{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hhboorstein/ds_portfolio/blob/main/Santander_Customer_Satisfaction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ts2yVY_0HlvI"
      },
      "source": [
        "<a name='top'></a>\n",
        "\n",
        "![Santander Logo](https://www.santanderbank.com/documents/330006/330008/logo+%281%29.png/ca903dab-71ae-9b33-0bab-05c7d754b18f?t=1600208431481&download=true)\n",
        "\n",
        "# Santander Customer Satisfaction\n",
        "\n",
        "\n",
        "Data is from the 2016 [Banco Santander competition](https://www.kaggle.com/competitions/santander-customer-satisfaction/overview) on Kaggle.\n",
        "\n",
        "Cyphered customer data is supplied with the goal of predicting customer satisfaction. The target flag is encoded:\n",
        "* 1: Unsatisfied,\n",
        "* 0: Not expilcitly unsatisfied.\n",
        "\n",
        "(From the perspective of philosophy of logic, the above encoding reflects our rejection of the Law of Excluded Middle for this binary classification. That is to say, it is possible for a customer to be labelled in class 0 without being unconditionally satisfied. Ultimately though, this is just semantic quibbling in the spirit of Wittgenstein, not Russell.)\n",
        "\n",
        "### About the Author\n",
        "\n",
        "[LinkedIn](https://www.linkedin.com/in/hi-im-harrison/)\n",
        "\n",
        "[Portfolio](https://internetweb.site/portfolio)\n",
        "\n",
        "\n",
        "###Table of Contents\n",
        "Note on HTML jump links: [Open in nbviewer](https://nbviewer.org/github/hhboorstein/ds_portfolio/blob/main/Santander_Customer_Satisfaction.ipynb) to use jump links.\n",
        "\n",
        "[Setup](#setup)\n",
        "\n",
        "[Model Evaluation Functions](#model-eval-funcs)\n",
        "\n",
        "[Vanilla Models](#vanilla)\n",
        "\n",
        "[Oversampled Models](#oversampled-models)\n",
        "\n",
        "[Model Tuning](#tuning)\n",
        "\n",
        "[Concluding Remarks](#conclusion)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CWQyn57LVKwv"
      },
      "source": [
        "<a name='setup'></a>\n",
        "# Setup\n",
        "\n",
        "[Skip to data preprocessing.](#data-pre)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SelQk3qOJr9E"
      },
      "source": [
        "## Update Libraries\n",
        "\n",
        "Running the lastest builds of:\n",
        "* scikit-learn\n",
        "* imbalanced-learn\n",
        "* XGBoost\n",
        "* LightGBM\n",
        "* CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-IejLDqKNjE",
        "outputId": "561aa5f1-a6f9-4fb3-b1ad-c30c639db677"
      },
      "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: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.22.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install -U scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOkqVqMTKRHp",
        "outputId": "e865399d-b929-47db-805a-01032a51f9c5"
      },
      "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: xgboost in /usr/local/lib/python3.10/dist-packages (1.7.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.22.4)\n"
          ]
        }
      ],
      "source": [
        "! pip install -U xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evCTben5KWEJ",
        "outputId": "97a9c1f6-ad2d-4d1e-d720-80770e45acc2"
      },
      "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: lightgbm in /usr/local/lib/python3.10/dist-packages (3.3.5)\n",
            "Requirement already satisfied: scikit-learn!=0.22.0 in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.2.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from lightgbm) (0.40.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.22.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn!=0.22.0->lightgbm) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn!=0.22.0->lightgbm) (1.2.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install -U lightgbm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3W96An6KZP2",
        "outputId": "3a2ed691-350f-4a23-a702-2a782ed5bdfe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting catboost\n",
            "  Downloading catboost-1.1.1-cp310-none-manylinux1_x86_64.whl (76.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.22.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.13.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.10.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->catboost) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.4)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.0.9)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (8.4.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.39.3)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.2)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.1.1\n"
          ]
        }
      ],
      "source": [
        "! pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3j7prw7RNYi",
        "outputId": "94fcecda-a01a-49b4-a5d0-dce9209de053"
      },
      "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: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install -U imbalanced-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BI_lF_AVKaQB"
      },
      "source": [
        "## Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4ISiBdbYEV1B"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import metrics\n",
        "\n",
        "from xgboost import cv as XGB_CV\n",
        "from xgboost import DMatrix\n",
        "\n",
        "#plotting\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# cluster analysis\n",
        "from sklearn.cluster import KMeans\n",
        "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer\n",
        "\n",
        "# models\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.ensemble import StackingClassifier, VotingClassifier\n",
        "\n",
        "# processing pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "kjOHUTn2S925"
      },
      "outputs": [],
      "source": [
        "sns.set_theme()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uPuN6xcKo-6"
      },
      "source": [
        "## Read Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We import our data, stored as an Apache parquet file."
      ],
      "metadata": {
        "id": "kTD0fmopsoBp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "JZyiqHNRGpea"
      },
      "outputs": [],
      "source": [
        "bank=pd.read_parquet('santander_train.parquet')\n",
        "data=bank.copy()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bank.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hwe2j-l9szkm",
        "outputId": "d179600c-5844-4082-c813-eb7ff56de4fd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(76020, 371)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have around 76k records with 371 features."
      ],
      "metadata": {
        "id": "VcW4ivg7s14H"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xq6MF_qAHH9-",
        "outputId": "82cafab4-a327-4d99-bb05-8a2dccbf7ee7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
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              "35732                  1391.55                      0.0   \n",
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              "\n",
              "       imp_op_var40_comer_ult3  imp_op_var40_efect_ult1  \\\n",
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              "14162                      0.0                     0.0   \n",
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              "24191                      0.0                     0.0   \n",
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              "70885                      0.0                     0.0   \n",
              "72468                      0.0                     0.0   \n",
              "\n",
              "       saldo_medio_var33_ult3  saldo_medio_var44_hace2  \\\n",
              "14162                     0.0                      0.0   \n",
              "35732                     0.0                      0.0   \n",
              "24191                     0.0                      0.0   \n",
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              "70885                     0.0                      0.0   \n",
              "72468                     0.0                      0.0   \n",
              "\n",
              "       saldo_medio_var44_hace3  saldo_medio_var44_ult1  \\\n",
              "14162                      0.0                     0.0   \n",
              "35732                      0.0                     0.0   \n",
              "24191                      0.0                     0.0   \n",
              "10440                      0.0                     0.0   \n",
              "46585                      0.0                     0.0   \n",
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              "36671                      0.0                     0.0   \n",
              "70885                      0.0                     0.0   \n",
              "72468                      0.0                     0.0   \n",
              "\n",
              "       saldo_medio_var44_ult3          var38  TARGET  \n",
              "14162                     0.0   46969.410000       1  \n",
              "35732                     0.0  307194.780000       0  \n",
              "24191                     0.0  109659.060000       0  \n",
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              "46585                     0.0  117310.979016       0  \n",
              "46064                     0.0  103667.040000       0  \n",
              "27661                     0.0  195088.740000       0  \n",
              "36671                     0.0   59630.010000       0  \n",
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              "\n",
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              "      <th>ID</th>\n",
              "      <th>var3</th>\n",
              "      <th>var15</th>\n",
              "      <th>imp_ent_var16_ult1</th>\n",
              "      <th>imp_op_var39_comer_ult1</th>\n",
              "      <th>imp_op_var39_comer_ult3</th>\n",
              "      <th>imp_op_var40_comer_ult1</th>\n",
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              "      <th>...</th>\n",
              "      <th>saldo_medio_var33_hace2</th>\n",
              "      <th>saldo_medio_var33_hace3</th>\n",
              "      <th>saldo_medio_var33_ult1</th>\n",
              "      <th>saldo_medio_var33_ult3</th>\n",
              "      <th>saldo_medio_var44_hace2</th>\n",
              "      <th>saldo_medio_var44_hace3</th>\n",
              "      <th>saldo_medio_var44_ult1</th>\n",
              "      <th>saldo_medio_var44_ult3</th>\n",
              "      <th>var38</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14162</th>\n",
              "      <td>28459</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>46969.410000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35732</th>\n",
              "      <td>71476</td>\n",
              "      <td>2</td>\n",
              "      <td>33</td>\n",
              "      <td>0.0</td>\n",
              "      <td>930.21</td>\n",
              "      <td>1391.55</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>307194.780000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24191</th>\n",
              "      <td>48386</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>109659.060000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10440</th>\n",
              "      <td>20945</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>71302.530000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46585</th>\n",
              "      <td>93165</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>117310.979016</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46064</th>\n",
              "      <td>92159</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>103667.040000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27661</th>\n",
              "      <td>55359</td>\n",
              "      <td>2</td>\n",
              "      <td>62</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>195088.740000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36671</th>\n",
              "      <td>73262</td>\n",
              "      <td>2</td>\n",
              "      <td>41</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>59630.010000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70885</th>\n",
              "      <td>141557</td>\n",
              "      <td>158</td>\n",
              "      <td>65</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>83606.940000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>72468</th>\n",
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              "      <td>2</td>\n",
              "      <td>24</td>\n",
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              "      <td>74988.840000</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 371 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca2ed0eb-8fdd-4947-8649-3a7fed44b837')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ca2ed0eb-8fdd-4947-8649-3a7fed44b837');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "bank.sample(10,random_state=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The only recognizable columns are the ID column and the TARGET column. The rest of the attributes are cyphered, according to the source linked above."
      ],
      "metadata": {
        "id": "CxcOaMOSs8xE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sogfh3ulRCMD",
        "outputId": "3b92d6a5-359f-4986-9147-8fd52fad3b81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 76020 entries, 0 to 76019\n",
            "Columns: 371 entries, ID to TARGET\n",
            "dtypes: float64(111), int64(260)\n",
            "memory usage: 215.2 MB\n"
          ]
        }
      ],
      "source": [
        "bank.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data frame requires around 215 MB of memory."
      ],
      "metadata": {
        "id": "HOb5dBYLvZlp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jy56djZGSqGL",
        "outputId": "a6f50f30-1a61-48d4-de87-d126443152cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# null check\n",
        "bank.isna().sum().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dysTNfsESuy6",
        "outputId": "2102759b-bd4e-478a-aa49-047fa0969f6f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# duplicate row check\n",
        "bank.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BXQQjapTMeA"
      },
      "source": [
        "There are no null entries or duplicated rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGPHqYQzHIck",
        "outputId": "35f34e93-842e-4333-a250-c3ff4b5ed17c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.960431\n",
              "1    0.039569\n",
              "Name: TARGET, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "bank['TARGET'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This dataset is imbalanced, with only 4% of records in the positive class. Thankfully, most of our customers are not unsatisfied. On the other hand, this imbalance will make detection and modeling more delicate."
      ],
      "metadata": {
        "id": "qb8DS3GRvuP4"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpBid-fmVbZ4"
      },
      "source": [
        "<a name='data-pre'></a>\n",
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finding Superfluous Columns"
      ],
      "metadata": {
        "id": "7Hl49dVX0Yw7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find constant columns\n",
        "const_col=[]\n",
        "\n",
        "for col in bank.columns:\n",
        "  if bank[col].std()==0:\n",
        "    const_col.append(col)"
      ],
      "metadata": {
        "id": "MENhFdIa9MbX"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# find duplicate columns\n",
        "dup_bool=bank.T.duplicated()\n",
        "\n",
        "dups=[]\n",
        "for idx in dup_bool.index:\n",
        "  if dup_bool[idx]==True:\n",
        "    dups.append(idx)"
      ],
      "metadata": {
        "id": "19b7iNcKEolq"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "remove=const_col+dups\n",
        "print(f'There are {len(remove)} columns to remove.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8ytQEkhFDMx",
        "outputId": "963b52c6-58fb-44dd-f836-1d38caf5b6df"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 96 columns to remove.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We found 96 columns that were either constant or a duplicate of another column. Note that this is an *inclusive* OR, so it is possible that fewer than 96 columns will be removed."
      ],
      "metadata": {
        "id": "DXh3XQviHqbe"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVsOr4qjDKVR"
      },
      "source": [
        "### Split\n",
        "\n",
        "We separate the data into predictive features and our target. Then we split into training data and validation data. There is no need to reserve data for final evaluation, as we have that data stored in a separate file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "EK8p53ZrS7fE"
      },
      "outputs": [],
      "source": [
        "X=bank.drop(['ID','TARGET']+remove,axis=1)\n",
        "y=bank['TARGET']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "sV0lQ4qbVpqP"
      },
      "outputs": [],
      "source": [
        "# split into training and validation sets\n",
        "X_train,X_val,y_train,y_val=train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.3,\n",
        "    stratify=y,\n",
        "    random_state=57\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We reserve 30% of the data for our validation set."
      ],
      "metadata": {
        "id": "jWhhv-yCw-_N"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mae64YbuDO7v"
      },
      "source": [
        "### Scaling\n",
        "\n",
        "In order to demonstrate the PCA transformation that follows, we first need to scale our data. Scaling will shortly be incorporated into a preprocessing pipeline, rendering this section redundant."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "jwBC3s6OSKQ2"
      },
      "outputs": [],
      "source": [
        "scaler=StandardScaler().set_output(transform='pandas')\n",
        "\n",
        "X_ts=scaler.fit_transform(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "nYPklpf3OjmK",
        "outputId": "438ff0eb-9bb1-49ca-ac02-c8ad9ac526ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           count          mean       std        min       25%  \\\n",
              "var3                     53214.0  5.608072e-18  1.000009 -27.956373  0.035750   \n",
              "var15                    53214.0 -2.281951e-16  1.000009  -2.172472 -0.785866   \n",
              "imp_ent_var16_ult1       53214.0 -6.008649e-18  1.000009  -0.049542 -0.049542   \n",
              "imp_op_var39_comer_ult1  53214.0 -5.207496e-18  1.000009  -0.219330 -0.219330   \n",
              "imp_op_var39_comer_ult3  53214.0  1.215082e-17  1.000009  -0.220725 -0.220725   \n",
              "\n",
              "                              50%       75%         max  \n",
              "var3                     0.035750  0.035750    0.042356  \n",
              "var15                   -0.477731  0.446674    5.530898  \n",
              "imp_ent_var16_ult1      -0.049542 -0.049542  117.774053  \n",
              "imp_op_var39_comer_ult1 -0.219330 -0.219330   26.065719  \n",
              "imp_op_var39_comer_ult3 -0.220725 -0.220725   38.221859  "
            ],
            "text/html": [
              "\n",
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              "    <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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>var3</th>\n",
              "      <td>53214.0</td>\n",
              "      <td>5.608072e-18</td>\n",
              "      <td>1.000009</td>\n",
              "      <td>-27.956373</td>\n",
              "      <td>0.035750</td>\n",
              "      <td>0.035750</td>\n",
              "      <td>0.035750</td>\n",
              "      <td>0.042356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>var15</th>\n",
              "      <td>53214.0</td>\n",
              "      <td>-2.281951e-16</td>\n",
              "      <td>1.000009</td>\n",
              "      <td>-2.172472</td>\n",
              "      <td>-0.785866</td>\n",
              "      <td>-0.477731</td>\n",
              "      <td>0.446674</td>\n",
              "      <td>5.530898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imp_ent_var16_ult1</th>\n",
              "      <td>53214.0</td>\n",
              "      <td>-6.008649e-18</td>\n",
              "      <td>1.000009</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>-0.049542</td>\n",
              "      <td>117.774053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imp_op_var39_comer_ult1</th>\n",
              "      <td>53214.0</td>\n",
              "      <td>-5.207496e-18</td>\n",
              "      <td>1.000009</td>\n",
              "      <td>-0.219330</td>\n",
              "      <td>-0.219330</td>\n",
              "      <td>-0.219330</td>\n",
              "      <td>-0.219330</td>\n",
              "      <td>26.065719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imp_op_var39_comer_ult3</th>\n",
              "      <td>53214.0</td>\n",
              "      <td>1.215082e-17</td>\n",
              "      <td>1.000009</td>\n",
              "      <td>-0.220725</td>\n",
              "      <td>-0.220725</td>\n",
              "      <td>-0.220725</td>\n",
              "      <td>-0.220725</td>\n",
              "      <td>38.221859</td>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e737a016-9ed2-4909-857f-afa46e9826b5 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-e737a016-9ed2-4909-857f-afa46e9826b5');\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",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "X_ts.describe().T.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that every attribute has mean approximately 0 and standard deviation approximately 1."
      ],
      "metadata": {
        "id": "cq-qm_FMx2WG"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-fe7VLUOsag"
      },
      "source": [
        "### PCA\n",
        "\n",
        "Principal Component Analysis is an implementation of eigen decomposition. In effect, it is a coordinate transformation, where the new axes reflect explained variance in the data. Moreover, these axes, or *components*, are ordered decreasingly by explained variance. Many of the components can thus be discarded (off the end), as they generally do not contribute much to the explanation of variance. In this way, PCA can be used for dimension reduction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzlM9U3aO7RX",
        "outputId": "37ecf702-1a2f-4ca6-81f7-87b6a775a030"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of features: 306.\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of features: {X_ts.shape[1]}.')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before dimension reduction, we have 306 features in our dataset."
      ],
      "metadata": {
        "id": "IWYBu3_K9K47"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "owwShlu5OuZ2",
        "outputId": "d9e56c2d-f7ba-47a0-f3ca-2433256ceab9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=37)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# major reduction test\n",
        "pca37=PCA(n_components=37)\n",
        "\n",
        "pca37.fit(X_ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Q4yJlY4hSaoY",
        "outputId": "a5cf0d5f-7759-4673-e1f9-bb975435204c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.title('Cumulative variance explained by eigenvectors',fontsize=15)\n",
        "plt.step(\n",
        "    np.arange(1,38),\n",
        "    np.cumsum(pca37.explained_variance_ratio_),\n",
        "    where='mid'\n",
        ")\n",
        "plt.xlabel('Number of Eigenvectors')\n",
        "plt.ylabel('Cumulative Variance');"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find that 37 components explain about 75% of our variance: not enough. Moreover, we can see graphically that the right side of the curve is still increasing, not levelling off. We'll need more components.\n",
        "\n",
        "We know that 306 features can explain 100% of the variance in our data. Can we get away with fewer?"
      ],
      "metadata": {
        "id": "ZDbBc3zP9UMA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Z20gUwbKTRzs",
        "outputId": "d2be951a-21c1-4645-8fa5-537a9ff67355"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=123)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=123)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# about 1/3 the size\n",
        "pca123=PCA(n_components=123)\n",
        "\n",
        "pca123.fit(X_ts)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evr=pca123.explained_variance_ratio_\n",
        "\n",
        "plt.title(f'Cumulative explained variance (reaches {np.round(sum(evr)*100,2)}%)',fontsize=15)\n",
        "plt.step(\n",
        "    np.arange(1,124),\n",
        "    np.cumsum(evr),\n",
        "    where='mid'\n",
        ")\n",
        "plt.xlabel('Number of Eigenvectors')\n",
        "plt.ylabel('Cumulative Variance');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "sFY6BVIMIkIU",
        "outputId": "60c5ae04-2d92-4b0f-d601-875c53a9e33a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can explain nearly 100% of the variance in our data with just a third of the components. This decreases the memory required to store the data and massively reduces computation time during training."
      ],
      "metadata": {
        "id": "3BmMSRgX-jNL"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bu_nTk0DOx9h"
      },
      "source": [
        "### Pipeline\n",
        "\n",
        "We now incorporate scaling and PCA into a preprocessing pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "4Qy6LqeYWFGB"
      },
      "outputs": [],
      "source": [
        "# preprocessing pipe\n",
        "pre=Pipeline(\n",
        "    steps=[\n",
        "        ('Scaler',StandardScaler()),\n",
        "        ('Dimension_Reduction',PCA(n_components=123))\n",
        "    ]\n",
        ").set_output(transform='pandas')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "6s6SJaRwfKCS"
      },
      "outputs": [],
      "source": [
        "X_ts=pre.fit_transform(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "XdM2_T0ofeXD"
      },
      "outputs": [],
      "source": [
        "X_vs=pre.transform(X_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We fit the pipeline on our training data and then use it to transform our validation data. This approach ensures the integrity of our analysis by preventing data leakage."
      ],
      "metadata": {
        "id": "rkqI98DO_Kc-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZUH2b-OvbVQ",
        "outputId": "a9dacf5b-0a85-4d46-b07d-8efaf94f99f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Memory usage reduced to 40.39% of original data frame.\n"
          ]
        }
      ],
      "source": [
        "a=X_ts.memory_usage().sum()/X_train.memory_usage().sum()\n",
        "print(f'Memory usage reduced to {np.round(a*100,2)}% of original data frame.')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As expected, PCA reduced memory usage by roughly 60%."
      ],
      "metadata": {
        "id": "5Xww8236_7rD"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooG22ZP-DQtI"
      },
      "source": [
        "### Oversampling"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjISpRmiADrI",
        "outputId": "ccaac3cc-558a-45e4-bb87-9b734d150a3f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.960424\n",
              "1    0.039576\n",
              "Name: TARGET, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Only around 4% of the supplied data belongs to the positive class. We oversample to balance the classes using the SMOTE method."
      ],
      "metadata": {
        "id": "-O1NqbVw0pcK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "AyJOkNk9DIL_"
      },
      "outputs": [],
      "source": [
        "smote=SMOTE(\n",
        "    sampling_strategy='not majority',\n",
        "    random_state=1,\n",
        "    k_neighbors=5\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "BMYKdo3EDkyz"
      },
      "outputs": [],
      "source": [
        "# oversampled training data\n",
        "Xt_over,yt_over=smote.fit_resample(X_ts,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# re-scale data\n",
        "Xt_over=scaler.fit_transform(Xt_over)"
      ],
      "metadata": {
        "id": "EEpF_emLGECK"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLWgvJEFEQ0P",
        "outputId": "24e25c63-9e38-4d48-b135-08b60b9140ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.5\n",
              "1    0.5\n",
              "Name: TARGET, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "yt_over.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Target classes are now balanced."
      ],
      "metadata": {
        "id": "PxhUDXU004xW"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wdsB24ZGLjY"
      },
      "source": [
        "<a name='model-eval-funcs'></a>\n",
        "# Model Evaluation Functions\n",
        "\n",
        "This first function collects training and validationscores for a given model. It provides an option to output the scores in an easy-to-read Pandas DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "DOFTLP2xGOGs"
      },
      "outputs": [],
      "source": [
        "def get_scores(model,sample=None,output=None):\n",
        "  '''Collect model scores.'''\n",
        "  \n",
        "  # define training data\n",
        "  if sample=='over':\n",
        "    X_t=Xt_over\n",
        "    y_t=yt_over\n",
        "  else:\n",
        "    X_t=X_ts\n",
        "    y_t=y_train\n",
        "\n",
        "  # predictions\n",
        "  y_t_hat=model.predict(X_t)\n",
        "  y_v_hat=model.predict(X_vs)\n",
        "\n",
        "  # collect scores\n",
        "  train_scores=[\n",
        "      metrics.recall_score(y_t,y_t_hat),\n",
        "      metrics.fbeta_score(y_t,y_t_hat,beta=2),\n",
        "      metrics.f1_score(y_t,y_t_hat),\n",
        "      metrics.roc_auc_score(y_t,y_t_hat),\n",
        "      metrics.zero_one_loss(y_t,y_t_hat)\n",
        "  ]\n",
        "\n",
        "  val_scores=[\n",
        "      metrics.recall_score(y_val,y_v_hat),\n",
        "      metrics.fbeta_score(y_val,y_v_hat,beta=2),\n",
        "      metrics.f1_score(y_val,y_v_hat),\n",
        "      metrics.roc_auc_score(y_val,y_v_hat),\n",
        "      metrics.zero_one_loss(y_val,y_v_hat)\n",
        "  ]\n",
        "\n",
        "  # output scores in pandas df\n",
        "  if output=='pandas':\n",
        "    df=pd.DataFrame(\n",
        "        [train_scores,val_scores],\n",
        "        columns=[\n",
        "            'Recall',\n",
        "            'F_beta',\n",
        "            'F1',\n",
        "            'AUC',\n",
        "            '0-1_Loss'\n",
        "        ],\n",
        "        index=['train','val']\n",
        "    )\n",
        "    return df\n",
        "\n",
        "  return [train_scores,val_scores]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next function displays a confusion matrix of model predictions on validation data."
      ],
      "metadata": {
        "id": "gYgS_vgO2HSu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "D3cpPjO7JJjP"
      },
      "outputs": [],
      "source": [
        "def confusion_heatmap(model,show_scores=True):\n",
        "  '''Heatmap of confusion matrix for\n",
        "  model performance on validation data.'''\n",
        "\n",
        "  actual=y_val\n",
        "  predicted=model.predict(X_vs)\n",
        "\n",
        "  # generate confusion matrix\n",
        "  cm=metrics.confusion_matrix(actual,predicted)\n",
        "  cm=np.flip(cm).T\n",
        "\n",
        "  # heatmap labels\n",
        "  labels=['TP','FP','FN','TN']\n",
        "  cm_labels=np.array(cm).flatten()\n",
        "  cm_percents=np.round((cm_labels/np.sum(cm))*100,3)\n",
        "  annot_labels=[]\n",
        "  for i in range(4):\n",
        "    annot_labels.append(str(labels[i])+'\\nCount:'+str(cm_labels[i])+'\\n'+str(cm_percents[i])+'%')\n",
        "  annot_labels=np.array(annot_labels).reshape(2,2)\n",
        "\n",
        "  # print figure\n",
        "  plt.figure(figsize=(8,5))\n",
        "  plt.title('Confusion Matrix (Validation Data)',fontsize=20)\n",
        "  sns.heatmap(data=cm,\n",
        "              annot=annot_labels,\n",
        "              annot_kws={'fontsize':'x-large'},\n",
        "              xticklabels=[1,0],\n",
        "              yticklabels=[1,0],\n",
        "              cmap='Greens',\n",
        "              fmt='s')\n",
        "  plt.xlabel('Actual',fontsize=14)\n",
        "  plt.ylabel('Predicted',fontsize=14)\n",
        "  plt.tight_layout();\n",
        "\n",
        "  # scores\n",
        "  if show_scores==True:\n",
        "    scores=['Accuracy','Precision','Recall','F1']\n",
        "    score_list=[metrics.accuracy_score(actual,predicted),\n",
        "                metrics.precision_score(actual,predicted),\n",
        "                metrics.recall_score(actual,predicted),\n",
        "                metrics.f1_score(actual,predicted)]\n",
        "    df=pd.DataFrame(index=scores)\n",
        "    df['Val. Scores']=score_list\n",
        "    return df\n",
        "  return\n",
        "\n",
        "# alias function name to something shorter\n",
        "ch=confusion_heatmap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fg1nn8kDmnb9"
      },
      "source": [
        "<a name='vanilla'></a>\n",
        "# Vanilla Models\n",
        "\n",
        "Summary of vanilla model testing: No model performed well with default configurations on the regular data. Performance gains are only observed once we train on oversampled data.\n",
        "\n",
        "[Jump to models trained on oversampled data.](#oversampled-models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_MgWkfR-YNES"
      },
      "outputs": [],
      "source": [
        "models=[\n",
        "    'RandomForest',\n",
        "    'AdaBoost',\n",
        "    'XGBoost',\n",
        "    'LightGBM',\n",
        "    'CatBoost'\n",
        "]\n",
        "\n",
        "datasets=['train','val']\n",
        "\n",
        "# generate MultiIndex object\n",
        "mi=pd.MultiIndex.from_product(\n",
        "    iterables=[models,datasets],\n",
        "    names=['model','data']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m-kU0mHuHPzM"
      },
      "outputs": [],
      "source": [
        "# build comparison table\n",
        "tab=pd.DataFrame(\n",
        "    columns=[\n",
        "        'Recall',\n",
        "        'F_beta',\n",
        "        'F1',\n",
        "        'AUC',\n",
        "        '0-1_Loss'\n",
        "    ],\n",
        "    index=mi\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_w2bRRFQbKkF"
      },
      "source": [
        "The value of $\\beta$ in the $F_\\beta$ score allows us to bias the score between precision and recall. With $\\beta=2$, we give recall twice the importance of precision."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCU4ZpqbJn4B"
      },
      "source": [
        "## Dummy Classifier\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "PYWe_PjaJINh",
        "outputId": "0756fed4-040d-4f46-9e12-ddba2e67cdde"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DummyClassifier(random_state=1, strategy='stratified')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier(random_state=1, strategy=&#x27;stratified&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier(random_state=1, strategy=&#x27;stratified&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "d=DummyClassifier(\n",
        "    strategy='stratified',\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "d.fit(X_ts,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11Vdlk-sI1ij"
      },
      "source": [
        "We fit a dummy classifier to set a performance baseline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
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        "id": "ZxADgGp8JXN0",
        "outputId": "5f4da47d-881e-46a1-c21a-6aee2545b83e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.925809\n",
              "Precision     0.042824\n",
              "Recall        0.041020\n",
              "F1            0.041903"
            ],
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              "\n",
              "  <div id=\"df-7c1c2093-451f-4437-b500-c32805a0a010\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.925809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.042824</td>\n",
              "    </tr>\n",
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              "      <th>Recall</th>\n",
              "      <td>0.041020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.041903</td>\n",
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          },
          "metadata": {},
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        {
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          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(d)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjNIZRsyJRiD"
      },
      "source": [
        "Predictably, this classifier yields high accuracy on our highly imbalanced data set. Its failing, however, is 4% recall."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKNuMMqtKOrH"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "h4kTWw1AmnIG",
        "outputId": "57f1c62b-f2a6-4c65-dd04-af96b0e7ddad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_jobs=-1, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=-1, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "rf=RandomForestClassifier(\n",
        "    random_state=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf.fit(X_ts,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mQiw0G8gY7Y2",
        "outputId": "1ad8612f-3332-4df4-cc0e-e9fb1744dff2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.902659  0.919868  0.946949  0.951251  0.004003\n",
              "val    0.042129  0.050211  0.070501  0.517914  0.043936"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec19e30b-64fc-46d6-87f8-4b267a7200f2\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.902659</td>\n",
              "      <td>0.919868</td>\n",
              "      <td>0.946949</td>\n",
              "      <td>0.951251</td>\n",
              "      <td>0.004003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.042129</td>\n",
              "      <td>0.050211</td>\n",
              "      <td>0.070501</td>\n",
              "      <td>0.517914</td>\n",
              "      <td>0.043936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec19e30b-64fc-46d6-87f8-4b267a7200f2')\"\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",
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              "  <style>\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ec19e30b-64fc-46d6-87f8-4b267a7200f2 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-ec19e30b-64fc-46d6-87f8-4b267a7200f2');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "tab.loc['RandomForest']=get_scores(rf)\n",
        "\n",
        "tab.loc['RandomForest']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJp1fh5IJsLz"
      },
      "source": [
        "Random forest fares better on recall, though this is merely due to overfitting. Validation AUC barely clears the baseline 50%."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "v5jlFJPJKAmc",
        "outputId": "0d68e2f8-aef3-4807-dfdf-2bc87d5e8e5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.956064\n",
              "Precision     0.215909\n",
              "Recall        0.042129\n",
              "F1            0.070501"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36a41486-cced-4344-80bc-f7650e82dbec\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.956064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.215909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.042129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.070501</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36a41486-cced-4344-80bc-f7650e82dbec')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-36a41486-cced-4344-80bc-f7650e82dbec 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-36a41486-cced-4344-80bc-f7650e82dbec');\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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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(rf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g84NdUq3J8sH"
      },
      "source": [
        "We can see from the confusion matrix that the number of true positives detected is **the same** as the dummy classifier. Thus, the only improvement made here is in the classification of true negatives."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8Fae4a0KQdU"
