notebooks/threshold-queue-rate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab958018",
   "metadata": {},
   "outputs": [],
   "source": [
    "from equalityml import FAIR, discrimination_threshold, paired_ttest, binary_threshold_score\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.base import clone\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "%matplotlib inline\n",
    "random_seed = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450e2392",
   "metadata": {},
   "source": [
    "### 1. Train a Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "417cb257-bbcf-4103-b0ec-9d9e13e52189",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset Parameters\n",
    "target_var = 'two_year_recid'\n",
    "features = ['juv_fel_count', 'decile_score', 'juv_misd_count', 'juv_other_count', 'priors_count', 'sex', 'age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90e2c41e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/compas-scores-two-years.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "033f1b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[target_var]\n",
    "X = data[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37c48622-835b-4256-966e-2d38f411a1da",
   "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>juv_fel_count</th>\n",
       "      <th>decile_score</th>\n",
       "      <th>juv_misd_count</th>\n",
       "      <th>juv_other_count</th>\n",
       "      <th>priors_count</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   juv_fel_count  decile_score  juv_misd_count  juv_other_count  priors_count  \\\n",
       "0              0             1               0                0             0   \n",
       "1              0             3               0                0             0   \n",
       "2              0             4               0                1             4   \n",
       "3              0             8               1                0             1   \n",
       "4              0             1               0                0             2   \n",
       "\n",
       "    sex  age  \n",
       "0  Male   69  \n",
       "1  Male   34  \n",
       "2  Male   24  \n",
       "3  Male   23  \n",
       "4  Male   43  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "392e1749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define which columns should be encoded\n",
    "columns_to_encode = ['sex']\n",
    "\n",
    "# Instantiate encoder\n",
    "ohe = OneHotEncoder(sparse=False, drop = 'if_binary')\n",
    "\n",
    "# Scale \n",
    "encoded_data = ohe.fit_transform(X[columns_to_encode])\n",
    "\n",
    "# Concatenate (Column-Bind) Processed Columns Back Together\n",
    "encoded_df = pd.DataFrame(encoded_data, columns = columns_to_encode)\n",
    "\n",
    "processed_X = pd.concat([X.drop(columns=columns_to_encode), encoded_df], axis=1)\n",
    "processed_X = pd.DataFrame(processed_X, columns = X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22d95660-9706-4fca-9700-7675de5e2e18",
   "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>juv_fel_count</th>\n",
       "      <th>decile_score</th>\n",
       "      <th>juv_misd_count</th>\n",
       "      <th>juv_other_count</th>\n",
       "      <th>priors_count</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   juv_fel_count  decile_score  juv_misd_count  juv_other_count  priors_count  \\\n",
       "0              0             1               0                0             0   \n",
       "1              0             3               0                0             0   \n",
       "2              0             4               0                1             4   \n",
       "3              0             8               1                0             1   \n",
       "4              0             1               0                0             2   \n",
       "\n",
       "   sex  age  \n",
       "0  1.0   69  \n",
       "1  1.0   34  \n",
       "2  1.0   24  \n",
       "3  1.0   23  \n",
       "4  1.0   43  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "721e8581",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and testing dataset split\n",
    "X_train, X_test, y_train, y_test = train_test_split(processed_X, y, test_size=0.2, random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c28d445",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a machine learning model (for example LogisticRegression)\n",
    "orig_ml_model = LogisticRegression()\n",
    "orig_ml_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ced551e-100f-486c-9890-d01e2cd47d92",
   "metadata": {},
   "source": [
    "### 2. Optimal Discrimination Threshold for Queue Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69cfde39-494f-455c-9c78-7d517caf0dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  6.08it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHFCAYAAAAe+pb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACPFUlEQVR4nOzdeXhU9dn/8feZPetk3/ewhR2CQEBEVFBU1NbWvYhFK7U+FtCqaOtWH/lpq/VxQdviUi3urba2qODCJouQENYAIfu+J5M9s5zfH5GRIWELSSaT3K/rmqudk+85556Mk/lwzndRVFVVEUIIIYQYgjTuLkAIIYQQwl0kCAkhhBBiyJIgJIQQQoghS4KQEEIIIYYsCUJCCCGEGLIkCAkhhBBiyJIgJIQQQoghS4KQEEIIIYYsCUJCCCGEGLIkCIl+pSjKGT02bNjAhg0bUBSFjz76yN1lA/RJPY899hiKopxR24SEBBYtWnTadif+Ls1mMxdeeCH//e9/e3S8E7W0tPDYY4+xYcOGM95n9+7dzJ49G7PZjKIoPP/882d93rOhKAp33313n57jRG+++SaKopCfn39W+z311FN88sknXbYf++/tbH7Px9dx7KHT6YiMjOSGG24gOzv7rI7lyc7msyWGNp27CxBDy7Zt21ye//73v+ebb77h66+/dtk+evRoMjIy+rO0QeUnP/kJ9957Lw6Hg9zcXJ588kkWLFjAp59+yhVXXHFOx25paeHxxx8H4MILLzyjfX7+85/T3NzMe++9R2BgIAkJCedUw0B0xRVXsG3bNiIjI89qv6eeeoqf/OQnXHPNNS7bJ0+ezLZt2xg9enSP6nnjjTcYNWoUbW1tfPvtt/zv//4v33zzDYcOHSIwMLBHx/Qkt99+O5dddpm7yxAeQIKQ6FfTp093eR4aGopGo+myvTe0tLTg7e3d68f1BOHh4c7f6YwZM0hLS2PYsGE8//zz5xyEemL//v3ccccdzJ8/v1eOZ7VanVc7BorQ0FBCQ0N77Xj+/v7n9LkYO3YsU6ZMAToDq91u59FHH+WTTz7htttu660yz0hrayteXl79es6YmBhiYmL69ZzCM8mtMTHgWa1WHn74YaKiovD39+eSSy7h8OHDLm0uvPBCxo4dy6ZNm5gxYwbe3t78/Oc/B8BisXDfffeRmJiIwWAgOjqapUuX0tzc7HKMDz/8kGnTpmE2m/H29iYpKcl5jLOtB+D1119nwoQJmEwmgoKC+NGPfkRWVtYZvd7777+fiIgIvL29Of/88/nuu+/O5lfWRXJyMqGhoRQUFJyyXWFhIbfccgthYWEYjUZSUlJ49tlncTgcAOTn5zu/7B9//HHn7ZeT3WI7dpvGZrPxyiuvONsfs3//fq6++moCAwMxmUxMnDiRv/3tby7HOHaL6O233+bee+8lOjoao9HI0aNHz+E3ArW1tdx1111ER0djMBhISkri4Ycfpr293aVdfX09ixcvJigoCF9fX6644gpyc3NRFIXHHnusy2s9/tbY7t27ufLKK52/z6ioKK644gqKi4uBzlt4zc3N/O1vf3P+bo5dZTvZrbEdO3awYMECgoODMZlMJCcns3Tp0tO+3mOhqKKiwmX7rl27uOqqqwgKCsJkMjFp0iQ++OCDLvtv2bKFtLQ0TCYT0dHR/O53v2P16tVdXnNCQgJXXnkl//znP5k0aRImk8l5BbG8vJw777yTmJgYDAYDiYmJPP7449hsNpdzvfLKK0yYMAFfX1/8/PwYNWoUDz30kPPnLS0tzs/0sc/XlClTePfdd51turs15nA4eOaZZxg1ahRGo5GwsDAWLlzofD+OOfb3ZOfOncyaNcv59+D//b//5/wsiMFj4PxzSoiTeOihh5g5cyarV6/GYrHwwAMPsGDBArKystBqtc52ZWVl3HLLLdx///089dRTaDQaWlpamD17NsXFxTz00EOMHz+eAwcO8Mgjj7Bv3z6+/PJLFEVh27ZtXH/99Vx//fU89thjmEwmCgoKutyyO9N6Vq5cyUMPPcSNN97IypUrqamp4bHHHiMtLY2dO3cyfPjwk77eO+64g7feeov77ruPuXPnsn//fn784x/T2NjY499hXV0dNTU1pzxvVVUVM2bMoKOjg9///vckJCTwn//8h/vuu4+cnBxWrVpFZGQkn3/+OZdddhmLFy/m9ttvBzjplZBjt4vS0tKct+uOOXz4MDNmzCAsLIwXXniB4OBg/v73v7No0SIqKiq4//77XY61YsUK0tLSePXVV9FoNISFhfX499HW1sacOXPIycnh8ccfZ/z48WzevJmVK1eSmZnp7E/lcDhYsGABu3bt4rHHHnPerjqTWy7Nzc3MnTuXxMREXn75ZcLDwykvL+ebb75xvpfbtm3joosuYs6cOfzud78DOq8EncwXX3zBggULSElJ4bnnniMuLo78/HzWrVt32nry8vIAGDFihHPbN998w2WXXca0adN49dVXMZvNvPfee1x//fW0tLQ4A+7evXuZO3cuI0aM4G9/+xve3t68+uqr/P3vf+/2XBkZGWRlZfHb3/6WxMREfHx8KC8vZ+rUqWg0Gh555BGSk5PZtm0bTz75JPn5+bzxxhsAvPfee9x11138z//8D3/84x/RaDQcPXqUgwcPOo+/fPly3n77bZ588kkmTZpEc3Mz+/fvp6am5pS/g1/+8pf85S9/4e677+bKK68kPz+f3/3ud2zYsIGMjAxCQkKcbcvLy7n55pu59957efTRR/n4449ZsWIFUVFRLFy48LS/b+FBVCHc6NZbb1V9fHy6/dk333yjAurll1/usv2DDz5QAXXbtm3ObbNnz1YB9auvvnJpu3LlSlWj0ag7d+502f7RRx+pgLp27VpVVVX1j3/8owqo9fX1J631TOupq6tTvby8urQrLCxUjUajetNNNzm3Pfroo+rxH8OsrCwVUJctW+ay75o1a1RAvfXWW09a3zGAetddd6lWq1Xt6OhQs7Ky1Pnz56uA+vLLLzvbxcfHuxzvwQcfVAF1x44dLsf75S9/qSqKoh4+fFhVVVWtqqpSAfXRRx89bS3H1/SrX/3KZdsNN9ygGo1GtbCw0GX7/PnzVW9vb+d7cez3fsEFF5zT+Y736quvqoD6wQcfuGx/+umnVUBdt26dqqqq+t///lcF1FdeecWl3cqVK7v8Dt544w0VUPPy8lRVVdVdu3apgPrJJ5+cslYfH59u39djr/ubb75xbktOTlaTk5PV1tbWkx7vWB3bt29XrVar2tjYqH7++edqRESEesEFF6hWq9XZdtSoUeqkSZNctqmqql555ZVqZGSkarfbVVVV1Z/+9Keqj4+PWlVV5Wxjt9vV0aNHu7xmVe3870qr1Tr/eznmzjvvVH19fdWCggKX7cc+ewcOHFBVVVXvvvtuNSAg4KSvT1VVdezYseo111xzyjYn+2zdddddLu127NihAupDDz3k3Hbs78mJn4XRo0erl1566SnPKzyP3BoTA95VV13l8nz8+PEAXW7zBAYGctFFF7ls+89//sPYsWOZOHEiNpvN+bj00ktdbjucd955AFx33XV88MEHlJSU9Liebdu20dra2uV2UWxsLBdddBFfffXVSY/9zTffAHDzzTe7bL/uuuvOqj/MqlWr0Ov1GAwGUlJS2Lp1K0888QR33XXXSff5+uuvGT16NFOnTnXZvmjRIlRV7fbq2Ln4+uuvufjii4mNje1yvpaWli4d66+99tpePbePjw8/+clPupwbcL5HGzduBDp//8e78cYbT3uOYcOGERgYyAMPPMCrr77qckWjJ44cOUJOTg6LFy/GZDKdtv306dPR6/X4+flx2WWXERgYyL/+9S/nf0dHjx7l0KFDzv/Wjv98XH755ZSVlTlv+W7cuJGLLrrI5YqJRqPp8ns5Zvz48S5XnqDzszhnzhyioqJcznWs39ix3/XUqVOpr6/nxhtv5F//+hfV1dVdjj916lQ+++wzHnzwQTZs2EBra+tpfx/HPlsnfi6nTp1KSkpKl89lREREl8/C+PHjT3t7WXgeCUJiwAsODnZ5bjQaAbr88etutE5FRQV79+5Fr9e7PPz8/FBV1flH9oILLuCTTz7BZrOxcOFCYmJiGDt2rEufgzOt59jl+e7qiYqKOuXl+2M/i4iIcNmu0+m6nPdUrrvuOnbu3MmuXbs4fPgwNTU1zlsvpzr3yWo+vrbecrbnO9vRWKc7d0RERJc+JGFhYeh0Oue5a2pq0Ol0BAUFubQLDw8/7TnMZjMbN25k4sSJPPTQQ4wZM4aoqCgeffRRrFbrWddcVVUFcMYdgN966y127tzJ119/zZ133klWVpZLgDvWV+i+++7r8vk4FpiPfT5qamq6fc0n+z2c7LP46aefdjnXmDFjXM71s5/9jNdff52CggKuvfZawsLCmDZtGuvXr3ce64UXXuCBBx7gk08+Yc6cOQQFBXHNNdeccnqAs/1cdvd5MxqNZxS6hGeRPkJi0OhuzpCQkBC8vLx4/fXXu93n+H/hXn311Vx99dW0t7ezfft2Vq5cyU033URCQgJpaWlnXMexP6BlZWVdflZaWupyzpPtW15eTnR0tHO7zWY7qyASGhrq7Bx7poKDg09aM3DKunvibM/Xm3PCBAcHs2PHDlRVdTluZWUlNpvNee7g4GBsNhu1tbUuYai8vPyMzjNu3Djee+89VFVl7969vPnmmzzxxBN4eXnx4IMPnlXNx/phndix92RSUlKc/w3MmTMHu93O6tWr+eijj/jJT37ifI0rVqzgxz/+cbfHGDlyJND5ezixkzWc/Pdwss/i+PHj+d///d9u9zkWgAFuu+02brvtNpqbm9m0aROPPvooV155JUeOHCE+Ph4fHx8ef/xxHn/8cSoqKpxXhxYsWMChQ4e6Pf7xn8sTw+TpPpdicJMrQmJQu/LKK8nJySE4OJgpU6Z0eXQ3n43RaGT27Nk8/fTTQOfIn7ORlpaGl5dXl46kxcXFzttBJ3NsxNCaNWtctn/wwQddRtb0tosvvpiDBw92mb/prbfeQlEU5syZA5z8ilxPzvf11187g8/x5/P29u6TKRWOP3dTU1OXiQzfeust588BZs+eDcD777/v0u699947q/MpisKECRP405/+REBAgMvv+EyvMowYMYLk5GRef/31LiPbzsQzzzxDYGAgjzzyCA6Hg5EjRzJ8+HD27NnT7WdjypQp+Pn5AZ2/h6+//trlNpXD4eDDDz884/NfeeWV7N+/n+Tk5G7PdXwQOsbHx4f58+fz8MMP09HRwYEDB7q0CQ8PZ9GiRdx4440cPnyYlpaWbs9/7Lb5iZ/LnTt3kpWVdcrPpRjc5IqQGNSWLl3KP/7xDy644AKWLVvG+PHjcTgcFBYWsm7dOu69916mTZvGI488QnFxMRdffDExMTHU19fzf//3f+j1eueX4ZkKCAjgd7/7HQ899BALFy7kxhtvpKamhscffxyTycSjjz560n1TUlK45ZZbeP7559Hr9VxyySXs37+fP/7xj6ccTdQbli1bxltvvcUVV1zBE088QXx8PP/9739ZtWoVv/zlL519Pvz8/IiPj+df//oXF198MUFBQYSEhJz1JImPPvqos9/II488QlBQEGvWrOG///0vzzzzDGaz+ZxeT05OTrezgI8ePZqFCxfy8ssvc+utt5Kfn8+4cePYsmULTz31FJdffjmXXHIJAJdddhkzZ87k3nvvxWKxkJqayrZt25yBSaM5+b8l//Of/7Bq1SquueYakpKSUFWVf/7zn9TX1zN37lxnu3HjxrFhwwY+/fRTIiMj8fPzc16JOdHLL7/MggULmD59OsuWLSMuLo7CwkK++OKLLuH5RIGBgaxYsYL777+fd955h1tuuYU///nPzJ8/n0svvZRFixYRHR1NbW0tWVlZZGRkOIPOww8/zKeffsrFF1/Mww8/jJeXF6+++qpzCopT/R6OeeKJJ1i/fj0zZszgnnvuYeTIkbS1tZGfn8/atWt59dVXiYmJ4Y477sDLy4uZM2cSGRlJeXk5K1euxGw2O/vyTZs2jSuvvJLx48cTGBhIVlYWb7/9NmlpaSedO2zkyJH84he/4MUXX0Sj0TB//nznqLHY2FiWLVt22tcgBim3dtUWQ96ZjBr78MMPXbbn5eWpgPrGG284t82ePVsdM2ZMt8dpampSf/vb36ojR45UDQaDajab1XHjxqnLli1Ty8vLVVVV1f/85z/q/Pnz1ejoaNVgMKhhYWHq5Zdfrm7evLlH9aiqqq5evVodP36885xXX321c2TMMSeObFFVVW1vb1fvvfdeNSwsTDWZTOr06dPVbdu2dRnldTKcZsTUMd0dr6CgQL3pppvU4OBgVa/XqyNHjlT/8Ic/OEcPHfPll1+qkyZNUo1G4xmNZjtZTfv27VMXLFigms1m1WAwqBMmTOjyezzZ7/105zvZ49hIr5qaGnXJkiVqZGSkqtPp1Pj4eHXFihVqW1uby7Fqa2vV2267TQ0ICFC9vb3VuXPnqtu3b1cB9f/+7/+c7U4cNXbo0CH1xhtvVJOTk1UvLy/VbDarU6dOVd98802X42dmZqozZ85Uvb29VUCdPXu2y+s+ftSYqqrqtm3b1Pnz56tms1k1Go1qcnKyyyjDY3WcOFJSVVW1tbVVjYuLU4cPH67abDZVVVV1z5496nXXXaeGhYWper1ejYiIUC+66CL11Vdfddl38+bN6rRp01Sj0ahGRESov/nNb5yj7I4fbRkfH69eccUV3b4vVVVV6j333KMmJiaqer1eDQoKUlNTU9WHH35YbWpqUlVVVf/2t7+pc+bMUcPDw1WDwaBGRUWp1113nbp3717ncR588EF1ypQpamBgoGo0GtWkpCR12bJlanV1tbNNd58tu92uPv300+qIESNUvV6vhoSEqLfccotaVFTk0u5kf09uvfVWNT4+vtvXJjyXoqqq2o+5SwghPN4777zDzTffzLfffsuMGTPcXY7bzJs3j/z8fI4cOeLuUoToMbk1JoQQp/Duu+9SUlLCuHHj0Gg0bN++nT/84Q9ccMEFQyoELV++nEmTJhEbG0ttbS1r1qxh/fr1vPbaa+4uTYhzIkFICCFOwc/Pj/fee48nn3yS5uZmIiMjWbRoEU8++aS7S+tXdrudRx55hPLychRFYfTo0bz99tvccsst7i5NiHMit8aEEEIIMWTJ8HkhhBBCDFkShIQQQggxZEkQEkIIIcSQNeQ6SzscDkpLS/Hz8+vVKfuFEEII0XdUVaWxsZGoqKgzmsTzTA25IFRaWtpltWshhBBCeIaioqIzXnz4TAy5IHRs7ZyioqI+X7JACCFE38jMzGT27Nls3LiRiRMnursc0Q8sFguxsbHO7/HeMuSC0LHbYf7+/hKEhBDCQ40cOZJnn32WkSNHyt/yIaa3u7UMuXmELBYLZrOZhoYG+fAIIYQQHqKvvr9l1JgQQgiPU1dXx4cffkhdXZ27SxEeToKQEEIIj5OXl8d1111HXl6eu0sRHk6CkBBCCCGGLAlCQgghhBiyJAgJIYQQYsiSICSEEMLjeHl5MWnSJLy8vNxdivBwQ24eISGEEJ4vJSWFjIwMd5chBgG5IiSEEEKIIUuCkBBCCI+ze/dujEYju3fvdncpwsO5NQht2rSJBQsWEBUVhaIofPLJJ6fdZ+PGjaSmpmIymUhKSuLVV1/t+0KFEEIMKKqq0tHRwRBbHEH0AbcGoebmZiZMmMBLL710Ru3z8vK4/PLLmTVrFrt37+ahhx7innvu4R//+EcfVyqEEEKIwcitnaXnz5/P/Pnzz7j9q6++SlxcHM8//zzQ2Vlu165d/PGPf+Taa689q3OX1LVgsQ/MvuKKouBv0uFr1PX64nJCCCGE+MHATAInsW3bNubNm+ey7dJLL+W1117DarWi1+u77NPe3k57e7vzucVi6dzv+c1ojN59W/A50moUArz0mL30BPoYSAj2YXi4L8PDfBke5kd0oBdajQQlIYQQoqc8KgiVl5cTHh7usi08PBybzUZ1dTWRkZFd9lm5ciWPP/54l+0GnQatbuD0FT/+LrfDoWJzqNgdKjXNHdQ0d0B1M+kFrosLmnQaJscHMiM5mLTkYMbHBKDXDpzXJIQQfSUlJYX9+/eTlJTk7lKEh/OoIAR0uVV0rKPcyW4hrVixguXLlzufWywWYmNjyfjdXPz9/fuu0HPUZrVT2dhOVWMb1U0dlDe0kVvVxNGqZgprmilraKPN5mBrTg1bc2oAMOk1pMYFkpYczJSEICbGBmDSa938SoQQovd5eXkxZswYd5chBgGPCkIRERGUl5e7bKusrESn0xEcHNztPkajEaPR2B/l9SqTXktckDdxQd3fvrPZHRwotbApu4ptOTXsLW6gqd3Gtzk1fPt9MNJpFMZGm5maGMRNU+NICPHpz5cghBB9pqCggN///vf87ne/Iz4+3t3lCA/mUUEoLS2NTz/91GXbunXrmDJlSrf9gwYznVbDhNgAJsQG8D8XDcfhUDlYZuGbw5Vsy6lhf2kDllYbmUX1ZBbV8/qWPG6ZHs/SS4YT4G1wd/lCCHFOampqeO2117jrrrskCIlz4tYg1NTUxNGjR53P8/LyyMzMJCgoiLi4OFasWEFJSQlvvfUWAEuWLOGll15i+fLl3HHHHWzbto3XXnuNd999110vYcDQfH/1Z2y0mf+5aDiW1g42Z1ezLaeG9MI6ssoaeXNrPh/uKuLn5yeyZHYSPsahFR6FEEKIE7k1CO3atYs5c+Y4nx/ry3Prrbfy5ptvUlZWRmFhofPniYmJrF27lmXLlvHyyy8TFRXFCy+8cNZD54cCfy8DV4yP4vxhoRws67yF9snuEsoa2njx66O8+10ht89KYsGEKCL8TTL6TAghxJCkqENsWk6LxYLZbKahoWFAd5bubQ0tVo5WNfGPjGL+s6cUS5sNgNT4QH6aGsOwMF+iAryI8DehkVAkhBjgMjIySE1NJT09ncmTJ7u7HNEP+ur726P6CImeM3vrSY0PZGy0PzeeF8uLXx9lfVYF6QV1HC5v5NrUaCbFBuBn0jMmykyE2eTukoUQ4qTCw8N58MEHu0ypIsTZkitCQ5SqqnxxoIIn/nOA0vo2AMZF+/PTKbGYvfSE+hoZE23G7CX9iIQQQrhfX31/y+x7Q5SiKFw2NoJ1S2dz43mxaDUK+0osPP35IQ6UNlDV1M7GI1XsKaqn3WZ3d7lCCOGisbGRDRs20NjY6O5ShIeTIDTE+Zp0rLx2PO//YjpxQd40t9v5y6Y8/pFeTIfNTn5NM19nVVJY0yKrPAshBozs7GzmzJlDdna2u0sRHk6CkABgSkIQ65ZdwI8nRQOwKbuaZ9cdoayhlQ67g91FdXx7tAZLm9XNlQohhBC9R4KQcDLptTx3/UT++NMJ+Jl0lDW08ey6I2QUdq5xVtPczobDVRwstWB3yNUhIYQQnk+CkOjiJ6kxfPzLGYyO9MdqV3l7WwG7C+uBzk7W2ZWNbMquoqnd5t5ChRBCiHMkQUh0a1i4Hx/cOZ2ZycE4VHhrWz57i+udP7e0Wtl0pIrS+lb3FSmEGLL0ej3R0dFDbnkl0ftk+Lw4pdYOO4v/tpOtOTVoNQqLz09gTJTZpU1yqC+jI/1lIkYhhBB9RobPC7fwMmh5Y9F5TE8Kwu5QeW1LPlllFpc2OVVNbDlaTZtVhtkLIYTwLBKExGkZ9Vr+dttUpiYcC0N5HC53nbujrqWD7bk1WO0ON1UphBhK9u3bR0xMDPv27XN3KcLDSRASZ8So1/LWz6cyJT4Qq13lr5tzu4ShhlYru/LrcMiIMiFEH7NarZSUlGC1ypQe4txIEBJnzGTQ8vfbp5J6ijBU2dhG5nGdqoUQQoiBTIKQOCsmvY41t09lclzAScNQUW1Ll35EQgghxEAkQUicNZNex1uLpzIhxnzSMHSkopG86mY3VSiEEEKcGQlCokd8jXpeX3QeY6P8nWFoX0mDS5u9xfUcrWySNcqEEL1u+PDhfPPNNwwfPtzdpQgPJ/MIiXOSV93M3e9kcKC081bYrOEhXDUhCoPuh4wd6mtkcnwgJr3WXWUKIYTwcDKPkBiQEkN8ePKasVw4MhSAzdnVPLf+iMuM01VN7XxzqFJmoRZC9JqSkhJWrFhBSUmJu0sRHk6CkDhnE2MDuGNWEktmJ+Fr7Fys9bn1R9icXe28LdZhd7Azv5bMonpZsFUIcc4qKir4f//v/1FRUeHuUoSH07m7AOH5FEUhNT6Qlg470YFevLOjkKyyRj5KL6a0vpXrpsSgKJ3LbxTUNNPSYWNaYjBaWZJDCCGEm8kVIdErTHotM5KDCfcz8YsLkvjRpCgUBbbm1PDvPWUuHaarGtvZmV8rEy8KIYRwOwlCotf4GHXMHBaCj0HHhSPDuOG8WAC+PlTJl1mVLm0rLG2kF9bJiDIhhBBuJUFI9KpjYchLr2V6UjDXTIoC4D97y9icXe3StrS+lQwJQ0KIHggODmbx4sUEBwe7uxTh4WT4vOgTze02vj1aTavVztp9ZXxxoLND48+mxzElIcilbVyQNxNjA5z9iIQQQogTyfB54VGOXRky6bXMHxvBBcNDAFizo5D9J0y8WFjbQlZZY3eHEUKIbrW2tnLgwAFaW2VaDnFuJAiJPuNj1DEtMQidVsOPJkdzXkIgDhXe3JpPQY3r8hvZlY0U1rS4qVIhhKfJyspi7NixZGVlubsU4eEkCIk+FeBtYGJsABpF4capcaRE+mG1q/xlUx7VTe0ubTOL66lqbD/JkYQQQojeJ0FI9LmYQG9GhPuh1SgsmpFATKAXTe02/rwxl+Z2m7Odqqrsyq+lsc3qxmqFEEIMJRKERL8YFeFHVIAXJr2WX1yQRKC3nsrGdv66OY8Om8PZrsPuYEduLe02uxurFUIIMVRIEBL9QlEUJsUGYPbSY/bSs2R2Ml56LXnVzfx9e4HL5IrNHTa+y6uVpTiEECelKAoGg0FGm4pzJkFI9BudVsO0xGCMOi0RZhO3z0pEq1HYU9zAJ5klLvMJ1TZ3sEtmnxZCnMSkSZNob29n0qRJ7i5FeDgJQqJfeRm0pCUHY9BqGBbmyy3T4gDYeKSarw9VubQtt7Sxu6heJlwUQgjRZyQIiX5n9tIzLalz0dXJ8YFcM7Fz9ul/7yllZ36tS9viuhb2nTDvkBBCZGVlMXnyZBk+L86ZBCHhFkE+BqYmBKEoCnNGhXHhyFAA3tlRyKEyi0vbvOpmDpZaujuMEGKIam1tZffu3TKhojhnEoSE24T5m5gcFwDA1ROjmBwXgEOF17/Np6jWdXLF7MpGsitk9mkhhBC9S4KQcKuYQG/Gx3ROuHjztDhGhPvSbnPw6sbcLpMrHiyzdJmRWgghhDgXEoSE2yWG+DAmyoxOq2Hx+YlEB3ROuPh/X2V3CT57ihuotLS5qVIhhBCDjQQhMSAMC/NlXLQZk17LktlJRAd40dhm48Wvj7KnqN7ZTlVVdubX0dAis08LMZQlJibywQcfkJiY6O5ShIdT1CE2NtlisWA2m2loaMDf39/d5YgT5Fc3s6e4njarnb9tLeBgmQUFuGpiFHNGhjonTzPptVwwPBQvg9a9BQshhOgXffX9LVeExICSEOLDpNhATHott89KZNbwEFTgX5mlfLCr2DnbdJvVzva8Gqx2x6kPKIQYlCoqKnjuueeoqKhwdynCw0kQEgNOXLA3k+MC0Wk1XDs5mh9NikYBtubU8MqGHJraOhdqtbRa2SmzTwsxJJWUlHDvvfdSUlLi7lKEh5MgJAak2CBvJsUGoNFouHBkKIvPT8Sg05Bd2cQf1x2m8Pvh9VWN7WQU1sns00IIIXpEgpAYsI6FIYBxMWaWzx1OqJ+RuhYr//dlNttzawAoqW+VpTiEEEL0iAQhMaDFBnXeJgOINHuxfO5wxkb5Y3OovPtdER/sKsJmd1BU28LeYlmKQwghxNmRICQGvOPDkLdBx+JZiVw+NgIF+PZoDas25NDcbiO/ppn9si6ZEEOC2WxmwYIFmM1md5ciPJwMnxceo6i2hYzCOufzA6UW3tqWT5vVQaifkTsvSCLUz8iIcD9SIuW9FUKIwUSGz4shLzbImykJQWi+n0toTJQ/Sy8ZTpC3nqrGdv60/gg5VU0cqWhkb3G9jCYTYhCzWq1UVVVhtcrkquLcSBASHiU6wIu05GD02s7/dCPNXiybO4K4IG+aO+y8/E0O6QV15FU3sym7iuZ2m5srFkL0hX379hEWFsa+ffvcXYrwcBKEhMcJ8TVy/vAQvPSds0r7e+n5n4uGMT7GjN2h8ta2AjYcrqKh1crGI1WUNbS6uWIhhBADlQQh4ZH8TXpmDQ/F36QHwKDTcNuMBC4cGQrAJ7tL2FfcgNXu4Lu8WvaXNMitMiGEEF1IEBIey8ugZeawEEJ9jQBoNArXTIxi5rBgVOCtbQUU13VOvJhT1cQ+GVEmhBDiBBKEhEcz6DRMTwomKcQXAEVRuHZyDCPD/eiwO/jLpjwaWjs7U+bXNFNY0+LOcoUQQgwwEoSEx9NoFMbFmJkYG4CiKGg1CotmxhPub6Sh1cpfN+fSYetcnHVPcT0NLTLKRAhPN2HCBBoaGpgwYYK7SxEeToKQGDTig32YkRyMUafB26DjF7OS8DFoKaptZc2OAhyqikNV+S6/1hmMhBCeSavV4u/vj1ardXcpwsNJEBKDSoivkQtGhOJr1BHiZ2Tx+YloNQqZRQ38Z08ZAC0dNtILZKFWITxZdnY2l156KdnZ2e4uRXg4CUJi0PE26Jie1HllKDnMlxvOiwXgq0OVfJVVAUBlYxtHKprcWaYQ4hw0Njaybt06Ghsb3V2K8HBuD0KrVq0iMTERk8lEamoqmzdvPmX7NWvWMGHCBLy9vYmMjOS2226jpqamn6oVnsLHqGNqYjBajcLUxCCumhAFwL/3lLH1aDUAh8otVFja3FmmEEIIN3NrEHr//fdZunQpDz/8MLt372bWrFnMnz+fwsLCbttv2bKFhQsXsnjxYg4cOMCHH37Izp07uf322/u5cuEJgnwMzsVaL04J45KUMAA+2FVMRkHnmmXpBXU0yezTQggxZLk1CD333HMsXryY22+/nZSUFJ5//nliY2N55ZVXum2/fft2EhISuOeee0hMTOT888/nzjvvZNeuXf1cufAUUQFejInqXJ36yvGRzjmG3t5ewIFSC1a7g515tVjt0nlaCCGGIrcFoY6ODtLT05k3b57L9nnz5rF169Zu95kxYwbFxcWsXbsWVVWpqKjgo48+4oorrjjpedrb27FYLC4PMbQMC/MlMcQHRVH4SWoMqfGBOFR449s8jlY2YWmzkllU7+4yhRBnITY2lpdeeonY2Fh3lyI8nNuCUHV1NXa7nfDwcJft4eHhlJeXd7vPjBkzWLNmDddffz0Gg4GIiAgCAgJ48cUXT3qelStXYjabnQ/50AxN46LNRAV4oVEUbp4Wx5gof6x2ldWbcymtb6W0vpXsCul0KYSnCA0N5Ve/+hWhoaHuLkV4OLd3llYUxeW5qqpdth1z8OBB7rnnHh555BHS09P5/PPPycvLY8mSJSc9/ooVK2hoaHA+ioqKerV+4RkURWFKfCAxgd6dEy7OSCApxIdWq4NXN+ZS29zBwTILldJ5WgiPUFtby9///ndqa2vdXYrwcG4LQiEhIWi12i5XfyorK7tcJTpm5cqVzJw5k9/85jeMHz+eSy+9lFWrVvH6669TVlbW7T5GoxF/f3+XhxiaFEVhclwA8cE+GHQa7rggkQh/Ew2tVl7dmENzu006TwvhIfLz8/nZz35Gfn6+u0sRHs5tQchgMJCamsr69etdtq9fv54ZM2Z0u09LSwsajWvJx2YVlcnxxJlQFIUJMWaSQnzxNuhYMjsJs5eeCks7f92cR1O7jR25NTLztBBCDBFuvTW2fPlyVq9ezeuvv05WVhbLli2jsLDQeatrxYoVLFy40Nl+wYIF/POf/+SVV14hNzeXb7/9lnvuuYepU6cSFRXlrpchPIyidK5NNjzMj0AfA0tmJ+Gl15JX3cxb2/JpaLWyM78Wh0PCtRBCDHY6d578+uuvp6amhieeeIKysjLGjh3L2rVriY+PB6CsrMxlTqFFixbR2NjISy+9xL333ktAQAAXXXQRTz/9tLtegvBgo6P8sTk6r/zcPiuRVzbksK/Ewse7S/hJagy7i+pJjQ90c5VCCCH6kqIOsXtKFosFs9lMQ0OD9BcSOBwqW3NqqGluJ7Oonje/zUcFrj8vlhnJwYyK8GdkhJ+7yxRCnODw4cMsWrSIN998k5EjR7q7HNEP+ur72+2jxoRwJ41GYUpCICa9lomxAVw+LgKAj9KLya1q4lC5haLaFjdXKYQ40ciRI9m2bZuEIHHOJAiJIc+k13JeQhAaRWHu6HAmxgZgd6i8/m0+dc0dZBbVU9PU7u4yhRBC9AEJQkLQuS7ZuGgziqJw07RYogJMNLbZeG1LHm1WOzvz62iz2t1dphDiexkZGSiKQkZGhrtLER5OgpAQ30sI8SEuyBujTsvt5yfiY9BSVNfKezuLaLN2zjEkI8mEEGJwkSAkxHHGxwQQ5GMg2NfIbTMT0CidK9R/faiK6qZ2DpbJWnVCCDGYSBAS4jhajcLUxCD8TDqGh/vxo0nRAHy6p5R9JQ3kVDVRWt/q5iqFEEL0FglCQpzAqNMyPSkYk17LrOEhzBwWjAq8ta2A4roWMovqaWyzurtMIYQQvUCCkBDd8DbomJ4UjEGn5drJMYwI96XD5uCvm/KobmpnV34dNrsswyGEu4wePZrs7GxGjx7t7lKEh5MgJMRJmL30TE0MQq/VcNvMBML9jdS3Wlm9uTMM7cyXztNCuIvJZGLYsGGYTCZ3lyI8nAQhIU4hxNdIanwg3gYdv5iVhI9BS2FtC2t2FFBuaSW9sE4W/BXCDfLy8rjlllvIy8tzdynCw0kQEuI0ogK8GB3pT4ifkZ+fn4hWo5BZ1MDn+8sprW8ls6je3SUKMeTU1dWxZs0a6urq3F2K8HAShIQ4A8PD/Yg0ezEszJfrp8QA8MWBCg6UWiisbWF/SYObKxRCCNETEoSEOEOT4gLwM+mYlhTM+cNCAPj79gJqmtrJqWricHmjmysUQghxtiQICXGG9FoN5yUEodNo+NGkKOKDvGnpsPPG1nxsdgeHyi0U1DS7u0whhBBnQYKQEGfBz6RnUlwAOq2GRTMT8DZoKapt5Z+7SwDYW9xAXXOHm6sUYvCLjIzk0UcfJTIy0t2lCA8nQUiIsxQV0NlXKMjHwMK0eBTg26M17MqvxaGq7Myvpd0mC7QK0ZciIyN57LHHJAiJcyZBSIgeGB3pT4ivkZRIf+aNCQfg/Z3FlDW00mq1k54vw+qF6EsWi4UvvvgCi0XW/xPnRoKQED2gKAqT4wIxaDVcNiaCkeF+dNg7Z55uarNR1dROVpl0nhairxw9epTLLruMo0ePursU4eEkCAnRQ14GLRPjAtBoFBamxRPsY6CmuYPVW/Kw2h1kVzbKAq1CCDHASRAS4hxEmr1ICPbB16TjFxck4aXXklfdzJodhThUld2FskCrEEIMZBKEhDhHY6PN+Jl0RJhNLD4/Aa1GYXdhPWv3lWNzOGSBViGEGMAkCAlxjrQahdS4IDSKwvBwP244LxaA9Qcr2J5bg6XNyp5imXlaiN5kNBpJTk7GaDS6uxTh4XTuLkCIwcDsrWd0lD/7SxqYmhhEdVM7Xxyo4P2dRQR4GwAI9jGQEOLj5kqFGBzGjBkjHaVFr5ArQkL0kuRQX6IDvACYPzaC1PhAHCqs3pzL4fJG9pU0UN8iky0KIcRAIkFIiF40OS6Q6AAvFEXhpqmxjI70x2pX+evmXA6UNrAzv44Om/QXEuJc7d27l9DQUPbu3evuUoSHkyAkRC/SaBRnGNJpNSw+P4Fx0cfCUB7f5dWwu1AmWxTiXNlsNqqrq7HZbO4uRXg4CUJC9DKNRiE1/ocwdNvMRCbGmrE7VF7bkscXB8o5UtHk7jKFEEIgQUiIPqEoP4QhrUZhYVoCk+MCcKjw5tZ83ttZSHlDm7vLFEKIIU+CkBB95FgYCvc3odUo/Gx6POcldHagfmdHIesPlmORyRaFEMKtJAgJ0YeOrUnmpdei0SjcNDWOxBAf2m0O3t5ewI7cGuk8LUQPjBgxgq1btzJixAh3lyI8nAQhIfqYQadhSkIQiqJ0hqFpcei1Ckcqmlh3oIJdBbXSeVqIs+Tr60taWhq+vr7uLkV4OAlCQvSDIB8DoyP9AQjzM7JgQhQA/9pTSlaZhYNlFneWJ4THKS4uZvny5RQXF7u7FOHhJAgJ0U+SQ30I9zcBMGt4CMmhPnTYHLy7o5AjFY0U1ba4uUIhPEdlZSV/+tOfqKysdHcpwsNJEBKinyiKwqS4gM7+QkrnLTKDTsPRqma2ZFeTWVRPTVO7u8sUQoghRYKQEP3IqNOSGh+IoiiE+Bq56vtbZP/eU0qFpY2d+bU0t8sEcUII0V8kCAnRz4J9jaRE+AEwc1gww8N8sdpV3tlRSKvVzo48GUkmhBD9RYKQEG4wLMyXMD8TGkXhxqmxGHQacqs7b5E1ttnYVVCLwyEjyYQ4mZCQEO666y5CQkLcXYrwcBKEhHADRVGYHB+ASa8l2NfIgvGRAHy6t4yapnaqGtvZV9Lg5iqFGLji4uJ4+eWXiYuLc3cpwsNJEBLCTY7vL3T+8BCSQjpHkX2wqxhVVcmvaSavutndZQoxILW0tJCRkUFLi4y2FOdGgpAQbhTia2RkuB8aReGGqbHoNAqHyhv5Lq8WgP0lDTS0yDIcQpzo0KFDpKamcujQIXeXIjycBCEh3GxEuC+hvkbC/U3MHxcBwMe7S2loteJQVXYV1GK1S+dpIYToCxKEhHCzzv5CgRh1WuaMDCM20ItWq52P0jtvkTW129hbXO/uMoUQYlCSICTEAGDSa5kcF4BWo3Dj1Dg0CuwtbiCzqLPDdHFdKwU10l9ICCF6mwQhIQaIMH8TyaG+RAd6MXd0OAAf7CqivqUDgH0lDTS0Sn8hIQA0Gg1+fn5oNPI1Js6N/BckxAAyOtKfAG8D80aHExPoRUuHnbe3FeBwqNgdKukFtdikv5AQTJw4EYvFwsSJE91divBwEoSEGEA0GoXU+EBMeh23psU71yJbf7ACgMY2G+kFdTLZohBC9BIJQkIMML5GHeNjzIT5m/hpagwAnx0oJ6eqCYBySxu7i+pRVQlDYug6ePAgY8aM4eDBg+4uRXg4CUJCDECxQd7EBHoxNTGIKfGBqCq8ta2Alo7OBVmL61rYX2Jxc5VCuE9bWxsHDx6kra3N3aUIDydBSIgBanxMAD4GHT+dEkOIr4H6FivvflfkvBKUW93E4fJGN1cphBCeTYKQEAOUXqthUlwAXgYdt85IQKtR2FvcwJaj1c42h8ot5H5/y0wIIcTZkyAkxAAW7GtkeJgvcUHeXPn9wqz/SC9h5/dLcEDnsPrS+lZ3lSiEEB5NgpAQA9zIcD/MXnrmjAxlRnIwKrBmR6FLGNpdWE9jm8wxJIaOpKQk/vWvf5GUlOTuUoSHkyAkxACn0XQuwaHTavjplJhuw5DN4WBnvqxJJoaOgIAArrrqKgICAtxdivBwEoSE8AD+Jj2jI/3RKMpJw1Bjm409RfVurVOI/lJeXs7KlSspLy93dynCw0kQEsJDJIb4EOpn7DYM7crvDEMl9a0crZTO02LwKy0t5aGHHqK0tNTdpQgPJ0FICA+hKAqT4wIxaDVdwtA73xVRUNMCwMEyC9VN7e4tVgghPITbg9CqVatITEzEZDKRmprK5s2bT9m+vb2dhx9+mPj4eIxGI8nJybz++uv9VK0Q7mXSa5maGIRWozjD0LhoM3aHyuvf5tHUZkNVVXbl19Fmtbu7XCGEGPDcGoTef/99li5dysMPP8zu3buZNWsW8+fPp7Cw8KT7XHfddXz11Ve89tprHD58mHfffZdRo0b1Y9VCuFewr5GpCUEoSmcYunlaHGF+RupbrPxtWz4Oh0q7zc62nBrabRKGhBDiVBTVjQsWTZs2jcmTJ/PKK684t6WkpHDNNdewcuXKLu0///xzbrjhBnJzcwkKCurROS0WC2azmYaGBvz9/XtcuxDuVlLf6uwbVNbQynPrsumwO7gkJYwFE6IAMHvpmZEcgkHn9ou/QvSq3NxcHnjgAZ5++mkZQj9E9NX3t9v+OnZ0dJCens68efNcts+bN4+tW7d2u8+///1vpkyZwjPPPEN0dDQjRozgvvvuo7X15JPJtbe3Y7FYXB5CDAbRAV5MjA0AINLsxY1TYwH4MquSvcX1ADS0WtmeWyPD6sWgk5SUxIcffighSJwztwWh6upq7HY74eHhLtvDw8NPOhwyNzeXLVu2sH//fj7++GOef/55PvroI371q1+d9DwrV67EbDY7H7Gxsb36OoRwp/hgH8ZEmQGYHB/I7BGhAPx9eyGVls7FKOtaOvgurxa7Q1arF4NHR0cHxcXFdHR0uLsU4eHcfr1cURSX56qqdtl2jMPhQFEU1qxZw9SpU7n88st57rnnePPNN096VWjFihU0NDQ4H0VFRb3+GoRwp2FhvgwL8wXg6olRJIf60G5z8Nq3+c4+QtVN7ezIq5EwJAaN/fv3Exsby/79+91divBwbgtCISEhaLXaLld/Kisru1wlOiYyMpLo6GjMZrNzW0pKCqqqUlxc3O0+RqMRf39/l4cQg83oSH+iArzQahQWzUjA36SjvKGND3YWO1err2psJ6OwDjd2CxRCiAHHbUHIYDCQmprK+vXrXbavX7+eGTNmdLvPzJkzKS0tpanphwnjjhw5gkajISYmpk/rFWIgOzbHUKC3AX8vPbfOSECjwK6COrbm1Djblda3cqi80Y2VCiHEwOLWW2PLly9n9erVvP7662RlZbFs2TIKCwtZsmQJ0Hlba+HChc72N910E8HBwdx2220cPHiQTZs28Zvf/Iaf//zneHl5uetlCDEgaDUKUxOD8DboGBbmy5XjO0eO/SOjhMLaFme7IxWNFNa0nOwwQggxpLg1CF1//fU8//zzPPHEE0ycOJFNmzaxdu1a4uPjASgrK3OZU8jX15f169dTX1/PlClTuPnmm1mwYAEvvPCCu16CEAOKSa9lelIQBq2Gi0aFOidbfGNLHs3tNme7PcX1Mvu0EELg5nmE3EHmERJDQVVjO9tya2hut/LsuiNUN3UwOsqfO2Ylovl+MIJBq2HWiFB8jTo3VyvE2XM4HFitVvR6PRqN28f9iH4w6OYREkL0nVA/IxNizHgbdNw2MwGdRuFgqYWvsiqdbTrsDnbkyuzTwjNpNBqMRqOEIHHO5L8gIQap+GAfkkN9iQn05iepnYMJ1u4ro+i4/kJN7TZ25dfhkGH1wsMcOXKECy+8kCNHjri7FOHhJAgJMYiNifInzM/E9KQgJsaacaidky0eP9N0dVM7e76fiVoIT9HU1MTGjRtdRhEL0RMShIQYxBRFYUpCIP5een46JRY/k45ySxtr95W5tCusbeFopQyrF0IMPRKEhBjk9FoNUxODCfI2cMN5nUvMfHOoipxK139JHyi1UNZw8nX7hBBiMJIgJMQQ4GvUMSUhiHExAUxLDEIF1uwopN3q2lE6vaCOhhare4oUQgg3kCAkxBAR6mckJcKPH0+OJtBbT01zB59klrq0sTtUtufV0GaVkWRiYIuLi+Ovf/0rcXFx7i5FeDgJQkIMIcmhvoT7m7h5WueXx9acGrLKLC5t2qx2MgpkTTIxsIWEhHD77bcTEhLi7lKEh5MgJMQQotEoTIwNYESEPxeM6PwCeWtbgcuQeoCqpnbyqpvdUaIQZ6S6uprVq1dTXV3t7lKEh5MgJMQQE+BtIDnUhwXjo0gM8aGlw86qDTldwtDBMguNbdJfSAxMhYWF3HHHHS7LMAnRExKEhBiCRkX4E+htYMnspJOGIbtDJaOwXiZbFEIMaj0OQvX19axevZoVK1ZQW1sLQEZGBiUlJb1WnBCib2g1ChNiAzDptacMQ/UtHWRXyoR1QojBq0dBaO/evYwYMYKnn36aP/7xj9TX1wPw8ccfs2LFit6sTwjRR0L9jMQH+3QJQy9/4xqGDlc0Ut/S4cZKhRCi7/QoCC1fvpxFixaRnZ2NyWRybp8/fz6bNm3qteKEEH1rdKQ/Jr3WJQy1Wu38eVOus3+QqqpkFNZhl1tkYgDx9fVl9uzZ+Pr6ursU4eF6FIR27tzJnXfe2WV7dHQ05eXl51yUEKJ/GHQaJsQEADjDUITZRGObjTU7CnF8P4S+sc3G/pIGN1YqhKsRI0awYcMGRowY4e5ShIfrURAymUxYLJYu2w8fPkxoaOg5FyWE6D8RZhMxgV5AZxi6NS0evVYhq6yRjYernO3ya5q7jCwTwl0cDgft7e04HI7TNxbiFHoUhK6++mqeeOIJrNbOS+eKolBYWMiDDz7Itdde26sFCiH63thoM0adFoCoAC+umRQNwKd7y1zCz57ieiwypF4MAJmZmZhMJjIzM91divBwPQpCf/zjH6mqqiIsLIzW1lZmz57NsGHD8PPz43//9397u0YhRB8z6rSMjzE7n89MDmZ8jBm7Q+XNrfnOJTfsDpVd+bVY7fKvcCHE4KDryU7+/v5s2bKFr7/+moyMDBwOB5MnT+aSSy7p7fqEEP0kKsCL6AAvSupbURSFG86LpbC2heqmDj5KL+aW6fFAZ3+hPUX1TEkIcnPFQghx7s46CNlsNuflyIsuuoiLLrqoL+oSQrjB2Ggz1U3ttNsc+Bh1LEyL58Wvj7Izv46R4X6cl9gZfkrqWwmsaiI5VEbsCCE821nfGtPpdMTHx2O3y+rUQgw2Jr2WsdE/3CJLDvVl/pgIAD5ML6a6sd35swOlFqqb2rscQwghPEmP+gj99re/dZlRWggxeMQEehMd4OV8Pnd0OMmhPrTbHLy1rcA5n5CqqnyXV0tts0y2KPrf2LFjKSoqYuzYse4uRXg4RVXVs54lbdKkSRw9ehSr1Up8fDw+Pj4uP8/IyOi1AnubxWLBbDbT0NCAv7+/u8sRYkCy2h1szq6isc0GQG1zB898fphWq515o8O5Ynyks61eqyEtKZhAH4O7yhVCDAF99f3do87S11xzTa8VIIQYePRaDeclBLE5uxqr3UGQj4Hrz4vlza35rD9YwcgIP4aFdfYPstodbMutkTAk+lVubi4PPPAATz/9NElJSe4uR3iwHl0R8mRyRUiIM1fW0Mp3eT/cAn9nRyE78moJ8NJz/2Uj8TH+8G8pvVbDjORgArwlDIm+l5GRQWpqKunp6UyePNnd5Yh+0Fff3z1efR4gPT2dv//976xZs4bdu3f3Vk1CiAEi0uzFiHA/5/NrJ0cT6mekvtXKB7uKOP7fUVa7g605NTS0yISLQgjP0aMgVFlZyUUXXcR5553HPffcw913301qaioXX3wxVVVVpz+AEMJjjIrwI8yvc3Flo17LwrR4NApkFjWwLdd1wITV7mBXQS02mXBRCOEhehSE/ud//geLxcKBAweora2lrq6O/fv3Y7FYuOeee3q7RiGEGymKQmp8ID6GzttgcUHeXPl9Z+kPdhWxLafGpX1Tu439pV3XIhRCiIGoR0Ho888/55VXXiElJcW5bfTo0bz88st89tlnvVacEGJgMOg0pCUHY9J3rkc2Z1QY0xKDUFV4b2cR6w5UuNwmK6hpprS+1V3liiEgKiqKp556iqioKHeXIjxcj4KQw+FAr9d32a7X62UlYCEGKR+jjhnJwRh1WjSKwo1TY5k7OhyA/+4r458ZJTiOC0N7iuqda5QJ0dsiIiJYsWIFERER7i5FeLgeBaGLLrqIX//615SWljq3lZSUsGzZMi6++OJeK04IMbD4mfSkJQdj0GpQFIUrx0fy48nRKMCm7Gre2lrg7B/UYXeQUVjHEBuYKvpJfX09//73v6mvr3d3KcLD9SgIvfTSSzQ2NpKQkEBycjLDhg0jMTGRxsZGXnzxxd6uUQgxgJi9OsOQXtv552P2iFAWpsWj1SjsLqrnja35zvBT1dhObnWzO8sVg1Rubi5XX301ubm57i5FeLgeTagYGxtLRkYG69ev59ChQ6iqyujRo2X1eSGGiABvA9OTgtmWU4PN4WByfCA+Rh1/3ZzL/hILO/JqmZ4UDMDBUgshvkbMXl1vpwshhLv1KAgdM3fuXObOndtbtQghPEiQj4HJ8QHOCRdHRvgxf2wE/95Txie7S0mJ9Mfspcfx/Zpks4aHODtbCyHEQNGjW2P33HMPL7zwQpftL730EkuXLj3XmoQQHuLECRcvHBlGbKAXrVY7H+4qdt4ia+mwsSu/DodD+gsJIQaWHgWhf/zjH8ycObPL9hkzZvDRRx+dc1FCCM9x/ISLWo3CjdPi0Ciwr6SBzKJ6Z7ua5nZ2H/dciHNhMpkYPXo0JpPJ3aUID9ejIFRTU4PZbO6y3d/fn+rq6nMuSgjhOU6ccDE6wMs5rP6j9BKa2m3OtsV1LWRXNLqlTjG4jB49mgMHDjB69Gh3lyI8XI+C0LBhw/j888+7bP/ss89kFWAhhiCDTsN5iUFoNQoA80aHE2E20dRu4+OMEpe2B8sslDXIZItCiIGhR0Fo+fLl3H///Tz66KNs3LiRjRs38sgjj/DAAw+wbNmy3q5RCOEBzF56JsQEAKDTarhpaiyKArsK6jhwwpIb6QV11Ld0uKFKMVhkZmbi7+9PZmamu0sRHq5HQejnP/85zz77LK+99hpz5sxhzpw5rFmzhldffZU77rijt2sUQniI2CBvkkN9AYgP9mH2iFAA3t9ZRGvHD7NM2x0qW3NqqGpsd0udwvM5HA4aGxtlNQNxznoUhFpbW7n11lspLi6moqKCvXv3cvfddxMeHt7b9QkhPMyYKH9n5+krxkUS4mugodXKvzJdb5FZ7Q6259ZQImuSCSHcqEdB6Oqrr+att94COtcXu+SSS3juuee45ppreOWVV3q1QCGEZ1EUhSkJgfib9Bh0Gm6cGgfAttxaDpW53iJzqCq78mvJrWpyR6lCCNGzIJSRkcGsWbMA+OijjwgPD6egoIC33nqr2/mFhBBDi16rYWpiEEadhmFhvlwwPAToXKm+u4VY95U0cPCEfkRCCNEfehSEWlpa8PPrnERt3bp1/PjHP0aj0TB9+nQKCgp6tUAhhGfyMeqYkhCERlG4ckIkwT4G6lqs/CuztNv22ZWN7C9p6OcqhacaNWoU6enpjBo1yt2lCA/X4+Hzn3zyCUVFRXzxxRfMmzcPgMrKSvz9/Xu1QCGE5wrxNTIhJgCjTsuNU2MB2JpTw5GTzCWUU9VEcV1Lf5YoPJS3tzeTJ0/G29vb3aUID9ejIPTII49w3333kZCQwLRp00hLSwM6rw5NmjSpVwsUQni2uGBvEoJ9GB7ux8xhnQuxvvtdEe3d3CIDyCyqp6HV2p8lCg9UWFjIr371KwoLC91divBwPQpCP/nJTygsLGTXrl0uEytefPHF/OlPf+q14oQQg8PoKH9Mei1XTYgiyFtPbXMHn2SWOtciO57d0dmBusMmw6LFyVVXV7Nq1SpZzUCcsx4FIYCIiAgmTZqERvPDIaZOnSr3a4UQXei1GsZFmzHptdzw/SiyrTk1fJlV2W37pnYbuwvrug1KQgjRm3ochIQQ4mxEBXgR7m9iZIQf10yMAuA/e8vYkt39v+jLLW1kV8qweiFE35IgJIToN+NjzGg1CnNGhTHPuTBrMbvy67ptn1VmocLS1p8lCiGGGAlCQoh+423QMSqic+qNy8dFMGt4CCqwZkfBSYfOf5dXS6WEIXGCsLAwli1bRlhYmLtLER5OUYfYTXiLxYLZbKahoUGG+gvhBg6HysbsKiytVhyqyjs7CtmZX4dOo7BkdhLDw/267KNRFKYmBhHub3JDxUKIgaCvvr/lipAQol9pNIpzlXqNonDj1DjGRftjc6j8ZXMeedXNXfZxqCrf5dVS3iBXhkSnpqYmtm3bRlOT9CMT50aCkBCi3wX5GJyr1Gs1CrfOSGBEuC8dNgevbMihoKb7MLQzv5ayBlmkVcCRI0eYMWMGR44ccXcpwsNJEBJCuMXoSH9CfY1A5/D6O2YlMSzMl/bvw1BRbdcZpjvDUJ1cGRJC9BoJQkIIt9BoFFITAvE26AAw6DT8YlYiSSE+tFodrNqQQ0ld16s/qqqyq6CWhhaZfVoIce7cHoRWrVpFYmIiJpOJ1NRUNm/efEb7ffvtt+h0OiZOnNi3BQoh+oxRp2VqYhC67ydmNeq13Dk7iYRgb1o67Ly84Sil9V3DkN2hsj2vptuV7IUQ4my4NQi9//77LF26lIcffpjdu3cza9Ys5s+ff9q1YxoaGli4cCEXX3xxP1UqhOgrZi89k+MDnM9Nei1LZicTG+RFc7udl74+2m2foTarne25NdjsshTHUKTT6QgJCUGn07m7FOHh3Dp8ftq0aUyePJlXXnnFuS0lJYVrrrmGlStXnnS/G264geHDh6PVavnkk0/IzMw843PK8HkhBqbD5Y0cKrc4n7d02HhlQy6FtS0YdBp+PjOBlMiun9moAC+mxAeiKEp/liuE6GeDbvh8R0cH6enpzJs3z2X7vHnz2Lp160n3e+ONN8jJyeHRRx/t6xKFEP1oRLgv0QFezufeBh13z0lmVIQfHTYHf9mUy6782i77lda3cqi8sT9LFUIMIm4LQtXV1djtdsLDw122h4eHU15e3u0+2dnZPPjgg6xZs+aML4e2t7djsVhcHkKIgUdRFCbHBRJx3KSJRr2WO2YlkhofiEOFt7cX8s2hrgu1HqlopLCm6ygzMXgdOHCAYcOGceDAAXeXIjyc2ztLn3g5W1XVbi9x2+12brrpJh5//HFGjBhxxsdfuXIlZrPZ+YiNjT3nmoUQfUOjUTgvIYgwvx/CkE6r4ZbpccweEQrAJ5ml/CuztMvK9JnF9TLH0BDS3t5OTk4O7e3t7i5FeDi3BaGQkBC0Wm2Xqz+VlZVdrhIBNDY2smvXLu6++250Oh06nY4nnniCPXv2oNPp+Prrr7s9z4oVK2hoaHA+ioqK+uT1CCF6h0bTuZxGqJ/xh22Kwo8mRbFgQiQAXx+q5L2dRTgcP4QhVVXZlV9HVaN8MQohzpzbgpDBYCA1NZX169e7bF+/fj0zZszo0t7f3599+/aRmZnpfCxZsoSRI0eSmZnJtGnTuj2P0WjE39/f5SGEGNi0GoVpicGE+P4QhhRF4ZKUcG6cGouiwPbcWv62Ld9l1NixpThqmzvcUbYQwgO5ddzh8uXL+dnPfsaUKVNIS0vjL3/5C4WFhSxZsgTovJpTUlLCW2+9hUajYezYsS77h4WFYTKZumwXQni+zjAUxHd5tVQ1/XCVZ3pSMCa9lre2FZBZ1EBrRx6Lz0/AqNcCYHM42JFbw4xhIZi99O4qXwjhIdwahK6//npqamp44oknKCsrY+zYsaxdu5b4+HgAysrKTjunkBBi8NJpNaQlB1NU28rBsgbabZ1XfybGBuCl17J6Sx6HKxpZtSGHX1yQhI+x809ah93BtpwaZg0PcW4Tg8uwYcP4/PPPGTZsmLtLER7OrfMIuYPMIySEZ2q32ckqa3SZXDG/ppk/b8ylpcNOdICJuy8a5lyyA8BLr2XGsBB8JQwJ4fEG3TxCQghxNow6LRNjA5g1PBQ/U2ewSQj24X8uGoafSUdJfRt/3phLu+2HZTdarXa2ZFdjaZN1yQabsrIyHnvsMcrKytxdivBwEoSEEB4lyMfAjOQQTN/3CYoK8OKuC5PxNmjJr2nhtc2uHajbbXa2Hq2moVXC0GBSVlbG448/LkFInDMJQkIIj2PSa5mWGIRW0znnWFSAF3dekIRBp+FwRSN/21aA/bih9e02R2cYkhXrhRAnkCAkhPBIAd4GJsUFOp8nhPhw+/mJaDUKe4sbOucZOq4LZIfdwbc51TK0XgjhQoKQEMJjRQd4MTLCz/l8ZIQfi2bEo1Hgu7xaPtntOgO19fvRZJWNbe4oVwgxAEkQEkJ4tJHhfi6LtY6PCeDGqXEAbDxSxYbDVS7tO+cZqqWkXpbj8GSBgYHcfPPNBAYGnr6xEKcgw+eFEB7P7lDZcrSa+pYfbnt9faiSf2WWArBoRgKT4gK67DchJoCEEJ/+KlMIcQ5k+LwQQpyEVqMwPSkI/+Nmkp4zMpRZw0MA+Pv2AnKqmrrst6e4niMVjf1Wp+g9bW1tHD16lLY2uc0pzo0EISHEoGDUaUlLCsbf1BmGFEXhx5OiGRftj82hsnpzHhWWrl+aWWUW9pc0dFnNXgxsBw8eZPjw4Rw8eNDdpQgPJ0FICDFomPRa0pKDnRMuajQKC9MSiA/2pqXDzqsbc7F0M59QTlUTu4vqXVazF0IMDRKEhBCDikmvZUbyD8tqGHQafjEriRBfA7XNHfx5Uy6tHfYu+xXVtrAzv9Zl/iEhxOAnQUgIMegcC0M+36875mvScefsZHyMWorrWvnzJtelOI4pt7SxLacG63EzUwshBjcJQkKIQcnL0HmbzKjrXIojzM/IXRcm46XXkFfdzOrNed0Gnprmdr49Wk2btWtQEkIMPhKEhBCDlo9Rx/SkH5biiAn05s7ZyRh0Go5UNPH6t67rkh3T0GplS3Y1jbJY64A1efJkVFVl8uTJ7i5FeDgJQkKIQS3A28B5CUEoSmcYSgzx4RezEtFrFQ6WWnh7e2G3/YKaO2xsya6muqm9v0sWQvQjCUJCiEEv3N/EhBiz8/nwcD8Wf78uWWZRPe/s6D4MdXy/JEdRbUt/livOwOHDh0lLS+Pw4cPuLkV4OAlCQoghIT7Yx2VdspRIf279fl2yXQV1vLYljw5b19tkDlUlo7COQ+WW/ixXnEZzczPbt2+nubnZ3aUIDydBSAgxZIyK8CfxuCU1JsQE8POZnbfJDpRaWLUhh+Z2W7f7Hi5vJKOwTiZeFGKQkSAkhBhSxscEMCbqh3WKxsWY+eXsZLz0WvKqm3nhq6PUNXd0u2/nXEN1MvGiEIOIBCEhxJAzLMyPqYk/jCZLDvPlnouHYfbSU25p4/kvsylv6H4Nq7KGVrbn1XQ72kwI4XkkCAkhhqRIsxezhoVi0nfOMxQV4MXSS4YT5mekvtXK818e4UBpQ7f7VjW2sz23tts+RaJ/JCQk8Pbbb5OQkODuUoSHU9QhdsPbYrFgNptpaGjA39//9DsIIQa1Nqud7bk1NHy/BllTu43Vm/PIq25GAS4bG8G8MeFovh9+fzyzl95l0kYhRN/pq+9vuSIkhBjSTHot5w8LIdLsBYCvUcfdc5KZOSwYFfhsfzmrN+fR0tG1E3VDq5VtOXKbzB2qqqp4+eWXqaqqcncpwsNJEBJCDHk6rYbzEgIZHubnfH7dlFhunBqLTtM5ouzZdUcorW/tsm9Dq5X0AhlN1t+Kioq4++67KSoqcncpwsNJEBJCCEBRFEZH+TM5LtB5G2x6UjBLLxlOoLee6qYOnv8ym5yqpi77llva2F8i8wwJ4YkkCAkhxHFig7yZkRyMUadxPr/v0pEkh/rQbnPwyoYcDpU3dtkvt7qJ3G5CkhBiYJMgJIQQJwj2NTJreCj+Xnqgs9/QktnJpET6YbWr/GVTLvtKuo4o219qOemweyHEwCRBSAghuuFj1DFrWAjRAZ2dqA06Dbefn8j4GDN2h8rrW/LIKKhz2UdVVdIL6mhokVXr+5qfnx/z5s3Dz8/v9I2FOAUZPi+EEKeRXdHIwbLOPkB2h8o7OwrZVVCHAlx/XixpycEu7Q1aDdOSggnyMbihWiEGJxk+L4QQbjI83I+0pGD0Wg1ajcLN0+OYkdw5vP69nUX8M6PYZfX6DruDrTnVVFrkNllfsdvtWCwW7Ha7u0sRHk6CkBBCnIEwfxMXjAjFz6RDoyhcNyWGS8eEA7DxSDUvf3MUS+sPt8TsDpUdebUU17W4q+RBbc+ePZjNZvbs2ePuUoSHkyAkhBBnyNeo4/xhoYT5mVAUhcvHRXL7rERMeg05Vc38cd0R8qqbne0d3/cZOn6bEGJgkSAkhBBnwaDTMD0piORQXwDGRZtZPncEEf4mGlqtvPj1Ub49Wu2yz97ieo5Wdh1yL4RwPwlCQghxlhRFYWy0mUmxnZMvhvubWDZ3OBNjO0eUfbCrmI93l+A4rt/QgVJLtzNTCyHcS4KQEEL0UFxw5+SLBq0Gk17LohkJXDEuEoANh6t4/ds82m0/dObNKJSh9UIMNBKEhBDiHAT7Gjl/eAheei2KojBvTDi3zohHp1HYV2Lhxa+OOle27+xAXUObVUY6natx48ZRWVnJuHHj3F2K8HAShIQQ4hz5mfRcMOKHmagnxwVy90XD8DFqKapr5bn1PyzY2mq1szO/1mW4vTh7er2e0NBQ9Hq9u0sRHk6CkBBC9AKTXsv5w0II9TUCkBjiw/JLRhDmZ6S+xcr/fZXtHD1W29xBZlG9G6v1fDk5OVx11VXk5OS4uxTh4SQICSFEL9FrNUxPCiYmsHNZjhA/I0svGU5SiA9tVgervsnh8PcLthbXtTj/vzh7DQ0NfPrppzQ0dF3zTYizIUFICCF6kUajMDku0Dm83seo45cXJjMqwo8Ou4O/bMpl//cLth4qt3Co