XGBoost_Credit_Default.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "XGBoost Credit Default.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMf0fUFor+Pw/FQyMCFcc3n",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/XGBoost_Credit_Default.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Te2NyO5OCDXc",
        "outputId": "7ebc4e21-445d-4968-f1e6-f3098a8e36c3"
      },
      "source": [
        "!pip install xgboost==1.3.3"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost==1.3.3 in /usr/local/lib/python3.7/dist-packages (1.3.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.3.3) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.3.3) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCSZmSMrsGGq",
        "outputId": "18896e13-80b8-4965-a18a-d231533dbc81"
      },
      "source": [
        "!wget -O credit_default.xls https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-05 23:21:37--  https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5539328 (5.3M) [application/x-httpd-php]\n",
            "Saving to: ‘credit_default.xls’\n",
            "\n",
            "credit_default.xls  100%[===================>]   5.28M  9.47MB/s    in 0.6s    \n",
            "\n",
            "2021-04-05 23:21:38 (9.47 MB/s) - ‘credit_default.xls’ saved [5539328/5539328]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfU9QIv0whiH"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "import xgboost as xgb\n",
        "import numpy as np\n"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PNgdm76QB9Cq",
        "outputId": "050fe044-5dad-45b9-ce4b-a8887ea60fb3"
      },
      "source": [
        "xgb.__version__"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.3.3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "RenQPA14yC6d",
        "outputId": "2be97f8a-b290-46ea-f7c5-642e0cc41167"
      },
      "source": [
        "data=pd.read_excel('credit_default.xls',skiprows=1)\n",
        "data"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
              "      <th>PAY_2</th>\n",
              "      <th>PAY_3</th>\n",
              "      <th>PAY_4</th>\n",
              "      <th>PAY_5</th>\n",
              "      <th>PAY_6</th>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <th>default payment next month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>20000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-2</td>\n",
              "      <td>-2</td>\n",
              "      <td>3913</td>\n",
              "      <td>3102</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>120000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2682</td>\n",
              "      <td>1725</td>\n",
              "      <td>2682</td>\n",
              "      <td>3272</td>\n",
              "      <td>3455</td>\n",
              "      <td>3261</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29239</td>\n",
              "      <td>14027</td>\n",
              "      <td>13559</td>\n",
              "      <td>14331</td>\n",
              "      <td>14948</td>\n",
              "      <td>15549</td>\n",
              "      <td>1518</td>\n",
              "      <td>1500</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>50000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46990</td>\n",
              "      <td>48233</td>\n",
              "      <td>49291</td>\n",
              "      <td>28314</td>\n",
              "      <td>28959</td>\n",
              "      <td>29547</td>\n",
              "      <td>2000</td>\n",
              "      <td>2019</td>\n",
              "      <td>1200</td>\n",
              "      <td>1100</td>\n",
              "      <td>1069</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8617</td>\n",
              "      <td>5670</td>\n",
              "      <td>35835</td>\n",
              "      <td>20940</td>\n",
              "      <td>19146</td>\n",
              "      <td>19131</td>\n",
              "      <td>2000</td>\n",
              "      <td>36681</td>\n",
              "      <td>10000</td>\n",
              "      <td>9000</td>\n",
              "      <td>689</td>\n",
              "      <td>679</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29995</th>\n",
              "      <td>29996</td>\n",
              "      <td>220000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>188948</td>\n",
              "      <td>192815</td>\n",
              "      <td>208365</td>\n",
              "      <td>88004</td>\n",
              "      <td>31237</td>\n",
              "      <td>15980</td>\n",
              "      <td>8500</td>\n",
              "      <td>20000</td>\n",
              "      <td>5003</td>\n",
              "      <td>3047</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29996</th>\n",
              "      <td>29997</td>\n",
              "      <td>150000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1683</td>\n",
              "      <td>1828</td>\n",
              "      <td>3502</td>\n",
              "      <td>8979</td>\n",
              "      <td>5190</td>\n",
              "      <td>0</td>\n",
              "      <td>1837</td>\n",
              "      <td>3526</td>\n",
              "      <td>8998</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29997</th>\n",
              "      <td>29998</td>\n",
              "      <td>30000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>37</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3565</td>\n",
              "      <td>3356</td>\n",
              "      <td>2758</td>\n",
              "      <td>20878</td>\n",
              "      <td>20582</td>\n",
              "      <td>19357</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22000</td>\n",
              "      <td>4200</td>\n",
              "      <td>2000</td>\n",
              "      <td>3100</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>29999</td>\n",
              "      <td>80000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1645</td>\n",
              "      <td>78379</td>\n",
              "      <td>76304</td>\n",
              "      <td>52774</td>\n",
              "      <td>11855</td>\n",
              "      <td>48944</td>\n",
              "      <td>85900</td>\n",
              "      <td>3409</td>\n",
              "      <td>1178</td>\n",
              "      <td>1926</td>\n",
              "      <td>52964</td>\n",
              "      <td>1804</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29999</th>\n",
              "      <td>30000</td>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>47929</td>\n",
              "      <td>48905</td>\n",
              "      <td>49764</td>\n",
              "      <td>36535</td>\n",
              "      <td>32428</td>\n",
              "      <td>15313</td>\n",
              "      <td>2078</td>\n",
              "      <td>1800</td>\n",
              "      <td>1430</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30000 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID  LIMIT_BAL  SEX  ...  PAY_AMT5  PAY_AMT6  default payment next month\n",
              "0          1      20000    2  ...         0         0                           1\n",
              "1          2     120000    2  ...         0      2000                           1\n",
              "2          3      90000    2  ...      1000      5000                           0\n",
              "3          4      50000    2  ...      1069      1000                           0\n",
              "4          5      50000    1  ...       689       679                           0\n",
              "...      ...        ...  ...  ...       ...       ...                         ...\n",
              "29995  29996     220000    1  ...      5000      1000                           0\n",
              "29996  29997     150000    1  ...         0         0                           0\n",
              "29997  29998      30000    1  ...      2000      3100                           1\n",
              "29998  29999      80000    1  ...     52964      1804                           1\n",
              "29999  30000      50000    1  ...      1000      1000                           1\n",
              "\n",
              "[30000 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mv_bpVYb0m4W"
      },
      "source": [
        "data=data.rename({'default payment next month':'default'}, axis=1)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "4I5COf3QyIHl",
        "outputId": "6ad2a13e-0142-4d70-efa3-ded0c915624f"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        "sns.set_palette(\"hls\", 3)\n",
        "fig, ax = plt.subplots(figsize=(20,5))\n",
        "ax = sns.countplot(x=\"SEX\", hue=\"default\", data=data)\n",
        "\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x()+p.get_width()/2.,\n",
        "            height + 3,\n",
        "            '{:1.2f}'.format(height/data.shape[0]),\n",
        "            ha=\"center\") "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "Di7OxK5C0Yse",
        "outputId": "32cc42dd-c391-400e-9223-2fd2f8119404"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        "sns.set_palette(\"hls\", 3)\n",
        "fig, ax = plt.subplots(figsize=(20,5))\n",
        "ax = sns.countplot(x=\"EDUCATION\", hue=\"default\", data=data)\n",
        "\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x()+p.get_width()/2.,\n",
        "            height + 3,\n",
        "            '{:1.2f}'.format(height/data.shape[0]),\n",
        "            ha=\"center\") "
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "posx and posy should be finite values\n",
            "posx and posy should be finite values\n",
            "posx and posy should be finite values\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "lQPlkBD91d6D",
        "outputId": "e2f448ba-2533-42e4-c25d-2d374e3a16ca"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        "sns.set_palette(\"hls\", 3)\n",
        "fig, ax = plt.subplots(figsize=(20,5))\n",
        "ax = sns.countplot(x=\"MARRIAGE\", hue=\"default\", data=data)\n",
        "\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x()+p.get_width()/2.,\n",
        "            height + 3,\n",
        "            '{:1.2f}'.format(height/data.shape[0]),\n",
        "            ha=\"center\") "
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "bElHmSnH4ktG",
        "outputId": "62e97e13-0448-4991-a222-d128e669164e"
      },
      "source": [
