{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e8eef48",
   "metadata": {},
   "source": [
    "# Lesson 21: Ensemble learning demonstration\n",
    "\n",
    "This notebook demonstrates key concepts and tools for training ensemble models.\n",
    "\n",
    "1. Baseline models\n",
    "    - Logistic regression\n",
    "    - Decision tree\n",
    "2. Parallel ensembles\n",
    "    - Voting ensemble\n",
    "    - Bagging ensemble\n",
    "    - Random forest\n",
    "3. Serial (sequential) ensembles\n",
    "    - AdaBoost\n",
    "    - Gradient boosting\n",
    "    - Stacking ensemble\n",
    "4. Model comparison\n",
    "    - Score comparison\n",
    "    - Confusion matrix comparison\n",
    "5. Model metric optimization\n",
    "    - ROC_AUC optimized thresholds\n",
    "    - F1 optimized thresholds\n",
    "\n",
    "## Notebook set up\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "463812a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split, cross_validate, TunedThresholdClassifierCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Ensemble models\n",
    "from sklearn.ensemble import (\n",
    "    AdaBoostClassifier,\n",
    "    BaggingClassifier,\n",
    "    GradientBoostingClassifier,\n",
    "    RandomForestClassifier,\n",
    "    StackingClassifier,\n",
    "    VotingClassifier,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7012bb8b",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60a2f9f5",
   "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",
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       "\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>feature_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_11</th>\n",
       "      <th>feature_12</th>\n",
       "      <th>feature_13</th>\n",
       "      <th>feature_14</th>\n",
       "      <th>feature_15</th>\n",
       "      <th>feature_16</th>\n",
       "      <th>feature_17</th>\n",
       "      <th>feature_18</th>\n",
       "      <th>feature_19</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6850</th>\n",
       "      <td>2.056957</td>\n",
       "      <td>0.222830</td>\n",
       "      <td>-1.914398</td>\n",
       "      <td>1.095708</td>\n",
       "      <td>-2.389691</td>\n",
       "      <td>-1.113905</td>\n",
       "      <td>1.296463</td>\n",
       "      <td>-5.769138</td>\n",
       "      <td>-1.742394</td>\n",
       "      <td>-1.717438</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903576</td>\n",
       "      <td>-1.730474</td>\n",
       "      <td>-0.211586</td>\n",
       "      <td>0.402758</td>\n",
       "      <td>-2.171021</td>\n",
       "      <td>1.242654</td>\n",
       "      <td>-0.993714</td>\n",
       "      <td>1.083335</td>\n",
       "      <td>1.735312</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5856</th>\n",
       "      <td>1.776417</td>\n",
       "      <td>-3.711362</td>\n",
       "      <td>-0.042654</td>\n",
       "      <td>-4.450805</td>\n",
       "      <td>0.283605</td>\n",
       "      <td>0.062951</td>\n",
       "      <td>-2.952074</td>\n",
       "      <td>-4.635668</td>\n",
       "      <td>0.133406</td>\n",
       "      <td>-1.665485</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.160329</td>\n",
       "      <td>1.273855</td>\n",
       "      <td>-2.754988</td>\n",
       "      <td>-5.904544</td>\n",
       "      <td>-0.580578</td>\n",
       "      <td>0.952436</td>\n",
       "      <td>-0.566598</td>\n",
       "      <td>-1.590927</td>\n",
       "      <td>0.632844</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2483</th>\n",
       "      <td>-0.017753</td>\n",
       "      <td>1.031828</td>\n",
       "      <td>-2.485858</td>\n",
       "      <td>-0.667876</td>\n",
       "      <td>-1.021137</td>\n",
       "      <td>-0.692445</td>\n",
       "      <td>0.630700</td>\n",
       "      <td>2.300769</td>\n",
       "      <td>-1.097844</td>\n",
       "      <td>0.717599</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.728046</td>\n",
       "      <td>0.328055</td>\n",
       "      <td>0.403882</td>\n",
       "      <td>1.735435</td>\n",
       "      <td>0.162798</td>\n",
       "      <td>-1.306485</td>\n",
       "      <td>-0.760256</td>\n",
       "      <td>0.780826</td>\n",
       "      <td>-1.476193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4915</th>\n",
       "      <td>-0.495843</td>\n",
       "      <td>2.895735</td>\n",
       "      <td>-2.537299</td>\n",
       "      <td>-2.609338</td>\n",
       "      <td>-0.875950</td>\n",
       "      <td>2.935914</td>\n",
       "      <td>3.253340</td>\n",
       "      <td>6.378967</td>\n",
       "      <td>-0.058042</td>\n",
       "      <td>-1.338881</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.579209</td>\n",
       "      <td>1.383636</td>\n",
       "      <td>0.879382</td>\n",
       "      <td>2.941039</td>\n",
       "      <td>-0.171335</td>\n",
       "      <td>0.508342</td>\n",
       "      <td>-0.704955</td>\n",
       "      <td>1.260240</td>\n",
       "      <td>-2.110639</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>0.899913</td>\n",
       "      <td>-0.241683</td>\n",
       "      <td>-1.206857</td>\n",
       "      <td>-4.108075</td>\n",
       "      <td>0.786454</td>\n",
       "      <td>-0.923499</td>\n",
       "      <td>-1.404900</td>\n",
       "      <td>-6.952240</td>\n",
       "      <td>-1.282080</td>\n",
       "      <td>-1.646648</td>\n",
       "      <td>...</td>\n",
       "      <td>3.731664</td>\n",
       "      <td>1.857395</td>\n",
       "      <td>-0.758554</td>\n",
       "      <td>4.081466</td>\n",
       "      <td>2.046172</td>\n",
       "      <td>0.938040</td>\n",
       "      <td>-0.088168</td>\n",
       "      <td>-0.283488</td>\n",
       "      <td>0.257320</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
       "6850   2.056957   0.222830  -1.914398   1.095708  -2.389691  -1.113905   \n",
       "5856   1.776417  -3.711362  -0.042654  -4.450805   0.283605   0.062951   \n",
       "2483  -0.017753   1.031828  -2.485858  -0.667876  -1.021137  -0.692445   \n",
       "4915  -0.495843   2.895735  -2.537299  -2.609338  -0.875950   2.935914   \n",
       "4598   0.899913  -0.241683  -1.206857  -4.108075   0.786454  -0.923499   \n",
       "\n",
       "      feature_6  feature_7  feature_8  feature_9  ...  feature_11  feature_12  \\\n",
       "6850   1.296463  -5.769138  -1.742394  -1.717438  ...    0.903576   -1.730474   \n",
       "5856  -2.952074  -4.635668   0.133406  -1.665485  ...   -1.160329    1.273855   \n",
       "2483   0.630700   2.300769  -1.097844   0.717599  ...   -0.728046    0.328055   \n",
       "4915   3.253340   6.378967  -0.058042  -1.338881  ...   -1.579209    1.383636   \n",
       "4598  -1.404900  -6.952240  -1.282080  -1.646648  ...    3.731664    1.857395   \n",
       "\n",
       "      feature_13  feature_14  feature_15  feature_16  feature_17  feature_18  \\\n",
       "6850   -0.211586    0.402758   -2.171021    1.242654   -0.993714    1.083335   \n",
       "5856   -2.754988   -5.904544   -0.580578    0.952436   -0.566598   -1.590927   \n",
       "2483    0.403882    1.735435    0.162798   -1.306485   -0.760256    0.780826   \n",
       "4915    0.879382    2.941039   -0.171335    0.508342   -0.704955    1.260240   \n",
       "4598   -0.758554    4.081466    2.046172    0.938040   -0.088168   -0.283488   \n",
       "\n",
       "      feature_19  target  \n",
       "6850    1.735312       0  \n",
       "5856    0.632844       0  \n",
       "2483   -1.476193       0  \n",
       "4915   -2.110639       0  \n",
       "4598    0.257320       0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate synthetic dataset, similar to Lesson 20: classification but\n",
    "# more challenging with: more features, redundant features, and noise\n",
    "data = make_classification(\n",
    "    n_samples=10000,\n",
    "    n_features=20,\n",
    "    n_informative=10,\n",
    "    n_redundant=5,\n",
    "    n_clusters_per_class=3,\n",
    "    weights=[0.9, 0.1],\n",
    "    flip_y=0.1,\n",
    "    class_sep=0.5,\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 and test sets\n",
    "train_df, test_df = train_test_split(df, random_state=315)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2867da3d",
   "metadata": {},
   "source": [
    "### Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f93530f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_ci(values, z=1.96):\n",
    "    '''Calculate the mean and 95% confidence interval for a given array of values.'''\n",
    "\n",
    "    mean = values.mean()\n",
    "    margin = z * values.std(ddof=1) / (len(values) ** 0.5)\n",
    "\n",
    "    return mean, mean - margin, mean + margin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa06f29",
   "metadata": {},
   "source": [
    "## 1. Baseline models\n",
    "\n",
    "Scikit-learn [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db77210f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression - AUC: 0.663 (95% CI: 0.642, 0.683), F1: 0.305 (95% CI: 0.291, 0.318)\n"
     ]
    }
   ],
   "source": [
    "# Logistic regression baseline\n",
    "lr_clf = LogisticRegression(random_state=315, max_iter=1000, class_weight='balanced')\n",
    "\n",
    "lr_scores = cross_validate(\n",
    "    lr_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "lr_auc = lr_scores['test_roc_auc']\n",
    "lr_f1 = lr_scores['test_f1']\n",
    "\n",
    "lr_auc_mean, lr_auc_low, lr_auc_high = mean_ci(lr_auc)\n",
    "lr_f1_mean, lr_f1_low, lr_f1_high = mean_ci(lr_f1)\n",
    "print(\n",
    "    f'Logistic Regression - AUC: {lr_auc_mean:.3f} (95% CI: {lr_auc_low:.3f}, {lr_auc_high:.3f}), '\n",
    "    f'F1: {lr_f1_mean:.3f} (95% CI: {lr_f1_low:.3f}, {lr_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "lr_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "lr_pred = lr_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1adac4",
   "metadata": {},
   "source": [
    "Scikit-learn [Decision Tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8697878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree - AUC: 0.600 (95% CI: 0.588, 0.612), F1: 0.307 (95% CI: 0.287, 0.327)\n"
     ]
    }
   ],
   "source": [
    "# Decision tree baseline\n",
    "dt_clf = DecisionTreeClassifier(random_state=315, class_weight='balanced')\n",
    "\n",
    "dt_scores = cross_validate(\n",
    "    dt_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "dt_auc = dt_scores['test_roc_auc']\n",
    "dt_f1 = dt_scores['test_f1']\n",
    "\n",
    "dt_auc_mean, dt_auc_low, dt_auc_high = mean_ci(dt_auc)\n",
    "dt_f1_mean, dt_f1_low, dt_f1_high = mean_ci(dt_f1)\n",
    "print(\n",
    "    f'Decision Tree - AUC: {dt_auc_mean:.3f} (95% CI: {dt_auc_low:.3f}, {dt_auc_high:.3f}), '\n",
    "    f'F1: {dt_f1_mean:.3f} (95% CI: {dt_f1_low:.3f}, {dt_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "dt_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "dt_pred = dt_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7a6b8c",
   "metadata": {},
   "source": [
    "## 2. Parallel ensembles\n",
    "\n",
    "### 2.1. Voting ensemble\n",
    "\n",
    "Combines predictions from multiple independent models by majority vote (hard voting) or by averaging predicted probabilities (soft voting).\n",
    "\n",
    "Scikit-learn [Voting Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8cf9ed05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting - AUC: 0.764 (95% CI: 0.752, 0.776), F1: 0.360 (95% CI: 0.343, 0.377)\n"
     ]
    }
   ],
   "source": [
    "# Create voting classifier with soft voting\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[\n",
    "        ('lr', LogisticRegression(random_state=315, max_iter=1000, class_weight='balanced')),\n",
    "        ('dt', DecisionTreeClassifier(random_state=315, class_weight='balanced')),\n",
    "        ('svm', CalibratedClassifierCV(SVC(random_state=315, class_weight='balanced'), ensemble=False))\n",
    "    ],\n",
    "    voting='soft'\n",
    ")\n",
    "\n",
    "voting_scores = cross_validate(\n",
