Fraud_Detection_Christian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Preconditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score, confusion_matrix, f1_score, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "N_JOBS= 8\n",
    "RSEED = 12345\n",
    "PRIMARY_SCORE = \"f1\"\n",
    "COUNTER_TYPE_MAPPING = {\"ELEC\": 0, \"GAZ\": 1}\n",
    "MAX_FEATURES_PER_ROW = 6\n",
    "TARGET_LABELS = {0: \"Regular\", 1: \"Fraud\"}\n",
    "COLUMNS_ONE_HOT_ENCODING = [\n",
    "    \"disrict\", \n",
    "    \"region\", \n",
    "    \"client_catg\", \n",
    "    \"tarif_type\", \n",
    "    \"reading_remarque\",\n",
    "\t\"counter_type\", \n",
    "    \"counter_code\", \n",
    "    \"counter_statue\", \n",
    "    \"counter_coefficient\"\n",
    "]\n",
    "\n",
    "\n",
    "def evaluate_model(y_true, y_predict, y_predict_proba):\n",
    "    print(f\"F1: {f1_score(y_true, y_predict):.2f}\")\n",
    "    print(f\"Recall: {recall_score(y_true, y_predict):.2f}\")\n",
    "    print(f\"TNR: {recall_score(y_true, y_predict, pos_label=0):.2f}\")\n",
    "    print(f\"ROC-AUC: {roc_auc_score(y_true, y_predict_proba):.2f}\")\n",
    "    print(confusion_matrix(y_true, y_predict))\n",
    "\n",
    "\n",
    "def train_and_predict(classifier, X_train, y_train, X_test, y_test, scaler=None):\n",
    "    if scaler is not None:\n",
    "        scaler.fit(X_train)\n",
    "        X_train = scaler.transform(X_train)\n",
    "        X_test = scaler.transform(X_test)\n",
    "\n",
    "    classifier.fit(X_train, y_train)\n",
    "    print(\"[TRAIN]\")\n",
    "    evaluate_model(y_train, classifier.predict(X_train), classifier.predict_proba(X_train)[:,1])\n",
    "    print(\"\\n[TEST]\")\n",
    "    evaluate_model(y_test, classifier.predict(X_test), classifier.predict_proba(X_test)[:,1])\n",
    "\n",
    "\n",
    "def feature_importance(tree, feature_names):\n",
    "    fig, ax = plt.subplots()\n",
    "    pd.Series(tree.feature_importances_, index=feature_names).plot.bar(ax=ax)\n",
    "    ax.set_title(\"Feature importances using MDI\")\n",
    "    ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Client and Invoice data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clients = pd.read_csv(\"data/train/client_train.csv\")\n",
    "df_invoices = pd.read_csv(\"data/train/invoice_train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clients\n",
    "\n",
    "- client_id: Unique id for client\n",
    "- disrict: District where the client is\n",
    "- region: Area where the client is\n",
    "- client_catg: Category client belongs to\n",
    "- creation_date: Date client joined\n",
    "- target: Fraud: 1, no fraud: 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disrict</th>\n",
       "      <th>client_id</th>\n",
       "      <th>client_catg</th>\n",
       "      <th>region</th>\n",
       "      <th>creation_date</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>31/12/1994</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_1</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>29/05/2002</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_10</td>\n",
       "      <td>11</td>\n",
       "      <td>301</td>\n",
       "      <td>13/03/1986</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_100</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "      <td>11/07/1996</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_1000</td>\n",
       "      <td>11</td>\n",
       "      <td>303</td>\n",
       "      <td>14/10/2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135488</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_99995</td>\n",
       "      <td>11</td>\n",
       "      <td>304</td>\n",
       "      <td>26/07/2004</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135489</th>\n",
       "      <td>63</td>\n",
       "      <td>train_Client_99996</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>25/10/2012</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135490</th>\n",
       "      <td>63</td>\n",
       "      <td>train_Client_99997</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>22/11/2011</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135491</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_99998</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>22/12/1993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135492</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_99999</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>18/02/1986</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135493 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        disrict           client_id  client_catg  region creation_date  target\n",
       "0            60      train_Client_0           11     101    31/12/1994     0.0\n",
       "1            69      train_Client_1           11     107    29/05/2002     0.0\n",
       "2            62     train_Client_10           11     301    13/03/1986     0.0\n",
       "3            69    train_Client_100           11     105    11/07/1996     0.0\n",
       "4            62   train_Client_1000           11     303    14/10/2014     0.0\n",
       "...         ...                 ...          ...     ...           ...     ...\n",
       "135488       62  train_Client_99995           11     304    26/07/2004     0.0\n",
       "135489       63  train_Client_99996           11     311    25/10/2012     0.0\n",
       "135490       63  train_Client_99997           11     311    22/11/2011     0.0\n",
       "135491       60  train_Client_99998           11     101    22/12/1993     0.0\n",
       "135492       60  train_Client_99999           11     101    18/02/1986     0.0\n",
       "\n",
       "[135493 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 135493 entries, 0 to 135492\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   disrict        135493 non-null  int64  \n",
      " 1   client_id      135493 non-null  object \n",
      " 2   client_catg    135493 non-null  int64  \n",
      " 3   region         135493 non-null  int64  \n",
      " 4   creation_date  135493 non-null  object \n",
      " 5   target         135493 non-null  float64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 6.2+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "disrict          0\n",
       "client_id        0\n",
       "client_catg      0\n",
       "region           0\n",
       "creation_date    0\n",
       "target           0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_clients)\n",
    "df_clients.info()\n",
    "display(df_clients.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invoices\n",
    "\n",
    "- client_id: Unique id for the client\n",
    "- invoice_date: Date of the invoice\n",
    "- tarif_type: Type of tax\n",
    "- counter_number: Counter number\n",
    "- counter_statue: Takes up to 5 values such as working fine, not working, on hold statue, ect\n",
    "- counter_code: Counter codes\n",
    "- reading_remarque: Notes that the STEG agent takes during his visit to the client (e.g: If the counter shows something wrong, the agent gives a bad score)\n",
    "- counter_coefficient: An additional coefficient to be added when standard consumption is exceeded\n",
    "- consommation_level_1-4: Consumption levels\n",
    "- old_index: Old index\n",
    "- new_index: New index\n",
    "- months_number: Month number\n",
    "- counter_type: Type of counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>tarif_type</th>\n",
       "      <th>counter_number</th>\n",
       "      <th>counter_statue</th>\n",
       "      <th>counter_code</th>\n",
       "      <th>reading_remarque</th>\n",
       "      <th>counter_coefficient</th>\n",
       "      <th>consommation_level_1</th>\n",
       "      <th>consommation_level_2</th>\n",
       "      <th>consommation_level_3</th>\n",
       "      <th>consommation_level_4</th>\n",
       "      <th>old_index</th>\n",
       "      <th>new_index</th>\n",
       "      <th>months_number</th>\n",
       "      <th>counter_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
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       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14302</td>\n",
       "      <td>14384</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2013-03-29</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12294</td>\n",
       "      <td>13678</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <td>14624</td>\n",
       "      <td>14747</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2015-07-13</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14747</td>\n",
       "      <td>14849</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15066</td>\n",
       "      <td>15638</td>\n",
       "      <td>12</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4476744</th>\n",
       "      <td>train_Client_99998</td>\n",
       "      <td>2005-08-19</td>\n",
       "      <td>10</td>\n",
       "      <td>1253571</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3197</td>\n",
       "      <td>3732</td>\n",
       "      <td>8</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476745</th>\n",
       "      <td>train_Client_99998</td>\n",
       "      <td>2005-12-19</td>\n",
       "      <td>10</td>\n",
       "      <td>1253571</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3732</td>\n",
       "      <td>3938</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476746</th>\n",
       "      <td>train_Client_99999</td>\n",
       "      <td>1996-09-25</td>\n",
       "      <td>11</td>\n",
       "      <td>560948</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13884</td>\n",
       "      <td>14143</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476747</th>\n",
       "      <td>train_Client_99999</td>\n",
       "      <td>1996-05-28</td>\n",
       "      <td>11</td>\n",
       "      <td>560948</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>603</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13281</td>\n",
       "      <td>13884</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476748</th>\n",
       "      <td>train_Client_99999</td>\n",
       "      <td>1996-01-25</td>\n",
       "      <td>11</td>\n",
       "      <td>560948</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12765</td>\n",
       "      <td>13281</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4476749 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  client_id invoice_date  tarif_type  counter_number counter_statue  counter_code  reading_remarque  counter_coefficient  consommation_level_1  consommation_level_2  consommation_level_3  consommation_level_4  old_index  new_index  months_number counter_type\n",
       "0            train_Client_0   2014-03-24          11         1335667              0           203                 8                    1                    82                     0                     0                     0      14302      14384              4         ELEC\n",
       "1            train_Client_0   2013-03-29          11         1335667              0           203                 6                    1                  1200                   184                     0                     0      12294      13678              4         ELEC\n",
       "2            train_Client_0   2015-03-23          11         1335667              0           203                 8                    1                   123                     0                     0                     0      14624      14747              4         ELEC\n",
       "3            train_Client_0   2015-07-13          11         1335667              0           207                 8                    1                   102                     0                     0                     0      14747      14849              4         ELEC\n",
       "4            train_Client_0   2016-11-17          11         1335667              0           207                 9                    1                   572                     0                     0                     0      15066      15638             12         ELEC\n",
       "...                     ...          ...         ...             ...            ...           ...               ...                  ...                   ...                   ...                   ...                   ...        ...        ...            ...          ...\n",
       "4476744  train_Client_99998   2005-08-19          10         1253571              0           202                 9                    1                   400                   135                     0                     0       3197       3732              8         ELEC\n",
       "4476745  train_Client_99998   2005-12-19          10         1253571              0           202                 6                    1                   200                     6                     0                     0       3732       3938              4         ELEC\n",
