notebook.ipynb

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   "source": [
    "# Table of contents\n",
    "* [Chapter 1 - Introduction](#ch1)\n",
    "* [Chapter 2 - Preprocessing](#ch2)\n",
    "    * [Chapter 2.1 - Importing the libraries](#ch21)\n",
    "    * [Chapter 2.2 - Description of the dataset](#ch22)\n",
    "    * [Chapter 2.3 - Discretization](#ch23)\n",
    "* [Chapter 3 - Data exploration](#ch3)\n",
    "* [Chapter 4 - Naïve Bayes Network](#ch4)\n",
    "    * [Chapter 4.1 - Considerations](#ch41)\n",
    "* [Chapter 5 - Improving the Naïve Bayes network with Hill Climbing](#ch5)\n",
    "* [Chapter 6 - Running Hill Climbing Unconstrained](#ch6)\n",
    "    * [Chapter 6.1 - How Bayesian Networks are represented](#ch61)\n",
    "    * [Chapter 6.2 - First run of the algorithm](#ch62)\n",
    "    * [Chapter 6.3 - Adding a blacklist](#ch63)\n",
    "    * [Chapter 6.4 - Manually adding edges to the network](#ch64)\n",
    "* [Chapter 7 - Domain Knowledge Network](#ch7)\n",
    "    * [Chapter 7.1 - Motivational Background](#ch71)\n",
    "    * [Chapter 7.2 - Construction of the network](#ch72)\n",
    "    * [Chapter 7.3 - Changing the estimator](#ch73)\n",
    "* [Chapter 8 - Markov Blanket feature selection algorithm](#ch8)\n",
    "* [Chapter 9 - Network analysis](#ch9)\n",
    "* [Chapter 10 - Inference](#ch10)\n",
    "    * [Chapter 10.1 - How the combinations of evidences affect the prediction](#ch101)\n",
    "    * [Chapter 10.2 - Practical use of the model](#ch102)\n",
    "* [Chapter 11 - Conclusion](#ch11)\n",
    "    * [Chapter 11.1 - What's the best model](#ch111)\n",
    "    * [Chapter 11.2 - What we have learned](#ch112)\n",
    "    * [Chapter 11.3 - Future developments](#ch113)\n",
    "* [Chapter 12 - References](#ch12)\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "<a class=\"anchor\" id=\"top\"></a>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# 1. Introduction\n",
    "###### [Back to top](#top)\n",
    "<a class=\"anchor\" id=\"ch1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea029484",
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   "source": [
    "# Heart Disease Risk Assessment using Bayesian Networks\n",
    "\n",
    "### Authors: Matteo Fasulo, Luca Tedeschini, Antonio Gravina, Luca Babboni\n",
    "\n",
    "### Course: Fundamentals of Artificial Intelligence and Knowledge Representation (Mod. 3) - Alma Mater Studiorum Università di Bologna\n",
    "\n",
    "### Overview\n",
    "\n",
    "**Cardiovascular diseases** (CVDs) are the number one cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 32% of all deaths worldwide. Four out of 5 CVD deaths are due to heart attacks and strokes, and one third of these deaths occur prematurely in people under 70 years of age [[1]](#1). \n",
    "\n",
    "The main risk factors for **heart disease** are unhealthy diet, physical inactivity, tobacco use and harmful use of alcohol. The effects may include raised blood pressure, raised blood glucose, raised blood lipids, and overweight and obesity [[1]](#1). Since the 1930s, research has identified diverse **CVD risk factors** [[2]](#2). Notably, the World Health Organization categorizes factors as modifiable (influenced by lifestyle) and non-modifiable [[3]](#3). Interestingly, modifiable factors can be significantly impacted by individual choices and interventions. \n",
    "\n",
    "In this notebook, we will use a **Bayesian Network** to assess the **risk of heart disease** in a patient based on these risk factors. We will face the modelling of **causal relations** between the risk factors and the disease, and we will use the network to make **predictions** about the risk of heart disease in a patient. The aim is to learn a model which **given** the risk factors of a patient as input (**evidence**), can predict the probability of the patient having heart disease. The causal relations between the risk factors and the disease will be both learned from data and from **domain knowledge** found in the **literature** and in **medical guidelines**.\n",
    "\n",
    "The work was inspired by a paper [[4]](#4) in which the authors used a Bayesian Network to assess the risk of heart disease in a patient. Their work guided us in the choice of the risk factors and in the structure of the network. However, we will use a different dataset and we will use a different approach to learn the parameters of the network.\n",
    "\n",
    "A demo of the final model built in this notebook can be found at the following link: [Heart Disease Risk Assessment](https://heart-disease-risk.streamlit.app/)\n",
    "\n",
    "---"
   ]
  },
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   "cell_type": "markdown",
   "id": "ed6cc115-1304-43ca-9053-f3fdafa42f8e",
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   "source": [
    "# 2. Preprocessing\n",
    "###### [Back to top](#top)\n",
    "\n",
    "<a class=\"anchor\" id=\"ch2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50257e62-b885-46e3-8660-17e275a1261f",
   "metadata": {},
   "source": [
    "## 2.1 Importing the libraries\n",
    "<a class=\"anchor\" id=\"ch21\"></a>"
   ]
  },
  {
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   "id": "facda2b6-3bb3-45aa-b7cc-b0885b1b87f6",
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   "source": [
    "#os is used to access the csv file \n",
    "import os\n",
    "#Warnings are used to ignore the warnings prompted by pgmpy\n",
    "import warnings\n",
    "\n",
    "#Itertools is used to create combinations on data structures\n",
    "from itertools import combinations, product\n",
    "\n",
    "#Pandas is used to work with dataframes\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "\n",
    "#PyImpetus is used to get the most important features\n",
    "warnings.simplefilter(action='ignore', category=UserWarning)\n",
    "from PyImpetus import PPIMBC\n",
    "\n",
    "#Matplotlib and networkx are used to plot the graph\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "#Pgmpy modules used throughout the code\n",
    "from pgmpy.models import BayesianNetwork\n",
    "from pgmpy.estimators import HillClimbSearch, MaximumLikelihoodEstimator, BayesianEstimator\n",
    "from pgmpy.inference import VariableElimination\n",
    "from pgmpy.readwrite import XMLBIFWriter\n",
    "\n",
    "#Sklearn modules used throughout the code to work with the data\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 2.2 Description of the dataset\n",
    "<a class=\"anchor\" id=\"ch22\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "The original dataset [[5]](#5) was retrieved from **Kaggle**. The creator combined **5 heart disease datasets** on over **11 common features** commonly know to be strictly related to the prediction of possible heart disease. \n",
    "\n",
    "The dataset contains the following **attributes**:\n",
    "\n",
    "* **Age**: age of the patient [years]\n",
    "\n",
    "* **Sex**: sex of the patient\n",
    "    * *M*: Male\n",
    "    * *F*: Female\n",
    "* **ChestPainType**: chest pain type \n",
    "    * *TA*: Typical Angina\n",
    "    * *ATA*: Atypical Angina\n",
    "    * *NAP*: Non-Anginal Pain\n",
    "    * *ASY*: Asymptomatic\n",
    "    \n",
    "* **RestingBP**: resting blood pressure [mm Hg]\n",
    "\n",
    "* **Cholesterol**: serum cholesterol [mm/dl]\n",
    "\n",
    "* **FastingBS**: fasting blood sugar \n",
    "    * *1*: if FastingBS > 120 mg/dl\n",
    "    * *0*: otherwise\n",
    "\n",
    "* **RestingECG**: resting electrocardiogram results \n",
    "    * *Normal*: Normal\n",
    "    * *ST*: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "    * *LVH*: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "\n",
    "* **MaxHR**: maximum heart rate achieved [Numeric value between 60 and 202]\n",
    "\n",
    "* **ExerciseAngina**: exercise-induced angina \n",
    "    * *Y*: Yes\n",
    "    * *N*: No\n",
    "\n",
    "* **Oldpeak**: oldpeak = ST [Numeric value measured in depression]\n",
    "\n",
    "* **ST_Slope**: the slope of the peak exercise ST segment \n",
    "    * *Up*: upsloping\n",
    "    * *Flat*: flat\n",
    "    * *Down*: downsloping\n",
    "    \n",
    "* **HeartDisease**: target variable \n",
    "    * *1*: heart disease\n",
    "    * *0*: Normal\n",
    "\n",
    "\n",
    "For the moment we consider these features *as they are*, but in the [Chapter 7](#ch7) we will provide **medical documentations** on how each feature influences (directly or indirectly) the target variable (i.e having an heart disease)"
   ]
  },
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   "id": "1e66305c",
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   "source": [
    "## 2.3 Discretization\n",
    "<a class=\"anchor\" id=\"ch23\"></a>"
   ]
  },
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    "Firstly, all the continuous variables are discretized using the `cut` function of the `pandas` library.\n",
    "\n",
    "The discretization is performed in such a way to be as close as possible to the standard medical guidelines [[29]](#29) [[30]](#30) [[32]](#32); for example, the `RestingBP` (indicating the resting blood pressure) is discretized in 3 categories: `90-120`, `120-140` and `140+` according to the following intervals: $[90, 120]$, $(120, 140]$, $(140, +\\infty)$.\n",
    "\n",
    "The same procedure is done for the other continuous variables such as `Cholesterol` and `Oldpeak`.\n",
    "\n",
    "We have also dropped some rows where the `Cholesterol` or the `RestingBP` are equal to $0$, because we considered them `nan` values."
   ]
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   "source": [
    "def clean_data(data: pd.DataFrame):\n",
    "    \"\"\"\n",
    "    Cleans the given DataFrame by performing various data transformations.\n",
    "\n",
    "    Args:\n",
    "        data (pd.DataFrame): The input DataFrame to be cleaned.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: The cleaned DataFrame.\n",
    "\n",
    "    \"\"\"\n",
    "    data = data[~(data['Cholesterol'] == 0) & ~(data['RestingBP'] == 0)]\n",
    "    df = data.copy()\n",
    "    df[\"Age\"] = pd.cut(x=data[\"Age\"], bins=[20, 40, 50, 60, 70, np.Inf], labels=[\n",
    "        \"20-40\", \"40-50\", \"50-60\", \"60-70\", \"70+\"])\n",
    "\n",
    "    df[\"RestingBP\"] = pd.cut(x=data[\"RestingBP\"], bins=[90, 120, 140, np.Inf], labels=[\n",
    "        \"90-120\", \"120-140\", \"140+\"])\n",
    "\n",
    "    df[\"Cholesterol\"] = pd.cut(x=data[\"Cholesterol\"], bins=[\n",
    "        -np.Inf, 200, 240, np.Inf], labels=[\"<=200\", \"200-240\", \"240+\"])\n",
    "\n",
    "    df[\"MaxHR\"] = pd.qcut(x=data[\"MaxHR\"], q=4, labels=[\n",
    "                          \"low\", \"medium\", \"high\", \"very-high\"])  # binning using quartiles\n",
    "\n",
    "    df[\"Oldpeak\"] = pd.cut(x=data[\"Oldpeak\"], bins=[-np.Inf, 0.5, 1, 2, np.Inf], labels=[\n",
    "        \"<=0.5\", \"0.5-1\", \"1-2\", \"2+\"])\n",
    "    df['FastingBS'] = df['FastingBS'].map({0: 'N', 1: 'Y'})\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2bdf5486",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 746 observations and 12 attributes\n"
     ]
    },
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20-40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>120-140</td>\n",
       "      <td>240+</td>\n",
       "      <td>N</td>\n",
       "      <td>Normal</td>\n",
       "      <td>very-high</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;=0.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40-50</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>140+</td>\n",
       "      <td>&lt;=200</td>\n",
       "      <td>N</td>\n",
       "      <td>Normal</td>\n",
       "      <td>high</td>\n",
       "      <td>N</td>\n",
       "      <td>0-5-1</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20-40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>120-140</td>\n",
       "      <td>240+</td>\n",
       "      <td>N</td>\n",
       "      <td>ST</td>\n",
       "      <td>low</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;=0.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40-50</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>120-140</td>\n",
       "      <td>200-240</td>\n",
       "      <td>N</td>\n",
       "      <td>Normal</td>\n",
       "      <td>low</td>\n",
       "      <td>Y</td>\n",
       "      <td>1-2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50-60</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>140+</td>\n",
       "      <td>&lt;=200</td>\n",
       "      <td>N</td>\n",
       "      <td>Normal</td>\n",
       "      <td>low</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;=0.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG  \\\n",
       "0  20-40   M           ATA   120-140        240+         N     Normal   \n",
       "1  40-50   F           NAP      140+       <=200         N     Normal   \n",
       "2  20-40   M           ATA   120-140        240+         N         ST   \n",
       "3  40-50   F           ASY   120-140     200-240         N     Normal   \n",
       "4  50-60   M           NAP      140+       <=200         N     Normal   \n",
       "\n",
       "       MaxHR ExerciseAngina Oldpeak ST_Slope  HeartDisease  \n",
       "0  very-high              N   <=0.5       Up             0  \n",
       "1       high              N   0-5-1     Flat             1  \n",
       "2        low              N   <=0.5       Up             0  \n",
       "3        low              Y     1-2     Flat             1  \n",
       "4        low              N   <=0.5       Up             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(f'data{os.sep}heart.csv')\n",
    "df = clean_data(data)\n",
    "df.to_csv(f'data{os.sep}heart_cleaned.csv')\n",
    "\n",
    "print(\"The dataset contains %s observations and %s attributes\" % df.shape)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2502c9",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5c084e-bf95-4b5b-8063-16d7ec314cbb",
   "metadata": {},
   "source": [
    "# 3. Data exploration\n",
    "<a class=\"anchor\" id=\"ch3\"></a>\n",
    "###### [Back to top](#top)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af185db",
   "metadata": {},
   "source": [
    "### Heart Disease Distribution in the Dataset\n",
    "\n",
    "The dataset, after the preprocessing, contains 746 samples and 11 features plus the target variable.\n",
    "\n",
    "The distribution of the target variable `HeartDisease` is shown in the following plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d27c23d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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++te/4re//S1GjBiBtLS0kF/bPTVy5Eh8//vfx44dO5Ceno7nnnsO1dXVeP7559vPueiiizBkyBB8//vfxy9+8QvI5XI899xzSE1NRWlpaYf762ltSqUSjzzyCNasWYN58+bhuuuua5+SmJubizvvvLNXz4dIEtJMeqB4dGra1o4dO8JeP2/evA5TEk958803xTlz5oh6vV7U6/Vifn6++KMf/Ug8evRo+zmHDh0SFyxYIBoMBjElJUW85ZZbxH379nWY5iaKwSlder0+7OPb7Xbx+uuvFy0Wiwig2+mJp6axARAFQRBNJpM4duxY8ZZbbhG3b98e9jb4zpREt9st/uIXvxAnTpwoGo1GUa/XixMnThSffvrpkNvt2bNHvPLKK8Xk5GRRrVaLOTk54ooVK8RNmza1n9PY2CiuWbNGTElJEQ0Gg7ho0SLxyJEjYk5Ojrhq1ar2837729+KM2bMEC0Wi6jVasX8/HzxoYceEj0eT4fHLCwsFFeuXClmZGSISqVSzMrKEpcuXSq+8cYbXb4up57nqYtMJhMtFos4efJk8Y477hAPHjwYcv6ZUxLr6urEH/3oR2J+fr6o1+tFs9kszpw5U3zttdc63K6qqkpcsmSJaDQaRQDtUwC7eq91NiVxyZIl4ocffihOmDBBVKvVYn5+vvj666+H3H7Xrl3izJkzRZVKJQ4ZMkR84oknwt5nZ7WdOSXxlFdffVWcPHmyqFarRavVKt5www1ieXl5h3M6e/92NlWSqL9x7wMiIiICwDEFRERE1IahgIiIiAAwFBAREVEbhgIiIiICwFBAREREbRgKiIiICABDAREREbVhKCAiIiIADAVERETUhqGAiIiIADAUEBERURuGAiIiIgLAUEBERERtGAqIiIgIAEMBERERtWEoICIiIgAMBURERNSGoYCIiIgAMBQQERFRG4YCIiIiAsBQQERERG0YCoiIiAgAQwERERG1YSggIiIiAAwFRERE1IahgIiIiAAwFBAREVEbhgIiIiICwFBAREREbRgKiIiICABDAREREbVhKCAiIiIADAVERETUhqGAiIiIADAUEBERURuGAiIiIgLAUEBERERtGAqIiIgIAEMBERERtWEoICIiIgAMBURERNSGoYCIiIgAMBQQERFRG4YCIiIiAsBQQERERG0YCoiIiAgAQwERERG1YSggIiIiAIBC6gKIKMY1VwJiABBkwYtcBSh1gFIjdWVEFGWCKIqi1EUQUQz7w3igqTT0uEwBKPWA2gCo9G0XA6AxA/pUwJAOGNODfxrSAUNa8E+Fuv+fAxH1CFsKiGKUzelFTbMLVc0u1DS70ejwoNXth93thd3tR6vbh1a3Dy1tfzo8fnj9AYgiEBBFBEQRH/98HkwaZd8UGPABblvwEgldCmAdCliHA9ZhQPLw03/XWvqkVCLqGYYCIonUNLtwvNaOE7WtKG1woMrmQnXzqYsbTq//rB9DDESh0Ghz1AUv5TtCr9NagdR8IHMiMGgSkDkJSBkJyDj8iag/MBQQ9aFAQMSJulYcr2lBYW0rCmvsKGwLAi1un9TlhfB6PBDFAARBACBAJpNBBkDorwKcDUDptuDlFKUeyBjfMSikjgJk8v6qimjAYCggiqKyBgf2lTdhf7kN+8qacLCyGfYY/PIPp6m+Bm8/9wQc9macigGCIOAGNMAkZWHeVqDs6+DlFKUOSB8XDAk5s4HhFwTHMhDRWWEoIOoll9ePHcUN2FHciP3lTfi23Ib6Vo/UZfVaS1M9aipLoTeaIchkgCgiIAYAiP3YVNBDXgdQ/k3w8s3fg4MeB58D5C0E8i4C0sdIXSFRXGIoIOohnz+AfeU2bDteh62Fddhd2gSPLxY77c+OMSkFCsXpwYmyRjkQ608z4ANKtgQvn9wHmAcDIxYEA8KwecGZEUTULYYCoi4UVLfgy4I6bCusw/YTDTE5DoDCsJUBu54PXuTqYBdD3kXAyEXB2Q5EFBZDAdF3iKKI3aVN+OhgFT48WIXieofUJdHZ8ruBE58FLx/+KjgNMu+iYFdD7nlcN4HoOxgKaMDz+QP46kQ9PjxYhY8PVaO62S11SdSXGk4A258JXjQWYMI1wNRVQPpYqSsjkhxDAQ1I/oCILwtq8e6+Smw6XAOb0yt1SSQFVxPwzd+Cl6ypwJRVwLirgqs0Eg1ADAU0oByvseONXeV4a085WwSoo4pdwcuH/weMuxKYshrInip1VUT9iqGAEp7N6cV/91XijV3l2FvWJHU5FOs8dmD3v4KX9HHB1oMJK7gEMw0IDAWUsLYer8N/vinFx4eq4U7AqYPUD6oPABt+AXx8DzDmsmBAyD1X6qqI+gxDASUUh8eHN3dX4F/bilFQY5e6HEoUPhew/9XgJXkEMGUlMPkmQGeVujKiqGIooIRQ3ujAuq3FeHVnGVpcXEuA+lD9ceDje4HPHwVm/gCY/WNAmyR1VURRwVBAcW13aSP++eUJfHiwGv6AKHU5NJB4WoAvHwsus3zOD4Fzbue4A4p7DAUUl7Yer8MfPynAN8UNUpdCA527Gfj8EeDrZ4BZtwcDAjdnojjFUEBxZUtBHf646Rh2FDdKXQpRR24bsHkt8PVfgVk/Bs65DVAbpa6KKCIMBRQXviyoxR8/KcDOEoYBinGuJuCz3wJfPx0cbzDjB1wMieIGQwHFtC+O1eKPmwqwi2GA4o2zAdj0APDVX4DZPwFm3MrdGinmMRRQTDpQYcNv3z+Er09wzADFOUc98Mn9wLY/A+feAUy/GVDppK6KKCyGAoop1c0uPPrhUazfXQ5OJqCE4qgLLoK0/W/AxQ8Do5dJXRFRCIYCiglOjx9/+6IQf//iBBwev9TlEPWd5nLg1RuBkRcDlzwKWAZLXRFRO4YCkpQoili/uwKPfXQUJ20uqcsh6j/HNgBFXwDz/ic4W0HOj2OSHt+FJJlvy2349dvfYn+5TepSiKThbQU+uS+4fPLSJ4Eh50hdEQ1wDAXU7xweHx7/6BjWbSvmKoREAFBzCHhuMTD5RmDhA9xTgSQjk7oAGlg+O1qDhU98gWe3FDEQEHUgAnteBP48Hdj7b6mLoQGKLQXUL2pb3PjNfw/ivf0npS6FKLY56oC3fxgMBpc/DViGSF0RDSBsKaA+JYoiXvmmFBc+vpmBgCgSxV8Cfz0X2PsfqSuhAYQtBdRnappduOu1vfjyeL3UpRDFJ3cz8PZtwZkKS//AsQbU59hSQH1i44EqLHzicwYComg49A7w9Czg+CdSV0IJjqGAosrh8eHu1/bgtpd2webySV0OUeKwVwEvXQW8fxfgdUpdDSUodh9Q1Owra8LtL+1Ahc0jdSlEiWvHP4GSr4BrXgSSh0tdDSUYthTQWfMHRDz50WFc8fRWBgKi/lBzEPj7/GC3AlEUMRTQWam3u7H8L1/gj5+e4AZGRP3J3Qy8thLY+H+An111FB0MBdRru4vrseCxT7Gnwi51KUQD19d/AdYtAZo55ZfOHkMB9crfPz2Cq//2FRpdAalLIaKyr4G/nQec+FzqSijOMRRQRFxeH77/j8/xu48K4RcFqcsholNaa4GXrgR2Pid1JRTHOPuAeqyoxoab/r4F5ewtIIpNAR/w3p1AXQFw0UOAjL/7KDJ8x1CPbNxbjEv+8AUDAVE8+Ppp4JXrADf/h6XIMBRQt57euAe3v3IAzgDfLkRx49hG4LlFgK1c6koojvBTnjoVCATw8+c/w+83VyAAjh8gijvVB4B/XACU75K6EooTDAUUVrPdgeWPv4/1Rx0AAwFR/LJXB6csHvtI6kooDjAUUIiC0pNY+tgG7K7n24MoIficwTEG+16VuhKKcfzUpw62fVuAFc9sQ6lLI3UpRBRNAR/w1g+Ar56WuhKKYQwF1O7dL/fg+y8fQGOAgYAoMYnAh78CPlsrdSEUo7hOAUEURby0cRt+u7kGbkEldTlE1Nc+fxjw2IFFD0ldCcUYhoIBzuv14Zn1m/DUbge8DAREA8dXfw4Gg6V/AAQOJqYghoIBrNXhxJP/2Yh1xwT4GAiIBp5d6wBBBix9UupKKEYwFAxQjbZmrH3hPbxZoYNfUEpdDhFJZedzgFwFXPyI1JVQDGAoGIDqG2144Pl38F61GX6BbwGiAW/7M4BMwTEGxFAw0NTWN+I3z72LD+qSEBDkUpdDRLHiqz8HWwwW3Cd1JSQhTkkcQKpq6/Gb597ChjoLAwERhdryBKcrDnAMBQNEZXUtfvfcW9hYa2WXARF17vOHga1PSV0FSYShYAAoP1mNR55/ExvqrfDJOKiQiLrx8b3A/telroIkwFCQ4EorTuLxdW9gY0MKvDK11OUQUVwQgXduB05slroQ6mcMBQmsoqoGf35xPT5sSIFbppW6HCKKJ34P8OpNQNW3UldC/YihIEHV1Dfgry+/iQ31SXDIDVKXQ0TxyN0MvHw10FQmdSXUTxgKElBDkw1/f/lNbKjUoEVulrocIopnLSeBl64CnI1SV0L9gKEgwbTYW/HsK29hwwkP6pVpUpdDRImg7ijwyg2A3yt1JdTHGAoSiNPlwrrX38WHh2pRocmRuhwiSiQlW4ENv5S6CupjDAUJwuv14d9vbcDGXcdRpMsHwF3PiCjKdj4b3ESJEhZDQQIQRRHrN3yC97ftQaFxAvz8ZyWivvLBL4DS7VJXQX2E3x4J4LNtO/Dup9tQaJgAl8jVComoD/k9wGs3Ac2VUldCfYChIM7tO3QMr/73QxzXjIJN5FoERNQP7NXAqzcCPrfUlVCUMRTEsbLKKvzrjf/imNeKalikLoeIBpKKXcB7P5e6CooyhoI41WhrxnOvvo2jdS4UKznTgIgksPcl4Ns3pK6CooihIA653R68+OZ72H+8FCcM4yBypgERSeW9O4HGYqmroChhKIgzgUAAb3zwMbbt2ofKpMlwBDiwkIgk5G4G3rwZ8PukroSigKEgznyxfTc2bt6GFutonPTppC6HiAgo3wFsXit1FRQFDAVxpLCkDG988DGcmhQc8qZIXQ4R0WlbngCKt0hdBZ0lhoI40dxix0vr30dNkwOH5MM5joCIYosYANbfCjgapK6EzgJDQRwIBAJ4/b2PcbjgBKqTJsARkEtdEhFRqOYK4N2fSF0FnQWGgjiw+aud2PzVDohpI1Hi4TgCIophR94Ddj4ndRXUSwwFMa6gqBRvvP8x5DoT9rrTpS6HiKh7G/8PqD0qdRXUCwwFMczWNo7A1mJHoToPbpH/XEQUB3xOYP0tQCAgdSUUIX7LxChRFLH+g004crwIYsZolLk1UpdERNRzJ/cFt1qmuMJQEKN27D2AzV/vRHJGNnbYk6Quh4gocp/+Fmitk7oKigBDQQyqa2jCmx9sgkwQsNefDS+7DYgoHrmagE/uk7oKigC/bWJMIBDA+g2bUFxRCV9KHio9aqlLIiLqvT0vA2U7pK6CeoihIMZ8vXs/vvxmNzIHZWGH3Sx1OUREZ0kEPriLgw7jBENBDKmuq8f6DZugUipwLJAJFxcpIqJEcHIfsItrF8QDhoIY4ff78eYHm1B2shr69BwcdnCRIiJKIJseBFrrpa6CusFQECO+2rUfX+3ci5ysQfiqOYl7GxBRYuGgw7jAUBADmppb8N+PP4dKpUSlLBW1XpXUJRERRd+el4DynVJXQV1gKIgBGz/bgpKKSqRlZmFHi1HqcoiI+ogIvM9Bh7GMoUBiBUWl+HTbDqSnpmBPqwVuDi4kokR2ci+we53UVVAnGAok5PX68M6Hn8He6oTSlIKjHFxIRAPBpgcAR4PUVVAYDAUS2rZrL/YcPILcwYOws8XEwYVENDA4G4FP7pe6CgqDoUAi9Y02/PfjL6DVaGCXG1Hs0kpdEhFR/9nzIlBfKHUVdAaGAols+HQLyk9WYfCgDOxoNkldDhFR/xIDwJYnpK6CzsBQIIETJeX44ptdyEhLQaVXi5Pc34CIBqJ9rwK2cqmroO9gKOhnoihi4+fb0GxvhdViYSsBEQ1cAS+w9Smpq6DvYCjoZwePFWLHvgMYPCgDhS4dGnxKqUsiIpLO7n8B9lqpq6A2DAX9yO/3Y+NnW+H1+GA0GLCHCxUR0UDncwJf/0XqKqgNQ0E/2n3gCPYdPobBWRkocmnQ7FdIXRIRkfR2PAs4m6SugsBQ0G/cbg82fLYFgiBAr9NiP1sJiIiC3M3AN/+QugoCQ0G/2b73WxwtLMaQrEyUudSo51gCIqLTvn4a8LRKXcWAx1DQDxxOFzZu3gq1WgWNWoV9doPUJRERxRZnA7BrndRVDHgMBf1gx74DKC6rxODMDFS5VajiugRERKG2/RnweaSuYkBjKOhjbrcHm7Zsh1qtglKpYCsBEVFnWiqBvS9LXcWAxlDQx/YcPIITpeXIzkxHvVeBMrdG6pKIiGLX1j8CAb/UVQxYDAV9yOfzYdOWbyCTyaFWqXCArQRERF1rLAIOvCl1FQMWQ0Ef2n+kAEcKi5CdmQ5XQMAJF1sJiIi6tY1LH0uFoaCPBAIBfLp1B8RAADqtBgUOHfwiX24iom5VfRu8UL/jt1QfOXy8CAeOFiB7UAZEETjcqpe6JCKi+LH3P1JXMCAxFPQBURSxedsOeDw+GPQ6VLjVXNKYiCgS377OAYcSYCjoAxVVNdh/+Bgy0pIBAIcdOokrIiKKM601wPFNUlcx4DAU9IGd+w7C1mJHktmEVr8MpRxgSEQUuX3/lrqCAYehIMpaHU5s2bEHFrMJgiDgSKseIgSpyyIiij9HN3D3xH7GUBBl+w8fw8maOqSnJEMUgWPsOiAi6h2fCzj4ltRVDCgMBVEkiiK27NgDmVwGpVKBSo8KrQG51GUREcWvfa9IXcGAwlAQRYUlZThyvBiZaakAgONsJSAiOjtlXwMNJ6SuYsBgKIiib/YeQKvDCaNeB58IFHOAIRHR2WNrQb9hKIgSW4sd2/d8i2SrBYIgoMSlgZcrGBIRnb19rwCiKHUVAwK/taLk28MFqKlvRFpyEgDghFMrcUVERAmiqQQo2SZ1FQMCQ0EUiKKIHfsOQiGXQy6XwxMQUM6uAyKi6NnHZY/7A0NBFJysqcPRwiKktrUSFLs08HNtAiKi6Dn0DuB1Sl1FwmMoiIJvjxSgqbkFSWYTAKCIXQdERNHlbgYKPpK6ioTHUHCWAoEAvtl7AFqtBoIgwBcQUOlWS10WEVHi4V4IfY6h4CyVVlShpLwSqclWAECFR8WuAyKivlD4mdQVJDyGgrN08Fgh7K0OGPXBhYrKOMCQiKhv2EqBugKpq0hoDAVnwe/3Y/veb6HX6SAIwdYB7ohIRNSHCj+VuoKExlBwFkorqlBxsqZ91kGdVwEH9zogIuo7DAV9iqHgLBwvKUOrwwG9LjjbgF0HRER9rOhLwOeRuoqExVBwFvYfOga1WsWuAyKi/uJtBcq2S11FwmIo6KX6RhsKS8pgtZgBAE6/DHVepcRVERENAOxC6DMMBb10vLgUTS0tsJiMAIBKjwoipyISEfU9hoI+w1DQS4cLiiBAgFweHFhYzQWLiIj6x8l9QGu91FUkJIaCXnC53dh/5BjMba0EAHDSo5KwIiKigUQETnAho77AUNALJ0orUNfQBKsluNeBOyCg0aeQuCoiogGEWyn3CYaCXjheXAaP1wuNOthlUOVRARxPQETUfyp2Sl1BQmIo6IUjx4ugVZ8eQ1DF8QRERP2r+iC3Uu4DDAURam6xo7TiJEwmQ/uxKo4nICLqXwEfULlX6ioSDkNBhEorq2BrscNsDIYCX0BAPdcnICLqf+xCiDqGggiVVlTB5/NBpQwGgRqvEgGOJyAi6n/lDAXRxlAQoYKiEqhUp1sG2EpARCQRhoKoYyiIgMPpQmFJWXvXAcBQQEQkmeZyoKVK6ioSCkNBBMoqq2BraYWJoYCIKDZUH5C6goTCUBCB0soqeDye9vUJ/CJg46JFRETSqT0qdQUJhaEgAiXlle17HQBAIwcZEhFJq+aw1BUkFIaCHhJFESdKyqHXaduP1bOVgIhIWrVHpK4goTAU9FCjrRmNtmYY9Lr2Yw0cT0BEJC12H0QVQ0EPVdfWw+5wQq/9TksBQwERkbTczYCtXOoqEgZDQQ9V1zUEFy36zhoFTew+ICKSXg27EKKFoaCHTlbXAsLpQYWegABXQN7FLYiIqF9wXEHUMBT00Imycug0mva/t/gZCIiIYkJTidQVJAyGgh5wOF2oqqmHXn96PEEzuw6IiGJDU5nUFSQMhoIeqKlrQKvDCYPu9MwDthQQEcUIG0NBtDAU9EB9kw0utxsatar9GFsKiIhihEShQBAEvP3225I8dl9hKOiBJlszRAAy2emXiy0FREQxwmUDXM1Ru7vVq1dDEAQIggClUon09HQsXLgQzz33HAKBQPt5J0+exMUXXxy1x40FDAU90NBkA8SOx1rYUkBEFDui3FqwePFinDx5EsXFxdiwYQPOP/983HHHHVi6dCl8Ph8AICMjA+q2vXASBUNBD1TV1ndYn0AUATtbCoiIYkeUFzBSq9XIyMhAVlYWpkyZgv/7v//DO++8gw0bNmDdunUAOnYfeDwe/PjHP0ZmZiY0Gg1ycnKwdu3a9vtramrCzTffjNTUVJhMJlxwwQXYt29f+/WFhYW47LLLkJ6eDoPBgOnTp+OTTz7pUNPTTz+NvLw8aDQapKenY/ny5e3XBQIBrF27FkOHDoVWq8XEiRPxxhtvRPy8GQq6IYoiqmrrOowncAVk3AiJiCiW9MO4ggsuuAATJ07E+vXrQ6576qmn8O677+K1117D0aNH8fLLLyM3N7f9+quvvho1NTXYsGEDdu3ahSlTpuDCCy9EQ0MDAMBut+OSSy7Bpk2bsGfPHixevBjLli1DaWkpAGDnzp346U9/igceeABHjx7Fxo0bMXfu3Pb7X7t2Lf71r3/hmWeewcGDB3HnnXfixhtvxOeffx7Rc2QbeDecLjeaW1qh0ZxuInIFmKWIiGKKvbZfHiY/Px/79+8POV5aWoq8vDzMmTMHgiAgJyen/botW7bgm2++QU1NTXt3w2OPPYa3334bb7zxBm699VZMnDgREydObL/Ngw8+iLfeegvvvvsufvzjH6O0tBR6vR5Lly6F0WhETk4OJk+eDABwu9343e9+h08++QSzZs0CAAwbNgxbtmzB3/72N8ybN6/Hz4+hoBsNTTY4XW6kWC3tx5wMBUREscVR1y8PI4oiBCG0pXj16tVYuHAhRo0ahcWLF2Pp0qW46KKLAAD79u2D3W5HcnJyh9s4nU4UFhYCCLYU3H///Xj//fdx8uRJ+Hw+OJ3O9paChQsXIicnB8OGDcPixYuxePFiXHHFFdDpdDh+/DgcDgcWLlzY4f49Hk97cOgphoJuNDW3hExHZEsBEVGMcdT3y8McPnwYQ4cODTk+ZcoUFBUVYcOGDfjkk0+wYsUKLFiwAG+88QbsdjsyMzOxefPmkNtZLBYAwN13342PP/4Yjz32GEaMGAGtVovly5fD4/EAAIxGI3bv3o3Nmzfjo48+wr333ov7778fO3bsgN1uBwC8//77yMrK6nD/kQ6EZCjohq25BYFAAArF6ZeKoYCIzsbDW9z41SY37pipwh8Wa1DcFMDQP9rDnvvaci2uHht+R1bhN+Gn4f1+gRq/OFcNt0/Ezf914Z0jXmQYZHh6iQYLhp3+LHt0qxultgD+dIk27P3ElX4IBZ9++im+/fZb3HnnnWGvN5lMuOaaa3DNNddg+fLlWLx4MRoaGjBlyhRUVVVBoVB0GGfwXVu3bsXq1atxxRVXAAi2HBQXF3c4R6FQYMGCBViwYAHuu+8+WCwWfPrpp1i4cCHUajVKS0sj6ioIh6GgG61OV8gxJ2ceEFEv7ajw42+7PJiQfvrHxWCTgJN3GTqc9/ddXjy6zY2L8zr/mD7zNhsKfPj+uy5cNUbZfh+7Kv346vt6bDjuw/VvOlF9twGCIKCoMYB/7PZi5636KD47CTmbonp3brcbVVVV8Pv9qK6uxsaNG7F27VosXboUK1euDDn/iSeeQGZmJiZPngyZTIbXX38dGRkZsFgsWLBgAWbNmoXLL78cv//97zFy5EhUVlbi/fffxxVXXIFp06YhLy8P69evx7JlyyAIAu65554OayK89957OHHiBObOnYukpCR88MEHCAQCGDVqFIxGI+6++27ceeedCAQCmDNnDmw2G7Zu3QqTyYRVq1b1+HkzFHTD4XCGHGNLARH1ht0j4ob1TvxjmRa//cLdflwuE5Bh6NhP/dYRL1aMUcKg6nymU4ah42fRO0d9OH+oHMOSgscP1/lx6SgFxqYFj/3iYzfqHCJS9QJ++L4TjyxQw6ROkJlUrqao3t3GjRuRmZkJhUKBpKQkTJw4EU899RRWrVrVYSG7U4xGI37/+9+joKAAcrkc06dPxwcffNB+7gcffIBf//rXWLNmDWpra5GRkYG5c+ciPT0dQDBUfO9738Ps2bORkpKCX/7yl2huPt0SZLFYsH79etx///1wuVzIy8vDf/7zH4wdOxZAcGBiamoq1q5dixMnTsBisbRPpYyEIIqi2P1pA9dL69/DB59txZi8Ye3HNjUkociVAM1tlPD23XsRzLrwTc9nKis8jP/8+UFkDBkOheL0bS5t/AcMgeitFjeQrXrbCatGwJOLNZi/rhWTMuT4w2JNyHm7Kv2Y9o9WbP2eDrMH9+y3W7U9gOwn7Xjhci2uHx/89/vbTg9e3O/Fxzfp8GGhD7e/70LFzw3497c+vH7Ii7ev1XVzr3FEYwb+t1TqKuIeWwq60WhrgVLR8WVys6WAiCL0ygEvdp/0Y8ct3TfXP7vHg9Epsh4HAgB4YZ8XRhVw5ejTt/neZCX2V/sx5mk7UnQCXrtai0YXcO9mFzav0uP/ferCKwe8GG6V4blLtcgyxfFnm7sluLJcmJkB1HMMBd2wtdihVHZ8mXwi33RE1HNltgDu2OjCxzfpoFF0/fnh9Ir497de3DM3slHjz+3x4obxyg73r5QL+MuSjq2aa95x4qczVNhT5cfbR3zYd5sBv9/qxk83uvDmijhuORADgM8NKENbXqjnGAq6IIoimlvsIS0FfoYCIorArpN+1LSKmPK31vZjfhH4osSPP3/jgfv/GSGXBT9X3jjkhcMLrJzYs24fAPiyxIej9QG8urzrbs3Pinw4WOPHP5dp8IuP3bgkTwG9SsCKsUr8eZ2jd08ulvg9DAVniaGgCx6PFy63O7SlQKJ6iCg+XThUgW9/2LHbYM07TuSnyPHLc1XtgQAAnt3jxaWjFEjV97wp/9k9XkzNlGFiRuczo1w+ET/6wIWXr9RCLhPgDwRb2wHAGwD8gQQYXub3Sl1B3IvjDqS+1+p0wuv1saWAiM6KUS1gXJq8w0WvFJCsDR4/5XhDAF+U+HHzFFXY+8n/sx1vHe74xdfsFvH6IW+ntznlwc+DLQOTM4OPd+4QOdYf8WJ/dbC14twhCfAb0e/u/hzqUgK8C/qOy+2Bz+9nKCCifvHcHg+yTQIuGh7+F//R+gBs7o6/6F854IUoAteN67y74UCNH68d8mHvD063Viwfo8DmYgXOe74Vo5Jl+PdVcTye4BS/R+oK4h5DQRf8fj8CgUDInFSGAiI6W5tXh85C+N2FGvzuws77xMX7TCHHbp2qwq1Tu24lGJcmR8FPOi50JBMEPL1Ei6eXJND0anYfnDV2H3TB5/MjEBAhk3UMAQwFREQxiC0FZ42hoAu+Uy0FwhktBRLVQyQNhmCKEwwFZ42hoAs+nw8BsWNLQXC0Lj8kaeBwCwnUvEyJTeBX2tniK9gFX5gxBYIACEiAqTs0ILR6zn4CbYvccvaFEPUHWc/XdqDwGAq6cCoUCGcsm8kXjeJFcX1r9yd1g6GA4oa86wGX1D3OPuiCz+eHIAihoUAQ43qwYdOWl2Hb+p8OxxTWbGTd8gz8zhbYtrwMZ/Ee+JtrIdOaoRt5Dizn3QiZuvM125u2vIzWw1/C31ILQaaAKmMELHNXQj1oFABA9HlRv/EpOAq+hlyfBOtFt0ObO6n99rbtb8LfXAvrwtv65DkPVMV1Dswefnb30SJPik4xRH1NzpaCs8VQ0AW/P/yQwkRoKVCmDEH6NQ+dPtDWReK318Nvb0DS+d+DMnkIfM01aPjwL/C31CP1is634FRas2BdeBsUlgyIXjdadr6D6lfvQdYP/gG5zoyWfRvhqTqOjBsfg/PELtT991Fk//glCIIAb1MV7Ps+ROaqP/Txsx54ImkpECAAEE4vc9emRcZQQHGCLQVnjaGgC53tKi0IIuJ+WIFMDrkh9MNelZrb4ctfmZQJy9yVqHvvMYgBPwRZ+EVV9GPmd/h70gU3w77/I3hqiqDNnQRvfRm0I2ZClZoDhSUDTZufQ8DZDLnOjIaPnkbS/NWQqRNg8ZQYU1TX81CgNRihVKnh9bihUJ7+cGX3AcUNhoKzxlDQBblMFva7PxFaCnyNlSj/y0oIciVUWflImrcKClNa2HMD7lbIVLpOA8GZRL8XLXs3QlDroUobCgBQpQ1F64HPEPC64SraDbnBCpnWBPvBzyAoVNCNnB2150anFUcQCgxmK9QaLTwuJ7R6Y/txj0wLt6CBWnT1RYlE0cPug7PGUNAFQSYLO/lQJsR3M4E6cxSSL7kTSmsW/PYG2Lb+B1Uv/xKDvveXkF/rfocNtm2vwDBpcbf36zj+Dere/T1ErxtyQxLSr3kQcp0ZAGAYvxCemmJUPns75FoTUi77JQIuO2xbXkb6dWvR+MWLcBz+AgpLBpIvuQMKY0qfPPeBpqTBEXYBrnDUGi2M5iTU15wMua5FngS1L/Q4UUxhS8FZS4QfvV3avHkzBEFAU1NTxLeVy2UQRTGkGyHeXzTt8GnQ58+BKm0otMOmIu3q+xFwtaL1yJYO5wXcDtS88Rsok4fAcu713d6vZsgEZK55Chk3PgrN0KmofecR+FubAACCXIHki36I7NueReaqJ6HJHovGT5+FceoyeKpPwFnwFTLX/AnqQflo/OTvffG0BySPL4BKm7PH5yenZ8HjDj2/RWaJYlVEfUCm5LbJURDR99vq1ashCAIefvjhDsfffvvtkBH6iUAuk4d9Xso4byk4k0xjgNKaBV9TZfuxgNuBmtfuhUylRdqVv4Yg775RSabSQJk0COqsfKRccgcEmQz2/R+FPddVsh/e+hIYpyyFq3Q/tMOmQabSQJc/B67Sb6P23Cg4A6GnktIyEfCFDrDlDASKeVqL1BUkhIh/9Go0GjzyyCNobGyMWhEeT2wuTSmTCZAJAgJn7DOukgUkqqhvBDxO+JpOQq63Bv/udqD6tXsAuQKpV90DQdHLJjlRhBhmgxLR50HDx39F8qIfB8cpiAGIgbYvooAfophYr6/UIpmBYDRbIYYJvRxsSDFPy+AaDRGHggULFiAjIwNr167t9Jw333wTY8eOhVqtRm5uLh5//PEO1+fm5uLBBx/EypUrYTKZcOutt2LdunWwWCx47733MGrUKOh0OixfvhwOhwMvvPACcnNzkZSUhJ/+9Kcdpgq++OKLmDZtGoxGIzIyMnD99dejpqYm0qcVllwuBwQBIhIrFDR++ixcpd/CZ6uGq/wwatc/BAgy6MfMCwaCV++B6HUj+eI7ILqd8Nsb4bc3nv7iBlDxj9vgOLYNABDwuND4+QtwVxyBz1YDd9Vx1H3wB/ha6qEbNSfk8Zu2vQLtsGlQpQcn0KuzxsBxbBs8NUVo2f0eNFmj++eFGCAiG2yYBJlMDr+v40qIbCmgmMdQEBURDzSUy+X43e9+h+uvvx4//elPkZ2d3eH6Xbt2YcWKFbj//vtxzTXXYNu2bbj99tuRnJyM1atXt5/32GOP4d5778V9990HAPjyyy/hcDjw1FNP4ZVXXkFLSwuuvPJKXHHFFbBYLPjggw9w4sQJXHXVVTj33HNxzTXXAAC8Xi8efPBBjBo1CjU1Nfj5z3+O1atX44MPPjiLlyVIJgiQyQSIARH4zsB7VZx3H/ha6lD330fhdzZDrjVDnT0GGTc9DrnODFfpfnhOHgUAVP79lg63y7rtWSjM6cH7aChHwB1slhZkMngbylH79qa2+zRBlZGHjBsegSo1p8N9eGqL4TjyJTJX/6n9mC7/XLjKvkXVy7+EMjkLKct+0ZdPf8CJqKXAkgy1um0GguH0DASOKaCYp7FIXUFCEMTOJuOHsXr1ajQ1NeHtt9/GrFmzMGbMGDz77LN4++23ccUVV0AURdxwww2ora3FRx+d7kv+n//5H7z//vs4ePAggGBLweTJk/HWW2+1n7Nu3TqsWbMGx48fx/DhwV+Qt912G1588UVUV1fDYAjuBb548WLk5ubimWeeCVvjzp07MX36dLS0tMBgMGDz5s04//zz0djYCIvFEtGLU1BUit8+9Q8MycqAWnW6Cf1rmwkHWg1d3JIodgxP1WPTXfN7dK7H7cI/f3cXAMCSkt7huisbnoZG7PmgRaJ+NeFa4Mq/SV1F3Ov1QPpHHnkEL7zwAg4fPtzh+OHDh3Huued2OHbuueeioKCgQ7P/tGnTQu5Tp9O1BwIASE9PR25ubnsgOHXsu90Du3btwrJlyzBkyBAYjUbMmzcPAFBaWtrbp9ZOrVJCLpPB7+/YXaCJ8+4DGljKGpzwB3qW/VVqDUxJyfC4Q9ck4LgCimnsPoiKXoeCuXPnYtGiRfjVr37Vq9vr9aHr6CuVHReeEAQh7LFAIPil3NraikWLFsFkMuHll1/Gjh072lsfojF4Ua1WQaGQhyx3zFBA8cTjD6CyKbJpiW5X6IwFjiugmMZQEBVntXjRww8/jEmTJmHUqFHtx0aPHo2tW7d2OG/r1q0YOXJkcOBeFB05cgT19fV4+OGHMXjwYADB7oNo0ajVUMjl8J4x6IqhgOJNUV0rBlt7tox0UmoGAv7Q9zj3QKCYZsqUuoKEcFbr8IwfPx433HADnnrqqfZjd911FzZt2oQHH3wQx44dwwsvvIA///nPuPvuu8+62DMNGTIEKpUKf/rTn3DixAm8++67ePDBB6N2/xpVsKXAx5YCinORDDY0mK0It+wIuw8oplmGSF1BQjjrxfkeeOCB9uZ8AJgyZQpee+01vPLKKxg3bhzuvfdePPDAAx1mHkRLamoq1q1bh9dffx1jxozBww8/jMceeyxq969SKaFSKuE7YzEXvTz87olEsSqSjZFMFitkMjl8vo5rTLD7gGIaQ0FURDT7YCD6zZPPoLyqBjlZp5umAiLw/MlMiGF3RiCKPeePSsXza2b06NyGmpP41xP/DwZzEnQGU/txhejBioY/dXFLIokIMuD/1XBDpCiI92X8+5zZaIDP23FMgUxgawHFl+L6ni91bLSc3i3xu3yCCk6B21tTDDJmMhBECUNBN8xGIzxnDDQEAANDAcWR8kZHj6clKlVqGC3JcLvCbIzELgSKRew6iBqGgm6YTYawI7GNDAUUR7x+EeWNPW8tSMnMDmkpADjYkGIUQ0HUMBR0w2TQh90pkS0FFG8iGWxoSU6HGOC0RIoTlpzuz6EeYSjohtlkBCDizPGYRkVolwJRLItkYySjxQoxzPueLQUUk9hSEDUMBd2wmIxQKhTweDtOz2JLAcWbSAcbyhUK7pZI8YGhIGoYCrphMuqhUavgdndcNpljCijeRNJ9YDRbodboQpY7trOlgGIRQ0HUMBR0w2I0Qq1Ww3VGKDDI/VAKXNmQ4kekqxqq1Bp43KHTEh1C6L4lRJIR5IA5W+oqEgZDQTfUahUsJmNIKBAEwMJxBRRHyhud8IaZSROOUqWC2ZoKjyvcbonsQqAYkjaaaxREEUNBD6SnWOH2uEOOW5XeMGcTxSZ/QERZQ8/HFSRnZMMTdrdESxSrIjpLWVOkriChMBT0QFqKFR5PaABIYksBxZlIuhAsyWkQwyx4xJYCiilZ06SuIKEwFPRAktmEcNvGsaWA4k1RXWQzEMJOS+RaBRRLsqZKXUFCYSjogdRkK2SCAP8ZWyhb2VJAcaYkgpaC4LREJfwhuyVaolwVUS8p9cExBRQ1DAU9kJqcBL1Wi1Znx0FXGnkAWhmnJlL8iGRaosEc3BjpzD0Q7HILuLUqxYRBkwCZXOoqEgpDQQ+kWpNg0GvhcISuBc8uBIonkYwpMJqtUIXZLdEvKOGUGaJdGlHk2HUQdQwFPaBUKpCVmY7WMKEgmaGA4khlkwseX8+mJSqUSliS08LvlshxBRQLGAqijqGgh3KyMuH2eEKOp6tCjxHFKn9ARGkE0xJTMrLhdYdbq8ASxaqIeimbMw+ijaGgh9JTkiECISOxg6GAPawUPyLZGMlsTYUoho6b4bREkpwhgysZ9gGGgh5KTU4KuzGSRibCzFkIFEciGldgsUIUQ8MwWwpIcuw66BMMBT2UlmKFXqeFwxnalMouBIonkW6MpFAo4TsjDHNMAUmOKxn2CYaCHkoym5CSZEGzPfQDlaGA4klELQVJyW0zEDqOQ2jhtESSGscT9AmGgh4SBAGj84aitTV0kBZDAcWT4ghWNTSYkqDW6kJ2SwwICjhkxmiXRtQzggwYxJaCvsBQEIEhgzIBhPavWhR+aLiIEcWJkzYn3L6evV/lCkXn0xI52JCkkpwHaExSV5GQGAoiMHhQOrQaNVqdoR+QbC2geBEQgdL6CHZLTO9kWiLHFZBUhpwjdQUJi6EgAoPS02A2GdHSEtonm6UO3VqZKFZFMtjQkpwKUQxd8IgzEEgyoy6RuoKExVAQAaVSgbzcIWEHG2YzFFAc4bREilsqAzBsvtRVJCyGgggNzx0MX5j+WJPCD5Oc6xVQfIhkC2WD2QqlUgWft2MXGbsPSBLDLwCUGqmrSFgMBRHKzkyHUimHxxO658FgTWi/K1EsimRVQ5MlOC0xdLdEMwIQol0aUdfyl0pdQUJjKIjQ4Mx0mAwGNLW0hFzHLgSKF5F0H+jNbdMSXZyWSBKTKYCRi6SuIqExFETIaNAjb+gQNNlCQ0Gm2g05l3ShOFDV7ILL28NpiXI5klIyQkIBwGmJ1M9yzgW0FqmrSGgMBb0wduRweL3ekIFXCiEYDIhinShG1lqQnJ4FT9hpiZYoVkXUDXYd9DmGgl4YkTsEOq0GrY7QX07sQqB4EdFuiZ1OS2RLAfUXAchfInURCY+hoBcGD0pHekoyGpqaQ67L4WBDihORzUAIfvmHTktkKKB+MmgSYM6SuoqEx1DQC3K5HBPHjIS9NfSXllHhR6qSqxtS7CuJZK0CsxVKlRpeT8eWMK5VQP2GrQT9gqGgl0YMzYEgCPD5QtcmGKYN7VYgijURbaFsSYZKrQnZGMku47RE6iccT9AvGAp6aXhONixmIxqbQ2chBEMBZyFQbItoWqLJAo1OD88Z+36IghytMm5MQ33MOhxIGy11FQMCQ0EvWUxG5OUOQWOYcQV6eYAbJFHMq252w+Hp2Sqcp6Ylut2clkgSyOdeB/2FoeAsTBg9El6vF4FA6KjsYRxwSHGgOILBhtb0rPC7JXJcAfU1dh30G4aCszBm5HCYTUY0helCGKp1QmAXAsW4SLoQLNbU4AIHZ+AeCNSnDBlA9gypqxgwGArOQnqKFSOH5aCuoSnkOp08gAx2IVCMi2SwocGSBBGclkj9bMpKQMavqv7CV/osCIKAqeNHw+vzhe1CGM5ZCBTjIlnAyGhJ5rRE6l8yBTDte1JXMaAwFJyl0XnDYDEa0GgLHXA4TOuEUggNC0SxIpLuA6PZCpVGG7IHQqvMjAA/Sqgv5C8BTJlSVzGg8P/ks5SWbMXovGFhuxBUMpFrFlBMi2RVQ73JAq1WH7KFsijIYJeZo10aETD9FqkrGHAYCqJg8th8+Px++P2hrQL5up5/6BL1tzq7G3Z3z6YlymQyJKV2tluiJcqV0YCXOhoYep7UVQw4DAVRMGbkcCSZTWiw2UKuS1V5kcxljymGRTKuIDkju5NpiRxsSFE2/ftSVzAgMRREQXKSGePzR6CuvjHs9WwtoFgWybgCU1IKwq3WyZYCiiqVEZh4rdRVDEgMBVEyY9J4CIIAtye0VWC41gkFBxxSjIpsBkISACFkWqKdaxVQNE28FlAbpa5iQGIoiJJxI4dj8KAMVNXWh1zHAYcUyyIZbNg+LfGMLoRmthRQNM3gAEOpMBREiVqtwrnTJ6GlpTXkVxQAjGYXAsWoiKclarUhMxAcMhP8/DihaBg6F0gdJXUVAxb/L46iyWPzYTGFX7MgVeVFhsod5lZE0oqk+0BnNEOj1YdsocxpiRQ1nIYoKYaCKBqUnorx+XmoDtOFAAATDPZ+roioe/WtHjS7vD06VyaTwZo2KKSlAADsnIFAZ8uUFVywiCTDUBBFgiBg5pQJkHUy4HCw2g2LomcfvkT9KbJpiVnwukNbvTgtkc7a1DWATC51FQMaQ0GUjRs1HFmZ6WFbCwSBrQUUmyLZGMlkSYYghI6b4WBDOityFTB1ldRVDHgMBVGmUasxZ/okNLe0drpJkk7ml6Ayos4VRzQDwQqIodMSuYUynZXxVwOGNKmrGPAYCvrAjMnjkWw1ozbMYkZyARjH1gKKMRHNQLBYoVSr4TljWiK7D6jX5Cpg3i+lroLAUNAn0pKtOHfaJNTWN4Sdnpivc3D3RIopkYQCg9kKtUYLj6tj64JDZoQf7A+mXpiyEkjKkboKAkNBn5kzfTLMnWyprJKJGK3v+YcwUV+LZKCh3miGRm+Ex3XGHgiCwOWOKXJKHTD3f6SugtowFPSRwYMyMHXCWJysqQt7/QR9K1sLKGY0OrywOXo2M0YQBFhTM+F2hY5DsMssUa6MEt6MWwBjutRVUBuGgj4iCALmzpwCjVqFFnvorzCNPICxbC2gGFIUQRdCckYWfN7QabdsKaCIqM3AuT+Tugr6DoaCPjRyWA7G5+ehoqom7PXjDXao2FpAMSKSLgSTJRnhd0vkYEOKwOwfAzqr1FXQdzAU9CGZTIZ550yDIAhwntn/CkAtEzGeMxEoRkSyVoHRYoUoImTaLUMB9ZguBTjndqmroDMwFPSxCfl5GDU8B2WV1WGvH6dvhYbrFlAM6M0MhDN3S2zhmALqqfN+DqgNUldBZ2Ao6GNKpQIXnTcLYiAQtrVAKRMxma0FFAMi6j5ISoZKHW63RCN8UES7NEo0pixg2velroLCYCjoB1PGj8bovGEoq6gKe32+vhVGua+fqyLqKJLuA63eCK3BGLJbIgQBdg42pO7M/QWg1EhdBYXBUNAPFAoFLpo3GyKAVmfo7nJyAZhmCl3PgKg/Nbt8aGgNnVEQjiAISE4fBE+Y3RI5A4G6lD4uuFgRxSSGgn4yacxIjM/P67S1YLjWhQxV6M5zRP0pktYCa9ogeMPsBso9EKhLlzzKnRBjGENBP1EoFFg8fzYUCjmaw6xbAACzzDYIYaZ5EfWXkoj2QEgO+35lSwF1avzVQM5sqaugLjAU9KNxo0ZgyrjRKKusCrsnQrLSh9G6nu9WRxRtka1VYAUEgdMSqUdElRG46LdSl0HdYCjoRzKZDBefPwcGnTbsnggAMNXUzCmKJJmi+p6HUoPZCpVaGzLYkNMSKRxh3i8AY4bUZVA3GAr62YjcwZg1ZQIqq2pCfmEBwQWNphlbJKiMKLKWAqMlGSqNJmSwoVNm4LRE6kBMGcmFiuIEQ0E/EwQBl1w4F2kpVlRW14Y9Z5TOgRRlz0aBE0VTJKFAqzdAZzCHzkAQBHYhUAfCxY8AcqXUZVAPMBRIICM1GRefPwe25hZ4PKE70wkCMNtsQ7i15Yn6Uovbhzp7z2bBnJqWeOYCRgAHG9J3TLwOGH6B1FVQDzEUSGT+rGkYNWIoissrw16fpvJy0CFJIpLWAmtaZvjdEjktkQCIhnRg8Vqpy6AIMBRIRKvR4NKF8yAIAppbwi9zPMPUDANXOqR+FslaBaaklLDH2VJAACAsfRLQMiDGE4YCCU0aMwozJ49HaUX4KYpKmYi5liawG4H6U2QbIyVBEAQE/B1nzHBMAYnjlgP5S6QugyLEUCAhmUyGSxfOgzXJjKra+rDnDFJ7kM9uBOpHxXU9f7+ZLMlQabTwnLlbIkPBgBbQJkO45FGpy6BeYCiQWHZmOhbNnYX6hiZ4vKGDDgF2I1D/ingL5TBrFbhkengFVbRLozghW/oEoLNKXQb1AkNBDFhw3jkYnTcUJ0rKw16vkomYY7b1c1U0UEUy0FCj00NnNIWfgcBFjAakwOjLgLGXS10G9RJDQQzQ67RYvmQhtBo1ahsaw56TrXFjpK7nH9ZEvdXq8aOmxdX9iTg1LTGbuyUSAMBvGATZpU9JXQadBYaCGDEmbxgWnncOqmvq4PWG7yo4x9QMI7sRqB9EMq6g02mJHFcwoIiCHPIV6wCtRepS6CwwFMQIQRBwyQXnYdTwoThR2nk3wgVJjZBxNgL1sUiXOxbCHGf3wcASmPs/wJCZUpdBZ4mhIIYY9DosX7IAapUSdQ1NYc9JVXkxzRR+MyWiaCmKaAvlJECQcVriAObJnA75vP+RugyKAoaCGDNu1AhcOGcmTtbUwusL31UwXt+Kweqe9fkS9UakLQVqTZjdEhkKBgSv0gjVdS8CMn6dJAL+K8YYQRCw9MK5GDUsp9PZCIIAzLM0QcctlqmPRLKqodFshUqjDZmB4Jbp4OG0xIQmQoDsir8BpkypS6EoYSiIQUaDHlcvvQgatQrVnSxqpJEHcH5SIwSOL6A+UFLvCLvKZjhqrQ4GkyX8DATugZDQ3FNugXwMVy1MJAwFMWp8fh6WLZiHusYmOJzhuwoy1R5MNrb0c2U0EDi9flQ393y3RGvaoJDuA4DTEhNZa9pUaJY+LHUZFGUMBTFs8fxzMXPSOJwoKUMgEAh7zmSDHVkcX0B9IJIuhOT0QfCFWZGT4woSk0uVDN3qNwCZXOpSKMoYCmKYSqXEdZdfjCFZmThRWhH2HEEALkhqhInrF1CUlUS43HHYaYkMBQnHByVkN7wKgcsYJySGghiXnpKMay5dDJkgdLraoVom4iJrA5RC+NYEot6IdFqiIJPBf+a0RK5VkFBEAK0LHoYqZ7rUpVAfYSiIA1PG5ePi889FTW09XO7w/bwWpQ8XcOAhRVGk0xJVam3IYEM7xxQklNq8a2Gec7PUZVAfYiiIA4IgYNnCeZgyfjQKizsfXzBY48Y0DjykKIlkqWOj2Qq1VguPq+NtgtMS1dEujSRQmzQFqdf9VeoyqI8xFMQJjVqN6y+/BFkZ6SgsKe90uthEox0jtD3/MCfqTElDa0TTEvWmJLjdoYNeOS0x/tWph8Byy9sQuEBRwuO/cBzJykjDTVcthUatQmV1bafnnWdpQqoydIMaoki4vAGctPV8ZktKelZISwHAaYnxrkmWBNWqt6HUmaUuhfoBQ0GcmThmJK5eshCtrQ402sLvgSAXgIXWBu6oSGctknEFSWmZ8IdZmpszEOJXq6iF+8p/wTRouNSlUD9hKIhDF5w7AxfNm42KkzWdLmykkwdwcXI9tFwKmc5CRDMQzFaEm5fIUBCf3KIC1fMfQ/q4uVKXQv2IoSAOyWQyLF+yAOdMGY8TJWXwdbJxkknhx6Lkek5VpF6LbAaCFTJBDr+/4/uR0xLjj18UcHzMnRg6/wapS6F+xlAQpzRqNW66ahlGDstBQVFppwPCUpQ+LLQ2QM6pitQLRZHMQLAEN0Y6c1piM1sK4kpABA4NvhH5y38FQQi3JBUlMoaCOJacZMaqqy9FitWCok5WPASAQWoP5nMNA+qF4ogWMApuoXzmbolemQYuQRPt0qgPiCKwL+VyjLrpMcjlXMJ4IGIoiHPDcwbjxiuXQqGQo6yyqtPzhmpdmG229WNllAhKGxwIBHoWJlVqDYzmpLC7JdrZWhAXdpsuwqg1f4ZKzRA3UDEUJIDpE8fi+ssvgcfj7XSrZQAYrXdgqjH8jAWicDy+ACptoV/ynUnOyOYWynHqG/V5GLHmaegMRqlLIQkxFCSIeedMxfIlC9Bka0Z9Y+ctApONdkxhMKAIRLKyYVJqBgL+0BkvXKsgtm2XTUfuyr/AbE2VuhSSGENBghAEARefPwfLFs5DdV0dmlvsnZ47xWhniwH1WOTTEkO7GxgKYtd2cSIyr/sj0rJypC6FYgBDQQKRyWS48uILsXDOLJRWVKHV0Xmz72SjHdMYDKgHIp2WKMjkIYsYca2C2PR1YCJSr30KQ/LGSl0KxQiGggSjUChw3eWLcd7MKThRWt7prooAMMlox3QGA+pGJKHAYLZCrdHBfcZyxxxTEFtEEdjsn4rUa/6AYaMnSV0OxRCGggSkUaux8qqlmD5xLI6fKO0yGEw02jHDxFkJ1LlIug9MSclQqTUhgw29MjVcgjbapVEv+EUBH/lnYtDVD2P42ClSl0MxhqEgQRkNetxy/VWYNnEsjhd1HQwmGFoxk8GAOlHW4IC/h9MSlSo1TEnJIWsVAOxCiAU+UYb3fbORc9VvMHLCDKnLoRjEUJDAzEYDbrnhKkyfNA4FJ0rhdHUeDMYbWjHXwgWOKJTXL6KiMcJpiW6GgljjFhV4y3MeRlzxf8ifPEvqcihGMRQkOLPRgFuuuxIzJ49DYXEpnK7Ot8IdqXNikbWBeyVQiEi6EJJS0hHwh76HuAeCdBwBFdZ752HMVf+DMdPmSF0OxTCGggHAZDTg5uuuxIzJ43G8qKzLYJCtceOS5HpouLsifUdkMxCSgTAtTmwpkEat34jXA4sx9dr/xVgGAuoGQ8EAYWprMZg1dQIKi8s63XIZAFJVXlyaUgeTPPzuizTwRLQHgjkJcrkCPp+3w3GuVdD/jnvS8K58Kebe+AuOIaAeYSgYQIwGPb5/7RU4Z8oEnCgpg72185XqTAo/lqXUIVXp6ccKKVZFOi0x3G6JbCnoX9ududisX4ZFK+/E0PwJUpdDcYKhYIA5NSvh/NnTUVJeiUZb5+sUaOUBXJJcj8HqzlsVaGAoro9sC2V1mFDgE1RwCrpol0Zn8EOG91rG4lDyYixd+RNkDxsldUkURxgKBiC9Tos1Ky7H0gVzUV1Th5q6zjdRUspEXGRtwHh958smU+Ira3DAF2bwYDjBaYkpnJYoARc0eLlpChqzL8Slq36KjMFDpS6J4gxDwQClUilx7aWLcc2li9Fsb0VZZRVEMfx0REEAZpqbMc/SCDmnLA5IvoCI8gimJaZ0tlsixxX0mUZY8FzDNBjGXYLLVv8MyelZUpdEcYihYACTy+VYumAuVl99GSCKOFFS3mkwAIA8nRNLUuqg58yEASmSaYnm5DSIgTDTEtlS0CdKxEysa5yOvLlXY8mNt8OUlCx1SRSnGAoGOEEQMH/WNNxyw3IYDTocLSxGIMyH+SlpKi8uT61FhqrzhZAoMUUy2NBksUKEGBIyuQdCdAUgYLsnD285z8Gcy9fggituglrD5aSp9xRSF0CxYdqEMTDqdXjulbdxuOAERgwdArVKFfbcUwMQv2k24UCroZ8rJalENAPBYoVcoYDf54VCefp9xO6D6LELRrxjG4Nm8yhccu1NnHJIUcGWAmo3angufvr96zE+Pw/HCkvQYu/8S0AmAOeYm3FBUgNUXAFxQCiKaAZCcttuiZyW2BeOK0bimdpp8GVNx+VrfsZAQFHDUEAdZGWk4SdrrsOFc2ai7GRVlzMTAGCY1oUrUmuRzu6EhBfRqoZma9jdEv2CEg6BrUu95RY02CQ/H6+dzMHQiefiijV3InPIcKnLogTCUEAhjAY9vnfNZbh22WK0OlwoLCnrcpyBUeHHkuR6TDa0cEOlBFbR5IS3h9MSFUolLMlpnWyMZIlyZQPDScUQ/Nu3GDvrNJg672IsvfFHMFlTpC6LEgxDAYWlUCiwbOE83HbT1bCYjDhcUASPx9vp+TIBmGpqwZLkeui5PHJC8gdElDX0vAvBmp7VybREdiFEwgcFdqjPwwu1Y9DsU+L8y27EgqtWQ63lQlAUfRxoSJ0SBAHTJ45FRmoyXnjjvzhwpACDB2XAbDJ2epsMtQdXptbiyyYLil0cBZ1oiutbMSy1Z83/luQ0BMJMcWVLQc81ylPxsTgbx8sdGJKXj/nLrkP28Hypy6IExlBA3Ro8KAN3fO96vP7+x9j81U40NrcgJysTgiCEPV8tE7HA2oijDhe228zwiGyQShRFdZEtdwwxOC3xu+8VTkvsXgACDqun4MO6TIgCcM7CyzBr4RXQ6jkeg/oWQwH1iNGgx+qrL8XIoTl444OPcfBYIUbkDoZGre70NqN0TgxWu7HVZkYJWw0SQmRbKFuhUCjh83qhVHFaYk+1yozYLJ+HfeVupA8ehLmXrMCI8dM6DeFE0cRQQD0mk8kwZ8Zk5A4ehH+/vQF7DhxGWkoyUpM7/+Wnkwew0NqIE04nvrKZ4QzI+7FiiraItlC2JAd3S3Q7O4QCu9wCEQC/4joKQMAJ9Vh8ZBuOFqcXE2ZdgLmXrOBgQupXDAUUsezMdPz0e9fhvU++wMbNW9Foa8bwnGzI5Z1/4Q/TujBI7cZ2mxkFTg6QildFkSxgZEpq2y3RAb3R3H7cLyjhkBmgD3CTrVOqFdnYrpiFA+U2WFKMWHzZcoybMbfL/6eI+gJDAfWKRq3GVZcswIjcIXjl3Y04VHACOdmDYDLoO7+NTMS8pCYM1zqxxWaG3c+3X7ypbHLC7fNDrej+y0qhVMKcnI6TpYUh17XIkhgKANhlZuzWnYdvm01orq5D3vhpmL/sOqQOGiJ1aTRA8VOZek0QBEwaOwrZmel44/2PsG3XftTVNyInO7PLXzjZGjeuUtVir92AA3YD/GxIjhsBMbiN8oi0zmegfFdKRhZKCw6EHG+RJyHDVxbt8uKGV1DhoHYGDsgnoLy0CFqdF+dfdiOmzl0MparzcTpEfY2hgM5aitWCW29Yjklj8/HWhk9x+NgJZA1KR5LZ1OltlDIR000tyNc5sL3ZxOmLcaSoruehwJKcBlEMt1uiJcpVxYfguIFx2KedjdpmFxqqCzBkxBjMu/Q6DB4+WuryiBgKKDpkMhnOmTIBI4fl4J0PN+Pz7TtR19CEoYMHQaHo/G1mVPixwNqISncrvraZ0eBT9mPV1BsRbYxkToIoInRa4gBcwKhaMRi79PNR5dGj5kQJ1BotZi64DLMWXg6doWchi6ivMRRQVFktZqxecSkmjh2J9Rs24fDxImSmpSLFaunydoPUHlyeWosjDh12txjh4iyFmBXpDASFUgWf19OhWXwgrVXQIrNgj24uimW5qCkvRsDfgLzxUzHzgmXIGjaKUw0ppjAUUNQJgoAp40ZjRM5gvLfpC3y6dQfqGhqRkz0IWk3n/aUyARijd2C41om9LUYccujg58JHMSeyUHB6Y6TvhgK73IwABMgSeK8Mj6DCQe05OKyahLqaatibj2BQ7gjMvGAZRk6YAXkXLWhEUuG7kvqMyWjAdZddjIljRuHdjzbjwNHj0GjUGDIoo8uBiGqZiJnmZow32LHXbsDRVj0HI8aQ4ghWNTSYkqDR6uF2OaE3WdqPBwQFnDID9IGWPqhQWl5BhUL1eBzQTEddsxMNJYdhSc3AgitXYcI550Oj63yGDpHUGAqoTwmCgLEjhyMvdwi+2r0P72/6EocKTiAt2Yq0FGuXTac6eQCzzc2YYLBjb4sRxxw6BBgOJFdpc8Ll9UOj7L6LR65QwJyShsrigpDrWuRJCRUKHDIDjmom47h6IlpcXtQUFkOj02P6+Usxbf7FSEpJl7pEom4xFFC/UKmUmHfONEwcMwqffPk1Nm3ZjkPHCjE4K7PLtQ0AwCAPYI7FhokGO/bYDShw6CAyHEhGFIHSBgdGpvdwWmJ6NoqPfBtyvEWWhAyURru8ftcoT8Vh7TSUqEbB6/OjprQEAb8fIydOx4wLliFr6EiOG6C4wVBA/cpiMmL5koWYPnEs3tv0Jb7ZewBVNbUYkpXZ5T4KQHCmwty2cLDPbkChQ8duBYkU1bX2OBSYk1MBJN60xJPKHBzWTEOVKheBQAANVRWwNzcha+hIzLxgKfImzOCKhBR3GApIEjnZg/DDm67GrKkT8P6mL3G0sAgKuQKDszKg/s46+eGY28LBNGMLDrfqccihg5uzFfpVRBsjmYMzDRJhWqIfcpSo83FYMxU2RSpEUYStrhpNddVISs3AwuVrMGHmfKi1XMqb4hNDAUlGJpNhyrjRGDdyBHbuP4iPvvgKBUWlUKmUGJyZAZWq6zULdPIApppaMNHYggKHDgfsBti4dHK/iHhaoiLMtMQ4ainwCGoUqCfgmHYKnDID/H4fGqvKYW9qgDEpGTMvvBRT5y2GJTlN6lKJzgo/QUlyKpUSs6dNwtTxY/DN3gP46IuvcLykFFq1GtmZGVAqu36bKgRgtN6BfJ0DpW41vrUbUOXhUrF9KZKNkYxmK1QaLdwuR8dpiTJLzE9LtMtMOKqZgkLNePgEFbweN+qrT8DtaIU1LRPTLr0eo6fMRlJqhtSlEkUFQwHFDLVahfNmTsG0iWOwfc8BfPTFNhwrKoFOq0FWelq3LQeCAORo3MjRuNHgVeCoQ4fjDh3cXOsg6iKZlqg3J0Gt1cHjcgHfWfk6IMjhkJlgCNj6oMLe8whqlKnyUKzOR7ViCCAIcDnsqKsqgigGkJ6Vi4mzLsDIiTM67P5IlAgYCijmaDUazJ81DTMmjcPXu/fjky+/RmFJGRQKBQZlpEKv7X6fBKvSh1nmZswwNaPYpcHRVj0qPSqAAxOjorrFBafHD62qB9MS5XIkpWSg7MSRkOta5JaYCAV+yFGhGoZi1WhUqoYiICggiiLszY1orDkJhVKF3JFjMeGc8zF87BRuWkQJi6GAYpZOq8EF587ArKkTsOfAEWz+aieOFhbD5/cjIy0FFpOx26lecgEYrnVhuNaFFp8cxxw6HHNq0cqxB2dFFIGShlbkZ3S+6dV3JWdk48ThvSHHW2RJyERJlKvrmQAE1CgHo1g1GmWqPHhlwS/6QCAAW30VbHW10BlNGDf9PIybMQ+DR4zhbAJKePxkpJin1Wgwe9okzJw8HgePFeLL7bux79AxVJysQWpyElKTkyCTdd9FYFT4MdXUginGFlR6VChyalHs0nCfhV4qrut5KDBbUyCKoWMHpJiBUC9PR7F6NErUo+CSGdqP+30+NNSeRKutESZrCs5ZeCnGTJ2D9OxcrjNAAwZDAcUNuVyOCaNHYnx+HkrKT2Lrzj34atd+HDpWCL1eh4zUZGg1mm7vRxCALLUHWWoPZos2VHlUOOHUosSlgZMBoceKIlnu2JwEAeGmJVqiX9gZfFCgWjkEFaphqFQOhUN+OsiIogi7rRG2hlr4/T4kpw3CzAuWIn/yLM4koAGJoYDijiAIyB08CLmDB+GiubOx69tD2LpjL0oqTsLr8yHVakFyUhLk8u5bD2RCcIfGQWoPzhVtOOlRobitBcHBgNClSNYqMFmSoVCp4fW4oVKfDm59FQrsMjMqlUNRoRqGauVgBITTH3WiKMJhb4atvgYetwsGkwUjxk3ByPHTMWzMZG5jTAMaQwHFtdTkJCyefy4uPHcmjhQWYdf+w9i5/yCOFBZBrVQiIy0FBn3PFpIRvhMQZok21HuVKHOrUe5Wo8aj4tLKZyiKYK0CQ9u0RI/L2SEU2GXR2S3RIehRq8xGjTIb1YrBaFYkh5zjcrbCVl8Dp70FWr0RWUNHIn/SOcgZOQ5JqRnsIiACQwElCKVSgfH5eRifn4dLL5qHfYeOYdvOfSgsKUNxWSXMJgNSrEldbt38XYIApKi8SFF5MdlohzsgoNKtRqVbjQq3Gs0cqBhRS4HeZIFGq4PH5QTMp8cRiIIcrTIzjIGmiB7bLjOhRpmNGkU2apXZnY5N8LhdsDXUorW5CWq1FimDBmP0olnIGTkOqYOG9GgsCtFAwk82SjhWixnnz56OeedMRWFJOfYePIKd+w+hvLIKHq8XRoMBKVYLdNruxx+copaJGKp1YajWBQCw+2U42daCUONVosGrHHAtCTUtbrS6fdCru/8YCU5LzETp8UMh17XILV2GglaZEY3yNDQo0tCoSEODPB1OefgmflEU4Wq1o8XWAKe9BXKlEtbUDEyZcxGGjhqPzJwRkCv4sUfUGf7fQQlLJpMhb+gQ5A0dgssuOh+FJWU4eKwQO/cfQmVVDdweD4wGPVKsSREFBCC4c2Oezok8nRMA4BOBuraAUO1RodajGhBjEorrWzF2UM8W8EnOyELhod0hx1vkSYC3GF4o0SJPQrPcikZFalsQSIdH1vW6FH6/D3ZbI1qaGuDzeqDVGZCUmoGpcxcje9hIZA/L57oCRD0kiOHmCRElMK/Xh8KSMhw+fgI79h5EZU0t3G4PNBo1kswmmI3GHg1S7I7dL0ODV4kmnwKNXiUafQo0+RTwJdAKi3+5fgqWTMjs0bm7Pt+Ij954Djkjx3U4rvW3AECnv/7PFAgE4LQ3o7XFBmdrCwSZDAajBVlDRyJ31HgMys1DSuZgrilA1AtsKaABR6lUIH/EUOSPGIqlF87DidJyHC8uw75DR1FWWYWqmjoAgMlogMVkhF6n7dUgNIM8AIPcjSFwtx8TRcDulweDQltIaPXLYffL0eqXx3xgkAlAhkmDwVYdcpJ1yDD3/Be4MckKIHRaYndhwO/3wWFvhqPFBpejFQIEaA1GJKWkY+KsC5A1NA+DcvJgMMffrotEsYYtBURtRFFEbUMjTpSUo6CoFAeOHEddYyMcThfkcjmMBj2MBj0MOm2fDVBzBQS0tgWEU0HB6ZfDLQrwBGTwnPqz7b/PdhyDABFyQYRCEKESRGhkAWjlfuja/tTKAtDKAmiqq8LwzGQ88NNVUCt69wu8suQ4/v3Ub5CSkQ2VJnyXQMDvh8vZCmerHS6HHT6vBzKZDFqDCdbUTAzJG4v07BykDcqBOTmNAwWJoowtBURtBEFAWrIVaclWnDNlAnw+H8pP1qCorALHi0tRUFSKxiYbyk9WASKgUavbg4JapYzKlDaNTIRG5kOy0tftuaIIeEUBHlGAXxQgAhBP/XnmfwOQA1DIAlC0hYDgpWd1VWv8cNvqIDuL3xAmSzLUGi3cbieUag18Xg/cLgdcra1wOloQ8PshCAI0OgN0BiOGjBiD9OwcpGRkI21QDoxJyZw2SNTHGAqIOqFQKNoXSTp/9nT4/X5U1dajoqoGFVXVOHaiFOUnq1FWWQWPxwtBALQaNXRaLXRaDXRaTZ/2awsCoBJEqPph62GNRo3m5hY02GxITwldA6AndEYzNDoDqsqK0KCshFKpgkqjhb5t8aDUQUNgTc2AJSUDluQ0zhIgkgC7D4jOgr3VgcrqWpSfrEZVbR1KKk6iqqYeDqcTDqerbb1/AVqNCjqtFmq1CmqVCmqVMi6avv3+AFxuN+ytDthaWvB/P74Z+SOG9vr+jh/YhfrqSpiSkmG0JMOUlAKDuWd7VxBR32MoIIoyt9uDusYm1DU0oba+ATX1DSipOInqmnq4PB643R64PR4AwXEMMpkMKqUSarUKKqUSCoUcCoUCCrk8eFHIo/qlGQgE4A8E4PX64PV64fH64PF64fF64fX64PP5IAhC+4BAjVoNjUYFq8WMn6y5DmnJ1qjVQkSxhaGAqJ94PF4021vR3GIP/mkP/tnY1Iya+gbUNzTB7nDC5/PB5/fD5/PD5/fD7/cj0PYFfapHPdz/tu2HBLSfJwAQBeH0lSIgyATIZDIoFQqoVEqolApo1RpYzEYkmU1Isphg0Olg0GthMZlgtZhgMRlh0OvYp0+U4BgKiGKEKIrweLxweTxwud1wuz1wuYP/7XIHWxhcHg+8Xh9EUURADEAUg7cTA4Hgnwi2BMhlMiiUCqiUSigViuBFqYCi7b9VSgV0Wg30bV/+apWKX/hExFBAREREQRzdQ0RERAAYCoiIiKgNQwEREREBYCggIiKiNgwFREREBIChgIiIiNowFBAREREAhgIiIiJqw1BAREREABgKiIiIqA1DAREREQFgKCAiIqI2DAVEREQEgKGAiIiI2jAUEBEREQCGAiIiImrDUEBEREQAGAqIiIioDUMBERERAWAoICIiojYMBURERASAoYCIiIjaMBQQERERAIYCIiIiasNQQERERAAYCoiIiKgNQwEREREBYCggIiKiNgwFREREBIChgIiIiNowFBAREREAhgIiIiJqw1BAREREABgKiIiIqA1DAREREQFgKCAiIqI2DAVEREQEgKGAiIiI2jAUEBEREQCGAiIiImrDUEBEREQAGAqIiIioDUMBERERAWAoICIiojYMBURERASAoYCIiIja/H8CRzjVolXfUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"HeartDisease\"].value_counts().plot(kind=\"pie\", autopct='%1.1f%%', startangle=90, explode=[\n",
    "    0, 0.1], shadow=True, labels=['Normal', 'Disease'], label='', title=\"Heart Disease Distribution\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39c048c",
   "metadata": {},
   "source": [
    "The dataset is slightly imbalanced, with 390 samples of `HeartDisease` equal to 0 (Normal) and 356 samples of `HeartDisease` equal to 1 (affected with Heart Disease).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8678453",
