38RDo9EY7Zp90/PnPkZNlN987fd59uwJAQEv+fbb14eGFAW02mhevvSnbdsO2NrasH//bmbNmka2bNnp0aMPVavWwMtrCGvXrmTTpnVMnDiOAgUK4uU1hJIlSyXotVM7jSb2740pv4vJHoyhoaFqZ+cYMY/DwsKMgjGuee3s7AgLCzP5dTNmTFiDyrc5OVmz3t4e1/BwhqRPT6aT/yND9uwftK7U7EPHN7VLjHGJiIjA398CS0sNVlaf3laBhYWhbisrO9q370hISDDDhg1iyZKVFCxYiMuX/2X27OksW7aKIkU+A2DDhnU8eHAPKysLLC0N7/nN96559VfOysoCd/csPHv2RH1eURT8/F5gYaEhc2Y3bG1tefbsMfnzv74T1ZMnj6hc+Wt1GUtLC/X/NRqNWnNM/RpN3GPv7p6VFy+eGb32ixc+6vKWlpbo9Vr1+ZCQoDfqdidHjpxs2bJDXV9oaCj+/n64uWXk1q2bfP11VVq3bktISDDbt29j7NiRHDr0K/fu3aVpU0+6d+/Jy5cvWbFiKaNGDeGXX4597I8rRXvf51+v12BhYUH69I7Y2dl9+Ot88JIfyMHBgfDwcKNpMY8dHR2Nptvb2xMREWE0LSIiItZ8CeHn9+Gd1Dds2MLly3/z+fwF+OTIQdas2bh0Sa5bBMO3sIwZnT9qfFOjxByX6Ogo9Ho9Op3ySXZ31+uN6+7SpQcXL/7FmDEjWLFiHUFBwWg0Flha2qDV6rl8+V+2bNmITqdDq9Wj0xmWfXMdMVuLWq2e+vU9mDlzKpUrV6VIkc9Yv341vr6+6PUKej3Uq9eQhQvnkz17LrJkcWfnTm/u3LnD2LET1HXqdHr1/xVFMapZr1dQlLjHvkGDxqxfv44SJUqTNWs21q9fzYsXL9Tlc+fOw4kTv1G79reEhISwefNGte4vv/yKefNms3btajw9WxIZGcmUKT/y9OlTVq5cz65dO/jvv+tMmjQDV9f02Ns7Ym9vj0ZjxcqVK7C2tmLkyB+wt3fE1tYOFxfXT/LzkVBWVhbvfX863f/bu9Oops51D+D/JIyCFgsUHKpVFDkKEjBMooepCMigWKDOWl1UtFgFbO11qkL1IF6g1YplYWvtdahXioBFQLx1aGud9VipVUFRECuCoARFEvLeD5ScRMAmDDsVnt9afMjOzs6T/8reT97sTV4GmUyG6uo6aGtLlO5r3idVeq52V9lOw4cPR01NDSorK+VXUBUXF8Pc3Fzp6wsAsLS0RGFhodKyoqIiWFtbq/28jKHdB6jx490RHByIbdu+UNoe+Y+O5NuddUYu3S1XgUCAdes+waxZU/H5558hOvpDBAeHIDIyHI2NMvTv3x8hIVORmvo5Hj6s+svteXv74tGjGqxZ81+ora2Fh4cXLCz+M83be+8twVdfpWLJkoV4/PgxLCyGISlpCwYNGtzh1xIWNg1VVZVYuHAe+HwBvLy88dprZtDS0gYAREQsRmJiPIKCfGBiYorQ0Gny85UGBob49NMUfP55Mvbs+QaNjTKMGSPCxo1JAIAFCyKRlLQRs2aF4dmzZxg8eAji4xOhq6uLDz9cicTEeISGToJEIoGV1T8QFxff4dfTXXR0v+Mxxv1uN336dJibmyM2NhbV1dVYuHAhfHx8sHjxYqX1iouLERwcjPj4eEyYMAGHDx/GRx99hKysLAwZMkSt56ysbP8ndx4PMDHpDV/fifKG/j8cnZ/5u2vOpiP5dkedmYtE0oCqqnswNu4HbW2dzilQw1T59P8yKCy8gn79+uHVV40BNI02AwLexNq16+Hg4Kz29rpLLl1BlWxetK8075Oq0MgJi82bN0MqlcLLywthYWEYP348Fi1aBACws7NDdnY2gKaLcrZu3YrU1FQ4ODggJSUFW7ZsUbspdpZdu/YhN/f/qCkSQgA0/ZtHXNwaiMViSKVSfPvtbgDAqFGjNVwZ6QiNjBg1oTNGjDQqaomyaR2NGF+su4yM6urESEpKwOnTJ9HQIIGl5QhERkbByuof7dped8mlK3A5YqSfhCOEkHYyMDDE6tWxmi6DdLKX79pvQgghpAvRiFENH320DDU1NTAyMsK//vXfmi6H9DA95KwHIe3WWfsIjRjVcOjQ9/juu//FoUPfa7oU0oM0T9nU0PBMw5UQ8vfWvI8IBB0b89GIkZC/OT5fAH19Q4jF1QAAHR1d+S+/vKxkMh4aG2kE/DzKpW0vyoYxhoaGZxCLq6Gvb9jh342lxqiGgwfzIJU2yj/BE8KVPn1eBQB5c3zZ8fn8Nn9/tCejXNqmSjb6+obyfaUjqDGqYfDgN+hfEohG8Hg8vPKKMXr37ovGRqmmy+kQHg/o29cA1dV1tD8poFzapko2AoFWp80wQo2RkJcIn88Hn/9y/y8jj9c0GYC2toQagALKpW1cZ0MX3xBCCCEKaMSohp9//hH19c+gq6sLV9fxmi6HEEJIF6DGqIaFC8Nx7145+vXrj3//m6adIoSQ7qjHNMaOXN3e/FhDQ0P07t0bhoaGNGP9nzpzpvruhHJpG2XTOsqlbZ2RjTqP7TE/Ik4IIYSogi6+IYQQQhRQYySEEEIUUGMkhBBCFFBjJIQQQhRQYySEEEIUUGMkhBBCFFBjJIQQQhRQYySEEEIUUGMkhBBCFFBj/FNVVRUWLVoEkUgEJycnrF+/HlJp6/PeHT9+HIGBgRAKhfDz88PRo0c5rpZb6mSzd+9e+Pj4wM7ODj4+Pti9ezfH1XJHnVyaXb9+Hba2tjh9+jRHVWqGOtmcOXMGoaGhsLOzg5ubG1JTUzmuljvq5LJz5054enrC3t4egYGByM/P57ha7j18+BDe3t4v3D84Of4ywhhjbObMmSwmJoY9efKE3blzh/n7+7O0tLQW6926dYvZ2NiwgoICJpFIWE5ODhs9ejT7448/NFA1N1TNpqCggIlEInbx4kUmk8nYhQsXmEgkYnl5eRqouuupmkuzJ0+esICAAGZpaclOnTrFYaXcUzWboqIiZmtryzIyMphMJmNXr15ljo6OLDc3VwNVdz1Vczl27BhzcXFhxcXFjDHG8vLymJWVFSstLeW6ZM6cO3eOvfnmmy/cP7g6/lJjZIyVlJQwS0tLpXBzcnKYu7t7i3WTkpLYO++8o7Rs/vz57LPPPuvyOjVBnWx27drFUlNTlZa99957LC4ursvr5Jo6uTRbvnw5+/TTT7t9Y1Qnm9jYWBYdHa207ObNm6yioqLL6+SaOrl89dVXzNnZmRUVFTGZTMYKCgqYjY0Nu3fvHpclcyYjI4O5u7uznJycF+4fXB1/6atUADdu3ICRkRHMzMzkyywsLFBeXo7Hjx8rrVtUVARLS0ulZcOGDcPvv3fPaajUyWbGjBl499135berqqpw9uxZWFtbc1YvV9TJBQAyMzNx+/ZtREZGclmmRqiTzeXLlzFw4EBER0fDyckJfn5+OHPmDExNTbkuu8upk4u/vz9MTEwwceJEjBo1CkuWLEF8fDzMzc25LpsT48aNQ0FBASZOnPjC9bg6/lJjBFBXVwd9fX2lZc23nzx58pfr6unptVivu1AnG0UPHjxAeHg4rK2tERAQ0KU1aoI6uRQXFyM5ORmJiYkQCASc1agp6mTz6NEjfPPNNwgKCsLPP/+M2NhYbNy4EXl5eZzVyxV1cpFIJLCyssL+/ftx6dIlxMbGYuXKlbh27Rpn9XLJ1NQUWlp/PQsiV8dfaowAevXqhadPnyota75tYGCgtFxfXx/19fVKy+rr61us112ok02zS5cuISQkBEOGDMG2bdtUesO/bFTN5dmzZ4iKisKKFSvQv39/TmvUFHXeMzo6OvDy8oK7uzu0tLTg4OCASZMmITc3l7N6uaJOLnFxcRg+fDhGjx4NHR0dvPXWWxAKhThw4ABn9f4dcXX8pcYIYPjw4aipqUFlZaV8WXFxMczNzdG7d2+ldS0tLXHjxg2lZUVFRRg+fDgntXJNnWwAID09HXPnzsWcOXOQmJgIHR0dLsvljKq5/PrrrygpKcHKlSshEokgEokAABEREVi7di3XZXNCnfeMhYUFGhoalJY1NjaCdcNpYtXJpby8vEUuWlpa0NbW5qTWvyvOjr+desbyJTZt2jQWFRXFamtr5VeLbd68ucV6RUVFzMbGhuXk5MivirKxsWE3b97UQNXcUDWbvLw8NmrUKHbixAkNVMk9VXN5Xne/+IYx1bM5efIkGzlyJMvMzGQymYydOXOGCYVCduTIEQ1U3fVUzSU5OZk5OTmxK1eusMbGRpabm8tsbGzYb7/9poGqufWi/YOr4y81xj89ePCALV68mDk6OjJnZ2cWHx/PpFIpY4wxoVDIsrKy5OueOHGCBQUFMaFQyPz9/dmxY8c0VTYnVM0mICCAWVlZMaFQqPS3evVqTZbfZdR5zyjqCY1RnWyOHTvGpkyZwuzs7JiXlxfbu3evpsrucqrmIpFI2ObNm5mHhwezt7dnwcHBPeYD5/P7hyaOvzzGuuF3FoQQQkg70TlGQgghRAE1RkIIIUQBNUZCCCFEATVGQgghRAE1RkIIIUQBNUZCCCFEATVGQgghRAE1RkJ6sNraWjx8+FDTZaispKRE0yWQHoAaI+mxPD09YWNjAzs7O6W/efPmqfT4ESNGvHCm8fYoKyvDiBEjIBQK5fUIhUL4+PggMzOzw9vPzs6Gv7+//La3t7f8tyefv6+zZGRkwMrKSiljW1tbeHh4IDExUeXfRd29ezdWr17d6fUR8rzuN+0BIWpYt24dpkyZoukyWvj+++8xcOBAAABjDIcOHcKyZctgZmYGFxeXdm83KCgIQUFB8tvV1dVt3teZ+vfvjx9++EF+u7GxEcePH8f777+PQYMGITQ09C+38TKNbMnLjUaMhLTh/v37WLp0KTw9PWFrawsvLy+kp6e3um5+fj78/f0xZswY+Pn5ISUlRX5fZWUlli1bBldXV4wbNw5r1qyBWCxWuQ4ejwd/f3/06dMHV69eBQDcvXsXS5cuhYuLC1xdXRETE4OKigoAgFQqxdq1a+Hq6gonJydMnz4d58+fB9A0evP09AQA+Pj4AADCw8ORlpamdN+MGTOQlJSkVEdoaCi2b98OACgsLMSsWbPg4OCACRMm4Ouvv1ZrRgyBQABPT09YWlrKX1NDQwM2btwIPz8/2NnZwcXFBXFxcWCM4cCBA0hNTcW5c+fkM5SIxWLExsbCzc0NLi4uiIqKUpq5gpD2osZISBtWrVoFbW1t5OTk4MKFC5g5cybi4uJQV1entF59fT0++OADrFmzBufPn0diYiLS0tJw+fJlyGQyLFq0CHw+H/n5+Th48CAqKiqwZs0alet4+vQp9uzZA7FYDGdnZ0gkEsybNw8CgQCHDx+Wz10YEREBqVSKrKwsXLx4Ebm5uTh58iQcHBywbt26FtvNz88HAKSlpSE8PFzpvtDQUGRnZ0MmkwFomh7p6tWrmDx5Mu7fv485c+bA19cXJ0+eREpKCvbs2YN9+/ap/JoaGhqQm5uLa9euwc3NDQCwc+dO/Pjjj9i5cycuXryIlJQUfPvttzh16hSCg4OxYMECiEQinDt3DgCwYsUK3L59GxkZGThy5AgMDQ0RGRnZLaesItyir1JJj7Zu3Tps2LBBadmJEyfQq1cvfPLJJzAwMIC2tjbKy8thYGCA+vp6PHr0qMXEqHp6ekhPT4dMJoO9vT3Onz8PPp+Py5cvo7CwEDt27JA/Zvny5fD19cXq1avRt2/fVusKCgoCn9/0uVUgEGDIkCFITk7GyJEj8csvv6C0tBTfffcdDA0N5a/D0dERV65cgZ6eHsrKypCeno5//vOfWLJkCaKiotTKxdfXF+vXr8fp06fh4uKCjIwMuLm5wcTEBGlpabCwsMCMGTMAAMOGDcP8+fOxa9cuTJ06tdXtlZeXQyQSgTGGhoYGMMYwduxYbNmyRd4Yw8LCEBwcDGNjY1RUVMgnoL1//36L7VVVVSE/Px+5ubkwNjYG0NQoRSIRCgsLYW1trdbrJUQRNUbSo3388cdtnmMsLS1FQkICSkpK8MYbb2Dw4MEAIB9FNdPT08PevXuRkpKCmJgYiMVi+Pj4YNWqVSgrK0NjY6P84N9MR0cHpaWlbTbG7Oxs+TnG51VVVaFv377ypggAhoaGMDIywt27d+Hv7w+JRIL9+/cjKSkJxsbGiIiIwLRp01TORU9PD4GBgcjMzISjoyOys7MRFxcHoOlr3MLCQvlXms2ZCASCNreneI6xuLgYS5cuhY6ODsaPHy9f5+nTp4iNjcXZs2dhbm6OkSNHgjHWIu/mGoCmZqpIIBCgrKyMGiPpEGqMhLRCIpFgwYIFiI6OxvTp08Hj8XDlyhVkZ2e3WFcsFqOiogKJiYkAgKtXryI6OhpffPEFvL29oaenh9OnT8sbR0NDA0