{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Libraries"
      ],
      "metadata": {
        "id": "8Vjfbt-sDp13"
      },
      "id": "8Vjfbt-sDp13"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gxk7Ljc_AEgq",
        "outputId": "1b1cf91b-93a9-4efd-ac82-0794de0f475f"
      },
      "id": "Gxk7Ljc_AEgq",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: shap in /usr/local/lib/python3.10/dist-packages (0.42.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shap) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from shap) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from shap) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from shap) (1.5.3)\n",
            "Requirement already satisfied: tqdm>=4.27.0 in /usr/local/lib/python3.10/dist-packages (from shap) (4.65.0)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.10/dist-packages (from shap) (23.1)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.10/dist-packages (from shap) (0.0.7)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap) (0.56.4)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap) (2.2.1)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba->shap) (67.7.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2022.7.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (1.3.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (3.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install sklearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFbt2zHi4buJ",
        "outputId": "d5e28347-1660-4ec5-b7aa-1fa6201a9c35"
      },
      "id": "tFbt2zHi4buJ",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.10/dist-packages (0.0.post7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install optuna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZjaFtQeIToL",
        "outputId": "1d0aaf6e-2337-4faf-f981-1b71f8293eac"
      },
      "id": "tZjaFtQeIToL",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: optuna in /usr/local/lib/python3.10/dist-packages (3.2.0)\n",
            "Requirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (1.11.1)\n",
            "Requirement already satisfied: cmaes>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from optuna) (0.10.0)\n",
            "Requirement already satisfied: colorlog in /usr/local/lib/python3.10/dist-packages (from optuna) (6.7.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from optuna) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (23.1)\n",
            "Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (2.0.19)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from optuna) (4.65.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from optuna) (6.0.1)\n",
            "Requirement already satisfied: Mako in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (1.2.4)\n",
            "Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (4.7.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna) (2.0.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "c5e31cf2",
      "metadata": {
        "id": "c5e31cf2"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import graphviz\n",
        "import xgboost as xgb\n",
        "import shap\n",
        "from math import sqrt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_squared_log_error\n",
        "from sklearn.model_selection import train_test_split, KFold\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "%matplotlib inline\n",
        "import lightgbm as lgbm\n",
        "from lightgbm import  log_evaluation, early_stopping\n",
        "import optuna\n",
        "from optuna.integration import LightGBMPruningCallback\n",
        "\n",
        "import pickle\n",
        "import sklearn"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ce7d0a75",
      "metadata": {
        "id": "ce7d0a75"
      },
      "source": [
        "## Data Processing and Feature Selection\n",
        "\n",
        "For the feature selection, I started off with dropping columns that have low correlation (< 0.4) with SalePrice. I then dropped columns with low variances (< 1). After that I checked the correlation matrix between columns to dropped selected columns that have correlation greater than 0.5 but with consideration for domain knowledge. After that I checked for NAs in the numerical columns. Then, based on the result, I used domain knowledge to fill the NAs with appropriate value. In this case, I used 0 to fill the NAs as it was the most relevant value. As for the categorical NAs, they were replaced with ‘None’. Once, all the NAs were taken cared of, I used LabelEncoder to encode the categorical values. I, then, checked for correlation between columns and dropped them based on domain knowledge."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "link to the data: https://drive.google.com/drive/folders/1oml9pTxlzrMBt7qZRe2KSV8dkNkbEXvK?usp=sharing"
      ],
      "metadata": {
        "id": "Ku3MSqwIF58K"
      },
      "id": "Ku3MSqwIF58K"
    },
    {
      "cell_type": "markdown",
      "id": "74abfbd7",
      "metadata": {
        "id": "74abfbd7"
      },
      "source": [
        "#### Importing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "id": "e13fb5d4",
      "metadata": {
        "id": "e13fb5d4"
      },
      "outputs": [],
      "source": [
        "dataset = pd.read_csv('train.csv')\n",
        "testset = pd.read_csv('test.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5e94266",
      "metadata": {
        "id": "f5e94266"
      },
      "source": [
        "#### Examining train dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "id": "d916ab5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d916ab5d",
        "outputId": "894f2207-a7db-43e0-db8b-77c5a352e3a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  SalePrice      1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ]
        }
      ],
      "source": [
        "dataset.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43ab061c",
      "metadata": {
        "id": "43ab061c"
      },
      "source": [
        "#### Setting y to the label column (numpy array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "id": "ac8eb354",
      "metadata": {
        "id": "ac8eb354"
      },
      "outputs": [],
      "source": [
        "y = dataset['SalePrice'].values\n",
        "#type(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d1f6fcaa",
      "metadata": {
        "id": "d1f6fcaa"
      },
      "source": [
        "#### Making a new dataframe without SalePrice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "id": "5bba9f18",
      "metadata": {
        "id": "5bba9f18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c929913f-a696-439a-cfae-fa954fcc42b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC Fence MiscFeature  \\\n",
              "0         Lvl    AllPub  ...           0        0    NaN   NaN         NaN   \n",
              "1         Lvl    AllPub  ...           0        0    NaN   NaN         NaN   \n",
              "2         Lvl    AllPub  ...           0        0    NaN   NaN         NaN   \n",
              "3         Lvl    AllPub  ...           0        0    NaN   NaN         NaN   \n",
              "4         Lvl    AllPub  ...           0        0    NaN   NaN         NaN   \n",
              "\n",
              "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
              "0       0      2    2008        WD         Normal  \n",
              "1       0      5    2007        WD         Normal  \n",
              "2       0      9    2008        WD         Normal  \n",
              "3       0      2    2006        WD        Abnorml  \n",
              "4       0     12    2008        WD         Normal  \n",
              "\n",
              "[5 rows x 80 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d9854b8f-c859-437b-a9e7-6ce616c87a53\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>...</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 80 columns</p>\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ],
      "source": [
        "X_start = dataset.drop(['SalePrice'], axis = 1)\n",
        "X_start.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e0e3e2d",
      "metadata": {
        "id": "0e0e3e2d"
      },
      "source": [
        "#### Checking for columns with low correlation (< 0.4) with SalePrice and dropping them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "id": "213d8d98",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "213d8d98",
        "outputId": "91191569-2e1e-41c9-d33b-7ebfe99e0443"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n"
          ]
        }
      ],
      "source": [
        "price_corr = dataset.corr()['SalePrice']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "id": "dd70b06c",
      "metadata": {
        "id": "dd70b06c"
      },
      "outputs": [],
      "source": [
        "low_corr = price_corr[abs(price_corr) < 0.4].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "id": "e027ed66",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e027ed66",
        "outputId": "0d8e31f9-a8d1-4c4e-884b-8726ae16f83d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BsmtFinSF1       0.386420\n",
              "LotFrontage      0.351799\n",
              "WoodDeckSF       0.324413\n",
              "2ndFlrSF         0.319334\n",
              "OpenPorchSF      0.315856\n",
              "HalfBath         0.284108\n",
              "LotArea          0.263843\n",
              "BsmtFullBath     0.227122\n",
              "BsmtUnfSF        0.214479\n",
              "BedroomAbvGr     0.168213\n",
              "ScreenPorch      0.111447\n",
              "PoolArea         0.092404\n",
              "MoSold           0.046432\n",
              "3SsnPorch        0.044584\n",
              "BsmtFinSF2      -0.011378\n",
              "BsmtHalfBath    -0.016844\n",
              "MiscVal         -0.021190\n",
              "Id              -0.021917\n",
              "LowQualFinSF    -0.025606\n",
              "YrSold          -0.028923\n",
              "OverallCond     -0.077856\n",
              "MSSubClass      -0.084284\n",
              "EnclosedPorch   -0.128578\n",
              "KitchenAbvGr    -0.135907\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ],
      "source": [
        "low_corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "id": "978aa742",
      "metadata": {
        "scrolled": true,
        "id": "978aa742"
      },
      "outputs": [],
      "source": [
        "for i in low_corr.index:\n",
        "    X_start.drop(i, axis = 1, inplace = True)\n",
        "    testset.drop(i, axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "id": "568174fb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "568174fb",
        "outputId": "f8133fc0-fffb-4159-a45c-6eaad9b8c72a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave   NaN      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave   NaN      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1  ... GarageCars GarageArea GarageQual  GarageCond  \\\n",
              "0      CollgCr       Norm  ...          2        548         TA          TA   \n",
              "1      Veenker      Feedr  ...          2        460         TA          TA   \n",
              "2      CollgCr       Norm  ...          2        608         TA          TA   \n",
              "3      Crawfor       Norm  ...          3        642         TA          TA   \n",
              "4      NoRidge       Norm  ...          3        836         TA          TA   \n",
              "\n",
              "   PavedDrive  PoolQC Fence MiscFeature SaleType SaleCondition  \n",
              "0           Y     NaN   NaN         NaN       WD        Normal  \n",
              "1           Y     NaN   NaN         NaN       WD        Normal  \n",
              "2           Y     NaN   NaN         NaN       WD        Normal  \n",
              "3           Y     NaN   NaN         NaN       WD       Abnorml  \n",
              "4           Y     NaN   NaN         NaN       WD        Normal  \n",
              "\n",
              "[5 rows x 56 columns]"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>TA</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Norm</td>\n",
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              "      <td>TA</td>\n",
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              "      <td>NaN</td>\n",
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              "<p>5 rows × 56 columns</p>\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0892d33d-d5c4-435f-af63-314218ecf0c7 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a4e9db73-a46a-4f5d-b941-57018afa0b3c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4e9db73-a46a-4f5d-b941-57018afa0b3c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ],
      "source": [
        "X_start.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42da68a9",
      "metadata": {
        "id": "42da68a9"
      },
      "source": [
        "#### Checking for columns with low variance (< 1) and dropping them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "id": "e761e84e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e761e84e",
        "outputId": "201cc253-d8b7-406b-d050-7e56fe613b0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The default value of numeric_only in DataFrame.var is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n"
          ]
        }
      ],
      "source": [
        "variance = X_start.var()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "id": "64855097",
      "metadata": {
        "id": "64855097"
      },
      "outputs": [],
      "source": [
        "low_var = variance[(variance) < 1].sort_values(ascending = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
      "id": "32be86a0",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32be86a0",
        "outputId": "4c06b2ff-a83e-4c08-df45-04b3c93f49d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FullBath      0.303508\n",
              "Fireplaces    0.415595\n",
              "GarageCars    0.558480\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ],
      "source": [
        "low_var"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "id": "28340bfa",
      "metadata": {
        "id": "28340bfa"
      },
      "outputs": [],
      "source": [
        "for i in low_var.index:\n",
        "    X_start.drop(i, axis = 1, inplace = True)\n",
        "    testset.drop(i, axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 192,
      "id": "e79a1ccd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "e79a1ccd",
        "outputId": "10fe8613-484e-4b6d-83ab-83036f4bbe1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave   NaN      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave   NaN      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1  ... GarageFinish GarageArea GarageQual  GarageCond  \\\n",
              "0      CollgCr       Norm  ...          RFn        548         TA          TA   \n",
              "1      Veenker      Feedr  ...          RFn        460         TA          TA   \n",
              "2      CollgCr       Norm  ...          RFn        608         TA          TA   \n",
              "3      Crawfor       Norm  ...          Unf        642         TA          TA   \n",
              "4      NoRidge       Norm  ...          RFn        836         TA          TA   \n",
              "\n",
              "   PavedDrive  PoolQC Fence MiscFeature SaleType SaleCondition  \n",
              "0           Y     NaN   NaN         NaN       WD        Normal  \n",
              "1           Y     NaN   NaN         NaN       WD        Normal  \n",
              "2           Y     NaN   NaN         NaN       WD        Normal  \n",
              "3           Y     NaN   NaN         NaN       WD       Abnorml  \n",
              "4           Y     NaN   NaN         NaN       WD        Normal  \n",
              "\n",
              "[5 rows x 53 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-34dffb6b-0a6a-49b6-90b5-15f3a9ec1b78\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .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>MSZoning</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>...</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>RFn</td>\n",
              "      <td>548</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>...</td>\n",
              "      <td>RFn</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>RFn</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Unf</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>RFn</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 53 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34dffb6b-0a6a-49b6-90b5-15f3a9ec1b78')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-c43cb295-764c-4190-aeb4-0917b27ee71a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c43cb295-764c-4190-aeb4-0917b27ee71a')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c43cb295-764c-4190-aeb4-0917b27ee71a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-34dffb6b-0a6a-49b6-90b5-15f3a9ec1b78 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-34dffb6b-0a6a-49b6-90b5-15f3a9ec1b78');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ],
      "source": [
        "X_start.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d3cd6a1",
      "metadata": {
        "id": "4d3cd6a1"
      },
      "source": [
        "#### Checking to correlation between columns and dropping selected columns based on domain knowledge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 193,
      "id": "9be646b3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9be646b3",
        "outputId": "83c0b2ce-34c9-4c8e-f76a-662bf6d9b04c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n"
          ]
        }
      ],
      "source": [
        "correlation = X_start.corr().abs()\n",
        "corr_list = (correlation.where(np.triu(np.ones(correlation.shape), k=1).astype(bool))\n",
        "            .stack())\n",
        "high_corr = corr_list.loc[corr_list > 0.5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "id": "7aa53645",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aa53645",
        "outputId": "36ce4b77-3389-46ab-f029-d7ea28129501"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OverallQual   YearBuilt       0.572323\n",
              "              YearRemodAdd    0.550684\n",
              "              TotalBsmtSF     0.537808\n",
              "              GrLivArea       0.593007\n",
              "              GarageYrBlt     0.547766\n",
              "              GarageArea      0.562022\n",
              "YearBuilt     YearRemodAdd    0.592855\n",
              "              GarageYrBlt     0.825667\n",
              "YearRemodAdd  GarageYrBlt     0.642277\n",
              "TotalBsmtSF   1stFlrSF        0.819530\n",
              "1stFlrSF      GrLivArea       0.566024\n",
              "GrLivArea     TotRmsAbvGrd    0.825489\n",
              "GarageYrBlt   GarageArea      0.564567\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ],
      "source": [
        "high_corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 195,
      "id": "2d30f2f6",
      "metadata": {
        "id": "2d30f2f6"
      },
      "outputs": [],
      "source": [
        "drop_hico = ['GarageArea', 'TotRmsAbvGrd', '1stFlrSF', 'GarageYrBlt', 'YearRemodAdd']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "id": "1c29f6db",
      "metadata": {
        "id": "1c29f6db"
      },
      "outputs": [],
      "source": [
        "X_start = X_start.drop(drop_hico, axis = 1)\n",
        "testset = testset.drop(drop_hico, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "id": "46e4fdc1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "46e4fdc1",
        "outputId": "8e9eb31c-4b3a-474d-b3da-3b9c080351f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave   NaN      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave   NaN      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1  ... GarageType GarageFinish GarageQual  GarageCond  \\\n",
              "0      CollgCr       Norm  ...     Attchd          RFn         TA          TA   \n",
              "1      Veenker      Feedr  ...     Attchd          RFn         TA          TA   \n",
              "2      CollgCr       Norm  ...     Attchd          RFn         TA          TA   \n",
              "3      Crawfor       Norm  ...     Detchd          Unf         TA          TA   \n",
              "4      NoRidge       Norm  ...     Attchd          RFn         TA          TA   \n",
              "\n",
              "   PavedDrive PoolQC Fence MiscFeature SaleType SaleCondition  \n",
              "0           Y    NaN   NaN         NaN       WD        Normal  \n",
              "1           Y    NaN   NaN         NaN       WD        Normal  \n",
              "2           Y    NaN   NaN         NaN       WD        Normal  \n",
              "3           Y    NaN   NaN         NaN       WD       Abnorml  \n",
              "4           Y    NaN   NaN         NaN       WD        Normal  \n",
              "\n",
              "[5 rows x 48 columns]"
            ],
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              "\n",
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              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
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              "      <td>Veenker</td>\n",
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              "      <td>RFn</td>\n",
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              "      <td>TA</td>\n",
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              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>WD</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 48 columns</p>\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "    domScope.querySelector('#df-247ed91d-59bc-4243-8145-2dc4eb2eed0a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-559aeac5-e80b-4b60-8e69-0d427684a5b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-559aeac5-e80b-4b60-8e69-0d427684a5b6');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ],
      "source": [
        "X_start.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1827825",
      "metadata": {
        "id": "f1827825"
      },
      "source": [
        "#### Identifiying numerical and categorical values for replacing NAs with appropriate values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "id": "6fe2d4e7",
      "metadata": {
        "id": "6fe2d4e7"
      },
      "outputs": [],
      "source": [
        "numerical = X_start.select_dtypes(include=['number'])\n",
        "categorical = X_start.select_dtypes(include=['object'])\n",
        "t_numerical = testset.select_dtypes(include=['number'])\n",
        "t_categorical = testset.select_dtypes(include=['object'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "id": "6ab315bc",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6ab315bc",
        "outputId": "8365b300-4efa-431b-d68c-3500218b316b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   OverallQual  YearBuilt  MasVnrArea  TotalBsmtSF  GrLivArea\n",
              "0            7       2003       196.0          856       1710\n",
              "1            6       1976         0.0         1262       1262\n",
              "2            7       2001       162.0          920       1786\n",
              "3            7       1915         0.0          756       1717\n",
              "4            8       2000       350.0         1145       2198"
            ],
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              "      <th></th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>GrLivArea</th>\n",
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              "      <td>7</td>\n",
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              "      <td>196.0</td>\n",
              "      <td>856</td>\n",
              "      <td>1710</td>\n",
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              "      <td>162.0</td>\n",
              "      <td>920</td>\n",
              "      <td>1786</td>\n",
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              "      <td>1717</td>\n",
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              "      <td>8</td>\n",
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              "      <td>350.0</td>\n",
              "      <td>1145</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b588e77f-cab8-45c6-8473-fa0112dd9e0c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ],
      "source": [
        "numerical.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "SEZM6dlIcePb",
        "outputId": "340a48cf-2142-45c4-b6f3-47c093798734"
      },
      "id": "SEZM6dlIcePb",
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0          RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1          RL   Pave   NaN      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2          RL   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3          RL   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4          RL   Pave   NaN      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "...       ...    ...   ...      ...         ...       ...       ...       ...   \n",
              "1455       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1456       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1457       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1458       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1459       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "\n",
              "     Neighborhood Condition1  ... GarageType GarageFinish GarageQual  \\\n",
              "0         CollgCr       Norm  ...     Attchd          RFn         TA   \n",
              "1         Veenker      Feedr  ...     Attchd          RFn         TA   \n",
              "2         CollgCr       Norm  ...     Attchd          RFn         TA   \n",
              "3         Crawfor       Norm  ...     Detchd          Unf         TA   \n",
              "4         NoRidge       Norm  ...     Attchd          RFn         TA   \n",
              "...           ...        ...  ...        ...          ...        ...   \n",
              "1455      Gilbert       Norm  ...     Attchd          RFn         TA   \n",
              "1456       NWAmes       Norm  ...     Attchd          Unf         TA   \n",
              "1457      Crawfor       Norm  ...     Attchd          RFn         TA   \n",
              "1458        NAmes       Norm  ...     Attchd          Unf         TA   \n",
              "1459      Edwards       Norm  ...     Attchd          Fin         TA   \n",
              "\n",
              "     GarageCond PavedDrive PoolQC  Fence MiscFeature SaleType SaleCondition  \n",
              "0            TA          Y    NaN    NaN         NaN       WD        Normal  \n",
              "1            TA          Y    NaN    NaN         NaN       WD        Normal  \n",
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              "3            TA          Y    NaN    NaN         NaN       WD       Abnorml  \n",
              "4            TA          Y    NaN    NaN         NaN       WD        Normal  \n",
              "...         ...        ...    ...    ...         ...      ...           ...  \n",
              "1455         TA          Y    NaN    NaN         NaN       WD        Normal  \n",
              "1456         TA          Y    NaN  MnPrv         NaN       WD        Normal  \n",
              "1457         TA          Y    NaN  GdPrv        Shed       WD        Normal  \n",
              "1458         TA          Y    NaN    NaN         NaN       WD        Normal  \n",
              "1459         TA          Y    NaN    NaN         NaN       WD        Normal  \n",
              "\n",
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              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GdPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>Unf</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Edwards</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>Fin</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1460 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1a9dd5b-f9c2-4df9-a8bb-13d2134d3950')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ef3336d2-8486-42bc-947c-c6e10a47a52e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef3336d2-8486-42bc-947c-c6e10a47a52e')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ef3336d2-8486-42bc-947c-c6e10a47a52e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1a9dd5b-f9c2-4df9-a8bb-13d2134d3950 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1a9dd5b-f9c2-4df9-a8bb-13d2134d3950');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "id": "075dca0e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "075dca0e",
        "outputId": "c4c35672-237b-49ec-9e15-a7a0796d4bff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MasVnrArea'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ],
      "source": [
        "num_na = numerical.columns[numerical.isnull().any()]\n",
        "num_na"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58ba1209",
      "metadata": {
        "id": "58ba1209"
      },
      "source": [
        "#### Based on domain knowledge, NAs in MasVrArea is replaced with 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 202,
      "id": "765e417a",
      "metadata": {
        "id": "765e417a"
      },
      "outputs": [],
      "source": [
        "for n in [num_na]:\n",
        "    X_start[n] = X_start[n].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 203,
      "id": "87c1a73e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87c1a73e",
        "outputId": "ee52da65-9f9e-4ac7-c637-2ce93296d25e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MasVnrArea', 'TotalBsmtSF'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ],
      "source": [
        "t_num_na = t_numerical.columns[t_numerical.isnull().any()]\n",
        "t_num_na"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 204,
      "id": "07bd4e08",
      "metadata": {
        "id": "07bd4e08"
      },
      "outputs": [],
      "source": [
        "for n in [t_num_na]:\n",
        "    testset[n] = testset[n].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 205,
      "id": "a003e75b",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "a003e75b",
        "outputId": "15edb7ed-f711-4c5a-fd22-2042d18c195a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave   NaN      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave   NaN      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1  ... GarageType GarageFinish GarageQual GarageCond  \\\n",
              "0      CollgCr       Norm  ...     Attchd          RFn         TA         TA   \n",
              "1      Veenker      Feedr  ...     Attchd          RFn         TA         TA   \n",
              "2      CollgCr       Norm  ...     Attchd          RFn         TA         TA   \n",
              "3      Crawfor       Norm  ...     Detchd          Unf         TA         TA   \n",
              "4      NoRidge       Norm  ...     Attchd          RFn         TA         TA   \n",
              "\n",
              "  PavedDrive PoolQC Fence MiscFeature SaleType SaleCondition  \n",
              "0          Y    NaN   NaN         NaN       WD        Normal  \n",
              "1          Y    NaN   NaN         NaN       WD        Normal  \n",
              "2          Y    NaN   NaN         NaN       WD        Normal  \n",
              "3          Y    NaN   NaN         NaN       WD       Abnorml  \n",
              "4          Y    NaN   NaN         NaN       WD        Normal  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
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              "\n",
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>...</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>Unf</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbf186b0-cf5a-447f-8d13-019b95ae7275')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-20d38d62-4291-4adb-92a9-3d3674dd45de\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20d38d62-4291-4adb-92a9-3d3674dd45de')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-20d38d62-4291-4adb-92a9-3d3674dd45de button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fbf186b0-cf5a-447f-8d13-019b95ae7275 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fbf186b0-cf5a-447f-8d13-019b95ae7275');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ],
      "source": [
        "categorical.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d02aa749",
      "metadata": {
        "id": "d02aa749"
      },
      "source": [
        "#### For categorical NAs, they were replaced with None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 206,
      "id": "2345bc44",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2345bc44",
        "outputId": "69a47b07-1cda-448f-96e8-9a025854875c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Alley', 'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure',\n",
              "       'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'FireplaceQu',\n",
              "       'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC',\n",
              "       'Fence', 'MiscFeature'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ],
      "source": [
        "cat_na = categorical.columns[categorical.isnull().any()]\n",
        "cat_na"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 207,
      "id": "76063429",
      "metadata": {
        "scrolled": true,
        "id": "76063429"
      },
      "outputs": [],
      "source": [
        "for c in [cat_na]:\n",
        "    X_start[c] = X_start[c].fillna('None')\n",
        "    categorical[c] = categorical[c].fillna('None')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 208,
      "id": "52ec4ee2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52ec4ee2",
        "outputId": "11f2be4c-01bd-42db-8b9c-58b585fccdaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MSZoning', 'Alley', 'Utilities', 'Exterior1st', 'Exterior2nd',\n",
              "       'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',\n",
              "       'BsmtFinType2', 'KitchenQual', 'Functional', 'FireplaceQu',\n",
              "       'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC',\n",
              "       'Fence', 'MiscFeature', 'SaleType'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ],
      "source": [
        "t_cat_na = t_categorical.columns[t_categorical.isnull().any()]\n",
        "t_cat_na"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 209,
      "id": "ec3ffa70",
      "metadata": {
        "id": "ec3ffa70"
      },
      "outputs": [],
      "source": [
        "for c in [t_cat_na]:\n",
        "    testset[c] = testset[c].fillna('None')\n",
        "    t_categorical[c] = t_categorical[c].fillna('None')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 210,
      "id": "ed9753fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ed9753fe",
        "outputId": "b778d498-c7de-41ea-f803-67d1aa14bc19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave  None      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave  None      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave  None      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave  None      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave  None      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1  ... GarageType GarageFinish GarageQual GarageCond  \\\n",
              "0      CollgCr       Norm  ...     Attchd          RFn         TA         TA   \n",
              "1      Veenker      Feedr  ...     Attchd          RFn         TA         TA   \n",
              "2      CollgCr       Norm  ...     Attchd          RFn         TA         TA   \n",
              "3      Crawfor       Norm  ...     Detchd          Unf         TA         TA   \n",
              "4      NoRidge       Norm  ...     Attchd          RFn         TA         TA   \n",
              "\n",
              "  PavedDrive PoolQC Fence MiscFeature SaleType SaleCondition  \n",
              "0          Y   None  None        None       WD        Normal  \n",
              "1          Y   None  None        None       WD        Normal  \n",
              "2          Y   None  None        None       WD        Normal  \n",
              "3          Y   None  None        None       WD       Abnorml  \n",
              "4          Y   None  None        None       WD        Normal  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>...</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>Unf</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>None</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
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              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    fill: #1967D2;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-420d645c-ef17-4add-ad0e-9c5f9ff7698f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d9a20dcb-a484-4486-9438-87d5f11a7257 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d9a20dcb-a484-4486-9438-87d5f11a7257');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ],
      "source": [
        "categorical.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "08eb4efb",
      "metadata": {
        "id": "08eb4efb"
      },
      "source": [
        "#### Checking to see if they are any NAs left"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "id": "68242cc3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68242cc3",
        "outputId": "c7180e00-8016-49e7-d433-e9853bea2d51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "X_start.isnull().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "id": "fff1637b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fff1637b",
        "outputId": "80a55569-6e4b-4bbc-b0bd-83fcc1d72619"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ],
      "source": [
        "testset.isnull().values.any()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e572f249",
      "metadata": {
        "id": "e572f249"
      },
      "source": [
        "#### Label encoding of categorical data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 213,
      "id": "13b81bb3",
      "metadata": {
        "scrolled": true,
        "id": "13b81bb3"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "for feature in categorical.columns:\n",
        "    X_start[feature]=le.fit_transform(X_start[feature])\n",
        "    categorical[feature]=le.fit_transform(categorical[feature])\n",
        "    testset[feature]=le.fit_transform(testset[feature])\n",
        "    t_categorical[feature]=le.fit_transform(t_categorical[feature])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "id": "eef8457a",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eef8457a",
