lecture10-user-modelling-2021.ipynb

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   "source": [
    "<p style=\"font-size:20px\">ELEC-E7890 - User Research<br></p>\n",
    "<p style=\"font-size:30px\"><b>Lecture 10 - User modeling</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aini Putkonen**, Aalto University\n",
    "\n",
    "*Materials adapted from [Aurélien Nioche](https://github.com/AurelienNioche/LectureUserResearch/blob/master/OLD_lecture10.ipynb)*. If you have any questions or suggestions for the materials, please contact aini.putkonen@aalto.fi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<h2><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Learning objectives</h2>\n",
    "    \n",
    "<ul>\n",
    "<li>Explain what <b>user models</b> are. Name some examples of user models and their <b>application contexts</b>.</li>\n",
    "<li>Understand the basic principles of <b>designing a user model</b> that describes <b>some phenomenon</b> taking place in <b>a given task</b>.</li>\n",
    "<li>Be able to <b>fit a model</b> to some data.</li>\n",
    "<li>Be able to <b>conduct a parameter recovery study</b> and explain why their results matter for the <b>interpretation of a user model</b>.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python setup <a class=\"tocSkip\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# Import the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import string  # For adding letters in the figures\n",
    "import scipy.special as sps  # For gamma function\n",
    "from tqdm import tqdm\n",
    "import sys\n",
    "import scipy\n",
    "from itertools import product\n",
    "\n",
    "%config InlineBackend.figure_format='retina' # For not burning your eyes\n",
    "sns.set_theme(context=\"notebook\", style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a folder for the bkp\n",
    "BKP_FOLDER = \"bkp\"\n",
    "DATA_FOLDER = \"data\"\n",
    "\n",
    "# Create this folder if it doesn't exist\n",
    "os.makedirs(BKP_FOLDER, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are user models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Goal</h4> \n",
    "Explain what <b>user models</b> are. Name some examples of user models and their <b>application contexts</b>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following example (adapted from [Rich, 1979](https://doi.org/10.1207/s15516709cog0304_3)):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family:Arial; color:Black; font-size: 18px; padding-left: 120px; padding-right: 120px;text-align: center;line-height:150%;\">A person walks into a library and asks for recommendations of materials related to computers from the librarian. In order to provide useful recommendations, the librarian must have more information about this individual (e.g, occupation, age, interests and language preferences). They must construct <b>a model</b> of the person.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then consider the following example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family:Arial; color:Black; font-size: 18px; padding-left: 120px; padding-right: 120px;text-align: center;line-height:150%;\">The same individual is buying a book related to computers in a web shop. They type the key word in the search bar and the web shop provides them several options to choose from. The web shop acts in the same role as the librarian: it holds <b>a model of the user</b> and provides recommendations based on it.</p>"
   ]
  },
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   "source": [
    "That is, user models are simply **models that systems hold of users** ([Fischer, 2000](https://doi.org/10.1023/A:1011145532042)).\n",
    "\n",
    "More specifically, a system collects **data collected about a user**, which is then processed to **a user model** to perform **adaptation**, especially in the context of HCI (e.g., see the user modeling-adaptation loop from Knutov, 2012, adapted from Brusilovsky, 1996, and also presented in De Bra, 2017)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal of user modeling\n",
    "\n",
    "User models may have various goals, including ([Webb, 2000](https://doi.org/10.1023/A:1011117102175); [De Bra, 2017](https://doi.org/10.1109/MIS.2017.3711638)):\n",
    "\n",
    "1. Describing **cognitive processes** of the underlying user behavior\n",
    "2. Analyzing user's **behavioral patterns**\n",
    "3. Being the basis for **adapting** interactive systems\n",
    "4. **Distinguishing user's skills** from expert skills\n",
    "5. Capturing user's **characteristics**\n",
    "\n",
    "Have a look at the references for more comprehensive analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of user models\n",
    "\n",
    "User models are ubiquitous today, taking many forms and using several techniques. For instance, user models can be used in recommender systems, menu adaptation, news applications and search engines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User research and user modeling\n",
    "\n",
    "User research and user modeling are related concepts: user research can be used to build user models.\n",
    "\n",
    "The diagram below illustrates the relationship between **user models** and **user research**. Assume that we are attempting to better understand user behavior in a streaming platform, specifically when the user is choosing content to view. \n",
    "\n",
    "**User research**: We decide to perform a quantitative study of the categories of films users most often want to watch. Hence, we (1) collect data by recording user behavior in a task and then (2) produce descriptive statistics of this dataset. This data can then be further analyzed, and some hypotheses of user behavior are formed. \n",
    "\n",
    "**User modeling**: User modeling builds on user research. The hypotheses formed about user behavior are summarized into (3) a user model. Then this user model can be (4) used to produce data in similar tasks as the real users encountered. If the user model is accurate at capturing user behavior, (5) the data produced by it should produce similar descriptive statistics as the ground truth data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/user-modelling-vs-research.jpg\" width=\"800\">\n",
    "<em>A perspective on the relationship between user research and user modeling.</em>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Checkpoint</h4> \n",
    "What is a user model in the context of this lecture?\n",
    "<br> a) A model that a user holds of a system they are using (e.g., how a computer operates)\n",
    "<br> b) A model the system holds of the user (i.e, how the system thinks the user behaves)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenges in user modeling\n",
    "\n",
    "User modeling is not an easy task due to the complexity of behavior users can exhibit. Advances in computational techniques allow flexibility in user modeling, but also introduce challenges, some of which are listed below (adapted from [De Bra, 2017](https://doi.org/10.1109/MIS.2017.3711638); [Webb, 2000](https://doi.org/10.1023/A:1011117102175)). \n",
    "\n",
    "### User models should be *traceable (scrutable)* \n",
    "\n",
    "The user should be able to trace what the system knows about them and how the model is constructed. \n",
    "\n",
    "*Update 20/10/2021: This section previously contained the word 'tractable', but here we actually refer to the ability to 'trace' a user model.* \n",
    "\n",
    "### Research results in user modeling should be *repeatable* in various contexts\n",
    "\n",
    "User modeling research can take place in various application contexts (e.g., recommendation systems, menu adaptation, intelligent tutoring systems, social media, etc.). A user model should produce results that are stable in similar applications, and ideally, across contexts. \n",
    "\n",
    "### *Computational complexity* should not limit the use of a user model\n",
    "\n",
    "Certain user modeling approaches may be highly costly to run, which can make them inconvenient to use in live systems.\n",
    "\n",
    "### The need for *large datasets* \n",
    "\n",
    "Some data-driven approaches require large datasets, which may be infeasible to collect within an interactive system. Similarly, obtaining labeled data can be a challenge."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model types\n",
    "\n",
    "Different model types address the challenges presented in the previous section. Two important distinctions are the difference between **theory-based and data-driven** models as well as **individual-level and aggregate models**.\n",
    "\n",
    "### Theory-based vs. data-driven models\n",
    "\n",
    "**Theory-based models** are based on some model with (mostly) interpretable parameters. The benefits of theory-based models are that they are more traceable and require smaller datasets than data-driven models. The downside is that the given theory-based model needs to be a good description of the underlying phenomenon to produce meaningful results.\n",
    "\n",
    "In **data-driven models** the relationships between different features are learned (though this definition does not necessarily describe all data-driven models). The benefit of these models is, for instance, their predictive accuracy. On the other hand, data-driven model may come with the downside of computational complexity.\n",
    "\n",
    "Note that models also exist between theory-based and data-driven models.\n",
    "\n",
    "### Individual-level vs. aggregate models\n",
    "\n",
    "In HCI applications, modeling the behavior of an individual user is often critical as their preferences may deviate from the average. However, if it is safe to assume that the behavior across individuals is similar, adopting an aggregate model can also be acceptable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Checkpoint</h4> \n",
    "If you were trying to model a user in the following cases, what kind of a user model would you use (theory-based or data-driven / individual or aggregate / something else) and why?  \n",
    "<br>a) A recommender system in an e-commerce application that sells books, if we know that the customer base is fairly homogeneous?\n",
    "<br>b) A candidate for a mortgage, given we have information about their age, occupation, gender they identify as, postcode and credit information?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Designing a user model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Goal</h4> \n",
