{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f777a869",
   "metadata": {},
   "source": [
    "# Lesson 20: Classification demonstration\n",
    "\n",
    "This notebook demonstrates key concepts and tools for training classification models.\n",
    "\n",
    "1. **What is classification**\n",
    "\n",
    "    - Binary classification\n",
    "    - Class probabilities\n",
    "    - Cross-entropy (log_loss)\n",
    "\n",
    "2. **Classification metrics**\n",
    "\n",
    "    - Accuracy\n",
    "    - Two ways to be right, two ways to be wrong (confusion matrix)\n",
    "    - Metrics derived from TP, FP, TN & FN\n",
    "    - ROC & PR curves\n",
    "\n",
    "3. **Dealing with imbalanced classes**\n",
    "\n",
    "    - Class weighting\n",
    "    - Oversampling\n",
    "    - Undersampling\n",
    "\n",
    "4. **Decision thresholds**\n",
    "\n",
    "    - Manually setting decision thresholds\n",
    "    - Optimizing thresholds with `TunedThresholdClassifierCV`\n",
    "\n",
    "5. **Classification models**\n",
    "\n",
    "    - Logistic regression\n",
    "    - Naive Bayes\n",
    "    - K-nearest neighbors\n",
    "    - Decision trees\n",
    "    - Support vector machines\n",
    "\n",
    "6. **Multiclass & multilabel classification**\n",
    "\n",
    "## Notebook set up\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a664f2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.datasets import make_classification, make_multilabel_classification\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, TunedThresholdClassifierCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    ConfusionMatrixDisplay,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    roc_auc_score,\n",
    "    RocCurveDisplay,\n",
    "    average_precision_score,\n",
    "    PrecisionRecallDisplay,\n",
    "\n",
    ")\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd3aadd",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "764cfd57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3237</th>\n",
       "      <td>0.468666</td>\n",
       "      <td>-1.293372</td>\n",
       "      <td>1.589576</td>\n",
       "      <td>-1.032292</td>\n",
       "      <td>0.268626</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>-2.015761</td>\n",
       "      <td>-0.057455</td>\n",
       "      <td>0.183112</td>\n",
       "      <td>-1.668115</td>\n",
       "      <td>-1.482481</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>0.295756</td>\n",
       "      <td>-1.540530</td>\n",
       "      <td>2.277648</td>\n",
       "      <td>-1.760406</td>\n",
       "      <td>0.110130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>-1.199452</td>\n",
       "      <td>-0.239735</td>\n",
       "      <td>0.803799</td>\n",
       "      <td>-1.597170</td>\n",
       "      <td>-0.914508</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>-0.673379</td>\n",
       "      <td>-0.383835</td>\n",
       "      <td>-0.861746</td>\n",
       "      <td>0.245781</td>\n",
       "      <td>-0.452229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature_0  feature_1  feature_2  feature_3  feature_4  target\n",
       "3237   0.468666  -1.293372   1.589576  -1.032292   0.268626       1\n",
       "2220  -2.015761  -0.057455   0.183112  -1.668115  -1.482481       0\n",
       "1604   0.295756  -1.540530   2.277648  -1.760406   0.110130       0\n",
       "1963  -1.199452  -0.239735   0.803799  -1.597170  -0.914508       0\n",
       "2329  -0.673379  -0.383835  -0.861746   0.245781  -0.452229       0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate synthetic dataset with 5000 samples and 5 features\n",
    "# Weights [0.9, 0.1] create class imbalance (90% class 0, 10% class 1)\n",
    "data = make_classification(\n",
    "    n_samples=5000,\n",
    "    n_features=5,\n",
    "    weights=[0.9, 0.1],\n",
    "    random_state=315\n",
    ")\n",
    "\n",
    "# Create dataframe with feature columns\n",
    "df = pd.DataFrame(data[0], columns=[f'feature_{i}' for i in range(data[0].shape[1])])\n",
    "df['target'] = data[1]\n",
    "\n",
    "# Split into train (75%) and test (25%) sets\n",
    "train_df, test_df = train_test_split(df, random_state=315)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c71e3999",
   "metadata": {},
   "source": [
    "## 1. What is classification?\n",
    "\n",
    "Classification models predict a categorical label from input features.\n",
    "\n",
    "### 1.1. Binary classification\n",
    "\n",
    "The simplest type of classification is to predict a binary label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c544660f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual:      [0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "Predictions: [0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Train logistic regression classifier\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "# Generate binary predictions for test set\n",
    "test_predictions = model.predict(test_df.drop(columns=['target']))\n",
    "\n",
    "print(f'Actual:      {test_df[\"target\"].values[:30]}')\n",
    "\n",
    "# Display first 30 predictions vs actual labels\n",
    "print(f'Predictions: {test_predictions[:30]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f113d3a",
   "metadata": {},
   "source": [
    "### 1.2. Class probabilities\n",
    "\n",
    "Under the hood, the model is predicting the probability of each class and then applying a default threshold of 0.5 to decide the predicted class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9631f7f4",
   "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>Probability</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.033855</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000118</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.045244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.361296</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.007032</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.032096</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.899164</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.073542</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.006653</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Probability  Label\n",
       "0     0.033855      0\n",
       "1     0.000118      0\n",
       "2     0.045244      0\n",
       "3     0.012696      0\n",
       "4     0.361296      0\n",
       "5     0.007032      0\n",
       "6     0.032096      0\n",
       "7     0.899164      1\n",
       "8     0.073542      0\n",
       "9     0.006653      0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get probability predictions for both classes\n",
    "test_probabilities = model.predict_proba(test_df.drop(columns=['target']))\n",
    "\n",
    "# Create dataframe with class 1 probabilities and true labels\n",
    "probabilities_df = pd.DataFrame({\n",
    "    'Probability': test_probabilities[:, 1],  # Probability of class 1\n",
    "    'Label': test_df['target']\n",
    "})\n",
    "\n",
    "\n",
    "probabilities_df.reset_index(inplace=True, drop=True)\n",
    "probabilities_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc0fbcb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAXpFJREFUeJzt3XlcVGX7P/DPzLAMoIKIbIoKPpmKAiqiaKEliZpLi1tR7pqCadGmiSIJ2aaZCRKWVkruqS0+lprGk6KUG4qKG0qmoIQsgjAyc35/+Jvz5cgigwMDh8/79fIlc59rzlwzMMPFfe5FIQiCACIiIiKZUJo6ASIiIiJjYnFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDsqFQKDBz5kyjne/rr7+GQqHAX3/99cDY/v37o3///uLty5cvQ6FQ4OuvvxbbFi5cCIVCYbT8DKV/PpcvXxbb7s+7NikUCixcuFC8rX89srOz6+Tx27VrhwkTJtTJY9XE2rVr0bFjR5ibm8POzs7U6Rid/j3xySef1PpjlH3fUePE4oZqlf4Xqv6fWq1Ghw4dMHPmTGRlZZk6PZN7//33sX37dlOnYZCDBw9i4cKFyM3NNXUq5dTn3Kpy9uxZTJgwAe3bt8eqVasQHx9v6pSIGjQzUydAjcN7770Hd3d3FBcX448//sDKlSuxc+dOnDp1CtbW1qZO76H9+uuvD4wJDw/HnDlzJG3vv/8+Ro4ciWeeeaaWMqtadfK+38GDBxEZGYkJEyYY1MNw584dmJnV7kdOVbmlpaVBqayff8/t378fOp0On332Gf7zn/+YOh2iBo/FDdWJwYMHw9fXFwAwZcoUtGjRAkuXLsWOHTvwwgsvVHifwsJC2NjY1GWaNWZhYfHAGDMzs1r/5W6o6uT9MHQ6HTQaDdRqNdRqda0+1oNYWlqa9PGrcuPGDQAw6uWooqIiWfzhQFQT9fPPGJK9J598EgCQnp4OAJgwYQKaNGmCixcvYsiQIWjatCmCg4MB3Cty3njjDbi5ucHS0hKPPvooPvnkE1S2oX1CQgIeffRRqNVq9OjRA4mJiZLjV65cQUhICB599FFYWVmhRYsWGDVqlGQsSllFRUV45ZVX0KJFCzRr1gzjxo3DrVu3JDHVGbty/5gbhUKBwsJCfPPNN+JluwkTJmDfvn1QKBTYtm1buXN89913UCgUSEpKqvKxUlNT8eSTT8LKygqtW7dGVFQUdDpdubiK8v7888/h6ekJa2trNG/eHL6+vvjuu+/E5/DWW28BANzd3cW89a+dftxTQkICPD09YWlpiV27donHyo650cvOzsbo0aPRrFkztGjRArNnz0ZxcbF4vKpxFGXP+aDcKhpzc+nSJYwaNQr29vawtrZG79698fPPP0ti9u/fD4VCgU2bNiE6OhqtW7eGWq3GgAEDcOHCBUns+fPn8fzzz8PZ2RlqtRqtW7fG2LFjkZeXVy53vXbt2iEiIgIA0LJly3KvU2xsrPhaurq6IjQ0tNxlt/79+6NLly44cuQIAgICYG1tjXfffbfSxwTuXQobOXIk7O3toVar4evrix9++EESk5OTgzfffBNdu3ZFkyZN0KxZMwwePBgnTpwod77i4mIsXLgQHTp0gFqthouLC5577jlcvHixXGx8fDzat28PS0tL9OzZE3/++WeVuerl5ubi9ddfR7t27WBpaYnWrVtj3LhxVY7bSklJwYQJE+Dh4QG1Wg1nZ2dMmjQJ//77rySuoKAAr732mnhuR0dHPPXUUzh69KgYU5PvL5lG/fozkhoN/QdeixYtxLbS0lIEBQXhsccewyeffAJra2sIgoDhw4dj3759mDx5Mnx8fPDLL7/grbfewj///INPP/1Uct7ff/8dGzduxKxZs2BpaYnY2FgMGjQIycnJ6NKlCwDgzz//xMGDBzF27Fi0bt0aly9fxsqVK9G/f3+cPn263F+7M2fOhJ2dHRYuXIi0tDSsXLkSV65cEX/p1dTatWsxZcoU+Pn5Ydq0aQCA9u3bo3fv3nBzc0NCQgKeffZZyX0SEhLQvn17+Pv7V3rezMxMPPHEEygtLcWcOXNgY2OD+Ph4WFlZPTCnVatWYdasWRg5cqRYZKSkpODw4cN48cUX8dxzz+HcuXNYv349Pv30Uzg4OAC490tZ77fffsOmTZswc+ZMODg4oF27dlU+5ujRo9GuXTssXrwYhw4dwvLly3Hr1i18++23D8y3rOrkVlZWVhb69OmDoqIizJo1Cy1atMA333yD4cOHY8uWLeVe+w8++ABKpRJvvvkm8vLy8NFHHyE4OBiHDx8GAGg0GgQFBaGkpASvvvoqnJ2d8c8//+Cnn35Cbm4ubG1tK8xj2bJl+Pbbb7Ft2zasXLkSTZo0gZeXF4B7BVtkZCQCAwMxY8YM8efvzz//xIEDB2Bubi6e599//8XgwYMxduxYvPTSS3Bycqr0tUpNTUXfvn3RqlUr8Wdk06ZNeOaZZ7B161bxuV+6dAnbt2/HqFGj4O7ujqysLHzxxRfo168fTp8+DVdXVwCAVqvF0KFDsXfvXowdOxazZ89GQUEBdu/ejVOnTqF9+/biY3/33XcoKCjAK6+8AoVCgY8++gjPPfccLl26JHk+97t9+zYef/xxnDlzBpMmTUL37t2RnZ2NH374AVevXhW/3/fbvXs3Ll26hIkTJ8LZ2RmpqamIj49HamoqDh06JL6Hp0+fji1btmDmzJno3Lkz/v33X/zxxx84c+YMunfvXuPvL5mIQFSL1qxZIwAQ9uzZI9y8eVP4+++/hQ0bNggtWrQQrKyshKtXrwqCIAjjx48XAAhz5syR3H/79u0CACEqKkrSPnLkSEGhUAgXLlwQ2wAIAIS//vpLbLty5YqgVquFZ599VmwrKioql2dSUpIAQPj222/L5d6jRw9Bo9GI7R999JEAQNixY4fY1q9fP6Ffv37i7fT0dAGAsGbNGrEtIiJCuP8tZ2NjI4wfP75cPnPnzhUsLS2F3Nxcse3GjRuCmZmZEBERUS6+rNdee00AIBw+fFhyX1tbWwGAkJ6eXmneI0aMEDw9Pas8/8cff1zuPHoABKVSKaSmplZ4rGzu+tdj+PDhkriQkBABgHDixAlBECp+LSs7Z1W5tW3bVvJa61+n//3vf2JbQUGB4O7uLrRr107QarWCIAjCvn37BABCp06dhJKSEjH2s88+EwAIJ0+eFARBEI4dOyYAEDZv3lzusR9E/1rcvHlTbLtx44ZgYWEhDBw4UMxFEARhxYoVAgBh9erVYlu/fv0EAEJcXFy1Hm/AgAFC165dheLiYrFNp9MJffr0ER555BGxrbi4WPLYgnDv+2FpaSm89957Ytvq1asFAMLSpUvLPZZOpxPvB0Bo0aKFkJOTIx7fsWOHAED48ccfq8x5wYIFAgDh+++/f+BjlP1Zqej9vn79egGAkJiYKLbZ2toKoaGhlT7+w3x/qe7xshTVicDAQLRs2RJubm4YO3YsmjRpgm3btqFVq1aSuBkzZkhu79y5EyqVCrNmzZK0v/HGGxAEAf/9738l7f7+/ujRo4d4u02bNhgxYgR++eUXaLVaAJD0YNy9exf//vsv/vOf/8DOzk7SBa03bdo0yV+UM2bMgJmZGXbu3Gngq1B948aNQ0lJCbZs2SK2bdy4EaWlpXjppZeqvO/OnTvRu3dv+Pn5iW0tW7YUL/NVxc7ODlevXq32ZYKK9OvXD507d652fGhoqOT2q6++CgC1+vrqz+/n54fHHntMbGvSpAmmTZuGy5cv4/Tp05L4iRMnSsYoPf744wDu9W4AEP9y/+WXX1BUVPTQ+e3ZswcajQavvfaaZCD01KlT0axZs3KXzywtLTFx4sQHnjcnJwe//fYbRo8ejYKCAmRnZyM7Oxv//vsvgoKCcP78efzzzz/iOfWPrdVq8e+//6JJkyZ49NFHJe+VrVu3wsHBQfzelXV/7+aYMWPQvHlz8fb9r2Nltm7dCm9v73I9ahU9Rlll3+/FxcXIzs5G7969AUDyHOzs7HD48GFcu3atwvMY+/tLtYvFDdWJmJgY7N69G/v27cPp06dx6dIlBAUFSWLMzMzQunVrSduVK1fg6uqKpk2bSto7deokHi/rkUceKffYHTp0QFFREW7evAng3qydBQsWiGN4HBwc0LJlS+Tm5lZ47fz+czZp0gQuLi6VjtExho4dO6Jnz55ISEgQ2xISEtC7d+8Hzqa5cuVKha/Do48++sDHfeedd9CkSRP4+fnhkUceQWhoKA4cOGBQ7u7u7gbF359r+/btoVQqa/X1Be69ThW9JpX9bLVp00ZyW/8LWj/+yt3dHWFhYfjyyy/h4OCAoKAgxMTE1Hg8hv7x78/RwsICHh4e5fJr1apVtQaIX7hwAYIgYP78+WjZsqXkn37sj36As06nw6effopHHnlE8l5JSUmRPK+LFy/i0UcfrdaA+Qe9jpW5ePGieGnZEDk5OZg9ezacnJxgZWWFli1bij+jZZ/DRx99hFOnTsHNzQ1+fn5YuHChpOAy9veXaheLG6oTfn5+CAwMRP/+/dGpU6cKp+SW/SuxNr366quIjo7G6NGjsWnTJvz666/YvXs3WrRoUeGgW1MZN24cfv/9d1y9ehUXL17EoUOHHthr87A6deqEtLQ0bNiwAY899hi2bt2Kxx57TPylVx3VGdtTlfv/Cq/sr3J9T1xdUalUFbYLZQa2L1myBCkpKXj33Xdx584dzJo1C56enrh69Wqt51fd113/M/7mm29i9+7dFf7TF9Dvv/8+wsLCEBAQgHXr1uGXX37B7t274enpWeP3SnVeR2MaPXo0Vq1ahenTp+P777/Hr7/+Kg5yL/scRo8ejUuXLuHzzz+Hq6srPv74Y3h6ekp6h035/SXDcEAx1Wtt27bFnj17UFBQIOm9OXv2rHi8rPPnz5c7x7lz52BtbS0OLN2yZQvGjx+PJUuWiDHFxcWVLvx2/vx5PPHEE+Lt27dv4/r16xgyZEiNn5deVd3pY8eORVhYGNavX487d+7A3NwcY8aMeeA527ZtW+HrkJaWVq2cbGxsMGbMGIwZMwYajQbPPfccoqOjMXfuXKjVaqOvsnz+/HlJb8+FCxeg0+nEgcj6v+zv//7c33MBVP163q9t27YVviaV/WxVV9euXdG1a1eEh4fj4MGD6Nu3L+Li4hAVFWXQefSPn5aWBg8PD7Fdo9EgPT0dgYGBNcpPfy5zc/MHnmPLli144okn8NVXX0nac3NzJQN427dvj8OHD+Pu3btVDgp+GO3bt8epU6cMus+tW7ewd+9eREZGYsGCBWJ7Re8PAHBxcUFISAhCQkJw48YNdO/eHdHR0Rg8eLAYY6zvL9Uu9txQvTZkyBBotVqsWLFC0v7pp59CoVBIPnQAICkpSXId/e+//8aOHTswcOBA8S9GlUpV7q/Ezz//vNKegPj4eNy9e1e8vXLlSpSWlpZ77JqwsbGptKhycHDA4MGDsW7dOiQkJGDQoEGVzggpa8iQITh06BCSk5PFtps3b0oucVXm/umxFhYW6Ny5MwRBEF8D/dpDxloFOCYmRnL7888/BwDx9W3WrBkcHBzKTemPjY0tdy5DchsyZAiSk5Ml0+oLCwsRHx+Pdu3aGTRuCADy8/NRWloqaevatSuUSiVKSkoMOhdwb5yahYUFli9fLvl5/eqrr5CXl4enn37a4HMCgKOjI/r3748vvvgC169fL3dcf/kWqPi9snnzZnFMjt7zzz+P7Ozscu9TwHg9Ms8//zxOnDhR4RIJlT2G/j1///Fly5ZJbmu12nKXlxwdHeHq6ip+74z9/aXaxZ4bqteGDRuGJ554AvPmzcPly5fh7e2NX3/9FTt27MBrr70mmWIKAF26dEFQUJBkKjgAREZGijFDhw7F2rVrYWtri86dOyMpKQl79uyRTEsvS6PRYMCAARg9ejTS0tIQGxuLxx57DMOHD3/o59ejRw/s2bMHS5cuhaurK9zd3dGrVy/x+Lhx4zBy5EgAwKJFi6p1zrfffhtr167FoEGDMHv2bHEqeNu2bZGSklLlfQcOHAhnZ2f07dsXTk5OOHPmDFasWIGnn35a7DnTD9ieN28exo4dC3NzcwwbNqzGCy6mp6dj+PDhGDRoEJKSkrBu3Tq8+OKL8Pb2FmOmTJmCDz74AFOmTIGvry8SExNx7ty5cucyJLc5c+Zg/fr1GDx4MGbNmgV7e3t88803SE9Px9atWw2+RPrbb79h5syZGDVqFDp06IDS0lKsXbsWKpUKzz//vIGvyr1B4HPnzkVkZCQGDRqE4cOHiz9/PXv2fKhLlDExMXjsscfQtWtXTJ06FR4eHsjKykJSUhKuXr0qrmMzdOhQvPfee5g4cSL69OmDkydPIiEhQdKTBNz7Of32228RFhaG5ORkPP744ygsLMSePXsQEhKCESNG1DhXvbfeegtbtmzBqFGjMGnSJPTo0QM5OTn44YcfEBcXJ/l50WvWrBkCAgLw0Ucf4e7du2jVqhV+/fVXcX0tvYKCArRu3RojR46Et7c3mjRpgj179uDPP/8Ue3iN/f2lWmaiWVrUSOinU//5559Vxo0fP16wsbGp8FhBQYHw+uuvC66uroK5ubnwyCOPCB9//LE4/VMPgBAaGiqsW7dOeOSRRwRLS0uhW7duwr59+yRxt27dEiZOnCg4ODgITZo0EYKCgoSzZ8+Wmyqsz/33338Xpk2bJjRv3lxo0qSJEBwcLPz777+Sc9Z0KvjZs2eFgIAAwcrKSgBQblp4SUmJ0Lx5c8HW1la4c+dOla9hWSkpKUK/fv0EtVottGrVSli0aJHw1VdfPXAq+BdffCEEBAQILVq0ECwtLYX27dsLb731lpCXlyc5/6JFi4RWrVoJSqVSck7996AiqGQq+OnTp4WRI0cKTZs2FZo3by7MnDmz3HMtKioSJk+eLNja2gpNmzYVRo8eLdy4caPcOavK7f7vryAIwsWLF4WRI0cKdnZ2glqtFvz8/ISffvpJEqOfCn7/FOD7v8eXLl0SJk2aJLRv315Qq9WCvb298MQTTwh79uyp8PUoq6Kp4HorVqwQOnbsKJibmwtOTk7CjBkzhFu3bkli+vXr98Ap/Pe7ePGiMG7cOMHZ2VkwNzcXWrVqJQwdOlTYsmWLGFNcXCy88cYbgouLi2BlZSX07dtXSEpKKvdzIwj3vkfz5s0T3N3dBXNzc8HZ2VkYOXKkcPHiRcnr9fHHH5fLpaLvY0X+/fdfYebMmUKrVq0ECwsLoXXr1sL48eOF7OxsyWOUfd9dvXpVePbZZwU7OzvB1tZWGDVqlHDt2jXJY5aUlAhvvfWW4O3tLTRt2lSwsbERvL29hdjYWPE8D/P9pbqnEIRaGsVFRA+ttLQUrq6uGDZsWLlxD0REVDGOuSGqx7Zv346bN29i3Lhxpk6FiKjBYM8NUT10+PBhpKSkYNGiRXBwcKhwcUEiIqoYe26I6qGVK1dixowZcHR0NHiPJSKixo49N0RERCQr7LkhIiIiWWFxQ0RERLLS6Bbx0+l0uHbtGpo2bWr0ZeSJiIiodgiCgIKCAri6uj5wkc1GV9xcu3YNbm5upk6DiIiIauDvv/9G69atq4xpdMWNfgn5v//+G82aNTNxNkRERFQd+fn5cHNzk2yiXJlGV9zoL0U1a9aMxQ0REVEDU50hJRxQTERERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkxaTFTWJiIoYNGwZXV1coFAps3779gffZv38/unfvDktLS/znP//B119/Xet5EhERUcNh0uKmsLAQ3t7eiImJqVZ8eno6nn76aTzxxBM4fvw4XnvtNUyZMgW//PJLLWdKREREDYVJN84cPHgwBg8eXO34uLg4uLu7Y8mSJQCATp064Y8//sCnn36KoKCg2kqTiIiIGpAGtSt4UlISAgMDJW1BQUF47bXXKr1PSUkJSkpKxNv5+fm1lR4RUYNRXFyMjIwMU6dBZbRp0wZqtdrUachCgypuMjMz4eTkJGlzcnJCfn4+7ty5Aysrq3L3Wbx4MSIjI+sqRSKiBiEjIwPTpk0zdRpURnx8PDp06GDqNGShQRU3NTF37lyEhYWJt/Pz8+Hm5mbCjIiITK9NmzaIj483dRoP5cqVK4iOjsa8efPQtm1bU6fz0Nq0aWPqFGSjQRU3zs7OyMrKkrRlZWWhWbNmFfbaAIClpSUsLS3rIj0iogZDrVbLppegbdu2snkuZBwNap0bf39/7N27V9K2e/du+Pv7mygjIiIiqm9MWtzcvn0bx48fx/HjxwHcm+p9/PhxcZDb3LlzMW7cODF++vTpuHTpEt5++22cPXsWsbGx2LRpE15//XVTpE9ERET1kEmLm7/++gvdunVDt27dAABhYWHo1q0bFixYAAC4fv26ZDS/u7s7fv75Z+zevRve3t5YsmQJvvzyS04DJyIiIpFJx9z0798fgiBUeryi1Yf79++PY8eO1WJWRERE1JA1qDE3RERERA/C4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkK2amToCotmg0GuzYsQPXrl2Dq6srRowYAQsLC1OnRUREtYzFDclSXFwcNm/eDK1WK2kbNWoUpk+fbsLMiIiotvGyFMlOXFwcNmzYAEEQJO2CIGDDhg2Ii4szUWZERFQXWNyQrGg0GmzatAkA4Ofnh5iYGOzcuRMxMTHw8/MDAGzatAkajcaUaRIRUS1icUOysm3bNuh0OrRv3x7vv/8+PD09YW1tDU9PT7z//vvw8PCATqfDtm3bTJ0qERHVEhY3JCsnT54EAEyePBlKpfTHW6lUYvLkyZI4IiKSHw4oJlmxsrICAGRmZkKr1SIlJQU5OTmwt7eHl5cXMjMzJXFERCQ/LG5IVgYOHIjdu3dj1apV2LBhA27cuCEec3R0REFBgRhHRETyxMtSJCvdu3eHhYUF7ty5g1u3buGFF17A2rVr8cILL+DWrVu4c+cOLCws0L17d1OnSkREtYQ9NyQ71tbW0Gg0uHv3LtavX4/169dLjtvY2JgoMyIiqgvsuSFZSUlJQW5uLqZOnQpHR0fJMScnJ0yZMgW3bt1CSkqKiTIkIqLaxp4bkpWcnBwAwLPPPouxY8eWG1BcUlKCL7/8UowjIiL5YXFDsmJvbw8ASE9Ph6enJ7p16yY5np6eLokjIiL54WUpkhUvLy84OzsjISEBOp1Ockyn0yEhIQEuLi7w8vIyUYZERFTbWNyQrKhUKoSEhCApKQnh4eFITU1FUVERUlNTER4ejqSkJMyYMQMqlcrUqRIRUS3hZSmSnYCAAERGRiI2NhahoaFiu4uLCyIjIxEQEGDC7IiIqLaxuCFZCggIQN++fcsNKGaPDRGR/LG4IdlSqVTlBhQTEZH8ccwNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQpXKCbZ0mq13H6BiKgRYnFDspSYmIjY2FhkZmaKbc7OzggJCeHGmUREMsfLUiQ7iYmJiIiIgIeHB2JiYrBz507ExMTAw8MDERERSExMNHWKRERUi1jckKxotVrExsbC398fUVFR8PT0hLW1NTw9PREVFQV/f3+sXLkSWq3W1KkSEVEtYXFDspKSkoLMzEwEBwdDqZT+eCuVSgQHB+P69etISUkxUYZERFTbWNyQrOTk5AAA3N3dKzyub9fHERGR/LC4IVmxt7cHAKSnp1d4XN+ujyMiIvlhcUOy4uXlBWdnZyQkJECn00mO6XQ6JCQkwMXFBV5eXibKkIiIapvJi5uYmBi0a9cOarUavXr1QnJycpXxy5Ytw6OPPgorKyu4ubnh9ddfR3FxcR1lS/WdSqVCSEgIkpKSEB4ejtTUVBQVFSE1NRXh4eFISkrCjBkzuN4NEZGMmXSdm40bNyIsLAxxcXHo1asXli1bhqCgIKSlpcHR0bFc/HfffYc5c+Zg9erV6NOnD86dO4cJEyZAoVBg6dKlJngGVB8FBAQgMjISsbGxCA0NFdtdXFwQGRnJdW6IiGTOpMXN0qVLMXXqVEycOBEAEBcXh59//hmrV6/GnDlzysUfPHgQffv2xYsvvggAaNeuHV544QUcPny4TvOm+i8gIAB9+/blCsVERI2QyYobjUaDI0eOYO7cuWKbUqlEYGAgkpKSKrxPnz59sG7dOiQnJ8PPzw+XLl3Czp078fLLL1f6OCUlJSgpKRFv5+fnG+9JUL2mUqnQrVs3U6dBMpSVlYW8vDxTp9HoXblyRfI/mZatrS2cnJxMnQYAExY32dnZ0Gq15V4IJycnnD17tsL7vPjii8jOzsZjjz0GQRBQWlqK6dOn49133630cRYvXozIyEij5k5EjVdWVhZeenkc7mpKHhxMdSI6OtrUKRAAcwtLrFv7bb0ocBrU3lL79+/H+++/j9jYWPTq1QsXLlzA7NmzsWjRIsyfP7/C+8ydOxdhYWHi7fz8fLi5udVVykQkM3l5ebirKcEdj37QqW1NnQ5RvaAszgMu/Y68vLzGXdw4ODhApVIhKytL0p6VlQVnZ+cK7zN//ny8/PLLmDJlCgCga9euKCwsxLRp0zBv3rxyK9ICgKWlJSwtLY3/BIioUdOpbaGzcTB1GkRUAZNNBbewsECPHj2wd+9esU2n02Hv3r3w9/ev8D5FRUXlChj9AFFBEGovWWqQtFotjh07hr179+LYsWPcT4qIqJEw6WWpsLAwjB8/Hr6+vvDz88OyZctQWFgozp4aN24cWrVqhcWLFwMAhg0bhqVLl6Jbt27iZan58+dj2LBhnAVDEomJiYiNjUVmZqbY5uzsjJCQEE4FJyKSOZMWN2PGjMHNmzexYMECZGZmwsfHB7t27RKv12VkZEh6asLDw6FQKBAeHo5//vkHLVu2xLBhwziYjCQSExMREREBf39/zJ8/H+7u7khPT0dCQgIiIiK41g0RkcwphEZ2PSc/Px+2trbIy8tDs2bNTJ0OGZlWq0VwcDA8PDwQFRUlKY51Oh3Cw8ORnp6OdevWsbePauTcuXOYNm0aCjsP55gbov9PWZgNm9M/ID4+Hh06dKiVxzDk97fJt18gMqaUlBRkZmYiODi43PgspVKJ4OBgXL9+HSkpKSbKkIiIahuLG5KVnJwcAIC7u3uFx/Xt+jgiIpKfBrXODdGD2NvbAwDS09PRsWPHctsvpKenS+KIiEh+WNyQrHh5ecHZ2RnLly/HrVu3cOPGDfGYo6MjmjdvDhcXF3h5eZkwSyIiqk28LEWyolKp0L9/f6SlpSE7O1tyLDs7G2lpaejXrx8HExMRyRiLG5IVrVaLXbt2AQDMzc0lx/S3d+3axQX9iIhkjJelSFaOHz+