pLS+WNVl0DBgxAdXU1xGKxvDnW1taiuroapqamuHXrFkaNGoXJkyejvr4eeXl5WL58uVIjU0VYWBimTZsGb29v8Hg8eRMzNzeHk5MTvvzyS/m61dXVLb5ibouFhQXS0tIQHByMlStXIiEhAUDTV9evvPIKfvrpJ+jq6kImk8HBwaHVbZiZmQEAcnNzYWpqKl9eVFSE119/Xa3XScjz6BwjIa2QSCSor6+Hnp4eeDweysvLsWnTJvl9iurq6hAeHo6DBw+CMYbXXnsNfD4fffv2xejRozF48GDEx8ejrq4O9fX12LBhA+bOnYvGxsZ21WZjY4Nhw4bh448/Rm1tLWpra7F27VoMGjQI9vb2OHr0KCIjI1FWVgY9PT0YGRlBS0sLvXv3brEtHR0d1NbWtvo8VlZWGDp0KDZs2IDg4GB5Yw8MDMSlS5eQnZ0NqVSKiooKREREID4+XuXXYG5ujvj4eGRlZeHAgQMAmj5g6Orqgs/nQywWIyEhAWKxWJ63rq4uxGIxGGMwMzODu7s71q9fj+rqakgkEmzbtg0hISF4/PixupESooQaIyGt6NWrFzZs2ICtW7fCzs4Os2fPhqurK0xMTHD9+nWldc3MzLB582akpaXB3t4eAQEBcHZ2xty5c6GlpYXU1FRUVlZiwoQJGDduHO7cuYMdO3ZAV1e3XbU1b1MqlcLHxwceHh6QSCTYsWMHtLS0MHv2bLi7u2Pq1KkQCoXYtGkTkpOTYW5u3mJbb7/9NmJiYpCcnNzqc4WFhaG8vBwhISHyZQMGDMD27duxb98+jB07FpMmTcLQoUPVaowA4ObmJr+gqbS0FKtWrcLvv/8OR0dH+Pr6QiwWY/z48fK8PTw8UFNTgzFjxuDx48dISEhAnz59MHnyZDg7O+P48ePYvn270giSkPbgMbqEixBCCJGjESMhhBCigBojIYQQooAaIyGEEKKAGiMhhBCigBojIYQQooAaIyGEEKKAGiMhhBCigBojIYQQooAaIyGEEKKAGiMhhBCigBojIYQQouD/AfO7OP8XtijjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# separate the features for training \n",
    "X=data_final[feature_list_for_training]\n",
    "# separate the labels for the training \n",
    "y=data_final['died_within_60_days']\n",
    "\n",
    "# add the constant to the x features\n",
    "X=sm.add_constant(X)\n",
    "# split the dataset into train and tes set \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification\n",
    "prediction = list(map(round, yhat))\n",
    "\n",
    "# If I understand this – you rounded the yhat to create a binary variable for comparison? \n",
    "# Then you created the confusion matrix for these predictions?\n",
    "# Can you try calculating the auc for y_test using yhat as a continuous measure?\n",
    "\n",
    "\n",
    "# Usually, we’d choose the cutoff so that the outcome is more balanced, using either the youden index \n",
    "# or a cutoff that ensures that the sum 1’s based on the predicted scores = the sum of observed scores=1. \n",
    "# I think this is leading to the poor calibration we see in the confusion matrix.\n",
    "\n",
    "\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1257d795",
   "metadata": {},
   "source": [
    "# summary of the model trained by imbalanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51da1fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Logit Regression Results                           \n",
      "===============================================================================\n",
      "Dep. Variable:     died_within_60_days   No. Observations:                23022\n",
      "Model:                           Logit   Df Residuals:                    22967\n",
      "Method:                            MLE   Df Model:                           54\n",
      "Date:                 Tue, 01 Aug 2023   Pseudo R-squ.:                  0.1658\n",
      "Time:                         13:33:06   Log-Likelihood:                -6303.4\n",
      "converged:                        True   LL-Null:                       -7555.9\n",
      "Covariance Type:             nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "const                                -7.3378      0.251    -29.222      0.000      -7.830      -6.846\n",
      "spec_pat_num_age                      0.0465      0.003     17.339      0.000       0.041       0.052\n",
      "patient_gender_M                      0.4518      0.050      9.050      0.000       0.354       0.550\n",
      "collected_in_ed                       1.5123      0.123     12.308      0.000       1.271       1.753\n",
      "collected_in_hospital                 1.2802      0.135      9.507      0.000       1.016       1.544\n",
      "collected_in_dsl                     -0.0331      0.150     -0.221      0.825      -0.327       0.260\n",
      "dsl_resident_during_collection       -0.6216      0.139     -4.474      0.000      -0.894      -0.349\n",
      "symptomatic_during_collection_U       0.3685      0.066      5.576      0.000       0.239       0.498\n",
      "symptomatic_during_collection_Y       0.7049      0.069     10.218      0.000       0.570       0.840\n",
      "interp_result_Positive                1.2830      0.071     18.055      0.000       1.144       1.422\n",
      "y_m__2020-10                          0.7667      0.112      6.855      0.000       0.547       0.986\n",
      "y_m__2020-11                          0.8569      0.106      8.074      0.000       0.649       1.065\n",
      "y_m__2020-12                          0.7813      0.105      7.432      0.000       0.575       0.987\n",
      "y_m__2020-3                           1.5306      0.112     13.721      0.000       1.312       1.749\n",
      "y_m__2020-4                           1.0853      0.082     13.185      0.000       0.924       1.247\n",
      "y_m__2020-5                           0.7339      0.100      7.330      0.000       0.538       0.930\n",
      "y_m__2021-3                           0.3826      0.366      1.046      0.295      -0.334       1.099\n",
      "y_m__2020-7                           0.7661      0.127      6.019      0.000       0.517       1.016\n",
      "y_m__2020-8                           0.5552      0.172      3.230      0.001       0.218       0.892\n",
      "y_m__2020-9                           0.5347      0.164      3.251      0.001       0.212       0.857\n",
      "y_m__2021-1                           0.7068      0.123      5.743      0.000       0.466       0.948\n",
      "y_m__2021-2                           0.7133      0.221      3.228      0.001       0.280       1.146\n",
      "chf_2_years_full_1                    0.1817      0.061      2.958      0.003       0.061       0.302\n",
      "htn_unc_2_years_full_1               -0.2345      0.052     -4.467      0.000      -0.337      -0.132\n",
      "cancer_mets_2_years_full_1            0.4581      0.165      2.774      0.006       0.134       0.782\n",
      "fluid_elec_dis_2_years_full_1         0.1784      0.058      3.086      0.002       0.065       0.292\n",
      "card_arrh_2_years_full_1              0.0388      0.058      0.666      0.506      -0.076       0.153\n",
      "valv_dis_2_years_full_1               0.1064      0.129      0.827      0.408      -0.146       0.358\n",