      },
      "source": [
        "## AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "XdAmsC2cKSG1",
        "outputId": "7a639d8c-841a-4d73-d80b-71b277812e98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "abc=AdaBoostClassifier(random_state=1)\n",
        "\n",
        "abc.fit(X_ts,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "_n1E3fHsKSDo",
        "outputId": "8ac51a13-08d3-4978-88d9-f4b25be112fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.006648  0.008286  0.013146  0.503226  0.039501\n",
              "val    0.002217  0.002765  0.004396  0.500972  0.039726"
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              "      <th>train</th>\n",
              "      <td>0.006648</td>\n",
              "      <td>0.008286</td>\n",
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              "      <td>0.503226</td>\n",
              "      <td>0.039501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.002217</td>\n",
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              "      <td>0.500972</td>\n",
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              "          document.querySelector('#df-10f3820c-06f1-4ce8-a2af-6432d6cc9f9e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-10f3820c-06f1-4ce8-a2af-6432d6cc9f9e');\n",
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              "            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": 40
        }
      ],
      "source": [
        "tab.loc['AdaBoost']=get_scores(abc)\n",
        "\n",
        "tab.loc['AdaBoost']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jzdwYjSKQ8Y"
      },
      "source": [
        "On the one hand, AdaBoost is not suffering the same overfitting issues as random forest. On the other, its performance is horrid."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "V-8Fx3HOKk37",
        "outputId": "c754b3c4-0532-4809-bf00-a9bc1008571a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.960274\n",
              "Precision     0.250000\n",
              "Recall        0.002217\n",
              "F1            0.004396"
            ],
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              "      <th>Val. Scores</th>\n",
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              "      <th>Accuracy</th>\n",
              "      <td>0.960274</td>\n",
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              "      <td>0.250000</td>\n",
              "    </tr>\n",
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              "      <th>Recall</th>\n",
              "      <td>0.002217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.004396</td>\n",
              "    </tr>\n",
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              "\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "          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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(abc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P56ED7kdKhhd"
      },
      "source": [
        "The confusion matrix shows that AdaBoost is just predicting 0 for essentially every observation, with only eight predicted in the positive class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "So3Wm2GyKSqp"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "DnKltk3mH3sE",
        "outputId": "2e8fcc0d-251d-4722-816d-60fccc1d936a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=1, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=1, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "xgb=XGBClassifier(\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "xgb.fit(X_ts,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "nzrrRT0vI1Ti",
        "outputId": "5c61d2bd-060b-4004-f707-84b62f2cdb89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.405508  0.459931   0.57586  0.702695   0.02364\n",
              "val    0.011086   0.01365  0.020899  0.504516  0.041086"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae050586-472b-4517-8388-2b570abb3766\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.405508</td>\n",
              "      <td>0.459931</td>\n",
              "      <td>0.57586</td>\n",
              "      <td>0.702695</td>\n",
              "      <td>0.02364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.011086</td>\n",
              "      <td>0.01365</td>\n",
              "      <td>0.020899</td>\n",
              "      <td>0.504516</td>\n",
              "      <td>0.041086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae050586-472b-4517-8388-2b570abb3766')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae050586-472b-4517-8388-2b570abb3766 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-ae050586-472b-4517-8388-2b570abb3766');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "tab.loc['XGBoost']=get_scores(xgb)\n",
        "\n",
        "tab.loc['XGBoost']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "uw1YgMjlJo_u",
        "outputId": "0e953d4a-34a4-4e6f-aa7b-691aa4aee309"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.958914\n",
              "Precision     0.181818\n",
              "Recall        0.011086\n",
              "F1            0.020899"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3610e6c-07ef-45d8-87d7-4c8d107e1451\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.958914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.181818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.011086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.020899</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3610e6c-07ef-45d8-87d7-4c8d107e1451')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a3610e6c-07ef-45d8-87d7-4c8d107e1451 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-a3610e6c-07ef-45d8-87d7-4c8d107e1451');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(xgb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SPeodnQLDFF"
      },
      "source": [
        "Overfitting plagues XGBoost too. We have yet to see a validation AUC appreciably climb above 50%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAHFoYu1cxUK"
      },
      "source": [
        "## LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "7qwyERtfc0l7",
        "outputId": "50b9db4d-50ee-4ac6-f190-cdd6e22d7075"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "lg=LGBMClassifier()\n",
        "\n",
        "lg.fit(X_ts,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "bbiCmidLdoCr",
        "outputId": "f6638e12-518f-48ac-f70f-a0ea0335a0f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train   0.17189  0.205892  0.292762  0.585896  0.032867\n",
              "val    0.009978    0.0124  0.019502  0.504715  0.039683"
            ],
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              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
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              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
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              "    <tr>\n",
              "      <th>data</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.17189</td>\n",
              "      <td>0.205892</td>\n",
              "      <td>0.292762</td>\n",
              "      <td>0.585896</td>\n",
              "      <td>0.032867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.009978</td>\n",
              "      <td>0.0124</td>\n",
              "      <td>0.019502</td>\n",
              "      <td>0.504715</td>\n",
              "      <td>0.039683</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-ef057e45-4774-4f71-8968-353864c0616c 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-ef057e45-4774-4f71-8968-353864c0616c');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "tab.loc['LightGBM']=get_scores(lg)\n",
        "\n",
        "tab.loc['LightGBM']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "ew9LLnIOdnt_",
        "outputId": "653c5283-8d8b-4d6c-d55f-9ec110a9cb1a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.960317\n",
              "Precision     0.428571\n",
              "Recall        0.009978\n",
              "F1            0.019502"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
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              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.960317</td>\n",
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              "      <th>Precision</th>\n",
              "      <td>0.428571</td>\n",
              "    </tr>\n",
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              "      <th>Recall</th>\n",
              "      <td>0.009978</td>\n",
              "    </tr>\n",
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              "      <th>F1</th>\n",
              "      <td>0.019502</td>\n",
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              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "            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",
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          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(lg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2c9q4HZCL4j_"
      },
      "source": [
        "Comparable performance can be observed in default LightGBM, with overfitting and poor recall and AUC."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_6Q8JlRc0_u"
      },
      "source": [
        "## CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4JiH5_G8c2iT",
        "outputId": "245a22e1-5d86-47e9-8b26-0b8bddce700d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f378bdf7790>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "cb=CatBoostClassifier()\n",
        "\n",
        "cb.fit(X_ts,y_train,verbose=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "2-uROvu8d9pt",
        "outputId": "f3853904-7775-4ca4-faeb-32e697067bd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.213675  0.253464    0.3517  0.606808  0.031176\n",
              "val    0.008869  0.011004  0.017223  0.504001  0.040033"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.213675</td>\n",
              "      <td>0.253464</td>\n",
              "      <td>0.3517</td>\n",
              "      <td>0.606808</td>\n",
              "      <td>0.031176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.008869</td>\n",
              "      <td>0.011004</td>\n",
              "      <td>0.017223</td>\n",
              "      <td>0.504001</td>\n",
              "      <td>0.040033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8b1e5f7-f79d-453c-9342-c343e92a4bc5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a8b1e5f7-f79d-453c-9342-c343e92a4bc5 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-a8b1e5f7-f79d-453c-9342-c343e92a4bc5');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "tab.loc['CatBoost']=get_scores(cb)\n",
        "\n",
        "tab.loc['CatBoost']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "j82uidoJeC3u",
        "outputId": "415308de-1a9b-4bde-b609-3a3b7e67e4af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.959967\n",
              "Precision     0.296296\n",
              "Recall        0.008869\n",
              "F1            0.017223"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-903cc095-916e-40a8-ada1-6cd46a0653a6\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.959967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.296296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.008869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.017223</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-903cc095-916e-40a8-ada1-6cd46a0653a6')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-903cc095-916e-40a8-ada1-6cd46a0653a6 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-903cc095-916e-40a8-ada1-6cd46a0653a6');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(cb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4NO8MppMNbr"
      },
      "source": [
        "CatBoost is similarly deficient."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmuAjVateXXJ"
      },
      "source": [
        "## Comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "CW1pia7beWRM",
        "outputId": "f5b0cf04-0bcb-4251-cb24-dacd82e94978"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Recall    F_beta        F1       AUC  0-1_Loss\n",
              "model        data                                                   \n",
              "RandomForest train  0.902659  0.919868  0.946949  0.951251  0.004003\n",
              "             val    0.042129  0.050211  0.070501  0.517914  0.043936\n",
              "AdaBoost     train  0.006648  0.008286  0.013146  0.503226  0.039501\n",
              "             val    0.002217  0.002765  0.004396  0.500972  0.039726\n",
              "XGBoost      train  0.405508  0.459931   0.57586  0.702695   0.02364\n",
              "             val    0.011086   0.01365  0.020899  0.504516  0.041086\n",
              "LightGBM     train   0.17189  0.205892  0.292762  0.585896  0.032867\n",
              "             val    0.009978    0.0124  0.019502  0.504715  0.039683\n",
              "CatBoost     train  0.213675  0.253464    0.3517  0.606808  0.031176\n",
              "             val    0.008869  0.011004  0.017223  0.504001  0.040033"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea40dc4d-5514-44c0-abb6-ee35aa11c054\">\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></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">RandomForest</th>\n",
              "      <th>train</th>\n",
              "      <td>0.902659</td>\n",
              "      <td>0.919868</td>\n",
              "      <td>0.946949</td>\n",
              "      <td>0.951251</td>\n",
              "      <td>0.004003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.042129</td>\n",
              "      <td>0.050211</td>\n",
              "      <td>0.070501</td>\n",
              "      <td>0.517914</td>\n",
              "      <td>0.043936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">AdaBoost</th>\n",
              "      <th>train</th>\n",
              "      <td>0.006648</td>\n",
              "      <td>0.008286</td>\n",
              "      <td>0.013146</td>\n",
              "      <td>0.503226</td>\n",
              "      <td>0.039501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.002217</td>\n",
              "      <td>0.002765</td>\n",
              "      <td>0.004396</td>\n",
              "      <td>0.500972</td>\n",
              "      <td>0.039726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">XGBoost</th>\n",
              "      <th>train</th>\n",
              "      <td>0.405508</td>\n",
              "      <td>0.459931</td>\n",
              "      <td>0.57586</td>\n",
              "      <td>0.702695</td>\n",
              "      <td>0.02364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.011086</td>\n",
              "      <td>0.01365</td>\n",
              "      <td>0.020899</td>\n",
              "      <td>0.504516</td>\n",
              "      <td>0.041086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">LightGBM</th>\n",
              "      <th>train</th>\n",
              "      <td>0.17189</td>\n",
              "      <td>0.205892</td>\n",
              "      <td>0.292762</td>\n",
              "      <td>0.585896</td>\n",
              "      <td>0.032867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.009978</td>\n",
              "      <td>0.0124</td>\n",
              "      <td>0.019502</td>\n",
              "      <td>0.504715</td>\n",
              "      <td>0.039683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">CatBoost</th>\n",
              "      <th>train</th>\n",
              "      <td>0.213675</td>\n",
              "      <td>0.253464</td>\n",
              "      <td>0.3517</td>\n",
              "      <td>0.606808</td>\n",
              "      <td>0.031176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.008869</td>\n",
              "      <td>0.011004</td>\n",
              "      <td>0.017223</td>\n",
              "      <td>0.504001</td>\n",
              "      <td>0.040033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea40dc4d-5514-44c0-abb6-ee35aa11c054')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ea40dc4d-5514-44c0-abb6-ee35aa11c054 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-ea40dc4d-5514-44c0-abb6-ee35aa11c054');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "tab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5P_7OZhXMTYR"
      },
      "source": [
        "Hyperparameter tuning will not garner the performance improvements we need. Let's instead train the models on oversamed data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "008abU3IIEIG"
      },
      "source": [
        "<a name='oversampled-models'></a>\n",
        "# Oversampled Models\n",
        "\n",
        "Data oversampling using SMOTE (Synthetic Minority Oversampling TEchnique).\n",
        "\n",
        "[Jump to model comparison.](#oversampled-comp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y1XE7mXtHAx-"
      },
      "outputs": [],
      "source": [
        "# build comparison table\n",
        "tab_over=pd.DataFrame(\n",
        "    columns=[\n",
        "        'Recall',\n",
        "        'F_beta',\n",
        "        'F1',\n",
        "        'AUC',\n",
        "        '0-1_Loss'\n",
        "    ],\n",
        "    index=mi\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qoQkGLQNHJJh"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ocAoAMlQIG72",
        "outputId": "aaa7bfe3-75bd-4206-e7c8-175c34265e07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_jobs=-1, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=-1, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "rf_over=RandomForestClassifier(\n",
        "    random_state=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf_over.fit(Xt_over,yt_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "gZ14VLPOHjxU",
        "outputId": "e23f82db-840c-45eb-8eab-5deff3548add"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.990158  0.990224  0.990323  0.990324  0.009676\n",
              "val    0.296009  0.239935  0.186844   0.60945  0.101903"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-900afa7c-e0a9-429d-936d-33d4cd3dec8d\">\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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.990158</td>\n",
              "      <td>0.990224</td>\n",
              "      <td>0.990323</td>\n",
              "      <td>0.990324</td>\n",
              "      <td>0.009676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.296009</td>\n",
              "      <td>0.239935</td>\n",
              "      <td>0.186844</td>\n",
              "      <td>0.60945</td>\n",
              "      <td>0.101903</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-900afa7c-e0a9-429d-936d-33d4cd3dec8d')\"\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",
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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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-900afa7c-e0a9-429d-936d-33d4cd3dec8d 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-900afa7c-e0a9-429d-936d-33d4cd3dec8d');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "tab_over.loc['RandomForest']=get_scores(rf_over,sample='over')\n",
        "\n",
        "tab_over.loc['RandomForest']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "vW6IYVQwIeO_",
        "outputId": "c12cb4f2-d51b-4865-ca46-a898eec4001f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.898097\n",
              "Precision     0.136503\n",
              "Recall        0.296009\n",
              "F1            0.186844"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c763c059-818a-4ca8-abbc-4feb5347b53a\">\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",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.898097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.136503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.296009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.186844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c763c059-818a-4ca8-abbc-4feb5347b53a')\"\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",
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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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c763c059-818a-4ca8-abbc-4feb5347b53a 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-c763c059-818a-4ca8-abbc-4feb5347b53a');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(rf_over)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_tBAAWdHtuC"
      },
      "source": [
        "## AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "I2Wsn4GhHtQ8",
        "outputId": "5d6dd6ec-8c98-461b-e5a8-71ac3ac0ad06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "abc_over=AdaBoostClassifier(random_state=1)\n",
        "\n",
        "abc_over.fit(Xt_over,yt_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "48yVgBgtHw7-",
        "outputId": "6c8f76ce-df9d-4545-f398-491b365a4613"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.775906  0.770682  0.762978  0.758961  0.241039\n",
              "val    0.722838   0.32441  0.177584  0.729274  0.264799"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.775906</td>\n",
              "      <td>0.770682</td>\n",
              "      <td>0.762978</td>\n",
              "      <td>0.758961</td>\n",
              "      <td>0.241039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.722838</td>\n",
              "      <td>0.32441</td>\n",
              "      <td>0.177584</td>\n",
              "      <td>0.729274</td>\n",
              "      <td>0.264799</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daad208e-07f7-4653-adb7-4d04d83193be')\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-daad208e-07f7-4653-adb7-4d04d83193be 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-daad208e-07f7-4653-adb7-4d04d83193be');\n",
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              "            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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "tab_over.loc['AdaBoost']=get_scores(abc_over,sample='over')\n",
        "\n",
        "tab_over.loc['AdaBoost']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "0y3t8xYJHw2c",
        "outputId": "6d78ef6a-6c23-419a-cd01-ca737e1419de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.735201\n",
              "Precision     0.101227\n",
              "Recall        0.722838\n",
              "F1            0.177584"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fbca6235-50df-4a6e-b19a-9287faa5efab\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.735201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.101227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.722838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.177584</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbca6235-50df-4a6e-b19a-9287faa5efab')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fbca6235-50df-4a6e-b19a-9287faa5efab 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-fbca6235-50df-4a6e-b19a-9287faa5efab');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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Ypk2bxvHjxzPdEk/n5eXFe++9x8yZM5WcbUj7w2toaMjWrVu5cuUKN2/eVPJqC8LIyIiuXbuyceNGdu/erRHsZxwcmtcc8FatWvH2229jamqqsb1mzZq0adOGYcOGMXjwYO7cucOyZcs0gksrKyusrKw0Auy8/k74+/tTokQJNm/ejIODg7K9Xr16eToewNLSkgULFjBs2DBCQ0P55ptvWL58OZC2HkT6APnGjRvz/vvv57nejEJDQ3n48CEANWrUyLU97dq14+DBg1y/fh0fH58sx0zcvn1bSTXp1KmTxvuqIK+HNqSntkHa59e6des0el2bN29OgwYNcr2bVr16dU6dOpVlus9bb73F4MGDGTNmDKdPn+bnn3+md+/e6OvrA2kXQFWrVuX69evKMRUrVnzhNSLSPxfTA/0NGzZovJb169enU6dO9O/fnydPnrBgwQK6dOmS7cXylStXaN26NT/99JNGMN+oUSNsbGxYvHgxgYGBnDp1KstUu8KS8e/NgwcPMu1fs2YNFStWzLS9QYMG9OzZk+3btzNlyhQuXLjA2bNnNX63np+QIX3gcHb09fU5dOhQlkF5kyZNeOedd1i6dCk///wzu3fvZuzYsVm2LavHduPGDQn2xWtNBugWARmDT230dm/cuFG5FTp79uws/3i2atVK6Rm7evUqV69ezba+WrVqKXMXZ6RSqRgxYgSQdhvfy8urwG1/UeHh4cqsP40aNcqx7PNpLLnZvn27Mp7i66+/1gj009WsWVNZNCk4ODjHRayKFy/OtGnTNAL9dBkHW6f3DmtDeiDv5eXFo0ePlO2HDx8mPj5eCTrzomTJkpkCy4wsLS359NNPAbh8+bJy10obJkyYoBHov4jGjRszevRoAE6ePMmGDRuIi4tj4sSJJCUlYWlpyfz58194sbqgoCDl+7y8nzOmfWWcQz+jnObW1+XrAbBp0ybl82bmzJlZplf07t2bVq1a5ViPnZ1djnn9RkZGfPXVV0Baylthrr599OhRQkJCABg7dmyWF21lypRR2hMXF5fjis3GxsbMnTs3y177oUOHKh0D2nzP50XGz8LIyMhM+3MKpgHeeecd5bnJy8J9OVGpVLkG5B9//DG2trao1eocx1YBGhdeGT/zhHgdSbBfBMTExCjf5/RHO6/SB/FVqVIlx17P9957L9MxWenRo0eWgSlo9p7o8gPV1tZW+YO5e/durS4Wk/7cWFlZ5djrlnGsRU7PZ5cuXbK9Ve/o6Kj02mrz+XR2dlb+kGYMHNO/79KlC8bGxi9Ud2xsLP7+/ty9exdvb2+8vb017mpkvHNVEIaGhnTt2lUrdY0bN0753Z0/fz4TJkzA19cXSFvdtkyZMi9c97Nnz5Tv8zIotWXLlhQrVgxIS2lKT2dIl5qayr59+wAoVaqUMsA0Oy/r9Uh35swZIO0OTU7z/mdMu8mLxMREAgMDuXfvnvI4Mj432n4cGaW/f1UqFf369cu2XJcuXbC0tNQ4JistWrTI9sLPwsJCCapf9mdoxjtEGf8OZUWtVvPkyRN8fX2V18Pb21tJudP265GamkpwcDA+Pj7Kue7fv69MXpDb+TJeyDx58kSrbRPiZZM0niIgY09Yeg/yi0pMTFRux9atWzfHsjVq1MDQ0JCkpCS8vb2zLZfTVIwZP1Bz+2NRmIyMjOjWrRu7d+/m8OHDdOrUiS5dutC0aVOcnZ3zFHRlJ/25qVmzZrapOQDFihWjTJkyBAQE5Ph85rbegLW1NbGxsVp/Pnv27MmyZcvYu3cvn3zyCY8fP1by0/O7EuuzZ89YvXo1hw8fxs/PL1OAmpG2epIrVqz4whckz0sffNu3b1/i4uKUVKa33367wPPXR0REKN9nzEnOjoGBAW+//TarV68mICCAy5cva9ydOnv2rNLL3KNHjyzvOOji9YC0zxs/Pz8g9/EJuX0eQdqFyrp169i/fz/37t0jJSUl27LavkOR0d27d4G0HP+cxrEYGRlRo0YNLly48MKfofDf78nL/gzNeL7sxor99ddfbNy4kYsXL+bYPm28Hmq1mj179rBt2zauXr1KfHz8C58v42d+Qf+uCqFrEuwXARkD5tDQ0ALVlTHQyC2FwNDQEBsbG548eaJx3PNymo0kY+Ch68VLpk+fTmRkJCdPniQgIAA3Nzfc3NzQ09OjZs2adO3alf79+ys9cXmV/tzkJSWjePHiBAQE5Ph85nb3Jv051fbz2atXL5YtW8aDBw+4cuUK58+fR61WU6ZMGRo2bJjneq5fv46rq2uexz5oazXeglywZcXR0RFXV1dlOkA7Ozu+/fbbAteb8YIkp2Alo969e7N69Wog7W5LxmA/pxQe0N3rAWnvjfQLi9zeH+l3L7Lj7+/P8OHD8ff3z9O5C3OV5/TnMq/veeCVfM/nJmPA/PyFqVqtZurUqWzbti1PdeX1dz07CQkJfPLJJ7i7u2vlfBl/P2SNF/G6k9/gIiDjTDg3btzQWr3Zpd4UVRYWFixfvpyrV69y8OBBZa7mlJQUrl+/zvXr1/n999/5+eefcXZ2znf9r/vzmT6vu6enJ7t371amh+zZs2eeH1tiYiKff/454eHhGBoaMmTIENq3b0/FihWxtrZW0pMePXpEhw4dAHLsZc6P9MGY2hIdHa1MeQtpgc+NGzcKPIDV1tZW+T6nADCjGjVqULVqVby9vTl06BDTpk3DyMiI2NhYjh49CqSlzD0/V74uX4/nFfT98dVXX+Hv749KpaJv3750794dJycn7OzsMDQ0RKVSkZqaquSIF9bjyOh1f8/n5ubNm8r3z99x3LZtmxLo16hRg+HDh1O3bl1ljEj6+/Grr77KdqxJfvz6669KoN+kSRMGDRpErVq1KFasGCYmJsoF0eDBg/M0tiHjxa+2OwqEeNkk2C8CqlSpgq2tLWFhYVy+fJno6OgXnn4zY+/M06dPcyybnJysfCDmJd2gMKlUKtRqda49W3mZX71u3bpKykB0dDQXLlxg586dHDlyhNDQUMaNG8exY8fyPH+6tbU1T548yfX5hP9yQ3X9fGand+/eeHp6sn37dqXnKz8rsZ47d07JK/7222+zXRMivzMe6cLMmTOVOcTNzc2JiYnh66+/Zs+ePQUKDjKmfeQ12Ie012bBggVERkZy4sQJunTpwtGjR5Xf+ax69XX9emR8nnJ7f+S0//79+1y+fBmADz/8MNtpOl/W71X63dai8J7PSfp4CyDT3b30GdoqVKjApk2bsv28zM/veHbUarVyYdGoUSPWrFmT7QD5vJ4v44DjrCZVEOJ1IgN0iwCVSkWfPn2AtGA2p2kwc2NkZKQM9spphh1I69VJn8GmsKbEzKv0cQtZzQiRTq1WK1Ma5pWFhQXt2rVj2bJlDB06FEj745weWORF+nNz8+bNHAf+hoaGEhgYqHHMq6Zr164YGRkpgX69evVyHUOQUcYFy3IaLJtxCsSs6LrH9ODBg0pv5LvvvsuPP/4IwOPHj7NdWTevypUrp6RtZDWdYXZ69Oih9JamL2CX/r+hoSFvv/12pmO09Xq8KGNjY+Xz5tq1azmWzWl/xsfRrVu3bMu9rN+rKlWqAGmpRRkHXD8vKSlJmRXoVX3PZ8fb21sZVFy6dOlMg6vTxy20a9cu20BfrVZr5W50eHi4ctGU0wq5MTExykD63GR876W/nkK8riTYLyJGjBihBAhLly7l/v37eTouNTU108q26WkId+/ezTHgz5iLqevlxNPnw87pj7m7u3uOFwO5yfgY8zOYLP24yMhIjhw5km25bdu2KakFun4+s2NlZUWHDh0wMjLCyMgo3wNzM17sZDfoLTU1NdcL1oyzET2/OmlhCw4OVnLzK1asyJQpU2jbti0DBgwAYP/+/ZneU/lhaGiozIKVWwCcUYkSJZTfG3d3d41grGXLllkOFNXW61EQ6W329vbWSAt53vbt27Pdl3Egbk6DKTdt2pRjWzKOlyjI71X6Y1Kr1Tm2+/Dhw0RFRWkc8zqIj49n0qRJyufVqFGjMuW1p78mOd1NPX78eK4z3aS/Jjm9Hnl9/bdu3ZrnmdbS33uGhoZaWa9ECF2SYL+IKFmyJNOmTQPSPlyHDh2qzJSSnXv37vH+++/j5uamsX3gwIFKz8i0adOIjo7OdKyHh4cS7GdMe9GV9GXi//nnnyx73Z88ecKsWbOyPf7Ro0e5Pl+nT59Wvs/PYjvvvPOOciE2f/58goODM5W5ffu2sjhTyZIllfzoV9GiRYu4du0a165dy3HVz6xknHc7Y757Rj/++GOuvX0ZVzbN792aglCr1UyaNImIiAgMDAz4/vvvlekHJ0+erNzlmDlzpnKX5kWkD7C9c+dOvoLO9IuvpKQkvvjiCyUIyu6iTFuvR0EMGDBA6VGfNm1alsHhnj17OHXqVLZ1ZJxfPbv56jds2JDrCtjpg2WhYNNYdujQQfkdXb58ubKgWUaPHz9WVnE1NTWlb9++L3y+l+nevXsMGjRIuTBr0qRJplW14b/X5OTJk1mmTz18+JDvvvsu1/OlvyY5vc8zrrGwb9++LN8zV69eZcmSJbmeL2N5SFsALau1H4R4nUjOfhHyzjvvEBQUxNKlSwkNDWXo0KG0bNmSdu3a4eTkhJWVFREREfj6+nLq1Cn+/vtvUlJSNAb4AlSrVo2RI0fi5ubG7du36dOnD6NHj6ZGjRrExcVx8uRJ1q1bR0pKCoaGhsycOVNHj/g//fv3Z+PGjSQnJzN27Fg++ugjGjZsSFJSEp6enqxevZqkpCQqVqyYZWpEYGAgw4YNo3LlynTo0IE6deoof6yDgoI4cOAABw8eBNIGm+Vn1VU7Ozu+/PJLZs6cSVBQEH379mX06NE0aNCA5ORkzpw5g5ubG7GxsahUKmbNmpXjFJ2vs5YtW2Jvb09oaCiLFy/G39+fjh07Ymtry8OHD9myZQtnz55VBgJnJ+MA6blz5zJmzBiKFy+uBI1lypQplBk0Vq9erfSWjx07VuMi19TUlO+//56BAwcSFRXFpEmTcswdzkn79u356aefSEpK4tKlS7Ro0SJPx3Xs2FEZP5CeRmFtbZ3tgmfaej0Konr16gwePJg///yT69ev88477zB69GiqVq1KVFQUhw4dYsuWLdSuXTvbO3c1a9ZUBihv3ryZyMhIevXqRfHixQkKCmLPnj0cPnw418dRo0YNjI2NSUhIYMmSJRgYGODg4KC8hiVLlszTWB0jIyNmzZrFmDFjiI6OZuDAgbi6utK8eXP09fXx8vJi5cqVyuxpX331VY5TdL5MoaGhGtOAxsXFERERwZ07dzh37hynT59WevTr16/PkiVLsvy8Sh9DEhISQv/+/ZXXNCEhgXPnzrFmzRoSExOpVatWjheTzs7O+Pv7c+LECTZt2kSDBg2U3n4LCwvs7e3R09OjR48erF+/njt37jBw4EBGjhxJhQoViI6O5tSpU2zYsAEzMzNKlCiRa3pcdHS00rP/Kne8CJFXEuwXMR9//DFVqlRh3rx5BAQE4OHhgYeHR7blq1Spwpdffplp+8SJE4mLi2PDhg08fPhQuWuQkaWlJYsXL85ydciXLf1xzJ07l4iICObOnaux38bGhp9//pklS5bk+EF/7949jfzf5zk6OrJs2bJ85/YOHjyYqKgolixZwtOnTzO1D/4LEFq3bp2vul8nZmZmzJ8/n48//piEhAQ2b97M5s2bNco0adKE6dOnZ5ljnq5ChQp07dqVgwcPZvk7fvz48XzdfcmLO3fusHDhQiAtABk7dmymMnXq1OHjjz9m8eLFXLhwATc3N2W13fyoWbOmEgTt3bs3z8G+iYkJnTt31ujdzmkRNm29HgU1efJkQkJCOHLkCD4+Pnz99dca+8uWLcvixYuzDbxUKhULFixg+PDhREREcPDgQeXiPF3VqlVZsmQJb731VrbtsLCwYOjQofz222/cuHFDY0VqgLVr1+a6KFm6Nm3aMHfuXKZPn05MTAxLly5l6dKlGmX09fX57LPP8n2HrDBt3LiRjRs35ljGzs6O4cOH8/7772d7UT1s2DDOnDmDh4cHDx484JtvvtHYb2Jiwvz58zl16lSOwb6rqyuHDx8mMTEx09S2ffr0Yd68eQCMHz8eT09Pbt26xfXr15kwYYJGWRsbG5YtW8bSpUtzDfaPHj1KQkICBgYGdO/ePceyQrwOJNgvgjp16kSbNm04fPgw7u7uXLt2jWfPnhETE4OFhQVlypShXr16dO7cmaZNm2YZuOrp6fHtt9/SvXt3Nm3axOXLl3n69ClGRkaUK1eO1q1bM3z48FemNwrSxi04OTmxevVqrl27RlxcHCVKlKB169a8//77ODg4ZHtso0aNWLduHR4eHly5coWgoCCePn1KYmIi1tbWVK9enY4dO9K3b99sA6fcjBkzhjZt2rB+/XrOnTtHSEgIenp6lC5dGhcXF4YPH671APVV9NZbb7F9+3ZWrlzJuXPnCAsLw9LSksqVK9OjRw/69euXpxSY77//ntq1a3P48GF8fX2JiYkptHnGExMTmThxIomJiZiZmbFgwYJsp/L84IMP+Pvvv7l8+TJLliyhZcuWL3RB3L9/f6ZPn87Ro0f57rvv8vx716dPH41gP7dxFdp6PQrC0NCQZcuWsXv3brZs2cKdO3dITk7GwcGBjh07MmrUqFxnq6lRowa7du1ixYoV/P3334SEhGBubk758uXp2rUrgwcPztOiahMnTqRixYrs2rWLe/fuERUVlePiXDnp06cPjRs3Zs2aNZw+fZrHjx+TmppKiRIlaNasGUOGDKFatWovVPfLoKenh7m5OZaWljg4OFCrVi0aNWpEmzZtcv19NDQ0ZMWKFWzcuJFdu3Zx//591Go1JUuWpHnz5gwbNgwnJ6cc07Mg7XXdvHkzbm5ueHp6Kp/Lz7O0tGTjxo388ccfHDx4ED8/P/T19SldurTy9yp99dzcpK843aFDB43ULiFeVyr1y5hsWAghRL7ExMTQoUMHnj17xvfff1/glXmFELkLCAigY8eOpKSksHnzZurXr6/rJglRYDJAVwghXkHm5uZ89NFHAKxYsULnK0wL8SZYsWIFKSkpvPXWWxLoiyJDgn0hhHhFDRgwgHLlynHv3j0OHTqk6+YIUaQ9fvyYHTt2oK+vn+VYNiFeV5KzL4QQryhDQ0PmzZvH2bNnXzhvXAiRN48fP+bDDz+kXLlyr/RYCiHyS3L2hRBCCCGEKKIkjUcIIYQQQryx/Pz8mD59Or169aJmzZrZTjUcGRnJ7NmzadmyJXXq1KFDhw78/vvvGmUSExOZP38+Li4u1K9fn5EjR+Lj45Oprvv37zNy5Ejq16+Pi4sLCxYsyHKmqa1bt9K5c2fq1KlDz549OXnyZL4fn6TxCCGEEEKIN9bdu3c5deoU9erVIzU1laySXmJjYxk6dCj6+vpMmTIFe3t7Hjx4QHR0tEa52bNnc+DAASZPnkzJkiVZvnw5I0aMYP/+/VhaWgIQERHB8OHDqVixIsuWLSM4OJh58+YRHx/P9OnTlbr279/PtGnTGDNmDM2aNePAgQN88sknrF+/Pl8DyCWNRwghhBBCvLFSU1OVlbInT57M9evXlfUW0i1evJh9+/axZ88ezMzMsqwnKCiIdu3a8e2339K/f38AwsPDadu2LR999JGyyOKKFStYvnw5J0+exMbGBoDNmzfz3XffcfLkSUqWLAlA586dqV27Nj/++KNyjgEDBmBpacmqVavy/PikZ/85McmRum6CEKKI2++3R9dNEEK8Ad5zGqLrJihUHbW3aKT6qL/W6gKUQD8n27ZtY/DgwdkG+gAeHh6kpqbSpUsXZZuNjQ0uLi64u7srwb67uzvNmzdXAn2Arl278u2333L69Gn69u3Lo0ePePDgQaaZobp166ak/OR1sUXJ2RdCCCGEECIb/v7+PHnyBFtbW8aMGUPt2rVp0qQJU6dOJSYmRinn4+ODvb19phW/nZycNPL2fXx8cHR01ChjZWVF8eLFlXLp/1eqVClTXUlJSTx69CjP7ZeefSGEEEIIUbhUKq1V1b59+xz3Hz9+XGvnAnj69CkA8+fPp1OnTqxatYoHDx7w448/Ehsby8KFC4G0AbzpefkZWVlZERERofwcGRmJlZVVpnLW1tZKufT/ny+X/nPG+nIjwb4QQgghhChc2swlecnLjqSvYF6pUiXmz58PQPPmzTEwMGDq1KmMHz+ecuXKvdxG5YME+0IIIYQQ4rWh7Z773KSn5TRt2lRje7NmzYC02XzKlSuHlZVVptl5IK0nP2Nqj5WVFVFRUZnKRUREKOXS/4+KiqJ48eIadWXcnxeSsy+EEEIIIQqXSqW9r5esXLlyOQ6GTUhIAMDR0ZGnT59mSrF5Pkff0dEx09z7UVFRPHnyRCmX/v/z5Xx8fDA0NMzXnQQJ9oUQQgghROFSafHrJTMyMsLFxYWzZ89qbD9z5gwAtWrVAqBly5bo6elx5MgRpUxERAQeHh60atVK2daqVSvOnDmj9NIDHDp0CD09PVxcXIC0C4yKFSty6NAhjXMeOHCA5s2b53kmHpA0HiGEEEII8QaLi4vj1KlTAAQEBBAdHa0E2U2aNMHOzo5PPvmEAQMGMGHCBPr06YOfnx8//vgjPXr0oHz58gCUKlWKfv36sWDBAvT09ChZsiQrVqzA0tKSAQMGKOcbMGAA69at4+OPP+bDDz8kODiYBQsWMGDAAGWOfYBx48YxceJEypcvT9OmTTlw4ABXr17lzz//zNfjk0W1niPz7AshCpvMsy+EeBleqXn2u1fQWl3q/X5aqwvSptbMboaftWvXKrn6Z8+e5YcffsDb2xtra2t69OjB+PHjNXrZExMTWbRoEbt37yYmJoYGDRowdepUnJycNOq9f/8+s2bNwsvLC3Nzc3r16pWpLoCtW7eyatUqAgMDqVSpEl988QVt27bN1+OTYP85EuwLIQqbBPtCiJfhlQr2e2gx2N+r3WC/qJOcfSGEEEIIIYooydkXQgghhBCFSwez6Ig0EuwLIYQQQojCJbG+zkgajxBCCCGEEEWU9OwLIYQQQojCpSdd+7oiwb4QQgghhChcEuvrjAT7QgghhBCicMkAXZ2RnH0hhBBCCCGKKOnZF0IIIYQQhUs69nVGgn0hhBBCCFG4ZICuzkgajxBCCCGEEEWU9OwLIYQQQojCJR37OiPBvhBCCCGEKFwyG4/OSBqPEEIIIYQQRZT07AshhBBCiMIlA3R1RoJ9IYQQQghRuCTW1xlJ4xFCCCGEEKKIkp59IYQQQghRuGSArs5IsC+EEEIIIQqXxPo6I2k8QgghhBBCFFHSsy+EEEIIIQqXzMajMxLsCyGEEEKIwiWxvs5IGo8QQgghhBBFlPTsCyGEEEKIwiWz8eiMBPtCCCGEEKJwSS6JzshTL4QQQgghRBElPftCCCGEEKJwSRqPzkiwL4QQQgghCpfE+jojaTxCCCGEEEIUUdKzL4QQQgghCpek8eiMBPtCCCGEEKJwSS6JzkiwL4QQQgghCpf07OuMXGcJIYQQQghRREnPvhBCCCGEKFzSsa8z0rMvhBBCCCEKl55Ke19a5ufnx/Tp0+nVqxc1a9bk7bffzrH8sWPHqFatWpbloqKimDJlCk2aNMHZ2ZlPP/2UkJCQTOU8PT3p378/devWpW3btqxcuRK1Wq1RRq1Ws3LlStq0aUPdunXp378/V65cyffjk2BfCCGEEEK8se7evcupU6eoUKECTk5OOZaNj49nzpw5FCtWLMv9n3/+OadPn2bGjBn88MMP+Pr6Mnr0aJKTk5Uyfn5+uLq6Urx4cVasWMHw4cNZunQpv//+u0Zdq1atYunSpYwYMYIVK1ZQvHhxRo0axaNHj/L1+CSNRwghhBBCFK5XeIBuu3bt6NChAwCTJ0/m+vXr2ZZdsWIFDg4OlC1bNlM5Ly8vPDw8cHNzo2XLlgBUqlSJbt26ceTIEbp16waAm5sbtra2LFy4ECMjI5o3b86zZ89Yvnw5Q4cOxcjIiISEBFasWMGoUaMYMWIEAA0bNqRLly64ubkxY8aMPD8+6dkXQgghhBCFS6XFLy3T08tbOPzw4UP++OMPpk6dmuV+d3d3rKyscHFxUbY5OjpSo0YN3N3dNcq1b98eIyMjZVu3bt2IjIzEy8sLSEvziY6OpmvXrkoZIyMjOnbsqFFXnh5fvkoLIYQQQgjxBvrf//5Hr169qF69epb7fXx8qFSpEqrn7mI4Ojri4+MDQGxsLI8fP8bR0TFTGZVKpZRL///5ck5OTgQGBhIfH5/ndksajxBCCCGEKFTPB8AF0b59+xz3Hz9+XGvnSnfixAm8vLw4dOhQtmUiIyOxtLTMtN3a2lpJ+YmKigLAyspKo4yRkRGmpqZEREQodRkZGWFsbKxRzsrKCrVaTUREBCYmJnlquwT7QgghhBCiUGkz2H/ZEhISmDNnDuPGjcPOzk7Xzck3CfaFEEIIIcRrozB67nOyZs0a9PT06N69O5GRkQAkJSWRmppKZGQkJiYmGBkZYWVlRVBQUKbjIyIisLa2BlB6/tN7+NMlJiYSFxenlLOysiIxMZGEhASN3v3IyEhUKpVSLi8k2BdCCCGEEIXqNe7Yx8fHBz8/P5o3b55pX+PGjZkxYwYDBw7E0dGRs2fPolarNe5k+Pr6UrVqVQDMzMwoXbq0kpOfsYxarVZy9NP/9/X11Rgj4OPjg4ODQ55TeEAG6AohhBBCiEKmp1Jp7etlGz16NGvXrtX4atmyJWXKlGHt2rW0a9cOgFatWhEREcHZs2eVY319fbl58yatWrVStrVq1Yrjx4+TlJSkbDtw4ABWVlY4OzsD0KBBAywsLDh48KBSJikpiSNHjmjUlRfSsy+EEEIIId5YcXFxnDp1CoCAgACio6OVgbhNmjTByckp02JbO3fuJDg4mKZNmyrbnJ2dadmyJVOmTGHSpEkYGxuzaNEiqlWrRqdOnZRyrq6u7N27lwkTJjBw4EC8vb1xc3Nj/PjxynScxsbGfPjhhyxbtgw7OzuqVq3Kxo0bCQ8Px9XVNV+PT4J9IYQQQghRqF7lAbqhoaF89tlnGtvSf167dq1GQJ+bxYsXM3fuXKZPn05ycjItW7Zk6tSpGBj8F3JXqFABNzc35s2bxwcffICdnR2ffvopo0aN0qhr9OjRqNVqfv/9d549e0aNGjVwc3OjXLly+Xp8KrVarc7XEUVcTHKkrpsgdKBBrcYvdFzDxg1YtXoFAN079uRx4GON/Xp6elhaWlLJqRLtOrTlnff6Ymqa9zw7UTTt99uj6yaIV8COhbvxOnY113JdP+hEi95pwYbbpLU8uOansV+lAmMzY+zL2FOtSRWa9miMmaVpobRZvF7ecxqi6yYoTCY20Fpd8T94aq2uN4H07AsB2NtnPZVWREQkycnJGBsbY2Fhnmm/lbVVpm2mpqaYmaX9oU1KSiYiIoIrnle44nmFXdt3sfKP5dhlcz4hxJtH30AP0xyCcyMTw0zbDIwMMDFPm6EjNUVNbGQsAd6BBHgHcumgJyPmDqF42WKF1mYhxOtDgn0hgKPuh7PcPnrEh1y+6EmnLh34bs6MPNU1dOQQxnz8gfJzeHg469ds5PdVf+Bz35fZM+awcNkP2mi2EKIIKFejHK7zh+XrmDqtatL3i17Kz/GxCXgeucLRP44TGRrFlnk7+GjZ6Fc6dUK8WeR3UXdkNh4hCpmNjQ0ffzaWnn16APDXiVM8CXmi41YJIYoSEzNjWvRuSqv+LQEI8gnm0e0AHbdKiP+oVNr7Evkjwb4QL0mXbv+NxL9187YOWyKEKKrqtqmtfB9473EOJYUQbwpJ4xHiJSleooTyfUx0jA5bIoQoqqzsLZXvE2ITdNgSITRJGo/uSLAvxEsS9Pi/JbQtrSxzKCmEEC8m/EmE8r2Jucz8JV4dEuzrjgT7QrwkO7ftAtKm46xVu6ZuGyOEeGU8uvWI+YMXZrmvSsPK9P2iZ57runzIS/m+bLUyBW6bENqiQoJ9XZFgX4hClJSYxKOHj/hz7QaOHz0BQKcuHbG1s9Vxy4QQr4qU5FSiw7JO7YuLjsv1+NSUVMKCwrh85Apnd50HoFyNspSpUlqr7RRCvJ4k2BdCy1b+soqVv6zKcl+denX4etqkl9wiIcSrrGKdCvmeetPr2NVsF+QqVtae9yb11UbThNAaSePRHQn2hdCyjItq6enrY2FhQSXHirTt0IbOXTtpLJkthBAvIuOiWiqVCiNTI+wd7KnWpDL129fLciEuIXRJYn3dkahDCC17flEtIYTQtucX1RJCiOxIsC+EEEIIIQqVnnTt64wE+0IIIYQQolBJzr7uyAq6QgghhBBCFFHSsy+EEEIIIQqV9OzrjgT7QgghhBCiUEmsrzuSxiOEEEIIIUQRpVKr1WpdN+JVEpMcqesmCCGKuP1+e3TdBCHEG+A9pyG6boKi+LcuWqvryXentVbXm0DSeIQQQgghRKGSnH3dkTQeIYQQQgghiijp2RdCCCGEEIVKevZ1R4J9IYQQQghRqCTY1x1J4xFCCCGEEKKIkp59IYQQQghRqKRjX3ck2BdCCCGEEIVK0nh0R9J4hBBCCCGEKKKkZ18Uqri4ePbt3oeH+2nu3rlLeHg4qFTY2dlRo2Z12rRvTfuO7TAxMdF1Uwtk+c8rARg8dCCWVpYFri8qMopN6zfz10l3/B/5k5SYiH0xe6pWr0rrNq3o2adHpmO6d+zJ48DHOdb7+cRPGTZyaKbtvj4POHHsJJcvXube3fuEh4VjYmJMhYoVaN2uNf0HvquVxyXEi0qMT+LK8X/wvniPIN9gYiNjARXmNuY4VC5FjebVqOVSA0NjQ103tUBO/HkKgOa9m2Jq8WKfi2HB4SwcuSzXcmMWu1KmqoPGNs+j/7BzUd4WfbMpacOEP8Zl2p6clMzF/Ze5/vdNQh49JTEuEWMzY0qUL07tt2rQqGtDDAz1s63X++I9Lhy4hP+dQOKj4zGzNqNi7fK06NOMss+1V7w+pGdfdyTYF4Xm1El3/jdjDk+fhirbTE1N0dPTIzAgkMCAQI4fPcHShT8xa+53NGnWWIetLZiVv6wCoGfvtwscFF++5MmkL77mWegzAIyMjDAyMiTAP5AA/0Du3rmXZbCfzsrKCkPDrN/apqammbZd8fyHUUPfV35WqVRYWFoQEx3Djes3uXH9Jtu3bGfZ8iVUrlK5QI9NiBdx+7w3u5fuJzosWtlmZGKISqUiPDic8OBwbp6+zZE/TtBvQi8c61fSYWsL5uQGdwCcO9Z74WA/Iwtb82z36RlkvrlvaGSQ4zEA0WExADg4lcq0LyYihj+m/Emwb4iyzcTcmLioOPxuPMTvxkMuH7nCyDlDMLMyy3T83p8PcmH/JQBUeipMzIyJDovm2qkb3Pj7Jt3HdqVJ94Y5tk+8mvQk2NcZCfZFodizcy8zp88mNTWVipUq4PrhKFzeaoGNjQ0AUVHRXDh7gc0btnDp4mU8L3u91sG+tty6eZtPx3xOXFwcrdu24oOPRlOjZnUg7Tm79s81/vH6J8c6fliygEZN8v7HMDk5GQMDA9p3bEf3nt1o0NAZM3Mz4uPjOXnsL36Yv5DgoBA+++gLtu/d8trfhRGvF8+j/7BryV7UqWqKlbWn9YCWVG1UWQkU42Piue/ly/l9F/G96seD6w9f62Bf2yat/yJf5eu0rkWd1rWy3R94/zG/jvsNSLsged7+5YcJ9g3BwMiA7mO6UK9tbQyNDZU7MwdWHCHIJ5jDbsfoM76nxrFnd19QAv3W/VvSsl9zTMxNiI2M5fi6U1zYf4l9vx6kZMXiVKhVPl+PS4g3mQT7Quu8b3szZ+Y8UlNTadnKhQWL5mUKEC0tLWjfqR3tO7Xj8MEjhASHZFPbmyMlJYUZ38wkLi6Orm93Yfa8mRq3PS0tLWjRsjktWjbX6nnLVyjHjn1bKVuurMZ2ExMTur7dheIlivPByDE8DnzM0UPH6NH7ba2eX4jsPPYJYu9P+1GnqqnauDIDpvTLlKZjYm5CrZY1qNWyBtdO3SAyNFJHrX0zXDl2FQBzG3OqNNK805eclMzN07cBaPWeC426OCv7jEwMadK9EdFhMZzc4M7NM7c1gv2UlFT+2vg3kHbB0WF4W2WfmZUZPT7uypNHT/G9+oAjvx9n9I8jC+0xisIhHfu6I8G+0Lqfl/5KYmIiJUqW4H8LZufaE9y5ayfUarXGtsTERLZs2MqRQ0d54PuApKRkSpUuSctWLRk+aijFihfLVM/yn1ey8pdV9OjVne/mzMjyXN9OmcHe3fv54KPRjPn4A2X7pQuX+WDkGEo7lGb/0T1c8fyH339bzfV/rhEXF0/5CuXp0683/Qe9qxGAp9eX7u1OvTTOl1Nbnvf3KQ/uet/FxMSYr76e+NLyG0uULJHj/kZNGuJQxoHAgEBu3bwtwb54aY6v/YvkpBSs7C1596s+uebj12ldK9NnSXJSMuf3XuK6+w2e+IeSkpyCdXErqjWuQst+LbC0s8hUz4k/T3FygzvOHerS94temfYD7Fi4G69jV2k7qBXthrRWtvtefcDvk9dhU8KaCas/xe/GI9y3nMb/jj+J8UnYl7GjUZcGNH27kcZ7PL2+dM/n3OfUlpclJTmFq39dB6Bem9ro62umAcVFxZOSnAJkneID4FClNABJCcmo1WrlOQi8G/jvOAxo0btplse69GmK79UHPLzlz7PHz7ArbVfwByVeGsnZ1x0J9oVWhQSH4OF+GoABg/tjaZn5D2lWMn4IhD0L4+MPxnH71h0gLWfd0NAQvwcP8Xuwgb279rF0+WLq1quj/QdAWgrSrG//R2pqKuYW5iQkJHDX+y4L5nzPo4eP+PLrCUpZC0sL7O3tCP03v97G1gZ9PT2N/Rk1qJWWqvT8xQbAwX2HAGju0hxrG+tCeWwvytrGmsCAQFJSUnTdFPGGiHwaiffFuwA069kEE/O8pY9l/CyJiYhhzdQNPL4fBICBoT76BvqEBjzjTMB5vI79w9CZAylXvWx21RWI59F/2L1kL2q1GmMzY5ITkwn2DWH/r4d4FviMbh92Vsoam5lgYWuu5MObWZmhp6/S2J/RtG6zADJdbBQm70v3iIlIC8jrd6ibab+5jTmGxgYkJSQTeD+Iak2rZioTeDdtEoHSjiU1Xqvw4Ajl+2Jl7bM8f7Fy/3Xy3PP0pUl3CfZfJyok2NcVCfaFVl26eFnpWWvdttUL1THt62+5fesOVlZWfDPja9p1aIu+vj43r99kxrRZ3PO+x4RxE9myezO2tjZabD2EhYXxv+/m0q//O7z/4Sjsi9kTFRnFrz+tYNP6zWxav5m+7/bGqbITAF9+PZEvv56oBPF/bl6DQ5kXmy3i6j9pvXrVqlclJDiEFb+swsP9NOFh4djZ29KwcUOGjxpGlao5D5L9cf5CgoNDiI6Kxtraiuo1q9Pt7a506toRff3sZ8DITkR4BPfv3gegchWn/D8wIV6A7zU/0jvpqzfLHDTmxfYfdvP4fhCmFib0HNedmi2qo6evR4B3IDsX7yX4QQgbZm3hk1/GYG6debBoQcRExLJn2X4ad29EmwEtsbC1IC46nhN//sW5PRc5t+cCDbs4U7JC2p217mM6031MZyWIH7PEFduSNgVux8ovfifE7wkpKalY2FpQvmZZmnRr+EI57+kpPKUcS1LaMXPPvZ6eigadnDm/9yLuW05jaWeZKWfffctp9A306TSqvebBGeLA1FTNuzPK9pRU5fuQh0/y3X4h3lQyz77QKt/7D4C03viKlSrk+3jPy16c8TgLwJzvZ9OxcwclQK1Zuya/rvoJKysrQkOfsenPTVprd7r4uHje7tmNSd98iX2xtN4lSytLvpoykcpVK6NWqzl+5ITWz5uQkEBwUNq4hcjIKAa8M5id23YRER6BsYkxwUEhHNh7kMHvDuXwgSM51nXntjfx8fEYGxsTGvqM03+f4ZtJ0xjj+hFRkVH5btuq5W4kJiZibm5Oh07tcz9ACC148vApkNYbn11Pb04eXH/I3ctpF6nvTupL7bdqovdv2kmZqg6M+N9gTC1MiA6L4dyeC9pr+L+SEpKo374ub4/tgoVt2h0+UwsTuo/pQsmKJVCrUfLbC9Oj2wGo9NIi6fDgcK6evM5vX67hwIrDmVKechIbGcudC2l3Wpw7ZB6Ym67TyPbUdKlOcmIyu5fuY2afefzv3QXM6juPfb8cpGLt8oyaN5RKdStqHGdTwkb5/kk2gXzGAD/qWf4/y4RuqVQqrX2J/JGefaFVERFpt2KtrCxf6A15/MhxAGrWqpHlQFT7Yva8078vf6xazdHDxxg7bkzBGpyFkaNHZLm9TdtW3PO+x/17Pi9ct+eNi1luzxiEb1q/GTMzU+b+8D/ad2yHgYEB9+7eY+b0/3H96nVmTJ1J9ZrVqFBR82KqTbvWNGjkTMPGDZRZjx4HBrFpw2bWr9nA5YueTJrwNb+s+inP7T3jcZZN6zcD8OHHH2BrZ5vPRyzEi4mNigPAxML0hT5LbnjcAqBMldJUaZj5jpSFrQWNuzXEfctprv99k/ZD2xSovVlp9Z5LlturN6tK8IMQgh+8+MQEsw5My3afgaEBTbo3ok7rWpR2KoWxqRFqtZrH94M4sf4Ud87f5ezuC5jbmNO6f8s8ne/qqRukJKegp69H3Ta1sy1nZGJIvy/7cKL0X3hsS+u4iY9JAECthoS4RGL+zc3PyKFyKcyszIiNjMVj+9lMdx7UarVSH0BiXGKe2i1eHRKk685r2bMfFhbGxYtZB03i9XbrZlqefqMmjbIt06Rp2j6/Bw+Ji43T6vmtra0zzUqTLn0ga2Sk9mf7yNjDlpqayhdfjadz104YGKRdj1euUplFy37AzMyMhIQENqzdmKmOL7+eQPuO7ZRAH6C0QynGT/yMr6ZMBODcmfOcPX0uT23yuefDN19NIzU1lbdat2TwsIEFeIRCvFyP76flhj/fg5yRY720faEBoSTGazd4NLU0xa501hfHVvZWAMRHx2v1nOks7Szo8XFXKtYuj7GpEZAWaDlULs2QbwdQq2UNANw3exCXxzZcOZY25W+VRk5Y2GQ/D/9T/1B+/mgFZ3acw+Wd5oxbPoZpOyYzbvkYXN5pTuDdQDbO2sKF/Zc1jtM30KfVey0AuH3Om93L9vPscRgpySk8efSULfN24H8nAP1/1waQwFFok5+fH9OnT6dXr17UrFmTt9/WnIgiOjqaZcuW0a9fPxo1akSLFi0YM2YMd+7cyVRXVFQUU6ZMoUmTJjg7O/Ppp58SEpL5wt7T05P+/ftTt25d2rZty8qVKzPdbVOr1axcuZI2bdpQt25d+vfvz5UrV/L9+F7LYP/ChQsMGzZM180QWbC2ThtYGhkZla9bxOnCw8IAKFGyeLZlSpQsCaS9CcLCw/PfyByYmWeft2tklPZHMzk5WavnBDA1+2+xKwtLiyxnvLEvZk+X7mkD+i6cz9/F7rsD+iljCdz/+jvX8gH+AXz0wTgiIiKoW78O836YI39cxUtlZpn2noiPjnuhz5L0gaRW9tkvcmdVLC3oVqshNiJzb3NBpAfZWTEwSruIT8mQg/4ydf43Xz4xPgmfK765lg95+ISAfwfW5pTCk5qSyvpZmwkNfEbbwa3p4tqBEuWLY2RiSInyxeni2oE2g1qhVsPh348RExGjcXyLPs1o2Kk+AJcOerLI9Sdm9JzD0g9/5frfN2nY2ZlS/44VMNHCgmPi5VKptPelbXfv3uXUqVNUqFABJ6fMdwIDAwPZvHkzLi4uLF68mFmzZhEVFUX//v25f/++RtnPP/+c06dPM2PGDH744Qd8fX0ZPXq0Ruzg5+eHq6srxYsXZ8WKFQwfPpylS5fy+++/a9S1atUqli5dyogRI1ixYgXFixdn1KhRPHr0KF+PT9J4hFZVcqoIpE2d+cDXj0qOFV+onsSEN+