3OLOcoUY8iQICSFELzs2vH50ZOd6SAadhjtmJTIu2ozNofLaljx2F3Yu2Hq4vJEDpXJVQwh3kSAkhBB9ZHi4H5NiA1EUBZ1Ww20zE0iND8Shwt+2FbAjtwaAo5VN7CtuYIitgS3EgCBBSAgh+lBcsDdTE4LQahS0GoVbpsWRlhSMqsK73xU5O03nVjexR8LQGYuOjubZZ58lOjra3aUID6eoQ+xTZ7FYMJvNNDQ04O/v7+5yhBBDRG1zB9/l1dJus6OqnbNPb82pQadR+OWFyQwL6+xTFBPoxcTYQLQaxc0VCzGw9NX3t1wREkKIfhDkY+CCESH4e+lRFIWfpsY4+wyt3pzrnGeouK6Vb49Wy6SLp1FXV8eHH35IXV2du0sRHk6CkBBC9BNvg47zh4UQ4W9Co1FYmBZPYogPrVYHr27Mpa65A4C6lg42HamS5ThOIS8vj+uuu468vDx3lyI8nAQhIYToR3qthqmJQQwL83WOJgv3N9LQauXVjbk0t9uAzhmoNx+tokQWahWiT0kQEkKIfqYoCmOizEyMDcDXpGfJ7GTMXnrKLW38ZVMuTW2dYcjuUNmVX8vBUossySFEH5EgJIQQbhIf7MN5CYGE+BpZMjsJL72W/JoWnl13mJK6H64EZVc28vWhSsoa5OqQEL1NgpAQQrhRpNmLtORg4oN9+PUlwwjxNVDbYuX5L7Nd1iNr6bDxXV4tW3OqaWyTvkNeXl5MmjQJLy8vd5ciPJwMnxdCiAGgodXKtpwaapvb+dvWAg5XdK5DNm90OPPHRaBRfhhOrygKw0J9SYn0Q1FkmL0YGmT4vBBCDGJmLz2zhocQ5mfiztlJXDgyFIB1BytYvTnP2YkaQFVVsisb2ZZTQ7tNhtkLcS4kCAkhxADhY9Rx/vAQQv2M/GhSNDdPi0OnUThQauHpzw+TXeG6Wn1VUzsbDw/NYfa7d+/GaDSye/dud5ciPJwEISGEGEBMei0zkkOIDvBiamIQy+YOJ8yvc3j9y9/k8OmeUpcRZMeG2RfVtrix6v6nqiodHR2yJIk4ZxKEhBBigNFqFFLjAxkR7kdMoDf3XTqCtKQgVODLrEqe/zKb6sZ2Z3u7QyWjsI79JbJWmRBnS4KQEEIMQIqikBLpz6TYQEx6HTdMjeO2mQl46bUU1rbwh3WHySqzuOyTU9VERmE9DplzSIgz5vYgtGrVKhITEzGZTKSmprJ58+aTtv3nP//J3LlzCQ0Nxd/fn7S0NL744ot+rFYIIfpXXLA305M6V6+fGBvA/ZeNJDHEhzargz9vymXjkSqXq0DFdS3szK+VCRiFOENuDULvv/8+S5cu5eGHH2b37t3MmjWL+fPnU1hY2G37TZs2MXfuXNauXUt6ejpz5sxhwYIF0llOCDGohfmZSEsKRqfREORj4O45yUxNDEJV4Z8ZJXyYXuwSfMotbWzLqaHD5nBj1X0rJSWF/fv3k5KS4u5ShIdz6zxC06ZNY/LkybzyyivObSkpKVxzzTWsXLnyjI4xZswYrr/+eh555JEzai/zCAkhPFVdcwfbc2vosDtQVZWvD1Xx6Z5SVGB4mC+3zUzAx6hztvf30pOWFIxJr3Vf0UL0kkE3j1BHRwfp6enMmzfPZfu8efPYunXrGR3D4XDQ2NhIUFBQX5QohBADSqCPgRnJIRh1GhRF4eKUMBbPSsSg05Bd2cRz64+QW9XkbG9ptbI5u5ra71e1H0wKCgq4/fbbKSgocHcpwsO5LQhVV1djt9sJDw932R4eHk55efkZHePZZ5+lubmZ66677qRt2tvbsVgsLg8hhPBUZm89M4aFYNR1XuUZF21m6SXDCfLWU93UwQtfHeWT3SXO22ItHTa2HK3mSEXjoBpRVlNTw2uvvUZNTY27SxEezu2dpU+cHl5V1TOaMv7dd9/lscce4/333ycsLOyk7VauXInZbHY+YmNjz7lmIYRwJ39T5yzUvt/fBosO8OI3l43s7DcEfHO4ij+uO0x+TTPQ+Xc1q8zCtpwa2qwyE7UQx3NbEAoJCUGr1Xa5+lNZWdnlKtGJ3n//fRYvXswHH3zAJZdccsq2K1asoKGhwfkoKio659qFEMLdjs1CHexjBMDboOPmaXHcMSsRf5OOCks7z3+Zzb/3lDqvDlU1tbPhcCUVljZ3li7EgOK2IGQwGEhNTWX9+vUu29evX8+MGTNOut+7777LokWLeOedd7jiiitOex6j0Yi/v7/LQwghBgOjTsuM5GBiAn9YgX1stJkH548iNT4QVYWvsipZuTaLfcWdky222xxsz61x6UskxFDm1ltjy5cvZ/Xq1bz++utkZWWxbNkyCgsLWbJkCdB5NWfhwoXO9u+++y4LFy7k2WefZfr06ZSXl1NeXk5DQ4O7XoIQQriVRqOQGh/EyAg/5zYfo46FafHcfn4igd56alusrN6Sx1825TlnpN5X0uDRM1GHh4fz4IMPnvYOghCn49bh89A5oeIzzzxDWVkZY8eO5U9/+hMXXHABAIsWLSI/P58NGzYAcOGFF7Jx48Yux7j11lt58803z+h8MnxeCDFYFdW2kFlUj+O4P+vtNjvrDlTwzeEq7A4VnUbh0jHhXDI6HI2iEGn2IjU+EK3m9H0zhXCnvvr+dnsQ6m8ShIQQg1lDi5Xv8mtp6bC5bK+wtPFRejFHKjpviU2JD+SmaXFoNQqB3gamJQU5R6J5gsbGRtLT00lNTcXPz+/0OwiPN+jmERJCCNH7zN56LhgRQqif0WV7uL+Juy5M5obzYtEosKugjr9uzqXdaqeupYPNR6ppared5KgDT3Z2NnPmzCE7O9vdpQgPJ0FICCEGGaNOS1pSMCPCXa+UKIpCWnIwd8xKwqDVkFXWyMvf5NDUbqO5w8aW7CrqBuHki0KcigQhIYQYhI6tXj8tMbjLLa/RUf786qJkfAxaCmpb+L8vs6lt7qDd5uDbnGrKG2R4vRg6JAgJIcQgFmE2MWdUKJFmL5ftCcE+/PqS4QR466lsbOe59UfIKrNgd6h8l19LfnWzmyoWon9JEBJCiEHOqNMyNTGIyXGB6LU//NkP9zex7JLhRJpNNLbZeHVjLh+lF9NutbOnuJ6DpZYBO7xer9cTHR2NXq93dynCw8moMSGEGEJaOmzsLqynuqndua3D5uDTvaVsOlINQJifkZ+lxRMX5E2Qj4HJcYEuq9oL4Q4yfL6XSBASQgx1qqpyoNRCzgmzSx8qs/DOd0U0tFrRKDBvTARzU8Iw6XWMizYTF+ztpoqFkOHzQggheomiKIyNNneZSHFUpD8PXDaSibEBOFT4fH85T39+mAOlDewuqmNnfi3ttoGxaOu+ffuIiYlh37597i5FeDgJQkIIMUTFBHoza1goXvofRpX5GHUsmhHPwrR4/Ew6KhvbWbUhhze+zedgaQPfHKqiuK7FjVV3slqtlJSUYLVa3V2K8HBy01cIIYYws7ee2SNDSc+vo+r7fkOKopAaH0hKpB+f7S9nc3Y1mUX1HCyzcPnYCFqtNgprWxgfE4Cv9B0SHk6uCAkhxBBn1GlJSw5mXLQZneaHrwVvg45rJ8fwm3kjSQzxocPm4JPMUt74Np/i2ha+OVTJ4fJGHI4h1dVUDDIShIQQQqAoCkmhvlw4MpRQX9flOaIDvbjn4mH8dEoMWo3C3uIG/vRlNlWNbRwqt/DN4UqZhFF4LBk1JoQQoov86mYOllmw2h0u2/Oqm3ltSx6NbTZ8DFoWzUxwLuUR6mtkTJQZs3ffz+0ji64OPTJ8vpdIEBJCiDPT2mFnb3E95RbXqz31LR2s3pJHUW0rGgWumhDFBSNCnSPQ4oK8SYn0x6T3nNXsxcAnw+eFEEL0Ky+DlmlJwaTGB2LU/fB1EeBt4J6LhjMlPhCHCp9klrLys0Psyq/F4VAprG3hy6wKDpQ29Nlw+5KSElasWEFJSUmfHF8MHXJFSAghxGm12+wcKLVQVPvD0HlVVdlytIbP9pfR3N4ZeCL8TVw2NoIJsWY0ioJOoyEp1IfkUF8Mut77t3dGRgapqamkp6czefLkXjuuGLj66vtbxj2ehN1ul/kpxJCm1+vRauXWhuhk1GmZHBdITKAX+0saaGyzoSgKs4aHcF5CIJuOVHd2mra08ebWfKICTMwb3RmIjlQ0klfdzLAwX5JDfV0mcRTC3SQInUBVVcrLy6mvr3d3KUK4XUBAABERESiKfHGJTmF+Ji4cYSS3upkjFY1Y7Q5Mei3zxoQza3gIGw5XsuFIFaX1nYEo3N/IvNHhTIoLJKvMQmFNC+NjzIT5m9z9UoQAJAh1cSwEhYWF4e3tLV8AYkhSVZWWlhYqKysBiIyMdHNFYiDRaBSGhfkSE+jVGW6+v13mZdAyf1wkF4wIZdORKjYeqaLC0s7b2wv5bH85c0eHMyU+kG25NUQFeDE2yoyXQa46CveSIHQcu93uDEHBwcHuLkcIt/Ly8gKgsrKSsLAwuU0mujDptUyKCyQxxIejlU2UNrShqio+Rh3zx0Vy4cgwNmdXs+FwJdVNHbz7XRFr95Vz4chQZiQHU2lpZ1SkH0khPmf9j87g4GAWL14sf6vFOZPO0sdpa2sjLy+PhIQE55eAEENZa2sr+fn5JCYmYjLJrQxxas3tNvKqmymoacHm+GH+oXarnW9zavjmUCWWNhsAXnot5w8P4YIRISQG+zIxTpbrEKcmnaX7kdwOE6KTfBbE2fAx6hgbbWZEuB8FNc3kVDXRbnNg1Gu5aFQYFwwPYWd+HV8fqqSysZ31ByvYcLiS2SNCmdcQznkJwSQEn1mXhNbWVnJzc0lKSpJ/uIpzIkFICCFErzLoNAwP9yMhxIe86mZyKpvosDvQaTWkJQczLSmIfSUNfHWwkoLaFr7MqmRrTg2XpITz40nRTEsKPm3foaysLBk+L3qFBCEhhBB9Qq/VMCLcj4RgH3Krm8itasZqd6BRFCbEBDA+2syBUguf7i2jvKGNf+8pZdORKi4dG86C8VGMjw3A39T3y3WIoU2CkBBCiD5l0GkYFeFPYogPOZXN5FY3YXeoKIrC2GgzoyP92VVQx9p9ZdS1WHl/ZzEf7ipmeJgf5w8L5sepMYyO9JdbtaJPyBIbYkhaunQp11xzjVvOvWrVKmfn49TUVDZv3nzO+zU2NrJ06VLi4+Px8vJixowZ7Ny502X/xx57DEVRXB4RERG9+tqEOBWjTsvoKH8uSQknKcQXzffBRqNRmJoYxMNXpPCjSdFEB5hwqHC4opHXvs3nyhe3cPkLm/lHRhGNbTLRrehdEoTEoGKz2c6o3c6dO5k6dWofV9PV+++/z9KlS3n44YfZvXs3s2bNYv78+RQWFp7Tfrfffjvr16/n7bffZt++fcybN49LLrmkyzpMY8aMoayszPnYt29fn71WIU7GpNcyLsbMJSnhxAf/MHRer9Vw4chQ7r9sFL+9IoUFEyKJDfJCVSGrrJF7P9jLjX/ZzpodBRTVtWIwGOQqkThnEoQGmUOHDjFnzhxMJhMjRozg888/R6PRkJ6ezueff46Xl5dLWMjKykJRFKqrq53bioqKuPnmmwkMDCQwMJCbbrqJuro658/P5DinO0ZvyM/PR1EUPvroIy644AKMRiMff/wxAE888QTjxo3Dx8eH8PBwfvnLX2K1WrFarRgMBrZu3crDDz+MoihMmzbtjF/7uXruuedYvHgxt99+OykpKTz//PPExsbyyiuv9Hi/1tZW/vGPf/DMM89wwQUXMGzYMB577DESExO7HFen0xEREeF8hIaG9tprE+JseRm0TIwNYG5KOIkhPs4rRAChfkYuSQnnvnkjeeTKFNKSgtEosL/Uwm8/3s+abIU/f3OYDnMclZbO+YuE6AkJQqehqiotHTa3PM72g3348GGmTp3KlClTOHDgAH/4wx9YuHAhGo2GMWPGkJmZyZgxY9DpfugalpmZSXR0NCEhIQAcPXqU1NRUkpOT2bZtG19++SU5OTn85je/cdnnVMc5k2P0hszMTACefvppfve733HgwAHmzZuHqqrY7Xb+/Oc/c/DgQd58800++ugjVq9ejVarZcuWLc79y8rK+OKLL874tR/z1FNP4evre8rHibe8Ojo6SE9PZ968eS7b582bx9atW0/6Ok+3n81mw263d5nnx8vLy/laj8nOziYqKorExERuuOEGcnNzT/EbFqJ/eBm0jI8JYO7o8G7XIgv2NXLD1FgenD+KCTFmVOC7/Dqe+PQAz3xxmA92FfFVViVHKxv7bLV7MXhJZ+nTaLXaGf3IF24598EnLsXbcOZv0T333MO1117LH/7wBwCSk5N555132L9/PyaTiT179jBx4kSXfXbv3s2ECROcz5csWcIvf/lLHn/8cee2+++/3yUMnO44Z3KM3rBnzx58fHz48MMPSUhIcPnZ8eeOj49n7ty5HDp0CI1GQ2lpKcHBwS6v+2zrXrJkCdddd90p64uOjnZ5Xl1djd1uJzw83GV7eHg45eXlJz3O6fbz8/MjLS2N3//+96SkpBAeHs67777Ljh07GD58uLP9tGnTeOuttxgxYgQVFRU8+eSTzJgxgwMHDsjsvGJAMOm1znmIyhvaKG1opaqxHcf3/ygM9zfx8/MTKahp5r1120l/8wnar7yP9IJYkkN9mD0ylIkxAYyI8GN4mJ8s7irOiAShQaKoqIh169axZ88el+0Gg8H5hZ+Zmcldd93l8vPMzEymTJkCQEFBAV999RVbt27l2Wefdbax2+3Exsa67HOy45zpMY732GOPuYSP7uzcudNZ5/HnvOqqq7qEoIKCAv7whz+wYcMGSkpKsFqttLW1sXLlSqBr+Dub135MUFAQQUFBp6z5ZE7s06Cq6hn1czjVfm+//TY///nPiY6ORqvVMnnyZG666SYyMjKc7efPn+/8/+PGjSMtLY3k5GT+9re/sXz58h69FiH6gkGnIS7Ym7hgb6x2BxWWNkrr2yj//hZYfLAPP5kYxrZnchgZaiQfyKlqJqeqmRBfA1eOjyItKYgx0WZiAr3d/XLEACdB6DS89FoOPnGp2859pjIyMtDr9YwePdpl+759+7jppptobW0lOzvb5UqOw+EgIyODxYsXA51XWIKCgtixY0fXWr6fufV0xzmTY5zo7rvv5oYbbjjl6zsx7Byr98EHH3TZVl1dzdSpU5kzZw7PPfcc0dHROBwOpkyZ4qw5MzOzSxA627qfeuopnnrqqVPW/NlnnzFr1izn85CQELRabZerP5WVlV2u9hzvTPZLTk5m48aNNDc3Y7FYiIyM5PrrrycxMfGkx/Xx8WHcuHFkZ2ef8nUI4U56rYaYQG9iAr1ps9opqGmhoKbZ+fMrx0cSHD+SzdnVbM2pobqpgze35rPhcCXXTIpmSnwQY6P9CfA2uPFViIFMgtBpKIpyVren3EWj0WC327HZbM6+O1988QV79uzh6aefJicnB7vdzsiRI537fPHFF9TU1DhDgV6vp7GxkcjISHx8fLo9z+mOk5eXd9pjnCgkJMTZR+lMWSwW8vPzmTRpksv2tWvXYrPZePfdd51XS15++WU6OjqcQWjfvn386Ec/ctnvTF778Xpya8xgMJCamsr69etdzr9+/Xquvvrqkx7nbPbz8fHBx8eHuro6vvjiC5555pmTHre9vZ2srCyXsCbEQGbSaxkZ4ceIcF/U6h/WmgrwNrBgQhTzRofz9aFKvj5URX5NC89/mc2k2ACuHB/JxLhAUiL9POLvuehn6hDT0NCgAmpDQ0OXn7W2tqoHDx5UW1tb3VDZuSkpKVGNRqO6dOlSNScnR/3oo4/UpKQkFVDLysrU0tJSVVEU9dNPP1VVVVW3bdumJiUlqV5eXqrNZlNVVVVramrU4OBg9Uc/+pG6e/duNTs7W/3ss8/Ue+65x3me0x3nTI7RGzZt2qRqtdou79Wnn36q6nQ69ZNPPlGPHDmiPvvss2pISIgaHR3tbBMfH68uW7ZMLSkpUevr68/4tfeG9957T9Xr9eprr72mHjx4UF26dKnq4+Oj5ufnO9u8+OKL6kUXXXRW+33++efqZ599pubm5qrr1q1TJ0yYoE6dOlXt6OhwHuPee+9VN2zYoObm5qrbt29Xr7zyStXPz8/l3Cfy5M+EGNzS09NVQP18w7fq5/vL1E92Fzsfb36bq1736lY14YH/qPHfP6Y8uV69/tWt6rNfHFKzShtUh8Ph7pcgztKpvr/PhQSh43j6H/23335bjYmJUX18fNSf/OQn6jPPPKOGhYU5f/7kk0+qgYGBamxsrPqzn/1MfeCBB9QpU6a4HGPHjh3qhRdeqPr7+6t+fn7qpEmT1Oeee86lzemOcybHOFcvvviiOmbMmC7bHQ6Heuedd6p+fn5qWFiYunz5cvWuu+5Sr7jiCmebt99+W42KilIBdfny5f1at6qq6ssvv6zGx8erBoNBnTx5srpx40aXnz/66KNqfHz8We33/vvvq0lJSarBYFAjIiLUX/3qV86Qd8z111+vRkZGqnq9Xo2KilJ//OMfqwcOHDhlrZ7+mRCDV21trfrBBx+otbW1aofNru4tqncJQ5/sLlZf/jpbvfRPG10CkTMY/X6d+ueNR9WWdpu7X4o4Q30VhBRVHVqTL1gsFsxmMw0NDfj7+7v8rK2tjby8POfsvZ7u/vvvZ8+ePc7h4UKcrcH2mRCDW31LB5lF9TS0us4+3dJhI6+6hbzqZnKrmiisbcFq7/zqM3vpueOCRBbNSMTXKLfNBrJTfX+fC3nXB7G9e/d26RQshBCDQUVFBWvWrOHmm292DhoI8DYwe0QoFZZ2cquaqGpqB8DboGNMlD9jojq/PG12Bzvz61h/sIKa5g7++MUR/rwxl8XnJ7JkdjKmsxioIjyfTKg4iO3bt0+CkBBiUCopKeHee+/tsoyMoihEmE3MGBbChSPCiA3ydpmxGkCn1ZCWHMzDV6Rw87Q4wvyMNLbZeP7LbC5+diNfHCjH4RhSN0uGNLkiNIid+AdCCCGGErO3nslxgYyO9OdIRSMFNS3OyRkBtN8v9jolPpCMwjr+lVlKSX0rS95OZ/bIUH5xQRLDQn0J9DGg18p1g8FKgpAQQohBzaTvXMJjWJgvRyo6+wgd3z1Wo1GYkhDE6Ch/Ptldyo68WjYcrmJvUT3XnxfLqEh/fI06Arz1mL0MBHjrCfaRBV8HCwlCQgghhgRvg46JsZ2B6HB5I6X1rS5XiLwNOm6aFsfkuADe31lEbYuVVzbmMjrSnysnRBId4EVxXSvQOeFtfLAP8cHe0qfIw0kQEkII4XHMZjMLFizAbDaf9b6+Rh2p8YGMizZTUt9KYW0L9S0dzp+PivTnwfmj+O++MjZnV3OwzEJWmYXU+EAuHxdBsK+RVqudQ+UWDlc0EuFvIiHYm1A/o1wl8kAyfP44MlRYCFfymRBDhaXNSlFtC6X1bbR02JzbKxvbWbu3jN1F9UBnv6K0pGBmDgsmKsB1+R2jTktMoBcxgV6ypEcfkOHzQgghxPesViv19fUEBASg1+vP+Xj+Jj1josyMiTJT39JBaX0bZQ2dt8EWzUzg4toWPt1TxuGKRrYcrWbL0WpiAr04L6Gzs7WvSUe7zU5OVRM5VU34GnXEBHqTEOKNUSe3zgYyCUJCCCE8zr59+0hNTSU9PZ3Jkyf36rEDvA0EeBsYHeVPQ6uV/OpmtBqFu+Ykc6Sikc3Z1RwotVBc10pxXQn/yiwhJdKf0VH+pET4EexrpKndxqFyC0crm0gI8WZYmK8EogFKgpAQQghxEmYvPRNiA0iJ9KewthkvvZYR4X40t9tIL6jju/xaimpbOVBq4UCpBYAwPyOjIvxIifRnRLgvRyubyKtuJiHYh2FhvtK5eoCRICSEEEKchkGnYViYH8mhvpQ1tFHZ2E6E2cQFI0Ipa2hlX3EDh8obyatuprKxncrGdjZlV2PSaxgTZWZCrJnWDjt51c0E+RgI8jEQ7NN55cmgkzmK3EmCkBBCCHGGFEUhKsDL2VG63WanrtnKrOGhVFraqbC0caSykUNljRwotdDQaiW9oI70gjoMWg0jI/yIC+rcPybQC7OXHrN3ZygK8jEQ4muUK0b9TGLoILN06VKuueaafj3nqlWrnKOKUlNT2bx580nbJiQkoChKl8evfvWrbtuvXLkSRVFYunRpj48hhBB9xajTEmE2MSbKzJxRYSyYEMXN0+K5e84wHr96DEsvGc6FI0MJ9NbTYXewr6SB/+4r56+b83j03wd5+OP9PP3ZId7ZUcjm7Cq+OFDOlwcryCiso6CmGUublSE2uLvfyRWhQWbnzp1cccUV/Xa+999/n6VLl7Jq1SpmzpzJn//8Z+bPn8/BgweJi4vrtj673e58vn//fubOnctPf/rTbtv+5S9/Yfz48T0+hhBicJowYQINDQ34+Pi4uxQXPkYdyaG+JIf60mFzUN3UzpyRYVQ1tpFV3kh2RRMl9a2U1rdSYWmjucPOofJGDpU38mF6MSkRfkyOD2RstD9F33eu1ms1BHp3XjGKDvTC1yhf3b1J5hE6jifPmWK1WvHx8cFqtTq3TZ06lR07dvTpeadNm8bkyZN55ZVXnNtSUlK45pprWLly5Wn3X7p0Kf/5z3/Izs52mYisqamJyZMns2rVKp588kkmTpzI888/f1bHEOfOkz8TQgw0LR02qhrbqW5qp6qxg6Z2K+UNbRwqbySjsI7S+jZnW71WYViYL6Mi/EmJ9CPsuMkaQ/2MJAT7EOFvQqMZOn/zZB4hcUparZYtW7Ywbdo0MjMzCQ8PP+MvrqeeeoqnnnrqlG0+++wzZs2a5bKto6OD9PR0HnzwQZft8+bNY+vWrac9b0dHB3//+99Zvnx5lwDzq1/9iiuuuIJLLrmEJ598skfHEEIMXtnZ2dx999289NJLDB8+3N3lnBFvg474YB3xwZ1XsSxtVqob2zkvIYj5YyMpqmsmo6Ce9II6apo7yCprJKuskY93Q6C3npERfsQGehMd6EVxbQv+Xgbig72J8DcR4K2Xv4E9JEFokNBoNJSWlhIcHMyECRPOat8lS5Zw3XXXnbJNdHR0l23V1dXY7XbCw8NdtoeHh1NeXn7a837yySfU19ezaNEil+3vvfceGRkZ7Ny5s8fHEEIMbo2Njaxbt47GxkZ3l9Jj/iY9/iY9SaG+2B0q1U3tpCWFUNbQSl51M4fKG8kqs5BT1Uxdi5XtubVspxYABQj2NRAT6E1ckDdJoT5MjAkg9vulPvxN5z7J5FAhQegMlZWVUVZW5rItMDCQxMRE2traOHjwYJd9jk3ydfjwYZqbm11+lpCQQFBQEFVVVRQVFbn8zM/Pr0f/wtm9e/dZhyCAoKAggoKCznq/Y078V4iqqmf0L5PXXnuN+fPnExUV5dxWVFTEr3/9a9atW3dGV7S6O4YQQngarUYh3N9EuL+J8TFmGlqt1LdYaWyzUdXYRnphPYfLLZTWt1FS30pDq5Xqpg6qmzrI/H75DwUIN5uIC/JmWKgvk+ICmJIQSFSAl0zmeAoShM7Qn//8Zx5//HGXbTfffDN///vfKS4uJjU1tcs+x7pfLVq0iO3bt7v87O233+aWW27hgw8+4O6773b52bx58/jiiy/OusbMzMwuQejAgQPccccddHR0MGvWLLKysvj8889d2vT01lhISAharbbL1Z/KysouV4lOVFBQwJdffsk///lPl+3p6elUVla6/D7tdjubNm3ipZdeor29Ha1We8pjCCGEJ1MUxTm7dSczF6WE026zOwNSQXUz+0st5FY3UVjTQkFtC/UtnX2Oyhva+C6vlne+K0SrUYgJ8GJkhB8zhgVzSUo40QFechvtOBKEztCdd97JVVdd5bItMDAQgJiYGNLT00+675tvvtntFSGA6667jrS0NJef+fn59ajGffv28aMf/cj53GazsXjxYt544w1SUlK48sorGTduXJf9enprzGAwkJqayvr1613Ou379eq6++upTHu+NN94gLCysywi3iy++mH379rlsu+222xg1ahQPPPCAMwSd6hhCCDEYGXVawvy0hPmZGBHux9wxEbR22Klu6uyAfbSyiSMVjRTUtFBY2/lo6bBTUNsZlNYdrOD3/8kiOdSH8xKCuHBkKKMj/dHrNCgoaJTOK1O+Rh067dCZXUeC0BmKjIwkMjKy25+ZTKZTrnUzcuTIk/4sNDSU0NDQc64PwOFwsHfvXkpLS/Hx8eGLL75gypQppKSkOOs4cSg6nNutseXLl/Ozn/2MKVOmkJaWxl/+8hcKCwtZsmQJAC+99BIff/wxX331lUudb7zxBrfeeis6net/gn5+fowdO9Zlm4+PD8HBwS7bT3UMIcTgFxsby0svvURsbKy7S3ErL4OW2CBvYoO8mRQXSHN758i0ysZ2KiytVFraKaxt4WhVE4fKGqlp7uBIRRNHKppYs6PzilGEv4noQC+iA0xEBXgR7m8iKdSHSLMXYX5G/AZ5fyO3f4OsWrWKP/zhD5SVlTFmzBief/75Lrdgjrdx40aWL1/OgQMHiIqK4v7773d+6Q51Tz75JA888AB/+tOfWL58OV5eXi63yvbv38/PfvazXj3n9ddfT01NDU888QRlZWWMHTuWtWvXEh8fD3R2qM7JyXHZ58svv6SwsJCf//znPT5vbxxDCOG5QkNDZRLVbvgYdfgYdSSE+GB3qFQ1tlNu6bxd1ma1UdXUwaEyC4fKG8mpaqLN6qCkvpWS+laX4+i1CsE+RkL8DET6exEd6EVMkBcJQd4khfoSF+Q9aK4auXUeoffff5+f/exnLpPxrV69+qST8eXl5TF27FjuuOMO7rzzTr799lvuuusu3n33Xa699tozOudgnUeoO8899xzl5eU888wzfP7551x11VU0NjZiNBrdXZrwEIPtMyEGj9raWtauXcvll19+ToM9hgpVVWnpsFPfaqWhxfp9X6N2yho6O1+X1LVSUt9GWUMrtc0dOE6TDPRahVA/I9EBXsQH+zAizJfh4X6E+5sI9TMS5GNA28tzHPXVPEJuDUJnOxnfAw88wL///W+ysrKc25YsWcKePXvYtm3bGZ1zKAWhyspK5s+fj91u5+qrr+bjjz9m79697i5LeJDB9pkQg0dGRgapqamkp6efsmuCOLU2qx1LmxVLq+37/+38/1VNbVQ3tlPV1EFNUzv1LVbqWjqoa+lsc7rgoFHA7KUn6PuFZY8tNBvg3TllgFGnQa899lAwnPhcq0Gvc33e1tLE+KSowTOhYk8m49u2bRvz5s1z2XbppZfy2muvYbVa0eu73sdsb2+nvb3d+dxisfRC9Z4hLCzM2Yl7x44dHDlyxM0VCSGEGEhMei0mvZawE8bodNgctHbYabHaaO2w02F3YLWpWB0O2jrsVFjaKP1+hFq5pY0KSxs1TR1YWq00tdtwqFDXYqWuxQo0d3vus+Vob+mV45zIbUGoJ5PxlZeXd9veZrNRXV3dbWfmlStXdhn2PhTt3bu32xFjQgghxIkMOg0GnQYzZ99R2mZ3UNPUQbmljaqmNupbbNQ2d15Vqm+x0tBmxWp3YLOpWO0OrA4HHTYHNoeKze7AalexOTp/Zneo2OwqNoeDNvpmLiS3d5Y+28n4umvf3fZjVqxYwfLly53PLRbLkBxlcMcdd7i7BCGEEEOATqsh3Gwi3Ny7t9MtFgvmp3v1kIAbg1BPJuOLiIjotr1OpyM4OLjbfYxGo3QOFkKIQcbHx4fp06cPuNXnhedx29i34yfjO9769euZMWNGt/ukpaV1ab9u3TqmTJnSbf8gIYQQg9PIkSPZtm3bKedpE+JMuHUSgOXLl7N69Wpef/11srKyWLZsmctkfCtWrGDhwoXO9kuWLKGgoIDly5eTlZXF66+/zmuvvcZ9993nrpcghBBCCA/m1j5Cp5uMr6ysjMLCQmf7xMRE1q5dy7Jly3j55ZeJiorihRdeOOM5hIQQQgwOMnxe9Ba3d5a+6667uOuuu7r92Ztvvtll2+zZs8nIyOjjqoQQQggxFAyO+bF7mRvnmBRiQJHPghBisJMgdJxjHa5bWvpm0iYhPM2xz4IMRhBCDFZuvzU2kGi1WgICAqisrATA29v7lHMaCTFYqapKS0sLlZWVBAQEoNX2zURmQgjhbhKEThAREQHgDENCDGUBAQHOz4QQA8no0aPJzs4mJibG3aUIDydB6ASKohAZGUlYWBhWq9Xd5QjhNnq9Xq4EiQHLZDIxbNgwd5chBgEJQieh1WrlS0AIIQaovLw8fve73/H73/+exMREd5cjPJh0lhZCCOFx6urqWLNmDXV1de4uRXg4CUJCCCGEGLIkCAkhhBBiyBpyfYSOTRBnsVjcXIkQQoieampqcv6v/D0fGo69z7090auiDrGpY3Nzc0lOTnZ3GUIIIYTogZycHJKSknrteEPuilBQUBAAhYWFmM1mN1cjLBYLsbGxFBUV4e/v7+5yhjR5LwYOeS8GDnkvBo6Ghgbi4uKc3+O9ZcgFIY2ms1uU2WyW/6gHEH9/f3k/Bgh5LwYOeS8GDnkvBo5j3+O9drxePZoQQgghhAeRICSEEEKIIWvIBSGj0cijjz6K0Wh0dykCeT8GEnkvBg55LwYOeS8Gjr56L4bcqDEhhBBCiGOG3BUhIYQQQohjJAgJIYQQYsiSICSEEEKIIUuCkBBCCCGGrEEZhFatWkViYiImk4nU1FQ2b958yvYbN24kNTUVk8lEUlISr776aj9VOvidzXvxz3/+k7lz5xIaGoq/vz9paWl88cUX/Vjt4He2n41jvv32W3Q6HRMnTuzbAoeQs30v2tvbefjhh4mPj8doNJKcnMzrr7/eT9UObmf7XqxZs4YJEybg7e1NZGQkt912GzU1Nf1U7eC1adMmFixYQFRUFIqi8Mknn5x2n175/lYHmffee0/V6/XqX//6V/XgwYPqr3/9a9XHx0ctKCjotn1ubq7q7e2t/vrXv1YPHjyo/vWvf1X1er360Ucf9XPlg8/Zvhe//vWv1aefflr97rvv1CNHjqgrVqxQ9Xq9mpGR0c+VD05n+34cU19fryYlJanz5s1TJ0yY0D/FDnI9eS+uuuoqddq0aer69evVvLw8dceOHeq3337bj1UPTmf7XmzevFnVaDTq//3f/6m5ubnq5s2b1TFjxqjXXHNNP1c++Kxdu1Z9+OGH1X/84x8qoH788cenbN9b39+DLghNnTpVXbJkicu2UaNGqQ8++GC37e+//3511KhRLtvuvPNOdfr06X1W41Bxtu9Fd0aPHq0+/vjjvV3akNTT9+P6669Xf/vb36qPPvqoBKFecrbvxWeffaaazWa1pqamP8obUs72vfjDH/6gJiUluWx74YUX1JiYmD6rcSg6kyDUW9/fg+rWWEdHB+np6cybN89l+7x589i6dWu3+2zbtq1L+0svvZRdu3ZhtVr7rNbBrifvxYkcDgeNjY29vsDeUNTT9+ONN94gJyeHRx99tK9LHDJ68l78+9//ZsqUKTzzzDNER0czYsQI7rvvPlpbW/uj5EGrJ+/FjBkzKC4uZu3ataiqSkVFBR999BFXXHFFf5QsjtNb39+DatHV6upq7HY74eHhLtvDw8MpLy/vdp/y8vJu29tsNqqrq4mMjOyzegeznrwXJ3r22Wdpbm7muuuu64sSh5SevB/Z2dk8+OCDbN68GZ1uUP2pcKuevBe5ubls2bIFk8nExx9/THV1NXfddRe1tbXST+gc9OS9mDFjBmvWrOH666+nra0Nm83GVVddxYsvvtgfJYvj9Nb396C6InSMoiguz1VV7bLtdO272y7O3tm+F8e8++67PPbYY7z//vuEhYX1VXlDzpm+H3a7nZtuuonHH3+cESNG9Fd5Q8rZfDYcDgeKorBmzRqmTp3K5ZdfznPPPcebb74pV4V6wdm8FwcPHuSee+7hkUceIT09nc8//5y8vDyWLFnSH6WKE/TG9/eg+mdeSEgIWq22S5KvrKzskhqPiYiI6La9TqcjODi4z2od7HryXhzz/vvvs3jxYj788EMuueSSvixzyDjb96OxsZFdu3axe/du7r77bqDzy1hVVXQ6HevWreOiiy7ql9oHm558NiIjI4mOjsZsNju3paSkoKoqxcXFDB8+vE9rHqx68l6sXLmSmTNn8pvf/AaA8ePH4+Pjw6xZs3jyySflLkI/6q3v70F1RchgMJCamsr69etdtq9fv54ZM2Z0u09aWlqX9uvWrWPKlCno9fo+q3Ww68l7AZ1XghYtWsQ777wj99x70dm+H/7+/uzbt4/MzEznY8mSJYwcOZLMzEymTZvWX6UPOj35bMycOZPS0lKampqc244cOYJGoyEmJqZP6x3MevJetLS0oNG4fnVqtVrgh6sRon/02vf3WXWt9gDHhkK+9tpr6sGDB9WlS5eqPj4+av7/b+/+Y6Ku/ziAPy/g6I4jDigRxo9b/AoUp0hUYIKbG46lQGqUTCEMNNYgy2O53EJX6TQgMVKXBI1l1hJY0QLk4g6Y5NS4jDz5cQGmsbVIFwJx/Hh9/2h88uDA4wuC3b0e2/3xeX/en/fr/QO4196fz3FdXURE9MYbb9DWrVuF+uMfv9u1axdduXKFioqK+OPzc2Sma3Hq1CmytbWlwsJC6unpEV63bt1aqCFYlJmux0T8qbG5M9O16OvrI09PT9q0aRP9/PPPpNFoyN/fn1566aWFGoLFmOlaFBcXk62tLX344Yek1+upsbGRwsLCKDw8fKGGYDH6+vqoubmZmpubCQDl5eVRc3Oz8K8M7tX7t8UlQkREhYWF5OPjQ2KxmEJDQ0mj0QjnkpOTKSoqyqi+Wq2mFStWkFgsJoVCQceOHZvnHluumaxFVFQUAZj0Sk5Onv+OW6iZ/m7ciROhuTXTtdDpdLR27VqSSCTk6elJr732Gg0MDMxzry3TTNeioKCAgoODSSKRkLu7OyUlJdH169fnudeWp66ubtr3gHv1/i0i4r08xhhjjFkni3pGiDHGGGNsJjgRYowxxpjV4kSIMcYYY1aLEyHGGGOMWS1OhBhjjDFmtTgRYowxxpjV4kSIMcYYY1aLEyHG7lPR0dF49dVXhWOFQoH333//nsXLycnB8uXLZ9VGV1cXRCIRtFrtnPRpOiUlJZDL5fc8zv0Uey7WSK1WQyQS4datW1PWWci5ZWy+cSLE2H/EhQsXkJ6efs/a3717N1Qq1aza8PLyQk9PD5YuXTpHvfqHqSQwMTERbW1tcxrH3NiMMcthUd8+z5gle+SRR+5Ju0SE0dFRyGQyyGSyWbVlY2ODxYsXz1HPpieRSCCRSOYl1lwwGAwQi8UL3Q3G2AS8I8TYfaC/vx/btm2DTCaDu7s7cnNzJ9WZuDORk5MDb29v2Nvbw8PDA5mZmcK5oaEhZGdnw8vLC/b29vD390dRURGAf2+NVFdXIywsDPb29mhoaJh02yUlJQXx8fF499134ebmBrlcjn379mFkZARKpRIuLi7w9PTExx9/LFwz8dbYeCyVSoWwsDBIpVJERESgtbVVuEav1yMuLg5ubm6QyWR4/PHHUVtbK5yPjo5Gd3c3du3aBZFIBJFIBMD07Ztjx47B19cXYrEYgYGBKC0tNTovEolw8uRJJCQkQCqVwt/fH1999dWU6zJV7HHV1dUICgqCTCbDunXr0NPTM2n+Dhw4AA8PDwQEBAAAbty4gcTERDg7O8PV1RVxcXHo6uoSrlOr1QgPD4eDgwPkcjkiIyPR3d1tFLe0tBQKhQJOTk54/vnn0dfXJ5wbGhpCZmYmFi1ahAcffBCrVq3ChQsXphzj+Fx6e3tDKpUiISEBvb2909ZnzJJwIsTYfUCpVKKurg7l5eWoqamBWq3GpUuXpqz/5ZdfIj8/HydOnEB7ezsqKioQEhIinN+2bRtOnz6NgoIC6HQ6HD9+fNJuT3Z2Ng4cOACdTodly5aZjPPdd9/ht99+Q319PfLy8pCTk4NnnnkGzs7OOH/+PHbu3ImdO3fi119/nXZ8b775JnJzc3Hx4kXY2toiNTVVOHf79m3ExsaitrYWzc3NiImJwfr163Ht2jUAQFlZGTw9PbF//3709PQYJRt3Ki8vR1ZWFl5//XW0tLRgx44dePHFF1FXV2dUb9++fXjuuedw+fJlxMbGIikpCX/++afJNqeLPTAwgPfeew+lpaWor6/HtWvXsHv3bqPrVSoVdDodzp49i8rKSgwMDGDNmjWQyWSor69HY2OjkEQZDAaMjIwgPj4eUVFRuHz5MpqampCenm6UgOn1elRUVKCyshKVlZXQaDQ4ePCgcD47OxtnzpzBJ598gh9++AF+fn6IiYmZcoznz59HamoqMjIyoNVqsWbNGrz99tsm6zJmkWb7bbGMsdnp6+sjsVhMp0+fFsp6e3tJIpFQVlaWUObj40P5+flERJSbm0sBAQFkMBgmtdfa2koA6OzZsybjjX/Dc0VFhVH5xG+XT05OJh8fHxodHRXKAgMD6emnnxaOR0ZGyMHBgT777DMiIurs7CQA1NzcbBSrtrZWuOabb74hADQ4ODjlnAQHB9PRo0dNjn1ccXExOTk5CccRERGUlpZmVGfz5s0UGxsrHAOgvXv3Cse3b98mkUhE33777ZR9mSo2AOro6BDKCgsLyc3NTThOTk4mNzc3GhoaEsqKioooMDCQxsbGhLKhoSGSSCRUXV1Nvb29BIDUarXJvrz11lsklUrpr7/+EsqUSiU98cQTwnjs7Ozo008/Fc4bDAby8PCgQ4cOEdG/a3Lz5k0iInrhhRdo3bp1RnESExON5pYxS8Y7QowtML1eD4PBgKeeekooc3FxQWBg4JTXbN68GYODg3j00UeRlpaG8vJyjIyMAAC0Wi1sbGwQFRU1bdywsLC79m3JkiV44IF//0y4ubkZ7TzZ2NjA1dUVv//++7Tt3Lnj5O7uDgDCNf39/cjOzkZwcDDkcjlkMhmuXr0q7AiZS6fTITIy0qgsMjISOp1uyr44ODjA0dHxrv03RSqVwtfXVzh2d3ef1E5ISIjRc0GXLl1CR0cHHB0dhWeyXFxc8Pfff0Ov18PFxQUpKSnCrtiRI0cm7YApFAo4OjqajKvX6zE8PGw0D3Z2dggPD580D+N0Op3Rzx6ASceMWTJOhBhbYEQ042u8vLzQ2tqKwsJCSCQSZGRkYPXq1RgeHjb7AWIHB4e71rGzszM6FolEJsvGxsbMbmf8Ns/4NUqlEmfOnME777yDhoYGaLVahISEwGAwmDWOiX25ExFNKvt/+m+KqXYmruXEOR4bG8PKlSuh1WqNXm1tbdiyZQsAoLi4GE1NTYiIiMDnn3+OgIAAfP/992b1fzy+OfNw5znGrBknQowtMD8/P9jZ2Rm92d28efOuHw2XSCTYsGEDCgoKoFar0dTUhJ9++gkhISEYGxuDRqO5112fEw0NDUhJSUFCQgJCQkKwePFio4eHAUAsFmN0dHTadoKCgtDY2GhUdu7cOQQFBc2qf+bENldoaCja29uxaNEi+Pn5Gb2cnJyEeitWrMCePXtw7tw5LF26FKdOnTKrfT8/P4jFYqN5GB4exsWLF6ech+DgYKOfPQCTjhmzZJwIMbbAZDIZtm/fDqVSCZVKhZaWFqSkpBjdkpqopKQERUVFaGlpwS+//ILS0lJIJBL4+PhAoVAgOTkZqampqKioQGdnJ9RqNb744ot5HJX5/Pz8UFZWBq1Wix9//BFbtmyZtEOjUChQX1+PGzdu4I8//jDZjlKpRElJCY4fP4729nbk5eWhrKxs0gPMM2VObHMlJSXh4YcfRlxcHBoaGtDZ2QmNRoOsrCxcv34dnZ2d2LNnD5qamtDd3Y2amhq0tbWZncw5ODjg5ZdfhlKpRFVVFa5cuYK0tDQMDAxg+/btJq/JzMxEVVUVDh06hLa2NnzwwQeoqqqa1TgZ+y/hRIix+8Dhw4exevVqbNiwAWvXrsWqVauwcuXKKevL5XJ89NFHiIyMxLJly6BSqfD111/D1dUVwD8fI9+0aRMyMjLw2GOPIS0tDf39/fM1nBnJz8+Hs7MzIiIisH79esTExCA0NNSozv79+9HV1QVfX98p/59SfHw8jhw5gsOHD2PJkiU4ceIEiouLER0dPav+mRPbXFKpFPX19fD29sazzz6LoKAgpKamYnBwEA899BCkUimuXr2KjRs3IiAgAOnp6XjllVewY8cOs2McPHgQGzduxNatWxEaGoqOjg5UV1fD2dnZZP0nn3wSJ0+exNGjR7F8+XLU1NRg7969sxonY/8lIuIbxIwxxhizUrwjxBhjjDGrxYkQY4wxxqwWJ0KMMcYYs1qcCDHGGGPManEixBhjjDGrxYkQY4wxxqwWJ0KMMcYYs1qcCDHGGGPManEixBhjjDGrxYkQY4wxxqwWJ0KMMcYYs1qcCDHGGGPMav0Pv3pQSXkoSJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrimination Threshold = 0.747\n"
     ]
    }
   ],
   "source": [
    "decision_maker=['queue_rate', 'limit', '0.1']\n",
    "scoring=\"accuracy\"\n",
    "# Discrimination Threshold\n",
    "orig_threshold = discrimination_threshold(orig_ml_model, \n",
    "                                         processed_X,\n",
    "                                         y,\n",
    "                                         decision_maker=decision_maker,\n",
    "                                         metrics=[decision_maker[0]], \n",
    "                                         show=True,\n",
    "                                         random_seed=random_seed)\n",
    "print(f\"Discrimination Threshold = {orig_threshold:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4304469c-4f45-4e5d-800d-853fcf7bb81f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original accuracy score = 0.584\n"
     ]
    }
   ],
   "source": [
    "orig_acc_score = binary_threshold_score(orig_ml_model, \n",
    "                                        X_test, \n",
    "                                        y_test,\n",
    "                                        scoring=scoring,  \n",
    "                                        threshold=orig_threshold)\n",
    "print(f\"Original accuracy score = {orig_acc_score:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7089a26b-0199-4210-b1fb-9eb4a71f1495",
   "metadata": {},
   "source": [
    "### 3. Fairness Metric Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99c9479d",
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data = pd.concat([X_test, y_test], axis=1)\n",
    "training_data = pd.concat([X_train, y_train], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e0e58a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate FAIR object\n",
    "fair_object = FAIR(ml_model=orig_ml_model,\n",
    "                   training_data=training_data,\n",
    "                   testing_data=testing_data, \n",
    "                   target_variable=target_var,\n",
    "                   protected_variable=\"sex\", \n",
    "                   privileged_class=1.0,\n",
    "                   threshold=orig_threshold,\n",
    "                   random_seed=random_seed) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c2babfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Fairness Metric = 0.110\n"
     ]
    }
   ],
   "source": [
    "metric_name='statistical_parity_ratio'\n",
    "orig_fairness_metric_score = fair_object.fairness_metric(metric_name)\n",
    "print(f\"Original Fairness Metric = {orig_fairness_metric_score:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12be4fed-1ee1-4173-b2a8-fa233299cadd",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 4. Compare Performance of Each Mitigation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "96389970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available bias mitigation methods for 'statistical_parity_ratio' are:\n",
      "1 'disparate-impact-remover'\n",
      "2 'resampling'\n",
      "3 'resampling-preferential'\n",
      "4 'reweighing'\n"
     ]
    }
   ],
   "source": [
    "# Print available bias mitigation methods for statistical_parity_ratio\n",
    "fair_object.print_bias_mitigation_methods(metric_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0b07834-61b4-4296-9226-a6aa549f9f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          accuracy  statistical_parity_ratio  \\\n",
      "reference                 0.583507                  0.109817   \n",
      "disparate-impact-remover  0.589744                  0.267955   \n",
      "resampling                0.584893                  0.375637   \n",
      "resampling-preferential   0.587665                  0.718405   \n",
      "reweighing                0.583507                  0.368745   \n",
      "\n",
      "                          discrimination_threshold  \n",
      "reference                                 0.747475  \n",
      "disparate-impact-remover                  0.707071  \n",
      "resampling                                0.727273  \n",
      "resampling-preferential                   0.717172  \n",
      "reweighing                                0.727273  \n"
     ]
    }
   ],
   "source": [
    "df = fair_object.compare_mitigation_methods(metric_name=metric_name, \n",
    "                                            scoring=scoring, \n",
    "                                            show=True,\n",
    "                                            compute_discrimination_threshold=True,\n",