        "data=data.drop(['ID'], axis=1)\n",
        "data"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
              "      <th>PAY_2</th>\n",
              "      <th>PAY_3</th>\n",
              "      <th>PAY_4</th>\n",
              "      <th>PAY_5</th>\n",
              "      <th>PAY_6</th>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-2</td>\n",
              "      <td>-2</td>\n",
              "      <td>3913</td>\n",
              "      <td>3102</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>120000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2682</td>\n",
              "      <td>1725</td>\n",
              "      <td>2682</td>\n",
              "      <td>3272</td>\n",
              "      <td>3455</td>\n",
              "      <td>3261</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29239</td>\n",
              "      <td>14027</td>\n",
              "      <td>13559</td>\n",
              "      <td>14331</td>\n",
              "      <td>14948</td>\n",
              "      <td>15549</td>\n",
              "      <td>1518</td>\n",
              "      <td>1500</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46990</td>\n",
              "      <td>48233</td>\n",
              "      <td>49291</td>\n",
              "      <td>28314</td>\n",
              "      <td>28959</td>\n",
              "      <td>29547</td>\n",
              "      <td>2000</td>\n",
              "      <td>2019</td>\n",
              "      <td>1200</td>\n",
              "      <td>1100</td>\n",
              "      <td>1069</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8617</td>\n",
              "      <td>5670</td>\n",
              "      <td>35835</td>\n",
              "      <td>20940</td>\n",
              "      <td>19146</td>\n",
              "      <td>19131</td>\n",
              "      <td>2000</td>\n",
              "      <td>36681</td>\n",
              "      <td>10000</td>\n",
              "      <td>9000</td>\n",
              "      <td>689</td>\n",
              "      <td>679</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29995</th>\n",
              "      <td>220000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>188948</td>\n",
              "      <td>192815</td>\n",
              "      <td>208365</td>\n",
              "      <td>88004</td>\n",
              "      <td>31237</td>\n",
              "      <td>15980</td>\n",
              "      <td>8500</td>\n",
              "      <td>20000</td>\n",
              "      <td>5003</td>\n",
              "      <td>3047</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29996</th>\n",
              "      <td>150000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1683</td>\n",
              "      <td>1828</td>\n",
              "      <td>3502</td>\n",
              "      <td>8979</td>\n",
              "      <td>5190</td>\n",
              "      <td>0</td>\n",
              "      <td>1837</td>\n",
              "      <td>3526</td>\n",
              "      <td>8998</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29997</th>\n",
              "      <td>30000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>37</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3565</td>\n",
              "      <td>3356</td>\n",
              "      <td>2758</td>\n",
              "      <td>20878</td>\n",
              "      <td>20582</td>\n",
              "      <td>19357</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22000</td>\n",
              "      <td>4200</td>\n",
              "      <td>2000</td>\n",
              "      <td>3100</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>80000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1645</td>\n",
              "      <td>78379</td>\n",
              "      <td>76304</td>\n",
              "      <td>52774</td>\n",
              "      <td>11855</td>\n",
              "      <td>48944</td>\n",
              "      <td>85900</td>\n",
              "      <td>3409</td>\n",
              "      <td>1178</td>\n",
              "      <td>1926</td>\n",
              "      <td>52964</td>\n",
              "      <td>1804</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29999</th>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>47929</td>\n",
              "      <td>48905</td>\n",
              "      <td>49764</td>\n",
              "      <td>36535</td>\n",
              "      <td>32428</td>\n",
              "      <td>15313</td>\n",
              "      <td>2078</td>\n",
              "      <td>1800</td>\n",
              "      <td>1430</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30000 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       LIMIT_BAL  SEX  EDUCATION  ...  PAY_AMT5  PAY_AMT6  default\n",
              "0          20000    2          2  ...         0         0        1\n",
              "1         120000    2          2  ...         0      2000        1\n",
              "2          90000    2          2  ...      1000      5000        0\n",
              "3          50000    2          2  ...      1069      1000        0\n",
              "4          50000    1          2  ...       689       679        0\n",
              "...          ...  ...        ...  ...       ...       ...      ...\n",
              "29995     220000    1          3  ...      5000      1000        0\n",
              "29996     150000    1          3  ...         0         0        0\n",
              "29997      30000    1          2  ...      2000      3100        1\n",
              "29998      80000    1          3  ...     52964      1804        1\n",
              "29999      50000    1          2  ...      1000      1000        1\n",
              "\n",
              "[30000 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQkc15jQlXf2",
        "outputId": "c9769559-bdf5-4562-e078-678096618a79"
      },
      "source": [
        "data['default'].value_counts()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    23364\n",
              "1     6636\n",
              "Name: default, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-WNRVCUVNut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b3066a3-f5c1-4b06-f53f-ef66ad9f9302"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30000 entries, 0 to 29999\n",
            "Data columns (total 24 columns):\n",
            " #   Column     Non-Null Count  Dtype\n",
            "---  ------     --------------  -----\n",
            " 0   LIMIT_BAL  30000 non-null  int64\n",
            " 1   SEX        30000 non-null  int64\n",
            " 2   EDUCATION  30000 non-null  int64\n",
            " 3   MARRIAGE   30000 non-null  int64\n",
            " 4   AGE        30000 non-null  int64\n",
            " 5   PAY_0      30000 non-null  int64\n",
            " 6   PAY_2      30000 non-null  int64\n",
            " 7   PAY_3      30000 non-null  int64\n",
            " 8   PAY_4      30000 non-null  int64\n",
            " 9   PAY_5      30000 non-null  int64\n",
            " 10  PAY_6      30000 non-null  int64\n",
            " 11  BILL_AMT1  30000 non-null  int64\n",
            " 12  BILL_AMT2  30000 non-null  int64\n",
            " 13  BILL_AMT3  30000 non-null  int64\n",
            " 14  BILL_AMT4  30000 non-null  int64\n",
            " 15  BILL_AMT5  30000 non-null  int64\n",
            " 16  BILL_AMT6  30000 non-null  int64\n",
            " 17  PAY_AMT1   30000 non-null  int64\n",
            " 18  PAY_AMT2   30000 non-null  int64\n",
            " 19  PAY_AMT3   30000 non-null  int64\n",
            " 20  PAY_AMT4   30000 non-null  int64\n",
            " 21  PAY_AMT5   30000 non-null  int64\n",
            " 22  PAY_AMT6   30000 non-null  int64\n",
            " 23  default    30000 non-null  int64\n",
            "dtypes: int64(24)\n",
            "memory usage: 5.5 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iy20fkLlED-e"
      },
      "source": [
        "cred_def_X = data.copy()\n",
        "cred_def_y = cred_def_X.pop('default')"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qnGcjQCEUbh"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(cred_def_X, cred_def_y, train_size=0.75, test_size=0.25)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zdKKR1ktGXXh"
      },
      "source": [
        "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
        "dtest = xgb.DMatrix(X_test, label=y_test)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3NpphjNEgyE"
      },
      "source": [
        "num_round = 200\n",
        "maxdepth = 6\n",
        "param = {\n",
        "  'colsample_bylevel': 1,\n",
        "  'colsample_bytree': 1,\n",
        "  'gamma': 0,\n",
        "  'learning_rate': 0.1, \n",
        "  'random_state': 1010,\n",
        "  'objective': 'binary:hinge', \n",
        "}"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbjxW3fmFyB3",
        "outputId": "f3ab8962-4058-4d6b-b0de-f296143f284c"
      },
      "source": [
        "param['tree_method'] = 'gpu_hist'\n",
        "param['grow_policy'] = 'depthwise'\n",
        "param['max_depth'] = maxdepth\n",
        "param['max_leaves'] = 0\n",
        "param['verbosity'] = 0\n",
        "param['gpu_id'] = 0\n",
        "param['updater'] = 'grow_gpu_hist'\n",
        "param['predictor'] = 'gpu_predictor'\n",
        "\n",
        "gpu_result = {} \n",
        "\n",
        "# Training with the above parameters\n",
        "bst=xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=gpu_result, verbose_eval=20)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\ttest-error:0.77947\n",
            "[20]\ttest-error:0.18867\n",
            "[40]\ttest-error:0.18133\n",
            "[60]\ttest-error:0.18040\n",
            "[80]\ttest-error:0.18067\n",
            "[100]\ttest-error:0.18120\n",
            "[120]\ttest-error:0.18027\n",
            "[140]\ttest-error:0.18107\n",
            "[160]\ttest-error:0.18040\n",
            "[180]\ttest-error:0.18053\n",
            "[199]\ttest-error:0.18107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "LuYLWo0WGs5e",
        "outputId": "c96763c0-5031-46e8-8331-293ed1fcb032"
      },
      "source": [
        "xgb.plot_importance(bst)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21b1e2f3d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zy1oowORPyef",
        "outputId": "ba0f438e-5f91-4a2d-b425-757227c5c24e"
      },
      "source": [
        "bst.predict(dtest)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "peCn_CpJU64l"