      "pcd_2_years_full_1                    0.1569      0.113      1.389      0.165      -0.064       0.378\n",
      "pvd_2_years_full_1                    0.1028      0.077      1.335      0.182      -0.048       0.254\n",
      "htn_c_2_years_full_1                 -0.2289      0.239     -0.958      0.338      -0.697       0.239\n",
      "paral_2_years_full_1                 -0.2457      0.127     -1.934      0.053      -0.495       0.003\n",
      "oth_neur_dis_2_years_full_1          -0.0366      0.049     -0.746      0.456      -0.133       0.060\n",
      "cpd_2_years_full_1                    0.1349      0.058      2.323      0.020       0.021       0.249\n",
      "diab_2_years_full_1                   0.1020      0.054      1.881      0.060      -0.004       0.208\n",
      "hypothyroidism_2_years_full_1         0.0372      0.073      0.513      0.608      -0.105       0.179\n",
      "ren_fail_2_years_full_1               0.0885      0.069      1.282      0.200      -0.047       0.224\n",
      "liver_dis_2_years_full_1              0.4144      0.140      2.970      0.003       0.141       0.688\n",
      "pep_ulc_exc_bld_2_years_full_1        0.2526      0.172      1.465      0.143      -0.085       0.591\n",
      "aids_hiv_2_years_full_1              -0.7965      1.042     -0.764      0.445      -2.839       1.246\n",
      "lymph_2_years_full_1                  0.0381      0.222      0.171      0.864      -0.398       0.474\n",
      "tumour_no_mets_2_years_full_1         0.1920      0.078      2.452      0.014       0.038       0.346\n",
      "rheum_col_vasc_dis_2_years_full_1    -0.1958      0.055     -3.538      0.000      -0.304      -0.087\n",
      "coag_2_years_full_1                   0.0936      0.120      0.781      0.435      -0.141       0.328\n",
      "obes_2_years_full_1                  -0.2036      0.144     -1.417      0.156      -0.485       0.078\n",
      "wt_loss_2_years_full_1                0.0123      0.099      0.124      0.902      -0.182       0.207\n",
      "anemia_2_years_full_1                 0.0723      0.073      0.985      0.325      -0.072       0.216\n",
      "alc_abuse_2_years_full_1             -0.3102      0.125     -2.473      0.013      -0.556      -0.064\n",
      "drug_abuse_2_years_full_1             0.0179      0.132      0.135      0.893      -0.242       0.278\n",
      "psych_2_years_full_1                  0.0467      0.056      0.834      0.404      -0.063       0.156\n",
      "depress_2_years_full_1               -0.0568      0.050     -1.130      0.259      -0.155       0.042\n",
      "num_admits_1_year                     0.0218      0.028      0.769      0.442      -0.034       0.077\n",
      "num_scu_admits_1_year                 0.1386      0.168      0.824      0.410      -0.191       0.468\n",
      "num_procs_dad_1_year                  0.0117      0.022      0.538      0.590      -0.031       0.054\n",
      "num_procs_nacrs_1_year               -0.0007      0.001     -0.645      0.519      -0.003       0.002\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# print summary of the model trained with Imbalanced data\n",
    "print(log_reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9306a96f",
   "metadata": {},
   "source": [
    "# Adjusted Odds Ratios for the data with imbalanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0fea4826",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming you have already fitted your logistic regression model and stored it in log_reg_model\n",
    "# For example: log_reg_model = sm.Logit(y, X).fit()\n",
    "\n",
    "# Get the summary of the logistic regression model\n",
    "log_reg_summary = log_reg.summary()\n",
    "\n",
    "# Convert the summary to HTML\n",
    "html_summary = log_reg_summary.tables[1].as_html()\n",
    "\n",
    "# Read the HTML table into a DataFrame\n",
    "df_summary = pd.read_html(html_summary, header=0, index_col=0)[0]\n",
    "\n",
    "# Calculate the odds ratio and add it to the DataFrame\n",
    "df_summary['Odds Ratio'] = df_summary['coef'].apply(lambda x: round(np.exp(x), 2))\n",
    "df_summary['[0.025'] = df_summary['[0.025'].apply(lambda x: round(np.exp(x), 2))\n",
    "df_summary['0.975]'] = df_summary['0.975]'].apply(lambda x: round(np.exp(x), 2))\n",
    "df_summary=df_summary[['Odds Ratio','[0.025', '0.975]','P>|z|']]\n",
    "# Display the DataFrame with the odds ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b095ff58",
   "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>Odds Ratio</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>y_m__2020-3</th>\n",
       "      <td>4.62</td>\n",
       "      <td>3.71</td>\n",
       "      <td>5.75</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collected_in_ed</th>\n",
       "      <td>4.54</td>\n",
       "      <td>3.56</td>\n",
       "      <td>5.77</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interp_result_Positive</th>\n",
       "      <td>3.61</td>\n",
       "      <td>3.14</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collected_in_hospital</th>\n",
       "      <td>3.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>4.68</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-4</th>\n",
       "      <td>2.96</td>\n",
       "      <td>2.52</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-11</th>\n",
       "      <td>2.36</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-12</th>\n",
       "      <td>2.18</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-10</th>\n",
       "      <td>2.15</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-7</th>\n",
       "      <td>2.15</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-5</th>\n",
       "      <td>2.08</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2021-2</th>\n",
       "      <td>2.04</td>\n",
       "      <td>1.32</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2021-1</th>\n",
       "      <td>2.03</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.58</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symptomatic_during_collection_Y</th>\n",
       "      <td>2.02</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-8</th>\n",
       "      <td>1.74</td>\n",
       "      <td>1.24</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-9</th>\n",
       "      <td>1.71</td>\n",
       "      <td>1.24</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cancer_mets_2_years_full_1</th>\n",