   "metadata": {},
   "source": [
    "### Train-Test Split\n",
    "\n",
    "We have split the dataset into `train` and `test` for the sake of evaluating our classificator.\n",
    "\n",
    "The training set contains $85%$ of the samples, while the test set contains $15%$ of the samples and the split is performed using the `train_test_split` function of the `sklearn` library. Thanks to the `stratify` parameter of the procedure, we have mantained the same distribution of the target variable (`Heart disease`) in both the train and test set.\n",
    "\n",
    "The training set will be used to learn the parameters of the Bayesian Network, while the test set will be used to evaluate the performance of the model in terms of accuracy and other metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a50f29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_variable = \"HeartDisease\"\n",
    "X, y = df.drop(columns=target_variable), df[target_variable]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, shuffle=True, stratify=y)\n",
    "train = pd.concat([X_train, y_train], axis=1)\n",
    "test = pd.concat([X_test, y_test], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528f1f16",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b9950b",
   "metadata": {},
   "source": [
    "# 4. Naïve Bayes Network\n",
    "<a class=\"anchor\" id=\"ch4\"></a>\n",
    "###### [Back to top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9d2710-00a5-4183-b571-16322028c50b",
   "metadata": {},
   "source": [
    "A [Naïve Bayes classifier](https://www.ibm.com/topics/naive-bayes) is a specific type of **classifier** based on a Naïve Bayes network. It is characterized by its semplicity, and it is built upond a **simple assumption**: all the features are conditional independent if the target feature is given.\n",
    "\n",
    "They are widely used in Machine Learning thanks to their **computational efficiency** and their **ease of implementation**. They can also be expanded with other connections, and we will see that using an **Hill Climbing** algorithm in the next chapter.\n",
    "\n",
    "Creating a Naïve Bayes network in `pgmpy` is quite simple: once it is clear which are the features and which is the target variable, a simple list comprehension can build the network structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2a8d9a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "network = [(target_variable, x) for x in df.columns[:-1]]\n",
    "naive_bayes = BayesianNetwork(network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "37225be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.nx_agraph.graphviz_layout(naive_bayes, prog=\"dot\")\n",
    "nx.draw(naive_bayes, pos, with_labels=True, node_size=1000,\n",
    "        font_size=8, arrowsize=20, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3449fcd",
   "metadata": {},
   "source": [
    "Once the structure of the network is built, the next step is letting it learn the parameters (_CPDs_) from the dataset. The choice is [between two algorithms](https://towardsdatascience.com/maximum-likelihood-vs-bayesian-estimation-dd2eb4dfda8a):\n",
    "\n",
    "* **Maximum Likelihood Estimation**: Given the likelihood function $\\mathcal{L}(\\theta | D) = f(D | \\theta) = \\prod{}_{i=1}^{N}f(x_i | \\theta)$, the MLE algorithm tries to fit the parameter $\\theta$ that maximize the likelihood function:\n",
    "$$ \\hat{\\theta} = \\argmax_\\theta \\prod_{i=1}^N f(x_i | \\theta) = \\frac{\\partial}{\\partial \\theta} \\prod_{i=1}^N f(x_i|\\theta)$$ \n",
    "Since computing the derivatives of many products can get really complex, in the real world scenarios the _log likelihood function_ is used. This is done because the logs of products is the sum of the logs, and that simplifies the computation, and also the $\\argmax$ of a function doens't change if we are applying the log, since it is strictly monotonic and scaled. So the real computed formula is:\n",
    "$$\\text{log likelihood} : \\int(\\theta) = \\ln \\prod_{i=1}^Nf(x_i | \\theta) = \\sum_{i=1}^N \\ln f(x_i | \\theta)$$\n",
    "Therefore:\n",
    "$$ \\hat{\\theta} = \\argmax_\\theta \\int (\\theta)$$\n",
    "\n",
    "This algorithm works the best if the dataset is big, since its outcome solely depends on the _observed_ data. It is also adviced when there is uncertainty about the prior.\n",
    "\n",
    "* **Bayesian Estimation**: The equation used for Bayesian estimation is the following:\n",
    "$$ \\overbrace{\\mathbb{P}(\\theta | D)}^\\text{posterior distribution} = \\frac{\\overbrace{\\mathbb{P}(D|\\theta)}^{\\text{likelihood function}} \\overbrace{\\mathbb{P}(\\theta)}^{\\text{prior distribution}}}{\\int \\mathbb{P}(D|\\theta) \\mathbb{P}(\\theta) d \\theta}$$\n",
    "The formula is quite similar to the *Bayes' theorem*, but instead of working with numerical values it uses models and pdfs. The Bayesian estimator tries to compute a distribution over the parameter space, called *posteriod pdf*, and denoted as $\\mathbb{P}(\\theta | D)$. This distribution represents how strongly we believe each parameter values is the one that generated our data, after taking into account both the observed data and the prior knowledge.\n",
    "\n",
    "The bayesian estimation works better if the priors of the networks *makes sense*.\n",
    "\n",
    "\n",
    "Since we are working on a naïve network, the priors are probably not describing well how the features affect each other, and we have proceeded using the **Maximum Likelihood Estimation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37fac87",
   "metadata": {},
   "source": [
    "To test the performance of the network, we have used `KFold` to compute an average of the `roc_auc` score given different **permutations** of the dataset, to get a more precise idea on the performances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17cf0e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(5, shuffle=True, random_state=42)\n",
    "\n",
    "def bayesian_kfold(df, model, metric):\n",
    "    score = []\n",
    "    predictions = []\n",
    "    for train, test in kfold.split(df):\n",
    "        model.cpds = []\n",
    "        model.fit(df.iloc[train, :], estimator=MaximumLikelihoodEstimator)\n",
    "        y_pred = model.predict(df.drop(columns=target_variable, axis=1).iloc[test, :])\n",
    "        score.append(\n",
    "            metric(df[target_variable].iloc[test], y_pred[target_variable]))\n",
    "        predictions.append(y_pred)\n",
    "    return sum(score) / len(score), predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3671def0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "roc_auc_value, _ = bayesian_kfold(df, naive_bayes, roc_auc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "aa11e5b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc_auc score for the naive bayesian network is 0.84\n"
     ]
    }
   ],
   "source": [
    "print(f\"The roc_auc score for the naive bayesian network is {roc_auc_value:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06f5610",
   "metadata": {},
   "source": [
    "## 4.1 Considerations\n",
    "<a class=\"anchor\" id=\"ch41\"></a>\n",
    "With the Naïve Bayesian classifier we get an overall good `roc_auc` score and we mantain a good computational performace. Is it possibile to improve the network? Does it make sense to add others **causal** link between features?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1bcd3b-5da0-44f2-bf31-61793ebcb6d8",
   "metadata": {},
   "source": [
    "# 5. Improving the Naïve Bayes network with Hill Climbing\n",
    "<a class=\"anchor\" id=\"ch5\"></a>\n",
    "\n",
    "The first approach we use to learn the structure of the Bayesian Network is the **Hill Climbing** algorithm.\n",
    "\n",
    "The Hill Climbing algorithm is a greedy search algorithm which starts from an empty network (or an already built one) and adds or removes edges to maximize a score [[14]](#14). The score is usually the **Bayesian Information Criterion (BIC)**, which is a trade-off between the likelihood of the data and the complexity of the model. Since it is a *local search algorithm*, it strongly depends on the starting network, and there is a very high risk of being stuck on a local Maximum. To avoid this, the algorithm is usually run multiple times with different initializations and the best network is selected.\n",
    "\n",
    "The Hill Climbing algorithm is implemented in the `pgmpy` library. We have compared different scoring method to see which one works the best with our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a88361bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "scores = {} # Dictionary to store the roc_auc_score for each scoring method\n",
    "networks = {} # Dictionary to store the network structure for each scoring method\n",
    "\n",
    "for scoring in ['k2score', 'bdeuscore', 'bdsscore', 'bicscore', 'aicscore']:\n",
    "    network = HillClimbSearch(df, use_cache=False).estimate(scoring_method=scoring)\n",
    "    networks[scoring] = network    \n",
    "    model = BayesianNetwork(network)\n",
    "    scores[scoring], _ = bayesian_kfold(df, model, roc_auc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "92cb3c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,15))\n",
    "i = 1\n",
    "for scoring in networks:\n",
    "    plt.subplot(3,2,i)\n",
    "    pos = nx.nx_agraph.graphviz_layout(networks[scoring], prog=\"dot\")\n",
    "    nx.draw(networks[scoring], pos, with_labels=True, node_size=1000,\n",
    "        font_size=8, arrowsize=20, alpha=0.8) \n",
    "    plt.title(scoring) \n",
    "    i += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4cb586c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k2score</th>\n",
       "      <th>bdeuscore</th>\n",
       "      <th>bdsscore</th>\n",
       "      <th>bicscore</th>\n",
       "      <th>aicscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ROC AUC</th>\n",
       "      <td>0.871053</td>\n",
       "      <td>0.864054</td>\n",
       "      <td>0.864054</td>\n",
       "      <td>0.853694</td>\n",
       "      <td>0.85557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          k2score  bdeuscore  bdsscore  bicscore  aicscore\n",
       "ROC AUC  0.871053   0.864054  0.864054  0.853694   0.85557"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scores, index = ['ROC AUC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b884bbd0",
   "metadata": {},
   "source": [
    "The performances are slighly higher w.r.t the Naïve Bayesian Classifier, but the network doesn't have any clear semantic. For example, the nodes `Age` and `Sex` should not have parent nodes, because they are not influenced by any other feature (but it makes sense that they influence other features). This means that Hill Climbing learns a structure that may even have good performance, but it does not have (in most cases) semantical correctness."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec50e431",
   "metadata": {},
   "source": [
    "From the comparison of the scoring parameters of the Hill Climbing above we can notice that they have an overall similar `ROC AUC`. However, the networks are different from each other and each network is not fully explainable. This means that:\n",
    "- the choice of the scoring method does not influence so much the performance, even if the structures of the networks are different\n",
    "- since the semantic is problematic, we need to add some constraint through blacklisting/whitelisting. This means that the Hill Climbing will not explore the connections written in the blacklist while they will use the connections reported in the whitelist.\n",
    "\n",
    "Given these consideration we can choose as the scoring method for the Hill Climbing the `bdeuscore`, since it has good performance it is more general w.r.t `k2score`.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9323df8b-7e56-4def-a1be-64cfbc8c9597",
   "metadata": {},
   "source": [
    "# 6. Running Hill Climbing Unconstrained\n",
    "<a class=\"anchor\" id=\"ch6\"></a>\n",
    "###### [Back to top](#top)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b9ea04",
   "metadata": {},
   "source": [
    "## 6.1 How Bayesian Networks are represented\n",
    "<a class=\"anchor\" id=\"ch6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c94b8ebf",
   "metadata": {},
   "source": [
    "The libraries used in this notebook represent the bayesian network as a **Directed Acyclic Graph (DAG)**.\n",
    "\n",
    "A DAG is a graph that has a **topological ordering**, which means that the edges are oriented: they have a starting node and a destination node. This is important because the edges in the Bayesian Network represent the causal relation between the variables: the edge goes from the cause to the effect."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58638edb",
   "metadata": {},
   "source": [
    "## 6.2 First run of the algorithm\n",
    "<a class=\"anchor\" id=\"ch62\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3445e2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "hc_unconst = HillClimbSearch(df, use_cache=False).estimate(scoring_method='bdeuscore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5190a7dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.nx_agraph.graphviz_layout(hc_unconst, prog=\"dot\")\n",
    "nx.draw(hc_unconst, pos, with_labels=True, node_size=1000,\n",
    "        font_size=8, arrowsize=20, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a9dac2",
   "metadata": {},
   "source": [
    "From the above graph, we can see that the structure of the Bayesian Network learnt from the data has some edges that are meaningless.\n",
    "\n",
    "For example, the edges that points to `Age` and `Sex` does not make sense, because those features are not a consequence of other features, but it is valid the vice versa.\n",
    "\n",
    "This is because the Hill Climbing algorithm is not aware of the domain knowledge and it tries to maximize the score by adding or removing edges. In the next section, we will use the domain knowledge to remove the edges that are not meaningful and to add the edges that are meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656e20f7",
   "metadata": {},
   "source": [
    "## 6.3 Adding a blacklist\n",
    "<a class=\"anchor\" id=\"ch63\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a65648",
   "metadata": {},
   "source": [
    "In this section we will add a blacklist to the Hill Climbing algorithm that will limit the creation of unexplainable networks. The blacklist is passed as a parameter to the Hill Climbing algorithm.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5e4387ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "black_list = [(target_variable, 'Cholesterol'), \n",
    "              ('Oldpeak', target_variable),\n",
    "              ('ST_Slope', target_variable),\n",
    "              ('ST_Slope', 'MaxHR'),\n",
    "              ('ExerciseAngina', 'Cholesterol'),\n",
    "              ('ST_Slope', 'Oldpeak'),\n",
    "              ('ExerciseAngina', 'RestingECG'),\n",
    "              ('ExerciseAngina', 'ChestPainType')] + [(x, 'Sex')for x in df.columns] + [(x, 'Age') for x in df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f93c0b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "hc_const = HillClimbSearch(df, use_cache=False).estimate(\n",
    "    scoring_method='BDeuScore', black_list=black_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5da662a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.nx_agraph.graphviz_layout(hc_const, prog=\"dot\")\n",
    "nx.draw(hc_const, pos, with_labels=True,\n",
    "        node_size=1000, font_size=8, arrowsize=20, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4582f298",
   "metadata": {},
   "source": [
    "As we can see the new structure of the Bayesian Network still has some edges that are not meaningful.\n",
    "\n",
    "Note that `Cholesterol` now is a singleton node, meaning that it is not connected to any other node. This is because the Hill Climbing algorithm removed the edge between `Cholesterol` and `HeartDisease` and there are no other edges that connect `Cholesterol` to the other nodes.\n",
    "\n",
    "This is semantically wrong because `Cholesterol` is a risk factor for heart disease and it should be connected to the `HeartDisease` node, but this correlation was probably not captured by the algorithm while analyzing the data.\n",
    "\n",
    " We will use the references found in the literature and in the medical guidelines to adjust the edges."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cca0266",
   "metadata": {},
   "source": [
    "## 6.4 Manually adding edges to the network\n",
    "<a class=\"anchor\" id=\"ch64\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788f746f",
   "metadata": {},
   "source": [
    "We are going to add some edges that make sense by referring medical documentation. The list is the following:\n",
    "- Sex → Cholesterol [[7]](#7)\n",
    "- Cholesterol → Heart Disease [[8]](#8)\n",
    "- Oldpeak → ST_Slope [[9]](#9)\n",
    "- RestingECG → Heart Disease [[9]](#9)\n",
    "- RestingBP → Heart Disease [[10]](#10)\n",
    "- Cholesterol → RestingBP [[11]](#11)\n",
    "- FastingBS → Heart Disease [[12]](#12)\n",
    "- Age → Cholesterol [[13]](#13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e6993928",
   "metadata": {},
   "outputs": [],
   "source": [
    "hc_const.add_edge('Sex', 'Cholesterol')\n",
    "hc_const.add_edge('Cholesterol', target_variable)\n",
    "hc_const.add_edge('Oldpeak', 'ST_Slope')\n",
    "hc_const.add_edge('RestingECG', target_variable)\n",
    "hc_const.add_edge('RestingBP', target_variable)\n",
    "hc_const.add_edge('Cholesterol', 'RestingBP')\n",
    "hc_const.add_edge('FastingBS', target_variable)\n",
    "hc_const.add_edge('Age', 'Cholesterol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5553c1d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L48ePExwcTGhoKEqlkrZt2+Lr60uLFi1QKg0zI9uc6zS9Xs+xY8dYu3Ytt2/fpm7duowePZo2bdqIxFIQ/oNIKI1MkiQ2nH/AjyfuIpPJcLG1KJSKS5IkkjI1SJLE+HaVGd68glErTDFjXBDyT6/Xc/HixdxxkZmZmTRs2BBfX186deqEg4Phd4r5e50GYG8BVlZWhXLdgqrTJEni3LlzrF27litXrlC1alVGjx5N586dRQ+KILyESChNgCRJ7Lj0iJVHw1Fp9bjaWRpsULskSej0EgkZaqyUct7rVJUBjU1rhqOYMS4IeRMREUFwcDD79u0jPj4eT09PfHx88PHxwcPDo9DjeVqnfbnnEmmZ2VTzLIOF8t9niueHVqc3SJ0mSRJ//vkn69at4/z585QvX55Ro0bh4+Mjek8E4R9EQmlCzkcksOjgLe4nZGBnpcTBSlmgrYZZWVncj4rFoYQbld0cmN61usHHTBYEMWNcEJ6XmJjIgQMHCA4O5ubNmzg6OtKtWzd8fHyoU6eO0btof//9d9765FscWw8lS2lvkDpNkiTSVFoyVFoqutoZtE67fv06/v7+HDt2DDc3N0aMGEGfPn2wtrY2yPUEwdyIhNLEpGZrWH3iLr9eiSZbo8PR2gJbS0W+KmFJkshU60jN0pCS9IRKssf8Mv9dHExovbhX9aIZ4zVq1KBDhw5ixrhQ5KlUKk6cOEFwcDDnzp1DLpfTtm1bfHx8aNWqlcm0miUlJTFkyBC8vLz4atFSfj5175k6Ta/OwtbW9j/Xt3yZ3DotW4O1hYI+9cvyTjuvQqnTIiIiCAgIYP/+/Tg5OeHn58eAAQOwt7c3+LUFwZSJhNJEnY9IIPDcfcKiUsjW6MhOT8POSo5byZKvfA6tTk+aSku2Roe1hYJ6ZZ2oZ53Emq8/YtmyZbRu3dqAn8DwxIxxoTjQ6/VcvnyZ4OBgDh8+TEZGBvXq1cPX15cuXbrg6Pj8FqnGJEkSH3zwAX/99RdbtmyhVKlSwP/qtD/uJxCXkEhJFydKOtrlaXjPi+q0kS0r5nkjhoIQFRVFYGAgv/32G1ZWVgwcOBA/Pz8xHEcotkRCacIkSeJWXBq7Lz1gzf6L2DqXxNrGBiQJS6UCS6UcuQxkyJCQ0Eug1upRa3UgkyEDnG0t6FbLnR71PKhWOucJeuLEicTExLBt2zYsLY23tEdBEjPGhaLmwYMHueMiY2Ji8PDwwNfXF29vbzw9PY0d3ktt2bKFxYsXs3z5clq1avXMa5IkMXvJavZefkTJhp1JzdYi5bzw2nWasXskHj9+zKZNm/jll1+QJIl+/foxbNgwsa2jUOyIhNIMHDlyhBmfzmHOsjUk6qy4HZ/GtehUUjI16JGQJJDJQI4MJ1sLans4Ur20A1XcHKhb1gknm2cTqYiICAYPHszEiRMZOXKkkT6V4YgZ44K5Sk5O5uDBgwQHB3Pt2jUcHBzo0qULPj4+1K9f3+jJ03+5desWo0aNYsCAAUydOvW51/V6Pb6+vnTo0IF3Jn/A1agUwuPS8l2nmYJ/buvYs2dPRowYIbZ1FIoNkVCagTlz5nDz5k22bduW+zdJkkjJ0qDW6tHoJSzkMiyVcpxsXm3ZoSVLlrB792527dqV2yVVFP19xvjx48cJDw8XM8YFk6JWqzl9+jRBQUGcOXMGgFatWuHr60ubNm3MphchMzOTYcOGYWNjg7+//wvjPnfuHJMnTyYwMJBatWo981p+6zRT8c9tHbt27cro0aOpXLmysUMTBIMSCaWJ0+v1dO3alb59+zJx4sQCO29aWhr9+vWjRYsWfPHFFwV2XlMnZowLpkCSJMLCwggODubQoUOkpaVRu3bt3HGRLi4uxg4xzz7//HMOHz7Mpk2bXtol/+mnn3L79m22b99uVkni61CpVPz6668EBgYSGxtLu3btGDNmDLVr1zZ2aIJgECKhNHFXrlxh7NixrFu3jnr16hXouX/99VfmzZvH2rVrqV+/foGe2xyIGeNCYXv06BEhISGEhITw6NEj3N3dc9eLrFixorHDe2379+9n1qxZzJ07lx49erzwPenp6XTt2pXx48czYsSIQo7QeDQaTe62jpGRkTRt2pQxY8bQuHFjUb8IRYpIKE3cypUr+e233zhw4ECBz1jW6/WMGjUKvV5PYGBgsZ4RLWaMC4aSmprKoUOHCA4OJiwsDFtbWzp37kyPHj1o0KCB2d9Xjx49ws/Pj7Zt2zJv3ryXJkm7d+/m66+/Jjg4uEgPs3kZvV7P0aNHWbduHbdv36ZevXqMHj2a1q1bi8RSKBJEQmniBg4cSO3atZkzZ45Bzh8WFsaYMWP49NNP6du3r0GuYW6ezhg/fvw4J06cEDPGhTzTaDScPXuW4OBgTp06hU6no0WLFvj6+tK2bdsisxi2RqNh7NixpKSksHnzZuzs7F763rFjx2Jra8vKlSsLMULTI0kSZ8+eZe3atYSFhYltHYUiQySUJiwqKorevXuzYMECOnbsaLDrzJkzhzNnzrBr1y6TW9PO2P5txnj79u1p0aIFtra2xg5TMAGSJHHt2jVCQkI4cOAAKSkp1KhRAx8fH7p3706JEiWMHWKBW7FiBZs2bWLdunX/Ojbw4cOH9O3bly+//JJu3boVYoSm65/bOnp6ejJq1Ci8vb3FA6tglkRCacK2bNnCihUrOHLkiEGTlidPntCvXz969erF9OnTDXYdc/d0xvjTST1ixrgAEB0dzb59+wgODiYyMhI3Nze8vb3x9fXFy8vL2OEZzPnz55k0aRLvvffef46J/OGHH9i6dSsHDx7EysqqkCI0H3/f1rF06dK52zqK70owJyKhNGETJkxAoVAUShdRYGAgq1atYsuWLWJ5i1ckZowXX+np6Rw+fJiQkBD++OMPbGxs6NixI76+vjRp0qTId10mJiYyePBgqlWrxooVK/718+r1enr16kWLFi349NNPCzFK8/P3bR2dnZ3x8/Ojf//+YltHwSyIhNJEpaen06lTJ6ZNm8bAgQMNfj21Ws3gwYMpXbo033//vRgknkdixnjRp9VqOX/+PMHBwZw4cQKtVkvTpk3x9fWlffv2xWaxfL1ez5QpU7h58yZbtmzB1fXftz28ePEi48ePL7arSbyOqKgo1q9fz969e7GysmLQoEEMGTJE9IAIJk0klCbq0KFDfPzxx+zdu5cyZcoUyjXPnDnD+++/b/Axm0Xdv80Yb9++PXXq1CnyLVhFhSRJ3Lx5k5CQEPbv309SUhJVqlTB19eX7t27F8vZyhs3bmTZsmWsXLmSFi1a/Of7586dy5UrV9i1a5d4qMqj+Pj43G0dAbGto2DSREJpombPnk14eDhbtmwp1OtOmTKFO3fusHPnziIzE9WY1Go1v//+O8eOHRMzxs1IXFwc+/btIyQkhIiICFxdXfH29sbHx4dq1aoZOzyjuX79OmPGjGHIkCG8//77//n+zMxMunXrxqhRoxg7dmwhRFg0JScns3XrVrZt20Z2dnbuto7lypUzdmiCkEsklCZIp9PRpUsX+vfvz4QJEwr12pGRkQwcOJAxY8Ywbty4Qr12USdmjJu2zMxMjhw5QkhICBcvXsTS0pKOHTvi4+ND06ZNUSgUxg7RqDIzMxk6dCgODg6sXbv2lR6EgoKCmDt3bqH2tBRlGRkZ7Ny5k02bNpGcnJybrItx74IpEAmlCfrzzz95++23CQgIoE6dOoV+/ZUrV7JlyxZ++eUX8SNgIGLGuGnQ6XSEhoYSHBzMsWPHUKvVNGnSBF9fXzp27CgS/L+ZPXs2x48fZ9OmTZQvX/6Vjhk/fjwAP/74oyFDK3aebuu4fv164uLiaN++PWPGjHluf3RBKEwioTRBK1asICgoiP379xtlrF1mZib9+vWjfv36fPvtt4V+/eLoRTPGGzRokDtjXCT2Bev27duEhISwb98+EhIS8PLywsfHB29vb0qXLm3s8ExOSEgIs2fP5osvvsDHx+eVjomJiaFnz558/vnn+Pr6GjjC4kmj0bBv3z4CAgKIjIykWbNmjBkzhkaNGonxqkKhEwmlCerfvz/16tVj9uzZRovh6Q/IDz/8wBtvvGG0OIqjxMRETpw4wbFjx/j999/RaDRixngBePz4Mfv37yckJITw8HBcXFzo3r07Pj4+1KhRQ3ynLxEZGcmwYcPo0KEDn3/++Ssft2bNGtavX8+BAwdES6+B6fV6jhw5gr+/f+62jmPGjKFVq1bivhYKjUgoTczTHSUWLVpE+/btjRaHJEmMHTuW9PR0tmzZUuzHjxmLmDGeP1lZWRw7doyQkBBCQ0NRKpW0b98eHx8fmjdvjlKpNHaIJk2j0TBmzBjS09PZtGnTKyeGkiTl9nLMnTvXsEEKuSRJ4syZM6xbt46wsDCqVavG6NGj6dSpk6gnBIMTCaWJ2bx5M6tWreLIkSNGX9fuxo0bjBgxgunTpzNo0CCjxiKIGeOvSq/Xc/HiRYKDgzl69ChZWVk0atQIX19fOnXqJBaJzoNly5axdetW/P39qVmz5isfd+XKFcaOHcuPP/5IkyZNDBih8CKSJPHHH3+wdu1aQkNDc7d19PHxEQ9RgsGIhNLEjB8/HktLS1asWGHsUAD48ssvOXz4MLt27cLFxcXY4Qj/72Uzxlu3bk2HDh2K5YzxiIgIgoOD2bdvH/Hx8Xh6euLr64u3tzceHh7GDs/snD17lvfee48PPviAoUOH5unY+fPnc+HCBX799VfRMmZk169fZ926dRw/fhx3d3dGjBhB7969xbaOQoETCaUJSUtLo3PnzsyYMYP+/fsbOxwAkpKS6Nu3L127duWTTz4xdjjCCxTnGeOJiYns37+f4OBgbt26haOjY+64yNq1a4vxY68pISGBwYMHU7NmTZYtW5anpFClUtG1a1eGDBmSO8tbML6IiAj8/f05cOAAzs7ODB06lP79+2NnZ2fs0IQiQiSUJuTgwYN88sknBAcHm9RM061bt7J48WI2bNhAjRo1jB2O8B+ioqJyk8srV64UuRnjKpWKEydOEBwczLlz51AoFLRp0wZfX19atmwpuv3zSa/XM2nSJO7evcuWLVsoUaJEno4/cOAAn376Kbt3737l5YWEwvPo0SMCAwPZu3cv1tbWDBo0iMGDBxfZh06h8IiE0oTMmjWLiIgINm/ebOxQnqHVavHz88Pe3p61a9eKVh8z8m8zxtu3b4+Xl5dZ/Hvq9Xr+/PNPgoODOXLkCBkZGdSrV48ePXrQuXNnHB0djR1ikREYGMjKlSv57rvvaNq0aZ6Pnzx5MpmZmaxdu9YA0QkFJT4+no0bN7Jr1y4A3nzzTYYNG1YstxMVCoZIKE3E091xBg4caJLdRKGhoUyYMIF58+bh7e1t7HCE1/B0xvjx48c5ffo0mZmZlC9fPje5NMUZ4/fv3yckJISQkBBiY2MpW7Zs7rhI0fpV8P766y/Gjh3LsGHDmDx5cp6Pj4+Pp0ePHnzyySf06dOn4AMUClxycjJbtmxh27ZtqFQqevbsyciRIylbtqyxQxPMjEgoTcQff/zBuHHjCAwMNNndDmbOnElYWBi7du0qdhM+ihpTnjGenJzMwYMHCQoK4vr16zg4ONClSxd8fX2pV6+eWbSomqP09HT8/PxwcXFh7dq1rzUbeP369axevZqDBw+K2fRmJiMjgx07drBp0yZSUlLo3r07o0aNwsvLy9ihCWZCJJQmYtmyZezbt499+/aZXCvRU9HR0fTv3x8/Pz8mTZpk7HCEAvJ0xvjTcZfGmDGuVqs5deoUwcHBnDlzBoDWrVvj6+tL69atsbS0NOj1iztJkpg1axanT59m8+bNr9U6JUkSAwcOpFq1anz55ZcGiFIoDNnZ2fz6668EBgYSFxdHhw4dGD16tMk2dAimQySUJqJfv340atSIWbNmGTuUf/XTTz/h7+/Ptm3b8PT0NHY4QgErzBnjkiRx5coVQkJCOHToEGlpadSuXRtfX1+6du0qJgkUsMjISN577z3eeeed54at7N27l88//5wvv/ySbt26vdb5r1+/zogRI1i1ahXNmzcviJAFI/rnto7NmzdnzJgxNGzYUPQSCC8kEkoTEBkZSb9+/ViyZAlt27Y1djj/Kjs7m/79+1O1alWWLl1q7HAEAzPEjPGHDx/mjouMioqiTJkyeHt74+vrS4UKFQzwKQSA3bt38+6772Jtbc3IkSP58MMPsbW15cGDBwwbNowuXbrka7vXb7/9lhMnThAUFGSyvSxC3un1eg4fPoy/vz/h4eHUr1+fMWPG0LJlS5FYCs8QCaUJ2LhxI99//z1Hjx7F2tra2OH8pyNHjvDhhx+yfPlyWrVqZexwhELyshnj7du3p0OHDv86Yzw1NZVDhw4RHBxMWFgYdnZ2dO7cGV9fXxo0aCASkEKwZMkSFi1ahJ2dHWlpaTRu3Jivv/6ab7/9luzsbDZu3Pjau3Op1Wq6d+9O3759X2syj2D6JEni9OnTrFu3jqtXr1KtWjXGjBlDx44dRfkVAJFQmoRx48Zha2vLsmXLjB3KK5EkiXfffZf4+Hi2bdsm1v0rhl5lxrhOp+PMmTOEhIRw6tQpdDodLVq0wNfXl3bt2omdOgrZ+PHj2bNnD+XKlUOtVhMfHw+Ag4MDQUFBVK9e/bXPffToUWbOnMmOHTuoVKlSQYUsmCBJkrh06RLr1q0jNDSUChUqMGrUKLy9vcW2jsWcSCiNLDU1lc6dO/PRRx/Rr18/Y4fzyu7cuZM7OWfEiBHGDkcwon/OGI+Ojkar1aLVarGwsKBRo0b07NmTbt265XmRbKHgtG3blvv37+Pm5gbk7Mx17949HBwcGDJkCPPmzXvt7VWnTp1KQkIC69evL8iQBRP3119/4e/vz4kTJ8S2jgKindrIzp49i16vp3Xr1sYOJU+qVKnCgAEDWLNmDU+ePDF2OIIRWVpaUqlSJdzd3bG3t0eSJORyOba2tlhYWBAdHc21a9f4888/yczMNHa4xVJaWhoxMTG5P/RarZaYmBhcXFwoVaoUO3fupHfv3oSGhub53ImJiZw+fZoePXoUdNiCiatTpw6LFy9m69atNGjQgEWLFtGzZ08CAwNFWS+GRAulkX3yySdERkayceNGY4eSZ6mpqfTt25fWrVvz+eefGzscoZClpaVx5MgRgoOD+fPPP7GxsaFTp074+PjQpEkTZDLZC2eMN2vWjA4dOhTpPcZNzZUrV+jZsyeOjo5YW1sTGRmJSqXCy8sLhUKBVqslNjYWBwcHpk6dyrvvvvvK5968eTMrV67kwIEDYseiYu7hw4e52zra2NgwePBgBg8ejJOTk7FDEwqBSCiNSKvV0rlzZ/z8/Bg3bpyxw3ktu3bt4quvvmLdunXUq1fP2OEIBqbVajl37hzBwcGcPHkSrVZL06ZN8fX1pX379v86qaOo7zFuynbt2sWECRPw8PAgMTGRx48fU6FChdz1RXU6HQkJCWRlZVGrVi0OHTr0ymt/+vn5Ua5cORYsWGDIjyCYkafbOv7yyy/I5XLefPNNhg4dKrZ1LOJEQmlEFy9eZPz48WzYsIGaNWsaO5zXotfrGT58OHK5nPXr14vZfkWQJEncuHGDkJAQDhw4QFJSElWrVsXX15du3bq91o/E0xnjx48fJzQ0NE8zxoW8W7RoEYsXL6ZEiRLcv3+fkiVLUqpUKfR6PYmJiWRnZ+Pm5sbYsWMZOXLkK7cc3759Gz8/P5YuXUqbNm0M+yEEs5OUlMSWLVvYvn07KpWKXr16MXLkSDw8PIwdmmAAIqE0oiVLlnDo0CFCQkLM+sfzypUrjB07ls8++4zevXsbOxyDkCSJlCwNKq0erV5CKZdhpZTjZGNh1v92/yY2NpZ9+/YREhLCvXv3cHV1xdvbGx8fH6pVq1Zg1zHHPcYLW37vv3feeYc9e/agUqlQKpWUL1+elJQUMjMzKVGiBCNHjmTs2LGULFkyT3EtWbKE/fv3ExISImb4Ci+Vnp7Ojh072Lx5s9lt61gc6/7XJRJKI5EkiX79+vHGG2/wySefGDucfPvss884f/48u3btwsHBwdjh5FtKloawR8nciU/nVlwa16NTScnSoEdCkkAmAzkynGwsqOXhSPXSDlRxs6deOWecbMx3GaWMjAyOHDlCSEgIly5dwtLSko4dO+Lj40PTpk1RKBQGvf6L9hh3dXXN3WO8SZMmxWKZqoK+/1q3bs3Vq1fR6XSULFmS7OxsnJycGDZsGG+99dZrDTfQarV4e3vj7e3N1KlTC+JjC0VcdnY2e/bsITAwkPj4eDp27Mjo0aNNqoeuuNb9BUEklEZy//59+vfvX2S6iuLj43nzzTfp27ev2f64SJLEzdg0Qq7GsP9aLClZGnJKh4SlUoGlUo5cBjJkSEjoJVBr9ai1OkCGTAZONhZ0r+2Ob70yVC/tYBZPsDqdjgsXLhAcHMzx48dRq9U0adIEX19fOnbsaPB9vF9Gr9cTFhbGsWPHnttjvH379rRs2dJosRmCoe6/1NRUatSowePHj3FwcMDV1ZWBAwfyzjvv5Gv71JMnTzJ16lQ2b95coC3WQtGn0WgICQkhICCAhw8f0qJFC0aPHk2jRo1e6fiYmBj27NnD2LFjX3ms778prnV/QRMJpZEEBgayevVqjh49WmTW7AoICOD7779n69atZtGV8XfnIxJYf+4+V6NSyNbosLZQ4GClRKl49a5WrU5Pmkqbe3y9sk6MbFmRZpVcDRj563m6Z3dwcDD79u0jMTERLy8vfH196d69O6VLlzZ2iM/4+x7jx48f5/bt20Vqxrgh77/M+2F4e3tja2vL6NGjmTBhApUrV853zDNnzuTRo0ds3rw53+cSiqen2zquW7eOO3fu0KBBA8aMGUOLFi3+NSH7+OOP2bhxIxMnTuSjjz7KVwzFre43JJFQGsnbb7+Ng4MDS5YsMXYoBUatVjNw4EA8PDz47rvvzOIJLTVbw+oTd9lzORqVVoejtQW2lop8xS5JEplqHanZGqyUCvo08OCddpVxtDZ+d0h8fDz79+8nODiYu3fvUqJECbp164avry/Vq1c3i38zKDozxgvj/mtfyY6wrYv44rNPaNiwYYHEnZKSQrdu3Xjvvffw8/MrkHMKxZckSZw6dYp169bx119/Ub16dcaMGUOHDh2eGz/98OFDOnfuTGJiInZ2dnz//ff4+Pjk+ZrFre4vDCKhNIKUlBS6dOnCxx9/TN++fY0dToE6deoUH3zwAQsXLqRDhw7GDudfnYtIYPHBWzxIyMDWSomDlbJAEypJkkhTaclQaanoase0rtVp4fV6T6zx8fEsXryYiRMn5rmbMjMzk+PHjxMcHExoaCgWFha0b98eHx8fmjdvbvaTKV40Y7x69ep06NDBpGeMm9P990/bt29n8eLF7Nu3T+x+JBQYSZK4ePEi/v7+hIaGUrFiRUaNGkX37t1z66l58+bx3Xff4eHhQVxcHK6urvzyyy9UrVr1la9jzmXPlImE0ghCQkKYPXs2+/fvz/OsSlMnSRLvv/8+9+/fZ8eOHSbZnS9JEjsuPWLl0XBUWj2udpZ56t7IK61OT0KGGiulnMkdqzKgcbk8VV5JSUmMHDmSCxcuMG3aNGbOnPmfx+j1ei5evEhQUBDHjh0jKyuLRo0a4evrS6dOnbC3t8/PRzJZL5sx/nQ5ovzOGE9OTubu3bs0btz4tc9hbvffi4wYMYKSJUsWqR4WwbRcvXoVf39/Tp48SZkyZRgxYgTNmzenW7duZGVl4erqil6vJzo6mgYNGrBjx47/nBBaFMqeKRMJpRF8/PHHREVFERgYaOxQDOLBgwcMGjSIt956i7feesvY4TxDkiQCzz1g9cm7yGQyXGwLZ+kHSZJIytQgSRLj21VmePMKr3Td9PR0Ro8ezalTpwCoVKkSp06demmr4t27d3PHRT5+/BhPT0969OhB9+7di93ab2q1mosXL+bOGE9MTMz3jPH58+ezfv16Pv/889fq6jW3++9FIiIiGDhwIAsWLKBjx44FHKkgPCs8PJyAgAAOHTpEcnIycXFxVKpUKbcOVKvVxMfHM2jQIJYuXfrSB8aiUPZMnUgoC5lGo6Fz584MGzaMt99+29jhGMyKFSvYtm0bv/zyC+7u7sYOJ9f2iw9Zdvg2crkMF9v8zw7Mq6RMNXq9xJTO1RjYpPy/vjcrK4vx48dz4MCB3JbslJQUNm3aRNu2bXPfl5CQwIEDBwgKCuL27ds4OTnljousVatWka288qIgZozr9XratGnDtWvXcHZ2Zs6cOYwePTpPcZjT/fcyK1asYM+ePezfv79AZtgKwqu4evUqXbp0ISkpCSsrK1xcXChRogQKhYK0tDTS09OZP3/+S8tkUSh7ps68B0+ZoT///JOMjIxnEoKiaOzYsQQHB7N8+XK+/vprY4cD5IybWXk0/P+fTo3zQ+hia0lihpqVR8PxLGFL85eMq1Gr1XzwwQccOHAAV1dXrK2tAXjy5AlBQUE0bdqUEydOEBwczPnz51EoFLRp04Z33nmHli1bFou1GvNCLpfToEEDGjRowJQpU7hz5w7Hjh3j+PHjuYnR0xnjbdq0wcXF5blzhIWF8ejRIzw8PMjIyGDu3LmoVCreeeedV0razen+exm9Xs++ffvo3r27SCaFQnXq1ClkMhlVq1YlJSWFhIQEEhMTcxPL7Oxsvv76a2rXrk3Tpk2fObYolD1zIFooC9nixYs5cuQIwcHBRb7lKDg4mDlz5rB69ep8jTkrCKnZGsYE/M6DhExKO1oZ9buXJIn4NBWeJWxZN+qN52YA6nQ6ZsyYwZYtW3B2dsbOzi73tZiYGLRaLZUrV0atVlOvXj169OhB586dcXR0LOyPUiS8bMb403GXT2eML126lAULFuDh4YFMJiMhIQGdTsf06dOZPHnyv95T5nT//Ztz584xefJkAgMDqVWrlgGjFIT/SU9Pp0OHDsTGxuYuaabVaklKSiIxMRFJknByckKj0VClShV2796d+76iUvbMgUgoC5EkSfTp04fmzZvz8ccfGzscg9Pr9YwdO5asrCw2bdpk8F1W/s3CAzfZfvEhJe2tDDoI+1VpdXoep6sY3MST6d2q5/5dr9cze/Zs1q1bh4ODAw4ODqhUKlJSUkhNTUWtVqPX6xk9ejSffvop5cqVM+KnKHoSExM5efIkx44de2bGePv27QkICOD+/fvPLEmUmJiIRqPh/fffZ9q0aS/9sTKX+++/fPLJJ9y5c4dt27YV+QdiwXT4+/vz6aef4ubm9tz48af70ScmJqLVapHL5XTq1IkdO3ZgaWlZZMqeOTD+t1uM3Lt3j6ioqCLf3f2UXC5nxowZ3L17l19++cVocZyPSGDP5Whs87hYrSEpFXLsrJTsuRLFhXsJQM4Dx4IFC/D398fGxgatVsu9e/eIiIggKSkJOzs7KlWqhLOzM5mZmSKZNIASJUrQp08fli9fzuHDh/nqq6+oUKECq1ev5uLFi6SmphIfH09WVlbu+y0tLVm6dClfffUVer3+uXOay/33X9LT0zl+/Dg9evQQyaRQaDQaDWvWrCE9PZ3Y2FgePnzIw4cPefToEVFRUcTExKBWq7Gzs8PKygq1Ws2+ffuYMmVKkSl75kKMoSxEp06dwtramjfeeMPYoRSaWrVq0atXL3788Ue6du1a6LuZSJLE+nP3UWl1uNhaF+q1/4uDlZLY1GzWn71P04olWL58OYsXL0aj0ZCcnJzzHgcHSpYsib29fe6PuIODA2fOnCEqKoqyZcsa8RMUbXZ2dnTt2pWuXbtSrlw5PvvsM6ysrEhOTiYhIQGlUom9vX3uUIPvv/8ejUbD7Nmzc2eamtP9919J4qFDh3L37xaEwtSiRQtq1aqFhYVF7n8KhQKlUpn7v0//b8hZ7aJ7d+8iU/bMhUgoC9HJkydp1qxZsRvMPnHiRA4fPswPP/xQ6F39N2PTuBqVgqN14SwRkRcymQxHawvColKY8eUyVs3/GI1Gg42NDQ4ODjg6OmJlZYWFxbOxOzg4EB0dTUhISJFeKcCUHD58GCsrq9yllzIzM0lLSyMtLY3k5GTkcjkWFhasXLmS9PR0vv32WxQKhdncf7fi0qjh/u9jcPfu3Uvz5s0pVapUIUUoCGBhYcGiRYvyfNyNmFQWbLhYJMqeuTCNNuBiIDk5matXr9KuXTtjh1LoSpQowTvvvMOuXbu4detWoV475GoM2RodtpbGG7/5b2wtFWRrdCQ5VKJdu3b4+PhQv359nJyc0Gq1JCcnExMTQ1RUFNHR0Tx69Ij4+Hiys7PZtWsXYgi04UVFRREWFvbMYvC2traULl2aKlWqUKFCBezt7dFoNKSkpLBixQoGDRoEmM/9FxwW86/vi4yMJCwsjB49ehRSZIKQP0Wl7JkT0UJZSM6cOYMkSbRu3drYoRjFwIED2b17NwsXLuTnn38ulCfGlCwN+6/FYm2Rv/1ZDUkmk2FtoSBS78LOPUE42eTM+lOr1Tx+/Ji4uDhiY2Nz/zc2NpbIyEiioqJIS0tDq9WKJYIM7Pjx42RkZFCyZEkyMjJQqVSo1ercCQCSJGFlZYWjoyOlSpXC1taWpk2bmtX9t/9aLG+18cq9//4pODgYe3v7YvlALJifolT2zIlIKAvJyZMnqV27drHd91apVDJ9+nQmTpzIwYMH6datm8GvGfYomZQsDa52pj3EwMFKSUKGmqtRKbSukrOAuaWlJWXLlv3XMZJ6vT5f2wgKr+bkyZPodDoSExOxsrLCxsaG2rVrU7NmTby8vKhYsSKVKlWiQoUKueuFApwKf2y299/f6fV6goOD6dKli0lupSoI/2TOdb85EwllIdBoNJw7d45Ro0YZOxSjerpw9PLly2nTps1/7kySX3fi05EkTGZ238soFXIk4E58Wp4qFZFMFg4/Pz9atmxJpUqVqFixImXLln2lJbCKyv136dIlYmNj6dmzZ+EHJwivoaiUPXMjEspC8Mcff5CZmUmbNm2MHYrRffDBB/Tv35+AgAAmTJhg0GvdiksDcsYYhq7+EL1GTdN3FyJX5Nz2yZE3ubp1ER6NO1O50+DXukbYlgV4NOlCyaoN/3fdkHXYu5WnbJMuxF09w92jW7B2KoWk12Fh60DVriOwKVH62RNJ0v/HK5iadu3avbCrV6fTsXbtWg4cOJA707R27dq0atWKn376Cc/+H/H0/nsVcVfPkBD+J7X6TXqtOJ+E/4mlnSOOHpXzfvC/3H8//fQTcXFx1K1b97XiEgRD69mzJxYWFrk9BA8lF6jTJ0/neHDmN8o380auzOl6fnB6DzYu7rjVbv7acWWnPOH3nz7BrlROT5Neq8GtdnM8W+SMRU55eIt7h7azcL+C4NJ2ODg4sGjRIrPtyRQJZSE4efIk7u7uVKlSxdihGJ2HhwcjRoxg/fr19OrVy2BrKUqSxPXoVCyV/2tJsnIsQeKdK5SsnrNrT1zYaezdKxrk+n/nXL5GbpIQcWw7d49uoU7/Kc+8x1Kp4Hp0KpIkmeyYH+FZX3zxBampqfj7++Po6IgkSRw5coTU1FSA5+4/Q0sI/xN7t/KvlVBayGUvvP8yMzO5ePFi7u5AgmCqvvnmG6pVq4YkSfT+7gwJGeo8HR955jfKNu6cm1BWaN2nQOJSWFrRaNQcALSqTC6umYVr1UbYlijN9d3fU77nZDy9qrB9QisiIyOfGTZjbkRCaWCSJHHq1Cnatm0rKuT/N2rUKPbu3cuSJUtYsmSJQa6RkqUhJUuDpfJ/XR6l67Qi9uppSlZvjFaVSWr0XUrVbIpOrSLj8SPuHNyITqtG0mooVatZ7lNk+P71yJRKqnQeiiYrg8sb5lPNexRO5fO+y4FzhZok3fvrub9bKuUkZ+bE7GykvWaFV/fw4UMOHz5McHBw7jqUMpmMzp07c+nSJbJUaq6GrCczJgKFTKKa92gcylQCIO7aOaJC9wNg6VCCqt1GYOXw/N7hcdfOEfPHUfR6HQoLKyp3HoK9myep0Xe5e2gTkiQh6XWUadgBaydXEu9cIfn+deL+OotHww6412/70nPEXT1D3LWzKK3tyEqMo2wHPyKjshg4+DsUMglHR0c++ugjrl+/jkqlolKlSoX35QpCPqRkabh3+QyPLx/lPnqQJCq06YNrlQYARJ4LIv76eeSKnMSxVt+JPDwfAsCVzd8ik8upM+AD7p3YmdvT9ODMb2QmRKPXqMlOfoyFnRM1e7+LhY0dep2WiCNbSX5wA6W1LY5lq5Ae94B6Q2Y+F5tOrQJAaWmNVpWNVpWFraNzbt1foUKFwvmSDEQklAYWERFBdHS06O7+G2tra6ZMmcLHH3/M2bNnadmyZYFfQ6XVo0dC+bck3rFcFWIuH0eVlkTi3SuUrN4EmTynBcnaqSR1B01DrrRAp1FzZdPXOFeoiaNHZSp39uPyxq94fPMi8dfO4l6v9TPJ5L2j24g889v/rp2aiL1b+edikvR6EsL/pFSN5xe2l8tAi4Ra+/xOK4LpuXnzJp6eni9dqP/+/fu4tPfGq/NQUq6f5v6pPdQd+AEZj6O4d3wHDUd8hpWDC5Hnggk/sP65FuvUR3d4fOMC9YbMRK60IOXhbW4FraHxmC94eD6Esk274VazGQCarAwsbOwoUaV+7g/gf50DIC06goaj5mBbwp2U5ETCAubw446NNGtYm3379vHhhx/i4uJCrVq1it3auYL5+eijj7C2tiZbo4MSTak15CPsrCzITnnC5Y1f4VKxNjqNmkehB2g2YTEKC0t0GhUymZyq3UYQe+Uk9f0+RGn94rH9aTH3aDjiMyxs7Ln522pirxynfHNfYq+cJCspjkZjPgfg2s7lzxynU6v4I+BzJL2erKQ4yjXthpVjTpe2R6OOXAucg22Zyqyzvkn/3r54enoa9osyIJFQGtjJkyextbWlcePGxg7FpHTu3JkdO3awePFi3njjjQJf+karl5AkkPFsq7BbrebE/XWWhPA/qdHjbeJvXABAp1Fz7/h20uMeIpPJUKUlkRH/EEePysiVFtTsNZ4/N8zD0aMy5Zr5PHPOSh0HPTeG8u+SH97kj4DPUaUmorS2pcHwT5+LV4YMSQKNXqwrWRSUKVuO1NKVkCHDwcOLR6EHAEiJvIlLpTq5LZJlGrQn8uxepH9s2Zhw5zIZ8Y+4vPGr3L9ps9LRadQ4e9bg4dkgshLjcK5QE6dyVV8Yw7+dA8CxbBVsS7gDkBFzH2vXspSr5AWAt7c38+bN4/bt24wZM4YLFy4U0DcjCIbxtMs7KjmLnvO2EL57JbrMZGRyBdrsDLJTnmDjUhobl9LcCl6DS8XalKhc74W9Ay/iUrE2FjY5a9E6eFQm48kjAJIjb+BWu3nu2Hy3Oi2JCzuVe9zfu7w1WRlc3bYIhzKVcK3SgMqdBuNavyMJ968T+TASPz8/Vq1aRYMGDQrwmyk8IqE0sFOnTtG8eXPxhP8PMpmMGTNmMHToULZt28awYcMK9PxKuQyZDKR/TIpwq92CPwPn5VQsf5sYc//ULixs7Gk0ajYyuYLre75Dr9Xkvp6ZGIvCwgp1ZhqSTotM+eoJ8NMxlDqNihu//sCdQxup2Wv8M++RkJDJcsayCaavRo0aREZGkpKSgpOT03OvW1tZkSZ7+u+qQJJe3PL8smEwEhKl67SkYtt+z71WtkkXXKs2JOn+de6f3IVdybJU6fp8+fm3cwAoLKyeeS88e/8lJCTg7OxMkyZNREIpmA2lXMaDA2vxbN2PcnVyWvHPrXgfvU6DTC6nwbBPSI26Q8rDW1ze+BU1eryNU/lq/3leufJ/v+Eyufy5h8Dc13h5HW5hY4dLxdok3buW2wVv6VgC19ot+eSdlqxZtYRDhw6ZbUJp2nPqzVxiYiJXr14V3d0vUbVqVfr3789PP/1EQkJCgZ7bSilHjox/NvhZObhQsW0/KrV785m/a7MzsXRwQSZXkJkYS/L9G7mvZacmcPfQRuoMnIZjGS8ijm59rZgUFlZU7TaSpIi/SI+LfOY1vQRyZM+M+RRMV/ny5enUqRNffPEFaWk5s6MlSeLo0aNERUUhl8leeP85edYg6d5fqNKSAIi5fBznCjWQ/WMJKNfK9Ym/do7s1JxyIen1pMXcA3IebqydSlKmflvKN/chNSYCeDouK+uVzvFPtu5eZCdEERWZ8/qBAwdIT0/H29vbrCcJCMWPlVKOLjsTC8ecZXjirp1Dm50BgE6djTojBafy1fBs2ROnslVIj8+pixWW1mhVmXm+nrNnTeKvh6LXadHrtMRdO/fS9+q1GlKjwrEpURqdOpvEiKvo9BJyZEg6Dffv3zfYRNXCIFooDejMmTMAtGrVysiRmK7x48ezf/9+vvvuO2bPnl1g53WyscDJxiJnpt8/1mJ2r/v8bkWeLXpwK3gN8X+dxdrZDSfPnDGSkl7Hrb0/UaFNH+xKeuDVcRBXNn3N4xuhlKrZNM9xWTm4UPaNrjw4vYfab76X+3e1Vk9Je8sisVtCcTF79mzWrl3LyJEjUSqV6PV6GjVqRMuWLbFUynPvv7/ffnalylKp/QCu7VwGPJ2UM/K5czuVr0al9gO4sfs7JL0evV5LCa96OJSpRPQfR0iJvIVMrkAml+PVYSCQ0/p+e58/CeF/5k7Kedk5/kmytKNu73Es+no+Op0OjUZDiRIlxNqTgtlxsrGgehc/woNWE2Vrh7Nnjdwxi1pVFjf2fI9Oo0YmA2uX0pSukzOGv+wbXflr+1LkFpbUGfDBK1/PvX5bMh4/4tK62SitbHFwr4g6PTn39adjKAH0Og3O5WtQpkF7JJ2WmMvHSTm4GRtrKyaFutCiRQsGDhxYcF9GIZNJYjNgg5k5cyZPnjxh3bp1//3mYuyXX37h66+/Zv369dSuXbvAzvvRrjCO3IjD3cmmwM5pKLEpWXSuVZqv+9YzdihCATHn+2/+/PlcuHCBX3/9VSygL5idwi57OnU2Cktr9Dott4LWYO9egfLNvF/p2KJU94uawkDUajXnz58X3d2voG/fvlSrVo0FCxagf8m4lNdRvbQD/Mt4FpMik/1/vEJRYa73X3Z2NocOHcLX11ckk4JZKuyyd3XbYv4I+Jw//OeisLLGo1GnVz+4CNX9osvbQC5dukRmZiZt27Y1digmTy6XM2PGDN5++22Cg4MLrJutips9MhlodXqT3oJLq9MjA6q4FY1KRchhrvff8ePHycjIoEePHsYNTBBeU2GXvRet3PEqilrdb7q1nJk7deoUHh4eeHl5GTsUs9CwYUO6devGypUrSU9PL5Bz1ivnjJONBWkqbYGcz1DSVFqcbCyoW/b52cKC+TLX+y8oKIgGDRqY9eQAoXgz17Jn7kRCaQCSJHHixAnatGkjdsfJg/fff5/MzEzWrFlTIOdzsrGge213sjU6THWosCRJZGt0dK/tLibkFDHmeP/Fx8cTGhoqWicFs+ZkY0HHqiXIyFabTdkrCkRCaQDh4eHExcWJ7u48cnNzY8yYMWzZsoX79+8XyDl96pbB2kJBplpXIOcraJlqHdYWCnzrlTF2KIIBmNv9t2/fPpRKJZ07dzZyZILwehITE1m1ahW/ffc56SlJZKpNs5WyKNb9IqE0gFOnTmFra0ujRo2MHYrZGTZsGO7u7ixatKhAniwtMuIhMZInKRkm96QqSRKp2RrqlXMuMoOyhWfVcHegXlknUrM1Jn//SZLE3r176dixI/b29sYOTxDyJDo6mm+//ZYePXqwfft2BnVrQ9vaFUjN1pp82SsqREJpACdPnqRFixYFvp1gcWBpacm0adM4f/48p06d+u8DXiIpKYlvvvkGPz8/bCIvYGOpNLnxNGkqLdYWCka2qCCGRhRRMpmMkS0rYqVUmPz9d/36de7fvy+6uwWzEhERwezZs+nTpw8HDx5kzJgxBAUFMWXK+4zrUN0syl5RIRLKApaQkMC1a9dEd3c+tGnThhYtWrB48WLUanWejtVoNGzcuJG+ffuyf/9+pkyZQsj6FfR/owKZKi1aXcEtS5QfWp2eDJWWPvXL0qySq7HDEQyoWSVX+jTwMPn7LygoCDc3N5o2zfuC/YJQ2P766y+mTZvGwIEDuXjxIh988AFBQUG89dZbODo6AuZT9ooKkVAWsNOnTyOXy8XuOPkgk8mYNm0asbGxbNy48ZWOkSSJ48ePM3DgQFasWIG3tzd79uzBz88PCwsL3mlXmQqudiRkGH+QtiRJJGSoqehqx7h2YhWA4sDU7z+1Ws2BAwfw8fERa08KJkuSJC5cuMD48eMZNWoU9+7dY/bs2fz6668MGTIEG5vnFzI39bJXlIiao4CdPHmSunXr4uzsbOxQzFrFihUZMmQI69atIz4+/l/fe/v2bd59912mT59O2bJl2bp1Kx9++OEz/waO1hZM61odK6WcpEyNgaP/d0mZGqyUcqZ3rY6jtRgWURyY+v136tQpUlNTRXe3YJL0ej1Hjx5lxIgRTJw4kfT0dL799lt27txJr169/nV4mamXvaJEJJQFSK1Wc+HCBdHdXUDefvttbG1tWb58+QtfT0xMZP78+QwdOpTHjx+zfPlyVq5c+dK1P1t4uTK5Y1UkSSIpM29d6QUlKTPnKXlyx6o09ypa3R3CvzPl+y8oKIjatWtTsWJFo8QlCC+i0Wj47bffGDBgADNnzsTW1pZVq1axYcMGOnXq9Mqt6aZc9ooSsVNOAfr999/Jzs4W2y0WEDs7OyZPnsznn39O//79adiwIZCTuG/evJl169ahVCqZPn06b775Jkrlf9/OAxqXI1uj48cTd0nMUONia1Eog6JzKrKcmb7j21VmQGOxaHRxZIr3X2JiImfOnGHGjBkGj0MQXkV2djZ79uxhw4YNuUvwzZ07l7p16772OU2x7BU1IqEsQCdPnqRs2bJUqlTJ2KEUGb6+vuzcuZOFCxeyYcMGjh8/zvLly4mLi2PAgAGMGzcudwD2q5DJZAxvXgFrCwUrj4YTn6bC1c7SoNtzaXV6EjLUWCnlvNe5GgMalzfYtQTT9vf776tf/yAjy4IyLvZGvf/27duHQqGgW7duBotBEF5FamoqO3bsYMuWLaSmptK9e3dGjhxJ5cqV831uUfcbnkgoC4gkSZw6dYpOnToVqWUAjE0ulzNz5kwGDhxIt27dSE5OpnXr1qxYseK1u+dkMhkDm5THs4Qtiw7e4n5CBnZWShyslAX6bydJEmkqLRkqLRVd7ZjetXqR7eoQXp1MJsMp4TrS+UDcOo/lcbrKqPdfUFAQbdu2zdODmSAUpCdPnrB582Z27tyJVquld+/eDB8+HA8PjwK9jqj7DUsklAXk9u3bxMfHi+7uAvb48WN27txJcnIyjx8/ZsuWLXTq1KlAzt3cy5V1o95g9Ym7/HolmtjUbBytLbC1VOSrcpEkiUy1jtRsDdYWCgY38eSddl44FMFB2ELexcbG8vXXX9OjVSs+fL+LUe+/27dvEx4ezoQJE/LzkQThtURFRbF+/XqCgoJQKpUMGDAAPz8/XF0Nm3yJut8wZJKx59EXET///DMbN27k8OHDYkHzAqBSqdi4cSMBAQFYWloyfPhw1q1bR48ePZg5c2aBX+98RAKB5+4TFpVCtiZnSywHK2WeukO0Oj1pKm3u8fXKOjGyZcUit9aY8Pr0ej3jx48nKiqKrVu34uCQs0uGse6/JUuWsH//fkJCQl5pDLIgFITw8HACAgI4dOgQTk5ODBkyhAEDBuSWh8Ik6v6CI2qQAnLq1Clatmwpksl8kiSJQ4cOsWLFCp48ecLgwYMZO3YsDg4OWFhYsHz5cvr27UvVqlUL9LrNvVxpVqkEt+LSCA6LYf+12Jx1y3KCwlKpwFIpRy4DGTIkJPQSqLV61FodyGTIAGdbC/o2KEuPeh5UK20vhj8Iz9i4cSN//vknP/744zM/nq9y/0k6DarsbFycnQvk/tNqtezbtw8fHx+RTAqFIiwsDH9/f06dOoW7uzvTp0+nV69eWFtbGy2mvNb9ySnJKBRKlFY2ou7/B9FCWQAeP36Mt7c3X3zxBT4+PsYOx2z99ddfLFmyhLCwMNq3b897772Hp6dn7usajYYhQ4ZQokQJVq9ebdACm5Kl4WpUCuFxadyOT+NadCopmRr0SEgSyGQgR4aTrQW1PRypXtqBKm4O1C3rhJONeKgQnnfr1i1GjhyJn58f77333r++90X338PYJ6RnZFLa3b1A7r+TJ08ydepUtmzZUuAPaILwlCRJnDt3joCAAP744w8qVarEqFGj6Natm0k+yPxX3Z/w5AkKhRyvcu6i7v8HkVAWgN27d/P111/nNt8LeRMfH8+qVasICQmhatWqTJ06lTfeeOOF7z1//jyTJk3i66+/pkuXLoUWoyRJpGRpUGv1aPQSFnIZlko5TjaFs/SEYN6ys7MZPnw4FhYWucM48kKSJJasWs3hY8dZs86/QO6/mTNn8ujRIzZv3vxaxwvCv9Hr9Rw5coSAgABu3bpFrVq1GDNmDG3btjWr3Zj+Wfcv+vYbUlOSWPPdclH3/4PpPR6YoVOnTlG/fn2RTOZRVlYWGzZsYP369djZ2TFr1ix69er1r5VN8+bNadeuHcuWLaN169Yv3GrLEGQyGc62eUsCBOGpFStWEBUVxcaNG/OcTELO/WchqbFBTVnn/N/zKSkpnDx5kvfffz/f5xKEv1Or1YSEhBAYGEhkZCRNmzbl+++/54033jDLBOyfdX8ZFzti7982y89iaCKhzCeVSsWFCxd45513jB2K2dDr9ezfv59Vq1aRlJSEn58fY8aMwc7O7pWOnzp1KgMGDGD9+vWMHz/ewNEKQv6cPXuW7du3M3PmzJfu4vQqdDodCoWiQGI6cOAAkiTRvXv3AjmfIGRmZrJ79242bdpEfHw8HTp0YP78+dSqVcvYoRUoZ2dnkpOTjR2GSRIJZT6FhoaiUqnEdouvKCwsjEWLFnH9+nU6derEe++9R9myZfN0jrJlyzJs2DACAwPp1atXga9VJggFJSkpiblz59KyZUsGDBiQr3MVZEIZFBREq1atcHFxKZDzCcVXamoqW7duZevWrWRkZODt7c3IkSOL7AYfTxNKSZJEK+U/iIQyn06dOkX58uWpUKGCsUMxaTExMaxcuZKDBw9So0YNfvrpJxo1avTa5xs9ejRBQUEsXbqUhQsXFmCkglAwJEli/vz56PV65syZk+8fn4JKKCMiIrh+/ToLFizI97mE4is+Pp5Nmzaxa9cu9Ho9ffr0YdiwYZQpU8bYoRmUs7MzGo2GzMzMV+5VKy5EQpkPT3fH6dKli3hSeYnMzEwCAgLYuHEjjo6OzJkzB19f33wPyraxsWHKlCl88sknXLhwgWbNmhVQxIJQMPbs2cOJEydYvHhxgSzUXFAJZVBQEE5OTmITBuG1REZGEhgYSHBwMFZWVgwZMoTBgwdTokQJY4dWKJydnQFITk4WCeU/iIQyH27evMnjx49Fd/cL6PV6goKC+O6770hLS2P48OGMHDkSW1vbArtGly5d2LFjB4sWLWLLli0muQSFUDxFRkayePFi+vbtS7t27QrknFqtNt/3uE6nIyQkhG7duok1c4U8uX37Nv7+/hw5cgRnZ2fGjx9P//79i11S9feEMq/DtYo68QucD6dOncLe3p4GDRoYOxST8scff7B48WJu3bpFt27dmDx5Mu7u7gV+HZlMxowZMxg2bBjbt2/Hz8+vwK8hCHml1Wr57LPPKFWqFB988EGBnje/LZQXLlzgyZMn9OjRo4CiEoq6P//8E39/f86ePYuHhwczZ86kZ8+eWFlZGTs0o3g67jgpKcnIkZgekVDmw8mTJ2nZsqVoGft/UVFRLF++nKNHj1K7dm3WrVtHvXr1DHrNatWq0a9fP1avXk337t2LTbeLYLrWrFnDjRs38Pf3L9AW+YLo8g4KCsLLy4uaNWsWUFRCUSRJEmfOnMHf358rV65QuXJl5s2bR9euXQtsYpi5ero8oJjp/TzzWV3UxMTHx3Pz5k3R3Q1kZGSwYsUK+vfvz19//cW8efPw9/c3eDL51LvvvotCoeC7774rlOsJwsuEhYWxbt06xo0bR+3atQv03PlNKNPS0jh+/Dg9evQQY76FF9LpdBw4cAA/Pz+mTJmSs6D+kiVs2bIFb2/vYp9MAlhaWmJraysSyhcQTWuv6fTp08jlclq2bGnsUIxGr9ezZ88efvjhB7Kyshg9ejTDhw8vtMXGn3JycuLdd99lwYIFvPnmm0Vu3TPBPGRkZDBr1izq1KnD6NGjC/z8+U0oDx06hFarFdvDCs9Rq9UEBQWxfv16oqKiaNGiBTNmzKBhw4bi4eMFnJ2dRZf3C4iE8jWdPHmSBg0a4OjoaOxQjCI0NJSlS5cSHh6Oj48PkyZNws3NzWjx9OvXj127drFw4ULWrl1rVlt7CUXDokWLSE5O5ocffjBIS45Op8vX8JqgoCCaN29OyZIlCzAqwZxlZmayc+dONm/eTEJCAh07duSbb74RQyL+g4uLi2ihfAGRUL6G7OxsQkNDmTBhgrFDKXSRkZEsW7aMkydPUq9ePdavX1/gXXuvQ6FQMGPGDMaNG8e+ffvw9fU1dkhCMXLkyBH27t3LnDlzDDbzU6vVvvZEiMjISMLCwvjqq68KOCrBHCUnJ7Nlyxa2b99OVlYWPj4+jBw5Uqyn/IrEbjkvJhLK1xAaGoparS5W4ydTU1NZs2YN27dvp1SpUnz11Vcmt/5mo0aN6Nq1KytWrKB9+/bFbjkLwTji4+P58ssv6dSpk0FnT+enyzsoKAh7e/sCW8JIME9xcXFs3LiR3bt3Azk9O0OHDqV06dJGjsy8ODs78+jRI2OHYXJEQvkaTp48iaenJ56ensYOxeB0Oh27du3ixx9/RK1WM27cOIYOHWqyS0a8//779OvXj7Vr1/Lee+8ZOxyhiNPr9cydOxcrKys++eQTgz5gvW5CqdfrCQkJoWvXriZbbgXDevDgAevXryckJARbW1uGDx/OoEGDctdUFPLG2dmZq1evGjsMkyMSyjzS6/WcOnUKb29vY4dicOfOnWPp0qXcu3ePnj17MmHCBJMff1W6dGnGjBnDzz//TO/evUUXjmBQW7duJTQ0lO+++y53ORFDed2E8tKlS8TGxoq1J4uhp8tXHTt2DFdXVyZNmkS/fv0KdDmr4kiMoXwxkVDm0c2bN0lISCjS3d337t1j6dKlnD17lkaNGrFhwwZq1Khh7LBe2fDhw/n1119ZvHgxy5cvN6lueaHouHPnDitXrsTPz69Qtv583YQyKCgIT09P6tata4CoBFMjSRKXLl3C39+fCxcuUK5cOT7++GN69OiBpaWlscMrEpydnUlNTS2w7VCLCpFQ5tHJkydxdHSkfv36xg6lwKWkpPDTTz+xY8cO3N3dWbBgAR06dDC7hMzS0pKpU6cyffp0Tp8+LfYsFgqcWq1m1qxZVKhQgUmTJhXKNV9n68XMzEyOHDnCmDFjzK4cC3nztPfM39+fv/76i6pVq/LVV1/RqVMnkfQUsKdDBVJSUsRmGn8jEso8ero7TlEqoFqtlh07dvDzzz+j1WqZNGkSgwcPNuun2Xbt2tGsWTMWL15Ms2bNzPqzCKZn1apVPHjwgA0bNhTavfU6Wy8eOXIElUol1p4swp4uRh4QEEBERAQNGjRg+fLltGzZUjxEGMjf9/MWCeX/iIQyD+Li4rh9+zajRo0ydigFQpIkTp8+zbJly3j48CF9+vRh/PjxRaKAyGQypk+fzuDBg9m8eXOR+TcTjC80NJTNmzczdepUqlSpUmjXfZ3utaCgIJo0aYK7u7uBohKMRaVS8dtvv7Fhwwaio6Np2bIlH3/8MQ0bNjR2aEXe0/28xTjKZ4mE8gUkSUKtVj83I/LUqVMoFApatGhhpMgKzt27d1myZAkXLlygadOmfPPNN1StWtXYYRWoSpUqMWjQINauXYuPj49RF14XioaUlBTmzJlD06ZNGTx4sMGuk5GRAeSsr/r0v7wubB4dHc2lS5f44osvDBWmYATp6em5i5EnJyfTuXNnFi1aRLVq1YwdWrHx9xZKjUZDcnJy7n8lSpSgcuXKxg3QSGSSJEnGDsLU7Nq1i9mzZ9OoUSM6depEmzZtqFSpEu+//z5qtZoff/zR2CG+tqSkJH788Ud2795NuXLlmDJlCm3atCmyXSPp6en069ePZs2aMW/ePGOHI5gxSZL48MMPuXjxIlu3bjXYA0poaCijRo1Co9Egk8lyy2Z0dDT29va4ubmhVCpRKBQolUocHBxo2rQpXbp0oWXLlrlJ588//8yGDRs4cOBAoW+HKhS8xMREtmzZwo4dO1CpVPTo0YMRI0ZQvnx5Y4dWpCUmJrJ3716SkpJITk4mMTGRuLg49u3bR6lSpVAqlWi1WnQ6HTqdjrp16xISEmLssI1CtFC+gI2NDQkJCRw4cIBDhw5hY2ODl5cXd+7cYezYsa81ON7Y1Go127ZtY82aNchkMqZMmcKAAQOwsLAwdmgGZW9vz6RJk/jiiy/o379/kZxMJRSO4OBgjh49yrfffmvQ1m4vLy/kcjkpKSk4OTkhSRKSJKHX69FqtaSlpfG0HSApKYkqVaqwevVqAgICqFChAv369cPX15fg4GA6deokkkkzFxMTw4YNG9izZw8KhYI333yToUOHUqpUKWOHViycO3eOuXPnkpmZiVwuR6/Xo1QqUavVpKSk4OzsjIWFBVZWVjx58oSOHTsaO2SjES2ULxAeHk6XLl2wtbXF2tqazMxMEhISSE5OplSpUpQpU4YuXbrQtm1bWrVqZfD15/JDkiROnDjBsmXLiI6Opn///owbN65YLWir1+sZNWoUOp2ODRs2iH2+hTyLiopiyJAhdOrUiTlz5hj8el9//TXLly/Hw8Mj934NDw/H2dk5N5FIT08nKyuLCRMmsHTpUlxdXUlNTUWj0aBUKsnIyGD69Om8++67RWJcdHETERHB+vXr2b9/P3Z2dgwZMoRBgwbh6Oho7NCKFbVaTd++fbl06RJly5bN7TGIiIjAzs4ud5ehp8sIHT58mEqVKhkzZKMRCeULqNVq6tevj1qtzh18GxMTQ0ZGBuXKlSMtLQ2VSoVcLsfZ2ZnvvvuODh06GDnq592+fZvFixdz6dIlmjdvztSpU/Hy8jJ2WEZx9epVRo8ezSeffEK/fv2MHY5gRnQ6HW+//TZPnjxhy5YthbKlZ3R0NB07dkStVucmg+Hh4bi4uFCyZEkkSSIqKoouXbowadIk+vfvj4uLC5aWlkiSxIMHD0hPT8fFxQVnZ2e6du1K7969adOmTZHvlTB3165dw9/fn+PHj+Pm5sawYcPo06ePWIzciC5cuMDgwYNRKpW5DUgPHjzAwsICDw8PIOehs3v37vj7+xszVKMSTTUvYGlpSeXKlVGpVLl/S0tLw9HREWtra0qVKoWHhwc6nQ5HR0eTS9ISExOZP38+Q4cOJSEhgeXLl7Ny5UqTi7Mw1a1blx49evDdd9+Rmppq7HAEM/J0Xb958+YV2v7wHh4eDBw4kMzMTPR6PQB/f/ZPT0/H2tqa999/P3dMpVarzX1dpVJRunRp3N3dUalUbNu2jREjRtCpUyfi4+ML5TMIr06SJEJDQ3n33XcZOXIkERERfPbZZ+zZswc/Pz+RTBpZs2bN6N+/P2lpaeh0OiBnwtzTMve0gcmQE/XMgUgoX6JOnTq5N0tWVhY6nQ57e3sgpws1OjqasmXLsnbtWpPZ3k+tVhMQEECfPn04evQo06dPZ+vWrbRq1arITrrJi0mTJqHRaFi9erWxQxHMxLVr1/jpp58YM2ZMoY+/HTNmDA4ODqSkpAA5SYdMJkOSJFJSUujatStNmjShdOnSWFhYoNFogJyHX71ej5OTEwqFghIlSlCiRAk0Gg1ly5Y16SE6xY1er+fo0aOMHDmSCRMmkJqayjfffMPOnTvp3bu3WD/XhMyYMYMyZcrw5MkTgNyVFyBntrenp6dJ9lQWJpFQvkSVKlVyK+/09HQUCgW2trZIkkRsbCylSpXi559/pnbt2sYOFUmSOHLkCP379+eHH36gd+/e7Nmzh0GDBpnd5CFDKlmyJG+//TY7duzgzp07xg5HMHGZmZnMmjWLmjVr8tZbbxX69StWrEifPn3IyMjIbZ2UyWSkpqZia2vLe++9B4CtrS0ODg65D8DJycnY2trmJiPp6ekkJibSs2dP1q5d+9xyaELh02q17N27l4EDBzJz5kysra1ZuXIlGzdupHPnzmKctwlyc3Pjgw8+QKfTkZ2djVKpRKfTodfr0Wg0DBkypNg/AIi79iUqV66MXC5Hp9ORlpaGvb19bjLp5OTEDz/8QOPGjY0dJjdu3GDcuHF8+OGHVK5cme3btzNt2jQxcPslBg8eTLly5Vi0aBFi+LDwb5YuXcrjx4+ZN2+e0R7M3nrrLezs7EhNTc29X9PS0ujZsyd16tTJfV+ZMmXQarVotVoyMjJyWyFTU1NJTU1l8ODBfP/996Lr1Miys7PZtm0bffr04fPPP8fT05N169bx008/0aJFC9GTZOKGDBlCkyZNePLkCXK5PHfVBXt7e/r27Wvs8IxOJJQvUaVKFaysrMjIyEClUmFnZ0dcXBy2trYsX76c1q1bGzW+x48f8/nnnzNixAhSUlJYtWoVS5cuNZnud1NlYWHB9OnTuXjxIkePHjV2OIKJOnHiBLt372batGl4enoaLY7q1avj7e2d242dmZmJvb09EydOfOZ95cqVQ6PRkJKSgkwmw9HRkaSkJDIyMhgzZgyLFy8u9q0nxpSWlsa6devo2bMnixcvpkGDBmzdupUlS5ZQr149Y4cnvCILCwtmz56Nra0tKpUKSZJITU2lU6dOYj1QxDqUL1W2bFkcHByIjY1FJpORnZ2NpaUlCxcupGvXrkaLS6VSsXHjRgICArCysuKjjz6iT58+RWpvcUNr2bIlbdu2ZenSpbRq1Qpra2tjhySYkISEBObNm0e7du3o06ePscNh3LhxBAcHk5ycTHZ2NqNGjXpuV5QyZcogk8lITk7GwcGBxMREdDod77//PtOnTxddqEaSkJDApk2b2LlzJ1qtlp49ezJixAjKli1r7NCE19SkSRMGDRrE6tWr0ev1KBQKBg0aZOywTIKoZV5CLpdTo0YN0tPTkcvlKBQK5s+fb7QfGEmSOHDgAP369ePnn3/mzTffZPfu3bz55psimXwNH3zwAQkJCQQGBho7FMGESJLE559/jlwuZ9asWSbRBVmvXj06dOiARqPBzs6Od99997n3uLm5odPpUKlUubPCP/30U2bMmCGSSSOIjo7mm2++oWfPnuzcuZP+/fuzd+9ePv74Y5FMFgFTp07F3d0dtVqNh4cHbdu2NXZIJkG0UP6LKlWqoFKpcHJy4uOPP8bPz88ocfz1118sWbKEsLAw2rdvz/vvvy+a1/OpfPnyDBs2jICAAHr06JG7lphQvO3YsYOzZ8+yYsWK3DVoTcFbb73F5s2bad++/QuHtZQuXZqsrCz0ej12dnbMnz/faPVVcXb37l0CAgI4cOAAjo6OvPXWWwwYMAAHBwdjhyYUoFKlSjFx4kSmTp1Kp06dxOTX/1esvwVJkkjJ0qDS6tHqJZRyGVZKOU42FshkMpRKJUqlkkmTJjFu3LgCuWZ0dDSOjo65SxD9m7i4OFatWsW+ffuoVq0aP/74I02aNCmQOAQYPXo0QUFBLFu2jAULFhg7HMHIIiIiWLZsGQMHDqRly5bGDgf4Xx3lVbshU2fNY9DAgSRnqnPrqKecnZ1Rq9U4OTmxbNkyevXqZcSoi5+wsDACAgI4efIkpUuXZtq0afTu3VsMpynC3n77bcJu3mXoWxOISs56Ln8ojorVTjkpWRrCHiVzJz6dW3FpXI9OJSVLgx4JSQKZDOTIcLKxoJaHI+42esIvnuaraeNwss3/gPbHjx/To0cPatWqxbp1615602VlZREYGEhgYCB2dnZMmDCBXr16ia4rA9i/fz+zZs3i+++/p2nTpsYORzASjUbDyJEj0Wg0bNiwwWiJQF7rqOqlHajiZo+9KgGfLh1YunRpsV9cubBIksT58+fx9/fnjz/+oGLFiowcOZLu3buL3YiKoNctm/XKOeNkUzzuhyKfUEqSxM3YNEKuxrD/WiwpWRpyPrGEpVKBpVKOXAYyZEhI6CVQa/WotTpAhkwGTjYWdK/tjm+9MlQv7fBaTx+SJPHee++xdetWbGxsCAgIeG4Teb1ez/79+1m5ciXJyckMHTqU0aNHF9ruHMWRJEm89dZbpKWlsXnzZtF1UUytWLGCzZs3s379eqpXr16o1zaVOkp4NU8XIw8ICODmzZvUqlWLUaNG0b59e/HQX8SIspk3RTqhPB+RwPpz97kalUK2Roe1hQIHKyVKxasXeq1OT5pKm3t8vbJOjGxZkWaVXPMUy969e5k4cSI2NjakpqZSt25dgoKCcpfyuHLlCosXL+b69et07tyZ9957T4zrKyS3bt1i2LBhTJs2TbTuFEOXLl1i/PjxTJ48mREjRhTqtU2pjhL+nUajYd++fQQEBBAZGckbb7zB6NGjeeONN4p0klBcibKZd0UyoUzN1rD6xF32XI5GpdXhaG2BraUiX4VekiQy1TpSszVYKRX0aeDBO+0q42j9303Z8fHx+Pr6Eh0dTZkyZVCpVCQkJPDNN9/QuXNnVq5cyaFDh6hRowbTpk2jYcOGrx2n8Hq++uorDh48yO7du01qMoZgWGlpaQwePJiyZcvy448/FloLk6nVUcLLZWVlsXv3bjZu3Eh8fDzt27dn9OjRJrFLmlDwRNl8fUUuoTwXkcDig7d4kJCBrZUSBytlgT49SpJEmkpLhkpLRVc7pnWtTguvlz9tSJLEpEmT2LlzJx4eHrlL/MTExCCXy3F3d8fV1ZVJkybh4+MjukyMJDk5mb59+9K5c2c+/fRTY4cjFJJPP/2UM2fOsHXrVtzd3QvlmqZWRwkvlpqayrZt29i6dStpaWl4e3szcuRIvLy8jB2aYCCibOZPkUkoJUlix6VHrDwajkqrx9XOMk9N03ml1elJyFBjpZQzuWNVBjQu98Ib77fffmPSpEnY2NjkLh2RnJxMfHw82dnZdO/ena1bt4ot0UzA9u3bWbhwIYGBgdSsWdPY4QgG9nRC1pdffkm3bt0Mfj1TraOEZz1+/JhNmzaxa9cutFotffr0YdiwYWIIUhEmymbBKBIJpSRJBJ57wOqTd5HJZLjYFs60fUmSSMrUIEkS49tVZnjzCs9cNy4uLrer28PDg8zMTOLi4sjOzsbR0RFLS0usrKwICgp6bucLofDpdDr8/Pyws7Nj7dq1RaKACy8WExPD4MGDadOmDfPnzzf49Uy1jhL+5+HDhwQGBuaObR84cCBDhgyhRIkSxg5NMCBRNgtOkUgot198yLLDt5HLZbgUwPI+eZWUqUavl5jSuRoDm+QsOC5JEhMnTmTXrl24urqSkJBAWloaNjY2lC5dGhsbGyRJIioqip49e/LTTz+Z/c1UFFy8eJHx48fzxRdf4OPjY+xwBAPQ6/WMHz+e6OhotmzZUiiLTptiHSXkuH37NgEBARw+fBhnZ2f8/Pzo37//K60VLJg/UTYLjtknlOciEpi58wp6CUrYFf7N8FRihhq5DBb2r09zL1f27NnDpEmT0Gg0ZGRkoFAocHNzw8nJ6Znj0tPTycrKYsOGDWL7JhPx0UcfcfnyZXbt2iWGIhRB69evZ9WqVaxevZpGjRoZ/HqmWkcVd5cvX8bf358zZ87g4eHB8OHD6dWrF1ZWVsYOTSgkomwWLLNOKFOzNYwJ+J0HCZmUdrQyagufJEnEp6nwLGHLV93K071TO6KiolAoFLi4uODo6IgkSeh0OvR6/TP/paSk0Lp1a0JCQowWv/A/MTEx9O/fn8GDBzN58mRjhyMUoJs3bzJq1CiGDRvGpEmTDH49U62j1o16o8jNMH0VkiRx9uxZ/P39uXz5Ml5eXowaNYquXbuKNWiLGVE2C55Zl6DVJ+7yICGDkvbGvRkAZDIZrnaW3E/IYOh8fx49eoSVlRUODg4olUqys7NRKBTI5XIsLS2xs7PDzs4OR0dHHBwcqFKlilHjF/6nTJkyjBo1irVr19K7d288PT2NHZJQALKzs5k1axaVK1fmnXfeKZRrmmod9dOJCKZ3K9wF3I1Jr9dz+PBhAgICuH37NnXq1GHJkiW0bt1arKxRTImyWfDMtoXyfEQC03ZcQamQmVQ2n5qtITMrm26OcXSuXxFbW1vs7e1zE0hbW1tsbGyMfgML/06lUtG/f38qV67MsmXLjB2OUAC+/fZbfv31VzZt2kSlSpUMfj1TrqN0eonFA+oX2QWWn1Kr1QQFBREYGMijR49o1qwZo0ePpnHjxqIOLsZE2TQMs2yhlCSJ9efuo9LqcLE1zp67L+NgpSRDpSSxZF3atWskKi0zZWVlxQcffMDMmTM5ffo0rVu3NnZIQj6cOXOGHTt28OGHHxZKMmnqdVRsajbrz96nacUSRbKOyszMZNeuXWzcuJGEhAQ6dOjAV199Ra1atYwdmmBkomwajlkmlDdj07galYKjdeFM788LmSzniScsKoVbcWnUcHc0dkjCa+rQoQNNmzZlyZIlNG3aNHebTMG8JCYm8vnnn9OqVSv69+9fKNcUdZRxJCcns23bNrZt20ZGRga+vr6MGDGCihUrGjs0wUSIsmk4Zjl4JORqDNkaHbaWCmOH8kK2lgqyNTqCw2KMHYqQDzKZjOnTp/Po0SO2bt1q7HCE1yBJEvPnz0eSJObMmVNoPyCijipc8fHxLFmyhB49ehAYGIivry+//fYbs2fPFsmk8AxRNg3H7BLKlCwN+6/FYm2Rv701DUkmk2FtoWD/tVhSsjTGDkfIBy8vLwYNGsSaNWt4/PixscMR8mj37t2cPHmSzz77rNAWqBZ1VOGJjIzkiy++oFevXuzdu5ehQ4cSFBTEtGnTKF26tLHDE0yMKJuGZXYJZdijZFKyNDhYmXZvvYOVkpQsDVejUowdipBP48aNw9LSkpUrVxo7FCEPIiMjWbJkCf369SvUNV5FHWV4N2/e5MMPP+TNN9/kzJkzTJgwgaCgIN59911cXFyMHZ5gokTZNCyzSyjvxKcjSRh0n82CoFTIkYA78WnGDkXIJwcHByZNmkRISAhhYWHGDkd4BVqtllmzZuHm5sYHH3xQqNcWdZRhSJLEH3/8waRJkxg2bBg3b97k448/Zu/evYwYMQI7OztjhyiYOFE2DcvgaXrPnj2xsLDA2vp/s6m++OKL11538VZcGvDvKx0l3LlMSuRNvDoOfq1r5F4reC0J4X/SbMJiFJavsXuCJP1/vDnmzZuHt7c3TZo0yVdcQuHr1asXv/zyCwsXLmT9+vVi7ToT9/PPP3Pr1i38/f2xsbEx2HX+Xr9lZ2fTq1cvbjk24r/qqH/SZmcSc/k45Zv/b7vP2/sDcKvVHGfPGq8dX3LkLa7tXIZNCXcAJL0Oj8adKVM/p8U26vB65u28x8EanqhUKho3bszMmTNNapFvvV7P6dOnCQgIICwsjKpVq/Lll1/SuXNnFArTHAcnFL4XlcVRo0Y9855XyR8KqywCWDuVpFbfiQBkJcVz78ROMuIiUVrbkq2V2Jvqy6iWUwC4cOECa9asIS4uDicnJ2QyGX369KFfv36vHVNBK5Ra45tvvqFatWr5Po8kSVyPTsVSqUDS65DJX1yZuFZpgGuVBvm6llaVReLdMOzcyvHk1kVK122V53NYKhVcj05FkiRkMhmfffZZvmISjEculzNjxgzGjBnDb7/9Rp8+fYwdkvAST7fUGz9+fKEsE/O0fouPj2fAgAHIWyuwdCybp3NoVZk8vLDvmR+xat1HFUh8NiXcaTRqDgCqtCR+/+lj3Go2RWFpjUIux7leJzb5z0Wj0fD222/zyy+/MGjQoAK5dn7odDoOHjxIQEAAd+/epX79+ixbtoxWrVqZ7Pg3wbj+WRbfeOMNateuDTybP/ybwiqLf6fOSCFs8zd4tu5DrT4TAIhPSCLy4RUkSeLChQvMnTuXBQsWUK9evZzX4+PZvXt3gcRVUIzyGPrgwQPeffddfv75Z8qWLcuGDRsIDQ1l+fLlJCUlsXDhQqKjo1GpVLRr144JE3K+YG/fHtyx8CI96hYOJctQzXs090/tJiniKjKZHEt7J+oM+IC4q2dICP+TWv0mkZUYx+1969CpVUiShGuVBlRs2xe9TsuD03tIfnATSa/DxqU0VboOx8Imp9vk8Y1QnCvUpFTNpkRdPPhMQnlqwVtUaNOXhPA/0WalU75lD9zr5qxTmBp1hzuHNiHp9ViV8iQ87gEnGnxF+1bNGTduHH5+frRv3565c+diaWnJw4cPiYuLo3Llynz11VdYWFgQGhrKDz/8gEqlQqvVMnToUHr37l34/1DCM+rVq4ePjw+rVq2iU6dOODg4GDsk4R8yMjKYPXs2devWfa51wtDc3NzwKOfJtcdxOFo5cuPwDrJTnqDXanLrHUmv5+7RLSQ/uIFcrkQml1N/6MfcObgBnVrFHwGfI5PLaTjiM8K2LMCjSRdKVm3IrZB1yBVKspPjUaUmYVvKgxo930GuUKJTZ3N7fwAZ8Y+wsLXH1tUDvU5LdZ8xz8WoVWWhsLDKfRhXyGVkqnWkZGlwtrWkcePGPHjwoFC/t39Sq9X89ttvBAYGEh0dTcuWLfnoo49o2LChUeMSzIebmxsVK1YkJiYGd3d3Fi5cyP3IR5y/FoVz5fqU6Njf6GXx76L/OIpjuWq5PQcAtvYO6Ku3IiVLw88//8zbb7+dm0w+/YyFtePXqyqUhPKjjz56psvb39+f999/n48++ogpU6awY8eO3G7EOXPmMGbMGBo1aoROp2PKlCkcPnyYzp07o5dAk51OrSEfY2dlwYMzv5GVGEvDEZ8hV1qgznx+vEH0n0cpUbke5Zv7AqDJygDgUegBFBZWNBwxC4DIs3t5cHoPVboMBSA27BQVWvfGuUJN7hzaSGZiLLZ/a6qWK5U0HDGLzIQYLm/4ktK1WyBJEjd+W01137E4e9Yg9u41nlw7i0arf+H3cuvWLVavXo2lpSVvvfUWR48epVu3btSoUYO1a9cil8tJTU3Fz8+PFi1a4ObmVjD/IMJrmzx5MsePH+enn35i2rRpxg5H+IcFCxaQnJzMjz/+WOjDEu7fv09ySjJ21apy/2AAXq164FS+OpJex7VfVvD45kVsXNxIfnCDxqO/QCaXo1VlIpMrqNJ1OH8EfP7C1ounMuIfUnfwdOQKC8K2fMuT25dwq9mMB2f3Ilda0njsPPQaFZc3fo29e4Xc47ISY/kj4HMknZas5MdU7jQEuTJndxCZDPRIqLV6UlNTOXv2LH5+fgb/rl74+TIy2LlzJ5s2bSIpKYnOnTuzcOFCqlc3z23oBOO5f/8+KSkpNG7cmM8++4wxY8ZQtkot3vzhFA/2/mD0sviUc8VaeLUfQHrcA1wq1n7mGnIZaP+/bN68eZOZM2cW/BdVwIzW5d2tWzcuXrzIpEmT+OGHH3BxcSErK4vQ0FASExNz35eZmZn7xCxJEi41WyCX5fxQJN4No1K7N3MrR0vb51uMnMpV497xHejUapzKV8O5Qk0AEsL/RKfK4sntP3LOrdNi5ZSz1VHG40eoM1JwqVgbmVyOW63mxIWdplL7/y2K7FarOQC2rmWQyeWoM1LRZKUjlytyx1k4la+BpVNJtC/Z3bJDhw65iXadOnV49OgRACkpKcybN48HDx6gVCpJSUnhzp07IqE0AaVKleKtt95i1apV9O3bFy8vL2OHJPy/Q4cOERwczNy5c/Hw8Ci063700UfI5XIePHjAqHcmsTHOkrTIm9zNTs99j06dTVZSLC6VaiHp9dzeH4CzZ3VKeNVDZvVqia9r1YYoLHLGcjuUqUR2cs4yVskPblC54yBkMhkKS2tK1XyDrKT43OP+2eV9ZdPXOLhXxN69AjIg/tIhxo25g52Vkk6dOtGzZ88C+mZeTVJSElu2bGH79u1kZ2fTo0cPRowYgaenZ6HGIZi/v5fFqVOnYm1tnZtTZKp13Lr7BDQqkyiL/0WGDEkCjf75/GHWrFlERETw5MkTdu/ebTIT0ow28lqn03H37l0cHR2Jj8/5wp9uKx4QEPDCXUlkMhlKSyukPAx4L1m9MY5lK5N0/zrRfx4l6tIh6vSfAkDlzn64VKr93DGxYafRqbP5/eePc+LS6UDSU7Ft39yuIrnib/t/ymRIet1z55GQQCZD+ZLxPn//jHK5HJ0u5xxff/01rVq1YsGCBchkMoYOHYparX7lzywY1uDBg9mzZw+LFi3iu+++E+O5TEB8fDxff/01nTt3xtfXt1Cv/fSBOTQ0lImT30NqPxEJaDDsk9yH3b9rPPpzUh7eJjnyJvdP7qLekJkvHQ/+d8+cSyZH0r+45wNefj9aObjgUMaL5Ac3sHevgAS4Ne7CTz9+hptj4W5DFxsby4YNG9izZw9yuZx+/foxdOhQ8eAsvLa/l8UpU6bwxhtvADk5RVK2nv6rz2KpkGNrmZP6GLMs/p196QqkRt2lbJMuuX+TkJDJwEIuo3r16ly7di23tX7+/PkANGnSBP1Lr134jDZVdeXKlVSoUIE1a9awbNkyHj58iK2tLU2aNCEgICD3fY8fP85NOOWynKz9acLuWqU+UZcOo9fmLP75oi7vrMQ4LGwdKV2nJZXa9Sct+l7OsVUbEnXpEDqNCgCdRkXGk2j0Oi3x18/RYNgnNH3nW5q+8y3NJizCytGVxLv/vmSMbQl39HodKQ9vAZDy8Dbq5MdYKPP2NaemplKmTBlkMhl//PEH4eHheTpeMCxLS0umTZtGaGgox44dM3Y4xZ5er2fu3LlYW1vzySefGC3Bb9q0KX369iP23G84lKvGwwv7cl9TpSWhSktCnZmGTqPGpVJtKrbth7VTSTITYlBY2qDXatDrtHm+rrNnDeL+OoskSejU2Ty5+ftL36tVZZIe9wAb1/+f9S2BHBmWeayj8uPevXvMnTuX3r17s2/fPkaOHElQUBAffPCBSCaFAtG0aVP69+/P999/n5tTWCnlyJGRnZZsEmXx7zwadiDl0W1ir57O/Zs6K5PEq6ewVMp56623+Pnnn7l69Wru61lZWXmOz9CMMoZy/PjxnD17lsDAQKytrZk6dSofffQR/v7+zJ8/n6VLlzJw4EBkMhk2NjZ88sknuLm5YaGQ42CtRKXVgxWUa+bN/VO7+TNwHjK5ImdSzv+3Pj715PZF4q9fyHnqkCSqdB0GQPlm3Yk8q+Hyhq94+vtTrqk3mU+isHZ0xda1zDPnKVWrGbFXT+Na9eUDw+VKC2r0HMfdQ5uQJAlFiXI4lCxDmZJ5W2h38uTJfPPNN6xZs4Zq1apRp06dPB0vGF6rVq1o3bo1S5cupVWrVlhZvcayUkKB2LJlC6GhoXz//fc4Ohp379tJ777D0nXbKOc7gcwbp7i0bjYgQ2FhSZVuw0EvEX5gPZJehyRJOJatgkulOsgVSkrXbsEf/nNRWFrRcMSrrwjh2bIn4fsDuLT2Myxs7LFzK4fSyjb39b+P29LrNLjVap67CoZOL2FrqcDJ5vmW1IJ2/fp1/P39OX78OCVLluT999+nT58+2Nra/vfBgpBHb731Fn369GHFihVs27aNcaOGcedOAhqZktq+I41eFgEUllbU9/sIS3tn6g/5kHsnf+Hh2SAUllao9TK8mnbGycaCli1b8tlnn7F8+XIeP36Mi4sLFhYWzJw502S6uwFkkvSSAX4m6qNdYRy5EYe7k+HWlssPnTobhWVO8nwv/CaZJ9dx/dzRZxJqoWiIjIxk4MCBjB07lrffftvY4RRLt2/fZuTIkQwcOLDQFzB/mcKuo/Q6LUgScqUFOrWKv3YsxaNRR0rVbPqfx8amZNG5Vmm+7lvvP9/7OiRJ4vfffycgIIDQ0FDKly/PyJEj8fHxeeGwJkEwlLS0NN7beJ4rj3WULWFvkGvkpyz+k6HLpiGYzuq1r6h6aQeO3Ij/7zcayZNbl4i6eAiQyNbBxEkzRTJZRHl6ejJ06FD8/f3p0aMHZcqU+e+DhAKjVquZNWsWFSpUYOLEicYOJ1dh11Ha7Eyu7VyGJEm5SxSVrPHGqx0sk1G9dMEvf6XX6zl58iT+/v5cu3aNatWq8fXXX9OpUyexKYBgcJIkERMTw+XLl7ly5QpXrlzh7t27ZJZrilSzC2CYhDJfZfGfDFQ2DcnsEsoqbvbIZKDV6U1y+6TSdVtRum4rtDo9CRlqurZvYOyQBAMaO3YswcHBLF++nG+++cbY4RQrq1at4tGjR2zYsMGkWrsKu46ytHOk4cjZeT5Oq9MjA6q4FdyPllar5cCBA6xfv56IiAgaNWrEihUraNGihZi8JhiMTqcjPDw8N4G8fPkyjx/nzLyuVKkS9evXZ9iwYahLePHN8WiDlc3XLYv/ZIiyWRjMLqGsV84ZJxsL0lRaXGxN50fkn9JUWpxsLKhb1snYoQgGZGtry3vvvcfs2bO5ePGi2FazkJw/f57Nmzczbdo0KleubOxwnlEc6yiVSsWvv/7Khg0biImJoU2bNsyaNeuZhZgFoaBkZmZy9erV3OTx6tWrZGVlYWFhQa1atfDx8aF+/frUr18fJ6f/3d8pWRp+uPC4WJXNwmR2CaWTjQXda7uz5ffI3C0NTU1sbBwZkpKBjcsWymB3wbi8vb3ZuXMnCxcuZPPmzWJ/YQNLSUlh7ty5NGvWzCS2CPwnc6ijJEkiW6Ojb4P81VFpaWns2LGDLVu2kJKSQpcuXViyZAlVq1YtwGiF4i4+Pv6Z1sfw8HD0ej2Ojo40aNCAt99+m/r161OzZs1/7a0oTmXTGMwuoQTwqVuG3ZejyFTrsLMyvY8gKZSoUzP4dcUcUk9XZ9CgQTRt2tQkb14h/2QyGTNmzGDEiBH88ssvDBw40NghFVmSJPHll1+iVquZO3euyY7HM/U6KlOtw9pCgW+91xv3m5iYyKZNm9i5cydqtZqePXsyYsQIypUrV8CRCsWNXq/n7t27ucnjlStXiImJAaB8+fI0aNCAAQMGUL9+fSpUqJDnOqCol01jMr1v8xXUcHegXlknQu8nYmupMKlETZIksLSlcyMPurV9m+3btzFx4kQqVKjAoEGD6NGjh1gmowiqWbMmffr04YcffqBr1644OzsbO6Qiae/evRw9epQFCxZQqlQpY4fzUqZeR6Vma2hayTXPg/6jo6MJDAzkt99+Q6lU0r9/f/z8/ChZsqSBohWKuuzsbK5du5abPIaFhZGeno5CoaBGjRp07NiR+vXr06BBA0qUKJHv6xXVsmkKzG7ZoKcu3Etg6vYrKBUyHK1Np1k4NVuDTi+xeEB9mlVyRZIkLl++zLZt2zh6NGf5oB49ejBo0CAqVKjw3ycUzEZSUhJ9+/ala9eufPLJJ8YOp8h59OgRfn5+dO7cmdmz8z/w3dDMpY56FREREQQEBLB//34cHR0ZMmQIAwYMMPq6n4L5SUxM5PLly7kJ5M2bN9HpdNjb21OvXj0aNGhAgwYNqFWrlsFWSClKZdOUmG1CCbDwwE22X3xISXsrk5jxrdXpeZyuYnATT6Z3q/7c6/Hx8fzyyy/s3r2bxMTE3DFgrVu3NtmuOyFvtm7dyuLFi9m4cWPuNllC/ul0Ot5++20SEhLYsmWL2bTyz911iZ1/RlPO1RELpfHH1v5XHfVPV69exd/fn5MnT1K6dGmGDx9O7969sbExzXWABdMiSRL3799/Zvmehw8fAlCmTBkaNGiQ2/ro5eVVqL+D5pY/mAOzTihTszWMCfidyMRM3BysjNp0LUkS8WkqPEvYsm7UG//61KNWqzl8+DDbt2/nr7/+wsPDgwEDBtC7d2/xxG/mtFotfn5+ODg4sGbNGpPqTjFnP//8Mz///DNr1qwxm5nDR44c4YuvF/C41gDsSlegtKO1WdRRkiRx4cIF/P39uXTpEhUqVGDUqFF0794dCwvTac0RTI9areb69eu5yePly5dJTU1FLpdTrVq13OSxfv36Rt9m01zzB1Nm1gklwLmIBGbuvIJeghJ2xlsGIDFDjVwGC/vXp7nXqzdVX79+nW3btnHw4EHkcjne3t4MHDiQatWqGTBawZBCQ0OZMGEC8+fPp3v37sYOx+xdvXqVsWPHMnbsWN555x1jh/OfMjMzWbx4Mb/++iudOnWi67CJzN13x+TrKL1ez7FjxwgICODGjRvUrFmT0aNH0759e9GDIrxQcnIyYWFhuV3YN27cQKPRYGNjQ926dXO7r+vUqWOSvQrmnj+YGrNPKAG2X3zIssO3kctlRllbKilTjV4vMaVzNQY2Kf9a50hMTGTPnj3s3LmT+Ph4GjZsyMCBA+nQoQNKpVnOnSrWZs6cydWrV/nll19MsiI1F5mZmfj5+eHs7MyaNWtMvizcuHGDTz/9lPj4eGbMmEGvXr2QyWQmXUdpNBr27dvH+vXrefDgAU2aNGH06NFiZQrhGZIk8fDhw2daH+/fvw9AqVKlcpPH+vXrU7VqVbNZPs2Uy6a5KRIJpSRJbDj/gB9P3EUmk+Fia1EoFaEkSSRlapAkifHtKjO8eYV8X1en03HixAm2bt3KH3/8QalSpXjzzTfp169fgcxwEwpHdHQ0/fv3Z+jQoSa1LaC5mT9/PgcOHGDTpk14enoaO5yX0uv1bNy4ke+//54qVarw5ZdfPjPpzhTrqKysLPbs2cOGDRuIj4+nXbt2jB49mjp16hg8LsH0aTQabt269czyPYmJichkMipXrvxM93WZMmXM9uHDFMumuSoSCSXk/OPsuPSIlUfDUWn1uNpZGnSg7dOtFa2Uct7rVJUBjQv+yeLOnTts376dkJAQtFotXbp0YdCgQdSuXdvsb7zi4KeffsLf35/t27dTvrx5P3kaw7Fjx5gxYwazZs2iT58+xg7npR4/fsycOXMIDQ1lxIgRvPvuuy8ca2gqdVRqairbt29ny5YtpKWl0b17d0aNGoWXl5fBYhFMX1paGlevXs3tvr527RoqlQorKytq166d2wJZt25dHBzMb0mbf2MqZdPcFZmE8qnzEQksOniL+wkZ2FkpcbBSFmjyJUkSaSotGSotFV3tmN61usHHPKSmprJ37162b99OVFQUtWrVYtCgQXTp0sWk9jAWnpWdnU3//v2pVq0aS5YsMXY4ZuXJkycMGjSIhg0bsnDhQpN9gDpx4gRffPEFFhYWfPHFFzRt2vQ/jzFWHfXkyRM2bdrEL7/8glarpXfv3gwfPhwPD48Cu7ZgHiRJIiYm5pnWx7t37yJJEiVKlHim9bF69erFZjJWUcwfClORSyghZ/bW6hN3+fVKNNkaHY7WFvlewFSSJDLVOlKzNVhbKOhTvyzvtPPCoRBnY+n1es6ePcv27ds5e/Yszs7O9OvXjzfffJPSpUsXWhzCqzty5AgffvghK1asoGXLlsYOxyxIksR7773H7du32bZtm0kuEp+dnc2yZcvYuXMnbdu2Zfbs2XmKszDrqJQncQQGBrJ3714sLS0ZMGAAfn5+YghNMaLT6QgPD39m+Z74+HgAKlas+MzyPeXKlTPZB7jCUFTzh8JQJBPKp85HJBB47j5hUSlka3K2M3KwUuapKVur05Om0uYeX6+sEyNbVjT6oqORkZHs2LGD3377jaysLNq3b8+gQYNo1KhRsa4MTI0kSbz77rvEx8ezbdu2YvOknx/btm1j4cKFrFy5khYtWhg7nOeEh4fz6aef8ujRI6ZOncqbb7752mXOkHVUCW0iAQEBHDp0CGdn59zFyO3t7V8rVsF8ZGZm8tdff+V2X//1119kZmZiYWFBzZo1c7uv69WrZ5IPbKagKOcPhlKkE0rI+UG/FZdGcFgM+6/FkpKlQcp5AUulAkulHK1GjUKhQKFQoJdArdWj1upAJkMGONta0K2WOz3qeVCttL1JJWyZmZmEhISwfft2IiIiqFy5MoMGDcLb21ssPmwi7ty5g5+fH5MnT2b48OHGDsekRUREMGzYMPr27cuMGTOMHc4zJEli69atrFixgooVK/Lll18WyLjDV6mj5DKQIUNC+s86Kiv2LgEBAZw+fZoyZcrkLkZuZWWV71gF0xQfH/9M6+Pt27fR6/U4Ojo+0/pYs2ZNMUwqDwq6bJpa/lDQinxC+XcpWRquRqUQHpfG7fg0rkWnkpKpIS4+HktLS5ycnZAjw8nWgtoejlQv7UAVNwfqlnXCyca0W5YkSeLixYts27aNkydPYmtrS69evRg4cCDlypUzdnjF3sKFC9m7dy+7du0S+x6/hFqtZtSoUWi1WjZs2GBSCVBiYiJz587l7NmzDBkyhMmTJxvkh/lldZQeCUkCmYwX11Eejly7fBF/f3/+/PNPvLy8GDVqFF27djX5pZaEvNHr9dy9e/eZ5XtiYmIAKFeu3DMJZIUKFcQaogXktcumGeQPBaVYJZT/JEkSKVka3pkwicpVqjLlvclYKuU42RTOsgGGEhMTw/+1d9/hUZVpH8e/Z1p6IxACIRAgFClJUERAmnRpUiQgIh0LrljXvqgo6Nq7gkoXktAUBBQVwUJTWoDQUyghCakzaVPP+wcwa16KQMqk3J/r4lo3M3POPeEJ88tTV6xYwerVqzGZTHTu3JlRo0bRsWNH+cfFRYxGI8OGDaNr1668/PLLri6nUvrwww9ZunQpixYtqlQb+2/dutX5d/byyy9X6FzYi/9GWWwOrA4VvUYp8W+Uw+Hgp59+YsGCBRw9epTWrVszadIkunbtKj/r1URxcTEHDx50hsf4+Hjy8/PRarW0bNmyxAKawMDqOZRaGf3Tz2ZNVKMD5UUTJ06kcePGzJgxw9WllCmz2cwPP/xAbGwsR44coWHDhowcOZLBgwfLPCoXWLVqFbNnz2b+/Pm0bdvW1eVUKn/99RcPPfQQjzzyCOPGjXN1OcD5HtOPPvqIZcuW0blzZ15++eVKs5DFYrGwfv16Fi1axMmTJ+nQoQMTJ06kffv2NfbDrLrIzs52zn3ct28fhw8fxm634+XlVSI8tm7dGnd3d1eXK4STBEpgypQphISE8Morr7i6lHKhqir79+8nNjaWn376CYPBwMCBA4mOjpa95yqQw+HgvvvuQ6vVsmDBAulBusBoNDJ69GhCQ0P57LPPKsX3JTExkRdeeIHk5GSmT5/OqFGjKkVdhYWFrF69miVLlpCZmckdd9zBhAkTaNWqlatLEzdAVVWSk5NLbN9z6tQpAOrVq1ciQDZt2rRStEEhrkQm1wBarRa73e7qMsqNoihEREQQERHB448/zqpVq1i5ciUrVqzg1ltvJTo6mm7dulWZo7KqKo1Gw9NPP83kyZP57rvvGDJkiKtLcjlVVXnjjTcoLCxk5syZLv/AVFWVlStX8u6779KgQQMWLlxYKYbf8/LyiI2NJSYmhoKCAgYMGMD48eMJCwtzdWniOlgsFg4dOuQMj3v37sVoNKLRaGjWrBmdO3d2hsigoCBXlyvEdZEeSuDhhx/G19eX119/3dWlVBir1covv/xCTEwM8fHxBAcHc/fddzN06FDZRqKc/ec//2H79u2sWrWq2p04cb3Wr1/PjBkzmD17Nn379nVpLbm5ucycOZNff/2Vu+++m8cee8zlQ4oZGRksWbKE1atX43A4GDZsGGPHjiU4ONildYlrk5eXV6L3MSEhAavVioeHB23btnX2PrZt2xZPT09XlytEqUigBKZPn467uztvvvmmq0txicOHDxMXF8f3338PQL9+/YiOjuamm25ycWXVU0ZGBiNGjGDYsGE88cQTri7HZVJTU7nnnnvo3r07M2fOdGktO3fuZMaMGVitVl566SW6devm0npOnjzJwoULWbduHe7u7owaNYrRo0cTEBDg0rrElamqyunTp0v0PiYnJwNQp04dZ3iMjIykefPmMiIkqh0JlOD8UK/px+Pl5uby7bffsnz5ctLS0oiIiCA6OppevXrJhtxlbMGCBXz22WcsW7asRs5jdTgcPPDAA5w9e5aYmBiXLRKzWq189tlnLFq0iA4dOvDKK69Qp04dl9QCcOTIERYsWMBPP/1EQEAAY8eOZcSIEXh5ebmsJnF5VquVI0eOlNi+Jzs7G0VRaNKkSYnte+rVqyeLpUS1J4ES+Pe//43FYuGDDz5wdSmVgsPh4NdffyUuLo6dO3dSq1YtRowYwfDhw136YVudWCwWoqOjqV+/Pp988kmN+7CZP38+n376KXPmzOHmm292SQ0pKSm88MILHD9+nGnTpjF27FiXzOFUVZU9e/awYMECtm7dSv369Rk/fjyDBw+WTagrEZPJxP79+50rsA8ePIjZbMZgMNCmTZsSw9e+vr6uLleICieBEnj22WfJz8/n448/dnUplU5iYiLLly/nu+++w2Kx0KtXL0aNGkVERESNC0Fl7bfffuPxxx/n7bffpkePHq4up8IcOnSICRMmMG7cOB5++OEKv7+qqqxZs4a33nqLoKAgZs2a5ZLpHaqq8vvvvzN//nzi4+MJDw9nwoQJ9OnTR4ZDXUxVVc6ePVui9/HEiROoqkpAQECJ3scWLVrICI4QSKAE4IUXXiA7O5vPPvvM1aVUWvn5+Xz33XfExcVx8uRJmjdvzujRo+nXr1+lOtGkKlFVlUcffZTk5GSWL19eI76PxcXF3HvvvXh4eDB//vwK/yA2Go3MmjWLn3/+mbvuuosnn3yywhdD2O12fvzxRxYsWMDx48eJiIhg4sSJdOnSRX5JcxG73c6xY8dKLKDJyMgAICwsrMT2PaGhofL3JMRlSKAEZsyYQVpaGnPnznV1KZWew+Fgx44dxMbG8scff+Dj48PQoUO5++67qV+/vqvLq3JSUlIYNWoUU6dOZfLkya4up9y98cYbrF27lq+//rrCt7zZvXs3L774IkVFRbz44ov06tWrQu9vsVhYu3YtixYt4syZM3Tq1ImJEyfSrl07CSgVrLCwkAMHDjiHrw8cOEBhYSF6vZ6bbrqJqKgooqKiiIiIkF0vhLhGsg8loNPpsNlsri6jStBoNHTq1IlOnTpx+vRpli9f7txouWvXrowaNYpbb71VPiCvUaNGjRgzZgzz5s1j0KBB1K1b19UllZvff/+dFStW8Oyzz1ZomLTZbMydO5f58+fTrl07Xn311Qr9PhcWFrJixQq+/vprsrOz6dWrF//9739p2bJlhdVQ02VkZJTofTx69CgOhwNfX18iIyOZPHkykZGRtGrVSuatCnGDpIcSmDVrFkePHmXhwoWuLqVKKioqYsOGDcTGxnLixAnCwsKIjo5m0KBBsrfaNSgoKGD48OHccsstzJ4929XllIvs7GxGjx5Nq1ateO+99yrsF47Tp0/zwgsvcOjQIR588EEmTJhQYQtvcnJyiImJIS4ujqKiIgYOHMj48eNp2LBhhdy/pnI4HCQmJpbYvufs2bMANGjQoMTwdVhYmMs30xeiupBAyflhuAMHDrBkyRJXl1KlXVytGhsbyy+//IK7uzuDBw8mOjqaRo0aubq8Sm3dunW89NJLzJ0712WrnsuLqqo88cQTHDhwgNjY2Ao5D1tVVTZs2MAbb7xBQEAAs2bNok2bNuV+X4D09HQWL17M6tWrURSF4cOHM3bsWDn5pJwUFxdz8OBBZ3iMj48nPz8frVZLixYtnMPXkZGRBAYGurpcIaotCZTAW2+9xe7du1m2bJmrS6k20tPTWblyJatXryYnJ4eOHTsyatQobr/9dukRuAyHw8HkyZMpLi5myZIl1WqV78qVK3n99dd59913K2TD8Pz8fF5//XV++OEHBgwYwDPPPFMh+zgmJyezcOFC1q9fj5eXF6NHj2bUqFH4+fmV+71rkuzs7BK9j4cPH8Zut+Pl5UVERIQzPLZu3RoPDw9XlytEjSGBkvMbmm/fvp24uDhXl1LtWCwWfvrpJ2JiYkhISCAkJISRI0cyZMgQ2avt/0lISGD8+PE8/fTTjBw50tXllImUlBTGjBnDwIEDef7558v9fvHx8bz44ovk5eXx3HPP0b9//3K/Z0JCAgsWLOCXX36hdu3ajB07lmHDhsl0jzKgqirJyckl5j+eOnUKgODg4BLb9zRt2lR+WRXChSRQAh9++CGbN29m1apVri6lWjt48CCxsbFs3LgRrVbLgAEDiI6OplmzZq4urdJ49dVX+eWXX1i9enWV79my2WxMnDiRgoICvv7663LtLbLb7cybN48vvviCNm3a8Nprr5XrrgOqqrJr1y7mz5/Pjh07CA0NZdy4cQwcOFAWdZSCxWLh0KFDJQJkXl4eGo2G8PDwEsPX1XkBmxBVkQRK4JNPPmHjxo18++23ri6lRsjOzmb16tWsWLGCc+fOcfPNNxMdHU2PHj3Q6Wr2xgPZ2dkMGzaMO++8k2effdbV5ZTKp59+ysKFC5k/fz6tWrUqt/ucPXuWF198kf379zN58mSmTJlSblMGHA4Hv/32G/Pnz+fAgQM0b96cCRMm0Lt3b+kduwF5eXklwmNCQgJWqxUPDw/atm3r7H1s06aNHD8pRCUngRL4/PPP+e677/juu+9cXUqNYrPZ2Lx5M3FxcezevZugoCBGjBjBsGHDKmThRmW1dOlS3n//fZYsWULz5s1dXc4N2bNnD/fffz8PPfQQkyZNKrf7bNy4kdmzZ+Pt7c1rr71GVFRUudzHZrOxceNGFixYQGJiIu3atWPixIl06tRJtsi6Rqqqcvr06RIBMikpCYDatWuXGL5u3rx5tZpHLERNIIES+OKLL1i1ahUbNmxwdSk11tGjR1m+fDnr16/H4XDQp08fRo0aRevWrV1dWoWz2Wzcc889+Pv7M3fu3CoXWPLz87nnnnsICgriiy++KJeeu8LCQt566y3Wrl1L3759ee655/Dx8Snz+5jNZtasWcPixYtJTU2lS5cuTJw4kcjIyDK/V3Vjs9k4cuRIiQU02dnZADRt2tQZHqOioqhXr16Va+dCiJIkUALz5s1j2bJl/Pjjj64upcYzGo2sWbOGuLg4UlNTadWqFaNGjaJPnz41am7ajh07ePjhh5k9ezZ9+/Z1dTnXZcaMGWzevJmYmJhymcd48OBBXnjhBbKysnjmmWcYOHBgmYeR/Px8VqxYwdKlS8nNzaVPnz5MmDBB5vtehclkYv/+/c4AeeDAAcxmMwaDgTZt2jh7INu2bSsL8oSohiRQAosWLWLBggVs2rTJ1aWICxwOB3/88QexsbFs376dgIAAhg0bxt13311j9vP797//zcGDB1m5cmWV2f5k48aNPP/888ycOZMBAwaU6bUdDgeLFi3is88+o0WLFsyaNYvQ0NAyvUd2djbLli0jLi4Oi8XC4MGDue+++8r8PlWdqqqkpaWV6H08ceIEqqoSEBBQYvPwli1bVviZ7UKIiieBkvNz1j7//HN+/fVXV5ciLiMlJYW4uDjWrl1LcXExPXr0YPTo0dX+DOTU1FTuvvtuxo4dy7Rp01xdzj9KT09n9OjRdOzYkdmzZ5fp301GRgYzZsxg165djB8/ngceeKBMQ0pqaipLlizhm2++QafTMWLECMaMGUOdOnXK7B5Vmd1u59ixY+zbt88ZIDMyMoDzx4f+ff5jaGhotf65FEJcngRKIDY2lg8//JA//vjD1aWIqygsLOS7774jLi6O5ORkwsPDiY6O5s4776wyPXjX6/PPP2fRokUsX76ckJAQ59eLior4+uuvCQ0NpV+/fi6s8DyHw8G0adM4efIkMTExZTqkuWnTJl577TXc3d2ZOXMm7du3L7NrJyYmsmDBAr7//nt8fHy45557iI6OrvFDsoWFhRw4cIC9e/eyd+9eDhw4QGFhITqdjlatWjkDZEREBAEBAa4uVwhRCUigBFasWMHbb7/N9u3bXV2KuAaqqvLnn38SGxvLr7/+ire3N0OGDGHkyJE0aNDA1eWVqeLiYkaMGMFNN93E22+/jaqq/Pjjj7z++uskJCQwYMAA5s+fXyG1pKSkcOTIEfr06XNJD9TixYv54IMP+Oyzz7j11lvL5H5FRUW89957rFq1ijvuuIMXX3yxzPbmPHDgAPPnz2fLli0EBQVx3333MXTo0Gr7i8k/ycjIKNH7ePToURwOB76+vkRGRjp7H1u1alWj5jILIa5dzd707wKtVovdbnd1GeIaKYpChw4d6NChA6mpqaxYsYJvvvmGpUuXcvvttxMdHU3Hjh2rxb6A7u7uPPbYYzz33HPExcWxYcMGfv75Z6xWK4qikJOTU2G1LFiwgHnz5jFo0CBeeeUVateuDZxfof/JJ58wduzYMguTR44c4fnnnyctLY3nn3+eYcOGlXoY9eIvIvPnz+fPP/+kYcOGzJgxgzvvvLNGzfFzOBwkJiaW2L4nNTUVgAYNGhAZGcmIESOIjIwkLCysWvwcCSHKn/RQAmvWrGHmzJns3LlT/vGsooqLi/nhhx+IjY3l6NGjNGzYkJEjRzJ48GC8vb1dXV6p5Obm0rdvXw4dOoSnpye1atXCy8uLc+fO0bhxYzZv3lwhddx77718//336PV6GjduzOuvv06nTp2477770Gq1LFy4sNS9Vw6Hg2XLlvHRRx/RpEkTZs+eTVhYWKmvuXnzZhYsWEBCQgItW7Zk4sSJ3HHHHTXi5724uJiEhATn8PX+/fsxmUxoNBpatmxZ4vSZwMBAV5crhKiiJFAC69evZ8aMGWzbtq1G9VRUR6qqsm/fPuLi4vj5558xGAwMHDiQ6OhomjRp4uryrovdbmflypW88847JCYmYjQaqV+/vvNDPysri4CAAHbv3l0h9XTo0IGMjAz8/f1JT0/H3d2d5s2bk5uby9KlS2natGmprp+ZmcnLL7/M9u3bnQuRShNQbTYbGzZsYOHChSQnJ3PLLbcwceJEbrvttmq9aCQ7O7tE7+OhQ4ew2+14eXkRERHhDI+tW7eusUP8QoiyJ0Pe4DyRwW63S6Cs4hRFcfa4nDt3jlWrVrFy5UpWrFjBrbfeyqhRo+jWrVul75natWsXs2bNYseOHQCEhITg5uZGZmYmfn5+6HQ6tFotBQUF2Gy2cj+yMjc3l5ycHAwGAzqdjvr165Oens6mTZto3bo1RUVFpbr+b7/9xiuvvIJGo+Hjjz+mY8eON3yt4uJivvnmGxYvXkx6ejrdunXjpZdeom3btqWqsTJSVZWUlBRneNy3bx8nT54EIDg4mMjISAYOHEhkZCTh4eGVvt0LIaou6aEEfvrpJ5599lm2bNki58VWQ1arlZ9//pnY2Fj2799PcHAwI0eOZOjQoWW2yKMspaam0rdvX06dOkVISAienp7A+V94Tpw4gY+PD/Xq1cNkMmG32/nzzz/LfaXtvn37GDx4ML6+vri7u2O320lMTESn06HX6/Hz8+Opp55i0qRJ13Vkntls5oMPPiAuLo4uXbowY8aMGz5202QysXz5cpYuXYrRaKR///6MHz++1D2nlYnFYuHw4cPO4et9+/aRl5eHRqMhPDy8xPB13bp1XV2uEKIGkR5KSvZQiupHr9fTv39/+vfvT0JCAnFxccyZM4c5c+bQv39/Ro0aRYsWLVxdplO9evV48skneeedd8jMzATA09MTrVZLnTp1SEtLw9/fH61Wi8ViwWQylXugTElJwWKxOIegz549i6qqhIaGotVqncPVW7Zs4Y033rim1fYnTpzg+eef59SpUzz99NOMHDnyhoais7Ky+Prrr1mxYgU2m4277rqL++67r1xO6aloeXl5JVZfHzp0CIvFgoeHB23btiU6Otp5+oz8MiyEcCUJlPwvUNpsNhdXIspbq1atePnll3n00Uf55ptvWLFiBWvWrCEiIoLRo0dzxx13uHzag6IozkUj//nPf9i0aRNGo5GgoCACAgLIzc0lPT2d4OBgHA4HRqOx3GtKSUlBo9Gg0WjIzc3FZDLRoEED51B77dq1OXfuHBs2bODQoUOsWLGCxo0bX/ZaqqqyfPly3n//fUJDQ1m8ePEN9SKeOXOGRYsWsXbtWnQ6HSNHjmTMmDFVdmGJqqqcPn26RIBMSkoCzn9/o6Ki6N27N1FRUTRv3vy6eoKFEKK8SaAE54ei9FDWHAEBAUycOJFx48axZcsW4uLieP7556lduzbDhw9n+PDhzm1xXCUsLIyFCxeyfPly3njjDVJTU/H396du3bqkpKRQUFCA3W6vkECZnJyMw+HAYrGQnp6Ov78/Pj4+wPlNsLOzs3F3d6dt27YcOnSIJ554gpiYGNzc3EpcJycnh1deeYXff/+d6OhoHn300Uue80+OHz/OggUL2LhxI35+fkyZMoWRI0c666kqbDYbR44ccYbHvXv3kp2dDUDTpk1p164dEydOJCoqinr16lXrhURCiKpPAiUy5F2TabVaevbsSc+ePTlx4gRxcXEsWrSIefPm0atXL0aNGkXbtm1d9mGu0WgYNWoUXbt25ZVXXmH9+vU4HA68vb3JzMzEy8sLk8lU7nUcOXIEnU5HamoqWq2WunXrYjabyczMRKfT0aVLF6ZNm8bzzz+Pqqps376d999/n2eeecZ5je3bt/PSSy9ht9t577336Nq163XVEB8fz/z58/ntt98IDg7mqaeeYsiQIbi7u5f12y0X+fn5xMfHOwPkgQMHMJvNGAwG2rRpw1133UVUVBRt27at8Sf1CCGqHlmUA/z11188+OCDfPvttyWOtxM1k8lkYu3atSxfvpxTp07RsmVLoqOj6dev33X3ppUlVVVZu3Ytr776KikpKZhMJvR6PV999RUjR478x9fmFVkx2xzYHCo6jYKbToOfh/4fw7LdbiciIoL09HSKiopo0KAB+fn5ALRu3ZonnniCvn378uWXX/LSSy8RFBTkDLmLFi2iQ4cOfPLJJ3z99dd07NiRV1555ZqHpS+G0/nz57N7924aN27MhAkT6NevX7mvbC8NVVVJS0srsX3P8ePHUVWVgIAA58kzkZGRtGzZ0uXTLIQQorQkUAJ79uxh6tSprFq1ioYNG7q6HFFJOBwOtm/fTkxMDFu3bsXPz4+hQ4dy9913U69ePZfVde7cOWbPns2iRYswGo3MmjWLp59+usRz8oqsxJ/O5XhGPkfSTSSkGskrsuJARVVBUUCDgp+Hnlb1fWlR14fwIG8iGvjj51Ey3Jw+fZqOHTuSmZmJp6cnbm5uNG7cmEcffZThw4ej1+vJycmhV69enDt3jrp166KqKqmpqdSvX5+wsDDOnDnD9OnTGT169DVtXeNwONi0aRPz58/nyJEjtGrVikmTJlXaLZ8cDgfHjh0rMXydkZEBQKNGjZzhMSoqitDQUBm+FkJUO5X3V/wKJItyxOVoNBo6d+5M586dOXXqFMuXL2flypUsXryYbt26MWrUKNq3b1/h4aBOnTq8++679O3blwkTJpCVlQWc7xU7nGZi/f6zfH8wjbwiK+d/XVQx6LQYdBp0ioKCgoqKQ4WsAgs/H0rn50MZKAr4eejp3zqYgRH1aFHXB0VROHLkiPMeDRs25KGHHmLs2LElVhXPmzeP1NRUgoODgfMLi9zd3YmPj+fYsWMsWbKEIUOG/ON7s1qtrF+/noULF3Ly5Ek6dOjAp59+yq233lqpQlhhYSEHDhxw9j7u37+fwsJCdDodrVq1on///kRGRhIREVHuK/CFEKIykB5KICEhgXHjxrFs2TKaNWvm6nJEJVZYWMiGDRuIjY0lMTGRJk2aEB0dzYABA5z7RV6L3377jYKCAvr371+qehwOBxqNhu2JWSzclsz+M3kUW+2467X4uOnQaa+9N89md2Ay25yvjwjxY3znMHZ+t5TnnnuO6dOn89RTT12yT2R6ejq9e/fGaDRSp04d7HY7Z8+exWg04nA4sFqtdOvWjV9++eWK9y4qKmL16tUsWbKEjIwM7rjjDiZOnEirVq1u+HtTls6dO1di8/AjR47gcDjw9fUlMjLS2fvYqlWrUh8/KYQQVZEESs4vOLj33ntZsmQJLVu2dHU5ogpQVZVdu3YRFxfH5s2b8fDwYMiQIYwcOfIfp01YrVbuuOMOzp49y5w5c+jdu/cN12EstjJnywm+2ZuK2WbH112Pp0Fbqt48VVUptNgxFltx02m5K7I+994cREjQ5Tccf/XVV/nkk0+oX78+xcXFnDlzBlVVcXd3Jzc3F7vdjlarZdWqVQwaNKhk/UYjMTExxMTEUFBQwJ133sn48eOvuOVQRXA4HCQlJTkD5N69e0lNTQXOn1j09+HrsLCwSjkEL4QQFU0CJee3IRk9ejQLFiygTZs2ri5HVDFpaWmsXLmS1atXk5ubS+fOnRk1ahSdOnW6bNj4/vvvmTx5Mlarlbp167Js2bIbanfbErN4Z+MRUrIK8HTT4eOmK9NhYVVVMZltFJhthAV68WTfFnRqUnIxzcmTJ+nTpw8WiwW73U5WVhaenp74+vpy+vRp4PzUAbPZTFhYGAkJCbi7u5ORkcHXX3/NqlWrcDgcDB06lLFjx7pkbqrZbObgwYPOABkfH4/JZEKj0dCyZUvn6TMREREu30pKCCEqKwmUQFJSEiNHjuSrr74iMjLS1eWIKspisbBx40ZiY2M5dOgQDRo0IDo6msGDB5fYI3Hs2LH8/PPP1KtXj9TUVMLDw4mLi7vmk11UVWX5rtN8tOkYZpuDQC/DdQ1tXy+b3UFWgQU3nYZHejZj5C0NnMH1mWeeYd68edjtdsxmM0FBQXh7e3PixAmsVis6nQ5VVbHZbKiqyrRp0wgODua7777Dzc2N6OhoRo8efcPHLd6I7OzsEpuHHz58GJvNhpeXFxEREc7ex9atW+Ph4VFhdQkhRFUmgRI4deoUw4YNY+7cudx8882uLkdUcaqqcuDAAWJjY/npp5/Q6XQMGDCA6OhoLBYLgwYNQq/X4+Pjg81mIy0tjY4dO7JkyRK8vb3/8dqLtqUw59cTKIpCgOc/b/tTVu8pp9CKqqo82L0p93VsxOHDh+nZsyc5OTkYDAZq1aqF3W7n3LlzWCwW58k6iqKgKAo2mw1fX1/at2/Pvffey913313uxwWqqkpKSkqJ7XtOnjwJQHBwcInte8LDw2X4WgghbpAESiA1NZUhQ4bw6aef0qFDB1eXI6qRrKwsVq1axcqVK8nMzERVVZKSkmjcuLEzCJrNZs6dO8fQoUP55JNPrnqkXtxfp3j/p6NoNAoBnhW/+COn0ILDofJY7+Z89u9x/P7777i5uREQEICbmxsFBQXk5ubi6+uLl5cXqqqSn59PYWEhqqri7+/PmjVraN++fbnUZ7FYOHz4sHPrnvj4eHJzc9FoNISHhzuHryMjI6lbt2651CCEEDWRBEogIyODAQMG8NFHH9GpUydXlyOqIZvNxrfffsuUKVMoLCzE3d2dgIAAAgIC0Gq1FBQUkJeXx7/+9S+ee+65y/Y6bkvM4ukV+3CoUMvLdSuJswssaBRonrMTQ24Kffr0oXHjxuzYsYPXXnsNLy8vFEUhMzOToqIi3NzcCAwMxM3NjWPHjlG/fn1OnDhRJr2BRqOxRO9jQkICFosFDw8P2rZt61yB3bZt23LvDRVCiJpM9qFEjl4U5U+n02E0GnF3dyc4OBij0UhmZiaZmZn4+vpSq1YtPD09+fzzzwkLC2PMmDElXm8stvLOxiMUWx3U9XXdaT0AAZ56Mkxm8hp1Y95Lt+Lrruevv/7i3XffxWKxUFhYiNlsxsPDg9DQULy9vSkoKCApKQmbzcaZM2fYu3fvdU8vUVWVM2fOlAiQiYmJANSuXZuoqCgeeeQRoqKiaN68+VV7eoUQQpQtCZRIoBTlz263s3jxYhRFwcvLCy8vL4KCgsjNzSUnJ4e8vDw8PDxQFIUZM2YQEhJC9+7dna+fs+UEKVkF1PZ2c/kG34qiEOhlIDmrgLlbEhnVypOxY8dy8uRJNBoNXl5eNGrUyLkvZ05ODmfOnMFut2MwGLBYLHz++efMnTv3qvex2WwcPXrUOXy9b98+5wbrTZs2JSoqigkTJhAVFUW9evVc/n0RQoiaTAIlEihF+duyZQvHjh0rsZpZq9USGBhIYGAg+fn5ZGVlOYe+7733XlasWEHHjh3ZnpjFN3tT8bzOjcrLk06rwctNxzf7zrDuq2UkJSWh1+vx8PBAq9WSlZVFdnY2BQUFFBYWAue3D7Lb7aiqyrp165ybsl+Un59PfHy8cwX2gQMHKC4uxmAw0KZNG4YMGUJUVBRt27bF19fXVW9dCCHEZUigRI5eFOVv6dKlmEwmFEUhJycHh8OBqqrOVdCqqqLVavH29sZsNpOZmcm///1vfv31VxZuS8ZssxPg6e7qt1GCj5uONGMxTXrdx6IRfQgI8Mdms2Gz2cjLy2PBggXs2rWLwMBAPD09UVUVVVUxGo0EBASQnp5eYvue48ePo6oqAQEBREZG8uCDDxIZGUnLli3R6/X/XJAQQgiXkUDJ+fltID2UovwUFhbSsmVL/Pz88Pf3d/7x9fXFx8cHb29vfHx8nH90Oh3h4eEcTjOx/0wevu4Vsz3Q9VAUBV93PYl5NiLv6k/L4PO9hklJSUybNo2DBw9Sv35959B3cXExRUVFaDQakpKSuPPOO9HpdDRq1IjIyEjuueceoqKiCA0NrXTvVQghxNVJoESGvEX5W7p06Q29bsHGIxRb7fh7VM4eOk+DFqPRyrr4s7QM9mXr1q1Mnz6dU6dO4ePjQ2FhoXO1t8PhQFEUDAYDOp2O8ePHM2HCBAICAlz9NoQQQpSSBEpwzuOSQCkqk7wiK98fTMNdX7qzucuToii467V8fzAN45+r+fT9d8jPz0dRFPLz89FqtXh4eFC7dm08PDycC48unvctYVIIIaoHCZSc/1C8uGBAiMoi/nQueUVWAl245+S18HHTkVVg4fO4DeTn56PT6dDpdHh7e+Pr64u7u/slgVhRFHbu3OmiioUQQpQ1CZQX6HQ6WZQjKpXjGfmoKpVmZfeV6LQaVODVD+YS4Z7Dnj172LlzJ3/99RdGo5Hs7GxUVcXd3R1PT0/c3d3x8PBg3759FBUVyXnZQghRDUigvECr1UoPpShzgwcP5p133qF58+bOr91///2MGTOGHj16XPW1R9JNwD8fZFWcl0lO0gHqRf3vevHL3sRszEbr5oHdUoy7X21C2vehVtMIALKO7yXv5GGa9Bx9I2/rUqpKUo6Z+4d1pGPHjjz00ENYLBYSEhLYs2cPf/31F9u2bSM7O5ucnBysViuenp4cPHiw3I5hFEIIUXEkUF4ggVJUJqqqkpBqxKC7+mkvqsNOcV4WZ/duKREoARr3HEXtZu0AyD15hMNr5xDe+15qt7iFwPAoAsOjyqxeg05LQqoRVVWdw9sGg8F5dvbEiRNxOBwcO3aM3bt3s2vXLg4ePCin2QghRDUhgfICCZSiohUWFvLuu+9y9OhRLBYLbdu25emnn0av1/PF/EXsmP81qHb0Oj1Ne43GNyQcgJ1znqFOi1vJPXkYj1p1yU9LwWzMYveCV3DzrUXr4Y9cci//hi1odPsQTu3YQO0Wt5C+/w+yju2h1fB/UZSdztEN87BbzKiqSmB4FGHdhuGw20j5/RtyUw6jOux4BNQlvO996D28yEjYQequn3DYbaCqBN02mNzGEeQVWVm5bDEbNmzAYDg/9/Odd96hXr16HD58mA8//JCCggLsdjtPPvkk7dq1q9DvuRBCiPIhgfICrVYrcyhFuXj22Wdxd//fpuSnTp0C4L333qNdu3a8+OKLqKrKa6+9xrJlyxg3bhzde/WleVYoBq0GW2YKR9fPp/2U15zXsBbnE3XfCyiKQu7JIyRuiuHmCS9dtQ6fek1I3BR7yddT92yiVtMIQjsOPH/togIATu/8Aa3ejXbjXgTg5Na1pPz+DeF97iWgcWvq3NQBRVEozstk16JZ3DThNbKyc1m8eDE//PADbm5uFBcXo9FoMJlMvPbaa3z44YfUrl2b3Nxc7r33XiIiIggKCirdN1gIIYTLSaC8QKfTSQ+lKBdvvPHGJXMoATZv3kx8fDxff/01cH7j74tDwEeOHuXYindRzQXodDqKstOwWy1o9ed7/eq2uf36txJSLz8f069Bc5I2L8duseAX2hz/RjcBkHVsD3ZzEZlHd59/ud2Gm1/g+Vpzz5Hy+xeYTTkoGi324gLMeVkYPL1o2LAh//nPf+jYsSNdunQhKCiIP//8kzNnzjB9+vQS905JSZFAKYQQ1YAEygukh1JUNFVVeeutt2jYsGGJr1utVma/9DwhXSfjX78JBtXKtg8eQbXb4EKg1Brcrvt+prRkPGuHXPL12i1uwTekKTnJCaTu2cSZXT/S5u7HAGjaewwBjVtf8prDa+fSuPsIarc4v6Dm9w+mg8OKm07LggUL2LdvH7t27WLChAnMmjULVVVp2rQp8+bNu+66hRBCVH6Vez+SCiRzKEVF69GjBwsWLHC2O6PRyKlTp7BYLNhtNtx8AnGokLp701WvozO4YzcXXfU5eaeOcvKPbwm97c5LHivKTkfv6UvdNp1p3P1uTKlJAAQ2a8eZXT9it5oBsFvNFGSmAmArLsTNrzYA6Qe3YSsuQEHBZikmKyuLdu3aMWXKFKKiojhy5AgRERGcOXOmxN6TR48exWq1XuN3SwghRGUmPZQXyJC3qGhPPPEEH3/8Mffccw8ajQatVsujjz5KaGgo/3p4Gk+/9V8cek8atOl41et4BTXAs3Z9ds2bgbt/HeeinKRNsZz8Yw12qxl330DC+40nMDzyktdnHv2LjIQdKBotqCrhfccCEHpbf05utbJ38Wwujq436HAnXrXr06TXaA5/+zlaNw/8G7ZE7x2Ar7sOrd3Mv595hqKiIhRFoWHDhgwaNAhvb28++OAD3n//fd577z1sNhvBwcG88847ZftNFUII4RKKql5hYlUNM2rUKDp06MCTTz7p6lKEAODZVfH8fCidYL/Kv/F3Wl4RvVvV5fVhEa4uRQghhAvIkPcFMuQtKpsWdX2AynmG9yUU5UK9QgghaiIJlBfI0YuisgkP8kZRwGZ3uLqUq7LZHShAeJAESiGEqKkkUF4gPZSismle2w0DNozFlXvhislsw89DT9sQP1eXIoQQwkUkUF4ggVJUFqmpqXz44YfcM2IoOft/ochio7JOdVZVlWKrnf6tg/Hz0Lu6HCGEEC4iq7wvkEApXElVVXbt2kVsbCxbtmzBy8uLoUOH0u6OQTz/fQqFFjtebpXvx7XQYsddr2VgRD1XlyKEEMKFKt8nlItIoBSuUFxczPfff09MTAzHjx+nSZMmPPvss9x55514eHigqioR+3PZmZyNp0F7/afjlCNVVTEWW+nQOFAW5AghRA0ngfICWZQjKlJaWhrLly9n9erVmEwmunbtyhNPPMGtt95aIjQqisL4zmHsO52HyWzD173yDCubzDbc9VrGd2pUqYKuEEKIiieB8gLpoRTlTVVV9uzZQ0xMDJs3b8bT05O77rqL6OhoQkIuPRLxotsaBzI0qj5xf53CU69Fp3X91Geb3UGB2cbo9g25rXGgq8sRQgjhYhIoL5CzvEV5MZvN/PDDD8TExHD06FHCwsJ4+umnGTBgAJ6entd0jQe6N2VHUjYnswsJ8nFzaY+gqqpkFVgIC/Ti/u5NXFaHEEKIykMC5QVarRaz2ezqMkQ1kp6e7hzWNhqNdOnShUcffZQOHTpcdyD0ddfzZN8WPL1iHzmFVmp5Gcqp6n+WU2jFTafhqb4tKtUQvBBCCNeRQHmBDHmLsqCqKnv37iUmJoZffvkFDw8P7rrrLkaOHEloaGiprt2pSSCP9GzG+z8dJafQQoBnxYfKnEILqqrySK/mdGwiQ91CCCHOk0B5gSzKEaVhsVicq7WPHj1Ko0aN+Pe//83AgQOveVj7Woy8pQHFVjufbzlBdoGFAE99hQx/q6pKTqEVVVV5sHtTRt7SoNzvKYQQouqQQHmB9FCKG5GRkcGKFStYtWoVubm5dOnShenTp9OhQwc0mrJfPKMoCvd1bIS7XstHm46RYTIT6GUo14U6NruDrAILbjoN03s3Z+QtpetpFUIIUf1IoLxAFuWIa6WqKvHx8cTExPDzzz/j7u7OkCFDiI6OpmHDhuV+f0VRiG4fSsNanry98QjJWQV4uenwcdOVaW+lqqqYzDYKzDbCAr14qm8LGeYWQghxWRIoL5AeSvFPLBYLGzduJCYmhsOHD9OwYUOefPJJBg0ahJeXV4XX07FJIPMm3MqcLSf4dl8qacZifN31pd4AXVVVCi12jMVW3PVaRrdvyAPdm+AjC3CEEEJcgQTKCyRQiis5d+6cc1g7JyeHzp078+GHH9KxY8dyGda+Hr7uev7dryVdm9Vh0bZk4s/kYTSeD4I+bjq0GgWz2Yy7u/s/Xstmd2Ay2yi2nj9OsUNYLcZ3DpN9JoUQQvwjCZQX6HQ6CZTCSVVV9u/f7xzWdnNzY/DgwURHR9OoUSNXl3eJjk0Cua1xLY6km1gXf5bvD6aRVWChqKiIwsIigoNqY9BpSD1zhlq1AvD09MShgsXmwGKzg6KgAP6eeoZFhTAooj7N63rLCThCCCGuiQTKC2SVt4Dzw9o//fQTMTExJCQkEBoayuOPP87gwYNdMqx9PRRFoWWwLy2DfZnStQn7z+Tx6ZJVHC8uINC7PnmFVmwOlWKrHZ3dgQaFQG8Drev70qKuD+FBPrQN8cPPQ4a2hRBCXB8JlBfIkHfNlpmZycqVK1m5ciXZ2dl06tSJDz74gE6dOrl8WPtG+Hno6RJem0Wpf9ErMJDZ024nr8hK/4GDmDBpCsPv6oNBp8HPo2K2HRJCCFG9SaC8QAJlzXTgwAFiYmL48ccfMRgMzmHtsLAwV5dWJpKTk7nllltQFAV/TwNaSwEBBpUg33+eUymEEEJcKwmUF0igrDmsVqtzWPvgwYOEhITw6KOPMmTIELy9vV1dXpkxGo1kZ2fTuHFjV5cihBCimpNAeYEEyuovKyvLOaydlZXFbbfdxnvvvcftt99eJYe1/0lycjJAteltFUIIUXlJoLxAFuVUXwkJCcTExLBx40Z0Oh2DBg0iOjqaJk2auLq0cpWcnIyiKBWy2boQQoiaTQLlBdJDWb1YrVY2bdpETEwM+/fvp379+jzyyCMMHjwYX19fV5dXIZKSkqhXr9417UEphBBClIYEygskUFYP2dnZrFq1ihUrVpCZmUmHDh1499136dKlS7Uc1r6a5ORkGe4WQghRISRQXiCBsmo7dOiQc1hbo9EwcOBARo0aRdOmTV1dmsskJSXRrVs3V5chhBCiBpBAeYEEyqrHZrM5h7Xj4+OpV68e06ZN46677qoxw9pXYrFYSE1NlRXeQgghKoQEygt0Oh0OhwOHw1HjhkarmpycHFavXs3y5cs5d+4c7du35+2336Zbt27yd3fByZMncTgcEiiFEEJUCAmUF2i1WgAJlJXY4cOHiY2N5YcffkBRFAYMGMCoUaMIDw93dWmVjmwZJIQQoiJJoLzgYqC02+3odPJtqSxsNhu//PILMTEx7Nu3j+DgYB588EHuuusu/Pz8XF1epZWUlIS/vz/+/v6uLkUIIUQNIMnpgr8HSuF6F4e1V6xYQUZGBjfffDNvvvkm3bt3d/5diSuTFd5CCCEqkgTKCy6GFNnc3LWOHj1KTEwM33//PYBzWLtZs2YurqxqSUpKonXr1q4uQwghRA0hgfKCi8PcEigrnt1uZ/PmzcTExLBnzx6CgoK4//77GTZsmAxr3wCHw0FKSgoDBw50dSlCCCFqCAmUF8iQd8XLy8tzrtZOT0+nXbt2/Pe//6VHjx4yrF0KaWlpmM1mWeEthBCiwkigvOBiD6UEyvJ37NgxYmJi2LBhAwD9+vVj9OjRtGjRwsWVVQ+ywlsIIURFk0B5gfRQli+73c6WLVuIiYlh9+7dBAUFMWXKFIYNG0ZAQICry6tWkpOTcXNzIzg42NWlCCGEqCEkUF4ggbJ8GI1GvvnmG+Li4khLSyMqKoo33niDHj16yPZM5SQpKYlGjRrJfqpCCCEqjHyiXyCrvMvW8ePHiY2NZf369TgcDvr378+oUaNo2bKlq0ur9mTLICGEEBWtRgbK/Px8Tpw4gd1ux2azYbfbOX78OCaTid9//53jx49jt9udfyIiImSBwzVwOBz8+uuvxMTE8Ndff1GnTh0mT57M0KFDqVWrlqvLqzGSkpKIiori119/xeFwlHgsLy+PgwcPsnnz5hJfDwkJka2ZhBBC3DBFVVXV1UVUtP/85z8sXrzYObytqipWq5WcnBz8/f2dvZWqqqLVapk0aRKvvvqqK0uu1IxGI99++y3Lly8nNTWViIgIRo8eTc+ePWVYu4Ll5ubSu3dv7rzzTpYuXUpxcXGJx8+dO4e3tzceHh4lvt62bVvn3p9CCCHE9aqRn/Z9+vRh4cKFGAwGvLy8UBQFi8WCyWSiVq1aeHp6AlBQUIDZbGbo0KGuLbiSSkxMJCYmhvXr12O32+nbty9vvPEGrVq1cnVpNdbFFd533XUXa9aswWw2U7t2befjubm5+Pr6Oo9ktNvtnDt3jmHDhrmgWiGEENVFjQyUXbt2pUuXLmzatAl/f38URcHhcKAoClqtFo1Gg6qq5OXl0adPH26++WZXl1xpOBwOfv/9d2JiYti5cye1a9dmwoQJDB8+XIa1K4Hk5GQ0Gg0RERFMnDiR//73v852DTj/+2LPcVZWFg0aNOCee+5xZdlCCCGquBoZKBVF4bHHHmPr1q3k5+fj4+ODoijA+WFugMLCQtzc3HjwwQedj9VkJpOJNWvWEBsbS2pqKm3btmXWrFn07NkTvV7v6vLEBUlJSdSvXx+DwcC4ceOYP38+2dnZ1KlT55Ln2mw2bDYbU6dOxdfX1wXVCiGEqC5qZKAEuPXWW+nVqxfr1q3D29v7kkCZm5vL7bffTufOnV1ZpsslJSURGxvLd999h81mo2/fvrz++utyTnQl9fcV3rVq1XL2Utrt9ktOH8rKyiIkJER6J4UQQpRajQ2UiqLw6KOP8ssvv2AymUosUigqKkKn09XY3kmHw8Eff/xBTEwMO3bsoFatWowbN44RI0YQGBjo6vLEVSQlJdGrVy/n/79SL6X0TgohhChLNXrn44iICAYMGIDRaHR+TVVVsrOziYyMpGfPni6sruLl5+ezdOlShg8fzuOPP47JZOLVV19l3bp13H///RImKzmz2czZs2dL7EF5sZfSYrGU2LRfeieFEEKUpRrbQ3nRI488woYNGzCZTABYLBY0Gg0PPvhgjTlpJDk5mbi4ONauXYvFYqFPnz689tprtGnTxtWlieuQkpKCqqqX7Jn6915KwLn/qvROCiGEKCs1PlC2aNGC4cOHs3DhQufK7ltuuYX+/fu7urRy5XA42LZtGzExMWzbto1atWoxduxYhg8fftkFHKLyu7hl0P8/JefvcylVVcVkMtG4cWPpnRRCCFFmanygBHj44Yf55ptvyM3NBeD++++vthtyFxQUsHbtWmJjYzl16hQ33XQTM2fOpHfv3hgMBleXJ0ohOTmZWrVqXbbX8WIvZVZWFgaDQXonhRBClKmaMab7D8LCwoiOjsbhcFC/fn2GDBni6pLK3MmTJ3nrrbe48847ee+997jpppuYN28eixYtYsCAARImq4GrneF9sZdSVVUCAgKkd1IIIUSZqp7dcDfgoYce4qOPPmLEiBHVJlw5HA62b99OTEwMW7dudQaJESNGEBQU5OryRBlLSkqibdu2V3x83LhxvPLKK9x1113SOymEEKJMSaDk/Mpu71pB/PpnPIF16pBuLMZNp8HPQ18ltw0qLCzku+++IyYmhpMnT9KyZUteeeUV+vTpU23CsijJ4XCQkpJyxd51VVXRuHuzZedeAmoFVvk2LoQQonKpkYEyr8hK/OlcjmfkcyTdREKqkbwiKw5UVDURRQENCn4eelrV96VFXR/Cg7yJaOCPn0flPRXm1KlTxMXF8e2331JcXEzv3r15+eWXadu2rYSGau7s2bNYLBbnkPfV2/iJKtvGhRBCVE6KevFomGpOVVUOp5lYv/8s3x9MI6/Iyvl3rmLQaTHoNGgUUFBQUXGoYLE5sNjsgIKigJ+Hnv6tgxkYUY8WdX0qRUhTVZUdO3YQExPDH3/8gZ+fH8OHD+fuu++WYe0a5Pfff+fRxx7j/flx7DxrrVZtXAghROVXIwLl9sQsFm5LZv+ZPIqtdtz1WnzcdOi0174myWZ3YDLbnK+PCPFjfOcwbmtcus2+VVVly5YtdOjQAU9Pz2t+XWFhIevWrSM2Npbk5GSaN2/OPffcQ79+/WRYuwZ6dU4MS/88Q2CzdhTbKlcbF0IIUf1V60BpLLYyZ8sJvtmbitlmx9ddj6dBW6peF1VVKbTYMRZbcdNpGRpVnwe6N8XX/caGCRctWsSrr77KI488wvTp0//x+adPn3YOaxcVFdGzZ09Gjx5NZGSk9CbVQBfb+MItCZhtDkLr1q50bVwIIUT1V20D5bbELN7ZeISUrAI83XT4uOnKNHCpqorJbKPAbCMs0Isn+7agU5Pr68n55ZdfmDp1KpmZmTRv3pwtW7aUOFP87/f6888/iYmJ4bfffsPX19c5rF23bt2yekuiivl7G8/PzcJdp9AgJKTMrl8WbVwIIUTNUO0CpaqqLN91mo82HcNscxDoZbiuYb/rZbM7yCqw4KbT8EjPZoy8pcE1BddDhw4xevRoMjIyqFOnDllZWXz00UcMHz7c+ZyioiLWr19PTEwMSUlJNG/enNGjR9OvXz/c3NzK7T2Jyu3SNq7nxPHjBAYGUrt27TK/3422cSGEEDVHtQqUqqqyaFsKc349gaIoBHhWzJYoqqqSU2hFVVUe7N6U+zo2uup9MzIyiI6O5vDhw9SvXx+NRsOZM2fo0KED33zzDampqc5h7cLCQnr06MHo0aNp166dfJDXcJdr43a7nWPHjhIS0qDc9pe83jYuhBCiZqlW2wYt33WaOb+eQKNRCPCsuIUpiqJQy8tATqGFz7ecwF2vJbp96GWfW1RUxCOPPMLhw4cJDg5Goznfe+rv78/OnTu59957OXbsGD4+PowYMYK7776bevXqVdh7EZXb5dq4xWIBKNde6+tp40IIIWqeahMotyVm8dGmYxd6bVyzyjnA00B2gYWPNh2jYS1POv6/+WYOh4Pnn3+eLVu2ULt2bXQ6HQ6HA6PRSFZWFvn5+fz111+8+eab9O/fH3d3d5e8D1E5XamNm81mQMFgKP9FM//UxoUQQtRM1eIsb2OxlXc2HqHY6iDA07UrUQM89ZhtDt7eeARjsbXEYx9//DFxcXH4+fmh1WrJyMjg+PHjnD17Fjc3N+cCmw4dOkiYFCVcrY1bLBYMBj2KUjE/zldr40IIIWqmahEo52w5QUpWAbW9DS6f16UoCoFeBpKzCpi7JdH59TVr1vDee+8BkJeXx/Hjx8nJycHf35/w8HAaNGhAUFAQRqOR2NhYV5UvKqmrtXGz2YzBUHGLtK7UxoUQQtRcVT5Qbk/M4pu9qXhe5ybO5Umn1eDlpuObfWfYkZTFtm3bePjhh0lLSyMnJweLxUK9evVo1qwZQUFB6PXne5w0Gg0Gg4GlS5dSWFjo4nchKot/auMWixk3t4qd5vH/27gQQoiarXIksBukqioLtyVjttnxcatc00F93HQUW+0s3JrM0KFDSUtLQ6vV4uXlhZubGyaTibS0NNLS0sjIyCArK4ucnBy0Wi2nT59mw4YNrn4LohL4pzbucDiwWq0V2kN50d/beDXaLEIIIcQNqFwp7DodTjOx/0wevu4Vsz3Q9VAUBV93PfFn8njkP6/jZc1Dr9eTn5/v/JOTk0NeXh5GoxGTyUR+fj42mw0vLy/27dvHiBEjXP02hIv9UxuviBXeV/L3Nn4k3UTL4PLZskgIIUTlV6UD5fr9Zym22vH3qJxHwnkatBiNVjya387jfVv84/NVVaWoqIj8/HwCAgIqoEJR2f1TGz+/whuXnd9+sY2viz8rgVIIIWqwKjvknVdk5fuDabjrS3ducXlSFAV3vZbvD6aRV/TPq2EVRcHT07PEvEpRc11LG7dYLOh0OrRabQVXd971tnEhhBDVU5UNlPGnc8krsla6uZP/n4+bjrwiK/vP5Lm6FFHFXEsbr+gV3pcjbVwIIUSVDZTHM/JRVSrNyu4r0Wk1qMDxDJOrSxFVzLW08fMrvF0bKKWNCyGEqNzde1dxJN2Ew24lafN3ZB7dhaLVoihaGnToS902t5N78giJm2K4ecJLl7zWbilm6/v/ouvTX5ZLbZfcW1U5ki4ftjWJ3W7nq6++4ocffkCr1aLT6WjdujW33347c+fOZenSpf94jfNt5sqrp1VVxXj6GBpfHwgOdn79tzen4Fk7BEVRUFWVhp0HU6dl+6vea/eCV4gc8wxaw5U31D/6/QLy01IAKMxKxd23Nhr9+bmbdQdOlzYuhBA1WJUMlKqqkpBqJH3zEtw0KjdPeBmtwY3ivEwOrHgf1eHA3T/I1WU6GXRaElKNqKpaaed7irI1c+ZMjEYj8+fPx9fXF1VV+fnnnzEajdf0+ott3KC78txIq9WKOSOZogJPiLq9xGORY55B5+6J6WwS8cvexL9hC/SePle81uV+8fr/mvef4PzvnXOeoeVdD+Ad1BCA7AKLtHEhhKjBqmSgzCuykp56GlPSPtpMexvthTlk7n61aXJHNMd/WEzzgVNKvObs3i2c/vMHtHo3aje/ucRjv705hdCOA8lOjMdhtdCw82CCWncEwHQ2iaQtK7FbilAdDkI7DqROy/aoDjsHVnyIrTgfh82KV50GNOs33lnLRTZzEclrPuFcvabkTeyAv4vOGRcV59SpU/z000+sW7cOX9/zK58VRaF3797s2rULm83GG2+8wb59+7Db7bz88su0atUKgG3btvHll19iKihix7FMGnQdAeGtKcpO5+iGedgtZlRVJTA8Co/QluSf2I3dw4Pd6UkENmtHo9uHlKjFp15jtAZ3ivMyObVjA3mnjqI67GgN7jTrPx7PWud7Nn97cwqdpn+Izt2TnXOeoW7rTuQkJ2AtMFI3ogsNOw267Hs1nU3iyLqvaDH2JXILreQVWXniXw8yZcoUwsLCGDNmDEOHDmX79u04HA6eeuopOnToUOK9ms1mNBoN06dPp337q/ekCiGEqJyqZKA02xwUnDuJu39d9B7eJR7zqdcUsykHa+H/ht8Kzp0h5Y9vuXn8DAze/iT/uurSiyrne2mKcs+xd9Fr+DYIR+fmybEfFtHm7kcxePtjLTSxZ+FMfEOaYvD2p+Xgqeg9vFFVlRM/LiF198+EdhzwvzqN2SSs/pjakXfg2+I2LDZHuX1PROVx+PBhGjZsiL+//2UfT05OZsaMGTz77LOsXLmSTz/9lI8//pgzZ84wd+5cPv74Y/LtWgb/9xtOrHiH4IfeJHXPJmo1jSC040AArEUFGAuL8QlvT6C/D0173XPZe+UkJ+CwW/GoVZcGt91JkzuiATh3aCeJPy+jzcjHL/s6W3EhUWOfx1po4s8vnqNum9tx87l0Kyufeo3Re3iRf+oQHg1aknDoMDk5OXTq1ImzZ8+Sn59P48aNeeyxx9i/fz9PPPEE3377LTk5Oc736uXlxalTp5gyZQpr16512RZIQgghblyVDJQ2hwoqXOvAWu7JQ9Rq3BaDtz8A9aJ6cGr7+hLPCY7oCoCHfx38GjQj79RR9J7eFOed48CKD0o8tyg7DYOXH2f+/JHsxHhUhwO7uRDfkHDnc6wFecQve5PwfuNwr9+CYpsdq0NOExEQGhpKmzZtAGjbti2LFy8GYOvWrZw6dYqpU6dSaLGTdCITRaPBbMzGr0FzkjYvx26x4BfaHP9GN2HOyUOr03G5n4R9S/+LotGgc/ek1bBH0Ll5kpGwg9TdP2O3FIOqYi0uuGKNdVrdBoDe0wd3vzoU52VeNlAC1L+lN6l7N9MwpCWrV61g5MiRzmFvrVbL4MGDne+1Tp06HD58mBMnTjjf60UajYa0tDQaNmx4/d9UIYQQLlUlA6VOo+ARFEpxbjrWovwSvZSmsydw8wm46nwxrmWOl6KACl61Q4i897lLHk4/uI28U4eJuOdpdG4enNn1E3kph52Pa9098fAPIicxnrr1mqEooNfI3LKaoGXLlpw8eZK8vDz8/Pwuefzvq7K1Wi12ux04P2/ytttuY9asWaQbi7l7zlYMWg0eBh0eteriG9KUnOQEUvds4syuH/FuPxT9FeZYXpxDeVGxMYsTPy0l6r4X8AgIIj/jFPHL3rzie9Do/rcPqqLRoDqu3Lteu/nNHP9lOUXnTrHzrz+Y8ey/r/zNAedioYvvVQghRNVXuffcuQI3nQbPgGD8mkRy7IdF2K3nTwspzssk8Zc4QjsPLvF8/4Y3kZO0H0vB+X3yzu7ZfMk10w9sdV4j7/Qx/Bo0w6d+U4pzz5GTnOB8Xn7GSRx2G/biQnQe3ujcPLBbip2vv0ij1XPT0GmY83NJ+nExikPFoKuS325xnUJDQ+nVqxczZ87EZDo/9UJVVTZt2sSZM2eu+LpOnTqxc+dOjh07hptOgwYF09kkAIqy09F7+lK3TWcad78bU2ri+T0oPbyxm4v+sSa7uQhFq8Xg7Yeqqpzd80vZvFlA0Wip3bYbSWs/pVuPHvj4/O+XObvdzrp16wA4ePAg586do0WLFiXe60UHDx4ss5qEEEJUrCrZQ+nnocfPQ0/9nuMw7lnP7vkvX9g2SEODDv0JbtuF3JNHnM/3qhNCw9vvYt/S/152UQ6A6rCze8ErOKwWmva6B3e/2gC0HvEoiZuXk/RLHKrDjptvLVoN+xdBbTqRdXwvf33xAnpPH/waNMOcl1XimhqtjpaD7id+7TzSNi3A+8nu5fuNEZXGjBkz+Oqrrxg/fjw6nQ6Hw8HNN99M586dr/ia0NBQZs2axezZsykuLubY4bNoAkKo26gZmUf/IiNhB4pGC6pK415jyHHYqdPiFk7+uJDdC1657KKci7zqNKBOy1vZPe8ldB5eBDZrV6bv169FJ9K3fcu4MaNLfN3b25sTJ05wzz33YLfbmTVrFp6ennh6epZ4r1arlRYtWkiPpRBCVFGKqqpVcmLfs6vi+flQOsF+HqW+1t9XuJaHtLwiereqy+vDIsrl+qJ6ulobLywsICUlhSZNmrp8Y3OAo7u34pX6F7u/j3N+LTU1lTFjxrB582bXFSaEEKJCVNkx2BZ1fbj2ZTkupigX6hXi2l2tjZvNFkCpFCuiDyx/j/Rtq4me8ICrSxFCCOEiVXLIGyA8yBtFAZvdUerjF8vrxBw4X58ChAdJoBTX52pt/PwZ3oZKsYl4y+GPklVgoUv7yBJfr1+/vvROCiFEDVFleygjGvjj56HHZLa5upSrMplt+HnoaRty6WpfIa7mam3cYrHgVgl6J0HauBBCiCocKP089PRvHUyx1U5lnQaqqirFVjv9Wwfj56H/5xcI8TdXa+NmsxlDJZg7KW1cCCEEVOFACTCgbT3c9VoKLXZXl3JZyafPUlxgokNwlZ1ZIFzscm3c4XBgs1krxWKcQosdd72WgRH1XF2KEEIIF6rSgbJlsA8RIX4Yi62VrpdSVVUUgydkpfDk1DG89tprpKamurosUcVcro1bLOf3XXX1ghxVVTEWW4lo4C+LzoQQooar0oFSURTGdw7DTaetdHMpTWYbfj6efPr4KB7517/49ddfGTZsGDNnzrzq5tZC/N3l2vj5Fd7g5ubaQGky23DXaxnfqVGlWBwkhBDCdap0oAS4rXEgQ6PqU2i2YbNf+Xi4imSzOygw2xgaGUK3lvW57777WLNmDdOnT+f33393BsvTp0+7ulRRBfz/Nm42m9Hp9Gg0lz92sSL8vY3f1jjQZXUIIYSoHKrsxuZ/Zyy2MmnBn5zMLiTIx82lvSWqqpJhMtOwlifzJtyKr3vJhQrFxcWsWrWKhQsXkpOTw8CBA5k0aRKhoaEuqlhUBX9v4xZjJg67nUaNGrmkln9q40IIIWqeahEoAbYlZvH0in04VKjl5bqhwOwCCxoF3ro7ko5NrtxzYzabWbVqFQsWLCAnJ4c777yTyZMn07BhwwqsVlQlF9t4ekYGfu46goODXVLHtbZxIYQQNUe1CZQAcX+d4v2fjqLRKAR4VnyozCm04HCoPNa7OdHtr63H0Ww2s3r1ahYsWEB2djb9+/dnypQpEizFZcXsTOH5ZX/g5+NDg6CACr//jbRxIYQQ1V+1CpSqqrJ4ewqfbzmBoigEeOorZPhbVVVyCs+vwn2we1Pu63j9ixQsFoszWGZlZdGvXz8mT55MWFhY+RQtqqSUlBQGPPpfPG4ejJubW5Vq40IIIaqvKr8o5+8UReG+jo14rHdzNApkmMzlvlDHZneQYTKjUeDxPs0Z1ynshj5oDQYDo0aN4ttvv+Wpp55i165djBw5khdffJGkpKRyqFxURcnJybif3slDXRtVuTYuhBCi+qpWPZR/tz0xi7c3HiE5qwAvNx0+broy/RBUVRWT2UaB2UZYoBdP9W1RpvPJLBYL3377LfPnz+fcuXP06dOHKVOm0KRJkzK7h6h6Fi5cyLx589i8eTM7krKrdBsXQghRfVTbQAnnV8bO2XKCb/elUmy14+uux9OgLdWHrqqqFFrsGIutuOu1DI0M4YHuTfApp5WuFouFtWvXMm/ePDIyMiRY1nAvv/wyycnJLFiwAKgebVwIIUTVV60D5UXbE7NYtC2Z+DN5FFvPHxXn46ZDp732EX+b3YHJbHO+PiLEj/GdwypsDz6LxcJ3333HvHnzSE9Pp3fv3kyZMoWmTZtWyP1F5TBhwgTCwsJ4+eWXS3y9OrRxIYQQVVeNCJRwvtflSLqJdfFn+f5gGnlFVtTzD2DQaTHoNGgUUFBQUXGoYLE5sNjsoCgogL+nnn6tghkUUZ/mdb1dMo/MarWydu1a5s+fz9mzZ+nVqxdTp04lPDy8wmsRFUtVVXr06MGkSZMYP378ZR+vDm1cCCFE1VNjAuXf5RVZ2X8mj2PpJo5mmDiYaiSv0IoDFVUFRQENCn6eelrX96VFXR/Cg3xoG+KHn0flGPazWq2sW7eOefPmkZqaSs+ePZk6dSrNmjVzdWminGRmZtK/f3/eeecdunfvftXnVoc2LoQQouqokYHy/1NVlbwiKxabA6tDRa9RMOg0+HlUzJYspWGz2Vi3bh1fffUVqamp3HHHHUydOpXmzZu7ujRRxv78808eeughVq1add37lFblNi6EEKLyk0BZTdhsNtavX89XX33FmTNn6NGjB1OnTqVFixauLk2UkeXLl/POO+/wxx9/oNW67hxvIYQQ4v+rVvtQ1mQ6nY4hQ4awcuVKXnrpJY4fP869997LE088weHDh11dnigDSUlJNGzYUMKkEEKISkcCZTWj0+kYPHgwK1eu5JVXXiEpKYmxY8fyxBNPcOjQIVeXJ0ohOTlZTk4SQghRKUmgrKa0Wi0DBw5kxYoVzJw5k+TkZO677z4ee+wxEhISXF2euAESKIUQQlRWEiirOa1Wy4ABA1i+fDkzZ87k5MmTjBs3jkcffZSDBw+6ujxxjQoKCsjIyJBAKYQQolKSQFlDXAyWK1as4LXXXuP06dOMHz+e6dOnc+DAAVeXJ/5BSkoKAI0bN3ZxJUIIIcSlJFDWMBqNhv79+7N8+XJmzZrF2bNnmTBhAo888gjx8fGuLk9cQVJSEgCNGjVycSVCCCHEpSRQ1lAajYZ+/foRGxvL7NmzSU9PZ9KkSfzrX/+SYFkJJScnU7duXTw9PV1dihBCCHEJCZQ1nEajoW/fvsTExPD666+TkZHBpEmTmDZtGnv37nV1eeKCpKQkmT8phBCi0pJAKYDzwbJPnz7ExMTwxhtvkJWVxZQpU5g2bRp79uxxdXk1XnJyssyfFEIIUWlJoBQlaDQaevfuzbJly3jzzTfJzs5m6tSpPPjgg+zevdvV5dVINpuNU6dOSQ+lEEKISksCpbgsjUZDz549Wbp0KW+++SZGo5H777+fBx54gF27drm6vBrl9OnT2O126aEUQghRaUmgFFd1MVguWbKEt99+G5PJxAMPPMD999/PX3/9hRwFX/4urvCWQCmEEKKykkAprolGo6FHjx58/fXXvPPOOxQUFPDggw9y//338+eff0qwLEfJycn4+PgQEBDg6lKEEEKIy5JAKa6Loih0796dJUuW8O6771JUVMRDDz3E1KlT2blzpwTLcpCUlETjxo1RFMXVpQghhBCXJYFS3BBFUejWrRuLFy/mvffew2w2M23aNCZPnsyOHTskWJYhOcNbCCFEZSeBUpSKoih07dqVRYsW8f7772Oz2Xj44YeZPHky27dvl2BZSqqqSqAUQghR6UmgFGVCURS6dOnCwoUL+fDDD7Hb7fzrX/9i0qRJbN26VYLlDTp37hyFhYUSKIUQQlRqEihFmVIUhc6dO7NgwQI+/PBDAKZPn87EiRMlWN6A5ORkQFZ4CyGEqNwkUIpycTFYzps3j48//hiNRsP06dMZP348v//+uwTL/0dVVTZt2sS2bdtIS0vD4XAA5xfk6PV66tev7+IKhRBCiCtTVPlkFxVAVVV27tzJ3Llz2bdvH61atWLq1Kl06dJFVi8DRqORjh07YjQacXNzw8fHh2bNmpGVlYXJZOKNN96gcePGNGjQAIPB4OpyhRBCiBIkUIoKpaoqf/75J3PnzmXv3r3cdNNNTJ06la5du9b4YDlixAh+++03atWqhdlsxmKxkJOTg0ajwdfXFzc3N9zd3WnUqBE333wz//nPf/Dw8HB12UIIIYQMeYuKpSgKHTp04IsvvuCzzz7D3d2dJ554gvvuu48tW7bU6KHwfv36odFo8PLyIjAwkHr16qHX66lVqxZBQUF4eHhgsVjYunUrmzdvrtHfKyGEEJWL9FAKl1JVlV27djF37lx2795N8+bNuf/+++nevXuN67E8duwYffv2xc3NDW9vbxwOB0eOHCEkJARfX18AcnJyUFWVBQsW0K1bNxdXLIQQQpwnPZTCpRRFoX379sydO5c5c+bg4+PDU089xb333sumTZuci1NqgvDwcJo1a4bJZALAbDYDOOdMms1mCgsLmTp1qoRJIYQQlYoESlFp3HLLLcyZM4e5c+fi5+fH008/XaOCpaIoDBgwALvdjqqqWCwW4HygdDgcnDt3jltvvZXHH3/cxZUKIYQQJcmQt6i09uzZwxdffMHOnTtp2rQp999/P3fccQcaTfX9PWjfvn0MGTIELy8v8vPzMRqNhIeHk56ejq+vL6tXr6Z58+auLlMIIYQoQQKlqPT27dvH3Llz2bFjB02bNmXKlCn06tWrWgZLh8NBt27dSElJcfZQBgQEkJ+fz5tvvsmYMWNcXKEQQghxqer3iSyqncjISD755BPmzZtHnTp1eO655xg9ejQbN26sdkPhGo2GO++8E6vVitlsRqfTkZOTw6BBgxg9erSryxNCCCEuS3ooRZUTHx/Pl19+ydatW2ncuDFTpkyhT58+1abHcuvWrYwaNYrMzEzc3d1p1aoVa9asITAw0NWlCSGEEJclgVJUWQcOHGDu3Lls3bqVsLAwpkyZQt++fat8sLRarbRr147Dhw9Tt25dYmJi6Nq1q6vLEkIIIa6oan/yihqtTZs2fPjhhyxcuJCQkBBefPFFRo4cyYYNG6r0ULherycyMhKNRsOUKVMkTAohhKj0pIdSVBsJCQl88cUX/PbbbzRs2JApU6bQr18/tFqtq0u7KlVVySuyYrY5sDlUdBqFLZt+5uP33mTjDz/g7u7u6hKFEEKIq5JAKaqdhIQEvvzyS3799VcaNmzI5MmT6d+/f6UJlnlFVuJP53I8I58j6SYSUo3kFVlxoKKqoCigQcHPQ0+r+r60qOtDeJA3EQ388fPQu7p8IYQQ4hISKEW1dfjwYb744gu2bNlCaGgokyZNYsCAAdcULDdt2sSpU6cYP358mdSiqiqH00ys33+W7w+mkVdk5fxPnopBp8Wg06BRQEFBRcWhgsXmwGKzAwqKAn4eevq3DmZgRD1a1PWpcUdTCiGEqLwkUIpq78iRI3zxxRds3ryZBg0aOIOlTqe77PMLCwvp2bMnaWlpfPbZZ9x5552luv/2xCwWbktm/5k8iq123PVafNx06LTXPoXZZndgMtucr48I8WN85zBuaywrv4UQQrieBEpRYxw9epQvv/ySTZs2ERISwuTJky8bLBcuXMizzz6LqqrUq1eP1atXExYWdt33MxZbmbPlBN/sTcVss+PrrsfToC1Vz6KqqhRa7BiLrbjptAyNqs8D3Zvi6y5D4UIIIVxHAqWocY4dO8YXX3zBpk2bqF+/PpMmTWLQoEHodDoKCwvp1asXJ0+epG7dupw5c4ZOnToRExNzXYtjtiVm8c7GI6RkFeDppsPHTVemQ9SqqmIy2ygw2wgL9OLJvi3o1ER6K4UQQriGBEpRYx0/fpwvv/ySn376iXr16jFp0iRycnJ44YUXCAoKQq/XYzabyczM5MEHH+Sll176x2uqqsryXaf5aNMxzDYHgV6G6xravl42u4OsAgtuOg2P9GzGyFsayNxKIYQQFU4CpajxTpw4wZdffsnGjRtJTExEo9EQFhbmDGa5ublYrVY++eQTBg0adMXrqKrKom0pzPn1BIqiEOCpr5Bwp6oqOYVWVFXlwe5Nua9jIwmVQgghKpQESiEueOedd3jppZdwOBwYDAYCAwPx9/dHURTOnj1L7dq1Wb16NU2aNLns6+P+OsX7Px1Fo1EI8DRUcPWQU2jB4VB5rHdzotuHVvj9hRBC1FxyUo4QQFFREatWrcLPz4+mTZvi4eFBWloax48fJycnh6CgINLT03nssccoLCy85PXbErP4aNOxCz2TFR8mAQI8DSiKwkebjrE9McslNQghhKiZJFAKAaxcuZKkpCQCAwNxc3MjJCSEJk2a4OnpSVpaGomJiRgMBnbu3Mns2bP5e8e+sdjKOxuPUGx1EODp2tXWAZ56zDYHb288grHY6tJahBBC1BwSKEWNV1xczOeff46iKBgM/+tdvBgsmzZtipeXF1lZWRiNRj7//HNWrlzpfN6cLSdIySqgtrfB5XMXFUUh0MtAclYBc7ckurQWIYQQNYfMoRQ13qpVq5g+fTpWq/Wqp+jY7XYKCwspKirC09OTffv2ka768uTyfei0SqXaC9JYbMXuUHlnZKRsfi6EEKLcXf6oECFqkDZt2jBt2rQSX7vY03i5/83NzeX06dMEBATw5roTmG12AjyvfY/KiuDjpiPNWMzCrcl0CKvl8p5TIYQQ1Zv0UApxgw6dNTJ18V8YtBq83Crf72YFZhsWu4Mvx7WnZbCvq8sRQghRjckcSiFu0Pr9Zym22vE0XHmY3JU8DVqKrXbWxZ91dSlCCCGqOQmUQtyAvCIr3x9Mw11furO5y5OiKLjrtXx/MI28IlnxLYQQovxIoBTiBsSfziWvyIpPJRzq/jsfNx15RVb2n8lzdSlCCCGqMQmUQtyA4xn5qCrlek53WdBpNajA8QyTq0sRQghRjVXuT0MhKqkj6SagiqxnU9UL9QohhBDlo3KP1wlRyWzatIl58+ax+XA6RcVmMvxrodHpsOSfH1IuyDiFZ+0QFI0GrcGNyDHPXvY6tuJCTvy0lPyMkyiKgqqqhHYcSFCr20jf/wdZx/bQavi/yqRmg05LQqoRVVUr7XxPIYQQVZsESiGuUWZmJrNmzeKTufM4tSoRjUaBnDN41W3oDGq/vTmFyDHPoHP3vOq1kn9fjd7Tm5snvoKiKNgtxc5QWtYMOg25hVbyiqz4u+iccSGEENWbBEohrlF2djZarRY3Lx8cqOgUBc/gRjd0LYsxB9+QcGcQ1Rrc8ah1+c3RT+/4nvSDWwEFr6AGhPe5F52bJyl/rKHg3GlsxYVY8nPxCAii+YBJ6D28cdhtpPz+Dbkph7HZrOj9gsi6pzX+nkE3+vaFEEKIK5I5lEJco/DwcKKiohgbPZzEtZ+T9tdGzKacG7pW/fa9ObVjA3sWzuT4T1+TdXzfZZ+XnbiftAO/EznmWW6Z9ApavRvJW/53jrjx9DFaDp5K+ymv4eZTi+RfVwFweucPaPVutBv3Im3GzsAtsD5ffTn3hmoVQggh/on0UApxjTQaDW+++SZ/7j/CuLdiMJ08yK4/N9Bu3It4BNS9rmv5N2xJhwffJO/0EYynT3B842Jykg4Q3ufeEs/LTU6gTssOziH0elE9OPTtZ87HazWJwODlB0BwVDcOrf4UgKxje7Cbi8g8uhu7w4HNZiOjduvSvH0hhBDiiiRQCnGdmjVtTFDbbmgiu5O85iOyju+jwa19r/s6WoMbtZpEnP8THsGBuPcuCZSX+Kc1NX9bdNO09xgCGrcm32zD4VB5++Hbr7tGIYQQ4lrIkLcQ1ygjI4N9+/bh56HHz0NPYb6J4txMPPzrXPe1cpIOYi0qcP7//LQU3C9zHf+wVmQe/hObuQiAtL1bCAj7X09jdtJ+LAXG84/F/4Z/o5sACGzWjjO7fsRuNWOxOfDRO8g6e+q66xRCCCGuhfRQCnGN7HY7X3zxBampqRw/V0xabgGhEZ0JbNbuuq9VkHmaxF/igPNb+ei9/GkxaOolz6vVpC2F586w7+vX+fuinIt8Q8I58t1czKb/LcoBCL2tPye3Wtm7eDbFNjvBvu4cbfEoTZs2vdG3L4QQQlyRoqpqFdmdWYjKY/4fSXy6+QTBfpdfmV0RUv5Yg624kKa9Rl/1eWnGYh7u0ZQJnRtXUGVCCCFqGhnyFuIGhAd5oyhgsztcXcpV2ewOFCA8yMfVpQghhKjGZMhbiBsQ0cAfPw89JrONgCtsFp6fcZKj6+df8vW6bToT0r5PqWtodPuQf3yOyWzDz0NP2xC/Ut9PCCGEuBIJlELcAD8PPf1bB7Psz5NXPNLQO6ghN094yQXVnaeqKsVWO8OiQvDz0LusDiGEENWfDHkLcYMGtK2Hu15LocXu6lIuq9Bix12vZWBEPVeXIoQQopqTQCnEDWoZ7ENEiB/GYiuVbW2bqqoYi61ENPCnRV2ZPymEEKJ8SaAU4gYpisL4zmG46bSYzDZXl1OCyWzDXa9lfKdGlx2OF0IIIcqSBEohSuG2xoEMjapPodlWaVZ82+wOCsw2hkaGcFvjQFeXI4QQogaQQClEKT3QvSmNAr3IKrC4fOhbVVWyCiyEBXpxf/cmLq1FCCFEzSGBUohS8nXX82TfFrjpNOQUWl1aS06hFTedhqf6tsDXXVZ2CyGEqBgSKIUoA52aBPJIz2aoqkpOocUlNeQUnu8hfaRnMzo2kaFuIYQQFUcCpRBlZOQtDXiwe1McDpXsChz+VtXz93M4VB7s3pSRtzSokPsKIYQQF8lZ3kKUIVVVWb7rNB9tOobZ5iDQy4BOW36/t9nsDrIKLLjpNEzv1YyRt4SW272EEEKIK5FAKUQ52J6Yxdsbj5CcVYCXmw4fN12Zbt+jqioms40Cs42wQC+e6ttChrmFEEK4jARKIcqJsdjKnC0n+HZfKsVWO77uejwN2lIFS1VVKbTYMRZbcddrGRoZwgPdm+AjC3CEEEK4kARKIcrZ9sQsFm1LJv5MHsXW88ch+rjprmso3GZ3YDLbnK+PCPFjfOcw2WdSCCFEpSCBUogKoKoqR9JNrIs/y/cH08grsqKefwCDTotBp0GjgIKCiopDBYvNgcVmB0VBAfw99fRrFcygiPo0r+stJ+AIIYSoNCRQClHB8oqs7D+Tx7F0E0czTBxMNZJXaMWBiqqCooAGBT9PPa3r+9Kirg/hQT60DfHDz0OGtoUQQlQ+EiiFcDFVVckrsmKxObA6VPQaBYNOg5+HXnohhRBCVAkSKIUQQgghRKnIxuZCCCGEEKJUJFAKIYQQQohSkUAphBBCCCFKRQKlEEIIIYQoFQmUQgghhBCiVCRQCiGEEEKIUpFAKYQQQgghSkUCpRBCCCGEKBUJlEIIIYQQolQkUAohhBBCiFKRQCmEEEIIIUpFAqUQQgghhCgVCZRCCCGEEKJUJFAKIYQQQohSkUAphBBCCCFKRQKlEEIIIYQoFQmUQgghhBCiVCRQCiGEEEKIUpFAKYQQQgghSkUCpRBCCCGEKBUJlEIIIYQQolQkUAohhBBCiFKRQCmEEEIIIUpFAqUQQgghhCgVCZRCCCGEEKJUJFAKIYQQQohSkUAphBBCCCFK5f8A457FhGlNTKwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.nx_agraph.graphviz_layout(hc_const, prog=\"dot\")\n",