        "outputId": "922baeea-e003-4d46-b721-a919becaa213"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MSZoning  Street  Alley  LotShape  LandContour  Utilities  LotConfig  \\\n",
              "0         3       1      1         3            3          0          4   \n",
              "1         3       1      1         3            3          0          2   \n",
              "2         3       1      1         0            3          0          4   \n",
              "3         3       1      1         0            3          0          0   \n",
              "4         3       1      1         0            3          0          2   \n",
              "\n",
              "   LandSlope  Neighborhood  Condition1  ...  GarageType  GarageFinish  \\\n",
              "0          0             5           2  ...           1             2   \n",
              "1          0            24           1  ...           1             2   \n",
              "2          0             5           2  ...           1             2   \n",
              "3          0             6           2  ...           5             3   \n",
              "4          0            15           2  ...           1             2   \n",
              "\n",
              "   GarageQual  GarageCond  PavedDrive  PoolQC  Fence  MiscFeature  SaleType  \\\n",
              "0           5           5           2       3      4            1         8   \n",
              "1           5           5           2       3      4            1         8   \n",
              "2           5           5           2       3      4            1         8   \n",
              "3           5           5           2       3      4            1         8   \n",
              "4           5           5           2       3      4            1         8   \n",
              "\n",
              "   SaleCondition  \n",
              "0              4  \n",
              "1              4  \n",
              "2              4  \n",
              "3              0  \n",
              "4              4  \n",
              "\n",
              "[5 rows x 43 columns]"
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              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e732e5de-a4b2-4db1-bf70-cfdd21fafce2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
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              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7719fcb9-98e6-4871-8e3d-bf44003ce9f3')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-7719fcb9-98e6-4871-8e3d-bf44003ce9f3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e732e5de-a4b2-4db1-bf70-cfdd21fafce2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e732e5de-a4b2-4db1-bf70-cfdd21fafce2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ],
      "source": [
        "categorical.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e81d0983",
      "metadata": {
        "id": "e81d0983"
      },
      "source": [
        "#### Checking for correlation between categorial columns and dropping highly correlated items based on domain knowledge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 215,
      "id": "d41f8b8a",
      "metadata": {
        "id": "d41f8b8a"
      },
      "outputs": [],
      "source": [
        "cat_corr = categorical.corr().abs()\n",
        "cat_corr_list = (cat_corr.where(np.triu(np.ones(cat_corr.shape), k=1).astype(bool))\n",
        "            .stack())\n",
        "high_cat_corr = cat_corr_list.loc[cat_corr_list > 0.5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 216,
      "id": "39405297",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39405297",
        "outputId": "42f584d3-7cb9-4a58-da96-675c65245b2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exterior1st  Exterior2nd    0.854163\n",
              "ExterQual    BsmtQual       0.572327\n",
              "             KitchenQual    0.641584\n",
              "GarageQual   GarageCond     0.618383\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ],
      "source": [
        "high_cat_corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 217,
      "id": "1b8d38aa",
      "metadata": {
        "id": "1b8d38aa"
      },
      "outputs": [],
      "source": [
        "drop_hcc = ['GarageCond', 'BsmtQual', 'KitchenQual', 'Exterior2nd']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 218,
      "id": "afd1fc17",
      "metadata": {
        "id": "afd1fc17"
      },
      "outputs": [],
      "source": [
        "X_start = X_start.drop(drop_hcc, axis = 1)\n",
        "testset = testset.drop(drop_hcc, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 219,
      "id": "6cdfd930",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6cdfd930",
        "outputId": "b4c7e16f-d7c4-48de-acd8-ec8f4f6c40c8"
      },
      "outputs": [
        {
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              "   MSZoning  Street  Alley  LotShape  LandContour  Utilities  LotConfig  \\\n",
              "0         3       1      1         3            3          0          4   \n",
              "1         3       1      1         3            3          0          2   \n",
              "2         3       1      1         0            3          0          4   \n",
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              "4         3       1      1         0            3          0          2   \n",
              "\n",
              "   LandSlope  Neighborhood  Condition1  ...  FireplaceQu  GarageType  \\\n",
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              "4          0            15           2  ...            5           1   \n",
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              "   GarageFinish  GarageQual  PavedDrive  PoolQC  Fence  MiscFeature  SaleType  \\\n",
              "0             2           5           2       3      4            1         8   \n",
              "1             2           5           2       3      4            1         8   \n",
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              "4             2           5           2       3      4            1         8   \n",
              "\n",
              "   SaleCondition  \n",
              "0              4  \n",
              "1              4  \n",
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              "4              4  \n",
              "\n",
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              "      </button>\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
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              "\n",
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              "\n",
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              "    domScope.querySelector('#df-f1f61ead-e62f-4c95-af37-24376c06aaeb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8d78740-914a-4dcb-a31c-6aa39f00a7e9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8d78740-914a-4dcb-a31c-6aa39f00a7e9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ],
      "source": [
        "X_start.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e30cfa3b",
      "metadata": {
        "id": "e30cfa3b"
      },
      "source": [
        "#### Normalizing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 220,
      "id": "b8b4eb75",
      "metadata": {
        "id": "b8b4eb75"
      },
      "outputs": [],
      "source": [
        "X_start = (X_start - X_start.min()) / (X_start.max() - X_start.min())\n",
        "testset = (testset - testset.min()) / (testset.max() - testset.min())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 221,
      "id": "ea423b42",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ea423b42",
        "outputId": "ff80a5e4-6845-403e-d6d5-ea10c0681c03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
              "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
              "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0   2008        WD         Normal     208500  \n",
              "1   2007        WD         Normal     181500  \n",
              "2   2008        WD         Normal     223500  \n",
              "3   2006        WD        Abnorml     140000  \n",
              "4   2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 81 columns]"
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              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86ed353a-50ed-410e-8d01-c8633829733f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-2663d23d-cf0d-4085-845c-a50fc58068a1\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2663d23d-cf0d-4085-845c-a50fc58068a1')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-2663d23d-cf0d-4085-845c-a50fc58068a1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86ed353a-50ed-410e-8d01-c8633829733f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86ed353a-50ed-410e-8d01-c8633829733f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ],
      "source": [
        "dataset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c19de74",
      "metadata": {
        "id": "8c19de74"
      },
      "source": [
        "#### Using Decision Tree (Random Forest) to selected the 10 best features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 222,
      "id": "66b2d593",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "66b2d593",
        "outputId": "81b481f3-76e2-4d4e-b635-f798becdae93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=10, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=10, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=10, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model = RandomForestRegressor(random_state=1, max_depth=10)\n",
        "model.fit(X_start,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 223,
      "id": "adbbd88b",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "adbbd88b",
        "outputId": "be757af5-74d9-4685-81a3-0327fb5ea21c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "features = X_start.columns\n",
        "importances = model.feature_importances_\n",
        "indices = np.argsort(importances)[-10:]  # top 10 features\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 224,
      "id": "4a35608d",
      "metadata": {
        "id": "4a35608d"
      },
      "outputs": [],
      "source": [
        "feat = dict(reversed(sorted(zip(model.feature_importances_, X_start.columns.values))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 225,
      "id": "ef61f8c5",
      "metadata": {
        "id": "ef61f8c5"
      },
      "outputs": [],
      "source": [
        "feat10 = [feat[x] for x in list(feat)[:10]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "id": "801cbef5",
      "metadata": {
        "id": "801cbef5"
      },
      "outputs": [],
      "source": [
        "t_drop = [feat[x] for x in list(feat)[10:]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "id": "ced0290a",
      "metadata": {
        "id": "ced0290a"
      },
      "outputs": [],
      "source": [
        "for i in t_drop:\n",
        "    testset.drop(i, axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 228,
      "id": "8e3418e7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e3418e7",
        "outputId": "66d01093-6eaa-48a9-e320-7ebf3c8643d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1459, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ],
      "source": [
        "testset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 229,
      "id": "6a091d90",
      "metadata": {
        "id": "6a091d90"
      },
      "outputs": [],
      "source": [
        "X = X_start[feat10].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "id": "7c9dcad9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7c9dcad9",
        "outputId": "8be66e5c-f7e6-495f-a8a8-e10501324143"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   OverallQual  GrLivArea  TotalBsmtSF  YearBuilt  MasVnrArea  Neighborhood  \\\n",
              "0     0.666667   0.259231     0.140098   0.949275     0.12250      0.208333   \n",
              "1     0.555556   0.174830     0.206547   0.753623     0.00000      1.000000   \n",
              "2     0.666667   0.273549     0.150573   0.934783     0.10125      0.208333   \n",
              "3     0.666667   0.260550     0.123732   0.311594     0.00000      0.250000   \n",
              "4     0.777778   0.351168     0.187398   0.927536     0.21875      0.625000   \n",
              "\n",
              "   BsmtFinType1  GarageType  BsmtExposure  SaleCondition  \n",
              "0      0.333333    0.166667          0.75            0.8  \n",
              "1      0.000000    0.166667          0.25            0.8  \n",
              "2      0.333333    0.166667          0.50            0.8  \n",
              "3      0.000000    0.833333          0.75            0.0  \n",
              "4      0.333333    0.166667          0.00            0.8  "
            ],
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              "\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.259231</td>\n",
              "      <td>0.140098</td>\n",
              "      <td>0.949275</td>\n",
              "      <td>0.12250</td>\n",
              "      <td>0.208333</td>\n",
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              "      <td>0.8</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.174830</td>\n",
              "      <td>0.206547</td>\n",
              "      <td>0.753623</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.273549</td>\n",
              "      <td>0.150573</td>\n",
              "      <td>0.934783</td>\n",
              "      <td>0.10125</td>\n",
              "      <td>0.208333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.260550</td>\n",
              "      <td>0.123732</td>\n",
              "      <td>0.311594</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.351168</td>\n",
              "      <td>0.187398</td>\n",
              "      <td>0.927536</td>\n",
              "      <td>0.21875</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.8</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9cdc04c8-88c7-4e07-8941-d0168f744b7d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cdc04c8-88c7-4e07-8941-d0168f744b7d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 231,
      "id": "6a79fe62",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6a79fe62",
        "outputId": "839a1387-8afb-4a0a-d250-9e9d45b5c58d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([208500, 181500, 223500, ..., 266500, 142125, 147500])"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "514a6436",
      "metadata": {
        "id": "514a6436"
      },
      "source": [
        "#### Spiliting the training data for testing purposes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "id": "c2f3a67e",
      "metadata": {
        "id": "c2f3a67e"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Getting the categorical features"
      ],
      "metadata": {
        "id": "iVgcgKCMdI7h"
      },
      "id": "iVgcgKCMdI7h"
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_features = []\n",
        "for col in X.columns:\n",
        "    if X[col].dtype == \"object\":\n",
        "        categorical_features.append(col)"
      ],
      "metadata": {
        "id": "2njzGJJPdBRt"
      },
      "id": "2njzGJJPdBRt",
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cat feature indices on X\n",
        "cat_indices = []\n",
        "for c in categorical_features:\n",
        "    if c in X.columns:\n",
        "        idx = list(X.columns).index(c)\n",
        "        cat_indices.append(idx)"
      ],
      "metadata": {
        "id": "Qmp6fnpMda-z"
      },
      "id": "Qmp6fnpMda-z",
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##XGBoost Model"
      ],
      "metadata": {
        "id": "5KMnVh6V-UZw"
      },
      "id": "5KMnVh6V-UZw"
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model = xgb.XGBRegressor(objective=\"reg:squarederror\",max_depth=3)\n",
        "xgb_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "2Y82Jh1S-T3F",
        "outputId": "b0f9fd47-1682-4f46-9045-32cab7f19e64"
      },
      "id": "2Y82Jh1S-T3F",
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### testing the model"
      ],
      "metadata": {
        "id": "0AeHS0rYX3XR"
      },
      "id": "0AeHS0rYX3XR"
    },
    {
      "cell_type": "code",
      "source": [
        "xgbt_pred = xgb_model.predict(X_test)\n",
        "print(\"MAE test score:\", int(mean_absolute_error(y_test, xgbt_pred)))\n",
        "print(\"MSE test score:\", int(mean_squared_error(y_test, xgbt_pred)))\n",
        "print(\"RMSE test score:\", int(sqrt(mean_squared_error(y_test, xgbt_pred))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ouVv4khX6xq",
        "outputId": "8a393a1f-7ed7-4650-d145-d300093e58ca"
      },
      "id": "7ouVv4khX6xq",
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE test score: 18490\n",
            "MSE test score: 840217398\n",
            "RMSE test score: 28986\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N334wHmUZy4g",
        "outputId": "c39f23ca-48ec-4591-c3e9-05f128a5aa8c"
      },
      "id": "N334wHmUZy4g",
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181370.38356164383"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Discussion: Mean absoute error and (root) mean square error difference between the predicted price and the ground truth are shown to show how the model performs."
      ],
      "metadata": {
        "id": "NFAMhHHKYMn8"
      },
      "id": "NFAMhHHKYMn8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SHAP for XGBoost"
      ],
      "metadata": {
        "id": "Svs-DEZFPn6q"
      },
      "id": "Svs-DEZFPn6q"
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.TreeExplainer(xgb_model)\n",
        "shap_interaction = explainer.shap_interaction_values(X_train)\n",
        "# Get SHAP values\n",
        "shap_values = explainer(X_train)"
      ],
      "metadata": {
        "id": "H3A1aeT-APja"
      },
      "id": "H3A1aeT-APja",
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Waterfall plot for first observation\n",
        "shap.plots.waterfall(shap_values[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JSsYo_9JVS0D",
        "outputId": "d44afdd1-e950-47d3-bfbc-85f7e16a0ecc"
      },
      "id": "JSsYo_9JVS0D",
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion**\n",
        "\n",
        "This waterfall plot shows which feature contributes how much to the predicted price. The Overall Quality has the highest attribution to the predicted price in this model, followed by Year Built, Total Basement Squre Foot, etc. But the Masonry veneer type and Basement Exposure, which refers to walkout or garden level walls, reduces the predicted price."
      ],
      "metadata": {
        "id": "yOKtazz2agEE"
      },
      "id": "yOKtazz2agEE"
    },
    {
      "cell_type": "code",
      "source": [
        "#Mean SHAP\n",
        "shap.plots.bar(shap_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "U4HyunoGV-Yj",
        "outputId": "3cd8bfe4-bca7-4b22-843c-413f095b2ba4"
      },
      "id": "U4HyunoGV-Yj",
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 1 Axes>"
            ],
            "image/png": 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m5sbgwYMtrjk4OFCzZk2OHz/O6NGjuXLlCh99ZLlLQqZMmShZsiRbt25l1apVVu1HRkZabGOckJQpjWVNUaM0UQYMGMCdO3cS1UZchg8fjp2dHb6+vnFuFTx48GA2b95M0aJFqV+/fnR51MYAixYtsoonsbH7+vpalYmIiIhIEjl41vj58wb4ZKzlq/PPz9/upLbQrxHs/gs6T4cDZ40zVIZ8bFlv2TfGLmAnL0GX6TD2NyiXD7YNtpw21qg8pDAZJ9l/McU4jT5zOuP+LnWfHk+rt+FWCIz5zbh/0loonh02D4D6ZZ9+fywmsw19O921axfvvfcet27dokKFClSsWJFUqVKxZ88efv/9dxwcHJg7dy516tSxunfDhg1Uq1YNJycnHjx4wPHjxy129gI4ceIE5cuX5/bt29SoUYPixYvz6NEj/v77bzZt2kS9evWiRyzatGnD1KlTOXLkSMzCpMcOHjxIqVKlcHFxoVmzZqRNm5YdO3awc+dOXF1defToEdevX4+uX7hwYa5cuWJRVrNmTdauXWuVHMQ+mf69996jaNGi3L9/n3Xr1rFv3z5y5szJtm3b8PaO2e/6xo0bZMuWjcjISJo3b07atGnZsGEDt2/f5tSpU9SoUSP6/Jfly5dTr149MmbMSJMmTbC3t2fTpk2cOXOGBw8ekDVrVo4cORLdtqenJ97e3hZlCQkODsbd3Z07udqQ+vT1p98gIiIiYkvyZoKN/WNGIyTZ2MzULzDORTl+/Dh+fn6sXbuWkSNHEhkZiaenJw0aNGDQoEHkyJEjznvffvttsmTJwoULFyhWrJhVkgKQL18+Dh48SI8ePdi4cSO///47dnZ2pE+fnsqVKyf6XJJixYoxb948evfuzdSpU0mRIgWFChVi7dq1tGvXjitXrjz3Z9CmTRtKlSpF37592bp1K8uWLePhQ2PP6nfeeYfffvvNampZ+vTpWbhwIV26dGHatGk4OjpSuXJlli9fbpHQANStW5dJkyYxfPhwxo8fj729Pf/73//4448/qFix4nPHLSIiIiKSlGxqREXidubMGcqXL8+tW7dYsGAB77///tNvSiYaUREREZGXmkZUbIbNrFGR+OXMmZNNmzbh7u5OkyZNoqdxiYiIiIi8qmxq6pfEL1++fFy7di25wxARERER+U9oREVERERERGyOEhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjnb9khcjW3pIaZ/cUYiIiIg8m+wZkjsCeUyJirwYEz4Ht9TJHYWIiIjIs3NxSO4IBCUq8qJkTAuplaiIiIiIyPPRGhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5ihRERERERERm6NERUREREREbI4SFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGxOquQOQF5Rl29ByMPkjkJEROTV5uIA7i7JHYXIC6FERV6MDlPhQnByRyEiIvLqyp4BprVXoiKvLCUq8mKcuwGnryd3FCIiIiLyktIaFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGyOEhUREREREbE5SlRERERERMTmaHtiEREREYmx4TDM3gLbjkPgTfBOA1ULwcAmkDGtdf0dJ+DrX2D/35DaGRqWhSHNwNXJst6+M9B7Nuw4CWYzlMkLI5pDsewxde6HwfSNsGwPHPkHQh5ArozQprrxSpkypu6lW0a/f542/pwyBeTxgfa1oHllMJme/qxhEdBnLsz8A4LuQZGsMKgJVC/2fDFJktKIShLz9PSkcOHCyR2GiIiIyPPpMRM2H4UPSsG4z6BxOViwA4p3gytBlnUPnoW3+xlf5ke3hNbVYMo6aPCdZb39Z6B8b/j7KvRtCH0awl+XoZIfnLwYU+/vK9DxJyOR6VIXvvvUONjyyynQ6gfLNm8EG4lU/TJGvUFNIWMaaDHeSIgSo8V4GL0CmlWEsa2MZKf2YCNJe56YJEmZzGazObmDeBFCQkIYPnw4y5cv5+zZs9y7dw8HBwd8fHwoU6YMHTt25K233kp0e56enjg5OXH+/Pmn1vP29ubIkSP/Kv6IiAi8vLwICgqiXbt2TJw48V+1918JDg7G3d2dO7nakFoHPoqIiLw4eTPBxv7gE8coR0Iq+0G2DODfMe7rW45C+fyQIoVlWSU/6F3fSAii1B5kJCsnxhujKQA/rYPPJ8LaPvBOMaPs3UGw8xT89QOkczPKLt+CPB2MOou/NspuBMPV21DwDcuYWk0wRjX++sEYzUhInSGwKQDuzEx4tGPPX1CqB4xsDt3qGWUPwqFQZ8jgDjuGJl1M8lxeyRGVQ4cOkS9fPgYNGkRkZCTNmzdn4MCBdOrUibx587Js2TJKly7NX3/9leR9//PPP+zZs+dftzNz5kyCgoLw8vJiyZIlREZGJkF0IiIiIk9RsaBlkhJVltYVjgfGlAXfh3WH4ONKMUkKGNOuXB1hwfaYsq3HoVqRmCQFjGlklQrCb3shJNQoS5/aOiEAY3QHLPuPTzZPY4Qn/GHC9RbtNEZQ2rwTU+ZoD5+9DTtPwoUbSReTPJdXbo3K3bt3qV27NlevXmXixIm0a9fOqk5ISAg9e/bE9JS5i2FhYURERODq6pro/p2dnZ9eKRGmTZuGl5cXAwcOpE2bNixevJgGDRok6t6bN2+SLl26JIlDREREhJBQY21G+liJxpF/4OEj+F9Oy7r2dsa6kwNnY8rCIsDJ3rpdZwcjoQg4D6Xzxt//ldvGz/Spra+FhsG9MCO+P47C9E1QJg84OST8TAf+Nta0pH7iu9tbuY2fB89ClvTPF5MkiVduRGXIkCFcunSJTz75JM4kBcDV1ZXx48eTK1eu6LI2bdpgMpnYsWMHjRs3Jm3atDg5ObFmzZpn6v/JNSp58uTB3d2d8PBwq7qzZs3CZDLx9ddfW5SfPXuWXbt2Ua9ePT799FNSp07N5MmTE+xv48aNvPnmmzg7O5M/f/7o6/v376d69ep4eHiQKlUqPD09adasGbdv37ZoZ8+ePXzwwQdkyZIFR0dHHBwcyJkzJwMHDnym5xcREZFX0Pe/GQlFo/IxZZcfr1fJmMa6fsY0xgL3KHkzwa5T8OhRTFl4BOw+Zfz54i3iFR5h9J/dC0rmsr4+diV4toDs7Yw1J6XzwLyuT3+my0Hxxw6W8T9rTJIkXrkRlRUrVgDg6+v7XPc3a9YMe3t7PvvsM0wmE1mzZv1X8TRq1IhBgwYxa9YsWrVqZXHN39+fFClS0L59e4vycePGYTab6dChA/b29tSqVYulS5dy/fp1PD09rfq4evUqderUoWrVqtStW5e7d+8CsH79eurWrYuzszMNGzYkc+bMHDp0iAULFrBv3z4OHTqEg4Px24aVK1eyb98+KleuTI4cOQgJCeG3336jT58+XLt2jfHjx/+rz0FERESSQcRDuHPfuiwswlh7EVtaV+spX2CsT+m/wNjNq2qsDYNCH/8S1sHO+h5Hu5jrAF/WhC8mw2c/wtf1IDISBi2Cy7ct24pLh5/g2AVY2RtSxbHmpEl5Y1TnerAxjezqnYTbix1/fLH/25gkSbxyIyrnzp3DycmJIkWKWJRHREQQGBho8Yr6Qh+bq6srR44cYeTIkYwYMYKSJUv+q3i++OILUqVKxYwZMyzKb968ydatWylZsqRVMrRgwQIKFixIoUKFotsIDw+PN1m4fv0633zzDStWrKBv3758952x00br1q1JkyYNJ0+eZMqUKfTp04fFixczfvx4Tp48yejRo6Pb8PX15fz588ycOZP+/fszatQojh07RqFChZg2bRphYWH/6nMQERGRFycwMNBiw5/g4GACAgJg+wljtCH2a8dJmLfNqvzAsnU8ijXicezYMe7sPgYfDIdCb3Cxfz2LPu6bH9cNi4gu27Fjh/GHB8ZUr+j37WpAr4+InP0HFOwEhX3hzBWutyxnXHd1jPM5HgycB1PXGVsj137Tso+oPi+ehmpFoUkFmO3LVVcwV+trTAl7/BxBQTG7lUX34WQPYRExn1V0p4+fJ9ZUNYs+R/4KU9cR2b9xdEzx9vHk30fsuJ98jife79q1y+rvI6n7sHWv3IhKaGgoHh4eVuV79uyhfPnyFmW+vr4WX9YBOnbsiL19HHMon5OPjw+lS5dm586dXL16FS8vLwAmT55MeHg4zZs3t6i/YsUKLl26ZDHKUqlSJbJly8acOXMYMGCAVR8uLi588803FmXbtm3jn3/+oW3btoSGhhIYGLPQq3bt2jg4OLBu3Tp69uwJYPGZhYSEcOfOHcxmM5UqVSIgIIA///zT6vMTERER25A5c2aL96lTpzZ+4RkUAuv6Wlbu6m+cjdL9fYvi4uXzW+ySVcAtA9TsBe4usKo3mZ44Q8U5p4/xh8sxX57Lli0bU+aTNuY9wOBmpOj2Phy9AO7OUDgrnr1mGdfy+Fg/h/9GHPsuNJKcb2PW6Vq0Gcd7ry/rQY0BsOUY1ChOgQIFLK5H95ExDVy8FfNZRYl6nli7qUX34b/R2L65XQ1S9GkY81nF18djVn0k4jlKly5t8f5F9GHrXrlExcnJidDQUKvyggULMmfOHAD27t1rlaBEeRFnoLRo0YJt27YxadIk+vY1/rGYN28eLi4utGzZ0qLupEmTSJkyJWXKlOHAgQPR5RUqVGDmzJls2bKFihUrWtyTMWNG7Owshy73798PGAlRfOtbbt68Gf3noKAg2rdvz++//25RHuX6dW01LCIi8tJJ42qMNjxZljGNdXlsN+/CO/2N0ZIN/eI+6LHQG8a0p71noGG5mPLwCGMheuyy2H2Xj1lLy/rDkDkd5MtkWW/ZHmj9I3xYCn74/KmPaSFqytaTU96eVCy7sY1x8H3LBfVR62ZiH0T5b2OS5/LKJSrZsmXj6NGjHD582GL6l4eHB02aNAGw+lIfm5ubW7zXntcnn3yCr68v8+fPp2/fvhw/fpyAgADef/99nJxiTm0NCgpiw4YNPHr0iKpVq8bZ1oQJE6wSFUdHx3j7btKkCXXq1InzWuz1LrVq1WLPnj3UqVOHSpUqkSFDBlKmTMmyZcuYP3++xdCjiIiIvMLuPTDOR7l4CzYNgNw+cddzdzG2HJ71B/g1ALfH32lm/mHswNWgTML9zN9mnCr/3afWZ7Y0Hg0VC8Bs37jXzQBcvwOe7tbl09Ybp9KXyBFTdiPYeL3haew0Bo8PilwGU36POUclLMLYNaxUbssdvxIbkySpVy5RqVOnDkePHmXMmDFMnz49ucMBwN7enpo1a7Jw4UIOHTrElClTMJvNtGnTxqLejz/+SFhYGB06dCBfvnxW7fz444+sXr2a0NBQiwQnLgULFgQgZcqU0QlafK5evcqePXuoXr06y5Yts7i2evXqxDyiiIiIvCqafW8chtjqbeOMkNjnhLg6Qr1SMe8HN4WyvaDSt8Z5JIE3YdRy4xDHmiVi6m05CgMWwjtFjbNUdp0yDkusWRw6vRdT759rUHcomDASiYVPrKkokhWKZHvc9yJjDU7N4kYCcusuLN5lJD8da1sewjhhlbEhwKYBUPnx9KhSeaBBWeg5G64FQy5vmLEJzl2DaV8+X0ySpF65RKVXr1788ssvzJw5k1KlSsW5RbHZbP7P42rXrh0LFy7khx9+YOXKlfj4+FCrVi2LOrNmzcLFxYVRo0bFuU4mJCSEb775hmnTptGhQ4cE+6tSpQpZsmRh8eLFdOvWjaJFLYd3w8PDuXHjBj4+PqRKZfzP4MnP5cyZMyxduvR5HldEREReVgcfn3/y8wbjFVtWT8tEpUROWN8PevwCvtPBzdE4MHHox5b3ZUpnHK44chncDYXsGYwT7rvUsdw16+y1mClb7adax9a3YUxS8O6bcOYq/LzR2PHL0c5IGqZ3gE+rJO5Zf/kK/ObCzM0QdM+4/7dexgGXzxOTJCmTOTm+tb9ghw4d4t133+XixYsULlyYSpUqkTFjRu7cucPJkydZv349oaGhjBkzhq+++gowzlGZOnUqR44csVqIBMY0qfDwcJo1axZnn76+vuTOnRtPT0+8vb05cuSIVZ2sWbNy/fp1QkND6dChg8UuXnv27KFUqVLUrFkz3lGMK1eukDlzZooWLcq+ffui44qvv02bNlGnTh0ePnxInTp1KFiwIPfu3ePMmTNs3LiRHj16RC+mf/PNNzlw4ADvvfceJUuW5Ny5cyxcuJAMGTJw5swZFi5cSP369Z/yyRs7Tri7u3MnVxtSn9a6FhERkRcmbybY2N9i0bfIq+SVG1EBKFq0KMePH2f48OEsX74cf39/7t+/j4ODAz4+PtSrV49OnTo989bDwcHBTJw4Mc5r7733Hrlz507w/vr16zN69GhMJpPV2Sljx46NrhMfb29vihYtyoEDBzhx4kSc08Niq1KlCrt376ZXr15s3ryZpUuX4ujoiJeXF++//z4ffPBBdN1ly5bRtm1btm7dyurVq8mYMSPdunXDzs6OXr16JdiPiIiIiEhSeyVHVCT5aERFRETkP6IRFXnFacsCERERERGxOUpURERERETE5ihRERERERERm6NERUREREREbI4SFRERERERsTlKVERERERExOYoUREREREREZvzSh74KDYgW3pIaZ/cUYiIiLy6smdI7ghEXiglKvJiTPgc3FIndxQiIiKvNheH5I5A5IVRoiIvRsa0kFqJioiIiIg8H61RERERERERm6NERUREREREbI4SFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGyOEhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5qRK7gDkFXX5FoQ8TO4oROR15+IA7i7JHYWIiDwHJSryYnSYCheCkzsKEXmdZc8A09orUREReUkpUZEX49wNOH09uaMQERERkZeU1qiIiIiIiIjNUaIiIiIiIiI2R4mKiIiIiIjYHCUqIiIiIiJic5SoiIiIiIiIzVGiIiIiIiIiNkfbE4uIiDyLy7dg7ErY/RfsPQ0hD2DTAKhcyLLe/TCYvhGW7YEj/xj1cmWENtWNV8qUMXX7zYP+C+Lvc9tgKJc/5v2EVfDDavj7KqRPDY3KwcAm4OIYU+dEIPy8EX4/CGeugKsjlMgB/RvD/3I9/TmPnod+82Hf33AlCJwdoEAW6P4+1ClpXT8yEib/brxOXgJneyiaDca0hKLZn96fiMgTlKg8wWQyUaNGDdasWZPcoYiIiC06eQmGL4XcGaFwVth5Mu56f1+Bjj/B24WhS11I7QRrD8KXU2DXKZjxVUzdD0sbScyTes02EpySsRKLHr/AiF+hfhno9B4cuwDjV8HRC7C2T0y9n9bDtA3wUWn4sibcuW8kEaW/gTV+UK1ows/5z3W4GwqfVgaftEbitXgX1B0Kk9tBm3cs67f6AWZvgeaVoUMtuBcGB87CtTsJ9yMiEo9kSVRMJlOi6x45coRChQolWCcgIIBx48bRpEkTqlSp8m/Ds+Lp6cmNGzei35tMJlKnTk3mzJlp3bo1nTt3TvI+E+vrr78mTZo09OzZ0+paREQEY8aMwd/fn0uXLnHv3j3c3NzInDkzb731FuPGjcPZ2RmAoUOH0qtXr3j7OXPmDDly5HhhzyEiYjMq+0G2DODfMe7rb+aEmzMgrRss2gEN4klUvNPAkTFQ8I2YsrY1oNUEY6TFr0FMclIkm/GK7cINCLwJrauBvZ1RdvkWjF4Bn1SCXzrF1M3jYyRFK/6MGe1oUgH6NQJXp5h6rd6G/B2NkZKnJSq13zResXWoBW92N2KInags2A4zNsGSr+GD0gm3KyKSSMmSqIwcOdLi/ZYtW1ixYgV16tShYsWKFtcyZ8781PZOnDjB1KlTyZ49+wtJVADSpk0bnQw8evSICxcusGTJEnx9fbl48aLVM/1Xpk+fjre3d5yJSvXq1fnjjz/Inz8/LVu2JG3atJw/f57Dhw8zY8YM+vXrF52oRGnQoAFvvfWWVVsZMmR4Yc8gIvJScXN6eh0wpmSlT21d/kEpI1E5Hhj3KEqUuVvBbIZmsf67uPMUPHwEjctb1m1c3khU5m2LSVTezGndZjo3qFAANgck7hmelDIlZEkPf562LB+9HN7KbSQpkZEQGm45DU1E5DkkS6LSrVs3i/cRERGsWLGCMmXKWF2zFS4uLlax9ezZk6xZs7Jw4cJkS1Tis379ev744w/KlSvHtm3brK5fvHiR9OnTW5VXqlSJ9u3b/xchioi8nq7cNn7GlcTENnuLkRRULBBTFhZh/HSyt6zr7GD83Pd3IvoPenrfsd17YCQed+7D8j9h9X5jTUyU4Puw57QxvazXLGMaWsgDyO4Fwz6GhuXib1tEJAE2vevX7du3+fTTT/Hy8sLOzg4PDw/eeecdjh49Gl1n6NChNGjQAIBevXphMpkwmUwULlwYMEY/2rdvT8GCBfHw8CBVqlSkT5+eevXqcfHixX8Vn7e3N3Z2dqRKZZnv7dixg4oVK5I2bdrouIsUKcL06dMt4jaZTMyfP5927dqRPn167O3tyZMnD6tWrQJg8eLFFChQAAcHBzw8PPjiiy8s+jGZTNy4cYOAgIDo5zaZTAQEBBAQYPy27MkRqiiZMmXCwcHhXz2/iIg8o/AI+P4340t8yQQWtB89D4f/gSblIfZ06bw+xs/tJyzrbz1m/Lx4M+H+tx4zRmUaPUPy0NUfPFtAri+h2wxjRGjC5zHXz1wxRn7mbTMW749oDrM7g2dqaDwa1uxPfF8iIrHY7GL6sLAwypYty/Hjx6lQoQKVKlXi1KlTLFmyhHLlyrF7927y5s1L7dq1OXXqFP7+/hZTx3x8jH/MQ0NDmT59OlWqVKFmzZq4urqyd+9efvvtN/bv38/Jkydxcnr6MP6jR48IDAwEjBGgc+fOMWLECB48eMCnn34aXe/ixYvUqlULgIYNG5ItWzauX7/OgQMH2L59Oy1btrRot3fv3kRGRtKiRQvCw8Px9/fno48+YuzYsXTp0oWPPvqI+vXr8+uvvzJp0iRy5swZPbIzcuRIBgwYgJubG76+vtFtZs6cmfz5jd1hli9fzldffYW3t3eiPvc7d+5EP2eU1KlTkzr1M/z2TUTkZRHx0BgpeLIsLAJuBFuWp3WFFP/y93sdfjIWv6/sDalSxl9v9hbjZ7MnftlUIieUym0s5s+UFqoUNqaQfTEZ7FIZIx/xuXYbmo6B7Bng63qJj7nze8bC/Uu3YMEOeBQJ4Q9jroc8MH7evAu7hkGpPMb7uiUh+xcwaBHULJH4/kREHrPZEZXhw4dz/PhxmjVrxpYtWxg4cCDz589nypQp3Llzhw4dOgBQtGhR3n33XYDoqWPdunWjadOmADg7O3P16lVWrlzJqFGj6N+/PytXrqRfv35cuHCBqVOnJiqeS5cukSVLFrJkyUKOHDmoWrUqGzZsoF+/fvj5+UXXW7VqFcHBwQwcOJCpU6fSu3dvvv/+e/744w9++uknq3YjIyM5evQo3333HePGjWPUqFE8ePCAL774gqVLlzJjxgwGDBjA7t27cXd3t4i3W7duODg4kDZt2ujn7tatGx4eHtSoUYNSpUpx9OhRsmbNSvHixWnVqhXTpk0jODjYKo4ovXv3jn7OqFfUZy0i8rIJD7f84r5r1y4ePXoU/f7c7DXGaEHs146TxujAk+XnjU1VduzYkWCfT/Zx7NgxgoKCYOSvMHUdd7q9y/lCntHXg4ODo0fBATCbeTB9PRR6I3qBvUWfi78mOEc6Y5et7O2gzhBuVM3Lw8JZjC2IgcDAQM6fPx/Tx+Xr3H/bz9jFa9k34Opk9RxPvo9+jnyZoVpRjv3Pi6CZXxqJSZ0hBF64YPTxeBpaZFZPAlxifd6uTlwt9YYxLezho4T7ePKzeszqOZ78rOJoU32oD/WR+D5snclsNpuTO4ioHaeGDBkSvSi8ZMmS7Nu3j0uXLlmNBmTPnp0rV64QEhJCypQpWbRoEQ0aNLC4Py4RERHcunUr+mfRokVp3Lgxc+fOja4T1/bEnp6emEwmxo4dC4DZbObChQvMnj2bgIAABg0aFL1j1uLFi6lfvz41a9Zk5syZca4Dif3M/fv3p0+fmO0kr1y5QsaMGcmfPz/Hjh2zuKdMmTIEBARw9+5di9i8vb05cuSIVR9hYWH079+fRYsWcebMGSIjIwFwdHTkyy+/ZNSoUVbxtG7dmqpVq1q0ky9fPooXLx7v5xpbcHAw7u7u3MnVhtSnryfqHhGRFyJvJtjY39haNz5BIbDvjGVZV39jx67u71uWl88Pjk+sDVm0Axp8F/c5KrH5bzQSi7bvwMS2Cce97ThU6A1DP4ZvPoy/3l+XjPUuuTMa8fp8BpnTwZ4RlvXCI6DOUPjjqLF9caWCCff/NFN+h7aT4MR44zO+dAsytYbSeWDnMMu638w0Rn9uzwR3l3/Xr4i8dmx26telS5dIkyZNnFOWcubMyblz5wgMDCRr1qxPbWvcuHFMmDCBv//+2yIzBWMdTGI4OjrSpEkTizJfX19y5crFgAED+PTTT8mUKRMfffQR77zzDmvWrMHb25vcuXNTvnx5WrVqRZkyZazazZs3r8X7qOfNlCmTVV13d3dCQkISFS+Ag4MDQ4YMYciQIdy9e5eNGzeydOlS5s2bx+jRo8mcObPFlDGAYsWKWT2niMgrK42r9Ta9aVwhY5qnb9+bWMv2QOsf4cNS8MPnT68/e4uxLqVphYTr5fYxXmBMJ7scBC2e2PkyMhKaj4MNh2FBt3+fpEDM9LKoKXM+acHbAy7esq576ZaR3CV2pzQRkVhsdupXUpk4cSKdOhl7zffq1Ytp06YxZ84cZs6cCRijI8/L3t6e0qVLExYWxrp166LL165dy7Zt2+jYsSPu7u7MnDmT8uXLx3lOScqUcc9Rjq/8ebm5ufH+++/j7+/Pzz//DMCMGTOStA8REXnClqPGgvKKBWC279PXuEQ8hIU7jNGbNzwTrhslMhK+/sXY+atdDctrHX+C+dvhxzbGoZLxuRFsnGR/Pyym7NrtuOP7ZbMx3atArOMDGpUzzn1Zd9CyzWV/QtVC/35tj4i8lmx2RCVTpkzs3buXq1ev4uXlZXHt77//xsnJKfqMlYQOkJw1axZ2dnbs3bvXYkH4nj17kiTOhw+NBYVPrvsoV64c5coZu6pcvXqV4sWLM3r0aAYNGkSKJPwH+1kOz4xSo4bxH7Lr1zU1S0TkuQxaaPw8esH4OfMPY8oWwLfGTpT8c804xd2EsRh94RNzw4tktT7kce1BY1F6swRGUzpNgwfhUCw7RDyCOVuMdSAzOlomN9+vgB/XQJm8RhIz6w/Ldj4oFXPWyYRV0H+B5RS2tpMgONRIsjKlNaaZzd4CJy7CqBaWB0n2/MhYaP/RSOhSx5jmNWmtkdgMaRb/s4iIJMBmE5X33nuPP//8k+7du/PLL79El8+YMYOzZ89SrVq16FEHd3d3AG7etN6WMUWKFJhMJospX5GRkQmewp5YISEhbN++HYDy5Y3Dty5duoSXl5fFiIiXlxcZM2bkypUr0afDJxVHR8c4F8fv378fk8kU59qSadOmAcZaHxEReQ5+cy3f/7wh5s9RicrZazHTo9rHsXFL34bWicrsLcbuXQ3Kxt938ezGFsezt0IKk3HQ4oZ+xg5gsR08Z/zcedJ4PenspIQPZWxUHqath4lrjeTJzQnezAHDP4G6TxwM7OUB2wYb2xeP+c1IUMrkhVmdoKj+WyMiz8dmE5UePXowb948Zs6cyYULFyhfvjx//fUXixcvxt3dnfHjx0fXLVWqFI6OjsyaNQsXFxfSpEmDj48PDRs25KOPPmLbtm289dZbNGjQgIiICFavXs2DBw+eKZ579+7x3XffAUaic/HiRZYuXcq1a9d47733KFHC2Hrx+++/5+eff6Zq1arkypULe3t7tmzZwv79+6lUqVKSJikARYoUYdWqVXz22WcULFiQFClS0KJFC3bu3EnHjh0pXLgwZcqUIUuWLISEhPDnn3+yefNmHB0dGTx4cJLGIiLyStg88Ol1zEueXqdyocTVi21ul6fXaVHVeD2Nf0fjlRj9Ghuv2BqXN16JlcMblvRIfH0Rkaew2UTFwcGBHTt20KlTJ1avXs3WrVtxdnamcuXKfP/99+TLly+6rpubGxMnTmTAgAEMGTKEhw8fUqhQIRo2bEjnzp0JDg5mypQpjBw5EhcXFypUqMDkyZPjXLAen1u3btG9e/fo9/b29mTJkgU/Pz/69u0bXV6rVi0OHTrEli1bWLZsGSlSpMDLy4suXbowcGAi/uP3jH744QeaNWvG/PnzuX//PmazmWrVqvHee+9x9uxZNm/ezOLFi7lz5w5ms5n06dNTu3Zt+vfvn+idvERERERE/ms2sT2xvDq0PbGI2IzEbE8sIiI2S9twiIiIiIiIzVGiIiIiIiIiNkeJioiIiIiI2BwlKiIiIiIiYnOUqIiIiIiIiM1RoiIiIiIiIjZHiYqIiIiIiNgcmz3wUV5y2dJDSvvkjkJEXmfZMyR3BCIi8i8oUZEXY8Ln4JY6uaMQkdedi0NyRyAiIs9JiYq8GBnTQmolKiIiIiLyfLRGRUREREREbI4SFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGyOEhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5ihRERERERERm5MquQOQV9TlWxDyMLmjEHlxXBzA3SW5oxAREXllKVGRF6PDVLgQnNxRiLwY2TPAtPZKVERERF4gJSryYpy7AaevJ3cUIiIiIvKS0hoVERERERGxOUpURERERETE5ihRERERERERm6NERUREREREbI4SFRERERERsTlKVERERERExOZoe2IREVuy7wz0nQd7z0DIA8jhBa2rQfuakDJlTL3522DFXtj9F5y+DJUKwuaB1u1tDoAqfeLua+dQKJ035n1lP/jjqHW9GsVgTTxtRPHfCC0nxH99VidoVinua9X7wfrD0L4WTPg84X5EROS1oUTlNbJo0SIaNGjAkCFD6NmzJwABAQEULlyYzz//nClTpiRzhCKvuX1noGxPyJ0RenwAzvaw+gB0mgZnrsDYz2LqTlxr1C+ZC27efXrbX71r1I0tV0brepnTwdCPLct80j69/YoFYGYn6/IxK+DQOXi7SNz3LdkFO089vX0REXntvJaJSoUKFdi+fTtLly7l/ffft7q+bNkyPvjgA8qVK8fWrVv/09g8PT25ceNG9HuTyUTq1KnJnDkzrVu3pnPnzi88hqtXr+Ln58c777xD/fr1X3h/Iq+Nyn6QLQP4d4z7+uTfjZ9bBkFaN+PPbWtApW/Bf5NlojKzE2RKCylSQKE4EoQnVcgP9cs+vZ67M3wcz8hHQnJ4G6/YQsPgyylQtTB4p7G+50E4dPWHHvWgz7xn71NERF5pr2WiMnv2bAoXLkzbtm2pVKkSHh4e0deCg4Np27Ytbm5uzJ07N1niS5s2bfSIx6NHj7hw4QJLlizB19eXixcvMnLkyOdq94MPPuDu3bs4ODgkWO/69etMnToVQImKyH8p+D442oOHi2V5xjRw8pJlWZb0z97+3VBwsodUKROu9/CRkUS4Oj17H7Gt2Gv02axi3NdH/AqRkdDtfSUqIiJi5bVcTP/GG28wcuRIrl69SqtWrSyutWrViqtXrzJy5EgyZ878n8QTFhZGSEhI9HsXFxe6detGt27d6NGjBxMmTODPP/8kZcqULFy48Ln7SZkyJa6urtjZ2SVF2CKS1CoXMpKVtpPgeCD8cw0mrYUlu6Hnh/+u7ZYTIHUzcGxkrFnZezrueqcug0sTcGsG3q3Abw5EPHy+PmdvMRKjD0tbXzt/HYYtgeHNwSnhX56IiMjr6bVMVADatGlD9erV+fXXX5k/fz5grOFYsmQJ77zzDm3atGH//v1Ur14dDw8PUqVKhaenJ82aNeP27dsWbe3Zs4cPPviALFmy4OjoiIODAzlz5mTgQOuFrW3atMFkMrFjxw4aN25M2rRpcXJyYs2aNQnG6+3tjZ2dHalSWQ6CeXp6UrhwYav6ixYtwmQyMXTo0ATL4rovqr2pU6diMpkwmUx4enomGJ+IJIHPq0GHWjBjMxT4CrK1gw5TYdxn0Om952vTPhV8VNqYNrbsGxjUFI78AxW+hQN/W9bN6Q29P4K5XeCXr6BUbhi0CD7+/tn7vXUX1hyAOv8DtzhGZrr6Q/Hs0Lj88zyViIi8Bl7LqV9RZs6cSYECBWjfvj1vvvkmX375JWnSpGHWrFmsX7+eunXr4uzsTMOGDcmcOTOHDh1iwYIF7Nu3j0OHDkVPoVq5ciX79u2jcuXK5MiRg5CQEH777Tf69OnDtWvXGD9+vFXfzZo1w97ens8++wyTyUTWrFmjrz169IjAwEAAIiIiOHfuHCNGjODBgwd8+umnL/QzefPNN+nevTsjR46kXLly1KtXD4DUqVO/0H5FXjkRD+HOfeuysAi4EWxZntbVWGuSMqWRLNQoBg3KGNPA5m6Fjj+BtwfUK/XscZTNZ7yi1H0L6peBIr7Qc5blbl7T2lve+0llaDMRpq4D35OWO4Q9zaKdEP4w7mlfm47A4l2we9gzPYqIiLxeXtsRFQAvLy/GjRvHzZs3KVGiBDdu3GD8+PF4enrSunVr0qRJw8mTJ5kyZQp9+vRh8eLFjB8/npMnTzJ69Ojodnx9fTl//jwzZ86kf//+jBo1imPHjlGoUCGmTZtGWFiYVd+urq4cOXKEkSNHMmLECEqWLBl97dKlS2TJkoUsWbKQI0cOqlatyoYNG+jXrx9+fn4v9DPJnj07zZs3B6BAgQLRU9DatGnzQvsVeVnt2LEj7vfbT4BnC8vXjpMwb5tV+cWdh4x7hi0hctgSjvnVgOZVoGE5WPoNdwr7QPupxtqROPq8HxqK2WyOfn/s2DGCgoKi3wcGBnL+/Pno98EZXLhdOS9sCoBHcbcZ/b5rXQAuTF/Jo8d1E9PHw1828dDdCWqVsGzz4SP4ahp8UokdEdct+rxy5coz9REcHExAQEDcccfzfteuXepDfagP9aE+XhImc+z/ur2m6taty4oVK6hTpw7Lly9n27ZtVKhQgbZt2/Ltt99a1I2MjCRPnjyULVuWjRs3WrUVEhLCnTt3MJvNDBs2jB9++IGtW7dSvrwxvaFNmzZMnTqVyZMnx/nl39PTE5PJxNixYwEwm81cuHCB2bNnExAQwKBBg+jVq5dFfW9vb44cOWLRTlxbESd2e+J/s2VxcHAw7u7u3MnVhtSnrz/9BpGXUd5MsLF/wtv2BoUY2wfH1tXf2P2q+xO7DZbPb4yevNHG2J1rtq/l9TEroMt0+OuHuLcULtQJ0qeO+xyV+Hz9C4z8Fe7MgtTO8de7H2asWfGtA6NbJq7t89eNaWttqsOkdpbXft4A7SbD5gGWn1/2dtC8MvRvDBncwVnrVkREXnev9dSvKGXKlGHFihWUKVMGgP379wMwefJkJk+eHOc9N2/ejP5zUFAQ7du35/fff7coj3L9uvUX9rjWlURxdHSkSZMmFmW+vr7kypWLAQMG8Omnn5IpU6anP5iIJJ80rlCtqHVZxjTW5VGu3oZHkdblUYvZHz6yvva8/r5iJEeujk+vB+D5DNM/524FsznuaV/nrxvPU66X9bVfNhuvpT2eb5qbiIi8UpSoJKBJkybUqVMnzmuxF5fXqlWLPXv2UKdOHSpVqkSGDBlImTIly5YtY/78+RbDdlHc3NyeKRZ7e3tKly7NwoULWbduHS1atACMc1biEhER8Uzti4gNyOMD6w4ZBzime/xvxKNHsGCHsSA9p3fC98fl+h3wdLcsO3QWlu+FWsWNtTFg7DbmYGe8opjNxmJ6MNbNRLkfZiQc6VMbryfN2QpvpDdGip7UuDwUy25d/sFwqF0CPq9uLOIXEZHXnhKVOBQsWBAwtvN9cmTjSVevXmXPnj1Ur16dZcuWWVxbvXp1ksb18KHxW9Xg4JiFuG5ubhbvo5w69fwnPceX/IjIC/bNB/DxWCjVw5g25WQPc7cZU8gGNQW7WP9kbzkKW44Zf74eDPfCYNDj7csrFoCKxr9jNBpltFM2nzGl6tgFmLLOOPV+2Ccx7e3/G5qMhiYVIJc3hIbD0t3GWps21aFEzpi6e/4ytjju2xD6NbZ8hoB/4PA/8M2HENe/JfkyG6+4ZPfSSIqIiERTohKHKlWqkCVLFhYvXky3bt0oWtRymkZ4eDg3btzAx8cnervgJ5f6nDlzhqVLlyZZTCEhIWzfvh0ger0LQNasWdmyZQt//fUXuXMbv4W8f/8+06dPf+6+og7AjL1gS0T+A80qGSMUQ5fAyGXGKEfeTDCprXFCfWwbj0D/BZZlfo8Pqe3bMCZRqVfKOM9k9HIIDjWmcH1Y2qgTe71LVk+oUMBITq7chhQmyJ/Z6LvNO4l/htlbjZ9NKzzTo4uIiDxJiUocUqRIwYwZM6hTpw6lSpWiTp06FCxYkHv37nHmzBk2btxIjx496NmzJ+nSpaN48eLR2xmXLFmSc+fOsXDhQry8vCwOckyse/fu8d133wHG4v2LFy+ydOlSrl27xnvvvUeJEjG76HTu3JlNmzZRuXJlPv74Y8LDw1myZAmOjk+Zd56ATJky4e3tzdq1a+nRowcZM2bEzc2Nzz777LnbFBESt9i9RnHj9TT9GluPZsTlq3eN19Nk94IF3Z5eD4yDKc1L4r429GPj9azia09ERF5bSlTiUaVKFXbv3k2vXr3YvHkzS5cuxdHRES8vL95//30++OCD6LrLli2jbdu2bN26ldWrV5MxY0a6deuGnZ2dxQ5diXXr1i26d+8e/d7e3p4sWbLg5+dH3759LerWrVuXYcOGMXbsWEaNGkXatGlp2rQpZcuWpVGjRs/9/NOnT8fX15fvv/+e8PBw0qdPr0RFRERERP4z2p5YkpS2J5bXQmK2JxYREZF/5bU+8FFERERERGyTEhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5ujAR3kxsqWHlPbJHYXIi5E9Q3JHICIi8spToiIvxoTPwS11ckch8uK4OCR3BCIiIq80JSryYmRMC6mVqIiIiIjI89EaFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGyOEhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5ihRERERERERm6NERUREREREbE6q5A5AXlGXb0HIw+SOQl41Lg7g7pLcUYiIiMh/QImKvBgdpsKF4OSOQl4l2TPAtPZKVERERF4TSlTkxTh3A05fT+4oREREROQlpTUqIiIiIiJic5SoiIiIiIiIzVGiIiIiIiIiNkeJioiIiIiI2BwlKiIiIiIiYnOUqIiIiIiIiM1RoiIiArD+EFTtA+4fg1tTeLMbzN9mXe9uKHz9C2RvBw4NIVNrqD8C7ofF1Ll8C76ZCVX6GG2ZPoTNAXH3GxkJk9ZCsS7g2hS8WkKtgbDjxLM/w7bjRl+mD+GGzjESEZGXmxKVBAQEBGAymWjTpk1yhyIiL9L0DfDOALBLBUOawchPoWIBuHDDst6de1ChN/y8AZqUh4lt4ava8CACwiJi6p28BMOXwsWbUDhrwn13nwFfTDbqjW4BXevCqUtQyQ/2/JX4Z4iMhI4/gYtj4u8RERGxYUmSqCxatAiTyWTxsrOzI0OGDNSqVYtdu3YlRTfPLCAggDZt2rBp06Y4rz0Zc+zX999//6/6Hjp0aILtx34VLlz4X/WVFMaNG0etWrXIli0bKVOmxGQyERAQz2+ARV4mlf2gxfj4r5+7Bu2nQsfasLYPtK8F7WrAmFbQrZ5l3Z6z4J/r8OcIGPIxtHobenwIv/WGNK4x9d7MCTdnwKkfoEud+Pt++AgmroX6ZWBmJ2jzDnz9AazvZ1ybvSXxzzllnZFYtX478feIiIjYsCQ9mb5KlSrUrl0bgPv373P48GGWL1/O5s2b2bt3LwULFkzK7p7qxIkTTJ06lezZs1OlSpU46xQvXpymTZtalVevXp38+fNz9+5d7O3tn7nv2rVrY2dnZ1E2ZswYLl26xMiRIy3KfXx8nrn9pDZ16lROnjxJ9uzZ8fb25tKlS8kdksh/Y9JaeBQJAxob70NCjVEJk8my3u17MH2TMYKS3QvCI8AMONhZNYmbU+L6jngIoeHg5WFZnsEdUqQAp0T+23PrLnw7x3iGa3cSd4+IiIiNS9JEpVixYnTr1s2irGfPngwbNgx/f3+rL+i2IHv27FYxx+bq6hrvtYQULVqUokWLWpTNmDGDS5cuJdhfclmwYAE5c+bE3t6ejz76iCVLliR3SCL/jfWHIV8mWLXfmIZ18ZYxOtK+JvRvbCQMYKz/eBAOuTIaa1J+3QORZiiTB35oA8WyP3vfTg5QKjf4bzLaqVDASIgGLoQ0LtCmeuLa8ZsL3h7Q9h3jXhERkVfAC1+jkjlzZgAcHByiy0aMGEHu3LlxdnbGwcGBDBkyUK1aNc6fPx9dp3Dhwnh6ehIQEEC5cuVwdnbGxcWFmjVrEhQUxKNHj+jQoQMZMmTAzs6OHDlysHz58uj7hw4dSoMGDQDo1avXc02zimuNSuyyn3/+mVy5cmFnZ4eHhwfNmjUjPDw80e1PmjQJk8nEgAED4ryeJUsWvL29iYyMtPhMDh06RJkyZXB2dsbJyYly5cpx6NAhq/sjIyPp06cPOXPmxMHBAUdHR4oUKcLChdZfZPLnz/9cI0ciL72/LsOFm9BygjGVa1F3qFUcBi2C3nMs64Ex/evCTfjlK/jhczhzFar2NRbQP49ZnSGvD3w8FrK2haJdYP/fsH0I5PB++v2Hz8Hk32F0S0iZ8vliEBERsUFJOqJy//59AgMDAQgJCWH37t0MHjwYNzc3WrVqBRhJSo8ePShQoACdOnXCycmJ8+fPs3nzZgIDA3njjTei2wsLC6Nq1aq8+eabdO/enb1797Jq1So+/PBD0qRJw+HDh2nevDkRERFMnz6dpk2b8s8//5AuXTpq167NqVOn8Pf3p06dOlSsWBGwnmb14MGD6JijODg44OnpmeCzbtq0iXnz5tGwYUN8fHxYuXIlc+bMIU2aNEyYMCFRn1erVq345ptvmD17Nn369LG4tmrVKgIDA2nfvj0pUsTkk2FhYbz99tsUKFCArl27curUKZYsWUKlSpXYv38/OXLkiK77zjvvsHHjRipWrEijRo0ICwtj6dKlNG7cmNu3b/P5558nKk6Rl0bEQ7hz37osLMJ6F6y0rsZoScgDYyH6sI+N9SYAH5WBWyEw9jfo9ZExlSsk1LhmMsGGfuD6eHpX8exQpif8sAYGWU8jfSo3JyiYBcrkhbcLw5XbMGwJ1BsGWwdD+tQJ3//VNKhVAt4p9ux9i4iI2LAkHVGZPHkyWbJkIUuWLOTPn58WLVqQMmVK1q9fH/0Fevny5Tg6OnLgwAGGDh1Knz59+Omnnzh9+jRly5a1aO/u3bs0a9aM1atX079/f1auXEm5cuX4448/OHz4MEePHuW7775j7NixjBo1inv37jF+vLFotmjRorz77rsAlClThm7dutGtWzer9SirVq2KjjnqVb58+ac+6/nz59mxYwc//fQTAwYM4M8//yRLlizMmjUr0Z+Xvb099erV49SpU+zcudPi2o8//kiKFCno3Lmz1WdStWpVtmzZwsCBA5k/fz7jx4/nzp07+Pr6RtebNGkSGzZsoG/fvmzevJkhQ4YwatQoTpw4Qfbs2endu3f0SI3IyyL2/2YDAwMtRmGDg4M5O2s1eLawfO04CfO2WZefv8GuXbswOz1eY9KkAseOHSMoKOjx+/IQGs7VNY83A3m8XiSofM6YJAXYEXnTWLPyeDvhHTt2WMR86pTlzl0WfTx8RESl3txNEQkTPocPShPcrCynfmxmjNSM/DXONqPfz98GO05y8JNiPHr0yKLO7du3E/ysntwsI94+Htu1a5dFHxbPoT7Uh/pQH+rjpezD1pnMZrP53zayaNEiGjRoQJ06dWjSpAkAoaGhHD58mOnTp+Pg4MCmTZsoWLAgtWrV4vfff2fixIm0bt3aYrQgtsKFC3Ps2DHu3LljsU6ke/fufPfddwwYMAA/P7/o8itXrpAxY0aaNGnCnDlzLOIaMmQIPXv2tGg/ICCAwoULU6ZMGTp27GhxLW3atNSoUSO6zueff86UKVMs7nv77bdZv369xX3169dn8eLF3Lp1izRp0sT5TAEBAcT+yA8fPkyxYsVo1KgRc+fOBYwvGBkzZqRIkSLs3r3b6v4zZ85YjJwAZMqUiXv37kV/OSlfvjz79u3jxIkTpHxiOsjAgQOZMmUKf/75J//73/+s4oxao3LkyBEKFSpkdT0hwcHBuLu7cydXG1Kfvv5M94okKG8m2NgffNLGXycoBPadsSzr6g/eaaD7+5bl5fODoz3kaW9M63ow33Jh/Jr9UGsQ/PoNvP8WzNkCzb6Hbz6EoR9btlW6B4Q9hAOjrGNatAMafAebBkDlJ/7/tPEIvN0Xfu8D1YtZXivqa4y2bBsS//O+0QYq5IfBzWLKvl8BY1fC/u+MRfoJfV4iIiI2LEmnfuXKlSs6UYlSo0YNateuTYcOHdi0aRODBw9m//79tG3blq5du1K0aFFq1KjBl19+Sbp06Szu9fDwsFrMnjat8R/dPHnyWJR7extzuWNnmokRldw8q6xZrc9GiIrt0qVLcSYqcSlSpAjFihVj5cqVhIWF4eDgwI8//siDBw/47LPPrOo7OztbJSkA2bJlY8eOHdy+fRsPDw/OnTvHgwcPyJYtW7x9BwYGxpmoiLy00rhCtaLWZRnTWJdHeTOnkahcvGm5JuTS439LPFPH1AOj3pMuBRkL8p/V1dvGz0dxjG5GPDK2KE7IhRswZ6vxelKJblA0Gxwc/exxiYiI2IAkTVTiUqtWLZycnNi3bx8AJUqU4OzZsyxYsIC1a9fy559/0qdPH8aMGcPGjRspVqxY9L3xjbYAVqMEUZJggChR4uv/eWL47LPP6NChA9OmTePLL79k1qxZuLu706JFi+eOz2w24+bmxuTJk+OtU7p06eduX+SV0aicMTVs2oaYkYnISJi+0VjHEpWg5M1kfPFf9qex3iVq7cjvB42EoWPtZ+87z+M1c/O2Q80SMeX7zxiHRsbe9et+GJy/bvQb1ffSHtZtztsG87cbi/0zp7O+LiIi8pJ44YkKGPPKHzx4EP3e2dmZFi1aRH8R9/f3p2XLlgwYMCBJt8U1PXkOgo1q3bo1vXr1Yvr06ZQsWZLjx4/zySefxLkL1/379/n777+tRlXOnTtH6tSp8fDwAIwdw/bs2UPNmjUTPboj8lp6/y14uwgMXWIkIEWzGVsPbzsOk9tZTgcb0xKq94fyvY2tgO/cg9ErjITjixqW7Q56vLve0QvGz5l/GG0CfGvsSMibOaF6UZixCYLvGwviLwfB+FXGmpjO78W0t+cvqNIH+jaEfo/PfKlXyvp5Dp41ftYq8fSF+CIiIjbshW9PPGfOHMLCwsiVKxeA1Q5bQPSOXLEXfyYFd3d3AG7ejGOqhg1xcHCgbt267Nu3jx49jN+QPrmIPrZvvvnG4v2kSZO4dOkSlSpVii775JNPMJvN8e7sdfbs2X8fuMirwGSCX3sYBzku/xN8pxs7b816fFJ8bFUKwxo/44yTXrONhKLeW/DHQIsF9oBxtonfXGOEA+DnDTFlsS37xjio8eQl6DLd2GmsXD7YNtgYxREREXlNJemIysGDB/nuu+8AY9vfgIAAfv31V1KmTBl9VkjFihVxc3Pjrbfe4o033iAoKIhFixZhMplo3rx5UoZDqVKlcHR0ZNasWbi4uJAmTRp8fHxo2LBhkvaTFLp06cLs2bPZtGkThQoVokSJEnHWc3NzY+PGjVSqVImKFStGb0/s7u7OmDFjouu1b9+e3377jcWLF5M/f36qVauGp6cnFy5cYN++fQQGBnLt2rXo+kuXLmXdunUA0TtGDBkyJHqE5scff3xBTy7ygm0e+PQ6rk7w/WfG62mqFY1/vUts5kSODjs5gF9D45WQyoUS12a/xjEjLiIiIi+xJE1UNm3axKZNmwBj2pWrqyv/+9//8PPzo0YNY1pEixYtWLp0KQsXLiQkJARXV1dy587NqFGjog9oTCpubm5MnDiRAQMGMGTIEB4+fEihQoVsMlEpXrw4hQsX5vDhw3zyySfx1nNwcGD9+vW0a9eO7777DrPZzP/+9z9+/PFHcubMaVF39erVjBgxghkzZvDTTz/x8OFDPDw8yJs3L99++61V3alTp1qURe1CBkpUREREROS/lSTbE0vSKFmyJMeOHePKlSu4ublZXS9cuDBXrlzh+nXb3fZX2xPLC5OY7YlFRETklfHC16hI4hw4cIB9+/ZRq1atOJMUEREREZHXyX+y65fEb/Xq1Rw8eJCpU6eSKlUq+vXrl9whiYiIiIgkOyUqyWzs2LH8/vvvZMiQgbFjxz7zSfAiIiIiIq8iJSrJbM2aNYmue+TIkRcYiYiIiIiI7dAaFRERERERsTlKVERERERExOZo6pe8GNnSQ0r75I5CXiXZMyR3BCIiIvIfUqIiL8aEz8EtdXJHIa8aF4fkjkBERET+I0pU5MXImBZSK1ERERERkeejNSoiIiIiImJzlKiIiIiIiIjNUaIiIiIiIiI2R4mKiIiIiIjYHCUqIiIiIiJic5SoiIiIiIiIzVGiIiIiIiIiNkeJioiIiIiI2BwlKiIiIiIiYnOUqIiIiIiIiM1RoiIiIiIiIjZHiYqIiIiIiNgcJSoiIiIiImJzUiV3APKKunwLQh4mdxSvJxcHcHdJ7ihERERE/hUlKvJidJgKF4KTO4rXT/YMMK29EhURERF56SlRkRfj3A04fT25oxARERGRl5TWqIiIiIiIiM1RoiIiIiIiIjZHiYqIiIiIiNgcJSoiIiIiImJzlKiIiIiIiIjNUaIiIiIiIiI2R9sTi8jz+fxH+Gk9vPsm/NY7pvzmXfh5A6zYC8cDIeIh5MsEvnWgUXnLNv78C2Zshk0BcO4apHOD0nlgUFPI42NZd+o6mPUHnLgIt++BT1qoXBD6NoJsGRKO9dw1yN4u/uutq8HUL40/bw6AKn3irrdzKJTOm3BfIiIikiRsdkSlZs2amEym575/0aJFmEwmhg4dmqj6np6eFC5c+Ln7ex7PGuN/bejQoZhMJhYtWpTcoYit2Xsa/DeBo731tZ0nofccSOsK39aHwc3A2QEaj4a+8yzrDl8Ki3fC24VhbCtoUx22HIMS3SDgH8u6B/6G7F7wdT2Y2BY+rgirD0DJr+HSrYTj9UwNMztZv5pVNK6/U8z6nq/eta6fK2NiPyERERH5l557RGXRokU0aNAAgP79+9Onj/VvIE0mE2+99Ra7d+9+/ghF5L9R2c8YmfDvmHA9sxm+mgbNK8OGw9bXC2aBvyZA1lijHF/WhGr9jMTk63rg4miUd6kLc3zB3i6mbqNyUNgXhi2FWZ1jyn9sa91XvVLwv+7wy2b45sP4Y3ZxhI8rWZf7b4LUzlDnf9bXKuSH+mXjb1NEREReqCQZUfn++++5e/duUjQVbdmyZUnepogkgZmbIeA8DG4a9/XsXpZJCoDJBPXegrAI+PtqTHnZfJZJCkBuHyPZOR749FiipnzdvpfY6GNcvmVMOfuwVNwjQwB3Q+Hho2dvW0RERP61f52o5MyZk6CgIHr16pUU8URzcHDA1dU1Sdu0FREREQQHByd3GCLP7m4o9JgJvT4C7zTPdu+V28bP9G4J1zOb4eptSJ867us378K128b0s5bjjbK3n2Pa5rztEBkZM/3rSS0nQOpm4NjIWLOy9/Sz9yEiIiLP7V8nKnXq1CFnzpxMmzaNixcvPrX+unXrKFeuHG5ubtjZ2eHj40P79u0JDw+3qBffGpXFixeTN29e7O3t8fDwoGHDhuzYsQOTyUSbNm3i7HPIkCFkyZIFOzs70qdPT+fOneONb8OGDRQtWhRHR0dcXV2pUaMG//zzj1W9wMBAPvzwQ9KlS0eqVKlIly4dH374odVnELXOY+7cuXzxxRd4eXnh6OjIDz/88FwxTpkyhfz58+Pg4ICjoyP58+dnypQp/7rugAEDyJQpE3Z2dnh7e/P1119jNpvj/ZzkNTVgATg5GAvjn8Wtu8bC+woFIGPahOvO3gIXbxlTwOKSqTV4tTLWpuw4CeM+g+rFni2eqH4ypoGqTyQ59qngo9Iw9jNY9o2xsP/IP1DhW2OdjIiIiPwn/vWuXyaTiYEDB9K0aVO6du3KvHnz4q07ffp02rRpg7e3Ny1atCBdunTs2rWLiRMncuTIEbZs2ZJgX8uWLaNx48a4uLjQsmVLPDw8WL58Oc2bN4/3Hn9/f4KCgqhfvz4eHh4sXryYsWPH8sYbb9ClSxeLutevX6du3bpUrlyZunXrsn//flavXk358uU5evQoqVOnjq5XsmRJrl69Sq1atShRogQHDhzg119/Zffu3Rw+fJh06dJZtN2rVy8ePnxIo0aNcHd3p0iRIoSGhj5TjH5+fgwaNAgfHx/atTN2MFq0aBFt27bl8uXL9O3b97nqfv3114wcOZJs2bLx1Vdfcf/+fX766Sc8PDwS/PuQl1jEQ7hz37osLAJuPDHal9YVUqSAU5dg7EqY6wsOT0zXSkhkJDT73pieNf6zhOueCIT2U6FMXvi0ctx1Vn8LDyKMqWGz/oB7YYmPJcqpS7DvjJFwpXji9zVl8xmvKHXfgvploIgv9JwFa+LZEUxERESSVJKsUWnSpAnFixdn8eLFHD16NM46ISEh+Pr6kidPHs6cOcP48ePp168fa9asoWvXrmzduvWpu0t169YNgD/++IPJkyczfPhwDh48iJ1d/F+arl27xtGjR/nxxx8ZMmQIf/75J25ubkyePNmq7tWrV2nfvj0rV65k4MCBrFy5kq5duxIYGMi3334bXe/rr7/mypUr9O7dO7rub7/9Rq9evbh06RJff/21VdthYWEcPXqUcePGMXDgQN59991nivHKlSuMHDkSLy8vDh06xNixYxk7diyHDh0iQ4YMDB06lKtXrz5z3atXrzJ27FgyZcrEoUOHGDVqFBMnTmTnzp1cunQpwb8PsU3h4eE8ehSzruLYsWMEBQVFvw8MDOTqkj/As4Xla8dJmLfNuvz8DXbs2AGdpkHZvPBRGXbt2vXUPs6fP2+86fgTrDlAYL+6UDR7dJ0dO3ZYxP3ninXw7mBwd4ZF3dn1559x91GlMNQqQWDDElwa2wz6L4AJqwgODiYgIMCizSf7iH4/2/ilyJEiaRL3HLkywvtvYd4UQMChw3G3Gc/7Z/qs4NmeQ32oD/WhPtSH+vgXfdg6k/k55/dE7frl6+vL6NGj2bhxI9WqVaNatWr8/vvvRuOxdv2aNm0arVu3ZvDgwVYjIJcvX+att97i008/xd/fHzCmfq1duzZ6+tHZs2fJkSMHlSpVYvPmzRb3jxs3jk6dOvH5559HT22Kiq9FixZMnz7don6pUqU4duyYxWJ9T09P7t27x82bN3Fycoouv3//PunTpydz5sycOnUKgMyZMxMcHMyNGzewt49ZhBseHk769Onx8PCI/h/O0KFD6dWrF927d2fEiBFxfoaJiTHqGb/55hur7Yx79OjBiBEjmDBhAu3bt3+uur1792bQoEEWdevWrcuKFStYuHAh9evXJzGCg4Nxd3fnTq42pD59PVH3SBLKmwk29jfOGElIUIgxohBbV39j3Un39y3Ly+c3kpi3+8KSr6F4jljXehl9TmtvjLykdra8t/986Dcfhn0MPRLYlevOPajcB85fh62DoUCWpz5qtLI9jXUtO4cl/p7c7SFlCjgxPvH3fP0LjPwV7syyfk4RERFJckl24GPVqlWpWrUq69evZ8uWLVSsaLlA9ciRIwD07t2b3r17x9UE16/H/8X2+PHjAOTKlcvqWtGiReO9L2fOnFZladKkISQkxKrcy8vLIkkBcHZ2xsvLiytXrkSXXbt2jdy5c1skKQD29vZkzpyZM2ee+AIIFChQ4F/FGNVmsWLFrOpGlf3111/PXPf0aWOBcFxnyOTPn58VK1bEG7e8xNK4QrWi1mUZ01iXg5FAAHw4wvraxVvGYYpjWkLnWGtXflhtJCmd30s4SXkQDnWGGtOx1vd7tiQFIDTcmLKWWLtPwenLMKDxs/Xz9xVjdzBXx2e7T0RERJ5Lkp5MP3r0aN588026dOnC3r17La5FjYz4+vpSsmTJOO/Pli1bUoYDQMqUKZO8zeeR0A5mthKjSLyqFoalPazL20yErJ7Quz4UzhpTPn+bcdZKs4owumX87T56BI1GGYdELvvGWJsSl4ePjB3H0jzx/6M9fxkL3ZtWsCw/EWgcMvmGp3Vbc7YaP5vGs9vX9Tvg6W5ZdugsLN8LtYpbr2kRERGRFyJJE5UiRYrwwQcfsHDhQqv1JvnyGYtTXV1dadKkyTO3HXV/1AhAbIcOHXqOaK1dvXqV0NBQq6lfV69eJXPmzNFlXl5eXLhwgfDwcKupX4GBgXh5eSVJPLHlzp0bgIMHD9KoUSOLa4cPH7ao8yx1o0aojhw5YlU3ahRLhDc84/7S3/ln8PIwDl6MsucvaD4O0rka2wbPfmKTjLJ5IYe38eeu/rD8T+PAxVshxuL42KIOaQx5AFnaGDuBFcwCLg5w5DxM32isafFrYHlf/q+gUkHYPNCy/NEjmL8dSueBnN5xP2ujUeBkbyyoz+AOxy7AlHXgbA/DPknoUxIREZEklOS/Ghw9ejROTk707NnTorxJkyakTp2aH3/8Mc5F2nfv3uXmzZvxtpsjRw5y5szJ9u3bLRKTsLAwJk6cmCSxh4aGWuyGBdC3b19CQ0OpUaNGdFm1atW4e/cugwcPtqg7aNAg7t69S/Xq1ZMkntgaNGiAg4MD/v7+Fp/TzZs38ff3x8HBgYYNGz5z3YYNG2Jvb4+/v7/F2S4nT56MXmsk8kyOXYDwh3A9GFr9AJ+MtXxtORZT9+A54+eKvdb1PhkbU8/ZHlq/bZxl0n8BdJwGv+2FJuVh33fGIZGJsf6wcUbLkyMwsdUrBTfuwujl8OUUI7H5sDTsHQn5M8d/n4iIiCSpJB1RAWOheYsWLaySBw8PD3744QdatWpFvnz5eP/998mdOzdBQUGcOnWKzZs3M2PGjAQXbQ8fPpxGjRpRqVIlGjdujLu7O8uXLyciwpifHte5K8/Cy8uLH374gaNHj/Lmm2+yb98+Vq9ejY+PDwMHxvxmdsSIEaxZs4aBAweyf/9+ihcvzoEDB1i5ciU+Pj5Wi+aTgpeXF927d2fQoEEULVo0+nNatGgRV65coV+/fnh6ej5zXW9vbzp06MDo0aMpWrQoH330Effv32fevHn4+Phw9uzZJH8WsVFPjj4kxjnr3fNoUdV4JWWf9nbw/VO2No7NvCTu8hrF478W5at3jZeIiIgkqyRPVACGDRvG3LlzuX37tkX5xx9/TPbs2enbty8rV64kODgYFxcXMmbMSPPmzSlbtmyC7X700UfMmTMHPz8/pk2bhouLC++88w4tW7akdu3aVgvhn5WnpyezZ8+mS5curF+/nlSpUvH2228zZcoUizNFPD092b17N506deKPP/5g1apVuLu7U69ePcaNG2d1hkpSGThwIJkyZeL777+PTgSzZ8/O5MmTrQ67fJa6o0aNws3NjSlTpjB27FjSpUtH69at8fDwiHfjAxERERGRF+m5tye2JT/++CPt27dnzJgxCZ46Ly+etidOZondnlhERETExr1U29dERkZabSscFhbG+PHjSZEiBXXr1k2myEREREREJCm9kKlfL8r9+/fJlCkTtWrVIl++fNy4cYPffvuNf/75h08++YQcOXI8vREREREREbF5L1Wi4uDgQNmyZdm0aRNLly7FbDaTKVMmvv32W4vF7iIiIiIi8nJ7qRIVOzs7Vq9endxhiIiIiIjIC/ZSrVEREREREZHXgxIVERERERGxOS/V1C95iWRLDyntkzuK10/2DMkdgYiIiEiSUKIiL8aEz8EtdXJH8XpycUjuCERERET+NSUq8mJkTAuplaiIiIiIyPPRGhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5ihRERERERERm6NERUREREREbI4SFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGxOquQOQF5Rl29ByMPkjuLV5OIA7i7JHYWIiIjIC6VERV6MDlPhQnByR/HqyZ4BprVXoiIiIiKvPCUq8mKcuwGnryd3FCIiIiLyktIaFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGyOEhUREREREbE5SlRERERERMTmKFERERERERGbo3NURAS2HIXvlsGBs3A9GDxcoFg28GsA5fJb1o2MhCnrYNJaOH0FXBygRA6jbtl8MfU2B0CVPnH3t3MolM5r/PncNcjeLv7YWleDqV/Gf/3CDfh5A6zcB39dhpQpoNAb8G19qFY04ef+/Ef4aT28+yb81jvhuiIiIvKfUqIiInDqEqRIAe1qgLcHBIXArC1Q0Q9W9oKaJWLqdp8Bo1fAx5Xgy5pw+x5M/h0q+cH2IfBWbsu2v3oXSuayLMuVMebPnqlhZifrmNYcgNlb4J1iCce+bA8MXwr1SsGnVeDhI/hlM1TvDz+3h5Zvx33f3tPgvwkc7RNuX0RERJKFTSUqISEhDB8+nOXLl3P27Fnu3buHg4MDPj4+lClTho4dO/LWW28ld5gvhKenJzdu3EhU3YULF1K/fv0XHJG8Uir7QbYM4N8x7uutqxuv2L6sBTm+gO9/i0lUHj6CiWuhfhnL5KJBWaPu7C3WiUqF/FC/bPyxuTgaSc+T/DdBameo87+En61KITg/BdKnjilrVwOKdYE+8+JOVMxm+GoaNK8MGw4n3L6IiIgkC5tJVA4dOsS7777LxYsXKVSoEM2bN8fHx4e7d+9y+PBhli1bxuzZszl58iS5c+d+eoMvmcGDBxMcHBz9/ujRo/j7+1OuXDnq1atnUffNN9/8j6OT15KzgzHacfteTFnEQwgNBy8Py7oZ3I0RGad4RifuhhrXUqVMXN+Xb8GmAGhe6ekjHgXfsC5zsIPaJYyRn7uh4OZkeX3mZgg4D0u+VqIiIiJio2wiUbl79y61a9fm6tWrTJw4kXbtrOerh4SE0LNnT0wmU5L1GxERQWhoKKlTp3565ResTZs2Fu8XLVqEv78/BQoUoFu3bskUlbx2gu9D+EO4EWxMnwo4D70+irnu5AClchujHWXyQIUCRiIzcCGkcYE21a3bbDkBQh4Ya0cqFICRzeF/uazrxTZvu7EWplnF53+WK7eNZMv5iUTnbij0mGk8l3ea529fREREXiib2PVryJAhXLp0iU8++STOJAXA1dWV8ePHkytXzBec06dP06RJE7Jnz46Liwt2dnZkzpyZTp06ERERYXH/0KFDMZlMzJ07ly+++AIvLy8cHR354YcfAJg9ezaVK1cmQ4YM2Nvb4+zsTIkSJVi8eHGc8fz4449ky5YNOzs70qdPT9u2bZk7dy4mk4mhQ4da1L1//z4dOnQgS5Ys2NnZ4ezsTKlSpdi4ceMzfU7ZsmUjffr0PHr0yOrahAkTMJlMjBgxAjASnahYevbsScaMGbGzsyNjxoz07Nkzzvb3799P9erV8fDwIFWqVHh6etKsWTNu3779THHKS6zhd+DZAvJ/BaOWQ9t3jEXysc3qDHl94OOxkLUtFO0C+/821qfk8I6pZ58KPioNYz+DZd/AoKZw5B+o8C0c+DvhOGZvgYxpoGrh53uO05dhyW6j/5RPjOIMWGAkXL51nq9tERER+U/YxIjKihUrAPD19X2m+3bu3MmGDRuoWrUquXLlIjw8nA0bNjBu3DjOnj3L8uXLre7p1asXDx8+pFGjRri7u1OkSBEApk2bxp07d6hXrx5ZsmQhMDCQRYsW0bBhQxYvXmwx/WrcuHF07twZLy8v2rVrR6pUqVi8eDEbNmyw6i8sLIzSpUtz/Phx3nnnHVq2bMnt27eZP38+tWrV4rfffqN69Th+Cx2Hpk2bMnToUObOncvHH39scW369Ok4OTnxxRdfWJT/9NNPBAUF0bBhQ9zc3Pj1118ZNmwYt27dYvLkydH11q9fT926dXF2dqZhw4ZkzpyZQ4cOsWDBAvbt28ehQ4dwcHBIVJySzCIewp371mVhEcZISWxpXY0pW1GGfQJd68KFmzBjkzG68vCJxNjNCQpmgTJ54e3CxsjFsCVQbxhsHRyzVqRsPstdwOq+ZaxtKeILPWfBmnh2BDt1CfadMRKJFM/xu5T7YdDgO2Oq2bBPrNseuxLm+hrTw0RERMRm2cSIyrlz53BycopOGqJEREQQGBho8bp792709Xr16nHlyhXmzZvHoEGDGDFiBPv27aNatWr89ttvnDlzxqqvsLAwjh49yrhx4xg4cCDvvvsuAEuWLOHAgQNMmTIFPz8/Jk+ezN69e3F2dmbw4MHR94eHh9OvXz/c3Nw4cOAA48ePZ8yYMRw6dMhijUmUb7/9liNHjjB9+nRWrlzJgAEDGDduHEePHsXNzY0uXbok+nPq1KkT9vb2TJ061aL8xIkTHDhwgBo1auDm5mZx7eLFi+zcuZNJkyYxcuRIDh8+TK5cufjpp584efJkdL3WrVuTJk0aTp48yZQpU+jTpw+LFy9m/PjxnDx5ktGjRyc6TnmxwsPDLd7v2LHD4v2xqUuMUZHYrx0nYd426/LzNwgODiYgIMC4uVh2qF6MHfmcYF1f2PMXtBgf08fDR1CtH1dC7/JobCv4oDR8UZPTkz7GfOYKjPwVgMDAQM6fPx8dU3QfuTLC+2/BpgB2bN0W93PM3gLAkSJpLEYPjx07RlBQUPT7OPs4dBgaj4JjF2BRd3acO2HRR9Cn30HZvPBRGQB27dqFOfZnl5g+oj6reD7/J9/v2rXr2Z9DfagP9aE+1If6+A/6sHUms9lsfnq1FytlypR4eHhw8+ZNi/Lt27dTvnx5izJfX984vzSHhoYSFBREZGQk8+bNo3v37vz000989tlngDH1q1evXnTv3j16elR8goKCuH//PmazmQ8//JBjx44REhICwOrVq6lduzYff/wxM2fOtLivc+fOjB07liFDhkRPr8qZMycPHjxg9+7dVv20bduWNWvWcPv2basEY9GiRTRo0IDPP/+cKVOmRJfXrFmTDRs2cO7cOTJlygRAu3btmDx5MuvWraNatWoW99etW5dly5ZZtD1mzBi6dOmCn58fAwYMYNu2bVSoUIG2bdvy7bffWtSNjIwkT548lC1bNlFT1YKDg3F3d+dOrjakPn39qfXlGeXNBBv7g0/a+OsEhRgjErF19TfWY3R/37K8fP6EF6v3mgXDlsK9OcZ0qY1H4O2+8HsfqF7Msm5RX2O0ZduQhJ/h61+MhObOLGNXryflbm+sZzkxPuF24tJqgrF+ZnZnaFLB8lpU7Eu+huI5YsrL9zI+12ntjRGmuGISERGR/5xNTP1ycnIiNDTUqrxgwYLMmTMHgL1791olKOHh4XTu3JmlS5dy9epVnsy5nkx8AAoUKBBnDAcPHsTX15c9e/Zw/77ltJnYC/ijRiHy5cvHk+JqOzAwkPDwcLJkyRJnv2CMesTVXlzat2/P2rVrGTt2LCNGjCAyMpIlS5aQLVu26CQltrjaLVHC2Go2asRp//79AEyePNliOlhscX2WYqPSuFofdJjG1Vjz8bQDEJ8UGm5s5Xs31EhUrt42yh9FWteNeGQ9TSwuf18xkiNXR+tru08Z60sGNH62OME432X6Rvi+lXWSAnD+ceL8YRy/qLh4yzh0ckxL6Ky1KyIiIrbAJhKVbNmycfToUQ4fPmwx/cvDw4MmTZoAYGdnPZ+8WbNmLFq0iMqVK9O5c2e8vb2xt7dn9+7djB07Ns5F566urlZlQUFBVK1alQcPHvDxxx9TrFgx3N3dSZEiBcOGDePw4effvtRsNvPGG28wbNiweOtkzpw50e3VqVOHLFmyMG/ePEaMGMH8+fO5fv261a5hz6NJkybUqRP3lzRPT89/3b7YsGu3IYOHZdnte7B4F2RJH3Mtj4/xc952y0Mg95+Bk5csd/26fgc83S3bPHQWlu+FWsXjXn8yZ6vxs2k8u33dDzMSjvSpLc9NGfkrfLfM2Mmr03tx31u1MCztYV3eZiJk9YTe9aFw1rjvFRERkf+cTSQqderU4ejRo4wZM4bp06cn+r5Vq1ZRsGBBNm3aZFF+4sSJeO6I28KFCwkKCmLw4MH06tXL4lq/fv0s3ufJkyfePo4dO2ZVljFjRu7cuUPDhg1J+eTuQ8+pWbNmDBs2jDVr1jB16lTs7Ozo2DHug/ziijNqBCVnzpyAMXIFxhS8qMRQXjO1BkHmdMbWwxnc4fwNY3TiUhDMj7WO6s2cUL2osdA++L5xavzlIBi/yli83jlWktBolFFWNp/R5rELMGWdsV3wk4vcAR49gvnboXQeyOltfR2MNTNV+kDfhtDv8ajL0l3GdLLcGSF/Zpj1h+U91Ysa57684Wm8ntT5Z+N6vVLP8IGJiIjIi2YTi+l79eqFj48PM2fOZNKkSXHWiWspTYoUKazKb9++bbXY/GlSpUoVZx+zZ8/m1KlTFmXVqlXDw8OD5cuXc+XKlejyoKCg6GlqsX300UfcuXOH7t27x9n32bNnnylWMBbV29nZMWTIELZu3UrFihXx8vKKs+7atWstFs2Hhoby448/kiJFCpo2bQpAlSpVyJIlC4sXL+bQoUNWbYSHh3Pp0qVnjlNeIq3ehlshMOY3+GIKTFoLxbPD5gHWp8ov+8aYmnXyEnSZDmN/g3L5YNtgY61HlHql4MZdGL0cvpxiJCEfloa9I42E4knrDxtTy5rGMW0rIYfOGT//ugyfjLV+HQ98tvZERETEJtjEiIqbmxurVq3i3Xff5YsvvuDHH3+kUqVK0aMRJ0+eZP369aRIkYJs2bJF31e1alWWL19O5cqVqVq1KleuXGHRokXPfIDju+++i7u7O0OGDOHs2bNkyZKFgwcPsnr1at544w2LHRXs7e3x8/Oja9euFC9enAYNGpAqVarofq9fv06KWFNahg4dyh9//MGYMWPYunUrFSpUwN3dnX/++Yft27fj4ODwzFPLvL29qVSpEuvXrweI9+wZgEyZMlGmTBkaNWqEm5sbS5cu5fTp03z22WfR61dSpEjBjBkzqFOnDqVKlaJOnToULFiQe/fucebMGTZu3EiPHj3iPX9FXgKbByZ8vX0t45UYTg7g19B4JeSrd41XYtUoDuYlCdepXMi6Tr/GMaMrz+Nc3OuyREREJHnZRKICULRoUY4fP87w4cNZvnw5/v7+3L9/HwcHB3x8fKhXrx6dOnWiZMmS0ffMnDmTL774grVr17J9+3bSp09P48aNKVeuHI0bJ/6Li5eXF8uXL8fX15e5c+fy6NEjcufOzbx585g0aZJFogLQpUsX7O3tGTlyJBMnTsTd3Z2PPvqI4sWL88UXX+Dk5BRd18HBgZ07d9K3b18WLlwYfcBk2rRpKViwIC1btnyuz6t9+/asX78eb29vPvzww3jrtW7dmuDgYPz9/blx4wbp06fn66+/Zvjw4Rb1qlSpwu7du+nVqxebN29m6dKlODo64uXlxfvvv88HH3zwXHGKiIiIiDwPm9ie+FXRtWtXRo8ezcqVK6ldu/YL7WvNmjXUqlWLDh06MH689TauUdsTx94q+b+g7YlfsMRsTywiIiLyCrCJNSovm9DQUCIiIizKgoKCmDlzJq6urlSpUuWFxzB69GhSpkyJr6/vC+9LREREROS/ZjNTv14mR44c4b333qNWrVrkyJGDS5cusXTpUq5fv06fPn0spn4lpdu3b+Pv78+RI0dYv349derUIUeOHE+/UURERETkJaNE5TlkypSJQoUKsXLlSu7cuUPKlCnJmjUr3377LV999dUL6zcwMBBfX18cHByoUKEC06ZNe2F9iYiIiIgkJ61RkSSlNSovmNaoiIiIyGtCa1RERERERMTmKFERERERERGbozUq8mJkSw8p7ZM7ildP9gzJHYGIiIjIf0KJirwYEz4Ht9TJHcWrycUhuSMQEREReeGUqMiLkTEtpFaiIiIiIiLPR2tURERERETE5ihRERERERERm6NERUREREREbI4SFRERERERsTlKVERERERExOYoUREREREREZujREVERERERGyOEhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOamSOwB5RV2+BSEPkzsK2+LiAO4uyR2FiIiIyEtBiYq8GB2mwoXg5I7CdmTPANPaK1ERERERSSQlKvJinLsBp68ndxQiIiIi8pLSGhUREREREbE5SlRERERERMTmKFERERERERGbo0RFRERERERsjhIVERERERGxOUpURERERETE5ihRERERERERm6NzVEReZluOwnfL4MBZuB4MHi5QLBv4NYBy+a3r7zgBX/8C+/+G1M7QsCwMaQauTjF1NgdAlT5x97dzKJTOa/z5fhhM3wjL9sCRfyDkAeTKCG2qG6+UKROO/eZd+HkDrNgLxwMh4iHkywS+daBRecu6IaEwchnsPgV7TkNQCEzvAC2qJvqjEhERkZeLEhWRl9mpS5AiBbSrAd4exhf4WVugoh+s7AU1S8TUPXgW3u4H+TPB6JYQeNNIcv66DKv9rNv+6l0omcuyLFfGmD//fQU6/gRvF4YudSG1E6w9CF9OgV2nYMZXCce+8yT0ngO1S8C39SFVSli8ExqPhmOB0L9xTN0bd2HAAngjPRTNZiRTIiIi8kp75kRl0aJFNGjQwLKRVKlIkyYNb775Jn379qV06dJJFmBiBQQEMG7cOJo0aUKVKlWsrhUuXDjB+0+cOEHevHlfZIgiz66yH2TLAP4d477eurrxiu3LWpDjC/j+N8tEpddsSOMCmwcaoykA2Tzh84nw+0F4p5hlOxXyQ/2y8cfmnQaOjIGCb8SUta0BrSYYIy1+DSwTmycVzAJ/TYCsGWLFXhOq9YPhS+HreuDiaJRnTAOXpxl97j0NJb+Ov10RERF5JTz3iEqVKlWoXbs2APfv3+fw4cMsX76czZs3s3fvXgoWLJhkQSbGiRMnmDp1KtmzZ7dKVKIUL16cpk2bxnktY8YEvlCJvEycHcAzNdy+F1MWfB/WHTKmVUUlKQDNK4PvdFiw3TpRAbgbCk72xmjHk9KnNl5P+qCUkagcD0w4UcnuZV1mMkG9t2DjEfj7KhTOapQ72BlJioiIiLw2njtRKVasGN26dbMo69mzJ8OGDcPf35+RI0f+6+CSWvbs2a1ift3dvHmTdOnSJXcY8m8F34fwh3AjGH7ZDAHnoddHMdeP/AMPH8H/clreZ28HxbIba1ye1HKCse4kZQqoUABGNof/5bKu96Qrt42fcSUxiRF9v9vz3S8iIiKvhCTd9Stz5swAODg4RJeNGDGC3Llz4+zsjIODAxkyZKBatWqcP38+uk7hwoXx9PQkICCAcuXK4ezsjIuLCzVr1iQoKIhHjx7RoUMHMmTIgJ2dHTly5GD58uXR9w8dOjR6OlqvXr0wmUyYTKanTveKi5+fHyaTiS+++MKi/PTp06ROnZpMmTJx+/bt6H5NJhNz586lTZs2pE+fHjs7O7Jmzcr3338fZ/tTpkwhf/78ODg44OjoSP78+ZkyZYpVvRUrVvC///0PDw8P7OzsSJs2LSVLlmTVqlXRdWrWrInJZIqzH5PJRM2aNaPfBwQEYDKZaNOmDePGjSNnzpzY29vTrFmz6Dpz586lRIkSODs7Rz9H3759E/vRSXJq+B14toD8X8Go5dD2HWPqVZTLQcbPjHGMSmRMA5duxby3TwUflYaxn8Gyb2BQUyPRqfAtHPg74TjCI4wpZ9m9rNe3JMatu/DTeiMxypj22e8XERGRV8Zzj6jcv3+fwMBAAEJCQti9ezeDBw/Gzc2NVq1aAUaS0qNHDwoUKECnTp1wcnLi/PnzbN68mcDAQN54I2Zue1hYGFWrVuXNN9+ke/fu7N27l1WrVvHhhx+SJk0aDh8+TPPmzYmIiGD69Ok0bdqUf/75h3Tp0lG7dm1OnTqFv78/derUoWLFigD4+PhYxPzgwYPomGOzs7PDy8uYhjJw4EA2bdrElClTqFmzJu+//z6PHj3iww8/JDQ0lBUrVuDh4WFx/7fffsuDBw+iv/QvWrQIX19fQkND6dmzZ3Q9Pz8/Bg0ahI+PD+3atYuu27ZtWy5fvhydFPz555/Ur18fDw8PPvnkE7y9vbly5Qq7d+9mz5490VPunse6deuYM2cODRo0IHv27NHP0r9/f/r370+ePHlo27Ytrq6ubNy4kQEDBnD69Glmz5793H1KIkU8hDv3rcvCIoyRktjSuhqL6KMM+wS61oULN2HGJmN05eGjmOuh4cZPBzvrfh3tYq4DlM1nvKLUfQvql4EivtBzFqyJZ0cwgA4/wbELsLJ33NPFEhIZCc2+N6asjf/s2e4VERGRV4/5GS1cuNAMxPnKnDmzeffu3dF1y5UrZ3Z0dDSHhYUl2GahQoXMgLlz584W5eXKlTObTCZzzpw5zQ8ePIgunzJlihkw9+3b1yquIUOGWLV/5MiReGMGzFmyZLGof/nyZXP69OnN6dKlM1++fNncrl07M2Du2bOnRb0hQ4aYAXO6dOnM165diy6/du2aOV26dGYXFxdzcHBwdJsODg5mLy8v8/Xr16PrXr9+3ZwhQwazg4OD+cqVK2az2Wz+5ptvzIB59erVCX5uNWrUMMf3VwiYa9SoYfUZpEiRwrxz506LuqdPnzanSpXKXKVKFat2GjRoYDaZTOaDBw8mGEuUO3fumAHznVxtzGY+0CvqlbeD2XzxpvnAgQPmhw8fRn9eR48eNd+6dct4s+lI4ts7ezW6je3bt1v8Hezass0cWfArs/mj4dF93J3+u3HflqPmCxcumP/555/o+uH1hpjD039i0caTbW7fvt1sbjzKbLZvYDY/fGjeuXOn1XPc6zfH6GPgAqs+7ty5Yz5y5EiCfVz6sL9x/y+bzGazOc4+bt26ZTb/+ZfZzAfmm6MWPXMfT76Pt4/Hnuc51If6UB/qQ32oj5elD1tnMpvN5mdJbKJ2/apTpw5NmjQBIDQ0lMOHDzN9+nQcHBzYtGkTBQsWpFatWvz+++9MnDiR1q1bkyJF3DPNChcuzLFjx7hz5w6urq7R5d27d+e7775jwIAB+PnFbJ965coVMmbMSJMmTZgzZ45FXEOGDLEYxYCYXb/KlClDx47Wuye5u7tbjVKsWLGCevXqkStXLk6fPs1bb73Fzp07LeoMHTqUXr160bFjR8aNG2dxrWPHjkyYMAF/f38+/fRTxo0bR6dOnfjmm28YOnSoRd0ePXowYsQIJkyYQPv27Rk+fDjffPMNLVq0YPz48RafSWw1a9Zk7dq1xPVXaDKZqFGjBmvWrLH6DHbs2GFRN2pt0bx58yhXrpzFta1bt9K0aVP69euXqGlgwcHBuLu7cydXG1Kfvv7U+q+NvJlgY3/wSWA6U1AI7DtjWdbV31hE3v19y/Ly+cHRPv62es2CYUvh3hxwcoDtx6F8b5jfFRpa/h1TobdxJsq+7xJ+hq9/gZG/wp1ZlgvyAfw3QqsfjClnE9sm3E5c+s+HfvNh2MfQ48OE60bt+qVzVERERF5pzz31K1euXNGJSpQaNWpQu3ZtOnTowKZNmxg8eDD79++nbdu2dO3alaJFi1KjRg2+/PJLqwXcHh4eVl/I06Y1vtTlyZPHotzb2xuAoKCgZ4o5KrlJjDp16vDpp58yffp03N3dWbRoUbx1CxUqZFUWtT7mr7/+AuDMGeMLaLFixazqRpVF1e3YsSPz5s3D39+fOXPmkC9fPipXrkybNm3+9W5qOXLksCo7ceIEAI0bN7a6FuXKlSv/ql9JhDSuUK2odVnGNNblTxMaDmbz4127HKDQG8ZUrL1nLBOV8AjjfJUnk5e4/H3FSI5cHS3Ll+2B1j/Ch6Xgh8+fLU6AH1YbSUrn956epIiIiMhrI0kPfKxVqxZOTk7s27cPgBIlSnD27FkWLFjA2rVr+fPPP+nTpw9jxoxh48aNFl/a4xttAUgZzwnXzzgY9ExCQ0PZsmULAPfu3ePkyZNkypTphfUXm7OzMwcOHGDNmjUsW7aMXbt28eOPPzJx4kTGjRsXvb4lvoX04eHhcZZHtf2kqM9x+PDhZMmSJc77ChQo8KyPIf+Fa7chg4dl2e17sHgXZEkfc83dBaoVgVl/GIvs3R6fRD/zD2NnrwZlYu6/fgc83S3bPHQWlu+FWsUt18ZsOWoc0FixAMz2tbwWW8RDOHMF3J0tF8nP3wZfTYNmFY1DKEVEREQeS/KT6SMjI3nw4EH0e2dnZ1q0aEGLFi0A8Pf3p2XLlgwYMIAlS5YkWb/xfWl/Xq1ateLMmTN07tyZadOm8fHHH3Ps2DGrhfRgTKt60pEjRwDInTu3xc+DBw/SqFEji7qHDx+2qBOlZs2a0Tt3nThxgpIlSzJ48ODoRCUqlkuXLllsHBDVXmLlymXszuTl5ZXoESexEbUGQeZ0UCo3ZHCH8zeMM0wuBcH8LpZ1BzeFsr2g0rfQ5h3jZPpRy43zU2IfDNlolHF2Stl8RpvHLsCUdeBsbyzaj/LPNag7FEwYi+0XWk4ppEhWKJLN+PPFW8aOZJ9WiTm8cs9f0HwcpHM1TrefvcXy/rJ5IYd3zPsJq4wkLGqHshV7jWcA6FjbSMZERETklZGk2xPPmTOHsLCw6C++ce2wFbUjV9QWv0nF3d34DfDNmzf/dVszZsxg/vz51KhRgzFjxvD9999z+fLleL/Ez5kzh+vXY9ZjXL9+nblz5+Ls7MwHH3wAQIMGDXBwcMDf398ixps3b+Lv74+DgwMNGzYE4v7c8uTJQ+rUqbl79250Wd68eQGspqUNGDDgmZ73888/J1WqVAwaNMii/djPc//+/TjulGTX6m24FQJjfoMvpsCktVA8O2weYH2qfImcsL6fkYT4Tocpv8Nnb8Oi7pb16pX6f3v3HRbF1bYB/F46LF1AQBA01oD1VbGDJRA7NiJqYok1JrZobESNGNFYor7ms0XsFewRbIgSQX3t0SiWiAUrKkUBqef7Y2V13QWWll31/l3XXLBnnpnzzB432YeZMwM8fQEs2AN8swLYGg10awycmQvUdHoTF/dEdpey9ExgxErgy0WKy46TBed+5Z7s7mQJKbL5Le9uH3VFMX7ebuDHzcDSA7LXO07KXv+4GUhMVd4/ERERvdeKfUblwoULmDdPNvn21atXuHz5Mnbt2gVdXV35F+WWLVvCzMwMjRo1QsWKFZGYmIjQ0FBIJBJ89dVXpXMEr3l4eMDIyAgbNmyAVCqFlZUVHB0d5V/+ASAuLk6e87u6deuGypUr459//sF3330He3t7bN68GYDs7Mr+/fsREhKChQsXYvTo0QrbWlhYoG7duujRowcAWeHw7NkzBAYGwtxc9tC78uXLY/z48Zg5cybq1KmjEPvo0SNMnz4dtra2AICxY8ciJiYGXl5eqFy5MoQQCA8Px4MHD9C3b195v99++y3mzZuHiRMn4sqVK7C2tkZERESRi8Dq1avjp59+QkBAACpXrozOnTvD1dUVT548wd9//43o6GicPXtW5VwcKmNHAwteP6KdbFFX85pAdFDBMSM7yJbCeLkDQs2zoq52yrH9WxdtMvzt5erHEhER0Xuv2IVKZGQkIiMjAcguuzI1NUWDBg3w448/wsfHBwDQv39/7Ny5EyEhIXj58iVMTU1RtWpVzJ8/X/6AxtJiZmaGpUuXYsaMGZg1axays7Ph7u6uUKicP38e58+fV7l9hQoV4OLigu7duyM9PR27d++GldWbh+OtXbsWZ86cweTJk9G6dWvUrl1bvm7mzJk4cuQINm7ciOTkZDg4OGD+/PkYO1bx0pvAwEBUqFABCxcuxNKlSwEAlSpVwvLlyzFkyBB5nJ+fH548eYIDBw4gOTkZBgYGcHR0xIwZMzBlyhR5nI2NDUJCQjB27FisWrUKRkZG8PLywp49e+Q3HFDX5MmT4e7ujjlz5iAkJARpaWkwMzODs7Mzvv32W7i6uhZpf0REREREJVHk2xPTG3m3Jw4JCZGfIfnY8fbE+VDn9sREREREJFeqc1SIiIiIiIhKAwsVIiIiIiLSOixUiIiIiIhI67BQKYFJkyZBCMH5KUREREREpYyFChERERERaR0WKkREREREpHWK/RwVogK52gC6BprOQntUstN0BkRERETvFRYqVDaWDAbMzDWdhXaRGmo6AyIiIqL3BgsVKhsO1oA5CxUiIiIiKh7OUSEiIiIiIq3DQoWIiIiIiLQOCxUiIiIiItI6LFSIiIiIiEjrsFAhIiIiIiKtw0KFiIiIiIi0DgsVIiIiIiLSOixUiIiIiIhI67BQISIiIiIircNChYiIiIiItA4LFSIiIiIi0josVIiIiIiISOvoaToB+kA9fA68zNZ0FjJSQ8BCquksiIiIiKgIWKhQ2fh2JXAvRdNZAJXsgFUjWKgQERERvWdYqFDZuP0UuJmg6SyIiIiI6D3FOSpERERERKR1WKgQEREREZHWYaFCRERERERah4UKERERERFpHRYqRERERESkdVioEBERERGR1uHtiYmK4+FzYNE+4NQN4MxN4OUrIHIG4OWuHOv1I3Dsb+V2n7rA/qlvXh+9DLSaqhwHACeCgMbVFdtiYoEf1gHnbgHmJoBfU2BWH8DUuPD8k1OBn7cDO08B8c8AO3OgbR1gmh9Q0fZN3PQtwE/blLc31AdebS28HyIiIqJiYqFCVBzXHgBzdgJVHYBaLsCJawXHO5UDgvoqtjlaq44d2QFoWEWxrYqD4usLcUCb6UDNCsCCAbJiY95u4MZDIPzHgnPJzQU++wm4Eg988zlQzQG4+Qj4v/3AgfPA1f8CZu8UO0uHAqZGb17r8mQsERERlS2NFyqhoaHo2bMnAKBbt27Yvn27UsydO3fwySefICcnB+7u7rh06VKp59GyZUv8+eefOHLkCFq1aqUyJjc3F/b29nj58iUeP34MMzOzUs8jT7Vq1XDjxg20b98e+/btK7N+KB9ePwKudsCa71Sv/88nwLO1gLUZEBoD9CykULEwAfp6qtd3i5pAj6YFx0zeCFhJgaOBsrMpAOBqCwxeChy8AHjXzX/bk9eB0zeBJYOBEe3etFd3BAb+Bhy+CHRtrLhNjyaAjbl6+RMRERGVAq35s6i+vj4OHDiAtLQ0pXWLFy+GEAI6OmWX7uDBgwEAS5cuzTcmNDQUCQkJ+Oyzz8q0SImOjsaNGzdQvnx5REREICkpqcz6omIyM5YVKUWRnQO8TFcv9kW6LF6VlDTg0EVZ4ZNXpADAV16ysx7bogved8rrz1h5C8V2ByvZT2MD5W2EkG0nhFrpExEREZWU1hQqzZs3R2pqKn7//XeldSEhIWjUqBH09fXLrP/evXujXLlyCA8PR3q66i+TK1euBAB88803pdbvixcvkJmZqdC2ePFiGBkZITg4GBkZGfi///s/tff37NmzUsuNStH1h4DUHzDrA9gPBH7cBGRlq44dsAQw7wMYfSGbs3LmpuL6S3dkRUyDTxTbDfSBupWA83EF59KgCiA1An7cDBy5BNx/JptD88N62SVnbesob1N5OGDRV5Z/34XA4yR1j5yIiIioWLSmUKlbty5cXFywbt06hfb9+/fj3r17GDBggNI2GzduhJeXF+zs7GBgYAATExPUr19f5eVjMTExaNmyJaytraGvrw9LS0vUrl0bq1evBgDo6urC19cXL1++RHBwsNL2T58+RVRUFJydneHj4yNvl0gk+Pzzz7Fv3z64u7vD0NAQpqam6NChAxITExX28fnnn0MikeDu3bto164dLCwsYGFhgevXr8tj0tPTER4eDi8vL7Rv3x6VKlXChg0bVL5ntWrVgq2tLS5evIgWLVrA1NQUNjY28vX//PMPfH19YWNjAz09PVhZWaFTp064c+eOwn5u3rwJf39/VKpUCVKpFPr6+nBycsKoUaOQlZWlsm8qgk/sgSndgc1jgXUjAY+qwMxQ2Rf+txnoAd0bA4u+BnZPBGb2lhUlLQKA87fexD18/e8q7wzI2xysgAfPC87HxhzYOhZITgPaTAOcBssudXO0Ao78BOjpvom1MgW+bQcsHwaEjgcGtQG2RgMtprw5M0NERERUBjQ+R+VtX3zxBebOnYsbN26gatWqAGSXYpmbm6N///4YOXKkQvyqVauQnJwMX19fODs7Iz4+HqGhofDz88P27dvh6+sLALh//z7atZNdi+/n5wdXV1ckJCTg/PnziI6OlhdBI0eORHBwMNauXYsRI0Yo9PXbb78hMzMTvXr1Usr72rVr+OKLL9C5c2f06NEDUVFRCAsLw1dffYW9e/cqxXt6esLa2hojRoxAamoqLC0tFY7pxYsXGDRokPw9mT17Nk6fPo2GDRsq7evVq1do3bo1ateujbFjx+Lx48cAgKtXr6JZs2bIzs6Gr68vqlSpghs3biA0NBSNGzfGX3/9BVtb2d2dTpw4gYiICLRu3RpVqlRBZmYmIiIisHjxYsTFxWHPnj2Fjt17LStb9qX93baMLOBpimK7tSlQ1EsQVyn+W8KXXsCQpcDKQ8CYa2/u5tW0hmzJ07mRbG5I7THApA1v7hCW/voMnKGKM4xG+m/WF8TWAqhXSVaEuFWUTc7/ZZfsbE7I+Ddxozoqbte9CdCoKtBnoWzy/cRuhfdFREREVBxCw0JCQgQAMWbMGBEfHy/09PTEiBEjhBBCpKSkCGNjY+Hv7y+EEMLQ0FC4u7vLt01MTFTa361bt4Spqalo0KCBvG3FihUCgFi0aFGh+dSuXVvo6OiImzdvKrRXq1ZN6OnpiXv37im0AxASiUSEhYUptDds2FDo6OiI58+fy9t8fHwEANGmTZt8+//Pf/4jbG1tRU5OjhBCiHv37gldXV3Rt29fpVh3d3cBQAwYMEBpXePGjYWZmZmIjY1VaD948KDQ0dERgwcPlrelpKTI+3tb27ZthUQiUXovCpKcnCwAiOQqQ4RAV80v1b8V4v6zgpOOvKT+/uIeK28fEi1bF3lJ7fdJxMbLtgncVnhsr/lCGPQUIjtbsb+ov5Vje84Vwl7534OCfx4KYdJLiNAYxfY1R2T7DTtbeE72A4RoM63wOCIiIqJi0ppLvwCgQoUKaNKkifzSrRUrViA9PR3ffaf6zktvn4lITEzE/fv3oa+vj5o1a+Lq1avyddbWstvAhoeH4+nTpwXm0K9fP+Tm5mLx4sXytv/973+4fv06mjRpAicnJ6VtqlevLj9jk6dFixbIzc3FlStXlOKnTJmisu/Y2FicO3cOXbp0kd84wMnJCR4eHti7d6/SXJY8QUFBCq8TEhJw6tQptGjRAlKpFPHx8fKlZs2asLe3R1RUlDzezMxM3l96ejoePHiA+Ph4+Pj4QAiBo0ePquz3fRMTE6P6dR1X4NA0XFnkh5z9PwKHpgG1XfCyaRW82PG97PWhaUjYNAJ3M1/It09JScHly5fV6+O1kydPIicnB3CWXaL37MYdhUsE4+PjcffuXYU+EowFkJkNpGYAAC49vS9b+foSMIU+HibipYWhrI/Xrly5otBHyuLdEK8ygY4NFI+j8+szdtGxhR7HSysjiOdv3ot3+1B1HMV+r9gH+2Af7IN9sA/2USZ9aD1NV0pvn1ERQojVq1cLAGLXrl2idu3aomrVqvLYd8+onD9/Xnh5eQkTExMBQGGRSCQK/Xh7ewsAQldXV9SoUUMMGjRIxMS88xdlIcSLFy+EiYmJcHR0lLf16dNHABBr1qxRigcg2rZtq9Q+a9YsAUCEhobK2/LOqCQnJ6t8L4YNGyYAiA0bNohz587Jl4kTJwoAYsWKFQrx7u7uwszMTGk/4eHhSu/Hu4udnZ08PiMjQwwfPlzY29sLiUSiFDtnzhyV+aryXp5RUcUzQIh+i9WLLc4ZlUu3ZdvMCi08tvscIYy+ECLvrFfSSyH0eggxfq1iXEamEKb+QgxcUvD+hvyfEJJuQqS+Umx/nCjLacK6grfPzRXCtp8Q3j8VnjsRERFRMWnVHBUA6NOnD8aOHYvp06fj0qVLmDpV9ZO6ExMT0bp1a7x69Qp9+/ZF3bp1YWFhAR0dHcyePRt//fWXQvyBAwcQHR2N0NBQnDhxAuvXr0dwcDAmTJiAWbNmyeNMTU3h4+ODnTt3Yu/evfj888/xxx9/wNraGn379n03DQCyifj5ESpu52purvw8itzcXPmZpPz6Wb16tfw2ynkMDQ3z7bNt27YYOHCgyn1JpVL573369EFoaCi8vLwwevRo2Nvbw8DAAKdOncKiRYsUqnkqopQ02VySt+eTCCGbTA/Ink6fJyFZNnfkbRfjgD1ngHb13syNsZACbWsDG44BP/Z883DG9ceAl6+Ank3ebJ+WAdxNkE2gz3sOSjVHWQ7booH+rd/Ebj4u+1mvUsE5Ld0PJKQAn9cr0ltBREREVBRaV6jo6+ujc+fOWLt2LQwMDPDtt9+qjAsJCUFiYiJ+/vlnTJ48WWHd9OnTVW7TrFkzNGvWDADw+PFj1KtXDwsWLMDMmTMVntEyYsQI7Ny5E8uXL0dCQgKSk5MxYMCAAguSksp7Roufnx9atmyptH7z5s04ceIE4uLiUKlSJRV7eKN27dqQSCTIysqCv79/oX2HhYXBzc0NkZGRCu2xsbFFO4iPzcwQ2c+/78l+rj8GHH99yWGA7CGmOHcL8F8A+LcAqtjLJrrvPAVExwJDPgPqv3WL4S/my55h0rQGYGcBXLkHrDgEmBgAs79U7Pvn3kDTyYBnADDEW/Zk+vl7ZA96/Lz+m7j/3ZDd4niaHzD99Y0g+reWPcV+6DLZrYzdnGV5/n5Y9ntXjzfbuwwFvmgG1HKRTdQ/HgtsOS67DfJQ71J7K4mIiIjepXWFCgBMmDABBgYGqFKlisLtdt+mpydL/d0zFhs3blS43S8APHjwAOXLl1coNMqXLw8HBwc8evQIqampCg9wbNOmDVxdXREREYFHjx5BIpFg1KhRpXV4Kq1YsQI6OjqYO3cuKlasqLTe3t4e0dHRWLx4MX799dcC91WhQgU0bNgQf/75J8LCwtC+fXuF9bm5uYiPj5f3o6Ojo/Q+JiUlyZ8bQ/n4cbPi6+CIN7/nFSoutkCLT2XFyaMkQEcC1HQClg2VFRhv8/UANkYBC/YAKemArTnQrbGsyKjioBhb/xPg8HRgwjpgzGrAzAj4ug0QpPpsnIJyZsCZucDULcDeM8CyA7K2gW2AWX1kz2PJ06clEHMN2H4SeJUlO54ffIEpPQAT5bN5RERERKVFKwuVmjVrYsWKFQXGdOjQARYWFpg1axbi4uLg7OyMCxcuIDw8HBUrVlSYXLRw4UIEBwfLb79rYGCAqKgonDt3Dp6eniqfMu/v74+goCCcPXsWtWrVQp06Kh6CV0oeP36MqKgouLm5qSxSAMDX1xfm5ubYtm1boYUKAKxduxbNmzdH586d4ePjg3r16iEnJwe3bt1CZGQkfH195e9x69atsWfPHnh5eaF169Z49OgRQkNDVV6i9tE4Glh4jNhReEyl8sC2cer1ObKDbFFX85pAdFDBMV7uqvOsUE75tsmqrCy9h5sSERERFYVWFirqKF++PPbs2YMxY8Zg8+bNyMnJQdWqVbFlyxYsW7ZMoVBp164dLl68iKioKOzevRs6OjooX748xo4di8BA1V9IR44ciXnz5iErKyvfOSOl5b///S+ysrLQoUP+X1J1dXXh6emJvXv3Yu/evejUqVOB+6xRowYuXLiACRMm4MiRIzh48CD09fVhY2MDLy8vhQdorl+/HsOHD5fP47GxsUGvXr3QrFkzlc+NISIiIiIqaxKharY3UTGlpKTAwsICyVWGwPxmgqbTAapXkD1t3dFa05kQERERURFo1XNUiIiIiIiIABYqRERERESkhVioEBERERGR1mGhQkREREREWoeFChERERERaR0WKkREREREpHVYqBARERERkdZ5bx/4SFrO1QbQNdB0FkAlO01nQERERETFwEKFysaSwYCZuaazkJEaajoDIiIiIioiFipUNhysAXMtKVSIiIiI6L3DOSpERERERKR1WKgQEREREZHWYaFCRERERERah4UKERERERFpHRYqRERERESkdVioEBERERGR1mGhQkREREREWoeFChERERERaR0WKkREREREpHVYqBARERERkdZhoUJERERERFqHhQoREREREWkdPU0nQB+oh8+Bl9mazUFqCFhINZsDERERERULCxUqG9+uBO6laK7/SnbAqhEsVIiIiIjeUyxUqGzcfgrcTNB0FkRERET0nuIcFSIiIiIi0josVIiIiIiISOuwUCEiIiIiIq3DQoWIiIiIiLQOCxUiIiIiItI6LFSIiIiIiEjrsFAhKoqHz4GJ64FWUwGz3oCkG3D0cuHbJaUCdv1l8aExiuv6/1fWnt9y/9mb2IMXgK9/A9xHAbo9ANehxT+Wfx4BRl/I+jhzs+DYwf8ni+v4c/H7IyIiIioCrS1UatWqBVtbW02nUaZCQ0MhkUgQFBRUYFtBLl++DIlEgiFDhpRVmvS2aw+AOTtlxUMtF/W3m7oZSMtUvW6oN7B+lOKybiRgYgh86gxUKPcmdtOfssXCBHC0KtmxjAkG9HQLjztzE1gTCRgZlKw/IiIioiIolULl4sWL6NSpEypUqABDQ0NIpVJUqFABPj4+CAkJKY0uiu3GjRvo27cvXFxcYGRkBH19fdja2qJt27bYunWrRnNT1+XLlzFkyBBERkZqOpUPn9ePsjMc+fnPJ8CztcD134CxndTb5+U7wNIDwARf1eubVAf6eioulcoDaRlAnxaKsbP6ACkbgOggoI6rev2rcuA8cOACMKZjwXFCACNXAV95AeUtit8fERERURGV+Mn0hw4dQseOHaGrq4sOHTrAzc0NaWlpuHnzJk6cOIEtW7agZ8+epZFrka1evRrffPMNsrOz4enpiT59+sDExARxcXE4dOgQevXqhfT0dPTv318j+anStWtXvHjxAoaGhvK22NhYrFy5EpUqVUKrVq0U4j/99FO8ePECBgb8a/e/wsy46NuMCga6egAtPlV/m01RgEQC9G6p2O5oXfT+35WVLctpVEfgE/uCY9cfBS7fBXb8AET8VfK+iYiIiNRU4kIlICAAmZmZOHr0KDw9PZXW37p1q6RdFEtMTAyGDRsGqVSKsLAwNG7cWGF9bm4uFixYABMTE43klx9dXV2YmpqqHa+jo1OkePqXhcQAMdeAq4uB20/U2yYrG9gWAzStDrjalX5OC/8AEl8CAT2AHSfzj3uRDkxYD0zuDtiX8DIzIiIioiIq8aVf8fHxMDU1VVmkAEDlypXlvy9evBiNGzeGjY0N9PX1YWZmhqZNmyIqKkrt/s6dO4fPPvsMlpaW0NPTg62tLfr06YOkpCSFuB9++AGZmZlYtGiRUpECyL7gjxs3Dn5+fvK2zMxMjBo1Ck5OTtDX14epqSmaNWuG48ePK2z79ryQ4OBgVKlSBfr6+rC0tESfPn2Qmak8F2HFihWoVKkS9PX1Ua5cOXz99dcq496doxIUFCQ/IzV58mRIJBJIJBLUqlVLKZe3leWxkJrSM4Bxa2SXVxWl4DhwAXj2AujTstDQInuUCASGAIH+gHkhRfqMbYCxITBGzUvciIiIiEpRic+oVKhQAQ8ePMDSpUsxfPjwAmOXL18OCwsL+Pn5wcHBATdv3sT27dvh7e2NmJgY1K9fv8DtDx8+jM6dO8PExAR+fn5wcnLCxYsXsW3bNpw9exYXL16EoaEhXr58iVOnTqFcuXL48ssv1T4Wb29vHDt2DPXq1cOXX36JR48eYevWrWjbti3CwsLQunVrhfjIyEhs2bIFfn5+cHR0xL59+7Bp0yZYWVlhyZIl8rhly5bhm2++gY2NDYYOHQo9PT2EhISoNeekffv2uH79OtasWYNOnTqhZUvZl1dHR0eNHMsHJSsbSE5TbsvIAp6mKLZbmwI6RazrZ+8EsnJkZySKYtOfgL4e4NesaNupY8J6oHJ5YFDbguOuPwAW7QM2jwEM9Us/DyIiIqJClLhQmTZtGrp06YJvvvkGM2bMQN26ddGwYUO0b99e6UxGdHQ0LC0tFdqGDx+OFi1aIDAwEDt37iywr0GDBsHKygp//fUXypV7cyekZcuWYfjw4ViwYAEmTZqECxcuIDs7G1WqVFH7ODZt2oRjx47B09MTR44cgc7rL6X9+/dH69at8d133+Hvv/9W2Obu3bs4e/Ys3N3dAQDTp0+Hq6srNmzYIP9yn5WVhYCAAEilUpw7dw5OTk4AgClTpqBmzZqF5lWnTh106NABa9asQZMmTTBu3DiNHcsHJzpWdpvhd8VcA7YonnlC3LKinRW5/QSYuwv4bTBgWoR5LS/Tgd3/A3zqAuXM1N9OHSevAeuPARHTCy+6Rq2SXXrWvUnp5kBERESkphJf+tWhQwccPXoUPj4+SEtLw/79+xEYGIgmTZrAzc0NFy9elMfmFSm5ubl4+vQp4uPj4eTkBEdHR/z1V8ETdY8fP447d+6gU6dOSE9PR3x8vHxp3749DA0NcejQIQDA8+fPAaBIczfy7k42Y8YM+Rd7APD09ETjxo1x5coV3L17V2GbFi1ayL/YA7LLyRo1aoTk5GQkJiYCACIiIvDs2TN07txZXqQAgK2tLfz9/dXOryjK6ljeN1lZWQqvY2IUn19y6tUT4NA0+XJlkR9EbRfAuy5waBru/N4fL3Z8L1tvb4n4+HiF9y0tLV2pT3kfUzcDFcrhrFmGrGi5/QR4lAQAyH2cJHudm4srV64ovL/Pg/e/vtuX7MxZSkoKLl++rLqPfF6fPHkSOTk58tfyPn5YD7SoiYdGubgffV6Ww+szR3dOXgDuJsg2OHIJ2H8eGNVBnvvZ7eFAdg6QngncfoLTEcdU9/Hau+9VqR4H+2Af7IN9sA/2wT5KpQ+tJ0rZ5cuXxZw5c8Snn34qAAhnZ2eRlpYmhBAiIiJCNGrUSBgaGgoACoutra3Cftzd3YWNjY389aJFi5S2eXepXbu2EEKIP//8UwAQjRs3Vjvv//znP0IikYhXr14prRswYIAAIMLDw4UQQly6dEkAEAMHDlSKHTx4sAAgLl++LIQQ4tdffxUARGBgoFLs8uXLBQAxa9YseVtISIhabXnychk8eHCZH4s6kpOTBQCRXGWIEOiquaX6t0Lcf6Z23nKeAUL0W6xebEi0rK/IS6r3U1iOiS+Vt/t8hhCm/kKkKo+dkg4zhXAZol6uQshiC8rHoo8sbnVE4bn/ukf9fomIiIiKocSXfr3Lzc0Nbm5uGDduHNzd3XH16lUcPHgQ1apVQ/v27WFiYoKhQ4fCzc0NpqamkEgkmDBhAtLTlf86rYq/vz86dVI9uTfvAZF169aFnp4ebty4UWrHpYqubv4PyxNClGnfpe1DOhatMLO38jyXy3eBHzcDP/jKnp0iNVRcn5AMHP4L8G8ue9hjSWRly548b2ECOLy+pfGK4bKzNW87cgn4bxgwrx9Qo4KsrXUtYOcE5X0OWQq42AJTehTtYZdERERExVDqhUoeHR0d1KlTB1evXsWdO3cQExODjIwMrF+/Xum5KsOHD4e+fsETdt3c3ADIvlAXdsmUqakpPDw8EB0djY0bN6JPnz6F5luxYkWcPXsWp06dkk9Yz3P9+nUAsmeWFFWNGjUAAFevXlVad+nSJbX2IZFIitRnWR0LvTbz9UNM/74n+7n+GHD89fgGvP633VzF/CNLqexnwyqAr4fy+q3RssurCrrb11+3gT2nZb/ffCS7GUBePnVcgU4NZb/ffw7UHAn0awWs+U7W5l1XeX9JqbKfnm5Ag9dzuiraypZ3jQ4Gyluqzp2IiIiolJV4jsqmTZtU3sL2xYsXiI6OBgDUr19f/hf7d/86P2PGDCQnJxfaT6tWreDs7Izt27crzHvJk5mZiQcPHshfz5kzBwYGBhg1ahROnz6tcp8LFizAtm3bAAA9evQAIJtEnpubK4+Jjo7GiRMn8Omnn6JixYqF5vmuNm3awNraGnv27EF8fLy8/enTp9i8ebNa+7CwkD0R/NmzZ2rFl9Wx0Gs/bpYteRPugyPetJXExijAzgJoWzv/mHO33vR17b6s0Mh7vb2AZ6IQERERvWdKfEZl4sSJ+Oabb9CyZUu4u7tDKpXi7t272Lt3Lx4+fAhvb280b94cpqammD9/PoYNG4Y///wT1tbWiImJwYkTJ1C+fHmFyUKq6OjoYO3atejUqRM8PDzQqVMnuLm5ITU1Ff/88w+OHDmCCRMmYNKkSQCAZs2ayW8L3LRpU7Rq1QqNGjWCsbExbt++jcOHD+P27dtYt24dAKB3795YsWIFIiMj0bBhQ/j4+Mhv6auvr4///ve/xXp/9PX1MWPGDHz33XeoX78+vvjiC+jp6WHbtm0wNzdXq/jw8PCAkZERNmzYAKlUCisrKzg6Oio8A+ZtZXUsH4WjgYXHiB3F27eXe8Hbnphd+D76t5YthXG1Uy9PdfcHALeXqxdHREREVApKXKgEBQVh+/btOHfuHCIjI5GWlgZjY2NUrlwZI0aMwMSJEwHI5o1s2bIFU6ZMwcqVK6GjowN3d3ccOHAAw4YNw6NHjwrtq1WrVjh16hQmT56Mo0ePYufOnTAyMkL58uXRpUsXdO3aVSF+wIABaN68OaZPn44///wTUVFRyMnJgZWVFerUqYO5c+fKzz4AwMGDBzF+/HiEhoZi7ty5MDQ0RJ06dfDLL7+gefPmxX6PRowYAR0dHfzyyy9YtmwZzM3N4evrizZt2qh1WZqZmRmWLl2KGTNmYNasWcjOzoa7u3u+hUpZHgsRERER0b9BIjhTmkpRSkoKLCwskFxlCMxvJmgukeoVgCM/AY7WmsuBiIiIiIqtxHNUiIiIiIiIShsLFSIiIiIi0josVIiIiIiISOuwUCEiIiIiIq3DQoWIiIiIiLQOCxUiIiIiItI6LFSIiIiIiEjrlPiBj0QqudoAugaa67+Sneb6JiIiIqISY6FCZWPJYMDMXLM5SA012z8RERERFRsLFSobDtaAuYYLFSIiIiJ6b3GOChERERERaR0WKkREREREpHVYqBARERERkdZhoUJERERERFqHhQoREREREWkdFipERERERKR1WKgQEREREZHWYaFCRERERERah4UKERERERFpHRYqRERERESkdVioEBERERGR1mGhQkREREREWoeFChERERERaR0WKkREREREpHVYqBARERERkdZhoUJERERERFqHhQoREREREWkdFipERERERKR1WKgQEREREZHWYaFCRERERERaR0/TCdCHRQgBAEhJSdFwJkRERERUEDMzM0gkEk2nkS8WKlSqnj17BgBwdnbWcCZEREREVJDk5GSYm5trOo18sVChUmVtbQ0AuHv3LiwsLDScDRVXSkoKnJ2dce/ePa3+DxgVjOP4YeA4fhg4jh+GD20czczMNJ1CgVioUKnS0ZFNe7KwsPggPsAfO3Nzc47jB4Dj+GHgOH4YOI4fBo7jv4OT6YmIiIiISOuwUCEiIiIiIq3DQoVKlaGhIaZNmwZDQ0NNp0IlwHH8MHAcPwwcxw8Dx/HDwHH8d0lE3v1kiYiIiIiItATPqBARERERkdZhoUJERERERFqHhQoREREREWkdFipUKmJjY/HZZ59BKpXC3t4eP/zwAzIzMzWd1kdpzZo1kEgkSsvEiRMV4latWoVq1arByMgIderUwR9//KG0r+TkZHz99dewtraGmZkZevTogYcPHyrFxcTEoEmTJjA2NoaLiwvmzJkDTn8rmps3b2LYsGGoW7cu9PT04O7urjLu3x43IQRmz56NihUrwtjYGE2aNMHJkydL56A/QOqMo5eXl8rPaGxsrEIcx1EzQkJC0KVLFzg5OUEqlaJu3boIDg5Wek/5WdRu6owjP4vvAUFUQs+fPxcODg6iZcuWYv/+/WLVqlXCwsJCjBgxQtOpfZRWr14tAIj9+/eLEydOyJe7d+/KYzZv3iwkEokICAgQR44cEUOHDhV6enrixIkTCvvy8fERTk5OYuvWrWL37t3C3d1d1KlTR2RlZcljbty4IUxNTUXXrl3F4cOHxYIFC4SBgYGYO3fuv3bMH4Jdu3YJJycn0b17d1GrVi3h5uamFKOJcQsKChIGBgZiwYIF4vDhw6Jr167CzMxM/PPPP2XzRrzn1BlHT09P0axZM4XP54kTJ0R6erpCHMdRMxo3bix69eoltmzZIiIiIsTEiROFjo6OmD59ujyGn0Xtp8448rOo/VioUInNmjVLSKVS8ezZM3nb8uXLha6urrh//74GM/s45RUqCQkJ+cZUq1ZN+Pv7K7Q1adJEtGvXTv46JiZGABAHDhyQt8XGxgqJRCK2bt0qbxsyZIhwcXERGRkZ8rZJkyYJS0tL8erVq9I4pI9CTk6O/Pd+/fqp/IL7b49benq6MDc3F5MmTZLHZGRkCBcXFzF8+PASHO2HS51x9PT0FB06dChwPxxHzVH1387BgwcLc3Nz+fjys6j91BlHfha1Hy/9ohILDw9H27ZtYW1tLW/z8/NDbm4uDh48qMHMSJVbt27h+vXr8PPzU2jv1asXIiIikJGRAUA2rpaWlvjss8/kMdWrV0fdunURFhYmbwsPD4evry8MDAwU9pWUlIQTJ06U8dF8OHR0Cv7PsSbGLSYmBikpKQp9GhgYoFu3bgr7ojcKG0d1cRw1x8bGRqmtXr16SElJQWpqKj+L74nCxlFdHEfNYqFCJRYbG4saNWootFlaWsLBwUHpOk/697i5uUFXVxeVK1dGUFAQcnJyAEA+Ju+OWc2aNZGZmYm4uDh5XPXq1SGRSJTi8vaRmpqKe/fuKe2rRo0aKq/zpeLTxLgV1Ofdu3eRnp5eSkf38Tl27BikUimMjIzg6emJqKgohfUcR+1y/PhxVKhQAWZmZvwsvsfeHsc8/CxqNxYqVGKJiYmwtLRUareyssLz58///YQ+cg4ODvjpp5+wbt06hIeHo3379ggICMCoUaMAyMYLgNKYWVlZAYB8zNQZ16SkJJX7MjAwgImJCce/FGli3BITE2FoaAgjIyOlfQkh5DlR0Xh6emLRokXYv38/1q5di7S0NLRt21bhDCTHUXscP34cW7Zswbhx4wDws/i+enccAX4W3wd6mk6AiEqXj48PfHx85K+9vb1hbGyMX3/9FVOmTNFgZkQEAD/99JPC644dO8LNzQ2BgYG8/EPLxMfH44svvkCrVq0wcuRITadDxZTfOPKzqP14RoVKzMrKCsnJyUrtiYmJCvNWSHP8/PyQk5ODCxcuyP/q9+6Y5f0lJ2/M1BnXvL8evRuXmZmJtLQ0jn8p0sS4WVlZISMjA69evVLal0QikedEJSOVStGhQwecPXtW3sZx1LykpCS0a9cO5cqVw/bt2+Xzj/hZfL/kN46q8LOofVioUInVqFFD5T3HHz58qHQdJmle3pi8O2axsbEwMDBA5cqV5XHXrl1Tugf823OSpFIpnJ2dlfaVtx3Hv/RoYtzyfl67dk1pX3nPAKCywXHUrPT0dHTs2BHJyckIDw+HhYWFfB0/i++PgsZRXRxHzWKhQiXWrl07HD58WH6NJiB70JKOjg68vb01lxjJbdmyBbq6uqhXrx4qV66MatWqISQkRCFm69ataNOmjfyOJe3atUNiYiIiIiLkMdevX8f58+fRvn17eVu7du2we/duZGVlKezL0tISTZs2LeMj+3hoYtyaNm0Kc3NzhT6zsrKwY8cOhX1RyaSmpuKPP/5Aw4YN5W0cR83Jzs6Gn58frl69iv3796NChQoK6/lZfD8UNo6q8LOohTRxT2T6sOQ98NHT01McOHBABAcHC0tLSz7wUUO8vb3F7Nmzxb59+8S+ffvE0KFDhUQiEaNHj5bHbNq0SUgkEjF16lQRGRkphg0bJvT09ERMTIzCvnx8fISzs7PYtm2b2LNnj6hVq5bKh1xJpVLRvXt3ERERIRYuXMgHPhZDamqqCAkJESEhIcLLy0s4OzvLXz958kQIoZlxCwoKEoaGhmLhwoUiIiJCdO/enQ8nK0Bh4xgVFSU6deokgoODxZEjR8SGDRtEvXr1hIGBgTh16pTCvjiOmjF48GABQMyfP1/pQYB5z8PgZ1H7FTaO/Cy+H1ioUKm4cuWKaNOmjTA2NhZ2dnZi3LhxCg89on/PyJEjRdWqVYWxsbEwNDQUtWrVEosWLRK5ubkKcb///ruoUqWKMDAwELVq1RJ79+5V2ldSUpIYOHCgsLS0FKampqJbt24qH+IZHR0tPDw8hKGhoXBychJBQUFK/VHB4uLiBACVS2RkpDzu3x633NxcMWvWLOHk5CQMDQ2Fh4eH0pcxeqOwcbxx44bw8fER9vb2Ql9fX1haWor27dsrfTESguOoKS4uLvmOYVxcnDyOn0XtVtg48rP4fpAI8c5Fd0RERERERBrGOSpERERERKR1WKgQEREREZHWYaFCRERERERah4UKERERERFpHRYqRERERESkdVioEBERERGR1mGhQkREREREWoeFChERERERaR0WKkREpHWePHkCCwsLrFy5UqG9f//+cHV11UxSH4jp06dDIpHg9u3b/0p/a9asUeovPT0djo6O+Omnn/6VHIjo/cRChYiItE5AQABsbW0xYMAAteIfPXqEcePGwd3dHWZmZjA3N0fVqlXRq1cv7NixQyHWy8sLpqam+e4r74v8mTNnVK5PTEyEsbExJBIJ1q9fn+9+XF1dIZFI5IuBgQFcXV0xaNAg3Lt3T63j+lAZGxtj4sSJmDt3Lh4+fKjpdIhIS7FQISIirRIfH4/g4GB899130NPTKzT+zp07qFOnDn777Tc0btwYs2fPRlBQEDp27IjY2FisXr26VPPbuHEjMjIyUKlSJQQHBxcY6+TkhPXr12P9+vVYtGgRPDw8EBwcDA8PDzx9+rRU83rffP3115BIJFiwYIGmUyEiLVX4/wGIiIj+RcuXL4dEIoG/v79a8fPmzcOTJ0+wa9cudOnSRWn9o0ePSjW/VatWoVWrVujSpQtGjx6NW7duoXLlyipjLSws0LdvX/nr4cOHw87ODkuWLMHq1asxfvz4Us3tfSKVStGtWzesWbMGM2fOhKGhoaZTIiItwzMqRETvubw5ABEREZgxYwZcXFxgbGwMDw8PnDx5EgBw7NgxNG/eHFKpFA4ODggMDFS5rzNnzqBr166wsbGBoaEhqlevjp9//hnZ2dkKcf/73//Qv39/VKtWDSYmJjAzM0OzZs2wc+dOpX32798fEokEycnJ8i/qRkZGaNasGU6dOqUUHxISggYNGsDOzk6t479x4wYAoE2bNirX29vbq7UfdZw7dw4XLlxAv3790Lt3b+jp6RV6VuVdPj4+AICbN2/mGxMeHg6JRILFixerXN+kSRPY2toiKysLQNHGQ5W8MVJFIpGgf//+Su1bt25F8+bNYWZmBhMTE3h4eCA0NFSt/vK0a9cOT58+RWRkZJG2I6KPAwsVIqIPxMSJE7Fr1y6MGjUK06ZNw61bt+Dt7Y1du3ahW7duaNGiBebNm4caNWpg6tSp2LBhg8L2+/btQ7NmzXD9+nV8//33WLx4MZo0aYKpU6cqnd3YuXMnYmNj4efnh0WLFmHKlCl4/vw5unXrhk2bNqnMz8fHB/Hx8Zg6dSomTZqEy5cvo0OHDnjx4oU85vHjx7h27RoaNWqk9nF/8sknAICVK1dCCKH2dk+fPlW5pKWl5bvNqlWrYGpqiu7du8PGxgYdO3bE2rVrkZubq3a/eYWVjY1NvjHe3t6wt7fHunXrVG5/8uRJ9O7dG/r6+gCKNx4lERAQgF69esHMzAyBgYGYPXs2TExM0LNnT/z2229q76dJkyYAgKNHj5Z6jkT0ARBERPReW716tQAg6tWrJzIyMuTtu3fvFgCEnp6eOH36tLw9IyND2Nvbi8aNG8vb0tPTRfny5UWLFi1EVlaWwv4XLFggAIjIyEh528uXL5XySE1NFdWqVRM1a9ZUaO/Xr58AIIYPH67Qvm3bNgFALFu2TN525MgRAUAsWrRI5bH269dPuLi4KLT9888/wtzcXAAQzs7Oonfv3uLXX38VZ86cUbkPT09PAaDQ5e33LO89srS0FP369ZO37dq1SwAQYWFhSv24uLiIGjVqiISEBJGQkCBu3bolgoODhYWFhdDT0xOXLl1SmV+ecePGCQDi77//VmgPCAgQAMTZs2flbUUZj2nTpgkAIi4uTt6WN0aqAFA45rNnzwoAYtKkSUqxXbp0EWZmZiIlJUXelvfv8+3+3qanpyc6duyoch0Rfdx4RoWI6AMxfPhwGBgYyF+3aNECAODh4YEGDRrI2w0MDNCoUSP5X/YB4NChQ3j8+DEGDBiApKQkhTMM7du3BwAcPHhQHi+VSuW/p6Wl4dmzZ0hLS0Pr1q1x9epVpKSkKOU3ZswYhdetW7cGAIU8EhISAADW1tZqH3flypVx8eJFjBgxAgCwadMmjBkzBg0aNEDt2rVx9uxZpW2MjIxw6NAhlcuXX36psp8dO3YgKSkJ/fr1k7e1b98etra2+V7+FRsbC1tbW9ja2qJy5coYOHAgbGxssHv3bri7uxd4XHn9vH1WRQiBDRs2wN3dHfXr15e3F2c8imvjxo2QSCTo16+f0tmozp0748WLFzhx4oTa+7O2tsaTJ09KLT8i+nBwMj0R0Qfi3QndVlZWAIBKlSopxVpZWeHZs2fy11evXgUADBw4MN/9P378WP77kydPEBAQgN27d6v8kpmUlARzc/MC8ytXrhwAKOSRN09CFOESLkB2K+AlS5ZgyZIlePjwIY4fP47169dj79696NixI/7++2+F4kdXVxdt27ZVua/jx4+rbF+1ahVsbW3h5OSkML/E29sbISEhePr0qdLlXK6urvJnwRgYGMDR0RFVqlRR65jyipGNGzdi1qxZ0NHRQVRUFG7fvo1ffvlFIbY441FcV69ehRACNWrUyDfm7X8rhRFC5Ds/hog+bixUiIg+ELq6ukVqf1teYTB37lzUrVtXZYyjo6M81tvbG1evXsWoUaPQoEEDWFhYQFdXF6tXr8amTZtUztnIL4+3ixJbW1sAwPPnzwvNOT8ODg7o2bMnevbsiT59+mDTpk0ICwtTuPtWUcXFxSEyMhJCCFSrVk1lzIYNGzB69GiFNqlUmm9BpI6vvvoKo0ePxpEjR9C2bVusW7cOurq6CsdS3PF4W36Fwrs3UcjrTyKRIDw8PN8xdXNzU/sYExMT5eNORPQ2FipERISqVasCUO+L9V9//YWLFy9i6tSpSk8W//3330uUR94X3LcvByuJxo0bY9OmTbh//36J9rN69WoIIbBy5UpYWloqrQ8ICEBwcLBSoVJSvXv3xvjx47Fu3To0a9YMoaGh+Oyzz+Dg4CCPKY3xyDvb9Pz5c4UzT7du3VKKrVq1Kvbv34+KFSuiZs2axTksudu3byM7O7vQy+CI6OPEOSpERAQfHx/Y2dlh9uzZKs9mpKeny+/OlfdX9Hcvz7p8+bLat8PNj62tLdzc3OS3VVbH0aNHkZ6ertSem5uLvXv3AgA+/fTTYueUm5uLNWvWoFatWhg0aBB69OihtPj7++PSpUs4ffp0sftRxdbWFu3atcOOHTuwceNGpKSkKMyRAUpnPPLOEh0+fFihff78+UqxeXN4Jk+ejJycHKX1RbnsK2+cPT091d6GiD4ePKNCRESQSqVYt24dfH19Ub16dQwcOBBVqlRBUlISYmNjsWPHDuzcuRNeXl6oWbMm3Nzc8MsvvyAtLQ3Vq1fH9evXsXz5ctSqVUvl5PWi6NmzJwIDA/Hw4UOFMwf5mTdvHqKjo9GpUyfUr18fFhYWePToEbZv346zZ8+iVatW6NChQ7HzOXjwIO7du4evv/4635ju3btj+vTpWLVqFRo2bFjsvlTp168f9uzZg++//x4WFhbw9fVVWF8a4+Hv74/JkydjyJAhiI2NhbW1Nfbv34+nT58qxTZs2BDTp0/H9OnTUbduXfTs2ROOjo54+PAhzp49i7CwMGRmZqp1bGFhYbCxsUGrVq3UiieijwsLFSIiAiA7q3L69GnMnj0bGzZsQEJCAqysrPDJJ59g7NixqF27NgDZX/D37duHcePGYe3atUhNTYW7uzvWrl2LixcvlrhQGTx4MGbOnIlNmzbh+++/LzQ+ICAAISEhiIqKwoEDB/D8+XNIpVLUrFkT8+fPx4gRI6CjU/wLCFatWgUA6NatW74x7u7uqFatGrZs2YJff/0VxsbGxe7vXR07doS1tTWeP3+OQYMGwcjISGF9aYyHubk5wsLCMHbsWMyaNQumpqbo1q0bNmzYIL8pw9umTZuGBg0aYPHixVi4cCFSU1NhZ2cHd3f3fB9S+a7U1FTs2LEDw4cP51PpiUgliSjqrVWIiIjK2LBhw3Dw4EFcu3ZN/lBDQPYE9aNHj+L27duaS46KZM2aNRgwYADi4uLg6uoqb897MOWNGzfUOnNGRB8fzlEhIiKtM2PGDDx79gyrV6/WdCpUBtLT0zF79myMHz+eRQoR5YuXfhERkdaxs7NDcnKyptOgMmJsbIyHDx9qOg0i0nI8o0JERERERFqHc1SIiIiIiEjr8IwKERERERFpHRYqRERERESkdVioEBERERGR1mGhQkREREREWoeFChERERERaR0WKkREREREpHVYqBARERERkdZhoUJERERERFrn/wHUDhBz22Z3qQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion**\n",