    "Understand the basic principles of <b>designing a user model</b> that describes <b>some phenomenon</b> taking place in <b>a given task</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lecture, we will focus on a simple **theory-based approach** to user modeling, using techniques from computational cognitive modeling. This section of the lecture is based on a tutorial by [Wilson & Collins (2019)](https://doi.org/10.7554/eLife.49547). We will work through a user modeling example by asking the following question:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family:Arial; color:Black; font-size: 30px; padding-left: 120px; padding-right: 120px;text-align: center;line-height:150%;\">How do people choose from a pool of options?</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to answer this question, we need to specify which task we are modeling and with which model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task definition\n",
    "\n",
    "Specifically, imagine that a user is choosing a movie among a pool of options. Each movie is either a 'success' or a 'failure', that is, the user either likes the movie or hates it. We are interested in learning how individuals make choices between these options.\n",
    "\n",
    "The above task can be modelled as a *bandit*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task: <a href=\"https://en.wikipedia.org/wiki/Multi-armed_bandit\">Bandit task</a></b>\n",
    "\n",
    "Parameters: \n",
    "* Number of option ($N$)\n",
    "* Distribution of probability over these options ($\\{p_{\\text{reward}}(i)\\}_{i\\in N}$)\n",
    "* Number of trials ($T$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameters of the bandit task\n",
    "\n",
    "N = 2 # Number of options\n",
    "P = np.array([0.5, 0.75]) # Probability of different options\n",
    "T = 500 # Number of trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will consider that each agent has the opportunity at each $t\\in T$ to:\n",
    "<ul>\n",
    "    <li>Choose: we will define for each model a \"decision rule\", more precisely, we model a probability distribution over the action choices such as:\n",
    "\\begin{equation}\n",
    "\\forall i: p_{\\text{choice}}(i) \\in [0, 1] \\wedge \\sum_{i}^N p_{\\text{choice}}(i) = 1\n",
    "\\end{equation}\n",
    "</ul>\n",
    "<ul>\n",
    "    <li>Learn: we will define for each model an \"updating rule\".\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GenericPlayer:\n",
    "    \n",
    "    \"\"\"\n",
    "    Generic Player\n",
    "    \"\"\"\n",
    "    \n",
    "    param_labels = ()\n",
    "    fit_bounds = ()\n",
    "\n",
    "    def __init__(self):\n",
    "        self.options = np.arange(N)\n",
    "        \n",
    "    def decision_rule(self):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def learning_rule(self, option, success):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def choose(self):\n",
    "        p = self.decision_rule()\n",
    "        return np.random.choice(self.options, p=p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Win-Stay Lose-Switch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>M1. <a href=\"https://en.wikipedia.org/wiki/Win%E2%80%93stay,_lose%E2%80%93switch\">Win-Stay-Lose-Switch</a> (Noisy variant)</b>\n",
    "\n",
    "<img src=\"img/wsls.png\" width=\"200\">\n",
    "\n",
    "<ul>\n",
    "    <li>Decision rule ($\\epsilon$)\n",
    "<ul>\n",
    "\\begin{equation}\n",
    "p_{\\text{choice}}(i) = \n",
    "\\begin{cases}\n",
    "1 - \\epsilon + \\epsilon / N & \\text{if } r_{t-1} = 1 \\wedge c_{t-1} = i,\\\\\n",
    "\\frac{1 - \\epsilon}{N-1} + \\epsilon / N & \\text{if } r_{t-1} = 0 \\wedge c_{t-1} \\neq i\\\\\n",
    "\\epsilon / N & \\text{otherwise.}\\\\\n",
    "\\end{cases}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\epsilon \\in [0, 1]$ is a free parameter describing the probability to choose randomly instead of applying the \"Win-Stay-Lose-Switch' rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WSLS(GenericPlayer):\n",
    "    \"\"\"\n",
    "    Win-Stay-Lose-Switch\n",
    "    \"\"\"\n",
    "\n",
    "    param_labels = \"epsilon\",\n",
    "    fit_bounds = (0., 1),\n",
    "\n",
    "    def __init__(self, epsilon):\n",
    "        super().__init__()\n",
    "        self.epsilon = epsilon\n",
    "\n",
    "        self.c = -1\n",
    "        self.r = -1\n",
    "\n",
    "    def decision_rule(self):\n",
    "\n",
    "        if self.c == -1:\n",
    "            return np.ones(N) / N  # First turn\n",
    "\n",
    "        p = np.zeros(N)\n",
    "\n",
    "        p_apply_rule = 1 - self.epsilon # 1 - epsilon: apply the rule\n",
    "        p_random = self.epsilon / N # epsilon: pick up randomly\n",
    "        if self.r:\n",
    "            p[self.options != self.c] = p_random\n",
    "            p[self.c] = 1 - np.sum(p)  # p_apply_rule + p_random\n",
    "        else:\n",
    "            p[self.options != self.c] = p_apply_rule / (N - 1) + p_random\n",
    "            p[self.c] = 1 - np.sum(p)  # p_random\n",
    "\n",
    "        return p\n",
    "\n",
    "    def learning_rule(self, option, success):\n",
    "        self.r = success\n",
    "        self.c = option"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Checkpoint</h4> \n",
    "What features does a modeler need to take into account when designing candidate models (one or more)? \n",
    "<br>a) Previous scientific research on the phenomenon underlying behavior in the chosen task\n",
    "<br>b) Available data  \n",
    "<br>c) Modeling goal (e.g., prediction or interpreting behavior)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a user model to data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Goal</h4> \n",
    "Be able to <b>fit a model</b> to some data\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we look at how to fit a model to given data using [maximum likelihood estimation](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a likelihood function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Determine the (log) likelihood of observing some data given your model and a specific parametrization:\n",
    "\n",
    "\\begin{equation}\n",
    "\\log p(D \\mid M, \\theta) = \\sum_{d \\in D} \\log p(d \\mid M, \\theta)\n",
    "\\end{equation}\n",
    "\n",
    "*Note: For several reasons (including numerical precision on computers but also easier algebraic manipulation), it is preferable to use the log-likelihood than the likelihood.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_likelihood(model, param, data):\n",
    "\n",
    "    # Create the agent\n",
    "    agent = model(*param)\n",
    "\n",
    "    # Data container\n",
    "    ll = np.zeros(T)\n",
    "\n",
    "    # Simulate the task\n",
    "    for t in range(T):\n",
    "\n",
    "        # Get choice and success for t\n",
    "        c, s = data.choice[t], data.success[t]\n",
    "\n",
    "        # Look at probability of choice\n",
    "        p_choice = agent.decision_rule()\n",
    "        p = p_choice[c]\n",
    "\n",
    "        # Compute log\n",
    "        ll[t] = np.log(p + np.finfo(float).eps)\n",
    "\n",
    "        # Make agent learn\n",
    "        agent.learning_rule(option=c, success=s)\n",
    "\n",
    "    return np.sum(ll)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose a way to search for the best parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: Here, we will use the <a href=\"https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html\">function 'minimize' from the SciPy library</a>. However, there is multiple of other ways to do it.*, see for instance <a href=\"https://ipvs.informatik.uni-stuttgart.de/mlr/marc/teaching/13-Optimization/06-blackBoxOpt.pdf\">here</a> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(param, model, data):\n",
    "    # Since we will look for the minimum, \n",
    "    # let's return -LLS instead of LLS\n",
    "    return - log_likelihood(model=model,\n",
    "                            data=data,\n",
    "                            param=param)\n",
    "\n",
    "def optimize(model, data):\n",
    "\n",
    "    # Define an init guess\n",
    "    init_guess = [(b[1] - b[0])/2 for b in model.fit_bounds]\n",
    "\n",
    "    # Run the optimizer\n",
    "    res = scipy.optimize.minimize(\n",
    "        fun=objective,\n",
    "        x0=init_guess,\n",
    "        bounds=model.fit_bounds,\n",
    "        args=(model, data))\n",
    "\n",
    "    # Make sure that the optimizer ended up with success\n",
    "    assert res.success\n",
    "\n",
    "    # Get the best param and best value from the \n",
    "    best_param = res.x\n",
    "    best_value = res.fun\n",
    "\n",
    "    return best_param, best_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will explore how the model fitting procedure described above works in practice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data\n",
    "\n",
    "First, assume that some data has already been collected in the described bandit task and we can use it directly in model fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>choice</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  choice  success\n",
       "0     0       1     True\n",
       "1     1       1     True\n",
       "2     2       1     True\n",
       "3     3       1    False\n",
       "4     4       1     True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the data\n",
    "wsls_data = pd.read_csv(os.path.join(DATA_FOLDER, \"wsls-data.csv\"), \n",
    "                         index_col=[0])\n",
    "# Print...\n",
    "display(wsls_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit model to data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model fitting procedure is defined above, hence we can simply fit the model to the data as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for parameter: 0.25651301500287144\n",
      "Corresponding log-likelihood: 191.83954810197105\n"
     ]
    }
   ],
   "source": [
    "# Optimize\n",
    "best_param, best_value = optimize(model=WSLS, data=wsls_data)\n",
    "\n",
    "print (f\"Estimate for parameter: {best_param.item()}\")\n",
    "print (f\"Corresponding log-likelihood: {best_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore parameter space\n",
    "\n",
    "Let's have a closer look what happens under the hood by exploring log-likelihoods different parameter values have. First, define the parameter values to explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\epsilon$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   $\\epsilon$\n",
       "0    0.000000\n",
       "1    0.001001\n",
       "2    0.002002\n",
       "3    0.003003\n",
       "4    0.004004"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the model\n",
    "model = WSLS\n",
    "\n",
    "# Choose the number of parameters to explore\n",
    "grid_size = 1000\n",
    "\n",
    "# Set the values to use for each parameter\n",
    "parameter_values = np.atleast_2d([np.linspace(*b, grid_size) \n",
    "                                  for b in model.fit_bounds])\n",
    "\n",
    "# Create a grid (for one parameter models this is step is not necessary)\n",
    "grid = np.asarray(list(product(*parameter_values)))\n",
    "\n",
    "# Create a dataframe that stores it\n",
    "grid_df = pd.DataFrame(grid, columns=(r\"$\\epsilon$\",))\n",
    "\n",
    "# Print...\n",
    "display(grid_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then calculate $P(D)$ for each of these values of $\\epsilon$ using the log-likelihood sum (LLS). That is, consider that we have dataset $D$ that contains data from each time step $t$. At each time step $t$ choice $d$ is recorded. Thus, the following is calculated:\n",
    "\n",
    "\\begin{aligned}\n",
    "\\text{log} p(D) &= \\text{log}[p(d_1) \\times p(d_2) ... \\times p(d_t)] \\\\\n",
    "\\text{log} p(D) &= \\text{log} p(d_1) + \\text{log} p(d_2) + ... + \\text{log} p(d_t) \\\\\n",
    "\\text{log} p(D) &= \\sum \\text{log} p(d)\n",
    "\\end{aligned}\n",
    "\n",
    "This is implemented in the `log_likelihood` function above. The aim is to maximize LLS, which is equivalent to minimizing negative LLS.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing data for parameter space exploration...\n",
      "100%|███████████████████████████████████████| 1000/1000 [00:19<00:00, 50.05it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>$\\epsilon$</th>\n",
       "      <th>log-likelihood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2307.486964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001001</td>\n",
       "      <td>-487.304645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002002</td>\n",
       "      <td>-443.161107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003003</td>\n",
       "      <td>-417.429330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004004</td>\n",
       "      <td>-399.235777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   $\\epsilon$  log-likelihood\n",
       "0    0.000000    -2307.486964\n",
       "1    0.001001     -487.304645\n",
       "2    0.002002     -443.161107\n",
       "3    0.003003     -417.429330\n",
       "4    0.004004     -399.235777"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results container\n",
    "row_list = []\n",
    "\n",
    "print(\"Computing data for parameter space exploration...\")\n",
    "# Loop over each value of the parameter grid for all parameters\n",
    "for _, param in tqdm(grid_df.iterrows(), total=len(grid_df), file=sys.stdout):\n",
    "\n",
    "    # Compute the log-likelihood\n",
    "    ll = log_likelihood(\n",
    "        data=wsls_data,  # THIS IS SPECIFIC\n",
    "        model=model,              # THIS IS SPECIFIC\n",
    "        param=param)\n",
    "    \n",
    "    # Backup\n",
    "    row_list.append({\n",
    "        r\"$\\epsilon$\": param[r\"$\\epsilon$\"],\n",
    "        \"log-likelihood\": ll})\n",
    "\n",
    "# Create a dataframe\n",
    "ll_df = pd.DataFrame(row_list)\n",
    "\n",
    "# Save it for later use\n",
    "ll_df.to_csv(os.path.join(BKP_FOLDER, \"log_likelihood_grid-bhv_single.csv\"))\n",
    "\n",
    "# Print...\n",
    "display(ll_df.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned, the log-likelihoods above are negative, in contrast to the ```best_value``` returned earlier. Note that the ```objective``` function returns the negative LLS, so we find the minimum which corresponds to the best fit parameter value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for parameter: 0.25625625625625625\n",
      "Corresponding log-likelihood: -191.8395849044937\n"
     ]
    }
   ],
   "source": [
    "row_max_ll = ll_df.loc[ll_df[\"log-likelihood\"] == ll_df[\"log-likelihood\"].max()]\n",
    "best_param_manual, best_value_manual = row_max_ll[\"$\\epsilon$\"].item(), row_max_ll[\"log-likelihood\"].item()\n",
    "\n",
    "print (f\"Estimate for parameter: {best_param_manual}\")\n",
    "print (f\"Corresponding log-likelihood: {best_value_manual}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, plotting LLS for each value of $\\epsilon$ shows the maximum where $\\epsilon \\approx 0.26$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 216,
       "width": 683
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize=(11,3))\n",
    "\n",
    "for i in range(2):\n",
    "    sns.lineplot(x=r\"$\\epsilon$\", y=\"log-likelihood\", data=ll_df, ax=axs[i])\n",
    "\n",
    "    sns.scatterplot(x = [best_param_manual], y = [best_value_manual], ax = axs[i], color = \"red\", \\\n",
    "                    marker = 'X', s = 200)\n",
    "\n",
    "axs[0].set_ylim(-2500,0)\n",
    "axs[1].set_ylim(-500,0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this data was originally generated with $\\epsilon \\approx 0.3$, so the estimate is close to ground truth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting results\n",
    "\n",
    "Recall that in the model description we noted that $\\epsilon$ is a free parameter describing the probability to choose randomly instead of applying the \"Win-Stay-Lose-Switch\" rule. The model fitting results can be interpreted in the light of this information. That is, the probability that an individual will choose randomly is around 0.26 according to our estimate. Given the task is to choose between movies in a streaming platform, the user behavior can be interpreted as follows: at probability 0.26 the user will choose randomly, otherwise if they like a movie, they will choose it again - and if the hate it, switch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Checkpoint</h4> \n",
    "What kind of a result does the model fitting presented in this notebook yield?\n",
    "<br>a) A probability distribution of the parameter of interest  \n",
    "<br>b) A point estimate of the parameter of interest\n",
    "<br>c) A point estimate of the parameter of interest with a quantification of uncertainty of this measure\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating user behaviour\n",
    "\n",
    "A benefit of a computational model of user behavior is that it is easy to simulate behaviors using this model, which is demonstrated below (in code and with a plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_simulation(model, param):\n",
    "\n",
    "    # Create the agent\n",
    "    agent = model(*param)\n",
    "\n",
    "    # Data containers\n",
    "    choices = np.zeros(T, dtype=int)\n",
    "    successes = np.zeros(T, dtype=bool)\n",
    "\n",
    "    # Simulate the task\n",
    "    for t in range(T):\n",
    "\n",
    "        # Determine choice\n",
    "        choice = agent.choose()\n",
    "\n",
    "        # Determine success\n",
    "        success = P[choice] > np.random.random()\n",
    "\n",
    "        # Make agent learn\n",
    "        agent.learning_rule(option=choice, success=success)\n",
    "\n",
    "        # Backup\n",
    "        choices[t] = choice\n",
    "        successes[t] = success\n",
    "\n",
    "    return pd.DataFrame({\n",
    "        \"time\": np.arange(T),\n",
    "        \"choice\": choices, \n",
    "        \"success\": successes})\n",
    "\n",
    "\n",
    "# Seed the pseudo-random number generator\n",
    "np.random.seed(0)\n",
    "\n",
    "# We will experiment with Win-Stay Lose-Switch\n",
    "model = WSLS\n",
    "\n",
    "# Get data\n",
    "param_single = np.array([0.1])\n",
    "bhv_single = run_simulation(model=model, param=param_single)\n",
    "\n",
    "# Backup\n",
    "bhv_single.to_csv(os.path.join(BKP_FOLDER, \"bhv_single.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>time</th>\n",
       "      <th>choice</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>495</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>496</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>497</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>499</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     time  choice  success\n",
       "0       0       1     True\n",
       "1       1       1     True\n",
       "2       2       1     True\n",
       "3       3       1    False\n",
       "4       4       1     True\n",
       "..    ...     ...      ...\n",
       "495   495       0     True\n",
       "496   496       0    False\n",
       "497   497       1     True\n",
       "498   498       1    False\n",
       "499   499       0    False\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the data\n",
    "bhv_single = pd.read_csv(os.path.join(BKP_FOLDER, \"bhv_single.csv\"), \n",
    "                         index_col=[0])\n",
    "# Print...\n",
    "display(bhv_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 538,
       "width": 735
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "fig, axes = plt.subplots(nrows=2, figsize=(12, 9))\n",
    "\n",
    "# Show choices\n",
    "sns.swarmplot(data=bhv_single, x='time', y='choice', ax=axes[0], orient=\"h\")\n",
    "\n",
    "# Show success\n",
    "sns.swarmplot(data=bhv_single, x='time', y='success',\n",
    "              ax=axes[1], palette={True: \"green\", False: \"red\"}, orient=\"h\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sanity checking a user model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Goal</h4> \n",
    "Be able to <b>conduct a parameter recovery study</b> and explain why their results matter for the <b>interpretation of a user model</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting a model to data, a necessary question that needs to be asked is: *How confident can we be in our estimate?* This section presents a technique called **parameter recovery**, which addresses this question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is parameter recovery?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameter recovery is used in judging whether a given model and estimation technique provide accurate and consistent results when fitted to data (see, e.g., [Wilson & Collins (2019)](https://doi.org/10.7554/eLife.49547) or [Heathcote et al. (2015)](https://doi.org/10.1007/978-1-4939-2236-9_2)).\n",
    "\n",