       "4476746  train_Client_99999   1996-09-25          11          560948              0           203                 6                    1                   259                     0                     0                     0      13884      14143              4         ELEC\n",
       "4476747  train_Client_99999   1996-05-28          11          560948              0           203                 6                    1                   603                     0                     0                     0      13281      13884              4         ELEC\n",
       "4476748  train_Client_99999   1996-01-25          11          560948              0           203                 6                    1                   516                     0                     0                     0      12765      13281              4         ELEC\n",
       "\n",
       "[4476749 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4476749 entries, 0 to 4476748\n",
      "Data columns (total 16 columns):\n",
      " #   Column                Dtype \n",
      "---  ------                ----- \n",
      " 0   client_id             object\n",
      " 1   invoice_date          object\n",
      " 2   tarif_type            int64 \n",
      " 3   counter_number        int64 \n",
      " 4   counter_statue        object\n",
      " 5   counter_code          int64 \n",
      " 6   reading_remarque      int64 \n",
      " 7   counter_coefficient   int64 \n",
      " 8   consommation_level_1  int64 \n",
      " 9   consommation_level_2  int64 \n",
      " 10  consommation_level_3  int64 \n",
      " 11  consommation_level_4  int64 \n",
      " 12  old_index             int64 \n",
      " 13  new_index             int64 \n",
      " 14  months_number         int64 \n",
      " 15  counter_type          object\n",
      "dtypes: int64(12), object(4)\n",
      "memory usage: 546.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "client_id               0\n",
       "invoice_date            0\n",
       "tarif_type              0\n",
       "counter_number          0\n",
       "counter_statue          0\n",
       "counter_code            0\n",
       "reading_remarque        0\n",
       "counter_coefficient     0\n",
       "consommation_level_1    0\n",
       "consommation_level_2    0\n",
       "consommation_level_3    0\n",
       "consommation_level_4    0\n",
       "old_index               0\n",
       "new_index               0\n",
       "months_number           0\n",
       "counter_type            0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_invoices)\n",
    "df_invoices.info()\n",
    "display(df_invoices.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning & Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndf[\"invoice_month_ts\"] = invoice_date.dt.strftime(\"%Y%m\").astype(int)\\n\\ndf_y = df[df[\"target\"] == 0]     .groupby(\"invoice_month_ts\")     .agg(consumption_mean=(\"consumption\", \"mean\"), consumption_std=(\"consumption\", \"std\"))     .reset_index()\\n\\ndf = df.merge(df_y, on=\"invoice_month_ts\")\\ndf[\"consumption_std\"] = df[\"consumption_std\"].fillna(0)\\ndf[\"over_consumption\"] = (df[\"consumption\"] - df[\"consumption_mean\"])\\n'"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_invoices.merge(df_clients, on=\"client_id\")\n",
    "\n",
    "# Convert target from float to int\n",
    "df[\"target\"] = df[\"target\"].astype(int)\n",
    "\n",
    "# Sanitize counter type and status\n",
    "df[\"counter_type\"] = df[\"counter_type\"].map(COUNTER_TYPE_MAPPING).astype(int)\n",
    "df[\"counter_statue\"] = df[\"counter_statue\"].replace(\"A\", 10).astype(int)\n",
    "\n",
    "# Derive consumption from old and new counter indices\n",
    "df[\"consumption\"] = df[\"new_index\"] - df[\"old_index\"]\n",
    "# Also derive sum of all consommations\n",
    "#df[\"consommation_total\"] = df[\"consommation_level_1\"] + df[\"consommation_level_2\"] + df[\"consommation_level_3\"] + df[\"consommation_level_4\"]\n",
    "\n",
    "# Derive date parts from invoice_date & creation_date\n",
    "creation_date = pd.to_datetime(df[\"creation_date\"], format=\"%d/%m/%Y\")\n",
    "invoice_date = pd.to_datetime(df[\"invoice_date\"], format=\"%Y-%m-%d\")\n",
    "\n",
    "df[\"creation_date\"] = creation_date.dt.strftime(\"%Y%m%d\").astype(int)\n",
    "df[\"creation_year\"] = creation_date.dt.year\n",
    "df[\"creation_month\"] = creation_date.dt.month\n",
    "\n",
    "df[\"invoice_date\"] = creation_date.dt.strftime(\"%Y%m%d\").astype(int)\n",
    "df[\"invoice_year\"] = creation_date.dt.year\n",
    "df[\"invoice_month\"] = creation_date.dt.month\n",
    "\n",
    "\"\"\"\n",
    "df[\"invoice_month_ts\"] = invoice_date.dt.strftime(\"%Y%m\").astype(int)\n",
    "\n",
    "df_y = df[df[\"target\"] == 0] \\\n",
    "    .groupby(\"invoice_month_ts\") \\\n",
    "    .agg(consumption_mean=(\"consumption\", \"mean\"), consumption_std=(\"consumption\", \"std\")) \\\n",
    "    .reset_index()\n",
    "\n",
    "df = df.merge(df_y, on=\"invoice_month_ts\")\n",
    "df[\"consumption_std\"] = df[\"consumption_std\"].fillna(0)\n",
    "df[\"over_consumption\"] = (df[\"consumption\"] - df[\"consumption_mean\"])\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all rows where year < 2005, because there was no fraud detection\n",
    "# Remove all rows where counter_statue > 5\n",
    "df = df[(invoice_date.dt.year >= 2005) & (df[\"counter_statue\"] <= 5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"client_id\", \"months_number\", \"counter_number\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The resulting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4454591 entries, 0 to 4476745\n",
      "Data columns (total 23 columns):\n",
      " #   Column                Dtype\n",
      "---  ------                -----\n",
      " 0   invoice_date          int64\n",
      " 1   tarif_type            int64\n",
      " 2   counter_statue        int64\n",
      " 3   counter_code          int64\n",
      " 4   reading_remarque      int64\n",
      " 5   counter_coefficient   int64\n",
      " 6   consommation_level_1  int64\n",
      " 7   consommation_level_2  int64\n",
      " 8   consommation_level_3  int64\n",
      " 9   consommation_level_4  int64\n",
      " 10  old_index             int64\n",
      " 11  new_index             int64\n",
      " 12  counter_type          int64\n",
      " 13  disrict               int64\n",
      " 14  client_catg           int64\n",
      " 15  region                int64\n",
      " 16  creation_date         int64\n",
      " 17  target                int64\n",
      " 18  consumption           int64\n",
      " 19  creation_year         int32\n",
      " 20  creation_month        int32\n",
      " 21  invoice_year          int32\n",
      " 22  invoice_month         int32\n",
      "dtypes: int32(4), int64(19)\n",
      "memory usage: 747.7 MB\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>tarif_type</th>\n",
       "      <th>counter_statue</th>\n",
       "      <th>counter_code</th>\n",
       "      <th>reading_remarque</th>\n",
       "      <th>counter_coefficient</th>\n",
       "      <th>consommation_level_1</th>\n",
       "      <th>consommation_level_2</th>\n",
       "      <th>consommation_level_3</th>\n",
       "      <th>consommation_level_4</th>\n",
       "      <th>old_index</th>\n",
       "      <th>new_index</th>\n",
       "      <th>counter_type</th>\n",
       "      <th>disrict</th>\n",
       "      <th>client_catg</th>\n",
       "      <th>region</th>\n",
       "      <th>creation_date</th>\n",
       "      <th>target</th>\n",
       "      <th>consumption</th>\n",
       "      <th>creation_year</th>\n",
       "      <th>creation_month</th>\n",
       "      <th>invoice_year</th>\n",
       "      <th>invoice_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19941231</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14302</td>\n",
       "      <td>14384</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19941231</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19941231</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12294</td>\n",
       "      <td>13678</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19941231</td>\n",
       "      <td>0</td>\n",
       "      <td>1384</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19941231</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14624</td>\n",
       "      <td>14747</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19941231</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19941231</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14747</td>\n",
       "      <td>14849</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19941231</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19941231</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15066</td>\n",
       "      <td>15638</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19941231</td>\n",
       "      <td>0</td>\n",
       "      <td>572</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
       "      <td>1994</td>\n",
       "      <td>12</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476741</th>\n",
       "      <td>20111122</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>20111122</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476742</th>\n",
       "      <td>20111122</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2562</td>\n",
       "      <td>2874</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>20111122</td>\n",
       "      <td>0</td>\n",
       "      <td>312</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476743</th>\n",
       "      <td>20111122</td>\n",
       "      <td>11</td>\n",
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       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>20111122</td>\n",
       "      <td>0</td>\n",
       "      <td>578</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476744</th>\n",
       "      <td>19931222</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3197</td>\n",
       "      <td>3732</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19931222</td>\n",
       "      <td>0</td>\n",
       "      <td>535</td>\n",
       "      <td>1993</td>\n",
       "      <td>12</td>\n",
       "      <td>1993</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476745</th>\n",
       "      <td>19931222</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3732</td>\n",
       "      <td>3938</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>19931222</td>\n",
       "      <td>0</td>\n",
       "      <td>206</td>\n",
       "      <td>1993</td>\n",
       "      <td>12</td>\n",
       "      <td>1993</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4454591 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         invoice_date  tarif_type  counter_statue  counter_code  reading_remarque  counter_coefficient  consommation_level_1  consommation_level_2  consommation_level_3  consommation_level_4  old_index  new_index  counter_type  disrict  client_catg  region  creation_date  target  consumption  creation_year  creation_month  invoice_year  invoice_month\n",
       "0            19941231          11               0           203                 8                    1                    82                     0                     0                     0      14302      14384             0       60           11     101       19941231       0           82           1994              12          1994             12\n",
       "1            19941231          11               0           203                 6                    1                  1200                   184                     0                     0      12294      13678             0       60           11     101       19941231       0         1384           1994              12          1994             12\n",
       "2            19941231          11               0           203                 8                    1                   123                     0                     0                     0      14624      14747             0       60           11     101       19941231       0          123           1994              12          1994             12\n",
       "3            19941231          11               0           207                 8                    1                   102                     0                     0                     0      14747      14849             0       60           11     101       19941231       0          102           1994              12          1994             12\n",
       "4            19941231          11               0           207                 9                    1                   572                     0                     0                     0      15066      15638             0       60           11     101       19941231       0          572           1994              12          1994             12\n",