    "nx.draw(hc_const, pos, with_labels=True,\n",
    "        node_size=1000, font_size=8, arrowsize=20, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b543a88f",
   "metadata": {},
   "source": [
    "The structure now starts to have a more realistic semantic meaning but it is still not explainable due to the presence of some edges that are not meaningful. For example, the edge between `FatingBS` and `RestingECG` is meaningless because the two variables are not related to each other. \n",
    "\n",
    "Inferring the structure of the network from the data is not enough to obtain a meaningful one, we need to use domain knowledge to build a network that is semantically correct from scratch. In the next section we will do that.\n",
    "\n",
    "> Just for comparison, here are the performance of this constrained network compared to the results. Firstly we need to convert the estimated DAG to a Bayesian Network and then we will learn the parameters of the network from the data using the `fit` method of the `BayesianNetwork` class using the `MaximumLikelihoodEstimator` as the estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dd6d7817",
   "metadata": {},
   "outputs": [],
   "source": [
    "hc_const_model = BayesianNetwork(hc_const.edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "de65eed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "hc_const_model.cpds = [] # Clear the cpds\n",
    "hc_const_model.fit(train, estimator=MaximumLikelihoodEstimator)\n",
    "hc_const_model.get_cpds()\n",
    "assert hc_const_model.check_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c13a5110",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "accuracy, _ = bayesian_kfold(df, hc_const_model, accuracy_score)\n",
    "roc_auc, _ = bayesian_kfold(df, hc_const_model, roc_auc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "89fc3829",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuarcy: 0.831\n",
      "ROC AUC: 0.830\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuarcy: {accuracy:.3f}')\n",
    "print(f'ROC AUC: {roc_auc:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe32d78",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77321de6-b51d-48cd-b8ef-d917909a5ebf",
   "metadata": {},
   "source": [
    "# 7. Domain Knowledge Network\n",
    "<a class=\"anchor\" id=\"ch7\"></a>\n",
    "###### [Back to top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f829dc7",
   "metadata": {},
   "source": [
    "## 7.1 Motivational background\n",
    "<a class=\"anchor\" id=\"ch71\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf5736f",
   "metadata": {},
   "source": [
    "In this paragraph we will build a fully explainable network with the literature references reported in the section 11. The aim is to build a network that is fully explainable where each edge has a clear semantic meaning.\n",
    "\n",
    "We have decided to pursue this direction because:\n",
    "\n",
    "- Using a structure learning algorithm on this relatively small dataset could lead to overfitting. We have seen that in the sixth paragraph.\n",
    "- In general we think that the people that partecipate in this kind of survey are not a good representation of the whole population (for example, there isn't even one underage person). In other words, we think that the dataframe could be biased by definition, and letting an algorithm learn on such biased dataset could lead to overfitting.\n",
    "- Generally, a good `roc_auc` score is anything above 0.80[[31]](#31). We assume that the network we are going to build can easily achieve that score (since even the Naive Bayes one can get these kind of results), and even if it doesn't reach the score obtained before, it is fully explainable and more general, and it can be used in a real world scenario.\n",
    "- Bayesian Networks are based on the conditional independence assumption: if some edge doesn't represent an actual causal-effect relation the whole structure may be weak"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1ba19a",
   "metadata": {},
   "source": [
    "## 7.2 Construction of the network\n",
    "<a class=\"anchor\" id=\"ch72\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cabe0b",
   "metadata": {},
   "source": [
    "The references for the Bayesian Network with Domain Knowledge are the following:\n",
    "\n",
    "* Age → Cholesterol [[13]](#13)\n",
    "* Age → RestingECG [[15]](#15)\n",
    "* Age → MaxHR [[16]](#16)\n",
    "* Age → RestingBP [[17]](#17)\n",
    "* Age → FastingBS [[18]](#18)\n",
    "* Age → HeartDisease [[19]](#19)\n",
    "* Sex → Cholesterol [[7]](#7)\n",
    "* Sex → MaxHR [[20]](#20)\n",
    "* Sex → ExerciseAngina [[21]](#21)\n",
    "* Sex → HeartDisease [[22]](#22)\n",
    "* RestingECG → HeartDisease [[9]](#9)\n",
    "* MaxGR → ExerciseAngina [[23]](#23)\n",
    "* Cholesterol → HeartDisease [[24]](#24)\n",
    "* Cholesterol → RestingBP [[25]](#25)\n",
    "* RestingBP → FastinBS [[26]](#26)\n",
    "* FastingBS → HeartDisease [[12]](#12)\n",
    "* ExerciseAngina → HeartDisease [[27]](#27)\n",
    "* ExerciseAngina → Oldpeak [[27]](#27)\n",
    "* ExerciseAngina → ST_Slope [[27]](#27)\n",
    "* HeartDisease → Oldpeak [[9]](#9)\n",
    "* HeartDisease → ST_Slope [[9]](#9)\n",
    "* HeartDisease → ChestPainType [[9]](#9)\n",
    "* Oldpeak → ST_Slope [[9]](#9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5cf4a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_kg_model = BayesianNetwork([\n",
    "    ('Age', 'Cholesterol'),\n",
    "    ('Age', 'RestingECG'),\n",
    "    ('Age', 'MaxHR'),\n",
    "    ('Age', 'RestingBP'),\n",
    "    ('Age', 'FastingBS'),\n",
    "    ('Age', target_variable),\n",
    "    ('Sex', 'Cholesterol'),\n",
    "    ('Sex', 'MaxHR'),\n",
    "    ('Sex', 'ExerciseAngina'),\n",
    "    ('Sex', target_variable),\n",
    "    ('RestingECG', target_variable),\n",
    "    ('MaxHR', 'ExerciseAngina'),\n",
    "    ('Cholesterol', target_variable),\n",
    "    ('Cholesterol', 'RestingBP'),\n",
    "    ('RestingBP', 'FastingBS'),\n",
    "    ('FastingBS', target_variable),\n",
    "    ('ExerciseAngina', target_variable),\n",
    "    ('ExerciseAngina', 'Oldpeak'),\n",
    "    ('ExerciseAngina', 'ST_Slope'),\n",
    "    (target_variable, 'Oldpeak'),\n",
    "    (target_variable, 'ST_Slope'),\n",
    "    (target_variable, 'ChestPainType'),\n",
    "    ('Oldpeak', 'ST_Slope'),\n",
    "]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b05375f",
   "metadata": {},
   "source": [
    "After manually adding the edge, we get the following network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "443f622a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.nx_agraph.graphviz_layout(domain_kg_model, prog=\"dot\")\n",
    "nx.draw(domain_kg_model, pos, with_labels=True,\n",
    "        node_size=1000, font_size=8, arrowsize=20, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa842ac5",
   "metadata": {},
   "source": [
    "## 7.3 Changing the estimator\n",
    "<a class=\"anchor\" id=\"ch73\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930fe191",
   "metadata": {},
   "source": [
    "As stated in the paragraph 4, until now we have used the `MaximumLikelihoodEstimator` to estimate the CPDs of our networks since they lack semantic meaningfulness.\n",
    "\n",
    "Now that we focus on the semantic correctness of the Bayesian Network, `BayesianEstimator` is a better choice, since it's used whene there is prior knowledge of the domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe6c9e5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "domain_kg_model.cpds = []\n",
    "domain_kg_model.fit(train, estimator=BayesianEstimator, prior_type=\"BDeu\")\n",
    "domain_kg_model.get_cpds()\n",
    "assert domain_kg_model.check_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1a03a6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/estimators/base.py:170: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data.groupby([variable] + parents).size().unstack(parents)\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy/factors/discrete/DiscreteFactor.py:491: RuntimeWarning: invalid value encountered in divide\n",
      "  phi.values = phi.values / phi.values.sum()\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "\n",
    "accuracy, _ = bayesian_kfold(df, domain_kg_model, accuracy_score)\n",
    "roc_auc, y_pred = bayesian_kfold(df, domain_kg_model, roc_auc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d565d109",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuarcy: 0.846\n",
      "ROC AUC: 0.846\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuarcy: {accuracy:.3f}')\n",
    "print(f'ROC AUC: {roc_auc:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "119d15d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir('model'):\n",
    "    os.mkdir('model')\n",
    "domain_kg_model.save(f'model{os.sep}heart_disease_model.bif')\n",
    "writer = XMLBIFWriter(domain_kg_model)\n",
    "writer.write_xmlbif(f'model{os.sep}heart_disease_model.xml')\n",
    "\n",
    "if os.path.isdir('HeartDisease-Dashboard'):\n",
    "    domain_kg_model.save(\n",
    "        f'HeartDisease-Dashboard{os.sep}model{os.sep}heart_disease_model.bif')\n",
    "    writer.write_xmlbif(\n",
    "        f'HeartDisease-Dashboard{os.sep}model{os.sep}heart_disease_model.xml')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a409f8a5",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba2e424",
   "metadata": {},
   "source": [
    "# 8. Markov Blanket feature selection algorithm\n",
    "\n",
    "<a class=\"anchor\" id=\"ch8\"></a>\n",
    "###### [Back to top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff49640b",
   "metadata": {},
   "source": [
    "In this section we will use a Markov Blanket based feature selection algorithm that selects a subset of features by considering their performance both individually as well as a group. This allows the algorithm to not only select the best set of features, but also select the best set of features that play well with each other. The Markov Blanket of a node is the set of nodes that are conditionally independent of the node given the other nodes in the network. In other words, the Markov Blanket of a node is the set of nodes that are the parents, children, and parents of children of the node. The Markov Blanket of a node is the smallest set of nodes that contains all the information about the node. \n",
    "\n",
    "`PyImpetus` [[28]](#28) is a library that implements a Markov Blanket based feature selection algorithm. By first using a classifier to determine the features that are most important, and then using a Markov Blanket based feature selection algorithm to select the best set of features, we can create a network that is semantically correct and has the best performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2ffd78",
   "metadata": {},
   "source": [
    "First we need to encode the categorical variables using the `OrdinalEncoder` of the `sklearn` library (since for this task we need numeric values). Then we will use the `PyImpetus` library to select the best set of features. We will use the `SVC` as the classifier to determine the features that are most important. Then we will use the `MarkovBlanket` (MB) class of the `PyImpetus` library to select the best set of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ae954b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = OrdinalEncoder().set_output(transform='pandas')\n",
    "\n",
    "X_enc, _ = encoder.fit_transform(X), y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2752fc",
   "metadata": {},
   "source": [
    "Here the library is used to select the best set of features by means of a `SVC` classifier with fixed `random_state` (for reproducibility) and balanced `class_weight`. The algorithm needs also a `p_val_thresh` below which a feature will be considered as a candidate for the final MB, as well as a number of simulations (`num_simul`) and a simulation size (`simul_size`). \n",
    "\n",
    "* `simul_type` set to 1 means that the algorithm will use a stratify split to divide the dataset in training and test set, and it will use the training set to select the best features.\n",
    "* `sig_test_type` instead, is the type of significance test to use which is either `non-parametric` or `parametric`. The parametric tests use a tail paired t-test to compare the performance of the features, while the non-parametric tests use the Wilcoxon test to compare the performance of the features.\n",
    "* `cv` is the cross validation strategy to use. In this case a `StratifiedKFold` which is a stratified k-fold cross validation strategy. We put it to 5 so that each fold contains 20% of data, resembling our starting `train_test_split`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5dbbfbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_mb = PPIMBC(model=SVC(random_state=42, class_weight=\"balanced\"), p_val_thresh=0.05, num_simul=100,\n",
    "               simul_size=0.20, simul_type=1, sig_test_type=\"non-parametric\", cv=5, random_state=42, n_jobs=-1, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bc8c0791-7102-4ab5-982e-6cdb21100daa",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "model_mb.fit_transform(X_enc, y.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d46fab1",
   "metadata": {},
   "source": [
    "So the most important features are the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "34e42fa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most important features are: ['ST_Slope', 'ChestPainType', 'Sex', 'ExerciseAngina', 'RestingECG', 'Oldpeak']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"The most important features are: {model_mb.MB}\")\n",
    "\n",
    "model_mb.feature_importance()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef306e6",
   "metadata": {},
   "source": [
    "Now we can use the variables selected by the Markov Blanket based feature selection algorithm to build a Bayesian Network that is semantically correct and has the best performance. We will start by taking all the edgs of the Domain Knowledge network and remove the ones that are not selected by the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5580ba6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = model_mb.MB + [target_variable]\n",
    "edges = [x for x in list(domain_kg_model.edges()) if x[0] in all_nodes and x[1] in all_nodes]\n",
    "\n",
    "reduced_network = BayesianNetwork(edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6365de3",
   "metadata": {},
   "source": [
    "In this way we can build a network that is semantically correct (since it is a subset of the edges of the previous network) and has the best performance (since it uses the best set of features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "673c1870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.nx_agraph.graphviz_layout(reduced_network, prog=\"dot\")\n",
    "nx.draw(reduced_network, pos, with_labels=True,\n",
    "        node_size=1000, font_size=8, arrowsize=20, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9906dc94",
   "metadata": {},
   "source": [
    "We notice that the network is way simpler than the others we built before.\n",
    "\n",
    "We can then use the `fit` method of the `BayesianNetwork` class using the `BayesianEstimator` as the estimator to learn the parameters of the network from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "057213b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "reduced_network.cpds = []\n",
    "reduced_network.fit(train[list(reduced_network.nodes)], estimator=BayesianEstimator, prior_type=\"BDeu\")\n",
    "reduced_network.get_cpds()\n",
    "assert reduced_network.check_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c66a4e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_vars = list(reduced_network.nodes)\n",
    "removed_vars.remove(target_variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645e5275",
   "metadata": {},
   "source": [
    "And now we can make predictions about the risk of heart disease in a patient using the network by testing it on the test set with reduced dimensionality."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64677dfb",
   "metadata": {},
   "source": [
    "The network then can be compared in terms of `accuracy` and `ROC AUC` with the previous networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "50eaf4c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy-0.1.24-py3.11.egg/pgmpy/factors/discrete/DiscreteFactor.py:488: RuntimeWarning: invalid value encountered in divide\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy-0.1.24-py3.11.egg/pgmpy/factors/discrete/DiscreteFactor.py:488: RuntimeWarning: invalid value encountered in divide\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy-0.1.24-py3.11.egg/pgmpy/factors/discrete/DiscreteFactor.py:488: RuntimeWarning: invalid value encountered in divide\n",
      "/home/matteo/Documenti/VSCODE/BayesianClassifier/.venv/lib/python3.11/site-packages/pgmpy-0.1.24-py3.11.egg/pgmpy/factors/discrete/DiscreteFactor.py:488: RuntimeWarning: invalid value encountered in divide\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "\n",
    "accuracy, _ = bayesian_kfold(df.loc[:, list(reduced_network.nodes)], reduced_network, accuracy_score)\n",
    "roc_auc, y_pred = bayesian_kfold(df.loc[:, list(reduced_network.nodes)], reduced_network, roc_auc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "47e361d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuarcy: 0.854\n",
      "ROC AUC: 0.855\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuarcy: {accuracy:.3f}')\n",
    "print(f'ROC AUC: {roc_auc:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3d28e4",
   "metadata": {},
   "source": [
    "As we can see the network built using the Markov Blanket based feature selection algorithm has the best performance in terms of `accuracy` and `ROC AUC`. This is because the algorithm selects the best set of features that play well with each other. The network is also semantically correct because it is a subset of the edges of the previous network. \n",
    "\n",
    "As a result, the network achieves 0.86 `ROC AUC` and 0.85 `accuracy` on the test set. However the network is too simplistic and it does not take into account the majority of the features. The features not included in the network may have complex interactions with the other features and may be important for the prediction of the risk of heart disease in a patient even if they are not selected by the algorithm.\n",
    "\n",
    "**Thus the further analysis will be performed on the network built using the Domain Knowledge and not on the network built using the Markov Blanket based feature selection algorithm.**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89d5c7b-90c9-4712-ba8a-38988397297d",
   "metadata": {},
   "source": [
    "# 9. Network analysis\n",
    "\n",
    "<a class=\"anchor\" id=\"ch9\"></a>\n",
    "###### [Back to top](#top)\n",
    "\n",
    "Thanks to `pgmpy` it is possible to investigate several **properties of the network**. In particular the interest is to find the **conditional independencies** and **active trails** with respect to some given evidence or to ask for the **Markov Blanket** of a node. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "366dce64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There can be made 9885 valid independence assertions with respect to the all possible given evidence.\n",
      "For instance, any node in the network is independent of its non-descendents given its parents:\n",
      " \n",
      "(Age ⟂ Sex)\n",
      "(Sex ⟂ Age, RestingECG)\n",
      "(ChestPainType ⟂ ExerciseAngina, FastingBS, RestingECG, Cholesterol, RestingBP, Sex, Oldpeak, ST_Slope, MaxHR, Age | HeartDisease)\n",
      "(RestingBP ⟂ ExerciseAngina, MaxHR, Sex, RestingECG | Age, Cholesterol)\n",
      "(Cholesterol ⟂ ExerciseAngina, MaxHR, RestingECG | Age, Sex)\n",
      "(FastingBS ⟂ ExerciseAngina, Sex, MaxHR, Cholesterol, RestingECG | Age, RestingBP)\n",
      "(RestingECG ⟂ ExerciseAngina, RestingBP, Sex, MaxHR, FastingBS, Cholesterol | Age)\n",
      "(MaxHR ⟂ FastingBS, RestingBP, RestingECG, Cholesterol | Age, Sex)\n",
      "(ExerciseAngina ⟂ RestingBP, FastingBS, Cholesterol, Age, RestingECG | MaxHR, Sex)\n",
      "(Oldpeak ⟂ FastingBS, RestingECG, Cholesterol, Sex, RestingBP, ChestPainType, MaxHR, Age | ExerciseAngina, HeartDisease)\n",
      "(ST_Slope ⟂ FastingBS, Cholesterol, RestingECG, RestingBP, Sex, ChestPainType, MaxHR, Age | ExerciseAngina, Oldpeak, HeartDisease)\n",
      "(HeartDisease ⟂ MaxHR, RestingBP | ExerciseAngina, Sex, FastingBS, Age, RestingECG, Cholesterol)\n",
      "\n",
      "But we can also find some other independencies in the network given some evidence. For example:\n",
      "\n",
      "(Age ⟂ ChestPainType | HeartDisease)\n",
      "(Sex ⟂ ChestPainType | HeartDisease)\n",
      "(ChestPainType ⟂ MaxHR | ExerciseAngina, Sex, Age)\n",
      "(RestingBP ⟂ ChestPainType | HeartDisease)\n",
      "(Cholesterol ⟂ ChestPainType | HeartDisease)\n",
      "(FastingBS ⟂ ChestPainType | HeartDisease)\n",
      "(RestingECG ⟂ Sex)\n",
      "(MaxHR ⟂ ChestPainType | HeartDisease)\n",
      "(ExerciseAngina ⟂ ChestPainType | HeartDisease)\n",
      "(Oldpeak ⟂ ChestPainType | HeartDisease)\n",
      "(ST_Slope ⟂ ChestPainType | HeartDisease)\n",
      "(HeartDisease ⟂ MaxHR | ExerciseAngina, Sex, Age)\n"
     ]
    }
   ],
   "source": [
    "print(f'There can be made {len(domain_kg_model.get_independencies().get_assertions())}',\n",
