{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3415114e-9577-4487-89eb-4931620ad9f0",
   "metadata": {},
   "source": [
    "# Predict Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f271eb45-1470-4764-8c2e-31374efa1fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n",
    "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
    "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n",
    "\n",
    "import pickle\n",
    "import warnings\n",
    "#import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fecb606-22e5-4dee-8efa-f8dff0832299",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae591854-3003-4c75-a0c7-5abf04246e81",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59dd4694-a812-4923-b995-a2ee86c74f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filesystem object\n",
    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "017f7e9a-3ba0-40fa-bdc8-51b98cc1fdb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_test():\n",
    "    BUCKET = \"projet-bdc2324-team1/Generalization/musique\"\n",
    "    File_path_train = BUCKET + \"/Train_set.csv\"\n",
    "    File_path_test = BUCKET + \"/Test_set.csv\"\n",
    "    \n",
    "    with fs.open( File_path_train, mode=\"rb\") as file_in:\n",
    "        dataset_train = pd.read_csv(file_in, sep=\",\")\n",
    "        # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n",
    "\n",
    "    with fs.open(File_path_test, mode=\"rb\") as file_in:\n",
    "        dataset_test = pd.read_csv(file_in, sep=\",\")\n",
    "        # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n",
    "    \n",
    "    return dataset_train, dataset_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c479b230-b4bd-4cfb-b76b-d9faf6d95772",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_train, dataset_test = load_train_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c24c446d-4e1c-4ac1-a048-f0b8d8559f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                    0\n",
       "nb_tickets                     0\n",
       "nb_purchases                   0\n",
       "total_amount                   0\n",
       "nb_suppliers                   0\n",
       "vente_internet_max             0\n",
       "purchase_date_min              0\n",
       "purchase_date_max              0\n",
       "time_between_purchase          0\n",
       "nb_tickets_internet            0\n",
       "street_id                      0\n",
       "structure_id              327067\n",
       "mcp_contact_id            135224\n",
       "fidelity                       0\n",
       "tenant_id                      0\n",
       "is_partner                     0\n",
       "deleted_at                354365\n",
       "gender                         0\n",
       "is_email_true                  0\n",
       "opt_in                         0\n",
       "last_buying_date          119201\n",
       "max_price                 119201\n",
       "ticket_sum                     0\n",
       "average_price             115193\n",
       "average_purchase_delay    119203\n",
       "average_price_basket      119203\n",
       "average_ticket_basket     119203\n",
       "total_price                 4008\n",
       "purchase_count                 0\n",
       "first_buying_date         119201\n",
       "country                    56856\n",
       "gender_label                   0\n",
       "gender_female                  0\n",
       "gender_male                    0\n",
       "gender_other                   0\n",
       "country_fr                 56856\n",
       "nb_campaigns                   0\n",
       "nb_campaigns_opened            0\n",
       "time_to_open              224310\n",
       "y_has_purchased                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "825d14a3-6967-4733-bfd4-64bf61c2bd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_target_split(dataset_train, dataset_test):\n",
    "    features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n",
    "            'time_between_purchase', 'nb_tickets_internet', 'fidelity',  'is_email_true', 'opt_in', #'is_partner',\n",
    "            'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n",
    "    X_train = dataset_train[features_l]\n",
    "    y_train = dataset_train[['y_has_purchased']]\n",
    "\n",
    "    X_test = dataset_test[features_l]\n",
    "    y_test = dataset_test[['y_has_purchased']]\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69eaec12-b30f-4d30-a461-ea520d5cbf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d039f31d-0093-46c6-9743-ddec1381f758",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape train :  (354365, 17)\n",
      "Shape test :  (151874, 17)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape train : \", X_train.shape)\n",
    "print(\"Shape test : \", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d6de94-4e11-481a-a0ce-412bf29f692c",
   "metadata": {},
   "source": [
    "### Prepare preprocessing and Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b808da43-c444-4e94-995a-7ec6ccd01e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 0.5481283836040216, 1.0: 5.694439980716696}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Weights\n",
    "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n",
    "                                            y = y_train['y_has_purchased'])\n",
    "\n",
    "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n",
    "weight_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b32a79ea-907f-4dfc-9832-6c74bef3200c",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n",
    "            'time_between_purchase', 'nb_tickets_internet', 'fidelity',  'is_email_true', 'opt_in', #'is_partner',\n",
    "            'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    #(\"imputer\", SimpleImputer(strategy=\"mean\")),  \n",
    "    (\"scaler\", StandardScaler()) \n",
    "])\n",
    "\n",
    "categorical_features = ['opt_in']  \n",
    "\n",
    "# Transformer for the categorical features\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")),  # Impute missing values with the most frequent\n",
    "    (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
    "])\n",
    "\n",
    "preproc = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numeric_transformer, numeric_features),\n",
    "        (\"cat\", categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9809a688-bfbc-4685-a77f-17a8b2b79ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set loss\n",
    "balanced_scorer = make_scorer(balanced_accuracy_score)\n",
    "recall_scorer = make_scorer(recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f9b2bbf-5f8a-4ac1-8e6c-51bd0dd8ac85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_confusion_matrix(y_test, y_pred):\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def draw_roc_curve(X_test, y_test):\n",
    "    y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n",
    "\n",
    "    # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n",
    "    \n",
    "    # Calcul de l'aire sous la courbe ROC (AUC)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    plt.figure(figsize = (14, 8))\n",
    "    plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n",
    "    plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
    "    plt.xlabel('Taux de faux positifs (FPR)')\n",
    "    plt.ylabel('Taux de vrais positifs (TPR)')\n",
    "    plt.title('Courbe ROC : modèle logistique')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf400c70-0192-42cc-9919-f61bae8382b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_features_importance(pipeline, model):\n",
    "    coefficients = pipeline.named_steps['logreg'].coef_[0]\n",
    "    feature_names = pipeline.named_steps['logreg'].feature_names_in_\n",
    "    \n",
    "    # Tracer l'importance des caractéristiques\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.barh(feature_names, coefficients, color='skyblue')\n",
    "    plt.xlabel('Importance des caractéristiques')\n",
    "    plt.ylabel('Caractéristiques')\n",
    "    plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "def draw_prob_distribution(X_test):\n",
    "    y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.hist(y_pred_prob, bins=10, range=(0, 1), color='blue', alpha=0.7)\n",
    "    \n",
    "    plt.xlim(0, 1)\n",
    "    plt.ylim(0, None)\n",
    "    \n",
    "    plt.title('Histogramme des probabilités pour la classe 1')\n",
    "    plt.xlabel('Probabilité')\n",
    "    plt.ylabel('Fréquence')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "206d9a95-7c37-4506-949b-e77d225e42c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter\n",
    "param_grid = {'logreg__C': np.logspace(-10, 6, 17, base=2),\n",
    "              'logreg__penalty': ['l1', 'l2'],\n",
    "               'logreg__class_weight': ['balanced', weight_dict]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ff2f7bd-efc1-4f7c-a3c9-caa916aa2f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;vente_internet_max&#x27;,\n",
       "                                                   &#x27;purchase_date_min&#x27;,\n",
       "                                                   &#x27;purchase_date_max&#x27;,\n",
       "                                                   &#x27;time_between_purchase&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;fidelity&#x27;, &#x27;is_email_true&#x27;,\n",
       "                                                   &#x27;opt_in&#x27;, &#x27;gender_female&#x27;,\n",
       "                                                   &#x27;gender_male&#x27;,\n",
       "                                                   &#x27;gender_other&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                                  1.0: 5.694439980716696},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;vente_internet_max&#x27;,\n",
       "                                                   &#x27;purchase_date_min&#x27;,\n",
       "                                                   &#x27;purchase_date_max&#x27;,\n",
       "                                                   &#x27;time_between_purchase&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;fidelity&#x27;, &#x27;is_email_true&#x27;,\n",
       "                                                   &#x27;opt_in&#x27;, &#x27;gender_female&#x27;,\n",
       "                                                   &#x27;gender_male&#x27;,\n",
       "                                                   &#x27;gender_other&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                                  1.0: 5.694439980716696},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;,\n",
       "                                  &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;,\n",
       "                                  &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;,\n",
       "                                  &#x27;time_between_purchase&#x27;,\n",
       "                                  &#x27;nb_tickets_internet&#x27;, &#x27;fidelity&#x27;,\n",
       "                                  &#x27;is_email_true&#x27;, &#x27;opt_in&#x27;, &#x27;gender_female&#x27;,\n",
       "                                  &#x27;gender_male&#x27;, &#x27;gender_other&#x27;, &#x27;nb_campaigns&#x27;,\n",
       "                                  &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse_output=False))]),\n",
       "                                 [&#x27;opt_in&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;, &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;, &#x27;time_between_purchase&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;fidelity&#x27;, &#x27;is_email_true&#x27;, &#x27;opt_in&#x27;, &#x27;gender_female&#x27;, &#x27;gender_male&#x27;, &#x27;gender_other&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>[&#x27;opt_in&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                 1.0: 5.694439980716696},\n",
       "                   max_iter=5000, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['nb_tickets', 'nb_purchases',\n",
       "                                                   'total_amount',\n",
       "                                                   'nb_suppliers',\n",
       "                                                   'vente_internet_max',\n",
       "                                                   'purchase_date_min',\n",
       "                                                   'purchase_date_max',\n",
       "                                                   'time_between_purchase',\n",
       "                                                   'nb_tickets_internet',\n",
       "                                                   'fidelity', 'is_email_true',\n",
       "                                                   'opt_in', 'gender_female',\n",
       "                                                   'gender_male',\n",
       "                                                   'gender_other',\n",
       "                                                   'nb_campaigns',\n",
       "                                                   'nb_campaigns_opened']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  ['opt_in'])])),\n",
       "                ('logreg',\n",
       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                                  1.0: 5.694439980716696},\n",
       "                                    max_iter=5000, solver='saga'))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pipeline\n",
    "pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preproc),\n",
    "    ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n",
    "                                  max_iter=5000))  \n",
    "])\n",
    "\n",
    "pipeline.set_output(transform=\"pandas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed415f60-9663-4179-877b-233faf6e1645",
   "metadata": {},
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b467511-2ae5-4a16-a502-397c3460471d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;vente_internet_max&#x27;,\n",
       "                                                   &#x27;purchase_date_min&#x27;,\n",
       "                                                   &#x27;purchase_date_max&#x27;,\n",
       "                                                   &#x27;time_between_purchase&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;fidelity&#x27;, &#x27;is_email_true&#x27;,\n",
       "                                                   &#x27;opt_in&#x27;, &#x27;gender_female&#x27;,\n",
       "                                                   &#x27;gender_male&#x27;,\n",
       "                                                   &#x27;gender_other&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                                  1.0: 5.694439980716696},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;,\n",
       "                                                   &#x27;total_amount&#x27;,\n",
       "                                                   &#x27;nb_suppliers&#x27;,\n",
       "                                                   &#x27;vente_internet_max&#x27;,\n",
       "                                                   &#x27;purchase_date_min&#x27;,\n",
       "                                                   &#x27;purchase_date_max&#x27;,\n",
       "                                                   &#x27;time_between_purchase&#x27;,\n",
       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
       "                                                   &#x27;fidelity&#x27;, &#x27;is_email_true&#x27;,\n",
       "                                                   &#x27;opt_in&#x27;, &#x27;gender_female&#x27;,\n",
       "                                                   &#x27;gender_male&#x27;,\n",
       "                                                   &#x27;gender_other&#x27;,\n",
       "                                                   &#x27;nb_campaigns&#x27;,\n",
       "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;opt_in&#x27;])])),\n",
       "                (&#x27;logreg&#x27;,\n",
       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                                  1.0: 5.694439980716696},\n",
       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;,\n",
       "                                  &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;,\n",
       "                                  &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;,\n",
       "                                  &#x27;time_between_purchase&#x27;,\n",
       "                                  &#x27;nb_tickets_internet&#x27;, &#x27;fidelity&#x27;,\n",
       "                                  &#x27;is_email_true&#x27;, &#x27;opt_in&#x27;, &#x27;gender_female&#x27;,\n",
       "                                  &#x27;gender_male&#x27;, &#x27;gender_other&#x27;, &#x27;nb_campaigns&#x27;,\n",
       "                                  &#x27;nb_campaigns_opened&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse_output=False))]),\n",
       "                                 [&#x27;opt_in&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;, &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;, &#x27;time_between_purchase&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;fidelity&#x27;, &#x27;is_email_true&#x27;, &#x27;opt_in&#x27;, &#x27;gender_female&#x27;, &#x27;gender_male&#x27;, &#x27;gender_other&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;opt_in&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                 1.0: 5.694439980716696},\n",
       "                   max_iter=5000, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['nb_tickets', 'nb_purchases',\n",
       "                                                   'total_amount',\n",
       "                                                   'nb_suppliers',\n",
       "                                                   'vente_internet_max',\n",
       "                                                   'purchase_date_min',\n",
       "                                                   'purchase_date_max',\n",
       "                                                   'time_between_purchase',\n",
       "                                                   'nb_tickets_internet',\n",
       "                                                   'fidelity', 'is_email_true',\n",
       "                                                   'opt_in', 'gender_female',\n",
       "                                                   'gender_male',\n",
       "                                                   'gender_other',\n",
       "                                                   'nb_campaigns',\n",
       "                                                   'nb_campaigns_opened']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  ['opt_in'])])),\n",
       "                ('logreg',\n",
       "                 LogisticRegression(class_weight={0.0: 0.5481283836040216,\n",
       "                                                  1.0: 5.694439980716696},\n",
       "                                    max_iter=5000, solver='saga'))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6356e870-0dfc-4e60-9e48-e2de5e7f9f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8489010627230468\n",
      "F1 Score: 0.4775997086140958\n",
      "Recall Score: 0.7887218045112782\n"
     ]
    }
   ],
   "source": [
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Calculate the F1 score\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy Score: {acc}\")\n",
    "\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"F1 Score: {f1}\")\n",
    "\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f\"Recall Score: {recall}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09387a09-0d53-4c54-baac-f3c2a57a629a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "580b58d7-596f-4207-8c99-4365aba2bc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Csv+DDz7wOLZJkyZs27bN0pRNT09n+fLlluPGjBlDeno6JSUlZX5dW7duXaHPU0REpCpohJCIiEgV6NWrF6+88gpTpkyhe/fu/OMf/6B9+/YUFxezdu1aXn/9dTp06MB5551H69at+fvf/86LL76I0+lk5MiR7Nmzh3vvvZeGDRtaGiCXX345l112GVOmTOGiiy5i7969TJs2jdq1a1e4xlWrVnHNNdcwYcIE9u/fz913301SUhJTpkwBzDldXnzxRSZPnszRo0cZP348iYmJHDlyhF9++YUjR47wyiuvnNHX58knn+Tss8/m4Ycf5v/+7/8AmDRpEmvWrOHpp59mz549XHXVVdSpU4etW7cyffp0du7cyQcffECzZs1Kn2fChAnce++9PPzww2zZsoWrr76a5s2bk5eXx08//cRrr73GxIkTT7v0/Pfff8+QIUO47777yj2P0Jl6/PHHGTZsGIMGDeKWW24hODiYl19+mQ0bNjBjxozSEV333HMPX3zxBYMHD+a+++4jPDyc//znPx5zNjVp0oSHHnqIe++9l927dzNq1Cji4uI4fPgwP/30ExERETz00ENl1nL8sXfffTe7du3i3HPPJTY2lsOHD/Pzzz8TERHBgw8+WO7Pzel0Mm3aNC699FLGjBnDtddeS2FhIU899RQZGRk88cQTpcc+9NBDjB49mhEjRnDDDTdQUlLCU089RWRkJEePHj3t65x33nl06NCBHj16ULt2bfbu3ctzzz1H48aNadmyJQAdO3YE4Pnnn2fy5MkEBQXRunXrMkfm/PWvf2X69On89a9/5dFHH6Vly5bMmzePr7/+2uPYyy+/nNdee43LLruMv/3tb6SnpzNt2jSio6Mtx11yySX873//Y9SoUdxwww307NmToKAgDhw4wKJFixg7diwXXHBBub+2IiIiVcK++axFRERqvnXr1hmTJ082GjVqZAQHBxsRERFG165djfvuu89ITU0tPa6kpMR48sknjVatWhlBQUFGQkKCcdlllxn79++3PJ/b7TamTZtmNGvWzAgNDTV69OhhfPfdd6dcZWzWrFkeNR1fZWzBggXG5ZdfbtSqVcsICwszRo0aZWzfvt3j+O+//94YPXq0ERcXZwQFBRlJSUnG6NGjy3zuEx1fZeypp54q8/4JEyYYgYGBxo4dOyz7582bZ4waNcqIj48vfb3LL7/c2Lhx4ylf6/vvvzfGjx9v1KtXzwgKCjKio6ONXr16GU899ZSRlZV12jqPf63uv//+0x5nGOYqU+3bt/fY37hxY2P06NEe+wGPFcB++OEHY/DgwUZERIQRFhZmnHPOOcaXX37p8dhly5YZ55xzjhESEmLUrVvXuPXWW43XX3+9zNWzZs+ebQwaNMiIjo42QkJCjMaNGxvjx483vvnmm9JjTl5lrCKPLcvJq4yd+Hxnn322ERoaakRERBhDhgwxli1b5vH4zz77zOjYsaMRHBxsNGrUyHjiiSeMf//730ZsbKzluJNXGXvmmWeM3r17GwkJCaWPvfrqq409e/ZYHnfnnXca9evXN5xOp6XOslYKO3DggHHRRRcZkZGRRlRUlHHRRRcZy5cv91hlzDAM45133jHatm1rhIaGGu3atTNmzpzpscqYYRhGcXGx8fTTTxudO3c2QkNDjcjISKNNmzbGtddeW2bOREREqpvDMMoYXy0iIiIiUo2Ki4vp0qULSUlJXnGZ3549e2jatClvvfWWxwqAIiIiNYEuGRMRERGRanf11VczbNgw6tWrR0pKCq+++iqbN2/m+eeft7s0ERERv6CGkIiIiIhUu+zsbG655RaOHDlCUFAQ3bp1Y968eQwdOtTu0kRERPyCLhkTEREREREREfEzWnZeRERERERERMTPqCEkIiIiIiIiIuJn1BASEREREREREfEzfjeptNvtJjk5maioKBwOh93liIiIiIiIiIhUCsMwyM7Opn79+jidpx8D5HcNoeTkZBo2bGh3GSIiIiIiIiIiVWL//v00aNDgtMf4XUMoKioKML840dHRNlfz58yfP59zzz3X7jJEvIYyIWKlTIh4Ui5ErJQJEStfz0RWVhYNGzYs7X2cjt8tO5+VlUVMTAyZmZk+3xByuVwEBvpdT0/klJQJEStlQsSTciFipUyIWPl6JirS89Ck0j5s1qxZdpcg4lWUCRErZULEk3IhYqVMiFj5UybUEBIRERERERER8TNqCPmwdu3a2V2CiFdRJkSslAkRT8qFiJUyIWLlT5lQQ8iHxcTE2F2CiFdRJkSslAkRT8qFiJUyIWLlT5lQQ8iHrVixwu4SRLyKMiFipUyIeFIuRKyUCRErf8qEGkIiIiIiIiIiIn5Gy877sPT0dOLj4+0uQ8RrKBMiVsqEiCflQsRKmRCx8vVMaNl5P7Fp0ya7SxDxKsqEiJUyIeJJuRCxUiZErPwpE2oI+bADBw7YXYKIV1EmRKyUCRFPyoWIlTIhYuVPmVBDyIeFh4fbXYKIV1EmRKyUCRFPyoWIlTIhYuVPmdAcQiIiIiIiIiIiNYDmEPITM2bMsLsEEa+iTIhYKRMinpQLEStlQsTKnzKhhpCIiIiIiIiIiJ9RQ8iHtWrVyu4SRLyKMiFipUyIeFIuRKyUCRErf8qEGkI+LDEx0e4SRLyKMiFipUyIeFIuRKyUCRErf8qEGkI+bOnSpXaXIOJVlAkRK2VCxJNyIWKlTIhY+VMm1BASEREREREREfEzWnbehx0+fJg6derYXYaI11AmRKyUCRFPyoWIlTIhYuXrmdCy835i586ddpcg4lWUCRErZULEk3IhYqVMiFj5UybUEPJhe/futbsEEa+iTIhYKRMinpQLEStlQsTKnzKhhpAPCw4OtrsEEa+iTIhYKRMinpQLEStlQsTKnzJh6xxCS5Ys4amnnmL16tUcOnSIzz77jHHjxp32Md9//z033XQTGzdupH79+tx2221cd9115X7NmjSHkIiIiIiIiIjIcT4zh1Bubi6dO3fmpZdeKtfxu3fvZtSoUfTr14+1a9dy11138e9//5tPPvmkiiv1TrNmzbK7BBGvokyIWCkTIp6UCxErZULEyp8yEWjni48cOZKRI0eW+/hXX32VRo0a8dxzzwHQtm1bVq1axdNPP81FF11URVV6L5fLZXcJIl5FmRCxUiZEPCkXIlbKhFQWwzAodLkpcRu43Abu3/4tcRsUFJeQU+j67bjfjsco3TZOeA7zPizHcsKxZd1vGIbnPqwHn+r+E59z0ebDFKfB6CIX4cG2tkuqhU99hitWrGD48OGWfSNGjODNN9+kuLiYoKAgj8cUFhZSWFhYup2VlVXldVaXZs2a2V2CiFdRJkSslAkRT8qFiJUvZMIwjNKmwfGmg8t98i/7huUX+7IaCyc2CMpqDpT5+BMmWCnr2JxCFw6H53F/1Ow4sdFhfd1TH2ucUPjx/S63m+wCF8UlZiPGMMBtGJQYBm6D0qZMRl4Rrt/uP948Of51dRvWz8ld+vX+/X4w+PVgpvmaJQYhQU5cJQYut5tth3OICQsiM7/45P86nxGbl8nUpR/Q1lXE7aNuYEpesRpC3iYlJYU6depY9tWpUweXy0VaWhr16tXzeMzjjz/Ogw8+6LF/1qxZhIeHc+GFF/Ltt9+SmZlJYmIiPXv2ZM6cOQB069YNt9vNunXrABg7dixLly4lPT2duLg4+vfvz+zZswHo1KkTQUFBrF69GoDRo0ezatUqDh8+THR0NMOHD+fjjz8GoH379kRGRvLTTz8BZlNrw4YNHDx4kIiICMaMGcPMmTMBaN26NQkJCSxbtgyAoUOHsm3bNvbt24fb7ebss89m5syZuN1umjdvTlJSEkuWLAFg4MCB7Nu3j127dhEYGMiECRP45JNPKCoqonHjxjRv3pzvvvsOgL59+5Kamsq2bdsAmDRpEp9//jl5eXk0aNCAdu3asWDBAgB69epFZmYmmzZtAmDChAnMnz+f7Oxs6tatS7du3Zg3bx4AZ511FgUFBfz6668AXHDBBSxevJhjx46RkJBAr169+PLLLwHo2rUrAGvXrgXgvPPOY8WKFaSlpREbG8vAgQP57LPPAOjYsSOhoaGsXLkSgFGjRrFmzRpSUlKIiori3HPPLR3q165dO2JiYlixYgUAw4cPZ9OmTRw4cIDw8HDGjh3LjBkzAGjVqhWJiYksXboUgMGDB7Nz50727t1LcHAwF110EbNmzcLlctGsWTMaNWrE4sWLAejfvz8HDx5k586dOJ1OJk6cyKeffkphYSGNGjWiVatWfPPNNwD06dOHtLQ0tm7dCsDEiROZM2cOubm5JCUl0aFDB77++msAzj77bHJycti4cSMA48ePZ8GCBWRlZVGnTh169OjB3LlzAejevTvFxcWsX78egHHjxrFkyRKOHj1KfHw8ffv25fPPPwegS5cuOJ1O1qxZA8CYMWP4+eefSU1NJSYmhiFDhvDpp58C0KFDB8LDw/n5558Bc3TfL7/8QnJyMpGRkYwaNYqPPvoIgDZt2hAXF8fy5csBGDZsGFu2bGH//v2EhYUxbtw4PvzwQwzDoGXLltStW5cffvgBgEGDBrFnzx52795NUFAQ48eP5+OPP6a4uJimTZvSpEkTFi1aBEC/fv1ISUlh+/btOBwOLrnkEnbu3MmuXbto2LAhbdq0YeHChQD07t2bo0ePsmXLFgAuvvhi5s2bR05ODvXr16dz58589dVXAPTs2ZO8vDw2bNgA4NPvESEhIVx44YV6j8B/3yMiIiLIysrSe8Rv7xGzZ88mPz9f7xF+/h5RUFBARESE3iP0c4TPvkcUlcDQ4SNYs3YtqampREZFM2DAAL74cg7Gb9+HYeERrFm7lmK3g+5n92Ld5h1sTz6KMyiEDu3bs2btWgwDEmrXBuCtHz/BMKBZ8xYcSUsjIyODgMBAmrdszZK1W4kOKiE6phbh4REcTE7GbUDdevXIys5hX3ouDoeDJg3qcSD5EDuynDSIDsQZGMSeowU4MIgIDsDtNihxuzGAwMBAXC6X2YBwOHA4HOZ9v23D8aaFeVu83+maQU4MQoMDCXaUYLjdOBxOwsJCyMvLxwEEBQXhdDooKioCIDwsjKKiIkpKSnA6HYSHR5CTk4MDg+DgYAICAsjPz8fhgPDwCIqLinC5inE4nERFRZL92yCQ4JBgggKDyMvLBSAyIoLi4mKKiopwOBzExMSQl57GRT/P5eolM4ksyMWNg++Hn4c7uxOrdhz2yfeIvLy8cv+/2Tqp9IkcDscfTirdqlUrrrzySu68887SfcuWLaNv374cOnSIunXrejymrBFCDRs2rBGTSs+YMYNJkybZXYaI11AmRKyUCRFPyoX4isVbU9l8KBu3YV56U2IYLNl2hDX7MuwuzWc5HJS2mBwOxwm3f/uX3w9wnLz/FI/P/u0yqMSokN/uL/tYSw0nPO+pauI0tZ74GgDBgU5iwoIIcDoI+K3B5nRAgNOB0+EwmypBAUSEBOJ0HK/ht2dxgPO328ef+3hNDofDY19aTiHNakdSJzqEsOBAgpwO83WcDupGhxIdGkRIkBOnw0Hgb/u9kmHAF1/ALbfAjh3mvi5d4NlnmZGS4tPniYpMKu1TI4Tq1q1LSkqKZV9qaiqBgYHEx8eX+ZiQkBBCQkKqozwREREREZE/lFVQjOH+fftARh570vIoLnFTXOJmS0o2by7dXaHndPz2i73zt1/2j/8eXlDspkl8OPuO5lEnOpRWdaKoFR6EA/P4PXt206xZM5zHGwMnPN6BA5fbTUZeMc1rR+J0/tZo+K3J4PjtdlZBMfERIcRFBBMU4KTQVUL9WmEEBzpxADFhQaUNiBMbDMfrLuu+480KjwYFZgMkKMB5QmPl+HOd1DhxeGkzQuy1bx9ceSX8NsqVOnXg0UfhiisgIAB+G/XpD3yqIXTisODjFixYQI8ePcqcP6im69+/v90liHgVZULESpkQ8aRcSHVxlbjJKXSxYmc6OYUuXG6DI9mFPLtwW7mfY1i7OsSFB+N0ms2a+IhgLunZiPjI4N+aP783gM7UwYO1SUpKOuPHi/icWrXg118hJARuvhnuuAOiokrv9qfzhK0NoZycHHYcH56Fuaz8unXriIuLo1GjRtx5550cPHiQd999F4DrrruOl156iZtuuom//e1vrFixgjfffLP0um1/c/DgQb15i5xAmRCxUiZEPCkXUhVyCl0s3X6E577ZTlpOEYWuErILKrZ6V2iQkx6N4wgMcBDodDK4TSJ/ObtRFVX8O2VCaryCAvjwQ5g82Rw6Fh0NH3wALVtC48Yeh/tTJmxtCK1atYpBgwaVbt90000ATJ48mbfffptDhw6xb9++0vubNm3KvHnzmDp1Kv/5z3+oX78+L7zwgl8uOQ+wc+dOevbsaXcZIl5DmRCxUiZEPCkXcqayCorZkZpD8W8rXB3MyOeDn/aRlV/M7vRcTjUza7OECBrFhxMU4CQowMGx3GLa14/m9pFtLFMmBwY4q+XzOJkyITWWYcCsWXD77bBnD4SHw8UXm/cNHXrKh/lTJmxtCA0cOJDTzWn99ttve+wbMGBA6WoG/s7ptOekIeKtlAkRK2VCxJNyIX/EMAxSsgrIKXCxaGsqCzYeZv3BTIpc7tM+LijAwcSzGnLp2Y0JCnAQGx5MYICT6NBAr57LRpmQGmnlSpg6FX5bZZOkJAgOLtdD/SkTXrPKWHWpyIzbIiIiIiJSsxmGwTebU/m/H3aRV1TC4awCUrMLyzw2JNBJUq0wc7RPoIOWiVG0SIxkbJf6NIgNr+bKRcTDgQNw113w3nvmdng43HabuZpYRIS9tVWTGrvKmFh9+umnXHjhhXaXIeI1lAkRK2VCxJNyIccVFJfwxbpk3lq+h82HsjzuN1fGgqAAJxd0TeKibg1oVSfSq0f7nAllQmqUiy+GFSvM25dfDo89Bg0aVOgp/CkTagj5sMLCsv9yIeKvlAkRK2VCxJNy4Z/ScgqZteoAK3alk5pVQFZ+McmZBZZjejaNo1lCBANb16Zn03jiIsp3eYmvUybEp7ndUFICx1cdf+QRuO8+mD4dzjrrjJ7SnzKhhpAPa9So6lcdEPElyoSIlTIh4km5qPlSswtYs/cYyRkFzPv1EEdyCtmbnnfK45vVjuCZCZ3p2ii2Gqv0HsqE+Kzly+HGG2HsWLj7bnPf4MEwaJC5mtgZ8qdMqCHkw1q1amV3CSJeRZkQsVImRDwpFzVPalYBCzcfZtGWI+xOy2Hnkdwyj0uqFUaLxEhGdaxLyzpR1IsJpU5UKE5nzboErKKUCfE5e/eaK4fNnGluHzhgzhEUEmJu/8nLOv0pE2oI+bBvvvmGSZMm2V2GiNdQJkSslAkRT8qFb8svKmHZjjS2p+bw/o97OZiRf8pj+7ZIIKlWGO3qRzOwdW0ax/vHhLIVpUyIz8jOhscfh2efhcJCs/Fz9dXw8MO/N4MqgT9lQg0hERERERHxSiv3HGXFznSKXG7W7j/Gsh3ppzz2wm5JGAYMaZvI6I71atzkzyJ+7Ztv4LLL4PBhc3vQILMx1KWLrWX5OjWEfFifPn3sLkHEqygTIlbKhIgn5cK7FbncpGQWMGPlPt5YsguX2/A4JijAQXGJwcQeDWmeGMGojvW05PufoEyIT2jSBI4ehRYt4Omn4fzz//SlYafiT5lQQ8iHpaWl+dWEVyJ/RJkQsVImRDwpF9WvyOUmNbuAjLxiDmbkU+hyU+xyk55byPKd6cRHhFBU4ubLX5LLfHxokJPGcRGM6VSPTg1r0b9lgkb/VCJlQrzSjh2wYAFMmWJut2gBCxdCr14QXLUrAPpTJtQQ8mFbt26lW7dudpch4jWUCRErZULEk3JR9bIKivllfwbfbk5l/YEM1uzLOKPn6d+qNref25r29WMqt0CxUCbEq2RkmEvHv/ACuFxwzjlw/PtzwIBqKcGfMqGGkIiIiIiInJHsgmLmrj/EF78kk1VQzJHsQg5nFZ7y+Cbx4dSNCSU4MIDgAAdhwYGEBDppVy+aoEAnrhI3k3o2IjQooBo/CxGxncsFr78O998PaWnmvnPPhchIe+uq4RyGYXhemFuDZWVlERMTQ2ZmJtHR0XaX86e43W6cTqfdZYh4DWVCxEqZEPGkXPx5e9NzWbYjnQWbUli89UiZx8SGB9GmbjQut5ubhrXm7KZxfr+8u7dSJsR2X38NN90EmzaZ223bwjPPwMiRtpTj65moSM9DI4R82Jw5czj//PPtLkPEaygTIlbKhIgn5aLi8otK+H5bKtsO5zDv10NsScm23B8TFsTwdnUIDw5gbNckmsRHEBdRtXN8SOVRJsRWublw+eVw5AjEx8ODD8Lf/w5BQbaV5E+ZUEPIh+Xm5tpdgohXUSZErJQJEU/KRfks2XaERVtTWbMvg1/2Z3jc3yA2jKFt69CqThTjuzcgONB3/5ru75QJqXYZGRATY64SFhEBjz8OGzfCvfdCbKzd1flVJtQQ8mFJSUl2lyDiVZQJEStlQsSTv+aioLiEA8fyTnn/wYwCNhzMLJ0E+kh22fMAXdu/GX85uxGN4yOqqlSpZv6aCbFBURG8/DI89BC89hpMmGDuv/pqe+s6iT9lQg0hH9ahQwe7SxDxKsqEiJUyIeLJX3KxJy2XTYeySM7I55XFO0nPLarQ450OiIsI4d9DWtC+fjSdGtQiKECjgGoif8mE2MgwYM4cuPlm2L7d3Pf++783hLyMP2VCDSEf9vXXXzNp0iS7yxDxGsqEiJUyIeLJ13ORmV/MjtQccgpduA0Dt9vgaG4Rs1YdIKugmILiEvakn3okUGz4qefliA4LolODWozpVI9ezeOJDrVvDg+pPr6eCfFy69ebE0Z/+625nZgIjz4KV15pb12n4U+ZUENIRERERMSLFJe4ycov5lBmAZsOZfHlL8kcyytiw8GsCj/XOc3iABjcJpG/9WuGw6GVvkSkmjzxBNx9N7jdEBxsNobuvBN8fLXvmkQNIR929tln212CiFdRJkSslAkRT3blwjAMMvKKycwvxuV2U1xiUORysyUliyPZhRS53Gw7nMP8jSnler529aJxOiHA4SAwwEmrOlE0iA3j7KZxhAQGkBAVTL2YsCr+rKQm0LlCqkz37mYzaMIEePJJaNrU7orKxZ8yoYaQD8vJybG7BBGvokyIWCkTIp4qMxe5hS7Sc4oodJXw68FMDhzLZ3tqDrXCgnC53RS5DA5nFXA0t4hNhyo+uicqNJCEyBB2p+Vyw5CW9G+VQPPakdQK15LuUnl0rpBKYRjw8ceQmQnXXGPuGzYMfv0VfGxOHn/KhBpCPmzjxo106tTJ7jJEvIYyIWKlTIh4OpNcHMstYuWeo2xPzWHToSx2puawJSX7jF4/PDiAoADnbx8OGsWF0zg+nJDAAEICnUSHBTGyQ10ax0doKXepFjpXyJ+2ahVMnQpLl0JkJIwZA3Xrmvf5WDMI/CsTagiJiIiIiABut0FyZj7fbzvC4cwC3l6+h6wC1x8+rlZ4ECGBTgKdTurXCiUyJJBujWIJCnQS6HTgNgx6No2nZWIkESH68VtEaoiDB+Guu+Ddd83tsDBznqCoKHvrknJzGIZh2F1EdcrKyiImJobMzEyifXwyq+LiYoKCtPqDyHHKhIiVMiHi6XguDMNgV1ou//luByt2pZNT4CK78PTNn3Fd6pMQGUKhy02z2hG0rx/DWU1iNVGz+DSdK6TC8vLg6afNeYHyflvV8LLL4LHHoGFDe2urBL6eiYr0PPQnCh+2YMECRo8ebXcZIl5DmRCxUibE3+UUuth4MJMdR3LIzDcndN60bRd7C8PZd/TUS7M3iA2jyOWme+NYWiRGcnXfppq3R2osnSukwg4cgIcfBpcLevWC556Dnj3trqrS+FMm1BDyYVlZFZ+cUKQmUyZErJQJ8Rdut8HqfcfYm57Hip3pbEnJYvvhHIpK3Kd4hLUZ1KpOJDcMaUWHpGjqRIcSGhRQ9UWLeAmdK6Rcdu/+fZWwVq3gkUegSRO4+GKoYaMk/SkTagj5sDp16thdgohXUSZErJQJqUkMwyA9t4hf9meQklXA91uPcDAjn43Jf/yDe+P4cHILXbSpG40j/xh/GdiZro1iiQoN1Jw+4vd0rpDT2rsX7rgDPvrInDy6a1dz/+2321tXFfKnTOgM6MN69OhhdwkiXkWZELFSJsTX5BW5SM0qZN/RPPam55KcWcCyHWmsP5BZ7ufomBRDt0a1aJEYSddGsbSrF43T+ftfr7Oysnx+HkmRyqRzhZQpJweeeAKeeQYKCsxRQIsW/d4QqsH8KRNqCPmwuXPnMmnSJLvLEPEayoSIlTIh3qTI5Sa7oJhth3PIyCsiPbeI+RtSWLvvGPnFJbgrsMxJ4/hwujWKJSIkgK4NY2kUH06XhrUICvjjZdqVCxErZUIs3G545x1z9bCUFHPfgAEwfbpfNIPAvzKhhpCIiIiInLESt8GxvCLSc4rYdSSH/cfyOHAsnz3peaTnFJKeU0R2QTG5RSUVet5+LRNIiAyhQWwYbsOgUVw4IzvWIzrUd1d+ERHxeiNHwoIF5u3mzeGpp2DcuBo3T5CY1BDyYd27d7e7BBGvokyIWCkTUhkMw8DlNtidlsvn6w6SmV/MztRc1h/IICYsiCM5hRSXVGB4DxDodNCvZQLxkSEcPJZPz6Zx9G9Vm+a1I4gMCSSwHCN9zpRyIWKlTIjF2LHw449w771w/fUQEmJ3RdXOnzKhhpAPKy4utrsEEa+iTIhYKRNyKkUuN2k5hRzLK2JHag6GAXvT83C53aTlFDF3fTJZBS6cDjAA4xT9nhNH/cSEBREeHEBeUQlju9Snfq0wQgKdtKkbTb2YUCJDA6kVFlSlzZ7yUC5ErJQJP5aZaa4W1qePOQoI4O9/hwkToHZtW0uzkz9lQg0hH7Z+/Xrat29vdxkiXkOZELFSJqTQVcKavRnsPJJDkcvN0h1pfLcltdyPP3lenwaxYTSKC+fcDnUJCXRSNyaMhrFhNIgNJzjQ3kZPeSkXIlbKhB9yueD//g/uuw+OHIFPP4VRoyA4GAID/boZBP6VCTWERERERHxYRl4Rn69LZs2+YziAw1mFrN53jCKX+w8fGx8RbF72lV3I2c3iSM0upFfzeGpHhpBfVELflgkk1QojONBJaFAAoUEBVf8JiYhI1Vm4EKZOhY0bze02bcyVxII0P5s/chjGqQYB10xZWVnExMSQmZnp80uO5ufnExYWZncZIl5DmRCxUiZqLsMwWLs/g3eW7+Hzdcl/eHyt8CBiw4Pp0TiWJgkRtKsXzcDWtXH44SShyoWIlTLhJ7Zvh5tugjlzzO24OHjwQbj2WjWDTuLrmahIz0MjhHzYkiVLGDFihN1liHgNZULESpmoOQzDYM2+DGau3MfstckUlXiO/mkQG8ZZTeLo1Tye2PBgggIcNE2IoGFsOE6n/zV+TkW5ELFSJvzE7t1mMygwEP71L/NysdhYu6vySv6UCTWEfNjRo0ftLkHEqygTIlbKhG9Lyynk+W+2c+BYHou2HjnlcS9M6sqYjvXU9Ckn5ULESpmooYqLYf16OL5i1vDh5gTSEyZAq1b21ubl/CkTagj5sPj4eLtLEPEqyoSIlTLhO1wlbn7ec5Qftqex60gOX288fMpjz21flyv6NKFt3WhiwjXMv6KUCxErZaKGMQyYOxduuQWSk81LxerUMe+7+257a/MR/pQJzSHkw/Ly8ggPD7e7DBGvoUyIWCkT9nGVuClwuckuKCanwEVqdiGFrhKO5RZT4CrBVWLgchvkFbr4bN1Bdh3JPeVzBQc4mT6xC0PaJmpS50qgXIhYKRM1yIYN5jxBCxea27Vrw8cfQ//+9tblY3w9E5pDyE98/vnnTJo0ye4yRLyGMiFipUxUjpxCFztSc9iWks2vBzPJKyohKMBBcYmBy+3mp11HCXA6KCpxU1ziJiOv+Ixfq1/LBLo1iqV2VAj9WibQOD6iEj8TAeVC5GTKRA2QmmrOCfTGG+B2m8vH33gj3HUXxMTYXZ3P8adMqCEkIiIicpJCVwkfrTrA8h1pfLUh5U8/X53oEA5nFdKnRTwRwYEEBTgJDHAQ4HQQFx5MbEQw1/RrSkigRgCJiEgF5ORAu3aQnm5ujx8PTz4JzZrZW5f4BDWEfFiXLl3sLkHEqygTIlbKxB8rcrlJzS7gwLF8Vu89Rm6hi0/XHCQ1uwD3SRfVR4cG4jagee0IOjWoRf1aYQQFOAh0OggODKBzwxhCAp0EBTgJCwogOiyI4ACnJnv2MsqFiJUy4eMiI+HSS2HpUpg+XZeHVQJ/yoQaQj7M6XTaXYKIV1EmRKz8NRP7j+axdn8GWfnF5Ba6yMgv5tcDmRzOKiA5I7/08q6CYs+l20/WJD6cm4e3ZmjbOoQFa/ROTeCvuRA5FWXCx6xZY04Y/eyzcLxx8cQTEBIC+r+sFP6UCTWEfNiaNWto3bq13WWIeA1lQsTK3zKx4WAml7/5E8fOYA6f2PAgjuUVk1QrjF7N4ykucfPkRZ00iXMN5G+5EPkjyoSPOHTIXCXs7bfNlcTuugvmzTPvCwuztbSaxp8yoYaQiIiI+LSPVu7noTmbyCl0Wfb3bZFAQmQwUaFBRIcFcjS3mOa1I2hfP4YGsWEEBzoJdDqIiwjG4dBlXSIi4oXy883RQI8/Drm/rUh56aXmtsifpGXnfVh2djZRUVF2lyHiNZQJEauanAnDMHhmwTZ+3JXOqr3HLPfNv7Efber69jleqk5NzoXImVAmvNhnn5mrhe3bZ26fcw489xycfbadVdV4vp6JivQ8/OfiuBro559/trsEEa+iTIhY1YRM5BS6OJSZz770PHak5rAlJYsVO9MZ+PRiXlq0w9IMeuuKs9jzxGg1g+S0akIuRCqTMuHFDh40m0ENG8IHH8Dy5WoGVQN/yoQuGfNhqampdpcg4lWUCRErX83ElpQsvt2cyjebD7N2X8YfHv/m5B4MbpOoy76kXHw1FyJVRZnwIvv3Q3Ly702fa6815wu65hrNE1SN/CkTagj5sJiYGLtLEPEqyoSIla9lYktKFi98u515v6Z43BceHEBQgLmke3CAg6jQIO4Z05Z+LWvbUKn4Ml/LhUhVUya8QE4OTJsGTz0FSUmwcaO5alhQEFx/vd3V+R1/yoTmEPJhhYWFhISE2F2GiNdQJkSsfCkT/126m4fmbCrdjgwJZFi7OlzUrQF9WsRr9I9UGl/KhUh1UCZs5HbDu++aK4YdOmTu698fZsyA+vXtrc2P+XomNIeQn/j000/tLkHEqygTIla+komcQpelGfTg+e3Z8OAIpk/sQt+WCWoGSaXylVyIVBdlwiY//AA9e8KVV5rNoGbN4JNPYPFiNYNs5k+Z0CVjIiIiYqtr3llZenvev/vRrr5vj+AVERE5rbVrzZFAANHRcM898O9/m5eJiVQjNYR8WIcOHewuQcSrKBMiVt6aiRK3QXGJG5fb4IdtR/hx11EAhrero2aQVDlvzYWIXZSJauJ2g/O3C3S6doUxY6BBA3jwQUhMtLc2sfCnTKgh5MPCw8PtLkHEqygTIlZ2ZMIwDIpLDNJzC9maks3OI7l8svoABa4Sdh3JxeEwF0wpy8uXdqveYsUv6VwhYqVMVLGSEnjzTXj6aVi69Pfmz+zZEBBga2lSNn/KhBpCPuznn3+mefPmdpch4jWUCRGrysyE221wKKuA3EIXu47kMn/DIQwgt7CEVXuPEhEcyJGcQopc7tM+T1nNoFrhQXw2pQ+BAZraUKqezhUiVspEFfr2W5g6FX791dx+8UV4+GHztppBXsufMqGGkIiIiJ8yDIP03CIKXW4KikvYmJzF1pQscgtLcLnd7EnLI7+4hPyiEjYdyjrtc2XkFZe5v1ujWjSKC6dHkzg6JMUQFRpIXHgwgQGO0mXkA5yaNFpERGqQbdvgllvgyy/N7dhYuP9+mDLF3rpETqJl531YRkYGtWrVsrsMEa+hTIiYDMPgWF4xh9OOkWMEUVBcwvoDmeQVuVi+M53CYjd5RS72pOed0fPXjwnlaF4RozvWp1ODGMKCAogOC6R9/RjCggMIDnQSHOAkNEh//RTvo3OFiJUyUYkMA26/HaZPB5cLAgPNJtD990NcnN3VSTn5eiYq0vPQCCEf9ssvvzBgwAC7yxDxGsqE+BPDMCh0uVm55yjrD2Tyw/YjhAUFsGjrkTN6vqiQQLILXQBMGdicoAAnwYFO8opcNIoLp3ntSDo1qEVwoC7rEt+mc4WIlTJRiRwOKCgwm0GjR5vzBrVpY3dVUkH+lAk1hHxYcnKy3SWIeBVlQmqCtJxCdqflsnbfMYpcbpbvTCe30BzNc3xlLrfbwOUu3wDf4AAnRSVuOjesxS/7M7i6b1NaJEbSNCGCyJBAWteNIkhz94gf0blCxEqZ+BMMA+bNgyZNoH17c9/995sriA0fbmtpcub8KRNqCPmwyMhIu0sQ8SrKhPiC1OwCVu85Rl6ROWdPeHAAR/OK2HIoizX7Mir8fOHBAbgNg0GtE3G5DcZ1SSLA6aB3i3i+Xzif884bXfmfhIgP07lCxEqZOEMbNsDNN8OCBTBkCCxcaI4Qio9XM8jH+VMmNIeQDyspKSFAs9OLlFImxNsUukr4Yl0y2QXm3D3fbjl8yiXXTxYTFoTbMBjfvQEZecWM6liP1nWiCAhwEOBwEOB0EOh0UCs8CIej7EmZlQkRT8qFiJUyUUFHjpijgF57DdxuCAqCG26Axx4zb4vP8/VMaA4hP/HRRx8xadIku8sQ8RrKhNgpLaeQp+ZvZceRHFbvPWaZk6csZzeNIzosiNSsAs5pHk+Aw0Gz2pEMaZNIbERwpdSkTIh4Ui5ErJSJciosNJeNf+QRyMw09114IUybBn6yRLm/8KdMqCEkIiJSTgXFJaRmFbLjSDar9hxjyfYjbDhY9nLsJzeDLu7RgLiIEHo0jmVI28RTjuoRERERL/Tee3Drrebtrl3NlcT8ZOJhqbnUEPJhbTRjvYiFMiGVIaugmB2pOaTnFLEpOYsfth9h79E8jmQXluvxXRvV4sKuSSTFhtGqThT1Y8JwOu1p/igTIp6UCxErZeI08vMhLMy8PXkyfPABXH45/PWv4MOXFMnp+VMm1BDyYXFxcXaXIOJVlAkpj5xCF3vScknJLGDehkNEhQSSVeBi3q+HKHS5K/Rc7epFc0HXJDokxdCxQQyRId51WlUmRDwpFyJWykQZDh2Ce+6BZctg/XoIDjbnB/ruO7srk2rgT5nwrp9cpUKWL19O48aN7S5DxGsoEwKQW+hiR2oOe9JzWbItjbBgJ7uO5LIxOYvM/OIKPVfrOlHUCg/ib/2a0ax2BLXCg6kVFmTbiJ+KUiZEPCkXIlbKxAny881LwR57DHJzzX0LFpjLyIvf8KdMqCEkIiI1wk+70rn2/dVk5JW/6dO5YS1yC11c0DWJoAAHbgPGd29AQmRIFVYqIiIiXsUwYOZMuP122LfP3Hf22WZzqFcve2sTqUJadt6HpaWlkZCQYHcZIl5DmfAfbrfB9tQc0nIKKXK52Xc0j4fnbMLl/v2U1jAujMy8YurXCmNslyQCnNC6bjSdkmJOu1R7TaJMiHhSLkSs/D4TWVkwciQsX25uN2gATzwBkyaB02lvbWILX8+Elp33E1u2bKFv3752lyHiNZSJmq2guIT3f9zLW8v2cDAjv8xjBrWuzVV9m9KrWTyBAfohTpkQ8aRciFj5fSaiosyP8HC44w64+Wbztvgtf8qEGkI+bP/+/XaXIOJVlAnfZhgGBcVusguKyS50kV3g4nBWAVn5xRzNLeLNpbtJLWOlrw5J0YQEBtC7eTw3DGmpRtAJlAkRT8qFiJXfZSI3F559Fq67DmrXBocDXnnFnDg6Kcnu6sQL+FMm1BDyYWHHl0AUEUCZ8GYlboOMvCKO5RWTmV/EsdxidhzJYdWeo2w7nENWQTE5BS7LJV9lSaoVxr8Gt+CsJnE0jg8nSM2f01ImRDwpFyJWfpMJtxveew/uuguSk82VxF5+2byvaVN7axOv4jeZQHMI2V2OiIjPyyl0sXxHGqnZhWQVFJOV7yIzv4jM/GIy8orZnZbLocyCcj+fwwGRIYFEhQQSFRrE0bwiakeGMOnsRkzs0ZDgQDWBREREpAKWLoWpU2HVKnO7aVN4+mm48EJ76xKpAppDyE98+OGHXHLJJXaXIeI1lInqk19Uwivf7+SztQfYf7Ts+XzKEhUaSGx4MLHhQdSvFUb3xrF0TIohLiKYyFCzARQeFOAzy7p7O2VCxJNyIWJVozOxe7e5ctisWeZ2VBTcfTfccAOEhtpbm3itGp2Jk6gh5MP8bHCXyB9SJqqOq8TNg19uYu/RPPKLXKzcc8zjmO6NY2maEEF0aBC1woOICTP/jQwJJCQwgJ5N4zS6p5opEyKelAsRqxqdienTzWaQ0wlXXw0PPwx16thdlXi5Gp2Jk6gh5MNatmxpdwkiXkWZqByuEjfpuUWs259BXpGLbzansnR7Gpn5xR7HOhzw3MQujOlUnwCN6vE6yoSIJ+VCxKpGZaKkBI4dg+NLht93H+zfDw88AJ0721qa+I4alYk/oIaQD6tbt67dJYh4FWXizHzw0z5+PZjBwk2ppOV4ruJ1orOaxHJln6bUCg+icXwESbX8Z9I9X6RMiHhSLkSsakwmvvvOnCeoTh34+mvzr1YJCfDZZ3ZXJj6mxmSiHDR234f98MMPdpcg4lWUifIxDIMNBzP5fN1BnvhqC3d99iszft7v0QyqGx1KSKCT4e3q0LdFAt/dPIBZ1/VmVMd69G6eoGaQD1AmRDwpFyJWPp+J7dth7FgYMgTWr4eVK81RQSJnyOczUQEaISQiUsOVuA32Hc3j5UU7yCsuYePBTPak51mO6d44lt7N42kUF06fFgnUjgrRku4iIiLivY4dM+cEeuklKC6GgACYMgXuvx/i4+2uTsQnqCHkwwYNGmR3CSJeRZmwyi8q4cXvtvPfZbspKHZ73F87KoTQICdX92nK5N5NcDg0B1BNo0yIeFIuRKx8MhO//GKOCEpPN7dHjTKXkW/b1t66pEbwyUycITWEfNiePXv86vpGkT+iTPzu+21HuHf2BvYdtY4E6tkkjou6J9G9cRwtEiNtqk6qizIh4km5ELHyyUy0bQtxceZ8Qc8+CyNG2F2R1CA+mYkzpIaQD9u9ezfnnHOO3WWIeA1/zkR6TiF3f7aBLSlZFBS7SckqAKBeTCgPnN+eQa0TteS7H/LnTIicinIhYuUTmdi0CZ5/Hl58EYKDzY+vv4aGDSFQv9JK5fKJTFQSpceHBQUF2V2CiFepyZk4Pg/QjtQcMvKKyC108e6KvUSEBFLkcrP1cLbleKcDrujdlJuGtyIyRG/1/qomZ0LkTCkXIlZenYm0NHPJ+FdfNZeUb9sWbrzRvK9pUzsrkxrMqzNRyRyGYRh2F1GdsrKyiImJITMzk+joaLvLERE/YhgGGXnFpOcWUVBcQlZBMVn5xWTmF5OWU8SBY3m4SgxK3AYlhkFaTiHLdqTTNCGC5Ix8Cl2e8wCdKNDp4JFxHWhfP4Y6MSEkRoVW02cmIiIiUomKiszJoh96CDIzzX0XXADTpkGLFvbWJuLlKtLz0J+NfdjHH3/M+PHj7S5DxGt4WyZ+3n2UBRtTKHS5WX8ggwPH8knPLarw8+xOywUgJNBJi8RIEiJDCAl0EhTopGFsOH1bJBAc6KR57QjiI0Mq+9MQH+ZtmRDxBsqFiJVXZcIw4Isv4JZbYMcOc1+XLjB9OgwcaGdl4ke8KhNVTA0hH1ZcXGx3CSJexY5MZBUUsyctl+wCF++t2EtWQTHbDmeTlnP6xk/d6FAiQwOJCQsiJiyI6NBAGsaFExYcQKDTQYDTSaDTQXGJm7DgAPq2SKBBbDgBTq0EJuWn84SIJ+VCxMrrMvHSS2YzqE4dePRRuOIKc0l5kWridZmoQmoI+bCmum5WxKK6MnEku5Dvtx1h4aYUvtmcSon71FfedmoQw8BWtQkOdJJbVMLVfZuSoFE8Uk10nhDxpFyIWNmeiZQUc5LouDhwOMxVw2bMgDvvhKgoe2sTv2R7JqqRGkI+rEmTJnaXIOJVKjsTxSVu9qTlsjE5i9V7j7H+QAZ7j+aRkWf9q0FQgIPiEoPODWux/XA2D4/tQGRoIA1iw2hfP6ZSaxKpCJ0nRDwpFyJWtmWioMC8FOyxx2DyZHNkEEDHjuaHiE386TyhhpAPW7RoEZMmTbK7DBGvUVmZ+L8fdvHjrqP8tDud7ALXKY/r1zKBjkkx3DqiNQ6HLuUS76PzhIgn5ULEqtozYRgwaxbcfjvs2WPuW7sWXC4tIS9ewZ/OE0qciPi1Ipebz9YeYMXOdL7eeJj84hKPYxrFhTOkbSJx4cE0T4ykY1IMDWLD1AQSERERqYiVK2HqVFi2zNxOSoInnoC//AWcTntrE/FDagj5sH79+tldgohXKU8m3G6DN37YxdHcIvYdzeOrDSllHhfgdPDsxZ05q0kc9WuFVXapItVC5wkRT8qFiFW1ZeLdd81LwwDCw80RQrfcYt4W8SL+dJ5QQ8iHpaSk0KBBA7vLEPEap8pEbqGLbzYf5q1le1i3P8Pj/qAAB0Pb1qFOdCj9WyXQtWEssRHB1VCxSNXSeULEk3IhYlVtmRg5EmrVgvPPN+cNSkqq+tcUOQP+dJ5QQ8iHbd++nR49ethdhojXOJ6JzPxiVu89yo7UHJbvTGfx1iMex9aPCWV8j4b0aR5Pm3rRxIQF2VCxSNXSeULEk3IhYlUlmXC74YMPYNEiePNNc1/t2uZy8vHxlftaIpXMn84Tagj5MM1fIvK7ErfBz+nBPHDPVxS63GUec0HXJKJDAxnbNYlujWKruUKR6qfzhIgn5ULEqtIzsXw53HijOV8QwMSJMHy4eVvNIPEB/nSecBiGYdhdRHXKysoiJiaGzMxMoqOj7S5HRM5QZn4xi7akMmd9MtsO57DvaJ7HMU3iwxnRvi5D29Whc4NaBAdqskIRERGRKrF3rzkv0MyZ5nZkJNx1lzmJdGiovbWJ+JGK9Dw0QsiHzZ49m3Hjxtldhki1yC10sWbfMTYczGL9gYwyJ4MOcBjUqxXOPaPbcm6HejZUKeJddJ4Q8aRciFj96Uzk58Mjj8Azz0BhITgccPXV8PDDULdupdUpUl386TyhhpAPy8/Pt7sEkSqVX1TCl+uT2XUkl1e/31nmMcPa1eHK3k1Iig3jh6+/4LK/jKnmKkW8l84TIp6UCxGrP52JgAD46COzGTRoEDz7LHTpUim1idjBn84Tagj5sIYNG9pdgkilyS10sXZfBj/tTufF73YQExZEZn6xx3H9WibQt0UCHZNi6Nk0jsCA3y8D299ImRA5kc4TIp6UCxGrM8rE0qVw9tkQFATBwfDKK5Cba64g5kfzr0jN5E/nCTWEfFibNm3sLkHkjBiGQWp2IdsOZ3Mku5BvNh9m3q/WS8CON4MCnA4iggO4vFdjBrRKpGfTuFM+rzIhYqVMiHhSLkSsKpSJHTvg1lth9mx4/nn497/N/UOHVkltInbwp/OEGkI+bOHChUyaNMnuMkTKpbjEzZe/JPPWsj3sTsslp9BV5nGdG8QwsHUiAJf3akx8RHC5Z/pXJkSslAkRT8qFiFW5MpGRYc4T9MILUFxsXiaWmlot9YlUN386T6ghJCJVKjkjn399sIY1+zI87qsbHUqJYRAXHkzPpnFcN7A5SbXCqr9IEREREfHkcsEbb8B990Famrnv3HPNCaTbtbO3NhH509QQ8mG9e/e2uwSRMqVkFvDtlsO8vGgnBzOsk7I1igtnZMe63DCkJeHBlfsWpEyIWCkTIp6UCxGr02bin/+E1183b7dta04Yfe651VOYiE386TyhhpAPO3r0KI0bN7a7DBHWH8hg+c50lu9MZ+2+Y2QXWC8HiwkLonfzeG4c2orWdaOqrA5lQsRKmRDxpFyIWHlkwjB+nxj6n/805wu67z649loI1K+PUvP503nC+ceHVK2XX36Zpk2bEhoaSvfu3fnhhx9Oe/z//vc/OnfuTHh4OPXq1ePKK68kPT29mqr1Llu2bLG7BPFjaTmFPDZvM63u/orzX1rGE19tYcm2I6XNoIZxYdx+bhvm39iPdfcN45XLuldpMwiUCZGTKRMinpQLEavSTKSnw/XXwy23/H5np06wb5/ZGFIzSPyEP50nbE31zJkzufHGG3n55Zfp06cPr732GiNHjmTTpk00atTI4/ilS5fy17/+lenTp3Peeedx8OBBrrvuOq655ho+++wzGz4DEf9iGAbfbk7luW+3seFgluW+4EAn0aFBjOxQl9Gd6nFOs3ibqhQRERGR8nK6XDB9Ojz0kDl5dGAg3HgjHF96OyTEzvJEpAo5DMMw7Hrxs88+m27duvHKK6+U7mvbti3jxo3j8ccf9zj+6aef5pVXXmHnzp2l+1588UWmTZvG/v37y/WaWVlZxMTEkJmZSXR09J//JGxUUlJCQECA3WWIH9iXnscjczexYNPhMu9/68qzGNiqdrlXA6sqyoSIlTIh4km5EPmNYcCXX2LccguO7dvNfZ06mfMEDRlib20iNvL180RFeh62XTJWVFTE6tWrGT58uGX/8OHDWb58eZmP6d27NwcOHGDevHkYhsHhw4f5+OOPGT169Clfp7CwkKysLMtHTTFv3jy7S5AaLKugmI9W7afVPV/R/6lFlmaQwwGPXdCR3Y+PYs8ToxnUOtH2ZhAoEyInUyZEPCkXIsDu3TBsGIwdazaDEhPNyaPXrFEzSPyeP50nbLtkLC0tjZKSEurUqWPZX6dOHVJSUsp8TO/evfnf//7HxIkTKSgowOVycf755/Piiy+e8nUef/xxHnzwQY/9s2bNIjw8nAsvvJBvv/2WzMxMEhMT6dmzJ3PmzAGgW7duuN1u1q1bB8DYsWNZunQp6enpxMXF0b9/f2bPng1Ap06dCAoKYvXq1QCMHj2aVatWcfjwYaKjoxk+fDgff/wxAO3btycyMpKffvoJgBEjRrBhwwYOHjxIREQEY8aMYebMmQC0bt2ahIQEli1bBsDQoUPZtm0b+/bt49ChQ4B56Z3b7aZ58+YkJSWxZMkSAAYOHMi+ffvYtWsXgYGBTJgwgU8++YSioiIaN25M8+bN+e677wDo27cvqampbNu2DYBJkybx+eefk5eXR4MGDWjXrh0LFiwAoFevXmRmZrJp0yYAJkyYwPz588nOzqZu3bp069atNERnnXUWBQUF/PrrrwBccMEFLF68mGPHjpGQkECvXr348ssvAejatSsAa9euBeC8885jxYoVpKWlERsby8CBA0svDezYsSOhoaGsXLkSgFGjRrFmzRpSUlKIiori3HPPZdasWQC0a9eOmJgYVqxYAZhNx02bNnHgwAHCw8MZO3YsM2bMAKBVq1YkJiaydOlSAAYPHszOnTvZu3cvwcHBXHTRRcyaNQuXy0WzZs1o1KgRixcvBqB///4cPHiQnTt34nQ6mThxIp9++imFhYU0atSIVq1a8c033wDQp08f0tLS2Lp1KwATJ05kzpw55ObmkpSURIcOHfj6668BcyRdTk4OGzduBGD8+PEsWLCArKws6tSpQ48ePZg7dy4A3bt3p7i4mPXr1wMwbtw4lixZwtGjR4mPj6dv3758/vnnAHTp0gWn08maNWsAGDNmDD///DPbDhxhdXYU3xywZiYqxMmF9XNoFuli7OiR/PLLL3z44TIiIyMZNWoUH330EQBt2rQhLi6utLE7bNgwtmzZwv79+wkLC2PcuHF8+OGHGIZBy5YtqVu3buncYYMGDWLPnj3s3r2boKAgxo8fz8cff0xxcTFNmzalSZMmLFq0CIB+/fqRkpLC9u3bcTgcXHLJJezYsYMZM2bQsGFD2rRpw8KFCwHzvePo0aOl1wNffPHFzJs3j5ycHOrXr0/nzp356quvAOjZsyd5eXls2LABwKffI0JCQrjwwgv1HoH/vkekpaWRlZVVqe8RqampxMTEMGTIED799FMAOnToQHh4OD///DMAI0ea7xHJycle9R4xe/Zs8vPz9R7h5+8RBw8eLJ2/0t/fI6ri5wi9R/jGe8Tsr79mzPLlBAYFsWHoULZedBGu8HCGHj3q9+8RoJ8j/P094uDBg3Tt2tVn3yPy8vIoL9suGUtOTiYpKYnly5fTq1ev0v2PPvoo7733XpkTOW3atImhQ4cydepURowYwaFDh7j11ls566yzePPNN8t8ncLCQgoLC0u3s7KyaNiwYY24ZOz7779nwIABdpchNcT2w9nc9sl61u7LsOwf2aEuozrWY0ynel4xCuh0lAkRK2VCxJNyIX6psNBcLWzixN/3ffkldOjA9/v2KRMiJ/D180RFLhmzbYRQQkICAQEBHqOBUlNTPUYNHff444/Tp08fbr31VsDsgkVERNCvXz8eeeQR6tWr5/GYkJAQQmroRGidO3e2uwSpAfak5XLei0vJLvx9qfhezeK5sk8ThrStQ4DTu5tAJ1ImRKyUCRFPyoX4FcOATz6B224zLxOLiYFzzzXvO+88ADrHxtpYoIj38afzhG1zCAUHB9O9e/fSYVbHLVy4kN69e5f5mLy8PJxOa8nHJ3uycW5s2xwfdiZyJvYfzeOqt1cy5NnvLc2gGX87hxl/P4fh7ev6VDMIlAmRkykTIp6UC/Ebq1fDgAEwYYLZDKpfH4qLPQ5TJkSs/CkTti47f9NNN3H55ZfTo0cPevXqxeuvv86+ffu47rrrALjzzjs5ePAg7777LmBe5/m3v/2NV155pfSSsRtvvJGePXtSv359Oz8VEZ9RXOLm5UU7eX3JTnKLSgDo2yKBu0e3pW09376MUkRERMTvJSfDXXfBu++aI4TCwuDWW81RQhERdlcnIl7E1obQxIkTSU9P56GHHuLQoUN06NCBefPm0bhxYwAOHTrEvn37So+/4ooryM7O5qWXXuLmm2+mVq1aDB48mCeffNKuT8FWPXv2tLsE8SHzN6SweGsqH67cX7ovIjiAe8a045KzGnr9/EDloUyIWCkTIp6UC6nRDANGjoTfJv3lssvgscegYcNTPkSZELHyp0zY2hACmDJlClOmTCnzvrfffttj3/XXX8/1119fxVX5horMHi7+61huEc8s3Mr7P+6z7O/VLJ7X/9qdqNAgmyqrfMqEiJUyIeJJuZAax+02G0EBAeBwwAMPwFNPwXPPQTl+sVUmRKz8KRO2zSEkf97xpepETuWX/Rl0fXihpRl0ZZ8mfHNTf2b8/Zwa1QwCZULkZMqEiCflQmqUFSugVy949dXf940bB8uWlasZBMqEyMn8KRO2jxASkcqVmVfM2v3HWL4zndeX7CrdP65LfZ69uAtOH5soWkREREROsncv3HEHfPihuX34MFx7LQQGmqOERETKwWH42fJcWVlZxMTEkJmZSXS0b0+gW1hYSEhIiN1liJfIKijmXx+sZcm2Ix73fXDN2fRukWBDVdVLmRCxUiZEPCkX4tOys+GJJ+DZZ6GgwGz+XHklPPII1Kt3Rk+pTIhY+XomKtLz0CVjPuzbb7+1uwTxIp0eWGBpBo3rUp/7xrTj57uH+EUzCJQJkZMpEyKelAvxWfPnQ6tW5iTRBQUwcKC5tPybb55xMwiUCZGT+VMmdMmYD8vMzLS7BPECRS43d3yyvnS7We0I5l7fj7DgABursocyIWKlTIh4Ui7EZ9Wta14a1rw5PP00jB1bKZeHKRMiVv6UCTWEfFhiYqLdJYjNCopLaHPv/NLt2lEhfHvTgBqxhPyZUCZErJQJEU/KhfiMnTth6VKYPNnc7tIFvvrKHBlUiZezKBMiVv6UCc0h5MOys7OJioqyuwyx0e0fr2fmqv0AjO/egKcndLa5InspEyJWyoSIJ+VCvF5mpjkn0AsvmEvKb9gArVtX2cspEyJWvp4JzSHkJ+bMmWN3CWKzlXuOAjC8XR2/bwaBMiFyMmVCxJNyIV7L5TKXj2/Z0rwkrKgIBg8GZ9X+yqZMiFj5UyZ0yZiID0vNLgTguoHNba5ERERERM7YggVw002wcaO53bq1uZLYyJFaRl5EqowaQj6sW7dudpcgNjmaW8SEV5eTU+gCoEGtMJsr8g7KhIiVMiHiSbkQr5ORAePHm0vKx8XBAw/AdddBUFC1vLwyIWLlT5lQQ8iHud1uu0sQm/zfD7vYeSQXgJaJkSRGh9pckXdQJkSslAkRT8qFeIXsbDg+R0mtWnD//bB/P9x3n9kUqkbKhIiVP2VCcwj5sHXr1tldgthk2c50AMZ2qc+Cqf1trsZ7KBMiVsqEiCflQmxVXGxOFt24sXmZ2HE33wzPPVftzSBQJkRO5k+ZUENIxAdtPpQFwOA2iX67xLyIiIiIzzAMmDMHOnaEG26AY8fgzTftrkpE/JyWnfdheXl5hIeH212GVLN1+zMY959lAGx8cAQRIbry8zhlQsRKmRDxpFxItduwwZwweuFCc7t2bXNZ+auvhoAAe2tDmRA5ma9nQsvO+4mlS5faXYLYYP/RPACCAhxqBp1EmRCxUiZEPCkXUq0efhg6dzabQcHBcNttsH07/P3vXtEMAmVC5GT+lAn9NunD0tPT7S5BbPD0gq0ADGydaHMl3keZELFSJkQ8KRdSrdq1A7fbXEXsySehWTO7K/KgTIhY+VMm1BDyYXE2TDon9krLKWRvujlCKCasepYi9SXKhIiVMiHiSbmQKmMY8NlnUFQEl1xi7rvwQli9Grx4GWtlQsTKnzKhOYR8WH5+PmFhYXaXIdXo83UHueHDdQBsefhcQoO8Y6ixt1AmRKyUCRFPyoVUiTVrYOpUWLIE4uNhxw5zOXkfoEyIWPl6JjSHkJ+YPXu23SVINTtwLB+AC7smqRlUBmVCxEqZEPGkXEilSk6GK6+EHj3MZlBoKPzjHxDkOyO5lQkRK3/KhC4ZE/EhX6xLBqBRvO/Oei8iIiLi8/Lz4Zln4IknIDfX3PeXv8Djj0OjRvbWJiJSTmoI+bBOnTrZXYJUoxe+3c7Ww9k4HHBh1wZ2l+OVlAkRK2VCxJNyIZVi61a47z5z3qBzzoHp081/fZAyIWLlT5lQQ8iHBfnQUFQ5c/lFJTw2bzPv/bgXgEGtEzVC6BSUCRErZULEk3IhZ+zAAWjw2x/lunSBu+6C9u3NCaQdDltL+zOUCRErf8qE5hDyYatXr7a7BKkihmHw0ar99HjkG9reN7+0GdQgNoxXL+tuc3XeS5kQsVImRDwpF1Jh+/fDpZeaS8Zv2/b7/kcegUmTfLoZBMqEyMn8KRMaISTihR7/aguvL9lVuh0a5KRfy9o8f0kXggPVxxURERGpcjk5MG0aPPUUFBSYjZ+FC6FVK7srExGpFFp23odlZWX5/Ocgnub9eogp/1sDwKiOdXn8wk5Ehwbi8PG/PlUHZULESpkQ8aRcyB9yu+G99+DOO+HQIXNf//7mPEHdutlbWxVQJkSsfD0TWnbeT6xatcruEqSSvf/j3tJmUGJUCP/5SzdiwoLUDConZULESpkQ8aRcyGkZBgwZAldcYTaDmjaFTz6BxYtrZDMIlAmRk/lTJtQQ8mGHDx+2uwSpREeyC7ln9obS7W9vHqBGUAUpEyJWyoSIJ+VCTsvhgOHDISrKvFxs82a48EKfnyfodJQJESt/yoQaQj7Ml4exiadXv99Zenv1PUOJCvWf2e0rizIhYqVMiHhSLsQiKwtuvx2+/fb3fVOnwo4dcOutEBJiX23VRJkQsfKnTGgOIR9WXFzsV0vi1XSX/t+PLNuRTqcGMXzxr752l+OTlAkRK2VCxJNyIQCUlMCbb8I998CRI9ChA6xbBwEBdldW7ZQJEStfz4TmEPITH3/8sd0lSCXZlJzFsh3pAAxvV8fmanyXMiFipUyIeFIuhG++ga5d4dprzWZQq1bw+OPg9M9fjZQJESt/yoR/vuuJeJGdR3IY9cIPpdsDWiXaWI2IiIhIDbVtG5x/PgwbBr/+CrGx8NxzsGEDjBlTo+cJEhEpS6DdBciZa9++vd0lSCV4b8Xe0tvPTexChyTfvpTRTsqEiJUyIeJJufBjv/wCX34JgYEwZQrcfz/Exdldle2UCRErf8qEGkI+LDIy0u4SpBIs2X4EgH8Oas64rkk2V+PblAkRK2VCxJNy4UeKi2HrVnN+IIDx4+GOO2DyZGjTxt7avIgyIWLlT5nQJWM+7KeffrK7BPmTcgtd7DqSC8AlZzWyuRrfp0yIWCkTIp6UCz9gGDBvHnTqBIMGQWamud/hMOcKUjPIQpkQsfKnTKghJGKj3EIXYP580iA2zOZqRERERHzcxo1w7rkwejRs2WLu27TJ3ppERLyUGkI+bMSIEXaXIH/Soq2pAMRHBOPQRIZ/mjIhYqVMiHhSLmqoI0fMeYE6dYIFCyAoCG69FXbsgF697K7OqykTIlb+lAk1hHzYhg0b7C5B/qR3lpsTSndtFGtzJTWDMiFipUyIeFIuaqCjR6F1a3jlFXC74cILYfNmmDYNYmLsrs7rKRMiVv6UCU0q7cMOHjxodwnyJxiGwaZDWQBM6N7A5mpqBmVCxEqZEPGkXNRAcXEwdqy5itizz8LAgXZX5FOUCRErf8qERgj5sIiICLtLkD/hzaW7S2/3b1XbxkpqDmVCxEqZEPGkXNQAa9fC8OHm5WDHvfACrFypZtAZUCZErPwpEw7DMAy7i6hOWVlZxMTEkJmZSXR0tN3l/ClutxunUz09X+R2G4x4bgnbU3NIqhXGsjsG211SjaBMiFgpEyKelAsfdugQ3H03vP22uZLYhAnw0Ud2V+XzlAkRK1/PREV6Hr77WQozZ860uwQ5AztSs2l21zy2p+YA8N8rzrK5oppDmRCxUiZEPCkXPig/Hx59FFq