        "\n",
        "This mean SHAP value plot tells us which features are the most important by find the mean values across all observations instead of positive and negative offsets. You can see from the above plot that Overall Quality, Above grade (ground) living area square feet, and Total square feet of basement area made the most significant impact on the model’s predictions."
      ],
      "metadata": {
        "id": "IJaRTohdbojk"
      },
      "id": "IJaRTohdbojk"
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_values, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TFrp6L5Oajkf",
        "outputId": "6f1c08e5-0ac3-400f-c54b-55909d5bad69"
      },
      "id": "TFrp6L5Oajkf",
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion**\n",
        "\n",
        "This summary plot visualises all of the SHAP values. On the y-axis, the values are grouped by feature and higher feature values are redder. This plot highlights important relationships: for example, for the Overall Quality and Above grade (ground) living area square feet, as the feature value increases the SHAP values increase. But for the Basement Exposure, which refers to walkout or garden level walls, has the opposite relationship.\n",
        "From these Beeswarm plots, we can also see where the high density SHAP values are because the points are vertically stacked."
      ],
      "metadata": {
        "id": "fC9O1U9qbtIj"
      },
      "id": "fC9O1U9qbtIj"
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_interaction, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vqDFIMa_UAVB",
        "outputId": "9ca925b9-c0a2-4e05-aa7f-a6f770220fe2"
      },
      "id": "vqDFIMa_UAVB",
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1150x660 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion**\n",
        "\n",
        "This summary plot gives additional insight through visualizing the relationship between features and their SHAP interaction values. As we can see, certain features tend to have a more significiant impact on the prediction, and the distributions of the plots tell us which interactions are more significant than others. For example, Overall Quality, Above Ground Living Area, Total Basement Square Foot, and Neighborhood."
      ],
      "metadata": {
        "id": "vTmWVA3BbrGc"
      },
      "id": "vTmWVA3BbrGc"
    },
    {
      "cell_type": "code",
      "source": [
        "# OverallQual-GrLivArea depenence plot\n",
        "shap.dependence_plot(\n",
        "    (\"GrLivArea\", \"OverallQual\"),\n",
        "    shap_interaction, X_train,\n",
        "    display_features = X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WIGMO0jjw7iT",
        "outputId": "8129b6dc-f64a-4430-e73c-43ab1df6f03a"
      },
      "id": "WIGMO0jjw7iT",
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 750x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion**\n",
        "\n",
        "Given a relatively high overall quality, bigger above ground living area increase the SHAP interaction value. However, when the overall quality of the home is low or medium, change in SHAP interaction value is not as pronounced."
      ],
      "metadata": {
        "id": "L_66Y-b_zE1Y"
      },
      "id": "L_66Y-b_zE1Y"
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Tuning XGBoostWIthOptuna"
      ],
      "metadata": {
        "id": "7tJx-XGEnUKy"
      },
      "id": "7tJx-XGEnUKy"
    },
    {
      "cell_type": "code",
      "source": [
        "def objective(trial):\n",
        "    param = {\n",
        "        'max_depth': trial.suggest_int('max_depth', 1, 10),\n",
        "        'learning_rate': trial.suggest_float('learning_rate', 0.01, 1.0),\n",
        "        'n_estimators': trial.suggest_int('n_estimators', 50, 1000),\n",
        "        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),\n",
        "        'gamma': trial.suggest_float('gamma', 0.01, 1.0),\n",
        "        'subsample': trial.suggest_float('subsample', 0.01, 1.0),\n",
        "        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.01, 1.0),\n",
        "        'reg_alpha': trial.suggest_float('reg_alpha', 0.01, 1.0),\n",
        "        'reg_lambda': trial.suggest_float('reg_lambda', 0.01, 1.0),\n",
        "        'random_state': trial.suggest_int('random_state', 1, 1000)\n",
        "    }\n",
        "    model = xgb.XGBRegressor(**param)\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    return mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "IxteJgb5na3W"
      },
      "id": "IxteJgb5na3W",
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the study\n",
        "study = optuna.create_study(direction='minimize', study_name='regression')\n",
        "study.optimize(objective, n_trials=100)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_tPseNvn5x1",
        "outputId": "22369445-a65c-435b-9cba-15273929b2cf"
      },
      "id": "J_tPseNvn5x1",
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:50:25,430] A new study created in memory with name: regression\n",
            "[I 2023-07-23 03:50:25,525] Trial 0 finished with value: 13358775628.102783 and parameters: {'max_depth': 8, 'learning_rate': 0.7967697991002506, 'n_estimators': 76, 'min_child_weight': 6, 'gamma': 0.729547355260913, 'subsample': 0.1921452589342656, 'colsample_bytree': 0.4922451273737446, 'reg_alpha': 0.18890583791277304, 'reg_lambda': 0.6744945519044583, 'random_state': 210}. Best is trial 0 with value: 13358775628.102783.\n",
            "[I 2023-07-23 03:50:25,730] Trial 1 finished with value: 1431332444.0111012 and parameters: {'max_depth': 1, 'learning_rate': 0.752928204647888, 'n_estimators': 581, 'min_child_weight': 10, 'gamma': 0.6755342827153574, 'subsample': 0.7126149213544195, 'colsample_bytree': 0.10003821342444509, 'reg_alpha': 0.839033660752902, 'reg_lambda': 0.749196748998681, 'random_state': 573}. Best is trial 1 with value: 1431332444.0111012.\n",
            "[I 2023-07-23 03:50:26,579] Trial 2 finished with value: 1173600769.2003253 and parameters: {'max_depth': 8, 'learning_rate': 0.1120472937924404, 'n_estimators': 892, 'min_child_weight': 7, 'gamma': 0.7331961141108392, 'subsample': 0.3001645164232275, 'colsample_bytree': 0.3796290634565319, 'reg_alpha': 0.5030814552481127, 'reg_lambda': 0.8967047555746749, 'random_state': 191}. Best is trial 2 with value: 1173600769.2003253.\n",
            "[I 2023-07-23 03:50:27,584] Trial 3 finished with value: 5083715790.81351 and parameters: {'max_depth': 6, 'learning_rate': 0.8870515311158333, 'n_estimators': 780, 'min_child_weight': 3, 'gamma': 0.23575821943996272, 'subsample': 0.5108016790427551, 'colsample_bytree': 0.7294777069879226, 'reg_alpha': 0.5345817815000341, 'reg_lambda': 0.04143768875673282, 'random_state': 717}. Best is trial 2 with value: 1173600769.2003253.\n",
            "[I 2023-07-23 03:50:27,764] Trial 4 finished with value: 2391933106975.5684 and parameters: {'max_depth': 8, 'learning_rate': 0.9445356199555149, 'n_estimators': 372, 'min_child_weight': 5, 'gamma': 0.37870415928863604, 'subsample': 0.07553471369725709, 'colsample_bytree': 0.06781057245384632, 'reg_alpha': 0.8605984091482731, 'reg_lambda': 0.6643262105910627, 'random_state': 623}. Best is trial 2 with value: 1173600769.2003253.\n",
            "[I 2023-07-23 03:50:28,343] Trial 5 finished with value: 1880490058.277797 and parameters: {'max_depth': 8, 'learning_rate': 0.5775881911353088, 'n_estimators': 395, 'min_child_weight': 7, 'gamma': 0.4248015557184492, 'subsample': 0.3921411621135158, 'colsample_bytree': 0.8035745031935722, 'reg_alpha': 0.6726836344059888, 'reg_lambda': 0.4274056197298031, 'random_state': 200}. Best is trial 2 with value: 1173600769.2003253.\n",
            "[I 2023-07-23 03:50:28,817] Trial 6 finished with value: 2124584220.395222 and parameters: {'max_depth': 7, 'learning_rate': 0.5260519710920132, 'n_estimators': 828, 'min_child_weight': 7, 'gamma': 0.7779867401210132, 'subsample': 0.2298332040727324, 'colsample_bytree': 0.037870129583385786, 'reg_alpha': 0.1747916635363485, 'reg_lambda': 0.8294986465543339, 'random_state': 505}. Best is trial 2 with value: 1173600769.2003253.\n",
            "[I 2023-07-23 03:50:28,885] Trial 7 finished with value: 1131401701.4092727 and parameters: {'max_depth': 3, 'learning_rate': 0.2006893172171373, 'n_estimators': 101, 'min_child_weight': 8, 'gamma': 0.7651026557915684, 'subsample': 0.8206816335017874, 'colsample_bytree': 0.4358142986301366, 'reg_alpha': 0.2844236008634488, 'reg_lambda': 0.3292144650070812, 'random_state': 761}. Best is trial 7 with value: 1131401701.4092727.\n",
            "[I 2023-07-23 03:50:29,304] Trial 8 finished with value: 1075198937.832628 and parameters: {'max_depth': 9, 'learning_rate': 0.03076528081351939, 'n_estimators': 420, 'min_child_weight': 2, 'gamma': 0.06641851002469942, 'subsample': 0.4206969151159552, 'colsample_bytree': 0.21569340103145157, 'reg_alpha': 0.3350534837266884, 'reg_lambda': 0.22237511768572263, 'random_state': 332}. Best is trial 8 with value: 1075198937.832628.\n",
            "[I 2023-07-23 03:50:29,925] Trial 9 finished with value: 3380586033.6119704 and parameters: {'max_depth': 4, 'learning_rate': 0.8871737220863664, 'n_estimators': 856, 'min_child_weight': 7, 'gamma': 0.11850947042562304, 'subsample': 0.418152864298001, 'colsample_bytree': 0.5258620839136356, 'reg_alpha': 0.48814542024471125, 'reg_lambda': 0.967336797990327, 'random_state': 361}. Best is trial 8 with value: 1075198937.832628.\n",
            "[I 2023-07-23 03:50:30,730] Trial 10 finished with value: 1080014577.335862 and parameters: {'max_depth': 10, 'learning_rate': 0.0117036664855701, 'n_estimators': 605, 'min_child_weight': 1, 'gamma': 0.07448899005330267, 'subsample': 0.594724426986589, 'colsample_bytree': 0.26919779232428587, 'reg_alpha': 0.014901372860685436, 'reg_lambda': 0.22059663502016508, 'random_state': 915}. Best is trial 8 with value: 1075198937.832628.\n",
            "[I 2023-07-23 03:50:31,517] Trial 11 finished with value: 1050446468.4021653 and parameters: {'max_depth': 10, 'learning_rate': 0.010975488117546969, 'n_estimators': 588, 'min_child_weight': 1, 'gamma': 0.014895146571431872, 'subsample': 0.5766755078928658, 'colsample_bytree': 0.23431159989907344, 'reg_alpha': 0.016779847508752038, 'reg_lambda': 0.20476235869174775, 'random_state': 989}. Best is trial 11 with value: 1050446468.4021653.\n",
            "[I 2023-07-23 03:50:32,064] Trial 12 finished with value: 947512599.4291106 and parameters: {'max_depth': 10, 'learning_rate': 0.26580915858097814, 'n_estimators': 361, 'min_child_weight': 1, 'gamma': 0.02364471476265273, 'subsample': 0.9926245831684262, 'colsample_bytree': 0.2351620538258153, 'reg_alpha': 0.014534293078725568, 'reg_lambda': 0.15399571012885543, 'random_state': 10}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:32,379] Trial 13 finished with value: 1146195803.8526082 and parameters: {'max_depth': 10, 'learning_rate': 0.286390561975062, 'n_estimators': 229, 'min_child_weight': 4, 'gamma': 0.01818036105522899, 'subsample': 0.9874703635022171, 'colsample_bytree': 0.2506150567813622, 'reg_alpha': 0.010399107016737116, 'reg_lambda': 0.1488883114274443, 'random_state': 31}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:34,258] Trial 14 finished with value: 1968169465.467209 and parameters: {'max_depth': 10, 'learning_rate': 0.30643453148766026, 'n_estimators': 677, 'min_child_weight': 1, 'gamma': 0.2370552192832142, 'subsample': 0.9975682341934219, 'colsample_bytree': 0.9890487692592766, 'reg_alpha': 0.09998108486713554, 'reg_lambda': 0.010061474235377482, 'random_state': 981}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:34,478] Trial 15 finished with value: 1348365740.2980075 and parameters: {'max_depth': 5, 'learning_rate': 0.366546523368411, 'n_estimators': 275, 'min_child_weight': 3, 'gamma': 0.9842213137985811, 'subsample': 0.7911338810965404, 'colsample_bytree': 0.17812096094639873, 'reg_alpha': 0.12773464647987995, 'reg_lambda': 0.40530414770276185, 'random_state': 833}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:37,575] Trial 16 finished with value: 1050024715.911841 and parameters: {'max_depth': 6, 'learning_rate': 0.18238115573336816, 'n_estimators': 1000, 'min_child_weight': 2, 'gamma': 0.21411674908277722, 'subsample': 0.6327636832269649, 'colsample_bytree': 0.3035543550744624, 'reg_alpha': 0.27409995644484825, 'reg_lambda': 0.5092041914402162, 'random_state': 23}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:37,969] Trial 17 finished with value: 1095103975.7038558 and parameters: {'max_depth': 2, 'learning_rate': 0.4085287592589871, 'n_estimators': 739, 'min_child_weight': 3, 'gamma': 0.20884537847683043, 'subsample': 0.8884065734387456, 'colsample_bytree': 0.33776638953790317, 'reg_alpha': 0.2910507228350392, 'reg_lambda': 0.5194027355770358, 'random_state': 2}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:38,904] Trial 18 finished with value: 979096047.2173654 and parameters: {'max_depth': 6, 'learning_rate': 0.17742032542316297, 'n_estimators': 968, 'min_child_weight': 2, 'gamma': 0.17576888871263258, 'subsample': 0.6985816108849419, 'colsample_bytree': 0.3397953667467378, 'reg_alpha': 0.22217777885513967, 'reg_lambda': 0.5236074549985792, 'random_state': 113}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:39,220] Trial 19 finished with value: 1204782905.7458966 and parameters: {'max_depth': 4, 'learning_rate': 0.19432714755401403, 'n_estimators': 496, 'min_child_weight': 4, 'gamma': 0.3628249777834368, 'subsample': 0.732295078759021, 'colsample_bytree': 0.1521673452379504, 'reg_alpha': 0.3886055720826832, 'reg_lambda': 0.5882733993945581, 'random_state': 128}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:39,476] Trial 20 finished with value: 1562426872.87379 and parameters: {'max_depth': 5, 'learning_rate': 0.47059199198757673, 'n_estimators': 237, 'min_child_weight': 2, 'gamma': 0.13131748576148322, 'subsample': 0.8754384103429292, 'colsample_bytree': 0.39302849610020724, 'reg_alpha': 0.19875719755642143, 'reg_lambda': 0.28537233550083924, 'random_state': 377}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:40,398] Trial 21 finished with value: 1169474806.89473 and parameters: {'max_depth': 6, 'learning_rate': 0.16203709824811388, 'n_estimators': 962, 'min_child_weight': 2, 'gamma': 0.1776954781310937, 'subsample': 0.6520718006129285, 'colsample_bytree': 0.32561613968855474, 'reg_alpha': 0.2372847376720465, 'reg_lambda': 0.4888724540658396, 'random_state': 103}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:41,099] Trial 22 finished with value: 2028304795.1432145 and parameters: {'max_depth': 7, 'learning_rate': 0.2759069523483352, 'n_estimators': 997, 'min_child_weight': 2, 'gamma': 0.2939735904730363, 'subsample': 0.6594327239413929, 'colsample_bytree': 0.1345088711520272, 'reg_alpha': 0.1388914683099335, 'reg_lambda': 0.381594566365143, 'random_state': 86}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:42,885] Trial 23 finished with value: 1008765915.3980786 and parameters: {'max_depth': 7, 'learning_rate': 0.1541027917545994, 'n_estimators': 921, 'min_child_weight': 4, 'gamma': 0.13948100595015103, 'subsample': 0.744044452719014, 'colsample_bytree': 0.31052521110810105, 'reg_alpha': 0.09583804784721306, 'reg_lambda': 0.5424844973457668, 'random_state': 310}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:47,154] Trial 24 finished with value: 1287620420.7597928 and parameters: {'max_depth': 7, 'learning_rate': 0.08964493241653905, 'n_estimators': 892, 'min_child_weight': 4, 'gamma': 0.15043959907315713, 'subsample': 0.9077745021131225, 'colsample_bytree': 0.18329241512420538, 'reg_alpha': 0.09525603481079073, 'reg_lambda': 0.5772844453984262, 'random_state': 292}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:50,557] Trial 25 finished with value: 1573997204.246409 and parameters: {'max_depth': 9, 'learning_rate': 0.2553749557868986, 'n_estimators': 716, 'min_child_weight': 5, 'gamma': 0.29603002595091954, 'subsample': 0.749171325296448, 'colsample_bytree': 0.0404065598499537, 'reg_alpha': 0.09492491942639343, 'reg_lambda': 0.45675973495270644, 'random_state': 274}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:50,912] Trial 26 finished with value: 1004155089.1916193 and parameters: {'max_depth': 4, 'learning_rate': 0.1097771739661613, 'n_estimators': 477, 'min_child_weight': 3, 'gamma': 0.09924112953484646, 'subsample': 0.7798650026663239, 'colsample_bytree': 0.288299402624383, 'reg_alpha': 0.207528289001098, 'reg_lambda': 0.3402897576660874, 'random_state': 437}. Best is trial 12 with value: 947512599.4291106.\n",
            "[I 2023-07-23 03:50:51,342] Trial 27 finished with value: 943428979.7956787 and parameters: {'max_depth': 4, 'learning_rate': 0.09075173987012278, 'n_estimators': 487, 'min_child_weight': 1, 'gamma': 0.06486303166546555, 'subsample': 0.829822714404179, 'colsample_bytree': 0.44470461739596995, 'reg_alpha': 0.21062251791035907, 'reg_lambda': 0.3588766726596445, 'random_state': 454}. Best is trial 27 with value: 943428979.7956787.\n",
            "[I 2023-07-23 03:50:51,561] Trial 28 finished with value: 803017332.4859939 and parameters: {'max_depth': 2, 'learning_rate': 0.22825631358101134, 'n_estimators': 289, 'min_child_weight': 1, 'gamma': 0.04048260124452186, 'subsample': 0.8454572801771427, 'colsample_bytree': 0.5160525873736304, 'reg_alpha': 0.33036226958685433, 'reg_lambda': 0.3113889269075163, 'random_state': 133}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:51,761] Trial 29 finished with value: 878339955.5584278 and parameters: {'max_depth': 1, 'learning_rate': 0.33085726472077115, 'n_estimators': 285, 'min_child_weight': 1, 'gamma': 0.01019209205215249, 'subsample': 0.9388369966328152, 'colsample_bytree': 0.512606611748263, 'reg_alpha': 0.36819943370328456, 'reg_lambda': 0.13447256761427628, 'random_state': 236}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:51,888] Trial 30 finished with value: 1091986508.7110748 and parameters: {'max_depth': 1, 'learning_rate': 0.33282590692053693, 'n_estimators': 106, 'min_child_weight': 1, 'gamma': 0.0761549779532428, 'subsample': 0.844760412659112, 'colsample_bytree': 0.5268779787130669, 'reg_alpha': 0.38060643747147777, 'reg_lambda': 0.27505759798576557, 'random_state': 449}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:52,125] Trial 31 finished with value: 846998605.5822263 and parameters: {'max_depth': 2, 'learning_rate': 0.2403710152607737, 'n_estimators': 317, 'min_child_weight': 1, 'gamma': 0.013951746497672855, 'subsample': 0.954212241022039, 'colsample_bytree': 0.46704328480870794, 'reg_alpha': 0.34826924478278365, 'reg_lambda': 0.12785140203376283, 'random_state': 234}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:52,353] Trial 32 finished with value: 1416939086.85185 and parameters: {'max_depth': 2, 'learning_rate': 0.23154386918461162, 'n_estimators': 311, 'min_child_weight': 10, 'gamma': 0.027185458142165504, 'subsample': 0.9317761251265642, 'colsample_bytree': 0.4716026024419009, 'reg_alpha': 0.36168361437337687, 'reg_lambda': 0.08880571101352427, 'random_state': 228}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:52,578] Trial 33 finished with value: 1123914494.6338341 and parameters: {'max_depth': 2, 'learning_rate': 0.3518366581817015, 'n_estimators': 311, 'min_child_weight': 1, 'gamma': 0.09173596024190558, 'subsample': 0.848226673786386, 'colsample_bytree': 0.5782498630923767, 'reg_alpha': 0.41425525899854865, 'reg_lambda': 0.1054711116371114, 'random_state': 159}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:52,751] Trial 34 finished with value: 1082619436.026067 and parameters: {'max_depth': 1, 'learning_rate': 0.08767637015775898, 'n_estimators': 178, 'min_child_weight': 1, 'gamma': 0.010889915920123283, 'subsample': 0.9398227260448312, 'colsample_bytree': 0.6142288973136291, 'reg_alpha': 0.45542925477136015, 'reg_lambda': 0.165609615941053, 'random_state': 594}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:52,930] Trial 35 finished with value: 922389883.012298 and parameters: {'max_depth': 3, 'learning_rate': 0.402119567541931, 'n_estimators': 161, 'min_child_weight': 3, 'gamma': 0.076734274493939, 'subsample': 0.8129707513480259, 'colsample_bytree': 0.4222077846591877, 'reg_alpha': 0.3173527616218791, 'reg_lambda': 0.06316185394283076, 'random_state': 244}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:53,103] Trial 36 finished with value: 998365552.4907157 and parameters: {'max_depth': 3, 'learning_rate': 0.39160542605728493, 'n_estimators': 157, 'min_child_weight': 3, 'gamma': 0.13881951532824366, 'subsample': 0.9283331399875381, 'colsample_bytree': 0.4061955350970683, 'reg_alpha': 0.3267276941347129, 'reg_lambda': 0.05283712000589164, 'random_state': 254}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:53,278] Trial 37 finished with value: 1288881166.0866423 and parameters: {'max_depth': 3, 'learning_rate': 0.4518417237039595, 'n_estimators': 141, 'min_child_weight': 2, 'gamma': 0.5387916700823745, 'subsample': 0.7911141047771864, 'colsample_bytree': 0.45719882967238495, 'reg_alpha': 0.5587040260764715, 'reg_lambda': 0.08939622356311284, 'random_state': 198}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:53,459] Trial 38 finished with value: 1261277367.8646688 and parameters: {'max_depth': 1, 'learning_rate': 0.32634516207721853, 'n_estimators': 211, 'min_child_weight': 3, 'gamma': 0.08066282699491213, 'subsample': 0.8694355448138072, 'colsample_bytree': 0.5972050961669122, 'reg_alpha': 0.4450134996461808, 'reg_lambda': 0.01858644878444808, 'random_state': 165}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:53,698] Trial 39 finished with value: 1075438326.7309587 and parameters: {'max_depth': 2, 'learning_rate': 0.568217005361835, 'n_estimators': 338, 'min_child_weight': 5, 'gamma': 0.17563996623728334, 'subsample': 0.9456714411085874, 'colsample_bytree': 0.48122615222554843, 'reg_alpha': 0.30635701525674813, 'reg_lambda': 0.27906251454889686, 'random_state': 384}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:54,001] Trial 40 finished with value: 1185267353.3165824 and parameters: {'max_depth': 3, 'learning_rate': 0.41861803765439076, 'n_estimators': 424, 'min_child_weight': 6, 'gamma': 0.05954039334304945, 'subsample': 0.8952121945939637, 'colsample_bytree': 0.3856912860835059, 'reg_alpha': 0.34111691896455143, 'reg_lambda': 0.11615607134097027, 'random_state': 538}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:54,234] Trial 41 finished with value: 868211447.1797655 and parameters: {'max_depth': 2, 'learning_rate': 0.2297538110517792, 'n_estimators': 284, 'min_child_weight': 1, 'gamma': 0.05812314527862125, 'subsample': 0.8182713333795353, 'colsample_bytree': 0.43986452762337425, 'reg_alpha': 0.26585624106323313, 'reg_lambda': 0.1943914932460164, 'random_state': 672}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:54,342] Trial 42 finished with value: 1470741241.8085845 and parameters: {'max_depth': 1, 'learning_rate': 0.2161738326509871, 'n_estimators': 53, 'min_child_weight': 2, 'gamma': 0.13557841195760661, 'subsample': 0.7802203611368886, 'colsample_bytree': 0.5243899992201165, 'reg_alpha': 0.2564243237325311, 'reg_lambda': 0.1935527317421192, 'random_state': 227}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:54,576] Trial 43 finished with value: 984047185.5520055 and parameters: {'max_depth': 2, 'learning_rate': 0.30485663654195305, 'n_estimators': 263, 'min_child_weight': 1, 'gamma': 0.10454793082300662, 'subsample': 0.8288005138093263, 'colsample_bytree': 0.41569034998625787, 'reg_alpha': 0.31592494645334657, 'reg_lambda': 0.05720139010672769, 'random_state': 660}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:54,907] Trial 44 finished with value: 1238838306.8326883 and parameters: {'max_depth': 3, 'learning_rate': 0.24073529147554648, 'n_estimators': 419, 'min_child_weight': 8, 'gamma': 0.045208352311177515, 'subsample': 0.9607243023146911, 'colsample_bytree': 0.5080010137355667, 'reg_alpha': 0.39851150278484476, 'reg_lambda': 0.2374191947986503, 'random_state': 689}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:55,080] Trial 45 finished with value: 869343946.0758811 and parameters: {'max_depth': 2, 'learning_rate': 0.3635492246444517, 'n_estimators': 200, 'min_child_weight': 1, 'gamma': 0.0117916056134975, 'subsample': 0.8938285763879928, 'colsample_bytree': 0.36421477014454956, 'reg_alpha': 0.2680039448443297, 'reg_lambda': 0.1355621429538259, 'random_state': 799}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:55,284] Trial 46 finished with value: 1480480575.0424953 and parameters: {'max_depth': 1, 'learning_rate': 0.34682791008220176, 'n_estimators': 286, 'min_child_weight': 9, 'gamma': 0.01016690212328739, 'subsample': 0.9043246830703774, 'colsample_bytree': 0.37281245515073147, 'reg_alpha': 0.26238123869767044, 'reg_lambda': 0.15854134390847105, 'random_state': 828}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:55,472] Trial 47 finished with value: 942135640.5432421 and parameters: {'max_depth': 2, 'learning_rate': 0.24586187829506342, 'n_estimators': 203, 'min_child_weight': 1, 'gamma': 0.05089219053658702, 'subsample': 0.9940770332561759, 'colsample_bytree': 0.49121994724612017, 'reg_alpha': 0.35301152787115153, 'reg_lambda': 0.12597025286177205, 'random_state': 760}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:55,747] Trial 48 finished with value: 1249915573.8559966 and parameters: {'max_depth': 2, 'learning_rate': 0.30748155340916605, 'n_estimators': 354, 'min_child_weight': 2, 'gamma': 0.013601520525004848, 'subsample': 0.8608993204474512, 'colsample_bytree': 0.6609319157712088, 'reg_alpha': 0.5279133381009251, 'reg_lambda': 0.18280144607562543, 'random_state': 822}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:55,974] Trial 49 finished with value: 881452830.9914647 and parameters: {'max_depth': 1, 'learning_rate': 0.2789086660634279, 'n_estimators': 389, 'min_child_weight': 1, 'gamma': 0.09493449953570687, 'subsample': 0.9396926155144201, 'colsample_bytree': 0.5580358165490081, 'reg_alpha': 0.28123993838341615, 'reg_lambda': 0.24933609698086417, 'random_state': 921}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:56,316] Trial 50 finished with value: 844127202.3257238 and parameters: {'max_depth': 2, 'learning_rate': 0.14291247509019506, 'n_estimators': 538, 'min_child_weight': 1, 'gamma': 0.11485295052763933, 'subsample': 0.9643949273359286, 'colsample_bytree': 0.44483536521300004, 'reg_alpha': 0.42788038359981545, 'reg_lambda': 0.3099102183959052, 'random_state': 738}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:56,706] Trial 51 finished with value: 838281002.648213 and parameters: {'max_depth': 2, 'learning_rate': 0.12846601787673406, 'n_estimators': 624, 'min_child_weight': 1, 'gamma': 0.05676721107885606, 'subsample': 0.9688509942192477, 'colsample_bytree': 0.4530773846958263, 'reg_alpha': 0.41629293314243365, 'reg_lambda': 0.3121491825398558, 'random_state': 747}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:57,059] Trial 52 finished with value: 1074470435.6796782 and parameters: {'max_depth': 2, 'learning_rate': 0.14651056529489265, 'n_estimators': 579, 'min_child_weight': 2, 'gamma': 0.05867225567763762, 'subsample': 0.9671127671038111, 'colsample_bytree': 0.4495828019574595, 'reg_alpha': 0.4313278849948316, 'reg_lambda': 0.32354832403003336, 'random_state': 750}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:57,448] Trial 53 finished with value: 829419394.532645 and parameters: {'max_depth': 3, 'learning_rate': 0.05267935526096662, 'n_estimators': 546, 'min_child_weight': 1, 'gamma': 0.11341168842334415, 'subsample': 0.89107224682366, 'colsample_bytree': 0.37035408357099664, 'reg_alpha': 0.49591301740154425, 'reg_lambda': 0.20905220752270096, 'random_state': 638}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:57,904] Trial 54 finished with value: 890264814.3327471 and parameters: {'max_depth': 3, 'learning_rate': 0.055700987244721736, 'n_estimators': 638, 'min_child_weight': 1, 'gamma': 0.11046879302484197, 'subsample': 0.9823822310406881, 'colsample_bytree': 0.43346218559266464, 'reg_alpha': 0.48357206025536864, 'reg_lambda': 0.22060767005031962, 'random_state': 628}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:58,378] Trial 55 finished with value: 961864297.4745786 and parameters: {'max_depth': 4, 'learning_rate': 0.1380751848137636, 'n_estimators': 559, 'min_child_weight': 2, 'gamma': 0.17306164038949126, 'subsample': 0.9985348211688041, 'colsample_bytree': 0.3539463895838222, 'reg_alpha': 0.5664312958560371, 'reg_lambda': 0.3144040809852279, 'random_state': 711}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:58,756] Trial 56 finished with value: 827340186.0592088 and parameters: {'max_depth': 3, 'learning_rate': 0.0526695390557677, 'n_estimators': 529, 'min_child_weight': 1, 'gamma': 0.22221772435482554, 'subsample': 0.8811909723516651, 'colsample_bytree': 0.47311202135457575, 'reg_alpha': 0.47003920894536766, 'reg_lambda': 0.3946609108577867, 'random_state': 559}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:59,400] Trial 57 finished with value: 926882094.2996271 and parameters: {'max_depth': 5, 'learning_rate': 0.03898753462716947, 'n_estimators': 541, 'min_child_weight': 2, 'gamma': 0.2246338181759675, 'subsample': 0.8698445347328294, 'colsample_bytree': 0.5536296769055342, 'reg_alpha': 0.4768335272569495, 'reg_lambda': 0.4013177087673725, 'random_state': 553}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:50:59,852] Trial 58 finished with value: 947530404.6682284 and parameters: {'max_depth': 3, 'learning_rate': 0.054896662508824294, 'n_estimators': 639, 'min_child_weight': 2, 'gamma': 0.20665416280091456, 'subsample': 0.910962580966061, 'colsample_bytree': 0.4778669038118349, 'reg_alpha': 0.41741529638743896, 'reg_lambda': 0.3678127116863012, 'random_state': 617}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:02,835] Trial 59 finished with value: 849919204.7206304 and parameters: {'max_depth': 3, 'learning_rate': 0.13355605834185333, 'n_estimators': 514, 'min_child_weight': 1, 'gamma': 0.11895844561261955, 'subsample': 0.9601642533662235, 'colsample_bytree': 0.4015537698091197, 'reg_alpha': 0.5022301661931432, 'reg_lambda': 0.30515746551957146, 'random_state': 506}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:03,740] Trial 60 finished with value: 1034478110.2865614 and parameters: {'max_depth': 4, 'learning_rate': 0.013395343050246838, 'n_estimators': 622, 'min_child_weight': 2, 'gamma': 0.2513181986732955, 'subsample': 0.8977608841753912, 'colsample_bytree': 0.26460105228155595, 'reg_alpha': 0.6025809646488401, 'reg_lambda': 0.24398965153226926, 'random_state': 646}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:04,696] Trial 61 finished with value: 922037281.291017 and parameters: {'max_depth': 3, 'learning_rate': 0.12922450331989382, 'n_estimators': 519, 'min_child_weight': 1, 'gamma': 0.1145097586216742, 'subsample': 0.9657899090839362, 'colsample_bytree': 0.38908494960327794, 'reg_alpha': 0.5156106801219986, 'reg_lambda': 0.29781782482544206, 'random_state': 498}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:05,416] Trial 62 finished with value: 860897298.9307096 and parameters: {'max_depth': 3, 'learning_rate': 0.19237289759686726, 'n_estimators': 460, 'min_child_weight': 1, 'gamma': 0.14943902574602885, 'subsample': 0.9563788603225715, 'colsample_bytree': 0.33652841279657286, 'reg_alpha': 0.4774852696493106, 'reg_lambda': 0.33876628547612053, 'random_state': 589}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:05,866] Trial 63 finished with value: 817775855.3814925 and parameters: {'max_depth': 2, 'learning_rate': 0.11282391214784698, 'n_estimators': 682, 'min_child_weight': 1, 'gamma': 0.11888182946288398, 'subsample': 0.8591730306906321, 'colsample_bytree': 0.4051258074471627, 'reg_alpha': 0.4498494835088153, 'reg_lambda': 0.41589285164159917, 'random_state': 501}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:06,308] Trial 64 finished with value: 871364012.8509103 and parameters: {'max_depth': 2, 'learning_rate': 0.17126047366704045, 'n_estimators': 801, 'min_child_weight': 1, 'gamma': 0.15760990672116354, 'subsample': 0.8523173432376081, 'colsample_bytree': 0.47284952626456733, 'reg_alpha': 0.39932880668136395, 'reg_lambda': 0.43337365011229995, 'random_state': 49}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:07,651] Trial 65 finished with value: 1369516531.8729794 and parameters: {'max_depth': 2, 'learning_rate': 0.07978563200903718, 'n_estimators': 696, 'min_child_weight': 2, 'gamma': 0.18235125332427676, 'subsample': 0.9145934436345735, 'colsample_bytree': 0.2990993841152652, 'reg_alpha': 0.44823083133629926, 'reg_lambda': 0.3739517793415861, 'random_state': 716}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:07,971] Trial 66 finished with value: 906064185.1451782 and parameters: {'max_depth': 1, 'learning_rate': 0.10829540070897742, 'n_estimators': 673, 'min_child_weight': 1, 'gamma': 0.046697407760618675, 'subsample': 0.8775088081895384, 'colsample_bytree': 0.4345417439343953, 'reg_alpha': 0.422394994960249, 'reg_lambda': 0.43545889585478037, 'random_state': 882}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:08,498] Trial 67 finished with value: 851261707.3206787 and parameters: {'max_depth': 4, 'learning_rate': 0.06396505557566783, 'n_estimators': 582, 'min_child_weight': 2, 'gamma': 0.09629937886623656, 'subsample': 0.7138527374344859, 'colsample_bytree': 0.5034551359312326, 'reg_alpha': 0.3802079049695372, 'reg_lambda': 0.2709518876046476, 'random_state': 495}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:08,942] Trial 68 finished with value: 912676065.954303 and parameters: {'max_depth': 2, 'learning_rate': 0.038024343348931317, 'n_estimators': 766, 'min_child_weight': 1, 'gamma': 0.1268420127155547, 'subsample': 0.7618219432954974, 'colsample_bytree': 0.36395026475116105, 'reg_alpha': 0.3579934905516452, 'reg_lambda': 0.3365779164869576, 'random_state': 784}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:09,199] Trial 69 finished with value: 1269622005.9129474 and parameters: {'max_depth': 1, 'learning_rate': 0.1078508870790293, 'n_estimators': 452, 'min_child_weight': 3, 'gamma': 0.041854761090696566, 'subsample': 0.8234706413440012, 'colsample_bytree': 0.40664065675517835, 'reg_alpha': 0.39962337048205004, 'reg_lambda': 0.4679731160631324, 'random_state': 418}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:09,910] Trial 70 finished with value: 882656634.1867883 and parameters: {'max_depth': 5, 'learning_rate': 0.20687026031147532, 'n_estimators': 661, 'min_child_weight': 2, 'gamma': 0.25619973084459086, 'subsample': 0.9248389650689874, 'colsample_bytree': 0.537301998611726, 'reg_alpha': 0.45951778510341645, 'reg_lambda': 0.3997778170017247, 'random_state': 561}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:10,298] Trial 71 finished with value: 873039236.5868208 and parameters: {'max_depth': 3, 'learning_rate': 0.14642019740490078, 'n_estimators': 530, 'min_child_weight': 1, 'gamma': 0.10725446608376957, 'subsample': 0.962754935301451, 'colsample_bytree': 0.4052634977659061, 'reg_alpha': 0.5086739343270337, 'reg_lambda': 0.3056692322660538, 'random_state': 510}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:10,686] Trial 72 finished with value: 864485241.9067278 and parameters: {'max_depth': 3, 'learning_rate': 0.1759476364536814, 'n_estimators': 510, 'min_child_weight': 1, 'gamma': 0.12724040678326115, 'subsample': 0.9958061634910597, 'colsample_bytree': 0.4653381981009436, 'reg_alpha': 0.4982188471340591, 'reg_lambda': 0.25218427818026456, 'random_state': 481}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:11,045] Trial 73 finished with value: 823430674.2623389 and parameters: {'max_depth': 2, 'learning_rate': 0.12089070342841081, 'n_estimators': 609, 'min_child_weight': 1, 'gamma': 0.08164674399699703, 'subsample': 0.8783304536059544, 'colsample_bytree': 0.3324078091747319, 'reg_alpha': 0.42682339342787823, 'reg_lambda': 0.35549171213155284, 'random_state': 606}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:11,399] Trial 74 finished with value: 844811867.5356687 and parameters: {'max_depth': 2, 'learning_rate': 0.07879310963831845, 'n_estimators': 623, 'min_child_weight': 1, 'gamma': 0.07508983406395499, 'subsample': 0.8064952696330252, 'colsample_bytree': 0.32065252394885624, 'reg_alpha': 0.4317377363520104, 'reg_lambda': 0.36790717850155574, 'random_state': 608}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:11,783] Trial 75 finished with value: 872985883.8873688 and parameters: {'max_depth': 2, 'learning_rate': 0.07737078412186676, 'n_estimators': 614, 'min_child_weight': 1, 'gamma': 0.19852554171551282, 'subsample': 0.8054892222075271, 'colsample_bytree': 0.2796255389759927, 'reg_alpha': 0.46125271633558057, 'reg_lambda': 0.35750716837200236, 'random_state': 610}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:12,817] Trial 76 finished with value: 1594135356.5153487 and parameters: {'max_depth': 1, 'learning_rate': 0.11186997074253195, 'n_estimators': 728, 'min_child_weight': 2, 'gamma': 0.1482787006077954, 'subsample': 0.8497724568541213, 'colsample_bytree': 0.32063638945675654, 'reg_alpha': 0.4207014947732519, 'reg_lambda': 0.4074364898824646, 'random_state': 531}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:16,445] Trial 77 finished with value: 1378469060.6799598 and parameters: {'max_depth': 2, 'learning_rate': 0.010070164716752858, 'n_estimators': 594, 'min_child_weight': 2, 'gamma': 0.07538073286120492, 'subsample': 0.8763303075267022, 'colsample_bytree': 0.339363218099496, 'reg_alpha': 0.3770628590147054, 'reg_lambda': 0.3524247781339273, 'random_state': 686}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:17,719] Trial 78 finished with value: 928385953.1229744 and parameters: {'max_depth': 2, 'learning_rate': 0.04877975883727603, 'n_estimators': 551, 'min_child_weight': 1, 'gamma': 0.0756351757896608, 'subsample': 0.7609458366387089, 'colsample_bytree': 0.2329156216999634, 'reg_alpha': 0.5401171499468669, 'reg_lambda': 0.36965878921177614, 'random_state': 580}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:19,511] Trial 79 finished with value: 997129934.428752 and parameters: {'max_depth': 1, 'learning_rate': 0.07945234567028185, 'n_estimators': 696, 'min_child_weight': 1, 'gamma': 0.19569958012128785, 'subsample': 0.840721655675042, 'colsample_bytree': 0.3096162786341509, 'reg_alpha': 0.44114277292570386, 'reg_lambda': 0.3854728017029599, 'random_state': 633}. Best is trial 28 with value: 803017332.4859939.\n",
            "[I 2023-07-23 03:51:20,900] Trial 80 finished with value: 790385115.243746 and parameters: {'max_depth': 2, 'learning_rate': 0.15808118499582519, 'n_estimators': 762, 'min_child_weight': 1, 'gamma': 0.16361083785027813, 'subsample': 0.9158919008817531, 'colsample_bytree': 0.37827172048287455, 'reg_alpha': 0.4700943005519576, 'reg_lambda': 0.3267660568241752, 'random_state': 742}. Best is trial 80 with value: 790385115.243746.\n",