O3NxcdO3aFUuXLsWpU6fEMTddunRBWFgYTp48iePHj6NHjx6mTpeIiGoBe25IVo4fPw4AmDhxIszNzdGtWzcMGDAA3bp1g7m5OSZMmCCJIyIi+WFxQ7LUyNamJCKiMljckKz4+PgAAL7++mvodDrJMZ1Oh6+//loSR0RE8sPihmTFx8cHdnZ2OHnyJObNm4fU1FQUFRUhNTUV8+bNw8mTJ9G8eXMWN0REMsYBxSQrKpUKYWFhiIiIwNGjR5GUlCQes7S0hEKhwOuvv86p4EREMsaeG5KdgIAAREZGonnz5pJ2e3t77ghORNQIsOeGZCkgIAB9+/Ytt/0Ce2yIiOSPxQ3JlkqlQrdu3UydBhER1TFeliIiIiJZYc8NyZZWq+VlKSKiRojFDclSYmIiYmNjkZmZKbY5OzsjJCSEA4rJKJR3ck2dAlG9Ud/eDyxuSHYSExMREREBf39/zJ8/H+7u7khPT0dCQgIiIiI4Y4qMwio90dQpEFElWNyQrGi1WsTGxsLf3x9RUVFQKu8NK/P09ERUVBTCw8OxcuVK9O3bl5eo6KHccQ+AzsrO1GkQ1QvKO7n1quBncUOykpKSgszMTMyfP18sbPSUSiWCg4MRGhqKlJQUzqSih6KzsoPOxsHUaRBRBThbimQlJycHAODu7l7hcX27Po6IiOSHxQ3Jir29PQAgPT29wuP6dn0cERHJj8HFTb9+/fDtt9/izp07tZEP0UPx8vKCs7MzEhISKtwVPCEhAS4uLvDy8jJRhkREVNsMLm66deuGN998E87Ozpg6dSoOHTpUG3kR1YhKpUJISAiSkpIQHh4u2RU8PDwcSUlJmDFjBgcTExHJmMHFzbJly3Dt2jWsWbMGN27cQEBAADp37oxPPvkEWVlZtZEjkUH0G2deunQJoaGhGDJkCEJDQ5Gens5p4EREjUCNZkuZmZnhueeew3PPPYcbN24gPj4e8+fPx7vvvoshQ4Zg1qxZePLJJ42dK1G1ceNMIqLG66GmgicnJ2PNmjXYsGEDHB0dMWHCBPzzzz8YOnQoQkJC8MknnxgrTyKDceNMIqLGyeDi5saNG1i7di3WrFmD8+fPY9iwYVi/fj2CgoKgUCgAABMmTMCgQYNY3BAREVGdM7i4ad26Ndq3b49JkyZhwoQJaNmyZbkYLy8v9OzZ0ygJEhERERnC4OJm7969ePzxx6uMadasGfbt21fjpIiIiIhqyuDZUhEREcjNzS3Xnp+fz0HEREREZHIGFze///47NBpNufbi4mL873//M0pSRERERDVV7ctSKSkpAABBEHD69GlkZmaKx7RaLXbt2oVWrVoZP0OiGtJqtZwKTkTUCFW7uPHx8YFCoYBCoajw8pOVlRU+//xzoyZHVFOJiYmIjY2VFOHOzs4ICQnhIn5ERDJX7eImPT0dgiDAw8MDycnJkllSFhYWcHR05F/FVC8kJiYiIiIC/v7+mD9/Ptzd3ZGeno6EhARERERwlWIiIpmrdnHTtm1bACi3GSFRfaLVahEbGwt/f39ERUVBqbw3rMzT0xNRUVEIDw/HypUr0bdvXxbjREQyVa3i5ocffsDgwYNhbm6OH374ocrY4cOHGyUxoppISUlBZmYm5s+fLxY2ekqlEsHBwQgNDUVKSgpXLyYikqlqFTfPPPMMMjMz4ejoiGeeeabSOIVCAa1Wa6zciAyWk5MDAHB3d6/wuL5dH0dERPJTrangOp0Ojo6O4teV/WNhQ6Zmb28P4N4YsYro2/VxREQkPwavc0NUn3l5ecHZ2RkJCQnlxofpdDokJCTAxcUFXl5eJsqQiIhqW7UuSy1fvrzaJ5w1a1aNkyF6WCqVCiEhIYiIiEB4eDiCg4Mls6WSkpIQGRnJwcRERDJWreLm008/rdbJFAoFixsyuYCAAERGRiI2NhahoaFiu4uLC6eBExE1AtUqbiobv0BUXwUEBKBv375coZiIqBHimBsiIiKSlWr13ISFhWHRokWwsbFBWFhYlbFLly41SmJED4PbLxARNV7VKm6OHTuGu3fvil9XRqFQGCcroofA7ReIiBq3ahU3+/btq/Brovqm7PYLkZGROHXqFJKSkmBvb4/IyEhERERw+wUiIpmr9t5SFfn7778BAG5ubkZJhuhh6bdfGDZsGF5++eVyl6WGDh2KgwcPcvsFIiIZM3hAcWlpKebPnw9bW1u0a9cO7dq1g62tLcLDw8VLV0Smot9WYdWqVfDw8EBMTAx27tyJmJgYeHh44Msvv5TEERGR/Bjcc/Pqq6/i+++/x0cffQR/f38AQFJSEhYuXIh///0XK1euNHqSRNVlZ2cHAOjatWuFu4LPnj0bJ0+eFOOIiEh+DC5uvvvuO2zYsAGDBw8W27y8vODm5oYXXniBxQ0RERGZlMGXpSwtLdGuXbty7e7u7rCwsDBGTkQ1lpubCwA4deoUwsPDkZqaiqKiIqSmpiI8PBynTp2SxBERkfwYXNzMnDkTixYtQklJidhWUlKC6OhozJw506jJERlKv9v3lClTcOnSJYSGhmLIkCEIDQ1Feno6pkyZIokjIiL5qdZlqeeee05ye8+ePWjdujW8vb0BACdOnIBGo8GAAQOMnyGRAfS7gqempmLt2rU4deqUuP1Cly5dEBERwV3BiYhkrlrFja2treT2888/L7nNqeBUX5TdFTwiIgLBwcHw9/dHeno6IiIiuCs4EVEjUK3iZs2aNbWWQExMDD7++GNkZmbC29sbn3/+Ofz8/CqNz83Nxbx58/D9998jJycHbdu2xbJlyzBkyJBay5EaFv2u4DExMZJdwZ2dnbk6MRFRI/BQi/g9rI0bNyIsLAxxcXHo1asXli1bhqCgIKSlpcHR0bFcvEajwVNPPQVHR0ds2bIFrVq1wpUrVzitlyrE7UCIiBqnGhU3W7ZswaZNm5CRkQGNRiM5dvTo0WqfZ+nSpZg6dSomTpwIAIiLi8PPP/+M1atXY86cOeXiV69ejZycHBw8eBDm5uYAUOHMLWrcuLcUEVHjZvBsqeXLl2PixIlwcnLCsWPH4OfnhxYtWuDSpUuStW8eRKPR4MiRIwgMDPy/ZJRKBAYGIikpqcL7/PDDD/D390doaCicnJzQpUsXvP/++9BqtZU+TklJCfLz8yX/SL7K7i0VFRUFT09PWFtbi4v4+fv7Y+XKlVX+zBARUcNmcHETGxuL+Ph4fP7557CwsMDbb7+N3bt3Y9asWcjLy6v2ebKzs6HVauHk5CRpd3JykuwHVNalS5ewZcsWaLVa7Ny5E/Pnz8eSJUsQFRVV6eMsXrwYtra24j8OfpY3/d5SwcHB4urEekqlEsHBwbh+/TpSUlJMlCEREdU2g4ubjIwM9OnTBwBgZWWFgoICAMDLL7+M9evXGze7++h0Ojg6OiI+Ph49evTAmDFjMG/ePMTFxVV6n7lz5yIvL0/8p9/sk+RJv2eUu7s7NBoNNm/ejM8++wybN2+GRqOBu7u7JI6IiOTH4DE3zs7O4iylNm3a4NChQ/D29kZ6ejoEQaj2eRwcHKBSqZCVlSVpz8rKgrOzc4X3cXFxgbm5uWQab6dOnZCZmQmNRlPhCsmWlpawtLSsdl7UsOkX51u6dCl+++036HQ68djKlSvx5JNPSuKIiEh+DO65efLJJ/HDDz8AACZOnIjXX38dTz31FMaMGYNnn3222uexsLBAjx49sHfvXrFNp9Nh79694oac9+vbty8uXLgg+YV17tw5uLi4cOsHAnBvET8bGxvs2bOnXLEtCAL27NkDGxsbLuJHRCRjBvfcxMfHi8VFaGgoWrRogYMHD2L48OF45ZVXDDpXWFgYxo8fD19fX/j5+WHZsmUoLCwUZ0+NGzcOrVq1wuLFiwEAM2bMwIoVKzB79my8+uqrOH/+PN5//33MmjXL0KdBMqXValFUVATg3g7hkyZNgr+/P5KSkrB69WrcunULRUVF0Gq1XMiPiEimDC5ulEqlZKDm2LFjMXbs2Bo9+JgxY3Dz5k0sWLAAmZmZ8PHxwa5du8RBxhkZGZLHcnNzwy+//ILXX38dXl5eaNWqFWbPno133nmnRo9P8rNt2zYIggAnJycoFAosWbJEPObi4gInJydkZWVh27ZtGDNmjAkzJSKi2lKjdW5u3bqFr776CmfOnAEAdO7cGRMnTqzROIaZM2dWuuHm/v37y7X5+/vj0KFDBj8ONQ4nT54EALz22mvw8/NDSkqKuLeUl5cXDh06hHnz5uHkyZMsboiIZMrgMTeJiYlwd3fH8uXLcevWLdy6dQvLly+Hu7s7EhMTayNHomqzsrICAFy/fr3C4/plBvRxREQkPwb33ISGhmL06NFYuXKlOGZBq9UiJCQEoaGh4l/ORKYwcOBA7N69G/Hx8di4caNkNp6Tk5O4FtPAgQNNlSIREdUyg3tuLly4gDfeeEMyGFOlUiEsLAwXLlwwanJEhurevTssLS1RXFxc4TIDxcXFsLS0RPfu3U2UIRER1TaDi5vu3buLY23KOnPmDLy9vY2SFNHDMDOrukPyQceJiKhhq9anfNml6mfNmoXZs2fjwoUL6N27NwDg0KFDiImJwQcffFA7WRJV0/Hjx1FYWAgHBwfk5ORI1kRSKpWwt7dHdnY2jh8/jh49epgwUyIiqi3VKm58fHygUCgki6K9/fbb5eJefPFFzkAhkzp+/DiAe3uX+fv7w8/PD5aWligpKUFycrK4KSuLGyIi+apWcZOenl7beRAZhb4A79y5M6KjoyXrJI0YMQIzZ87E6dOnDdoqhIiIGpZqFTdt27at7TyIjKJp06YAgJKSkgqPFxcXS+KIiEh+ajSy8uLFi1i2bJlkEb/Zs2ejffv2Rk2OyFD6hSQvXryIefPm4aWXXoK7uzvS09Oxbt06XLp0SRJHRETyY3Bx88svv2D48OHw8fFB3759AQAHDhyAp6cnfvzxRzz11FNGT5KouhwcHMSvjxw5Io6xASDZXLVsHBERyYvBxc2cOXPw+uuvl5sZNWfOHLzzzjssbsikvLy84OzsDKVSKa5GrKfVauHq6gpBELgrOBGRjBlc3Jw5cwabNm0q1z5p0iQsW7bMGDkR1ZhKpUL//v2xYcMGNG/eHE899RRcXV1x7do17N69G9euXcPYsWO5Izg9NGVxnqlTIKo36tv7weDipmXLljh+/DgeeeQRSfvx48fh6OhotMSIakKr1WL//v149NFHkZubKynEnZ2d8eijj+L333/H1KlTWeBQjdja2sLcwhK49LupUyGqV8wtLGFra2vqNADUoLiZOnUqpk2bhkuXLqFPnz4A7o25+fDDDxEWFmb0BIkMkZKSgszMTMyfPx8dO3Ystyv42bNnERoaipSUFHTr1s3U6VID5OTkhHVrvxX3KSPTuXLlCqKjozFv3jzO6q0HbG1t4eTkZOo0ANSguJk/fz6aNm2KJUuWYO7cuQAAV1dXLFy4ELNmzTJ6gkSGyMnJAQC4u7tDpVKVK2Dc3d0lcUQ14eTkVG8+xOneciUdOnQwdRpUjxhU3JSWluK7777Diy++iNdffx0FBQUAuGYI1R/6Kd7p6enw9PQsd1y/ICWnghMRyZdBG2eamZlh+vTpkoXQWNhQfaKfLZWQkCDZVwoAdDodEhIS4OLiwtlSREQyZvBlKT8/Pxw7dozXN6leUqlUCAkJQUREBN599120atUKJSUlsLS0xD///IPDhw8jMjKSg4mJiGTM4OImJCQEb7zxBq5evYoePXrAxsZGcpx/EZOpBQQEoE+fPjhw4EC5Y3379kVAQIAJsiIiorpicHEzduxYAJAMHtbvGK5QKKDVao2XHVENxMXF4cCBA+V2slcoFDhw4ADi4uIwffp0E2ZIRES1yeDihjuEU32m0WjEtW169+5dbm+ppKQkbNq0CZMmTZJsx0BERPJhcHHDsTZUn23btg06nQ7t27dHdHQ0lMp7Y+Y9PT0RHR2NKVOm4NKlS9i2bRvGjBlj4myJiKg2GDRbSi8tLQ0zZ87EgAEDMGDAAMycORNpaWnGzo3IYCdPngQATJkyRSxs9JRKJSZPniyJIyIi+TG4uNm6dSu6dOmCI0eOwNvbG97e3jh69Ci6dOmCrVu31kaORNVmZWUFALh+/XqFx/WbaerjiIhIfgy+LPX2229j7ty5eO+99yTtERERePvtt/H8888bLTkiQw0cOBC7d+/GmjVr8PTTT+P06dPi9gudO3fG119/LcYREZE8GVzcXL9+HePGjSvX/tJLL+Hjjz82SlJ1obCwsMK1TlQqFdRqtSSuMkqlUtIDYEhsUVGRZCZPWQqFAtbW1jWKvXPnTrnF68oqO3XfkNji4uIqZ8IZEmttbQ2FQgEAKCkpQWlpqVFirays0L17d9jY2CAvLw8DBw6UPD+lUgmdTgdra2v4+PiI7RqNBnfv3q30vGq1WvxZMST27t270Gg0lcZaWlrCzMzM4NjS0lKUlJRUGmthYQFzc3ODY7VarbhAZ0XMzc3FQdiGxOp0Oty5c8cosWZmZrC0tAQACIKAoqIio8Qa8r7nZ0TFsab4jLj/eVfnM0J/ufpB72VDYvkZcU9dfEZUm2CgwYMHC6tXry7Xvnr1amHgwIGGnq7O5eXlCQAq/TdkyBBJvLW1daWx/fr1k8Q6ODhUGuvr6yuJbdu2baWxnTt3lsR27ty50ti2bdtKYn19fSuNdXBwkMT269ev0lhra2tJ7JAhQ6p83coaOXJklbG3b98WY8ePH19l7I0bN8TYkJCQKmPT09MFQRCElStXCq1bt64y9tSpU+J5IyIiqoxNTk4WYz/66KMqY/ft2yfGrlixosrYn376SYxds2ZNlbGbNm0SYzdt2lRl7Jo1a8TYn376qcrYFStWiLH79u2rMvajjz4SY5OTk6uMjYiIEGNPnTpVZeybb74pxqanp1cZGxISIsbeuHGjytjx48eLsbdv364yduTIkZKf4api+Rlx7199+Yzw9/cX0tLSBEGo/meEIAjCm2++WWUsPyPu/atPnxH63995eXnCgxjcczN8+HC88847OHLkCHr37g0AOHToEDZv3ozIyEj88MMPkliiuqTVarF//37Y29vj6tWrVcYREZE8KQShkr7MStw/A6XSE9fTBf3y8/Nha2uLa9euoVmzZuWOs8u54tiGclnqxIkTeP3117Fs2TJ4eHjg1KlT4pibLl26IC0tDWFhYVi2bBl69OgBgF3O9anLmZel+BkBVP99f/78ecyePRurVq1Chw4deFlK5p8R+t/feXl5Ff7+LsvgnpuqftAbEhsbm3JbR1QWZ8g5q6vsh40xYw2ZBWRIbNkPc2PGWlpair+AjBGbk5MDAOjQoQOsra3Rp08fyXFPT0+oVCrk5uaKbRYWFtW+nmtIrLm5ufihYMxYMzMz8UPMmLEqlaraP8OGxCqVylqJVSgUtRIL1N77np8RhsdW9b4vWwQ9KPZ+tfW+52eE4bGGvO+ry+Dihqg+s7e3B3BvJe2OHTsiJSVF7Lnx8vISV9jWxxERkfywuCFZ8fLygrOzM5YvX47c3FxkZWWJx5ycnGBnZwcXFxdu8EpEJGM1WqGYqL5SqVTo378/0tLScPPmTcmxmzdvIi0tDf369atwGQAiIpIHFjckK1qtFrt27QJQfnyY/vYvv/xSLwe7ExGRcbC4IVk5fvy4OFj4/sF3+tu3bt3C8ePH6zgzIiKqK9Uac5Ofn1/tEz5oehZRbTp69Kj4ta+vL1566SW4u7sjPT0d69atQ1JSkhinnwpORETyUq3ixs7OTjLdrirs7idT0g8gbteuHaKjo8V1Kjw9PREdHY1Jkybh8uXLkoHGREQkL9Uqbvbt2yd+ffnyZcyZMwcTJkyAv78/ACApKQnffPMNFi9eXDtZEhmosmK8ukU6ERE1XNUqbvr16yd+/d5772Hp0qV44YUXxLbhw4eja9euiI+Px/jx442fJVE1OTk5Abi3zs28efPKXZbSr3OjjyMiIvkxeJ2bpKQkxMXFlWv39fXFlClTjJIUUU11794dCQkJAO6Nq9GPsQEgWbm0e/fudZ4bERHVDYNnS7m5uWHVqlXl2r/88ku4ubkZJSmimvLx8YGdnR0AlNtrR3/bzs4OPj4+dZwZERHVFYN7bj799FM8//zz+O9//4tevXoBAJKTk3H+/Hls3brV6AkSGUKlUiEsLAwLFiyoNCYsLIyL+BERyZjBPTdDhgzBuXPnMGzYMOTk5CAnJwfDhg3DuXPnMGTIkNrIkcggAQEBeO+999C8eXNJu729Pd577z0EBASYKDMiIqoLNdpbys3NDe+//76xcyEymoCAAPTs2RNffPEFrl69itatW+OVV14xaJdjIiJqmGq0QvH//vc/vPTSS+jTpw/++ecfAMDatWvxxx9/GDU5opqKi4vD0KFDsX37dvz111/Yvn07hg4dWuFgeCIikheDi5utW7ciKCgIVlZWOHr0KEpKSgAAeXl57M2heiEuLg4bNmxAs2bNMHr0aLz22msYPXo0mjVrhg0bNrDAISKSOYOLm6ioKMTFxWHVqlWSvXv69u0rWfqeyBQ0Gg02b94MGxsbWFhYYNOmTVi2bBk2bdoECwsL2NjYYPPmzdBoNKZOlYiIaonBxU1aWlqFAzJtbW3FDQuJTGXHjh3QarUoLCxE+/btERMTg507dyImJgbt27dHYWEhtFotduzYYepUiYiolhhc3Dg7O+PChQvl2v/44w94eHgYJSmimtKPAfP19UVUVBQ8PT1hbW0NT09PREVFwdfXVxJHRETyY3BxM3XqVMyePRuHDx+GQqHAtWvXkJCQgDfffBMzZsyojRyJDNahQwdx00w9pVKJRx55xEQZERFRXTG4uJkzZw5efPFFDBgwALdv30ZAQACmTJmCV155Ba+++mpt5EhUbZ06dQIA7Ny5E6WlpZJjpaWl+O9//yuJIyIi+TG4uFEoFJg3bx5ycnJw6tQpHDp0CDdv3sSiRYtqIz8igzg6OgIAcnNzMWrUKPz444/Izs7Gjz/+iFGjRonjwvRxREQkPwYv4jdp0iR89tlnaNq0KTp37iy2FxYW4tVXX8Xq1auNmiCRIby8vODs7AylUonMzEwsWbJEPKZSqeDq6gpBEODl5WXCLImIqDYZ3HPzzTff4M6dO+Xa79y5g2+//dYoSRHVlEqlQkhICK5fvw5fX194eXmhbdu28PLyQo8ePXD9+nXMmDGDe0sREclYtXtu8vPzIQgCBEFAQUEB1Gq1eEyr1WLnzp3s6qd6ISAgAH369MGBAwfKHevbty/3liIikrlqFzd2dnZQKBRQKBTo0KFDueMKhQKRkZFGTY6oJuLi4nDgwAHY2dnBx8cHVlZWuHPnDo4fP44DBw4gLi4O06dPN3WaRERUS6p9WWrfvn3Yu3cvBEHAli1b8Ntvv4n//vjjD2RkZGDevHk1SiImJgbt2rWDWq1Gr169kJycXK37bdiwAQqFAs8880yNHpfkp+wKxWq1Gvv378d///tf7N+/H2q1misUExE1AtXuuenXrx8AID09HW3atIFCoTBKAhs3bkRYWBji4uLQq1cvLFu2DEFBQUhLS6vyMtfly5fx5ptv4vHHHzdKHiQPZVco9vb2xvz58+Hu7o709HQkJCTg4MGDYtyoUaNMnC0REdUGgwcU//bbb9iyZUu59s2bN+Obb74xOIGlS5di6tSpmDhxIjp37oy4uDhYW1tXOetKq9UiODgYkZGRXBWZJLhCMRERGVzcLF68GA4ODuXaHR0dDd4VXKPR4MiRIwgMDPy/hJRKBAYGIikpqdL7vffee3B0dMTkyZMf+BglJSXIz8+X/CP54wrFRESNl8HFTUZGBtzd3cu1t23bFhkZGQadKzs7G1qtFk5OTpJ2JycnZGZmVnifP/74A1999RVWrVpVrcdYvHgxbG1txX9ubm4G5UgNC1coJiIig4sbR0dHpKSklGs/ceIEWrRoYZSkKlNQUICXX34Zq1atqrD3qCJz585FXl6e+O/vv/+u1RzJtLhCMRERGbxC8QsvvIBZs2ahadOm4nohv//+O2bPno2xY8cadC4HBweoVCpkZWVJ2rOysuDs7Fwu/uLFi7h8+TKGDRsmtul0untPxMwMaWlpaN++veQ+lpaWsLS0NCgvariqWqFYqVRyhWIiokbA4OJm0aJFuHz5MgYMGAAzs3t31+l0GDdunMFjbiwsLNCjRw/s3btXnM6t0+mwd+9ezJw5s1x8x44dcfLkSUlbeHg4CgoK8Nlnn/GSE4krFEdERMDPzw+WlpYoKChA06ZNUVJSguTkZERGRnKFYiIiGTO4uLGwsMDGjRuxaNEinDhxAlZWVujatSvatm1bowTCwsIwfvx4+Pr6ws/PD8uWLUNhYSEmTpwIABg3bhxatWqFxYsXQ61Wo0uXLpL729nZAUC5dmq8AgICMGbMGGzevBlarVZsV6lUGDNmDFcoJiKSOYOLG70OHTpUuFKxocaMGYObN29iwYIFyMzMhI+PD3bt2iUOMs7IyCg364WoKomJidi4cSN69+4t9t7oe202btyIzp07s8AhIpIxhSAIwoOCwsLCsGjRItjY2CAsLKzK2KVLlxotudqQn58PW1tb5OXloVmzZqZOh4xMvwaSh4cHoqKiJIWxTqdDeHg40tPTsW7dOl6aImrgzp07h2nTpiE+Pt4of2xT/WbI7+9q9dwcO3YMd+/eFb+ujLFWLSaqqZSUFGRmZmL+/PkVrnMTHByM0NBQpKSkoFu3bibKkoiIalO1ipt9+/ZV+DVRfZOTkwMAcHd3h1arRUpKCnJycmBvbw8vLy9xjSZ9HBERyU+Nx9wQ1Uf29vYAgG3btuGHH36QLDPg5OSE4cOHS+KIiEh+qlXcPPfcc9U+4ffff1/jZIgelpeXF+zs7CpcwTorKwurVq2CnZ0d17khIpKxahU3tra24teCIGDbtm2wtbUVNyE8cuQIcnNzDSqCiGrLnTt3ANxb2DEgIAAdO3bE2bNnkZiYiNLSUvE4ERHJU7WKmzVr1ohfv/POOxg9ejTi4uLE2SZarRYhISGcfUQmd/ToUZSUlMDc3BylpaX47bff8NtvvwG4N6DY3NwcJSUlOHr0KHr27GnibImIqDYYvIDM6tWr8eabb0qm0apUKoSFhWH16tVGTY7IUL/++isA4O7du7CwsJAcMzc3F2f96eOIiEh+DC5uSktLcfbs2XLtZ8+eFfd5IjKVoqIi8evu3bsjJiYGO3fuRExMDLp3715hHBERyYvBs6UmTpyIyZMn4+LFi/Dz8wMAHD58GB988IG4ZQKRqeh3pler1Vi0aJG4/5mnpycWLVqEoUOHori4uNZ3sCciItMxuLj55JNP4OzsjCVLluD69esAABcXF7z11lt44403jJ4gkSGaNGkCACguLkZ4eDh69eolbr9w+PBhFBcXS+KIiEh+DC5ulEol3n77bbz99tvIz88HAA4kpnqj7FiwQ4cO4dChQw+MIyIieanRjpSlpaXYs2cP1q9fL265cO3aNdy+fduoyREZysfHx6hxRETU8Bjcc3PlyhUMGjQIGRkZKCkpwVNPPYWmTZviww8/RElJCeLi4mojT6Jq6dq1K5RKJXQ6HXr27AkrKysUFBSgadOmuHPnDv78808olUp07drV1KkSEVEtMbi4mT17Nnx9fXHixAnJoMxnn30WU6dONWpyRIZKTU0VZ+39+eefFcbodDqkpqZy40wiIpkyuLj53//+h4MHD5ZbQ6Rdu3b4559/jJYYUU2U3RBTP5C4otvcOJOISL4MHnOj0+mg1WrLtV+9ehVNmzY1SlJENWVnZwcAaNOmjWTbEODeNiJt2rSRxBERkfwY3HMzcOBALFu2DPHx8QAAhUKB27dvIyIiAkOGDDF6gkQ1kZGRgd69e+OFF16QTAWvbPYUERHJR43WuRk0aBA6d+6M4uJivPjiizh//jwcHBywfv362siRqNrKXm46duyYpJixtLSsMI6IiOTF4OLGzc0NJ06cwMaNG3HixAncvn0bkydPRnBwMKysrGojR6Jqy83NNWocERE1PAYVN3fv3kXHjh3x008/ITg4GMHBwbWVF1GN6BeUtLOzw8aNG3H69Gnk5OTA3t4enTt3xpgxY5Cbm8uFJ4mIZMyg4sbc3Fxcvp6oPtKvmp2bm4uFCxfCz88PlpaWuHz5MjZt2iT22OjjiIhIfgy+LBUaGooPP/wQX375pbgpIVF9oZ8F5ezsjOTkZCQlJYnHVCoVnJ2dkZmZydlSREQyZnB18ueff2Lv3r349ddf0bVrV9jY2EiOf//990ZLjshQDg4OAIDMzEw0a9YM7u7uEAQBCoUC6enpyMzMlMQREZH8GFzc2NnZ4fnnn6+NXIgemqenJ1QqFZRKJfLz83HixAnJcXNzc+h0Onh6epooQyIiqm0GFzdr1qypjTyIjCI1NRVarbbChSaBe4Pi9XHcfoGISJ6qvUKxTqfDhx9+iL59+6Jnz56YM2cO7ty5U5u5ERnsxo0bRo0jIqKGp9o9N9HR0Vi4cCECAwNhZWWFzz77DDdu3MDq1atrMz8ig6Smpopf9+7dG7169apwheLU1FQEBQWZKk0iIqpF1S5uvv32W8TGxuKVV14BAOzZswdPP/00vvzySyiVBm9RRVQrsrOzAQA2NjaIioqSzOgbNmwYRowYgcLCQjGOiIjkp9rFTUZGhmTvqMDAQCgUCly7dg2tW7euleSIDKXf9buwsBDz588X17kpKSlBcnIyCgsLJXFERCQ/1S5uSktLoVarJW3m5ubiAE2i+qBDhw44cuQIzM3NcejQIck6N0qlUvyZ7dChgwmzJCKi2lTt4kYQBEyYMEGy+WBxcTGmT58uWeuG69yQKfn6+mL9+vW4e/cuVCoVvLy84ODggOzsbKSkpIjFuK+vr4kzJSKi2lLt4mb8+PHl2l566SWjJkP0sLp27QqlUgmdTgetVotjx46Vi1EqlejatasJsiMiorpQ7eKG69tQQ5CamgqdTldljE6n4zo3REQyxmlOJCtlZ0GZm5tLjllYWFQYR0RE8sLihmQlJycHAODk5AR7e3vJsebNm8PJyUkSR0RE8sPihmQlPz8fAJCVlQUPDw/ExMRg586diImJgYeHB7KysiRxREQkPwbvLUVUnykUCvFrQRBw7tw5XLlyBSUlJRAEocI4IiKSFxY3JCtNmzYFcG/3+uTkZHG7BeDeLClbW1vk5eWJcUREJD8sbkhW9ONscnNz0bx5czz11FNwdXXFtWvXsHv3bty6dUsSR0RE8sPihmSlbNFSVFSETZs2ibfLLkDJ4oaISL44oJhkycHBARqNRtJ29+5dODg4mCgjIiKqK+y5IVnJzc0FcG8dGzs7OwwcOFC8LPXrr7+K69vo44iISH5Y3JCs2NnZAQDatGkDjUYjuSzl7OyMNm3aICMjQ4wjIiL54WUpki2tVlvlbSIikif23JCs6C83ZWRkQKmU1u7//vuvuO8UL0sREckXe25IVspebrp/A82yt3lZiohIvthzQ7JStoDp2bMnrKyscPv2bTRp0gR37tzBn3/+WS6OiIjkhcUNycqJEyfEr/WFTGVxPXv2rIuUiIiojvGyFMmKfmNMY8UREVHDw54bkpUWLVqIX+svSxUUFKBp06aSy1Jl44iISF5Y3JCsFBYWil9XdVmqbBwREckLL0uRrOTk5Bg1joiIGh4WNyQrarVa/LrsRpn33y4bR0RE8sLihmTF2toaAGBmZgZbW1vJMTs7O5iZmUniiIhIfjjmhmRFpVIBAEpLS6HRaNC/f3+o1WoUFxfj+PHjKC0tlcQREZH8sLghWWnVqpX4dW5uLvbv3//AOCIikpd6cVkqJiYG7dq1g1qtRq9evZCcnFxp7KpVq/D444+jefPmaN68OQIDA6uMp8ZlxIgR4p5SFhYWkmP6MTdKpRIjRoyo89yIiKhumLy42bhxI8LCwhAREYGjR4/C29sbQUFBuHHjRoXx+/fvxwsvvIB9+/YhKSkJbm5uGDhwIP755586zpzqI5VKJQ4W1mg0kmMlJSUA7g0m5mUpIiL5Mnlxs3TpUkydOhUTJ05E586dERcXB2tra6xevbrC+ISEBISEhMDHxwcdO3bEl19+CZ1Oh71799Zx5lQfpaSkoKioqMqYoqIipKSk1FFGRERU10xa3Gg0Ghw5cgSBgYFim1KpRGBgIJKSkqp1jqKiIty9exf29va1lSY1INnZ2eLXCoVCcqzs7bJxREQkLyYdUJydnQ2tVgsnJydJu5OTE86ePVutc7zzzjtwdXWVFEhllZSUiJcjACA/P7/mCVO9V7ZoMTMzw927dyu8zeKGiEi+GvRsqQ8++AAbNmzA/v37K12UbfHixYiMjKzjzMhUzp8/L37dvXt39O7dG5aWligpKcGhQ4dw+PDhcnFERCQvJi1uHBwcoFKpyu3QnJWVBWdn5yrv+8knn+CDDz7Anj174OXlVWnc3LlzERYWJt7Oz8+Hm5vbwyVO9VbZgejHjx8XixlAukJxZQPWiYio4TPpmBsLCwv06NFDMhhYPzjY39+/0vt99NFHWLRoEXbt2gVfX98qH8PS0hLNmjWT/CP5Kjv9u+zlyPtv3z9NnIiI5MPks6XCwsKwatUqfPPNNzhz5gxmzJiBwsJCTJw4EQAwbtw4zJ07V4z/8MMPMX/+fKxevRrt2rVDZmYmMjMzcfv2bVM9BapHOnToYNQ4IiJqeEw+5mbMmDG4efMmFixYgMzMTPj4+GDXrl3iIOOMjAxxUTYAWLlyJTQaDUaOHCk5T0REBBYuXFiXqVM9ZGdnZ9Q4IiJqeExe3ADAzJkzMXPmzAqP3b98/uXLl2s/IWqwqjsbjrPmqLErLi5GRkaGqdN4KFeuXJH839C1adOm0skxZJh6UdwQGcvNmzeNGkckVxkZGZg2bZqp0zCK6OhoU6dgFPHx8bxkbiQsbkhWHB0djRpHJFdt2rRBfHy8qdOgMtq0aWPqFGSDxQ3Jio+PDxISEqoVR9SYqdVq9hKQbLG4ISKiBkej0WDHjh24du0aXF1dMWLECC7xQCIWNyQrx44dq3Zcz549azkbIqoNcXFx2Lx5M7RaraRt1KhRmD59ugkzo/qCxQ3JSlpamlHjiKh+iYuLw4YNG9C8eXNMnjwZ/v7+SEpKwldffYUNGzYAAAscMv0ifkTGVN1uaXZfEzU8Go0GmzdvRvPmzbF582YMHToULVq0wNChQyXtGo3G1KmSibG4IVlr2bIlOnTogJYtW5o6FSJ6SDt27IBWq8XkyZNhZia98GBmZoZJkyZBq9Vix44dJsqQ6gteliJZKXsNHri3nk1Fa9rcH0dE9d+1a9cAoNK9B/Xt+jhqvNhzQ7Jy6dIlo8YRUf3h6uoKAEhKSqrwuL5dH0eNF4sbkhWVSmXUOCKqP0aMGAGVSoWvvvoKpaWlkmOlpaVYvXo1VCoVRowYYaIMqb5gcUNERA2ChYUFRo0ahVu3bmHUqFH48ccfkZ2djR9//FHSzgkDxDE3JCsuLi7IysqqVhwRNTz6ad6bN2/GkiVLxHaVSoWxY8dyGjgBYHFDMmNlZWXUOCKqf6ZPn45JkyZxhWKqFIsbkhUWN0SNg/4SFVFFOOaGZKW4uNiocURE1PCwuCFZuX8GxcPGERFRw8PLUiQrly9fNmocEdVPWq0WKSkpyMnJgb29Pby8vLjEA4lY3JCsFBYWGjWOiOqfxMRExMbGIjMzU2xzdnZGSEgIAgICTJgZ1Re8LEWywkX8iOQtMTERERER8PDwQExMDHbu3ImYmBh4eHggIiICiYmJpk6R6gEWNyQrgiAYNY6I6g+tVovY2Fj4+/sjKioKnp6esLa2hqenJ6KiouDv74+VK1dy7zhicUPycvfuXaPGEVH9kZKSgszMTAQHB0OplP76UiqVCA4OxvXr15GSkmKiDKm+4JgbkpWmTZtWa5p306ZN6yAbIjKmnJwcAIC7u3uFA4rd3d0lcdR4sbghWWndujVu3rxZrTgialjs7e0BANu2bcOPP/5YbkDxsGHDJHHUePGyFMlKx44djRpHRPWHl5cX7OzssGrVKri7u0sGFLu7u2PVqlWws7ODl5eXqVMlE2PPDclKdS838bIUUcMmCALOnTuHK1euoKSkRJwkoFAoTJwZ1QcsbkhWLly4YNQ4Iqo/UlJSkJubi8DAQOzbtw+HDh0Sj6lUKgQGBmLPnj1ISUlBt27dTJgpmRqLG5IVFjdE8qUfKLxnzx74+/vDz88ParUaxcXFSE5Oxp49eyRx1HixuCFZycvLM2ocEdUfdnZ2AICuXbsiOjpaMh18xIgRmD17Nk6ePCnGUePFAcUkKzqdzqhxRETU8LC4IVmpzho3hsQRUf2Rm5sLADh16hTCw8ORmpqKoqIipKamIjw8HKdOnZLEUePFy1IkK1yhmEi+9OvXTJkyBT/++CNCQ0PFYy4uLpgyZQpWrVrFdW6IxQ0RETUMXl5ecHZ2RmpqKtauXYtTp06JKxR36dIFERERcHFx4To3xMtSRETUMKhUKoSEhCApKQkRERGwsLCAv78/LCwsEBERgaSkJMyYMQMqlcrUqZKJseeGiIgajICAAERGRiI2NrbcZanIyEgEBASYMDuqL1jcEBFRgxIQEIC+ffuW2ziTPTakx+KGiIgaHJVKxVWIqVIcc0NERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrHCdG6pUcXExMjIyTJ2GQWxsbFBYWFituHPnztVBRsbVpk0bqNVqU6dBRFSvsbihSmVkZGDatGmmTqNWFBYWNsjnFh8fjw4dOpg6DSKieo3FDVWqTZs2iI+PN3UaBikuLsasWbMeGLd8+fIG2QPSpk0bU6dARFTvsbihSqnV6gbZS9CxY0ecPXu2yuNeXl51mBEREdUlDigm2YmLi0PHjh0rPNaxY0fExcXVcUZEZGxarRbHjh3D3r17cezYMWi1WlOnRPUIe25IluLi4nD79m3MmzcPJ06cgLe3N6Kjo9GkSRNTp0ZEDykxMRGxsbHIzMwU25ydnRESEoKAgAATZkb1BXtuSLaaNGmC0NBQAEBoaCgLGyIZSExMREREBDw8PBATE4OdO3ciJiYGHh4eiIiIQGJioqlTpHqAxQ0RETUIWq0WsbGx8Pf3R2RkJDQaDZKSkqDRaBAZGQl/f3+sXLmSl6iIl6WIiKhhSElJQWZmJoYNG4aXX3653GWpYcOG4eDBg0hJSUG3bt1MmCmZGoubWpKVlYW8vDxTp9HoXblyRfI/mZatrS2cnJxMnQY1UDk5OQCAL7/8Ev7+/pg/fz7c3d2Rnp6OhIQEfPnll5I4arxY3NSCrKwsvPTyONzVlJg6Ffr/oqOjTZ0CATC3sMS6td+ywKEasbOzAwB06dIFUVFRUCrvjazw9PREVFQUZs+ejZMnT4px1HixuKkFeXl5uKspwR2PftCpbU2dDlG9oCzOAy79jry8PBY39NA0Gg1+/PFHXLt2Da6urhg2bJipU6J6hMVNLdKpbaGzcTB1GkREspCbmwsAOHnyJAYNGiQ5FhMTUy6OGq96MVsqJiYG7dq1g1qtRq9evZCcnFxl/ObNm9GxY0eo1Wp07doVO3furKNMiYjIVOzt7Y0aR/Jl8uJm48aNCAsLQ0REBI4ePQpvb28EBQXhxo0bFcYfPHgQL7zwAiZPnoxjx47hmWeewTPPPINTp07VceZERFSX7l953NfXF1OnToWvr2+VcdT4mPyy1NKlSzF16lRMnDgRwL2VZX/++WesXr0ac+bMKRf/2WefYdCgQXjrrbcAAIsWLcLu3buxYsWKeresvvJOrqlTIKo3+H6gh7V161bx6169emHcuHFwd3eHj48PVCoVDh8+LMa99NJLpkqT6gGTFjcajQZHjhzB3LlzxTalUonAwEAkJSVVeJ+kpCSEhYVJ2oKCgrB9+/YK40tKSlBS8n+zlvLz8x8+8WqySudKmURExvLTTz8BADp37owrV66IK5ADgIuLCzp16oQzZ87gp59+YnHTyJm0uMnOzoZWqy03c8LJyanSXZ0zMzMrjC+7mFNZixcvRmRkpHESriZbW1uYmVug9K6mTh+XqL4zM7eArS1nEFLN6P9Q9fX1xfjx45GSkoKcnBzY29vDy8sLa9aswZkzZyR/0FLjZPLLUrVt7ty5kp6e/Px8uLm51epjOjk5IWHd2ga/iF9JSUmlRSOZhrOzMywtLU2dRo1xET96GB07dkRSUhI2bNiAcePGSVYhLi0txaZNm8Q4atxMWtw4ODhApVIhKytL0p6VlQVnZ+cK7+Ps7GxQvKWlpUl+GTg5OcniQ7xr166mToGICAAwb948DB06FBqNBiNHjsTkyZPh7++PpKQkfPXVV9BoNGIcNW4mnS1lYWGBHj16YO/evWKbTqfD3r174e/vX+F9/P39JfEAsHv37krjiYhIHpo0aSL2yuTm5mLJkiUYOXIklixZIq5t07FjRzRp0sSEWVJ9YPKp4GFhYVi1ahW++eYbnDlzBjNmzEBhYaE4e2rcuHGSAcezZ8/Grl27sGTJEpw9exYLFy7EX3/9hZkzZ5rqKRARUR2Ji4ur9LJTx44d692sWTINk4+5GTNmDG7evIkFCxYgMzMTPj4+2LVrl3hJJyMjQ9w/BAD69OmD7777DuHh4Xj33XfxyCOPYPv27ejSpYupngIREdWhuLg43L59G4sXLxa3X5g7dy57bEikEARBMHUSdSk/Px+2trbIy8tDs2bNTJ0OERERVYMhv79NflmKiIiIyJhY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZMvv1CXdMvyJyfn2/iTIiIiKi69L+3q7OxQqMrbgoKCgAAbm5uJs6EiIiIDFVQUABbW9sqYxrd3lI6nQ7Xrl1D06ZNoVAoTJ0O1bL8/Hy4ubnh77//5l5iRDLD93fjIggCCgoK4OrqKtlQuyKNrudGqVSidevWpk6D6lizZs344UckU3x/Nx4P6rHR44BiIiIikhUWN0RERCQrLG5I1iwtLREREQFLS0tTp0JERsb3N1Wm0Q0oJiIiInljzw0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljckKzFxMSgXbt2UKvV6NWrF5KTk02dEhE9pMTERAwbNgyurq5QKBTYvn27qVOieobFDcnWxo0bERYWhoiICBw9ehTe3t4ICgrCjRs3TJ0aET2EwsJCeHt7IyYmxtSpUD3FqeAkW7169ULPnj2xYsUKAPf2FXNzc8Orr76KOXPmmDg7IjIGhUKBbdu24ZlnnjF1KlSPsOeGZEmj0eDIkSMIDAwU25RKJQIDA5GUlGTCzIiIqLaxuCFZys7OhlarhZOTk6TdyckJmZmZJsqKiIjqAosbIiIikhUWNyRLDg4OUKlUyMrKkrRnZWXB2dnZRFkREVFdYHFDsmRhYYEePXpg7969YptOp8PevXvh7+9vwsyIiKi2mZk6AaLaEhYWhvHjx8PX1xd+fn5YtmwZCgsLMXHiRFOnRkQP4fbt27hw4YJ4Oz09HcePH4e9vT3atGljwsyovuBUcJK1FStW4OOPP0ZmZiZ8fHywfPly9OrVy9RpEdFD2L9/P5544oly7ePHj8fXX39d9wlRvcPihoiIiGSFY26IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0QkC19//TXs7Owe+jwKhQLbt29/6PMQkemwuCGiemPChAl45plnTJ0GETVwLG6IiIhIVljcEFGDsHTpUnTt2hU2NjZwc3NDSEgIbt++XS5u+/bteOSRR6BWqxEUFIS///5bcnzHjh3o3r071Go1PDw8EBkZidLS0rp6GkRUB1jcEFGDoFQqsXz5cqSmpuKbb77Bb7/9hrffflsSU1RUhOjoaHz77bc4cOAAcnNzMXbsWPH4//73P4wbNw6zZ8/G6dOn8cUXX+Drr79GdHR0XT8dIqpF3DiTiOqNCRMmIDc3t1oDerds2YLp06cjOzsbwL0BxRMnTsShQ4fEnd/Pnj2LTp064fDhw/Dz80NgYCAGDBiAuXPniudZt24d3n77bVy7dg3AvQHF27Zt49gfogbMzNQJEBFVx549e7B48WKcPXsW+fn5KC0tRXFxMYqKimBtbQ0AMDMzQ8+ePcX7dOzYEXZ2djhz5gz8/Pxw4sQJHDhwQNJTo9Vqy52HiBo2FjdEVO9dvnwZQ4cOxYwZMxAdHQ17e3v88ccfmDx5MjQaTbWLktu3byMyMhLPPfdcuWNqtdrYaRORibC4IaJ678iRI9DpdFiyZAmUyntDBTdt2lQurrS0FH/99Rf8/PwAAGlpacjNzUWnTp0AAN27d0daWhr+85//1F3yRFTnWNwQUb2Sl5eH48ePS9ocHBxw9+5dfP755xg2bBgOHDiAuLi4cvc1NzfHq6++iuXLl8PMzAwzZ85E7969xWJnwYIFGDp0KNq0aYORI0dCqVTixIkTOHXqFKKiouri6RFRHeBsKSKqV/bv349u3bpJ/q1duxZLly7Fhx9+iC5duiAhIQGLFy8ud19ra2u88847ePHFF9G3b180adIEGzduFI8HBQXhp59+wq+//oqePXuid+/e+PTTT9G2bdu6fIpEVMs4W4qIiIhkhT03REREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrLC4ISIiIln5f8+gAUM+BbWYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize predicted probability distributions by true class label\n",