       "...               ...         ...             ...           ...               ...                  ...                   ...                   ...                   ...                   ...        ...        ...           ...      ...          ...     ...            ...     ...          ...            ...             ...           ...            ...\n",
       "4476741      20111122          40               0             5                 9                    1                   118                     0                     0                     0        406        524             1       63           11     311       20111122       0          118           2011              11          2011             11\n",
       "4476742      20111122          11               0           207                 9                    1                   312                     0                     0                     0       2562       2874             0       63           11     311       20111122       0          312           2011              11          2011             11\n",
       "4476743      20111122          11               0           207                 9                    1                   578                     0                     0                     0       8505       9083             0       63           11     311       20111122       0          578           2011              11          2011             11\n",
       "4476744      19931222          10               0           202                 9                    1                   400                   135                     0                     0       3197       3732             0       60           11     101       19931222       0          535           1993              12          1993             12\n",
       "4476745      19931222          10               0           202                 6                    1                   200                     6                     0                     0       3732       3938             0       60           11     101       19931222       0          206           1993              12          1993             12\n",
       "\n",
       "[4454591 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.info()\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "### Unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "client_catg: [11 12 51]\n",
      "\n",
      "consommation_level_1: [     0      1      2 ...  98889 126118 999910]\n",
      "\n",
      "consommation_level_2: [     0      1      2 ... 785757 819886 999073]\n",
      "\n",
      "consommation_level_3: [    0     1     2 ... 45360 50695 64492]\n",
      "\n",
      "consommation_level_4: [     0      1      2 ... 284765 343568 547946]\n",
      "\n",
      "consumption: [-999025 -998916 -998539 ...  625521  629631  633228]\n",
      "\n",
      "counter_code: [  5  10  16  25  40  65 101 102 201 202 203 204 207 210 214 222 227 303\n",
      " 305 307 403 407 410 413 420 433 442 450 453 467 483 506 532 565 600]\n",
      "\n",
      "counter_coefficient: [ 0  1  2  3  4  5 10 11 20 30 33 40]\n",
      "\n",
      "counter_statue: [0 1 2 3 4 5]\n",
      "\n",
      "counter_type: [0 1]\n",
      "\n",
      "creation_date: [19770205 19770209 19770210 ... 20190821 20190905 20190910]\n",
      "\n",
      "creation_month: [ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "\n",
      "creation_year: [1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990\n",
      " 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004\n",
      " 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018\n",
      " 2019]\n",
      "\n",
      "disrict: [60 62 63 69]\n",
      "\n",
      "new_index: [      0       1       2 ... 2721031 2800280 2870972]\n",
      "\n",
      "old_index: [      0       1       2 ... 2581026 2721031 2800280]\n",
      "\n",
      "reading_remarque: [6 7 8 9]\n",
      "\n",
      "region: [101 103 104 105 106 107 199 206 301 302 303 304 305 306 307 308 309 310\n",
      " 311 312 313 371 372 379 399]\n",
      "\n",
      "target: [0 1]\n",
      "\n",
      "tarif_type: [ 8  9 10 11 12 13 14 15 18 21 24 27 29 40 42 45]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns.sort_values():\n",
    "    print(f\"{col}: {np.sort(df[col].unique())}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Q86anpxdxRLnz8fGRh4dHoeb19fUt9LyALUpCHrAn9od7iouLU8uWLRUdHa0PP/zQPL0gDx2Kj49XcHCwxZWpMTExGjt2rA4ePKg77rijwHEU5YNjCnP8vP6luTDHI2c/jpXk/cGDpopWbGysvvrqK33wwQe65ZZbzOOe+vn5ma8UGzFihKpUqaJhw4ZJknr16qWePXvqk08+UcuWLbVu3TolJiYqLi5O0rUi+pAhQ/Trr79qzpw5MplM5uVWqFBBpUuXdsCWFv48urDvffKAJWffH0BOfvrpJ+3du9diWmJion766Sc1adLEQVEBtsvv+O/n56fOnTtr0qRJqlChgsqVK6fx48crMjLSXAQ/cOCAnn76acXExKhPnz7mZXh5ealy5cqSpG7dumnJkiV655131LlzZ23bti3bOOr2Zu/zAcm5j4Hsj5xRSM9HbsWiG9udoWh05coVtW3b1tFhFLv169erTJkyjg4Dbqak5AF7YX+4p7Vr1+rXX3/VZ599lq2tIA8dyu2BQpJyfTBRbopqrDvDMDRz5kz98ccfRbK8grjttts0aNAgpzxBZH/gRsuWLZMk9ezZ02L6xIkT1alTJ0nSyZMn5en5zw2uUVFRmjx5sqZNm6apU6fqtttu06xZs8wPKDt16pR++OEHSdJjjz1msdyFCxeqefPmxbY9uTEMQ4MHD1ZiYqLd1hkaGqoZM2Y45fue/QHkLysrS2PHjs2xbezYsfryyy8tciNQkhTk+D9q1Ch5enpqyJAhFs9Guu6bb77RmTNn9OWXX+rLL780T69Ro4b5PKBWrVqaM2eOJk6cqIULF6pq1aoaP3687r777uLexBw54vgnOe8xkP2ROwrpAAAgTydPntRbb72lTz75RD4+Po4Op8geKGQYRqHHZi6scuXKKSIiwilPDkv6/uAhg0Vr//79+fZZtGhRtmlt27bN9eKOmjVrFmi5AODMtm3bposXL+bYdvHiRW3bts3iLj2gJCnIcdrHx0djxoyxKJ7faPDgwRo8eHC+y2nevLlWr15tbYiAQ1FId0EzY87Ix8socH/DkDKyijGgm5T2lKz5vpxu8tCgzZWLLyAAQJ727t2r1NRU81Uo0rWi5c6dO7VkyRLNmzcv34cOBQQEKCEhwWK51x8wdPODifJTlA8UmjFjhlW3LF65ckUdO3aUJK1atcrqhyE5++2K7A+4Gw8PD6vf95Jt731nft8XZn+QByDJfD6QnJysBg0a6I033lB4eHiu/c+fP6/33ntP3377rc6ePasaNWpo1KhRatmypR2jLpwqVarY1A7A+TjifEBy3mMg+yN3FNLzsWHDhjyHMXDG4Qt8vAz5WFlfsO6tbW8F/1EAjjFjxgzNnDnTYlrdunX19ddfS5LS09M1adIkrVu3zuLWr5uHeXBWJTEPFCf2h/v517/+pTVr1lhMe+2111SvXj0988wzqlatmvmhQw8++KCk7A8dioiI0OzZs5Wamip/f39J1x4oVK5cOdWvX9+u23MjDw+PQg8Z5uvr63LDjbE/4I5sed9LrvfeJw/AWuvWrdPEiRMVGxurxo0ba8GCBerXr5++/vpr8zH/RhkZGerTp4/8/f31/vvvq0qVKjpx4kS2IeKc1V9//ZVve1BQkJ2iAVBUOB+wxP7IGYX0AsitaESxCPjH7bffrvnz55tf33i16IQJE7Rx40ZNmzZNfn5+GjdunAYNGqTly5c7ItRCIQ9YYn+4l3LlypnHOL6ubNmyqlixonl6fg8diomJUf369TVixAi98sorSk5O1rRp09S9e3eHPWAQAADYbv78+erSpYs6d+4s6drDCjds2KCVK1fq2WefzdZ/5cqVOnfunJYvXy5vb29J14Z+KimqVatmUzsAoOSikA6gSHh5eeU4PMOFCxe0cuVKTZ48WS1atJB0rbDerl07xcfHm4tsAEq2/B465OXlpdmzZ2vs2LHq2rWrypQpo44dO2rIkCEOjBoAANgiIyNDe/fu1YABA8zTPD09FR0drd27d+c4zw8//KCIiAjFxcXp+++/V+XKlfXII4/omWeesXroNpPJZFP8hZGVlfe4qFlZWQ6JC/9g/wMoLhTSC+jmqy+56hKwdPToUcXExMjHx0cREREaNmyYqlevrsTERGVmZlo8cCcoKEjVq1cvVCHdkSdF33//ve677z6L1+58kuZM+8Od/x0c5eaHDOb30CFJqlGjhubOnVvcoQEAADv5+++/ZTKZsg3h4u/vr8OHD+c4z7Fjx7Rt2za1b99eH330kZKSkhQbG6urV69q0KBBVq3fEQ+ZTkxMzLP9f//7n86dO2enaAAA9kQh3QoUz4GchYeHa+LEiapbt66Sk5M1a9Ysde/eXWvWrFFKSoq8vb2zjXno7++v5ORkq9fliJPlG02ZMsX8d3x8vOMCcRLsDwAAAFjDMAz5+/tr3Lhx8vLyUmhoqE6dOqV58+ZZXUgPCwsrsgeQF1SFChUshrS82V133aW6devaMSLczGQyOfx7IwDXRCEdgM1atmxp/rtBgwZq3LixWrVqpfXr11v9lOb8OOJkGc6Pk2UAAAD7q1Spkry8vJSammoxPTU1VQEBATnOExgYqFKlSlmc09erV0/JycnKyMiw6tkpXl5edv9uULduXXl6euY4xIunp6fq1q3L9xUAcFEU0gEUufLly+u2225TUlKSoqOjlZmZqfPnz1tclZ6amprjmOr5ccTJMgAAAIDsSpcurUaNGmnr1q1q06aNpGtjhG/dulU9evTIcZ6oqCh99dVXysrKkqenpyTpjz/+UGBgYIl4APn27dtzHSc9KytL27dvtxjWEgDgOjwdHQAA13Pp0iUdO3ZMgYGBCg0Nlbe3t7Zu3WpuP3z4sE6cOMGDRgEAAIASrk+fPlqxYoVWrVqlQ4cOaezYsbp8+bI6deokSRoxYoTFcIBPPvmkzp49q7feektHjhzRhg0bNGfOHHXv3t1Rm2CVKlWq2NQOACi5uCIdgM3efvtttWrVStWrV9fp06c1Y8YMeXp66pFHHpGfn586d+6sSZMmqUKFCipXrpzGjx+vyMhICukAAABACdeuXTudOXNG06dPV3Jysho2bKiPP/7YPLTLyZMnzVeeS1K1atU0b948TZw4UY8++qiqVKmiXr166ZlnnnHUJljl1KlT+bYHBQXZKRoAgD1RSAdgs7/++ksvv/yyzp49q8qVK+vOO+/UihUrVLlyZUnSqFGj5OnpqSFDhigjI0MxMTEaM2aMg6MGAAAAUBR69OiR61AuixYtyjYtMjJSK1asKO6wikXz5s3zHCO9efPmDogKAGAPFNIB2Oy9997Ls93Hx0djxoyheA4AAACgRDt27FieY6QfO3ZMt912m32DAgDYBWOkAwAAAAAAAACQBwrpAAAAAAAABVCnTh2FhYXl2BYeHq46derYOSIAgL1QSAcAAAAAACgADw8PjRw5Uh4eHhbTPT09c5wOAHAdFNIBAAAAAAAKqGbNmurWrZvFtG7duqlGjRoOiggAYA8U0gEAAAAAAKzw9NNPq3z58pKk8uXLq1evXg6OCABQ3CikAwAAAAAAWMHX11evvvqqqlSpoldffVW+vr6ODgkAUMxKOToAAAAAAACAkiY6OlrR0dGODgMAYCdckQ4AAAAAAGClQYMG6d5779WgQYMcHQoAwA4opAMAAAAAAFghKSlJiYmJkqTExEQlJSU5OCIAQHGjkA4AAAAAAGCFZ555Js/XAADXY3Mh/dSpUxo+fLiaN2+u8PBwtW/fXnv27DG3X7p0SXFxcbrnnnsUHh6udu3aadmyZbauFgAAAAAAwO6WLVum9PR0i2np6enUOgDAxdlUSD937pyefPJJeXt7a+7cuVq7dq1GjhypChUqmPtMmjRJmzZt0rvvvqt169bp6aef1rhx4/T999/bHDwAAAAAAIC9XL16VXPmzMmxbc6cObp69aqdI3IeW7ZsUdeuXbVlyxZHhwIAxcKmQvrcuXNVtWpVTZw4UeHh4apVq5ZiYmJUu3Ztc5/du3erQ4cOat68uWrWrKmuXbuqQYMGSkhIsDl4AAAAAAAAe5k/f75N7a7qypUrGjdunE6dOqVx48bpypUrjg4JAIqcTYX0H374QaGhoRoyZIhatGihDh06aMWKFRZ9IiMj9cMPP+jUqVMyDEPbtm3TkSNHFBMTY/X6TCYT/+Xxnztw520HAAAAADhWpUqVbGp3VfPmzdPly5clSZcvX9Ynn3zi4IgAoOiVsmXmY8eOadmyZerTp4+ee+457dmzR+PHj5e3t7c6duwoSXrjjTf0xhtv6J577lGpUqXk4eGh8ePHq2nTplav78ax12Hp5vHZXFVCQoJ8fHwcHQYAAAAAwA2lpqba1O6Kjh8/rv/85z8W01asWKFHH31UNWvWdFBUAFD0bCqkG4ah0NBQvfzyy5KkO+64Q7///ruWL19uLqQvWrRI8fHx+vDDD1W9enX99NNPio2N1a233qro6Gir1hcWFiYvLy9bQnZZ13/5dXXh4eEqU6aMxTSTycSPLAAAAACAYufv729Tu6sxDENxcXE5tsXFxWnOnDny8PCwc1QAUDxsKqQHBgYqKCjIYlq9evX0zTffSLo2RtZ7772nmTNn6t5775UkNWjQQPv27dO8efOsLqR7eXlRSM+Fu+wX3gMAAAAAAEcJDw+3qd3V/PHHHzpw4ECObQcOHNAff/yhunXr2jkqACgeNo2RHhUVpSNHjlhM++OPP1SjRg1J155mnZmZme3XRy8vLxmGYcuqAQAAAAAA7OqLL76wqd3VnDhxwqZ2AChJbCqkP/300/rll180e/ZsHT16VGvWrNGKFSv01FNPSZLKlSunZs2a6d1339X27dt17Ngxff7551q9erXatGlTJBsAAAAAAAAcZ8mSJWrdurXCwsL0xBNPKCEhoUDzrV27ViEhIRo4cGAxR1h0KlSoYFO7q6lWrZpN7QBQkthUSA8PD9fMmTO1du1aPfLII/rggw80atQoPfroo+Y+U6dOVVhYmIYPH66HH35YH330kV566SU9+eSTNgcPAAAAAAAcZ926dZo4caJeeOEFrVq1Sg0aNFC/fv3yfejm8ePH9fbbb6tJkyZ2irRo9O3b16Z2V5Pf+OeMjw7Aldg0RroktWrVSq1atcq1PTAwUBMnTrR1NQBKiI8++khTpkxRr1699Prrr0uSevbsqR07dlj069q1a64PpQEAAABQMsyfP19dunRR586dJUmxsbHasGGDVq5cqWeffTbHeUwmk4YPH67Bgwfr559/1vnz5+0Zsk08PT3l7e2tzMzMbG3e3t7y9LTpekUAgBOzuZAOANclJCRo+fLlCgkJydbWpUsXDRkyxPy6TJky9gwNAAAAQBHLyMjQ3r17NWDAAPM0T09PRUdHa/fu3bnON2vWLPn7++uJJ57Qzz//XOj1m0ymQs9bWFu2bMmxiC5JmZmZ+t///qfo6Gg7R+U4+f0bmEwmu/87OeJ9AcA9UEgHUCQuXbqkV155RePHj9eHH36Yrd3X11eBgYEOiAwAAABAcfj7779lMpnk7+9vMd3f31+HDx/OcZ6ffvpJn332mVavXm3z+vfs2WPzMqz1999/59seHx9vn2CcwF9//ZVn+/79+3Xu3Dk7RQMAxYtCOoAiERcXp5YtWyo6OjrHQvqaNWv05ZdfKjAwUK1atdLAgQMLdVU6VxcgJ7wvitfSpUu1bNky/fnnn5Kk22+/XQMHDlTLli0lFWz4phMnTmjs2LHavn27ypYtqw4dOmjYsGEqVYpTEQAA3MXFixc1YsQIjRs3TpUrV7Z5eWFhYfLy8iqCyAru0qVLebZXrFhRERER9gnGCVy9elXvvvturu1t2rSx+/meyWRyyI8sAFwf314B2Gzt2rX69ddf9dlnn+XY/sgjj6h69eq69dZbtX//fk2ePFlHjhzRzJkzrV4XJ0SA/VWtWlXDhw9XnTp1ZBiGVq9ebX6g2O233y4p7+GbTCaTBgwYoICAAC1fvlynT5/WyJEj5e3trZdfftnu2wMAAIpGpUqV5OXlle3BoqmpqQoICMjW/9ixY/rzzz/1/PPPm6dlZWVJku644w59/fXXql27doHX7+XlZfdCeo0aNfJtt3dMjvTVV1/l2f7111/rscces1M0AFC8KKQDsMnJkyf11ltv6ZNPPpGPj0+Ofbp27Wr+OyQkRIGBgerdu7eSkpKsOlGWHHPVCZwfV50Ur9atW1u8fumll7Rs2TLFx8ebC+l5Dd+0efNmHTx4UPPnz1dAQIAaNmyoF198UZMnT9agQYNUunTpYt8GAABQ9EqXLq1GjRpp69atatOmjaRrhfGtW7eqR48e2frXq1dPa9assZg2bdo0Xbp0Sa+//rqqVq1ql7htkd9QJn/99ZeCgoLsFI3jPfLII5o+fXqOd4iWKlVKjzzyiAOiAoDiQSEdgE327t2r1NRUderUyTzNZDJp586dWrJkifbs2ZOt8N24cWNJ0tGjR60upDviqhMA/zCZTPr666+VlpamyMhI8/S8hm+Kj49XcHCwxZVpMTExGjt2rA4ePKg77rjD7tsBAACKRp8+fTRy5EiFhoYqPDxcCxYs0OXLl83fD0aMGKEqVapo2LBh8vHxUXBwsMX85cuXl6Rs051VfsX+kvBjQFHy8vLSnXfemW2YP0m68847+e4GwKVQSAdgk3/961/Zrip57bXXVK9ePT3zzDM5njjt27dPknj4KFCC7N+/X926dVN6errKli2rWbNmqX79+pLyH74pJSUl2+3d118nJydbHYsjx8S/cd0mk8ntx+d3pv3h7v8WAOAo7dq105kzZzR9+nQlJyerYcOG+vjjj83H+pMnT8rT09PBURadEydO5NvuTlekZ2Zm5lhEl6Tt27crMzNT3t7edo4KAIoHhXQANilXrly2q0fKli2rihUrKjg4WElJSVqzZo1atmypihUrav/+/Zo4caKaNm2qBg0aOChqANaqW7euVq9erQsXLuibb77RyJEjtXjxYtWvX79Ih28qCEcO45Oenm7+OyEhIdchrdwF+wMAIEk9evTIcSgXSVq0aFGe806aNKk4Qio2p0+ftqnd1UyfPj3f9mHDhtkpGgAoXhTSARQrb29vbd26VQsXLlRaWpqqVaumBx54QAMHDnR0aACsULp0adWpU0eSFBoaqj179mjhwoWKi4vL1vfm4ZsCAgKUkJBg0SclJUVS4e5MceSzEi5fvmz+Ozw83OKhqu7ImfYHz0oAANhDfucu7nbXbc2aNW1qB4CShEI6gCJ341Un1apV0+LFix0YDYDikJWVpYyMjBzbbh6+KSIiQrNnz1Zqaqr8/f0lSVu2bFG5cuXMw8NYw5HPSrhxvTyzgf0BAIC7++OPP2xqB4CShEI6AADI05QpU3TPPfeoWrVqunTpkr766ivt2LFD8+bNK9DwTTExMapfv75GjBihV155RcnJyZo2bZq6d++u0qVLO3jrAAAACu6vv/6yqd3VVKhQwaZ2AChJKKQDAIA8paamauTIkTp9+rT8/PwUEhKiefPm6a677tLJkyfzHb7Jy8tLs2fP1tixY9W1a1eVKVNGHTt21JAhQxy4VQAAANaLj4/Pt71Lly72CQYAYFcU0gEAQJ4mTJiQa1tBh2+qUaOG5s6dW5RhAQAA2B0PG7VUqVIlm9oBoCTxdHQAAAAAAAAAJUFaWppN7a7m6NGjNrUDQElCIR0AAAAAAKAA+vTpY1O7q6ldu7ZN7QBQklBIBwAAAGBhzpw56ty5syIjI9WiRQsNHDhQhw8fzne+9evX66GHHlJYWJjat2+vjRs3WrT/97//Vd++fdW8eXOFhIRo3759xbUJAFAsjhw5YlO7q9m7d69N7XAuBTn+p6enKzY2Vs2bN1dkZKQGDx6slJQUiz7jx49Xp06dFBoaqsceeyzHdf3222966qmnFBYWppYtWzIMJEoECukAAAAALOzYsUPdu3fXihUrNH/+fF29elX9+vXLc8iCXbt2adiwYXr88ce1evVq3XfffXrhhRd04MABc5+0tDRFRUVp+PDh9tgMAChylStXtqnd1VStWtWmdjiXghz/J0yYoB9//FHTpk3TokWLdPr0aQ0aNCjbsjp37qx27drluJ6LFy+qX79+ql69uj7//HONGDFCM2fO1L///e9i2zagKPCwUQAAAAAW5s2bZ/F60qRJatGihfbu3aumTZvmOM/ChQt19913q3///pKkoUOHasuWLVq8eLHi4uIkSR06dJAkHT9+vPiCB4BilJGRYVO7qzl37pxN7XAu+R3/L1y4oJUrV2ry5Mlq0aKFpGuF9Xbt2ik+Pl4RERGSpNGjR0uSzpw5o/3792dbz5dffqnMzExNmDBBpUuX1u233659+/Zp/vz56tq1a/FuJGADCukAAAAA8nThwgVJUoUKFXLtEx8fr969e1tMi4mJ0XfffVcsMZlMpmJZrrXrNplMDo3F0ZxpX7jzvwPsZ926dfm2P/XUU3aKxvEuX75sUzuc283H/8TERGVmZio6OtrcJygoSNWrV7copOcnPj5eTZo0