    "    voting_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "voting_auc = voting_scores['test_roc_auc']\n",
    "voting_f1 = voting_scores['test_f1']\n",
    "\n",
    "voting_auc_mean, voting_auc_low, voting_auc_high = mean_ci(voting_auc)\n",
    "voting_f1_mean, voting_f1_low, voting_f1_high = mean_ci(voting_f1)\n",
    "print(\n",
    "    f'Voting - AUC: {voting_auc_mean:.3f} (95% CI: {voting_auc_low:.3f}, {voting_auc_high:.3f}), '\n",
    "    f'F1: {voting_f1_mean:.3f} (95% CI: {voting_f1_low:.3f}, {voting_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "voting_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "voting_pred = voting_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f472d779",
   "metadata": {},
   "source": [
    "### 2.2. Bagging ensemble\n",
    "\n",
    "Trains multiple instances of the same model on different random subsets of the training data (with replacement), then averages their predictions to reduce variance.\n",
    "\n",
    "Scikit-learn [Bagging Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e226745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging - AUC: 0.783 (95% CI: 0.764, 0.802), F1: 0.244 (95% CI: 0.217, 0.271)\n"
     ]
    }
   ],
   "source": [
    "# Create bagging classifier with decision trees\n",
    "bagging_clf = BaggingClassifier(\n",
    "    estimator=DecisionTreeClassifier(random_state=315, class_weight='balanced'),\n",
    "    n_estimators=100,\n",
    "    random_state=315,\n",
    "    oob_score=True\n",
    ")\n",
    "\n",
    "bagging_scores = cross_validate(\n",
    "    bagging_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "bagging_auc = bagging_scores['test_roc_auc']\n",
    "bagging_f1 = bagging_scores['test_f1']\n",
    "\n",
    "bagging_auc_mean, bagging_auc_low, bagging_auc_high = mean_ci(bagging_auc)\n",
    "bagging_f1_mean, bagging_f1_low, bagging_f1_high = mean_ci(bagging_f1)\n",
    "print(\n",
    "    f'Bagging - AUC: {bagging_auc_mean:.3f} (95% CI: {bagging_auc_low:.3f}, {bagging_auc_high:.3f}), '\n",
    "    f'F1: {bagging_f1_mean:.3f} (95% CI: {bagging_f1_low:.3f}, {bagging_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "bagging_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "bagging_pred = bagging_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11de28e",
   "metadata": {},
   "source": [
    "### 2.3. Random forest\n",
    "\n",
    "An extension of bagging that uses decision trees as base models and introduces additional randomness by considering only a random subset of features at each split.\n",
    "\n",
    "Scikit-learn [Random Forest Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dcfadcb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest - AUC: 0.799 (95% CI: 0.780, 0.818), F1: 0.444 (95% CI: 0.404, 0.484)\n"
     ]
    }
   ],
   "source": [
    "# Create random forest classifier\n",
    "rf_clf = RandomForestClassifier(\n",
    "    n_estimators=500,\n",
    "    random_state=315,\n",
    "    oob_score=True,\n",
    "    class_weight='balanced'\n",
    ")\n",
    "\n",
    "rf_scores = cross_validate(\n",
    "    rf_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "rf_auc = rf_scores['test_roc_auc']\n",
    "rf_f1 = rf_scores['test_f1']\n",
    "\n",
    "rf_auc_mean, rf_auc_low, rf_auc_high = mean_ci(rf_auc)\n",
    "rf_f1_mean, rf_f1_low, rf_f1_high = mean_ci(rf_f1)\n",
    "print(\n",
    "    f'Random Forest - AUC: {rf_auc_mean:.3f} (95% CI: {rf_auc_low:.3f}, {rf_auc_high:.3f}), '\n",
    "    f'F1: {rf_f1_mean:.3f} (95% CI: {rf_f1_low:.3f}, {rf_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "rf_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "rf_pred = rf_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd04cae2",
   "metadata": {},
   "source": [
    "## 3. Serial (sequential) ensembles\n",
    "\n",
    "### 3.1. AdaBoost\n",
    "\n",
    "Sequentially trains weak learners where each new model focuses more on the examples that previous models misclassified by adjusting sample weights.\n",
    "\n",
    "Scikit-learn [AdaBoost Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25afd924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost - AUC: 0.684 (95% CI: 0.661, 0.707), F1: 0.086 (95% CI: 0.068, 0.104)\n"
     ]
    }
   ],
   "source": [
    "# Create AdaBoost classifier\n",
    "adaboost_clf = AdaBoostClassifier(\n",
    "    n_estimators=100,\n",
    "    random_state=315\n",
    ")\n",
    "\n",
    "adaboost_scores = cross_validate(\n",
    "    adaboost_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "adaboost_auc = adaboost_scores['test_roc_auc']\n",
    "adaboost_f1 = adaboost_scores['test_f1']\n",
    "\n",
    "adaboost_auc_mean, adaboost_auc_low, adaboost_auc_high = mean_ci(adaboost_auc)\n",
    "adaboost_f1_mean, adaboost_f1_low, adaboost_f1_high = mean_ci(adaboost_f1)\n",
    "print(\n",
    "    f'AdaBoost - AUC: {adaboost_auc_mean:.3f} (95% CI: {adaboost_auc_low:.3f}, {adaboost_auc_high:.3f}), '\n",
    "    f'F1: {adaboost_f1_mean:.3f} (95% CI: {adaboost_f1_low:.3f}, {adaboost_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "adaboost_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "adaboost_pred = adaboost_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78cb3b9b",
   "metadata": {},
   "source": [
    "### 3.2. Gradient boosting\n",
    "\n",
    "Builds models sequentially where each new model is trained to predict the residual errors of the previous ensemble, using gradient descent to minimize a loss function.\n",
    "\n",
    "Scikit-learn [Gradient Boosting Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c97f3753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting - AUC: 0.736 (95% CI: 0.718, 0.755), F1: 0.226 (95% CI: 0.202, 0.250)\n"
     ]
    }
   ],
   "source": [
    "# Create Gradient Boosting classifier\n",
    "gb_clf = GradientBoostingClassifier(\n",
    "    n_estimators=100,\n",
    "    learning_rate=0.1,\n",
    "    max_depth=3,\n",
    "    random_state=315\n",
    ")\n",
    "\n",
    "gb_scores = cross_validate(\n",
    "    gb_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "gb_auc = gb_scores['test_roc_auc']\n",
    "gb_f1 = gb_scores['test_f1']\n",
    "\n",
    "gb_auc_mean, gb_auc_low, gb_auc_high = mean_ci(gb_auc)\n",
    "gb_f1_mean, gb_f1_low, gb_f1_high = mean_ci(gb_f1)\n",
    "print(\n",
    "    f'Gradient Boosting - AUC: {gb_auc_mean:.3f} (95% CI: {gb_auc_low:.3f}, {gb_auc_high:.3f}), '\n",
    "    f'F1: {gb_f1_mean:.3f} (95% CI: {gb_f1_low:.3f}, {gb_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "gb_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "gb_pred = gb_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a0f3be",
   "metadata": {},
   "source": [
    "### 3.3. Stacking ensemble\n",
    "\n",
    "Trains multiple diverse base models, then uses their predictions as input features for a meta-model that learns how to best combine them.\n",
    "\n",
    "Scikit-learn [Stacking Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html) implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "193054bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacking - AUC: 0.811 (95% CI: 0.794, 0.828), F1: 0.521 (95% CI: 0.507, 0.534)\n"
     ]
    }
   ],
   "source": [
    "# Create stacking classifier\n",
    "stacking_clf = StackingClassifier(\n",
    "    estimators=[\n",
    "        ('rf', RandomForestClassifier(n_estimators=50, random_state=315, class_weight='balanced')),\n",
    "        ('dt', DecisionTreeClassifier(max_depth=5, random_state=315, class_weight='balanced')),\n",
    "        ('svm', CalibratedClassifierCV(SVC(random_state=315, class_weight='balanced'), ensemble=False))\n",
    "    ],\n",
    "    final_estimator=LogisticRegression(random_state=315, max_iter=1000, class_weight='balanced'),\n",
    "    cv=7\n",
    ")\n",
    "\n",
    "stacking_scores = cross_validate(\n",
    "    stacking_clf,\n",
    "    train_df.drop('target', axis=1),\n",
    "    train_df['target'],\n",
    "    cv=7,\n",
    "    scoring=['roc_auc', 'f1'],\n",
    "    return_train_score=False\n",
    ")\n",
    "\n",
    "stacking_auc = stacking_scores['test_roc_auc']\n",
    "stacking_f1 = stacking_scores['test_f1']\n",
    "\n",
    "stacking_auc_mean, stacking_auc_low, stacking_auc_high = mean_ci(stacking_auc)\n",
    "stacking_f1_mean, stacking_f1_low, stacking_f1_high = mean_ci(stacking_f1)\n",
    "print(\n",
    "    f'Stacking - AUC: {stacking_auc_mean:.3f} (95% CI: {stacking_auc_low:.3f}, {stacking_auc_high:.3f}), '\n",
    "    f'F1: {stacking_f1_mean:.3f} (95% CI: {stacking_f1_low:.3f}, {stacking_f1_high:.3f})'\n",
    ")\n",
    "\n",
    "stacking_clf.fit(train_df.drop('target', axis=1), train_df['target'])\n",
    "stacking_pred = stacking_clf.predict(test_df.drop('target', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce015bfd",
   "metadata": {},
   "source": [
    "## 4. Model comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "118b48f3",
   "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>Model</th>\n",
       "      <th>AUC Mean</th>\n",
       "      <th>AUC 95% CI Low</th>\n",
       "      <th>AUC 95% CI High</th>\n",
       "      <th>F1 Mean</th>\n",
       "      <th>F1 95% CI Low</th>\n",
       "      <th>F1 95% CI High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.662758</td>\n",
       "      <td>0.642092</td>\n",
       "      <td>0.683424</td>\n",
       "      <td>0.304572</td>\n",
       "      <td>0.291414</td>\n",
       "      <td>0.317729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.599774</td>\n",
       "      <td>0.587684</td>\n",
       "      <td>0.611864</td>\n",
       "      <td>0.307292</td>\n",
       "      <td>0.287241</td>\n",
       "      <td>0.327343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Voting</td>\n",
       "      <td>0.764078</td>\n",
       "      <td>0.752256</td>\n",
       "      <td>0.775899</td>\n",
       "      <td>0.360355</td>\n",
       "      <td>0.343349</td>\n",
       "      <td>0.377361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.783047</td>\n",
       "      <td>0.763948</td>\n",
       "      <td>0.802146</td>\n",
       "      <td>0.243632</td>\n",
       "      <td>0.216746</td>\n",
       "      <td>0.270519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.798929</td>\n",
       "      <td>0.780129</td>\n",
       "      <td>0.817729</td>\n",
       "      <td>0.444362</td>\n",
       "      <td>0.404471</td>\n",
       "      <td>0.484254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.683823</td>\n",
       "      <td>0.661132</td>\n",
       "      <td>0.706513</td>\n",
       "      <td>0.085703</td>\n",
       "      <td>0.067820</td>\n",
       "      <td>0.103587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.736354</td>\n",
       "      <td>0.717980</td>\n",
       "      <td>0.754728</td>\n",
       "      <td>0.225941</td>\n",
       "      <td>0.201503</td>\n",
       "      <td>0.250379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stacking</td>\n",
       "      <td>0.811252</td>\n",
       "      <td>0.794148</td>\n",
       "      <td>0.828356</td>\n",
       "      <td>0.520599</td>\n",
       "      <td>0.506728</td>\n",
       "      <td>0.534470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  AUC Mean  AUC 95% CI Low  AUC 95% CI High   F1 Mean  \\\n",
       "0  Logistic Regression  0.662758        0.642092         0.683424  0.304572   \n",
       "1        Decision Tree  0.599774        0.587684         0.611864  0.307292   \n",
       "2               Voting  0.764078        0.752256         0.775899  0.360355   \n",
       "3              Bagging  0.783047        0.763948         0.802146  0.243632   \n",
       "4        Random Forest  0.798929        0.780129         0.817729  0.444362   \n",
       "5             AdaBoost  0.683823        0.661132         0.706513  0.085703   \n",
       "6    Gradient Boosting  0.736354        0.717980         0.754728  0.225941   \n",
       "7             Stacking  0.811252        0.794148         0.828356  0.520599   \n",
       "\n",