2hLfeMptBl1wCTz1ld2U1gjIhYuVPmdAcQiLVKDWrgEte/6l0+4reTWhdN8rGikRERES82KefwtSpsG+fuX322TB9ulYOExGpBGoI+bDWrVvbXYJUgGEY3DzrF9JyCgFYfMtAmiT4z/Wp1UGZELFSJkQ8KRc+ZvNmsxnUoAE88QRMmgQ+fCmHN1ImRKz8KRNqCPmwhIQEu0uQCvh8XTI/bE8D4LXLu6sZVAWUCRErZULEk3Lh5Q4cMJeR79TJ3L7pJggNhX/8A8LD7a2thlImRKz8KRNqr/uwZcuW2V2ClFNmXjF3ffYrADcNa8WI9nVtrqhmUiZErJQJEU/KhZfKzYX774dWreDyy6GkxNwfFgY336xmUBVSJkSs/CkTGiEkUsWOXyqWV1RCk/hwrhvQ3O6SRERERLyD2w3vvw933gnJyea+6GhIT4fERHtrExGp4TRCyIcNHTrU7hKkHG77eD3fbD4MwFV9mxIcqNhVFWVCxEqZEPGkXHiRpUvNSaInTzabQU2awKxZsGSJmkHVSJkQsfKnTOg3Ux+2bds2u0uQP3Dbx78wa/UBAK7q05S/9mpib0E1nDIhYqVMiHhSLrzEsmXQrx+sWgVRUeaE0Zs3w/jx4HDYXZ1fUSZErPwpE2oI+bB9x5ffFK/0j/dX89GqA6Xbd4xsY2M1/kGZELFSJkQ8KRc2Mozfb/fubTaE/v532L4dbr/dnDxaqp0yIWLlT5nQHEI+LCQkxO4SpAy5hS6e+GoLX21IKd2349GRBAao/1rVlAkRK2VCxJNyYYOSEvjvf+HFF83LxKKjzVFA334LQUF2V+f3lAkRK3/KhMMwTmzV13xZWVnExMSQmZlJdHS03eVIDZOSWcCQZxaTW2SujNGsdgTf3jQAh4Y+i4iIiD/67juYOhXWrze3H38c7rjD3ppERGqwivQ8NGTBh82cOdPuEuQEu47kMPL5JaXNoBuGtGT+Df3VDKpGyoSIlTIh4km5qCbbt8O4cTBkiNkMqlULpk+Hm26yuzI5iTIhYuVPmdAlYz7M7XbbXYKc4KJXlnMsrxiA//ylG6M71bO5Iv+jTIhYKRMinpSLKmYYcOut8MILUFwMAQEwZQrcfz/Ex9tdnZRBmRCx8qdMqCHkw5o3b253CfKb177fWdoMemFSVzWDbKJMiFgpEyKelIsq5nDAkSNmM2jUKHj6aWjb1u6q5DSUCRErf8qELhnzYUlJSXaXIMAnqw/w+FdbAJjYoyHnd65vc0X+S5kQsVImRDwpF1Vg/nzYtev37cceM/fNnatmkA9QJkSs/CkTagj5sCVLlthdgt9bfyCDm2f9Urr9yAUdbKxGlAkRK2VCxJNyUYk2bYKRI82PW2/9fX9SEowYYV9dUiHKhIiVP2VCDSGRM7TrSA7nv7QMAKcDtj86kiAtLS8iIiI1XVoa/Otf0KmTORIoKAiaNgU/mndDRKQm0BxCPmzgwIF2l+DXHvhyU+ntBVP7qxnkBZQJEStlQsSTcvEnFBXBSy/BQw9BZqa5b9w4eOopaNHC1tLkzCkTIlb+lAn9BuvD9u3bZ3cJfqnEbTD5vz+zZNsRAO4b044WiVE2VyWgTIicTJkQ8aRc/Akvvww332w2g7p0ge++g88+UzPIxykTIlb+lAk1hHzYrhMn75NqceBYHiOfX8L3vzWDhrZN5Kq+TW2uSo5TJkSslAkRT8pFBRUV/X7773+Hs86C//s/WLUKBg2yry6pNMqEiJU/ZUKXjPmwwED991Wn+RsOcd37a0q3bz+3DdcNaGZjRXIyZULESpkQ8aRclNPhw3DPPbB2Lfz0EwQEQHi4edvhsLs6qUTKhIiVP2XCYRiGYXcR1SkrK4uYmBgyMzOJjo62uxzxEWv3HWP8qysocZtx+fi6XvRoEmdzVSIiIiKVrKAAnnvOXDo+O9vc9803MGSIrWWJiEj5VKTnoUvGfNgnn3xidwl+oaC4hMvf/JkSt0FIoJPND52rZpCXUiZErJQJEU/KxSkYBsyaBW3bwp13ms2gnj1h2TI1g2o4ZULEyp8y4T9joWqgohOv6ZYqc96LS8kpdAHww22DCAsOsLkiORVlQsRKmRDxpFyUIT3dXC1s6VJzOykJnngC/vIXcOrvxzWdMiFi5U+ZUEPIhzVu3NjuEmq8177fyfbUHACmDm1FYnSozRXJ6SgTIlbKhIgn5aIMcXHgcplzBN12G9xyC0RE2F2VVBNlQsTKnzKhhpAPa968ud0l1Ghf/JLM419tAeD8zvX512AtqertlAkRK2VCxJNyAeTlwQsvwJQpEB1tThL93/9CVBQ0aGB3dVLNlAkRK3/KhMaA+rDvvvvO7hJqtBe+3Q7AeZ3r8/wlXQhwakUNb6dMiFgpEyKe/DoXbje8/z60amXOE/T447/f17atmkF+yq8zIVIGf8qERgiJlGHVnqPs+O1SsSt6N8ah5VVFRETEly1fDjfeCCtXmtuNG8NZZ9lakoiI2EsjhHxY37597S6hRnK7Dca/ugKAbo1q0b2xVhTzFcqEiJUyIeLJ73Kxdy9ccgn06WM2gyIjzZFBW7bAhRfaXZ14Ab/LhMgf8KdMVKghtHXrVh544AGGDBlC8+bNqVevHp06dWLy5Ml88MEHFBYWVlWdUobU1FS7S6iRvtl8uPT2LcNb21iJVJQyIWKlTIh48rtcPPAAzJxpzhN0zTWwfTvccQeEaqEMMfldJkT+gD9lolwNobVr1zJs2DA6d+7MkiVLOOuss7jxxht5+OGHueyyyzAMg7vvvpv69evz5JNPqjFUTbZt22Z3CTVOidvgXzPWAtA4PpzeLRJsrkgqQpkQsVImRDzV+FyUlEBm5u/bDz8Mo0bBmjXwxhtQt659tYlXqvGZEKkgf8pEueYQGjduHLfeeiszZ84kLu7Ul8+sWLGC6dOn88wzz3DXXXdVWpEi1eW+zzdQ5HID8PwlXW2uRkRERKQCFi+GqVOhRQuYNcvc16ABzJ1ra1kiIuKdHIZhGH90UFFREcHBweV+0ooeX52ysrKIiYkhMzOT6Ohou8sRL7J0exqXvfkTAFf3bcq9Y9rZXJGIiIhIOezYAbfeCrNnm9u1aplzBNWpY2dVIiJig4r0PMp1yVh5mzsHDx6s0PHy53z++ed2l1CjPP+tOTTwwq5J3DO6rc3VyJlQJkSslAkRTzUqFxkZcMst0K6d2QwKCIB//tOcJ0jNICmnGpUJkUrgT5molFXGUlJSuP7662nRokVlPJ2UU15ent0l1Bivfb+TlXuOAXDpOY20zLyPUiZErJQJEU81JhcrV0LLlvDMM1BcDOeeC+vXw0svQYLmQJTyqzGZEKkk/pSJcjeEMjIyuPTSS6lduzb169fnhRdewO12c99999GsWTN+/PFH/vvf/1ZlrXKSBg0a2F1CjTDj5308/tUWAK7o3UTLzPswZULESpkQ8VRjctGuHYSEQNu2MG8efPWVuU+kgmpMJkQqiT9lolyTSgPcddddLFmyhMmTJzN//nymTp3K/PnzKSgo4KuvvmLAgAFVWaeUoZ1O+n/a419t5rXvdwFwYbckHji/vc0VyZ+hTIhYKRMinnw2F5s3w2uvwbPPgtMJERHw7bfQrBkEBdldnfgwn82ESBXxp0yUe4TQ3Llzeeutt3j66af54osvMAyDVq1a8d1336kZZJMFCxbYXYJPe2ze782gbo1q8dT4zjZXJH+WMiFipUyIePK5XKSnw/XXQ8eO8Pzz8O67v9/XurWaQfKn+VwmRKqYP2Wi3COEkpOTSztlzZo1IzQ0lGuuuabKChOpSs8u2MrrS8xm0OhO9fjPX7rZXJGIiIjICYqK4OWX4cEHzcmjAc4/H3r3trUsERGpOcrdEHK73QSd8BeIgIAAIiIiqqQoKZ9evXrZXYLPemnRDgASIkPUDKpBlAkRK2VCxJPX58IwYM4cuPlmc7UwgE6dYPp0GDzY3tqkRvL6TIhUM3/KRLkbQoZhcMUVVxASEgJAQUEB1113nUdT6NNPP63cCuWUMjMz7S7BJ7lK3LgN8/ZbV5xlbzFSqZQJEStlQsSTT+Ti0UfNZlBionn7yivNJeVFqoBPZEKkGvlTJso9h9DkyZNJTEwkJiaGmJgYLrvsMurXr1+6ffxDqs+mTZvsLsEn/bT7aOntlnUibaxEKpsyIWKlTIh48spcHD4MOTnmbYcDnnsO7rjDbApdc42aQVKlvDITIjbyp0yUe4TQW2+9VZV1iFSbJduPANCvZQKhQfoBS0RERGxSUGBOFP3oo/Dvf8Mjj5j7zznH/BAREalCDsMwjPIevHfvXhYsWEBxcTEDBw70yeXYsrKyiImJITMzk+joaLvL+VNcLheBgeXu6clvrnzrZxZtPcLUoa24YWhLu8uRSqRMiFgpEyKevCIXhgGffAK33Qa7d5v7+vaF7783l5QXqUZekQkRL+LrmahIz6PcZ5wlS5bQvn17rr32Wv71r3/RpUsXZsyY8aeLlTM3f/58u0vwSUu2pwGQEBVscyVS2ZQJEStlQsST7blYvRoGDIAJE8xmUP368M47agaJbWzPhIiX8adMlPusc++99zJo0CAOHDhAeno6V111FbfddltV1iZ/IDs72+4SfFLD2DAA6kSF2lyJVDZlQsRKmRDxZGsuXn8devSAH36AsDC47z7Ytg3++lc1g8Q2OleIWPlTJso9DurXX39lyZIl1K9fH4BnnnmGN954g2PHjhEbG1tlBcqp1a1b1+4SfM6x3CL2Hs0DoH2Sb18yKJ6UCRErZULEk625GDkSwsPhwgvhscegYUP7ahH5jc4VIlb+lIlyN4QyMjJITEws3Y6IiCA8PJyMjAw1hGzSrVs3u0vwObvScjEMSIwKoV5MmN3lSCVTJkSslAkRT9WWC7cbZsyAVatg+nRzX8OGsHMn+NEvG+L9dK4QsfKnTFRobOqmTZtYv3596YdhGGzevNmyT6rPvHnz7C7B5+xJywUgMTrE5kqkKigTIlbKhIinasnFihXQqxdcdpm5hPzy5b/fp2aQeBmdK0Ss/CkTFZo6e8iQIZy8KNmYMWNwOBwYhoHD4aCkpKRSCxSpTAs2pQAQFKDr9EVERKSS7d0Ld9wBH35obkdGwl13Qdeu9tYlIiJShnI3hHYfXxJTvMZZZ51ldwk+5fUlO/l642EA7hndzuZqpCooEyJWyoSIpyrJRV6eOSfQM89AQQE4HHDllfDII1CvXuW/nkgl0rlCxMqfMlHuhtA777zDLbfcQnh4eFXWIxVQUFBgdwk+Y+76Qzw2bwsA/Vom0K1RLXsLkiqhTIhYKRMinqokF4YBb71lNoMGDoRnn9WoIPEZOleIWPlTJsp93cyDDz5ITk5OVdYiFfTrr7/aXYLXc7sNnv9mO//8YA0AUSGB/PeKs3A4HDZXJlVBmRCxUiZEPFVaLn76yZw4GiAiAv7zH/jsM/juOzWDxKfoXCFi5U+ZKHdD6OS5g0R8wZ2f/sr0b7YB4HTAktsGaf4gEREROXM7d8JFF8E558D77/++f9w480N/dBIRER9RoUmlNarCu1xwwQV2l+D1tqRkAdCmbhRf/KsvwYFqBtVkyoSIlTIh4umMc5GZac4J9MILUFQETifs2lW5xYnYQOcKESt/ykSFfjseMmQI3bp1O+2HVJ/FixfbXYLXO5iRD8Ddo9uqGeQHlAkRK2VCxFOFc+FywauvQsuW8PTTZjNo+HBYvx4eeKAqShSpVjpXiFj5UyYqNEJoxIgRREZGVlUtUkHHjh2zuwSvlpyRT1pOEQBN4iNsrkaqgzIhYqVMiHiqcC6uugree8+83aaNuZLYyJG6NExqDJ0rRKz8KRMVagjdeuutJCYmVlUtUkEJCQl2l+DVlu1IA6BZQgQN47Q6nj9QJkSslAkRTxXOxbXXwrx55miga6+FoKAqqUvELjpXiFj5UybK3RDS/EHep1evXnaX4NU2JpvzB3VvHGtzJVJdlAkRK2VCxNNpc3H0KDz4IMTHw333mfv69IG9e82VxERqIJ0rRKz8KRNaZcyHffnll3aX4NWy8osBaFpbP8D5C2VCxEqZEPFUZi6Ki83Jolu0MP99/HE4cuT3+9UMkhpM5woRK3/KRLkbQrt37/aroVPi+w5nFwBQKyzY5kpERETEKxkGzJkDHTvCDTfAsWPm7TlzoHZtu6sTERGpUuVqCD3xxBPUrl0bp/OPD//pp5+YO3duuQt4+eWXadq0KaGhoXTv3p0ffvjhtMcXFhZy991307hxY0JCQmjevDn//e9/y/16NUnXrl3tLsFrGYbBlkPZALSpF2VzNVJdlAkRK2VCxFNpLnbuhBEj4LzzYOtWswH02muwdi0MGWJvkSLVSOcKESt/ykS55hDatGkTjRo1YsKECZx//vn06NGD2r/91cTlcrFp0yaWLl3K+++/z6FDh3j33XfL9eIzZ87kxhtv5OWXX6ZPnz689tprjBw5svT1ynLxxRdz+PBh3nzzTVq0aEFqaioul6ucn674i4MZ+aTnFhHgdNCuXrTd5YiIiIi3CQyEH36A4GC48Ua46y6IibG7KhERkWpTrhFC7777Lt999x1ut5tLL72UunXrEhwcTFRUFCEhIXTt2pX//ve/XHHFFWzZsoV+/fqV68WfffZZrr76aq655hratm3Lc889R8OGDXnllVfKPH7+/Pl8//33zJs3j6FDh9KkSRN69uxJ7969y/8Z1yBr1661uwSv9ebS3YC5wlhoUIDN1Uh1USZErJQJkRMUFsIXX/yei8aN4e23YfNmePJJNYPEb+lcIWLlT5ko9ypjnTp14rXXXuPVV19l/fr17Nmzh/z8fBISEujSpUuF5xcqKipi9erV3HHHHZb9w4cPZ/ny5WU+5osvvqBHjx5MmzaN9957j4iICM4//3wefvhhwsLCynxMYWEhhYWFpdtZWVkVqlN8j6vEzcJNhwG03LyIiIi/Mwz49FO47TbYtYuEBx74/b6JE20rS0RExG7lbggd53A46Ny5M507d/5TL5yWlkZJSQl16tSx7K9Tpw4pKSllPmbXrl0sXbqU0NBQPvvsM9LS0pgyZQpHjx495TxCjz/+OA8++KDH/lmzZhEeHs6FF17It99+S2ZmJomJifTs2ZM5c+YA0K1bN9xuN+vWrQNg7NixLF26lPT0dOLi4ujfvz+zZ88GzIZZUFAQq1evBmD06NGsWrWKw4cPEx0dzfDhw/n4448BaN++PZGRkfz0008AjBgxgg0bNnDw4EEiIiIYM2YMM2fOBKB169YkJCSwbNkyAIYOHcq2bdvYt28fAQHmyJeZM2fidrtp3rw5SUlJLFmyBICBAweyb98+du3aRWBgIBMmTOCTTz6hqKiIxo0b07x5c7777jsA+vbtS2pqKtu2bQNg0qRJfP755+Tl5dGgQQPatWvHggULAHMZvszMTDZt2gTAhAkTmD9/PtnZ2dStW5du3boxb948AM466ywKCgr49ddfAbjgggtYvHgxx44dIyEhgV69epXO4n78Ws3jHdnzzjuPFStWkJaWRmxsLAMHDuSzzz4DoGPHjoSGhrJy5UoARo0axZo1a0hJSWHG/igOHHMA0IVd/PJLMDExMaxYsQIwm46bNm3iwIEDhIeHM3bsWGbMmAFAq1atSExMZOnSpQAMHjyYnTt3snfvXoKDg7nooouYNWsWLpeLZs2a0ahRIxYvXgxA//79OXjwIDt37sTpdDJx4kQ+/fRTCgsLadSoEa1ateKbb74BoE+fPqSlpbF161YAJk6cyJw5c8jNzSUpKYkOHTrw9ddfA3D22WeTk5PDxo0bARg/fjwLFiwgKyuLOnXq0KNHj9J5u7p3705xcTHr168HYNy4cSxZsoSjR48SHx9P3759+fzzz82vTZcuOJ1O1qxZA8CYMWP4+eefSU1NJSYmhiFDhvDpp58C0KFDB8LDw/n5558BGDlyJL/88gvJyclERkYyatQoPvroIwDatGlDXFxcaWN32LBhbNmyhf379xMWFsa4ceP48MMPMQyDli1bUrdu3dK5wwYNGsSePXvYvXs3QUFBjB8/no8//pji4mKaNm1KkyZNWLRoEQD9+vUjJSWF7du343A4uOSSSwgMDGTGjBk0bNiQNm3asHDhQgB69+7N0aNH2bJlC2Beejpv3jxycnKoX78+nTt35quvvgKgZ8+e5OXlsWHDBgCffo8ICQnhwgsv1HsE1veIqKgozj33XGbNmgVAu3btaux7RFxcHFlZWXqP+O09Yvbs2eTn5+s9wo/eIza8+y7NXnyRxN/+b/Nq1cKZnc2GDRv0HqGfI/Qegfke0bVr19LvYX97j9DPEXqPKOs9wuVyceDAAZ99j8jLy6O8HIZN68knJyeTlJTE8uXL6dWrV+n+Rx99lPfee6/0i3mi4cOH88MPP5CSkkLMb8N6P/30U8aPH09ubm6Zo4TKGiHUsGFDMjMziY727bllFi5cyLBhw+wuw2sYhsGNM9fx+bpkAF76S1fGdKpvc1VSnZQJEStlQvxWcjLcfTe88445Qig0FG69FW67jYUrVigXIifQuULEytczkZWVRUxMTLl6HhUeIVRZEhISCAgI8BgNlJqa6jFq6Lh69eqRlJRU2gwCaNu2LYZhcODAAVq2bOnxmJCQEEJCQiq3eC+RlpZmdwle5cEvN5U2g/7aq7GaQX5ImRCxUibEL7ndMGgQ/DYSgUsvhccfh4YNAeVC5GTKhIiVP2WiXJNKV4Xg4GC6d+9eOszquIULF55ykug+ffqQnJxMTk5O6b5t27bhdDpp0KBBldbrjWJjY+0uwWvM33CIt5fvAeD8zvV5aGwHewsSWygTIlbKhPgNwzAbQQBOJ9x5J5xzDvz4I7z/fmkzCJQLkZMpEyJW/pQJ2y4ZA3Pum8svv5xXX32VXr168frrr/PGG2+wceNGGjduzJ133snBgwdLl7HPycmhbdu2nHPOOTz44IOkpaVxzTXXMGDAAN54441yvWZFhk95u4KCAkJDQ+0uw3bJGfn0m7aIErfBiPZ1eO3yHnaXJDZRJkSslAnxCz/+CFOnwpQpcPnl5j63GxwO8+MkyoWIlTIhYuXrmahIz+NPjxDKyspi9uzZbN68ucKPnThxIs899xwPPfQQXbp0YcmSJcybN4/GjRsDcOjQIfbt21d6fGRkJAsXLiQjI4MePXpw6aWXct555/HCCy/82U/DJx2f9MyfpecUMubFpZS4DQKcDp6b2NXuksRGyoSIlTIhNdr+/eblYL16mU2hRx+1jhIqoxkEyoXIyZQJESt/ykSF5xC6+OKL6d+/P//617/Iz8+nR48e7NmzB8Mw+PDDD7nooosq9HxTpkxhypQpZd739ttve+w7cTZv8W/FJW7OevQb3L+NcZtzfV/CggPsLUpERESqVk4OTJsGTz0FBQVm42fyZLMh5LRtNgQRERGfU+Gz5pIlS+jXrx9gds4MwyAjI4MXXniBRx55pNILlFPr2LGj3SXYpqC4hFb3fFXaDHpkXAfa1vPtSwDlz/PnTIiURZmQGmfuXGjVCh5+2GwG9e8Pq1bBW29B/fItJqFciFgpEyJW/pSJCjeEMjMziYuLA2D+/PlcdNFFhIeHM3r0aLZv317pBcqp+fJ1jX+GYRhMnbmO47Nf3TWqDZed09jeosQr+GsmRE5FmZAaJzoaDh2Cpk3h449h8WLo1q1CT6FciFgpEyJW/pSJCjeEGjZsyIoVK8jNzWX+/PkMHz4cgGPHjvnVF84brFy50u4SbPHwnM18tSEFgGsHNOPv/ZvbXJF4C3/NhMipKBPi83btglmzft/u1w8++ww2b4aLLjrlPEGno1yIWCkTIlb+lIkKN4RuvPFGLr30Uho0aED9+vUZOHAgYF5K5k9Dq8Qen6w+wH+X7Qbg8nMac+fItjZXJCIiIpUuKwtuvx3atjXnB9q///f7xo2DkBDbShMREakpyrXsfFZWlmW5stWrV7Nv3z6GDRtGZGQkAHPnzqVWrVr06dOn6qqtBDVp2fnMzExiYmLsLqPaLN+Zxl/e+AmABrFhLLl1EE5nxf8yKDWXv2VC5I8oE+JzSkrgzTfhnnvgyBFz37Bh8Mor0LxyRgQrFyJWyoSIla9notKXnY+NjSU1NRWAwYMH07x5cy644ILSZhDA6NGjvb4ZVNOsWbPG7hKqzaHMfK57bzUATgfMvb6fmkHiwZ8yIVIeyoT4lG+/ha5d4dprzWZQ69YwZw58/XWlNYNAuRA5mTIhYuVPmSjXsvORkZGkp6eTmJjI4sWLKS4uruq6pBxSUlLsLqFabDiYyZgXlwIQEuhk5T1DiQ4Nsrkq8Ub+kgmR8lImxGccPgyjR0NhIcTGwgMPwD/+AUGVf75XLkSslAkRK3/KRLkaQkOHDmXQoEG0bWvO13LBBRcQHBxc5rHfffdd5VUnpxUVFWV3CVXG7TZ4Yv4W3vhhFyde1Pi/a85WM0hOqSZnQuRMKBPi1fLyIDzcvF2njjlnUEYG3H8//LaibVVQLkSslAkRK3/KRLnmEMrPz+edd95h586dPPPMM/ztb38j/PgJ/CTTp0+v9CIrU02aQ8jlchEYWK6ens9pcsdcy3a9mFBuGd6ai7o3sKki8QU1ORMiZ0KZEK9UXAyvvgoPPmheEnbOOdX68sqFiJUyIWLl65moSM+jXJ9lWFgY1113HQCrVq3iySefpFatWn+6UPlzZs2axaRJk+wuo9L1ecI6ymzJrYNoFF92A1LkRDU1EyJnSpkQr2IYMG8e3HILbNli7nvllWpvCCkXIlbKhIiVP2Wiwm2vRYsWVUUdIgD8d+luDmbkA9CsdgTf3TzQ3oJERETkz9uwAW6+GRYsMLcTEuDhh+Gaa+ytS0RExI+VqyF000038fDDDxMREcFNN9102mOfffbZSilM/li7du3sLqFSlbgNHpqzqXT76xv721iN+KKalgmRP0uZEK9w333w6KPgdpuTRN9wA9x9N9g02ly5ELFSJkSs/CkT5WoIrV27tnRlsTVr1uBwaLlvbxATE2N3CZXqto/Xl95eefdQggKcNlYjvqimZULkz1ImxCs0aWI2gy64AKZNgxYtbC1HuRCxUiZErPwpE+VqCJ14mdjixYurqhapoBUrVtCkSRO7y6gUr36/k0/WHADgvjHtqB0VYnNF4otqUiZEKoMyIdXOMODzzyEgAM47z9w3eTK0bQu9etlb22+UCxErZULEyp8yUeEhGFdddRXZ2dke+3Nzc7nqqqsqpSjxL1/8kswTX5mTS57VJJar+ja1uSIRERGpsHXrYPBgcyTQlCnmsvJgNoe8pBkkIiIivyvXsvMnCggI4NChQyQmJlr2p6WlUbduXVwuV6UWWNlq0rLz6enpxMfH213GGcsvKuG1JTt57pvtALSvH82c6/vqkkQ5Y76eCZHKpkxItUhJgXvugf/+1xwhFBpqTiB9110Q7n2rhCoXIlbKhIiVr2eiIj2Pco8QysrKIjMzE8MwyM7OJisrq/Tj2LFjzJs3z6NJJFVr06ZNf3yQl8kvKuHDn/fR8f6vaXvf/NJmEMD7V5+tZpD8Kb6YCZGqpExIlcrPh8ceg5Yt4c03zWbQJZeYS8o/8ohXNoNAuRA5mTIhYuVPmSj3svO1atXC4XDgcDho1aqVx/0Oh4MHH3ywUouT0ztw4IDdJVTIwYx8+jzxncf+S85qyD1j2hEZUu5vR5Ey+VomRKqaMiFVavVqc7UwgJ49Yfp06N3b3prKQbkQsVImRKz8KRPl/g180aJFGIbB4MGD+eSTT4iLiyu9Lzg4mMaNG1O/fv0qKVLKFu6lf3k7lZs/WmfZ/muvxtw1qi2hQQH2FCQ1jq9lQqSqKRNS6Q4fhjp1zNt9+8K//gXnnAOTJoHTN1YHVS5ErJQJESt/ykSF5xDau3cvjRo18tlLe2rSHEK+ZPmONP7yfz8B8ME1Z9O7RYLNFYmIiEi57d8Pd94Js2fD1q2QlGR3RSIiIlKGSp9DaP369bjdbgAyMzP59ddfWb9+fZkfUn1mzJhhdwnlNnvdQcBcRUzNIKkqvpQJkeqgTMiflpsL998PrVvD//5nbn/1ld1V/SnKhYiVMiFi5U+ZKNclY126dCElJYXExES6dOmCw+GgrIFFDoeDkpKSSi9SfFuJ22D2umQAzu+ivyiKiIh4Pbcb3nvPXCks2TyH07evOU9Qjx721iYiIiKVolwNod27d1O7du3S2+Idyprc2xu9+v1OilzmCLMR7erYXI3UZL6SCZHqokzIGXG7YcAAWLrU3G7aFKZNg4suAh+dMuBEyoWIlTIhYuVPmShXQ6hx48Zl3hZ7JSYm2l3CH1p/IIOnvt4KwFV9mpIYHWpzRVKT+UImRKqTMiFnxOmEPn3gl1/gnnvg3/+G0Jpz/lYuRKyUCRErf8pEhZeDeOedd5g7d27p9m233UatWrXo3bs3e/furdTi5PSWHv/LnRc73gyKCg3kjpFtbK5GajpfyIRIdVImpFyysswJo3/++fd9d98N27fDbbfVqGYQKBciJ1MmRKz8KRMVbgg99thjhIWFAbBixQpeeuklpk2bRkJCAlOnTq30AsV37UnL5YftaQB8NqUPwYG+sRytiIiIXygpgTfegJYt4Ykn4MYb4fgckVFRvy8vLyIiIjVSuS4ZO9H+/ftp0aIFALNnz2b8+PH8/e9/p0+fPgwcOLCy65PTGDx4sN0lnNZdn/0KQM8mcbRIjLS5GvEH3p4JkeqmTMgpffcdTJ0Kx1eIbdXKnEDaDygXIlbKhIiVP2WiwkM2IiMjSU9PB2DBggUMHToUgNDQUPLz8yu3OjmtnTt32l3CKaVkFrBil/l9clXfJvYWI37DmzMhYgdlQjxs3w7jxsGQIWYzqFYtc+WwX3+FMWNqxKTRf0S5ELFSJkSs/CkTFW4IDRs2jGuuuYZrrrmGbdu2MXr0aAA2btxIkyZNKrs+OQ1vnrPpy1+SMQyoFR7EuR3q2V2O+AlvzoSIHZQJ8fD99/D55xAQANdfDzt2mJeKBQfbXVm1US5ErJQJESt/ykSFG0L/+c9/6NWrF0eOHOGTTz4hPj4egNWrVzNp0qRKL1BOLdhLf3gzDIM3ftgFwKSejWyuRvyJt2ZCxC7KhOBywbZtv29feaXZCPr1V3jhBfjt5zh/olyIWCkTIlb+lAmHYRyfPdA/ZGVlERMTQ2ZmJtHR0XaXUyO9/+Ne7pm9AYC19w4jNsJ/AiUiIuI15s+Hm26CnBzYuhV+WxREREREaq6K9DzOaNmnjIwMnnnmGa655hr+9re/8eyzz5KZmXlGxcqZmzVrlt0leDiWW8RDX24C4IreTdQMkmrljZkQsZMy4ac2bYKRI82PzZshLw82brS7Kq+hXIhYKRMiVv6UiQo3hFatWkXz5s2ZPn06R48eJS0tjenTp9O8eXPWrFlTFTXKKbhcLrtL8PDOij0UlbgJCwrg9nPb2F2O+BlvzISInZQJP5OWBv/6F3TqZI4OCgqCm2825wnq0cPu6ryGciFipUyIWPlTJiq87PzUqVM5//zzeeONNwgMNB/ucrm45ppruPHGG1myZEmlFylla9asmd0lWBzJLuS5b7YDcNu5rQkLDrC5IvE33pYJEbspE34kJQXatoWMDHN73Dh46ilo0cLOqrySciFipUyIWPlTJircEFq1apWlGQQQGBjIbbfdRg/99alaNWrkXRM2Pzl/S+nty89pbGMl4q+8LRMidlMm/EjdujBoEOzeDc8+a96WMikXIlbKhIiVP2WiwpeMRUdHs2/fPo/9+/fvJyoqqlKKkvJZvHix3SWUyity8fHqAwBc1acpgQFnND2VyJ/iTZkQ8QbKRA32yy8wZgz8P3v3HR5FvbZx/LvpjSxNCB2kSJcmCNKbgCKICHY5x4YNAcuxCxasgIfjET0ior4KiCIoIkWlCkoXMDTpJQEhkARC+rx/DCwMGyAbkp3d7P25rlzszs7uPEm4N+ThV/bvP3Ns4kRYtUrNoItQLkSslAkRq0DKhMe/tQ8cOJB77rmHqVOnsnfvXvbt28eUKVO49957te18AHvky7UABDngXz2vsLkaERGRYioxEe67D5o2hR9+gBdfPPNYyZIQrOnaIiIikj8eTxl75513cDgc3HXXXa7FlkJDQ3nwwQd54403Cr1AOb/27dvbXQIAL87cyC+bDwHwcp+GhIfoH6NiD1/JhIivUCaKkfR0GDsWRo0yt5EHGDgQXnjB3rr8kHIhYqVMiFgFUiY8HiEUFhbGv//9b44ePcq6detYu3YtSUlJjB07lvDw8KKoUc5j/9nDxG2yevdRPlu+G4DLy0Zzh9YOEhv5QiZEfIkyUUxMn24uGP3ss2Yz6KqrYOlSmDIFqle3uzq/o1yIWCkTIlaBlIkCL/QSFRVFyZIlKV26NFFRUYVZk+TT9u3bbb3+/mMnue+zVQCUiQ5j/vAOttYjYncmRHyNMlFM/PYb7NoFlSrB55+b96+5xu6q/JZyIWKlTIhYBVImPG4IZWdn88ILL+B0OqlevTrVqlXD6XTy/PPPk5WVVRQ1ynkEBdm7cPMLMzaSdCITgOkPtSE4yGFrPSJ2Z0LE1ygTfmrfPtiy5cz9554zp4pt2QJ33AH6vl4S5ULESpkQsQqkTDgMwzA8ecLgwYP59ttvefnll2ndujUAy5cvZ8SIEfTp04cPPvigSAotLCkpKTidTpKTk4mNjbW7HL/1+uxNfLh4BwCf/rMlHepcZnNFIiIifi4tDd5+G958E5o1gyVLwKH/bBEREZH886Tn4XHra/LkyUyaNIkHHniAxo0b07hxYx544AEmTpzI5MmTC1y0eG769Om2Xfv3nUkAhAY71AwSn2FnJkR8kTLhJ3Jz4f/+D+rUgREj4ORJ8/jRo7aWVVwpFyJWyoSIVSBlwuOGUEREBNXzWMCwevXqhIWFFUZNkk8ZGRm2XTs9KweAt/tfaVsNIueyMxMivkiZ8APLlsHVV8Odd8L+/VCtGkydao4OKl3a7uqKJeVCxEqZELEKpEx43BB6+OGHeeWVVyxfpIyMDF577TUeeeSRQi1OLqxq1aq2XXtzYioA5WMjbKtB5Fx2ZkLEFykTPu6nn8zFoVeuhJgYeP112LwZBgzQVLEipFyIWCkTIlaBlIkQT5+wdu1afv75ZypXrsyVV5qjQ/744w8yMzPp0qUL/fr1c50bSEOt7FCnTh3brh0XG0FiSjphIYGz4Jb4PjszIeKLlAkfZBhnmj2dOkGTJtCiBbzyCsTF2VpaoFAuRKyUCRGrQMqEx7/NlyxZkptuuonrr7+eKlWqUKVKFa6//nr69euH0+m0fEjR+umnn2y7dmJKOgAlo0Jtq0HkXHZmQsQXKRM+JCcHJkyA1q0h3fwZSnCwuYX8Rx+pGeRFyoWIlTIhYhVImfB4hNAnn3xSFHWIHzm9fhBAiXCP/wqJiIgElgULYNgw+OMP8/7//gdDhpi3w8Ptq0tEREQCmub7+LFrrrnGluumZZ5pCJWN0T9kxXfYlQkRX6VM2GzbNujbFzp3NptBTieMHg2DB9tdWUBTLkSslAkRq0DKhBpCfuzw4cO2XDcrJxeAkCAHQUFa9FJ8h12ZEPFVyoRNcnLg8cehQQOYOdOcGvbQQ/DXXzB8OGhXVlspFyJWyoSIVSBlQg0hP7ZlyxZbrpuZbTaEQoP110d8i12ZEPFVyoRNgoNh