SHapley_Additive_exPlanations_(SHAP)_for_Bipolar_Disease_Prediction.ipynb

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      "source": [
        "# SHAP: Understanding the Functioning of an ML-based (XGBoost) Bipolar Disorder Crisis Predictor Model"
      ]
    },
    {
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      "source": [
        "In this tutorial, you will learn how to apply eXplainable methods to machine learning models for tabular data. The goal is to understand how the models operate and what are the main criterias used for the decision making. Since we are working with tabular data, we will consider a  very familiar use-case.\n",
        "\n",
        "In particular, we are going to focus on a specific explanation model called SHapley Additive exPlanations, SHAP (https://arxiv.org/pdf/1705.07874.pdf). It will be implemented using the framework developed by Lundberg in https://github.com/slundberg/shap. \n",
        "\n",
        "Based on game theory, SHAP considers each feature as a \"player\" in a cooperative game. In a nutshell, what SHAP does is quantifying the contribution that each feature brings to the prediction given by the model.\n",
        "\n",
        "In this notebook we will learn how the SHAP values are computed mathematically with a simple example and then focus on a critical aplication of Machine Learning models: Bipolar Disorder Crisis Prediction.\n",
        "\n"
      ]
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        "### Shapley values: Definition\n",
        "\n",
        "One of the main contribution of SHAP is to consider the explanation as a model itself. The explanation model is a linear function of a binary variable:\n",
        "\n",
        "\n",
        "\\begin{equation}\n",
        "g(z') = \\phi_0 + \\sum_{i=1}^{M}\\phi_i z'_i\n",
        "\\end{equation}\n",
        "\n",
        "where $z' \\in \\{0,1\\}^M $, M is the number of simplified features, and $\\phi_i \\; in \\; \\mathbb{R}$. *z'* represents the simplified features vector, $\\phi_i $ is the feature attribution for feature *i*.\n",
        "\n",
        "\\begin{equation}\n",
        "\\phi_i = \\sum_{S \\subseteq F \\ \\{i\\}} \\frac{\\mid S\\mid! (\\mid F\\mid - \\mid S\\mid - 1)!}{\\mid F\\mid !} [f_{S \\bigcup \\{i\\}}(x_{S \\bigcup \\{i\\}}) - f_S(x_S)]\n",
        "\\end{equation}\n",
        "\n",
        "where $f$ represents the model function, and $S \\subseteq F $, where $F$ is the set of all features and $S$ a subset of $F$. Shap values are a weighted average of all possible difference subsets. $\\mid S \\mid$ represents the number of elements of subset $S$.\n",
        "\n",
        "\n",
        "To be able to understand the Shap values, what they mean and how they are computed, we are going to consider a simple example: Let's imagine we have a model (any machine learning model) that outputs the  probability of not paying your credit given a set of 3 features (for instance age, localization and income).\n",
        "\n",
        "SHAP values consider each combiniation or coalition of players (i.e., features) to determine the importance of a single player (i.e., features). As we are considering a simple set of 3 features, the number of different models with different sets of features we have to build are $2^3$. It is important to remark that all models are constructed with the same hyperparameters.\n",
        "\n"
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        "![shap_scheme.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "354fff2c-1b7f-40db-8420-200f6858ab6d",
      "metadata": {
        "id": "354fff2c-1b7f-40db-8420-200f6858ab6d"
      },
      "source": [
        "The output of the empty set ($\\phi_0)$ is the number of total positive class cases over the number of data points. In this simple example the number of positive classes is 0.2. Now let's take a look on how the prediction of the model changes when we add the feature income for a single datapoint $x$. The considered set is the empty set. \n",
        "\n",
        "$S_0 = \\{\\emptyset\\}$\n",
        "\n",
        "$f_{S_0}(x_{S_0}) = 0.2$\n",
        "\n",
        "$f_{S_0 \\bigcup \\{work\\}}(x_{S_0 \\bigcup \\{work\\}}) = 0.25$\n",
        "\n",
        "Now we consider all subsets of $F$ that that do not contain the feature *income*  ($S \\subseteq F \\ \\{i\\}$) and check the difference on the model's output when we consider the same subset, but adding the feature income. \n",
        "\n",
        "$S_1 = \\{gender\\}$\n",
        "\n",
        "$f_{S_1}(x_{S_1}) = 0.3$\n",
        "\n",
        "$f_{S_1 \\bigcup \\{work\\}}(x_{S_1 \\bigcup \\{work\\}}) = 0.25$\n",
        "\n",
        "\n",
        "$S_2 = \\{Marriage\\}$\n",
        "\n",
        "$f_{S_2}(x_{S_2}) = 0.4$\n",
        "\n",
        "$f_{S_2 \\bigcup \\{work\\}}(x_{S_2 \\bigcup \\{work\\}}) = 0.15$\n",
        "\n",
        "$S_3 = \\{Marriage, gender\\}$\n",
        "\n",
        "$f_{S_3}(x_{S_3}) = 0.2$\n",
        "\n",
        "$f_{S_3 \\bigcup \\{work\\}}(x_{S_3 \\bigcup \\{work\\}}) = 0.05$\n",
        "\n",
        "Once we find all model outputs for all different susbsets, we compute a weighted average of the model's output, with and without the feature.\n",
        "\n",
        "The criteria the weights have to meet are the following:\n",
        "  1. $\\sum_{i} w_i = 1$\n",
        "  2. $w_1 = w_2 + w_3 = w_4$"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99f5df47-c84f-46ad-ae4f-d9147e2739c7",
      "metadata": {
        "id": "99f5df47-c84f-46ad-ae4f-d9147e2739c7"
      },
      "source": [
        "As mentioned above we are just considering the impact of the feature *income* in the model's outcome. To obtain the Shap values for the rest of the features we repeat the same strategy.\n",
        "\n",
        "Let's apply the formula to obtain the Shap values $\\phi_i$. Since we have four subsets that do not contain the feature *income*, we are going to feature attribution for each subset.\n",
        "\n",
        "Feature attribution for $S_0$:\n",
        "\n",
        "\\begin{equation}\n",
        "\\frac{\\mid S_0\\mid! (\\mid F\\mid - \\mid S_0\\mid - 1)!}{\\mid F\\mid !} [f_{S_0 \\bigcup \\{i\\}}(x_{S_0 \\bigcup \\{i\\}}) - f_{S_0}(x_{S_0})] = \\frac{0! (3 - 0 - 1)!}{3!} [0.25-0.2] = \\frac{1}{3}0.05\n",
        "\\end{equation}\n",
        "\n",
        "Now we repeat the same computation for $S_1$ , $S_2$, and $S_3$\n",
        "\n",
        "For $S_1$: $\\frac{1}{6}(-0.05)$\n",
        "\n",
        "For $S_2$: $\\frac{1}{6}(-0.25)$\n",
        "\n",
        "For $S_3$: $\\frac{1}{3}(-0.15)$\n",
        "\n",
        "$\\phi_i = \\frac{1}{3}0.05 + \\frac{1}{6}(-0.05) + \\frac{1}{6}(-0.25) + \\frac{1}{3}(-0.15) = -\\frac{1}{6}0.5$"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bad3f012-94a9-446e-be04-7e9af081c8ac",
      "metadata": {
        "id": "bad3f012-94a9-446e-be04-7e9af081c8ac"
      },
      "source": [
        "## Use Case: Bipolar Disease Crisis Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2dc650d8-fd27-48c1-822d-2dfa8ec7ea15",
      "metadata": {
        "id": "2dc650d8-fd27-48c1-822d-2dfa8ec7ea15"
      },
      "source": [
        "Now that we know how to compute Shap values to understand the model, we are going to focus on a critical use case that directly impacts us in our daily economic activity: Bipolar Disease Crisis Prediction.\n",
        "\n",
        "Bipolar disorder, formerly called manic depression, is a mental health condition that causes extreme mood swings that include emotional highs (mania or hypomania) and lows (depression) which often come accompanied by different features (i.e., physical and psychological features).\n",
        "\n",
        "In this notebook we will create a Bipolar Disease Crisis Prediciton Model using the data available [here](https://datasets.simula.no/depresjon/) and then apply XAI method SHAP to help psychiatrists to understand the causes behind a potential patient tendency towards a mania or depression episode.\n",
        "\n",
        "\n",
        "The use case is organized as follows:\n",
        "1. We load the processed data.\n",
        "2. We develop a Machine Learning based model for the bipolar disease prediction: the [XGBoost](https://arxiv.org/abs/1603.02754).\n",
        "3. We apply SHAP Values (https://github.com/slundberg/shap) to explain the ML model outputs\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "################## Downdload the git repository ############\n",
        "! git clone https://github.com/ayoubelqadi/xai-tutorial.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbLe0jswg700",
        "outputId": "52309af8-45f0-468c-cb84-1e0dac0af910"
      },
      "id": "SbLe0jswg700",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'xai-tutorial'...\n",