       "   F1 95% CI Low  F1 95% CI High  \n",
       "0       0.291414        0.317729  \n",
       "1       0.287241        0.327343  \n",
       "2       0.343349        0.377361  \n",
       "3       0.216746        0.270519  \n",
       "4       0.404471        0.484254  \n",
       "5       0.067820        0.103587  \n",
       "6       0.201503        0.250379  \n",
       "7       0.506728        0.534470  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collect results\n",
    "results = {\n",
    "    'Model': ['Logistic Regression', 'Decision Tree', 'Voting', 'Bagging', 'Random Forest', 'AdaBoost', 'Gradient Boosting', 'Stacking'],\n",
    "    'AUC': [lr_auc, dt_auc, voting_auc, bagging_auc, rf_auc, adaboost_auc, gb_auc, stacking_auc],\n",
    "    'F1': [lr_f1, dt_f1, voting_f1, bagging_f1, rf_f1, adaboost_f1, gb_f1, stacking_f1],\n",
    "    'Predictions': [lr_pred, dt_pred, voting_pred, bagging_pred, rf_pred, adaboost_pred, gb_pred, stacking_pred]\n",
    "}\n",
    "\n",
    "results_df = pd.DataFrame({\n",
    "    'Model': results['Model'],\n",
    "    'AUC Mean': [mean_ci(auc)[0] for auc in results['AUC']],\n",
    "    'AUC 95% CI Low': [mean_ci(auc)[1] for auc in results['AUC']],\n",
    "    'AUC 95% CI High': [mean_ci(auc)[2] for auc in results['AUC']],\n",
    "    'F1 Mean': [mean_ci(f1)[0] for f1 in results['F1']],\n",
    "    'F1 95% CI Low': [mean_ci(f1)[1] for f1 in results['F1']],\n",
    "    'F1 95% CI High': [mean_ci(f1)[2] for f1 in results['F1']]\n",
    "})\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c055ca",
   "metadata": {},
   "source": [
    "### 4.1. Score comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a279fc04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4.8))\n",
    "\n",
    "# AUC boxplot\n",
    "auc_data = [auc for auc in results['AUC']]\n",
    "\n",
    "axes[0].boxplot(\n",
    "    auc_data,\n",
    "    tick_labels=results['Model'],\n",
    "    patch_artist=True,\n",
    "    vert=False\n",
    ")\n",
    "\n",
    "axes[0].set_title('AUC scores by model')\n",
    "axes[0].set_xlabel('AUC')\n",
    "\n",
    "# F1 boxplot\n",
    "f1_data = [f1 for f1 in results['F1']]\n",
    "\n",
    "axes[1].boxplot(\n",
    "    f1_data,\n",
    "    tick_labels=results['Model'],\n",
    "    patch_artist=True,\n",
    "    vert=False\n",
    ")\n",
    "\n",
    "axes[1].set_title('F1 scores by model')\n",
    "axes[1].set_xlabel('F1')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b539a53",
   "metadata": {},
   "source": [
    "### 4.2. Confusion matrix comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6944af4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*2.6, nrows*2.6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Model Comparison - Normalized')\n",
    "fig.supylabel('True Label')\n",
    "fig.supxlabel('Predction')\n",
    "\n",
    "for i, (model_name, pred) in enumerate(zip(results['Model'], results['Predictions'])):\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        pred,\n",
    "        normalize='true',\n",
    "        ax=axes[i],\n",
    "        colorbar=False\n",
    "    )\n",
    "\n",
    "    axes[i].set_title(model_name)\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99654169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*2.6, nrows*2.6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Model Comparison - Raw Class Counts')\n",
    "fig.supylabel('True Label')\n",
    "fig.supxlabel('Prediction')\n",
    "\n",
    "for i, (model_name, pred) in enumerate(zip(results['Model'], results['Predictions'])):\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        pred,\n",
    "        ax=axes[i],\n",
    "        colorbar=False,\n",
    "        normalize='true'\n",
    "    )\n",
    "\n",
    "    axes[i].set_title(model_name)\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e23b03a",
   "metadata": {},
   "source": [
    "## 5. Model metric optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c2598f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9ee276",
   "metadata": {},
   "source": [
    "### 5.1. ROC_AUC optimized thresholds\n",
    "\n",
    "This section uses a threshold tuner to search for a cutoff that improves the model's decision boundary. The key point is that the tuner evaluates candidate thresholds after converting the predicted probabilities into binary labels. That means the optimization is no longer the usual threshold-independent ROC AUC on the raw probability scores. Instead, it becomes a search over thresholds where the score can change depending on the cutoff being used.\n",
    "\n",
    "In other words, this is a threshold-tuning exercise, not the standard ROC AUC calculation you would normally use for ranking quality. Outside of a threshold tuner, ROC AUC is usually computed from the predicted probabilities directly and is not tied to a specific cutoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62d69883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression new threshold: 0.476\n",
      "DecisionTreeClassifier new threshold: 0.010\n",
      "VotingClassifier new threshold: 0.224\n",
      "BaggingClassifier new threshold: 0.132\n",
      "RandomForestClassifier new threshold: 0.268\n",
      "AdaBoostClassifier new threshold: 0.449\n",
      "GradientBoostingClassifier new threshold: 0.126\n",
      "StackingClassifier new threshold: 0.456\n"
     ]
    }
   ],
   "source": [
    "model_thres_results = {}\n",
    "\n",
    "for model in [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf]:\n",
    "    TunedThreshCV = TunedThresholdClassifierCV(estimator=model, scoring='roc_auc') \n",
    "    TunedThreshCV.fit(X=train_df.drop('target', axis=1)[:num_samples], y=train_df['target'][:num_samples])\n",
    "    \n",
    "    model_thres_results[model] = TunedThreshCV.best_threshold_\n",
    "    print(f'{type(model).__name__} new threshold: {TunedThreshCV.best_threshold_:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cd6e93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc optimized -  Logistic Regression 0.476\n",
      "roc_auc optimized -  Decision Tree 0.01\n",
      "roc_auc optimized -  Voting 0.224\n",
      "roc_auc optimized -  Bagging 0.132\n",
      "roc_auc optimized -  Random Forest 0.268\n",
      "roc_auc optimized -  AdaBoost 0.449\n",
      "roc_auc optimized -  Gradient Boosting 0.126\n",
      "roc_auc optimized -  Stacking 0.456\n"
     ]
    }
   ],
   "source": [
    "preds = {}\n",
    "\n",
    "for model_name, model in zip(results['Model'], [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf]):\n",
    "    print(\"roc_auc optimized - \", model_name, round(model_thres_results[model], 3))\n",
    "\n",
    "    preds[model] = model.predict_proba(test_df.drop(['target'][:num_samples], axis=1))[:, 1] > model_thres_results[model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b91913f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*2.6, nrows*2.6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Model comparison - Optimized Threshold (ROC_AUC) Normalized')\n",
    "fig.supylabel('True label')\n",
    "fig.supxlabel('Predction')\n",
    "\n",
    "for i, (model_name, model) in enumerate(zip(results['Model'], [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf])):\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        preds[model],\n",
    "        normalize='true',\n",
    "        ax=axes[i],\n",
    "        colorbar=False\n",
    "    )\n",
    "\n",
    "    axes[i].set_title(model_name)\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6479f4e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GQ5oMDAwwfPhwJCQk4PTp0xq/69q1q3SVGQDq1KkDAPjyyy817tWvU6cO0tLSEBoaqvF7R0dHjfaYm5ujV69euHbtGp4/fw4g615n9YPIMjMzERkZCVNTU1SqVCnHZdC7d28YGRm9dlmo23z48GGtrr5q6r/V6NGjNcrVV8ey35fq4eGhcQXPzs4OlSpVwuPHj1/blvyIj48HgNc+aEo9LC4uTqO8Xr168PLykr6XK1cO7du3x+HDh5GZmVngNvXr10+j23mdOnUghEC/fv2kMrlcjk8++SRfy+LXX3/FihUrMHfuXPj4+AAArl+/jkePHuGLL75AZGSkFBOJiYn49NNPcebMGahUKmRmZuLw4cPo0KEDypUrJ42zSpUqWg8UKyxff/21xvdGjRohMjJS6+/QpEkTeHh4SN/zE+uWlpYICQnBpUuX3ro9+V2/X3XgwAGULl0aXbp0kcqMjY0xcODAN7YLACIjI6Gvrw9TU9Mch69btw52dnawt7fHJ598guPHj2P8+PEabc1LLLw6PC4uTpr3wnhQ26u2bNmCTz75BOXLl5fG37p167fu2l7Q9lpZWSE5OTnXbRsR0ZswOSeiIsfOzg7NmjXD1q1bsWvXLmRmZmocjL4qODgYjo6OWgdRVapUkYar/9XT09PqnlmpUiWN7+Hh4YiJiZHupXz106dPHwBZ94bnR0BAAKpVq/baOsHBwahQoYLWU7mzz4faq4kP8DLpLVu2bI7l0dHRGuXly5fXup+4YsWKACDd66xSqbBo0SJUqFABCoUCtra2sLOzw82bNzXuD1dzdXV97Tyq64wePRpr166Fra0tfH19sXz5co3xqf9W6gNuNQcHB1haWr5xWQBZB8nZ5zm78PBwPH/+XPokJCTkWle9fqkTk5zklrRUqFBBq27FihWRlJSE8PDw17bxdfKzDrxpWahdv34dX3/9NXr06KGRkD169AhA1gmY7HGxdu1apKamIjY2FuHh4UhOTs5xnrPHWmHJvhysrKwAaK/z2dfP/MT6hAkTYGpqitq1a6NChQoYMmRIrt2X39Se/K7frwoODs4xdgtr2bZv3x5Hjx7F/v37pfe2JyUlaWyX8hILrw43MzODubl5nn6THzExMThw4ACaNGkCf39/6dOgQQNcvnwZDx8+LPC4C9pe8f/PduDT2omooHjPOREVSV988QUGDBiA58+fo2XLllqvl3pXVCoVgKwr0Lk98bd69ervpS2vI5fL81Uusj0QLC9mz56N77//Hn379sWMGTNgbW0NPT09jBw5UlpOr3rTVXO1BQsWwM/PD3v27MGRI0cwfPhwzJkzBxcuXNB4wFJeD3ALOs+1atXSSISmTJkiPSQtO/VJkps3b6JmzZo51rl58yYAaFydfZfysw7k5e8fHR2Nzp07o2LFili7dq3GMPXfe968ebnOv6mpaZ4fTFaY8vr3z75+5ifWq1SpggcPHmDfvn04dOgQdu7ciRUrVmDy5MmYNm1agdqjiwTOxsYGGRkZub5uzMnJSXpYXKtWrWBra4uhQ4fCx8cHnTp1ApB1sqd06dLS+p6bmzdvokyZMlKi6+joiNu3bxfavGzfvh2pqalYsGABFixYoDV8y5Yt0t9G/WT63N6jnpSUpPH0+sqVKwPIen5Cbut7TqKjo2FsbJznbSERUXZMzomoSOrYsSMGDRqECxcu4Pfff8+1nrOzM44dO6Z1sHn//n1puPpflUqFgIAAjatMDx480Bif+knumZmZeX6i8Zu4u7u/8aDU2dkZN2/ehEql0rhKlX0+Cou/vz+EEBoJgvpKk4uLCwBgx44d8PHxwbp16zR+GxMTIz20rKA8PT3h6emJ//3vfzh37hwaNGiAVatWYebMmdLf6tGjR1JSDGQ9qC8mJqbQlsWWLVs0DtbVr3zKScuWLSGXy7Fp06ZcH6z266+/Ql9fH5999plGufqq86sePnwIY2Nj6aFvur7SplKp0LNnT8TExODYsWMwNjbWGK7ucWJubv7auLCzs4ORkVGO85w91nQtv7FuYmKCzz//HJ9//jnS0tLQqVMnzJo1C5MmTcrXK9neZv12dnbG7du3tWI3r8tWnXQGBgbm6STjoEGDsGjRIvzvf/9Dx44dpWm2adMGP//8M86ePYuGDRtq/e7vv/9GUFAQBg0aJJW1adMGa9aswfnz51GvXr08tfd1tmzZgmrVqmHKlClaw1avXo2tW7dKybl6mea2nB48eKCx3NXxvnnz5nw9FC4wMFDjb0pElF/s1k5ERZKpqSlWrlyJqVOnom3btrnWa9WqFTIzM7Fs2TKN8kWLFkEmk0lPfFf/m/1p74sXL9b4LpfL0blzZ+zcuTPHhLog3ZA7d+6MGzdu5PgUZvXVtFatWuH58+caJyIyMjKwdOlSmJqaokmTJvme7us8e/ZMoz1xcXH49ddfUbNmTekpx3K5XOtq3/bt27XuX8+PuLg4ZGRkaJR5enpCT09PuuraqlUrANp/m4ULFwJAjk/YL4gGDRqgWbNm0ud1yXnZsmXRp08fHDt2LMf3mK9atQonTpxAv379NK7+A8D58+c17tF/+vQp9uzZgxYtWkhXWU1MTABknfjQhWnTpuHw4cPYtm1bjrcneHl5wd3dHfPnz8+x+786LuRyOXx9fbF79248efJEGn7v3j0cPnz43c1AAeQn1iMjIzWGGRoawsPDA0IIpKen52u6b7N+t2rVCs+ePcOOHTukMvUrzfJCnRRfvnw5T/X19fUxZswY3Lt3T+MVYePGjYORkREGDRqktWyioqLw9ddfw9jYWHpFIACMHz8eJiYm6N+/P8LCwrSmFRAQgCVLluSpXU+fPsWZM2fQrVs3dOnSRevTp08f+Pv7499//wUAlC5dGjVr1sTmzZu1YuzKlSu4cOGCtI8AsuJ9wIABOHLkCJYuXao1fZVKhQULFmi9ivPq1auF+jR6Iip5eOWciIqs3Lqavqpt27bw8fHBd999h6CgINSoUQNHjhzBnj17MHLkSOmKX82aNdGjRw+sWLECsbGxqF+/Po4fPw5/f3+tcf7www84efIk6tSpgwEDBsDDwwNRUVG4evUqjh07pvXe8DcZN24cduzYga5du6Jv377w8vJCVFQU9u7di1WrVqFGjRoYOHAgVq9eDT8/P1y5cgUuLi7YsWMH/vnnHyxevLjQH6RUsWJF9OvXD5cuXUKpUqXwyy+/ICwsDOvXr5fqtGnTBtOnT0efPn1Qv3593Lp1C1u2bHltEvsmJ06cwNChQ9G1a1dUrFgRGRkZ2LRpk5QoAUCNGjXQu3dvrFmzBjExMWjSpAkuXryIjRs3okOHDtJDyt63RYsW4f79+xg8eDAOHTokXSE/fPgw9uzZgyZNmuTYvbZatWrw9fXVeJUaAI3u0OoHxn333Xfo3r07DAwM0LZtWylpf5du3bqFGTNmoHHjxnjx4gU2b96sMfzLL7+Enp4e1q5di5YtW6Jq1aro06cPypQpg9DQUJw8eRLm5ub466+/pPk6dOgQGjVqhMGDB0snmapWrfrGrtDvW15jvUWLFnBwcECDBg1QqlQp3Lt3D8uWLUPr1q3zHZtvs34PGDAAy5YtQ69evXDlyhWULl0amzZt0urpkBs3NzdUq1YNx44dQ9++ffP0Gz8/P0yePBk//vgjOnToACDrOQobN25Ez5494enpiX79+sHV1RVBQUFYt24dIiIisG3bNo1nfLi7u2Pr1q34/PPPUaVKFfTq1QvVqlVDWloazp07J706Mi+2bt0qvUYzJ61atYK+vj62bNkiPSxz4cKF8PX1Rc2aNeHn5wdHR0fcu3cPa9asQenSpTFp0iSNcSxYsAABAQEYPnw4du3ahTZt2sDKygpPnjzB9u3bcf/+fXTv3l2qf+XKFURFRaF9+/Z5mgciohy978fDExHl5NVXqb1O9lepCSFEfHy8GDVqlHB0dBQGBgaiQoUKYt68eRqvfBJCiOTkZDF8+HBhY2MjTExMRNu2bcXTp09zfK1UWFiYGDJkiChbtqwwMDAQDg4O4tNPPxVr1qyR6uT1VWpCCBEZGSmGDh0qypQpIwwNDYWTk5Po3bu3xiucwsLCRJ8+fYStra0wNDQUnp6eWuNWT/PVVxsJkfurgnJaruplePjwYVG9enWhUChE5cqVtX6bkpIixowZI0qXLi2MjIxEgwYNxPnz57Ve7/S61xRlf5Xa48ePRd++fYW7u7tQKpXC2tpa+Pj4iGPHjmn8Lj09XUybNk24uroKAwMDUbZsWTFp0iSNV8K9Oi/ZZW9jYUlNTRWLFi0SXl5ewsTERBgbG4uPP/5YLF68WKSlpWnVByCGDBkiNm/eLCpUqCAUCoX46KOPcnzl1YwZM0SZMmWEnp6exmvVcnuVWvZYUb/KKzw8XKO8d+/ewsTERKtd6nVe/TfK7fOqa9euiU6dOgkbGxuhUCiEs7Oz6Natmzh+/LhGvdOnTwsvLy9haGgo3NzcxKpVq6T25UdeXqWWfX7Vy+fV19Kp/w45yUusr169WjRu3Fiab3d3dzFu3DgRGxtboPbkdf3OaT0ODg4W7dq1E8bGxsLW1laMGDFCHDp0KE+vUhNCiIULFwpTU1Ot1x6+bhlNnTo1x/HfvHlT9OjRQ5QuXVpadj169BC3bt3KdfoPHz4UAwYMEC4uLsLQ0FCYmZmJBg0aiKVLl2rNf248PT1FuXLlXlvH29tb2Nvba7xa8sKFC6JNmzbCyspK6OvrizJlyoj+/fuLkJCQHMeRkZEh1q5dKxo1aiQsLCyEgYGBcHZ2Fn369NF6zdqECRNEuXLltPY7RET5IROiAE8JIiKiD5aLiwuqVauGffv26bopxZ5MJsOQIUO0brsg0pXY2Fi4ublh7ty5Gq/do4JLTU2Fi4sLJk6ciBEjRui6OUT0AeM950REREQlhIWFBcaPH4958+bl+NYFyr/169fDwMBA6x33RET5xSvnREQlDK+cvz+8ck6UP7Gxsbm+8kxN/dBKIqLihg+EIyIiIqIiYcSIEdi4ceNr6/C6EhEVV7xyTkRERERFwt27d/Hs2bPX1snLe+mJiD5ETM6JiIiIiIiIdIwPhCMiIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOREREREREZGOMTknIiIiIiIi0jEm50REREREREQ6xuSciIiIiIiISMeYnBMRERERERHpGJNzIiIiIiIiIh1jck5ERERERESkY0zOiwlvb294e3sX2vhcXFzg5+dXaOMjQCaTYerUqbpuBtFrBQUFQSaTYcOGDfn6XWFvg4jow+Ln5wcXFxddN4OIdIS5Q+Fgcl7INmzYAJlMhsuXL+u6KW907tw5TJ06FTExMe90Oi4uLpDJZNLHxMQEtWvXxq+//vpOp0v0IVJvQ9QfpVIJR0dH+Pr64qeffkJ8fLyum1hkZN+25PbJ74kGouKiXbt2MDY2fu12o2fPnjA0NERkZOQbx/fs2TNMnToV169fL8RWEtHrZD8ukMlksLe3h4+PDw4ePKjr5lEh09d1A6hwHDlyJN+/OXfuHKZNmwY/Pz9YWlpqDHvw4AH09Arv3E3NmjUxZswYAMB///2HtWvXonfv3khNTcWAAQMKbTpFWXJyMvT1GXKUN9OnT4erqyvS09Px/PlznDp1CiNHjsTChQuxd+9eVK9e/Z1M19nZGcnJyTAwMMjX7wqyDXpbixcvRkJCgvT9wIED2LZtGxYtWgRbW1upvH79+u+9bURFQc+ePfHXX3/hzz//RK9evbSGJyUlYc+ePfjss89gY2PzxvE9e/YM06ZNg4uLC2rWrKkx7Oeff4ZKpSqsphNRNurjAiEEwsLCsGHDBrRq1Qp//fUX2rRpo+vmFXruUFIxUygmDA0NC3V8CoWiUMdXpkwZfPnll9J3Pz8/uLm5YdGiRe89OU9MTISJicl7nSYAKJXK9z5N+nC1bNkSn3zyifR90qRJOHHiBNq0aYN27drh3r17MDIyKvTpqq/W51dhb4PyokOHDhrfnz9/jm3btqFDhw6v7V6rq20A0fvWrl07mJmZYevWrTkm53v27EFiYiJ69uz51tPK7wk9Isqf7McF/fr1Q6lSpbBt27YikZwXdu5QUvH0ho5cu3YNLVu2hLm5OUxNTfHpp5/iwoULWvVu3ryJJk2awMjICE5OTpg5cybWr18PmUyGoKAgqV5O93suXboUVatWhbGxMaysrPDJJ59g69atAICpU6di3LhxAABXV1epm4x6nDndNxITE4NRo0bBxcUFCoUCTk5O6NWrFyIiIvI9/3Z2dqhcuTICAgI0ylUqFRYvXoyqVatCqVSiVKlSGDRoEKKjo7XqTZ06FY6OjjA2NoaPjw/u3r2r1W51V6DTp09j8ODBsLe3h5OTkzT84MGDaNSoEUxMTGBmZobWrVvjzp07GtN6/vw5+vTpAycnJygUCpQuXRrt27fXWP6XL1+Gr68vbG1tYWRkBFdXV/Tt21djPDndc56X9UA9D//88w9Gjx4NOzs7mJiYoGPHjggPD8/rIqdioGnTpvj+++8RHByMzZs3awy7f/8+unTpAmtrayiVSnzyySfYu3ev1jjeFMc53XOelxjIaRv04sUL6eBBqVSiRo0a2Lhxo0Yd9fTmz5+PNWvWwN3dHQqFArVq1cKlS5feboEh60SgqakpAgIC0KpVK5iZmUmJSF63N0DethVERY2RkRE6deqE48eP48WLF1rDt27dCjMzM7Rr1w6PHz9G165dYW1tDWNjY9StWxf79++X6p46dQq1atUCAPTp00frtpHs95znN7a3b98ODw8PKJVKVKtWDX/++SfvYyd6DUtLSxgZGWn0ypw/fz7q168PGxsbGBkZwcvLCzt27ND6bXJyMoYPHw5bW1tpGxAaGprjseqpU6fwySefQKlUwt3dHatXr8bUqVMhk8k06uV2DJ6X49e8HteXBLxyrgN37txBo0aNYG5ujvHjx8PAwACrV6+Gt7c3Tp8+jTp16gAAQkND4ePjA5lMhkmTJsHExARr167N05mpn3/+GcOHD0eXLl0wYsQIpKSk4ObNm/j333/xxRdfoFOnTnj48KFWF1A7O7scx5eQkIBGjRrh3r176Nu3Lz7++GNERERg7969CAkJ0ehCmhcZGRkICQmBlZWVRvmgQYOwYcMG9OnTB8OHD0dgYCCWLVuGa9eu4Z9//pHOzE+aNAlz585F27Zt4evrixs3bsDX1xcpKSk5Tm/w4MGws7PD5MmTkZiYCADYtGkTevfuDV9fX/z4449ISkrCypUr0bBhQ1y7dk06IOjcuTPu3LmDYcOGwcXFBS9evMDRo0fx5MkT6XuLFi1gZ2eHiRMnwtLSEkFBQdi1a9drl0Fe1wO1YcOGwcrKClOmTEFQUBAWL16MoUOH4vfff8/XsqcP21dffYVvv/0WR44ckXqd3LlzBw0aNECZMmUwceJEmJiY4I8//kCHDh2wc+dOdOzYEUDB4/hNMZCT5ORkeHt7w9/fH0OHDoWrqyu2b98OPz8/xMTEYMSIERr1t27divj4eAwaNAgymQxz585Fp06d8Pjx47e+IpeRkQFfX180bNgQ8+fPh7GxMYC8b2/yuq0gKop69uyJjRs34o8//sDQoUOl8qioKBw+fBg9evRAXFwc6tevj6SkJAwfPhw2NjbYuHEj2rVrhx07dqBjx46oUqUKpk+fjsmTJ2PgwIFo1KgRgDffNpKX2N6/fz8+//xzeHp6Ys6cOYiOjka/fv1QpkyZd7dgiD4wsbGxiIiIgBACL168wNKlS5GQkKDRM3XJkiVo164devbsibS0NPz222/o2rUr9u3bh9atW0v1/Pz88Mcff+Crr75C3bp1cfr0aY3hateuXcNnn32G0qVLY9q0acjMzMT06dNzzRdykpfj1/we1xdrggrV+vXrBQBx6dKlXOt06NBBGBoaioCAAKns2bNnwszMTDRu3FgqGzZsmJDJZOLatWtSWWRkpLC2thYARGBgoFTepEkT0aRJE+l7+/btRdWqVV/b1nnz5mmNR83Z2Vn07t1b+j558mQBQOzatUurrkqleu10nJ2dRYsWLUR4eLgIDw8Xt27dEl999ZUAIIYMGSLV+/vvvwUAsWXLFo3fHzp0SKP8+fPnQl9fX3To0EGj3tSpUwUAjXar/x4NGzYUGRkZUnl8fLywtLQUAwYM0BjH8+fPhYWFhVQeHR0tAIh58+blOn9//vnnG//mQggBQEyZMkX6ntf1QD0PzZo101jWo0aNEnK5XMTExLx2uvRhycs2xMLCQnz00UfS908//VR4enqKlJQUqUylUon69euLChUqSGV5iePAwEABQKxfv14IkbcYEEJ7G7R48WIBQGzevFkqS0tLE/Xq1ROmpqYiLi5OY3o2NjYiKipKqrtnzx4BQPz111+vne6rctqm9e7dWwAQEydO1Kib1+1NXrcVREVVRkaGKF26tKhXr55G+apVqwQAcfjwYTFy5EgBQPz999/S8Pj4eOHq6ipcXFxEZmamEEKIS5cuaWwfXtW7d2/h7Owsfc9PbHt6egonJycRHx8vlZ06dUoA0BgnUUmkPi7I/lEoFGLDhg0adZOSkjS+p6WliWrVqommTZtKZVeuXBEAxMiRIzXq+vn5aR2rtm3bVhgbG4vQ0FCp7NGjR0JfX19kTyOz5w55PX7Nz3F9ScBu7e9ZZmYmjhw5gg4dOsDNzU0qL126NL744gucPXsWcXFxAIBDhw6hXr16Gg9dsba2ztO9YZaWlggJCSmUbqEAsHPnTtSoUUO6Aveq7N1acnLkyBHY2dnBzs4Onp6e2LRpE/r06YN58+ZJdbZv3w4LCws0b94cERER0sfLywumpqY4efIkAOD48ePIyMjA4MGDNaYxbNiwXKc/YMAAyOVy6fvRo0cRExODHj16aExLLpejTp060rSMjIxgaGiIU6dO5djVFYD0ML19+/YhPT39jcsCyN96oDZw4ECNZd2oUSNkZmYiODg4T9Ok4sPU1FR6+nJUVBROnDiBbt26IT4+XlqXIyMj4evri0ePHiE0NBRAweI4LzGQkwMHDsDBwQE9evSQygwMDDB8+HAkJCTg9OnTGvU///xzjZ406qtyjx8/zvM0X+ebb77R+J7X7U1etxVERZVcLkf37t1x/vx5jVtRtm7dilKlSuHTTz/FgQMHULt2bTRs2FAabmpqioEDByIoKAh3794t8PTfFNvPnj3DrVu30KtXL5iamkr1mjRpAk9PzwJPl6i4Wb58OY4ePYqjR49i8+bN8PHxQf/+/TV6ar76LJro6GjExsaiUaNGuHr1qlR+6NAhAHjjcXRmZiaOHTuGDh06wNHRUSovX748WrZsmed2v+n4tSDH9cUZk/P3LDw8HElJSahUqZLWsCpVqkClUuHp06cAgODgYJQvX16rXk5l2U2YMAGmpqaoXbs2KlSogCFDhuCff/4pcLsDAgJQrVq1Av++Tp06OHr0KA4dOoT58+fD0tIS0dHRGg+RevToEWJjY2Fvby8l8upPQkKCdL+cOpizLwdra2utbvJqrq6uGt8fPXoEIOse3uzTOnLkiDQthUKBH3/8EQcPHkSpUqXQuHFjzJ07F8+fP5fG1aRJE3Tu3BnTpk2Dra0t2rdvj/Xr1yM1NTXX5ZGf9UCtXLlyGt/V85qfhImKh4SEBJiZmQEA/P39IYTA999/r7UuT5kyBQCk9bkgcZyXGMhJcHAwKlSooPXk1ipVqkjDX/Uu1299fX2NZ00Aed/e5HVbQVSUqU/qq587ExISgr///hvdu3eHXC5HcHBwrvsjQDte8+NNsZ3bPj23MqKSqnbt2mjWrBmaNWuGnj17Yv/+/fDw8MDQoUORlpYGIOtCUd26daFUKmFtbQ07OzusXLkSsbGx0niCg4Ohp6endWycPd5evHiB5OTkt47Ngm4DXndcX5zxnvNiqkqVKnjw4AH27duHQ4cOYefOnVixYgUmT56MadOmvff22NraolmzZgAAX19fVK5cGW3atMGSJUswevRoAFkPg7C3t8eWLVtyHEd+7m/JLvtTrdWve9m0aRMcHBy06r/6cI2RI0eibdu22L17Nw4fPozvv/8ec+bMwYkTJ/DRRx9BJpNhx44duHDhAv766y8cPnwYffv2xYIFC3DhwgWNKwFv49Ur/68SQhTK+OnDEBISgtjYWGknpl6Xx44dC19f3xx/87YHuG+KgcLwLtdvhUKhdZIgr9ub/GwriIoqLy8vVK5cGdu2bcO3336Lbdu2QQhRKE9pfxPuu4jeDT09Pfj4+GDJkiV49OgRoqKi0K5dOzRu3BgrVqxA6dKlYWBggPXr10sn5nSB24D84VHFe2ZnZwdjY2M8ePBAa9j9+/ehp6eHsmXLAsh637C/v79WvZzKcmJiYoLPP/8cn3/+OdLS0tCpUyfMmjULkyZNglKpzFN3dDV3d3fcvn07z/XfpHXr1mjSpAlmz56NQYMGwcTEBO7u7jh27BgaNGjw2ldEOTs7A8haDq+e9YuMjMzzVTZ3d3cAgL29vXTS4E31x4wZgzFjxuDRo0eoWbMmFixYoPHE7Lp166Ju3bqYNWsWtm7dip49e+K3335D//79tcaXn/WA6FWbNm0CACkRV98WYWBg8MZ1+W3iOC8x8CpnZ2fcvHkTKpVKIzG+f/++NFyX8rq9ye+2gqio6tmzJ77//nvcvHkTW7duRYUKFaSnrzs7O+e6P1IPB/J2G1t+vbpPzy6vxztEJVVGRgaArB51O3fuhFKpxOHDhzUeHr1+/XqN3zg7O0OlUiEwMBAVKlSQyrPHm729PZRK5TuPzcI4ri9O2K39PZPL5WjRogX27Nmjce9XWFgYtm7dioYNG8Lc3BxA1sH3+fPncf36daleVFRUrld6XhUZGanx3dDQEB4eHhBCSPdFq9/zGxMT88bxde7cGTdu3MCff/6pNaygZ74mTJiAyMhI/PzzzwCAbt26ITMzEzNmzNCqm5GRIbXz008/hb6+PlauXKlRZ9myZXmetq+vL8zNzTF79uwc7xNXv+IhKSlJ60mR7u7uMDMzk7qtR0dHay0D9XMCcuvanp/1gEjtxIkTmDFjBlxdXaUrXvb29vD29sbq1avx33//af3m1deVFCSO8xIDOWnVqhWeP3+u8TTWjIwMLF26FKampmjSpMnrZ/Ydy+v2Jq/bCqKiTr3NmDx5Mq5fv65x1bxVq1a4ePEizp8/L5UlJiZizZo1cHFxgYeHB4D8HTfklaOjI6pVq4Zff/0VCQkJUvnp06dx69atQpsOUXGTnp6OI0eOwNDQEFWqVIFcLodMJkNmZqZUJygoCLt379b4nfrk/ooVKzTKly5dqvFdLpejWbNm2L17N549eyaV+/v74+DBg4U2H4VxXF+c8Mr5O/LLL79ID1x41YgRIzBz5kwcPXoUDRs2xODBg6Gvr4/Vq1cjNTUVc+fOleqOHz8emzdvRvPmzTFs2DDpVWrlypVDVFTUa89gt2jRAg4ODmjQoAFKlSqFe/fuYdmyZWjdurV0r6qXlxcA4LvvvkP37t1hYGCAtm3bSjvfV40bNw47duxA165d0bdvX3h5eSEqKgp79+7FqlWrUKNGjXwvo5YtW6JatWpYuHAhhgwZgiZNmmDQoEGYM2cOrl+/jhYtWsDAwACPHj3C9u3bsWTJEnTp0gWlSpXCiBEjsGDBArRr1w6fffYZbty4gYMHD8LW1jZPZ/bNzc2xcuVKfPXVV/j444/RvXt32NnZ4cmTJ9i/fz8aNGiAZcuW4eHDh/j000/RrVs3eHh4QF9fH3/++SfCwsLQvXt3AMDGjRuxYsUKdOzYEe7u7oiPj8fPP/8Mc3NztGrVKtc25HU9oJLp4MGDuH//PjIyMhAWFoYTJ07g6NGjcHZ2xt69e6FUKqW6y5cvR8OGDeHp6YkBAwbAzc0NYWFhOH/+PEJCQnDjxg0ABYvjvMRATgYOHIjVq1fDz88PV65cgYuLC3bs2IF//vkHixcvlrZDupLX7U1etxVERZ2rqyvq16+PPXv2AIBGcj5x4kRs27YNLVu2xPDhw2FtbY2NGzciMDAQO3fulHq/uLu7w9LSEqtWrYKZmRlMTExQp04drXtX82v27Nlo3749GjRogD59+iA6OhrLli1DtWrVNBJ2opJMfVwAZN0PvnXrVjx69AgTJ06Eubk5WrdujYULF+Kzzz7DF198gRcvXmD58uUoX748bt68KY3Hy8sLnTt3xuLFixEZGSm9Su3hw4cANHvITJ06FUeOHEGDBg3wzTffIDMzU4rNVy8evo3COK4vVnT1mPjiKrfXHag/T58+FUIIcfXqVeHr6ytMTU2FsbGx8PHxEefOndMa37Vr10SjRo2EQqEQTk5OYs6cOeKnn34SAMTz58+letlfY7R69WrRuHFjYWNjIxQKhXB3dxfjxo0TsbGxGuOfMWOGKFOmjNDT09N4BVH21yEIkfUat6FDh4oyZcoIQ0ND4eTkJHr37i0iIiJeu0ycnZ1F69atcxy2YcMGrdeyrFmzRnh5eQkjIyNhZmYmPD09xfjx48WzZ8+kOhkZGeL7778XDg4OwsjISDRt2lTcu3dP2NjYiK+//lrr75Hba6lOnjwpfH19hYWFhVAqlcLd3V34+fmJy5cvCyGEiIiIEEOGDBGVK1cWJiYmwsLCQtSpU0f88ccf0jiuXr0qevToIcqVKycUCoWwt7cXbdq0kcahhmyvp1D/9k3rQW7zcPLkSQFAnDx5Msd5ow9T9m2IoaGhcHBwEM2bNxdLliyRXkGWXUBAgOjVq5dwcHAQBgYGokyZMqJNmzZix44dGvXeFMfZX6WWlxgQQnsbJIQQYWFhok+fPsLW1lYYGhoKT09PrVcwqaeX06vacoqZ18ntVWomJia5/iYv2xsh3rytIPoQLF++XAAQtWvX1hoWEBAgunTpIiwtLYVSqRS1a9cW+/bt06q3Z88e4eHhIb1KSR3Tub1KLa+x/dtvv4nKlSsLhUIhqlWrJvbu3Ss6d+4sKleu/FbzTPShyym3UCqVombNmmLlypUarylbt26dqFChglAoFKJy5cpi/fr1YsqUKVqvPUtMTBRDhgwR1tbWwtTUVHTo0EE8ePBAABA//PCDRt3jx4+Ljz76SBgaGgp3d3exdu1aMWbMGKFUKjXq5fYqtbwcv+b1uL4kkAnBu/E/NCNHjsTq1auRkJCQ60MWSqKYmBhYWVlh5syZ+O6773TdHCIiInoLNWvWhJ2dHY4eParrphAVe9evX8dHH32EzZs3v/FhkR06dMCdO3ekN5q8CyX1uJ73nBdxycnJGt8jIyOxadMmNGzYsEQn5tmXCwAsXrwYAODt7f1+G0NEREQFlp6eLj3YSu3UqVO4ceMG9+lE70Bux9F6enpo3Ljxa+s+evQIBw4cKNTY5HH9S7znvIirV68evL29UaVKFYSFhWHdunWIi4vD999/r+um6dTvv/+ODRs2oFWrVjA1NcXZs2exbds2tGjRAg0aNNB184iIiCiPQkND0axZM3z55ZdwdHTE/fv3sWrVKjg4OODrr7/WdfOIip25c+fiypUr8PHxgb6+Pg4ePIiDBw9i4MCBWm8LcnNzg5+fH9zc3BAcHIyVK1fC0NAQ48ePL7T28Lj+JSbnRVyrVq2wY8cOrFmzBjKZDB9//DHWrVundVarpKlevTr09fUxd+5cxMXFSQ+TmDlzpq6bRkRERPlgZWUFLy8vrF27FuHh4TAxMUHr1q3xww8/wMbGRtfNIyp26tevj6NHj2LGjBlISEhAuXLlMHXq1By7j3/22WfYtm0bnj9/DoVCgXr16mH27Nkar2F7Wzyuf4n3nBMRERERERHpGO85JyIiIiIiItIxJudEREREREREOsZ7zt+CSqXCs2fPYGZmBplMpuvmEAEAhBCIj4+Ho6Mj9PR4/q2gGN9UFDG+Cwfjm4oixnfhYHxTUZTX+GZy/haePXum9URDoqLi6dOncHJy0nUzPliMbyrKGN9vh/FNRRnj++0wvqkoe1N8Mzl/C2ZmZgCA4KsuMDflGc631fOxj66bUCykJ6bhaOdN0vpJBcP4LlxdGjbVdROKhQxVGk5H/Mr4fkuM78JV76f+um5CsZCZloJHa6Yzvt8S47twNZ7TT9dNKBYy01Jwd8uMN8Y3k/O3oO4qY26qB3MzBv/bMjAx1HUTihV25Xo7jO/Cpa/H+C5MjO+3w/guXHKFUtdNKFYY32+H8V245IaM78L0pvjmGktERERERESkY0zOiYiIiIiIiHSMyTkRERERERGRjjE5JyIiIiIiItIxJudEREREREREOsbknIiIiIiIiEjHmJwTERERERER6RiTcyIiIiIiIiIdY3JOREREREREpGNMzomIiIiIiIh0jMk5ERERERERkY4xOSciIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOREREREREZGOMTknIiIiIiIi0jEm50REREREREQ6xuSciIiIiIiISMeYnBMRERERERHpGJNzIiIiIiIiIh1jck5ERERERESkY0zOiYiIiIiIiHSMyTkRERERERGRjjE5JyIiIiIiItIxJudEREREREREOsbknIiIiIiIiEjHmJwTERERERER6RiTcyIiIiIiIiIdY3JOREREREREpGP6um4A5c2tCybYvsIej24ZIyrMAFPWBaJ+y1gAQEY6sOHH0rh0whz/BRvCxFyFjxrFo9+3z2DjkCGNY+uSUrh4zByP7xhB31Bg1/1bWtPxdaypVTZpRRC8O8S8q1nTOZGkQtraJGT+nQoRrYJeBX0YDjeFvIoBACDjdCrS9yRD9TADiBNQrrOCvIJ26GTeTkfaz4lQ3UsH9GTQK68P5QILyBSy9z1L9IF5XXwDQHS4PtbNcsSV02ZIjJWjWt0EDJkZgjJuaVKdtBQZ1kxzxKm9VkhPlcHLOx7D5oTAyu7lNqCkxXe3voGo3/QFnFwSkZaqh3s3LPHLkgoIDTYBAJiap+PLbwLwcd1I2DmkIDbaEOdP2WHTCnckJRhojKtZ22fo+GUwyjgnISlRjrNHS2HFD1V0MVv0gXlTfL9qyQQnHNhki0HTQtFpQLhU/uimEdbNcsTDG8bQkws0bBWDQVOfwchEBQAIuKPEH8tK4fZFE8RF66OUUxpa94pAx/4R72UedcXeNAEjG19AQ9cnUOpn4GmMBb4/5IO7YfYAgG/qX8JnlfzhYJ6A9Ew93A2zw9K/6+DW81LSOKrYh2Nk4wuo6vACKiHDsYdumHeqAZLTDXKbLJHkTfE9f2Q5HP3DWuM3Xt5xmL31sda40lJlGNG6Ih7fNcKKIw/gXi05qzxFhp8mlsWjm0Z48kiJOs3iMHV94LudMR0b6H0Jg7yvaJQFRVii87LuAAAnq1iMbHEeNcs9h4F+Js77l8XcAw0RlWgs1Tc3SsH4lmfRqFIwhJDh+F03zD/UAMlpRTu2mZx/IFKS9OBWNRm+PaIwvZ+rxrDUZD343zLGFyPD4OaRjIRYOVZOLoMpfm5YduihVC8jTYbGbWNQ5ZNEHN5mk+u0xix6gk984qTvpuaZhT9DRUjqjwlQBWZA8Z0ZZLZyZBxJQcroWBj9agU9OzlEioC8ugH0myqQNjchx3Fk3k5HyrhYGPQ0hmKkKSAHVP4ZAPNyyoPXxbcQwLS+rpDrC0xd/xjGpirsWmOHiZ+Xx8+n70NpnHVwvmpqGVw8Zo7/rQ6CiXkmln/nhOn9XLBor7/G+EpSfFf7OBr7fi+Lh3fMIdcX6D3UH7NWXsWgTvWRmiKHjV0qbOxSsXZRRTx5bIJSpVMw9Lt7sLFLxexxNaTxdPwyGB2/CsYviyrg/m0LKI0yUcoxWYdzRh+S18X3q/45aIH7V0xg45CmUR75XB8Tu7ujSbsYDJkVgqQEPayaXAbzR5bD9z8HAQD8bxrD0jYDE5YFw84xHXcvm2DJuLLQ0wPa9y2eCbqZIhUbe+zGpaeOGLyzNaKTjVDOMhZxKQqpTnCUBWYfb4SQWHMo9TPwldcNrOq6D23WfoHoZCPYmSRiTde/cPiBO+YcbwgTRTrG+/yDmS1PYMxeXx3OHX0o8hLfn/jEYcyiJ9J3A0ORY711Mx1h45COx3eNNMpVKhkMlSq07xeOs/stC63tRZ3/CysM/rWt9D1TlXVQrTRIx/Kv9uNhmA2+3pg1/Juml7Doi4PwW9sJQmTVm9npOGzNkjDk1zbQl6swpf1J/K/taXy3s9n7n5l8YLd2AMuXL4eLiwuUSiXq1KmDixcv6rpJWmo1jYffhOdokMPZdhNzFX74PQBN2sWgbPlUVPFKwpBZIXh00xgvQl6eHeo17jk6DQyHa+WU107L1DwT1vYZ0sdQmfNGpDgQqQKZZ1Jh+I0J5DUNoeckh2FfE+iVkSNjd9ZyMvBVwtDPBHIvw1zHk7YsAQadjWD4pTH0XPWhV04f+k2VkBkyO9e1Dz2+Qx8rcO+KCYb9EIJKNZNRtnwqhv0QgtQUGU7+aQkASIzTw+Ft1hg0NRQ1GyagQvVkjF74BHcvm+LeFWON8ZWk+J489GMc+8sRTx6bIvChGRZOqQr70imo4JF1ciI4wBSzxtbAxTN2eB5ijBuXrLFxWXnUaRwOPXnWSQ9Ts3R8NdgfC76vilOHSuN5iDGCHpnh39P2upw1+n8fenyrRfxngBX/K4MJy4Ohn+2yyb/HLKCvLzB0dgjKlk9FpZrJGP5jCM7ut0RoYNZ+ybdHFL6ZEYrq9RJR2jkNn3aORovPI/HPQYt3OWs61bf2NYTFm2Dyoaa4/bwUQmPNcT64LEJiX87zgfsV8e8TJ4TGmiMg0hrzTjWAmSINFe0iAQCN3YORodLDrGONERRthTvP7THzaGM0r/gYZS1z/3vR+1Fc4tvAUGjsd80stU+KXzphhiunzTBgcqjWMKWxCsN/CEGrnlGwts/QGl5cZar0EJlgLH1ikrJOWtQs9xylLeMxdbcP/F/YwP+FDab86QMPx3DUcs1afi620WhQ4Slm7G2C26GlcP1Jacw92BAtqvnD1ixRl7P1RiU+Of/9998xevRoTJkyBVevXkWNGjXg6+uLFy9e6LppbyUxTg6ZTMDEIv9XxZZ9VwZdq1bDsFYVcHibNUTxPXYHMgWQCe0kWgFk3krP0yhEtAqquxmQWekh+ZtoJLaPQPKwGGTezNvv6d0pDvGdnpa1bhoqVFKZnl7Wzv7OJVMAwKObxshI18NHjV727ChXIRX2ZdJw74qJxvhKVHxnY2KadVATH5t7lzYTs3QkJepDlZm1e/yobiT09AAb+1Ss2nkOvx46g0k/3oRtqdef5KR3rzjENwCoVMDc4eXQ5ZsXcKmkvV6lp8qgbyCg98oRm6Eya3tw56JpruNNjJfnmAQUF97lg3DnuT3mtz2MU4PX4/evtqOz591c6+vrZaJL9buISzHEg/Cs3oOG8kykZ+pBvNLNLSUj6+zIR2X+e7czQK9VXOIbAG6eN0U3z6ro17AyfprohLgoucbw6HB9LB5XFuOXBkNhVIJ2ym9QzjoWh8b8ij0jtmBmp2NwsIgHABjIMyEApGW8XI6pGfpQCRlqlsuK2+plwxCXbIh7z16eSL/42AkqIYNnmaK9DpX45HzhwoUYMGAA+vTpAw8PD6xatQrGxsb45ZdfdN20AktLkWHdLEd4d4iGiZnqzT94Ra9x/+G7VcGY81sAGraKxdJvnbBnne07aqnuyYz1oFdVH2kbk6CKyITIFMg4kgLVnQyIyLwtO9WzrIOftPWJ0G9rBOU8C+hV1EfKqBionpacM5xFUXGI77LlU2BfJg2/zCmN+Bg50tNk+H2ZPSL+M0RUWNZBZNQLfRgYqmCa7WScpV06ol68vAxX0uL7VTKZwKCxD3DnmiWCA3JOaMwt09BjQCAO7nSSyhyckiHTE/i8byDWzK+IWeOqw9QiHbNWXoG+fv62r1S4ikN8A8Afy+0hlwt06Jdz9/MaDRMQHW6A7SvskJ4mQ3yMHL/MdgQAjfh+1Z1Lxji91wqteka+s3brmpNFHLrVvIMn0Rb4ekcb/HGjKiY0PYt2Ve9r1GvsFoQLw3/G5VFr8KXXTQza0RYxyVlX4C4+KQMbk2T41boGfb1MmClSMbLxBQCAnUnSe58neqm4xPcn3nEYtyQYP/4RgH7f/Ydb503x3ZduyPz/3bUQWfelt/4qEhVr8HYptdshpTB1tw+Gbm6NH/Y1hqNVPNb22QNjwzTcCimFlDQDDG9+AUqDdCgN0jGyxXno6wnYmmbFrY1pEqISNW8PyFTpIS5ZARvToh3bJfqe87S0NFy5cgWTJk2SyvT09NCsWTOcP39eq35qaipSU1Ol73FxcVp1dC0jHZg1yAUQwLAfQvL9+56jwqT/l/dMRkqSHravtEeHYvxQGcX/zJH6QzySO0UBckCvgj7knyqgepDHxPr/j88N2ilh0EoJAJBXNEDmlTRkHEiB4aDcr2zQu1Nc4lvfAJi8LhALR5dDFw9P6MkFPmoUj1pN4/J91bskxrfa4En34Vw+AWP71MpxuJFJBqb9dA1PHptgy2o3qVwmAwwMBFbNrYxrF7Kutv04yRNbjp5G9VpRuHq+ZJzcKGqKS3w/ummE3WvtsPzwA8hyuQvKpVIKxi4OxpppZfDLHEfI5QLt+0bAyi49x98E3VdiWh83fDn6Oby849/tDOiQnkzgznM7/HS2LgDg/gs7lLeNQtcad7H3TmWp3qWnZdD1126wMkpGp+r3ML/tEfTc0glRScYIiLTG9wd9MNbnHIY3+hcqlQxbr3kiItEIKj40RmeKS3wD0HjgqmuVFLh6JMOvngdunjPFR40SsGedLZIT9PD5sLDcR1ICnfMvJ/3fP8wGt0LtsX/kFjSvGoA916pgwvbmmNT6b3SvcwsqIcPhW+Vx75mtdL/5h6xEJ+cRERHIzMxEqVKlNMpLlSqF+/fva9WfM2cOpk2b9r6al2/qxDws1BBz//DP91XznFT+OAlbFzsgLVUGQ0Xx7GqjV0YOo6WWEMkCIlEFPVs5UqbEQc9R/uYfA5DZZHVA0XPRDCc9Z32ownhlTVeKU3xXqJ6MlcceIDFOD+npMljaZGJ46wqoWD3r7K+1fQbS0/SQECvXuHoeE27w2vvTSkJ8A8A3E+6jdqNwjO9XC5EvlFrDjYwzMGP5VSQl6WPG6BrIzHjZqSw6Iuue3iePX94eEBdtiLgYQ9g5sGu7rhSX+L71ryliIvTxZa2qUpkqU4afpzli9892+PViVjftpp1i0LRTDKLD9aE0VkEmA3atsUNp51SN8QU/VGBCN3e0/DICX4ws3gf74YnGeBxppVEWGGmJZhU0n4KdnG6ApzEWeBpjgZv/OeCvflvRsdp9rLv4MYCs+9IP3K8Ia+Mk6QntX3ndREiM+fuZEdJSXOI7J6Wd02BhnYFnQQp81CgB1/8xw70rJmjjUkOj3tCWFdG0UzTGLXmSy5hKloQUBYIjLVDWOuvEy4WAsmj/0xewNE5GhkoPCSkKHB67ESG3s+I2MsEY1iaaPRHkeiqYG6UiMsFYa/xFSYnv1p4fkyZNQmxsrPR5+vSprpskUSfmoYEK/PC7P8ytC+c+s4A7RjC1zCjWB+5qMiMZ9GzlEPEqZF5Kg7xh7g+A0/hdaT3IbPWgeqK5zEVIJvQcGGIfiqIc32om5ipY2mQi9LEhHt0wRj3frJ1UhepJ0DdQ4drZl700nvor8CLUEFW8cn/wSfGPb4FvJtxHvaYvMGmQF8KeGWnVMDLJwMyVV5GRrofpI2siPU3zpNzd65YAACeXl8vR1Dwd5pZpePGf9vioaCqq8d2scxRWHX+AlUdffmwc0tDlmxeYtTVAq76VXQaMTFQ4vccSBgoVPm788jkTQQ+UGN+lPJp3jUKfic/f52zoxPVQB7hYx2iUOVvF4r+41/dW05MJGOprHyNFJRkjOd0AvpX8kZYpx4Vgpxx+TUVRUY3vnIQ/M0BctBzW9lnPJRo8IwQrj72M/5mbsk4ufbsqCH4T+NwDNSPDdDhZxyEiW2Idk2SEhBQFarmGwtokGWceuAAAbj4tBXOjNFQu/fKVlLVcQ6EnE7gVWrQf6Fqir5zb2tpCLpcjLEzz7HJYWBgcHBy06isUCigUCq3y9yE5UQ/PAl9O+/lTQwTcNoKZZQasS6VjxgBX+N8ywvRfH0OVKZPuQzOzzJRe2fAixADxMfp4EWoAVSYQcDvrwNLRNRVGJipcOGKO6HB9VPFKgoFChatnzPDbT/bo8nW4doOKkYyLaYAA9MrKIUIzkbYyAXrl5ND//y7qIk4FVZgKIiJrZy6eZCATgMxaD3o2epDJZDDoboS09UnQK68PvfL6yDiUAlVwBhTTeeZdV4pLfNs7pePMXxawsMmEfZk0BN5TYtVkJ9T7LFbqsmpiroJvjyismVoGZpaZMDHLepVaFa9EVPHKurpeEuN78KT78G75HNNH1UByoj6sbLKuMiYm6CMtVQ4jkwzMWnEVCmUm5n1XDcYmGTA2yeppEBttCJVKhtAnJjh/0g6Dxj3A0pkeSErQh9+wRwgJMsHNy1avmzy9Q8UpvrOfTNfXB6zsM1C2/Mur4nt+sYXHJ4kwMsmK3bUzHNH322dST5mg+0qM7+qOT7zj0WlQuHQMoCcXsLQpng+F23SlBn7t8Sf617mCww/Kw9MhDF1q3MW0I00AAEYG6RhQ5wpOBbggPNEElkYp6F7zNuxNE3Hkgbs0nu4f3cKNUAckpRugrnMIRjc5jyVn6iA+VTfrCxWf+DazysTmBQ5o2DoGVvYZ+C/IEGtnOsLRNVXaf9s7pQN4+QBhpUlWj0tH5zTYOb4sD36oQEaaHuKj5UhK1JOO4dXvQi9uRrY4jzMPnPFfrCnszJIwyPsSVCoZDt0qDwBoW/M+AiOsEJOohGfZMIz97B9sPV8dwZGWAICgCCv886gsvm93GrP3NYK+ngrjW53FkdvlERFv8pop616JTs4NDQ3h5eWF48ePo0OHDgAAlUqF48ePY+jQobptXDYPbxhjfJfy0vfVU8sAAJp3i8KXY57jwpGsV4cMbl5Z43dzd/ijRv2sM+u/zi+No39YS8MGt6ikUUduIPDXBlusnqqAEICjSxoGTX2GlsX4gTIAgAQV0tYkQoSrIDPTg7yJIQwHmECmn3XfSsY/aUib8/K+vdRp//+0SD9jGPbNCnCDbsYQaUDa0gSIeBX03PWhXGgJvTJ56xpPha+4xPfYxU8QFWaA1VPLICZCH9b2GWjWNUqry+rXU7POCM8Y4IL0VBk+8Y7H0DkvnztREuO7Tbes+Z+79opG+cLJVXHsL0eUrxyHytWzXn/zy1//aNTxa9VQujI+//tqGDj2Aab+dA1CJcOtK1b4fsjHGt3f6f0qTvGdFw+uG2PTAgekJOrBqXwqhs99imZdoqXhf++zRGykAY7vtMbxnS/386Wc0qSu8cXNnef2GLXHFyMa/YtB9a4gNNYMc080wIF7FQFkvRPZxToGC6oegZVRMmJSlLjz3B5+v3VAQOTLZeTp8AKD61+CsUE6AqOsMONoY+y7W0lXs0UoPvE9bM5TBN5T4uh2VyTGyWFTKgMfN4lD7/HP891j7fsv3REW8rJHp/oY/vCz628/E0WQvXkCZnc5BgujFEQnGeH6Ewf4re0ovU7NxTYGQ5v9CwujVDyLMcMvf3+MLeera4zjf7s+xYRWZ7Gy1z4IIcPxe66Yd7ChLmYnX2RClKQX6Wj7/fff0bt3b6xevRq1a9fG4sWL8ccff+D+/fta97pkFxcXBwsLC0Q/dIO5GQ/S3lYn/+a6bkKxkJ6YhgOfrUNsbCzMzUv2lXvGd9HR6qMWum5CsZChSsPxF2sZ32B8FyXV5w/WdROKhczUFNxf9i3jG4zvosRr6je6bkKxkJmWglvrv3tjfJfoK+cA8PnnnyM8PByTJ0/G8+fPUbNmTRw6dOiNgU9ERR/jm6j4YnwTFV+MbyqpSnxyDgBDhw4tct1kiKhwML6Jii/GN1Hxxfimkoh9PYiIiIiIiIh0jMk5ERERERERkY4xOSciIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOREREREREZGOMTknIiIiIiIi0jEm50REREREREQ6xuSciIiIiIiISMeYnBMRERERERHpGJNzIiIiIiIiIh1jck5ERERERESkY0zOiYiIiIiIiHSMyTkRERERERGRjjE5JyIiIiIiItIxJudEREREREREOsbknIiIiIiIiEjHmJwTERERERER6RiTcyIiIiIiIiIdY3JOREREREREpGNMzomIiIiIiIh0jMk5ERERERERkY4xOSciIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOREREREREZGO6eu6AcVBx4qe0JcZ6LoZHzx5eVNdN6FYyMhM1XUTipWuPi2gr6fQdTM+eJlhobpuQrGQKdJ13YRihfvvwlHW7Zmum1AsZKhScV/XjShGGN+Fw8HtP103oVjIUKXiVh7q8co5ERERERERkY4xOSciIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOREREREREZGOMTknIiIiIiIi0jEm50REREREREQ6xuSciIiIiIiISMeYnBMRERERERHpGJNzIiIiIiIiIh1jck5ERERERESkY0zOiYiIiIiIiHSMyTkRERERERGRjjE5JyIiIiIiItIxJudEREREREREOsbknIiIiIiIiEjHmJwTERERERER6RiTcyIiIiIiIiIdY3JOREREREREpGNMzomIiIiIiIh0jMk5ERERERERkY4xOSciIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOREREREREZGOMTknIiIiIiIi0jEm50REREREREQ6pq/rBlDBVKuTgK6Dw1HBMwk2DhmY2tcF5w9ZAADk+gJ+E/5DrabxKO2chsQ4PVz72wzrZpdGVJiBNI4ybqkY8P0zeNRKhL6BQOA9JX6dWxo3zpnqarbeu249H6J+42dwck5AWqoe7t22xi+rqiL0qVkOtQWmzz2PT+q+wIxva+P8WUdpyKDhN+HhGQkX13g8CTbFsH5N399MULHTtbc/6vuE/f96Kce9W1ZYv7QSQp+8jM05Ky+guleUxu8O7CqL5T94St8rVImB39AHKF85FhDAg7uWWL+0MgIfmb+3edG1120rAaBByxi07hWJCp7JMLfOxDfNK+LxHSONcbTsGQmfjtEo75kMEzMVOlWuhsQ4+fueFSomCmOdnLvDHzXqJ2qU7f/VBj9NdHov81AUdP3yIeo3+Q9OzvH/v520xvqVHhr77zlLz6L6R5Eavzuw2wXL59fQGp+ZeRqWbTgJW/sUdPusFRITDLTqEL3J28a3mWUGvhr7HB83SYC9Yxpio/Rx7pAFNs51QFJ8ydnvFEZ8N2v5BKO+u5bj+L9o8xliYxTvbgbeQom+cn7mzBm0bdsWjo6OkMlk2L17t66blGdKYxUe31Fi2bfaO2KFkQrlPZOxdXEpDPGtgOn9XeDknoppGwI16k3f+Bh6coEJXd0x9LOKeHzXCNN/DYSVXfr7mg2dq1YzAvv+dMXorxvju9ENINcXmLXgHBTKDK26HboGQECW67iOHnDGmRNl3mVzKR8+5Pj2/DgK+7c7Y0y/+vjfsNrQl6swc+lFrfXy0J9l8WXLT6XPL0srS8OURhmY/tMlhD9XYnSf+hg3sB6SE/Ux46eLkMtV73uWdOZ120r18DsXTbBuduncx2GkwuVTZvhtqf27aibl04cc34WxTgLAgc3W6F7DQ/qsnfn6+sWN50eR2L/LFWMGNcb/RtWHvr7AzEXntbeTe53xZTtf6fPLCo8cxzdi4jUEBpScE5dFWUmOb+tS6bAplYGfp5fGoKaVMH9kWXziHYfRC56+y2YXOYUR32eOl9EY9mU7X1z51x43r9kU2cQcKOFXzhMTE1GjRg307dsXnTp10nVz8uXySXNcPpnzTiQpXo5J3d01ypZ/VwZLDz6CXZk0hIcawtw6A07uaVg0piwC72WdsftlVmm084uES+UURIeXjDPGk8fV1/i+cPbH+O2vg6hQKQa3b9hK5W7lY9Dpc3+MGOiNLbsPaY1n9U/VAQAWlvfg4h77bhtNefIhx/fkEbU1vi+cXh3bjhxH+SpxuHPNWipPSZEjOjLnHYyTSwLMLdKxeXVFRLzIivGta8tjxbazsC+djP9CTN7dDBQhr9tWAsDxnVnLs5RTWq51/lxrBwCoXi+hcBtHBfYhx3dhrJMAkJqsV2L21TmZPKaexveFsz/Ctn2HUL5SDO68sv9OSZEjOkr52nG16hAIE7N0bFtfCbXqvXgn7aW8K8nxHfzACDMGuEjf/wtWYMOPpTF+6RPoyQVUmblfJCpOCiO+09LkSIt62dvA3DIV1T8Ox5IfPno3jS4kJTo5b9myJVq2bKnrZrwXJuaZUKmAxNislTQuSo6n/go06xqNR7eMkJ6mh9ZfRSI6XB+Pbhq9YWzFl4lpVq+B+DhDqUyhyMD4yVewYnGNN+7gqegoTvFtYpp1pjghVvNA3OezZ/BpGYroSAUu/m2P39ZVQGpqVoyHBpsiNsYALdo/xR/ry0NPLtCiXQiePDZF2H8lN8apeChO8V1QPp2i0bRzNKJfGODCUXNsXVwKqcklt0OkiUnW/jvhlf03APg0D4FPixBERylw8R8H/LahIlJTXx7+lnWJQw+/Bxg9qDEcHJPea5spZ4xvTSbmmUhK0CsxiXlOChrfr/r0s6dITZHjn5OOOQ4vKkp0cp5fqampSE1Nlb7HxcXpsDV5Z6BQod93/+HUbkskJajPIMkw8XM3TPklCLsf3YZQATER+viupysSYkvmaiGTCQwadgt3blojOPDlWc8Bw27j3m1rXDhbsroMljRFNb5lMoGBo+/iznUrBD9+ea/V6cOOePHcCJHhCriWj0efoQ/g5JyIWRO8AADJSfqY9HVd/G/eFXTv6w8AePbUBN8Prw1VZsk9gKeSqajGd0Gd/NMKL0IMEBlmANcqKej33X9wck/FjP4uum6aTshkAgOH39baf58+6oQXz40RGaGEq3ss+nxzF07lEjDru6zeSfoGmRg/9Qp+WVEV4WHGTM4/UMUtvl9lbp2BL0aG4eBmG103RWcKGt/ZtWgdjNPHnJCWVrTv3S+ZWVgBzZkzB9OmTdN1M/JFri/w3epgQAYs1XhQjMDQ2aGIidDHmI7lkZYiw2c9ojBtQxCGt6qAqBclr6vc4FE34Owah7FDG0tldRr8hxofh2NYPx8dtozeh6Ia39+MvwNntwSMG1hXo/zQ7nLS/4MDzBEVqcCcFRfhUCYRz0NNYKjIxIj/3cTdm1aY+7+a0JMLdOoZiKmLLmGUXwOkpRbtnRNRYSqq8V1QB7e8PFAPum+EqBf6mLv9MUo7p+K/4KJ7L+W78s3om3B2i8O4wY00yg/tdZH+H/zYHFGRSsz56RwcHBPx/JkJ/Abdw9MgM5w8UvY9t5gKU3GLbzVj00zM+DUQTx4qsWmBg66bozMFje9XVa4ahXKuCVgw0+t9NPmt8PJJPkyaNAmxsbHS5+nTov1whqzEPAilyqRhUne3V66aAzUbJqB2szjM+cYZdy+ZwP+WMZZ964S0FBmadYt6zViLp29G3kDt+mGYOLIhIsNfdvmt8XE4SjsmYvv+/fjrxB78dWIPAODbGRfxw5K/ddVcegeKYnx/PfYOajd8gUmD6yDyxeu7oj+4bQkAcCybdeXH2/cZ7EsnY/H06nh0zxIPblth3vc14eCYjLqNw95104mKlKIY34Xp/lVjAICjS+obahY/X4+6idr1n2PS8AYa+++cPLhrBQBwdMp60n0Nr3A09AnF3lN7sffUXsxa/A8AYNu+g+jZ9/67bTgVmuIY30YmmZi19TGSE/UwrZ8LMjNKZpf2t4nvV/m2DUbAQwv4P7B8F80sVLxyng8KhQIKxYdxRlqdmJdxTcP4Lu6Ij9b8UyuMsp7WrMr20GaVkEGvRMW/wDcjb6Jeo/8wcURDhP2neaZt+5aKOLzPRaNs5cYT+HmZJ/49V3LPYhZHRSu+Bb4eexf1vJ9j0jd1EfbM+I2/cKuY1Y0vKiJrHhTKTAghgxAv66gEIAQg0xM5jYKo2Cpa8V343KulAEAJ6/Um8PWoW6jX+D9MGtZAa/+dE7cKWQ9sjfr/B2nO+q42FIpMaXiFKtEY9e11jB/SEP+FloyHZhYHxS2+jU2zEvP0NBmm+LkiPbUkXkt9+/hWUxploGHTUGxclfObGooaJucfKKVxJhxdXz7p0aFsGtyqJiM+Ro6oMAN8/3MQynsmY3IvV+jJhfR6tPgYOTLS9XDvigkSYuUYt+QptiwqhdQUPbTsGQmHsmm4eLzkvEpk8Kib8G72FNO/rYvkJH1YWWcd4CQmGCAtLesJkDk9BC48zEhjQ1G6TAKMjDJgZZ0KhUIFt/IxAIAnQebIyCiJG1V6G4PH30ET32eYMdYra720yboalpigj7RUORzKJMLb9xkun7NHXKwBXMvHY8Coe7h11RpB/lnxe+1fW/Qddh+Dx9/BX3+4QKYn0LVXADIzZbh5ueTcu/a6bWV4qCHMLDNgVyYdNqWytpFl3bO2AdEv9KUnYVvZpcPKPgOOrll/B9fKyUhKlCM81ADxMdyNUv687TpZ2jkVPh1jcPG4GeKj9eHqkYxBU5/h5nkT6e0rJcHgMTfRpFkIZkyqk+P+28ExEd7NQ3D5QinExRrC1T0WA4bfxq1rNggKyHrvdPaur+aWWX+Xp8FmfM85FcjbxrexaSZmb3sMhZEKc4e5wNg0E8amWSeQYiP1oVKVjCtohRHfao2bhkIuFx/M7Ssl+qgiISEB/v7+0vfAwEBcv34d1tbWKFeu3Gt+qXsVayRj3s4A6fvX054BAI78boXNCxxQzzfrKtrKYw81fjeusztunjdFXJQ+vvvCDX4T/8OPfwRAbiAQ/ECJqX1c8Phuydm5t+mY9e73uUvPapQvnP0Rjh1yzvN4Roy/huofRUrfl/1yCgDg1605Xjzn2Xdd+JDju3WXJwCAH1f/q1G+aFp1HNvvhIx0PdSsHYn2PYKgVGYiPEyJf0464LdfXr5CMSTYFNPGeOGL/v6Yv+4chEqGgIfmmDyiFqIjS85bB163rVwwqhzqtojD2MUvu0B+uypr2W9aUAqb//8ev9a9IvHVmJe3AizYnTW++SPL4ugfL19tR+/Phxzfb7tOZqTL8FGjeHTsHw6lsQrhzwxw9oAFti0u9X5nRMdadwwCAPy47B+N8kWzPsKxg+WQkaGHmp+Eo323gKzt5Asj/HPKEb9trKiD1lJ+lOT4Lu+ZjCpeWbenbTiveWtFr9pVEBai+bTy4qow47tFm2CcO+34wZxwkwkhSmz/xlOnTsHHR/tBX71798aGDRve+Pu4uDhYWFjAG+2hL/sw/uBFmby8q66bUCxkZKbi+OOfEBsbC3PzktMLIrvCiu9mZb6Gvl7x6S6nKxkhobpuQrGQIdJxCnsY39x/Fyn6bi66bkKxkKFKxbHApYxvxneRwvguHHmN7xJ95dzb2xsl+NwEUbHG+CYqvhjfRMUX45tKMt4MS0RERERERKRjTM6JiIiIiIiIdIzJOREREREREZGOMTknIiIiIiIi0jEm50REREREREQ6xuSciIiIiIiISMeYnBMRERERERHpGJNzIiIiIiIiIh1jck5ERERERESkY0zOiYiIiIiIiHSMyTkRERERERGRjjE5JyIiIiIiItIxJudEREREREREOsbknIiIiIiIiEjHmJwTERERERER6RiTcyIiIiIiIiIdY3JOREREREREpGNMzomIiIiIiIh0jMk5ERERERERkY4xOSciIiIiIiLSMSbnRERERERERDrG5JyIiIiIiIhIx5icExEREREREekYk3MiIiIiIiIiHdPPS6UzZ84UaOSNGzcu0O+IiIiIiIiISpI8Jefe3t6QyWR5HqkQAjKZDJmZmQVuGBEREREREVFJkafk/OTJk++6HUREREREREQlVp6S8yZNmrzrdhARERERERGVWG/9QLj//vsPN27cQGJiYmG0h4iIiIiIiKjEKXByvmfPHlSuXBlOTk74+OOP8e+//wIAIiIi8NFHH2H37t2F1UYiIiIiIiKiYq1Ayflff/2FTp06wdbWFlOmTIEQQhpma2uLMmXKYP369YXWSCIiIiIiIqLirEDJ+fTp09G4cWOcPXsWQ4YM0Rper149XLt27a0bR0RERERERFQSFCg5v337Nrp165br8FKlSuHFixcFbhQRERERERFRSVKg5NzY2Pi1D4B7/PgxbGxsCtwoIiIiIiIiopIkT69Sy87HxwcbN27EyJEjtYY9f/4cP//8M9q0afO2bSvy1PfaZyAdEG+oTG8kMlN13YRiIUOVtRxffRYE5Z8U36o0HbekeMgQ6bpuQrGQgazlyPh+O9x/FzIV99+FQb2/YXy/HcZ3IWN8F4q8xneBkvNZs2ahbt26qFWrFrp27QqZTIbDhw/jxIkTWL16NYQQmDJlSkFG/UGJj48HAJzFAR23pJh4rOsGFC/x8fGwsLDQdTM+WOr4PvXfLzpuCZE2xvfb4f67kAXqugHFC+P77TC+Cxnju1C9Kb5looCn5+7cuYMRI0bg5MmTGmcAvL29sXz5clSpUqUgo/2gqFQqPHv2DGZmZpDJZLpuTq7i4uJQtmxZPH36FObm5rpuzgfrQ1mOQgjEx8fD0dERenoFfltiicf4Llk+lOXI+C4cjO+S5UNZjozvwsH4Llk+lOWY1/gucHKuFh0dDX9/f6hUKri5ucHOzu5tRkfvQFxcHCwsLBAbG1ukV9qijsuRiiKul4WDy5GKIq6XhYPLkYoirpeFo7gtxwJ1a3+VlZUVatWqVRhtISIiIiIiIiqRCtxnJjw8HGPHjoWHhweMjY1hbGwMDw8PjB07FmFhYYXZRiIiIiIiIqJirUDJ+Z07d+Dp6YmFCxfCwsICXbt2RdeuXWFhYYGFCxeievXquH37dmG3lQpIoVBgypQpUCgUum7KB43LkYoirpeFg8uRiiKul4WDy5GKIq6XhaO4LccC3XPu7e2NO3fu4MCBA1pd2i9evIhWrVrB09MTJ0+eLLSGEhERERERERVXBbpyfvHiRYwYMSLHe81r166NESNG4N9//33rxhERERERERGVBAVKzu3t7aFUKnMdrlQqYW9vX+BGEREREREREZUkBUrOR44ciZUrV+L58+daw549e4aVK1di5MiRb9s2IiIiIiIiohIhT69SW7hwoVaZqakpypcvj44dO6J8+fIAgEePHmH37t0oX7483vL16UREREREREQlRp6unI8dO1brc+vWLSQlJWHLli2YNm0apk2bhq1btyIpKQk3b97E2LFj33XbKQ+WL18OFxcXKJVK1KlTBxcvXtR1kz44Z86cQdu2beHo6AiZTIbdu3fruklEABjfhYHxTUUV4/vtMb6pqGJ8v73iGt95Ss4DAwPz/Xn8+PG7bju9we+//47Ro0djypQpuHr1KmrUqAFfX1+8ePFC1037oCQmJqJGjRpYvny5rptCJGF8Fw7GNxVFjO/CwfimoojxXTiKa3wX6FVq9GGoU6cOatWqhWXLlgEAVCoVypYti2HDhmHixIk6bt2HSSaT4c8//0SHDh103RQq4RjfhY/xTUUF47vwMb6pqGB8F77iFN8FeiAcFX1paWm4cuUKmjVrJpXp6emhWbNmOH/+vA5bRkRvi/FNVHwxvomKL8Y3vUmeHgiXk5s3b2Lp0qW4evUqYmNjoVKpNIbLZDIEBAS8dQOpYCIiIpCZmYlSpUpplJcqVQr379/XUauIqDAwvomKL8Y3UfHF+KY3KdCV81OnTqF27drYt28fHB0d8fjxY7i5ucHR0RHBwcEwNTVF48aNC7utRERERERERMVSgZLzyZMnw83NDQ8ePMD69esBAN9++y3Onj2Lc+fOISQkBN26dSvUhlL+2NraQi6XIywsTKM8LCwMDg4OOmoVERUGxjdR8cX4Jiq+GN/0JgVKzq9evYp+/frB3NwccrkcAJCZmQkg6yEHgwYNwvfff194raR8MzQ0hJeXF44fPy6VqVQqHD9+HPXq1dNhy4jobTG+iYovxjdR8cX4pjcp0D3n+vr6MDMzAwBYWlrCwMBA4/H/bm5uuHv3buG0kAps9OjR6N27Nz755BPUrl0bixcvRmJiIvr06aPrpn1QEhIS4O/vL30PDAzE9evXYW1tjXLlyumwZVSSMb4LB+ObiiLGd+FgfFNRxPguHMU2vkUBeHl5ibFjx0rfq1evLjp37ix9b9++vXB1dS3IqKmQLV26VJQrV04YGhqK2rVriwsXLui6SR+ckydPCgBan969e+u6aVTCMb7fHuObiirG99tjfFNRxfh+e8U1vgv0nvPJkyfjl19+QVBQEPT19bFx40b06dMH7u7uAICAgADMmTMHEyZMeKsTB0REREREREQlQYGS8/T0dMTFxcHa2hoymQwAsHnzZuzcuRNyuRxt2rSBn59fYbeViIiIiIiIqFgqUHJORERERERERIWnQE9rJyIiIiIiIqLCk6entTdt2jTfI5bJZBqvCSAiIiIiIiKinOUpOVepVNK95XnF3vJEREREREREecN7zomIiIiIiIh0jPecExEREREREekYk3MiIiIiIiIiHWNyTkRERERERKRjTM6JiIiIiIiIdIzJOeWZn58fXFxcdN0MInqHNmzYAJlMhqCgIF03hYiyyWk/LJPJMHXqVJ20pzji8iTSdOrUKchkMuzYseO19Xj8UDiYnBdB6pVb/dHX10eZMmXg5+eH0NBQXTevyMi+nF79TJw4UdfNy9Hs2bOxe/duXTeDSogVK1ZAJpOhTp0672V6fn5+WtuusmXLonv37rh79+57acPr3L17F1OnTuWBA+VbYGAghg4diooVK8LY2BjGxsbw8PDAkCFDcPPmTV03753bunUrFi9enOf6Li4uGtsCpVKJChUqYNy4cYiKinp3Dc2jAwcOMAGnIu/WrVvo0qULnJ2doVQqUaZMGTRv3hxLly6V6vC4svjJ03vOcxMaGoozZ87gxYsX6Ny5M5ycnJCZmYnY2FhYWFhALpcXVjtLpOnTp8PV1RUpKSm4cOECNmzYgLNnz+L27dtQKpW6bl6RoV5Or6pWrZqOWvN6s2fPRpcuXdChQwddN4VKgC1btsDFxQUXL16Ev78/ypcv/86nqVAosHbtWgBARkYGAgICsGrVKhw6dAh3796Fo6PjO29Dbu7evYtp06bB29ubvYAoz/bt24fPP/8c+vr66NmzJ2rUqAE9PT3cv38fu3btwsqVKxEYGAhnZ2edtC85ORn6+m91OPdGW7duxe3btzFy5Mg8/6ZmzZoYM2YMACAlJQVXrlzB4sWLcfr0aVy8ePEdtTRvDhw4gOXLl+eYoL+P5Un0JufOnYOPjw/KlSuHAQMGwMHBAU+fPsWFCxewZMkSDBs2DEDROq786quv0L17dygUCl035YNWoK2PEAJjxozBsmXLkJGRAZlMBk9PTzg5OSEhIQEuLi6YPn16vjbipK1ly5b45JNPAAD9+/eHra0tfvzxR+zduxfdunXTceuKjleXU2FKTEyEiYlJoY+X6H0IDAzEuXPnsGvXLgwaNAhbtmzBlClT3vl09fX18eWXX2qU1a1bF23atMH+/fsxYMCAd94GosISEBCA7t27w9nZGcePH0fp0qU1hv/4449YsWIF9PRe3xHxXe5PiurJ+jJlymhsC/r37w9TU1PMnz8fjx49QoUKFXTYutwV1eVJJcusWbNgYWGBS5cuwdLSUmPYixcvdNOoN5DL5bwwWwgK1K193rx5WLJkCcaOHYujR49CCCENs7CwQKdOnbBz585CayRladSoEYCsgwW1tLQ0TJ48GV5eXrCwsICJiQkaNWqEkydPavw2KCgIMpkM8+fPx5o1a+Du7g6FQoFatWrh0qVLWtPavXs3qlWrBqVSiWrVquHPP//MsU2JiYkYM2YMypYtC4VCgUqVKmH+/Pka6wSQdQ/X0KFDsX37dnh4eMDIyAj16tXDrVu3AACrV69G+fLloVQq4e3tXajdTk+cOIFGjRrBxMQElpaWaN++Pe7du6dRZ+rUqZDJZLh79y6++OILWFlZoWHDhtLwzZs3w8vLC0ZGRrC2tkb37t3x9OlTjXE8evQInTt3hoODA5RKJZycnNC9e3fExsZKyyAxMREbN26Uuvr5+fkV2nwSvWrLli2wsrJC69at0aVLF2zZskWrzp07d9C0aVMYGRnByckJM2fOhEql0qq3Z88etG7dGo6OjlAoFHB3d8eMGTOQmZmZp7Y4ODgAgNbVqMePH6Nr166wtraGsbEx6tati/3792v9/sWLF+jXrx9KlSoFpVKJGjVqYOPGjVr1fvvtN3h5ecHMzAzm5ubw9PTEkiVLAGTdBtO1a1cAgI+PjxSDp06dytM8UMk0d+5cJCYmYv369VqJOZC1Tg8fPhxly5aVyvz8/GBqaoqAgAC0atUKZmZm6NmzJwDg77//RteuXVGuXDkoFAqULVsWo0aNQnJysta487ofzuke6dDQUPTt2xelSpWCQqFA1apV8csvv2jUUd9H+scff2DWrFlwcnKCUqnEp59+Cn9/f6met7c39u/fj+DgYCluCtrzJLdtQV720wBw7do1tGzZEubm5jA1NcWnn36KCxcuaNRJT0/HtGnTUKFCBSiVStjY2KBhw4Y4evQogKy/z/Lly6Vlp/6oZV+e6uMDf39/+Pn5wdLSEhYWFujTpw+SkpI0pp2cnIzhw4fD1tYWZmZmaNeuHUJDQ3kfO+VbQEAAqlatqpWYA4C9vT2A1x9XBgcHY/DgwahUqRKMjIxgY2ODrl275nh8HRMTg1GjRsHFxQUKhQJOTk7o1asXIiIicm1famoq2rRpAwsLC5w7dw5Azvecu7i4oE2bNjh79ixq164NpVIJNzc3/Prrr1rjvHnzJpo0aaJxTLJ+/foSdx97ga6c//zzz+jVqxdmz56NyMhIreHVq1fHwYMH37pxpEm9YlpZWUllcXFxWLt2LXr06IEBAwYgPj4e69atg6+vLy5evIiaNWtqjGPr1q2Ij4/HoEGDIJPJMHfuXHTq1AmPHz+GgYEBAODIkSPo3LkzPDw8MGfOHERGRqJPnz5wcnLSGJcQAu3atcPJkyfRr18/1KxZE4cPH8a4ceMQGhqKRYsWadT/+++/sXfvXgwZMgQAMGfOHLRp0wbjx4/HihUrMHjwYERHR2Pu3Lno27cvTpw4kaflEhsbq7UBsbW1BQAcO3YMLVu2hJubG6ZOnYrk5GQsXboUDRo0wNWrV7UOMLp27YoKFSpg9uzZ0gmGWbNm4fvvv0e3bt3Qv39/hIeHY+nSpWjcuDGuXbsGS0tLpKWlwdfXF6mpqRg2bBgcHBwQGhqKffv2ISYmBhYWFti0aRP69++P2rVrY+DAgQAAd3f3PM0jUX5t2bIFnTp1gqGhIXr06IGVK1fi0qVLqFWrFgDg+fPn8PHxQUZGBiZOnAgTExOsWbMGRkZGWuPasGEDTE1NMXr0aJiamuLEiROYPHky4uLiMG/ePK366njMzMzE48ePMWHCBNjY2KBNmzZSnbCwMNSvXx9JSUkYPnw4bGxssHHjRrRr1w47duxAx44dAWQd7Hp7e8Pf3x9Dhw6Fq6srtm/fDj8/P8TExGDEiBEAgKNHj6JHjx749NNP8eOPPwIA7t27h3/++QcjRoxA48aNMXz4cPz000/49ttvUaVKFQCQ/iXKyb59+1C+fPl8P7chIyMDvr6+aNiwIebPnw9jY2MAwPbt25GUlIRvvvkGNjY2uHjxIpYuXYqQkBBs375d+n1e98M5CQsLQ926daWT4nZ2djh48CD69euHuLg4rV6NP/zwA/T09DB27FjExsZi7ty56NmzJ/79918AwHfffYfY2FiEhIRI+3VTU9M3tiM9PV3aFqSkpODatWtYuHAhGjdurHErWl7303fu3EGjRo1gbm6O8ePHw8DAAKtXr4a3tzdOnz4t/Y2mTp2KOXPmSPvbuLg4XL58GVevXkXz5s0xaNAgPHv2DEePHsWmTZveOB9q3bp1g6urK+bMmYOrV69i7dq1sLe3l7Y3QFbi/8cff+Crr75C3bp1cfr0abRu3TrP0yBSc3Z2xvnz53H79u1cb9V83XHlpUuXcO7cOXTv3h1OTk4ICgrCypUr4e3tjbt370rbpISEBDRq1Aj37t1D37598fHHHyMiIgJ79+5FSEiIdDz9quTkZLRv3x6XL1/GsWPHpOOK3Pj7+6NLly7o168fevfujV9++QV+fn7w8vJC1apVAWSdUFSfOJ80aRJMTEywdu3aktlFXhSAQqEQa9asEUIIERERIWQymTh+/Lg0fNWqVUKpVBZk1CSEWL9+vQAgjh07JsLDw8XTp0/Fjh07hJ2dnVAoFOLp06dS3YyMDJGamqrx++joaFGqVCnRt29fqSwwMFAAEDY2NiIqKkoq37NnjwAg/vrrL6msZs2aonTp0iImJkYqO3LkiAAgnJ2dpbLdu3cLAGLmzJka0+/SpYuQyWTC399fKgMgFAqFCAwMlMpWr14tAAgHBwcRFxcnlU+aNEkA0Kj7uuWU0+fVebG3txeRkZFS2Y0bN4Senp7o1auXVDZlyhQBQPTo0UNjGkFBQUIul4tZs2ZplN+6dUvo6+tL5deuXRMAxPbt21/bZhMTE9G7d+/X1iF6W5cvXxYAxNGjR4UQQqhUKuHk5CRGjBgh1Rk5cqQAIP7991+p7MWLF8LCwkIr/pKSkrSmMWjQIGFsbCxSUlKkst69e+cYj2XKlBFXrlzR+L16+n///bdUFh8fL1xdXYWLi4vIzMwUQgixePFiAUBs3rxZqpeWlibq1asnTE1NpW3HiBEjhLm5ucjIyMh1uWzfvl0AECdPnnzN0iPKEhsbKwCIDh06aA2Ljo4W4eHh0ufVGFHHwcSJE7V+l1MszZkzR8hkMhEcHCyV5XU/LETW/nXKlCnS9379+onSpUuLiIgIjXrdu3cXFhYWUhtOnjwpAIgqVapoHEcsWbJEABC3bt2Sylq3bq013ddxdnbOcVvQoEEDrXbldT/doUMHYWhoKAICAqSyZ8+eCTMzM9G4cWOprEaNGqJ169avbd+QIUM0jhVelX15qo8PXj2mEkKIjh07ChsbG+n7lStXBAAxcuRIjXp+fn5a4yR6kyNHjgi5XC7kcrmoV6+eGD9+vDh8+LBIS0vTqJfbcWVO25rz588LAOLXX3+VyiZPniwAiF27dmnVV6lUQoiX24rt27eL+Ph40aRJE2FrayuuXbumUV99XP7q8YN6W3DmzBmp7MWLF0KhUIgxY8ZIZcOGDRMymUxjnJGRkcLa2jpPOUFxUqBu7fb29lpdel915coVlCtXriCjplc0a9YMdnZ2KFu2LLp06QITExPs3btX48y5XC6HoaEhAEClUiEqKgoZGRn45JNPcPXqVa1xfv755xpX3tVd5R8/fgwA+O+//3D9+nX07t0bFhYWUr3mzZvDw8NDY1wHDhyAXC7H8OHDNcrHjBkDIYRW74lPP/1U40q1+ix3586dYWZmplWubtObLF++HEePHtX4vDovfn5+sLa2lupXr14dzZs3x4EDB7TG9fXXX2t837VrF1QqFbp164aIiAjp4+DggAoVKki3D6iX1eHDh7W6uRG9b1u2bEGpUqXg4+MDIKvr2+eff47ffvtN6op+4MAB1K1bF7Vr15Z+Z2dnJ3W/fdWrV9Pj4+MRERGBRo0aISkpCffv39eoq1QqpTg8fPgwVq9eDVNTU7Rq1QoPHz6U6h04cAC1a9fWuH3E1NQUAwcORFBQkPR09wMHDsDBwQE9evSQ6hkYGGD48OFISEjA6dOnAQCWlpZITEyU4p/obcXFxQHI+Sqxt7c37OzspI+6m/SrvvnmG62yV2MpMTERERERqF+/PoQQuHbtGoD87YezE0Jg586daNu2LYQQGvstX19fxMbGah0b9OnTRzqOALSPCwqqTp060rZg3759mDVrFu7cuYN27dpJ3fjzup/OzMzEkSNH0KFDB7i5uUn1SpcujS+++AJnz56V/l6Wlpa4c+cOHj169Fbtzy778UGjRo0QGRkpTffQoUMAgMGDB2vUUz+4iyg/mjdvjvPnz6Ndu3a4ceMG5s6dC19fX5QpUwZ79+594+9f3dakp6cjMjIS5cuXh6WlpcY2YOfOnahRo4bUW+1Vr97uAWT1VG3RogXu37+PU6dOafXOzY2Hh4e0XQGyjjUqVaqksY05dOgQ6tWrpzFOa2vrHI9JirsCJeedOnXCqlWrNBaq+g945MgRjXv7qODUSeeOHTvQqlUrRERE5Ni9Y+PGjahevbp0b5WdnR32798v3ev8quwnTdSJenR0NICse1QA5PiglkqVKml8Dw4OhqOjo0ZiDbzsJqoeV27TVh90vHqv3qvl6ja9Se3atdGsWTONz6vTz95udRsjIiKQmJioUZ79qe+PHj2CEAIVKlTQOBCzs7PDvXv3pIdyuLq6YvTo0Vi7di1sbW3h6+uL5cuX5/g3IHqXMjMz8dtvv8HHxweBgYHw9/eHv78/6tSpg7CwMBw/fhxAVnzkJc6BrO6kHTt2hIWFBczNzWFnZyc96Cn7Oi6Xy6U4bNGiBQYOHIhjx44hNjYWkyZNkuoFBwfnGpvq4a+2M/sDt7LXGzx4MCpWrIiWLVvCyckJffv2lQ6WiQpCvW9LSEjQGrZ69WocPXoUmzdvzvG3+vr6OXZBf/LkiZSImpqaws7ODk2aNAHwMpbysx/OLjw8HDExMVizZo3WPqtPnz4AtB8m9abjgoKytbWVtgWtW7fGt99+i7Vr1+LcuXPSGx3yup8ODw9HUlJSrvVUKpV00Wj69OmIiYlBxYoV4enpiXHjxhXK6+7ycvykp6endRzxPt6SQcVTrVq1sGvXLkRHR+PixYuYNGkS4uPj0aVLlze+njQ5ORmTJ0+Wnglla2sLOzs7xMTEaOy3AwIC8vyGo5EjR+LSpUs4duyY1B09L3K6YGtlZaWxjQkODs4xVkpi/BTonvNp06bh5MmTqFmzJho1agSZTIYff/wR33//Pc6fP4+PPvoI3377bWG3tcSpXbu29BTyDh06oGHDhvjiiy/w4MED6Uz+5s2b4efnhw4dOmDcuHGwt7eHXC7HnDlzNB4cp5bbUxRFtge4vQu5TVuXbcou+/22KpUKMpkMBw8ezLGdr15RWbBgAfz8/LBnzx4cOXIEw4cPx5w5c3DhwoU83SdIVBhOnDiB//77D7/99ht+++03reFbtmxBixYt8jy+mJgYNGnSBObm5pg+fTrc3d2hVCpx9epVTJgwIccHyGXn5OSESpUq4cyZM/mal/ywt7fH9evXcfjwYRw8eBAHDx7E+vXr0atXrxwfHkf0JhYWFihdujRu376tNUzdwyu3hxQpFAqtE0qZmZlo3rw5oqKiMGHCBFSuXBkmJiYIDQ2Fn59fnmLpTdTj+PLLL9G7d+8c61SvXl3j+/vcB3/66acAgDNnzryzK8qNGzdGQECAtC9eu3YtFi1ahFWrVqF///4FHm9ROlahksXQ0BC1atVCrVq1ULFiRfTp0wfbt29/7RtYhg0bhvXr12PkyJGoV68eLCwsIJPJ0L179wJva9q3b4/ffvsNP/zwA3799dc3vqVCjbGTPwVKzi0sLHDhwgUsWLAAO3bsgFKpxOnTp+Hu7o4pU6Zg3LhxOT5UiApOnXD7+Phg2bJlmDhxIgBgx44dcHNzw65duzS6nxT0lUnq97Tm1B3swYMHWnWPHTuG+Ph4javn6m6uunrnq5p6+tnbDWS10dbW9o2vtnF3d4cQAq6urqhYseIbp+np6QlPT0/873//w7lz59CgQQOsWrUKM2fOBKDdRYiosG3ZsgX29vY5drPdtWsX/vzzT6xatQrOzs55ivNTp04hMjISu3btQuPGjaXywMDAfLUrIyND4wqks7NzrrGpHq7+9+bNm1CpVBoHAjltZwwNDdG2bVu0bdsWKpUKgwcPxurVq/H999+jfPnyjD/Kt9atW2Pt2rW4ePGixi0gBXHr1i08fPgQGzduRK9evaTy7Ldi5Gc/nJ2dnR3MzMyQmZkp9SIrDIUVOxkZGQBe9kbI635aqVTC2Ng413p6enoavfCsra3Rp08f9OnTBwkJCWjcuDGmTp0qJefvYlvg7OwMlUqFwMBAjV4Prz75nuhtqS/a/ffffwByX5d37NiB3r17Y8GCBVJZSkoKYmJiNOq5u7vneAIyJx06dECLFi3g5+cHMzMzrFy5sgBzkDNnZ+ccY6Ukxk+BurUDWVcY//e//+H69etITExEcnIybt++jcmTJzMxf0e8vb1Ru3ZtLF68GCkpKQBeno169ezTv//+i/PnzxdoGqVLl0bNmjWxceNGjW4vR48e1epC06pVK2RmZmLZsmUa5YsWLYJMJkPLli0L1IbC8uq8vLoxun37No4cOYJWrVq9cRydOnWCXC7HtGnTtM7wCSGktxXExcVJBx1qnp6e0NPTQ2pqqlRmYmKitWEkKizJycnYtWsX2rRpgy5dumh9hg4divj4eOzduxetWrXChQsXcPHiRen34eHhWq9cy2kbk5aWhhUrVuS5XQ8fPsSDBw9Qo0YNqaxVq1a4ePGixrYqMTERa9asgYuLi3RvbatWrfD8+XP8/vvvUr2MjAwsXboUpqamUpfg7G8O0dPTk64QqmNQfTKOMUh5NX78eBgbG6Nv374ICwvTGp6fKz85xZIQQnrdn1p+9sM5TaNz587YuXNnjgfc4eHheW7vq0xMTArlNq2//voLAKRtQV7303K5HC1atMCePXs0eiuEhYVh69ataNiwIczNzQFobwtMTU1Rvnx5rX0xULjbAl9fXwDQ2jYuXbq00KZBJcfJkydz3L6on8OgvsUjt+NKuVyu9fulS5dqvQK1c+fOuHHjRo6vasxp+r169cJPP/2EVatWYcKECXmenzfx9fXF+fPncf36daksKioqx9fAFncFunJOujNu3Dh07doVGzZswNdff402bdpg165d6NixI1q3bo3AwECsWrUKHh4eOd4nlxdz5sxB69at0bBhQ/Tt2xdRUVFYunQpqlatqjHOtm3bwsfHB9999x2CgoJQo0YNHDlyBHv27MHIkSOLxGvC5s2bh5YtW6JevXro16+f9IoWCwuLPL1z1N3dHTNnzsSkSZMQFBSEDh06wMzMDIGBgfjzzz8xcOBAjB07FidOnMDQoUPRtWtXVKxYERkZGdi0aZN0oKTm5eWFY8eOYeHChXB0dISrq2u+X9FDlJu9e/ciPj4e7dq1y3F43bp1YWdnhy1btmD16tXYtGkTPvvsM4wYMUJ6lZr6SrVa/fr1YWVlhd69e2P48OGQyWTYtGlTrklJRkaGdB+uSqVCUFAQVq1aBZVKpdGjZ+LEidi2bRtatmyJ4cOHw9raGhs3bkRgYCB27twpXSUfOHAgVq9eDT8/P1y5cgUuLi7YsWMH/vnnHyxevFjqtdO/f39ERUWhadOmcHJyQnBwMJYuXYqaNWtK96fXrFkTcrkcP/74I2JjY6FQKNC0aVPpnbFE2VWoUAFbt25Fjx49UKlSJfTs2RM1atSAEAKBgYHYunUr9PT08nTrUuXKleHu7o6xY8ciNDQU5ubm2LlzZ473dud1P5yTH374ASdPnkSdOnUwYMAAeHh4ICoqClevXsWxY8cQFRWV7+Xg5eWF33//HaNHj0atWrVgamqKtm3bvvY3oaGh0rYgLS0NN27cwOrVq2Fra6vRpT2v++mZM2fi6NGjaNiwIQYPHgx9fX2sXr0aqampmDt3rlTPw8MD3t7e8PLygrW1NS5fvowdO3Zg6NChGvMDAMOHD4evry/kcjm6d++e7+WSfRl17twZixcvRmRkpPQqNfWDMNlzh/Jj2LBhSEpKQseOHVG5cmWkpaXh3Llz+P333+Hi4iI9QyK348o2bdpg06ZNsLCwgIeHB86fP49jx47BxsZGYzrjxo3Djh070LVrV/Tt2xdeXl6IiorC3r17sWrVKo2T6mpDhw5FXFwcvvvuO1hYWBTKrczjx4/H5s2b0bx5cwwbNkx6lVq5cuUQFRVVsuKnII9479Onzxs/2V85QXmnfhXBpUuXtIZlZmYKd3d34e7uLjIyMoRKpRKzZ88Wzs7OQqFQiI8++kjs27dP9O7dW+O1J+pXqc2bN09rnMjhFR87d+4UVapUEQqFQnh4eIhdu3ZpjVOIrFcfjRo1Sjg6OgoDAwNRoUIFMW/ePOn1C69OY8iQIRplubXp1Vc2FHQ5verYsWOiQYMGwsjISJibm4u2bduKu3fvatRRvyolPDw8x3Hs3LlTNGzYUJiYmAgTExNRuXJlMWTIEPHgwQMhhBCPHz8Wffv2Fe7u7kKpVApra2vh4+Mjjh07pjGe+/fvi8aNGwsjIyMBgK9Vo0LVtm1boVQqRWJiYq51/Pz8hIGBgYiIiBA3b94UTZo0EUrl/7V3n+FRVG8fx3+b3nsjhRZ6C0gTBQFFERBFwIItgiKKoAioIEoRFLv8BRRURB8biAUbKIKgoFioIk1qKCGNJKSQvvO8WFlcEwQlMMnm+7kuXuzs2ck9w947c885c8bLiImJMaZOnWrMmzev3GNLfvjhB+PCCy80vL29jejoaPsjXfS3x5JV9Ci1gIAA47LLLiuXC4ZhGHv27DEGDhxoBAUFGV5eXkaHDh2ML774oly71NRUY/DgwUZYWJjh4eFhtGzZ0pg/f75Dmw8//NC44oorjIiICMPDw8OoXbu2MWzYMOPIkSMO7V577TWjfv36hqurK49VwxnbvXu3cc899xgNGjQwvLy8DG9vb6NJkybG3XffbWzatMmhbWJiouHr61vherZt22b06NHD8PPzM8LCwoyhQ4camzdvNiSV+06f6XG4omN4amqqce+99xpxcXGGu7u7ERUVZVx22WX2x+AaxqmPtSeOzX+NJy8vz7