+3bIyoIePWD9evjvf6FsWbsrE5QLkXMpEyJWgZQJ/UYvHjualglAjmHYXImIiIiPmDsXEhLO3H/nHZg9G378EerXt68uERERkfNwGMal/1Z/7NgxSpYsWQjlFL2UlBScTifJycnExsbaXc4lyc3NJSjI+z29nzcd5J5PVwGw643rvH59kfOxKxMivkqZ8IJNm+CJJ8zmzz/+ARMn2l2RXIRyIWKlTIhY+XsmPOl5ePxZvvnmm0ydOtV1f8CAAZQpU4ZKlSrxx+ndM8QrZs2aZct1k09mAVBKW86Lj7ErEyK+SpkoQkeOwKOPQqNGZjMoJARKlwaNnvV5yoWIlTIhYhVImfC4IfThhx9SpUoVAObPn8/8+fP58ccf6dmzJ08++WShFyjnd+LECVuuezwjG4D6Ff17hJUUP3ZlQsRXKRNFIDMT3n0XatWC994zF5Du0wfi481pYg5ttuDrlAsRK2VCxCqQMhHi6RMSEhJcDaFZs2YxYMAAunfvTvXq1WnVqlWhFyjnV6lSJVuueyzNHCFUtXS0LdcXOR+7MiHiq5SJIvD22/D88+btxo1h7FhzW3nxG8qFiJUyIWIVSJnweIRQqVKl2Lt3LwBz5syha9euABiGQU5OTuFWJxfUsGFDW6574NhJAEpqypj4GLsyIeKrlIlCkp195vbDD5vbyf/vf7BmjZpBfki5ELFSJkSsAikTHjeE+vXrx2233Ua3bt04cuQIPXv2BGDdunXUqlWr0AuU85s7d64t1916MBWAkpFqCIlvsSsTIr5KmbhEBw/C/fdDt25n1gYqWRI2bID77jO3lxe/o1yIWCkTIlaBlAmPp4yNHTuW6tWrs3fvXt566y1iYmIAcyrZQw89VOgFim9JPpnFnwdSALj68jI2VyMiIlIE0tPh3/+G116DVPM/QVi+HNq0MW9rnSAREREpBjxuCIWGhvLEE0+4HR86dGhh1CMesGPNpikr9pCRnUu5EuE0rOT0+vVFLkTrmIlYKRMeMgz4+mt46inYtcs81ry5uU7Q6WaQ+D3lQsRKmRCxCqRM5Ksh9N1339GzZ09CQ0P57rvvLnjuDTfcUCiFycUdP37cq9fLzsll0rJdADzauRbBQfofUvEt3s6EiK9TJjxw8CD07w9Ll5r3K1aE11+HO+6AII9n2IsPUy5ErJQJEatAykS+GkJ9+/YlMTGRcuXK0bdv3/Oe53A4tLC0F/355580btzYa9ebsnIvCcnpOCND6d+8iteuK5Jf3s6EiK9TJjxQpgwcOwaRkfDkk+YooWjtplkcKRciVsqEiFUgZSJfDaHc3Nw8b0tgWbPnKAB3XF2VyDAtpCkiIn4sLQ0++AAeeggiIiAkBD7/3GwMVdF/eoiIiEjx5zCM09tmBIaUlBScTifJycnExsbaXc4lycrKIjTUezt9XfXaT/ydmsGrfRtyx9XVvHZdkfzydiZEfJ0ykYfcXJg8GZ5+GvbtgzfegH/9y+6qxIuUCxErZULEyt8z4UnPo0CT4k+cOMHs2bP54IMPGDdunOVDvGfevHm2XDegOojiV+zKhIivUibOcXqnsDvuMJtBVatC7dp2VyVeplyIWCkTIlaBlAmPdxlbu3YtvXr1Ii0tjRMnTlC6dGkOHz5MVFQU5cqVY8iQIUVRp+QhJSXFq9dLPpkFQNXSUV69rkh+eTsTIr5OmThl925zRNCUKeb9mBh45hkYNsxcM0gCinIhYqVMiFgFUiY8HiE0bNgwevfuTVJSEpGRkfz222/s3r2b5s2b88477xRFjXIe5cuX9+r1Skaaw+ZKR4V59boi+eXtTIj4OmXilMcfN5tBDgf885+wdSs8+6yaQQFKuRCxUiZErAIpEx43hNatW8fjjz9OcHAwwcHBZGRkUKVKFd566y2effbZoqhRzqNFixZevd7JTHMHuehwLSgtvsnbmRDxdQGbiZwcOHvL2FGjoFs3WL0aPv4YKlSwrzaxXcDmQuQ8lAkRq0DKhMcNodDQUBwOB2B2zvbs2QOA0+l03Rbv+OGHH7x2reycXFIzsgFwRvrvAltSvHkzEyL+ICAzsWgRXHUVDB165lidOjBvHjRtaltZ4jsCMhciF6BMiFgFUiY8XkOoadOmrFq1ijp16tCpUydefPFFDh8+zOeff06jRo2KokbxAcdPNYMASkSoISQiIj5m+3Z46imYPt28v2sXvP02lCpla1kiIiIivsrjEUKjRo2iwqmh1q+88gplypThwQcf5NChQ/zvf/8r9ALl/Jo3b+61ax05kQlAifAQwkIKtDmdSJHzZiZE/EFAZCI52WwE1a9vNoOCguDBB2HLFjWDJE8BkQsRDygTIlaBlAmPRggZhsFll11GgwYNALjsssuYPXt2kRQmF5eVleW1ayUcSwcgJsLjQWUiXuPNTIj4g2KfiWXLoG9f+Ptv83737jBmDJz6d4pIXop9LkQ8pEyIWAVSJjwa6mEYBrVr12bfvn1FVY94YP369V671tK/DgNntp4X8UXezISIPyj2mahXD3JzoW5d+OEHmDNHzSC5qGKfCxEPKRMiVoGUCY8aQkFBQdSuXZsjR44UVT3igwzDYOa6/QA80L6mzdWIiEjA2rwZnnsODMO8X6oULFgA69dDr17mtvIiIiIiki8Owzj9r6r8+eGHH3jjjTcYP348DRs2LKq6ikxKSgpOp5Pk5GRiY2PtLueSnDx5ksjIyCK/zqz1B3jky7UArHiuC+VKRBT5NUUKwluZEPEXxSYTSUkwciS8/z5kZ5trBd14o91ViZ8qNrkQKSTKhIiVv2fCk56Hx6sD33HHHaxYsYIrr7ySyMhISpcubfkQ71m8eHGRXyMn12DEd/EAdK5bTs0g8WneyISIP/H7TGRlwbhxUKuW+Wd2NvTurWlhckn8PhcihUyZELEKpEx4vELw2LFjcWhItk9ISkoq8muM+3kbh49nAPDC9fWL/Hoil8IbmRDxJ36bCcMw1wR64glztzCARo3MBaO7drW3NvF7fpsLkSKiTIhYBVImPG4IDRo0qAjKkIIoU6ZMkb5+dk4u//55GwD/uKY6NcpGF+n1RC5VUWdCxN/4bSZyc+Hpp81m0GWXwauvwj33QHCw3ZVJMeC3uRApIsqEiFUgZcLjNYQ6derEHXfcQf/+/XE6nUVVV5EpTmsIpaWlERUVVWSv//L38Uz8dScAG0deS0y4tpwX31bUmRDxN36Vib//hhIlIOLU1OT58+Gnn+DZZ8EP/70hvsuvciHiBcqEiJW/Z6JI1xBq1KgRzz//PHFxcdx0003MmDGDzMzMAhcrBTdz5swiff2fNh0EYFCb6moGiV8o6kyI+Bu/yERGBrz99pl1gk7r1g3efFPNICl0fpELES9SJkSsAikTHjeExo0bx/79+5k5cyYlSpTg7rvvJi4ujvvvv59FixYVRY1ig9W7k9iTlAbAo51r2VyNiIgUO4Zh7hZWvz489RSkpMCcOWe2lBcRERGRIuVxQwggKCiI7t27M2nSJA4ePMiHH37IihUr6Ny5c2HXJxfQpEmTInvtL37bA8CVlZ2UiQkvsuuIFKaizISIP/LZTKxZA506wU03wY4dUKECTJpkThHTxhVSxHw2FyI2USZErAIpE5c0DygxMZEpU6bwf//3f6xfv56rrrqqsOqSfAgKKlA/76KWbPub6Wv3A9C/eeUiuYZIUSiqTIj4K5/MxHvvwZAh5kigiAh48klzhFBMjN2VSYDwyVyI2EiZELEKpEx4/JmmpKTwySef0K1bN6pUqcL48ePp3bs3W7du5ffffy+KGuU81qxZU+ivmXwyi4f+z3zdEhEh3NqyaqFfQ6SoFEUmRPyZT2aia1cICYHbbjN3EXv5ZTWDxKt8MhciNlImRKwCKRMejxAqX748pUqVYsCAAYwaNUqjgoqZV2fFk5qRTXhIEHOGtickOHC6oyIiUsgMA6ZMgU2bzMYPQN268NdfUFX/4SAiIiJiJ4+3nZ83bx5du3b122FUxWnb+dTUVEqUKFFor7fvaBpt31wAwCeDrqJT3XKF9toi3lDYmRDxd7Zm4rffYNgw80+HA9atg8aN7alF5Cz6WSFipUyIWPl7Jop02/nu3bv7bTOouFmxYkWhvt6z324EoEHFWDWDxC8VdiZE/J0tmdi7F26/HVq3NptB0dHm6KDatb1fi0ge9LNCxEqZELEKpExc0qLSYq9Dhw4V2muNnb+VxVv/BuCJ7lcU2uuKeFNhZkKkOPBqJk6cgDffhLffhvR0c1TQoEHw6qtQsaL36hC5CP2sELFSJkSsAikTagj5MafTWSivMz/+IP/+eRsAg9pU1+gg8VuFlQmR4sKrmcjMhP/+12wGtW8PY8dCs2beu75IPulnhYiVMiFiFUiZ8HgNIX9XnNYQysjIIDw8/JJeY/vfx+nz3q8cz8imXe2yfH5Pq0KqTsT7CiMTIsVJkWdi7Vpo0sQcDQTwxRcQGQk33njmmIiP0c8KEStlQsTK3zNRpGsInS09Pf1Sni6XaPr06Zf8Go9/9QfHM7KpVDKS/96u/8kV/1YYmRApToosEzt2QP/+5gig7747c/z226FfPzWDxKfpZ4WIlTIhYhVImfC4IZSbm8srr7xCpUqViImJYceOHQC88MILfPzxx4VeoBSdjxbvYN3eYzgcMHZgE2IjQu0uSUREfFlKCvzrX1CvHnzzDQQFwYYNdlclIiIiIgXgcUPo1VdfZdKkSbz11luEhYW5jjdq1IgJEyYUanFyYQ0bNryk53+7dj8AhgEta5QujJJEbHWpmRApbgotEzk58L//Qa1a8NZb5npB3bqZW8k//3zhXEPES/SzQsRKmRCxCqRMeNwQ+uyzz/jf//7H7bffTnBwsOt448aN2bx5c6EWJxcWFRVV4OeeyMgmPiEFgM/vaVlYJYnY6lIyIVIcFVombr0VHngA/v4b6tSBWbNg7lxo1KhwXl/Ei/SzQsRKmRCxCqRMeNwQ2r9/P7Vq1XI7npubS1ZWVqEUJfmzYsWKAj/3RGa263bbWmULoxwR211KJkSKo0LLxD/+AaVKwbvvwsaNcN11WidI/JZ+VohYKRMiVoGUCY+3nW/QoAFLliyhWrVqluPTpk2jadOmhVaYFK3j6WZDyOEAh/5RLyIipyUlwcsvQ40a8Nhj5rGePWHXLvDz3TlFRERE5AyPG0IvvfQSd955J/v37yc3N5fp06ezZcsWPvvsM2bNmlUUNcp59OzZs8DPzck1AHP9IJHi4lIyIVIceZSJrCz44AMYMcJsCsXGwt13Q8mS5uNqBkkxoZ8VIlbKhIhVIGXC4yljvXv3ZurUqcyePRuHw8GLL77Ipk2b+P777+nWrVtR1Cjn8ccffxT4uVk5ZifoshLhhVWOiO0uJRMixVG+MmEYMHs2NG4MQ4aYzaCGDeHrr880g0SKEf2sELFSJkSsAikTHo8QArj22mu59tprC7sW8dCBAwcK/NzTI4RCgzRdTIqPS8mESHF00Uxs2waPPmouEA1Qtiy88grcey+EFOifCCI+Tz8rRKyUCRGrQMqE/rXnx2JiYgr83KzcXACCg9UQkuLjUjIhUhxdNBPZ2fDTTxAaCkOHwnPPgdPpldpE7KKfFSJWyoSIVSBlwmEYF19FplSpUvleeDgpKemSiypKKSkpOJ1OkpOTifXz9RBycnIIDg4u0HNX7ExiwIfLubxsNL880bFwCxOxyaVkQqQ4cstERgYsXgxnT/H+5BNo3x5q1vR+gSI20M8KEStlQsTK3zPhSc8jX2sIvfvuu4wdO5axY8fy/PPPA+a0sREjRjBixAjX9LEXXnjB42Lff/99atSoQUREBM2bN2fJkiX5et6vv/5KSEgITZo08fiaxcVXX31V4OemZ+UAEBbi8TJSIj7rUjIhUhy5MmEY8O230KAB9OgBGzacOekf/1AzSAKKflaIWCkTIlaBlIl8TRm7++67XbdvuukmXn75ZR555BHXsSFDhvDee+/x008/MWzYsHxffOrUqQwdOpT333+fa665hg8//JCePXsSHx9P1apVz/u85ORk7rrrLrp06cLBgwfzfT05IzPbnDIWHuq/nU8REcmHtWth+HBYuNC8HxcHBw5Ao0a2liUiIiIi9vJ4eMjcuXPp0aOH2/Frr72Wn376yaPXGjNmDPfccw/33nsv9erV491336VKlSqMHz/+gs974IEHuO2222jdurVH1ytu6tatW+DnZp9aQ0iLSktxcimZECl2EhLoPnUqNG9uNoMiIsw1grZuBW0MIQFMPytErJQJEatAyoTHDaEyZcrw7bffuh2fMWMGZcqUyffrZGZmsnr1arp372453r17d5YtW3be533yySds376dl156KV/XycjIICUlxfJRXJQuXbrAz808te18iBaVlmLkUjIhUqxkZ8PVV1Nm5kxzutgtt8DmzfDqq1CihN3VidhKPytErJQJEatAyoTHu4yNHDmSe+65h4ULF7pG6Pz222/MmTOHCRMm5Pt1Dh8+TE5ODuXLl7ccL1++PImJiXk+Z9u2bTz99NMsWbKEkHxuh/v6668zcuRIt+PTpk0jKiqKfv368fPPP5OcnEy5cuVo2bIls2bNAqBZs2bk5uaybt06APr06cPSpUs5cuQIpUuXpn379syYMQOAxo0bExoayurVqwG47rrrWLVqFQcPHiQ2Npbu3bvz9ddfA9CgQQNiYmL4/fffAXN01caNG9m/fz/R0dFcf/31TJ06FYArrriCsmXL8uuvvwLQtWtXtm7dyp49e0hISGD48OFMnTqV3NxcatasSaVKlVi8eDEAHTt2ZM+ePezYsYOQkBBuvvlmvvnmGzIzM9kTFAfAkUOHmDx5Mm3btuXQoUNs3boVgFtvvZWZM2eSlpZG5cqVqV+/PvPmzQOgdevWJCcnEx8fD8DNN9/MnDlzSE1NJS4ujmbNmjF79mwArrrqKtLT09lwar2KG2+8kYULF3L06FHKli1L69at+f777wFo2rQpAGvXrgWgd+/eLF++nMOHD1OqVCk6duzoakY2atSIiIgIVq5cCUCvXr1Ys2YNiYmJlChRgh49ejBt2jQA6tevj9PpZPny5YDZdIyPj2ffvn1ERUXRp08fJk+eDECdOnUoV64cS5cuBaBz585s376d3bt3ExYWxk033cS0adPIzs7m8ssvp2rVqiw8NQ2jffv27N+/n+3btxMUFMTAgQOZPn06GRkZVK1alTp16rhG0V1zzTUcPnyYLVu2ADBw4EBmzZrFiRMnqFSpEg0bNmTuqW2gW7VqxfHjx/nzzz8B6N+/P/PmzSMlJYXy5cvTokULfvjhBwCaN29OVlYW69evB6Bv374sXryYpKQkypQpQ9u2bZk5cyYATZo0ISgoiDVr1gBw/fXXs2LFCg4dOoTT6aRLly5Mnz4dgIYNGxIVFcWKFSsA6NmzJ3/88QcHDhwgJiaGXr16ueba1q1bl9KlS7sau926dWPz5s3s3buXyMhI+vbty5QpUzAMg9q1axMXF+daO6xTp07s2rWLnTt3EhoaSv/+/fn666/JysqiRo0aVK9enQULFgDQrl07EhMT2bZtGw6Hg1tuuYXp06cTFxdHlSpVqFu3LvPnzwegTZs2JCUlsXnzZgAGDBjA7NmzOX78OBUrVuTKK6/kxx9/BKBly5akpaWxceNGAL9+jwgPD6dfv34Feo+oVq0aNWvW5JdffgHQe4Q/vEec+jvb/+abmTdvHhU6dKDi4sWUmDCB7/7+G5Yto3lGRkC/R8yYMYOTJ0/qPSLA3yP2799Pjx49Au89Qv+O0HvEed4j1q1bR2RkJKD3iID+d4TeI1zvEfv37+eWW27x2/eItLQ08itfu4yd6/fff2fcuHFs2rQJwzCoX78+Q4YMoVWrVvl+jQMHDlCpUiWWLVtmmfr12muv8fnnn7u+mKfl5ORw9dVXc8899zB48GAARowYwYwZM1xfoLxkZGSQkZHhup+SkkKVKlWKxS5jkydP5tZbby3Yc1fs4ZnpG+harxwT7r6qkCsTscelZELEr61YAcOGwb/+BTfcYB7LyWHylCncevvt9tYm4mP0s0LESpkQsfL3THiyy5jHI4TA7CJ+8cUXBSrutLJlyxIcHOw2GujQoUNuo4YAUlNTWbVqFWvXrnUtaJ2bm4thGISEhDBv3jw6d+7s9rzw8HDCw8MvqVZf1e3sbYM9lHwyC4DYiNDCKkfEdpeSCRG/tG8fPPMM/N//mfdHjIDevcHhgOBgummtIBE3+lkhYqVMiFgFUiZs23M8LCyM5s2bu4ZZnTZ//nzatGnjdn5sbCwbNmxg3bp1ro/Bgwe7hjl6MjqpuDh3FJUn/k41R02VjAorrHJEbHcpmRDxKydOwEsvQZ06Z5pBgwbBrFlmM+gUZULEnXIhYqVMiFgFUiYKNEKosAwfPpw777yTFi1a0Lp1a/73v/+xZ88e15SwZ555hv379/PZZ58RFBREw4YNLc8vV64cERERbscDxd69ewv83N1HzHmFcc7iOXpKAtOlZELEb3z/PQwebG4dD9CuHYwda+4mdg5lQsSdciFipUyIWAVSJmxtCA0cOJAjR47w8ssvk5CQQMOGDZk9ezbVqlUDICEhgT179thZok87vfhbQfyx7xgA9Sr49zpKIme7lEyI+I2gILMZVKMGvP029OtnGRV0NmVCxJ1yIWKlTIhYBVImCrSotD/zZIGl4up4RjYNXzJXlV/3YjdNGxMR8WU7d0J8PFx3nXnfMGDKFLjxRoiIsLc2EREREfEpnvQ8PF5D6ODBg+d97PT2c+IdU6ZMKdDzko5num6rGSTFSUEzIeKTUlLMBaPr1YPbb4fDh83jDgfcemu+mkHKhIg75ULESpkQsQqkTHjcEGrUqBHfffed2/F33nknIBd2tlNBB3dlZOcAEBNu64xBkUIXYAMepbjKyYGPPoLateGNNyAjA1q0gNRUj19KmRBxp1yIWCkTIlaBlAmPG0L/+te/GDhwIIMHD+bkyZPs37+fzp078/bbbzN16tSiqFHOo3bt2gV6XkZ2LqCGkBQ/Bc2EiM/45Rdo1gzuvx8OHTJ3EfvuO5g/31wzyEPKhIg75ULESpkQsQqkTHjcEXj88cfp2rUrd9xxB40bNyYpKYmrr76a9evXU758+aKoUc4jLi6uQM9LOmFOGQsOynsRUhF/VdBMiPiEPXuge3dzhFDJkua28g89BGEFn9qrTIi4Uy5ErJQJEatAyoTHI4QALr/8cho0aMCuXbtISUlhwIABagbZYMmSJQV6XtCp3Wj2HztZmOWI2K6gmRCxTUbGmdtVq8Ijj8Cjj8Jff8HQoZfUDAJlQiQvyoWIlTIhYhVImfC4IfTrr7/SuHFj/vrrL9avX8/48eN59NFHGTBgAEePHi2KGqWQZeWaU8YaVAzMXdZERGyXnQ3//S9UqwYbN545PnYsjBsHZcrYV5uIiIiIBASPG0KdO3dm4MCBLF++nHr16nHvvfeydu1a9u3bR6NGjYqiRjmPTp06Feh5OTnmIlkhwQUaICbiswqaCRGvmjMHGjc2RwMdPAjvvXfmMUfhTuVVJkTcKRciVsqEiFUgZcLjjsC8efN44403CA0NdR2rWbMmS5cu5YEHHijU4uTCdu3aVaDnZZ8aIRSqNYSkmCloJkS8Ij4eevY0PzZtMkcBvf++tSFUyJQJEXfKhYiVMiFiFUiZ8Lgh1KFDh7xfKCiIF1544ZILkvzbuXNngZ53MMVcs6KQ/yNaxHYFzYRIkXvuOXNU0Jw5EBoKjz9urhP04IMQUnQ7PioTIu6UCxErZULEKpAy4fG/Ql9++eULPv7iiy8WuBjxzNmjtDwRfWq7+T1JaYVZjojtCpoJkSJXtqy5e1jfvvD221Crllcuq0yIuFMuRKyUCRGrQMqEwzAMw5MnNG3a1HI/KyuLnTt3EhISQs2aNVmzZk2hFljYUlJScDqdJCcnExsbmIsqf/n7Hp79dgPd6pfno7ta2F2OiEjxYhjw3XdQogR07mwey8yE33+Hdu3srU1EREREijVPeh4eTxlbu3at5WPjxo0kJCTQpUsXhg0bVuCixXNff/11gZ6XlpkNQFiIFpWW4qWgmRApNH/8AV26mCOBHnoIsrLM42FhtjSDlAkRd8qFiJUyIWIVSJkolI5AbGwsL7/8stYQ8rKs079oeGjbweMAhGuXMSlmCpoJkUuWmAj33QdNm8KCBRAeDv36mdvL20iZEHGnXIhYKRMiVoGUiUJbyfLYsWMkJycX1stJPtSoUaNAz8vMyS3kSkR8Q0EzIVJg6ekwdiyMGgXHzWY7AwfCG29A9eq2lgbKhEhelAsRK2VCxCqQMuFxQ2jcuHGW+4ZhkJCQwOeff06PHj0KrTC5uOoF/GVj95ETAHSqW64QqxGxX0EzIVJgCxfCs8+at1u2NJtDbdrYWtLZlAkRd8qFiJUyIWIVSJnweM7Q2LFjLR/jxo1j4cKF3H333fzvf/8rihrlPBYsWODxcwzDYPcRc3exqqWjCrskEVsVJBMiHktKOnP72mvh7rvh889h+XKfagaBMiGSF+VCxEqZELEKpEx4PEJo586dRVGHeMnBlAyOnMjE4YA65UvYXY6IiP/Yv98cDTRrFmzdCmXKgMMBkybZXZmIiIiIiMe0qrAfa1eAHWs2J6YA5uigyLDgwi5JxFYFyYTIRaWlwciRUKcOfPaZOULohx/sripflAkRd8qFiJUyIWIVSJko0KLSK1euZNq0aezZs4fMzEzLY9OnTy+UwuTiEhMTqVy5skfPWbP7KKDpYlI8FSQTIueVmwtffglPP22ODgJzStjYseZ6QX5AmRBxp1yIWCkTIlaBlAmPRwhNmTKFa665hvj4eL799luysrKIj4/nl19+wel0FkWNch7btm3z+DkHUzIAiA4rtA3mRHxGQTIhkqesLGjbFu6802wGVasGU6fC0qV+0wwCZUIkL8qFiJUyIWIVSJnwuCE0atQoxo4dy6xZswgLC+Pf//43mzZtYsCAAVStWrUoapTzcDgcHj8nISUdgPZ1LivsckRsV5BMiOQpNBSuvBJiYswt5TdvhgEDzDWD/IgyIeJOuRCxUiZErAIpEw7DMAxPnhAdHc2ff/5J9erVKVu2LAsWLKBRo0Zs2rSJzp07k5CQUFS1FoqUlBScTifJycnExsbaXY7XdX5nITsOn+D/7mlF29pl7S5HRMQ3pKbC66/DHXdA/frmsSNHzJFCcXH21iYiIiIikk+e9Dw8HiFUunRpUlNTAahUqRIbN24E4NixY6SlpRWgXCmoGTNmePycv4+bU8binOGFXI2I/QqSCQlwOTnw8cdQu7bZEHr88TOPlSnj980gZULEnXIhYqVMiFgFUiby3RD65z//SWpqKu3atWP+/PkADBgwgMcee4z77ruPW2+9lS5duhRZoeLu5MmTHp2fnpVDano2AJfFRBRFSSK28jQTEuAWLIAWLeDee+HgQahVCwYPBs8Gzvo0ZULEnXIhYqVMiFgFUibyvbLwp59+yhtvvMF7771Herq5Ds0zzzxDaGgoS5cupV+/frzwwgtFVqi4q1KlikfnHzy1flBYSBAlIrSotBQ/nmZCAtRff8GTT8Lp//1xOuGll+DhhyEszNbSCpsyIeJOuRCxUiZErAIpE/nuCpxeaqh06dKuY0FBQTz11FM89dRThV+ZXFTdunU9On9Pkjmlr2rpKIKCAmehLAkcnmZCAtTMmWYzKDjYHBE0YgSULZ5rqikTIu6UCxErZULEKpAy4dEaQoG02rY/OD11L7+2HTwOQOmo4vU/4CKneZoJCRDZ2bBr15n7jz5qThNbvx7ee6/YNoNAmRDJi3IhYqVMiFgFUiY8mjdUp06dizaFkpKSLqkgKTr7j5lzIZNPZtlciYiIl8ydC8OHQ26u2QAKDTWnhX30kd2ViYiIiIjYyqOG0MiRI3E6nUVVi3ioTZs2Hp2fnZMLwOWXRRdFOSK28zQTUoxt2mTuGPbjj+b90qXNY40b21uXlykTIu6UCxErZULEKpAy4VFD6JZbbqFcuXJFVYt4KCkpiWrVquX7/IVb/wagVY3SFzlTxD95mgkpho4cMdcEGj/e3FI+JMScIvbCC1CqlN3VeZ0yIeJOuRCxUiZErAIpE/leQ0jrB/mezZs3e3R+yVNrBwUHe7R0lIjf8DQTUszs2mVuHf/ee2Yz6IYb4M8/YcyYgGwGgTIhkhflQsRKmRCxCqRMeLzLmPivQ6e2na9foYTNlYiIFIFq1aBZMzh82GwCdelid0UiIiIiIj7LYQRYpyclJQWn00lycjKxsbF2l3NJcnJyCA4Ozvf51Z/+AYBfHu/A5ZfFFFVZIrbxNBPi59avN6eHTZhgrhEE8Pff5m39PQCUCZG8KBciVsqEiJW/Z8KTnofmDvmx2bNn5/vcrFMLSgM4I0OLohwR23mSCfFjBw/C/fdD06bw7bfw8stnHrvsMjWDzqJMiLhTLkSslAkRq0DKhEeLSotvOX78eL7PTU3Pdt2OVUNIiilPMiF+KD0d/v1veO01SE01j918Mzz2mL11+TBlQsSdciFipUyIWAVSJtQQ8mMVK1bM97kns3Jct0O1qLQUU55kQvzM9OnwxBOwc6d5v0ULGDsW2ra1ty4fp0yIuFMuRKyUCRGrQMqEOgN+7Morr8z3uRmnGkIlItQDlOLLk0yIn5k712wGVawIn34Kv/+uZlA+KBMi7pQLEStlQsQqkDKhhpAf+/HHH/N97ukRQuEhWltDii9PMiE+7sABcxv5015+GUaOhK1b4a67IEg/vvJDmRBxp1yIWCkTIlaBlAn9izpApJw01xCKCVdDSER8WFqa2fypXRsefvjM8fLl4cUXITravtpERERERIoRzR/yYy1btsz3uenZ5gihGE0Zk2LMk0yIj8nNhcmT4emnYd8+89jRo+bi0SVK2FubH1MmRNwpFyJWyoSIVSBlQiOE/FhaWlq+z004lg5AhKaMSTHmSSbEhyxfDq1bwx13mM2gqlVhyhT49Vc1gy6RMiHiTrkQsVImRKwCKRNqCPmxjRs35vvc0GAHALuTAucvtwQeTzIhPuL776FNG1ixAmJizC3lN2+GgQPB4bC7Or+nTIi4Uy5ErJQJEatAyoTmDwWIP/YdA6BljdL2FiIicrbu3aFWLWjfHl59FSpUsLsiEREREZGA4DAMw7C7CG9KSUnB6XSSnJxMbGys3eVckoyMDMLDw/N17gOfr2LunwdpV7ssn9/TqogrE7GHJ5kQG+TmmlvGf/EFzJkDIaf+TyItDaKi7K2tmFImRNwpFyJWyoSIlb9nwpOeh6aM+bGff/453+eGBJvf6jrltR6HFF+eZEK8bPFiuOoq+Oc/4eef4bPPzjymZlCRUSZE3CkXIlbKhIhVIGVCDSE/lpycnO9zDyabi0o3rVqyiKoRsZ8nmRAv2b4dbroJOnSANWvA6YR33oHbb7e7soCgTIi4Uy5ErJQJEatAyoTWEPJj5cqVy/e5h1IzACgfG1FU5YjYzpNMSBHLyoLnnoN//xsyMyEoCB54AEaOhMsus7u6gKFMiLhTLkSslAkRq0DKhBpCfqxly5b5Os8wDPac2l2sbIz/zoUUuZj8ZkK8ICQEVq40m0Hdu8Po0dCwod1VBRxlQsSdciFipUyIWAVSJjRlzI/NmjUrX+edyMxx3S5XQg0hKb7ymwkpIvPnQ1KSedvhgHHj4IcfzAWk1QyyhTIh4k65ELFSJkSsAikTaggFgBMZ2a7bUWHBNlYiIsXS5s1w/fXmSKBXXjlzvFEj6NXLbA6JiIiIiIhPUUPIjzVr1ixf5x0/1RAqER6CQ7+YSTGW30xIIUlKgsceMxs/P/xgThMLDbW7KjmLMiHiTrkQsVImRKwCKRNaQ8iP5ebm5uu85JNZAKSeNVJIpDjKbybkEmVlwfjxMGIEHD1qHuvdG95+G664wtbSxEqZEHGnXIhYKRMiVoGUCY0Q8mPr1q3L13nH081GkNYPkuIuv5mQSzRihDky6OhRc3TQ/Pnw3XdqBvkgZULEnXIhYqVMiFgFUibUEAoAf+w9BkBMuAaEiUgBnf0/JUOGQK1a8OGHsHYtdO1qX10iIiIiIlIgDsMwDLuL8KaUlBScTifJycnExsbaXc4lSUtLIyoq6oLnGIbBDe/9yob9yVx9eWmm3N/aS9WJeF9+MiEeOnQIXnwREhNhxowzx3NzIUj/p+DrlAkRd8qFiJUyIWLl75nwpOehf837saVLl170nDfmbGbD/mQAHulUu6hLErFVfjIh+ZSRYa4JVLu2ORJo5kw4e/ismkF+QZkQcadciFgpEyJWgZQJ/Yvejx05cuSCj2fl5PLhoh0AdK9fnra1y3qjLBHbXCwTkg+GAd98A/Xrw1NPQUoKNGsGixZBkyZ2VyceUiZE3CkXIlbKhIhVIGVCi8r4sdKlS1/w8aXbDrtujxnYpIirEbHfxTIhF7F/P9x2GyxebN6vUAFGjYK77tKIID+lTIi4Uy5ErJQJEatAyoQaQn6sffv2F3x846mpYm1qltGC0hIQLpYJuYiyZWHvXoiIgCefNEcIxcTYXZVcAmVCxJ1yIWKlTIhYBVIm9F++fmzG2Qu85mHR1r8BaFkjcDqcEtgulgk5x8mT8N57kJ1t3g8Phy+/hC1b4OWX1QwqBpQJEXfKhYiVMiFiFUiZ0LCRYqxkVCgA2TkBtZGciFyMYcCUKfCvf5kjgoKD4cEHzceuvtre2kRERERExCvUEPJjjRs3vuDjKenm//rXiSvhjXJEbHexTAjw228wbJj5J0CVKhAXZ29NUmSUCRF3yoWIlTIhYhVImdCUMT8WGhp63sdOZuawalcSAGWiw7xVkoitLpSJgLd3L9x+O7RubTaDoqPh1VfN6WE33mh3dVJElAkRd8qFiJUyIWIVSJlQQ8iPrV69+ryP/eeXbeSeminWpEpJ7xQkYrMLZSLg3XefuT6QwwH/+Ads2wbPPQeRkXZXJkVImRBxp1yIWCkTIlaBlAlNGSuGTmRk8/7C7QAMalOdaO0wJhJ4cnMhM9PcMQzM7eMzMmD0aGjWzN7aRERERETEdg7DMAJqxeGUlBScTifJycnExsbaXc4lSUlJyfNz+Cn+IPd+tgqAra/2JCxEA8EkMJwvEwFn8WJznaCOHc0GkAQsZULEnXIhYqVMiFj5eyY86XmoU+DHVq1alefxtXuPAtC4slPNIAko58tEwNixA/r3hw4dYM0a+OwzOH7c7qrERgGfCZE8KBciVsqEiFUgZULdAj928ODBPI+HBpvf1uMZ2d4sR8R258tEsZecDE89BfXqwTffQFAQPPAA/PknxMTYXZ3YKGAzIXIByoWIlTIhYhVImdDiMn7sfMO/DqVmANClbjlvliNiO38e2llgixbBzTfD33+b97t2hTFjoFEje+sSnxCQmRC5COVCxEqZELEKpExoDSE/lpWVleeWeHdNXMHirX/zSt+G3Hl1NRsqE7HH+TJRrCUkQJ06ULGiuV7QddeZO4mJEKCZELkI5ULESpkQsfL3TGgNoQDx9ddf53l88VZzpED1MlHeLEfEdufLRLGydSu8/vqZ+xUqwC+/wMaNcP31agaJRUBkQsRDyoWIlTIhYhVImVBDqBgrHR1mdwkiUliOHjV3DmvQAJ59Fn766cxjV10Ffvy/GCIiIiIi4n1aQ8iPNWjQwO1Ydk6u63a5EhHeLEfEdnllwu9lZcEHH8CIEZCUZB677jqoWtXWssQ/FMtMiFwi5ULESpkQsQqkTKgh5Mdi8tg96ERGjuu2M1IjBiSw5JUJv2UY8OOP8PjjsHmzeaxhQ3PB6G7d7K1N/EaxyoRIIVEuRKyUCRGrQMqEpoz5sd9//93tWEp6lut2WIi+vRJY8sqE38rKgocfNptBZcvC+PGwdq2aQeKRYpUJkUKiXIhYKRMiVoGUCY0QKmZOZGbbXYKIFNThw1CyJISEQFiYuWvY8uXw3HPmcRERERERkUKiISR+7Nprr3U79ndqBgBxsVo/SAJPXpnwCxkZ8M47ULMmfPzxmeP9+sHbb6sZJAXmt5kQKULKhYiVMiFiFUiZUEPIj23cuNHtWK5h/pmYku7lakTsl1cmfJphwLffmjuHPfkkpKSY90UKid9lQsQLlAsRK2VCxCqQMqGGkB/bv3+/27ETGeaUsWZVS3q5GhH75ZUJn7V2LXTqZI4C2r4d4uJg4kT44Qe7K5NixK8yIeIlyoWIlTIhYhVImdAaQn4sOjra7VhCsjkyKMfwdjUi9ssrEz5p9GhzRJBhQESEuZPY009DAO1oIN7hN5kQ8SLlQsRKmRCxCqRMOAzDCKjWQUpKCk6nk+TkZGJjY+0u55Lk5uYSFGQd5PX5b7t5YcZGapeLYf7wDjZVJmKPvDLhk1atgpYt4ZZb4I03oGpVuyuSYspvMiHiRcqFiJUyIWLl75nwpOfhv5+lMHXqVLdjuw6fAKBBRf9udokURF6ZsJ1hwNSp5qLRp7VoAdu2wZdfqhkkRconMyFiM+VCxEqZELEKpExoylgxExUWDMDfxzNsrkREWLEChg2DZcsgNBRuvNHcSQzO/CkiIiIiImIDjRDyY1dccYXbsaxTiwfVKBs48x5FTssrE7bYtw/uvBNatTKbQVFR8MILUKGC3ZVJgPGZTIj4EOVCxEqZELEKpExohJAfK1u2rNuxTQkpAMRGhHq7HBHb5ZUJrzpxAt5+G956C06eNI/dfTe89hpUqmRvbRKQbM+EiA9SLkSslAkRq0DKhEYI+bFff/3V7Vi5EuEAHDme6e1yRGyXVya8KjnZXCvo5Elo2xZWroRJk9QMEtvYngkRH6RciFgpEyJWgZQJjRAqZjKycwGoXV7bV4t4RXw81K9v3q5Y0dxSvkwZuOkmcDjsrU1EREREROQ8NELIj3Xt2tXt2MmsHAAiTy0uLRJI8spEkdm5EwYMgAYNYMGCM8cfeAD691czSHyCVzMh4ieUCxErZULEKpAyoYaQH9u6davbsZSTWQDEhGvwlwSevDJR6FJS4JlnoF49mDYNgoLM3cREfJBXMiHiZ5QLEStlQsQqkDKhhpAf27Nnj9uxE5nZgBaVlsCUVyYKTU4OTJgAtWvDG29ARgZ06QJr18K//lV01xW5BEWaCRE/pVyIWCkTIlaBlAkNI/Fj4eHhbseOpZkjhGIj1RCSwJNXJgpNv37w3Xfm7Tp1zMWjr79eU8PEpxVpJkT8lHIhYqVMiFgFUiYchmEYdhfhTSkpKTidTpKTk4mNjbW7nELX7JX5JJ3I5MfH2lGvQvH7/ERsM2UKPPggvPQSPPQQhIXZXZGIiIiIiIiFJz0PTRnzY1OnTnU7lnTC3G4+IlSLSkvgySsTBXL0KAwfDp98cubYwIGwYwcMHapmkPiNQsuESDGiXIhYKRMiVoGUCU0Z82O5ubmW+9k5Z+6XiNC3VgLPuZnwWHY2fPihOQroyBEoV87cSSw62pwaVqpU4RQq4iWXnAmRYki5ELFSJkSsAikTGiHkx2rWrGm5n5Ke7brt1BpCEoDOzYRH5syBxo3hkUfMZlD9+vDZZ2YzSMRPXVImRIop5ULESpkQsQqkTKgh5McqVapkuZ+anuW6HRqsb60EnnMzkS9bt0LPnubHpk1Qpgy8/z788Qdce23hFyniRQXKhEgxp1yIWCkTIlaBlAl1DfzY4sWLLfdTTpojhMrHBs6q6CJnOzcT+ZKcbI4OCg2Fxx+Hv/4yF48O0bRL8X8FyoRIMadciFgpEyJWgZQJ/cZTjKRmmCOESkRoupjIeWVmwu+/Q7t25v2rroL//Ad69IBateytTURERERExEs0QsiPdezY0XI/4Vg6AFFh2mFMAtO5mbAwDJg5Exo0gK5dzR3DTnvkETWDpFi6YCZEApRyIWKlTIhYBVIm1BDyY3v27LHcDwl2ALD7SJod5YjY7txMuPzxB3TpAn37mlPCSpWCnTu9WpuIHc6bCZEAplyIWCkTIlaBlAk1hPzYjrNHOAC7DpuNoKuql7ajHBHbnZsJDh6E++6Dpk1hwQIID4dnn4Vt28wGkUgx55YJEVEuRM6hTIhYBVImbG8Ivf/++9SoUYOIiAiaN2/OkiVLznvu9OnT6datG5dddhmxsbG0bt2auXPnerFa3xJyzqK3EaHmt/NQarod5YjYzpKJjAxo0gQmTDCniw0cCJs3w2uvQYkSttUo4k3n/pwQEeVC5FzKhIhVIGXC1obQ1KlTGTp0KM899xxr166lXbt29OzZ87xDtBYvXky3bt2YPXs2q1evplOnTvTu3Zu1a9d6uXLfcPPNN1vuZ+XkAlDzshg7yhGx3c39+5+5Ex5u7hZ21VWwdClMmQLVq9tWm4gdzv05ISLKhci5lAkRq0DKhK0NoTFjxnDPPfdw7733Uq9ePd59912qVKnC+PHj8zz/3Xff5amnnuKqq66idu3ajBo1itq1a/P99997uXLf8M0331ju7zq1dlBsROB0NEVcVq7kcP36sHDhmWNPPw2//QbXXGNbWSJ2OvfnhIgoFyLnUiZErAIpE7Y1hDIzM1m9ejXdu3e3HO/evTvLli3L12vk5uaSmppK6dLnXzMnIyODlJQUy0dxkZmZabkfGmx+O1PTs+0oR8Qe+/bBXXdBy5aU3bwZnn/+zGNhYRBk+8xYEduc+3NCRJQLkXMpEyJWgZQJ24aSHD58mJycHMqXL285Xr58eRITE/P1GqNHj+bEiRMMGDDgvOe8/vrrjBw50u34tGnTiIqKol+/fvz8888kJydTrlw5WrZsyaxZswBo1qwZubm5rFu3DoA+ffqwdOlSjhw5QunSpWnfvj0zZswAoHHjxoSGhrJ69WoArrvuOlatWsXBgweJjY2le/fufP311wA0aNCAmJgYfv/9dwCuvfZaNm7cyP79+4mOjub6669n6tSpAFxxxRWULVuWX3/9FYCuXbuydetW9uzZ42puTZ06ldzcXA4mlwPgyP4dTJ68iY4dO7Jnzx527NhBSEgIN998M9988w2ZmZlUq1aNmjVr8ssvvwDQtm1bDh06xNatWwG49dZbmTlzJmlpaVSuXJn69eszb948AFq3bk1ycjLx8fGAOaRuzpw5pKamEhcXR7NmzZg9ezYAV111Fenp6WzYsAGAG2+8kYULF3L06FHKli1L69atXSO8mjZtCuCaAti7d2+WL1/O4cOHKVWqFB07duTbb78FoFGjRkRERLBy5UoAevXqxZo1a0hMTKREiRL06NGDadOmAVC/fn2cTifLly8HzKZjfHw8+/btIyoqij59+jB58mQA6tSpQ7ly5Vi6dCkAnTt3Zvv27ezevZuwsDBuuukmpk2bRnZ2NpdffjlVq1Zl4akRKe3bt2f//v1s376doKAgBg4cyPTp08nIyKBq1arUqVOHn376CYBrrrmGw4cPs2XLFgAGDhzIrFmzOHHiBJUqVaJhw4au9bFatWrF8ePH+fPPPwHo378/8+bNIyUlhfLly9OiRQt++OEHAJo3b05WVhbr168HoG/fvixevJikpCTKlClD27ZtmTlzJgBNmjQhKCiINWvWAHD99dezYsUKDh06hNPppEuXLkyfPh2Ahg0bEhUVxYoVKwDo2bMnf/zxBwcOHCAmJoZevXrx1VdfAVC3bl1Kly7taux269aNzZs3s3fvXiIjI+nbty9TpkzBMAxq165NXFyca+2wTp06sWvXLnbu3EloaCj9+/fn66+/Jisrixo1alC9enUWLFhgfr2bN4fRoyk/aRIhp960t159NfG33ELZpUupW7cu8+fPB6BNmzYkJSWxefNmAAYMGMDs2bM5fvw4FStW5Morr+THH38EoGXLlqSlpbFx40YAv36PCA8Pp1+/fq73iJo1a1KpUiUWL14MoPeIAHiPyMrKIiUlJSDfI9q1a0diYiLbtm3D4XBwyy23MGPGDE6ePEmVKlX0HhHA7xFJSUls3LhR7xEB/u8IvUeceY+IjY11/R3We4T+HaH3iAUkJSWxb98+v32PSEvL/67jDsMwjHyfXYgOHDhApUqVWLZsGa1bt3Ydf+211/j8889dX8zzmTx5Mvfeey8zZ86ka9eu5z0vIyODjIwM1/2UlBSqVKlCcnIysbGxl/6J2OjgwYOWhtojX65h1voEXry+Pv9sW8PGykSK2IwZ8MgjsH+/ef+aa2DsWA5WrerWZBYJZOf+nBAR5ULkXMqEiJW/ZyIlJQWn05mvnodtcynKli1LcHCw22igQ4cOXfSLP3XqVO655x6++uqrCzaDAMLDw4mNjbV8FBenO+6nncgwp4rFaA0hKe7S0sxmULVqMHUqLFkCV13llgmRQKdMiLhTLkSslAkRq0DKhG0NobCwMJo3b+4aZnXa/PnzadOmzXmfN3nyZAYNGsSXX37JddddV9Rl+pUVO5MAiA5TQ0iKmd274dQQbwBuvRU+/tjcRn7AAHA47KtNRERERETED9m62urw4cOZMGECEydOZNOmTQwbNow9e/YwePBgAJ555hnuuusu1/mTJ0/mrrvuYvTo0Vx99dUkJiaSmJhIcnKyXZ+Crdq2bWu5X71sNAA59swCFCl8qanw7LNwxRVmEyg11TzucMA//wkREZbTz82ESKBTJkTcKRciVsqEiFUgZcLWhtDAgQN59913efnll2nSpAmLFy9m9uzZVKtWDYCEhAT27NnjOv/DDz8kOzubhx9+mAoVKrg+HnvsMbs+BVsdOnTIcv/PA+Yi0xWdEXmdLuI/cnLMEUC1a8Prr0NGBtSvD0ePXvBp52ZCJNApEyLulAsRK2VCxCqQMmH7fswPPfQQu3btIiMjg9WrV9O+fXvXY5MmTXKtqg6wcOFCDMNw+5g0aZL3C/cBp1fpP61UVCgAYSG2f1tFCm7BAmjRAu69Fw4ehFq1zEWkf/4Zqla94FPPzYRIoFMmRNwpFyJWyoSIVSBlQovNFCPpWbkAlIoKs7kSkQLauhU6dzZvO53w4ovmbmJh+jstIiIiIiJSmGzbdt4unmzB5k+ycnKp/dyPAKx+vitlYsJtrkgkn7KyIDT0zP1BgyA6GkaOhLJlbStLRERERETE3/jFtvNy6WbOnOm6nZqe7brtjAzN63QR35KdDe+/DzVqwM6dZ45/8gn8978FagadnQkRUSZE8qJciFgpEyJWgZQJNYT8WFpamuv2sbRM1+2QYH1bxcfNnQtXXgkPPwz798O4cWceu4Qt5M/OhIgoEyJ5US5ErJQJEatAyoQ6B36scuXKrtsZ2bk2ViKST5s2wXXXQY8eEB8PZcrAe+/BW28VysufnQkRUSZE8qJciFgpEyJWgZQJLSrtx+rXr++6ffTUCKEqpSPtKkfkwp5+Gt55x9xSPiQEHn0UXngBSpUqtEucnQkRUSZE8qJciFgpEyJWgZQJjRDyY/PmzXPdzsox1wbfm3TSrnJELiwy0mwG9eljjg4aM6ZQm0FgzYSIKBMieVEuRKyUCRGrQMqERggVE0knMgBoXq1wf8EWKRDDgFmzoFw5aNXKPPbkk9C+PXTqZG9tIiIiIiIiohFC/qx169au24nJZkOocilNGRObrV8P3brBDTeYi0bnnlrfKiqqyJtBZ2dCRJQJkbwoFyJWyoSIVSBlQg0hP5acnOy6fXqEUNmYcLvKkUB38CDcfz80bQo//wzh4WZjKCvLayWcnQkRUSZE8qJciFgpEyJWgZQJNYT8WHx8vOv2ur3HAIiNCLWpGglY6enw5ptQuzZ89JE5Iujmm80dxV5/3WwMecnZmRARZUIkL8qFiJUyIWIVSJnQGkLFRJlo85fuk1k5NlciAee778wdxABatICxY6FtW3trEhERERERkQtyGIZh2F2EN6WkpOB0OklOTiY2Ntbuci5JdnY2ISFmT+/G939l7Z5j/OfWpvS+sqLNlUmxl5ICp/OTmwv9+0PfvnDHHRBk38DDszMhIsqESF6UCxErZULEyt8z4UnPQ1PG/NicOXNct4+eyASgfGyEXeVIIDhwAAYNgnr1IDXVPBYUBNOnw1132doMAmsmRESZEMmLciFipUyIWAVSJtQQ8mOpp38hB05kmlPFYsL9t5MpPiwtDV55xVwn6NNPzcaQD75Rnp0JEVEmRPKiXIhYKRMiVoGUCXUP/FhcXJzrdsaptYPCQ9Xjk0KUmwuTJ5trBO3bZx5r08ZcJ6hlS3try8PZmRARZUIkL8qFiJUyIWIVSJlQQ8iPNWvWzHU7PTsXgPAQNYSkkKSnQ6dO8Ntv5v1q1czdxAYMAIfD3trO4+xMiIgyIZIX5ULESpkQsQqkTKh74Mdmz54NQE6uQeaphlBkaLCdJUlxEhEBNWpATAyMGmVuIz9woM82g+BMJkTEpEyIuFMuRKyUCRGrQMqEGkLFQFZOrut2uBpCUlDHj8MLL8CuXWeOjR4N27bBM89AZKRtpYmIiIiIiEjh0pQxP3bVVVcBkJF9piEUGuy7ozfER+XmmgtFP/ssJCaaDaApU8zHKlSwtzYPnc6EiJiUCRF3yoWIlTIhYhVImVBDyI+lp6cD1hFCYcEa9CUeWLQIhg2DtWvN+zVrmtPC/NTpTIiISZkQcadciFgpEyJWgZQJdQ/82IYNGwA4eWrL+YjQIBw+vL6L+JDt2+Gmm6BjR7MZ5HTCO+/An3/CjTfaXV2Bnc6EiJiUCRF3yoWIlTIhYhVImdAIoWLgRGY2ANFh+nZKPn32GUyfDkFB8MADMHIkXHaZ3VWJiIiIiIiIlzgMwzDsLsKbUlJScDqdJCcnExsba3c5lyQ9PZ2IiAhW7z7KTeOXUaV0JEue6mx3WeKLsrPh0CGoWNG8f/w4DB4MTz8NDRvaW1shOp0JETEpEyLulAsRK2VCxMrfM+FJz0NTxvzYwoULAUjPMqeMRYVqhJDkYf58aNoU+vQxF5AGcyv5//u/YtUMgjOZEBGTMiHiTrkQsVImRKwCKRNqCPmxo0ePApCRbTaEwkL07ZSzbN4M118P3bvDxo2wY4e5g1gxdjoTImJSJkTcKRciVsqEiFUgZUIdBD9WtmxZADJPbTuvhpAAkJQEjz0GjRrBDz9ASAgMHQp//QVXXGF3dUXqdCZExKRMiLhTLkSslAkRq0DKhOYY+bHWrVsDkHZql7HI0GA7yxFfsGULtG4Np7vavXvD228X+0bQaaczISImZULEnXIhYqVMiFgFUiY0pMSPff/998CZhlBUmBpCAa92bahZ01wbaP58+O67gGkGwZlMiIhJmRBxp1yIWCkTIlaBlAk1hIqBPUlpAESqIRR4Nm6E224zdw0Dcxv5mTNh7Vro2tXe2kRERERERMRnqSHkx5o2bQpAaLADgJSTWXaWI9506JC5bfyVV8LkyfDWW2ceq1jRXDcoAJ3OhIiYlAkRd8qFiJUyIWIVSJkIzN8ai5k/D6QAUK1MtM2VSJHLyIBx4+DVVyHF/L7Tvz8MGmRrWSIiIiIiIuJfNELIj61duxaAxOR0ABwOO6uRIjd9OtSvD089ZTaDmjWDRYtg2jS4/HK7q/MJpzMhIiZlQsSdciFipUyIWAVSJtQQKgackaEAVCoZaXMlUqSmTYMdO6BCBZg0CVauhPbt7a5KRERERERE/JDDMAzD7iK8KSUlBafTSXJyMrGxsXaXc0mOHz9OTEwMPd5dzObEVD77Z0va17nM7rKksCQkmH9WqGD+uXs3TJwITz4JMTH21eXDTmdCREzKhIg75ULESpkQsfL3THjS89AIIT+2fPlyAFLTswEoEaEloYqFkyfNNYJq14bHHz9zvFo1GDlSzaALOJ0JETEpEyLulAsRK2VCxCqQMqEOgh87fPgwAPuPnQQgKkzfTr9mGDBlCvzrX7B3r3ls1y5IT4eICFtL8xenMyEiJmVCxJ1yIWKlTIhYBVImNELIj5UqVYqc3DMz/k6vJSR+6LffoE0buO02sxlUpQp88QX8+quaQR4oVaqU3SWI+BRlQsSdciFipUyIWAVSJrSGkB9LT0/naIZB69d/AWD7qF4EB2mrMb/z1VcwcKB5Ozoann4ahg+HqCh76/JD6enpRKiBJuKiTIi4Uy5ErJQJESt/z4TWEAoQ3377LcknswAIcqBmkL/q1QsqVYJBg2DrVnj+eTWDCujbb7+1uwQRn6JMiLhTLkSslAkRq0DKhBad8XNHT5gNoaql1UDwC7m58NlnMGMGTJ8OQUHmItHx8eDnI9ZERERERETEf2iEkB9r1KgRh49nAFAiQusH+bzFi+Gqq+Af/4CZM+Hrr888pmZQoWjUqJHdJYj4FGVCxJ1yIWKlTIhYBVIm1BDyYxEREWRk5wK4GkPig3bsgP79oUMHWLPGbP689Rb06WN3ZcWOP8/1FSkKyoSIO+VCxEqZELEKpEyoIeTHVq5cSe6pNcHjnIHzl9ZvpKebW8jXqwfffGNODxs8GLZtgyefhPBwuyssdlauXGl3CSI+RZkQcadciFgpEyJWgZQJrSHk544czwQgLlYNIZ8TFgY//QSZmdCtG4weDQE0/FBERERERER8l0YI+bFevXpxNM1sCGWemjomNvvlFzhxwrwdFATvvw+zZsHcuWoGeUGvXr3sLkHEpygTIu6UCxErZULEKpAyoYaQH1uzZg0RocEAOBzact5WW7ZA797QpQu8/faZ461awXXXgb4/XrFmzRq7SxDxKcqEiDvlQsRKmRCxCqRMqCHkxxITE8nIzgGgehltO2+LpCQYOhQaNjRHAgUHm2sHiS0SExPtLkHEpygTIu6UCxErZULEKpAyoTWE/FiJEiVYvzcZgPBQ9fa8KisLPvgARowwm0JgjgR65x2oW9fW0gJZiRIl7C5BxKcoEyLulAsRK2VCxCqQMuEwjFPbVAWIlJQUnE4nycnJxMbG2l3OJcnOzuap6RuZvmY/t7asyuv9tEaN1wwbBu++a95u0ADGjIHu3W0tScxMhISozy1ymjIh4k65ELFSJkSs/D0TnvQ8NKzEj02bNo20DHPKWP2K/t3c8gtn906HDIHKlWH8eFi3Ts0gHzFt2jS7SxDxKcqEiDvlQsRKmRCxCqRM+G/bSwBcu4w5I0NtrqQY+/tvePFFSEuDTz81j9WoATt3gh93jkVERERERCRwaYSQH6tfvz6rdx8FoES4GhOFLiPDXBOoVi1zvaDPPoOtW888rmaQz6lfv77dJYj4FGVCxJ1yIWKlTIhYBVIm1BDyY06nkwolIwDtal6oDAO+/dZcG+jJJyElBZo2hYULoU4du6uTC3A6nXaXIOJTlAkRd8qFiJUyIWIVSJlQQ8iPLV++nL1JJwG4rES4zdUUE3v2QOfO0K8fbN8OcXEwcSKsXAkdOthdnVzE8uXL7S5BxKcoEyLulAsRK2VCxCqQMqE5L34uyAG5BkSGBttdSvFQqhRs2gQREfD44/D00xATY3dVIiIiIiIiIoVKI4T8WNdu3cg9tfFVrBaVLpiTJ2HCBMjNNe+XKAFffgmbN8Orr6oZ5Ge6a7c3EQtlQsSdciFipUyIWAVSJtQQ8mPrNsS7bkeFaYSQRwwDpkyBunXhvvvM26d17gzVqtlXmxRYfHz8xU8SCSDKhIg75ULESpkQsQqkTGjKmB/bufcAUALQlDGPrFgBw4bBsmXm/cqVITra3pqkUOzbt8/uEkR8ijIh4k65ELFSJkSsAikTGiHkx4zQSABiwkNwaJuxi9u7F+64A1q1MptBUVHw8suwZQv06WN3dVIIoqKi7C5BxKcoEyLulAsRK2VCxCqQMuEwDMOwuwhvSklJwel0kpycTGxsrN3lXJIN+5Lp/d5SAHa9cZ3N1fiBdu1gqfn14u674bXXoFIle2sSERERERERKSSe9Dw0QsiPzZozF4BKJSNtrsRH5eZCZuaZ+6++ajaFVq6ESZPUDCqGJk+ebHcJIj5FmRBxp1yIWCkTIlaBlAk1hPxY5qmNsWLCtRSUm19/NaeGvfHGmWMdOsCiRdCihX11iYiIiIiIiPgANYT8WImyFQFIz86xuRIfsmsXDBwIbdvCqlUwfjxkZJx5XGstFWt16tSxuwQRn6JMiLhTLkSslAkRq0DKhBpCfiymhLnDWHCQmhykpMAzz5jbyH/1FQQFwf33w7p1EB5ud3XiJeXKlbO7BBGfokyIuFMuRKyUCRGrQMqEGkJ+bP2mvwCoX8G/F8e+ZD/9BLVrm9PDMjKgSxdYuxY+/BDKl7e7OvGipacXDRcRQJkQyYtyIWKlTIhYBVImtPiMHzt5aqZYbGSovYXY7fLL4dgxsyk0ejRcf72mhomIiIiIiIhcgEYI+bEyFaoCUDLQGkLbtsG4cWfuX345/PwzbNwIvXurGRTAOnfubHcJIj5FmRBxp1yIWCkTIlaBlAk1hPzY7zuOAOAMlIbQ0aMwfDg0aACPPQYrVpx5rG1bCAuzrzbxCdu3b7e7BBGfokyIuFMuRKyUCRGrQMqEGkJ+LMJIByAjO9fmSopYVha89545JWzsWPN+z55QsqTdlYmP2b17t90liPgUZULEnXIhYqVMiFgFUia0hpAfyzCCAahTPsbmSorQjz/C44/Dpk3m/fr1YcwYuPZae+sSnxSmUWIiFsqEiDvlQsRKmRCxCqRMOAzDMOwuwptSUlJwOp0kJycTG+vfu3NdO3YxWw6m8sW9rbimVlm7yyl8aWlQowYcOgRlysArr8B990GI+pgiIiIiIiIi5/Kk56EpY35sy8FUAKLCgm2upBAlJcHpHmVUFLz5pjlC6K+/4MEH1QySC5o2bZrdJYj4FGVCxJ1yIWKlTIhYBVIm1BDyY5HB5tpB4SHFoCGUmWlOBatZE6ZOPXN80CB45x2tFyT5kp2dbXcJIj5FmRBxp1yIWCkTIlaBlAk1hPyUYRhk5JrfvtLRfjzH0TBg5kxz57DHH4djx+DLL+2uSvzU5ZdfbncJIj5FmRBxp1yIWCkTIlaBlAk1hPxU8sksck/NrCoZ5afbzv/xB3TpAn37mlPCypeHCRPg22/trkz8VNWqVe0uQcSnKBMi7pQLEStlQsQqkDKhhpCfOpqWBUBosIOIUD+cMvbmm9C0KSxYAOHh8MwzsG0b3HMPBPvh5yM+YeHChXaXIOJTlAkRd8qFiJUyIWIVSJnQCr1+6kSGOa8xK8dPN4lr1cqcLjZwILzxBlSvbndFIiIiIiIiIgFDDSE/9ffxDABqlI22uZJ8MAz46is4ehQGDzaPdewIf/4J9evbWpoUL+3bt7e7BBGfokyIuFMuRKyUCRGrQMqEGkJ+Ki0jB4D9R0/aXMlFrFwJw4bBr7+a28jfcANUrGg+pmaQFLL9+/dTqVIlu8sQ8RnKhIg75ULE6lIykZOTQ1ZWViFXJGKv/fv3U6ZMGbvLuKCwsDCCgi59BSA1hPzUodR0ABpUirW5kvPYtw+efRY+/9y8HxUFTz0FTqe9dUmxtn37dlq2bGl3GSI+Q5kQcadciFgVJBOGYZCYmMixY8eKpigRGwUFBbFz5067y7igoKAgatSoQVjYpe04roaQnzIM658+Iy0N3n7bXDT65KnRS3feCaNGQeXK9tYmxV5hdMlFihNlQsSdciFiVZBMnG4GlStXjqioKBwORxFUJmKPY8eOUbJkSbvLOK/c3FwOHDhAQkICVatWvaT8OQzD51oKRSolJQWn00lycjKxsT46uiYfxszbwrhf/uLWllV5vV8ju8s5Y/t2cypYZiZccw2MHQtXXWV3VSIiIiIiUghycnLYunUr5cqV8/lpNSLFVXJyMgcOHKBWrVqEhoZaHvOk56H/IvFTqad2GSsZFXqRM73gr7/O3K5Z0xwN9NVXsGSJmkHiVdOnT7e7BBGfokyIuFMuRKw8zcTpNYOioqKKohwR2x09etTuEi7q9FSxnJycS3odNYT81Olt56PDgu0rYtcuc9v4OnVg1aozxx9/HG6+GTR0VLwsIyPD7hJEfIoyIeJOuRCxKmgmNE1Miqvc3Fy7S7iowsqfGkJ+KuWk2RByRtowQig11Vwwum5dcyQQwOLF3q9D5BxVq1a1uwQRn6JMiLhTLkSslAkRq/DwcLtL8Bo1hPxUSro5VDPWmw2hnByYMAFq14bXX4eMDOjUCdasgeHDvVeHyHnUqVPH7hJEfIoyIeJOuRCxUiYkLx9//DHdu3e3uwxb2N0QysjIoGrVqqxevbrIr6WGkJ86fmrKWIkIL24U16sX3HcfHDwItWrBjBnw88/QpIn3ahC5gJ9++snuEkR8ijIh4k65ELEKpEwMGjQIh8OBw+EgJCSEqlWr8uCDD+a5ZsyyZcvo1asXpUqVIiIigkaNGjF69Og812xZsGABvXr1okyZMkRFRVG/fn0ef/xx9u/f741Pq9BlZGTw4osv8sILL9hdSpExDIMRI0ZQsWJFIiMj6dixI3/++SdgLsp8Pu+++y5XXHEFkZGRVKlShWHDhpGenu56vHr16q6/Y2d/PPzww65z8nrc4XDw9ttvA2ZD6oknnuBf//pXEX32Z6gh5KfW70sGIDLUiw2hm24CpxNGj4Y//4Q+fbROkIiIiIiI+I0ePXqQkJDArl27mDBhAt9//z0PPfSQ5Zxvv/2WDh06ULlyZRYsWMDmzZt57LHHeO2117jllls4e6PuDz/8kK5duxIXF8c333xDfHw8H3zwAcnJyYwePdprn1dmZmahvdY333xDTEwM7dq1u6TXOb0AuS966623GDNmDO+99x4rV64kLi6Obt26kZqaet7nfPHFFzz99NO89NJLbNq0iY8//pipU6fyzDPPuM5ZuXIlCQkJro/58+cDcPPNN7vOOfvxhIQEJk6ciMPh4KabbnKdc/vtt7NkyRI2bdpUBJ/9WYwAk5ycbABGcnKy3aVcklav/WRU+9csY8XOI0VzgaNHDePxxw3j66/PHMvONoy//y6a64kUgt27d9tdgohPUSZE3CkXIlaeZuLkyZNGfHy8cfLkSdex3Nxc40RGli0fubm5+a797rvvNvr06WM5Nnz4cKN06dKu+8ePHzfKlClj9OvXz+353333nQEYU6ZMMQzDMPbu3WuEhYUZQ4cOzfN6R48ePW8tR48eNe677z6jXLlyRnh4uNGgQQPj+++/NwzDMF566SXjyiuvtJw/duxYo1q1am6fy6hRo4wKFSoY1apVM55++mmjVatWbtdq1KiR8eKLL7ruT5w40ahbt64RHh5uXHHFFcZ///tfy/m9e/c2nnjiCcuxFStWGF27djXKlCljxMbGGu3btzdWr15tOQcwxo8fb9xwww1GVFSU65rfffed0axZMyM8PNyoUaOGMWLECCMrK8v1vNGjRxsNGzY0oqKijMqVKxsPPvigkZqaet6v3aXKzc014uLijDfeeMN1LD093XA6ncYHH3xgpKen5/m8hx9+2OjcubPl2PDhw422bdue91qPPfaYUbNmzQv+Pe3Tp4/b6xqGYXTs2NF44YUX8nxOXjk8zZOehxeHl0hhSj21hlCpqLDCfeHsbPjoI3jxRTh8GKpVg+uvh/BwCA6GsmUL93oihejw4cNaGFHkLMqEiDvlQsSqMDJxMiuH+i/OLaSKPBP/8rVEhRXs19odO3YwZ84cQkPPrMs6b948jhw5whNPPOF2fu/evalTpw6TJ09m4MCBTJs2jczMTJ566qk8X79kyZJ5Hs/NzaVnz56kpqbyf//3f9SsWZP4+HiCgz3bQfrnn38mNjaW+fPnu0YtvfHGG2zfvp2aNWsC8Oeff7Jhwwa+/vprAD766CNeeukl3nvvPZo2bcratWu57777iI6O5u677wZgyZIl3H777ZZrpaamcvfddzNu3DgARo8eTa9evdi2bRslSpRwnffSSy/x+uuvM3bsWIKDg5k7dy533HEH48aNo127dmzfvp3777/fdS5AUFAQ48aNo3r16uzcuZOHHnqIp556ivfff/+8n3vPnj1ZsmTJBb8+x48fz/P4zp07SUxMtKyRFB4eTocOHVi2bBl33HFHnusItW3blv/7v/9jxYoVtGzZkh07djB79mzX1+1cmZmZ/N///R/Dhw8/765gBw8e5IcffuDTTz91e6xly5YX/RwvlRpCfupEpjl3NTykEGf9zZ1rbhl/au4k9eqZ08MCaJV18W9btmyhWbNmdpch4jOUCRF3yoWIVaBlYtasWcTExJCTk+Na+2XMmDGux7du3QpAvXr18nx+3bp1Xeds27aN2NhYKlSo4FENP/30EytWrGDTpk2uRb0vv/xyjz+X6OhoJkyYQFjYmUECjRs35ssvv3St//PFF19w1VVXua7zyiuvMHr0aPr16wdAjRo1iI+P58MPP+Tuu+/m2LFjHDt2jIoVK1qu1blzZ8v9Dz/8kFKlSrFo0SKuv/561/HbbruNf/7zn677d955J08//bSraXL55Zfzyiuv8NRTT7kaQkOHDnWdX6NGDV555RUefPDBCzaEJkyYwMmTJ/P9tTpbYmIiAOXLl7ccL1++PLt37yY9PZ3o6Gi3591yyy38/ffftG3bFsMwyM7O5sEHH+Tpp5/O8zozZszg2LFjDBo06Ly1fPrpp5QoUcL1/ThbpUqV2LVrV/4/sQJQQ8gP5eSembMaHV4I38KtW2HYMJg927xfpgyMHAn33w+hNmxrLyIiIiIifiMyNJj4l6+17dqe6NSpE+PHjyctLY0JEyawdetWHn30UbfzjLPWCTr3+OnRHmff9sS6deuoXLnyJe/w1qhRI0szCMy1ZyZOnMgLL7yAYRhMnjzZ1XD5+++/2bt3L/fccw/33Xef6znZ2dk4nU4AV5MlIiLC8rqHDh3ixRdf5JdffuHgwYPk5OSQlpbGnj17LOe1aNHCcn/16tWsXLmS1157zXXsdDMuLS2NqKgoFixYwKhRo4iPjyclJYXs7GzS09M5ceJEno0ZMJsll+rc793Fvp8LFy7ktdde4/3336dVq1b89ddfPPbYY1SoUCHPBbg//vhjevbs6dZcO9vEiRO5/fbb3b7eAJGRkaSlpXnwGXlODSE/lJmd67pdKCOE9u41m0EhIfDoo/DCC1Cq1KW/roiXDRw40O4SRHyKMiHiTrkQsSqMTDgcjgJP2/K26OhoatWqBcC4cePo1KkTI0eO5JVXXgFwNWk2bdpEmzZt3J6/efNm6tev7zo3OTmZhIQEj0YJRUZGXvDxoKAgt4ZUXgs059Usue2223j66adZs2YNJ0+eZO/evdxyyy2AOVUNzGljrVq1sjzv9HS1MmXK4HA43HZeGzRoEH///Tfvvvsu1apVIzw8nNatW7stZn1uTbm5uYwcOTLPETARERHs3r2bXr16MXjwYF555RVKly7N0qVLueeeey64KPWlTBmLi4sDzJFCZ3/fDh06RPny5SldunSez3vhhRe48847uffeewGzIXfixAnuv/9+nnvuOYKCzvxuvnv3bn766SemT59+3vqWLFnCli1bmDp1ap6PJyUlcdlll13wc7xU2mXMD53dEAoNLsC3MDMTVqw4c79LFxg1ypwqNmaMmkHit2bNmmV3CSI+RZkQcadciFgFeiZeeukl3nnnHQ4cOABA9+7dKV26dJ47hH333Xds27aNW2+9FYD+/fsTFhbGW2+9ledrHzt2LM/jjRs3Zt++fa6pZ+e67LLLSExMtDSF1q1bl6/Pp3LlyrRv354vvviCL774gq5du7qmRpUvX55KlSqxY8cOatWqZfmoUaMGAGFhYdSvX5/4+HjL6y5ZsoQhQ4bQq1cvGjRoQHh4OIcPH75oPc2aNWPLli1u16tVqxZBQUGsWrWK7OxsRo8ezdVXX02dOnVc34sLmTBhAuvWrbvgx/nUqFGDuLg41w5gYK73s2jRItq0aUNycnKez0tLS7M0fcBspBmG4dbA++STTyhXrhzXXXfdeev4+OOPad68OVdeeWWej2/cuJGmTZue9/mFwT/auGKRkZ3juh0a7MEQRcOA77+HJ56AAwdg2zY43RE9a6s8EX914sQJu0sQ8SnKhIg75ULEKtAz0bFjRxo0aMCoUaN47733iI6O5sMPP+SWW27h/vvv55FHHiE2Npaff/6ZJ598kv79+zNgwAAAqlSpwtixY3nkkUdISUnhrrvuonr16uzbt4/PPvuMmJiYPBtLHTp0oH379tx0002MGTOGWrVqsXnzZhwOBz169KBjx478/fffvPXWW/Tv3585c+bw448/Ehsbm6/P6fbbb2fEiBFkZmYyduxYy2MjRoxgyJAhxMbG0rNnTzIyMli1ahVHjx5l+PDhAFx77bUsXbrUsrZPrVq1+Pzzz2nRogUpKSk8+eSTFx3pBPDiiy9y/fXXU6VKFW6++WaCgoJYv349GzZs4NVXX6VmzZpkZ2fzn//8h969e/Prr7/ywQcfXPR1L2XKmMPhYOjQoYwaNYratWtTu3ZtRo0aRVRUFLfddptr1NNdd91FpUqVeP311wFzUfExY8bQtGlT15SxF154gRtuuMGyIHhubi6ffPIJd999NyEhebdcUlJSmDZtWp5/P05bsmSJa+RakbnoPmTFTHHYdn7n38eNav+aZdR5dlb+n/THH4bRubNhmG0hwyhXzjAWLiy6IkVssGjRIrtLEPEpyoSIO+VCxMrTTFxou2tfl9e284ZhGF988YURFhZm7Nmzx3Vs8eLFRo8ePQyn02mEhYUZ9evXN9555x0jOzvb7fnz5883rr32WqNUqVJGRESEUbduXeOJJ54wDhw4cN5ajhw5YvzjH/8wypQpY0RERBgNGzY0Zs068/vd+PHjjSpVqhjR0dHGXXfdZbz22mt5bjufl6NHjxrh4eFGVFRUntu3f/HFF0aTJk2MsLAwo1SpUkb79u2N6dOnux7ftGmTERkZaRw7dsx1bM2aNUaLFi2M8PBwo3bt2sa0adOMatWqGWPHjnWdAxjffvut2/XmzJljtGnTxoiMjDRiY2ONli1bGv/73/9cj48ZM8aoUKGCERkZaVx77bXGZ599ZgDG0aNHz/v1u1S5ubnGSy+9ZMTFxRnh4eFG+/btjQ0bNhiGYRgpKSmGYRhGhw4djLvvvtv1nKysLGPEiBFGzZo1jYiICKNKlSrGQw895Fbn3LlzDcDYsmXLea//4Ycfun2Nz7Zs2TKjZMmSRlpaWp6PF9a28w7DOM9qWcVUSkoKTqeT5OTkfHdYfc32v4/TZfQiYiOCWT+ix4VPPnjQXBPo448hN9fcMWzYMHNEkJ9+/iLnk5SUdN45vyKBSJkQcadciFh5mon09HR27txJjRo18lwIV4qHAQMG0LRpU54JwJkk2dnZ5x3Z4y0333wzTZs25dlnn83z8Qvl0JOeh9YQ8kOndxnLyT7/IlsAnDgBDRrARx+ZzaCbb4ZNm+D119UMkmJp7ty5dpcg4lOUCRF3yoWIlTIheXn77beJiYmxuwxbnG8NIW/JyMjgyiuvZNiwYUV+La0h5Ieyc8yG0EW7edHRcPfdsHgxjB0LbdsWeW0iIiIiIiLi36pVq8ajjz5qdxkBKTw8nOeff94r19IIIT90eoRQZHiY9YHVq6FjR1iz5syx116D339XM0gCwrnbZ4oEOmVCxJ1yIWKlTIhYBdLIKNsbQu+//75r3lvz5s1ZsmTJBc9ftGgRzZs3JyIigssvvzxfK5AXN9m5p7edP7X804EDMGgQXHUVLFoEZ88zjIiAINu/zSJecfz4cbtLEPEpyoSIO+VCxEqZELHKycm5+EnFhK2dgqlTpzJ06FCee+451q5dS7t27ejZsyd79uzJ8/ydO3fSq1cv2rVrx9q1a3n22WcZMmQI33zzjZcrt9fpEUKhaanwyitQuzZ8+qm5f9gdd5hrBokEoD///NPuEkR8ijIh4k65ELFSJkSsTp48aXcJXmPrGkJjxozhnnvu4d577wXg3XffZe7cuYwfP57XX3/d7fwPPviAqlWr8u677wJQr149Vq1axTvvvMNNN93kzdJtlZGdy7Vbl/Hyzx9CyhHzYOvW8O670LKlrbWJiIiIiIiIiO+zbYRQZmYmq1evpnv37pbj3bt3Z9myZXk+Z/ny5W7nX3vttaxatYqsrLx33MrIyCAlJcXy4e+OZ2Rz2YljlE85AlWrwpQp8OuvagZJwOvfv7/dJYj4FGVCxJ1yIWKlTIhYlSpVyu4SvMa2EUKHDx8mJyeH8uXLW46XL1+exMTEPJ+TmJiY5/nZ2dkcPnyYChUquD3n9ddfZ+TIkW7Hp02bRlRUFP369ePnn38mOTmZcuXK0bJlS2bNmgVAs2bNyM3NZd26dQD06dOHpUuXcuTIEUqXLk379u2ZMWMGAI0bNyY0NJTVq1cDcN1117Fq1SoOHjxIbGws3bt35+uvvwagQYMGxMTE8PvvvwNmU2vjxo3s37+f6Ohorr/+eqZOnQrAFVdcQdmyZfn1118B6Nq1K7v/2szy1l34ICyNwV+9z9TvviN3yhRq1qxJpUqVWLx4MQAdO3Zkz5497Nixg5CQEG6++Wa++eYbMjMzqVatGjVr1uSXX34BoG3bthw6dIitW7cCcOuttzJz5kzS0tKoXLky9evXZ968eQC0bt2a5ORk4uPjAbj55puZM2cOqampxMXF0axZM2bPng3AVVddRXp6Ohs2bADgxhtvZOHChRw9epSyZcvSunVrvv/+ewCaNm0KwNq1awHo3bs3y5cv5/Dhw5QqVYqOHTvy7bffAtCoUSMiIiJYuXIlAL169WLNmjUkJiZSokQJevTowbRp0wCoX78+TqeT5cuXA2bTMT4+nn379hEVFUWfPn2YPHkyAHXq1KFcuXIsXboUgM6dO7N9+3Z2795NWFgYN910E9OmTSM7O5vLL7+cqlWrsnDhQgDat2/P/v372b59O0FBQQwcOJDp06eTkZFB1apVqVOnDj/99BMA11xzDYcPH2bLli0ADBw4kFmzZnHixAkqVapEw4YNXVuAtmrViuPHj7uG8/bv35958+aRkpJC+fLladGiBT/88AMAzZs3Jysri/Xr1wPQt29fFi9eTFJSEmXKlKFt27bMnDkTgCZNmhAUFMSaU4uQX3/99axYsYJDhw7hdDrp0qUL06dPB6Bhw4ZERUWxYsUKAHr27Mkff/zBgQMHiImJoVevXnz11VcA1K1bl9KlS7sau926dWPz5s3s3buXyMhI+vbty5QpUzAMg9q1axMXF+daO6xTp07s2rWLnTt3EhoaSv/+/fn666/JysqiRo0aVK9enQULFgDQrl07EhMT2bZtGw6Hg1tuuYUPP/yQMmXKUKVKFerWrcv8+fMBaNOmDUlJSWzevBmAAQMGMHv2bI4fP07FihW58sor+fHHHwFo2bIlaWlpbNy4EcAv3yO2bt3Knj17CA8Pp1+/fkydOpXc3Fy9RwTge0Rqaiq33HKL3iNOvUfMmDGDkydP6j0iwN8jDh48SNeuXfUeoX9H6D0C8z1i69atGIa5JEV+3iOWL19O9erVyczMJDs7m4yMDBwOB6VLl+bo0aPk5uYSHh5OeHi46z/jS5QoQVZWFunp6QCUKVOGY8eOkZOTQ1hYGJGRka6tvmNiYsjJyXFN2yldujTJycnk5OQQGhpKVFSU69zo6GgMwyAtLQ0wf5FPTU0lOzub0NBQoqOjOXbsGABRUVEArnNLlizJiRMnyMrKIiQkhBIlSnD06FHXuQ6HgxMnTgDgdDpJS0sjKyuL4OBgnE4nSUlJAERGRhIcHOxai8npdHLy5EkyMzMJDg6mZMmSHDlizuqIiIggNDSU1NRUAGJjY8nIyCAjI4OgoCBKlSpFUlIShmEQHh5OWFiY69yzv4YX+3rHxMSQnZ3t+nqf/TW82Ne7VKlSpKSkuL7eZ38NL/T1DgkJISYmxvL1PvtreKGvd2RkJEFBQZav99lfwwt9vWNjY0lPTyczM9Pta3ihr/fpr+HZX+/TX8Ps7GxKlSp13q93WFgYERERlq/3+f7Onvv1jo6OJjc31/L1Pt/f2XO/3iVLluT48eNkZ2eTm5uLYRjMmjWLrKwsy3vE6fPzw2GcTr+XHThwgEqVKrFs2TJat27tOv7aa6/x+eefu95wz1anTh3+8Y9/8Mwzz7iO/frrr7Rt25aEhATi4uLcnnP6G35aSkoKVapUITk5mdjY2EL+rLxr8uTJ3HrrrXaXIeIzlAkRK2VCxJ1yIWLlaSbS09PZuXOna2MgkeLmyJEjlClTxu4yLuhCOUxJScHpdOar52HblLGyZcsSHBzsNhro0KFDbqOATouLi8vz/JCQkPN+w8LDw4mNjbV8FBfn+zqJBCplQsRKmRBxp1yIWCkT3lO9enXXeriBqGPHjgwdOtR131e/HqGhoXaX4DW2NYTCwsJo3ry5ayjmafPnz6dNmzZ5Pqd169Zu58+bN48WLVoE1DfttBYtWthdgohPUSZErJQJEXfKhYhVIGVi0KBBOBwOHA4HISEhVK1alQcffNA1dam4GjFihOvzdjgcOJ1O2rVrx6JFi2yta+XKldx///221pCX6Ohou0vwGlu3nR8+fDgTJkxg4sSJbNq0iWHDhrFnzx4GDx4MwDPPPMNdd93lOn/w4MHs3r2b4cOHs2nTJiZOnMjHH3/ME088YdenYKvT871FxKRMiFgpEyLulAsRq0DLRI8ePUhISGDXrl1MmDCB77//noceesjusopcgwYNSEhIICEhgeXLl1O7dm2uv/5619o2drjssstcazv5ktNr+AQCWxtCAwcO5N133+Xll1+mSZMmLF68mNmzZ1OtWjUAEhIS2LNnj+v8GjVqMHv2bBYuXEiTJk145ZVXGDduXEBtOS8iIiIiIuKTTpw4/8epBY7zde6pBXcvem4BhIeHExcXR+XKlenevTsDBw50LXoPkJOTwz333EONGjWIjIzkiiuu4N///rflNQYNGkTfvn155513qFChAmXKlOHhhx+27Hx96NAhevfuTWRkJDVq1OCLL75wq2XPnj306dOHmJgYYmNjGTBgAAcPHnQ9PmLECJo0acLEiROpWrUqMTExPPjgg+Tk5PDWW28RFxdHuXLleO211y76eYeEhBAXF0dcXBz169dn5MiRHD9+3LUZAMCYMWNo1KgR0dHRVKlShYceesi1eDPA7t276d27N6VKlSI6OpoGDRq4NgEAiI+Pp1evXsTExFC+fHnuvPNODh8+fN6azp0y5nA4mDBhAjfeeCNRUVHUrl2b7777zvIcT68hF2ZrQwjgoYceYteuXWRkZLB69Wrat2/vemzSpEmunRdO69ChA2vWrCEjI4OdO3e6RhMFoubNm9tdgohPUSZErJQJEXfKhYhVoWYiJub8H+f+J365cuc/t2dP67nVq+d93iXasWMHc+bMsSw/kpubS+XKlfnqq6+Ij4/nxRdf5Nlnn3XthHfaggUL2L59OwsWLODTTz9l0qRJTJo0yfX4oEGD2LVrF7/88gtff/0177//PocOHXI9bhgGffv2JSkpiUWLFjF//ny2b9/OwIEDLdfZvn07P/74I3PmzGHy5MlMnDiR6667jn379rFo0SLefPNNnn/+eX777bd8f94ZGRlMmjSJkiVLcsUVV7iOBwUFMW7cODZu3Minn37KL7/8wlNPPeV6/OGHHyYjI4PFixezYcMG3nzzTWJOfR8SEhLo0KEDTZo0YdWqVcyZM4eDBw8yYMCAfNcFMHLkSAYMGMD69evp1asXt99+u2uHscK6xsUE0pQx27adl0t3dgdaRJQJkXMpEyLulAsRq0DLxKxZs1zbhJ/eln3MmDGux0NDQxk5cqTrfo0aNVi2bBlfffWVpfFQqlQp3nvvPYKDg6lbty7XXXcdP//8M/fddx9bt27lxx9/5LfffqNVq1YAfPzxx9SrV8/1/J9++on169ezc+dOqlSpAsDnn39OgwYNWLlyJVdddRVgNqgmTpxIiRIlqF+/Pp06dWLLli3Mnj2boKAgrrjiCt58800WLlzI1Vdffd7Pe8OGDa7mTVpaGiVKlGDq1KmWTZfOXvC5Ro0avPLKKzz44IO8//77gDmi6aabbqJRo0YAXH755a7zx48fT7NmzRg1apTr2MSJE6lSpQpbt26lTp06F/y+nDZo0CDXrnejRo3iP//5DytWrKBHjx6Fdo2LsWkjdluoIeTH1q9fT4MGDewuQ8RnKBMiVsqEiDvlQsSqUDNx1vQiN8HB1vtnjZZxE3TORJZduwpc0rk6derE+PHjSUtLY8KECWzdupVHH33Ucs4HH3zAhAkT2L17NydPniQzM5MmTZpYzmnQoAHBZ31OFSpUYMOGDQBs2rSJkJAQy4LddevWpWTJkq77mzZtokqVKq5mEED9+vUpWbIkmzZtcjWEqlevTokSJVznlC9fnuDgYILO+hqVL1/eMvooL1dccYVr+lVqaipTp07l5ptvZsGCBa46FyxYwKhRo4iPjyclJYXs7GzS09M5ceIE0dHRDBkyhAcffJB58+bRtWtXbrrpJho3bgzA6tWrWbBggavpdLbt27fnu1lz+vXAHKlTokQJ1+dWWNe4mLS0NCIjIwvltXyd7VPGREREREREpBiIjj7/R0RE/s8995fx851XoBKjqVWrFo0bN2bcuHFkZGRYRgR99dVXDBs2jH/+85/MmzePdevW8Y9//IPMzEzL65y7y7XD4SA3Nxc4M8LE4XCctw7DMPJ8/NzjeV3nQtc+n7CwMGrVqkWtWrVo2rQpb7zxBpUqVXKt4bN792569epFw4YN+eabb1i9ejX//e9/gTOjyO6991527NjBnXfeyYYNG2jRogX/+c9/AHMkU+/evVm3bp3lY9u2bZZlYS7mQp9bYV1DztAIIT/Wt29fu0sQ8SnKhIiVMiHiTrkQsQr0TLz00kv07NmTBx98kIoVK7JkyRLatGlj2Xls+/btHr1mvXr1yM7OZtWqVbRs2RKALVu2WHavql+/Pnv27GHv3r2uUULx8fEkJydbppYVpeDgYE6eWsB71apVZGdnM3r0aNfoo3PXTQKoUqUKgwcPZvDgwTzzzDN89NFHPProozRr1oxvvvmG6tWrExJSNG0Gb1wDzOmAgUIjhPzY4sWL7S5BxKcoEyJWyoSIO+VCxCrQM9GxY0caNGjgWpemVq1arFq1irlz57J161ZeeOEFVq5c6dFrXnHFFfTo0YP77ruP33//ndWrV3PvvfdapiF17dqVxo0bc/vtt7NmzRpWrFjBXXfdRYcOHSxTzQpLdnY2iYmJJCYmsm3bNl599VXi4+Pp06cPADVr1iQ7O5v//Oc/7Nixg88//5wPPvjA8hpDhw5l7ty57Ny5kzVr1vDLL7+4mlcPP/wwSUlJ3HrrraxYsYIdO3Ywb948/vnPf5KTk1Mon4M3rgHmlLpAoYaQHzu92rqImJQJEStlQsSdciFipUzA8OHD+eijj9i7dy+DBw+mX79+DBw4kFatWnHkyBHLaKH8+uSTT6hSpQodOnSgX79+3H///ZQrV871uMPhYMaMGZQqVYr27dvTtWtXLr/8cqZOnVqYn5rLn3/+SYUKFahQoQJNmjThq6++Yvz48dx1110ANGnShDFjxvDmm2/SsGFDvvjiC15//XXLa+Tk5PDwww9Tr149evTowRVXXOFacLpixYr8+uuv5OTkcO2119KwYUMee+wxnE6nZb2jS+GNa4DZPAsUDiOQltAGUlJScDqdJCcnW1ZU90fz5s2je/fudpch4jOUCRErZULEnXIhYuVpJtLT09m5cyc1atQg4tx1gUSKgeTkZJxOp91lXNCFcuhJz0MjhPxY27Zt7S5BxKcoEyJWyoSIO+VCxEqZELHKaxez4koNIT82c+ZMu0sQ8SnKhIiVMiHiTrkQsVImRKzOXvy7uFNDSEREREREREQkwKgh5MeaNGlidwkiPkWZELFSJkTcKRciVsqEiFVUVJTdJXiNGkJ+rDBXUhcpDpQJEStlQsSdciFiVdBMBNjeRBJAHA6H3SVcVGHlTz8R/diaNWvsLkHEpygTIlbKhIg75ULEytNMhIaGApCWllYU5YjY7sSJE3aXcFGZmZkABAcHX9LrsBIrCwAAFwJJREFUhBRGMSIiIiIiIlL8BQcHU7JkSQ4dOgSY02v8YUSFSH5lZmaSnp5udxnnlZuby99//01UVBQhIZfW0nEYATbWLyUlBafTSXJyMrGxsXaXc0lSU1MpUaKE3WWI+AxlQsRKmRBxp1yIWBUkE4ZhkJiYGFC7MUngyM3N9fnpxUFBQdSoUYOwsDC3xzzpeWiEkB9bsWIFXbp0sbsMEZ+hTIhYKRMi7pQLEauCZMLhcFChQgXKlStHVlZWEVUmYo/ly5fTunVru8u4oLCwsEJpWqkh5MdOD9MUEZMyIWKlTIi4Uy5ErC4lE8HBwZe8homIr0lMTCQiIsLuMrzCt8dByQU5nU67SxDxKcqEiJUyIeJOuRCxUiZErAIpE1pDyI9lZGQQHh5udxkiPkOZELFSJkTcKRciVsqEiJW/Z8KTnodGCPmx6dOn212CiE9RJkSslAkRd8qFiJUyIWIVSJkIuDWETg+ISklJsbmSS5eWllYsPg+RwqJMiFgpEyLulAsRK2VCxMrfM3G69vxMBgu4KWP79u2jSpUqdpchIiIiIiIiIlIk9u7dS+XKlS94TsA1hHJzczlw4AAlSpTA4XDYXU6BpaSkUKVKFfbu3ev3ayGJFAZlQsRKmRBxp1yIWCkTIlbFIROGYZCamkrFihUvujV9wE0ZCwoKumiXzJ/Exsb67V9UkaKgTIhYKRMi7pQLEStlQsTK3zOR353StKi0iIiIiIiIiEiAUUNIRERERERERCTAqCHkp8LDw3nppZcIDw+3uxQRn6BMiFgpEyLulAsRK2VCxCrQMhFwi0qLiIiIiIiIiAQ6jRASEREREREREQkwagiJiIiIiIiIiAQYNYRERERERERERAKMGkIiIiIiIiIiIgFGDSEf9v7771OjRg0iIiJo3rw5S5YsueD5ixYtonnz5kRERHD55ZfzwQcfeKlSEe/wJBPTp0+nW7duXHbZZcTGxtK6dWvmzp3rxWpFip6nPydO+/XXXwkJCaFJkyZFW6CIl3maiYyMDJ577jmqVatGeHg4NWvWZOLEiV6qVsQ7PM3FF198wZVXXklUVBQVKlTgH//4B0eOHPFStSJFa/HixfTu3ZuKFSvicDiYMWPGRZ9TnH/PVkPIR02dOpWhQ4fy3HPPsXbtWtq1a0fPnj3Zs2dPnufv3LmTXr160a5dO9auXcuzzz7LkCFD+Oabb7xcuUjR8DQTixcvplu3bsyePZvVq1fTqVMnevfuzdq1a71cuUjR8DQTpyUnJ3PXXXfRpUsXL1Uq4h0FycSAAQP4+eef+fjjj9myZQuTJ0+mbt26XqxapGh5moulS5dy1113cc899/Dnn38ybdo0Vq5cyb333uvlykWKxokTJ7jyyit577338nV+cf89W9vO+6hWrVrRrFkzxo8f7zpWr149+vbty+uvv+52/r/+9S++++47Nm3a5Do2ePBg/vjjD5YvX+6VmkWKkqeZyEuDBg0YOHAgL774YlGVKeI1Bc3ELbfcQu3atQkODmbGjBmsW7fOC9WKFD1PMzFnzhxuueUWduzYQenSpb1ZqojXeJqLd955h/Hjx7N9+3bXsf/85z+89dZb7N271ys1i3iLw+Hg22+/pW/fvuc9p7j/nq0RQj4oMzOT1atX0717d8vx7t27s2zZsjyfs3z5crfzr732WlatWkVWVlaR1SriDQXJxLlyc3NJTU3VP/qlWChoJj755BO2b9/OSy+9VNQlinhVQTLx3Xff0aJFC9566y0qVapEnTp1eOKJJzh58qQ3ShYpcgXJRZs2bdi3bx+zZ8/GMAwOHjzI119/zXXXXeeNkkV8TnH/PTvE7gLE3eHDh8nJyaF8+fKW4+XLlycxMTHP5yQmJuZ5fnZ2NocPH6ZChQpFVq9IUStIJs41evRoTpw4wYABA4qiRBGvKkgmtm3bxtNPP82SJUsICdGPfyleCpKJHTt2sHTpUiIiIvj22285fPgwDz30EElJSVpHSIqFguSiTZs2fPHFFwwcOJD09HSys7O54YYb+M9//uONkkV8TnH/PVsjhHyYw+Gw3DcMw+3Yxc7P67iIv/I0E6dNnjyZESNGMHXqVMqVK1dU5Yl4XX4zkZOTw2233cbIkSOpU6eOt8oT8TpPfk7k5ubicDj44osvaNmyJb169WLMmDFMmjRJo4SkWPEkF/Hx8QwZMoQXX3yR1atXM2fOHHbu3MngwYO9UaqITyrOv2frvwh9UNmyZQkODnbr3B86dMitO3laXFxcnueHhIRQpkyZIqtVxBsKkonTpk6dyj333MO0adPo2rVrUZYp4jWeZiI1NZVVq1axdu1aHnnkEcD8ZdgwDEJCQpg3bx6dO3f2Su0iRaEgPycqVKhApUqVcDqdrmP16tXDMAz27dtH7dq1i7RmkaJWkFy8/vrrXHPNNTz55JMANG7cmOjoaNq1a8err77q96MhRDxV3H/P1gghHxQWFkbz5s2ZP3++5fj8+fNp06ZNns9p3bq12/nz5s2jRYsWhIaGFlmtIt5QkEyAOTJo0KBBfPnll5r7LsWKp5mIjY1lw4YNrFu3zvUxePBgrrjiCtatW0erVq28VbpIkSjIz4lrrrmGAwcOcPz4cdexrVu3EhQUROXKlYu0XhFvKEgu0tLSCAqy/ooYHBwMnBkVIRJIiv3v2Yb4pClTphihoaHGxx9/bMTHxxtDhw41oqOjjV27dhmGYRhPP/20ceedd7rO37FjhxEVFWUMGzbMiI+PNz7++GMjNDTU+Prrr+36FEQKlaeZ+PLLL42QkBDjv//9r5GQkOD6OHbsmF2fgkih8jQT53rppZeMK6+80kvVihQ9TzORmppqVK5c2ejfv7/x559/GosWLTJq165t3HvvvXZ9CiKFztNcfPLJJ0ZISIjx/vvvG9u3bzeWLl1qtGjRwmjZsqVdn4JIoUpNTTXWrl1rrF271gCMMWPGGGvXrjV2795tGEbg/Z6thpAP++9//2tUq1bNCAsLM5o1a2YsWrTI9djdd99tdOjQwXL+woULjaZNmxphYWFG9erVjfHjx3u5YpGi5UkmOnToYABuH3fffbf3CxcpIp7+nDibGkJSHHmaiU2bNhldu3Y1IiMjjcqVKxvDhw830tLSvFy1SNHyNBfjxo0z6tevb0RGRhoVKlQwbr/9dmPfvn1erlqkaCxYsOCCvyME2u/ZDsPQ2D8RERERERERkUCiNYRERERERERERAKMGkIiIiIiIiIiIgFGDSERERERERERkQCjhpCIiIiIiIiISIBRQ0hEREREREREJMCoISQiIiIiIiIiEmDUEBIRERERERERCTBqCImIiIiIiIiIBBg1hERERMRnTZo0iZIlS17y64wYMYLy5cvjcDiYMWPGJb+er9q1axcOh4N169Zd8LyOHTsydOhQ1/20tDRuuukmYmNjcTgcHDt2rEDXv/POOxk1alSBnnspnnjiCYYMGeL164qIiPgzNYREREQCkMPhuODHoEGD7C6x0GzatImRI0fy4YcfkpCQQM+ePe0uqchUqVKFhIQEGjZsCMDChQvzbPBMnz6dV155xXX/008/ZcmSJSxbtoyEhAScTqfH116/fj0//PADjz76qOtYx44d8/z7lZ2d7fZ4eHg4derUYdSoUeTk5FjqP/1RpkwZOnfuzK+//mq59lNPPcUnn3zCzp07Pa5bREQkUKkhJCIiEoASEhJcH++++y6xsbGWY//+97/tLrHQbN++HYA+ffoQFxdHeHi4zRUVneDgYOLi4ggJCbngeaVLl6ZEiRKu+9u3b6devXo0bNiQuLg4HA6Hx9d+7733uPnmmy2vC3DfffdZ/m4lJCRY6jv9+JYtWxgyZAjPP/8877zzjuU1tmzZQkJCAgsXLuSyyy7juuuu49ChQ67Hy5UrR/fu3fnggw88rltERCRQqSEkIiISgOLi4lwfTqcTh8Phuh8aGsrgwYOpXLkyUVFRNGrUiMmTJ1ueX716dd59913LsSZNmjBixAjAHNkRFhbGkiVLXI+PHj2asmXLkpCQcN66Jk2aRNWqVYmKiuLGG2/kyJEjbud8//33NG/enIiICC6//HJGjhzpGnFyrhEjRtC7d28AgoKCXI2OlStX0q1bN8qWLYvT6aRDhw6sWbPG9by8pl4dO3YMh8PBwoULAXj55ZepWLGipcYbbriB9u3bk5ubm2c9gwYNom/fvowcOZJy5coRGxvLAw88QGZmpuucjIwMhgwZQrly5YiIiKBt27asXLnS9fjRo0e5/fbbueyyy4iMjKR27dp88sknbnXv2rWLTp06AVCqVCnLyK+zp4x17NiR0aNHs3jxYhwOBx07dgTg/fffp3bt2kRERFC+fHn69++f5+cEkJuby7Rp07jhhhvcHouKirL8fYuLi8vz8erVq/PII4/QpUsXt2l95cqVIy4ujkaNGvH888+TnJzM77//bjnnhhtucPt7KiIiIuenhpCIiIhYpKen07x5c2bNmsXGjRu5//77ufPOO91+Ab+Q0w2HO++8k+TkZP744w+ee+45PvroIypUqJDnc37//Xf++c9/8tBDD7Fu3To6derEq6++ajln7ty53HHHHQwZMoT4+Hg+/PBDJk2axGuvvZbnaz7xxBOuZsnp0SkAqamp3H333SxZsoTffvuN2rVr06tXL1JTU/+/vTsPiarr4wD+VRutJp8WknambCpK0rISp0XDFk2KtMAIm5oWC0m0hTYql6AyyZIKk5IsNcw/1CIjWxWNKCotMYdKSbNoRTNsUqfxPH+E9+064/b0vMX7zvcD88dZ7v2dc+cPxx/nnNvlOe7evRsjR47EunXrAABJSUkoLCxEWloabG3b/4l169Yt6PV65OfnIyMjAzk5OYiJiZHat2/fjqysLJw7dw7FxcVQq9Xw9fVFbW0tAGDv3r0oLy/H1atXodfrcfLkSQwcONAszogRI5CVlQXgPytsLK38ys7ORkhICDQaDd6+fYvs7Gw8fPgQ4eHh2LdvH549e4a8vDx4eXm1O6fS0lJ8/vwZU6dO7drD60CvXr1gNBotthkMBun7VCgUsjYPDw/U1NSgurr6l8dARERkFQQRERFZtZSUFNG3b98O+/j7+4utW7dKZZVKJY4ePSrr4+bmJqKioqRyU1OTmDx5sggKChIuLi5i3bp1HcZYvny58PPzk9UtW7ZMNrZZs2aJAwcOyPqkpaWJIUOGtHvfnJwc0dlPnu/fvwtHR0dx+fJlIYQQL1++FABESUmJ1Keurk4AEPn5+VJdZWWlcHR0FDt27BC9e/cW6enpHcZZtWqVGDBggPj69atUd/LkSdGnTx9hMplEQ0ODUCgU4vz581J7c3OzGDp0qIiLixNCCLFo0SKxevVqi/dvO+78/HwBQNTV1cn6eXt7i4iICKkcEREhvL29pXJWVpb466+/xJcvXzqcT6ucnBxhZ2cnWlpazOIoFAqhVCqlz5YtWyyOw2QyiatXrwp7e3uxfft22fhbr7WxsREAxJQpU0Rzc7MsVn19vQAgCgoKujRmIiIia9fxBnMiIiKyOiaTCbGxscjMzMSbN2/Q1NSEpqYmKJXKbt3H3t4e6enpcHV1hUqlMtti1pZer0dgYKCsTqPRIC8vTyo/evQIDx48kK0IMplMaGxshMFgQO/evbs0tg8fPiAyMhK3b9/G+/fvYTKZYDAY8OrVq65PEICzszMOHz6MDRs2YNmyZQgODu70Gjc3N9k4NRoNGhoaUFNTg/r6ehiNRsyYMUNqVygU8PDwgF6vBwCEhoZi6dKlKC4uxvz58xEQEIDp06d3a9ydmTdvHlQqFZydneHn5wc/Pz8EBga2+3y/ffsGBwcHi2cPBQcHY/fu3VK57VvjEhMTkZycLG2b02q1iIqKkvUpKiqCUqlESUkJduzYgbNnz5qtEOrVqxeAH6uIiIiIqHNMCBEREZFMfHw8jh49ioSEBEycOBFKpRKbNm2SnXNja2sLIYTsOkvbfO7evQsAqK2tRW1tbYdJpbb3s6SlpQUxMTFYsmSJWVvPnj07vb6VTqfDx48fkZCQAJVKBQcHB2g0GmmOrVu+fh5Te9uYCgsLYWdnh6qqKnz//r3TA53bY2NjI8Vrm1gRQkh1CxYsQHV1Na5cuYKbN29izpw52Lhxo9lBzL/C0dERxcXFKCgowPXr1xEZGYno6Gg8ePDALKEDAAMHDoTBYEBzczPs7e1lbX379oVarW43VmvCyMHBAUOHDoWdnZ1Zn1GjRqFfv34YO3YsGhsbERgYiLKyMtkB4a1b6pycnP7hrImIiKwLzxAiIiIimaKiIixevBgrVqyAm5sbnJ2d8eLFC1kfJycn2eHQX758MXvld2VlJTZv3ozTp0/D09MTK1eubPewZQCYMGEC7t27J6trW3Z3d8ezZ8+gVqvNPh2d22NpjuHh4fD394eLiwscHBzw6dMn2fwAyOb48wHTrTIzM5GdnY2CggLU1NTIXuXenidPnuDbt2+yOfbp0wfDhw+HWq2Gvb097ty5I7UbjUY8fPgQ48ePl41Pp9MhPT0dCQkJOHXqlMVYrcmZ1te4d0ePHj0wd+5cxMXFobS0FFVVVbh9+7bFvpMmTQIAlJeXdztOa8JoxIgRFpNBbWm1WrS0tCAxMVFWX1ZWBoVCARcXl26PgYiIyBoxIUREREQyarUaN27cwN27d6HX67Fhwwa8e/dO1sfHxwdpaWkoKipCWVkZVq1aJftn3mQyQavVYv78+Vi9ejVSUlJQVlaG+Pj4duOGh4cjLy8PcXFxeP78OU6cOCHbLgYAkZGRSE1NRXR0NJ4+fQq9Xo/MzEzs2bOn23NMS0uDXq/H/fv3ERwcLG05An5sP/L09ERsbCzKy8tRWFhoFuP169cIDQ3FoUOHMHPmTJw9exYHDx40S2K11dzcjLVr10oHQ0dFRSEsLAy2trZQKpUIDQ3Ftm3bkJeXh/LycoSEhMBgMGDt2rXSM7h06RIqKirw9OlT5ObmypJFP1OpVLCxsUFubi4+fvyIhoaGLj2f3NxcHDt2DI8fP0Z1dTVSU1PR0tKCcePGWezv5OQEd3d3WSLrv8XW1habNm1CbGysbHtYUVERZs2aJfseiYiIqH1MCBEREZHM3r174e7uDl9fX8yePRuDBw9GQECArM+uXbvg5eWFhQsXwt/fHwEBARg9erTUvn//flRVVUkrVwYPHozk5GTs2bPH4kobAPD09ERycjKOHz+OSZMm4fr162ZJGF9fX+Tm5uLGjRuYNm0aPD09ceTIEahUqm7N8cyZM6irq8PkyZOh1Wql17y37WM0GjF16lRERETI3ngmhIBOp4OHhwfCwsIA/Dh3JywsDCtWrOgw8TJnzhyMGTMGXl5eCAoKwqJFixAdHS21x8bGYunSpdBqtXB3d0dFRQWuXbuG/v37A/ix6mfXrl1wdXWFl5cX7OzscOHCBYuxhg0bhpiYGOzcuRODBg2SxtqZfv36ITs7Gz4+Phg/fjySkpKQkZHR4eqb9evX4/z58126/69as2YNjEYjTpw4IdVlZGQgJCTkt8QnIiL6f2AjurJhn4iIiIh+mU6nw+fPn3Hx4sU/PZR/XWNjI8aNG4cLFy5Ao9H81thXrlzBtm3bUFpa+o/PcCIiIrI2XCFERERERL+sZ8+eSE1NlZ3F9Lt8/foVKSkpTAYRERF1A/9qEhEREdG/wtvb+4/EDQoK+iNxiYiI/pdxyxgRERERERERkZXhljEiIiIiIiIiIivDhBARERERERERkZVhQoiIiIiIiIiIyMowIUREREREREREZGWYECIiIiIiIiIisjJMCBERERERERERWRkmhIiIiIiIiIiIrAwTQkREREREREREVuZvZzb0bn+ygIMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc_curve(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ca5d0a55-adbb-47a0-a4c8-6af9ca75ca9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_features_importance(pipeline, 'logreg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f3782ec2-9f2c-4c23-9691-79413c4e04be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_prob_distribution(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f0535de-34f1-4e97-b993-b429ecf0a554",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train['y_has_purchased']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f7fca463-d7d6-493b-8329-fdfa92457f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:  {'logreg__C': 0.0009765625, 'logreg__class_weight': 'balanced', 'logreg__penalty': 'l1'}\n",