            "remote: Enumerating objects: 94, done.\u001b[K\n",
            "remote: Counting objects: 100% (94/94), done.\u001b[K\n",
            "remote: Compressing objects: 100% (70/70), done.\u001b[K\n",
            "remote: Total 94 (delta 20), reused 86 (delta 18), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (94/94), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "######### Change  directory for running the data processing scripts\n",
        "import os\n",
        "os.chdir('xai-tutorial/notebooks')"
      ],
      "metadata": {
        "id": "M4KPfalEhStp"
      },
      "id": "M4KPfalEhStp",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3cfc9b22-89df-4a14-9fce-ed1af6bb908e",
      "metadata": {
        "id": "3cfc9b22-89df-4a14-9fce-ed1af6bb908e"
      },
      "outputs": [],
      "source": [
        "%run ../src/setup.py\n",
        "%run ../src/process_data.py"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "54bdfb76-f161-44fa-9af8-0b150b37e201",
      "metadata": {
        "id": "54bdfb76-f161-44fa-9af8-0b150b37e201"
      },
      "source": [
        "### Setting the Environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c765b0b3-6af7-426c-848b-9732a3ba9749",
      "metadata": {
        "id": "c765b0b3-6af7-426c-848b-9732a3ba9749"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "pd.set_option(\"max_rows\", 500)\n",
        "pd.set_option(\"max_columns\", 500)\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.rc(\"font\", size=16)\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn import model_selection\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c834b6a6-1b7a-4ab4-b537-5ef157596930",
      "metadata": {
        "id": "c834b6a6-1b7a-4ab4-b537-5ef157596930"
      },
      "outputs": [],
      "source": [
        "path_features = \"../data/features.csv\"\n",
        "path_target = \"../data/target.csv\"\n",
        "features = pd.read_csv(path_features)\n",
        "target = pd.read_csv(path_target)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#####Transform age and education range\n",
        "def transform_range(df: pd.DataFrame):\n",
        "\n",
        "  df[\"age\"] = df[\"age\"].apply(lambda x: (int(x.split(\"-\")[0]) + int(x.split(\"-\")[1]))/2 if isinstance(x, str) else x)\n",
        "  df[\"edu\"] = df[\"edu\"].apply(lambda x: (int(x.split(\"-\")[0]) + int(x.split(\"-\")[1]))/2 if isinstance(x, str) else x)\n",
        "  \n",
        "  return df"
      ],
      "metadata": {
        "id": "tMuiVkk_ljX2"
      },
      "id": "tMuiVkk_ljX2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = transform_range(features)"
      ],
      "metadata": {
        "id": "p-Pc4lGCnIX-"
      },
      "id": "p-Pc4lGCnIX-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features.sample(n=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "5NXfC2UMnieP",
        "outputId": "be71cbe0-a873-4f97-fddc-1224ea8b9e01"
      },
      "id": "5NXfC2UMnieP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    days  gender   age  melanch  inpatient   edu  marriage  work  madrs1  \\\n",
              "8     13       2  47.0      NaN        2.0   8.0       1.0   2.0    26.0   \n",
              "18    13       1  32.0      1.0        1.0   8.0       1.0   2.0    27.0   \n",
              "12    14       2  37.0      2.0        2.0  13.0       2.0   2.0    18.0   \n",
              "\n",
              "    madrs2  activity_mean  activity_std  \n",
              "8     26.0        289.151       348.722  \n",
              "18    25.0        171.471       220.950  \n",
              "12    13.0        390.093       383.131  "
            ],
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      "metadata": {
        "id": "24f285f7-0ecc-4781-a8ae-1eaf9035b8ed"
      },
      "source": [
        "**Target Distribution**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a505d4e-08c5-416c-b334-58206a15141a",
      "metadata": {
        "id": "9a505d4e-08c5-416c-b334-58206a15141a"
      },
      "source": [
        "- Bipolar II: **1**\n",
        "\n",
        "- Unipolar Depressive: **0** "
      ]
    },
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          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "fig, ax = plt.subplots(figsize=(12,12))\n",
        "counter = target[\"afftype\"].value_counts()\n",
        "counter.rename(index={1.0: \"Bipolar II\", \n",
        "                       0.0: \"Unipolar Depressive\"},\n",
        "               inplace=True\n",
        "              )\n",
        "ax.set_xlabel(\"# Cases\", size=16)\n",
        "counter.plot(kind=\"barh\", ax=ax)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = features, target"
      ],
      "metadata": {
        "id": "45HGygs5olA8"
      },
      "id": "45HGygs5olA8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77edd7a1-3fa3-4470-9ce0-612519c50f21",
      "metadata": {
        "id": "77edd7a1-3fa3-4470-9ce0-612519c50f21"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = model_selection.train_test_split(x, \n",
        "                                                                    y, \n",
        "                                                                    test_size=0.3, \n",
        "                                                                    random_state=80,\n",
        "                                                                    stratify=target)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "cUBU51BqjUas",
        "outputId": "a3a7ead7-8c74-4565-df95-6873297d2374"
      },
      "id": "cUBU51BqjUas",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    days  gender   age  melanch  inpatient   edu  marriage  work  madrs1  \\\n",
              "7      5       2  27.0      NaN        2.0  13.0       1.0   2.0    20.0   \n",
              "15    16       1  47.0      2.0        2.0  13.0       1.0   2.0    13.0   \n",
              "18    13       1  32.0      1.0        1.0   8.0       1.0   2.0    27.0   \n",
              "8     13       2  47.0      NaN        2.0   8.0       1.0   2.0    26.0   \n",
              "13    14       1  62.0      2.0        2.0   8.0       2.0   2.0    28.0   \n",
              "1     18       2  42.0      2.0        2.0   8.0       2.0   2.0    24.0   \n",
              "16    13       1  52.0      2.0        2.0   8.0       1.0   2.0    17.0   \n",
              "5      7       1  37.0      2.0        2.0   8.0       1.0   2.0    18.0   \n",
              "2     13       1  47.0      2.0        2.0   8.0       2.0   2.0    24.0   \n",
              "10    14       1  47.0      2.0        2.0   8.0       1.0   2.0    24.0   \n",
              "20    14       1  67.0      2.0        1.0   NaN       2.0   2.0    29.0   \n",
              "21    16       1  32.0      2.0        1.0  18.0       2.0   2.0    29.0   \n",
              "0     11       2  37.0      2.0        2.0   8.0       1.0   2.0    19.0   \n",
              "11    12       2  42.0      2.0        2.0   8.0       2.0   2.0    25.0   \n",
              "14    13       2  57.0      2.0        2.0  13.0       1.0   1.0    14.0   \n",
              "\n",
              "    madrs2  activity_mean  activity_std  \n",
              "7     16.0        316.545       503.483  \n",
              "15    17.0        338.069       424.913  \n",
              "18    25.0        171.471       220.950  \n",
              "8     26.0        289.151       348.722  \n",
              "13    19.0        205.845       262.523  \n",
              "1     11.0        153.673       330.808  \n",
              "16    15.0        147.850       211.517  \n",
              "5     15.0        361.821       382.775  \n",
              "2     25.0        265.300       399.881  \n",
              "10    24.0        300.669       369.657  \n",
              "20    28.0        287.349       279.429  \n",
              "21    23.0        440.540       415.779  \n",
              "0     19.0        276.751       357.105  \n",
              "11    21.0        276.281       320.105  \n",
              "14    18.0        227.998       292.819  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85c5d183-c66e-4625-90cf-78e51d51a280\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>days</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>melanch</th>\n",
              "      <th>inpatient</th>\n",
              "      <th>edu</th>\n",
              "      <th>marriage</th>\n",
              "      <th>work</th>\n",
              "      <th>madrs1</th>\n",
              "      <th>madrs2</th>\n",
              "      <th>activity_mean</th>\n",
              "      <th>activity_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>316.545</td>\n",
              "      <td>503.483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>338.069</td>\n",