    "sns.boxplot(data=probabilities_df, x='Label', y='Probability')\n",
    "\n",
    "# Show default decision threshold at 0.5\n",
    "plt.axhline(y=0.5, color='black', linestyle='--', label='Decision threshold')\n",
    "\n",
    "plt.ylabel('Predicted probability')\n",
    "plt.title('Probability distributions for each class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b114af",
   "metadata": {},
   "source": [
    "### 1.3. Cross-entropy (log loss)\n",
    "\n",
    "Binary cross-entropy measures how well predicted probabilities match the true labels:\n",
    "\n",
    "$$\\text{Log Loss} = -\\frac{1}{N} \\sum_{i=1}^{N} [y_i \\log(p_i) + (1 - y_i) \\log(1 - p_i)]$$\n",
    "\n",
    "where $y_i$ is the true label (0 or 1), $p_i$ is the predicted probability of class 1, and $N$ is the number of samples.\n",
    "\n",
    "| **True label** | **Predicted prob.** | **$-y_i \\log(p_i)$** | **$-(1-y_i) \\log(1-p_i)$** | **Total loss** |\n",
    "|----------------|---------------------|----------------------|----------------------------|----------------|\n",
    "| 0 | 0.1 | 0 | 0.105 | 0.105 |\n",
    "| 0 | 0.9 | 0 | 2.303 | 2.303 |\n",
    "| 1 | 0.1 | 2.303 | 0 | 2.303 |\n",
    "| 1 | 0.9 | 0.105 | 0 | 0.105 |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d4aa01f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss example calculations:\n",
      "\n",
      "Example 1: True label = 0, Predicted probability = 0.0339, Log loss = 0.0344\n",
      "Example 2: True label = 0, Predicted probability = 0.0001, Log loss = 0.0001\n",
      "Example 3: True label = 0, Predicted probability = 0.0452, Log loss = 0.0463\n",
      "Example 4: True label = 0, Predicted probability = 0.0127, Log loss = 0.0128\n",
      "Example 5: True label = 0, Predicted probability = 0.3613, Log loss = 0.4483\n",
      "\n",
      "Average log loss for these 5 examples: 0.1084\n"
     ]
    }
   ],
   "source": [
    "# Calculate log loss for first 5 examples to demonstrate the formula\n",
    "n_examples = 5\n",
    "example_probabilities = test_probabilities[:n_examples, 1]\n",
    "example_labels = test_df['target'].values[:n_examples]\n",
    "\n",
    "print('Log loss example calculations:\\n')\n",
    "\n",
    "for i in range(n_examples):\n",
    "\n",
    "    prob = example_probabilities[i]\n",
    "    label = example_labels[i]\n",
    "    \n",
    "    # Apply log loss formula based on true label\n",
    "    if label == 1:\n",
    "        loss = -np.log(prob)  # Loss for positive class\n",
    "    else:\n",
    "        loss = -np.log(1 - prob)  # Loss for negative class\n",
    "    \n",
    "    print(f'Example {i+1}: True label = {label}, Predicted probability = {prob:.4f}, Log loss = {loss:.4f}')\n",
    "\n",
    "# Calculate average log loss across all examples\n",
    "avg_log_loss = np.mean(\n",
    "    [-np.log(example_probabilities[i]) if example_labels[i] == 1 \n",
    "     else -np.log(1 - example_probabilities[i]) \n",
    "     for i in range(n_examples)]\n",
    ")\n",
    "\n",
    "\n",
    "print(f'\\nAverage log loss for these {n_examples} examples: {avg_log_loss:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9e9257",
   "metadata": {},
   "source": [
    "## 2. Classification metrics\n",
    "\n",
    "### 2.1. Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d001a8f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy: 0.9584\n",
      "Constant 0 prediction accuracy: 0.9040\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy of trained model\n",
    "model_accuracy = accuracy_score(test_df['target'], test_predictions)\n",
    "\n",
    "# Compare to baseline that always predicts class 0\n",
    "constant_zero_predictions = np.zeros(len(test_df))\n",
    "constant_zero_accuracy = accuracy_score(test_df['target'], constant_zero_predictions)\n",
    "\n",
    "print(f'Model accuracy: {model_accuracy:.4f}')\n",
    "\n",
    "print(f'Constant 0 prediction accuracy: {constant_zero_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfc9709",
   "metadata": {},
   "source": [
    "### 2.2. Two ways to be right, two ways to be wrong\n",
    "\n",
    "1. **True positive** (TP): model predicts 1 and the label is 1\n",
    "2. **True negative** (TN): model predicts 0 and the label is 0\n",
    "3. **False positive** (FP): model predicts 1 and the label is 0 (Type I error)\n",
    "4. **False negative** (FN): model predicts 0 and the label is 1 (Type II error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c4c1cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare confusion matrices for baseline and trained model\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 3.5))\n",
    "\n",
    "# Confusion matrix for constant 0 baseline\n",
    "axes[0].set_title(f'Constant 0 predictions\\n(accuracy: {constant_zero_accuracy:.2%})')\n",
    "\n",
    "disp_constant = ConfusionMatrixDisplay.from_predictions(\n",
    "    test_df['target'],\n",
    "    constant_zero_predictions,\n",
    "    normalize='true',\n",
    "    ax=axes[0],\n",
    "    colorbar=False\n",
    ")\n",
    "\n",
    "# Confusion matrix for trained model\n",
    "axes[1].set_title(f'Model predictions\\n(accuracy: {model_accuracy:.2%})')\n",
    "\n",
    "disp_model = ConfusionMatrixDisplay.from_predictions(\n",
    "    test_df['target'],\n",
    "    test_predictions,\n",
    "    normalize='true',\n",
    "    ax=axes[1],\n",
    "    colorbar=False\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a72dbc",
   "metadata": {},
   "source": [
    "### 2.3. Metrics derived from TP, FP, TN & FN\n",
    "\n",
    "| **Metric** | **Formula** | **Interpretation** |\n",
    "|------------|-------------|--------------------|\n",
    "| **Precision** | $TP / (TP + FP)$ | Fraction of positive predictions which are correct |\n",
    "| **Recall** | $TP / (TP + FN)$ | Fraction of positive labels correctly identified |\n",
    "| **F1-Score**| $2 \\times \\frac{P \\times R}{P + R}$ | Equally weighted combination of precision and recall |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f83ff750",
   "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>Metric</th>\n",
       "      <th>Constant 0</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.958400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Precision</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.798246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Recall</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.758333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F1 Score</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Metric  Constant 0     Model\n",
       "0   Accuracy       0.904  0.958400\n",
       "1  Precision       0.000  0.798246\n",
       "2     Recall       0.000  0.758333\n",
       "3   F1 Score       0.000  0.777778"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate metrics for baseline constant 0 predictions\n",
    "constant_zero_precision = precision_score(test_df['target'], constant_zero_predictions, zero_division=0)\n",
    "constant_zero_recall = recall_score(test_df['target'], constant_zero_predictions)\n",
    "constant_zero_f1 = f1_score(test_df['target'], constant_zero_predictions)\n",
    "\n",
    "# Calculate metrics for trained model\n",
    "model_precision = precision_score(test_df['target'], test_predictions)\n",
    "model_recall = recall_score(test_df['target'], test_predictions)\n",
    "model_f1 = f1_score(test_df['target'], test_predictions)\n",
    "\n",
    "# Compare all metrics side by side\n",
    "metrics_df = pd.DataFrame({\n",
    "    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1 Score'],\n",
    "    'Constant 0': [constant_zero_accuracy, constant_zero_precision, constant_zero_recall, constant_zero_f1],\n",
    "    'Model': [model_accuracy, model_precision, model_recall, model_f1]\n",
    "})\n",
    "\n",
    "\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe9f759",
   "metadata": {},
   "source": [
    "### 2.4. ROC & PR curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b93b9163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate AUC (area under ROC curve) score\n",
    "auc_score = roc_auc_score(test_df['target'], test_probabilities[:, 1])\n",
    "\n",
    "# Plot ROC curve showing TPR vs FPR at different thresholds\n",
    "display = RocCurveDisplay.from_predictions(test_df['target'], test_probabilities[:, 1])\n",
    "display.ax_.set_title(f'ROC curve (AUC = {auc_score:.4f})')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9839d30e",
   "metadata": {},
   "source": [
    "The ROC curve plots the true positive rate against the false positive rate at various decision thresholds. A perfect classifier would have a curve that passes through the top left corner (100% TPR, 0% FPR). The AUC (area under curve) score summarizes performance across all thresholds, with 1.0 being perfect and 0.5 being random guessing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "020c2312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate average precision score\n",
    "avg_precision = average_precision_score(test_df['target'], test_probabilities[:, 1])\n",
    "\n",
    "# Plot precision-recall curve at different thresholds\n",
    "display = PrecisionRecallDisplay.from_predictions(test_df['target'], test_probabilities[:, 1])\n",
    "display.ax_.set_title(f'Precision-recall curve (AP = {avg_precision:.4f})')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fcc26d",
   "metadata": {},
   "source": [
    "The precision-recall curve shows the trade-off between precision and recall at different thresholds. This curve is particularly useful for imbalanced datasets where the positive class is rare. A perfect classifier would have a curve hugging the top right corner (100% precision and 100% recall). The average precision score summarizes the curve as a weighted mean of precisions at each threshold.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317a759d",
   "metadata": {},
   "source": [
    "## 3. Dealing with imbalanced classes\n",
    "\n",
    "### 3.1. Class weighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53496020",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model with class weighting to handle imbalance\n",
    "# 'balanced' mode automatically adjusts weights inversely proportional to class frequencies\n",
    "model_weighted = LogisticRegression(class_weight='balanced')\n",
    "\n",
    "model_weighted.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "test_predictions_weighted = model_weighted.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8104cf",
   "metadata": {},
   "source": [
    "### 3.2. Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa40f482",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create oversampler to duplicate minority class samples\n",
    "oversampler = RandomOverSampler(random_state=315)\n",
    "\n",
    "# Resample training data to balance classes\n",
    "X_train_oversampled, y_train_oversampled = oversampler.fit_resample(\n",
    "    train_df.drop(columns=['target']),\n",
    "    train_df['target']\n",
    ")\n",
    "\n",
    "# Train model on balanced oversampled data\n",
    "model_oversampled = LogisticRegression()\n",
    "\n",
    "model_oversampled.fit(X_train_oversampled, y_train_oversampled)\n",
    "test_predictions_oversampled = model_oversampled.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a7e828",
   "metadata": {},
   "source": [
    "### 3.3. Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab8f66ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create undersampler to remove majority class samples\n",
    "undersampler = RandomUnderSampler(random_state=315)\n",
    "\n",
    "# Resample training data to balance classes\n",
    "X_train_undersampled, y_train_undersampled = undersampler.fit_resample(\n",
    "    train_df.drop(columns=['target']),\n",
    "    train_df['target']\n",
    ")\n",
    "\n",
    "# Train model on balanced undersampled data\n",
    "model_undersampled = LogisticRegression()\n",
    "\n",
    "\n",
    "model_undersampled.fit(X_train_undersampled, y_train_undersampled)\n",
    "test_predictions_undersampled = model_undersampled.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f87dba6",
   "metadata": {},
   "source": [
    "### 3.4. Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18afdfad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare all four approaches for handling imbalanced classes\n",
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(12, 6))\n",
    "\n",
    "models_list = [\n",
    "    model,\n",
    "    model_weighted,\n",
    "    model_oversampled,\n",
    "    model_undersampled\n",
    "]\n",
    "\n",
    "predictions_list = [\n",
    "    test_predictions,\n",
    "    test_predictions_weighted,\n",
    "    test_predictions_oversampled,\n",
    "    test_predictions_undersampled\n",
    "]\n",
    "\n",
    "titles = [\n",
    "    'Baseline',\n",
    "    'Class weighting',\n",
    "    'Oversampling',\n",
    "    'Undersampling'\n",
    "]\n",
    "\n",
    "# Loop through each model and create confusion matrix and probability distribution plots\n",
    "for i in range(4):\n",
    "\n",
    "    # Top row: confusion matrices\n",
    "    acc = accuracy_score(test_df['target'], predictions_list[i])\n",
    "    axes[0, i].set_title(f'{titles[i]}\\n(accuracy: {acc:.2%})')\n",
    "    \n",
    "    ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        predictions_list[i],\n",
    "        #normalize='true',\n",
    "        ax=axes[0, i],\n",
    "        colorbar=False\n",
    "    )\n",
    "    \n",
    "    # Bottom row: probability distributions\n",
    "    model_probabilities = models_list[i].predict_proba(test_df.drop(columns=['target']))\n",
    "    \n",
    "    model_probabilities_df = pd.DataFrame({\n",
    "        'Probability': model_probabilities[:, 1],\n",
    "        'Label': test_df['target']\n",
    "    })\n",
    "    \n",
    "    sns.boxplot(data=model_probabilities_df, x='Probability', y='Label', orient='h', ax=axes[1, i])\n",
    "    axes[1, i].axvline(0.5, color='black', linestyle='--')\n",
    "    axes[1, i].set_ylabel('Predicted probability')\n",
    "    axes[1, i].set_title('')\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a11cd9a",
   "metadata": {},
   "source": [
    "## 4. Decision thresholds\n",
    "\n",
    "### 4.1. Manually setting decision thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a718540",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train logistic regression model with balanced class weighting\n",
    "threshold_model = LogisticRegression(class_weight='balanced', random_state=315)\n",
    "threshold_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "# Get probability predictions\n",
    "threshold_probabilities = threshold_model.predict_proba(test_df.drop(columns=['target']))\n",
    "\n",
    "# Create predictions with different thresholds\n",
    "thresholds = [0.25, 0.5, 0.75, 0.9]\n",
    "threshold_predictions = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "\n",
    "    # Apply threshold to class 1 probabilities\n",
    "    preds = (threshold_probabilities[:, 1] >= threshold).astype(int)\n",