       "      <td>1.58</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_gender_M</th>\n",
       "      <td>1.57</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liver_dis_2_years_full_1</th>\n",
       "      <td>1.51</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2021-3</th>\n",
       "      <td>1.47</td>\n",
       "      <td>0.72</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symptomatic_during_collection_U</th>\n",
       "      <td>1.45</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pep_ulc_exc_bld_2_years_full_1</th>\n",
       "      <td>1.29</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tumour_no_mets_2_years_full_1</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chf_2_years_full_1</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fluid_elec_dis_2_years_full_1</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pcd_2_years_full_1</th>\n",
       "      <td>1.17</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_scu_admits_1_year</th>\n",
       "      <td>1.15</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpd_2_years_full_1</th>\n",
       "      <td>1.14</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pvd_2_years_full_1</th>\n",
       "      <td>1.11</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diab_2_years_full_1</th>\n",
       "      <td>1.11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valv_dis_2_years_full_1</th>\n",
       "      <td>1.11</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coag_2_years_full_1</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ren_fail_2_years_full_1</th>\n",
       "      <td>1.09</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anemia_2_years_full_1</th>\n",
       "      <td>1.07</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psych_2_years_full_1</th>\n",
       "      <td>1.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec_pat_num_age</th>\n",
       "      <td>1.05</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_arrh_2_years_full_1</th>\n",
       "      <td>1.04</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypothyroidism_2_years_full_1</th>\n",
       "      <td>1.04</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lymph_2_years_full_1</th>\n",
       "      <td>1.04</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drug_abuse_2_years_full_1</th>\n",
       "      <td>1.02</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_admits_1_year</th>\n",
       "      <td>1.02</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procs_dad_1_year</th>\n",
       "      <td>1.01</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt_loss_2_years_full_1</th>\n",
       "      <td>1.01</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procs_nacrs_1_year</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collected_in_dsl</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oth_neur_dis_2_years_full_1</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depress_2_years_full_1</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rheum_col_vasc_dis_2_years_full_1</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obes_2_years_full_1</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>htn_c_2_years_full_1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>htn_unc_2_years_full_1</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paral_2_years_full_1</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alc_abuse_2_years_full_1</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsl_resident_during_collection</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aids_hiv_2_years_full_1</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Odds Ratio  [0.025  0.975]  P>|z|\n",
       "y_m__2020-3                              4.62    3.71    5.75  0.000\n",
       "collected_in_ed                          4.54    3.56    5.77  0.000\n",
       "interp_result_Positive                   3.61    3.14    4.15  0.000\n",
       "collected_in_hospital                    3.60    2.76    4.68  0.000\n",
       "y_m__2020-4                              2.96    2.52    3.48  0.000\n",
       "y_m__2020-11                             2.36    1.91    2.90  0.000\n",
       "y_m__2020-12                             2.18    1.78    2.68  0.000\n",
       "y_m__2020-10                             2.15    1.73    2.68  0.000\n",
       "y_m__2020-7                              2.15    1.68    2.76  0.000\n",
       "y_m__2020-5                              2.08    1.71    2.53  0.000\n",
       "y_m__2021-2                              2.04    1.32    3.15  0.001\n",
       "y_m__2021-1                              2.03    1.59    2.58  0.000\n",
       "symptomatic_during_collection_Y          2.02    1.77    2.32  0.000\n",
       "y_m__2020-8                              1.74    1.24    2.44  0.001\n",
       "y_m__2020-9                              1.71    1.24    2.36  0.001\n",
       "cancer_mets_2_years_full_1               1.58    1.14    2.19  0.006\n",
       "patient_gender_M                         1.57    1.42    1.73  0.000\n",
       "liver_dis_2_years_full_1                 1.51    1.15    1.99  0.003\n",
       "y_m__2021-3                              1.47    0.72    3.00  0.295\n",
       "symptomatic_during_collection_U          1.45    1.27    1.65  0.000\n",
       "pep_ulc_exc_bld_2_years_full_1           1.29    0.92    1.81  0.143\n",
       "tumour_no_mets_2_years_full_1            1.21    1.04    1.41  0.014\n",
       "chf_2_years_full_1                       1.20    1.06    1.35  0.003\n",
       "fluid_elec_dis_2_years_full_1            1.20    1.07    1.34  0.002\n",
       "pcd_2_years_full_1                       1.17    0.94    1.46  0.165\n",
       "num_scu_admits_1_year                    1.15    0.83    1.60  0.410\n",
       "cpd_2_years_full_1                       1.14    1.02    1.28  0.020\n",
       "pvd_2_years_full_1                       1.11    0.95    1.29  0.182\n",
       "diab_2_years_full_1                      1.11    1.00    1.23  0.060\n",
       "valv_dis_2_years_full_1                  1.11    0.86    1.43  0.408\n",
       "coag_2_years_full_1                      1.10    0.87    1.39  0.435\n",
       "ren_fail_2_years_full_1                  1.09    0.95    1.25  0.200\n",
       "anemia_2_years_full_1                    1.07    0.93    1.24  0.325\n",
