chapter02/model-perf-comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/packtpublishing/Machine-Learning-for-Imbalanced-Data/blob/main/chapter02/model-perf-comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "JNsTImaZ3idL"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import (\n",
    "    train_test_split,\n",
    "    RepeatedStratifiedKFold,\n",
    "    cross_validate,\n",
    ")\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN, BorderlineSMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.datasets import fetch_datasets\n",
    "from imblearn.pipeline import Pipeline\n",
    "from xgboost import XGBClassifier\n",
    "from numpy import mean\n",
    "from collections import Counter\n",
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0jay5T6y4Qd2"
   },
   "source": [
    "## Synthetic data set prep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "W6XlXjJx3B-o"
   },
   "outputs": [],
   "source": [
    "from imblearn.datasets import make_imbalance\n",
    "from collections import Counter\n",
    "from sklearn.datasets import make_moons\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def make_moons_data(n_samples, noise, random_state=10):\n",
    "    X, y = make_moons(n_samples=n_samples, noise=noise, random_state=random_state)\n",
    "    X = pd.DataFrame(X, columns=[\"feature 1\", \"feature 2\"])\n",
    "\n",
    "    def ratio_func(y, multiplier, minority_class):\n",
    "        target_stats = Counter(y)\n",
    "        return {minority_class: int(multiplier * target_stats[minority_class])}\n",
    "\n",
    "    multiplier = 0.1\n",
    "\n",
    "    X_resampled, y_resampled = make_imbalance(\n",
    "        X,\n",
    "        y,\n",
    "        sampling_strategy=ratio_func,\n",
    "        random_state=0,\n",
    "        **{\"multiplier\": multiplier, \"minority_class\": 1},\n",
    "    )\n",
    "    return X_resampled, y_resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "aKjR2jG63I9g",
    "outputId": "7449a7c2-8a5e-4d81-fd82-42da1b8c56a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 550, 1: 55})\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "X1, y1 = make_moons_data(n_samples=1100, noise=0.1, random_state=14)\n",
    "print(Counter(y1))\n",
    "\n",
    "ax = X1.plot.scatter(\n",
    "    x=\"feature 1\",\n",
    "    y=\"feature 2\",\n",
    "    c=y1,\n",
    "    colormap=\"viridis\",\n",
    "    colorbar=False,\n",
    ")\n",
    "sns.despine(ax=ax, offset=10)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "kBtOELuZ3Q-o"
   },
   "outputs": [],
   "source": [
    "synthetic_datasets = []\n",
    "synthetic_datasets.append(\n",
    "    {\"data\": np.array(X1), \"target\": np.array(y1), \"DESCR\": \"synthetic data\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kt0BU1tJ4UeS"
   },
   "source": [
    "## Real world data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "oTR8eXKJ3q9F"
   },
   "outputs": [],
   "source": [
    "# these datasets are baked into imbalanced-learn\n",
    "dataset_list = [\"thyroid_sick\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JplOW1dI3q6k",
    "outputId": "ecd9bfbb-0bf7-47dd-b59b-e95998f49625"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 550, 1: 55}) synthetic data\n",
      "Counter({0: 3541, 1: 231}) thyroid_sick\n"
     ]
    }
   ],
   "source": [
    "# print class imbalance of each dataset\n",
    "all_datasets = []\n",
    "all_datasets = synthetic_datasets\n",
    "le = LabelEncoder()\n",
    "for dataset in dataset_list:\n",
    "    data = fetch_datasets()[dataset]\n",
    "    data[\"target\"] = le.fit_transform(data[\"target\"])\n",
    "    all_datasets.append(data)\n",
    "\n",
    "for item in all_datasets:\n",
    "    print(Counter(item[\"target\"]), item[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "Q6K7v2VK59WL"
   },
   "outputs": [],
   "source": [
    "# function to train random forests and evaluate the performance\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "\n",
    "def run_model(classifier, X_train, y_train, oversampler=None):\n",
    "    model = Pipeline(\n",
    "        steps=[(\"minMaxScaling\", scaler), (\"over\", oversampler), (\"model\", classifier)]\n",
    "    )\n",
    "    folds = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=4)\n",
    "    cv_results = cross_validate(\n",
    "        model, X_train, y_train, scoring=\"average_precision\", cv=folds, n_jobs=-1\n",
    "    )\n",
    "    print(\n",
    "        \"model: \",\n",
    "        type(classifier).__name__,\n",
    "        \" | cv_results:\",\n",
    "        cv_results[\"test_score\"].mean(),\n",
    "    )\n",
    "    return cv_results[\"test_score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "lOQgLZiR6xQE"
   },
   "outputs": [],
   "source": [
    "oversampler_map = {\n",
    "    \"Nosampling\": None,\n",
    "    \"RandomOverSampler\": RandomOverSampler(sampling_strategy=\"auto\", random_state=0),\n",
    "    \"SMOTE\": SMOTE(random_state=0),\n",
    "    \"ADASYN\": ADASYN(\n",
    "        sampling_strategy=\"auto\",  # samples only the minority class\n",
    "        random_state=0,  # for reproducibility\n",
    "        n_neighbors=5,\n",
    "        n_jobs=4,\n",
    "    ),\n",
    "    \"Borderline-SMOTE\": BorderlineSMOTE(\n",
    "        sampling_strategy=\"auto\",  # samples only the minority class\n",
    "        random_state=0,  # for reproducibility\n",
    "        k_neighbors=5,\n",
    "        m_neighbors=10,\n",
    "        kind=\"borderline-1\",\n",
    "        n_jobs=4,\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k_ouwEoy59Tr",
    "outputId": "43e40e6b-133f-419d-a0fe-00865e892f8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nosampling  | Dataset description:  synthetic data\n",
      "model:  LogisticRegression  | cv_results: 0.7712053119679325\n",
      "model:  RandomForestClassifier  | cv_results: 0.8878080751580462\n",
      "model:  XGBClassifier  | cv_results: 0.9807069387416533\n",
      "\n",
      "RandomOverSampler  | Dataset description:  synthetic data\n",
      "model:  LogisticRegression  | cv_results: 0.7712732178674747\n",
      "model:  RandomForestClassifier  | cv_results: 0.933366035910007\n",
      "model:  XGBClassifier  | cv_results: 0.981636151819638\n",
      "\n",
      "SMOTE  | Dataset description:  synthetic data\n",
      "model:  LogisticRegression  | cv_results: 0.7695915524496135\n",
      "model:  RandomForestClassifier  | cv_results: 0.911883035401193\n",
      "model:  XGBClassifier  | cv_results: 0.9813158362620099\n",
      "\n",
      "ADASYN  | Dataset description:  synthetic data\n",
      "model:  LogisticRegression  | cv_results: 0.7416527893362935\n",
      "model:  RandomForestClassifier  | cv_results: 0.9504081847731581\n",
      "model:  XGBClassifier  | cv_results: 0.9827732239759731\n",
      "\n",
      "Borderline-SMOTE  | Dataset description:  synthetic data\n",
      "model:  LogisticRegression  | cv_results: 0.7148121374104714\n",
      "model:  RandomForestClassifier  | cv_results: 0.9647119505074051\n",
      "model:  XGBClassifier  | cv_results: 0.9816732098311045\n",
      "\n",
      "Nosampling  | Dataset description:  thyroid_sick\n",
      "model:  LogisticRegression  | cv_results: 0.5415331143687857\n",
      "model:  RandomForestClassifier  | cv_results: 0.7762961940616745\n",
      "model:  XGBClassifier  | cv_results: 0.9613875372152264\n",
      "\n",
      "RandomOverSampler  | Dataset description:  thyroid_sick\n",
      "model:  LogisticRegression  | cv_results: 0.615777204097425\n",
      "model:  RandomForestClassifier  | cv_results: 0.7887468651088209\n",
      "model:  XGBClassifier  | cv_results: 0.967430372961738\n",
      "\n",
      "SMOTE  | Dataset description:  thyroid_sick\n",
      "model:  LogisticRegression  | cv_results: 0.6219410880490295\n",
      "model:  RandomForestClassifier  | cv_results: 0.7777116389416264\n",
      "model:  XGBClassifier  | cv_results: 0.9536485701551576\n",
      "\n",
      "ADASYN  | Dataset description:  thyroid_sick\n",
      "model:  LogisticRegression  | cv_results: 0.6228985588170769\n",
      "model:  RandomForestClassifier  | cv_results: 0.7840987136696074\n",
      "model:  XGBClassifier  | cv_results: 0.9508895307260407\n",
      "\n",
      "Borderline-SMOTE  | Dataset description:  thyroid_sick\n",
      "model:  LogisticRegression  | cv_results: 0.6157092856564513\n",
      "model:  RandomForestClassifier  | cv_results: 0.7857142685838759\n",
      "model:  XGBClassifier  | cv_results: 0.9594857471166574\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# now we train several models, with the different OverSampling techniques and\n",
    "# with cross-validation for each dataset\n",
    "\n",
    "# save results\n",
    "pr_mean_dict_lr = {}\n",
    "pr_mean_dict_rf = {}\n",
    "pr_mean_dict_xgb = {}\n",
    "\n",
    "\n",
    "for data in all_datasets:\n",
    "    pr_mean_dict_lr[data[\"DESCR\"]] = {}\n",
    "    pr_mean_dict_rf[data[\"DESCR\"]] = {}\n",
    "    pr_mean_dict_xgb[data[\"DESCR\"]] = {}\n",
    "    # separate dataset into train and test\n",
    "    # X_train, X_test, y_train, y_test = train_test_split(\n",
    "    #     data['data'],\n",
    "    #     data['target'],\n",
    "    #     test_size=0.3,\n",
    "    #     random_state=0,\n",
    "    #     stratify=data['target']\n",
    "    # )\n",
    "\n",
    "    # train model on data without re-sampling\n",
    "    # with cross-validation\n",
    "    for oversampler in oversampler_map.keys():\n",
    "        print(oversampler, \" | Dataset description: \", data[\"DESCR\"])\n",
    "        X, y = data[\"data\"], data[\"target\"]\n",
    "\n",
    "        # set up the classifier\n",
    "        lr = LogisticRegression(random_state=0, max_iter=2000)\n",
    "        aps_mean_lr = run_model(lr, X, y, oversampler_map[oversampler])\n",
    "        pr_mean_dict_lr[data[\"DESCR\"]][oversampler] = aps_mean_lr\n",
    "\n",
    "        rf = RandomForestClassifier(\n",
    "            n_estimators=100, random_state=39, max_depth=3, n_jobs=4\n",
    "        )\n",
    "        aps_mean_rf = run_model(rf, X, y, oversampler_map[oversampler])\n",
    "        pr_mean_dict_rf[data[\"DESCR\"]][oversampler] = aps_mean_rf\n",
    "\n",
    "        xgb = XGBClassifier()\n",
    "        aps_mean_xgb = run_model(xgb, X, y, oversampler_map[oversampler])\n",
    "        pr_mean_dict_xgb[data[\"DESCR\"]][oversampler] = aps_mean_xgb\n",
    "\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 706
    },
    "id": "SW1tjApdsSeG",
    "outputId": "1a78ab89-7bef-4d2f-d3bf-aeb76a3a6a43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Logistic Regression, Average Precision\n"
     ]
    },
    {
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       "      <th>synthetic data</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>Nosampling</th>\n",
       "      <td>0.771</td>\n",
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      "text/plain": [
       "                   synthetic data  thyroid_sick\n",
       "Nosampling                  0.771         0.542\n",
       "RandomOverSampler           0.771         0.616\n",
       "SMOTE                       0.770         0.622\n",
       "ADASYN                      0.742         0.623\n",
       "Borderline-SMOTE            0.715         0.616"
      ]
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     "text": [
      "\n",
      "\n",
      "\u001b[1m Random Forest, Average Precision\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>synthetic data</th>\n",
       "      <th>thyroid_sick</th>\n",
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       "  </thead>\n",
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       "      <th>Nosampling</th>\n",
       "      <td>0.888</td>\n",
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       "      <th>RandomOverSampler</th>\n",
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       "      <td>0.912</td>\n",
       "      <td>0.778</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borderline-SMOTE</th>\n",
       "      <td>0.965</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-646bcb34-ef34-4a80-a8c2-86f0b094fc0d')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-646bcb34-ef34-4a80-a8c2-86f0b094fc0d button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-646bcb34-ef34-4a80-a8c2-86f0b094fc0d');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-980dd196-9d7f-4c5a-a551-7437f332f45c\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-980dd196-9d7f-4c5a-a551-7437f332f45c')\"\n",
       "            title=\"Suggest charts.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-980dd196-9d7f-4c5a-a551-7437f332f45c button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                   synthetic data  thyroid_sick\n",