sWrbm5OWZmZsTGxlK2XNlsB9JW/Dd1JzgoOF/1q1QqatWuSWBAIAH+ATmWDQ4KZozrR4QEh1CtelWW/bpE42JEiJehePm0mVeSk1J46h9K8XKZp9vNi6Qk7V+cv+5sS9libm1GTEQsYUFhuZb3+rdX39TSlGpNsh8sfc/zPk8fhaJSQfNsps9s0bspJ/48RWJcIvev+FK39X8pQSqVit6f96CmSw08j14h+EEIyUkp2DvY0bBTfeq0rsWPI5YCYO8gM/G8bl7lDqN27drRoUMHACZPnsz169c19pctW5ajR49qrELfrFkz2rVrx4YNG5g2LS2tzsvLCw8PD9zc3GjZMi1FrlKlSnTr1o0jR47QrVs3ANzc3LC1tWXhwoUYGRnRvHlznj17xvLlyxk6dChGRkYkJCSwYsUKRo0axYgRIwBo2LAhXbp0wc3NjRkzZuT58b1SwX6PHj3yVC4mJib3QkInGjZqgEqlQq1Wc+qke76DfRtbW/D14/HjoGzLhASnBboqlQrbDCkrBv8GyAmJ2V8oREe/mr87KpUKx8qOXL96PffCaQcUSjuePnnKGNePCPAPpKJjRX5Z9ROWVtn3jApRWCrWqYBKldbrfvucd76DfXNrM576hxIRkn3aXeTTtH0qFZhlmI0nfSBvUmL2FwrpeehFXWpKKv+cTPtcqtu6FgaG2c/o9eRR2qBqMyuzbO9sGJsZKxcaGafbzKhq48pUbZx51rHYyFjCQ9KOKazpUsWbSU8v50QXM7PMd/3Nzc0pX768RoqOu7s7VlZWuLj8N17H0dGRGjVq4O7urgT77u7udOzYUckYAOjWrRsrVqzAy8uLpk2b4unpSXR0NF27dlXKGBkZ0bFjR44ePZq/x5ev0oXMx8cHPT09ateuneNX2bLyJn9VlSxVEpdWab/kmzdsJjo6Ok/Hpd+mr1GzGgCelzyzvXV/4XzacuoVKpZ/Lv0lLSgNCcp60JtarebWjVt5ak9+pfdYvEi6QbqmzZoA4P/IP9v57H19HwDg4FA6X3Wr1WpuXL+Zdmw2U4OGhYUz9v2P8XvwkLLlyrDC7RcZkCt0xrqYFVUaVQHg3N6LxMfmLbhOfw+Wdkp7jzy47pft+9LnnwcA2Jexx8jkvz+66SkikU+znvFFrVYTeO9xntqTX8p1fAE+S3ITFhSmpDnZlLLJsex9Lx+iQtOeh5xSeOC/z8HYqDiSEpKyLJMYn6QsnpVTqlNWrp26AaTN5+/kXClfxwrdK2qz8URGRnL37l0cHR2VbT4+PlSqVClTGx0dHfHxSZvcIzY2lsePH2scl15GpVIp5dL/f76ck5MTgYGBxMfnfczPK9WzX6VKFSpUqMDcuXNzLHf48GEZoPsK+3jcGC6cvUBwUAhTvprG94vmYWxsnG35I4eOEhwUzNARQ2jfqT0b/9zM/Xs+/HXiFG3bt9EoG/o0lO2bdwDQsXMHjX1Vqqbl2d24fpMnT55S/LlVdg/sO0hQPtNf8srcwpzoqGiiovJ2cZOVrm934Y/fVhMdFc2enXvp06+3xv7Qp6Ec2n8YQLmgSpdxJcqsbN+yg8CAQADeap15hpCoqGg+/mAc9+/5UKpUSZb//ivFS2Q/bkKIl6HDsDb4XPEh8mkkW+fvZMA3/TA0yv7P1jX3G0Q+jcSlb3NqtazBuT0XCPF7wq2zd6jZorpG2eiwaC4eSBskWvutmhr7SlZMG4wf4B1I1LMoLO007279c/IaEU+0P1Af0nq+42MSiIuJ50UvtXP7PDi65iQAhsYGONbLOWj2Op42t37x8sUoUzXnNURKVfp3PFWqmsuHvWjWs0mmMpcPeynXMWWrlcmxvowinkZycmPaeCOXPs3QN8j/miFCt7QZpLdvn/M00MePH9faubLz/fffo1KpGDjwv8krIiMjsbTMfDfc2tpaSQ2Kikq7eLaystIoY2RkhKmpqTKrYWRkJEZGRpniJysrK9RqNREREZiY5G3syivVs1+3bl2uXr2ae0EK1oMqCle1GtWYPPUrVCoVHqc8GNhvCPv3HiAi/L9btlFR0Rw/eoIPRoxh8oQpxMSk9fQ0aOisTLn53dRZHDt8XOnlvnnjFh+NHkdkZCT29nYMHDpA47z1nOtRvERxkpKSmPLlN0puelxcPNu27GD2t3Myvbm0xckp7cp73+79Oa4y26BWYxrUaszyn1dm2ufoVIlefXsCsOj7xRw5dFQZ0HP/3n2++PRL4uLisLKyYsiwQRrHLpjzA9/P/QGvy1c0rvaDHgexdOEy5v/veyBtliOXtzSD/bjYOD4b+zm3b96mWPFiLP/913zfORCiMJR2KsXbH3VFpQLvi3f5ZdxKrpy4qkzLCRAfE8+N07f4ffJatszbQUJsWhpfxdrllSk3dy3ey3WPm8qiTAF3H7N66nriouOxsDWneS/NoLR8zXJY2luSkpzClvk7lbz2xPgkLh64zO6l+zEtpAGiJcqnXWRfOX5NYxGp503rNotp3WZx4s9Tmfb9PmktpzZ7EPwgRKlDrVYTeP8xG2ZtUXrIW/ZroQyEzkp8TDy3zqbNNuLcPudefUhLvUpPtzryx3FO7zirzPYTFx3P6R1nObo6LQgrX6NspouHIN9gTm5wJ9gvhJTktM/RxPgkrp66zqoJfxATHkP5GmVp0bdZrm0RojBt376dLVu2MH36dEqVyrzA3KvmlerZf//992ndOvdlv1u3bv1SrtrEi+v9Ti+sbaz533dzeeDzgGmTvwXS8t5UKpXGuIvSDqVp3PS/qTZnzf2Oj0Z/wp3b3nz1xWSMjY0xMDBQjrGysuKHpd9rTDEJYGBgwKRvvuSr8ZO5fNGTHp17Y2FhTnx8PMnJKfTq25PUlBT27t6v/cfbrxf/XLnKhnUb2b5lB7Z2tqhUKjp0asf4Lz/Pcz1fTZmI/yN/Lp6/xOQJUzA2NsbQyJDof+8YWFha8P3i+Zl63WNjYti7ez8b/9yMnp4eFhYWpKamaIxRaNi4Ad8vmpfpnMePnuCKV9oAvJjoGFyHvp9t++o61+XHJd/n+fEIUVANOztjamnKnp8O8PRRKNt/2A2AkakRKtLmbU9nU8JamU4T4J2JvVj9zXqCfILZPGc7BkYG6OvrKceYWpgwcOp7mFlp5uPq6+vx9tgubJqzjQfX/Fg46ieMzYxJSkgiNSWVBp3qo05NxetY3jqn8vt4H97y5+yu81w8cBlza3NUKqjVsgZd3u+YpzrCQyI4tuYkx9acRN9AT2l7UsJ/YxCa9WxM20E5r3J+/e+bJCcmo6enon77OrmeV09fjwHf9GP1N+uJCo3i0G/HOPTbMYxNjTReJzsHO96d1DfT8XFRcZz48xQn/jyFSk+FiZkx8bEJqP9dUdexXkUGTnsPff1Xqp9S5JE2s290GQOeOnWK6dOn89FHH9GnTx+NfVZWVgQFZR5zGBERocxWmN7zn97Dny4xMZG4uDilnJWVFYmJiSQkJGj07kdGRqJSqZRyefFKBfvly5enfPncl/A2MTGhTJm83/4TutG2fRuaNW/K3t378HA/zV3vu4SHRaBSpeWN16xVg3Yd2tKuY1uNQSq2dras3vA7WzZu5fCBI/g9eEhSUhLlK5SnZSsXhrsOy5Sik65dh7b8vGoZbiv+4NaNW6SkpFK1WlX6DehH7749+XbKjEJ5rL369CQ1JZWd23bhc9+X4KBg1Go14WHh+arH2NiYX3/7mR3bdrFv9z587vuSmJBIufLlcHmrBcNGDqFU6cy9CO/0fwcbO1uuel0lKCiIiPAIUlPVlCpVkhq1a9ClW2fad2yX5SCk1NT/eg/j4uKIi8t+7YLIiMJJXRAiJzVbVKdyA0e8jl3F++JdgnxDiI2MRaUCm5I2lKlSmpotqlPTpToGhv/9WTO3NueDhaO4sO8i107d4Kl/KCnJabO7VG1Shbf6Nc+UopPxnMNnD+bUJg8C7waiTk2ltGNJGndvRMNO9dmxcHehPNYGneqTmqrm8iFPQh49JfJpRNrUoFksRJWdzq4duO/lg793INFh0cRFxaFvkLYScfma5WjUpQHlquf+NzT9YsbJ2THb5+l5JcoXZ9wvH3J+3yXunPfmaUAoiXGJmFiYULxcMWo2r0bj7o2yzNcvXq4Yrd5zwffqA54FhRMfHY+5tTkOlUtTv10d6rSulefnQLx6XpVc+4K4cuUKn332Gb179+azzz7LtN/R0ZGzZ89mSqXz9fWlatW0mazMzMwoXbq0kpOfsYxarVZy9NP/9/X1pXr1/9IQfXx8cHBwyHMKD4BKLfkwGmKSJZgRQhSu/X57dN0EIcQb4D2nIbpugqL64q65F8qj258f1Fpdz0ufenPfvn0a2+/du8fgwYNxdnbmp59+Uha9zMjLy4sBAwbwxx9/0KJF2iJxvr6+dO3alYULFyqz8UyfPp3Tp09z6NAhDA0NAVi0aBEbNmzg9OnTytSbLVq0YMiQIYwfPx6ApKQkOnfuTKtWrV7fqTeFEEIIIUTR8yr37MfFxXHqVNr4l4CAAKKjozl06BAATZo0Qa1W4+rqirGxMcOHD9eYh9/CwoLKldOminV2dqZly5ZMmTKFSZMmYWxszKJFi6hWrRqdOnVSjnF1dWXv3r1MmDCBgQMH4u3tjZubG+PHj1cyHYyNjfnwww9ZtmwZdnZ2VK1alY0bNxIeHo6rq2u+Hp/07D9HevaFEIVNevaFEC/Dq9SzX3Npd63VdfNT7Y698/f3z3aGn7Vr1wIwbNiwLPc3adKEdevWKT9HRUUxd+5cjh5Nm2SjZcuWTJ06lZIlS2oc5+npybx587h16xZ2dnYMHjyY0aNHa1wUqdVqVq5cyYYNG3j27Bk1atTg66+/xtnZOV+PT4L950iwL4QobBLsCyFeBgn2BUgajxBCCCGEKGSvcBZPkSfBvhBCCCGEKFSvcs5+USeT1QohhBBCCFFESc++EEIIIYQoVNKzrzsS7AshhBBCiEIlwb7uSBqPEEIIIYQQRZT07ItX0uPAIE4cO8GFcxe5e+cuoaHPMDQ0pEzZMri81ZyBQwdSvHixF6o7NTWVvbv3cXDfIbzv3CU6KgoTU1MqVqxAq7atGDikP+bm5pmOCwwI5O1OvXKt/8/Na6hZu2aW+8LDw/lzzQbcT7rj7x+Avp4+5SqUo1OXjgwaOkBZTON58fHx/PrTCo4cPMKz0DBKO5Sm9zu9GDZyCHp6WV+znztzno9Gf8KAwf35asrEXNstRFEUHhLBzTO38bniS5BvMNFhMegb6mNbyoaqjSrTvFcTLO0sMx0XFhzOwpHLcq1/zGJXylR1yFeb4qLjuXL8Kv7eAQT7hhAdHkN8dByGJkYUK2NPtaZVaNajMSbmJtnWEfUsijM7z3P38n3CgsJISU7BzMoMhyqladjZmRrNquW5PRFPI1n64a8kxiUCMGreUCrVrZipXGpKKn9vO4PnkStEPInEwtaCum1q0W5IawwMsw4ngv1C+HXcKio3dGLItwPy3CZR9EjHvu5IsC9eOUGPg3i7U08yLgFhYWFOXFw8d73vctf7Lju27uL7xfNp3LRRvuqOi4vn84/Hc/H8pf/qtrQgJjqG69ducP3aDXZu28WK33+hbLmy2dZjb2+X7b6sltAG8L7tzSdjPuPpk6cAmJmZkZKawu2bt7l98zYH9x1ixR+/YGNjo3GcWq1mwqdfcvb0OQBMTU156PeQpQuX8TggkK+nT850rsTERObNXkCxYvaMHTcm27YKUZRFPIlg4cilZFxNxtjMmKT4RIJ9Qwj2DeHSQU8GfPMujvUqZluPhW3mi/90egb5v0H+1P8pB1YcVn7WN9DD0MSI+Oh4/O8E4H8ngAv7LzN81iBKViyR6fhHt/1ZN30jcdHxaW3QU2FobEjUs2junL/LnfN3qd++Ln2/6Jmn1Il9vxxUAv2c7P3lIJcOegJgZGJIxJMI/t56huAHIQz9bmDWdf98ED19PbqP6ZJr/aJokzQe3ZFgX7xyUlNTAWjZuiU9e71Nk2aNsbK2IikxiQvnLzBv9gIC/AOZ8OlEduzbRrF89PD/tvw3Lp6/hEql4pPPPqLfgH5YWlqQlJjEiWMnmTNrHo8DHzNz+v9Y+cev2dZz1P1wtvuyEhsTy+cff8HTJ0+pWKkC386aRj3neqjVai6cv8iMb77jrvddpnw5jV9WafYmnjtznrOnz1HaoTTLli/B0akSXpev8OnYz9m2ZQeDhg2kQsUKGses/m0ND/0eMnv+TCwtLfLVViGKitTUtCi/auMqOHesi1O9SphampKclILPFV/2/XqQsKBwNszawmcrP8LSLuv3yqT1X2i1XWaWprz1bgsq1q6AQ5XSmFuboVKpSEpI4vY5bw6uOkJUaBQb/7eVT5ePRU//vwuKlOQUNs/dQVx0PLalbOk1rhsV61ZEX1+PqGfR/LXxby7sv8SV41dxrF8R5/b1cmzLrbN3uH3Om7LVyuB/JyDbck/8n3L5kCcmFiYM/W4g5WuUJdgvhLXTNuJ98R73vXxwcnbUOMbr+D88uP6Q9sPaYFvSpkDPmSgCJNjXGcnZF68cSysrNm77k6W/LKJD5/ZYWVsBYGhkiMtbLiz9dQnGxsZER8ewfevOfNV9cH9akN6zTw9Gjh6hBMKGRoZ07taJCV+NB+DShUtERmhvNeVdO3YTFBSMvr4+Pyz5nnrOaX+AVSoVTZs1Yfb8WQCcO3OOc2fOaxx74dxFAIaPGoqjUyUAnBvWp0+/XqjVai5euKxR3v+RP3/8toZGjRvS7e2uWnsMQrxuTC1MGLtsNEO/G0DtljUxtTQFwMBQn6qNKzP0u4EYGBmQEJvAxYOXc6lNe+zL2NNpZHuqNq6MhY250uNpaGxInda1eGdibwBCA57x8Ja/xrF+Nx4R8SQCgL5f9MTJ2RH9fy8GLO0s6PFxVyrWSbv4v3n6do7tSIhLZP/yQxiZGtHl/Y45lvX95wFqNTTq7Ez5Gml3PUtWKMFb/ZoDcP+Kr0b5uOh4Drsdx76MHS3faZHbUyKEKEQS7ItXjqWlBVWrV812fyXHitSpWxuAWzdu5avuZ6HPAKhWPet81hq1aijfx8fH56vunJz++wwAzVo0VQL2jBo2aqCce98ezWXAIyLS/rCXKVtGY3u58uUACA8L19i+YM4PpKSkMHnqV1ppuxCvKxNzE0o7lsp2f/FyxShXPe19FXjv8ctqVq4yjgGICo3S2BcdHq18X9op68fmULk0AEnxSTme5/i6v4h4Ekm7wa2wKpZ53EJGsZFxANiWstXYblfaTmN/umNrThATHsPbY7tiYKifY93izaBSqbT2JfJHgn3xWrK2sQb+S/nJK4cyaX8E79y+k+X+9IsHe3s7SpTMnCv7oh4/DgLIlG6TUcVKafvOP9ezb22d9lgD/DVvsfs/Suvxs/n3uQA4fvQEHu6nGTxsEI6VNW+pCyEyS+/tV6eqcyn58jy6+Uj53raUjca+jOkwj+8HZXl8+oVL6crZX+gE3n/M+T0XKFGhOM16Nc21TWZWac9TWFCYxvZn//6cvh8gwDuQiwc9qf1WTSo3kM8hkUal0t6XyB/J2RevneTkZP7x+gcAp8pO+Tq2T7/eLPp+CXt27qV8+XKaOfvHT/LjgkWoVCo+//KzHOsZPmgUPvd8SE5Oxr6YPfWc69LvvXdwblg/y/Iq0j6dcro4SUlJASA09Bnh4eHKQN3GTRux5ve1rPl9HY2bNqaSY0X+8fqHndt2oVKplEHKcbFx/DhvIaVKleSDse/n63kR4k2UkpLKw5tpF80lKhTPttzKL34nxO8JKSmpWNhaUL5mWZp0a0iFWuW12pbosGjuXrrPsTUnAChb1SHTTD9lqpahlGNJgnyC2bFwT6ac/VOb/ubBNT8s7S1p+U7zLM+Vmqpmz9L9qNVqenzcTUkDykn67DyXDntR06U65aqXJeThEzy2pd21dKxXSal7788HMDI2pOvonFODhBAvhwT74rWzZeNWnj4NRU9Pjx69uufr2EFDBxLgH8iWjVtZtvhnli3+GQtLC2JjYklNTaVOvTq4fjCSVm3eyrGea/9cw+LffP/AgEACAwI5uO8QA4cMYOLkLzLdZiztUApfH1987vtkW6fP/f9yXp8+eaoE+81dmtG0eRPOn73AOz3exczMjNjYWADeea+vcrdgxS+rCAoK5vvF8zE1M81UvxBC04W9F4kOi0alp8K5Q/YDWR/dDsDE3BhSIDw4nPDgcK6evE7zXk3o+kGnAqUV/DHlT3yey3eHtOD6vUl9MtWtp6di4Dfvsn7mZkL8nrD6m/XKbDwJcYkYGhtQr10dOo1oh7l11rMInd97kYC7j3HuUJeKtfN2wVK8XDEadqrP5SNXWPnFHxiZGJL4b5pQlYZOODmnBfsXD1wm4O5jurzfEatiVvl5KkQRJ+k3uiPBvniteN+5y0+Lfwag/6B3852qoq+vz8TJX1C2bBmWLlpGcnIK0VH/5cDGxsQQFhaW5bFGRka8O6Afnbt1onr1apiZm6FWq7l96w4rfl6J+19/s/HPTdjZ2+H6wUiNY5u1aMYZj7NcPH+JmzduUTPD2ACA03+f5p73PeXnmJhY5XuVSsXCZT/yy9JfOXr4GM9Cn1G2XFl6v9OTEa7DAbh/7z4b1m2kRcvmtO/YDoBN6zezaf0WHgc+plTpUvQf9C6DhmY9PZ4Qb5og32CO/tuD3vTtxpQor9mzb2BoQJPujajTuhalnUphbGqEWq3m8f0gTqw/xZ3zdzm7+wLmNua07t/yhdthamGCha05yYnJxMckAOBYryJdRnfEwjbr2YHsStsy4n9D2LFwN/c8fUhNVZPw79SZKcmpJMYnEhcdn2WwHfk0kuNrT2JqYUJn1w75amvPcd2xKWmD59ErRD6NxKqYFXVa16L9kNaoVCqiw2M4tvbkv6lBTQC4euo67ptPExoQirmNOc4d6tFmUKs83U0QRYsE+7qjUmeczFwQk6y9GViEdj158pRRQ1wJ8A+kRq0a/L5uFcbGxvmq4+mTp3wxbiLXr92gR6/uDB4+mHLlyvLkyVOOHznOquW/ER+fwMj3hzNu/Cf5qnvSF19z9PAxTE1NOXRiP5ZW/w14i46O5p0e7/Ek5AklS5Xgqylf0qx5U1LVqfz9lwcL5nxPdHQMycnJAKzZ+IcyCDkv3h/+ATeu3WTzzo2Ur1COVb/+xq8/rcChjAMNGzlz+ZIXgQGBfDRuDO+Pcc3X4xLat99vj66b8EaLehbFqomrCQsKx6Fyad7/YQSGRvnr+9o0Zxs3PG5hZGLIxLWfY2qR/QJYeRUbFcd195scX/cXcdFxdH2/I817Z86nv33em60LdqJvoE/H4W2p0tAJU0tTnjx6yl8b/+bOhbsYmxkzYs4Qyj6XBrRx9lZunrlNj4+70qT7f+uUZFxELLtFtXKz7YddXD15jVHzh1OxdnmuHL/K9h93Y2FrTuUGTgTcDeTJw6c07FSf3p/3yHf9Iv/ecxqi6yYoGrm9o7W6Lrlu11pdbwK5tBavhYjwCD4e/QkB/oGUr1Cepb8synegDzB9ygyuX7tB73d68d2cGVStVgVTM1PKVyjHyNEjmPLtFADW/L6O+/fu56vuT79IuziIi4tTpstMZ2FhwaJlP2BrZ0twUAgTPv0Sl8ateKtJG6Z8NRWAMZ98oJTPz9z4+3bvx/OSF8Ndh1G+QjnCwsL5bcXvlChZgg1b1/HdnBms27wGe3s7flvxO2HPzd4jxJskNiqO1d+sJywoHHsHO4Z+NyDfgT5A51HtAUiMT8oyDedFmFma0qR7Q4b/bxAq4OCqI5lmCQoLCmPT/7aRFJ/IoKnv0rhbQ2xK2mBsZkzZamUYMmMAjvUrkRCbwP5fD2kce+e8NzfP3KZMldI06tpQK21O9+CaH/+cuEb99mmpQSnJKRz+/RiGxgZ8sHAU70zoxdgl71OyYgkuH7lCkG+wVs8vXn0yG4/uSLAvXnlRUdF8/ME47t29T6nSpfj1t5+xL2af73p87vkoc9gPHpZ1OsvbPbthY2NNamoq7if/zlf9ZcqWwdYubVo6f//Mi9PUrF2TbXu2MHrs+9SuW5tSpUtRuWplBg7pz6YdGyhWLG1xMAMDAxzKOGQ6PitRkVEs/nEpZcuVYeT7aSk958+cJykpiS7dOitrFNja2tD17S4kJiZy4eyFfD0uIYqK+Jh41k5dT4jfE6yLWzNizpBsU2VyY1vKFnNrMyDzDDUF5eBUmvK1yqNWg+fRfzT2Xdh/mZTkFBwql1bm039ei39TaPzvBBD17L80xb2/HEKlgk6jOpCUkERCXKLylZTw3zSdSQnJmbblJCU5hb2/HMTEwoROo9JSgwLuPiY6LIaqjasoMwgZGhvSqIszAN4X72VXnSiiZDYe3ZGcffFKi4uN49Mxn3Hzxi2KFbNnudvPlHbIfjq5nPj6PFC+dyhTJttyZcqWITw8gsBA7c+7bWtrw9hPPmTsJx9m2nf7Vtp0oNVrVMPIyChP9f20+GeehT5jxuzFyp2Ox/+2u0xZzQuG9Hn5HxfC4xLiVZcYn8i66RsJuPsYC1sLRs4ZjE0J69wP1BEr+7Q0wGePNS8knjx6CoBNDivS2pb+by788JBwZWXg9MW4/vh6XY7nXvftRgAq1qmA6/xhubb1zM7zhPg94e2PumJhY66cFzJPHZo+L394SESu9QohtEN69sUrKz4+ns8+/oJ/rlzFxsaaX91+pnyFF5/qTqX3X3dA0OOs56eG/+bENzM3y1f9Af4BhD1L+8NcJo898+mSEpM4fjRtsGCX7p3zdMzN6zfZvnUnbTu0oWUrl0z7ExISNX+OT8hXm4QoKpISkvhzxmYe3vLHzMqUEXMGY18m/3cHMwoLCiMmIm0gvc1zAa02hAWHA2BkYqixPf1zLOJJ9uPLwoP/C6SNTPPWcfCiIp5E8NdGdxwql6Zxt8ypQcmJKRo/JyXm7W6BKHokjUd3pGdfvJKSEpOY+NlXXLpwCUsrS35e9VO+59R/XtVqVZTvd27byRdfjc9U5tRJd2WV3Tp1NAfIqtXqHD9kflr8CwAmJsbK3Pd5tfyXlTwJeUKxYvb06J37wLXU1FTmzJyHkbEREydN0NhX2iFt4bBbNzVXF75x/Wba/n8XFhPiTZCclMLG2VvxvfoAEwsThs8eTMkKuS+Yl9v7/eiakwAYGhsoc8znVUpKao6z0Ty4/hD/22nz/z8/NWapSiW5fc6bwHuPCbz/GAenzO/nS4c9ATAxN6Z42WLK9lkHpmV7zhcdoLt/xWGSEpPp8Uk39DJ0qNiUsAEyr0wc4B2Ytr/kq3tXRRQOCdJ1R3r2xSsnJSWFKV9N5YzHWczNzVm2fAk1albP07GBAYE0qNWYBrUas2fnXo19ZcuVpVmLZgBsWLeJZYt+UgL72JhY9uzcy4xvZgLgUMaB1m1baRw/esSH/L7qD+7dvacsgJU+9eaET7/k8MEjAAx3Ha6s8JvR6t/WcPzoCSLC/+t1e+D7gO+mzeKPVavR19dn6nff5Glw7rbN27l54xajP3TNlNbUtHkTDA0NOX7kBKf/Tlvw5vTfpzlx7CRGRkY0adYk1/qFKApSU1LZumAHdy/fx9jUiGEzB+JQOW8Xu79PWsupzR4EPwghNSVtMTy1Wk3g/cdsmLWFa6duANCyXwvMLDOva+E2aS3Tus3CbdLaTPs2z9nG0TUnCLz3mJTk/3q+o8NjOLvrPH/O2IRaDdbFrXDuWF/j2Aad6mNgqE9qSiobZm7h1tk7JCWmzeIV8SSCXYv3cutMWkpgk+6N0CvEKS69L97j1pk7NOrsnGnWnzJVSmNuY87Dm4/wPPoParVaWVkXoGqjyoXWLiGEJunZF6+cK17/KCktycnJTBg3MduyJUuV5M8tmf+YZue7Od8yZtRH+Pr48sdva/jjtzWYm5sTExOjlLG3t+OHJQswNNK8ff44MIifFv/CT4t/wcDAAAsLc+Lj44nPkB4zYHD/bFevPe1xhsuL0v7QmZqaokZNfFw8AGZmZkz77ptcF/MCeBb6jJ+X/kolx0oMGZF5WjVbO1tGjh7Byl9WMW7MZ5iYGCtt/GjcGGxtbXI9hxBFwcObj7h5+jaQ1pu+YdaWbMtaF7NizJL/3rvhIREcW3OSY2tOom+gh7GZMUkJSSQlJCtlmvVsTNtBrbKqLkdx0fG4bz6N++bT6OmpMDY3ITUllYTY/z5L7B3sGPxtf4yfS8OxLWlD3y96sWPhbiKeRLJh1hZUeioMjQyURa4AqjWpQtvBrfPdtrxKSkxm36+HMLc2o+OIdpn26xvo02lEO3Yu3svORXvY98sB5blr2Kk+pSqVLLS2iVeT9OzrjgT74pWjTv1v6YeEhAQSErLPNTfK5/SbxYsXY/3WtezYupMTx05y/64P0dHRWFiYU7Z8Od5q5cKAwf2VWXUy+nzip5w7c54b124Q+jSUiIhIDA0NqVipAvWc69H33T45zo0/YHB/7IvZc/P6LUKfhqKn0sOpsiMt3mrBwMH9KVU6bwOPF/2whKjIKL5fNB9Dw6zfwmM+/gALCwu2bNxK0OMgypYry4DB78miWuKNknEZmeTEZKITk7Mta/Dce6mzawfue/ng7x1IdFg0cVFx6BvoU6ysPeVrlqNRlwaUq579QP+cdHHtwJ0Ld/G95kd4cDgxETGoU9VY2VtSyrEkNZpXp167OtlOCZq+0Ne5PRfwufqA8OAIkpNSsLA1p7RTaeq3q0Od1rUKNbhy3+xBWFAYfT7vgWkWdzYg7S6EvqE+7ltOExrwDKtiVjTomLaolnjzSLCvO7Ko1nNkUS0hRGGTRbWEEC/Dq7SoVot1A7RW15mhm7RW15tAevaFEEIIIUShko593ZFgXwghhBBCFCpJ49EdmY1HCCGEEEKIIkp69oUQQgghRKGSnn3dkWBfCCGEEEIUKgn2dUeCfSGEEEIIUagk1tcdydkXQgghhBCiiJKefSGEEEIIUagkjUd3JNgXQgghhBCFS4J9nZE0HiGEEEII8cby8/Nj+vTp9OrVi5o1a/L2229nWW7r1q107tyZOnXq0LNnT06ePJmpTFRUFFOmTKFJkyY4Ozvz6aefEhISkqmcp6cn/fv3p27durRt25aVK1eiVqs1yqjValauXEmbNm2oW7cu/fv358qVK/l+fBLsCyGEEEKIQqVSqbT2pW13797l1KlTVKhQAScnpyzL7N+/n2nTptG1a1dWrVpF/fr1+eSTTzIF359//jmnT59mxowZ/PDDD/j6+jJ69GiSk5OVMn5+fri6ulK8eHFWrFjB8OHDWbp0Kb///rtGXatWrWLp0qWMGDGCFStWULx4cUaNGsWjR4/y9fgkjUcIIYQQQhQqvVc4i6ddu3Z06NABgMmTJ3P9+vVMZZYuXUr37t35/PPPAWjWrBne3t78/PPPrFq1CgAvLy88PDxwc3OjZcuWAFSqVIlu3bpx5MgRunXrBoCbmxu2trYsXLgQIyMjmjdvzrNnz1i+fDlDhw7FyMiIhIQEVqxYwahRoxgxYgQADRs2pEuXLri5uTFjxow8Pz7p2RdCCCGEEG8sPb2cw+FHjx7x4MEDunbtqrG9W7dunD17lsTERADc3d2xsrLCxcVFKePo6EiNGjVwd3dXtrm7u9O+fXuMjIw06oqMjMTLywtIS/OJjo7WOKeRkREdO3bUqCtPjy9fpYUQQgghhMinVzmNJzc+Pj5AWi99Rk5OTiQlJSlpNT4+PlSqVClTGx0dHZU6YmNjefz4MY6OjpnKqFQqpVz6/8+Xc3JyIjAwkPj4+Dy3X9J4hBBCCCFEodLTYpDevn37HPcfP35ca+cCiIiIAMDKykpje/rP6fsjIyOxtLTMdLy1tbWSGhQVFZVlXUZGRpiammrUZWRkhLGxcaZzqtVqIiIiMDExyVP7pWdfCCGEEEKIIkp69oUQQgghRKHSZvqNtnvuc2NtbQ2k9coXL15c2R4ZGamx38rKiqCgoEzHR0REKGXSe/7Te/jTJSYmEhcXp1FXYmIiCQkJGr37kZGRqFQqpVxe5CvYz+22SXZUKhXHjh17oWOFEEIIIcTr7XVOJUnPm/fx8dHIoffx8cHQ0JBy5cop5c6ePYtarda4uPH19aVq1aoAmJmZUbp0aSUnP2MZtVqt1J/+v6+vL9WrV9c4p4ODQ55TeCCfz71arc70lZiYSEBAAAEBAQQHB5OQkEBwcLCyLTExkdTU1PycRgghhBBCiFdCuXLlqFixIocOHdLYfuDAAZo3b67MqtOqVSsiIiI4e/asUsbX15ebN2/SqlUrZVurVq04fvw4SUlJGnVZWVnh7OwMQIMGDbCwsODgwYNKmaSkJI4cOaJRV17kq2f/xIkTGj9HRkYyYsQIKlasyOeff46zszN6enqkpqbi6enJkiVLiI2NZfXq1flqlBBCCCGEKDq0OUBX2+Li4jh16hQAAQEBREdHK4F9kyZNsLOzY9y4cUycOJHy5cvTtGlTDhw4wNWrV/nzzz+VepydnWnZsiVTpkxh0qRJGBsbs2jRIqpVq0anTp2Ucq6uruzdu5cJEyYwcOBAvL29cXNzY/z48cqFg7GxMR9++CHLli3Dzs6OqlWrsnHjRsLDw3F1dc3X41Opn1+bNx+mT5+Op6cnu3fvRl9fP9P+5ORkevXqRcOGDZk5c+aLnualikmO1HUThBBF3H6/PbpughDiDfCe0xBdN0Hx9u78Bag52dfLTWt1Afj7+2ebqr527VqaNm0KwNatW1m1ahWBgYFUqlSJL774grZt22qUj4qKYu7cuRw9epTk5GRatmzJ1KlTKVmypEY5T09P5s2bx61bt7Czs2Pw4MGMHj1aI/1HrVazcuVKNmzYwLNnz6hRowZff/210vufVwUK9l1cXOjTpw8TJ07Mtsz333/P7t278fDweNHTvFQS7AshCpsE+0KIl0GCfQEFnI0nOjo602ji50VFReVaRgghhBBCFF2vchpPUVegwdGVK1fmwIEDPHz4MMv9Dx484MCBA1SpUqUgpxFCCCGEEK+x13kF3dddgXr2x44dyyeffELv3r3p168fDRs2xN7entDQUC5dusT27duJi4tj7Nix2mqvEEIIIYQQIo8KFOx36NCBefPmMWvWLNauXcu6deuUfWq1GgsLC+bOnfvC8/MLIYQQQojX3+s8z/7rrsAr6Pbu3ZsOHTpw7Ngx7ty5Q1RUFJaWllSrVo0OHTpgYWGhjXYKIYQQQojXlOTs606Bg30ACwsLevfurY2qhBBCCCGEEFqilWAfICYmhgcPHhAXF0ejRo20Va0QQgghhHjNycBa3SlwCpW/vz9jx46lSZMm9OvXj2HDhin7Ll++TLdu3Th//nxBTyOEEEIIIV5TeiqV1r5E/hQo2A8MDKR///64u7vTvn176tevT8Y1uurVq0dYWBj79+8vcEOFEEIIIcTrSaXFL5E/BQr2ly1bRkREBOvWrWPp0qW4uLho7DcwMKBRo0Z4enoWqJFCCCGEEEKI/CtQsP/333/TsWNHGjRokG0ZBwcHgoODC3IaIYQQQgjxGpM0Ht0p0ADdiIgIypQpk2MZtVpNYmJiQU4jhBBCCCFeYxKk606BevaLFSuGn59fjmW8vb0pXbp0QU4jhBBCCCGEeAEFCvZbtGjByZMnuX37dpb7L126xLlz52jdunVBTiOEEEIIIV5jKpVKa18ifwqUxjN27FgOHz7MkCFDcHV1VXr5T506hZeXF6tXr8bW1hZXV1etNFYIIYQQQrx+JI1HdwoU7JctWxY3NzfGjx/PkiVLUKlUqNVqxowZg1qtxsHBgSVLllCiRAlttVcIIYQQQgiRRwVeQbdevXocOXKEkydP8s8//xAREYGFhQV169alffv2GBkZaaOdQgghhBDiNSX9+rpT4GAf0ubT79ixIx07dtRGdUIIIYQQogiRNB7dKdAA3WHDhrFr164cy+zevZthw4YV5DRCCCGEEEKIF1CgYP/ChQv4+/vnWCYwMJCLFy8W5DRCCCGEEOI1Jotq6Y5W0nhyEhcXh4FBoZ9GCCGEEEK8omTKTN3JdxQeGBio8XNUVFSmbQApKSkEBQVx+PDhXFfZFUIIIYQQQmhfvoP9du3aKVdnKpWKtWvXsnbt2mzLq9VqvvrqqxdvoRBCCCGEeK1J+o3u5DvY7927tzKf/q5du6hevTo1atTIVE5PTw9ra2uaNWtGq1attNJYIYQQQgjx+pFQX3fyHezPmzdP+f7ChQv07dtXZtsRQgghhBDiFVSgkbMnTpzQVjuEEEIIIUQRJWk8ulOgqTfv3bvH2rVrefbsWZb7Q0NDWbt2Lffv3y/IaYQQQgghxGtMpt7UnQIF+ytXrmTVqlXY2Nhkud/GxgY3Nzd+++23gpxGCCGEEEII8QIKlMZz6dIlmjdvjp5e1tcM+vr6NG/eXBbVEkIIIYR4g8k8+7pToGD/6dOnlC5dOscyJUuW5MmTJwU5jRBCCCGEeI0VKJVEFEiBgn1TU1NCQ0NzLBMaGoqxsXFBTiOEEEIIIV5j0rOvOwW60KpVqxbHjh0jMjIyy/0REREcPXqUmjVrFuQ0QgghhBBCiBdQoGB/0KBBhIeHM2zYsEx5+RcuXGDYsGFERkYyZMiQAjVSCCGEEEK8vmQ2Ht0pUBpPhw4dGDFiBKtXr2bYsGEYGRlRrFgxnj59SmJiImq1GldXVzp06KCt9gohhBBCiNeMBOm6U6BgH2Dy5Mk0bdqUDRs2cO3aNYKDg7G0tKRZs2YMGjSI1q1ba6OdQgghhBBCiHwqcLAP0LZtW9q2bauNqoQQQgghRBHzKg/QPX78OMuXL+fevXuYm5vTsGFDJk6cSLly5TTKbd26ld9++43AwEAqVarE+PHjM8W/UVFRzJ07l2PHjpGUlMRbb73F1KlTKVGihEY5T09P5s+fz61bt7C3t2fgwIGMHj26UJ4nlVqtVmu91tdYfEqsrpsghCjiTLtU1XUThBBvAPVRf103QfHV6claq2uByzyt1XX+/HlGjBhB79696dGjB+Hh4SxZsoTU1FT27t2LiYkJAPv372fChAmMGTOGZs2aceDAAbZv38769eupX7++Up+rqyv37t1j0qRJGBsbs3jxYvT09Ni+fTsGBml97H5+fvTu3RsXFxcGDx7MnTt3+OGHHxg/fjyurq5ae2zptNKzL4QQQgghxOtm//79ODg4MGfOHKVX3c7OjuHDh3P9+nUaNWoEwNKlS+nevTuff/45AM2aNcPb25uff/6ZVatWAeDl5YWHhwdubm60bNkSgEqVKtGtWzeOHDlCt27dAHBzc8PW1paFCxdiZGRE8+bNefbsGcuXL2fo0KEYGRlp9THmK9ivXr06enp67N+/n0qVKlG9evU83W5QqVTcvHnzhRsphBBCCCFeX69qGk9ycjLm5uYa7bO0tAQgPfnl0aNHPHjwgC+//FLj2G7durFgwQISExMxMjLC3d0dKysrXFxclDKOjo7UqFEDd3d3Jdh3d3enY8eOGkF9t27dWLFiBV5eXjRt2lSrjzFfwX7jxo2BtMW0Mv4shBBCCCFEdl7V2Xj69u3L7t27Wb9+PT179iQ8PJyFCxdSs2ZNGjRoAICPjw+Q1kufkZOTE0lJSTx69AgnJyd8fHyoVKlSpgsbR0dHpY7Y2FgeP36Mo6NjpjIqlQofHx/dBvvr1q3L8WchhBBCCCEKU/v27XPcf/z48TzX1ahRI3766ScmTJjAzJkzAahRowa//fYb+vr6QNoisQBWVlYax6b/nL4/MjJSuSuQkbW1NdevXwfSBvBmVZeRkRGmpqZKXdpUoEW1hBBCCCGEyI1Ki/+0ydPTk6+++or33nuPNWvWKINzP/jgA+Lj47V6Ll2RAbpCCCGEEKJQaTNnPz8997mZPXs2zZo1Y/Lk/2YLql+/Pm3atGH37t30798fa2trIK1Xvnjx4kq5yMhIAGW/lZUVQUFBmc4RERGhlEnv+U/v4U+XmJhIXFycUk6b8hXsf/311y90EpVKxZw5c17oWCGEEEIIIQrD/fv3M6UFlSpVCltbWx4+fAig5Nf7+Pho5Nr7+PhgaGiozMfv6OjI2bNnUavVGhc3vr6+VK2aNuWymZkZpUuXVnL4M5ZRq9WZcvm1IV/B/s6dO7PcrlKpyGq6/vTtEuwLIYQQQry5XtUBug4ODplmjAwICCAsLIwyZcoAUK5cOSpWrMihQ4fo0KGDUu7AgQM0b95cmVWnVatW/PLLL5w9e5YWLVoAaUH8zZs3ef/995XjWrVqxfHjx/nyyy8xNDRU6rKyssLZ2VnrjzFfwf7zt01SU1P53//+xz///MOwYcNo1KgR9vb2hIaGcvHiRdatW0f9+vWZMmWKVhsthBBCCCFeH6pXdJjogAEDmDNnDrNnz6Zdu3aEh4fz66+/Ym9vT9euXZVy48aNY+LEiZQvX56mTZty4MABrl69yp9//qmUcXZ2pmXLlkyZMkVZVGvRokVUq1aNTp06KeVcXV3Zu3cvEyZMYODAgXh7e+Pm5sb48eO1Psc+FHAF3ZUrV7J69Wp27dqVaRlggODgYHr37s2oUaMYPXp0gRr6ssgKukKIwiYr6AohXoZXaQXd6ee/1VpdM5t+p7W61Go1mzZtYuPGjTx69Ahzc3Pq16/P+PHjcXJy0ii7detWVq1aRWBgIJUqVeKLL76gbdu2GmWioqKYO3cuR48eJTk5mZYtWzJ16lRKliypUc7T05N58+Zx69Yt7OzsGDx4MKNHjy6U9QgKFOx36tSJt956i2nTpmVbZubMmXh4eHDkyJEXPc1LJcG+EKKwSbAvhHgZXqVgf8aFGdqrq4n26noTFGg2nqCgoFxvNxgbG2c5MlkIIYQQQrwZXtUVdN8EBUqgKlWqFMeOHSMhISHL/XFxcRw9epRSpUoV5DRCCCGEEEKIF1CgYL9fv348evSIgQMHcuzYMcLCwgAICwvj2LFjDBo0iICAAN59912tNFYIIYQQQrx+XtVFtd4EBUrjef/993nw4AE7duxg3LhxAOjp6ZGamgqkDXro27evxnRDQgghhBDizfKqTr35JihQsK+np8ecOXPo3bs3O3fu5M6dO0RHR2NhYUH16tXp1asXTZs21VZbhRBCCCGEEPlQoGA/XZMmTWjSpIk2qhJCCCGEEEWMDNDVHa0E+0IIIYQQQmRH7xVdVOtNUOBnPjk5mdWrV9OvXz8aNGhAzZo1lX23bt1ixowZ+Pr6FvQ0QgghhBDiNaVSqbT2JfKnQD378fHxjBo1Ci8vL2xtbbGwsCAuLk7ZX7ZsWXbs2IG1tTXjx48vcGOFEEIIIYQQeVegnv3ly5fj6enJF198wenTpzNNsWlpaUnjxo3x8PAoUCOFEEIIIcTrS3r2dadAPfsHDx6kadOmjB49Gsh68EW5cuW4detWQU4jhBBCCCFeY3oyP77OFKhnPzAwkNq1a+dYxtzcnKioqIKcRgghhBBCCPECCtSzb25uzrNnz3Is8+jRI+zs7ApyGiGEEEII8RqT9BvdKVDPfv369Tlx4gSRkZFZ7n/8+DGnTp2iUaNGBTmNEEIIIYR4jempVFr7EvlToGDf1dWVyMhIRowYweXLl0lOTgYgLi6Os2fP4urqSkpKCiNHjtRKY4UQQgghhBB5V6A0nsaNGzNt2jTmzJnDkCFDlO0NGjQAQF9fn2+//TbXvH4hhBBCCFF0qWSArs4UeAXdQYMG0bRpUzZu3MjVq1eJiIjA3NycevXqMWjQIKpUqaKNdgohhBBCiNeUnkpW0NWVAgX7Fy9exMLCgho1ajB16lRttUkIIYQQQgihBQW6zBo2bBibN2/WVluEEEIIIUQRJItq6U6Bevbt7e0xNjbWVluEEEIIIUQRJDn7ulOgnv0WLVpw4cIF1Gq1ttojhBBCCCGE0JICBfsTJkwgPDycadOmER4erqUmCSGEEEKIokTm2dedAqXxfPnll1haWrJ9+3b27NlD2bJlsbe3z5RPpVKpWLNmTYEaKoQQQgghXk+SxqM7BQr2L1y4oHyfmJiIj48PPj4+mcrJYAohhBBCCCFevgIF+7dv39ZWO4QQQgghRBEl6Te680I5+15eXgwbNgxnZ2caNmzIyJEjuXr1qrbbJoQQQgghigCVSk9rXyJ/8t2zf+fOHUaMGEFCQoKy7ezZs3h5ebF161ZZMVcIIYQQQohXRL4vj1auXElCQgJjxozh9OnTnD59mo8++oj4+HhWrVpVGG0UQgghhBCvMZUW/4n8yXfP/uXLl2nYsCGff/65su3TTz/lwoULXLx4UZttE0IIIYQQRYDk7OtOvnv2nz59Sr169TJtr1u3Lk+fPtVKo4QQQgghRNGhUqm09iXyJ9/BfnJyMmZmZpm2m5mZkZycrJVGCSGEEEIIIQquQFNvCiGEEEIIkRs9ybXXmRcK9vfu3cs///yjse3hw4cAjB49OlN5lUrFypUrX+RUQgghhBDiNSfpN7rzQsG+n58ffn5+We77+++/M22TF1gIIYQQQoiXL9/B/vHjxwujHUIIIYQQooh61RfD2rlzJ2vWrOH+/fuYmZlRp04dfvrpJ0xMTAA4ceIEixcvxtfXFwcHBz744APeeecdjToSExNZtGgRe/bsISYmBmdnZ6ZNm4ajo6NGufv37zN79my8vLwwNzenV69efP755xgZGRXKY8t3sF+mTJnCaIcQQgghhCiiXuWc/V9//ZVVq1YxZswY6tevT1hYGGfPniUlJQWAS5cu8cknn9CvXz+mTJnCuXPn+OabbzA3N6dLly5KPbNnz+bAgQNMnjyZkiVLsnz5ckaMGMH+/fuxtLQEICIiguHDh1OxYkWWLVtGcHAw8+bNIz4+nunTpxfK45MBukIIIYQQ4o3k4+PDTz/9xC+//ELr1q2V7Z07d1a+//XXX6lbty4zZ84EoFmzZjx69IilS5cqwX5QUBDbtm3j22+/pV+/fgDUqVOHtm3bsmnTJmVM66ZNm4iJieGnn37CxsYGgJSUFL777js+/PBDSpYsqfXH+GrfUxFCCCGEEK+9V3We/R07dlC2bFmNQD+jxMREzp8/r9GDD9CtWzfu37+Pv78/AB4eHqSmpmqUs7GxwcXFBXd3d2Wbu7s7zZs3VwJ9gK5du5Kamsrp06e1+Mj+I8G+EEIIIYQoVCot/tOmf/75h6pVq/LLL7/QvHlzateuzYABA5RZJx8+fEhSUlKmvHsnJycg7c5A+v/29vZYW1tnKpdeJr3c83VZWVlRvHhxjXLaJGk8QgghhBDitdG+ffsc9+dnMpknT55w/fp1vL29+fbbbzE1NWX58uWMGjWKI0eOEBERAaQF5Bml/5y+PzIyUsnLf75cepn0cs/XBWBtba1RTpsk2BdCCCGEEIXqVZ2GXa1WExsby5IlS6hevToA9erVo127dvz555+0bNlSxy0sOAn2hRBCCCFEodLmbDzanAbeysoKGxsbJdCHtFz7mjVrcu/ePbp37w5AVFSUxnGRkZEAStqOlZUV0dHRmeqPjIzUSO2xsrLKVBek3SF4PgVIWyRnXwghhBBCvJEqV66c7b6EhATKly+PoaFhpnz69J/T8+8dHR15+vRpplSc53P0HR0dM9UVFRXFkydPMuXya4sE+0IIIYQQolCpVHpa+9Kmtm3bEh4ezq1bt5RtYWFh3Lhxg1q1amFkZETTpk05fPiwxnEHDhzAycmJsmXLAtCyZUv09PQ4cuSIUiYiIgIPDw9atWqlbGvVqhVnzpxR7gwAHDp0CD09PVxcXLT62NJJGo8Q/5o2ZTp7du3NtdyXkycyZNhgAFyHv8+li5cBaNehHYuW/pjtccOHjOSK5xXGfPQhYz8Zo51GCyFeC+qj/i903F//nKXtxHcB8F13loqlygGwcNtKJqyYme1xjzZcpGzx0oz4fjxrjmx9oXMLoU3ankVHWzp06ECdOnX49NNPGT9+PMbGxqxcuRIjIyMGDRoEwNixYxk2bBgzZsyga9eunD9/nn379rFo0SKlnlKlStGvXz8WLFiAnp4eJUuWZMWKFVhaWjJgwACl3IABA1i3bh0ff/wxH374IcHBwSxYsIABAwYUyhz7IMG+EJkYGBjkmDdnamqa5fYTx05w88ZNataqWVhNE0K8poKehWS53c7SBiNDI+IS4omIicy0/1lUeJbHje0xjB+3rSQwNEibzRTijaOnp8fKlSuZO3cu06dPJykpiUaNGrF+/XqKFy8OQKNGjVi2bBmLFy9m27ZtODg4MHv2bLp27apR19SpUzE3N+fHH38kJiaGBg0a8Mcff2jM0mNtbc2aNWuYNWsWH3/8Mebm5vTr14/x48cX2mOUYF+I59R3rofbmt9e6NiflvzMLyt/1nKLhBCvu9L9G2S5/eQPW2lTrzmbT+1h5Pdf5Lk+U2MTpg7+lI+WTtFWE4UoVK/qbDwAdnZ2fP/99zmWad++fa5TfhoZGTFp0iQmTZqUYzknJydWr16d32a+MMnZF0ILXFq2QKVScdrjDJ6XvXTdHCFEEXbgwgkAXLsMUNJ6hHjVvaqLar0JJNgXQguqVq9Kx84dAfh5qfTsCyEKz56zRzh/ywsjQyNmDM373QAhxJtJgn0htOSjT8agr6/PpYuXOXvmnK6bI4QowqauXgDA4PZ9qFbOScetESJ3KpVKa18ifyTYF0JLKjlWonuPbkBa7r4QQhSWY55/89c/ZzHQN2Dm8Im6bo4QudJDpbUvkT8yQFeI51zx+od2b3XIcp/LWy7MmvNdtsd++NGHHNh3kOvXrnPy+Enatm9bWM0UQrzhpv6xAI/FO+n3VnfqOdXkn/s3dd0kIcQrSHr2hXhOcnIyoaGhWX5FRWaeGi+jsmXL0Oed3gD8vOxX1Gr1S2ixEOJNdPrGRQ5eOImenh6zR3yl6+YIkSNJ49Ed6dkX4jmNGjd84ak3AUaPGc2eXXu5632XQwcO0bV719wPEkKIFzB19QK6NmnL28060KxGA87d8tR1k4TIkkr6l3VGnnkhtKxkyRK8O6AfAL/+vIKUlBQdt0gIUVR53r3GDo+DAMweKb374tUlPfu6I8G+EIXAdfQoTE1N8Xvgx55de3XdHCFEETZ9zQ+kpKTQ3rklbeu30HVzhBCvGAn2hSgEdnZ2DB46CICVv64kKTFJxy0SQhRVNx7cYdNfewD438icV+4UQldkUS3dkWBfiEIyfOQwLC0tCAx8zLat23XdHCFEETZj3UKSkpNoXrMh3Zu213VzhMhET6XS2pfIHwn2hSgkVtZWDBs5DAC3lW4kxCfouEVCiKLqXoAva45uA2DWiC8lr1kIoZBgX4hCNGToYGxtbXjy5Cm3bt7SdXOEEEXYzHWLSEhMwLlybcoUK6Xr5gihQdJ4dEeCfSEKkZm5GSPfH6nrZggh3gCPngSy8sAGXTdDiCzJbDy6I8G+EIWs/8D3KF6iuK6bIYR4A/xvw1Ji4mJ13QwhxCtEpZYlPjXEp8iHpBCicJl2qarrJggh3gDqo/66boLi0KM9WqurS7meWqvrTSAr6AohhBBCiEIl6Te6I2k8QgghhBBCFFHSsy+EEEIIIQqVnsyiozMS7AshhBBCiEIlaTy6I2k8QgghhBBCFFHSsy+EEEIIIQqVLIalOxLsCyGEEEKIQiVpPLojaTxCCCGEEEIUUdKzLwpVXFwce3fv5W/303jf9iY8PBxUYGdnR81aNWjbri0dOrXHxMRE100tkF9/Wg7A4GGDsbKyfKE6oqOjOXniL86ePseN6zcIevz/9u47vqb7j+P4K3tIgkRsSuw9a4va81e7pcMs2qKotkbN0hq1aalZqlWj1AqqaK3YsVdJqBUjZMuQe39/XLlcScykiXg/PfJ4JOd8z/d8721v8jnf8/l+TiBGoxHPrJ6Ur1Ced95rT7HiRZM8vnG9Jly9eu2x5/j0s3507NIhyf0XL1xk/twF7Nm9l6CgIDJlykj5iuXp1KUjxUsUf67XJfI0nBwc6Vi/LU0q1aFMgeJkcXPHaDRyI/gWB/85xu+7N/HbDh+iYqJSe6gvZPj7nwIwZeVcQiJCn6sPKysrapWuyutFylCxcGleL1yG/DnyAvDh1IH8sG7xU/fVumZTOtRvTYVCpcnilpk74SGcv3aRbYd3M3XVPG6F3LZo7+GWmdplq1GxcBleL1yGCoVLkTGDGwCOTQoQHRudYuPeNmE5b5Sp+tg2M1b/SO8ZQ57mpct/zErzy6lGwb6kmL+2/c2o4aO5deuWeZuTkxPW1tZcvXKVq1eu8ucfW5gyaSpfjx1N5SqVUnG0L2bW9z8A8GbLN5872G/f5h3+/feS+WdHJ0essOLypctcvnSZ9WvX0+fTT+jYOelgHcDNzQ07O7tE9zk5J31R5bt7D3179yPqrimYcnV14datIDZt+IMtm7cycvQImr3Z9DlemcjjNatSj9l9x5HDI5t5W/jdCAwGA/lz5CV/jry08W7KuA8G8/64T9h2eHcqjvbFjOhgCvZ//GPZcwf7bs6ubJuw7IXG4eKUgd+Gz6ZBhVoAxMXFERIRhmdGD7K7Z6V6idfZuP+vBMH+e3VbMeXjkak2boCQiFDuRid+0RcaGfbC/UvKsFYaT6pRsC8pYvWqNYwYOhKDwUC+/Pno1uMDanhXJ1OmTACEhYWx13cvS35ZyoF9Bzh04NBLHewnh3v37lG0WFFatWlJTe8a5MyVE4PBwLl/zjF+7AT2793PpG8n4+WVn5q1aibZz6RpE3m9UsVnOvetm7f4rO/nRN2Nokq1KgwfOZScuXISFHSbCeMm4rPOhxFDR1K0WFEKFirwoi9VxKxjg7bM+3QCNjY2nP73HKN/mcaGfVu5HRYMmALEeuVr0qt5J2qXrYZ3qSovdbCfXMLvRnDon2McOHuU/WePMKnHMIuLpcextrZm/eiFeJeuwsXrlxkw9xvW+P7B3ego7GztKJQrP61qNCYo7E6CY43ApRtX2X/2CAfOHsFoNDKm66D/ZNzx+nw/nIV/LH+mY0ReZQr2JdmdOX2G0SO/xmAwUNO7BhOmfJsgTcfV1ZV6DepRr0E9Nm7YxPXA66k02rTj67GjKV+xvMU2a2trChcpzIyZ02jf5h38/QP4ccGixwb7z2P+3AWEh4eTPXt2Jk+diHMGZwA8PNwZPeYrLgQEcPLEKb6f8T2Tpk5M1nPLq6u0VzFm9RmDjY0N6/duoc1XPRKk6YRGhrFypw8rd/rwVq3/kdszRyqNNu0IiQjFrXlRjEajeduYLgOf+vhPW3fHu3QVrt+5SfW+Lbly60H6X+y9WE5ePMvJi2cTPXbG6gVMWzXP/HOt0o9Pq0nOccvLTdV4Uo8SqCTZzZj2PTExMWTNlpUx337zxHz8Ro0b0qHT+xbbYmJiWPTjT7z79vtUr1STSuWq8GaTFnw7bgK3bt5KtJ+ZM2ZRpng5hg4eluS5hg4eRpni5cw59vH27ztAmeLlaFyvCQB+hw7T66NPqFWtNpXKVaFty7dY8vOvFn+kHu4vXpP6TSlTvJz563FjedSjgf7DHB0dadi4IQCnTpx66j6fhsFgYKPPJgDeatfGHOjHs7Gx4f2Opv8+2//aQXh4eLKeX15dozt9gaO9I5dvXuOdb3o9MR9/2d9rmbRitsU2ezt7+rXuxp5pawn+/SSR685xev7fTOwxjGyZPRPtZ/j7n2LcfJkFn09K8lwLPp+EcfNlc459vFqlq2LcfJmAn3wBqFaiImtH/cjNFUeJXHeOw7P+oGfzTkn2F+/C4j0YN182fz1uLIl59HfR07K1seWztj0AGLFokkWg/zQMBsNznTfe845bXn5WVlbJ9iXPRjP7kqyuX7/Bjr93APDOe+1xdX26/PWHP7y3b9/mo249OX3qNAD29vbY2dlx8cJFLl64yJpVa/nuh+mULlM6+V8AphSkkcO+wmAw4OKSgejoaM6e+YexX4/j0r+X+GLQ5+a2Li4ueHh4EBQUBEDmzJmwtrax2P+w+AuDDz/uwUe9PnymcWXMlBGAOEPcc72upJw/728ef7Ua1RJtU7V6FQBiY2PxO+iX7HcW5NWT0yM7TSvXBWDa7/OfK9c6S0Z3No35mfKFSgEQFRNFTGwsRfIUoEieAnRq8BZNhnRg76lDyTr2eB0btGXup99ibWVNaGQYTg6OlClQnBm9RlMwZz76zRxhbhsSEUbg7Rtkd88KwM3gIIvPckiE5euPvzAYsWgSI396tguBx6lfwZtsmT0xGAz8+teaZOtX5Ek0s596NLMvyerAvgPmmZs3atd6rj6GDBzK6VOncXNz49tJ49lzcDe79+/kl2WLKVS4EKGhofTt9Sl37iTMJ31Rt+/cYdSI0bR9uw1b/t7Mzr072LFnO+3fbQfAL4uXcO6f8+b2AwZ/wdYdf5p//nnZz2zd8af5a8DgL5JtbAf3HwSgYMGCj2337dgJvFG9NhVKv06dmvXo2aMXPus2EBeX+EWC/zl/wHTB5VXAK9E2mTNnxt3D3dT+fMDzvgQRszfKVMXa2vQnaI3vH8/Vx6IvplK+UCluhwbTdlQPMvyvMBlbFKNizyYc9T+Fu1smfh8xFw+3zMk5dAA8M3rwQ5+xzFz7EzneLk/mliXI1KI401bNB+CTFl0o/lphc/u+3w8nx9sP7t693qspOd4ub/7q+/3wZB9jYqoWM43hwvVLhESE0qt5Zw7P+oPIdee4vfI42yYsp0P9Nml69vSzNj248usBon38ubH8CH+O/5UPm72Pg51Dag9NJE1SsC/JKsDfFAja29uTL3++Zz7+0IFD7NppWnw3dsIYGjSqj42Naaa8RMkS/DB3Jm5ubgQFBfHL4iXJNu54UXej+F/zZgwaMhCPLB4AuLm5MvDLARQqXAij0cifm/98Qi/J79TJU2zdsg2A5i3ffGzbM6fPcDcqCgcHB4KCgti5YxeDvhhM9y49CA1NOHsaXy3Jzc0NB4ek/1h6eppSIm7evPm8L0PErFhe00VrVEwUZy6df0LrhGqUrETjSrUBaD+mJyu2rzenmBw8e5T6A9pzOzSY7O5Z+aRl1+Qb+H0ZnJxZ9OdvfPLdUG4Emz5DIRGh9Pl+GEf9T2FtbU3rmk2S/bwvqlCu/ADcCrnDyuFzmN5rFKXyF+VudBSuzi68UaYqC7+Ywophs80XY2lNyfxFcXfNRERUJJ6ZPKhbrgYz+4xh34x15PHMmdrDkyQojSf1pM1Psry0goODAVPg+DwfyM1/mALpEiWLUz2RlBKPLB60fbsNAH9s3Pz8A32Mrt26JLr9jTqmOxXn/3n2wCTekZN+HDnp90wpPBEREQz64kvi4uIoVrwYrdq0TLRd7bq1mTh1An/v3sbeg77s3r+TjX/60LFzB6ytrTmw/yCff5rwTsPdyLsAODg+flbMycm09iLyfnuRFxE/234nLOS5jm/jbSoDu//MYf448HeC/TeCbzFr/U8AvOXd7DlH+XhjlsxIdPvq3aY1MCXzFXnuvq3q58aqfu5kTeEByORiSgesUKgULao34od1i8natgwerUvi0aoU3yyZDkCrGo0Z3L53sp77Rf11xJf3x/Uh+1vlcGpaEPdWJcnatgyD5o0hKiaK0l7F8Pl6EXa2iZceltRlnYz/5NmkyZz9mzdvsmvXLvz9/c3BY6ZMmfDy8qJ69ermGUZJf06dNOXpv17p9STbVKr8OvPmzOfihYtERt7F2dkp2c6fMWNGcufJnei+rFlNubahoc9XG/t53Lt3j4GfDybAPwBXN1fGTRiDrW3iH9uH1xLEy5EzB59+3o9cuXPxzagx7Nm9h927fKlW/ekraIikReULlgRg22HfJNts9dvF4Pa9KZzbC2dHJyKjku9CNSj0DgGB/ya670pQIACZ7wfWaYm1tWkSxsbGhh3H9vLh1AfVcEIjw/hy/jgK5cpPW+9mfNq6G+OWfk/svdjUGq6FxC58bgYHMfbX7zjqf4r1Xy+iZP6idGrwFnN8fk6FEYqkTWnq8ig2NpbRo0dTu3ZtBg0axO+//46fnx9+fn78/vvvDBo0iNq1azN69Gju3buX2sOVRMTX0Q8NDX2uqgvxefhZs2VNsk227KaazEajkeBkztvP8EglmofFp7j8V//vGQwGhg4ezva/tuPo5Mi076byWr7Xnquvt9q1JWcu0+3tv7dZzoI63b9Yio5K+smXAHfvP2wrOS+u5NUVFGr67GZ2fb6A2DOjKc0uPrBOzOX7lWasra3J4ub+XOdJSlhk0lWpomJMn6W0OMMcfjfS/P3U++sLHhVf8SizayYq3F/8nNb57NvK30f3APC/KvVSeTSSGKXxpJ40FexPmTKF1atXM2zYMPbt28f27dtZu3Yta9euZfv27ezfv5/hw4ezZs0apkyZktrDlUTk9zLlg8bExHAh4MJz9xMd/fjAM70zGo2MHvkNPut8sLOzY/K0SZSvUO7JBybBysqKkiVLAHDl8hWLffF3ykJDQx/7vsfn6uvOmiSHU/+eA8DR3pEieZ7/QW2O9lqU+SyuBj14pklSayXOXPY3f/8y5cDvPeUHgFeOvKk8EkmMVTL+S0kRERF4e3tTpEgRjh07ZrFv+fLlNGzYkFKlSvHmm2+ybdu2BMeHhYUxePBgKlWqRLly5fjkk0+4ceNGgnaHDh3i7bffpnTp0tSuXZvZs2enWGnaNBXsr169mkGDBvHWW28lWrLRxcWFtm3bMmDAAH7//ff/foDyRBVfr2C+6v5rW8I82ifJnNmUxxt4LenZuvgHcFlZWZEp84MqGza2poW8jwtYw8Jejhrx48d8y2/Lf8PW1pZxE8emaNpNfAUeo9GI/3n/RNvcuXOH20G377fPn2JjkVfH30f3mBfUvlm1wTMffzPEVC42b9akg9HcWUwP4DIYDNwKvW3efi/OdHfO8THVWzI6uz3zmF4Gxy+cfqb2RlQXX14t33//faLV69avX8/QoUNp3Lgxc+bMoWzZsvTq1YvDhw9btOvbty+7du1ixIgRTJgwgYCAALp162aRFXDx4kW6du2Kp6cnP/zwAx07dmTatGnMn5/43bYXlaaC/YiICLJnz/7EdtmzZyciIuI/GJE8q2zZs1HDuwYAS37+9akfwBR/NVuseFEADuw/mOQV7r69+wF4Ld9rFikl8ReI168nvIKOP0dyP5AqXvwFTnJclU+ZNJVfFi/B2tqaUd98Rd16dV64T6PRyPHjJwDImTuXxT6vAvnx8DClRPju3pPo8Xvub7ezs6PcC9xhEIl35dY1fPZtBaB38864Ors84QhLh84dB6BWqSpJtqlTrjoAZy/7W+TrB0eY1t087mm8FQqnTPpK/AVOaqUi/Hlop/n7pO6oFH1o+4XAy4m2SYsqFzP9bgoIvJTKI5HEvAxpPOfPn+eXX36hd++Ei9OnTZtG06ZN6du3L1WqVOGrr76iVKlSfPfdd+Y2fn5+7Ny5k6+//pomTZpQt25dpk6dypkzZ/jjjwclhufNm0fmzJmZNGkSVatWpVOnTnTp0oVZs2YRExOT7K8rTQX7ZcuWZdasWYSFJf1wlfDwcGbNmkW5cgo40qpen/TE3t6e64HXGfj54Cem5Gza8AeLfjRVzajfwJRref7cebZt+StB26BbQSxfugKABo3qW+wrVNhUyu/EsROJlodcv9aHwMCk7xi8CBeXDACEJVLa8ln8MHM2C+b+iJWVFUNHDqFJs8ZPddyTLjKWL13B1StXAfC+fzEWz9ramkZNTE/nXbZkWYJqOwaDgZ8WmRa71artneBBYSLPa8iP44mKiSJP1pz8MmjGE+ukt/VuxqdtugOwYvt6wFSGsXm1hgnaZs2UhQ+bmp78vGz7Oot9xwLuFwIoXMb8kKuHvVu3FXmz5kqwPTnEPzwsU4bUuXNw/uoFdp84AECflolXHuvXuhsA14Kuc+jcsUTbpDUNK75BrdKmC7/1e7ek8mgkMS9DGs/o0aNp164d+fNb3sG+dOkSFy5coHFjy7/JTZo0wdfX1xygb9++HTc3N6pXr25u4+XlRbFixdi+fbt52/bt26lbty729vYWfYWGhuLn55fsrytNBftDhw4lICCAWrVq0atXLyZNmsScOXOYM2cOkydPpnfv3tSqVYuLFy8yZMiQ1B6uJKFosSIMGjoQKysrdvy9g7dbt2fdmvWEBD8osRcWFsafm7fQtVM3vug/gMgI06Kx8hXLm0tuDh8ygs2bNptvp508cZIPu31EaGgoHh4evPv+OxbnLVuuLJ5ZPYmNjWXgZ4O4fD83/e7duyxfuoKvho/CzS1l/sAWKGiaCVu3Zl2SD68C0xN0yxQvx8wZsxLsW7zoZ76fPhOAgV8OoFXrxEtsJmbs1+MY9814Dh30Iyoqyrw98FogUyZNZezX4wB4vfLr5jsvD+vyQWdcXFy4di2QT/v059pV08LG27dvM3TwME4cO4GdnR0f9fzoqcck8iRHzp+k5/QhGAwGmlWph9+sjbxbtxWZXTOZ27g5u9KyRmO2fruMZUNn4epkutjceXwfG/aZ8mXn959I65pNzXXhyxcqxR9jf8HdLROBt28wdeVci/PuOr6fK7cCcbB3YMng78iXPQ8ATg6OdG/6LnP6jed2aHCKvOYTF88C0KF+m8fWsTduvoxx82WGv/9povvdnF3xcMts/orvK4Ojs8V2ezv7BMcOmPsNcXFx1CxVmZl9xpjLoLo6uzC68xe0vV+qdOTiyeY7EfGsrKws+s+Y4UHK7cPbk3qQ2fOOe2C7nszrP4F65Wvi4pTBvD1LRnc+f+sjVg6fA8Dpf88xf9PSJN9XkaRs3LiRs2fP0rNnzwT7/P1NKa6PXgQUKFCA2NhYLl26ZG6XP3/+BHcfvLy8zH1ERkZy7do1vLy8ErSxsrIyt0tOaar0ppeXF+vXr2fJkiXs2LGDFStWmMscurm54eXlRY8ePWjXrl2KBW2SPFq1bkmmjJkYNXI0Af4BfDnQdHHm7OyMlZWVRRpWzpw5qFTlQanN0WNH8eEHH3Pm9Bk+6/cFDg4O2Nramo9xc3Nj8vSJ5so/8WxtbRk0ZCCf9f2cA/sP0rRBM1xcXIiKiuLevXu0aNUcg8HAmt/XJvvrbdm6JYf9jrB40c8sX7oCd/fMWFlZUa9BPfp/kfgf60dNGDcRMM20z545h9kz5yTZ9pdli8me40HKW2RkJGt+X2tO/3FxccFgMFikUVV8vQITJ3+baH9ZPLMwYcq39O3dD99dvjSq1wRXVxfCwyMwGo3Y2toyYtRwChZ6/oWUIomZv/FXgkLv8EPfsRTLW4jFA6cBpmo3RqMRt4eCyQuBl9h6eJf55w7j+/DH2F8oV7AkK4b9wN3oKGLvxZqPuR0aTMsRH3A7LNjinHGGOHrNGMKKoT/wRpmqBPzkS0hEKM4OTtjZ2jFvw6/Y2FjTqcFbyf56525YQvUSr9OvdTc+bPY+N4JvYTQaWbFjPZ/PHv3U/az+aj5vlEm4lmdij2FM7DHM/HOnb/ux8I/lFm12Ht9HzxlfMqPXaD5s9j7dGr9DcEQoGTO4YmtjCgumrprHD+sWJ+g/b9ZcXFiceLrflV8PWPxsVT9hGePnHbeDnQNdGrWjS6N2GAwGQiPDMBqNFheGR/1P8eawzsTEJn8ahLy45JyRr1u37mP3b9nybHd37t69y9ixY+nXr1+id69DQkyTlY/GnvE/x+8PDQ1NdM1pxowZOX7clHoYn73yaF/29vY4OTmZ+0pOaSrYB9OL79GjBz169EjtocgLqlOvNlWqVWHt6rXs+HsHZ8/+Q/CdYKysrMiZKyclShSnbv061K1veSvL3d2dn5Ys5NdflrJpwyYuBFwkNjaWvK/lxbtWTTp17ZhkRZi69eowa873zJk9j5PHTxIXF0eRooVp+3ZbWrZuwdDBwxI97kWZLiTi+G35KvzP+xMYeN1UGvT+cyKeRnwqjsFgICgo6LFt4x6ZbWv7dhsyZ87MkcNHuHYtkJDgEAwGA9mzZ6d4yeI0btKIeg3qPnYmsWq1Kiz77VfmzVnAHt893A66jYeHB+Urlqdz144UL1H8qV+LyLNYvXsTmw9tp2P9tjStXJfS+YuRJaM7RqORgGv/cuCfo6zcuYGVOzdYBHK3Qm5T9ZPm9GzekXZvvEmR3AWwt7Pj7GV/1u/dwvhlMwm8nfgant93baTBoHf4sv0nVCxcGhtrGw6fP8HMtT+xYNNSFnyevA+zivfjpmXYWNvQrfE7FH+tEHk8c5pKg2ZM3tKgT/LDusUcPHuM/m26U7NUZTwzunM7NJi9p/34bs1CNh346z8dz5Ms374OWxsbqhWvSIGcr+Hhlhk7G1uuBgVy+PxJVmxfz89bVynQT8vScMnMmTNn4uHhQevWrVN7KCnCyphSdX5eUlFxkU9uJCLyApwaFU7tIYjIK8C4Oe0ssD5wa3ey9VUxS7Vk6+vKlSs0bNiQ7777zrwe9ODBg3z44YcsWrSIkiVLcuDAAbp3786GDRss0m927dpFly5d8PHxoUCBAvTp04fAwECWLrVMJevfvz+XLl1i2bJlREZGUq5cOUaOHEm7du3MbWJiYihdujTDhw+nffv2yfb6IA3O7IuIiIhI+pLS9fGf1+XLl4mNjaV79+4J9nXo0IEyZcowcaIpzdbf398i2Pf398fOzo48eUzrfry8vPD19cVoNFrk7QcEBFC4sGmSx9nZmRw5ciTIzQ8ICMBoNCbI5U8OaWqBroiIiIikP2m19GaxYsVYtGiRxdegQYMAGDlyJMOHDydPnjzky5ePjRs3Whzr4+ND1apVzanI3t7ehISE4Ovra24TEBDAyZMn8fb2Nm/z9vZmy5YtxMbGWvTl5uaWItUmNbMvIiIiIq8kNzc3KleunOi+EiVKUKKE6enzvXv35rPPPiNv3rxUrlwZHx8fjh49yuLFDxaylytXjho1ajB48GAGDBiAg4MDkydPpkiRIjRo8ODhgV27dmXt2rX079+f9u3bc/bsWebNm0e/fv0s1jAmFwX7IiIiIpKi0moaz9Nq1qwZd+/eZc6cOcyePZv8+fMzY8aMBDPxU6ZMYcyYMQwbNox79+5Ro0YNhgwZgq3tg5D7tddeY968eYwdO5bu3bvj7u7OJ598QpcuiT/74kVpge4jtEBXRFKaFuiKyH8hLS3QPRy0L9n6KutRKdn6ehUoZ19EREREJJ1SGo+IiIiIpKjkXlgrT0/BvoiIiIikqJc9Z/9lpjQeEREREZF0SjP7kiZFRESwf+9+jh8/wcnjJzlx/CTBwcEA/L5uJfm98r9Q/+Hh4fw4byF/bt7CtavXcHBwoEjRIrzVrg31G9Z/7LHXrl5j4YJF7Nq5m+uB17F3sKdAAS+avdmM1m1bYW395Gvo48dOsHzpcvbvO8Ctm7dwcHAge45slCtfjlZtWlG0WBGL9nFxcSyY+yO/r1xNYGAgWbJ40KhpIz7u9VGSZbrO/XOet1u3o3rNakz7burTvzki6cSzLE6s1b8N24/uSbDdztaOvq0+4J06LSiYMx/34u5x6t9zLNi0jDk+P7/wGB3tHenR9F3aeDelSO4CuDpn4PqdW5y8+A9/+u1g0orZjz2+dc2mdKjfmgqFSpPFLTN3wkM4f+0i2w7vZuqqedwKuW3R3sMtM7XLVqNi4TK8XrgMFQqXImMGN9NYmhQgOjb6sefr1uRd+rTsQsGc+bgdFszvuzcxeP44gsNDEm3vmcmDM/P/JiDwEq/3aorBYHiGd0fSE83spx5V43mEqvGkDVv/3Ea/Tz5NdN+LBvvXA6/TuUNXrly+ApieZhcTE8O9e/cAeKtdW74cNjjRY/fs3kP/vp8THh4OgKurC9HRMcTExABQuUolps+choODQ5LnnzJpKgvnLzL/0XN1dSEqKtr8cI2en3xM9w+7WRzz1fDR/Lb8NwCcnJy4e/cuADW9azBj1vREz9OlQ1dOHD/JyrW/kStXzie/MfKfUTWe/8a1pYceu9/N2RVnRyeiY6LJ2a4Ct8OCLfa7Oruw9dulVCxcBoCIu5HY2tjgYG/6fK/13UzLER8QZ4h7rvEVy1uItaMWUCBnPgBiYmOIjL5LJpeMANyLu4ddo3yJHuvilIHfhs+mQYVagGlCICQijIwZXLGxsQGgRt+W7Dqx3+K4Pi27MuXjkYn2+aRgf2C7nozpanrYUMTdSDI4OQNw6J9jVO3TnJjYmATHLPxiCu/VbUW1vi3Ye+rx/z0k+aWlajzH7yTff/+SmcsnW1+vAs3sS5rl7uFOiRLFKVGyBFmzZeWr4aNeuE+j0Uj/vp9z5fIVcubKyZjxX1O2XFmio6NZsvhXpk6exrJfl1O0WFFat21lcWzgtUA+62cK9MuULc3QkUMpVKgg9+7dY9uWbYwc9hV79+xj3DffMmzkkETPP23ydBbM/RFHR0d6fNyd5i2b4+HhjsFg4HrgdXZs34lHFg+LYy4EXGDlipW4urny3azplClbhnP/nOfjHj3ZsX0ne3bvoUq1KhbHrF29loMHDtHrk54K9OWVlePtxwcEfrM2UbZACdbt3ZIg0AeY0288FQuXISj0Dh3H92X93i1YW1vzXt1WzOozlv9Vrc/Ijv0ZsmD8M48tt2cOtk1YRrbMnuw8vo9B88ay68R+jEYjzo5OlC9YilY1Gid6rLW1NetHL8S7dBUuXr/MgLnfsMb3D+5GR2Fna0ehXPlpVaMxQWF3EhxrBC7duMr+s0c4cPYIRqPRHMA/TsYMbgx9ty+x92JpOeID1u/dQm7PHKwbtZDyhUrxfr3WzNuwxOKYmqUq06F+G+b4/KJAXzSzn4oU7EuaVKu2N3XqbTH/fOXK1WTpd9uWvzh29BjW1tZMnjbJnC7j4OBAp64duXnzJosX/cz302fyZvP/YWdvZz72p4WLCQsLx8XFhanfTSFz5swA2NraUr9hfaKiohgyaBirflvF+x3fTXD34eiRYyyY9yNWVlZMmjaR6jWqmfdZW1uTI2cO3mrXNsGY9+3dh9FopHWbVpQpa5phLFioAJ26dGTcN+PZu2efRbAfGhrGpAlTeC3fa3Tq0jFZ3jeR9KZMgeKULWB6MubCzcsT7C9boARvv/EmAJ0nfMr6vabfRwaDgUWbV5DJJSNTPx5Jv1bdmLpqHjeDg57p/LP6jCVbZk+2Hd5Nw0HvEnsv1rwvMuouO4/vY+fxxOuSf9q6O96lq3D9zk2q923JlVvXzPti78Vy8uJZTl48m+ixM1YvYNqqeeafa5Wu+lTjrVq8As6OTiz7e635vbh88xpDfhzP2lE/UrdcdYtg39bGlu97f82tkNsMnPvNU51DRFKGFuhKmhR/Gzq5+azzAaBy1coJ8uIBOnTugJWVFbdu3WLfXss/tDt37AKgSbPG5kD/YU3/15TM7pkxGAysv3+eh82fuwCDwUD9hvUtAv0nCQ425cLmyp3LYnvevHnu7w+22D59ynRuB91m0JCBFhcrIvJAx/qmC+vrd27is3drgv3v1GkJwOl/z7HWd3OC/bPX/0xweAjOjk5JzsAnpbRXMZpWrgvAx9MHWwT6T2JrY8tnbXsAMGLRJItA/2k8b868h5vpd57/tX8ttp+7egGALG7uFtv7te5GyfxFGThvTKJ3TeTVY2VllWxf8mwU7MsrZf++AwBUq574bFa2bFkpULAAAPv2Wua6Xrtq+qOaL3++RI+1trbmtXyvAbBn916LfeHh4ez4ewcATZo2eqYxZ8pkyt+NX2MQ79IlUy5mxvv7AU4cP8GKZb/RoFEDqj6S2iMiJjbWNrxTpwUAv2z9PdGc+9plTb8j/ji4PdE+omKi2HHMNCFQp2z1Zzr/u3VMKYJ+545z+t9zz3Rs/QreZMvsicFg4Ne/1jzTsS8iKNSUEuSVI6/F9gI5XrPYD6YUpWHv9cX35MEEqT3y6rJKxn/ybBTsyysjKOi2eRY8PqBPjFcBLwDOn/e32B4/mxAXl/RivLh7pn3+jxx74tgJ8wLgosWKsnP7Tj7o3J3qlWpSpUI13mr5Nj/MnE1ERESCPl+v9DoAv61YydEjR839/zhvIQCVq1QGTDN2X3/1DU5OTnw2oH+SYxR51TWuVJtsmT2BxFN4AIrmKQjAiYtnkuzn5L//AFA8b6FnOn/V4qa1BH7njpMxgxvfdh+C/6LdRK0/z7Wlh/ht+Byql3g98WOLmY69cP0SIRGh9GremcOz/iBy3TlurzzOtgnL6VC/TbLPfvqePEhk1F1aVm9kviuRK0sORnX6HIAtfrvMbad+/BWO9g58PC3xQgci8t9Szr68Mm7dvGn+PmtWzyTbxe+7dfOWxfYcOXMQ4B+QIJCPd+/ePf69aLrFHRERQWREJM4ZTNUqLl58cOt73Zr1zJj2HWCqxBMXF8eZM2c5c+YsG9ZvZPb8WWTNmtXcPr9Xflq2bsGq337n/fYdLarxVK9RjSpVTcH+8qUrOHH8JP2/+JRs2R4cLyKWOjV4C4DD509w5PzJBPvdnF1xccoAwNWg60n2E78vh0e2Zzp/oVwP1vMc+M6HgrnyERMbQ0RUJNnds9KqRmNaVGvI53NGJyi9GX/srZA7rBw+hxbVG2EwGAgOD8UtgwtvlKnKG2Wq0rxaQ9qO6pFspS5DIkL5Zsl0Rnf+gnWjFxJ+N8L8Hh0+f4Kf/jRVC2v0em1a1WjMtFXzOXz+RLKcW9IHzcinHs3syysjPkAGHlsa09HREYDISMsyrPFpMRvWb+T69RsJjlu5YiUhIQ9qTUdEPpilDwsLM3///YyZlC1fllVrf2Pn3h3s3r+Tb8Z9jZOTEwH+AXw5YGiCvoeOGELPTz4mV+5cxMbGki17Njp27sCkaROxsrIiKOg206fOoGChgrzzXnvTOH020qbFW7xetjIN6zTmu+nfm+8uiLyqMrtmotn9memFfyQ+qx9fUhLgbnRUkn1FRpl+p7g4OifZJjGZXEx17TvWb0verDn5eNpg3JoXw71VSfK9V4V1e/7E2tqab7sNwbt0lUeONaXtVShUihbVG/HDusVkbVsGj9Yl8WhVim+WmErxtqrRmMHtez/TuJ7k61+m8dHUQZy8eBZ7Wzuu37nJ7PU/U+fzt4mOjcbBzoEZvUYRePsGQ3/8FoC65WrgO20NkevOEbjMj2k9R5HhGd8vSR+Us596FOyLPKX3Or6Hs7MzUVFRfNy9J3v37CM6Oprg4GCWLlnGxPGTsbV9cLPM2urBx8toePA4iwwZnJk2Y4o5XcjOzo6m/2tC3/59AFP1nWNHj1uc28bGhu4fdsPnj3UcPLqfP7Zu5NPP+5kvTCZ9O4nwsHC+HDoIW1tb1q5Zx8DPBnE76DYNGzfAOYMzs2fOYfTIr1Ps/RF5GbSv3RwHewdi78Xy89ZVqTKG+N8NNjY2jFv6PTPXLjLXt794/TJtvurBvzeuYG1tzYC3P7Y81trKfOyOY3v5cOpAc758aGQYX84fx/Lt6wD4tHU37GyTd5H+rHU/UeKDOjg08SL7W+XoMWUAd+4vwB38Tm8K5MzHZ7NHExoZxhtlqrHhm58olrcgv+3w4ULgZXq36MyqEXOTdUwi8ngK9uWV4eTkZP4+OjrpB8dERZlm8pydLWefcuXKyfiJY3FycuLcP+fo3qUHlcpVoVa12nwzagzuHu507NzB3N7VzfXBuZ0fnLvJ/5paLKqN17ptK/MY9+3Zm2B/Ug4eOMi6Nev5X/NmlK9YntjYWCZ/OwVHR0d++nURo8eMYsnynylUuBCrfvudf87+89R9i6Q38VV4NuzflmS5zIi7D+7qOTk4JtmXs6Pp8xoe9WwPYwx/qP+pD5XBjBcdG83MtYsAeKN0VYunclseOz/R/uNTfzK7ZqJCoVLPNLbnVTBXfr5460P+OuLLz1tWAjCh+xDsbO1oMPBd3h/3CVX7vMmmA39Rv4I3zarU+0/GJWmHFuimHgX78srwfChP/8aNm0m2i9+XxTNLgn01a9Vk5ZoVvN/xPYoVL0b27NkpWqwo3Xp8wNLffsXOzjSLlj17duzt7c3HPbxGIN/9ij2PsrOzI3ee3AAEBiadJ/yw2NhYvv5qDK5urvTt3xeAkydOEhQUhHetmuYHajk6OtKqjamU4Pb7VYFEXjVF8xakUtGyACz8Y0WS7UIjwwi/a0rDy/mYfPz4fdcek9efmKtBgYCpgs3DVWweduayaW2Qs6OTueyl6dgH5zpz6fxjjwXI4/nfPFRvRq9RWFtZmxflZnfPSoXCpdl3+jD7TvsBpocazlj9I4B5ka+8OhTspx4t0JVXhru7O5kzZ+LOnWDOnzufZK37+AW4Be6n2TwqZ66cSVa7OXXqFACly5a22F6wUMFnGuvT5iQuXvQz58+dZ/DQQXh4mOpcX71fIjRnEnX540uIirxq4hfmBoXeYe2ehLXzH3bq3394vUhZSryW8Hkc8eKr8MRX5Xlaxy+coWT+ok/d3mh8kAZ4/MLpZzqXEeOTG72gtt7NaFjxDcYvncmp++/Fa9lMExcBgYnX5X8ta+4UH5eImGhmX14p8WUs9+zek+j+69dvcP6cabasUpVKz9R3cHCwub7+o7X08+XPR7bsplnACxcuJnp8bGwsl+/Xzs+Z88mzcYHXAvnh+9kUL1GMtm+3SbA/5pFUpajHpC6JpHfW1ta8V9dU337JttVPfJDVtsO+ANSvUDPR/Q52DtQsZfodscVv5zON5c/77T3cMlvM2j+saB5TeeDQiDCL2f8/Dz04V5E8iZcQLvrQ9guBl59pbM/KxSkDkz4czqUbVxm5eFKC/Y72lsUQnOyTTouS9E0LdFOPgn15pTRuanrSpe/uPZw5nbB+9k8//oTRaMTTMwuVKiVe5zoxRqORcV+PJzo6mkKFC+H9hrfFfisrK5r9rykAPmvXExIckqCP35avfFBS0/vJD+kZN+ZboqOj+XLYlxY5vTlz5gDg5MlTFu1PHDOVwcuZ67+5rS+SltQrX5NcWbIDSVfhediSbb8DUCxvoURTTro1eYdMLhmJjLrLql0bn2ksq3ZuICwyHIC+rT5IsN/BzoEPm70PwKaDf1vM7J+/eoHdJ0wPB+zTskui/fdr3Q0wpRcdOnfsmcb2rEZ26E9uzxz0nTnCXJ0ITAuNAcoVLGkRnL1epAxgek6AvGqskvFLnoWCfUmz7ty5Y/4KDQk1bw8LC7PY92gd6TLFy1GmeDlmzpiVoM/add+gVOlSGAwG+n3S3/yQqpiYGBYuWMTPP/0CwEe9PsLOPmEVi2mTp7Nr527Cw8PN206dPE3fXv3wWb8BRydHvvp6BDY2NgmO7dS1E+4e7oSFhdOnVz9zulBsbCw+6zYwddI0ABo2bkChJ6T97Ny+k61/bqVVm5aULFXCYl/xEsVx93Dn8KHDrF61BqPRyInjJ1i+zJSjXNO7xmP7FkmP4hfmnrhwhgNnjzyx/eHzJ1h6/wm1P34+mcaV6gCmOwTv12vNuA9MuemTV85JdKHvtgnLMW6+zLYJCS8sbocFM+bXGQB88dZHfPS/DjjYmWbA82bNxfKhs8ibNRfRMdGM/nlqguMHzP2GuLg4apaqzMw+Y8x3B1ydXRjd+QvaejcDYOTiyQl+P1pZWZnvKHi4ZSZjhgeFBB7entQdh4eVzFeU3i06s2HfNlbu9LHYF3j7Bn7njpM3ay6GvNsHG2sbvHK8xqB2vQDw2bf1if2LSPKwMj48ZSBExT1bVQVJOWWKl3uqdj6b15sXoj583Icf9+CjXh8maH898DqdO3TlyuUrgKnqTkxMjLkGfdu32zBk+JeJnqtxvSbmnHgXFxdiYmKIiYkBILN7ZsZPHEelyknfETh29Dgfd+9JaKjp4sXVzZXoqGhzH+XKl2PGrGm4uLgk2Ud0dDSt3mxDREQEa9b/jltGtwRtfl+5muFDRgCmxbnxFYZatm7BiFHDk+xb/htOjQqn9hBeKa7OLgQu9cPZ0Ykv5nzNt8tmPvVxW79dSsXCptnoiLuR2NhY43g/FWWt72ZajviAOEPCp2pvm7CcN8pU5a8jvtT+rG2i/S8aMJX367UGICY2hvC7kbi7ZQIgOiaajt/2M19wPKpHs/eY0Ws0tja2xMXFERwRSsYMrtjamJbiTV01j77fJ/ysv5YtNxcWJ57G+Cir+o/Pq98xeSUVC5emZLd6nL+fi/+wuuVqsHHMYmxtbLkbHWWubLT54HYaDHznqcYgL8a4OWXTuJ7F+bBnW2/yOAVcn37Ni2iBrryCsmXPxrKVv7Jg7o9s+XMrV69cxdnZmSLFivDW221p0Kh+ksd2+6gb27dt58yZswTdCsLBwZ4CBQvwRu1atH+3XaIlNR9WqnRJfluznAVzf2TH3zu5fv069vb2FC9ZnKbNGtOydUtzRZ+kzJ09j8uXLjNy9IhEA32AFq2aY2dnx/y5C7h44SLZsmejecs36fFR9ye/QSLpzFu1/oezoxNxcXEsvl8W8mmERYZTrU8L+rXuRvvazSmYMx/RsTH4nTvBgk3LmOPz8wuNq8O4Pqzb8yfdm75L2QIlcHF05uL1y2zx28mE5T+YF7sm5od1izl49hj923SnZqnKeGZ053ZoMHtP+/HdmoVsOvDXC43tSTo3fJsaJSsx8qfJiQb6YFrL0HRIR0Z1/JzSXkW5cecWS/9ey6B5Y1J0bJI2qYpO6tHM/iM0sy8iKU0z+yLyX0hLM/v+YQnXyT0vL9ekq2RJQprZFxEREZEUpZn91KNgX0RERERSlEpmph5V4xERERERSac0sy8iIiIiKUppPKlHwb6IiIiIpCgF+6lHaTwiIiIiIumUZvZFREREJEVpgW7qUbAvIiIiIilKaTypR8G+iIiIiKQozeynHuXsi4iIiIikU5rZFxEREZEUpTSe1KOZfRERERFJYVbJ+JV8NmzYwEcffYS3tzdly5alefPmrFixAqPRaNFu+fLlNGzYkFKlSvHmm2+ybdu2BH2FhYUxePBgKlWqRLly5fjkk0+4ceNGgnaHDh3i7bffpnTp0tSuXZvZs2cnOF9yUrAvIiIiIq+kH3/8EScnJwYOHMjMmTPx9vZm6NChfPfdd+Y269evZ+jQoTRu3Jg5c+ZQtmxZevXqxeHDhy366tu3L7t27WLEiBFMmDCBgIAAunXrxr1798xtLl68SNeuXfH09OSHH36gY8eOTJs2jfnz56fYa7QypuSlxEsoKi4ytYcgIumcU6PCqT0EEXkFGDdfTu0hmF2L/DfZ+srhnDfZ+rp9+zbu7u4W24YOHYqPjw/79+/H2tqahg0bUrJkSSZOnGhu065dO1xdXZkzZw4Afn5+tGvXjnnz5lGjRg0A/P39adKkCZMmTaJJkyYADBs2jJ07d7Jx40bs7e0BmDRpEkuWLGHXrl3mbclJM/siIiIikqKsrKyS7Ss5PRroAxQrVozw8HAiIyO5dOkSFy5coHHjxhZtmjRpgq+vLzExMQBs374dNzc3qlevbm7j5eVFsWLF2L59u3nb9u3bqVu3rkVQ36RJE0JDQ/Hz80vW1xZPwb6IiIiIyH0HDx4kW7ZsuLi44O/vD0D+/Pkt2hQoUIDY2FguXboEmGbx8+fPn+BixMvLy9xHZGQk165dw8vLK0EbKysrc7vkpmo8IiIiIpLCkm9Gvm7duo/dv2XLlufu+8CBA/j4+DBgwAAAQkJCAHBzc7NoF/9z/P7Q0FBcXV0T9JcxY0aOHz8OmBbwJtaXvb09Tk5O5r6Sm4J9EREREUlRL0PhzcDAQPr160flypXp0KFDag8n2SjYFxEREZGXxovM3CclNDSUbt26kSlTJqZPn461tSnTPWPGjIBpVt7T09Oi/cP73dzcCAwMTNBvSEiIuU38zH/8DH+8mJgY7t69a26X3JSzLyIiIiIpLG3W2QeIioqiR48ehIWFMXfuXIt0nPj8+kfz6f39/bGzsyNPnjzmdgEBAQnq5QcEBJj7cHZ2JkeOHAn6ij/u0Vz+5KJgX0RERERSVFqtxnPv3j369u2Lv78/c+fOJVu2bBb78+TJQ758+di4caPFdh8fH6pWrWququPt7U1ISAi+vr7mNgEBAZw8eRJvb2/zNm9vb7Zs2UJsbKxFX25ubpQrVy5ZX1s8pfGIiIiIyCtp5MiRbNu2jYEDBxIeHm7xoKzixYtjb29P7969+eyzz8ibNy+VK1fGx8eHo0ePsnjxYnPbcuXKUaNGDQYPHsyAAQNwcHBg8uTJFClShAYNGpjbde3albVr19K/f3/at2/P2bNnmTdvHv369UuRGvugh2oloIdqiUhK00O1ROS/kJYeqnUj6mqy9ZXVMWey9VWnTh2uXLmS6L4tW7aQO3duAJYvX86cOXO4evUq+fPn59NPP6V27doW7cPCwhgzZgybN2/m3r171KhRgyFDhiS4W3Do0CHGjh3LqVOncHd3591336Vbt27JftcinoL9RyjYF5GUpmBfRP4LaSnYvxl1Ldn68nTMkWx9vQqUsy8iIiIikk4pZ19EREREUpTVS1FpP33SzL6IiIiISDqlYF9EREREJJ1SGo+IiIiIpKiUqjQjT6aZfRERERGRdEoz+yIiIiKSorRAN/VoZl9EREREJJ3SzL6IiIiIpDDN7KcWBfsiIiIikqIU6qcepfGIiIiIiKRTmtkXERERkRSl0pupR8G+iIiIiKQwBfupRWk8IiIiIiLplGb2RURERCRFaV4/9SjYFxEREZEUpnA/tSiNR0REREQkndLMvoiIiIikKFXjST2a2RcRERERSacU7IuIiIiIpFNK4xERERGRFGWlBbqpRsG+iIiIiKQwBfupRWk8IiIiIiLplGb2RURERCRFaV4/9SjYFxEREZEUpdKbqUdpPCIiIiIi6ZRm9kVEREQkhWlmP7Uo2BcRERGRFKVQP/UojUdEREREJJ3SzL6IiIiIpDDN7acWBfsiIiIikqJUjSf1KI1HRERERCSdUrAvIiIiIpJOKY1HRERERFKUlXL2U42V0Wg0pvYgREREREQk+SmNR0REREQknVKwLyIiIiKSTinYFxERERFJpxTsi4iIiIikUwr2RURERETSKQX7IiIiIiLplIJ9EREREZF0SsG+iIiIiEg6pWBfRERERCSdUrAvIiIiIpJOKdgXEREREUmnFOyLiIiIiKRTCvZFRERERNIpBfsiIiIiIumUgn0RERERkXRKwb6IiIiISDqlYF9EREREJJ1SsC/yHC5evMiwYcNo3rw5xYsXp1mzZqk9JBFJh86fP0/nzp0pW7Ys1atXZ/z48cTExKT2sETkJWKb2gMQeRn9888//P3335QpUwaDwYDRaEztIYlIOhMSEkLHjh3Jly8f06dP5/r164wdO5aoqCiGDRuW2sMTkZeEgn2R51CnTh3q1asHwMCBAzl+/Hgqj0hE0ptff/2ViIgIZsyYQaZMmQCIi4tj5MiR9OjRg2zZsqXuAEXkpaA0HpHnYG2tj46IpKzt27dTtWpVc6AP0LhxYwwGA7t27Uq9gYnIS0URi4iISBrk7++Pl5eXxTY3Nzc8PT3x9/dPpVGJyMtGwb6IiEgaFBoaipubW4LtGTNmJCQkJBVGJCIvIwX7IiIiIiLplIJ9ERGRNMjNzY2wsLAE20NCQsiYMWMqjEhEXkYK9kVERNIgLy+vBLn5YWFh3Lx5M0Euv4hIUhTsi4iIpEHe3t7s3r2b0NBQ87aNGzdibW1N9erVU3FkIvIyUZ19kedw9+5d/v77bwCuXLlCeHg4GzduBKBSpUq4u7un5vBEJB1o164dP/30Ez179qRHjx5cv36d8ePH065dO9XYF5GnZmXUoz9Fntnly5epW7duovsWLVpE5cqV/+MRiUh6dP78eUaNGoWfnx8ZMmSgefPm9OvXD3t7+9Qemoi8JBTsi4iIiIikU8rZFxERERFJpxTsi4iIiIikUwr2RURERETSKQX7IiIiIiLplIJ9EREREZF0SsG+iIiIiEg6pWBfRERERCSdUrAvIiIiIpJOKdgXEUmnBg4cSJEiRbh8+XJqD0VERFKJgn0RkYcMGjSIIkWKULlyZWJiYl6or+nTp1OkSBH27t2bTKMTERF5Ngr2RUTuCw8PZ+PGjVhZWREcHMyff/6Z2kMSERF5IQr2RUTu27BhA5GRkXTq1Alra2tWrFiR2kMSERF5IbapPQARkbRixYoV2Nra8sEHH3D69Gl8fX25cuUKuXLlStB2//79LFiwgMOHDxMaGoqHhwelSpWiU6dOVKxYkffff599+/YB0KFDB/NxuXLlYuvWrQAUKVKESpUq8dNPPyXov06dOgDmtgABAQEsX74cX19frl69SmRkJDlz5qR+/fp89NFHZMiQIVnfDxERefkp2BcRAc6dO8fhw4epVasWWbJkoUWLFvj6+rJy5Up69+5t0XbhwoWMGTMGR0dH6tWrR86cObl+/ToHDx5k06ZNVKxYkZYtWwKwb98+WrZsab5gcHV1fe4xbt68md9++43KlStTqVIlDAYDR44cYc6cOezfv5/FixdjZ2f3/G+CiIikOwr2RUTAnLLTvHlzAOrXr8/IkSNZuXIlPXv2xNralPV4+vRpxo4di6enJ0uWLCF37tzmPoxGIzdu3ACgVatWXLlyxRzsV65c+YXH2Lx5czp16oS9vb3F9hkzZjB9+nQ2bNjAm2+++cLnERGR9EM5+yLyyouNjWX16tW4uLhQr149ADJkyEC9evW4evUqu3fvNrf99ddfMRgM9O3b1yLQB7CysiJbtmwpNs5s2bIlCPQB3nvvPQB8fX1T7NwiIvJyUrAvIq+8LVu2cPv2bRo1aoSDg4N5e4sWLQAsFuoePXoUgBo1avynYwTTnYMVK1bw7rvvUqlSJYoVK2YuEwqY7yqIiIjEUxqPiLzy4oP5+OA+XtWqVcmWLRtbtmwhODiYTJkyER4ejpWVFZ6env/5OEePHs3ixYvJkSMHderUwdPT0zzTP2PGjBd+LoCIiKQ/CvZF5JV27do1du3aBTxIh0nMmjVr6NChA66urhiNRm7evPnCKTtWVlbcu3cv0X1hYWEWi3mDgoL4+eefKVKkCEuXLsXJycm87+bNm8yYMeOFxiIiIumTgn0ReaWtXLkSg8FAhQoVyJ8/f4L9cXFxrFq1ihUrVtChQwdKly7N8ePH2blzJ61bt35s3/GLeg0GQ6L7M2bMyPXr1xNsv3z5MqGhoRbB/qVLlzAajVSrVs0i0Ac4cODAE1+niIi8mhTsi8gry2g0snLlSqysrBg3bhx58uRJtN2FCxfw8/Pj2LFjtGvXjqVLlzJlyhSqVKliUYM/vhpP/Ix/pkyZANPdg8SULFmSnTt3sm/fPipVqgRATEwMY8eOTdA2Z86cAPj5+WEwGMwXEoGBgUyaNOn53gAREUn3FOyLyCtrz549XL58mUqVKiUZ6IOpjKafnx8rVqxg5MiRDB48mNGjR9OsWTPq1q1Lrly5uHnzJgcOHKBWrVp8+eWXAFSuXBkrKysmTZrEP//8g6urK25ubuZ0oc6dO7Nr1y66d+9O06ZNcXJyYteuXbi5uSVYE5A1a1YaNmzIpk2baN26NVWqVCEoKIi//vqLKlWq8O+//6bcGyUiIi8tVeMRkVdW/MLc+AdgJaVJkyY4Ojqyfv16oqKieO+991i4cCGVK1dmx44dzJs3j127dlG0aFEaN25sPq5gwYKMGTOGzJkzs3jxYqZOncr8+fPN+2vUqMGUKVPIkycPq1evZuPGjVSvXp358+cnWmJzzJgxdOnShZCQEBYvXsyRI0fo1KkTEydOTKZ3RERE0hsro9FoTO1BiIiIiIhI8tPMvoiIiIhIOqVgX0REREQknVKwLyIiIiKSTinYFxERERFJpxTsi4iIiIikUwr2RURERETSKQX7IiIiIiLplIJ9EREREZF0SsG+iIiIiEg6pWBfRERERCSdUrAvIiIiIpJOKdgXEREREUmnFOyLiIiIiKRT/wc1qCRkmH0z5QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(abc_over)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BvW-j00HxOu"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "nrFJ7o_PHzpG",