      "Best cross-validation score: 0.65\n",
      "Test set score: 0.64\n"
     ]
    }
   ],
   "source": [
    "# Cross validation\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, param_grid, cv=3, scoring=recall_scorer, error_score='raise',\n",
    "                          n_jobs=-1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and the best score\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n",
    "\n",
    "# Evaluate the best model on the test set\n",
    "test_score = grid_search.score(X_test, y_test)\n",
    "print(\"Test set score: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "56bd7828-4de1-4166-bea0-5d5e152b9d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = grid_search.predict(X_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "319fe0eb-4d4a-492c-bd50-3f08ab483021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc_curve(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab122f66-1591-43ea-a364-2564f09b2bb3",
   "metadata": {},
   "source": [
    "# Segmentation du score de prédiction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "210b931c-6d46-4ebf-a9c7-d1ee05c3fadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Création d'un dataframe avec le score\n",
    "dataset_for_segmentation = dataset_test[['customer_id'] + numeric_features + categorical_features]\n",
    "\n",
    "y_predict_proba = pipeline.predict_proba(X_test)[:, 1]\n",
    "\n",
    "dataset_for_segmentation['prediction_probability'] = y_predict_proba\n",
    "\n",
    "# Arrondir les valeurs de la colonne 'prediction_probability' et les multiplier par 10\n",
    "dataset_for_segmentation['category'] = dataset_for_segmentation['prediction_probability'].apply(lambda x: int(x * 10))\n",
    "\n",
    "dataset_for_segmentation['prediction'] = y_pred\n",
    "\n",
    "def premiere_partie(chaine):\n",
    "    if chaine:\n",
    "        return chaine.split('_')[0]\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "dataset_for_segmentation['company_number'] = dataset_for_segmentation['customer_id'].apply(lambda x: premiere_partie(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "969f1f92-d715-4d74-85a7-437e72838cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>nb_tickets</th>\n",
       "      <th>nb_purchases</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>nb_suppliers</th>\n",
       "      <th>vente_internet_max</th>\n",
       "      <th>purchase_date_min</th>\n",
       "      <th>purchase_date_max</th>\n",
       "      <th>time_between_purchase</th>\n",
       "      <th>nb_tickets_internet</th>\n",
       "      <th>fidelity</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>nb_campaigns</th>\n",
       "      <th>nb_campaigns_opened</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.113637</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>1.586366</td>\n",
       "      <td>0.005821</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>548.790455</td>\n",
       "      <td>548.773103</td>\n",
       "      <td>-0.977118</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.000776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>13.984219</td>\n",
       "      <td>1.302720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.810841</td>\n",
       "      <td>0.128432</td>\n",
       "      <td>9.611292</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>0.018186</td>\n",
       "      <td>525.437516</td>\n",
       "      <td>525.275222</td>\n",
       "      <td>-0.729328</td>\n",
       "      <td>0.054312</td>\n",
       "      <td>0.111832</td>\n",
       "      <td>0.245480</td>\n",
       "      <td>0.495929</td>\n",
       "      <td>0.258591</td>\n",
       "      <td>18.413562</td>\n",
       "      <td>3.718711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.159419</td>\n",
       "      <td>0.339253</td>\n",
       "      <td>15.182143</td>\n",
       "      <td>0.337577</td>\n",
       "      <td>0.323824</td>\n",
       "      <td>501.529129</td>\n",
       "      <td>501.415505</td>\n",
       "      <td>-0.554439</td>\n",
       "      <td>0.969939</td>\n",
       "      <td>0.304757</td>\n",
       "      <td>0.392570</td>\n",
       "      <td>0.297258</td>\n",
       "      <td>0.310173</td>\n",
       "      <td>17.395042</td>\n",
       "      <td>2.608084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.153080</td>\n",
       "      <td>0.744161</td>\n",
       "      <td>27.820044</td>\n",
       "      <td>0.734881</td>\n",
       "      <td>0.600982</td>\n",
       "      <td>287.051054</td>\n",
       "      <td>286.675385</td>\n",
       "      <td>0.105360</td>\n",
       "      <td>1.776035</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>0.288813</td>\n",
       "      <td>0.253244</td>\n",
       "      <td>0.457943</td>\n",
       "      <td>16.790421</td>\n",
       "      <td>4.173954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.044749</td>\n",
       "      <td>0.777640</td>\n",
       "      <td>27.353145</td>\n",
       "      <td>0.754549</td>\n",
       "      <td>0.079213</td>\n",
       "      <td>297.179255</td>\n",
       "      <td>295.019902</td>\n",
       "      <td>1.898178</td>\n",
       "      <td>0.293760</td>\n",
       "      <td>0.894877</td>\n",
       "      <td>0.666980</td>\n",
       "      <td>0.301424</td>\n",
       "      <td>0.031596</td>\n",
       "      <td>16.954707</td>\n",
       "      <td>6.060621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.237988</td>\n",
       "      <td>0.958520</td>\n",
       "      <td>46.637380</td>\n",
       "      <td>0.807655</td>\n",
       "      <td>0.484785</td>\n",
       "      <td>387.464785</td>\n",
       "      <td>380.145068</td>\n",
       "      <td>7.111357</td>\n",
       "      <td>2.080397</td>\n",
       "      <td>1.164958</td>\n",
       "      <td>0.497758</td>\n",
       "      <td>0.259769</td>\n",
       "      <td>0.242473</td>\n",
       "      <td>27.006406</td>\n",
       "      <td>12.457719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.592233</td>\n",
       "      <td>1.102881</td>\n",
       "      <td>49.989226</td>\n",
       "      <td>0.878014</td>\n",
       "      <td>0.599906</td>\n",
       "      <td>268.627019</td>\n",
       "      <td>250.949344</td>\n",
       "      <td>17.539247</td>\n",
       "      <td>2.525994</td>\n",
       "      <td>1.420921</td>\n",
       "      <td>0.534607</td>\n",
       "      <td>0.304259</td>\n",
       "      <td>0.161134</td>\n",
       "      <td>14.073285</td>\n",
       "      <td>4.604134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.747016</td>\n",
       "      <td>1.391266</td>\n",
       "      <td>40.710335</td>\n",
       "      <td>0.914702</td>\n",
       "      <td>0.160990</td>\n",
       "      <td>309.716173</td>\n",
       "      <td>274.795570</td>\n",
       "      <td>34.796876</td>\n",
       "      <td>0.844250</td>\n",
       "      <td>1.963028</td>\n",
       "      <td>0.650364</td>\n",
       "      <td>0.263464</td>\n",
       "      <td>0.086172</td>\n",
       "      <td>26.186317</td>\n",
       "      <td>8.891703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.698276</td>\n",
       "      <td>1.567006</td>\n",
       "      <td>63.033699</td>\n",
       "      <td>0.907915</td>\n",
       "      <td>0.334248</td>\n",
       "      <td>326.485952</td>\n",
       "      <td>257.940194</td>\n",
       "      <td>68.425460</td>\n",
       "      <td>2.794279</td>\n",
       "      <td>2.413009</td>\n",
       "      <td>0.606583</td>\n",
       "      <td>0.251567</td>\n",
       "      <td>0.141850</td>\n",
       "      <td>30.987461</td>\n",
       "      <td>11.676332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.505956</td>\n",
       "      <td>3.211571</td>\n",
       "      <td>107.288514</td>\n",
       "      <td>1.011628</td>\n",
       "      <td>0.157119</td>\n",
       "      <td>369.696066</td>\n",
       "      <td>209.280306</td>\n",
       "      <td>160.348544</td>\n",
       "      <td>3.514464</td>\n",
       "      <td>5.394498</td>\n",
       "      <td>0.669314</td>\n",
       "      <td>0.223766</td>\n",
       "      <td>0.106920</td>\n",
       "      <td>45.928247</td>\n",
       "      <td>18.241634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2262.859155</td>\n",
       "      <td>45.619718</td>\n",
       "      <td>11051.732394</td>\n",
       "      <td>1.464789</td>\n",
       "      <td>0.154930</td>\n",
       "      <td>467.111875</td>\n",
       "      <td>31.146796</td>\n",
       "      <td>435.950994</td>\n",
       "      <td>54.295775</td>\n",
       "      <td>64.704225</td>\n",
       "      <td>0.507042</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.197183</td>\n",
       "      <td>53.352113</td>\n",
       "      <td>26.070423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           nb_tickets nb_purchases  total_amount nb_suppliers  \\\n",
       "                 mean         mean          mean         mean   \n",
       "category                                                        \n",
       "0            0.113637     0.006274      1.586366     0.005821   \n",
       "1            0.810841     0.128432      9.611292     0.125295   \n",
       "2            1.159419     0.339253     15.182143     0.337577   \n",
       "3            2.153080     0.744161     27.820044     0.734881   \n",
       "4            2.044749     0.777640     27.353145     0.754549   \n",
       "5            3.237988     0.958520     46.637380     0.807655   \n",
       "6            3.592233     1.102881     49.989226     0.878014   \n",
       "7            3.747016     1.391266     40.710335     0.914702   \n",
       "8            5.698276     1.567006     63.033699     0.907915   \n",
       "9           14.505956     3.211571    107.288514     1.011628   \n",
       "10        2262.859155    45.619718  11051.732394     1.464789   \n",
       "\n",
       "         vente_internet_max purchase_date_min purchase_date_max  \\\n",
       "                       mean              mean              mean   \n",
       "category                                                          \n",
       "0                  0.000647        548.790455        548.773103   \n",
       "1                  0.018186        525.437516        525.275222   \n",
       "2                  0.323824        501.529129        501.415505   \n",
       "3                  0.600982        287.051054        286.675385   \n",
       "4                  0.079213        297.179255        295.019902   \n",
       "5                  0.484785        387.464785        380.145068   \n",
       "6                  0.599906        268.627019        250.949344   \n",
       "7                  0.160990        309.716173        274.795570   \n",
       "8                  0.334248        326.485952        257.940194   \n",
       "9                  0.157119        369.696066        209.280306   \n",
       "10                 0.154930        467.111875         31.146796   \n",
       "\n",
       "         time_between_purchase nb_tickets_internet   fidelity gender_female  \\\n",
       "                          mean                mean       mean          mean   \n",
       "category                                                                      \n",
       "0                    -0.977118            0.001585   0.000776      0.000000   \n",
       "1                    -0.729328            0.054312   0.111832      0.245480   \n",
       "2                    -0.554439            0.969939   0.304757      0.392570   \n",
       "3                     0.105360            1.776035   0.659878      0.288813   \n",
       "4                     1.898178            0.293760   0.894877      0.666980   \n",
       "5                     7.111357            2.080397   1.164958      0.497758   \n",
       "6                    17.539247            2.525994   1.420921      0.534607   \n",
       "7                    34.796876            0.844250   1.963028      0.650364   \n",
       "8                    68.425460            2.794279   2.413009      0.606583   \n",
       "9                   160.348544            3.514464   5.394498      0.669314   \n",
       "10                  435.950994           54.295775  64.704225      0.507042   \n",
       "\n",
       "         gender_male gender_other nb_campaigns nb_campaigns_opened  \n",
       "                mean         mean         mean                mean  \n",
       "category                                                            \n",
       "0           0.000032     0.999968    13.984219            1.302720  \n",
       "1           0.495929     0.258591    18.413562            3.718711  \n",
       "2           0.297258     0.310173    17.395042            2.608084  \n",
       "3           0.253244     0.457943    16.790421            4.173954  \n",
       "4           0.301424     0.031596    16.954707            6.060621  \n",
       "5           0.259769     0.242473    27.006406           12.457719  \n",
       "6           0.304259     0.161134    14.073285            4.604134  \n",
       "7           0.263464     0.086172    26.186317            8.891703  \n",
       "8           0.251567     0.141850    30.987461           11.676332  \n",
       "9           0.223766     0.106920    45.928247           18.241634  \n",
       "10          0.295775     0.197183    53.352113           26.070423  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouper le DataFrame par la colonne 'category' et calculer la moyenne pour chaque groupe\n",
    "summary_stats = dataset_for_segmentation.groupby('category')[numeric_features].describe()\n",
    "\n",
    "# Sélectionner uniquement la colonne 'mean' pour chaque variable numérique\n",
    "mean_stats = summary_stats.loc[:, (slice(None), 'mean')]\n",
    "\n",
    "# Afficher le DataFrame résultant\n",
    "mean_stats"
   ]
  }
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