UenSpc3TYmJiNHfuXJ07dy7P842bOctxx9HHQGfgTPujuNZNIR0AAABArrKysjRhwgRFRUUpODg4134pKSkKCAiwmObv759t3NSismfPnmJZbkGkp6eb/05ISJCPj4/DYnE09gXcTfPmzfO8q6Z58+Z2jMbxypQpY1M7nFdOx/+UlBR5e3urfPnyFn39/f2VnJxc4GWnpKSoZs2aFtOun0OkpKRYVUjnfMB5uMP+oJAOAAAAIFexsbH6/ffftXTpUkeHYiEsLExeXl4OWfeNV1iGh4e7daHImfaFyWRyaEEF7uHSpUs2tbsaxox3Xc56/L8Z5wPOw5n2R3GdE1BIBwAAAJCjuLg4bdiwQYsXL873gXEBAQHZrj5PTU3NdpV6UfHy8nLYF+cb1+vIOJwB+wLuxs/Pz6Z2oCTI7fgfEBCgzMxMnT9/3uKq9NTUVAUGBhZ4+TmdM1x/be15A+cDzsMd9oenowMAAAAA4FwMw1BcXJy+/fZbLViwQLVq1cp3noiICG3bts1i2pYtWwo8XioAlAT5PSzZ3R6m7OHhYVM7nEt+x//Q0FB5e3tr69at5mmHDx/WiRMnrDreR0RE6KefflJmZqZ52pYtW1S3bl2rhnUB7I1COgAAAAALsbGx+vLLLzVlyhTdcsstSk5OVnJysq5cuWLuM2LECE2ZMsX8ulevXtq0aZM++eQTHTp0SDNmzFBiYqJ69Ohh7nP27Fnt27dPhw4dkiQdOXJE+/bts2pcVQBwpBMnTtjU7moqVapkUzucS37Hfz8/P3Xu3FmTJk3Stm3blJiYqFGjRikyMtKikH706FHz8f3KlSvat2+f9u3bp4yMDElS+/bt5e3trddff12///671q1bp4ULF6pPnz6O2GygwBjaBQAAAICFZcuWSZJ69uxpMX3ixInq1KmTJOnkyZPy9PznupyoqChNnjxZ06ZN09SpU3Xbbbdp1qxZFg8o/eGHH/Taa6+ZX7/00kuSpEGDBmnw4MHFtj0AUFTyu1rW3a6mPXv2rE3tcC4FOf6PGjVKnp6eGjJkiDIyMhQTE6MxY8ZY9B89erR27Nhhft2hQwdJ0vfff6+aNWvKz89P8+bNU1xcnDp16qRKlSpp4MCB6tq1azFuHWA7CukAAAAALOzfvz/fPosWLco2rW3btmrbtm2u83Tq1Mn8RRwASqJq1aopISEhz3Z3Ur9+fZva4VwKcvz38fHRmDFjshXPb5TTOcLNGjRo4PQPMgVuxtAuAAAAAAAABeDj42NTu6v56aefbGoHgJKEQjoAAAAAACi0JUuWqHXr1goLC9MTTzyR5xXbK1as0FNPPaWmTZuqadOm6t27d579nc2XX35pU7urqVixok3tAFCSUEgHAAAAAACFsm7dOk2cOFEvvPCCVq1apQYNGqhfv35KTU3Nsf/27dv18MMPa+HChVq+fLmqVaumvn376tSpU3aOHEWhQYMGNrUDQEnCGOkAAAAASjTDMHTlyhW7re/Gddlzvb6+vvLw8LDb+oCCmD9/vrp06aLOnTtLkmJjY7VhwwatXLlSzz77bLb+U6ZMsXg9fvx4ffPNN9q6dav5gYTOrHLlyjpz5kye7e7kxodOF6YdKGr2PCfgfMD9UEgHUKQ++ugjTZkyRb169dLrr78uSUpPT9ekSZO0bt06i6d6BwQEODhaAADgCq5cuZLnQ06LU8eOHe22rvXr16tMmTJ2Wx+Qn4yMDO3du1cDBgwwT/P09FR0dLR2795doGVcvnxZV69eVYUKFYorzCKVX7HMnsU0Z1C9enWb2oGi5qhzAs4H3AOFdABFJiEhQcuXL1dISIjF9AkTJmjjxo2aNm2a/Pz8NG7cOA0aNEjLly93UKQAAAAAbPX333/LZDLJ39/fYrq/v78OHz5coGVMnjxZt956q6Kjo61ev8lksnoeW2VmZubb7oi4HCUrKyvfdnvvD3fa/wDsi0I6gCJx6dIlvfLKKxo/frw+/PBD8/QLFy5o5cqVmjx5slq0aCHpWmG9Xbt2io+PV0REhIMiBgAAruhixJMyPO3wNccwrv2/mG+t9si6qnLxy4p1HYCjfPTRR1q3bp0WLlwoHx8fq+ffs2dPMUSVt4IU0uPj4+0TjBP466+/8mzfv3+/zp07Z6doAEt2OSfgfMCtUEgHUCTi4uLUsmVLRUdHWxTSExMTlZmZaXGFSVBQkKpXr16oQjpXFyAnvC8AANcZnqUkL29Hh1FkDEcHAOShUqVK8vLyyvZg0dTU1HyHcZw3b54++ugjzZ8/v9APpAwLC5OXl1eh5i1O7nSxUFZWlmbNmqW0tLRsbWXLltUDDzxg93HSTSaTQ35kgfNxpXMCzgecA4V0ADZbu3atfv31V3322WfZ2lJSUuTt7a3y5ctbTPf391dycrLV6+KECAAAAHAOpUuXVqNGjbR161a1adNG0rXC6tatW9WjR49c55s7d65mz56tefPmKSwsrNDr9/LycspCujPGVFyOHTuWYxFdktLS0vTnn3/qtttus29QAFBMKKQDsMnJkyf11ltv6ZNPPinU7ZjWctarTuBYXHUCAMA/DMOw2wMPb1yPvR+y6OvrK49ivpUe+evTp49Gjhyp0NBQhYeHa8GCBbp8+bI6deokSRoxYoSqVKmiYcOGSbo2nMv06dM1ZcoU1ahRw3xxTdmyZXXLLbc4bDtQOIaR93Wy+bUDKF7ucE5gz/MBCukAbLJ3716lpqaaT5Sla0XNnTt3asmSJZo3b54yMzN1/vx5i6vSU1NTFRgYaPX6nPWqEwAAAGdx5coVtW3b1u7r7dixo13Xt379epUpU8au60R27dq105kzZzR9+nQlJyerYcOG+vjjj81Du5w8edJiaI/ly5crMzNTQ4YMsVjOoEGDNHjwYLvGDtvlV7zixy7AsdzhnMCe5wMU0gHY5F//+pfWrFljMe21115TvXr19Mwzz6hatWry9vbW1q1b9eCDD0qSDh8+rBMnTrjV2IEAAACAq+rRo0euQ7ksWrTI4vUPP/xgj5BgJ3Xq1FFYWFiOd4eGh4erTp06DogKAIoHhXQANilXrpyCg4MtppUtW1YVK1Y0T+/cubMmTZqkChUqqFy5cho/frwiIyMppANAMeD2TQA3mhlzRj5exTu0wvWRG+zxsUw3eWjQ5srFvyIABeLh4aGRI0dm+yHl+nSO14DzcKVzAkedD1BIB1DsRo0aJU9PTw0ZMkQZGRmKiYnRmDFjHB0WABdB4dgSt28CuJGPlyEflxoVj/GWAWdTs2ZNPfnkk1q2bJl52pNPPqkaNWo4MCoAN3OtcwLHnA9QSAdQ5G6+fdPHx0djxoyheA6gWFA4BgAAcKyuXbtq+fLlMgxDHh4e6tKli6NDAoAiRyEdAADARXH7JgAAsIeVK1fK+P8nBYZh6PPPP1ffvn0dHBUAFC0K6QAAwGVcjHhShmcxn97YqXLskXVV5eKX5d8xD9y+CQAAitvx48e1ePFii2mLFy/WAw88oJo1azooKgAoehTSAQCAyzA8S0le3o4Oo0hQNgYAAM7OMAy9//775qvRr8vKytL777+vd955hweOAnAZnrYu4NSpUxo+fLiaN2+u8PBwtW/fXnv27LHoc+jQIT333HO68847FRERoc6dO+vEiRO2rhoAANjB0qVL1b59e0VFRSkqKkpdu3bVxo0bze3p6emKjY1V8+bNFRkZqcGDByslJcViGSdOnNCzzz6rxo0bq0WLFnr77bd19epVe2+K20k3udZ/AADAuSQlJWnnzp05tu3cuVNJSUl2jggAio9NV6SfO3dOTz75pJo3b665c+eqUqVKOnr0qCpUqGDuk5SUpKeeekqdO3fWkCFDVK5cOf3+++/y8fGxOXgAAFD8qlatquHDh6tOnToyDEOrV6/WCy+8oFWrVun222/XhAkTtHHjRk2bNk1+fn4aN26cBg0apOXLl0uSTCaTBgwYoICAAC1fvlynT5/WyJEj5e3trZdffrlogzVlFu3yHKmQ23LjFWGDNvsXVTRO5ear3gAAgGPUrFlTHh4eOR6bPTw8GNoFgEuxqZA+d+5cVa1aVRMnTjRPq1WrlkWf9957T/fcc49GjBhhnla7dm1bVgsAAOyodevWFq9feuklLVu2TPHx8apatapWrlypyZMnq0WLFpKkCRMmqF27doqPj1dERIQ2b96sgwcPav78+QoICFDDhg314osvavLkyRo0aJBKly5tU3w3fnHz+2W5TctyVhSOAQCAM/rf//6X63mKYRj63//+p3vuucfOUQFA8bCpkP7DDz8oJiZGQ4YM0c6dO1WlShU99dRT6tKli6RrY2Jt2LBB/fv3V79+/fTrr7+qZs2aGjBggNq0aWP1+kwm7unNjbvsG5PJlG1b3WXbAcAZmEwmff3110pLS1NkZKQSExOVmZmp6Ohoc5+goCBVr17dXEiPj49XcHCwAgICzH1iYmI0duxYHTx4UHfccYfVMeT12hXldPzLTVZWlvnvmTGpLvOw0XTTP1fYZ2VlueX7AAAAZ/O///0v33YK6QBchU2F9GPHjmnZsmXq06ePnnvuOe3Zs0fjx4+Xt7e3OnbsqNTUVKWlpWnu3LkaOnSohg8frk2bNmnQoEFauHChmjVrZtX6bh57Hf9IT093dAh2kZCQwLBAAOAA+/fvV7du3ZSenq6yZctq1qxZql+/vvbt2ydvb2+VL1/eor+/v7+Sk5MlSSkpKRZFdEnm19f7WOPm84Ebj4EXGndzmYeNypRpvsJ+z549BT7+3bg/fLzkMoX0G3E+AACAczh58qRN7QBQkthUSDcMQ6GhoebxTe+44w79/vvvWr58uTp27Gi+Iuq+++5T7969JUkNGzbUrl27tHz5cqsL6WFhYfLycsFvg0Xg8uXLjg7BLsLDw1WmTBmLaSaTiR9ZAKCY1a1bV6tXr9aFCxf0zTffaOTIkVq8eLFDYrn5fMDiGOjl7TqF9BvkdPzLjTucE3A+AACAczh48KBN7QBQkthUSA8MDFRQUJDFtHr16umbb76RJFWqVEmlSpXK1icoKEg///yz1evz8vKikJ4Ld9kvvAcAwDFKly6tOnXqSJJCQ0O1Z88eLVy4UG3btlVmZqbOnz9vcVV6amqqAgMDJV27+jwhIcFieSkpKZJk7mONm48F7nBcsOb4x/4AAAD2Uq1aNR06dCjPdgBwFZ62zBwVFaUjR45YTPvjjz9Uo0YNSde+dIeFheXZBwAAlDxZWVnKyMhQaGiovL29tXXrVnPb4cOHdeLECUVEREiSIiIidODAAaWmppr7bNmyReXKlVP9+vXtHToAAACKiIeHh03tAFCS2FRIf/rpp/XLL79o9uzZOnr0qNasWaMVK1boqaeeMvfp16+f1q9frxUrVujo0aNavHixfvzxRz355JM2Bw8AAIrflClTtHPnTh0/flz79+/XlClTtGPHDrVv315+fn7q3LmzJk2apG3btikxMVGjRo1SZGSkuZAeExOj+vXra8SIEfrtt9+0adMmTZs2Td27d1fp0qUdu3EAAAAotOPHj9vUDgAliU1Du4SHh2vmzJmaOnWqZs2apZo1a2rUqFF69NFHzX3uv/9+jR07Vh999JHGjx+vunXravr06WrSpInNwQMAgOKXmpqqkSNH6vTp0/Lz81NISIjmzZunu+66S5I0atQoeXp6asiQIcrIyFBMTIzGjBljnt/Ly0uzZ8/W2LFj1bVrV5UpU0YdO3bUkCFDHLVJAAAAKAImk8mmdgAoSWwqpEtSq1at1KpVqzz7PP7443r88cdtXRUAAHCACRMm5Nnu4+OjMWPGWBTPb1ajRg3NnTu3qEMDAACAA/n6+iozMzPPdgBwFTYN7QIAAAAAAAD3ZBiGTe0AUJJQSAcAAAAAAIDVLl68aFM7AJQkFNIBAAAAAAAAAMgDhXQAAAAAAFBoS5YsUevWrRUWFqYnnnhCCQkJefZfv369HnroIYWFhal9+/bauHGjnSIFAKDwKKQDAAAAAIBCWbdunSZOnKgXXnhBq1atUoMGDdSvXz+lpqbm2H/Xrl0aNmyYHn/8ca1evVr33XefXnjhBR04cMDOkQMAYJ1Sjg4AAAAAAACUTPPnz1eXLl3UuXNnSVJsbKw2bNiglStX6tlnn83Wf+HChbr77rvVv39/SdLQoUO1ZcsWLV68WHFxcTbHYxiGzp49a9U8WVlZOn/+vM3rvu7IkSN5tpcvX16entZf11ixYkV5eHhYNQ/7AwCKDoV0ADZbunSpli1bpj///FOSdPvtt2vgwIFq2bKlJKlnz57asWOHxTxdu3YtkhNlAAAAAI6RkZGhvXv3asCAAeZpnp6eio6O1u7du3OcJz4+Xr1797aYFhMTo++++87q9ZtMJovXhmHoxRdf1N69e61eVlHq06dPsSw3NDRU06ZNK3Dx2F33x83vCwAoKhTSAdisatWqGj58uOrUqSPDMLR69WrzrZ233367JKlLly4aMmSIeZ4yZco4KlwAAAAAReDvv/+WyWSSv7+/xXR/f38dPnw4x3lSUlIUEBCQrX9KSorV69+zZ4/Fa8MwdOnSJauXU1JcvHhR8fHxVhXS2R8AUHQopAOwWevWrS1ev/TSS1q2bJni4+PNhXRfX18FBgY6IjwAAAAALigsLExeXl4W0z7++ONiHcrk+pA0efn444/zbLfnUCbuuD9MJlO2H1kAoChQSAdQpEwmk77++mulpaUpMjLSPH3NmjX68ssvFRgYqFatWmngwIGFuiqd2/SQE94XAAAA9lepUiV5eXlle7BoampqtqvOrwsICMh29Xle/fPi5eWVrZB+fR3WuvXWW62eJzf169cvsmUVheLcH4sWLVLPnj3zbK9Vq5bV6wcAZ0QhHUCR2L9/v7p166b09HSVLVtWs2bNMp9APvLII6pevbpuvfVW7d+/X5MnT9aRI0c0c+ZMq9fDlQUAAACAcyhdurQaNWqkrVu3qk2bNpKuXc28detW9ejRI8d5IiIitG3bNotx0rds2aKIiAg7RGy78ePHa/To0Xm2u5OaNWva1A4AJQmFdABFom7dulq9erUuXLigb775RiNHjtTixYtVv359de3a1dwvJCREgYGB6t27t5KSklS7dm2r1pPT7ZsAt28CAAA4Rp8+fTRy5EiFhoYqPDxcCxYs0OXLl9WpUydJ0ogRI1SlShUNGzZMktSrVy/17NlTn3zyiVq2bKl169YpMTFRcXFxjtyMAqtWrZpN7a4mKSkp3/Y6derYKRoAKF4U0gEUidKlS5tPkEJDQ7Vnzx4tXLgwxxPixo0bS5KOHj1qdSE9t9s3AQAAANhfu3btdObMGU2fPl3Jyclq2LChPv74Y/NwIidPnrQY/zoqKkqTJ0/WtGnTNHXqVN12222aNWuWgoODHbUJVvnzzz/zbQ8KCrJTNI5Xu3ZtNW3aVDt37szW1qxZM6u/7wGAM6OQDqBYZGVlKSMjI8e2ffv2SRIPHwUAAABcQI8ePXIdymXRokXZprVt21Zt27Yt7rCKxf79+/Ntv+eee+wUjeN5eHjovvvuy7GQft9991n9cFQAcGYU0gHYbMqUKbrnnntUrVo1Xbp0SV999ZV27NihefPmKSkpSWvWrFHLli1VsWJF7d+/XxMnTlTTpk3VoEEDR4cOAAAAAAWW33cYd/uOk5WVpQ8++CDHtlmzZun++++3uCMBAEoyCukAbJaamqqRI0fq9OnT8vPzU0hIiObNm6e77rpLJ0+e1NatW7Vw4UKlpaWpWrVqeuCBBzRw4EBHhw0AAFyRKdPRERQtV9seoIRr0aKFTe2uZvv27Tp//nyObefPn9f27dvdbp/AibjSMdSVtqUEo5AOwGYTJkzIta1atWpavHixHaMBAADuxjAM899+vyx3YCTF68btBOAY//vf//Jtb9mypZ2icbymTZva1A4UNXc4J+B8wHG4vwYAAAAAAKAA/vOf/9jU7mpyGhvdmnYAKEm4Ih0AAABAiXbjw+wuNO4meXk7MJoiZso0X1HHQ/sAx0tMTLSp3dXceeedNrUDRc1lzwk4H3AKXJEOAAAAwMKcOXPUuXNnRUZGqkWLFho4cKAOHz6c73zr16/XQw89pLCwMLVv314bN260aDcMQ++//75iYmIUHh6u3r17648//ija4L28Xe8/AHBSH374oU3tcC4FOf6np6crNjZWzZs3V2RkpAYPHqyUlBSLPidOnNCzzz6rxo0bq0WLFnr77bd19epViz7bt29Xx44dFRoaqvvvv1+ff/550W+Qo4/fnA+4HArpAAAAACzs2LFD3bt314oVKzR//nxdvXpV/fr1U1paWq7z7Nq1S8OGDdPjjz+u1atX67777tMLL7ygAwcOmPvMnTtXixYt0tixY7VixQqVKVNG/fr1U3p6uj02CwBQxFatWmVTO5xLQY7/EyZM0I8//qhp06Zp0aJFOn36tAYNGmRuN5lMGjBggDIzM7V8+XJNmjRJq1at0vTp0819jh07pgEDBqh58+b64osv9PTTT2v06NHatGmTXbcXsBaFdAAAAAAW5s2bp06dOun2229XgwYNNGnSJJ04cUJ79+7NdZ6FCxfq7rvvVv/+/RUUFKShQ4fqjjvuMD903DAMLVy4UM8//7zatGmjBg0a6J133tHp06f13Xff2WvTAABALvI7/l+4cEErV67Uq6++qhYtWig0NFQTJkzQ7t27FR8fL0navHmzDh48qHfffVcNGzZUy5Yt9eKLL2rJkiXKyMiQJC1fvlw1a9bUq6++qqCgIPXo0UMPPvigPv30UwdtOVAwFNIBAAAA5OnChQuSpAoVKuTaJz4+Xi1atLCYFhMTY/5iffz4cSUnJys6Otrc7ufnp8aNG2v37t1Wx2QymSz+cwc3b3Ne/7kDd91uALCXm4//iYmJyszMtDiWBwUFqXr16ubjfXx8vIKDgxUQEGDuExMTo4sXL+rgwYPmPnmdM1jDHY8F1pwPuPP+KA48bBQAAABArrKysjRhwgRFRUUpODg4134pKSkWX5olyd/f3zxuanJysnlabn2ssWfPHvPf7jI0TEJCgnx8fPLtx/4AYC/33nuvNmzYkGc7Sqacjv8pKSny9vZW+fLlLfr6+/ubj/M5nQ9cf51fn4sXL+rKlSvy9fUtcJw3ng9I7nEMtOb4x/4oWhTSAQAAAOQqNjZWv//+u5YuXeroUCyEhYXJy8tLknT58mUHR2Mf4eHhKlOmTL793HV/mEymbAUVoKht2LAhz+JwXkVlVzR27Ng898fYsWPtFguKlrMe/2924/mA5B7HwIKeD0juuz+K65yAoV0AAAAA5CguLk4bNmzQggULVLVq1Tz7BgQEZLuyPDU11XzFWWBgoHlabn2s4eXlZfGfO7h5m/P6zx2463bD8XIrlrtbEf069ofrye34HxAQoMzMTJ0/f96if2pqqvk4n9P5wPXX+fUpV66cVVejS+55LLDmfMCd90dxoJAOAAAAwIJhGIqLi9O3336rBQsWqFatWvnOExERoW3btllM27JliyIiIiRJNWvWVGBgoLZu3Wpuv3jxon755RdFRkYWafwAAMB6+R3/Q0ND5e3tbXEsP3z4sE6cOGE+3kdEROjAgQMWP5xv2bJF5cqVU/369c198jpnAJwVQ7sAAAAAsBAbG6uvvvpKH3zwgW655RbzmKZ+fn7mK8VGjBihKlWqaNiwYZKkXr16qWfPnvrkk0/UsmVLrVu3TomJiYqLi5MkeXh4qFevXvrwww9Vp04d1axZU++//75uvfVWtWnTxjEb6gbSXewZY662PSi5bh7ixd2vvmZ/uIb8jv9+fn7q3LmzJk2apAoVKqhcuXIaP368IiMjzUXwmJgY1a9fXyNGjNArr7yi5ORkTZs2Td27d1fp0qUlSd26ddOSJUv0zjvvqHPnztq2bZvWr1+vOXPmOGrT3YIrHUMdtS0U0gEAAABYWLZsmSSpZ8+eFtMnTpyoTp06SZJOnjwpT89/bnCNiorS5MmTNW3aNE2dOlW33XabZs2aZfGA0meeeUaXL1/Wm2++qfPnz+vOO+/Uxx9/zAMji5hhGOa/B232z6NnyXbjdgKOQLHYEvuj5CvI8X/UqFHy9PTUkCFDlJGRoZiYGI0ZM8bc18vLS7Nnz9bYsWPVtWtXlSlTRh07dtSQIUPMfWrVqqU5c+Zo4sSJWrhwoapWrarx48fr7rvvtsNWuhd3OCew5/kAhXQAAAAAFvbv359vn0WLFmWb1rZtW7Vt2zbXeTw8PPTiiy/qxRdftCk+AHAGW7Zs0fvvv68XX3xR0dHRjg4HsFlBjv8+Pj4aM2aMRfH8ZjVq1NDcuXPzXE7z5s21evVqa0MEHIpCOgAAyNOcOXP03//+V4cPH5avr68iIyM1fPhw1atXz9ynZ8+e2rFjh8V8Xbt2NQ/pIEknTpzQ2LFjtX37dpUtW1YdOnTQsGHDVKoUpyMAUJQ8PDzMf8+MSZWPCz1nLN30zxV1N24nYG9XrlzR1KlTlZKSoqlTpyoqKsrqhyQCQHFz1XMCR50P8M0VAADkaceOHerevbvCwsJkMpk0depU9evXT2vXrlXZsmXN/bp06WJxy2aZMmXMf5tMJg0YMEABAQFavny5Tp8+rZEjR8rb21svv/yyXbcHANyJj5dc5ksz4EyWLFlifphiamqqli5dqr59+zo4KgDIHecEtvPMvwsAAHBn8+bNU6dOnXT77berQYMGmjRpkk6cOKG9e/da9PP19VVgYKD5v3LlypnbNm/erIMHD+rdd99Vw4YN1bJlS7344otasmSJMjIy7L1JAAAAhXb8+HEtXbrUPC6vYRhaunSpjh8/7uDIAADFiUI6AACwyoULFyRJFSpUsJi+Zs0aNW/eXI888oimTJmiy5cvm9vi4+MVHBysgIAA87SYmBhdvHhRBw8etE/gAACgSJ09e1bDhg1TVFSUmjRpolGjRunSpUt59h83bpwefPBBhYeH695779X48ePN5xYlgWEYev/993OdzkNwAcB1MbQLAAAosKysLE2YMEFRUVEKDg42T3/kkUdUvXp13Xrrrdq/f78mT56sI0eOaObMmZKklJQUiyK6JPPr5ORkq2IwmUx5vnZFJpOpwNvprvvDHbYbAJzN8OHDlZycrPnz5yszM1OjRo3Sm2++qSlTpuTY//Tp0+bh3erXr68///xTY8eO1enTpzV9+nQ7R184SUlJ2rlzZ7bpJpNJO3fuVFJSkurUqeOAyAAAxY1COgAAKLDY2Fj9/vvvWrp0qcX0rl27mv8OCQlRYGCgevfuraSkJNWuXbtIY9izZ4/F6/T09CJdvjNKSEiQj49PgfqyPwAA9nDo0CFt2rRJn332mcLCwiRJo0eP1rPPPqsRI0aoSpUq2eYJDg7WjBkzzK9r166toUOH6pVXXtHVq1dLxAPIa9euraZNm+qnn36yuPr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      "text/plain": [
       "<Figure size 1500x1500 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = df.columns.drop(\"target\")\n",
    "nrows = math.ceil(len(cols)/MAX_FEATURES_PER_ROW)\n",
    "fig, ax = plt.subplots(nrows, MAX_FEATURES_PER_ROW, figsize = (15,15))\n",
    "\n",
    "for i,col in enumerate(cols):\n",
    "    nrow = math.floor(i/MAX_FEATURES_PER_ROW)\n",
    "    ncol = i%MAX_FEATURES_PER_ROW\n",
    "    f = sns.boxplot(y=df[col], x=df[\"target\"], ax=ax[nrow][ncol])\n",
    "    f.set_xlabel(col)\n",
    "    f.set_ylabel(None)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(wspace = 0.9, top = 0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr(\"spearman\")\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-Hot-Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(drop_first=True, data=df, columns=COLUMNS_ONE_HOT_ENCODING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting & Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"target\"]\n",
    "X = df.drop(\"target\", axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, test_size = 0.3, random_state = RSEED)\n",
    "\n",
    "#sampler = SMOTE(random_state=RSEED, sampling_strategy=0.1)\n",
    "#X_train, y_train = sampler.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline model: Coin toss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.14\n",
      "Recall: 0.50\n",
      "ROC-AUC: 0.50\n",
      "[[2048286 2053193]\n",
      " [ 176742  176370]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.random.randint(2, size=len(df))\n",