      },
      "source": [
        "from sklearn.utils import class_weight\n",
        "class_weights = list(class_weight.compute_class_weight('balanced',\n",
        "                                             np.unique(y_train),\n",
        "                                             y_train))"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOCa6-v2VPoR",
        "outputId": "670864b4-a5e3-4dba-eabb-51c726f169d7"
      },
      "source": [
        "class_weights"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6421965977851353, 2.258129265355279]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pQIpESGVHjU"
      },
      "source": [
        "weights = np.ones(X_train.shape[0], dtype = 'float')"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIRsouIuV3yW"
      },
      "source": [
        "for i, val in enumerate(y_train):\n",
        "    weights[i] = class_weights[val]"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ca6YIYl3V_fC",
        "outputId": "423db61d-7048-462b-dbea-e5afd3aad079"
      },
      "source": [
        "weights"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.25812927, 0.6421966 , 0.6421966 , ..., 0.6421966 , 0.6421966 ,\n",
              "       0.6421966 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJhvsG7FQgKB"
      },
      "source": [
        "xgb_clf = xgb.XGBClassifier(n_estimators=200, learning_rate=0.05, max_depth=6, objective='binary:hinge', random_state=10)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOeFJqMVSHY2",
        "outputId": "b14b6df3-b6b7-4d41-d300-740840e23070"
      },
      "source": [
        "xgb_clf.fit(X_train, y_train, sample_weight=weights)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
              "              importance_type='gain', interaction_constraints='',\n",
              "              learning_rate=0.05, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
              "              n_estimators=200, n_jobs=2, num_parallel_tree=1,\n",
              "              objective='binary:hinge', random_state=10, reg_alpha=0,\n",
              "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
              "              tree_method='exact', use_label_encoder=True,\n",
              "              validate_parameters=1, verbosity=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-kuMKQRSQdU"
      },
      "source": [
        "predictions = xgb_clf.predict(X_test)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0R1dXhUSphJ",
        "outputId": "060a65d8-8d7c-4faa-97cb-ad79c9c39c7a"
      },
      "source": [
        "cm = confusion_matrix(y_test, predictions)\n",
        "print(cm)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4551 1295]\n",
            " [ 623 1031]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBeMlsgkSvky",
        "outputId": "b80e8d37-ba83-48ef-e9be-881c7ff2c994"
      },
      "source": [
        "print('classification_report :\\n',classification_report(y_test, predictions))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classification_report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.78      0.83      5846\n",
            "           1       0.44      0.62      0.52      1654\n",
            "\n",
            "    accuracy                           0.74      7500\n",
            "   macro avg       0.66      0.70      0.67      7500\n",
            "weighted avg       0.78      0.74      0.76      7500\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "oV2lNzibWOuY",
        "outputId": "c547a2ab-8ddc-47ee-de2d-9eda9c40fb56"
      },
      "source": [
        "xgb.plot_importance(xgb_clf)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21b1d2f650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABMUAAAJiCAYAAADHZ3UNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVjVZf7/8ReLoCyGIpqpoJZg6riiSCoQhuLGUllkjWOljiszpuOGM1bfHLNoA3Mpx7HFtMHDAYVEzYJyyq1Sc2nml6bJ5IKIioJHjvD7o/EznTRFYzl6no/rmus65/587vvzvo/XPdfpxf35HKeKiooKAQAAAAAAAA7EubYLAAAAAAAAAGoaoRgAAAAAAAAcDqEYAAAAAAAAHA6hGAAAAAAAABwOoRgAAAAAAAAcDqEYAAAAAAAAHA6hGAAAAAyLFi1SUlJSbZcBAABQ7ZwqKioqarsIAACAW0FkZKROnDghFxcXoy0nJ0dNmjT5VWM+99xzuueee6qixJtKamqqDh06pOTk5NouBQAA3IJca7sAAACAW8miRYvsKsCyWq1ydb35vvJZrdbaLgEAANziuH0SAACgmhUXF2vmzJnq3bu3+vTpo1deeUUXL16UJH3//fcaPny4QkJCFBISosmTJ+vMmTOSpD/96U/64YcfNGbMGHXp0kVvvvmmtmzZorCwMJvxIyMj9dlnn0n6cXdVYmKipkyZoq5du8psNl/1+j+XmpqqKVOmSJLy8/MVFBQkk8mk8PBwde/eXStWrNCuXbs0ZMgQBQcH69lnnzX6pqenKyEhQc8++6y6deum6Ohoff7558bxY8eOacyYMerRo4eioqL0j3/8w+a6P6175cqVWrx4sdauXasuXbooJiZGkmQymTRgwAB16dJFffv21cqVK40xLn02S5cuVWhoqHr37i2TyWQcP3/+vJ5//nnde++96tatmx555BGdP39ekrRjxw4lJCQoODhYMTEx2rJly3X+KwMAgJvNzfdnQwAAgJvM9OnT5evrq/Xr16u0tFS///3v1bRpUyUkJKiiokK///3v1b17d509e1YTJ05UamqqkpKS9OKLL+qLL76wuX2yMmHNxo0b9dprr+mFF17QhQsXNHny5F+8fmXs3LlT69ev17Zt2zR27Fj16dNHy5Ytk9VqVVxcnKKjo9WjRw9J0q5duxQdHa3Nmzdrw4YNmjBhgjZu3CgfHx899dRTatOmjT799FMdOHBAjz/+uFq0aKHQ0NAr1l1UVHTZ7ZO+vr5avHixWrRooW3btmnUqFH6zW9+o/bt20uSTpw4oeLiYn3yySf67LPPlJiYqPvuu0+33Xab5s2bp2+//VYrV65Uo0aNtHPnTjk7O+vYsWP6/e9/rxdeeEF9+vTR559/rsTERK1du1YNGza8rn9rAABw82CnGAAAQBUaP368goODFRwcrHHjxunEiRPKy8vTzJkz5eHhIV9fX40YMULZ2dmSpICAAPXq1Utubm5q2LChHn/8cW3btu1X1dC5c2fdd999cnZ21tmzZ696/crOyd3dXb1795aHh4cGDx4sX19fNWnSRMHBwdq7d69xbsOGDfW73/1OderU0cCBA9WqVSvl5ubqyJEj+vLLLzVlyhS5u7vr7rvv1tChQ5WZmXnFuuvWrXvFWiIiIuTv7y8nJyf16NFDvXr10vbt243jrq6uGj9+vOrUqaPw8HB5eHjou+++U3l5uUwmk5KSktSkSRO5uLioa9eucnNzU2ZmpsLCwhQeHi5nZ2f16tVLHTp0UF5e3g18+gAA4GbBTjEAAIAq9Prrr9s8U2zXrl2yWq3q3bu30VZeXq6mTZtK+nFn05w5c7R9+3adO3dOFRUVql+//q+q4fbbbzde//DDD1e9fmX4+voar93d3S97X1JSYrxv0qSJnJycjPd33HGHjh8/ruPHj+u2226Tl5eXzbHdu3dfse5fkpeXp9dff10HDx5UeXm5zp8/r8DAQOO4j4+PzTPU6tWrp5KSEhUVFclisahFixaXjfnDDz8oJydHH3/8sdFmtVoVEhJyzXoAAMDNi1AMAACgGt1+++1yc3PT5s2br/jA+5dffllOTk5as2aNfHx89OGHH9o8p+vn6tWrZzwHS5IuXryokydP2pzz01DqWtevaseOHVNFRYVRw5EjRxQZGanGjRvr9OnTOnv2rBGMHTlyxOaXOX9a95XeX7hwQYmJiZo3b5769u2rOnXqaNy4carMj6k3aNBA7u7uOnz4sNq2bWtzrGnTpoqNjdVzzz13Q3MGAAA3J26fBAAAqEaNGzdWr1699Pzzz+vs2bMqLy/X999/r61bt0qSzp07Jw8PD3l7e+vYsWNasmSJTf9GjRrp8OHDxvtWrVrJYrEoNzdXZWVlWrhwoS5cuHDD169qJ0+e1Ntvv62ysjKtXbtW+/fvV3h4uJo2baouXbro5ZdflsVi0TfffKNVq1YZD9C/El9fX/3nP/9ReXm5pB9DsQsXLqhhw4ZydXVVXl6e/vnPf1aqLmdnZz3wwAOaO3eujh07posXL+qrr77ShQsXFBMTo48//liffvqpLl68KIvFoi1btujo0aNV8pkAAAD7RCgGAABQzV544QWVlZVp4MCB6t69uxITE1VQUCBJmjBhgvbu3avg4GCNHj1a/fr1s+k7evRoLVy4UMHBwfrb3/4mb29vzZ49W7NmzVJYWJjq1at3zdsOr3b9qtaxY0cdOnRIPXv21KuvvqqUlBQ1aNBA0o+74v7zn/+oT58+mjBhgiZOnGhzq+nPRUdHS5JCQkIUHx8vLy8vzZo1S3/84x/VvXt3ZWVlKTIystK1TZs2TYGBgXrwwQfVo0cPJScnG7eSLliwQIsXL1ZoaKjCw8P1t7/9zQjjAADArcmpojL7zQEAAIBrSE9PV1pamlasWFHbpQAAAFwTO8UAAAAAAADgcAjFAAAAAAAA4HC4fRIAAAAAAAAOh51iAAAAAAAAcDiEYgAAAAAAAHA4hGIAAAAAAABwOK61XQD+p6jonMrLecQbYG98fb1UWHi2tssA8DOsTcA+sTYB+8TahCNydnZSgwaev3icUMyOlJdXEIoBdoq1Cdgn1iZgn1ibgH1ibQK2uH0SAAAAAAAADodQDAAAAAAAAA6HUAwAAAAAAAAOh1AMAAAAAAAADodQDAAAAAAAAA6HUAwAAAAAAAAOh1AMAAAAAAAADodQDAAAAAAAAA6HUAwAAAAAAAAOh1AMAAAAAAAADodQDAAAAAAAAA6HUAwAAAAAAAAOh1AMAAAAAAAADodQDAAAAAAAAA7HtbYLAAAAAAAAuNUcOfKDXnrpee3e/bXc3NwUERGpxMTJ2rPna02ZkmhzbmlpqZ57bp4iIvqqoqJCb765UB98sEalpSVq0yZITz01Ta1b31lLM7l1sVMMAAAAAACgir300vNq0KChMjNz9Pe/L9eOHV/KbF6lTp26aMOGT43/zZv3iurV81BIyD2SpI8++lDZ2av1+utv6oMPPlKHDh313HN/qeXZ3JoIxQAAAAAAAKrYkSM/KDLyPrm7u8vXt5FCQu7Rd9/tv+y8nJxs3XtvX9WrV++//f6jjh07q1mz5nJxcVG/fgN08OB3NV2+Q6jV2ycjIyPl5uYmd3d3WSwWBQcHa/bs2VqzZo1yc3OVkpKi/Px8PfDAA9qyZctl/YOCgvTll1/K09Oz0td88cUX9dZbbykvL0++vr42tVy4cEF5eXlycXGRJKWnp2vGjBn685//rHr16untt9+WJB05ckR169ZVgwYNJEnPPvusDh48qCVLlmj//v2aOXOmHnvssev+PHx9va67D4Ca4efnXdslALgC1iZgn1ibgH1ibVY/q8WiojMXJElDhz6iDz9cry5dglVcfEabN/9TI0eOtTm/tLRUH3+8UfPmvWy03Xdff3300Yf6/vtDuuOOZsrJyVJISGiNzsNR1PozxVJSUhQYGKiLFy/q0Ucf1YYNG6rtWhcvXlRmZqa6du2qzMxMPfHEEzbHGzdurE2bNik8PFySZDab1b59e0nSAw88oAceeECSNH36dHXo0MEm+KpXr55eeeUVvfHGGzdc38EpE2U9UXDD/QEAAAAAQO25a9lKST+GYp07d9Xq1Wb17x+uixcvasCAwQoLi7A5Py/vI/n4+KhLl25Gm69vI3Xs2FnDhj0gFxcXNW7cRK+9trAGZ+E4aj0Uu8Rischisah+/fo6f/58tVwjLy9P/v7+SkxM1NNPP31ZKBYfH6/09HSFh4fr8OHDKikpUWBgYKXGvnSeszN3pAIAAAAA4Kj8/LxVXl6uhx76gx566CE9+WSazp07p5kzZ2rZskWaOnWqce7GjTm6//54NW5c32h75ZVXtH//v5SXl6dGjRpp9erVmjRpnLKzs41bLFE1aj0US0xMlLu7u77//nv17t1bvXv3Vnp6erVcy2Qy6f7771dwcLDKysq0c+dOderUyTjeo0cPvffeezp9+rTMZrPi4uK0Z8+eaqkFAAAAAADcegoKinXq1Cn98MMPio6O0+nTFkmuuu++gXrzzQV6/PEfb6E8duyotm7dqj/+cZoKCoqN/rt27VafPpFycfFUUVGp+vSJ0pw5c7R9+y61bduulmZ1c3J2drrqo6pqfVtTSkqKMjMztXnzZlksFi1btqxarlNYWKitW7cqOjpakhQXFyeTyWRzjpOTkwYMGKDs7GxlZ2dr8ODB1VILAAAAAAC4dfn4+Khp02Yym1fJarWquLhYa9dm6c472xjnrFv3gTp06KhmzZrb9G3btp0+/vhDnTxZqPLycuXkZMtqtapZsxY1PY1bXq3vFLvE3d1dERERys3NNYKrqpSZmSmr1aqYmBhJktVqVWlpqWbOnKm6desa58XHx2vo0KHq3r278SD9mtIyObVGrwcAAAAAAKqO1WIxXv/1ry/otdde0vLlb8nFxVldu3ZXYuJTxvGcnGwNG/bby8Z49NHfqaioSI8/PkylpefVvHlzPffcC/L25ocSqprdhGLl5eXatm2bWrZsWS3jp6en6/XXX9c999xjtD355JPKyclRXFyc0daiRQtNmjRJHTt2rJY6rqaw8KzKyytq/LoArs7Pz9tmOzMA+8DaBOwTaxOwT6zNmtemTZDmz//lH+N77z3TFdvd3d01efI0TZ48rbpKw3/Veih26ZliZWVlatOmjcaPH6+NGzfanHPmzBmFhYUZ71u3bm3cZhkdHS0nJydJP/4C5Lp16y67xs6dO3Xq1Cn17NnTpn3IkCEymUw2oZgkPfzww9c9j6ysLL3wwgs6c+aMNm7cqDfeeENLly7VXXfddd1jAQAAAAAAoHo5VVRUsDXJTrBTDLBP/FUNsE+sTcA+sTYB+8TahCOy+wftAwAAAAAAADWt1m+frEqFhYV64oknLmuPiorShAkTaqEiAAAAAAAA2KNbKhTz9fVVZmZmbZcBAAAAAAAAO8ftkwAAAAAAAHA4hGIAAAAAAABwOIRiAAAAAAAAcDiEYgAAAAAAAHA4hGIAAAAAAABwOIRiAAAAAAAAcDiEYgAAAAAAAHA4hGIAAAAAAABwOK61XQAAAAAAALUtKqqPzXuLxaL4+Ac1adJUHTnyg4YOjVG9evWM448++juNGDFSknThwgUlJ89Vbu5Hqlu3roYN+60SEh6r0foBXD9CMQAAAACAw9uw4VPjdUlJiWJj++vee++zOWft2o/l6nr5f0YvXfqG8vMPa9WqNTp5slCJiWPUsmVr9ex5T7XXDeDG2dXtk5GRkfr3v/9t0/bb3/5WH3/8sSQpNTVVQUFBys3NNY6fO3dOXbp00f3332+0BQUF6dy5cxo/frxiY2MVGxuroKAgDRkyRLGxsXryySd/sYbU1FSFhoYqNjZWMTExevjhh/XNN9/YnLN//34FBQVp2bJll/WdN2/eDc4eAAAAAGAP8vI+ko9PQ3Xq1KVS569dm6URI0aqfv36atmylYYMidPatWuquUoAv9ZNt1Osffv2MpvNioiIkCTl5OSoVatWVzz39ddfN14HBQVp5cqV8vT0vOY14uLiNG3aNEnS8uXLlZycrCVLlhjHV61apZ49e8pkMmnEiBE3Ppmf8fX1qrKxAFQtPz/v2i4BwBWwNgH7xNrEzcRqsajozAWbtrVrsxQdPVBOTk427Q8+OEROTk7q3j1E48b9QT4+Pjpz5owKC0/orrvaGOfddVcbffppbk2UD+BXuOlCsR49eig3N1enT5/WbbfdpoyMDMXHx8tsNlfL9c6ePavbbrvNeG+1WrVmzRotX75co0aN0q5du9SxY8cqudbBKRNlPVFQJWMBAAAAAK7trmUrJf0vFDt69Ih27PhS06f/2Wi77TYfLVnytu66K1BnzpzWyy/P07PPztLLL89XaWmJJMnT83+bHLy8vFRSUlJjcwBwY266UMzJyUkDBw5Udna2+vTpo5KSEgUGBlbpNTIyMvTZZ5/p9OnTslqteuedd4xjubm5CggIUEBAgOLj42UymaosFAMAAAAA1Lyf7m5MS3tH3bp1U6dObX9yhrcCAppIkpo2baDnnntWvXv3Vr16TmrRorEkqV49J/n6/jiOi0u56tf3trtdk/ZWD1DbbrpQTJLi4+M1ZcoUnThxQnFxcVU+/k9vn8zIyNCkSZOUkZEhSTKZTIqPjzfOi4uL08yZM+Xu7l7ldQAAAAAAql9BQbHx2mQy67HHfmfT9nMnT54z+nl5ecnXt5G2bPlS3bv3lCR9+eUutWgRcNUxapqfn7dd1QPUBGdnp6s+quqmDMVatGghNzc3/eMf/9CaNWsuezh/VYqOjta0adN08uRJlZeXa9OmTdq3b58WLFggSSotLdW6desUExPzq6/VMjn1V48BAAAAAKg8q8VivP766506