    "    threshold_predictions.append(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "511c00b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKgAAAJRCAYAAABhkI/OAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAzfdJREFUeJzs3Xd4U2X/x/FPutK9KaVQZtl7CTgAFREcqDyKKCjgenxcOBFFpgKKAxfDBYriQBT3QhyAgshSkL13GaWDls7cvz/6a2zooIWmSdr367q4NGfdd5Lmk3O+Oec+FmOMEQAAAAAAAOAiXq7uAAAAAAAAAKo3ClQAAAAAAABwKQpUAAAAAAAAcCkKVAAAAAAAAHApClQAAAAAAABwKQpUAAAAAAAAcCkKVAAAAAAAAHApClQAAAAAAABwKQpUAAAAAAAAcCkKVB7ml19+kcVi0fz5813dFUnO6c+4ceNksVjKtKzFYtG4ceMqrG0AFY/cckRuAa5BFjkiiwD3RmY5IrOqBwpUbsBisZTp3y+//OLqruL/ffHFF+rQoYP8/f1Vt25djR07Vrm5uaddb9OmTRoxYoTatWunkJAQ1apVS5dffrlWrlxZZNmCwD71n7+/vzOeElAu5JbnOdPc2rVrV4nv74cfflgJPQdKRhZ5njPNopL2iwr+/fbbb/Zlhw4dWuwyzZo1c+ZTA06LzPI8Z5pZkrRt2zZde+21ioiIUGBgoM4//3z9/PPPTu6xZ/NxdQcgvfvuuw6P58yZo4ULFxaZ3rx5c23cuLEyu4ZifPvtt7r66qvVs2dPvfLKK1q3bp2eeuopHT58WDNmzCh13TfffFNvvfWW/vOf/+iuu+5SSkqKXnvtNXXt2lXfffedevXqVWSdGTNmKDg42P7Y29u7wp8TUF7klmc5m9wqcMMNN+iyyy5zmNatWzdndBcoM7LIs5xNFvXv318JCQlFpj/++OM6ceKEOnfu7DDdarXqzTffdJgWFhZ29k8COAtklmc5m8zau3evunXrJm9vbz3yyCMKCgrS7Nmz1bt3by1atEjdu3evpGfhWShQuYHBgwc7PF6+fLkWLlxYZLqksw6qjIwMBQYGntU2qruHH35Ybdq00Q8//CAfn/yPUGhoqCZNmqThw4eX+uvcDTfcoHHjxjkUnG655RY1b95c48aNK7ZAde211yo6OrrinwhwFsgtz3I2uVWgQ4cOxb6/gCuRRZ7lbLKoTZs2atOmjcO0vXv3at++fbrtttvk5+fnMM/Hx4fMgtshszzL2WTW008/reTkZK1fv15NmzaVJN1+++1q1qyZHnjgAa1atapSnoOn4RI/D2Wz2TRx4kTVqVNH/v7+uvjii7Vt2zaHZXr27KlWrVpp1apV6t69uwIDA/X4449LkrKysjR27FglJCTIarUqPj5eI0aMUFZWlsM2Fi5cqPPPP1/h4eEKDg5W06ZN7dsob38k6eOPP1bHjh0VEBCg6OhoDR48WPv37z/t883KytIDDzygGjVqKCQkRP369dO+ffvK85JViA0bNmjDhg2644477CElSXfddZeMMae9Jrtjx44OxSlJioqK0gUXXFDil5AxRqmpqTLGnP0TAFyI3PLM3CosPT1d2dnZzugmUGnIIs/PogIffPCBjDEaNGhQsfPz8vKUmpp6xn0G3AGZ5ZmZtWTJErVv395enJKkwMBA9evXT6tXr9bWrVud1ndPxhlUHurpp5+Wl5eXHn74YaWkpGjKlCkaNGiQ/vjjD4fljh07pr59+2rgwIEaPHiwatasKZvNpn79+mnp0qW644471Lx5c61bt05Tp07Vli1b9Nlnn0mS/vnnH11xxRVq06aNJkyYIKvVqm3btjlc41+e/rz99tsaNmyYOnfurMmTJysxMVEvvfSSfvvtN61Zs0bh4eElPt/bbrtN7733nm688Uade+65+umnn3T55ZeX+fU6evRomZYLCQmR1Wotcf6aNWskSZ06dXKYHhcXpzp16tjnl9ehQ4dKPEuqYcOGOnHihIKCgnT11Vfr+eefV82aNc+oHcCVyC3Pzq3x48frkUcekcViUceOHTVx4kT17t27TOsC7oQs8uwsKmzu3LmKj48v9lKZjIwMhYaGKiMjQxEREbrhhhv0zDPPFPmhEHB3ZJZnZlZWVpYiIiKKTC84q23VqlVq3LhxmfparRi4nbvvvtuU9Nb8/PPPRpJp3ry5ycrKsk9/6aWXjCSzbt06+7QePXoYSWbmzJkO23j33XeNl5eXWbJkicP0mTNnGknmt99+M8YYM3XqVCPJHDlypMS+lrU/2dnZJiYmxrRq1cqcPHnSvtxXX31lJJkxY8bYp40dO9bh+a9du9ZIMnfddZdD2zfeeKORZMaOHVti/wpIKtO/2bNnl7qdZ5991kgye/bsKTKvc+fOpmvXrqfty6kWL15sLBaLGT16tMP0F1980dxzzz1m7ty5Zv78+Wb48OHGx8fHNG7c2KSkpJS7HcCZyK2qm1u7d+82vXv3NjNmzDBffPGFefHFF03dunWNl5eX+eqrr077PIDKRBZV3Sw61fr1640kM2LEiCLzRo4caR599FHz0UcfmQ8++MAMGTLESDLnnXeeycnJKVc7gDORWVU3s6688koTHh5uUlNTHaZ369bNSDLPPffcaZ9LdcQZVB5q2LBhDtfaX3DBBZKkHTt2qFWrVvbpVqtVw4YNc1j3448/VvPmzdWsWTOHCvNFF10kSfr555917rnn2ivbn3/+uYYNGyYvr5KvCD1df1auXKnDhw9r3LhxDnehu/zyy9WsWTN9/fXXGj9+fLHb/uabbyRJ9913n8P0+++/X++//36JfSps4cKFZVquZcuWpc4/efKkJBVbbff39y/3aeSHDx/WjTfeqAYNGmjEiBEO84YPH+7w+D//+Y/OOeccDRo0SNOnT9fIkSPL1RbgauSWZ+ZW3bp19f333ztMu+mmm9SiRQs99NBD5fpVE3AHZJFnZtGp5s6dK0nFXt43efJkh8cDBw5UkyZNNGrUKM2fP18DBw4sV1uAK5FZnplZ//vf//Tll1/q+uuv18SJExUUFKTp06fb795esH04okDloerWrevwuOD0wePHjztMr127dpFBI7du3aqNGzeqRo0axW778OHDkqTrr79eb775pm677TaNHDlSF198sfr3769rr722SGidrj+7d++WJIdrcAs0a9ZMS5cuLfG57t69W15eXmrUqJHD9OK2VZLiBh8/EwEBAZJU5JptScrMzLTPL4v09HRdccUVSktL09KlS8t0yvmNN96ohx56SD/++CMFKngccsvzc6tAZGSkhg0bpqefflr79u1TnTp1zrqfQGUhizw/i4wxev/999WqVasiA6eX5IEHHtDo0aP1448/UqCCRyGzPDOz+vbtq1deeUUjR45Uhw4dJEkJCQmaOHGiRowYweXGJaBA5aG8vb2LnW5OGUi7uA+OzWZT69at9cILLxS7jfj4ePu6ixcv1s8//6yvv/5a3333nT766CNddNFF+uGHHxz6UNb+uMqhQ4fKtFxYWFipYVOrVi1J0sGDB+2vU4GDBw/qnHPOKVM72dnZ6t+/v/7++299//33Dr9+nE58fLySkpLKvDzgLsit8nG33DpVwbaSkpIoUMGjkEXl445Z9Ntvv2n37t1FzpQqTUBAgKKiotiHgschs8rHnTLrnnvu0bBhw/T333/Lz89P7dq101tvvSVJatKkSZn6Wd1QoKqGGjVqpL/++ksXX3yxLBZLqct6eXnp4osv1sUXX6wXXnhBkyZN0qhRo/Tzzz+Xqzpdr149SdLmzZvtp5QW2Lx5s31+SevabDZt377doXq+efPmMrdfEDCnM3v2bA0dOrTE+e3atZMkrVy50iGUDhw4oH379umOO+44bRs2m00333yzFi1apHnz5qlHjx5l6puUH/y7du1S+/bty7wOUBWQWyWrjNwqzo4dOySpxF9lgaqILCpZZWbR3LlzZbFYdOONN5Z5nbS0NB09epTMQrVCZpWssjIrKChI3bp1sz/+8ccfFRAQoPPOO69M61c3JV9ciiprwIAB2r9/v954440i806ePKn09HRJKvYXpoIPanGnOpamU6dOiomJ0cyZMx3W/fbbb7Vx48ZSxzDp27evJOnll192mP7iiy+Wuf2FCxeW6d+ll15a6nZatmypZs2a6fXXX1deXp59+owZM2SxWHTttdfap6WkpGjTpk1KSUlx2Ma9996rjz76SNOnT1f//v1LbOvIkSNFps2YMUNHjhxRnz59yvrUgSqB3HJdbhWXRfv379esWbPUpk2bMu8IAlUBWeTafShJysnJ0ccff6zzzz+/yKVGUv6lN2lpaUWmP/nkkzLGsA+FaoXMcn1mFfb777/r008/1a233qqwsLAyP6fqhDOoqqGbbrpJ8+bN05133qmff/5Z5513nvLy8rRp0ybNmzdP33//vTp16qQJEyZo8eLFuvzyy1WvXj0dPnxY06dPV506dXT++eeXq01fX18988wzGjZsmHr06KEbbrjBfrvR+vXr64EHHihx3Xbt2umGG27Q9OnTlZKSonPPPVeLFi3Stm3bytx+RV2LLEnPPvus+vXrp969e2vgwIFav369Xn31Vd12221q3ry5fbkFCxZo2LBhDtX5F198UdOnT1e3bt0UGBio9957z2Hb11xzjYKCgiTl/4Jw/fXXq3Xr1vL399fSpUv14Ycfql27dvrvf/9bYc8H8ATk1tk5m9waMWKEtm/frosvvlhxcXHatWuXXnvtNaWnp+ull16qsD4CnoAsOjtnk0UFvv/+ex07dqzYwdGl/Mt72rdvrxtuuEHNmjWzr/PNN9+oT58+uuqqqyrs+QDujsw6O2eTWbt379aAAQPUr18/xcbG6p9//tHMmTPVpk0bTZo0qcL6WNVQoKqGvLy89Nlnn2nq1KmaM2eOFixYoMDAQDVs2FDDhw+3Xw/br18/7dq1S7NmzdLRo0cVHR2tHj16aPz48WdU8R06dKgCAwP19NNP69FHH1VQUJCuueYaPfPMM/Y7R5Rk1qxZqlGjhubOnavPPvtMF110kb7++usi1wNXhiuuuEKffvqpxo8fr3vvvVc1atTQ448/rjFjxpx23bVr10qSli1bpmXLlhWZv3PnTnuBatCgQfr999/1ySefKDMzU/Xq1dOIESM0atQoBQYGVuhzAtwduXV2zia3evfurZkzZ2ratGk6fvy4wsPD1b17dz3xxBP2QT+B6oIsOjtnk0UF5s6dK19fX1133XXFzg8PD9cVV1yhhQsX6p133lFeXp4SEhI0adIkPfzww6XenQyoasiss3M2mRUaGqpatWrp1VdfVVJSkmrXrq377rtPo0aNUkhISCX03jNZjLuMZgYAAAAAAIBqiZ8QAAAAAAAA4FIUqAAAAAAAAOBSFKgAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSFKgAAAAAAADgUhSoKtGUKVPUrFkz2Ww2V3cFVdB3332n4OBgHTlyxNVdgYcio+BMI0eOVJcuXVzdDXgo8gnONHPmTNWtW1dZWVmu7go8EPkEZ6pu+USBqpKkpqbqmWee0aOPPiovL152Z9q6dasGDhyoOnXqKDAwUM2aNdOECROUkZFhXyYjI0PTpk1T7969VatWLYWEhKh9+/aaMWOG8vLyTtvGL7/8IovFUuK/iRMn2pddtGiRbrnlFjVp0kSBgYFq2LChbrvtNh08eLDIdl977TU1aNBAkZGRuummm5Samuow32azqX379po0aVKRdfv06aOEhARNnjy5PC8XIImMqkyrVq1Snz59FBoaqpCQEPXu3Vtr164tdtnff/9d559/vgIDAxUbG6v77rtPJ06cKHebS5cutefT0aNHi8z/8ccfdeGFFyo6Olrh4eE655xz9O677zosk5WVpXvvvVc1atRQnTp19NRTTxXZzr59+xQcHKzffvutyLz7779ff/31l7744oty9x/VG/lUecqTTwWSk5MVExMji8Wi+fPnn7aNt99+u9R9qLlz59qX/fTTT3X99derYcOGCgwMVNOmTfXQQw8pOTnZYZvGGI0fP161a9dWTEyM7r//fmVnZzssc+LECdWuXVvvv/9+kT4NHTpU2dnZeu21107bf6Aw8qnylDWfevbsWWy29OnTp0ztJCYmatiwYYqJiVFAQIA6dOigjz/+uMhy5JOTGFSKqVOnmtDQUHPy5ElXd6VK27NnjwkPDzf16tUzkydPNq+99poZOnSokWT69etnX27dunXGYrGYXr16mSlTppiZM2eaa665xkgyN99882nbOXTokHn33XeL/Ovdu7eRZFasWGFftmPHjqZBgwZmxIgR5o033jCPPfaYCQkJMTVr1jQHDx60L7dkyRJjsVjM8OHDzUsvvWRiY2PNHXfc4dDuzJkzTYMGDUxmZmax/Zo+fboJDAw0qamp5X3pUM2RUZVj1apVxt/f3zRu3Ng899xzZsqUKaZ+/fomNDTUbNq0yWHZNWvWGH9/f9O+fXszY8YMM2rUKGO1Wk2fPn3K1WZeXp5p166dCQoKMpLMkSNHHOZ//vnnxmKxmHPPPde88sor5tVXXzXdu3c3kswLL7xgX+7JJ580oaGh5plnnjFPPPGE8fX1Ne+//77DtgYOHGhuuOGGEvsyYMAAc8EFF5Sr/wD5VDnKk0+F3XvvvfZ8+fjjj0/bzvbt24vdh+rQoYPx9vZ22DeKiooyrVu3NqNHjzZvvPGGue+++4yfn59p1qyZycjIsC/37rvvGj8/PzN69Gjz9NNPm5CQEDNp0iSHdkeOHGnOPffcEvs1YsQIU69ePWOz2U77HIAC5FPlKE8+9ejRw9SpU6dIxixatOi07aSkpJiEhAQTEhJinnjiCYd9orlz5zosSz45BwWqStKmTRszePBgV3fjrJw8edLk5eW5uhulmjhxopFk1q9f7zD95ptvNpJMUlKSMcaYI0eOFFnGGGOGDRtmJJmtW7eeUfsJCQmmcePGDtN+/fXXIq/br7/+aiSZUaNG2ac9+uij5sILL7Q/nj17tomNjbU/Pn78uImOjjaffPJJie0nJiYab29v89Zbb51R/1F9kVGV47LLLjMRERHm6NGj9mkHDhwwwcHBpn///g7L9u3b19SqVcukpKTYp73xxhtGkvn+++/L3OaMGTNMVFSUGT58eLEFqksuucTExcU5FL5zcnJMo0aNTJs2bezTunTpYsaPH29/PGTIEDNw4ED74yVLlpigoCCzd+/eEvsyf/58Y7FYzPbt28vcf4B8qhzlyacC69atMz4+PmbChAllLlAVJyMjw4SEhJhLLrnEYfrPP/9cZNl33nnHSDJvvPGGfdr1119vhg0bZn88duxY07VrV/vjbdu2mYCAAPPnn3+W2IeVK1caSWU6iAUKkE+Vozz51KNHD9OyZcszamfKlClFciAvL8907tzZxMbGmqysLPt08sk5OA+xEuzcuVN///23evXqVWTec889p3PPPVdRUVEKCAhQx44dSzw9+r333tM555yjwMBARUREqHv37vrhhx8clvn222/Vo0cPhYSEKDQ0VJ07d3Y4VbB+/foaOnRokW337NlTPXv2tD8uuITtww8/1BNPPKHatWsrMDBQqampSkpK0sMPP6zWrVsrODhYoaGh6tu3r/76668i283MzNS4cePUpEkT+fv7q1atWurfv7+2b98uY4zq16+vq666qtj1wsLC9N///leStGnTJu3Zs6fY16Wwgkviatas6TC9Vq1a8vLykp+fnyQpOjpaLVu2LLL+NddcI0nauHHjads61YoVK7Rt2zYNGjTIYXr37t2LnPLbvXt3RUZGOrRz8uRJRURE2B9HRkY6XJY4btw4tW7dWv379y+xDzExMWrTpo0+//zzcvcf1RcZVXkZtWTJEvXq1UtRUVH2abVq1VKPHj301Vdf2S/fS01N1cKFCzV48GCFhobal7355psVHBysefPmnbYtSUpKStITTzyhCRMmKDw8vNhlUlNTFRERIavVap/m4+Oj6OhoBQQE2KeVllE2m03Dhw/XiBEjVKdOnRL7U/A3RkahrMgn98unwoYPH65rrrlGF1xwwWm3X5ovv/xSaWlpRfahCr+uBYrbVzvdPtRDDz2kgQMHqlOnTiX2oWPHjoqMjCSfUGbkk3vnU25ubrmHRViyZIlq1Kihiy66yD7Ny8tLAwYM0KFDh/Trr7/ap5NPzkGBqhL8/vvvkqQOHToUmffSSy+pffv2mjBhgiZNmiQfHx9dd911+vrrrx2WGz9+vG666Sb5+vpqwoQJGj9+vOLj4/XTTz/Zl3n77bd1+eWXKykpSY899piefvpptWvXTt99990Z9/3JJ5/U119/rYcffliTJk2Sn5+fduzYoc8++0xXXHGFXnjhBT3yyCNat26devTooQMHDtjXzcvL0xVXXKHx48erY8eOev755zV8+HClpKRo/fr1slgsGjx4sL799lslJSU5tPvll18qNTVVgwcPliQ1b95cN99882n7WxAUt956q9auXau9e/fqo48+0owZM3TfffcpKCio1PUPHTokKb+AVV4FYyacunNVnBMnTujEiRMO7XTu3FnfffedfvjhB23dulXPP/+8zjnnHEnShg0bNHPmTL344oun3XbHjh3tf3NAWZBRlZdRWVlZDkWfAoGBgcrOztb69eslSevWrVNubm6RnRU/Pz+1a9dOa9asKdPrM3r0aMXGxtp3BIvTs2dP/fPPPxo9erS2bdum7du368knn9TKlSs1YsQI+3KdO3fW66+/rnXr1mnZsmX64IMP7Bn11ltv6ejRo3rkkUdK7U9YWJgaNWpU7BhVQHHIJ/fLpwIff/yxfv/9d02ZMuVMXh4Hc+fOVUBAQKk/whUobl+tc+fO+uCDD7R8+XKtW7dOr732mj2fFi5cqJ9++qnY8TtP1aFDB/IJZUY+uW8+bdmyRUFBQQoJCVFsbKxGjx6tnJycs2pHyh8HqzTkUwVw7Qlc1cMTTzxhJJm0tLQi8wpfn2qMMdnZ2aZVq1bmoosusk/bunWr8fLyMtdcc02R0y8LrkNNTk42ISEhpkuXLkWugS58rWq9evXMkCFDivSjR48epkePHvbHP//8s5FkGjZsWKSPmZmZRfqxc+dOY7VazYQJE+zTZs2aVWQMk1P7tHnzZiPJzJgxw2F+v379TP369e3LSXLoX2mefPJJExAQYCTZ/xW+lK4kWVlZpkWLFqZBgwYmJyenTG0VyM3NNTVr1jTnnHNOmfuoU07TzM3NNf3797f3OT4+3vz999/GGGN69+5t7rzzzjJte9KkSUaSSUxMLNdzQPVFRlVeRrVu3do0adLE5Obm2qdlZWWZunXrGklm/vz5xhhjPv74YyPJLF68uMg2rrvuOofLf0vy119/GW9vb/vlgGPHji32Er8TJ06YAQMGGIvFYs+fwMBA89lnnzkst3fvXtOyZUv7MhdccIFJS0szycnJpkaNGubDDz88bZ+Myc+z5s2bl2lZgHxyv3wyJv+1r1u3rnnsscccnvOZXOJ37Ngx4+fnZwYMGFCm5W+99Vbj7e1ttmzZYp+Wmppqzj//fHs+tWzZ0uzbt8/k5OSYFi1amKeffrpM277jjjtMQEBAuZ8DqifyyT3z6ZZbbjHjxo0zn3zyiZkzZ47p16+fkVSmjLn33nuNl5eX2bVrl8P0gQMHGknmnnvuKXV98unscQZVJTh27Jh8fHwUHBxcZF7hCu3x48eVkpKiCy64QKtXr7ZP/+yzz2Sz2TRmzJgil4pZLBZJ+dXXtLQ0jRw5Uv7+/sUucyaGDBlSpIpstVrt/cjLy9OxY8cUHByspk2bOvT7k08+UXR0tO69994i2y3oU5MmTdSlSxeHO7YkJSXp22+/1aBBg+zLGWP0yy+/lKnP9evXV/fu3fX666/rk08+0S233KJJkybp1VdfLXW9e+65Rxs2bNCrr74qHx+fMrVVYNGiRUpMTCzT2VOLFy/W+PHjNWDAAIfTR729vfXJJ59o69atWrlypbZs2aLWrVvriy++0IoVK/Tkk09q//79uvLKKxUXF6crr7zS4deMAgWnkBZ3py6gOGRU5WXUXXfdpS1btujWW2/Vhg0btH79et188832u3qePHnS4b+FL7sr4O/vb59fmvvuu099+/ZV7969S13OarWqSZMmuvbaa/XBBx/ovffeU6dOnTR48GAtX77cvlydOnW0Zs0arVmzRv/8849++eUXBQcHa/z48WratKmuv/56LV26VF26dFF8fLzuu+++InepkfIzinxCWZFP7pdPkvT0008rJydHjz/+eBlfjZLNnz9f2dnZZdqHev/99/XWW2/poYceUuPGje3TQ0JC9Ouvv+qff/7R2rVrtXbtWtWuXVvTp09XVlaWHnjgAW3YsEEXXnihateurcGDBxe5U7KUn08nT550uPwGKAn55J759NZbb2ns2LHq37+/brrpJn3++ee6/fbbNW/ePIf9muLcdttt8vb21oABA/T7779r+/btmjx5shYsWFCknVORTxXEtfWx6uF///uf8fHxKXbel19+abp06WKsVqvDGT8Wi8W+zJ133mm8vLwcBmU71dNPP12mwb3LW12fM2dOkWXz8vLMCy+8YBISEoy3t7dDvwsP8t2sWTNz3nnnldofY4yZNm2asVgs9kr1zJkzjaRS7xhTkg8++MAEBAQUGaR36NChJjAw0GFgvcIKBsR78skny92mMfmDsHt7e5tDhw6VutzGjRtNZGSkadeuXZnutJeVlWUSEhLM1KlTjTHGnH/++ea6664zK1euNNdee22xvzhMnz7dSDIbNmw4k6eCaoiMKl1FZpQxxjz++OPG19fX3qdOnTqZUaNGGUlmwYIFxpizP4Pqww8/NL6+vmbz5s32aSWdQfXf//7XtG3b1uFX0+zsbNO4cePTnhW6ceNGY7VazapVq8yxY8dMaGiomTRpklm2bJlp1qyZGTNmTJF1BgwYYGJiYkrdLlCAfCqdK/Jp586dJiAgwMyaNavIcz6TM6i6d+9uIiMjTXZ2dqnLLV682Pj7+5tLL720TGe6HzlyxERERJgFCxaY7Oxs06BBA3PvvfeaP//801xwwQXF3rV5xIgRRlKRM0uA4pBPpXNFPpVk06ZNZT7W+/jjj01UVJS9ndjYWDNjxgwjyQwfPrzYdcinisMZVJUgKipKubm5SktLc5i+ZMkS9evXT/7+/po+fbq++eYbLVy4UDfeeKOMMU7pS0mV9ry8vGKnF3cN7qRJk/Tggw+qe/fueu+99/T9999r4cKFatmypWw2W7n7NHDgQPn6+tor7AW/3jdt2rTc25o+fbrat29fZJDefv36KSMjo9hxW95++209+uijuvPOO/XEE0+Uu82TJ09qwYIF6tWrV5HB2Qvbu3evevfurbCwMH3zzTcKCQk57banTp0qHx8f3XPPPdq7d6+WLl2qKVOmqGPHjpoyZYp+/fVX7du3z2Gd48ePSzqzcbRQPZFRpavIjJKkiRMnKjExUUuWLNHff/+tP//8096vJk2aSMof+FOS/ZfBwg4ePKi4uLhS23jkkUd03XXXyc/PT7t27dKuXbuUnJwsKT+LCs6+zM7O1ltvvaXLL7/c4ddbX19f9e3bVytXriz2LKgCDzzwgAYPHqwOHTro66+/VmRkpB577DF17dpVI0aMcPjltMDx48fJJ5QZ+VQ6V+TTmDFjVLt2bfXs2dOeLwXjrhw5ckS7du0q83PZs2ePlixZouuuu06+vr4lLvfXX3+pX79+atWqlebPn1+mM91Hjx6tDh066Oqrr9by5ct18OBBTZkyRZ06ddL48eP14YcfFunn8ePHFRgYWOx7B5yKfCqdK/KpJPHx8ZJUZEys4lx77bU6cOCAVqxYoWXLlmn37t1q2LBhie2QTxWrfNcx4Yw0a9ZMUv6dHtq0aWOf/sknn8jf31/ff/+9w2Ucs2fPdli/UaNGstls2rBhg9q1a1dsG40aNZIkrV+/XgkJCSX2JSIiwn6QUljhD97pzJ8/XxdeeKHeeusth+nJyckOBx2NGjXSH3/8oZycnFJ3OiIjI3X55Zdr7ty5GjRokH777bcyDQZenMTERIe7JBQoGBQvNzfXYfrnn3+u2267Tf3799e0adPOqM0vvvii2DvPFHbs2DH17t1bWVlZWrRokf3gszQHDx7UU089pY8//lg+Pj72A8qCA9OC/+7fv9+hILdz505FR0erRo0aZ/R8UP2QUZWXUQUiIiJ0/vnn2x//+OOPqlOnjv29aNWqlXx8fLRy5UoNGDDAvlx2drbWrl3rMK04e/fu1fvvv+9wh58CHTp0UNu2bbV27VodO3ZMubm5xe7A5uTkyGazlbhz+9VXX+n333/X1q1bJUkHDhxwyLa4uDjt37+/yHo7d+5U27ZtS+0/UIB8cr982rNnj7Zt21bsc77rrrsk5R9IlXTn0MI++OADGWNK3Yfavn27+vTpo5iYGH3zzTfFXk51qr/++kuzZs2yD2h84MABRURE2C+RiouLU3Z2to4cOeLw4+LOnTvVvHnz024fkMgnd8ynkuzYsUOSynx85Ofnp86dOzu0I6nIHRvJp4rHGVSVoFu3bpKklStXOkz39vaWxWJx2PnftWuXPvvsM4flrr76anl5eWnChAlFKqkFVfjevXsrJCREkydPVmZmZrHLSPmBsnz5codfxL/66ivt3bu3zM/H29u7SPX/448/LnIg8p///EdHjx4tduynU9e/6aabtGHDBj3yyCPy9vbWwIEDHeaX9RakTZo00Zo1a7RlyxaH6R988IG8vLwcvjwWL16sgQMHqnv37po7d26Ra78L5OTkaNOmTcWeySDlX28cGBhov63oqdLT03XZZZdp//79+uabbxyuSS7NyJEj1b17d/Xp00eS7AG1adMmSf/evjQ2NtZhvVWrVtn/5oCyIKMqL6OK89FHH+nPP//U/fffb8+hsLAw9erVS++9957DL7PvvvuuTpw4oeuuu84+LSMjQ5s2bXIY12nBggVF/l1//fWSpDlz5mjq1KmSpJiYGIWHh2vBggUOr/mJEyf05ZdfqlmzZsX+Upedna0HH3xQTzzxhGJiYiTlZ9S2bdvsPwRs3LixSD6lpKRo+/btOvfcc8/otUL1Qz65Xz499dRTRfLlySeflCSNGDFCCxYssN81ubh8Kuz9999X3bp1HQ44Czt06JB69+4tLy8vff/992U+uBw+fLhuu+02tWrVSlJ+Ph05csR+9sTGjRvl4+NT5GzO1atXk08oM/LJ/fIpNTVVWVlZRfr01FNPSZIuvfRS+/TT5VOBrVu3aubMmbriiisczqAin5ykMq8nrM5atWplbrjhBodpixYtst8JacaMGWb8+PEmJibGtGnTxpz61owePdpIMueee6557rnnzCuvvGJuvvlmM3LkSPsyb775ppFkWrVqZSZNmmRmzJhh7rzzTodrWL/77jv7dcQzZswwDz/8sImNjTWNGjUq9vrk4sYSGDNmjJFkhg4dal5//XVz7733msjISNOwYUOHbeTm5pqePXsaSWbgwIFm2rRpZsqUKaZ3795F7g6VlZVlv9a3b9++RdpUGe/w8Ouvvxpvb28TExNjJkyYYKZNm2b69u1rJJnbbrvNvtyuXbtMWFiYCQgIMNOmTTPvvvuuw7+//vrLvuzOnTuNpGKv6z527Jjx9fU1AwcOLLFPV111lZFkbrnlliLtlHS99B9//GGsVqvD+DHGGNOpUyfTvn178+qrr5p27dqZLl26OMxPTEw03t7e5s033zztawUURkZVXkZdfPHF5plnnjFvvvmmue2224y3t7fp06dPkTELVq1aZaxWq2nfvr2ZMWOGGTVqlPH39ze9e/d2WK7gtRg7dmypbZc0BtVTTz1lJJn27dubqVOnmueee840b97cSDLvvfdesduaMmWKady4scO4GYmJiSYwMNBcd911ZurUqSYyMtKMGDHCYb358+cbSWbbtm2ne6kAO/LJ/fLpVCU959Lyad26dUaSw/twqrZt2xpJZsSIEUX2oX744Ydi15k3b56JiIhwGHc0MzPT1K5d21x00UXm1VdfNfXr1y9yR6+VK1caSebHH38s9bkChZFP7pVPP//8s4mNjTUPPPCAmTZtmnnuuefMeeedZySZO+64w2GbJeVT8+bNzZgxY8ybb75pRo0aZSIjI029evXMvn37HJYjn5yDAlUleeGFF0xwcHCRQc3eeust07hxY2O1Wk2zZs3M7Nmz7QcRp5o1a5Zp3769sVqtJiIiwvTo0cMsXLjQYZkvvvjCnHvuuSYgIMCEhoaac845x3zwwQcOyzz//POmdu3axmq1mvPOO8+sXLmyxAH0iguvzMxM89BDD5latWqZgIAAc95555lly5YV2YYx+bdYHTVqlGnQoIHx9fU1sbGx5tprrzXbt28vst277rrLSDLvv/9+kXllDS9j8os7ffv2NbGxscbX19c0adLETJw4sUh4qdDAf6f+KxxUpRWoCgb7++KLL0rsT7169Upsp169ekWWt9lspkuXLubBBx8sMm/btm2me/fuJjg42HTv3r3I6zhjxgwTGBhYpgHYgcLIqMrJqG3btpnevXub6Oho+2s6efLkEgdIXbJkiTn33HONv7+/qVGjhrn77ruLfL7PtkBljDFz584155xzjgkPDzcBAQGmS5cuDrdsLuzQoUMmJCSk2Nz79ttvTbNmzUx4eLi5+eabTXp6usP866+/3px//vml9hM4FfnknvlU2JkUqEaOHGkkmb///rvE7Za2r1bcc8rIyDD16tUzL7/8cpF5f/75p+nQoYMJCQkxV155pTl8+LDD/EcffdTUrVvX2Gy20z5foAD55F75tGPHDnPdddeZ+vXrG39/fxMYGGg6duxoZs6cWeSzXVI+DRw40MTHxxs/Pz8TFxdn7rzzTpOYmFhs38mnimcxxkkjtcFBSkqKGjZsqClTpujWW291dXfc0gMPPKC33npLhw4dUmBgoKu745Hat2+vnj172i/fAcqKjDo9MursHDp0SA0aNNCHH36oq666ytXdgQchn06PfDo7WVlZql+/vkaOHKnhw4e7ujvwIOTT6ZFPZ6e65RNjUFWSsLAwjRgxQs8+++wZ3QWhqsvMzNR7772n//znPwTXGfruu++0detWPfbYY67uCjwQGVU6Mursvfjii2rdujXFKZQb+VQ68unszZ49W76+vrrzzjtd3RV4GPKpdOTT2atu+cQZVHCpw4cP68cff9T8+fP12WefafXq1SXexQIAKhsZBcBdkU8A3BX5hDPl4+oOoHrbsGGDBg0apJiYGL388ssEFwC3QkYBcFfkEwB3RT7hTHEGFQAAAAAAAFyKMagAAAAAAADgUtXuEj+bzaYDBw4oJCREFovF1d0BqhRjjNLS0hQXFycvL+rf5UU+Ac5DPp0d8glwLjLqzJFPgHNVZj5VuwLVgQMHFB8f7+puAFXa3r17VadOHVd3w+OQT4DzkU9nhnwCKgcZVX7kE1A5KiOfql2BKiQkRJK0e3V9hQbz64QnuPyuIa7uAsooNzdTK36ZbP+coXzIJ89zbZ8rXN0FlFGuLUu/7nqNfDpD5JPn+U+bTq7uAsoh1+RocdYCMuoMkE+e55omrV3dBZRDrnK0VN9USj5VuwJVwWmfocFeCg0hwDyBj6+/q7uAcuL06jNDPnkeH2+rq7uAciKfzgz55Hl8LH6u7gLOABlVfuST5/Gx+Lq6CyiP/7+tXmXkE59gAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuJSPqzuAkmWc8NI7U2rp92/DlHzMR41antT/ntynpu1OKjdHevuZWvrzp1Ad3O2noFCb2l+QplsfP6Co2FxJ0qG9fnp/ak2t/S1Yx4/4Kqpmji7qf1w3DE+Ur59x8bOrem68bK0u6LhLdWulKCvbW/9sq6nX53fW3kPh9mV8fXJ118A/dOE5O+Tnk6c/19fRi++dq+OpgfZlmtY/ojuu/VNN6h+VMdKmnTX02sfnaPveKBc8KyDfuuVB+nh6jLauC1RSoq/GvrVT5/ZNsc83RprzbKy+ez9KJ1K91aJTuu57eq9qN8y2L/P+SzW14sdQ7fgnQD5+Rp9uWleknTVLgvXOlFratclf/oE29bouScNGHpQ331ZnpVXbo/rPwK1KaJqiqOhMPfn4OVq2NM4+/9zuB3TZVTuV0CRZoWE5uueWntqxLdxhG32u3KWevfYqoUmKAoNydd1llyn9hF8lPxOgqIrIp33brXrjyTht+DNIuTkWNWh+UjePOKR2550o0l5qkrf+d0lTHT3op082rlNwWF6lPM/q6ro7D+iWEXv12exYvfZkPfv0Zu3TNOShfWrW7oRsedL2jUF6YkgzZWfx+zvcx+nyaek3Yfp6TpS2rgtU2nEfTf9hsxq1OumwjW/ei9LPCyK0bV2AMk54l5g7f/wYqrlTa2rnxgD5WW1q3TVd42bvdPpzrG5adTmh6+46osatMxQVm6txt9TXsu/Cil32vqf36fKbj2nmmDgteLNGJffUM5HgbmzqQ/FavThYI17ZrZmLNqljjzSNvD5BRw/6Kuukl7atC9SN9ydq2vdbNObNndq33aqxQxva19+7zSqbTRr+zD69/vMm/Xfcfn39bpRmT67lwmdVdbVtekif/dRCdz/VT48831c+3jZNefA7+fvl2Je5+4bl6tZ2j8ZPv1j3P3OFosIzNOHuH+3z/a05eubB75SYFKS7nuqn+yZfqYxMX0158Dt5e9tc8bQASVJmhpcatjypeybtK3b+vGkx+nxWDd379F699NUW+Qfa9PiNjZSdabEvk5ttUfcrk3X5kKPFbmP7P/4afVNDdbowVdN+2KzHZ+7S8h/C9NbEuGKXR9n5++dp5/YwTZ/apoT5ufrn7yjNntmyxG1Y/XO1akVNffReE2d1EzgjFZFPY4Y0kC1PeubjbXr1u81q2OKkxtzcQEmHi1bHX3iorho0z3Ta88G/mrQ5octuOKwdGwMdpjdrn6an3t6s1UvDNPyalrrv6lb6ck5NGX5/hZs5XT5lZnip5TnpuvXxAyVv46SXOvVM1cB7E0tcZsnXYZpyX131vj5JMxZu1gufb9WF1xw/6/6jKP9Am3b8469XH69T6nLn9klRs47pOnqQX1nLwyMLVNOmTVP9+vXl7++vLl26aMWKFa7uUoXLOmnR0m/CddsTB9W6a7pqN8jWTQ8fUlz9LH01J0pBoTY9/dF29eiXrPiELDXvmKG7J+7T1r8DdXifrySp84VpevjFverYM0216mWr26WpuvbOw/rt2+IrvDg7j07to+9/a6JdByK0fW+Unp7VXbHRJ9Skfv7BeFBAti67YIumf9hVazbFacvuaD0zq7taNT6s5g0PS5Lq1kpWWHCWZi/oqL2HwrXrQITe+byDIsNOqmZUmiufHsqoquZT54vSNPTRQzqv0K9+BYyRPnuzhm4Yfkjn9klVwxaZGvHybh1L9NXvhX5RuvmRQ+p/xxE1aFb8gd2vX0SoQfNMDX4wUbUbZKtNt3Td9sQBfflOtDJOeOTXldtY+UdNzXmzhZYtKb7Y99MPdfXBO820ZlXJv+59/nGCPp7bRJv+iXBWN+Fk5FPx+ZRyzFv7d/hrwD2H1bBFpmo3zNYtow4q66S3dm3yd9jel+9EKT3VW9feebhSnlt15h+Yp0embtdLjzfQiRRvh3n/fWK3Pn+7pj6eGac9WwO1f2eAlnwTpZxsvis8WVXMqNLySZJ6XXtcgx9MVPvuRc/WLND/9iO6/t7DatYxo9j5ebnSzDG1dfsTB3TFzcdUp1GW6jXJUo9+yRXxFHCKlT+H5l/lVMJZU5IUFZuju57ar2furqfcXEuJy6Eoj0vxjz76SA8++KDGjh2r1atXq23btrr00kt1+HDV2lHIy7PIlmeRn9XxrBmrv03/rAgudp30VG9ZLEZBpZxqnp7mrZBwTkWvDEEB+ZcOpKZbJUlN6h2Vr49Nqzb8e4C491C4Dh0NVstGif//OEwpaVZd1n2zfLzz5Oebq8u6b9auA+E6dDSk8p8EyqW65NOpDu3xU9JhX3W44N+dq6BQm5q1z9DGVUFl3k5OtkW+p2Sen79N2Zle2vp3YAlrASgL8qnkfAqNzFOdRpn68eNIZWZ4KS9X+vrdKIVH56hxm38vtdm9xar3p8bqkZd2y+Jxe9Ce5+7xu/Tnz+Fa+5vjQWBYVI6atU9XyjFfPf/xP3p/xSpN+WCDWnbihzxPVl0zqiJsXReoowf9ZPGS7rqkiW5o11KjBjUsUmBH5bBYjEa8vEfzZ9TQ7i28B+XlcV+vL7zwgm6//XYNGzZMLVq00MyZMxUYGKhZs2YVu3xWVpZSU1Md/nmCwGCbmndM1/svxurYIR/l5UmLPonQxlVBSkoseppgdqZFb02MU8+rjysopPhLwfbv9NPns2rospuKv7wGFcdiMbrnhuVat7Wmdu2PlCRFhmUoO8dL6SetDsseTw1QZFj+DvDJTD/dP+VyXdJ1u7577W19M+MdndNqn0ZOvVQ2m8d9XKud6pJPpyq4BCa8Ro7D9PAaOcVeHlOSTj3StHFlkH5eEK68POnoQV/NnRqb30YxuQeg7MinkvPJYpGe/mi7tq8P0NWNW+uKBm316esxmjh3h/1HvewsiybfVV+3jT6gmDqO20LF63HFMTVqla7ZU+KLzKsVn38W7qDh+/XdRzEaPbSZtv0TpMnvblRcfS699FTlyaiqkk8V5dDu/PEg33s+Vjfcn6gJc3YoOCxPj/wnQanHvU+zNiragLsPKy9P+uytaFd3xSN51BFvdna2Vq1apV69etmneXl5qVevXlq2bFmx60yePFlhYWH2f/HxRb/o3NWIV3bLGOnGDq10Rf22+uytaPW8+niRX+1yc6SJ/60vGenep4u/vvnoQV+NGtRI3a9I1mWDkpzf+Wpu+ODf1KD2cU2YeVG51vPzzdWIYUu0fltN3f1UP9076Urt3B+hycN/kJ9vrpN6i4pQ3fLJGTr2TNNtow/o5ZHxuqJ+W91yfjOdc1H+TidnKwBnjnwqnTHSq4/XUXh0rp5fsE0vf71F5/ZJ0dihDXTs/4vjsyfXUt2ETF38H8Z0cbboWln675hdmvJAQrGX7BV8H3zzQYwWzq+h7RuC9PpT9bRvp796X8fZNp6ovBlVnfKpLGz/f27CDcMTdcHlKWrc5qQemrpHFou05Ktwl/atuklonaGrbzuq5+6vK4lL+86ER+3yHz16VHl5eapZs6bD9Jo1a+rQoUPFrvPYY48pJSXF/m/v3r2V0dUKEVc/W899uk2fb/