    "That is, consider data produced by a model with some parameters $\\theta$. The same model is then fitted to this generated data to obtain inferred parameters $\\hat{\\theta}$. The generating parameters $\\theta$ should be close to the inferred parameters $\\hat{\\theta}$. In our example case, this would mean the following: consider that there is an individual producing data with given value of $\\epsilon$ in WSLS. We take the data that this individual has generated and fit WSLS to it. We should obtain a value of $\\hat{\\epsilon}$ that is close to the original one. If this is not the case, the parameters of the model do not recover.\n",
    "\n",
    "Inadequate parameter recovery is a warning sign in modeling. It suggests that a given model specification or parameter inference technique might not be suitable for the given modeling task. \n",
    "\n",
    "This can be also understood by thinking about a case when parameter recovery has not been verified and the modeler directly proceeds to fitting the model to data. The modeler assumes that the chosen model is a good description of the underlying phenomenon and thus trusts that the inferred parameters are accurate. However, since the ground truth parameters are unknown, there is no way of verifying the accuracy of the inferred parameters. Here parameter recovery can help. By considering data generated by known parameters, we can assess whether it is theoretically possible to recover the parameters and obtain consistent results.\n",
    "\n",
    "As a caveat, even though unsuccessful parameter recovery is a warning sign suggesting that the given model should not be fitted to data with the chosen inference technique, good parameter recovery does not guarantee that results are reliable. However, they can increase our confidence in the inference results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running a parameter recovery simulation\n",
    "\n",
    "In this section we run a simple parameter recovery simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/parameter-recovery-illustration.jpg\" width=\"600\">\n",
    "<em>Parameter recovery simulation conceptually and in code.</em>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 30/30 [00:10<00:00,  2.97it/s]\n"
     ]
    }
   ],
   "source": [
    "# Seed the pseudo-random number generator\n",
    "np.random.seed(1284)\n",
    "\n",
    "# Select one model\n",
    "model = WSLS \n",
    "\n",
    "# Define the number of agents to simulate\n",
    "n = 30 \n",
    "\n",
    "# Data container\n",
    "row_list = []\n",
    "\n",
    "# For each agent...\n",
    "for i in tqdm(range(n), file=sys.stdout):\n",
    "\n",
    "    # Generate parameters to simualte\n",
    "    param_to_sim = [np.random.uniform(*b)\n",
    "                    for b in model.fit_bounds]\n",
    "\n",
    "    # Simulate\n",
    "    d = run_simulation(model=model, param=param_to_sim)\n",
    "\n",
    "    # Optimize\n",
    "    best_param, best_value = optimize(model=model, data=d)\n",
    "\n",
    "    # Backup\n",
    "    for j in range(len(param_to_sim)):\n",
    "        row_list.append({\n",
    "            \"Parameter\": model.param_labels[j],\n",
    "            \"Used to simulate\": param_to_sim[j],\n",
    "            \"Recovered\": best_param[j]})\n",
    "\n",
    "# Create dataframe and save it\n",
    "df = pd.DataFrame(row_list)\n",
    "df.to_csv(os.path.join(BKP_FOLDER, \"likelihood_explo.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Parameter</th>\n",
       "      <th>Used to simulate</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epsilon</td>\n",
       "      <td>0.774765</td>\n",
       "      <td>0.721443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epsilon</td>\n",
       "      <td>0.739455</td>\n",
       "      <td>0.789579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epsilon</td>\n",
       "      <td>0.062072</td>\n",
       "      <td>0.060120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epsilon</td>\n",
       "      <td>0.129357</td>\n",
       "      <td>0.148297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epsilon</td>\n",
       "      <td>0.059156</td>\n",
       "      <td>0.060120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Parameter  Used to simulate  Recovered\n",
       "0   epsilon          0.774765   0.721443\n",
       "1   epsilon          0.739455   0.789579\n",
       "2   epsilon          0.062072   0.060120\n",
       "3   epsilon          0.129357   0.148297\n",
       "4   epsilon          0.059156   0.060120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the dataframe and display it\n",
    "df = pd.read_csv(os.path.join(BKP_FOLDER, \"likelihood_explo.csv\"), index_col=[0])\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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jtHwrYCsQcgEAADqZMEeIEhwNx8CqB9XyC492TfB6vbLba2/163A45HA4qh9UCzQsVwAAAEAtVQ+qGcbRh8w+/3y98vLy6oyrelAtrJHA3F4IuQAAAKgjOdGp5IRwbd26VZWVbm3fvr1W0K35oFogCrzYDQAAgHZ3KD9XB3Z9pXFn9NfOjMPKyXcpPDxcdruhOOePO545Qu1NX6wdEHIBAABQS3Z2dvVWvVFRUUrp21/XX3aRunSJqPdBtUAU2NUBAACgTdUMuNLRh86ObvSQ0M6VtQxrcgEAACCpbsANtK16W4KQCwAAgA4VcCWWKwAAAHR6ZWVlWrhwYZMBt8LtaXQTiUASmFUBAACgzXTp0kXnnnuuli9fXm/AdVd6lZnrUnpWkQqKy+X1mbLbDMVFB26XBUIuAAAAdNppp8lms6lHjx51Au7W9HztzCiQWWOXX6/PVH5hmQ4VlanQVaEhKfEBFXQJuQAAAJAkDRs2rM6xzFxXnYBbk2lKOzMKFOsMU0rvWP8W2AI8eAYAANDJZGdn6+WXX1ZFRUWj4yrcHqVnFTUYcKuYppSeVaQKt8fCKluHkAsAANCJVHVR2LlzpxYtWtRo0C0ucauguLxZ1y1wlau4xG1Vma1GyAUAAOgkjm0Tlp+fr6KiogbH+3ymvL4mpnF/4PWa8jVzbFsg5AIAAHQCDfXBTUxMbPA1Npshu81o1vXtdkO2Zo5tC4RcAACADu54N3qIjnQoLjq8WfeIc4YrOtLR6lqtQsgFAADowFqzk1mYI0QpSTEympigNQwpJSkmoDaGIOQCAAB0UFZs1Zuc6FRqn7gGg65hSKl94pSc6LSiZMsETtwGAACAZXJyclodcCXJEWrXkJR4xTrDju545iqX12vKbjcU52THMwAAALSh6OhoxcbGqqys7LgDbhVHqF0pvWOVlBCl4hK3fD5TNpuh6EhHQC1RqCkwqwIAAECrVAXbpUuX6vzzzz/ugFtTmCNECQEaao8VHFUCAACgxaqCbmfEg2cAAAAdQHZ2trZt29beZQQMZnIBAACCXFUXhfLycvl8Pg0dOrS9S2p3zOQCAAAEsZptwkzT1HvvvaeKior2LqvdEXIBAACCVH19cCdPnqywsLB2rqz9EXIBAACCkBUbPXRkhFwAAIAgQ8BtGiEXAAAgiBBwm4eQCwAAECQIuM1HyAUAAAgSeXl5Ki8vl0TAbQp9cgEAAILE0KFDZZqm3n//fU2ePJmA2whCLgAAQBA55ZRTlJaWpvDw8PYuJaCxXAEAACBA5eTkVK+/rYmA2zRCLgAAQADKzs7W/PnztXDhwnqDLhpHyAUAAAgwNbsoHDhwQK+//np7lxR0CLkAAAABpL42Yeedd147VxV8ePAMAAAgQDTVB7fC7VFxiVs+nymbzVB0pENhDuJcffhTAQAACACNBVx3pVeZuS6lZxWpoLhcXp8pu81QXHS4UpJilJzolCPU3s7vILAQcgEAANpZUwF3a3q+dmYUyDR/fI3XZyq/sEyHispU6KrQkJR4gm4NrMkFAABoR00tUcjMddUJuDWZprQzo0CZua62KjkoEHIBAADa0apVqxpdg5ueVdRgwK1imlJ6VpEq3B5/lxs0CLkAAADt6KqrrlLv3r3rBFxJKi5xq6C4vFnXKXCVq7jE7a8ygw5rcgEAANpReHi4Jk+erKKiInXv3r3WOZ/PlNfXxDTuD7xeU75mju0MCLkAAABtqLy8vM62vOHh4fVu1WuzGbLbjGYFXbvdkM1mWFZnsGO5AgAAQBvJzs7WzJkztWnTpmaNj450KC66bvitT5wzXNGRjtaU16EQcgEAANpAVReF0tJSvf3229q8eXOTrwlzhCglKUZGExO0hiGlJMWwMUQNhFwAAAA/O7ZNWHh4uBITE5v12uREp1L7xDUYdA1DSu0Tp+REp1XldgjEfQAAgOPUnG12m+qD2xRHqF1DUuIV6ww7uuOZq1xerym73VCckx3PGkLIBQAAaKHmbrPb2oBbxRFqV0rvWCUlRDUZqnEUfyoAAAAt0NxtdpO62i0JuDWFOUKUQKhtFv6UAAAAWqA52+xu/CZTmRFlstuPLiGwIuCiZXjwDAAAoJmas82ux+PR15s369s9eerRM4mA204IuQAAAM3UnG12Q0JCNGDAAO0/WKBu3XsTcNsJyxUAAACaqbnb7Hbv3l2GYWhQ2iD17JnQBpXhWMzkAgAANFPVNrvN0bNnd0VFRfq5IjSEkAsAANBM9W2z63K59PXXm1VZWVnrONvsti9CLgAAQDMdu82uy+XS5s1bVFhYoM2bfwy6bLPb/gi5AAAALVC1zW7JkSPavHmLPJ6jwba8vEIVFRVssxsg+OsFAABACzhC7UrqaldmRJmSe8Zp/8HD8pmGTht+qpJ7xbPNboAg5AIAALRA1Va9drtdyT2TdGpqmgalDVJUVCTb7AYQPgUAAIBmqgq4VVv17s/ap3PHj6VNWABiTS4AAEAzHBtw2ckssBFyAQAAmpCbm0vADTKEXAAAgCbExMQoPj5eEgE3WBByAQAAmhAWFqZf/OIXSk1NJeAGCR48AwAAaIawsDBNnDixvctAMzGTCwAAcIzs7Gxt2rSpvctAKzCTCwAAUEPNLgper1enn356e5eE48BMLgAAwA+ObRO2atWq6n9GcCHkAgAAqOE+uF26dGnnynA8WK4AAAA6vbbY6KHC7VFxiVs+nymbzWALYD/jTxYAAHRq/g647kqvMnNdSs8qUkFxubw+U3abobjocKUkxSg50SlHqN2Se+FHhFwAANBptUXA3Zqer50ZBTLNH497fabyC8t0qKhMha4KDUmJJ+hajDW5AACgU2qLJQqZua46Abcm05R2ZhQoM9dl2T1xFCEXAAB0SoWFhaqoqJDkvzW46VlFDQbcKqYppWcVqcLtsezeIOQCAIBO6sQTT9RVV12lyMhIv2zVW1ziVkFxebPGFrjKVVzitvT+nR1rcgEAQKd10kknacCAAXI4HJZf2+cz5fU1MY37A6/XlK+ZY9E8zOQCAIBO4eDBgyopKalz3B8BV5JsNkN2m9GssXa7IVszx6J5CLkAAKDDy87O1ksvvaSXXnqp3qDrD9GRDsVFhzdrbJwzXNGR/gnbnRUhFwAAdGg1uyjk5ubqtddek9nU02AWCHOEKCUpRkYTE7SGIaUkxbAxhMUIuQAAoMOqr03YhRdeKKOp5GmR5ESnUvvENRh0DUNK7ROn5ERnm9TTmfBXBgAA0CG1RR/cpjhC7RqSEq9YZ9jRHc9c5fJ6TdnthuKc7HjmT4RcAADQ4QRCwK3iCLUrpXeskhKiVFzils9nymYzFB3pYImCH/EnCwAAOpRACrg1hTlClECobTOsyQUAAB1GoAZctD1CLgAA6DDWrl1LwIUklisAAIAO5Morr1RZWZlycnIIuJ0cIRcAAHQYDodDEydOVGFhoRITE9u7HLQjlisAAICgVVpaWueYw+Eg4IKQCwAAglN2draeeeYZrV+/vr1LQQAi5AIAgKBTs4vCu+++qy+//LK9S0KAIeQCAICgUl+bsN69e7dzVQg0hFwAABA06IOL5uoQ3RU+/fRTzZ49Wzt27FBlZaVOPvlkTZ8+XWeffXazr/H111/rueee06ZNm1RaWqoePXpo/Pjx+tWvfqWYmBg/Vg8AAJqDgIuWCPqZ3GXLlmnatGnatGmThg4dqmHDhmnTpk2aPn26lixZ0qxrrFy5UpMmTdJHH32kE044QWeffbYqKir00ksv6eqrr9bhw4f9/C4AAEBjCLhoqaCeyc3JydEjjzwip9Opl19+WampqZKkLVu2aNq0aXrsscd0zjnnqHv37g1ew+Px6JFHHpHP59Mzzzyjn/70p5KkiooK/frXv9bq1as1a9YsPfTQQ23yngAAQG0EXByPoJ7JXbx4sdxut6ZOnVodcCVp6NChmj59uioqKpqczd2xY4fy8/M1aNCg6oArSWFhYbr99tslSV988YV/3gAAAGiU2+3W4sWLCbhosaAOuevWrZMknXfeeXXOVR1bu3Zto9ew2Y7+ERw6dEgej6fWuYKCAkliTS4AAO3E4XDo0ksvld1uJ+CiRYI25JqmqV27dslms6l///51zp9wwgmy2WzatWuXTNNs8DoDBgxQz549lZOTo/vuu08ZGRkqKyvTZ599pj/+8Y+y2WyaNm2aP98KAABoRFpamq699loCLlokaNfkFhUVye12q2vXrnI4HHXOh4SEKC4uTocOHVJJSYmioqLqvU5oaKhmzpypO+64QytWrNCKFSuqzyUmJmru3Lk666yz/PY+AABAbaZpyjCMWsdqLksEmiNoZ3Jrrs1pSHh4uCSppKSk0Wv16dOn+lchQ4cO1bhx45SQkKDc3FzNnTtXhYWFltUNAECgqnB7lFdQqpxDJcorKFWF29P0iyyWnZ2tF154QS6Xq83vjY4laGdyq9bSNkdjyxUKCgo0ceJE5eTk6MUXX9TIkSMlHV3oPmPGDC1dulR33HGHFi1a1OqaAQAIRO5KrzJzXUrPKlJBcbm8PlN2m6G46HClJMUoOdEpR6jd73XU7KLw0ksv6YYbbpDT6fT7fdExBe1MbkREhKSjrb4aUl5eXmtsfebNm6fdu3fr9ttvrw640tGF7o888oj69eunL774gj2xAQAdkrvSq63p+drwzUHlF5bJ6zs6MeT1mcovLNOGbw5qa3q+3JVev9ZxbJuwkpKSJn8TCzQmaENuVFSUIiIiVFBQUKcrgnS0/21BQYHCwsIUHR3d4HU2bNggSfWuuw0NDdXo0aMlSdu3b7eocgAAAkdmrks7MwrU0C89TVPamVGgzFz/LR9oqA9ujx49/HZPdHxBG3INw9CAAQPk9Xq1d+/eOuf37Nkjn8/X5EL14uJiSZLdXv+vYaqOV1ZWtq5gAAACTIXbo/SsogYDbhXTlNKzivyyRpeNHuAvQRtyJWnMmDGSjm7Le6yqY2PHjm30GlXtx9asWVPnnNfr1eeffy5JGjRoUKtqBQAg0BSXuFVQXN6ssQWuchWXuC29PwEX/hTUIXfChAkKCwvTnDlztG3bturjW7du1dy5cxUeHq6JEydWH8/IyFB6enqtJzavvfZaSdLs2bP11VdfVR/3eDx68skntXPnTg0cOFBnnnlmG7wjAADajs9nVq/BbYrXa8rXzLHNQcCFvwVtdwVJSkpK0v33368ZM2bouuuuq35wbP369fJ4PHriiSfUrVu36vFTp07V/v379Ze//EUTJkyQdHSm9+abb9bzzz+vSZMm6dRTT1XXrl317bff6sCBA4qPj9c///nPBpczAAAQrGw2Q3ab0ayga7cbstmMJsc1R15eHgEXfhfUIVeSJk2apF69emnu3LnauHGjHA6Hhg8frttuu02jRo1q1jXuueceDR8+XAsXLtTWrVu1bds2JSYm6he/+IVuueUWJSYm+vldAADQ9qIjHYqLDld+YVmTY+Oc4YqOrLv50vGIjY1Vz549tXv3bgIu/CboQ64kjRs3TuPGjWty3IcfftjqawAA0FGEOUKUkhSjQ0VljT58ZhhSSlKMwhzWxIbQ0FBdf/31evPNN/WTn/yEgAu/6BAhFwAAHJ/kRKcKXRUNthEzDCm1T5ySE63dlCE0NFRXX321pdcEaiLkAgDQiTlC7RqSEq9YZ9jRHc9c5fJ6TdnthuKc1ux4lp2drYyMjFqbLgH+RsgFAKCTc4TaldI7VkkJUSouccvnM2WzGYqOdLR6iULNLgper7d6kyXA34K6hRgAALBOmCNECXER6t4tUglxEZYGXElat24dW/WizRByAQCA5RrqgxsZGdnOlaGzIOQCAABLsdEDAgEhFwAAWIaAi0BByAUAAJYg4CKQEHIBAECrEXARaAi5AACg1UpKSlRZWSmJgIvAQMgFAACtNmDAAF133XVyOp0EXAQENoMAAACWGDBggO666y6Fhoa2dykAM7kAAKDlDh48qOLi4jrHCbgIFIRcAADQItnZ2XrppZc0f/78eoMuEAgIuQAAoNlqdlE4fPiwlixZItM027ssoA5CLgAAaJb62oRdcsklMgyjnSsD6iLkAgCAJtEHF8GGkAsAABpFwEUwIuQCAIAGEXARrAi5AACgXgRcBDNCLgAAqNdnn31GwEXQYsczAABQr8suu0xlZWXKysoi4CLoEHIBAEC9QkJCdO2116qwsFDx8fHtXQ7QIixXAAAAkqQjR47UORYSEkLARVAi5AIAAGVnZ2vWrFlau3Zte5cCWILlCgAAdHI1uyh8+OGHCgkJ0ejRo1Xh9qi4xC2fz5TNZig60qEwR+3o0JwxQHvgv0IAADqx+tqEpQxIVfr+QqVnFamguFxenym7zVBcdLhSkmKUnOiUJGXmuhod4wi1t+dbQydHyAUAoJOqL+BOnXaTsg57tTMjX6b541ivz1R+YZkOFZXpcFG5oiJC9fXOvAbHFLoqNCQlnqCLdsOaXAAAOqF6A+7UqTpSGaKdGQW1wmtNpil9sf2g9uceUVgDAdY0pZ0ZBcrMdfmrfKBJhFwAADqZhnYyi43rpvSsogYDriR5vD4dKirX95mFiokKa3CcaUrfZxQor6BUOYdKlFdQqgq3x+q3AjSI5QoAAHQijW3Vm1dQqoLi8kZfX+H2qqzCo+z8Eg0ZUH9rMa/Xp6IStzIOFssZ6dC+bBfrddHmCLkAAHQSHo9Hr7zySoNb9fp8pry+RqZxJZmmKdOUKj0+GTLqnPd6fcopKNWhonLJlGQeHcN6XbQ1lisAANBJhISE6Morr1RoaGidgCtJNpshu61ucK3JMAwZhhQaYpOpuoG4qMRdHXDrG8N6XbQVZnIBAOhE+vXrp4kTJyo8PLxWwJWk6EiH4qLDlV9Y1uDrwxx2dQkLUXxsF1W4vbXOeby+o8sdfsi1PeMj64yRjgbd9KwiJSVE0VMXfsNMLgAAHZhZz1Nk/fr1qxNwJSnMEaKUpBgZjUzmhtht6hYTroHJsSo6UlHrXNV6XenorPCApLpjqhS4ylVc4m7BOwFahpALAEAHlZ2dreeff16FhYXNfk1yolOpfeIaDLqGIZ1xUg/1ToxSRWXtWdqq9bo2m6FhqYmy2QyV1zOTK0lerylfE+t/gdbgdwQAAHRANbsozJ8/X1OnTlVsbGyTr3OE2jUkJV6xzrCju5m5yuX1mrLbDcU5a+945gi11xoTGmJT355OpfSOlc1mKOdwaYP3sdsN2ZpY/wu0BiEXAIAO5tg2YRUVFSorK2tWyJWOhteU3rFKSohScYlbPp8pm81QdKSj1hraY8eYppSR61JWjqvBGdwqcc5wRUc6jvs9Ak0h5AIA0IE01ge3pcIcIUpo4sGwY8eUVFQqPauw0dcYhpSSFMNDZ/Ar1uQCANBBWBlwj1dz1vSm9omrXvIA+At/hQIAoAMIhIArNX9NLxtBwN8IuQAABLlACbhVmrumF/An/ksDACCI5efnB1TArak5a3oBf2FNLgAAQSwuLk59+/aVFFgBF2hv/PUKAIAgZrfbdfXVV2v58uUaOXIkARf4ASEXAIAgZ7fbdcUVV7R3GUBAYbkCAABBJDs7W+vWrZNpsiUu0BhmcgEACEAVbk+dzgSFBYeqHzLzeDw655xzZDTUkBbo5Ai5AAAEEHelV5m5rqM9ZovL5fWZstsMhYf4ZJbnq1dSH+3etVMbNmzQ6aefLqeTTRWA+hByAQAIEO5Kr7am52tnRoFqrkYoLCrW5s1bZPq8OmPoCTrxpMEa85PRBFygEazJBQAgQGTmuuoEXJfLpc2bt8jjqZTX59PG7Vk66dTRdFEAmkDIBQAgAFS4PUrPKmow4EpSSEiohgwZogOH3apwe9qpUiA4sFwBAAAL1PegWEu2sC0ucauguLz63+sLuKecMlROp1MFrnIVl7jZTQxoBF8dAAC0QkMPisVFhyslKUbJiU45Qu1NXsfnM+X1HZ3GbSzgSpLXa8rno4UY0BhCLgAAx6mhB8W8PlP5hWU6VFSmQleFhqTENxl0bTZDdpshr8+U1+uVafok1Q24kmS3G7LZaB0GNIY1uQAAHKf6HhSryTSlnRkFysx1NXmt6EiH4qLDJUmxsbEaMmSIwsLC6wRcSYpzhis60tHq+oGOjJALAMBxqO9BsfqYppSeVdTkg2JhjhClJMWoam+H2NhYjRw5ok7ANQwpJSmmRet9gc6IkAsAwHE49kGxxlQ9KNaQ7OxsHT58WMmJTqX2iasOujZb7R/ThiGl9olTciL9cYGm8NdAAACOQ80HxZrS2INi2dnZWrBggUJDQzV16lQNSYlXrDPs6INsrnJ5vabsdkNxzpY9yAZ0doRcAACOQ80HxZrS0INiVQG3rKxMZWVlWrJkiW699Val9I5VUkJUq1qSAZ0dXy0AAByHqgfF8gvLmhxb34NiNQOuJHXp0kVXXHGFjB/WKoQ5QuiDC7QCa3IBADgOxz4o1pD6HhSrL+BOmTKFrXoBCxFyAQA4Tsc+KHas+h4UI+ACbYPfgwAAcJwcofYWPShGwAXaDiEXAIBWcITam/WgGAEXaFuEXAAALNDUg2JffvklARdoQ4RcAADawMUXX6zy8nLt3r2bgAu0AUIuAABtwGaz6ec//7kKCgrUrVu39i4H6PDorgAAgB8UFxfLNGtvFGGz2Qi4QBsh5AIAYLHs7Gw999xzWrlyZZ2gC6BtEHIBALBQzS4Kn3zyidasWdPeJQGdEiEXAACL1NcmLC0trZ2rAjonQi4AABagDy4QWAi5AAC0EgEXCDyEXAAAWoGACwQmQi4AAMeJgAsELkIuAADHwev1asmSJQRcIEARcgEAOA52u11XXXWVwsLCCLhAAGJbXwAAjlNSUpImT54su91OwAUCDCEXAIBmMk1ThmHUOpaUlNRO1QBoDCEXAIBGVLg9Ki5x68iREu3ena6hg9OU0C2uvcsC0ARCLgAA9XBXepWZ61J6VpEyD+Trq41fy2aY2rQzTxeNH6EBfeLlCLW3d5kAGkDIBQDgGO5Kr7am52tnRoGKi13avHmLPJ5KSdLujFx98nWmyiqlISkEXSBQ0V0BAIBjZOa66g24ISGhOuWUoYqMitLOjAJl5rrauVIADSHkAgBQQ4Xbo/SsogYDrtPplCSZppSeVaQKt6c9ywXQAEIuAAA1FJe4lXkgv9GAW6XAVa7iEnd7lAmgCYRcAABqOHKkRF9t/LrJgCtJXq8pn89s6xIBNAMhFwCAHxw+fFjf7fhONuNocG0s4EqS3W7IZjPqPQegfRFyAQD4QWxsrKLCQ9S7R9cmA64kxTnDFR3paMMKATQXLcQAAPiBzWbTWaNHyheyXTv3lysyKqrBsYYhpSTFKMzBj1IgEDGTCwBADTabTaNOO1HDT06W0cBKBMOQUvvEKTmx4VleAO2LkAsA6LSys7O1atUqmWbth8ccoXYNSYnXiJN7KD62i+z2o2nXbjcUH9tFI07uwUYQQIDjdywAgE4pOztbCxYsUFlZmdxuty688EIZNaZuHaF2pfSOVVJClIpL3PL5TNlshqIjHSxRAIIAM7kAgE6nZsCVpC1btqi4uLjesWGOECXERah7t0glxEUQcIEgQcgFAHQqxwbcLl26aMqUKYqJiWnnygBYiZALAOg0Ggq4PXv2bOfKAFiNkAsA6BQIuEDnQsgFAHR4BFyg8+kQq+c//fRTzZ49Wzt27FBlZaVOPvlkTZ8+XWeffXazr1FSUqJ58+bp3XffVVZWlrp06aLhw4frV7/6lYYMGeLH6gEA/kTABTqnoJ/JXbZsmaZNm6ZNmzZp6NChGjZsmDZt2qTp06dryZIlzbpGYWGhrr/+es2aNUslJSUaO3asevToodWrV2vixInasmWLn98FAMBfTNOs7oNLwAU6j6AOuTk5OXrkkUfkdDr1xhtvaM6cOZo3b55efvllRUVF6bHHHlNOTk6T1/nLX/6iHTt26OKLL9bKlSv17LPP6u2339Z9990nt9utP/zhD23wbgAA/tCrVy9NmTJFcXFxjQbcCrdHeQWlyjlUoryCUlW4PW1cKQArBXXIXbx4sdxut6ZOnarU1NTq40OHDtX06dNVUVHR5GzugQMH9NZbbyk5OVmPP/64HA5H9bmbbrpJJ598ssrKynT48GG/vQ8AgH/16tVLd9xxR70B113pVfr+Qq3ZtF8ffpmpVV9m6sMvM7Vm036l7y+Uu9LbDhUDaK2gDrnr1q2TJJ133nl1zlUdW7t2baPX+OCDD2SapiZNmlQr4FZZtmyZ/ve//6lr164WVAwA8Lfs7Gzl5ubWOW63192C113p1db0fG345qDyC8vk9R1d1uD1mcovLNOGbw5qa3o+QRcIQkH74Jlpmtq1a5dsNpv69+9f5/wJJ5wgm82mXbt2yTTNWls11rR9+3ZJ0pAhQ1RSUqJ33nlH27ZtU0hIiEaNGqVzzz23wdcCAAJL1UNmNptNN9xwgxITExsdn5nr0s6MAv2wZLcO05R2ZhQo1hmmlN6x1hcMwG+CNuQWFRXJ7Xara9eu9c7AhoSEKC4uTocOHVJJSYmioqLqvU5GRoakow+fXXrppdq/f3/1uUWLFmnUqFF69tlnG3w9ACAwHNtF4bXXXtPtt98um63+X1pWuD1KzypqMOBWMU0pPatISQlRbOkLBJGgXa5QsxVMQ8LDwyUdbQ/WEJfLJUl64IEHFBsbq1dffVVfffWVXn75ZaWlpemzzz7TI488YmHlAACr1dcm7Oc//3mDAVeSikvcKigub9b1C1zlKi5xW1IrgLYRtCG3sW9cxzIb+Wu62330m1ZoaKjmz5+vYcOGKSoqSqeddprmzZunyMhIrVixQnv27Gl1zQAA6x1vH1yfz6xeg9sUr9eUr5ljAQSGoA25ERERkqSKiooGx5SXl9caW5+q2d5LLrlE0dHRtc4lJCRo/PjxMk1TGzZsaG3JAACLtWajB5vNkN3WvGcu7HZDtmaOBRAYgjbkRkVFKSIiQgUFBfJ46vYy9Hg8KigoUFhYWJ3wWlNV14TevXvXe77qeEFBgQVVAwCs0tqdzKIjHYqLDm/W2DhnuKIj6z7/ASBwtWoF/ZtvvmlJEVdccUWLX2MYhgYMGKAtW7Zo7969GjBgQK3ze/bskc/nq9U/tz6pqan6/PPP6203I0l5eXmSpG7durW4RgCAf1ixVW+YI0QpSTE6VFTW6MNnhiGlJMXw0BkQZFr1Ffu73/3OkvZaxxNyJWnMmDHasmWLVq5cWSfkrly5UpI0duzYRq9x9tlna8GCBVq5cqXuvvtuhYT8+Efidru1fv16SdJpp512XDUCAKy3efPmVgXcKsmJThW6KhpsI2YYUmqfOCUnOltbMoA21qqQe9JJJ9UbcgsLC6tbcSUlJSktLU3R0dEqLy9Xenq6du7cKcMwdMopp6hXr17Hff8JEyZo7ty5mjNnjn7yk59o8ODBkqStW7dq7ty5Cg8P18SJE6vHZ2RkqLKyUomJiXI6j37DGj16tAYNGqTvvvtOf/7zn/Xggw/KbrfL5/PpySefVFZWls4666x6e/ECANrHBRdcoIqKCn333XfHHXAlyRFq15CUeMU6w5SeVaQCV7m8XlN2u6E4Z7hSkmKUnOiUI7TuRhIAApthNtZ64Djk5eXpmmuukSQ9/vjjGjlyZJ0x33zzjX7zm9+ouLhYr732mpKTk4/7fosXL9aMGTMUGhpafa/169fL4/HoiSee0OWXX149dvz48dq/f7/+8pe/aMKECdXH09PTdcMNNygvL0+9e/fWiSeeqJ07dyojI0M9e/bUokWLlJSUdNw11mfy5MnasGGDRowYoYULF1p6bQDoDEzTVGFhoeLi4iy5XoXbo+ISt3w+UzaboehIB0sUAAu0V+ax/MGzf/zjH8rJydFzzz1Xb8CVpJNPPlnPPfecioqK9Ne//rVV95s0aZJmz56tU045RRs3btS2bds0fPhwvfjii7UCbmNSUlL05ptvavLkyZKkNWvWyOPxaNKkSVq6dKnlARcA0DKFhYV12kEahmFZwJWOrtFNiItQ926RSoiLIOACQc7yr+A1a9ZowIABGjRoUKPj+vfvr9TU1Oo1r60xbtw4jRs3rslxH374YYPn4uPj9Yc//EF/+MMfWl0PAMA6VQ+ZDR48WBdddBFbrQNoFstncsvLy+X1eps1trS0tNGNGgAAnVvNLgpffPFF9UPFANAUy0Nu//79tXv3bm3cuLHRcatWrVJGRobS0tKsLgEA0AHU1yas6gFjAGiK5SH3uuuuk2mauu222/Tmm29Wb5tbpaSkRAsXLtS9994rwzB0ww03WF0CACDIWdEHF0DnZvma3Kuuukqffvqp3nnnHT3wwAN66KGH1Lt3b0VERKikpET79++X1+uVaZq66aabdN5551ldAgAgiBFwAVjBL4+O/v3vf9cZZ5yhuXPnav/+/dq7d2+t8wMHDtRdd92l888/3x+3BwAEKQIuAKv4JeQahqHrr79e119/vfbu3at9+/bJ5XIpOjpa/fr1a1VfXABAx0TABWAlvzcBPOGEE3TCCSf4+zYAgCDm8/n0+uuvE3ABWMbyB89q2rFjh+bMmaPf//73uuuuuyQdffDsjTfeqPNAGgCg87LZbLr66qvVpUsXAi4AS/hlJreoqEgPPvigVq1aJeno1otVzbszMzP14IMPaubMmXr++edpIQYAkCT16NFDN9xwg0zTbFHAZTteAPWx/LuA2+3WjTfeqG+++UYREREaNWqUtm7dqry8PElHA290dLRycnI0efJkvfnmm+rVq5fVZQAAApzP55PNVvsXij169Gj2692VXmXmupSeVaSC4nJ5fabsNkNx0eFKSYpRcqJTjlC71WUDCBKWL1dYtGiRvvnmG51xxhlauXKlZs2apaSkpOrzJ554oj788EOdccYZcrlcmjNnjtUlAAACXHZ2tmbNmqWDBw8e1+vdlV5tTc/Xhm8OKr+wTF7f0d0zvT5T+YVl2vDNQW1Nz5e7snk7cALoeCwPuf/9738VEhKiv/3tb+ratWu9Y6KiovS3v/1NISEhWrdundUlAAACWFUXhUOHDumll146rqCbmevSzowCNbQzvGlKOzMKlJnramW1AIKV5SF3z549GjBggLp3797ouO7du6t///7KycmxugQAQIA6tk2YdHQZW0tUuD1KzypqMOD+eF0pPatIFW7P8ZQKIMhZHnINw6j1zasxPp9PDofD6hIAAAHIqj64xSVuFRSXN2tsgatcxSV08wE6I8tDbr9+/ZSVlaWsrKxGx2VkZGjXrl3q37+/1SUAAAKMlRs9+Hxm9Rrcpni9pnzNHAugY7E85F566aXyer26//77VVhYWO+YwsJC3XvvvZKkn/3sZ1aXAAAIIFbvZGazGbLbjGaNtdsN2Zo5FkDHYnkLsYkTJ2rFihX66quvdNFFF2nUqFHVs7ovvviidu/erQ8++EBFRUUaOHCgJk2aZHUJAIAA4Y+teqMjHYqLDld+YdNL4+Kc4YqOZFkc0BlZPpPrcDg0d+5cnXPOOTp8+LBWrFih3NxcmaapJ598UkuXLlVRUZHOOOMMzZs3T2FhYVaXAAAIAIWFhZYHXEkKc4QoJSlGRhMTtIYhpSTFsDEE0En55Ss/JiZGs2fP1rZt27Rq1Sqlp6fryJEj6tKli/r27atzzjlHI0aM8MetAQABIiYmRoMHD9YXX3xh+Va9yYlOFboqGmwjZhhSap84JSc6LbkfgOBjechdvHixUlJSdOaZZ2rw4MEaPHiw1bcAAAQBwzB00UUXyeFwaPDgwZYFXElyhNo1JCVesc6wozueucrl9Zqy2w3FOdnxDIAfQu7s2bN15MgRrVmzRtHR0VZfHgAQRAzD0Pnnn++XaztC7UrpHaukhCgVl7jl85my2QxFRzpYogDA+jW5RUVF6tevHwEXADqZ7Oxsvffeey3e3KG1whwhSoiLUPdukUqIiyDgApDkh5ncE088Ubt27VJBQYHi4uKsvjwAIADV7KJQUVGhyy67TEZTT4YBgB9ZPpP75z//WU6nU9dff72WLl2q77//XoWFhSorK2vwfwCA4HVsm7DvvvuuwT7pANBWLJ/J/b//+z8ZhqF9+/bp4YcfbnK8YRjavn271WUAANpAQ31w+U0egPZmecj9/vvvWzS+rdduAQCs4Y+NHgDAKpaH3FWrVll9SQBAgCHgAgh0lofc3r17W31JAEAAIeACCAaWP3h2rEOHDunLL7/U6tWrJUk+n09Hjhzx920BAH5AwAUQLPzWTHDVqlWaNWuWvv32W0k/PmCWlZWlK664Qtdee63uuecehYTQzxAAgoXdbpfNdnR+hIALIJD5ZSb32Wef1R133FHdNcFut1c/YLZ//36VlpZq/vz5uuWWW+T1ev1RAgDADxITE3XDDTcoPj6egAsgoFkecj/77DM9++yzioyM1KOPPqr169dr6NCh1edHjhypxx9/XBEREfr000/1yiuvWF0CAMCPEhMTdfvttxNwAQQ0y0PuSy+9JMMw9Ne//lXXXXddne19bTabrrjiCv3zn/+UaZp6++23rS4BAGCR7OxsHThwoM7xqiULABCoLP8u9fXXX6tHjx4aN25co+PGjBmjXr16adeuXVaXAACwQNVDZgsWLKg36AJAILM85JaUlDR7p5uuXbvK4/FYXQIAoJVqdlEoLy/X0qVLeYYCQFCxPOQmJiZqz549TYZXt9utPXv2KCEhweoSAACtUF+bsGuuuUZ2u72dKwOA5rM85J511lkqLy/X7NmzGx03a9YslZSUaPTo0VaXAAA4TvTBBdBRWN6k9uabb9by5cs1a9Ys7d+/XxdffLHKy8slSUVFRUpPT9err76q5cuXKywsTDfeeKPVJQAAjgMBF0BHYnnITUpK0syZM3X33XfrP//5j958883qc2eeeaYkyTRNhYWF6YknnlC/fv2sLgEA0EIEXAAdjV96wIwZM0ZvvfWWJk6cqB49esg0zer/xcXF6corr9SyZct04YUX+uP2AIAWIOAC6Ij8tqdu79699fDDD+vhhx9WSUmJjhw5ooiICDmdTn/dEgBwHLZv307ABdDhWD6T+/e//13p6em1jkVGRqp79+4EXAAIQOPHj9fIkSMJuAA6FMtncufMmaO5c+dq0KBBuvzyy3XJJZcoPj7e6tsAACxiGIYuvPBCjR49WjExMe1dDgBYwvKZ3Msvv1yRkZH69ttv9cQTT+icc87RL3/5Sy1fvrz612EAgPZz+PBh+Xy+WscMwyDgAuhQLJ/JfeKJJ+R2u7V69Wr997//1dq1a/Xxxx/rk08+UXh4uH7605/qsssu0+jRo2UYhtW3BwA0ouohs4EDB+qKK66QzeaX548BoN355cEzh8OhCy64QBdccIGOHDmiDz74QCtWrNDnn3+ut956S2+//bbi4+N1ySWX6LLLLtOJJ57ojzIAADXU7KKwZcsWhYeH66KLLmrvsgDAL/zWXaFKVFSUJkyYoAkTJujw4cN6//339b///U9ffPGF5s+fr5deeknbt2/3dxkA0KnV1yZs2LBh7VwVAPiP30NuTZWVlTJNs/rXY6ZptuXtAaBTog8ugM7I7yH38OHDeu+99/TOO+9o48aN1ZtC9OnTR5dddpkuv/xyf5cAAJ0WARdAZ+WXkOtyufTBBx/onXfe0fr16+X1emWapmJiYnThhRfqiiuu4NdkAOBnBFwAnZnlIfe2227TJ598Ur00ITQ0VOeee64uv/xyjR07Vg6Hw+pbAkCHUOH2qLjELZ/PlM1mKDrSoTDH8X2bJuAC6OwsD7mrV6+WJJ166qm67LLLdNFFFyk2Ntbq2wBAh+Gu9Coz16X0rCIVFJfL6zNltxmKiw5XSlKMkhOdcoTam3090zT1n//8h4ALoFOzPOT+6le/0mWXXaa+fftafWkA6HDclV5tTc/XzowC1XwW1+szlV9YpkNFZSp0VWhISnyzg65hGLrmmms0f/58eb1eAi6ATsnykHvnnXfW+nfTNJWXl6eSkhJFRESoe/fuVt8SAIJWZq6rTsCtyTSlnRkFinWGKaV3bLOvGx8fr6lTp6qyspKAC6BT8lt3hV27dulf//qX1q5dq5KSkurj4eHhGj16tO68804NGjTIX7cHgIBX4fYoPauowYBbxTSl9KwiJSVENbhG1+v1ym6vPdMbHx9vVakAEHT8sp/j//73P1111VV69913deTIkeq2YaZpqqysTKtWrdI111yjd955xx+3B4CgUFziVkFxebPGFrjKVVzirvdcdna2nnnmGWVlZVlZHgAENctD7t69e3XvvfeqvLxc48eP1/z58/XFF1/om2++0eeff6558+Zp3LhxcrvdevDBB7Vv3z6rSwCAoODzmfL6mrcpjtdrylfP2KouCoWFhVq4cCFBFwB+YHnInTNnjioqKjRt2jTNmjVLZ555ppxOp+x2u2JjY3XWWWfpueee0w033KCysjItXLjQ6hIAICjYbIYiw0PUo2uE+vRwqm+PaPXp4VT3rhEKd9ReemC3G7LZjFrHjm0TZrPZ6ixZAIDOyvI1uZ999pmioqJ09913Nzrunnvu0euvv65169ZZXQIABIWI8BB1jemiben5ys4vUaXHp9AQm3rGR2pAUqxiogzlHC6VJMU5wxUd+WOfcfrgAkDjLJ/JzcvLU79+/Zrc9MHhcKh///46ePCg1SUAQMBzV3r13b4Cbd2Vp4wclyo9PklSpcenjIMufbQxSwXFFereNUKGIaUkxVQ/dEbABYCmWR5yIyIidOjQoWaNPXTokMLDw60uAQACXlXrMGeEQ91iwqXaKxHk85natDNXpmnqpH7dlJzolETABYDmsjzkDh48WNnZ2VqzZk2j49asWaMDBw5o8ODBVpcAAAGtZuswu92m7nER6p0QpYjwEBk/fFc2bFK4w67CIxUakBQrR6idgAsALWB5yL322mtlmqZ++9vf6r333qt3zLvvvqt7771XhmHo6quvtroEAAhox7YOs9tt6hodrr49o9WvZ4z69Tr6/317RsvrM1VaXqni4mICLgC0gOUPnv30pz/VJZdcov/+97+6++679ac//UlpaWmKjIzUkSNHtGPHDhUUFMg0TV188cW68MILrS4BAAJaQ63DQuw2hXSpPfdQ1TrMGevU8OHD9cknnxBwAaAZ/LLj2V//+ledcMIJevHFF3Xo0CF9+umntc5HRUVp2rRpuu222/xxewAIaDabIbvNaFaP3KrWYYZh6LzzzlNoaKjS0tIIuADQBL+EXMMwdMcdd+imm27Sl19+qb1796qkpEQRERHq16+fTjvtNEVERPjj1gAQ8KIjHYqLDld+YVmTY2u2DjMMQ+ecc46fqwOAjsEvIbfKkSNHNGbMGI0ZM6b62JdffqmMjAwNGjTIn7cGgIAV5ghRSlKMDhWVyWxgMtflcungwWyNvOLs6tZhAIDms/zBM0nKycnRjTfeqHHjxqm4uLjWuTlz5ujKK6/UtGnT6JELoNNKTnQqtU+cDKPuOZfLpa1btyo5IVyZu7fJ5/O1fYEAEOT8shnENddcU70ONzc3t9b57t27Kzw8XJ999pkmT54sl8tldQkAEPAcoXYNSYnXiJN7KD62i+z2o2m3rLRExYcPaNwZ/RXqOaT1n32igoKCdq4WAIKP5SF31qxZysnJ0ZlnnqnVq1drwIABtc7PmDFDq1ev1llnnaWsrCw9//zzVpcAAEHBEWpXSu9YjR3WW+NPS9aok7qpl7NUPSPLtOe7L3UgK0NTpkxRt27d2rtUAAg6lofctWvXqkuXLvrnP/+phISEesfExsbqr3/9qxwOhz744AOrSwCAoBLmCJGnvEivvfyC1n+8Sru+/05er5c2YQDQCn5ZrtCvXz/FxsY2Oq5r167q16+fDhw4YHUJABBU2MkMAKxneciNjY1t9vqxI0eO0EoMQKdGwAUA/7A85J500kk6ePCgVq5c2ei4Tz75RFlZWTr55JOtLgEAgkJLAm6F26O8glLlHCpRXkGpKtyeti4XAIKK5SH3+uuvl2mauu+++7RkyRKVl5fXOu92u7Vs2TLdfffdMgxD119/vdUlAEBQCAsLU2hoqKSGA6670qv0/YVas2m/PvwyU6u+zNSHX2Zqzab9St9fKHeltz1KB4CAZ3mH8XPOOUfXX3+9XnnlFT366KP605/+pN69eysyMlKlpaXav3+/PB6PTNPUNddco/PPP9/qEgAgKHTt2lVTp07VkiVLdMUVV9QbcLem52tnRkGtTSO8PlP5hWU6VFSmQleFhqTEyxFqb+PqASCw+WUbnUceeURDhgzRv//9b+3bt0/79u2rdb5nz566+eabmcUF0Ol17dpVt956q4x6doXIzHXVCbg1maa0M6NAsc4wpfSO9W+hABBk/LZX5IQJEzRhwgTt27dPGRkZKiwsVJcuXXTCCSfU6Z0LAJ1Bdna23G63+vbtW+t4fQG3wu1RelZRgwG3imlK6VlFSkqIYvtfAKjB798R+/btW+cbOgB0NlUPmXm9Xk2aNKnJ74vFJW4VFJc3OqZKgatcxSVuJRByAaCa5Q+e1bRjxw7NmTNHv//973XXXXdJkkpKSvTGG2/I7Xb789YAEDBqdlGoevjW42m8O4LPZ8rra2Ia9wderylfM8cCQGfhl7/2FxUV6cEHH9SqVaskSaZpVv86LjMzUw8++KBmzpyp559/Xmlpaf4oAQACQn1twq677jqFhDT+7ddmM2S3Gc0Kuna7IZut7pIHAOjMLJ/JdbvduvHGG7Vy5Up16dJF48ePV2JiYvV50zQVHR2tnJwcTZ48mR3PAHRYrdnoITrSobjo8GbdJ84ZruhIR6tqBYCOxvKQu2jRIn3zzTc644wztHLlSs2aNUtJSUnV50888UR9+OGHOuOMM+RyuTRnzhyrSwCAdtfanczCHCFKSYpRPc+k1WIYUkpSDA+dAcAxLA+5//3vfxUSEqK//e1v6tq1a71joqKi9Le//U0hISFat26d1SUAQLuyaqve5ESnUvvENRh0DUNK7ROn5ERna0sGgA7H8r/679mzRwMGDFD37t0bHde9e3f1799fu3fvtroEAGg3VgVcSXKE2jUkJV6xzjClZxWpwFUur9eU3W4ozhmulKQYJSc62QgCAOphecg1DKP6m3tTfD6fHA7WkQHoOHbt2mVJwK3iCLUrpXeskhKiVFzils9nymYzFB3pYIkCADTC8u+Q/fr103fffaesrKxaa3GPlZGRoV27dmnw4MFWlwAA7eYnP/mJKisr9cUXX7Q64NYU5gihDy4AtIDla3IvvfRSeb1e3X///SosLKx3TGFhoe69915J0s9+9jOrSwCAdmMYhsaNG6fbb7/dsoALAGg5y6cFJk6cqBUrVuirr77SRRddpFGjRikrK0uS9OKLL2r37t364IMPVFRUpIEDB2rSpElWlwAAbSY/P19xcXGy239cF2sYhpxOHgYDgPZk+Uyuw+HQ3Llzdc455+jw4cNasWKFcnNzZZqmnnzySS1dulRFRUU644wzNG/ePIWFhVldAgC0iezsbM2bN09Lly6V1+tt73IAADX4ZYFXTEyMZs+erW3btmnVqlVKT0/XkSNH1KVLF/Xt21fnnHOORowY4Y9bA0CbqNlF4bvvvtPy5ct1xRVXtHdZAIAf+PUphsGDBzf6YFlmZqZmzJjBhhAAgkp9bcJGjhzZzlUBAGqyJOS6XC6tWLFC6enpMk1TQ4YM0c9+9rMG24N5PB7NmTNH//73v1VRUWFFCQDQJqzsgwsA8J9Wh9x3331XDz74YJ3euLNmzdK///1v9evXr9bxzz//XDNmzNCePXtkmqZCQ0NbWwIAtAkCLgAEj1Y9eLZt2zbdc889Ki0tld1u14ABAzRw4EBJR/vg3nrrraqsrJQkud1u/fGPf9S0adOqA+4ZZ5yh//znP61/FwDgZwRcAAgurZrJnTdvnnw+n4YOHap//OMf1Zs/7N69W3fddZfS09O1YsUKjRs3Trfccos2b94s0zQVHx+v++67T5dddpklbwIA/ImACwDBp1UzuVu3bpXdbtdTTz1Va3ez/v37669//atM09Tq1at177336uuvv5YkXXPNNXrvvfcIuACCgmmaevvttwm4ABBkWhVy8/Pz1adPH/Xu3bvOuRNPPFGJiYlavXq1Pv74YyUkJOiFF17QjBkzFBUV1Zrb1vHpp59qypQpGjlypIYPH67Jkydr7dq1rbrmL3/5S6WlpWn9+vUWVQkgGBmGoWuvvVaxsbEEXAAIIq1arlBeXq64uLgGzycmJio3N1d9+/bVggUL1L1799bcrl7Lli3TAw88IIfDoTPPPFM+n0/r16/X9OnTNWPGDF177bUtvubLL7+sdevWWV4rgOAUGxurqVOnqqysjIALAEGi1d0VDMNo8JzD4ZBhGPr973/vl4Cbk5OjRx55RE6nUy+//LJSU1MlSVu2bNG0adP02GOP6ZxzzmnRvfft26e//vWvltcKIHh4PB6FhNT+9hgbG6vY2Nj2KQgA0GKWb+tbn9GjR/vluosXL5bb7dbUqVOrA64kDR06VNOnT1dFRYWWLFnS7Ot5vV7df//9Cg0NrXU9AJ1Hdna2Zs6cqT179rR3KQCAVmiTkOuvXrhVSwrOO++8OueqjrVkbe7cuXO1adMmPfTQQ+rWrZs1RQIIGlVdFIqLi/Xyyy8TdAEgiLVJyPUH0zS1a9cu2Ww29e/fv875E044QTabTbt27ZJpmk1e77vvvtMzzzyjCy64QJdeeqk/SgYQwI5tExYSEqLw8PB2rgoAcLxavSbX7XbrwIEDDZ6Tjv7waCxo9urVq8X3LSoqktvtVteuXevdPjgkJERxcXE6dOiQSkpKGu3o4Ha79dvf/lbR0dF69NFHW1wLgOBGH1wA6HhaHXK3bdumc889t9Ex48ePb/CcYRjavn17i+9b84dRQ6pmYZoKuU8//bR27typWbNmqWvXri2uBUDwIuACQMfU6pDbnKUA/ni9zdb8lRaN3eOrr77SCy+8oMsuu6zetb0AOi4CLgB0XK0KuatWrbKqjhaLiIiQJFVUVDQ4pry8vNbYY5WWlup3v/udEhIS9NBDD1lfJICARcAFgI6tVSG3vp3O2kpUVJQiIiJUUFBQb09Lj8ejgoIChYWFKTo6ut5rvPLKK8rIyFBaWppmzJhR69yuXbskSbNnz9bSpUt13XXX6fTTT/fPmwFgiQq3R8Ulbvl8pmw2Q9GRDoU56n6bO3LkCAEXADq4Vi9XaC+GYWjAgAHasmWL9u7dqwEDBtQ6v2fPHvl8vkb73ZaWlkqSduzYoR07dtQ75tNPP5V0tNcvIRcITO5KrzJzXUrPKlJBcbm8PlN2m6G46HClJMUoOdEpR6i9enxUVJRGjRqlDz/8kIALAB1U0IZcSRozZoy2bNmilStX1gm5K1eulCSNHTu2wdffeeeduvPOO+s9N3XqVH322WdasGCBRo4caV3RACzlrvRqa3q+dmYUqObye6/PVH5hmQ4VlanQVaEhKfG1gu7ZZ5+tkJAQ9evXj4ALAB1Q0PbJlaQJEyYoLCxMc+bM0bZt26qPb926VXPnzlV4eLgmTpxYfTwjI0Pp6elyuVztUS4AP8jMddUJuDWZprQzo0CZuXW/7kePHk3ABYAOKqhDblJSku6//34dOXJE1113nW666SbddNNNuv7661VSUqIZM2bU2rls6tSpuuiii/S///2vHasGYJUKt0fpWUUNBtwqxcUurVr3tUrL3W1TGACg3QX1cgVJmjRpknr16qW5c+dq48aNcjgcGj58uG677TaNGjWqvcsD4EfFJW4VFJc3Osblcmnz5i2yGaa+OiFGo04fUudBVQBAx9MhvtOPGzdO48aNa3Lchx9+2Oxrzp8/vxUVAWgLPp8pr6/hadyqgOvxVEo6uolMYWGh4uPj26pEAEA7CerlCgA6N5vNkN1m1Hvu2IAb0aWLUgcOJOACQCdByAUQtKIjHYqLDq9z/NiAGxISqnFjRqhnYlxblwgAaCeEXABBK8wRopSkGBk1JnPrC7jDTj1Fg1N71rsxBACgYyLkAghqyYlOpfaJk2E0HHCHn5ys5ERnO1cKAGhLTGsACGqOULuGpMQrLMSndz/cIZtx9EG0iC5dNG7MCA1O7VlnxzMAQMdHyAUQ9ByhdvXtHqWB3W06sU+KHGFhSh04UD0T41iiAACdFMsVAHQITqdT48eNVXbmLg0Z1E8nJCUQcAGgE+MnAIAOIzo6Wr/85S9lGPW3FQMAdB7M5AIIStnZ2dq1a1ed4wRcAIBEyAUQhLKzs7VgwQK9+uqr9QZdAAAIuQCCSlXALSsrk8fj0VtvvaXKysr2LgsAEGAIuQCCRs2AK0ldunTRxIkTFRoa2s6VAQACDSEXQFCoL+BOmTJFPXv2bOfKAACBiJALIOARcAEALUXIBRDQCLgAgONByAUQsAi4AIDjRcgFELAyMjIIuACA48KOZwAC1siRI+X1erVu3ToCLgCgRQi5AALa6NGjdcoppygyMrK9SwEABBGWKwAIGHl5efVu7EDABQC0FCEXQEDIzs7WCy+8oFdeeYUdzAAArUbIBdDuanZR2L17t9588832LgkAEOQIuQDaVX1twn7yk5+0c1UAgGBHyAXQbuiDCwDwF0IugHZBwAUA+BMhF0CbI+ACAPyNkAugTRFwAQBtgZALoM2Ypql33nmHgAsA8DtCLoA2YxiGrrnmGnXr1o2ACwDwK7b1BdCmnE6npk6dqpKSEvXo0aO9ywEAdFCEXAB+5Xa75XA4ah1zOp1yOp3tVBEAoDNguQIAv8nOztbMmTO1Y8eO9i4FANDJEHIB+EVVF4UjR47otdde086dO9u7JABAJ0LIBWC5Y9uEORwOlicAANoUIReApeiDCwAIBIRcAJYh4AIAAgUhF4AlCLgAgEBCCzEgQFW4PSouccvnM2WzGYqOdCjMEZhfsgRcAECgCcyfmEAn5q70KjPXpfSsIhUUl8vrM2W3GYqLDldKUoySE51yhNrbu8xqpaWlBFwAQMBhuQIQQNyVXm1Nz9eGbw4qv7BMXp8pSfL6TOUXlmnDNwe1NT1f7kpvO1f6o4iICJ1zzjmSCLgAgMDBTC4QQDJzXdqZUSDTrP+8aUo7MwoU6wxTSu/YNq2tMSNHjpTdblfv3r0JuACAgEDIBQJEhduj9KyiBgNuFdOU0rOKlJQQFVBrdE8//fT2LgEAgGosVwACRHGJWwXF5c0aW+AqV3GJ288V1S87O1tLliyR290+9wcAoDkCZxoI6OR8PrN6DW5TvF5TvmaOtVLNLgplZWWaOHGiHA5Hm9cBAEBTmMkFAoTNZshuM5o11m43ZGvmWKsc2yYsJydHhYWFbVoDAADNRcgFAkR0pENx0eHNGhvnDFd0ZNvNoDbUBzcxMbHNagAAoCUIuUCACHOEKCUpRkYTE7SGIaUkxbTZQ2ds9AAACEaEXCCAJCc6ldonrsGgaxhSap84JSc626QeAi4AIFjx4BkQQByhdg1JiVesM+zojmeucnm9pux2Q3HOtt3xjIALAAhmhFwgwDhC7UrpHaukhCgVl7jl85my2QxFRzrabInCwYMHCbgAgKBGyAUCVJgjRAnttNmD0+mU0+lUWVkZARcAEJRYkwugjsjISN1www3q27cvARcAEJSYyQVQr8jISE2dOlVGU+0eAAAIQMzkAlB2dra+/fbbOscJuACAYEXIBTq5qi4KS5cu1fbt29u7HAAALEHIBTqxmm3CfD6fVqxYIbfb3d5lAQDQaoRcoJOqrw/uL37xCzkcbbddMAAA/kLIBTohNnoAAHR0hFygkyHgAgA6A0Iu0IkQcAEAnQUhF+gkCLgAgM6EkAt0EgcPHiTgAgA6DXY8A4JYhduj4hK3fD5TNpuh6EiHwhz1f1kPGzZMXq9Xq1atIuACADo8Qi4QhNyVXmXmupSeVaSC4nJ5fabsNkNx0eFKSYpRcqJTjlB7ndedfvrpOvnkk9WlS5d2qBoAgLZDyAWCjLvSq63p+dqZUSDT/PG412cqv7BMh4rKVOiqUJ/4UEU7IxUWFlbr9QRcAEBnQMgFgkxmrqtOwK3JNKWN32QqM6JMpQWZ+vnPf14n6AIA0NHx4BkQRCrcHqVnFTUYcCXJ5XJp09eb9e2ePPlMm9544422KxAAgADBTC4QRIpL3CooLm/wvMvl0ubNW+TxVGr/wcM6NTVNp548oA0rBAAgMBBygSDi85ny+uqfxq0ZcCXJZxoalDZIPXsmtGWJAAAEBJYrAEHEZjNktxl1jh8bcENCQnXa8FMVFRXZ1iUCABAQCLlAEImOdCguOrzWsfoC7imnDFVyr3hFRzrao0wAANodIRcIImGOEKUkxcj4YTK3oYAbHe1USlJMgxtDAADQ0RFygSCTnOhUap84GYaUnr673oCb2idOyYnOdq4UAID2wzQPEGQcoXYNSYlXrDNMMZEhWvvJFyp2leq04acquVd8ozueAQDQWRBygSDkCLUrpXeskhKidGpqotxutyIjIhQd6WCJAgAAIuQCQaWioqLW7mVhjhD1Sohux4oAAAhMrMkFgkR2draefvppbd26tb1LAQAg4BFygSCQnZ2tBQsWqLS0VMuWLdM333zT3iUBABDQCLlAgKsKuGVlZZKk8PBwde3atZ2rAgAgsBFygQB2bMDt0qWLpkyZop49e7ZzZQAABDZCLhCgCLgAABw/Qi4QgAi4AAC0DiEXCDAEXAAAWo+QCwSQ8vJyLVy4kIALAEArEXKBABIeHq7zzz9fhmEQcAEAaAV2PAMCzLBhw2Sz2ZSYmEjABQDgOBFygQB0yimntHcJAAAENZYrAO0oOztbixYtUnl5eXuXAgBAh0LIBdpJVReFXbt2aeHChQRdAAAsRMgF2sGxbcIOHz6soqKidq4KAICOg5ALtLGG+uB27969nSsDAKDjIOQCbYiNHgAAaBuEXKCNEHABAGg7hFygDRBwAQBoW4RcwM9ycnIIuAAAtDFCLuBn0dHRiouLk0TABQCgrRByAT/r0qWLJk+erJSUFAIuAABthG19gTZQFXQBAEDb6BAh99NPP9Xs2bO1Y8cOVVZW6uSTT9b06dN19tlnN/saa9as0YIFC7R161aVlpYqISFBY8aM0e23364ePXr4sXp0NNnZ2crLy9PQoUPbuxQAADqtoF+usGzZMk2bNk2bNm3S0KFDNWzYMG3atEnTp0/XkiVLmnWN559/XjfffLM+/fRT9evXrzocL1myRFdeeaXS09P9+RbQgVR1UfjPf/6jzZs3t3c5AAB0WkE9k5uTk6NHHnlETqdTL7/8slJTUyVJW7Zs0bRp0/TYY4/pnHPOaXQnqV27dumpp55SRESEXnjhBQ0bNkySVFlZqT//+c96+eWX9fvf/77ZgRmd17Ftwt5//32lpaUpPDy8nSsDAKDzCeqZ3MWLF8vtdmvq1KnVAVeShg4dqunTp6uioqLJcPrWW2/J5/Np2rRp1QFXkkJDQ/X73/9eXbt21ddff639+/f77X0g+NXXB3fy5MkEXAAA2klQh9x169ZJks4777w656qOrV27ttFrhIaGKi0tTWeccUa955KSkiRJubm5rS0XHRQbPQAAEHiCdrmCaZratWuXbDab+vfvX+f8CSecIJvNpl27dsk0TRmGUe917rrrLt111131nistLdWuXbskiYfPUC8CLgAAgSloZ3KLiorkdrsVGxsrh8NR53xISIji4uJUVlamkpKS47rHnDlzVFpaqiFDhhBaUAcBFwCAwBW0IbdmsGhI1XrI4wm5a9as0b///W/ZbDb99re/Pb4i0WERcAEACGxBG3JttuaXbppmi6790Ucf6c4775TX69Xdd9+tkSNHtrQ8dHCHDh1SeXm5JAIuAACBKGjX5EZEREiSKioqGhxTFUKqxjbH66+/rkceeUQej0d33HGHbr755tYVig5p8ODB8vl8eu+99zR58mQCLgAAASZoQ25UVJQiIiJUUFAgj8ejkJDab8Xj8aigoEBhYWGKjo5u1jWfeuopzZ49W4Zh6IEHHtDUqVP9UDk6iqFDhyotLU1hYWHtXQoAADhG0C5XMAxDAwYMkNfr1d69e+uc37Nnj3w+X63+uQ0xTVMPPvigZs+eLYfDoX/84x8EXNSSk5NTvf62JgIuAACBKWhDriSNGTNGkrRy5co656qOjR07tsnrPP7443r99dcVFRWlefPm6aKLLrK2UAS17OxszZ8/v9aDZgAAILAFdcidMGGCwsLCNGfOHG3btq36+NatWzV37lyFh4dr4sSJ1cczMjKUnp4ul8tVfWzt2rWaP3++QkJC9O9//1sjRoxo0/eAwFazi0J2draWLl3a3iUBAIBmCNo1uZKUlJSk+++/XzNmzNB1111X3QVh/fr18ng8euKJJ9StW7fq8VOnTtX+/fv1l7/8RRMmTJAkzZw5U5LUrVs3vfrqq3r11Vfrvddtt92mlJQUP78jBJL62oSdf/757VwVAABojqAOuZI0adIk9erVS3PnztXGjRvlcDg0fPhw3XbbbRo1alSjry0sLNTWrVslHV1zuXz58gbHXn311YTcToQ+uAAABLegD7mSNG7cOI0bN67JcR9++GGtf4+NjdWOHTv8VRaCFAEXAIDgF9RrcgGrEXABAOgYCLnADwi4AAB0HIRc4AerV68m4AIA0EEQcoEf/PznP1dycjIBFwCADqBDPHgGWCEsLEy/+MUvVFRUpMTExPYuBwAAtAIzuei0Gtqml4ALAEDwI+SiU8rOztYzzzyjr776qr1LAQAAfkDIRadT1UWhtLRUy5cv16ZNm9q7JAAAYDFCLjqV+tqE9ejRo52rAgAAViPkotOgDy4AAJ0HIRedAgEXAIDOhZCLDo+ACwBA50PIRYdGwAUAoHMi5KLDcrvdWrRoEQEXAIBOiJCLgFXh9iivoFQ5h0qUV1CqCrenRa93OBy6+OKLZbPZCLgAAHQybOuLgOOu9Coz16X0rCIVFJfL6zNltxmKiw5XSlKMkhOdcoTam3Wtk046SVdffbViY2MJuAAAdCKEXAQUd6VXW9PztTOjQKb543Gvz1R+YZkOFZWp0FWhISnx9QZd0zRlGEatYyeeeGKtf69we1Rc4pbPZ8pmMxQd6VCYgy8FAAA6En6yI6Bk5rrqBNyaTFPamVGgWGeYUnrH1jqXnZ2t999/X1dffbUiIyPrvNbKGWIAABDYWJOLgFHh9ig9q6jBgFvFNKX0rKJaa3Sruijs3btXL730kkpKSmq9pmqGeMM3B5VfWCav7+hNqmaIN3xzUFvT8+Wu9Fr+vgAAQNsj5CJgFJe4VVBc3qyxBa5yFZe4JdVtE+ZyueRyuWqNb+4McWauq/4BAAAgqBByETB8PrN6hrUpXq8pn89ssA9ujx49qse2ZoYYAAAEJ0IuAobNZshuM5oeKMluN1RWVtqsjR6Od4YYAAAEL0IuAkZ0pENx0eHNGhtu92nDZ+uatdHD8cwQAwCA4EZ3BQSMMEeIUpJidKioTKYphTvsio0Kk