        "outputId": "cb1fbfeb-b27e-4d78-a9c5-ba21cf7f23ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=1, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=1, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "xgb_over=XGBClassifier(\n",
        "    tree_method='gpu_hist',\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "xgb_over.fit(Xt_over,yt_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "hYVbrsx-Hz-d",
        "outputId": "9d54ba32-6a6a-4c30-ced0-356faafd1cc4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.950184  0.938345  0.921131  0.918643  0.081357\n",
              "val     0.51663  0.316533  0.200215  0.683283  0.163247"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d38023e-8b68-4c53-bd41-60643fc7a623\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.950184</td>\n",
              "      <td>0.938345</td>\n",
              "      <td>0.921131</td>\n",
              "      <td>0.918643</td>\n",
              "      <td>0.081357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.51663</td>\n",
              "      <td>0.316533</td>\n",
              "      <td>0.200215</td>\n",
              "      <td>0.683283</td>\n",
              "      <td>0.163247</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d38023e-8b68-4c53-bd41-60643fc7a623')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4d38023e-8b68-4c53-bd41-60643fc7a623 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-4d38023e-8b68-4c53-bd41-60643fc7a623');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "tab_over.loc['XGBoost']=get_scores(xgb_over,sample='over')\n",
        "\n",
        "tab_over.loc['XGBoost']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "NZFBaMBjJupX",
        "outputId": "1340f71d-7faf-403a-b778-cf79c49a5e5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.836753\n",
              "Precision     0.124167\n",
              "Recall        0.516630\n",
              "F1            0.200215"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80445804-38eb-4da9-abdb-dcade853103a\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.836753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.124167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.516630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.200215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80445804-38eb-4da9-abdb-dcade853103a')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80445804-38eb-4da9-abdb-dcade853103a 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-80445804-38eb-4da9-abdb-dcade853103a');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(xgb_over)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4J_h_AwlKoJk"
      },
      "source": [
        "Notice here that far fewer false positives yields a higher F1 score than the previous AdaBoost model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4VcPKBFH0Xx"
      },
      "source": [
        "## LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "B9NrBhASJ9fG",
        "outputId": "037c98c7-68f1-4574-acb3-25dcfb1d09d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "lg_over=LGBMClassifier(\n",
        "    n_jobs=-1,\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "lg_over.fit(Xt_over,yt_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "xnTA1uC0ia2z",
        "outputId": "64ddd542-0576-4bfb-fc65-b2571c4c4081"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.897511  0.885921  0.869087  0.864806  0.135194\n",
              "val    0.615299  0.340825  0.204194  0.716822  0.189687"
            ],
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              "\n",
              "  <div id=\"df-f4f44160-83ed-4dfd-8e74-a743b5bcc1bf\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.897511</td>\n",
              "      <td>0.885921</td>\n",
              "      <td>0.869087</td>\n",
              "      <td>0.864806</td>\n",
              "      <td>0.135194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.615299</td>\n",
              "      <td>0.340825</td>\n",
              "      <td>0.204194</td>\n",
              "      <td>0.716822</td>\n",
              "      <td>0.189687</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      fill: #174EA6;\n",
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              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4f44160-83ed-4dfd-8e74-a743b5bcc1bf 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-f4f44160-83ed-4dfd-8e74-a743b5bcc1bf');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "tab_over.loc['LightGBM']=get_scores(lg_over,sample='over')\n",
        "\n",
        "tab_over.loc['LightGBM']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "uAhLSRLZLL6g",
        "outputId": "a77bbe1d-8061-4842-cbbd-9bccfec484e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.810313\n",
              "Precision     0.122408\n",
              "Recall        0.615299\n",
              "F1            0.204194"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-920a6343-a0ce-4782-823e-8e6d9f06e530\">\n",
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              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.810313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.122408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.615299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.204194</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-920a6343-a0ce-4782-823e-8e6d9f06e530')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      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",
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              "      height: 32px;\n",
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              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-920a6343-a0ce-4782-823e-8e6d9f06e530 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-920a6343-a0ce-4782-823e-8e6d9f06e530');\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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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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Hjh1L8Ehcy8PDg6+++oqpU6cqOdug+cNramrK1q1buXbtGnfu3FHyajPCzMyMVq1asXHjRnbt2qUT7McfHJraHPAGDRrw+eefY2FhobO9XLlyNGrUiF69etGjRw/u37/PokWLdIJLa2trrK2tdQLs1P5MeHt7kzdvXjZv3kzBggWV7ZUrV07V8QBWVlbMmTOHXr164e/vz08//cSyZcsAzXoQ2gHyNWrUYMCAAamuNz5/f3+ePXsGQNmyZVNsT5MmTThw4AC3bt3C09Mz0TET9+7dU1JNmjdvrvN7lZHXQx+0qW2gef9au3atTq9rnTp1qFq1aopP08qUKcOpU6cSTfepX78+PXr0YPDgwZw9e5YlS5bQoUMHjI2NAc0NUKlSpbh165ZyTPHixdO9RoT2fVEb6G/YsEHntaxSpQrNmzenS5cuvH79mjlz5tCyZcskb5avXbtGw4YNWbx4sU4wX716dXLmzMn8+fPx8fHh1KlTiabaZZb4f2+ePHmSYP/q1aspXrx4gu1Vq1alXbt2bN++nQkTJnDp0iXOnz+v87P1/oQM2oHDSTE2NubgwYOJBuU1a9bkiy++YOHChSxZsoRdu3YxZMiQRNuW2LXdvn1bgn3xUZMBullA/OBTH73dGzduVB6FTp8+PdE/ng0aNFB6xm7cuMGNGzeSrK98+fLK3MXxqVQq+vTpA2ge43t4eGS47ekVGBiozPpTvXr1ZMu+n8aSku3btyvjKX788UedQF+rXLlyyqJJvr6+yS5iZWdnx6RJk3QCfa34g621vcP6oA3kPTw8eP78ubL90KFDvHv3Tgk6UyNfvnwJAsv4rKysGDFiBABXr15Vnlrpw+jRo3UC/fSoUaMGAwcOBODEiRNs2LCBiIgIxowZQ3R0NFZWVsyePTvdi9W9evVK+Tw1v8/x077iz6EfX3Jz6xvy9QDYtGmT8n4zderURNMrOnToQIMGDZKtx9bWNtm8fjMzM3744QdAk/KWmatvHzlyBD8/PwCGDBmS6E1boUKFlPZEREQku2Kzubk5M2fOTLTXvmfPnkrHgD5/51Mj/nthcHBwgv3JBdMAX3zxhfK9Sc3CfclRqVQpBuRDhw4lV65cqNXqZMdWATo3XvHf84T4GEmwnwWEhYUpnyf3Rzu1tIP4Pvvss2R7Pb/66qsExySmbdu2iQamoNt7Ysg31Fy5cil/MHft2qXXxWK03xtra+tke93ij7VI7vvZsmXLJB/V29vbK722+vx+Ojo6Kn9I4weO2s9btmyJubl5uuoODw/H29ubhw8f8uDBAx48eKDzVCP+k6uMMDU1pVWrVnqpa/jw4crP7uzZsxk9ejReXl6AZnXbQoUKpbvugIAA5fPUDEqtV68eefLkATQpTdp0Bq24uDj27t0LQP78+ZUBpkn5r14PrXPnzgGaJzTJzfsfP+0mNaKiovDx8eHRo0fKdcT/3uj7OuLT/v6qVCo6d+6cZLmWLVtiZWWlc0xi6tatm+SNn6WlpRJU/9fvofGfEMX/O5QYtVrN69ev8fLyUl6PBw8eKCl3+n494uLi8PX1xdPTUznX48ePlckLUjpf/BuZ169f67VtQvzXJI0nC4jfE6btQU6vqKgo5XFspUqVki1btmxZTE1NiY6O5sGDB0mWS24qxvhvqCn9schMZmZmtG7dml27dnHo0CGaN29Oy5YtqVWrFo6OjqkKupKi/d6UK1cuydQcgDx58lCoUCFevHiR7PczpfUGbGxsCA8P1/v3s127dixatIg9e/YwbNgwXr58qeSnp3Ul1oCAAFatWsWhQ4d4+vRpggA1Pn31JBcvXjzdNyTv0w6+7dSpExEREUoq0+eff57h+euDgoKUz+PnJCfFxMSEzz//nFWrVvHixQuuXr2q83Tq/PnzSi9z27ZtE33iYIjXAzTvN0+fPgVSHp+Q0vsRaG5U1q5dy759+3j06BGxsbFJltX3E4r4Hj58CGhy/JMbx2JmZkbZsmW5dOlSut9D4d+fk//6PTT++ZIaK3by5Ek2btzI5cuXk22fPl4PtVrN7t272bZtGzdu3ODdu3fpPl/89/yM/l0VwtAk2M8C4gfM/v7+GaorfqCRUgqBqakpOXPm5PXr1zrHvS+52UjiBx6GXrxk8uTJBAcHc+LECV68eIGrqyuurq4YGRlRrlw5WrVqRZcuXZSeuNTSfm9Sk5JhZ2fHixcvkv1+pvT0Rvs91ff3s3379ixatIgnT55w7do1Ll68iFqtplChQlSrVi3V9dy6dYv+/funeuyDvlbjzcgNW2Ls7e3p37+/Mh2gra0tP//8c4brjX9DklywEl+HDh1YtWoVoHnaEj/YTy6FBwz3eoDmd0N7Y5HS74f26UVSvL296d27N97e3qk6d2au8qz9Xqb2dx74IH/nUxI/YH7/xlStVjNx4kS2bduWqrpS+7OelMjISIYNG8bp06f1cr74Px+yxov42MlPcBYQfyac27dv663epFJvsipLS0uWLVvGjRs3OHDggDJXc2xsLLdu3eLWrVusXLmSJUuW4OjomOb6P/bvp3Zed3d3d3bt2qVMD9muXbtUX1tUVBQjR44kMDAQU1NTvv76a5o2bUrx4sWxsbFR0pOeP3+Os7MzQLK9zGmhHYypL6GhocqUt6AJfG7fvp3hAay5cuVSPk8uAIyvbNmylCpVigcPHnDw4EEmTZqEmZkZ4eHhHDlyBNCkzL0/V74hX4/3ZfT344cffsDb2xuVSkWnTp1o06YNDg4O2NraYmpqikqlIi4uTskRz6zriO9j/51PyZ07d5TP33/iuG3bNiXQL1u2LL1796ZSpUrKGBHt7+MPP/yQ5FiTtPjzzz+VQL9mzZp0796d8uXLkydPHrJly6bcEPXo0SNVYxvi3/zqu6NAiP+aBPtZwGeffUauXLl4+/YtV69eJTQ0NN3Tb8bvnXnz5k2yZWNiYpQ3xNSkG2QmlUqFWq1OsWcrNfOrV6pUSUkZCA0N5dKlS+zYsYPDhw/j7+/P8OHDOXr0aKrnT7exseH169cpfj/h39xQQ38/k9KhQwfc3d3Zvn270vOVlpVYL1y4oOQV//zzz0muCZHWGY8MYerUqcoc4jly5CAsLIwff/yR3bt3Zyg4iJ/2kdpgHzSvzZw5cwgODub48eO0bNmSI0eOKD/zifXqG/r1iP99Sun3I7n9jx8/5urVqwAMGjQoyWk6/6ufK+3T1qzwO58c7XgLIMHTPe0MbcWKFWPTpk1Jvl+m5Wc8KWq1WrmxqF69OqtXr05ygHxqzxd/wHFikyoI8TGRAbpZgEqlomPHjoAmmE1uGsyUmJmZKYO9kpthBzS9OtoZbDJrSszU0o5bSGxGCC21Wq1MaZhalpaWNGnShEWLFtGzZ09A88dZG1ikhvZ7c+fOnWQH/vr7++Pj46NzzIemVatWmJmZKYF+5cqVUxxDEF/8BcuSGywbfwrExBi6x/TAgQNKb+SXX37J77//DsDLly+TXFk3tYoUKaKkbSQ2nWFS2rZtq/SWahew0/5vamrK559/nuAYfb0e6WVubq6839y8eTPZssntj38drVu3TrLcf/Vz9dlnnwGa1KL4A67fFx0drcwK9KH+ziflwYMHyqDiAgUKJBhcrR230KRJkyQDfbVarZen0YGBgcpNU3Ir5IaFhSkD6VMS/3dP+3oK8bGSYD+L6NOnjxIgLFy4kMePH6fquLi4uAQr22rTEB4+fJhswB8/F9PQy4lr58NO7o/56dOnk70ZSEn8a0zLYDLtccHBwRw+fDjJctu2bVNSCwz9/UyKtbU1zs7OmJmZYWZmluaBufFvdpIa9BYXF5fiDWv82YjeX500s/n6+iq5+cWLF2fChAk0btyYrl27ArBv374Ev1NpYWpqqsyClVIAHF/evHmVn5vTp0/rBGP16tVLdKCovl6PjNC2+cGDBzppIe/bvn17kvviD8RNbjDlpk2bkm1L/PESGfm50l6TWq1Ott2HDh0iJCRE55iPwbt37xg3bpzyftWvX78Eee3a1yS5p6nHjh1LcaYb7WuS3OuR2td/69atqZ5pTfu7Z2pqqpf1SoQwJAn2s4h8+fIxadIkQPPm2rNnT2WmlKQ8evSIAQMG4OrqqrO9W7duSs/IpEmTCA0NTXCsm5ubEuzHT3sxFO0y8devX0+01/3169dMmzYtyeOfP3+e4vfr7NmzyudpWWzniy++UG7EZs+eja+vb4Iy9+7dUxZnypcvn5If/SH6448/uHnzJjdv3kx21c/ExJ93O36+e3y///57ir198Vc2TevTmoxQq9WMGzeOoKAgTExMmDt3rjL94Pjx45WnHFOnTlWe0qSHdoDt/fv30xR0am++oqOj+f7775UgKKmbMn29HhnRtWtXpUd90qRJiQaHu3fv5tSpU0nWEX9+9aTmq9+wYUOKK2BrB8tCxqaxdHZ2Vn5Gly1bpixoFt/Lly+VVVwtLCzo1KlTus/3X3r06BHdu3dXbsxq1qyZYFVt+Pc1OXHiRKLpU8+ePeOXX35J8Xza1yS53/P4ayzs3bs30d+ZGzdusGDBghTPF788aBZAS2ztByE+JpKzn4V88cUXvHr1ioULF+Lv70/Pnj2pV68eTZo0wcHBAWtra4KCgvDy8uLUqVOcOXOG2NhYnQG+AKVLl6Zv3764urpy7949OnbsyMCBAylbtiwRERGcOHGCtWvXEhsbi6mpKVOnTjXQFf+rS5cubNy4kZiYGIYMGcK3335LtWrViI6Oxt3dnVWrVhEdHU3x4sUTTY3w8fGhV69elCxZEmdnZypWrKj8sX716hX79+/nwIEDgGawWVpWXbW1tWXs2LFMnTqVV69e0alTJwYOHEjVqlWJiYnh3LlzuLq6Eh4ejkqlYtq0aclO0fkxq1evHrlz58bf35/58+fj7e1Ns2bNyJUrF8+ePWPLli2cP39eGQiclPgDpGfOnMngwYOxs7NTgsZChQplygwaq1atUnrLhwwZonOTa2Fhwdy5c+nWrRshISGMGzcu2dzh5DRt2pTFixcTHR3NlStXqFu3bqqOa9asmTJ+QJtGYWNjk+SCZ/p6PTKiTJky9OjRg3Xr1nHr1i2++OILBg4cSKlSpQgJCeHgwYNs2bKFChUqJPnkrly5csoA5c2bNxMcHEz79u2xs7Pj1atX7N69m0OHDqV4HWXLlsXc3JzIyEgWLFiAiYkJBQsWVF7DfPnypWqsjpmZGdOmTWPw4MGEhobSrVs3+vfvT506dTA2NsbDwwMXFxdl9rQffvgh2Sk6/0v+/v4604BGREQQFBTE/fv3uXDhAmfPnlV69KtUqcKCBQsSfb/SjiHx8/OjS5cuymsaGRnJhQsXWL16NVFRUZQvXz7Zm0lHR0e8vb05fvw4mzZtomrVqkpvv6WlJblz58bIyIi2bduyfv167t+/T7du3ejbty/FihUjNDSUU6dOsWHDBrJnz07evHlTTI8LDQ1VevY/5I4XIVJLgv0sZujQoXz22WfMmjWLFy9e4ObmhpubW5LlP/vsM8aOHZtg+5gxY4iIiGDDhg08e/ZMeWoQn5WVFfPnz090dcj/mvY6Zs6cSVBQEDNnztTZnzNnTpYsWcKCBQuSfaN/9OiRTv7v++zt7Vm0aFGac3t79OhBSEgICxYs4M2bNwnaB/8GCA0bNkxT3R+T7NmzM3v2bIYOHUpkZCSbN29m8+bNOmVq1qzJ5MmTE80x1ypWrBitWrXiwIEDif6MHzt2LE1PX1Lj/v37zJs3D9AEIEOGDElQpmLFigwdOpT58+dz6dIlXF1dldV206JcuXJKELRnz55UB/vZsmWjRYsWOr3byS3Cpq/XI6PGjx+Pn58fhw8fxtPTkx9//FFnf+HChZk/f36SgZdKpWLOnDn07t2boKAgDhw4oNyca5UqVYoFCxZQv379JNthaWlJz549+euvv7h9+7bOitQAa9asSXFRMq1GjRoxc+ZMJk+eTFhYGAsXLmThwoU6ZYyNjfnuu+/S/IQsM23cuJGNGzcmW8bW1pbevXszYMCAJG+qe/Xqxblz53Bzc+PJkyf89NNPOvuzZcvG7NmzOXXqVLLBfv/+/Tl06BBRUVEJprbt2LEjs2bNAmDUqFG4u7tz9+5dbt26xejRo3XK5syZk0WLFrFw4cIUg/0jR44QGRmJiYkJbdq0SbasEB8DCfazoObNm9OoUSMOHTrE6dOnuXnzJgEBAYSFhWFpaUmhQoWoXLkyLVq0oFatWokGrkZGRvz888+0adOGTZs2cfXqVd68eYOZmRlFihShYcOG9O7d+4PpjQLNuAUHBwdWrVrFzZs3iYiIIG/evDRs2JABAwZQsGDBJI+tXr06a9euxc3NjWvXrvHq1SvevHlDVFQUNjY2lClThmbNmtGpU6ckA6eUDB48mEaNGrF+/XouXLiAn58fRkZGFChQACcnJ3r37q33APVDVL9+fbZv346LiwsXLlzg7du3WFlZUbJkSdq2bUvnzp1TlQIzd+5cKlSowKFDh/Dy8iIsLCzT5hmPiopizJgxREVFkT17dubMmZPkVJ7ffPMNZ86c4erVqyxYsIB69eql64a4S5cuTJ48mSNHjvDLL7+k+ueuY8eOOsF+SuMq9PV6ZISpqSmLFi1i165dbNmyhfv37xMTE0PBggVp1qwZ/fr1S3G2mrJly7Jz506WL1/OmTNn8PPzI0eOHBQtWpRWrVrRo0ePVC2qNmbMGIoXL87OnTt59OgRISEhyS7OlZyOHTtSo0YNVq9ezdmzZ3n58iVxcXHkzZuX2rVr8/XXX1O6dOl01f1fMDIyIkeOHFhZWVGwYEHKly9P9erVadSoUYo/j6ampixfvpyNGzeyc+dOHj9+jFqtJl++fNSpU4devXrh4OCQbHoWaF7XzZs34+rqiru7u/K+/D4rKys2btzI33//zYEDB3j69CnGxsYUKFBA+XulXT03JdoVp52dnXVSu4T4WKnU/8Vkw0IIIdIkLCwMZ2dnAgICmDt3boZX5hVCpOzFixc0a9aM2NhYNm/eTJUqVQzdJCEyTAboCiHEByhHjhx8++23ACxfvtzgK0wL8SlYvnw5sbGx1K9fXwJ9kWVIsC+EEB+orl27UqRIER49esTBgwcN3RwhsrSXL1/yzz//YGxsnOhYNiE+VpKzL4QQHyhTU1NmzZrF+fPn0503LoRInZcvXzJo0CCKFCnyQY+lECKtJGdfCCGEEEKILErSeIQQQgghhMiiJNgXQgghhBAii5JgXwghhBBCiCxKBui+Jzwm1NBNEEJkcY+D7xu6CUKIT0BF22qGboJC1Ux/i0aqj3jrra5PgfTsCyGEEEIIkUVJz74QQgghhMhcKpWhW/DJkmBfCCGEEEJkLsklMRj51gshhBBCCJFFSc++EEIIIYTIXJLGYzAS7AshhBBCiMwlsb7BSBqPEEIIIYQQWZT07AshhBBCiMwlaTwGI8G+EEIIIYTIXJJLYjDyrRdCCCGEECKLkp59IYQQQgiRuSSNx2Ak2BdCCCGEEJlLYn2DkTQeIYQQQgghsijp2RdCCCGEEJnLSLr2DUWCfSGEEEIIkbkk1jcYCfaFEEIIIUTmkgG6BiM5+0IIIYQQQmRR0rMvhBBCCCEyl3TsG4wE+0IIIYQQInPJAF2DkTQeIYQQQgghsijp2RdCCCGEEJlLOvYNRoJ9IYQQQgiRuWQ2HoORNB4hhBBCCCGyKOnZF0IIIYQQmUsG6BqMBPtCCCGEECJzSaxvMJLGI4QQQgghRBYlPftCCCGEECJzyQBdg5FgXwghhBBCZC6J9Q1G0niEEEIIIYTIoqRnXwghhBBCZC6ZjcdgJNgXQgghhBCZS2J9g5E0HiGEEEIIIbIo6dkXQgghhBCZS2bjMRgJ9oUQQgghROaSXBKDkW+9EEIIIYQQWZQE+0IIIYQQInOpVPr70LOnT58yefJk2rdvT7ly5fj8888TLRccHMz06dOpV68eFStWxNnZmZUrV+qUiYqKYvbs2Tg5OVGlShX69u2Lp6dngroeP35M3759qVKlCk5OTsyZM4eoqKgE5bZu3UqLFi2oWLEi7dq148SJE2m+PknjEUIIIYQQmesDTtl/+PAhp06donLlysTFxaFWqxOUCQ8Pp2fPnhgbGzNhwgRy587NkydPCA0N1Sk3ffp09u/fz/jx48mXLx/Lli2jT58+7Nu3DysrKwCCgoLo3bs3xYsXZ9GiRfj6+jJr1izevXvH5MmTlbr27dvHpEmTGDx4MLVr12b//v0MGzaM9evXU6VKlVRfnwT7QgghhBDik9WkSROcnZ0BGD9+PLdu3UpQxsXFhbCwMHbv3k327NkBqFWrlk6ZV69esW3bNn7++Wc6d+4MQMWKFWncuDGbNm1i4MCBAGzatImwsDAWL15Mzpw5AYiNjeWXX35h0KBB5MuXD4CFCxfSpk0bRo4cCUDt2rV58OABS5YsYcWKFam+PknjEUIIIYQQmesDTuMxMko5HN62bRtffPGFEugnxs3Njbi4OFq2bKlsy5kzJ05OTpw+fVrZdvr0aerUqaME+gCtWrUiLi6Os2fPAvD8+XOePHlCq1atdM7RunVrzp8/n2jKT1Ik2BdCCCGEEJnLSI8f/zFvb29ev35Nrly5GDx4MBUqVKBmzZpMnDiRsLAwpZynpye5c+fGxsZG53gHBwedvH1PT0/s7e11ylhbW2NnZ6eU0/5fokSJBHVFR0fz/PnzVLdf0niEEEIIIUTm0mOPfNOmTZPdf+zYMb2dC+DNmzcAzJ49m+bNm7NixQqePHnC77//Tnh4OPPmzQM0A3i1efnxWVtbExQUpHwdHByMtbV1gnI2NjZKOe3/75fTfh2/vpRIsC+EEEIIIUQS4uLiAE0v++zZswGoU6cOJiYmTJw4kVGjRlGkSBFDNjFZEuwLIYQQQojMpcdUe3333KdEm5bz/oDc2rVrA5rZfIoUKYK1tXWC2XlA05MfP7XH2tqakJCQBOWCgoKUctr/Q0JCsLOz06kr/v7UkJx9IYQQQgiRuYxU+vv4jxUpUgQzM7Mk90dGRgJgb2/PmzdvEqTYvJ+jb29vn2Du/ZCQEF6/fq2U0/7/fjlPT09MTU3T9CRBgn0hhBBCCCGSYGZmhpOTE+fPn9fZfu7cOQDKly8PQL169TAyMuLw4cNKmaCgINzc3GjQoIGyrUGDBpw7d07ppQc4ePAgRkZGODk5AZobjOLFi3Pw4EGdc+7fv586deoke/PxPknjEUIIIYQQmSsTpszUl4iICE6dOgXAixcvCA0NVYLsmjVrYmtry7Bhw+jatSujR4+mY8eOPH36lN9//522bdtStGhRAPLnz0/nzp2ZM2cORkZG5MuXj+XLl2NlZUXXrl2V83Xt2pW1a9cydOhQBg0ahK+vL3PmzKFr167KHPsAw4cPZ8yYMRQtWpRatWqxf/9+bty4wbp169J0fSp1YsuEfcLCYxLmWgkhhD49Dr5v6CYIIT4BFW2rGboJCtWIinqrS73wpt7qAs3UmknN8LNmzRolV//8+fP89ttvPHjwABsbG9q2bcuoUaN0etmjoqL4448/2LVrF2FhYVStWpWJEyfi4OCgU+/jx4+ZNm0aHh4e5MiRg/bt2yeoC2Dr1q2sWLECHx8fSpQowffff0/jxo3TdH0S7L9Hgn0hRGaTYF8I8V+QYF+ApPEIIYQQQohMpvqA03iyOgn2hRBCCCFEppJg33BkNh4hhBBCCCGyKOnZF0IIIYQQmUo69g1Hgn0hhBBCCJGpjCTaNxhJ4xFCCCGEECKLkp59IYQQQgiRqWSAruFIsC8E4Fg+fXMRV6tRjb9WuQDQutnnvPR5qbPfyMgIKysr7B1K0MS5CV981QkLC4sMt1cI8fFbPG0ZJ/efTrFcn+968nnXVgBM/nYadzzu6uxXqVRY5LCgYJH8VHOqSsvOzbGyscyUNguRXhLsG44E+0IAuXPnTnR7UFAQMTExmJubY2mZ8I+njY11gm0WFhZkz54dgOjoaIKCgvBwv4aH+zV2bN/Jir+XY5vbVr8XIIT4aJmYGGNpnXRwbm5hnmCbmZkp2S017zNxcXEEB4bw6K4nj+56cmTXcX5eNIFCxQpmWpuFEB8PCfaFAI6ePpzo9gF9vuHq5as0b9mMqTN+SVVdvfr2ZPDQQcrXgYGBrF+9AdcVK/F87Mm0KdP5Y9E8vbRbCPHxK1WxFFOXTkrTMXWd6zBs0mDl6/CwcI7vOcX6PzcR8DqAPyYtYu7qGdKbKj4Y8rNoODJAV4hMljNnToZ+9y3tOrYD4OTxU/j5vTZwq4QQWUn2HNn5vGsrOvVuD8CTh095cOuhgVslxL9UKv19iLSRYF+I/0jL1i2Uz+/duZtMSSGESJ96zeoqn3ve8zJgS4QQHwpJ4xHiP5I3b17l87DQMAO2RAiRVdna/TseKDwswoAtEUKXpPEYjgT7QvxHXr38d6YeS2srA7ZECJFVvfF9o3yewyqHAVsihC4J9g1Hgn0h/iP/bNsJaKbjLF+hvGEbI4T4YDy4+YABbYYkus+xTmWGThyc6L7EHN11Qvn8s/IOGW6bEPqiQoJ9Q5FgX4hMFB0VzbNnz1m3Zh3HjhwDoHnLZtja5jJwy4QQH4qYmFgCA4IS3RcaknLKX2xsHH4+vhzfc4p9Ww4AUKrCZziUsddrO4UQHycJ9oXQs+VLXVi+1CXRfRUrV2TCpB//4xYJIT5k5RzLpnnqzZP7Tye5IFfBogUYNW24PpomhN5IGo/hSLAvhJ7FX1TLyNgIS0tLStiXoIlzY1q0ao6JifzaCSEyJv6iWiojFRbZLchfOD/VnKrQqHUDzLMlXIhLCEOSWN9wJOoQQs/eX1RLCCH07f1FtYQQIikS7AshhBBCiExlJF37BiPBvhBCCCGEyFSSs284soKuEEIIIYQQWZT07AshhBBCiEwlPfuGI8G+EEIIIYTIVBLrG46k8QghhBBCCJFFqdRqtdrQjfiQhMeEGroJQogs7nHwfUM3QQjxCahoW83QTVDY/eykt7pe/3JWb3V9CiSNRwghhBBCZCrJ2TccSeMRQgghhBAii5KefSGEEEIIkamkZ99wpGdfCCGEEEJkKpVKpbcPfXv69CmTJ0+mffv2lCtXjs8//zzZ8kePHqV06dKJlgsJCWHChAnUrFkTR0dHRowYgZ+fX4Jy7u7udOnShUqVKtG4cWNcXFx4fxitWq3GxcWFRo0aUalSJbp06cK1a9fSfH0S7AshhBBCiE/Ww4cPOXXqFMWKFcPBwSHZsu/evWPGjBnkyZMn0f0jR47k7NmzTJkyhd9++w0vLy8GDhxITEyMUubp06f0798fOzs7li9fTu/evVm4cCErV67UqWvFihUsXLiQPn36sHz5cuzs7OjXrx/Pnz9P0/VJGo8QQgghhMhUH3IWT5MmTXB2dgZg/Pjx3Lp1K8myy5cvp2DBghQuXDhBOQ8PD9zc3HB1daVevXoAlChRgtatW3P48GFat24NgKurK7ly5WLevHmYmZlRp04dAgICWLZsGT179sTMzIzIyEiWL19Ov3796NOnDwDVqlWjZcuWuLq6MmXKlFRfn/TsCyGEEEKITPUhp/EYGaUuHH727Bl///03EydOTHT/6dOnsba2xsnp32lG7e3tKVu2LKdPn9Yp17RpU8zMzJRtrVu3Jjg4GA8PD0CT5hMaGkqrVq2UMmZmZjRr1kynrlRdX5pKCyGEEEII8Qn69ddfad++PWXKlEl0v6enJyVKlEhwQ2Jvb4+npycA4eHhvHz5Ent7+wRlVCqVUk77//vlHBwc8PHx4d27d6lut6TxiEwVERHBnl17OXv6LA/uPyAwMBBUKmxtbSlbriyNmzaiabMmZMuWzdBNzZBlS5YD0KNnd6ysrdJVh88LH9o0b5tiuXWb11K+QrkE2wf0+Yarl68me2yXbl8xfuK4BNt379jNzxN/SfZYCwsLzl1xS7F9QmSGyHeRnNx/Gvdz13j66BnBgSGoVCpsclljX6YENRtUp1ajmphnM0u5sg/Y5r+2AfB5l1bksMqRrjr8/QJwO3yWh3ce4+31gqC3QYSHRpDd0oIiJQpTu0ktmrVvgqmZaZJ1vH71ht0b9nHtwnX8ff0xNTelcPHCNGxVH+f2jRPtCfV7+ZpvO32X6nb++c8C7ArYJdjufv4ah/85ysPbjwgLDcfaxoqyjmVo27U1Jcsln08tPlz67JFv2rRpsvuPHTumt3NpHT9+HA8PDw4ePJhkmeDgYKysEsYANjY2SspPSEgIANbW1jplzMzMsLCwICgoSKnLzMwMc3NznXLW1tao1WqCgoJSHTtJsC8yzakTp5k+ZTpv3vgr2ywsLDAyMsLnhQ8+L3w4duQYC+YtZPrMqdSsXdOArc2Y5UtdAGjXoW26g/34cufOneQ+E5Pkf20tLXNgbp74G0AOy+SDBxMTE2xsbBLdZ5H9474hEx+vK2eusmy2K4H+gcq2bBbmqFRG+L18jd/L11w4cQnbJRsZ/vO3VKxe3nCNzaCtrv8A0LhNw3QH+3ev32Ptko3K16ZmpphnMyMkKJQ71+5x59o9Dv9zlEkLfiR3XtsEx9+4dJPfJswnPCwCgOyW2YmKjOb+zQfcv/mA88cv8ONvYzEz172xMjIyIqdt4u8fWqEhYcREx5DT1gZbu4TnXjH3bw79c+T/9anIniM7gQGBnD1ynvPHL9L/+z606OSc5u+JMDyjDzlpPwWRkZHMmDGD4cOHY2ub8Of2QyfBvsgUu3fs5pfJ04iLi6N4iWIMGDQAp/p1yZkzJ6C5s714/hKbN2zmyuWruF/1+KiDfX07evpwuo8dO34M7Tq2S9exlR0r89cql3SfWwh9O7HvFH/OcCEuTk3BogXo3LcjjnUqY2WjuakOCw3n5uVbHNh2mNvud7hz7e5HHezrQ558efiyfyfKO5alWMliWNlYAhAeFs7ZoxdYvXAd3k9esGjqn0xZ/JPOsW98/fntpwWEh0VQqsJnDB4/gKIORYiNieXS6Sv8OXMFN6/c5u8/1jBo/ID3zpubv/b9mWS7YmJi+KbtUIIDQ6jfwgljE2Od/fu2HFQC/S/6dKBdj8/JYZmdkKAQNrls49A/R3D9/W+KOhSmbOXE0yjEpyEzeu6Ts3r1aoyMjGjTpg3BwcEAREdHExcXR3BwMNmyZcPMzAxra2tevXqV4PigoCClI03b86/t4deKiooiIiJCKWdtbU1UVBSRkZE6vfvBwcGap5pJdMwlRoJ9oXf37z3g16kziYuLo14DJ+b+MSfBoyYrKyucmzfFuXlTDh04jK+vr4FaK4T4UD15+BSXOSuJi1NTtW4VRv86MkGaTg7L7NRuXJPajWty9uh5/P0CDNTaD0eZSqUoU6lUgu3Zc2SnWfsmGKlU/DlzBbeu3uaNrz958v37JHHPxv2Eh4aTPYcF4+eOxjqnJtXA2MSYOk1qEfkuksXTlnFszwk+79qKQsULpbpd7mc9CA7UBDgNWzfQ2RcbE8v2v3cAUK9ZXboN+krZZ2VjxcCxfXnx9AW3rt5h3ZJN/OoyJdXnFR+Gj7hjH09PT54+fUqdOnUS7KtRowZTpkyhW7du2Nvbc/78edRqtU7akpeXF6VKaX4ns2fPToECBZSc/Phl1Gq1kqOv/d/Ly0tnjICnpycFCxZMU/qzBPtC75YuXEpUVBR58+VlxpxfU/yBbNGqeYKFJKKioti8YQuHDx7midcToqNjyF8gP/Ua1KNPv17ksUs4v+2yJctZvtSFtu0/Z+qMxPPPJ0/4mT279jLo228YPHSQsv3KpSsM7DuIAgULsP/IXq65X2PlX39z8/pNIiLeUbRYUTp17kiX7l/p/AJr69N6P+c+ubYIIZK3cfkWoqOisbWz5btfhqWYj+/kXCfBe0l0VDQHtx/m7NEL+Dz1ISY6hjz581C1bhXaf92WXLlzJqhn81/b2Or6D41aN2DYpMGJnmvxtGWc3H+aL/t3osuAzsr2W+53mDJ0Onb58/DnjoXcu36ff9bs5uHth0RGRFKgaAGc2zWmZefmOu8l2vq03s99T64taeUQL+894M1bnWDf4/w1AOq3cFIC/fgatKzHmsUbCH4bzOlDZ3WC8pSc3H8GgBKlilO8ZFGdfY/veSo3Am26tEz0+M+7tubW1Tvcv/mAV96+5C+cL9XnFob3Ma+gO3DgQDp27KizzcXFBS8vL2bOnEnx4sUBaNCgAUuXLuX8+fPUrVsX0ATrd+7cYcCAf5+ENWjQgGPHjjF27FhMTTVjZ/bv34+1tTWOjo4AVK1aFUtLSw4cOKAE+9HR0Rw+fJgGDXRvllMiwb7QKz9fP86c1gzi7Naja6IDVRIT/00gIOAtQ78Zyr279wHNoBVTU1OePnnK0ydP2bNzD4uWLaRS5Yr6vwA0KUhTf55OXFwcOSxzEBkZycMHD5k9Yw7Pnz1n7I9jlLKWVpbkzp0bf3/NuIScuXJibGSssz8+x/LVABLcbAghdPn7BeB+7hoArb9qQQ7L7Kk6Lv57SdDbYKaPnIXXgyeAJnfdxMQEn2cv8Xn2kpP7TjNh3g+UqvCZvpsP/D8FaeYK1HFqLHJYEBUVzdNHz3Cdt5pX3r70HdVLKZvd0oKctjYEBmgG51nntNIZBJvd0kKn7s51ugMkuNlIjfs3Hiif531vgOybV28AKFi0QKLHGhkZUbBIfoLfBnPj8q1UB/vBgcG4n9NMKdiodf0E+1///7wABYsVTLSOQvG2X790U4L9j4yKDzfYj4iI4NSpUwC8ePGC0NBQZSBuzZo1cXBwSLDY1o4dO/D19aVWrVrKNkdHR+rVq8eECRMYN24c5ubm/PHHH5QuXZrmzZsr5fr378+ePXsYPXo03bp148GDB7i6ujJq1ChlOk5zc3MGDRrEokWLsLW1pVSpUmzcuJHAwED69++fpuuTYF/o1ZXLV5SetYaN03bnqTXpx8ncu3sfa2trJk6ZQBPnJhgbG3P71h1+mfQLDx884vvho9m6azO5cuXSZ/N5+/Yt03+ZQecunRk4qD+58+QmJDiEpYv/ZNP6zWxcv4lOX3bEoaTml/6HH8fyw49jlSB+/ea1FCyU+B+qtOjVvQ+ejzyJiYkhd57cVHGsTOevvsCxmmOKx65ZtY5FC5YQ+DYQSytLSpX6jKbNm9K+Y7sEo/rf9/jRY75o9yUvvF9gbGxMgYIFqF2nFt2+7kqhwql/XC9ERt32uKO8l9SoVzVddSya+ideD55gaZWDb8b1p1ajmhgbG/HoridLf13Os8fPmTNuHvPWz060FzsjggNDWD7blRYdnfmib0dy2toQFhLGphXbOLD1EPu3HsK5fROK2BcGoN+o3vQb1VsJ4metnJ4gEM+I6OgYAl4HcOnUZTa5aGb8qdOkVsIBtf+/WYqLi0uyrthYzT5vrxepPr/bkfPExMRiYmJMveZOCfbHDwTjYhM/d1xsrPK5t5d3qs8tREr8/f357jvdp2nar9esWaMT0Kdk/vz5zJw5k8mTJxMTE0O9evWYOHGizuQaxYoVw9XVlVmzZvHNN99ga2vLiBEj6Nevn05dAwcORK1Ws3LlSgICAihbtiyurq4UKVIkTdcnwb7QK6/HTwBNb3zxEsXTfLz7VQ/OuZ0DYObcX6lbr66yr3yFcvy5Yimd2nbG39+fjes28e3wIfpotuJdxDs6ftGB8T/9oGyzsrZi3IQfuHr5Kg8fPOLo4WNKsJ9Zbl6/qTwV0M5ctH/vAbp/3Y0x40cn+zj08aPHmim8slsQ+DaQSxcvc+niZbZt3saCJfMpUDDxHjuAwLeBBAUGYW1tTVhYKI8fPebxo8ds37qdyb9MotXnrZI8Vgh9evHEB9D0xifV05ucO9fuce3CdQBGTh1GldqVlX0ly9ozeeEERnYdQ2BAEPu3HKLrN1/qp+H/F/kukqbtGtN/dB9lWw6rHPT/vje33e/w7PFzLpy4pAT7mWVY51G8eqE7JkqlUlGnSS2+/Snh00W7/Hl48dQnyUA+NiaWl881AxAjwiOICH+Xqpm6Tu7T9Jo61q2CTa6EN1Z5Cvybmun9xDvRAbjPn/zbprfxZmYSH4cPOY2ncOHC3L9/P03HzJo1K9HtVlZWzJgxgxkzZiR7fNWqVdmyZUuyZVQqFYMGDWLQoIxlAsiiWkKvAoMCAbC2tkrXL/bRw0cBKFe+nE6gr5U7T246d/kCgCOHjqS/ocnoN7BvotsbNm4IaILp9PK4fRWP21cTTeExMzPjq65f4rrmL85eOsOZC6c4f/UsG7auo0EjzVOSDes2snLF34nWXb1GNabNnMqRk4e44H6O0+dPcuz0EYaNHIqZmRkPHzxi+JDviI6KTnCsXV47Bg8dxLZdW7jofp6T545z9rIbC/9cgL2DPe/eRTL5p5+5esU93dcuRFqEBGnyt3NY5UjXe8mFExcBcChrrxPoa+W0taFZR81c3eeOXchAS5PWqVfis2LVqK95EvjM83m66952fgPbzm9IMYXHOpcVOW1tyBYvIK/TtBbdB3dJNEivXEuTHnnm8LlEBzsf3X2c0OBQ5et34REptvXZ4+d43n8CQKNWiT/xtS9dHOucmrTPXev3JdivVqvZte7f8VERqTiv+LB8yCvoZnUfZbD/9u1bLl++bOhmiExw7849AGrUrJ5kmRq1agDw9Mkzvb/h29jYULhI4j1tefPlBSA4OCTR/RmVxy4PP04aT9VqjmTPoclPVqlUlC1XlgVL/qBZC83c0q4uKwlJpA2Dhw7i83ZtyGOXR3kztM1tS/+B/fht/hxAc6Oye+eeBMfWcarDoG+/waGkg7LQjpmZGfUb1GPV+pUUKVqEmJhYFv6xKFOuXQh90waXFaomXIBOq2I1zRSdL5+/4l1E6lejTA1La0vyFUo8p1w7v3xYSJhez5mYGSum8te+P1l3bCV/7fuTboO+wv2sB6O/HpfoTc7nXVuTLXs2oiKj+HXUbG5euU1UZBQhQSEc3H6E1QvXYxJvykxVIotrvU878NjKxpKqTomnIpqYmNChp+bm6MqZqyyf9Re+L3yJiYnhxZMX/DFpEQ9vP1LOrVJ9lOGLEAbxUf62XLp0iV69eqVcUPznctrkBDQB8fuzYqTG27dvAcibL+lc1Xz/D7rVajVvAwPTfI7kaIPsxJj/f9BMTEyMXs+ZWt99PwLQDCS6eOFSmo6t37A+Vatr8p5PnzydQmldVlZW9P9Gk0d48/pN5TUSIjMp8+iHhKXrvST4rWYubFu7pMf1aBeUUqvVhATq9yY+udQWM3PNDXVMTGySZTJDTlsbvujTgZG/DCMqKpol05cn6L3PW8CO76cNJ5uFOc88n/PL8F/p3qgPfVsO4q/f/ianrQ1tu7dRyqc0cDo2No4zh88CUK+5E6amSWcPt+3WmqZtGwFwZNdxhnYeRdf6vfiu21jOHbtA03aNKf5ZMc15rVI3YFt8OFQq/X2ItPkog33x4SrhUBzQTJ35xOtJuuuJjIzST4OykEKFC5HLVhO4vPBO/cA4rYoVKwDgnY5jK/z/WLVajY+3T5qPFyKtChXX5OlHR0Xj8zT9P3NRiaStfeqq16+GXf48RL6L5OzR8wn2V63ryB/r59C2W2vsSxcnT77clChVnC/6dGDu6hmY/D9gz5Mvt/IkMCnXL93g7ZtAIPFZeOJTqVQMmfANE+b9QO3GNSlYtAB5C9hRqWZFRk0bzpAfBxL0/5u4AoXzp+PKhSFJGo/hfFADdNu2bZtyISAsLPMffYr0qVa9GiqVCrVazakTpylhXyJNx+fKlYsnXk959TLhCnRavr5+gOaNI9f/V+QFMDbWPN6NjEr6RiE0NDTJfUKID0d5x7LKe8llN/c0Ld4EYJ3LGp9nL3nzyj/JMtpebZVKhVXOf6cJ1r6XRCXzXhIeGp6m9nxobO1sef3qDb4vEl/Q0K6AHb1HfJ3oPq//p0ilZsrSk/s0TxKL2hfBoYx9qtpWtU4VqtapkmB7SFCIMkVnqYqZM12qEFnRB9Wz7+npiZGRERUqVEj2o3DhzJ29QKRfvvz5qNdAM63apg2bUx1cax/TlymnmYHh6hX3JB/dX76oGa9RrHhRLLL/O/e0dk5/v1eJ//FSq9XcvX03Ve1JK21PQ3rSDVLrhfcL3gZoUmjSM73nzZu3AChUOO3H3vr/sQAF9DC1qBApyZ03N47/D/gObD1EeFjqgmvt76B96eIA3Ll2N8nfy5tXbwNQoEh+sln8m3ajTU0JSGI1XrVajed9r1S1J63+i/cSgNcvXwPoXHdqhASFcOPSTQDqNU84iUJ8YSFhXDlzFYCGKfTqp4bbEc1TCJtc1lSqUSHD9Yn/lvTsG84H1bP/2WefUaxYMWbOnJlsuUOHDskA3Q/Y0OHfcvH8JXxf+TLhh5+Y+8ecZOd3P3zwMK9e+dKrT0+cmzuzcd0mHj96zMnjJ2nctLFOWf83/mzbvB2AZi2a6ewrWaokALdv3eH169fY2enm/e/fe4BXSdwIZFQOyxyEhoQSEpL+vN/3l9d+36L5SwDIls2cmv8fpJzaY8+eOYf7/2fSqdegXpqODQ0N5e+/VgFQoWJ5bG31u7aBEEnpNuhLbl65hb9fAPMnL2HMjO8wM096Fd1zRy/wxs+fdt3bULtxLfZvOcRzT28un75CzYa6vzOBAUEc2XEMgLpNa+vsK+qgWd310V1P3r55S648uj/zZw6d5Y1v0k8MMsIihwXhoeGEhaT/yUFsTCzG8QbRvu/0ITcC3mg6DspWLp3qetVqNa7zVhMVFU1RhyJUc0p+/YOzRy8QFRWNkbERDVoknFs/Lfz9/Nm28h8A2nZvozNnufg4SJBuOB9Uz36lSpW4ceNGqspmdq+HSL/SZUszfuI4VCoVZ0650a1zd/bt2U9QYJBSJiQkhGNHjjOwzzeMG/2j0mtXtZqjMuXmlIlTOXLoKLH/X0jlzu27DBk4lODgYHLnzk33nt10zlvFsTJ2ee2Ijo7mx7E/KXntERERbNuynWk/T8faWr8L52hpV9bbu2uf0t7EOJavhmP5aixbsjzBvgF9vsF1xUoePXyk1KFWq7l39x7fjxjNoQOHAOjTvw82OXUXwln5199MmfgLF85d0ElzCwh4yyrX1YwZqVn1t3iJYnTo1F7n2Jc+L+nVrTc7tu/kpc9LZXt0VDRnz5yj79f9ePrkKUZGRgwfOSwt3xYhMqREqeIMGN0HlUqF+zkPxvaewOmDboQE/fvEMCw0nAsnL/Hz0OnMm7RQmaGrXJUyypSbS3914fzxi8piUI/veTLtu5mEhoSR09aGNl1a6py3TKVS2ObJRUx0DH9MXoyvjyZ1MPJdJId3HGPZzBVYWuXIlGsuUkLz5PrUgTNKexPTuU53Otfpzua/tiXYN2nIVHas2c1zL2+dOl6/esMW1+0s/dUFAPsyJRKdHWf9n5u4duG6ztMUz/tPmD1uHm6Hz2GezZyhEwdhbJx8CKGdhadyzYoJbpgS8/TRM7a6/sNzT29lIoTId5G4HTnHT99MIehtMKUrlqJt19Yp1iWE+NcHdWs8YMAAGjZsmGK5hg0bcuzYsf+gRSK9On7RgZw5bZj+ywy8PJ8wcfwkALJnz45KpdIJSAsULKBMpwkwbeZUvh04lPv37vPD95rlpk1MTJRjrK2t+X3hb+SMl68Pmqnbxv/0A2NHjePq5at83qIdlpY5ePfuHTExsbTv1J642Fj27NqLvnXs3IHr166zfu0Gtm3ZTi7bXKhUKpybN+X7saNSVcdLn5csnr+ExfOXYGJiorT93btIpUzXHl34ZsjABMdGR0Wza8dudu3YjUqlIoelZm7y+FN0flaqJPMX/6EsxR3fzRu3uHlDk6pjbm6OhUU2QkPDlD+42Syy8dPkCdSsXTNN3xchMqppu8ZY2ViyfLYrL576sPCXpQBky54NFSqd6Xft8udRptMEGD55CNO+m8mTh0/5/acFmJmZYmxiohxjaZWDsbNGKTP/aBmbGNN/dB9+/2k+dzzuMvSLkWTPYUHkuyhiY2Np8nkj4uLilGBWv9fbiPs3H7Bv8wGO7DiKta0NKqB241r0HtEjVXW8ffOW9X9uYv2fmzAxMcYihwXRUdG8i/j3vaRkWXvGzR2DUSJTZ7odPseONbsByJ7DgujoGGV9Dutc1nw/bXiK+fc+z17y4NZDABq3TvnvOkBIcCib/9rG5r+2YWSkInuO7ISHhRMXp+ncq1CtPD/MHpXsUwvx4ZKOfcP5oIL9okWLUrRo0RTLZcuWjUKF0jZYS/z3GjdtTO06tdmzaw9nTrvx8MEjAt8GolJpcs7LlS9LE+cmNG3WRCcAtbXNxeoNf7N54xYO7T/M0ydPiY6OpmixotRvUI/e/XslSNHRauLchKUrluC63JU7t+8SGxtHqdKl+bJrZzp0as/kCT9nyrW279iO2NhYdmzbgedjL3xf+aJWqwl8G5jqOkaN+Y4L5y5y6+Zt/N/4ExQUhKmpKcVLFKOyYxW++LIjFStVTPTYZi2ciY2J5fq16zx/7k1QYBAxMTHksctD6TKlcW7elNaft0o00LfNbcsPE8Zyzf0aD+4/4G1AIKGhoWSzsKBo0aLUrF2DL7t+ScFkVt4VIjPVbFiDSjUrcnL/GdzPefD00TPNVJkqFXkL2GFfpgS1G9WkVqMaOrPD2OSyZsaKXzi4/Qhnj57H5+lLYmJiKFAkP1XrOtLh68+T7HGu1agGE+f/yD+rd/L4rhdxcXEUL1WM5h2dadq2EYunLcuUa9XcSKg5uus43k9e4O/rr5kaNCj1KYJDJw3m2oXr3PG4h7+fP8GBIaj+/70qUbo4dZrUok6T2kn2zHfu25ErZ915+vAZgf6BmJqZUqREIarXr0arzi2wsrFMsQ2nDpwBNFNkVq+ffLqPVuHihejYqx233e/g+8KPsNBwrHPZYF+6BA1b1cPJuU6qvwfiwyNpPIajUks+jI7wGJmtRQiRuR4Hp21ZdiGESI+KttUM3QRFmfmt9FbXvZEH9FbXp+CD6tkXQgghhBBZj/TsG44E+0IIIYQQIlNJsG84H9RsPEIIIYQQQgj9kZ59IYQQQgiRqaRj33Ak2BdCCCGEEJlK0ngMR9J4hBBCCCGEyKKkZ18IIYQQQmQq6dk3HAn2hRBCCCFEppJg33AkjUcIIYQQQogsSnr2xQfppc9Ljh89waULl3hw/wH+/gGYmppSqHAhnOrXpXvPbtjZ2enlXOFh4XRq1xnfV74A/DL9Z9p1bJegnJ+vHwf2HeTWzVs8fuTJ24AAQkNDsbS0xL6kA87Nm/LFl50wMzNL9bnXr9nAb7N/B6BAwQLsP7I30XI+Pi9ZMG8hF85eIDIykpKflWTg4AE0bNwgyboXzV/MyhV/89v8uTRt1iTVbRIiK4kIi+CW+x0e3XnM43tePL77mJAgzUrpCzbOpVDxQoket3jaMk7uP52qczRu04ChEwdnuK1//7GGfVsOAlDOsSxTl05K9bGXTl1mzvg/lK+3nd+QoIzfy9d82+m7VNf55z8LsCvw7/tsbGwcO9fu5sTeU7zx8ydn7pzUa1aHLgM6Y2pmmmgdzz29Gdv7R6rUrsz4uWNSfW6R9UjHvuFIsC8+OK9evqJN87ao1Wplm6VlDiIi3vHwwUMePnjIP1t38Nv8OdSoVSPD51uycKkS6CfH/aoH839foHxtZmZGtmzZCAwMwv2KO+5X3Nm6aRvL/lpK3nx5U6zP95UvSxf9mWK5t2/f0vfrfvj5+mFkZISZmSm3b91m1PDvmTl3Bi1aNU9wjJenF2tXrcOpvpME+uKTdvPKLZ0gOLWyW1qQ09Ymyf0xMbGEBmtuGkqULpHu9mk9vufJge2H03VsRPg7XOetTrGckZFRstcEEBoSRkx0DDltbbC1s9XZ99fclRzZdRyAbBbmvHn1hp1r9/Ds8XMm/P5DovWtmLsSI2Nj+n3fO5VXI7IqSeMxHAn2xQcnLi4OgPoN69G2fVtq1a6JtY010VHRXLx4iVnTZ/PC+wXfjxjNjr3/kMcuT7rPdffOXTZv3ELFShW4eeNWsmXzF8jPoG+/oVr1qpQqXQqbnJo/mqGhoRw+cJjf5/yBl6cXk36czPKVy1I89+wZcwgPD0/x3OtWr8fP14+6TnWY9dtMcljmYMvGrcyeMYc/fptP85bNEryJzpo+GyMjI8ZNGJuK74IQWZtNLmscytrjUNYeWztbls/6K8Vj+o3qTb9RSQeoezbuZ/XCdZiYmlC/ed0MtS8uLo7ls11RocK+TAk873ml6fhNLlvx9wvgs/IleXj7UZLl8uTLzV/7ku5giImJ4Zu2QwkODKF+CyeMTYyVfS+e+nB09wlyWGVnwu8/ULpiKZ57ejN91Czcz13jxqWbVKpZUae+k/tPc+faPboN+oq8BfTzJFZ8xCTYNxjJ2RcfHCtrKzZu28DCpQto1sIZaxtrAEzNTKlX34lFfy7A3Nyc0NAwtm/9J93niYuLY/qUGQBMmPxjiuWrOFZm8NBB1KhVQwn0ASwtLen0ZSfGjB8NwKWLl3n18lWydZ08fooTx07SxLkxdeslHyhcunAJgO9/+B4rayuMjIzo2qMLZcuVwfeVL0+fPNUpf2DvAS5dvEyf/r0pUrRIitclRFZWrV41XPcvY8LvP9BlQGcqvxeQptepA2cAqFrXESsbqwzVdWDrITzvedHqy+YUtU/b76znfS8ObDuEfZkSOLfP2FM897MeBAeGANCwtW6K4K2rt1Gr1Ti3a0LpiqUAKGJfmPZftwXg5pXbOuXDQsJYu3gDBYsWoF2PzzPULiFExkiwLz44VlZWlC5TKsn9JexLULFSBQDu3L6b7vNsWr+JO7fv0LlLZ8qULZPuerTKVyivfP769esky4WHhTP719lYWFgwZtzoFOsNCgwCoHAR3dxibSAf+DZQ2RYaGsq8uX9QpEhh+g7ok4bWC5E1GRvr/8/ck4dPefJQc5PduE3S42ZSw9/Pn00u27DNk4suAzqn6VjtEwHUar4Z2w8jo4z1nJ7cr7mBKVGqOMVLFtXZpx3nkLegbopi/sL5AAgOCtHZvmHZZoLeBtN/dB9MTSWJQGjSePT1IdJGgn3xUbLJmROAuLjYdB3v5+vH0kXLyJ07N0NHDNFLm65fu658XrBgwSTLLV28jFevfBk4eAAFChZIsV7tUwTv5y90tj9/7q2zHzTjD9688eeHn37A3Nw8Te0XQqSOduCudS5rHOtUzlBdrvPWEBEeQe/vvsYih0Wajj2w7TCP73ri3L4JJcs5ZKgdwYHBuJ/zAKBR6/oJ9lvZWALg5+Ons93X21dnP8Cju54c2XmMuk1r6+1Jivj4qVT6+xBpI8G++OjExMRw3eMaACVLlkxXHbNnzCEsLIxRY0diZZX+R/DRUdG88H7B2lXrmDdXMwiwWQtncufJnWj5e3fvsWn9JkrYl+Dr3j1SdQ7tIOR5c/8gJDiEuLg4tmzayt3bd8mXPx/FSxRX6t66aRtNnBtTr75Tuq9JCJG02JhYzhw6C0D9ZnUxMUl/r/WVM1e5dOoylWpUwMm5TpqO9fcLYNPyrVjnsqb74C7pboOW25HzxMTEYmJiTL3mCd8/KlQtB8DR3cd5cOshAN5PXrBrvWYGsYrVNU9b4+LiWDF3JebZstF7xNcZbpcQIuPk2Zr46GzZuIU3b/wxMjLi8/ZpzwU9deI0x4+eoHqNarRp2zpdbWjXsr3Ss66lUqlo1sKZKdN+TvSYuLg4pv38K7GxsUyYNB5T08Snqnvf1716sGfXXs65naORUxPMzEx59y4SgO++H4FKpUKtVjNj6kzMzMwYO16mtxMis3icv0bQ22AAGmUgheddxDv+mrcaE1MTBozpm+bjXeetJiI8gn7f98LS2jLlA1Jwct8pABzrVsEml3WC/YWKF6Jp20Yc23OSCQN/JpuFOe8iNO9DVWpXplINTbB/eMcxHt/1pPeIHuTOa5ugHvHpkvQbw5FgX3xUHtx/yKL5iwHo0v0rHErap+n4iPAIZv06GxMTE8ZPHJ/uduS0zUV4eAQRERGEh4cD0KxlM4YO/5bsObIneszmjVu4c/sOrT9vRfWa1VN9Ltvctqxat1Izz/75i0S+i6Rc+XIMGNSPxk0bA/DP1h3cvHGLEaOGk79AfsLDwlm8cAmHDxwhJCSEz0p9xrDvvqV23drpvmYhBJz8/8DcYiWLUqJU8XTXs3nFNt68esMXfTpQsGjK6XzxaZ8IlKlcmkatMzZmAODZ4+d43n8CQKNWSdf3zbgB2BWw4/jekwT4BZA7ry1OznXoMvBLVCoVQQFBbFy+maL2RWj9ZUsA3I6c45/Vu3j5/BU2uaxp1LoBX/brpDPTj/g0SLBvOBLsi4/G69ev+X7EaN69i6Rs+bJ89/2INNexdPGfvHr5ij79eqf5RiG+NRtWKZ/7v/Fn5z+7cHVZyanjp5g28xeatWimU97P7zVLFy7F0sqSUWNGpvl8hQoXYs682Ynue/v2LYvmL1ZSg9RqNd9/N4aL5y9SoWJ5ahevxakTpxg+ZARLXZboZW0CIT5FIUGhXHFzBxLPa08trwdP2LflIHkL2NGpd4c0Hat9ImBsbMzAMX31EkBpxyBY2VhS1ckxyXLGxkZ07tuRzn07Jrp/9aL1hIdGMG5OX4xNjDl14AyLpv5JTlsb6jatzeN7nmz7ewdv37xlyIRvMtxuIUTqSM6++CgEBQbx7cChvPB+QdFiRVm0dEGaB6Dev3ufjes2kT9/Pr4ZMlBvbcudJzf9v+nHzLkziIyMZPJPU/Dz1R3ENvvXOYSGhvHt8CEZWhcgMQt+X0hQUBA/ThyHqakpp0+e4eL5i9RvWI/VG1YxfdY0Fi5dQExMLH/8Nl+v5xbiU3L26HliomMwNjamQYt66aojLi6O5bP+Ii42jn6jemGeLfUrbsO/TwRaf9WCYu/NmJMesbFxnDmsGYNQr7lTumfOueNxl9MH3WjYqj7lqpQhJiaGtYs3YGZuxoy/pjJ88hBmr/yVog5FOLbnJE8fPctw28XH5UOejefp06dMnjyZ9u3bU65cOT7/XDdFODQ0lEWLFtG5c2eqV69O3bp1GTx4MPfv309QV0hICBMmTKBmzZo4OjoyYsQI/Pz8EpRzd3enS5cuVKpUicaNG+Pi4qKzmCiAWq3GxcWFRo0aUalSJbp06cK1a9fSfH0S7IsPXkhICN9+M4xHDx+Tv0B+lv21NMkBsMmZM+s3YmNjGfrdUNRqNeFh4TofWlHR0YSHhRMREZGm+hs2bkCBggV4F/GOgwcOKdsvX7zM8aPHcSjpwOft2iQ4b3R0NIBOm2JiYlJ1zmvu19i9cw+t2rRUeuzPnNKkGXzZ9UuMjDS/4o7VHClbrgx379zjzes3abouIYSGtge8Su1K2KSwEm1ydTy660nlWhUpX608EeHvdD5iYzUzjMXFxcXbpllo8OXzV+zbchCbXNa0694mwbHRUf++byjbopN/L7l+6QZv3wQC6X9aERMTw4rf/iaHVXZ6Du0GwOO7ngQGBFHNyVFZUMs8m5myFoD7uWvpOpf4eH3Is/E8fPiQU6dOUaxYMRwcEs5s5ePjw+bNm3FycmL+/PlMmzaNkJAQunTpwuPHj3XKjhw5krNnzzJlyhR+++03vLy8GDhwoM7f9adPn9K/f3/s7OxYvnw5vXv3ZuHChaxcuVKnrhUrVrBw4UL69OnD8uXLsbOzo1+/fjx//jxN1ydpPOKDFhEewfDB33Hn9h3y5MnNMtc/UzVdZWJe+rwEYNKPk5mUTLlff5nBr7/MoEDBAuw/sjdN58ibNy8vfV7i/ezfwbva8z5+9JgGtRsleeyrl69wqqn5Y/vL9J9p17FdsueKiYlhxrRZ5MiRne/HjkpwvkKFEs7Lf/fOPV6+fKn3pwtCZHXeT17w6I7mj3pG8uRfv9LcbF+/eJOeTfslWe7e9fvK/ilLJlKhajn8XwcQFxtH0NtgBrYdmux5tMd+2b9TsvP3n9ynuYEpal8EhzLpS23cu+kAzz29GTCmr3ITpL3OpOblf/0q6bVIhPivNWnSBGdnZwDGjx/PrVu6q9oXLlyYI0eOYGHx7/S4tWvXpkmTJmzYsIFJkzRRhYeHB25ubri6ulKvnubpX4kSJWjdujWHDx+mdWvNpCCurq7kypWLefPmYWZmRp06dQgICGDZsmX07NkTMzMzIiMjWb58Of369aNPnz4AVKtWjZYtW+Lq6sqUKVNSfX3Ssy8+WO/eveO7oSO5fu06OXPasMz1T4oVy/hj68zk4+MDQPbsiQ/S1aeN6zbx8MFDhiSRGhQVFanztXYGHyFE2ml79S2tLalev5qBW6MfYSFhXDlzFYCG6ezVf+Prz7aV/2BfpgTNOzZNsD86Kkrn66jIqARlxKfhQ07j0T4FT0r27Nl1An2AHDlyULRoUZ0UndOnT2NtbY2T07/T19rb21O2bFlOnz6tU65p06aYmf2bxte6dWuCg4Px8NCsd+Hu7k5oaCitWrVSypiZmdGsWTOdulJDevbFByk6KprR343l8qUrWFlbsXTFEhxKZmzRmJR66R3La/6AJ9WrHhMTk+yc2vv37ue1n6a3yrHav4Pc2nVsl2wv/bIly1m+1CVNTxL8/F6zfKkLpUqXoku3r3T2aZ983Ll9V1kZODY2lvv3NLmFBQqk78mIEJ+quLg4Th/U5LU7OdfJ0IqwXQZ0TranffG0ZZzcf5pyjmWZulT3GWSFquXYdn5Dksee2HeKJdOXAyRbTuvs0QtERUVjZGxEgxbpW5tj5R9riIqM+v8Kvv8GTHb5NR0Qnve8dMo/vuOp2f//1B7x6chqs/EEBwfz8OFD6tatq2zz9PSkRIkSCa7V3t4eT0/Nz354eDgvX77E3t4+QRmVSoWnpye1atVSyr9fzsHBgdWrV/Pu3TuyZcuWqrZKsC8+OLGxsfz4w0+ccztHjhw5WLxsEWXLlU3VsT4vfGjTvC2QulSYtOjfeyANGzWgYeOGFC9RDGNjzdRxL31esnvnHlxdNLl2ZcuXpX7D9A3eS63fZv1GeHg4Eyb/qLRDy6mBE9u3/sPKFX9To2Z1ChYqyF/LXfF95UvZcmUkhUd8coIDg5XPQ4PDlM/DQsN19llaWybaw3fj8i0CXgcAqc9rn/ztNO543E00aP9QaJ9WVK5ZkVx5cqX5ePfz17h06jLNOjRNsIKvQ1l7bHJZc+/GA07sO0Wj1g14fM+LwzuPAlC1TpUMt198upo2TfgUKb5jx45lehvmzp2LSqWiW7duyrbg4OBEF+q0sbFRUoNCQkIAsLbWXc/CzMwMCwsLgoKClLrMzMwSTEZibW2NWq0mKChIgn3x8brmcZ1jRzS/qDExMXw/fHSSZfPlz8f6LWv/k3a99nvNovmLWTR/MSYmJlha5iAyMkpnIG/5CuWZv3heio8EM+LCuQscOXSUDl+0p3KVSgn2N2rckOo1qnHl8lXateqAubk5kZGRmJgYp2vaTyE+dv1aDU50+4SBugvgLf1ngTKYND5tUFy4eCE+K5++Vbs/ND7PXior4TZu3TDNx0dFRuH6+yqsc1nTY0jCFXxNTEzo8W1Xlv7qwpLpy1kx928lhadp20Z6mUlIfFyyUs/+9u3b2bJlC7NmzSJ//vyGbk6KJNgXHxx1XJzyeWRkJJGRSeeam5mnbdq6jJj66xTOup3D/aoHvq98eRvwFiMjFQULFaRM2TI0a+FMsxbOCXra9SkqKoqZ02djY2PDiFHDEy2jUqmYv+QPFv2xmCOHjxIaEkr5CuUZ9t23Mse+EGkUHhbOpVNXgPTntX+ITv1/cbAcVtmpXr9qmo//Z/UufF/48e1P3yS5gm+TzxthYmrCzjV78Hn+ktx5bWncpiFf9uuUobaLj5M+g/3/ouc+KadOnWLy5Ml8++23dOyou+aEtbU1r169SnBMUFAQNjaawevann9tD79WVJSm81BbztramqioKCIjI3V694ODg1GpVEq51FCp35/U8xMXHhNq6CYIIbK4x8EJ52YWQgh9q2j74Qwmr7u2q97qOtdzk97qep92Np69exOOobt27Rp9+vShTZs2/Prrrwn2L1iwgPXr13Px4kWdm5tOnTpRqlQpZs2aBUCjRo1o3rw5EyZMUMrcv3+fdu3asWbNGmrVqsX58+fp06cPu3btokyZMkq5WbNmcfjwYY4fP57qa5LZeIQQQgghRKb6kOfZT41Hjx4xaNAgateuzS+//JJomQYNGhAUFMT58+eVbV5eXty5c4cGDRrolDt27Jiyzg7A/v37sba2xtFRM8FH1apVsbS05MCBA0qZ6OhoDh8+rFNXakgajxBCCCGEyFQfcs5+REQEp06dAuDFixeEhoZy8OBBAGrWrIlaraZ///6Ym5vTu3dvnXn4LS0tKVlSM5bH0dGRevXqMWHCBMaNG4e5uTl//PEHpUuXpnnz5sox/fv3Z8+ePYwePZpu3brx4MEDXF1dGTVqlDIdp7m5OYMGDWLRokXY2tpSqlQpNm7cSGBgIP3790/T9Ukaz3skjUcIkdkkjUcI8V/4kNJ46q3vlnKhVHLrsVFvdQF4e3snOcPPmjVrAOjVq1ei+2vWrMnatf9OFBISEsLMmTM5cuQIMTEx1KtXj4kTJ5IvXz6d49zd3Zk1axZ3797F1taWHj16MHDgQJ2bIrVajYuLCxs2bCAgIICyZcvy448/Kr3/qSXB/nsk2BdCZDYJ9oUQ/4UPKdivv6G73uo60z3ldSTEvySNRwghhBBCZKoPOY0nq5NgXwghhBBCZCqJ9Q1HZuMRQgghhBAii5KefSGEEEIIkakkjcdwJNgXQgghhBCZS4J9g5E0HiGEEEIIIbIo6dkXQgghhBCZStJ4DEeCfSGEEEIIkamMJNY3GEnjEUIIIYQQIouSnn0hhBBCCJGpJI3HcCTYF0IIIYQQmcpIgn2DkTQeIYQQQgghsijp2RdCCCGEEJlK0ngMJ03BftOmTdN1EpVKxdGjR9N1rBBCCCGE+LhJKonhpCnYV6vVCbZFR0fz+vVrTWUmJuTMmZPAwEBiYmIAsLOzw9TUVA9NFUIIIYQQQqRFmoL948eP63wdHBxMnz59KF68OCNHjsTR0REjIyPi4uJwd3dnwYIFhIeHs2rVKn22WQghhBBCfERkgK7hZOipym+//UZUVBSrVq2iWrVqGBlpqjMyMqJ69er8/fffvHv3jrlz5+qlsUIIIYQQ4uOjUqn09iHSJkPB/rFjx2jUqBHGxsaJ7jcxMaFRo0YJnggIIYQQQgghMl+GZuMJDQ0lJCQk2TIhISEplhFCCCGEEFmXpPEYToZ69kuWLMn+/ft59uxZovufPHnC/v37+eyzzzJyGiGEEEII8RGTNB7DyVDP/pAhQxg2bBgdOnSgc+fOVKtWjdy5c+Pv78+VK1fYvn07ERERDBkyRF/tFUIIIYQQQqSSSp3YfJppsHPnTqZNm0ZYWJjO3ZZarcbS0pKJEyfSoUOHjLbzPxMeE2roJgghsrjHwfcN3QQhxCegom01QzdB0XHPQL3VtaPtCr3V9SnI8Aq6HTp0wNnZmaNHj3L//n1CQkKwsrKidOnSODs7Y2lpqY92CiGEEEKIj5Tk7BtOhoN9AEtLy4+q914IIYQQQohPgV6CfYCwsDCePHlCREQE1atX11e1QgghhBDiIycDaw0nQ7PxAHh7ezNkyBBq1qxJ586d6dWrl7Lv6tWrtG7dmosXL2b0NEIIIYQQ4iNlpFLp7UOkTYaCfR8fH7p06cLp06dp2rQpVapUIf5438qVK/P27Vv27duX4YYKIYQQQoiPk0qPHyJtMhTsL1q0iKCgINauXcvChQtxcnLS2W9iYkL16tVxd3fPUCOFEEIIIYQQaZehYP/MmTM0a9aMqlWrJlmmYMGC+Pr6ZuQ0QgghhBDiIyZpPIaToQG6QUFBFCpUKNkyarWaqKiojJxGCCGEEEJ8xCRIN5wM9eznyZOHp0+fJlvmwYMHFChQICOnEUIIIYQQIlM8ffqUyZMn0759e8qVK8fnn3+eaLmtW7fSokULKlasSLt27Thx4kSCMiEhIUyYMIGaNWvi6OjIiBEj8PPzS1DO3d2dLl26UKlSJRo3boyLiwvvr3OrVqtxcXGhUaNGVKpUiS5dunDt2rU0X1+Ggv26dety4sQJ7t27l+j+K1eucOHCBRo2bJiR0wghhBBCiI+YSqXS24e+PXz4kFOnTlGsWDEcHBwSLbNv3z4mTZpEq1atWLFiBVWqVGHYsGEJgu+RI0dy9uxZpkyZwm+//YaXlxcDBw4kJiZGKfP06VP69++PnZ0dy5cvp3fv3ixcuJCVK1fq1LVixQoWLlxInz59WL58OXZ2dvTr14/nz5+n6fpU6vdvI9LA29tbWUyrf//+eHp6snfvXpYtW4aHhwerVq3CwsKCXbt2kTdv3vSe5j8VHhNq6CYIIbK4x8H3Dd0EIcQnoKJtNUM3QdHr8DC91bWm+WK91QUQFxeHkZGm/3v8+PHcunWLvXv36pRp0aIFFSpU4Pfff1e2de3aFSsrK1asWAGAh4cHXbt2xdXVlXr16gHg6elJ69atmTdvHq1btwZg8uTJuLm5cfDgQczMzACYN28eGzdu5OzZs5iZmREZGUndunXp0aMH33//PQBRUVG0bNmSBg0aMGXKlFRfX4Z69gsXLoyrqyvW1tYsWLCAvXv3olarGTx4MMuWLcPW1hYXF5ePJtAXQgghhBCfFm2gn5Tnz5/z5MkTWrVqpbO9devWnD9/Xhmbevr0aaytrXVmp7S3t6ds2bKcPn1a2aadsl4b6GvrCg4OxsPDA9Ck+YSGhuqc08zMjGbNmunUlRoZXkG3cuXKHD58mBMnTnD9+nWCgoKwtLSkUqVKCS5ECCGEEEJ8ej7m4bmenp4AlChRQme7g4MD0dHRPH/+HAcHBzw9PSlRokSCVCN7e3uljvDwcF6+fIm9vX2CMiqVCk9PT2rVqqWUf7+cg4MDq1ev5t27d2TLli1V7c9wsA+a+fSbNWtGs2bN9FGdEEIIIYTIQvQ5G0/Tpk2T3X/s2DG9nQs0s08CWFtb62zXfq3