       "Nosampling                  0.888         0.776\n",
       "RandomOverSampler           0.933         0.789\n",
       "SMOTE                       0.912         0.778\n",
       "ADASYN                      0.950         0.784\n",
       "Borderline-SMOTE            0.965         0.786"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m XGBoost Classifier, Average Precision\n"
     ]
    },
    {
     "data": {
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       "\n",
       "  <div id=\"df-293fd829-7e5a-419c-8314-d85c1cc3ccc2\" class=\"colab-df-container\">\n",
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       "<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",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>synthetic data</th>\n",
       "      <th>thyroid_sick</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nosampling</th>\n",
       "      <td>0.981</td>\n",
       "      <td>0.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomOverSampler</th>\n",
       "      <td>0.982</td>\n",
       "      <td>0.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMOTE</th>\n",
       "      <td>0.981</td>\n",
       "      <td>0.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADASYN</th>\n",
       "      <td>0.983</td>\n",
       "      <td>0.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borderline-SMOTE</th>\n",
       "      <td>0.982</td>\n",
       "      <td>0.959</td>\n",
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       "\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "    }\n",
       "  </style>\n",
       "\n",
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       "        document.querySelector('#df-293fd829-7e5a-419c-8314-d85c1cc3ccc2 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-293fd829-7e5a-419c-8314-d85c1cc3ccc2');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "            title=\"Suggest charts.\"\n",
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       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
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       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-ee812eb7-b064-499a-87ec-aa336295586d button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                   synthetic data  thyroid_sick\n",
       "Nosampling                  0.981         0.961\n",
       "RandomOverSampler           0.982         0.967\n",
       "SMOTE                       0.981         0.954\n",
       "ADASYN                      0.983         0.951\n",
       "Borderline-SMOTE            0.982         0.959"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)\n",
    "\n",
    "print(\"\\033[1m Logistic Regression, Average Precision\")\n",
    "df_lr = pd.DataFrame(pr_mean_dict_lr)\n",
    "display(df_lr.round(3))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"\\033[1m Random Forest, Average Precision\")\n",
    "df_rf = pd.DataFrame(pr_mean_dict_rf)\n",
    "display(df_rf.round(3))\n",
    "print(\"\\n\")\n",
    "\n",
    "\n",
    "print(\"\\033[1m XGBoost Classifier, Average Precision\")\n",
    "df_xgb = pd.DataFrame(pr_mean_dict_xgb)\n",
    "display(df_xgb.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69WZiqBO5VrA"
   },
   "source": [
    "Points to note:\n",
    "1. Please note that the models used here are not tuned with the best hyperparameters.\n",
    "Tuning XGBoost parameters would likely make it better than logistic regression here (for example, try with hyperparameters of {'n_estimators': 20, 'max_depth': 5, 'learning_rate': 0.2})\n",
    "2. There is not much difference in the performance of different over-sampling techniques.\n",
    "3. Barring a few cases, usually, oversampling methods have performed better than no sampling at all\n",
    "4. As observed above, as the separation between classes increases(separation 2 vs. separation 3), oversampling techniques tend to show bigger performance gains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "xpffeudTvJuW",
    "outputId": "f519d2d5-aa98-4f26-a045-ba17fdb59f7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "\n",
      "Table 1: Logistic Regression average preicision scores on synthetic data\n",
      "----------------------------------------------------------------\n",
      "\n",
      "                     lr\n",
      "Nosampling         0.77\n",
      "RandomOverSampler  0.77\n",
      "SMOTE              0.77\n",
      "ADASYN             0.74\n",
      "Borderline-SMOTE   0.71\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Table 2: Logistic Regression average preicision scores on thyroid_sick\n",
      "----------------------------------------------------------------\n",
      "\n",
      "                     lr\n",
      "Nosampling         0.54\n",
      "RandomOverSampler  0.62\n",
      "SMOTE              0.62\n",
      "ADASYN             0.62\n",
      "Borderline-SMOTE   0.62\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Figure 1: Logistic Regression average preicision scores on synthetic data\n",
      "----------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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fv35yI9EODg65uhnR0NAQ//zzD968efPNbY6KisL58+cxcOBA/PLLL3L7crucm6enJ0xNTWFubo6mTZsiLCwMixcvRvfu3WVldu3ahaZNm8LIyEj2XIuOjkaLFi2Qnp6O8+fPAwCOHz8OdXV1DBkyRHasiooKRo4cmeX5vx4V3rVrFwwMDNCyZUu5c9WpUwe6uro4e/YsgP89vw4fPozU1FSldRsaGiIxMVFuWlVOcvs8+NLw4cPlvm/atCmePn2a7Xni4+MBAHp6erlu29e0tbVl/09MTER0dDQaN24MQRBw48YNWRkNDQ0EBQUpTNWRys21/BZ79+5FRkYGXFxc5B5Tc3NzVKhQQfaYSmlqamLAgAE51hsREYGbN2/C3d0dxYsXl22vXr06WrZsKftd8iVlj1VMTIzs8VDGwMAAABAYGJjltBQDAwN06tQJ27dvhyAIADJHhnfs2IHOnTsr3Lvg6uoq966SdLQ2p+dNXurIy9+JvNizZw9MTEwwatQohX3Z/d45deoUUlJSMGrUKLlyX99EDmQ+B1RUMqNseno6YmJiZNMVpdPycpKbn4+CxOBN9ANbtWoVTp48id27d6Nt27aIjo6GpqambP+TJ08gCAJmzJgBU1NTuS9PT08A/7vxadasWYiNjUXFihVha2uLiRMn4vbt24XeB1dXV9jZ2WHw4MEoUaIEevbsiZ07d+YYwl+8eAEVFRVYW1vLbTc3N4ehoSFevHhRKO2V1lupUiWFfTY2NrL90n+/bl9W2762cOFC3L17F2XKlEH9+vXh5eWVpz+2X5Ie9+Vb23k1dOhQnDx5EocOHZLNf/x6fvLjx49x/PhxhedaixYtAPzvufbixQtYWFgo3AiX1XVRU1OTvZj88lxxcXEwMzNTOF9CQoLsXA4ODujWrRu8vb1hYmKCTp06YePGjXL3AYwYMQIVK1ZEmzZtULp0aQwcOBDHjx/P9nrk9nkgpaWlBVNTU7ltRkZGWYZcKX19fQCZc/7z6+XLl7LQKZ1fLp13LZ17rKmpiQULFuDYsWMoUaIE7O3tsXDhQkRGRsrqyc21/BaPHz+GIAioUKGCwmMaFhYme0ylSpUqlasb+rJ7rCpXrozo6GgkJibKbf/6Bao0uGb3eJUrVw7jxo3D+vXrYWJiAmdnZ6xatUp2jaX69euHly9f4sKFCwAyg+bbt2/h5uamUGd+2pHXOvLydyIvwsPDUalSpTzfQCt9vCpUqCC33dTUVO4FBJA5bW3p0qWoUKECNDU1YWJiAlNTU9y+fVvhumclNz8fBYlzvIl+YPXr15etatK5c2c0adIEvXv3xsOHD6GrqysLrxMmTJDdtPM1adixt7dHeHg4Dhw4gBMnTmD9+vVYunQp/vrrL9kSYBKJRDZK86WcbhDLjra2Ns6fP4+zZ8/iyJEjOH78OHbs2IFmzZrhxIkTOc5r/a9+AIeLiwuaNm2Kffv24cSJE/jjjz+wYMEC7N27F23atBG9PRUqVJAF6Pbt20NVVRVTpkyBk5OT7DmYkZGBli1bYtKkSUrrqFixYr7O/eWollRGRgbMzMywdetWpcdIQ65EIsHu3btx+fJlHDp0CIGBgRg4cCAWL16My5cvQ1dXF2ZmZrh58yYCAwNx7NgxHDt2DBs3bkS/fv3g7++frzZ/Lb/zs21sbAAAd+7cQefOnfN8fHp6Olq2bIn3799j8uTJsLGxgY6ODl6/fg13d3e5F7hjx45Fhw4dsH//fgQGBmLGjBnw8fHBmTNnUKtWrVxdy2+RkZEBiUSCY8eOKb1eX9f/5UhlQcvq8VL2++9Lixcvhru7u+z36OjRo+Hj44PLly/LXjw6OzujRIkS2LJlC+zt7bFlyxaYm5vLfr4Koh15qSMvfye+N/PmzcOMGTMwcOBAzJ49G8WLF4eKigrGjh2bq3dQ8/LzUVAYvIn+I1RVVeHj4wMnJyf8+eefmDJlCsqXLw8AUFdXV/pL/WvFixfHgAEDMGDAACQkJMDe3h5eXl6y4G1kZKR01DU3o8vZBWQVFRU0b94czZs3x5IlSzBv3jxMmzYNZ8+ezbLdZcuWRUZGBh4/fix3c8/bt28RGxtbaB/QIa334cOHaNasmdy+hw8fyvZL/33y5IlCHcq2KWNhYYERI0ZgxIgRePfuHWrXro25c+fKgnduX3RInwd3797NVfncmDZtGtatW4fp06fLRoetrKyQkJCQ43OtbNmyOHv2rMLyb7m9LtJznTp1CnZ2drkKYA0bNkTDhg0xd+5cbNu2DX369EFAQIDsua2hoYEOHTqgQ4cOyMjIwIgRI7B27VrMmDFDaejI7fPgWzVp0gRGRkbYvn07PDw88hzg79y5g0ePHsHf3x/9+vWTbc9qWo2VlRXGjx+P8ePH4/Hjx6hZsyYWL16MLVu2yMrkdC3zy8rKCoIgoFy5cvl+kabMl4/V1x48eAATE5MCXZ7S1tYWtra2mD59OkJCQmBnZ4e//voLc+bMAZD5u7p3797w8/PDggULsH//fgwZMiTfL86+dfAhL38n8nIuKysr/PPPP0hNTc3TZwpIH6/Hjx/L2gZkTpn7eqR/9+7dcHJygq+vr9z22NhY2U382bU7rz8fBYFTTYj+QxwdHVG/fn0sW7YMSUlJMDMzg6OjI9auXYuIiAiF8lFRUbL/x8TEyO3T1dWFtbW13NvIVlZWePDggdxxt27dytUKJNI/bF9/cuX79+8Vyko/4CC7t7Dbtm0LAAqf3LhkyRIAmXNtC0PdunVhZmaGv/76S659x44dQ1hYmOy8JUuWRLVq1bBp0yYkJCTIyp07dw537tzJ9hzp6ekKb3GamZmhZMmScufU0dHJ1VuhpqamsLe3x4YNG/Dy5Uu5fXkZOfuSoaEhhg0bhsDAQNmnwbm4uODSpUsIDAxUKB8bG4u0tDQAmSN+qampWLdunWx/RkaGbFm03HBxcUF6ejpmz56tsC8tLU32PPvw4YNCH79+fn393FdRUUH16tXlynwtt8+Db1WsWDFMnjwZYWFhmDx5stLHa8uWLbhy5YrS46Vh7svjBEGQWyoRyFyRJikpSW6blZUV9PT0ZP3LzbX8Fl27doWqqiq8vb0VziMIgsLjlFsWFhaoWbMm/P395X7/3L17FydOnJD9LvlW8fHxsue4lK2tLVRUVBSuj5ubGz58+IBhw4YhISFBbo38vMrqd2tu5eXvRF7O1a1bN0RHR+PPP/9U2Jfd750WLVpAXV0dK1eulCun7FN6VVVVFeratWuXwpz0rNqd25+PgsQRb6L/mIkTJ6JHjx7w8/PD8OHDsWrVKjRp0gS2trYYMmQIypcvj7dv3+LSpUv4999/ZeudVqlSBY6OjqhTpw6KFy+Oq1evypazkxo4cCCWLFkCZ2dnDBo0CO/evcNff/2FqlWrZnvTEQDUqVMHQOZIac+ePaGuro4OHTpg1qxZOH/+PNq1a4eyZcvi3bt3WL16NUqXLo0mTZpkWV+NGjXQv39//P3334iNjYWDgwOuXLkCf39/dO7cGU5OTvm+hqmpqbLRqS8VL14cI0aMwIIFCzBgwAA4ODigV69esmXkLC0t8fvvv8vKz5s3D506dYKdnR0GDBiADx8+4M8//0S1atXkwvjXPn78iNKlS6N79+6oUaMGdHV1cerUKYSGhmLx4sWycnXq1MGOHTswbtw41KtXD7q6uujQoYPSOlesWIEmTZqgdu3aGDp0KMqVK4fnz5/jyJEj+f4Y5TFjxmDZsmWYP38+AgICMHHiRBw8eBDt27eHu7s76tSpg8TERNy5cwe7d+/G8+fPYWJigs6dO6N+/foYP348njx5AhsbGxw8eFD2Iiw3o2oODg4YNmwYfHx8cPPmTbRq1Qrq6up4/Pgxdu3aheXLl6N79+7w9/fH6tWr0aVLF1hZWeHjx49Yt24d9PX1ZYFr8ODBeP/+PZo1a4bSpUvjxYsXWLlyJWrWrJnlUmnq6uq5fh58q4kTJ+LevXtYvHgxzp49i+7du8Pc3ByRkZHYv38/rly5gpCQEKXH2tjYwMrKChMmTMDr16+hr6+PPXv2KIwcPnr0CM2bN4eLiwuqVKkCNTU17Nu3D2/fvkXPnj0BIFfX8ltYWVlhzpw5mDp1Kp4/f47OnTtDT08Pz549w759+zB06FBMmDAhX3X/8ccfaNOmDRo1aoRBgwbJlhM0MDAosE98PHPmDH777Tf06NEDFStWRFpaGjZv3gxVVVV069ZNrmytWrVQrVo17Nq1C5UrV0bt2rXzfV7p79bRo0fD2dkZqqqqsscst3L7d6JmzZpQVVXFggULEBcXB01NTTRr1gxmZmYKdfbr1w+bNm3CuHHjcOXKFTRt2hSJiYk4deoURowYgU6dOilti3SNex8fH7Rv3x5t27bFjRs3cOzYMblRbCBz2tusWbMwYMAANG7cGHfu3MHWrVvlRsqBzOeWoaEh/vrrL+jp6UFHRwcNGjTI9c9HgSrwdVKIqNBJlxNUtqxXenq6YGVlJVhZWcmWRwsPDxf69esnmJubC+rq6kKpUqWE9u3bC7t375YdN2fOHKF+/fqCoaGhoK2tLdjY2Ahz584VUlJS5OrfsmWLUL58eUFDQ0OoWbOmEBgYmKvlBAUhc7m5UqVKCSoqKrKlBU+fPi106tRJKFmypKChoSGULFlS6NWrl8KSdMqkpqYK3t7eQrly5QR1dXWhTJkywtSpU+WWxBKEvC8nCEDpl5WVlazcjh07hFq1agmamppC8eLFhT59+gj//vuvQn0BAQGCjY2NoKmpKVSrVk04ePCg0K1bN8HGxibL65WcnCxMnDhRqFGjhqCnpyfo6OgINWrUEFavXi13TEJCgtC7d2/B0NBQbolCZcsJCoIg3L17V+jSpYtgaGgoaGlpCZUqVRJmzJiR7fWQ1pXVMnPu7u6Cqqqq8OTJE0EQBOHjx4/C1KlTBWtra0FDQ0MwMTERGjduLCxatEjuuRQVFSX07t1b0NPTEwwMDAR3d3chODhYACAEBATIPR7ZPXZ///23UKdOHUFbW1vQ09MTbG1thUmTJglv3rwRBEEQrl+/LvTq1Uv45ZdfBE1NTcHMzExo3769cPXqVVkdu3fvFlq1aiWYmZkJGhoawi+//CIMGzZMiIiIkJVRtkyfIOTueZBVH75eTi8n0nYWL15cUFNTEywsLARXV1chKCgo23bev39faNGihaCrqyuYmJgIQ4YMEW7duiX3HImOjhZGjhwp2NjYCDo6OoKBgYHQoEEDYefOnbJ6cnMtBSH/ywlK7dmzR2jSpImgo6Mj6OjoCDY2NsLIkSOFhw8fyso4ODgIVatWzfW1EwRBOHXqlGBnZydoa2sL+vr6QocOHYT79+8rbdPXy5tKf+d+vRzql54+fSoMHDhQsLKyErS0tITixYsLTk5OwqlTp5SWX7hwoQBAmDdvnsK+7H7uvr6+aWlpwqhRowRTU1NBIpHIrmle6hCE3P2dEARBWLdunVC+fHlBVVVV7rmmbLnZT58+CdOmTZP9jjY3Nxe6d+8uhIeHK70mUunp6YK3t7dgYWEhaGtrC46OjsLdu3eFsmXLKiwnOH78eFk5Ozs74dKlS0rbcuDAAaFKlSqCmpqa3HM/Nz8fBUkiCPl8n5GIiPKlZs2aMDU1LdR5hD+i/fv3o0uXLrh48SLs7OyKujlEhWr58uX4/fff8fz5c4WVR+i/i3O8iYgKSWpqqsKcz6CgINy6dUvho5V/Nl9/HHN6ejpWrlwJfX39b3rbnehHIAgCfH194eDgwND9k+EcbyKiQvL69Wu0aNECffv2RcmSJfHgwQP89ddfMDc3V/iAjp/NqFGj8PnzZzRq1AjJycnYu3cvQkJCMG/evEJdJo6oKCUmJuLgwYM4e/Ys7ty5gwMHDhR1k0hknGpCRFRI4uLiMHToUAQHByMqKgo6Ojpo3rw55s+fDysrq6JuXpHatm0bFi9ejCdPniApKQnW1tb49ddf5W7mJfqvef78OcqVKwdDQ0OMGDECc+fOLeomkcgYvImIiIiIRMA53kREREREImDwJiIiIiISAW+uJPpOZGRk4M2bN9DT0/vmjwAmIiIicQiCgI8fP6JkyZJQUcl+TJvBm+g78ebNG5QpU6aom0FERET58OrVK5QuXTrbMgzeRN8JPT09AJk/uPr6+kXcGiIiIpElJgIlS2b+/80bQEenaNuTS/Hx8ShTpozs73h2GLyJvhPS6SX6+voM3kRE9PNRVf3f//X1f5jgLZWbaaK8uZKIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCtaJuABHJsz5/Gyo6ukXdDCIi+olEOtUs6ib8FDjiTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8M4niUQCR0fHQj2Hl5cXJBIJgoKCRD83ERERERWs7zJ4P3/+HBKJROFLR0cH1atXh7e3NxISEoq6mfSFxMREzJs3D7Vr14auri40NTVRunRpNG3aFFOnTkV4eLhceUdHR9njevjw4SzrbdCggazc1y9ApG7cuIEBAwagfPny0NbWhoGBAerWrYtZs2YhLi5Orqz0xUxuv7y8vAAA7u7uOZb18/P7lktIRERE/3FqRd2A7FhZWaFv374AAEEQEBUVhWPHjsHLywvHjx/HxYsXoaqqWsStFF9YWBiKFStW1M2Q+fjxI5o0aYLbt2/D2toaffv2hbGxMaKjo3HlyhXMnz8fVlZWsLKyUjhWTU0NGzZsQPv27RX23bt3D1euXIGamhrS0tKUnnvWrFnw8vKCmpoanJ2d4eLigs+fPyMoKAienp5Ys2YNDh48iHr16gGA0ncKbt68iQMHDsDBwUFh/9ffDxo0CKVLl1balpo1ayrdTkRERAR858Hb2tpaNuIolZycjEaNGuHy5cs4d+4cmjVrVjSNK0I2NjZF3QQ5y5Ytw+3btzF48GD8/fffkEgkcvufPXuG5ORkpce2adMGhw8fRlRUFExNTeX2+fr6QkVFBc7Ozjhy5IjCsatWrYKnpyfKly+PI0eOKFyXtWvXYuTIkWjTpg1u3LiBMmXKwNHRUSFM+/n54cCBA3B0dFR4vn1t8ODBaNiwYbZliIiIiJT5LqeaZEdTUxNOTk4AgOjoaIX9d+/ehYuLC8zMzKCpqYly5cph7NixiImJUShraWkJS0tLxMbG4rfffkOZMmWgpqYmN2Vg/fr1qFatGrS0tFCmTBlMmjQJSUlJWbbv48eP8PT0RNWqVaGtrQ1DQ0M4Ozvj4sWLCmWl0y2SkpIwffp0WFlZQV1dPcfwp2yOt3QqxLNnz7BixQrY2NhAU1MTZcuWhbe3NzIyMpTWdeDAATRv3hxGRkbQ0tJCtWrVsGjRIqSnp2fbhi9dunQJADBy5EiF0A0A5cqVy/LFwsCBA5GamorNmzfLbU9NTcWWLVvQqlUrpSPMHz58wNSpU6GhoYFDhw4prX/YsGGYPHkyYmJiMG3atFz3h4iIiKgw/HDBOyUlBUFBQZBIJApv7V+8eBENGjTAvn370Lx5c4wbNw5ly5bF8uXL0aBBA6VBPTk5Gc2aNcOJEyfQsWNHjBw5EiVKlAAAzJ49G0OGDEF0dDSGDBmCHj16YMeOHejRo4fStr1//x6NGjXCrFmzYGRkhOHDh6Nbt264du0anJycsH//fqXHdevWDX5+fnBycsKYMWNQrly5fF+fiRMnYvbs2WjUqBGGDx8OIHNe84wZMxTKTp06FZ07d8bDhw/RtWtXjBgxAtra2pg4cSJ69uyZ63MaGxsDAB49epTn9jZs2BBVqlTBxo0b5bYfOnQIUVFRGDhwoNLjdu/ejY8fP6Jr166oUqVKlvVPnDgRWlpaCAgIwKdPn/LcPiIiIqKC8l1PNXny5Ils9FcQBERHRyMwMBCvX7/GwoULUbFiRVnZjIwMuLu749OnTzh+/DicnZ1l+yZNmoQ//vgDkydPhq+vr9w5IiMjUaNGDQQHB0NbW1vu3LNmzUKpUqVw/fp1mJmZAcgMsfXr11fa3lGjRuHevXtYt24dBg8eLNvu4+ODunXrYujQoWjdujW0tLTkjnvz5g1u376N4sWL5+9CfeH69eu4ffs2LCwsAAAzZsxAhQoVsHLlSnh6ekJDQwMAcPLkScyfPx/Ozs7Ys2cPdHR0AGRe5xEjRuCvv/7Cnj170K1btxzP2aNHD2zZsgWDBw/GlStX0KpVK9SpU0cWyHMycOBATJgwAaGhobK52L6+vjA2NkanTp1w+vRphWNCQkIAAM2bN8+2bkNDQ9SuXRshISG4du0amjZtmqs2ZWX9+vU4fvy40n1TpkxReGyzk5ycLDcFJz4+/pvaRkREVBBCQ0Ph6emJkJAQpKamwtbWFuPGjYOLi0uujre0tMSLFy+yLXP+/Hm5v8krV67E9StXcA3AfQDpuro4e/bsf24Vt+86eIeHh8Pb21the/v27dGiRQu5bcHBwQgPD0ebNm3kQjcAzJw5E76+vti2bRvWrFkjC59SCxculAvdALBt2zakpaVh3LhxstANAPr6+pg+fTrc3NzkykdHR2PHjh1o1qyZXOgGADMzM0ycOBGjR4/GqVOnFG4k9Pb2LpDQDWQGbWnoBgATExN06tQJ/v7+ePjwIWxtbQEAf/75JwDg77//loVuIHMay/z587F27Vps3749V8G7Y8eOWLx4MTw9PbF48WIsXrwYQObNsa1bt8aYMWNQoUKFLI93c3PD1KlTsWHDBtSrVw9v3rxBYGAgfvvtN4XHSioyMhIAUKZMmRzbJy0TERGRY9mcfP3C7Utjx47NU/D28fFR+vwmIiIqKmfPnoWzszO0tLTQs2dP6OnpYc+ePXB1dcWrV68wfvz4HOsYO3YsYmNjFbZHR0dj1apVMDIykg20SY0ePRoAYAHAFEBkAfTle/RdB29nZ2e50cWYmBgEBwdjzJgxsLOzw5kzZ9CgQQMAmUvKAcpXrdDV1UXdunVx4sQJufAJAFpaWnLfS926dQsAlI6QKtsWGhqK9PR0JCcnK52j/fjxYwDAgwcPFIJ3ViPo+VGnTh2FbdI50l/+EFy+fBk6OjrYsGGD0nq0tbXx4MED2XHLli1TKPNlP8eNG4chQ4bg+PHjCAkJwdWrV/HPP/9g1apV8PX1xY4dO9CxY0el5zIzM0O7du0QEBCApUuXwt/fH+np6VlOMylKly5dKrCbK6dOnYpx48bJvo+Pj8/VCwkiIqLCkJaWhiFDhkBFRQXnz5+XTemdOXMm6tevDw8PD3Tv3h1ly5bNtp6xY8cq3S4dmOvbt6/CQNXhw4dRp3JlmFtZYTiAtd/ame/Udx28v2ZsbIyOHTuiWLFiaNmyJaZPn46TJ08C+N/b9NL52V+TjgJ//Xa+mZmZ0hsCpes/fznaLaXsHO/fvweQOfIeHBycZR8SExNzVV9+6evrK2xTU8t8mL+8YfL9+/dIS0vLdsRV2tbY2Fil5b5+gaGnp4cePXrI5sDHxcXBw8MDq1evxqBBg/D69essR7AHDhyI/fv3Y8+ePdi4cSPq1KmD6tWrZ9k2c3NzAMCrV6+yLCMlLfPlOwHfA01NTWhqahZ1M4iIiAAAZ86cQXh4OAYMGCB3H52BgQE8PDzg7u4Of39/zJw5M1/1S981HjRokMK+du3aAUoy0n/ND3dzJQDZKHdoaKhsmzRwvn37Vukx0qkJXwdTZaEbyHySAcC7d+8U9ik7h7Te8ePHQxCELL88PT0Vjs2qDYVJX18fxsbG2bb12bNnADLnainbnxMDAwP8+eefKFu2LKKjo3Hnzp0sy7Zt2xYWFhaYPHkyHj9+rPSH8kuNGzcGAKXzv78UGxuL69evQ0NDQ+m7AURERJRJ+kF1rVq1UtgnncZ77ty5fNUdEhKCsLAw1K1bFzVq1Mh3G390P2Tw/vDhAwDILZFXq1YtAFD66YaJiYm4evUqtLW1UalSpVydQ/qkuHDhgsI+Zdvq1asHiUQiW1rve9egQQPExMTIpsAUFuknjuZEVVUV/fr1w+vXr6GlpYVevXplW7579+7Q1dXF3r17ZVNilFm8eDGSkpLg6ur6XX3oEBER0fdGmgmU3Zdlbm4OXV3dfOcG6Wj31/fB/Wx+yOC9ZMkSAIC9vb1sm52dHaysrHDs2DGcOnVKrvycOXMQExODXr16ZTnV4Wu9e/eGqqoqlixZIjfqHR8fjzlz5iiUNzc3h4uLC0JCQvDHH38oHRH+559/vpsl7aQ3MQwcOFDpGueRkZEICwvLVV1r166Ve/fhS/v370dYWBgMDQ1RrVq1bOsZN24c9u3bh8DAQBgaGmZb1sjICHPnzkVKSgo6dOigdClDX19f+Pj4wNjYGHPnzs1VX4iIiH5W0mm20nf9v6avry8rkxcJCQnYuXMnihUrluPA2n/ddz3H+8vlBIHMecnBwcG4fv06jIyMsGDBAtk+FRUV+Pn5wdnZGW3btkWPHj1QtmxZXLp0CUFBQbCyssL8+fNzfW5ra2vMnDkTnp6eqF69OlxcXKCmpoY9e/agevXqePjwocIxq1evxsOHDzFp0iRs3rwZjRo1gqGhIV69eoWrV6/i8ePHiIiI+C5GXlu3bo0ZM2Zg9uzZsLa2RuvWrVG2bFnExMTgyZMnuHDhAubMmYPKlSvnWNexY8cwfPhwWFtbw87ODiVLlkRiYiJu3LiBCxcuQEVFBatXr85xPrOZmRk6d+6c6z6MHj0a0dHRmD17NmxtbdG6dWtUrlwZSUlJCAoKwq1bt1CiRAkcPHiwwG5azG45wYYNG6J169YFch4iIqL/ih07diAhIQH9+/dXei/az+S7Dt5fLyeoqamJ0qVL49dff8WUKVPwyy+/yJVv0qQJLl++jFmzZuHEiROIi4tDyZIlMWbMGEyfPh0mJiZ5Ov/MmTNRsmRJLF26FGvXroWZmRl69uyJWbNmKQ3PxYsXR0hICP7880/s2LEDW7duRUZGBszNzVGjRg3MmDEjz20oTLNmzYK9vT1WrFiB06dPIzY2FsbGxihXrhy8vLzQp0+fXNWzYMEC2NnZ4eTJkzh//rxs2b5SpUqhf//+GDVqVKHNr541axY6deqEFStW4Ny5cwgMDISGhgasra3h5eWFMWPG5Dh6nhfZLSc4ZswYBm8iIvphSUe6sxrVjo+Ph5GRUZ7r5TST/5EIublLjogKXXx8PAwMDGB66AJUdHSLujlERPQTiXSqCQ8PD/j4+GD79u0Kn2AdGRkJCwsLNGvWLMeFDb50//59VK1aFTY2NjlPYU1MBHR1ZcsJ/igfoCP9+x0XF5fjiP4POcebiIiIiAqWg4MDAODEiRMK+wIDA+XK5FZ2Swj+jBi8iYiIiAjNmzdH+fLlsW3bNty8eVO2PS4uDvPmzYOGhgb69esn2x4REYEHDx5kOTUlNTUVmzdvhrq6utxxPzMGbyIiIiKCmpoa1q9fj4yMDNjb22Po0KEYP348atSogUePHmHevHmwtLSUlZ86dSoqV66Mffv2Ka3v4MGDiIqKQocOHZR+IOGX5s+fD/dhw+AO4OyX29zd4e7ujv379xdEF4vcd31zJRERERGJx8nJCRcvXoSnpyd27NiB1NRU2NraYsGCBXB1dc1TXXm5qfL48eMKH84jnd4CZH6YX15WPvte8eZKou8Eb64kIqKiEulUs6ibILu5EgCQkADk4gP4vge8uZKIiIiI6DvD4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiYPAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISARqRd0AIpL3xL469PX1i7oZREREVMA44k1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiUCvqBhCRPOvzt6Gio1vUzSAiIsqzSKeaRd2E7xpHvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8KZvMnDgQEgkEhgbGyM5OVlpGUdHR0gkEtmXuro6jI2NUbNmTQwaNAjHjx9HRkZGjudq1qwZJBIJqlWrlm05QRCwZcsWNGvWDMbGxtDQ0ECJEiVQq1YtjBgxAufOnQMAvHjxAgYGBihRogSioqKU1pWUlIQqVapAXV0dV69eBQC4u7vL+nLp0iWlx7Vu3RoSiQTPnz/PsV9ERET0c2Dwpnz7+PEjdu7cCYlEgvfv32P//v3Zlh8/fjw8PT0xdepU9OnTB6ampti6dSvatGmDpk2b4uXLl1ke+/TpUwQFBUEikeDevXv4559/siw7cOBAuLm54ebNm2jXrh3Gjx+PXr16wdDQEL6+vli3bh0AoGzZsli2bBnevXuH4cOHK61r+vTpCAsLw7Rp01C3bl2F/ZMnT862z0RERERSakXdAPpx7dixA4mJiRg3bhyWLVsGX19fuLq6Zll+woQJMDc3l9sWHR2N0aNHY/v27XB2dsbVq1eho6OjcOyGDRsgCAImTJiARYsWwdfXFw0aNFAod+HCBfj5+aFmzZo4d+4c9PX15fbHxsbi/v37su8HDBiA/fv3Y+/evdiyZQv69u0r2xccHIylS5eibt26mD59usK5rKyscOHCBRw6dAgdOnTI+kIRERERgSPe9A18fX2hpqaGSZMmwcnJCadPn8aLFy/yVIeJiYlsWsiDBw+watUqhTLp6enw8/ODsbEx5s6dC2trawQEBCAxMVGhrHTqR//+/RVCNwAYGhqicePGctvWrVsHU1NTjBo1Cq9fvwYAfPr0Ce7u7tDQ0MCmTZugpqb4GtXT0xNqamrw8PDI1VQZIiIi+rkxeFO+3L9/H5cvX0arVq1QokQJ9OvXDxkZGdi4cWOe61JRUcG0adMAZI6ify0wMBCvX7+Gq6srNDQ04Obmho8fP2LXrl0KZY2NjQEAjx49yvX5zczMsHbtWsTGxmLQoEEAgEmTJuHJkyfw8fFB5cqVlR5XoUIFDBkyBHfv3oW/v3+uz0dEREQ/JwZvyhdfX18AgJubGwCga9eu0NHRwcaNG/M1+mtnZwc1NTXcvHkTaWlp2Z6rb9++kEgksu1fcnZ2hr6+Pv766y/06dMHu3fvztUofJcuXeDm5obAwEAMGDAAq1evhqOjI8aMGZPtcZ6entDV1cXMmTORlJSU2+4CAJKTkxEfHy/3RURE9F8RGhqKtm3bwtDQEDo6OmjYsCF27tyZ6+MtLS3lFmdQ9nXhwoVs6/j1119lZSMjI7+1S9+MwZvyLDU1FZs3b4a+vj46d+4MANDV1UWXLl3w8uVLnDp1Ks91ampqwtjYGBkZGXj//r1se1RUFA4dOoSKFSuiYcOGAIDy5cvDzs4OFy9exMOHD+XqKV26NPbs2YMyZcpg27Zt6NGjBywtLWFmZgZXV1ecOXMmyzasXLkSZcqUgZ+fH/T09ODn5weJRJJtu0uUKIFx48bh33//xYoVK/LUZx8fHxgYGMi+ypQpk6fjiYiIvldnz56V/a12cXHB8OHDERkZCVdXVyxevDhXdYwdOxaenp4KXyNHjgQAGBkZoV69elkef/LkSfz1119K7x0rKgzelGcHDhxAVFQUevToAS0tLdn2fv36AYDSkej88vf3R2pqqmy0++tzbdiwQeGYFi1aIDw8HCdPnsSMGTPQtm1bJCcnY+fOnWjevDk8PDyUnsvAwABTp04FAAwfPhxly5bNVRsnTJgAU1NTzJ8/Hx8+fMh136ZOnYq4uDjZ16tXr3J9LBER0fcqLS0NQ4YMgYqKCs6fP4+///4bixcvxq1bt1CxYkV4eHjk6t3osWPHwsvLS+GrXLlyADLfAf8yh3wpLi4OAwcORPfu3ZWuSlZUGLwpz6TBWhp+pZo3b45SpUrhwIEDcqPWuZGcnIyYmBioqqqiePHicueSSCQKwdvFxQVaWlrYtGmTwtQUAFBTU0OLFi0wa9YsHDlyBDExMVi3bh3U1NTg4+OD69evK22Htra23L+5oaenhxkzZuDDhw/w8fHJ9XGamprQ19eX+yIiIvrRnTlzBuHh4ejduzdq1qwp225gYAAPDw+kpKR8071R0hwivS9LmTFjxuDz589KF20oSgzelCevXr3CiRMnAAAODg5y86xUVVXx+vVrJCcnY8uWLXmqNzg4GGlpaahZs6ZsBZGQkBA8ePAAgiAozPMyNDREUlISIiMjcfTo0RzrV1NTw+DBg9G7d28AmW+BFaThw4fDysoKK1eu5Mg1ERH91IKCggAArVq1Utjn7OwMALIPs8urkJAQhIWFoW7duqhRo4bSMocOHYK/vz9WrlwJMzOzfJ2nsHAdb8oTPz8/ZGRkoEmTJqhUqZLC/rS0NPj7+8PX1xejR4/OVZ0ZGRmYO3cuAKBXr16y7dJXtG3atEHJkiUVjouNjcWePXvg6+uLjh075upcurq6uSqXV+rq6pgzZw569eqFmTNnFso5iIiIfgSPHz8GkLn619fMzc2hq6srK5NX0mwwePBgpftjYmIwZMgQdO7cWS5TfC8YvCnXBEHAxo0bIZFI4O/vj/Llyyst9+jRI1y6dAlXr17NcV6V9AN0zpw5gypVquDXX38FACQkJGDnzp3Q0dHBzp07lQbmjIwMlC1bFkePHkVkZCTMzc1x/PhxJCcno127dgprbz958kS2BGGTJk3ycwmy5erqikWLFmHTpk25nh9ORET0XxMXFwcgc2qJMvr6+rIyeSHNBsWKFcsyVI8YMQIpKSlYs2ZNnusXA4M35dqZM2fw7NkzODg4ZBm6gcxPg7x06RJ8fX3lgveiRYugq6uLjIwMxMfH4/79+7hw4QKSkpJgZ2eH7du3o1ixYgAy1/NOSEhA//79sxylVlFRQb9+/TBv3jz4+/tj8uTJePDgAX7//XeYmJjA3t4eVlZWEAQBT548wdGjR5GSkoJff/1V6adefiuJRIL58+ejZcuWePbsWYHXT0RE9DP7Mhsouy9qx44d2LlzJzZt2qTwSdnfCwZvyjXp2zvu7u7ZlnN1dcWYMWOwfft2LFmyRLZdunyQmpoa9PT08Msvv6B3795wcXFBy5YtoaLyv1sOcnsud3d3zJs3Dxs2bMDkyZPRp08f6OrqIjAwEHfu3MHJkyeRlJQEExMTtGrVCu7u7ujWrVs+ep87LVq0QKtWrWTz4ImIiH420pHurEa14+PjYWRklOd6s5tm8v79e4wcORLt2rVTWJDheyIRBEEo6kYQUeYvIgMDA5geugAVncKZi05ERFSYIp1qwsPDAz4+Pti+fTt69uwpvz8yEhYWFmjWrBlOnz4tf3BiIiB9lzshAfhi/e379++jatWqsLGxQVhYmMJ5b968iVq1auWqjTdu3JBbbeVbSf9+x8XF5bhCGUe8iYiIiKjAODg4wMfHBydOnFAI3oGBgbIyeZHTEoLGxsZZ7jty5AgiIyPRu3dvaGtrw9jYOE/nLkgc8Sb6TnDEm4iIfnSRTjWRlpaGSpUq4fXr17h8+bJsdDkuLg7169fH8+fP8fDhQ1haWgIAIiIiEBcXBwt9fRiUKpVZ0Rcj3qmpqShVqhRiY2Px77//5nmJQEdHR5w7dw4RERGFMvc7LyPeXMebiIiIiAqMmpoa1q9fj4yMDNjb22Po0KEYP348atSogUePHmHevHmy0A1kfpJz5cqVse/QIaX1HTx4EFFRUejQocN3ty53XnGqCREREREVKCcnJ1y8eBGenp7YsWMHUlNTYWtriwULFsDV1TVPdeW0dvePhFNNiL4TnGpCREQ/ukinmvk/OJubK79nnGpCRERERPSdYfAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCtaJuABHJe2JfHfr6+kXdDCIiIipgHPEmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiEagVdQOISJ71+dtQ0dEt6mYQERGJqtjnz3ha1I0oZBzxJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBmwpVYmIi5s2bh9q1a0NXVxeampooXbo0mjZtiqlTpyI8PFxW1tHRERKJBBKJBIcPH86yzgYNGsjKBQUFKS1z48YNDBgwAOXLl4e2tjYMDAxQt25dzJo1C3FxcXJlvby8ZPXl5svLywsA4O7unmNZPz+/b72ERERE9B+hVtQNoP+ujx8/okmTJrh9+zasra3Rt29fGBsbIzo6GleuXMH8+fNhZWUFKysruePU1NSwYcMGtG/fXqHOe/fu4cqVK1BTU0NaWprS886aNQteXl5QU1ODs7MzXFxc8PnzZwQFBcHT0xNr1qzBwYMHUa9ePQCZgf9rN2/exIEDB+Dg4KCw/+vvBw0ahNKlSyttS82aNZVfHCIiIvrpMHhToVm2bBlu376NwYMH4++//4ZEIpHb/+zZMyQnJysc16ZNGxw+fBhRUVEwNTWV2+fr6wsVFRU4OzvjyJEjCseuWrUKnp6eKF++PI4cOQIbGxu5/WvXrsXIkSPRpk0b3LhxA2XKlIGjo6NCmPbz88OBAwfg6OgoG+HOyuDBg9GwYcNsyxARERFxqgkVmkuXLgEARo4cqRC6AaBcuXIKwRgABg4ciNTUVGzevFlue2pqKrZs2YJWrVopHWH+8OEDpk6dCg0NDRw6dEhp3cOGDcPkyZMRExODadOm5bdrRERERHnG4E2FxtjYGADw6NGjPB3XsGFDVKlSBRs3bpTbfujQIURFRWHgwIFKj9u9ezc+fvyIrl27okqVKlnWP3HiRGhpaSEgIACfPn3KU9uIiIiI8ovBmwpNjx49AGROxZgwYQJOnDiBmJiYXB07cOBA3L17F6GhobJtvr6+MDY2RqdOnZQeExISAgBo3rx5tnUbGhqidu3aSE1NxbVr13LVnuysX78eXl5eSr+SkpKyPC45ORnx8fFyX0RERCQvNDQUbdu2haGhIXR0dNCwYUPs3Lkz18dbWlrmuBjChQsXZOVzs+jCoEGD8tUXzvGmQtOxY0csXrwYnp6eWLx4MRYvXgwAsLKyQuvWrTFmzBhUqFBB6bFubm6YOnUqNmzYgHr16uHNmzcIDAzEb7/9Bg0NDaXHREZGAgDKlCmTY9ukZSIiIvLTNTm+vr5Z7hs7diy0tLSU7vPx8YG3t/c3n5+IiOi/6uzZs3B2doaWlhZ69uwJPT097NmzB66urnj16hXGjx+fYx1jx45FbGyswvbo6GisWrUKRkZGsgUXAOWLLkitX78er1+/hrOzc366w+BNhWvcuHEYMmQIjh8/jpCQEFy9ehX//PMPVq1aBV9fX+zYsQMdO3ZUOM7MzAzt2rVDQEAAli5dCn9/f6Snp2c5zaQoXbp0KV83V06dOhXjxo2TfR8fH5+rFw1EREQ/g7S0NAwZMgQqKio4f/68bKWwmTNnon79+vDw8ED37t1RtmzZbOsZO3as0u3SAcG+ffvKDZIpW3QBAN6+fYu5c+fC2NgYnTt3zk+XONWECp+enh569OiBpUuX4sKFC4iKisKIESOQlJSEQYMGISUlRelxAwcORGxsLPbs2YONGzeiTp06qF69epbnMTc3BwC8evUqxzZJy1hYWOSjRwVDU1MT+vr6cl9ERESU6cyZMwgPD0fv3r3lluc1MDCAh4cHUlJS4O/vn+/6pe9Y53baiL+/P9LS0uDm5pblu+85YfAm0RkYGODPP/9E2bJlER0djTt37igt17ZtW1hYWGDy5Ml4/Phxjj8YjRs3BgCcPn0623KxsbG4fv06NDQ0UKdOnfx1goiIiAqV9EPyWrVqpbBPOtXj3Llz+ao7JCQEYWFhqFu3LmrUqJGrY6RBffDgwfk6J8DgTUVEIpFAR0cn2zKqqqro168fXr9+DS0tLfTq1Svb8t27d4euri727t2LBw8eZFlu8eLFSEpKgqurK4oVK5av9hMREVHhevz4MQAovR/M3Nwcurq6sjJ5ldcQfeHCBTx69AgNGzZE1apV83VOgMGbCtHatWvlViX50v79+xEWFgZDQ0NUq1YtyzrGjRuHffv2ITAwEIaGhtmez8jICHPnzkVKSgo6dOigdBlDX19f+Pj4wNjYGHPnzs1Tf4iIiEg8cXFxADLfKVdGX19fViYvEhISsHPnThQrVizHQT2pghjtBnhzJRWiY8eOYfjw4bC2toadnR1KliyJxMRE3LhxAxcuXICKigpWr14NTU3NLOswMzPL0w0Mo0ePRnR0NGbPng1bW1u0bt0alStXRlJSEoKCgnDr1i2UKFECBw8eLLAbGdevX4/jx48r3dewYUO0bt26QM5DRERE327Hjh1ISEhA//79c3V/VXx8PHbt2gVdXV24urp+07kZvKnQLFiwAHZ2djh58iTOnz8vW7qvVKlS6N+/P0aNGlUoc6xnzZqFTp06YcWKFTh37hwCAwOhoaEBa2treHl5YcyYMTmOnudFdssJjhkzhsGbiIgoH6Qj3VmNasfHx8PIyCjP9eZ19Fr6gXuDBg2Crq5uns/3JYkgCMI31UBEBSI+Ph4GBgYwPXQBKjrf9oNNRET0oyn2+TOetstcKAEJCfCYOxc+Pj7Yvn07evbsKVc2MjISFhYWaNasWY6LKnzp/v37qFq1KmxsbBAWFparY+rXr4/Q0NAslw+W/v2Oi4vLcQSdc7yJiIiI6Lvj4OAAADhx4oTCvsDAQLkyuZXXJQTv3LmD0NBQVK1aNV+f2fE1Bm8iIiIi+u40b94c5cuXx7Zt23Dz5k3Z9ri4OMybNw8aGhro16+fbHtERAQePHiQ5dSU1NRUbN68Gerq6nLHZSevQT0nDN5ERERE9N1RU1PD+vXrkZGRAXt7ewwdOhTjx49HjRo18OjRI8ybNw+Wlpay8lOnTkXlypWxb98+pfUdPHgQUVFR6NChA8zMzHI8f0pKCrZs2aIQ8L+pTwVSCxERERFRAXNycsLFixfh6emJHTt2IDU1Fba2tliwYEGeVxjJ602V+/fvR0xMDFxcXGBsbJzntivDmyuJvhO8uZKIiH5mX99ciRw+aO97wZsriYiIiIi+MwzeREREREQiYPAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEoFbUDSAieU/sq0NfX7+om0FERCSuxMSibkGh44g3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiUCtqBtARPKsz9+Gio5uUTeDiIhIVMU+f8bTom5EIeOINxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiYPAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYngPx+8LS0tYWlpWdTNoO/c8+fPIZFI4O7uXtRNISIiov+oPAdvaUD58ktdXR2lSpWCi4sLrl69Whjt/M+Ji4vD7NmzUa9ePRgaGkJLSwvlypVD//79cf369aJuXpaCg4PRo0cPlCpVChoaGjAyMoKNjQ169+4Nf3//om4eERER0XdLLb8HWllZoW/fvgCAxMREXLt2Dbt27cL+/ftx6tQp2NvbF1gj/2tCQ0PRsWNHREZGolq1aujXrx+KFSuGsLAwBAQEYPPmzfD09ISnp2dRN1WOn58fBg4cCDU1NbRt2xYVKlSARCLBw4cPcfToUZw/fx79+/cv6mYSERERfZfyHbytra3h5eUlt23+/PmYOnUqZsyYgXPnzn1r2/6TXr58idatWyM2NhZr1qzB8OHD5fY/fPgQ7dq1g5eXF0xNTTFixIgiaqm8T58+YfTo0dDT00NISAiqVq0qtz81NRVBQUFF0zgiIiKiH0CBzvEeNGgQAODatWty2zds2IBOnTrB0tISWlpaKF68OJydnXH27FmFOoKCgiCRSODl5YWrV6+iZcuW0NPTg4GBAbp06YLnz58rPfeBAwdQr149aGtro0SJEhgyZAg+fPiQZVujo6MxduxYlCtXDpqamjAzM4OLiwvu3r2rUNbd3R0SiQRPnz7FokWLULFiRWhra6NKlSoICAgAAKSkpGDatGmyPlavXh3Hjh1TqMvDwwPv37/H1KlTFUI3AFSqVAkHDhyAuro6pk6diri4OADA5s2bIZFIMGvWLKX9uX79OiQSCfr06SO3/d27d/j9999hbW0NTU1NmJiYoFu3bkr7KZ0PHxsbi99++w1lypSBmpoa/Pz8cPfuXXz8+BFOTk4KoRsA1NXV0bJlS7ltcXFxWLBgARwcHFCyZEloaGigZMmS6NevH8LDwxXq8PLygkQiQVBQEDZu3AhbW1toa2ujXLlyWLFiBQBAEAQsXrwYlSpVgpaWFipUqIBNmzYp1PXlY7Zw4UJUqFBBNp1n1qxZSE1NVXodlfn48SM8PT1RtWpVaGtrw9DQEM7Ozrh48aJCWUdHR0gkEiQlJWH69OmwsrKCurq6wotUIiIi+vnke8Q720rV5KsdOXIkatSogRYtWsDU1BSvX7/G/v370aJFC+zduxedOnVSqCM0NBQLFy6Ek5MThg0bhhs3bmD//v24c+cO7t69Cy0tLVnZTZs2oX///tDX14ebmxsMDQ1x+PBhtGjRAikpKdDQ0JCrOyoqCo0aNUJ4eDgcHR3Rs2dPPHv2DLt378aRI0cQGBiIJk2aKLRp3Lhx+Oeff9ChQweoqqoiICAAvXv3hpGREVauXIn79++jXbt2SEpKwrZt29CpUyeEhYXBysoKQOaUnJ07d0JLSwsTJkzI8vpVrVoVXbt2xY4dO7Br1y4MHjwYXbt2xa+//oqtW7di5syZCsds3rwZAODm5ibbJu3fv//+i1atWqFz58549+4d9uzZg8DAQJw+fRoNGjSQqyc5ORnNmjVDQkICOnbsCDU1NZQoUQLGxsYAgKdPnyI9PR2qqqpZtl8qLCwMM2fOhJOTE7p06QIdHR08ePAA27Ztw5EjR3D9+nWULVtW4bhly5YhKCgInTp1QrNmzbBnzx6MGTMGxYoVw40bN7Bnzx60b98ezZs3R0BAAPr37w9LS0ul05vGjh2L4OBguLi4QFdXF4cOHYKnpydu376N3bt359iH9+/fw97eHvfu3YOdnR2GDx+O+Ph4HDhwAE5OTti1axc6d+6scFy3bt1w69YttG7dGoaGhihXrlyO5yIiIqL/tgIN3uvXrwcAhdB6//59heARERGBunXrYuLEiUqD99GjRxEQEABXV1fZtn79+mHz5s3Yv38/evbsCQCIj4/HqFGjoKOjg9DQUFSsWBEAMHfuXLRo0QIREREK4W7y5MkIDw/H1KlTMW/ePLlztmvXDgMGDMDDhw+hoiL/hkBYWBhu374NU1NTAMCAAQPQoEED9OzZE9WqVcOdO3ego6MDAHB2doarqyuWL18uG629evUqUlNTUb9+fRgaGmZ7LZs3b44dO3bg0qVLGDx4MHR0dNClSxds2bIFV65cQf369WVl09PTsX37dpibm8uNOvfr1w8RERE4fvw4nJ2dZdunT5+OunXrYsiQIbh9+7bceSMjI1GjRg0EBwdDW1tbtl0QBNSpUwfXrl2Do6Mj+vfvj4YNG6Jy5cpZhvDKlSsjIiICxYsXl9t+9uxZtGjRAnPmzMG6desUjrtw4QKuX7+O8uXLAwAmTJgAa2trTJgwASVKlMCdO3dkj4G0HYsWLVIavC9fvoxbt26hdOnSADKfFy1btsSePXuwZ88edOvWTfkD8P9GjRqFe/fuYd26dRg8eLBsu4+PD+rWrYuhQ4eidevWci8EAeDNmze4ffu2Qt+/lJycjOTkZNn38fHx2baFiIjoZxQaGgpPT0+EhIQgNTUVtra2GDduHFxcXHJ1vKWlJV68eJFtmfPnz6Np06YAMt+B9/b2zrb8wIED4evrm7sOfCHfwfvJkyeyt8+lN1eePXsWJUqUwB9//CFXVtlon4WFBbp164aVK1fixYsXCuHY3t5eLnQDmZ3cvHkzQkNDZcF7//79svAtDd1A5tSHuXPnyi6iVEpKCrZv3w5jY2NMnz5dbl/btm3RsmVLnDx5EsHBwQrHTps2TRb4AKB+/fooX748nj59irlz58pCN5A54qmuro5bt27JtkVGRgIAypQpo3A9viYtExERIdvm5uaGLVu2YMuWLXLB+8SJE3j79i3Gjh0rC8E3btxASEgIBg4cKBe6AaBixYoYMmQIlixZgrt376JatWpy+xcuXCgXugFAIpFg9+7d6Nu3Ly5evCibZlGsWDE0atQIffv2hZubm1wINzAwUNo36XSVU6dOKd0/ZswYWeiWXosmTZrgzJkzWLFihdxj0KBBA5QvX17uOn9dlzR0A4CGhobseeHn55dt8I6OjsaOHTvQrFkzudANAGZmZpg4cSJGjx6NU6dOoX379nL7vb29sw3dQGZ4z+kHm4iI6Gd29uxZODs7Q0tLCz179oSenh727NkDV1dXvHr1CuPHj8+xjrFjxyI2NlZhe3R0NFatWgUjIyPUq1dPtt3R0THLutavX4/Xr18rZKvcynfwDg8PVwgN5ubmuHDhAqytreW2P336FD4+Pjhz5gxev34tN8oHZI4Ofh2869Spo3BOaYD68uJJA9fXIRkAGjVqpDDt5cGDB0hKSoKTkxOKFSumcIyTkxNOnjyJmzdvKtRZs2ZNhfIWFhZ4+vSpwj5VVVWYmZnhzZs3CsfkV/PmzWFhYYGAgAAsWbJE1rctW7YAkJ9mcvnyZQDA27dvlc4vfvDggezfL4O3lpYWbG1tlZ7f0tISFy9exM2bN3Hq1ClcvXoVwcHBOH36NE6fPo1Nmzbh2LFj0NTUlB0TFBSEZcuW4Z9//kF0dDTS0tJk+76eAiSV1XXObt8///yjtK7snhc3btxQeoxUaGgo0tPTkZycrPQaPn78GEDmNfw6eH/5wigrU6dOxbhx42Tfx8fH5+pFGRER0c8gLS0NQ4YMgYqKCs6fPy/LADNnzkT9+vXh4eGB7t27K522+qWxY8cq3b548WIAQN++feXeuXZ0dFQavt++fYu5c+fC2NhY6TTT3Mh38HZ2dsbx48cBZM6Z9vf3x+TJk9GxY0dcuXIFurq6ADJHxuvXr4/4+Hg4OTmhQ4cO0NfXh4qKCoKCgnDu3DmFIA4A+vr6io39/6CZnp4u2ya9+dDMzEyhvKqqqmxuspT07fwSJUoo7Zc04Cl72z+7NmW178ub+MzNzQEAr169UnruL0nLSNsDZPand+/eWLx4MQIDA9GuXTskJCRg//79qFKlCmrXri0r+/79ewDAkSNHcOTIkSzPk5iYKPe9mZkZJBJJtm2rWbOmXAAOCgpC3759cfbsWaxevRq///47AGDXrl1wdXWFrq4unJ2dYWlpiWLFikEikcDPzy/Lt33yc52/DPRfUvY4S58X0udOVqTXMDg4GMHBwVmW+/oaZnXer2lqasq9SCEiIqL/OXPmDMLDwzFgwAC53GFgYAAPDw+4u7vD399f6b1vuSGdKiJdHCQn/v7+SEtLg5ubW5aDhzkpkFVNTE1NMWHCBHh4eCAsLExuCsfSpUvx4cMH+Pn54eTJk1i2bBlmzZoFLy8v2NjYfPO5pdMZ3r17p7AvPT0dMTExctukwe3t27dK65NOB1EW8L5V3bp1oa6ujmvXruUY+k6fPg0gc3T2S9JRbeko9549e/Dp0ye50W7gf+1fuXIlBEHI8uvrdbdzCt3KODo6Yvbs2QAyf0ikvLy8oKWlJVvj/Y8//oC3t7dsuxiUPc7S50VWU2GkpNdw/Pjx2V5DZeut5+c6EhER0f9Ilylu1aqVwj7pVI/8Ll8dEhKCsLAw1K1bFzVq1MjVMdKg/vX007wo0OUEPTw8ULJkSaxevVq27J902bivb6AUBCHbUcTckl6sCxcuKOy7dOmSwkiojY0NtLS0EBoaik+fPikcI32QlU1p+FY6Ojro0aMHkpKSZG9vKBMWFoZ9+/ZBT08P3bt3l9tXo0YN2Nra4sCBA/j48SO2bNmidBlB6Wolly5dKvB+KCN9h+NL4eHhqFy5MipUqCC3PSIiAk+fPhWlXdk9L2rVqpXtsfXq1YNEIhHtGhIREdH/SKd0fp0jgMxZBLq6urIyeZXXEH3hwgU8evQIDRs2VLqscm4VaPDW1tbG5MmTkZqaKhsBlc67+XrN4/nz5ytdSzqvOnXqBH19fWzYsAGPHj2SbU9NTVW4eRLInFfcq1cvREdHw8fHR27f8ePHERgYCGtra9jZ2X1z25SZN28ejIyMMG/ePNkqMF96/PgxOnXqhJSUFMyfP1/p6idubm74/PkzVqxYgTNnzsDBwUFhbnD9+vXRoEEDbN++HTt27FCoIyMjI0+vEp89e4Y///wTHz9+VNj36dMnLF++HID8ijZly5bFkydP5Eadk5KS8Ouvv+ZpHe1vsXz5cvz777+y76XrrQOZa31nx9zcHC4uLggJCcEff/wBQRAUyvzzzz9KX8ARERHRt5HODsjqHWp9ff0cZxAok5CQgJ07d6JYsWLo1atXro4piNFuoBDW8R46dCgWLFiATZs2wcPDA8OHD8fGjRvRrVs3uLi4wNjYGJcvX8b169fRrl27bOcf54aBgQFWrFgBd3d31KtXDz179oSBgQEOHz4MbW1tuTnSUgsWLMC5c+cwZ84chISEoEGDBnj+/Dl27dqFYsWKYePGjQpLCRaUsmXL4ujRo+jUqROGDBmClStXwtHRUfaR8ceOHUNqaiq8vLyy/NTK3r17Y8qUKfD29kZGRobCNBOp7du3w8nJCT179sSyZctQu3ZtaGtr4+XLl7h06RKioqKQlJSUq3bHxcVh1KhRmDhxIpo0aYJq1apBW1sbr1+/xpEjRxATE4M6depg1KhRsmNGjRqFUaNGoVatWujevTvS0tJw8uRJCIKAGjVqZLkSSUFq2LAhatSoAVdXV+jo6ODQoUN4+PAhunbtmuNSggCwevVqPHz4EJMmTcLmzZvRqFEjGBoa4tWrV7h69SoeP36MiIgIpTfqEhER0fdnx44dSEhIkH0GTE7i4+Oxa9cu6OrqKqy4l1cFni61tLQwdepUpKWlwdvbG7Vq1cKJEydQu3Zt7N27Fxs2bIChoSGCg4NRt27dAjln//79sW/fPlSoUAH+/v7w9/eHnZ0dTp06pXTyu6mpKf755x+MHj0a4eHhWLRoEU6ePInOnTvjn3/+UfrhOQWpYcOGePDgAby8vGSfDLlkyRLcuHEDPXv2lK1XmZVSpUqhWbNmSE1NhZaWlsJ0FKly5crhxo0bmD59OhISErBx40asXbsWN2/ehL29PbZv357rNleuXBl79uxBv379EBUVhS1btmDhwoU4dOgQKleujCVLluDixYtyAXTkyJH466+/ULx4caxbtw779u2Dg4MDLl26lOM65gVl2bJlmDRpEk6ePIkVK1YgKSkJXl5eue578eLFERISgoULF0JDQwNbt27FypUrcfnyZVStWhWbNm2CiYlJIfeCiIjo5yMd6c5qVDs+Pj7H+7WUyevodUBAAD59+iRbMOJbSARl758T/eCkdzo/e/YMlpaWRd2cXJH+AjE9dAEqOt/2g01ERPSjKfb5M562a5z5TUICPObOhY+PD7Zv3y77/BapyMhIWFhYoFmzZrIFKXLj/v37qFq1KmxsbBAWFparY+rXr4/Q0FBcunQJDRs2VNgv/fsdFxeX4wh64cynICIiIiL6Bg4ODgAyPyjwa4GBgXJlciuvSwjeuXMHoaGhqFq1qtLQnVcM3kRERET03WnevDnKly+Pbdu24ebNm7LtcXFxmDdvHjQ0NNCvXz/Z9oiICDx48CDLqSmpqanYvHkz1NXV5Y7LTl6Dek4YvImIiIjou6Ompob169cjIyMD9vb2GDp0KMaPH48aNWrg0aNHmDdvntx00qlTp6Jy5crYt2+f0voOHjyIqKgodOjQQekHL34tJSUFW7ZsUQj434LBm/6T/Pz8IAjCDzO/m4iIiBQ5OTnh4sWLsLOzw44dO7BmzRqUKFECAQEBGD9+fJ7qyutNlfv370dMTAw6d+6s8Eno+cWbK4m+E7y5koiIfmZf31wJHZ2ibVAu8eZKIiIiIqLvDIM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiYPAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRqBV1A4hI3hP76tDX1y/qZhAREYkrMbGoW1DoOOJNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiYPAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREIlAr6gYQkTzr87ehoqNb1M0gIiISVbHPn/G0qBtRyDjiTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8Kb/ND8/P0gkEvj5+cltt7S0hKWlZZG0iYiIiH5ODN4/gOfPn0MikUAikcDZ2VlpmcuXL0MikcDd3V3cxhERERFRrqgVdQMob06cOIEzZ86gWbNmRd2UH9rp06eLuglERET0k+GI9w/E0tISKioqmDx5MgRBKOrm/NCsrKxgZWVV1M0gIiKinwiD9w+kUqVKcHNzw9WrV7Fz585cHfPixQsMGjQIpUqVgoaGBkqXLo1Bgwbh5cuXCmUjIiIwZswYVKhQAdra2jA0NETlypUxfPhwxMXFyco9evQIkyZNQu3atWFsbAwtLS1UrFgRU6ZMQUJCgkK9jo6OkEgkSE5OhoeHB3755Rdoa2ujTp06OHXqFAAgLi4OI0eORMmSJaGlpYVGjRrhypUrCnVJ52bHxsZi2LBhMDc3h5aWFmrVqoXt27fn9lIqnePt5eUFiUSCoKAgbNu2DTVr1oS2tjYsLCwwZswYfP78WaGetLQ0+Pj4wMrKClpaWrC2toaPjw+ePn3KqT9EREQkh1NNfjCzZs1CQEAApk+fjq5du0JdXT3Lso8ePUKTJk0QFRWFDh06oGrVqrh79y42bNiAQ4cO4eLFi6hYsSIA4NOnT7Czs8Pz58/RqlUrdOnSBSkpKXj27Bk2b96MCRMmwMDAAACwd+9e+Pr6wsnJCY6OjsjIyMDly5exYMECnDt3DufPn1faLldXV9y5cwcdO3bE58+fsXXrVrRv3x7BwcEYOnQoUlJS0KNHD0RFRWHHjh1o3bo1nj17JjuvVEpKClq0aIGEhAS4ubkhMTERO3fuRO/evREdHY1Ro0Z90zX+888/cfz4cXTq1AnNmjXD8ePHsWLFCkRHR2Pr1q1yZQcOHIjNmzejfPnyGDlyJJKTk7F06VJcunTpm9pARERE/z0c8f7B/PLLLxg1ahSePHmCtWvXZlt2+PDhiIqKwtq1a3Hw4EH4+Pjg0KFDWLVqFaKiovDrr7/Kyp4+fRrPnj3DmDFjcPz4cSxcuBDLli3DgQMH8PbtW5QpU0ZW1s3NDREREdi9ezeWLFmCZcuW4fLly/D29sbly5ezHI2PiYnB7du38eeff8LX1xebNm1CcnIyWrRoASsrK9y4cQPLly/Htm3b4OPjgw8fPsDX11ehnoiICOjo6OD27dv4448/sHr1aty8eRMmJiaYOHEiXr9+nc+rm+nUqVO4du0atm7diiVLluDatWuoWLEiAgIC8ObNG7lrtnnzZtSsWRN37tzB4sWL8eeff+LWrVtKR+u/lpycjPj4eLkvIiIikhcaGoq2bdvC0NAQOjo6aNiwYa7f+Qcy3+WWLlKR1deFCxdk5aXvgGf3NWjQoHz1hSPePyAPDw+sX78es2fPhru7O3R1dRXKvHz5EmfPnkWVKlUwZMgQuX3Dhw/HypUrcebMGbx69UouVGtrayvU9XX9pUqVUtqu3377DZ6enjh16hT69OmjsH/u3LnQ0dGRfd+9e3eoq6sjNjYWixYtgpra/56OvXr1wuTJk3Hr1i2l55o3bx40NDRk35cuXRpjxozBjBkzEBAQgPHjxys9LjfGjBmDSpUqyb7X1tZGr1694O3tjWvXrqFkyZIAgC1btgAAZs6ciWLFisnKS6emeHh4ZHseHx8feHt757udRERE/3Vnz56Fs7MztLS00LNnT+jp6WHPnj1wdXXFq1evcvX3fuzYsYiNjVXYHh0djVWrVsHIyAj16tWTbXd0dMyyrvXr1+P169dZrjKXEwbvH5CRkRGmTJmCKVOmYNGiRfDy8lIoc/PmTQCAg4MDJBKJ3D4VFRXY29vjwYMHuHnzJsqUKQN7e3tYWFhg/vz5uHXrFtq3bw8HBwdUrlxZ4XhBELBx40b4+fnh7t27iIuLQ0ZGhmz/l6PCX6pZs6ZCO8zMzPDp0yf88ssvcvssLCyyrEtNTQ2NGjVS2N60aVMAwI0bN5SeP7fq1KmjsK106dIAIPeDK31R0KRJE4XydnZ2OZ5n6tSpGDdunOz7+Ph4uRdBREREP7O0tDQMGTIEKioqOH/+vCxHzJw5E/Xr14eHhwe6d++OsmXLZlvP2LFjlW5fvHgxAKBv377Q0tKSbXd0dFQavt++fYu5c+fC2NgYnTt3zk+XONXkRzV69GiULl0aixcvxrt37xT2S6ctlChRQunx0mArLWdgYIDLly+jX79+uHz5MkaMGIGqVauibNmyWL16tcK5Bw0ahOfPn6Njx46YNGkSPD094enpCSBzCoUy+vr6CtvU1NSy3A4AqampCvtMTEygoqL41JX29csbQfMju/akp6fLtsXHx0NFRQUmJiZZtiU7mpqa0NfXl/siIiKiTGfOnEF4eDh69+4tN3hnYGAADw8PpKSkwN/fP9/1S6ez5nbaiL+/P9LS0uDm5ib3rntecMT7B6WtrQ1vb28MGjQI3t7ecHNzk9svDXFv375VenxkZKRcOSBz/rifnx8yMjJw+/ZtnDhxAitWrMDIkSNhZGSEXr164d27d1i1ahWqV6+OS5cuyU2xiIyMFGXqRHR0NDIyMhTCt7SvX9+MWVj09fWRkZGB6OhomJqaKm0LERER5U9QUBAAoFWrVgr7pFM9zp07l6+6Q0JCEBYWhrp166JGjRq5OkYa1AcPHpyvcwIc8f6h9e/fH1WrVsW6devw5MkTuX3SV4bnz59XWPNbEAScP39ertyXVFRUULNmTUyaNEm2RN/BgwcBAE+fPoUgCGjRooVc6AYgd2NCYUpLS1O6aoj0/LVq1RKlHdIf1ODgYIV9ISEhorSBiIjov+rx48cAgAoVKijsMzc3h66urqxMXuU1RF+4cAGPHj1Cw4YNUbVq1XydE2Dw/qGpqqpi3rx5SE1NVZjn/csvv8DJyQn37t3Dhg0b5Pb9/fffCAsLQ7NmzWRziu/du6d0lFa6TTr3STqPKiQkRG5e97///oupU6cWWN9yIn2L6cvzL1++HJqamujZs6cobZDeQDpr1iy5Nb4jIyOxfPlyUdpARET0XyWdOprVO9n6+vr5ml6akJCAnTt3olixYujVq1eujimI0W6AU01+eB07dkSTJk1w8eJFhX1r1qxBkyZNMGTIEBw6dAhVqlTBvXv3cPDgQZiammLNmjWysidPnsTEiRNhZ2eHihUrwtjYGE+fPsXBgwehpaWFkSNHAsicG96tWzfs2bMHdevWRfPmzfH27VscPnwYzZs3R3h4eKH32cLCAomJiahevTo6dOggW8c7JiYGK1asyHLVlYLWokUL9O7dG9u2bYOtrS06d+6M5ORk7Ny5Ew0aNMChQ4eUzkUnIiKiorNjxw4kJCSgf//+ubq/Kj4+Hrt27YKuri5cXV2/6dxMBf8BCxYsULq9UqVKuHr1Ktzd3XHlyhX88ccfCA0NxYABAxAaGir78Bwgc67UyJEjER8fj71792Lp0qW4evUqXF1dce3aNdStW1dW1s/PD+PHj8eHDx+wcuVKXL58GePGjcO2bdsKva8AoKGhgZMnT8LBwQGbN2/Ghg0bULp0aWzbtu2bPzwnr/z9/TF79mxkZGRg5cqVOHr0KMaOHYvp06cDUH6jJhEREeVMOtKd1ah2fHx8vu7ryuvodUBAAD59+gRXV1elSzjnhUT4egIw0XdM+jHvz58/L9J25GT9+vUYMmQIVq9eLfdBRdmR/gIxPXQBKjrf9oNNRET0oyn2+TOetmuc+U1CAjzmzoWPjw+2b9+uMI00MjISFhYWaNasGU6fPp3rc9y/fx9Vq1aFjY0NwsLCcnVM/fr1ERoaikuXLqFhw4YK+6V/v+Pi4nIccOOIN9E3iIyMVLh59fXr15gzZw5UVVXRvn37ImoZERHRj83BwQEAcOLECYV9gYGBcmVyK69LCN65cwehoaGoWrWq0tCdV5zjTfQN5s+fjyNHjqBp06YwMzPDy5cvcfjwYXz8+BFeXl78QBwiIqJ8at68OcqXL49t27Zh9OjRspXY4uLiZJ9g3a9fP1n5iIgIxMXFwcLCQukUlNTUVGzevBnq6upyx2Unr0E9JwzeRN+gdevWuH//Po4cOYIPHz5AS0sL1atXx4gRI9C7d++ibh4REdEPS01NDevXr4ezszPs7e3lPjL+xYsXWLRokWwKKpD5idD+/v7YuHEj3N3dFeo7ePAgoqKi0LVrV5iZmeV4/pSUFGzZskUh4H9TnwqkFiKRfG9zu1u3bo3WrVsXdTOIiIj+k5ycnHDx4kV4enpix44dSE1Nha2tLRYsWJDnFUbyelPl/v37ERMTAxcXFxgbG+e57crw5kqi7wRvriQiop/Z1zdXQkenaBuUS7y5koiIiIjoO8PgTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kREREREImDwJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBGpF3QAikvfEvjr09fWLuhlERETiSkws6hYUOo54ExERERGJgMGbiIiIiEgEDN5ERERERCJg8CYiIiIiEgGDNxERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiYPAmIiIiIhIBgzcRERERkQgYvImIiIiIRMDgTUREREQkArWibgARZRIEAQAQHx9fxC0hIiIqAomJ//t/fDyQnl50bckD6d9t6d/x7DB4E30nYmJiAABlypQp4pYQEREVsZIli7oFefbx40cYGBhkW4bBm+g7Ubx4cQDAy5cvc/zB/dHFx8ejTJkyePXqFfT19Yu6OYXmZ+knwL7+F/0s/QR+nr7+LP0ExO2rIAj4+PEjSubixQKDN9F3QkUl85YLAwOD//wvRCl9ff2foq8/Sz8B9vW/6GfpJ/Dz9PVn6ScgXl9zO2DGmyuJiIiIiETA4E1EREREJAIGb6LvhKamJjw9PaGpqVnUTSl0P0tff5Z+Auzrf9HP0k/g5+nrz9JP4Pvtq0TIzdonRERERET0TTjiTUREREQkAgZvIiIiIiIRMHgTEREREYmAwZuIiIiISAQM3kQiWrVqFSwtLaGlpYUGDRrgypUr2ZbftWsXbGxsoKWlBVtbWxw9elSkln67vPT13r176NatGywtLSGRSLBs2TLxGvqN8tLPdevWoWnTpjAyMoKRkRFatGiR43Pge5KXvu7duxd169aFoaEhdHR0ULNmTWzevFnE1n6bvP6sSgUEBEAikaBz586F28ACkpd++vn5QSKRyH1paWmJ2Npvk9fHNDY2FiNHjoSFhQU0NTVRsWLFH+J3cF766ejoqPCYSiQStGvXTsQW519eH9Nly5ahUqVK0NbWRpkyZfD7778jKSlJpNb+P4GIRBEQECBoaGgIGzZsEO7duycMGTJEMDQ0FN6+fau0fHBwsKCqqiosXLhQuH//vjB9+nRBXV1duHPnjsgtz7u89vXKlSvChAkThO3btwvm5ubC0qVLxW1wPuW1n7179xZWrVol3LhxQwgLCxPc3d0FAwMD4d9//xW55XmX176ePXtW2Lt3r3D//n3hyZMnwrJlywRVVVXh+PHjIrc87/LaV6lnz54JpUqVEpo2bSp06tRJnMZ+g7z2c+PGjYK+vr4QEREh+4qMjBS51fmT174mJycLdevWFdq2bStcvHhRePbsmRAUFCTcvHlT5JbnTV77GRMTI/d43r17V1BVVRU2btwobsPzIa993bp1q6CpqSls3bpVePbsmRAYGChYWFgIv//+u6jtZvAmEkn9+vWFkSNHyr5PT08XSpYsKfj4+Cgt7+LiIrRr105uW4MGDYRhw4YVajsLQl77+qWyZcv+MMH7W/opCIKQlpYm6OnpCf7+/oXVxALzrX0VBEGoVauWMH369MJoXoHKT1/T0tKExo0bC+vXrxf69+//QwTvvPZz48aNgoGBgUitK1h57euaNWuE8uXLCykpKWI1sUB868/p0qVLBT09PSEhIaGwmlhg8trXkSNHCs2aNZPbNm7cOMHOzq5Q2/k1TjUhEkFKSgquXbuGFi1ayLapqKigRYsWuHTpktJjLl26JFceAJydnbMs/73IT19/RAXRz0+fPiE1NRXFixcvrGYWiG/tqyAIOH36NB4+fAh7e/vCbOo3y29fZ82aBTMzMwwaNEiMZn6z/PYzISEBZcuWRZkyZdCpUyfcu3dPjOZ+k/z09eDBg2jUqBFGjhyJEiVKoFq1apg3bx7S09PFanaeFcTvJF9fX/Ts2RM6OjqF1cwCkZ++Nm7cGNeuXZNNR3n69CmOHj2Ktm3bitJmKTVRz0b0k4qOjkZ6ejpKlCght71EiRJ48OCB0mMiIyOVlo+MjCy0dhaE/PT1R1QQ/Zw8eTJKliyp8ALre5PfvsbFxaFUqVJITk6GqqoqVq9ejZYtWxZ2c79Jfvp68eJF+Pr64ubNmyK0sGDkp5+VKlXChg0bUL16dcTFxWHRokVo3Lgx7t27h9KlS4vR7HzJT1+fPn2KM2fOoE+fPjh69CiePHmCESNGIDU1FZ6enmI0O8++9XfSlStXcPfuXfj6+hZWEwtMfvrau3dvREdHo0mTJhAEAWlpaRg+fDg8PDzEaLIMgzcRURGYP38+AgICEBQU9EPdoJYXenp6uHnzJhISEnD69GmMGzcO5cuXh6OjY1E3rcB8/PgRbm5uWLduHUxMTIq6OYWqUaNGaNSokez7xo0bo3Llyli7di1mz55dhC0reBkZGTAzM8Pff/8NVVVV1KlTB69fv8Yff/zx3Qbvb+Xr6wtbW1vUr1+/qJtSKIKCgjBv3jysXr0aDRo0wJMnTzBmzBjMnj0bM2bMEK0dDN5EIjAxMYGqqirevn0rt/3t27cwNzdXeoy5uXmeyn8v8tPXH9G39HPRokWYP38+Tp06herVqxdmMwtEfvuqoqICa2trAEDNmjURFhYGHx+f7zp457Wv4eHheP78OTp06CDblpGRAQBQU1PDw4cPYWVlVbiNzoeC+DlVV1dHrVq18OTJk8JoYoHJT18tLCygrq4OVVVV2bbKlSsjMjISKSkp0NDQKNQ258e3PKaJiYkICAjArFmzCrOJBSY/fZ0xYwbc3NwwePBgAICtrS0SExMxdOhQTJs2DSoq4sy+5hxvIhFoaGigTp06OH36tGxbRkYGTp8+LTeC9KVGjRrJlQeAkydPZln+e5Gfvv6I8tvPhQsXYvbs2Th+/Djq1q0rRlO/WUE9phkZGUhOTi6MJhaYvPbVxsYGd+7cwc2bN2VfHTt2hJOTE27evIkyZcqI2fxcK4jHND09HXfu3IGFhUVhNbNA5KevdnZ2ePLkiexFFAA8evQIFhYW32XoBr7tMd21axeSk5PRt2/fwm5mgchPXz99+qQQrqUvrARBKLzGfk3UWzmJfmIBAQGCpqam4OfnJ9y/f18YOnSoYGhoKFuOy83NTZgyZYqsfHBwsKCmpiYsWrRICAsLEzw9PX+o5QTz0tfk5GThxo0bwo0bNwQLCwthwoQJwo0bN4THjx8XVRdyJa/9nD9/vqChoSHs3r1bbgmvjx8/FlUXci2vfZ03b55w4sQJITw8XLh//76waNEiQU1NTVi3bl1RdSHX8trXr/0oq5rktZ/e3t5CYGCgEB4eLly7dk3o2bOnoKWlJdy7d6+oupBree3ry5cvBT09PeG3334THj58KBw+fFgwMzMT5syZU1RdyJX8PnebNGkiuLq6it3cb5LXvnp6egp6enrC9u3bhadPnwonTpwQrKysBBcXF1HbzeBNJKKVK1cKv/zyi6ChoSHUr19fuHz5smyfg4OD0L9/f7nyO3fuFCpWrChoaGgIVatWFY4cOSJyi/MvL3199uyZAEDhy8HBQfyG51Fe+lm2bFml/fT09BS/4fmQl75OmzZNsLa2FrS0tAQjIyOhUaNGQkBAQBG0On/y+rP6pR8leAtC3vo5duxYWdkSJUoIbdu2Fa5fv14Erc6fvD6mISEhQoMGDQRNTU2hfPnywty5c4W0tDSRW513ee3ngwcPBADCiRMnRG7pt8tLX1NTUwUvLy/ByspK0NLSEsqUKSOMGDFC+PDhg6htlgiCmOPrREREREQ/J87xJiIiIiISAYM3EREREZEIGLyJiIiIiETA4E1EREREJAIGbyIiIiIiETB4ExERERGJgMGbiIiIiEgEDN5ERET/cX5+fjA0NCzwskSUNwzeRERUpC5dugRVVVW0a9euqJsiColEIvsyMDCAnZ0dzpw5U6jndHV1xaNHjwq8LBHlDYM3EREVKV9fX4waNQrnz5/HmzdvCvVcgiAgLS2tUM+RGxs3bkRERASCg4NhYmKC9u3b4+nTp0rLpqamfvP5tLW1YWZmVuBliShvGLyJiKjIJCQkYMeOHfj111/Rrl07+Pn5yfb17t0brq6ucuVTU1NhYmKCTZs2AQAyMjLg4+ODcuXKQVtbGzVq1MDu3btl5YOCgiCRSHDs2DHUqVMHmpqauHjxIsLDw9GpUyeUKFECurq6qFevHk6dOiV3roiICLRr1w7a2tooV64ctm3bBktLSyxbtkxWJjY2FoMHD4apqSn09fXRrFkz3Lp1K8d+GxoawtzcHNWqVcOaNWvw+fNnnDx5EkDmiPiaNWvQsWNH6OjoYO7cuQCAAwcOoHbt2tDS0kL58uXh7e0t9yIiNjYWw4YNQ4kSJaClpYVq1arh8OHDABSnj9y6dQtOTk7Q09ODvr4+6tSpg6tXryotCwBr1qyBlZUVNDQ0UKlSJWzevFluv0Qiwfr169GlSxcUK1YMFSpUwMGDB3O8DkQ/GwZvIiIqMjt37oSNjQ0qVaqEvn37YsOGDRAEAQDQp08fHDp0CAkJCbLygYGB+PTpE7p06QIA8PHxwaZNm/DXX3/h3r17+P3339G3b1+cO3dO7jxTpkzB/PnzERYWhurVqyMhIQFt27bF6dOncePGDbRu3RodOnTAy5cvZcf069cPb968QVBQEPbs2YO///4b7969k6u3R48eePfuHY4dO4Zr166hdu3aaN68Od6/f5/ra6CtrQ0ASElJkW3z8vJCly5dcOfOHQwcOBAXLlxAv379MGbMGNy/fx9r166Fn5+fLJRnZGSgTZs2CA4OxpYtW3D//n3Mnz8fqqqqSs/Zp08flC5dGqGhobh27RqmTJkCdXV1pWX37duHMWPGYPz48bh79y6GDRuGAQMG4OzZs3LlvL294eLigtu3b6Nt27bo06dPnq4D0U9BICIiKiKNGzcWli1bJgiCIKSmpgomJibC2bNn5b7ftGmTrHyvXr0EV1dXQRAEISkpSShWrJgQEhIiV+egQYOEXr16CYIgCGfPnhUACPv378+xLVWrVhVWrlwpCIIghIWFCQCE0NBQ2f7Hjx8LAISlS5cKgiAIFy5cEPT19YWkpCS5eqysrIS1a9dmeR4Awr59+wRBEITExERhxIgRgqqqqnDr1i3Z/rFjx8od07x5c2HevHly2zZv3ixYWFgIgiAIgYGBgoqKivDw4UOl59y4caNgYGAg+15PT0/w8/PLVdnGjRsLQ4YMkSvTo0cPoW3btnJ9mj59uuz7hIQEAYBw7Ngxpecg+lmpFWHmJyKin9jDhw9x5coV7Nu3DwCgpqYGV1dX+Pr6wtHREWpqanBxccHWrVvh5uaGxMREHDhwAAEBAQCAJ0+e4NOnT2jZsqVcvSkpKahVq5bctrp168p9n5CQAC8vLxw5cgQRERFIS0vD58+fZSPeDx8+hJqaGmrXri07xtraGkZGRrLvb926hYSEBBgbG8vV/fnzZ4SHh2fb9169ekFVVRWfP3+GqakpfH19Ub169Szbe+vWLQQHB8tGuAEgPT0dSUlJ+PTpE27evInSpUujYsWK2Z5Xaty4cRg8eDA2b96MFi1aoEePHrCyslJaNiwsDEOHDpXbZmdnh+XLl8tt+7L9Ojo60NfXV3iHgOhnx+BNRERFwtfXF2lpaShZsqRsmyAI0NTUxJ9//gkDAwP06dMHDg4OePfuHU6ePAltbW20bt0aAGRTUI4cOYJSpUrJ1a2pqSn3vY6Ojtz3EyZMwMmTJ7Fo0SJYW1tDW1sb3bt3l5vukZOEhARYWFggKChIYV9Oy/EtXboULVq0gIGBAUxNTRX2f93ehIQEeHt7o2vXrgpltbS0ZNNVcsvLywu9e/fGkSNHcOzYMXh6eiIgIEA2hSc/vp6qIpFIkJGRke/6iP6LGLyJiEh0aWlp2LRpExYvXoxWrVrJ7evcuTO2b9+O4cOHo3HjxihTpgx27NiBY8eOoUePHrKAV6VKFWhqauLly5dwcHDI0/mDg4Ph7u4uC5oJCQl4/vy5bH+lSpWQlpaGGzduoE6dOgAyR9g/fPggK1O7dm1ERkZCTU0NlpaWeTq/ubk5rK2tc12+du3aePjwYZbHVK9eHf/++y8ePXqU61HvihUromLFivj999/Rq1cvbNy4UWnwrly5MoKDg9G/f3/ZtuDgYFSpUiXX7SeiTAzeREQkusOHD+PDhw8YNGgQDAwM5PZ169YNvr6+GD58OIDM1U3++usvPHr0SO6GPj09PUyYMAG///47MjIy0KRJE8TFxSE4OBj6+vpyQfFrFSpUwN69e9GhQwdIJBLMmDFDbnTWxsYGLVq0wNChQ7FmzRqoq6tj/Pjx0NbWhkQiAQC0aNECjRo1QufOnbFw4UJUrFgRb968wZEjR9ClSxeF6SLfYubMmWjfvj1++eUXdO/eHSoqKrh16xbu3r2LOXPmwMHBAfb29ujWrRuWLFkCa2trPHjwABKJRPYOgdTnz58xceJEdO/eHeXKlcO///6L0NBQdOvWTem5J06cCBcXF9SqVQstWrTAoUOHsHfvXoVVYIgoZ1zVhIiIROfr6yubavG1bt264erVq7h9+zaAzBU47t+/j1KlSsHOzk6u7OzZszFjxgz4+PigcuXKaN26NY4cOYJy5cple/4lS5bAyMgIjRs3RocOHeDs7Cw3nxsANm3ahBIlSsDe3h5dunTBkCFDoKenBy0tLQCZUymOHj0Ke3t7DBgwABUrVkTPnj3x4sULlChR4lsujwJnZ2ccPnwYJ06cQL169dCwYUMsXboUZcuWlZXZs2cP6tWrh169eqFKlSqYNGkS0tPTFepSVVVFTEwM+vXrh4oVK8LFxQVt2rSBt7e30nN37twZy5cvx6JFi1C1alWsXbsWGzduhKOjY4H2kehnIBGE/1+3iYiIiLL077//okyZMjh16hSaN29e1M0hoh8QgzcREZESZ86cQUJCAmxtbREREYFJkybh9evXePToUZZrXhMRZYdzvImIiJRITU2Fh4cHnj59Cj09PTRu3Bhbt25l6CaifOOINxERERGRCHhzJRERERGRCBi8iYiIiIhEwOBNRERERCQCBm8iIiIiIhEweBMRERERiYDBm4iIiIhIBAzeREREREQiYPAmIiIiIhIBgzcRERERkQj+D/L6UCauJXThAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "\n",
      "Figure 2: Logistic Regression average preicision scores on thyroid_sick\n",
      "----------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we plot the performance of the model in the\n",
    "for i, dataset in enumerate(all_datasets):\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    print(\n",
    "        \"Table \"\n",
    "        + str(i + 1)\n",
    "        + \": Logistic Regression average preicision scores on \"\n",
    "        + str((all_datasets[i][\"DESCR\"]))\n",
    "    )\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    pr_mean_s_lr = pd.Series(pr_mean_dict_lr[dataset[\"DESCR\"]]).round(2)\n",
    "    df = pd.DataFrame([pr_mean_s_lr], index=[\"lr\"]).T\n",
    "    print(df)\n",
    "\n",
    "\n",
    "for i, dataset in enumerate(all_datasets):\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    print(\n",
    "        \"Figure \"\n",
    "        + str(i + 1)\n",
    "        + \": Logistic Regression average preicision scores on \"\n",
    "        + str((all_datasets[i][\"DESCR\"]))\n",
    "    )\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    pr_mean_s_lr = pd.Series(pr_mean_dict_lr[dataset[\"DESCR\"]]).round(2)\n",
    "    df = pd.DataFrame([pr_mean_s_lr], index=[\"lr\"]).T\n",
    "\n",
    "    colors = []\n",
    "    colors.append(\"#17becf\")\n",
    "    for j in range(1, 5):\n",
    "        if df[\"lr\"][j] > df[\"lr\"][0]:\n",
    "            colors.append(\"r\")\n",
    "        else:\n",
    "            colors.append(\"#17becf\")\n",
    "\n",
    "    r = np.arange(5)  # plotting for 5 different overampling techniques\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(r, df[\"lr\"], align=\"center\", color=colors)\n",
    "    ax.set_xlabel(\"Average Precision\")\n",
    "    plt.axvline(df[\"lr\"][0], color=\"r\")\n",
    "    plt.yticks(\n",
    "        [r + 0.1 for r in range(5)],\n",
    "        [\"Nosampling\", \"RandomOverSampler\", \"SMOTE\", \"ADASYN\", \"Borderline-SMOTE \"],\n",
    "        fontsize=14,\n",
    "    )\n",
    "    maximum = df[\"lr\"].max()\n",
    "    plt.xlim(0, maximum + 0.10 * maximum)\n",
    "    plt.title(\n",
    "        \"Results of Logistic Regression Classifier on %s\" % (all_datasets[i][\"DESCR\"])\n",
    "    )\n",
    "    ax.bar_label(ax.containers[0], fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "jvpIAEqFv03Q",
    "outputId": "9afc6b73-ed51-47f5-9194-42fa8f5bbe74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "\n",
      "Table 1: Random Forest average preicision scores on synthetic data\n",
      "----------------------------------------------------------------\n",
      "\n",
      "                     rf\n",
      "Nosampling         0.89\n",
      "RandomOverSampler  0.93\n",
      "SMOTE              0.91\n",
      "ADASYN             0.95\n",
      "Borderline-SMOTE   0.96\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Table 2: Random Forest average preicision scores on thyroid_sick\n",
      "----------------------------------------------------------------\n",
      "\n",
      "                     rf\n",
      "Nosampling         0.78\n",
      "RandomOverSampler  0.79\n",
      "SMOTE              0.78\n",
      "ADASYN             0.78\n",
      "Borderline-SMOTE   0.79\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Figure 1: Random Forest average preicision scores on synthetic data\n",
      "----------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "\n",
      "Figure 2: Random Forest average preicision scores on thyroid_sick\n",
      "----------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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fpceF+TzavXs3bG1t0a1bN0WZgYEBhgwZkpfuKSnI5/i2bduQmZmJKVOmKL1XAagdkvLTTz9h1KhRmDVrFiZPnpzvGD83TLyJKM9+/fVXHDhwAJs3b0abNm2QkJAAXV1dxfrbt29DCIEffvhBkUjKF/lMB8+ePQPwYYaUxMRElC9fHi4uLhg3bhwuX75c5H3w8vKCq6srBg8eDGtra/Ts2RObNm3KNYG6d+8eNDQ04OzsrFRuY2MDMzMzRTJbEFkTyrCwMFSqVElxgWZWb9++xZQpU2BnZwddXV2ULFkSlpaWSExMRFJSkkq7X331ldJjeZLx8uVLRZ8AoFy5ckr1LC0tlRISed0KFSqo7EP+E/vH/f943/LhIHZ2dirlmZmZauP/mKamJpo3b6601K5dW2n/H8eoo6ODsmXLqsRXunRplYvObt26hatXr6q8dsuXLw/g/1+7BX0NZcfExATAh7HVBZWZmYl58+ahXLlySq+Ny5cvKx3bCRMmwMjICPXq1UO5cuUwfPhwlWFWP/30E65cuQI7OzvUq1cPQUFBhU5G5fLzGSHn6OiYp7bv3buHcuXKqSRzeX2Nfvz+KKiP25W3nbVdMzMztG/fXmls/dq1a1G6dGnFF72sPj4Ghfk8unfvHpydnVWSXHXv79wU5HM8NjYWGhoaKhfiqnP06FFMmDABEyZM+KLHdWfFMd5ElGf16tVTzGrSqVMnNG7cGL1798aNGzdgZGSkSDzGjh0LT09PtW3I/1A0adIEsbGx2L59O/bv348VK1Zg3rx5+O233zB48GAAH85+iP+NZ8zq/fv3Be6Dvr4+jh07hiNHjmDXrl3Yu3cvNm7ciKZNm2L//v25nlkryI0gciNPKOU8PT1RsWJFDB06VOmCsxEjRiAsLAx+fn5o2LAhTE1NIZPJ0LNnT7VJX3Z9UXdMP7Xs9l2cMWX18Zca4EPy6uLigp9//lntNvIvDYV9DX3MxMQEpUqVwpUrV/Lfkf+ZPn06fvjhBwwcOBBTp05FiRIloKGhAT8/P6XXRqVKlXDjxg389ddf2Lt3L7Zs2YLFixdjypQpCA4OBvDhbOzXX3+NP//8E/v378fs2bMxa9YsbN26Fa1bty5wjADy9Rkhp+65+hSK6rWY13b79euHP/74A9HR0XBxccGOHTswbNgwlS8OQPbHoCg+j/IjL5/jhVGlShUkJiZizZo1GDp0aJ6/hH3OeMabiApEU1MTM2bMwKNHj7Bo0SIAQNmyZQF8+Hn34zOT8iXrz+klSpTAgAEDsH79ejx48ADVqlVTuvukubm52hvg5OXsck5/kDQ0NNCsWTP8/PPPuHbtGqZNm4bDhw8rhhKoY29vj8zMTJWfVJ8+fYrExETY29vnGlNe2draYvTo0di5cydOnjypKN+8eTP69++PuXPnolu3bmjRogUaN25c4JsEyWP+uE/x8fEqZ/3s7e1x48YNlTauX7+u1FZxke//4xjfvXuHuLi4PMXn5OSEFy9eoFmzZmpfu1nPCOb2GspvQtSuXTvExsbixIkT+dpObvPmzfDw8EBoaCh69uyJli1bonnz5mpfG4aGhvDy8kJYWBju37+Ptm3bYtq0aUo3bLK1tcWwYcOwbds2xMXFwcLCAtOmTStQbFnl9zMiP+zt7XHr1i2VL6Gf+jX6qZLdVq1awdLSEmvXrsWff/6JN2/e5PmmQ4X5PLK3t0dsbKzKFwF17++8yO1z/GNOTk7IzMxUGZ6mTsmSJXHw4EFoa2ujWbNmRTL8SWpMvImowNzd3VGvXj3Mnz8fqampsLKygru7O5YuXYrHjx+r1M86w8Hz58+V1hkZGcHZ2VlpSisnJydcv35dabtLly7laQYS+Ty3HyceL168UKkrv8FJTtNptWnTBgAwf/58pXL52dG2bdvmGlN+jBgxAgYGBpg5c6aiTFNTU+WP5cKFCwv8C0Dz5s2hra2NhQsXKrX7cR+BD/0/ffq0UmKYkpKCZcuWwcHBIU8/Gxel5s2bQ0dHBwsWLFDqS2hoKJKSkvL0/PTo0QMPHz7E8uXLVda9fftWMaNGXl5D2b3+sjN+/HgYGhpi8ODBePr0qcr62NhY/PLLL9lur+618ccff6iMl/74faejo4PKlStDCIH09HS8f/9eZdiPlZUVSpUq9UmmfczPZ0R+tWnTBk+ePFGatScjIwMLFy6EkZER3NzcCtx2Vvl9brOjpaWFXr16YdOmTQgPD4eLiwuqVauWp20L83nUpk0bPHr0CJs3b1aUvXnzpkA3Z8rL5/jHOnXqBA0NDYSEhKh8SVL3a0OZMmVw8OBBvH37Fi1atFDZ55eGQ02IqFDGjRuH7t27Izw8HL6+vvj111/RuHFjuLi4wMfHB2XLlsXTp09x4sQJ/PPPP4p5hStXrgx3d3fUrl0bJUqUwNmzZxVTmMkNHDgQP//8Mzw9PTFo0CA8e/YMv/32G6pUqaK4IC078rG/33//PXr27AltbW20b98eISEhOHbsGNq2bQt7e3s8e/YMixcvRpkyZZQuHPxY9erV0b9/fyxbtgyJiYlwc3PD6dOnsWrVKnTq1AkeHh6f4Gj+PwsLCwwYMACLFy9GTEwMKlWqhHbt2mHNmjUwNTVF5cqVceLECRw8eBAWFhYF2oelpSXGjh2LGTNmoF27dmjTpg0uXLiAPXv2KKZglJs4cSLWr1+P1q1bY+TIkShRogRWrVqFuLg4bNmyRe3P41KytLTEpEmTEBwcjFatWqFDhw64ceMGFi9ejLp16+bpxjt9+/bFpk2b4OvriyNHjsDV1RXv37/H9evXsWnTJuzbtw916tTJ02vIyckJZmZm+O2332BsbAxDQ0PUr18/25/KnZycsG7dOnh5eaFSpUpKd66Mjo5WTIuXnXbt2iEkJAQDBgxAo0aN8Pfff2Pt2rWKM8xyLVu2hI2NDVxdXWFtbY2YmBgsWrQIbdu2hbGxMRITE1GmTBl069YN1atXh5GREQ4ePIgzZ84oLmQtrLx+RuTXkCFDsHTpUnh7e+PcuXNwcHDA5s2bERUVhfnz5xfq4tWsatSoAU1NTcyaNQtJSUnQ1dVF06ZNYWVlle+2+vXrhwULFuDIkSOYNWtWnrcrzOeRj48PFi1ahH79+uHcuXOwtbXFmjVrCnQDqbx8jn/M2dkZ33//PaZOnYqvv/4aXbp0ga6uLs6cOYNSpUphxowZarfZv38/3N3d4enpicOHDyuujfjiFMNMKkT0hZFP63XmzBmVde/fvxdOTk7CyclJMeVXbGys6Nevn7CxsRHa2tqidOnSol27dmLz5s2K7X788UdRr149YWZmJvT19UXFihXFtGnTlKagE0KI33//XZQtW1bo6OiIGjVqiH379uVpOkEhhJg6daooXbq00NDQUEwteOjQIdGxY0dRqlQpoaOjI0qVKiV69eolbt68metxSE9PF8HBwcLR0VFoa2sLOzs7MWnSJKVp0YQo2HSC6sTGxgpNTU3F9GIvX74UAwYMECVLlhRGRkbC09NTXL9+XWVqueyeL/lUf1mnC3v//r0IDg4Wtra2Ql9fX7i7u4srV66otCmPp1u3bsLMzEzo6emJevXqib/++kvtPv744w+l8uxikk81ltv0jnk9posWLRIVK1YU2trawtraWnz77bfi5cuXSnXc3NyynVLv3bt3YtasWaJKlSpCV1dXmJubi9q1a4vg4GCRlJQkhBB5fg1t375dVK5cWWhpaeV5asGbN28KHx8f4eDgIHR0dISxsbFwdXUVCxcuVHqdqZtOcMyYMYrn0dXVVZw4cUJlKrmlS5eKJk2aCAsLC6GrqyucnJzEuHHjFH1LS0sT48aNE9WrVxfGxsbC0NBQVK9eXSxevFgpzsJMJyhE3j4jcvrcyc7Tp08V7xEdHR3h4uKism95TLNnz1bZXt3niDrLly8XZcuWFZqamkrvKXXHQIjsp0YVQogqVaoIDQ0N8c8//6isy+n9kdfPI3X7vnfvnujQoYMwMDAQJUuWFKNGjRJ79+7N93SCefkc/3g6QbmVK1eKmjVrKt5nbm5u4sCBA4r16o7lqVOnhLGxsWjSpIna6VW/BDIhJL6ihYiIiIgAADVr1kSJEiVw6NCh4g6FJMAx3kRERETF4OzZs7h48SL69etX3KGQRHjGm4iIiEhCV65cwblz5zB37lwkJCTgzp070NPTK+6wFN6+fZvr3PolSpRQmQufcscz3kREREQS2rx5MwYMGID09HSsX7/+s0q6AWDjxo2wtbXNcYmOji7uML9IPONNRERERAqPHz/G1atXc6xTu3ZtlTvcUu6YeBMRERERSYBDTYiIiIiIJMAb6BB9JjIzM/Ho0SMYGxt/slsSExERUdESQuD169coVapUrjcTY+JN9Jl49OgR7OzsijsMIiIiKoAHDx6gTJkyOdZh4k30mZDfzvjBgwdf7q1wiYiI8iIlBShV6sP/Hz0CDA2LN55CePXqFezs7BR/x3PCxJvoMyEfXmJiYsLEm4iI/t00Nf///yYmX3TiLZeXYaK8uJKIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJKBV3AEQ0UdMTYs7AiIiIukYGSk/FqJ44pAAz3gTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4ExERERFJgIk3EREREZEEmHgTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4ExERERFJgIk3EREREZEEmHgTEREREUmAiXcByWQyuLu7F+k+goKCIJPJEBERIfm+iYiIiOjT+iwT77t370Imk6kshoaGqFatGoKDg5GcnFzcYVIWKSkpmD59OmrVqgUjIyPo6uqiTJky+PrrrzFp0iTExsYq1Xd3d1c8r3/99Ve27davX19R7+MvIHIXLlzAgAEDULZsWejr68PU1BR16tRBSEgIkpKSlOrKv8zkdQkKCgIAeHt751o3PDy8MIeQiIiI/uW0ijuAnDg5OaFPnz4AACEE4uPjsWfPHgQFBWHv3r2IjIyEpqZmMUcpvZiYGBgYGBR3GAqvX79G48aNcfnyZTg7O6NPnz6wsLBAQkICTp8+jZkzZ8LJyQlOTk4q22ppaWHlypVo166dyrqrV6/i9OnT0NLSQkZGhtp9h4SEICgoCFpaWvD09ESPHj3w9u1bREREIDAwEEuWLMGOHTtQt25dAFD7S8HFixexfft2uLm5qaz/+PGgQYNQpkwZtbHUqFFDbTkRERERAEB8huLi4gQA4enpqbIuNTVV1KxZUwAQhw4dKoboPgAg3NzcinQfgYGBAoA4cuRIke6nsEJCQgQAMXjwYJGZmamy/s6dOyImJkapzM3NTQAQ7du3F9ra2uLZs2cq240ePVpoaGiItm3bqj0OixYtEgBE2bJlVdoXQojffvtNaGpqCgsLC3H//v1s4w8LCxMARGBgYLZ1+vfvLwCIEydOZFunsJKSkgQAkQQIwYULFy5cuPxXly+M4u93UlKudT/LoSY50dXVhYeHBwAgISFBZf2VK1fQo0cPWFlZQVdXF46OjvDz88Pz589V6jo4OMDBwQGJiYn47rvvYGdnBy0tLaUhAytWrEDVqlWhp6cHOzs7jB8/HqmpqdnG9/r1awQGBqJKlSrQ19eHmZkZPD09ERkZqVJXPtwiNTUVkydPhpOTE7S1tRXDG7Kjboy3fChEXFwcFixYgIoVK0JXVxf29vYIDg5GZmam2ra2b9+OZs2awdzcHHp6eqhatSrmzJmD9+/f5xhDVidOnAAADB8+HDKZTGW9o6MjKlasqHbbgQMHIj09HWvWrFEqT09Px++//46WLVuqPcP88uVLTJo0CTo6Oti5c6fa9ocOHYoJEybg+fPn+P777/PcHyIiIqKi8MUl3u/evUNERARkMpnKT/uRkZGoX78+/vzzTzRr1gz+/v6wt7fHL7/8gvr166tN1NPS0tC0aVPs378fHTp0wPDhw2FtbQ0AmDp1Knx8fJCQkAAfHx90794dGzduRPfu3dXG9uLFCzRs2BAhISEwNzeHr68vunbtinPnzsHDwwPbtm1Tu13Xrl0RHh4ODw8PjBo1Co6OjgU+PuPGjcPUqVPRsGFD+Pr6AvgwrvmHH35QqTtp0iR06tQJN27cQJcuXTBs2DDo6+tj3Lhx6NmzZ573aWFhAQC4efNmvuNt0KABKleujLCwMKXynTt3Ij4+HgMHDlS73ebNm/H69Wt06dIFlStXzrb9cePGQU9PDxs2bMCbN2/yHR8RERHRJyPBGfh8kw81cXJyEoGBgSIwMFBMmTJFDBs2TDg5OQk9PT0xe/ZspW3ev38vnJycBACxd+9epXXjxo0TAMTAgQOVyu3t7QXwYUjLmzdvlNbdunVLaGlpidKlS4unT58qypOSkkSFChUEoDrUpHfv3gKAWL58uVL506dPhZ2dnbC0tBRv375VlMuHW9SoUUM8f/5c5ThkN9RE3b7lQyEcHR3Fo0ePFOXx8fHCzMxMGBsbi7S0NEX5/v37FX1PTk5WlGdmZgpfX18BQGzevFklJnW2b98uAAhjY2MxZswYsW/fPpGQkJDjNvK+P378WMyZM0cAEKdPn1asb9OmjbCwsBBpaWli6NChKsfB29tb7bFWp1GjRgKAOHbsmNr1+RlqMmjQIMVr8uMl63ObF6mpqSIpKUmxPHjw4MNPVcX9Ex8XLly4cOFSnMtHTp8+LVq3bi1MTU2FgYGBqF+/vti4cWOe/97K872clo9zhLdv34qQkBBRqVIloaurK8zMzESrVq1EZGSkSvv5GWqi2rvPgDzxzm5p166duHDhgtI2x44dEwBE69atVdp7/fq1KFGihNDT01NKPuVPxKVLl1S2CQ4OFgDE3LlzVdatWbNGfJz8xsfHC01NTdG0aVO1fVqwYIEAIHbu3Kkokyef27dvV7tNQRLvlStXqrQjX3f58mVFWYcOHQQAce/ePZX6iYmJQiaTia5du6qNS525c+cKIyMjpefJyclJDB8+XNy8eVOlftbE++nTp0JbW1v4+voKIYR4+PCh0NTUFKNGjRJCCLWJd6tWrYS6L1nqeHl5CQDZvknzk3jntLx8+TLXWLKSP78fL0y8uXDhwoXLf3rJ4vDhw0JbW1sYGxsLHx8f4e/vr8jf5syZk6e/t/PmzVN7wmz48OECgDA3N1c6efb27VvFSbtq1aoJPz8/4e3tLYyNjYWmpqbYtm2bUvv5Sbw/61lNPD09sXfvXsXj58+fIyoqCqNGjYKrqysOHz6M+vXrA/gwpRygftYKIyMj1KlTB/v378eNGzfg4uKiWKenp6f0WO7SpUsAgK+//lplnbqyM2fO4P3790hLS1M7RvvWrVsAgOvXr6vM4FGvXj2V+gVVu3ZtlTL5GOnExERF2cmTJ2FoaIiVK1eqbUdfXx/Xr19XbDd//nyVOln76e/vDx8fH+zduxfR0dE4e/YsTp06hV9//RWhoaHYuHEjOnTooHZfVlZWaNu2LTZs2IB58+Zh1apVeP/+fbbDTIrTiRMn0KBBg0/S1qRJk+Dv7694/OrVK9jZ2X2StomIiL50GRkZ8PHxgYaGBo4dO6YYYjxlyhTUq1cPAQEB6NatG+zt7XNsx8/PT2353LlzAQB9+vSBnp6eonzRokWIjo5G9+7dsX79esUMepMnT0atWrXg4+ODpk2bwtjYON99+qwT749ZWFigQ4cOMDAwQIsWLTB58mQcOHAAwIekBYBifPbHbG1tlerJWVlZqb0gUD7/s5WVlco6dft48eIFACAqKgpRUVHZ9iElJSVP7RWUiYmJSpmW1oenOesFky9evEBGRgaCg4OzbUsea2Jiotp6H3/BMDY2Rvfu3RVj4JOSkhAQEIDFixdj0KBBePjwIXR0dNTua+DAgdi2bRu2bNmCsLAw1K5dG9WqVcs2NhsbGwDAgwcPsq0jJ68jfw18LnR1daGrq1vcYRAREX2WDh8+jNjYWAwYMEDpuj5TU1MEBATA29sbq1atwpQpUwrUfmhoKIAPUwVntX37dgAf8pys01Y7OTlh4MCBmD9/PjZv3owBAwbke59f3MWVABRnuc+cOaMokyecT58+VbvNkydPlOrJqUu6gQ9PKgA8e/ZMZZ26fcjbHTNmDIQQ2S6BgYEq22YXQ1EyMTGBhYVFjrHGxcUB+DD7i7r1uTE1NcWiRYtgb2+PhIQE/P3339nWbdOmDWxtbTFhwgTcunVL5U3wsUaNGgEADh06lGO9xMREnD9/Hjo6Omp/DSAiIqLPk/zGeS1btlRZ5+npCQA4evRogdqOjo5GTEwM6tSpg+rVqyutk+eM6ia7kJcdPny4QPv9IhPvly9fAoDSFHk1a9YEALV3N0xJScHZs2ehr6+PChUq5Gkf8ifh+PHjKuvUldWtWxcymUwxtd7nrn79+nj+/LliCExRkd9xNDeampro168fHj58CD09PfTq1SvH+t26dYORkRG2bt2qGBKjzty5c5GamgovL6/P6qZDRERElDN5jlKuXDmVdTY2NjAyMipwHiM/2z148GCVdSVLlgQAxQnIrORlBZnJDfhCE++ff/4ZANCkSRNFmaurK5ycnLBnzx4cPHhQqf6PP/6I58+fo1evXtkOdfhY7969oampiZ9//lnprPerV6/w448/qtS3sbFBjx49EB0djdmzZ6s9I3zq1KnPZkq7kSNHAvgwxEPdHOdPnjxBTExMntpaunSp0q8PWW3btg0xMTEwMzND1apVc2zH398ff/75J/bt2wczM7Mc65qbm2PatGl49+4d2rdvr/YNEBoaihkzZsDCwgLTpk3LU1+IiIjo8yAf9isfhfAxExMTRZ38SE5OxqZNm2BgYKD2RF/r1q0BfLg7dtZhunFxcYrpj7NeN5cfn/UY79u3byuNI37x4gWioqJw/vx5mJubY9asWYp1GhoaCA8Ph6enJ9q0aYPu3bvD3t4eJ06cQEREBJycnDBz5sw879vZ2RlTpkxBYGAgqlWrhh49ekBLSwtbtmxBtWrVcOPGDZVtFi9ejBs3bmD8+PFYs2YNGjZsCDMzMzx48ABnz57FrVu38Pjx48/izGurVq3www8/YOrUqXB2dkarVq1gb2+P58+f4/bt2zh+/Dh+/PFHVKpUKde29uzZA19fXzg7O8PV1RWlSpVCSkoKLly4gOPHj0NDQwOLFy/OdTyzlZUVOnXqlOc+jBw5EgkJCZg6dSpcXFzQqlUrVKpUCampqYiIiMClS5dgbW2NHTt2fLKLFlesWKF0wW9WDRo0QKtWrT7JfoiIiKhobNy4EcnJyejfv7/aa+NGjx6NjRs3YuPGjbh+/TqaNm2KxMREbNmyBQ4ODrh8+TI0NAp47jpP87BILLvpBHV1dYWTk5P49ttv1U6DJ4QQly9fFt26dRMlS5YU2trawt7eXowaNUrEx8er1LW3txf29vY5xrJ8+XJRuXJloaOjI8qUKSPGjh0r3rx5IwD1t4x/8+aN+Omnn0Tt2rWFoaGh0NfXF46OjqJTp05i9erVIj09XVFXPqVedgoynWBcXFye2xFCiAMHDoj27dsLS0tLoa2tLWxsbETDhg3F1KlTc7zNelbXr18XP/30k2jRooVwdHQUenp6Qk9PTzg5OYn+/fuLs2fPqmyTdTrB3KibTjCrs2fPin79+gl7e3uhq6srjI2NRc2aNUVQUFCepvj7VNMJyqc/LCjeMp4LFy5cuHCB4u9it27dBAC1eYQQQhgZGQk7O7t8/71t2LChACCOHz+ebZ2XL1+K0aNHCwcHB6GtrS1Kly4txo8fr5i+ukmTJqp/v/MwnaBMCCEKlrIT0af06tUrmJqaIgmA6vdvIiKi/4j/paYBAQGYMWMG1q9fr3JH7SdPnsDW1hZNmzbNdaKFrK5du4YqVaqgYsWKeR5Sm1V4eDgGDBgAf39/xXSEir/fSUlqz6Bn9UWO8SYiIiKifzc3NzcAwP79+1XW7du3T6lOXmU3hWBerV27FgBUvgjkFc94E30meMabiIgIijPeGRkZqFChAh4+fIiTJ08q5vJOSkpCvXr1cPfuXdy4cQMODg4AgMePHyMpKQm2trZqL8hMT09H6dKlkZiYiH/++UftvVrkXr16pXL2et68efD390fnzp2xdetWpbp5PeP9WV9cSURERET/TVpaWlixYgU8PT3RpEkT9OzZE8bGxtiyZQvu3buHOXPmKJJu4MMdoVetWoWwsDB4e3urtLdjxw7Ex8ejS5cuOSbdAFC6dGl4eHigXLlykMlkiIiIwLlz51CnTh3FWfMC9anAWxIRERERFSEPDw9ERkYiMDAQGzduRHp6OlxcXDBr1ix4eXnlq62c5u7+WJ8+fXDkyBEcOnQIMpkM5cuXx+zZszFixIhC3XWaQ02IPhMcakJERATFUJMvBS+uJCIiIiL6zDDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJaBV3AET0kaQkwMSkuKMgIiIqOikpgJHRh/8nJwOGhsUbj0R4xpuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJaxR0AESlzPnYZGoZGxR0GERFRkTF4+xZ3ijuIYsAz3kREREREEmDiTUREREQkASbeREREREQSYOJNRERERCQBJt5ERERERBJg4k1EREREJAEm3kREREREEmDiTUREREQkASbeREREREQSYOJNRERERCQBJt5ERERERBJg4k1EREREJAEm3kREREREEmDiTUREREQkASbeVCgDBw6ETCaDhYUF0tLS1NZxd3eHTCZTLNra2rCwsECNGjUwaNAg7N27F5mZmbnuq2nTppDJZKhatWqO9YQQ+P3339G0aVNYWFhAR0cH1tbWqFmzJoYNG4ajR48CAO7duwdTU1NYW1sjPj5ebVupqamoXLkytLW1cfbsWQCAt7e3oi8nTpxQu12rVq0gk8lw9+7dXPtFRERE/w1MvKnAXr9+jU2bNkEmk+HFixfYtm1bjvXHjBmDwMBATJo0Cd988w0sLS2xdu1atG7dGl9//TXu37+f7bZ37txBREQEZDIZrl69ilOnTmVbd+DAgejbty8uXryItm3bYsyYMejVqxfMzMwQGhqK5cuXAwDs7e0xf/58PHv2DL6+vmrbmjx5MmJiYvD999+jTp06KusnTJiQY5+JiIiI5LSKOwD6cm3cuBEpKSnw9/fH/PnzERoaCi8vr2zrjx07FjY2NkplCQkJGDlyJNavXw9PT0+cPXsWhoaGKtuuXLkSQgiMHTsWc+bMQWhoKOrXr69S7/jx4wgPD0eNGjVw9OhRmJiYKK1PTEzEtWvXFI8HDBiAbdu2YevWrfj999/Rp08fxbqoqCjMmzcPderUweTJk1X25eTkhOPHj2Pnzp1o37599geKiIiICDzjTYUQGhoKLS0tjB8/Hh4eHjh06BDu3buXrzZKliypGBZy/fp1/Prrryp13r9/j/DwcFhYWGDatGlwdnbGhg0bkJKSolJXPvSjf//+Kkk3AJiZmaFRo0ZKZcuXL4elpSVGjBiBhw8fAgDevHkDb29v6OjoYPXq1dDSUv2OGhgYCC0tLQQEBORpqAwRERH9tzHxpgK5du0aTp48iZYtW8La2hr9+vVDZmYmwsLC8t2WhoYGvv/+ewAfzqJ/bN++fXj48CG8vLygo6ODvn374vXr1/jjjz9U6lpYWAAAbt68mef9W1lZYenSpUhMTMSgQYMAAOPHj8ft27cxY8YMVKpUSe125cqVg4+PD65cuYJVq1bleX9ERET038TEmwokNDQUANC3b18AQJcuXWBoaIiwsLACnf11dXWFlpYWLl68iIyMjBz31adPH8hkMkV5Vp6enjAxMcFvv/2Gb775Bps3b87TWfjOnTujb9++2LdvHwYMGIDFixfD3d0do0aNynG7wMBAGBkZYcqUKUhNTc1rdwEAaWlpePXqldJCRET0X3XmzBm0adMGZmZmMDQ0RIMGDbBp06Y8b+/g4KA0mYO65fjx40rbZGRkYOXKlWjYsCEsLS1hbGyMypUrY/z48Xjy5Mmn7iITb8q/9PR0rFmzBiYmJujUqRMAwMjICJ07d8b9+/dx8ODBfLepq6sLCwsLZGZm4sWLF4ry+Ph47Ny5E+XLl0eDBg0AAGXLloWrqysiIyNx48YNpXbKlCmDLVu2wM7ODuvWrUP37t3h4OAAKysreHl54fDhw9nGsHDhQtjZ2SE8PBzGxsYIDw+HTCbLMW5ra2v4+/vjn3/+wYIFC/LV5xkzZsDU1FSx2NnZ5Wt7IiKif4sjR44o/rb36NEDvr6+ePLkCby8vDB37tw8teHn54fAwECVZfjw4QAAc3Nz1K1bV2kbLy8vDBo0CAkJCejZsyeGDh2KEiVKYPbs2ahVq9YnT76ZeFO+bd++HfHx8ejevTv09PQU5f369QMAtWeiC2rVqlVIT09XnO3+eF8rV65U2aZ58+aIjY3FgQMH8MMPP6BNmzZIS0vDpk2b0KxZMwQEBKjdl6mpKSZNmgQA8PX1hb29fZ5iHDt2LCwtLTFz5ky8fPkyz32bNGkSkpKSFMuDBw/yvC0REdG/RUZGBnx8fKChoYFjx45h2bJlmDt3Li5duoTy5csjICAgT79e+/n5ISgoSGVxdHQE8OEX86x5y+nTp7F161bUq1cP165dw8KFCzFnzhxERkZi5MiRePz4MZYtW/ZJ+8rEm/JNnljLk1+5Zs2aoXTp0ti+fbvSWeu8SEtLw/Pnz6GpqYkSJUoo7Usmk6kk3j169ICenh5Wr16tMjQFALS0tNC8eXOEhIRg165deP78OZYvXw4tLS3MmDED58+fVxuHvr6+0r95YWxsjB9++AEvX77EjBkz8rydrq4uTExMlBYiIqL/msNHjyI2Nha9e/dGjRo1FOWmpqYICAjAu3fvCnUtlTxvkV/HJXfnzh0AH07YaWtrK61r164dAGR7n4+CYuJN+fLgwQPs378fAODm5qY0bkpTUxMPHz5EWloafv/993y1GxUVhYyMDNSoUUMxg0h0dDSuX78OIYTKuC0zMzOkpqbiyZMn2L17d67ta2lpYfDgwejduzeADz9pfUq+vr5wcnLCwoULeeaaiIgoHyL+N+66ZcuWKus8PT0BQHHzu/yKjo5GTEwM6tSpg+rVqyutq1KlCgDg4MGDSE9PV1r3119/AfhwUvFT4jzelC/h4eHIzMxE48aNUaFCBZX1GRkZWLVqFUJDQzFy5Mg8tZmZmYlp06YBAHr16qUol39Dbd26NUqVKqWyXWJiIrZs2YLQ0FB06NAhT/syMjLKU7380tbWxo8//ohevXphypQpRbIPIiKif6Nbt28D+DBb2MdsbGxgZGSEW7duFahteS4xePBglXUuLi4YNWoUfvnlF1SuXBmtW7eGrq4uTpw4gXPnziE4OFhxLdunwsSb8kwIgbCwMMhkMqxatQply5ZVW+/mzZs4ceIEzp49q/Zuj1nJb6Bz+PBhVK5cGd9++y0AIDk5GZs2bYKhoSE2bdqkNmHOzMyEvb09du/ejSdPnsDGxgZ79+5FWloa2rZtqzL39u3btxVTEDZu3LgghyBHXl5emDNnDlavXp3n8eFERET/dUn/m9XL1NRU7XoTExMkJSXlu115LmFgYKB0Yi+r+fPnw9HREePGjcPChQsV5e3bt0eXLl3yvc/cMPGmPDt8+DDi4uLg5uaWbdINfLgb5IkTJxAaGqqUeM+ZMwdGRkbIzMzEq1evcO3aNRw/fhypqalwdXXF+vXrYWBgAODDfN7Jycno379/tmepNTQ00K9fP0yfPh2rVq3ChAkTcP36dYwePRolS5ZEkyZN4OTkBCEEbt++jd27d+Pdu3f49ttv1d71srBkMhlmzpyJFi1aIC4u7pO3T0RERHmXNZdQdx1VZmYmfH19sX79eixcuBAdO3aEgYEBoqKiMHLkSDRo0ABHjhxRmQmlMJh4U57Jf67x9vbOsZ6XlxdGjRqF9evX4+eff1aUy6cD0tLSgrGxMb766iv07t0bPXr0QIsWLaCh8f+XHOR1X97e3pg+fTpWrlyJCRMm4JtvvoGRkRH27duHv//+GwcOHEBqaipKliyJli1bwtvbG127di1A7/OmefPmaNmypWIcPBEREeXM9H9JcXZntV+9egVzc/N8t5vTMBPgw8xoy5cvxy+//IKhQ4cqylu3bo3NmzejRo0aCAgIwIEDB/K97+zIhBDik7VGRAX26tUrmJqawnLncWgYFs1YdCIios+Bwdu3uNO2EQAgYOxYzJgzB+vXr0fPnj2V6j158gS2trZo2rQpDh06lOf2r127hipVqqBixYqIiYlRW6dr167YunUrLl++DBcXF5X1JUqUgIaGBhISEnLcl/zvd1JSUq4zlHFWEyIiIiIqNm7/u+5K3a/F+/bt+1DHzS1fbWY3hWBW7969A6B+ysC0tDS8fv0aurq6+dpvbph4ExEREVGxaebujrJly2LdunW4ePGiojwpKQnTp0+Hjo6O0r1DHj9+jOvXr2c7NEV+h21tbW2Ve45k5erqCgCYPn060tLSlNYFBQUhIyMDHh4eheiZKibeRERERFRstLS0sGLFCmRmZqJJkyYYMmQIxowZg+rVq+PmzZuYPn06HBwcFPUnTZqESpUq4c8//1Tb3o4dOxAfH4/27dvDysoq2/0OGzYMFStWxKFDh1CxYkV8++238Pf3R/369TFz5kxYWloiJCTkk/aViTcRERERFSsPDw9ERkbC1dUVGzduxJIlS2BtbY0NGzZgzJgx+Wort4sq5UxMTHDy5ElMmjQJBgYGCA8Px6JFixAfHw9fX1+cP38+x1ncCoIXVxJ9JnhxJRER/VdkvbgSycmAoWHxBlQIvLiSiIiIiOgzw8SbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCSgVdwBEJGy202qwcTEpLjDICIiKjopKcUdQbHgGW8iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgloFXcARKTM+dhlaBgaFXcYRERERcbg7VvcKe4gigHPeBMRERERSYCJNxERERGRBJh4ExERERFJgIk3EREREZEEmHgTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4ExERERFJgIk3EREREZEEmHgTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4U5FKSUnB9OnTUatWLRgZGUFXVxdlypTB119/jUmTJiE2NlZR193dHTKZDDKZDH/99Ve2bdavX19RLyIiQm2dCxcuYMCAAShbtiz09fVhamqKOnXqICQkBElJSUp1g4KCFO3lZQkKCgIAeHt751o3PDy8sIeQiIiI/iW0ijsA+vd6/fo1GjdujMuXL8PZ2Rl9+vSBhYUFEhIScPr0acycORNOTk5wcnJS2k5LSwsrV65Eu3btVNq8evUqTp8+DS0tLWRkZKjdb0hICIKCgqClpQVPT0/06NEDb9++RUREBAIDA7FkyRLs2LEDdevWBfAh4f/YxYsXsX37dri5uams//jxoEGDUKZMGbWx1KhRQ/3BISIiov8cJt5UZObPn4/Lly9j8ODBWLZsGWQymdL6uLg4pKWlqWzXunVr/PXXX4iPj4elpaXSutDQUGhoaMDT0xO7du1S2fbXX39FYGAgypYti127dqFixYpK65cuXYrhw4ejdevWuHDhAuzs7ODu7q6STIeHh2P79u1wd3dXnOHOzuDBg9GgQYMc6xARERFxqAkVmRMnTgAAhg8frpJ0A4Cjo6NKYgwAAwcORHp6OtasWaNUnp6ejt9//x0tW7ZUe4b55cuXmDRpEnR0dLBz5061bQ8dOhQTJkzA8+fP8f333xe0a0RERET5xsSbioyFhQUA4ObNm/narkGDBqhcuTLCwsKUynfu3In4+HgMHDhQ7XabN2/G69ev0aVLF1SuXDnb9seNGwc9PT1s2LABb968yVdsRERERAXFxJuKTPfu3QF8GIoxduxY7N+/H8+fP8/TtgMHDsSVK1dw5swZRVloaCgsLCzQsWNHtdtER0cDAJo1a5Zj22ZmZqhVqxbS09Nx7ty5PMWTkxUrViAoKEjtkpqamu12aWlpePXqldJCRET0X3XmzBm0adMGZmZmMDQ0RIMGDbBp06Y8b+/g4JDrpAfHjx9X2iYjIwMrV65Ew4YNYWlpCWNjY1SuXBnjx4/HkydPPnUXOcabik6HDh0wd+5cBAYGYu7cuZg7dy4AwMnJCa1atcKoUaNQrlw5tdv27dsXkyZNwsqVK1G3bl08evQI+/btw3fffQcdHR2128jfIHZ2drnGJq/z+PHjgnRNSWhoaLbr/Pz8oKenp3bdjBkzEBwcXOj9ExERfemOHDkCT09P6OnpoWfPnjA2NsaWLVvg5eWFBw8eYMyYMbm24efnh8TERJXyhIQE/PrrrzA3N1dMrCDn5eWFrVu3wtnZGT179oSuri5OnjyJ2bNn4/fff8f58+dhY2PzqbrJxJuKlr+/P3x8fLB3715ER0fj7NmzOHXqFH799VeEhoZi48aN6NChg8p2VlZWaNu2LTZs2IB58+Zh1apVeP/+fbbDTIrTiRMnCnRx5aRJk+Dv7694/OrVqzx9aSAiIvo3ycjIgI+PDzQ0NHDs2DHFjGBTpkxBvXr1EBAQgG7dusHe3j7Hdvz8/NSWy0/89enTR+lk2OnTp7F161bUq1cPkZGR0NbWVqwbNWoUFixYgGXLlmHKlCmF62AWHGpCRc7Y2Bjdu3fHvHnzcPz4ccTHx2PYsGFITU3FoEGD8O7dO7XbDRw4EImJidiyZQvCwsJQu3ZtVKtWLdv9yL+RPnjwINeY5HVsbW0L0KNPQ1dXFyYmJkoLERHRf83ho0cRGxuL3r17K03Da2pqioCAALx79w6rVq0qcPvyX6YHDRqkVH7nzh0AQPPmzZWSbgCKKY3j4+MLvF91mHiT5ExNTbFo0SLY29sjISEBf//9t9p6bdq0ga2tLSZMmIBbt26pvGE+1qhRIwDAoUOHcqyXmJiI8+fPQ0dHB7Vr1y5YJ4iIiOiTiPjfuOuWLVuqrPP09AQAHD16tEBtR0dHIyYmBnXq1EH16tWV1lWpUgUAcPDgQaSnpyutk9/IL7frxvKLiTcVC5lMBkNDwxzraGpqol+/fnj48CH09PTQq1evHOt369YNRkZG2Lp1K65fv55tvblz5yI1NRVeXl4wMDAoUPxERET0ady6fRsA1F73ZWNjAyMjI9y6datAbcvPdg8ePFhlnYuLC0aNGoXTp0+jcuXKGDlyJMaNG4fGjRtj2bJlCA4ORqdOnQq03+xwjDcVmaVLl6JWrVoqFzIAwLZt2xATEwMzMzNUrVo12zb8/f3RoEEDlChRAmZmZjnuz9zcHNOmTcOoUaPQvn177Nq1C+XLl1eqExoaihkzZsDCwgLTpk0rUL+IiIjo00n636xepqamatebmJggKSkp3+0mJydj06ZNMDAwyPbk3fz58+Ho6Ihx48Zh4cKFivL27dujS5cu+d5nbph4U5HZs2cPfH194ezsDFdXV5QqVQopKSm4cOECjh8/Dg0NDSxevBi6urrZtmFlZZWvb5sjR45EQkICpk6dChcXF7Rq1QqVKlVCamoqIiIicOnSJVhbW2PHjh2f7ELGFStWYO/evWrXNWjQAK1atfok+yEiIqK827hxI5KTk9G/f3+111FlZmbC19cX69evx8KFC9GxY0cYGBggKioKI0eORIMGDXDkyBG1JxALiok3FZlZs2bB1dUVBw4cwLFjxxRT95