       "psych_2_years_full_1                     1.05    0.94    1.17  0.404\n",
       "spec_pat_num_age                         1.05    1.04    1.05  0.000\n",
       "card_arrh_2_years_full_1                 1.04    0.93    1.17  0.506\n",
       "hypothyroidism_2_years_full_1            1.04    0.90    1.20  0.608\n",
       "lymph_2_years_full_1                     1.04    0.67    1.61  0.864\n",
       "drug_abuse_2_years_full_1                1.02    0.79    1.32  0.893\n",
       "num_admits_1_year                        1.02    0.97    1.08  0.442\n",
       "num_procs_dad_1_year                     1.01    0.97    1.06  0.590\n",
       "wt_loss_2_years_full_1                   1.01    0.83    1.23  0.902\n",
       "num_procs_nacrs_1_year                   1.00    1.00    1.00  0.519\n",
       "collected_in_dsl                         0.97    0.72    1.30  0.825\n",
       "oth_neur_dis_2_years_full_1              0.96    0.88    1.06  0.456\n",
       "depress_2_years_full_1                   0.94    0.86    1.04  0.259\n",
       "rheum_col_vasc_dis_2_years_full_1        0.82    0.74    0.92  0.000\n",
       "obes_2_years_full_1                      0.82    0.62    1.08  0.156\n",
       "htn_c_2_years_full_1                     0.80    0.50    1.27  0.338\n",
       "htn_unc_2_years_full_1                   0.79    0.71    0.88  0.000\n",
       "paral_2_years_full_1                     0.78    0.61    1.00  0.053\n",
       "alc_abuse_2_years_full_1                 0.73    0.57    0.94  0.013\n",
       "dsl_resident_during_collection           0.54    0.41    0.71  0.000\n",
       "aids_hiv_2_years_full_1                  0.45    0.06    3.48  0.445\n",
       "const                                    0.00    0.00    0.00  0.000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.sort_values('Odds Ratio', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0b4f2b",
   "metadata": {},
   "source": [
    "# train the model by balanced  classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a926053a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are no longer any imbalanceness:\n",
      "0    50.0\n",
      "1    50.0\n",
      "Name: died_within_60_days, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.544549\n",
      "         Iterations 6\n",
      "Confusion Matrix : \n",
      " [[1636  667]\n",
      " [  79  176]]\n",
      "Test accuracy =  0.7083659108678655\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.71      0.81      2303\n",
      "           1       0.21      0.69      0.32       255\n",
      "\n",
      "    accuracy                           0.71      2558\n",
      "   macro avg       0.58      0.70      0.57      2558\n",
      "weighted avg       0.88      0.71      0.77      2558\n",
      "\n",
      "F1 Score: 0.3205828779599271\n",
      "AUC: 0.70%\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# only balnce the classes with out normalization \n",
    "\n",
    "X=data_final[feature_list_for_training]\n",
    "\n",
    "\n",
    "X=sm.add_constant(X)\n",
    "#split to test and training \n",
    "# x_train : features for the training the model\n",
    "# x_test :features for testing the model \n",
    "\n",
    "# y_train: the labels for the training the model\n",
    "# y_test: the labels for the testing  the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "\n",
    "#only oversample for the trian sets\n",
    "\n",
    "scaler_PT = PowerTransformer() \n",
    "over_sampler = RandomOverSampler(random_state=42)\n",
    "X_train, y_train = over_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are no longer any imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification\n",
    "prediction = list(map(round, yhat))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4792024",
   "metadata": {},
   "source": [
    "# Adjusted Odds Ratios for the data with balanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09e73b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming you have already fitted your logistic regression model and stored it in log_reg_model\n",
    "# For example: log_reg_model = sm.Logit(y, X).fit()\n",
    "\n",
    "# Get the summary of the logistic regression model\n",
    "log_reg_summary = log_reg.summary()\n",
    "\n",
    "# Convert the summary to HTML\n",
    "html_summary = log_reg_summary.tables[1].as_html()\n",
    "\n",
    "# Read the HTML table into a DataFrame\n",
    "df_summary = pd.read_html(html_summary, header=0, index_col=0)[0]\n",
    "\n",
    "# Calculate the odds ratio and add it to the DataFrame\n",
    "df_summary['Odds Ratio'] = df_summary['coef'].apply(lambda x: round(np.exp(x), 2))\n",
    "df_summary['[0.025'] = df_summary['[0.025'].apply(lambda x: round(np.exp(x), 2))\n",
    "df_summary['0.975]'] = df_summary['0.975]'].apply(lambda x: round(np.exp(x), 2))\n",
    "df_summary=df_summary[['Odds Ratio','[0.025', '0.975]','P>|z|']]\n",
    "# Display the DataFrame with the odds ratio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76fe051c",
   "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>Odds Ratio</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>collected_in_ed</th>\n",
       "      <td>5.01</td>\n",
       "      <td>4.34</td>\n",
       "      <td>5.79</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-3</th>\n",
       "      <td>4.95</td>\n",
       "      <td>4.43</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interp_result_Positive</th>\n",
       "      <td>4.16</td>\n",
       "      <td>3.87</td>\n",
       "      <td>4.49</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collected_in_hospital</th>\n",
       "      <td>3.62</td>\n",
       "      <td>3.09</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-4</th>\n",
       "      <td>3.28</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symptomatic_during_collection_Y</th>\n",
       "      <td>2.31</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-7</th>\n",
       "      <td>2.16</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-10</th>\n",
       "      <td>2.14</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-12</th>\n",
       "      <td>2.05</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-5</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-11</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2021-1</th>\n",