    "evaluate_model(y, y_pred, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest with 100 estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRAIN]\n",
      "F1: 0.99\n",
      "Recall: 0.99\n",
      "TNR: 1.00\n",
      "ROC-AUC: 1.00\n",
      "[[2870274     761]\n",
      " [   3495  243683]]\n",
      "\n",
      "[TEST]\n",
      "F1: 0.64\n",
      "Recall: 0.51\n",
      "TNR: 0.99\n",
      "ROC-AUC: 0.96\n",
      "[[1222065    8379]\n",
      " [  52412   53522]]\n"
     ]
    }
   ],
   "source": [
    "train_and_predict(\n",
    "    RandomForestClassifier(n_estimators=100, random_state=RSEED, n_jobs=N_JOBS), \n",
    "    X_train, y_train, X_test, y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRAIN]\n",
      "F1: 0.99\n",
      "Recall: 0.99\n",
      "TNR: 1.00\n",
      "ROC-AUC: 1.00\n",
      "[[2870353     682]\n",
      " [   3525  243653]]\n",
      "\n",
      "[TEST]\n",
      "F1: 0.63\n",
      "Recall: 0.64\n",
      "TNR: 0.97\n",
      "ROC-AUC: 0.81\n",
      "[[1190166   40278]\n",
      " [  38606   67328]]\n"
     ]
    }
   ],
   "source": [
    "train_and_predict(\n",
    "    DecisionTreeClassifier(random_state=RSEED), \n",
    "    X_train, y_train, X_test, y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRAIN]\n",
      "F1: 0.00\n",
      "Recall: 0.00\n",
      "ROC-AUC: 0.65\n",
      "[[2870651     384]\n",
      " [ 246710     468]]\n",
      "\n",
      "[TEST]\n",
      "F1: 0.00\n",
      "Recall: 0.00\n",
      "ROC-AUC: 0.65\n",
      "[[1230303     141]\n",
      " [ 105710     224]]\n"
     ]
    }
   ],
   "source": [
    "train_and_predict(\n",
    "    LogisticRegression(random_state=RSEED, n_jobs=N_JOBS), \n",
    "    X_train, y_train, X_test, y_test,\n",
    "    StandardScaler()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRAIN]\n",
      "F1: 0.14\n",
      "Recall: 0.07\n",
      "ROC-AUC: 0.78\n",
      "[[2868691    2344]\n",
      " [ 228876   18302]]\n",
      "\n",
      "[TEST]\n",
      "F1: 0.13\n",
      "Recall: 0.07\n",
      "ROC-AUC: 0.77\n",
      "[[1229381    1063]\n",
      " [  98332    7602]]\n"
     ]
    }
   ],
   "source": [
    "train_and_predict(\n",
    "    XGBClassifier(random_state=RSEED), \n",
    "    X_train, y_train, X_test, y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 247178, number of negative: 2871035\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.116372 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2267\n",
      "[LightGBM] [Info] Number of data points in the train set: 3118213, number of used features: 95\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.079269 -> initscore=-2.452319\n",
      "[LightGBM] [Info] Start training from score -2.452319\n",
      "[TRAIN]\n",
      "F1: 0.04\n",
      "Recall: 0.02\n",
      "ROC-AUC: 0.73\n",
      "[[2870630     405]\n",
      " [ 242218    4960]]\n",
      "\n",
      "[TEST]\n",
      "F1: 0.04\n",
      "Recall: 0.02\n",
      "ROC-AUC: 0.73\n",
      "[[1230266     178]\n",
      " [ 103891    2043]]\n"
     ]
    }
   ],
   "source": [
    "train_and_predict(\n",
    "    LGBMClassifier(random_state=RSEED, n_jobs=N_JOBS), \n",
    "    X_train, y_train, X_test, y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent (SGD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[TRAIN]\n",
      "F1: 0.00\n",
      "Recall: 0.00\n",
      "ROC-AUC: 0.50\n",
      "[[2871035       0]\n",
      " [ 247178       0]]\n",
      "\n",
      "[TEST]\n",
      "F1: 0.00\n",
      "Recall: 0.00\n",
      "ROC-AUC: 0.50\n",
      "[[1230444       0]\n",
      " [ 105934       0]]\n"
     ]
    }
   ],
   "source": [
    "train_and_predict(\n",
    "    CalibratedClassifierCV(SGDClassifier(max_iter=5, random_state=RSEED, n_jobs=N_JOBS)),\n",
    "    X_train, y_train, X_test, y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "### Manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntree = DecisionTreeClassifier(\\n    criterion=\"gini\",\\n    splitter=\"best\",\\n    max_depth=None,\\n    max_features=None,\\n    max_leaf_nodes=None,\\n    min_samples_split=2,\\n    min_samples_leaf=1,\\n    min_impurity_decrease=0,\\n    random_state = RSEED\\n)\\nscores = cross_val_score(tree, X_train, y_train, cv=5, n_jobs=N_JOBS, scoring=PRIMARY_SCORE)\\nsns.barplot(x=[1,2,3,4,5], y=scores)\\n'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "tree = DecisionTreeClassifier(\n",
    "    criterion=\"gini\",\n",
    "    splitter=\"best\",\n",
    "    max_depth=None,\n",
    "    max_features=None,\n",
    "    max_leaf_nodes=None,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    min_impurity_decrease=0,\n",
    "    random_state = RSEED\n",
    ")\n",
    "scores = cross_val_score(tree, X_train, y_train, cv=5, n_jobs=N_JOBS, scoring=PRIMARY_SCORE)\n",
    "sns.barplot(x=[1,2,3,4,5], y=scores)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automatic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nparam_grid = {\\n    \"max_depth\": [None, 5, 10, 20, 50, 100],\\n    \"max_features\": [\"auto\", \"sqrt\", None],\\n    \"max_leaf_nodes\": [None, 5, 10, 20, 50, 100],\\n    \"min_samples_split\": [2, 5, 10],\\n    \"min_samples_leaf\": [1, 2, 3],\\n    \"min_impurity_decrease\": [0, 0.01, 0.1]\\n}\\n\\nestimator = DecisionTreeClassifier(random_state = RSEED)\\n\\nrs = RandomizedSearchCV(estimator, param_grid, n_jobs = N_JOBS, \\n                        scoring = PRIMARY_SCORE, cv = 2, \\n                        n_iter = 10, verbose = 5, random_state=RSEED)\\n\\nrs.fit(X_train, y_train)\\nprint(rs.best_params_)\\n\\ny_train_pred = rs.best_estimator_.predict(X_train)\\ny_train_proba_pred = rs.best_estimator_.predict_proba(X_train)[:, 1]\\n\\ny_test_pred = rs.best_estimator_.predict(X_test)\\ny_test_proba_pred = rs.best_estimator_.predict_proba(X_test)[:, 1]\\n\\nprint(\"TRAIN:\")\\nevaluate_model(y_train, y_train_pred, y_train_proba_pred)\\n\\nprint(\"\\nTEST:\")\\nevaluate_model(y_test, y_test_pred, y_test_proba_pred)\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "param_grid = {\n",
    "    \"max_depth\": [None, 5, 10, 20, 50, 100],\n",
    "    \"max_features\": [\"auto\", \"sqrt\", None],\n",
    "    \"max_leaf_nodes\": [None, 5, 10, 20, 50, 100],\n",
    "    \"min_samples_split\": [2, 5, 10],\n",
    "    \"min_samples_leaf\": [1, 2, 3],\n",
    "    \"min_impurity_decrease\": [0, 0.01, 0.1]\n",
    "}\n",
    "\n",
    "estimator = DecisionTreeClassifier(random_state = RSEED)\n",
    "\n",
    "rs = RandomizedSearchCV(estimator, param_grid, n_jobs = N_JOBS, \n",
    "                        scoring = PRIMARY_SCORE, cv = 2, \n",
    "                        n_iter = 10, verbose = 5, random_state=RSEED)\n",
    "\n",
    "rs.fit(X_train, y_train)\n",
    "print(rs.best_params_)\n",
    "\n",
    "y_train_pred = rs.best_estimator_.predict(X_train)\n",
    "y_train_proba_pred = rs.best_estimator_.predict_proba(X_train)[:, 1]\n",
    "\n",
    "y_test_pred = rs.best_estimator_.predict(X_test)\n",
    "y_test_proba_pred = rs.best_estimator_.predict_proba(X_test)[:, 1]\n",
    "\n",
    "print(\"TRAIN:\")\n",
    "evaluate_model(y_train, y_train_pred, y_train_proba_pred)\n",
    "\n",
    "print(\"\\nTEST:\")\n",
    "evaluate_model(y_test, y_test_pred, y_test_proba_pred)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x = df_invoices.merge(df_clients, on=\"client_id\")\n",
    "creation_date = pd.to_datetime(df_x[\"creation_date\"], format=\"%d/%m/%Y\")\n",
    "invoice_date = pd.to_datetime(df_x[\"invoice_date\"], format=\"%Y-%m-%d\")\n",
    "\n",
    "df_x[\"target\"] = df_x[\"target\"].astype(int)\n",
    "df_x[\"counter_type\"] = df_x[\"counter_type\"].map(COUNTER_TYPE_MAPPING).astype(int)\n",
    "df_x[\"counter_statue\"] = df_x[\"counter_statue\"].replace(\"A\", 10).astype(int)\n",
    "df_x[\"consumption\"] = df_x[\"new_index\"] - df_x[\"old_index\"]\n",
    "df_x[\"consommation_total\"] = df_x[\"consommation_level_1\"] + df_x[\"consommation_level_2\"] + df_x[\"consommation_level_3\"] + df_x[\"consommation_level_4\"]\n",
    "\n",
    "df_x[\"invoice_date\"] = invoice_date.dt.strftime(\"%Y%m%d\").astype(int)\n",
    "df_x[\"invoice_year\"] = invoice_date.dt.year\n",
    "df_x[\"invoice_month\"] = invoice_date.dt.month\n",
    "df_x[\"invoice_month_ts\"] = invoice_date.dt.strftime(\"%Y%m\").astype(int)\n",
    "\n",
    "df_x[\"creation_date\"] = creation_date.dt.strftime(\"%Y%m%d\").astype(int)\n",
    "df_x[\"creation_year\"] = creation_date.dt.year\n",
    "df_x[\"creation_month\"] = creation_date.dt.month\n",
    "\n",
    "df_x = df_x[(invoice_date.dt.year >= 2005) & (df_x[\"counter_statue\"] <= 5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_y = df_x \\\n",
    "    .groupby([\"invoice_year\", \"target\"]) \\\n",
    "    .agg(customers=(\"client_id\", \"nunique\")) \\\n",
    "    .reset_index()\n",
    "\n",
    "df_y[\"target\"] = df_y[\"target\"].map(TARGET_LABELS).astype(str)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax = sns.lineplot(df_y, x=\"invoice_year\", y=\"customers\", hue=\"target\", marker=\"o\")\n",
    "ax.set_ylim(0, None)\n",
    "\n",
    "plt.legend(title=None)\n",
    "plt.title(\"Active customers\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"No. customers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JMW9ftWpVgeWdAB555BE2bdrE119/jbe3tzmpKUyXLl1QFIWZM2fm25fbS5Vbmuzrr7+22D9//nyL/cVx9epVi9tubm7UqlXLooRSzZo1OXnypMXrdfjwYXbt2lXsxyuKy5cvm2fkg1o944cffqBhw4bmnk4XFxcArl27dsfztW/fnoSEBH799VfztpycHBYsWICrq6t51n1pWbJkicUY1UWLFpGTk2N+H7Rr1w53d3dmz55d4FjWorwvC6LVauncuTN//fWXRfm4XLnvo9xvK/KONU5PT89XJi4lJSVfb3XDhg0BzO+P3Ha4dciGv78/DRs25IcffrDYd/ToUTZt2lSi9+rtPPjgg1y6dImlS5fm25eZmWmueiKEPZKeWyEqiH/++cfcq5ZX06ZNLcZmFsW4ceN44okn6N69O48//jg1a9YkMTGRPXv2cPHiRX766adC7+/o6Mh//vMfJk6cyDPPPMODDz7IuXPnWLFixW1XTHv44Yd5//33Wbt2Lf369StS+ahWrVrxyCOPsGDBAuLj42nXrh0mk4mdO3cSHR3NU089RVhYGD179mTJkiWkpqbSokULYmNjWblyJZ07d7boLSyqbt260bJlS+655x68vb2JjY1lzZo1PPXUU+Zj+vTpw1dffcXAgQPp06cPSUlJLF68mODgYK5fv17sx7yTOnXq8OabbxIbG4uvry/Lly8nKSnJomRYw4YN0el0zJkzh2vXruHo6EirVq3w9fXNd76+ffuyZMkSxowZw4EDBwgMDGTNmjXs2rWLsWPH5hvjercMBgPPPvssDz74IHFxcSxcuJBmzZpx3333AeqY2vHjxzNq1Ch69erFQw89hI+PD+fPn2f9+vU0bdqUd955p0SP/eqrr7Jp0yb69+9vLouVkJDAb7/9xsKFC/H09KRt27YEBATw5ptvcvLkSXQ6HcuXL6dKlSoWvbcrV65k0aJFdO7cmVq1anH9+nWWLl2Ku7u7OVF3dnYmODiY1atXU6dOHby9vWnQoAEhISGMGjWKF154gb59+9KnTx9zKTAPD48CJ06W1COPPMLq1asZN24c27Zto2nTphiNRk6ePMlvv/3G3LlzbWJRGCHKgiS3QlQQn3zySYHbp0yZUuzkNjg4mOXLlzNz5kxWrlxJcnIyPj4+NGrUiKFDhxbpHE899RSKojB//nymTZtGWFgYn3/+OZMmTSpwjG7VqlVp27Yt69ev55FHHilyrFOmTCE0NJRly5bx3nvv4eHhQXh4OFFRUeZjJk2aRFBQECtXrmTdunVUrVqVwYMHlzhZ6N+/P3/++SebNm0iOzubgIAAXn75ZQYOHGg+pn79+kybNo1PPvmEKVOmEBwczHvvvcfPP//M9u3bS/S4halTpw5vv/027733HnFxcQQFBfHxxx9bjJv28/NjwoQJzJ49mzfffBOj0cg333xTYHLr7OzMggUL+OCDD1i5ciVpaWnUrVuXKVOm0KtXr1KP/5133mHVqlV88sknGAwGunXrxltvvWXxlXn37t3x9/fniy++YN68eWRnZ1OtWjWaN29+VzFVq1aNpUuXMmPGDFatWkVaWhrVqlWjffv25rGmer2emTNnMmHCBGbMmIGfnx/PPPMMnp6eFuNWW7ZsSWxsLL/++iuJiYl4eHgQGRnJBx98YPF7OGnSJCZOnMiUKVMwGAwMGzaMkJAQ2rRpw9y5c/nkk0/45JNPcHBwoEWLFrz++uvF/j0ujFar5bPPPuOrr77ixx9/ZO3atbi4uBAUFET//v0LnNQmhL3QKLY2a0EIUWGZTCZat27N/fffX+CqZEOHDuXo0aMWX6+LO+vUqRMNGjS442putmjFihW88cYbLFu2THoKhRDlQsbcCiFKJCsrK9/Ywx9++IHk5GRatmyZ7/jLly8Xu9dWCCGEKC4ZliCEKJE9e/YwZcoUHnjgAby9vTl48CDLli0jJCTEon7tmTNn2LVrF8uWLcPBwYG+fftaMWohhBD2TpJbIUSJBAYGUr16dRYsWEBKSgpeXl488sgjjBw50qKY/Y4dO3jjjTcICAhg6tSpxV5sQAghhCgOGXMrhBBCCCHshoy5FUIIIYQQdkOSWyGEEEIIYTdkzC1q+aKcnBy0Wq0sUyiEEEIIYYMURcFkMuHg4GCxbPitJLlFXXKyoGUZhRBCCCGEbYmIiLCYuHwrSW7BnP1HRESg0+msHE3FYjQaiY2NldfOBknb2CZpF9slbWObpF1skzXaJfcxC+u1BUluAcxDEXQ6nfzilJC8drZL2sY2SbvYLmkb2yTtYpus0S53GkIqE8qEEEIIIYTdkORWCCGEEELYDUluhRBCCCGE3ZAxt+KuOTs7WzsEIYQQolgURSEnJwej0WjtUCqk3NctMzOz1Mbc6nQ6HBwc7rosqyS3ouSy09HqHAirWRUtRsjOAkdXa0clhBBCFCo7O5sLFy6Qnp5u7VAqLEVRcHBwID4+vlTXCHB1daVGjRqFlvq6E0luRcnkZMKm6Wi2zUaXmQzO3hA9GNq9Cg7SkyuEEMI2mUwm4uLi0Ol0BAQE4OjoKAs4lYCiKGRkZODi4lIqr5+iKGRnZ5OQkEBcXBwNGjS4Y8mv25HkVhRfdjpsmg7rp93clpl883bbl6UHVwghhE3Kzs7GZDJRs2ZNXF3lf1VJ5a4W5uzsXGofDlxcXNDr9cTHx5OdnV3iYY8yoUwUn84Bts0ueN+22ep+IYQQwoaVtFdQlK3SaBdpWVF8mSlqT22B+5IhM7U8oxFCCCGEMJPkVhSfs5c6xrbAfd7g7Fme0QghhBBCmElyK4rPmKNOHitI9GB1vxBCCCHs0pgxYxg6dKi1w7gtGRwpis/RFWJeBcUE2+eoQxGcvaHlCzKZTAghhCgjY8aMYeXKlQA4ODhQrVo1HnjgAUaMGIGTk5OVo7MdktyKkkmOh4AmKK8exJSVhtbZE83xdfDTMOg9D6SsihBCCFHq2rVrx5QpU8jJyeHAgQOMHj0ajUbD66+/bu3QisVgMKDX68vk3DIsQZTM0d9g8ZPw03AOn07ElH4VVgyG/csh9ntrRyeEEEIUi6IopGfnlOtFUZRix+no6Iifnx81atSgc+fOtGnThs2bNwNqDd/Zs2fTqVMnIiMj6dGjB7/99pvF/f/44w+6dOlCREQE/fv3Z+XKlYSGhpKaqk4G//TTT3nkkUcs7vPVV1/RqVOn28b0zz//0K9fP5o3b050dDSDBw/m9OnT5v1nz54lNDSUX3/9laeeeoqIiAhWrVpV7OdeVNJzK0rmxJ8AKIHNyMzMBPdq0P41+ONdWDsOwrqBo5uVgxRCCCHuTFEU+szaws74q+X6uM1rV+H7F1uXuE7s0aNH2b17NwEBAQDMnj2bn376iQkTJlCnTh127NjB66+/jo+PDy1btuTMmTOMGDGC/v3789hjj3Ho0CGmTZt2h0e5s4yMDAYMGEBoaCjp6enMmDGDoUOH8uOPP1qU9vrggw8YM2YMDRs2LNNhFJLciuIzZED8FgCUeh3h7I3lC1sNhZ1fq0MWNk6HTm9aL0YhhBCiGCrKYLq///6bqKgocnJyyM7ORqvV8vbbb5Odnc3s2bOZP38+UVFRANSsWZOdO3eyZMkSWrZsyZIlS6hbty6jR48GoF69ehw9epRZs2bdVUxdu3a1uD158mRat27N8ePHCQkJMW9/5pln6NKly109