    "                                            decision_maker=decision_maker)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f42439",
   "metadata": {},
   "source": [
    "### 5. Apply Resampling-PreferentialMitigation and Re-evaluate F1 Score and Fairness Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c52cefb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mitigation_method = \"resampling-preferential\"\n",
    "mitigation = fair_object.bias_mitigation(mitigation_method = mitigation_method)\n",
    "\n",
    "# extract modified data \n",
    "X_train = mitigation['training_data'][features]\n",
    "# drop y \n",
    "y_train = mitigation['training_data'][target_var]\n",
    "\n",
    "# Train the second machine learning model using the mitigated dataset\n",
    "ml_model = clone(orig_ml_model)\n",
    "ml_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6c866ae1-f21d-4131-af51-b2c5dfe0f698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrimination Threshold = 0.717\n"
     ]
    }
   ],
   "source": [
    "# We can also find the optimal discrimination threshold for this new model\n",
    "threshold = discrimination_threshold(ml_model, \n",
    "                                     X_test,\n",
    "                                     y_test,\n",
    "                                     decision_maker=decision_maker,\n",
    "                                     metrics=[decision_maker[0]], \n",
    "                                     show=True,\n",
    "                                     model_training=False,\n",
    "                                     random_seed=random_seed)\n",
    "print(f\"Discrimination Threshold = {threshold:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67ac3dbe-f8e1-4b47-bb97-5c9ab6b1e2b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mitigated accuracy score = 0.588\n"
     ]
    }
   ],
   "source": [
    "acc_score = binary_threshold_score(ml_model, \n",
    "                                   X_test, \n",
    "                                   y_test, \n",
    "                                   scoring=scoring, \n",
    "                                   threshold=threshold)\n",
    "print(f\"Mitigated accuracy score = {acc_score:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21ef4274-d148-4ac6-9b12-c74c88d272e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate FAIR object\n",
    "fair_object = FAIR(ml_model=ml_model,\n",
    "                   training_data=training_data,\n",
    "                   testing_data=testing_data, \n",
    "                   target_variable=target_var,\n",
    "                   protected_variable=\"sex\", \n",
    "                   privileged_class=1.0,\n",
    "                   threshold=threshold,\n",
    "                   random_seed=random_seed) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7a2d35a-14cd-409f-a16f-ef0d0b7d286c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mitigated Fairness Metric = 0.834\n"
     ]
    }
   ],
   "source": [
    "fairness_metric_score = fair_object.fairness_metric(metric_name)\n",
    "print(f\"Mitigated Fairness Metric = {fairness_metric_score:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b795994-5432-46c0-9a3a-24ee5c630535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Accuracy score = 0.584 and Mitigated Accuracy score = 0.588\n",
      "Orginal Fairness Score = 0.110 and Mitigated Fairness Score = 0.834\n"
     ]
    }
   ],
   "source": [
    "print(f\"Original Accuracy score = {orig_acc_score:.3f} and Mitigated Accuracy score = {acc_score:.3f}\")\n",
    "print(f\"Orginal Fairness Score = {orig_fairness_metric_score:.3f} and Mitigated Fairness Score = {fairness_metric_score:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619cc2f7-5d99-4f61-bc56-ac54c23f167b",
   "metadata": {},
   "source": [
    "### 6. Paired t Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "88ca3648-ce64-4299-bf45-87cb62b96b36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:01<00:00,  3.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5x2cv paired t-test using fairness metric. (chi2, p) = (-3.0180421672451976, 0.029482434359630622)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Paired t test between original Machine Learning model and \"applying the mitigation method\".\n",
    "# Fairness metric comparison\n",
    "results = paired_ttest(orig_ml_model,\n",
    "                        processed_X,\n",
    "                        y,\n",
    "                        method=\"5x2cv\",\n",
    "                        threshold=orig_threshold,\n",
    "                        fair_object=fair_object,\n",
    "                        mitigation_method=mitigation_method,\n",
    "                        scoring=metric_name,\n",
    "                        random_seed=random_seed)\n",
    "print(\"5x2cv paired t-test using fairness metric. (chi2, p) =\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e10d12b3-02ef-4037-b07c-ef71c517e5ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:05<00:00,  1.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5x2cv paired t-test using fairness metric. (chi2, p) = (-3.2759037872842516, 0.022054119905021447)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Paired t test between original Machine Learning model and \"applying the mitigation method\".\n",
    "# Fairness metric comparison computing the discrimination threshold\n",
    "results = paired_ttest(orig_ml_model,\n",
    "                        processed_X,\n",
    "                        y,\n",
    "                        method=\"5x2cv\",\n",
    "                        fair_object=fair_object,\n",
    "                        mitigation_method=mitigation_method,\n",
    "                        scoring=metric_name,\n",
    "                        compute_discrimination_threshold=True,\n",
    "                        decision_maker=decision_maker,\n",
    "                        random_seed=random_seed)\n",
    "print(\"5x2cv paired t-test using fairness metric. (chi2, p) =\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfc306a6-14a8-4caf-a0b0-de3a145f7f62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:02<00:00,  2.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5x2cv paired t-test using accuracy metric. (chi2, p) = (3.8293465061149528, 0.012255835188739286)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Paired t test between original Machine Learning model and \"applying the mitigation method\".\n",
    "# Accuracy comparison\n",
    "results = paired_ttest(orig_ml_model,\n",
    "                        processed_X,\n",
    "                        y,\n",
    "                        method=\"5x2cv\",\n",
    "                        threshold=orig_threshold,\n",
    "                        fair_object=fair_object,\n",
    "                        mitigation_method=mitigation_method,\n",
    "                        scoring=scoring,\n",
    "                        random_seed=random_seed)\n",
    "print(\"5x2cv paired t-test using accuracy metric. (chi2, p) =\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "134de341-d38f-4e88-ad69-6fabadca5b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:04<00:00,  1.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5x2cv paired t-test using accuracy metric. (chi2, p) = (7.837206195313964, 0.0005427188750539392)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Paired t test between original Machine Learning model and \"applying the mitigation method\".\n",
    "# Accuracy comparison computing the discrimination threshold\n",
    "results = paired_ttest(orig_ml_model,\n",
    "                        processed_X,\n",
    "                        y,\n",
    "                        method=\"5x2cv\",\n",
    "                        fair_object=fair_object,\n",
    "                        mitigation_method=mitigation_method,\n",
    "                        scoring=scoring,\n",
    "                        compute_discrimination_threshold=True,\n",
    "                        decision_maker=decision_maker,\n",
    "                        random_seed=random_seed)\n",
    "print(\"5x2cv paired t-test using accuracy metric. (chi2, p) =\", results)"
   ]
  }
 ],
 "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": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}