       "      <td>1.88</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2021-2</th>\n",
       "      <td>1.84</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cancer_mets_2_years_full_1</th>\n",
       "      <td>1.68</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symptomatic_during_collection_U</th>\n",
       "      <td>1.56</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-9</th>\n",
       "      <td>1.55</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2020-8</th>\n",
       "      <td>1.55</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_gender_M</th>\n",
       "      <td>1.52</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_m__2021-3</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liver_dis_2_years_full_1</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pep_ulc_exc_bld_2_years_full_1</th>\n",
       "      <td>1.27</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tumour_no_mets_2_years_full_1</th>\n",
       "      <td>1.27</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fluid_elec_dis_2_years_full_1</th>\n",
       "      <td>1.23</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpd_2_years_full_1</th>\n",
       "      <td>1.22</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chf_2_years_full_1</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ren_fail_2_years_full_1</th>\n",
       "      <td>1.16</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anemia_2_years_full_1</th>\n",
       "      <td>1.13</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diab_2_years_full_1</th>\n",
       "      <td>1.13</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pvd_2_years_full_1</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pcd_2_years_full_1</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_arrh_2_years_full_1</th>\n",
       "      <td>1.08</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valv_dis_2_years_full_1</th>\n",
       "      <td>1.06</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec_pat_num_age</th>\n",
       "      <td>1.05</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procs_dad_1_year</th>\n",
       "      <td>1.05</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypothyroidism_2_years_full_1</th>\n",
       "      <td>1.05</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_admits_1_year</th>\n",
       "      <td>1.04</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lymph_2_years_full_1</th>\n",
       "      <td>1.03</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psych_2_years_full_1</th>\n",
       "      <td>1.01</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_procs_nacrs_1_year</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drug_abuse_2_years_full_1</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_scu_admits_1_year</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oth_neur_dis_2_years_full_1</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coag_2_years_full_1</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt_loss_2_years_full_1</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depress_2_years_full_1</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>htn_c_2_years_full_1</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obes_2_years_full_1</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collected_in_dsl</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>htn_unc_2_years_full_1</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rheum_col_vasc_dis_2_years_full_1</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alc_abuse_2_years_full_1</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paral_2_years_full_1</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aids_hiv_2_years_full_1</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsl_resident_during_collection</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Odds Ratio  [0.025  0.975]  P>|z|\n",
       "collected_in_ed                          5.01    4.34    5.79  0.000\n",
       "y_m__2020-3                              4.95    4.43    5.52  0.000\n",
       "interp_result_Positive                   4.16    3.87    4.49  0.000\n",
       "collected_in_hospital                    3.62    3.09    4.25  0.000\n",
       "y_m__2020-4                              3.28    3.05    3.52  0.000\n",
       "symptomatic_during_collection_Y          2.31    2.15    2.48  0.000\n",
       "y_m__2020-7                              2.16    1.92    2.43  0.000\n",
       "y_m__2020-10                             2.14    1.93    2.37  0.000\n",
       "y_m__2020-12                             2.05    1.85    2.26  0.000\n",
       "y_m__2020-5                              2.00    1.83    2.19  0.000\n",
       "y_m__2020-11                             2.00    1.81    2.22  0.000\n",
       "y_m__2021-1                              1.88    1.67    2.13  0.000\n",
       "y_m__2021-2                              1.84    1.49    2.26  0.000\n",
       "cancer_mets_2_years_full_1               1.68    1.42    2.00  0.000\n",
       "symptomatic_during_collection_U          1.56    1.46    1.67  0.000\n",
       "y_m__2020-9                              1.55    1.34    1.81  0.000\n",
       "y_m__2020-8                              1.55    1.33    1.82  0.000\n",
       "patient_gender_M                         1.52    1.45    1.59  0.000\n",
       "y_m__2021-3                              1.42    1.04    1.94  0.029\n",
       "liver_dis_2_years_full_1                 1.38    1.19    1.59  0.000\n",
       "pep_ulc_exc_bld_2_years_full_1           1.27    1.06    1.52  0.010\n",
       "tumour_no_mets_2_years_full_1            1.27    1.17    1.37  0.000\n",
       "fluid_elec_dis_2_years_full_1            1.23    1.16    1.30  0.000\n",
       "cpd_2_years_full_1                       1.22    1.15    1.29  0.000\n",
       "chf_2_years_full_1                       1.21    1.14    1.29  0.000\n",
       "ren_fail_2_years_full_1                  1.16    1.08    1.24  0.000\n",
       "anemia_2_years_full_1                    1.13    1.05    1.21  0.001\n",
       "diab_2_years_full_1                      1.13    1.07    1.19  0.000\n",
       "pvd_2_years_full_1                       1.10    1.02    1.19  0.014\n",
       "pcd_2_years_full_1                       1.10    0.98    1.24  0.100\n",