tb7638R698s1W5ORbVqpdlX6agOJW430+TP9xe7NlTxw75aMR1jdSiU7qGP+s5z99T3Tfod3Vru1cPTLlcR4//e3lTUkqg/HxtCgrIclg+IvSkklICJEm9um5Xzag0PTOruzbvqqGNO2L01GsXKrZGms5rv7tSnwfKp7rlU2GRMfnF0+Qjvg7Tk4/42ueV1X/+e0Sfblqn9/78Rx+vX69uffLHbCicewDKh3wqPZ/WLg3Wih9D9diMXWp5Troatzmpeyfvk5+/0Y/zIv9/mRAt+SpcfePbqm98W40c0EiSdF2rVprzbGwlPqOqr3GrdEVE5+rVL9bpqy1/6Kstf6hN1zT1G3JIX235Q8lH89/LPVsDHNbbsy1AMXHZxW0Sbq68GVVV8qmiRNbMz7K6jf89g9DPahRbL0uH9/uWtBqcoHWXdIVH5+q9Pzfomz1/6Zs9fyk2Pke3jz2gd/7Y4OrueYQqf82E1WqV1Wo9/YJuzD/QJv9Am9KSvbXq11Dd9kT+XR4KilP7d1o1Zf42hUYWHVvq6EFfjbiukRq3zq+ke3lUSdLTGN03aJnO77BLDzxzeZExo7bsjlZOrpc6tjigxasaSJLiY5MVG31C/2zP/0K2+uXKGIvDXWhsxiIZycvCrWmqmqqQT5IUWzdbkTE5WrM02H5r5PQ0L21aE6grbi7/JcUWixQVm7+z9fOCCNWIy1ZC65OnWQtARapO+ZR1Mn/n6NR9JC+Lke3/v3pHv7lT2Zn/LrB5baBeeLCunl+wVXH1KYpUpLW/h+nOPq0dpj04ZYf2bvfXx6/F6eAeq44e8lWdho7fC3UaZOrPX8MrsadwlaqSTxWlcZsM+Vpt2rfdqlZd0iXlHycm7vVTTS5JrlQ/fhKh1Uscx4ue9P4OLfokQj98FOmiXnkWjypQRUdHy9vbW4mJjrfYTExMVGxs1fv1auUvITJGim+Upf07/fTmk7UVn5Cp3tcfU26O9OTtDbRtXYAmzNkhW57FPpZCSHiefP2Mjh701SPXJiimdrZuH3NAKcf+fbvLe1YDTu/+wb/r4q7b9cTLlygj01cRofl32kg/6afsHB+ln/TTN0ua6H/X/6HUdKsyTvrp3kG/a/22GG3cESNJWvlPbd05YIXuH/y7Pl3UQl4W6YbL/lKezUtrNhV/9y24h6qeTyfTvXRg5787g4f2+mn7+gCFhOcqpk6Orr7tiD54qaZqN8hSbN1svTOllqJq5ujcPv/etebwPl+lJfvo8H5f2fKk7evzf/2Oa5ClgKD8sz8/nl5DnS5Mk8VL+u2bMM2bFqNRM3fLmyEUzop/QK7iav87SHTNWhlqmJCstFQ/HTkcqOCQbMXUzFBkdP6vr3Xq5i97PMlfx5PyB/iMiMxURGSm4mrn7/zWb5iqkxk+OpwYqBNpfpX8jFAe5FPp+dS8Y7qCw/L07PC6GvTAIVn9jb6dG6VDe/10zsX5lxmfWoRKScrfp6rbOEvBpdycBuV3Mt1bu7c43hgjM8NLacm+9umfvFFLg+/fr52bArV9Q5B69T+iOo1OauLdjV3RZZylqpxRp8un1OPeOrLfz3458d7t+ctGxOTYj9eSDvvo+GFfHdiZ/127c5O/AoNsqlE7W6EReQoKsenym47p3edjVSMuRzF1sjV/Rv6xxQVXJFfis60e/APzFNfg3++E2PhsNWx5UmnJ+e9l2nHHEkturkXHD/tq33YGTC8LjypQ+fn5qWPHjlq0aJGuvvpqSZLNZtOiRYt0zz33uLZzTpCe6q3Zk2vp6EFfhYTn6bzLkjVs5EH5+OaH2/If8u9qctclzRzWmzJ/m9qee0KrF4fowE6rDuy0alDHlg7LfH9gbWU9jWrjqos2SpJeHPm1w/Sn3+qu739rIkma9kFXGfOHxt+1SL6+efpzfW29+O559mX3HgrX4y9doiFXrdG0UV/KZrNo254ojXihj5JSuIuZO6vq+bTlr0CNuDbB/vi1cbUlSZcMSNLDL+7RgLsPKzPDSy+NiNeJVG+17JyuiXN3yM//3zP/5jxXSwvn/fvr0V29m0r6N7Mk6c+fQ/XBy7HKybaoYYuTGjd7pzpfxJ2Zzlbjpsf1zMu/2R/fce96SdLCb+M1dXJHdT3voB58fI19/shxKyVJc2c31dzZzSVJl121U4OGbbYv8+yrSyVJL0xqrx+/q+f054AzRz6Vnk9hUXma+P52vf10LT06IEF5ORbVa5qpcbN3qlFLBt12R5/NriVfq9Edo/YoJDxXOzYGatTNzXVwDweAnqgqZ9Tp8mn5D2F6/oG69vmT/1dfkjT4wUO66eH8yxu/nhOt9174t1D38DX5hdiHpu5R7+vzxxa+ffR+eXsbTbmvrrIzvdS0fYae+Xg7d293giZtT+rZT7bbH985Pv/qph8+inB4L3FmLMYYj7pu6KOPPtKQIUP02muv6ZxzztGLL76oefPmadOmTUWuWy5OamqqwsLCdHxLQ4WGcL2bJ7jwlttd3QWUUW5Opn7/caxSUlIUGhrq6u5UOvKp+rms+zWu7gLKKDcvS4t2vEw+kU/VRt+GXV3dBZRDrsnWT5nzyKgzyCjyyfNcGtfO1V1AOeSaHP2izyslnzzqDCpJuv7663XkyBGNGTNGhw4dUrt27fTdd9+VaecKAJyJfALgrsgnAO6MjAIgeWCBSpLuuecejz/dE0DVRD4BcFfkEwB3RkYB4BxIAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4lI8zNvrFF1+Uedl+/fo5owsAUCIyCoC7Ip8AuCvyCYCzOaVAdfXVV5dpOYvFory8PGd0AQBKREYBcFfkEwB3RT4BcDanFKhsNpszNgsAFYKMAuCuyCcA7op8AuBslToGVWZmZmU2BwDlQkYBcFfkEwB3RT4BqChOL1Dl5eXpySefVO3atRUcHKwdO3ZIkkaPHq233nrL2c0DQKnIKADuinwC4K7IJwDO4PQC1cSJE/X2229rypQp8vPzs09v1aqV3nzzTWc3DwClIqMAuCvyCYC7Ip8AOIPTC1Rz5szR66+/rkGDBsnb29s+vW3bttq0aZOzmweAUpFRANwV+QTAXZFPAJzB6QWq/fv3KyEhoch0m82mnJwcZzcPAKUiowC4K/IJgLsinwA4g9MLVC1atNCSJUuKTJ8/f77at2/v7OYBoFRkFAB3RT4BcFfkEwBn8HF2A2PGjNGQIUO0f/9+2Ww2ffrpp9q8ebPmzJmjr776ytnNA0CpyCgA7op8AuCuyCcAzuD0M6iuuuoqffnll/rxxx8VFBSkMWPGaOPGjfryyy91ySWXOLt5ACgVGQXAXZFPANwV+QTAGZx+BpUkXXDBBVq4cGFlNAUA5UZGAXBX5BMAd0U+AaholVKgkqSVK1dq48aNkvKvWe7YsWNlNQ0Ap0VGAXBX5BMAd0U+AahITi9Q7du3TzfccIN+++03hYeHS5KSk5N17rnn6sMPP1SdOnWc3QUAKBEZBcBdkU8A3BX5BMAZnD4G1W233aacnBxt3LhRSUlJSkpK0saNG2Wz2XTbbbc5u3kAKBUZBcBdkU8A3BX5BMAZnH4G1a+//qrff/9dTZs2tU9r2rSpXnnlFV1wwQXObh4ASkVGAXBX5BMAd0U+AXAGp59BFR8fr5ycnCLT8/LyFBcX5+zmAaBUZBQAd0U+AXBX5BMAZ3B6gerZZ5/Vvffeq5UrV9qnrVy5UsOHD9dzzz3n7OYBoFRkFAB3RT4BcFfkEwBncMolfhEREbJYLPbH6enp6tKli3x88pvLzc2Vj4+PbrnlFl199dXO6AIAlIiMAuCuyCcA7op8AuBsTilQvfjii87YLABUCDIKgLsinwC4K/IJgLM5pUA1ZMgQZ2wWACoEGQXAXZFPANwV+QTA2Zx+F7/CMjMzlZ2d7TAtNDS0MrsAACUiowC4K/IJgLsinwBUFKcPkp6enq577rlHMTExCgoKUkREhMM/AHAlMgqAuyKfALgr8gmAMzi9QDVixAj99NNPmjFjhqxWq958802NHz9ecXFxmjNnjrObB4BSkVEA3BX5BMBdkU8AnMHpl/h9+eWXmjNnjnr27Klhw4bpggsuUEJCgurVq6e5c+dq0KBBzu4CAJSIjALgrsgnAO6KfALgDE4/gyopKUkNGzaUlH8tclJSkiTp/PPP1+LFi53dPACUiowC4K7IJwDuinwC4AxOL1A1bNhQO3fulCQ1a9ZM8+bNk5RfdQ8PD3d28wBQKjIKgLsinwC4K/IJgDM4vUA1bNgw/fXXX5KkkSNHatq0afL399cDDzygRx55xNnNA0CpyCgA7op8AuCuyCcAzuD0MageeOAB+//36tVLmzZt0qpVq5SQkKA2bdo4u3kAKBUZBcBdkU8A3BX5BMAZnF6gOlW9evVUr169ym4WAMqEjALgrsgnAO6KfAJQEZxSoHr55ZfLvOx9993njC4AQInIKADuinwC4K7IJwDOZjHGmIreaIMGDcrWuMWiHTt2VHTzpUpNTVVYWJh66ir5WHwrtW2cGe+mCa7uAsooNy9Li7ZOVUpKikJDQ13dnRK5a0aRT57HO6Fsf0twvdy8LC3a8TL5dIbIJ8/jUzvO1V1AOeTasvTjgdfcOqPcPZ8utA4gnzyEV3iYq7uAcsi1ZWvR4TcrJZ+ccgZVwR0dAMAdkVEA3BX5BMBdkU8AnM3pd/EDAAAAAAAASkOBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5VKQWqJUuWaPDgwerWrZv2798vSXr33Xe1dOnSymgeAEpFRgFwV+QTAHdFPgGoaE4vUH3yySe69NJLFRAQoDVr1igrK0uSlJKSokmTJjm7eQAoFRkFwF2RTwDcFfkEwBmcXqB66qmnNHPmTL3xxhvy9fW1Tz/vvPO0evVqZzcPAKUiowC4K/IJgLsinwA4g9MLVJs3b1b37t2LTA8LC1NycrKzmweAUpFRANwV+QTAXZFPAJzB6QWq2NhYbdu2rcj0pUuXqmHDhs5uHgBKRUYBcFfkEwB3RT4BcAanF6huv/12DR8+XH/88YcsFosOHDiguXPn6uGHH9b//vc/ZzcPAKUiowC4K/IJgLsinwA4g4+zGxg5cqRsNpsuvvhiZWRkqHv37rJarXr44Yd17733Ort5ACgVGQXAXZFPANwV+QTAGSzGGFMZDWVnZ2vbtm06ceKEWrRooeDg4MpotojU1FSFhYWpp66Sj8X39CvA5bybJri6Cyij3LwsLdo6VSkpKQoNDXV1d8rFHTKKfPI83gkNXN0FlFFuXpYW7XiZfDpD5JPn8akd5+ouoBxybVn68cBrHpdR7pRPF1oHkE8ewis8zNVdQDnk2rK16PCblZJPTj+DqoCfn59atGhRWc0BQLmQUQDcFfkEwF2RTwAqktMLVBdeeKEsFkuJ83/66SdndwEASkRGAXBX5BMAd0U+AXAGpxeo2rVr5/A4JydHa9eu1fr16zVkyBBnNw8ApSKjALgr8gmAuyKfADiD0wtUU6dOLXb6uHHjdOLECWc3DwClIqMAuCvyCYC7Ip8AOIOXqxoePHiwZs2a5armAaBUZBQAd0U+AXBX5BOAs+GyAtWyZcvk7+/vquYBoFRkFAB3RT4BcFfkE4Cz4fRL/Pr37+/w2BijgwcPauXKlRo9erSzmweAUpFRANwV+QTAXZFPAJzB6QWqsLAwh8deXl5q2rSpJkyYoN69ezu7eQAoFRkFwF2RTwDcFfkEwBmcWqDKy8vTsGHD1Lp1a0VERDizKQAoNzIKgLsinwC4K/IJgLM4dQwqb29v9e7dW8nJyc5sBgDOCBkFwF2RTwDcFfkEwFmcPkh6q1attGPHDmc3AwBnhIwC4K7IJwDuinwC4AxOL1A99dRTevjhh/XVV1/p4MGDSk1NdfgHAK5ERgFwV+QTAHdFPgFwBqeNQTVhwgQ99NBDuuyyyyRJ/fr1k8Visc83xshisSgvL89ZXQCAEpFRANwV+QTAXZFPAJzJaQWq8ePH684779TPP//srCYA4IyRUQDcFfkEwF2RTwCcyWkFKmOMJKlHjx7OagIAzhgZBcBdkU8A3BX5BMCZnDoGVeHTPQHA3ZBRANwV+QTAXZFPAJzFaWdQSVKTJk1OG2BJSUnO7AIAlIiMAuCuyCcA7op8AuAsTi1QjR8/XmFhYc5sAgDOGBkFwF2RTwDcFfkEwFmcWqAaOHCgYmJinNkEAJwxMgqAuyKfALgr8gmAszhtDCquTQbgzsgoAO6KfALgrsgnAM7ktAJVwR0eAMAdkVEA3BX5BMBdkU8AnMlpl/jZbDZnbRoAzhoZBcBdkU8A3BX5BMCZnHYGFQAAAAAAAFAWFKgAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSFKgAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSFKgAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSFKgAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSPq7uAM6cl5fR4IcO6eL/JCuiRo6OJfpq4bxIvf9ijCSLq7tX7bRqc0T/uX6LEpokKyo6U08+0VXLfqttn3/uBft12ZU7lNAkWaFh2brntou1Y3u4fX5wSLYGD92gDp0SVaNmhlKSrVr2W5zendVSGem+LnhGwJkjn9xLq7ZH9Z+BW5XQNCU/nx4/R8uWxtnnn9v9gC67auf/51OO7rmlp3ZsC7fPDw7J1uBbNqpD5yP/5tOSWnr3rebkE9ze9fck6rzLUhSfkKXsTC9tWBmotybW0r7t/vZlfK023TH2gHr2S5av1WjVLyF65bHaSj7K33dlaNk+Sf+5aYcSmqUqqkaWnny4g5b/WrPQEkaD/7tVl169T0HBOdr4d4SmPd1SB/YG2ZcIDs3WnY9sUJfzD8tmLPr9p1i99nxzZZ7kcAfu7fJBibpi8GHF1M6SJO3ZGqC5L9fWyl/DVbN2lt5Z+lex6028O0FLvomszK5C+fu4g+7crgsvO6iIqGwlHbHqxy/j9MEbDVSwjzvov9vV/dJDqhGbqZwcL23bGKo5ryZo8/ow13beA3jUGVSLFy/WlVdeqbi4OFksFn322Weu7pJLDbj7sK4YckzTRtXW7T2a6a2JtXTdXYd11a1HXd21asnfP087t4dr+kvtSpifq3/WR2v2662KnR8VdVJR0Sf15szW+t8tl2jqM53UqXOi7n9klRN7jYpERv2LfHIv+fkUpulT25QwP1f//B2l2TNbFjs/KjpTUdGZenN6S/1vyMWaOrmDOnVJ1P2PrnFmt1GBqnM+temWri/fjtb9VzTWYwMbytvHaNIHO2QNyLMvc+e4A+p6Saqe+m89Pdy/kSJr5mjMW7tc1+lqxj8gTzu3hGrGlBbFzr/25h268vrdmja5pR4c1k2ZJ7315Ct/ytfv3/fwkSf/Ur2GJ/TEPedo/AMd1bJ9ku59fH1lPQWcheqcT5J09JCfZj0Tr3v7tdJ9V7XU2mWhGvv6VtVrnKEjB/10Q+d2Dv/mvFBbGSe89OcvFDtc4dqhu3TZtfs04+lm+m//czXr5cb6z5Bd6nfDXvsy+3cHasYzzXTXdd30yLDOOnzAX09NX63QiGwX9twzeNRPCunp6Wrbtq1uueUW9e/f39XdcbkWndK17PswrVgUKklK3OenC69OVtN2GS7uWfW0ckWsVq6ILXH+TwvrSZJiaqYXO3/3rjBNHNvN/vjQgWC981ZLPfL4n/Lysslm86h6crVERv2LfHIvK/+oqZV/1Cxx/k8/1JUkxcSWkE87QzVxdBf740MHgvTOGy30yBOr5OVtky2PfHJ31TmfRg1q6PD4+fvrat76f9S4zUmt/yNYgSF5uvSGJD19d1399VuIJOmFB+P15uLNatYhXZtWBxW3WVSgVb/X0Krfa5Qw1+iqG3bro1mNtHxxfo49P7aN5n7/k7r1SNTihXGKr39Cnc49quE3n6ttG/MP2l97roXGvbhSb73UTElH/UvYNtxBdc4nSfpjUYTD43eei9cVgw6rWft07d4aqONH/Rzmn3vpcS35OlKZGd6V2U38vxZtk7X81xr6c2l+Zh0+GKCefQ6pScsU+zK/fFfLYZ3Xn2+qS685oAaN0/TXiqhK7a+n8agCVd++fdW3b19Xd8NtbFgZpL6Dj6l2wyzt32FVwxYn1fKcdL02Lu70K8MjBAXlKCPDh+KUhyCj/kU+VX32fKI45RHIp38FheafdZOWnH9w17hNhnz9jNYsCbEvs3ebvxL3+ap5xwwKVC4WW/ukIqOztHZFtH1aRrqvNv8TpmZtkrV4YZyatT6uE6k+9uKUJK1ZESVjs6hpq2Qt+6XkHxDheuTTv7y8jC64LEnWAJs2rg4uMj+hVboSWmZo2ph6LugdJGnDX+Hq+599ql03Xfv3BKlBkzS1aJesN55vUuzyPj429e2/TyfSfLRzS0ixy+BfHlWgOhNZWVnKysqyP05NTXVhbyrWR6/GKDAkT28u3iRbnuTlLb39dKx+XhBx+pXh9kJDs3TDTZv07VcNXN0VOAn5BE8VGpalG4Zs1rdf1Hd1V+AkVTWfLBajO8fv1/oVgdq9OUCSFBmTq+wsi9JTHc9GSD7io8iYHFd0E4VEROX/HR4/5ngWSfIxq31eRFS2ko9bHebb8ryUluprXwZVR1XMp/pNMzT1kw3ys9p0MsNbT97ZWHu2BRRZ7tIBR7R7q782rqbQ4Sofz66vwOBcvbbgd9nyLPLyNpozLUG/fOt41tQ5FxzRo0+vk9U/T0lHrRp1ZwelJvuVsFUUqPI/e06ePFlhYWH2f/Hx8a7uUoXp3i9ZF/VP1tN319XdlzbRc8Pjde2dR9TruiRXdw1nKSAwR+Of/k17dodo7tvFj8cAz0c+wRMFBOZo/DPLtWdXiObObubq7sBJqmo+3TNpv+o1y9Tk/3H2AeCpqmI+7dvhr7sub6Xh17TU1+/F6KHndqhuwkmHZfysNl141TF9P6+ky2FRGS7onagL+x7UlMdb674bu+iFMS3V/6bduvjKAw7L/fVnpO4Z2FUPDe2sVb9H6bEpfyuMMahOq8oXqB577DGlpKTY/+3du/f0K3mI20cf1EevxujXzyO0a1OAFn0SqU/fqKGB9x52dddwFgICcvTkM0uVkeGjJ0d3Ux6Xz1RZ5BM8TUBAjp58bll+Pj3RhXyqwqpiPt09cZ+6XJKqEdc20tGD//6KnXTYR35WY7/0r0B4jVwlHeYufq52/Fj+mVERUY4HduFRWfZ5x4/5KTzC8UwpL2+bQkJz7Mug6qiK+ZSb46WDu/21bX2QZj8br50bA3X1sEMOy1xwWZKs/jYt+jS6hK2gMtx6/xZ9PLuBFn8fq13bQvTT13H6bG5dDRi202G5rExvHdwbqM3rwvXS+JbKy7Po0mv2u6jXnqPKX+JntVpltVbNLyarv03G5jjNlpd/+jo8U0Bgjp6aslQ5OV6aMOpc5eQw+GFVRj7BkwQE5uip537Pz6fHuignm3yqyqpWPhndPXG/zu2TokeuTVDiXsfntfXvQOVkW9T+/DQt/SZcklSnUaZq1snRxlWBLugvCju0P0BJR61q2/mYdmzJv/FGQFCOmrZM0Tfz82/wsGldhIJDc5XQLEXbNuWPQ9W20zFZvIw2rw93VdfhJFUrn4pn8TLy9XPcZ7p0wBEtXxSulCQK565k9bfJdsrurM1mkddpfrPzski+vrbSF0LVL1BVZcsXhmrgfYd1eL+fdm/2V6NWJ9X/v0f0w4eRru5ateTvn6u42ifsj2vWylDDRslKS/PTkcOBCg7JVkxMhiKj80/XrVM3TZJ0PMlfx4/7KyAwRxOfXSqrNVfPTuqmwMBcBQbmSpJSUqyy2SyV/6SAM0Q+uRf/gGLyKSFZaamF8qlmhiKjMyVJdermL3s8yV/Hk/4/n57/XVb/PD37VCcFBuUqMOj/8ymZfIJ7u2fSfl14zXGNG9ZAJ094KaJG/rhS6Wneys70Ukaat77/IFJ3jDugtGQfpad56e6J+7VhZSADpFcS/4BcxcX/e5fX2LgMNWySqrQUXx1JDNDnH9TTwFu26cDeQB3aH6ib7tyipKNWLfs1/65+e3cFa+Xv0bp31HpNm9xS3j5G/3tkgxb/UIs7+MHtDXtkr/78NUxH9lsVEJynC/sdU5uuaRo15N8by9Sql6lW56Rp9LDiB+JG5fljcbQG3rpTRw76a/f2YDVqlqZrBu/WD5/VliRZ/fM08LYdWv5rDR0/alVoeI6uGLBXUTFZWrKw5DsqI59HFahOnDihbdu22R/v3LlTa9euVWRkpOrWrevCnrnG9Cdqa8iIQ7pn8j6FR+XqWKKvvnk3SnOn8ofvCo2bHtczLy62P77j7r8lSQu/q6epz3RS13MP6MGRq+zzR45ZIUma+3ZzzX2nhRIaJ6tZi/zxeWbN/d5h20MH9tHhRHaS3R0Z9S/yyb00bnpcz7z8m/3xHfeulyQt/DZeUyd3VNfzDurBx9fY548ct1KSNHd2U82d3VwJTZLVrOVxSdKsDxc6bHvogEt0+BD55O6qcz5dOfSYJOm5T7c7TH/u/ngtnJdfNJ85Lk42I41+Y5d8rUYrfwnRq4/VrvS+VleNm6fo6ddW2B/f/uAmSdKPX9XW1PFtNH9OQ/kH5Onex9crKDhXG/6K0Oj7Ojucyfns6Lb63yMbNHH6Chlj0W8/1dRrzzGOpyeozvkkSeFROXrk+R2KqJGjjDRv7dwUqFFDmmrN0n/vSnnpdUd09KCfVi8JK2VLqAwzn2mmm+7arrsf36SwiGwlHbHq2/l19P7rDSVJNptUp36GRl35t8LCs5Wa4qst/4TpkVs6ac+OondmhCOLMcZjrrf45ZdfdOGFFxaZPmTIEL399ttl2kZqaqrCwsLUU1fJx8LpkZ7Au2mCq7uAMsrNy9KirVOVkpKi0NBQV3en0p1tRpFPnsc7gbtseorcvCwt2vEy+XQK8qnq8qkdd/qF4DZybVn68cBr1TKjKiqfLrQOIJ88hFc4hTZPkmvL1qLDb1ZKPnnUGVQ9e/aUB9XTAFQzZBQAd0U+AXBX5BOAAtx+BwAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC5FgQoAAAAAAAAuRYEKAAAAAAAALkWBCgAAAAAAAC7l4+oOVDZjjCQpVzmScXFnUCYmL8vVXUAZ5f7/e1XwOUP5kE+eh3zyHLk28ulskE8eyEY+eZJcW7YkMupM2PPJ5Li4Jygrr///e4dnqMx8qnYFqrS0NEnSUn3j4p6gzLa6ugMor7S0NIWFhbm6Gx6HfPJAO1zdAZQX+XRmyCcPdMDVHcCZIKPKryCflmQvcHFPUGaHXd0BnInKyCeLqWZlepvNpgMHDigkJEQWi8XV3akwqampio+P1969exUaGurq7uA0qur7ZYxRWlqa4uLi5OXFFcTlRT7BHVTV94t8OjvkE9xBVX6/yKgzRz7BXVTV96wy86nanUHl5eWlOnXquLobThMaGlqlPgxVXVV8v/jV78yRT3AnVfH9Ip/OHPkEd1JV3y8y6syQT3A3VfE9q6x8ojwPAAAAAAAAl6JABQAAAAAAAJeiQFVFWK1WjR07Vlar1dVdQRnwfqE64e/ds/B+oTrh792z8H6hOuHv3fPwnp29ajdIOgAAAAAAANwLZ1ABAAAAAADApShQAQAAAAAAwKUoUAEAAAAAAMClKFABAAAAAADApShQVQHTpk1T/fr15e/vry5dumjFihWu7hJKsHjxYl155ZWKi4uTxWLRZ5995uouAU5FPnkO8gnVERnlOcgoVDfkk+cgnyoOBSoP99FHH+nBBx/U2LFjtXr1arVt21aXXnqpDh8+7OquoRjp6elq27atpk2b5uquAE5HPnkW8gnVDRnlWcgoVCfkk2chnyqOxRhjXN0JnLkuXbqoc+fOevXVVyVJNptN8fHxuvfeezVy5EgX9w6lsVgsWrBgga6++mpXdwVwCvLJc5FPqA7IKM9FRqGqI588F/l0djiDyoNlZ2dr1apV6tWrl32al5eXevXqpWXLlrmwZwCqO/IJgDsjowC4K/IJ1RkFKg929OhR5eXlqWbNmg7Ta9asqUOHDrmoVwBAPgFwb2QUAHdFPqE6o0AFAAAAAAAAl6JA5cGio6Pl7e2txMREh+mJiYmKjY11Ua8AgHwC4N7IKADuinxCdUaByoP5+fmpY8eOWrRokX2azWbTokWL1K1bNxf2DEB1Rz4BcGdkFAB3RT6hOvNxdQdwdh588EENGTJEnTp10jnnnKMXX3xR6enpGjZsmKu7hmKcOHFC27Ztsz/euXOn1q5dq8jISNWtW9eFPQMqHvnkWcgnVDdklGcho1CdkE+ehXyqOBZjjHF1J3B2Xn31VT377LM6dOiQ2rVrp5dfflldunRxdbdQjF9++UUXXnhhkelDhgzR22+/XfkdApyMfPIc5BOqIzLKc5BRqG7IJ89BPlUcClQAAAAAAABwKcagAgAAAAAAgEtRoAIAAAAAAIBLUaACAAAAAACAS1GgAgAAAAAAgEtRoAIAAAAAAIBLUaACAAAAAACAS1GgAgAAAAAAgEtRoAIAAAAAAIBLUaDCWRs6dKiuvvpq++OePXvq/vvvr/R+/PLLL7JYLEpOTi5xGYvFos8++6zM2xw3bpzatWt3Vv3atWuXLBaL1q5de1bbAVB+5FPpyCfAtcio0pFRgOuQT6Ujn5yDAlUVNXToUFksFlksFvn5+SkhIUETJkxQbm6u09v+9NNP9eSTT5Zp2bIEDoCqhXwC4M7IKADuinxCVefj6g7Aefr06aPZs2crKytL33zzje6++275+vrqscceK7Jsdna2/Pz8KqTdyMjICtkOgKqLfALgzsgoAO6KfEJVxhlUVZjValVsbKzq1aun//3vf+rVq5e++OILSf+esjlx4kTFxcWpadOmkqS9e/dqwIABCg8PV2RkpK666irt2rXLvs28vDw9+OCDCg8PV1RUlEaMGCFjjEO7p57+mZWVpUcffVTx8fGyWq1KSEjQW2+9pV27dunCCy+UJEVERMhisWjo0KGSJJvNpsmTJ6tBgwYKCAhQ27ZtNX/+fId2vvnmGzVp0kQBAQG68MILHfpZVo8++qiaNGmiwMBANWzYUKNHj1ZOTk6R5V577TXFx8crMDBQAwYMUEpKisP8N998U82bN5e/v7+aNWum6dOnl7svQHVCPp0e+QS4Dhl1emQU4Brk0+mRT56LAlU1EhAQoOzsbPvjRYsWafPmzVq4cKG++uor5eTk6NJLL1VISIiWLFmi3377TcHBwerTp499veeff15vv/22Zs2apaVLlyopKUkLFiwotd2bb75ZH3zwgV5++WVt3LhRr732moKDgxUfH69PPvlEkrR582YdPHhQL730kiRp8uTJmjNnjmbOnKl//vlHDzzwgAYPHqxff/1VUn7I9u/fX1deeaXWrl2r2267TSNHjiz3axISEqK3335bGzZs0EsvvaQ33nhDU6dOdVhm27Ztmjdvnr788kt99913WrNmje666y77/Llz52rMmDGaOHGiNm7cqEmTJmn06NF65513yt0foLoin4oinwD3QUYVRUYB7oF8Kop88mAGVdKQIUPMVVddZYwxxmazmYULFxqr1Woefvhh+/yaNWuarKws+zrvvvuuadq0qbHZbPZpWVlZJiAgwHz//ffGGGNq1aplpkyZYp+fk5Nj6tSpY2/LGGN69Ohhhg8fbowxZvPmzUaSWbhwYbH9/Pnnn40kc/z4cfu0zMxMExgYaH7//XeHZW+99VZzww03GGOMeeyxx0yLFi0c5j/66KNFtnUqSWbBggUlzn/22WdNx44d7Y/Hjh1rvL29zb59++zTvv32W+Pl5WUOHjxojDGmUaNG5v3333fYzpNPPmm6detmjDFm586dRpJZs2ZNie0C1Qn5VDzyCXAPZFTxyCjA9cin4pFPVQdjUFVhX331lYKDg5WTkyObzaYbb7xR48aNs89v3bq1wzXJf/31l7Zt26aQkBCH7WRmZmr79u1KSUnRwYMH1aVLF/s8Hx8fderUqcgpoAXWrl0rb29v9ejRo8z93rZtmzIyMnTJJZc4TM/Ozlb79u0lSRs3bnTohyR169atzG0U+Oijj/Tyyy9r+/btOnHihHJzcxUaGuqwTN26dVW7dm2Hdmw2mzZv3qyQkBBt375dt956q26//Xb7Mrm5uQoLCyt3f4Dqgnw6PfIJcB0y6vTIKMA1yKfTI588FwWqKuzCCy/UjBkz5Ofnp7i4OPn4OL7dQUFBDo9PnDihjh07au7cuUW2VaNGjTPqQ0BAQLnXOXHihCTp66+/dggNKf+a64qybNkyDRo0SOPHj9ell16qsLAwffjhh3r++efL3dc33nijSJh6e3tXWF+BqoZ8Kh35BLgWGVU6MgpwHfKpdOSTZ6NAVYUFBQUpISGhzMt36NBBH330kWJiYopUmAvUqlVLf/zxh7p37y4pv4q8atUqdejQodjlW7duLZvNpl9//VW9evUqMr+gup+Xl2ef1qJFC1mtVu3Zs6fEqnzz5s3tgwEWWL58+emfZCG///676tWrp1GjRtmn7d69u8hye/bs0YEDBxQXF2dvx8vLS02bNlXNmjUVFxenHTt2aNCgQeVqH6jOyKfSkU+Aa5FRpSOjANchn0pHPnk2BkmH3aBBgxQdHa2rrrpKS5Ys0c6dO/XLL7/ovvvu0759+yRJw4cP19NPP63PPvtMmzZt0l133aXk5OQSt1m/fn0NGTJEt9xyiz777DP7NufNmydJqlevniwWi7766isdOXJEJ06cUEhIiB5++GE98MADeuedd7R9+3atXr1ar7zyin1QujvvvFNbt27VI488os2bN+v999/X22+/Xa7n27hxY+3Zs0cffvihtm/frpdffrnYwQD9/f01ZMgQ/fXXX1qyZInuu+8+DRgwQLGxsZKk8ePHa/LkyXr55Ze1ZcsWrVu3TrNnz9YLL7xQrv4AKBn5RD4B7oyMIqMAd0U+kU8exbVDYMFZCg+gV575Bw8eNDfffLOJjo42VqvVNGzY0Nx+++0mJSXFGJM/YN7w4cNNaGioCQ8PNw8++KC5+eabSxxAzxhjTp48aR544AFTq1Yt4+fnZxISEsysWbPs8ydMmGBiY2ONxWIxQ4YMMcbkD/r34osvmqZNmxpfX19To0YNc+mll5pff/3Vvt6XX35pEhISjNVqNRdccIGZNWtWuQfQe+SRR0xUVJQJDg42119/vZk6daoJCwuzzx87dqxp27atmT59uomLizP+/v7m2muvNUlJSQ7bnTt3rmnXrp3x8/MzERERpnv37ubTTz81xjCAHnAq8ql45BPgHsio4pFRgOuRT8Ujn6oOizEljHwGAAAAAAAAVAIu8QMAAAAAAIBLUaACAAAAAACAS1GgAgAAAAAAgEtRoAIAAAAAAIBLUaACAAAAAACAS1GgAgAAAAAAgEtRoAIAAAAAAIBLUaACAAAAAACAS1GgAgAAAAAAgEtRoAIAAAAAAIBLUaACAAAAAACAS/m4ugNnw2az6cCBAwoJCZHFYnF1dwCUkzFGaWlpiouLk5dX1aqXk0+A5yOjALgr8gmAuzqbfPLoAtWBAwcUHx/v6m4AOEt79+5VnTp1XN2NCkU+AVUHGQXAXZFPANzVmeSTRxeoQkJCJOU/8dDQUBf3xjXS09MVFxcnKT/Mg4KCXNwjoOxSU1MVHx9v/yxXJeRT1Ub2Vg9kFOBeyN5/kU8ATuUuGXk2+eTRBaqCUz5DQ0OrbXh5e3vb/z80NLRaf1HDc1XF07fJp6qN7K1eyCjAPZC9RZFPAAq4W0aeST5VrQuWAQAAAAAA4HEoUAEAAAAAAMClKFABAAAAAADApShQAQAAAAAAwKU8epB0SP7+/lqxYoX9/wEAzkf2AkDlI3sBoGRVISMpUHk4b29vde7c2dXdAIBqhewFgMpH9gJAyapCRnKJHwAAAAAAAFyqWp5BlZiYqJSUFJe1HxYWppo1a1bItrKzs/XSSy9JkoYPHy4/P78K2S4A16hK+VSVkb2ojsgnuBrZi5K4Op8kMgquVxUy0mKMMa7uxJlKTU1VWFiYUlJSFBoaWqZ1EhMTNfimm5WTneXk3pXM18+q996dUyEBlp6eruDgYEnSiRMnFBQUdNbbBCrLmXyGPQX5VLWRvdUDGfUv8gnugOz9F/n0L3fIJ4mMguu5S0aeTT5VuzOoUlJSlJOdpZMNe8jmH2af7nUyWQE7F+tkg+6yBYQ7rX2vzBRpx69KSUkhvAA4IJ8AuKuS8kmqnIwinwCUxNX5JJFRQEWpdgWqAjb/MNmCootODwgvdjoAVBbyCYC7KimfJDIKgGuRT4DnY5B0AAAAAAAAuBQFKgAAAAAAALgUBSoAAAAAAAC4FAUqAAAAAAAAuFS1HSS9qvD399fPP/9s/38AgPORvQBQ+cheAChZVchIClQeztvbWz179nR1NwCgWiF7AaDykb0AULKqkJFc4gcAAAAAAACX4gwqD5eTk6PXX39dknTHHXfI19fXxT0CgKqP7AWAykf2AkDJqkJGUqDycNnZ2brnnnskSUOHDvXIP0IA8DRkLwBUPrIXAEpWFTKSS/wAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSFKgAAAAAAADgUhSoAAAAAAAA4FIUqAAAAAAAAOBSPq7uAM6O1WrVV199Zf9/AIDzkb0AUPnIXgAoWVXISApUHs7Hx0eXX365q7sBANUK2QsAlY/sBYCSVYWM5BI/AAAAAAAAuFS1KVBlZmZqy5YtysrKcnVXKlROTo7efvttvf3228rJyXF6ewWvY2ZmptPbAqqLqppPla0y86mysxdwpczMTO3evdvV3fBo7D9VDLIXpyKfzh75VHVUhYysNgWqPXv26I477tChQ4dc3ZUKlZ2drWHDhmnYsGHKzs52ensFr+OePXuc3hZQXVTVfKpslZlPlZ29gCvt2bNHEydOdHU3PBr7TxWD7MWpyKezRz5VHVUhI6tNgQoAAAAAAADuiQIVAAAAAAAAXIoCFQAAAAAAAFyKAhUAAAAAAABcigIVAAAAAAAAXKrcBaoePXpozpw5OnnypDP6AwBnjHwC4K7IJwDujIwC4A7KXaBq3769Hn74YcXGxur222/X8uXLndEvlJHVatW8efM0b948Wa1WV3cHcCnyCZWF7EV5kU/A2SN7nYeMAjxfVcjIcheoXnzxRR04cECzZ8/W4cOH1b17d7Vo0ULPPfecEhMTndFHlMLHx0fXXXedrrvuOvn4+Li6O4BLkU+oLGQvyot8As4e2es8ZBTg+apCRp7RGFQ+Pj7q37+/Pv/8c+3bt0833nijRo8erfj4eF199dX66aefKrqfAFAm5BMAd0U+AXBnZBQAVzurQdJXrFihsWPH6vnnn1dMTIwee+wxRUdH64orrtDDDz9cUX1EKXJzc/Xxxx/r448/Vm5urqu7A7gN8gnORPbibJBPwJkheysHGQV4pqqQkeU+7+vw4cN69913NXv2bG3dulVXXnmlPvjgA1166aWyWCySpKFDh6pPnz567rnnKrzDcJSVlaUBAwZIkk6cOOGxp/IBFYF8QmUhe1Fe5BNw9she5yGjAM9XFTKy3D2uU6eOGjVqpFtuuUVDhw5VjRo1iizTpk0bde7cuUI6CABlRT4BcFfkEwB3RkYBcAflLlAtWrRIF1xwQanLhIaG6ueffz7jTgHAmSCfALgr8gmAOyOjALiDco9BNXbsWCUnJxeZnpqaqosuuqgi+gQAZ4R8AuCuyCcA7oyMAuAOyl2g+vXXX5WdnV1kemZmppYsWVIhnQKAM0E+AXBX5BMAd0ZGAXAHZb7E7++//5YkGWO0YcMGHTp0yD4vLy9P3333nWrXrl3xPYRbsdlskqQ33nhDe/bsUW5urvz9/RUfH6/o6GilpqZq9erVys3NVVBQkFq0aKH09HQFBASoTZs26tevnzZs2KCVK1dq5cqVSkpKks1mU82aNRUeHq7U1FSdPHlSDRo0UP369bVmzRrt3btXFotFvr6+stlsysjIUGBgoOrXr69mzZpp5cqVMsaoYcOGatasmf755x+tXLlSaWlp8vLyUmRkpKxWq3x9fZWamipJCgwMVMeOHdW5c2e1aNFCCxYs0MKFC5WTk6O6devqvPPOU0ZGhkJDQ5WcnKzjx49ry5YtysrKktVqVaNGjbR//35lZWUpPj5e//3vfxUQEKC8vDytXr1aP/zwg06ePKkWLVpIkjZs2CBfX18dOnRIx48fV3BwsJo2bSpvb295eXmpadOmSklJ0datW5WYmKjY2Fhdeuml6tChgyRp7dq1Wr16tRITE2WMkc1ms/dLkho0aKCEhASdPHlSxhiFhoYqNDRUGzZs0I