jpppuMoKCgCh/n9nd/f5Sai4uLERERYQwaNMjhMasnnMlx2jAMY8OGDUbPnj0NPz8/w8fHx+jevbvx448/OrSZNm2a0aFDByMoKMj+//TEE084PIKqtLTUGDlypBEeHm5YLBaHx6r9fX+e6vygosdG5efnG/fee68REhJi+Pn5Gf369TN27txpSDKeeuqpf9xnwF8tXbrUGDJkiNGkSRPDz8/P8PDwMBo0aGCMHDnSSE1Ntbc71XllVlaW/bjp5+dn9OzZ09ixY4dRp06dcueeR48eNUaMGGHExMQYHh4eRmxsrJGYmGh/7OGpfiseeughQ5Ixa9YswzBO/Si1ih5t2LVrV6Nr164OyzZu3Gh06dLF8PT0NGJjY43p06cbL730kiHJSElJ+Y97svqxGMa/vxu/bt265a5glJWV6ciRIyorK1N4eLh8fX3P+jEcAAAAQHW1adMmtWnTRu+8806NfCwUcDZGjRqluXPnKi8v75QTyzmb/zSs/VSzk5aUlGju3LmaMWMGz5oFAABAjVFQUFBu3qUZM2bIxcXFYUJNAOX9PX+OHj2qt99+W507d64xhbkk/aee89MZPny4kpKS9OWXX1b2qgEAAIAqZ8qUKVq/fr26d+8uNzc3+2MdT8ydAeDUWrdurW7duqlp06ZKTU3VvHnzlJycrBUrVtSoi1vnZEK4hIQEvf322+di1QAAAECVc9FFF+mbb77R1KlTlZeXp9q1a2vy5MmaMGGC2aEBVV7v3r314Ycf6tVXX5XFYtEFF1ygefPm1ajCXDpHPecDBw7U6tWrK3zsCAAAAAAAcPSfes4ff/zxCpdnZ2fr+++/14YNGzRu3LizCgwAAAAAgJriP/Wcn3j+7N8FBwcrPj5ed955p4YOHVqznkkHAAAAAMB/dE6GtdcUVqtVycnJ8vf350IEqgzDMJSbm6vo6OhTXkjD6ZHfqIrI78pBfqMqIr8rB/mNquhM8/tfD2svKCjQhAkT1L17d/Xt2/esgqzukpOTFRcXZ3YYQIUOHjyo2NhYs8OotshvVGXk99khv1GVkd9nh/xGVXa6/P7Xxbm3t7fmzp2rZs2anVVgzsDf31+SlLShrgL8uMJ5tvoMv83sEJxCaWmhfln1lP37if+G/K5c1zZuZXYITqHUKNEafUl+nyXyu3JdNONOs0NwCmXFhfrj9cfJ77NEfleuAQMGmh2CUygtK9L32/532vz+TxPCtW3bVr///vt/CsyZnBgqE+DnogB/kv9subl5mR2CU2Eo19khvyuXm8Xd7BCch0F+ny3yu3K5enL8rkzk99khvyuXm6un2SE4ldPl93/6xs6YMUMLFizQ66+/rtLS0v8UGAAAAAAAsDnjnvPvv/9eTZs2VXh4uBITE+Xi4qJhw4bpvvvuU0xMjLy9vR3aWywWbd68udIDBgAAAADA2Zxxcd69e3e98847GjRokEJDQxUWFqbGjRufy9gAAAAAAKgRzrg4NwxDJ566tmrVqnMVDwAAAAAANQ6zJAAAAAAAYLJ/VZwzeyQAAAAAAJXvXxXnt9xyi1xdXc/on5vbf3pKGwAAAAAANc6/qqB79OihRo0anatYAAAAAACokf5VcZ6YmKibbrrpXMUCAAAAAECNxIRwAAAAAACYjOIcAAAAAACTUZwDAAAAAGCyM77n3Gq1nss4AAAAAACoseg5BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTuZkdAM7Mlp98tejlCO3a4qPMVHdNmrdPF/U65tDmwC5PzZsWrd9+8lNZqVSnUZEee22fImJL7G22rfPRm0/X0o4NPnJ1leo3L9CT7+2Rp7dhb/Pz8gC9+2Kk9m33loenVS0vzNfk+fvO27aaaVDvzbrrunX6cFlzzX7/QvvyZvGpumPAejWtny6r1aLdB0L00PNXqrjElkLvP7tQUWF5Dut6dVE7vb8k4bzGj+pnwcwI/bAkSAd3e8rDy6pm7Y7rjgnJimtQZG9TXGjRq1OiteqzYJUUWdS2W65GTj+k4PDScuvLyXTVPZc3VsYRD320fYv8AstOrqfIondfjNS3H4UoK91NIRGluvmBFPUclHlettVsLi6GbhmTosv6Zyk4vERHU931zaIQvTcjUpJFkjTmxSRdcX2Ww+fWrfTXhFviTYgYzuBMjt8n/O/hWC15O0zDphxW/6Hp9uW7fvPWvCei9cdmH7m4GurcO1vDJifL29cqyZb3T42oo33bvZWb5arA0FJ16nlMg8cfka+/9bxspxki/PI0qutPurj+AXm5lepgdqAmLu2ubSkRkqS7L/5VVzbZrSj/PJVYXbQtJVyzVnfUliOR9nXUCc7WA93WqnVMitxdy7QrPVSz13TQrwdizNosVCOny++e0a0r/Nydjx7WdcPTtflHPz00sEGFbV5aslONWxco5aCHEjs2K/f+jM//UNO2xytlO6qaPn12qU+f3YqMzJckJSUF6r33mmvdumhJ0tNPr1CrVukOn/nyy3jNmtXeYVmPHnvVv/9OxcTk6vhxd61eHaeXX253fjbiP6I4ryYKj7uofvMC9RyUqcfvqFfu/eT9Hhrdr6GuvPGobh2bIh//MiXt9JKH18mie9s6H024OV43jkjV8GmH5epqaO82b1n+Mn5i9ZeBmvFgnAaPO6LWF+eprEzav8P7fGyi6RrXS1ffbju050CIw/Jm8al6evTXeu/LBM18p5PKrBbFx2XKMCwO7d74+AJ98V1j++uCQvfzEjeqt9/W+qnv7Rlq1Pq4ykqlN5+qpUcGxeu173bIy8d2Uj1ncox+WR6gR+ful29AmWZPiNXjd9TVi5/tLre+F8bUVr2mhco44lHuvSeG1VV2hpseeP6AousVKzPVTYbVUq6ds7r+3jRddVuGnhtVW0k7vdQwoUBjXjig/BxXffpGuL3dr9/66/nRte2vS4przj5C5Tvd8fuEH5YGasd6X4VGFTssP5ripnE3xqvr1dm694lDOp7nojkTY/TcqNp67LX9kiSLi9Sp5zHd/vARBYaWKnmfp2Y9EqvcbDeNfznpXG6eafw9i/TmzYu17kC07l3UR1kF3qodfEw5hZ72NkmZgZq+vIsOZQfIy61Ut7TfrFeu/0J9X71JWQW2c5uZA5YoKStQQxderaJSV93c9jfN7L9EfV67WUfzfczaPFQTp8vv9zf97vD6128D9OKYOHXuYyvgm7XLL9fmrWdqadMaPzVKKHBY/tTC3arTuND+OiC4/AV6Z5GR4aP58xN0+LC/LBZDPXrs18SJazRiRE8dOBAoSVq6tL7efrul/TNFRY5l7bXX7lD//js1b16Cdu4Mladnmb3Yr8ooziXNnj1bzz77rFJSUpSQkKCZM2eqQ4cOZofloP2luWp/ae4p33/zqVrqcGmO7nzsiH1ZdF3HA/zcyTHqd0e6bhiZZl/21965slJpzsQYDX00WVfedLInrU6jIjk7L88STbhrlZ57s7Nu7bvJ4b17B/2sj5c3d+gFP5gSVG4dxwvdlZXDgbyqqer5/eR7ex1ej5lxQDe0bKldv3mr5YX5ys9x0dfvh2jc7CS17mwbnTH6hQMa2rWptq/3cbhq/vlbocrPcdXND6To128DHNb760p/bfnJT2+u3aaAYFtvelSc42+Es2vWLl9rvw7ULytsB/bUQ57qfk2WGrd27HkoKbYoK52La9VBVc9v6fTHb0nKOOKulx+N0RPv7dXEW+s7vPfz8kC5uRka8eQhufx5Mf2+pw/p7sua6PA+D8XUK5Z/UJn6Jh61fyYytkR9EzO06JWISt+eqmJIx41KzfHVxKWX2pcdPub4u7d0eyOH1899e7H6t9qhhuFH9cuBWAV5F6hOyDFN+qqbdqWHSpL+9/2FuvGCrWoQlklxbjJnyO+QCMcCeu3XgUq4OE+16tiOv+4ehkOb0hJp7dcBumZIhix/uy4cEFxWbn3O6uefHUeuvPVWK/Xps1tNmmTYi/OiIjdlZVXcgejnV6zbbtuiKVO6aNOmKPvy/fuDzlnMlaXG33O+cOFCjR49WpMmTdKGDRuUkJCgnj17Ki0t7fQfriKsVumXFQGKqV+kRwbV1/Utm+u+Pg3149JAe5vsDDft2OCroNBSjerbUDe0aq6x/Rvo95997W12bfFRxhEPWVyk4Zc30qDWzTXh5vrav8PLjM06r0bd+qN+2hynDdscfwyC/AvULD5d2Tlemjnhc300413NePhLtWiYUm4dN/X5TYtnvqNXJ3+iG678TS4uzjuUsLqojvmdn+MqSfIPshXQu37zUWmJi9p0OXnbRO2GRYqIKdb29SfzN+kPT733YpQe/F+Sw2iYE35aFqiGrY5r0csRuumCZhrSuYlenRKtooKa0yu8bZ2vWnfOVUx9W89D/WYFat4hX7+u9Hdo16pTnhZu/l2vf79dI6cflL8T905UZ9UxvytitUrP3FdbA+9JU92/9IqdUFJkkZu7YS/MJcnDy3Z82fqLX4XrPJriph+WBqlVp7wK33cGXRvs19bUCD179ddaee98LUxcpP6ttp2yvZtLmQYkbFNOoYf++LMQzy7w0r6jQerb/A95u5fI1WLVwIRtOprvrW0p4adcF849Z8nvv8pKd9MvKwLU88ajp2yzdlmgcrPcdMUN5W83m3R7PV3fsrlGX9NAa78OqODTzsnFxaquXZPk5VWqHTvC7Mu7d0/SggUf65VXlur22zfL0/PksbpNmxS5uBgKDS3Q3LlL9Pbbn2r8+B8UFlb1e85rfHH+wgsvaOjQoRo8eLCaNWumOXPmyMfHR2+88YbZoZ2x7Aw3FeS7auGsCLXrnqvp7+/VxVce0+N31tVva20n70eSbENc334hSr1uPqon3t2rBi2Pa9wN8Tq81/Zeyp9t3nk+SoNGperx/9srv8AyPTiggXKyXM3ZuPOge4c9aljnqF77sPw9KLXCbVdDE/tt1JffNdbDL/TUH0mhev7BpYqJPHlP0cffNNPjr3TX6Kd76/NVTXTzVZt19/W/nLdtQMWqW35brdKcSTFq3j5PdZvYTtIz09zk7mF1uHdckoLCS5SZZhv8VFxk0fThdXXnY8kOc0z81ZEkD2391Vf7d3pp4rz9unvKYa35Mkgzx8ee242qQhbOitB3nwbr9e926Mv9mzT765365PVwrfzk5K0s61YG6Nn76+jhG+I174laanlhnp54e69cXIx/WDPMUN3y+1Q+mB0hV1dD/e7IqPD9hM55ykp316KXw1VSbFFutqveeNJ23+WJ34ATpt9TR1fXb6WbLmghH78yPfDcwXMev1lig3J0feutOpAVqHsWXaUPNjXXw5etUd/mOxzaXRK/X2tHvaZfx7yqW9v9prs/6KvsghO9bRbdtbCvmkRm6MdRr+uXMa/q1vabNXxRH+UWeZb/ozhvnCW//+qbD0Lk7Vemzr0rnnNCkr5+P1Rtu+UqPPrksdzbp0x3TTqsR1/dr6lv71XzDvmaMqSe0xfodetm6+OPP9Rnny3SiBHrNHVqZ3uv+apVdfTMMxdq3LhL9cEHTXXZZfv14IM/2T8bFZUni0W64YZtmju3jZ544mL5+xfrySdXyc2t7FR/skqo0cPai4uLtX79eo0fP96+zMXFRT169NDatWvLtS8qKlJR0ckh3jk5OeclztMx/uyg7dQzR/3vsk2OEN+iQNvW+erL/wtTq075sv7ZpvctR9XzRtvVuAYtC7Rpjb++XhCqIY8csbcZdH+quvx5L8yYFw/olrbNtfqLIPW59dRX+qqr8JA8jbjpJz34XC+VlJZPhxMn5F+saqKv1tiGx+0+EKYLmiWrV5c/9PqHtoknFi07ec/L3kMhKi1z1ejb1ui1D9urpNR5L2xUZdUxv2c9EqukHd56fvGuf/W5+dNrqXaDQl02IOuUbQyrZLFI42YlyTfAlux3TT6saUPrauT0Qw6TQjqrS/pm69L+WXrq3jpK+sNL8c0LdPeUwzqa6q7li2wF+nefBdvb79/hrX3bvfXW2u1qdVGeNq3xP9WqcZ5Vx/yuyK7fvLX49XDN/npnuSGsJ9RtXKixM5L06pQYvTE9Wq6uhq4ZkqHg8JJynxk25bBuHp2iw3s99cb0Wpo7JUYjpx869xtiAheLoa0p4Zq52jZ56460cDUIy9R1rbfp861N7O1+PRCj69+8XkHeBRqQsF3PXr1Mt7zTX5nHfSQZeuTy1crM99bg9/qpsNRN/Vtt10sDluqm/xugjHzfU/x1nEvOkt9/9/WCEF16bZbDfFB/lZ7srvWr/PXI3P0OywNDyzRg2MnJzxq3LtDRVHcteiVCnXpWzW2tDIcO+evee3vK17dEnTsf1JgxP+uhhy7VgQOBWrr05CR6+/cHKTPTW089tVK1auXqyBF/ubgYcne3as6cC7RhQy1J0tNPd9K7736qVq3S7Muqohrdc56RkaGysjJFRkY6LI+MjFRKSvlhy9OnT1dgYKD9X1xc3PkK9R8FhJTJ1c1QnUaOw+HiGhYq7bDtvsnQSNtQj3JtGpxsE/Jnm9oNT7bx8DQUVafI3sbZNKqToZDAQr06ebGWv/6Glr/+hlo3SVH/Hlu1/PU3lHXMdnV9f3KQw+cOHAlSZMiph8Zs3xMuNzdDUWH/fJ8hzp3qlt+zHonRz98E6JkPdztcMQ+JKFVJsYvyjjle5MlOd7ffe7Zpjb9WfxGkXnEJ6hWXoHHX22YWv65FC/3fs7Z7rUIiSxUaVWIvzCVbrhuGRRlHnDO//27oY8m23vPPgrV/h7dWfBSij18L140jUk/5mZQDnso+6qrous4/90Z1Ut3y+1S2/Oyn7Aw33dK+uT1/Uw956LUp0bqtw8nZmS/tn60Fm7fqvQ1btWjr77p1bIqOHXVTrTqO38uQiFLVblikTj1zdP/Th/TFW2E6muqc/TDpeT7aezTYYdneo0GqFeA4lL+gxF0HswO15UiUJn/VXaWGi/q1tPWud6h9WJfEJ+nhzy/XpsO1tCM1XE9+c4kKS910dYud521b4MhZ8vuvtvzsq0N7vHTlTafu6Fq2MET+waXqdMWpe9ZPaNLmuI7sd+7RHaWlrjpyxF+7d4fozTcTtHdvkK655o8K2+7YYbtVpVYtW/5nZtrO30/0tEvSsWNeysnxUERE1Z7h3jl/sc+R8ePHa/To0fbXOTk5VeIHwN3DUKOE4zq0xzFJD+/1tA9xjYwrVmhUcYVt2v05kUXDVsfl7mnVoT2eatHRVniWlkipBz0UeYqhstXdhu3RGvzotQ7LHr5jtQ4cCdT7S1opOd1f6Vk+ioty/KGMjTymX7ac+v++Qe2jKrNalJVTM2a6dwZm5bdhSLMnxOjHrwL17Ie7FVXbcZK2hq2Oy83dqo1r/OwjWg7u9lTaYQ81bWvL08de36fiwpPXWndu8tELo2vr+U922SeGbN4+X6s/D1JBvov98UuH9njKxcVQWC3nzO+/8/S2lnvKgrXMUuE9+ieE1SpWQHCZMlNrxgUMZ1VVj989BmTqgi6OF3Efuam+LhuQVeE9pycen/j1+yFy97TqgktOfU+58WfnXEmxc/bDbDocpbrB2Q7L6oQcU3JOxffhn+AiQx5/Dmv1drftT+vffhcMQ7JYnH80kbOoqvn9V1+/H6qGrY4rvnn5eSUk23du2cIQ9RiYJbczONzs2eqtkIiacew+wWIx5O5e8ZD0+HjbyMETRfm2bbZ702Njc5SRYZvY0c+vSAEBxUpLq9oTPdbo4jwsLEyurq5KTXXsNUlNTVVUVFS59p6envL0NOcqVUG+i5L3nfzbKQc9tOd3b/kHlSoitkTXDU/Tk3fXUYsL85RwUZ7WrQzQT9/YTvYl23DWgfek6+3nolS/WYHqNy/Q8kUhOrjHS4/++SgWX3+r+tx6VG8/H6Xw6BJFxBbrwz9neu1yVfb53uTzoqDQQ/sPOz46rbDITTl5XvblC5e21O39NmjPwRDtPhCqnhfvUu1axzR59mWSbI9aa1o/XZt21NLxQnc1j0/T8EE/a/naeOUdd+6rmlVZdcnvWY/EauUnwZo8f6+8/az2e0h9/cvk6W3IN8CqnoMy9erkGPkHlcnX3/YotaZt8+0ztf/9yQzHMm3rqN2wyH6vevdrs/Tui5F6/oHaunXsEeVkuun1adG64sbMGjGkXZJ++iZAN96XqrTD7kra6aX4FgXqf1eali2wXXH38inTLaNTtGZJkLLS3FSrbrHunJCs5P2eWv8dQ9qrkuqS39Lpj98BIY4nm25uUnBEqcPTVD59I0zN2uXL29eqDd/76/Wp0RrySLI9v39Z4a+sdHc1bn1cXr5WJe300utTo9W8fZ7TPpXhnXUJeuvmT3THheu1bEcDtaiVqoGttunxZV0lSd7uJbrzwvVatbuuMvJ9FeRdqBvb/K4I/3x9s8M2umhzcqRyCj01rfcKzf2xnYpK3dQ/YZtiAnO1ek8dMzevRnOm/Jak/FwXff95oO6alHzK9Wxa46eUA54V9qx/80Gw3NwNxbewPVrth6WBWrYgRKOceE6J22/frHXraiktzUc+PqXq1i1JrVql6dFHu6lWrVx165akX3+NVk6Oh+rVO6ZhwzZoy5Zw+2zshw8H6McfYzRs2Ea99FJ7HT/upsGDf9OhQ/7avDnyn/+4yWp0ce7h4aG2bdtqxYoV6tevnyTJarVqxYoVGjFihLnB/c0fm3300MCT91fMnWybVfzy6zM1dsYBXdzrmO576pAWzIrUK4/FKrZ+kR57bZ+9B1yS+g9NV0mhRXMmxSg321X1mxVq+vt7HE7shz5me/75M/fVVnGhixq3Oa6nF+2xzxxdE330TQt5uJfp3kE/y9+3SHsOhmjsc1cqOd02EUdJqasu7bhXt/fbKHe3Mh1J99eHy1po0dctTI68Zqsu+f3FW7aruw8OaOiwfMyLB+w9Z3dPPiwXi6GpQ+uqpMiidt1yNeJf3kfq7WvV9AV79PKjsRp5ZWP5B5fqkquzdftDR07/YSfx8qOxSnzoiEY8eUhBoaU6muquJe+E6d0XbQdqq9Wiek0Ldfl1++QbUKajqW7a8F2A3no2yml7H6ur6pLf0umP32di5yYfvf18lArzXRTboEj3PXNQPQaenGPCw8vQ0ndDNXdyjEqKLQqPLtbFvY7phhHVd2br09maEqHRi3vqvkt+1rCL1uvwMX898+3FWrLNNj9MmdWieqHZurrFMgV5Fyi70Etbj0Ro8Hv9tOeo7eJ7doG3hn/YRyO7/KLXbvxMbi5W7ckI0f0fX6k/0sP+6c/jHHK2/P7u02DJsKh7v1PPC/PV+6Fq1i5PtRtWfAvVezOilHrIXa5utltSH5mzX12uOv3w9+oqKKhQY8f+pJCQQuXnu2vfviA9+mg3bdwYpbCwfLVpk6p+/f6Ql1ep0tN9tGZNnBYsaO6wjuefv1B33bVBU6Z8J8OwaMuWCD36aFeVlVXt47nFMIya0WVyCgsXLlRiYqLmzp2rDh06aMaMGfrggw+0Y8eOcve6/F1OTo4CAwOV9Ud9BfhX7f/o6qD74DvNDsEplJYW6sflk3Xs2DEFBDj3TJ6nQ35XHT1j2pgdglMoNUq0ylhMfov8rkoSnhludghOoayoUNtffoT8FvldlfTqNcjsEJxCaVmRvt3yzGnzu0b3nEvSDTfcoPT0dE2cOFEpKSlq3bq1vvrqq9MmPoCqj/wGnBf5DTgv8hs1VY0vziVpxIgRVW6YDIDKQX4Dzov8BpwX+Y2aiLEeAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTuZkdgDO4tlFLuVnczQ6j2vOOPGB2CE6h1FpsdghOZWCvvnJz9TQ7jOrP2Gt2BM7BMMyOwKlw/K4csbFJZofgFEqtRdpudhBOhPyuHG5RmWaH4BRczvD8nJ5zAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwAAAABgMopzAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwAAAABgMopzAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwAAAABgMopzAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwAAAABgMopzAAAAAABMRnEOAAAAAIDJKM4BAAAAADCZm9kB4L9p0TFP1w1PV8OWxxUaVarJQ+pq7VeB9vdvGZOibtdkKzy6RCXFFu3e4q35T0Vp50Zfe5u3ft6mqLgSh/XOezJKH8yKPG/bYbbrh+zTRZemKbZuvoqLXLR9c5De+F9DHU46uZ9GTNimNh0zFRJepMICV23bHKT5/2uoQ/t9y63PP7BYsxf+pLDIIl3XpZvy89zP5+bASVx/805ddEmyYmvn2b6Xv4fqjbnNdfigv71NVHSe7hz+u5q3PCp3d6vW/xKpV/7XStlZXpKkiKh8DbptpxIuSFdwSKEyM7z17TdxWvh2Y5WW1tzrshX97knSZ2+G6sOXI/R/v2yv8HPT7qqj1V8EnePoUBNUxvHbP6hUw6cdVsfLc2RYpTVLgvTKY9EqPO5qxiaZ4rrE3bqoe6pi6+SpuMhV27cEa/7Mxjp8wM/eZsS4LWrd4ahCwgpVWOCm7b8Faf6sJjqUdLJNQvsM3TrsD9WJz1VRoatWfBmrt15pJGtZzf2dxH/3T/nt6mbo9oePqP2luapVp1j5OS7auNpf856spcxU2/liq055evajPRWue2Svhvpjs8952xYzXTd4b7nz8/kvNXI4Pz/J0JSZG9Tu4qOaOrq1floVIUmq1zBX1w3ep2atsxQQVKK0I95a8mGsPnu/zvndmH+pRv/yfP/99+rbt6+io6NlsVi0ePFis0M6Y14+Vu3d6qVZj8RW+P7hvZ6aPSFGwy5tpDH9GijloIemv79XgSGlDu3eeiZKNyY0s//7dF7Y+Qi/ymhxQZa+WBin0bd10IR72srVzdATr2yQp1eZvc3u7QF6cXJzDet/kR4dfoEsFkPTXt4gFxej3PpGTdqmfbv8yi3H+Ved87tFQoa++KS+Rt/TVRPGdJarm1VPPPeDPL1s+evpVaonnvtRhiGNf6Czxo64RG5uVk2a/pMsFtv3Mq52nlxcDM18rrXuSeyhV2e1VO+r9ylx6FYzN8109/Vq5PCbN+6G+pKk1Z8HKT3Z3eG9GxOa6f+ejdTxPBf9+q3/adaM86k653dlHL8fnnVAdRoXavyN9TUxsZ5adszTqGcPna9NqBJaXpCpLxfV0Zg7LtKjIzvIzdWqaTN/sf9OStLuHYF6cWor3X3DJXrsvvayWKSpM3+xH7/rNczRlBfXaf3acN13a2c99UgbdeySqsH37jRrsyDnzW9Pb6satCzQezMidW/Phnr8zrqKjS/SlDf32dtsW+dT7ji09N0QHUny0B+bvc/nppiqZdssfflBnMYkdtSj97STm5uhaS+vd8jvE/rdfECGYSm3vEGzHGVneui5R1tq+HUXaeG8ekocsUtX3XDgfGzCf1aje87z8/OVkJCgIUOGqH///maH86+sWxmgdSsDTvn+yk+CHV6/OjlavW7KVL1mBdq05uRJZkGei7LSa27v7sQRFzi8fmFScy349js1bJaj3zfY9uFXH5/8gU074q3/m91AL3/wkyKiC5Ry6OQVzN7XHZSvf6nef7W+2nc+en42AKdUnfN74kMXO7x+YXpbLfhsiRo2ytbvv4WpWYujiojK14g7u6vguC1/n5/eVh988YUSLkjXpvURWv9LpNb/cnIUTMoRX328MFe9r9mnea+0PK/bU5Ucy3Q87N0wIk3J+zz021pfSZZyv4cX9Tqm7z8PqlE9ktVBdc7vsz1+xzUoVPtLczXiyoba9ZvtGPTyozGa+s4+vfp4tL0HztlNvL+Dw+sXHm+l95etUIOmOdq6MUSS9NXi2vb3045I/zenkWa/t0YRtY4r5bCvuvQ4on27/fX+vIaSpCOHfPXGzCYa9+RGvfd6QxUcr9GnyaZx1vw+nuuq8TfGOyybPSFGM5fuUnhMsdIPe6i0xEVZ6Sf7Tl3dDHXqmaNP3wiTVL4AdVYTR7R1eP3CpBZ6/9tVatAsR1s3hNiX12+Uo2tv2a9Rt1yod775zuEz33wa4/A65bCPmrQ6posuTdUXC2urqqrRvzq9evVSr169zA7jnHNzt6r3LUeVd8xFe7c5XnW7fkSabhqVqrRkd638JFgfvxoua1nNSf6/8/WzXZHLPVbxyY2nV5kuvzpZRw55KyPFy748rn6ebhq6Vw/c1kFRMQXnJVb8M2fKb18/2zDs3FwPSZK7h1UyLCopOXkALy52kWG1qHnLo9q0PqLi9fiWKi/H49wHXE24uVt16YAsfTw3XBWd9DRoeVwNWhRq9il6OGEeZ8rvf1LR8btpu3zlZrvaC3NJ2rDaX4ZVatLmuH78yxD5muTE8TvvlMfvUl3e95BSDnsrI9W2L909rCoudhxEWlzkKk8vqxo0OaYtG0LPbdCoUE3Jb0nyDSiT1SrlH6v4AnCnK47JP7hUyxYGV/h+TeHrXz6/Pb3K9OCTW/TKU02VddTzzNbjV3rKc/yqokYX586uY48cjX8lSZ7eVmWmumn8jfHK+Uuv0afzwrV7i7dys13VrF2+Bo9PUUhEiV6dEvMPa3VeFouhYWN3auvGICXtcRya3ue6gxoyape8fcp0cJ+PJtxzgf2+XTd3qx6evkXzZjRSeoo3xTkqlcViaNiI37T1txAl7bNdjd+xNUSFha4aMmyr3nqtmWSRBg/bKlc3Q8GhhRWup1ZMnvr236PXX2lxPsOv0i66Mkd+AWVa9kFIhe9fOShTSX94atu6iu5xA86dfzp+h4SXKvuo4+mbtcyi3Gw3hUSUn0+hJrBYDN01epu2bgpW0l7HW1D6DEjS4JE7bMfv/b6aMKKD/fi94acwXXPjPnW9Ilmrl9dScGiRBt25S5IUElZ03rcDNYu7p1V3TDiiVYuDdDyv4uK856BMrV/lr4wjNffCusVi6K6xO/48Pz+Z30PH7NT2zUH66buKOyT+rmmrbHW5PEWT729zrkKtFBTn/0JRUZGKik7+WOfk5JgYzelt+sFXwy9vpICQUvW6OVMT5ibpvj4NdOyo7YrRx6+G29vu2+6tkhKL7n/6kOZPr6WS4po3HcHw8TtUp0Gexg5uX+69lUujtPHnEIWEFav/bfs1/unfNHZwe5UUu2rwfbt0cJ+vVi6pZULUqCxVNb+HP7BZderlauzIS+zLco556slJHTRi9GZdPWCPDKtF330bq107gyq87yo0rEBTn/lRa1bF6Osv6p3P8Ku0noOO6teVARUOA/bwsqr7tVl6b0bNmSDTmVXV/D6V0x2/4eieh7aqTv08PXjXheXeW/lVtDb+EqbgsCINuHmvxj+5UWOHdlJJsas2/hyuN2Y20b3jfteYyZtVUuKiBfMaqEWbLFmtJmwI/pPqlt+Sbbj6hLlJkkWaOa7i0VlhtYrVtluunhxWtScwO9fuGbdddeLz9OCQk7eydLwkTa3aZ+q+QeVzviJ14nP12Isb9d6r8dr4U9WeX4vi/F+YPn26pkyZYnYYZ6yowFXJ+12VvN9TOzb46o0123XloEwtPMVs7Ds3+MrNXYqMK9ahPV4VtnFW9zy8Qx26pOuhO9rraFr5bT+e567jee5KPuCrHb8F6oPvV+qiS9P03Ve11Kp9puo2yFPnX5fbGv85IdeCld9pwbx6endOfLn1oeqpivl9z/2b1aFTih4a2UVH0x1vSdm4LlJ33HSFAgKLVFZmUX6eh975eIlSkh1HvoSEFuipGau1fWuIXnqual8tPp8iYorVpkuept5Zt8L3u/TJlqe3oeWLKu5VR/VSFfP7n/zT8Tsz3U1BoY6TIrm4GvIPKlVmWs0r3u8eu1UdOqfp4WEX6mha+Qmzjue763i+u5IP+mrnliAtXPGNLuqWqu+WRUuSFr9XX4vfq6eQsCLl5borslaBbh+xUymHa8as2M6guuW3rTDfr8iYYj10ffwpe82vuCFLuVluWrusZt6qIkl3P7xdHbqk6+E7Hc/PW3XIVK3Y4/rgu5UO7R95dpO2bgzW+LtOdrTF1cvTE3PW66uPY7VwXv3zFvt/RXH+L4wfP16jR4+2v87JyVFcXJyJEf07FhfJ3bP8DOMn1G9eoLIyKTujJn0tDN3z8E51ujRN44a2VWryGcyE+WfHpLu7bV8+MTZBnp4nL7E3an5MD0zZpgfvaKcjBzm4VxdVK78N3XP/b+rUJVnj7u+i1JRTD6vOOWa7zyqhTbqCgov00w8nR3CEhtkK811/BOvFp9pW2KteU11xY6ayM9z08/KKJ+7pOShTPy0LKDeBHKqnqpXf/95fj9/b1/nKP6hMDVoe1+4ttmNM6855srhIOzbWpGOOobvHblOnbikaf8+FSk0+g223GJLFkLv737vFLcrMsJ34d70iWWkpXtqzs+YWRNVNdcrvE4V5TL1iPTQwXrlZpzrGGLrihkwt/zBYZaU18dht6O6Hd6hT9zSNH9quXH5/OL+eln3i2Bnx8qK1eu35xvrl+5Mjg2vXz9OTc9dpxRfR+r/ZDc9L5GeLs45/wdPTU56eZzbhwLnm5VOm6HrF9tdRccWq37xAudmuysl01U33p2ntMttwzYCQUl09OENhUSVa/XmQJKlp23w1aXNcm3/00/E8FzVte1x3T0nWtx8FK+9YzflaDB+/Q916pejxBxJUkO+m4FDbsKj8PDcVF7kqKua4LumZqg1rQ3Usy932/PLB+1Rc5Kpf19iGxfx1xnZJCgiy/b8c3OvLc86rkaqU38Mf2Kxulx3S4xMuVEGBm4JDbPeR5+e5q7jYdoX98l5JOpDkr2PZHmraPFPDRv6mxYsa2J+FHhpWoKf+t1ppKT6a93ILBQadHPKXlVmzRsb8ncXy50nPouAKJ8CMrluklhfm67FbuAXAWVSl/D7b4/fB3V769Vt/jXrukGY+HCtXd0P3Tjuk7z4NqjEztUvS8Ie2qmvPZE0d21YFxys4fkcfV5fLk7Xx53Ady/JQWEShrkvcYzt+/3jy5L3/LXu1fm2YDMOii7qlaGDiHj31SBtZrTWxIKqeqkt+Z6a667HX9qtBywJNvK2eXFwNBYf/OeFrtqtK/zLJa+vOeapVp1hfvVczR28NH7ddXXulaOoDrSvM76yjnhVOApee4m0v5OvE5+rJueu0YW2YFr9Tx76OsjKLcrKr7j38NacKq0BeXp52795tf71v3z5t2rRJISEhql276k6xL0mNEgr07Ed77K/vnpIsSVq2MFgvjYtVbIMiPXbdfgWElCk3y1V/bPbRmGsbKOkP20l5SbFFXa/J1i1jUuTuYSjloIc+fjXM4T70muCq623PhX3m9fUOy1+Y2FzLP49WcbGLmrfJ0jU3HZBfQImyj3ro9w3BGnN7ex3LqrqJjeqd31f1sz3z9JmXVjssf2H6BVr+le3es5i4XCUO3Sr/gGKlpfho4TuN9ckHDext27RLU0xsvmJi8/X2R185rKd312vP8RZUbW0uyVNkbIm+XlDxTMw9b8xUxhF3rf+OZ5tXVdU5v8/2+C1JT4+orXufOKynPtgjwyqtWRKolx+tWZO59hloe1bx03N/dlj+4pRWWv5lrO343TpL19y433b8zvTU7xtDNPaOTjqWdfKkvt1F6bph8G65u1u1b1eApo5tq/Vrz2yCKZwbzprf7zwfpU49bffDv7L8D4fPPTggXr+tPTkZ8ZWDMrX1Vx8d3F0zL6b3+fP8/OnX1zksf3FScy3//Mx+6y7ukaqgkBJd2ueILu1zxL48NdlLQ6665B8+aS6LYRinHufs5FatWqXu3buXW56YmKg333zztJ/PyclRYGCguukauVlqztXqc8U1koNhZSi1FmtF2us6duyYAgJO/SxdZ1dZ+X1Z/P1yc60aV+Srs7Jde80OwSmUGiVapU/Jb47fVYpbbM26MHCulFqLtPzwHPKb/K5S3KKYGLUylFqLtTz1tdPmd43uOe/WrZtq8LUJwKmR34DzIr8B50V+oyarec/LAgAAAACgiqE4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZBTnAAAAAACYjOIcAAAAAACTUZwDAAAAAGAyinMAAAAAAExGcQ4AAAAAgMkozgEAAAAAMBnFOQAAAAAAJqM4BwAAAADAZG5mB1CdGYYhSSpViWSYHIwTMKzFZofgFEr/3I8nvp/4b+z5bS0yORLnUGaUmB2CUyiVbT+S32eH43cl43eyUnD8rhzkdyXj/LxSnGl+U5yfhdzcXEnSGi0xORInkWZ2AM4lNzdXgYGBZodRbZ3I7+/2zTE5EqA88vvscPyuZIfNDsC5kN9nh/yuZKlmB+BcTpffFoPLc/+Z1WpVcnKy/P39ZbFYzA7nlHJychQXF6eDBw8qICDA7HCqreqyHw3DUG5urqKjo+Xiwp0r/xX5XbNUl/1IflcO8rtmqS77kfyuHOR3zVJd9uOZ5jc952fBxcVFsbGxZodxxgICAqr0l7a6qA77kSvuZ4/8rpmqw34kv88e+V0zVYf9SH6fPfK7ZqoO+/FM8pvLcgAAAAAAmIziHAAAAAAAk1Gc1wCenp6aNGmSPD09zQ6lWmM/oirie1k52I+oivheVg72I6oivpeVw9n2IxPCAQAAAABgMnrOAQAAAAAwGcU5AAAAAAAmozgHAAAAAMBkFOcAAAAAAJiM4tzJzZ49W3Xr1pWXl5c6duyoX375xeyQqp3vv/9effv2VXR0tCwWixYvXmx2SIAk8rsykN+oqsjvs0d+o6oiv8+es+Y3xbkTW7hwoUaPHq1JkyZpw4YNSkhIUM+ePZWWlmZ2aNVKfn6+EhISNHv2bLNDAezI78pBfqMqIr8rB/mNqoj8rhzOmt88Ss2JdezYUe3bt9esWbMkSVarVXFxcRo5cqTGjRtncnTVk8Vi0SeffKJ+/fqZHQpqOPK78pHfqCrI78pHfqOqIL8rnzPlNz3nTqq4uFjr169Xjx497MtcXFzUo0cPrV271sTIAJwt8htwXuQ34LzIb5wOxbmTysjIUFlZmSIjIx2WR0ZGKiUlxaSoAFQG8htwXuQ34LzIb5wOxTkAAAAAACajOHdSYWFhcnV1VWpqqsPy1NRURUVFmRQVgMpAfgPOi/wGnBf5jdOhOHdSHh4eatu2rVasWGFfZrVatWLFCnXq1MnEyACcLfIbcF7kN+C8yG+cjpvZAeDcGT16tBITE9WuXTt16NBBM2bMUH5+vgYPHmx2aNVKXl6edu/ebX+9b98+bdq0SSEhIapdu7aJkaEmI78rB/mNqoj8rhzkN6oi8rtyOG1+G3BqM2fONGrXrm14eHgYHTp0MH766SezQ6p2Vq5caUgq9y8xMdHs0FDDkd9nj/xGVUV+nz3yG1UV+X32nDW/ec45AAAAAAAm455zAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwAAAABgMopzAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwAAAABgMopzAAAAAABMRnEOAAAAAIDJKM4BAAAAADAZxTkAAAAAACajOAcAAAAAwGQU5wAAAAAAmIziHAAAAAAAk1GcAwDghOrWravbb7+90tZ3++23q27dupW2PgAA4IjiHACAc+DNN9+UxWKx//Py8lKjRo00YsQIpaammh1ehZKTkzV58mRt2rTJ7FAAAKhx3MwOAAAAZ/b444+rXr16Kiws1Jo1a/TKK69oyZIl+v333+Xj42N2eA6Sk5M1ZcoU1a1bV61bt3Z477XXXpPVajUnMAAAagCKcwAAzqFevXqpXbt2kqQ777xToaGheuGFF/Tpp59q0KBB5drn5+fL19f3fId5Wu7u7maHAACAU2NYOwAA59Gll14qSdq3b59uv/12+fn5ac+ePerdu7f8/f118803S5KsVqtmzJih5s2by8vLS5GRkRo2bJiysrIc1mcYhqZNm6bY2Fj5+Pioe/fu2rp1a4V/Ozs7Ww888IDq1q0rT09PxcbG6rbbblNGRoZWrVql9u3bS5IGDx5sH47/5ptvSqr4nvP8/HyNGTNGcXFx8vT0VOPGjfXcc8/JMAyHdhaLRSNGjNDixYvVokULeXp6qnnz5vrqq6/OdncCAOA06DkHAOA82rNnjyQpNDRUklRaWqqePXuqc+fOeu655+xD3YcNG6Y333xTgwcP1n333ad9+/Zp1qxZ2rhxo3744Qd7T/bEiRM1bdo09e7dW71799aGDRt0xRVXqLi42OHv5uXlqUuXLtq+fbuGDBmiCy64QBkZGfrss8906NAhNW3aVI8//rgmTpyou+66S126dJEkXXTRRRVuh2EYuvrqq7Vy5Urdcccdat26tb7++ms9+OCDOnz4sF588UWH9mvWrNHHH3+s4cOHy9/fXy+99JIGDBigAwcO2PcFAAA1mgEAACrd/PnzDUnG8uXLjfT0dOPgwYPGggULjNDQUMPb29s4dOiQkZiYaEgyxo0b5/DZ1atXG5KMd99912H5V1995bA8LS3N8PDwMPr06WNYrVZ7u0ceecSQZCQmJtqXTZw40ZBkfPzxx+ViPfHZX3/91ZBkzJ8/v1ybxMREo06dOvbXixcvNiQZ06ZNc2g3cOBAw2KxGLt377Yvk2R4eHg4LNu8ebMhyZg5c+Yp9iAAADULw9oBADiHevToofDwcMXFxenGG2+Un5+fPvnkE8XExNjb3HPPPQ6fWbRokQIDA3X55ZcrIyPD/q9t27by8/PTypUrJUnLly9XcXGxRo4cKYvFYv/8qFGjysXx0UcfKSEhQddee2259/762TO1ZMkSubq66r777nNYPmbMGBmGoaVLl5bbD/Hx8fbXrVq1UkBAgPbu3fuv/zYAAM6IYe0AAJxDs2fPVqNGjeTm5qbIyEg1btxYLi4nr427ubkpNjbW4TO7du3SsWPHFBERUeE609LSJElJSUmSpIYNGzq8Hx4eruDgYIdle/bs0YABA856e05ISkpSdHS0/P39HZY3bdrUIbYTateuXW4dwcHB5e6hBwCgpqI4BwDgHOrQoYN9tvaKeHp6OhTrkm0yuIiICL377rsVfiY8PLxSYzwfXF1dK1xu/G3yOAAAaiqKcwAAqpj4+HgtX75cF198sby9vU/Zrk6dOpJsPe3169e3L09PTy/XIx0fH6/ff//9H//uvxneXqdOHS1fvly5ubkOvec7duxwiA0AAJwZ7jkHAKCKuf7661VWVqapU6eWe6+0tFTZ2dmSbPdxu7u7a+bMmQ490DNmzCj3uQEDBmjz5s365JNPyr134rMnnq9+Yv3/pHfv3iorK9OsWbMclr/44ouyWCzq1avXadcBAABOouccAIAqpmvXrho2bJimT5+uTZs26YorrpC7u7t27dqlRYsW6X//+58GDhyo8PBwjR07VtOnT9dVV12l3r17a+PGjVq6dKnCwsIc1vnggw/qww8/1HXXXachQ4aobdu2yszM1GeffaY5c+YoISFB8fHxCgoK0pw5c+Tv7y9fX1917NhR9erVKxdj37591b17d02YMEH79+9XQkKCli1bpk8//VSjRo1ymPwNAACcHsU5AABV0Jw5c9S2bVvNnTtXjzzyiNzc3FS3bl3dcsstuvjii+3tpk2bJi8vL82ZM0crV65Ux44dtWzZMvXp08dhfX5+flq9erUmTZqkTz75RG+99ZYiIiJ02WWX2Sekc3d311tvvaXx48fr7rvvVmlpqebPn19hce7i4qLPPvtMEydO1MKFCzV//nzVrVtXzz77rMaMGXNudw4AAE7IYjATCwAAAAAApuKecwAAAAAATEZxDgAAAACAySjOAQAAAAAwGcU5AAAAAAAmozgHAAAAAMBkFOcAAAAAAJiM4hwAAAAAAJNRnAMAAAAAYDKKcwAAAAAATEZxDgAAAACAySjOAQAAAAAwGcU5AAAAAAAmozgHAAAAAMBk/w/dU7yAfyA11gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1040x520 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*2.6, nrows*2.6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Model comparison - Optimized Threshold (ROC_AUC)')\n",
    "fig.supylabel('True label')\n",
    "fig.supxlabel('prediction')\n",
    "\n",
    "for i, (model_name, model) in enumerate(zip(results['Model'], [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf])):\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        preds[model],\n",
    "        #normalize='true',\n",
    "        ax=axes[i],\n",
    "        colorbar=False\n",
    "    )\n",
    "\n",
    "    axes[i].set_title(model_name)\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783e5fef",
   "metadata": {},
   "source": [
    "### 5.2. F1 optimized thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "646ffb2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression new threshold: 0.485\n",
      "DecisionTreeClassifier new threshold: 0.000\n",
      "VotingClassifier new threshold: 0.224\n",
      "BaggingClassifier new threshold: 0.209\n",
      "RandomForestClassifier new threshold: 0.268\n",
      "AdaBoostClassifier new threshold: 0.449\n",
      "GradientBoostingClassifier new threshold: 0.126\n",
      "StackingClassifier new threshold: 0.590\n"
     ]
    }
   ],
   "source": [
    "model_thres_results = {}\n",
    "\n",
    "for model in [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf]:\n",
    "    TunedThreshCV = TunedThresholdClassifierCV(estimator=model, scoring='f1') \n",
    "    TunedThreshCV.fit(X=train_df.drop('target', axis=1)[:num_samples], y=train_df['target'][:num_samples])\n",
    "    \n",
    "    model_thres_results[model] = TunedThreshCV.best_threshold_\n",
    "    print(f'{type(model).__name__} new threshold: {TunedThreshCV.best_threshold_:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e7704432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 optimized -  Logistic Regression 0.485\n",
      "f1 optimized -  Decision Tree 0.0\n",
      "f1 optimized -  Voting 0.224\n",
      "f1 optimized -  Bagging 0.209\n",
      "f1 optimized -  Random Forest 0.268\n",
      "f1 optimized -  AdaBoost 0.449\n",
      "f1 optimized -  Gradient Boosting 0.126\n",
      "f1 optimized -  Stacking 0.59\n"
     ]
    }
   ],
   "source": [
    "preds = {}\n",
    "for model_name, model in zip(results['Model'], [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf]):\n",
    "    print(\"f1 optimized - \", model_name, round(model_thres_results[model], 3))\n",
    "\n",
    "    preds[model] = model.predict_proba(test_df.drop(['target'][:num_samples], axis=1))[:, 1] > model_thres_results[model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34dbecc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*2.6, nrows*2.6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Model comparison - Optimized Threshold (F1) Normalized')\n",
    "fig.supylabel('True label')\n",
    "fig.supxlabel('Predction')\n",
    "\n",
    "for i, (model_name, model) in enumerate(zip(results['Model'], [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf])):\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        preds[model],\n",
    "        normalize='true',\n",
    "        ax=axes[i],\n",
    "        colorbar=False\n",
    "    )\n",
    "\n",
    "    axes[i].set_title(model_name)\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "562c405a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*2.6, nrows*2.6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "fig.suptitle('Model comparison - Optimized Threshold (F1)')\n",
    "fig.supylabel('True label')\n",
    "fig.supxlabel('Predction')\n",
    "\n",
    "for i, (model_name, model) in enumerate(zip(results['Model'], [lr_clf, dt_clf, voting_clf, bagging_clf, rf_clf, adaboost_clf, gb_clf, stacking_clf])):\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_predictions(\n",
    "        test_df['target'],\n",
    "        preds[model],\n",
    "        #normalize='true',\n",
    "        ax=axes[i],\n",
    "        colorbar=False\n",
    "    )\n",
    "\n",
    "    axes[i].set_title(model_name)\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.12.3)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}