8NhlyFDpkxVuL3Kzi2Q6StWxr7dkpreyaxqhrg5QdduN2Rr5mwyAAAIXIRcBJTkRKcKXRUqOlIhr9fUjowCZeeXqNLjU2iITQmxYUpNipCt3KeSkpJm7WRWNUOcX1jW5P3jnOGKjnRY+ZYAAEA7YLkCAooj1K4TT+gqm2FowzcHlZnrUqXHJ8MmhYbYJMOmL7buUWZ+hU48aXCztuqtmiE2mpigNQwpJSmGjSEAAOgA+GmOgJN9qETZh0qU3MOpCre3ehezMIddIXabYqP6aseO73TdJaObvVVv1QxxQ23EDENK7ROn5ESnxe8GAAC0B0IuAkrNdl8hdptCutT9ZYPdbtdJJ52sA4fdGuj2NGvm1RFq15CUeMU6w47ueOYql9drym43FOdkxzMAADoaQi4CyrHtvqo2dujVq1edsVXtvhKaubzAEWpXSu9YJSVEqbjELZ/PlM1mKDrSwRIFAAA6GH6yI6DUbPflcrm0efMWeTyVMk1TvXv3rjX2eNt9hTlCmh2MAQBAcOInPdpEhdvTrNnTqnZfhUXF1QFXkvbs2avExESFhoZWj6XdFwAAaAghF37lrvQqM9d1dB1scbm8PlN2m6G46PrXwUZHOhQe4qsVcENCQnXKKUNrBVyJdl8AAKBhhFz4jbvSq63p+XU6Gnh9pvILy3SoqEyFrgoNSYmvDrqFBYdklufL9Hkl/Rhwnc7aXQ9o9wUAABpDn1z4TWauq8GWXZJkmtLOjAJl5rokSdnZ2Zo/f75ch7N0xtAT5HA4Ggy4tPsCAACNYRoMflGzFVhjTFNKzypSZKhHCxYsUFlZmXbv2qkTTxqsW3/xMx047KbdFwAAaDFCLvzi2FZgjck8kC+vK0NlZUe33Q0PD9eYnxzd6GFgMx9YAwAAqIm0AL+o2QqsKSWlZZLzaJeELl261Nqql3ZfAADgeLAmF35R1QqsObp3T1BK/36KioqqFXABAACOF1Nk8IvoSIfiosOVX1jW5Ng4Z7j6JnXXXXfdJYeDlmAAAKD1CLnwizBHiFKSYnSoqKzOw2dHjhyRw+GQw+GwtBVYczecAAAAHR8JAH6TnOhUoauiVhuxqq16HQ6Hhg07RYMHdG91K7CWbjgBAAA6PkIu/MYRateQlHjFOsOUnlWkzAP52rx5i2yGqe7dItU9skKD+3drVQA9ng0nAABAx0fIhV85Qu1K6R2ryFCPvK4M9Tw7VT6vR6WuAg07qU+rlxM0d8OJWGeYUnrHtupeAAAgeBBy4XfZ2dnVGz1IP7YJ6969e6uu29INJ5ISolijCwBAJ0ELMfhVQwHXijZhLdlwosBVruISd6vvCQAAggMhF37jz4ArtWzDCa/XlK+ZYwEAQPAj5MIv/B1wpZZtOGG3G7I1cywAAAh+hFz4xbp16/wacKUfN5xojjhnuKIj2WgCAIDOgpALv7jyyivVv39/vwVc6ccNJ4wmJmit3HACAAAEB37qwy9CQ0N1/fXXq7CwUAkJCX67T30bTtRkGFJqn7hWbzgBAACCCyEXligpKVFkZGStY6GhoX4NuFLdDScKXOXyek3Z7YbinOx4BgBAZ0XIRatVPWR29tlna9SoUW1+/6oNJ5ISolRc4pbPZ8pmMxQd6WCJAgAAnRQJAK1Ss4vC+++/L7vdrhEjRrRLLWGOECUQagEAgHjwDK1QX5uw5OTkdq4KAACAkIvj1BZ9cAEAAI4Xv9tFix1PwK1we1gvCwAA2gwpAy3S0oDrrvQqM9d1tPNBcbm8PlN2m6G4aDofAAAA/yHkotmOJ+BuTc+v08PW6zOVX1imQ0VlKnRVaEhKPEEXAABYijW5aJbKykq9/PLLLVqikJnranCTBkkyTWlnRoEyc13+KBkAAHRihFw0S2hoqC677DLZ7fZmr8FNzypqMOBWMU0pPatIFW6PxRUDAIDOjOUKaLaBAwfq+uuvV2RkZJNdFIpL3CooLm/WdQtc5SoucdPjFgAAWIZUgQaZpinDMGodGzBgQLNe6/OZ8vqamMb9gddrytfMsQAAAM3BcgXUKzs7W3PnzlVxcfFxvd5mM2S3GU0PlGS3G7I1cywAAEBzEHJRR1UXhf3792v+/PnHFXSjIx2Kiw5v1tg4Z7iiIx0tvgcAAEBDWK7QiVW4PSoorlB5pUeGpIjwUJneMr3xxhvVXRTKyspUWlqq6OjoFl07zBGilKQYHSoqa/ThM8OQUpJi2BgCAABYimTRCbkrvdqXXazNu/L0fUahXKVuhdhtSogNU58Eh4aefrb2pX+jA1kZmjJlinr06HFc90lOdKrQVdFgGzHDkFL7xCk50dnKdwQAAFAbIbeTcVd6tfn7PK37er/yCsukH8LnkdIype/ep/XyadSpfZSafKJ+ev756p4Yf9z3coTaNSQlXrHOsKM7nrnK5fWastsNxTnZ8QwAAPgPIbeTycx16YvtB2sF3Ap3hXIO5srn80qS1m/Zr/59zlCZN7TV93OE2pXSO1ZJCVEqLnHL5zNlsxmKjnSwRAEAAPgNKaMTqXB79H1GgQ4VlTcYcG02u+ITE7T34BF1i4tUz26RloTRMEcIfXABAECbobtCJ1Jc4taB/BKVVRzdXcxd6a4TcLv3SFSYI0zZ+SWq9HhVXOJuz5IBAACOCyG3E/H5THk8vuqHwEJDQhUWdrR1V82AK0mVHp9kGmzSAAAAghIhtxOx2QyFhNhUtYmZYRhKTExUZGRUrYArSaEhNskw2aQBAAAEJUJuJxId6VCv+Eh1CftxbaxhGEpIiK8VcCWpZ3ykQkPsbNIAAACCEiG3EyksOKS4CJ+6xYRLjUzQ2myGBibHKjGuCx0QAABAUCLkdhLZ2dmaP3++dm7boNTeXZQQ26XeoGuzGRqelqjeiVHqFR/V9oUCAABYgGm6TiA7O1sLFixQWVmZdu/aqTCHQxeNHqttuw9rV2ahikvdCrHZ1CshUif3j1ffnk716BrJJg0AACBoEXI7uJoBV5LCw8M15iej1bNnvPr1ilVBcYXKKz0yJEWEh7JJAwAA6BBIMx3YsQG3S5cumjJlinr27Cnp6AYNPeL5TwAAAHQ8JJwOpMLtqd46t6ysVBs++0x2+9ElB8cGXAAAgI6MkNsBuCu9ysx1KT2rSAXF5SosKtb27d+oR3y0UgedLm/ZoR+WKBBwAQBA50DIDXLuSq+2pudrZ0aBTFNyuVzavHmLPJ5K7cks1/6cQl13xXh1i09s71IBAADaDC3Eglxmrqs64EqSx+ORz+eVJIWEhGrIkCHKzCtXZq6rHasEAABoW4TcIFbh9ig9q6g64EpSXFycBg8eLIcjTKecMlROp1OmKaVnFanC7Wm/YgEAANoQyxWCWHGJWwXF5XWOd+3aVWeeOVI2249/hylwlau4xK0E2oMBAIBOgJncIObzmSosKlZ5ed2gWzPgSpLXa8rnM+uMAwAA6IgIuUGsrKxU27d/o6+/3lxv0K3Jbjdks9Wzjy8AAEAHRMgNUtnZ2drw2Tr1iI9WeXmZvvnmG5lmwzO1cc5wRUc62rBCAACA9kPIDUJVO5ll7Nut1D5d5XA4lJqaKsOof6bWMKSUpBi26wUAAJ0GqSfIHLtVr7fskK67Yrwy88pV30SuYUipfeKUnOhs40oBAADaDyE3iBwbcLt06aIxPxmtbvGJ6lm145mrXF6vKbvdUJwzXClJMUpOdMoRam/n6gEAANoOITdI1Bdwp0yZUr1Vb0rvWCUlRKm4xC2fz5TNZig60sESBQAA0CmRgIJAUwG3SpgjhD64AAAA4sGzoPD55583GXABAADwI6b9gsBll12m8vJyZWRkEHABAACagZAbBOx2u6655hoVFBQoPj6+vcsBAAAIeCxXCEAul6vOMbvdTsAFAABoJkJugMnOzta//vUvffTRR+1dCgAAQNAi5AaQml0UPvroI3388cftXRIAAEBQ6hBrcj/99FPNnj1bO3bsUGVlpU4++WRNnz5dZ599drOvsWfPHj3zzDP66quvVFhYqD59+uiaa67RpEmTZLP5/+8C9bUJS0lJ8ft9AQAAOqKgn8ldtmyZpk2bpk2bNmno0KEaNmyYNm3apOnTp2vJkiXNusZ3332nq666SitWrFCvXr00ZswYHTx4UP/v//0/3XfffX5+B83vgwsAAIDmCeqZ3JycHD3yyCNyOp16+eWXlZqaKknasmWLpk2bpscee0znnHOOunfv3uA1TNPUfffdpyNHjujJJ5/U5ZdfLkk6fPiwpk6dquXLl+v888/XBRdc4Jf3UFFRQcAFAACwWFDP5C5evFhut1tTp06tDriSNHToUE2fPl0VFRVNzuZ+8skn2rFjh0aMGFEdcCWpa9euevTRRyVJCxcu9Ev90tGgTsAFAACwVlCH3HXr1kmSzjvvvDrnqo6tXbv2uK8xfPhwdevWTV999ZWOHDnS2nLr5fP5JBFwAQAArBS0Idc0Te3atUs2m039+/evc/6EE06QzWbTrl27ZJpmg9fZtWuXJNWaCa6pX79+8vl8Sk9Pt6bwehBwAQAArBW0a3KLiorkdrvVtWtXORyOOudDQkIUFxenQ4cOqaSkRFFRUfVeJzc3V5KUkJBQ7/mq4/n5+RZVftS+ffskSQUFBfrqq6+0bds2S68PAAAQCL799ltJP2afthK0IbfmOtaGhIeHS1KjIbfqOlVjG7pGaWnpcddan6rrVVZWavPmzZZeGwAAINBYnaWaErQhtyW9axtbrmC32yVJhmE0eo2qtbNWSUpKUlZWliIiItS3b19Lrw0AABAo9u3bp9LSUiUlJbXpfYM25EZEREg62oKrIeXl5bXG1qdqJrhqbEPXiIyMPK46G/Lmm29aej0AAAD8KGgfPIuKilJERIQKCgrk8XjqnPd4PCooKFBYWJiio6MbvE5iYqKkhtfc5uXlSWp4zS4AAAACT9CGXMMwNGDAAHm9Xu3du7fO+T179sjn8zXYNaHKwIEDJf3YZaEm0zS1e/du2e12ttgFAAAIIkEbciVpzJgxkqSVK1fWOVd1bOzYsc26xqpVq+qc27hxow4fPqzTTjutwQfXAAAAEHiCOuROmDBBYWFhmjNnTq0WXFu3btXcuXMVHh6uiRMnVh/PyMhQenq6XC5X9bERI0Zo4MCB+uSTT/Taa69VHz98+LD++Mc/SpKmTZvWBu8GAAAAVjHMxloPBIHFixdrxowZCg0N1ciRIyVJ69evl8fj0RNPPFFrq97x48dr//79+stf/qIJEyZUH9+yZYtuuOEGlZaW6pRTTlFiYqI2bNigoqIiXXPNNfrTn/7U5u8LAAAAxy9ouytUmTRpknr16qW5c+dq48aNcjgcGj58uG677TaNGjWqWdcYOnSoli5dqpkzZ2r9+vX6/vvv1bdvX/3mN7/R1Vdf7ed3AAAAAKsF/UwuAAAAcKygXpMLAAAA1IeQCwAAgA6HkAsAAIAOh5ALAACADoeQCwAAgA6HkAsAAIAOh5ALAACADoeQCwAAgA6HkAsAAIAOh5ALAACADoeQa7FPP/1UU6ZM0ciRIzV8+HBNnjxZa9eubdE19uzZo9/85jcaO3asTjnlFF166aVauHChfD6fn6rGsaz4HNesWaObbrpJI0aM0ODBgzVu3Dg9/PDDOnjwoJ+qRn2s+CyP9ctf/lJpaWlav369RVWiKVZ8jiUlJZo5c6Z+9rOfaciQIRoxYoRuvfVWbd261U9Voz5WfJZff/21brnllurvr+edd57+/Oc/q6ioyE9VozHLli1TWlqavvzyyxa9LicnRw8//LDOPfdcDR06VBdccIFmzZolt9ttSV2GaZqmJVeCli1bpgceeEAOh0NnnnmmfD6f1q9fr8rKSs2YMUPXXnttk9f47rvvNGnSJB05ckTDhw9Xt27dtH79ehUXF+vSSy/V3/72tzZ4J52bFZ/j888/r7///e+y2WwaOnSounXrpm+//VYHDhxQ165dtWjRIqWkpLTBu+ncrPgsj/Xyyy/rj3/8oyRpwYIFGjlypNVl4xhWfI6FhYWaMmWKduzYoe7du2vo0KHKyMjQjh075HA4tHjxYg0dOrQN3k3nZsVnuXLlSv3617+Wx+PRKaecovj4eG3dulW5ubnq27evXn31VXXt2rUN3g0kadOmTbrxxhtVWlqqxYsX6/TTT2/W6w4ePKhrr71WBw8e1EknnaTk5GRt3LhReXl5GjFihF544QWFhoa2rjgTljh48KA5ePBg87TTTjN37NhRfXzz5s3m8OHDzSFDhpgHDx5s9Bo+n8+89NJLzdTUVPPNN9+sPn7o0KHq4++9957f3gOs+Ry///57c9CgQeapp55qbty4sfq42+02H330UTM1NdW85ppr/PYecJQVn+Wx9u7da5566qlmamqqmZqaan7++edWl41jWPU53nfffWZqaqp59913mxUVFdXH586da6amppqXXnqpX+rHj6z4LCsrK83Ro0ebgwYNMt9///3q4+Xl5eYtt9xipqammjNmzPDbe0Bt7733njls2LDq74lffPFFs19b9XnNmjWr+lhJSYk5depUMzU11Zw3b16r62O5gkUWL14st9utqVOnKjU1tfr40KFDNX36dFVUVGjJkiWNXuOTTz7Rjh07NGLECF1++eXVx7t27apHH31UkrRw4UK/1I+jrPgc33rrLfl8Pk2bNk3Dhg2rPh4aGqrf//736tq1q77++mvt37/fb+8D1nyWNXm9Xt1///0KDQ2tdT34lxWf44EDB/TWW28pOTlZjz/+uBwOR/W5m266SSeffLLKysp0+PBhv70PWPNZ7tixQ/n5+Ro0aJB++tOfVh8PCwvT7bffLkn64osv/PMGUO3gwYO67777dNddd8nn8yk+Pr5Fr9+9e7c++ugj9enTR7feemv18YiICD322GOy2+1atGhRq+sk5Fpk3bp1kqTzzjuvzrmqY02tOWrsGlVLF7766isdOXKkteWiAVZ8jqGhoUpLS9MZZ5xR77mkpCRJUm5ubmvLRSOs+Cxrmjt3rjZt2qSHHnpI3bp1s6ZINMmKz/GDDz6QaZqaNGlSrYBbZdmyZfrf//7Hr7j9zIrP0mY7GlsOHTokj8dT61xBQYEkKSYmptW1onH//Oc/9dZbb2nw4MFasmSJ+vfv36LXf/zxxzJNU+PGjav+TKv06tVLJ510kvbv369du3a1qk5CrgVM09SuXbtks9nq/aBPOOEE2Ww27dq1S2YjS6CrPsyGZon69esnn8+n9PR0awpHLVZ9jnfddZfefvttjRo1qs650tLS6s+5R48e1hWPWqz6LKt89913euaZZ3TBBRfo0ksv9UfJqIdVn+P27dslSUOGDFFJSYmWLl2qRx55RH/605+0cuXKZv03gNax6rMcMGCAevbsqZycHN13333KyMhQWVmZPvvsM/3xj3+UzWbTtGnT/PlWIKl///564okntHTpUqWlpbX49VU/BwcOHNjg9SVp586dx1+kpJBWvRqSpKKiIrndbnXt2rXeWYKQkBDFxcXp0KFDKikpUVRUVL3XqZrZS0hIqPd81fH8/HyLKkdNVn2OjZkzZ45KS0s1ZMgQ9ezZ04qyUQ8rP0u3263f/va3io6Orl42hLZh1eeYkZEh6ejDZ5deemmtpUKLFi3SqFGj9Oyzzx7X1zSax6rPMjQ0VDNnztQdd9yhFStWaMWKFdXnEhMTNXfuXJ111ll+ex846uabb27V66vyTmJiYr3nrco7zORaoKysTJLUpUuXBseEh4dLOtrCpqnrVI1t6BqlpaXHVScaZ9Xn2JA1a9bo3//+t2w2m377298eX5FoFis/y6efflo7d+7UjBkz+HV2G7Pqc3S5XJKkBx54QLGxsXr11Vf11Vdf6eWXX1ZaWpo+++wzPfLIIxZWjmNZ+TXZp08fXXrppbLb7Ro6dKjGjRunhIQE5ebmau7cuSosLLSsbvhHW+UdQq4Fjl1P0pjGfg1jt9slSYZhNHoN+uX6h1WfY30++ugj3XnnnfJ6vbr77rtpO+VnVn2WX331lV544QVddtll9a4jhH9Z9TlW9dwMDQ3V/PnzNWzYMEVFRem0007TvHnzFBkZqRUrVmjPnj2trhn1s+qzLCgo0PXXX69XXnlFL774opYuXarZs2frww8/1NVXX61PP/1Ud9xxhxUlw4/aKu8Qci0QEREhSaqoqGhwTHl5ea2x9an6G27V2IauERkZeVx1onFWfY7Hev311/WrX/1KFRUVuuOOO1r9ax40zYrPsrS0VL/73e+UkJCghx56yPoi0SSrviarZoUuueQSRUdH1zqXkJCg8ePHyzRNbdiwobUlowFWfZbz5s3T7t27dfvtt9eaLHA4HHrkkUfUr18/ffHFFy3elABtq7l5pyU/a+vDmlwLREVFKSIiQgUFBfJ4PAoJqf3H6vF4VFBQoLCwsDrfYGtKTEzUt99+q/z8/Ho3CsjLy5PU8JpdtI5Vn2NNTz31lGbPni3DMPTAAw9o6tSpfqgcx7Lis3zllVeUkZGhtLQ0zZgxo9a5qocmZs+eraVLl+q6665rdgN0NJ9VX5NVy0x69+5d7/mq41VP58N6Vn2WVX8RqW/dbWhoqEaPHq09e/Zo+/btfE0GsKq1uA2tua3KOw2t2W0uZnItYBiGBgwYIK/Xq71799Y5v2fPHvl8viZ7a1Y9ZVhfywzTNLV7927Z7XZ2yvITqz5H6ejn9eCDD2r27NlyOBz6xz/+QcBtQ1Z8llVrwXbs2KHly5fX+l/VN+BPP/1Uy5cvr36wCday6muy6nxDbfuqPk9aw/mPVZ9lcXGxpB9/3X2squOVlZWtKxh+1VjekVTdRaq1PckJuRYZM2aMpKPbDR6r6tjYsWObdY1Vq1bVObdx40YdPnxYp512Gk8A+5EVn6MkPf7443r99dcVFRWlefPm6aKLLrK2UDSptZ/lnXfeqR07dtT7v6r2cAsWLNCOHTs0YcIEP7wDSNZ8TZ599tnV44/trep2u7V+/XpJ0mmnndbqetEwKz7LqtZSa9asqXPO6/Xq888/lyQNGjSoVbXCv6r+W1i9enWddbcHDhzQt99+q969e2vAgAGtug8h1yITJkxQWFiY5syZo23btlUf37p1q+bOnavw8HBNnDix+nhGRobS09Orn/qVpBEjRmjgwIH65JNP9Nprr1UfP3z4sP74xz9KEv3//MyKz3Ht2rWaP3++QkJC9O9//1sjRoxo0/eAo6z4LNH+rPgcR48erUGDBmnv3r3685//LK/XK+noQy1PPvmksrKydNZZZ7W4oT1axorP8tprr5V0dKnQV199VX3c4/HoySef1M6dOzVw4ECdeeaZbfCO0BwHDhxQenp6rR0Fk5OTNWbMGO3evVtPP/109fHS0lL94Q9/kNfrtSTvGCZdsC2zePFizZgxQ6GhodUL4tevXy+Px6Mnnnii1la948eP1/79+/WXv/yl1izQli1bdMMNN6i0tFSnnHKKEhMTtWHDBhUVFemaa67Rn/70pzZ/X51Naz/Hq666Slu3blX37t0bDbi33XYbS0/8zIqvyfpMnTpVn332mRYsWECnjDZgxeeYnp6uG264QXl5eerdu7dOPPFE7dy5UxkZGerZs6cWLVpUvRsh/MeKz/Lvf/+7nn/+eRmGoVNPPVVdu3bVt99+qwMHDig+Pl4vvfRSq2cA0TKTJ0/Whg0btHjx4jproavO3XHHHbrzzjurj2dmZur6669XXl6eUlNT1a9fP23cuFF5eXk6++yz9dxzz9VZu91SPHhmoUmTJqlXr16aO3euNm7cKIfDoeHDh+u2226rd/er+gwdOlRLly7VzJkztX79en3//ffq27evfvOb3+jqq6/28zuA1LrPsbCwUFu3bpUk5eTkaPny5Q2Ovfrqqwm5fmbF1yTanxWfY0pKit58883qdlNr1qxRQkKCJk2apNtuu40HetuIFZ/lPffco+HDh2vhwoXaunWrtm3bpsTERP3iF7/QLbfc0uqHldA2kpOTq/PO2rVrtW/fPiUnJ2vKlCm64YYbWh1wJWZyAQAA0AGxJhcAAAAdDiEXAAAAHQ4hFwAAAB0OIRcAAAAdDiEXAAAAHQ4hFwAAAB0OIRcAAAAdDiEXAAAAHQ4hFwAAAB0OIRcAAAAdDiEXAAAAHQ4hF0BAycrKUlpamtLS0pSVldXk+PHjxystLU3Lli1rg+paZuTIkUpLS9P69etb9Lr09HQ/VXR81q9fr7S0NI0cObK9S6njmWeeUVpamu666y7Lrhlof/4Ajg8hFwACxN69ezV16lQ9/fTT7V1Kp5SXl6f/+7//03333dfepQCwACEXAALE8uXL9dlnn7V3GXUMHTpU77zzjl577bX2LsWv1q1bp3fffVemabZ3KQAsENLeBQAAAluXLl2UkpLS3mUAQIswkwsAAIAOh5lcAB3SkSNH9MILL+h///uf9u7dK7vdrp49e+onP/mJfvnLX6p79+71vmb+/Pl6//33lZGRIbvdrtTUVE2YMEE///nPZbfb67zG7Xbr5Zdf1htvvKHMzEw5nU5deOGF+vWvf92ietPS0qr/+f3331daWppGjBihhQsXVh/Pzc3VCy+8oNWrV+vAgQNyOBxKTU3VlVdeqQkTJigkpPnf0g8dOqTnn39eH330kfbv3y+Hw6Hk5GSNGzdOU6dOVWxsbPXY9evXa8qUKYqNja31EN348eO1f/9+ffHFF/r44481f/587dy5U2FhYRoxYoTuvfde9e3bV5mZmfrnP/+pTz75RCUlJerXr59uuukmXX755bVqqrre7NmzNW7cuDo1jxw5UoWFhVqwYEGzHoLLzMzUggUL9Pnnn2v//v1yu92KjY3VKaecosmTJ+vMM8+sc29J+uabb5SWlqbevXvrww8/rB5T9VkvX75cu3fvlmma6tevny655BL94he/UFhYWLP//AH4HyEXQIdTXl6uyZMna/v27QoLC9MJJ5wgm82mPXv2aMGCBfrvf/+r1157TcnJydWvycrK0o033qh9+/YpJCREJ5xwgnw+nzZt2qRNmzbpgw8+0L/+9S85HI7q15SWlurWW2/V+vXrZbPZNHDgQFVUVGjBggXasGGDKisrm13z8OHDlZ2drezsbMXGxqp///5KTU2tPr9p0ybdeuutKiwslMPh0MCBA1VSUqKNGzdq48aNWrFihf71r38pMjKyyXsdOnRIV111lQ4cOKDIyEgNGDBAHo9Hu3bt0nfffad33nlHS5cuVUxMTLNq/8c//qFXXnlF8fHx6tu3r9LT0/XBBx9o06ZNeuqpp3TbbbepoqJC/fr1U15ennbs2FH9cNexQdcqH3/8sX71q1+pvLxcTqdTffr0UUVFhTIzM7Vy5UqtWrVKf/vb33TJJZdIkgYPHqzQ0FDt3btXERERGjRokBISEqqvV1hYqOnTp2vLli2y2WxKTk5WeHi4duzYoe3bt2vFihWaN2+e4uLi/PJ+ALQcyxUAdDhvvPGGtm/frtNOO01r167V8uXL9dZbb2n16tU69dRTdfjwYT333HPV471er+68807t27dP48eP15o1a7RixQq9++67WrFihVJSUrRu3Tr99a9/rXWff/3rX1q/fr169+6tt99+W2+//bbef/99zZ8/X/v371dJSUmza37llVf085//XNLRGctXXnlFDz30kKSjAasq4F544YVat26dli1bpvfff19Lly5Vz5499fnnn+vRRx9t1r3mzp2rAwcO6MILL9THH3+sN998U//973/13nvvqW/fvtq3b58WLVrUotrvvfderV27Vm+99ZbeeustRUREKC8vT1OmTNHQoUO1Zs0avf3221q7dq3OP/98SdL8+fObfY+WcLvdeuCBB1ReXq6pU6fq008/1Ztvvql3331XH330kUaPHi3TNPWvf/2r+jUzZ87ULbfcIknq16+fXnnlFc2cObP6/O9+9ztt2bJFw4YN0/v/v727jamy/uM4/gaDAEkEtCDuRA83gssRxyk2w1EDZ2K6FY1WjJtlQtFWI8nZg0qDJ2Hmim5dZaU5mIEkJsUMcUYoYnJnB4MDqOBBCI7M5CDwf+Cu68+JAxwL0519X090183vujnXg8/14/v7XYcPU1ZWxoEDBygvL0er1dLQ0MCWLVtuyfUIIf4ZCblCCJvz+++/AxAbG2v2Z3dPT09ee+01oqOj8fHxUZf/+OOPNDY2EhgYyI4dO5gzZ466TqPRsGPHDuzt7dm7dy89PT0ADA4OqqUEubm5BAUFqftERUWxefPmabueb775hr6+PoKDg8nLyzO7pgceeID8/Hzs7OwoKSnh3LlzU7an0+kAiI+Px8XFRV3u5+dHVlYWMTExN9UjuXz5cp577jm1nGPBggXExMQA4OjoyLvvvouHhwcADg4OJCcnAzd+p1sxk0F9fT1Xr17lvvvuY9OmTWa973PmzOGFF14AoLW1lZGRkSnbq6ur48iRI7i7u5Ofn4+/v7+6ztvbm507d+Li4kJ5eTlnz56d9usRQvwzEnKFEDZHCSGfffYZpaWlDAwMqOsiIiL45JNP1KADUF5eDsCjjz5qsa4yODiY4OBghoaGqKqqAuDEiRNcu3YNLy8vi/Wha9aswcnJaVqup6KiAoCEhASLdbdhYWFERkYyOjrKzz//PGV7yv155513qKioYHBwUF0XGxvLhx9+yNNPP231+T388MPjlikvEYsWLRpX9qC8RAwPD/PXX39ZfRxrPfjgg9TU1FBWVmaxjtrZ2RmAkZERs2ufiPJ8REVFqWF9LE9PT6KiogA4evTovzl1IcQ0kppcIcQdxd7+5t69lZ7Asfs9+eST7Nu3j/b2dl5++WUcHByIiIhgxYoVxMTEoNFozNpQvnD1ww8/UFNTY/E4XV1dwI3eP4C2tjaACafWUmqBp6NnT6/XA7Bw4cIJtwkLC+PkyZPqtpNJTU2ltLSU1tZWNmzYgLOzM1qtlhUrVvDII4/g6+t7U+dnaRCfg4MDgMVQODao38o5aZ2cnGhoaKCxsZH29nba29vR6XTqbwhY1ZOrPB8nT54kMTHR4jbK1/nGti2EuL0k5Aoh7ihjez9NJtOU21+9ehX4f+8cgJubG4WFhXz00UccPHiQS5cuUV1dTXV1NXl5eURERLBt2zY17Co9vR0dHXR0dEx6vCtXrpj9O/a4fzdr1qwpz98aSm2vq6vrhNsoZQfW1AH7+flRXFxMfn4+ZWVl/Pnnn1RWVlJZWUlOTg7R0dFs3brVYni1ZLJ7YGdnZ1Ub0+3EiRPk5ubS0NBgdi4BAQHEx8dz4MABq9tSng+DwYDBYJh0W+W5EELcfhJyhRB3FHd3dxwcHBgaGqK3t5f58+dPuO3g4CBGoxHAbCQ83Ai62dnZZGdnc/bsWaqqqqisrKSqqora2lpSUlIoKyvD2dlZDWnvvfceq1atsuo8lQCrhGxLrl27ZlVbU3FxccFoNJqVXfydsm5sje1kvLy8eOutt3jjjTeoq6vjl19+4ejRo5w6dYqKigo2btzI/v37b0tInah319r7qdPpSE1NxWQyodVqefzxxwkJCWHBggW4urrS2tp6UyFXeT42bdpEWlqa1fsJIW4vqckVQtxR7Ozs1EFcTU1Nk27b1NTEyMiIOn2Xoru7m19//VUNoKGhoSQnJ7Nr1y6KiopwcnLCYDCoc74GBAQA0NLSMuGxamtr0el0atAKDAwEJh48NTIyYlXpgDWUYzU2Nk64jdJjqVzLZDo7Ozl+/Dijo6PY29uzePFiNm7cyJ49e/j888/VY1kziG06KfWzlnrwjUaj1SH3q6++wmQyERUVxe7du0lISGDx4sVqT7hSemIta56PxsZGmpqaJn0REUL8tyTkCiHuOMrI/K+//nrSkoV9+/YBsGzZMu655x51+bPPPktSUhJHjhwZt09QUJDZwCeAlStXAlBUVGRxIFJHRwfPPPMM8fHx1NbWAqDVanFzc6Onp0cdmDTWTz/9pPYyW0vpNf17aI6OjgagoKCA69evj9uvvr6e06dPA/DQQw9NegyTycSaNWtISUnht99+G7deq9Wq9bTK/fmvKL3jlupax36UYSrKRx1CQkIsDjwrLCxU/z/2GieqB1eej7KyMnp7e8etv3LlCsnJyaxbt45Dhw5ZfZ5CiFtLQq4Q4o6TkpLC3Llz0ev1pKWlqQN/FEajkby8PPbv38+MGTPIysoyW69M8J+bm0tdXZ26fGRkhC+//JLz58/j4uJCZGSkuv28efNoa2sjMzOT7u5udR+9Xk9GRgbXr19n4cKF6ih6BwcH0tPTAXj99dc5deqUus/p06etnrN2LOVDDhcvXjRbnpiYiIeHBzqdjqysLPr6+tR1Z86cITMzk9HRUVatWkV4ePikx3B0dCQ2NlY977GB0mQysX37doaGhvDx8Rk3QO9Wi4iIAG683Iz9zY8fP05ubq7V7cybNw+A0tJSdYAgQH9/Pzk5OXz//ffqsrEvNUqph8FgMHu5Wrp0KUuWLMFoNPL888+btXnp0iUyMjLo7+9n7ty5xMfHW32eQohbS2pyhRB3HFdXVz7++GNefPFFqqurWb16NT4+Pnh6ejIwMEBbWxvDw8M4Ozuzffv2ccFuw4YNHDt2jNraWp544gl8fX1xc3Ojs7OT3t5e7O3tefPNN9X5Zh0dHfnggw9IS0ujoqKClStXotFoGBoaQq/XMzw8jJeXl9nHAwCSkpKoq6vj4MGDJCYmqqHw3LlzBAQEMHv27HEBfTLKp33r6+uJi4sjJCSEnTt34uHhwfvvv096ejqHDh2ivLxc/eKZUhKxdOlStm7datVxsrOzqampobm5mcceeww/Pz9mzpxJR0cHRqORu+++m5ycnJv6TPB0SE5OpqSkhMuXL7N27Vo0Gg0DAwOcP3+eiIgIZs6cybFjx6ZsJyUlhZKSEgwGA6tXryYwMBA7Ozv0ej0mk4nQ0FC6urro6+vDYDCo9dxBQUHY2dnR3d1NXFwcXl5e7N27F4C8vDzS0tI4c+YMcXFxaDQa7O3taWlpYWhoCFdXVz799NNpmzZOCPHvSU+uEOKOFB4eTlFREVu2bEGr1TI8PExTUxP9/f2EhoaSkZHB4cOH1dKGsRwdHdm1axcvvfQSYWFh9Pb2otPpcHR0JD4+nsLCQtauXWu2j0ajobi4mPT0dObPn49er6e9vR1/f39SU1P57rvvuP/++832mTFjBnl5ebz99tssWrSIixcv0tPTw7p169izZ8+ksyFYsnz5cl555RW8vLy4cOECDQ0N6hRXkZGRlJSUkJSUhLe3N83NzfT19bFkyRJycnL44osvrJ7NYfbs2Xz77bekpqYSGBhIV1cXzc3NzJo1i4SEBEpKSli2bNlNnft08PX1pbCwkPXr1+Pu7s4ff/zBXXfdRWZmJrt377Y4h7ElyuwR69evx9vbG71eT2dnJ6GhoWzevJmCggK1rGNsSUtgYCDbtm3D39+f7u5uOjo6uHz5MnBjmrSCggJeffVVwsPDuXDhAi0tLdx777089dRTFBcXTzrFmxDiv2c3eisnKRRCCCGEEOI2kJ5cIYQQQghhcyTkCiGEEEIImyMhVwghhBBC2BwJuUIIIYQQwuZIyBVCCCGEEDZHQq4QQgghhLA5EnKFEEIIIYTNkZArhBBCCCFsjoRcIYQQQghhcyTkCiGEEEIImyMhVwghhBBC2BwJuUIIIYQQwuZIyBVCCCGEEDZHQq4QQgghhLA5EnKFEEIIIYTNkZArhBBCCCFsjoRcIYQQQghhc/4Hr1iJKrjGPp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 350,
       "width": 348
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "param_names=WSLS.param_labels\n",
    "param_bounds=WSLS.fit_bounds\n",
    "n_param = len(param_names)\n",
    "\n",
    "# Define colors\n",
    "colors = [f'C{i}' for i in range(n_param)]\n",
    "\n",
    "# Create fig and axes\n",
    "fig, axes = plt.subplots(ncols=n_param,\n",
    "                         figsize=(5, 5))\n",
    "\n",
    "for i in range(n_param):\n",
    "\n",
    "    # Select ax\n",
    "    if n_param > 1:\n",
    "        ax = axes[i]\n",
    "    else:\n",
    "        ax = axes\n",
    "    \n",
    "    # Get param name\n",
    "    p_name = param_names[i]\n",
    "    \n",
    "    # Set title\n",
    "    ax.set_title(p_name)\n",
    "\n",
    "    # Create scatter\n",
    "    sns.scatterplot(data=df[df[\"Parameter\"] == p_name], \n",
    "                    x=\"Used to simulate\", y=\"Recovered\", \n",
    "                    alpha=0.5, color=colors[i], \n",
    "                    ax=ax)\n",
    "\n",
    "    # Plot identity function\n",
    "    ax.plot(param_bounds[i], param_bounds[i], \n",
    "            linestyle=\"--\", alpha=0.5, color=\"black\", zorder=-10)\n",
    "    \n",
    "    # Set axes limits\n",
    "    ax.set_xlim(*param_bounds[i])\n",
    "    ax.set_ylim(*param_bounds[i])\n",
    "\n",
    "    # Square aspect\n",
    "    ax.set_aspect(1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting results\n",
    "\n",
    "The plot given above gives us a rough idea of how well the parameters recover. That is, if parameter recovery is sufficient, the points should lie along the line of equality ($x=y$). For a more systematic analysis, calculating some indicator of the correlation between generating and inferred parameters is necessary (e.g., the Pearson correlation coefficient)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other sanity checks\n",
    "\n",
    "In addition to parameter recovery, some sensible checks are presented in Wilson & Collins (2019). For instance, it is sensible to:\n",
    "\n",
    "- Simulate the candidate model across range of parameter values\n",
    "- Visualize simulated behavior of different candidate models\n",
    "- Run a model comparison\n",
    "- Make predictions with the candidate model\n",
    "\n",
    "But note that this list is not comprehensive and the work of a modeler is never complete!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h4><img src=\"img/logo/goal.png\" width=\"20\" align=\"left\"><a class=\"tocSkip\"></a>&nbsp;Checkpoint</h4> \n",
    "Which of these statements is true (one or more):   \n",
    "<br>a) If parameter recovery is insufficient, a model or the task needs to be reconstructed\n",
    "<br>b) If parameter recovery is sufficient, we can be fairly confident in our inference results  \n",
    "<br>c) Parameter recovery should always be verified before fitting a model to data\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "In this lecture, we have:\n",
    "\n",
    "- Looked at what **user models** are, as well as named some examples of user models and their **application contexts**.\n",
    "- Built understanding of the basic principles of **designing a user model** that describes **some phenomenon** taking place in **a given task**.\n",
    "- **Fitted a model** to some data.\n",
    "- Conducted a **parameter recovery study** and explained why its results matter for the **interpretation of a user model**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "P. De Bra, \"Challenges in User Modeling and Personalization,\" in IEEE Intelligent Systems, vol. 32, no. 5, pp. 76-80, September/October 2017, https://doi.org/10.1109/MIS.2017.3711638.\n",
    "\n",
    "Brusilovsky, P. Methods and techniques of adaptive hypermedia. User Model User-Adap Inter 6, 87–129 (1996). https://doi.org/10.1007/BF00143964\n",
    "\n",
    "Fischer, G. User Modeling in Human–Computer Interaction. User Modeling and User-Adapted Interaction 11, 65–86 (2001). https://doi.org/10.1023/A:1011145532042\n",
    "\n",
    "Heathcote A., Brown S., Wagenmakers EJ. (2015) An Introduction to Good Practices in Cognitive Modeling. In: Forstmann B., Wagenmakers EJ. (eds) An Introduction to Model-Based Cognitive Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2236-9_2\n",
    "\n",
    "Knutov, E. (2012). Generic adaptation framework for unifying adaptive web-based systems. Technische Universiteit Eindhoven. https://doi.org/10.6100/IR732111\n",
    "\n",
    "Kobsa, A. Generic User Modeling Systems. User Modeling and User-Adapted Interaction 11, 49–63 (2001). https://doi.org/10.1023/A:1011187500863\n",
    "\n",
    "Rich, E. (1979). User modeling via stereotypes*. Cognitive Science, 3(4), 329–354. https://doi.org/10.1207/s15516709cog0304_3\n",
    "\n",
    "Webb, G.I., Pazzani, M.J. & Billsus, D. Machine Learning for User Modeling. User Modeling and User-Adapted Interaction 11, 19–29 (2001). https://doi.org/10.1023/A:1011117102175\n",
    "\n",
    "Wilson RC, Collins AG. Ten simple rules for the computational modeling of behavioral data. Elife. 2019 Nov 26;8:e49547. https://doi.org/10.7554/eLife.49547. PMID: 31769410; PMCID: PMC6879303."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more comprehensive account of the techniques introduced in this lecture are available in previous year's lecture materials by Aurélien Nioche (available [here](https://github.com/AurelienNioche/LectureUserResearch/blob/master/OLD_lecture10.ipynb) and in [Wilson & Collins (2019)](https://doi.org/10.7554/eLife.49547)."
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