              "      <td>424.913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>171.471</td>\n",
              "      <td>220.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>47.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>289.151</td>\n",
              "      <td>348.722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>205.845</td>\n",
              "      <td>262.523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>42.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>153.673</td>\n",
              "      <td>330.808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>147.850</td>\n",
              "      <td>211.517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>37.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>361.821</td>\n",
              "      <td>382.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>265.300</td>\n",
              "      <td>399.881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>300.669</td>\n",
              "      <td>369.657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>67.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>287.349</td>\n",
              "      <td>279.429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>440.540</td>\n",
              "      <td>415.779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>37.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>276.751</td>\n",
              "      <td>357.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>42.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>276.281</td>\n",
              "      <td>320.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>57.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>227.998</td>\n",
              "      <td>292.819</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85c5d183-c66e-4625-90cf-78e51d51a280')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85c5d183-c66e-4625-90cf-78e51d51a280 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85c5d183-c66e-4625-90cf-78e51d51a280');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3b415a5-9e29-4233-8222-56c3f4f9b69c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3b415a5-9e29-4233-8222-56c3f4f9b69c",
        "outputId": "a789273b-63bd-42c9-8650-fa3443278f0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(learning_rate=0.05, max_depth=8, n_estimators=200,\n",
              "              scale_pos_weight=2.142857142857143, use_label_encoder=True)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "proportion_class = counter[\"Unipolar Depressive\"]/counter[\"Bipolar II\"]\n",
        "\n",
        "params_xgb = {\n",
        "    \"n_estimators\": 200,\n",
        "    \"max_depth\": 8, \n",
        "    \"learning_rate\": 0.05,\n",
        "    \"scale_pos_weight\": proportion_class\n",
        "}\n",
        "xgb = XGBClassifier(**params_xgb, use_label_encoder=True)\n",
        "xgb.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6d9552f-477e-43a7-9240-71a9fd1dfea2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "b6d9552f-477e-43a7-9240-71a9fd1dfea2",
        "outputId": "6c233566-4b6e-4414-90c2-1dbd1d833d84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Confusion Matrix (Train set)')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ],
            "image/png": 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ju3b16lVYW1sjMjJS623cFAoFnj17pnXSrfyUK1cOwKu81tffIqQ/bKAWgaOjI+7cuYPHjx8X6nVpaWmq1+va74MHD5CamqoWOrp+eSnPlL3+i0bfJk2aBDc3N+zduxdhYWEICwuDlZUV2rRpg4kTJ2oNa6X09HQAut+v8he5tlB/23t+lxtZ9+vXDydPnkRERAT27t0LR0dHtG/f/q2vmTNnDjZu3AhHR0e0atUKTk5OqjN/ymuMikLZ21ZY1atXh4eHB3799Ve4u7ujVatWhXq9svainjVMTU2FIAh49uyZ2gRHb+Pv749Vq1Zh3bp12Lt3L8LDwwEAderUwdixY/H+++8XqZbXLVmyBKtXr0ZERISq4VmiRAl06tQJEyZMUH3eyj8KlGfvdcnMzCx0DS9fvgSg/cwwEekXs5hZXJyz+HVv64XV9+em7TuhbFgW5DthZ2eHnTt3YtmyZThx4oSqB7d8+fLo378/goKCVI3m5ORk5OTk5Pu3RmZmZqEbqMrPXdvILjI+NlCLoFGjRrhw4QIuXLiAjz76qMCvUwbd06dPtV4QrxyuZKgpr5XDTnTdX0xbOFlaWmLQoEEYNGgQkpKSEBsbi0OHDiEyMhK3b9/GoUOHtA4dAaAapvH06VOt65VT8Is5xXfbtm3h5OSEn376CY8ePcLQoUO1DolRSkpKwubNmyGXy7F9+3a1oSfAq1lsi6qok+iEhYXh119/Rfny5XHr1i2sWrVKYwjW2yiHgykbaoWl/Axq166tmt22INq1a4d27dohMzMTv/32G06dOoXt27dj6NCh2L9/f6F7K99UsmRJjBo1CqNGjcLDhw9x6dIl7Nu3DwcOHEBCQgK2bdsG4N/v288//wxPT893OuablJ+prjPZRKQ/zGJmsVJxzOLX6XoPhvzc3oWzszNmz54NQRBw+/ZtXLhwAVu3blXdz/g///kPgFffXUEQcPHiRb3XkJycDCsrK7XhymQ6eA1qEfTp0wfW1tY4cuRIvtepvT7ttnIWMW3Tj//999949OgR3Nzc9HrD5dcp96ttWvH09HTV0EZdKlSoAH9/fyxduhTNmzfHP//8g5s3b+rc3s7OTjU7qrZ9Kz+H2rVrF/xNvCNLS0v07dsXjx49gkwmy/ePmvv37yMvLw/vv/++xi/2R48eIT4+XusxAMOcTb9y5QqWLVuG6tWrIyIiAtWrV8fy5ctx+fLlAu+jVq1asLS01LidSkHZ2tqiVq1auHXrVpGCtXTp0mjRogUmTZqEoUOHQqFQIDo6WrVe+cfbu5ydd3FxQY8ePbB27VpUq1YNsbGxqmG3yltRxMbGFnh/Ba3p7t27sLCwyHdGUSJ6d8xiZjFQfLO4IIryuYlJJpOhVq1aCAwMxPr16wEAUVFRqvUNGzZESkoKbt26VeB9WlhY5Pszz8jIwOPHj+Hh4cEZ900UG6hF4ObmhpEjR0KhUOCrr77C77//rnW76OhofPHFF6rnffv2BQD89NNPePbsmWp5bm4u5s2bh7y8PHz44YcGq9vOzg41atTAlStX1MI8NzcXc+bMUQ1PVMrOztb6R7xCoVBdZ/Dm9X1v6tu3LwRBwPz589V+YTx79gwrV65UbSOmwMBArFixAmvXrlWbylwb5XT0sbGxavVnZGRg8uTJWs+AK8/GKSdA0JeUlBSMHTsWFhYWWLx4MSpWrIglS5bA0tIS48aNK3BjsUyZMvD09MTNmzc1fuYFNWjQICgUCnz77bdaz/anpKTgjz/+UD2/dOmS1s8qKSkJgPqQWOXnV5iJhp49e4Y///xTY3lmZiYyMzNhZWWlGjLUp08flC1bFiEhIVon+cjLy9P4w7UgNWVnZ+PGjRvw8vIy2B+2RPQvZjGzuLhncX6K8rkZ2q1bt7T25iuXvf73gPJWRFOmTNE6lD8zM1PjWmh7e3s8e/bsrZ/p77//jtzc3ALdDoiMg0N8i2jYsGHIycnBihUr8OGHH8Lb2xt169aFra0tnj59isuXL+PevXtqF5P7+Pjgiy++wJo1a9CtWzd06tQJpUqVwpkzZ3Dz5k00atQIQ4YMMWjdQ4YMwXfffYdPPvkEH3zwAUqUKIGYmBgoFAp4enoiLi5Ote3Lly/Rv39/VKtWDXXq1EHlypWRlZWFc+fO4c6dO/D19dU64cLrPv/8c0RHRyMqKgo9e/ZEmzZt8PLlS0RGRiIpKQlffPEFGjdubND3/CYHBwfVPdLy4+joiK5du+LQoUPo1asX3n//faSlpeHcuXOwsbGBl5eXxsQS1atXh5OTEw4dOgQrKytUrlwZMpkMPXv21Lj/WmF8++23ePDgASZPnqzqAfD09MTEiRMxY8YMTJw4EaGhoQXaV6dOnfDHH3/gwoULaNeuXaFr+fDDD/HHH39g27Zt6NixI1q1agUXFxekpKQgPj4ely5dQp8+fTBjxgwAwA8//IDHjx/Dx8cHrq6usLa2Vh3f1dUVXbt2Ve27RYsWWLt2LaZMmQJ/f3/Y2tqibNmy+Oyzz3TW8/jxY/Tq1QtyuRweHh5wcXFBeno6Tp06hSdPniAwMFB19rh8+fJYtmwZRowYgX79+qFFixZwd3eHTCbDo0eP8OuvvyI5OVntj92C1KT8f9SpU6dCf55EVDTMYmZxcc7i/BTlczO0//73v1iwYAEaNmyI9957DxUqVMCjR48QFRUFCwsLtf97LVq0wNixY/Hjjz+iU6dOaNOmDdzc3JCZmYkHDx7g0qVL8PHxwdq1a9Ve8/vvv6u+0zY2NvD09ISvr69aDcCr+THINLGB+g5GjhyJzp07Y9u2bYiJicHevXuRnZ0Ne3t7eHp64osvvkDPnj3VXvPNN9+gdu3a2LJlC/bv34+cnBxUrVoV//nPf/D555/rvIZEXz788EMIgoANGzZg3759KFeuHPz8/DB69GgEBwerbVuqVCmMGzcOMTEx+PXXX3H8+HHY2tqiatWqmDZtWoHOttrY2GD9+vVYv349IiIisGXLFlhaWsLT0xPffvstunXrZqi3qjezZs1ClSpVcPjwYWzduhUODg7w9fVFcHCwxmcGvBpWFBISgkWLFiEyMhIZGRkQBAGNGjUqcihu3rwZx48fh6+vLwIDA9XWffrppzh//jyOHTuGDRs2qN38XJcPP/wQy5cvx/79+4scilOnTkWbNm2wY8cOnDt3DmlpaShXrhxcXFwwZMgQ9OjRQ7Xt0KFDcfz4cVy7dg3nz5+HTCZD5cqVMWzYMAwcOFA1mx7w6hYGEydOxM6dO7Fx40YoFAq4urq+tYHq6uqKUaNG4eLFi4iJicHz589hb2+P6tWrY+zYsWoNYOBVgB04cADr1q3D2bNncfnyZVhbW6NSpUpo3ry5RiOzIDXt378f1tbWBu15KU7OnDmD1atX486dO0hJSYGDgwO8vb0xatSofK9XTklJwfz583H8+HFkZWWhYcOGmDRpEjw8PESqnsTELGYWF+cszk9hPzdDa926tWqeiKioKKSnp6NSpUp4//33MWjQIPj4+Kht/9VXX8HHxwebN29GbGwsTpw4ATs7Ozg5OaFfv34a392goCCkpqbi5MmTuHLlCnJzc9G7d29VAzUvLw8HDhyAp6cnvL29RXvfUvLo0SOsXr0a165dQ1xcHF6+fImoqCi4ubmpbZeVlYUlS5bg4MGDSE1NhZeXF8aNG4cmTZrkewyZIAiCod4AEZmu77//Hvv27cOJEyf0ch+24iwpKQm+vr7o1q0bZs2aZexyJCEiIgJ//PEHGjRoAAcHBzx48ACrV6/Gw4cPcfDgQZ1/YAqCgP79+yMhIQHjx49H2bJlERYWhlu3buHnn3/m7QSIyKQwi8V14sQJBAUFYf78+RonrqhgYmJiMHr0aNSpUwd5eXk4e/as1gbq2LFjcfr0aYwfPx5VqlTB1q1bER0djfDwcNXoA13YQCUqpp4+fQp/f3/07t0bU6ZMMXY5Zm3WrFnYvXs3jhw5Uqj74FHh3L17F507d8aECRPw+eefa93m+PHjGDFiBDZu3Ki6v2NaWhr8/PzQo0cPTJ48WcySiYjeilksHkEQ0KdPH1hYWGD37t2cIKmI8vLyVJNH7tq1C5MnT9ZooMbFxaFnz56YPXu2apRHTk4OunbtiurVq+c7DJ6TJBEVUxUrVsSCBQtQqVKld5oxt7gTBAGOjo6qz5IMRznpyZs3c3/diRMnVEO1lcqUKYP27durzQ5JRGQKmMXiefLkCXx9ffHDDz+wcfoOlI3Tt4mKioK1tTW6dOmiWmZlZYWuXbvi7NmzajOra8NrUImKMT8/P/j5+Rm7DLMmk8nw1VdfGbsMycrNzUVubi4ePHiARYsWwdHR8a3Xy92+fVvrbX7c3d2xf/9+ZGRkwNbW1pAlExEVCrNYHJUqVcKoUaOMXUaxcPv2bbi6umrMMO7u7g6FQoG///4btWrV0vl6NlCJiMhkffTRR6pbFlWrVg0bN25EhQoVdG6fkpKi9fpUZe9ramoqG6hEREQGlJKSojYBppIyi5W3yNLF7BuoKYMLNkU5kSGVXhBm7BKIAADWFWvodX+Kp4a5ifzeqEsIDw9XPQ8ICEBAQIDGdgsWLEB6ejru37+PdevWYfDgwdi2bZvGZAxkWgz1vSEqjFKVWxu7BCIAQE52gl73Z+xsNjSzb6ASEZH5KWjoKe/v2KBBA7Rp0wa+vr4ICwtT3WP3TWXLlkVqaqrG8uTkZNV6IiIi0qSvBmnZsmWRkKDZKFdmsbbe1ddxkiQiItItL9cwjyIoW7Ysqlatin/++UfnNu7u7rh165bG8jt37qBy5coc3ktERObPhLJZG3d3dyQkJODFixdqy+/cuQNra2tUq1btra9nA5WIiMzC06dP8ddff6Fq1ao6t/Hz88Pjx49x8eJF1bL09HScPHlSdaN2IiIiMhxfX18oFApERkaqluXk5ODw4cNo1aoVbGxs3vp6DvElIiLdBOPc9mDEiBGoXbs2PDw8YGdnh3v37mHDhg2wtLTE4MGDAQAXL17EoEGDMHv2bPTq1QvAq1D09vbGN998g/Hjx6Ns2bIICwuDIAj44osvjPJeiIiI9MpI2aykbHheu3YNABAdHQ0HBwc4ODigadOmqF27Nrp06YLZs2cjJycHbm5u2L59O+Lj47Fw4cJ8988GKhERmZwGDRogMjIS69evh0KhgLOzM5o1a4avvvpKNUGSIAjIzc1Vu3eghYUFQkNDMW/ePEyfPh1ZWVlo2LAhNm3aBBcXF2O9HSIiIsn4+uuv1Z5Pnz4dANC0aVNs3rwZADBnzhwsXrwYS5YsQWpqKjw9PbFmzRrUqVMn3/3LBEEQ9F+2eDiLL5kCzuJLpkLvs/g+vKHX/SlZu3gZZL9kGjiLL5kCzuJLpkLvs/hKPJvZg0pERDoJRh5GREREROqkns2cJImIiIiIiIhMAntQiYhItzxpn6UlIiIyOxLPZvagEhERERERkUlgDyoREekm8etciIiIzI7Es