VlFIciuKL34zGLPAMwh8G8DZvep2vTN0mQRL+8PmTyDqKahS27qxCiGEEHeg0Wj4/sXWZBiM5fq4LnpdsXtto6OjGT9+PBkZGXz11VfodDq6du3KsWPHyMjI4LnnnrM43mAw0LBhQwDi4uIIDw+32B8ZGXl3TwI4deoUn3zyCXv37uXq1avm4RYXLlywSG5vfeyyIsmtKL4bQxKof2/+iWMNu0OddnBqA6x9Bx7/utzDE0IIIYpLo9Hg6mj7aZGLiwu1a6sdR5MnT+aRRx7h+++/NyeRs2fPplq1ahb3cXR0LPL5NRpNvrHAOTmFl/h88cUXCQwMZNKkSfj7+2MymXj44YcxGAwWx5XXcscyoUwU38m/1Z/1CxhcrtHAA1NBo4WDP8CpjeUZmRBCCFFpaLVaBg8ezIwZM6hfvz6Ojo6cP3+e2rVrW1xq1KgBQN26ddm/f7/FOWJjYy1u+/j4kJiYaJHgHjp06LYxXL16lbi4OIYMGULr1q2pX78+KSkppfgsi0+SW1E81y7Cpf2ABureW/Ax1cOh2bPq9dVjwFS+X/MIIYQQlcUDDzyAVqtlyZIlPPfcc0yZMoWVK1dy+vRpDhw4wIIFC8y1cfv27UtcXBzvv/8+cXFx/Prrr+Z9ucMjoqOjuXLlCnPmzOH06dN89913bNiw4baP7+Xlhbe3N0uWLCE+Pp4tW7YwderUsn/ihZDkVhRPbq9tjcbg5nv74zq+qS7TeykWdi8ol9CEEEKIysbBwYGnnnqKuXPnMnjwYF566SVmz57NQw89xPPPP8/ff/9NUFAQoE4wmzFjBmvXrqVHjx4sWrSIF198Ebg5dKF+/fqMGzeOhQsX8sgjj7Bv375843jz0mq1fPzxxxw4cICHH36YKVOmMGrUqLJ/4oXQKCUpsmZnjEYje/bsoUmTJuh0OmuHY9tWDIZ9i9UVyjqPK/y12/o5/DYGXKvCf3aCi7dVQq6s5H1tm6RdbJe0jW0q7XbJzMwkLi6OunXr4uzsXAoRVmyff/45ixcvZv369cW6n6IopKen4+rqWuJSZgUprH2K+l6QnltRdIqSZzJZxzsf3+J5qBoC6Ynwz/tlG5sQQggh7ui7775j3759nDlzhh9++IF58+bx6KOPWjusUmX70wKF7bh0AK5fBr0r1Iy+8/E6PTwwBb7tDdtmqeNwqzYo8zCFEEIIUbD4+Hg+//xzUlJSCAgIYMCAAQwePNjaYZUqSW5F0Z38S/1ZJwYciriySHBnaNAVjq2BNW/Ck0vLLj4hhBBCFGrs2LGMHTvW2mGUKRmWIIoud0hCvSIMScir62TQOqgJ7rG1pR+XEEIIIcQNktyKojFkqiuTQcH1bQtTNRii1dmY/PYGGA2FHy+EEEIIUUKS3IqiOb0FcjLBIwD8Qot//w6j1KoJScdg+5zSj08IIYQQAkluRVHlrZJQkpIfzl5w39vq9b+nwvXE0otNCCGEEOIGSW5F0eROJivukIS8ovpD9QjISoG//ls6cQkhhBBC5CHJrbiztMtw8cba03U7lPw8Wh08ME29vvOrm+cUQgghhCglktyKO8tdcrd6JLj73d256rSFe3qCYlInl8kCeUIIIUSFMmbMGIYOHWrtMG5LkltxZydKYUhCXve/Cw7OcGoDHPqpdM4phBBC2LkxY8YQGhqa7xIfH2/t0GyKLOIgClfcJXeLwrsWtBkO/7wHv7+lLvKgl/W9hRBCiDtp164dU6ZMsdjm4+NjcTs7OxtHR8fyDMumSHIrCnf5EKRdBAcXqNmq9M4b8zLs/haST8OWmdB+ZOmdWwghhCguRQFDevk+pt612BWIHB0d8fOzHCLYv39/GjRogE6n46effiIkJIQFCxYwf/58VqxYwZkzZ/Dy8qJjx468/vrruLm5AfDpp5+ybt06fvzxR/O5vvrqK7755hv+/FPt2DIajbz33nssX74cnU5H7969UWx8SKEkt6Jw5iV325Zu76qjmzo8YcXzsOEjaPIEeAaU3vmFEEKIolIU+LIrnNlWvo9bsxU891vJSmzeYuXKlfTr149FixaZt2k0Gt58802CgoI4c+YMEyZM4P3332f8+PFFPu+XX37JypUrmTx5MvXr1+fLL79k7dq1tGpVih1epczqY24vXbrEyJEjiY6OJjIyku7duxMbe3MWvaIozJgxg5iYGCIjI3n22Wc5deqUxTmSk5N57bXXaNq0Kc2bN2fs2LFcv369nJ+JnSrpkrtFEdEHakaD4Tqsm1D65xdCCCGK7O4TzPLw999/ExUVZb4MHz4cgDp16jBq1Cjq1atHvXr1AHj22Wdp1aoVQUFBtG7dmpdffpnVq1cX6/G+/vprBg0aRJcuXahfvz4TJkzAw8Oj1J9XabJqz21KSgr9+vUjOjqaOXPmUKVKFeLj4/Hy8jIfM2fOHBYsWMDUqVMJCgpixowZDBw4kF9//RUnJycARo4cSUJCAvPnz8dgMDB27FjeeecdPvzwQ2s9NfuQkwWnNqnXS2syWV4aDTwwBeZ0gn2LocXzULNF6T+OEEIIURiNRu1BrQDDEqKjoy16Xl1cXHjttde455578h27efNmZs+ezcmTJ0lLS8NoNJKVlUVGRgYuLi53fKxr166RkJBA48aNzdscHBwIDw+36aEJVk1u58yZQ/Xq1S0GRtesWdN8XVEUvvnmG4YMGULnzp0BeO+992jTpg3r1q2jW7dunDhxgg0bNrBs2TIiIiIAeOuttxg0aBCjRo2iWrVq5fuk7MnprZCTAe7Vwb9h2TxGYDNo8iTs+Q5+GwMD14LW6l8oCCGEqGw0GnXInI1zcXGhdu3aBW7P6+zZswwePJh+/frxyiuv4OXlxc6dO3nzzTcxGAy4uLig0WjyJak5OTllGn95sGpy++effxITE8Pw4cPZsWMH1apV44knnuDxxx8H1IZJSEigTZs25vt4eHjQuHFjdu/eTbdu3di9ezeenp7mxBagTZs2aLVa9u3bx/3331/keIxGY+k9OTugOf4HWsBU714Uk6nAY3Jfs7t67e59E+3BH9Gc+xfT3sUokX1Lfi5hViptI0qdtIvtkraxTaXdLkajEUVRzJeKJDfeW+MuaPv+/ftRFIXRo0ejvdFplDskIfe5+/j4kJiYiMlkQnOjB/nQoUPm/e7u7vj5+bF3716aN28OqMnvgQMHaNSoUYGxlMZzVBQFo9GYr82L+h6wanJ75swZFi1axIABA3jxxReJjY1l0qRJ6PV6evbsSUJCAgC+vr4W9/P19SUxMRGAxMTEfCUwHBwc8PLyMt+/qPKO9RUQduBX3IB4XT2u7NlT6LF3+9pVq9+PoENzyPntLQ5k18LkcOevS0TRyPvaNkm72C5pG9tUmu3i4OBARkYGptt03Niq3IQvPd1y+ITJZMJgMFhsr1atGgaDgS+//JL27duzZ88e82SzjIwMHBwciIiI4MqVK3z++efcd999bN68mX/++Qc3Nzfzufr168cXX3xB9erVqVOnDt9++y2pqanmRDMjI6NUn2NWVhYGg4HDhw+X+BxWTW4VRSE8PJxXX30VgEaNGnHs2DEWL15Mz549yz2eiIgIdDpduT+uTbqeiG7VMQBq3fs0tdz9CzzMaDQSGxt7969deEOUi2txvHqKxql/oHR6q+TnEkApto0oVdIutkvaxjaVdrtkZmYSHx+Pi4sLzs4Vq8a6TqdDp9Ph6upqsV2r1aLX6y22N2nShDFjxjBv3jxmzpxJ8+bNee211xg9ejQuLi64uroSHh7OuHHjmD17NnPnzuX+++/nueeeY+nSpeZzDRo0iOTkZMaNG4dWq6VXr1507tyZtLQ0APPwhtKS+1yCg4PztU/ue+FOrJrc+vn5Ub9+fYtt9erVY82aNeb9AElJSfj730yukpKSCAsLA6Bq1apcuXLF4hw5OTmkpKTkqwN3J7lvGgHEb1B/VotA51Xjjoff9Wunc4Uu/4UlT6Ld+hk0fwaq1Cn5+YSZvK9tk7SL7ZK2sU2l1S46nQ6NRmO+VCTTpk0rcPu3335b4PYBAwYwYMAAi22PPvqoxe1+/frRr18/i21DhgwxX9fr9bz55pu8+eabFscoikJ6enqpv46557ub9rbqzJ2mTZsSFxdnse3UqVMEBgYCEBQUhJ+fH1u2bDHvT0tLY+/evURFRQEQFRVFamoq+/fvNx+zdetWTCYTkZGR5fAs7JR5yd0yKAF2O2HdoG4HMGbB72+X3+MKIYQQwm5YNbl95pln2Lt3L7NmzSI+Pp5Vq1axdOlSnnjiCUDN3p9++mk+//xz/vjjD44cOcKoUaPw9/c3V0+oX78+7dq14+2332bfvn3s3LmTiRMn0q1bN6mUUFJlseRuUWg08MBU0Gjh0E8Q90/5PbYQQggh7IJVhyVERkYyc+ZMPvroIz777DOCgoIYO3YsPXr0MB/zwgsvkJGRwTvvvENqairNmjVj7ty55hq3AB988AETJ07kmWeeQavV0qVLF956S8ZslljCEbh2HhycoVbr8n3sao2g+UDYMQd+ewMG/wNa+XpQCCGEEEVj9eV3O3bsSMeOt+8d1Gg0jBgxghEjRtz2GG9vb1mwoTTlLrlbqzXorVC1oONYiP0eLu2HXV9D8+fKPwYhhBBCVEhSLV/kZx6SUAarkhWFq4+a4AL8MREyrlonDiGEEHarotW4rSxKo10kuRWWcrLg1Eb1urWSW1B7a/3CIOMKrH/PenEIIYSwK3q9HiBfrVhhG3LbJbedSsLqwxKEjTmzXV1b280fquVfp7rc6PTwwBRY0BO2fwHNngW/UOvFI4QQwi7odDq8vb25fPkyAK6urhWuJJgtUBSFrKwstFptqbx+uaXFLl++jLe3912VfZPkVljKWyXB2r/s9TtByINwdDWsGQtPLbduPEIIIexC9erVAcwJrig+RVEwGAzo9fpS/XDg7e1tbp+SkuRWWMqdTGbNIQl5df0vHF+nXo7+DiFdrB2REEKICk6j0VCjRg38/f0xGAzWDqdCMhqNHD58mODg4FJb9ESv15fKuSS5FTddT4Lze9Tr9e61ZiQ3+daHVkNg8yew5g01LgdHa0clhBDCDshqdCVnNBoBcHZ2trnXUCaUiZvi/gYU8L8HPO7uK4FS1f51cPODpOPq+FshhBBCiNuQ5FbcZI0ld4vC2RPuG6deXz8N0hKsG48QQgghbJYkt0KlKLab3AI0eRJqNIasVPhrkrWjEUIIIYSNkuRWqBKPQepZ0DlBrTbWjiY/rRYemKZe3/k1XNhn3XiEEEIIYZMkuRUq85K7rcDR1bqx3E7t1hDeG1DgtzFqb7MQQgghRB6S3AqVtZfcLarOE8DBBeI3wcEfrB2NEEIIIWyMJLcCcrJtY8ndovCuCW1HqNd/fxsMGdaNRwghhBA2RZJbAWd3QHYauFaFauHWjubO2o4Az0BIOQObZ1o7GiGEEELYEEluheWSu9oK8JZwdIX731Wvb/wIUs5ZNx4hhBBC2IwKkMmIMmdrS+4WRXhvqNkKDOmwbry1oxFCCCGEjZDktrJLvwLndqnXbWXJ3aLQaODBqYAGYpfCme3WjkgIIYQQNkCS28oubj2ggF9D8AywdjTFExAFUU+q11ePBpPJuvEIIYQQwuokua3sbHlVsqLo9A44esD5XbBvsbWjEUIIIYSVSXJbmVksuVuBxtvm5VENOryuXl83HrKuWTUcIYQQQliXJLeV2ZWTkHIadI5Q2waX3C2q6BfBpx6kXYINH1o7GiGEEEJYkSS3lVluCbCa0eDoZt1Y7oaDE3T5r3p9y2dq0i6EEEKISkmS28qsoiy5WxShD0K9jmDMVlcuE0IIIUSlJMltZWU0QNwG9bo9JLcaDTwwBTQ6OPwznPzb2hEJIYQQwgokua2szv4L2dfA1ReqR1o7mtLh3xBaPK9e/+0NMOZYNx4hhBBClDtJbiur3CEJ9e6tGEvuFtW9Y8ClClw+CDvnWzsaIYQQQpQzO8pqRLHkLrlbr4LWt70dVx/o+KZ6/a/J6gpsQgghhKg0JLmtjDKuwrmd6vWKunhDYZoNUFdcy7gC66dZOxohhBBClCNJbiujuH9AMUHVUPAKsnY0pU/nAA9OVa9vnwOXD1s3HiGEEEKUG0luK6OKvuRuUdS7F8IeBsUIa95QV2MTQgghhN2T5LYysqf6toXpMlFdfe3En3B0jbWjEUIIIUQ5kOS2srlyEpLjQauH2m2tHU3Z8qkHrV5Sr695A3KyrRuPEEIIIcqcJLeVTd4ld53crRtLeWg/EtyrqUn9tlnWjkYIIYQQZcyqye2nn35KaGioxeWBBx4w78/KymLChAlER0cTFRXFf/7zHxITEy3Ocf78eQYNGkTjxo1p3bo106ZNIydHivffVmUYb5uXkwfcN069vv49SLts3XiEEEIIUaas3nPboEEDNm7caL4sXLjQvG/y5Mn89ddfTJ8+nQULFnD58mWGDRtm3m80Ghk8eDAGg4HFixczdepUVq5cySeffGKNp2L7jDlqpQSoPMktQON+EBClrsj2x7vWjkYIIYQQZcjqya1Op8PPz8988fHxAeDatWssX76cMWPG0Lp1a8LDw5k8eTK7d+9mz549AGzcuJHjx4/z/vvv07BhQzp06MCIESP47rvvyM6W8ZX5nNsJWanqCl41mlg7mvKj1cIDN+rd7v4Wzu+xajhCCCGEKDsO1g4gPj6emJgYnJycaNKkCa+99hoBAQHs378fg8FAmzZtzMfWr1+fgIAA9uzZQ5MmTdizZw8hISFUrVrVfExMTAzjx4/n+PHjNGrUqFixGI3GUntetkhzfB1awFS3g1oZqxSeb+5rZvOvXWBzNOF90O5fhrJ6NKZnfgGNxtpRlakK0zaVjLSL7ZK2sU3SLrbJGu1S1MeyanIbGRnJlClTqFu3LgkJCXz22Wc8+eSTrFq1isTERPR6PZ6enhb38fX1JSEhAYDExESLxBYw3849pjhiY2NL+EwqhtDYX3AHTjvUJ+lG73dpqQivnb76Y9xz6Gd0Z7YS/+t0rgZWjqEZFaFtKiNpF9slbWObpF1sky22i1WT2w4dOpivh4WF0bhxYzp27Mjq1atxdnYu93giIiLQ6XTl/rjlIjMF7c/qSl01OzxNTe+apXJao9FIbGxshXntNIZXYP0U6h7/ktpdBoPe1dohlZmK1jaVhbSL7ZK2sU3SLrbJGu2S+5h3YvVhCXl5enpSp04dTp8+TZs2bTAYDKSmplr03iYlJeHn5weovbT79u2zOEduNYXcY4pDp9PZ7y/O6U3qal2+DdD51in101eY1y5mBOz5Fk3KGXRb/wf3jrZ2RGWuwrRNJSPtYrukbWyTtIttssV2sfqEsryuX7/OmTNn8PPzIzw8HL1ez5YtW8z7T548yfnz52nSpAkATZo04ejRoyQlJZmP2bx5M+7u7gQHB5d3+LatspUAux29C9x/o2LCxo8h5ax14xFCCCFEqbJqcjtt2jS2b9/O2bNn2bVrF8OGDUOr1fLwww/j4eFB7969mTp1Klu3bmX//v2MHTuWqKgoc3IbExNDcHAwo0aN4vDhw2zYsIHp06fz5JNP4ujoaM2nZnsqy5K7RXFPT6jVBnIyYO04a0cjhBBCiFJk1WEJFy9e5NVXXyU5ORkfHx+aNWvG0qVLzeXAxo4di1arZfjw4WRnZxMTE8O4cTeTEZ1Ox6xZsxg/fjx9+/bFxcWFnj17Mnz4cGs9Jdt0JQ6uxoHWAerEWDsa69No4MGpMLsD7F8GLZ6H2q2tHZUQQgghSoFVk9uPP/640P1OTk6MGzfOIqG9VWBgIHPmzCnt0OzLyRtDEoJaqit2CajRGJr2h13fwG9j4IW/1Hq4QgghhKjQ5L95ZSBDEgrW6W1w8oQLe2DvwjseLoQQQgjbJ8mtvausS+4Whbs/dBilXl83ATKvWTceIYQQQtw1myoFJsrA+d2QmQLOXhAQZe1obE/LwXDib2jxHOgc4HqC+loZc8DRfmvgCiGEEPZKem7tXe6QhHr3gta26tDZBAdH6Pu1+iHgwzB4PxjebwCbpkNOprWjE0IIIUQxSc+tvcudTFZPhiQUKDsdNn0C/7x/c1tmMqyfpl5v+7L04AohhBAViPTc2rPMVDizXb0u420LpnOAbbML3rdttrpfCCGEEBWGJLf27NRGdcldn/pQpY61o7FNmSlqT22B+5LheiIknSjPiIQQQghxFyS5tWfmEmDSa3tbzl7g7H2bfd7q/nn3w5z7YOfXkCUVFYQQQghbJsmtPZP6tndmzIHowQXvix4Ml/arvbvn/oVVw+GDUPhxmDrcQ1HKN1YhhBBC3JEMKLRXV+PhygnQ6GTJ3cI4ukK7V9Xr22arQxGcvdXEtt2r4OAMrx6CvYvU1cySjsPuBerFLwyaPg2R/wduvtZ8FkIIIYS4QZJbe2VecreF+tW6uD0HZ7UqQvuR6iQ8Z08wGtTtoC720HYEtBkOp7eqSe6BlZBwGNaMhbXjIKybmujW6yjL+AohhBBWJMmtvZIhCcWTW+7Lrar6U+eY/xiNBmq3Vi8PToXYZWoP7vndcPAH9eJVC6KegqgnwSuovKIXQgghxA3SxWSPTEY4uV69LpPJyoazF7QYCIP+hsEboOUgdVvKafh7MnwcDt/2hoM/Qk62taMVQgghKg3pubVH5/eoY0edvCCgqbWjsX81IqHG+3D/u3DoZ9j1NZzaAMfXqRfXqtCkH0Q9DX4h1o5WCCGEsGuS3Noj85K77WURgvKkd4HIx9RL0gnY/S3s+Q7SLsHmT9VLzVbq2Nx7HgVHN2tHLIQQQtidImU+33zzTbFP3KtXL9zd3Yt9P1EKZMld6/OtD53HQcc34djv6tjco2vgzFb1sno0RPRRE92AKHU8rxBCCCHuWpGS28mTJ1O9enW0RZwFfvHiRTp27CjJrTVkXYMz29TrMpnM+nQOEPaQekm9AHsXwq4FcDUOds5XL9XC1SQ34jFw9bF2xEIIIUSFVuTvrJcvX46vb9FqeUZFRZU4IHGXTm0CU4663K5PXWtHI/LyrAHtXoO2r0D8JrWk2MEf1YUiVo+C39+GRj0gqj/UaSclxYQQQogSKFJyO2zYMFxdXYt80hdffBEvL6mtahVSAsz2abVQt516eeg92Pe9muheioXY79VLlTpqktvkSTUpFkIIIUSRFKlraNiwYbi4uBT5pIMHD8bT07PEQYm7IMltxeJSBaIHwYsb1LJizZ8DJ0+4egr+nAgfN4KFfeHwL+rCEkIIIYQolEyltyfJZyDpGGi06tfaouLQaNSJZQFR0GWSOlxh1wI4vRmO/qZe3KtBkyfUHl3f+taOWAghhLBJxU5uExMTmTZtGlu2bOHKlSsoimKx/9ChQ6UWnCim3CoJgc3BxduqoYi74OimJrFNnoCEo2qlhb2L1JJiGz9WL7Vj1ElojXqoJchuw9nZuRwDF0IIIayv2MntmDFjuHDhAi+99BL+/v5lEZMoKRmSYH/8QqDLROj0ttp7u3uBujBE/Eb18uvrEPk4NO0PNRrfvF92OlqdA2E1q6LFCNlZN5cYFkIIIexYsZPbnTt3snDhQho2bFgW8YiSMhnh5N/qdVly1/44OKq9tI16QMpZ2HOjpFjKadgxR73UaAxthkNYN9g0Hc222egyk8HZG6IHQ7tXwUF6coUQQti3YtcaqlGjRr6hCMIGXNgLGVfVyUiBzawdjShLXkHQYRSM2Av9V8I9vUDnqL4H9C6w4UNYP01dghnUn+unwYaPIDvdmpELIYQQZa7Yye3YsWP58MMPOXv2bFnEI0oqd0hCnXag01s3FlE+tFp1CMpj8+HVw9DtY3VVuu1zCj5+22xZjlkIIYTdK9J/uhYtWqDJszxoeno6999/P87Ozuj1lonU9u3bSzdCUTQyJKFyc/OFFs/B9YSbPba3ykyGzFRwq1qekQkhhBDlqkjJ7dixY8s6DnE3stLg9Fb1ukwmq9ycvdQxtgUluM7e4Cz1p4UQQti3IiW3PXv2LOs4xN2I3wwmA3jXAp961o5GWJMxR508tn5a/n3Rg9X9Osfyj0sIIYQoJ0UegPfUU0/RqlUrWrVqRePGjfMNRxBWlLcEWJ7hI6IScnRVqyKAOsY2t1pCyxcg+kX1Q5AQQghhx4qc3AYFBbFixQpmzpyJs7MzTZo0ITo6mlatWhEZGYlOpyvLOEVhpL6tyMvBGdq+jNJ+JKb0ZLQu3mji1sOXXcGjBjy1XCYdCiGEsFtFTm6nTp0KwNmzZ9m6dSs7duxg6dKlzJgxA1dXV5o2bUqrVq14/vnnyyxYUYCUc5B4RF1yt257a0cjbIWjKyajkcOnEwgL80XnUf3Ge+Uo/DYGun1o7QiFEEKIMlHsUmBBQUH06dOHadOm8ddff7F27Vr69+/P7t27+fBD+YdZ7nKX3A1oCi5VrBuLsDmZmZnqleoR0OsLQAM75t6+XJgQQghRwRU7uQU4d+4cK1eu5I033uCZZ57h66+/Jjw8nKFDh5Y4kC+++ILQ0FD++9//mrdlZWUxYcIEoqOjiYqK4j//+Q+JiYkW9zt//jyDBg2icePGtG7dmmnTppGTk1PiOCoc85AEKQEm7qDhw9B5nHp99eib7x0hhBDCjhR5WMIPP/zAtm3b2L59O1evXiUqKoqWLVvy2GOPERERcVcTzPbt28fixYsJDQ212D558mTWr1/P9OnT8fDwYOLEiQwbNozFixcDYDQaGTx4MFWrVmXx4sVcvnyZ0aNHo9frefXVV0scT4VhMuWpbyvjbUURtH0ZEo7A3kWw9Fl44Q+o2sDaUQkhhBClpsg9t2PGjGHbtm08//zzbNu2jXnz5jF48GCaNm16V4nt9evXef3115k0aRJeXl7m7deuXWP58uWMGTOG1q1bEx4ezuTJk9m9ezd79uwBYOPGjRw/fpz333+fhg0b0qFDB0aMGMF3331HdnZ2iWOqMC7ug/QkcHSHoBbWjkZUBBoNdJ8BNaMhKwUW9oX0K9aOSgghhCg1Re65HTduHNu3b+ezzz7jo48+olmzZrRs2ZIWLVoQHh5usYJZcbz77rt06NCBNm3a8Pnnn5u379+/H4PBQJs2bczb6tevT0BAAHv27KFJkybs2bOHkJAQqla9ueJSTEwM48eP5/jx4zRq1KhYsRiNxhI9B2vRHP8TLaDUjsGEFqwQf+5rVtFeu8rgtm2jcYDHvkE77z40V06gLH0G0xPfSwWFciK/M7ZL2sY2SbvYJmu0S1Efq8jJbb9+/ejXrx8Ax48fZ/v27Wzfvp25c+eSnZ1N06ZNiY6OZuDAgUUO8pdffuHgwYMsW7Ys377ExET0ej2enpYrKvn6+pKQkGA+Jm9iC5hv5x5THLGxscW+jzU12PcTnsAZpwYk3OjNtpaK9tpVJrdrG5cm4wjd+B90p/7hynfPczriZamTXI7kd8Z2SdvYJmkX22SL7VLk5Dav4OBggoODeeKJJ7h06RILFy7k22+/ZcOGDUVObi9cuMB///tfvvzyS5ycnEoSRqmLiIioOPV6Delofz0AQGD7/gT6BlslDKPRSGxsbMV67SqJO7dNE6jmirL0KfziV+HbMAalxQvlHWalI78ztkvaxjZJu9gma7RL7mPeSbGT26SkJLZt22aeXHbq1CkcHBzMizoU1YEDB0hKSqJXr14WQe/YsYPvvvuOefPmYTAYSE1Ntei9TUpKws/PD1B7afft22dx3txqCrnHFIdOp6s4vzgnt4ExG7xqovMLsXqPW4V67SqZQtum0cPQeTysG4d2zVh1clnwfeUaX2UlvzO2S9rGNkm72CZbbJciJ7fjx49n+/btxMXFodPpiIyMpGvXrkRHR9O0adNi9762atWKVatWWWx74403qFevHi+88AI1atRAr9ezZcsWunbtCsDJkyc5f/48TZo0AaBJkybMmjWLpKQkfH19Adi8eTPu7u4EB1unJ7Pc5C0BJl8li7vRdgQkHFYrKHw/AJ5fB34h1o5KCCGEKJEiJ7eHDh2ic+fO5mTWxcXlrh7Y3d2dkBDLf6Curq54e3ubt/fu3ZupU6fi5eWFu7s7kyZNIioqypzcxsTEEBwczKhRo3j99ddJSEhg+vTpPPnkkzg6Ot5VfDZPltwVpSW3gsKVODizFRb1hef/AFcfa0cmhBBCFFuRk9slS5YAkJaWdtvENj4+ntq1a5dOZMDYsWPRarUMHz6c7OxsYmJiGDdunHm/Tqdj1qxZjB8/nr59++Li4kLPnj0ZPnx4qcVgk1IvQMIhQAN1O1g7GmEPHJyg77cwpxNcOQnfPwNPrZAKCkIIISqcYo+5HTRoEPPnz883DOHkyZM8++yz/PPPPyUOZsGCBRa3nZycGDdunEVCe6vAwEDmzKlkS4mal9yNkt41UXrc/aDfIviyK8T9A7++Dg9/LMNehBBCVCjFXn7X1dWVYcOGWSxxe+LECZ5++mm6dOlSqsGJ25Ald0VZqR4OvecCGtg5H7Z/Ye2IhBBCiGIpdnI7c+ZMrl27xsiRI1EUhaNHj9K/f3+6devGW2+9VRYxirxkyV1R1kIfhPsnqNd/GwPH/7BuPEIIIUQxFDu5dXZ25osvviAuLo4RI0bw7LPP8sgjj/DGG2+URXziVpf2w/UE0LtBUEtrRyPsVZvh0PgJUExqBYWEo9aOSAghhCiSIiW3aWlpFhetVsvHH3/Mvn376NKlC0OHDjXvE2Usd7xtnRhwsPOKEMJ6NBroPh1qtoKsFFj4OKRfsXZUQgghxB0VaUJZ8+bN0RQwqURRFBYvXsySJUtQFAWNRsOhQ4dKPUiRh5QAE+UlbwWFq3Gw9Gnov1IqKAghhLBpRUpuv/nmm7KOQxSFIQPit6jXJbkV5cHdD55YDPO6wKkN8OtIeHi6VFAQQghhs4qU3LZsKWM7bUL8ZjBmgWegukyqEOWh2j3Qex4s+j/Y+RX4NYRWL1o7KiGEEKJARRpze/jwYUwmU5FPeuzYMYtSYaKUyJK7wlpCH4D731Wvr3kDjq2zbjxCCCHEbRQpue3ZsyfJyclFPmnfvn25cOFCSWMSt3PixmSyelLfVlhBm/9Ak6fUCgrLBkDCEWtHJIQQQuRTpGEJiqIwffr02y67eyuDwXBXQYkCXLsIlw8AGkluhXVoNPDwR3DlBJzeAgv7wgt/yip5QgghbEqRktsWLVoQFxdX5JM2adIk3/K84i7lLtxQozG4+Vo1FFGJmSsodLxZQeGpFVKWTgghhM0oUnK7YMGCso5D3IksuStshVtV6LcE5t1/s4JC9xkyDlwIIYRNKPYKZcIKFOXmeFspASZsQbVGagUFNLDra9g2y9oRCSGEEIAktxXDpQNw/TLoXaFmtLWjEUIV+gB0maheXzNWKigIIYSwCZLcVgS5S+7WbquOeRTCVrQeJhUUhBBC2BRJbisCWXJX2KrcCgq12kBWKix8HNKvWDsqIYQQlVixk9v09PSyiEPcjiFTXZkMZDKZsE0OTtB3AXjXhqunYEl/yMm2dlRCCCEqqWInt23btuWNN97g33//LYt4xK1Ob4GcTPCoAX5h1o5GiIK5VYUnloCjB8RvVCsoKIq1oxJCCFEJFTu5ff/990lJSeHZZ5+la9eufPHFF1y6dKksYhNgOSRBSi0JW+bfEPp8CRqtWkFh6+fWjkgIIUQlVOzktnPnzvzvf//jn3/+4f/+7/9YtWoVnTp1YvDgwfz+++/k5OSURZyVlyy5KyqSkC5w/40KCr+/CcfWWjceIYQQlU6JJ5T5+PgwYMAAVq1axZgxY9i8eTPDhw+nXbt2zJgxg4yMjNKMs3JKuwyXYtXr9e61aihCFFnroRDV/0YFhefg8mFrRySEEKISKdIKZQVJTExk5cqVrFy5kvPnz9O1a1f69OnDxYsXmTt3Lnv37uXLL78szVgrn9wld6tHgrufVUMRosg0Guj2EVw5CfGbYFFfeP5PWTZaCCFEuSh2cvv777+zYsUKNm7cSP369XniiSfo0aMHnp6e5mOaNm3KQw89VKqBVkrmVclse0iCs7OztUMQtsbBER5fAHM6qhUUlvaH/j+o24UQQogyVOxhCW+88Qb+/v4sXLiQH3/8kaeeesoisQXw9/fnxRdfLLUgKyVFsfn6thnZORgVDX4162FUNKRny3hrkYebb54KCpvgl1elgoIQQogyV+ye240bN+Li4lLoMc7OzgwbNqzEQQng8iFIuwgOzlCzlbWjySfLYGTW+pPM3xxHakYOni4ODGhTl5furY+TXmft8ISt8G8Ij81XF3fYvUC93XqotaMSQghhx4rdc2s0GklLSyvwkp0thdtLTd4ld/W29bV/RnYO//v7BDP+OEZqhtpbm5qRw4w/jvG/v09ID66w1OB+6DJJvf77W3D0d+vGI4QQwq4Vu+e2efPmaAqpt1q9enV69uzJsGHD0Gpldd8Ss+EhCTqtlvmb4wrcN39zHEM7BpdzRMLmtXoJEg7Drm/UCgrPr1V7cYUQQohSVuzkdurUqXz88cf07NmTyMhIAPbt28cPP/zAkCFDuHLlCl9++SWOjo4y7rakDJlwapN63QYnk13LNJh7bG+VmpHDtUwDvu5O5RyVsGkaDTz0ISSdVFcwW9gXXvhLKigIIYQodcVObleuXMno0aMtqiF06tSJkJAQlixZwtdff02NGjWYNWuWJLcldWYb5GSAezXwb2TtaPLxcNbj6eJQYILr6eKAh7PeClEJm+fgCI9/A3M7SQUFIYQQZabY4wZ2795No0b5E65GjRqxZ88eAJo1a8aFCxfuOrhKy4aX3FUUheOX03imdZ0C9z/Tug6bTySy4VhC+QYmKgY3X+i3BJw8b1RQeEUqKAghhChVxU5ua9SowbJly/JtX7ZsGdWrVwcgOTk5X3kwUQy5ya0NLrk7fd0x/rNoN8+2qcPw+4LxdFE7/z1dHBhxXwOea1uXiT8fov+87bzz436ZXCby8w+DPl+CRgu7v4Utn1k7IiGEEHak2MMSRo0axYgRI/jnn3+IiIgAYP/+/Zw8eZJPPvkEgNjYWFnEoaSuJ8LFfep1G1ty97O/jjPjj2MA/H0kgRc71GdYxwakpGfh5epEjsmEBmgb7MuJhDS+2RLPP0cT+PDxJjSrXcW6wQvb0uB+6PJfWPMGrH0bqjaAkK7WjkoIIYQdKHbP7X333cdvv/1Ghw4dSElJISUlhfbt27N69Wo6dlR7Gp944gneeOONUg+2UshdcrdaBHhUs2ooec3dcJL31xwBYMyDYfRuFoSrowM6jcLlMyfRaRRcHR1wcXTg3UfCWTCwJdU9nTmVlM5jszbz3m+Hyc4xWflZCJvSagg0fQYUEywbqNZ2FkIIIe5SsZJbg8HAM888Q05ODq+99hozZ85k5syZvPbaawQFBRX7wRcuXEj37t1p2rQpTZs2pW/fvqxfv968PysriwkTJhAdHU1UVBT/+c9/SExMtDjH+fPnGTRoEI0bN6Z169ZMmzaNnJwK/FW4ecnde60aRl7fbDnFpF/UxOOVziG82KG+xf7MzMx892nXwI81r7SnV1QgJgX+9/cJHvlsE4cupJZLzKIC0GjgoQ+gdgxkX1MrKFxPvPP9hBBCiEIUK7nV6/UcOXKk1B68evXqjBw5khUrVrB8+XJatWrF0KFDOXZM/ep78uTJ/PXXX0yfPp0FCxZw+fJli5XPjEYjgwcPxmAwsHjxYqZOncrKlSvNwyMqHBtccnfx9tO88+MBAIZ2rM/w+4pew9bLRc9HfZsw66mm+Lg5cuhCKj1mbuTzv09gNMkkIoFaKaHvAqhSF5LjYUl/yJHFYIQQQpRcsYcl9OjRo8AJZSXRqVMnOnToQJ06dahbty6vvPIKrq6u7Nmzh2vXrrF8+XLGjBlD69atCQ8PZ/LkyezevdtclWHjxo0cP36c999/n4YNG9KhQwdGjBjBd999VzFXS0s4AtfOg84JarW2djSs2HWWN1bGAvB8TF1GdgktdAGP23kgvAZrXm5P54bVMBgVpv12mMdnb+FU4vXSDllURK4+8MSNCgqnN8PPUkFBCCFEyRV7QpnRaGTRokVs3ryZ8PBwXFxcLPaXdKyt0Wjkt99+Iz09naioKPbv34/BYKBNmzbmY+rXr09AQAB79uyhSZMm7Nmzh5CQEKpWrWo+JiYmhvHjx3P8+PECS5bdKQZr0hz/Ay2g1G6NSesIVozn530XGPn9XhQF+kfXYswDIZhM+cfM5r5md3rtfFwdmPVkE5bvPsfEnw+xM/4qD87YwBsPhvJEy5olSppF4YraNjbBJxh6zUW7+P/Q7PkWU9UQlNbD7ny/CqhCtUslI21jm6RdbJM12qWoj1Xs5Pbo0aPmpDEuznIJ1pIkKEeOHOH//u//yMrKwtXVlc8++4zg4GAOHTqEXq/PV1LM19eXhAS1hmpiYqJFYguYb+ceUxyxsbHFvk9pCt7zI17AOedQLt3onbaGbecy+WBLMiYFOtd1oUfNLPbu3VvofYr62gVr4f37fPhsRwr7E7J556eDrNh2nJdaeOHroiuN8MUtrP2+Lrqq+DcaQs0Dn6FZN46TqTpSqln/G4yyUnHapfKRtrFN0i62yRbbpdjJ7YIFC0o1gLp16/LDDz9w7do11qxZw+jRo/n2229L9TGKKiIiAp3OSglWThba39Q3SI22T1KjerhVwvjrSAIfb9uFSYFHmwTwfu8ItNrbf2gxGo3ExsYW+7Xr3Frh663xvL/mKHsuZTNy3VXGd29Ej8Y1pBe3lJS0bayqcWNMTmlod31N/T1TMA34zSZX6bsbFbJdKglpG9sk7WKbrNEuuY95J8VObnPFx8dz+vRpWrRogbOzM4qilCgpcXR0pHbt2gCEh4cTGxvLN998w4MPPojBYCA1NdWi9zYpKQk/Pz9A7aXdt2+fxflyqynkHlMcOp3Oer84p3eCIR3c/NHViABtsYdD37UNxxJ4aeFuDEaFhyNr8MFjjXHQFS2O4r52Oh08364+94ZW47Wle9h7NoVXv9/HusOXmfRoBD5usiRrabHq+7okun0IV06iObUB3e9vqkv2OrpCZgo4e4ExR71dwVW4dqlEpG1sk7SLbbLFdil2BnX16lWeeeYZunbtyqBBg8xf/48dO5apU6fedUAmk4ns7GzCw8PR6/Vs2bLFvO/kyZOcP3+eJk2aANCkSROOHj1KUlKS+ZjNmzfj7u5OcHDRZ/XbBHOVhI5WSWy3nkzihW/+JTvHRNd7qvFx3yZFTmzvRrC/O8uHtOHV+0Nw0Gr4NfYiXT7+hz8OXSrzxxY2SqdXE9p6ndSVzLbMhPcbwPvB6s9N0yEnf/k5IYQQAkqQ3E6ZMgUHBwf+/vtvnJ2dzdsfeughNmzYUKxzffjhh+zYsYOzZ89y5MgRPvzwQ7Zv30737t3x8PCgd+/eTJ06la1bt7J//37Gjh1LVFSUObmNiYkhODiYUaNGcfjwYTZs2MD06dN58skncXSsYD1/Vlxyd2f8FZ77ageZBhOdwvz5tF9T9OWQ2OZy0GkZfl8Dfhjalgb+7iSmZTHw638ZvWwf1zIN5RaHsCGuPtD7C9g2C/55HzKT1e2ZybB+Gmz4CLLTrRmhEEIIG1XsYQmbNm1i3rx5VK9e3WJ7nTp1OH/+fLHOlZSUxOjRo7l8+TIeHh6EhoYyb9482rZtC6i9wVqtluHDh5OdnU1MTAzjxo0z31+n0zFr1izGjx9P3759cXFxoWfPngwfPry4T8u6rifBhRsTtuqXb3K790wyz365g/RsIzHBVfnfk01xdCj/nmOA8EAvVv0nhg9/P8LcjXEs+fcMG48n8sFjjWld39cqMQkrcvaC7XMK3rdtNrQfWb7xCCGEqBCKndymp6db9NjmSk5OLnZv6eTJkwvd7+TkxLhx4ywS2lsFBgYyZ85t/gFWFHF/A4o6ccaj+p2OLjUHzqfQf942rmXlEF3XhzlPN8dZb91xM856HW92a0TnhtV47fu9nL2aQb85WxkYU5fXu4ZaPT5RjjJTbvbY5tuXDJmp4Fa14P1CCCEqrWJ30TVv3pwffvjBYpvJZGLu3LlER0eXVlyVi3nJ3fJblezIxWs8NXcbqZk5NKtdhS+fbYGLo+0kjtH1fPnt5fb0a1kTgHkb43j4043sO5ts3cBE+XH2Amfv2+zzBmfPgvcJIYSo1Iqd3L7++ussXbqU559/HoPBwPvvv8/DDz/Mv//+y8iR8jVhsSlKnuS2fIYknEhI48m527iabqBxkBfzB7TAzanEhTPKjLuTA1N6RfLls83x83Di+OU0ev5vMx+vPYrBmH9BCWFnjDkQPbjgfS1fgIRj6jFCCCFEHsVObkNCQlizZg3NmjXjvvvuIyMjg/vvv5+VK1dSq1atsojRviUeg9SzoHOEWm3ufPxdik+6zhNztpKYlkWjGp5881w0ns76Mn/cu9EprBq/v9yebpE1MJoUZvxxjF7/28zxy9esHZooS46u0O5V6DD6Zg+usze0HwXRL8KyZ+GbHpByzopBCiGEsDUl6q7z8PBgyJAhpR1L5XTyRq9trdZlXrvz7NV0npizjUupWYRUc+fb56PxcrXtxDZXFTdHPnuiKV3vOc/bP+wn9lwKD32ykVFdQ3mubd1CF5oQFZiDM7R9WZ08lpmqDkUwGiBuA6Seh8SjMKstPPo5hD5o7WiFEELYgBIlt6mpqezbt4+kpCQURbHY9+ijj5ZGXJWHub5t2Y63vZCSwRNztnEuOYN6fm5893yrCrlQQo/GAUTX9WHUsn2sP5rApF8OsfbgJT54rDE1fSp+YX9RgNwPfbmTx3SOEPoADP4Hlj0HF/bAov+D6CFw/wRwcLJaqEIIIayv2Mntn3/+yciRI0lPT8fd3d1iVTKNRiPJbXHkZKs9UFCm420vX8vkyTnbOH0lnVo+rix8vhV+HhU3Aajm6cxXA1qwcPtp/vvLIbbFXeHBGRt4++GGPN68pizfW1n41oeBa2HdeNj6GWz7HE5vhj7z1X1CCCEqpWInt9OmTaN37968+uqruLi4lEVMlcfZHWC4Dq5VoVpEmTxEUloWT87ZxsnE6wR6u7DwhWiqe+Uv5VbRaDQanoyuTUxwVUZ+v5cdp64yenksvx+4xJTeEfh7VPznKIrAwREemAz1OsDKF9V60bPbQ7ePoHFfa0cnhBDCCoo9oezSpUs8/fTTktiWBvOqZPeWyZK7yenZPDVvO8cup1Hd05mFL0QTVMW+vrqv7evG4kGteePBMBx1Wv44fJmuH//Dr7EXrB2aKE8hXWHIJqgdA9lpsHIQrBwCWWnWjkwIIUQ5K3ZGFRMTQ2xsbFnEUvmcLLv6tqmZBp7+cjuHLqRS1d2J716IpravW6k/ji3QaTUM7lCfn/7TlkY1PLmabuCl73YxYvFuUtJl+d5KwzMAnvkJ7h0LGi3sXQhf3AsX9lk7MiGEEOWo2MMSOnTowPvvv8+JEycICQnBwcHyFPfdd1+pBWfX0q/AuV3q9VIeb5uWlcOzX25n39kUfNwcWfhCNPX93Ev1MWxRWHVPfhjalk//PMZnfx3nxz3n2XbyCu/1iaR9iJ+1wxPlQauDe0dDnRhY/jwkHYO5naHrf6HF8yDjsYUQwu4VO7l9++23Afjss8/y7dNoNBw6dOjuo6oM4tYDCviFqT1OpSQ9O4fnvtrBrtPJeLno+XZgNCHVPErt/LbO0UHLa11C6RTmz2tL93Iy8TpPf7mdp1rVYuxDDXF1tL3FKspSQUtlVwp12qrDFH54CY6uhl9Hwsm/ocen4Opj7eiEEEKUoWL/pz98+HBZxFH5lMGSu5kGIy988y/b467g4eTAgoEtaRRQOZcojapVhV+Gt2Pab4f5avMpvt16mg3HEvno8cY0q23/yU1Gdg46rRa/mvUwKhqysnMqXWKPqw/0WwTbZsPat+Hwz3B+D/SZB7VaWTs6IYQQZaT0ZzGJO7NYcrd0ktusHCNDvt3JpuNJuDrq+Oq5lkQGeZfKuSsqF0cd43vcw3fPRxPg5Ux8UjqPzdrC1NWHycoxWju8MpNlMDJr/Uma/3ctLSf/SfP/rmX2+pNkGez3Od+WRgOtXlRLhvnUU1cDnP8Q/PM+mCrh6yGEEJVAkZPbF154gWvXbi53+sUXX5Cammq+ffXqVR566KHSjc5eXYkDJ3fwqAG1737JXYPRxLCFu/nrSALOei3zn21Bs9pVSiFQ+9A2uCq/vdKe3k2DMCkwa/0JHpm5iYPnU+985womLcvA//4+wYw/jpGakQNAakYOM/44xv/+PkF6do6VI7SSgCbqog+RfUExwp+TYMGjcO2itSMTQghRyor8PeXGjRvJzs423541axYPPvggnp7q195Go5G4uLjSj9DeZKeDVwD0WwzufqDc+S6FyTGaeHnxHtYevISjg5a5T7cgup5v6cRqRzyd9Xz4eGO63FONsStiOXzxGo98tpGXO4cwuH09DEYTOq2Wa5kGPJz15JhMVvsaX1EU0rONJGcYSE7PJiXdcOO6geSMG7dvXE9ON5ByY59GA3+81oH5mwv+PZy/OY6hHYPL+dnYECcP6PWFWnrvl9cg7h/4vC30nA0NOls7OiGEEKWkyP+9b11m99bboghyMmHTdHUMYGYyOHtD9GBo9yo4FH/ij9Gk8PqyffwSewG9TsPsp5oR06BqaUdtV7reU51mtaswdkUsvx+8xIpd53gyuhZfborjq82nSM3IwdPFgQFt6vLSvfVx0utK/Fgmk8K1rJwbyWn2jYTUQEr6zetqcpr/tsFY/N+v0GoeJKVlm3tsb5WakcPV9GwURaG6VyWuU93kCQhqAd8PgEux8F1vaPMf6PSOuiiEEEKICq2SzTCxoux0NbFdP+3mtszkm7fbvgyORV9gwWRSGLsilpW7z+Gg1TDziaZ0DPMvzYjtVlV3J2b3b8aKXeeo4qpn3sY4Pv3zuHl/7tf4AIM71MNRpyU1M4fk9OwbyWmeZNXcc5qdJzm90eOaYcB0F58B9ToN3q6OeLvo8XbV4+XiiLer/ubtPPu8XRzxcdPj5+GMp4tDgQmup4sDHs4OxEz7i5Bq7vRuGsSDETVwd6qEfwaqNoDn16kTzbZ/AZs/hfjN0Hse+NS1dnRCiFtU2sovokSK/F9No9GgkRqRJadzUHtsC7JtNrQfWeRTKYrCOz/tZ8m/Z9BqYPr/NaHrPdVLKdDKQaPR0LtZENk5Jl5euqfAY+ZvjmNwh3q0nPwHV65nF3hMUbjodTeS05uJqJqc3rzu7XLLbVc9LnpdsX/nMrJzGNCmrjk5z+vZNnWIPZvC1fRstp68wtaTV3jnxwM8EF6dXk0DaVO/KjptJfod1zvDQ+9D3Q7w41A4t1Ndurf7dAjvbe3ohBAA2elodQ6E1ayKFiNkZxWrI0hUTsUaljBmzBgcHdWv7bKzsxk/frx5Gd6843FFATJT1J7aAvclQ2YquN15SIGiKEz8+RDfbj2NRgMfPt6YhyNLr05uZXMt01Do1/hXrmfj5+7ElevZeDg5qAnojSTUK08vquXtmwmrp4se57sY2lBcLo4OvHRvfUBNzgsaZrFxdCd+2H2O5TvPcjLxOit3n2Pl7nNU93Tm0ahA+jQLJNi/8tRGpuHDUKOxuujDma2w7Dk4uR4emCr/RCsh6SG0ITeG8mm2zUZXCkP5ROVR5OS2Z8+eFrd79OiR75hHH330rgOyW85e6i9mQQmuszc437keraIovLfmCF9uUicMTe0VQc+ooFINs7LxcNYX+jW+v4czi16IxsNFj15XMSrnOel1DO5Qj6Edg0lJz8LL1Ykck8k8fjjQ24WhHYN56d767DmTzIpd5/hp73kupmYya/0JZq0/QWSQF72bBtG9cQA+bpVgHKp3TXj2F1g/Ff75AHZ9DWe2QZ/5UK2RtaMT5UF6CG1LKQ/lE5VLkZPbKVOmlGUc9s+Yo37izPuLmit6sLpfV3gSMeOPY3z+9wkAJj5yD31b1CqLSCsVo8l026/xB7SpS47JhI+7kxUiuzuujg4YjUYunzmJT1gYjgVUftBoNETVqkJUrSq89XBD/jp8mWU7z/H3kcvsO5vCvrMpTPz5IB3D/OndNIhOYf44OlSMBL9EdA7Q6S2o0w5WvAAJh2FOR7UHt9mzsnSvPZMewvKjKJCdBtcT4HrijUsCpCfevJ2TBT1nFT6UL+ZVOPgTeAWqNaxdpPyluKkSziSxEkdX9Q8llKhawv/+Ps70dWoC9la3hvRvXadMw60sivI1fkWWmZlZpOOcHHQ8EF6DB8JrkJSWxU97z7Ni1zliz6Ww9uAl1h68hLernh6NA+jdNIjIIC/7HYNfrwO8uAl+eBGOr4OfX1aX7u0+A1y8rRycKHX22EOYna5+WMtMUb81NOaU7XPIvn4zMU2/kazmTV7N25LUn8asws/n30g9rrChfNcvwd9T4PJBdZtLFahSV010fW78zL3t7i8fTisZSW7Lk4Oz+oey/Uh1jK2zJxgNd0xs5244yXu/HQFg1AOhPN+uXjkEW3nk/Ro/b53bip7YlpSvuxMD2tZlQNu6HLl4jRW7z/LD7nNcSs3imy3xfLMlnmB/d3o1DaRnVCA17LGsmLsfPPE9bJkJf0yAgz/A+V3qMIWg5taOTpQm7R0m+8a8rI7HNhnVBFHvduPnjYvFNpc812/ZVl5l5kqj5KQhI09SWlDv6i2JqyG9+HHqXdV5Jq5Vwc3vxsVX/ekRCB7VCx/K5+YP3rUg/QqkXYSMq+rl/K4CHstNTXir1LmR/OZJgD0DQVs5/9bbM0luy1vup+fcyWN3GIqwYMspJv1yCIAR9zXgpXsrcRH+MpS7YIPvjSEIjrIyNQCh1T1448GGjOoaxqbjiSzfdZY1By5y/HIa7/12hPfXHKFt/ar0ahrIA+HVrbbwRZnQaqHtcKjdFpYNgOR4+LIrdHob2gxX94uKKf0KHFkN53erNY4L7SFMgEsHbvYQlpTW4TZJsCs4ut3Ylns9T9Ksd7nNtlsSba3uzr3QzQfCpf239Kbe2ruaqA4bKC4HZzUxdb2RoLpVvSV5zXu7qvqcCpOdXvhQPkWBJ5bcOPY6XD0FV06qK4BeOQlXb/xMOQuG6+rzvrQ//7l0juBdO39vr09ddbvUvq6Q7Og/kf1ZsuM0b/94AIAh99bn5c4NrByRqKx0Wg3tQ/xoH+LHtUwDq2MvsnzXWbbFXWHj8UQ2Hk/krR/282B4DXo3C6RVXV+09lJWLKgZvLgBVo2AAyth3Th1dbOes9SvO0XFkJYAh3+GQz+p7WfKUROx+ycU3kPoXg3ajlB7BbOvq72ahvQb19PVJMyQnuf69Rs/M9TrphuTVU05kJWiXsqCRwD8Z2fhvdBtR6jjydOT7nw+nePNRNTtRoJa2G1H99L96r84Q/kc3aDaPerlVjnZkHz6ZrKbNwFOjgdjNiQdUy+30mjBM+hm4muRANe9c4J+q/IeLlKJSXJro1buPsuYFbEAPNe2LqO6htrvGEdRoXg463m8RU0eb1GTM1fSWbn7HMt3nSU+KZ3lu86yfNdZAr1d6BkVSK+mgdTzc7d2yHfP2UsdklCvI6weDSf+gFkx6tK99TtaOzpxO6nn4dAqdeLR6c2gmG7u878HGvW4cw+hyQSN/6/kMeRkq0muIeOW5LegbQUlyemWSXRuUm3IUK/nruHu4n3ncarpSeo3EVmpeXpUffMkqnl6WJ08rT9O9cZQPqX9SEzpyWhdvdEUYSif5TkcoWqwermVyQip5/L39l45pf40XIeU0+olbn3++7tXy9/b61NXve3qY3lsKa9QKgonya0N+nnfeV5buhdFgada1eLthxtKYitsUk0fV4bf14D/dApmZ/xVlu86x8/7znMuOYOZfx1n5l/HiarlTa+mQXSPrIG3awX+ik+jgWbPQM2W6tK9CYdgQU/1n9O9b4BOb+0IBcDVeLV39uBPcHa75b6AKGjYAxo9Ar71b26/i8m+d+TgqF7KYja/oqhJU3a6+tOtauG90B7Voe+C0o+jLDm6YjIaOXw6gbAwX3SOpfg3RKtTx+1611InkualKOqHhVt7e3MT4IyrkHZJvZzekv/czt43E96YV+HQj7D+vZv7K/qkRRsnya2NWXPgIiMW78GkwOPNg3i3R7gktsLmaTQamtfxoXkdH8Z1b8S6Q5dYsesc648msPt0MrtPJzNx1UE6N/KnV1QQHUL9Kkzd4Hz8G8ILf8KasbBzPmz4EE5thN5z1X+SovwlHlcn/R36CS7stdxXM1pNaBt2hyq1C75/afQQWoNGc2Os7o1JnXfqhS5CyUlbVdTKL6VGo1GHHbn7Q61W+fdnXFUT3lt7e6/GwbULavJ6fpc69KHHp7Dti4IfZ9ts9UNU3Ea13rZXkExwKwWS3NqQvw5fZtjCXRhNCj2jApnSK9J+xi2KSsNZr+PhyAAejgzg8rVMftpznuW7znHoQiq/xl7k19iL+Lo50qOJWlbsngDPivcBztFVXaa3Xgf4abi64MOsGOgxU/2qW5QtRVEneB38SU1o80720mjVr94bPQJhD4NnjaKdsyx7CMvLXZacFMXgUgUCq0Bg0/z7zBPc4tTxtRnJhQ8XSbsEq19X38davfohzDzUIc9YX+9a4FDx6q5bgyS3NmLjsUQGf7sTg1GhW0QN3u8TiU4SW1HB+Xs483y7ejzfrh4Hz6eyYtdZfthznsS0LOZvOsX8TacIreZB72aBPNokEH/PCvbP956e6lfdywbCuX9haX9o8Tx0+S/oK9hzsXWKAhf2qAntwR/hyomb+7QOULeD+sEitJtayq2Eyr2HsLSVsOSkKEW3TnAzZt+5rJmju9qrbsyGpOPqJR8NeNUEnzr5x/pWqQtOdjC/oZRIcmsDtp1M4vlvdpCdY+L+RtWY/n9NcKioX9kKcRuNAjxpFNCIMQ+GseFYIst2nWXtwUscuXSNyb8eZurqw7Rr4EevpoF0vac6zjfqDGdk56DTai1qENtUybEqdeC53+DPSeqEkR1z4fRWdQKaX4i1o6vYTCY4u0PtnT30kzrrPZfOCep3UntoQx+QFaryKmbJSVHG7rRCqaLA82tvTHA7f8vktribwx+y0/JMcPsn/7nc/AtexMKnrvr7UQbfkDk72+aHJhv6D1E57Yy/ynNf7SDTYOLeUD9mPhFVccciClEEDjotHcP86RjmT0qGgV/2XWDFrrP8G3+V9UcTWH80AQ8nB55tU4chHesze/1J2189TqdXS0rVbQcrX1TraX7RAR56H5o8af1Z5xWJyQjxm28ktKvU8Yu59K7Q4H51DG1IV3DysF6cQhRVUYeLaHXquFvvmsDtJrjlKWmWNwHOuALXL6uXM1vzx+DsdfsV3DyqF/9vVHY6Wp0DYTWrosUI2Vk2NSlOkttyZtkL5UByejbVvVyo7uXErKea4eRgQ/+whShjXi56noiuxRPRtTiVeJ0Vu8+xYtdZzl7NILKmF5//fYJP/7z59VxqRg4z/lDrUQ7uUM+2enABgjurS/euHKQu2fvjUPXnwx9bJGK22tthNUaDWmrp4E9w+Bd1QYFcjh5qz2yjR6D+fTb1D1SIIrvb4SIWE9yi8+/PSC64t/fKyRsT3FLUYT0X9uS/r9715upt5lXccldwC1Jr8+Z1o6yZZttsdDY6rtuq/xlmz57N77//zsmTJ3F2diYqKoqRI0dSr97N5WWzsrKYOnUqv/76K9nZ2cTExDBu3DiqVq1qPub8+fOMHz+ebdu24erqyqOPPsprr72Gg4Nt/ePLMhiZdUsv1DOt67B8SGucHLTmr2GFqIzqVHXj1ftDePm+Buw+fZV7Ar147fu9BR47f3McQzva6Gp9HtXgqZWw6WP4878Q+z2c2wn/9x1417Hp3o5yZciEk3+pCe2RX9R/vrmcvdXJYI16QL17ZRKNsA9lOVzExRtcotQ5ALfKTr+5gptFAnwSUs6odZMvHyx4FT6tg+UKbi2eh/3LbL6smVWzv+3bt/Pkk08SERGB0Wjko48+YuDAgfzyyy+4uqovzuTJk1m/fj3Tp0/Hw8ODiRMnMmzYMBYvXgyA0Whk8ODBVK1alcWLF3P58mVGjx6NXq/n1VdftebTs5CRncOs9SfNvU6g9kJ9+udxtBoNgzvUK+TeQlQeWq2GZnV8SErLIjUjp8BjUjNySLqexekr6TStVcX2hvJotdDuNagdA8sHqv8g3Pxh08dotn1hs70dZS77Ohxfp04IO/o7ZF+7uc/N72ZCW6ed1A0WorQ4ukK1RurlVjnZaoKbbyGLGz2/xmx18uaVE+qKfp3HF17WrP3IMn0qRWXV5HbevHkWt6dOnUrr1q05cOAALVq04Nq1ayxfvpwPPviA1q1bA2qy+9BDD7Fnzx6aNGnCxo0bOX78OPPnz6dq1ao0bNiQESNG8MEHHzBs2DAcbaSci06rZf7muAL32XQvlBBW4uGsx9PFocAE19PFAS8XPUO+3YVWAz0aB9K7WSCNathYWbFa0erSvYnHYNss+Of9m/tssLejTGSmwtE1ahH7Y+sgJ+PmPo8ANZlt2EOtJSr1PYUoXw6O6oImeRc1yZU7wS034c2+rtb3LaysWWbqzZ5pK7Kp7+2vXVM/xXt5eQGwf/9+DAYDbdq0MR9Tv359AgICzMntnj17CAkJsRimEBMTw/jx4zl+/DiNGhXwSeU2jEZjKT2T/FIzcwrthbqWacDbxaaao0hyX7OyfO1EyVT0tskxmhjQpq7Ftx25BrSpy8mENAAS07L5clMcX26KI7SaOz2jAunRuAbVbKWsmKMn2oAmaL57rOD922ajtH0Zvu0DignFyUMtC+TkYXlxdFf3OXmAo2eefe5qz3A50+ZkgoNeHU7g7AU5Bky5PdAZV9EcXY3m0E9w8m80xmzz/RTv2igNu6OEdYfAZmpdWlBXkbXye7Wi/87YK2kXK/IIUC+12gKgxYimkLJmirMnpjJsp6K+B2wmmzKZTEyePJmmTZsSEqKWz0lMTESv1+Pp6WlxrK+vLwkJCeZj8ia2gPl27jFFFRsbW9LwC+Xg4EBYo/BCe6HcnRzYv38/OTkFJ8C2rqxeO3H3KmrbODs7M7h9CKAwf/OpPNUS6jC4fV3ijh/l8weqsPdSFn+dyuTf85kcuZTG1N+O8N5vR4is5si9dVxoGeCMk4P1enOdnZ0Jq1lVHYpQkMxkNOkJ6hr3lw9SkkhNWieMeleMDm4YHVwxObhizL3oc7e5YXRwuXmM+Xh1m8nBFZPO+Y6zpp2dnQkLrgubp6PZ9oV55rcSPRhNq2GkrxiO2/Gf0Cg3/wllutXkao32XA1oT4ZnsPoYiUDivhI827JXUX9n7J20i/U1qBOIR/RgNAWUNVOiB5N69QrH489ZITJLNpPcTpgwgWPHjrFw4UKrxRAREYFOVzZfi2XlFN4LZTSZCA8PL5PHLktGo5HY2Ngyfe1EydhL2wxuX4+hHRuY69wajEacHLSEhYUB0BwYCKRkGPg19iIr95xjZ3wyey5ls+dSNu5OaTwYXp2eUYG0qF3FKqv+aTEWWsRdca+OqePbaDKSICsNsq6pl2z1pyYrzXz95r40NDnqggNaUxbarCz0WVfvKk5FozX3EuftNVYcb/YSa6L6w+aP0dwyoUSzfhoaxYR7sz5wbCWK/z3mHlq9Xxj+Gg3+dxVd2bOX3xl7I+1iY2JeRQE0ecqaKdGDIeZV3LV6mlQp+SIqd5L7XrgTm0hu3333Xf7++2++/fZbqlevbt5etWpVDAYDqampFr23SUlJ+Pn5mY/Zt8/y039iolpGJveYotLpdGX2i+Oq0/HSveqYFpuv2VkCZfnaibtT0dvG9Ubsvu7qjHlHh4Inj/m463iqdR2eal2HU4nXWbn7HCt2n+XMlQy+33mO73eeI9DbhV5NA+kZFUg9v3JczSc7q9Ai7hqTEV3Dh4p/3pxstbB7VuqNpDc3MU61SILzbcubQOfuQ0GjmNRhBnkrF8DN3mRXX7jvndtPKNk+B147AsP3oPGpW6JeaFtQ0X9n7JW0i43Q6aDtyyjtR2JKT0br6o3GaAC9M7bSOlZNbhVFYeLEiaxdu5YFCxZQs2ZNi/3h4eHo9Xq2bNlC165dATh58iTnz5+nSZMmADRp0oRZs2aRlJSEr68vAJs3b8bd3Z3gYNuapOWk1zG4Qz2Gdgy2WG2poie2QtiaOlXdeOX+EF7u3IAdp66yYtdZftl3gXPJGXz653E+/fM4TWt506tpEA9H1sDbtYwnnha1iHtxOTiCgw+4+txdfIqilgPKm+zmS4JTQe+i1tMsbEJJdppaNkhYjc2v6icqPkdXTEYjh08nEBbmi85GJu/nsuq7fcKECfz888/873//w83NzTxG1sPDA2dnZzw8POjduzdTp07Fy8sLd3d3Jk2aRFRUlDm5jYmJITg4mFGjRvH666+TkJDA9OnTefLJJ22mUkJeuX9gzL1Q2FgJIyHsiEajoWVdH1rW9WF8j3tYe/ASy3ed5Z+jCew6ncyu08m8u+og9zX0p3fTIDqE+pVdWbEbRdzz9XbYQhkwjQYc3dSLR/XCjzVmFzrEAmfP/NtFuSmonrq9fEMobE9mZqa1QyiQVZPbRYsWAdC/f3+L7VOmTKFXr14AjB07Fq1Wy/Dhwy0Wccil0+mYNWsW48ePp2/fvri4uNCzZ0+GDx9efk9ECGHznPU6ujcOoHvjAC5fy+SnPedZvuschy6ksnr/RVbvv4iPmyM9GgfQu2kQ4YFlUFbMxns7isSYU+gQC4w5pVucXhTZ7eqp2/SqfkKUAau+y48cOXLHY5ycnBg3bpxFQnurwMBA5syZU5qhCSHsmL+HM8+3q8fz7epx8HwqK3efZeXu8ySmZfHV5lN8tfkUDfzd6dU0iJ5RgVT3Kt3eVVvt7SiSshpiYSNsaWnk7BwTyenZXEnP5sr1bK5eN3AlPZur12/cTr/5UzHB90NaF1pP/cUO9XlzZSzuTg74eThRzdOZap7O+Hs44e/pJImvsBvyThZCVGqNAjxpFNCI0Q+EseF4Iit2neP3Axc5djmNab8d5r01h4kJrkqvpoF0vae6JABgHmJB+5Fq0XZnT7CVIRYllDtO1a9mPYyKhqzsnFJt6xyjieQMwy2JqeFmgno925y4Xk1Xj7uWVfTSkKHVPEhKy77jqn7/nrrKkUvXCjzGw8kBf08n/D2cqebphL858XWmWu5PKyXBtvShQ9xkq+0if6WFEAJw0GnpGOpPx1B/UjMNrI69wPKd59h+6gobjiWy4Vgiro77eTC8Br2bBtKqnq9VyorZjNwV1XJXI6rAQxGKO07VZFJIySioF9UyWb2ariaqV65nk5JhKFFsWg1UcXWkipsjPq6OVHHT4+PmSBVXR4ufVW/0xBZWT93P3Yknomty+koGl1IzuXwti8upmVxKzSLDYORaVg7XEnI4kXC90JjczUmwk7nnt5qnszkZzt3m5nT3KUZZf+gQJWPr7WI7kQghhI3wdNbTt0Ut+raoxemkdHNZsfikdJbvOsvyXWcJ8HKmZ9NAekYFEexfjmXFRKkqbJyqgsJ9YdWY+ddxdXjAjV7V5PRsTErJHs/bVX8jSc1NTPV5Etc8P90cqeKqx9NZX+QPURnZOYXXU1cUnmmTv5KFoiikZeVwKTWLy9cyuXzjp3o7i0upmSTc+JmebSQtK4e0hBxOFiUJvjHkwdwb7OGcr3fY/TZJsL1NjrOXKhYVoV0q3qsqhBDlqJavKyM6N2D4fcHsOn2VZTvP8fO+85xPyeSzv07w2V8naFzTm95NA+keGUAVt4rbg1lZXM/K4cD5VI5dukbvZkG3Haf61eZTvNihPjvjr3Llena+/R7ODvl6Uau46vMkp+pPHzc9VVwd8XLR41BW1TgAF0eHEtVT12g0eDjr8XDW3/GDmpoE502A1euXbvQCX741Cc7K4WRi4Umwm6Mu3xCIvi1qsmrfeT7547j5uIo8Oa4iJIRFUVEmLVo/AiGEqAA0Gg3NavvQrLYP47o34o9Dl1mx6yx/H01g75lk9p5JZuLPB+kY6k/vZkF0DPW/7YITtjpOzR5dyzRw8HwqsedS2H8uhdhzKZxMvI6iqONU24f4FTpO9VpmDu/2uAetVnMziXXT4+3ieNv2taayrqfu7uSAu5879e+wCEpaVo55yEP+3uAbCXFqJtezjVzPNhKXeJ24G0mwj5sjr3YJ4avNpwo89/zNcQzuUI9Wk//gelYOegctep0GvU6Lo06r/sy7zUHdVvAxWvQOGvO23OMcHW4ep3fQ4njjvnrzfS3P7Zjnvvo899VpNSVKCBVFwaRAjsmEyWT506go+baZFIUck4KxoIty5+05JgXTbY7N3afXaXm2bZ1CJy0O7Wgb6wtIciuEEMXkrNfRLbIG3SJrkJiWdaOs2FkOnE/l94OX+P3gJbxd9fRoHECvpkE0DvJCo9HY/Di1iu5apoH951LNSez+cynEJamJ7K2qezrTsIYnfh5OhY5TreLqyMONA8oh+tJjC/XUc5PgO60EmJsE5x3+YFIUktMNhX7ouHI9Gy8XPRdTMyGrLJ5B6ajq7sg/ozoWmhAO7lCPdtP+JDEt2yLptDWh1TzoFlnjDh8GDeb3nTXJX1UhhLgLVd2deC6mLs/F1OXwxVRW7jrHyt3nuHwti2+2xPPNlnjuDfXj035RzN0QV+G/lrQVqZkG9pt7Y9WENu42X38HeDkTHuhFRKAX4UFehAd44eeh/gO+0zjVHJNJFtspQ7dLgrNzTIV+6PD3cGb+gOZk5SgYjCayc0wYjCYMxpu3s42520wYchSL2+p+5ca+G9tyb996TE6ebXmOyX1M9ae6PeeWpNTXzemOVSyuXM/G1dGBDENGkV83B60G3S0XB60GrebGz7z7NLcck/dY3c37WJ5Pi06D+lMLOq0GD2eHO34Y9HDWF/k5lCVJboUQopSEVffkjYc8GfVAGBuPJ7Ji11nWHLjIk9G1+OKfk3z6Z8HjBwe1r1cqM8vtVUqGgQM3emNze2RPJaUXeGygtwvhgZ5qInvjUrWQnqSSjlMVZctoMt3xQ0eAt6sVIiucyaRgMKnJbnaOiRyTCW8Xxzsm6nOeboZGc0vimScRzZuoWrNKS0X5MCh/TYUQopTptBo6hPjRIcSPtEwDep2W177fW+CxuV9Ldvl4Pc56HdU8nanu6Ux1r5s/q3k6U8PLuVIkwCnpBoskNvZcCqev3D6RjQj0IiLoRiIb4Fmir0TzjlNNSc/Cy9WpVMepiuKrqB86tFoNTlodTg7AjbdiURLCWr5u5RtoCVWUdrH/v5RCCGFF7s56ktKy7vi1pAYN+86mACm3PZeHkwPVvNRENzcJrublTI08SbCvm2OFqb+bnJ6dL5E9c6Xgr2Zr+riYe2MjAtWhBaVZmcLV0QGj0cjlMyfxCQvDUcZCW529fOioKAlhUVWEdpHfXiGEKGMezvo7fi35cd8mnEvO4GJKBhdTM7mYok6wuZCSwaXULNKyctQi+5fTOH457baPpddp8Pe42fOb2+tbLbcn2NOZal5OODnc3T+i4tbsvHo9fyJ79mrBiWwtH1fLRDbQE2/X8imxVqGXRrZD9vKho6yrWJQ3W28X24pGCCHsUFHGD6rLAHve9hxpWTlcTMlUL6lqfdGLKZlcSLlxPTWTxLQsDEaFc8kZnEsufHKKj5vjjd5fJ6p7udwYApHn+o3VrjSa/L3Ad6rZmZSWRey5FA6cTyX2rJrI3i6e2r6u5iQ2t0fWy9U2JqUI22EPHzpsoYpFabPVdpHkVgghylhpfC3p7uRAsL97oUX2DUYTl69lcTHlZvJ7sYCf2TkmrtxYNvbQhds/prNeSw0vF6p5OpmHQDzeLIgf9xZcXF9RFJrVrsIz83cUeL66Vd3MY2MjAr24J9ALLxdJZIUQpUuSWyGEKAflMU5Nr9MS6O1CoLfLbY9RbtQQvTXpVYdA3OwFTk43kGkw5SuuP+K+Brctrv/VllO8eG99fNwc8XbR5xlW4MU9gZ542kiZICGEfZPkVgghyoktjFPTaDRUcXOkipsjDWvcfhhEpsFoMQTiQkomJtOdi+tfzzKyYdS9uDlJIiuEsA5JboUQopzZ6ji1vJz1OupUdaNOVcsSRXcqru/lorfJZWmFEJWH/AUSQghRZLmT4wqSOzlOCCGsSXpuhRBCFJm91ewUQtgfSW6FEEIUi73V7BRC2BdJboUQQhSbPdbsFELYB/lrJIQQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5YNbndsWMHL774IjExMYSGhrJu3TqL/YqiMGPGDGJiYoiMjOTZZ5/l1KlTFsckJyfz2muv0bRpU5o3b87YsWO5fv16OT4LIYQQQghhK6ya3KanpxMaGsq4ceMK3D9nzhwWLFjA+PHjWbp0KS4uLgwcOJCsrCzzMSNHjuT48ePMnz+fWbNm8e+///LOO++U11MQQgghhBA2xKrJbYcOHXjllVe4//778+1TFIVvvvmGIUOG0LlzZ8LCwnjvvfe4fPmyuYf3xIkTbNiwgUmTJtG4cWOaN2/OW2+9xS+//MKlS5fK++kIIYQQQggrc7B2ALdz9uxZEhISaNOmjXmbh4cHjRs3Zvfu3XTr1o3du3fj6elJRESE+Zg2bdqg1WrZt29fgUlzYYxGY6nFX1nkvmby2tkeaRvbJO1iu6RtbJO0i22yRrsU9bFsNrlNSEgAwNfX12K7r68viYmJACQmJuLj42Ox38HBAS8vL/P9iyM2NraE0Qp57WyXtI1tknaxXdI2tknaxTbZYrvYbHJrDREREeh0OmuHUaEYjUZiY2PltbNB0ja2SdrFdknb2CZpF9tkjXbJfcw7sdnk1s/PD4CkpCT8/f3N25OSkggLCwOgatWqXLlyxeJ+OTk5pKSkmO9fHDqdTn5xSkheO9slbWObpF1sl7SNbZJ2sU222C42W+c2KCgIPz8/tmzZYt6WlpbG3r17iYqKAiAqKorU1FT2799vPmbr1q2YTCYiIyPLPWYhhBBCCGFdVu25vX79OqdPnzbfPnv2LIcOHcLLy4uAgACefvppPv/8c2rXrk1QUBAzZszA39+fzp07A1C/fn3atWvH22+/zYQJEzAYDEycOJFu3bpRrVo1az0tIYQQQghhJVZNbvfv38/TTz9tvj1lyhQAevbsydSpU3nhhRfIyMjgnXfeITU1lWbNmjF37lycnJzM9/nggw+YOHEizzzzDFqtli5duvDWW2+V+3MRQgghhBDWZ9XkNjo6miNHjtx2v0ajYcSIEYwYMeK2x3h7e/Phhx+WRXhCCCGEEKKCsdkxt0IIIYQQQhSXJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHshiS3QgghhBDCbkhyK4QQQggh7IYkt0IIIYQQwm5IciuEEEIIIeyGJLdCCCGEEMJuSHIrhBBCCCHsht0kt9999x2dOnUiIiKCxx57jH379lk7JCGEEEIIUc7sIrn99ddfmTJlCkOHDmXlypWEhYUxcOBAkpKSrB2aEEIIIYQoR3aR3M6fP5/HH3+c3r17ExwczIQJE3B2dmb58uXWDk0IIYQQQpQjB2sHcLeys7M5cOAAgwcPNm/TarW0adOG3bt3F+kciqKYz6XT6cokTntlNBoBee1skbSNbZJ2sV3SNrZJ2sU2WaNdch8zN2+7nQqf3F69ehWj0Yivr6/Fdl9fX06ePFmkc5hMJgAOHjxY6vFVFvLa2S5pG9sk7WK7pG1sk7SLbbJGu+TmbbdT4ZPb0uDg4EBERARarRaNRmPtcIQQQgghxC0URcFkMuHgUHj6WuGT2ypVqqDT6fJNHktKSqJq1apFOodWq8XR0bEswhNCCCGEEOWowk8oc3R05J577mHLli3mbSaTiS1bthAVFWXFyIQQQgghRHmr8D23AAMGDGD06NGEh4cTGRnJ119/TUZGBr169bJ2aEIIIYQQohzZRXL70EMPceXKFT755BMSEhJo2LAhc+fOLfKwBCGEEEIIYR80yp3qKQghhBBCCFFBVPgxt0IIIYQQQuSS5FYIIYQQQtgNSW6FEEIIIYTdkORWCCGEEELYDUluRYnMnj2b3r17ExUVRevWrXnppZeKvNyxKD9ffPEFoaGh/Pe//7V2KAK4dOkSI0eOJDo6msjISLp3705sbKy1w6rUjEYj06dPp1OnTkRGRtK5c2c+++yzO65dL0rfjh07ePHFF4mJiSE0NJR169ZZ7FcUhRkzZhATE0NkZCTPPvssp06dsk6wlUhh7WIwGHj//ffp3r07TZo0ISYmhlGjRnHp0iUrRizJrSih7du38+STT7J06VLmz59PTk4OAwcOJD093dqhiRv27dvH4sWLCQ0NtXYoAkhJSaFfv37o9XrmzJnDL7/8wujRo/Hy8rJ2aJXanDlzWLRoEe+88w6//vorI0eOZO7cuSxYsMDaoVU66enphIaGMm7cuAL3z5kzhwULFjB+/HiWLl2Ki4sLAwcOJCsrq5wjrVwKa5fMzEwOHjzIkCFDWLFiBTNnziQuLo4hQ4ZYIdKb7KLOrSh/8+bNs7g9depUWrduzYEDB2jRooWVohK5rl+/zuuvv86kSZP4/PPPrR2OQP3HXL16daZMmWLeVrNmTStGJAB2797Nfffdx7333gtAUFAQv/zyC/v27bNuYJVQhw4d6NChQ4H7FEXhm2++YciQIXTu3BmA9957jzZt2rBu3Tq6detWnqFWKoW1i4eHB/Pnz7fY9vbbb/PYY49x/vx5AgICyiPEfKTnVpSKa9euAUgvlI1499136dChA23atLF2KOKGP//8k/DwcIYPH07r1q159NFHWbp0qbXDqvSioqLYunUrcXFxABw+fJidO3fSvn17K0cm8jp79iwJCQkWf9M8PDxo3Lgxu3fvtmJk4lZpaWloNBo8PT2tFoP03Iq7ZjKZmDx5Mk2bNiUkJMTa4VR6v/zyCwcPHmTZsmXWDkXkcebMGRYtWsSAAQN48cUXiY2NZdKkSej1enr27Gnt8CqtQYMGkZaWxoMPPohOp8NoNPLKK6/Qo0cPa4cm8khISADA19fXYruvry+JiYnWCEkUICsriw8++IBu3brh7u5utTgkuRV3bcKECRw7doyFCxdaO5RK78KFC/z3v//lyy+/xMnJydrhiDwURSE8PJxXX30VgEaNGnHs2DEWL14sya0VrV69mlWrVvHhhx8SHBzMoUOHmDJlCv7+/tIuQhSDwWBgxIgRKIrChAkTrBqLJLfirrz77rv8/ffffPvtt1SvXt3a4VR6Bw4cICkpiV69epm3GY1GduzYwXfffUdsbCw6nc6KEVZefn5+1K9f32JbvXr1WLNmjZUiEqCO2xw0aJB5zGZoaCjnz59n9uzZktzaED8/PwCSkpLw9/c3b09KSiIsLMxaYYkbDAYDL7/8MufPn+frr7+2aq8tSHIrSkhRFCZOnMjatWtZsGCBTIyxEa1atWLVqlUW29544w3q1avHCy+8IImtFTVt2tQ8rjPXqVOnCAwMtFJEAtTZ3hqNxmKbTqeTUmA2JigoCD8/P7Zs2ULDhg0BdWzn3r176devn5Wjq9xyE9v4+Hi++eYbqlSpYu2QJLkVJTNhwgR+/vln/ve//+Hm5mYeD+Xh4YGzs7OVo6u83N3d8417dnV1xdvbW8ZDW9kzzzxDv379mDVrFg8++CD79u1j6dKlvPvuu9YOrVLr2LEjs2bNIiAgwDwsYf78+fTu3dvaoVU6169f5/Tp0+bbZ8+e5dChQ3h5eREQEMDTTz/N559/Tu3atQkKCmLGjBn4+/ubqyeIslFYu/j5+TF8+HAOHjzI7NmzMRqN5nzAy8sLR0dHq8SsUeTjqSiB29VOnTJlisVX4sL6+vfvT1hYGG+++aa1Q6n0/vrrLz766CNOnTpFUFAQAwYM4PHHH7d2WJVaWloaM2bMYN26deavvLt168bQoUOt9o+5stq2bRtPP/10vu09e/Zk6tSpKIrCJ598wtKlS0lNTaVZs2aMGzeOunXrWiHayqOwdhk2bBj33Xdfgff75ptviI6OLuvwCiTJrRBCCCGEsBtS51YIIYQQQtgNSW6FEEIIIYTdkORWCCGEEELYDUluhRBCCCGE3ZDkVgghhBBC2A1JboUQQgghhN2Q5FYIIYQQQtgNSW6FEEIIIYTdkORWCCGEWWhoKOvWrbN2GEIIUWKS3AohhA0YM2YMoaGhvPPOO/n2TZgwgdDQUMaMGVNqj/fpp5/yyCOPlNr5hBDCVkhyK4QQNqJGjRr8+uuvZGZmmrdlZWXx888/ExAQYMXIhBCi4pDkVgghbESjRo2oUaMGv//+u3nb77//To0aNWjYsKF5W3Z2NpMmTaJ169ZERETQr18/9u3bZ96/bds2QkND2bJlC7169aJx48b83//9HydPngRgxYoVzJw5k8OHDxMaGkpoaCgrVqww3//q1asMHTqUxo0b06VLF/74449yePZCCFE6JLkVQggb0rt3b4tEc/ny5fTq1cvimPfee481a9YwdepUVq5cSe3atXn++edJTk62OO7jjz9mzJgxLF++HJ1Ox9ixYwF46KGHeO6552jQoAEbN25k48aNPPTQQ+b7zZw5kwcffJCffvqJ9u3bM3LkyHznFkIIWyXJrRBC2JAePXqwc+dOzp07x7lz59i1axc9evQw709PT2fx4sWMGjWKDh06EBwczMSJE3FycmLZsmUW53rllVdo2bIlwcHBDBo0iN27d5OVlYWzszOurq7odDr8/Pzw8/PD2dnZfL+ePXvy8MMPU7t2bV599VXS09MteoaFEMKWOVg7ACGEEDf5+Phw7733snLlShRF4d5778XHx8e8//Tp0xgMBpo2bWreptfriYyM5MSJExbnCg0NNV/38/MDICkp6Y7jd/Pez9XVFXd3d65cuXJXz0sIIcqL9NwKIYSNyR2asHLlSnr37l3i8zg43Oy/0Gg0AJhMpjveT6/XW9zWaDRFup8QQtgCSW6FEMLGtGvXDoPBQE5ODjExMRb7atWqhV6vZ9euXeZtBoOB2NhYgoODi/wYer1eElYhhF2SYQlCCGFjdDodq1evNl/Py9XVlX79+vHee+/h5eVFQEAAc+fOJTMzkz59+hT5MQIDAzl79iyHDh2iWrVquLu74+joWKrPQwghrEGSWyGEsEHu7u633Tdy5EgURWHUqFFcv36d8PBw5s6di5eXV5HP37VrV9auXcvTTz9NamoqU6ZMyVeVQQghKiKNoiiKtYMQQgghhBCiNMiYWyGEEEIIYTckuRVCCCGEEHZDklshhBBCCGE3JLkVQgghhBB2Q5JbIYQQQghhNyS5FUIIIYQQdkOSWyGEEEIIYTckuRVCCCGEEHZDklshhBBCCGE3JLkVQgghhBB2Q5JbIYQQQghhN/4fQLF0akjGT4EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_y = df_x \\\n",
    "    .groupby([\"invoice_month\", \"target\"]) \\\n",
    "    .agg(\n",
    "        consumption=(\"consumption\", \"sum\"), \n",
    "        customers=(\"client_id\", \"nunique\"),\n",
    "        invoice_months=(\"invoice_month_ts\", \"nunique\")) \\\n",
    "    .reset_index()\n",
    "\n",
    "df_y[\"consumption\"] = df_y[\"consumption\"]/(df_y[\"customers\"]*df_y[\"invoice_months\"])\n",
    "df_y[\"target\"] = df_y[\"target\"].map(TARGET_LABELS).astype(str)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax = sns.lineplot(df_y, x=\"invoice_month\", y=\"consumption\", hue=\"target\", marker=\"o\")\n",
    "ax.set_ylim(0, None)\n",
    "\n",
    "plt.legend(title=None)\n",
    "plt.title(\"Energy consumption per customer\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Energy [kWh]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_y = df_x \\\n",
    "    .groupby([\"invoice_year\", \"target\"]) \\\n",
    "    .agg(\n",
    "        consumption=(\"consumption\", \"sum\"), \n",
    "        customers=(\"client_id\", \"nunique\")\n",
    "    ) \\\n",
    "    .reset_index()\n",
    "\n",
    "df_y[\"consumption\"] = df_y[\"consumption\"]/df_y[\"customers\"]\n",
    "df_y[\"target\"] = df_y[\"target\"].map(TARGET_LABELS).astype(str)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax = sns.lineplot(df_y, x=\"invoice_year\", y=\"consumption\", hue=\"target\", marker=\"o\")\n",
    "ax.set_ylim(0, None)\n",
    "\n",
    "plt.legend(title=None)\n",
    "plt.title(\"Energy consumption per customer\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Energy [kWh]\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}