            "[I 2023-07-23 03:51:21,333] Trial 81 finished with value: 846641932.115281 and parameters: {'max_depth': 2, 'learning_rate': 0.15664683112654143, 'n_estimators': 755, 'min_child_weight': 1, 'gamma': 0.1552545657911443, 'subsample': 0.9176317191794969, 'colsample_bytree': 0.3710631887014635, 'reg_alpha': 0.47149185410341626, 'reg_lambda': 0.32027114518594596, 'random_state': 739}. Best is trial 80 with value: 790385115.243746.\n",
            "[I 2023-07-23 03:51:21,872] Trial 82 finished with value: 941591363.8999944 and parameters: {'max_depth': 3, 'learning_rate': 0.10650264774385165, 'n_estimators': 808, 'min_child_weight': 1, 'gamma': 0.09063838147152457, 'subsample': 0.7998329626795674, 'colsample_bytree': 0.34715571979506527, 'reg_alpha': 0.4261796694147809, 'reg_lambda': 0.27884326297923, 'random_state': 729}. Best is trial 80 with value: 790385115.243746.\n",
            "[I 2023-07-23 03:51:22,281] Trial 83 finished with value: 786666806.8943005 and parameters: {'max_depth': 2, 'learning_rate': 0.12725278676355972, 'n_estimators': 645, 'min_child_weight': 1, 'gamma': 0.03913120415720357, 'subsample': 0.8728192285358491, 'colsample_bytree': 0.4267925489922823, 'reg_alpha': 0.3947596527993052, 'reg_lambda': 0.4185596117742408, 'random_state': 782}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:22,772] Trial 84 finished with value: 966046757.759296 and parameters: {'max_depth': 2, 'learning_rate': 0.1977922973386229, 'n_estimators': 865, 'min_child_weight': 1, 'gamma': 0.04322352068295238, 'subsample': 0.879573172272397, 'colsample_bytree': 0.4303879050827212, 'reg_alpha': 0.3925148613393772, 'reg_lambda': 0.43174281766866696, 'random_state': 856}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:23,081] Trial 85 finished with value: 1644341468.8222575 and parameters: {'max_depth': 1, 'learning_rate': 0.16565359178365427, 'n_estimators': 565, 'min_child_weight': 2, 'gamma': 0.16356126725750758, 'subsample': 0.9271223338458014, 'colsample_bytree': 0.38408437309241655, 'reg_alpha': 0.33259834884440487, 'reg_lambda': 0.3434443943149819, 'random_state': 795}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:24,365] Trial 86 finished with value: 934485081.3540016 and parameters: {'max_depth': 9, 'learning_rate': 0.12650123755461715, 'n_estimators': 704, 'min_child_weight': 1, 'gamma': 0.1292668178930977, 'subsample': 0.9011347503283749, 'colsample_bytree': 0.49888880255160156, 'reg_alpha': 0.49220546486665023, 'reg_lambda': 0.47430513014497894, 'random_state': 768}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:24,825] Trial 87 finished with value: 1006104077.3723383 and parameters: {'max_depth': 3, 'learning_rate': 0.2112772472845656, 'n_estimators': 647, 'min_child_weight': 6, 'gamma': 0.09797303774957261, 'subsample': 0.8429130374374598, 'colsample_bytree': 0.4519227602648159, 'reg_alpha': 0.3680982318481479, 'reg_lambda': 0.30124785841574087, 'random_state': 689}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:25,237] Trial 88 finished with value: 1120273531.3220508 and parameters: {'max_depth': 2, 'learning_rate': 0.09785189955902697, 'n_estimators': 672, 'min_child_weight': 2, 'gamma': 0.035925372130455324, 'subsample': 0.8676018850383689, 'colsample_bytree': 0.41290703018880265, 'reg_alpha': 0.3018300138794683, 'reg_lambda': 0.4116887560494319, 'random_state': 661}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:25,544] Trial 89 finished with value: 886967145.0084268 and parameters: {'max_depth': 1, 'learning_rate': 0.12455462596707151, 'n_estimators': 602, 'min_child_weight': 1, 'gamma': 0.03890258345692197, 'subsample': 0.9340965083511117, 'colsample_bytree': 0.452920644943771, 'reg_alpha': 0.4518855943869224, 'reg_lambda': 0.4584403463633648, 'random_state': 703}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:25,964] Trial 90 finished with value: 821897536.6776905 and parameters: {'max_depth': 2, 'learning_rate': 0.18100639017404488, 'n_estimators': 483, 'min_child_weight': 1, 'gamma': 0.06514089660918937, 'subsample': 0.8871404408412114, 'colsample_bytree': 0.48280619765637134, 'reg_alpha': 0.40851209704872926, 'reg_lambda': 0.3895759724331621, 'random_state': 892}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:29,088] Trial 91 finished with value: 865697483.5615878 and parameters: {'max_depth': 2, 'learning_rate': 0.18291168315005238, 'n_estimators': 741, 'min_child_weight': 1, 'gamma': 0.06952958029958861, 'subsample': 0.8921562250151566, 'colsample_bytree': 0.5034196541971969, 'reg_alpha': 0.3423230722010233, 'reg_lambda': 0.3882661384879453, 'random_state': 883}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:31,833] Trial 92 finished with value: 826131233.4238601 and parameters: {'max_depth': 2, 'learning_rate': 0.14898997379315107, 'n_estimators': 481, 'min_child_weight': 1, 'gamma': 0.11428993068325304, 'subsample': 0.8580246768922913, 'colsample_bytree': 0.4195931254831141, 'reg_alpha': 0.3988277854619899, 'reg_lambda': 0.333084660882269, 'random_state': 949}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:36,080] Trial 93 finished with value: 1205588252.2599497 and parameters: {'max_depth': 2, 'learning_rate': 0.06052840817926507, 'n_estimators': 480, 'min_child_weight': 2, 'gamma': 0.030111780025956426, 'subsample': 0.8245480138402248, 'colsample_bytree': 0.42086175605049747, 'reg_alpha': 0.4053913080343975, 'reg_lambda': 0.41618963508505014, 'random_state': 938}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:38,384] Trial 94 finished with value: 820452995.3952287 and parameters: {'max_depth': 3, 'learning_rate': 0.031920113341493625, 'n_estimators': 496, 'min_child_weight': 1, 'gamma': 0.08707826588014594, 'subsample': 0.7820901150063315, 'colsample_bytree': 0.37718850127638937, 'reg_alpha': 0.4703941543225648, 'reg_lambda': 0.338231619131769, 'random_state': 932}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:42,786] Trial 95 finished with value: 880483120.5132921 and parameters: {'max_depth': 4, 'learning_rate': 0.031490770406476645, 'n_estimators': 433, 'min_child_weight': 2, 'gamma': 0.1786941398740436, 'subsample': 0.7792433974252151, 'colsample_bytree': 0.3828100823806276, 'reg_alpha': 0.4688442752813897, 'reg_lambda': 0.3888041067305561, 'random_state': 963}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:44,276] Trial 96 finished with value: 814408057.5225784 and parameters: {'max_depth': 3, 'learning_rate': 0.09678145370854105, 'n_estimators': 497, 'min_child_weight': 1, 'gamma': 0.139400969235499, 'subsample': 0.8618853390802512, 'colsample_bytree': 0.35336318448466814, 'reg_alpha': 0.3824257803871188, 'reg_lambda': 0.33189494899805205, 'random_state': 966}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:45,008] Trial 97 finished with value: 918912995.4592662 and parameters: {'max_depth': 3, 'learning_rate': 0.16400333221120914, 'n_estimators': 387, 'min_child_weight': 1, 'gamma': 0.1414722816164909, 'subsample': 0.8604875649369355, 'colsample_bytree': 0.35046487243969415, 'reg_alpha': 0.3732428397960651, 'reg_lambda': 0.3356727785909611, 'random_state': 971}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:48,290] Trial 98 finished with value: 1081950576.1354778 and parameters: {'max_depth': 3, 'learning_rate': 0.08967783233632456, 'n_estimators': 451, 'min_child_weight': 2, 'gamma': 0.09506268555912553, 'subsample': 0.8255198293297393, 'colsample_bytree': 0.3963177957868931, 'reg_alpha': 0.33035206951989066, 'reg_lambda': 0.4374320223249043, 'random_state': 941}. Best is trial 83 with value: 786666806.8943005.\n",
            "[I 2023-07-23 03:51:48,657] Trial 99 finished with value: 1272539335.2747402 and parameters: {'max_depth': 1, 'learning_rate': 0.18562319667616517, 'n_estimators': 505, 'min_child_weight': 5, 'gamma': 0.23198970066364516, 'subsample': 0.8387404643002384, 'colsample_bytree': 0.29259404981131126, 'reg_alpha': 0.38742599282951734, 'reg_lambda': 0.35228098146591624, 'random_state': 877}. Best is trial 83 with value: 786666806.8943005.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best parameters\n",
        "print('Best parameters', study.best_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGIewYgytyMR",
        "outputId": "c96bccc0-d878-42ea-be91-6cd75766234f"
      },
      "id": "kGIewYgytyMR",
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters {'max_depth': 2, 'learning_rate': 0.12725278676355972, 'n_estimators': 645, 'min_child_weight': 1, 'gamma': 0.03913120415720357, 'subsample': 0.8728192285358491, 'colsample_bytree': 0.4267925489922823, 'reg_alpha': 0.3947596527993052, 'reg_lambda': 0.4185596117742408, 'random_state': 782}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best value\n",
        "print('Best value', study.best_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fpv7PjBR3ze9",
        "outputId": "29048cbc-0a4c-4d25-d26c-c4c08491f1dd"
      },
      "id": "Fpv7PjBR3ze9",
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best value 786666806.8943005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best trial\n",
        "print('Best trial', study.best_trial)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LEp_GVU32Uq",
        "outputId": "a600b857-131e-4e62-e4f7-8e7cfdd961fa"
      },
      "id": "-LEp_GVU32Uq",
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best trial FrozenTrial(number=83, state=TrialState.COMPLETE, values=[786666806.8943005], datetime_start=datetime.datetime(2023, 7, 23, 3, 51, 21, 877354), datetime_complete=datetime.datetime(2023, 7, 23, 3, 51, 22, 280771), params={'max_depth': 2, 'learning_rate': 0.12725278676355972, 'n_estimators': 645, 'min_child_weight': 1, 'gamma': 0.03913120415720357, 'subsample': 0.8728192285358491, 'colsample_bytree': 0.4267925489922823, 'reg_alpha': 0.3947596527993052, 'reg_lambda': 0.4185596117742408, 'random_state': 782}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'max_depth': IntDistribution(high=10, log=False, low=1, step=1), 'learning_rate': FloatDistribution(high=1.0, log=False, low=0.01, step=None), 'n_estimators': IntDistribution(high=1000, log=False, low=50, step=1), 'min_child_weight': IntDistribution(high=10, log=False, low=1, step=1), 'gamma': FloatDistribution(high=1.0, log=False, low=0.01, step=None), 'subsample': FloatDistribution(high=1.0, log=False, low=0.01, step=None), 'colsample_bytree': FloatDistribution(high=1.0, log=False, low=0.01, step=None), 'reg_alpha': FloatDistribution(high=1.0, log=False, low=0.01, step=None), 'reg_lambda': FloatDistribution(high=1.0, log=False, low=0.01, step=None), 'random_state': IntDistribution(high=1000, log=False, low=1, step=1)}, trial_id=83, value=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Optimized XGBoost"
      ],
      "metadata": {
        "id": "i-ug2rcd2pvm"
      },
      "id": "i-ug2rcd2pvm"
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_optimized = xgb.XGBRegressor(**study.best_params)\n",
        "xgb_optimized.fit(X_train, y_train)\n",
        "y_pred = xgb_optimized.predict(X_test)\n",
        "\n",
        "print('MSE: ', mean_squared_error(y_test, y_pred))\n",
        "print('RMSE: ', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqY6jB4L36Gb",
        "outputId": "f39d9893-e914-4e69-e5e3-8f8b451a77bb"
      },
      "id": "GqY6jB4L36Gb",
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE:  786666806.8943005\n",
            "RMSE:  28047.58112376717\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SHAP for XGBoost Optimized"
      ],
      "metadata": {
        "id": "I0XgxDM41l-2"
      },
      "id": "I0XgxDM41l-2"
    },
    {
      "cell_type": "code",
      "source": [
        "explainer_xgb = shap.TreeExplainer(xgb_optimized)\n",
        "shap_interaction_xgb = explainer_xgb.shap_interaction_values(X_train)\n",
        "# Get SHAP values\n",
        "shap_values_xgb = explainer_xgb(X_train)"
      ],
      "metadata": {
        "id": "loSq74Q11r0h"
      },
      "id": "loSq74Q11r0h",
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Waterfall plot for first observation\n",
        "shap.plots.waterfall(shap_values_xgb[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "xv6hYqbs2IG_",
        "outputId": "aeec53d3-81d9-426e-8f04-aadef58b7cdb"
      },
      "id": "xv6hYqbs2IG_",
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mean SHAP\n",
        "shap.plots.bar(shap_values_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "B9k8qXVS2KiB",
        "outputId": "7e90bb0a-e1a7-4aa6-d277-e0f5cf70e54d"
      },
      "id": "B9k8qXVS2KiB",
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_values_xgb, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "8I2xcCxy2RdB",
        "outputId": "c4004ed0-43db-44b9-f1c2-e8cf55ecdebe"
      },
      "id": "8I2xcCxy2RdB",
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_interaction_xgb, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "xcZVe5ro2WXe",
        "outputId": "5cca3601-af21-4e39-d5a7-ade4f864f7c6"
      },
      "id": "xcZVe5ro2WXe",
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1150x660 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Light Gradient Boosting Machine (LGBM) (Baseline)"
      ],
      "metadata": {
        "id": "hgySYyBhq4x5"
      },
      "id": "hgySYyBhq4x5"
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the Baseline."
      ],
      "metadata": {
        "id": "Cl9L02DTIups"
      },
      "id": "Cl9L02DTIups"
    },
    {
      "cell_type": "code",
      "source": [
        "reg_lgbm_baseline = lgbm.LGBMRegressor()  # default - 'regression'"
      ],
      "metadata": {
        "id": "eVJIIDpWGp5M"
      },
      "id": "eVJIIDpWGp5M",
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg_lgbm_baseline.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "6v5xNY0zGzNl",
        "outputId": "765049ba-0f60-4960-9bff-50707dd8780e"
      },
      "id": "6v5xNY0zGzNl",
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_predict = reg_lgbm_baseline.predict(X_test)"
      ],
      "metadata": {
        "id": "6KH3ayV5H1pC"
      },
      "id": "6KH3ayV5H1pC",
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"MAE test score:\", int(mean_absolute_error(y_test, lgbm_predict)))\n",
        "print(\"MSE test score:\", int(mean_squared_error(y_test, lgbm_predict)))\n",
        "print(\"RMSE test score:\", int(sqrt(mean_squared_error(y_test, lgbm_predict))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djmKFJ7yQSHq",
        "outputId": "80a65946-81a3-479f-90f2-79d0496fa82b"
      },
      "id": "djmKFJ7yQSHq",
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE test score: 18418\n",
            "MSE test score: 1163546281\n",
            "RMSE test score: 34110\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7gvE4wWQf7z",
        "outputId": "8f37555c-2153-49f4-a193-bdfed3b0a9e5"
      },
      "id": "K7gvE4wWQf7z",
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181370.38356164383"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion:**\n",
        "\n",
        "I did XGBoost for milestone-2 and switch to LGBMRegressor for milestone-3 and the baseline model is already better than the XGBoost, with RMSE = 26233."
      ],
      "metadata": {
        "id": "NHphf01WyY6s"
      },
      "id": "NHphf01WyY6s"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###SHAP for LGBM baseline"
      ],
      "metadata": {
        "id": "kuRo5MwFpEIN"
      },
      "id": "kuRo5MwFpEIN"
    },
    {
      "cell_type": "code",
      "source": [
        "explainer_lgbm_baseline = shap.TreeExplainer(reg_lgbm_baseline)\n",
        "shap_interaction_lgbm_baseline = explainer_lgbm_baseline.shap_interaction_values(X_train)\n",
        "# Get SHAP values\n",
        "shap_values_lgbm_baseline = explainer_lgbm_baseline(X_train)"
      ],
      "metadata": {
        "id": "4mN5YD7VpIM_"
      },
      "id": "4mN5YD7VpIM_",
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Waterfall plot for first observation\n",
        "shap.plots.waterfall(shap_values_lgbm_baseline[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "g5mesgOG1sev",
        "outputId": "dd7d6e13-b79a-4d06-cc97-0dffefea0c02"
      },
      "id": "g5mesgOG1sev",
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mean SHAP\n",
        "shap.plots.bar(shap_values_lgbm_baseline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "YuPkn4ma15D-",
        "outputId": "5259e3aa-0828-4017-9059-55eb48126915"
      },
      "id": "YuPkn4ma15D-",
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_values_lgbm_baseline, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "9Q3rWf--1-XD",
        "outputId": "d7931d0a-f9e6-4b45-dedf-4a7fb06fbfb0"
      },
      "id": "9Q3rWf--1-XD",
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_interaction_lgbm_baseline, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "53n5XJ9p2Emp",
        "outputId": "f33556e3-41a4-418f-d17b-09d85e159c36"
      },
      "id": "53n5XJ9p2Emp",
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1150x660 with 7 Axes>"
            ],
            "image/png": 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AhQsXMnz4cFq1asWkSZOoqanhnXfeoW/fvqxdu9YnuXXNNdfQpk0bXn755Wbf5+fxXuce7z2Cv8+6octYt24dw4YNIyEhgcmTJ+NyuXjhhRd8KgXAie+7dRRF4frrr+fVV1+luLiYyMhIz7TvvvuO8vJyz3H2yHUqisLjjz9Ofn4+b775JhdddBHr16/HbDYDtYnM4cOH06NHD55//nk0Gg1Tp05lyJAhLF26lF69ejXoc290gavcJMTpU1paqgLq5Zdffsz5LrvsMhVQy8vL1euuu06NjY1VnU6nZ3pOTo6q0WjUF154wVN24YUXql26dFGtVqunzO12q3369FHbtGnjKaur+tuvXz+vZaqqqlZXV/vEsmLFChVQP/30U09ZQ5qkjR49WgXUkpKSerfzm2++UQH17bffrne5qnq4yu+RVf79xfrll1+qgPrbb7/5bO/Z2iStrKxMBdTRo0f7TCspKVELCgo8f3Wf2Y033qgC6hNPPOHznoZUwz76My0rK1ONRqP6yCOPeM336quvqoqiqPv27fMqHzlypNq3b1/P6w8//FDV6XQ+TTTri3Pp0qUqoE6bNs2rfP78+T7l/vaTO+64Qw0KCvL6rZxtjm6SNnv2bBVQ//73v3vNd/XVV6uKoqi7d+/2lNXXJO1kjw+Az19SUpK6Zs0ar2V269ZNHTFixDG3r255L7/8sqespKRENZvNqqIo6vTp0z3l27dv92macqymIhMmTFABNSIiQr3iiivU119/Xd22bZvPfHXb6e/vbD3e1Ofee+9V9Xq9GhwcrF533XWqqjb8nGS1WlWXy+W1vIyMDNVoNHqd4+o+71atWvnsi/V9FxqNRn3ppZd8ln30+aTO0fuJv/OHNEmrX93ndfSf0WhU//e//3nme+CBB9TQ0FCfaxB/yxo6dKjqdrs95RdccIGqKIp65513esqcTqeanJzs9b00pEna0X911zD+jmEDBw70OdbZbDY1Pj5eveqqq/xugzRJa5gjmzLu2bNH1el06v333++ZfmSTtMzMTFWr1fr8rjdt2qTqdDqv8qOvS7/++msVUN98801PmcvlUocMGeLzG647xxx5DFJVVT3nnHPUHj16eF7XHU/MZrN68OBBT/nKlStVQH3ooYc8Zd27d1djY2PVoqIiT9mGDRtUjUajTpgwwVNWt3/WHUubuxO5zm3oPcKxPuuGLmPUqFFqUFCQV1PKXbt2qTqdzus67Hj23SMd3SRtx44dKqC+//77XvNddtllampqqud4WXccS0pKUsvLyz3zffXVVyqgvvXWW6qq1p6X27Rp43Osra6uVtPS0tSLL77Yb1yBcPY/hhOC2qefACEhIcecr256eXk51157Lfn5+V5Vo2fNmoXb7ebaa68Fap+SLVq0iDFjxniesBYWFlJUVMTQoUPZtWuXz8gTt912G1qt1qusLtMMtc1CioqKaN26NeHh4axdu/aUb2vdtLp5j8eRsVqtVgoLCz2d0R5vrGeyuuYY/kY2GzRoEDExMZ6/uuY6dY5+Qnai6pqQfPXVV15PwWbMmMH5559Py5YtPWVFRUUsWLCA6667zlN21VVXoSgKX331ld/lHx3nzJkzCQsL4+KLL/bs64WFhfTo0YPg4GAWL17smffI/aTut9G/f3+qq6vZvn37SW/7meLHH39Eq9Vy//33e5U/8sgjqKraoBGMTvb4YDKZ+Pnnn/n5559ZsGABU6ZMITg4mEsvvZSdO3d65gsPD2fLli3s2rXrL5d5ZAez4eHhtGvXDovFwpgxYzzl7dq1Izw8nL179/7l8gCmTp3Kf/7zH9LS0vj222959NFH6dChAxdeeKHfEXyee+45z3bV/dU1tWsuXnrpJaKiotBoNLzxxhvHdU4yGo2e2lgul4uioiKCg4Np166d3/3qxhtv9NoXj3TkdzFjxgyuu+46nn76aa+mTqLxvfvuu57v4fPPP2fw4MHceuutfPPNN0Dtb7WqqsqrmWl9brnlFq/aAL1790ZVVW655RZPmVarpWfPng3+jdf5+uuvvX63fzWiXnBwsNfTe4PBQK9evY57vaJ+rVq14oYbbuDDDz8kJyfHZ/o333yD2+1mzJgxXuf/+Ph42rRp43X+P9r8+fPR6/XcdtttnjKNRnPMDvHvvPNOr9f9+/f3+32PHj2apKQkz+tevXrRu3dvfvzxRwBycnJYv349N910k1etkK5du3LxxRd75jvWupurE7nOPd57BH+fdUOW4XK5WLhwIaNHj/aqEde6dWufWocns+8eqW3btvTu3dvreFVcXMy8efMYP368T+2pCRMmeN2LXX311SQkJHj2ufXr17Nr1y7GjRtHUVGRJ66qqiouvPBCfvvttybTebw0SRPNQkMTJEcmW+rak86YMcPTbGvGjBl0796dtm3bArB7925UVeXZZ5/l2Wef9bvM/Px8r5NZWlqazzw1NTX84x//YOrUqWRlZXnd/JeVlR3Hlnpva31VLOu280T6BSkuLmby5MlMnz6d/Px8r2nHG+uZrO5zrqys9Jk2ZcoUKioqyMvL86miqtPpPH1XnQrXXnsts2fPZsWKFfTp04c9e/awZs0a3nzzTa/5ZsyYgcPh4JxzzmH37t2e8rqT39EXbv7i3LVrF2VlZfXuN0fuD1u2bOGZZ55h0aJFPn2dNKf9ZN++fSQmJvokcOuaY+3bt+8vl3GyxwetVstFF13kVXbppZfSpk0bnnzySb7++mugdoSlyy+/nLZt29K5c2eGDRvGDTfc4NOcxGQy+VT5DgsLIzk52eeCKSwszKd/q/rU3UDcc889FBUVsWzZMj744APmzZvH2LFjWbp0qdf8Xbp08dmu5iY0NJR27dpRWFhIXFwcq1atavA5ye1289Zbb/Hee++RkZHh1T9IVFSUz/v8nbvqHP1djBkzhrKyMp544gnGjRvnt4mAOPV69erl1XfdddddxznnnMO9997LyJEjufvuu/nqq68YPnw4SUlJXHLJJYwZM4Zhw4b5LOvIBw5Q+1sGPH2VHFne0N94nQEDBhAdHd3g+f0dWyIiIs6K4bybkmeeeYbPPvuMV155xSfZu2vXLlRVpU2bNn7fW9cc3Z99+/aRkJDg09S4devWfuf3d46JiIjwu5/5i6dt27aeB2F159h27dr5zNehQwcWLFhAVVUVFovFU36sY11zciLXucd7j+Dvs27IMvLz86mpqfG7Dx1ddjL77tEmTJjAvffey759+0hJSWHmzJk4HA5uuOEGn3mPXp+iKLRu3drTN1/dw7n6mmZC7fYeq7nu6SIJI9EshIWFkZCQ8JcXFxs3biQpKYnQ0FCg9snFt99+y3vvvUdeXh7Lli3j5Zdf9sxfl/l99NFHGTp0qN9lHn3g8veE9r777mPq1Kk8+OCDXHDBBYSFhaEoCmPHjj3u7HLHjh2ZPXs2GzduZMCAAfVuJ+DpMLm+fof8dTA4ZswYli9fzt/+9je6d+9OcHAwbrebYcOGNZlM+OlQt0/561yxrq23vw5bj3yqfyqMGjWKoKAgvvrqK/r06cNXX32FRqPxdC5cp+6JiL9OhaG2P5MjO9D2F6fb7SY2Nrbep8F1F3ilpaUMHDiQ0NBQXnjhBdLT0zGZTKxdu5bHH3+8We0np8KpPD7USU5Opl27dvz222+esgEDBrBnzx7mzJnDTz/9xMcff8wbb7zBBx984FWj6Ogakn9VfmSCq6GioqK47LLLuOyyyxg0aBC//vqr5wJN1O94zkkvv/wyzz77LDfffDMvvviip/+nBx980O9+VV/tovpceOGFfP/996xatYoRI0Yc13lGnBoajYbBgwfz1ltvsWvXLjp16sT69etZsGAB8+bNY968eUydOpUJEybwySefeL33eH7nJ/IbPx6n8tgi6teqVSuuv/56PvzwQ5544gmvaW63G0VRmDdvnt/vw18tlBNV3/d9uhzvse5sdSLXucd7j+Dvsz7V9xmnct8dO3YsDz30ENOmTeOpp57i888/p2fPnn4Tkg2JC2oHZerevbvfeU7l7+pkSMJINBsjR47ko48+4vfff/c76sHSpUvJzMz09HIPtbU3PvnkE3755Re2bduGqqqe5mhwOOGi1+tP6kn3rFmzuPHGG/nXv/7lKbNarZSWlh73skaNGsXLL7/Mp59+6jdh5HK5+OKLL4iLi/NMr8teH72+o2s/lJSU8MsvvzB58mSee+45T3lDmrCcjUaMGMHHH3/MqlWrAtYxncViYeTIkcycOZN///vfzJgxg/79+3tV0c3IyGD58uXce++9Pp24u91ubrjhBr744gueeeaZY64rPT2dhQsX0rdv32NeUC1ZsoSioiK++eYbr30wIyPjBLfyzJWSksLChQupqKjwqmVU1yzvyARIfTfUp/L4cCSn0+nz5DAyMpKJEycyceJEKisrGTBgAJMmTfJKGJ2ME+kUv2fPnvz666/k5ORIwugvHM85adasWQwePNinY+rS0tLjqv1RH6fTCRx+Ot3Q80xDNacBFk7G0d+DwWBg1KhRjBo1Crfbzd13382UKVN49tln663xcTzkezmzPfPMM3z++eeeTtHrpKeno6oqaWlpnlr2DZWSksLixYuprq72qmV0ZG3nE+Xv+nPnzp2ejqzrzhk7duzwmW/79u1ER0d71S4S3o7nOvdU3CM0dBmxsbGYTCa/+9DRZSez7x4tMjKSESNGMG3aNMaPH8+yZct8avTXF7OqquzevdtTa7tuYJPQ0NAmX1ta+jASzcbf/vY3zGYzd9xxh8/QjMXFxdx5550EBQXxt7/9zVN+0UUXERkZyYwZM5gxYwa9evXyqj4ZGxvLoEGDmDJlit823wUFBQ2KTavV+jwpe+edd07oyev555/PJZdcwtSpU/n+++99pj/99NPs3LmTxx57zDPUbkpKClqt1qu2AcB7773nEyf4PtWr72B5tnvssccICgri5ptvJi8vz2f66Xr6ee2115Kdnc3HH3/Mhg0bvJKacLh20WOPPcbVV1/t9TdmzBgGDhz4l31IQO1TH5fLxYsvvugzzel0em4E/e0ndrvdZ39qDi699FJcLhf/+c9/vMrfeOMNFEXxamtvsVj8JoFO5fGhzs6dO9mxYwfdunXzlB19XAwODqZ169bYbLYTXs/R6i7Mj97O3Nxctm7d6jO/3W7nl19+QaPRnJKb2bPd8ZyT/O1XM2fO9Ntf1ImoO//U7WOhoaFER0f/5Xmmoerbl8RhDoeDn376CYPBQIcOHXx+4xqNxnPzcqp+53UJgab4vezfv9+nD73CwkK2b99OdXW1p6yur72jR2FsDtLT07n++uuZMmUKubm5nvIrr7wSrVbL5MmTfY4bqqr6HfK8ztChQ3E4HHz00UeeMrfb7dO/44mYPXu21zFr1apVrFy50nNuTUhIoHv37nzyySde++TmzZv56aefuPTSS/9yHXv27GHPnj1eZTk5OWzfvh2Hw+EpKysrY/v27WdVs/vjuc49FfcIDV1GXVP72bNnk52d7SnfvXu3T9+QJ7Pv+nPDDTewdetW/va3v6HVahk7dqzf+epG8Ksza9YscnJyPPtmjx49SE9P5/XXX/fb7K+h95Cng9QwEs1GmzZt+OSTTxg/fjxdunThlltuIS0tjczMTP773/9SWFjIl19+6cn4Qu1T2iuvvJLp06dTVVXF66+/7rPcd999l379+tGlSxduu+02WrVqRV5eHitWrODgwYNs2LDhL2MbOXIkn332GWFhYXTs2JEVK1awcOFCv/1INMSnn37KkCFDuPzyyxk3bhz9+/fHZrPxzTffsGTJEq6//noeeughz/xhYWFcc801vPPOOyiKQnp6Ot9//71P2+HQ0FDPUNoOh4OkpCR++umnZllzBGr3qS+++ILrrruOdu3aMX78eLp164aqqmRkZPDFF1+g0Wga3GeRw+Hg73//u095ZGQkd999d73vu/TSSwkJCeHRRx9Fq9Vy1VVXeU2fNm0a3bt39+l7os5ll13Gfffdx9q1azn33HPrXc/AgQO54447+Mc//sH69eu55JJL0Ov17Nq1i5kzZ/LWW29x9dVX06dPHyIiIrjxxhu5//77URSFzz77rFk2Hxg1ahSDBw/m6aefJjMzk27duvHTTz8xZ84cHnzwQa/jTY8ePVi4cCH//ve/SUxMJC0tjd69e5/08cHpdPL5558DtRfpmZmZfPDBB7jdbp5//nnPfB07dmTQoEH06NGDyMhIVq9ezaxZs7j33ntP2efRvXt3tFot//znPykrK8NoNDJkyBAOHjxIr169GDJkCBdeeCHx8fHk5+fz5ZdfsmHDBh588MFTUuulOWjoOWnkyJG88MILTJw4kT59+rBp0yamTZvm1TS1oZYuXYrVagVqH8DMnTuXX3/9lbFjx9K+fXvPfLfeeiuvvPIKt956Kz179uS3337z6nj9eKSnpxMeHs4HH3xASEgIFouF3r17N+v+R+bNm+dJiOTn5/PFF1+wa9cunnjiCUJDQ7niiisoLi5myJAhJCcns2/fPt555x26d+/u6VftZJnNZjp27MiMGTNo27YtkZGRdO7cmc6dO5+S5Z+MCRMm8Ouvv3qdi/7zn/8wefJkFi9ezKBBg4DapMPgwYN5/vnnmTRpUmCCDaCnn36azz77jB07dtCpUyeg9vf297//nSeffJLMzExGjx5NSEgIGRkZfPvtt9x+++08+uijfpc3evRoevXqxSOPPMLu3btp3749c+fOpbi4GDi5WmmtW7emX79+3HXXXdhsNt58802ioqJ47LHHPPO89tprDB8+nAsuuIBbbrmFmpoa3nnnHcLCwhr0/db1Y3pk86snn3ySTz75hIyMDE9tpm+//ZaJEycydepUbrrpphPepqbkeK5zT8U9wvEsY9KkSfz000/07duXu+66y/NwrnPnzqxfv94z38nsu/6MGDGCqKgoZs6cyfDhw+vt1zMyMpJ+/foxceJE8vLyePPNN2ndurWn83eNRsPHH3/M8OHD6dSpExMnTiQpKYmsrCwWL15MaGgo3333XYPjalSNPg6bEE3Mxo0b1euuu05NSEhQ9Xq9Gh8fr1533XXqpk2b/M7/888/q4CqKIp64MABv/Ps2bNHnTBhghofH6/q9Xo1KSlJHTlypDpr1izPPEcOX3q0kpISdeLEiWp0dLQaHBysDh06VN2+fbuakpLiNcx2fcNmHzl8aZ2Kigp18uTJaqdOnVSTyeQZvvbZZ5/1uw0FBQXqVVddpQYFBakRERHqHXfcoW7evNlnyNODBw+qV1xxhRoeHq6GhYWp11xzjZqdnd2gYZHPVrt371bvuusutXXr1qrJZFLNZrPavn179c4771TXr1/vme/GG29ULRaL32XUN/w5oKanp6uqeuzPdPz48SqgXnTRRV7la9asOeb3rqq1Q45yxDC0x4pTVVX1ww8/VHv06KGazWY1JCRE7dKli/rYY4+p2dnZnnmWLVumnn/++arZbFYTExPVxx57TF2wYMFZP9TxPffcox59aq2oqFAfeughNTExUdXr9WqbNm3U1157zWsYVVWtHYZ+wIABqtlsVgHPb/9kjw9H70+hoaHqhRdeqC5cuNBr/X//+9/VXr16qeHh4Z59+KWXXlLtdrvX8vztG0cOv3yklJQUdcSIEV5lH330kdqqVStVq9V64i0vL1ffeustdejQoWpycrKq1+vVkJAQ9YILLlA/+ugjr8+qbjtnzpzp/0toZvx99g05J1mtVvWRRx5RExISVLPZrPbt21ddsWKFz/D1x/q866Yd+WcwGPzuO6paO1zwLbfcooaFhakhISHqmDFj1Pz8/AadP46OS1VVdc6cOWrHjh09wygfea5qTuo+ryP/TCaT2r17d/X999/3/H5mzZqlXnLJJWpsbKxqMBjUli1bqnfccYeak5Pjs6yjr1XqhsEuKCjwKvd3TFi+fLnao0cP1WAweH239S2jjr9jWH3HlvqufVRVVWfOnOn3XDNw4ECf43NdTEfOWxfHkfvk2ehY16V1546jP/uvv/5a7devn2qxWFSLxaK2b99eveeee9QdO3Z4vffo76agoEAdN26cGhISooaFhak33XSTumzZMhVQp0+f7vVef+eYuu+pTkZGhgqor732mvqvf/1LbdGihWo0GtX+/furGzZs8Hn/woUL1b59+6pms1kNDQ1VR40apW7dutXvOo7eP1NSUny2p+7zOfIYVfd5no3HoYZe5zb0HuFYx4KGLkNVVfWXX35RzznnHNVgMKjp6enqxx9/rD7yyCOqyWTyWW5D9t0jderUyeecU+fuu+9WAfWLL77wmVZ3/Pjyyy/VJ598Uo2NjVXNZrM6YsQIdd++fT7zr1u3Tr3yyivVqKgo1Wg0qikpKeqYMWPUX375xe+6A0FR1Wb4yFeIZigrK4s+ffrgdDpZsWKFzwgoQgghhBBCnA6zZ8/miiuu4Pfff693QA4hjtfo0aPZsmVLo/av+tBDD/Hf//6X3Nxcn9H/lixZwuDBg5k5cyZXX311o8VwOkkfRkI0E0lJScyfPx+r1crw4cOPexhcIYQQQgghjldNTY3Xa5fLxTvvvENoaOgxm8ILcSxH71e7du3ixx9/9DQvbQxWq5XPP/+cq666yidZdLaSPoyEaEb8dXophBBCCCFEY7nvvvuoqanhggsu8PSpuXz5cl5++WUZxl6csFatWnHTTTfRqlUr9u3bx/vvv4/BYPDqw+pUyc/PZ+HChcyaNYuioiIeeOCBU76OpkoSRkIIIYQQQgghGsWQIUP417/+xffff4/VaqV169a88847p3RQBdH8DBs2jC+//JLc3FyMRiMXXHABL7/8Mm3atDnl69q6dSvjx48nNjaWt99+m+7du5/ydTRV0oeREEIIIYQQQgghhPAifRgJIYQQQgghhBBCCC+SMBJCCCGEEEIIIYQQXiRhJIQQQgghhBBCCCG8SKfXQjRhDoeDqVOnAjBx4kT0en2AIxJNnewz4njJPiNOhOw34njJPiOOl+wz4njJPnPqSQ0jIYQQQgghhBBCCOFFEkZCCCGEEEIIIYQQwoskjIQQQgghhBBCCCGEF0kYCSGEEEIIIYQQQggvkjASQgghhBBCCCGEEF4kYSSEEEIIIYQQQgghvEjCSAghhBBCCCGEEEJ4kYSREEIIIYQQQgghhPAiCSMhhBBCCCGEEEII4UUSRkIIIYQQQgghhBDCiySMhBBCCCGEEEIIIYQXSRgJIYQQQgghhBBCCC+SMBJCCCGEEEIIIYQQXiRhJIQQQgghhBBCCCG8SMJICCGEEEIIIYQQQniRhJEQQgghhBBCCCGE8CIJIyGEEEIIIYQQQgjhRRJGQgghhBBCCCGEEMKLJIyEEEIIIYQQQgghhBdJGAkhhBBCCCGEEEIIL5IwEkIIIYQQQgghhBBeJGEkhBBCCCGEEEIIIbxIwkgIIYQQQgghhBBCeJGEkRBCCCGEEEIIIYTwIgkjIYQQQgghhBBCCOFFEkZCCCGEEEIIIYQQwoskjIQQQgghhBBCCCGEF0kYCSGEEEIIIYQQQggvkjASQgghhBBCCCGEEF4kYSSEEEIIIYQQQgghvEjCSAghhBBCCCGEEEJ4kYSREEIIIYQQQgghhPAiCSMhhBBCCCGEEEII4UUSRkIIIYQQQgghhBDCiySMhBBCCCGEEEIIIYQXSRgJIYQQQgghhBBCCC+SMBJCCCGEEEIIIYQQXiRhJIQQQgghhBBCCCG8SMJICCGEEEIIIYQQQniRhJEQQgghhBBCCCGE8CIJIyGEEEIIIYQQQgjhRRJGQgghhBBCCCGEEMKLJIyEEEIIIYQQQgghhBdJGAnRyCqr3VRWuwMdhhBCCCGEEEII0WC6QAcgxNnKanNz0xM5lFeqAMREapjyQjwmo+RpxemxeaedWT+V4nCojBwczIDzggIdkmjKMvXMvOgXrAU2YrqGM+StXljizIGOSjRhMRmlGKxOsDtBrw90OKKJK61wk1UcT7CpKtChiDOEK7uS1JlW3KUGykr+JOqh3igGbaDDEk1diQbydVTl1BDeUs5NJ0vuXIVoJOMezvYkiwAKit088kp+ACMSzUl+WRRPv1nCuq02Nu+y88qHxXy/pDLQYYmmqkJB81UYNVnVqHYX+auLmDl6SaCjEk1VtQ3txZNp9395xM6qwt3pIdiRFeioRBP2+5pq7n/4IPm/t2DD8u68O6080CGJJs5tdTLtxpX8p8XlvN19JPdsjGHlbUsCHZZo4ja8sZWotwyEz9Dz7ZCf2frpnkCHdMaTGkZCNAK3W8Vq9y3POOg8/cGIZmlTRkefss+/LGLkoOAARCOaOvcvIRz9zNZVUENxRgWRaSEBiUk0XdYPFvJM8EVsvaw1ABFVZfzjyTm0/ObuAEcmmiKnU2X2P3Zy4fq9KIfK9mXls6Xf+XRqbQxobKLp2vbFHr5u3QmU2r2mIsjMq/Y0viqsQhdtCXB0oikqXZlL3utrKQ0Nqy1QVda8sJb0y1pgDDcENrgzmNQwEqIR7N3vJ1t0hLx1Rez57gBOm+s0RSSaG1eV7/MApdwWgEjEmcC8F0wOB+0K8uicm0VKSRGK283eHVIrTfj6dr2GrQmtPa9LLGG8TdcARiSasqJCB+mb93mSRQApeUXsXFIQsJhE0/fVHr0nWVTHajCQlSvXzsK/3U8sI7cuWQSgKNhVDYWbSwIX1FlAahgJ0Qj2HKwnYaSqfNbjO+xlDk9R76e70Hlim9MUmWgukgqK2ZWY6HWxlX4wj5KDyUQkS19GwpsdPamVhXw58Hwy4mKILq/gwvVbWL+6ip7DAh2daGp+MqT5lO2IaBGASMSZwFBahcHpe5MfuTcPSD79AYkzgr60EEgk1FpJdGUJ+yITAXCGSZ80wr+cvdUoWjOqprZOjAqgKPKA/iRJwkiIRpDeop4q1ipeySKAlS9tosP4dLQGqfAnTp0aow6jw4nNUHthpbhV9A4nLpMc9oWvUJuVTwb3JTciHIDC0BC+7nMeI7dlA62P+V7R/JS7jRzdhtGN4n9m0ewF2ezo3C6cmsM7jQq03JMF9AhYXKJpC7bbuG7tPMau/wm920VhUBgvX3gzpupQQAZkEL5qVIVySxCZCdGoikJKbiE6hwOl6tgtP8SxyR2qEI3AEuT/p6Vz+89wb5+R0ZjhiGZod2KsJ1kEoGoUVnZsRVCwJIyEr+zwMHDB4NVbuHTZOrrvyEQF8rSmQIcmmiCz1epTpnPJE1zhn0OrIdKdTYWx9pxk12r4plc7luVLklHUz6S6uGHtPPSHrp2jq8t4+LdpFLnlvCT8y4yJ4pfzOrEzJZFdLRNY1LMTVUEmbOGyz5wMSRgJ0Qjm/lLhtzzIX0/YQN6f0o5fnFoOje+ws1aDnjf+kJs64WtPbDQXbN5JZEUVZruD9Ox8ztmVSU2N+tdvFs2OTaMD9fC+oagqilv2FeFfVYGV1YltCLHV1rA2uNz0236AKR3bBjgy0ZT1yNrhU5Zclk8LR3EAohFnguWd2uLSHr7+dWs0bElrwXxHaACjOvNJwkiIRlBj9X/h3H37Xv9v0MhTNnFqhVX6Jid1Lhe/zCigqNodgIhEU5aSX4LmqMNWcl4xcSVyYS582bU6r/7RVEXB6JBRQIV/pduKaJlf41UWV15NaJk0ExH1K9P4NjsrDArD7paa0sI/u16Hwe6gy+79DFi3jc57DmBwOlm9sSrQoZ3RJGEkRCO4dJD/4T6Da/yPUhWaKkOdi1PIBSaHb00iHQoGp5tF62W0NOHNbPO9cXPotORGRQUgGtHU+UsO6dxu3C6pZSR8WfdUgZ8+rrpn5J7+YMQZ48vkC1iSeriPK6tWzzt9r6HKIP0XCf9aH8il34YdtD2QS0xpBe3253Dujkx6L98W6NDOaJIwEqIRpLc0+C0vs/g/ySX3i2vMcERzU6khotz/cOh6lwutn9pHonlz6jRUG72PWztSEtGYfZs2ChFf6FvzLK64DGuJJKOFr8IKF8E2736vFFWl1CL9ioj6uQqs7Na2Y01EF/YFJbEi6jx+TunIqkypJS38WL2bpMISIiqrvYrNdgcJ+WUBCursIAkjIRrBpu01fsszkuP9ltvKHX7LG6ris03sC/sXmYZ/kj3wc9x26aemWQtxk5Rf5NXHSB2XRoPz130BCEo0ZSHVVralJrIlNYm9iTH83rUtOVHhGArKAx2aaILMNisal6v2GKOqGOwOYopLcUs/RsKPVdVmfuvQmgqTETfg1CjsSoqlWiPDo4v6tcs8wKXZi+hRsomU6iwG5y/npfmfs98h3TgIP/75LXsT/T+Az1b8P8gXDSONQIVoBKu3+I4gA1AcEuS3vGxvGQxJOKF1VczYStGE7z2v7b8d4EDc26SUPHRCyxNnAQ1sbJPq1cdIHUVV2ZchtQCEt/zwMM7ZuY+MxBjyI8KILK8ipricrGD/zWtF87a+dRoXr9laO5CDAgaHk/WtW6Ja5KJc+LLtKyXSbSDEWnvu0bhVWmcXEBQpHdGK+rUpOUBijXezxQFZ68mqLAbCAhOUaLrWZxCVXEmwzUql8XDtRa3qJksj56aTIQkjIRpB5kH/NYY679nvt1xrOvGfYtENc33K1FIrjpxy9AlyMdYcWStMWE0GIksrQIHisBAAjHY7HfZmUZIeHtgARZOjcYNGVUnPyic9Kx+AwrBgtrZKCnBkokmxObD/+2dS8hPQu1zsTYpF63bTMq+Idgdy0GvOCXSEogmKLKnBaPdtJl3fyLFCAERV+444rFXd9DiYBaSd/oBE02Z3ct6eDDrl5JIRHkWF0USorYZIazWze7QLdHRnNEkYCdEIIkJ1gG/SyGL1n0gyRxpPfGUO/00Aqr/aTtgDvU58ueKMZXA5UIGE4lLa78uhzGLGodMSVVaJqkBpsFTnFt6KwixEH9XvVVFoMG6NBodDRa+XfabZc7upPudF8reZ0AyP5+fzuuDW1vZssKNlAoPXbCXnYA1pbWQQB+FNUVV0fpormm12VFVF8VMbVogybTh2RY9BPXztXGgMpyg8MoBRiSZLo1BuMmJwuWhXlO8pzgkLpcIszV9PhvRhJEQjiI7wf/GzP9b/Sc4YfmIJI3dN/X0fmQa0OKFlijOfxujCplWoNNdWyTXZHNQYDRSGh5AfYsHkPLk+s8TZpzQ4iPzwEM/rgvAQdqQkAqCRfq8FwA9rKN3mRoeNwogQT7IIwG7Q1/aBlS19GAlfoUoBVp2GI7sqtms1GF1OdhVLB8bCP5vWxML4AZTrLLhQyDVG81tUXzKjpDma8KOsmpVtUqkwed9T7Y2Movve7AAFdXaQGkZCNAKn23/CKL7Ut3otQOnuCpL6xB7/enb6jlRTR98++riXJ858VTk16D8Ixz7AzYHYKCLKq9jUugUube1dvwM3Rq2D+wIcp2haOmZmowK54aE4dVoqgkwk5RWRFRuBViNP/wWoP6xBg4N4tmM1+vYHkRUTgSOzFAaG+L5ZNGvn5W3jgeG3sScuigt2HsCm17G0Q0suW7OdrgUOiJKstPBl1+nol7uSUGcVAHG2QuKt+azQSN96wo/SauwaI9+ccw4Dtu1E73aRFxyKVWOgZZH/+y/RMJIwEqIR5Bb6r8HhqufGy36Cw5zrWkfUO82ZVYGhtVTbbW6WTPgNt0PHNcs28HuXtqxrm+JV3V+PBrMMfS2OolHd/Na9A9XmIzqKdLlomZ2Pw5EmTdIEjiqwkEchyVy74k/ywkJZ1q4NNkNtVX+XVosxryrAUYqmqMgcyh9tkzHZnRyMDWFLUixFwRY2pCbgtDoDHZ5oojqUbafEEM7ymF6oKLQr38V5JWtZvqsACA90eKKpUVVC7VUE1djZGxXjNUkrI3ieFEkYCdEIjDr/rT2r/DyVBdAFndjTNcVQ//tsa3IlYXQ2q7bB7hxokwDm2uq3bpdK0QEbqqKwOzmO8mAzGnxv9MssJp8y0bxtSWtBldnktbe4tFpyYqNQHS7Qy+VCc1dYHY6LFFyY6L99F/lBFnpt30OFxcyGtBasbdmS3Gzfjo2F+LbjBVy1eitPfb8Yg8uNTatl8uVD2NoimXV/lnFBd+n3SnhTVRWnXmFh5CBPWXZQAoNyf6O6RJKMwpcbMDltVASZiKzwfngRVVUTmKDOEtKHkRCN4JxO/vskKg31f1FkKz6xPmVU5zHa/iuSTT9bqV8sxRV5E85uj+OOvAnX1CUAlOwuB6DcYuSPdiksSY+lOMg3SRldXI6zRi64BDhzK3GX2dgTH8neCN9q/lajgY0/5AQgMtHU2HMcuKg9t+0Nj8TgdtOyrJRO2TmMW7aKa1atpmCbVPsXvip0QTz1/WKWtm3FW0MHsLFlEs/PXcT439ZQ5pBktPC1dUclm6La+5RvD2tLSbA5ABGJpk4BMqLj2J6ahNVwuJPrKqOBrNgwXC65LzpRcpQW4hQrt6nYtf5zsXmR/jvqC0s/sfbY9h3192FkOP8UD4edXwYT3oKtB+HK3vDqBDDIqAOn3by1qOPfRosLFzpKrTFYbv4vmlHnUrgsB6dWw/rWMWxICMeh07A5LpRzs0oJsTtR3G7aHMil896D7Pw6hI7Xpwd6a0SAOPOryOk5FdeBClRgyzN3YTUYaFNUgd7lwqav/W0HV9dQmuUKbLCiSTDsOYAdBTdQZjLTqtT7/HPenky+O6dzYIITTdqAnZk8Mm40LmPttc6OFikkFRRw5fI/Wac5N8DRiaaoZv42dkXH0S7b+zhzIDyalIp8IDUgcYmmSwVqDGbsegMLenclorwSu05HucWMqlGIm1vKDVfU35WHqJ/UMBLiFBo+y0nYOy4u/dVAick3mWJw+q/VUbSt/MRWaK+/loguKujElulPeRXE3wwL1sOBQnjrh9rX4vRyunBf8Rp2gighiWrCCSOLEhKwfbCM4tdXUhBu5Jd2rXEcahZZbdDxe1o0v6ZFo6muos3BPBSgdI/UBGjOcnp/gutA7T6gAIXBZq78cyMvfzGLNz6Zzr3zFhJcXUWrg/kcDAsNbLCiSQjL344bUBUFjer7pFYDGOzW0x6XaPqsBjd2k/eDsayYGFa2TiUpp/4HX6L5cthgfrfWHD2GzILObeiWdTAwQYkmL6GsEK3TiVOnpSAyjLJQC6pWA4rC/AUlgQ7vjCUJo0Zy++23M2rUKK+ySZMm0bNnzwBF1HCjRo3i9ttvD3QYZ5znf3cyP/PQC0VhT5RvraG0rHy/763Yd2IdhWpj66+ZpDGdwgqEF06Co28QSqrgy6Wnbh3iL6lbDlBhiyCX9pSRSBFp5NGeUPKomLUTy55C8sJCuH/eSjrtz2PSV4v58IO5/G3OMkKraoiocbAlLYkqo56wOKlg2pw5M8sACKKYaPbyxSfvMum7BYRaaztE75iVw8RflrIvPpqavBPrlF+cRWwOtDjRAFpVRaO6sWu8+9Cr1Ou5bfVsSrZIAkAc5nCoZCaEovXTGmRFh3ZYamQQBuFruzWM3PBg3hhxAWvSEliXGs9bw3ujtyuU7c0NdHiiCVKA8b8u4ekF/yWuvNBnenXlMbrxEMfUJO4YKisrmT59OosXL+bAgQO4XC4SExPp168f119/PVFRUYEOsdFlZWXx2WefsXLlSvLz89Hr9aSlpTF06FCuvPJKDAb/nSWLpuOVVd6vC4JNrE0Mp1tOqedCqc0B/yc5zQmOQKRLrv/Jv7PKhj70FLXzXr3Hf/nHP8N1/U/NOsRfspe6KSWBEPIJJRcFN1VEoaLBuqEEi2Ki+948UDTcN38VukOjQrTLKeKxOcsItdbe+FeajKgLNsCd0nyk+VIJI5sIsgG4ZtvvONGTTWfchy4N2uUXkp6VS8yFqQGMUzQJ3/1JDbXXYmZK6FeYyZaw9lhqDFjsNkpNZnbGxNGjpADnqr3QSQZcELVKcqu4cuMy1qT28BmEIdRqY60xmqsDFJtouorX5THCXsang7qzLbl2xKsWBaUM2raPCu2JDRQjzl7VCzOwE03fkj9Y1aITeaHRPvM4FRnt9UQFPGG0b98+7rvvPnJychg8eDCXX345Op2OTZs28eWXXzJ37lzeeOMNunbtGuhQG82SJUt4+umn0Wq1jBgxgrZt22K1Wlm2bBmvv/46P/zwA2+99RaRkXIB1pTZ/SSu84ONuBQF7aHaOfUdqpIHxp/QOq1L99c7reCa2SQuuO6ElutlV3b90wpPsCldQ/y+Dca9ATnF4JjVeOtp6jLzYf662lHRcioxUUEU+zyTw8ilinA0OCg1RYBSW3FUd9QQonXJIoBgq42DO6xIuqiZefpz+GIp1ugYFAyEkec1WYcDC8VUEAtAjU5PenYBReYOgYhWNCVLt+FCSzCFRJMBbuhXUoAbhRw6khEUjU2nx6nowC410sRhpux8kgrdXLlyLbN79/CaZjUaqd6VD7QMTHCiyYpal8W+QT1waw43hjE5nBicLuJKpUm9OKzq2+0UXPktyZQcamLvv58irZ+m1KJhApowslqtPPTQQ+Tn5/PGG2/Qr18/z7Qrr7ySa665hrvvvptHHnmE6dOnB6SmkdPpxOVyYTT6H/XqZO3evZunn36a8PBwpkyZQnJysmfaddddx7fffstLL73EU089xfvvv48i2dEm488cF4O+Uql2gKGexp3BNieGuht3l7vehFFUmxPrI6To4YX1TrMvzDyhZfqYPL3+absbqVrwiu3Q/+nGWXagZeTBjiw4rzX8sqm2T6gbB8G2LCitgmm/wapd0D4J1u6FvDLPWw1AMLUnQhWFUhKoIRwNLhxYCDqO/kNs5TZKnl1E+KRBKPV00i7OYHmlUFQBHVvAn7ug1+OeSYbMQiKIRsG3M+u6MjewOyqaYLuNqleXULQrjvAHzkMbFQQHCuC9+bX78oCOcOcw0Mg+dFZ7+wciAQeHa62W64LZGNGJUl0EZpuLFiVFRFhLWLfSxSV3BC5U0bQ4P/mNGjWSUKvv+cml1eIulX6vhDfXxgMkVZWSExXMxZt3sTolifiyKlRgRdsWdF+6nZI95USkS/96AgrvmA+A9tD1S48DW0nNL6D/tl0YnQ5Wtm5FRnQ0lYaA15M5YwX0k5s9ezb79+/nhhtu8EoW1enYsSP33HMP//znP/nss8948MEHycjI4JprrmHcuHE8/PDDPu956qmnWLRoEfPmzSMiovbGqrCwkI8++ojff/+doqIiwsPD6d+/P3fddZdXrZ0pU6bw0UcfMWPGDObMmcPChQspLCzkvffeo2fPnvz000/MmzePnTt3UlxcTFBQEN27d+fOO++kTZs2J/QZTJkyBZvNxpNPPumVLKpzxRVXsHLlShYuXMiyZcs8n9OkSZP4/vvvWb16tc97evbsyciRI5k0aZKnbObMmSxZsoS9e/dSUlJCWFgYvXr14q677iIxMfGEYm/Otha66TXtcKbaX+0iVJVWRZWHX2sUyi1mwqpqfGbd+H+7GPJm7+OOw7m+oP6Jp6qp7qrd9U+rbqQnyRdNapzlBlr/p2trTh3t0U98yzJ8+7tSANehw3YRLak8VBOkjsnlRuN2ez2Rq1NlNGCxHf6+Ck3xrPx4C32mfEOoWgEhZnjySrjtkuPbJtG0uFzQ4X7YlVPvLBpUwijAgQE9h/cJNwqF2lj2hcdQEBSMVa+nW/YBUrP2Yd5cQs2Ln2KgAgNHdLY/Yznc83Htv3UaSI2tHUHxivMbawvF6fbiVwC4MKBQe97bHNOKFeF90NSdZ4IgqayELH17Dqzw7TtCNFOqiu3T31DVNqTl5df2hXjEg0+nAh2ysgIYoGhy3G4Ku71BC30U8974BJtWy5qkltToa7vmKLGY2RDXlg1DF9L17jac93CXAAcsAkl1uVELvO+rIipr+NvceSiHHtOfm7Gfpa1S+abfeYEI8awQ0EeCixYtAmprE9Vn1KhR6HQ6z7xpaWl07NiRBQsW4HJ5Px2trKzk119/pU+fPp5kUW5uLjfccAO//PILw4YN4/HHH+fSSy/lp59+4pZbbqGystJnnc8++yybNm1i/PjxPPjgg0RH17aD/Oqrr9BoNFxxxRU8/vjjXHHFFaxfv55bbrmF/fvrbxpUH5vNxrJly4iLi/ObMKszevRoAH755ZfjXkedzz//nPDwcK699loef/xxLr74YhYvXszNN99MaWnpCS+3uer+6V9nY9rml5NYeURnjkrdpbavjB+O/4Kp5pfM437PCdn1F7WIrKc4afT7tsZLRAXSnR/4TxYdByc6yonDhZYqfNtnAyRUlRFks6F1uchIiGJ7ywQ2pSV7JYvqZFmSKKnSQGFFbYLq9g9gyoKTilEE2I3vHDNZdCQddsqJxoERKyHk0xaDS0uR2UKNwUBYTTWptj1EsY8gygmmBD31j8yI011b6/DKV2HW8lO0QSLgnqutZVpFNJXEsC84mSxtB3oe2E+L0mLPgAg5IWFEVdagsR5jHxHNywVPsDM6BQvF5EREcPeCRfzz86/4x7RZXP/rcqIrKtmcksKT/8r762WJZsExfSVaVEyO2lvUjIgoT7IIIKKqhmqjCYCN7+1CdUszo+Ysb/ThLisc1LYGqiTWkyyq0y0rB41bzk0nKqA1jPbs2YPFYqFFixb1zmMymUhNTWX37t1UV1cTFBTEyJEjefXVV1mxYoVXomXhwoXYbDZGjhzpKXv11VdxOp1MmzaNuLg4T/lFF13ExIkTmTZtGnfc4V13Ojg4mPfeew+dzvvjeeeddzCbvTsRHjFiBOPGjeOLL77giSeeOK7tP3DgAHa7nbZt2x5zvg4davuP2L37GDU9/sL06dN9Yh8wYAB33303c+bM4cYbbzzhZTc3dpeK46/yRapKq1LfmkRbUhLpu9VPB9IncL4r+8dpuCEra8DobT+uhStPYW2Cl78+dctqSqb9dtKLsGHBiZlsOvndZRSgfX5tzSSbVsPMIb1RNRra7qs/gVBkjCSl+oghat/4Hu4YetKxigCZvfI4ZlYooSXFeHcganY6qDIaseoNhKn5R72jgT74Ca7ucxyxiCbpiNGIVKCMOJzVWtpV1u4XEdYajE4nu6NjcSsKTq3iO6KmaL5W7sKc1I5wDtL5QJRX33p9du0hPb+AL/p3Y/12RwCDFE1J+VurUI8401QafLsE0TsO3/hvn76XDuPST0tsomlRXW6s3x++pyoglUR24EbDluREFnTrTIXZxLl799F/yw6s+lM0EFAzFNAaRpWVlQQHB//lfBaLxTM/wNChQ9Hr9fzwww9e8/3444+EhYXRv39/z/y///47AwYMwGg0Ulpa6vlLTEwkOTmZlSt9L67HjRvnkywCPAkXVVWprKyktLSUiIgIUlJS2Lx58/Ft/BHb81efQd32V1Wd2NDrcDh2t9vtib1t27YEBwefUOynW3FxMTbb4do6lZWVVFQc7vTObrdTVFTk9Z6cnJxjvs7NzUU94sK2oeswaP/6linY5vR/Y6U59nv9bUd8vHeH2J7tMDRslIiT+qwa0t63sPzUfh9J/jurO15NaZ8BQHfyo3rYCQLAhZG/OnwrKKiHqv4Xh1nqnS/GdlTzkUPHt4B+VqdoHcerye0zJ7IOS8P729sZkYr7qGSRXaul1FR7voitLKf+epHH5na5mv5n5Wcdx+us2GeOtY4jbvCDKSI/KJhdUTGsTE5hW3QcO6JiyA051I+IorA6OQX3oRGMmtR2NOI6TsTZ+lkcvQ4V6JqzhxoifAZiAIgrK+fyjX80+e1oCvvN2fo5HL0OxaxDz+F+rcKtvg9fNerhp7aqu2luh+wzp28ddXQ4aju9jtTy/iWD2Z0QR154GPPO7crM83t6romb6nY0hX2mPgGtYRQcHOy3SdjR6hIldYmVsLAw+vXrx2+//eZJOmVnZ7Nu3Tquvvpq9Ho9AJmZmbjdbubMmcOcOXP8LjspKcmnrGVL/6M1bN++nQ8++IA1a9ZQU+N9APO3nL9Stz1/9RnUbf/JdPr9559/8tFHH7FlyxafHcnfj66pOXqEuKOTbAaDwefzSUhIOObroxMxx7MOowZsx6hlZNVpUPF9Gp9YWOr/DYrvOurk5no3CavbjrC/nY913t76gzjkpD4rcwNuPvu0O7Xfx3PXwscn3vyyTlPbZ7hnOLx0cqO9WSihjEQakus3uFwklJSRExlOYXgou5LjaH0wz2ufjK4pJLn6qFHwHhgR+M/qFK3jeDW5feZE1vHfe2HUyxyLCjgwkWFJo33J4eP/7pgIHrpuJPuiIkjPK2bMH5voURxLJNle721ILSPNXcOa/mflZx3H66zYZ461jpAQ0CrgUrFhZEdsPA5t7aVj5aFmIUey6fRNczsacR0n4mz9LI5eh3Jea1xr92PT6OvtV9GhaOna7nCTo6a4HU1hvzlbP4ej1xH6zmgKuu4jjCzKSCC1tJhyk5ES8+H54srLKTyUqO4wrhXKUQ9jm8J2yD5zetZhGt7Kcy8USu0D0OWtO/r05bklJdnr8VdT247GWsepEtCEUXp6OmvXruXAgQP1NkuzWq1kZmaSmJhIUFCQp3zEiBEsXryYhQsXMnr0aH788UdUVWXEiBE+yxg+fLhXM7Uj+Rv9zGTyvQjKzc3l9ttvx2KxcMstt5CamorJZEJRFP71r3/5JJAaokWLFhgMBnbu3HnM+bZv3w7g1Sl2faOlOZ2+7TO3bNnCvffeS3JyMvfeey+JiYkYjUYUReGpp57C7T5VvSM3Hz9eBRfOrH+6U6elyGwgusa735i4olK/83e4sdVxx2AenHLc72kU7Y4/WXpMLaIhyHD29WP093G1Nczemw9V1toaR1XWQ3fgCqD+ZdNEPVbi2EkFsbjRYaaMSqI9Ixc5NBr0R/ye752/kBevHoXVYGRjmxR2tYgnqMZKXHE5rQ7mklKdidImoXY0rWATPHEl3DWs8T4D0fhG9oTP7od7PoKKGr/7VAWhuAimV9YWgqgG4KMeg7n/2vGeDmnXpyWQExFCjU7HPX/OI1QtQkHFyDFqumo10DK6ttNraY529tj1LrS6m32mdE+y6FiMEfq/nEc0Eyv+QUabZwjNKsPhAq3L+3FHudHEmoR2/PPR2HoXIZoXXZckguOcVOYF4w4pROtSiHDovBJGDn3teerSL/r5JItE8xI39xr26f8J4GnKaHL43j/oXG5sp2w0oOYnoAmjwYMHs3btWmbPns19993nd57vv/8ep9PJ4MGDvcr79etHeHg4P/zwgydhlJqaSufOnT3zJCcnoygKTqeT3r2PfwSqIy1evJjq6mr+/e9/07NnT69pZWVlGAyGet5ZP6PRSN++fVm8eDHLly+nTx//F9izZ88G4NJLL/WUhYaGetYdFhbmKc/yM9rE/PnzcblcvP322141oWpqas6I2kVN0ZAUHS/1czJpOTjcEKKHiqOa4P/ZMpK0wgraFlV5LpDyw0NILSjxWV7XW09slD1izZBfT7LyVJ1DW0bD/npGvVEAfSMcRuY8CRdPPvXLDbQXrqv9O5aCMli5C9Ji4Y+d8Px0yCoGoJpggqjEzOHfbSi5FJFCJbHU6HVo7A60h6qnJlTn0bpgH5uTavtJqzEZqTEZ0aqgtVZz95YXGuf7E4F1/aDaP6jtT2ZnFvR5Eorrkj1GVFQi1cMdzfYs3OY1ehFAXngwVr2B568dx8vtrYTdeS5KnAWqamDNXkiMhFZx4GdUPnEWSYuHYBMO119f51Qb9Jw7LrXxYxJnBq2WVm+PwTDq74y99j40Oh0Dt+6kw8FsVKfKwbAI9E654RfeQnL/zX79P6jAzNaW8agO72OPDgc377xCkkUCRafBOCAZ228HKSceE+VcsvMP5nQaSKXpcHcMrQ/msCnVdzRy0TABvcobPXo0LVq0YNq0aSxf7tuB7/bt23n33XeJiIjghhtu8Jqm0+kYNmwY69evZ/78