ceK4IiNtf3Vyz57d8vb2UvPm/iouPqNXX01Wly7d5OX1439sR0cP0ltvLVVQUDsVFZ3UmjVmzZgxu0bnAeD63ZShmCQ99dRTOnTokBo0aFCt19myZYt8fHzk4+OjpUuXqn///kpOTjaOZ2VlKS0trUpCscLCsyovr/jV4wCoWvxVDbBPrE3APrE2cTNbuzZL4eH3ysPD9gfafvghX2+8sUBFRSfl6emp4OAQPf30HOP4k0/+XsnJc/Xgg0Pk7u6uRx8drp4976np8gFcJ7sLxR5//HG5uLgY7318fK54XpcuXdSlS+V+Hvd6XXqmWEVFhVxdXfXaa6/J2dlZ6enpxm2Vl/Tt21ezZ89Wfn6+JGnlypXKzs42jo8bN04JCQnVUicAAAAAoOpMnZp0xfaoqGhFRUX/Yj83NzfNnDlbM2eyOwy4mThVVFSwNclOsFMMsE/8xRuwT6xNwD6xNgH7xNqEI7rWM8Wca7AWAAAAAAAAwC7Y3e2TNWHfvn2aPn36Ze2PPfaYhg4dWgsVAQAAAAAAoCY5ZCh29913KzMzs7bLAAAAAAAAQC3h9kkAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgc19ouAAAAAABw84mK6mPz3mKxKD7+QU2aNFWStH37Vr388jwdO3ZU7dp1UFLS07r99qaSpDlzntaGDTmqU6eO0T8nJ1cuLi41NwEADo9Q7GdOnz6tPn366KGHHtKsWbOM9qysLC1dulRnz56Vl5eXPDw89OSTT+ree+9Vfn6++vXrpzZt2hjnN2jQQMuWLauFGQAAAABA9duw4VPjdUlJiWJj++vee++TJJ06dUpJSX/StGl/Vq9efbRkySL95S8z9MYby4w+w4YN1+jR42q6bAAwEIr9TFZWljp16qTs7GxNnTpVbm5uSktL09///nelpqbqzjvvlCT9+9//1meffWb08/b2VmZm5q+6tq+v16/qD6D6+Pl513YJAK6AtQnYJ9bmrc1qsajozAWbtry8j+Tj01CdOnUx3rdqdaciI38MyZ54YrQGDbpPhw4dVEBAy5ouGQCuiFDsZ0wmk/70pz9p8eLF2rhxowYMGKDU1FTNmTPHCMQkKTAwUIGBgVV67YNTJsp6oqBKxwQAAACAqnTXspWSbEOxtWuzFB09UE5OTpKk7747oLvu+t+dNPXq1VOzZs303Xf7jVDMbF4ls3mVmja9Q8OHP66IiL41NQUAkEQoZuObb77RqVOn1LNnTxUUFMhkMqlHjx46duyYOnXqdNW+xcXFio2NNd536tRJzz77bHWXDAAAAAC16ujRI9qx40tNn/5no620tEQ+Pg1szvPy8lJJSYkk6cEHEzRhwh/l6emlbds26y9/mamGDX3VsWPnGq0dgGMjFPuJVatWKTY2Vk5OTurXr5+ee+45nT9//rLzEhISdPbsWbm7u8tkMkmqmtsnAQAAAOBm8NNbZNPS3lG3bt3UqVNbo61hw9tktV6wOc9iOa/bb/eVn5+3/Py6G+0xMQO0ffvn2rbtn+rb1/bh/aha3NoM2CIU+68LFy4oKytLbm5uRrhVVlam1atXq0mTJvr666/Vq1cvSdLKlSv173//W2PGjKnNkgEAAACgVhQUFBuvTSazHnvsdzZtt9/eQjk5WUZbaWmpDh06JF/fO2zOu8RisUqyXPEYqoafnzefLxyOs7PTVZ/fTij2Xxs3blSrVq20YsUKo+2rr77StGnTNH78eM2dO1epqalq1aqVpB//T72qtUxOrfIxAQAAAKAqWS0W4/XXX+/UiRPHjQfqXxIWdq8WLHhNubkbFRraW3//+5u68842xvPEPv74Q4WE3KO6detq+/atWrdurebNe7kmpwEAhGKXmEwmDRkyxKatS5cuKi8vV6tWrTRq1ChNnjxZZ8+eVcOGDVWvXj1Nnz7dOPfnzxSTdN23UxYWnlV5ecWNTwJAteCvaoB9Ym0C9om16VjWrs1SePi98vDwtGlv0KCBnnvuBb3yygt69tm/qF279nrmmb8ax9PSVur55/9PFRVS06Z3aNq0JHXtGlzT5QNwcE4VFRWkMHaCUAywT3y5B+wTaxOwT6xNwD6xNuGIrnX7pHMN1gIAAAAAAADYBUIxAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgc19ouAAAAAEDNmjBhtPbu3S0XFxdJUqNGflqxIl1vv71U77zzd+O8ixfLVVZ2QWvWbJCPj4/OnDmt5OTntX37Vjk5ST16hGrKlOny9PSqrakAAHDDCMUAAAAABzRp0lQNGRJn0zZ8+BMaPvwJ4/3f/rZYO3d+JR8fH0nSG28sVHHxGaWlZaqiQkpKmqqlS9/QxIlP1WjtAABUBW6fBAAAAHCZiooK5eR8oAEDBhttR478oD59IuTp6SUvLy+FhUXou+8O1GKVAADcOLvaKRYZGSk3Nze5ubmpvLxcY8eO1aBBg7R//34NHDhQM2bM0IgRI3TgwAENHz5cJpNJTZo0kSRt27ZNSUlJysjIkIeHxy9eIy8vT6NHj9b8+fMVFRVltE+fPl1ms1lZWVlq06aNJOnw4cOKiopSv379NH78eE2dOlWSdPr0aZ09e1bNmjWTJD300EOqU6eO3nnnHWO8kSNHKjY29rrm7+vLtnPAXvn5edd2CQCugLUJVJ7VYlHRmQvG+8WL52vRolT5+wdo1Khx6to12Ob8nTu/0qlTJxUeHmm03X//UJnNabrvvv6SpLy8j9S7d1jNTAAAgCpmV6GYJKWkpCgwMFB79+5VQkKCQkNDtWrVKvXs2VMmk0kjRoxQ69atNXLkSM2ePVuLFi1SaWmpZs2apTlz5lw1EJMkk8lkjPXTUEyS2rdvL7PZbIRfZrNZ7dq1kyQFBQUpMzNTkpSenq7c3FylpKQYfbds2aJ33nlHPj4+Onr0qGJjY9WtWzc1b9680nM/OGWirCcKKn0+AAAAUFl3LVsp6cdQbOzYRLVq1UqurnW0ceN6TZv2lJYte0/Nmv3vu+vatVmKiOhr8/06KKitysqsGjSorySpW7fuio8fWqPzAACgqthdKHZJu3bt5Onpqfz8fK1Zs0bLly/XqFGjtGvXLnXs2FHDhw/Xhg0bZDabtWfPHoWHh6t79+5XHbOoqEibN2/WBx98oEGDBqmgoEB+fn7G8ejoaJlMJk2ePFnOzs7Kzs7Www8/rB07dlyz3pCQEOP17bffrsaNG+vo0aPXFYoBAAAA1enS7sqIiFCjbfjwR5SX96G+/nq7One+W5JUWlqq3NyNWrBggc2OzMTE0QoKCtKSJYtVUVGhefPmad68Z/Taa69d8ToA7AtrE7Blt6HY5s2bZbFYtH//fgUEBCggIEDx8fEymUzq2LGjnJ2dNXfuXA0bNkze3t4ym83XHHP16tWKiIhQo0aNFBUVJbPZrNGjRxvHPTw81LlzZ23atEnu7u4KDAw0Hip6PbZs2aIzZ86oQ4cO190XAAAAqC4FBcVXbC8rK1dx8Xnj+Pr1a+XtXV+tW7ez6bNv3z5NnDhF585dlCRFR8do3LiRNuf4+Xn/4nUA1B7WJhyRs7PTVR9VZXcP2k9MTFRsbKxSU1OVmpqq9evXKz4+XpIUFxennJwcWSwWSZK/v79CQ0OVkJCgunXrXnPs9PR0Y6z4+Hilp6dfdk58fLwyMjJkNpuNc6/Ht99+q2nTpumll16qVE0AAABATSouLtaWLZ/LYrHIarVq/fq12rnzS4WE/G/32Nq1WYqOHiQnJyebvm3btlNWVoYslvOyWM4rM9OsO+9sU9NTAACgStjdTrFLzxSTpBMnTmjTpk3at2+fFixYIOnHrdzr1q1TTEyMJMnFxUUuLi7XHHf37t369ttvlZSUZLQdP35cX3zxhbp162a0hYSE6JlnnlFZWZnmzJmj1atXV7r2gwcPavTo0XrmmWcUHBx87Q4/0zI59br7AAAAAJVh/e8flq1Wq958c6EOHTooFxdn+fu31Ny5yfL3D5AkFRQc15dfbtfkydMvG54tAhIAACAASURBVGPGjL/o1VeTFR8/SBUVFbr77vZKSnq6JqcBAECVsbtQ7KcyMjLUv39/JScnG21ZWVlKS0szQrHKMplMGjlypCZNmmS0LV68WCaTySYUc3Jy0owZM1RWViZX18p/PIcPH9aTTz6ppKQkhYeHX1dtlxQWnlV5ecUN9QVQfdhqDtgn1iZwYxo0aKAlS97+xeN+fo2Vl7flisfuuKOZXnjhleoqDQCAGmV3t0/+VHp6uoYMGWLT1rdvX+3evVv5+fmVHsdisSg7O/uyIG3w4MFat26dSkpKbNrDwsLUt2/f66r1xRdf1KlTp5SSkqLY2FjFxsbq008/va4xAAAAAAAAUDOcKioq2JpkJ9gpBtgndqMA9om1Cdgn1iZgn1ibcEQ33YP2AQAAAAAAgOpm188UuxFjxozRkSNHbNqaNm2qRYsW1VJFAAAAAAAAsDe3XChG+AUAAAAAAIBr4fZJAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgcQjEAAAAAAAA4HEIxAAAAAAAAOBxCMQAAAAAAADgc19ouAAAAALhZTZgwWnv37paLi4skqVEjP61Yka4vv9yuV199UcePH5Ozs4s6deqip56aKj+/xpKkM2dOKzn5eW3fvlVOTlKPHqGaMmW6PD29anM6AAA4FHaKAQAAAL/CpElTtWHDp9qw4VOtWJEuSWrZspVefnm+cnJylZGxVi1atFBy8lyjzxtvLFRx8RmlpWXq/fczdfLkSS1d+kZtTQEAAIdkV6FYZGSkoqOjFRMTo8GDBys7O1uStH//fgUFBWnZsmWSpAMHDqh37946duyY0Xfbtm3q16+fSkpKrnqNvLw8BQUFacOGDTbt06dPV1BQkP7f//t/Rtvhw4fVtm1bJSYm6l//+pdiY2MVGxuriIgIBQcHG++XL1+uTz75RDExMYqNjdWgQYP0yiuvqKKiooo+GQAAANxMGjb0VaNGfsZ7Z2cX/ec/+cb7I0d+UJ8+EfL09JKXl5fCwiL03XcHaqNUAAAclt3dPpmSkqLAwEDt3btXCQkJCg0N1apVq9SzZ0+ZTCaNGDFCrVu31siRIzV79mwtWrRIpaWlmjVrlubMmSMPD4+rjm8ymYyxoqKibI61b99eZrNZU6dOlSSZzWa1a9dOkhQUFKTMzExJUnp6unJzc5WSkmL0PXfunMxms1xcXFRWVqZHHnlEHTt2VN++fSs9d19ftssD9srPz7u2SwBwBaxN1AarxaKiMxeM94sXz9eiRany9w/QqFHj1LVrsCTp6NGjGjEiQefOnZOzs7OmTZtl9Ln//qEym9N03339JUl5eR+pd++wmp0IAAAOzu5CsUvatWsnT09P5efna82aNVq+fLlGjRqlXbt2qWPHjho+fLg2bNggs9msPXv2KDw8XN27d7/qmEVFRdq8ebM++OADDRo0SAUFBfLz+99f8KKjo2UymTR58mQ5OzsrOztbDz/8sHbs2HHNej09PY3XFotFZWVlcna+vo14B6dMlPVEwXX1AQAAQM26a9lKST+GYmPHJqpVq1Zyda2jjRvXa9q0p7Rs2Xtq1qy5br/9duXk5OrMmdNavdosf/+WxhhBQW1VVmbVoEE//gG1W7fuio8fWguzAQDAcdltKLZ582ZZLBbt379fAQEBCggIUHx8vEwmkzp27ChnZ2fNnTtXw4YNk7e3t8xm8zXHXL16tSIiItSoUSNFRUXJbDZr9OjRxnEPDw917txZmzZtkru7uwIDA+Xj41Ppmr/++mvNnDlThw4d0iOPPKKIiIgbmToAAADs3KVdihERoUbb8OGPKC/vQ3399XZ17ny3zbmPPZag2NhYffLJJ3J1dVVi4mgFBQVpyZLFqqio0Lx58zRv3jN67bXXanwu1YFdnIB9Ym0CtuwuFEtMTJS7u7u8vLyUmpqqd999V/Hx8ZKkuLg4xcXFaebMmXJ3d5e/v79CQ0PVoUMH1a1b95pjp6ena/r06ZKk+Ph4JSUl2YRil9rff/99ubm5KT4+XqdOnap07b/5zW+0Zs0anTx5UomJidq+ffs1d68BAADg5lNQUHzF9rKychUXn7/s+PHjp1VYWKhDh46qfv3btG/fPk2cOEXnzl2UJEVHx2jcuJG/OO7NxM/P+5aYB3CrYW3CETk7O131UVV2F4pdeqaYJJ04cUKbNm3Svn37tGDBAklSaWmp1q1bp5iYGEmSi4uL8RPYV7N79259++23SkpKMtqOHz+uL774Qt26dTPaQkJC9Mwzz6isrExz5szR6tWrr3sODRs2VFhYmHJycq4rFGuZnHrd1wIAAEDNsloskqTi4mLt3btbnTt3lYuLiz76aIN27vxSf/jDZOXlfaRWrVqreXN/nT59WqmprygwMEj1698mSWrbtp2ysjI0blyiJCkz06w772xTa3MCAMAR2V0o9lMZGRnq37+/kpOTjbasrCylpaUZoVhlmUwmjRw5UpMmTTLaFi9eLJPJZBOKOTk5acaMGSorK5Ora+U/nu+++04BAQFydnZWSUmJPvnkEw0ZMuS6aiwsPKvycn6xErA3/FUNsE+sTdQ2q9WqN99cqEOHDsrFxVn+/i01d26y/P0DtHXr55o//1UVFZ2Uh4enunTpqr/+9X/faWfM+ItefTVZ8fGDVFFRobvvbq+kpKdrbzIAADgguw7F0tPTNW3aNJu2vn37avbs2crPz1fz5s0rNY7FYlF2drZWrFhh0z548GDFxMRo1qxZNu1hYdf/yz8bN240fn2yvLxc9913n4YO5WGpAAAAt6oGDRpoyZK3r3jswQcT9OCDCb/Y9447mumFF16prtIAAEAlOFVUVLA1yU6wUwywT+xGAewTaxOwT6xNwD6xNuGIrvVMMecarAUAAAAAAACwC3Z9++SNGDNmjI4cOWLT1rRpUy1atKiWKgIAAAAAAIC9ueVCMcIvAAAAAAAAXAu3TwIAAAAAAMDhEIoBAAAAAADA4RCKAQAAAAAAwOEQigEAAAAAAMDhEIoBAAAAAADA4RCKAQAAAAAAwOEQigEAAAAAAMDhEIoBAAAAAADA4RCKAQAAAAAAwOG41nYBAAAAwM1iwoTR2rt3t1xcXCRJjRr5acWKdH355Xa9+uqLOn78mJydXdSpUxc99dRU+fk1tul/5sxpDRv2gFq0CNDChX+rjSkAAID/IhQDAAAArsOkSVM1ZEicTVvLlq308svz1aiRny5cuKAlSxYqOXmu5s17xea8hQtTFRDQSuXl5TVZMgAAuAK7CsUiIyPl5uYmNzc3lZeXa+zYsRo0aJD279+vgQMHasaMGRoxYoQOHDig4cOHy2QyqUmTJpKkbdu2KSkpSRkZGfLw8PjFa+Tl5Wn06NGaP3++oqKijPbp06fLbDYrKytLbdq0kSQdPnxYUVFR6tevn8aPH6+pU6dKkk6fPq2zZ8+qWbNmkqSHHnpIjz76qCTp5MmTGjx4sIKDg5WSknJd8/f19bqu8wHUHD8/79ouAcAVsDZR3awWi4rOXLjmeQ0b+tq8d3Z20X/+k2/T9vXXO3XgwH7FxMQrKyuzSusEAADXz65CMUlKSUlRYGCg9u7dq4SEBIWGhmrVqlXq2bOnTCaTRowYodatW2vkyJGaPXu2Fi1apNLSUs2aNUtz5sy5aiAmSSaTyRjrp6GYJLVv315ms9kIv8xms9q1aydJCgoKUmbmj19e0tPTlZube8XQ6+mnn1Z4eLjOnTt33XM/OGWirCcKrrsfAAAAqsddy1ZKsg3FFi+er0WLUuXvH6BRo8apa9dgSdLRo0c1YkSCzp07J2dnZ02bNsvoc/HiRb3yyguaOnWWDhz4tianAAAAfoHdPmi/Xbt28vT0VH5+vtasWaNnn31WFotFu3btkiQNHz5cxcXFMpvNeumllxQeHq7u3btfdcyioiJt3rxZL730kr766isVFNgGUNHR0dq4caMuXryoiooKZWdna/DgwZWuefXq1WrUqNE16wAAAMDNaezYRP3jH5kym9cqJuZ+TZv2lLEj7Pbbb1dOTq6ysz/UqFFj5e/f0ui3atVKtWvXQW3b3l1LlQMAgJ+zu51il2zevFkWi0X79+9XQECAAgICFB8fL5PJpI4dO8rZ2Vlz587VsGHD5O3tLbPZfM0xV69erYiICDVq1EhRUVEym80aPXq0cdzDw0OdO3fWpk2b5O7ursDAQPn4+FSq3mPHjmnZsmV65513tG7duhueNwAAAOzLT2/TjYgINV4PH/6I8vI+1Ndfb1fnznfbnP/YYwmKjY3VJ598osLCQqWn/0Pp6eny8fGWt3dd1anjckvf/nsrzw24mbE2AVt2F4olJibK3d1dXl5eSk1N1bvvvqv4+HhJUlxcnOLi4jRz5ky5u7vL399foaGh6tChg+rWrXvNsdPT0zV9+nRJUnx8vJKSkmxCsUvt77//vtzc3BQfH69Tp05Vqu4///nP+tOf/iRPT8/rnDEAAADsWUFB8S8eKysrV3Hx+cvOOX78tAoLC3Xo0FHt2PHjHQoDBgyUJFks52WxWBQaeo/M5g+MX7K8Vfj5eV/1MwNQO1ibcETOzk5XfX673YVil54pJkknTpzQpk2btG/fPi1YsECSVFpaqnXr1ikmJkaS5OLiUqkvErt379a3336rpKQko+348eP64osv1K1bN6MtJCREzzzzjMrKyjRnzhytXr26UnXv2LHDGPvcuXOyWCwaNWqU3nzzzcpNXFLL5NRKnwsAAIDqZ7VYjNfFxcXau3e3OnfuKhcXF3300Qbt3Pml/vCHycrL+0itWrVW8+b+On36tFJTX1FgYJDq179NPXveo7S0/32n3LhxgzZsyNHzz790ywViAADcTOwuFPupjIwM9e/fX8nJyUZbVlaW0tLSjFCsskwmk0aOHKlJkyYZbYsXL5bJZLIJxZycnDRjxgyVlZXJ1bXyH8/WrVuN11d7EP/VFBaeVXl5xXX1AVD9+KsaYJ9Ym6hpVqtVb765UIcOHZSLi7P8/Vtq7txk+fsHaOvWzzV//qsqKjopDw9PdenSVX/964/fYd3c3OTr28gYx8vLS66urjZtAACg5tl1KJaenq5p06bZtPXt21ezZ89Wfn6+mjdvXqlxLBaLsrOztWLFCpv2wYMHKyYmRrNmzbJpDwsL+3WFAwAA4JbToEEDLVny9hWPPfhggh58MKFS4wwcOEQDBw6pytIAAMANcKqoqGBrkp1gpxhgn9iNAtgn1iZgn1ibgH1ibcIRXeuZYs41WAsAAAAAAABgF+z69skbMWbMGB05csSmrWnTplq0aFEtVQQAAAAAAAB7c8uFYoRfAAAAAAAAuBZunwQAAAAAAIDDIRQDAAAAAACAwyEUAwAAAAAAgMMhFAMAAAAAAIDDIRQDAAAAAACAwyEUAwAAAAAAgMMhFAMAAAAAAIDDIRQDAAAAAACAw3Gt7QIAAACAqjZhwmjt3btbLi4ukqRGjfy0YkW6Tpw4oRdfnKNvvtmnwsITSktbraZN7zD6zZnztDZsyFGdOnWMtpycXGMcAABw6yAUAwAAwC1p0qSpGjIkzqbN2dlJISH36Le/fVxjxjxxxX7Dhg3X6NHjaqJEAABQiwjFAAAA4DAaNvTV/fcPldVqre1SAABALbOrUCwyMlJubm5yc3NTeXm5xo4dq0GDBmn//v0aOHCgZsyYoREjRujAgQMaPny4TCaTmjRpIknatm2bkpKSlJGRIQ8Pj1+8Rl5enkaPHq358+crKirKaJ8+fbrMZrOysrLUpk0bSdLhw4cVFRWlfv36afz48Zo6daok6fTp0zp79qyaNWsmSXrooYfUtWtXPfPMM9q3b5/Cw8OVkpJy3fP39fW67j4Aaoafn3dtlwDgClib+CmrxaKiMxeM94sXz9eiRany9w/QqFHj1LVrcKXGMZtXyWxepaZN79Dw4Y8rIqJvdZUMAABqkV2FYpKUkpKiwMBA7d27VwkJCQoNDdWqVavUs2dPmUwmjRgxQq1bt9bIkSM1e/ZsLVq0SKWlpZo1a5bmzJlz1UBMkkwmkzHWT0MxSWrfvr3MZrMRfpnNZrVr106SFBQUpMzMTElSenq6cnNzbYKvY8eOacaMGdq3b58+++yzG5r7wSkTZT1RcEN9AQAAHN1dy1ZK+jEUGzs2Ua1atZKrax1t3Lhe06Y9pWXL3lOzZs2vOsaDDyZowoQ/ytPTS9u2bdZf/jJTDRv6qmPHzjUwAwAAUJPsLhS7pF27dvL09FR+fr7WrFmj5cuXa9SoUdq1a5c6duyo4cOHa8OGDTKbzdqzZ4/Cw8PVvXv3q45ZVFSkzZs364MPPtCgQYNUUFAgPz8/43h0dLRMJpMmT54sZ2dnZWdn6+GHH9aOHTuuWW+TJk3UpEkT7d+//1fPHQAAADfm0u7BiIhQo2348EeUl/ehvv56uzp3vluSjNsnGzb0tNlx6Of3v++TMTEDtH3759q27Z/q27dPTZR/y2AXJ2