4dO3TixAnl5OTIYrEoJiZGcXFxSk5OVmBgoHr37q1WrVrpq6++0oEDBxQdHa2DBw9qy5YtMsYoPj7e/v5ERkYqMjJSNptNa9as0ebNm+Xv76/WrVurUaNGSk1NVWRkpNq0aSNvb+9K+7sknyD9m0+ffPKJduzYofT0dGVnZys8PFzR0dHy9fXVmjVrlJubq4iICLVp00ZpaWnKyspS06ZN1b59e0nSmjVrtHHjRvtnPCwsTHFxcZKkpKQkRUVFKTg42N7u4MGDFRAQ4JBPDRo00GWXXaZff/1VBw8elNVqVePGjZWWlqb169crOTlZNptNVqtVYWFhCgkJUWJionJzcxUfH6/OnTurRo0aioyM1MmTJzVnzhylpqYqKipKl112mWJjY2Wz2bR27VodPnxY4eHh9v76+fnJarXq6NGjJWbJ4cOHFRUVpfT0dCUlJclqtSo9PV379++Xj4+PWrRoodDQUB07dkw1atRQcHCwtm/fXqZ8MsbIYrHIZrNp3759slgsio6OVt26deXr66s2bdpIklatWqVVq1bZ3yer1aqaNWuqffv2ysjI0NGjRxUTE6N27dpJyv+c5+TkaM+ePcrMzJTValVAQICys7PVsmVLJSQkKDk5WcnJyQoODtamTZt09OhRBQQEKCEhQVFRUYqOjiaf4BIF+bR06VK99NJLOnr0qPLy8hQdHa3AwEAFBQVpw4YN9n2mrl27KisrS4cPH1ZsbKwuueQSeXl5ac2aNfa/7RMnTtj3rywWi7Zt2yZjjOLi4pSSkqKDBw8qNzdXVqtVPj4+ysrKkiRFR0era9euSkxM1NatWxUUFKT27dtrx44d2rBhgzIyMmSMccinlJQUSVJERIQiIyMVGxurdu3aKSsry55PDRo0UL9+/XTixAklJSUpOTlZR48edcgnf39/nXPOOVqxYoUyMzMd9qGys7O1YMECrVu3TlarVYGBgbJYLDLGKDU1VZs2bbIP1i1J48ePV1hYmHr06KE//vhDW7ZsUXBwsAYMGKBOnTrJ29vbvl/2/fff69ChQ/Lz81N4eLiMMWXKp4yMDPn7+8tisdiXrVOnjpKTk4vkkzFGAQEB2rFjhw4dOqTs7GzVqFFDrVu3dsin0NBQJSUlafv27Tp58qSioqLUsmVLxcTEVHo+FfRdIqOQ/7fw7LPP6ujRo/Ly8rIfMzRs2FCbN2/WihUr7McZzZs3V1JSkn2foG3btlq3bl2RY7yYmBh5e3vbj0Pat2+vkJAQ/fnnn9q5c6eMMQoJCZGXl5fS09MVFBTkkGlBQUHq2LGjkpOT9fvvvys9PV02m02+vr4KDg5WzZo1lZaWpqSkJPn7+ysqKkpNmzZVhw4dlJubq/nz5ysxMVE1atRQ586dFRUVpeTkZIdjwh07digxMVF+fn4lHuOVlE+S5Ofnp+XLlys3N1f169dXq1atdPToUUlS06ZNdfz4ca1atUppaWlq3ry57rzzTofjxlPzSZKSk5OVmZkpf39/NW7cWJGRkQoPD3c4Ls3MzFR2drb9tQ0PD3fYP121apUCAgKK5FPBc42IiFBMTEyJx3hWq1XR0dGVnk8WY4wpy4JeXl72N6G4VQICAvTKK6/olltuqdgeliI1NVVhYWFKSUlRaGhoqctu2bJFd9xxh0aNGqWJEycqvUU/2YKi7fO90o8qaMMXRaZXtIJ2Xn/9dTVp0uSst5eenm7/Qzxx4oSCgoLOepslWbx4sSZNmqTMzEynteHJmjVrpj179igjI6PCtunn5ycfH58K3WZliI2N1V133aXu3buXulx5PsOlIZ8qRkXnU2VavHixnn32WaWlpVVKe3l5eVq6dKkk6fzzz6/0A4ryslqt8vb2Jp9U9nySKiaj3DGfpDPLKEnF5lBlZJSn59NLL72kY8eOuborbqt27do6ePCgvZBXkrJmr5+fn/r3768vvvjCozKqsvNJcs+M8rR8KtyOJ2bUhx9+qJkzZ7q6G26rrPlUHs44bpScu39aWflU5kv8du7cqe3bt8sYoxUrVmjnzp32f/v371dqauoZB9e0adNUv359+fv7q0uXLlqxYsUZbac68vPz0+zZszV79mz5+fk5rZ3FixdrzJgxbluc8vIq99WqxYqKijrj9jdt2qSMjAyFhITYzywor4JbgcbExEiSsrOzXbpj5evrW+r8pk2bFpnWq1cvNWzYUGPHjtXixYud1TUH5FP1tnjxYo0dO7bSilOSZLFY1LRpUzVt2tThF/3iVFQ+1axZU4GBgeVap2D5rKwse5acac4VcGU+nfpaFzw+3XtQ+PsxLCyMfEKlKcgnTyqSnKlTD4ROd2BUcNBisVi0f/9+2Ww2hYWFSVKJ+7Sny95u3brJ29tb2dnZ+vDDD136ulut1jItFxERYf9/Ly+vSs0nyXkZRT55hsWLFxdbnCrrvsvpvn/dRcExVnmVJ59KU3ASSWRkpCTZjxsrWln3T8vy/p564ktl5VOZ95rr1aun+vXry2azqVOnTqpXr579X61atc64OvfRRx/pwQcf1NixY7V69Wq1bdtWl156qQ4fPnxG26tufH19NXToUA0dOvS0xYQzlZeXp2nTphWZHhERoejokn+JOF1ghYSEnHXfCpS3ol1c3wpOge/SpUux8wtOubRYLPZL86SiIfX2229r7dq18vLyUqdOnRzmFQ6DU98vHx8fGWPUtWtXeXl5FTv/TBUEanEsFovD6aA+Pj7y8fGRr6+vcnJy1Llz5yJtWywWnXPOOdq2bZu6du2qrl27ys/PT15eXvr55581ZswYdevWTTNmzFBeXt4Z97usyKfqKy8vT9OnTy/yN+rr63vWhZjSeHl5KTY2VrGxsaf9ki9vPhX39xoREVHks1pY4duBWywW1ahRQ76+vsrIyHDIEqvVqqSkJHl5eZV6q/BT8yc0NFRWq1VWq9V+yfWpy/v5+ZW401ba91OnTp1K3dkr/F1R8Ou+j4+PrFar/XJCY4zDa9O5c2f7pY5du3ZVRESEPcNTUlLUtWtX8glOV5BPnTt31smTJ+3Ti/uOL6/58+efbffOyKl/rwWfXavVWiRz8/Ly7JfGFXz+pPwiecGwC507d3Y4ayclJcXh81qgYH+zYFiEwtnboUMHe5Fn586d+vzzzx3W9fHxOeN88vLyKrJe4e+b4vavCvIpJyen2DMHwsPDHfLJarWqS5cu8vLyUmJiorp06VJp+SQ5J6PIJ8+Ql5enV1991WGal5eXunbtWuo+VHHfyyXNL4uKLHKVdIwXGRlZYjuFP8edOnWyZ4uvr2+x+RQREeGQA4WPh4vLmYiICGVmZtqPlwqv6+3tXWo+nc6pz6m0/VNvb29ZLBb79NDQ0GJ/+CvIp08//VTdunWzH+NVVj6V6Yj3iy++UN++feXr66svvvii1GX79etXrg688MILuv322zVs2DBJ0syZM/X1119r1qxZGjlypMOyWVlZ9uvnJdmvHS2PgwcPlnsdZ9i9e7eru1BmmzdvVmJiYpHpl1xyiebNm1fieu3bt9fq1atLnB8TE1OuMx6aNm2qzZs3l3n50hQXpllZWUpMTNTAgQP1xx9/FJlfcM1yx44dtXLlSvv0UweUHDt2rP2A9NR5BV/+K1euVE5OjsO87t2766efflLt2rW1fPnyIu3n5uaW/QmeokGDBlq7dm2x84wxqlmzpk6cOOHQzkUXXaSffvpJdevWVUhIiH766SeHdeLj47VixQp16dJFTZo0cejzl19+qUGDBunuu+/W33//bR/bxxnIp4rnaflUeLyMAt27d9eiRYsqrR/x8fHau3dvhWyruC/90+XtkSNH7P9vjNGRI0fsn+HCWVPwN1owFkFJTs2nDh066JdffpGkYr8PTl2+PPPr1avnkKmnKu67YsCAAXr//fclSY0bN9aWLVsUGhpqz7GC8SIkqUuXLlq+fLkGDBigefPm6dChQ7r++uu1bNmyapNPUsVklDvwxHw6tbBss9nO+lKRqVOnntX6Z+rUfCr4nBWMl3Wqgn2mgn0oSTp27Jg9nwrGaSmsuLwrvFz79u3teSRJwcHBuuWWW/T888/r0KFD+vbbbx3WPXX/qWPHjlq1apX9cWn51K5duyL7soW3Fx0dbR+fq0DhfCqcnQXq169v3ycryKfrr7/evu9ZsB/o7HySnJdR1TWfJM/LqFM/tzabzf53WZK+ffuWuk9S3mO8U4+tzkZJx3ilFUcLf47PO+88e1+Ky4bT5dOpx361a9fWwIED9fzzzxd7jJeXl3dWxZ4yjtZkb6vwOn369CnyXIwx9uewYcMGDR48WMuWLbPPr4x8KlOB6uqrr9ahQ4cUExOjq6++usTlLBZLuV7g7OxsrVq1So899ph9mpeXl3r16uXwQhSYPHmyxo8fX+btF2fWrFlntX5FmThxYoVsxxijpKQkSSq1MuwMpxsw8XRnLxR3p5DSdOvWrcIKVKXx9/cvdnpBf9u3b19qiBY+eDt1p6W09Zs2baqffvrJ4Qu6opwuvIp7Lwr3p+D/Cyvop7+/vxo0aOAw78CBA7r88sslyf736SzkU8WrqHxypWbNmjm1QHVq9tavX7/CClTFqVWrVrnXKe5zW1h5dh5LysWKcLrvguLmX3bZZfYDwLi4OPtgoQUKP7eCy2wKv4YF06pLPkkVk1HuwBPzadu2bRW+zQMHDlT4Np2hYJ/n1M9xQT4Vl0MFN6UojjFGu3bt0rFjx+z7vWlpaerWrZt9mdO9Nh06dHAoUJXmTPZlC+dTcdlZeJ+sIIsKXw5YsH/l7HySnJNR1TmfJM/MqFOd7ju/tM+oVP5jvNMdWzlb4f6e7tLc0z33U1ksFns+OeMY71TlqQ2c7rkkJSU5ZKtUOflUpgJV4V95KnJwsII7mNSsWdNhes2aNbVp06Yiyz/22GN68MEH7Y9TU1MVHx9frjZvueUWtzgIHDVqlOrVq3fW28nIyHC481R5xycpi82bN+v5558vMn3//v2lrne6wUDLeypjSV9qFa2kcbYK+rtmzZpS169Zs6b9bIbiTv0uaf2C4ltZxywoj/KMz1Jcf4orDBb0MzMzUzt37nSYFxcXZ59WcK21s5BPFa+i8qkylJRPxb1HFclms2n9+vWS8geh3LVrl1PbO5Oz605X0C/PKfjOHH/wdN8Fxc3/5ptv7P9fcDBaeIe68HMr2Jkq/BoWTKsu+SRVTEa5A0/Mp4SEhAovUhX+nnVnBfs8p36OC/IpJCSkyFlUpRWYbDab3nnnHUn/DgAcEhLisI94uoOu0s7uP9WZ7MsWzqfisrPwPllBFhU+cC3Yv3J2PknOyajqnE+SZ2bUqU73nX+6InB5j/FOd2zlbIX7e7oiUnl/HDDG2PPJGcd4pzp1/7S0S3RP91wiIyOLfM9URj6d+aA2LlAw/sXZOJNfoZ2hXr16FXYXvwKNGzd2yl38GjVqpPfee6/IZR0LFy5UdHR0sadnS6cPm/Jeh16RZ08VjFlSmNVqVXh4uJYvX17s/IKdlFWrVsnb21vGGNlsNvn5+Sk3N9f+xT5+/Hhdd911kooGtJeXl/0XgoIxngosXrxY3t7e2r9/v2JjY3Xs2DGH+T4+Pmd8mV9pO7EWi8XhvS24LnrJkiWSpD179hR5Ly0Wi/bu3Stvb2/98ccf+uOPP+yvg8Vi0ZVXXqkJEyaoVq1a9ls2V3Xkk2s0atRIc+fOLfJ5Wbx4saKioirtrlkVefZUwa3RC1u4cKFiY2Nls9mKzc4aNWrYC+MFt0Iv+AwXzhqr1ars7GxZLBaHMXFOdWo+rV692v73HR4erqSkJIf5vr6+9oOu4n45PXV7he3evVt+fn4l/uJa3POdN2+erFarsrKytHXrVkmOl4ScPHlSfn5+slgs+uOPP1SzZk398MMPkvLvQrNixYpqlU9SxWSUO/DEfDp1R97Ly8t+2/Uz9cADD+i333472y6W26n5VPDZtVqtCgsLK/J5XbVqlSwWi0MWR0VF2fMpOjq6yD7KwoULFRMT47Ct0vY3T5w4Yf9xJzY2Vn379nUYO9XHx8c+5krB2T2FlZZPa9euLZJPhffHiutTQT7l5OQUWwzbtWuXQz7FxsZq+fLl9rvp7d+/n3zyYJ6WUe+++67DZ83Ly0t//PGHw37FqU69jPZU5T3GK+sZjWVR0jFeWFiYjhw5UuxVJYU/x7/99pu8vLxks9mKzYaFCxeqZs2aOnbsmD0HCufTqXmxf/9+zZo1y+EY7+jRo/Z1vb297UWk8p55VtLzLYm3t7dsNpt9f+27774rsn7BOKMWi0UtWrTQuHHjHI7xKiOfylSgevnll8u8wfvuu6/My0ZHR8vb27tI4SMxMVGxsbFl3g6cy9vbW3fffbfGjBnjMP348eOlrne6D0tF3nGrIEjKqqTrk3Nzc4sdf0qSfewEY4zDzll2drbDIHRDhw5V27ZttWbNmiKnqxbu46mBl5ubKx8fHy1fvlwxMTHFzj9TxV1qWMAYYx+35dR2fHx89Oeffxa7zooVK4qMPSVJF198sSZMmKBly5Zp/PjxFXp70+KQT9Wbt7e37rrrLo0dO9Zhek5Ojtvc0r28+VTcZRTHjx93GCvuVMWNQSXl38Wv8F1isrKy7IW74j7bBU7Nn8LFH2NMkflnMwbV6U7rL/xdUbAjlZuba99ZKhgovfBrU/i5LV++3GGHMSwsTMuXLyef4HSF8ykgIMBeFK6IMaiuvfbaiuhiuZU2BtWpmVu4mFWwDyX9+4NfaGio/vzzT4cDpLCwMB0/frzID3wlFackxzOiGjRooKuuusph/un2n0rLJ5vNVuSgsfD2itu/Ksgnq9Va7FhKhV+L5cuXKyIiwr7vGRcXpz/++KNS8klyTkaRT57D29tb99xzj8Mxns1msxdMS1Lc93JJ88uiPOMoncm2srKylJSUVGI7hT/HhfdJcnJySsynwjlQ2hhUUv4+XFBQkJYvX67IyEiHdV01BpWXl1ex+VR4DKr+/fs7nAxTWflUpgJVWQditFgs5drB8vPzU8eOHbVo0SL7dc82m02LFi3SPffcU+btwPm6d++uCRMmaNKkSU691ONMVdRpyWd6QGuz2dSsWTPt2bNHaWlpZ3yqakFgFfzyYLVa5e3t7bLbJJ9up27Llv9r787jm64PP46/0yNpaWkKLbSg3AjKURA5RB14TkTBOZ2igOCBFx4T8RrOgs6JurnNDUXcxAMV57WpY/NgIgPLjSA3AoIo5SdHKT1oafP9/cEjXxt6Jek3zfebvp6Phw/T5JtvPt9v0hfJp98kW6qdN3/+fLVp00bTpk3TkCFDIjU0E33CkCFDNG3aND311FOWTnxbxao+1fTh5PXxt6NqSxo6cRfNPh3/RMz/cyifs1dYWEif0Gj8ffrTn/5U51GLseD4F1n1vejyvzgyDEMnnHCC9uzZY75QDOdIAkkBb6W57LLL9P7770ftOVSwnzdT9Q++hmE0Wp+kyDSKPjnLkCFDdMstt2jmzJkB5wf73MXKyaVICvcP/Vb1yT/R4//sJv/rxmj1KZj7t+rklNR4fQpqgiqS73GfNGmSxo0bp/79+2vgwIH64x//qOLiYvNbH2AfQ4YMUevWrXXLLbdowIAB2rVrlyoqKpSUlKR27dopMzNThYWFWrVqlSoqKpSSkqIePXqouLhYycnJysnJ0ciRI7VhwwatWLFCK1as0IEDB+Tz+ZSVlaX09HQVFhaqtLRUnTp1UseOHbV69Wp9++235uGGPp9PJSUlatasmTp27KiTTz5ZK1askGEY6ty5s04++WStX79eK1as0OHDhxUXF6eWLVvK4/GYX2ksHTuq4LTTTtOAAQPUo0cPvffee/rkk0909OhRtW/fXmeeeaZKSkqUlpamgoICHTx4UFu2bFFZWZk8Ho+6dOmi7777TmVlZWrXrp1uvvlmJScnq7KyUqtWrdLHH3+s0tJS9ejRQ9Kxb0FITExUfn6+Dh48qNTUVHXv3l3x8fHmVyYfOnRIW7duNf/CdOGFF6pfv36Sjh1ivmrVKu3du9d8a6F/XNKxvxh27dpVpaWlMgxDaWlpSktL04YNG7R9+3YVFRWZfwVo3bq12rZtq4KCAjVr1kw//elP1atXL3344Yf6/vvvlZmZqT179mjLli3mt/X575+WLVuqZcuW8vl8Wr16tTZv3qykpCT17t1bXbp0UWFhoVq2bKmcnJxG+cufRJ9wTNU+XXjhhdq+fbuKi4tVXl6u9PR0ZWZmKjExUatXr1ZFRYVatGihnJwcHT582PwigKqf57dx40bzd9zr9ZqfaXLgwAFlZGQoNTVVixYtknTsr8XJyckBferUqZOGDx+uzz//XHv27JHH49FJJ52kw4cPa926dSooKJDP5zMPO2/evLn27t2riooKtWvXTgMGDFCrVq3UsmVLlZaW6pVXXlFhYaEyMjI0fPhw8+1+X375pf7v//5P6enp5nj9Xw+8b9++Wlvyf//3f8rIyFBxcbEOHDggj8ej4uJifffdd0pISFCPHj2Ulpam/fv3q1WrVkpNTdW2bduC6pP/iCafz6fdu3ebbzls3769EhMTzcPCV65cqZUrV5r3k8fjUVZWlk499VSVlJRo3759at26tfr27StJWrt2rY4ePapdu3bpyJEj8ng85rf19ezZU127dlVBQYEKCgqUmpqqTZs2ad++fUpOTlbXrl2VkZGhzMxM+oRGV7VP1157rVauXGl+Tk9mZqaaNWumlJQUbdiwwXzOdPrpp5vfPJWdna0LLrhAcXFxWr16tfnYLioqMp9fuVwuff311zIMQ23bttWhQ4e0Z88e80iehIQEc8IkMzNTp59+uvbu3autW7cqJSVFp556qrZv364NGzaopKREhmEE9Mn/wqxFixZq2bKlsrOz1bdvX5WVlZl96tSpk0aOHKmioiIdOHBABQUF2rdvX0CfkpKSNHDgQC1btkxHjhwJeA5VXl6u9957T1999ZU8Ho+aNWtmHr1QWFioTZs2BXx+0+DBg+X1ejV06FAtXbpUW7ZsUWpqqq688kr1799f8fHxmjBhglatWqWPPvpI+fn5crvdSk9Pl2EYQfWppKRESUlJcrlc5rInnniiCgoKqvXJ/+2o27dvV35+vsrLy9WqVSv17t07oE9paWk6cOCAtm3bptLSUmVkZKhnz55q3bp1o/ZJilyj6JOz+P89v/322/XRRx9p3759iouLM18zdO7cWZs3b9ayZcvM1xmnnHKKDhw4YD4n6NOnj7766qtqr/Fat25tvqXZ5XLp1FNPVfPmzbV8+XLt2LFDhmGoefPmiouLU3FxsVJSUgKalpKSotNOO00FBQX64osvVFxcbL79LjU1VVlZWTp8+LAOHDigpKQkZWRkqHv37urXr58qKir09ttva+/evWrVqpUGDBhgfqNo1deE27dv1969e+V2u2t9jVdbn6Rjk7JLlixRRUWFOnbsqF69eplHVXXv3l0HDx7UypUrdfjwYZ1yyim65ZZbAl43Ht8n6diRlkeOHFFSUpJOOukktWzZUunp6QGvS48cOaLy8nJz36anpwc8P33ssceUnJxcrU/+bW3RooVat25d62s8j8ejzMzMRu+Ty7DBtOdf/vIXPfXUU8rPz1ffvn31zDPPaNCgQfVer7CwUF6vV4cOHVJaWlqdy27ZskU33XSTpkyZoscee0zFPUbKl5JpXh5XvE8pG96vdr7V/Lcza9Ysyz6Dyv/1xUVFRRH5DKqq/PvRqvGjaQvldzha6JNzNGafGru9iA67NyrcPknhNUpSjR1qjEbRJ0i0tyr6dIwd+lT1dpz4O06fYoddGtmQPgV1BNWkSZP06KOPKiUlJeAbFmry9NNPhzQA6dhsLYd8AggHfQJgV/QJgJ1FslH0CUA4gpqgWr16tfkBgnV9tk59X2UP67ndbv3lL38xTwNNDX1CNNBeBIM+AdaivdaiUUBsiYVGBjVB9dlnn9V4GtGXmJioiRMnRnsYQNTQJ0QD7UUw6BNgLdprLRoFxJZYaGTt3x8ZhG+//VbffvutVWMBAMvQJwB2RZ8A2BmNAhAtIU9QVVRU6Ne//rW8Xq86duyojh07yuv16qGHHjIPEUXjqays1IIFC7RgwYJ6v84XiHX0CY2F9iJU9AloONobOTQKcL5YaGRQb/Gr6o477tC7776rJ598UoMHD5Yk5eXlaerUqdq/f7+ee+45yweJ2h05ckTnnHOOJL7NBKBPaCy0F6GiT0DD0d7IoVGA88VCI0OeoHr99dc1d+5cXXTRReZ5OTk5ateuna6++mriBSBq6BMAu6JPAOyMRgGwg5Df4ufxeNSxY8dq53fq1MmxnxQPIDbQJwB2RZ8A2BmNAmAHIU9Q3X777Xr00UdVVlZmnldWVqbHHntMt99+u6WDA4BQ0CcAdkWfANgZjQJgB0G9xe/nP/95wM+ffvqpTjzxRPXp00eStGbNGpWXl+u8886zfoQAUAf6BMCu6BMAO6NRAOwmqAkqr9cb8PPll18e8HO7du2sGxEAhIA+AbAr+gTAzmgUALsJaoJq9uzZkR4HAISFPgGwK/oEwM5oFAC7Cflb/GAviYmJevLJJ83TAIDIo70A0PhoLwDULhYaGdYE1dtvv62///3v2rVrl8rLywMuW7VqlSUDQ3DcbrfuvffeaA8DsA36hMZAexEO+gQ0DO2NLBoFOFssNDLkb/F75plndN111ykrK0urV6/WwIEDlZGRoe3bt+uiiy6KxBgBICj0CYBd0ScAdkajANhByBNUzz77rGbNmqU///nPcrvduu+++/TJJ5/ozjvv1KFDhyIxRtShsrJSy5cv1/Lly1VZWRnt4QBRRZ/QWGgvQkWfgIajvZFDowDni4VGhjxBtWvXLp1xxhmSpOTkZB0+fFiSNHbsWL3xxhvWjs5C7du316xZs5SdnR3toVjqyJEjGjhwoAYOHKgjR45E/Pb8+7F9+/YRvy0gVPSpaWvMPjV2e+F8Tu2TdOx3a8qUKdEehqPx/MkatDdynNoo+tRw9Cl2xEIjQ56gys7O1oEDByQdezAvWbJEkrRjxw4ZhmHt6CyUlJSkbt26yePxRHsojubfj0lJSdEeClANfWra6BPszKl9ko79bnXo0CHaw3A0+gS7c2qj6FPD0SfYScgTVOeee67ef/99SdJ1112nu+++WxdccIGuuuoqXXbZZZYPEACCRZ8A2BV9AmBnNAqAHYT8LX6zZs2Sz+eTJE2cOFEZGRn64osvNHLkSN18882WDxAAgkWfANgVfQJgZzQKgB2EPEEVFxenuLgfD7waNWqURo0aZemgACAc9AmAXdEnAHZGowDYQcgTVJJ08OBB/e1vf9PGjRslST169NB1112nli1bWjo4AAgVfQJgV/QJgJ3RKADRFvJnUC1cuFCdOnXSM888o4MHD+rgwYN65pln1KlTJy1cuDASYwSAoNAnAHZFnwDYGY0CYAchH0E1ceJEXXnllXruuecUHx8vSaqsrNRtt92miRMn6quvvrJ8kKhdYmKicnNzzdNAU0af0FhoL0JFn4CGo72RQ6MA54uFRoY8QfX111/r7bffNsMlSfHx8Zo0aZJeeeUVSweH+rndbk2dOjXawwBsgT6hsdBehIo+AQ1HeyOHRgHOFwuNDPktfv369TPfl1zVxo0b1adPH0sGBQDhoE8A7Io+AbAzGgXADoI6gmrt2rXm6TvvvFN33XWXvv76a51++umSpCVLlmjGjBmaPn16ZEaJWvl8PvMfk1NOOSXg2zeApoA+IRpoL4JBnwBr0V5r0SggtsRCI4OaoOrbt69cLpcMwzDPu++++6otd8011+iqq66ybnSoV2lpqXr16iVJKioqUkpKSpRHBDQu+oRooL0IBn0CrEV7rUWjgNgSC40MaoJqx44dkR4HAISFPgGwK/oEwM5oFAC7CWqCqkOHDpEeBwCEhT4BsCv6BMDOaBQAuwn5W/wkadu2bfrjH/9ovr+xR48euuuuu9SlSxdLBwcAoaJPAOyKPgGwMxoFINpC/tSsjz76SD169NCyZcuUk5OjnJwcLV26VD179tQnn3wSiTECQFDoEwC7ok8A7IxGAbCDkI+geuCBB3T33XdX+zaHBx54QPfff78uuOACywYHAKGgTwDsij4BsDMaBcAOQj6CauPGjbrhhhuqnX/99ddrw4YNlgwKAMJBnwDYFX0CYGc0CoAdhHwEVatWrfTll1/qpJNOCjj/yy+/VOvWrS0bGIKTmJioyZMnm6eBpow+obHQXoSKPgENR3sjh0YBzhcLjQx5gmrChAm66aabtH37dp1xxhmSpMWLF+uJJ57QpEmTLB8g6uZ2u/XUU09FexiALdAnNBbai1DRJ6DhaG/k0CjA+WKhkSFPUP36179W8+bN9fvf/14PPvigJKlt27aaOnWq7rzzTssHGClxRw4F/lxaEPD/xrpdANahT9beLgDrxGqfpMZpFH0CIisWGhWtPtV22wBCF9IEVUVFhV5//XVdc801uvvuu3X48GFJUvPmzSMyuEjwer1KdHuk7Z/XeHnyjoURH0Oi2yOv12vJunw+n3bt2iVJat++veLiQv5YMSAm0CdrWNmnWEZ7EYqm0Ccp8o2iT6C9keH0RtmhTxKNQvTFQiNdhmEYoVyhWbNm2rhxozp06BCpMQWtsLBQXq9Xhw4dUlpaWtDX27t3rw4dit4st9frVVZWliXrKi4uVmpqqiSpqKhIKSkplqwXaAzh/g7Xhj41nJV9imW0t2mwslF26pMU3rbRJ0Qb7f0Rz6ECRbtPEo1C9NmlkQ3pU8hv8Rs4cKBWr15ti3iFKysri3gAMYg+AbAr+gTAzpzeKPoExIaQJ6huu+023XPPPdq9e7dOO+20arNyOTk5lg0OAEJBnwDYFX0CYGc0CoAdhPwWv5rex+hyuWQYhlwulyorKy0bXH2sPrTViexyGB8QDqt/h+kTGgvtbRqs/D22U58kGgVnor0/4jkUgOPZpZGN+ha/HTt2hHoVAGgU9AmAXdEnAHZGowDYQcgTVE59XzKA2EefANgVfQJgZzQKgB2EPEElSZs3b9af//xnbdy4UZJ0yimn6I477lD37t0tHRwAhIo+AbAr+gTAzmgUgGgLeYLqnXfe0ahRo9S/f38NHjxYkrRkyRL16tVLc+fO1eWXX275IFG7hIQE3XbbbeZpoCmjT2gstBehok9Aw9HeyKFRgPPFQiND/pD0Ll26aPTo0XrkkUcCzs/NzdWcOXO0bds2SwdYFz5AD3A2q3+H6RMAK1n5e2ynPkk0CnA6nkMBsKuG/A5X/7qGeuzZs0fXXntttfPHjBmjPXv2hLo6ALAMfQJgV/QJgJ3RKAB2EPIE1dlnn63//e9/1c5ftGiRfvKTn1gyKATPMAz98MMP+uGHHxTiwXBAzKFPaCy0F6GiT0DD0d7IoVGA88VCI0N+Y+LIkSN1//33a+XKlTr99NMlHXt/8ltvvaVp06bp/fffD1gWkVVSUqLWrVtLkoqKipSSkhLlEQHRQ5/QWGgvQkWfgIajvZFDowDni4VGhvwZVHFxwR105XK5VFlZGdaggsX7k6Xi4mKlpqZKcu6DEE2X1b/D9AmNhfY2DVb+HtupTxKNgjPR3h/xHArA8ezSyIb8Dod8BJXP5wv1KgDQKOgTALuiTwDsjEYBsIOQP4MKAAAAAAAAsBITVAAAAAAAAIgqJqgAAAAAAAAQVUxQAQAAAAAAIKpC/pB02EtCQoLGjRtnngYARB7tBYDGR3sBoHax0MigRl1YWBj0Cvkq0Mbl8Xj00ksvRXsYcIi9e/fq0KFDYV/f6/UqKyvLwhE1HH1CNNBe69En+gTUJ5rtpVE0CrCrqn361a9+JUnauXNn0Ne3U5+CmqBKT0+Xy+UKaoWVlZUNGhCAyNi7d6/GjL1WR8vLwl5HotujOa++YpuASfQJiAX0iT4BdkajaBRgV7HWp6AmqD777DPz9DfffKMHHnhA48eP1+DBgyVJeXl5evnll/X4449HZpSolWEYKikpkSQ1a9Ys6H9k0PQcOnRIR8vLVNp5qHxJ3nqXjystUPKOhSrtNES+5HTFHTkkbf9chw4dskW8/OgTooH2Wos+0ScgGNFqL42iUYBdVe1TpSdNvopySVJcgrvGRtq9T0FNUA0dOtQ8/cgjj+jpp5/W1VdfbZ43cuRI9e7dW7NmzTLf84jGUVJSotTUVElSUVGRUlJSojwi2J0vyStfSmbwyyenh7R8Y6NPiAbaGxn0CUBdot1eGgXArnxJXlUkNteamTdJkvrcOUvxbk/ty9u0TyF/i19eXp769+9f7fz+/ftr2bJllgwKAMJBnwDYFX0CYGc0CoAdhDxB1a5dO73wwgvVzv/rX/+qdu3aWTIoAAgHfQJgV/QJgJ3RKAB2EPJ3D/7hD3/Q5Zdfrn//+98aNGiQJGnZsmXaunWr3nnnHcsHCADBok8A7Io+AbAzGgXADkI+gmr48OHasmWLRowYoQMHDujAgQMaMWKEtmzZouHDh0dijAAQFPoEwK7oEwA7o1EA7CDkI6ikY4eA/va3v7V6LADQYPQJgF3RJwB2RqMARFvIR1BJ0v/+9z+NGTNGZ5xxhr777jtJ0quvvqpFixZZOjgACBV9AmBX9AmAndEoANEW8gTVO++8owsvvFDJyclatWqVysrKJEmHDh1ixj0K4uPjdcUVV+iKK65QfHx8tIcDRBV9QmOhvQgVfQIajvZGDo0CnM8V51J6twFK7zZArjhXtIcTlpAnqH7zm99o5syZeuGFF5SYmGief+aZZ2rVqlWWDg71S0pK0ltvvaW33npLSUlJ0R4OEFX0CY2F9iJU9AloONobOTQKcL64BLc6j7xdnUferrgEd7SHE5aQJ6g2b96sIUOGVDvf6/WqoKDAijEBQFjoEwC7ok8A7IxGAbCDkCeosrOz9fXXX1c7f9GiRercubMlgwKAcNAnAHZFnwDYGY0CYAchT1BNmDBBd911l5YuXSqXy6Xvv/9er732miZPnqxbb701EmNEHYqLi+VyueRyuVRcXBzt4QBRRZ/QWGgvQkWfgIajvZFDowDnqywv06rfjdOq341TZXlZtIcTloRQr/DAAw/I5/PpvPPOU0lJiYYMGSKPx6PJkyfrjjvuiMQYASAo9AmAXdEnAHZGowDYQcgTVC6XS1OmTNG9996rr7/+WkVFRerRo4dSU1MjMT4ACBp9AmBX9AmAndEoAHYQ8lv8rr/+eh0+fFhut1s9evTQwIEDlZqaquLiYl1//fWRGCMABIU+AbAr+gTAzmgUADsIeYLq5ZdfVmlpabXzS0tL9corr1gyKAAIB30CYFf0CYCd0SgAdhD0W/wKCwtlGIYMw9Dhw4eVlJRkXlZZWal58+apdevWERkkANSFPgGwK/oEwM5oFAA7CXqCKj093fzWjG7dulW73OVyadq0aZYODgCCQZ8A2BV9AmBnNAqAnQQ9QfXZZ5/JMAyde+65euedd9SyZUvzMrfbrQ4dOqht27YRGSRqFx8fr+HDh5ungaaIPqGx0V4Eiz4B1qG91qNRQOxwxbmU1qmPedqJgp6gGjp0qCRpx44dat++vVwuZ25wrElKStK//vWvaA8DEXDkyBHt2rVL7du3Dzjc2u6iMW76hMbW1Nvr1D5JjT92+gRYJ5j20qfQ0Cig8UT6dzwuwa2ul0+yfL1S4/Up5A9J/+9//6u333672vlvvfWWXn75ZUsGBUDatWuXbrrpJu3atSvaQwlJNMdNn4DG4dQ+SdEbO30CGgd9Cg+NAiKPPtUv5Amqxx9/XJmZmdXOb926tX77299aMigACAd9AmBX9AmAndEoAHYQ8gTVrl271KlTp2rnd+jQwZEzgU5XXFyslJQUpaSkqLi4ONrDAaKKPqGx0F6Eij4BDUd7I4dGAc5XWV6mL/84QV/+cYIqy8uiPZywhDxB1bp1a61du7ba+WvWrFFGRoYlg0JoSkpKVFJSEu1hAFFHn9CYaC9CQZ8Aa9DeyKBRQGzwVZTLV1Ee7WGELeQJqquvvlp33nmnPvvsM1VWVqqyslL//e9/ddddd2nUqFGRGCMABIU+AbAr+gTAzmgUADsI+lv8/B599FF98803Ou+885SQcOzqPp9P1157Le9PBhBV9AmAXdEnAHZGowDYQcgTVG63W2+++aYeffRRrVmzRsnJyerdu7c6dOgQifEBQNDoEwC7ok8A7IxGAbCDkCeo/Lp166Zu3bpZORYAsAR9AmBX9AmAndEoANEU1ATVpEmT9OijjyolJUWTJk2qc9mnn37akoEBQDDoEwC7ok8A7IxGAbCboCaoVq9eraNHj5qna+NyuawZFYIWFxenoUOHmqeBpoY+IRpoL4JBnwBr0V5r0SggtrhcLqWeeLJ52omCmqD67LPPajyN6EtOTtaCBQuiPQwgaugTooH2Ihj0CbAW7bUWjQJiS1yiW91GPRjtYTQIf3oAAAAAAABAVAV1BNXPf/7zoFf47rvvhj0YAAgVfQJgV/QJgJ3RKAB2E9QRVF6v1/wvLS1N8+fP14oVK8zLV65cqfnz58vr9UZsoKhZcXGxWrVqpVatWqm4uDjawwEaHX1CNNBeBIM+AdaivdaiUUBsqSwv09oZt2vtjNtVWV4W7eGEJagjqGbPnm2evv/++3XllVdq5syZio+PlyRVVlbqtttuU1paWmRGiTrt27cv2kMAooY+IVpoL+pDnwDr0V7r0Cgg9lSUHo72EBok5M+gevHFFzV58mQzXJIUHx+vSZMm6cUXX7R0cAAQCvoEwK7oEwA7o1EA7CDkCaqKigpt2rSp2vmbNm2Sz+ezZFAAEA76BMCu6BMAO6NRAOwgqLf4VXXdddfphhtu0LZt2zRw4EBJ0tKlSzV9+nRdd911lg/QCqWlpXr++ef17bffKikpSWeddZZat24tSSooKFDLli2Vk5MTcDjr2rVrdeDAgWqXhcKq9dS1zi5dujRofU5Q036UVO++DWX/+5fdt2+fCgoKlJ6erszMzFqvU9e6KysrtWrVKn300UfKz8+Xx+NR9+7dddppp6lv3741jr228xAaJ/bp+MdLdna2LrjgAiUkJDi+T1as0wnWrFmj0tLSgG0OZl8Eu7+qLpeeni6p5n+7gll3fX2qaew9e/bU+vXr6VMDObFP0rHnUDNnztTGjRuVlpamyy+/XG63mz45QF0dsOtzqPLycr377rtatGiRioqK1KVLFw0bNkz9+vUzx+63Zs0a9e/fnz5ZxImNCqVPkjUdiFRLmlqjnNgnK17jJScnW7gXY1PIE1S/+93vlJ2drd///vfas2ePJKlNmza69957dc8994S0roULF+qpp57SypUrtWfPHr333nv62c9+FuqQ6jRlyhQtXrw44Lzjf5ak7Oxs3XbbbZKkZ599Vvn5+dUuGzJkSNC3u3DhQkvWU986MzMzw1qXU9S0zVVfoPkdv29D2f81LVvXdepatyQ98cQT1T64c+XKlXr99dfVrFkz8x/O+rbH6t+FpsBpfVq4cGG1x8u6dev06aefBizn1D41dJ1Ocf/995tPXrKzs3X22WdrwYIFde6LYPdXXX0K5Tr+cX3wwQe19ik9PV3Dhg2rNvbjXxTSp/BY2Scpes+hqn6AskSf7KquDljVp9qWres6da17w4YNevPNN2UYhnnZN998o/nz56tZs2YaOXJkwL+P999/v9xuN32yiNOeQ4XSpyFDhljSgUi1pKk1yol9kqx5jZeRkVHf7mnyQn6LX1xcnO677z599913KigoUEFBgb777jvdd999Ic/yFhcXq0+fPpoxY0aowwiKP1z+cZ166qnq06ePeXnz5s0lSRMmTFDnzp318MMPKzc3V507d9aMGTM0b948zZgxQ507d1Zubq4WLlwY1O0uXLjQkvUEs86OHTuGvC6nqGmbJ0yYYD7uJkyYUOO+DWX/+5f1er1yuVwaNGiQJk+erEGDBkk69u0mVa9T17offvhhPfzwwwHh6tChg9q0aWP+XFJSEjD2urbn+eefb6Q9HTuc1KeFCxcGPF46deqkCy+8sNpyTu5TQ9Zpd4sWLTJPP/300+Y2e71ezZ07V16vt9Z9Eez+qrrchAkT5HK51Lt3b/Xu3VvSj4+NYBrlH1ddfSooKAgY+5QpUyRJaWlpcrlcmjJlCn1qACv7JDXecyhJOuGEE3TllVdWW4Y+2VN9HbCiT1Vvx6rnUHPnzjUnp9q3b6/LLrvMPNqgpKREc+fOrfZh3fTJOk56DhVKn3JzczVz5swGdyBSLWlqjXJqn6x6jXfCCSdIklatWtUYu9uRXEbVP1MEqaKiQgsWLNC2bdt0zTXXqHnz5vr++++Vlpam1NTU8AbicoU8u15YWCiv16tDhw5V+wertLRUF110kRISEpSRkaEuXbpo2rRpGjt2rDp27KgVK1aooqJCgwYN0q5duzR79mxdeumlkqQPP/xQiYmJ5rp8Pp8eeugh7dixQ3PmzKkz0pWVlRo9erQ6d+6s3/zmN4qLiwtrPcGus7i4WF27dlV5ebl27twZ9v63m5q22X9ep06dJB37q5p/P1bdtz