    "      'valid independence assertions with respect to the all possible given evidence.')\n",
    "print('For instance, any node in the network is independent of its non-descendents given its parents:\\n',\n",
    "      f'\\n{domain_kg_model.local_independencies(df.columns.tolist())}\\n')\n",
    "\n",
    "print('But we can also find some other independencies in the network given some evidence. For example:\\n')\n",
    "\n",
    "for node in df.columns.tolist():\n",
    "    for assertion in domain_kg_model.get_independencies(latex=False, include_latents=True).get_assertions():\n",
    "        if node in assertion.get_assertion()[0] and len(assertion.get_assertion()[2]) < 4 and len(assertion.get_assertion()[1]) < 3 and assertion not in domain_kg_model.local_independencies(df.columns.tolist()).get_assertions():\n",
    "            print(assertion)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dc522169",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thanks to this library it is also possible to find automatically the Markov blanket of any node in the network.\n",
      "\n",
      "Markov blanket of 'Age' is ['ExerciseAngina', 'FastingBS', 'HeartDisease', 'RestingECG', 'Cholesterol', 'RestingBP', 'Sex', 'MaxHR']\n",
      "Markov blanket of 'Sex' is ['ExerciseAngina', 'FastingBS', 'HeartDisease', 'Cholesterol', 'RestingECG', 'MaxHR', 'Age']\n",
      "Markov blanket of 'ChestPainType' is ['HeartDisease']\n",
      "Markov blanket of 'RestingBP' is ['FastingBS', 'Age', 'Cholesterol']\n",
      "Markov blanket of 'Cholesterol' is ['ExerciseAngina', 'FastingBS', 'HeartDisease', 'RestingECG', 'Sex', 'RestingBP', 'Age']\n",
      "Markov blanket of 'FastingBS' is ['ExerciseAngina', 'HeartDisease', 'RestingECG', 'Cholesterol', 'Sex', 'RestingBP', 'Age']\n",
      "Markov blanket of 'RestingECG' is ['ExerciseAngina', 'FastingBS', 'HeartDisease', 'Cholesterol', 'Sex', 'Age']\n",
      "Markov blanket of 'MaxHR' is ['ExerciseAngina', 'Age', 'Sex']\n",
      "Markov blanket of 'ExerciseAngina' is ['FastingBS', 'HeartDisease', 'MaxHR', 'RestingECG', 'Cholesterol', 'Sex', 'ST_Slope', 'Oldpeak', 'Age']\n",
      "Markov blanket of 'Oldpeak' is ['ST_Slope', 'ExerciseAngina', 'HeartDisease']\n",
      "Markov blanket of 'ST_Slope' is ['ExerciseAngina', 'Oldpeak', 'HeartDisease']\n",
      "Markov blanket of 'HeartDisease' is ['ExerciseAngina', 'FastingBS', 'RestingECG', 'Cholesterol', 'Sex', 'ChestPainType', 'ST_Slope', 'Oldpeak', 'Age']\n"
     ]
    }
   ],
   "source": [
    "print('Thanks to this library it is also possible to find automatically the Markov blanket of any node in the network.\\n')\n",
    "\n",
    "\n",
    "def markov_blanket(model, target_variable):\n",
    "    return f\"Markov blanket of \\'{target_variable}\\' is {domain_kg_model.get_markov_blanket(target_variable)}\"\n",
    "\n",
    "\n",
    "for column in df.columns:\n",
    "    print(markov_blanket(domain_kg_model, column))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e26a6f",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088be4b9",
   "metadata": {},
   "source": [
    "# 10. Inference\n",
    "\n",
    "\n",
    "<a class=\"anchor\" id=\"ch10\"></a>\n",
    "###### [Back to top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27b9394",
   "metadata": {},
   "source": [
    "## 10.1 How the combinations of evidences affect the prediction\n",
    "\n",
    "<a class=\"anchor\" id=\"ch101\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930a21e1",
   "metadata": {},
   "source": [
    "`pgmpy` also provides some methods to inference the model.\n",
    "\n",
    "In the following chapter we are gonna inference the model in order to understand how the evidence affects the results of the models.\n",
    "\n",
    "More specifically we are gonna explore how the following combination of evidences affects the prediction of the model:\n",
    "- `Age` of the patient\n",
    "- the combinations of `RestingBP` and `FastingBP` of the patient\n",
    "- the combination of the `Cholesterol` and `Sex` of the patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ad709281",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = [target_variable]\n",
    "\n",
    "def format_string(array):\n",
    "    string = str(array[0])\n",
    "    for item in array[1:]: \n",
    "        string += f', {item}'\n",
    "    return string\n",
    "\n",
    "def exact_inference(model, variables, evidence):\n",
    "    inference = VariableElimination(model)\n",
    "    result = inference.query(variables=variables, evidence=evidence)\n",
    "    return result\n",
    "\n",
    "def create_dictionary(df, columns):\n",
    "    dictionary = {}\n",
    "    for column in columns:\n",
    "        dictionary[column] = df[column].unique().tolist()\n",
    "    return dictionary\n",
    "\n",
    "def get_all_combinations(dictionary):\n",
    "    if len(dictionary) == 1:\n",
    "        return [{list(dictionary.keys())[0]: value} for value in dictionary[list(dictionary.keys())[0]]]\n",
    "    res = []\n",
    "    for k1, k2 in combinations(dictionary.keys(), 2):\n",
    "        for v1, v2 in product(dictionary[k1], dictionary[k2]):\n",
    "            res.append({k1: v1, k2: v2})\n",
    "    return res\n",
    "\n",
    "def he_prob_analysis(model, target, knowledge, df):\n",
    "    res = pd.DataFrame(columns=knowledge + [\"Prob\"]) \n",
    "    if len(knowledge) == 1:\n",
    "        print(f'How does {format_string(knowledge)} affect the {format_string(target)} probability?')\n",
    "    else:\n",
    "        print(f'How the does combination of the variables {format_string(knowledge)} affect the {format_string(target)} probability?')\n",
    "\n",
    "    all_values = create_dictionary(df, knowledge)\n",
    "    all_queries = get_all_combinations(all_values)\n",
    "\n",
    "    for query in all_queries:\n",
    "        result = exact_inference(model, target, query)\n",
    "        query[\"Prob\"] = result.values[1]\n",
    "        res.loc[len(res)] = query\n",
    "\n",
    "    return res.sort_values(by=knowledge[0], ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "152f2bae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How does Age affect the HeartDisease probability?\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "     Age      Prob\n",
       "0    70+  0.618510\n",
       "1  60-70  0.671738\n",
       "2  50-60  0.453654\n",
       "3  40-50  0.407760\n",
       "4  20-40  0.299332"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidences = [\"Age\"]\n",
    "he_prob_analysis(domain_kg_model, target, evidences, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "11bd811e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How the does combination of the variables RestingBP, FastingBS affect the HeartDisease probability?\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>Prob</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RestingBP FastingBS      Prob\n",
       "0    90-120         N  0.430367\n",
       "1    90-120         Y  0.490279\n",
       "2      140+         N  0.501145\n",
       "3      140+         Y  0.621003\n",
       "4   120-140         N  0.449936\n",
       "5   120-140         Y  0.547821"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidences = ['RestingBP', 'FastingBS']\n",
    "he_prob_analysis(domain_kg_model, target, evidences, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2e1fd5aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How the does combination of the variables Cholesterol, Sex affect the HeartDisease probability?\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Prob</th>\n",
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       "  </thead>\n",
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       "      <td>&lt;=200</td>\n",
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       "      <th>2</th>\n",
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       "      <td>M</td>\n",
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      ],
      "text/plain": [
       "  Cholesterol Sex      Prob\n",
       "0       <=200   M  0.471456\n",
       "1       <=200   F  0.224809\n",
       "2        240+   M  0.590717\n",
       "3        240+   F  0.292656\n",
       "4     200-240   M  0.521641\n",
       "5     200-240   F  0.170678"
      ]
     },
     "execution_count": 82,
     "metadata": {},
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    }
   ],
   "source": [
    "evidences =  ['Cholesterol', 'Sex']\n",
    "he_prob_analysis(domain_kg_model, target, evidences, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "16fea308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How the does combination of the variables ST_Slope, Oldpeak affect the HeartDisease probability?\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>Flat</td>\n",
       "      <td>0-5-1</td>\n",
       "      <td>0.612591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Flat</td>\n",
       "      <td>1-2</td>\n",
       "      <td>0.793428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Flat</td>\n",
       "      <td>2+</td>\n",
       "      <td>0.943565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Down</td>\n",
       "      <td>&lt;=0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Down</td>\n",
       "      <td>0-5-1</td>\n",
       "      <td>0.533319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Down</td>\n",
       "      <td>1-2</td>\n",
       "      <td>0.687560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Down</td>\n",
       "      <td>2+</td>\n",
       "      <td>0.823109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ST_Slope Oldpeak      Prob\n",
       "0        Up   <=0.5  0.080900\n",
       "1        Up   0-5-1  0.324528\n",
       "2        Up     1-2  0.319725\n",
       "3        Up      2+  1.000000\n",
       "4      Flat   <=0.5  0.740093\n",
       "5      Flat   0-5-1  0.612591\n",
       "6      Flat     1-2  0.793428\n",
       "7      Flat      2+  0.943565\n",
       "8      Down   <=0.5  0.000000\n",
       "9      Down   0-5-1  0.533319\n",
       "10     Down     1-2  0.687560\n",
       "11     Down      2+  0.823109"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evidences = ['ST_Slope', 'Oldpeak']\n",
    "he_prob_analysis(domain_kg_model, target, evidences, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0649f83",
   "metadata": {},
   "source": [
    "## 10.2 Practical use of the model\n",
    "\n",
    "<a class=\"anchor\" id=\"ch102\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c3f6a6",
   "metadata": {},
   "source": [
    "In this chapter, we delve into practical examples of interacting with the model. We explore various types of requests and how the model responds. Additionally, we introduce a new feature that dynamically suggests the next medical examination based on estimated heart health risks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d79644af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(model, query):\n",
    "    labels = df.columns.tolist()\n",
    "    labels.remove('HeartDisease')\n",
    "    variables = []\n",
    "\n",
    "    for label in labels:\n",
    "        if label not in query.keys():\n",
    "            variables.append(label)\n",
    "\n",
    "    base_result = exact_inference(model, target, query)\n",
    "    probs = base_result.values\n",
    "    probs = np.round(probs * 100, 2)\n",
    "\n",
    "    my_dict = {}\n",
    "    for col in df.drop('HeartDisease', axis=1).columns:\n",
    "        my_dict[col] = df[col].unique().tolist()\n",
    "    exam_df = pd.DataFrame(columns=['exam', 'outcome', 'prob'])\n",
    "\n",
    "    for var in variables:\n",
    "        for val in my_dict[var]:\n",
    "            query[var] = val\n",
    "            result = exact_inference(model, target, query)\n",
    "            exam_df.loc[len(exam_df)] = [var, val, round(result.values[1],2)]\n",
    "            del query[var]\n",
    "\n",
    "    exam_df.sort_values(by='prob', ascending=False,inplace=True)\n",
    "\n",
    "    print(f'Given the evidence: ')\n",
    "    for key, value in query.items():\n",
    "        print(f'{key}: {value}')\n",
    "    print()\n",
    "    print(f'The probability of having a heart disease is {probs[1]}%')\n",
    "    \n",
    "    if probs[1] == 100:\n",
    "            print(f'Exiting, probability has reached 100%')\n",
    "            return\n",
    "\n",
    "    print('The next exams suggested are:')\n",
    "    display(exam_df.head(3))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dff4d798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given the evidence: \n",
      "Age: 20-40\n",
      "Sex: M\n",
      "ChestPainType: ATA\n",
      "\n",
      "The probability of having a heart disease is 7.43%\n",
      "The next exams suggested are:\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exam</th>\n",
       "      <th>outcome</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oldpeak</td>\n",
       "      <td>2+</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ExerciseAngina</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ST_Slope</td>\n",
       "      <td>Flat</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              exam outcome  prob\n",
       "20         Oldpeak      2+  0.51\n",
       "16  ExerciseAngina       Y  0.25\n",
       "22        ST_Slope    Flat  0.24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "Now we are going to test the model with a new query adding the result of the suggested exams.\n",
      "Given the evidence: \n",
      "Age: 20-40\n",
      "Sex: M\n",
      "ChestPainType: ATA\n",
      "Oldpeak: 2+\n",
      "\n",
      "The probability of having a heart disease is 50.56%\n",
      "The next exams suggested are:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exam</th>\n",
       "      <th>outcome</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ST_Slope</td>\n",
       "      <td>Up</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ST_Slope</td>\n",
       "      <td>Flat</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>240+</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           exam outcome  prob\n",
       "17     ST_Slope      Up  1.00\n",
       "18     ST_Slope    Flat  0.64\n",
       "3   Cholesterol    240+  0.63"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "Now we are going to test the model with a new query adding the result of the suggested exams.\n",
      "Given the evidence: \n",
      "Age: 20-40\n",
      "Sex: M\n",
      "ChestPainType: ATA\n",
      "Oldpeak: 2+\n",
      "ST_Slope: Up\n",
      "\n",
      "The probability of having a heart disease is 100.0%\n",
      "Exiting, probability has reached 100%\n"
     ]
    }
   ],
   "source": [
    "query = {'Age': '20-40', 'Sex': 'M', 'ChestPainType' : 'ATA'}\n",
    "\n",
    "prediction(domain_kg_model, query)\n",
    "print('-'*20)\n",
    "\n",
    "print('Now we are going to test the model with a new query adding the result of the suggested exams.')\n",
    "query['Oldpeak'] = '2+'\n",
    "prediction(domain_kg_model, query)\n",
    "print('-'*20)\n",
    "\n",
    "print('Now we are going to test the model with a new query adding the result of the suggested exams.')\n",
    "query['ST_Slope'] = 'Up'\n",
    "prediction(domain_kg_model, query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7e0cf5",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df51309",
   "metadata": {},
   "source": [
    "# 11. Conclusion\n",
    "\n",
    "<a class=\"anchor\" id=\"ch11\"></a>\n",
    "###### [Back to top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a384d958",
   "metadata": {},
   "source": [
    "## 11.1. What's the best model?\n",
    "<a class=\"anchor\" id=\"ch111\"></a>\n",
    "\n",
    "As stated in the chapter 8.1, in the end we think that a fully explainable network is better than a network that has higher performance but doesn't make sense (in a semantically point of view) or it doesn't use all the feature. Because:\n",
    "* All the features are related to the heart disease (ch. 8.2 and its references), and even if they bring into the network some *noise* we think that it is actually better than not considering them at all\n",
    "* One could say that a smaller network performs better (computationally speaking) and it is easier to retrain, and that's correct, but since we are working with such a small dataset (so we don't have any memory problem) it's pointless to be constrained to such limitation when you could consider all the features in the dataset\n",
    "* The `roc_auc` score of `0.85` of the final network is a really good score, and trying to improve that would probably lead to overfitting on the dataset\n",
    "\n",
    "So we have chosen to keep the network created in the ch. 8, for all the reason stated above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6796cf67",
   "metadata": {},
   "source": [
    "## 11.2. What we have learned\n",
    "<a class=\"anchor\" id=\"ch112\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a85b32",
   "metadata": {},
   "source": [
    "From this deep analysis into the Bayesian Network, Bayesian Classification and the Heart disease field we have learned that:\n",
    "* Even a single edge of a Bayesian Network can change the results. It is very important to design the network with the help of a domain expert, or in their absence, by referincing trusted sources to explain why an edge has been added.\n",
    "* Structure learning algorithms that work on the whole dataframe can create a network that performs really well on a given scoring system, but they tend to learn correlation when there is actually none. Maybe if we had worked with a bigger dataset those algorithms could have learnt an actual causal-correlation between features, but the medical field is well known, for privacy issues, to not have really big datasets.\n",
    "* Medical field is a difficult one to work on without a domain expert. It is tough to fully know the problem and its implications, also because some correlations are subject of study, or even not proven. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d8690a",
   "metadata": {},
   "source": [
    "## 11.3. Future developments\n",
    "\n",
    "<a class=\"anchor\" id=\"ch113\"></a>\n",
    "\n",
    "### Dyanamic Bayesian Network\n",
    "Introducing the concept of time into our network could be more useful to follow a patient for a longer period of time. It could also be interesting to embedd the network into an ecosystem of wereables and smartphone, to keep the users always updated on their heart healt, while keeping their privacy because everything would run locally.\n",
    "\n",
    "### Hidden Markov Models (HMM) and Hidden Semi Markov Models (HSMM)\n",
    "If we had repeated clinical analysis of the same patient over time, we could consider implementing Hidden Markov Models (HMM) or Hidden Semi Markov Models (HSMM). These models are suitable for analyzing sequential data and can capture the temporal dependencies in the patient's health condition.\n",
    "\n",
    "HMMs are widely used in various fields, including speech recognition, natural language processing, and bioinformatics. They consist of a set of hidden states and observable symbols, with transition probabilities between states and emission probabilities for each symbol.\n",
    "\n",
    "HSMMs are an extension of HMMs that allow for variable-length durations in each state. This can be useful in modeling clinical data where the duration of a particular health state may vary.\n",
    "\n",
    "By incorporating HMM or HSMM into our Bayesian network, we can capture the dynamics of the patient's health condition over time and make more accurate predictions or assessments.\n",
    "\n",
    "Implementing HMM or HSMM would require collecting longitudinal data from patients and training the models on this data. The models can then be used to predict future health states or assess the progression of a disease.\n",
    "\n",
    "\n",
    "### Federated learning\n",
    "Related to the first point, it would be really interesting to introduce some kind of federate learning. For example, every parameter gathered from the hypotethical wereables or by the doctors / people using the tool could be added in the Knowledge Base to increase the performance of the whole network.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22578ca4",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3fcabd-e07a-4a2a-bb60-f316e8030c1b",
   "metadata": {},
   "source": [
    "# 12. References\n",
    "\n",
    "<a class=\"anchor\" id=\"ch12\"></a>\n",
    "###### [Back to top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594f9899",
   "metadata": {},
   "source": [
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    "Hirofumi Tanaka, Kevin D Monahan, Douglas R Seals, Age-predicted maximal heart rate revisited, Journal of the American College of Cardiology, Volume 37, Issue 1, 2001, Pages 153-156, ISSN 0735-1097, https://doi.org/10.1016/S0735-1097(00)01054-8.\n",
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    "Lv Y, Yao Y, Ye J, Guo X, Dou J, Shen L, Zhang A, Xue Z, Yu Y, Jin L. Association of Blood Pressure with Fasting Blood Glucose Levels in Northeast China: A Cross-Sectional Study. Sci Rep. 2018 May 21;8(1):7917. https://doi.org/10.1038/s41598-018-26323-6. PMID: 29784970; PMCID: PMC5962536.\n",
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    "Hänninen H, Takala P, Mäkijärvi M, Korhonen P, Oikarinen L, Simelius K, Nenonen J, Katila T, Toivonen L. ST-segment level and slope in exercise-induced myocardial ischemia evaluated with body surface potential mapping. Am J Cardiol. 2001 Nov 15;88(10):1152-6. https://doi.org/10.1016/s0002-9149(01)02052-5. PMID: 11703962.\n",
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    "Understanding Blood Pressure Readings - American Heart Association. https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings\n",
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    "Lipid Panel - hopkinsmedicine.org. https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/lipid-panel\n",
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    "\n",
    "<a id=32>[32]</a>\n",
    "Melissa Orlandi, Goffredo Orlandi, Vittorio Bini, Claudia Fiorillo, Matteo Becatti, Laura Stefani - The ST segment depression pattern in asymptomatic peri-menopausal female athletes, Helyon, Volume 6, Issue 8, 2020, e04738, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2020.e04738."
   ]
  }
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