dHxwcjJWVVYLjbWxsuHXrFqAZwJtYXWZmZlhYWOjUZWZmhrm5eYJzqtVqgoKCUh3sZyiNp1evXuzcuTPZMrt27aJXr14ZOY0QQgghhBAiHTLUs3/p0iVq1qyZbBkfHx8uX76ckdMIIYQQQoiPmD579vXdc58SGxsbQNMrb2dnp2wPDg7W2W9tbc2rV68SHB8UFKSU0fb8a3v4taKiooiIiNCpKyoqisjISJ3e/eDgYFQqlVIuNTLUs58aERERmJjoJVtICCGEEEJ8hD7kqTdTos2b1+bRa3l6emJqakqRIkWUcl5eXgnmy/fy8lLqyJ49OwUKFEhQl/Y4bTnt/15eXgnOWbBgwVSn8EA6gn0fHx/lAzR3JvG3aT+eP3/O5cuXOXToUIqr7AohhBBCCPEhKlKkCMWLF+fgwYM62/fv30+dOnWUyWgaNGhAUFAQ58+fV8p4eXlx584dGjRooGxr0KABx44dIzo6Wqcua2trHB0dAahatSqWlpYcOHBAKRMdHc3hw4d16kqNNHe5N2nSRLmrUqlUrFmzhjVr1iRZXq1W88MPP6T1NEIIIYQQIovQZxqPvkVERHDq1CkAXrx4QWhoqBLY16xZE1tbW4YPH86YMWMoWrQotWrVYv/+/dy4cYN169Yp9Tg6OlKvXj0mTJjAuHHjMDc3548//qB06dI0b95cKde/f3/27NnD6NGj6datGw8ePMDV1ZVRo0YpNw7m5uYMGjSIRYsWYWtrS6lSpdi4cSOBgYH0798/TdeX5kW1xo8fj0qlQq1Ws3PnTsqUKUPZsmUTlDMyMsLGxobatWun+Q7EkGRRLSFEZpNFtYQQ/4UPaVGtQcdH6q2u5U3m660u0CwSm9QMP2vWrKFWrVoAbN26lRUrVuDj40OJEiX4/vvvady4sU75kJAQZs6cyZEjR4iJiaFevXpMnDiRfPny6ZRzd3dn1qxZ3L17F1tbW3r06MHAgQN10pTUajUuLi5s2LCBgIAAypYty48//qj0/qdWhlbQbdKkCX369MlSs+1IsC+EyGwS7Ash/gsS7AvI4Gw8x48f11c7hBBCCCFEFvUhp/FkdRmajefRo0esWbOGgICARPf7+/uzZs0aHj9+nJHTCCGEEEKIj5iRSqW3D5E2GQr2XVxcWLFiBTlz5kx0f86cOXF1deWvv/7KyGmEEEIIIYQQ6ZChNJ4rV65Qp04djIwSv2cwNjamTp06sqiWEEIIIcQnzBDz4wuNDAX7b968oUCBAsmWyZcvH69fv87IaYQQQgghxEcs01dxFUnKULBvYWGBv79/smX8/f11lvkVQgghhBCfFunZN5wM3WiVL1+eo0ePEhwcnOj+oKAgjhw5Qrly5TJyGiGEEEIIIUQ6ZCjY7969O4GBgfTq1StBXv6lS5fo1asXwcHBfP311xlqpBBCCCGE+HjJbDyGk6E0HmdnZ/r06cOqVavo1asXZmZm5MmThzdv3hAVFYVaraZ///44Ozvrq71CCCGEEOIjI0G64WQo2AcYP348tWrVYsOGDdy8eRNfX1+srKyoXbs23bt3p2HDhvpopxBCCCGEECKNMhzsAzRu3JjGjRvroyohhBBCCJHFyABdw9FLsJ+VGKlkcighROaq1KW9oZsghPgEqI94G7oJCiMk2DcUiWyFEEIIIYTIotLUs1+mTBmMjIzYt28fJUqUoEyZMql6LKNSqbhz5066GymEEEIIIT5eksZjOGkK9mvUqAFoFtOK/7UQQgghhBBJkdl4DCdNwf7atWuT/VoIIYQQQgjx4ZABukIIIYQQIlOpZICuwUiwL4QQQgghMpXk7BtOmoL9H3/8MV0nUalUzJgxI13HCiGEEEIIIdInTcH+jh07Et2uUqlQq9VJbpdgXwghhBDi0yUDdA0nTcH+sWPHdL6Oi4vj119/5fr16/Tq1Yvq1auTO3du/P39uXz5MmvXrqVKlSpMmDBBr40WQgghhBAfD5Us7WQwaQr2CxUqpPO1i4sLN27cYNeuXeTNm1fZbm9vT40aNfjiiy/o0KEDBw8eZODAgfppsRBCCCGEECJVMnSbtW3bNlq1aqUT6MeXL18+WrVqxdatWzNyGiGEEEII8REzUqn09iHSJkOz8bx69QozM7Nky5ibm/Pq1auMnEYIIYQQQnzEZDYew8lQz37+/Pk5evQokZGRie6PiIjgyJEj5M+fPyOnEUIIIYQQQqRDhoL9zp078/z5c7p168bRo0d5+/YtAG/fvuXo0aN0796dFy9e8OWXX+qlsUIIIYQQ4uOj0uM/kTYZSuMZMGAAT5484Z9//mH48OEAGBkZERcXB4BaraZTp04MGDAg4y0VQgghhBAfJcm1N5wMBftGRkbMmDGDDh06sGPHDu7fv09oaCiWlpaUKVOG9u3bU6tWLX21VQghhBBCCJEGGQr2tWrWrEnNmjX1UZUQQgghhMhiZICu4egl2BdCCCGEECIpRrKolsFk+DsfExPDqlWr6Ny5M1WrVqVcuXLKvrt37zJlyhS8vLwyehohhBBCCPGRUqlUevvQt2PHjvHll1/i6OhIvXr1+O6773j+/HmCclu3bqVFixZUrFiRdu3aceLEiQRlQkJCmDBhAjVr1sTR0ZERI0bg5+eXoJy7uztdunShUqVKNG7cGBcXF9Rqtd6vDTIY7L97945evXoxe/ZsfHx8sLS01Glo4cKF+eeff9i5c2dG2ymEEEIIIYReXbx4kWHDhlGyZEmWLFnChAkTuHfvHv369ePdu3dKuX379jFp0iRatWrFihUrqFKlCsOGDePatWs69Y0cOZKzZ88yZcoUfvvtN7y8vBg4cCAxMTFKmadPn9K/f3/s7OxYvnw5vXv3ZuHChaxcuTJTrjFDaTzLli3D3d2d0aNHM2DAABYvXszSpUuV/VZWVtSoUQM3NzdGjRqV4cYKIYQQQoiPz4eas79v3z4KFizIjBkzlDba2trSu3dvbt26RfXq1QFYuHAhbdq0YeTIkQDUrl2bBw8esGTJElasWAGAh4cHbm5uuLq6Uq9ePQBKlChB69atOXz4MK1btwbA1dWVXLlyMW/ePMzMzKhTpw4BAQEsW7aMnj17prhgbVplqGf/wIED1KpVi4EDByb5aKVIkSK8fPkyI6cRQgghhBAfMSNUevvQp5iYGHLkyKETw1pZWQEo2SrPnz/nyZMntGrVSufY1q1bc/78eaKiogA4ffo01tbWODk5KWXs7e0pW7Ysp0+fVradPn2apk2b6gT1rVu3Jjg4GA8PD71eH2Qw2Pfx8aFChQrJlsmRIwchISEZOY0QQgghhBB616lTJx4/fsz69esJCQnh+fPnzJs3j3LlylG1alUAPD09AU0vfXwODg5ER0cr+f2enp6UKFEiQee3vb29Ukd4eDgvX77E3t4+QRmVSqWU06cMpfHkyJGDgICAZMs8f/4cW1vbjJxGCCGEEEJ8xPSZxtO0adNk9x87dizVdVWvXp3FixczevRopk6dCkDZsmX566+/MDY2BiAoKAgAa2trnWO1X2v3BwcHK08F4rOxseHWrVsASgf4+3WZmZlhYWGh1KVPGerZr1KlCsePHyc4ODjR/S9fvuTUqVNKvpMQQgghhPj0GKlUevvQJ3d3d3744Qe++uorVq9ezYIFC4iLi+Obb77RGaD7MctQz37//v3p1asXffr04aefflJGGkdERHDt2jWmTZtGbGwsffv21UtjhRBCCCHEpy0tPfcpmT59OrVr12b8+PHKtipVqtCoUSN27dpFly5dsLGxATS98nZ2dko5bWe3dr+1tTWvXr1KcI6goCCljLbn//0U96ioKCIiIpRy+pShYL9GjRpMmjSJGTNm8PXXXyvbtTlOxsbG/Pzzzynm9QshhBBCiKxLpeeBtfry+PHjBGlB+fPnJ1euXDx79gxAya/39PTUybX39PTE1NSUIkWKKOXOnz+PWq3WSVvy8vKiVKlSAGTPnp0CBQokyM338vJCrVYnyOXXhwwvqtW9e3d27dpFjx49qFixIkWLFqVs2bJ07dqVHTt28OWXX+qjnUIIIYQQ4iNlpDLS24c+FSxYkDt37uhse/HiBW/fvqVQoUKAZmbJ4sWLc/DgQZ1y+/fvp06dOsqsOg0aNCAoKIjz588rZby8vLhz5w4NGjRQtjVo0IBjx44RHR2tU5e1tTWOjo56vT7IYM/+5cuXsbS0pGzZskycOFFfbRJCCCGEECLTde3alRkzZjB9+nSaNGlCYGAgf/75J7lz59aZanP48OGMGTOGokWLUqtWLfbv38+NGzdYt26dUka7Au+ECRMYN24c5ubm/PHHH5QuXZrmzZsr5fr378+ePXsYPXo03bp148GDB7i6ujJq1Ci9z7EPoFJnYG3esmXL0qVLF6ZMmaLHJhnWu9hwQzdBCJHFWbQsZegmCCE+Aeoj3oZugmLhzT/0VteIivpbqFWtVrNp0yY2btzI8+fPyZEjB1WqVGHUqFE4ODjolN26dSsrVqzAx8eHEiVK8P3339O4cWOdMiEhIcycOZMjR44QExNDvXr1mDhxIvny5dMp5+7uzqxZs7h79y62trb06NFDWbdK3zIU7NerV482bdrw448/6rNNBiXBvhAis0mwL4T4L3xIwf6im/P1VtfwiiP1VtenIEOJT3Xr1uXSpUtk4H5BCCGEEEIIkUkyFOyPHj2awMBAJk2aRGBgoJ6aJIQQQgghspIPdZ79T0GGBuiOHTsWKysrtm/fzu7duylcuDC5c+dOkG+kUqlYvXp1hhoqhBBCCCE+Th/q1JufggwF+5cuXVI+j4qKwtPTM8G8oaDfJZKFEEIIIYQQqZOhYP/evXv6aocQQgghhMiiJP3GcNKVs+/h4UGvXr1wdHSkWrVq9O3blxs3bui7bUIIIYQQIgtQqYz09iHSJs09+/fv36dPnz5ERkYq286fP4+Hhwdbt27ls88+02sDhRBCCCGEEOmT5tsjFxcXIiMjGTx4MGfPnuXs2bN8++23vHv3jhUrVmRGG4UQQgghxEdMpcd/Im3S3LN/9epVqlWrxsiRI5VtI0aM4NKlS1y+fFmfbRNCCCGEEFmA5OwbTpp79t+8eUPlypUTbK9UqRJv3rzRS6OEEEIIIUTWoVKp9PYh0ibNwX5MTAzZs2dPsD179uzExMTopVFCCCGEEEKIjMvQ1JtCCCGEEEKkxEhy7Q0mXcH+nj17uH79us62Z8+eATBw4MAE5VUqFS4uLuk5lRBCCCGE+MhJ+o3hpCvYf/r0KU+fPk1035kzZxJskxdYCCGEEEKI/16ag/1jx45lRjuEEEIIIUQWJYthGU6ag/1ChQplRjuEEEIIIUQWJTn7hiO3WUIIIYQQQmRRMhuPEEIIIYTIVDJ+03Ak2BdCCCGEEJlKJWk8BiNpPEIIIYQQQmRR0rMvhBBCCCEylaTxGI4E+0IIIYQQIlPJbDyGI2k8QgghhBBCZFHSsy+EEEIIITKVLKplOBLsC/F/kyZMZvfOPSmWGzt+DF/36gFA/94DuHL5KgBNnJvwx8Lfkzyu99d9ueZ+jcHfDmLIsMH6abQQ4qOgPuKdruNOXj9P4zFfAuC19jzF8xcBYN42F0Yvn5rkcc83XKawXQH6zB3F6sNb03VuIfRJZuMxHAn2hXiPiYkJNjY2Se63sLBIdPvxo8e5c/sO5cqXy6ymCSE+Uq8C/BLdbmuVEzNTMyIi3xEUFpxgf0BIYKLHDWnbi9+3ueDj/0qfzRRCZEES7AvxniqOlXFd/Ve6jl28YAlLXZbouUVCiI9dgS5VE91+4retNKpch82ndtN37veprs/CPBsTe4zg24UT9NVEITKVzMZjOJJAJYQeONWri0ql4qzbOdyvehi6OUKILGz/peMA9G/ZVUnrEeJDp9LjP5E2EuwLoQelypSiWYtmACxZKD37QojMs/v8YS7e9cDM1IwpPVP/NEAI8WmSYF8IPfl22GCMjY25cvkq589dMHRzhBBZ2MRVcwDo0bQjpYs4GLg1QqRMpVLp7SMz7Nixgw4dOlCxYkVq1arFgAEDePfunbL/+PHjtGvXjooVK9KiRQu2b9+eoI6oqChmz56Nk5MTVapUoW/fvnh6eiYo9/jxY/r27UuVKlVwcnJizpw5REVFZcp1gQT7QuhNCfsStGnbGtDk7gshRGY56n6Gk9fPY2JswtTeYwzdHCFSZIRKbx/69ueffzJt2jRat26Nq6srU6dOpXDhwsTGxgJw5coVhg0bRpUqVVixYgWtWrXip59+4uDBgzr1TJ8+na1btzJq1CgWLVpEVFQUffr0ISQkRCkTFBRE7969iY6OZtGiRYwaNYotW7Ywa9YsvV+XlgzQFeI91zyu06S+c6L7nOo7MW3GL0keO+jbQezfe4BbN29x4tgJGjdtnFnNFEJ84ib+PQe3+TvoXL8NlR3Kcf3xHUM3SYiPjqenJ4sXL2bp0qU0bNhQ2d6iRQvl8z///JNKlSoxdapmutvatWvz/PlzFi5cSMuWLQF49eoV27Zt4+eff6Zz584AVKxYkcaNG7Np0yYGDhwIwKZNmwgLC2Px4sXkzJkTgNjYWH755RcGDRpEvnz59H6N0rMvxHtiYmLw9/dP9CMkOOHUePEVLlyIjl90AGDJoj9Rq9X/QYuFEJ+is7cvc+DSCYyMjJje5wdDN0eIZH2oaTz//PMPhQsX1gn044uKiuLixYtKUK/VunVrHj9+jLe3Zg0NNzc34uLidMrlzJkTJycnTp8+rWw7ffo0derUUQJ9gFatWhEXF8fZs2f1eGX/kmBfiPdUr1GN63c8Ev2Yv/iPFI8fOHgg5ubmPHzwkIP7D6ZYXggh0kubu/95bWdql018ek8hPgT6S+LRb+h6/fp1SpUqxdKlS6lTpw4VKlSga9euXL9+HYBnz54RHR2Nvb29znEODpqxMtqcfE9PT3Lnzp1gnR4HBwedvH1PT88EdVlbW2NnZ5dofr8+SBqPEHqWL19evuzamXWr1/PnkuU0b9kcY2NjQzdLCJEFuT+8yT9uB+hUrxXT+/6A8w9dDd0kIRKlzx75pk2bJrv/2LFjqa7r9evX3Lp1iwcPHvDzzz9jYWHBsmXL6NevH4cPHyYoKAjQBOTxab/W7g8ODsbKyipB/dbW1koZbbn36wKwsbHRKadP0rMvRCboP7AfFhYWPH3ylN079xi6OUKILGzy6t+IjY2lqWM9Glepa+jmCPFRUavVhIeHs2DBAlq2bEnDhg35809NGu66desM3Ty9kJ59ITKBra0tPXp25y8XV1z+dOHztm0M3SQhRBZ1+8l9Np3cTY+mHfm17zjqftfe0E0SIgF9LoaVlp77lFhbW5MzZ07KlCmjbMuZMyflypXj0aNHtGmj+fsdf0Yd0PTQA0rajrW1NaGhoQnqDw4O1kntsba2TlAXaJ4QvJ8CpC/Ssy9EJundtxdWVpb4+Lxk29aE8/EKIYS+TFk7j+iYaOqUq0abWsmnOAhhCEYqld4+9KlkyZJJ7ouMjKRo0aKYmpomyKfXfq3Nv7e3t+fNmzcJUnHez9G3t7dPUFdISAivX79OkMuvLxLsC5FJrG2s6dW3FwCuLq5Evos0cIuEEFnVoxderD6yDYBpfcZm2sJDQmQ1jRs3JjAwkLt37yrb3r59y+3btylfvjxmZmbUqlWLQ4cO6Ry3f/9+HBwcKFy4MAD16tXDyMiIw4cPK2WCgoJwc3OjQYMGyrYGDRpw7tw55ckAwMGDBzEyMsLJySlTrlGCfSEy0dc9e5ArV05ev37D3Tt3Uz5ACCHSaeraP4iMisSxZAUK5clv6OYIoUOlx3/65OzsTMWKFRkxYgT79+/n2LFjDB48GDMzM7p37w7AkCFDuHbtGlOmTOHixYssXLiQvXv3Mnz4cKWe/Pnz07lzZ+bMmcP27dtxc3Nj2LBhWFlZ0bXrvwPnu3btSo4cORg6dChubm5s376dOXPm0LVr10yZYx8k2BciU2XPkZ2+A/oauhlCiE/A89c+uOzfYOhmCJGoD3WefSMjI1xcXKhSpQqTJ0/m+++/x9LSkvXr12NnZwdA9erVWbRoEVevXqV///7s3buX6dOn06pVK526Jk6cSOfOnfn9998ZOnQoJiYm/P333zqz9NjY2LB69WqMjY0ZOnQov//+O507d2b8+PF6va74ZICuEJmsS7evWLt6Ha/9Xhu6KUKILO7XDQvp16ILOSyyG7opQnw0bG1tmTt3brJlmjZtmuKUn2ZmZowbN45x48YlW87BwYFVq1altZnpplLLEp863sWGG7oJQogszqJlKUM3QQjxCVAf8TZ0ExQHn+/WW10ti7TTW12fAunZF0IIIYQQmUoGjRuO5OwLIYQQQgiRRUnPvhBCCCGEyFRGep5FR6SeBPtCCCGEECJTSRqP4UgajxBCCCGEEFmU9OwLIYQQQohMpe/FsETqSbAvhBBCCCEylaTxGI6k8QghhBBCCJFFSc++yFQRERHs2bWHM6fP8uDeAwIDA0GlWa2uXPmyNG7SGOfmTcmWLZuhm5ohfy5eBkCPXj2wtrZKoXTifH39OLD3ALdu3uLxo8cEBLwlNDQUS0tLHEra49zcmc5ffYGZmVmq61y3Zj1zZ/0GQMGCBThwdH+CMpcvXWFAn4GprvP6HY9UlxUitSzMs9G72Ze0rtmEyg7lyGNti1qtxi/wDVcf3mTnuUNsP7Ofd1HvDN3UDPm55/cAzP/nL4LCgtNVh0qlomGlOtQoXZnqpSpRo1RlShQoCsDgBeNZvnddksf2bv4lq8b+karzeL18hn2vugm2mxibMKBVN3o07UiF4qUxNzXH+81L9l08xuzNS3kV4JdofVbZLWlftwXNqzWgRqnKFM1bCJVKhY//K07fvMjCHSu59vh20u1Ze57i+Ysk2+Yxy6fx+7blqbo+8d9SSf+ywUiwLzLNyROnmPbzdN68eaNss7CwwMjICJ8XPvi88OHo4WPMn7eAX2dNp1btmgZsbcYsW6r549KuY7t0B/vuV9354/f5ytdmZmZky5aNwMBArl5x5+oVd7Zu2soy12Xky5c3xfp8X/myZOHSFMuZmpqQO3fuZMsEBgYSGxtL2XJlU6xPiLT6vLYzLiNnUyB3PmVbaEQYcXFxlChQlBIFitK5QRtmD5hAz9kjOHHtnAFbmzFTemmC/VWHt6Q72LfObsWJ37ak69iIyHdJBuNa+W017y/uj24l2GeTw5p9v67GqXwNACKjIgmPjOCzQiUY2WkAvZw703LC11y+fy3BsVeXHuCzQiWUr8MiNCvWOxQsjkPB4vR0/oJxf81g3jaXZNsXEBxIVExUovvC3oUne6wwHCNJ4zEYCfZFpti1YzdTJv1CXFwcxUsUZ+CgAdRr4ETOnDkBCAkJ4eL5i2zcsJkrl67gfsX9ow729aFAgfwM/nYQ1WpUo3TpUtjktAEgNDSUQwcO89vs3/H09GLi+Ems+DvlnquZv84mPDycipUqcvPGzSTLVXGswvEzR5PcHxAQQLNGLQBo16FtGq9KiOT1bv4lrt//hrGxMfeePWL6hoUcuHScgJBAQBPYOletz7D2fWhcpS4NKtb+qIN9fQmNCMP94U2uPLjB5QfXmTdoss7NUlK2nNrDllN7ktxfxaE8HssOAZobkvetHPM7TuVrEBIeyqD549l6ei8xsTGUKmyPy6g5NKxUmz3T/qZ034YJbmZMjU1wf3iTvw5sZN/FYzzze4FKpaJC8TLMHzKFJo5O/D5oMnefPeLApeNJtrHTLwM5deN8itcqhNCQYF/o3f1795n+y6/ExcVRv0E9fps/N0GajpWVFc7NnXFu7szBA4fwfeVroNZ+OKo4VqGKY5UE2y0tLfniy06ojFT8Mmkqly5e4tXLV+QvkD/Juk4eP8mJYydo4tyEUqU+SzbYT8n+vQeIiYnBxMSEVm1aprseId5Xyb4sy76bibGxMfsuHqPz1EEJ0nSCw0P4x20//7jt56uGbSlsV8BArf1wBIUFY92+DGq1Wtk2s994vdTdu/mXAPi+fc2BSyd09lV2KEeneq0AGL18GhtP7FT2PfD2pP3kfjxec5Z8uewY+9VgJv49R+f4nrO/w+3WJZ1tarWam153aTOxN1eX7qdcsVKM/XJwssG++DjJbDyGIwlUQu8WL1xKVFQUefPlZebcGSnm47ds1YJefXrqbIuKimLNqrX06NITp5r1qelYm3atOzB39m+8ef0m0Xr+XLyMyuUcmTRhcpLnmjRhMpXLOSo59lqXL12hcjlHWjm3BsDD/RrDhoygYd3G1HSszZcdv2Lj+k06f1zj16fVulkbKpdzVD6Sa0taVahQXvncz+91kuXCw8KZOX02FhYW/DB+TIbPu3unphewQaMG5MqVK8P1CaE1vc8PZDPLhvfrl3SfMSzFfPwtp/YkSPEwMzVj1BcDubBwD4E77xC+9xH3Vp7i90GTyZfLLtF6fu75Peoj3vw9dl6S5/p77DzUR7yVHHuthpXqoD7ijddaTc9y3fLV2TNtFa+33SB87yOuLTvM0PZ9kqxP68m6C6iPeCsfybUlMe+/F+mDibEJ3Rt3AGD9sR3ExsXq7G9ZvTEAwWEhuB7cmOD4oLBg/j60GYCezl8k2P9+oB/fu6h3bD6pea+p9lnFdLVffNhUKpXePkTaSM++0CtfXz/OnDoDQPevu2Fllbr89fi/vAEBAQwZOJR7d+8Bmtx1U1NTnj55ytMnT9m9Yw9Lli+iUuVK+r8ANClIv0yeSlxcHJaWOYiMjOTB/YfM+nU2z58954cfxyplLS0tyZ07N/7+/gDkypUTIyNjnf3xaW8MBn87iCHDBqepXdc8riufFypUMMlySxYv5dWrV3z3/QgKFMxYL+iD+w+4f+8+AO3af56huoSIr2Du/LSp1RSA/7V33/E1X38cx183MmTHiD1D7RUjoTRqN7Gp4qdFzardYRWlFB1GUbRIFaVGlapdVTN2VK1qJbagQnZk/v64crm9iZk0xPvpkUfl+z3f8z33Pir53PP9nM+Zvtqf8OiIR+4jt2tONk38jqp3gsPYuFji4uMpXbgEpQuXoGvj1/Ab2Zl9Jw+n69hTdGncjnnvfIaVwYrw6Ajs7bJTuUQ5ZvYbT8kCxRg8e4ypbVhUBCGh10z58Ndv3TALpsOizF9/ygeDMQunMHbRo30QeFy+XvXIkyM3AN9uWWFxvmjeggCcuXKOpKSkVPs4deEMAEXyFKR04RL8eef7h3Ej4iYA2e75GSpZh2b2M49m9iVdHdx/0DTj9HK9uo/Vx8hhozh18hQuLi58NuVT9h7aw54Du1iyfDEvlHqB8PBwBvV7h5s3b6bn0AEIvXmTcWPG0679q2zdvoVd+3ayc+8OOnbqAMCSxUv5+6+7v7yGjhhilu/+3fLv+HXnL6avoSOGPNF44uPiuXjxEgsXLGLyZ8Zf+I2bNCJX7tQX1J48cYqli7/Hw6M4b3R5/YnuDXdn9XPkzEEdnzpP3J9Iipcr18LKyvgr6KeAzY/Vx8IhX1D1hYqEht+i3bjeODYvhWurslTv68fRoJPkdHFj9Zh55HJJ/ydS7q65+GrgJGavXUT+9lXJ0bo8bq3KMf1HfwAGtOpGuaKlTO0HzfqQ/O2rmr6v0a8p+dtXNX0NmvVhuo/xUXVpZEzhOXLmOEeDTlqcT/nZfr9g3Drb3XPli5Z+pPvXrVQTgGNn/7xvu6l9PuTait+5vT6IK8sOs+7jhXSs18r0/5OImNO/DElXwUHBgHE2vljxYo98/eGDh9m9y7j4btLnE2n8SiOy3fnlUb5Ceb6aNxsXFxdu3LjBksWWj5GfVGxMLM1bNmP4yGGmgNrFxZlhHwzlhVIvkJyczC9b0l7Mml6aNWlB5XKeVK/iRdPGzZj86RRux96mcZNGjB0/JtVrkpKSGDdmHImJiYwYPQIbG5snGkNCQgLrf94AgF9T3yfuT+ReZYuUBIyz8Y8y+5uiTgUvfL2MaSUdJ/Zl5Y51ptnmQ6eP0mhoR0LDb5EvZx4GtO6efgO/w9HegYW//MCAL0dx7ZYxtTAsKpyBs0ZzNOgkVlZWtH3JL93vm1FyOrvR7M6TlgWbUq/0c+7aJQBK5C+KrU3qJYDLFbn7ASd/rgdXDUvhWbICrWsb1wR9s3nZA9s62NkTG3ebfDnz4OdVnyUjZrL102W4Oro89D3lv6U0nsyjYF/S1a1btwBwcXF5rH+QWzYbA+nyFcpRu45lfedcuXPRrv2rAGzeuOXxB3of3Xt2S/X4y/WNTyrO/PXogUmK308E8vuJwAem8OTI6UauXLlwcHAwHWv8SiP6D+qHg6NDqtd8v2QZx4+dwK+ZHzW8qj/2GFPs3rXHlJ7UvKWq8Ej6SpltvxkR9ljXv+rTFIADfx5h88HtFuev3fqHOesWAfCaT8akoE1cOjPV42v2GKvZVCj2aDPb9zI0KoShUaH/LIWnY/1W2NnaEZ8Qz5J7Ft7eK+V9drR34O3mnS3Ou7vlouudBb4AzvaOD3VvJ3tHvhs+A+ts1hw6fZR561OfyFm9ZxNtxvYkV5sKOLUwPsUp8j8vPls+h8TERF6uXIvlo2Y/1D3lv2eVjn/k0TyVOfvXr19n9+7dBAUFmYJHNzc3PDw8qF27Nu7uqS+6kmffyRPGPP0aXjXSbOPlXYP5c/05d/Yc0dExODjYp9v9XV1dKVS4UKrn8uQxzlKFhz9ebexHsWjpQtPfb/xzgx9/WM28r+fz26/bGT9xHI1faWTW/tq1a3w5fRbOzk68+/7gdBnD2jspPC+UeoGy5cqkS58i6aVqyQoAbDuSdgnGXwN3M6Jjf0oV8sAhuz3RsTHpdv8b4TcJDjmf6rlLN0IAyOHkmm73y2hdGhknUTYc2Mb1WzdSbXPkzHHWBmyhea1GTOw+jKTkZJb8+iPh0ZF4l/Hki7fHkt3WztQ+6SEWEWezysaS4TMpW+QFbkbcosOEvhYLg1PcuwYixYXrlxkydzzBIeeZNWACjavVpVE1H7Yc2vEQr1rk+fBUBfvx8fF88sknfP/99yQmJuLu7o6rq/GHZVhYGNevXydbtmx06NCBYcOGYW39VA1fwFRHPzw8nOTk5Eee3U/Jw89zn02j8uYz1pJOTk7m1s2b6RrsO6Yxaw5gZ2f8JZaQkJBu93sYuXLnokfv7pQsVZKBfQcx6oPRVPasbLax1qSPPyEyMpKhI4aQ2z33E98zPCyc7b8Zf1mqtr5khBvhxn/rOZwfLyB2dzWm2aUE1qm5+M8VAKysrMjtkpPzsZce616piYiOTPNcbNxtAGysn43Ut7JFXqBG6SoALNhsuTD3Xl0/G8yGCYvxKlOFL94eyxdvjzWdS0hMYMCXo5k1YAIAtyLv/9TGYDCw4P2pNK/ViKiYaJqPepO/LwU/1muYvXYh77d7i+L5i9C8ZiMF+08hpd9knqcqWp42bRpr1qxh9OjR+Pr6WlRyiYyMZMOGDXz2mbFu+3vvPXlZQUlfxT2MuyPGxcVxNvis6ftHdfv27fQcVpbwcr26FCiQn8uXr7Bx/Ua6vGl8jL5/3wG2bvmVEiVL0LxlM6KjzHeQjI+PB4wfjlLO2drZ3vfD8sYNG4mLi8Pa2pqmzZ6dvGN5dpw8/zcA2W2zP3LVlnvdO5Msjyeltv6N8Jv8vPf+a5JCI25RZ3Br3mzyGm1f8qNkgWIkJCZy6K+jTP1hnmn9AsBfDwjcZw+YyOsN23A77jatx/Zg9/EDT/Q6Dpz+neL5i+CRv8gT9SMZQ9V4Ms9TFeyvWbOG4cOH06ZNm1TPOzk50a5dO6ysrJg6daqC/adQ9RrVMBgMJCcn89u27Y8c7OfIkYOzwWcJuZL2bF3KBlwGgwG3e+q+Z7M2LuS93weFiIi0Z+OeBXny5uHy5StcvHC3XveVy8bZyzN/n6GOt0+a1165EkKtGrUB+OjjsbRs3SLNtmt+NKbw1KpdK83KPyJPYvvRvSQlJWFlZUWLWo357MKj5VpfD7tBGUpSJE/aZWgL5TaWnk1KSuKf8FDT8YRE49O57DZpf1BwdXg+FnpaWVnxegPj79yl29YQnxD/wGviE+L5et13fL3uO4tzre9suhWfEM/B00fT7GPa22Pp3ex14hPi6TChr2biRTLQU7XKISoqinz50t4VNEW+fPmIior6D0YkjypvvrymEo1Lv/ueyMiHC65TSrql5IYfPHAozU1j9u8zzv4ULVbULIUn5UnQ1avX0rzHyeOW5eTSQ8rjyYzY6OZely5dBjBbuJvegoOCOfbHMQBaKoVHMsilf66w/s4uqf1bvomzg9MDrjB3+G/j/6N1K9ZMs019T+OH29MXg8zy9W9FGdfd3G833mqlMmZjp5SKQU9LSkOjqj4UzG38vbtgc+pVeB5Fx3otAfh579Y0U50mdh/OwNbdSUxMpMung1m9e+MT3xegRqnKAASHXEiX/iR9qRpP5nmqgv0qVaowZ84cIiLS3lwlMjKSOXPm4OnpmWYbyVz9BvTF1taWqyFXGfb+iAem5GzasJmFC4xVMxo1bggYZ6m3bf3Nou2Nf26wYtlKAItFqi+UMpbyO/7Hca5ft9xhdt3a9YSEpP3E4Ek4ORmrTkSEP/rGQCketBZg3dr1XL+zc65ntbv//7ds3cJU5Se1r7fe7g1AgQL5TcfuN6ufUlvfxcWFuo+5V4LIwxi54FNi42IpnKcAS4bPxO4+M+0A7Xya8c6rvQBYuWMdABWKl6Hli00s2uZxy81bTY07cy/f8bPZuT+C7xQCKFXZtMnVvTo1aEORPAUf/QU9hJTNw9yekhKRXRobF+YeP/snh+4zE/8wGlevS9s6fiQmJvLJ8lmpthnZaSDDOvQlKSmJXtOGsjSNyj+Pqnez1yl+J31n3b6t6dKnpC9DOv6RR/NUBfujRo0iODiYunXr0q9fP6ZMmcLcuXOZO3cuU6dOpX///tStW5dz584xcuTIzB6upKFM2dIMHzUMg8HAzu07ad+2Iz//tI6wW3cXa0VERPDLlq1079qTIe8ONeWSV61e1VRy88ORY9iyaQuJicbKDCeOn+Ctnn0IDw8nV65cdHrjf2b3reJZBfc87sTHxzPsveFcvGhcjBcTE8OKZSv56MNxuLhkzC/YEiVLAPDzTz+bxpuayuU8qVzOk9kz51ic69a5O/Pn+nPm7zNmfVy5fIU5X37FhyPHAFCufFl86r6Uvi/gjqSkJNb9vB6AV/yaYGubei1tkfTw+5kT9J0xkqSkJJrVbEjgnI10atCGHM5upjYuDs60ruPLr58tZ/moOTjbG58A7Dq2nw37twHg/+5k2r7U1LSpUtUXKrJ50hJyurgREnqNL1bNM7vv7mMHuPRPCHa2diwd8SXF8hUGwN4uO72admLu4E8JDb+VIa/5+LnTAHRu9Op9N4FK3nKR5C0X+fCNd1I97+LgTC6XHKavlL4cszuYHU+rHn5KH61eNNa2/3bLyoca/6s+Tend7HWzpyLubrkY0v5tfvxwHlZWVny+8qtUdy0e2Lo747oadyDv/+Uo/Dd+/1D3BJjedxzT3h5L7fI1yG6b3XS8kHt+JnYfzsx+4wFjBaaNB7Y9dL8iz4OnKmffw8ODdevWsXTpUnbu3MnKlStNZQ5dXFzw8PCgd+/edOjQIcOCNkkfbdq2xs3VjXFjxxMcFMwHw4wfzhwcHDAYDGZpWAUK5Mer5t1Sm+MnjeOtHm/z56k/eW/wEOzs7LC2tjZd4+LiwtQZk02Vf1JYW1szfOQw3hv0PgcPHKJp42Y4OTkRGxtLQkICrdq0JCkpyTRznZ5at23NkcDfWbzwO1YsW0nOnDkwGAw0bNyQd4ek/sv6365fu870qTOYPnUG1tbWODk5cfv2bWJi7qYflK9Ynukzp2XYTpH79u43rYlo0Srt2X+R9OK/8XtuhN/kq0GTKFvkBRYPmw4Yq90kJyfj4ni3UMPZkAv8emS36fvOnw5k86QleJaswMrRXxFzO5b4hHjTNaHht2g9pgehEbfM7pmYlEi/mSNZOeorXq5ci+BFAYRFheNgZ4+NtQ3zN3xPtmxWdG38Wrq/3nkbllK7fA0Gt+3JW83e4Nqtf0hOTmblznW8//X4h+5nzUf+vFy5lsXxyb1HM7n3aNP3XT8bzLdpVNh5rW5z7O2yk5CYwKJffnio+5YvWpoxnY0/027H3SYmLha3OyVGk5KS+HzFHIbNm5DqtVPeMu4SnJiYyKhOAxnVaWCa96nRrykXr18xfe/s4EjXxq+Z0n/CoiLIls3KbBOt334P4NVxvR/qdch/TzPymeepCvbBGMj17t2b3r31D/ZZV79hPWq+WJO1a9ayc/tOTp/+i1s3b2EwGChQsADly5ejQaP6NGjUwGwGOWfOnCxa+i3fL1nGpg2bOBt8jvj4eIoULYJP3Zfo2r1LmnstNGhYnzlzZzH36/mcOHaCxMRESpcpRbv27WjdthWjRoxO9bonZfwgkcgPK34k6EwQISFXjaVB7+wT8TA+mvARu3ft5vDBw4SEXOVm6E2srKwoULAAZcuVoVGTRjRucndH4YyQ8kGouEdxKlaqkGH3EbnXmj2b2HJ4B10ataOpdwMqFS9LbtecJCcnE3zlPAf/OsqqXRtYtWsDcfFxpuv+CQul1oCW9G3ZhQ4vt6B0oRLY2thw+mIQ6/Zt5dPlswkJTX0Nz+rdG2k8/H980HEA1UtVIptVNo6cOc7stYv4ZtMyvnk/YzazWrBpOdmsstHT93+UK/oChd0LGEuDuubMkPvdT0oKz5ZDO9N8n/7tp4DN5M2Rm9rla1DYPT/2dtkJvnKe7X/sZfbaRew/FZjmtSmTFNmyZUs1fepe2azMf87N+Xkx12+F8mL5ahRxL3jnaYaB89cucfD0UZZuW80PO9dn+LopeQLKtc80hmT9yzATmxj94EYiIk/A/pVSmT0EEXkOJG+5+OBG/5GD/+xJt76q534x3fp6Hjx1M/siIiIikrUojSfzKNgXERERkQylkpmZ56mqxiMiIiIiklmioqLw8fGhdOnS/PHHH2bnVqxYQZMmTahYsSItWrRg2zbLyk8RERGMGDECLy8vPD09GTBgANeuWa6JOXz4MO3bt6dSpUrUq1ePr7/+OsPWnCjYFxEREZEM9azU2Z81a1aqJbTXrVvHqFGj8PX1Ze7cuVSpUoV+/fpx5MgRs3aDBg1i9+7djBkzhs8//5zg4GB69uxptpfOuXPn6N69O+7u7nz11Vd06dKF6dOn4+/vnyGvSWk8IiIiIpKhnoWc/TNnzrBkyRKGDh3Khx9+aHZu+vTpNG3alEGDBgFQs2ZNTp8+zZdffsncuXMBCAwMZNeuXcyfP586deoAULx4cfz8/Ni8eTN+fn4AzJ8/nxw5cjBlyhRsbW2pVasWoaGhzJkzhzfeeCPd97jRzL6IiIiIPPfGjx9Phw4dKF68uNnxCxcucPbsWXx9fc2O+/n5ERAQQFycsSTwjh07cHFxoXbt2qY2Hh4elC1blh07dpiO7dixgwYNzMuO+/n5ER4eTmBg2uVrH5eCfRERERHJUAaDId2+MsLGjRs5ffo0ffv2tTgXFBQEYPEhoESJEsTHx3PhwgVTu+LFi1uM0cPDw9RHdHQ0V65cwcPDw6KNwWAwtUtPSuMRERERkQyVnmk8DRo0uO/5rVu3PlJ/MTExTJo0icGDB+Pk5GRxPiwsDDBu/HqvlO9TzoeHh+Ps7My/ubq6cuzYMcC4gDe1vmxtbbG3tzf1lZ40sy8iIiIiz63Zs2eTK1cu2rZtm9lDyRCa2ZenUlRUFAf2HeDYseOcOHaC48dOcOvWLQBW/7yK4h7F799BGg7sP0iPrj0f2O633b+SI0cOs2MJCQns3bOXnTt2cfT3o5w/d4HY2Fjc3NwoX7EcrVq3on7Demn2eeL4Cbb9+huHDx7mzJkgIsIjcHBwoETJEjRq0pBXX2uLnZ1dqtfGxsYya8ZsNm7YROiNUPIXyE+bV1vT5c3Opi3o/y1gz17e6tGHjp06MOyDoQ98zSJZzaPsHlr33VfZcXSvxfGXK7/I2y06U7NMVfK45SIhMZFz1y6yNXA301bNI+jKuUceV9G8hTi72PJe/1a9rx+HTh9N9VxOZzfeebUXLWo1xiNfERKTEvnrUjDLtq/lix/nExcfl2a/BoOBLo3b0al+ayp7lMPNyYWo2Gj+vBDET3s3M/1HfyJjolK9tqdfJwa27kbJAsUIjbjF6j2bGOH/CbciU5+NdHfLxZ/+2wkOuUCNfk1JSkp64OuWrCk9Z/Yfdeb+fi5duoS/vz9ffvmladY9Ojra9N+oqChcXV0B46y8u7u76drw8HAA03kXFxdCQkIs7hEWFmZqkzLzn3KvFHFxccTExJjapScF+/JU2hewn8ED3smw/q2srCyCebPzBssA+uOPJrBq5Y+m762trbGzs+Off/5h+7YdbN+2g0aNGzLxswnY2NiYXbtu7XpGDP3A7P6OTo7GxTiHAwk8HMjK5T8wZ95s8ubNY3ZtcnIygwe8y55dxq3G7e3tOX/uPNMmf8HlS5f5YPQIi7HGxcUxYdxEcufOTd8Bbz/cmyKSxYSEWta2vpeLgzMO2e25HXebY8GnLM5P6DaM4R37mb6PjInC1tqGckVLUa5oKXr4duTVj3qxfv+vGTLG+IT4VI9X8ijLhgmLKJArHwAR0ZFks8pGtVKVqFaqEp3qt6b++68RGnHL4lp7u+ysHbeABp51TMduRYbh4uCMd1lPvMt60tP3f9R/vz3BIefNrh3WoS8Tuw8HICommvy58tKneWe8y3hSa2DLVD9gfN5rFK6OLrw94wMF+s+5p3VTrYsXLxIfH0+vXr0sznXu3JnKlSszefJkwJiTf2+ufVBQEDY2NhQuXBgw5t0HBASQnJxs9nqDg4MpVaoUAA4ODuTPn98iNz84OJjk5GSLXP70oGBfnlo5c+WkfPlylK9Qnjx58/DRh+PSre98+fKy4Zf1j3RNQkIC7nncadO2NfUb1qd0mVIYDAauXbvGvK/9WbZkGVs2/0LBQgUZ/N4gi2uz22enaTM/XvF7hSqelbG1tSUqKoqfVq/liynTCToTxLsD32PR0m/NfkgE7NnLnl17KFAgP19+NROPEh4cPhRIv7f6s2LZSjp17kSxYkXN7uc/7xvOnzvPhE8+TjV/UOR5kL991fueD5yziSolyvPzvq0WgXGjaj6mQH/Jr6sZ4T+Jc1cvYjAY8C7jyZyBk6hcohyLh02n2Ou1CI+OSOUOTz7Gf3PM7sDacQsokCsfp87/TbfJ7xJw4hAA9T1rs+C9qVQuUY4lI2byyvDXLa4f1WkQDTzrkJSUxAj/T5i9diHh0RHYWNvQpo4vswdMoFi+wsx75zMaDGlvus7V0YVRnQYRnxBP6zE9WLdvK4Xc8/PzuG+p+kJF3mjYlvkblprd66WK3nRu9Cpz1y9h38nDj/HuSFbytJbeLFu2LAsXLjQ7dvLkSSZOnMjYsWOpWLEihQsXplixYmzcuJGGDRua2q1fv55atWqZqur4+Pgwa9YsAgICePHFFwFjEH/ixAl69Ohhus7Hx4etW7fy/vvvmyYH169fj4uLC56enun+GhXsy1Opbj0f6je8+5ju0qXLmTgao9c6vMaoMSMt6t/myZOHESOHERMdw0+rf+L7pcvo0+8tsmfPbmpT2bMy6zf9TK7cucyudXR0pGOnDjg6OjBqxIf8cfQPDh08RPUa1U1t9u3dD0DX7l3xKGH8xF+1midtXm3Nom8Xc2DfAbNg/+KFi/jP/YbqXtVp2twv3d8HkaygcolyVClRHoBvt6ywON+pfmsATl8M4o1PBphmpZOTk9l78jCtxnQneFEAOZzdqFflRdbs2fSfjLu7b0eK5ClIQmICrcf24NT5v03nfg3cTadJ/dkx5QeaVH+ZhlVf4pfDO82u/1/9VgB8s2k5nyz70nQ8PiGeZb/9RHZbOxa8P5X6nrVxc3I1pefUKlcNh+z2LN++lnX7jD+bL16/wsgFn955UlDbLNi3zmbNrP4f809YKMPmTciot0Pkibm4uODt7Z3qufLly1O+vPHnRP/+/XnvvfcoUqQI3t7erF+/nqNHj7J48WJTe09PT+rUqcOIESMYOnQodnZ2TJ06ldKlS9O4cWNTu+7du7N27VreffddOnbsyOnTp5k/fz6DBw9O9xr7oAW68pTKli1bZg/BQsVKFe77j7Bl6+YAxMbEEhwUbHauWLGiFoH+vXyb+po+3Z84ftLsXNidtQqFChU0O16kqPGx4a2bt8yOT/r4ExITExkxcljaL0bkOdelUTsArt68zvp9lmk4eXMY83KPBp1MNf3kbMgFboTfBIyz7f8V3xrGdUGbD+0wC/RT7PxjHwdP/w5A50avWpzPmyM3AIFnjqXa/6HTf5j+7mBnb/p7Lhdj2mPQFfPUnr8vnwUgt0tOs+OD2/akQvEyDJs/MdV0Inn+PO2lNx+kWbNmjBs3jp9//pnu3btz+PBhZs6caTETP23aNF588UVGjx7Nu+++S7Fixfj666+xtr47v160aFHmz59PSEgIvXr1wt/fnwEDBtCtW7cMGbtm9kXSiaubm+nviYmPlptqY2ODo6Mjt27dIulf16b0e/HiJbPjF85fvHP+7mKeX7ZsZeeOXXTt3pUSJUs80hhEnhfZrLKZZriX/LqaxKREizZnrxrrZlfyKIuVlZVFwF8sX2FTABz4d+qBc0Yomtf4of/PC2fSbHPq/Bmql6pMo6ovWZw7G3KRMkVK4lmiQqrXVitVETCuJbh84+5Cw5QPNh75i5i1L5G/qNl5gELu+Rn9+iACThyySO2R59fTmsaTGm9vb/7880+L4+3ataNdu3b3vdbZ2ZkJEyYwYcL9n2hVrVqV5cuXP9E4H5Zm9uW5FHrzJu3bdsS7Wi28q9WiuW9LPvpwHH+d/uux+zx0wJg3a21tTdFiRR7Q2tzff50xVRsq+YJ5kO7lXQOABfMXmJ4YHAk8wqqVP2IwGPCq6QVAdHQMn038nHz58tG7j+VCIxEx8vWqZ5q5Ty2FB2DehqUkJSVRqpAHi4ZOp2jeQoBxdrJm2aqsHjMfgK/XfcfJ84//c2PPF2sIW32S6J//JmjhHhYNnU7t8jXSbJ+cnAwYP7CkxfrOk9F8OfOQ09nN7NzcDUsAeLPJawxt3xcXB+OaHhtrG16r25ypb31IUlIS73093uy6gBOHiI6NoXXtV2jqbaxxXjB3fsZ1fR+ArYG7TW2/ePsjstva8fZ0y+IBIvLf08y+PJdiY2I5dfIULi4uxMTEcP7cec6fO8/qVWsYOHgAXbp1fqT+oqOi8Z/3DQANGtV/5EWxM6cbc2fz58+Hd03z3MEXa9eiZi1v9gbso1WzNjg4OJjKgr36WltTvv5Xs74iJCSEyV98joODPSKSuq6NXwPgyJnj/H7mRKptDp0+SrfJ7zJ7wET+V78V/6vfisiYKGyyWWNna0fQlXO8+9VHTFn59RONpVa5aqa8+OL5i1A8fxFeb9iGaavmMXj2GIv2565eulMN6IU0+yxXtJTp7/lz5TVLo5m2ah7F8xWhX8uuTOoxnEk9hnMrMgxneyeyZctGwIlDfLxkuikvP0VYVDgTls5g/JtD+Hn8t0TGROFk7wgY38dFv/wAwCs16tGmji/Tf/TnyJnjj/u2SBb0LM3sZzWa2ZfnirOzM127dWHpiu/YH7iXnXu3s+9wAP4L51HFszKJiYlM+Xwq63/e8Ej9jhv7MVdDruLk5MTAdwY+0rU/rFjFtq3bAHhv2HvY2JqX7TQYDEybOZXXu3QiT948xMXFUbhwYQYM6mcqu/n3X2dYvPA7atd5kYaNjLNuSxYvpblvS6pX9qL5Ky34btGSRxqXSFaUw9mNZndmpr/dnPqsfopvN6+g1ZjuXLv5DwBO9o7Y2Rr3wnCwsyensxu2No++mC427jZf/rSAlwa3wal5KXK0Lo9Ds5JU7fMKPwVsBmBQmx5mZT9TbD60HYD6VWpT9YWKFudfqVGPSh5lTd873wnIUyQlJTFo1oe8M2esqbSnm5OraZ2Us70j7m6pry/6eMl0+nwxnBPnTmNrbcPVm9f5et131H+/Pbfjb2NnY8fMfuMICb3GqAWfAdDAsw4B038i+ue/CVkeyPS+4/7TNQ7y9HjWc/afZYbklGeCAkBsYnRmD0FScenSZfwaNQWebFOt+4mPi6dHt14cOXyEvPnysvGX9WluWHWv+XP9mT51BgaDgU8+n0QT38YPvCbFwQMH6dOzL3FxcbT/X/vHXlTbrXN3jv1xnJWrl1OkaBG+mv01s2bMpkDBAlSvUY2DBw5x+dJl+g54m15vPXhTMclY9q+UenAjyRBvt+jCl/0/Jj4hnoIdq3P91o1U21lns2b2wIn08O3InuMHGe4/id/PnMAhuz0+Fb35rOdICucpwI6je2kwpAMJiQnpNsZlI2fzWt3mRMZEUahjDcKiwk3nnB2cODn/NwrmzseFa5fp/+UothzegZXBimY1GzKj7zhcHJxMH0K8+zdn/6lA0/V5c7izZqw/3mU9WbB5OVNWzuXMlbPkz5mXV19qyujXB+GQ3Z6JS2cywn/SI417bJf3GP36IF6fNIDvtq7i5covsnnSd0TfjmFtwC+8ULA43mU92XJoB42H/S993iy5r0fZWC6j/Rn2x4MbPaTSrpYfdCVtmtkXucPG1oa+/Y0bUF0Nucqpk5ab7PzbimUrmT51BgDvDnnnkQL948eOM7DvYOLi4qjfsD5Dh7//WONeu2Ythw4e5s3uXSlStAg3b95k7px55Mmbh2UrlzJuwkcsWb6YXLlyMXfOPG7evPngTkWyqJQqPBsObEsz0AcY0r4PPXw7cvzsn7z8Xjt2HN1LWFQ4V25cZdlvP9FgSHtibsfiU6kmPXw7pusYh94pVelk72i2+RUYN9Bq+WE3rt38h8J5CrB67Hyi1v5FxE9/snSEMR3ww4WTTe3/vbPtwqHT8C7rybwNS3nzs3f4I/gk0bExnLl8lk+WfUnvL4wTDkNe62OWDvQgJQsWZ8hrb/Hb7wF8t3UVAJ/3GomNtQ2Nh3XijU8GUGtgCzYd/I1G1XxoVrPhA3qUrMaQjn/k0SjYF7lHxUp3K1RcvHDpPi1h7U8/M2HcRAD69H2LN7pYbmCTltN/nqZPz75ERkZSq3YtPv180mOVGw0Pj2DK59MoVLgQ3Xq+CcDePXuJj4/Hr6kvLq4uAOTIkQO/Zr7ExcWxN2DfI99HJCsoU6QkXmWqAPDt5pX3bTuwVXcAZq1dmOpOtn9dCjbltbd8sUm6jvNsyAVT6tC/q9+AcT1BuR71GLtoKntPHubc1YscDTrJFz/Op3Lvxly5sytvXHwcZ6/endktW+QFGlerC8DUH+ameu/Fv/zAP2GhZMuWjeY1Gz30mGf2G4eVwcq0KDdfzjxUK1WJ/aeOmJ4sJCcnM3PNAgDTIl95fijYzzxaoCvyGDZv3MKHH4whKSmJzl3f4K2+vR/62uCgYHr36ENYWBhVq3kydfpkizz9hzVj2gxCb4Ty0fgx2NkZc4kvX74CQMFCBczaptTlv3LnvMjzJmVh7o3wm6zduyXNdjmd3chzpx59cMiFNNul1JwvdqdSz3/pRvhNxiyczJh7ZvFTVC1pTHEI/Ps4cfFxpuNli9xd1Bscct7iuhRBV86T2zUnxfI93Otq59OMJtVf5tNls02ViVKqF/37Pil1+Yvm+e/fM5HnlWb2Re7xx9G79bL/HSyn+G3bdoYPGUFiYiLt2r/Ku0Peeej+L5y/QM9uvQm9EUr5iuWZOWcG9vaPVznn+LHjrFz+A/Ub1uelupb1tG/fjjP7Pjb29mPdRyQrsLKy4vUGbQBYum1NqrP1KZLuWcpWJE/qPwfgbs37iJjIdBqlUbF8hR/qw0ZqbKxtaPuScefsJdtWm51LSr67V0CRPOab9N3L9Lqiox54Pyd7R6a89SEXrl1m7OIpFuez31nQnMLeNrtFG3k+aIFu5lGwL8+V+61Hj4+PZ9aM2QC4u+embLmyFm0C9uzl/cFDSEhIoEWr5qZqOA8j5EoIvbr15vq165QuXYo5X8/C0dHxwRemIikpiY8/moCtnS1Dhr1ndq5AgfyA5U68x48Zy+AVKJh28CKSVTWs+hIFc+cDHlyF51ZkGOfupL90a9Ih1YX6BXPn55UaLwOw79SRdB3rhG5DAYiOjeHXI7sf0NrcmM7vUDB3Pq7cuMqCTeYb9txbZrSnX+oLZJvVbGjag2DfPQt70zK287sUcs/PoNljiI6NMR1Pef88S1YwC85qlK4M3N20TJ4nhnT8kkehYF+eWjdv3jR9hYfdrUYRERFhdu7fO1tWLudJ5XKezJ45x6LPNi1eZcnipZw7e84U+CcmJnL4UCC9uvUm8LDxl9uAwf0tfsEHHj7CoP7GBbWv+DVh7PgxDz3DcONGKL26v8Xly1fwKOHBnPmzTfn0j2PFspUcP3aCXm/1JP+d4D6Fdy1vbGxs+GXzL+zeaQwUdu3YxdYtv2Jra4v3nU24RJ4nKQtzj5/9k4Onf39g+zk/LwLAq0wVVn04l1KFPABjlZ66lWqxccIiXB1diE+IZ9ZP31pcH7wogOQtF/nmfcvZ7t8mr2RYh76UL1ba7OdMlRLlWfXhPDrWawXAJ8tmcfOeGvkphrR/m9Z1fMlxz4ZZpQp5MO+dzxnRsT8JiQn0nDqE8OgI8zGFnGfTwd8AGNS6BxO6DTOV2XTM7kCXxu1Y8P5UY9sr501lQNNSoVgZ+rd6kw37t7Fq13qzcyGh1wj8+xhF8hRkZKeBZLPKhkf+ogzvYCwnun7/r/ftW0TSj0pv/otKbz49KpfzfKh267eso+A9s9Up1731dm/69HsrzT5tbW1xdHQgMjKK+HjjI31ra2v6Dnibbj3etLhPjzd7cWDfAQBy5HDD6j47WA4Z8T6v+N5dtDdn1lemDx9OTk6m/PrUNPFtzNARQ9I8f+NGKC2btsI9d26W/7gMGxvLfP/ZM+cwZ9ZXAGTPnp3Y2FgAld58Sqj05n/L2cGJkGWBOGS3Z8jcj/ls+ewHXpPNKhtLP/iSdj7NTMeiY2OwsbbGxtr4by4uPo6eU4ewcIvlYt/gRQEUy1eYBZuX8+Zn76R6LqWP8OhIHOzscch+N6Vv+o/+DJw1OtWxbft8BS9XrgVAZEwUBgw42htr10dER9Jz6hCW/fZTqtfmy5mHrZ9+b1ZpJzwqAhfHuxsBhoRew3fEGw/cFGvn1FVUL1WJCj0bcuZOLv69GnjWYePExVhnsybmdiz2dsYUHpXe/O88TaU3z0Q8uMLdwyrhXCbd+noeaIGuPFdGjRnJkcAjnDh+ktDQUCLCI7Czs6NY8WJUq16V1zq0o0TJEqlem3zPE4SbN2/d9z63/5Ufn5x09zN1ZGQkkZFp5/je7xzAlM+mEBEeweRpn6ca6AP06fcWTs5OLFuynCtXrlC4cGE6vt6BTm/oF6w8f16r2xyH7PYkJiay+E5ZyAdJTErktXFv0bqOL10avUqN0pXJ7ZKT+IQEgkMusO3IHqav9ufEudOPPJ73546nUVUfvMpUIV8Od3I6uxGXEM+p83+z+/hBvl7/nVlt/H+bvtqfkNBrVC9VmXw53UlKTuJY8Ck2HPiN6avnc/F62ovwQ0KvUe1tP3o17USbOr5UKFYaV0dnwqLC+fvSWdbt/5UZq/35Jyz0vq/hzSbtqVPBi7GLpqYa6ANsDdxF05FdGNflfSp5lOHazX9Ytn0tw+dPfKj3SbIWVdHJPJrZ/xfN7ItIRtPMvoj8F56mmf2giD/TrS8P59Lp1tfzQDP7IiIiIpKhNLOfeRTsi4iIiEiGUsnMzKNqPCIiIiIiWZRm9kVEREQkQymNJ/Mo2BcRERGRDKVgP/MojUdEREREJIvSzL6IiIiIZCgt0M08CvZFREREJEMpjSfzKNgXERERkQylmf3Mo5x9EREREZEsSjP7IiIiIpKhlMaTeRTsi4iIiEgGU7CfWZTGIyIiIiKSRWlmX0REREQylOb1M4+CfRERERHJUKrGk3mUxiMiIiIikkVpZl9EREREMphm9jOLZvZFREREJEMZ0vErPW3YsIE+ffrg4+NDlSpVaNmyJStXriQ5Odms3YoVK2jSpAkVK1akRYsWbNu2zaKviIgIRowYgZeXF56engwYMIBr165ZtDt8+DDt27enUqVK1KtXj6+//trifulJwb6IiIiIPJcWLFiAvb09w4YNY/bs2fj4+DBq1Ci+/PJLU5t169YxatQofH19mTt3LlWqVKFfv34cOXLErK9Bgwaxe/duxowZw+eff05wcDA9e/YkISHB1ObcuXN0794dd3d3vvrqK7p06cL06dPx9/fPsNdoSM7IjxLPoNjE6MwegohkcfavlMrsIYjIcyB5y8XMHoLJ1ZhL6dZXXvuC6dZXaGgoOXPmNDs2atQo1q9fz4EDB7CysqJJkyZUqFCByZMnm9p06NABZ2dn5s6dC0BgYCAdOnRg/vz51KlTB4CgoCD8/PyYMmUKfn5+AIwePZpdu3axceNGbG1tAZgyZQpLly5l9+7dpmPpSTP7IiIiIpKhDAZDun2lp38H+gBly5YlMjKS6OhoLly4wNmzZ/H19TVr4+fnR0BAAHFxcQDs2LEDFxcXateubWrj4eFB2bJl2bFjh+nYjh07aNCggVlQ7+fnR3h4OIGBgen62lIo2BcRERERuePQoUPkzZsXJycngoKCAChevLhZmxIlShAfH8+FCxcA4yx+8eLFLT6MeHh4mPqIjo7mypUreHh4WLQxGAymdulN1XhERERE5JnRoEGD+57funXrY/d98OBB1q9fz9ChQwEICwsDwMXFxaxdyvcp58PDw3F2drboz9XVlWPHjgHGBbyp9WVra4u9vb2pr/SmYF9EREREMpThGSi9GRISwuDBg/H29qZz586ZPZx0o2BfRERERJ4ZTzJzn5bw8HB69uyJm5sbM2bMwMrKmOnu6uoKGGfl3d3dzdrfe97FxYWQkBCLfsPCwkxtUmb+U2b4U8TFxRETE2Nql96Usy8iIiIiGcqQjn/SW2xsLL179yYiIoJ58+aZpeOk5Nf/O58+KCgIGxsbChcubGoXHBxsUS8/ODjY1IeDgwP58+e36Cvlun/n8qcXBfsiIiIi8lxKSEhg0KBBBAUFMW/ePPLmzWt2vnDhwhQrVoyNGzeaHV+/fj21atUyVdXx8fEhLCyMgIAAU5vg4GBOnDiBj4+P6ZiPjw9bt24lPj7erC8XFxc8PT0z4iUqjUdEREREnk9jx45l27ZtDBs2jMjISLONssqVK4etrS39+/fnvffeo0iRInh7e7N+/XqOHj3K4sWLTW09PT2pU6cOI0aMYOjQodjZ2TF16lRKly5N48aNTe26d+/O2rVreffdd+nYsSOnT59m/vz5DB48OENq7IM21bKgTbVEJKNpUy0R+S88TZtq3bh9Nd36ymWX98GNHlL9+vW5dCn1Db+2bt1KoUKFAFixYgVz587l8uXLFC9enHfeeYd69eqZtY+IiGDixIls2bKFhIQE6tSpw8iRIy2eFhw+fJhJkyZx8uRJcubMSadOnejZs2e67yGQQsH+vyjYF5GMpmBfRP4LCvYFlMYjIiIiIhnsWSi9mVVpga6IiIiISBalmX0RERERyWCa2c8sCvZFREREJEMp1M88SuMREREREcmiNLMvIiIiIhkqo8pKyoMp2BcRERGRDKZgP7MojUdEREREJIvSzL6IiIiIZCjN62ceBfsiIiIiksEU7mcWpfGIiIiIiGRRmtkXERERkQylajyZRzP7IiIiIiJZlIJ9EREREZEsSmk8IiIiIpKhDFqgm2kU7IuIiIhIBlOwn1mUxiMiIiIikkVpZl9EREREMpTm9TOPgn0RERERyVAqvZl5lMYjIiIiIpJFaWZfRERERDKYZvYzi4J9EREREclQCvUzj9J4RERERESyKM3si4iIiEgG09x+ZlGwLyIiIiIZStV4Mo/SeEREREREsigF+yIiIiIiWZTSeEREREQkQxmUs59pDMnJycmZPQgREREREUl/SuMREREREcmiFOyLiIiIiGRRCvZFRERERLIoBfsiIiIiIlmUgn0RERERkSxKwb6IiIiISBalYF9EREREJItSsC8iIiIikkUp2BcRERERyaIU7IuIiIiIZFEK9kVEREREsigF+yIiIiIiWZSCfRERERGRLErBvoiIiIhIFqVgX0REREQki1KwLyIiIiKSRSnYFxERERHJohTsizyGc+fOMXr0aFq2bEm5cuVo1qxZZg9JRLKgM2fO8Oabb1KlShVq167Np59+SlxcXGYPS0SeIdaZPQCRZ9Fff/3F9u3bqVy5MklJSSQnJ2f2kEQkiwkLC6NLly4UK1aMGTNmcPXqVSZNmkRsbCyjR4/O7OGJyDNCwb7IY6hfvz4NGzYEYNiwYRw7diyTRyQiWc33339PVFQUM2fOxM3NDYDExETGjh1L7969yZs3b+YOUESeCUrjEXkMVlb6pyMiGWvHjh3UqlXLFOgD+Pr6kpSUxO7duzNvYCLyTFHEIiIi8hQKCgrCw8PD7JiLiwvu7u4EBQVl0qhE5FmjYF9EROQpFB4ejouLi8VxV1dXwsLCMmFEIvIsUrAvIiIiIpJFKdgXERF5Crm4uBAREWFxPCwsDFdX10wYkYg8ixTsi4iIPIU8PDwscvMjIiK4fv26RS6/iEhaFOyLiIg8hXx8fNizZw/h4eGmYxs3bsTKyoratWtn4shE5FmiOvsijyEmJobt27cDcOnSJSIjI9m4cSMAXl5e5MyZMzOHJyJZQIcOHVi0aBF9+/ald+/eXL16lU8//ZQOHTqoxr6IPDRDsrb+FHlkFy9epEGDBqmeW7hwId7e3v/xiEQkKzpz5gzjxo0jMDAQR0dHWrZsyeDBg7G1tc3soYnIM0LBvoiIiIhIFqWcfRERERGRLErBvoiIiIhIFqVgX0REREQki1KwLyIiIiKSRSnYFxERERHJohTsi4iIiIhkUQr2RURERESyKAX7IiIiIiJZlIJ9EZEsatiwYZQuXZqLFy9m9lBERCSTKNgXEbnH8OHDKV26NN7e3sTFxT1RXzNmzKB06dLs27cvnUYnIiLyaBTsi4jcERkZycaNGzEYDNy6dYtffvkls4ckIiLyRBTsi4jcsWHDBqKjo+natStWVlasXLkys4ckIiLyRKwzewAiIk+LlStXYm1tTY8ePTh16hQBAQFcunSJggULWrQ9cOAA33zzDUeOHCE8PJxcuXJRsWJFunbtSvXq1XnjjTfYv38/AJ07dzZdV7BgQX799VcASpcujZeXF4sWLbLov379+gCmtgDBwcGsWLGCgIAALl++THR0NAUKFKBRo0b06dMHR0fHdH0/RETk2adgX0QE+Pvvvzly5Ah169Yld+7ctGrVioCAAFatWkX//v3N2n777bdMnDiR7Nmz07BhQwoUKMDVq1c5dOgQmzZtonr16rRu3RqA/fv307p1a9MHBmdn58ce45YtW/jhhx/w9vbGy8uLpKQkfv/9d+bOncuBAwdYvHgxNjY2j/8miIhIlqNgX0QETCk7LVu2BKBRo0aMHTuWVatW0bdvX6ysjFmPp06dYtKkSbi7u7N06VIKFSpk6iM5OZlr164B0KZNGy5dumQK9r29vZ94jC1btqRr167Y2tqaHZ85cyYzZsxgw4YNtGjR4onvIyIiWYdy9kXkuRcfH8+aNWtwcnKiYcOGADg6OtKwYUMuX77Mnj17TG2///57kpKSGDRokFmgD2AwGMibN2+GjTNv3rwWgT7A66+/DkBAQECG3VtERJ5NCvZF5Lm3detWQkNDeeWVV7CzszMdb9WqFYDZQt2jR48CUKdOnf90jGB8crBy5Uo6deqEl5cXZcuWNZUJBUxPFURERFIojUdEnnspwXxKcJ+iVq1a5M2bl61bt3Lr1i3c3NyIjIzEYDDg7u7+n49z/PjxLF68mPz581O/fn3c3d1NM/0zZ8584n0BREQk61GwLyLPtStXrrB7927gbjpMan766Sc6d+6Ms7MzycnJXL9+/YlTdgwGAwkJCamei4iIMFvMe+PGDb777jtKly7NsmXLsLe3N527fv06M2fOfKKxiIhI1qRgX0Sea6tWrSIpKYlq1apRvHhxi/OJiYn8+OOPrFy5ks6dO1OpUiWOHTvGrl27aNu27X37TlnUm5SUlOp5V1dXrl69anH84sWLhIeHmwX7Fy5cIDk5mRdffNEs0Ac4ePDgA1+niIg8nxTsi8hzKzk5mVWrVmEwGPjkk08oXLhwqu3Onj1LYGAgf/zxBx06dGDZsmVMmzaNmjVrmtXgT6nGkzLj7+bmBhifHqSmQoUK7Nq1i/379+Pl5QVAXFwckyZNsmhboEABAAIDA0lKSjJ9kAgJCWHKlCmP9waIiEiWp2BfRJ5be/fu5eLFi3h5eaUZ6IOxjGZgYCArV65k7NixjBgxgvHjx9OsWTMaNGhAwYIFuX79OgcPHqRu3bp88MEHAHh7e2MwGJgyZQp//fUXzs7OuLi4mNKF3nzzTXbv3k2vXr1o2rQp9vb27N69GxcXF4s1AXny5KFJkyZs2rSJtm3bUrNmTW7cuMFvv/1GzZo1OX/+fMa9USIi8sxSNR4ReW6lLMxN2QArLX5+fmTPnp1169YRGxvL66+/zrfffou3tzc7d+5k/vz57N69mzJlyuDr62u6rmTJkkycOJEcOXKwePFivvjiC/z9/U3n69Spw7Rp0yhcuDBr1qxh48aN1K5dG39//1RLbE6cOJFu3boRFhbG4sWL+f333+natSuTJ09Op3dERESyGkNycnJyZg9CRERERETSn2b2RURERESyKAX7IiIiIiJZlIJ9EREREZEsSsG+iIiIiEgWpWBfRERERCSLUrAvIiIiIpJFKdgXEREREcmiFOyLiIiIiGRRCvZFRERERLIoBfsiIiIiIlmUgn0RERERkSxKwb6IiIiISBalYF9EREREJIv6P+4I7/2TkRmFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(lg_over)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11M3GXcvLVtT"
      },
      "source": [
        "## CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ca_m0lTALM36",
        "outputId": "929997b6-b209-475a-86f8-9a88975dbcd9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f169c6741c0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "cb_over=CatBoostClassifier(\n",
        "    task_type='GPU',\n",
        "    gpu_ram_part=0.9,\n",
        "    gpu_cat_features_storage='GpuRam',\n",
        "    random_seed=1\n",
        ")\n",
        "\n",
        "cb_over.fit(Xt_over,yt_over,verbose=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "jN2_FjqjLinC",
        "outputId": "46fb70c5-e738-46ce-b592-85f72d22dc96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "data                                                   \n",
              "train  0.897218  0.886961  0.872008  0.868308  0.131692\n",
              "val    0.599778   0.34068  0.206725  0.713352  0.182057"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49cf7916-d1d8-4224-b929-ac0812bd8109\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.897218</td>\n",
              "      <td>0.886961</td>\n",
              "      <td>0.872008</td>\n",
              "      <td>0.868308</td>\n",
              "      <td>0.131692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.599778</td>\n",
              "      <td>0.34068</td>\n",
              "      <td>0.206725</td>\n",
              "      <td>0.713352</td>\n",
              "      <td>0.182057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49cf7916-d1d8-4224-b929-ac0812bd8109')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-49cf7916-d1d8-4224-b929-ac0812bd8109 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-49cf7916-d1d8-4224-b929-ac0812bd8109');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "tab_over.loc['CatBoost']=get_scores(cb_over,sample='over')\n",
        "\n",
        "tab_over.loc['CatBoost']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "BZWoBplzLubc",
        "outputId": "759a0e59-44d1-4d1d-da9d-d383ddce564a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.817943\n",
              "Precision     0.124885\n",
              "Recall        0.599778\n",
              "F1            0.206725"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd6751f4-173b-4679-81f7-0ec5a62b889a\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.817943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.124885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.599778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.206725</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd6751f4-173b-4679-81f7-0ec5a62b889a')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fd6751f4-173b-4679-81f7-0ec5a62b889a 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-fd6751f4-173b-4679-81f7-0ec5a62b889a');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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KlCkcOXIk1S3xZD4+Pnz66afMmDFDydmGpD+8hoaGbN26lUuXLnHt2jUlrzYnjIyMaNeuHRs3bmTXrl0awX7KwaFZzQFv3LgxH3/8MQULFtTYbm9vT9OmTenbty+9e/fm5s2buLq6agSX5ubmmJubawTYWf2ZCAwMpEiRImzevJkSJUoo22vWrJml4wHMzMyYP38+ffv25cWLF3z//fcsX74cSFoPInmAfL169Rg8eHCW603pxYsX3L9/H4AqVapk2p7mzZuzf/9+fH198fPzS3PMxI0bN5RUk9atW2v8XuXk9dCG5NQ2SHr/Wrt2rUava4MGDahdu3amd9MqV67MiRMn0kz3+eijj+jduzfDhg3j9OnTLF26lM6dO6Ovrw8kfQCqWLEivr6+yjHlypV74zUikt8XkwP9DRs2aLyWDg4OtG7dmh49evDs2TPmz59P27Zt0/2wfOnSJZo0acKvv/6qEczXrVsXS0tLFi1axKNHjzhx4kSaqXa5JeXfm3v37qXav3r1asqVK5dqe+3atenYsSPbt29n0qRJnD9/nrNnz2r8bL0+IUPywOH06Ovrc+DAgTSDckdHR7p168aSJUtYunQpu3btYvjw4Wm2La1ru3r1qgT74p0mA3TzgZTBpzZ6uzdu3KjcCp01a1aafzwbN26s9IxdvnyZy5cvp1tf1apVlbmLU1KpVPTv3x9Iuo3v4+OT47a/qeDgYGXWn7p162ZY9vU0lsxs375dGU8xceJEjUA/mb29vbJoUlBQUIaLWNnY2DBlyhSNQD9ZysHWyb3D2pAcyPv4+PDgwQNl+8GDB4mOjlaCzqwoWrRoqsAyJTMzM0aNGgXAxYsXlbtW2jB27FiNQP9N1KtXjyFDhgBw7NgxNmzYQFRUFOPGjSMuLg4zMzPmzZv3xovVPXnyRPk+K7/PKdO+Us6hn1JGc+vr8vUA2LRpk/J+M2PGjDTTKzp37kzjxo0zrMfKyirDvH4jIyO+++47ICnlLTdX3/b09OTp06cADB8+PM0PbSVLllTaExUVleGKzcbGxsyZMyfNXvs+ffooHQPa/J3PipTvhaGhoan2ZxRMA3Tr1k15brKycF9GVCpVpgH5V199ReHChVGr1RmOrQI0PnilfM8T4l0kwX4+EBERoXyf0R/trEoexPfhhx9m2Ov56aefpjomLR06dEgzMAXN3hNdvqEWLlxY+YO5a9curS4Wk/zcmJubZ9jrlnKsRUbPZ9u2bdO9VW9ra6v02mrz+axVq5byhzRl4Jj8fdu2bTE2Nn6juiMjIwkMDOT27dvcunWLW7duadzVSHnnKicMDQ1p166dVuoaOXKk8rM7b948xo4di7+/P5C0um3JkiXfuO6XL18q32dlUGqjRo344IMPgKSUpuR0hmSJiYn89ddfABQrVkwZYJqet/V6JDtz5gyQdIcmo3n/U6bdZEVsbCyPHj3izp07ynWkfG60fR0pJf/+qlQqunfvnm65tm3bYmZmpnFMWho2bJjuBz9TU1MlqH7b76Ep7xCl/DuUFrVazbNnz/D391dej1u3bikpd9p+PRITEwkKCsLPz0851927d5XJCzI7X8oPMs+ePdNq24R42ySNJx9I2ROW3IP8pmJjY5XbsTVq1MiwbJUqVTA0NCQuLo5bt26lWy6jqRhTvqFm9sciNxkZGeHi4sKuXbs4ePAgrVu3pm3btjg5OVGrVq0sBV3pSX5u7O3t003NAfjggw8oWbIkDx8+zPD5zGy9AQsLCyIjI7X+fHbs2BFXV1f27NnDiBEjePz4sZKfnt2VWF++fMmqVas4ePAgAQEBqQLUlLTVk1yuXLk3/kDyuuTBt127diUqKkpJZfr4449zPH99SEiI8n3KnOT0GBgY8PHHH7Nq1SoePnzIxYsXNe5OnT17Vull7tChQ5p3HHTxekDS+01AQACQ+fiEzN6PIOmDytq1a9m7dy937twhISEh3bLavkOR0u3bt4GkHP+MxrEYGRlRpUoVzp8//8bvofDfz8nbfg9Neb70xoodP36cjRs38s8//2TYPm28Hmq1mt27d7Nt2zYuX75MdHT0G58v5Xt+Tv+uCqFrEuznAykD5hcvXuSorpSBRmYpBIaGhlhaWvLs2TON416X0WwkKQMPXS9eMnXqVEJDQzl27BgPHz7Ew8MDDw8P9PT0sLe3p127dvTo0UPpicuq5OcmKykZNjY2PHz4MMPnM7O7N8nPqbafz06dOuHq6sq9e/e4dOkS586dQ61WU7JkSerUqZPlenx9fRk0aFCWxz5oazXenHxgS4utrS2DBg1SpgO0srJi2rRpOa435QeSjIKVlDp37syqVauApLstKYP9jFJ4QHevByT9biR/sMjs9yP57kV6AgMD6devH4GBgVk6d26u8pz8XGb1dx7Ik7/zmUkZML/+wVStVjN58mS2bduWpbqy+rOenpiYGEaMGMHJkye1cr6UPx+yxot418lPcD6Qciacq1evaq3e9FJv8itTU1OWL1/O5cuX2b9/vzJXc0JCAr6+vvj6+rJy5UqWLl1KrVq1sl3/u/58Js/r7u3tza5du5TpITt27Jjla4uNjWX06NEEBwdjaGjI559/TosWLShXrhwWFhZKetKDBw9o2bIlQIa9zNmRPBhTW8LDw5UpbyEp8Ll69WqOB7AWLlxY+T6jADClKlWqULFiRW7dusWBAweYMmUKRkZGREZG4unpCSSlzL0+V74uX4/X5fT347vvviMwMBCVSkXXrl1p3749dnZ2WFlZYWhoiEqlIjExUckRz63rSOld/53PzLVr15TvX7/juG3bNiXQr1KlCv369aNGjRrKGJHk38fvvvsu3bEm2bFs2TIl0Hd0dOSzzz6jatWqfPDBBxQoUED5QNS7d+8sjW1I+eFX2x0FQrxtEuznAx9++CGFCxfm1atXXLx4kfDw8DeefjNl78zz588zLBsfH6+8IWYl3SA3qVQq1Gp1pj1bWZlfvUaNGkrKQHh4OOfPn2fHjh0cOnSIFy9eMHLkSA4fPpzl+dMtLCx49uxZps8n/JcbquvnMz2dO3fG29ub7du3Kz1f2VmJ9e+//1byiqdNm5bumhDZnfFIF2bMmKHMIW5iYkJERAQTJ05k9+7dOQoOUqZ9ZDXYh6TXZv78+YSGhnL06FHatm2Lp6en8jOfVq++rl+PlM9TZr8fGe2/e/cuFy9eBGDo0KHpTtP5tn6uku+25off+Ywkj7cAUt3dS56hrWzZsmzatCnd98vs/IynR61WKx8s6taty+rVq9MdIJ/V86UccJzWpApCvEtkgG4+oFKp6NKlC5AUzGY0DWZmjIyMlMFeGc2wA0m9Oskz2OTWlJhZlTxuIa0ZIZKp1WplSsOsMjU1pXnz5ri6utKnTx8g6Y9zcmCRFcnPzbVr1zIc+PvixQsePXqkcUxe065dO4yMjJRAv2bNmpmOIUgp5YJlGQ2WTTkFYlp03WO6f/9+pTfyk08+4eeffwbg8ePH6a6sm1WlS5dW0jbSms4wPR06dFB6S5MXsEv+39DQkI8//jjVMdp6Pd6UsbGx8n5z5cqVDMtmtD/ldbi4uKRb7m39XH344YdAUmpRygHXr4uLi1NmBcqrv/PpuXXrljKouHjx4qkGVyePW2jevHm6gb5ardbK3ejg4GDlQ1NGK+RGREQoA+kzk/J3L/n1FOJdJcF+PtG/f38lQFiyZAl3797N0nGJiYmpVrZNTkO4fft2hgF/ylxMXS8nnjwfdkZ/zE+ePJnhh4HMpLzG7AwmSz4uNDSUQ4cOpVtu27ZtSmqBrp/P9Jibm9OyZUuMjIwwMjLK9sDclB920hv0lpiYmOkH1pSzEb2+OmluCwoKUnLzy5Urx6RJk2jWrBk9e/YEYO/eval+p7LD0NBQmQUrswA4pSJFiig/NydPntQIxho1apTmQFFtvR45kdzmW7duaaSFvG779u3p7ks5EDejwZSbNm3KsC0px0vk5Ocq+ZrUanWG7T548CBhYWEax7wLoqOjGT9+vPJ+NXDgwFR57cmvSUZ3U48cOZLpTDfJr0lGr0dWX/+tW7dmeaa15N89Q0NDraxXIoQuSbCfTxQtWpQpU6YASW+uffr0UWZKSc+dO3cYPHgwHh4eGtt79eql9IxMmTKF8PDwVMd6eXkpwX7KtBddSV4m/t9//02z1/3Zs2fMnDkz3eMfPHiQ6fN1+vRp5fvsLLbTrVs35YPYvHnzCAoKSlXmxo0byuJMRYsWVfKj86JffvmFK1eucOXKlQxX/UxLynm3U+a7p/Tzzz9n2tuXcmXT7N6tyQm1Ws348eMJCQnBwMCAn376SZl+cMKECcpdjhkzZih3ad5E8gDbmzdvZivoTP7wFRcXxzfffKMEQel9KNPW65ETPXv2VHrUp0yZkmZwuHv3bk6cOJFuHSnnV09vvvoNGzZkugJ28mBZyNk0li1btlR+RpcvX64saJbS48ePlVVcCxYsSNeuXd/4fG/TnTt3+Oyzz5QPZo6OjqlW1Yb/XpNjx46lmT51//59fvjhh0zPl/yaZPR7nnKNhb/++ivN35nLly+zePHiTM+XsjwkLYCW1toPQrxLJGc/H+nWrRtPnjxhyZIlvHjxgj59+tCoUSOaN2+OnZ0d5ubmhISE4O/vz4kTJzh16hQJCQkaA3wBKlWqxIABA/Dw8ODGjRt06dKFIUOGUKVKFaKiojh27Bhr164lISEBQ0NDZsyYoaMr/k+PHj3YuHEj8fHxDB8+nC+//JI6deoQFxeHt7c3q1atIi4ujnLlyqWZGvHo0SP69u1LhQoVaNmyJdWrV1f+WD958oR9+/axf/9+IGmwWXZWXbWysuLbb79lxowZPHnyhK5duzJkyBBq165NfHw8Z86cwcPDg8jISFQqFTNnzsxwis53WaNGjbC2tubFixcsWrSIwMBAWrVqReHChbl//z5btmzh7NmzykDg9KQcID1nzhyGDRuGjY2NEjSWLFkyV2bQWLVqldJbPnz4cI0PuQULFuSnn36iV69ehIWFMX78+AxzhzPSokULfv31V+Li4rhw4QINGzbM0nGtWrVSxg8kp1FYWFiku+CZtl6PnKhcuTK9e/dm3bp1+Pr60q1bN4YMGULFihUJCwvjwIEDbNmyhWrVqqV7587e3l4ZoLx582ZCQ0Pp1KkTNjY2PHnyhN27d3Pw4MFMr6NKlSoYGxsTExPD4sWLMTAwoESJEsprWLRo0SyN1TEyMmLmzJkMGzaM8PBwevXqxaBBg2jQoAH6+vr4+Pjg7u6uzJ723XffZThF59v04sULjWlAo6KiCAkJ4ebNm/z999+cPn1a6dF3cHBg8eLFab5fJY8hefr0KT169FBe05iYGP7++29Wr15NbGwsVatWzfDDZK1atQgMDOTo0aNs2rSJ2rVrK739pqamWFtbo6enR4cOHVi/fj03b96kV69eDBgwgLJlyxIeHs6JEyfYsGEDhQoVokiRIpmmx4WHhys9+3m540WIrJJgP5/56quv+PDDD5k7dy4PHz7Ey8sLLy+vdMt/+OGHfPvtt6m2jxs3jqioKDZs2MD9+/eVuwYpmZmZsWjRojRXh3zbkq9jzpw5hISEMGfOHI39lpaWLF26lMWLF2f4Rn/nzh2N/N/X2dra4urqmu3c3t69exMWFsbixYt5/vx5qvbBfwFCkyZNslX3u6RQoULMmzePr776ipiYGDZv3szmzZs1yjg6OjJ16tQ0c8yTlS1blnbt2rF///40f8aPHDmSrbsvWXHz5k0WLlwIJAUgw4cPT1WmevXqfPXVVyxatIjz58/j4eGhrLabHfb29koQtGfPniwH+wUKFKBNmzYavdsZLcKmrdcjpyZMmMDTp085dOgQfn5+TJw4UWN/qVKlWLRoUbqBl0qlYv78+fTr14+QkBD279+vfDhPVrFiRRYvXsxHH32UbjtMTU3p06cPv//+O1evXtVYkRpgzZo1mS5Klqxp06bMmTOHqVOnEhERwZIlS1iyZIlGGX19fb7++uts3yHLTRs3bmTjxo0ZlrGysqJfv34MHjw43Q/Vffv25cyZM3h5eXHv3j2+//57jf0FChRg3rx5nDhxIsNgf9CgQRw8eJDY2NhUU9t26dKFuXPnAjBmzBi8vb25fv06vr6+jB07VqOspaUlrq6uLFmyJNNg39PTk5iYGAwMDGjfvn2GZYV4F0iwnw+1bt2apk2bcvDgQU6ePMmVK1d4+fIlERERmJqaUrJkSWrWrEmbNm1wcnJKM3DV09Nj2rRptG/fnk2bNnHx4kWeP3+OkZERpUuXpkmTJvTr1y/P9EZB0rgFOzs7Vq1axZUrV4iKiqJIkSI0adKEwYMHU6JEiXSPrVu3LmvXrsXLy4tLly7x5MkTnj9/TmxsLBYWFlSuXJlWrVrRtWvXdAOnzAwbNoymTZuyfv16/v77b54+fYqenh7FixfH2dmZfv36aT1AzYs++ugjtm/fjru7O3///TevXr3CzMyMChUq0KFDB7p3756lFJiffvqJatWqcfDgQfz9/YmIiMi1ecZjY2MZN24csbGxFCpUiPnz56c7lecXX3zBqVOnuHjxIosXL6ZRo0Zv9IG4R48eTJ06FU9PT3744Ycs/9x16dJFI9jPbFyFtl6PnDA0NMTV1ZVdu3axZcsWbt68SXx8PCVKlKBVq1YMHDgw09lqqlSpws6dO3Fzc+PUqVM8ffoUExMTypQpQ7t27ejdu3eWFlUbN24c5cqVY+fOndy5c4ewsLAMF+fKSJcuXahXrx6rV6/m9OnTPH78mMTERIoUKUL9+vX5/PPPqVSp0hvV/Tbo6elhYmKCmZkZJUqUoGrVqtStW5emTZtm+vNoaGiIm5sbGzduZOfOndy9exe1Wk3RokVp0KABffv2xc7OLsP0LEh6XTdv3oyHhwfe3t7K+/LrzMzM2LhxI3/88Qf79+8nICAAfX19ihcvrvy9Sl49NzPJK063bNlSI7VLiHeVSv02JhsWQgiRLREREbRs2ZKXL1/y008/5XhlXiFE5h4+fEirVq1ISEhg8+bNODg46LpJQuSYDNAVQog8yMTEhC+//BIANzc3na8wLcT7wM3NjYSEBD766CMJ9EW+IcG+EELkUT179qR06dLcuXOHAwcO6Lo5QuRrjx8/5s8//0RfXz/NsWxCvKskZ18IIfIoQ0ND5s6dy9mzZ984b1wIkTWPHz9m6NChlC5dOk+PpRAiuyRnXwghhBBCiHxK0niEEEIIIcR7KyAggKlTp9KpUyfs7e3TnGo4KiqKn3/+mRYtWigzGi5fvjzVqsxhYWFMmjQJR0dHatWqxahRo3j69Gmq+ry9venRowc1atSgWbNmuLu783r/u1qtxt3dnaZNm1KjRg169OjBpUuXsn19EuwLIYQQQoj31u3btzlx4gRly5bFzs4uzTIzZsxgw4YNDBw4EDc3N7p06cKSJUtwdXXVKDd69GhOnz7N9OnTWbBgAf7+/gwZMkTjQ0FAQACDBg3CxsYGNzc3+vXrx5IlS1i5cqVGXStWrGDJkiX0798fNzc3bGxsGDhwYLZX+JY