       "card_arrh_2_years_full_1                 1.08    1.02    1.14  0.013\n",
       "valv_dis_2_years_full_1                  1.06    0.92    1.21  0.419\n",
       "spec_pat_num_age                         1.05    1.04    1.05  0.000\n",
       "num_procs_dad_1_year                     1.05    1.02    1.07  0.000\n",
       "hypothyroidism_2_years_full_1            1.05    0.98    1.12  0.206\n",
       "num_admits_1_year                        1.04    1.01    1.07  0.004\n",
       "lymph_2_years_full_1                     1.03    0.83    1.29  0.783\n",
       "psych_2_years_full_1                     1.01    0.96    1.07  0.765\n",
       "num_procs_nacrs_1_year                   1.00    1.00    1.00  0.010\n",
       "drug_abuse_2_years_full_1                0.99    0.88    1.13  0.932\n",
       "num_scu_admits_1_year                    0.98    0.82    1.16  0.805\n",
       "oth_neur_dis_2_years_full_1              0.96    0.91    1.01  0.083\n",
       "coag_2_years_full_1                      0.96    0.85    1.09  0.544\n",
       "wt_loss_2_years_full_1                   0.95    0.86    1.05  0.333\n",
       "depress_2_years_full_1                   0.91    0.87    0.96  0.000\n",
       "htn_c_2_years_full_1                     0.90    0.71    1.14  0.375\n",
       "obes_2_years_full_1                      0.86    0.76    0.98  0.019\n",
       "collected_in_dsl                         0.85    0.71    1.00  0.053\n",
       "htn_unc_2_years_full_1                   0.78    0.75    0.83  0.000\n",
       "rheum_col_vasc_dis_2_years_full_1        0.77    0.73    0.81  0.000\n",
       "alc_abuse_2_years_full_1                 0.74    0.66    0.83  0.000\n",
       "paral_2_years_full_1                     0.69    0.62    0.78  0.000\n",
       "aids_hiv_2_years_full_1                  0.60    0.25    1.40  0.233\n",
       "dsl_resident_during_collection           0.57    0.48    0.67  0.000\n",
       "const                                    0.01    0.00    0.01  0.000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.sort_values('Odds Ratio', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96f4bbb",
   "metadata": {},
   "source": [
    "# summary of the model trained by balanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a9e12cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Logit Regression Results                           \n",
      "===============================================================================\n",
      "Dep. Variable:     died_within_60_days   No. Observations:                41374\n",
      "Model:                           Logit   Df Residuals:                    41319\n",
      "Method:                            MLE   Df Model:                           54\n",
      "Date:                 Tue, 01 Aug 2023   Pseudo R-squ.:                  0.2144\n",
      "Time:                         13:33:06   Log-Likelihood:                -22530.\n",
      "converged:                        True   LL-Null:                       -28678.\n",
      "Covariance Type:             nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "const                                -5.1568      0.114    -45.066      0.000      -5.381      -4.933\n",
      "spec_pat_num_age                      0.0461      0.001     37.035      0.000       0.044       0.048\n",
      "patient_gender_M                      0.4172      0.025     16.916      0.000       0.369       0.466\n",
      "collected_in_ed                       1.6114      0.074     21.834      0.000       1.467       1.756\n",
      "collected_in_hospital                 1.2875      0.082     15.677      0.000       1.127       1.448\n",
      "collected_in_dsl                     -0.1683      0.087     -1.933      0.053      -0.339       0.002\n",
      "dsl_resident_during_collection       -0.5694      0.083     -6.825      0.000      -0.733      -0.406\n",
      "symptomatic_during_collection_U       0.4445      0.034     13.023      0.000       0.378       0.511\n",
      "symptomatic_during_collection_Y       0.8360      0.036     23.113      0.000       0.765       0.907\n",
      "interp_result_Positive                1.4262      0.038     37.631      0.000       1.352       1.501\n",
      "y_m__2020-10                          0.7613      0.052     14.505      0.000       0.658       0.864\n",
      "y_m__2020-11                          0.6942      0.052     13.360      0.000       0.592       0.796\n",
      "y_m__2020-12                          0.7162      0.051     14.080      0.000       0.616       0.816\n",
      "y_m__2020-3                           1.5990      0.056     28.520      0.000       1.489       1.709\n",
      "y_m__2020-4                           1.1875      0.037     32.532      0.000       1.116       1.259\n",
      "y_m__2020-5                           0.6944      0.046     15.046      0.000       0.604       0.785\n",
      "y_m__2021-3                           0.3485      0.160      2.182      0.029       0.035       0.662\n",
      "y_m__2020-7                           0.7690      0.060     12.737      0.000       0.651       0.887\n",
      "y_m__2020-8                           0.4408      0.081      5.470      0.000       0.283       0.599\n",
      "y_m__2020-9                           0.4412      0.078      5.691      0.000       0.289       0.593\n",
      "y_m__2021-1                           0.6326      0.062     10.197      0.000       0.511       0.754\n",
      "y_m__2021-2                           0.6074      0.107      5.690      0.000       0.398       0.817\n",
      "chf_2_years_full_1                    0.1945      0.031      6.296      0.000       0.134       0.255\n",
      "htn_unc_2_years_full_1               -0.2425      0.026     -9.391      0.000      -0.293      -0.192\n",
      "cancer_mets_2_years_full_1            0.5203      0.088      5.922      0.000       0.348       0.693\n",
      "fluid_elec_dis_2_years_full_1         0.2064      0.029      7.161      0.000       0.150       0.263\n",
      "card_arrh_2_years_full_1              0.0727      0.029      2.487      0.013       0.015       0.130\n",
      "valv_dis_2_years_full_1               0.0545      0.067      0.808      0.419      -0.078       0.187\n",
      "pcd_2_years_full_1                    0.0984      0.060      1.645      0.100      -0.019       0.216\n",
      "pvd_2_years_full_1                    0.0958      0.039      2.452      0.014       0.019       0.172\n",
      "htn_c_2_years_full_1                 -0.1056      0.119     -0.887      0.375      -0.339       0.128\n",
      "paral_2_years_full_1                 -0.3669      0.061     -6.057      0.000      -0.486      -0.248\n",
      "oth_neur_dis_2_years_full_1          -0.0419      0.024     -1.732      0.083      -0.089       0.006\n",
      "cpd_2_years_full_1                    0.1967      0.029      6.724      0.000       0.139       0.254\n",
      "diab_2_years_full_1                   0.1186      0.027      4.413      0.000       0.066       0.171\n",
      "hypothyroidism_2_years_full_1         0.0444      0.035      1.265      0.206      -0.024       0.113\n",
      "ren_fail_2_years_full_1               0.1480      0.035      4.267      0.000       0.080       0.216\n",
      "liver_dis_2_years_full_1              0.3203      0.073      4.392      0.000       0.177       0.463\n",
      "pep_ulc_exc_bld_2_years_full_1        0.2391      0.093      2.571      0.010       0.057       0.421\n",
      "aids_hiv_2_years_full_1              -0.5191      0.435     -1.194      0.233      -1.372       0.333\n",
      "lymph_2_years_full_1                  0.0310      0.112      0.276      0.783      -0.189       0.251\n",
      "tumour_no_mets_2_years_full_1         0.2359      0.039      5.997      0.000       0.159       0.313\n",
      "rheum_col_vasc_dis_2_years_full_1    -0.2606      0.027     -9.588      0.000      -0.314      -0.207\n",
      "coag_2_years_full_1                  -0.0374      0.062     -0.606      0.544      -0.158       0.084\n",
      "obes_2_years_full_1                  -0.1525      0.065     -2.340      0.019      -0.280      -0.025\n",
      "wt_loss_2_years_full_1               -0.0493      0.051     -0.969      0.333      -0.149       0.050\n",
      "anemia_2_years_full_1                 0.1203      0.037      3.231      0.001       0.047       0.193\n",
      "alc_abuse_2_years_full_1             -0.2995      0.059     -5.056      0.000      -0.416      -0.183\n",
      "drug_abuse_2_years_full_1            -0.0053      0.063     -0.085      0.932      -0.128       0.118\n",
      "psych_2_years_full_1                  0.0083      0.028      0.299      0.765      -0.046       0.063\n",
      "depress_2_years_full_1               -0.0897      0.025     -3.649      0.000      -0.138      -0.042\n",
      "num_admits_1_year                     0.0409      0.014      2.888      0.004       0.013       0.069\n",
      "num_scu_admits_1_year                -0.0218      0.088     -0.248      0.805      -0.194       0.151\n",
      "num_procs_dad_1_year                  0.0452      0.011      4.191      0.000       0.024       0.066\n",
      "num_procs_nacrs_1_year               -0.0014      0.001     -2.572      0.010      -0.002      -0.000\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# print summary of the model trained with balanced data\n",
    "print(log_reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714a2d42",
   "metadata": {},
   "source": [
    "# train the model by normalization and balanced  classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c1a5225",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Classes percentage in the training as there are no longer any imbalanceness:\n",
      "0    50.0\n",
      "1    50.0\n",
      "Name: died_within_60_days, dtype: float64\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.545353\n",
      "         Iterations 6\n",
      "Confusion Matrix : \n",
      " [[1633  670]\n",
      " [  79  176]]\n",
      "Test accuracy =  0.7071931196247068\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.71      0.81      2303\n",
      "           1       0.21      0.69      0.32       255\n",
      "\n",
      "    accuracy                           0.71      2558\n",
      "   macro avg       0.58      0.70      0.57      2558\n",
      "weighted avg       0.88      0.71      0.76      2558\n",
      "\n",
      "F1 Score: 0.31970935513169846\n",
      "AUC: 0.70%\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# only balnce the classes with out normalization \n",
    "X=data_final[feature_list_for_training]\n",
    "\n",
    "\n",
    "# normalize the x features \n",
    "X = scaler_PT.fit_transform(X)\n",
    "X=sm.add_constant(X)\n",
    "\n",
    "#split to test and training \n",
    "# x_train : features for the training the model\n",
    "# x_test :features for testing the model \n",
    "\n",
    "# y_train: the labels for the training the model\n",
    "# y_test: the labels for the testing  the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "\n",
    "\n",
    "#only oversample for the trian sets\n",
    "scaler_PT = PowerTransformer() \n",
    "over_sampler = RandomOverSampler(random_state=42)\n",
    "X_train, y_train = over_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "######################################\n",
    "\n",
    "print(\"### Classes percentage in the training as there are no longer any imbalanceness:\") \n",
    "print(y_train.value_counts()*100/len(y_train))\n",
    "\n",
    "\n",
    "# fit the model\n",
    "log_reg=sm.Logit(y_train,X_train ).fit()\n",
    "\n",
    "# after fitting the model, we need to test the model on the x_test (y_hat are the predicted probabilities)\n",
    "yhat = log_reg.predict(X_test)\n",
    "# recieve the labels from the y_hat for the classification\n",
    "prediction = list(map(round, yhat))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, prediction)\n",
    "print (\"Confusion Matrix : \\n\", cm)\n",
    "\n",
    "# accuracy score of the model\n",
    "print('Test accuracy = ', accuracy_score(y_test, prediction))\n",
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "\n",
    "print(\"F1 Score: {}\".format(f1_score(y_test, prediction)))\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, prediction)\n",
    "print('AUC: %.2f' % roc_auc + \"%\")\n",
    "\n",
    "\n",
    "\n",
    "# Visualisation with plot_metric\n",
    "bc = BinaryClassification(y_test, prediction, labels = [\"Class 1\", \"Class 2\"])\n",
    "\n",
    "# Figures\n",
    "plt.figure(figsize = (5,5))\n",
    "bc.plot_roc_curve()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f57b6e68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}