6fT126dKl3/0sy17djx46A2/Ivu337dnXq1EnffPONXn75ZV177bU13g9Hjx7VJZdcorKyMrlcLiUmJuq0007TY489Jkn61a9+pSVLlkiSPB6PWrRooVdeeUXXXnttrdvzy1/+UmvXrtXMmTN18sknN3ifbtmyRTfddJOKe4yUL6X+I+/iivcpZcP75vL+n2fNmqVu3brVezv1LSfV/TscLif0qbKyUtdcc4327t2ruLg4DRo0SI888ojGjh2rTp06yTAMLV26VIZhKDs7Wy+99JIj+xTuOu2usrJSV111lRYvXqwTTzxRCxcuVHJysrkvysrK5PF49Nprr5nbHGqjqvbG/2+Xfx9LMpd75ZVXlJubW+06Vddd9fEmSW63O6BPU6ZM0dKlS+Xz+RQXF6fWrVubfercubMeeeQRPfzww+b96HK5HNunqrcVjUZFok9S5J5DSdLpp5+uRx991HwMPvzww7rkkktUUVFBn2yotm22sk9WP4c6cuSIhg0bZv58+umn67e//a3i4uJUUVGhK664wjwCISMjQzt37tT27dt1ww036PHHH494nySeQ9npOVQofXr11Vf18MMPa9myZRo4cKAee+yxsDoQqZY0tUY5sU9Wv8bbtGmTbrnlFmVmZurNN9+05H6t2qcKd5q2zP2tJKnbqF8pLtFdbXm79ynkI6h27typ3r1769JLL9XEiRP1ww8/SDp2yNvkyZNDXV1IysrKVFhYGPBfbfx/PRk6dKj27t2r0aNHa926dcrPz9fYsWP1i1/8QtKxB9KePXv0wQcfqKysTGVlZVq3bl3AuuLi4jR69Gjt2bNHa9eurXOMa9euVX5+vkaPHh3w4A51PcGuMyUlRZ9++ql69+6trVu3Br1Ou6tpm/3njRkzRmPGjAnYj1X3rf/+rm//+9c3cODAarflX9Z/+Z49e/TPf/6z1vth3bp1KisrkyQZhqHy8nKNGTNGcXFx5gSEX1lZmfLz88311bY9/n98Fy1apC1btjT4v507d1py3+zcubNRbifcsTmhT2vXrjUnC3w+n8aMGWP2acyYMRo7dqz5V+T8/HzH9incddqd/7MMPv30Uy1fvtz8C79/X9xwww3Kz88P2OZQG1W1N/7Hhv86VZdbt25djdepuu6qjzdJ1fo0ZswY8wNwfT5fQJ9Gjx6thISEgPvRyX2KZqOi2ScpvOdQkjR27NiAx2BSUpL5HIo+2U9t22xln6x+DvXBBx8E/Dx27FhzmYSEBP30pz81L9u/f7/uv/9+5eTkaPz48Y3SJ55D2es5VCh9WrdunQYOHKjKykoNHDgw7A5EqiVNrVFO7FMkXuNJ0r59+zRv3jzL+xSX6NbJY6fq5LFTa5ycqotd+hTyZ1Dddddd6t+/v9asWRPwHsrLLrtMEyZMsHRwx3v88cc1bdq0oJbdvXu3JKl79+6aP3++OnXqpLy8PEnH3kqTlpamN954Q4cPH5Ykff/99+Z1Dxw4UG19/hnQmi6ryn+5f/lw1xPpddpdTdtc13nHnx/KvvJ4PDVex/+z/3L/Y6Smddf1mKm6jqpqWl/V9fgPAZ0zZ4751wA78P/FwI6c0qfjHy/H9+l49Mleattm//mDBw8O+NkvlEZV7UNNj42q+9V/e7U1qr4+1fWY8192/P1In0IXzT5J4T2Hkmp+DA4fPlxvvPGGJPpkN43RJ6ufQ1V9DNW0TNW30dS0Lqf0SaJRtYlUnw4cOGA+BpOSkmpcXzAdiFRLmlqjnNinSLzG8/v9739f06ZEjV36FPIE1f/+9z998cUXcrsDZ+Q6duyo7777zrKB1eTBBx/UpEmTzJ8LCwvVrl27Gpc98cQTtWLFCm3evFmStGPHDrVs2dI87f/8EP/nULVt29a8rn+5qnbs2FHrZVVVvY2ePXuGvZ5Ir9Puatrmqucdv9zx54eyr/yz4sdfx7+s/3L/Y6Smddf2mPEv519HVVXXV9N6/L9PY8aMseQDEnfu3GlJeKZMmaIOHTpE/HbC4ZQ+Hf94Ob5Px6NP9lLbNvvP9z9Zrul+rnq6rv1VtQ813V7V/VrTdWoa1/G3c/y6qjp+Xcffj07tk5W3Fapo9kkK7zmUVPNjcN68eeay9MleGqNPVj+HqvoYqmkZ/wd2H798Y/VJ4jmUnZ5DhdKnli1b6ptvvpF07K2kNQmmA5FqSVNrlBP7FInXeH733HOPunfvXu38UMVan0J+i5/P5wv4tgy/3bt3m5M9keLxeJSWlhbwX21uvvlmSdLnn3+urKwsvfbaa+rVq5f5fuS33npL0rEHVZs2bTRixAh5PB55PB716tUrYF0+n0+vvfaa2rRpU+/X1+bk5Cg7O1uvvfaa+ZaJcNYT7DqLiop05plnavny5eratWvQ67S7mrbZf57/L2JV92PVfeu/v+vb//71LVu2rNpt+Zf1X96mTRtdeumltd4PvXr1MmfQXS6X3G635syZI5/PJ5/Pp6VLl5rLejweZWdnm+urbXv+/e9/S5LOOussdevWrcH/1feiLVgdOnRolNsJh1P6lJOTo6ysLEnHDvWdM2eO2ac5c+bo1VdflcvlknTs20Oc2qdw12l3OTk5yszM1JlnnqmOHTuqpKTEPD87O1t/+9vflJ2dHbDNoTaqam/8jw3/daou16tXrxqvU3XdVR9vkqr1ac6cOQGHvlft02uvvaaKioqA+9HJfYpmo6LZJym851CS9OqrrwY8Bo8cOWI+h6JP9lPbNlvZJ6ufQ40YMSLg51dffdVcpqKiQh9//LF5WUZGhu69914tX75cL730UqP0iedQ9noOFUqfevXqpWXLlik+Pl7Lli0LuwORaklTa5QT+xSJ13iSlJmZqeHDh1veJ9/RMq2bdY/WzbpHvqPVJ8/qYpc+hTxB9dOf/lR//OMfzZ9dLpeKioqUm5ur4cOHWzm2BklOTtaZZ56piooK7du3T1988YXuu+8+tW7dWkuWLFFFRYWaN2+upUuX6uKLL9YjjzyisrIylZeXKzc3V+vXr1dJSYnWr1+vhx56SHl5ebr11lvr/SCz+Ph43XbbbcrLy9NDDz0U9nqCXefUqVN16NAhlZSUVHvPrJPVtM1lZWUaMWKE8vLylJeXp4svvlhlZWXV9u3EiROD2v/+21iyZIm8Xq/y8vL04IMP6oMPPtCDDz6oL774Ql6vV0uWLNGtt94qt9td6/2Qm5tb7f3JeXl5uu666zR69Gjzw/MkmdtRUVGhSy65xNwe/wfw+cf61VdfSVJM3a+R5pQ+xcfHa+LEiZKO/UOVl5enm266Sb1791ZeXp6WLFlifgbVJZdc4tg+hbtOu4uPj9eECRN06NAh7dy509zmTZs2yev16uDBg/J6vdq0aVON+yKYRlXtTW5urtm+u+66S3fddZe++OILXXzxxcrNza3xOlXXvWnTJvOrjiVV61NeXl7AEzev16utW7fqhhtu0BdffKFf/OIXysvL0/XXX69NmzbRpzA5pU/Sj8+hJGnJkiUaP368fvKTn+iLL77QsGHDVFFRIYk+2VFt22xln6x+DvXII48EbIP/MfeHP/xBI0aMCPiKdq/Xq507d6qkpERLliyhTxZySqNC6VNubq75OFmyZEnYHYhUS5pao5zYJ6tf4z377LOSpCuuuCIi96thSOWF+1ReuE+hfxWePYT8LX7ffvuthg0bJsMwtHXrVvXv319bt25VZmamFi5cqNatWwe9rqKiIn399deSpFNPPVVPP/20zjnnHLVs2VLt27ev9/rBfDp81a8hrUubNm106623SpKeffZZ5efnV7sslMOEFy5caMl66ltnq1at9Pbbb0s6tj9TUlLCWrdd1bTN/hdaVZ+wHL9vQ9n/NS1b13XqWrd07MMkq34NaVUpKSlKTEwMGHuLFi1kGEa17bn00ks1c+bMoL4pIRhN4RtonNanhQsX1vl48XNqnxq6TjsrLi42v9HorLPOMp9ktGnTRkOHDtWCBQvq3BfB7q+6+hTKdfzj+uCDD2p9vKWnp2vYsGHVxh4fHx/wV3Un96nqbTV2o6zsk2Sf51D0yZ7q6oBVfapt2bquU9e6N2zYoDfffFM1vTRJSUnRiBEj9Omnn+qdd96RdKy9brc74n2SeA5lx+dQofRpyJAhlnQgUi1pao1yYp8ka17jZWZmat++fRHr09HE5lrzzE2SpD53zlK8u/pnZNm9TyFPUEnHDrd98803tWbNGhUVFalfv34aPXq0+U1GwVqwYIHOOeecauePGzdOL730Ur3XD3bDS0tL9fzzz+vbb79VUlKSzjrrLDOyBQUFatmypXJycswXGJWVlVq7dq0OHDhQ7bJQWLWeutbZpUsXeb1eSbE5QSXVvB8l1btvQ9n//mX37dungoICpaenKzMzs9br1LXuyspKrVq1Sh999JHy8/Pl8XjUvXt3nXbaaerbt2+NY6/pvG3btgUdgWA0hSdXkvP6dPzjJTs7WxdccIESEhIc3ycr1mlXVSeoFi9erNLS0oBtDmZfBLu/qi5XdYI+2OscP666+lTT2Hv27Kn169fHRJ+q3lY0GmVVn6TGfQ41c+ZMbdy4UWlpabr88svldrvpkwPU1QG7PocqLy/Xu+++q0WLFqmoqEhdunTRsGHD1K9fP8XHx5uPW+lYe/v37x/xPkk8h7Lrc6hQ+iRZ04FItaSpNcqJfbLiNV5ycrJuueUWJqjqENKHpB89elQnn3yyPvzwQ40ePVqjR48O6caOd/bZZ9f4VxKrJScn65e//GXQy8fHx+vUU09t8O1atZ661lnfkRexoLb9WN++DWX/h3pf1bV8fHy8BgwYoAEDBtR6/XC2B3Vzap+Cebwcv7xT+tRU9OnTp9ofB4LZF8Hur3D2a23XCebxVtN1m+L9aiWr+yQ17nOou+++O6hl6ZO91NUBuz6HcrvdGjVqlEaNGlXrdf369Okjt9vd5O7XSHDqc6hQ+iRZ04FItaSpNcqJfbLiNd6WLVuCHktTFdKbsxMTE2v9BgQAiCb6BMCu6BMAO6NRAOwi5E8PnDhxop544gnzA+gAwC7oEwC7ok8A7IxGAbCDkN7iJ0nLly/X/Pnz9fHHH6t3797V3tbw7rvvWjY41M/lcqlHjx7maaApo09oLLQXoaJPQMPR3sihUYDzuVxSUkZb87QThTxBlZ6erssvvzwSY0EYmjVrpvXr10d7GIAt0Cc0FtqLUNEnoOFob+TQKMD54hI96nHd49EeRoOEPEE1e/bsSIwDABqMPgGwK/oEwM5oFAA7CPozqHw+n5544gmdeeaZGjBggB544AGVlpZGcmwAEBT6BMCu6BMAO6NRAOwk6Amqxx57TL/61a+UmpqqE044QX/60580ceLESI4NQSgpKVHPnj3Vs2dPlZSURHs4QFTQJzQ22otg0SfAOrTXejQKiB2+o2XaMPtBbZj9oHxHy6I9nLAE/Ra/V155Rc8++6xuvvlmSdKnn36qiy++WH/9618VFxfylwHCIoZhaMOGDeZpoCmiT2hstBfBok+AdWiv9WgUEDsMQzqy/3vztBMFXZ1du3Zp+PDh5s/nn3++XC6Xvv/++4gMDACCRZ8A2BV9AmBnNAqAnQQ9QVVRUaGkpKSA8xITE3X06FHLBwUAoaBPAOyKPgGwMxoFwE6CfoufYRgaP368PB6Ped6RI0d0yy23KCUlxTzv3XfftXaEAFAP+gTArugTADujUQDsJOgJqnHjxlU7b8yYMZYOBgDCQZ8A2BV9AmBnNAqAnQQ9QTV79uxIjgMAwkafANgVfQJgZzQKgJ0EPUEFe3K5XOrQoYN5GgAQebQXABof7QWA2rlckjst0zztRExQOVyzZs30zTffRHsYANCk0F4AaHy0FwBqF5foUa+bfh/tYTRI0N/iB6BxtW/fXrNmzVL79u2jPZSQOHXcAILn5N9zJ48dQP2c/Dvu5LEDqJ+Tf8cba+wcQQXYVFJSkrp16xbtYYTMqeMGEDwn/547eewA6ufk33Enjx1A/Zz8O95YY+cIKocrLS3VgAEDNGDAAJWWlkZ7OADQJNBeAGh8tBcAauc7Wq5Nr07Vplenyne0PNrDCQtHUDmcz+fTihUrzNMAgMijvQDQ+GgvANTOMAyV7N1hnnYijqACAAAAAABAVDFBBQAAAAAAgKhiggoAAAAAAABRxQQVAAAAAAAAoooJKgAAAAAAAEQV3+IXAzIzM6M9BABocmgvADQ+2gsAtUtIbh7tITQIE1QOl5KSoh9++CHawwCAJoX2AkDjo70AULt4t0c5E/8S7WE0CG/xAwAAAAAAQFQxQQUAAAAAAICoYoLK4UpLS3X22Wfr7LPPVmlpabSHAwBNAu0FgMZHewGgdr6j5doy93Ftmfu4fEfLoz2csPAZVA7n8/n0+eefm6cBAJFHewGg8dFeAKidYRgq2r3JPO1EHEEFAAAAAACAqGKCCgAAAAAAAFHFBBUAAAAAAACiigkqAAAAAAAARBUTVAAAAAAAAIgqvsUvBjRr1izaQ4CDxB05FNxypQWB/w/yekBTQXutR58A1Cea7aVRAOwq7sghxR0tU1yC+9jPJfsUd9RTfTmb94kJKodLSUlRcXFxtIcBB/B6vUp0e6Ttn4d0veQdC83TiW6PvF6v1UMDHIf2Wos+AQhGtNpLowDYVdU+JUv6yZmDj12w9aM6r2fXPjFBBTQRWVlZmvPqKzp0KPxZcq/Xq6ysLAtHBQD0CYC90SgAdhVrfWKCCmhCsrKybBMfAKiKPgGwMxoFwK5iqU98SLrDHTlyRBdffLEuvvhiHTlyJNrDAYAmgfYCQOOjvQBQu1hoJEdQOVxlZaXmzZtnngYARB7tBYDGR3sBoHax0EiOoAIAAAAAAEBUMUEFAAAAAACAqGKCCgAAAAAAAFHFBBUAAAAAAACiigkqAAAAAAAARJWjv8XPMAxJUmFhYZRHEj3FxcXm6cLCQsd+Wj+aJv/vrv93OZbQp9hGe5sGGgXYC+39EX0CcDy7NLIhfXL0BNXhw4clSe3atYvySOyhbdu20R4CEJbDhw/L6/VGexiWok9NB+2NfTQKsB/aewx9AlATOzQynD65DAdPu/t8Pn3//fdq3ry5XC5XncsWFhaqXbt2+vbbb5WWltZII2wcbJszxeq2hbJdhmHo8OHDatu2reLiYusdx/TpGLbNmdi2Y2jUMbH6eIjV7ZLYNqeiT8fQp2PYNueJ1e2SGq9Pjj6CKi4uTieeeGJI10lLS4u5B4sf2+ZMsbptwW5XrP3Vz48+BWLbnIlto1FVxerjIVa3S2LbnIo+0aeq2DbnidXtkiLfp9iabgcAAAAAAIDjMEEFAAAAAACAqGoyE1Qej0e5ubnyeDzRHorl2DZnitVti9XtiqRY3mdsmzOxbagqVvdZrG6XxLY5VSxvW6TE8j5j25wnVrdLarxtc/SHpAMAAAAAAMD5mswRVAAAAAAAALAnJqgAAAAAAAAQVUxQAQAAAAAAIKqYoAIAAAAAAEBUOXqCasaMGerYsaOSkpI0aNAgLVu2rM7l33rrLZ188slKSkpS7969NW/evIDLDcPQww8/rDZt2ig5OVnnn3++tm7dGslNqFUo2/bCCy/oJz/5iVq0aKEWLVro/PPPr7b8+PHj5XK5Av4bNmxYpDejmlC266WXXqo25qSkpIBlnHqfnX322dW2zeVy6eKLLzaXsct9tnDhQo0YMUJt27aVy+XSP/7xj3qvs2DBAvXr108ej0ddu3bVSy+9VG2ZUH9/nYY+HeOkPkmx2yj69CP6RJ/86JMz7zenNIo+hYc+HUOfnHm/0ScL+mQ41Ny5cw232228+OKLxvr1640JEyYY6enpxt69e2tcfvHixUZ8fLzx5JNPGhs2bDAeeughIzEx0fjqq6/MZaZPn254vV7jH//4h7FmzRpj5MiRRqdOnYzS0tLG2izDMELftmuuucaYMWOGsXr1amPjxo3G+PHjDa/Xa+zevdtcZty4ccawYcOMPXv2mP8dOHCgsTbJMIzQt2v27NlGWlpawJjz8/MDlnHqfbZ///6A7Vq3bp0RHx9vzJ4921zGDveZYRjGvHnzjClTphjvvvuuIcl477336lx++/btRrNmzYxJkyYZGzZsMP785z8b8fHxxn/+8x9zmVD3l9PQpx85pU+GEbuNok8/ok/0qSr65Mz7zSmNok+ho08/ok/OvN/oU8P75NgJqoEDBxoTJ040f66srDTatm1rPP744zUuf+WVVxoXX3xxwHmDBg0ybr75ZsMwDMPn8xnZ2dnGU089ZV5eUFBgeDwe44033ojAFtQu1G07XkVFhdG8eXPj5ZdfNs8bN26ccemll1o91JCEul2zZ882vF5vreuLpfvsD3/4g9G8eXOjqKjIPM8O99nxggnYfffdZ/Ts2TPgvKuuusq48MILzZ8bur/sjj7Vzq59MozYbRR9+hF9ok91oU/OvN+c0Cj6FBz6VDv65Mz7jT6F3idHvsWvvLxcK1eu1Pnnn2+eFxcXp/PPP195eXk1XicvLy9geUm68MILzeV37Nih/Pz8gGW8Xq8GDRpU6zojIZxtO15JSYmOHj2qli1bBpy/YMECtW7dWt27d9ett96q/fv3Wzr2uoS7XUVFRerQoYPatWunSy+9VOvXrzcvi6X77G9/+5tGjRqllJSUgPOjeZ+Fq77fNSv2l53Rp7rZsU9S7DaKPgWiT/SpLvTJmfdbrDSKPtGnutAnZ95v9Cn0Pjlygmrfvn2qrKxUVlZWwPlZWVnKz8+v8Tr5+fl1Lu//fyjrjIRwtu14999/v9q2bRvwABk2bJheeeUVzZ8/X0888YQ+//xzXXTRRaqsrLR0/LUJZ7u6d++uF198Uf/85z81Z84c+Xw+nXHGGdq9e7ek2LnPli1bpnXr1unGG28MOD/a91m4avtdKywsVGlpqSWPcTujT3WzY5+k2G0UfQpEn+hTXeiT8+63WGoUfaJPdaFPzrvf6FN491lCg0cLW5k+fbrmzp2rBQsWBHzY3KhRo8zTvXv3Vk5Ojrp06aIFCxbovPPOi8ZQ6zV48GANHjzY/PmMM87QKaecoueff16PPvpoFEdmrb/97W/q3bu3Bg4cGHC+E+8zoC6x1CepaTSKPqGpoE/ORKPQFNAnZ6JP4XHkEVSZmZmKj4/X3r17A87fu3evsrOza7xOdnZ2ncv7/x/KOiMhnG3z+93vfqfp06fr448/Vk5OTp3Ldu7cWZmZmfr6668bPOZgNGS7/BITE3XqqaeaY46F+6y4uFhz587VDTfcUO/tNPZ9Fq7aftfS0tKUnJxsyWPBzuhTzezcJyl2G0WfAtEn+lQT+hTeOq1Ao35En+hTTehTeOu0An36UWP2yZETVG63W6eddprmz59vnufz+TR//vyA2diqBg8eHLC8JH3yySfm8p06dVJ2dnbAMoWFhVq6dGmt64yEcLZNkp588kk9+uij+s9//qP+/fvXezu7d+/W/v371aZNG0vGXZ9wt6uqyspKffXVV+aYnX6fSce+GresrExjxoyp93Ya+z4LV32/a1Y8FuyMPlVn9z5Jsdso+hSIPtGn49EnZ95vUuw1ij7Rp+PRJ2febxJ9khrQp5A+Ut1G5s6da3g8HuOll14yNmzYYNx0001Genq6+RWVY8eONR544AFz+cWLFxsJCQnG7373O2Pjxo1Gbm5ujV9Dmp6ebvzzn/801q5da1x66aVR+zrLULZt+vTphtvtNt5+++2Ar6s8fPiwYRiGcfjwYWPy5MlGXl6esWPHDuPTTz81+vXrZ5x00knGkSNHbLtd06ZNMz766CNj27ZtxsqVK41Ro0YZSUlJxvr16wO23Yn3md9ZZ51lXHXVVdXOt8t95h/L6tWrjdWrVxuSjKefftpYvXq1sXPnTsMwDOOBBx4wxo4day7v/xrSe++919i4caMxY8aMGr+GtK795XT0yXl9CmfbnNIo+kSfqqJP9Knq9jvxfvOze6PoU+joE32quv1OvN/86FP4fXLsBJVhGMaf//xno3379obb7TYGDhxoLFmyxLxs6NChxrhx4wKW//vf/25069bNcLvdRs+ePY1//etfAZf7fD7j17/+tZGVlWV4PB7jvPPOMzZv3twYm1JNKNvWoUMHQ1K1/3Jzcw3DMIySkhLjpz/9qdGqVSsjMTHR6NChgzFhwoSo/GMWynb98pe/NJfNysoyhg8fbqxatSpgfU69zwzDMDZt2mRIMj7++ONq67LTffbZZ5/V+Pjyb8+4ceOMoUOHVrtO3759DbfbbXTu3NmYPXt2tfXWtb9iAX06xkl9MozYbRR9CrwOfaJPhkGfnHq/GYYzGkWfwkOfjqFPzrzfDIM+NbRPLsMwjNCOuQIAAAAAAACs48jPoAIAAAAAAEDsYIIKAAAAAAAAUcUEFQAAAAAAAKKKCSoAAAAAAABEFRNUAAAAAAAAiComqAAAAAAAABBVTFABAAAAAAAgqpigAgAAAAAAQFQxQYVGMX78eP3sZz9r0Dq++eYbuVwuffnll7Uus2DBArlcLhUUFEiSXnrpJaWnp5uXT506VX379m3QOADEFvoEwM5oFAC7ok+wGhNUqGb8+PFyuVxyuVxyu93q2rWrHnnkEVVUVER7aPU644wztGfPHnm93hovnzx5subPn2/+bEVUATQe+gTAzmgUALuiT3CChGgPAPY0bNgwzZ49W2VlZZo3b54mTpyoxMREPfjggwHLlZeXy+12R2mU1bndbmVnZ9d6eWpqqlJTUxtxRACsRp8A2BmNAmBX9Al2xxFUqJHH41F2drY6dOigW2+9Veeff77ef/99czb6scceU9u2bdW9e3dJ0ldffaVzzz1XycnJysjI0E033aSioqJq6502bZpatWqltLQ03XLLLSovLzcv+89//qOzzjpL6enpysjI0CWXXKJt27ZVW8emTZt0xhlnKCkpSb169dLnn39uXnb84Z/Hq3r459SpU/Xyyy/rn//8p/nXhAULFujcc8/V7bffHnC9H374QW63O2BmHkB00Cf6BNgZjaJRgF3RJ/pkd0xQISjJyclmaObPn6/Nmzfrk08+0Ycffqji4mJdeOGFatGihZYvX6633npLn376abUAzJ8/Xxs3btSCBQv0xhtv6N1339W0adPMy4uLizVp0iStWLFC8+fPV1xcnC677DL5fL6A9dx777265557tHr1ag0ePFgjRozQ/v37Q96myZMn68orr9SwYcO0Z88e7dmzR2eccYZuvPFGvf766yorKzOXnTNnjk444QSde+65Id8OgMiiT/QJsDMaRaMAu6JP9Ml2DOA448aNMy699FLDMAzD5/MZn3zyieHxeIzJkycb48aNM7KysoyysjJz+VmzZhktWrQwioqKzPP+9a9/GXFxcUZ+fr65zpYtWxrFxcXmMs8995yRmppqVFZW1jiOH374wZBkfPXVV4ZhGMaOHTsMScb06dPNZY4ePWqceOKJxhNPPGEYhmF89tlnhiTj4MGDhmEYxuzZsw2v12sun5uba/Tp06fGbfUrLS01WrRoYbz55pvmeTk5OcbUqVPr2XMAIo0+0SfAzmgUjQLsij7RJyfgCCrU6MMPP1RqaqqSkpJ00UUX6aqrrtLUqVMlSb179w54T/LGjRvVp08fpaSkmOedeeaZ8vl82rx5s3lenz591KxZM/PnwYMHq6ioSN9++60kaevWrbr66qvVuXNnpaWlqWPHjpKkXbt2BYxt8ODB5umEhAT1799fGzdutGzbk5KSNHbsWL344ouSpFWrVmndunUaP368ZbcBIHz0iT4BdkajaBRgV/SJPtkdH5KOGp1zzjl67rnn5Ha71bZtWyUk/PhQqRopK40YMUIdOnTQCy+8oLZt28rn86lXr14B72FuLDfeeKP69u2r3bt3a/bs2Tr33HPVoUOHRh8HgOroE30C7IxG0SjArugTfbI7jqBCjVJSUtS1a1e1b98+IFw1OeWUU7RmzRoVFxeb5y1evFhxcXHmB+xJ0po1a1RaWmr+vGTJEqWmpqpdu3bav3+/Nm/erIceekjnnXeeTjnlFB08eLDG21uyZIl5uqKiQitXrtQpp5wS1na63W5VVlZWO793797q37+/XnjhBb3++uu6/vrrw1o/AOvRJ/oE2BmNolGAXdEn+mR3TFChwUaPHq2kpCSNGzdO69at02effaY77rhDY8eOVVZWlrlceXm5brjhBm3YsEHz5s1Tbm6ubr/9dsXFxalFixbKyMjQrFmz9PXXX+u///2vJk2aVOPtzZgxQ++99542bdqkiRMn6uDBg2HHpWPHjlq7dq02b96sffv26ejRo+ZlN954o6ZPny7DMHTZZZeFtX4A0UWfANgZjQJgV/QJ0cAEFRqsWbNm+uijj3TgwAENGDBAV1xxhc477zz95S9/CVjuvPPO00knnaQhQ4boqquu0siRI833PMfFxWnu3LlauXKlevXqpbvvvltPPfVUjbc3ffp0TZ8+XX369NGiRYv0/vvvKzMzM6yxT5gwQd27d1f//v3VqlUrLV682Lzs6quvVkJCgq6++molJSWFtX4A0UWfANgZjQJgV/QJ0eAyDMOI9iAAO/rmm2/UpUsXLV++XP369Yv2cADARJ8A2BmNAmBX9MnemKACjnP06FHt379fkydP1o4dOwJm3AEgmugTADujUQDsij45A2/xA46zePFitWnTRsuXL9fMmTOjPRwAMNEnAHZGowDYFX1yBo6gAgAAAAAAQFRxBBUAAAAAAACiigkqAAAAAAAARBUTVAAAAAAAAIgqJqgAAAAAAAAQVUxQAQAAAAAAIKqYoAIAAAAAAEBUMUEFAAAAAACAqGKCCgAAAAAAAFH1/3mKpwpbbcnRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare predictions at different decision thresholds\n",
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(12, 6))\n",
    "\n",
    "# Loop through each threshold\n",
    "for i in range(4):\n",
    "\n",
    "    # Top row: confusion matrices\n",
    "    acc = accuracy_score(test_df['target'], threshold_predictions[i])\n",
    "    axes[0, i].set_title(f'Threshold = {thresholds[i]}\\n(accuracy: {acc:.2%})')\n",
    "    \n",
    "    ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        threshold_predictions[i],\n",
    "        ax=axes[0, i],\n",
    "        colorbar=False\n",
    "    )\n",
    "    \n",
    "    # Bottom row: probability distributions\n",
    "    probabilities_threshold_df = pd.DataFrame({\n",
    "        'Probability': threshold_probabilities[:, 1],\n",
    "        'Label': test_df['target']\n",
    "    })\n",
    "    \n",
    "    sns.boxplot(data=probabilities_threshold_df, x='Probability', y='Label', orient='h', ax=axes[1, i])\n",
    "    axes[1, i].axvline(thresholds[i], color='black', linestyle='--')\n",
    "    axes[1, i].set_ylabel('Predicted probability')\n",
    "    axes[1, i].set_title('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc61894e",
   "metadata": {},
   "source": [
    "### 4.2. Optimizing thresholds with TunedThresholdClassifierCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b883b8fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default threshold: 0.500\n",
      "Precision-optimized threshold: 0.989\n",
      "Recall-optimized threshold: 0.000\n",
      "F1-optimized threshold: 0.858\n"
     ]
    }
   ],
   "source": [
    "# Create base logistic regression model with balanced class weighting\n",
    "base_model = LogisticRegression(class_weight='balanced', random_state=315)\n",
    "\n",
    "# Optimize thresholds for different objectives using TunedThresholdClassifierCV\n",
    "# Default model (threshold = 0.5)\n",
    "default_model = LogisticRegression(class_weight='balanced', random_state=315)\n",
    "default_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "# Precision-optimized threshold\n",
    "precision_tuned_model = TunedThresholdClassifierCV(\n",
    "    estimator=base_model,\n",
    "    scoring='precision',\n",
    "    cv=5,\n",
    "    random_state=315\n",
    ")\n",
    "precision_tuned_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "# Recall-optimized threshold\n",
    "recall_tuned_model = TunedThresholdClassifierCV(\n",
    "    estimator=base_model,\n",
    "    scoring='recall',\n",
    "    cv=5,\n",
    "    random_state=315\n",
    ")\n",
    "recall_tuned_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "# F1-optimized threshold\n",
    "f1_tuned_model = TunedThresholdClassifierCV(\n",
    "    estimator=base_model,\n",
    "    scoring='f1',\n",
    "    cv=5,\n",
    "    random_state=315\n",
    ")\n",
    "f1_tuned_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "\n",
    "# Get optimized thresholds\n",
    "default_threshold = 0.5\n",
    "precision_threshold = precision_tuned_model.best_threshold_\n",
    "recall_threshold = recall_tuned_model.best_threshold_\n",
    "f1_threshold = f1_tuned_model.best_threshold_\n",
    "\n",
    "# Generate predictions\n",
    "default_preds = default_model.predict(test_df.drop(columns=['target']))\n",
    "precision_preds = precision_tuned_model.predict(test_df.drop(columns=['target']))\n",
    "recall_preds = recall_tuned_model.predict(test_df.drop(columns=['target']))\n",
    "f1_preds = f1_tuned_model.predict(test_df.drop(columns=['target']))\n",
    "\n",
    "tuned_predictions = [default_preds, precision_preds, recall_preds, f1_preds]\n",
    "tuned_thresholds = [default_threshold, precision_threshold, recall_threshold, f1_threshold]\n",
    "\n",
    "# Get probabilities for visualization\n",
    "threshold_probabilities = default_model.predict_proba(test_df.drop(columns=['target']))\n",
    "\n",
    "print(f'Default threshold: {default_threshold:.3f}')\n",
    "print(f'Precision-optimized threshold: {precision_threshold:.3f}')\n",
    "print(f'Recall-optimized threshold: {recall_threshold:.3f}')\n",
    "print(f'F1-optimized threshold: {f1_threshold:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c59fe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare predictions with tuned thresholds\n",
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(12, 6))\n",
    "\n",
    "titles_tuned = [\n",
    "    f'Default ({default_threshold:.3f})',\n",
    "    f'Precision-optimized\\n({precision_threshold:.3f})',\n",
    "    f'Recall-optimized\\n({recall_threshold:.3f})',\n",
    "    f'F1-optimized\\n({f1_threshold:.3f})'\n",
    "]\n",
    "\n",
    "# Loop through each tuned threshold\n",
    "for i in range(4):\n",
    "\n",
    "    # Top row: confusion matrices\n",
    "    acc = accuracy_score(test_df['target'], tuned_predictions[i])\n",
    "    axes[0, i].set_title(f'{titles_tuned[i]}\\n(accuracy: {acc:.2%})')\n",
    "    \n",
    "    ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        tuned_predictions[i],\n",
    "        ax=axes[0, i],\n",
    "        colorbar=False\n",
    "    )\n",
    "    \n",
    "    # Bottom row: probability distributions\n",
    "    probabilities_tuned_df = pd.DataFrame({\n",
    "        'Probability': threshold_probabilities[:, 1],\n",
    "        'Label': test_df['target']\n",
    "    })\n",
    "    \n",
    "    sns.boxplot(data=probabilities_tuned_df, x='Probability', y='Label', orient='h', ax=axes[1, i])\n",
    "    axes[1, i].axvline(tuned_thresholds[i], color='black', linestyle='--')\n",