+ezfv9+nFlF4eDh9+/Zl0aJFbNq0yWf5qqpSUuJ78+6P5tAF8ZHtBAG+/fZbn/aGx+OOO+7AaDTy8ssvk52d7TN9zpw5/Pzzz/Tq1csrUVXXbG7VqlVe83/++ec+y9Ae6k/g6Nj/7//+T2oXnYSnztdhf1iH+qiO8gd0mI7oCkTjdhNZZSOpwur1I9PUU4PEZT+x7yFm2qh6pxkva31Cy/Tx56v1Twv2rY13SlzUDVa/Cl1bQlLjVK9ssmLCamuJdGoJt1wEBz+GHf+Bzx/A+eh1OPG+cFLA089MqM2OVlXZFRnNmoRkfk9qhcvPTV5yfjEtghRJFjUHigLtkqHoM5j7JFx1Pqa7+6DTVHvNFlRPm3edy02/ezsR+Xx/tHGHLr4sZhjQCVonSLKoufjvPQQ7ywixWeudxa7VYnA6CEsMqnce0fzow0OZ2vNyqkNCqDSb+aFHN16/fDjf9u5BXmgQEdFyHhK+fu7Wli8G9KJ78Sa0R4xupXU7cWrtkiwSHlHvDAWjFgdG/jHoVrLMCTywcBZdMjJokVvIBZt2klhQgnoK+hJtrgJ6lDabzfz73//mvvvu48EHH2TIkCH06NEDrVbLli1b+PHHHwkKCuL111/3DG1/pJEjRzJ9+nT+8Y9/oNFoGD58uM88TzzxBLfeeiu33XYbI0aMoF27drjdbrKysvjtt9+49NJLfUZJ86dv37688847PPfcc4wZM4aQkBA2bNjA8uXLSU5OxuVyndBn0Lp1a1566SWefvppxo4dy8iRI2nbti1Wq5Xly5ezfPlyOnbsyCuvvOLVDG3o0KG89957vPTSS2RmZhIaGsqKFSsoLS31WcegQYP44osveOCBB7jiiivQ6/WsXLmS3bt3Ex4efkJxC19j28H/ttb+W1UUOueWYXF6J4Iiy/zX6CrLqCSsZchxr1PfIrzeaREvDDju5fkVG16bWvaX02p/ipujHalHa9jwZuMt/0zSNhHaJhJyuY2q12eiw+apQGbHTCXRmCigVJuAVadjf0RtG+g9iWlsb3Go6aKqonW5ab8/m9ScAhJ6NbNEnIBR58Go8zAA2ve+85rUPj+XTgfy2dLicNOQDgcLcOi0WLNqAEkCNGtX9MbsmEvX7INkhUdQYTASYrOidausTkuhKCyEomAzvbfvJbL7ife3KM4+mm7J7I3xvVbIiwojJTuP6v7H3yRfnP2K+rYnr1DPx/0u5/4lMygnHJdGQ7kliDJj/QN5iObH0DWWpA23UNz+FYpNUcxqP4y0nALaZhYABQCUWkw+tahFwwU8rZ+Wlsb06dP58ssvWbx4McuWLcPtdhMfH8+1117L9ddf7zdZBNC+fXvS09PZs2cPvXr1Ii4uzmee+Ph4Pv/8cz755BN+/fVX5s2bh8FgIC4ujv79+3PxxRc3KM7k5GTefvtt3n33XaZOnYpGo6Fbt25MmTKFV1991afX8uMxaNAgZsyYwWeffcayZcuYPXs2dntt+8sRI0bw3HPPeWoJ1QkODuatt97i3//+N1OnTsVsNjNkyBBefPFFn+Z73bt359VXX+Xjjz/mgw8+wGg00qtXLz788ENuu+22E45bePu/4Vr+t7U2cagqCqVBBizl3k9jlXqexh/4JZeWAxP8TjsW18HyeqcpulP45P/ibrBgg2/5P2/wLRONRhNspEYbRbkrCTNluNBTTQQqWgxp4ewrD8Oqq+2Xza7Tsql1i8OjQigKLq2G5LxiXECERmoXNm+qVwfWeredG39dzepWLcmIjSA1v5SBWzOZd14n4gt8+8YTzYxeh1ZjxeR20LN4LRaKcaOjjARcWRr+1XM45hob+6MiCG8XHuhoRROiDTHSuvAAG5LaeZVbamyEV9vkqb/wq3Oki305CpmRiTx8xYMklhdQYg6hTU4h59f89YAvonnRt4vCSAXD1m9g6oD+JBYUY3TW3pNp3G6C3A6pEH0SFPXodkqiSSgsLOT2228nPz+f//znP3Tv3j3QIYkG+P2gi2GzVDSVDi7YX4T2qF/XxSs3ElrtW6X/gsnd6Dg+3afc4XAwdepUACZOnOgZAdAzPbeSrIR3/MaStPdO9GmnZoh6yqogdiLYj7hx7JkOf752apYvGqxk8lIqJi3GzZH7gpOYH69jzfO/sbswFFWjoTjEwuKenXzef8Gmnbhdbvp3NzLko/6nL3DRpNhvfB/Dpz8DYNUYWJQ8gNTsajbFJVJpMqF3OkkrKeKfV4/g4vMMPHBn/F8sUZzt8oIewFRjJ4zDfV+pwC5jNx684RYcCoQX5DPz23MDF6RoklYnPsFLF92OzVjbRFrrdNF/ww4sVhu6Gztyx+O+1z+iecucvIQvFzhY2qmDV/n1vy4jqrOeod9cGaDIRFOVp7uPGT0H0G13ETsSE9DZXSQXFtOytITXRgwg9ZJWPHtPTKDDPCNJrq2Jio6O5r333iMyMpIHHniAzZs3Bzok0QD9krVUPqjjH2k1PskigJJg/806Enqf2AHMnX2MTssjTmH/QmEWKP4UJo2By3vBby9KsihAIp7vj9LB++Zd2zqGoEtaEXd/P9RDj1BCq2rQO7xrhiiqSmRZJZGVVYR1kSZpzZnhf3fivm8kbpMRRadlS0QaW+ISqDSZQFWJrq5kf1wUbkUhDsdfL1Cc9YLjwUKxV1ntuAe1NV2jS8poVVR/rVfRfE09byjnbNtNz6176L4zk6ErNxJVXkm1UY/FevyjDIuzX7khhCs2LMRk9+5fb1GX9hjUs2wUXXFKFBiCWZ/chs/OP4cB23YyZMcO2hYVsKlFHNPP68K918t174kKeJM0Ub/4+Hjmzp0b6DDECUhr4b+K9YHYKFLyi33LF+cQ0Tr0uNdj3+67rDru3CoINx/3MutlMcHzY0/d8sQJS95wC+X/WYP1l0wMvRIJva8HilZDy2Et4flNoCjo3G56bt/Lnx3Sceq0KKpKt52ZmBxO7BqFzje1DfRmiEBSFDRv3wxv34zG4cB93s+Um8yeaTmh4fzcpTUd9hwg6saugY1VNAlqu9YoGb4jsW5PqO2fpkVhGWnO+jvFFs1XcVAszsgKeu3xHtwl1FaNNHMQ/sQMacHPM9pi1+mJLyxBVRTyI8LIjoyirOoU1Z4XZ5WUmixMdjdL27Wm/7ntGLw9g/xQC0vbppJUUklEmPTFeKIkYSREI1i/xf+IQ1aT/2GJc1cV0vW2dn6nHYu7qLreaapD+qg5Wyl6LWEP9SLsoV5e5aYoIwoqKgoHYiLZ2ioJ56H+IcxWG8n5xbgBjVvFGKr3s2TRXJkcvgM3RFRUEV5ZxYDe0sGoAF10EHYsmDlcs7XCEMQX511McFUNoZXV2C9KCWCEoqlKzc9HW+MiPzSI2PLa65YKk56R27ay2eLbdFqIhN6xZIbHc8kfG7HYamsUlQeZ+fWc9mxOS2N0YMMTTVAQldgNWm74bTXTendiZq8uAIRVWbn+t7XARYEN8AwmCSMhGkFUhP8aRmEVVX7Lw9OPf4Q0AMvlbSm5f6Hfaa6cCugS63eaODspikKr2Br2FFjYnN6CarPRM63abGJF5zYMWr89gBGKpsql9R09pMagZ21aPEaDjCwiwB0cRAFtCCWXbUkxOFUDC9qeT0JOBWk5e8mOCof2UuVf+Op8cCdbYloz+eqBpBaU4dBqsOk1nJu9j3M6ncLm8+KsUuUKIslW4nkdWl1D2wM5aC45/ges4uynATplZxBTYOP++avYHxVKhdlI25wiwmr833+JhpE+jIRoBBf1CfZb7tD4v/FKHnRiHcpqW9TfjE2V4SObpfM/vYj21u1Yjb41iFxaLfujQqhIDAtAZKIpKwwPxaY//AypzGJG1RsotEgVbnHIxd3RYqeMJH6Nu4Bd5na0OlBCuwO5GJwuwqpqiIuWm3/hqzw4iP8bfA42g54dSdHsjY8kKyqcR68dgTHZ//WSEGHVvv3nBVVb6aFUBiAa0eQZ9dywZi5FwcH8ck47lnduzb7YCBxaDeVmOTedDKlhJEQjqLH5b5WvxX8Sp3xfFYnnH/96VGv9w13rwuTg2BxpU6P586EkQv6soSzEQlRpBR0yszDZHWRHR7C0aztG5B8IdJiiicmOiWRzegrxRaU4tRryIsMJr6yiz64DgIxgJMB8WTtchiCwu/2O9gkq50ptEeHHvvCWlFh8+1TcFRdNdHtJSgv/8iJCiCvzTg6VhFowlErn+sKXIyySKhss6N0FNBr0QFVYOO8Ov4Drf1kV6PDOaJIwEqIRrFjnf9SPYL8X2RCWdmJP2DTm+vuh0bY4sWZu4sxnMttwKQqWaiv9N2xH665NYIZV1WBwODCq0jGt8Ga02nBGhnMwLspT5lLdmKQvNHGIotMQPe1yCq6Zjern2YddpyMpTi4rha+E3DJa5RexPdG7NnWo1QFOFaRLPeHHhlYJBNuc5EaFU202ElxdQ1GwmRoZJU34UaOP5N89LgXN4QZUGiDc4WZlO+lf72RIkzQhGoGhnmtmfxfZADqT/z6PGqSeZeoTJGHUXGl0UGU2kpxf7EkW1UnJLSIjXpqkCW8pOQUUmw7ftRWb9XzfNRWrTpq2isMsV3cgceMtVJhra4s4NRrshzrWLw2xYLPLmFfCV8vifMb9uoXY0kpQVVBVEkoq6LcrG51ZkozCv747M1jXPo2cmAjKgoPIio3CZjIRqpHjjPCl1zspM/l21aF3qRRHyOAdJ0OO0kI0AsVPB7IAWpf/p/Uu54k/xQ97ZSBlj//qVRZ0bfsTXp4486lWBVWjwe2nzyy3RsEaKX1GCG+746NYmRJFkN2JokKVsfbyoNQiTYyEN0OXWHa0iCM7JoKMxFjcGoX4olJKg0xo9PIcUvhhMZNYVsV/P5pNbGU5WtVNTkg435zXJdCRiSZsX6xv/56qRkN2WEQAohFNXVDXcDplZpEf5b1/5FsMTFixCUgLTGBnATmzC9EIwkL8/7S0bv+JoZN5hh/xWB9i5lyNrn0U2pRQoj4eTuz0K05iieJMZ3UaaVlYxP74aKx67+cCJcFBjJRzpjhKdlQYqCrVBp0nWYSqEmmWywThS+92s6dFPDq3C6PDSW50BAaXG708+Rd+hMSbia0sp3N+DrHVVUTV1NA5P4ceezIDHZpowsw2P83nVRXlJB6yirPYE1fSMSubjnv240LFplUoMmoYs3Q1HSorAh3dGU1qGAnRCFKT/DfIjyou81teXXhyfcpYLmuD5bI2J7UMcfbQGF2M+nMd7w6/iMU9OtH6YC5mm52ikGDaHsyl2/jUQIcomphysx6OHllRUQgKls5FhC+nRkvPrXtokV+EokJOVDgbW7fAZJAEo/BlbB1BxFrfwRZ67z8YgGjEmSIvMgKNy41be/i4oqgqWosxgFGJJuuCdri1y7n6z7X02pmJW6OQUFFGQnkZO/pKy4uTIQkjIRpBXJT/n5a5niZpFfurGzMc0cwYdA7m9uwFikK12cjGNrWd/emcTmJKy9FF+o5WI5o3q963HzXFraLTyJNc4Su5oJiUvCLP68SiUux6HRqNPLgQvmLTzFT4qUutU+sf6VUIU5iOyirvMlWjIaWdNKsX/m1KSebqP9cRf0SNouXpaZjTwwMX1FlAHgUJ0QgO5vq/CHLq/HduHd8rujHDEc2MosD+mEifcodOy9bruxNrkY6MhbeYKt+ktapR2JTk24eEEFFHDXUNEFdcevoDEWeEuF4xHAgP58gGiypgD5KEkaifOUJf20n6kVQVJUjqOwj/aoxGXr9sGKtbpbA7NoaZ5/dk9vk90fZvGejQzmjyixOiEfy+uspveXGIhdBq3+Zn1XkyzLk4tcpMWsJt3hdaTkXhjZuks0jhq/feg/zUubVXWUx5JWVylSD80By6ias2GnDotIRV1aCrpwatEK7UKIqDglmXkExSeRkokBUajqmF7DOifmaXy7epNBCE9JUm/GuZXUBUVRUdsnKw2OyoGgWj3U7E9b0CHdoZTS4FhWgE+UX+L4L2xkeTekQ1/jqWWBmJSJxacYWFlIXHYDp0E+cG9NYaYoOldpHwFVbj4PYlq/howHmoGoUgm50Hfl7Bhl4dAh2aaIJyYyMoDrawPy4KFIWwymraZGYFOizRRFWZDKCqlARZKAk6PLx1fJ+kAEYlmrr48gr2c9Rw6IqC3i4104R/4TU1XLxrp+d1m9x8IssqCHd2DmBUZz5JGAnRCCLD/bf2LAkP8SlTtArRXaXWhzi1OuUX8VVKS2r0WgwuNzqXmy7ZVuwOFZNRkkbC2674WK7+cz3XL19PRkwEHbIL+Lzv+SS77IEOTTRBezomU2E/3MS6LDiI1R3TAxiRaMrCKqvRut24tEc0y1dVEkxSU0TUr+v+g6yKaudVpnO6CC8pB6QfI+ErwlbjUxZZU4NeaqWdFOnDSIhGUN8oaSgKwS2DPC+1Zg1Xzb8QxU+VWyFOxrq2ybQurqJLXjntCitJL6mm2myiosoV6NBEE9Qqv5CM0EjyzKGEl9nZEpNAcl4J5/cPD3RooglSo3w7znf46ThdCAC3RuOdLAJQFIwhhsAEJM4IpaEhnLN3n+e1xu3mut9XkBcSGsCoRFO2PzHGp8yq06GGSkuOkyE1jIRoBA5HPRMUhWsXDaOmyAaomKPkACYaR1G8gcijWoiUG3WEhchhX/iKKy7DqeooMx9OaEdWVDF0sFyYC1/aGBPkeNc+qzLX86BENHvBaaHo9ApOh/dT/pjhqYEJSJwRivq04qJnvmHY+k3kh4XSJjePxR3bERptDHRoookqvqI7ZdszCLPV9g3rBnZHxXBxF2nJcTKkhpEQjSA99dhPzcxRRkkWiUbVPjKDXVHBuA5VXqvSa6lsGYxBL7XZhK+96b6j6rkVsIRJEkD46nt+EMVHJIhsWg2WzpJcFP7pg3T0eqYbHHH6aXVZMrHdfY87QtS5clgo/xo1jB/P7cr+6EjeHTqE3y7oRlKcPPgS/t1wbzJz+p3HxrhEdkbF8EeLNCIf640pQpKMJ0N+cUI0gl5dfKvrA4waYvFbLsSpdr5xNxXn9WXu7iCMTjehIQq/jJObf+GffkAZ5dlmr1EcXX1aoDNKMyPh69ZztMzeE8EfO53o3G600Qbmj5Lji6hfh/GtiO0VwTdvzkWNdtHvmVGBDkk0cektDYwfHcLn3yWhqi0JDVZ4+f4Y6cZB1CsuSsfDc3rx5rNFGIpg3MP9SO4WFeiwzniSMBKikbz2WDSPv16I+9CAaZ1b67nrOqkSKU4PjaIy/TKFA1U6imqgRzxo5CJL1CM2uoj9VyRRuKklujIrSf3juOWZ1oEOSzRRJp3CvKsUnvt4PlVuEy/dfDEhZjm+iGMLTQ1G7WYLdBjiDHL1sGCKD36D1W7i3juvwWiUfq/EsRkNCrHt8wCI6yg1X08FSRgJ0Ug6tTHx/ZRkiktdmE0KZpO0ABWnX6twhVbhgY5CnAlaJmUx8ZlL0OulpohomDRtIWjBJFeTQohGote60Jur0GgkKS1EIMgpXohGFhkuTTqEEEIIIYQQQpxZpMqDEEIIIYQQQgghhPAiCSMhhBBCCCGEEEII4UUSRkIIIYQQQgghhBDCiySMhBBCCCGEEEIIIYQXSRgJIYQQQgghhBBCCC+SMBJCCCGEEEIIIYQQXiRhJIQQQgghhBBCCCG8SMJICCGEEEIIIYQQQniRhJEQQgghhBBCCCGE8CIJIyGEEEIIIYQQQgjhRRJGQgghhBBCCCGEEMKLJIyEEEIIIYQQQgghhBdJGAkhhBBCCCGEEEIIL5IwEuIM4qhy4rS6Ah2GEEIIIYQQQjQ5qgpulxLoMM4aukAHIIRoAAd8f98aChbloNEqpF6VypDJ3dBo5WB4tnO6VR75ycH09U70isp9F+h5fIAh0GGJs8waRypL7e34Zjbcc66bUenyPEkc21tr4bPdQzHa3OSuVHm2r4qiyDlJHJuhzEXkLgeOc/LQn5cc6HBEE+dyw0+2zmxwtmThPHjqfJXusXKcEfVbt6gY3SfxhJTZmLF2PZe+1I3IWLluPhmKqqpqoIMQQvjncDiYOnUq2UsS+DG0K2vTEgiyORi5did3jQil/23pgQ5RNLLRX9mZs87mVfbCRQaevdDod/66fQZg4sSJ6PX6Ro9RnNmmb7Fz3TzvBNH3V2gYIUkjUY/P1tjJuPl3Yu0ONC4Vp9WB5oNB3HlpSKBDE01YxbSNFN80F62z9nXQzd2I/O+owAYlmrQ759uZsllDYlkxJWYLitnAjlt1JIdI0kj4ysusZtOgD4m17iO2qpR1Ca0pa3MOY3+4NNChndHkalCIM8B34V35s3USLq2GiiAjX/brwrSlVYEOSzQyq1NlzhaHT/krv9kDEI04Wz2/3PfC+8nfpOmrqN+m17ZhSw7nQHos+9rGkdMxgaKX1gU6LNGEqXYXpQ/87EkWAVT/3wZsyw4ELijRpKmqyqJf81n9xuNk/f1O8iffyv0/z+Y/q+X8JPzLfGUp/Q8up2vePuIryxi+aw0he9ZSXe57LS0aThJGQpwBtiTH+pStjIgKQCTidMqtVMEFKApoNLX/VxSq5bwnTqGsMt+KxhlFUvlY1M9QbK89Hh3i1mlRFLmkFP7tKXRxwXtVdL5hArfcNI5/XzaCub3Oozg4mIoV2YEOTzRRNXaV/375HkEOF190u4hNca15ad50NIs3Bzo00UQdWLsHo8vpVTZ013oy9stD9pMhfRgJcQYwWR30zsylV0YONp2Wxe1b4nLJDd3ZrsqugsLhG7Mj/u9yq2g1UiVbnAJ2F8FuG3f88TNdcvezNK09Vo0BGBLoyEQT5dJqWBERytZgC2aXmz6lZbS1SyZb+LI6VM57p4qSapVRRRW0tNqwG01sTWlJZlwsY0MtRAc6SNEk6VQXmREtuPWqayk16zE7XFy1aRXnr14PdA9wdKIpytKYfcqq9EZ27qmiU+fw0x/QWUISRkKcAcas3E6XohLP6/SCUn5KSwakw8iz2dxdbnD7lmvdKg/9XzlPXRlMfKT29AcmzirnZO7ijR8+pWf2XgBuXPMbmWFRUHEBhPhefAmxMDaS1RFhntcHzEbGS8JI+LFgh4OSapV4q42WVu/++KpNJrJCwzg3QLGJpm1vjpOnh47lQHiwp+yD8/tTsWEV1wQwLtF0rYxtT2boClLLCz1l759zCWmVfi6mRYNJ/WEhzgAxVisAbo2CQ6/DpdfRoro6wFGJxlZso7aG0SGKqhJicxBWbePH9TaufbmI3GJpyy9OTu/9+z3JojotyothY2ZgAhJN3tYQCwAJlWUE262oisKSeKknInwVZNvoU1TKuAO5tWNdH2VNmTz0EP7t+2AbB8MsXmXVBh1zW3cNUESiqTsYFc/IMU/yyvmX83mn/tw04m7eOH8k28ukjszJkE9PiDOAXavBpdHg1Os8zZLSy6rIWFNCWo+IAEcnGsuK7ENPRFQVFIWIGjtGV21ZtUZDhk3l69+ruecyGZlInBjV7cau9X12pFVVMt0mUk9/SKKJK9laQlx1OZ/O/5S+ORnUaPW8dc5A/tvjYqi2QZD/ERxF8xRfaqV3STkLW8YRX22jTfnhvkRcqsqBcnnyL/xbs6QA9QrfpveVGkkyCv8irVY2pCbx9nkjAHArUBFmwlVcGtjAznBSw0iIJu6gI4ItMdFUBpm8OhlVgG9f2hG4wESjW5WtgEkLYQYUs4JGVTny+axTUdiW46z3/UL8FfufB1ia1ol8S6hX+brENPasKg1MUKJJW/RRJm8s/Za+ORkAmF0Onli9kOH7tlF8sCLA0Ymmxlpk5c+YcFKz8jlnyx6M+UXk67SU6LWEVNdgKK0JdIiiCVLdKpmWEOKLyrzK9Q4nLfPL6nmXaO5iC/I452Aug7bu48o/thNbWcUFuw/ilibTJ0USRkI0cZWlZmwhFnQu36dw7hxplnY2c6iAvnZ0NNWgpzA2mHyLEecRicP4CP+HcfcBPave2M6+X3NPU7TiTLRrQzVaVctFtz/LvHbdORgWyRfd+3LH1fehX7/3rxcgmp2svZVcuG+7T/lluzZxUA0KQESiKQveV0iPnfu4fP1O2uYX0+lAHr037uTXuEiMbhVHqT3QIYomSNEoLE+KY+aUGVy9ejPxpRVcsHs/n388i7b5hX+9ANEs9dqbwz3frubWXzZy5Z+7ePP/FnHulizSSiXJeDKkSZoQTVxYiZtoux2HRoPO5d1fjdZuJ2tXFUltLPW8W5zRFM9/auk0uIP0lLlVomrs6FQVjcO7Twi3S8U9IxLloJbN7GbT1L1Etg/lyukD0BmlGrfwtmCdjdZlWr7plsqltz4FgNbtZtS2LHZZohkQ4PhE06MrriQrNJq2Rd7DoW+LiqfszxK6tpOkkThsbUwEnQ/UJp8ttsOdXicVljEvJYEr8nYBbQIUnWjKLDYrCRVVvPL1z17lszq1C1BEoqmrxEKcUsHe9jHYTDpCSq103VfI3gtjAx3aGU1qGAnRxJWFmrBrFAoiw9HX2NC4XGicLgw1VkzVNjKzpJrl2UrBt+0+WoUkm50WNgfJdifb99j4+X8HmfNWJrvXlLFtWibafQoat4rWraJxuyneXs6Ob/ef/g0QTd62Yg2RVgdXbDlIt5xSuuaWMXJHLjqnyi8t2gc6PNEEHTSF8GHXIVTpDZ6ylQmpvHVuXzKqJCktvJVazOhcLrpnHaTnob/uWVmE1FhZExNBpz3bAh2iaIK2zM2iLD6cCmPtcUYF3Cg4NBpWpMkIwcK/YouZjPYxVIaZcBh1FMcFY401s9ktD9ZPhtQwEqKJ2xMcwp/RYbQsLSCSAmpqzFToQ8gKsdAlt5xwnVTnPlv5jicDcdV2OlZaPa/z9rv49Y88AP5cUESr0jw0bhWd241GBVUBu17Lvl/z6DQ27TRFLs4Eecvy6b9yFwaHk1BnNfmx4Szv1AGb3sDuIBMjCgqBuECHKZqYMoOZQreBS65/iit3bSQnKISPOvegWqelINzfUUs0ZxE1NUTYrUzv25M1qS25fN0GLt24mQ+/mcu0c7uwOLYNowMdpGhyXlnqYEdyEk9cfTGvfvkzilsBFCqMBkKt8qBU+JeVHE5kiXfzs6owEzqX9Pd5MiRhJEQTd1CJ5ObVC3jh16/RoOJG4bn+VzK3TX9SckrQrt8H/aSq5dnG7Wf4YcXlplOhd6eyelXFrtFgcLlAUXAU2QhyqWhUPEMYm9wu9GZ58i8OczvdLL9tKSaHg3ML9xJhr4J8uHDHRt65+FK2WpLZ7DYFOkzRBPXfsYfNLZKIqHaxO6kTAJfnFvFzbCihmcVAWGADFE1KmSWYH87pxPy0FvTM2McV6zZ4pt20egNzO0tNRuFrfmgURoeDp376CcV9+PolpMbOg4v/BFICF5xoslLLSzi6AZXW7eaCAxlI09cTJ03ShGjiIsvLeX7pt4DCSksPVuoGcMPSnfTJymF122TCowx/uQxx5smrctd2X3TEn6rVoBydR1IUKk16jC4nRocDVdHUNkdzutE7VXROFY1bJSxCf/o3QjRZFbvLsJa7SKwqrk0WHRLksHPp2lW0qKymyCFJRuErqaSQssgIrzKLy00ru52MvVLjVXiLKyxheqsUDuh0DNib6TP9/ExpLi18RRVVMn7tn6QU+I682DWrIAARiTOBweUk1FrpVdY5by8VyDXwyZCEkRBNXOesbErMFt5pdT3WqgTszhBySeGaxdvRKTqKk5MCHaJoBBsLAEXx/tMoZIUYvebTu9w49Qaq9XrcClQHm9HW1TCiNtekdalU7So/7dsgmq6Mj3eAqhLstPpMSy8qJLK4iMhS2WeEt/IDVURVVOHU1l4+qkf8VZmMhOTYjvV20QxtNIZQcWh/KbD4doiuQ5oXCV9jl26kc04WBnyPKdVmeVAq/Juf3oYWRXspCNGzMi2J3DAjpQY3G8zhgQ7tjCYJIyGasCd+U8ktSubFvlfTdW+x1zSz1UV4hY3Vy0oDE5xoVOvy/fcFkqnTY3C60LjdmJxOIg+NOlNjMODSaKkID/Yki+ooQNXBmkaOWJxJCj/dTKirkhKDb0eQpQYLByKC2JIUE4DIRFN2cMYedA5oUVBwOFmkgFuBiBo72SYZIU14q9hbW4MxyGFjYO56NBzZl4hKKLmBCUw0ad0P5NJ/QzbhlGLkiOsXxc2Sjq1wuaW/NOErOzSYZ0dcyx+t2pAfGsafqem8eeHlOGzuQId2RpOEkRBNlNut4nzpD575ZikrE9r6Gy8LRYV9fxSe9thE4ws1+PnGnW6sDhca1Y3F6cLsdKE51E+Rzu0GRcGl1eFWfN9bkC9P/sVh8eX5FOtD2J6QxMGgSE8H6xU6E3uC49kbHE6F36OOaM7yCp0UmiyEFJSjolK3iyhA65IqdsZFBjQ+0fSE51YSbnfwtzWLuDhrO3FkEEo+IRRSGuEgSC3764WIZie8ugaNzcC2oDR2pZrY0dpEUZyLf1xxHnO7tkXxOyyIaO7Cy8uwqN7XLjo0FATLKGknQzq9FiLAbKV2Mmfvx1HhoOXIZELTQgD4v5VWrv99IwCd8ssojQwiorja8z67XkuFRUdsgTQbORtd2EKt7bTarYLDDS4VbC4w6NlvMdPCaseOFodLg8XuwOB04VYUFI2G8lATYWU1ntt9u15LtaJlyTWLaH1jG5JHtgjotonAK9BFkB8ShEGnsC4+jZ3WBIxOJ9X62o6uq8LMdMs4AMQHNlDRpGzHhC45gezE6NpmskfQADatPIcU3irMJjo7rPTLzUAFqgnFShB67Fh1bua1vISbAh2kaFLsG7NxKTqKgwxsTI3lqz6dWdk6GUVVCa52kJyfh0Yjxxrh6+rtO5jTuzc7ooIpMhswO1ykl1TR/8ABQDrYP1GSMBIigGryapg9eAHuyto2/Bv+vYWB/9eX5MEJfLKshvectVW3R27aRWZ6DFaznpByK1aTntykUFqWl6G1SjXLs9G2YgVQweqq/TvCziATLay1ncs6tFocihNFUVAVBdXlwmrQ4oq0YLQ7cWsUDDYH0YWV5C+toGBpPr3ePI+EgXFoDFoUnQZtmNFPBOJs9kerVgQpTvQuNzaznpogA/sT4zHZHGirqsmNDmXcH8V/vSDRrMQeKKIYsLhdFKsq6hFJIzeQazLh3JWLro0kGkWtpJIy/mgRw6bklnTbV0EFh2uhmco1ZKSHoqoqip+asaJ5Ovj6KnSqi+k927MlIYYe2w/Q7mABnw46B6deQ6zVgdvuQmOQgRmENxMKWcEKhUG117XVBh1bYkIYslm6ZTgZkjASIoDmX7MEd6UDt1LbF4TGpbLkyQ1c/0cC5VmVrGmRwHn7c+iVvYGDiReS0yKcnEPvdSsKwU4rMZlSw+hs9PP+Q9WtVcCkrX2a71bB5sRx1IW10eVCBQwOByFlFaDX4dCD06gjtKQKs9XpNf+uiYsptFrR4UaPG1PveFp8dSn6lqGnZ+NEQFUV2siNCaZD3uGEkEZVCa2sZH+LRKq00Rgq7bQrKApglKIpKrcpnl6uC7UKoaqCXlVxKrAqOpxKrYY9OdBORi8Wh+isdnA5qVE1VHDU6Ho2NyklvqNgieatYnkeD465hPUtahPPS9u0pPuBXPROFzUGHQlWFWuBlaAkaWYkvGWHx5AZ4d002q3RsCqpdYAiOjtIfT4hAsSZV0VJtpVqk46qIAM1Zj1WvQYOVmDdX8m20Cg+HtyDr3p3IkQppNCs4lZqW227NBqceh2K202sNS/QmyIagdVJbZM0jXK46YdGAYOWIIeD/YbaIUK1bjdGpwu3qmKwWtE7nCgc6lpEUbCZakcTUVwqGoeb8LJqjFaVSoIoJZhSgqhemUvWTT951u2usGGduRnbvV+gDnsB3vkRnN61nMSZK3O/FZ3Lt2ai/tB3bHGpJJdUMf0cqb4tvNm27639R42dAqOeaWkJfJccwxdpCWwJD0bndqFGSeJZHFaBHne1nbfPvQj89ItmQkfR7L2nPzDRZG0nyJMsqrO+RTxGm4OIGhvBTpXKCmc97xbNmV5VMTl8940Qu4zGeDIkYXQKjBo1ittvvz3QYYgzTP78/Tj0GmwmPTUhQZRFhVERGYLVoOHT21bTsqKY+1fPZ3j2cn5rlcZ/O3bFbjTgMBpw6XWoikJJUBglSUFU7a8M9OaIU6xzNLX9Fh1NqyHF4cIElGo1xFRWE2y3o9EoVIaGUBAbheuItv02sx691Y3R6sZgV1GdGtwcrsbtQEcNRqoXH6R6TS6O9TkUpryOdcz/ob67GNuCAzjun4EaczMUloPLBV+vgEnT4ZeN9W9ARQ1MWQB/nwlbD5y6D0actC+WVbMhMsKnvCw02PNvBcgID/aZRzRfVquLIAzsSI5lY2Q47fYX4tQo5JqN2LS1x5QxW1ZRWC03cuKwcp1KVmg4bausZMZEeU1TgQq9kc03/BKY4ESTpHVavQsO9efocCtcuCeXPRYz2/65MjDBiSbLXuVAp8Dg3Qe9ykOsdkKsMvDLyTirmqTZbDbmzp3LL7/8wu7du6moqMBsNtOyZUt69uzJZZddRmpqaoOXN2rUKMxmM1999VXjBX0El8vFyJEjKSgo4M477+TWW289LesVgXEwswaXRkHrcqOvqMal1VAaGYo53EZ1mYM/332FMHs1BZYwlnbpRd/cLKrMZvSH+jVyarWoikJ+UCy752bS7d7OAd4icSq1DudQzSLvpJHGraJ319YOsWu1aF0ur8y/S6elMiSIsLLaJKLiVtEekXjS+hmK1oaOIGxk9ZxKuLmc4JoKFBRUTICKEwOuUhX9yH+hsYCyaNPhNz9+Bbxyg/cCiyug52OQcaj223PTYeajcNUFJ/ZhiFNq84piFqUkYnE4GLI/B62qUhgVQWFkOABWjcK+ICOJFdLmXxy2/oCTT3ufxy8dUjxl/Tfvo3V5FQa3G5O9mBd++R9FPVQ4b2gAIxVNReXaAtYlJ9Ivt5ALs/OZ1acH5+zcS8+MAxgdLmyKjtCSSvLNwbirHGgs+kCHLJqAzjkHiauoIi/EUpssOnQNY3PD/NhoepaUs3ldBQMDHKdoWgoWZhFaWsGV+/PolJHD5uQYwq12CsNDCLdW//UCRL3OmoTRwYMHeeihh8jIyODcc89l3LhxREdHU11dzc6dO5k7dy6ff/4533//PbGxsad03V9//fUp6axv+fLlFBQUkJyczHfffcctt9winQCe4dRKG+78SjRpkVTn1rDyzW3s2OtEY9Ji21qIATBX2TFba5sRhZbb2d42GRQ3M7sM45JtS0Hn5tE/5gHz2Bbbiq/OGYFTe+inq6ok5JZg/c0BkjA6q8RbAK3v779lRQ26I3I+/o4QTl3t/qECweVHPanz8wY3ClqcaHGj1tT1M6Giw46Zw6OtuVbuR8tRQyC/NhseuQxiwg6XfbzwcLIIai/4xr8hCaMAczvc7LljKbf+sI/+seHM6tqaN7p15Ok/NlNjNmO0O4ksqaDz/lWEFLQgJzydzHlZpA5PCnToognI2VnC4na1IywanC6uX7OT1NLKI0ZLC2Jdcg9arM8PXJCiSdn50ApaWVJoqVeZ1yqJlYnRvL7nIEYrgBYjKmmFxeyNjKB8aynh58UEOmQRYHszbaxPbstt63awuGU86+KiqNYfTiRW6nXkmI3kBcew9rbfOfejfgGMVjQlPy4rx1Jemxhql1tEu9zafhg/6dOFxEKpYXQyzoqEkdVq5cEHH+TgwYO89tprDB482Gcem83GF1988ZcJGKfTicvlwmhs+KhBBoPhuGP2Z86cOSQnJ/PQQw/xyCOPsGbNGnr27Nmg91ZVVWGxSOdvp5N1bR4l/1kPJg220BBy5xxE0SqEDm9BVX4Nmux8SjeX4HKpRJbZcLh0OIOCcLeJJaSyAlVrQlW0BB3RIbHR7iBxfz7TLjyPiIo4uu3ZQq/SLZ7pHfL30mvfBlaknkNyaRbn71lNZGkljq81uC/ahObbv0FxJcSFg+nU7JciMDYVakB11R6lnaA4XXQpqSLU4d2XULVWi8Xp3QRE43ZjN+pxazSEF3k3V3RqlUP919QdC1UUQIObIKy40KNQW2tJhxMtdjS4UFFwo0FFg+Kp0+QGtxu2HIBBRySMpv/uu0E2J/T6G6x4pXYfjQ71GZZbNA61tAbVqGVZ3DQsFTUkAUnllQzefZBN6Qk4DDr6rtxGZHHthVYElVyV8Rm/pp6PbU457shsNLdeBJOuBZ2MStMcqAeKUF/+Hvf8zaiFVVQYw/i9ywii+ndh6Ka9tM0vQavX4Trq+mdVi3MJ+mkVcZN/xPL8pQGKXjQV+7aW0TmthH9ddgFORaFlURnDN+/xmifUZiOi2sq+SWsI/2FYgCIVTUXhhE8oCG9Jh137+bpTGjaLkfiqGi7fsR+AX5LjKTMZSC8txDl9H58sy8eYGEznm1rT+fpWAY5eBJL+zyz8NYi+cPdBioNDTns8Z5OzImE0e/ZsMjMzmThxot9kEYDRaGTixIleZVOmTOGjjz5ixowZzJkzh4ULF1JYWMh7773X4EQN1DZdS0hI4MMPPwTgxhtvJCcnhx9//BGdzvsjXrFiBffddx8PP/ww48aN85QXFRWxdOlSbr31Vvr27UtkZCRz5szxG0fd+h5++GH+85//sGnTJsLCwpg7dy4A+/fv56OPPmLVqlWUlZURExPDRRddxO23347ZbPYsJzMzk+nTp7N27Vpyc3NxuVykpaVx9dVXM3r06AZvf3NU9MFGDt61BBUNKlCJgRqtEZeiULxzO9VBOsojDCjGSNrn5RJitwE2ik0mNFYnlToz1RYzWheA9w19THkVeqeTkpAg/jtwML3mHE4Y5VmiyDLHMWDHH1yUschzy+9Gg+MXI4bQ62vLNArcPwLeuPm0fB7i1NtV7K7N6agK2JyoGgWnWjt0tYba2kMORSHI4UBxq6ia2r1B53BidDhw6bRo7U5cikpFsI7gytpabE6NBgUnyhFN3XS4MGJDRSGM/RipoJKWBFHm2ccUVDSH6iIdpq2NZPIMGHREDbf6+iz6cw9E3QRlVdAmAf7vXujX4RR9YuJo7sIqam74Avf8rWQRgpEEr+kKkFRYzvb0ePalRqG3uwiptOGg9oHJgMw/qFEiUfLK4aVZqDV2lH/ddPo3RJw+qgojX8L9407cGA/1d6agq6yiS0Y2f6usIthW23loVXAQNUcljKoMZizVOjImbaPFgp2E/nY/ik66y2yOVLeK26kwdUh3nFoNuFXCq/w3c7VU27H+mIGj2IY+suEPbMXZQ622UxE/ifQKF3a9ylUPXkN+aO2D8NxQC990TOWBVVu5YUcGP6cm0yY3h9TKYlzZWja5W7L87xsxmDW0vSo1sBsiAmLlI6votGojK1M7YTqig2uXAhV6HedmZbHkvxkMuiUtgFGeuc6Ks/iiRYsATjjJ8eyzz7Jp0ybGjx/Pgw8+SHR09EnFM3LkSIqLi1mxYoXPtB9++AGtVsuwYcN8yt1uNyNGjECn0zFs2DAWLVpEZaX/zozz8vK46667SEhI4IEHHmDMmDEAbNu2jRtuuIF169Zx5ZVX8vjjj9OvXz+mT5/OPffcg/OImgirV69m7dq19OvXj/vvv5+77roLnU7H3//+d6ZOnXpSn8HZTHWrZD2wFPXQz0cBgrGjKhyqMaEQVO0iqKr2Jn/vof1pa2oLNnRqS0lcFJVhwVSaTZRbzD7LL7GYcRzqQHRLQgrOQx0Yq8D3bQdTYQqm374/vFoWaXDjxIyn1ohbhTe/h2/+aJTPQDS+JQc5NHz1ocSOW2VXiJkCvY4aRaFSUbC73VicTrRuN3qbndCKSoJrajwjpGncbtxaDTUWPYUxJoqijNQYdTjQolK3t6iYqcGBDgsFBFGKEwtanH5ar2kO1UfyLmPJVig6Ymhk2zE6vS2rqv3/rhy46lWQkSsaje2hObjm78BEIfkk+m2+eGQfWSWRQQBEkwPU7h9mtRg3h45Tb//QqPGKJuDfc1F/XI+KATc66o4SBeZQHAa9J1kEYKqxonE4CS0qIzKniOCSCgpMevLCIwENNSv2U/WOdEzbXGXe8xs2vY78sMO137PCQ7Er3rUUnYqGCsWIDjdF8/af7jBFE1E96r9QYacKCxtaxnmSRXUKLGayg83oVJVrN2+h9f+zd99xctX14v9f55zpbXsv2WTTO+n0EmogNBGRoqLXBtjFq3CVpvfqV39claJgQZELCKIQupHeQ3rv2Wy219nd6XPK74/Z7GYyG0ACmUnyfj4eyyP7mXLeZzg755z35/N5f9rbAKge6B26Tlp725pDHrfIPsu02PPgVqJuF9srCmn3ewYfsLDrJmPaulESJq2/fINIUK45P4wjYoTRjh078Hq9VFWl11kwDIOBgYG0NpfLhcvlSmvz+XzcfffdGaOBPqwzzzyT22+/naeffpoTTzxxqD0cDvPyyy9z3HHHUVhYmPaaJUuWcMwxx1BZWQmkkk4PPvggzz33HJdccknGNpqbm/mv//qvjCTZrbfeSnFxMffff3/aFLV58+Zx/fXX8+yzz7J48WIAzj333Iz3vvzyy/nKV77Cn/70J6666qqP7DM5ksS3BzET6YWDFcBp6USV4d5WR8wg4rMTt9vZWVHKxjG1Q4/pikpXcSGmqlDQG6KsMwhAQlN5aca4oak6RX0hdNOOjTg97jz6XX5sho7DTGTEpWGQxIOD8HDjc6vg4gUf3c6LQ6Zxb6kgm5K6GFIUkqrKtoCHolic89p7ATAUFRsGdsNAtdKPy6TLMfRaS1UwVIWo10agL4mBioZBCQOASgQvzYzGRRAFc4TEEFipDNZ+baCgwCsb/v1jraMPVu2C+eP/vdeJD0R/bgsANiIoaBiYaKRPaWwvHl4C3W7oVLODKoaXuE4dBamRlOjprxVHoEfeBMj4+4/gQbXMtDZNNyhp7hwqpO+KxqnGYlN9JcUrBtCxE396K75vSe2yo1H73xspCieo6wjSUJqPZpqolsG60nImdnXiMpPEVRtdDh+KoZJUbPQ+0UD5FeOyHbrIAuPtVLJQw0gNpR6Be/AcNL1x91BbUtNSo6w1BbpkoYajUTKcpCgRpNNVgGWatAU8lAcHF34BPAmdPo+L47s3s/nlDmZdKHUZ/11HxAijUCiEz5e5/O+uXbs4/fTT034effTRjOddfvnlH2liJC8vjxNPPJHXXnstLWH1wgsvEIvFOO+889Kev2bNGhoaGtLax48fz/jx44emmY20jb2Jn722b9/Otm3bOPvss0kmkwSDwaGfmTNn4na7efvt4REn+05Pi8fjBINB+vv7WbBgAeFwmIaGhoP5GD5SPT09xOPDBctCoVDaZ5tIJOju7k57TWtr63v+3tbWhrXPDfYH3Ya93DtiMWJ9vz8nY3AYvmqadBbkpT+mqpiaCorC5kmjWD5rPOsn1/Hcgsk0lqWSiTbd4JNvrGIb09jJeDbbx2IBuqoRUdPfDyBGAI39Ekn1ZVn9rD7Kbfy7cumY+TDbqNl7H2+SSiBaFlgWnqTOOYPJIoB+tzM1Pc1mI2GzpadzLGu/9A7YksMteUTZtwp2Ehed1GInhIWFwX49wew/VcDCSgVIZ2Cf537AOjeWTYNRqSKncsx89NtQ61NLWJvYcRDFQCWBRtRup7PAx8Yx5UTtDqp29TJqaxcz2tYwii1pqQJr8L8KYO1TePRI+6w+rCPucxhfgYIJ+1WC8EUTOGI6yX1Oc4ppZay6WNobIuqwAxZ5dKKNHV5G/Yj7rA7CkfpZ7LsN1ygvzmiS655eRnnPAPVtfQSiJn86eSabi0pp8BbT4s4n5HakjiXLxH9SRc7tx8e1jX/Xkfo57N2GUpQaFeIjjGGzqOtOX2BjWnsPhbEEnliUMR3D29ieXwpK6vuoXLOyvh8f5zb+XUfq57D/NgzFIKx5QANLN5ne1JkqzaEMd3F6kgkGbD5Kx/pydj9y4Zg5EMXadyuHqYULF6LrOq+88kpaezQaZe3atQBs27aNX/7yl3zjG9/gqqtSS0DvrWH0yCOPMGZMZqG0xYsX43a7eeSRR95z+/vXMAJ49dVX+fa3v80NN9zAxRdfDMCXv/xltm3bxvPPP499nwvvW2+9lWeeeYY///nPeDyeofbHH3+cP//5zzz00EOMGzcubXsFBQXcf//9aXEsXbqUH/zgB+8Z69y5c/nNb34DQCQS4d5772Xp0qW0t7dnPPfee+9l1qxZ7/l+R6vWm96h/dZ3h36Po9Fj8w6NDDJU6C51YdpUqoO9DBQE2FxXPfR8JZ6kuTJ9tT7FtJiweQfdfh8Ru41FyzeRH03/o39z5liibifjOho5Y8crOAhhoRKjAB2NPPb5IqkpgjX/CwWZyVSR+370us5tb5qQGPwxUwmj6X1hjgkOT1UtGgjjTQwnClXTxB2LoVgWimnh7+nHnkjd/KmGSVXzALakhZMk+YQx9hto6iVIPauxUIiRj4UTBQsdBwYOfOzBNnQzaWLhgJPGo7xy6/CblHwWutJHd47ohk/AT6740J+ReG/6i9uInPsHtFg/SRKsYz4qEHXZ2TamBO9AlIqmEOo+VwF1bKWezUO/p+q0aSgkMe/4Mtp1slz6Ea09CGO+ihmJo1OENfj9EMbNWm0yL87Ix6MGsBRwGBb5feGMt1CcdhZufIc8Xwzf2huxjS44xDshckF0a5C3JjxC2ObmhqtOIepNje5XzdRI1QvWbODMTdtp8xVQ3hYiX48yR/+qrA58lEq+tJ3oab8F4P87/jTcSScra0ppyvdR0xfimLYeYg4720qLGN3UyCdWrWV3oIg+h5dAJE5xX5Rpz5xD/sLq99mSOBI9MeNx7N2d7PaU4DD3GaJmWSgW9BV5Ka73cdXDsqreh3FEzDeqr69n5cqVNDc3p01Lc7vdzJ8/HwBNO3CP9/5T1D4Kxx13HAUFBTz99NNcfPHFtLW1sXLlSj7xiU+kJYsikQj/+te/0HWdK64Y+cZpyZIlfOc733nfmPfm/q688kqOPXbkIeCBwPD0gxtvvJHXX3+diy66iFmzZpGXl4eqqrzxxhs8+OCDmOYBxoQKKm6ZT+CsWjp/sw4iCZQ8B/bl/cTi4Cx30m8p9IUh2Z8g7HEwtrORnZWlJAYLhCqqSiA4QH/+cNX+ou5e7IZJebAfXVXIj8YJOez8fsE0lo2qoK63n0vXb6Xf72V9dT2NxWVM3dNAUU8/o2I7CXx+Ekw+E5ZtgxMnwxcWgluKRx6uTquB20xAUwETVAVv0qQgoQ/VH1JNE08ifVSZqaqYSmolNGXwd2dcR7XAMxDHM7jKmoKCHT0jYWRgJ4kLCxfgGloRzRoscL13FFsqhtSKadZPLk97D06YBI8vy9ypIi889n1YtRMWjIcFEw7yUxLvxXbaOHzbvo/+xAbo6Kf01jdpUWoxkxa+UARffyItWQTQRN1QwshEoZsCfCUOHE9/C23u2CzshTikyvKh/Y+otzyCfcUOjF4Dc3cI1eXH0xHm1DXwlc/No8Bp4/SdrfjCMWz7TFVMOmxUhFsp/t483DeciZr30V9ficODe3w+ZZ52/nfaQoI+F1WRGMe1dlAUT9DrdNBVEMBlpjofFMVkwvPnSbLoKGY/dSzqrhvoOO4u1pVXUdsX46SdzWnP6cwP4EWhrXoUr/fGqWwPMq6rG/f4QiYtOx9XXeAA7y6OdGf+62z+Pv+p9GQRgKLgiido9Pr51sPHZye4I8ARkTA67bTTWLlyJY8//jjXXntttsMBGCpc/dBDD9HU1MTzzz+PZVkZ09GWLl1KJBLh2muvpaamJuN9/vrXv/LMM8/w9a9/PS3RNJLa2lSNHFVVhxJlBzIwMMDrr7/OokWLuOGGG9IeW7ZshBs9kcF7XAXe44ZXHRr1Ps8f15/ktT80sOXpVoxYnGOXb+C1OVPQbSr+/jC+fVYPSWoqKibfueAU3hmV2sbGsiJeq6/hq2u3UNU/gK5ovFE+lglWJz1nz+GkPyz8OHZTZImlqGAYqemPDhUSJqWxBIaq0uWwU5RIYrNGqjSUogBYFo5YEptuYTMt7DroKGhYmKiEcOEiiU5qComDBAX0EKME235FBDQMFAzAARiDq6UNFn7ffwranV+Ep5aDvs972FR4/iaYPRZOnvKRfEbi/anV+TiuPR4HMPbKuXgXPUh7a4J23nvkYVjx8GLBSZz4H+W4f3b+oQlW5AafG37+WRSGLxIdgO0HK2m+fxtzIjGqWwdAVWitKSe/O4gjnqQ7z8vO8mLOrCrE+zPpxRUw6s4z2PJ0gJCmsXBPKx4jlVwsiCcoNA3aPHkkVQU1aeGaWZLlaEW2aXWFlK37Fsec/RL/mDkLV1Knun+ApKqyvbSIpC9Vm1UFuvPyuHbF2ajOI+JWVhwkd4mLqtl5NCzvw9DSS4SUBUMYDS1ZiuzIcETUMLrwwgupq6vjL3/5Cy+99FK2wxly7rnnAqkV0J555hlGjRrF1KlT057zxBNPkJeXx1VXXZVRb+n000/nggsuoK+vL2O63UgmTJhAfX09jz32GE1NTRmP67pOX19qTrC6d+Wt/WYkdnV18fjjj3+Y3RXvwx2wc+a3xvG1f53Eda+dRnJyMYX9YWwWeCKxoedZQG1/K09NGzOULNrLwKLf7cQ0LdptdmbuaKGqp4/aY2UI7pGmI2ylvqETZqqOkarQ77DRZ9cI2W302jU6nQ767PtdLFkWimGCZaHpBnbdGJwCAJYCJipJVBKoBPHRTj57V0rzEyWpBoZWANzvjXG/+22oKATs7D19WPVlMK8+/alVRfDPm2DC4IjPWWNg1f+XShaJrNHGFVO97etsH1VG2GEn6rVh7pdx3F5fyf+c9nWezV/MK6PHky/JIrHX9CIwYdqeFryDq6XpDjtdFSW01FWyp7KUuKrz/LEzsxunyBm+qxeAAv5EYihZtJelaqytqeBvx0wg6rZhL5LRaALUIh+TOztYtHorb9VU8tD0STw/ZRyxPH/a8yxUSRaJNN5Tq6kI9g/VLbIA3a6xra6MAVUW7TgYR8Rfmsvl4pe//CXf+ta3uP7665k9ezYLFiygqKhoqHjz0qVL0TSNsrKyf+u9g8Egv//970d87Pzzz6e0tHTExwAmTpzI2LFjefDBBwmHwxmjnxoaGli7di2LFy8+YNHtk046CZvNxhNPPMHpp5/+nrEqisKtt97KV7/6VT796U9z/vnnM2bMGGKxGE1NTbz44otcd911LF68GK/Xy4IFC3j22WdxOp1MmTKF1tZW/v73v1NVVTWUWBIfD5tTQ//cLAp/8S52oKWyFM9AGG80xoQ9bWwcX8FOX/rQWs00+cbqTZRFY2DXKDN1IvkuSltCFJxYnp0dER+bzpg1nNIfTOx2ux0YqsLchlbcRmr0jrr/EH5FIeF0kN/ahWqYxJ2psULuhEHcpeEO66lVzQapmNgxsMryKHzyQpyTCgl/+iGsp1akjV7Sjq/DNmcUvHEz/OBhrOU7UeaNRfmfT4E6QoLp1Gmw+Q4wzZEfF1nTdekc/m+bnSnNHZykNOMOGwQdDl6ZUMXm+nLm9YfoOy6Pwp1d2Q5V5JCyhZWsd4BTN9CsGHGHfWgKUVxTaQ34+MbSZ2n63sQsRypyis2GZhpDK3bu68VxNZy7bTNjb54t09HEEBsm9V09THmhA4Ct9VXsqS4dXGIDsCxqHLI8ukhXd+lYNv7vRpIue2rlPEVJFb8GNtfIfdLBOCISRgDV1dX85S9/YcmSJbzwwgs88MADhEIh3G43NTU1XHDBBVxwwQXU1dX9W+/b09PDb3/72xEfmz9//nsmjADOO+88fvnLX6KqKuecc07aY0888QQAp5566gFfHwgEmDNnDu+88w5tbW2Ul7/3AT9hwgT+7//+j/vuu49XX32Vxx57DK/XS0VFBYsXL2bu3LlDz73tttu44447eO2113j66aepqanhmmuuwWazccstt7zndsTBm3RKCW/82sHxazYTddgxVRVvLI6uKnz683P50zo/o0Jhdg8OwZ3SHUwli/bRUlKAicIk3xHzpywGFToBUwHFTFtidl5n71CyCMicrw1YioJht2EMzmI17DascIyCiE7UpeGJJdGwsGOQTwRFUxj97hXYB5dm8z/5OfQHp6D/6BmscBztkpnYf/3J1JuNLoWHv37AqXAZJFmUc+Z8qo7/ubOPk7ftpt9v52vnn0if0zH0eJfDxkWdvXRW5mcvSJFzqkrt2G2phRgSmsaqikIiqDR4XXQWeXFi8tvWFmbGOoHa7AYrckYRSTbZ7bjCUWK+4YVdnOEo/qTB7ppRjP/O1Pd4B3G0CfmKsYcMoj4nbeVFNFWVDiUbLVJrOE76Uv17voc4+pQU23m7vprpLZ1Y+61mHfS5D/Aq8UEcEaukCXG4evqaZdTc+za2fRMADpPRXddy1rW7eG18LeOaggTtNua1dXJyY+Yc3H63ixtfOB5VkxvzI8mbTTrH/8WE5OAKaYM+taOZ4vhwz5ozkcQbTy987YzGqGzqxJ7QMTSVqNdJQUcYf3/qeappUJiM4iQBqkr9m5/EN196X44WhmkRuKGf7z//Nq0+N7+Zk3mz9oU9bQRiUW5/anYWIhS5asknX2Jlj4tg/vAI2G67jYcqSzGK3Dz65//jhHtOp/zU96vqJ44WX7jwbV4tr+LszbsZHxxAd9iwJXTiwK5xo7CwuPtRqWsnhr1+ysNsaXHRWVlEd8BP72DH6V4m8Js/jMbpls5Ske78SzexcMduHEl9qG3A5eS58TW8+ICMfv2w5A5TiCw69+55eM+tJWlXUTBxqgkq7l2I5ncy1x0lEdbZ4HKimyYrC/KJ7LfaX9TpwJlISLLoCJTcW1xGTe8lafam13mI223sm/VXdYOy5i5siSQWqTpG/t4I5j7vU3FlPQWfHk/gW/OY0vslSRYdZTRVwW8avDyhNuP42suhJ2nyyCqLIp3DrtC3Xy2RoqTO2EgUwkm0pInvOBldJIYpDjs3v/QGu/N9rCwpoMfppKmylObR1aiApkttEZHOO91Hd0k+lqoOTcnfl2JZ2O1y3SsyFUUGeGb6RJqK8ul3u9hRWsxTMyYxqav5/V8sDkhSs0JkWf0TF5Jc10FycxfOE2vRylOrFy2a7+Wn63RQFNodDnDA3VPGs2hPM6XRON0OBz7NhtNIvM8WxOHIppK6mVcVsCsQM8CCFUX5VIXjFMcTWEBCVdEMA9tgQVFPXxhnPDE8fDs14R9HRAcFyi+sZepdx6JJz9xR7RMrNvPHU2Zy9VureGTyWLpdw8mhyX0DbAr4KIqFshihyEUdJQVYTZlJxvxEEgyT+2fP4iKn1KIR+3B4iaoeLl+2ha7iPHaOTS3SoQB23SC/S2pminR5504j/+UNdDvz8MdiBL2eVPJoULPHiaLJ94zIVBiKsqnayYPTJ6AkdVRNoyKaQLfkmvdgyKcnRA6wTyvFPi29HtaYS8bC+iD7Dh9p8nm4d8r4obowV27bTZVXVhY5Em3oBoYuiBTwKGBBaW+CbreboNMJlsX4niC63Y5us6FYFj4zlFFY1FIgWuvjvH+egbNM5nELGNMd5DvPvUNDQSELO7podrtodzmpC0WoiCd4sL6Ws7v0938jcVRpKQpga+hH33ehDstil8fFlevfJOHKz1psIjf15LlxD6TqLzZXlaQ/qCigjfAicVQbfUYNdY0v0laSj90wqerppc/jIWq3EbbZeLs0H8MEmxw7Yn+KRSIa49rXN1IaitLrdvKPGRPo9uZnO7LDmoznEyJHVQVUKortmQ/skwxoDfgoHy3TRo5Eu/v3axhc7aHN7ySkqaiWRVksjsM0UUwztQoNFknHyP0ARbMLJVkkhrSU5lHT08/q2grWFufz+Q2vccerS/jh2/+gtG8bhqqQPMDqneLo5T22HH8kil3XwbLQDIMu1eLK9S/zx+f+SG1YVtYT+ylxoOkWRV1RnPHMJPTAfvVphFBUFU1PkNA0Yg4HCgq+eILCWBwdiwG3HZuMMBIj0E2dz72zjtJQFICCaJwr313PhJ7WLEd2eJOrQSFy2NLPO7no5wPsMp0oqkrS40iNOEroYFqUJ3XGTMnLdpjiY1DqsYaXIY7rkDBBTS1f/XZJgEldfYwOhbEZBg5j+CI8EvCgt/di22f1tLjTRsXYwEibEUcp35UT6L+9HxS48c3HuHL9q0OPnbT7XdYVlTNWkRs5ke6qE13cfWuC0eFuKgbaGN3TyIyWdSiAoSgURGLv+x7i6FJc7gAdHEmTUbu7WJc//L1iAQay9o7ItGZ0OUosQTLPl9a+NeBDUSEZM7C7ZIiRSBdxesmPBdPaHIaJ15TvmYMhI4yEyGHjixTG57eT59BI5nvAYQOnDTwOZg6EcCoKtu292Q5TfAwcKqmr6UgSQklIGKk6RuEkmCZdDjv9ioInkV7DSgV6iwPEnTaSdo2o24HusKEGRhitJo5aN1zgY0udmxM2NnH2jlUZj39q+0ryEp1ZiEzksnyXgpIw6HMGOHHnW8wcTBYBrCmbzOtT52Q1PpF76jUdVyJVY2/MznZmrNpFQfcAvv4IIaeD4t6eLEcoclF7UQE9qsLqwgC6opBUFJYV57OqKI/8qI5ygAUbxNHNk7DQlcxjw/RJcvFgyAgjIXJcGQN071+nSFPpDnjojiWpcUuP7pHIa1dSI4xiI9SRMSxUFFo8LsaPcK2tWCb63qlplgWGRU938uMNWBxWnHkuflHcyLNhOzHNDQykPR70Bjgh1JKd4ERO6/N5WPDuTv5Zdhbjw1so6++kzV7Go3POZlGV+f5vII4q2wv8VPrdOBIGb06uxm6YHPvmVlqr81GMJG0HmEYtjm61fSHeHlXNWr+P5yvLQAFTUXCZJqN7QtgcMmpaZPJGI7SX+qhqH76mGfCrFDhkhNHBkG9pIXJcWbQf3JnZ8rJInDf8XlwnyZS0I9GiMSpwgJsvwwTDJGGzE7drOJPDyxIrWLiicdhn+K1qmtj9MsJIpMu750o2nvoam7Vz+OGrfxmaxtiYV8TPTl7ED5RmFmU5RpF7irt6CQzECAxAP1X0UwUxIGkxOrJ/8TVxtBu3p5tHTp3Ga2PL6fOlOr8qu/t54q4/UZjoYutYWbhDZLI7VRQ0Eg47JhapjJGJZZg0SH09cQD1kZ1cveF53q48hk5XMZO6tlPd0sw3Ft6S7dAOa/IXJ0SOKwlGGRPvY2f+cGLIrRvU9IexOeyYFZVZjE58XIo9SmrdYZsKyX0SR5YFeipB5FBg18QKztb6aVzXT3Gth1mnBFj9o1ZMLTXjWDEtLE1lwrlVWdgLkcsUTSWQ1Llr9nH8fcZUPrnubbo9Pv5yzEn0enysLJGC+iJTxOUYsb3L7aItIMeMSFfXGeR/x9QNJYsgtdre70+ex4+eepFqr0wVEZlqTi2ntdlD176jpUnVcTRlVJo4AK9p8MroyfzozPNpKCjh3M2r+PFzf8UXiWQ7tMOa/MUJkePCcRcndO8gkKxnj89FQTzJ/PZeZu96l79PPJ7acvkzPlIpWFjq4Egjc7AItmGCBXXJZKpUqN/BVTdPHnpNMplk1cqX0F70ocUduKp8HP+jGRRPkOHbYgTVPkJYNJdUc9MZlw41p1bek2mMIpPqUAj6XeQPDE+H3llVxPqqYirqPVmMTOQi3aGRHGE2yPaSIgBaKyZwzCGOSeS+U/5zMtfd2oc1Qj2agC7nJjGyJmcVV1/+GWL2VMfGPQvOoMVbwtlKPMuRHd7kTlOIHLe9ws/acBH//fxDrKqegQLUd2zhybqxjGndiNtem+0QxcfEplgkFQVc9tTKeEkDbBo4NJKxBA02lTxlhLULxiUxx/Xy2auvxm6XqWjiwFwX1vOp32/hd8dNR3XbMW0aGCbl3f34KrIdnchFJ/W387/nzuHYtbsp7RlgT3kBb84cg2JZLBglo0VEOruiMndLE8/NHpfWfsL2BkzgzSkTZeqryOD02SiIRHE5HIxv7WZSWw/N+T7eqi2nMDlCbUchgH9OGDOULNrrmcnTqTKkJuPBkISREDnOUxblx3f/lfqWPpJhF4plMaV3My1Oi3O8SeDsbIcoPiaaopDcu+Sww5b6AVCg2W5DBeIj9L4J8UGVjvWxWbXx03+8yl/nTaKxKMCk1m7mb22i7OwF2Q5P5KAxZ9dga1Z4/oTJae2FsQQ1HiksKtKVlNrZHPIPTSnSTIuLV63nsmVrue30U2mok2n1IpNqUzn33a1MLMrj3I27htpP2tbEvcdPz2JkIpdtL8rPaHPoOk67LMhwMCRhJESOm+JoIa/P4qXKE4faWrwVzN6zmjGvXZPFyMTHzWcziQGogKKkfrAgbmCpCpgWkyqlR198eCdNdHBLfQ3TG1q57l/LUS2wgDtOncUttSPXqhFHt9Lvz6H481tZWV5Ik98NQPVAlFP3dFJZJJeVIl1rXSENAz729n1Ypsn6ijKO/9LV9Hg9fLm3EyjMaowi95gJg7q2HsZ29qa1T+3oYXxnL1CancBETvPHdbzROGH3cD09eyRBQZ50ZhwMObMLkeOcepLt/vqM9h15YzmxviQLEYlDZVGdyv1rzNRiaaYJWENJI80wCZgWV50qNUPEh1eRp7I138M3zz+JuKKgmRZOy8TudaKGdUCmNIp0jgInW4v87Mz3DrXtzPdSMxCmtFyKXot0ms1I+93UNDZVlEJfgsrefj4T3AKMzk5wImepDo3iSg+h1sxixaP7wlmISBwOiiMJJr6zhajPSa/PxeSmLjYX5FE8XupeHQxJGAmR43SbyraSSsZ0py9XvKlMVr060p07TuX+VXpqlTRzn94RBaYF4Nrz/Mysl1Eg4uAomkps8J5O1xR0VAKGwbh8uUQQI9tclJfRtj3fl4VIRK47s3sPvpiLkGs4mTitZTf3/d/vUMIFjPv6+CxGJ3LZ2J/O4+Vr3iE/HB1qS2gqiYA7i1GJXOaJxThjW2Na2/TGDvKPG5uliI4MI1RLFULkFoXnJ6T3vhmKwssT6rITjjhkjqlQQbfSk0WDnv9JCecfKxdN4uBZRubcft2CujGuEZ4tBPTbM5OJI7UJkXfiaJ753R1MbW2jMBzik2vf4tn7/ofZXduYGV2Oxy3TqsXIqmbl8+eTZtKZ78MCwm4nyyePZu14mY4mRmY6Ms9DTsMgMKogC9EcOeTsLkSOU7BYU13Gr0+cxQm7mjAUlVfrq6FAkgVHumr/YM2i/clUbPERcmgKUSP9oFJdGooUVBcHUDQQJeS0D06RBSwLbzgO+LMal8g9SlU+M244kde++f/ISzSz77eKiomZjGUtNpHbAgUO3q0rpbXweAKxJDG7RnPARVWxJBnFyPrL8zAVUPe5pOnxujh2TlH2gjoCSMJIiBynKJBnJNlYWczGimJQAAUW2zLndYsji9uuUOjX6AmmLyFbVyiDQ8VHZ6zDZF1SGU5Eqgrj3JKVFAd2Wls79xUGQNlbyRhO39wIFGc1LpGbAl89AWvxJBj1pYwRs+o0mV4vDsxSoLEgvVbjKL+seCVGNmFOgBcax3H8pl14kjo9XhfPTh/PN8qlHuPBkLsOIQ4Di7UNaFigKaAqFMeSfOdUKS56NHjicgcO5/BXtccBT1wl/+/FR+drY5Lk+Wzgd0CeE3/AzrcnygW5OLD/mKrwhRfXUN4TojQY5srX1nOpTwrRigNTqouwPnNKWptVVwrnz81OQOKwcFyJCdY+SUbd5NpZcvsqRnbzIg/PjK7ivpNm8eAJx3DvCbOwH1eK0yYjpg+GjDAS4jBwQuU6zigZz0PvGvgUiy8udHPycVJg9GhwQq1G/w89/G2jgV2BiyZr2DU58YmPzmUX5bH5xpVsjldjKXD+NDuXLS7Ldlgih8371gTCL73IBX94HgC1xsepv1+Y5ahErjPu/iJvRduo3tRF9cL5aNdfCG7pABEHdv9FGqfe1UajUYxdgW8fZ+OiKTJaRIysukDj+WvcXP3HDvboXq6c7+F/zpX7pYMlCSMhDgOKAp9eHOAzF8tJ8mjktClcMV2+rsXHw+VWmTBtHaPjW7j88ssoLJJi1+K92Xx2Tnn+NB645c8oCfj0LZfglBt/8X5sGhtOG8WG00Zx9dVXodnlmka8t5o8hRsrnqNH9/AfV11KsV9WhhXvbW6NxndqXwHg6vOuxm6XTtaDJXcgQgghhMDhTOAPSDFR8cEla1NTF1WbTBERQnx8Cm0R8lxy4y9ENsgZXgghhBBCCCGEEEKkkYSREEIIIYQQQgghhEgjCSMhhBBCCCGEEEIIkUYSRkIIIYQQQgghhBAijSSMhBBCCCGEEEIIIUQaSRgJIYQQQgghhBBCiDSSMBJCCCGEEEIIIYQQaSRhJIQQQgghhBBCCCHSSMJICCGEEEIIIYQQQqSRhJEQQgghhBBCCCGESCMJIyGEEEIIIYQQQgiRRhJGQgghhBBCCCGEECKNJIyEEEIIIYQQQgghRBpJGAkhhBBCCCGEEEKINJIwEuIwENiZZP233mTXTW+it4ayHY44DPTpLmJJK9thiBxkmRa7VvezbVkQPWFmOxxxmFr2doimhlEkE1q2QxFCHKHCCYs3uuv4556JrNxjZDsccZgwDI14zJntMI4YtmwHIIR4b563bShvFGC0rmOP08myv7dz8XMLsVcFsh2ayEHbu0x+svt0GuOF3PbjCD8+x+LrJ8pJU6REB3Tu/doGgm1xALz5Nq762fgsRyUOJ/G4yXeu2YnVG0ezKunYVssbJe2c8qnqbIcmcphlWmz44y6CT45B90Lj/DD1M/KzHZbIYZGExWe/18SMzV58usGjy9aw5otj+dIlRdkOTeSwZ5YEWfHmCVimxo9bWrju25WUlNqzHdZhTUYYCZHj3K+6mbd1B6ZlUtobZPaGrbzzvXezHZbIUZc/EKMxXgjAQAK+8USUdxv1LEclcsWSX+4cShYBhIM6T93RmMWIxOHmj79uwtUZwmUZ2BXwJxP89eFezGAs26GJHPbyNcu5/9kYTc4K2vQK7rilia1rZcS0OLD7ng2yYH0TPj01sqg0FmfnvduwLBk9LUa2aUOEJX/vwzJTI1+b9iS5+5etWY7q8CcJIyFymGVZ2KMxzr3mCub/55dZ8L0v8dfZU9mwrj/boYkcFI5brG4zmdPSwZXrtnLGzj24kjp/eDWa7dBEjti0KpzR1rBFjg/xwW16uw9LU0FRADBVFY9l0vzfy7IcmchVsfYor29IEnM4SNpsJG02EjYb/7i3OduhiRy29oVu9p/wWhhP0NYQyUo8Ive99FxvRlvj7gSmKUnGgyFT0oTIYYYB1198JlvLigHo87j44eKFfPaNFXw5y7GJ3GOzTD61aQdTQxHCXi91AxFmrdhAa8UEwJ/t8EQO6NHsFJFMaxuwyVBt8cG5I3F0937TXFWV198Z4NPZCUnkuHh3HNNmcsNjD+M2UyMcW32F3Hv2oixHJnKZr3uExJBl0dRrUjH60Mcjcl+soQ9wpDdaFqZpoqpSb+/DkhFGQuSwrog1lCzay1IVXpgmNUdEpnhvgrEJnacnjOG50dU8Ma6Of06qZ9w66cUVKR0+Lz2u4Zv9kMNOi0+SieKDq+rsyWhzxhNsyC/IQjTicKB4bHzhlX/hMeMogAJUhnq45PVXsx2ayGE13f2YpkVcVel1pDo2lKSBYyCR5chErqrZ3QL7Tlm0LNzRCC3vZp63xAcnCSMhcpg5kMCpZ64K4TCVLEQjcp4Cr42qxB9PMKoniNswUO12nnZIgUiRktBU2vICbC0uYmtxIbsLC0jY5FJAfHBFPf04Y8M3bBZQ09JJa2lp9oISOU0LRXFZCSxgT2EJu4rLMBWFCe3SmSEObLfbx2sVpbQnLQaiOn+vquDFCWPodcoEGTGyVsXNDrvG5JbNnL/2n0xs3sjfKkpplFIeB0X+4oTIYWZnhLKgSmOhD4zhjLkRjb/Hq8TRKumxs2B7I6O7U3O4YzYbz08dz7aAL8uRiVyhGRamopCwD5/+7ZZFNOrC7ZaixeL9NZbk0+fzoFoWimVhqiqbxlTTKZ3+4gCeeiPE8XY7Dx1/Gq0FqQ6MglA/l73+EmVZjk3krjZD4dJV61EHL3/n7mnjD8fOYMlGg1MWZDc2kZteqB/FxS89wddXvzDUVtW+maZ5n8piVIc/6VYUIod15PshloSoDkkz9ZMw6HW5sh2ayEG7n20aShYBuHSd43bspjSZxDCk4J8A1bKGihXvSzdkbr/4YNpKC0FRMFUVQ9OwFAXDZiOvL7OguhAAkbVdvDl28lCyCKDXF+DVydOzGJXIdWNbOoaSRZC6aZ3X0Exfd/KArxFHN0ekn2vXvJjWdvmWd5jdviVLER0ZJGEkRA5zu1R6fJ70GzxFwbDJzZ3ItPuZpoy2olCYUZEYwS7p/hfgTOgjtiuyTLH4gFq97ow2W1JnbHtXFqIRh4MJbV105BVmtLfnSd0r8V4yOzdshklt10AWYhGHg2k79qBZFnG8DFCKTqr2VSBhZjmyw5skjITIYTV+iKojnDBleUgxAnNNY0Zbv9eDy7JwaHLMCPDH4ux/JBSEo1gRx4jPF2J/y0oL8PWHMC0wSdUXLezpozQqI4zEyBKWRWVPZtHZ0t7uLEQjDhdvjavGUBTemFDDX06azhsTanh5Yi1VoRFWTxMCcPeptCkT6GM0UUrpZgJdSjU7/dXZDu2wJjWMhMhhe3ZGGamHpaQ/DEjPnEhnRiO0VNRS0dWLAsRtNrbVVOJKJHHbJWEkoCnPR9im4bQsVMsiqSh4NZWkKZcD4oOZ1tmHqWlDI18tBdpLChnXtivLkYlcZevvZ9aO7TQVF9NQlqpaVNbby7G7VgAXZDc4kbO68wN856oz6MnzDrUpuskn3l6ZxahELqvu68RumbhpQcUgRh4Rq5Dg203AtGyHd9iSK0QhclikI0ptyGJXUf5w0WtNoXIgTLRxAHetLIctUizdJGr6UcIWNVuDmJqCI2agJTSaRhWQ1GzyhX+UMwyLqM2GpSjogKWoGKpCr8uJ0euEimxHKA4H1bE4fV4P5uDoV9UCVVVZU1PP5VmOTeSmqOHAZ/RwyZtv0O3zYaoqVf3NdBRpJA0LuyYrv4pMTsy0ZBGAZVPZbM+cFisEwJTG3bhoYTsTCVJIgCCVdNLeIIsFHQyZkiZEDntnbTeBZBLsGrg0cGpg19AdKu/cKD0sYljjeX/jjUmTmL9yF46kiStmoALjNrXijiVJ7MycDiCOLqv2JIkBPl3HaZq4TANvUkezLEKtMmJRfDCWqmJqamqEkaJgqgqmoqArkpIWIxtQ3ESw01sQo6XAxdZyH91FCV4eNZ3m7pHrqgmxoWLk81Kbw36IIxGHC6/Zw2rm00Y1MTx0UMlmxtHoyqyhJj44ObsLkcMS27oIJJzgHLw4Ny1Imhy/q4Wdms4p2Q5Q5IToy7tZvSVKbUVnWn0ri9SExpKuftY3mBwvC9IctUzT4uS7QiywIKntUzTfsvDHdbSQitktdYzE+ws5HOy/TqehgDIQzUo8Ivf1hwd4oXY+fzt5JmWhEP5omL/NPJZGt43CNRHqzsjLdogiB9UEwzSrCv2u4XOTL55kVpN0gImR7c4vRe9In30Rw4OzQ+peHQxJGAmRw95yVvPauLrhBlVB06AnP8BAVFaJECnhe5dzUsu7tBe7SNpUYj4njeOKifmcePtixF0aQen9P6otWZ8gmbRwDKURBykKcZuNou4QsSeqWVW5h3mfHZO1OEXus+sG2NO/T3psNjTf/mkkIVL0sMG2CbWctmM3AIaiUBTtYF44SDhamuXoRC6yYjqnbd3F8du3cvfJC1hRW4U3nuSUXZ3sLpYRsWJkr4ydzfEdezLag4pMqjoYcgchRA5rLsrsdTNsGtuK8ijsiGUhIpGLIo+uo1oPc+Gat3itYjbb68oxbalRJOE8F1gWDpcUvT6arWxIjlA+P0UBDFKrL771253MumwUNqd2gGeLo11tSysttdVYg0WvsSzKevt5esYYeqMWBW6pRyPSxVUvpeHhEWiaZaFgY0Z7K8r6LXD+gixGJ3JR591ruPallyiMRDhtyzYAwg4Hi770BYrtcn4SI+vOL6C7JEhR53Cnel+Bh+JEMotRHf4k3SZEDstTMm/yNcMkP5rgnQrplRNg7exkh6+AHkqJJkspi0aGkkVDFIWAJQmjo9m4YhVVVRjQ9jvtWxbepI6/L0pxWx8F7f00vtaenSDFYaErz0/xQJhoMsnS4jweqyxmTXGAonCUp56XqSIiU3NefkabJ6mzvaiMye+uOfQBiZw38MJO3IkED86dyU/POpV36mrwJhJ8atN26nv6sh2eyFGtXhcd1V5aagJ0F3voqPDSWeWmqjeY7dAOazLCSIgcVtXeT0mJh869Q/0ti4quENPau2kccGY3OJET2gYU1pZXMCYYBsAdT2Q8xwL2bI4w/8xDHJzIGWV+FU1TWRvwcl5bN2G7HcWyCCSS+OJJPNEEqmVhmSZrb1rFmNMrsx2yyFWaHV3X+Vt9NYnBelhvV5UxvidI/IkdcGFRlgMUuWZVTSmn725NazNUlR2VNTxX5uPqLMUlctejyQIevuZzrKlJnYt+efqJ3PrE84TcHlSHXP+KkcVtsLO8mMYZJaAo2HWd6Tt2Y4vKrIyDISOMhMhhLk3llB3tzG/sZlx7PwsaujljdzsFiTgTOnqzHZ7IAc2am4m9XWwrKuH1UWPYmVdISU/6sdHt89K1PZSlCEUuWNdlkVBVBhx2TMuiOBKjIhJDsyz2+DxgWtiTBq6YTmyXHCtiZF3rezh2+Ta2BHxDyaK9thXkscGQwuki04DLxjt15ewd52oqClG3C5epsrK0Nquxidz0+NixQ8mivf7fWaewp7CImFMSRmJkY7u7aSwvTS0UBCRtNjaNqqYjz/8+rxTvRUYYCZFjzJ1dbPzPl9i1J4m9cDR5njzO3rCdPq8HbzxBQThV6d8fk/m4Aor3dPNSaS35g8dDBCCcYGrPTl6bMZWI00G310OFIUXSj2adMYWkaaImLB4tS40AqUrojI+nahvtKi9mUmMbAIplYcQMNJfUiRDpGn+7HG8oSX5PZlLRYRiskWK0YgSzGrazZtwE+v0+VNPEVFMrvypAzR6ZxigyNecHMtpCLifeUJiY04FlWSiK1EsT+9HsGU0Rl5PQAas4ig9CRhgJkUOscJzfXvYq37PV8dfKUiq72+nwewm5XNT0BCkMR1JfeZZFSVt/tsMVOeDlnSZ50f2Sh4pCY2Eho7rayA/1o+gGnUgNo6PVu60mP9+QOt2bloWpKJiKQlCBMS1tHL99Fy7VRLelnmMBrc82ZjFikave2mLy8uwxXLhyG5W96Unoy99djTtpZCkykcu2lpTw+z8+hGYamJo21Pvvisep2rU7y9GJXDS6ox/VNNPaJjV1ctzarXiiUbmiERmMpEFpMLO+lSuRpMPvzkJERw4ZYTRozpw5nHfeedx8883ZDkUcxbbf9Co6nTzz8AMA/Kt+Di/Y5/HamGpCGgRicWY3d3Ly2zvQbfLnK+D5DWFOUYYXStdMg7EDO6lvbMYXt0jiZ1NRBX89dU5W4xTZ88knDMy4BaoCxvBl9hc276B678pFmkJ7ZSGVjV3kRaN0XvwE6hcnU3nvmZidYUI/+CfJ1xrQxhbi/e8zsU0vl97do9AuPCTyPXhJ8Of7niLodxGIxYnY7JRHu/hDkQaMyXaYIseM7gvj1g3qd2ynoaoaJwo1XV3UNrfz03OP5YpsByhyzgmNu5nS1Mw/5kyhI+BlemM7n3tpNQ7dYEJHN6qcf8R+jI1tVPWGaezrpz3gB0VBM0xGd3XT5JKE0cE4pHecc+Z88BuWJUuWUFn53kU3W1paePLJJznllFOYMGHCwYaXYfHixbS2DhfpUxSFwsJCRo0axSc+8QnOOuusj3ybH9SDDz6I3+9n8eLFGY8ZhsFzzz3H3//+d5qamhgYGCA/P5+amhqOOeYYPv/5z+NwpOoMPPnkk9xyyy0H3M5zzz1HcXHxx7YfIt3W15q57t1/0uv0sKaklk0lY2jywObywlRGwIR3qsuo39iBVSy1IgRMWtNAU6GX2p4wWBaLmv9Feaxj6PEIRUzqBr03+h7vIo5ECcPii88b7A6aYEtPFlWGI8PJokGGXaOjLEDxhjAuDIK/W4f/4nFEz/8TJFM9vcbWHsxn1uPOA9vtF2P//LGHcpdEFlmhOHN3bGBGd5DS/iCtlFMWiww93u/2MqGxOYsRilylo/LFxYt4cUwdnmQcy1K49eV2ZkYH6LJ7sx2eyEGL173BZu945m9pTZtMZJG6HzNNE1WViTJiWHRHPwk75IXDtDvsoCiM7g2SF4sRVGSK/cE4pAmjW2+9Ne33VatW8Y9//IOLLrqIY445Ju2xgoL3nwff0tLC7373OyorKz+WhBFAWVkZ1157LQCmadLR0cFTTz3FjTfeSFdXF1dckZ1+kYceeoiKiooRE0b/9V//xdKlS5kxYwZXXHEFgUCA9vZ2Nm/ezP33389ll102lDDa67LLLmPy5MkZ7+X3S5GwQ6FrWz+h9jjdIYO/jD2NLk8NDaPr2FpWxObKkuEnamCh8tDZ07lgxZbsBSxyw7YWPr3iLWrjjTR5i+m3+9OSRQBueohShMPQsxSkyJavLDW4fyOgqRBJDl5ppx5LHuBCW7EUnGaSGA7s6HQvfhCPnj4tQMeJ1deH/oUHUX02tFF5qTeeVAUBz8e6TyJ79N++ytzGDkzDRSvlmKRfgAeicdSYK0vRiVzW5PKzuiyffzzxa85uWE+/w8WvZp3J0qnjqJblrsV+4rrFzrwK6jvbaXUWEnMO16UxVYWC2AArmgzm1krCSAxzWQlKI038cszxmGrqYmdrfoBF2xp4clIdd2c5vsPZIU0YLVq0KO13wzD4xz/+wfTp0zMeyxVerzcjtosvvpizzz6bp556KmsJowPZtGkTS5cu5dRTT+XnP/95xuPBYBCfz5fRPnPmTE4//fRDEaLYR9/yTpad9hQF8QGaivLoCZRQ1z1AfleI+sb1rLlohN57FQYcdpYV+TEbu1Fr91vCuGewrkShJPuOaL9+Cusbf6TC7sVJmDHhEL3uvIynKVhYGLxbWUHX41twT6nAOy6zmKQ48ty/gb3dsZA02LebttPtYnOen4l9+9ShsSzsSZM+uwdXoh8dOzZ95OL6e3NPxqfuRGMg9Zuiwq+/ANecCdLze/iLJcCZ6qXFsgj//GViRiEWGnFNw26kVxGxgFZ/5neQOLpZ0Thvj63m7qd+x6KGdQDkJ6Lc9PYTXHbpNzl/xWpgXlZjFLlFUSyOb2jEntAJ57n59ZlzKQrH2FmSz67iPB7848OUnvgT2H1ztkMVOaTb7+OmMy4YShYBmKrKS2NqcMg1yUHJySIo0WiUP/zhDyxdupSOjg4CgQDz58/nq1/9KhUVFUD6VKpbbrll6N+zZs3i3nvvxTRN7rvvPt5++20aGxvp6+ujqKiIE044ga9+9avk5+d/6PgCgQBOpxO7Pb0S+44dO7j33ntZu3YtwWCQQCBAXV0dV111FSeccEJa3HfffTdr1qzhiSeeoLe3l7Fjx/Ld736XadOmsWLFCu6++262bNmC1+vlk5/8JP/xH/8xtJ29U/taW1vTpvktWbKExsbGtOfs72D2WxwcPRhj1xmPE1/TSVy1EUpqJBwaSbfCA9Om8MrYWq56exMmCgmbhls3qOrsg4nVQ+9R2d3P3G0tfHrZKvLiUZomPUhNpAVrTh3qht2wf/HjqkKoLoJptfClM2HuuEO81+IjtX43vLEZnl4BTy5HAXzJMH0uP39ccAX9Lj8/WPpLHMbwcdDmLeC+6TNQ1CRXPKJz3vJnOH5aKTMfPgXVLifQI1na/byaWe/hzxPG8N/vrE4b7u8PRlETFj1uDwXRKBoGaUOTSCUhVXQ0omjsndZmgWXA1+5N/QDUFsN158DVC6FYkpSHjdc3wpW/gt2dYNdgSg1me5hkRwBrcFRRZ4GfvFAMbywx9LLOfB8raoow3tyCdtzHM+pbHH7MT96ONeOrnN2wPuOxa996lV8cdxZXZiEukWWfvxMeeSOVkJ4+Ck6cBI8vg22tmKYdpzYOLJUHjp/Bm+NrsRkGZ2/ZyJy2nTy8YCILHtuGrl2K7c7Pw/ETYXpdtvdIZNnLb/TT4x6FN5G++ELcZsNpmAd4lfggci5hpOs61113HWvWrGHhwoVceeWVNDY28thjj/HOO+9w//33U1ZWxjHHHMPVV1/NfffdlzalrbCwEIBkMslf/vIXTjvtNE4++WRcLhcbN27kiSeeYPXq1TzwwAMZCZ+RmKZJMBgEUiOiurq6ePjhhwmHw1x88cVDzwsGg3z1q18F4BOf+ATl5eUEg0E2bdrE+vXrhxJGe915550YhsFll12Grus88MADXHfdddxyyy3cdtttXHTRRZxzzjksXbqU3/72t1RWVg6NdLr11lu5/fbbyc/P5/Of//zQexYUFFBdnUouvPDCC5xzzjkEAh/sIj0SiQzt514ulwuXS4aXf1S21P4RBhJEcBDEDSgQg6ZAgF+dNA8UhUucmwkW60TtCpuKSrh0+QrW1BexqbKcM1ft4IePvIrNTN0FeojixkmIEnzLd8BIa0Y096R+3tkGv38BvnsB/Pyzh3S/xUfk54/D9+5Pa3p11ERO2L2F5yedRmteOQD3zb+c8zY8T1GoixfHTeOai77A7sJSbl/yZ+Zvbubkb/8nVz+/ght+vYG670zLwo6IQ8G0Br8P9uZ5XHaIpE9LtIAeu53iRAIs8PVF0DCIFNipHAjhJo6Jho0EFgoq5mDqKIGTLlTeZ0Wsxi743l/gRw/Dy7fB/PEf9W6Kj9qjb8Klvxj+PWlgrW4gRgUaoJKkhD1U9YZpzi+jxTcaW1Ij6HPTVhiAmMWzn/kb591wPHz+lGzthcgVA1HMp1czubqDDk8+BfFI2sOm6WBzdQ10D0CRjIo+aoz+MjR0Dv/+5pbUzyA7Bh6zHx0PLfk+vPE4//z9HcxqaQIgarPhowfFhOQ1v8eOCbd9Gv7rk4d6T0QOWbLOoH+iPSNh5LIsQqZO8qT/wv7qj7MU3eEt5xJGTz75JGvWrOGqq67iG9/4xlD7/Pnz+eY3v8mdd97JbbfdRnV1NfPnz+e+++4bcUqbw+Hgueeey0h4TJ8+nR//+Me8/PLLnHHGGe8bT0NDQ8ZULafTyQ033MCFF1441LZmzRp6enr4n//5nw/0voZh8Kc//WkoaTV69Gi+853v8J//+Z/cd999Q/WELrjgAs477zweffTRoX1ctGgRv/nNbygsLMzY7ylTpnDiiSfy2muvsWjRIqZPn87UqVOZOnUq8+bNO2ACaP/6UgCf/exn+drXvva++yLeX3RDFwwksID+vcmiQXUd/cxq6KA22sMX1i1DBezEcZDquV346/X8YPFi3i2rpzPgpSIYAiCCmzBebEO9/x9gkdHbl8C150Bd6Ue9i+Lj1BtK3XTv57XRk8lLqDQWDI9C21EymhvO/Sb3H5O+UtF/nf0pmt79ClX93fzpzFl84p6/S8LoCLaqfb/vA4cGPgeEE0NfFZ/YtYciXccaHKodyvPgH4iCUyMccVGh9w5+s1jYSWKhYiOBm473TxbtK5aEGx+Ef938keyb+JhYFnzjD5nN2LCw4SRBHetwEwID8rt7qXC28eDkT2IpKnoigc002e0vhG/+SRJGAnpC9Ln8TGtq4l+jT6A++DdsVqqnv9tdwNqKqUxqaSaeHIszy6GKQ2TF9vRk0QhUFAJWN4+Nm0hSgc+ueHsoWQTg1nVMXNiIoOMAEnDro/ClM6A0/+ONX+Ssolic2Q2NrBpVQ34kNdI+5tQ4ZetmHh9bj/r6Jli5A2bVZznSw0/OzUd46aWXUFWVq6++Oq39hBNOYPz48bz66quY5vsPK1MUZSg5YhgGAwMDBINB5s6dC8D69ZlDY0dSWVnJXXfdxV133cWdd97JzTffzNSpU/npT3/KkiVLhp63ty7Qm2++SSgUet/3veSSS9JGOO0dITV16tS04tN2u50pU6YMTTX7IH7+859z/fXXU19fz4oVK/jjH//It7/9bc466yweeOCBEV/zxS9+cWg/9/7smxDLtp6eHuLx+NDvoVCIgYHh2huJRILu7u601+y7wt1Iv7e1tWFZwzdVH+c2jK4YACYK5gh/dtXdA3zx3RWogIKJneFh/pplccszz7C9uoAfXnlq2ut07EPv+oGYFslNe3L6szrQNv5dh/sxs+82EttbUvVE9jO3eTtBlwfLStUYUQ0Tm24QdGb2BUQcLlryChnX1UbSbqPfHD4Oj6TPSo6Z1DYUc4TaQz4HlHjAbcNmWRzT1Zv2sKWpRLyp82bSZsNEGUxHqyRxo+Mkhh+dD1HYektzzn5WcswMbqO3H1rTjwlg8PxioZBMJYsGvT56Jj8+8wu8NXEML40fzXfPPIFXRtdQ298DoWhO/P/MxWMmW3FmZRsNHSRtDiJ2F+9WT+K+Y67kpboTeGbcGTww/TISDjf9lkWXN/Dht3GYflb/riPlcxh4c+PIO5gmdX2yJb+Snz35HIs3ZN6zWdgGnzdYfD+pw66OQ7Yfcszk3jaqoknuevwxvvH2Kj6/bjNXbtzKDS+/yXeeW0J9dweaZcFbW3N+Pz7KbXxUFGvfrRxie+v53HTTTUOrfV1yySVEo1GefvrpjOf/8Ic/5Nlnn+Wf//wnhYWFLF++nK985Stpr9/X0qVLeeCBB9iyZQu6nj4Uf/Hixdx0001Dv8+ZM4fzzjuPm2++Oe05brebRx55JO21uq5zxRVX0NLSwpNPPjlUF+imm27i6aefxmazMXnyZObPn88ZZ5zBmDHDPf371jCaNy+9yN+cOXM455xzuO2229Lab775Zp566imWL1+eFltFRQX33nvvSB/tkFgsxubNm3njjTf461//SiQS4cc//jFnn312Wjw//elPpej1x2yd+itUy6KdAPo+q8tYwBe/eAb3Lfk709rb0dBxDdUFGXbK177G8tpRPHnrgxQPpB4fxR5cJAnQgMbIxWnTeF3Q+gfwuz+q3RKHQlKHuq9AS09aswW8UTORde7ZNI2tSxUdBgYcNu5aMAFznyJ/1cEu1v/821T/8B5sSXildyXTf33yIdwJcagpv9hvZTzDhP4EmBaaafLjN1dhN9MvAQo6+/ENxCjpClGq91JMEGW/JLeNCIXs/PeC+dypcJ+MWM15x34f3t6a0ZwggI4HD20ArC+r58ZF12Epw8fGFpeDnS47kf+9BvuoEth1xyELW+Qoy6Ld82X+MuNknpp4PIu37Up7uNHr5r7Z4+n/n8IsBSgOufYglH/+fZ6kEaKMIGUAOIgRID2ZrZDERj8mNuxEoSQAe36XKtQvjko3nfYyk9s72FlWldZ+wcp/ce/M4/h/r9+Lo+OPsijQh5BzI4w+Ki+++CI/+MEPAPjud7/L//7v/3LXXXdxxx2pC5iDyZPZbDbmzZtHNBpNG6l0yy238PDDD3PNNdeQl5fHAw88wKc//Wn++te/ZryHeoBq7Zqmjdj+YblcLmbOnMm1117L//t//w8gbWSUOHSqHj4HXdHIJzxYSBZ0TWFPZYDZre28PqoWAAM1Y3JZr9vN+opK3PEk3lgSsHC4orhIAuYHG18UcMMj35Fk0eHIbkv9vxtdltasAPltDiZv7cMfHU4y+hM6izc34UqmjjNn0uBbLz/PFVd8A8VQ+OmrrzP19hMP5R6IXBDVYTBBZKgqb5eXpD9uWjhiCfwDMdx6EgsFa4RvFx03+r8zo/3kyfCLzx1E4OKQ+fPXYXxlRrOdfiws9MGJQ6/Uz05LFgFUJ5LU9XVhaR745w2HJFyR4xQF9eaL6fX4qWjro8vnHbq+0TWNmGJjdGdXNiMUh1pZPvzk8vd5kkGnbbh0QgIXEbwYg+UcEqqCgk4zddiIwagS+Ot3JFl0lJvl62V3cXlG++7icvxxE8ePPy3Jog8p52oYVVVV8dZbbzEwMIDfn/4/defOnXi93qERPYqSuerLXs888wxOp5N77rknrW5PQ0PDRxLn3hFLkUh6Ab+xY8cyduxYPvOZzzAwMMBnP/tZ7rzzTi699NL3jPff9WHea9q0VL2Sjo6OjywO8cEVXjqewkvHE363Ha3Cw67vvMWKt3t56MSp7C7IZ/2oKk7a3IJXjeIxvFRGenAaOl1eL1+59FPE7Ha++9wrVBqdLJ01k1O3rUKLxbFNy0P75U3QE4T/fgy6QzCvHr52Lmhq6qc4kKpbZPtoE5LiEDp+Emy/K1XEPJGAiV/H1E2CViqJpCXTb+yndPShxA12FubjMky2eKdSmLTR9DmdwE3nZGMPRLbttwz6kvoaTOCUPW0oWGi6wUCem8KefvyEsFBRhuqjDZ9zVAxUdMzBtrQUt6ZBsR+m1sK3zoO5Y6WmxOFkfCVsuROau6G1J7WCkcsBcZ34ifeQiLjx0ItqZl6D6IrCMU2d2MP3giPnLi9FlhR99yyCr73F3I4uuksL6c3PQ7EsLFWlwLT47PJVgBTEP6rccAlcfyG8tB5qi1K19cZXwGNvw/Mrwelg9zMOxnYO1zqKEKBP81BqtTHgCfCWZwwLb5iEcvH1qRWBZdn0o17RF+ai/Hxb6jpkH2HFjSdiwvcvPsArxfvJuTP6KaecwhtvvMGf/vSntILLb7zxBlu2bOGcc84ZGp3j8aTqKPT19WW8z97n7FvvyLIs/vCHzIKO/654PM6bb74JwMSJE4di8Pv9aSOH/H4/VVVV7Nmzh3g8/pGuOOZ2u+nv789ob2xsRFEUampqMh57+eWXgVSBbZE93rmpG/xJfz2TScCM329l6R/WgmnQpflYPWo8u8eUYdeTzF27kYbCAGPagjz86oMcu6ORh+bNIeLx8mr5FL7a/4P0N7/kuEO/Q+LQUVWoKU79e8/vUG76K87fJ4ngpHZ7F72z/UO39XFVZaffh2FaKIArnuAXv5uF3/XRJa5FbitwQG+cVDFjC7CpoO9zTlQU4qqCfZ8p27rDRsjnoj/hoTgZIokdDQt1cLyRBSSxYZAH2FCvngPuwaTCBfPgpMn7hyEOR1VFqZ9BCuC9cSHLbtrGfefNoDm/EPtgSnGvHT4Xp/b1o0iySOxD1RRqO8M0lRSQ1FQGTJPiaJyETcOtaUSc0uN/VLLb4MyZ6W2fOy31A+ye+hj1nZ0oQELTaCoqpl918adRp/Kpja9Q8peLsJ05JuNtxdHL5nOy2+unKhEjEB9AsSCuOmhzF1HW+/71hcWB5dxZffHixTz11FP8+c9/pqWlhVmzZrFnzx7+9re/UVRUxLXXXjv03NGjR+P1evnb3/6Gy+XC7/dTWFjI3LlzWbhwIS+++CJf+cpXOPfcc9F1nVdeeYVYLPZvxRMOh3nmmWeAVMKps7OTZ599lubmZi666CJqa1PTiJ5++mkefPBBTj31VKqrq7HZbKxcuZK33nqLM8444yNfnn7atGk88cQT/OY3v2H06NEoisJJJ53E1q1bueGGG5g1axazZ8+mtLSUaDTKhg0bWLp0KV6vly9+8YsfaSzi4Ez7j/FM+49U79r/d8lrFCzrgTFlJG12XpkxhVNXbuXktXuIOW28WT0RfxSCPg3D7chy5CKrygtQ7vkK9gf+h0TSx8PTRtNYWUJ9KEJCUdnp9xKx27BbFsXBEPX9HZIsOsr87myFS5ZYkDQBBRwqGGrqd8tiWmcvZ+5qTnuNppuoukJJcgATFfW4MfDm9rRi/S5CYHOifv9stNsuPLQ7JbLG8aXjePYJhVW1o/nSC6sxgO015fQEPCwdXUmbz0VDkdSiEZk8hsH2Qj+l3QNMDw53doZsNpKWTCMSmcYFm8mjh52Bap4/Zh66LXXL6kwmeXjWcVxQlzn1SBzd+sv9LK8o5PKl93FcyxYA3qgcxzrvMbw+fhTvVzlLHFjOJYxsNht33nknf/jDH1i6dCkvvfQSfr+fhQsXcs0111BePvwF4XK5+MlPfsJvfvMbbr/9dhKJBLNmzWLu3LmcddZZRCIRHnzwQX71q1/h9/s56aSTuO6661i4cOEHjqe9vZ0f/ehHadscM2YM3//+97n44uGhbbNnz2bLli289tprdHV1oWkalZWVfPOb3+TSSy/9aD6cfVxzzTX09fXx6KOPMjAwgGVZLFmyhFmzZvH1r3+dZcuWsWTJEnp6erAsi7KyMhYvXsxnPvOZEUcfidzQt2Acs5c/yObuPAaKSvAZJuum1NHl91LW3oczmqQi2ktxRx+dpYH3f0NxxOuudfHbsVN5fOpYChI6fc70xYkD8STvel2cV5BzX/fiY/aJ8RrPfsLknIeNVO2iuMExXf0s6OzBsCxK+kLoNg1HYnDUkWnhCsVQdZO46sARsFP+r08T/cNKwje9AANxbNVevD8+D9viqSj+j7YjROQ2pdjHu5Or+dLSlQRCqc63uRt2YmgqDT4nbd5KOv0fYgU9ccTzxiNETItJvUH8iRBhuxdT1fDpOrsKfNkOT+Qg1UyNZX15yoyhZBGAZbcTVR3MqJd6nCLd2FKNT69/aShZBHB8yzb2jCsh6Jdprwcjq6ukCSHSbd+T4Gff2cI1rz/JlNYGlpVP4+VJs5i4rhFtv7/UHr+bL+y6MCtxityx/I4VnNowmpDDjmJZlMST+AanHeXpBmW6zqs+D/8Ra+VXv5mS5WhFNnzxOZ3fr9ChL87Znb2MGwgTdjhS9WmA/OAAE7ftRjNMbAY4IjGOvaKKUf8rq+iJdCd8bRdffWJ5RnuVsYNrLvgEM/vjPPjA1CxEJnLZssL/4dUx9Xxx/dPkxQeI2Ny8OPokthaPo31MPj97YFa2QxQ55n/n/o05zXt4bcI8LC29PlGj28k9T8oxI9KZsSRvjr2ZE5q3pbWvKB3FLSdcxZLHpmcpssOfVAgTIoeMrXEw4cIabjr7Cj5/+Xf477PPxUokUUdI64bsMmJEgLloCvbBWm2WotDhcrDL60QFynUdxbRIopAfN7IbqMia35ypMbsIXLrJRo+LHpdzKFkEEMz301pdQszrIuSxk3A7KPvBvCxGLHLVOIeRsYonwNieJv75fz/j2La2Qx6TyH3biyr4+srHyIsPAODRo5y9fSmWEac0L8vBiZzkS4Z4bNp8HPFExmNRTa5/RaakZiPoyBx51uwpYt6e9ixEdOSQhJEQOea7l+fz6G9r+daP6xkX7qbT6yXmTK/4bwEJp6x4JsDntWF67KnCxoMclkXN4EWWL5agNhxhjMwUOWrZVIUfzrPwGAZtTifOEe74B1wuUCywq0TddlylMtxfZJp3fCFJR/q5J5DsozLWSnV/D7WxpixFJnKZ7nDgsJJpbTbLZNzuHRQaMtFBZFpRWcPamlEkYgmeLi/h7nF1/L26nEhSx60nwTTf/03EUcVpV8DS2BkoHmrb7S+kXasjvz+cxcgOf5KiFSIHOR0Ks0fbuKOqAFc8TFeBh6JgBGfCxFRhVVUhfpf8+Qqo0iP0FXhxh+NUdvYTUW3YFeixa+TFkpQocF5HN2MvlZVojmaFfo2IppLQVDrtNkqSetrjrn16cb1u6UsSI/vEfDf/cMWpigWxxUwCeohJAxtQB8cdrZ83mQuyHKPIPVtLy0hu0rBb6SNdtZCTVRVFXJ2luETu2l5aTVGkl18cP4teR6oweovHzS6vhy9s2Zk2SlaIvdryy+kx63ixPA4K2GJunEkV17+56JVIJ3ecQuSwkMtFMk/jnekTqOzsxpXQCeb5sfUFyc9zvv8biCOepyvEhKYIjWX5FMd1NhR68RgGvaaJblOwmxo+TaVFitEe1V5rMNDVVCLotXw/C3v6yTMMdCBstzGzpxdIjV4c98lR2QtU5LTSKjcuxeCE5k37tOZhEmePv5SOaROzFpvIXRtKS3mzeC4nd7491LaHOkzTjVksoxlFpgWRHm6vmUiU9MRQr8vJ2qJ8SRiJES2rncHcUAP2hB3VtLAUSNg1XMiItIMhCSMhcliBCVEAm43WirKh9vH9/TiSyQO+Thw91LoifvGXh7jjghPocXk5sbuPKc1tlPeHAEgqCnFVpaFwQpYjFdmkqSbm4AV20G7jsdICAoZBWcJgbDRG0mZDSepgWdSfLytpigM7Zeem/VoU9jjHsKR2LpdMltFpIlNNVx+vjp/DHncN5MVp9pXhb4PTmt4BZS5Q9r7vIY4ugWkFjF/fzZrq4ozHWvMlyShG5tEN+n0eeksKCXvduGJxyls6sSJSx/NgyJldiBxWEuxDNVNFRvf+OBJJ/JEYtqgkjARo+U58dV4ufWsTJUmDolBkKFkEYLcsfIkktoj+Hu8ijnSLJjuxqXBiRxdO08RuQb5hUpVM4tUNejweFAtGzQhQPLUg2+GKHJaw2bCAHvJopIp2igna8tlaXsnxc73ZDk/kKCcxHlt4Ao/NWcjbE6ey9JSpDJTbmBqWYrQi04kX1nD21mbGd/WntZdE4zhsMlpEjKy0t5M9dVUMBHyYmkbE62FXfQ3PjavLdmiHNUkYCZHDjt21jnPXv4QJGIqCART0DWAzTFRdCkWKFP//ncdL42oB8MXimU/QVCYi87ePZtMqbCQ0japYgv/Y2cSFXb3M7wtRGYkR0TTaKvKxn9zJGb+R1dHEe3tm8iyaqaCRGnoooJVyehNFbK4oQVVlmogYQb6bd+vGZTT/fepJJKbVZiEgkevmTfEQ9LlxFDjAYwebCm4bsXIfvrh8z4iRJVQTS9tvoSBVJe6WzoyDIQkjIXLYQGkBr4+ZhbV3rrai0FxSSMxux3LLjFKRMnuiG++ZqWlEfR5XxuOmaTFjfGa7OLp8aopKo89Dl9+HioKKgqGquEwTpycOU+NoDrksEO9tbVEx7UpRWpsjaTJtq6yQJkY2x2ilNT9z4YUtJVUwvjwLEYlcp6kKraVu1ucHwLBAU8GEAVRMu6wSLEbWXlyVtmrwXvnJxAjPFh+UXBkKkcPi5x1Dpz/9whxFoc/rpmq2TBsRw+65rpBANE7Q42ZXUQEhp5N+jxvDsnintIjSSimSfrT78yVOxkXi6cVCFQWHZZEf6MteYOKwsmDnegxNY2ddGdvGVBB1pVYw8lgy7VWMrC7Ywo4iH/vexplAj9uBK9+erbBEjisOxyCx3/SzmIE7FMlOQCLn6Tqo+yWMFNPk+J3bsxTRkUGGKAiRw84Yr/LikiRJe/oFVUF/iEU/mpulqESuOnbdFjbXVYOq0VhRCoCmG0ze04oq3QNHPadTI2bLPO1bQH6l1BERH0xfQQXPnVFNzJ1KQq+bUsvJr2/gxI4NwMLsBidyUkkwiqbHeLO2kFHBCJYCu/M8XLhiA6MKpci+GJnNGHlUSGV/+BBHIg4X3oEBClSNvvwApqKgWhalbV2MU6Xu68GQWwghcljBKDeBzh4UczhbPrmhiYhl4vRIvlekm9OwE4dhEPUOryBi2DT6Sgrp65aTpWB4eut+FIcUERUfTKenbChZBKDbbaycOZpem6xcJEbWXlHOZ19dizcWZnl1AZtK/Jy4dRe+hNRiFAe2eOtbOPT01a388RiFVTLFXoysubSAku5eqptaqWjvZNTuZgLhMLZzM2uoiQ9OEkZC5DC73879E+pYo8IKj4tXfG7+UVnCI9MnZjs0kYN6p1QTc2VOPYu4XbSZMudfHDhhRFQS0OKDsZuZN/ltBQE25EktGjGy7RVl9OYXM60zzkVrGli4tQ3dnUdXQX62QxM5bGxPO79/+k8URVIjimr6evjzP/7IGK90gImR6fZUasMVT+AfCGPXU1OlvSXSoXEw5ApRiBzW22vgSyTZURAYauspK6Y8HM1iVCJXFZwxiq6dTvL3u6GzTINxJZIwEmAqoO57eFgWKpBMSB0R8cF0uGx49mvbmO/DXVGZlXhE7hsYXYXVHqUoEsWfSKKrCXpcLhKK9FuLA3u5chpf3vgEn970Oh3eAOWhPh6rPhWPR2oyipHZfQ72FBVQ09071NaW58eyyTXwwZBvaiFyWHIggSeZxFAgoaokVQUDKIqPsHS6OOqNubyepGFgWdbwKhGmiScWx6bJMrQilTBSBo8PZTBZZAFetxQRFR/M6rJCVhQESCqp89Fmv4c3Swpw2xzZDk3kqPNO8FEQj1EUi+MwTTy6QVUojCsqtWjEgb1bPY47JlxEp1aGLerguZKTuHbRRRl1sIXYa0q4n2Xj6ni3fhQ7S4tZObqGN8ePxmEZ7/9icUAywkiIHKa2hVAtE0NLZcYtFAzForonmN3ARG7Kd6EDNb1BYjYNCwW3rhORitdikKKqJCwLh2miAQYQ01SceZIwEh9MzOlmZZ6Pt4rzUbDQVZWiWIIJDGQ7NJGjCkbnEfN1pvVSK4Bdzk3iPczp7ubWU07jtpMXUhqOsrGsgPJwlFNmyPQiMbKa3laO6YqxclQdu0uL8EUjzG9qQplX9P4vFgckCSMhclhhvY89efv12ioK24oKshOQyGk+Uyem2UgoCq59CkXmx2JZjErkElNRQFWIqWpqFNrgKJEDlTYSYn9FiSTH9w6w1u8hZNOoisaZFQxx7J62bIcmcpQDIzUddr92XZVpIuLAdoyt5JvL16OpCk7TZGCHDSVp4Pu6rBIsRhYwwjxbMYFtAS8OyyLic3PihjUUHj8126Ed1iRhJEQO04o9xG2ZY2+7fPtXkBACNJ+duX39bPP7cZo6HsPEqxvUFEkvrkgx2eembTBLZEiySPwb3Ekdv2FRHu9Law9q0usvRubSk0xs3M322rqhtoSiMKm9BZiStbhEbot6HLixhlYK9id1TEVhV7dFVZZjE7lpd1ExG0rLAEgMtv16zly+M8WfvaCOAHIXIUQOa+vVM7v+LYsSQ+biikyaU6W0r5ex0Sg18SRFuoELqHCGsh2ayBF2e2Z2yJDhReLfcPbG9cM10gY54wmap1RnKSKR60JOF6ds3MiszRuxEnGIRVm4YhmnF8hUWHFghXqS/c9OqmVhtzJXahQCYGPd6Iy2Fn8Ac4TVPcUHJwkjIXJYb1MMvIOrQewtZGzTmJAvf7oik8OmcM66ZRSG+gGwGTonbVrDCZVSIVKkTJuWOQokUCKDjcUHNzUaZMbmXdgGp716ojHmr9nKjAvLsxyZyFWBUhevL5jNtD17+PYzT/HFl17EmzCp/vmp2Q5N5LBRZZmrd1oHaBcCYPK0woy20ZEIZKQexb9DrhKFyGHjx7upDIVpyfOBYYKiYLMs/nuRLCkqMimqSslFY/niH58m6PHhScTQ7CqFXzw/26GJHPGdqwv4XEOSSLeOBcSdGv99VYC1r2c7MnG4mH7DMbR9fyPVbd0kHDbc8SQ2Vees84qzHZrIUTanynE3TufZnzoxkhZgMfuTlZRNCGQ7NJHDTvtcLUveaEUzU51eFmC4HZTW+7IbmMhZnz3Tx99e72SN00VUU6mMJ/nSDAearBR8UCRhJEQO05wa1+vP8+fGE9hcUURJKMqted1Mmj8h26GJHJX360Us69pG5eoInhnjcP9wIWqtFEkXKT63yoM/KeOVtXH6wiYnT3eS7zUlYSQ+sOIrpnHsu+2sfrSNiOnAY49y8t/PwiYX5OI9TD69lKoZPh749eOoeXFO+vJx2Q5J5LgxMwKMv7yOTY80opoWuGws/sk0VJuMshcjKy6y8cevBbjjtxuJRF2ceUoFl1yUOepI/HskYSREjvNMCXFN5DkWTTqH/EkFeGslWSQOTHHY2HhuIRvPLeTqq6/AZpeh2yKdw65wxmzX0O/JpExZFP+e0b88neqfJ7nvD38krCr4p8iSxeL9efLt2Gukpp744M64to5G9VWsATuf+8aluH2u93+ROKqNrXcwc/IqAD558dXY7ZJgPFiSMBLiMGB5oPS0cuxy8y+EECJXqDKqSAjx8VJdBrgMbE4t26EIcVSSlJsQQgghhBBCCCGESCMJIyGEEEIIIYQQQgiRRhJGQgghhBBCCCGEECKNJIyEEEIIIYQQQgghRBpJGAkhhBBCCCGEEEKINJIwEkIIIYQQQgghhBBpJGEkhBBCCCGEEEIIIdJIwkgIIYQQQgghhBBCpJGEkRBCCCGEEEIIIYRIIwkjIYQQQgghhBBCCJFGEkZCCCGEEEIIIYQQIo0kjIQQQgghhBBCCCFEGkkYCSGEEEIIIYQQQog0kjASQgghhBBCCCGEEGls2Q5ACPHetB5wtCkke+PYS+3ZDkfkOMuyMLe7sFqcbMprYvKFtdicWrbDEjls9792M/b3QeyGTkf5NqrOn5ztkEQO61nfw7u3rSXUEsFR7iJxSizbIYnDxEBrDH2jH8VjYOomyCWNeB9m0oSVLtjtYFVkKzM+Pw5nwJHtsEQOi+wYoPbeBIG2GA27V1D/o7moch18UBTLsqxsByGEGNn2H61g53+vxVJUbHaVqX8+kbJP1mU7LJHDXrj+Xbru34krppNwaNhPruCCR09CUZRshyZy0MaHNxH96mNszqvHUBTGD+xmzJcnUfqT87MdmshB4ZYID5/2T5La4AB1y8JZGubKpZ/Ebpe7f3FgO1/tYPmXn8cRj2MB5qw6Lvi/U6VDQ7ynZz7/OsmlWymP9hB0+OgZP4rLXjwbRZVrGpFJ70/QWX4jZdFGFBRMoPHkcxj98mezHdphTaakCZGjwpuDbP3FRkIBB6E8O/1OlbVffhMjpmc7NJGjIt1x+n6/jd0lRbw+vZ6mogKUfzbR/FZntkMTOSry9SdYUziJuOZAV+1szBvLnrvXQVK+Z0SmN7+1jKSmohomNt0ARUFvdWPEjGyHJnLcxmufptNRyJ78aprzquhf38umB7ZnOyyRw0JtEYqfeItzmpZxTPd2Tm1dzbzlb7L9qT3ZDk3kqN4fv0BZtAVwY+FCwUntK8+T7JaRsAdDEkZC5Ki2x/cQ9tlIODWSDo24S6NfgfDmvmyHJnLUnr9s55ljp/LscZNZOamWJ06axotzxtP5ZGO2QxM5qkf1oZk6YwcamBLcii8ZZre3GuvljdkOTeSgnpVd1LQFmbm1lZlb2xi3uwtknLr4ABqVYjrKimgpK6a5ooS2klKa/7Qq22GJHNa/tpOpvbtIfckYgEVNuJPwP9ZnOTKRq3xPvU76XFcFBRtsbc5WSEcEqWEkRI5qXdeDYVPBslBMC0uBpEPFkItzcQDNr++hpXIqBbqOAujA+jGVJLqkF1eMzJuMsWBgLS1aFQOKl5ldWwg680hu78VxRrajE7mmuG2AvNBwT21eOE5dWxBFk+kh4sBM3SSYl8fY7Y3UBNvpdeSxfVQ1TYY326GJHKb2R7FbUWz0o2BhoaLjxxEKZzs0kaOc4dAIrSo2I3nIYzmSSMJIiBzV0xRBNS3UwQSRZYGpQFI3sxuYyFmrTBduc/j4sAFeyySqyTEjRuaLRdmdGIelKFgK7FDGUxNrQPd4kbKiYn/uSCKjzR+Ko9plwLo4sERzP8duX8NJbe+iYWKisD4ygTfGzMp2aCKH9ceglAG67aX02/0UxbsJGEE6fEXI0gxiJP2mnwDpMzFiOHFZIN0aH54kjITIVaHEULIIUl90qjW4YoQQI1DCUchPb7ObJraQ1KMRI+tOlpFw2LD2FhC1LBqtOkqL8vBkNzSRg+JOG45oetIoYZeixeK9xTsGOKF9BdrgtCIVmDawiZ3dtdkOTeQw2x9fZVXeLLb4x6YaLIuZfesp2CnT7MXINvVUEfA6mBDehYpFXLGz2juZKc06edkO7jAmXUJC5KhYLHPumQJoDvmzFSObvHPb0L+dsSQ23aC8vwflDSkQKUamK/bhZBGAomCi0vXA5uwFJXJWv99JQh0+B5lAj9eFZcpcaXFg5uo92K0EqSPGAiwUTKp7m7Icmchl/Wt6h5NFAIrCusAk6Bpp2pEQkLBgXWACb+VPY5O3hpeK5tHoK6NnfW+2QzusyQgjIXLU2sJ8Zna1pbUlbSqGISOMxAh6Q0xr2MMxVTuo2jZAbUsPuqbic/ZjuGUFIzEyTdFh/8lnCrS/2SpD/kUGW9Ki2+3FlUyiWBYxuw0tSWqVNGe2oxO5qqUhQj5WxpSQ0mRHVuIRh4dey5fRZqg2kna5fRUjy7OCjO7dTG0s1VE6KbyR5YHZuJOBLEd2eJO/OCFylF03iNtVHEkThVSfXMTrwGqRYn8ik7Wzk4CeZPEr79JFKQA2wyQW8dHnliSjGJnd0c+AN4+o14Zqgr8vTlG4H1skku3QRA5SdcA06fc5QQF3RMdKmqgy8lW8h874yNVD8pNyPSMOLJZ0oVgmljL8/WI3ktiiUsBYjGxMbBsBgkO/q1gcM7CauDE+e0EdAeQML0SOKg6G0UwLQwVdBUMFdzhBbGtntkMTOchUVJz0EsZDKc04CQ8N/vf1vd+rxdHIMkzWVNfTX+gi6bQRd9voLPdSauukItqa7fBEDjINi4F8J0mnRtKh0Z/vBFXBjEidNHFgtl399FCS1mai0OEsy1JE4nBgaSaBvhiamRolbTN07AMm+RGZkiZGZieW2WbpBEco8yE+OEkYCZGjnMnBaUSKMvSjWhZme3d2AxM5SQ1HMYDprKBLKSGs+jFVFUNRkNOkGJGioKnplwEK8NSEORTFZISRyBR3Zha4jrltKLJKmngPxR39bGUSLVQRx8EAATYwg1ZPabZDEzksanPjCkFRa4zitgiFrQk8EYMumyQaxch6Kcq45jVQibZlJpLEBydT0oTIUYYt88JcMS3au9zUZyEekdvaCosoJ8YqZRYxmwssUExQFIU+lxQXEZm6t/RhqAqqkX55ta6mnORqWflKZDK0kRNDphS9Fu9Bd4CBRiej6GQUkCp/bYsn3vuF4qiWtFRWTB+FkWdhT+rkNScYs6eLPldmbSMhANq1cjAclLIHGzoxPLRThTs4kO3QDmuSMBIiB3U/vRZ7PEavz4s3FsOhG/T5PGwdVcHESFe2wxM5yHh6IyHFR4u7Emtw1IhiWNjiJhpSw0hkCm3uZU+JlzFtw8P7o3Ybb42rZk1hDWdkMTaRm2IOG3bTSI16BbAsVNNE701Avie7wYmc1WH6yWMABRM7SZLYUVHxhuXcJA5s2aw6/mPFS9SuTV33NuUXsap0AprHneXIRK6yAWHyMenEIoGBiwGKCEgf2EGRhJEQOabp2w/xjfZRVFaXEayuxLBp2JM6cWdqJaNNisWMLMcock/LMztpdk6hM99HxGujvK8fV9TANFR2V0pvnMjk0xNM7dzG7489nZm72+nzOPnX1DEYFtx18mmSMBIZNMsgaVNxxQ0U08JUoTffSeyVZgKj87MdnshRZmiAAP1U0oKGiY5GOxX0a65shyZylNEb4dTGzdT0dmEBFgpVwW5aqztIJCqyHZ7IUXZTZyybB1dkVPASZjwb6ArPznJkhzdJGAmRS5q7ef1f7WxbcBy7xsJ406KitZfKtiD9Phc7R5fS2R3PdpQiB9m3dvJ/809kfFcrc7s24tWjNORXEDJ83HXqHL6d7QBFzokrGjN2dvHmZVW8Ma4GSwFb0iSpqER9/myHJ3KQQ9fpd7tJ7q1ZpCg4EzrRjhi8tQ3+/i7Ul8JnTgSPTIUVKSV9w8kiABsG5bTQGJiS5chErgq1J6jp6cRCgcHbfwsY093OGl9xVmMTuavA6mL/NRkdxAn1yfTXgyEJIyFyyaYmVpXU0+nxYU8mOf2drSxc1TD08PjtbWj5MoRbZGp2efHrES7b+k/8yVTB4sk9O3m7ZBoXrd2G3luKrUBu4MSwFkOjItzLBSvW8/fZ0wFIKio23WDurg5gQnYDFDnHFTPQtSSKlbqF01Uo7o/geG0j/OfzQ8+zbnwUpfkOcDmyF6zIGZaWOTVawyQUkHOSGJlZ5sUyh5NFKQq+eAwt2ZOtsESOczFSp7qCOyYr6x0MWdZCiBxiOFy8UTWaNq+Tdp+HYzc0pT1e2j2Ap1sq/YtMbfkBjt+zcShZtNeMnq1MbO/BSiazFJnIVbG3d2MqCt9+8l0+9eYGyoIhpjV2cPtflnLC5u1EdkmRSDEsETWI2e1oJmgWqBbYDeh3uXA8/SYGdpK40HFi9UQwz/p/2Q5Z5IjNVRWYgIlKKnWkYqCwvbgk26GJHJVQFMK2zFpFNtNgbHNLFiIShwONOGSsk2Zh141shHPEkBFGQuQQM26xsbgEFAV3UseT0DOeM2CTHluRKWb3oKi9Ge0OM4kzniC8s5v8UqllJIa19akkvGPwKHFOb2zkhJZ2FMViUqyZdeVFzGkN4xktU9NESu/uEIZdxZXUKQ6Hceo6UYedDq8PJxYGDhR0wETHhfbqpmyHLHJE3IQ+p5+8eHSwRUnd0hkyYlqMrHfbAP2WG4v0MUadbj+uuHScipEpGECSVIpDBUwsdOKKjGY8GDLCSIgcoiYT9DtTPSp9HicbqovSHreAPYXeLEQmcl3E5uPZqfNI9aFYpBYttmh0l9NdHOC3f5fhuCJd1csb6XEGWFk/npgtdTFlWQrryuvx2eJEfXKBJYa5PKk+xtpgL3nxGC5DpyAapaYviN0ysTGAnQFshLHTh4WGtb0ty1GLXFDQ30dgKFmUogILG9ZlJyCR8/q2ddHnVNlUVEnY5sQE+hxORkUbiXlkvIMYWbO7nEZXFamkURxIst09GkWRUfYHQ/7ihMghK/vsaJbJTS89zYVb1tGv+dhdWE9BbxzdptJfYGdCR3u2wxQ5aHz3TlaMPYsuXx7lob0jjSwGFB/+iM7qnVIsXaQr3NNJsFzFUtIT05phEkgo7HG4qclSbCJ3NPWbtIYsZle7cZpJ7Gb6qBBPMkkYJ3lE0PGgYKASRyOB9dBbKD+8KEuRi1yhxlIdGGF8JHDgJI6bCKVRqUUjRlbS0sWdU6bzo3ceZndBEWFbOZWRbpSEiduU6xkxsqCjhC986nLqB3YzrX0Pr9VNJJn088CSP2Q7tMOaJIyEyCHbHtvIpqX3MKa/a6htLjt5cOb5dPjzKGjtYVbLBqy+KEpe5txucXTqX9vDwm1voNvVfZJFKZMiO9g8UI+3P5id4ETOer16NHODLewsqU1rtwCbmUTtjgGy7PXRKmlYzPp9gvXNqXGLmgb/5xj5stGGicHwlFcTBzYGsOxymSng3YLRTKOdCMNTXH30E3FHKInqaG45TkS6V7Vi/ja7jJltp1HT3Y9mWuwsr6XVU0RpSBKNYmTL6upYX1PKekp5YvLcofZmh5/RWYzrcCdT0o4Cy5cvZ86cOTz55JNDbS0tLcyZM4d77rkni5GJ/c145a20ZBGAgxjzGtZjqSo9xXn4knEStz6XpQhFLur54sNsNWezeP1rGY85rCQ2w6SpJC8LkYmcZZqsqa6ktq8D335TRQy7xvKqUp5fHc5ScCIXfHOpPpQsAjAMaPF5MsqJqug4MQALlTA2elCJYKKi/PFfhzRmkZv8/aG0ZBFACD9rC0eRHJB6NCLTQ7sVLlu+kQUb9lDX1Muoll5mbmuh2VXByrKJ2Q5P5KhdhVUjtm+qqDzEkRxZjqqE0fe+9z3mzZvH6tWrR3x89erVzJs3j+9973uHNjBg8eLFzJkzZ+hn7ty5nHXWWXzpS1/i+eeff/83+AgMDAxwzz33sHz58kOyPZHOau8jpoz8J+lMJgAo0oO0FuTT++ruQxmayHXL9hDBj4NYxs1cu70YU9Gwx6S4qBhm3fEca6pL6XX4OG/zm0xr20lxJEhFqJtVVR4eOmYWu9b2ZztMkUX3rMhcdCFh0zBRBiukgYmCgokCaIP1i1R0NOKoJFC2yWpGAsZ3jVTLSmFsbyt7dspqjGKYldCxjr2F9aqb81dsJanbiOAkhJOEaaOqs5+kJiPSxMjGdg/gjyfS2kpDEVYWjZxIEh/MUfUX94Mf/IDVq1dz880389BDD+F2D0/picVi3HzzzeTn53PDDTdkJb6ysjKuvfZaAEzTpKOjg6eeeoobb7yRrq4urrjiig/1vrNmzeKNN97AZnvv/90DAwP87ne/A2DOnDkfalviIOzuwhtJ0OLLpzIUxALWlkxgddkk+l0+1KTBwu3LqBxopyVfelfEsCgeKmlE2Wf6kAW0OMpY6Z+GAszZ2pG1+ETuCf50KWdNmM0fZszje8ufZ37remiFPf5CvvOJ07nsrdX0lBa9/xuJI5aRMPHEdSYPhKkLxbEZBq/PmMC6qjLKgv1M6eihMByjuqsPy9JRSR8pogAWJiiXwMxRsOLnoKoQT0I4BoWyAt/R4pi2XQwo5SjWcKdYa8DHrYtPZf5vNvCzBRVZjE7kEuunT2K9vYOGd/4LxbLoooQtTMBCJYkNRzRJ0pPkj0vauPCMMgrdyvu/qThquOJRrl69lTcqSxiw27CpCletXMmvj53Pb7Id3GHsqEoYFRQUcMMNN/Dd736XX//61/znf/7n0GO//vWvaWpq4he/+AX5+fmHJB5d1zEMA6cztRKN1+tl0aJFac+5+OKLOfvss3nqqac+dMJIVdWhbYjcFf/kPVSGdf5+zBQu2riMXmc+q8sm0ucKAGBPJlhWNZ1PbXgGb1tTlqMV2RJOWDy+3WR1J0SSFv6BAT6tKowzG9KepwBrC8bRb/PgSJpM72pk2dPbmXfu2KzELbJo5Q7YsAdOmgyjSuG1TTjagvygbQnd9jKcpoEFNBRVMODy8uS9v8cXjHPbhRdnO3KRBa83mZz2VwM8diI+ByuKPUxYs4uq0ODURZeDVyfVs7M4j+++8S5xL1ih/cc2pgzdyq3eDYt+Amt2QVsw1VYagOW/gJrij3uXRJbZrQj/mlFOWQdMbu1kTXUZt517ElvLi+hr38nPsh2gyBnWTX/Hwo5uuTGwESBGLU10UwIoJAwVVVV4+PEBvrYhnw1fdVCXf1RNmBHvobqzA390gM++1kJJvId+u4+SSJBmjwM4I9vhHbaOur+wU045hUWLFvG3v/2NZcuWAakaP48++ijnnnsup5xyCo2Njfzwhz/krLPOYsGCBSxevJhf/epXRKPpdR4aGhr46U9/yqWXXspJJ53E8ccfz5VXXsnjjz+esd177rmHOXPmsGPHDm6//XYWLVrEcccdx7p1772kaCAQwOl0Yrfb09oXL17Ml770pYznj1SvaKS2kV53/vnnA/C73/1uaGrc4sWL3zM+8dFIbu8h3hjHQYzPrXqJvHiYuv5mLt38JO5k6rhTLWjyl9HlzkeJy/Sio044RuSCnxEv/CyzTvkGax5Yxd2r4edbPawZW4+NzGPCr0cwbCp9+W7mdq8j8Nk7ufHzLxGR4+fo0NkHk78Os6+Hz/wa6r4CysVw0g/xMMD/zTyR7aUV6IrK/ceey5+OP5/HZi/koWPP5v7TF3Hr0nvhbqmXdjTRTYsTHzZJWgqoCpgwpnOAqoH0658FwX6eG12DNxFidKgTBQ2L9I4pa/BnyPOrh5NFAB39UPslWLn9Y9obkRMicfxKkJJEB5d96RNMv+krXPWFi9haXkRpaIDJ7Z3ZjlDkioQO2EngwcABqJjYKKIfGwag4MBiwfpdNOYVEotbjP5Vgme2Ge/zxuJoEFzdzJbqfBbteYlj+tZRFWtl0sAubGqSMzZvoD8qx8mHdVSNMNrr+uuvZ8WKFdx6663cd9993HbbbZSWlnL99dezadMmvvKVr+D3+7n44ospLS1l69atPPzww6xZs4Z77713aGrX8uXLWblyJSeccAKVlZXEYjH+9a9/8eMf/5je3l6uvvrqjG3/8Ic/xOl0csUVV6AoCsXFwz1rpmkSDAYBMAyDrq4uHn74YcLhMBdf/PH29I4ePZpvf/vb3H777Zx66qmceuqpAHg8no91uyIlNO+XKKjYlVDaFbZbjzGuZydry6YA0Ot2oqsaEdVJSZZiFVky93t4NjXhAQrDIZ77/X8z41s/x6G7OG7POmI4cDE8b9tAodWVOkpMReHlScdy0pZlXPH043zh7pk89K2CLO2IOCRME479PuxoH/HhpKry7Quu5EfPP4OquNlZUj30mKHZiKgar407jsuuvRelPB8uXnCIAhfZ9LvV+1xQm6kfXyRzCWuHZaEpKv1uO9XROCoWBl4sNBSSWKiAhkZk8BXvMW3khBsh8tePcjdEDgmO/zZ5ZpSLNi/n2MadvFU7BgDFMrnx5WdJOI66vmtxAOas72OhYO53e6pi4SNEkNR1S8T0syXfO/T4uQ8m6fu+SsAp09OOZnd8/R1uXPvE0GgYQwHLMshPhmgvLGTCz/povbkwqzEero7KhJHf7+eHP/wh1113HZ/+9Kfp6+vjjjvuwOfzceutt1JcXMz999+P1zv8ZTRv3jyuv/56nn322aFRN+eeey6XXHJJ2ntffvnlfOUrX+FPf/oTV111VUbdIJ/Px9133z1iPaGGhgZOP/30tDan08kNN9zAhRde+BHt/ciKioo45ZRTuP322xk7dmzG1Djx8VJ7Q5iKiaEoaPuN7Nes1AV8XFOJ2iz8YZ1+vyTyjioNHbApfRqiCty89FGWjj2e+uguzH0GjIZVN+8WTiNqS9VpsydMNo4az5ztmxjXtYPljQa7O3RGlR6Vp4Cjw6sbD5gsAosOXz5dPj8/OeMc/vu5F0d8VoevJHWbf/PDkjA6Sty9evAflgVG6mS0o8iP3tSJzRo+OTU7HeTFY4zv6UHFxILB8tduIPW9o5IYbE/9l4yS/IOiyY9nZ0T27emir08ngILdVFn651/z0NR57Cgq5eINyxkT7GJnsVzPiBRrQxMw8vFgog39220kMh6//W2Dm0+Wa5qjVXLVbi7e/Hba1CmbZdDgqWRZzRgemz0NJS4jjD6sozatv2DBAi666CKCwSAXXnghCxYsYPv27Wzbto2zzz6bZDJJMBgc+pk5cyZut5u333576D32LZodj8cJBoP09/ezYMECwuEwDQ0NGdu9/PLLD1h8urKykrvuuou77rqLO++8k5tvvpmpU6fy05/+lCVLlnzkn8HhpKenh3h8uJczFAoxMDC8skYikaC7uzvtNa2tre/5e1tbG9Y+F8DZ3IYCaEqUN6vHp70+YnPy6qiprCsv4r55U7jm1ReJ4afdN7xEei7tx8e5jX/XEfU5REZedthuGPR4HOwoLEXDAHQskjisMMWJLrAsVMPEUDUsRWFV2SheGDsFZ9JEN+SY2d+R9Dkkou/9OZT39zC+o432QIAfn75wxOeM7m5I/UM3c+L/pxwzH/82jBFyOiGng6fqK0iSGnS00+VgjwWfbO6gyx9AwULHkfYaA21wLbUP7nD7rA7GkXbcHHAbsTg23WBHQS0GKq/UzWXAPYrSiJvVlbOJay6cSePgtnGYflb/riP1c9h3GxYmCklspCeE4tgZwDf4m4VfS48BICHXNBmO1M9hpG309vSSHwuzv6JEkJWFk1k6bhIRx3B5l1zdj2wfMweiWPtu5Sjz5JNPcsstt3DTTTexePFili5dyg9+8IP3fM3cuXP5zW9SddYjkQj33nsvS5cupb09syf33nvvZdasWUCqhtHvfvc7HnnkEcaMGZPx3MWLF+N2u3nkkUfS2nVd54orrqClpYUnn3xyqCD34sWLqaio4N577017/vLly/n/2bvvOLnKsv/jn3Om1+29pffeQwgJvSYYiqB0UYqgKGJ5eHyQoo8/lEdBBQSUjlQFEnovCQTSSO+bzfbep8+c8/tjk00ms0sLcM4m1/v14mX2nvaddXZmznXu+7qvuOKK3ufU31htbS0LFy7kBz/4AZdffnm/Y+Kb0T36VpQtu1hVVExmpJWxzbXEFBsfFo3hlWGzsSUgrTPE2LpKZjVup9KfzoSOXxsdW3yThv0Qdu7bmlgHZvzof/GGrfzg45f47tplHHgGf6V/ItXxQWydWkZ1hp3Tli/nlgUnUzQyiyd/LtNyD2nxRE//ou11qRdhoc2dzZMjjuc3C4+j1efmmB21TK1r65lRpOu4Q+384t17ccbCKI9eA+fN+8afgvjm3bg0zk3L6VnSGN83nh0Mc/XKLfjbA4xftxt3KErMqmLxJBjdsQsbMRQSqMTRsaASx0UrChF6Zhcp0F8BaeIg+ORPX/tzEwbJvpjF3mkM7mrhjRFHJl1U2NlI0BHgktXfNyicMBNt6vV7eppZieMigQ0dS89nFlloqKTRzjsjh3Dmdy7d75Y6LT93kOk+bOdBCODtQb/l6N0bk8Z2eYp4rXAOV1xyKmOVbjb8Sr77fhkyd28/e2tn559/PrNnz+7zOn6/v/ff//3f/83SpUtZtGgRU6ZMIS0tDVVVWbZsGf/617/QtNQvR06nM2Xs01itVmbMmMHjjz/Ohg0bOPLIng9bRel7nW4iIdPtBiLPpl/Qpf6E6TW76XA6iZFOt9XFlsxxFHXsmV2iKuzOKmRWy3oUXf5/Puys/AOc9Uf0D7YS1BT+eORp7MjOJ2BzcMFHLsKqDaeWfFYuT2tkxdCJjG3awAb3BO445gQmzcnjd+d4+3kQcciwWuDtm+G6B+H5FRDe99qw6AmqMgpRu6xk1ndy8XtrKWntxKbpROxWdLWVa1c+SxA3+i1noEix6LBx/WwLNy1PgKKArvX8L9DqctBhszJnT7EIwBbX0DsUunGSTgywkNhTLLITQScBqJ/Wvainsfbq277upyWMVH4XR2VfwTslR6dc1ODNosv6xb4Xi0OX+sFNxJyXYiWMnZ6ZFxHFQULPIIcmQCFotbN6yAyywxGaXQ7Q4R+nWaRYJJi+5GIap/+e3EgrAF1WDyszx9PsJN5DngAAxd5JREFUc2KLxfnol+nGBhzApGC0n9LSUqBnG/qZM2d+6nW7urpYunQpp5xyCtdff33SZXt3X/uqxOM9p/mCwWDvmN/vp7OzM+W6NTU1X/px+itCia+foijYjxpE04oG8kJtgMLOjCJiluTd8WIOKwo69rR433ckDl3pXnjjJhTAA9y4578NTTpNf++gxpXH0EBV0k3iMTuWmEaz5uOSde9yTPUvQZUvVYeNoix4/GcpwwqQm34NG4dk8cM1m7BoOopFRVN0nhg7jI8e+RUd5LKsaAKn/vqs1PsVhyy7RWHJt+D050DT9d6CkaYorMrJ4KxQclFaAQK4KaYKDRUdZc/yWB1wAGF6Zz5eMB/qW+H1dT0/Z/vhw9/Le9Khzu+mXUsjHrelXOQJhsDXbUAoYUoOGwo6Mdw0+tw8OGsR6/OHoUTCDK+rRXPaGFTeSkK1EFdV7FaFVZfaGJcn7yECvOOL2aqWUp4xiFanlwZnNo5EjGmNO7CHZ+NxSL+0L0v+wvYzcuRIhg4dyr///W+qq6tTLo/H43R0dAA9RSWAA1f0NTc389xzz31lmSKRCB988AEAo0aN6h0vLS2loqKCxsbG3rFoNMrTTz/9pR9rb0+mvgpR4uvneOQitN4v2qD2sVpU1XU8iTBDPdIkVPQYl6PgjQbZkD2aqLLvHEAMKw2RUgZtb6PLlk5mvEMOzESv4tOGUhwO4m8PU7qjlbLtbaS1R5hR34yOhffyppOR3vXZdyQOOacNsxK91grans8gXccejePXVUL21POMLaQTQ0VF21MsYr/+RXvuY8EUuPcKeO1G6HwMKv4OTQ/CsIKv/fkI47U5s+i2u0hr31ccssbi5NZ1MGlSunHBhOmoV85HBayJKLu8+fjrmjnj3bVM3NrEqE2NbM3NYlBNOT+fGqPiR3YpFokk6YkAY9rqmdhczVF1mzilcjUzqndz/PZdRkcb0GSG0X4UReHmm2/myiuv5Dvf+Q4LFy5kyJAhhMNhqqureeutt7j66qtZsGABHo+HWbNm8fLLL+NwOBg7dix1dXX85z//oaioqLew9EUEAgFeeukloKcQ1dTUxMsvv0xNTQ2LFi3qnQEF8O1vf5vXXnuNH/7wh5x55pnEYjFeeumlL7zkbX/p6emUlJTw2muvUVxcTGZmJi6Xi6OOOupL36f4/JTSTHLDdWi4UIFhbVWszh9BxOrovc74pu102934jh1pXFBhOsM7K6ktyqLV5qUjkY+agHoKieLEktDxBjQq/IOYZHRQYR63XcjgmS+TXRnpXTLk6Y4yQ4sTx4fVXUtmZrqRCYWBLKqC06kSRoEERO1WyjO9vDVjNCcvW4+6pw7UUxZSCeAinX0FRh2NnnOSFpToY2Db7+umz9XznzhsJKwwpHsXXdFMSqtbiNmspHUHCGbFmPHjE4yOJ0xE/X/fQXl3E6vCRcS6QyxcV957mSsWZ3RtM0V6FZefIb1oRCrFEgLAFwvji/W09Ajg4vsfvAlMNjDZwCYFowOMHDmSxx57jAceeID33nuPf//733g8HgoKCliwYAHTp0/vve4tt9zCX//6V95//31efPFFSkpK+OEPf4jVauWmm276wo/d0NDADTfc0Puz0+lkyJAh/OpXv+KMM85Iuu6kSZO48cYbuf/++7njjjvIzc3lzDPPZMyYMVx55ZVf+vnfcsst/OlPf+LOO+8kHA5TUFAgBaNvUM2YIZRsbGBdQRHj6iv51ra3WFoynlp/HsNbK8EB98xexC9unv7ZdyYOG65fnszQv6wgL9rODiYTIflgzJrQqSgcbFA6YUr56RQ2dBAj+SRDSV07kGBK7Q523/Dpm0CIQ9vV0yzctlIHqwKKwqZ8PzMznGwqK6C0oQ1PaG9Dax0vQZInrStoKFiOnZhcLBKHpW1ZBWR1x/BaWiiggU7dz6aiEYxv24EyKNvoeMJM/G6UDbfxybeXMnhn6sn3tFCEioJcA4KJgSCuxmmypJOd6EQBQoqdLlVhXEOF0dEGtMN6lzQhzGbVj55l/N9e4dcnn8+a4qFJl42orSUjASOryrlgw7cNSihMSdOI2s6nxeXhL4PPYFBApzXDS35DO6U1rXwyYzC6O851b/e9fbo4PK1Q/kIn/qQxFwG6h8bY6M1m9rNnM2uwo59bi0NdOK5z3BNxlu1pjZiuJvjDg68xeVMVIeyAgoKOhxDjWZvS3DqOiu2/jkb93+9+09GFyfxpwlMs2PURw7v3LQtpcmSy0TeO+U0/MS6YMK1t/7eait98Qqcnue9M2Goh11bOCRW/NCiZMLPXc+4gHM7GrkWx63G6VRdOPcxwbSODQn82Ot6AJad9hDCRiaU9TUK/tf4j1hYORtvTc8aSSDBmRwNpwRAl7bXGhhTmo6pYNI1fHXUGFYXj2RXraYq+dlwp7vZuykIBslVpLCqSOWxdEPPBfof6NVkZTG1YyxtlEyjsbgPyDcsnjOW0Kiw938buDp2YBoUkWPJ/QYIeO41+H85QjKL2NtwEUdDhgJKRiobu9BgTXpiK36YytLuCnlloPa+TnEgbebbGT7uZOIx1D8shKxBCURQ6XE5QFBRNQ7PpDGusNzqeMKlumxdrGKKqnSh2AMKKi+1FwxlkbLQBTQpGQpiIZVIJOhGmVm/jxpf+xQujZxJUXYzY3UhOR88BvxKULWhFqqDqYW3+MEbFknfQC6R7mfLBany/mW1QMmFW1iwHgSgQsaLoOgGvjaDXxrbuwXQ7/KQHw0ZHFCZQltZzgN+8MUR7hpumwvTey2rCGczavBVIkPyVUgeiqJfM/QaTCrMaqbSjohDGRYfix6934iKCNy4nMkTfunL8VBXlMKamCX8wTMxiwRmP8XrpELbZhjDE6IDClDyREJEDltq79AC5yHvNwZCCkRAmoisWtmaVYG3z0t2czZyllcTsKjGXCkrPOdy47HQl+rDVO5TccAhsyVsXK0BDbiYnnp5nTDBhWulTMgh94iCQnrzsrMvixaJDWFcOWLAmDmcWl4WYM/n9Jea0sqGwjMF1m/HqUXQsgI5CHBQLSok0phVQn+GiXB3GNnU0uqKi6BojtQ3kRJqNjiZMqpQoH2RlMLihDWc8jqrrtLvcZHYmsCSkm4ro2/D2Whot0GRPR1cULFqCwnALSkyW1x8MOfIUwkQUh8p673B2WIYQx4aigz2iYYv07EUTdtnoyJIp/iKViyCja5qIHzBuj8VQ7DFiXrshuYR5qWeMxxOIpoyHbQ6mV2/AKluei/1ZLKAc2KkIgh47bc4iNFQUYujoxLFiufvibz6jMKWo7u8tFgHoispWdRzdVjmIE33LKbBT1tyGNx7BThyHHiMjFCCjK0Bea53R8YRJ6boVS8xCbqCLnEAXOcEAIc2PwxIzOtqAJgUjIUxEGVVIJOJNGbdGE7Snu9AUIFcO/EWqglgLF2z9gBtefYDMrlYUXaO4rZGhjc3kB+tx2C1GRxRmM7yQyvxM8lu6sEfjOKJxCpq78HSFmbZjF5nZ8l4j9sko80Af+6RYY3Hil81DL8khjgNdtWK59mTUy482IKUwo7ymQG+xaC9dUQmS+n1HCAB7lpORjY1Y0FHoOWB16AnyOjtoy5AZ06JvLXYfoJFFI6V6OX5a0VEJWKQ4fTBkSZoQZpLjZ3AudLUkD7tjMcZX1FCT5id9uCwSEal2FI9i2vZVKGjc+ewf6dnW2sYTE05mfP0mrB7bZ96HOLwEAjEs6LT6XaR3R9AUhaZ0N2666Iy5P/sOxGHH2RUh7HP0zjSyRuNktASJ1HuxVf4RvaoVMtwoXum1J/ZRrRFUXUdT9p24UPUEDjVhYCphapEEjnjq68MTjbFp8DADAomBoM3lZUbkE9Jp6x2rp4QtaaMNTDXwyQwjIUxm0uOnoLr2+9PUdfxaGE80RllbO1lZUucVqTxFfhRiQBCFCAphVEKcu+4/bM0abnQ8YUJxbCjoRG1WGjM8NKe7iVstvD18JB+MLDQ6njChrJZuJm2uoqSmhaG7G5m6qZLcYAD7qHQAlJJMKRaJFDXZGQxKVGLRexZNW/Q4YxLraVeyDE4mzEpNdxDro2dn2Golq6vDgERiIMgLtCQViwByqcKbITOmD4YUjIQwGe+YdI5adzpRr4o/ESI/3oFjz5csRzyBZpeZIiJVztljgEjSxtYKcVbljmNF/jijYgkT8yailBekJY1FLBaenzyatITsKCJSueMx3JEYJY3t5LV2YdF1nIk4ed8danQ0YWJ54RghPY2J8Y1Mj6/kiPhyNN2LNXJg1z0helhtFnbnpLH/Iti4olKRm0F6OGBYLmFuGVprypgK+DXpYXQwZKqCECbkKvPSmu1iaFvyDiKaohBD1uGKVGmDPaS2o4WA3UvegWschQB8I9J4d3gRL44bwextVURsVt4aN4gav5eSFtm9SKSK2FJ7ocVVBXuRbMYg+jc40kkHVqoZjKJr6CjoqHgsTUZHEybmtgRZW5JPbkeAuKrS7POguVSyIp1GRxMmZdfDKWM6OnZditMHQwpGQphUW7qPmtwMihr3Ta3cUZZP2gjZplikClrdWLBjZd+uVxoK1d5CZlavA842LpwwJWexn6ArxIaSPDaU7WsiWtzcTswhzWhFqnavk8xACHd035fv+gwvY0IJHFIzEv1x6XgJ0o0Xfc/iBgcRarx+pLOI6M/g5mbKhxcScjtRdJ24zYJmUYlE5fBV9E3XLIAO+51CVdDR0mR1xsGQvzghTMoZiVHr8uNSItj1BAGbnV25OZw2OcPoaMKE7AU+6hhKNrtxESRkcfBh/nTS22IUdLR99h2Iw47VrhKypq5ML23sJD0gU/5FKkVR2F6YRUZ3CHs8QafbQdBho49WI0L0CpXkY6OJTFrptHpwJyLEdAslUflsEv2LKRY0iwXtgImN3VKdFv1oVkrI0+v267mjE8WGtVh21jsYUjASwqRK6ltwNyaIYSOGDWIwam012YMmGR1NmJCn2M1qawlN8UJshAnixFerkKZ3siOtGGktKg6kWlSm7axhe0Fu0vj0HbV02WSGkUjlD8RpSnfQ6u/ZRU8HfB1RFKWvBbFC9MhyRInbWvk4YyIJiwVF1ynuamBaoN7oaMLE2mNenLEoYdu+hsWKptFl9RmYSpiZTU/QxmD81GIlTBQPHRRjc0jT64Mh54SEMClHTE8Zc0YSKFrquBCke1g3qOfAP4YTWwIsuk6Tz81uV7HB4YQpKQrzdtZw1IbdKJqONZ7g9OVbmF7dSNWIEqPTCROyajq5DSHc3XGcoQRZTWFc4QRY5Ouk6F8808f69JEkLD1TRXRFocqfT4tVCtOif91OL21paST2vL9oqoJutaJG5P1G9E1RdKJ4aWYE9UyglaFEcIFFml4fDJlhJIRJhbJceNuSl4XELQox2SVN9GPdqAKmVlZji1rQUFDQsHvqyUiTgpHom1VP8J1PtvGDtz/BGYkRSHPTUJDOESfKgZxIZdXiaJqKJ7ivh5EtnkCRb5PiU6iFmXRbUxvptzgyKDQgjxgYOkp9xB024g4b6HtOlioKXboc/Iu+dVk8eOLtJCwWwjY73nCIKCquPOn/ejCkRCuESXWMyiHo3rdwWwdasxwEOqL930gc1k7s3sGQ6FbCWAljJ4SdYfWt+KfKznqib/PLV3DarjeIZlvYObIQxR1l/o7lNE+UGUYilW6FtGAISySBGtNwhqJ4ExEsfeyeJsRezun5OBOh1AuUxDcfRgwYZUfud+JCUUBRUDWNsS7ZxVP0Lc0XZ3XeYN4YNZY1ZSW8MmYCNe5sfJNzjI42oMk5ISFMyj88jcaNzThDCaxxjbDLSsxhwe6WP1vRt8CwQna+MwaNvQdvKrsZQcyVZmguYV4ZkW4yQ+18b/UzvWMBxU3elq1wfIGByYQZBe1WsgjgjsV796Fp9rvQdVkqLfqXPi6L0d2bWeufiKb0fD4NDu6iOU1mMor+jZyfy4ZV1TT59232MrZ2F+kTpGea6Fs0y0FWsJmTN72FVdcIWF28XzgZ5NjpoMgMIyFMypvtIOZyEHFbCfjtxG0qQb8Hp1vO5Iq+OWcMJc6Bs4kUfDFp9if6ti6nLGVsh7+QvITMZBSpvJFY72bFe//XGYmhJ6RgJPpn9dnYWZDDMS1vckTbMo5rep2CeB3vjxhndDRhYs0RN6evfZ85O9YxpnYXx2/6mDk711GbJTNgRd/ibjii9hOsugaAJx5idv06ojYpGB0MKRgJYVLpFp3uDB+dWX660zx05KYTt1lxKPLFXPTt+NwYPYsXkxWo4W8+jBgQfnjWOWzzlvS+asq9Bdw691QSU0cYmkuYk92R+rXRqumoVvk6KfqnKAr3TD2ON0ZMwalpNHuyuOGEM5gU7TI6mjAxXzCErlmYULOTedvXMqyplgQWMuwyw0j0LduSuvQ1LdqNPRgxIM2hQ8ptQpjUkGPyWP1wBZ0ZHrD3NPwr6uzGU+g2OpowKc+sIlxECWGn5/y/joM4vtOHGx1NmFRaTGfkf93EuOpaFEVnfVER/++p17AfOc3oaMKEvKcPgb+vSxpryXUalEYMJL/4aCm/n3UMPz/pHNJDYU5fu4V5J2QZHUuYWPqcfNqwE8aKSgINCzoqeaeUGh1NmJR67DhYsz5pLKZYUIbnG5To0CAFIyFMKnN6NtNOyGL7k5VE7VY8sTgz/zYT1S5L0kTfbHluss8opf0/FSRQUdFwj0rDv3Co0dGESd04OsING+tYVZaPMxrne++t5rgLB6MocgZXpJpw20ze+qAB36ZmLLpGc4ab+oulcbH4bDNvm8Mfv/8fKj05uOMRRqUH8V/7U6NjCROzTy5AmZCPvq6exN5FMYMzcZ0yzNhgwrSy/vtYav/+DoXddQBoQMXcuYzIkZPtB0PRpVOhEKYVi8V4+PcPYW1SOPO/vo230Gd0JGFy0XCEFy6/H/82jbGnzyD3hxOx+KWHkejfigd38PZjO7DoOsdfPZYJ35Kzt6J/iZjG7vcbCbSE+KDmTRS3xiWXXILNZjM6mjC56PpqPv7NQ4TSrMy740fY/XIQJz5dtLWbDy+4m6ztUYoXTsV//VGomfK6Ef1rWdfM8kufIq0pRNpZMxl90yysHpkjczDktyeEySXyNRL54MiRaf/isykWleY5FprnWJh3ySQschAnPsOk88pYE30LgNGnHmdwGmF2FpvKkGPyicVifPiAZnQcMYAoo/LYdFLP9tbzXfLZJD6b4nOw9fSenV4vueRYVPlOIz6Df3QaNT+wUoOPSy6ZiVUaXh806VIohBBCCCGEEEIIIZJIwUgIIYQQQgghhBBCJJGCkRBCCCGEEEIIIYRIIgUjIYQQQgghhBBCCJFECkZCCCGEEEIIIYQQIokUjIQQQgghhBBCCCFEEikYCSGEEEIIIYQQQogkUjASQgghhBBCCCGEEEmkYCSEEEIIIYQQQgghkkjBSAghhBBCCCGEEEIkkYKREEIIIYQQQgghhEgiBSMhhBBCCCGEEEIIkUQKRkIIIYQQQgghhBAiiRSMhBBCCCGEEEIIIUQSKRgJIYQQQgghhBBCiCRWowMIIfoRT6D8803mP7SO1iIfnBWCTJvRqcQAoCUUAq3p7NoSYsR4ec2Iz5a3vZWsqk44pR2Kc4yOI0yuPazzuw/h+cDJlFhaOTUIRWlGpxIDRTxi45E/17JtfZCcAjuLLslnyGiP0bGECf17O9zc/S00FCyb4NKJRicSprduN/MeXIerK4ZiGQyXHm90ogFP0XVdNzqEECJV/Lw7eP/jGOXZZRR0NDDXX4/vw1tAUYyOJkysqrybO/57O/GoHYAho91c/usyHE6ZUCr6EIygTfs56uZqAHQFlBvPgRvOMTiYMLNpdwdYVaVBNAF2C8PzVLb82I0qn0/iU8RiMR544AF2fjCRSJe3d1y1wM33jcSbJuexxT4PrNf43iuJfd97dZ1b51n4xQz5PiP6UV5PYvxPsQQjvUOJ356H5b/PNDDUwCd/cUKYUXUz/9hVxH+ffB43HncC1598Dv8v8wS0dzYanUyY3OKHm3qLRQDlm4N8+EabgYmEqf3f89RXdRO2WtFQaPCkUfWn16FeXjOib5/UxVm1K05pUzMLd22loKWN7TVx3tkZMzqaGABiYVtSsQhAS8DbS5oNSiTM6ro3Y0ypbeXSlTv4/sodzKpq5ub35H1G9K/z9teTikUA4dteMCjNoUNK+UKYUFt1kNvmHcvOLB8A1WkedmVM49wtmxl/tMHhhClFIxq3P97G32MZhMdmM6Klk4n17SjAxhWdzD8ty+iIwoRanlxJXqATi64BkN/dQbfNgf7RdpTTZxicTpjRx2/X8ZNPNmFx5xO3eBnW2E5H7W5Wv1HIMcOGGh1PmFw82vcy6YptoW84iTC7IRVtHFHVwt55izNrWrFoOlBoZCxhYus/aWcO8FHpJNpdaczd+RGhmI4seD04UjASwoRqcvLYmZk8AbDTaeetgqGMNyiTMLfb7mzktmYr569+i3HNNSgWBXtCoTJrPO0hl9HxhEnVBG1k7SkW7eWNRdhRH2GYQZmEuVlWVWNx5RK39HyFTKgW/NY0Mrc1A1IwEp8uEetncYN0yBAHmFzXxoGLXMfXtyEFI9GfuwYdwRL/LMIONwAvjzkGf2A3Nxuca6CTgpEQJpQWCeKNOpm7u4nCzhDNbjvLynLIXbEevjXX6HjCZEKBBI/uiLLyXzfgwErY6mBwSyUqOpryFreOvt7oiMKkdnpLmMDmlHHrW+vhcnmvEan0zc3o9tKkMUXXibfHDUokBpZ++lxJ+ytxAFc0DmpygdGR0Pq5thBgjbsJO/Z7jSgqrd7BxgU6REjBSAgTyqps4fxPQji0ng9Kb0ecwg2VnPDxv+F3chAnknVFdU7Z8AGvjTuFLXnDAcjsauHKDx4mJ9DK2a//G5hqbEhhSs2eLD7Jm4y/W8cdidDudTCsfT32HbVGRxMmpXd0k8hP/vqYsFipsroNSiQGEkXp+4Bf12SGkUjmC3TR7UveftGWkMK06F96JMGB1WeLMVEOKdL0WggTatrS1Fss2suu6VSomQYlEmbWFQOvxc2WvOHYwlHcgSCt3kz+cPTVvD5kLoMqKoyOKMwqYaOsoYvMQDfOeIz89m52po0jEJdljKJvDU4nAI5IjKK6FryBnt4zllCXkbHEAKFpVjRgY46f14bms7ogg6iqYLHIIYlI5ou2poxZtagBScRA4Yqk9kKzaAkDkhxa5N1ZCBNq29Hd53idWxoXi1SuUJzGtCLQdWzxOEGPGxSFmN3GixOPp8Kdb3REYVKDmhvRFIWdOQVUZuYAkNEdJmx3GJxMmFW7w8uonbu59KXFfPfDFzjv1Tc4YvUm9E7ZvUh8trim8EFJFq5YgsFt3ThjCd4anEetzAMQBzh2xwc4Ysk7Xo2pT11CLcRe0ytWpYyNrt9mQJJDiyxJE8KEQnWxngaQSvK0SltQvpCLVMFEgoTNiS0UJeg5YGaIovBh0QxpYCz61Oz28dcTFtHu6dmRsaSlkYUrl/GRM08a7Is+hRPpfGftvaTRAkAR4KtoY1PpqcYGE+bz8mp4+gPI8cMPT4LCDCq1TDKDUV4aUUhiz6yioS2dfBS0GxxWmI0rHuWn79zD+0NnErK5mFq1ljpfhtGxhInNqlxDWizEGyOPImxzMrJhB+eseR44y+hoA5oUjIQwoV0xBwG7xuqSPLJCUVpddrK6Agz35XKi0eGE6WSrEWKqQsJiwRJPkLAlv7V7u4MGJRNmt75kMEG3r/fnqqxc3hw1gfyWcgNTCTM7Y917vcWivfIpZ+TuCuAoQzIJE/rLC3DN/ft+vusVWP9n4t0O3hmS11ssAtiZ5cdfn7r8SBzeWtw+xjaUc/YnL/SObcg91sBEwuxCFg/HbXuf+ds/IGax4opHSMjsxYMmBSMhTKgmovP26Bx25uy/BC2LUbvXGpZJmJdPj6HqoFkt+Dq66ErbVwBwhsJMaFpnYDphZiFnaq+ixqwMxraGDUgjBoJWvwL1yWNWEtiVTmMCCXP6r0eTf+4Oo177AHXDTiFmTT2Aq3PLMliRLDOYWkTMDjQZkEQMFO+WTGFIeyVWPYE13tO7qM5VQrHBuQY6KRgJYUJpLa3szMlJGV8/aKgBaYTZaQko6GpFxcZxW1azZtBwKrLzUHWdsNPB1rxRyCtH9EXVEyQO+CoQtLuo82YblEiYXVVmPjrJ+9CErA46PH6jIgkzCqY2J1aXrCD049P7vHp+VwhI6/MycXhqdaYBVUljNX7p5Sn698iMWTw9bRzHlq8hK9jFkxOPoM5XgJw2PTjS9FoIExrRUE1OV+qOM0XtLX1cWxzuwnEru90ezln+NpuKyyjPK0SzWIhbewoB5VllBicUZqUn4j390vZwh0OMr9pGm012SRN9m1677YBNi8GWiOGPyS5p4jPEdYo6Q2QEkxsZWxMas6plSZpItiZ/Mgll36FqQlFodpUamEiYXXV2Go1+NxNrd3PCtnVcsOo9Grx2WkP6Z99Y9EtmGAlhQv54B7c9/yTfP/diYnsO+s9dvZyjKjaiafNR1QO/rovDmR7XSAtGyOtoY0vBAV+mFIUWXxo0tENeuhHxhInZdZXwfs31g04XRa0dZHZtBc4zLpgwrUa3m6hqZdng6dSmF5DX1cTUyk94Z/hoaSsqPpM1oXP6pircCQ2rphNTFXZkelHkeE4cYHh7JQG7mw0Fo3DGwoyv28JRlauBvmepCTGotZ2nHr2FzFAAgPPXLGVwaxPdF/6CzGHSMP3LkoKRECbU5XRx+tYPcP1LYW1hMcduX8nR5Wv5zfRzaOyKk59mMzqiMBFVi2G32tFQcMSjhO3OpMtDNhtBxYbboHzCvCK21L4hjWmZjGiSHRlF3z7MH0WDfxjBhA9nKEJNWgEfHTGBDmem0dHEANDusVLUkeidpWbXdEY1d7Ex2/eptxOHH6ue4OYTr6bVGUfRNYa2zuGcNS8ZHUuY2HHb1vcWi/aas3srrTWNIAWjL00KRkKYULsnnf89/hoitp4D/9dG5zK6sZo7p87l0tS2AOIwt6UNdmf6OfWHPyBq9zF/976mkJldHczfupSYdZJxAYVpOaNhQs7kUmJpSyOtrnwKDcokzG1ybRUdagn+eBd16T4yugO4wlZGRBuMjiYGgPzOCMoBhx8qoMnMaXGAd4ZO4aHJw2j29vRH84eD+ELN/EjXQZHXi0i1OzO1KBRXFOwt7d98mEOIFIyEMKFVJRN6i0UAIYebe444jxa3G728DnJkDbfY5/1mNy+OzCRs73lLb/E4GdLWzZDmGm565z7QVVSZ7y8O0BGI0+l0k9fdQas3DUXXmFO+gsl1K1mVP8HoeMKkYoqTqgwf9xw/lVafG1s8wekrtnDGllVGRxNmEe1/hqI13PcW1zEpAIgDrCtII7ctzPGf7KbN4+T90WW8MmokP5LXiujHxtyh1HvTyO/u6B3bnZFDWav02DsYUjASwoS6nKlTs9s8WUyuq4aPG2GmFIzEPmmdAcJ2b+/P1WluqtPcLC3N4s/PR9F1G47mIGTYDUwpzCbSGUXRNX769l10uv14IwHSwj1fqlwx2SJd9K0yI4N/HjuZVl/PzLSY1cIzs8dyZJXsQyP2UPvfU6dBdeJXVVya1jvW4bDij8a/iWRigNASOoX1ARZtXd47NndzJUtml4CmfeprTBy+jtpdSVYwuTg0tLWRyqdWU/r94wxKNfDJX5sQJuQJh1IHFZX737iPlemyUEQkC6h20FJnEGmqhZdHTcEX6aKxMWhAMmFmtrYojmiQToedP088iotPuIi/T5hPQlEo6ZAdGUXfAi4HjWnelPF3ho00II0wJUvq4YW+578tThf3FeexMs1LrcvBx1l+PvFIhz2RTFFg2vbkZa55nQEWrt5EIpYwKJUwu6xgJ7b9itF7NWyVXRgPhswwEsKELO3N4HLBftuJjmzYgT8epqBTzvyLZJZwAEuLjUS2J2Vdf3oogK4o+DrbQbrSiP3UVHZR7fEz8zv/Q42vZ93/c8OmsCG7iDO37OJog/MJc9JtLqwJjfgBRYGc7rBBiYTpHPA5pAMhMomSgb+7Ez0zk2UZab2Xu3SNoc1yQCf2iUc0LKnH/fgDCbRwDItDNn8RqRIoBGxOPLF9n0dxRaUtLk31D4bMMBLChGJxjTPWvExOVzOqlmBc7WbOW/lvrJpGZ5Z0+RfJut6qZnbFNrb/74958Z+/559P3sWidcuZWF3JsdvXsSl3OK++HvjsOxKHFfUfrxCKRXuLRXv9Y9xRvDRquEGphNl1ejKI99GgOOL0GJBGmNIBPYwUwEUrFgIMaW9LuXp+KMKOzNRZa+LwpeoJqrOSXxNxVWHJpDFEGuT7jOjb5OoNLC2dS5yeHWDj2KnzFNBtdRmcbGCTGUYHqK2tZeHChfzgBz/g8ssvNzqOOAyFt7dzwq6tTG2p4KiK5UmX1TmySMT7OOUiDmvVbTGe+OB+igLtDGvvmcJ9ycp3aFcyUXWFd4fNpbzCxcLuOG6vvO2LHtsbIhQojTC4LGk8plpYsPUT0BZInwiRwhWP9rlDUcApPdLEHs3JM6GjuGm2ltJtdXPBunU8NWY8QWvPZ5FN05jR3kV9mhzQiX306mY+HpXN7qZMJu6up9Xr4rkZo0iLdhKra4MROUZHFCa0tnACFy99i25KAA1QIOYiK1xvdLQB7UsfOaxcuZIrrrgiacxut5OTk8OUKVO48MILGTx48EEH/KJqa2tZsmQJ8+fPZ+TIkSmXLVy4sN/b/u53v2P8+PFf+rGXLFnCTTfd9LmuO2XKFO69994v/Vhfhddff50PPviALVu2UF5eTiKRYPHixRQWyrIVI0U211Ob6yHW4iVILg7aAZ0ofj7KnEr9J82cer70ihB7xOLkh6vJCwQJkYWNLqxECdgcPDp2Mhuzigh6c8lUFN5/s50TT882OrEwib+PmsNZb5TzxIQQnY59B2tnbl/FqNYq6A6DX3qLiGSnbVrKk5NG0+Ky8t01SxndWMPy0lEMb1UgFgebFKUPe537+jAmsNJqKaHKm0uT04c7HuXpp57h1pNOIoHCiEAIfyKB0gW6rqPIDlgCaN3ZzIWfvMm5F1ybNP7gY0voGj6UjHkjDEomzCw9FEZlb0/PnhNevkgYT6Tv3RnF53PQn+onnngic+bMASASibB9+3aef/553nrrLZ544gkKCgoOOuQXUVtby3333UdhYWFKwWivmTNncuqpp6aMT5gwgfz8fJYtW4bF8sVfWJMnT+bmm29OGrv//vupqKhIGc/MzPzC9/9Ve/rpp9m4cSPDhw+nuLiY3bt3Gx1JAO1dYSIWK7U+L5ee/n3WZGXjj0X52cfvU+53sE7L4BdGhxSmkXh8GYNa2/acTdFx0kJcUTn67F+wMn9f0X5KRxfpu6KcaFxUYTKr0/IpGKNx7ytP8fjY8exMz+XEig3c9MFzRFUHlQGFUr/RKYXZjKnezuVvr+OUiiVMq96+Z3QJb4yYS8vjcbIuPNLQfMIE1lf0/rNLyeWT7CHUeLN6xxzxKEOamtlalE+t3Y2jLYAvEuvjjsThqn5VHR8Uj+CSDz/imrffwxcJ89SUSUQUleC/lsF1JxgdUZhQkze1V1HIZmNXWhbTDMhzqDjogtGoUaM45ZRTksZKS0u57bbbeOuttzjvvPMO9iG+cqWlpSmZ9+dwOL7U/RYXF1NcXJw09txzz1FRUfGpj2eUm2++mezsbKxWK7feeqsUjEyi4e6lpHVbuGjRd1iZ3TPltsni5L/mHsfPV75GS6csERH7BO/8gJM3NQB2elqLKrwyaHxSsQhgrd9LzsZ2AxIKU2oPYNd1Hh05mI8zfSx+7m9kxVvwhUN02nygx8heugbOPsLopMJMIjHabDkU1e/iN9NOZttxl3Bk7Q7++N5TzNvxAR8/l8YcKRiJ2n19irqsfmo8ySdJI1Y7IYuFdSVZRKwq+mA4orxeZheJXtH11WzOns5TDz/aO3bF0g+5b+YM1G0bDUwmzKxbj7Js+BjmbN8E9DS8vn3WPIY3b/+MW4pP87UceWZn9yx5sNn2dbB/4YUXuPDCC5k/fz5HHnkkp59+Or/+9a9pa9v3oXLZZZexYMECamtrue6665g/fz5HH300N954I8FgEE3TuP/++1m4cCFHHHEE5513Hp988knv7ZcsWdK7TO6mm25i2rRpTJs2jcsuu+xzZ6+trWXatGncc889fY69//77XHjhhRxxxBGceOKJ3HHHHcTj8c99/2+//TbTpk3j2Wef7fPyb3/723zrW99C1/Wk30l1dTXXXnst8+bNY968eVx33XVUV1en3F7XdZ555hnOP/985syZw9y5c7n88stZuXJlynXz8/OxWmXquKms3klHTSedDgtrMpOXDumKwmMT57DTl0M4Kn2MRA/t4yos+t63cpWQ6mHJ6Hkp10soCtEEvF/++d+vxCHsrpcZu3sXlrjGqPZOjvzJjaTf8hC5Nz7E/x5/MY9OOgrXn543OqUwmbaFfyRii2CxN/Ls4jtZ/8gNTG/YxXdOuQyblqBpR4fREYUZfLyt9582PdBnz6vthVkEnDbiVgsJq4VlQwuo7JDt0kWPQHUnCzekFoZO2LqNTVklBiQSA4FThSuPPIqrFp7Ln+cez/TvXcHfJk5idu1mo6MNaAddMAqHw7S3t9Pe3k59fT3Lli3jrrvuIj09nWOOOQaAF198kRtvvBGHw8EVV1zBz372M04++WR2795Na2vyNpqhUIgrr7wSn8/H1VdfzdFHH80LL7zALbfcwh//+Efefvttvv3tb3PZZZfR0NDAtddeSyDQ0y1/8uTJXHLJJQAsWrSIm2++mZtvvpnvfe97SY8RjUZ7M+/9r7u7+zOf67Jly7j55ps54ogjuPbaaxkxYgSPPPIIDz/88Of+fc2dO5esrCwWL16cctn69espLy9n4cKFSWdZQqEQl19+OTabjauvvpqFCxeybNkyLr30Upqbm5Pu44YbbuAPf/gDJSUl/PjHP+byyy+nu7ubq666inffffdz5xQGOfoGOjzpOPQA6ZEIg1s6sMX3fYGqyc5Ec9u56L9rDQwpzCL4TiU6HhKoWAlitdbx1uhZ4M7DpiUXFbOiUXZm+nj0+XZjwgpzufd1EsC3Nu/kzenDqUvrWXvW4nHxl7lH4E2k0bQ1dTcjcRjbXov7tVWsKxrExZuWYdcSOBNxfrj2bca11LI+pxRbIGh0SmEGS1b1/jMvXk1WKLmQqGoau3LTcOz3/UZTFf6xTkcIgNLVm4jSV3sQjW0Fsoun6EMkxuimCs5oaGK318O/xozDqun8YPNWYuqXWz0kehz09JJ77rknaTYOwJAhQ/jHP/7RO9PonXfewePxcPfddyfNaDmwaTZAe3s7F154IRdeeGHvWFdXF2+88QajRo3igQce6L2PwYMH87Of/YxXXnmFM888k+LiYmbOnMkDDzzAhAkT+l0G9vzzz/P888lnTseNG8eDDz74qc+1vLycp556qrcp9Jlnnsk555zDk08+mVKU6o/VamXhwoU88MADlJeXM2TIkKRcFouFBQsWpPxOvvOd7/Czn/2sd2zKlCn8/Oc/59577+X6668HemYvvfzyy1x//fWcccYZvdc999xzueSSS/i///s/jjrqKJnya1axOHSGaPTlMWR3M4vvex5nPEGby8Etx8/k3ZEl4LKBohCoDBudVphA8K6PsaLQaUtQFqtFicOmvBG4NI3TG1pYmpFGm81KSThCWizGmvQMqnd3fvYdi0NfbSvHVG7GG7fxhHtq0kURq4UPho7EonRwYT83F4ehZz4kaPMyvT51av+pu9bijkQosLT2cUNx2Ona9x0ljosh9e1YMlS6XA6c0QQFrd3c8txytg8rpsHj5NXh+bS5HKyUjYzEHs3OHHwqxFUdq9Zz3KKjM6p9Kzua00HTZBdPkeyjbXQ6/OD0Mi0ch/CezyN/Bh8WTmaSoeEGtoP+S1u0aBF33nknd955J3/+85/50Y9+RHt7O9dccw11dXUAeL1ewuEwS5cu7V1q1R+LxcI555yTNDZp0iR0XefMM89MKjhNnjwZgKqqqi+Ued68eb2Z9/7385///DNvN3/+/KQdxBRFYdq0abS0tBAMfv6zat/61rdQFCWpaBUKhXj99dc54ogjyMlJ3SryoosuSvr56KOPpqysLGnW0EsvvYTH42H+/Pkps6fmzp1LbW0tlZWVnzunmbS2thKJRHp/7u7upqurq/fnaDRKS0tL0m32vv76+7m+vj7p9Wj4Y9is6KpCrb+InLoEzj1n3jJCEW5+5UNcPhuoCq5YDM1l+XKP8U08j6/pMb6oQ/X3sP9jWCbkA5AXr2dvGTgz2A5AUSTKOfVNXFFVxxnVVazze9F0KM1WTfc8vq7H+KIO1d9DX48Rz0vj2tWvcVzlStQDZqMpuk5pa0tSPz+zPg95zXxzj9FZ5McdD9Pq9HKgCn82pd3NWPdbom/W52H0a8aonN/oY7jtvWMWwpRqmxnVUsmo6lYGNXbgiCfIae+Z2Z8XCHPStjqsCY1ThpjseXyNj/FFHaq/h/4eY1N2GeOatpGpVeCkBQdt+KjCQYCjd3yUVCwy8/P4Kh/jizpUfw/9PsagXLr6+HxSdJ2ELXmyhKmfx1f4GF8VRf+sCk4/Vq5cyRVXXME111zDBRdckHTZhg0buPjiizn++OP5/e9/T2VlJT/60Y+oqakhLS2NKVOmMGfOHI4//ng8Hk/v7S677DJqamp48cUXk+5v73b1d911FzNmzEi6bNq0aSxYsIDf/OY3Sbl+85vfpMzUqa2tZeHChZx99tn88pe/7PN57b3OD37wAy6//PKksUsvvZQrr7wy6fr33HMP9913H0uWLOlzR7jLLruM1atXp/QQuuqqq9i2bRsvv/wyVquVxYsXc/PNN3Pbbbcxf/78pNtv376dt99+O+W+r7vuOt555x3ef/99XC4XZ599Nrt27erzee1133339Rba9nfrrbfy9NNPs3jx4qSimPiGXfl3Hng/h4kbG3qHduWksSsvg4fmT6TTr1JWG+K6y3JZODv1TVEcXvRYgqD9Cly09Fb/q9MK+NtR3yNscwLgD3Xy03fu5dgzfkJLTgErf51BTpat/zsVh4cNu2maeQNZwW6uXXghd8zdt3PoaZvWMW/rTs44I5shPzvawJDCVBIJ4u7v8ufxJ3Ll2jfwxntmkXTY3Ew9/39Y+uTv0bL8FFb+xeCgwnBn3ArPfpQ0pAM7mEwXPbulLT5qIu+PKcMTizOoLcC/xpVQ/Ws3Tpv01hRw90lLyKlp4KwNL6Vcti29gBFtdxqQSpjd7XNeoiK/LGnMHosye+urLNr6K4NSDXxfy7vyuHHj8Hq9vUWS0tJSnn76aT7++GNWrFjB6tWr+e1vf9tbbNl/ZzH1U6YX9nfZl6x5fWGflu2LZli0aBG/+tWvePfddzn22GN5/vnnycrK4sgjv/zuIrquk5GRwW9/+9t+rzN06NAvff/iG3D3FbjOeh429ux29efTZvLovIkAWBIJ/rjkEV4onsjC2UM+/X7EYUGxWbCcNZq31rRx7M6NKEBxRx3//drt/HPGAmr96fzqjQdxx8IUdLdxfomDnKxco2MLMxhXxgVnXcmSR/6P2xc/xKINH/NR6XAmV5dT2NLFHXNPoXH6KOSdRvSyWNA7H2PE4Hspj88gnQZAoS2WxzHbaoiqNtKPHW10SmEG04emFIwUIIdqOsjiH/Mmcu+cCega4LSSWxhjTGM7TpvfkLjCfI5yt/CsZxAtrrSkHlivlI2lKiOXEQZmE+aV3dWWUjAa0VjOx5mDWGRQpkPB11bGTyQSxGKx3p/tdjtHHnlkb0