5kNVCIi0i0v19gVEBER0eskns0c4ktEREREREQmgT2oRESkm8SHEREREZkdiWcze1CJiIiIiIjIJLAHlYiIdJP4VPZERERmR+LZzAYqERHpJEh8GBEREZG5kXo2c4gvERERERERmQT2oBIRkW4SH0ZERERkdiSezexBJSIiIiIiIpPAHlQiItJN4te5EBERmR2JZzN7UImIiIiIiMgksAeViIh0y8s1dgVERET0OolnMxuoRESkm8SHEREREZkdiWczh/gSERERERGRSWAPKhER6SbxqeyJiIjMjsSzmT2oREREREREZBLYg0pERLpJ/DoXIiIisyPxbGYDlYiIdJP4MCIiIiKzI/Fs5hBfIiIiIiIiMgnsQSUiIp0EQdr3WiMiIjI3Us9m9qASERERERGRSWAPKhER6SbxiRiIiIjMjsSzmQ1UIiLSTeITMRAREZkdiWczh/gSERERERGRSWAPKhER6SbxYURERERmR+LZzB5UIiIiIiIiMgnsQSUiIt3ypD2VPRERkdmReDazgUpERLpJfBgRERGR2ZF4NnOILxEREREREZkE9qASEZFuEp/KnoiIyOxIPJvZg0pEREREREQmwSgN1MzMTGzatAnBwcEIDAzEvXv3AIbRv8UAACAASURBVACHDh3CnTt3jFESERFpI+QZ5kEmh9lMRGQmJJ7Nog/xffjwIQIDA/Ho0SPUqFEDt27dQkZGBgAgJiYG586dw6xZs8Qui4iIqNhiNhMRkakQvQd17ty5sLGxwZEjR7B3714IgqBa16RJE1y+fFnskoiISJe8PMM8yKQwm4mIzIjEs1n0HtRz585hxowZcHV1RW6u+j18nJyckJiYKHZJRESkiwkFFhkOs5mIyIxIPJtF70FVKBSwtbXVui4tLQ2WlpYiV0RERFS8MZuJiMhUiN5A9fDwwNGjR7Wui46ORp06dUSuiIiIdBGEXIM8yLQwm4mIzIfUs1n0Ib5DhgxBcHAwAKBbt24AgNu3byMqKgp79uzBypUrxS6JiIioWGM2ExGRqZAJr8+EIJLt27dj0aJFyMjIUE3EYGtri/HjxyMgIKBQ+0oZ3MEQJRIVSukFYcYugQgAYF2xhl739+LUOr3uT6lUu88Nsl8qOn1ms+LpXUOUSFQopSq3NnYJRACAnOwEve5P6tkseg8qAHzyySfo2bMnrl69iqSkJNjb28Pb2xt2dnbGKIeIiHQxofuikWExm4mIzITEs9koDVQAKF26NFq2bGmswxMREdEbmM1ERGRsok+SdPz4cezZs0f1PCEhAQEBAfD29kZwcLDqxuBERGQCJH6vNXqF2UxEZEYkns2iN1B/+uknPHv2TPV87ty5ePToEQICAnDp0iWEhISIXRIREVGxxmwmIiJTIXoD9f79+/Dw8AAAvHz5EqdPn8bEiRMxceJEjBkzBseOHRO7JCIi0kXIM8yDTAqzmYjIjEg8m0W/BjUrKwslS5YEAPz666/Izc1Fq1atAADVq1dHYmKi2CUREZEuJjTkhwyH2UxEZEYkns2i96C6uroiNjYWABAVFYU6deqgTJkyAICkpCTVv4mIiEgczGYiIjIVovegBgQEYP78+Th27Bji4uIwbdo01bqrV6+iZs2aYpdERES6mNCQHzIcZjMRkRmReDaL3kAdOHAgypcvj99++w0DBgxAr169VOsyMjLQp08fsUsiIiIq1pjNRERkKmSCIAjGLuJdpAzuYOwSiFB6QZixSyACAFhXrKHX/b34ZZle96dUqnOwQfZLpkHx9K6xSyBCqcqtjV0CEQAgJztBr/uTejaLfg0qERERERERkTaiD/EFgPDwcGzfvh1//fUXsrOzNdbfuHHDCFUREZEGic8USP9iNhMRmQmJZ7PoDdT9+/dj5syZ6N27N+Li4tC3b1/k5OTgxIkTcHBwQPfu3cUuiYiIdJH4RAz0CrOZiMiMSDybRR/iu3HjRgwdOlQ1Q2D//v0xb948HD9+HCVKlIC9vb3YJRERERVrzGYiIjIVojdQ7927h8aNG8PCwgIWFhZQKBQAgHLlymHYsGHYtGmT2CUREZEueXmGeZBJYTYTEZkRiWez6A3UkiVLQhAEyGQyVKxYEffv31ets7W1RWJiotglERERFWvMZiIiMhWiX4Mql8vx999/o2XLlmjcuDFWrVoFNzc3WFpaYvny5ahRQ7+3SCAiondgpOtcIiMjcejQIVy7dg1JSUlwcXGBv78/hg4dCjs7u7e+1sPDQ+vy/fv3w8vLyxDlmj1mMxGRGZH4NaiiN1ADAgJUZ2a//vprDB48GP379wfw6iztihUrxC6JiIh0MdKQn3Xr1sHFxQWjR4+Gs7Mzrl+/jpCQEMTExGDHjh2wsHj7AKA+ffogICBAbdl7771nwIrNG7OZiMiMmNBwXEMQvYHapUsX1b+rVauGiIgIXL16FS9evIC3tzccHBzELomIiExMaGioWh40bdoU9vb2mDBhAmJiYtCiRYu3vr5SpUpo2LChocuUDGYzERGZCqPcB/V1pUuXRsuWLY1dBhERaWOkYUTaGkT16tUDADx+/FjscoodZjMRkQmT+BBf0SdJAoDMzExs2rQJwcHBCAwMxL179wAAhw4dwp07d4xREhERmbiLFy8CAGrWrJnvtjt27EDdunXRoEEDDBgwAJcvXzZ0eWaP2UxERKZA9B7Uhw8fIjAwEI8ePUKNGjVw69YtZGRkAABiYmJw7tw5zJo1S+yyiIhIGwNd5xIeHo7w8HDV84CAAI1rRl/3+PFjLFu2DC1btlT1pOrSo0cPtG/fHpUqVUJCQgLWrl2LgQMHYt26dWjWrJne3oOUMJuJiMwIr0HVr7lz58LGxgZHjhyBk5MT6tatq1rXpEkThISEiF0SERHpYqAQzK9B+rqMjAwEBQXB0tISc+bMyXf7BQsWqP7duHFj+Pn5oXv37liyZAm2b99e5JqljNlMRGRGJN5AFX2I77lz5zBq1Ci4urpCJpOprXNycuK91oiISOXly5cYNmwY4uPjsXbtWjg7Oxd6H3Z2dmjbti1+//13A1QoDcxmIiIyFaL3oCoUCtja2mpdl5aWBktLS5ErIiIinQTBaIdWKBQIDg7GtWvXsH79ep33Ny2oNxte9C9mMxGRGTFiNotB9B5UDw8PHD16VOu66Oho1KlTR+SKiIjI1OTl5WHcuHG4cOECVq5c+U63jElPT8epU6dQv359PVYoLcxmIiIyFaL3oA4ZMgTBwcEAgG7dugEAbt++jaioKOzZswcrV64UuyQiItLFSNe5TJ8+HZGRkRg2bBhKlSqFq1evqtY5OzvD2dkZCQkJ6NixI4YPH46RI0cCANauXYu//voLzZo1Q6VKlfDgwQOsW7cOT58+xcKFC43yXswBs5mIyIxI/BpU0Ruo/v7+mDp1KhYtWoQ9e/YAACZMmABbW1tMmTIFbdq0EbskIiIyMWfOnAEAhIaGIjQ0VG3dyJEjMWrUKAiCgNzcXAivDXWqXr06jh07hmPHjiE9PR12dnbw9vbGrFmz2IP6FsxmIiIyFTJBMM4g5szMTFy9ehVJSUmwt7eHt7c37OzsCr2flMEdDFAdUeGUXhBm7BKIAADWFWvodX8vtk7R6/6USn060yD7pXejr2xWPL1rgOqICqdU5dbGLoEIAJCTnaDX/Uk9m0XvQVUqXbo0WrZsaazDExFRQQjSHkZE6pjNRERmQOLZLPokScePH1cNHwKAhIQEBAQEwNvbG8HBwaobgxMREZE4mM1ERFQQsbGx+Pzzz9GiRQt4e3ujd+/e2L17t16PIXoD9aeffsKzZ89Uz+fOnYtHjx4hICAAly5d4s3AiYhMSV6eYR5kUpjNRERmxEjZHBcXh8GDB0OhUGDmzJkICQlBvXr18N1332Hbtm16e3uiD/G9f/++6l52L1++xOnTpzFv3jx07twZNWvWxKpVqzBhwgSxyyIiIiq2mM1ERJSfw4cPIy8vD6Ghoap7Z7///vv4888/8fPPP6N///56OY7oDdSsrCyULFkSAPDrr78iNzcXrVq1AvBq9sXExESxSyIiIl0kfjNweoXZTERkRoyUzQqFAlZWVqq8ULKzs0NqaqrejiP6EF9XV1fExsYCAKKiolCnTh2UKVMGAJCUlKT6NxERmQAO8S0WmM1ERGbESNncu3dvAMAPP/yAx48fIzU1FTt37sSFCxcwaNAgvb090XtQAwICMH/+fBw7dgxxcXGYNm2aat3Vq1dRs2ZNsUsiIiIq1pjNRESUH7lcjk2bNmHkyJGqa06tra0xbdo0dO3aVW/HEb2BOnDgQJQvXx6//fYbBgwYgF69eqnWZWRkoE+fPmKXREREurC3s1hgNhMRmREDZXN4eDjCw8NVzwMCAhAQEKB6fu/ePQQHB6NWrVqYPn06SpYsiaioKEybNg0lSpRAjx499FKHTBDM+wKjlMEdjF0CEUovCDN2CUQAAOuKNfS6vxdrx+l1f0qlhiw0yH7JNCie3jV2CUQoVbm1sUsgAgDkZCfodX/Gyubg4GBcv34dv/zyC6ytrVXLx44di7Nnz+L8+fOwsHj3K0hFvwaViIjMiJBnmAcREREVjZGy+ebNm/D09FRrnAJA/fr1kZycjKSkJL28PVGG+Hp5eSE8PBz169eHp6cnZDKZzm1lMhmuX78uRllERJQPIc+sB9nQWzCbiYjMk7Gy2dHRETdu3EB2djZsbGxUy//3v/+hRIkSKFeunF6OI0oDdcSIEXByclL9+20hSERERIbHbCYiosL49NNP8fXXXyMoKAiffPIJSpYsiRMnTiAiIgKDBg1Sa7S+C16DSqQHvAaVTIW+r0HNDP1ar/tTKj1sqUH2S6aB16CSKeA1qGQq9H0NqjGz+fTp01izZg1u3bqFrKwsVK1aFf369cPHH38MS0tLvdQh+iy+REREREREZH7atm2Ltm3bGvQYojZQr127hs2bN+PKlSt48uQJZDIZHB0d0ahRIwQGBqJ27dpilkNERPnhhEaSx2wmIjIzEs9m0Wbx3b59OwICAnD48GFUrFgRfn5+aN++PSpUqICIiAh89NFH2LVrl1jlEBERFXvMZiIiMjWi9KDevn0bs2bNgp+fH6ZNmwYHBwe19UlJSZg+fTpmzJgBHx8f1KxZU4yyiIgoP5zFV7KYzUREZkri2SxKD+qOHTtQvXp1LF68WCMAAaBChQr48ccf8d5772H79u1ilERERAWRl2eYBxkds5mIyExJPJtFaaBevnwZffv2fevMTlZWVujbty9iY2PFKImIiKhYYzYTEZEpEmWI74MHDyCXy/PdTi6XIz4+XoSKiIioQEzojCrpF7OZiMhMSTybRelBTU9Ph52dXb7b2draIiMjQ4SKiIiIijdmMxERmSJRelDzCtHKFwRpX/RrCqwat4ZNM19YvieHrKw98pISobhyFlkR24CXL4xdHhUj/42Jxdotu3Dn3j9ITUuDg305NKxXG8M//xQ1q1czdnkEAPydLFnMZuN6lPgE67bswh9xt/Dn7b/wMisLR3ZvgKuLk9p2WVnZWL56EyKOnkBaWgY8a9XA6OGfo3HDekaqnIoDN7fKWLRwGjr4tYZMJkPUiTMYM3Yq7t9/YOzSCJB8Not2H9Tvv/8etra2b92GZ2jFUeKDfshLSsTLPWuR9/wpLKu6o2SvAbDybIiMWcGS/9KT6UhJTUNtD3d83KcrytuXw8PHT7B28070/2o09m3+CZWdnfLfCRmWxIcRFXfMZuP5J/4hIk+cQR0Pd/g0qINzF69o3e77OYsRff4Sxo4YArfKzti+JwJDR0/G1lU/wlPOmZVJ/0qVKoljR3YiKzsLg4f8B4IgYMb08Th+dBe8G3VAZiY7M4xO4tksSgO1SZMmBdquTJkyaNy4sYGrocylkyGkpaie5/75PwgZaSj95QRYejZA7o2rRqyOipMuHduhS8d2asvqeXmge/8vcfTkWQz6pK9xCiMqBpjNxtW4YV1ER7yaHXn3gUitDdS4W3dx6NgpzPx2NHp39f//19VHr8+GImTNZoTMnyZmyVRMfDHkU9SoURW167bBnTv3AAC//34DcdfP4qsvA7FkaZhxCyTJE6WBunnzZjEOQwX0euNUKfevPwEAFvYVkSt2QUSvsS9XBgDeOrMoiUji91orzpjNxmVhkf80IKfOXoCVlRU+8GujWmZlZYkPOrTF2i07kZ2dDRsbG0OWScVQ927+iIm5omqcAsC9e/dx7twl9OjuzwaqKZB4NosySRKZPiuP+gCAvIf/GLkSKo5yc3OhUCjw9/0ETJ+/HBUrlNfoWSUiKm5u//U33FycUKpkSbXl7tWrQaHIwT/xD41UGUlZ7dpyXPvjT43lf1y/CS+v/Gf+JnpXol2DSqZLZl8BJXoPhOKPWOTeu2nscqgY+uTL0bj+5y0AQFW3yli7bC4qlLc3clUEABCkfZ0LkSlLSU1D2TKaMy2XK1tGtZ5I3xwc7JGcnKyx/PnzZJQvX84IFZEGiWczG6jFXYmSKB08A8jNxYu1C4xdDRVTc74fh4yMTMQ/eIQN2/fgq/98i00/LdKYzZKMQOLDiIiIiMyOxLOZQ3yLM2sb2H79AywcXZDx4yQIz58auyIqpmq+VxX163iiS8d2WLN0DjJfvMSazTuNXRYRkVGVLVMGqWnpGsuVPafKnlQifXr+PAX29pqjmMqXt8fz55rzmBDpG3tQiytLS5QeMRWW78mRsXAC8uL/MnZFRACAsmXsUMW1Mu4n8F5rpkCQ+FT2RKbMvXpVREWfw4uXL9WuQ71z7x9YW1uhqpuLEasjqbp+/Sbq1Na81rS2Vy3cuMFLwUyB1LNZ1B7U7OxsbNy4ETdv8sttVDIZSn31Lay8GiJj+VTk3r1h7IqIVJ4+e46//rmPKq78w4tIDMxm09WuVTPk5OTg6IkzqmU5ObmIjIpGy6Y+nMGXDOJgxFE0a+aD6tWrqpZVq+aGli2b4GDEMSNWRsWFqD2oNjY2WLRoEdauXSvmYekNJT8Lhk3Ttnh5cCuQ9RKWNbxU6/KeP+FQXxJN8KQZqC13h9y9OuxKl8a9+wnYHL4PVpaWGPhxH2OXR4Dkr3MhZrMxHT35quGpnCTuzIVLcLAvh/L25dDEuz685O74wK8N5i0LgyInF26VnRC+7xASHj7CvKnjjVk6SdiatVsxPGgQ9u5Zh++nzocgCJg+bTzu33+AsNW8PZVJkHg2iz7Et2bNmrh//36BbxBO+mdd/9VnX7L7p0D3T9XWvdy/CVk/bzJGWVQMNajjiSMnzmDjjr1QKHLgXKkimvjUxxeBAZwgiUhEzGbjGDN5ttrzHxauAAA09q6HDSHzXy37bgyWrdqI5as3IS09HR7uNRC66AfU9nAXvV4qHjIzX6Bjp35YtHAaNq5fBplMhhMnz2LM2KnIyMg0dnlUDMgEQRC1CX7y5EnMmjULK1asgIeHxzvvL2VwBz1URfRuSi/gTavJNFhXrKHX/WX88Jle96dkO3mLQfZLRaPvbFY8vauHqojeTanKrY1dAhEAICc7Qa/7k3o2i96Dunr1amRmZqJ3795wdXWFo6MjZDKZar1MJsOWLabx4RARFXsSH0ZErzCbiYjMiMSzWfQGqqWlJWrWrCn2YYmIiEgHZjMREZkK0Ruomzfz4moiIrMh8ans6RVmMxGRGZF4Not6mxkiIiIiIiIiXUTvQVVKSUnB33//jaysLI11nEWQiMhESPw6F1LHbCYiMgMSz2bRG6hZWVn49ttv8csvv0DXBMI3btwQuSoiItJKkPYwInqF2UxEZEYkns2iD/FduXIlYmJiMHfuXAiCgClTpuCHH35Ao0aNULVqVYSGhopdEhERUbHGbCYiIlMhegP1yJEjGDFiBLp27QoAaNCgAfr27YstW7bAw8MDZ86cEbskIiLSJU8wzINMCrOZiMiMSDybRW+gPnz4ELVq1YKlpSWsrKzw4sUL1bq+ffvil19+EbskIiKiYo3ZTEREpkL0Bqq9vT0yMjIAAC4uLoiLi1Ote/78OV6+fCl2SUREpIOQl2eQB5kWZjMRkfmQejaLPklSgwYNcOPGDbRt2xb+/v5YunQpMjIyYGlpifXr16NRo0Zil0RERLqY0JAfMhxmMxGRGZF4NoveQP3yyy/x8OFDAEBQUBD++ecfLFu2DLm5uWjYsCGmTZsmdklERETFGrOZiIhMhegN1Hr16qFevXoAADs7OyxfvhzZ2dnIzs6GnZ2d2OUQEdHbSPwsLb3CbCYiMiMSz2bRG6ja2NjYwMbGxthlEBER0f9jNhMRkTGI0kDdv39/obbv1auXgSohIqJCkfjNwIszZjMRkZmSeDaL0kCdOHFigbeVyWQMQSIiIgNjNhMRkSkSpYEaFRUlxmGIiEjfJH6dS3HGbCYiMlMSz2ZRGqiurq5iHIaIiPRMkHgIFmfMZiIi8yT1bDbaJEk3b97EpUuXkJycDHt7ezRt2hS1atUyVjlERETFHrOZiIiMTfQGak5ODiZOnIhDhw5BEP5t/ctkMnTr1g1z586FpaWl2GUREZE2Ej9LS68wm4mIzIjEs1n0BmpISAgiIyMRHByMHj16wNHREU+ePMGBAwewYsUKVKlSBcHBwWKXRUREVGwxm4mIyFSI3kA9cOAAhg0bhqCgINUyV1dXBAUFITc3F3v37mUIEhGZijxpT2VPrzCbiYjMiMSz2ULsAyYmJsLHx0frOh8fHyQmJopcERER6ZQnGOZBJoXZTERkRiSezaI3UCtVqoQrV65oXXflyhVUqlRJ5IqIiIiKN2YzERGZCtGH+Hbv3h2hoaGQyWRq17kcPnwYoaGh+PLLL8UuiYiIdDGhM6pkOMxmIiIzIvFsFr2BOmrUKMTHx2P58