0HiGEEEII8d5KTExUVsqeMGECvr6+ynoLyftr167NoEGDGDlypLJ9/PjxXLx4kcOHDwPg4+NDz5498fDwoFGjRgD4+fnh4uLCwoULcXFxAWDq1Kl4eXlx4MABZc2KhQsXsnHjRk6fPo2RkRExMTE0bNiQ3r1788033wBJa760bduWxo0bM3369CxfnwzQfU1UQoSumyCEyOf8w27ruglCiPeAvaWDrpugULXS3qKRas9ArdUFKIF+uudTq4mPj8fMzExju5mZmUbqzcmTJzE3N8fZ2VnZZmtrS5UqVTh58qQS7J88eZJWrVppLE7n4uKCm5sbPj4+ODk54e3tTXh4OO3atVPKGBkZ0apVKzw9PbN3fdkqLYQQQgghxHtEX1+frl27sm7dOi5fvkxERARnzpxh165dfP7550o5Pz8/ypcvj0ql0jje1tYWPz8/ACIjI3n8+DG2trapyqhUKqVc8v+vl7Ozs+PRo0dER0dnuf3Ssy+EEEIIIXLXawFwTrRo0SLD/UeOHNHauZJNmzaNadOm8cknnyjbhg4dyoABA5THoaGhqXr/ASwsLPD19QWSBvACmJuba5QxMjKiYMGChISEKHUZGRlhbGysUc7c3By1Wk1ISAgFChTIUtsl2BdCCCGEELlLm7kkOlh2ZMGCBRw/fpxZs2ZRrlw5Ll26xNKlSzE3N2fw4MFvv0HZIMG+EEIIIYR4Z+RGz31Gbt26xcqVK1m2bBnNmzcHoF69esTHx7N48WJ69uyJqakp5ubmPHnyJNXxISEhWFhYACg9/8k9/MliY2OJiopSypmbmxMbG0tMTIxG735oaCgqlUoplxWSsy+EEEIIIXKXSqW9r7fszp07AFSpUkVju729PbGxsQQFBQFJ+fX+/v6p5sv39/dXcu8LFSpE8eLFlZz8lGXUarVSLvl/f39/jXJ+fn6UKFEiyyk8IMG+EEIIIYTIbSotfr1lJUuWBODq1asa2319fVGpVJQoUQKAxo0bExISwtmzZ5Uy/v7+XLt2jcaNGyvbGjduzJEjR4iLi1O27du3D3Nzc2rVqgVA7dq1MTU1Zf/+/UqZuLg4Dh06pFFXVkgajxBCCCGEeG9FRUVx4sQJAB4+fEh4eDgHDhwAwNHRkWrVqlGtWjWmTZvGixcvKFOmDJcvX8bd3Z1u3bpRsGBBAGrVqkWjRo2YNGkS48ePx9jYmF9++YVKlSrRunVr5XyDBg1iz549jB07ll69enHr1i08PDwYM2aMMh2nsbExQ4cOxdXVFSsrKypWrMjGjRsJDg5m0KBB2bo+WVTrNTLPvhAit8k8+0KItyFPzbPfvqzW6lLvDdBaXQCBgYHpzvCzZs0anJycePbsGYsXL+bMmTO8ePGCYsWK8fHHHzNkyBCNlJqwsDDmzJmDp6cn8fHxNGrUiMmTJ1O0aFGNer29vZk7dy7Xr1/HysqK3r17M2TIEI1pO9VqNe7u7mzYsIGXL19SpUoVJk6cqPT+Z5UE+6+RYF8Ikdsk2BdCvA15KtjvoMVgf492g/38TnL2hRBCCCGEyKckZ18IIYQQQuQuHcyiI5JIsC+EEEIIIXKXxPo6I2k8QgghhBBC5FPSsy+EEEIIIXKXnnTt64oE+0IIIYQQIndJrK8zEuwLIYQQQojcJQN0dUZy9oUQQgghhMinpGdfCCGEEELkLunY1xkJ9oUQQgghRO6SAbo6I2k8QgghhBBC5FPSsy+EEEIIIXKXdOzrjAT7QgghhBAid8lsPDojaTxCCCGEEELkU9KzL4QQQgghcpcM0NUZCfaFEEIIIUTuklhfZySNRwghhBBCiHxKevaFEEIIIUTukgG6OiPBvhBCCCGEyF0S6+uMpPEIIYQQQgiRT0nPvhBCCCGEyF0yG4/OSLAvhBBCCCFyl8T6OiNpPEIIIYQQQuRT0rMvhBBCCCFyl8zGozMS7AshhBBCiNwluSQ6I0+9EEIIIYQQ+ZT07AshhBBCiNwlaTw6I8G+EEIIIYTIXRLr64yk8QghhBBCCJFPSbAvhBBCCCFyl0qlvS8tCwgIYOrUqXTq1Al7e3s+/vjjNMuFhoYya9YsGjVqRPXq1WnZsiUrV67UKBMbG8u8efNwdnbGwcGBAQMG4Ofnl6quu3fvMmDAABwcHHB2dmb+/PnExsamKrd161batGlD9erV6dixI8eOHcv29UkajxBCCCGEyF15uHv59u3bnDhxgpo1a5KYmIharU5VJjIykj59+qCvr8+kSZOwtrbm3r17hIeHa5SbNWsW+/btY8KECRQtWpTly5fTv39/9u7di5mZGQAhISH069ePcuXK4erqSlBQEHPnziU6OpqpU6cqde3du5cpU6YwbNgw6tevz759+xgxYgTr16/HwcEhy9cnwb4QQgghhMhdeXiAbvPmzWnZsiUAEyZMwNfXN1UZd3d3IiIi2L17N4UKFQLAyclJo8yTJ0/Ytm0b06ZNo3v37gBUr16dZs2asWnTJoYMGQLApk2biIiI4Ndff8XS0hKAhIQEfvjhB4YOHUrRokUBWLJkCe3bt2f06NEA1K9fn1u3brF06VJWrFiR5evLw5+zhBBCCCGEyF16epmHw9u2baNbt25KoJ8WLy8vEhMTadu2rbLN0tISZ2dnTp48qWw7efIkDRo0UAJ9gHbt2pGYmMjp06cBePDgAffu3aNdu3Ya53BxceHs2bNppvykR4J9IYQQQgiRu1Ra/HrLAgMDefbsGYULF2bYsGFUq1YNR0dHJk+eTEREhFLOz88Pa2trLCwsNI63s7PTyNv38/PD1tZWo4y5uTk2NjZKueT/y5cvn6quuLg4Hjx4kOX2SxqPEEIIIYTIXXrai9JbtGiR4f4jR45o7VwAz58/B2DevHm0bt2aFStWcO/ePX7++WciIyNZuHAhkDSANzkvPyVzc3NCQkKUx6GhoZibm6cqZ2FhoZRL/v/1csmPU9aXGQn2hRBCCCGESEdiYiKQ1Ms+b948ABo0aICBgQGTJ09mzJgxlC5dWpdNzJAE+0IIIYQQIndpcYCutnvuM5OclvP6gNz69esDSbP5lC5dGnNz81Sz80BST37K1B5zc3PCwsJSlQsJCVHKJf8fFhaGjY2NRl0p92eF5OwLIYQQQojc9Q7n7JcuXRojI6N098fExABga2vL8+fPU6XYvJ6jb2trm2ru/bCwMJ49e6aUS/7/9XJ+fn4YGhpm606CBPtCCCGEEEKkw8jICGdnZ86ePaux/cyZMwBUrVoVgEaNGqGnp8ehQ4eUMiEhIXh5edG4cWNlW+PGjTlz5ozSSw9w4MAB9PT0cHZ2BpI+YJQrV44DBw5onHPfvn00aNAgww8fr5M0HiGEEEIIkatUeXie/aioKE6cOAHAw4cPCQ8PV4JsR0dHrKysGDFiBD179mTs2LF06dKFgIAAfv75Zzp06ECZMmUAKFasGN27d2f+/Pno6elRtGhR3NzcMDMzo2fPnsr5evbsydq1a/nqq68YOnQoQUFBzJ8/n549eypz7AOMHDmScePGUaZMGZycnNi3bx+XL19m3bp12bo+lTqtZcLeY1EJEZkXEkKIHPAPu63rJggh3gP2lg66boJCf3RNrdWVsOhfrdUFSVNrpjfDz5o1a5Rc/bNnz7JgwQJu3bqFhYUFHTp0YMyYMRq97LGxsfzyyy/s2rWLiIgIateuzeTJk7Gzs9Oo9+7du8ycORMfHx9MTEzo1KlTqroAtm7dyooVK3j06BHly5fnm2++oVmzZtm6Pgn2XyPBvhAit0mwL4R4GyTYFyBpPEIIIYQQIpfl4SyefE+CfSGEEEIIkav0JNrXGZmNRwghhBBCiHxKevaFEEIIIUSuysuz8eR3EuwLATjY136j4+rUq4PH6hUAtGvZnsePHmvs19PTw8zMDFu78jRv1Zzun3ajYMGCOW6vEOLdt2TGbxzbeyLTcgNH96VDr/YATB7+A1e9r2nsV6lUFDQpSIkyxanrXBuXT9piZmGaK20W4k1JsK87EuwLAVhbW6e5PSQkhPj4eIyNjTE1Tf3H08LCPNW2ggULUqhQIQDi4uIICQnBx/sSPt6X2LFtJ7+vcsfK2kq7FyCEeGcZGOhjap5+cG5csECqbUbGhhQySXqfSUxMJDQ4jDvX7nLn2l08dx7hh6VTKFm2RK61WQjx7pBgXwjgyCnPNLcP6jeEi/9cpHW71syc/UOW6uo7oA/DRwxTHgcHB7Nu9Xo83Ffid9ePGdNmsejXhVpptxDi3VepRiVmLZuWrWOcWzZk1NQvlceR4ZEc2XOMtb9t5MWzl/w8eTE/r5krvakiz5CfRd2RAbpC5DJLS0tGfP0Vnbp0BOD40eM8ffpMx60SQuQnhUwL0aFXe7r37wKA/6173PSV9RxE3qFSae9LZI8E+0K8JW1d2irf37h2XYctEULkVx+1dla+97vhp8OWCCHyCknjEeItKVLURvk+PFxWahZCaJ+VzX/jgSIjonTYEiE0SRqP7kiwL8Rb8uTxE+V7MzMzHbZECJFfPQ96rnxvYlpIhy0RQpME+7ojwb4Qb8mf23YASdNxVq1eVcetEULkFTcv32RAuy/S3Fe7gQMjUwzEzYznziPK9xWrfpjjtgmhLSok2NcVCfaFyEVxsXHcv/+AdavXcfhQ0h/h1u1aY2VVWMctE0LkFfHxCQS/DElzX3hY5il/CQmJBD0M4sieY+zZvA+AStUrYlfFVqvtFEK8myTYF0LL3H5zx+039zT31ahZnUlTJr7lFgkh8rKqte2zPfXmsb0n0l2Qq2TZEoyd9bU2miaE1kgaj+5IsC+ElqVcVEtPXw8zU1PK25WneYtmtHFpg4GB/NoJIXIm5aJaKj09ChYqQPHSxanrXItm7ZtgXMBYxy0UQpPE+rojUYcQWvb6olpCCKFtry+qJYQQ6ZFgXwghhBBC5Co96drXGQn2hRBCCCFErpKcfd2RFXSFEEIIIYTIp6RnXwghhBBC5Crp2dcdCfaFEEIIIUSuklhfdySNRwghhBBCiHxKpVar1bpuRF4SlZD5aoVCCJET/mG3dd0EIcR7wN7SQddNUNhMc9ZaXc9+OK21ut4HksYjhBBCCCFyleTs646k8QghhBBCCJFPSc++EEIIIYTIVdKzrzsS7AshhBBCiFwlwb7uSBqPEEIIIYQQ+ZT07AshhBBCiFwlHfu6Iz37QgghhBAiV6lUKq19aVtAQABTp06lU6dO2Nvb8/HHH2dY/vDhw1SqVCnNcmFhYUyaNAlHR0dq1arFqFGjePr0aapy3t7e9OjRgxo1atCsWTPc3d15fTZ8tVqNu7s7TZs2pUaNGvTo0YNLly5l+/ok2BdCCCGEEO+t27dvc+LECcqWLYudnV2GZaOjo5k9ezYffPBBmvtHjx7N6dOnmT59OgsWLMDf358hQ4YQHx+vlAkICGDQoEHY2Njg5uZGv379WLJkCStXrtSoa8WKFSxZsoT+/fvj5uaGjY0NAwcO5MGDB9m6PknjEbkqKiqKPbv+wuukF7du3CY4OBhUYGVlhX3VKjRr3pQWrVtQoEABXTc1R5b9uhyA3n17Y25u9kZ1PHz4iPatMu5NAFi/ZR1Vq9lnqc6jh4/xzaixyuNL17zTLPfq1Sv+OXeBa1evcdX3Ktev3iA8PByAcz5nMTY2ztL5hMgtMdExHNt7gotnfLh3O4DQ4DBUKhUWhc2xq2yLY5N6NGjmhHEBI103NUc2rdgKQIeeLpiYmbxRHS+evuTkQS9uX7vDA79AQl6FEhkeSSHTQpS2LUXD5k607twSQyPDNM+/+fdtWTpPtdr2zFw2LdX26Kho9m45wNmj53h0/zGJiYkUKW6DU9N6dP68IyamhdKsLy42Dl/vq9y5dpfb1/y4c/0ur56/AmDKoonUbuCQ9SdB5Dl5eYBu8+bNadmyJQATJkzA19c33bJubm6UKFGCUqVKpSrn4+ODl5cXHh4eNGrUCIDy5cvj4uLCoUOHcHFxAcDDw4PChQuzcOFCjIyMaNCgAS9fvmT58uX06dMHIyMjYmJicHNzY+DAgfTv3x+AOnXq0LZtWzw8PJg+fXqWr0+CfZFrThw7wcxpP/L8+XNlW8GCBdHT0+PRw0c8eviIw4eOsGjhEn6cOxPH+o46bG3OuP3mDkDHLh3fONhPydraOt19BgZZ+7WNjIhk3uz5WSq7d88+Fsz9OUtlhXjb/jl1kWVz3Hn1IljZVqCgMSqVHk8fP+Pp42ecPXaOtUvX8/X0EdSoW013jc2h5EC7efsmbxzsX7t0nTW/rlceGxoZYlzAiLCQMK75XOeaz3UObPdkuutkrItYaRxboGABLK0s0q07Ua0m9FUoALaVy6fa/+zJc374+kce3nsEgJGxEfr6+jzwD+SBfyDH951k5rJpFCtZNNWxgfceMuPrOW90zSLv08vDwb6eXtYSXe7fv88ff/zBpk2bWLVqVar9J0+exNzcHGfn/1YLtrW1pUqVKpw8eVIJ9k+ePEmrVq0wMvqvc8LFxQU3Nzd8fHxwcnLC29ub8PBw2rVrp5QxMjKiVatWeHp6Zuv6JNgXuWLXjt38MGUGiYmJlCtfjiFDB+Hc2BlLS0sgKaft3NnzbNqwmQvnL3Dxgvc7Hexr25FT2ftFTstS198IehJE9RrVuHI5/V4KSOpxKVqsKFWr2WNf1R6VSoXrol9z3AYhcuroX8dZ+uNyEhPVlCxbgu4DulK7oQPmFkkfqiPCI7l8/gr7th7A1/sa13yuv9PBvjbYFPuAHoO7U7VWFcp9WA4zC1MAIsMj8Tp8lj8WryHw3kMW/7CUGUunaBzb+fMOdP68Q7p1/338PPPGJ3UMNG/fVGNfYmIi88b/zMN7jyhsbcmIKcNxcKqBnp4et6/d4ddZy7l/9wGzx87nl3Xz0TfQT1W/iZkJdpXLU6GKHRXs7Zg/YWEOnw0htOfHH3+kU6dOVK5cOc39fn5+lC9fPtVdDFtbW/z8/ACIjIzk8ePH2NrapiqjUqnw8/PDyclJKf96OTs7O1avXk10dHSWsyIk2Bdad/PGLX78YTaJiYk0atyIBYvmp/qBNDMzo2XrFrRs3YKD+w8S9CT14BXx5q5fu86m9Zuxr1qFbp90zTTY7/lZD3r3+Ux5/M/5C7ndRCEy5X/rHsvn/U5iopo6DWvx7ZxvUqXpmJgWokFzJxo0d8LL8wwvnr7QUWvzjso1KlG5RqVU2wuZFqJ15xbo6alY+qMbVy748jzoOR8UTTv3OC3H9p4AwLZSOcpWKKOx759TF7l7IylAGTX1Sxzq11T2fWhfgQnzxzGqxzc88A/kyF/Had25hcbxZSuUYa2nR55O9xBvTpsva4sWLTLcf+TIEe2d7P+OHj2Kj48PBw4cSLdMaGgoZmap7+5bWFgoKT9hYWEAmJuba5QxMjKiYMGChISEKHUZGRmlSqM1NzdHrVYTEhIiwb7QnaVLlhIbG0uRokWY89OPmf4wtmnXJtUI9NjYWDZv2MLB/Ye453+PuLg4ihUvxkdNGtF/YD8+sEn9x2nZr8tx+82dDp07MHP2D2mea8qkaezZuYehX37B8BHDlO3/nL/AkP5fULxEcfYf3ouP9yVWrljJlX99iYqKomy5MnTt3oUen/XQ+EOUXF+y13PuM2pLbklMTGTm9B9Rq9VMmjqJu3fuZnqMvn7qHjYhdG2D22biYuOwtrFizMxRmebjN2rVMNV7SVxsHPu2HeT04bM8vPeQ+Ph4Pij6AXWca9OlT0cKW1umqic5b71Z+yaMmvplmudaMuM3ju09QY/B3ek55BNlu+/Fq0z5cgY2xW1w3/kr1/+9wfbVu7jle5uY6GhKlC5Oq84taNe9jcZ7SXJ9yYZ2Galxvozakl0Vqvw3APHls1dZDvZDg0PxPuPz//Y0TbXf++wlAEqVK6kR6CcrXqoY9T6qy9lj5zi+72SqYD+rqRTi3fQuf4iLiYlh9uzZjBw5Eisrq8wPyGMk2BdaFRT0lFMnvADo9XnPND/hpiXlm8DLl6/4cshX3Lh+A0j6tGtoaEjAvQAC7gWwe8cefnVbQo2aNbR/ASSlIM2YOpPExERMTE2IiYnh1s3bzP1xPvfvP+C7id8qZU1NTbG2tubFi6TexMKFLdHT09fYn5KDfW2AVB82tGnT+s1c873GJz26U6161SwF+0LkNS+evuTi6aTAsn2PtukO6nxdyveSkFehzPj6R/xu3gOSctcNDAx4dP8xj+7v5dje40xeOIFK1Stqvf3w/xSk2W6oE9UUNClIbEwc9+7cZ8WCP3j84AmDvumvlC1kUghLKwuCXyb16plbmmkEv4VMNK+/i1MPgFQfNrLixpVbyvdFittk+biTB08TH5+AgYE+jds4p9r/7EnS+KySZUukW0fJckn7bl65SUx0DMYFZPD/+0KF9oL93Oi5z8jq1avR09Ojffv2hIYmjVmJi4sjMTGR0NBQChQogJGREebm5jx58iTV8SEhIVhYJI2FSY6Lknv4k8XGxhIVFaWUMzc3JzY2lpiYGI3e/dDQ0KTJCSzSH1vzOgn2hVZdOH9B6Vlr2qzJG9UxZcIUbly/gbm5OZOnf0+LVs3R19fnqu81pk+ezu1bdxgzYizbdm+hcOHC2mw+r169Ytb0H/mkR3eGDBuM9QfWhIaGscx1GRvXb2Ljuk107d6VCh8m9YyNn/Qt4yd9qwTx67aso2TJ9P/QZVXfXv24e8eP+Ph4rD+wxqFWTT7p0Z1adWpleFxQ0FOWLvmNwlaFGTl6RI7bIYSu+HpfVd5L6n1U943qWPzDUvxu3sPU3IRhE4ZQv6kT+vp63Ll+l19nLSfgzn3mfreAxRsXYG5pnnmF2RD6KpRlc1fQtmsrPhnQFUtrSyLCItjovoW9Ww6wd8sBWnVuQRnb0gAMHtufwWP7K0H8T3/MpkiJIlprT1xcPC+fveTv4+fZ6LYFgIYt6mOZxp2N9CTfeajdsFaaz1dyKJeYmJhuHQkJif8voybQ/yF2VWzTLStEXuHn50dAQAANGjRIta9evXpMnz6dXr16YWtry9mzZ1Gr1RodD/7+/lSsmNSpUKhQIYoXL67k5Kcso1arlRz95P/9/f01xgj4+flRokSJbM1iKPfMhFb5+/kDSb3x5cqXy/bx3he8Oe11BoC5C2bTum0rJcWkajV7lv++DHNzc168eMHGdZu01u5k0VHRfNzpYyZMHo/1B0kz4pibmzH+++/4sGIF1Go1Rzxzv0fh8r9XUOklvVE8eviIfX/tZ0CfQcyf81OqNIWU5v04j4iICEaP/RpzC+0GL0K8TYH3HgJJvfEZ9RSn55rPdXz+n1byzcxROLdogL5+0p+8ClXsmL7ke0zNTQh+GcJfm9PPwX1TMdExNHVpzJBxA5WA2sTMhMFjB1C2QhnUajVnj57T+nlfN7zbKLo49eDTRr0Z1mUkqxavJTYmloYt6jNi8vAs1xNw5z5+N5Pe35t/3DTNMjb/v0sQ6P8w3XoC/QOV71++eJXl84t3X15eVCszQ4YMYc2aNRpfjRo1omTJkqxZs4bmzZsD0LhxY0JCQjh79qxyrL+/P9euXaNx48bKtsaNG3PkyBHi4uKUbfv27cPc3JxatZI69WrXro2pqSn79+9XysTFxXHo0CGNurJCevaFVoUE//8WtLn5G/1Ceh5KCqTtq9nTsFHDVPutP7Cme49urFzxB4cOePLlyKz/scqqQUMGpLm9afOm3L51hzu33zwtJr157gGMjYz4tNcntG3XhspVKlPIpBBqtZob12+wfKkbJ46dZMPajVhZWTF46KBUx584doKjh49Rq7YDHTunP5uGEO+CsJCkdR5MzUze6L3kzP8D6QpVbKlV3yHVfktrS9p0acX21Ts5c+Qsnw39NEftTUu3fp3T3O74UV0C7tznvl/2FsZJace5zVkqZ25pTnRkNNHRMURHRgPg3KI+nw3rScFCWe8ZTO7VN7c0o45z2ncYHZxqcPBPTx4HPuHv4+ep31RzhrWAu/e5+P+cf4DoiOgsn1+8+/Jyzn5UVBQnTiT9jD98+JDw8HBlIK6joyN2dnapFtvasWMHQUFBODk5Kdtq1apFo0aNmDRpEuPHj8fY2JhffvmFSpUq0bp1a6XcoEGD2LNnD2PHjqVXr17cunULDw8PxowZo0zHaWxszNChQ3F1dcXKyoqKFSuyceNGgoODGTQodQyQkXcy2H/16hV37tyhXr16um6K0LIb164DUM8x/dv2jk6OrFzxBwH3AoiKjKJgoYJaO7+FhQWlSpdKc1+RIkm31JPz9bTtA5sPmDRlosY2lUpFFfsqLF66iG/HjMfzoCce7iv5tNenGvP5R0VGMXfWPAwMDJg0dWKeflMV4m1I7oWuVqdqumWq163K9tU7eXT/MdFR0RQoqL3F/UzNTdOcSx7A6v9z20eERWjtfOmZ5zFL+T74RTCH9xxj+6odnD91gVHTvsK5Req0hNclJCRy8mDSWKyPWjunu9ZHvY/qUu7Dsty7HcCvs5YTGRGFU+O6GBgacuWCLysWrERPpSI5ySf57qUQuvbixQu+/vprjW3Jj9esWaMR0Gdm0aJFzJkzh6lTpxIfH0+jRo2YPHmyxu9N2bJl8fDwYO7cuXzxxRdYWVkxatQoBg4cqFHXkCFDUKvVrFy5kpcvX1KlShU8PDwoXbp0tq7vnQz2z58/z+jRo7l+/bqumyJeY2GZNGAkNDQ0Vc5aVrx6FQxAkaLp56oWLZa0T61W8+rVK60G+68PgkvJyDjp03bKJa/fptFjR+F50JOoqCjO/32elq3/m8niN9dlPH78hD79PufDih/qpH1CaFPy3PDhYRFv9F4SGpz0odzKJv2ZM6yLJKXqqdVqQoPDtBrsFzRJ/33J6P8r18bHJ2jtfFlhaW1J9/5dKGtXhtnj5uM6YxmVq1dKtbDW6y79/a+yoFmz9umPxdLX12P8vLH8MOpHngQG4TrjN1xT7DcuYEzfEb1ZuWgNACZmWRt0LfKHvNwHVapUKW7evJmtY+bOnZvmdjMzM2bPns3s2bMzPL527dps2bIlwzIqlYqhQ4cydOjQbLXtdZKzL7SqvG3SioqxsbHc87/3xvXExsRqqUX5R8lSJSlslTQgOfDBf3mv9wPus2HdRqysreg7sA+REZEaX3Gx/z2X/22LS1W/EHlJqXIlgaSpMx8GPHrjeuRnPbV6H9XBprgNMdExeHmezrT8sX1J6Q1l7EpjVznjAbXFShZl4dr59B3RG/taVbApbkOpciVp2bEZC1bPoXzFckrZ4qWL5+g6xLvlXc7Zf9flqZ79Dh2ylmccEZH7tz7Fm6lbrw4qlQq1Ws3xYyeU4D+rChe25J4/PH78ON0yyQtwqVQqjdl4km+RxcbEpHtseFh4ttrzLggKekpCQgIvX7ykVZM2GZZtWK8RkLtTfwqhDVVr2SvvJf+cuqAE/1llbmnOw4BHynSQaUlegEulUmFu+V9aXPJA3rgMOh0iwyOz1Z68xtqmMM8eP+PJw6AMy0WERXD+ZNIie81csjbDWsFCBejSpyNd+nRMtS85Z9+isEW6aU5CCO3KUz37fn5+6OnpUa1atQy/SpVKO6da6F7RYkVp1Dhp/uVN6zcTHp614Dp5hpnK9lUAuPiPd7qzzpw/dx6AsuXKaqTwmJkl3fYPCkp7NV61Ws31q7mT+qX0NGQwU05OPQx8yKuXSbNXlCyVvcBHiHfNB0Wtqd0waSDo3i0HsxxcJ79v2FZK6mi46nMt3feSKxeuAlCiTHGNFB4TMxMAnj99me45kleK1bbk95LceydJ8vTxM4BMU5dOeZ4hLjYOPX09mrT7KMfn9fJMmm0trXn6Rf4mPfu6k6d69j/88EPKli3LnDlzMix38OBB/vnnn7fUKpFdX436inNnzxP0JIiJ305iwaKfUi33nNLB/YcIehJE3wF9aNW6BRvXbeTunbscO3Kc5i2baZR98fwF2zZvB6B121Ya+yr8P1f96pWrPHv2DBsbzcVi9u3Zl+ZiF9pgYmpCeFg4YaFhmRdOR2Z5ya6LfgWgQIECODr9Nzi9nmPdDGf52bVjN9O+nw5kPBuQEHlN72E9uPzPFV48fcHCqa58N2eMMnYmLacPn+V50HM69e5Aw+ZO7N2ynwd+gZw/eQGnJpoTOgS/CObgDk8AGr42SLWMXRkA7ly/y8vnr7D6QHM9jxMHvHge9EIbl5hKQZOCRIZH5mjwbkJ8AvoG6a+KfeLAKV4+S+o4sHeokmFdybPw1HKqmeZqw9lxaMdh7ly7i3EBYz7u0S5HdYl3jwTpupOnevZr1KjB5cuXs1Q2o7nGhW5VrlKJiVMmoFKpOHXCi57derF3915lWk5IWjnuiOcRBvf/gvFjJyipWbXr1sb5/1NuTp/8A54HD5OQkDSI7drVawwf8iWhoaFYW1vzWZ9eGud1qFUTmyI2xMXFMWHcJB4GJs31HBUVxbbN25gxbRbm5rkz97xdhaQpufbs/ktpb1oc7GvjYF+bZb8uT7VvcL8heLiv5M7tO0odarWaG9duMGbkWA7sOwhA/0H9lIHQ2pKYmMirV6+Ur5TpTsHBIRr7hHhbylcsxxffDkSlUnHxtDff9B3P8f2nlGk5ASLCIzl77BxThv/Agu8XEfX/6SXta1WhVgMHAH6dtYwzR/5WFnS6e92PH0b9SHhoBJZWFnzcUzPwrFKjElY2hYmPi2fhlCUEPUq6WxgTHcPBPz1ZNscNU3OTXLnmMrZJd66P7zuptDctXZx60MWpB5tWbE217/th09m+eicP/AI16nj25Dmbf9/Kr7OS3n/sKtumO40mwMP7j7jlexvIeGBuSod2HOb4vpME/39Ab/J51/y6Hrf5vwPQf9Tn6S4YFh4aTmhwqPKVLCoiSmO7riZKEOJdlKd69gcPHkyTJpm/oTRp0uStL5UssqdLt85YWFgw64cf8fe7x/cTpgBJK8epVCqNcRfFSxTHsf5/vW4z585k+OAvuXnjJt+O+Q5jY2MMDAyUY8zNzVnougBLS0uNcxoYGDBx8njGjf6Oi/9cpH3rDpiamhIdHU18fDydu3YiITGRPTv35Mr1/uvzL+vXbGDb5u1YWRUGlYpWrVvyzXdjslTHo0ePcV30K66LfsXAwEBpe3T0f3NR9+rdk6FffqH19j9+/IT2rT5Oc1+bZm01HsvdAfE2tezYHDMLM5bNWcHDe49YPP3/d7gKFUCFiqjIKKWsTXEbqtf9b6rNr6d9xQ+jfsT/1j1+mvQLRsaG6OsbKMeYmpswfv44zC3MNM6pb6DPkHED+WniQq56X2NYl5EUMilITHQsCQkJtOjQjMTERKXXW9vXe+PyLfZs2sfBHZ5YFLYAlYqGzZzo/3WfLNXx8vkr1v22kXW/bcTAQJ+CJoWIi40lOuq/8UwV7O2YtOA79PTS7/M7tvckkPQ8OTbO2irGN67cUp4XI2Mj9PX1lefbwECffqN607Zb63SP/6bvBJ79P8UopQXfL9J4PPO3qRlOqyryHunY1508FeyXKVOGMmXKZFquQIEClCwpOct5XfOWzWjQsD67d+3h1Akvbt+6TfCrYFQqFSVKlsC+qj0tWjWnRavmyiISAFZWhVmzcRWbN2zhwP6DBPgHEBcXR5myZfioSSP6D+qXKkXnv3M2Z9mKpfzu7sE13+skJCRQsXJFPu3Rnc7dOjNl0rRcudbOXTuRmJjIn1t34HfXjydPgpKmBg0OznIdY8aN5u+zf+N7+Sovnr8gJCQEQ0NDypUvh0OtmnT7pCvVa1bPlfYLkZc5NalHTcfqHNt7gounfbh35z5hIaGAiiLFbbCrYkuDpk7Ub+aI4f+ntQSwKGzO3N9nsn/bQbw8z/Aw4DHx8fEUL12cus616NynY6oUnWT1mzoybcn3bP1jB3ev+5GYmEj5imVp07U1LTs2Y8mM33LlWpM/SHjuOsoD/0CeB71Imho0JOspgqOmDMf773+55nOd50EvCA0ORaVKeq5sK5WnYYv6NEyxonBaEhMTObE/Kdh3btlQ43nNSDOXpJU9b129zcunr0hMTKR46eLUdKyOyydtKF1exty9rySNR3dUasmH0RCVIDP9CCFyl3/YbV03QQjxHrC3dNB1ExSVF2lvnMaN0fu1Vtf7IE/17AshhBBCiPxHevZ1R4J9IYQQQgiRqyTY1508NRuPEEIIIYQQQnukZ18IIYQQQuQq6djXHQn2hRBCCCFErpI0Ht2RNB4hhBBCCCHyKenZF0IIIYQQuUp69nVHgn0hhBBCCJGrJNjXHUnjEUIIIYQQIp+Snn2RJz1+9Jgjh49y/u/z3LpxmxcvXmBoaEip0iVx/siZz/r0wsbGJtv1XvW9xvGjx7nqe5UHAQ949SqYmJgYLAtbUrWqPR27dKR5y2ZpHjuo3xAu/nMxS+cZ9tVQhn01VHkcHx/P32fO4XXSi8v/XuZ+wAOio6OxtLSkanV7OnXplO55AR49fMTihUs4e/pvYmJiqFCxAl8MG0yTZk3SPWbJL66sXPEHPy/+iRatWmSp3ULkN1ERUVy5eJU71+5y54Yfd67dJSwkDADXzQspVa5kpnXERMdyaIcnZ46e49H9R0RFRmNpZUGp8qWoWa8anXp3eOP2ZbdutVrNtUvX+eeUN9f/vZF0TEQ0JuYmlP+wLI3bNKKpS2P09NLuy7tywRefv//l9rW7PHv8jJBXISQkJGJpbUmlah/SpktLqtWpmuaxCQmJ7Fi7iyN7jvE86AWFrS1p1NqZXkM+wdDIMM1j7vs9YGyf8dRq4MCkBd+98fMk3n3Ssa87KrVardZ1I/KSqIQIXTfhvffk8RPatWxPyh9NU1NToqKiSEhIAMDc3JyfF/9EPad62ap71vQf2bZlu/K4UKFCJCQkEBMTo2xr2boFc36ajaGh5h+vb0aN5V+fy+nWHRMbQ3hYOACLl/6iEYj/MHUmO7btUB4bGBhgbGxMRMR/P28tW7dkzk8/pjrvq1ev6NG1F0+DnqKnp4eRkRHR0dGoVCrmLphNm3ZtUrXF38+fT7v0xLG+I0vdXDN7WsRb5h92W9dNeG+cO/EPc79bkOa+rAT7D/wC+XHcfIIeBgFgYKCPUQFjIsMjAdDT12P7mY1v1LY3qXvrH3+yYflm5bGevh4FChZQjgGwd6jM9z+Pp5BpoVTnnPH1bHz+/ld5XMikILGxccTHxSvb2n/ajsFj+6c6dtkcdw7tPAJAgYLGREclvW/WaViLyb9MSPMavx82nTvX7uK66WeKlCiS4fMhtM/e0kHXTVA4LO+ktbouDdultbreB9KzL/KchMREAD5q0oiOnTviVN8Rcwtz4mLjOPf3eebMmsvDwIeMGTmWnXv/5AObD7Jcdw2HGpSzLUedurUpW7YshUyS/hg+efyEjes3sXrlGg4fOkLFSqv4YvgQjWMXLvk5w7rnzf6Jjes2YmVthfNHzhr74uPjsSliQ5dunWnesjmVKldEpVLx9OkzPNw92LxhC4cPHaZkqRKMGTda49i1q9bxNOgpDRs1YO6CuZiamrBl4xbm/jifhT8tonXb1qlyIefMnIuenh4TvpeeNCEsCltQoYotFeztsLKxYtkc9ywd9zzoOVO+nEHIqxAq16hEny97UblmJfT09IiOisbvhj9/Hz//Rm1607oT4hMwNTelWfsmOLeoT4Uqdugb6BMWEs7eLfvZ+sefXLt0g6Wz3fh29phUxzvUr4ljk3rYO1SmWMmiGBkboVarefTgMdtX7eTY3hPs3bKfClVsaerSWDnuYcAjPHcdxcTMhMm/TKBy9Yrc93vAjK/ncPGMD/+ev0xNxxoa5zq27wTXfK7z2bAeEugL6drXIenZf4307OteWFgYjx4+plLlimnu9/fzp2e3z4iJiUmVLpNT34+fzN49+yhVuhR/Hdyd5ePi4uJo3bQNr14F83m/3owbP1Zj/5XLvlSqXBEjI6M0j586aRq7d+6hQMECHD99lAIFCij7Pu/RF98rvmzfvQ27CrbK9l7dP+P6tRvs3Psn5cqXU7bv+2s/k777nqFffsHwEcOyfA3i7ZGe/bcnISERff3/UlqePnrK0C4jgcx79meNmcvFMz5Uq23P1CXfY2iovf6xN63b/9Y9ipYokmavPcDm37eyacU2ANx2/kqR4tlLd/x+6DSuXbpB9brVmLF0irL9wPZDuM33oPPnHeg38nNl+94t+/n951V07duJPl99pmyPCIvgq0/GYGJWiEUbFmj1uRNZl6d69t06a62uS0N3aq2u94EM0BV5jpmZWbqBPkB52/JUr1kdgOvXrmv13FWrJeWqPnv6LFvHeZ08zatXwQB07Jw6f7d6jWrpBvoAHbt0BCA6Khp/P3+NfSHBSfWWKq0ZlJQuUxpAOS9AeHg4C+f/QunSpRg4ZEC2rkGI/ChloJ8d/rfucfGMDwBffDdIq8FqTuouX7FcuoE+QLP2TZXv797wy3bb7KrYAfDy+UuN7cnjHIqWLKqxvVipYgCE/n9/snXLNhLyKoQh4wZKoC+ApNl4tPUlskeCffFOsrS0AJJ67bTp30tJuawlS5bI1nF7du0BoFLlSlSslP4HlfQkXw+kviYLS0sAAh881Nge+CAw1bFLF//G8+fPGf/9eIyNjbPdDiFEkpMHvYCk4Lp0+VLvTN1mFmbK94mJ2Xt/VKvV3PJNuutUtLhm2k1yvcnjC5IlP0553jvX73Jox2GcWzbAwUkztUe8v1Qq7X2J7JGP2+KdEx8fzyXvpKC8wod2Oa4vMiKSwMBAtm3ZzsH9hwDo0btHlo8PDg7m1ImkP94dOn/8Rm248P9ZfgwMDChbrozGPkenevhe8WXh/IXMWTAHU1MTtm3ZzrWr1ylarKiSwnPj2g22bNpK85bNaNTY+fVTCCGy4eaVpKC3fMVyRIRFsHXln5w9do6Xz19hamZCpRoV6djrY+wdKuepuq96X1O+L2NbOkvHRIRH8jDgEbvW7+HmlVsAuHzSVqNM8gw9nruOUr+pI5WqV+SBfyA71ialO9aoWw1I+oDhNu93jAsUYMDXfbPdfiGE9kmwL945mzdu4fnz5+jp6dGh05tNeRf0JIg2zdul2m5sbMzgoYPo0evTLNe1/68DxMXFYWBggMvHLtluS2REJH/8vgqAFq2aY2ZmprH/83692bNrD6e9ztC0YTNlNh6A0WO/RqVSoVar+XHGbIyMjPhu4rfZboMQQtPjB4+V78f1n8iTwCAMDPQxLliA4JchnDv+D+dPXKDfyN7Znnozt+pOTExk04qtAFSs9mGGdw2uXbrB90OnpdpeyKQgA0b3pY5zLY3tpcqVpGXHZhzefYwJg6dozMZTq4EDNR2TUisP/unJnet+9P+6D9ZFrLLcdpH/SfqN7kiwL94pt27ewvWXXwHo8VkPjQGr2aGnr4e1tTUAoaGhSrA+cMiAbAX68F8KT6OPnLGyKpzttsz64UeCngRhamrKqG9GpdpvZW3FqvV/sOjnJfx99m9iomOwr2bP4C8GKXPzb9/6J1cu+/L1N6MoVrwYkRGRuC5eyqH9hwgLC+PDSh8y4uuvaNCwfrbbJ8T7KOL/U1ke33cCPT09vvh2IC06NMPI2Iinj57ivuAPLp72ZrXreuyq2FGttr3O697gtoW7N/zQ19dn0Df9MyxrYGiApVVSCmBoSBiJCYkYFzCm9/CefNQ67TuDwyZ8QZHiNhzZc5wXT19gXcSaRq0a0uuLT1GpVAS/DGH98s2UsSvNx58mdaacOnSa7at38uj+YywKm9OsfVN6DOqGvoF+1p4skW9IsK87MhvPa2Q2nrzr2bNn9O89kIeBD7GvWoU/1q3USl56YmIiD+4/4I/fV7Hzz10UL14M1+WuWUoRunP7Lt07fQLwRotXrVzxB0t+cf3/nPlzaNOudbbb/+rVKzq7dMXa2orNOzZhYGDAsMFfcu7sOapVr0bZcmU4cewk0dHRLFuxNNtrEwjtk9l4dCers/F84vwZ8fFJ63p8MqArnw3TTO2LjYnlq09G8zzoBbUbODBl0cQstyE36j518DS/THNFrVbTb+TndP4863cEEuITuO/3gI3uW/nn1AXKVSjD5F8mZrtnfvH0XzlxwItZy6ZhX6sKx/edZPEPS7G0ssChfk3uXvfjgX8gLTs246vvZaawtyEvzcZT16Ob1uq6MGh75oWEQgboindCSHAIwwd/xcPAh5QpWwbXZUu0NgBVT0+PsuXKMn3WNPr0+5zHj58wecKULA1uS+7Vt7S0pHGTxpmU1rRt8zaW/JK04NU33415o0AfYNGCxYSEhDBxygQMDQ05efwk586e46MmjVizcRU/zpuF67LFxMfHs/CnX97oHEK8bwoU+m/62497pk75MzI2om3XVgD4el/L1mQB2q77gpc3S2YsRa1W0/7TdtkK9AH0DfQpX7EckxZ8S4NmTty7cx+3+R7ZquOqzzWO7z9FU5fG2NeqQnx8PKtd12FkbMQ8j1l8Pe0rflo1h7IVynB49zEC7tzPVv3i3Sez8eiOBPsizwsLC2P4F19x5/YdihcvhpvHMqw/sM6Vc/X8vCcAN67f4Mb1GxmWTUhIYN+efQC0dWmT7nLxaflr91/MnjkXgGFfDaVPv88zOSJtPt6X2L1zD+3at1N67E8ePwXApz0/RU8v6Ve8Vp1aVLGvzPVrN3j+7PkbnUuI90nhD5JS8kzNTTG3NE+zTImySbN2xcbEKlNTvu26L/9zhZ8mLSQ+PoHmHzdl0Df9styOtLh8krQi9z+nLqSaTjM98fHxuM33wMTMhL4jegNw57ofwS9DqNuotrKglnEBI1p1ag7AhdPeOWqnePfIbDy6I8G+yNOiIqMYMWwU13yv8cEHH7DcYznFSxTPtfMVKfLfAjQP7gdmWPbs6b959v/AuUMac+un59ABT6Z9/wOJiYn07d/njRcFi4+PZ87MOZiYmDD2u/9Wynz8OGnwX8lSmtOHJs/L//jRY4QQGSubxZlskmWnt1FbdV+7dIPZ434iNiYO55YN+HLS0Bz3elrZ/Je68yQwKIOS/9mzcS8P/ALpPayHMg7g2eOktUqKvrZybvK8/M+eSKeDEG+LBPsiz4qOjmbUV1/zr8+/WFpastxjWappKbXt0cNHyveFCqW/cA38l8JjV8GOqtWyNoDuxLETTPruexISEvikR3e+SRGkZ9fGdZu4dfM2X44czgc2H6TaHxsTq/E4Jibmjc8lxPumxv9nlwkPDSc0ODTNMg8Dkt4vChYqiJmF6Vut+9bVO/z4zVxiomOo91EdRv8w4o0XEEvp6aOnyvcFCxbIoGSS50HP2eKxHbvKtrT5f+pRSrExca89jk1VRrwf8nIaT0BAAFOnTqVTp07Y29vz8cea02iHh4fj6upK9+7dqVu3Lg0bNmTYsGHcvHkzVV1hYWFMmjQJR0dHatWqxahRo3j69Gmqct7e3vTo0YMaNWrQrFkz3N3deX0YrVqtxt3dnaZNm1KjRg169OjBpUuXsn19EuyLPCkuNo6xo8bxz7kLmJmbsez3pTmeUz8hISHVL9LrVq1cAyTNd1/TIf3FYMLCwjh+9AQAHTplbW79s2f+5tsx44mPj6dD5w5Mmpr1AX2ve/r0GcuXulGxUkV6fKY5e1Dx4kl3Pq6lWF04ISGBG9eT3pRy886IEPmFU1NHJbd+z6Z9qfbHxsRy8E9PABzq11BS5t5G3f637jFz9GwiI6Ko6ViDb2ePwcAg88n1Ev4/KDg9iYmJ7N64FwDzwuaULJf54oK/L1xFbEwsQ8cP0minTfGku6Svr+J759pdAIoUt0G8X/JysH/79m1OnDhB2bJlsbNLHWs8evSIzZs34+zszKJFi5g5cyZhYWH06NGDu3fvapQdPXo0p0+fZvr06SxYsAB/f3+GDBlCfHy8UiYgIIBBgwZhY2ODm5sb/fr1Y8mSJaxcuVKjrhUrVrBkyRL69++Pm5sbNjY2DBw4kAcPHmTr+iTYF3lOQkICE7+bxGmvM5iYmLDUzZUq9lWydOzDh49wsK+Ng31tdu3YrbHvyZMgPvukNzu37yToyX+3pxMTE7lx/SYTv/2eHdt2ANCzdw/MLdLOpQU4uP8QMTEx6Ovr075j5nPr+3hfYszIb4iNjaWtSxt+mDUtR29YP81ZQGRkJN9Pm4i+vuYUdo0aNwLAw30lD+4/ICEhgRXLfyfoSRBV7CuneRdAiPwsNDhU+QoP+2/GtYiwCI19KQflm1uY0a1fZwB2rt3N/m0HlV7pp4+f8dOkX3ge9AIDQwM+GdA11TknD/+BLk49mDz8h1T7clL3w4BH/DBqNuGhEVStbc/En77N8nih6//eYMqXM/DyPEPwyxBle0J8Ajcu32TWmLn4/J20YOEnA7pm+gHm4hkfzh3/h5adWvChfQWNfRWq2GJR2IIbl29y9K/jqNVq7ly/y8EdhwGo07BWWlUKoRPNmzfnxIkTLFmyhKpVq6baX6pUKTw9PRkzZgyNGjWiRYsWuLu7Y2xszIYNG5RyPj4+eHl58eOPP+Li4kKLFi1YvHgxN2/e5NChQ0o5Dw8PChcuzMKFC2nQoAH9+/dn4MCBLF++nNjYpPeCmJgY3NzcGDhwIP3796dBgwYsXLgQS0tLPDyyN4Be5tkXec4ln0scPnQESMpLHzNibLplixYvyoYt67Jc9/VrN5g+ZQaQtIBWoUIFiYiIVH65ADp27sDosV9nWM+eXX8BUL9hfWxsMu+h+s31N6KjkhbCOnf2HK2atEm37HeTxtGmXfr7z575G8+DnnTp1pmaDjVT7W/avAl1Hety4fwFOrTthLGxMTExMRgYGPDNt2+eNiTEu6pfmyFpbp8weIrGY7cdrspgUoBu/ToT6P+QEwdO4f7TSlb+spoChQoQHpr0gcHA0IBRU7+kfMVy2W7Tm9b955pdhLxKCtQD7gQwrMuIdM/RqXeHVDPz+F68iu/FqwAUKGiMkbERkeGRylSgenoquvTtzMc9Us8SlFJsTCwrFvyBeWFz+nzZK9V+AwMD+nzVi19nLcd15jLc5nsoH2hadmxG2Qq5m5Ip8p68PItOZh9s00rrNTExoUyZMhopOidPnsTc3Bxn5//WqrC1taVKlSqcPHkSFxcXpVyrVq0wMjJSyrm4uODm5oaPjw9OTk54e3sTHh5Ou3b//S4aGRnRqlUrPD09s3V9EuyLPCcx8b9Um5iYmAxzzY2MjdLd97oiNjbM+3ku5/8+j++Vqzx/9pyQkBCMjIwoVboUNR1q0LFLR2rVdsiwnoB79/nXJ6n3q2PnrKXwpLymV6+CMywbHZ3+9cbGxjJn5lwsLCwY9c3INMuoVCoWL/2FJb+44nnwMOFh4VStXpURX38lc+wLkQ0qlYrRP4ygbqPaHNp5BP9b94iOisam2AdUr1uNzr07UNo2/VVqc6PulKmIyR8M0pPcwZDMrrIto6Z9yeV/fPG76U/wi2AiwiIxLmhM6RJFsHeoTMuOzSn3YdlM279t1U6CHgYxYvIwTM3THq/QokMzDAwN+HPNLh7df4x1EWuaf5y0qJZ4/+TlYP9NhIaGcvv2bRo2bKhs8/Pzo3z58qmu1dbWFj+/pJS2yMhIHj9+jK2tbaoyKpUKPz8/nJyclPKvl7Ozs2P16tVER0dToEDm42pAFtVKRRbVEkLkNllUSwjxNuSlRbUaru2ptboKrnqW4f4jR468cd0TJkzA19eXv/76K8NyU6ZM4a+//mL//v0UK5Y0y9SAAQPQ09NLlWYzY8YMTp8+zcGDBwkKCqJx48YsXLiQ9u3ba5SrVasWQ4cOZdiwYSxbtozffvuNK1euaJQ5cOAAX3/9NSdPnqRo0aJZuibp2RdCCCGEELkqP3Xsb9++nS1btjB37lwl0M/LJNgXQgghhBC5SptpPDnpuc+pEydOMHXqVL788ku6dOmisc/c3JwnT56kOiYkJAQLi6Q1KMzMzICkWf1Sio2NJSoqSilnbm5ObGwsMTExGBsbK+VCQ0NRqVRKuayQ2XiEEEIIIYTIxKVLl/j666/p3LkzX3+deiIPW1tb/P39U03z7e/vr+TeFypUiOLFiys5+SnLqNVqpVzy//7+/hrl/Pz8KFGiRJbz9UGCfSGEEEIIkcvy8jz7WXHnzh2GDh1K/fr1+eGH1FPqAjRu3JiQkBDOnj2rbPP39+fatWs0btxYo9yRI0eIi/tv0bl9+/Zhbm5OrVpJ09LWrl0bU1NT9u/fr5SJi4vj0KFDGnVlhaTxCCGEEEKIXJWXZ+OJiorixImkhTIfPnxIeHg4Bw4cAMDR0RG1Ws2gQYMwNjamX79++Pr6KseamppSoULSOhO1atWiUaNGTJo0ifHjx2NsbMwvv/xCpUqVaN26tXLMoEGD2LNnD2PHjqVXr17cunULDw8PxowZo0zHaWxszNChQ3F1dcXKyoqKFSuyceNGgoODGTRoULauT2bjeY3MxiOEyG0yG48Q4m3IS7PxNNnUW2t1nei5Xmt1AQQGBtKiRYs0961ZswaAvn37prnf0dGRtWvXKo/DwsKYM2cOnp6exMfH06hRIyZPnpxq5hxvb2/mzp3L9evXsbKyonfv3gwZMkTjQ5Farcbd3Z0NGzbw8uVLqlSpwsSJE5Xe/6ySYP81EuwLIXKbBPtCiLdBgn0BksYjhBBCCCFyWV5O48nvJNgXQgghhBC5S4J9nZHZeIQQQgghhMinpGdfCCGEEELkKknj0R0J9oUQQgghRK7Sk1hfZySNRwghhBBCiHxKevaFEEIIIUSukjQe3ZFgXwghhBBC5Co9CfZ1RtJ4hBBCCCGEyKekZ18IIYQQQuQqSePRnWwF+y1atHijk6hUKg4fPvxGxwohhBBCiHebpJLoTraCfbVanWpbXFwcz549S6rMwABLS0uCg4OJj48HwMbGBkNDQy00VQghhBBCCJEd2Qr2jx49qvE4NDSU/v37U65cOUaPHk2tWrXQ09MjMTERb29vFi9eTGRkJKtWrdJmm4UQQgghxDtEBujqTo7uqixYsIDY2FhWrVpFnTp10NNLqk5PT4+6devyxx9/EB0dzU8//aSVxgohhBBCiHePSqXS2pfInhwF+0eOHKFp06bo6+unud/AwICmTZumuiMghBBCCCGEyH05mo0nPDycsLCwDMuEhYVlWkYIIYQQQuRfksajOznq2a9QoQL79u3j/v37ae6/d+8e+/bt48MPP8zJaYQQQgghxDtM0nh0J0c9+8OHD2fEiBF07tyZ7t27U6dOHaytrXnx4gUXLlxg+/btREVFMXz4cG21VwghhBBCCJFFKnVa82lmw86dO5k5cyYREREan7bUajWmpqZMnjyZzp0757Sdb01UQoSumyCEyOf8w27ruglCiPeAvaWDrpug6LJniNbq2tFhhdbqeh/keAXdzp0707JlSw4fPszNmzcJCwvDzMyMSpUq0bJlS0xNTbXRTiGEEEII8Y6SnH3dyXGwD2BqavpO9d4LIYQQQgjxPtBKsA8QERHBvXv3iIqKom7dutqqVgghhBBCvONkYK3u5Gg2HoDAwECGDx+Oo6Mj3bt3p2/fvsq+ixcv4uLiwrlz53J6GiGEEEII8Y7SU6m09iWyJ0fB/qNHj+jRowcnT56kRYsWODg4kHK8b82aNXn16hV79+7NcUOFEEIIIcS7SaXFL5E9OQr2XV1dCQkJYe3atSxZsgRnZ2eN/QYGBtStWxdvb+8cNVIIIYQQQgiRfTkK9k+dOkWrVq2oXbt2umVKlChBUFBQTk4jhBBCCCHeYZLGozs5GqAbEhJCyZIlMyyjVquJjY3NyWmEEEIIIcQ7TIJ03clRz/4HH3xAQEBAhmVu3bpF8eLFc3IaIYQQQgghxBvIUbDfsGFDjh07xo0bN9Lcf+HCBf7++2+aNGmSk9MIIYQQQoh3mEql0tqXyJ4cBfvDhw+nQIECfP755yxbtkzp5T9x4gSLFi1i8ODBFC5cmEGDBmmlsUIIIYQQ4t2Tl3P2AwICmDp1Kp06dcLe3p6PP/44zXJbt26lTZs2VK9enY4dO3Ls2LFUZcLCwpg0aRKOjo7UqlWLUaNG8fTp01TlvL296dGjBzVq1KBZs2a4u7trzGgJSanw7u7uNG3alBo1atCjRw8uXbqU7evLUbBfqlQpPDw8MDc3Z/Hixfz111+o1WqGDRvG8uXLsbKywt3dnSJFiuTkNEIIIYQQQuSK27dvc+LECcqWLYudnV2aZfbu3cuUKVNo164dK1aswMHBgREjRqQKvkePHs3p06eZPn06CxYswN/fnyFDhhAfH6+UCQgIYNCgQdjY2ODm5ka/fv1YsmQJK1eu1KhrxYoVLFmyhP79++Pm5oaNjQ0DBw7kwYMH2bo+lfr1jxFvID4+nmPHjvHvv/8SEhKCqakpNWrUoEWLFhgZGeW0+rcqKiFC100QQuRz/mG3dd0EIcR7wN7SQddNUPQ7NEJrda1u/avW6gJITExETy+p/3vChAn4+vry119/aZRp06YN1apV4+eff1a29ezZEzMzM1asWAGAj48PPXv2xMPDg0aNGgHg5+eHi4sLCxcuxMXFBYCpU6fi5eXFgQMHlDh54cKFbNy4kdOnT2NkZERMTAwNGzakd+/efPPNNwDExsbStm1bGjduzPTp07N8fTmajUepxMCAVq1a0apVK21UJ4QQQggh8pG8PBtPcqCfngcPHnDv3j2+/fZbje0uLi7Mnz+f2NhYjIyMOHnyJObm5hrrTtna2lKlShVOnjypBPsnT56kVatWGh3iLi4uuLm54ePjg5OTE97e3oSHh9OuXTuljJGREa1atcLT0zN715et0q/p27cvO3fuzLDMrl276Nu3b05OI4QQQgghhE74+fkBUL58eY3tdnZ2xMXFKWk1fn5+lC9fPtUgYltbW6WOyMhIHj9+jK2tbaoyKpVKKZf8/+vl7OzsePToEdHR0Vluf4569s+fP4+jo2OGZR49esQ///yTk9MIIYQQQoh3mDZ79lu0aJHh/iNHjmjtXJC0rhSAubm5xvbkx8n7Q0NDMTMzS3W8hYUFvr6+QNIA3rTqMjIyomDBghp1GRkZYWxsnOqcarWakJAQChQokKX2ayWNJyNRUVEYGOT6aYQQQgghRB4lU2bqTraj8EePHmk8DgsLS7UNICEhgSdPnnDw4MFMV9kVQgghhBAiK7Tdc58ZCwsLICnmtbGxUbaHhoZq7Dc3N+fJkyepjg8JCVHKJPf8J/fwJ4uNjSUqKkqjrtjYWGJiYjR690NDQ1GpVEq5rMh2sN+8eXPl05lKpWLNmjWsWbMm3fJqtZrvvvsuu6cRQgghhBD5RF4eoJuZ5Lx5Pz8/jRx6Pz8/DA0NKV26tFLu7NmzqNVqjTsZ/v7+VKxYEYBChQpRvHhxJSc/ZRm1Wq3Un/y/v78/lStX1jhniRIlspzCA28Q7Hfu3BmVSoVarWbnzp1UrlyZKlWqpCqnp6eHhYUF9evXp3Hjxtk9jRBCCCGEyCfe3VAfSpcuTbly5Thw4AAtW7ZUtu/bt48GDRoos+o0btyY3377jbNnz9KwYUMgKVi/du0agwcPVo5r3LgxR44c4dtvv8XQ0FCpy9zcnFq1agFQu3ZtTE1N2b9/vxLsx8XFcejQoWzH1dkO9ufOnat8f/78ebp27Sqz7QghhBBCiHdSVFQUJ06cAODhw4eEh4dz4MABABwdHbGysmLkyJGMGzeOMmXK4OTkxL59+7h8+TLr1q1T6qlVqxaNGjVi0qRJjB8/HmNjY3755RcqVapE69atlXKDBg1iz549jB07ll69enHr1i08PDwYM2aM8sHB2NiYoUOH4urqipWVFRUrVmTjxo0EBwczaNCgbF2fVhbVyk9kUS0hRG6TRbWEEG9DXlpUa/ixMVqra1mzX7RWF0BgYGC6M/ysWbMGJycnALZu3cqKFSt49OgR5cuX55tvvqFZs2Ya5cPCwpgzZw6enp7Ex8fTqFEjJk+eTNGiRTXKeXt7M3fuXK5fv46VlRW9e/dmyJAhGuk/arUad3d3NmzYwMuXL6lSpQoTJ05Uev+zKkfB/p07dzhz5gwff/wxVlZWqfa/ePGCvXv34uzsnO7yw3mNBPtCiNwmwb4Q4m3IS8H+V8e/0VpdS5su1Fpd74McLarl7u7OihUrsLS0THO/paUlHh4e/P777zk5jRBCCCGEEOIN5GgC/AsXLtCgQYN0lxnW19enQYMGsqiWEEIIIcR7TObZ150cBfvPnz+nePHiGZYpWrQoz549y8lphBBCCCHEOyxHqSQiR3IU7BcsWJAXL15kWObFixeplvoVQgghhBDvD+nZ150cfdCqWrUqhw8fVlYQe11ISAienp7Y29vn5DRCCCGEEEKIN5CjYP+zzz4jODiYvn37psrLP3/+PH379iU0NJTPP/88R40UQgghhBDvLj2VSmtfIntylMbTsmVL+vfvz6pVq+jbty9GRkZ88MEHPH/+nNjYWNRqNYMGDdJYbUwIIYQQQrxfJEjXnRwF+wATJkzAycmJDRs2cOXKFYKCgjAzM6N+/fp89tlnNGnSRBvtFEIIIYQQQmRTjoN9gGbNmqVaQUwIIYQQQgiQAbq6pJVgPz9RIT+MQojcVfWTj3XdBCHEe0DtGajrJij0JL7SGZn2VAghhBBCiHwqWz37lStXRk9Pj71791K+fHkqV66cpdsyKpWKa9euvXEjhRBCCCHEu0vSeHQnW8F+vXr1gKTFtFI+FkIIIYQQIj0yG4/uZCvYX7t2bYaPhRBCCCGEEHmHDNAVQgghhBC5SiZA0R0J9oUQQgghRK6SnH3dyVawP3HixDc6iUqlYvbs2W90rBBCCCGEEOLNZCvY37FjR5rbVSoVarU63e0S7AshhBBCvL9kgK7uZCvYP3LkiMbjxMREfvzxR/7991/69u1L3bp1sba25sWLF/zzzz+sXbsWBwcHJk2apNVGCyGEEEKId4dKlnbSmWwF+yVLltR47O7uzuXLl9m1axdFihRRttva2lKvXj26detG586dOXDgAEOGDNFOi4UQQgghhBBZkqOPWdu2baNdu3YagX5KRYsWpV27dmzdujUnpxFCCCGEEO8wPZVKa18ie3I0G8+TJ08wMjLKsIyxsTFPnjzJyWmEEEIIIcQ7TGbj0Z0c9ewXK1aMw4cPExMTk+b+qKgoPD09KVasWE5OI4QQQgghhHgDOQr2u3fvzoMHD+jVqxeHDx/m1atXALx69YrDhw/z2Wef8fDhQz755BOtNFYIIYQQQrx7VFr8J7InR2k8gwcP5t69e/z555+MHDkSAD09PRITEwFQq9V07dqVwYMH57ylQgghhBDinSS59rqTo2BfT0+P2bNn07lzZ3bs2MHNmzcJDw/H1NSUypUr06lTJ5ycnLTVViGEEEIIIUQ25CjYT+bo6Iijo6M2qhJCCCGEEPmMDNDVHa0E+0IIIYQQQqRHTxbV0pkcP/Px8fGsWrWK7t27U7t2bezt7ZV9169fZ/r06fj7++f0NEIIIYQQ4h2lUqm09iWyJ0c9+9HR0QwcOBAfHx8KFy6MqakpUVFRyv5SpUrx559/YmFhwZgxY3LcWCGEEEIIIUTW5ahnf/ny5Xh7e/PNN99w+vTpVFNsmpmZUa9ePby8vHLUSCGEEEII8e7Kyz37R44c4ZNPPqFWrVo0atSIr7/+mgcPHqQqt3XrVtq0aUP16tXp2LEjx44dS1UmLCyMSZMm4ejoSK1atRg1ahRPnz5NVc7b25sePXpQo0YNmjVrhru7O2q1WuvXBjkM9vfv34+TkxNDhgxJ9wUoXbo0jx8/zslphBBCCCHEO0wPlda+tOncuXOMGDGCChUqsHTpUiZNmsSNGzcYOHAg0dHRSrm9e/cyZcoU2rVrx4oVK3BwcGDEiBFcunRJo77Ro0dz+vRppk+fzoIFC/D392fIkCHEx8crZQICAhg0aBA2Nja4ubnRr18/lixZwsqVK7V6bclylMbz6NEjWrZsmWEZExMTwsLCcnIaIYQQQgghtG7v3r2UKFGC2bNnK53WVlZW9OvXD19fX+rWrQvAkiVLaN++PaNHjwagfv363Lp1i6VLl7JixQoAfHx88PLywsPDg0aNGgFQvnx5XFxcOHToEC4uLgB4eHhQuHBhFi5ciJGREQ0aNODly5csX76cPn36YGRkpNVrzFHPvomJCS9fvsywzIMHD7CyssrJaYQQQgghxDssr6bxxMfHY2JiolGvmZkZgJJW8+DBA+7du0e7du00jnVxceHs2bPExsYCcPLkSczNzXF2dlbK2NraUqVKFU6ePKlsO3nyJC1atNAI6l1cXAgNDcXHx0er1wc5DPYdHBw4evQooaGhae5//PgxJ06cUD4VCSGEEEKI94+eSqW1L23q2rUrd+/eZf369YSFhfHgwQMWLlyIvb09tWvXBsDPzw9I6qVPyc7Ojri4OCW/38/Pj/Lly6f6QGJra6vUERkZyePHj7G1tU1VRqVSKeW0KUdpPIMGDaJv377079+f77//XslHioqK4tKlS8ycOZOEhAQGDBiglcYKIYQQQoj3W4sWLTLcf+TIkSzXVbduXX799VfGjh3LjBkzAKhSpQq///47+vr6AISEhABgbm6ucWzy4+T9oaGhyl2BlCwsLPD19QVQUttfr8vIyIiCBQsqdWlTjoL9evXqMWXKFGbPns3nn3+ubE/+JKSvr8+0adOoVq1azlophBBCCCHeWSotD6zVFm9vb7777js+/fRTmjZtSnBwML/99htffPEFGzZsoECBArpuYo7leAXdzz77DCcnJzZu3Mjly5cJCQnBxMSEmjVr8tlnn/Hhhx9qo51CCCGEEOIdpafS3gq62em5z8ysWbOoX78+EyZMULY5ODjQtGlTdu3aRY8ePbCwsACSeuVtbGyUcslp7Mn7zc3NefLkSapzhISEKGWSe/5fn7wmNjaWqKgopZw25SjY/+effzA1NaVKlSpMnjxZW20SQgghhBAi1929ezdVWlCxYsUoXLgw9+/fB1Dy6/38/DRy7f38/DA0NKR06dJKubNnz6JWqzXy9v39/alYsSIAhQoVonjx4qly8/39/VGr1aly+bUhRx+z+vbty+bNm7XVFiGEEEIIkQ/l1dl4SpQowbVr1zS2PXz4kFevXlGyZEkgac2ocuXKceDAAY1y+/bto0GDBsqsOo0bNyYkJISzZ88qZfz9/bl27RqNGzdWtjVu3JgjR44QFxenUZe5uTm1atXS6vVBDnv2ra2tMTY21lZbhBBCCCFEPpRXc/Z79uzJ7NmzmTVrFs2bNyc4OJhly5ZhbW2tMdXmyJEjGTduHGXKlMHJyYl9+/Zx+fJl1q1bp5RJXoF30qRJjB8/HmNjY3755RcqVapE69atlXKDBg1iz549jB07ll69enHr1i08PDwYM2aM1ufYB1Cpc7A273fffcft27f5888/c2X5Yl2ITojUdROEEPlcwbYVdd0EIcR7QO0ZqOsmKFyvLNJaXSOrj9ZaXWq1mk2bNrFx40YePHiAiYkJDg4OjBkzBjs7O42yW7duZcWKFTx69Ijy5cvzzTff0KxZM40yYWFhzJkzB09PT+Lj42nUqBGTJ0+maNGiGuW8vb2ZO3cu169fx8rKit69ezNkyJBciadzFOwHBQXRs2dPnJ2dGTduHJaWllpsmm5IsC+EyG0S7Ash3oa8FOwv9V2stbq+qva11up6H+Qojefbb7/FzMyM7du3s3v3bkqVKoW1tXWqTyUqlYrVq1fnqKFCCCGEEOLdlFfTeN4HOQr2z58/r3wfGxuLn59fmit/5ZcUHyGEEEIIId4lOQr2b9y4oa12CCGEEEKIfEpPOn515o2m3vTx8aFv377UqlWLOnXqMGDAAC5fvqzttgkhhBBCiHxApdLT2pfInmz37N+8eZP+/fsTExOjbDt79iw+Pj5s3bpVVswVQgghhBAij8j2xyN3d3diYmIYNmwYp0+f5vTp03z55ZdER0ezYsWK3GijEEIIIYR4h6m0+E9kT7Z79i9evEidOnUYPXq0sm3UqFGcP3+ef/75R5ttE0IIIYQQ+YDk7OtOtnv2nz9/Ts2aNVNtr1GjBs+fP9dKo4QQQgghRP6hUqm09iWyJ9vBfnx8PIUKFUq1vVChQsTHx2ulUUIIIYQQQoicy9HUm0IIIYQQQmRGT3LtdeaNgv09e/bw77//amy7f/8+AEOGDElVXqVS4e7u/ianEkIIIYQQ7zhJv9GdNwr2AwICCAgISHPfqVOnUm2TF1gIIYQQQoi3L9vB/pEjR3KjHUIIIYQQIp+SxbB0J9vBfsmSJXOjHUIIIYQQIp+SnH3dkY9ZQgghhBBC5FMyG48QQgghhMhVMn5TdyTYF0IIIYQQuUolaTw6I2k8QgghhBBC5FPSsy+EEEIIIXKVpPHojgT7QgghhBAiV8lsPLojaTxCCCGEEELkU9KzL4QQQgghcpUsqqU7EuwL8X9TJk1l9849mZb7dsI4Pu/bG4BB/QZz4Z+LADRv2Zxflvyc7nH9Ph/AJe9LDPtyKMNHDNNOo4UQ7wS1Z+AbHXf837M0G/cJAP5rz1KuWGkAFm5zZ6zbjHSPe7DhH0rZFKf/T2NYfWjrG51bCG2S2Xh0R4J9IV5jYGCAhYVFuvsLFiyY5vajh49y7eo17Kva51bThBDvqCcvn6a53crMEiNDI6JiogmJCE21/2VYcJrHDe/Ql5+3ufPoxRNtNlMIkQ9JsC/Eaxxq1cRj9e9vdOyvi5fym/tSLbdICPGuK96jdprbjy3YStOaDdh8YjcDfvomy/UVNC7A5N6j+HLJJG01UYhcJbPx6I4kUAmhBc6NGqJSqTjtdQbviz66bo4QIh/bd/4oAIPa9lTSeoTI61Ra/CeyR4J9IbSgYuWKtGrTCoClS6RnXwiRe3afPcS56z4YGRoxvU/W7wYIId5PEuwLoSVfjhiGvr4+F/65yNkzf+u6OUKIfGzyqvkA9G7RhUql7XTcGiEyp1KptPYlskeCfSG0pLxtedp3cAGScveFECK3HPY+xfF/z2Kgb8CMfuN03RwhMqWHSmtfInsk2BfiNZd8/qX5Ry3T/JoyaVqGxw79cigGBgb4XvHl2JFjb6nFQoj30eQ/knr3u3/Unpp2MguYEDmxY8cOOnfuTPXq1XFycmLw4MFER0cr+48ePUrHjh2pXr06bdq0Yfv27anqiI2NZd68eTg7O+Pg4MCAAQPw8/NLVe7u3bsMGDAABwcHnJ2dmT9/PrGxsbl2bRLsC/Ga+Ph4Xrx4keZXWGjqqfFSKlWqJF26dQZgqesy1Gr1W2ixEOJ9dPrqP+w/fww9PT1m9f9O180RIkN5OY1n2bJlzJw5ExcXFzw8PJgxYwalSpUiISEBgAsXLjBixAgcHBxYsWIF7dq14/vvv+fAgQMa9cyaNYutW7cyZswYXF1diY2NpX///oSFhSllQkJC6NevH3Fxcbi6ujJmzBi2bNnC3LlztX5dyWTqTSFeU7denTeeehNgyLAh7N65h9u3bnNg3wHatW+nxdYJIcR/Jq+aTzvHZnxcvyX1q9Tm7+veum6SEGlS5dH+ZT8/P3799Vd+++03mjRpomxv06aN8v2yZcuoUaMGM2YkLWRXv359Hjx4wJIlS2jbti0AT548Ydu2bUybNo3u3bsDUL16dZo1a8amTZsYMmQIAJs2bSIiIoJff/0VS0tLABISEvjhhx8YOnQoRYsW1fo15s1nXoh3WNGiRfikZ9Iv+rKlbkrPgBBCaJv37Sv86bUfgFkDpHdf5F15tWf/zz//pFSpUhqBfkqxsbGcO3dOCeqTubi4cPfuXQIDk1bH9vLyIjExUaOcpaUlzs7OnDx5Utl28uRJGjRooAT6AO3atSMxMZHTp09r8cr+I8G+ELlg0JCBFCxYkIB7AezeuUfXzRFC5GNTVy8gISGBFrUa0cyhoa6bI8Q75d9//6VixYr89ttvNGjQgGrVqtGzZ0/+/fdfAO7fv09cXBy2trYax9nZJc2ClZyT7+fnh7W1NRYWFqnKpczb9/PzS1WXubk5NjY2aeb3a4Ok8QiRC6ysrOjd5zN+d/fAfZk7H3dor+smCSHyqav3brLp+G56t+jCjwPG0/DrTrpukhCpaHMxrBYtWmS4/8iRI1mu69mzZ/j6+nLr1i2mTZtGwYIFWb58OQMHDuTQoUOEhIQASQF5SsmPk/eHhoZiZmaWqn5zc3OlTHK51+sCsLCw0CinTdKzL0Qu6TegL2Zmpjx69JhtW1OP2hdCCG2ZvnYhcfFxNLCvQ3unjAMhIXRBT6XS2pc2qdVqIiMjWbx4MW3btqVJkyYsW5Y0wca6deu0ei5dkZ59IXKJuYU5fQf0ZemS3/Bw9+ADGxtdN0kIkU/deejPas9tDG7Xi5n9v5WFh0S+lp2e+8yYm5tjaWlJ5cqVlW2WlpbY29tz584d2rdPujOfckYdSOqhB5S0HXNzc8LDw1PVHxoaqpHaY25unqouSLpD8HoKkLZIz74QuejzPr0pXNiSZ8+ec/3adV03RwiRj81Y+wsxsTHUqlCNkh8U03VzhNCg0uI/bapQoUK6+2JiYihTpgyGhoap8umTHyfn39va2vL8+fNUqTiv5+jb2tqmqissLIxnz56lyuXXFgn2hchFhUwKMWDwAF03QwjxHnjw7BHu+zbouhlCpCmvzsbTrFkzgoODuX79vw65V69ecfXqVapWrYqRkRFOTk4cPHhQ47h9+/ZhZ2dHqVKlAGjUqBF6enocOnRIKRMSEoKXlxeNGzdWtjVu3JgzZ84odwYADhw4gJ6eHs7Ozlq9tmQS7AuRy3r0+hSbIpLCI4TIfT9uWEJEVKSumyHEO6Nly5ZUr16dUaNGsW/fPo4cOcKwYcMwMjLis88+A2D48OFcunSJ6dOnc+7cOZYsWcJff/3FyJEjlXqKFStG9+7dmT9/Ptu3b8fLy4sRI0ZgZmZGz549lXI9e/bExMSEr776Ci8vL7Zv3878+fPp2bNnrsyxD6BSyxKfGqIT5E1SCJG7CratqOsmCCHeA2rPQF03QXHgwW6t1dW2dEet1QXw8uVL5syZw7Fjx4iLi6Nu3bpMnDhRI8XnyJEjLFq0CH9/f0qUKMEXX3yhLJ6VLDY2ll9++YVdu3YRERFB7dq1mTx5sjJNZ7K7d+8yc+ZMfHx8MDExoVOnTowZMwYjIyOtXlcyCfZfI8G+ECK3SbAvhHgb8lKwfzBQe2vOtCnVQWt1vQ8kjUcIIYQQQoh8SqbeFEIIIYQQuUpPy7PoiKyTYF8IIYQQQuQqWftBdySNRwghhBBCiHxKevaFEEIIIUSu0vZiWCLrJNgXQgghhBC5StJ4dEfSeIQQQgghhMinpGdf5KqoqCj27NrDqZOnuXXjFsHBwaACKysr7KtWoVnzZrRs3YICBQrouqk5suzX5QD07tsbc3OzN6ojKOgp+//aj+8VX+7eucvLl68IDw/H1NQUuwq2tGzdku6fdst00Y2EhAR27dzNwX0HuX3rDqGhoRS2KkzZcmVxdKpH3/59Uj3fQU+CuPDPRa76XuWq71Vu3LhJdFQ01tbWHD11+I2uR4jsKGhcgH6tPsHFsTk17ez5wNwKtVrN0+DnXLx9hZ1nDrL91D6iY6N13dQcmdbnGwAW/fk7IRGhb1SHSqWiSY0G1KtUk7oVa1CvYk3KFy8DwLDFE3D7a12mdRSzKsKYrkNoW68ptsXLYGRgyPPQl1y4dZnf929kz1nPNI+zNi9MM4eG1K1Yk3oVa1KnYnUsTMwBKOBiR0xcTK61u0/LbjSwr0PtCtUp+UExPrAoTHxCAgFBgRzxOY3rrj+489A/02sXuqGS/mWdkUW1XiOLamnP8WMnmDltFs+fP1e2FSxYED09PSIiIpRtNkVs+HHuLJzqO+qimVpR074WAPs891KyZIk3qmP/vgNMGDdReWxkZISRkRHh4eHKNlvb8iz3WE7RokXSrOPp06eM+nI0169dB0BfXx8TExPCwsJI/lU/dPQARYtpLsk9f85PrF+7IVV9EuznDllUS9PH9VviPnoexa3/+7kMj4ogMTERc5P/Pjw/fP6EPvNGcezSGV00UyuSFzkq93l9AoLebMEjCxNzgndeS3NfVoJmpyq12TdrDVbmlgDEJ8QTGR2l8VyvPrSV/j+NSXXs110GsejLH9KsN7NgP6ftjtp7hwJGSR0VCQkJhESEYWFihr6+PgDRsdEMWDCWTcd2ZVjP+yQvLap14vEhrdXVpHhrrdX1PpCefZErdu3YzfQpP5CYmEi58uUYMnQwjRo7Y2lpCUBYWBjnzp5j44bNXDh/Ae8L3u90sK8NxYsXY9iXQ6lTrw6VKlXEwtICgPDwcA7uP8SCeT/j5+fP5AlTWPGHW6rjIyIiGNz/CwLuBWBXwY7/tXfn8TFeexzHP4kkJGSEiH2NorEHTSil9lqutYqqpVRVkVraWmotRVs7LWppLUVRraqdqjW2CmpfErtYksgiIpHk/jEyjEkQkk7E931f87rmec45z5m5N8nvOc/vnNOn3ydUfr0y9vb23L17lzOnz7BxwyYcMlo+GbCxsaFAgQKULO1BqdKluHH9BgvmPXl0UOR5dazXijl9x5EhQwZOXDjDqEVTWLv3L4LDbwFgcHKmToU36Nm0EzXLv071MpVf6GA/pUTcuc2B0/+y/9Rh9p06xIRuQ81ulpJil8GOX774nuwGF85eOUe3SQP4+5AvsXGx5MrmxpD3PqFHk050rNeKzX47WLDpV7P68cDF61fYd+oQ+08dIj4+njFdBiZ+sRTsN8DM1T+z+/gBdh3dz+WbgcTGxZLBNgOVPSrw7YeDqVKyIj9+Op59Jw9x9sq5p+6TSHqnYF9S3MkTJxk14ivi4uJ4o3o1xk361iJtxNnZmTr16lCnXh3WrV3PtcBrVupt2lHeszzlPctbHM+SJQstW7XAxtaGEUO+ZO+evQReDSR3ntxm5SZPmML5c+dxdy/CvJ9/xNn5wShdxowZKVW6FKVKl0r02n0/68NnAz41vV/52x8p86FEHqOsuwczPhlDhgwZWL1nM29/2c0iTScsMpwVO9awYsca3qnxP/K75bFSb9OO0NthGJq+ysMP5sd0HvBUdauV9qJQrvwAdPq2LzuO7DWduxZyg55TB1OqUAneLFeFFtUaWAT701b+yJTf5pje1yhb5T/pN0Dv74dZHIuNi2Xn0X28NfA9Li3eh7NTFtrWbMqonyc/dbvy39BqPNajBCpJcdOmfE90dDQ5c+VkzLejn5iP/1aD+nTo1N7sWHR0NPN/WkC71u2p6vUGXp6VadKwGd9+PY6bN24m2s70aTMoV9KTIYOGJnmtIYOGUq6kpynHPsG+vfspV9KTBnUaAuB34CA9u/tQ4/WaeHlWplXzd1j88xIezXpLaC9Bw7qNKFfS0/R6XF+Sq/RDgfr16zfMzgUHB7Ni+W8A9Pu8r1mg/zQSHoOL/JdGdfqcTA6ZuHTjKu+O7vnEfPylW1cxYfkPZscc7B3o07Iru6es4tbvx4j88wwn5m5lfLeh5Mrmlmg7w9r3JX7jJX78bEKS1/rxswnEb7xkyrFPUKNsFeI3XiJggS8Ar5eqxKqRP3Fj+WEi/zzDwRkb6NG0U5LtJTi3cDfxGy+ZXo/rS2KeNQM3V7Ycpn/7nTmSaJl/Th8GIHMmJ4tzcXFxz3TdBKmVORwWGc7p+/n6eZ/ySYH8t2xsbFLsJcmjkX1JUdeuXWf71u0AvPte26cOOh/+4Q0ODqZ71x6cOH4CMOau29vbc/7cec6fO88fv63iu5lTKVuubMp/AIyj2iOGfklcXBxZsmTm7t27nDp5mrFffc3FCxf5fOBnprJZsmTB1dWVoKAgALJlc8HWNoPZ+Ycl3Bh89HE3uvf8KFn9Ouh3yPTvR+cFbFy/iZiYGFxcXHi92uvJalfEGvK65qaRd20Apvw+l7DI8GS3kSNrdtaP+ZkKxcoAxpzt6JgYShQoSokCRelU7x0aDu7AnuMHUrTvCTrWa8Xsvt9ia2NLWGQ4jhkzUa5oSab1HMUreQvTZ/pwU9nQ2+EEBl8nd3bjfJsbt4KIjYs1O/+whBuD4fMnMGJB8m4EHufcQ/MEPF8pbTayn6BiMePv1gNJ3AykRdmdXSie3x2AgMCLVu6NJEYj+9ajkX1JUfv37jeN3LxZs8YztTF4wBBOHD+BwWDg2wnfsPufXezat4NFSxdSrHgxwsLC6N2zLyEhISnZdQCCQ0IYOXwUrVq/zeatG9mxZzvbd2+jbbs2ACxauJgzp8+ayvcf9LnZBNafl/7MX9s3mV79B33+XP2JiY7h0qXLzP9pAeO/Nf7Br1e/Lq45XM3KHTpoHIkrVqIY9+7dY+b0H2jaqDmvlfemxus1+fjDHvy9Zetz9UUkJb1Zrgq2tsY/QX/4PtvEvfmfT6ZCsTIEh92i1chuZP5fcbI286BSj4Yc9j9OdoMLvw+fjashW0p2HQC3rK7M/GQs01ctIE/rCmRrXgqXZiWZ8ttcAHyadaZkoQcTsXt/P4w8rSuY3r/WsxF5WlcwvRJLUUkNe0/4cfDsUQB++mwCtT2rkeH+AEWubG5M7TmKN8tV4fLNQMYtm/G4ptIENxdXGnrVYsPYRWRxzEzY7XDmbVhm7W6JpCka2ZcUFeBvfIzq4OBA4SKFk13/wP4D7NxhnHw3dtwYqj40Sl2qdClmzp5Os8YtCAoKYtHCxfTo9XGK9DtB1J0oWrzdnIGDH+SRGgzODPiiP/v3/cPpU6fZtHETrxQrmqLXfVTj+k24eNF8dMrGxoZ69esyYtRwi/IXzl8AwMnJkc4dPuDfw/+aVuIJDQ1l545d7Nyxi7bt2jDgi/6p2neRp+FR8BXAOBp/8uLZJ5S2VK20Fw28agLQdkwPNux/cDP7z6nD1O3fluNz/iZ39pz4NO/CsHnjUqbj92V2dGLWmkX4fDfEdCz0dhiffD+UN8tVoay7By3faMix86dS9LrPKz4+nhbDu/LHl3MpXeRVNn2zxGw1nsioO8zfuJyBc8ZyMzTY2t1N1Bfv+jDqfcuBFP+r52k7uifXbyWe6inWpfQb69HIvqSoW7duAWAwGJ7pB3vjBuMoeanSJc0C/QSuOVxp1fptADasS3wd6OfVpWvnRI+/Wcv4pOLs6eQHJgkOHfPj0DG/J6bwZMvugqurK05OD3Jm671Vl169e+KU2TKPNjzcmAKwfesOjvx7hG7dP2T77q1s372Vzds20bR5UwAW/7yEVX/8+cz9F0kpCaPtIeGhz1T/7eqNANh38qBZoJ/g+q2bzFi9AIB3qjd+xl4+3pjF0xI9vnLXegBKFy7xzG3b1M2PTd38KZrCkyAg8AJ1+rdl/f6/AeMKPQnLbtrb2ZHFMTPZnLOm+HVTSsSd2wQGX+fGrSDTsYCrF/D5bih7T/hZsWfyOLYp+B9JnjQ5sn/jxg127tyJv7+/KXh0cXHB3d2dqlWr4uaW+KQrefEdP2bM03/N67Uky3h5v8acWXM5f+48kZF3cHJyTLHrZ82alfwF8id6LmdOY65tWNizbYSTHAsWzzf9O+hmEL/9+juzf5jD339tZdSYkdR7q65Z+YRJc3FxcTT6XyM+7tXddM7VNTtffjWcM2fOcPTfo8z9YS7/a5I6wY/If6XCK6UB2HLQN8kyf/ntZFDbXhTP745TJkcio+6k2PWDwkIICLyQ6LnLQYEAZMuSNgPmxpXrsGjgNKLvxdBtUn/W7fub4PBbeBQsxpB2n9CiWgNqe1alzudt2X/q0JMb/I9N/m0Ok++vCOSYMRM1ylbh6w8G8eeoeSze8jsdvu7Nvdh7Vu6lSNqRpoL9mJgYvv76a5YsWUJsbCxubm5kzWr8ZRkaGsqNGzfIkCEDbdq0YcCAAdjZpanuC5jW0Q8LCyM+Pj7Zo/sJefg5k9g0CjBtCBUfH8+tkJAUDfYzJzJqniBjxowA3Lv33/4Rcc3hygfduvBK8Vf4pEdvhnwxlHKe5cw21nr4CUC79m0Tbad9h3YM+GwQ/v4B3LhxQzfNYlVBYcaf9WcdQXbLapy3khBYJ+bSzasA2NraksOQnQtRl5/pWokJj4xI8lxUtHFjKXs7+xS7XkopnLsAy4fMxN7OnkaftmL7v3tM5/adPEiToe+z8evF1KnwBlN7jqSKTxMr9vbJ7tyNYt2+Lew4spfDMzfStmYz9p44yKQVs63dNXmE0nisJ009C5k0aRIrV65k6NCh7N27l23btrFq1SpWrVrFtm3b2LdvH8OGDeOPP/5g0qRJ1u6uJKKIexHAuHTmuYBzz9zO3btJ78L4snqzZg3y5s1D1J0o1q1ZZ3bOLeeDwD2puRIPHw+8qn0NxLqOXzgDQCaHTJQo8OxzYDI5ZEypLr0UujfuQEaHjPxz+rBZoP+whEC5skeFJJcvTWsi7txm3sblAHR+q42VeyOJsUnB/0jypKlgf+XKlQwcOJB33nkn0SUbs2TJQqtWrejfvz+///77f99BeaJKr1U03b0/y+ov2bIZ83gDryY9WpewAZeNjQ0u2R6sspHBzriixONuFMLDkx6NexEkPPG4dNF8C/TkThjWCItY29bDu03pZ02q1Et2/RuhxnztgjnzJlkmfw7jBlxxcXHcDHsw2TQhxSOTfdI3ClmdDMnu04sgYWL045an9L/6ID2pcO4Cqd6nlHL5pvHvRtE8hazcE5G0JU0F+7dv3yZ37txPLJc7d25u3779H/RIkitX7lxUq14NME4GjYh4uuA6YblOj5KvArB/3z9Jbr6yd88+AAoVLmSWwpNwg3jt2vUkr3H86PGn6k9yJQTPqbVhTILLl68A5mk7AJUre5v+ndQTlQD/B8fz5NUupGJdl29eZc3evwDo1fR9nJ2yPKGGuYQ14GuUqZxkmVqeVQE4dcnfLF//1m3jvJvH7cZbsXiZZPXnaSXc4Fjrhjsu3nj9gjnzJVkmYYddeHy6UlpTJI/xxiTijuKDtEiballPmgr2y5cvz4wZM0wriyQmIiKCGTNm4OnpmWQZsa6ePj1wcHDgWuA1Bnw26IkpOevXbmD+T8ZVM+rWqwPA2TNn2bL5b4uyQTeDWPaL8VHto5NUixU3jlgd/fcoN27csKi7etUaAgOTfmLwPLJkyQxAeFjyNwZK8KS5AKtXreHG/Z1zPSua//+/klcl043ywvmLEq2/cMHPgHGlI1fX7M/cT5GUMvinb4iKjqJAzrwsGjiNjI8ZaQdoVb0xfd/+EIDl21YDULrIqzR9vb5F2ZwuOfiokXFn7qXbzFeg+jfg/kIAxcuZNrl6WLvaLR4bDD+PhM3DXDJb58nBIX/jgEfFYmUoX7RUomW6NnwXgFsRoZy4eOY/69vjZLB9/C7froZsvF/vHQC2J7JRmFif0nisJ00F+0OGDCEgIIAaNWrQs2dPJkyYwKxZs5g1axYTJ06kV69e1KhRg/PnzzN48GBrd1eS8KpHCQYOGYCNjQ3bt26ndcu2/PnHakJvPVhiLzw8nE0bN9OlU1c+79efyNuRAFSoVMG05OawwcPZuH4jsbHGXSaPHT3GR127ExYWhqurK+3av2t23fKe5XHL6UZMTAwDPh3IpUvGyXh37txh2S/L+XLYSAyG1PkDW/QVYxrNn3/8aepvYsqV9KRcSU+mT7PcrKZzhy7MmTWXs2fOmrVx9cpVZnw3k2GDhwNQspQH1Wu8YVbXzs6OT/r6ALB29VqmT5thevoVFBTMsMHDOfqvcSOdj3t251ExMTGEhISYXpGRxv894ok3Ox4WmvorEcnL49DZY/SYOpi4uDgaV66D34x1tKvdgmzOLqYyBidnmldrwF/fLmXpkBk4OxqfAOw4spe1e7cAMLffeFq+0ci0SVeFYmXYMHYR2Q0uBAZfZ/IjkzV3HtnH5ZuBZHTIyOJB35lSVRwzZuLDRu2Y1ecbgsNupcpnPnp/3f0Odd829Tcx8RsvEb/xEsPa9030vMHJGVdDNtMroa3MmZzMjjvYO5jVm7tuCVHRUdjb2bPyy7k0qVLPdJOV3y0Ps/p+S4tqDQD4ftV805OIBDY2NmbtZ838IOX24eNJbWT2rP0e0KYHP342gVqeVcmc6cGTTadMjjSv1oBdk1eSxzUXMfdiGL14apLfq8jLKE0tZ+Pu7s7q1atZvHgx27dvZ/ny5aZlDg0GA+7u7nTr1o02bdqkWtAmKaNFy+a4ZHVh5IhRBPgH8MUA482Zk5MTNjY2ZmlYefPmwavyg6U2R40dyUcffMzJEyf5tM/nZMyYETs7O1Mdg8HAxKnjTSv/JLCzs2Pg4AF82vsz9u/7h0b1GpMlSxaioqK4d+8ezVo0JS4ujj9+X5Xin7d5y+Yc9DvEwvk/s+yX5WTPng0bGxvq1KtDv88T/2P9qBvXbzBl4lSmTJyKnZ0dWbJk4e7du9y58yD9oFSZUkyZNinRIKFh4wacOX2GObPmMuP7mcyaOZssWbKYrYzU59PepjSrhx30O8QHnbpaHA8OCubNqrVM7/PmzcPaTWue6vOIPI2565YQFBbCzN5j8ShYjIUDpgDG9JH4+HjT+u8A5wIv8tfBnab3Hb75hA1jF+H5SmmWD53JnbtRxNyLMdUJDrtF8+EfEBx+y+yasXGx9Jw2mOVDZvJmuSoELPAl9HYYThkdsbezZ87aJWTIYEun+yPFKWn22sVULfUafVp25aPG7bl+6ybx8fEs376az34Y9dTtrPxyLm+Wq2JxfHy3oYzvNtT0vtO3fcx2lD1/7RIdv+nDvM8nUjBnPlZ+OZfY2FjuREeRxTGzqdwq340Mn2+5xn/BnPk4t3B3on26vGS/2XubupbLGD9rv+0y2NGp3jt0qvcOcXFxhEWGExsXh0tmAxkyGEf9Q2+H8f64fhw4/W+i/RPr0oi89aSpYB+MgVy3bt3o1q2btbsiz6lWnZpUfr0yq1auYvvW7Zw6dZpbIbewsbEhb768lCpVktp1a1G7bm0cHB6M4mTPnp0Fi+exZNEvrF+7nnMB54mJiaFgoYJUr/EGnbp0THLZyNp1ajFj1vfM+mEOx44cIzY2lhKvFqdV61Y0b9mMIYOGJlrveRlvJGL5ddlv+J/1JzDwmnFp0Pv7RDyNL0d/yc4dOzmw/wCBgdcICQ7B1taWvPny4lHyVerWr0u9+nVNf9gS49OnF5W8KrFk0RL+PXyEsNAwcuRwxbNiBdp3bEfZcmVT4NOKpKyVu9az8cA2OtZtRSPv2pQt4kGOrNmJj48n4OoF9p8+zIoda1mxYy3RMdGmejdDg6ni05QeTTvS5s0mlMhfFAd7e05d8mf1ns18s3Q6gcGJz+H5fec66g18ly/a+lCpeFky2Gbg4NmjTF+1gB/X/8KPn6X8ZlYAP61fSgbbDHRt8C4lCxWjgFte49KgWf+71LqlW1fhd+YIPs27ULNcFQrnKkBGewcCg69z4MwRFmz6lSVbVv5n/Xkac9ctISQilFrlX8ejYDFyZcuBwSkLweG3OHHxDOv3b2X22sVcC7FM4ZQ0Qrn2VmMTn9ozCl8wUbGR1u6CiKRzjm8Vt3YXROQlEL/x0pML/Uf239yVYm1VyvF6irX1MkhzI/siIiIikr4ojcd6FOyLiIiISKrSkpnWk6ZW4xERERERkZSjYF9EREREUtWLss7+7du3qV69OiVKlODff81Xdlq2bBn169enTJkyNGnShC1btljUDw8PZ9CgQXh5eeHp6YmPjw/Xr1suFHDgwAFat25N2bJlqVmzJj/88EOqbcypYF9EREREUtWLEux///33ie6Xs3r1aoYMGUKDBg2YNWsW5cuXp2fPnhw8eNCsXO/evdm5cyfDhw9n3LhxBAQE0LVrV7ONM8+fP0+XLl1wc3Nj5syZdOzYkSlTpjB37txU+UzK2RcRERGRl97Zs2dZtGgR/fv3Z9iwYWbnpkyZQqNGjejduzcAlStX5tSpU3z33XfMmjULAD8/P3bs2MGcOXOoVs24p02RIkVo2LAhGzZsoGHDhgDMmTOHbNmyMWHCBBwcHKhSpQrBwcHMmDGD9u3bmy1HnhI0si8iIiIiqcrGxibFXqll1KhRtGnThiJFipgdv3jxIufOnaNBgwZmxxs2bIivry/R0cb9P7Zt24bBYKBq1aqmMu7u7nh4eLBt2zbTsW3btlG7tvkeQw0bNiQsLAw/P78U/1wK9kVEREQkVaX1NJ5169Zx6tQpevToYXHO398fwOImoGjRosTExHDx4kVTuSJFiljckLi7u5vaiIyM5OrVq7i7u1uUsbGxMZVLSUrjEREREZEXRu3atR97fvPmzclq786dO4wdO5Y+ffqQJUsWi/OhoaEAGAwGs+MJ7xPOh4WF4ezsbFE/a9asHDlyBDBO4E2sLQcHBxwdHU1tpSQF+5Im3b59m3179nHkyFGOHTnG0SPHuHXrFgC//7mCIu5FHt9AEvbt3c8Hnbo+sdzfO/8iW7ZsiZ47f+48Py9YxJ7dewm8GkhsbCw5crhStnxZ3mnTikqvVUq03pbNW9i3dz9Hjx7j2tVAQkKMnydnTjcqVKpAm3db41HSI9G6UVFRfD91OuvWric4KJg8efPQ4u3mdHy/A7a2iT+g8921m48+6E7bdm0Y8EX/J35mkfQmObuH1uj3NtsO77Y47u1RgT4tPqBaaS9yGLIREhHK3pMHmfr7j2w6sD0lu8vE7sPp3eIDAP4+5EvNT1slWu6NMt78r3JdqpV+jRL5i+LslJmQ8FAO+R/j579+Y/7G5Umu6lGiQFFaVGvAm+WqULpwCXIYsnMnOoqTF/1Z6bue71bOI/R2WKJ1bW1t6d/6YzrXb0MBtzwEhtxg8ZaVDJs/nuiY6ETrlCxUHL/p61i3/2+aDu38DN+KpBepPbH2eUyfPh1XV1datmxp7a6kCgX7kibt8d1LH5++qda+ra1tksE8gK1N4gH0X5u2MOCzgdy9excAe3t77OzsuHo1kKtXA1m/dgNdP/qAnj6WjwEnTZjCuYBzpvfOBmfuRN7hwoWLXLhwkT9+X0Xvvp/QsXMHs3rx8fH08enHrh3GrcYdHR25cP4Ck8ZP5srlK3wxdJDFtaKjoxk9cgw5cuSgh8/HT/w+RNKjwGDL5e4eZnByximTI3ej73Ik4ITF+c9bf8yYzgOwtbUlLi6OWxFh5MianSZV6tGkSj2+WjSFwT9+kyJ9rVCsDD2bdnpiuUHv9uKr9x/cvN+LvUfEndvkzJaDuhWrU7didTrXb03jIZ0Ij4wwq/t6qUrsnPS76X1cXByht8MwODnj9Wp5vF4tz0eN2tPgi/YcPXfS4trf9xpNt8bvARBx5zaFcuVnQJselCnyKo0Hd0y0v9/7jOZebCw+3w19im9B0rOUzLVP7sj941y+fJm5c+fy3XffmUbdIyMjTf99+/ZtsmbNChhH5d3c3Ex1w8KMN8YJ5w0GA4GBgRbXCA0NNZVJGPlPuFaC6Oho7ty5YyqXkhTsS5qV3TU7pUqVpFTpUuTMlZMvh41MsbZz587F2k1rklUnJCSEwQOHcPfuXTxKejBoyEBKlymFra0tly5eYtL4yWzcsIlZM2ZT5fXKVKxU0ax+/bfqkTdfXjwrlCdv3rzYO9gTFxfHqZOnmTblO7Zv3c6EcRMpU7Y0FSpVMNXz3bWbXTt2kTdvHr6bOQ33ou4c+MePnh/1Ytkvy2nXoR2FCxcyu9bc2T9y4fwFRn/9VaKPFEVeBnlaV3jseb8Z6ylftBR/7tlMcPgts3P/q1KXrz8w3kjPXruYQXPHcuNWEFkcM9O7xQeM6NCPL9714ci5kyzZsvK5+mljY8PM3mOJj49n/6lDVCpeLsmy9hnsCQoLYd6GZSzd9if7Tx4iNi6WbM4u+DTrzOB2PlQvW5nZfb+l9ajuFnWjY6L5dccaFmxawbbDu7kdFUkmh0w0r/oWk7oPp0DOvPw58ic8utQkKjrKVLd4fne6NnyXkPBbNPyiA7uPH6BkoeKsG72QRt61qe1Zjc1+O8yu175OS2qUrcwXP37N+WtP/5RF0qe0OrJ/6dIlYmJi+PDDDy3OdejQgXLlyjF+/HjAmJP/cK69v78/9vb2FChQADDm3fv6+hIfH292cxMQEEDx4sUBcHJyIk+ePBa5+QEBAcTHx1vk8qcETdCVNKlGzeps2b6ZaTOm0r3nR1R+vbK1u8TWLdu4ffs2ABOnjKdsuTKmFJr8BfIz5tvRFCxo/IHfvPEvi/of9+pOsxZNKVS4EPYO9oDxCcOrHiWYOHk8+QvkB2Dl73+Y1duzey8Anbp0wr2o8ZdAhYqetHi7OfHx8ezbs8+s/KWLl5g760cqeVWi0f8aptTHF0lXyhUtSfmipQCYt3GZxfkRHfoBsOvofrpO+Iwbt4IA44j2qJ8n89OGpQCM7TKQDLYZnqsvvZp1plLxckz9/UeOJDKi/rDfdq6lSPsq9Jv5JXuOHyA2zrgeeEj4LUYsmMDIhZMBeKfG/yiYM59Z3dOXA3i185u8O7ona/f+xe0o4+hlVHQUi7f8zjv3bw4K5y7AOzUam9WtVb4qtra2zFqzmN3HDwBw7Pwpvlk6HYDantXMymfNbODbDwdz8uJZvl0641m+FpH/hIeHB/Pnzzd7DRw4EIARI0YwbNgwChQoQOHChVm3bp1Z3TVr1lClShXTqjrVq1cnNDQUX19fU5mAgACOHTtG9erVTceqV6/O5s2biYmJMWvLYDDg6emZ4p9Rwb6kSRkyPN8fz9QQFGT8Y+/i4kKevHksztvb21OsRDHAONknOewd7ClewnjXf+P6DbNzoffnKuTPb/6Hu2Ah443Frfu5/wnGfvU1sbGxDBo8IFl9EHmZdKxrzIe/FnKDNXvMb85zZ8+J5yulAZi0Ynai9Sf8alxXu1Cu/NQo9+yDEfly5GFkx0+5fDOQYfPHP7H8Yf/jFuk5D0u4CQGoWLys2bkrQYEEBF5Isu7Ww74EXDWer1jMvK6rwZj26B943uz4mSvnAMiRNbvZ8dGd+5Mrmxs9pw0m5l4MIml16U2DwYC3t7fZy8PDOH+uVKlSlCplHBTo1asXf/75J1OmTGHPnj0MGzaMw4cP8/HHD1JlPT09qVatGoMGDWLt2rX89ddf+Pj4UKJECerVq2cq16VLF4KDg+nXrx++vr7MmzePOXPm8NFHH6X4GvugYF/kqeXNlxeAW7ducfXKVYvz9+7d4/TJ0wB4lHw1WW3fvXuXE8eNOcP5Hgnqs7q4AHDp0mWz4xcvXLp//kF+36aNm9m+bQfvdXyPoq8UTVYfRF4WGWwz8G6tZgAs+ut30+h4godHxE9eOptoG6cvB5h22axboXqiZZ7GlB5fYsjsTN8ZI4i4c/uZ20kQFBZi+neGJCbvP7Z+uLF+hgzmdRPadc9tnjJYNG8hi+tWLF6Wbo3e45e//0jxSczy4krrS28+SePGjRk5ciR//vknXbp04cCBA0ybNs1iJH7SpEm8/vrrDB06lH79+lG4cGF++OEH7OweZM4XKlSIOXPmEBgYyIcffsjcuXPx8fGhc+fUmcSunH15KQWHhNC6ZVvOnTsHQM6cOXnNqxJt27WhWPFiidap8WYNcuTIwc2bN+nj0888Z//SZSaPn8yFCxd5pdgrNGvR7Kn6EXorlFOnTjNrxiyuXL5ChgwZaNX6bbMyXt6v8dOcn/hpzk94eb9GEfciHPQ7yIrlv2FjY4NXZS8AIiPv8O2YceTOnZtu3S1zD0XEqIFXTXJlM06ySyyF5+GVbJJK0bG1sTWNMJYqVPyZ+tG4ch1aVGvAxn+2sXTrqmdq41E1yj54yvCklKBHZXN2oXThEsa6AeZ1txwyLhDQtWFbVuxcy57jB3i14Ct8/o4x9SchX9/GxobpPqO5HRVJ3xlfPvPnELEmb29vTp60/Plp1aoVrVolvkpWAmdnZ0aPHs3o0aMfW65ChQosXbr0sWVSioJ9eSlF3YnixPETGAwG7ty5w4XzF7hw/gK/r1jJJ318LFbEAXBycmTq9Mn07tWX48eO075tB9NqPHfu3MHZOQut321Nr096PPYx3J9/rOaLAYMtjmd3zc6IUcNN6TwJXq9ahcpVvNntu4dmjVvg5ORkWing7Xdamibnzvx+JoGBgYyfPA4nJ8fn+XpE0rVO9d4B4ODZoxw6e8zi/IXrD56ilSxUHL8zRyzKeBR8xTRnJ49rzmT3wSmTI9N6juJu9F16TP0i2fUTY2Njw4iOxrkGvsf+4cSFM8mqP6TdJ2RyyETY7XCWb19tdu7kxbPMXruYDxq0ZfeUP4i4c5ssjpkBWLt3i2kE/6PG7XmtRHn6zhjBlSDLVUnk5ZVWJ+i+DJTGIy8VZ2dnOnXuyOJlP7PXbzfbd29lzwFf5s6fTXnPcsTGxjJh3ETW/Lk20folS5Vk1tyZlCxlzOeLiYkx5efHxNzjdkQEtyMe/yg+U6aMuLq6kt01uylYcHFx4dPP+/F61SoW5W1sbJg0bSLvdWxHzlw5iY6OpkCBAvj07mladvPM6bMsnP8zVau9Tp26xs1GFi1czP8aNKVSOS/+91YTfl6w6Nm+NJF0JJuzC429jT8j8zZYjuqDMY8/4Sag39sfJpoj3L/1gzxdZ0fLTXie5MuOn1IoV36+XTaD05cDkl0/MSM7fUal4uWIuRfDJ98PS1bdepVq4NPMmEIwbP54boYGW5TpNqk/g3/8Bv+r53Gws+fi9St8u3QGLUYY9y5xc3Hlq/c/59+AE0z5bS4Ard9swqGZG7mz+gznf97DiI6fPveEZnkxpdWc/ZeBTXxSO2+8pKJiI63dBUnE5ctXaFi3EfB8m2o9Tkx0DB90/pCDBw6SK3cu1m1aY7Fh1fKlvzJm1Fhcc7jS59PeVKxUgUyZHDl18iRTJk7l0MHDuOV046eFP1pMqE1MdHQ0R/49ypSJU/E74Mdr3q8xccr4ZC+X2blDF478e5Tlvy+lYKGCzJz+A99PnU7efHmp9FpF9u/7hyuXr9DD52M+/OjJm4pJ6nJ869nSPuT5fdykI9/1+oqYezHka1vJtMrOo1q+0YjlQ2cC8NuOdQz+6RtOXfInr2su+r3dDZ/mnYmOicbB3oHjF05TskvNp+5DuaIl2f/dGi5ev0LJD2qZLXH542cT6FTvncduqpWYNjWb8vOAqdja2vLpzJGMXz7zqet6FCzG9okrcDVkY5XvRpoMff+p6z5s3ueTeK92C2r0e5sdR/byXp2WLOg/mcDg66zfv5VKxctSqnAJZq9dTNcJnz3TNSR5krOxXGo7GfpvirVVImuZFGvrZaCRfZH77B3s6dHLOFp3LfCaacJsAr8DBxk5fBR2dnbMmjuTBg3fImfOnBgMzlR6rRKzf5qFu3sRbly/weQJU57qmg4ODlSo6Mnsn36gTNky7Nuzj++nTk9Wv1etXMU/+w/wfpdOFCxUkJCQEGbNmE3OXDn5ZfliRo7+kkVLF+Lq6sqsGbMJCQl5cqMi6VTCKjxr921JMtAH+HX7aobNM66O07zaWxyd/Rcx685x/uc9+DTvzNq9W1h9fxWfWxGJ7zibGBsbG37o/Q12Gezw+X6oWaD/rBp61WLeZxOxtbVl8m9zkhXoF85dgA1jF+FqyMauo/tpM/rZNuF7o4w3Heq+zfxNy9lxZC92Gez4tusXREbdobJPEzp924dKPRpx2P84HzRoS+nCyVvEQF58L/oE3ReZgn2Rh5QpW9r070sXzVe/WXQ/DeaN6tUo9MgmVmAM3N9pa8wF3vb3tiS3q0+MnZ2daWLu7yuefoOesLBwJoybRP4C+enc1Tgat3vXbmJiYmjYqAGGrAYAsmXLRsPGDYiOjma3756nbl8kPXm14Ct4vVoegHkblj+x/JcLJ1LFpwnzNizj6LmTnL92ia2Hd9NtUn8aDe5ANmfjSljJScPpWK8VXq+WZ/3+v9lycBeZMzmZvexsjVPpMtjamo49+oTxYbU8q7J86A842Dswd90SeicjfSdfjjxs/mYJ+d3y4HfmCA2/6EBkVPKWDQawy2DH972+IiT8Fp/P+gqASsXLkjt7Tv7cs8m0oVZUdBSz1hh/jza6n0olLw8F+9ajCboiT8nf3/gH/dGlMR+WkLoTFRVF0M0gcrjleOr2c+YyTvKLjIwkKCgYV9fsT6gBUydNJTgomC9HDSdjxowAXLm/LGi+/HnNyiasy5/YsqEiL4OEiblBYSGs2r3xqersPn7AtInUw2xtbSlbxDh3x/fYP0/dh0I5jZvn1a/0JhGrTiVZ7o0y3qbzb/ZrxdbDvhZlqpZ6jT9G/Ihjxkz88vcfdJ34+VP3I1c2NzZ/swT3PIU4fuE09Qa8S+jtp39C8bA+LbtSusirfDxlkOlpSaFcxs8ZEHjRrGzCuvyFcj05zVFEUoaCfZGH/Hv4waobjwbLtrbG0YSrV5MOlh8+55Q5c7KuffmhdfSfZjWdo0eOsnzpr9SqU4s3arxhcf7u3Wiz91FRd5PVH5H0xNbWlvdqtwBg8ZaVz73R01uV3iS7wYW70XctVq75L7xWojyrv5pHZkcn/vDdwHtjfYiLi3uquq6GbGz6ejElChTl7JVz1P68TaITcp9Gfrc8DH2vN/tPHWLGnwsszmdyyGj23jFjpme6jrz4NLHWepTGIy+Vx6XWxMTEmPLl3dxy4FHSw+x8wpKYO7fv4tq16xb1Y2Nj+f23PwAo+kpRs4D93r17j+1XVFQUSxb9AoBHSQ8cHR8f7MfFxfHVl6NxyOjA5wM+NTuX9/7uvseOHjc7fvTIUeP5fOY3MSIvgzoV3iBfjtxA0qvwPC2DkzPffmhcPnfOuiXJCpRHLJiATd38Sb4SdsD9+5Cv6dijo/pl3T1YN3ohWTMb2PDPVlqN/Ih7sY//HfNw39eP+ZnSRV7lwvXL1PqsNVeDrj11/x81+eMvcXTIRPfJg8x+vyak7lQsZj6R8rXi5QA4dy3tTByV/4pNCr4kORTsS5oVEhJieoWFPni8HB4ebnbu0dGsciU9KVfSk+nTZli02aLJ2yxauJjz586b/jDFxsZy4B8/PuzcDb8DfgD49OllkSebkFMfERFB964fs2/vfmJiYoiPj+dcwDn6+vTj6L/GgPrd99qa1V3z5xp69+rL1r+3mX2W6OhofHftpnPHDzh9yrj7brfuT14tZ9kvyzl65BgfftSVPPeD+wTeVbyxt7dn04ZN7Ny+E4Ad23aweeNfODg44H1/Ey6Rl0nCxNyj506y/9ShJ5bP6ZKDsR8MokKxMmS0N45O22Wwo4FXLXZO+o2ShYpz+nIAA+aMSbT+lnHLiN94iS3jnu/G4lHF87uzYewishtc+PuQL02HdiE6JvrJFTGu7b/6q3lULF6WK0GB1PqstdmeAsn11ms1aVGtAbPWLrL4TvefOsy1kBtUK+1Fx3rG775i8bJ81Pg9ANbcn9wsIqlPaTySZr1ZtVaix9u37Wj2fs3G1eR7ytFq/7P+fD36G8A4oTZzZiciIm4TE2N8pG9nZ0cPn49p0qyJRd3ynuXp93lfJo6bxNkzZ/mgU1fs7Oyws7MjKurBihotW7Xk7XdamtWNj4ctm7ewZfMWADJnzoy9vT3h4eHExsaa+tPv877UrP34JfyCgoKZOnka7u5F6NCpvcX57Nmz06VrZ2Z8P5OPu/UkU6ZMpv718PmYbNmyPdV3JZJeODtlodnr9QGYt/HJE3PBmG7Sv/XH9G/9MXFxcdyKCMPZKTP2dvaAcUOuRl90JDwyItX6nZj+rT827f5btogHAQt2JVl23LKZZivztKzWkGqljTf7BidndkxckWTdXcf+oeWIpAceMtpnZFrPkVwPucnAOWMtzt+LvceAOWP48dMJ/PTZRL7vNRqnTMYnlrPXLubIuRMWdSR9UxqP9SjYl5fKkOGDOeh3kGNHjxMcHEx4WDgZM2akcJHCVKxUgXfatKLoK0WTrN+hU3sqVqrAL4uX4ffPAa5du05sbCw5c+WkTNnStHy7BVXfqGpR740a1Rg87Av27t7L6dNnCLoZREREBJkzZ6ZgoYJ4eb9Gy1YtyF8g/xM/w4RvJxAeFs74SeOwt7dPtEz3nh+RxTkLvyxaytWrVylQoABt32tDu/bvPv2XJZJOvFPjfzhlciQ2NpaFm5MOcB92IzSIofPGUat8VYrnL0IOQ3ZCwkP599wJfvl7FXPXLSE2LjaVe27p4SeO2Q0ujy2bsMNtYnWzOGa2OP+w7M6Pb3vQu70omrcw74/ry62I0ETL/LR+KdExMQxo04Pi+Ypw8foVftywlC8XTHxs25I+aRUd69GmWo/Qploiktq0qZaI/BfS0qZa/uEnU6wtd+cSKdbWy0Aj+yIiIiKSqjSybz0K9kVEREQkVSln33q0Go+IiIiISDqlkX0RERERSVVK47EeBfsiIiIikqoU7FuP0nhERERERNIpjeyLiIiISKrSBF3rUbAvIiIiIqlKaTzWo2BfRERERFKVRvatRzn7IiIiIiLplEb2RURERCRVKY3HehTsi4iIiEgqU7BvLUrjERERERFJpzSyLyIiIiKpSuP61qNgX0RERERSlVbjsR6l8YiIiIiIpFMa2RcRERGRVKaRfWtRsC8iIiIiqUqhvvUojUdEREREXkpr166le/fuVK9enfLly9O0aVOWL19OfHy8Wblly5ZRv359ypQpQ5MmTdiyZYtFW+Hh4QwaNAgvLy88PT3x8fHh+vXrFuUOHDhA69atKVu2LDVr1uSHH36wuF5KUrAvIiIiIqnMJgVfKeenn37C0dGRAQMGMH36dKpXr86QIUP47rvvTGVWr17NkCFDaNCgAbNmzaJ8+fL07NmTgwcPmrXVu3dvdu7cyfDhwxk3bhwBAQF07dqVe/fumcqcP3+eLl264ObmxsyZM+nYsSNTpkxh7ty5Kfq5HmYTn5q3Ei+gqNhIa3dBRNI5x7eKW7sLIvISiN94ydpdMLkedSXF2sqZKW+KtRUcHEz27NnNjg0ZMoQ1a9awb98+bG1tqV+/PqVLl2b8+PGmMm3atMHZ2ZlZs2YB4OfnR5s2bZgzZw7VqlUDwN/fn4YNGzJhwgQaNmwIwNChQ9mxYwfr1q3DwcEBgAkTJrB48WJ27txpOpaSNLIvIiIiIi+lRwN9AA8PDyIiIoiMjOTixYucO3eOBg0amJVp2LAhvr6+REdHA7Bt2zYMBgNVq1Y1lXF3d8fDw4Nt27aZjm3bto3atWubBfUNGzYkLCwMPz+/lP54gIJ9ERERERGTf/75h1y5cpElSxb8/f0BKFKkiFmZokWLEhMTw8WLFwHjKH6RIkUs9hNwd3c3tREZGcnVq1dxd3e3KGNjY2Mql9K0Go+IiIiIpCqbFMy1r1279mPPb968+Znb3r9/P2vWrKF///4AhIaGAmAwGMzKJbxPOB8WFoazs7NFe1mzZuXIkSOAcQJvYm05ODjg6OhoaiulaWRfRERERF56gYGB9OnTB29vbzp06GDt7qQYjeyLiIiISKpKyZH95xm5T0pYWBhdu3bFxcWFqVOnYmtrHA/PmjUrYByVd3NzMyv/8HmDwUBgYKBFu6GhoaYyCSP/CSP8CaKjo7lz546pXErTyL6IiIiIvLSioqLo1q0b4eHhzJ492ywdJyG//tF8en9/f+zt7SlQoICpXEBAgMV6+QEBAaY2nJycyJMnj0VbCfUezeVPKQr2RUREROSldO/ePXr37o2/vz+zZ88mV65cZucLFChA4cKFWbdundnxNWvWUKVKFdOqOtWrVyc0NBRfX19TmYCAAI4dO0b16tVNx6pXr87mzZuJiYkxa8tgMODp6ZkaH1FpPCIiIiKSuh5dpSatGDFiBFu2bGHAgAFERESYbZRVsmRJHBwc6NWrF59++ikFCxbE29ubNWvWcPjwYRYuXGgq6+npSbVq1Rg0aBD9+/cnY8aMTJw4kRIlSlCvXj1TuS5durBq1Sr69etH27ZtOXXqFHPmzKFPnz6pssY+aFMtC9pUS0RSmzbVEpH/QlraVCvo7rUUa8s1Y64nF3pKtWrV4vLly4me27x5M/nz5wdg2bJlzJo1iytXrlCkSBH69u1LzZo1zcqHh4czZswYNm7cyL1796hWrRqDBw+2eFpw4MABxo4dy/Hjx8mePTvt2rWja9euqXZDpGD/EQr2RSS1KdgXkf9CWgr2g+9eT7G2smfMmWJtvQyUsy8iIiIikk4pZ19EREREUlnazNl/GSjYFxEREZFUpVDfepTGIyIiIiKSTmlkX0RERERSVVpdevNloGBfRERERFKZgn1rURqPiIiIiEg6pZF9EREREUlVGte3HgX7IiIiIpLKFO5bi9J4RERERETSKY3si4iIiEiq0mo81qORfRERERGRdErBvoiIiIhIOqU0HhERERFJVTaaoGs1CvZFREREJJUp2LcWpfGIiIiIiKRTGtkXERERkVSlcX3rUbAvIiIiIqlKS29aj9J4RERERETSKY3si4iIiEgq08i+tSjYFxEREZFUpVDfepTGIyIiIiKSTmlkX0RERERSmcb2rUXBvoiIiIikKq3GYz1K4xERERERSacU7IuIiIiIpFNK4xERERGRVGWjnH2rsYmPj4+3didERERERCTlKY1HRERERCSdUrAvIiIiIpJOKdgXEREREUmnFOyLiIiIiKRTCvZFRERERNIpBfsiIiIiIumUgn0RERERkXRKwb6IiIiISDqlYF9EREREJJ1SsC8iIiIikk4p2BcRERERSacU7IuIiIiIpFMK9kVERERE0ikF+yIiIiIi6ZSCfRERERGRdErBvoiIiIhIOqVgX0REREQknVKwL/IMzp8/z9ChQ2natCklS5akcePG1u6SiKRDZ8+e5f3336d8+fJUrVqVb775hujoaGt3S0ReIHbW7oDIi+j06dNs3bqVcuXKERcXR3x8vLW7JCLpTGhoKB07dqRw4cJMnTqVa9euMXbsWKKiohg6dKi1uyciLwgF+yLPoFatWtSpUweAAQMGcOTIESv3SETSmyVLlnD79m2mTZuGi4sLALGxsYwYMYJu3bqRK1cu63ZQRF4ISuMReQa2tvrREZHUtW3bNqpUqWIK9AEaNGhAXFwcO3futF7HROSFoohFREQkDfL398fd3d3smMFgwM3NDX9/fyv1SkReNAr2RURE0qCwsDAMBoPF8axZsxIaGmqFHonIi0jBvoiIiIhIOqVgX0REJA0yGAyEh4dbHA8NDSVr1qxW6JGIvIgU7IuIiKRB7u7uFrn54eHh3LhxwyKXX0QkKQr2RURE0qDq1auza9cuwsLCTMfWrVuHra0tVatWtWLPRORFonX2RZ7BnTt32Lp1KwCXL18mIiKCdevWAeDl5UX27Nmt2T0RSQfatGnDggUL6NGjB926dePatWt88803tGnTRmvsi8hTs4nX1p8iyXbp0iVq166d6Ln58+fj7e39H/dIRNKjs2fPMnLkSPz8/MicOTNNmzalT58+ODg4WLtrIvKCULAvIiIiIpJOKWdfRERERCSdUrAvIiIiIpJOKdgXEREREUmnFOyLiIiIiKRTCvZFRERERNIpBfsiIiIiIumUgn0RERERkXRKwb6IiIiISDqlYF9EJJ0aMGAAJUqU4NKlS9buioiIWImCfRGRhwwcOJASJUrg7e1NdHT0c7U1depUSpQowZ49e1KodyIiIsmjYF9E5L6IiAjWrVuHjY0Nt27dYtOmTdbukoiIyHNRsC8ict/atWuJjIykU6dO2Nrasnz5cmt3SURE5LnYWbsDIiJpxfLly7Gzs+ODDz7gxIkT+Pr6cvnyZfLly2dRdt++ffz4448cPHiQsLAwXF1dKVOmDJ06daJSpUq0b9+evXv3AtChQwdTvXz58vHXX38BUKJECby8vFiwYIFF+7Vq1QIwlQUICAhg2bJl+Pr6cuXKFSIjI8mbNy9169ale/fuZM6cOUW/DxERefEp2BcRAc6cOcPBgwepUaMGOXLkoFmzZvj6+rJixQp69eplVnbevHmMGTOGTJkyUadOHfLmzcu1a9f4559/WL9+PZUqVaJ58+YA7N27l+bNm5tuGJydnZ+5jxs3buTXX3/F29sbLy8v4uLiOHToELNmzWLfvn0sXLgQe3v7Z/8SREQk3VGwLyICppSdpk2bAlC3bl1GjBjBihUr6NGjB7a2xqzHEydOMHbsWNzc3Fi8eDH58+c3tREfH8/169cBaNGiBZcvXzYF+97e3s/dx6ZNm9KpUyccHBzMjk+bNo2pU6eydu1amjRp8tzXERGR9EM5+yLy0ouJiWHlypVkyZKFOnXqAJA5c2bq1KnDlStX2LVrl6nskiVLiIuLo3fv3maBPoCNjQ25cuVKtX7mypXLItAHeO+99wDw9fVNtWuLiMiLScG+iLz0Nm/eTHBwMG+99RYZM2Y0HW/WrBmA2UTdw4cPA1CtWrX/tI9gfHKwfPly2rVrh5eXFx4eHqZlQgHTUwUREZEESuMRkZdeQjCfENwnqFKlCrly5WLz5s3cunULFxcXIiIisLGxwc3N7T/v56hRo1i4cCF58uShVq1auLm5mUb6p02b9tz7AoiISPqjYF9EXmpXr15l586dwIN0mMT88ccfdOjQAWdnZ+Lj47lx48Zzp+zY2Nhw7969RM+Fh4ebTeYNCgri559/pkSJEvzyyy84Ojqazt24cYNp06Y9V19ERCR9UrAvIi+1FStWEBcXR8WKFSlSpIjF+djYWH777TeWL19Ohw4dKFu2LEeOHGHHjh20bNnysW0nTOqNi4tL9HzWrFm5du2axfFLly4RFhZmFuxfvHiR+Ph4Xn/9dbNAH2D//v1P/JwiIvJyUrAvIi+t+Ph4VqxYgY2NDV9//TUFChRItNy5c+fw8/Pj33//pU2bNvzyyy9MmjSJypUrm63Bn7AaT8KIv4uLC2B8epCY0qVLs2PHDvbu3YuXlxcA0dHRjB071qJs3rx5AfDz8yMuLs50IxEYGMiECROe7QsQEZF0T8G+iLy0du/ezaVLl/Dy8koy0AfjMpp+fn4sX76cESNGMGjQIEaNGkXjxo2pXbs2+fLl48aNG+zfv58aNWrwxRdfAODt7Y2NjQ0TJkzg9OnTODs7YzAYTOlC77//Pjt37uTDDz+kUaNGODo6snPnTgwGg8WcgJw5c1K/fn3Wr19Py5YtqVy5MkFBQfz9999UrlyZCxcupN4XJSIiLyytxiMiL62EibkJG2AlpWHDhmTKlInVq1cTFRXFe++9x7x58/D29mb79u3MmTOHnTt38uqrr9KgQQNTvVdeeYUxY8aQLVs2Fi5cyOTJk5k7d67pfLVq1Zg0aRIFChRg5cqVrFu3jqpVqzJ37txEl9gcM2YMnTt3JjQ0lIULF3Lo0CE6derE+PHjU+gbERGR9MYmPj4+3tqdEBERERGRlKeRfRERERGRdErBvoiIiIhIOqVgX0REREQknVKwLyIiIiKSTinYFxERERFJpxTsi4iIiIikUwr2RURERETSKQX7IiIiIiLplIJ9EREREZF0SsG+iIiIiEg6pWBfRERERCSdUrAvIiIiIpJOKdgXEREREUmn/g+q7gVxYi8mtQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ch(cb_over)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FxR9jZVL7iI"
      },
      "source": [
        "<a name='oversampled-comp'></a>\n",
        "## Comparison"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab_over"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "tx_W85KlGfKU",
        "outputId": "9752ae7d-1fac-4937-adb2-ee81206e9120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Recall    F_beta        F1       AUC  0-1_Loss\n",
              "model        data                                                   \n",
              "RandomForest train  0.990158  0.990224  0.990323  0.990324  0.009676\n",
              "             val    0.296009  0.239935  0.186844   0.60945  0.101903\n",
              "AdaBoost     train  0.775906  0.770682  0.762978  0.758961  0.241039\n",
              "             val    0.722838   0.32441  0.177584  0.729274  0.264799\n",
              "XGBoost      train  0.950184  0.938345  0.921131  0.918643  0.081357\n",
              "             val     0.51663  0.316533  0.200215  0.683283  0.163247\n",
              "LightGBM     train  0.897511  0.885921  0.869087  0.864806  0.135194\n",
              "             val    0.615299  0.340825  0.204194  0.716822  0.189687\n",
              "CatBoost     train  0.897218  0.886961  0.872008  0.868308  0.131692\n",
              "             val    0.599778   0.34068  0.206725  0.713352  0.182057"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43d454d6-2a80-4295-8273-96a76ec5616e\">\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></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th>data</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">RandomForest</th>\n",
              "      <th>train</th>\n",
              "      <td>0.990158</td>\n",
              "      <td>0.990224</td>\n",
              "      <td>0.990323</td>\n",
              "      <td>0.990324</td>\n",
              "      <td>0.009676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.296009</td>\n",
              "      <td>0.239935</td>\n",
              "      <td>0.186844</td>\n",
              "      <td>0.60945</td>\n",
              "      <td>0.101903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">AdaBoost</th>\n",
              "      <th>train</th>\n",
              "      <td>0.775906</td>\n",
              "      <td>0.770682</td>\n",
              "      <td>0.762978</td>\n",
              "      <td>0.758961</td>\n",
              "      <td>0.241039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.722838</td>\n",
              "      <td>0.32441</td>\n",
              "      <td>0.177584</td>\n",
              "      <td>0.729274</td>\n",
              "      <td>0.264799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">XGBoost</th>\n",
              "      <th>train</th>\n",
              "      <td>0.950184</td>\n",
              "      <td>0.938345</td>\n",
              "      <td>0.921131</td>\n",
              "      <td>0.918643</td>\n",
              "      <td>0.081357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.51663</td>\n",
              "      <td>0.316533</td>\n",
              "      <td>0.200215</td>\n",
              "      <td>0.683283</td>\n",
              "      <td>0.163247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">LightGBM</th>\n",
              "      <th>train</th>\n",
              "      <td>0.897511</td>\n",
              "      <td>0.885921</td>\n",
              "      <td>0.869087</td>\n",
              "      <td>0.864806</td>\n",
              "      <td>0.135194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.615299</td>\n",
              "      <td>0.340825</td>\n",
              "      <td>0.204194</td>\n",
              "      <td>0.716822</td>\n",
              "      <td>0.189687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">CatBoost</th>\n",
              "      <th>train</th>\n",
              "      <td>0.897218</td>\n",
              "      <td>0.886961</td>\n",
              "      <td>0.872008</td>\n",
              "      <td>0.868308</td>\n",
              "      <td>0.131692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.599778</td>\n",
              "      <td>0.34068</td>\n",
              "      <td>0.206725</td>\n",
              "      <td>0.713352</td>\n",
              "      <td>0.182057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43d454d6-2a80-4295-8273-96a76ec5616e')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
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              "    }\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43d454d6-2a80-4295-8273-96a76ec5616e 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-43d454d6-2a80-4295-8273-96a76ec5616e');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSgljix1ItuG"
      },
      "source": [
        "* Random forest is massively overfit.\n",
        "\n",
        "* AdaBoost appears to suffer the least from overfitting. Especially promising is the AUC scores. The validation AUC in particular is higher than any other model in the table.\n",
        "\n",
        "* XGBoost is plagued by overfitting, but the validation scores show promise. With tuning the overfitting might be controllable.\n",
        "\n",
        "* LightGBM and CatBoost show near-identical performance in every metric. Still these models require much tuning and improvement."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVXG2YN5yz5Z"
      },
      "source": [
        "<a name='tuning'></a>\n",
        "# Model Tuning\n",
        "\n",
        "[AdaBoost](#abc_tuning)\n",
        "\n",
        "[XGBoost](#xgb_tuning)\n",
        "\n",
        "[LightGBM](#lgbm_tuning)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcITk1AfE_4K"
      },
      "source": [
        "<a name='abc_tuning'></a>\n",
        "## AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7xYvIKKZFCe-"
      },
      "outputs": [],
      "source": [
        "params={\n",
        "    'n_estimators':np.arange(50,251,50),\n",
        "    'learning_rate':[0.5,1.0,2.0]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will vary the number of estimators and the learning rate of our AdaBoost classifier."
      ],
      "metadata": {
        "id": "cfgNMNcHulyr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwzHLWLaVBpV"
      },
      "source": [
        "### Base Estimator: Decision Stump"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HZo8CoNFCcD",
        "outputId": "1b3a721e-5651-493f-899a-4c4d8a5ba3f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 15 candidates, totalling 75 fits\n",
            "Fit completed in 48.8 minutes.\n"
          ]
        }
      ],
      "source": [
        "abc_tuned1=AdaBoostClassifier(random_state=1)\n",
        "\n",
        "go1=GridSearchCV(\n",
        "    estimator=abc_tuned1,\n",
        "    param_grid=params,\n",
        "    scoring=['recall','f1','roc_auc'],\n",
        "    refit='roc_auc',\n",
        "    cv=5,\n",
        "    n_jobs=-1,\n",
        "    verbose=1,\n",
        "    return_train_score=True\n",
        ")\n",
        "\n",
        "start=time.time()\n",
        "go1.fit(Xt_over,yt_over)\n",
        "print(f'Fit completed in {np.round((time.time()-start)/60,2)} minutes.')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_abc1=go1.best_params_\n",
        "\n",
        "best_abc1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmRyYoGuyX9X",
        "outputId": "c9428e74-64e6-4f4c-ba99-0f30f083ca86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 1.0, 'n_estimators': 250}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abc_tuned1=AdaBoostClassifier(\n",
        "    random_state=1,\n",
        "    **best_abc1\n",
        ")\n",
        "\n",
        "abc_tuned1.fit(Xt_over,yt_over)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "hUenIH_7yfNQ",
        "outputId": "8195b587-c0d6-4a2c-d41f-2ec1f5246bcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(n_estimators=250, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(n_estimators=250, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=250, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(abc_tuned1,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "HOqXBPzIyq_t",
        "outputId": "eecea521-98c6-475d-ec22-054fda78a927"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.792440  0.791087  0.789066  0.788164  0.211836\n",
              "val    0.667406  0.332010  0.189308  0.722856  0.226081"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbc62b1f-d019-44a8-9351-0a8c3185e63a\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.792440</td>\n",
              "      <td>0.791087</td>\n",
              "      <td>0.789066</td>\n",
              "      <td>0.788164</td>\n",
              "      <td>0.211836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.667406</td>\n",
              "      <td>0.332010</td>\n",
              "      <td>0.189308</td>\n",
              "      <td>0.722856</td>\n",
              "      <td>0.226081</td>\n",
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              "  </tbody>\n",
              "</table>\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",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dbc62b1f-d019-44a8-9351-0a8c3185e63a 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-dbc62b1f-d019-44a8-9351-0a8c3185e63a');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ch(abc_tuned1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "ezNtdbOVyyjw",
        "outputId": "95bbd0ae-d3ad-4a0e-d351-e5b4547bbc92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.773919\n",
              "Precision     0.110297\n",
              "Recall        0.667406\n",
              "F1            0.189308"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e04e83b-f6c7-4ee8-9510-e5da8f0fcdf8\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.773919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.110297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.667406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.189308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e04e83b-f6c7-4ee8-9510-e5da8f0fcdf8')\"\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",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e04e83b-f6c7-4ee8-9510-e5da8f0fcdf8 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-1e04e83b-f6c7-4ee8-9510-e5da8f0fcdf8');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUaTnrxqVFH2"
      },
      "source": [
        "### Base Estimator: XGBClassifier\n",
        "\n",
        "An attempt was made to train an AdaBoost Classifier with XGBoost as the base estimator. After 5h 47m the  training was terminated. Given limited computing resources, this configuration was too expensive to be feasable."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base Estimator: Logistic Regression"
      ],
      "metadata": {
        "id": "qPQDDXhl767j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LogisticRegression(\n",
        "    random_state=1,\n",
        "    max_iter=1000\n",
        ")\n",
        "\n",
        "lr.fit(Xt_over,yt_over)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "DCl06Vo_7-8L",
        "outputId": "b6933e3c-6c57-47cf-8f4d-2a55cace7c92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(lr,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "R6jTqnkYCgRG",
        "outputId": "49bbcba3-a066-4c2d-c4ea-e6ba01f5d133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.758551  0.749218  0.735643  0.727411  0.272589\n",
              "val    0.739468  0.307119  0.163621  0.719442  0.299000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec7ee540-99eb-476a-8bb7-e7f7c50a30b4\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.758551</td>\n",
              "      <td>0.749218</td>\n",
              "      <td>0.735643</td>\n",
              "      <td>0.727411</td>\n",
              "      <td>0.272589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.739468</td>\n",
              "      <td>0.307119</td>\n",
              "      <td>0.163621</td>\n",
              "      <td>0.719442</td>\n",
              "      <td>0.299000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec7ee540-99eb-476a-8bb7-e7f7c50a30b4')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "      background-color: #434B5C;\n",
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              "          document.querySelector('#df-ec7ee540-99eb-476a-8bb7-e7f7c50a30b4 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-ec7ee540-99eb-476a-8bb7-e7f7c50a30b4');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performance here is interesting: Recall and AUC good and *consistent*, indicating a reliable fit. However, precision, and consequently F1/$F_\\beta$, suffer massively when training is compared with validation.\n",
        "\n",
        "I conjecture this is due to training data being oversampled but not validation data. This sampling is of course intentional, as we generally only need the oversampled data for model training. However, in the case of logistic regression, training on oversampled data artificially inflates the regression intercept, meaning performance suffers on non-oversampled data."
      ],
      "metadata": {
        "id": "2ESdz_VE3ZjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# logistic regression on non-oversampled data\n",
        "logit=LogisticRegression(max_iter=1000)\n",
        "\n",
        "logit.fit(X_ts,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "r_NZysVE6lMW",
        "outputId": "727abd47-c1cc-40ba-d985-3d7824bcb469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(logit,output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "AMWaNFIH6vtW",
        "outputId": "612802aa-1321-48bf-9918-54cbaddfaded"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.006173  0.007679  0.012110  0.502812  0.039858\n",
              "val    0.017738  0.021786  0.033126  0.507773  0.040954"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.006173</td>\n",
              "      <td>0.007679</td>\n",
              "      <td>0.012110</td>\n",
              "      <td>0.502812</td>\n",
              "      <td>0.039858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.017738</td>\n",
              "      <td>0.021786</td>\n",
              "      <td>0.033126</td>\n",
              "      <td>0.507773</td>\n",
              "      <td>0.040954</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6c022ef-f323-40a7-ab52-05fe8adf488c')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a6c022ef-f323-40a7-ab52-05fe8adf488c');\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": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Do note that logistic regression still sees massive improvements when trained on oversampled data. A quick look at a model trained on the original scaled data finds that every metric is at rock bottom. For instance, AUC is at 0.5, no better than guessing."
      ],
      "metadata": {
        "id": "D0Mt2fbk7jYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ch(lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "pVeDg1Jd9akk",
        "outputId": "6e6e4681-d30e-4ef0-c2eb-8f17f46e4c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.701000\n",
              "Precision     0.091987\n",
              "Recall        0.739468\n",
              "F1            0.163621"
            ],
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              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.701000</td>\n",
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              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.091987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.739468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.163621</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3fa95eb-8e17-4d09-b724-ef7d3e998dee')\"\n",
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              "      \n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b3fa95eb-8e17-4d09-b724-ef7d3e998dee 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-b3fa95eb-8e17-4d09-b724-ef7d3e998dee');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While logistic regression suffers massive overpredictions of false positives, this model does have the advantage of providing stellar recall and AUC in both training and validation. We will try building a gradient booster on top of this logistic regression and optimize it for precision."
      ],
      "metadata": {
        "id": "fRZocav2-MIS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "abc_tuned3=AdaBoostClassifier(\n",
        "    estimator=LogisticRegression(\n",
        "        random_state=2,\n",
        "        max_iter=1000\n",
        "    ),\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "abc_tuned3.fit(Xt_over,yt_over)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "RiBEbI5Y-smv",
        "outputId": "89ea669a-e479-4124-deb7-8064cb2599d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   random_state=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(abc_tuned3,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "htexFZB6IWQ9",
        "outputId": "ce794524-bbb7-4cab-b9d7-bc79f3b485ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.740373  0.732576  0.721182  0.713763  0.286237\n",
              "val    0.722838  0.294410  0.155850  0.705900  0.309699"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.740373</td>\n",
              "      <td>0.732576</td>\n",
              "      <td>0.721182</td>\n",
              "      <td>0.713763</td>\n",
              "      <td>0.286237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.722838</td>\n",
              "      <td>0.294410</td>\n",
              "      <td>0.155850</td>\n",
              "      <td>0.705900</td>\n",
              "      <td>0.309699</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-564e9838-636c-4787-b8e9-1cd5e856401f')\"\n",
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              "  </svg>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-564e9838-636c-4787-b8e9-1cd5e856401f 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-564e9838-636c-4787-b8e9-1cd5e856401f');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ch(abc_tuned3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "DFkMSMmsIbWv",
        "outputId": "34c380a3-be98-4800-ed52-23c99f0f6ae6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.690301\n",
              "Precision     0.087341\n",
              "Recall        0.722838\n",
              "F1            0.155850"
            ],
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.690301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.087341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.722838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.155850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70c87c60-266f-4732-8c5e-15b49f7e006d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-70c87c60-266f-4732-8c5e-15b49f7e006d 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-70c87c60-266f-4732-8c5e-15b49f7e006d');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before tuning, the AdaBoost results are comparable to the logistic regression trained on the same data. Note that the subsequent boosting rounds did not reduce the high rate of false positives. This casts doubt on the possibility of remedying the poor precision with some hyperparameter tuning."
      ],
      "metadata": {
        "id": "xnRdmLhSm1PJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "go3=GridSearchCV(\n",
        "    estimator=abc_tuned3,\n",
        "    param_grid=params,\n",
        "    scoring='precision',\n",
        "    cv=5,\n",
        "    n_jobs=-1,\n",
        "    verbose=1,\n",
        "    return_train_score=True\n",
        ")\n",
        "\n",
        "go3.fit(Xt_over,yt_over)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "ZrpFM8kVInAR",
        "outputId": "ec2e5344-a454-4446-9829-70ac656b659a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 15 candidates, totalling 75 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000,\n",
              "                                                                       random_state=2),\n",
              "                                          random_state=1),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.5, 1.0, 2.0],\n",
              "                         'n_estimators': array([ 50, 100, 150, 200, 250])},\n",
              "             return_train_score=True, scoring='precision', verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000,\n",
              "                                                                       random_state=2),\n",
              "                                          random_state=1),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.5, 1.0, 2.0],\n",
              "                         &#x27;n_estimators&#x27;: array([ 50, 100, 150, 200, 250])},\n",
              "             return_train_score=True, scoring=&#x27;precision&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000,\n",
              "                                                                       random_state=2),\n",
              "                                          random_state=1),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.5, 1.0, 2.0],\n",
              "                         &#x27;n_estimators&#x27;: array([ 50, 100, 150, 200, 250])},\n",
              "             return_train_score=True, scoring=&#x27;precision&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   random_state=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LUf2tVyst6WN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27dcf6d2-4d8f-401c-a8d6-f76db0ebef1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 2.0, 'n_estimators': 150}"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "best_abc3=go3.best_params_\n",
        "\n",
        "best_abc3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8kZdwSLat-6L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "e6f12db8-f198-48ca-8a4e-d45cdc07b39d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   learning_rate=2.0, n_estimators=150, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   learning_rate=2.0, n_estimators=150, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(estimator=LogisticRegression(max_iter=1000, random_state=2),\n",
              "                   learning_rate=2.0, n_estimators=150, random_state=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=2)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "abc_tuned3=AdaBoostClassifier(\n",
        "    estimator=LogisticRegression(\n",
        "        random_state=2,\n",
        "        max_iter=1000\n",
        "    ),\n",
        "    random_state=1,\n",
        "    **best_abc3\n",
        ")\n",
        "\n",
        "abc_tuned3.fit(Xt_over,yt_over)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQAt06nPuNdr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "162fb2e2-6852-4542-ebbc-6e642ee1ff35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.741078  0.733895  0.723378  0.716610  0.283390\n",
              "val    0.717295  0.295218  0.156810  0.705639  0.305095"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08e41683-65d6-4f95-ab14-fe341fe039c0\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.741078</td>\n",
              "      <td>0.733895</td>\n",
              "      <td>0.723378</td>\n",
              "      <td>0.716610</td>\n",
              "      <td>0.283390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.717295</td>\n",
              "      <td>0.295218</td>\n",
              "      <td>0.156810</td>\n",
              "      <td>0.705639</td>\n",
              "      <td>0.305095</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08e41683-65d6-4f95-ab14-fe341fe039c0')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08e41683-65d6-4f95-ab14-fe341fe039c0 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-08e41683-65d6-4f95-ab14-fe341fe039c0');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "get_scores(abc_tuned3,sample='over',output='pandas')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scores remain effectively unchanged. AdaBoost with the base estimator still scored best on F1, but overall tuned performance leaves much to be desired.\n",
        "\n",
        "Consider, for example, the business ramifications of the AdaBoost model built on top of the logistic regression. With so many false positives, Santander would be investing in pleasing customers who are not unsatisfied. This is not a beneficial allocation of resources."
      ],
      "metadata": {
        "id": "057ZplYrnqbi"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-t9OZAFY01H"
      },
      "source": [
        "<a name='xgb_tuning'></a>\n",
        "## XGBoost\n",
        "\n",
        "We will next try to improve our XGBoost Classifier with several rounds of cross-validated tuning."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Grid Search"
      ],
      "metadata": {
        "id": "2xzZ3UpJE98I"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zf9fvQyyY6gY"
      },
      "outputs": [],
      "source": [
        "params={\n",
        "    'eta':np.linspace(0.05,0.3,6),\n",
        "    'max_depth':np.arange(2,5),\n",
        "    'min_child_weight':[1,2],\n",
        "    'subsample':np.linspace(0.5,0.9,4),\n",
        "    'colsample_bytree':np.linspace(0.5,0.9,4)\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We control the learning rate using `eta`.\n",
        "\n",
        "* To hopefully cut down on overfitting, we limit `max_depth`, which defaults to 6 in the standard XGBoost model.\n",
        "\n",
        "* We experiment with a more conservative algorithm by offering a larger value for `min_child_weight`. Options are 1, the algorithm default, and 2.\n",
        "\n",
        "* To build a more robust model, we limit both `subsample` and `colsample_bytree` to values less than 1. This subsamples data and features respectively during the tree building process to combat overfitting."
      ],
      "metadata": {
        "id": "ZND8DPcgs72P"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gme4Go--L_bs",
        "outputId": "6f431e11-5970-4619-e776-aa9559152fdd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 576 candidates, totalling 2880 fits\n",
            "Fit completed in 133.48 minutes.\n"
          ]
        }
      ],
      "source": [
        "xgb_tuned=XGBClassifier(\n",
        "    tree_method='gpu_hist',\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "go=GridSearchCV(\n",
        "    estimator=xgb_tuned,\n",
        "    param_grid=params,\n",
        "    scoring=['recall','f1','roc_auc'],\n",
        "    refit='roc_auc',\n",
        "    cv=5,\n",
        "    n_jobs=-1,\n",
        "    verbose=1,\n",
        "    return_train_score=True\n",
        ")\n",
        "\n",
        "start=time.time()\n",
        "go.fit(Xt_over,yt_over)\n",
        "print(f'Fit completed in {np.round((time.time()-start)/60,2)} minutes.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBnu-MPycNwG",
        "outputId": "4580e125-3ed8-4ce8-b316-4dfb5d303dc0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0.9,\n",
              " 'eta': 0.3,\n",
              " 'max_depth': 4,\n",
              " 'min_child_weight': 2,\n",
              " 'subsample': 0.7666666666666666}"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "best_xgb1=go.best_params_\n",