CfWJuALbsNxTZv3iyLxaL9+/crICBAAQEBio+Pl8lkUseOHeXs7Ky5c+dq2LBh8vb2ltlsvuaYq1evVkREhBo1aqSoqCiZzWaNHj3aOO7h4aHOnTtr06ZNcnd3V2BgoHx8fKpzmgAAAKhCBQXFV2wvKytXcfF54/ilUOzkyXNyd79yH0myWKySLL84Li7n5+fN5wXYIdYmHJGzs9NVH1XlXIO1VEpiYqJiY2OVmpqq1NRUrV+/XvHx8ZKkuLg45eTkyGKxSJL8/f0VGhqqhIQE1a1b95pjp6enG2PFx8crPT39snPi4+OVkZEhs9lsnAsAAICbR3FxsbZs+VwWi0VWq1Xr16/Vzp1fKiTkx91jFotFZWVlkqSysgvGd0tJ+vjjD1VSUqLy8nJt3bpZ69atVa9eYbUyDwAAUL3sbqfYpWeKSdKJEye0adMm7du3TwsWLJAklZaWat26dYqJiZEkubi4yMXF5Zrj7t69W99++62SkpKMtuPHj+uLL75Qt27djLaQkBA988wzKisr05w5c7R69eqqnN5VtUxOrbFrAQAA3Gqs/w23rFar3nxzoQ4dOigXF2f5+7fU3LnJ8vcPkCT17dvL6DNs2IOSpE2btkuS0tJW6vnn/08VFVLTpndo2rSkSj+gHwAA3FzsLhT7qYyMDPXv31/JyclGW1ZWltLS0oxQrLJMJpNGjhypSZMmGW2LFy+WyWSyCcWcnJw0Y8YMlZWVydW1Zj+ewsKzKi+vqNFrArg2tpoD9om1iV/SoEEDLVny9i8evxSAXcmCBUuqoyQAAGCH7O72yZ9KT0/XkCFDbNr69u2r3bt3Kz8/v9LjWCwWZWdnXxakDR48WOvWrVNJSYlNe1hYmPr2vb6f3s7Pz1dYWJief/555eXlKSwsTGlpadc1BgAAAAAAAGqGU0VFBVuT7AQ7xQD7xG4UwD6xNgH7xNoE7BNrE47opnvQPgAAAAAAAFDd7PqZYjdizJgxOnLkiE1b06ZNtWjRolqqCAAAAAAAAPbmlgvFCL8AAAAAAABwLdw+CQAAAAAAAIdDKAYAAAAAAACHQygGAAAAAAAAh0MoBgAAAAAAAIdDKAYAAAAAAACHQygGAAAAAAAAh0MoBgAAAAAAAIdDKAYAAAAAAACHQygGAACAX+3w4e8VGXmPnn32z0bbqlUrNXRojPr1C9eTT/5WO3fuMI69//5yDR0aq379whUbG62UlJdktVpro3QAAOCgCMUAAADwq7388jy1bdvOeL9nz24tWjRf//d/87RuXa4GD45VUtIUXbx4UZLUu3e4li59V+vX5+mdd97Xt9/+P61atbK2ygcAAA6oVkOxyMhIRUdHKzY2VtHR0Zo1a5bKysqUnp6uxMRESVJ+fr5CQkKu2D8oKEjnzp27rmu++OKL6tChgwoLCy+rpXfv3sYXNUlKT09XUFCQ3n33XZlMJsXGxio2NlY9evRQWFiY8X7nzp1auHChhgwZori4OMXGxuqDDz64zk8DAADg5vThh+vk5eWtbt26G21Hj/6gVq1aq23bu+Xk5KTo6EE6deqUioqKJEnNmjWXt7e3JKmiokJOTs7Kz8+vlfoBAIBjcq3tAlJSUhQYGKiLFy/q0Ucf1YYNG6rtWhcvXlRmZqa6du2qzMxMPfHEEzbHGzdurE2bNik8PFySZDab1b59e0nSAw88oAceeECSNH36dHXo0EGPPfaY0bd169YaO3asJOnYsWMaMGCAevXqpdtuu63S9fn6ev2q+QGoPn5+3rVdAoArYG3WHqvFoqIzF3Tu3FktWbJYKSkLtWZNhnG8Z897tHz529qzZ7fatr1b2dmZatMmUL6+vsY569fnKDl5rkpKzsnHx0cTJvyxNqYCAAAcVK2HYpdYLBZZLBbVr19f58+fr5Zr5OXlyd/fX4mJiXr66acvC8Xi4+OVnp6u8PBwHT58WCUlJQoMDKzU2Jf+0ilJJSUlcnJyUnl5+XXVd3DKRFlPFFxXHwAAgNpw17KVki7ozTcXafDgGDVu3MTmuIeHpyIiIjVu3JOSJC8vLyUnp8jJyck4p1+/aPXrF63Dh79XTk62GjZsWJNTAAAADq7WQ7HExES5u7vr+++/V+/evdW7d2+lp6dXy7VMJpPuv/9+BQcHq6ysTDt37lSnTp2M4z169NB7772n06dPy2w2Ky4uTnv27Kn0+CtWrNBbb72lo0eP6q9//asaNGhQHdMAAACwCydO5GvHju0ym81yc3OTp6e73N1d5efnrbS0NK1bl63s7GwFBARo06ZNmj79KZnNZjVpYhug+fm115EjBzV//kuaP39+rcwFVYtdnIB9Ym0Ctmo9FLt0+6TFYtHEiRO1bNky1a9fv8qvU1hYqK1bt2revHmSpLi4OJlMJptQzMnJSQMGDFB29o9f4FauXHldodgjjzyiRx55RP/61780ZcoUhYaGEowBAIBb1kcffaL8/HyFh0dIkkpLS3TxYrn+9a8YdejQUSEh98jLq5EKC8/p7ru7qEEDX+XlfaZ7773vsrGKis7qwIGDKigoruFZoKr5+Xnz7wjYIdYmHJGzs9NVH1VV66HYJe7u7oqIiFBubq6io6OrfPzMzExZrVbFxMRIkqxWq0pLSzVz5kzVrVvXOC8+Pl5Dhw5V9+7dbzjQCgoKUuPGjbV161b179+/0v1aJqfe0PUAAABqmtViUUzM/erbt5/RtmLFuzp69AdNnjxDmzf/U2+9tVQPPPCw7rijmbZv36LDhw+pdeu7JElr1mSod+8wNWjQUN99d0DvvLNMISE9a2s6AADAAdlNKFZeXq5t27apZcuW1TJ+enq6Xn/9dd1zzz1G25P/n717j4uq2v8//h7QGUQwCfF+IUwsJTXzhpKYpuIFhTyWJ/2See+o9DOtADUvJ1MTK1HTtI5WavbVAQnJC1ZqnpOKWiRqnaNdFG8h4RUcrr8//DqnSU0wYUbn9Xw8fDzYa++99mdRq3y8Z+01Q4dq48aNCgsLs7bVq1dP48aNU7NmzUrV/+HDh3X//Vf+knfs2DEdOnTIelxSWVkXVVRUXKp7AJQ9PlUDHBNz0/7c3NxsPlysVKmSjEaTvLy8FBLSS8ePZ2js2JG6cOGCqlevrhdfjFGDBr6SpP3707RkydvKzc1R1apeeuyxxzVs2Cg7jQQAADgju4diV/cUy8/PV6NGjTR69Gh99tlnNtecP39eHTt2tB77+flp+fLlkqSQkBDrhq2VKlXSpk2brnlGWlqazp49q3btbD99DA0NldlstgnFJOmpp54q9Tjmz5+vw4cPq0KFCnJ1ddWkSZPUsGHDUvcDAABwpxo6dKT1Z4PBoGHDRt0w6IqJmVJeZQEAAFyXobi4mKVJDoKVYoBjYjUK4JiYm4BjYm4Cjom5CWd0sz3FXMqxFgAAAAAAAMAh2P31ydspKytLQ4YMuaa9a9euGjNmjB0qAgAAAAAAgCO6q0Ixb29vJSYm2rsMAAAAAAAAODhenwQAAAAAAIDTIRQDAAAAAACA0yEUAwAAAAAAgNMhFAMAAAAAAIDTIRQDAAAAAACA0yEUAwAAAAAAgNMhFAMAAAAAAIDTIRQDAAAAAACA0yEUAwAAAAAAgNMhFAMAAHACx44dVefO7TV9+mRrW3Z2tqZOnaju3YMVEvKYpk2bZD23YMFbGjAgXF27dtTTT/fThg3r7VE2AABAmalg7wIAAABQ9t54Y7YeeKCJTdvEiS/qwQebyGxOlpubm3744bD1XKVKlTR79puqV6++Dh06qPHjx6pu3Xp66KHm5V06AABAmbBrKNa5c2cZjUaZTCZZLBa1atVKU6ZMUVJSkrZu3aq4uDhlZGSoX79+2rVr1zX3N27cWPv27VPlypVL/Mw5c+bo/fff17Zt2+Tt7W1TS15enrZt2yZXV1dJUnx8vKKjozV58mRVqlRJH3zwgSTp5MmTcnNzk5eXlyRp+vTpWrdunb766isZjUa5u7tr4sSJeuihh0r1+/D29ijV9QDKj4+Pp71LAHAdzM3rK7BYlH0+z3q8ZcsmeXh4KiCgmY4fz5Ak7d69U7/8clrz579j/buPv/8D1nuGDh1p/blp0wA1b95C6en7CcUAAMBdw+4rxeLi4uTv76/CwkINHDhQKSkpZfaswsJCJSYmqmXLlkpMTNSQIUNszlevXl07duxQcHCwJCkhIUFNmzaVJPXr10/9+vWTJEVFRSkgIECDBg2y3vvrr78qJiZGFStW1BdffKFx48Zpy5YtparvpwljVXAm888MEQAAQPcvXy3pSih26dJFvfvuO4qLW6SkpHXWaw4c2K/69Rtoxoyp2rnzX6pdu45Gj35eDz/8yDX9WSyX9d13BxUe3r+8hgAAAFDmHGZPMYvFIovFoipVqpTZM7Zt26b69esrMjJS8fHx15wPDw+3th87dkw5OTny9/cv3KszjgAAIABJREFUUd+PPfaYKlasKElq0aKFTp06paKiottXPAAAwC1YunSxevfuo+rVa9i0//LLL9q9e6datnxEn3yySQMGDFR09HidPXv2mj7mzJmphg391bZtYHmVDQAAUObsvlIsMjJSJpNJR48eVVBQkIKCgq4bWN0OZrNZTzzxhFq1aqX8/HylpaWpefP/vgLQpk0brVq1SufOnVNCQoLCwsJ04MCBUj9n5cqV6tSpk1xcHCZzBAAATsbHx1OHDh3SN9/sUUJCgoxGoypXNslkqiAfH09VreqhOnXq6Nln/0eS9Ne//kWrVr2vn3/+Xo0aPW7tZ/bs2Tp27Cd98MEH8vBgqweUDK82A46JuQnYsnsodvX1SYvForFjx2r58uVlslosKytLu3fv1uzZsyVJYWFhMpvNNqGYwWBQjx49lJycrOTkZK1evbrUoVhycrKSkpK0cuXK21o/AABAaWRmXtDnn29XRkaGgoM7SZJyc3NUWFik77/voyee6K+ioivXXVVYWKTz53Otbe+99462bt2qBQuWKDe3WLm5F673KMCGj4+nzb9XABwDcxPOyMXF8If7t9s9FLvKZDKpU6dO2rp1q0JCQm57/4mJiSooKFCfPn0kSQUFBcrNzVVMTIzc3Nys14WHh6t///5q3bq1dSP9kkpJSdGbb76p5cuXq1q1aqWu0Td2fqnvAQAA+L0Ci0WS1KfPE+rSpZu1/aOPVujUqRMaPz5arq4uWrBgnjZsWK9u3Xpo+/Yv9Msvv+ihh1pIkj78cJlSUjZq4cKluueeqnYZBwAAQFlymFCsqKhIqamp8vX1LZP+4+PjtXDhQrVv397aNnToUG3cuFFhYWHWtnr16mncuHFq1qxZqfr/4osvNHPmTC1btkx169a9pRqzsi6qqKj4lu4FUHb4VA1wTMzNm3Nzc7P58K9SpUoyGk3WD/5mz35Dc+fO0htvzFb9+r6aNWuuqla9EoC9885CVaxYUQMGhFvv/5//eVYREbZfVAQAAHCnsnsodnVPsfz8fDVq1EijR4/WZ599ZnPN+fPn1bFjR+uxn5+fli9fLkkKCQmRwWCQdOUveps2bbrmGWlpaTp79qzatWtn0x4aGiqz2WwTiknSU089VepxREdHq2LFioqMjLS2LV++vNSrzQAAAMrK0KEjbY6bN39YH3zw8XWv3bFjT3mUBAAAYDeG4uJiliY5CFaKAY6J1SiAY2JuAo6JuQk4JuYmnNHN9hTj6xEBAAAAAADgdOz++uTtlJWVpSFDrt3nomvXrhozZowdKgIAAAAAAIAjuqtCMW9vbyUmJtq7DAAAAAAAADg4Xp8EAAAAAACA0yEUAwAAAAAAgNMhFAMAAAAAAIDTIRQDAAAAAACA0yEUAwAAAAAAgNMhFAMAAAAAAIDTIRQDAAAAAACA0yEUAwAAAAAAgNMhFAMAAHe9Y8eOqnPn9po+fbK1bfPmjerXr7cefzxI0dHjdf78OZt7tmzZpIED/6LHHw/Sk0/2VVra1+VdNgAAAMoQoRgAALjrvfHGbD3wQBPr8Q8/HNGcOa9p8uTp+uSTzTKZ3DR37izr+dTUnVq0aL6io6do8+btWrhwqWrXrmOP0gEAAFBGCMUAAMBdbcuWTfLw8NQjj7S2tqWkbFSHDo+qRYuWcnd31/Dhz2nbti+Uk3NJkvTee0v07LPDFBDwkFxcXOTjU10+PtXtNQQAAACUgQr2LuC3OnfuLKPRKKPRqKKiIj333HPq1auXjhw5op49eyo6OlqDBw/WDz/8oIiICJnNZtWoUUOSlJqaqokTJ2rdunVyd3e/4TO2bdumESNGaMGCBeratau1PSoqSgkJCVq/fr0aNWokSTp27Ji6du2qbt26afTo0XrppZckSefOndPFixdVp86VT4yffPJJVapUSa+99pq1rW7dulq4cGGpxu/t7VGq6wGUHx8fT3uXAOA6bjQ3CywWZZ/P06VLF/Xuu+8oLm6RkpLWWc//+OMRBQQ0sx7XqVNXFStW1NGjR9Wokb++++6gOnToqKeeClNeXp4efTRYo0c/L5PJrczHBAAAgPLhUKGYJMXFxcnf318HDx7UgAEDFBgYqLVr16pdu3Yym80aPHiw/Pz8NGzYME2ZMkWLFy9Wbm6uJk2apBkzZvxhICZJZrPZ2tdvQzFJatq0qRISEqzhV0JCgpo0ufKqRePGjZWYmChJio+P19atWxUXF2e9Nz4+Xu3bt7dpK62fJoxVwZnMW74fAABccf/y1ZLytHTpYvXu3UfVq9ewOZ+bmysPD9sPoypX9lBOziVlZ/+qgoICbd36mRYufFcVKlRQdPQLWr78PY0cObocRwEAAICy5HCh2FVNmjRR5cqVlZGRoaSkJK1cuVLDhw/Xt99+q2bNmikiIkIpKSlKSEjQgQMHFBwcrNatW/9hn9nZ2dq5c6c+/fRT9erVS5mZmfLx8bGeDwkJkdls1vjx4+Xi4qLk5GQ99dRT+uabb8p6uAAA4DY7cyZD33yzRwkJCTIajapc2SSTqYJ8fDx1zz2ekgpsVprl5FxSnTo+ql3bW5L07LPP6MEH75MkDR8+TIsWLdKkSVH2GApwx2GFNeCYmJuALYcNxXbu3CmLxaIjR46oQYMGatCggcLDw2U2m9WsWTO5uLho5syZevrpp+Xp6amEhISb9vnJJ5+oU6dOqlatmrp27aqEhASNGDHCet7d3V0tWrTQjh07ZDKZ5O/vr6pVq5a45t27d6tv377y8PDQ8OHD1alTp1sZOgAAuA0+/3y7MjIyFBzcSZKUm5ujwsIiff99H7Vt215paenKzLwgSTp+PEN5eXny9KymvDwXVa9eQxcuXLaev3DhsgoKCq3HAG7Mx8eTuQI4IOYmnJGLi+EPt6pyuFAsMjJSJpNJHh4emj9/vlasWKHw8HBJUlhYmMLCwhQTEyOTyaT69esrMDBQAQEBcnO7+R4f8fHxioq68glveHi4Jk6caBOKXW3/+OOPZTQaFR4errNnz5ao7k6dOqlnz55yc3PTwYMHNXz4cH3wwQdq2LBhicfuGzu/xNcCAIAbK7BY1KfPE+rSpZu17aOPVujUqRMaPz5a2dm/atSoIUpL+1r+/g/ovffeUXDwY3J3ryxJ6tkzVGbz/6pdu/Zyda2gjz9epfbtH7XXcAAAAFAGHC4Uu7qnmCSdOXNGO3bs0KFDh/T2229LurIHyKZNm9SnTx9Jkqurq1xdXW/ab3p6ug4fPqyJEyda23755Rft3btXjzzyiLWtbdu2mjZtmvLz8zVjxgx98sknJar73nvvtf7cpEkTtWzZUt9++22pQrGsrIsqKiou8fUAygefqgGO6WZz083NzeZDs0qVKsloNMnLy0teXl6aMCFa06ZN0vnz59SqVRvFxEyxXjt48DCdPXtWf/3rEzIaTerc+XFFRAwp0/EAAACgfDlcKPZb69atU/fu3RUbG2ttW79+vdasWWMNxUrKbDZr2LBhGjdunLXtnXfekdlstgnFDAaDoqOjlZ+frwoVSv7rOX36tPWbMI8fP65vvvlGzz33XKlqBAAAZWfo0JE2x926hahbt5DrXluhQgVNmBClCRPYQwwAAOBu5WLvAv5IfHy8QkNDbdq6dOmi9PR0ZWRklLgfi8Wi5OTka4K03r17a9OmTcrJybFp79ixo7p06VKqWleuXKlevXqpT58+eu655/TCCy9Yv7kSAAAAAAAAjsVQXFzM+3oOgtcnAcfE65OAY2JuAo6JuQk4JuYmnNHNNtp36JViAAAAAAAAQFlw6D3FbsWoUaN08uRJm7ZatWpp8eLFdqoIAAAAAAAAjuauC8UIvwAAAAAAAHAzvD4JAAAAAAAAp0MoBgAAAAAAAKdDKAYAAAAAAACnQygGAAAAAAAAp0MoBgAAAAAAAKdDKAYAAAAAAACnQygGAAAAAAAAp0MoBgAAAAAAAKdTwd4FAAAA3Mj06ZO1d+9u5eZe1r33emvgwAiFhoZJktasWaNFixbr11+z9NBDLRQT84qqVfORJK1a9YE2bFivU6dOqWrVqgoP/4uefjrCnkMBAACAgyEUAwAADmvQoMGKiposo9Gon3/+SWPHjlSjRo2Vk3NJb7zxht56a5Hq1auvefNiNXXqRC1YsESSVFxcrEmTpqthw/t14kSGxo0bo+rVa+jxx7vbeUQAAABwFHZ9fbJz584KCQlR3759FRISokmTJik/P1/x8fGKjIyUJGVkZKht27bXvb9x48a6dOlSqZ45Z84cBQQEKCsr65pagoKCVFhYaG2Lj49X48aNtWLFCpnNZvXt21d9+/ZVmzZt1LFjR+txWlqaJOnDDz9USEiIQkND1bdv31LVBQAAruXn11BGo1GSZDBc+XP8eIb+9a8dCgkJkZ9fQ1WsWFGDBw/TN9/s0/HjGZKkgQOfUePGD6hChQqqX99Xjz4arP370+w5FAAAADgYu68Ui4uLk7+/vwoLCzVw4EClpKSU2bMKCwuVmJioli1bKjExUUOGDLE5X716de3YsUPBwcGSpISEBDVt2lSS1K9fP/Xr10+SFBUVpYCAAA0aNMh67+bNm7Vx40atXbtWHh4eOnPmTKnr8/b2uNWhAShjPj6e9i4BcBoFFouyz+dZj2NjZ2nDhiRZLBb5+zdWYGAHHTp0QMXFxdZrrv78ww+HVadOXZv+iouLlZb2tfr2faJ8BgAAAIA7gt1DsassFossFouqVKmiy5cvl8kztm3bpvr16ysyMlJTp069JhQLDw9XfHy8goODdezYMeXk5Mjf379Eff/jH//Q888/Lw+PK8FWtWrVSl3fTxPGquBMZqnvAwDgbnL/8tWS/huKTZgQpXHjXlR6+n59/fUeGY1GtW0bqGnTJqp79z6qV6+eli1bKoPBcN2/Q/zjH0tUVFSsnj37lOMoAAAA4OhuORQ7duyYDAaD6tate/OL/0BkZKRMJpOOHj2qoKAgBQUFKT4+/k/1eSNms1lPPPGEWrVqpfz8fKWlpal58+bW823atNGqVat07tw5JSQkKCwsTAcOHChR30eOHFFaWprmzZunvLw8DRgwQE8++WSZjAMAgLvd9VZn1qz5qLZvT1FKSpIiIiKUnX1aU6ZE6eLFi3rmmWdUuXJl+fvfZ3PvihUrtHnzp1q1apVq1vQuzyEATo0V1oBjYm4Ctkocir3wwgsaNGiQWrZsKbPZrGnTpsnFxUUTJ05U//79b7mAq69PWiwWjR07VsuXL1eVKlVuub8bycrK0u7duzV79mxJUlhYmMxms00oZjAY1KNHDyUnJys5OVmrV68ucShWWFiokydPatWqVcrOztZf//pX3XfffWrduvVtHwsAAHe7zMwL122/dOmyvv/+iDIzL2jgwIHq1u3K6q+jR3/W22+/LS+vWtZ7169P1HvvvaMFC5bI1bXyDfsEcHv5+Hgy3wAHxNyEM3JxMfzhVlUlDsW++uorzZo1S5K0fPlyLVu2TFWqVNHo0aP/VCh2lclkUqdOnbR161aFhIT86f5+LzExUQUFBerT58pfngsKCpSbm6uYmBi5ublZrwsPD1f//v3VunVreXl5lbj/2rVrq3fv3nJxcZG3t7fat2+vb7/9tlShmG/s/JIPCACAu1SBxSJJys7+VXv3pqp9+0dlMpm0Z89ubdmySVOnzpDFYtG//31SVavW1OnTp/X66zPUv/9frR+sbd68QUuWvK24uMXX7DEGAAAASKUIxfLz82U0GnX69GmdPXtWjzzyiCTd0oby11NUVKTU1FT5+vrelv5+Lz4+XgsXLlT79u2tbUOHDtXGjRsVFhZmbatXr57GjRunZs2alar/3r1768svv1Tr1q2Vk5OjvXv3qmvXrqXqIyvrooqKim9+IYByxadqgL0YtG6dWbGxM1VUVKyaNWsqMnK8goKCdeHCBY0fP15Hjx6Vu3tl9ewZqmHDRlnvXLp0kc6dO6vhwyOsbd269dCLL8bYYyAAAABwQCUOxR588EG98847On78uDp16iRJOn36tHVj+Vt1dU+x/Px8NWrUSKNHj9Znn31mc8358+fVsWNH67Gfn5+WL18uSQoJCZHBYJAkVapUSZs2bbrmGWlpaTp79qzatWtn0x4aGiqz2WwTiknSU089VepxDB48WJMnT1avXr0kSX379lWHDh1K3Q8AALjCy8tLCxYsue45T09PJSUl3TCwXrPmk7IsDQAAAHcBQ/Fvv8/8Dxw9elTz5s1ThQoV9NJLL8nb21sbN27U/v379eKLL5Z1nU6BlWKAY2KlGOCYmJuAY2JuAo6JuQlndLM9xUociqHsEYoBjom/QACOibkJOCbmJuCYmJtwRrdto/3i4mKtWbNGycnJ+vXXX5WUlKTU1FRlZmaqZ8+et6XYPysrK0tDhgy5pr1r164aM2aMHSoCAAAAAACAIypxKDZv3jz961//0jPPPKMpU6ZIkmrWrKmZM2c6TCjm7e2txMREe5cBAAAAAAAAB+dS0gsTEhK0ePFi9erVy7qxfd26dXXs2LEyKw4AAAAAAAAoCyUOxQoLC1W5cmVJsoZily5dkru7e9lUBgAAAAAAAJSREodiHTt21MyZM5WXlyfpyh5j8+bN02OPPVZmxQEAAAAAAABlocShWExMjM6cOaNHHnlEFy5c0MMPP6wTJ05owoQJZVkfAAAAAAAAcNuVaKP9wsJCbdy4UXPnztXFixd1/Phx1apVSz4+PmVdHwAAAAAAAHDblWilmKurq2bNmiWTySRvb281a9aMQAwAAAAAAAB3rBK/PvnYY4/p888/L8taAAAAAAAAgHJRotcnJclisSgyMlIPP/ywatasaf0GSkl6/fXXy6Q4AAAAAAAAoCyUOBTz9/eXv79/WdYCAABgNX36ZO3du1u5uZd1773eGjgwQqGhYZKkpKR1+uijD5SZmamHHmqhmJhXVK3ala0d9u3bo2XLlurf//5Onp5VtHZtkj2HAQAAAAdlKC4uLrZ3EbgiK+uiior4xwE4Gh8fT2VmXrB3GYDT+eGHI6pbt56MRqN+/vknjR07Uq+//pZyci7plVeitWLFh6pc2Vvz5sXqp59+1IIFSyRJBw+m6+jRn2WxWPThh8sIxYByxv83AcfE3IQzcnExyNvb44bnS7xS7KuvvrrhucDAwNJVBQAAcBN+fg2tPxsMV/4cP56hQ4cO6LHHHlejRo2UmXlBgwcPU1hYDx0/nqE6deqqSZMANWkSoNTUXXasHgAAAI6uxKHYxIkTbY6zs7OVn5+vGjVq6LPPPrstxXTu3FlGo1FGo1FFRUV67rnn1KtXLx05ckQ9e/ZUdHS0Bg8erB9++EEREREym82qUaOGJCk1NVUTJ07UunXr5O7ufsNnbNu2TSNGjNCCBQvUtWtXa3tUVJQSEhK0fv16NWrUSJJ07Ngxde3aVd26ddPo0aP10ksvSZLOnTunixcvqk6dOpKkJ598UgMHDtRXX32lOXPmyGKxSJLmzp2rBx54oMTj/6P0EoB9+fh42rsEwCkUWCzKPp9nPY6NnaUNG5JksVjk799YgYEddOjQAf12ofvVn3/44bDq1Klb7jUDAADgzlTiUOz33zxZWFioRYsWqXLlyre1oLi4OPn7++vgwYMaMGCAAgMDtXbtWrVr105ms1mDBw+Wn5+fhg0bpilTpmjx4sXKzc3VpEmTNGPGjD8MxCTJbDZb+/ptKCZJTZs2VUJCgjX8SkhIUJMmTSRJjRs3VmJioiQpPj5eW7duVVxcnPXe06dPa+LEiXr33Xfl5+eny5cvq6CgoFRj/2nCWBWcySzVPQAA3E3uX75a0n9DsQkTojRu3ItKT9+vr7/eI6PRqLZtAzV1aoy+++5/5OHhrWXLlspgMOjy5cv2KxwAAAB3HJdbvdHV1VWjRo3Su+++ezvrsWrSpIkqV66sjIwMJSUlafr06bJYLPr2228lSREREbpw4YISEhI0d+5cBQcHq3Xr1n/YZ3Z2tnbu3Km5c+fq66+/VmambQAVEhKizz77TIWFhSouLlZycrJ69+5donpXrVqlvn37ys/PT5Lk5uYmDw9WfgEA8Ge5urqqefMWysz8RQkJa9W6dVsNGTJSkZGR+stf+qhWrdpyd3dX9eo17F0qAAAA7iAlXil2Pf/85z9lMBhuVy02du7cKYvFoiNHjqhBgwZq0KCBwsPDZTab1axZM7m4uGjmzJl6+umn5enpqYSEhJv2+cknn6hTp06qVq2aunbtqoSEBI0YMcJ63t3dXS1atNCOHTtkMpnk7++vqlWrlqjew4cPq06dOoqIiND58+fVtm1bjR8/Xkaj8ZZ/BwAAOKMbva5csaKLfv31tHx8PDVq1FCNGjVUkvTjjz/qgw/+odatm+uee/57b9Wq7nJ1deH1Z8AOmHeAY2JuArZKHIoFBwfbBGC5ubnKy8vTK6+8clsLioyMlMlkkoeHh+bPn68VK1YoPDxckhQWFqawsDDFxMTIZDKpfv36CgwMVEBAgNzc3G7ad3x8vKKioiRJ4eHhmjhxok0odrX9448/ltFoVHh4uM6ePVuiugsLC7Vv3z4tW7ZMJpNJEyZM0JIlSzRmzJhS/gYAAHBumZkXlJ39q/buTVX79o/KZDJpz57dWr9+vaZOnaGMjDM6fvyY2rRpofT0w3r11Vf0l78MUF6eizIzL6ioqEj5+fk6c+a8CgoKlZFxRi4uLqpYsaK9hwY4Bb7hDnBMzE04o9v27ZNz5syxOa5UqZLuu+++2/6K4NU9xSTpzJkz2rFjhw4dOqS3335b0pUwbtOmTerTp4+kK69UuLq63rTf9PR0HT582OYLA3755Rft3btXjzzyiLWtbdu2mjZtmvLz8zVjxgx98sknJaq7du3aCggIkKfnleQ9JCTEugdZSfnGzi/V9QAA3G0K/u/LaiSD1q0zKzZ2poqKilWzZk1FRo5XUFCwLly4oGnTJunEieOqVMldPXuGatiwUdY+vvlmnyIj/3vcpUsHtWjRUgsWLCnn0QAAAMCRlTgU279/v4YOHXpN+7Jly/Tss8/e1qKuWrdunbp3767Y2Fhr2/r167VmzRprKFZSZrNZw4YN07hx46xt77zzjsxms00oZjAYFB0drfz8fFWoUPK3S3v37q0333xTI0aMUMWKFbVjx45SffOkJGVlXVRRUfHNLwRQrvhUDSh/Xl5eNwyxPD099f77q284N1u2bKUdO/aUdYkAAAC4w5V4o/2FCxdet33RokW3rZjfi4+PV2hoqE1bly5dlJ6eroyMjBL3Y7FYlJycfE2Q1rt3b23atEk5OTk27R07dlSXLl1KVWvLli316KOPKiwsTH369FFhYaFGjhxZqj4AAAAAAABQPgzFxcV/uDTpq6++kiQ999xzWrx4sX57eUZGht5++2198cUXZVulk2ClGOCYWCkGOCbmJuCYmJuAY2Juwhn96T3Fru7BZbFYFBMTY203GAzy8fHRpEmTbkOZAAAAAAAAQPm5aSj2+eefS5Jeeuklvf7662Ve0J81atQonTx50qatVq1aWrx4sZ0qAgAAAAAAgKO56euTKD+8Pgk4JpaaA46JuQk4JuYm4JiYm3BGf/r1yasuXryo+fPnKzU1VdnZ2TZ7i23duvVPFQkAAAAAAACUpxJ/++TUqVN18OBB/e1vf9PZs2c1adIk1apVS4MHDy7D8gAAAAAAAIDbr8Qrxf75z3/q008/lZeXl1xdXfX444/roYce0qhRowjGAAAAAAAAcEcp8UqxoqIieXp6SpLc3d114cIF+fj46Oeffy6z4gAAAAAAAICyUOKVYg888IBSU1MVGBioVq1aaerUqapcubJ8fX3LsDwAAAAAAADg9ivxSrFXX31VderUkSRNnDhRbm5uOn/+vF5//fUyKw4AAAAAAAAoCyVeKVavXj3rz97e3poxY0aZFAQAAAAAAACUtRKvFCsuLtb//u//KiIiQqGhoZKk1NRUffrpp2VWHAAAAAAAAFAWSrxSbN68efrXv/6lZ555RlOmTJEk1axZUzNnzlTPnj3LrEAAAHDnmj59svbu3a3c3Mu6915vDRwYodDQMG3evEFz5rxmva6oqEgWi0XvvvuhHnjgQeXl5WnevFht375VBQUFeuih5nrxxWj5+FS342gAAABwNynxSrGEhAQtXrxYvXr1ksFgkCTVrVtXx44dK7PiAADAnW3QoMFasyZJmzdv0+zZb2jp0kX67rtD6tath1JSvrT+GT8+SrVr11Hjxg9Iktas+Ujp6fv1/vsfad26DfL09NSbb86x82gAAABwNylxKFZYWKjKlStLkjUUu3Tpktzd3cumMgAAcMfz82soo9EoSTIYrvw5fjzjmus2bFivkJD/fvB28uQJtW3bTvfe6y2TyaQuXbrpxx+PlGvtAAAAuLuV+PXJ4OBgzZw5UzExMZKu7DE2b948PfbYY7f88M6dO8toNMpkMslisahVq1aaMmWKkpKStHXrVsXFxSkjI0P9+vXTrl27rrm/cePG2rdvnzWsK4k5c+bo/fff17Zt2+Tt7W1TS15enrZt2yZXV1dJUnx8vKKjozV58mRVqlRJH3zwgSTp5MmTcnNzk5eXlyRp+vTpat68uSRp165dGjx4sCZOnKhBgwaV6vfh7e1RqusBlB8fH097lwDcMQosFmWfz7Mex8bO0oYNSbJYLPL3b6zAwA421586dVJpaV8rOvoVa1vv3n01b16szpzJlIeHpzZv3qB27WzvAwAAAP6Mm4ZimZmZ8vHxUXR0tF5++WW1atVK+fn5evjhh9WhQwfNnj37TxUQFxcnf39/FRYWauDAgUpJSflT/f2RwsJCJSYmqmXLlkpMTNSQIUNszlevXl07duxQcHCwpCuvjDZt2lSS1K9fP/Xr10+SFBUVpYCAgGtCr4sXLyo2NlYdO3a8pfp+mjBWBWcyb+leAAAcxf3LV0v6byh/02uTAAAgAElEQVQ2YUKUxo17Uenp+/X113usK8eu2rgxWc2atVDt2nWsbXXr1lf16jUUFtZDrq6u8vNrqBdeeKm8hgAAAAAncNNQrHv37tq3b588PDy0cOFCDR8+XGPHjlWtWrXk4+Nz2wqxWCyyWCyqUqWKLl++fNv6/a1t27apfv36ioyM1NSpU68JxcLDwxUfH6/g4GAdO3ZMOTk58vf3L3H/s2bN0tChQ7V169bbXDkAAHeW662urFnzUW3fnqKUlCRFRERY21NSNmjkyJE298yePU0GQ7F27dold3d3LV26VFFR47RmzZqbPgeA/TE3AcfE3ARs3TQUKy4utjlOS0tTs2bNblsBkZGRMplMOnr0qIKCghQUFKT4+Pjb1v9vmc1mPfHEE9bVbmlpadbXHiWpTZs2WrVqlc6dO6eEhASFhYXpwIEDJep727ZtunDhgkJCQgjFAABOLzPzwnXbL126rO+/P2I9/+233+j06dNq1aqDzT3p6Qc0YsTflJ/vqnPnLOrRI1xxcXH6z3+OqWrVqpKu/MX+Rs8BYD/MTcAxMTfhjFxcDH+4VdVNQ7GrG96WlauvT1osFo0dO1bLly9XlSpVbvtzsrKytHv3buvrnmFhYTKbzTahmMFgUI8ePZScnKzk5GStXr26RKHY+fPnNXfuXC1btuxP1egbO/9P3Q8AgCMosFgkSdnZv2rv3lS1b/+oTCaT9uzZrS1bNmnq1BnWazdsSFZwcGe5u9vuD/rgg021cWOyHn64ldzc3JSQsEbVqvlYAzEAAADgz7ppKFZYWKidO3daV4wVFBTYHEtSYGDgny7EZDKpU6dO2rp1q0JCQv50f7+XmJiogoIC9enTR9KVceTm5iomJkZubm7W68LDw9W/f3+1bt3aupH+zfz73/9WZmam+vfvL0nKzs7WF198obNnz2rMmDElrjEr66KKiopvfiGAcsWnasCtMmjdOrNiY2eqqKhYNWvWVGTkeAUFXdm702Kx6IsvUvTqq69fc+fo0c/rrbdiNWBAuAoK8nXffQ312mtzynsAAAAAuIvdNBTz9va2fuOkJFWtWtXm2GAw6LPPPvvThRQVFSk1NVW+vr5/uq/riY+P18KFC9W+fXtr29ChQ7Vx40aFhYVZ2+rVq6dx48aV6hXRVq1a6auvvrIe32gjfgAAnImXl5cWLFhyw/Mmk0kbN2697rl77qmqKVNeLaPKAAAAgBKEYp9//nmZFnB1T7H8/Hw1atRIo0ePviZkO3/+vM03Ovr5+Wn58uWSpJCQEOsrnpUqVdKmTZuueUZaWprOnj2rdu3a2bSHhobKbDbbhGKS9NRTT92OoQEAAAAAAMBBGYp/v5M+7IbXJwHHxOuTgGNibgKOibkJOCbmJpzRzTbadynHWgAAAAAAAACHcNPXJ+8kWVlZGjJkyDXtXbt2LdWG9wAAAAAAALi73VWhmLe3txITE+1dBgAAAAAAABwcr08CAAAAAADA6RCKAQAAAAAAwOkQigEAAAAAAMDpEIoBAAAAAADA6RCKAQAAAAAAwOkQigEAAAAAAMDpEIoBAAAAAADA6RCKAQAAAAAAwOkQigEAcAeZPn2y+vbtrm7dgjVgwBNKSlp3zTXLli1VUFArpabusrbl5eXptdemqVu3YPXp012rV68oz7IBAAAAh1PB3gUAAICSGzRosKKiJstoNOrnn3/S2LEj1ahRYz3wwIOSpOPHM/TFF1vk7V3N5r5//GOJMjKOae3aJP36a5YiI0fJ19dP7dq1t8cwAAAAALuz60qxzp07KyQkRH379lVISIgmTZqk/Px8xcfHKzIyUpKUkZGhtm3bXvf+xo0b69KlS6V65pw5cxQQEKCsrKxragkKClJhYaG1LT4+Xo0bN9aKFStkNpvVt29f9e3bV23atFHHjh2tx2lpaYqKirJpW7RoUSl/GwAA3JyfX0MZjUZJksFw5c/x4xnW83PnztZzz41VxYoVbe7bsGG9Bg8epipVqsjX9z6FhoZpw4akcq0dAAAAcCR2XykWFxcnf39/FRYWauDAgUpJSSmzZxUWFioxMVEtW7ZUYmKihgwZYnO+evXq2rFjh4KDgyVJCQkJatq0qSSpX79+6tevnyQpKipKAQEBGjRokPXejz76SCNGjLBpKy1vb49bvhdA2fLx8bR3CXBiBRaLss/nWY9jY2dpw4YkWSwW+fs3VmBgB0nS559vkdFYUYGBQZJmW68/f/68srLO6P77G1nb7r+/kb78cmt5DQEAAABwOHYPxa6yWCyyWCyqUqWKLl++XCbP2LZtm+rXr6/IyEhNnTr1mlAsPDxc8fHxCg4O1rFjx5STkyN/f/8yqeV6fpowVgVnMsvteQCAO8P9y1dL+m8oNmFClMaNe1Hp6fv19dd7ZDQalZNzSUuWLNSbby685v7c3BxJUuXK//3wxcPDQzk5OWVeOwAAAOCo7B6KRUZGymQy6ejRowoKClJQUJDi4+PL5Flms1lPPPGEWrVqpfz8fKWlpal58+bW823atNGqVat07tw5JSQkKCwsTAcOHChx/8uWLdPHH3+sevXqafz48WrYsGFZDAMA4ISut1qxZs1HtX17ilJSknTixAmFh4epWbPGkiRXVxdVreouHx9PGY1FkqRKlQzy9vb8v/NFqlLF845fBXmn1w/crZibgGNibgK27B6KXX190mKxaOzYsVq+fLmqVKly25+TlZWl3bt3a/bsK6+ThIWFyWw224RiBoNBPXr0UHJyspKTk7V69eoSh2Ljxo2Tj4+PXFxctG7dOg0bNkxbtmyRq6vrbR8LAMD5ZGZeuG77pUuX9f33R/TNN/uUmXlaK1eukiSdPZutyMjnNXBghAYNGixv72ratWufWrduJ0nat+9b1avX4Ib93gl8fDzv6PqBuxVzE3BMzE04IxcXwx9uVWX3UOwqk8mkTp06aevWrQoJCbnt/ScmJqqgoEB9+vSRJBUUFCg3N1cxMTFyc3OzXhceHq7+/furdevW8vLyKnH/NWrUsP4cFhammTNn6tSpU6pTp06J+/CNnV/iawEAzqPAYpEkZWf/qr17U9W+/aMymUzas2e3tmzZpKlTZ+jZZ4epoKDAes/w4c9ozJhx1m+XDAnppfff/4caN26i7OxflZSUoOjoKXYZDwAAAOAIHCYUKyoqUmpqqnx9fcuk//j4eC1cuFDt2//3q+eHDh2qjRs3KiwszNpWr149jRs3Ts2aNStV/6dPn7YGY19++aVcXFxsgrKSyMq6qKKi4lLdA6Ds8akaHIdB69aZFRs7U0VFxapZs6YiI8crKCj4mitdXFzk6ekpd3d3SdLQoSMVGztTf/lLqEwmkwYOjLAGZgAAAIAzsnsodnVPsfz8fDVq1EijR4/WZ599ZnPN+fPn1bFjR+uxn5+fli9fLkkKCQmRwWCQJFWqVEmbNm265hlpaWk6e/as2rVrZ9MeGhoqs9lsE4pJ0lNPPVXqcbz88svKysqSwWCQh4eHFi1apAoV7P7rBQDcRby8vLRgwZISXbt2bZLNsdFoVEzMFMXEsDoMAAAAkCRDcXExS5McBCvFAMfESjHAMTE3AcfE3AQcE3MTzuhme4q5lGMtAAAAAAAAgEO4q97vy8rK0pAhQ65p79q1q8aMGWOHigAAAAAAAOCI7qpQzNvbW4mJifYuAwAAAAAAAA6O1ycBAAAAAADgdAjFAAAAAAAA4HQIxQAAAAAAAOB0CMUAAAAAAADgdAjFAAAAAAAA4HQIxQAAAAAAAOB0CMUAAAAAAADgdAjFAAAAAAAA4HQq2LsAAADuRNOnT9bevbuVm3tZ997rrYEDIxQaGqb8/HxNmzZR3313SKdOnVRc3GK1bNnqmvvz8/M1ePBflZOTo4SET+0wAgAAAMC5sVIMAIBbMGjQYK1Zk6TNm7dp9uw3tHTpIn333SFJUrNmLTR58t/l7e19w/tXrfpAVat6lVe5AAAAAH6HUAwAgFvg59dQRqNRkmQwXPlz/HiGKlasqCeffFrNm7eQi4vrde89ceK4Nm/eoEGDBpdjxQAAAAB+y2len+zcubOMRqOMRqOKior03HPPqVevXjpy5Ih69uyp6OhoDR48WD/88IMiIiJkNptVo0YNSVJqaqomTpyodevWyd3d/br9f/7553r99ddVWFiopk2baubMmapUqVKpavT29vjT4wRQNnx8PO1dAhxAgcWi7PN51uPY2FnasCFJFotF/v6NFRjYoUT9vPXWHI0YMVomk6msSgUAAABwE04TiklSXFyc/P39dfDgQQ0YMECBgYFau3at2rVrJ7PZrMGDB8vPz0/Dhg3TlClTtHjxYuXm5mrSpEmaMWPGDQOxS5cuafLkyVq5cqV8fX01ceJEvffeexozZkyp6vtpwlgVnMm8HUMFAJSB+5evlvTfUGzChCiNG/ei0tP36+uv91hXjv2Rbdu+UGFhkYKDH9O+fXvKsFoAAAAAf8SpQrGrmjRposqVKysjI0NJSUlauXKlhg8frm+//VbNmjVTRESEUlJSlJCQoAMHDig4OFitW7e+YX/bt29XQECAfH19JUkDBgxQVFRUqUMxAIDju96qwZo1H9X27SlKSUlSRESEtd3FxaCqVd2t9+Tk5GjJkgVasmSJfHw8VbWqu1xcDKxE/BP43QGOibkJOCbmJmDLKUOxnTt3ymKx6MiRI2rQoIEaNGig8PBwmc1mNWvWTC4uLpo5c6aefvppeXp6KiEh4Q/7O3nypGrXrm09rl27tk6ePFnWwwAA2EFm5oXrtl+6dFnff3/E5nxRUbHOns2xtv3nP98rIyNDf/3r05KufAPlpUsXFRjYXu+8s0y1atW+bt+4Ph8fzxv+8wBgP8xNwDExN+GMXFwMf7hVlVOFYpGRkTKZTPLw8ND8+fO1YsUKhYeHS5LCwsIUFhammJgYmUwm1a9fX4GBgQoICJCbm5udKwcAOJLs7F+1d2+q2rd/VCaTSXv27NaWLZs0deoMSVJeXp6Ki4slSQUFBbJYLDIajbrvvoaKj0+29pOe/q3eeON1/eMfK/gmSgAAAKCcOVUodnVPMUk6c+aMduzYoUOHDuntt9+WJOXm5mrTpk3q06ePJMnV1VWurtf/5rDfqlWrlnbt2mU9PnHihGrVqlXq+nxj55f6HgBA+SmwWP7vJ4PWrTMrNnamioqKVbNmTUVGjldQULAk6emn++nUqSsrhl944cqr9GvWfKJatWrL27uatT9PzypycXGxaQMAAABQPpwqFPutdevWqXv37oqNjbW2rV+/XmvWrLGGYiX16KOP6u9//7t++ukn+fr6avXq1erRo0epa8rKuqiiouJS3wegbLHUHL/n5eWlBQuW3PD82rVJJeqnZctWSkj49HaVBQAAAKAUXOxdgL3Ex8crNDTUpq1Lly5KT09XRkZGqfry8PDQ9OnTNXLkSHXt2lUXLlzQkCFDbme5AAAAAAAAuI0MxVc3PYHdsVIMcEysFAMcE3MTcEzMTcAxMTfhjG620b7TrhQDAAAAAACA83LaPcVuxahRo3Ty5Embtlq1amnx4sV2qggAAAAAAAC3glCsFAi/AAAAAAAA7g68PgkAAAAAAACnQygGAAAAAAAAp0MoBgAAAAAAAKdDKAYAAAAAAACnQygGAAAAAAAAp0MoBgAAAAAAAKdDKAYAAAAAAACnQygGAAAAAAAAp1PB3gUAAEomLy9Pc+fO0p49u3X+/HnVqVNXI0eOVmBgB6Wn79