    "    axes[1, i].set_ylabel('Predicted probability')\n",
    "    axes[1, i].set_title('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fec374d",
   "metadata": {},
   "source": [
    "## 5. Classification models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "89f7987e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize dataframes to store predictions and CV F1 scores\n",
    "predictions_df = test_df['target'].to_frame().rename(columns={'target': 'True_Label'})\n",
    "cv_scores_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98e4007",
   "metadata": {},
   "source": [
    "### 5.1. Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f090a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression - cross-validation F1 mean: 0.6945\n"
     ]
    }
   ],
   "source": [
    "# Train logistic regression model\n",
    "logistic_model = LogisticRegression(random_state=315, max_iter=1000, class_weight='balanced')\n",
    "\n",
    "# Evaluate performance with 7-fold cross-validation\n",
    "logistic_f1_scores = cross_val_score(logistic_model, train_df.drop(columns=['target']), train_df['target'], cv=7, scoring='f1')\n",
    "cv_scores_df['Logistic_Regression'] = logistic_f1_scores\n",
    "\n",
    "print(f'Logistic Regression - cross-validation F1 mean: {logistic_f1_scores.mean():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eeac77ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on full training set and collect predictions\n",
    "logistic_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "predictions_df['Logistic_Regression'] = logistic_model.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aafb16f",
   "metadata": {},
   "source": [
    "### 5.2. Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1839cdc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes - cross-validation F1 mean: 0.7225\n"
     ]
    }
   ],
   "source": [
    "# Train Gaussian Naive Bayes model\n",
    "nb_model = GaussianNB()\n",
    "\n",
    "# Evaluate performance with 7-fold cross-validation\n",
    "nb_f1_scores = cross_val_score(nb_model, train_df.drop(columns=['target']), train_df['target'], cv=7, scoring='f1')\n",
    "cv_scores_df['Naive_Bayes'] = nb_f1_scores\n",
    "\n",
    "print(f'Naive Bayes - cross-validation F1 mean: {nb_f1_scores.mean():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80052ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on full training set and collect predictions\n",
    "nb_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "predictions_df['Naive_Bayes'] = nb_model.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fb57d6",
   "metadata": {},
   "source": [
    "### 5.3. K-nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "273e414f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors - cross-validation F1 mean: 0.8558\n"
     ]
    }
   ],
   "source": [
    "# Train K-nearest neighbors model\n",
    "knn_model = KNeighborsClassifier()\n",
    "\n",
    "# Evaluate performance with 7-fold cross-validation\n",
    "knn_f1_scores = cross_val_score(knn_model, train_df.drop(columns=['target']), train_df['target'], cv=7, scoring='f1')\n",
    "cv_scores_df['K_Nearest_Neighbors'] = knn_f1_scores\n",
    "\n",
    "print(f'K-Nearest Neighbors - cross-validation F1 mean: {knn_f1_scores.mean():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a336f51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on full training set and collect predictions\n",
    "knn_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "predictions_df['K_Nearest_Neighbors'] = knn_model.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d29e30",
   "metadata": {},
   "source": [
    "### 5.4. Decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f37bf1c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree - cross-validation F1 mean: 0.7993\n"
     ]
    }
   ],
   "source": [
    "# Train decision tree model\n",
    "tree_model = DecisionTreeClassifier(random_state=315, class_weight='balanced')\n",
    "\n",
    "# Evaluate performance with 7-fold cross-validation\n",
    "tree_f1_scores = cross_val_score(tree_model, train_df.drop(columns=['target']), train_df['target'], cv=7, scoring='f1')\n",
    "cv_scores_df['Decision_Tree'] = tree_f1_scores\n",
    "\n",
    "print(f'Decision Tree - cross-validation F1 mean: {tree_f1_scores.mean():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1cc67716",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on full training set and collect predictions\n",
    "tree_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "predictions_df['Decision_Tree'] = tree_model.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef390f94",
   "metadata": {},
   "source": [
    "### 5.5. Support vector machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6501aff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine - cross-validation F1 mean: 0.7994\n"
     ]
    }
   ],
   "source": [
    "# Train support vector machine model with RBF kernel\n",
    "svm_model = SVC(random_state=315, probability=True, class_weight='balanced')\n",
    "\n",
    "# Evaluate performance with 7-fold cross-validation\n",
    "svm_f1_scores = cross_val_score(svm_model, train_df.drop(columns=['target']), train_df['target'], cv=7, scoring='f1')\n",
    "cv_scores_df['Support_Vector_Machine'] = svm_f1_scores\n",
    "\n",
    "print(f'Support Vector Machine - cross-validation F1 mean: {svm_f1_scores.mean():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2a997a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on full training set and collect predictions\n",
    "svm_model.fit(train_df.drop(columns=['target']), train_df['target'])\n",
    "predictions_df['Support_Vector_Machine'] = svm_model.predict(test_df.drop(columns=['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a011ae",
   "metadata": {},
   "source": [
    "### 5.6. Model comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e8962e8d",
   "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>True_Label</th>\n",
       "      <th>Logistic_Regression</th>\n",
       "      <th>Naive_Bayes</th>\n",
       "      <th>K_Nearest_Neighbors</th>\n",
       "      <th>Decision_Tree</th>\n",
       "      <th>Support_Vector_Machine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3467</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4739</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2096</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4663</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      True_Label  Logistic_Regression  Naive_Bayes  K_Nearest_Neighbors  \\\n",
       "3467           0                    0            0                    0   \n",
       "600            0                    0            0                    0   \n",
       "1148           0                    0            0                    0   \n",
       "3876           0                    0            0                    0   \n",
       "4739           0                    1            1                    0   \n",
       "2096           0                    0            0                    0   \n",
       "4663           0                    0            0                    0   \n",
       "1086           1                    1            1                    1   \n",
       "3358           0                    0            0                    0   \n",
       "1612           0                    0            0                    0   \n",
       "\n",
       "      Decision_Tree  Support_Vector_Machine  \n",
       "3467              0                       0  \n",
       "600               0                       0  \n",
       "1148              0                       0  \n",
       "3876              0                       0  \n",
       "4739              0                       1  \n",
       "2096              0                       0  \n",
       "4663              0                       0  \n",
       "1086              1                       1  \n",
       "3358              0                       0  \n",
       "1612              0                       0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display predictions dataframe\n",
    "predictions_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5841c2b6",
   "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>Logistic_Regression</th>\n",
       "      <th>Naive_Bayes</th>\n",
       "      <th>K_Nearest_Neighbors</th>\n",
       "      <th>Decision_Tree</th>\n",
       "      <th>Support_Vector_Machine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.679739</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.846847</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>0.744526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.642424</td>\n",
       "      <td>0.662069</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.728814</td>\n",
       "      <td>0.748201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.701987</td>\n",
       "      <td>0.729927</td>\n",
       "      <td>0.872727</td>\n",
       "      <td>0.778761</td>\n",
       "      <td>0.848000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.703448</td>\n",
       "      <td>0.864407</td>\n",
       "      <td>0.826446</td>\n",
       "      <td>0.835821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.731343</td>\n",
       "      <td>0.849558</td>\n",
       "      <td>0.862385</td>\n",
       "      <td>0.809160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.701987</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.886957</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.706667</td>\n",
       "      <td>0.706767</td>\n",
       "      <td>0.859649</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>0.781955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Logistic_Regression  Naive_Bayes  K_Nearest_Neighbors  Decision_Tree  \\\n",
       "0             0.679739     0.733333             0.846847       0.781818   \n",
       "1             0.642424     0.662069             0.810345       0.728814   \n",
       "2             0.701987     0.729927             0.872727       0.778761   \n",
       "3             0.717949     0.703448             0.864407       0.826446   \n",
       "4             0.710526     0.731343             0.849558       0.862385   \n",
       "5             0.701987     0.790698             0.886957       0.820513   \n",
       "6             0.706667     0.706767             0.859649       0.796610   \n",
       "\n",
       "   Support_Vector_Machine  \n",
       "0                0.744526  \n",
       "1                0.748201  \n",
       "2                0.848000  \n",
       "3                0.835821  \n",
       "4                0.809160  \n",
       "5                0.828125  \n",
       "6                0.781955  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display CV scores dataframe\n",
    "cv_scores_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7741e7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Melt CV scores dataframe for boxplot\n",
    "cv_scores_melted = cv_scores_df.melt(var_name='Model', value_name='F1 Score')\n",
    "\n",
    "# Create boxplot of cross-validation F1 scores\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.title('Cross-validation F1 score comparison')\n",
    "sns.boxplot(data=cv_scores_melted, x='Model', y='F1 Score', color='grey')\n",
    "plt.ylabel('F1 score')\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "601c3889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x240 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix comparison plot using predictions dataframe\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(9, 2.4))\n",
    "\n",
    "model_columns = [col for col in predictions_df.columns if col != 'True_Label']\n",
    "\n",
    "for i, col in enumerate(model_columns):\n",
    "\n",
    "    ConfusionMatrixDisplay.from_predictions(\n",
    "        predictions_df['True_Label'],\n",
    "        predictions_df[col],\n",
    "        ax=axes[i],\n",
    "        normalize='true',\n",
    "        colorbar=False\n",
    "    )\n",
    "    \n",
    "    # Format model name for display\n",
    "    model_name = col.replace('_', '\\n')\n",
    "    axes[i].set_title(model_name)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0d497e",
   "metadata": {},
   "source": [
    "## 6. Multiclass & multilabel classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd58f3d0",
   "metadata": {},
   "source": [
    "### 6.1. Multiclass classification\n",
    "\n",
    "Multiclass classification extends binary classification to predict one label from three or more classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d4367d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target class distribution:\n",
      "target\n",
      "1    255\n",
      "2    251\n",
      "3    250\n",
      "0    244\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Generate synthetic multiclass dataset with 4 classes\n",
    "multiclass_data = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=5,\n",
    "    n_informative=3,\n",
    "    n_redundant=1,\n",
    "    n_classes=4,\n",
    "    random_state=315\n",
    ")\n",
    "\n",
    "# Create dataframe\n",
    "multiclass_df = pd.DataFrame(\n",
    "    multiclass_data[0],\n",
    "    columns=[f'feature_{i}' for i in range(multiclass_data[0].shape[1])]\n",
    ")\n",
    "\n",
    "multiclass_df['target'] = multiclass_data[1]\n",
    "\n",
    "print(f'Target class distribution:\\n{multiclass_df[\"target\"].value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "96fe1b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual:      [0 0 1 1 2 2 0 0 1 2 0 1 1 1 2 2 3 3 0 0]\n",
      "Predictions: [2 0 3 1 2 2 0 0 3 2 0 1 1 1 2 2 3 2 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Split into train and test sets\n",
    "multiclass_train_df, multiclass_test_df = train_test_split(multiclass_df, random_state=315)\n",
    "\n",
    "# Train logistic regression model\n",
    "multiclass_model = LogisticRegression(random_state=315, max_iter=1000)\n",
    "multiclass_model.fit(\n",
    "    multiclass_train_df.drop(columns=['target']),\n",
    "    multiclass_train_df['target']\n",
    ")\n",
    "\n",
    "# Generate predictions\n",
    "multiclass_predictions = multiclass_model.predict(multiclass_test_df.drop(columns=['target']))\n",
    "\n",
    "print(f'Actual:      {multiclass_test_df[\"target\"].values[:20]}')\n",
    "print(f'Predictions: {multiclass_predictions[:20]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9254836e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize multiclass confusion matrix\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "    multiclass_test_df['target'],\n",
    "    multiclass_predictions,\n",
    "    normalize='true',\n",
    "    colorbar=False\n",
    ")\n",
    "\n",
    "plt.title('Multiclass confusion matrix')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0cff950",
   "metadata": {},
   "source": [
    "### 6.2. Multilabel classification\n",
    "\n",
    "Multilabel classification predicts multiple binary labels simultaneously. Each sample can belong to zero, one, or multiple classes at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "528f4a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilabel targets:\n",
      " [[1 1 1]\n",
      " [0 1 0]\n",
      " [0 0 0]\n",
      " [1 1 1]\n",
      " [1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "# Generate synthetic multilabel dataset with 3 labels\n",
    "multilabel_data = make_multilabel_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=5,\n",
    "    n_classes=3,\n",
    "    n_labels=2,\n",
    "    random_state=315\n",
    ")\n",
    "\n",
    "# Create dataframe for features\n",
    "multilabel_df = pd.DataFrame(\n",
    "    multilabel_data[0],\n",
    "    columns=[f'feature_{i}' for i in range(multilabel_data[0].shape[1])]\n",
    ")\n",
    "\n",
    "# Labels are stored as a 2D array (each row has multiple binary labels)\n",
    "multilabel_targets = multilabel_data[1]\n",
    "\n",
    "print(f'Multilabel targets:\\n {multilabel_targets[:5]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a559f9e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 samples:\n",
      "\n",
      "Actual labels:\n",
      "[[1 1 0]\n",
      " [1 1 0]\n",
      " [0 0 1]\n",
      " [1 0 1]\n",
      " [1 1 1]\n",
      " [1 0 0]\n",
      " [1 0 1]\n",
      " [1 1 0]\n",
      " [0 0 0]\n",
      " [1 0 1]]\n",
      "\n",
      "Predicted labels:\n",
      "[[1 1 1]\n",
      " [1 1 0]\n",
      " [0 1 1]\n",
      " [1 0 1]\n",
      " [1 1 1]\n",
      " [1 0 0]\n",
      " [1 1 1]\n",
      " [1 1 0]\n",
      " [0 0 0]\n",
      " [1 0 1]]\n",
      "\n",
      "Exact match accuracy: 0.5640\n"
     ]
    }
   ],
   "source": [
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    multilabel_df,\n",
    "    multilabel_targets,\n",
    "    random_state=315\n",
    ")\n",
    "\n",
    "# Train logistic regression for multilabel classification\n",
    "multilabel_model = MultiOutputClassifier(LogisticRegression(random_state=315, max_iter=1000))\n",
    "multilabel_model.fit(X_train, y_train)\n",
    "\n",
    "# Generate predictions\n",
    "multilabel_predictions = multilabel_model.predict(X_test)\n",
    "\n",
    "print('First 10 samples:')\n",
    "print('\\nActual labels:')\n",
    "print(y_test[:10])\n",
    "print('\\nPredicted labels:')\n",
    "print(multilabel_predictions[:10])\n",
    "\n",
    "# Calculate accuracy (exact match - all labels must be correct)\n",
    "exact_match_accuracy = accuracy_score(y_test, multilabel_predictions)\n",
    "print(f'\\nExact match accuracy: {exact_match_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bb11dc8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x240 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize per-label confusion matrices\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(6, 2.4))\n",
    "\n",
    "for i in range(3):\n",
    "\n",
    "    ConfusionMatrixDisplay.from_predictions(\n",
    "        y_test[:, i],\n",
    "        multilabel_predictions[:, i],\n",
    "        ax=axes[i],\n",
    "        normalize='true',\n",
    "        colorbar=False\n",
    "    )\n",
    "    \n",
    "    axes[i].set_title(f'Label {i}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "535c19b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}