uUICQlRLRcEAV27dsWIESPELomIiExMZGQkDh06hGvXriEpKQkuLi7w9/fH0KFDYWdn99bXZmVlYcmSJTh48CBSU1Ph5eWFcePGoUmTJiJVb36YzUREZCpkwuvzyYvo1q1buHjxIlJTU1GuXDk0adKkSPdaSxncwQDVERVO6QVhxi6BCABgXbGGXveXOrSTXvenVHbVkbeu79evH1xcXODn5wdnZ2dcv34dISEhqFGjBnbs2AELC91XqIwdOxanT5/G+PHjUaVKFWzduhXR0dEIDw+Hl5eXvt+KpOgrmxVP7xqgOqLCKVW5tbFLIAIA5GQn6HV/xspmsYjeg6pUq1Yt3vybiMjUGWkYUWhoKBwcHFTPmzZtCnt7e0yYMAExMTFo0aKF1tfFxcUhIiICs2fPRt++fQEATZo0QdeuXbF06VKEhoaKUr+5YjYTEZkBDvHVv9zcXOzfvx9Xr17F48eP4eTkBG9vb/Ts2ZM3AiciIrXGqVK9evUAAI8fP9b5uqioKFhbW6NLly6qZVZWVujatSvCwsKQnZ0NGxsb/RcsAcxmIiIyBaI3UBMSEjBkyBDcu3cPzs7OqFChAm7evIldu3Zh9erVWLNmDVxdXcUui4iItDGhs7QXL14EANSsWVPnNrdv34arqytKlSqlttzd3R0KhQJ///03ewi1YDYTEZkRE8pmQxD9NjMzZ85Eeno6tm3bhlOnTmHPnj04deoUtm7dirS0NMycOVPskoiIyMQ9fvwYy5YtQ8uWLVU9qdqkpKSgXLlyGsvt7e1V60kTs5mIiEyF6A3UCxcuYOzYsRo3BG/UqBHGjBmDmJgYsUsiIiIdhDzBII/w8HD06dNH9QgPD9dZQ0ZGBoKCgmBpaYk5c+aI+O6LD2YzEZH5MFQ2mwrRh/iWLl1a67VFAFChQgWULFlS5IqIiEhsAQEBCAgIyHe7ly9fYtiwYYiPj8fmzZvh7Oz81u3Lli2LhATN2RKTk5MBQGvvKjGbiYjIdIjeg9qjRw/s2LFD67odO3agV69eIldEREQ65QmGeRSAQqFAcHAwrl27hrCwMHh4eOT7Gnd3dyQkJODFixdqy+/cuQNra2tUq1atSB+D1DGbiYjMiBGzWQyi96BWrVoVkZGR6N69O/z9/VGhQgUkJSXhyJEjyMjIQJs2bbB7927V9h9++KHYJRIRkVKekQ6bl4dx48bhwoULWLVqFRo2bFig1/n6+mL58uWIjIxE7969AQA5OTk4fPgwWrVqxRl8dWA2ExGZESNls1hkgiCI2lz29PQs8LYymQw3btx46zYpgzu8a0lE76z0gjBjl0AEALCuWEOv+0sJ9NPr/pTKbY566/qpU6dix44dGDZsGNq3b6+2ztnZGc7OzkhISEDHjh0xfPhwjBw5UrV+9OjROHv2LMaPHw83Nzds374dJ0+exI4dO1CnTh2DvB9zp+9sVjy9+64lEb2zUpVbG7sEIgBATrbmpSfvwljZLBbRe1CjokzjjRMRUf6MNWnCmTNnAAChoaEIDQ1VWzdy5EiMGjUKgiAgNzcXb55nnTNnDhYvXowlS5YgNTUVnp6eWLNmDRunb8FsJiIyH6Y0oZEhiN5A5X3UiIgoPydOnMh3Gzc3N/z5558ay0uWLIlJkyZh0qRJhihNkpjNRERkKkRvoBIRkRmR+FlaIiIisyPxbBalgern54cVK1bA09MTvr6+kMlkOreVyWQ4fvy4GGUREVF+JD4RQ3HGbCYiMlMSz2ZRGqhNmzaFra2t6t9vC0EiIiIyPGYzERGZItFn8dU3zuJLpoCz+JKp0Pcsvs8/aqfX/SmV33XKIPsl08BZfMkUcBZfMhX6nsVX6tlsYewCiIiIiIiIiAAjTZKUnp6O06dP48GDB8jKylJbJ5PJMGLECGOURUREb5L4dS70L2YzEZGZkHg2i95AjY2NRVBQEFJTU7WuZwgSEZkOqd9rjV5hNhMRmQ+pZ7PoDdTZs2fD1dUV69atg1wuh42NjdglEBER0WuYzUREZCpEb6DevXsXS5YsQd26dcU+NBERFZbEhxHRK8xmIiIzIvFsFn2SJBcXF2RnZ4t9WCIiItKB2UxERKZC9AbqyJEjERYWhvT0dLEPTUREhSTkGeZBpoXZTERkPqSezaIP8T158iSSkpLg5+eHhg0boly5cmrrZTIZ5s2bJ3ZZRESkjQkFFhkOs5mIyIxIPJuNMosvANja2uLWrVsa62UymdglERERFWvMZiIiMhWiN1BPnDgh9iGJiKiITGnIDxkOs5mIyHxIPZtFvwaViIiIiIiISBtRelDPnz+vuwArK1SoUAE1atQQoxQiIioMiZ+lLc6YzUREZkri2SxKA3Xw4MGQyWQQBEFt+evXtDg6OmLMmDHo1auXGCUREREVa8xmIiIqitOnTyMsLAzXr1+HTCbDe++9h2+++QYtWrTQy/5FaaBu2rRJ57rc3FwkJibiyJEjmDRpEsqVK4f27duLURYREeVD6te5FGfMZiIi82TMbN6xYwdmzpyJTz/9FMOHD0deXh5u3LiBly9f6u0YMuHNU6dG9PXXXyMpKQlbtmwp8GtSBncwYEVEBVN6QZixSyACAFhX1O+QzES/tnrdn1KlqNMG2S/pX1GyWfH0rgErIiqYUpVbG7sEIgBATnaCXvdnrGyOj49Hly5dMGbMGAwaNMggNQAmNklSt27dcOPGDWOXI+4UTAAAIABJREFUQURERP+P2UxERACwZ88eWFhY4JNPPjHocUyqgWprawuFQmHsMoiI6P8JeYZ5kPlgNhMRmRZjZXNsbCxq1KiBQ4cOoUOHDqhduzY6duyIrVu36vX9iX4f1Lf57bffULlyZWOXQURERP+P2UxERACQmJiIxMREzJ8/H2PGjEGVKlUQGRmJGTNmICcnBwMHDtTLcUyigZqTk4MTJ05g9erVentjRESkB4Is/21IkpjNREQmykDZHB4ejvDwcNXzgIAABAQE/HtYQUBGRgbmzp0Lf39/AECLFi2QkJCAsLAwDBgwQG0m+KISpYHatm1bncXm5ubi+fPnyM3NRcuWLREUFCRGSUREVAAcjitdzGYiIvNkqGx+s0H6Jnt7ewBAy5Yt1Za3atUKZ86cQWJiIpycnN65DlEaqC1atNAZgpaWlqhQoQKaNWum8WaJiIjIMJjNRERUGO7u7rh69arO9RYW+pneqNAN1IyMDCQnJ6NSpUqwtrYu0Gvmzp1b6MKIiMj4hDwO8TUHzGYiouLDWNncsWNH7N69G2fPnsUHH3ygWn7mzBk4OzvD0dFRL8cpcDP35MmT6N27Nxo3boyOHTvi5s2bAIDvvvsOBw8e1EsxREREVHDMZiIiEkvbtm3RrFkzTJ06Fdu3b8fZs2cxefJknD17Fl9//bXejlOgBurx48cxfPhwlC9fHuPGjUNe3r8Dn93c3LB//369FURERKaDt5kxXcxmIqLiyVjZLJPJsHLlSnTp0gXLly/HsGHD8Ntvv2HhwoXo06eP3t5fgRqoISEh6NOnD9atW6cxk1+tWrVUZ2yJiEhaBEFmkAe9O2YzEVHxZMxstrOzw9SpU3Hu3Dlcu3YNBw8eRPfu3fX6/grUQL1z5w66dOkCABoTKpQrVw7Jycl6LYqIiIjejtlMRERSVKBJkuzs7PD8+XOt6xISEuDg4KDXooiIyDRwOK7pYjYTERVPUs/mAvWgtmzZEqtWrUJqaqpqmUwmQ3Z2NrZs2YI2bdoYrEAiIiLSxGwmIiIpKlAP6ujRo/HRRx/hgw8+UN3YOywsDH/++SfS0tKwYsUKQ9dJRERGwNvMmC5mMxFR8ST1bC5QD6qbmxv27duHdu3a4b///S8sLS1x+fJlNGzYELt27YKTk5Oh6yQiIqLXMJuJiEiKCtSDCgDOzs6YPXu2IWshIiITIwjGroDehtlMRFT8SD2bC9xAJSKi4kfqw4iIiIjMjdSzuUAN1EmTJr11vUwm4xlcIiIiETGbiYhIigrUQI2JidFYlpKSgoyMDJQtWxZlypTRe2FERGR8Uj9La86YzURExZPUs7lADdQTJ05oXX7p0iVMnToVCxcu1GtRRERE9HbMZiIikqICzeKrS5MmTTBo0CDMnDlTX/UQEZEJEQTDPMhwmM1ERNIm9Wx+50mSqlSpghs3buijFiIiMjFSH0YkVcxmIiLpkno2v1MPak5ODvbu3ct7rREREZkIZjMREZmzAvWgDhgwQGOZQqHAvXv3kJycjOnTp+u9MCIiMj5BkPZZWnPGbCYiKp6kns0FaqAKWgYl29raomPHjujatSuaNWum98KIiIhIN2YzERFJUYEaqJs3bzZ0HUREZIKEPGNXQLowm4mIiiepZ/M7T5JERETSlSfxYURERP/X3p3Hx3jv//9/TraGWGILJWiUprZIrKU0FS1VRWgr1M4pThBHtY46Wq2jlKqloWgrXVBNlcZarR/qVJ2ulh6trbYmltQaFSKSzO8P38zHSIZg1mse997mdjPX+ppI5+l9vd/X+wI8jdGz2WYDNSUl5ZYOFBsbe8fFAAAA28hmAIDR2WygjhkzpsgHMZlMhCAAGJDRJ2LwNGQzAMDo2WyzgbphwwZn1gEAAG6CbAYAGJ3NBmqVKlWcWQcAwA0Z/WHgnoZsBgAYPZt9XF0AAAAAAADSLcziu2XLFi1ZskSHDh3S5cuXC6xn2BEAGE8hj9qEGyGbAcD7GD2bi9SDunnzZj377LPKysrSwYMHVaNGDVWuXFknTpyQj4+PmjZt6ug6AQAuYM4zOeSFO0c2A4B3Mno2F6mB+vbbb6tnz5565513JEn/+Mc/tHDhQq1evVq5ublq1aqVQ4sEAADWyGYAgBEVqYF68OBBtW7dWj4+PjKZTMrNzZUkhYWFafjw4Zo7d65DiwQAuEae2eSQF+4c2QwA3sno2VykBqqPj498fX1lMplUtmxZHTt2zLIuJCREf/zxh8MKBAAABZHNAAAjKtIkSWFhYTp69KgkqV69evrwww/VsGFD+fn5KSkpiWnvAcCgjP4wcE9GNgOAdzJ6NhepgdqxY0cdOHBAkjR8+HD1799f0dHRkiRfX19NmzbNcRUCAFzG6DMFejKyGQC8k9Gz2WYDNSUlRe3atVOxYsXUs2dPy/J69epp1apV+uabb3Tp0iW1aNFCNWvWdEqxAAB4M7IZAGB0JrO58Db4/fffr+LFi6tt27aKjY3VAw884OzaiiSj/yOuLgFQ8TfecXUJgCTJv3wNux5vR/VOdj1evsgjKx1yXKPzlGy+cuqgq0sAVKwyM1nDPeRkH7Xr8YyezTZ7UN9//32lpKToq6++0ooVK1SpUiV16tRJsbGxCgsLc2aNAAAvdOLECb377rvatWuX9uzZo6ysLG3YsEGhoaE33TcmJsZyf+a15syZo0ce8dwLm2QzAMDobPag5rt06ZK+/PJLrVy5Ut99953MZrMiIiIUGxurDh06qFSpUs6qtVD0oMId0IMKd2HvHtTt1Trb9Xj5ov5YcdNtvv/+e40cOVJ169ZVXl6etmzZcksN1PzHrVwrLCxMpUuXvu263YW7ZzM9qHAH9KDCXdi7B9WV2ewMN22gXuvPP//UihUrtHLlSu3fv18BAQF6+OGH1aVLF7Vu3dqRddpEAxXugAYq3IW9G6jbqjomBBum3jwE8/Ly5ONz9WloS5cu1bhx426pgdqwYUOvmCjIHbOZBircAQ1UuAt7N1Bdmc3OUKTnoOYLCQnRs88+q1WrVunzzz9XbGys1q9fr6FDhzqqPgCAl8pvnOLGyGYAgJEU6TEz1/vvf/+rlStX6quvvpLZbFa1atXsXRcAwA3kefCz1jZt2qQGDRooNzdXderU0aBBgzz6/tObIZsBwDt4cjYXRZEbqAcOHFBKSopWrVql9PR0lShRQh06dFCXLl0UFRXlyBoBt8cwIrgLew8jcpTk5GQlJydb3sfFxSkuLs5ux2/durXq16+v0NBQnTp1SosXL9bQoUM1depUde7smKFRruCu2cx3ItzBdyFNXF0CgNtwwwbqmTNntGrVKq1YsUK7d++Wj4+PHnzwQY0ePVqPPPKIAgICnFUnAMAFzA66SmvvBun1XnrpJav3jz76qLp166bp06d7fAOVbAYA7+aobHYXNhuogwcP1rfffqucnBzVqlVLL7zwgjp27KgKFSo4sz4AAO6Yr6+vHnvsMU2bNk1//vmnQkJCXF3SbSGbAQBGZ7OB+ssvv6hHjx7q0qWL6tSp48yaAABuwoj3uZhMnvuZyGYAgBGz+Vo2G6jffPON/Pxuaw4lAIBBFPk5ZG4uJydHX3zxhSpXruzRvY1kMwDAKNlsi82UIwABAK62bt06SdKuXbskSf/5z39UtmxZlS1bVk2bNpUk1alTR7GxsZo0aZIkafXq1dqwYYOio6NVqVIlnT59WosXL9avv/6q6dOnu+aD2AnZDAAwOpIOAGCTq4cRjRgxwur9q6++Kklq2rSpFi5cKEnKzc1VXl6eZZvQ0FCdPn1aU6dOVUZGhooVK6Z69erpvffeU6tWzC4LAPBsrs5mR6OBCgBwW3v37r3lbSIjI/XRRx85qiQAAOBANFABADYZfSp7AAA8jdGzmQYqAMCmvJtvAgAAnMjo2WyzgXrs2LFbOlDlypXvuBgAAGAb2QwAMDqbDdSYmJhbelbc7t277VIQAMB9mGXsYUSehmwGABg9m202UCdNmuTRDzMHAMBoyGYAgNHZbKB27drVmXUAANxQntGfBu5hyGYAgNGzmUmSAAA25Rl8GBEAAJ7G6Nlc5Abq6dOntXr1ah06dEiXL1+2WmcymTRp0iS7FwcAAGwjmwEARlOkBurBgwfVvXt35eTk6NKlSypTpowyMjKUm5ur0qVLq0SJEo6uEwDgAkafiMGTkc0A4J2Mns0+Rdlo6tSpql+/vrZu3Sqz2ax3331XO3fu1MSJExUYGKg5c+Y4uk4AAHANshkAYERFaqDu2rVLPXr0UEBAgCQpLy9Pfn5+euqpp9SzZ0+99tprDi0SAOAaeQ564c6RzQDgnYyezUVqoGZmZio4OFg+Pj4qWbKkzp49a1lXv3597dq1y2EFAgCAgshmAIARFamBGhoaqpMnT0qSwsLCtG7dOsu6r7/+WiVLlnRMdQAAlzLL5JAX7hzZDADeyejZXKRJklq0aKGtW7eqffv26tevn5577jn9/PPP8vPz08GDBzVkyBBH1wkAcAF3GvIDa2QzAHgno2dzkRqoo0aNUnZ2tiTp8ccfV2BgoNauXausrCz16dNH3bp1c2iRAADAGtkMADCiIjVQAwICLJMwSFJMTIxiYmIcVhQAwD0Y/SqtJyObAcA7GT2bi9RAzXfmzBnt3LlT586dU+vWrRUcHKzLly/L399fPj5Fup0VAADYEdkMADCSIjVQzWazpk6dqkWLFunKlSsymUz67LPPFBwcrPj4eDVs2FBDhw51dK0AACdzp0kTYI1sBgDvZPRsLtKl1fnz52vx4sUaOnSoPv30U5nNZsu61q1b6+uvv3ZUfQAAF8ozOeaFO0c2A4B3Mno2F6kHdenSpRo6dKgGDx6s3Nxcq3XVqlXTH3/84ZDiAABA4chmAIARFamBmp6ergYNGhS6zt/fX5cuXbJrUQAA95Bn8GFEnoxsBgDvZPRsLtIQ34oVK2r//v2Frtu7d69CQ0PtWhQAALgxshkAYERFaqA+9thjmjNnjn7++WfLMpPJpEOHDikpKUmPP/64wwoEALiO2UEv3DmyGQC8k9GzuUhDfIcPH67t27erV69eqly5siRpxIgROn78uKKiojRo0CCHFgkAcA2jP2vNk5HNAOCdjJ7NRWqgBgYGauHChVq1apW2bNmi6tWrW6ax79ixo/z8bulxqgAA4A6RzQAAIypyevn6+io2NlaxsbFWy7Ozs/Xhhx+qb9++di8OAOBaeSZjT8Tg6chmAPA+Rs/mIt2DeubMGavnq0lSVlaWkpKSFBMTo9dff90hxQEAgMKRzQAAI7LZQM3OztbEiRMVFRWlBx98UM2aNdPHH38sSVqxYoUeeeQRTZ06VXfffbfee+89pxUMAHAeo0/E4GnIZgCA0bPZ5hDfOXPmaNGiRWrRooXq1KmjtLQ0TZo0SQcOHNDixYt1zz33aMKECYqJiXFmvQAAeC2yGQBgdDYbqGvXrtUzzzyjl19+2bLss88+07hx4/Tggw9q7ty5CggIcEqRAADXMPpMgZ6GbAYAGD2bbQ7xPX78uB599FGrZW3btpUk9evXjwAEAC+QZ3LMC7eHbAYAuFM2Dxw4UOHh4ZoxY4bdPp/NBmpOTo6CgoKsluW/L1u2rN0KAAAARUM2AwDcxerVq7V37167H/eGj5lJT09Xamqq5X1ubq5lealSpay2rVq1qt2LAwC4Vp7o7nQ3ZDMAeDd3yOaMjAxNnjxZL774okaNGmXXY9+wgZqQkFDo8qFDhxZYtnv3bvtUBAAAbCKbAQCuNm3aNNWqVUtPPPGE8xqokydPtuuJAACex52mnQfZDABwfTb/9NNPSklJ0YoVKxxyfJsN1C5dujjkhAAAz8GERu6FbAYAuDKbs7OzNX78eA0YMEA1atRwyDluOMQXAAAAAGB8ycnJSk5OtryPi4tTXFyc1TbvvfeesrKy9Pe//91hddBABQDYZPRnrQEA4Gkclc2FNUivdezYMc2bN08TJ05Udna2srOzLeuys7N1/vx5BQUFydfX947qoIEKAAAAALih1NRUXb58WS+88EKBdUlJSUpKSlJKSopq1659R+ehgQoAsMnVEzEAAABrrsrm2rVr66OPPiqwvE+fPurUqZOeeuopVatW7Y7PQwMVAGATkyQBAOBeXJXNpUqVUrNmzQpdV7lyZZvrbpWPXY4CAAAAAMAdogcVAGATkyQBAOBe3C2b9+7da9fj0YMKAAAAAHAL9KACAGxyt6u0AAB4O6NnMz2oAAAAAAC3QA8qAMAmM7P4AgDgVoyezTRQAQA2GX0YEQAAnsbo2cwQXwAAAACAW6AHFQBgk9Gv0gIA4GmMns30oAIAAAAA3AI9qAAAm8yuLgAAAFgxejbTQAUA2JRn8JkCAQDwNEbPZob4AgAAAADcAj2oAACbjD4RAwAAnsbo2UwPKgAAAADALdCDCgCwyehXaQEA8DRGz2YaqAAAm4w+UyAAAJ7G6NnMEF8AAAAAgFugBxUAYJMrp7I/ceKE3n33Xe3atUt79uxRVlaWNmzYoNDQ0Jvum5eXp3fffVfJyck6efKkwsLCNHToULVr184JlQMA4Dg8ZgYAABc4cuSIvvjiC5UqVUqNGze+pX1nzZqlxMRE9ezZU++++64iIyM1YsQIbd682UHVAgAAe6AHFQBgkysnYmjSpIm2bt0qSVq6dKm2bNlSpP1Onz6tBQsWaNCgQRo4cKAk6YEHHtCRI0c0bdo0RUdHO6xmAAAczeiTJNGDCgBwSz4+txdR33zzja5cuaJOnTpZLe/UqZP27dun1NRUe5QHAAAcgAYqAMAms4NejvT7778rICBA1atXt1peq1YtSdKBAwccXAEAAI7jidl8KxjiCwCwKc+tIqtoMjIyVKpUKZlM1rNIlC5dWpJ07tw5V5QFAIBdeGI23woaqAAAp0tOTlZycrLlfVxcnOLi4lxYEQAAcAc0UAEANjlqIgZHNkhLlSql8+fPy2w2W/WiZmRkSJKCg4Mdcl4AAJyBSZIAAPAgtWrVUnZ2tv744w+r5b///rsk6d5773VFWQAAoAic0oM6evToIm9rMpk0ZcoUB1YDACgqT7zLpVWrVvL399eqVas0bNgwy/KVK1fqvvvuU9WqVV1YnfsgmwHAM3liNt8KpzRQf/rppyJve/2kFgAA13H1MKJ169ZJknbt2iVJ+s9//qOyZcuqbNmyatq0qSSpTp06io2N1aRJkyRJ5cqVU79+/TR//nwFBQWpTp06Wrt2rb777jvNnTvXNR/EDZHNAOCZXJ3NjuaUBurGjRudcRoAgMGMGDHC6v2rr74qSWratKkWLlwoScrNzVVennVcjxw5UsWLF9dHH32kkydPKiwsTDNnzlTr1q2dU7gHIJsBAO6ISZIAADblubjjbO/evbe1ja+vr+Lj4xUfH++IsgAAcBlXZ7OjMUkSAAAAAMAtOKUHNTo6usj3r5hMJm3atMnBFQEAisLoDwP3ZmQzAHgmo2ezUxqozZs3Z4IFAPBAxo5A70Y2A4BnMno2O6WB+vrrrzvjNAAAoIjIZgCAO2KSJACATUafyh4AAE9j9GxmkiQAAAAAgFugBxUAYJPRJ2IAAMDTGD2baaACAGwydgQCAOB5jJ7NDPEFAAAAALgFpzZQs7OzNWnSJP3yyy/OPC0A4DblOegF90E2A4BnMXo2O7WBGhAQoOTkZF2+fNmZpwUAADaQzQAAd+L0Ib61a9fWvn37nH1aAMBtyJPZIS+4F7IZADyH0bPZ6Q3UMWPGaMGCBdq0aZPMZvf5QQAA4K3IZgCAu3D6LL4jRozQX3/9pfj4ePn5+als2bIymUyW9SaTSZs2bXJ2WQCAQtBU8Q5kMwB4DqNns9MbqM2bN7cKPQCA+3KnSRPgOGQzAHgOo2ez0xuor7/+urNPCQAAboBsBgC4C6c3UAEAnsNs+IFEAAB4FqNns8saqHv27NHBgweVnZ1dYF1sbKwLKgIAwLuRzQAAV3N6A/X8+fMaNGiQdu7cKUmW2QKvvfeFEAQA92D0+1xwFdkMAJ7D6Nns9MfMTJ8+XefOndOiRYtkNps1e/Zsffjhh+rYsaOqVq2qpUuXOrskAIANRn/WGq4imwHAcxg9m53eQN2yZYuGDBmiyMhISVKlSpXUrFkzTZ06Vc2bN9dHH33k7JIAAPBqZDMAwF04vYF68uRJhYaGytfXV3fddZcyMzMt69q2bavNmzc7uyQAgA1mB73gXshmAPAcRs9mpzdQy5cvr7/++kuSVLlyZe3YscOy7siRI84uxyv5NW6l4kPHq+Qbi1Vq/hqVmPS+7npqoBRYzNWlwQuFhlZW8ifv6PTJ3Tpzao+Wfvquqlat7OqyAK9CNrsHvg/hDko2r6fGaSkFXpG/LnZ1afASTp8kqVGjRtqxY4dat26tzp07a/bs2Tp69Kh8fX2VkpKimJgYZ5fkde56rJvyTv+prGULlHf2lHyr1VRgbB/53R+pzNcSJLM7XUOBkRUrFqj1X36qy9mX1X/gP2Q2mzXh1dH6/75aqqhGj+jixUuuLtHrudM9KXAcstn1+D6Eu/njpXeVuXO/5b05J9eF1eBaRs9mpzdQhw0bpj///FOSNHDgQJ07d05r165VVlaWYmJiNG7cOGeX5HUuzhon818Zlve5e3+ROfMvFX/2n/K9v4Fyd++4wd6A/fxtYE/VqFFNdeo9pAMHDkuS/ve/3drz2xYNera3Zs56x7UFwvAzBeIqstn1+D6Eu7m0P1WZ2/a5ugwUwujZ7PQGarVq1VStWjVJkr+/v8aMGaMxY8Y4uwyvdm3jNF/uob2SJJ/g8uL6GJyl4xNt9f332yz/GJOkw4dTtXXrj+rUsS3/IAOchGx2Pb4PAeAqp9+DCvfkFx4hSco7/oeLK4E3qVPnPu36dW+B5b/+tk+1a9/ngopwPbOD/gNgje9DuJsaic+p0ZFlivzfRwqb/ZwCKpd3dUn4f4yezU7pQZ09e3aRtzWZTBo6dKgDq8H1TMHldFeXvrry68/KPcxQDjhP2bLBOnfuXIHlZ8+eU5kypV1QEeA9yGb3wvch3EXO+UydmJeiv777VbkXLqp43Rq6e/iTKrlyin5r95xyThcciQfYEw1Ub3dXoIonTJByc3VpwRuurgaAmzH6fS7ejGwGUJhLvx5S2q+HLO8vfPerLnz/q2qvfkMhAzro2Bsfu7A6SMbPZqc0UPfs2eOM0+BW+QcoaMRE+VS4W5lTRsl89pSrK4KXOXs2Q8HBwQWWlykTrLNnuUILOBLZ7F74PoQ7u7jroLIOHlNQg1quLgVewOmTJMFN+Pqq+NDx8r3nPmVO+6fy0g7dfB/Azn77bZ/q1il4b1Wd2rW0ezfDzd2BO92TAhgZ34fwDGSCOzB6Nrusgbpp0yb98MMPysjIUOnSpdWsWTM9/PDDrirHu5hMKjZorPxqRypz5jjlHtzt6orgpVat/kpTp7yksLBqOnTo6gRd1auHqkWLJhr7r8kurg6S8YcRwRrZ7Dp8H8KdFY+4V4H3VtbZtVtdXQpk/Gw2mc1mpzbBL1y4oCFDhuinn36Sn5+fgoOvTgqQm5urxo0ba968eQoKCiry8TL6P+LAao0psPcI3RXTUVmrFitnx3dW6/LOnmSo720ot5hG/u0oXryYtv20XpeysvTy+Kkym8169ZXRKlkiSFGNHlFm5kVXl+hxcrKP2vV4fe950q7Hy/fh4WUOOS5uj72z2S+gigOrNSa+D+3vu5Amri7BI4UljtTlP9J1cddB5WZkqni9Gqo07EnlXbqs3Y89p5yzf7m6RI/TOC3FrsczejY7vQd1xowZ+vXXXzV16lR16NBBvr6+ys3N1Zo1a/TKK69oxowZPBDcwfwjrn5hB3bsKXXsabUuK+UjXV7xkSvKghe6ePGSHm3XTW9Oe0Ufvv+WTCaTNm7aoudGjecfY24iz7nXMOEiZLPr8X0Id3Fpzx8qG9tKIf07yKfYXco5eU7nvvhOx95cQuPUTRg9m53eg9qyZUs9++yz6tu3b4F1H374od577z198803RT4ePahwB/Sgwl3Yuwe1d/Wudj1evoVHljvkuLg99s5melDhDuhBhbuwdw+q0bPZ6T2o586dU82aNQtdV7NmzUKfAQYAcA1jX6NFPrIZADyH0bPZx9knDA0N1aZNmwpdt3nzZoWGhjq5IgCALXkyO+QF90I2A4DnMHo2O70HtXv37nr99dd18eJFdezYUSEhITp58qTWrl2rpUuXasyYMc4uCQAAr0Y2AwDchdMbqP369dOZM2f0/vvv6/PPP5ckmc1m+fv7a9CgQYXe/wIAcA2jP2sNV5HNAOA5jJ7NLnkO6nPPPaeBAwdqx44dlmetRUZGqnTp0q4oBwAAr0c2AwDcgUsaqJJUunRpRUdHu+r0AIAiMPrDwGGNbAYA92f0bHZJA/X8+fP64IMPtGPHDqWnp6tixYqKiopS3759VapUKVeUBAAohDtNmgDHIpsBwDMYPZudPovvnj171LZtW73zzju6fPmy7r33Xl2+fFnz589Xu3bttHfvXmeXBACAVyObAQDuwuk9qBMnTlRwcLCWLVumKlX+70HeaWlp+tvf/qaJEydq4cKFzi4LAFAIo0/EgKvIZgDwHEbPZqf3oP7vf//TiBEjrAJQuvoMtoSEBP3yyy/OLgkAAK9GNgMA3IXTe1CDg4MVEBBQ6LqAgAAFBwc7uSIAgC1Gn4gBV5HNAOA5XJXN69at05o1a7Rr1y6dPn1ad999t9q2bavBgwerRIkSdjuP03tQe/TooQULFujy5ctWy7OyspSUlKSePXs6uyQAALwa2QwAuJmkpCT5+Pho5MiReu+999SjRw8tWbJEAwYMUF6e/ZrNTu9BvXTpko4dO6aHH35Y0dHRKleunE6fPq3NmzcrMDBQFy9e1KxZsyRJJpNJCQkJzi4RAPD/mM3Gvs8FV5HNAOA5XJWmgA4zAAAgAElEQVTN8+bNU9myZS3vmzZtquDgYP3zn//U999/r+bNm9vlPE5voM6fP9/y55SUlALr582bZ/kzIQgArmX0qexxFdkMAJ7DVdl8beM0X/369SVJ6enpdjuP0xuoe/bscfYpAQDADZDNAIDb8cMPP0iS7r33Xrsd0+kNVACA52CSJAAA3Iu7ZHN6erreeusttWjRwtKTag80UAEAAADAyyUnJys5OdnyPi4uTnFxcYVum5mZqb///e/y9fXV5MmT7VqHUxqotWvXVnJysiIiInT//ffLZDLZ3NZkMum3335zRlkAgJsw+sPAvRnZDACeyVHZfKMG6bWysrI0ZMgQpaWlaeHChapUqZJd63BKA3Xo0KGqWLGi5c83CkEAgPtgkiTjIpsBwDO5MpuvXLmihIQE7dq1S++//77Cw8Ptfg6nNFCHDRtm+fPw4cOdcUoAAHADZDMA4Fbk5eXp+eef13fffaf58+crMjLSIefhHlQAgE08BxUAAPfiqmx+9dVXtW7dOg0ZMkTFihXTjh07LOsqVapkt6G+TmugXrhwQdu3b1dOTo6aNm2qoKAgHTx4ULNnz9bevXtVtmxZ9e7dW23btnVWSQAAeDWyGQBQVN98842kq8/Gvvb52NLVUTn2Go3jlAbqoUOH1L9/f6Wnp8tsNqt8+fKaN2+enn32WZnNZlWtWlX79u3TiBEjtGDBArVo0cIZZQEAbsJdprKH/ZHNAOCZXJXNGzdudMp5fJxxklmzZumuu+5SUlKSPv30U9WsWVNDhw5VnTp1tHnzZi1dulRff/21mjRponfeeccZJQEAisDsoP/gemQzAHgmo2ezUxqo27ZtU3x8vJo3b66IiAi99NJLSk9PV8+ePXXXXXdJkooVK6bevXtr//79zigJAACvRjYDANyRU4b4njp1StWqVbO8r1q1qiQpJCTEarsKFSrozJkzzigJAFAErpzK/vjx45o8ebK+/fZbmc1mtWjRQmPHjlXlypVvuq+tae9TUlJUu3Zte5fqkchmAPBMRn8EnFMaqHl5efL19bW8z//z9c9c4xlsAABJunTpkvr27auAgABNmTJF0tUhqX369NHKlStVvHjxmx6ja9euBR44fs899ziiXI9ENgMA3JHTZvFNT09XamqqJCk3N9eyrFSpUpZtTpw44axyAABF4Kqp7D/99FOlpqZq3bp1ql69uqSrvaLt2rVTcnKy+vfvf9NjhISEOOwZbUZBNgOA5zH6I+Cc1kBNSEgosGzo0KFW781mM1dqAQDauHGjGjRoYGmcSleHoDZs2FAbNmwoUgMVN0c2AwDcjVMaqJMnT3bGaQAAduaq+1x+//13tWnTpsDymjVrat26dUU6xieffKIFCxbI19dXDRo0UEJCgho3bmzvUj0W2QwAnol7UO2gS5cuzjgNAMDOHDXtfHJyspKTky3v4+LirO4XzcjIsBpmmq906dI6f/78TY/fqVMntW7dWiEhITp69KgWLFigvn37KikpSc2aNbPPh/BwZDMAeCZ3eiSMIzhtiC8AAPmub5Da2xtvvGH5c+PGjdWmTRt17NhRM2fO1JIlSxx2XgAAcGdooAIAbMpz0UQMpUqVKrSn1FbP6s2UKFFC0dHR+uyzz+xRHgAALuOqbHYWH1cXAADA9WrWrKn9+/cXWH7gwAHVrFnzto/LZD8AALg3GqgAAJvMDnrdTExMjHbu3Gl5BIokpaWladu2bYqJibnlz3HhwgV9/fXXioiIuOV9AQBwJ67KZmdhiC8AwCZXzRTYrVs3LV68WPHx8RoxYoRMJpNmzZqlSpUqWd27evToUT366KOKj4/XsGHDJEkLFizQoUOH1KxZM4WEhOjYsWNKSkrSqVOnNG3aNJd8HgAA7IVZfAEAcLLixYvrww8/1OTJkzV69GiZzWY1b95cY8eOVVBQkGU7s9ms3Nxcq4eWh4WFaf369Vq/fr0uXLigEiVKKCoqSq+99ho9qAAAuDkaqAAAm1x5lbZy5cpKTEy84TahoaHau3ev1bKYmJjbGgYMAIAnMHoPKvegAgAAAADcAj2oAACbzAafyh4AAE9j9GymgQoAsMnow4gAAPA0Rs9mhvgCAAAAANwCPagAAJvMBr9KCwCApzF6NtODCgAAAABwC/SgAgBsMvpEDAAAeBqjZzM9qAAAAAAAt0APKgDAJqPPFAgAgKcxejbTQAUA2GT0YUQAAHgao2czQ3wBAAAAAG6BHlQAgE1GH0YEAICnMXo204MKAAAAAHAL9KACAGwy+sPAAQDwNEbPZhqoAACb8gw+EQMAAJ7G6NnMEF8AAAAAgFugBxUAYJPRhxEBAOBpjJ7N9KACAAAAANwCPagAAJuMfp8LAACexujZTAMVAGCT0YcRAQDgaYyezQzxBQAAAAC4BXpQAQA2GX0YEQAAnsbo2UwPKgAAAADALdCDCgCwyej3uQAA4GmMns30oAIAAAAA3AI9qAAAm4x+nwsAAJ7G6NlMAxUAYJPRhxEBAOBpjJ7NDPEFAAAAALgFelABADaZzXmuLgEAAFzD6NlMDyoAAAAAwC3QgwoAsCnP4Pe5AADgaYyezTRQAQA2mQ0+UyAAAJ7G6NnMEF8AAAAAgFugBxUAYJPRhxEBAOBpjJ7N9KACAAAAANwCPagAAJuMfp8LAACexujZTAMVAGBTnsFDEAAAT2P0bGaILwAAAADALdCDCgCwyWzwiRgAAPA0Rs9melABAAAAAG6BHlQAgE1Gn4gBAABPY/RspgcVAAAAAOAW6EEFANhk9IeBAwDgaYyezTRQAQA2GX0YEQAAnsbo2cwQXwAAAACAW6AHFQBgk9EfBg4AgKcxejbTgwoAAAAAcAv0oAIAbDL6fS4AAHgao2czDVQAgE1GnykQAABPY/RsZogvAAAAAMAt0IMKALDJ6MOIAADwNEbPZnpQAQAAAABugR5UAIBNRp/KHgAAT2P0bKaBCgCwyWzwiRgAAPA0Rs9mhvgCAAAAANwCPagAAJuMPowIAABPY/RspgcVAAAAAOAW6EEFANhk9KnsAQDwNEbPZnpQAQAAAABugQYqAMAms4P+K4rjx48rISFBjRo1UsOGDTVs2DAdO3asSPtevnxZU6ZMUcuWLRUREaG4uDj9+OOPd/KjAADALXhqNhcVDVQAgE1ms9khr5u5dOmS+vbtq4MHD2rKlCmaOnWqjhw5oj59+ujixYs33X/s2LFaunSpEhISNH/+fFWoUEEDBw7U7t277fFjAQDAZTw1m4uKe1ABAG7n008/VWpqqtatW6fq1atLksLDw9WuXTslJyerf//+Nvfds2ePVq9erUmTJunJJ5+UJDVp0kQdOnTQrFmzNG/ePKd8BgAAjOROsvlW0IMKALDJVVdpN27cqAYNGlgCUJKqVq2qhg0basOGDTfcd8OGDfL399fjjz9uWebn56cOHTpoy5Ytys7Ovv0fCAAALuaJ2XwraKACANzO77//rvvuu6/A8po1a+r333+/6b5VqlRRsWLFCux75coVHTlyxK61AgDgDe4km28FQ3wBADa5aiL7jIwMlSpVqsDy0qVL6/z58zfdt3Tp0gWWBwcHW9YDAOCpPDGbb4XHN1BLv///uboEQDnvu7oCwDFyso865LjJyclKTk62vI+Li1NcXJxDzgXnc9TvDQDA+Nns8Q1UAIDnuVnolSpVqtCrsbau3l6/79GjBcP73LlzklRo7yoAAN7Okdl8K7gHFQDgdmrWrKn9+/cXWH7gwAHVrFnzpvsePXpUly5dKrCvv7+/1eQOAACgaO4km28FDVQAgNuJiYnRzp07lZqaalmWlpambdu2KSYm5qb7XrlyRevWrbMsy8nJ0dq1a9WyZUsFBAQ4rG4AAIzqTrL5VpjMRZlTGAAAJ7p48aI6d+6swMBAjRgxQiaTSbNmzVJmZqZWrlypoKAgSdLRo0f16KOPKj4+XsOGDbPsP3LkSG3ZskWjR49WaGiolixZok2bNumTTz5R3bp1XfWxAADwWEXN5jtFAxUA4JaOHTumyZMn69tvv5XZbFbz5s01duxYhYaGWrZJS0tTmzZtNGzYMA0fPtyyPCsrSzNmzNDq1at1/vx53X///Xr++efVrFkzV3wUAAAMoSjZfKdooAIAAAAA3IJX34M6ZswYPfTQQ4Wu+/777xUeHq6tW7fe1nHtOQ77emlpaQoPD9fy5csddo58vXv3Vnh4uMLDw1W7dm01adJEnTt31r///e9Cb5JOTExUeHi4w+uC/Sxfvtzyd5z/99yqVSuNGDFCBw8etNrWGX+/4eHhSkxMdOg5pML//4+JidHzzz/v8HMDsI1svjmy2fjI5v9DNnsfHjPjAPHx8erTp4+ry7Cb8PBwTZgwQZJ04cIF7d+/X8uWLdMnn3yisWPHqmfPnpZtn376abVq1cpVpeIOzJo1S5UqVVJubq5SU1P19ttvq1+/flqzZo1Kliwpib9fAJ6LbOa72xORzfBGNFAdoFq1aq4u4ZZkZ2ffcFbLoKAgRUZGWt63bNlSvXr10qhRozRx4kTVr19fERERkqRKlSqpUqVKDq8Z9le7dm3L4zcaNWqkkJAQ9e/fX9u2bVN0dLQkz/v7vdnvNgDvQTZ7znc3/g/ZDG/k1UN8b1X+EIM1a9aoffv2ioyMVNeuXfXTTz9ZbXf9MKL8YT+LFy/W5MmT1bx5czVo0ECDBw9WWlqa1b5XrlzRjBkzFBMTo3r16ikmJkYzZszQlStXbljbL7/8ooSEBD300EOKiIhQu3btNH36dGVlZVlt17t3b/Xo0UMbN25UbGys6tWrp48//viWfxb+/v4aP368fH19tXDhQsvywoaZfPjhh2rfvr0iIiLUpEkTde3aVevXr7fa5quvvlK3bt3UoEEDNW7cWAkJCTp27JjVNmvWrFGfPn30wAMPKCoqSrGxsfr8888L1Gav83m7EiVKSLr6eI58hf39hoeHa8aMGZo7d67l969nz57avXu31XZms1kffPCB2rVrp3r16qlly5aaMGGCLly4cMM6jhw5ohdeeEExMTGKiIhQmzZtNH78eGVkZFhtlz8saPv27erevbsiIiI0derUO/kRAPAAZPP/IZuNj2yGN6AH9Rb9/PPPOnTokEaMGKG77rpLs2bN0pAhQ7Rx40aVKlXqhvu+8847ql27tiZPnqzTp09rxowZGjhwoFavXi1/f39JV/9H/uKLLzR48GA1atRI27dv17x585SWlqY333zT5rGPHz+u+++/X126dFFQUJD279+vt99+W6mpqZoxY4bVtocPH9bEiRMVHx+vqlWrqnTp0rf1syhXrpzq1aunbdu22dxm5cqVmjJliuLj49W4cWNdvnxZe/fu1blz5yzbLFmyRK+88oq6du2qoUOHKjMzU4mJierVq5dWrlxp+TJOTU1Vu3btNGjQIPn4+OjHH3/UuHHjlJWVpR49etj9fN4mNzdXOTk5ysvLU2pqqqZPn65y5coVadbTlJQU3X333Xr55ZeVnZ2tWbNmqV+/fvryyy8VHBwsSZoxY4bmz5+vnj17qnXr1jpw4IBmzZqlPXv2aNGiRfLxKfx62Z9//qm7775bY8eOVenSpZWamqr58+dr0KBBSk5Ottr2r7/+0nPPPacBAwZo5MiRCgwMvPMfDAC3Rzb/H7LZWMhmeCMaqLfowoULSklJsQRH+fLl9dRTT2nz5s3q2LHjDfcNCgrS22+/bfmf/Z577tEzzzyjlJQUPf3009q3b59Wr15t9biEli1bytfXV7NmzdKzzz6r+++/v9Bjt2vXzvJns9mshg0bqkSJEvrnP/+pl19+WWXKlLGsP3v2rJKSklS7du07+llIUuXKlfXbb7/ZXL9jxw6Fh4dbPZ8wf0iKJGVmZmratGnq2rWrJk+ebFlev359tW/fXp999pn69esnSRoyZIhlfV5enpo2baqTJ09qyZIllhC05/m8Tfv27a3eh4SEaN68eUX6R0FWVpaSkpJUvHhxSbL0FHzwwQf6xz/+oXPnzikpKUldunTRyy+/LElq1aqVypQpo9GjR2vTpk1q06ZNocdu0qSJmjRpYnkfFRWlatWqqWfPnvrtt99Up04dy7qLFy/qjTfe0COPPHLLnx+A5yKbrZHNxkE2wxsxxPcWRUZGWl3VzB9Scfz48Zvu265dO6srUY0aNVKlSpW0Y8cOSdKPP/4oSerUqZPVfvnv89cX5sKFC5b/+evXr6+6detq9OjRMpvNOnLkiNW2VapUsUsASlcD12Qy2Vxfv3597d69W//+97+1detWXbp0yWr9jh07dOHCBXXq1Ek5OTmW1913362wsDCrIVqHDx/Wc889p1atWqlu3bqqW7euli5dqkOHDjnkfN5mzpw5+uyzz7R06VLNmTNHNWvW1KBBg3TgwIGb7hsdHW0JQEkKDQ1VgwYNLL/bO3fu1JUrVwr8bnfo0EF+fn43/N3Ozs7WvHnz9NhjjykiIkJ169a1TP5x7d+9dHV4W+vWrYv8mQEYA9lsjWw2DrIZ3sire1B9fX2Vl5dX6Lr85b6+vlbLrx9yk3+T9+XLl296vvLlyxdYVq5cOaWnp0uSZdx+hQoVrLbJf3/9uP5rvfjii9q6dasSEhJUu3ZtFStWTL/88osmTJhQoLbrj38njh8/fsPjxcbG6vLly/rss8/08ccfy8/PT9HR0RozZoxCQ0N1+vRpSbJ5ZTT/552ZmakBAwYoMDBQo0aNUrVq1eTv768lS5Zo2bJldj+fN6pVq5ZlIgbpag9BdHS0EhMTNXPmzBvuW65cuQLLypcvb3ncQf4wrut/V/z8/BQcHHzD3+3p06dr0aJFio+PV1RUlIKCgpSenq5hw4YV+N0uU6ZMgf9nAXgWsvnOkc3GQTbDG3l1A7VcuXI6e/ZsobOJ5QdTYcF1u06dOlVg2enTpy1XTPO/gE+dOmU12+DJkyet1l/v8uXL2rBhg4YNG6a+fftalu/bt6/Q7W90VfVWnD59Wrt27VKHDh1sbmMymdS9e3d1795dGRkZ+vbbb/X6669r5MiRWrp0qeUeiNdff101a9YssH9QUJCkq1dXjx49qsWLF6tx48aW9YsWLXLI+SAFBgaqatWq2rt37023zf/HxbVOnTqlihUrSpLl537q1CnVqlXLsk1OTo7OnTt3w398rFmzRp07d1Z8fLxl2XfffVfotvb63QbgOmTznSGbjY1shjfw6iG+zZo1U05OjjZu3Fhg3VdffaUKFSooLCzMbuf78ssvra4K//zzzzpx4oRlmvj8sfxr1qyx2m/VqlWSpKZNmxZ63OzsbOXm5srPz/p6Q2Gz6NnLlStX9Oqrryo3N1e9e/cu0j6lS5fW448/rvbt21uu3jVs2FBBQUE6cuSI6tevX+BVo0YNSbIMB8qfsEK6etV6w4YNDjkfrv7MU1NTVbZs2Ztuu3nzZl28eNHyPi0tTTt37rT8bjdo0ED+/v4FfrfXrl2rnJwcm7/b0tV7aK7/3V6+fPmtfBQAHoRsvn1ks/GRzfAGXt2D2qJFCz344IN68cUXdfDgQTVo0ECZmZlas2aNNmzYoMmTJ9ucvex2ZGZmKj4+Xt27d9eZM2c0ffp03XPPPYqNjZUk3XfffXriiSc0e/Zs5ebmKioqStu3b9fcuXP1xBNPFJhCPF/JkiUVGRmp999/XyEhISpTpoyWLVtmudJsj7rz71fIzMzUvn37tHz5ch06dEjjx49XvXr1bO770ksvWZ7VVq5cOR0+fFgrVqzQgw8+KOnqdOmjR4/WhAkTdObMGT300EMqWbKk0tPT9eOPP6pp06bq2LGjZWKJV199VQkJCbp48aLmzp2rMmXK6K+//rL7+bzR7t27dfbsWZnNZp08eVKLFi3SuXPn1KtXr5vuGxgYqAEDBuhvf/ubsrOz9dZbb6lEiRKW4VrBwcEaMGCA5s+fr2LFiik6OloHDhzQzJkz1ahRIz388MM2j92qVSulpKTovvvuU/Xq1fXVV19p+/btdvrUANwN2Vz0uslm4yOb4Y28uoFqMpn09ttva+7cuVqxYoXefvtt+fv7q3bt2pozZ47dZxsbNGiQ/vjjD40ZM0aXLl1Ss2bN9NJLL1ldeZw8ebJCQ0O1bNkyzZ07VyEhIfrb3/5mNfNdYd5880298sorevXVVxUYGKj27dvrX//6lwYPHnzHde/du1dxcXEymUwKCgpSaGiomjZtqunTp1sNCSlMw4YNtXz5cq1YsUJ//fWXQkJC1KlTJyUkJFi26d69u+6++2699957Wr16tXJzc1WxYkU1atTIMsSqbNmymj17tqZMmaKEhASFhISoT58+ysjI0OzZs+1+Pm80YsQIy5/Lli2rWrVq6b333lOrVq1uum9sbKyKFSumCRMm6OzZs6pfv75mzJhhGT4kSSNHjlTZsmW1ZMkSLVmyRMHBwYqNjdWoUaNu+I/NcePGyWw2W+61eeihh/Tmm2/q6aefvoNPC8Bdkc1FQzZ7B7IZ3shkNpvNri7C6NLS0tSmTRtNnDiR/3FhOOHh4RoyZIhGjhzp6lIAoMjIZhgZ2QxP5tX3oAIAAAAA3AcNVAAAAACAW2CILwAAAADALdCDCgAAAABwCzRQAQAAAABugQYqAAAAAMAt0EDFHVu+fLnCw8MVHh6uQ4cOFVj/ww8/WNZv3brVLudMS0tTeHi4li9ffsv79u7dW717977pdvk1h4eHq06dOoqJidGLL76oEydO3E7Jd1Tj999/r/DwcH3//fdFPsb58+eVmJioX3/99abHBwAYC9lsf2Qz4Bx+ri4AxhEUFKQVK1boH//4h9Xyzz//XEFBQcrMzHRRZbeva9euiouLU05Ojvbs2aPExERt27ZNK1asUGBgoNPqqFu3rpKTk1WzZs0i73P+/HnNnj1blSpVUt26da3WjR8/3t4lAgDcENnsOGQz4Bj0oMJu2rZtq5UrV+raiaGzsrL05Zdfql27di6s7PaFhIQoMjJSjRs3Vq9evTRmzBgdPnxY//nPf2zuk52dbfc6SpQoocjISJUoUcIux6tZs+YtBSoAwDORzVeRzYDnoIEKu+ncubOOHTumn3/+2bJs/fr1MpvNatu2baH7rFixQp06dVL9+vXVrFkzvfDCC/rzzz+ttrl06ZJeeeUVNWvWTFFRURoyZIjNoTw//PCD+vbtq6ioKEVGRmrgwIHat2+f3T5j/fr1JUlHjhyRdHU4To8ePbRx40bFxsaqXr16+vjjjyVJqampGjVqlB544AHVq1dPnTt31vr16wscc82aNXrsscdUr149dejQodBtbA0jWr9+vbp3766oqCg1bNhQTz31lDZs2KC0tDS1adNGkjRu3DjLcKj8YVeFDSM6ePCghg4dqsaNGysiIkLdunUrEPaJiYkKDw/X4cOHNWjQIEVFRal169aaPXu28vLybudHCgBwILKZbAY8DQ1U2E3lypXVpEkTrVixwrIsJSVFjzzyiIoXL15g++TkZI0ePVr33nuvZs+ereeff15btmxR7969rYYcvfzyy/rss8/Ur18/JSYmKiwsTM8//3yB43399dfq16+fihcvrjfeeEPTpk1TZmamevbsqePHj9vlM6alpUmSSpUqZVl2+PBhTZw4Ub169dKCBQv0wAMP6Pjx4+rWrZv27t2rF198UXPnzlWdOnU0fPhwbdiwwbLv1q1bNWrUKN1zzz2aPXu2Bg4cqNdee63Q+4Wut3DhQg0bNkzlypXT66+/rlmzZunRRx/V0aNHFRISotmzZ0uSBg8erOTkZCUnJ+vhhx8u9Fjp6el65plntGfPHr300kuaOXOmSpYsqcGDB2vz5s0Fth82bJgeeOABzZkzR23atFFiYqI+//zzW/lRAgCcgGwmmwFPwz2osKvOnTtrypQpGjdunDIyMvTf//5X7777boHtcnNzNWvWLDVt2lQzZsywLA8LC1PPnj21bNky9enTRwcPHtTq1as1cuRIDRo0SJLUsmVLXbx4UZ988onVMV977TU1adJEc+fOtSx74IEH1KZNGyUlJelf//rXbX2mnJwc5ebmavfu3ZoyZYqKFStmFSZnz55VUlKSateubVk2duxYmc1mLVy4UGXKlJEktWrVSidOnNBbb71luYL61ltvqUaNGnr77bfl43P1elGNGjUUFxensLAwmzVduHBB06dP16OPPmoJu/xz5Muvp2rVqoqMjLzhZ/zggw90/vx5JScnq3r16pKk6OhoPf7445o5c6aio6Ottu/fv7+efPJJSVKLFi30/fffa82aNZZlAAD3QTZfRTYDnoEeVNjVY489puzsbG3cuFGrVq1S+fLl1bx58wLbHTp0SKdPn1anTp2sljdu3FhVqlTRjz/+KEn65ZdflJeXp/bt21tt16FDB6v3hw8f1h9//KGOHTsqJyfH8goMDFRUVJR++umn2/o88+bNU926dRUREaG4uDj5+fnpnXfeUcWKFS3bVKlSxSoAJembb75RdHS0SpYsaVVPy5YttWfPHl24cEG5ubnatWuX2rVrZwlASYqMjFSVKlVuWNe2bdt08eJFdevW7bY+1/V+/PFHNWjQwBKAkuTr66snnnhCu3fv1oULF6y2v/5qb61atXTs2DG71AIAsC+y+SqyGfAM9KDCrkqUKKFHHnlEK1as0NGjR9WxY0erL/h8586dkyRVqFChwLry5ctb1uff81KuXDmrba5/f/r0aUnSv/71r0KvxlauXPk2Po305JNPqkePHvLz81OlSpUsV1yvVdhnOHPmjFJSUpSSklLocc+ePausrCxduXJF5cuXL7C+sGXXyv/5VKpUqSgf46YyMjIKBHl+HWazWRkZGVaTQJQuXdpqu4CAAIdMQAEAuHNk81VkM+AZaKDC7jp37qzBgwcrLy9P06dPL3Sb4OBgSdLJkycLrDt16pRl6vWQkBBJV0Pu2ntl8kPv+uONGtb4LicAAAOASURBVDWq0KvC/v7+t/FJrgZc/uQLtphMpgLLgoOD1ahRIz377LOF7lOxYkX5+vrK399fp06dKrD+1KlTN7xSmx/G6enpuu+++25YX1GULl3aZh0mk6lA6AEAPAvZTDYDnoIhvrC7Bx98UO3bt1f37t1Vq1atQrcJCwtT+fLltXbtWqvl27Zt09GjR9W0aVNJUkREhHx8fPTFF19YbbdmzRqr9zVq1FCVKlW0f/9+1a9fv8Dr/vvvt+MnvLlWrVpp7969qlWrVqH1BAQEyNfXV/Xq1dOXX35pNcvezp07dfTo0RsePyoqSsWLF9enn35qc5uAgABJVx8ncDNNmjTRzp07LRNNSFfvRVq7dq3q1Kljtyn0AQCuQTaTzYCnoAcVdufr62vz6uy12yQkJOjll1/W888/r06dOik9PV0zZ87UPffcY7mhv0aNGnriiSf01ltvKS8vT/Xr19eWLVsKTLFuMpk0fvx4xcfH68qVK2rfvr3KlCmjU6dOafv27apcubL69+/vsM98vYSEBD399NPq2bOnevXqpSpVquj8+fPat2+fUlNTNXnyZMt2AwYMUHx8vLp3764zZ84oMTGx0KFJ1ypRooRGjRqlf//73xo+fLg6duyooKAg7d69W3fddZd69+6t8uXLKzg4WGvWrFF4eLiKFSum0NDQQodC9evXT59//rkGDBig4cOHq0SJEvr44491+PBhzZ8/3yE/IwCA85DNZDPgKWigwmXi4uIUGBioBQsWKD4+XkFBQXrooYf0wgsvWA0ZmjBhgooXL66kpCRduXJFzZo107Rp0/TMM89YHS86OlqLFi3SvHnzNG7cOGVlZalChQpq0KCBHn/8cad+tsqVK2vZsmVKTEzU9OnTdfbsWQUHB6tWrVqKjY21bNeiRQtNmzZNiYmJGjZsmKpXr66xY8fqo48+uuk5evXqpfLly2vBggV6/vnn5efnp3vvvVfx8fGSJB8fH7322muaPn26+vfvr5ycHE2ePFldu3YtcKyKFSvq448/1rRp0/TKK68oOztbtWvX1vz58/XQQw/Z7wcDAHBrZDPZDLiayWw2m11dBAAAAAAA3IMKAAAAAHALNFABAAAAAG6BBioAAAAAwC3QQAUAAAAAuAUaqAAAAAAAt0ADFQAAAADgFmigAgAAAADcAg1UAAAAAIBboIEKAAAAAHAL/z8G+4oBQ/MY3wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_hat = xgb.predict(x_test)\n",
        "cf = metrics.confusion_matrix(y_pred=y_hat, y_true=y_test)\n",
        "cf_train = metrics.confusion_matrix(y_pred=xgb.predict(x_train), y_true=y_train)\n",
        "names_df = [\n",
        "    \"Unipolar Disease\",\n",
        "    \"Bipolar II\"\n",
        "]\n",
        "cf_df = pd.DataFrame(data=cf, columns=names_df, index=names_df)\n",
        "cf_train_df = pd.DataFrame(data=cf_train, columns=names_df, index=names_df)\n",
        "fig, ax = plt.subplots(ncols=2, figsize=(16,8))\n",
        "\n",
        "sns.heatmap(cf_df, fmt=\"d\",annot=True, ax=ax[0])\n",
        "ax[0].set_xlabel(\"Model Prediction\", size=16)\n",
        "ax[0].set_ylabel(\"Real Value\", size=16)\n",