e77y7S999/J1dXF7Vo8Yj+3/97UdWqVbPeO29erLZv36qCggI99FBzvfhitHx8qtt5VAAAAABgH6wUA4A7RGFhoapXr6EFC5Zo06atGj78Ob3ySrROnjyhCxfOq0+fJ7R27Sdau3a93N3d9dpr06z3rlnzkdLT9+v99z/SunUb5OnpqTffnGPH0QAAAACAfZXLSrHOnTvLaDTKZDJZ2xYuXKiIiAgZjUYZjUbl5ubq/vvv1/Dhw9WyZUtJ0vz585WTk6OXX37Zet+KFSuUnp6uWbNmSZJ+/PFHxcbG6rvvvtM999wjo9GoYcOG6fHHH7fe079/f+Xl5SkxMVHZ2dkaPHiwJCknJ0e//PKLfH19JUmdOnVS+/btNXv2bMXHx0uSzp07p9mzZ2vXrl2qUKGCvLy8NGHCBLVq1UqSFB8fr+joaL355pvq2bOntW3r1q2Ki4srm18oAKdUqVIlDR060nrcocOjql27tr7//pA6depic22/fk9pzJgR1uOTJ0+obdt2uvdeb0lSly7dNH/+G+VTOAAAAAA4oHJ7fTIuLk7+/v5/2L5582aNGDFC7733npo3b37TPn/55RcNGjRIL774ohYuXChJyszM1D//+U/rNf/5z3905swZVaxYUenp6QoICFBiYqIkadeuXTYB2NW233r++efl7++vzZs3y9XVVbt379aYMWP08ccfq0GDBpKkOnXqaN68eerWrZsqVLj1X6m3t8ct3wugbPn4eNrt2QUWi7LP513T/uuvWTp27Kjuu6/hNefS0vbpvvv8rMe9e/fVvHmxOnMmUx4entq8eYPatetQpnUDAAAAgCNzqD3FunXrpm+//VbvvfdeiVZZrVy5Um3btlVYWJi1zcfHx+bYbDarb9++MhqNMpvNCggIKHE9qamp+vHHH7V06VK5urpKktq0aaN+/frpnXfe0WuvvSZJCggIUF5entauXasBAwaUuP/f+2nCWBWcybzl+wHcne5fvlqSbShWUFCgadMmKySklxo08LU5d/jwf7Rs2buaNWuuta1u3fqqXr2GwsJ6yNXVVX5+DfXCCy+VQ/UAAAAA4JjKLRSLjIy0vj7p6upqszrrt5o3b67PP/+8RH0ePHhQHTrceKVDfn6+kpKS9NFHH6lixYoKCwtTVFSUzWucf+T7779X06ZNVbFiRZv2Fi1aaNGiRTZt48aN04gRI2wCOQC4XX67Uq2oqEjjx49X5cpueu21v9v8N+rnn3/WSy89r0mTJurxxzta22fPniaDoVi7du2Su7u7li5dqqiocVqzZk25jgO43ey5ihPAjTE3AcfE3ARs2f31yd8rLi62/mwwGK57zY3af2/r1q3y9fVV/fr1JUlNmjRRSkqKevfuXaL7f1vLzTRu3FitW7fWhx9+KG9v7xLfBwAlkZl5QdKV/y7NnDldJ0+eVmzsPJ09e1nSZUnSqVMnNWbMCEVEDFGHDl2s90hSevoBjRjxN+Xnu+rcOYt69AhXXFyc/vOfY6patao9hgT8aT4+njb/ngNwDMxNwDExN+GMXFwMf7hVlUO9PilJ+/fvV6NGjSRJXl5eOn78uM357Oxs3XvvvZKuhFz79++/YV9ms1mHDx9W586dJV3ZWN9sNpc4FHvggQf07rvvKj8/32YlxjfffKPGjRtfc/3zzz+vAQMGaMSIEdecKwnf2Pm3dB+Au1uBxWL9OTZ2pn766Ue99dbbMpncrO2Zmb8oMnKUnnjiSYWF/eWaPh58sKk2bkzWww+3kpubmxIS1qhaNR8CMQAAAABOy6FCsS1btuijjz7Se++9J0lq166dFi1apFOnTqlmzZo6e/asPv30U73yyiuSpKefflrh4eFKSkpSaGioJCkrK0tffvmlOnTooN27d2v79u3y8LiSClosFgUFBenEiROqXbv2Tetp3bq1GjRooDlz5ujll1+Wq6urUlNTtXbtWn388cfXXF+vXj11795d77//fqn2LrsqK+uiiopKvjoNQPlwlE/VTp06qcTEeBmNRvXt293a/uKLMcrIOKYTJ45r2bIlWrZsifVcSsqXkqTRo5/XW2/FasCAcBUU5Ou++xrqtdfmlPsYAAAAAMBR2GVPMUl69dVXre1Go1G5ublq2LChlixZYv3myYYNGyomJkZ/+9vfVFhYqOLiYg0aNEiBgYGSpBo1aujDDz9UbGys3nrrLbm7u8vd3V3Dhw9XQkKCOnbsaA3EJMlkMunxxx9XfHy8xowZU6K64+LiNHv2bHXt2lUVK1ZU1apVFRcXJ19f3+te/7e//U0JCQm38isCgD9Us2Yt7dix54bnhwy58SrVe+6pqilTXi2LsgAAAADgjmQoLs3GWShTrBQDHJOjrBQDYIu5CTgm5ibgmJibcEY321PMpRxrAQAAAAAAABwCoRgAAAAAAACcDqEYAAAAAAAAnA6hGAAAAAAAAJwOoRgAAADw/9u797Cq6nyP45/NRkAuiiEqZuJoYZJRKaaUiaGFVy52MytE0dSmsClPqVhe0tQZbEqU0HK0RmecIzdTU6fGmo6dTMrKo5IVZEmiImiS4MbNXuePxl3kFVP2zvV+PU/Pw/791vrt7yK/PrtPa/02AAAwHUIxAAAAAAAAmA6hGAAAAAAAAEyHUAwAAAAAAACmQygGAAAAAAAA0yEUAwAAAAAAgOkQigEAAAAAAMB0CMUAAAAAAABgOp6uLgAALlc1NTWaN2+OPvpoq44ePaorr2yjMWN+r6ioW3XixAlNn56mzz8v1P79pZo/P0tdukQ6z9227SMtXfqKvvjicwUENFF29hoXXgkAAAAAXH64UwwALpHa2lq1aNFSCxYs1saN72r06HF69tlJKi3dJ0mKiLhRzzzznIKCgk4518fHRwMHxumRR8Y3dNkAAAAAYAqmuVMsJiZGXl5e8vLyksPh0Lhx4zRw4EAVFRVpwIABmjRpkpKTk1VcXKykpCTl5OSoZcuWkqSCggKlpaUpPz9fvr6+p6y9bds2/fGPf9TRo0clSdHR0XrqqadksVjqVWNQkP+vv1AAl0RwcMB5H2u32XT4aI0aN26slJQxzvFbb71NrVu31u7dhQoJaa177x0mSfLwsJ6yRnh4Z4WHd1ZBwYe/vngAAAAAwClME4pJ0vz58xUWFqZdu3Zp6NChioqKUnZ2tnr06KGcnBwlJyerffv2GjVqlKZOnaqsrCxVV1drypQpmjVr1mkDMUny9/fXnDlz1K5dO9XU1Gj48OFavXq1EhIS6lXfngmPyX6o7GJcKgAXunrZSkk1p4xXVJRr795v9bvfdWj4ogAAAAAAdZjy8cnw8HD5+fmppKREa9as0YwZM2Sz2bR9+3ZJUlJSkiorK5WXl6d58+YpOjpa3bp1O+N6YWFhateunSTJy8tL4eHh2rdvX0NcCoDfCLvdrunTn1G/fgMVGtrO1eUAAAAAgOmZ6k6xk7Zs2SKbzaaioiKFhoYqNDRUiYmJysnJUUREhDw8PDR79mwNGzZMAQEBysvLO++1y8vLtXHjRi1evPgSXgEAd/fzxy0dDoeefPJJ+fn56Pnnn1OjRo3qHOvhYVFgoO9pH9EMDPSV1epRr8c3ATOhNwD3RG8C7oneBOoyVSiWmpoqb29v+fv7KyMjQ8uXL1diYqIkKSEhQQkJCZo8ebK8vb3Vtm1bRUVFqXPnzvLx8Tmv9X/44QeNGzdOI0eOVHh4+KW8FABurqysUpJkGIZmz56h0tIDSk9/SUeOHJd0vM6xDoehI0eqnOf83JEjVaqtdZx2DjC74OAAegNwQ/Qm4J7oTZiRh4flrPu3myoUO7mnmCQdOnRImzdvVmFhoTIzMyVJ1dXV2rhxo+Li4iRJVqtVVuupG2CfTnV1tcaOHatbb71VI0eOvKD62qVnXNB5ANyL3WZz/pyePlt79nytF1/MlLd33YC9pqZGhmH8eI7dLpvNJi8vL1ksFjkcDp04cUJ2u12GYchms8nDw+OUu8wAAAAAABfGVKHYz+Xn5ys2Nlbp6enOsbVr12rVqlXOUOx82Ww2jR07VjfccIPGjx9/wTWVl/8gh8O44PMBXBoX+n/V9u8v1erVufLy8lJ8fKxz/L/+a7LuvLO/hg27S/v3l0qSnnjiUUnSqlVvKCSktT79dJtSU8c6z+nT51bdeGMXLVjAo9kAAAAAcDGYNhTLzc3V008/XWesT58+mjp1qkpKStSmTZvzXis7O1tbt27VkSNHtHnzZklSv379NG7cuItaM4DfllatQrR580dnnM/OXnPGuS5dIs96LgAAAADg17EYJ5/dgctxpxjgnth/AXBP9CbgnuhNwD3RmzCjc+0p5tGAtQAAAAAAAABuwbSPT16IsWPHqrS0tM5YSEiIsrKyXFQRAAAAAAAALgShWD0QfgEAAAAAAFweeHwSAAAAAAAApkMoBgAAAAAAANMhFAMAAAAAAIDpEIoBAAAAAADAdAjFAAAAAAAAYDqEYgAAAAAAADAdQjEAAAAAAACYDqEYAAAAAAAATMfT1QUAwJnk5PxDb765VsXFX6lv31ilpU1zzh0/flwLFryod955S3a7XVdfHaaFC1+RJFVWVuqll9K1Zcv/SpISE+9WSsoYV1wCAAAAAMBNEYoBcFvNmwdr+PAUbd36gWw2W525P/5xlmpr7Vq+PFtNmjTRl19+4ZzLyHhBx48fV3b2Gh0+XKHx48epVasQDRwY19CXAAAAAABwUzw+CcBtRUfHqFev3mrSpGmd8W++2aPNm9/TU0+lqVmzZrJarbr22k7O+ffff08PPJAkHx8fhYS01qBB8Vq37o2GLh8AAAAA4MZMc6dYTEyMvLy85OXlJYfDoXHjxmngwIEqKirSgAEDNGnSJCUnJ6u4uFhJSUnKyclRy5YtJUkFBQVKS0tTfn6+fH19z/gehmFoxIgRKiws1IcffljvGoOC/C/4+oDLid1m0+GjNWec37Vrh1q1aqUlSxZp48Y3FRTUXCNHPqzevfs4jzEMo87PxcVFl7RmAAAAAMBvi2lCMUmaP3++wsLCtGvXLg0dOlRRUVHKzs5Wjx49lJOTo+TkZLVv316jRo3S1KlTlZWVperqak2ZMkWzZs06ayAmScuXL1fr1q1VWFh4QfXtmfCY7IfKLuhc4HJy9bKVks4cipWVHVRxcZGio2OUn79BO3Zs11NPPa527dqrXbvfqXv3W7R8+WuaMnW2LRAAACAASURBVGWaKioqtG7dG7LZjjfcBQAAAAAA3J6pQrGTwsPD5efnp5KSEq1Zs0YrVqzQ6NGjtX37dkVERCgpKUlvvfWW8vLytHPnTkVHR6tbt25nXXPPnj1at26d5syZo3/9618NdCXA5Ss4OMD5s6+vl3x8GjnHgoKaqlGjRpow4XF5enqqdeveys3toV27PlG3bhF67rlpeu655zRs2F0KDAxUXNxgrVu3rs6av6YeAO6D3gTcE70JuCd6E6jLlKHYli1bZLPZVFRUpNDQUIWGhioxMVE5OTmKiIiQh4eHZs+erWHDhikgIEB5eXlnXc/hcGjKlCmaOnWqPD1N+SsFLrqyskrnz1VVNTp+/IRzrGXLq2QYhsrKKp09V1Nj1w8/2P5zjFUTJ05znr9o0UKFhXWqs2Z9BAcHXPC5AC4dehNwT/Qm4J7oTZiRh4flrFtVmWqj/dTUVMXHxysjI0MZGRn65z//qcTERElSQkKCNmzY4PyGu7Zt2yoqKkpDhw6Vj4/PWdddsmSJunXrpk6dOp31OAD1Y7fbZbPZ5HA45HDUymazyW6368Ybu6hly1ZavnyZ7Ha7tm//VNu2fazu3aMkSd99V6Lvvz+i2tpaffDB+3rjjVwNH57i4qsBAAAAALgTU93WdHJPMUk6dOiQNm/erMLCQmVmZkqSqqurtXHjRsXFxUmSrFarrFbrOdf96KOPtHv3bq1evVp2u11Hjx5VTEyM3njjDfn7n//m+e3SMy7gqoDLj/0/4fRrry3R0qWvOMc3blyvESNGKyVljGbPnqe5c2dq+fJlatUqRFOmTFdoaDtJ0uefF2r+/Hn64YdKXXVVqJ59dqbat+/giksBAAAAALgpU4ViP5efn6/Y2Filp6c7x9auXatVq1Y5Q7HztWjRIufPJSUluuuuu7Rp06Z611Re/oMcDuPcBwImkZIyRikpY0471759By1atPS0c3363KE+fe64lKUBAAAAAH7jTPX45M/l5uZq8ODBdcb69OmjHTt2qKSkxEVVAQAAAAAAoCFYDMPg1iQ3wZ1igHtiU1LAPdGbgHuiNwH3RG/CjNhoHwAAAAAAAPgF0+4pdiHGjh2r0tLSOmMhISHKyspyUUUAAAAAAAC4EIRi9UD4BQAAAAAAcHng8UkAAAAAAACYDqEYAAAAAAAATIdQDAAAAAAAAKZDKAYAAAAAAADTIRQDAAAAAACA6RCKAQAAAAAAwHQIxQAAAAAAAGA6hGIAAAAAAAAwHU9XFwDAXHJy/qE331yr4uKv1LdvrNLSpkmSSkv36Z574tS4cWPnsQ88MFzJyaMkSWVlBzVv3hx99tmn8vHx0fDhI5WQcLcrLgEAAAAAcBkgFAPQoJo3D9bw4SnauvUD2Wy2U+bXr39Hnp6n/tU0Y8YzuvrqMM2c+Ud9/XWxUlPHqm3bdurSJbIhygYAAAAAXGZM8/hkTEyM+vXrp7i4OA0aNEjr1q2TJBUVFaljx45atmyZJKm4uFg9e/bUgQMHnOcWFBTozjvvVFVV1WnXPnjwoIYMGaL4+HgNHjxYqamp+v777y/5NQG/RdHRMerVq7eaNGl63udUVVXpk08+VlLSSHl6euqaa8LUu3cfrVu3+hJWCgAAAAC4nJnqTrH58+crLCxMu3bt0tChQxUVFaXs7Gz16NFDOTk5Sk5OVvv27TVq1ChNnTpVWVlZqq6u1pQpUzRr1iz5+vqedt1mzZppxYoVzse+nn/+eWVmZmrSpEn1qi8oyP9XXyPgjuw2mw4frTmvY+++e7AsFou6deuuRx4Zr8DAQEnGf2aNnx1pqLi46GKXCgAAAAAwCVOFYieFh4fLz89PJSUlWrNmjVasWKHRo0dr+/btioiIUFJSkt566y3l5eVp586dio6OVrdu3c64XqNGjdSoUSNJUm1traqqqhQQEFDvuvZMeEz2Q2UXfF2Au7p62UpJZw/FmjYN1Kuvvq6rrw7T0aPf64UX5mrGjCl64YUF8vX10/XX36Bly17VI4+M1549X+vddzf9JzADAAAAAKD+TBmKbdmyRTabTUVFRQoNDVVoaKgSExOVk5OjiIgIeXh4aPbs2Ro2bJgCAgKUl5d3XuvGx8ertLRUHTt21Msvv3yJrwL4bQkOrhsU+/p6ycen0c/GAxQa2lKSFBLSTDNnzlDPnj3VuLFF/v7+eumlP2vGjBm6++5Buuqqq5SQEK8vv/zylHUbqn4A7oHeBNwTvQm4J3oTqMtUoVhqaqq8vb3l7++vjIwMLV++XImJiZKkhIQEJSQkaPLkyfL29lbbtm0VFRWlzp07y8fH57zWX716tU6cOKGZM2fq73//u0aPHn0pLwf4TSkrq6zzuqqqRsePnzhl/KSKimPO86qrDXl5NdHMmenO+WnT0nT11dee8fyLKTg4oEHeB0D90JuAe6I3AfdEb8KMPDwsZ92qylSh2Mk9xSTp0KFD2rx5swoLC5WZmSlJqq6u1saNGxUXFydJslqtslqt9XqPRo0aKTExUc8880y9Q7F26Rn1Oh74rbD/7Fsm7Xa7amtr5XA45HDUymazyWq1avfuzxUQ4K82bdqqsvKoXnwxXTfd1FX+/j/+BbZnz9dq0aKFGjXy0qZNb6mgYIuWL8921SUBAAAAAH7jTBWK/Vx+fr5iY2OVnv7TnSdr167VqlWrnKHY+SotLVWTJk3k5+cnh8OhjRs3OsO3+igv/0EOh3HuA4HfsNdeW6KlS19xvt64cb1GjBittm1DtXhxpg4frpCfn58iI7tr2rRZzuO2bv1Ar7/+Fx0/flzXXNNR8+ZlqFmzZq64BAAAAADAZcC0oVhubq6efvrpOmN9+vTR1KlTVVJSojZt2pz3Wl9//bXmzJkjwzBkGIauvfZapaWlXeySgctCSsoYpaSMOe3cHXf0O+N59947TPfeO+xSlQUAAAAAMBmLYRjcmuQmuFMMcE/svwC4J3oTcE/0JuCe6E2Y0bn2FPNowFoAAAAAAAAAt2DaxycvxNixY1VaWlpnLCQkRFlZWS6qCAAAAAAAABeCUKweCL8AAAAAAAAuDzw+CQAAAAAAANMhFAMAAAAAAIDpEIoBAAAAAADAdAjFAAAAAAAAYDqEYgAAAAAAADAdQjEAAAAAAACYDqEYAAAAAAAATIdQDAAAAAAAAKZDKAYAAAAAAADT8XR1AQAuLzk5/9Cbb65VcfFX6ts3Vmlp0yRJpaX7dM89cWrcuLHz2AceGK7k5FGSpAULXtTmzf9WeXm5goOD9dBDI9S//yBXXAIAAAAAwAQIxQBcVM2bB2v48BRt3fqBbDbbKfPr178jT89T/+pp3Lix5s79s666qq0KC3fpyScfU5s2V+n6629oiLIBAAAAACZjmlAsJiZGXl5e8vLyksPh0Lhx4zRw4EAVFRVpwIABmjRpkpKTk1VcXKykpCTl5OSoZcuWkqSCggKlpaUpPz9fvr6+p12/pKRE06dP17fffiur1aoRI0bonnvuqVeNQUH+v/o6AVew22w6fLRGkhQdHSNJ+vzzXSorO3jea6SkjHH+fN11nXXDDTdqx47/IxQDAAAAAFwSpgnFJGn+/PkKCwvTrl27NHToUEVFRSk7O1s9evRQTk6OkpOT1b59e40aNUpTp05VVlaWqqurNWXKFM2aNeuMgZhhGHr00Uf16KOPqm/fvjIMQxUVFfWub8+Ex2Q/VPZrLxNocFcvWymp5ryOvfvuwbJYLOrWrbseeWS8AgMDTznGZjuuzz/fpcTE+gXLAAAAAACcL1NutB8eHi4/Pz+VlJRozZo1mjFjhmw2m7Zv3y5JSkpKUmVlpfLy8jRv3jxFR0erW7duZ1zvf//3f+Xn56e+fftKkiwWi4KCghrkWoDfiqZNA/Xqq68rO3uNliz5q6qqjmnGjCmnPfZPf5qtDh3C1L17VANXCQAAAAAwC1PdKXbSli1bZLPZVFRUpNDQUIWGhioxMVE5OTmKiIiQh4eHZs+erWHDhikgIEB5eXlnXe+rr75SYGCgUlNT9e2336pt27aaNGmSQkJCGuiKANcLDg6o89rX10s+Po1+Nh6g0NAfH0kOCWmmmTNnqGfPnmrc2CJ//58eHZ47d6727t2j119/vc64q/3y+gC4B3oTcE/0JuCe6E2gLlOFYqmpqfL29pa/v78yMjK0fPlyJSYmSpISEhKUkJCgyZMny9vbW23btlVUVJQ6d+4sHx+fs67rcDi0ZcsW/fd//7c6dOigpUuX6umnn9brr7/eEJcFuIWysso6r6uqanT8+IlTxk+qqDjmPK+62pAkLVmySO+++64WLFis6mpD1dWnP7ehBQcHnPE6ALgOvQm4J3oTcE/0JszIw8Ny1v3bTRWKndxTTJIOHTqkzZs3q7CwUJmZmZKk6upqbdy4UXFxcZIkq9Uqq9V6znVDQkJ03XXXqUOHDpKkuLg4zZ8/v971tUvPqPc5gDuw/+xbJu12u2pra+VwOORw1Mpms8lqtWr37s8VEOCvNm3aqrLyqF58MV033dTVeTfYX/+6VG+9tUELF76ipk1P3WcMAAAAAICLyVSh2M/l5+crNjZW6enpzrG1a9dq1apVzlDsfPXq1UsvvPCCDh48qBYtWuh//ud/1LFjx3rXVF7+gxwOo97nAe7ktdeWaOnSV5yvN25crxEjRqtt21AtXpypw4cr5Ofnp8jI7po2bZbzuEWLFqpRo0YaOjTROfbQQyOUlDSyQesHAAAAAJiDaUOx3NxcPf3003XG+vTpo6lTp6qkpERt2rQ577V8fX01ZcoUjR49WoZhKDAwUHPmzLnYJQO/CSkpY5SSMua0c3fc0e+M523e/NGlKgkAAAAAgFNYDMPg1iQ3wZ1igHti/wXAPdGbgHuiNwH3RG/CjM61p5hHA9YCAAAAAAAAuAXTPj55IcaOHavS0tI6YyEhIcrKynJRRQAAAAAAALgQhGL1QPgFAAAAAABweeDxSQAAAAAAAJgOoRgAAAAAAABMh1AMAAAAAAAApkMoBgAAAAAAANMhFAMAAAAAAIDpEIoBAAAAAADAdAjFAAAAAAAAYDqEYgAAAAAAADAdT1cXAODSycn5h958c62Ki79S376xSkubJknaseP/9OqrL2v37s9ltXroxhu76vHH/0vNmzeXJD35ZKq2b//Euc6JEyfUtm2oXn/9H664DAAAAAAALjpCMeAy1rx5sIYPT9HWrR/IZrM5xysrjyouboi6d+8hq9VTL7wwV88/P10vvJAhSZo3b36ddR599GF17dqtQWsHAAAAAOBS4vFJ4DIWHR2jXr16q0mTpnXGo6JuVUxMX/n5+cvHx0d33XWf/u//PjvtGqWl+7R9+6fq129gQ5QMAAAAAECDMM2dYjExMfLy8pKXl5ccDofGjRungQMHqqioSAMGDNCkSZOUnJys4uJiJSUlKScnRy1btpQkFRQUKC0tTfn5+fL19T1l7ZKSEt1555265pprnGPLli1Ts2bN6lVjUJD/r7tIQJLdZtPhozX1Ouezz7bpd79rf9q5DRvWKSLiRoWEtL4Y5QEAAAAA4BZME4pJ0vz58xUWFqZdu3Zp6NChioqKUnZ2tnr06KGcnBwlJyerffv2GjVqlKZOnaqsrCxVV1drypQpmjVr1mkDsZMCAgK0evXqX1XfngmPyX6o7FetAVy9bKWk8w/FvvrqSy1d+qrmzJl32vkNG9Zp+PCUi1QdAAAAAADuwVSh2Enh4eHy8/NTSUmJ1qxZoxUrVmj06NHavn27IiIilJSUpLfeekt5eXnauXOnoqOj1a0b+ynhtyM4OKDOa19fL/n4NDpl/JtvvtFTT43XlClp6tu31ynrfPTRRzp8uEJ33x0vPz+/S1qzu/vl7w6Ae6A3AfdEbwLuid4E6jJlKLZlyxbZbDYVFRUpNDRUoaGhSkxMVE5OjiIiIuTh4aHZs2dr2LBhCggIUF5e3jnXPHbsmIYMGSJJGjBggFJSUmSxWC71pQCnVVZWWed1VVWNjh8/UWd8//5SPfrow0pKGqlbb+1zyjmStHLlKt12W29VVTlUVXXqvFkEBwec9vcDwLXoTcA90ZuAe6I3YUYeHpazblVlqlAsNTVV3t7e8vf3V0ZGhpYvX67ExERJUkJCghISEjR58mR5e3urbdu2ioqKUufOneXj43PWdVu0aKF///vfCgoKUnl5ucaNG6emTZvqnnvuaYjLAs7IbrertrZWDodDDketbDabrFarDh+uUGrqWA0Zcq8SEu4+7bk223Ft2vSWnn8+vYGrBgAAAADg0jNVKHZyTzFJOnTokDZv3qzCwkJlZmZKkqqrq7Vx40bFxcVJkqxWq6xW6znX9fLyUlBQkCQpKChIgwcP1rZt2+odirVLz6jX8cDp2G0258+vvbZES5e+4ny9ceN6jRgxWhaLRfv2faelSxdr6dLFzvm33vof58/vvfeu/P0D1KVLZMMUDgAAAABAAzJVKPZz+fn5io2NVXr6T3fBrF27VqtWrXKGYuervLxcTZo0UaNGjVRdXa1Nmzapd+/e9a6pvPwHORxGvc8DziQlZYxSUsacdm7kyIfPeu4dd/TTHXf0uxRlAQAAAADgch6uLsBVcnNzNXjw4Dpjffr00Y4dO1RSUlKvtT7++GMlJiYqLi5Od911lzp16qQHH3zwYpYLAAAAAACAi8hiGAa3JrkJ7hQD3BObkgLuid4E3BO9CbgnehNmdK6N9k17pxgAAAAAAADMy7R7il2IsWPHqrS0tM5YSEiIsrKyXFQRAAAAAAAALgShWD0QfgEAAAAAAFweeHwSAAAAAAAApkMoBgAAAAAAANMhFAMAAAAAAIDpEIoBAAAAAADAdAjFAAAAAAAAYDqEYgAAAAAAADAdQjEAAAAAAACYDqEYAAAAAAAATIdQDDiD0tJ9mjAhVf363a64uFi98MJc2e32OsesX79WPXtGas2afBdVCQAAAAAALgShGHAG8+bNUbNmV2j16g1aunSFPv10m/Lysp3zR48e1V//ulS/+117F1YJAAAAAAAuhMtDsZiYGPXs2VO1tbXOsdzcXHXs2FHLly93jv3pT39S586dVV5efsr5/fr1U1xcnPr3769Vq1Y55z788EPdcMMNio+P16BBg/Tggw+qqKjIOT9x4sQ673G29zl06JAmTpyoPn36KD4+XoMHD9aMGTNUWVnpXKtXr16Kj493/rNhw4Zf/wuCy5SW7lNMTF95e3srKKi5une/RV9//dOfn0WLFujuu4eqadNAF1YJAAAAAAAuhKerC5CkFi1aaPPmzYqOjpYk5eXl6brrrnPO19bWavXq1erSpYtWr16tkSNH1jl//vz5CgsL0xdffKEhQ4aoV69eatmypSSpQ4cOys3NlfRj4DV79my9+uqrp63jTO9TXV2tBx54QAkJCZo1a5asVqtqamr0t7/9TeXl5QoICJAkPfzww3rwwQcv+PcQFOR/wefi4rDbbDp8tEaSdM899+vtt/+pm26KVGXlUW3Z8r5GjRonSdq1a4c+/7xQTz45UZs2veXKkgEAAAAAwAVwi1AsMTFRubm5io6O1t69e1VVVaWwsDDn/L///W+1bdtWqampmjZt2imh2ElhYWFq0qSJDhw44AzFfu7mm2/Wu+++e8Y6zvQ+a9euVWBgoMaNG+c81svLS8nJyRd2wWewZ8Jjsh8qu6hron6uXrZS0o+h2I03dtEbb+QpNjZatbW16t9/kHr16q3a2lrNmzdXf/jDU/LwcPnNlgAAAAAA4AK4RSh28803629/+5u+//575eXlKSEhQTt37nTO5+TkaMiQIYqMjNSJEyf02Wef6YYbbjhlnY8//ljNmjXTtddee8qcw+HQv/71Lw0YMOCMdZzpfXbu3KmIiIhzXsfixYvrPL45Z84cderU6Zznwb0EBwfI4XDo3nvH695771VKyiodO3ZMkydP1rJlWQoJCdF113XS7bffIkny8vJUQICPgoMDXFw5LiX+/QLuid4E3BO9CbgnehOoyy1CMYvFov79+2vdunVat26dVq5c6QzFysvLtXXrVs2dO1eSlJCQoJycnDqhWGpqqgzD0LfffquXXnpJXl5ezrmioiLFx8frwIED8vf3rxNa/dz5vM9J+fn5Wrp0qSorKzVhwgRn0PZrH5+Eeygrq9SRI0e0b98+9euXoO+/t0nyVN++A/TKK5lq0+YqffLJNr377r8lSUePfq+dO3dq27bP9MQTT7u2eFwSwcEBKiurdHUZAH6B3gTcE70JuCd6E2bk4WE561ZVbhGKST8+QnnPPfeoW7duatasmXN89erVstvtiouLkyTZ7XZVV1dr8uTJ8vHxkfTTnmLr16/XpEmT1KVLFzVv3lzST3uK1dTU6IknntC0adP00ksvnfL+Z3uf8PBw575k0o+BWUJCglJTU3X8+PGL9jtol55x0dbChbHbbJKkwMBAhYRcqby8bN1//4Oqrq7W+vVr1aHDNXryyYmqqbE5z0lLe0q9e8do0KAEV5UNAAAAAADqyW1Csauuukp/+MMfTnlMMTc3VwsXLtQtt9ziHEtJSdGGDRuUkFA3hOjfv7/Wr1+vRYsWKS0trc6cl5eXpk2bpjvvvFO7du1SeHj4eb/PoEGD9Oqrr2rx4sVKSUmR1WqVYRgXNRCTpPLyH+RwGBd1TVy455//o156aZ5WrHhNVquHunTpptTUJ/7zxQo/3Xbs6ekpPz9/+fvzRQkAAAAAAPxWuE0oJkn33XdfnddHjhzRkSNH1KNHjzrjgwcPVk5OzimhmCQ9+eSTGjJkiEaPHn3KXPPmzTVy5EgtWLBAmZmZzvHPPvvsnO+zfPlyzZs3T3fccYeaNGkiHx8fde7cWbfddpvz+F/uKTZ06FDdf//99fslwG1cc01HLViw+JzHnc8xAAAAAADAvVgMw+DWJDfBnWKAe2L/BcA90ZuAe6I3AfdEb8KMzrWnmEcD1gIAAAAAAAC4BUIxAAAAAAAAmA6hGAAAAAAAAEyHUAwAAAAAAACmQygGAAAAAAAA0yEUAwAAAAAAgOkQigEAAAAAAMB0CMUAAAAAAABgOoRiAAAAAAAAMB1CMQAAAAAAAJgOoRgAAAAAAABMh1AMAAAAAAAApuPp6gKAi+Httzdq6dJXdODAfl1xRZDS0qapRYuWuueeODVu3Nh53AMPDFdy8igXVgoAAAAAANwBodhprF+/XosWLZJhGLLZbLruuus0b948xcTEyMvLS97e3s5jFy5cKH9/f8XFxWnBggWKiIiQJGVlZWnnzp3KyMhw1WWYRkHBFr38coamT5+t8PDrVF5+SJJkt9slSevXvyNPT/6oAwAAAACAn5AU/MLBgwc1ffp05eXlKSQkRIZhqLCw0Dk/f/58hYWFnXLes88+q0mTJikvL09ff/21VqxYofz8/Hq9d1CQ/6+u3yzsNpsOH62RJC1ZslgjRoxS587XS5KCg1tIkkpL97msPgAAAAAA4N4IxX7h0KFD8vT0VGBgoCTJYrEoPDz8nOf17dtXGzZsUHp6ugoKCjRp0iQFBQXV6733THhM9kNlF1S32Vy9bKWkGtXW1urzz3fp1lt76b77ElRTU6PbbovW738/3nns3XcPlsViUbdu3fXII+Od/24BAAAAAIB5sdH+L1x77bWKiIhQ7969lZqaqmXLlunw4cPO+dTUVMXHxys+Pl5Dhgypc+4zzzyj7OxsXXnllRowYEBDl25Khw9XyG636913/6WFC1/V0qV/05df7tayZUvUtGmgXn31dWVnr9GSJX9VVdUxzZgxxdUlAwAAAAAAN8CdYr/g4eGhzMxMffHFFyooKNDbb7+tJUuWaM2aNZLO/PikJH3wwQfy9/dXcXGxampq5OXl1ZClm05wcIC8vBySpBEjhqtTp99JkkaPHqWXX35ZU6ZMVGhoS0lSSEgzzZw5Qz179lTjxhb5+/OoKuonODjA1SUAOA16E3BP9CbgnuhNoC5CsTMICwtTWFiYHnjgAQ0YMEBbt2496/EVFRV6/vnntXjxYv3lL3/R/PnzNWHChAaq1pzKyioleahFi5aqrDz+n9dSZeVx2e21ztcnVVQcc55XXW00dLn4DQsODjjlzxMA16M3AfdEbwLuid6EGXl4WM66fzuh2C8cOHBA+/bt00033SRJ2r9/vyoqKtSmTZuznjd9+nTde++9uvbaa5WWlqb4+HjFxsbq+uuvP+/3bpfON1WeL7vN5vx5wIDBysn5b/XocYusVk/94x9/0y233KadO3coIMBfbdq0VWXlUb34Yrpuuqkrd4kBAAAAAABCsV+y2+3KyMjQd999Jx8fHzkcDj3++OPOzfZTU1Pl7e3tPH7mzJnau3ev9uzZo/T0dElS06ZN9eyzz2ry5MnKyck578coy8t/kMPBHUz1lZw8SkeOHNH99w+Rl5e3YmL6KilppN577x0tXpypw4cr5Ofnp8jI7po2bZarywUAAAAAAG7AYhgGKYybIBQD3BO3mgPuid4E3BO9CbgnehNmdK7HJ/n2SQAAAAAAAJgOoRgAAAAAAABMh1AMAAAAAAAApkMoBgAAAAAAANMhFAMAAAAAAIDpEIoBAAAAAADAdAjFAAAAAAAAYDqEYgAAAAAAADAdQjEAAAAAAACYDqEYAAAAAAAATIdQDAAAAAAAAKZDKAYAAAAAAADTIRQDAAAAAACA6RCKAQAAAAAAwHQIxQAAAAAAAGA6hGIAAAAAAAAwHU9XF4CfeHhYXF0CgDOgPwH3RG8C7oneBNwTvQmzOdefeYthGEYD1QIAAAAAAAC4BR6fBAAAAAAAgOkQigEAAAAAAMB0CMUAAAAAAABgOoRiAAAAAAAAMB1CMQAAAAAAAJgOoRgAAAAAAABMh1AMAAAAAAAApkMoBgAAAAAAANMhFAMAAAAAAIDpEIq5ga+//lr33XefYmNjdd9992nPnj2uLgkweT2NBAAACF1JREFUhcOHD2v06NGKjY3V4MGD9eijj6qiokKS9OmnnyouLk6xsbEaOXKkysvLneedbQ7AxbVgwQJ17NhRX3zxhSR6E3A1m82mqVOn6s4779TgwYP1zDPPSDr751k+6wKX3jvvvKOEhATFx8crLi5O//znPyXRm8A5GXC5hx56yMjPzzcMwzDy8/ONhx56yMUVAeZw+PBhY8uWLc7Xc+bMMSZNmmTU1tYaffv2NQoKCgzDMIyFCxcaEydONAzDOOscgItrx44dRkpKinH77bcbu3fvpjcBN/Dcc88Zs2bNMhwOh2EYhlFWVmYYxtk/z/JZF7i0HA6HERkZaezevdswDMMoLCw0brzxRqO2tpbeBM6BO8VcrLy8XLt27dKgQYMkSYMGDdKuXbucd6sAuHQCAwPVvXt35+sbb7xR+/bt044dO+Tt7a3IyEhJ0tChQ7VhwwZJOuscgIunpqZGM2bM0LRp05xj9CbgWseOHVN+fr7Gjx8vi8UiSWrevPlZP8/yWRdoGB4eHqqsrJQkVVZWqkWLFjp8+DC9CZyDp6sLMLvS0lK1bNlSVqtVkmS1WtWiRQuVlpbqiiuucHF1gHk4HA79/e9/V0xMjEpLS9W6dWvn3BVXXCGHw6EjR46cdS4wMNAVpQOXpZdeeklxcXFq06aNc4zeBFxr7969CgwM1IIFC/Thhx/Kz89P48ePl4+Pzxk/zxqGwWdd4BKzWCx68cUX9cgjj8jX11fHjh3T4sWLz/rfmvQm8CPuFAMASc8995x8fX314IMPuroUwPQ++eQT7dixQ8OGDXN1KQB+pra2Vnv37lV4eLhyc3M1YcIEPfbYY6qqqnJ1aYCp2e12LVq0SJmZmXrnnXf08ssv6/HHH6c3gfPAnWIuFhISogMHDqi2tlZWq1W1tbU6ePCgQkJCXF0aYBpz587VN998o6ysLHl4eCgkJET79u1zzldUVMjDw0OBgYFnnQNwcRQUFKioqEh9+vSRJO3fv18pKSl66KGH6E3AhUJCQuTp6el83OqGG25Qs2bN5OPjc8bPs4Zh8FkXuMQKCwt18OBBde3aVZLUtWtXNW7cWN7e3vQmcA7cKeZiQUFB6tSpk9auXStJWrt2rTp16sQtq0ADeeGFF7Rjxw4tXLhQXl5ekqTOnTvr+PHj+uijjyRJK1euVL9+/c45B+DiePjhh7V582Zt2rRJmzZtUqtWrbRkyRKNGjWK3gRc6IorrlD37t31/vvvS/rxm+vKy8vVrl27M36e5bMucOm1atVK+/fvV3FxsSSpqKhI5eXlCg0NpTeBc7AYhmG4ugizKyoq0sSJE3X06FE1adJEc+fOVfv27V1dFnDZ+/LLLzVo0CC1a9dOPj4+kqQ2bdpo4cKF2rZtm6ZOnSqbzaYrr7xSf/rTn9S8eXNJOuscgIsvJiZGWVlZCgsLozcBF9u7d68mT56sI0eOyNPTU48//riio6PP+nmWz7rApffGG2/olVdecX4JRmpqqvr27UtvAudAKAYAAAAAAADT4fFJAAAAAAAAmA6hGAAAAAAAAEyHUAwAAAAAAACmQygGAAAAAAAA0yEUAwAAAAAAgOkQigEAAAAAAMB0PF1dAAAAAC6tmJgYHTp0SFar1Tm2YcMGtWzZ0oVVAQAAuBahGAAAgAlkZWXplltucWkNdrtdnp58/AQAAO6BxycBAAAgSaqoqNCYMWMUGRmpm2++WcOGDZPD4ZAklZaW6tFHH1WPHj3UvXt3zZgxQ5LkcDiUmZmp22+/XVFRUXrqqadUWVkpSSopKVHHjh21atUq9e7dW8OHD5ckZWdnq3///urWrZtSUlL03XffueaCAQCAqRGKAQAAQJK0dOlStWzZUh988IHef/99PfHEE7JYLKqtrdWYMWPUunVrbdq0Se+9954GDBggScrNzVVeXp5ef/11vf3226qqqnIGZicVFBTozTff1JIlS/T2229r0aJFWrBggT744AN17dpVTz75pCsuFwAAmByhGAAAgAn8/ve/V2RkpCIjI/XII4+c9hhPT0+VlZVp3759atSokSIjI2WxWLR9+3YdPHhQTz31lHx9feXt7a3IyEhJ0po1a5ScnKyrrrpKfn5+euKJJ/Tmm2/Kbrc7133sscfk6+srHx8frVy5Ug8//LA6dOggT09PjR07VoWFhdwtBgAAGhybOgAAAJjAwoULz7mnWEpKihYsWKCRI0dKku677z49/PDDKi0tVevWrU+7H9jBgwd15ZVXOl9feeWVstvtKi8vd461atXK+fO+ffv0/PPPa+7cuc4xwzB04MCBOusAAABcaoRiAAAAkCT5+/tr4sSJmjhxor744gsNHz5c119/vUJCQlRaWnrajfJbtGhR5y6vffv2ydPTU0FBQdq/f78kyWKxOOdDQkI0duxYxcXFNcxFAQAAnAGPTwIAAECS9M477+ibb76RYRgKCAiQ1WqVxWJRRESEgoODNW/ePFVVVclms+njjz+WJA0aNEivvfaa9u7dq2PHjunPf/6z+vfvf8ZvmRw6dKgWL16sL7/8UpJUWVmp9evXN9g1AgAAnMSdYgAAAJAkffPNN3ruuedUUVGhJk2a6P7771ePHj0kSVlZWZo5c6Zuv/12SdLgwYPVtWtX3XXXXTpw4IAefPBB2Ww29ezZU88888wZ3+OOO+7QsWPH9MQTT+i7775TQECAbrnlFvXv379BrhEAAOAki2EYhquLAAAAAAAAABoSj08CAAAAAADAdAjFAAAAAAAAYDqEYgAAAAAAADAdQjEAAAAAAACYDqEYAAAAAAAATIdQDAAAAAAAAKZDKAYAAAAAAADTIRQDAAAAAACA6RCKAQAAAAAAwHT+H7X4gM4lcdmWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6aywJS2WiY0"
      },
      "source": [
        "xgb_clf.save_model('cdefault.booster')"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuM5kpftZ4o9"
      },
      "source": [
        "bst = xgb.Booster({'nthread': 4}) \n",
        "bst.load_model('cdefault.booster')  "
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uR_5eYuMYlO6"
      },
      "source": [
        "xgb_loaded = xgb.XGBClassifier()\n",
        "xgb_loaded._Booster = bst"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YunPaI8ScmUN",
        "outputId": "abac7720-9975-46ae-f1df-494a8098639b"
      },
      "source": [
        "xgb_loaded.predict(X_test)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So7BIjwkbUci",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b24530d4-141d-4bf5-a422-a741114aa88d"
      },
      "source": [
        "!pip install shap"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: shap in /usr/local/lib/python3.7/dist-packages (0.39.0)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.7/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.1.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.4.1)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.41.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap) (0.22.2.post1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.0)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.51.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.0.1)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (54.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvXo86VSUvZ4"
      },
      "source": [
        "import shap"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bk9K-2ENU9qD"
      },
      "source": [
        "explainer = shap.TreeExplainer(xgb_loaded)\n",
        "shap_values = explainer.shap_values(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTOIkBKTU3Ds"
      },
      "source": [
        "shap.summary_plot(shap_values, X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DD4sDq-VNcF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}