UuXRr9+/fHiBEjimSMdUhICDp27IgFCxbg6NGj2LdvH3R0dODs7IygoCCMGjUq17Pn+ZHTdIKjRo1i4k1ERJQD0/8lxdmd1X716hXMzc3z3W5Ow0wAYOXKlVi+fDl++eUXDB06VFHeunVrbN68GTVq1EBAQAAOHDiQ731nRyaEEJ+sNSIqsFevXsHU1BSWO49Dw9CouMMhIiIqMgZv3+JO2w+TIgSMHYsZc+Zg/fr16Nmzp1K9J0+ewNbWFk2bNs118oSsrl27hipVqqBixYqIiYlRW6dr167YunUrLl++DBcXF5X1JUqUgIaGBhISEnLcl/zvd1JSUq4zlPHiSiIiIiIqNm6NGwMA9u/fr7Ju3759H+q4ueWrzeymEMxKPp2xuikD09LS8Pr16xyHwxYEE28iIiIiKjbN3N1RtmxZrFu3DhcvXlSUJyUlYfr06dDR0UG/fv0U5Y8fP8b169ezHZqSnp6ONWvWQFtbW2m7j7m6ugIApk+fjrS0NKV1QUFByMjIgIeHRyF6poqJNxEREREVGy0tLaxYsQKZmZlo0qQJhgwZgjFjxqB69eq4efMmpk+fDgcHB0X9SZMmoVKlSvjzzz/Vtrdjxw7Ex8ejffv2sLKyyna/w4YNQ8WKFXHo0CFUrFgR3377Lfz9/VG/fn3MnDkTlpaWCAkJ+aR9ZeJNRERERMXKw8MDkZGRcHV1xcaNG7FkyRJYW1tjw4YNGDNmTL7ayu2iSjkTExOcPHkSkyZNgoGBAcLDw7Fo0SLEx8fD19cX58+fR9myZQvcJ3V4cSXRZ4IXVxIR0X9F1osrkZwM5HJTvc8ZL64kIiIiIvrMMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgloFXcARKTsdpNqMDExKe4wiIiIik5KSnFHUCx4xpuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJaxR0AEX3E1LS4IyAiIpKOkZHyYyGKJw4J8Iw3EREREZEEmHgTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4ExERERFJgIk3EREREZEEmHgTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4ExERERFJgIk3EREREZEE/vWJt4ODAxwcHIo7DPrM3b17FzKZDN7e3sUdChEREf1L5TvxlicoWRdtbW2ULl0aPXr0wNmzZ4sizn+dpKQkTJ06FXXr1oWZmRn09PTg6OiI/v374/z588UdXraioqLQvXt3lC5dGjo6OjA3N0fFihXRu3dvrFq1qrjDIyIiIvpsaRV0QycnJ/Tp0wcAkJKSgnPnzuGPP/7Atm3bcPDgQTRp0uSTBflvc+bMGXTo0AFPnjxB1apV0a9fPxgYGCAmJgYbNmzAmjVrEBgYiMDAwOIOVUl4eDgGDhwILS0ttGnTBuXKlYNMJsONGzewe/duHDt2DP379y/uMImIiIg+SwVOvJ2dnREUFKRUNnPmTEyaNAk//PADjh49WtjY/pXu37+PVq1aITExEUuWLIGvr6/S+hs3bqBt27YICgqCpaUlhg0bVkyRKnvz5g1GjhwJY2NjREdHo0qVKkrr09PTERERUTzBEREREX0BPukY70GDBgEAzp07p1S+cuVKdOzYEQ4ODtDT00OJEiXg6emJI0eOqLQREREBmUyGoKAgnD17Fi1atICxsTFMTU3RuXNn3L17V+2+t2/fjrp160JfXx/W1tbw8fHBy5cvs401ISEBfn5+cHR0hK6uLqysrNCjRw9cuXJFpa63tzdkMhnu3LmDOXPmoHz58tDX10flypWxYcMGAMC7d+/w/fffK/pYrVo17NmzR6WtgIAAvHjxApMmTVJJugGgQoUK2L59O7S1tTFp0iQkJSUBANasWQOZTIaQkBC1/Tl//jxkMhm++eYbpfJnz55h9OjRcHZ2hq6uLkqWLImuXbuq7ad8PHxiYiK+++472NnZQUtLC+Hh4bhy5Qpev34NDw8PlaQbALS1tdGiRQulsqSkJMyaNQtubm4oVaoUdHR0UKpUKfTr1w+xsbEqbQQFBUEmkyEiIgJhYWFwcXGBvr4+HB0dsWDBAgCAEAJz585FhQoVoKenh3LlymH16tUqbWV9zn766SeUK1dOMZwnJCQE6enpao+jOq9fv0ZgYCCqVKkCfX19mJmZwdPTE5GRkSp13d3dIZPJkJqaismTJ8PJyQna2toqX1KJiIjoP0jkU1xcnAAgPD09VdY9e/ZMABCmpqZK5Xp6eqJ+/fpi0KBBYuLEiaJv377C2NhYaGhoiG3btinVPXLkiAAg2rRpI/T19UWbNm3EmDFjRNOmTQUA4eTkJN6+fau0zapVqwQAYWJiInx8fMS4ceNEpUqVRK1atYStra2wt7dXidPJyUkAEO7u7mLixInCy8tLaGpqCgMDA3H8+HGl+v379xcARMeOHYWNjY3w8fERvr6+wszMTMhkMrF3717Rtm1b4ejoKIYNGyYGDhwo9PT0hLa2trh9+7aineTkZKGtrS309PTEy5cvczzOXl5eAoBYvny5YltDQ0NRvnx5tfX9/PwEALFnzx5F2e3bt0WZMmUEANGyZUsxZswY0bdvX2FgYCAMDQ3FyZMnldqwt7cXNjY2ombNmqJcuXJi2LBhYuTIkWL37t3i9u3bAoBwcXERGRkZOcYud+LECaGjoyM8PT3FsGHDxLhx40T79u2FpqamKFGihLh7965S/cDAQMVxNjU1Ff369RMjR44UpUuXVhyLYcOGCWtrazFo0CDx7bffCnNzcwFAHD16VKkt+XPWvn17UaJECeHr6yvGjh0rKlSoIACIrl27KtWXv6779++vVP78+XNRpUoVAUC4uroKPz8/MXDgQGFhYSG0tLTEn3/+qVTfzc1N8fotXbq0GDRokBgzZowIDw/P9XglJSUJACIJEIILFy5cuHD5ry5fGMXf76SkXOvmu3c5Jd7Tp08XAETbtm2Vyu/cuaNS99GjR6JUqVKiXLlySuXyxBuA2LBhg9K6vn37CgBi/fr1irKkpCRhYmIiDA0NxY0bNxTl7969E02aNBEAVBLvAQMGCABi0qRJSuW7du0SAISzs7N4//69olyexJUvX148e/ZMUX7q1CkBQJiZmYnGjRuL5ORkxbqNGzcKAGLEiBGKsoiICCFP4HKzbNkyAUAMHDhQUdanTx8BQJw6dUqpbkZGhrC2thY2NjZKSXGjRo2Epqam2Lt3r1L9GzduCGNjY+Hi4qJUbm9vr3hu37x5o7QuMzNT1K5dWwAQjRs3FsuXLxd///13jkl4YmKieP78uUr54cOHhYaGhhg8eLBSuTzxLlGihIiNjVWU379/X+jo6AhTU1OV5+DkyZNCnmBnJX/OLC0txYMHDxTlaWlpitfF5s2bFeXZJd69e/cWWb8AyT19+lTY2dkJS0tLpS+C8sS7Ro0aavueVWpqqkhKSlIsDx48EEy8uXDhwoXLf375yOnTp0Xr1q2FqampMDAwEPXr1xcbN27M8W9sVvL8Jqfl2LFjStu8fftWhISEiEqVKgldXV1hZmYmWrVqJSIjI1XalyTxdnJyEoGBgSIwMFCMHTtWeHh4CADC2tpaXLt2LU9tjRgxQgBQOvMpT7ybNGmiUl++zt/fX1EmP9udNcGVO378uPg48U5LSxN6enrCwsJCpKSkqGzTokULlSdAnsStWrVKpX7ZsmWFujOuGRkZQltbW6kfGzZsEABEz549szki/2/Pnj0CgGjdurWibN++fWr7unv3bgFA+Pn5KcrOnz8vPk7cs/L39xcAxN9//60ok78wL126pHabuLg44erqqvRCNTAwEM2aNRNhYWF5PhMuhBAuLi7CwcFBqUyeeAcHB6vUl//ikd1z8NVXXymVyZ+zH3/8UaW+/HXRrl07pb59nHjHx8cLTU1N0bRpU7V9WLBggQAgdu7cqSiTJ97bt29X33E1/f14YeLNhQsXLlz+00sWhw8fFtra2sLY2Fj4+PgIf39/Rb4yZ86cXP/WCiHEvHnzFDlr1mX48OECgDA3N1c6ifb27VvRqFEjAUBUq1ZN+Pn5CW9vb2FsbCw0NTVVRmvkJ/Eu8MWVsbGxCA4OViqzsbHB8ePH4ezsrFR+584dzJgxA4cPH8bDhw+RlpamtP7Ro0ewt7dXKqtdu7bKPsuUKQMASExMVJRdunQJAPD111+r1G/YsCG0tJS7eP36daSmpsLDwwMGBgYq23h4eODAgQO4ePGiSps1atRQqW9ra4s7d+6orNPU1ISVlRUePXqksk1BNWvWDLa2ttiwYQN+/vlnRd9+//13AEDfvn0VdU+ePAkAePr0qdrxxdevX1f8W7VqVUW5np4eXFxc1O7fwcEBkZGRuHjxIg4ePIizZ88iKioKhw4dwqFDh7B69Wrs2bMHurq6im0iIiIwf/58nDp1CgkJCcjIyFCs09HRUbuf7I5zTutOnTqltq2cXhcXLlxQu43cmTNn8P79e6Slpak9hrdu3QLw4Ri2a9dOaV29evVybBsAJk2aBH9/f8XjV69ewc7OLtftiIiI/gsyMjLg4+MDDQ0NHDt2TJEDTJkyBfXq1UNAQAC6deumkkN+zM/PT2353LlzAQB9+vSBnp6eonzRokWIjo5G9+7dsX79emhqagIAJk+ejFq1asHHxwdNmzaFsbFxvvtU4MTb09MTe/fuBQDEx8dj1apVmDBhAjp06IDTp0/DyMgIAHD79m3Uq1cPr169goeHB9q3bw8TExNoaGggIiICR48eVUnEAcDExEQ12P8lmu/fv1eUyS8+tLKyUqmvqakJCwsLpbJXr14BAKytrdX2S57gyevlNabs1mW9iM/GxgYA8ODBA7X7zkpeRx4P8KE/vXv3xty5c7Fv3z60bdsWycnJ2LZtGypXroxatWop6r548QIAsGvXLuzatSvb/aSkpCg9trKygkwmyzG2GjVqKCXAERER6NOnD44cOYLFixdj9OjRAIA//vgDXl5eMDIygqenJxwcHGBgYACZTIbw8HDcu3dPbfsFOc5ZE/qs1D3P8teF/LWTHfkxjIqKQlRUVLb1Pj6G2e33Y7q6ukpfUoiIiOj/HT58GLGxsRgwYIBS3mFqaoqAgAB4e3tj1apVmDJlSoHaDw0NBfD/k4PIbd++HcCHSR/kSTfwYSrtgQMHYv78+di8eTMGDBiQ731+kllNLC0tMXbsWAQEBCAmJgaTJ09WrJs3bx5evnyJ8PBwHDhwAPPnz0dISAiCgoJQsWLFQu/b1NQUwIfZOz72/v17PH/+XKlMnrg9ffpUbXtPnjxRqvcp1alTB9ra2jh37lyuSd+hQ4cAfDg7m5X8rLb8LPeWLVvw5s0bpbPdwP/Hv3DhQgghsl0+nnc7t6RbHXd3d0ydOhXAhzeJXFBQEPT09BRzvM+ePRvBwcGKcimoe57lrwv5ayc78mM4ZsyYHI+huvnWC3IciYiI6P/Jpylu2bKlyjpPT08AKPD01dHR0YiJiUGdOnVQvXp1pXXyXNDR0VFlO3lZ1nwnPz7pdIIBAQEoVaoUFi9erJj2Tz5tXMeOHZXqCiFyPIuYV/KDdfz4cZV1J06cUDkTWrFiRejp6eHMmTN48+aNyjbyJ1ndkIbCMjQ0RPfu3ZGamqr4eUOdmJgY/PnnnzA2Nka3bt2U1lWvXh0uLi7Yvn07Xr9+jd9//13tNIL169cH8OEYSEH+C0dWsbGxqFSpEsqVK6dU/vjxY9y5c0eSuHJ6XdSsWTPHbevWrQuZTCbZMSQiIqL/Jx/S+XEeAXwYRWBkZKSok1/ys92DBw9WWVeyZEkAQFxcnMo6ednNmzcLtN9Pmnjr6+tjwoQJSE9PV5wBlY+7+XjO45kzZ6qdSzq/OnbsCBMTE6xcuVLpIKSnpyudeZfT0dFBr169kJCQgBkzZiit27t3L/bt2wdnZ2e4uroWOjZ1pk+fDnNzc0yfPh0rVqxQWX/r1i107NgR7969w8yZM2FmZqZSp2/fvnj79i0WLFiAw4cPw83NTWVscL169VC/fn2sX78eGzduVGkjMzMzX98S4+LisGjRIrx+/Vpl3Zs3b/DLL78AABo3bqwot7e3x+3bt5XOOqempuLbb7/N1zzahfHLL7/gn3/+UTyWz7cOfJjrOyc2Njbo0aMHoqOjMXv2bAghVOqcOnVK7Rc4IiIiKhz56IDsfqE2MTHJdQSBOsnJydi0aRMMDAzQq1cvlfWtW7cGAISEhCgNb46Li0NYWBgA5esN86PAY7yzM2TIEMyaNQurV69GQEAAfH19ERYWhq5du6JHjx6wsLDAyZMncf78ebRt2zbH8cd5YWpqigULFsDb2xt169ZFz549YWpqir/++gv6+vpKY6TlZs2ahaNHj+LHH39EdHQ06tevj7t37+KPP/6AgYEBwsLCoKHxSb+TKNjb22P37t3o2LEjfHx8sHDhQri7uytuGb9nzx6kp6cjKCgo27tW9u7dGxMnTkRwcDAyMzNVhpnIrV+/Hh4eHujZsyfmz5+PWrVqQV9fH/fv38eJEycQHx+P1NTUPMWdlJSEESNGYNy4cWjcuDGqVq0KfX19PHz4ELt27cLz589Ru3ZtjBgxQrHNiBEjMGLECNSsWRPdunVDRkYGDhw4ACEEqlevrrgwtig1aNAA1atXh5eXFwwNDbFz507cuHEDXbp0QdeuXXPdfvHixbhx4wbGjx+PNWvWoGHDhjAzM8ODBw9w9uxZ3Lp1C48fP1Z7oS4RERF9fjZu3Ijk5GT0799f7dDi0aNHY+PGjdi4cSOuX7+Opk2bIjExEVu2bIGDgwMuX75c8DwxT/OwZJHTPN5yCxcuFABE3759hRAfpgF0dXUVxsbGwszMTLRp00acO3dOMZ3akSNHFNvKpwwMDAzMdt8fz7UshBB//vmnqF27ttDV1RVWVlZi8ODB4sWLF8Le3l5lHm8hPkwVN3LkSGFvby+0tbVFyZIlRbdu3ZSm15OTT00XFxensk4+fZw62e1bCCFevHghgoKCRK1atYSJiYnQ0dERX331lejXr584e/as2m2yat68uQAg9PT0cpy+5sWLF2Ly5MmiatWqQl9fXxgZGYly5cqJ3r17i61bt+Y53tTUVLFlyxYxZMgQUb16dVGyZEmhqakpzM3NRePGjcXPP/+scmOjzMxM8dtvv4kqVaoIPT09YWNjIwYNGiSePXum9ripez3I5fc5kNePjY0VM2fOFM7OzkJHR0fY29uLoKAgkZaWplQ/p9fWmzdvxE8//SRq164tDA0Nhb6+vnB0dBSdOnUSq1evFunp6TnGkle8gQ4XLly4cOECxd/Fbt26CQDZ5kVGRkbCzs4u339vGzZsKACo3DAxq5cvX4rRo0cLBwcHoa2tLUqXLi3Gjx8vjh07JgDlaa+LdB5voi9BTon654qJNxcuXLhw4QLF38VJkyYJQPnGiXKPHz8WALK910Z2rl69KgCIihUrFuhvdVhYmACU7ymTn8S7aMZTEBEREREVgpubGwBg//79Kuv27dunVCevsptCMK/Wrl0LAOjZs2eBtmfiTURERESfnWbNmqFs2bJYt24dLl68qChPSkrC9OnToaOjg379+inKHz9+jOvXr2d7wWV6ejrWrFkDbW1tpe3UUXc/l3nz5uHgwYPo3Lkz6tatW6A+ffKLK4mIiIiICktLSwsrVqyAp6cnmjRpgp49e8LY2BhbtmzBvXv3MGfOHDg4OCjqT5o0CatWrUJYWJjamct27NiB+Ph4dOnSRe2NF7MqXbo0PDw8UK5cOchkMkRERODcuXOoU6eO4qx5QfCMN/0rhYeHQwih9IYkIiKiL4uHhwciIyPh6uqKjRs3YsmSJbC2tsaGDRswZsyYfLWV09zdH+vTpw9u3ryJ3377Db/99hsyMzMxe/ZsREZGwtzcvEB9AQCZEEIUeGsi+mRevXoFU1NTJAH49PdNJSIi+kJ8Yamp4u93UlKudz7nGW8iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCWgVdwBE9JGkJMDEpLijICIiKjopKYCR0Yf/JycDhobFG49EeMabiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCWsUdABEpcz52GRqGRsUdBhERUZExePsWd4o7iGLAM95ERERERBJg4k1EREREJAEm3kREREREEmDiTUREREQkASbeREREREQSYOJNRERERCQBJt5ERERERBJg4k1EREREJAEm3kREREREEmDiTUREREQkASbeREREREQSYOJNRERERCQBJt5ERERERBJg4k3/auHh4ZDJZAgPD1cqd3BwgIODQ7HERERERP9NTLy/AHfv3oVMJoNMJoOnp6faOidPnoRMJoO3t7e0wRERERFRnmgVdwCUP/v378fhw4fRtGnT4g7li3bo0KHiDoGIiIj+Y3jG+wvi4OAADQ0NTJgwAUKI4g7ni+bk5AQnJ6fiDoOIiIj+Q5h4f0EqVKiAvn374uzZs9i0aVOetrl37x4GDRqE0qVLQ0dHB2XKlMGgQYNw//59lbqPHz/GqFGjUK5cOejr68PMzAyVKlWCr68vkpKSFPVu3ryJ8ePHo1atWrCwsICenh7Kly+PiRMnIjk5WaVdd3d3yGQypKWlISAgAF999RX09fVRu3ZtHDx4EACQlJSE4cOHo1SpUtDT00PDhg1x+vRplbbkY7MTExMxdOhQ2NjYQE9PDzVr1sT69evzeijVjvEOCgqCTCZDREQE1q1bhxo1akBfXx+2trYYNWoU3r59q9JORkYGZsyYAScnJ+jp6cHZ2RkzZszAnTt3OPSHiIiIlHCoyRcmJCQEGzZswOTJk9GlSxdoa2tnW/fmzZto3Lgx4uPj0b59e1SpUgVXrlzBypUrsXPnTkRGRqJ8+fIAgDdv3sDV1RV3795Fy5Yt0blzZ7x79w5xcXFYs2YNxo4dC1NTUwDA1q1bERoaCg8PD7i7uyMzMxMnT57ErFmzcPToURw7dkxtXF5eXvj777/RoUMHvH37FmvXrkW7du0QFRWFIUOG4N27d+jevTvi4+OxceNGtGrVCnFxcYr9yr179w7NmzdHcnIy+vbti5SUFGzatAm9e/dGQkICRowYUahjvGjRIuzduxcdO3ZE06ZNsXfvXixYsAAJCQlYu3atUt2BAwdizZo1KFu2LIYPH460tDTMmzcPJ06cKFQMRERE9O/DxPsL89VXX2HEiBGYM2cOli5diu+++y7bur6+voiPj8fSpUsxZMgQRfnixYsxfPhwfPvtt4qxzocOHUJcXBz8/Pwwb948pXaSk5OVEum+ffvC398fOjo6SvVCQkIQGBiITZs24ZtvvlGJ5/nz57h8+TIMDQ0BAJ6envDy8kLz5s3RokULrFu3DlpaH16SNWrUwIQJExAaGgp/f3+ldh4/foxy5cohOjpaEUNAQABq1qyJcePGoUuXLihdunSuxzI7Bw8exLlz51ChQgUAwLRp01CjRg1s2LABs2fPRqlSpRTHbM2aNahRowaioqJgYGAAAPj+++9Rs2bNXPeTlpaGtLQ0xeNXr14VOGYiIqIv3ZkzZxAYGIjo6Gikp6fDxcUF/v7+6NGjR562d3BwwL1793Ksc+zYMXz99deKxxkZGVi9ejWWL1+O27dvIzU1FXZ2dmjXrh38/f1hY2NTqD59jENNvkABAQEwMzPD1KlT1Q7tAID79+/jyJEjqFy5Mnx8fJTW+fr6omLFijh8+DAePHigtE5fX1+lLSMjI+jq6ioey4etfEz+JUA+fORj06ZNUyTdANCtWzdoa2sjMTERc+bMUSTdANCrVy8AwKVLl9S2NX36dKUYypQpg1GjRiEtLQ0bNmxQu01ejRo1SpF0Ax+OSa9evZCZmYlz584pyn///XcAwJQpUxRJNwDF0JTczJgxA6amporFzs6uUHETERF9qY4cOQJXV1dERkaiR48e8PX1xZMnT+Dl5YW5c+fmqQ0/Pz8EBgaqLMOHDwcAmJubo27dukrbeHl5YdCgQUhISEDPnj0xdOhQlChRArNnz0atWrXw5MmTT9pPnvH+Apmbm2PixImYOHEi5syZg6CgIJU6Fy9eBAC4ublBJpMprdPQ0ECTJk1w/fp1XLx4EXZ2dmjSpAlsbW0xc+ZMXLp0Ce3atYObmxsqVaqksr0QAmFhYQgPD8eVK1eQlJSEzMxMxfpHjx6pjbtGjRoqcVhZWeHNmzf46quvlNbZ2tpm25aWlhYaNmyoUi7/BnvhwgW1+8+r2rVrq5SVKVMGAJCYmKgok38paNy4sUp9V1fXXPczadIkpbP5r169YvJNRET/ORkZGfDx8YGGhgaOHTumyBemTJmCevXqISAgAN26dYO9vX2O7fj5+aktlyfuffr0gZ6enqL89OnT2Lp1K+rVq4fIyEilX/dHjRqFBQsWYNmyZZgyZUrhOpgFz3h/oUaOHIkyZcpg7ty5ePbsmcp6+bAFa2trtdvLE1t5PVNTU5w8eRL9+vXDyZMnMWzYMFSpUgX29vZYvHixyr4HDRqEu3fvokOHDhg/frziWyUApeETWZmYmKiUaWlpZVsOAOnp6SrrSpYsCQ0N1ZeuvK9ZLwQtiJzief/+vaLs1atX0NDQQMmSJbONJSe6urowMTFRWoiIiP5rDh89itjYWPTu3VvpJJ2pqSkCAgLw7t07rFq1qsDth4aGAgAGDRqkVH7nzh0AQPPmzVWuTWvXrh0AID4+vsD7VYeJ9xdKX18fwcHBSE5ORnBwsMp6eRL39OlTtdvLfzrJmux99dVXCA8PR3x8PC5cuIBZs2YhMzMTw4cPV8wY8uzZM/z666+oVq0arl+/jvDwcMyYMQNBQUHw9fX91N1UKyEhQekMu5y8rx9fjFlUTExMkJmZiYSEhGxjISIiopxFHD8OAGjZsqXKOvmNA48ePVqgtqOjoxETE4M6deqgevXqSuuqVKkC4MMQ2Y9P9P31118AgGbNmhVov9lh4v0F69+/P6pUqaK4ICAr+TfGY8eOqcz5LYTAsWPHlOplpaGhgRo1amD8+PGKhHvHjh0APnw7FEKgefPmSuOaAeD4/944RS0jI0PtrCHy/eflwsZPQf4GjoqKUlkXHR0tSQxERERfulv/y2HKlSunss7GxgZGRka4detWgdqWn+0ePHiwyjoXFxeMGjUKp0+fRuXKlTFy5EiMGzcOjRs3xrJlyxAcHIxOnToVaL/ZYeL9BdPU1MT06dORnp6uMs77q6++goeHB65evYqVK1cqrVu2bBliYmLQtGlTxZjiq1evqj1LKy+Tj4mSj6+Kjo5WOuv8zz//YNKkSZ+sb7mR//SUdf+//PILdHV10bNnT0likM/cEhISojTH95MnT/DLL79IEgMREdGXLinLsFd1TExMCjSMNDk5GZs2bYKBgYFi0oaPzZ8/H/Pnz8e9e/ewcOFCzJkzB1FRUWjRogW6dOmS733mhhdXfuE6dOiAxo0bIzIyUmXdkiVL0LhxY/j4+GDnzp2oXLkyrl69ih07dsDS0hJLlixR1D1w4ADGjRsHV1dXlC9fHhYWFrhz5w527NgBPT09xRXBtra26Nq1K7Zs2YI6deqgWbNmePr0Kf766y80a9YMsbGxRd5nW1tbpKSkoFq1amjfvr1iHu/nz59jwYIFhZpKMD+aN2+O3r17Y926dXBxcUGnTp2QlpaGTZs2oX79+ti5c6fasehERERU9DZu3Ijk5GT0799f7XVUmZmZ8PX1xfr167Fw4UJ07NgRBgYGiIqKwsiRI9GgQQMcOXJEZSaUwmBW8C8wa9YsteUVKlTA2bNn4e3tjdOnT2P27Nk4c+YMBgwYgDNnzihungN8GEM1fPhwvHr1Clu3bsW8efNw9uxZeHl54dy5c6hTp46ibnh4OMaMGYOXL19i4cKFOHnyJPz9/bFu3boi7ysA6Ojo4MCBA3Bzc8OaNWuwcuVKlClTBuvWrSv0zXPya9WqVZg6dSoyMzOxcOFC7N69G35+fpg8eTIA9RdqEhER0f8z/d/fyuzOar969apA12/lNMwEAFauXInly5dj2rRpirthm5iYoHXr1ti8eTNSUlIQEBCQ7/3mRCY+HgBM9BmT3+b97t27xRpHblasWAEfHx8sXrwY3377bZ62kX+wWO48Dg1DoyKOkIiIqPgYvH2LO20bAQACxo7FjDlzsH79epXhok+ePIGtrS2aNm2quOlfXly7dg1VqlRBxYoVERMTo7ZO165dsXXrVly+fBkuLi4q60uUKAENDQ21kyhkJf/7nZSUlOsJN57xJiqEJ0+eqFy8+vDhQ/z444/Q1NRUTEdERERE6rn9734Y+/fvV1m3b9++D3Xc3PLVZnZTCGYlv1ZM3ZSBaWlpeP36tdINBD8FJt5EhTBz5kyUL18eAwcOxMSJE9G7d29UqlQJ9+7dww8//MAb4hAREeWimbs7ypYti3Xr1iluAAh8GHoiv1N1v379FOWPHz/G9evXsx2akp6ejjVr1kBbW1tpu4/Jb3Y3ffp0lXuQBAUFISMjAx4eHoXomSpeXElUCK1atcK1a9ewa9cuvHz5Enp6eqhWrRqGDRuG3r17F3d4REREnz0tLS2sWLECnp6eaNKkCXr27AljY2Ns2bIF9+7dw5w5cxRDTYEPd35etWoVwsLC4O3trdLejh07EB8fjy5dusDKyirb/Q4bNgyrVq3CoUOHULFiRbRq1Qr6+vqIiorC6dOnYWlpiZCQkE/b10/aGlER+9zGdrdq1QqtWrUq7jCIiIi+aB4eHoiMjERgYCA2btyI9PR0uLi4YNasWfDy8spXW7ldVClnYmKCkydPYtasWdi+fTvCw8Px/v17lClTBr6+vvj+++9RpkyZAvdJHV5cSfSZ4MWVRET0X5H14kokJwOGhsUbUCHw4koiIiIios8ME28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikoBWcQdARMpuN6kGExOT4g6DiIio6KSkFHcExYJnvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkoFXcARDRB0IIAMCrV6+KORIiIqIilpLy//9/9Qp4/774Yikk+d9t+d/xnDDxJvpMPH/+HABgZ2dXzJEQERFJqFSp4o7gk3j9+jVMTU1zrMPEm+gzUaJECQDA/fv3c33jfqlevXoFOzs7PHjwACYmJsUdTpH5L/STffx3YB//HdjH4iWEwOvXr1EqD18gmHgTfSY0ND5ccmFqavrZfah8aiYmJv/6PgL/jX6yj/8O7OO/A/tYfPJ6wowXVxIRERERSYCJNxERERGRBJh4E30mdHV1ERgYCF1d3eIOpcj8F/oI/Df6yT7+O7CP/w7s45dDJvIy9wkRERERERUKz3gTEREREUmAiTcRERERkQSYeBMRERERSYCJNxERERGRBJh4E0no119/hYODA/T09FC/fn2cPn06x/p//PEHKlasCD09Pbi4uGD37t0SRVpw+enj1atX0bVrVzg4OEAmk2H+/PnSBVoI+enj8uXL8fXXX8Pc3Bzm5uZo3rx5rs/75yI//dy6dSvq1KkDMzMzGBoaokaNGlizZo2E0RZMft+Tchs2bIBMJkOnTp2KNsBPID99DA8Ph0wmU1r09PQkjLZg8vs8JiYmYvjw4bC1tYWuri7Kly//2X++5qeP7u7uKs+jTCZD27ZtJYw4//L7PM6fPx8VKlSAvr4+7OzsMHr0aKSmpkoUbQEJIpLEhg0bhI6Ojli5cqW4evWq8PHxEWZmZuLp06dq60dFRQlNTU3x008/iWvXronJkycLbW1t8ffff0sced7lt4+nT58WY8eOFevXrxc2NjZi3rx50gZcAPntY+/evcWvv/4qLly4IGJiYoS3t7cwNTUV//zzj8SR509++3nkyBGxdetWce3aNXH79m0xf/58oampKfbu3Stx5HmX3z7KxcXFidKlS4uvv/5adOzYUZpgCyi/fQwLCxMmJibi8ePHiuXJkycSR50/+e1jWlqaqFOnjmjTpo2IjIwUcXFxIiIiQly8eFHiyPMuv318/vy50nN45coVoampKcLCwqQNPB/y28e1a9cKXV1dsXbtWhEXFyf27dsnbG1txejRoyWOPH+YeBNJpF69emL48OGKx+/fvxelSpUSM2bMUFu/R48eom3btkpl9evXF0OHDi3SOAsjv33Myt7e/otIvAvTRyGEyMjIEMbGxmLVqlVFFeInUdh+CiFEzZo1xeTJk4sivE+iIH3MyMgQjRo1EitWrBD9+/f/7BPv/PYxLCxMmJqaShTdp5HfPi5ZskSULVtWvHv3TqoQC62w78d58+YJY2NjkZycXFQhFlp++zh8+HDRtGlTpTJ/f3/h6upapHEWFoeaEEng3bt3OHfuHJo3b64o09DQQPPmzXHixAm125w4cUKpPgB4enpmW7+4FaSPX5pP0cc3b94gPT0dJUqUKKowC62w/RRC4NChQ7hx4waaNGlSlKEWWEH7GBISAisrKwwaNEiKMAuloH1MTk6Gvb097Ozs0LFjR1y9elWKcAukIH3csWMHGjZsiOHDh8Pa2hpVq1bF9OnT8f79e6nCzpdP8bkTGhqKnj17wtDQsKjCLJSC9LFRo0Y4d+6cYjjKnTt3sHv3brRp00aSmAtKq7gDIPovSEhIwPv372Ftba1Ubm1tjevXr6vd5smTJ2rrP3nypMjiLIyC9PFL8yn6OGHCBJQqVUrlS9XnpKD9TEpKQunSpZGWlgZNTU0sXrwYLVq0KOpwC6QgfYyMjERoaCguXrwoQYSFV5A+VqhQAStXrkS1atWQlJSEOXPmoFGjRrh69SrKlCkjRdj5UpA+3rlzB4cPH8Y333yD3bt34/bt2xg2bBjS09MRGBgoRdj5UtjPndOnT+PKlSsIDQ0tqhALrSB97N27NxISEtC4cWMIIZCRkQFfX18EBARIEXKBMfEmIpLIzJkzsWHDBkRERHwRF6zll7GxMS5evIjk5GQcOnQI/v7+KFu2LNzd3Ys7tEJ7/fo1+vbti+XLl6NkyZLFHU6RadiwIRo2bKh43KhRI1SqVAlLly7F1KlTizGyTyczMxNWVlZYtmwZNDU1Ubt2bTx8+BCzZ8/+LBPvwgoNDYWLiwvq1atX3KF8UhEREZg+fToWL16M+vXr4/bt2xg1ahSmTp2KH374objDyxYTbyIJlCxZEpqamnj69KlS+dOnT2FjY6N2Gxsbm3zVL24F6eOXpjB9nDNnDmbOnImDBw+iWrVqRRlmoRW0nxoaGnB2dgYA1KhRAzExMZgxY8ZnmXjnt4+xsbG4e/cu2rdvryjLzMwEAGhpaeHGjRtwcnIq2qDz6VO8J7W1tVGzZk3cvn27KEIstIL00dbWFtra2tDU1FSUVapUCU+ePMG7d++go6NTpDHnV2Gex5SUFGzYsAEhISFFGWKhFaSPP/zwA/r27YvBgwcDAFxcXJCSkoIhQ4bg+++/h4bG5zma+vOMiuhfRkdHB7Vr18ahQ4cUZZmZmTh06JDS2aWsGjZsqFQfAA4cOJBt/eJWkD5+aQrax59++glTp07F3r17UadOHSlCLZRP9VxmZmYiLS2tKEIstPz2sWLFivj7779x8eJFxdKhQwd4eHjg4sWLsLOzkzL8PPkUz+P79+/x999/w9bWtqjCLJSC9NHV1RW3b99WfHECgJs3b8LW1vazS7qBwj2Pf/zxB9LS0tCnT5+iDrNQCtLHN2/eqCTX8i9TQoiiC7awivniTqL/jA0bNghdXV0RHh4url27JoYMGSLMzMwUU3X17dtXTJw4UVE/KipKaGlpiTlz5oiYmBgRGBj4RUwnmJ8+pqWliQsXLogLFy4IW1tbMXbsWHHhwgVx69at4upCrvLbx5kzZwodHR2xefNmpem9Xr9+XVxdyJP89nP69Oli//79IjY2Vly7dk3MmTNHaGlpieXLlxdXF3KV3z5+7EuY1SS/fQwODhb79u0TsbGx4ty5c6Jnz55CT09PXL16tbi6kKv89vH+/fvC2NhYfPfdd+LGjRvir7/+ElZWVuLHH38sri7kqqCv1caNGwsvLy+pwy2Q/PYxMDBQGBsbi/Xr14s7d+6I/fv3CycnJ9GjR4/i6kKeMPEmktDChQvFV199JXR0dES9evXEyZMnFevc3NxE//79lepv2rRJlC9fXujo6IgqVaqIXbt2SRxx/uWnj3FxcQKAyuLm5iZ94PmQnz7a29ur7WNgYKD0gedTfvr5/fffC2dnZ6GnpyfMzc1Fw4YNxYYNG4oh6vzJ73syqy8h8RYif3308/NT1LW2thZt2rQR58+fL4ao8ye/z2N0dLSoX7++0NXVFWXLlhXTpk0TGRkZEkedP/nt4/Xr1wUAsX//fokjLbj89DE9PV0EBQUJJycnoaenJ+zs7MSwYcPEy5cvpQ88H2RCfM7n44mIiIiI/h04xpuIiIiISAJMvImIiIiIJMDEm4iIiIhIAky8iYiIiIgkwMSbiIiIiEgCTLyJiIiIiCTAxJuIiIiISAJMvImIiP7lwsPDYWZm9snrElH+MPEmIqJideLECWhqaqJt27bFHYokZDKZYjE1NYWrqysOHz5cpPv08vLCzZs3P3ldIsofJt5ERFSsQkNDMWLECBw7dgyPHj0q0n0JIZCRkVGk+8iLsLAwPH78GFFRUShZsiTatWuHO3fuqK2bnp5e6P3p6+vDysrqk9clovxh4k1ERMUmOTkZGzduxLfffou2bdsiPDxcsa53797w8vJSqp+eno6SJUti9erVAIDMzEzMmDEDjo6O0NfXR/Xq1bF582ZF/YiICMhkMuzZswe1a9eGrq4uIiMjERsbi44dO8La2hpGRkaoW7cuDh48qLSvx48fo23bttDX14ejoyPWrVsHBwcHzJ8/X1EnMTERgwcPhqWlJUxMTNC0aVNcunQp136bmZnBxsYGVatWxZIlS/D27VscOHAAwIcz4kuWLEGHDh1gaGiIadOmAQC2b9+OWrVqQU9PD2XLlkVwcLDSl4jExEQMHToU1tbW0NPTQ9WqVfHXX38BUB0+cunSJXh4eMDY2BgmJiaoXbs2zp49q7YuACxZsgROTk7Q0dFBhQoVsGbNGqX1MpkMK1asQOfOnWFgYIBy5cphx44duR4Hov8aJt5ERFRsNm3ahIoVK6JChQro06cPVq5cCSEEAOCbb77Bzp07kZycrKi/b98+vHnzBp07dwYAzJgxA6tXr8Zvv/2Gq1evYvTo0ejTpw+OHj2qtJ+JEydi5syZiImJQbVq1ZCcnIw2bdrg0KFDuHDhAlq1aoX27dvj/v37im369euHR48eISIiAlu2bMGyZcvw7NkzpXa7d++OZ8+eYc+ePTh37hxq1aqFZs2a4cWLF3k+Bvr6+gCAd+/eKcqCgoLQuXNn/P333xg4cCCOHz+Ofv36YdSoUbh27RqWLl2K8PBwRVKemZmJ1q1bIyoqCr///juuXbuGmTNnQlNTU+0+v/nmG5QpUwZnzpzBuXPnMHHiRGhra6ut++eff2LUqFEYM2YMrly5gqFDh2LAgAE4cuSIUr3g4GD06NEDly9fRps2bfDNN9/k6zgQ/ScIIiKiYtKoUSMxf/58IYQQ6enpomTJkuLIkSNKj1evXq2o36tXL+Hl5SWEECI1NVUYGBiI6OhopTYHDRokevXqJYQQ4siRIwKA2LZtW66xVKlSRSxcuFAIIURMTIwAIM6cOaNYf+vWLQFAzJs3TwghxPHjx4WJiYlITU1VasfJyUksXbo02/0AEH/++acQQoiUlBQxbNgwoampKS5duqRY7+fnp7RNs2bNxPTp05XK1qxZI2xtbYUQQuzbt09oaGiIGzduqN1nWFiYMDU1VTw2NjYW4eHhearbqFEj4ePjo1Sne/fuok2bNkp9mjx5suJxcnKyACD27Nmjdh9E/1VaxZjzExHRf9iNGzdw+vRp/PnnnwAALS0teHl5ITQ0FO7u7tDS0kKPHj2wdu1a9O3bFykpKdi+fTs2bNgAALh9+zbevHmDFi1aKLX77t071KxZU6msTp06So+Tk5MRFBSEXbt24fHjx8jIyMDbt28VZ7xv3LgBLS0t1KpVS7GNs7MzzM3NFY8vXbqE5ORkWFhYKLX99u1bxMbG5tj3Xr16QVNTE2/fvoWlpSVCQ0NRrVq1bOO9dOkSoqKiFGe4AeD9+/dITU3FmzdvcPHiRZQpUwbly5fPcb9y/v7+GDx4MNasWYPmzZuje/fucHJyUls3JiYGQ4YMUSpzdXXFL7/8olSWNX5DQ0OYmJio/EJA9F/HxJuIiIpFaGgoMjIyUKpUKUWZEAK6urpYtGgRTE1N8c0338DNzQ3Pnj3DgQMHoK+vj1atWgGAYgjKrl27ULp0aaW2dXV1lR4bGhoqPR47diwOHDiAOXPmwNnZGfr6+ujWrZvScI/cJCcnw9bWFhERESrrcpuOb968eWjevDlMTU1haWmpsv7jeJOTkxEcHIwuXbqo1NXT01MMV8mroKAg9O7dG7t27cKePXsQGBiIDRs2KIbwFMTHQ1VkMhkyMzML3B7RvxETbyIiklxGRgZWr16NuXPnomXLlkrrOnXqhPXr18PX1xeNGjWCnZ0dNm7ciD179qB79+6KBK9y5crQ1dXF/fv34ebmlq/9R0VFwdvbW5FoJicn4+7du4r1FSpUQEZGBi5cuIDatWsD+HCG/eXLl4o6tWrVwpMnT6ClpQUHB4d87d/GxgbOzs55rl+rVi3cuHEj222qVauGf/75Bzdv3szzWe/y5cujfPnyGD16NHr16oWwsDC1iXelSpUQFRWF/v37K8qioqJQuXLlPMdPRB8w8SYiIsn99ddfePnyJQYNGgRTU1OldV27dkVoaCh8fX0BfJjd5LfffsPNmzeVLugzNjbG2LFjMXr0aGRmZqJx48ZISkpCVFQUTExMlBLFj5UrVw5bt25F+/btIZPJ8MMPPyidna1YsSKaN2+OIUOGYMmSJdDW1saYMWOgr68PmUwGAGjevDkaNmyITp064aeffkL58uXx6NEj7Nq1C507d1YZLlIYU6ZMQbt27fDVV1+hW7du0NDQwKVLl3DlyhX8+OOPcHNzQ5MmTdC1a1f8/PPPcHZ2xvXr1yGTyRS/EMi9ffsW48aNQ7du3eDo6Ih//vkHZ86cQdeuXdXue9y4cejRowdq1qyJ5s2bY+fOndi6davKLDBElDvOakJERJILDQ1VDLX4WNeuXXH27FlcvnwZwIcZOK5du4bSpUvD1dVVqe7UqVPxww8/YMaMGahUqRJatWqFXbt2wdHRMcf9//zzzzA3N0ejRo3Qvn17eHp6Ko3nBoDVq1fD2toaTZo0QefOneHj4wNjY2Po6ekB+DCUYvfu3WjSpAkGDBiA8uXLo2fPnrh37x6sra0Lc3hUeHp64q+//sL+/ftRt25dNGjQAPPmzYO9vb2izpYtW1C3bl306tULlStXxvjx4/H+/XuVtjQ1NfH8+XP069cP5cuXR48ePdC6dWsEBwer3XenTp3wyy+/YM6cOahSpQqWLl2KsLAwuLu7f9I+Ev0XyIT437xNRERElK1//vkHdnZ2OHjwIJo1a1bc4RDRF4iJNxERkRqHDx9GcnIyXFxc8PjxY4wfPx4PHz7EzZs3s53zmogoJxzjTUREpEZ6ejoCAgJw584dGBsbo1GjRli7di2TbiIqMJ7xJiIiIiKSAC+uJCIiIiKSABNvIiIiIiIJMPEmIiIiIpIAE28iIiIiIgkw8SYiIiIikgATbyIiIiIiCTDxJiIiIiKSABNvIiIiIiIJMPEmIiIiIpLA/wHvVmC6M8awmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we plot the performance of the model in the\n",
    "for i, dataset in enumerate(all_datasets):\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    print(\n",
    "        \"Table \"\n",
    "        + str(i + 1)\n",
    "        + \": Random Forest average preicision scores on \"\n",
    "        + str((all_datasets[i][\"DESCR\"]))\n",
    "    )\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    pr_mean_s_rf = pd.Series(pr_mean_dict_rf[dataset[\"DESCR\"]]).round(2)\n",
    "    df = pd.DataFrame([pr_mean_s_rf], index=[\"rf\"]).T\n",
    "    print(df)\n",
    "\n",
    "\n",
    "for i, dataset in enumerate(all_datasets):\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    print(\n",
    "        \"Figure \"\n",
    "        + str(i + 1)\n",
    "        + \": Random Forest average preicision scores on \"\n",
    "        + str((all_datasets[i][\"DESCR\"]))\n",
    "    )\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    pr_mean_s_rf = pd.Series(pr_mean_dict_rf[dataset[\"DESCR\"]]).round(2)\n",
    "    df = pd.DataFrame([pr_mean_s_rf], index=[\"rf\"]).T\n",
    "\n",
    "    colors = []\n",
    "    colors.append(\"#17becf\")\n",
    "    for j in range(1, 5):\n",
    "        if df[\"rf\"][j] > df[\"rf\"][0]:\n",
    "            colors.append(\"r\")\n",
    "        else:\n",
    "            colors.append(\"#17becf\")\n",
    "\n",
    "    r = np.arange(5)  # plotting for 5 different overampling techniques\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(r, df[\"rf\"], align=\"center\", color=colors)\n",
    "    ax.set_xlabel(\"Average Precision\")\n",
    "    plt.axvline(df[\"rf\"][0], color=\"r\")\n",
    "    plt.yticks(\n",
    "        [r + 0.1 for r in range(5)],\n",
    "        [\"Nosampling\", \"RandomOverSampler\", \"SMOTE\", \"ADASYN\", \"Borderline-SMOTE \"],\n",
    "        fontsize=14,\n",
    "    )\n",
    "    maximum = df[\"rf\"].max()\n",
    "    plt.xlim(0, maximum + 0.10 * maximum)\n",
    "    plt.title(\"Results of Random Forest Classifier on %s\" % (all_datasets[i][\"DESCR\"]))\n",
    "    ax.bar_label(ax.containers[0], fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "8csnQInMwLdq",
    "outputId": "28764bad-a408-443f-b1e9-61e7a3897c6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "\n",
      "Table 1: XGBoost average preicision scores on synthetic data\n",
      "----------------------------------------------------------------\n",
      "\n",
      "                    xgb\n",
      "Nosampling         0.98\n",
      "RandomOverSampler  0.98\n",
      "SMOTE              0.98\n",
      "ADASYN             0.98\n",
      "Borderline-SMOTE   0.98\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Table 2: XGBoost average preicision scores on thyroid_sick\n",
      "----------------------------------------------------------------\n",
      "\n",
      "                    xgb\n",
      "Nosampling         0.96\n",
      "RandomOverSampler  0.97\n",
      "SMOTE              0.95\n",
      "ADASYN             0.95\n",
      "Borderline-SMOTE   0.96\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Figure 1: XGBoost average preicision scores on synthetic data\n",
      "----------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "\n",
      "Figure 2: XGBoost average preicision scores on thyroid_sick\n",
      "----------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we plot the performance of the model in the\n",
    "for i, dataset in enumerate(all_datasets):\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    print(\n",
    "        \"Table \"\n",
    "        + str(i + 1)\n",
    "        + \": XGBoost average preicision scores on \"\n",
    "        + str((all_datasets[i][\"DESCR\"]))\n",
    "    )\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    pr_mean_s_xgb = pd.Series(pr_mean_dict_xgb[dataset[\"DESCR\"]]).round(2)\n",
    "    df = pd.DataFrame([pr_mean_s_xgb], index=[\"xgb\"]).T\n",
    "    print(df)\n",
    "\n",
    "\n",
    "for i, dataset in enumerate(all_datasets):\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    print(\n",
    "        \"Figure \"\n",
    "        + str(i + 1)\n",
    "        + \": XGBoost average preicision scores on \"\n",
    "        + str((all_datasets[i][\"DESCR\"]))\n",
    "    )\n",
    "    print(\"----------------------------------------------------------------\\n\")\n",
    "    pr_mean_s_xgb = pd.Series(pr_mean_dict_xgb[dataset[\"DESCR\"]]).round(2)\n",
    "    df = pd.DataFrame([pr_mean_s_xgb], index=[\"xgb\"]).T\n",
    "\n",
    "    colors = []\n",
    "    colors.append(\"#17becf\")\n",
    "    for j in range(1, 5):\n",
    "        if df[\"xgb\"][j] > df[\"xgb\"][0]:\n",
    "            colors.append(\"r\")\n",
    "        else:\n",
    "            colors.append(\"#17becf\")\n",
    "\n",
    "    r = np.arange(5)  # plotting for 5 different overampling techniques\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(r, df[\"xgb\"], align=\"center\", color=colors)\n",
    "    ax.set_xlabel(\"Average Precision\")\n",
    "    plt.axvline(df[\"xgb\"][0], color=\"r\")\n",
    "    plt.yticks(\n",
    "        [r + 0.1 for r in range(5)],\n",
    "        [\"Nosampling\", \"RandomOverSampler\", \"SMOTE\", \"ADASYN\", \"Borderline-SMOTE \"],\n",
    "        fontsize=14,\n",
    "    )\n",
    "    maximum = df[\"xgb\"].max()\n",
    "    plt.xlim(0, maximum + 0.10 * maximum)\n",
    "    plt.title(\"Results of XGBoost Classifier on %s\" % (all_datasets[i][\"DESCR\"]))\n",
    "    ax.bar_label(ax.containers[0], fontsize=14, padding=2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "U-LfRhAiIQsN"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "provenance": []
  },
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3.9.8 64-bit",
   "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": "3.9.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}