        "\n",
        "best_xgb1"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find the model with the greatest AUC subsampled around 77% of the data for each training instance. This, along with the reduced column sample by tree, work to prevent overfitting."
      ],
      "metadata": {
        "id": "NQWnzNr1otNV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "M3htYATqeung",
        "outputId": "404b1a32-4553-40a3-eecb-57c6c8101c5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.9, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eta=0.3, eval_metric=None,\n",
              "              feature_types=None, gamma=None, gpu_id=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\n",
              "              max_leaves=None, min_child_weight=2, missing=nan,\n",
              "              monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, predictor=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.9, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eta=0.3, eval_metric=None,\n",
              "              feature_types=None, gamma=None, gpu_id=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\n",
              "              max_leaves=None, min_child_weight=2, missing=nan,\n",
              "              monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.9, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eta=0.3, eval_metric=None,\n",
              "              feature_types=None, gamma=None, gpu_id=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\n",
              "              max_leaves=None, min_child_weight=2, missing=nan,\n",
              "              monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, predictor=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "xgb_tuned1=XGBClassifier(\n",
        "    tree_method='gpu_hist',\n",
        "    random_state=1,\n",
        "    **best_xgb1\n",
        ")\n",
        "\n",
        "xgb_tuned1.fit(Xt_over,yt_over)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ch(xgb_tuned1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "0Dev4DKuz-fK",
        "outputId": "dcfe44e4-fd96-40c8-84d2-a9d91574c7b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.813207\n",
              "Precision     0.124048\n",
              "Recall        0.614191\n",
              "F1            0.206408"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa9d2810-573f-43f2-82f2-461163b48997\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.813207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.124048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.614191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.206408</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa9d2810-573f-43f2-82f2-461163b48997')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aa9d2810-573f-43f2-82f2-461163b48997 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-aa9d2810-573f-43f2-82f2-461163b48997');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find already fewer false positives than the AdaBoost classifier."
      ],
      "metadata": {
        "id": "RkZrpzy5pQz1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(xgb_tuned1,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "oIVeY5lHHZG6",
        "outputId": "c00f1711-1eb5-46da-fce0-8309622d158c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.892561  0.881539  0.865507  0.861304  0.138696\n",
              "val    0.614191  0.343077  0.206408  0.717797  0.186793"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3a4154f-6a8d-4308-8911-8719842bd32b\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.892561</td>\n",
              "      <td>0.881539</td>\n",
              "      <td>0.865507</td>\n",
              "      <td>0.861304</td>\n",
              "      <td>0.138696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.614191</td>\n",
              "      <td>0.343077</td>\n",
              "      <td>0.206408</td>\n",
              "      <td>0.717797</td>\n",
              "      <td>0.186793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3a4154f-6a8d-4308-8911-8719842bd32b')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b3a4154f-6a8d-4308-8911-8719842bd32b 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-b3a4154f-6a8d-4308-8911-8719842bd32b');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While validation recall is lower than the last AdaBoost model, both F1 and $F_\\beta$ are higher. Moreover, the 0-1 loss is lower than all AdaBoost models, meaning this model made fewer mistakes overall."
      ],
      "metadata": {
        "id": "ETwjy3bRpee6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CV Analysis\n",
        "\n",
        "Let's plot some findings from the cross validation performed during the grid search."
      ],
      "metadata": {
        "id": "WMBRrg_cFPzz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TbptM0cWnHK7"
      },
      "outputs": [],
      "source": [
        "results=go.cv_results_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "lxAW9U_IkeWH",
        "outputId": "c85a6efd-a76f-4179-9135-ab751eb10888"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# figure setup\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Mean Recall, ROC-AUC, and F1 for max_depth',fontsize=20)\n",
        "\n",
        "# recall training\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_train_recall']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(2,5),\n",
        "         a,\n",
        "         label='Train Recall',\n",
        "         linestyle='--',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# AUC training\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_train_roc_auc']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(2,5),\n",
        "         a,\n",
        "         label='Train AUC',\n",
        "         linestyle='--',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# F1 training\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_train_f1']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(2,5),\n",
        "         a,\n",
        "         label='Train F1',\n",
        "         linestyle='--',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# recall val\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_test_recall']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(2,5),\n",
        "         a,\n",
        "         label='Val. Recall',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# AUC val\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_test_roc_auc']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(2,5),\n",
        "         a,\n",
        "         label='Val. AUC',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# F1 val\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_test_f1']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(2,5),\n",
        "         a,\n",
        "         label='Val. F1',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# axes and legend\n",
        "plt.xlabel('Maximum Tree Depth')\n",
        "plt.ylabel('Mean Score')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that recall and F1 trade places as the dominant metric and `max_depth` increases. When `max_depth=2`, we find F1 is greater than recall (both in training and validation). This inverts when we increment `max_depth`.\n",
        "\n",
        "Notice further that the vertical gaps between training and validation averages increase with `max_depth`. This indicates that risk of overfitting increases with `max_depth`.\n",
        "\n",
        "ROC-AUC has the highest scores across the board. It too exhibits the tendancy toward overfitting as `max_depth` increases."
      ],
      "metadata": {
        "id": "-JmTPzzu3w5_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "jZ-vQ1ivfVyk",
        "outputId": "ed4c5e03-cf02-4431-b11a-337e97e4016d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Recall, ROC-AUC, and F1 for eta',fontsize=20)\n",
        "\n",
        "# recall train\n",
        "sns.lineplot(x=results['param_eta'],\n",
        "             y=results['mean_train_recall'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train Recall',\n",
        "             linestyle='--')\n",
        "\n",
        "\n",
        "# AUC train\n",
        "sns.lineplot(x=results['param_eta'],\n",
        "             y=results['mean_train_roc_auc'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train AUC',\n",
        "             linestyle='--')\n",
        "\n",
        "# F1 train\n",
        "sns.lineplot(x=results['param_eta'],\n",
        "             y=results['mean_train_f1'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train F1',\n",
        "             linestyle='--')\n",
        "\n",
        "# recall val\n",
        "sns.lineplot(x=results['param_eta'],\n",
        "             y=results['mean_test_recall'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. Recall')\n",
        "\n",
        "# AUC val\n",
        "sns.lineplot(x=results['param_eta'],\n",
        "             y=results['mean_test_roc_auc'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. AUC')\n",
        "\n",
        "# F1 val\n",
        "sns.lineplot(x=results['param_eta'],\n",
        "             y=results['mean_test_f1'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. F1')\n",
        "\n",
        "plt.xlabel('eta (learning rate)')\n",
        "plt.ylabel('Mean Score')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As learning rate increases, so too do all scores. Interestingly, recall and F1 start about matched (with `eta=0.05`) and then diverge as learning rate increases. AUC values are consistently higher than precision and recall, though one should be careful when directly comparing these metrics."
      ],
      "metadata": {
        "id": "BAwOxvo1qTJ1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gamma Tuning\n",
        "\n",
        "Now we will tune the `gamma` parameter. We use cross validation to prevent overfitting our model on validation data. "
      ],
      "metadata": {
        "id": "__XFRV_bE6Gl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "for i in range(9):\n",
        "  # collect params and set gamma\n",
        "  w=xgb_tuned1.get_params()\n",
        "  w['gamma']=2*i\n",
        "\n",
        "  # convert to XGB DMatrix format\n",
        "  dmat=DMatrix(\n",
        "      Xt_over,\n",
        "      yt_over,\n",
        "      enable_categorical=True\n",
        "  )\n",
        "\n",
        "  # cv\n",
        "  a=XGB_CV(\n",
        "      params=w,\n",
        "      dtrain=dmat,\n",
        "      num_boost_round=250,\n",
        "      nfold=5,\n",
        "      metrics={'auc'}\n",
        "  )\n",
        "\n",
        "  # subplot\n",
        "  plt.subplot(3,3,i+1)\n",
        "  plt.title(f'gamma={2*i}')\n",
        "  plt.plot(np.arange(250),a['train-auc-mean'],label='train')\n",
        "  plt.plot(np.arange(250),a['test-auc-mean'],label='test')\n",
        "  plt.legend(loc='lower right')\n",
        "\n",
        "clear_output()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "855fTQjq0kTy",
        "outputId": "8615b6be-e942-4048-d463-5d6bbb691d2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The shape of the plot for `gamma=8` looks good: high scoring and levels off around 100, which is our set number of estimators in the boosting model."
      ],
      "metadata": {
        "id": "16zuDd7PrOeY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "w=xgb_tuned1.get_params()\n",
        "w['gamma']=8\n",
        "\n",
        "xgb_tuned2=XGBClassifier(**w)\n",
        "\n",
        "xgb_tuned2.fit(Xt_over,yt_over)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "iYr-ZA0yCH7D",
        "outputId": "90d80ee6-a5a1-48f8-8a4b-e7461c17ec3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.9, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eta=0.3, eval_metric=None,\n",
              "              feature_types=None, gamma=8, gpu_id=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\n",
              "              max_leaves=None, min_child_weight=2, missing=nan,\n",
              "              monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, predictor=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.9, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eta=0.3, eval_metric=None,\n",
              "              feature_types=None, gamma=8, gpu_id=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\n",
              "              max_leaves=None, min_child_weight=2, missing=nan,\n",
              "              monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.9, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eta=0.3, eval_metric=None,\n",
              "              feature_types=None, gamma=8, gpu_id=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=4,\n",
              "              max_leaves=None, min_child_weight=2, missing=nan,\n",
              "              monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, predictor=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ch(xgb_tuned2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "exfesUxd_1V2",
        "outputId": "6a033550-d488-4d05-c1e7-b4b423ac0c02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.807594\n",
              "Precision     0.119930\n",
              "Recall        0.609756\n",
              "F1            0.200437"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aac855e0-b251-4efa-bbc3-b7534a77f6cb\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.807594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.119930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.609756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.200437</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aac855e0-b251-4efa-bbc3-b7534a77f6cb')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aac855e0-b251-4efa-bbc3-b7534a77f6cb 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-aac855e0-b251-4efa-bbc3-b7534a77f6cb');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gamma tuning has not made a perceptible difference."
      ],
      "metadata": {
        "id": "c33B_vRLrpYe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# gamma-tuned model\n",
        "get_scores(xgb_tuned2,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Z7zq4dZCITHv",
        "outputId": "1bae35bd-3501-4961-a005-dfb02839fb22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.892346  0.880714  0.863824  0.859327  0.140673\n",
              "val    0.609756  0.335611  0.200437  0.712749  0.192406"
            ],
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              "      <th>Recall</th>\n",
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              "      <td>0.892346</td>\n",
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              "      <td>0.609756</td>\n",
              "      <td>0.335611</td>\n",
              "      <td>0.200437</td>\n",
              "      <td>0.712749</td>\n",
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              "    [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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-975ebb09-b728-406b-8579-d5c0ec11829f 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-975ebb09-b728-406b-8579-d5c0ec11829f');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tuned model with default gamma\n",
        "get_scores(xgb_tuned1,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "XcI8lrQzFs_t",
        "outputId": "695b7b1f-6a9b-44e8-8cc7-17ac7e586750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.892561  0.881539  0.865507  0.861304  0.138696\n",
              "val    0.614191  0.343077  0.206408  0.717797  0.186793"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91d8ef74-8669-4ceb-a641-c3b96edd2eb2\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.892561</td>\n",
              "      <td>0.881539</td>\n",
              "      <td>0.865507</td>\n",
              "      <td>0.861304</td>\n",
              "      <td>0.138696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.614191</td>\n",
              "      <td>0.343077</td>\n",
              "      <td>0.206408</td>\n",
              "      <td>0.717797</td>\n",
              "      <td>0.186793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91d8ef74-8669-4ceb-a641-c3b96edd2eb2')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91d8ef74-8669-4ceb-a641-c3b96edd2eb2 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-91d8ef74-8669-4ceb-a641-c3b96edd2eb2');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing the first tuned XGBoost model with the gamma-tuned model, we see no improvement. If anything, scores have marginally slipped. "
      ],
      "metadata": {
        "id": "L0MBf1QTrv9_"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnsteXv7E4D8"
      },
      "source": [
        "<a name='lgbm_tuning'></a>\n",
        "## LightGBM"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tuning with Oversampled Data"
      ],
      "metadata": {
        "id": "65odVsiqXUUo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params={\n",
        "    'max_bin':[100,150,200],\n",
        "    'min_gain_to_split':[0.001,0.005,0.01],\n",
        "    'feature_fraction':np.linspace(0.5,0.9,4),\n",
        "    'max_depth':np.arange(3,10,2)\n",
        "}"
      ],
      "metadata": {
        "id": "MEd76l-is1MM"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We will experiment first with different values of `max_bin`, which controls the bucketing behavior of the model. Default is `max_bin=255`; lower values can negatively impact training accuracy but do generally cut down on overfitting.\n",
        "\n",
        "* With `min_gain_to_split`, we require a minimum gain to split two nodes. The default value is zero, so this should hopefully produce more meaningful divisions.\n",
        "\n",
        "* We subsample features in training instances using `feature_fraction`. This combats overfitting.\n",
        "\n",
        "* The general behavior of LightGBM is to let trees grow without limit. Restricting `max_depth` will contain tree growth with the goal of preventing overfitting."
      ],
      "metadata": {
        "id": "6H2rGFDzujda"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lg_tuned=LGBMClassifier(random_state=1)\n",
        "\n",
        "go=GridSearchCV(\n",
        "    estimator=lg_tuned,\n",
        "    param_grid=params,\n",
        "    scoring=['recall','f1','roc_auc'],\n",
        "    refit='roc_auc',\n",
        "    cv=5,\n",
        "    n_jobs=-1,\n",
        "    verbose=1,\n",
        "    return_train_score=True\n",
        ")\n",
        "\n",
        "start=time.time()\n",
        "go.fit(Xt_over,yt_over)\n",
        "print(f'Fit completed in {np.round((time.time()-start)/60,2)} minutes.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygH3HhDfonLE",
        "outputId": "3c09ce3e-a51f-489a-c0e7-b2820fa1187b"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n",
            "Fit completed in 82.99 minutes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_lg=go.best_params_\n",
        "best_lg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYeD-dtDz8qI",
        "outputId": "0a9c57a3-5919-47da-9e50-245481d6ccc1"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'feature_fraction': 0.7666666666666666,\n",
              " 'max_bin': 200,\n",
              " 'max_depth': 9,\n",
              " 'min_gain_to_split': 0.001}"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lg_tuned=LGBMClassifier(\n",
        "    random_state=1,\n",
        "    **best_lg\n",
        ")\n",
        "\n",
        "lg_tuned.fit(Xt_over,yt_over)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "kfg2sBCWUNhl",
        "outputId": "ca647b35-ec17-4454-faa0-24dae9652fd1"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(feature_fraction=0.7666666666666666, max_bin=200, max_depth=9,\n",
              "               min_gain_to_split=0.001, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(feature_fraction=0.7666666666666666, max_bin=200, max_depth=9,\n",
              "               min_gain_to_split=0.001, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(feature_fraction=0.7666666666666666, max_bin=200, max_depth=9,\n",
              "               min_gain_to_split=0.001, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ch(lg_tuned)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "LpzxmiVZUuhP",
        "outputId": "c75674ce-3145-40a9-eabd-5013782e8922"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.579409\n",
              "Precision     0.069717\n",
              "Recall        0.780488\n",
              "F1            0.128000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5ce3300-cd65-472c-b4f9-68b40e6a7e80\">\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>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.579409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.069717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.780488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.128000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5ce3300-cd65-472c-b4f9-68b40e6a7e80')\"\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",
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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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a5ce3300-cd65-472c-b4f9-68b40e6a7e80 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-a5ce3300-cd65-472c-b4f9-68b40e6a7e80');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(lg_tuned,sample='over',output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "gSQ_45K7Uvtx",
        "outputId": "4db462fd-5571-4743-d05a-c605adde6e13"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.893285  0.882073  0.865774  0.861509  0.138491\n",
              "val    0.780488  0.256822  0.128000  0.675808  0.420591"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8841bffd-14cd-4bb8-8fa3-eebf714e4489\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.893285</td>\n",
              "      <td>0.882073</td>\n",
              "      <td>0.865774</td>\n",
              "      <td>0.861509</td>\n",
              "      <td>0.138491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.780488</td>\n",
              "      <td>0.256822</td>\n",
              "      <td>0.128000</td>\n",
              "      <td>0.675808</td>\n",
              "      <td>0.420591</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8841bffd-14cd-4bb8-8fa3-eebf714e4489')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8841bffd-14cd-4bb8-8fa3-eebf714e4489 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-8841bffd-14cd-4bb8-8fa3-eebf714e4489');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results=go.cv_results_\n",
        "\n",
        "# figure setup\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Mean Recall, ROC-AUC, and F1 for max_depth',fontsize=20)\n",
        "\n",
        "# recall training\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_train_recall']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(3,10,2),\n",
        "         a,\n",
        "         label='Train Recall',\n",
        "         linestyle='--',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# AUC training\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_train_roc_auc']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(3,10,2),\n",
        "         a,\n",
        "         label='Train AUC',\n",
        "         linestyle='--',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# F1 training\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_train_f1']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(3,10,2),\n",
        "         a,\n",
        "         label='Train F1',\n",
        "         linestyle='--',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# recall val\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_test_recall']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(3,10,2),\n",
        "         a,\n",
        "         label='Val. Recall',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# AUC val\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_test_roc_auc']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(3,10,2),\n",
        "         a,\n",
        "         label='Val. AUC',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# F1 val\n",
        "a=pd.DataFrame(\n",
        "    data=np.array([\n",
        "        results['param_max_depth'],\n",
        "        results['mean_test_f1']\n",
        "    ]).T,\n",
        "    columns=['depth','']\n",
        ").groupby('depth').mean().values\n",
        "\n",
        "plt.plot(np.arange(3,10,2),\n",
        "         a,\n",
        "         label='Val. F1',\n",
        "         drawstyle='steps-mid')\n",
        "\n",
        "# axes and legend\n",
        "plt.xlabel('Maximum Tree Depth')\n",
        "plt.ylabel('Mean Score')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "9xm2HOAkZ8QI",
        "outputId": "1f02ce26-fc9e-43e4-8470-e21dd496ce6f"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To start, we see a jump in all scores as `max_depth` increases from 3 to 4. The gains continue as the parameter further increases, but the magnitude of improvements tapers off."
      ],
      "metadata": {
        "id": "gHb_dIeapTTe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The `is_unbalance` Parameter\n",
        "\n",
        "We now modify a previously untouched parameter from LightGBM. The `is_unbalance` flag modifies the model bulinding process to account for unbalanced training data. We can thus  train on the non-oversampled data again."
      ],
      "metadata": {
        "id": "uuQ_c7VsXYhj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lg1=LGBMClassifier(\n",
        "    random_state=1,\n",
        "    is_unbalance=True\n",
        ")\n",
        "\n",
        "lg1.fit(X_ts,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "k5yXyaz5U9JD",
        "outputId": "6949a3bb-6252-45e5-8257-74e307324658"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(is_unbalance=True, random_state=1)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(lg1,output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "4GoCIEkpWjKf",
        "outputId": "da749c52-9ba7-48eb-91c6-8e495ecb40a3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.922602  0.546308  0.338945  0.888749  0.142425\n",
              "val    0.587583  0.352816  0.220604  0.716797  0.164211"
            ],
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              "\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.922602</td>\n",
              "      <td>0.546308</td>\n",
              "      <td>0.338945</td>\n",
              "      <td>0.888749</td>\n",
              "      <td>0.142425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.587583</td>\n",
              "      <td>0.352816</td>\n",
              "      <td>0.220604</td>\n",
              "      <td>0.716797</td>\n",
              "      <td>0.164211</td>\n",
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              "  </tbody>\n",
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              "      display:flex;\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-239e586b-1f76-429e-b2fc-d6cde97a04a3 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-239e586b-1f76-429e-b2fc-d6cde97a04a3');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training scores are good and validation scores are some of the best we've seen."
      ],
      "metadata": {
        "id": "r0pLzCtMgmk0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ch(lg1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "io_pZJgVXGea",
        "outputId": "c53823fb-e6f3-4ef9-8136-d49e3ba7c568"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.835789\n",
              "Precision     0.135793\n",
              "Recall        0.587583\n",
              "F1            0.220604"
            ],
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.835789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.135793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.587583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.220604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f65f7d6-690a-4968-aecd-c4a8b96c7100 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-4f65f7d6-690a-4968-aecd-c4a8b96c7100');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the false positive rate is more than half that of the tuned LightGBM classifier above."
      ],
      "metadata": {
        "id": "P1UIVz6TeQe7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params={\n",
        "    'max_bin':[100,150,200,255],\n",
        "    'feature_fraction':np.linspace(0.5,0.9,4),\n",
        "}"
      ],
      "metadata": {
        "id": "BJpGwDYUYUjE"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will once again consider different values of `max_bin` and `feature_fraction`. This time, however, we will test `max_bin=255` as one of the options, which is the LightGBM default."
      ],
      "metadata": {
        "id": "qxrSmLf4g46O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lg1=LGBMClassifier(\n",
        "    random_state=1,\n",
        "    is_unbalance=True\n",
        ")\n",
        "\n",
        "go=GridSearchCV(\n",
        "    estimator=lg1,\n",
        "    param_grid=params,\n",
        "    scoring=['recall','f1','roc_auc'],\n",
        "    refit='roc_auc',\n",
        "    cv=5,\n",
        "    n_jobs=-1,\n",
        "    verbose=1,\n",
        "    return_train_score=True\n",
        ")\n",
        "\n",
        "start=time.time()\n",
        "go.fit(X_ts,y_train)\n",
        "print(f'Fit completed in {np.round((time.time()-start)/60,2)} minutes.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-tf5hC1ckQR",
        "outputId": "db82e404-c449-424d-b42c-50041174fcc7"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n",
            "Fit completed in 5.85 minutes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_lg1=go.best_params_\n",
        "best_lg1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAVgUkpQfdbZ",
        "outputId": "3946e9a6-b197-4a6e-8218-67879a5e1467"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'feature_fraction': 0.5, 'max_bin': 255}"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lg1=LGBMClassifier(\n",
        "    random_state=1,\n",
        "    is_unbalance=True,\n",
        "    **best_lg1\n",
        ")\n",
        "\n",
        "lg1.fit(X_ts,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "jWTypT0efgma",
        "outputId": "2174cfcd-513e-41fa-e0bc-83e3fee29d74"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(feature_fraction=0.5, is_unbalance=True, max_bin=100,\n",
              "               random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(feature_fraction=0.5, is_unbalance=True, max_bin=100,\n",
              "               random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(feature_fraction=0.5, is_unbalance=True, max_bin=100,\n",
              "               random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We fit the classifier with the updated parameters."
      ],
      "metadata": {
        "id": "7ASGS4m1k8Am"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ch(lg1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "CxdeCBKEf4I1",
        "outputId": "4c466472-7330-4eb0-ecf2-ad8d65c2835e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Val. Scores\n",
              "Accuracy      0.835087\n",
              "Precision     0.136352\n",
              "Recall        0.594235\n",
              "F1            0.221808"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72b01943-d71b-4962-a363-bcd18a715b8f\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Val. Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.835087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.136352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.594235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.221808</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72b01943-d71b-4962-a363-bcd18a715b8f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "      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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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-72b01943-d71b-4962-a363-bcd18a715b8f 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-72b01943-d71b-4962-a363-bcd18a715b8f');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_scores(lg1,output='pandas')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Ht45SGuef4-F",
        "outputId": "2204f970-eb76-4f95-c479-4a693ecacd9e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Recall    F_beta        F1       AUC  0-1_Loss\n",
              "train  0.924501  0.544037  0.336386  0.888652  0.144361\n",
              "val    0.586475  0.352103  0.220141  0.716197  0.164343"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38fa916f-df48-444b-8e3f-ae36b0c18d7e\">\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>Recall</th>\n",
              "      <th>F_beta</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC</th>\n",
              "      <th>0-1_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.924501</td>\n",
              "      <td>0.544037</td>\n",
              "      <td>0.336386</td>\n",
              "      <td>0.888652</td>\n",
              "      <td>0.144361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>val</th>\n",
              "      <td>0.586475</td>\n",
              "      <td>0.352103</td>\n",
              "      <td>0.220141</td>\n",
              "      <td>0.716197</td>\n",
              "      <td>0.164343</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38fa916f-df48-444b-8e3f-ae36b0c18d7e')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38fa916f-df48-444b-8e3f-ae36b0c18d7e 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-38fa916f-df48-444b-8e3f-ae36b0c18d7e');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No perceptable difference from the previous model. That is, tuning has not led to great gains. Let's get more detail with some plots."
      ],
      "metadata": {
        "id": "JZxp5dUHpwVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results=go.cv_results_\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Recall and ROC-AUC for Feature Fraction',fontsize=20)\n",
        "\n",
        "# recall train\n",
        "sns.lineplot(x=results['param_feature_fraction'],\n",
        "             y=results['mean_train_recall'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train Recall',\n",
        "             linestyle='--')\n",
        "\n",
        "\n",
        "# AUC train\n",
        "sns.lineplot(x=results['param_feature_fraction'],\n",
        "             y=results['mean_train_roc_auc'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train AUC',\n",
        "             linestyle='--')\n",
        "\n",
        "\n",
        "\n",
        "# recall val\n",
        "sns.lineplot(x=results['param_feature_fraction'],\n",
        "             y=results['mean_test_recall'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. Recall')\n",
        "\n",
        "# AUC val\n",
        "sns.lineplot(x=results['param_feature_fraction'],\n",
        "             y=results['mean_test_roc_auc'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. AUC')\n",
        "\n",
        "\n",
        "\n",
        "plt.xlabel('Feature Fraction')\n",
        "plt.ylabel('Mean Score')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "tQfzERmMj3dH",
        "outputId": "9a8b7d5a-e63a-47e9-80e8-8c5ae3d39b75"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find that recall and AUC are nearly constant across values for `feature_fraction`, betraying our prediction that a lower feature fraction would bring training and validation scores closer together."
      ],
      "metadata": {
        "id": "Ax-Pg8A6p5b-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.title('Recall and ROC-AUC for max_bin',fontsize=20)\n",
        "\n",
        "# recall train\n",
        "sns.lineplot(x=results['param_max_bin'],\n",
        "             y=results['mean_train_recall'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train Recall',\n",
        "             linestyle='--',\n",
        "             drawstyle='steps-mid')\n",
        "\n",
        "\n",
        "# AUC train\n",
        "sns.lineplot(x=results['param_max_bin'],\n",
        "             y=results['mean_train_roc_auc'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train AUC',\n",
        "             linestyle='--',\n",
        "             drawstyle='steps-mid')\n",
        "\n",
        "# recall val\n",
        "sns.lineplot(x=results['param_max_bin'],\n",
        "             y=results['mean_test_recall'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. Recall',\n",
        "             drawstyle='steps-mid')\n",
        "\n",
        "# AUC val\n",
        "sns.lineplot(x=results['param_max_bin'],\n",
        "             y=results['mean_test_roc_auc'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. AUC',\n",
        "             drawstyle='steps-mid')\n",
        "\n",
        "\n",
        "plt.xlabel('Max Bin')\n",
        "plt.ylabel('Mean Score')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "AUQC1EQBnEzC",
        "outputId": "b7cd75b5-e1ba-4fa4-e9c6-cdbdc5b468a1"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find a similar trend across values for `max_bin`. I predict that we're essentially maxed out on the performance of this architecture of classifier."
      ],
      "metadata": {
        "id": "IymvRZrOqIxX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.title('F1 for max_bin',fontsize=20)\n",
        "\n",
        "# F1 train\n",
        "sns.lineplot(x=results['param_max_bin'],\n",
        "             y=results['mean_train_f1'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Train F1',\n",
        "             linestyle='--',\n",
        "             drawstyle='steps-post')\n",
        "\n",
        "# F1 val\n",
        "sns.lineplot(x=results['param_max_bin'],\n",
        "             y=results['mean_test_f1'],\n",
        "             errorbar=('ci',0),\n",
        "             label='Val. F1',\n",
        "             drawstyle='steps-post')\n",
        "\n",
        "\n",
        "plt.xlabel('Max Bin')\n",
        "plt.ylabel('Mean Score')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "xZtFO9kqn63B",
        "outputId": "0487e7e8-820e-46fc-f72c-914c3eaddff8"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The pattern for F1 scores is also essentially constant."
      ],
      "metadata": {
        "id": "IXF_UsRpqeqq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name='conclusion'></a>\n",
        "# Concluding Remarks\n",
        "\n",
        "* With these data, boosting algorithms worked best.\n",
        "\n",
        "* With such prominent label imbalance in the target feature, models trained on the original data exhibited low-scoring recall, F1, and AUC.\n",
        "\n",
        "* Much better performance was achieved with models trained on oversampled data.\n",
        "\n",
        "* We tuned three boosting algorithms: AdaBoost, XGBoost, and LightGBM. All three offered slight gains at best, while still showing signs of weakness (such as overfitting).\n",
        "\n",
        "* The LightGBM model was also trained again on the non-oversampled data using a training flag for imbalanced data. Performance here was just as good or better than the others and training times were apprecibly reduced. This model is my favorite.\n",
        "\n",
        "## Next Steps\n",
        "\n",
        "* I think it would be interesting to explore the performance of a neural network trained to classify these data. I need to think more about how training on oversampled data would affect this architecture.\n",
        "\n",
        "* Previously, I implemented both a stacking classifier and a voting classifier with the goal of increasing overall scores. The former overfit and the latter offered no gains, though we can learn from the latter: As the voting classifier did not yield further correct labelling, this indicates that our highest-scoring classifiers are usually predicting the same incorrect label for the same data. Moving forward, it would be interesting to tune several models to maximize precision. Then we could combine those models with some of the high-recall models trained here to hopefully sniff out more correct classifications.\n",
        "\n",
        "[Return to the top.](#top)"
      ],
      "metadata": {
        "id": "35snRsqxj-Ez"
      }
    }
  ],
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      "provenance": [],
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      "gpuType": "A100",
      "authorship_tag": "ABX9TyPUNG050jZvpV7OZ1EF2fL+",
      "include_colab_link": true
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    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
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    "language_info": {
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