        "ax[0].set_title(\"Confusion Matrix (Test set)\", size=20)\n",
        "\n",
        "sns.heatmap(cf_train_df, fmt=\"d\",annot=True, ax=ax[1])\n",
        "ax[1].set_xlabel(\"Model Prediction\", size=16)\n",
        "ax[1].set_ylabel(\"Real Value\", size=16)\n",
        "ax[1].set_title(\"Confusion Matrix (Train set)\", size=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d9a1a246-4ac2-4bf5-9a8d-cdb3eb9db586",
      "metadata": {
        "id": "d9a1a246-4ac2-4bf5-9a8d-cdb3eb9db586"
      },
      "source": [
        "## SHAP Values"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39ba9956-ccec-4541-9023-b7806ffe1196",
      "metadata": {
        "id": "39ba9956-ccec-4541-9023-b7806ffe1196"
      },
      "source": [
        "Now we are going to use what we learnt from the SHAP values to understand how our model works. We use the framework developed by Lundberg et al. https://github.com/slundberg/shap "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Run if shap not installed\n",
        "! pip install shap "
      ],
      "metadata": {
        "id": "2wIa0erNpb32",
        "outputId": "be155bbd-6313-484f-c38a-48cd3415b06e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "2wIa0erNpb32",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: shap in /usr/local/lib/python3.7/dist-packages (0.40.0)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.51.2)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap) (21.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap) (1.0.2)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.7/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.4.1)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.64.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.21.6)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>20.9->shap) (3.0.9)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.34.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "36a2cfbd-ce50-4eac-bb5c-18b15b919374",
      "metadata": {
        "id": "36a2cfbd-ce50-4eac-bb5c-18b15b919374"
      },
      "outputs": [],
      "source": [
        "import shap"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec159fa2-3759-4faa-8927-841b96830b8a",
      "metadata": {
        "id": "ec159fa2-3759-4faa-8927-841b96830b8a"
      },
      "source": [
        "**We are going to explain the xgboost model. Since the model developed previously is a tree based we are going to use Tree explainer, a fast implementation to compute Shap values when models are tree based**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7ae0671-faf0-4abc-aa02-c581032d931e",
      "metadata": {
        "id": "f7ae0671-faf0-4abc-aa02-c581032d931e"
      },
      "outputs": [],
      "source": [
        "explainer = shap.TreeExplainer(xgb, x, model_output = \"raw\")\n",
        "shap_values = explainer.shap_values(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b6f5edc-ce7e-423d-9aba-ea1bad0f2281",
      "metadata": {
        "id": "7b6f5edc-ce7e-423d-9aba-ea1bad0f2281"
      },
      "source": [
        "**Local interpretability: How do we interpret the SHAP force plot?**\n",
        "\n",
        "\n",
        "The value we observe in bold is the natural logarithm of the odd ratio $ln(\\frac{P(Y=1)}{1-P(Y=1)})$. The base value is the odd ratio if we just consider the number of positive cases over the total of instances. The Shapley values can be understood as forces that push the output of the model. Forces of Shapley values in blue represent the features that increase the probability of default, while red values represent the features that are incrementing the probability of having a bipolar disease. \n",
        "\n",
        "A force plot allows us to study a concrete case in order to give a reason for diagnosing a potential bipolar disease."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd132f67-0be2-4895-82bb-bfcb7e457c87",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "bd132f67-0be2-4895-82bb-bfcb7e457c87",
        "outputId": "198b3544-31d2-47cc-f503-9e1a6978961d"
      },
      "outputs": [
        {
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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              " <script>\n",
              "   if (window.SHAP) SHAP.ReactDom.render(\n",
              "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": -0.6043063879809448, \"outValue\": 1.776260693249324, \"link\": \"identity\", \"featureNames\": [\"days\", \"gender\", \"age\", \"melanch\", \"inpatient\", \"edu\", \"marriage\", \"work\", \"madrs1\", \"madrs2\", \"activity_mean\", \"activity_std\"], \"features\": {\"1\": {\"effect\": 0.18341467885131185, \"value\": 2.0}, \"2\": {\"effect\": -0.19727548347278076, \"value\": 42.0}, \"5\": {\"effect\": 0.18031671186062423, \"value\": 8.0}, \"6\": {\"effect\": 0.01967227179557085, \"value\": 2.0}, \"8\": {\"effect\": 0.35140771368010476, \"value\": 25.0}, \"9\": {\"effect\": 0.36042833975939587, \"value\": 21.0}, \"10\": {\"effect\": -0.311144751496613, \"value\": 276.281}, \"11\": {\"effect\": 1.7937476002526553, \"value\": 320.105}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
              "    document.getElementById('iP9GHROEZ6NNHKOUVQPBT')\n",
              "  );\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "shap.initjs()\n",
        "patient_index = 11\n",
        "shap.force_plot(explainer.expected_value, shap_values[patient_index,:], x.iloc[patient_index,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f2e2f23-4b08-4c30-8b7a-3321d7269c51",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f2e2f23-4b08-4c30-8b7a-3321d7269c51",
        "outputId": "ac177075-324e-40e8-f155-5950a2a57861"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------Patient Real Diagnosis------\n",
            "Unipolar Depressive\n",
            "-------Patient Predicted Diagnosis------\n",
            "Probability of being diagnosed with a Unipolar Depressive \n",
            "0.9350000023841858\n"
          ]
        }
      ],
      "source": [
        "patient_index = 2\n",
        "\n",
        "# Patient real target value\n",
        "diagnosis_map = {1.0: \"Bipolar II\", \n",
        "                0.0: \"Unipolar Depressive\"}\n",
        "print(\"-------Patient Real Diagnosis------\")\n",
        "\n",
        "print(y.replace(diagnosis_map).iloc[patient_index].values[0])\n",
        "print(\"-------Patient Predicted Diagnosis------\")\n",
        "pred_class = xgb.predict(x.loc[patient_index:patient_index, :])[0]\n",
        "if pred_class==0.0:\n",
        "    pred_diagnosis = diagnosis_map[0.0]\n",
        "    p1 = xgb.predict_proba(x.loc[patient_index:patient_index, :])[0][0]\n",
        "    p1_round = round(p1,3)\n",
        "else:\n",
        "    pred_diagnosis = diagnosis_map[1.0]\n",
        "    p1 = clf_rf.predict_proba(x.loc[patient_index:patient_index, :])[0][1]\n",
        "    p1_round = round(p1,3)\n",
        "print(f'Probability of being diagnosed with a {pred_diagnosis} \\n{p1_round}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d8ce4d8-32f4-4592-b431-7228aeb11981",
      "metadata": {
        "id": "0d8ce4d8-32f4-4592-b431-7228aeb11981"
      },
      "source": [
        "**Global interpretability: SHAP Summary plot.**\n",
        "\n",
        "The SHAP summary plot allows us to understand the model by ranking the features from the most relevant to the least important ones. Each datapoint represents a different person. In this concrete use case, the most relevant feature are: the variation of the activity mesasurements obtained of the actigraph watch, the Montgomery Asberg Depression Rating Scale at the moment the actigraph was stopped. High values of these features impact negatively in the output of the model (the probability of having a bipolar disease increases). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "83dcec58-57a3-47fb-a372-90a585c78feb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "83dcec58-57a3-47fb-a372-90a585c78feb",
        "outputId": "9bef5a31-194e-4591-ca21-75de68dc8785"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x453.6 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "shap.summary_plot(shap_values, x, show=False)\n",
        "\n",
        "plt.savefig(\"shap_summary_plot.png\", bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8793127e-74c4-4209-a93d-a9849f8dd7af",
      "metadata": {
        "id": "8793127e-74c4-4209-a93d-a9849f8dd7af"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.5 64-bit ('3.9': conda)",
      "language": "python",
      "name": "python395jvsc74a57bd00cbbe149128e7864af0f5fd1a409e76d26140030e0601ee028f7d5be035d0348"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.5"
    },
    "colab": {
      "name": "SHapley Additive exPlanations (SHAP) for Bipolar Disease Prediction.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}