Fk6dKl/OQnP+Gxxx7rd7bPlzFQ+vPMnz+fzMxMnn/+eYYOHcratWu56KKL+ty1rKuri+bm5t6eUHvt2rWLzMxMXC4XACUlJVRWVjJ+/Hjcbvc38jzEV6/kzPF4nt3EJ7mDeotFAAmLhf856Rx+/f7LwHTjAgpTcf79++w6+WGUnRvptjsoz8xj8dip+CId/O/LT2Hds9yo3p/PGWNCBqcVZpITBqves/R1Xvlm5pX37CISR+GuxXeTeOwJI+MJE7I5rDiDDuJAM/t2Kjp9y07OvvCnvHWxy7hwwjyO7Ltw2Omy8+bgMu6dOhY9vKfhdTRBo9eON82LrusD5nu8+Hr5R6Wh7m5k+Pdu5Wer32B0824eHD2TJcOm8OzqZ4yOJ0xqcs1qbNYYH5VNQVMtFLfVcMa6Jfxx0jFGRxvQvpZuYcuXLycUCjFq1Cigp2nzgfZe1tHx1W7BurdQ8lXf71fNarVy2mmnsXz5cu677z4ATj/99H6v/9BDDyX9/Pbbb7N7927mzdu3ffapp56Kpmn87W9/6/M+DlwXKcyppNjJ29OH0pK6wpGA04XnSDnnL/aT5afL5iShKHijESbUV/LrN5/lmqUv9xaLoqqF0REoPX6QsVmFqUwOttDh9PX+HLS4SKASt1ixAHaPLF0UqarTUpdDN6ZlszsrB/ekUgMSCdPJ8vU5vCVtGMsGFZIyJz8YI6EqUiwSvdq+O5/Tdi8nPdhFvS2T58tm8U7JWDzRCM6zZ3z2HYjDUn6ohXPWLOaml//I9a/dznVv/52MUCeXrvrQ6GgD2kHPMNqyZQsvvdSzvjQajVJeXs6zzz6L1Wrt7fdz1VVX4fP5mDx5Mnl5eXR1dbFkyRIURel3J7Mva/DgwXg8Hp555hmcTic+n4/MzEymTzffjIxFixbxyCOP8OqrrzJlyhRKS/v+opWens5bb71FU1MTU6dOpbKykmeeeYasrKzePksAxx13HAsWLOCpp55iy5YtzJ07l/T0dBobG1m3bh3V1dVJjbZXr17N6tWrAdi8uefM8lNPPYXX2/Nl8Pvf//7X9dTFpyicmcf64rFc9e7dKNqZ6PsthfSHghybE/uUW4vDUYcnjVuP/jY/XLqYddnFOLQ4UxorsWkJdOCdIbOYkKhCzR9ldFRhItl6gCVjj+eU9W/yXu4cWh2Z2LQYFjXKuTufxSIHb6IPjdlWEtUKFq3nsD+uqlSX5nNEeQWKLXXTDnEYKuv7dVDWVYM31HdD1gn1bUDG1xhKDCRFRW6awx4+fvBerHvafvzs42X8a/RkJlyU2otVCIA3hh/JyVvfwx/pxhcJALA1dyjtmpthBmcbyA66YPTqq6/y6quvAj09ftLS0pg1axYXX3wxY8eOBeCss87i9ddf5z//+Q8dHR2kpaUxcuRIfvGLXzBt2rSDjZDE6XTyu9/9jrvvvps//elPRKNRpkyZYsqCUUlJCdOmTWPFihWfOrvI5XL1Pp+//e1v6LrO7Nmz+elPf5qyTO03v/kN06ZN49lnn+XBBx8kFouRlZXFqFGjuOqqq5Kuu2LFit7ZTXs9+uijvf+WgpExrHYLzS4HO3IKufqDd7hn1lFErVayuzs5cut2Kr5bxhijQwpT8RR4+Tg6mKyrTujZDUJVKehu59nFf2NmfTmTqzYx/HcnGh1TmMzGk2ajvFpPpMOFovcUh2KqjRg2VmeOl4Wvok87i7KJZBQzbHc9uqKwoyyfDr+HIyq3IculBQBOe5/DwwM7OWf1Vh6ffMDJC5cVXzTxDQQTA4Xfa6U7bsWq7yswlnZ2smD7Trz5cw1MJsxsQ+EwthWM5JhtS8kOtLI5bzgfl07imE9eNjragPald0kTX40f//jHrF+/npdffhmn05ly+WWXXUZdXR1LliwxIJ0wyrwrKhnf1IIVO2GLSqfTxqCmCpaUjeDBOa1MvWCS0RGFidx+xw6uL08jZE1eQjS2uZoND99AwOLC8rdLcF5xnEEJhRktfWwDbzzcTsnOxpTLVCXGJdvPMSCVMLuzznyfYjKTxnRgfMVyvr/qUmNCCXOJxcCe+v6ho3DNkdfw1NwJuNvDhFSV0lCExlwfWcEwK+/INyCsMCMtHKXBdxOuePKs+p3p+Yyq+wke59fSVUUMcN87Zx3+uCVpzBqPcsLHz3NC3Y3GhDoEyF+bgaqqqli+fDknn3xyn8Uicfj69icfY6XnDJ0zoZEbiBB25nH74mfY7c7+jFuLw42fYEqxCGBjdjGddidN9kwUX989JcTha8oYP7WeNCxaz5l9dzyILREFoFFeL6If3Vjp2d5qH0siQaXLY1AiYTq2/vqf6XwwtJBv7ahjYUML59Q1Mbu9k1N21NLslJ5pYh/Vaafdlvo5FNWcuC1aH7cQArRE6kzFmNVKtho1IM2h42vbJU30b8OGDezatYsnnngCm83G+eefb3QkYTKjm3exrmwcK4syafQ4yesOMb2mlaHtnWyJyZueSDairQl3NI+g3ZE0PqytAW80zI70DEqGZRmUTpiVPc2FI97MoGA9Izs2UhBuJIHKDv9Qlttltb/om9vqYfqOTwjaXezKKqWoo57xtZv5+/jjjY4mBoCyRBRnInlxg03TObKqBZDPKdFD13WqXXn4QmE8BNFQ6CCNgOJBVy1Ihz3Rl5y2WmLpg5LGCtuqiBwzse8biM9FCkYGeOaZZ3jxxRcpKirilltuobCw0OhIwmRG1W/huVGX0+jr2fWvOs1Ni8vK70NBxloCBqcTZhMZVMg5K1fywMg5vWP2RIy73nwEFciNtBDNy0I2vBb7U202Cro7yLA0UBDuWZZmQWNk53YcCSlMi76NbG1i0drX0RNeNKzYCOCgjUpHFjDF6HjCDLT+Z4C0+xWKg3rSAb8ORCxSAhD7KIpCbV4Gm3KzeXzuOCJWKwtXbqM0EEaV14roh251ceymN1g6/EgiVgdj6rcxe8dS7D/9sdHRBjQpGBngxhtv5MYbb/xc17333nu/3jDClN4ZOrq3WLRXRWYaGwoyyDtGWl6LZOqEEpZuzuK7VY0U165laGsN5279GH80DIBFTxC12qVgJJKoJem0+NIZ2lyRcplT73snIyHympuIJnLZ29UggRMNCxnRTmODiQHBpcapSPcyuD3YO7amIIPdfvmEEsmqChRuOPHE3t2CPxlSwNmfbOB7uo4iu3iKPig4eX3UfFS1p8SxNXco1b48rrbISbCDIT2MhDChan/ffYqeGDWcLLd8SIpkJWVOjqxsIycSY3hE4bL17/UWiwDW5Y5B8fS9a404vOmKQqM39f3GkpAeEaJvUyrrOPDrYxQ/m7JKjAkkzEft//BiaHuAtfkZPDahjLcG5/HE+FLeH5RLs0d6eYpknxRl9xaL9tpQkCXFItEvfzTcWywCSFishJxuMnyyx9fBkIKRECa0Mn906qCuszZvJJku+bMVyRztUfzROADWmI0AecRxEsdBkFw0uxN/hjQUFamyu1vYnV5E2LqvoNjmTKfZKU2vRd8a0/t+bZRnyPJ68dkiqo3B7QGaPU7W56fT4O2ZWVTQHTI4mTAbax9LG1VdTmaI/nmjXSljCdWCTc6ZHhQ58hTChDodqVOzrYkE4xrrDUgjzK7VaSOi9pxxG19VTgwf3RTTTQlR/Eyq3GlwQmFWMyqW0+Hy0eZMZ2v2cOo9+XjCXawpGGl0NGFSnS4VTdHZnpbPR7nD2JaWj0XpZGpdldHRxADgjCcYV9/OxLo2LJqGqumMbuxgZJMsaRTJJlc1Y03Ek8amVTQalEYMBDE1dSaRN9INWX4D0hw6pIeRECZ0xM5ythflUp6zb6nIhR+vZFhzm4GphGkp8G5GGse1tBPu4zSKLREzIJQYCBTFxjsjjqTT7WdK1XqqsvLYlV1ITU6e0dGESWVG2vgofxC17gIAqn3ZNLuddNvdn3FLcbjT/S4afHYU1cH8ikaO3N0EClg1nTcG5RgdT5hMq7uYC9ZsZVlZMQlVYWpNA3kBWY4m+ufsY1Za2OqEYQUGpDl0SMFICBP6VvlGMoIhdhXmsKkgn+M3b2VqeSUt3gyjowkTsumw2eehyuVkWEUGY2oqUHr3oNFZXVbKCYYmFGb14aDpoCisLpnI6pKebWcLOhoY2rjB4GTCrIo6Wljnn5Y0VufOJyssJzTEfkqyoao5aUj/4Yl4O8M8MmY0p26robgzRExVWVqaRZNPml6LZAmbBX/Mysk79s2u122yOEb0TyW1oKgi/YsOlvzVCWFCDcOHkhXSUOw5jOpQqM0bTpM7n01lZUZHEybki/dM2e62qMzeVYmVMBaCWAgBcSyK9IYQfWt2ps4KsWpxdOk/K/oRtvTdDMIZl531xH6evg5c+71Wxpag/WIRmQSIWFX+M7aUO2cM5+/Th7GqKIuMqMyEFQdQUg9T46rFgCBioKhOT+2lJ+WigyczjIQwoY7hWazIzke37Nm22GJh2eTxTN66zuBkwozCThsQobirk+FtrSRIPlNb0tZhTDBheuOr1rBx8BFo+30Jn1mxil3Dcg1MJcxsR3oJ3kCIbvu+9xl3LExldr6BqYTpzBwBNf+Al1dDpheOnwiaRsKuYEtouOIJOp12nLE4toRGXky2vRbJ3NEQndbkDTtcsXA/1xYCwg43Gd2dzNy5BW8kxKaiMjYVysn2gyUFIyFMKH1oFvqaA86sKApBh8eYQMLUvK6eKbiNbg9PjB7HhMYGxrQ09V6e391tVDRhcseVr2JaYyXvDZ2Nis6Emk3M3rWCe07/Hy4wOpwwpW6vj/m7NrGsZDRhxY5TjzKrYRuPjJhtdDRhNhle+O5R+37WNBR/kIVbaijuChFXFSyaTtiqkhjpNS6nMCV3MEinywfKnmVGuk5ua9On30gc1vzd3fzg3ZdwR3tmvI6rruDVcdOAccYGG+CkYCSECXnmD0JZVUNWsI0hLbup8+dRlVGEYksYHU2YUCwBWFWiWLny5IUAXLRuDbe/8TLLhpQyr3mVsQGFaYWsdibWbWFi3ZbesUdHzSIrGDQwlTCzmFXh+RlHUpGzr4loY2E2aQ5ZUiQ+W5YzQLSrZ5m0VetZLOKKa2R7pEuGSNaalrGvWASgKDRlyOxX0b8Zu7b0Fov2mr1jo0FpDh1SMBLChDIK3JQ1rePHy/7T26xtedEk1h8z7TNuKQ5HoZgO8eSdIR6aMJlVQ0vYMGwQf3v7Ca4yKJswtzVF48gLtJMR7TmASygKywqHMSncZXAyYVaa6qAyJ3nHmcrsfCZEqw1KJAaSgLvvHjT1TmmcJpJpSmr3mahVDl1F//yR1Bn19j19PsWXJ+V8IUxI02P84OMXkjr7z6r5BP/sUgNTCbPyOvp+K9+Q39P87/0jZKmI6NvaYVOx7vc+Y9F1bn/nCcbkyJdy0beWsuI+xzvzpIeR+Gw2XetjHyNwRKSHkUimhVNfE46gbOIh+hdwKSQOaJbekC7LXQ+WFIyEMKGSpiY8sdQdZyZ2NxqQRphdgU8h29vHV3DHnjO5mfJhKfo2pKseXzS5iahDizNjpHw9EH3LzlexR5KXLDoiAdLyDAokBhRfpO+z/SVBKRiJZHpMx9O5b8aIIxxBD8tsEdG/DcUjac5UqU9Lo9XjpTrbz+6cNKNjDXjyjVAIE+rOzaXV5U8ai6sqsRFDDEokzExVFf5zkRu3o6dopKCDz45iUbh4xdv8bPmrBicUZlWoBIiqybOJNBQcx401KJEwO3+Bneve+TvTdn9CbmcT03ev4edv343DZ3QyMRDo8b4PPfzuvuYdicNZR2YaWa0dDN+yi6Hbd1NUVU99bpbRsYSJJfLS+M/k42nNUAj4w6wZNJi3R841OtaAJ3POhTChvEEeHjr2XL715hNkhDoJ2FwsnnwKp83veymAEHOH2Gi/xc/H429i9K6d1KZlkh4OUNzRSuQXZxgdT5jU0UUdvD90FvN3fIBF19CBrqIC0gbLdBHRt+OHJMgNtHH+qn8njc8vkO2uxWdz+IOADgcsTJt0hMwCEMkKEh1UDirCFo2hahoRpwOHKs31Rf9mf2cwzz7YyFNTTu8dy8s2MNAhQgpGQpiQalE45k9Hcfftg9B2NNKd5uDsHw/HlyZ/sqJ/NovCrIsnYPnlOjLDAQB0jxPH948xOJkwK8+Pj2f+E9ezO6MImxYnv7ORtLsvMjqWMLFBJw0n6HLhDu3rJRK12ShcMMbAVGKgsDni+HJb6GrcdxSXmWtj4mz/p9xKHI5umFzHL1c5idodAMT1OP89o8HgVMLMjjw5m63rutm0umfZtNenctF/DTY41cCn6Lqe2oJeCGEKsViM+/7+KBZ7jO997xJsNpvRkYTJxWIxll7+W4asrKdk0hjUny2EifJhKfoXW7md7T/7G5a4xtBbvo/1mIlGRxJm98JKYhf9DVtrJ0G3Hft9P8T63aOMTiVMLhaL8cADD6BrMKZsEeWbw+QVOzjqlCzc3r53TxOHsVicxI/uo/7x1USsVsrOn4rlT5eARV4ron+xWIy7//IkiaiNK36yCJfLbnSkAU+mKwhhclaHTL8VX8z2WUVsn1XEJZdcgipFRvFZJg5i6Xk9PYuGzJVZIuJzOG0aVN7Nk/93N91ZLi48W3ZiFJ+fosKs49KZe7J8PolPYbOi/fX7vDTxnwBc/P2LsEixSHwOTl/PDCOrVXqjfRWkYCSEEEIIIb4Yu5WOfNmBUQjx9dItskeTEEaSv0AhhBBCCCGEEEIIkUQKRkIIIYQQQgghhBAiiRSMhBBCCCGEEEIIIUQSKRgJIYQQQgghhBBCiCRSMBJCCCGEEEIIIYQQSaRgJIQQQgghhBBCCCGSSMFICCGEEEIIIYQQQiSRgpEQQgghhBBCCCGESCIFIyGEEEIIIYQQQgiRRApGQgghhBBCCCGEECKJFIyEEEIIIYQQQgghRBIpGAkhhBBCCCGEEEKIJFIwEkIIIYQQQgghhBBJpGAkhBBCCCGEEEIIIZJYjQ4ghOifFtdwblRBAy2SAJvN6EhigAjHbcQTurxkxOeSvTVI9pYwcf86rGdNQrFajI4kzE7Xyd/Wji2agEhMPp+EEEKYgt5igbCKFtNAPpoOmqLrum50CCFEqkhDiKWzXiRWFQTAkuNgzoen4h7kNTiZMLPt1WGu+vNumsIZpLvgp2f5WDjLZXQsYWI7r3yR19+LASqaonNkXhcT3jzf6FjCzDqDtM/8Pxq3gIZKTlY3Wct+BCMLjU4mTKqzNsRff7mTjZoLZzzKabOcnPHTwUbHEia16endrLxrC6HWCPrQEMrRnXzviouxSWFafIpETOP5H62g8f0GACzZTs785ywyh/kNTjawyZI0IUxq3Y8+7C0WASSaIqy55H0DE4mB4L/vbqMpnAFAewhueqST3Y1xg1MJs4o3BXjz3ShjmyoZ01TB5IZyPqmy0v3KNqOjCRPruO4FNm/JoYU82shhW8tgGr/7tNGxhIn98dptfGL1oaITcDp5fJ3KmjdajI4lTKhuVQvv3biWYGMEPQ5sdaG/IQf84rOteKaqt1gEkGgO8/Qv1xmY6NAgS9KEMKnAq7s4cB5l+KNaY8KIAaGlU2N7u4UZleXMqdhOk8fHkjGTeP/5esp+UGx0PGFCm1+vIz3SxcacQb1jadFO3ny+gdNPGmFcMGFqlc/U0ZCdTXOGGxTIagvhWttNrtHBhClpkQS1cSsXv/wegxpaCDpsvD1pDIv/6WPycVlGxxMms/6x8tTB7Y5vPogYcFb/uzplNoy2pdWQLIcSKRgJYVaxOAcWjBQtYUwWMSB4g0EuXLmUH3y8bybags1rqY1PAykYiT4EWiM0O9OTvmB12P04G7oNyyTMb7cjHastwlG1O1GA7emF1GRkMsnoYMKU4qrCWe+vpLSxDQB3JMapH63ldW0wMM3YcMJ02nZ29f7bEY+SF2yny+Zk2y9WM/bPMw1MJkyvpitlyBsKGxDk0CIFIyFMqj4zDXu3hru7ZzlRyG2lLS3N4FTCzBIt3Vy0ahkAEYsVRdcp7Gwne+lK4FRjwwlTilstqIBVi2HVEoStTgBCijS9Fv3TbQmOrlrf+3NBoI33i0YbmEiYmVXXeotF+5u6Y4cBaYTZafGe9rolnY0cWbMJy552uw137KLtzEFkHJlnZDxhYgGrBYfNiiUWR1dUVEUn5LAbHWvAk4KRECbVkeZGTwNLTEPRdeJ2C0iLevEp2la349Lg3eJxVPmyUdAZ2l6PP9DJDKPDCVMKWRRmNKxkQusmrLpGtaeA14rmodVrRkcTJjakvS5lbGh7vQFJxECQ0BVCDhu2WJyq3CxckSj5bZ1Up2cYHU2YkKIqKFqCGfXbe4tFAHl6Ky3/XEPGkScZmE6YWYvLg+5Q2V2QS8JqIaelg9y21GK1+GKkYCSESekKoEPCtt9iEcWwOGIAqLvxLbpzB9Fm81PU2kHAYWdHegEdzkIpGIk+Jd5bz5SWDb0/FwfqmNOwgtes8wxMJcxO7+PDSM5niP7EInHenzia9WWFdHl6du0cVNdESA/xHYOzCfNRVAVnPIYrHqWZNNoVHy4i5OktaNulMC36l4hDRVlB789NWWlYE7Lxy8GSXdKEMKm+3uBU6WEk+pEIxRldvgzCKpN31TCkoZXxlfWMqm4gNyjrt0XfCl9fkTKWF6pnd7rXgDRiINhRH2d7RiEaoKH0Foq2ZhQZGUuY2Mcr2tlSmNtbLAKoKMghancbmEqYlaJCyOZgq6WUWiUfCypBvGxShqApcvAv+hdxOFPGmtNlh72DJTOMhDApRyxE3OFLHovLgb/om9rVTUVWMTktUfafipbTFaSrS97qRd+6dFfKWEK1oWsyX0T0rbs+RLfVTY2tEHcshqboVGWmU+PLNjqaMClfXRvd7tQDOV84ZkAaYXo6KDroCZUi9u1wFUeBDtlVT/TPGYkC4IhEsSUSdLuceEIRg1MNfHIUIYRJxS1WGjPSaMzyo+g6BU3t5LVFjY4lTEqxWWmyF5GNxq7cTBrSfai6TlFLB11W2Y5W9K3Jm0tNMJui7mYAuuxOLj/lUkZVdhqcTJhVvhbDVl2FO9ZzsK/qCsWtnezMlDP/om+emIa/O0hzZvLGHcXN7cYEEqam6zr2aJRsAknjVnTiVfLZJPqnxyJM3FpOVkcXChBwOmj3yEzGgyUFIyFMqjnDx/rhg7DFY+iKQkNmBrM2hIyOJUwqFIfK9CxCsRg12em947vyswjYZMcr0bcWVxqTfvC/HFW5gfRwkMXDp9Ds8bMoscXoaMKkfFqESDT5jK1F14lZpcme6FvIoTKsuo5dGX4q0ry4EgmmVTcwuKHB6GjChPQE+GKhPtt2hmNSmBb9s6kq2a0dvT97whHZJe0rIAUjIUyoqytGS6aPK99/ntnlG0moKm+OnMqqwsFGRxMmVXfDYt4cN4VjVm9MuSwzHOjjFkLA68NG0e5w8p9RyW3RqzJl9yLRN4clSthqxRlPPnDbVChbXYu+ZXV1sinLx6vFub1j9arCghWyJE2k0nUdtZ9V0UFF2u+K/nnCqcvP/N1BA5IcWuSvTggTen95N8dvXcXcneux6hqORJxTNn3EzMqtRkcTJnVr9wiCHjfNab6Uy/xh6X0l+nbaJ5uYUVGTMp4dkteM6NvmLjevjBmdNPbRoFLKs6S3iOhbnSeL58cNSxqr93vZVOQxKJEwu06Lg6DVBuhYiaKSwEqUgFVmi4j+BaypM+rbXKn908QXIzOMhDChysoQZ1eXp4xP273NgDRiIHh6+CgWrd/NiMoalk0cTWNmOvZYnEHV9XhicvAv+ja6thXVqYJFgYQOus6Za7egO+V8kujb0t+vps3t4Wdnnc642jqqMzL4pLiQU9ZtBkYZHU+YkKW9m1ZX6gy0Hdk5BqQRZqcEYzijUO9xM6ujHCtxdCCAD09Aioyify+MGMRlH66juLkbRzROwGXj7aICYqursE0pMTregCUFIyFMKPBuFfWODO6cM58VxUOYWlnDlcveIhiX5sWib6MqGvjuW++xa1g2O0sKe8c7vG6sm3eg6zqKIj1GRLKHj5rE0rGDQdexaBoJRaU128vEygqjowmTau+MUNrWxZSGdbwwehaQ4Lq3F9OuysG/6JuuJMgNhmnYv/msrtPlSDcskzCvaEMIOzC1cwtWepa+KoCXLopi0sBY9C+9M8z0rbU4COGlnUCXn2vebKftySHkSsHoS5OCkRAm5K7s4MzLrmRrfs82xS9MGMc7w4bzw9c/QroYib6c8dFWRrdW8ODwY5PG41YrzX4vmgYW6X0t9hOK6jx65MSeHxSFhMWCPR5nY0k2MyqqjA0nTMsZa6eou5HCYBt3vvwnEorK+szR1DoSRkcTJtUcc1DaFSZis9Fhs2LTdApCURLIUhGRKhbTceoJXHrq7Gg7skW66N85qzeQTwVlbO8dq9XKqPrYTe6n3E58OikYCWFCq0cUU5uexrxdTWSForQ7bHxSWoQV+UIu+pYWjqOqUfQ+9hXR0LFYZHaRSLajJkyr1wtAZneQLqeDqNXKMTs3897gISQ0HYsqrxuRbGZVBc5YnPFtmwGw6TC9eS3v5s+CREIq0yJFrCPMmOYuYo4hsOeAX9U0RjfVAOMMzSbMJ2KzYLXZCFiceBLJRaMgLh5aEeXcyTYcsjOjOMDIjlpK2JE0VsBuOtdI76uDIU0KhDChDgvMqWwhKxil2dXzJndkZQsxVf5kRapId4xdpdncftLZNDqTz9hqmsZLJQUGJRNm5vpoByPqGnnsnqe48aUP+NUbKzlnxWaO27yVkMvDmt2yfbFI5YkmKA7UpYyXdtf0WbAWIlLewZ9efoTvr3iT0rYmxtVX8vtX/8Xp2z4wOpowIVVRCHntvF80kZiyrwDdhZ82NY2Lnwwy765uNK2frdTEYSvhiKKS/LpQgAaP35hAhwiZYSSECY3bXs2HQ8fwyvA8gvaeP9OcQIRzLDaDkwkzWvpEDXndLTw1Yip0d3Hylh28O2oE7Q47RS1NDEYBhhodU5jMC60+7n7keR495mi6XK7e8YiaA4pC8+Z2GCx9aUSywR0NtDhdKeNhxQkJDSxyYkMky1q7gyZ3FujpzN3diDMaobi9C1c0YHQ0YVaKQo03lydGHEdBoIWA1cmU6m2o8Z6i9EeVCV7dGuPk0TJzROyzuriI4+tVVLTeMR1whOUE2MGQT3UhTCgeU1if6+8tFgE0eRwsHSUzRUSqDUvr2ZhdwLeXr+GKZStYl53NMeu2cvqqDXTbndRmphsdUZhQLBikNjs3qVgE8MGoMbjCEdLrWwxKJswsrlh4ftQ8wuq+z6eQxcEG31h0WTUt+rDek8dLo44i5HCBohB2OHlj+Gxa7D6jowkT2n9+SFo4TFF7J2Ma6siLt1Ck7NtB+Lb3pJ+RSBZVLWzNHEK1J4dmRwYBq4Ow6ic92m50tAFNZhgJYUKVaRkEbalT+yuyMw1II8yuriHBlMpGABrS0plZ3dBzgQYnb63g4cmjqWrXKEmXcwRinxEbd9NgT521GLNaKAkHabBIQ1qR6pVBU/iktJDIBjvOSM9Z2w15Q6ksKmTzlgBjJ6cZnFCYTUZHjMb05EOOqM1OtyXDoETCzHR6lhFZE3ECNjv13nTGNVYRJBuP3kyGFqHN6mRVvXynEck6HbncO/F0RjS0oACtbieaM8zw2jpGGR1uADPdX9qNN97ItGnTvvTtV65cybRp01iyZMnnuv6CBQu47LLLvvTjfRlfNOM3bcmSJUybNo2VK1caHeWw5YsFmFKZuktRTlcAvT1oQCJhZnqw56BNBzRL6nmAiXVNdDWGvuFUwuw6NgeYUr0Jazx5qvbM8q2kxzVUq0z1F6neGTae69/7F2mRfZ9F02s3MbR9F/a1srueSOVv6+xzPCfS+g0nEQNBVAUNiFusBO1OyjPzWF3Qs0dwED9TayvAZiUUkx5GIplqURi5p1gEkBkM06Wms7VI2jIcjC9cMNpb7Jg2bRrPPvtsn9eZNm0aP/nJTw42mxCHrdENNZQ2djOioaF37JSN6/nFu/+mcVeXgcmEGbV4HQBUZqXT6nUTt1oIe1yE3U4SFgvucIy3btticEphNt7OGsa37OTa155nVE0VBW2tnLJuBRd/+Aal9fWssrqNjihMpL5L54JfVxBHZ0RLTcrlpS01WLc1GZBMmF3EamdQSzXoOmldAVzhCMObd1Pn8RgdTZjMxtVdWFRLygFqjT8TjThbsvNYVjoCAE2RJvsiWbyPzYHyOrrxdTUbkObQcVBL0u69915OPvlknM6vbtr6r3/9a/7rv/7rK7s/IQaiYMLNjB11TN9RR4fPiiWuc3TlWsa21NC6chdMzjM6ojCJSFznzTGD2OVLpyInk5k1jWQnErDni1TcbuOoLdW8pRcbnFSYzfj6CsI2ldGNVYx+vYq4omLRNer8fkZv28WDW8YaHVGYyMy7Q1Rp2fyt4mOaHZlkHzA7JBLzULlkO2W/nY+qyoGc2GdLXgZnrt7ASJpQEwo6Oh66+Pf4cRxndDhhGrquc/8t5QzRk2cOeaPdnFD9Nrm0kNG6k//6IJsb5p1FXCYYiQN0uhwpY7UZPgY3ysmMg/Gll6SNGTOGpqYmHn/88a8yD1arFYcj9f/sQ0EikSAcDhsdQ5jcrqoQUasTFAVFUUjvTuALa2zKGIoOvPBMvdERhYks+aibCl86bw0rpcLnxheN9haLAFAUGvLTiScUcu+IsKwizq4W6UwrwBmP8vT4Kbw0dDSvlEzmmeFH8vSIOfzvnJNo0X2kfVyJJjuLCGBXS4LKLtDtFv42bxY/OuFSmp092xTrwKb0ETw+bjr/PXQ2+Td1sny3vG7EPq1eK63uDNREz2eTgkIQP94oVLfJ55HosWNzAEs0hq73vK/U+71Up/uZU7+c3HDPJgx2LcH/LHueE8rXY4nL+4xIduHqD8jv3ncywxmPsrE0g5oc6Zd2ML70DKPjjjsOXdd56KGHWLRoEenp6Z96/U2bNnH//fezZs0agsEgBQUFnHrqqVx00UVYrfti3Hjjjbzwwgsp/XNWrVrF3/72N7Zt24bX6+X4449n0aJFnHPOOfzgBz/g8ssvT3nMxYsX8+ijj1JVVUVWVhZnn302F110UZ/5tmzZwu23387GjRux2WzMnTuXa665hszM5CbD7e3t3HPPPbz33nu0tLSQlZXFUUcdxeWXX570O1iyZAk33XQTd955J+vXr2fJkiXU19fz61//moKCfTtdfd6M77zzDg8//DDbtm1DURSGDx/OhRdeyPz58w/qus8++yyPPvootbW15OXl8e1vfxuv19vn70h8/boiOkfd3MjJmX7cbV10pvnRLBbSOrvwNXZTaS/mNUs6d/wzxKpLU7c1FoeXru1taOc9Bxecw7D6NoY0tJMeSu1VtGx0CS8W5dPSqXHkQwkIxFg0xsKTF3iwWWQmwKFq49MVrLhjC526SnBsAROnp6G/WcmLARePjxzCz4YcjR50M2l3Oa54kLjaxsNzJ7FucDHvDi1lZv12Phr0P8y4fg6WSYPhKJlxdLj69pMRsKkQ1dicn8PmwQU8MXca03dVcuGyT7DGbdRkuNlRnEOTamP23QEWX+phwXDZW0VAXncnaYHUzyZbLEF7TTvFGVkGpBJm07CtC0dCJ6qqvDJpDE3+nuORTwbncO/i2yns2rdz57EVG3mneIRRUYVJTaqrQqWS5WUjafb6mV29hd27rMSsmtHRBrQv/UmuKApXX301V111Fffffz/XXnttv9ddunQpP//5zykpKeH888/H7/ezfv167rnnHrZt28att976qY/1ySefcPXVV+P3+7nooovw+Xy8/vrrrF27tt/b/Pvf/6a1tZWFCxfi8/l4+eWX+etf/0peXh4nnXRS0nUbGxu58sorOeaYYzj22GPZsmULixcvZvPmzTz88MO9S+66u7v53ve+R1VVFQsXLmTUqFFs3bqVZ555hhUrVvDQQw/hOWA99h133EE8HmfRokV4PB7KysqIRqNfKOPTTz/NrbfeyqBBg/j+978PwAsvvMB1113H9ddfzxlnnPGlrvuvf/2LP/3pT4wYMYKrrrqKcDjMo48+SkaGVGGNcvx93dSmpVGb1k6exYayZ6ZIW0Ya7micll1FbCkoY3UtTLk7wOorZf3/4ar6wyZeuHQZuieTH762hgWrdlKgVaPmBHh1+PFJ110xKJ+WnHQIxsFlBbeNZ7eFuem1ML89WQqPh6K3f72Grc9WsSs7gzfGDEdTVZ78BLKcxbw1JBtnLEFRlUZZtBrXnrO0Vk3ne++u4dkZY6jJ9PNkXjbrC0pYet1NZMS64YxZ8MzPk2ewicPCypaeYhEq4LH2vgZWDCnDRoz/PPBXTqtQGTTmJvDYwWph4ZNxnj5L4axRFmPDC8NV5qbT4msnryOQNL50cDGvPd/Bk+OkYCSgqTKEAqwtK+otFgHUpOVw14wF/PbNB3vHtmQVErXZicQ0HDbT7eEkDJC44SniqsJfj/0Wq0sGYdV1Fk+Ziae5jjqXHU3TZbn0l3RQp35mzpzJzJkzeeaZZ/jOd76TNHNmr0gkwi233MK4ceO4++67e2cTnXnmmQwfPpw///nPvY20+/OnP/0JRVH45z//SXFxTx+Os88++1N3N6uvr+eZZ57pnS1z+umnc9ppp/Hkk0+mFIyqq6u59tpr+e53v9s7NmTIEP785z/zxBNPcPHFFwPw0EMPUVlZyS9/+UvOPvvs3uuOGDGCP/zhDzz88MNceeWVSfcdDof517/+ldTnae/sqc+TsbOzk7/85S8UFxfz4IMP9l73rLPO4rzzzuP222/n+OOPx+fzfaHrdnV1cddddzF48GDuv//+3nwLFizgrLPO6vf3Kr5eH1Vq4LbTlpeFEkheelabm4lN0RneUM+qUcWsqZbljYezN3+1GhSVjfnZLHh3B4quMUpfj6MxAiiszxuLqmu8OGYom/P3zJRMaBCMgd8BPjt/WxaSgtEhKBFNsPXZKnTgw6FlaPs1gWzxuPHHNQqaO6nJ8jF9e23K7SfurmNXbgboGptyi1lfUMRRlVvhP8vhjbVw/KRv7skIw927IrbvB6uaUjD8YMgwHITJCwa557XH+f4Fl4NFgThc9HJCCkaC4vZmnps0jLNWbCUr2PPd5Y2Rg3h86hjiAWlEI3psXhsirihU52SmXLYpp7T33++XjOBf42aDonDz62F+d4ps0CBAv2UJL487hrumTaLJ7cCiaYxoDzLYamWrBa5aHOLub8lr5cs46JLsj370I2KxGHfffXefl3/00Ue0tLSwYMECuru7aW9v7/1vzpw5vdfpT0tLC5s2bWLevHm9xSLo6XX0ne98p9/bLViwIGlpldPpZPz48VRWVqZc1+PxJBWAoKcg5fF4ePvtt3vH3nnnHTIyMli0aFHSdc844wwyMjKSrrvXWWed1W9T8M+T8aOPPiIUCnHuuecmXdfr9XLuuecSDAZ7f39f5LrLly8nHA5z9tlnJ+XrawaWGbS2thKJRHp/7u7upqtr325h0WiUlpaWpNvU1dV96s/19fXo+zXWM8NjoCjgthJ02DiQLRZH0RXmVu/s2W90z5d2Mz6Pr+MxvqhD9few9zHC7T1j2/d8sbISw6pE6XS4Gd+4ie+uf5pzN/ybzHBjzw0TGs5IHLQ9921VCcWMfx5f52N8UYfK76F2Z8/uiglVocuV+vnjTGhUZ/nIawvQ4bSnXL65MCfpZ6umwd5NajfXfGPPQ14z5niMN8o1SOg9RaA+lrBaEnHcsZ7bnrV1Vc/gnrsORvddz+jn8XU9xpdxqP4u+nuMdQWDWDm4mBOu/i6XnXsyZ37/TH5y1gk4dA3bAQ2Ozfw8vsrH+KIO1d/D/o8RC8QJuBz4+uhNZItFufi0yzjp3J8z74JfE7HaQVFYUZ0w3fP4uh7jizpUfw/9PYYO/HHeMTS5e3ohJ1SVzZleOlx21ESMt8q1g36Mb+J5fJWP8VU56MXlo0aN4sQTT+SVV17hggsuYPjw4UmX79q1C4Cbb7653/s48Jezv9ranrOfZWVlKZf1NbZXUVFRylhaWhodHR19XtdmSz5At9vtFBUVUVOzb+vY2tpaRo8endRzCXqKV6WlpWzZkrptdWlpacrYF8m49/GHDBmSct29Y3uv82WuO2jQoH6vayYH9pI6sM+S3W4nKyt5SvOBM94O/Dk/P990j+G0Q9iisDE3nXPf2cToLXVYY3Fa8vx41AghuwWLGgR0iCVM+zy+jsf4og7V38Pex8gY4qN1Wxerh+UTtaqsGTyI/531B1rdPsY0VvDbNx5gcHsDbw0bhyWhobYHGdcdZKUnveeOogmOKFMNfx5f52N8UYfK76F4VAGqTcEa08nt6KIxzZd0edCi0G234XOHeSN3MN/avA2LrqMp8PDcSWwtykHRdPSEzvTqHQyr7aK3AnDMuG/sechrxhyP8ZNZFp5enwCb0ls33J+mWmj2+CjsbCduUUHXewpMQL5n3xdZo5/H1/UYX8ah+rvo7zGqHVkEnA4iDisfDC3pvdyiKrhsyQUjMz+Pr/IxvqhD9few/2OkFzlpqtIoCIXptNloc/Sc0BjVWMPvX3uaq04+m1eHjtt3Y13n2rkO8vOTP+OMfh5f12N8UYfq76G/x4gVZbAlK5sDtVpVUOCeb+07QWbm5/FVPsZX5SvpRnjllVfy5ptv8te//pW//OUvSZftrXpdc801jBjRd3OynJycPscPhsVijinQ/c0uAvNkFOax7Ao3Ux/RGFnZwsR1Vb3jOfWdKNYE15x7EpmJZuiO8uLFh+ZuguLzOe0fs/nX8a9T2trJH799BAGPB33P2uxNuYP4xQk/YEjTDgZXBsis2kZDXg6b0z3gsUE0gS8S4blLfJ/xKGIgUhSFk++ayYuXL2f+lh28Pm4kbR43tlic8bvqGeK38W5ZEZ3ZDk7ctZGaLC+dLj+binLZVJzDzB07QI8zo3IXP3znIxKaDXwu+O13YVz/J2rEoemIMisQhZgOig4HTErTFYXb557EH158gsfHzoTInrO4Fnj/u9L0WsDFH63gH3Nn9cyO3s/Q1nZuvlCWiIgeJeN9bK/qwAJMa6hn+K6dtNv9hB0WVhQO47mn7mNnpo/3SwZz2WmXAnDS6NRZsuLwZPvgBnJvD1CdmZY0PqG8DgdtzB8yzKBkA99X8kleVFTEWWedxeOPP56yu9neGTYul4uZM2d+4fveW0nbvXt3ymV9jX0ZNTU1xGKxpFlG0WiUmpqapBk4RUVF7N69m3g8njTLKB6PU1lZ2eeMoYO1dxleeXk5M2bMSLps7+ytvY/7Ra67938rKipSrlteXv5VPgXxBUwpsnLO0BAZL6a+tuO6hQ+HlzJ3SxcNv3CT65Mmf4czd5aT769egOO27dxW4UXVk0/978gu5rmhIxhfW8eEXS1sLsjBZtX5r7FRzplgY2JxWj/3LA4FJUfkcvm6BVQua+Rih4Wg1UbukAw63kuw9a06TrPVUlBfR3qsk+JQMy0JL/7qLgo6AuQF2vjRghM4qqWOrnPmM/KGcZCXDp7+T4CIQ9vl0+CelUBM65lBdEAfox1ZOWzJykYNOMCu4lE0dl5hI08+pwTg17tpdKYWhtSoxsjh6d98IGFKxSWOnmXzukZubTMJ3YUvEsMXidFJDsvzR3JE/VqOPW/PRkua7Hwl9lOazXc/eos/Hz+LmLVnUkZZcwfHbSjH5qgHjjY23wD2lX2SX3rppXg8npQZRrNnzyYzM5MHH3ywz+Vg4XCYQCCQMr5XdnY2Y8aM4d1336W6urp3PB6P8/jjj38l2QOBAE8//XTS2NNPP00gEEjain7evHm0tbXx3HPPJV33ueeeo62tjaOP/upfiDNnzsTlcvHkk08m/Z4CgQBPPvkkbrebWbNmfanrOhwOnn76acLhfc2TGxoaePXVV7/y5yE+vyfOdjG/fFPKeNxmZWhzC8+wUYpFote5PxrMjO01KeNxBRKKwsb8PL7//hqOqKil7ZZ0/vcUDxOL5Yzc4UBRFMqOzKNgejZDJ6fhS7NRvKCEY/88g0tuHcfOnDQGddeQG25hdNtujqteyYyGTTR4M8nvCtM0KIPpDx6HMiRfikWHudv2NsfX4VtrN6Rcnh7o4vKFV1ORVcg5oxV2X22XYpHo1aG7SfSxu2ICKHDJrkWix5CRHkAnq7kNb7inAVpesJkzyl/lkq3PMLlxKw+MOZJa/55lNwf0vxJifH09//PCMi56fx3XvPIx1736MTGXhXH1dZ99Y9Gvr2yucHp6OhdccAF///vfk8ZdLhc33XQT1113HWeeeSYLFy6kpKSErq4uKioqePvtt/njH//4qbukXXPNNVx11VVceumlnHXWWXi9Xl5//XXie5qiKQe5xW9xcTH33XcfO3fuZPTo0WzevJnFixczaNAgzj333N7rXXTRRbz55pv84Q9/YOvWrYwcOZKtW7fy/PPPU1ZWxoUXXnhQOfri8/n48Y9/zK233srFF1/MaaedBsALL7xAVVUV119/fe8ayC9yXb/fz5VXXsntt9/O9773PU455RTC4TD/+c9/KCkpYevWrV/5cxGfX3emFVe7FXuk5zWuAw0l6fz4veVk/HaiseGEqdgcVhKKzpCmVsr321mkzulAVxQm19Xg0yNMbmo1MKUwo9KuVtQDzhuNaavgk5wRnLKuHOdM2epa9PA6FL4zXuXx9Rqnb9rOrsxc1pXkgg4ZwSi13uFc8s4qHKcW8IcFsgxNJHPF4py0eTv/OGJq0vjM3bW4/ObrmymMkV3sZGi6hrq1HQCrFufkqndxJnqKR16tm+9uWc7l2nk9u39KrVEcoNPjZP244Si6TiShUTO8lME1Dcyul9loB+Mr/VQ///zzeeaZZ2hubk4anz17Ng899BAPPfQQL7/8Mm1tbfj9foqLiznvvPNSGmUfaOrUqfz1r3/lzjvv5IEHHsDn83H88cdz0kkncfHFF+NwHFwvl9zcXP7f//t/3H777bz66qvYbDZOOukkfvKTn+By7dty2uv18s9//pN77rmH9957j8WLF5OVlcWZZ57J5ZdfjsfjOagc/Tn77LPJzs7mkUce4b777gNgxIgR3HbbbUkzoL7odc8//3xcLhePPfYYd955J3l5eZx//vl4vd5PbVIuvn6dNpXusdn4WiNYYwk6Mt3EXBYK69qwHTHU6HjCZMbV1KN1dJEWCFCR6WZrRiaja2tp9ri49bWXaHSkkXPBp7/PisOPRUvdjdGia9i0OCUNnXhOm2JAKmFWj57t4JihCcKvOrhk2Qa0RAJ0HauuY0toBD0Ojh6e+poSIuRy8rv3nyGdNv41fhreSIQrVi5FDUsfPZHsij+P5oFZu9CAwkAjzkSUBFY0LFiJ4NbCTGqoZHVeKTaZxCgO0O3zoisKuqL0FBWB2twMYor0DT4Yiq4P3Pl8b775Jr/85S/53e9+x4knnmh0HCG+MveP+SeumJv67BwSFiuWRIK8llaIJ/hu+bmffQfisHLFgo8Z0dLNw+OGc/2rbzCjsg6FnplpLQ4Xzekwp/zHeN3ygSn2+cP33uWaBx5DZd/XgEZnOh96JqGEFNRXFrBorhzQiWS/m/cOaR1BrLHkra9d8RBn/W0anuPlpIZI9szoe1mwZQW7lDLqlFwsaJTp1bxTNpKLdl1gdDxhMneOXYwnEmV0UyWjO3cToedzSCWGn3oGX3kLNd400r0qbTfIZ5TY59dz32HrsMFJY2ndAc59/VWOa/+pQakGvgExb1jXdaLRaNJMong8zmOPPYbFYmHq1KmfcmshBp5GVyHpsQ5OXrmakNOJLxTClkjQ5JbdRESqfK9O8aYWCopzmVTT2DtLWwEyomE2+EqlWCRSjHMGWDx0DifsXImDCN2Ki+3RIXhjCRIKaNVdgHwZF8kafF68gUhKwag6PR3XuK9nS18xsHktISqUUirU0t6xzcoIajL8BqYSZhZ02PEHYr3FIgANG60UUuPPAIvKqBxZkyaSBWMa7mAEXzBMTmsXNXkZjN5cgyqz0Q7KgCgYRaNRFixYwEknnURZWRkdHR28/vrrbN++nYsuuojs7GyjIwrxlbKrCbocdlzxOK7u7t7xtHDQwFTCrGbYwpSU13B9WzWORCLpMouuY1EPbtmuODTtzsxhZKCceqWYRiWDgNLT2NqhRwlip8br+ox7EIcjb7CL3Xk5DK6px7anaNTkddPucZPITvvqdlMRhwxfQqHamg8HtBHxdMirRaSyoGPRNHISregcuMzVAlYL6DrXHilLYEWyDDXKOf95CS2+9wS7ThoNJDwxQ3MNdAOiYGS1WpkzZw7vvvtub3+ksrIyfvnLX3L22WcbnE6Ir17YoxKPp/55dh9kvy5xaJp4dC4dj2iUtoVIKCqW/VYaJxSFVp/XwHTCrGJZmUys38UuTwm3njCDd4YOoqS9g1+9tZRYzENWpnwZF6lml2/nnllHsX7aOLK7Amz3eShs7WBGQxNWBmyXA/E1Usvy6K6M4YhoOPUQUexoigVnPP7ZNxaHIYW4Q8FKgtgBBaP4nqki4/MUzho3IA5jxTdodG3tfsUiAIUucvAoqbsJi89vQPylWSwWfvOb3xgdQ4hvTEdhBq8WZXLu+k04EvtOyW3LyWGygbmEORVePIp3/2cVM2ur2JRfwNj6Oiy6TkJR2JxfgCZbiYg+HD3OQdDq5tqFJ/L+kDIAtuTlcNnZC7hk3VZum/b1bOQgBrYaTybnrF7DmPbN5IVaaXRlsSZrHNU5mSg2WfoqUlnHZpJYX8Wc2g+J21WsiTjVeikV9hFGRxMmtXzkKM7YuBxrPI7We7iq06XaeeLbds6ZMCAOYcU3LKc9CNiTxjQsNHgzGGdMpEOC/LUJYUI5gSBTAhp/PmI6F67ZgKLDyuJC7Ir8yYpUiqKQ3xFgV2YO1RmZNPr8+CJhuhxOYlYr6gHL1IQACJdmsS0zp7dYtFfUauWdwSV4HVJoFKliispJ1e+RE2oHoLi7hZxoO7cOvdDYYMK0QlYPs1vX8NKQubQ501F1jTGt25leWWF0NGFSIYeDbsVHDi1AFA2FCC5icYcUi0S/mrxpFHcE0PdbHG0jwubJ4znWwFwDnSweFsKENJvO2Ru38Kv3P6KwO0BBIMCp23bgC4eNjiZMqtvn4uHj5uJIRIlZrbR6vMSsPQtE1JgUjESqoSUO3hoyEk80mnJZUNH6uIUQkBuu7S0W7VXS0UhxZ4MxgYTpeT0qq3LH0eZMB0BTVDZkjURzyIw00QcVcto7UOIqbWTRSRqdZBDGjRPpRSP6t70gh+5MDcue14mNMJonzJGjZQOPgyEFIyFMaESRSmFHZ9KYqusUdbQbE0iY3luTx1LU3cTQjt1J4wpg1eXgX6TKdKvMLK/m2O3Jr5ncrgBH79xmUCphdhZ73zPPclq6+xwXoj4/gzp3Tsp4tT91TAi728LI3bVsK8gHFBLY9swY0UnpnC7EfsKoeEIKlYVpNOeqtKW5cAds5BdLf72DIXP6hDChkYOsRHWd/0yewBujRmDVNE5bt5HRdXIGV/Qt6HFy9KaVTGnZRKWvkICtp/+MOxYkv7vZ4HTCrJr9Pq796EP8Vivb0/1kRKLMrKpjaZ5sjy76Fps6kratG8jQ2nvHOkgnp0FmwIq+lagxmmNxgo7kw45um7ufW4jDmTPTQbSim3a7nVq/n7yuLmx6DDthOpDPJtE/XyTKu0NL+OfsSXS6HJy8cQc/fG8VueeMMTragCYFIyFMKDJlBM9OibF40vjesbuOnsu3P17NkQbmEuY1vquZ4tYWvLEg5+54kd2+IgBKumqps5d9xq3F4aqkq5GVI8cyuDvE4O4QAJrTyfjWVoOTCbM6+oqRbP/XaPKpx0sX3fioo5j8rhajowmTitocxNTUQw5fMGRAGmF2VocFeyxOWjhAUbARnx4ihhUNKxZVZhiJ/q0ryOWR6fvaWz80ayJtXjfPF6YbF+oQIEvShDChCXNyWFVanDK+tqTQgDRiIDjnSCuqliCBE6uuMayzkqGd1Si6k7SYLBURfRvdXE+HO3Vt//gGKRiJvnkLvCiOIDUMYivjqWEQGlactjajowmzskDMllow6vDKTowilaJC1GZlUn0VetxBJxmE8ALgUIMGpxNmtiXfnzL23pDU4ynxxUjBSAgTUq0W1D6X28qfrOhbzlUTsQV0NKzEcRPDQww3MZxELbJ2W/TNpkcobm1MHZcG+6If3U4Xy8dlkkUdoKESp4gdrBqea3Q0YVKeaBhFS50Zoivy2SRSKapCWjhIFCc9nRgBFCI4sPvlNSP6V9aR+n0mPRIwIMmhRY4+hTCp8dV1KPq+D0ZrIsG46joDEwlT87pwRaOE8JLARgIbMZzEsGPV4kanEya1fNAI5m5ZTW5nz4wiWzxGPBYgZLMZnEyYlRZNcMeRC2ksDTOTN5jOm+wus/D88LlGRxMm1eFyk9mVOjPEH5LZIqIPioIvEmZfsWgvFV+214hEYoCwhcET2bfzq6LrlLR0fsotxOchPYyEMKmStg6+tXoDW/NzUHWd0bWNZAbky5XoWzycoDBaS4I04jh7x61oaHJuQPTj45IyLjnnLC5Z8RE5HVU8NWECa4qLuePfLxsdTZhUPKYzprmboy+9gVGN1eiKQnV6Pjc/9RYw0eh4woRK9C7S6naz2jKETq8LdJ289i5sVjmQE6n0hEaL2wfUkVw00kmkSaN00T9P1ML1b3/EskFFBG02ptbU4wtFgHGfeVvRPykYCWFS1oRGbleA3K79plLqMhVX9M3mUNHRSfRxWUhxfON5xMDQlptOm9vNn+YdnTSu2OXrgehbTraN0z5cz/jyelptVvyhKINbNjGksd7oaMKkHGXp+LQ2MtrC6LqCmtBIbw1RPjrH6GjChNRYAl3VafR7yO0M0FM00nHTRSKebnA6YWYZkSiZoQgLNpf3jnXJjOmDJt8IhTArXe/5T1GSfxaiLzYr8WmD0Va1ktDtxLChkiBiVVjvG0yR0fmEKR2fHuaZrm6afPum+Ze2djBB7TIwlTAzV4aDjECA8cuaksZbsi0GJRJmlza5iOWZY6jKz+gd6/S7sAZkxyuRyprQiVjsrCoeQkawi6KONrKjzWQHutGGpjY1FmIvV1xDI7nnjjXW16lU8UXIOgUhTMqW2K9YBKAo2BPy5Ur0z/7rRWz0D6aTdEJ4COCnxZqD5pGzK6Jvw7IUXvrnvczbXk5eZzfHbNvJMw/fT7QozehowsTSO6MpY07pKyr6oVoVGjKTD/R1VcGWkAM5kSpms6CrPYeobW4fGwpK+ahwDBo23L8+xuB0wswSfgeqrhNTFGJqzzGUKy7vMwdLZhgJYVIWLQEkH+hb+9hlRIi91FmDyOkKJY2lhcN05rgMSiTMbtvoUo7o7OSFh+/tHQta7TybV8jxBuYS5ha22/AGkpvpxy3ylVL0Q9fRlAMbGINNixkQRphdX4f3YYsdy0/+f3v3HR5F2bUB/N6eZLPpDUhIIIUeepMiIEUB6UWQakHF9gEvKIodK76AHVB6eUVpEaQrAiJdaVIDSSCV9F62zPdHzMJkd0OCgZnA/bsuLs2zz86cWQ6b2bNP6QpVc46XJscEjQpQKKARBOCfSRlGLUe//lscYUQkU35F2TZtAbmZEkRCNYVFpYTGTlHR2c/JTm8ioH5uOtL0WiTovVCsUiNR74kUVye0SYyWOjSSsSxPF9w8QVoAkO7FwjTZJ1gAdUm53ToFAe4mFozIlsrJ9gO+oFRA80l/CaKhmsTZTm2oRM2C0b/Fr4OIZCqkmQAcSkWylzsEhQJ+mTmICM6QOiySMaWnCzL1LvC8aTc9CxRQdwuRLiiSNU2ED5pmXUS2xhWpei/4FV2DR34eMus/IHVoJGNGPxdcNgvwziyAAkC6hzMKfbh7Edmn0KjgnlUCKBQwatVQCAJ0hUaE9AqUOjSSodqtvZF2ptyXphziQJXg0tAbxrh0aG5awiPJz1vCiO4NLBgRyVT4F/0R2PptFMQ6QwDgpsmG5otXpQ6LZEypUiDnqXYwfXcMPvl5KNBqcSmoNnqMi5A6NJKpsG5BOO8ViIYZ8XA35gEAsrUuqP1yV4kjIzlrMjIYf30bjXy91trWeCA//JN9SrUSDZ8Ox8UFF2HUqaCwCNArBIR9wsI02YocF4qzP8TBVHjT5LSWXCSNbq3n2EDMvWpBUHIatCYTrnu5I6iTn9Rh1XgKQeC2S0RyZbyaij+nzIfSJKDFx5OhacgbcqpYUX4Rlr2wAbisRWCjOmg7uQH8m3tJHRbJWNS+NOQ8txqd4i/jnE8tJLz5GCaNryd1WCRjFpMFe974C5c2xwMA6vWqhZ4ft4aKa0WQA4JFQOzC8zix8BBMXgL6LBgMtwjPWz+R7ksZ0Tk4seQy8pILkOR8BYgsxBNPTISGW6TTLfy8IxNrfkiF0aJBm0hnPP+MH/QuHKL2b3CEEZGc1fLAyT7BAIAWof4SB0M1gUqrgrJjHtAR6DNxIG+u6Jb6dnTHNy8a8HVRN/zfuP7o56W99ZPovqZUK9H13UhEBx0HBKDHpL5QaVgsIscUSgUCnwrDLtV+AIBzPVeJIyI58wpzQ48PWsJoNGLp0r+lDodqkN49XJEQ9yMEAf8UGVks+rdYMCIiIrrPOSlNiHBJg7/BdicjIkcUvIskIiIZsrMxI90mltyIiIiIiIiIiEiEBSMiIiIiIiIiIhJhwYiIiIiIiIiIiERYMCIiIiIiIiIiIhEWjIiIiIiIiIiISIQFIyIiIiIiIiIiEmHBiIiIiIiIiIiIRFgwIiIiIiIiIiIiERaMiIiIiIiIiIhIhAUjIiIiIiIiIiISYcGIiIiIiIiIiIhEWDAiIiIiIiIiIiIRFoyIiIiIiIiIiEiEBSMiIiIiIiIiIhJRSx0AETlWYgbOmWrBRVEidShUQxxIANant4ECApomAw8ESR0RyV6JCfWOJ8M5twTomQbUryV1RCR3uYXI+egneP6RBR/nXKBfNlDHR+qoSOau5QIHS8Lgq8yVOhSqIU6mAuuK2kINCx7MBhrwbYYqIdXsiv/mP4JnPgN0ShM+fVCBF1qrpA6rxlIIgiBIHQQR2TqXLqDb9yZcL1QAAHoECtgyVA1njULiyEiudp8vxJmnViLTxQC1xQxDSSHaLR+PB+pppQ6N5CqvEMWdXofuVCwAwKzVQLV5JtC7haRhkbytfHwdJjXpiyKtFmqzGe8d24ZX1w4AlBy4TvZ9f96CMT+bYRZK72EGhgrYOEgNhYL3NGTfjhgLHllvhoDSHNGpBPw5Vo3GPswZcsxoNML5MxPM0Ija/xyjQMsAFo1uBwtGRDJVd4EJ1/LEbZ90VWB6O77ZkX1vjNuOP42B8MwvBgCYlAo0cEvBOysekTgykqucuVtx4ssD+OChwbiud8PQ04fxZMJJBJz/VOrQSKby/06A3yZPFOhuFKIVFguuNItGyCONJYyM5MpkEeD5hRl5RnH7tqFKPFyPRUayL3ihCVfLDUbrFgTsGckJMuTY6WQjIlfZFhXD3YGLTzN3bgdfNSIZis4UbIpFALD8bwHT2939eKhmuFTogSONgpDmpgcA6ItK4HSkUOKoSM62ny7A2EmzUKIu/Sbur8D6SN3nhvnShkUy9vsFk6hYBACCUomNZxSYwto02ZGUJ9gUiwBg9VkLC0bkULydmYtHk+9+HFSzHEmx334l++7GcS/huzSRDO2Isdhtj+WbHVXgire/tVgEAPlOWvwZHCxhRCR3P9ZuYi0WlVne5kGUmDn4mOyLdfEEyg9OFwT87eQtTUAke1nF9t9P/nTwwY4IAOzdCReb7noYVMOYHeQI72puHwtGRDKUmGv/ba3YfJcDoRrlqo+HTVucr20bURltXoFNm4uxBAei7QwHIAKQ5u4ClF93RqHAZR9PaQIi2YvJsn9PE5dzlwOhGsWppNimzVCYL0EkVJM4O1i2kwWj28eCEZEMFdkfYAQL3+2oAqkGF5u2PB0XvCbHAtLT4VEgnv/6+J/7UHCZn+TIvsSUYrsjjIoTMqUJiGQvxcFn/AKOFqEKGJW2a3YKZgc3yET/MHKEUbXjGkZEMpRdZL/dwrc7qoDFzm4zFiV3EyHH1jdug5Urv8DPjVsjxdUdQ04fxtaGrRB/09RGoptdvZQOKGqLGxUKpGdxVBrZl1UEqE0m1L+ehabXriPN4IJD4YEo0fBjCDlmVtkWjHJ1zhJEQjWJjnsDVTu+UxPJUH4JSr/BLVcA8MjPA8Bh/2SfUrDAAiUapMSjSK1BnLe/7UgAopvkQ4uRY6fgsRN/oHFKPD7uPggX/ALR3okDkMm+qwrbkYwAcE3vcXcDoRpDazGix99xGHHwrLXtwbNxWNS7OQAf6QKjGsdeEYnoZo5ShHc1t48FIyIZUqtgu0YEgKbJ18CCETnik5eDzcvnoN21ywCAqMZtMPLxlwFwWhrZZ8gvgkKpRLraB9FBQfDJzke0txk6rpdGDhQ6uO0WlLwdJ/sCs7LQ//hFUVtwWjZaxCWDBSMiqk5mB/cvnMx4+1gwIpKhHbH22428IacKzN7xg7VYBAADzx7DC3/sADBEuqBI1kJS0pDurEdUq0hrW/uYGBhSvQBw1yuylW1S2717VJVwQRqyz6RVo0StwpLuLXE20Be+OQUYdugsXIo4jZGIqleJg8oQF2i4fSwYEclMYp6A1CLYnZJ2JDhcmqCoRuh++YxN2yPn/wILRuRI02vXcKJOfby2fi/SDC4o0GnwY8em2BabhselDo5kqXZKFjT6AiR43BgZ4lpUiEZXkwEYpAuMZCtfpcRnfTvgmo87ACDB2w1fPdwWAZnZEkdGNQ6n2dMt8Lv16seCEZHMXM3555ehnSlpgoLvguSY8p8bqWOB9aEQBLROiIFfHm/IyTGv7GIU1NfggyFdAYUCCkFA85hk+BScBxAodXgkQ83i4/BDy9aitjwnZzRNuQaAX2qQrcQiJa75iIuJJpUKmXoniSKimkph4cQiqliJg4GLLDXePhaMiGSmbFMrjdEEY7kdRNTcTpQqcM6vDkaOnYJjQWEAgPZxF/HOjh/QTOK4SL7+rlUbx0Nv7HglKBQ4US8AvtHpEkZFcmYwFaBYa7sumlLBKWlkX0hMHCA0sfkiTGHhYmlUNQIXvaZbWHfx1n2oajhcgUhmLBYBEAS0uZxgM/S2/cV44EqyRJGR3M16+DEYlWp8uHU1XvtlA656+ODNPiOkDotk7HhoHdtGhQLH/YPufjBUI2RrnOCTky9qU5kt8MorlCgikrvj3oF2R02rOVqEiKpZZonUEdx7WDAikpmcEgFKi4CgjFw0i0spLRoJAvyyctHtfAwQz2/+yb462Rk49vmreHVPFN7f/j1OzpuOZIOH1GGRjCW7298ivU5G5l2OhGqK/fVD8fyOw6iVkQsAcM8vwlO/HMe+ME5HI/sS0u1PBinQckoaVY2+uEjqEEjmHO2SRrePU9KIZObEdcCiVGBb8zDk3jS//7qHARs7RGBWUS54i0X2zPp1g+gbW9/8XEw+uBMwjQXUHMZNtlSwf2elLy64y5FQTdEsKR69rhxHs6R4xLn7wz83A42yo/Gl7iGpQyOZUjp4nyk/7Z7oZgpBgFB+GiMAFBYDzjpJYiL503A7tGrHd2oimUnKMgEKtahYVOaalyeOqoEuEsRF8uebl2PT5peXDUtSJpRBPnaeQfc7R+uiaQR+RUf2uRXmokPacShhweW8ANTNvg6txYLGqfUBdJU6PJIh54I8QPC2mZYmcKNrugX/nEwUaHVQCgLqpyfjiqcfCjNMcK7DghE5wNWtqx2npBHJTN3kRIfbhhqKCrD8IkeKkH3bG7SwaYtq1Ab5ibl3PxiqEQxFxXbb01xd73IkVFPoTEZ836IjdB+uQfhrn8PpwzWY0n8snJF/6yfTfSnZooZbUblRi4KAgCxOfSXHgjJTkWLwQK6TC7Kd9fgrMBTdLp9Flo7j7MmBK8lQxiZIHcU9hwUjIplJ9LjxLZxo+1BBgE9+DoJ/OSJRZCRngiBgSesH8VnnR5DhrEeMpy8mD34Sh4PDka+z3dGICABcC27sP1svPQWuRaULFwdmp0oVEsncwbrhGDP6RRjVpYPUBaUS8x98FNsjmkscGclVUVIB3PJK0OlcHAwFxfDPysPYvScx/MSfUodGMpbr5GIzKm1//SYw5HHKNJUjCECraUDoZGiTuNZrdeOUNCKZKXDSAyhd2C//n29RnIwl+OjnVdgZFonJm9cBGCxhhCRHZrMFIVlp+L+BE/FV+/5wKyzGZT8v5Om0sJw7BbSwsxsW3dfySgRkuLugUUo81q34LxpfT0C+Rof3eg5BGhejJQcynPSAwvb7xj/qNZAgGqoJ6iYnIKgwGys7twUA5LrosKt5KN786bzEkZGc1c7MQabeIGrTmAREX8xEixBviaIiWXpjNfBXDABAY+Y2adWNI4yIZMZXyIeu2GgtFqnNJvQ99ycKtDqsX/lfrGvWXuIISY4KMo04USsYwamZuBTgi+P1AqEAEHktGXHHE6UOj2QoK6sI6Xo9lq39Co2vlw7h1huL8dG2/yGNu+uRA1qz7ZRppcWCWjmcXkT2aYtNiGrVVNSW7GnA6Tr8IoMcG3LiuM0SDc1jU5CUYnTwDLpv/Xez9X9HH/9DwkDuTSwYEclMxOlzKNaWDv7TmEz4c/4rWL9yLmbuiYKTxYzW8TESR0hy9P3qeLgUA3G+nta2TL0LdCYTLrnVljAykivj3rPQmo1od+2yzWNDTh+VICKqCQIz82zaLEolHj1+VoJoqCZwyytArlPpIsV1MjIRkZIEAEjw5lpp5Ni5QD+bKWkZBi2EEk5Jo3KKbhQRH//7kMO1YOn2SD4lrbi4GD/99BN++eUXREdHIzc3F87Ozqhbty7atGmDAQMGICQkROow74hHH30USUlJleq7YMECtGnT5g5HRJIrKsa1/Vfh1KE5Ol24hvGH98MnS7wobYuUq6VvhAruLkI3/Hg6F54q22lEcT6eaJj+twQRkdxdKHFCQFYmsrSu8CgRFwHqZHK0CNlXK7MAGqNJtCV6ROJ1KM3ctYjsy9Zp4Z+dgzc3/QJtsQYKwQI3Szrm9ekodWgkY3GeXhh28g+crhWMJDdP1M1MRYK3AdcVhVKHRjL2Y7MO/IxUzSQtGMXHx2PKlCmIiYlBq1atMHr0aPj4+KCgoAAXL17ETz/9hFWrVmHLli3w8/OTMtQ7Ytq0aSgouFElj4mJwdKlS9G9e3d0795d1LdevXp3OzySQv3JeCG1ACWJHqidlYcSeGJrnd7okHYUTbIvAADUggAUFAN6rjFCNyS5uOOpY3uxPyJc1N4m9ipaHN0FfDZQoshIrhplX0fP84k45tUa3ZP3Q4XSRfavutRBkt5H4uhIrtIMLvDNz0Wix43RjI3TY1GnIAkoLAacWTgiMSeTEZN/OQBtsQYAICiUyFb5Ytru3wBEShobyVRsClpdu4LFHXvCpCr9uHqmVjBCU5OgS+eOjPSPwmLgcrKoaV8w19OrbpIVjIqKivB///d/iI+Px5w5c2wKJEDp6KM1a9ZAUY1VQrPZDKPRCCcn6T9sd+vWTfTzsWPHsHTpUoSFhaFv377SBEXS2XgISMrEZc8GqJ0l/rb/hGczNM6+AAWAQpUazomZQHgtaeIkWep49QKeP/IzjtSNwNrWbWFRKtEqLh7v71yJi0oBTW99CLrPpOebcdGvFuL0uZjffAxUulzkqV3hnqnC2XpOGCN1gCRLWXqFqFgEAD81aYPPNy6G+feWUPVqIU1gJFth6SnwT9XjvF9tHA4PhHOJEZ3PX0VgKkcBkH3Xv9wNQeVlLRaVifH2R1FBrDRBkbxMXQLM22LTbFZp7HZXmM2QweSqGkmyV23Tpk2IjY3FxIkT7RaLAECn02HixImittTUVKxatQpHjx5FUlISiouLUadOHfTr1w9jx46FSqWy9t28eTPeeecdfPXVVzh9+jQ2b96M5ORkzJo1C48++igOHTqEqKgonD17FmlpadBoNGjSpAmeeOIJtG7d2iaeX375Bd999x3i4uLg6emJgQMHonnz5nj++efx1ltv4dFHH7X2LSkpwapVq7B9+3bEx8dDq9WiZcuWeOaZZ9CwYcNKv06jRo1CTk4ONm/eDKVSvOTU7t278eqrr+Ltt99G//79cezYMTz77LN46623kJ+fjx9++AHJyckICAjAiBEj8Nhjj9kc/+rVq/j2229x5MgRZGdnw9fXFz179sSkSZPg7Oxc6TipGnzxMwCgRGG7BXqxUgsLlFDCgng3T4RfTWXBiG6YugTv/roPGosFa374L6bsD4UANdomXcCRoFDkqtzRlNMYqZxNx/LQrPgKztY1YN4j3SD8kx+1srIw7K99QHoO4O0mcZQkKxYLGmSm4Ey9+uJmpRJGjQbCW2sBFoyonP0hEWjoXIyP+nWF5Z972T1Ng7Fkme36aUQAcCXqNAwN2tm0G4oLUTdqL/BWFwmiItmIu263WAQA/436H17+bQ/2BLfCZz274YqfFyAI1nscqjrJCka//vorAGDQoEFVet6lS5ewZ88edOvWDYGBgTCZTDh48CC+/PJLJCQk4PXXX7d5zmeffQaTyYTBgwdDr9cjODgYQGlBKTs7G3379oW/vz+uX7+OqKgoTJ48GQsWLEDLli2tx9i5cydef/11BAYG4umnn4ZKpcKWLVuwf/9+m/OZTCa8+OKLOHXqFPr27YsRI0YgLy8PGzduxJNPPolvv/0WjRs3rtT1Dho0CHPmzMHhw4fRsaN4rndUVBRcXV3Rs2dPUfvatWuRnp6OIUOGwMXFBTt27MCnn36KnJwcTJo0ydrv3LlzePbZZ2EwGDBkyBD4+fnh4sWL+P7773Hy5EksWrQIajUrsXfFpURgT+k6M2G5cTjl0QzCTdsW1ylIRKynD4Ky0+GXnwukZUsVKcnNhfjSX5qu7riud8OwcVOxv35jqMxmTDj2G7K1Ory1eSvQ+x1g19tSR0syEnHmPNZ06IEjLcNEN1JJHh6I9q8NNHwJSF0mXYAkO8a6T+Ps8Ddt1tHTmIwIykqH+uB1CaMjucrXGrCrWSC+2vgdHoo+g/O+tTHr4cewq3kYhhSXADrbL8ro/tbkcjS+adEd+uIi667BADBr93o0OXdBwshIFkKedfiQuzkHzTJycDIoAIbCQmiNJpRo+Hn235Ds1bt8+TL0ej3qlNtS02w2Izc3V9Tm5ORknULWqlUrREVFiaapjR49Gm+88QaioqLwzDPPwMdHvPZCUVER1qxZYzMNbdasWTajaIYOHYoRI0Zg6dKl1oKRyWTCvHnz4OnpieXLl8PNrfQb12HDhmHUqFE217Z27VocP34cX3zxhajIM2zYMIwcORLz58/HokWLKvU69e3bF59//jmioqJEx0pOTsbhw4cxZMgQm+u6evUqfvzxR/j7+wMARowYgSeffBKLFy/GwIEDre3vvvsufHx8sGLFCuj1euvz27Vrh+nTp2Pbtm2iUVN0B3200fq/BmMedOZiFKlv5OaOiBbo2+9ZZL05Aa7FRcDWP4GR/HaFAMxaAwDI1eowZeBE7K9fWow2q1RY3P4h9D9zDNk6A4Tdf5UOx71pFCbd3wZeOoE/60Xgt9AmNo+5FRcBaTnA3jPAg5zQSADMZlgSMnHF19dmtKJRrcEVbz9EpCaB3+FSeU0Sk/HowZ/Q7UrpTnrhacl4IO4iZj48GriaCoTXucUR6H6jFyxI9vAWFYuAf6Y3FuUDBUWAi/TLi5A8fdvuIUwa/ozUYdwzlLfucmfk5eXB1dV2O82YmBj07NlT9OfHH3+0Pu7k5GQtFhmNRmRnZyMrKwsdO3aExWLB2bO227oOGzbM7ppFNxeLCgoKkJWVBZVKhaZNm+Lvv2/sKnT+/Hmkpqaif//+1mIRALi4uGDIkCE2x922bRtCQkLQqFEjZGVlWf+YTCa0b98eJ0+eRFFRUaVeJ4PBgF69emHv3r3Iysqytm/evBkWiwUDB9ouZPvwww9bi0IAoNFoMHr0aJjNZuuIqOjoaFy6dAkPP/wwjEajKM4WLVrA2dkZhw4dqlSMd0NGRgaKi2/sFpaXlycqLJaUlCA9PV30nPI70JX/OTk5GcJN2y5Keo6SEmtbvEttUbEIAJokpGHU8YMwK5Sl/2idb3wbJ6vruIPnqKp79XUofw5zQeljgblZ2N7wxqjIMifqhOKaawAEABDkex3Mmbt/jhKVCpOO/Io212ynhfS4dKb0fwpLZH8d1XmOqrpXXwf758iAGoBZaVsS8izIQ93MtBpyHdLmjFRxSnkO35JUa7GojHdBHiYd2o2UuMQacx3VeY6quldfB0fnsAA4UTsE5W1r2KL0VsZkrhHXUZ3nqKp79XXIy8uDgIp91alPhY/L5TrkljOOKISbz3IXPfTQQzCZTNi7d6+ovbCwEKdOnQJQOv1s/vz5ePnllzF27FgApaN9li1bhq1bt+LatWsoH37Zej7AjTWM5s+fj86dO9vEEB8fj6+++gqHDh2yGdWkUChw9OhRAMCOHTvw+uuv44033rAp0Pz222/4z3/+I1rDqFOnTrf8y9qyZQsCAgJEbWVrED399NN45pkbVdGTJ0/iySefxNSpUzF69GgIgoCBAwfC1dUVa9assXn+tGnTbEY+Xbx4EaNHj8bYsWPx8ssvY9euXZg5c2aFMbZt2xbffPNNhX2ompyMAVpMAwDEuQRid61uNl281BeRYXDBqBMH4PLTK8Ajre5ykCRLhy8AHWYiS2OA1+zvIJT7MOeVV4gtX85Hx4h84I+PJAqS5OiUz2Rc9g/Ed+16wK2kGOubtYdPfg7e+GU9gtKvo3/ieSD3f1KHSXLi+Tjcpn6DXGcXa5PSYsYXm5Zg8sFdsABQChuki49k6cPOi/DKge0231Lnq5ygN67m+npk45JhIh578lX8GRgqap90cCe+3PAdNHyfub8pbAds3KzZ1E9xplZdcaMgQJhuf0FsqphkU9JCQ0Px559/IiEhQTQtzdnZGe3btwcA0QLWZebNm4e1a9eiV69eeOKJJ+Dp6Qm1Wo3z58/jiy++sCkgAbA7uqigoABPP/00CgsLMWrUKISFhUGv10OhUGDZsmXWYtHtCgsLw5QpUxw+7unp6fCx8po3b47Q0FBERUVh9OjROHLkCBITEzFjxozbjq/sdRozZozN2khlbh5NRXdY83pAv9bAz8cBdRHSXJ3hk1doffhEsD88NEa89PtWxHj5oYmnvoKD0X2lfQNgYFtsPesJfbERec7itSB0ZgEbIxuj4/YBEgVIcpXRsSkWe7bAyL8Oo9/F4/hm/SIkuHthXud+8MzJAf6aK3WIJDcpyxD0n2s4FxRkXffKolRhSdseGHriCAwDm8DlFoeg+8/ROqG4rndDQH6Ote2Stz/URiPqsVhEdnjV9UVQVrpNwehMQF3E9GiNCIniIplI+Bao87RNswDADDUejD5nt2BEt0eyglGPHj3w559/YtOmTXj++ecr/bytW7eiVatW+PDDD0Xt165dq9L5jxw5gtTUVLz55psYMED8Qar8qJratWsDAOLi4myOY68tKCgImZmZaNu2rc3OZrdr8ODB+PTTT3HmzBlERUVBp9PhkUcesds3JibGpu3KlSsAYC3O1a1b+o9IqVRaC3QksRmDgJ+PI6ppG8zr3h4PnY5FnYxcXKjtje0twvDezkvwzs+FU0kJEOIndbQkJ5tm4mr7tQAE0WK0TiUm5GtUiA3wAbT8VoXEmnfwx6fz1qBh+o0pIa6pSaiTbME3D/TGY2HciZHK0Wpwxc/fZreZ40GhmDj8OWx9xnapAaJovyBMHvw0fAty0OPyGZz3rYMvOj+Mx//cj8+kDo5kKXbxdMR9n2Xb7uWH8x1ZMLrv1fYGVr0MjBG/gygApBhc8VXn3tLEdY+SbA2jQYMGISQkBCtXrsSePXsq/TylUmkziqiwsFA0NasyykYvlT/WoUOHcObMGVFbo0aN4OPjgy1btiAn58a3IwUFBdiwwXZIZL9+/ZCeno7Vq1fbPXf5+YmV0bdvX+h0OqxcuRK//fYbevToAYPBYLfv9u3bkZKSYv3ZaDRizZo1UKlU1ql5DRo0QGhoKNavX4/4+HibY5hMJmRncyeuu6pLY6BVfZigwHU3V6zpEok5Azvhp7YN4VGUj3HH90IBwNVYDLg53/JwdH85E+iDPGcdFAKgEASozBaYlArkOGvxRJFtEZkIOh0i0sXz4dWCBfXzL+OKl7+DJxHZ/5a2SK0AWobafYzub9pCE84F1MGijr3w2JgpeLvPCKTr3XAyIETq0EimItt644JPgE17iVKJkjr80pQAPP4gIGwAiteKmn+v19D+NNdqGsRxP5JshJGTkxPmz5+PKVOmYPr06WjdujU6dOgAb29v5OfnIzY2Frt27YJKpRIt4PzQQw9hw4YNmDlzJtq1a4f09HRs3rwZ7u7uVTp/ixYt4O3tjfnz5yMpKcm6pfzWrVsRFhaG6Ohoa1+1Wo3/+7//w6xZszB+/HgMHDgQKpXKet6EhATRrm2jRo3C4cOH8dlnn+Ho0aNo27Yt9Ho9kpOTcfToUWi1WixcuLBK8bq5uaFHjx7Ytm0bANhd7LpM3bp1MWHCBAwdOhQuLi7Yvn07zp49i6eeesq6bpJCocC7776L5557DqNGjcKAAQNQv359FBUVIT4+Hr/++iteeOEF7pJ2NykUwG/v4cjn8RAUCrgX5qFYpUGRVgezAvg9pCEeOf8XFGYFXLTcHpLEsj0NpfOz/1nDyKwq/a93bhbauuZLGRrJlAEWGFUq6MwmUfvpwDpI0dv/QoKo28Uz2N6stfiGXBDQIfYS4NxWusBItjyKitHp4iWc9w8UtQfkZEoUEcmdRqWEoLRdmiRHp4dZw4mvdJNyI+gfiLskGm1/A6ek3S5JP3UGBgZi5cqV+Omnn/DLL79g1apVyMvLg7OzM4KCgjBw4EAMHDgQISEh1udMnToVer0eu3btwt69e+Hv74/BgwejcePGmDx5cqXPbTAY8OWXX+Lzzz/H2rVrYTab0bBhQ3z22WeIiooSFYyA0p3H1Go1vvvuOyxcuBBeXl4YOHAgwsPDMX36dOh0OmtftVqN+fPnY926ddi6dau1OOTr64smTZpYF+WuqiFDhmDbtm0ICgpC69atHfYbOXIk8vPzsXbtWiQnJyMgIMDuQtgNGjTA6tWrsXTpUuzbtw/r16+HXq9HrVq18Oijj6JtW9743XUGZ/i0CALOAtnON4b2p7u6Y+SYKVi95jMMO3kIULNgRGIvjQnC9sNmmFTi3Bhy5ij2tWmGwRLFRfIltAnFtojmGHTuuLXtqoc3vmv/EDSCuYJn0v2swfXr2K5Q3Lgh/+e/CoXthzsiAIgN8MLKqM+RbPDEusgOUFvMePLIr6iXlgKgm9ThkUwZiopQpBWvy6gvMUKtskgUEdUEgdnpcCsqQI5z+fVeuV7a7ZJsl7R7xapVqzB//nwsXboUzZo1u6PnOnPmDCZMmIDnn38eEydOtHm8bJe0m3dso5pnwYJLeC6vnt3HXtr3Mz7dsgIay/q7HBXJXVG+EQH/zUe2i3gNkQFnjuKt4Ay0eqefRJGRXGVklWDk9NNwLSnGgLPHcM3DG1937IMMvSs8sjNx/QOuYUS2Bo36A42yEzBl38+I9glAi8RYfNHpYVxx8cPCn/tKHR7JULPpV/DEoV8x5fetyHZyhtpc+oF/yqPjsGi9/fU4iVxn5yG/3MZFarMZe3zOovOTLSWKimTJdTSQXwQAMANQf7LW7rQ04T/8wv128FWrJKPRCKVSKdq5raCgAD/++CPc3d3RsGHDOx7DDz/8ALVazWLQPS5b+09FvNxwSoXFgqOB9ZHj5gZviWIj+RKUCvjnZtsUjM7UCkJ4E34vQLa8NBZkOuuxu0FzbGrWztrun5MJn4I8CSMjOWuVdAEz92+FxmKG3z+7Xk3/7Sd80bGPxJGRXNXKzcNF7wC80nc0Bp85ikQ3T3z+wMPoc+GE1KGRjBVobDfrMCsUEFrXlyAakrV5E4FJpZtW5Tk521/DiG4bC0aVlJCQgJdeegm9e/dG7dq1kZaWhp9//hkJCQl49dVXobHzplYdCgsLsW/fPly5cgXbtm3D4MGD4ePjc0fORfKQ5OUOZACRSXE4VTvE2v6ffZsx8sQfMNR2ky44ki2tTgWLnQX90l0MKGhUD1yRhmzonaAw2U49e/2XDYhx9wYQfvdjItm7EBAIjUWcN0oAOTpuxkD2Zfi6wSgEYsr+LVjQsRc8CvPx4fb/YVMDx8srEAl27mkEhQJ+gVzDiMp5uhew729g9T6kuDpY15iTqm4bC0aV5OHhgaZNm2Lbtm3IzMyESqVCWFgYXnjhBfTq1euOnTczMxOvv/46XFxc8NBDD+Gll166Y+cieXB2Lv0FOfb4Pjyy5nMcrhuOlgkxaJkYC7NCgfy2XaG9xTHo/qNSKux+G6cvLoKPhuvRkH2JHp6ol5aCWG+/0uUgFQpsa9gS3S6ekjo0kimjoIZJoYRauLGOiAWAR16JdEGRrPm7KrDVvSmOBYVCKVjgXlSAZa0fRGhq0q2fTPctrbEEJVqduFGhgLuZ9zRkx8r/AxY+hwFzc27ZlaqGBaNK8vDwwAcffHDXz1u7dm0cO3asUn3btGlT6b4kX5G5OYDFExd8auE/+7agSUq89bGz/oFo0oPbFpN9ajs3UeFpSVD5MWfIPs/CAvxdJ0TUtq1RK/hlc/cisu9YYCjO+dVGs5t+N2U663EgsAFelDAukq9WxRnY6ly7dKoIYF2MVuC0EarAI+dPIiqynajNtTAfAbAA0Nl/Et3fXHTQBbgDWXYe4/vNbbMd60dEkgr2VQEKBX5u1BKL23aH5Z83uFhPX0zvNwam0NoSR0hy5VZcCM1NW6TrTEZcc/cCvDghjeyLTI6z2763foO7HAnVFEqVBU1vKhYBgHdhPvSKfIkiIrmL8fWz+2Et0d1LgmiopsjSuaLN1WjrVCKdsQRjjh0A/DykDYxkTaliYai6cYQRkcyY6noAR4FUgweeGvEc3us5FH55OTgeWB8NryegsKE3p6SRXUkGTxhVN97Wi9UaxHn7SxgRyV1QRrrd9iIV16Mh+1olxNjdnLhFUuzdDoVqiMjsVKxR+UJQiL+nrpWdCcBPmqBI9g7WC4eTucRabAzMTsf3LR7ANxwpQhXgaJjqx9eUSGYUmtJfhKZ/PvjHefnhaN0wWJRKxHj4wq0eR4uQfcV21jBS3TTiiKi8ffWbQGsy2rQbjIUSREM1wa9hkTCWW4xWALC8xYPSBESyZ3BVwMlo+z7TMeacBNFQTVGiVlunLwLAZZ9ayHNykjAiqgkst+5CVcSCEZHMVPSPslCng4LfrJADBVrbGylBoZIgEqopauWkQVe+YCQIMFiKpAmIZE9vNmPCyOdRpNYgT6uDSaHEB90HIdq3ltShkUwl1wlEYfnFiwFkuXLXV6qAndtdk5r3NFSxuvxevdpxShqRzHg6KQFwBwiqOiejCQU68YRF94IiAPxGjuzrFHcFCd5+OFI3vLRBEDDg779wrmFDaQMj2WqcEoejgfXR+D9zEePtj9C0JHgV5OGBuPMAOkgdHsmQh7sGtXIykORWfs0ibnNNFbD3/Si/M6Vb6BMM/BQrdRT3FhaMiGSmsY8COgVQbO8+ir8oqQI9T0Tj5zYNYVaVjlPTGk0Y8cffADpJGxjJVvPkOEz7YjMOBEfgQEgDDD11GLVzszBs8TKpQyOZ8svOweGgcGS6uAIonSaSmZ+LR04dAwtGZI9aq0aD64koUamRrneD1mREt8t/I9onQOrQSNZYMaKq07C6Ue34khLJUIfawN4Ee4/wFyU51vtMNJpfS8UfDYKgNlvQ5VwcvHMLpA6LZOzPOiHoefkUOsVdRKe4iwCAGM8AdG/I2wOyL8k/wFosKpOhN+BC7boSRURyF56bCgFArex0+OblQBCAEqUa3S//DaCJ1OGRXAmC7e56AkelUcWU/KhU7XhHSCRDdd0A2C0YETlW4KxEcFo2gtOyrW0l3F6UKrC1SSsMPHsUDdKSAAAmhRLT+41GN94dkANJgQFQWCwQblr4WmGx4GztQAmjIjmL17vhmrsKV3wDYCgsQL5WhwsBgfAszpM6NJIxrcmMknLDRQyFxQBsN/ggKuPEFZqrHV9SIhnikn50O75v38Km7UADfutPjkVkZaLVlI/xxPBnMfORUWg27VNsbNYOJi6jRg64oQRtr0WL2h6KPoNUV3eJIiK5U5jMuOJbOv0s19kFFlXpXc6JgBAJoyK5axyfatMWGZciQSRUkzg7+BDFr09vH79DJJIhJ355QrfhVF1/5HgWwzdNgFGpQYlzCTa1DZc6LJKx6MBAFGidsLRdD1G7kl8nkQOR5hwsCI5A08Q4eBfmoUCtxS9hTdEs3/bDHREAeJhK7E4vqpt1HQBHppF97S7FI8PVGVd9PQAAzWOTEZyaJWlMJH9pJVJHcO9hwYhIhlwtJtj756nkjiJUgaeO/oopx34UtSXsVwIYJU1AJHu+Rdm2jYIAb2dWjMg+vxaBUJyx4Jx/EPyz85BmcIGgVMK7DrdIJ/s83HS2a9EAiA4IkiAaqil+axKCV6IO4LqbHlqTGc5GI758uK3UYZHMeWjtt/MT1O1jwYhIhhrHpwJCgM0Nlppvd1SBgX8fs207ewwsGJEjtUtyYSgqQK6Ti7Wt7bVoPBTcUMKoSM4CzYV4+pe/0DAhC25FJSjQqnEwog4Cu/oBMEgdHsmQzsEcEaWWH0PIsVg/T7w1ohvaRifCqFbiUHggDKYiqcMimeOM+urHd2oiGdL6u0CRIUAoVzByc1A1JyplW1DM0zpJEAfVFHsiWmDhsgXYH9oYZwOC0PfccWxo2h4Bes72J/siEpKgvHzd+rNLiQkPnYmDc2gJAI4YIVtutQyAYLH5EszHj7+fyDGTWok0Nz22tboxtd5i4SqfVDEdB0hXO76kRDLUqJ0XfOxshz6kIf/JkmP1stNQqL6xAFaxSg3vQu5CQ441LcjH6HFTcaRuOFyLi/BO7xE4E1BP6rBIxlRa+7+HXFWWuxwJ1RThnkq7K84+0ZrfglEF7ExjhIYFI6pYvtF+Oz9B3T6+dkQy1MpfgdaRrlBabtyAe+gEzO7MX5TkmMujreBsuvGbUmc2QT+otYQRkdyNDyiGc7ERx4NC8XPj1ijQOqHvtXipwyIZa9vIGUal7Qe5B5pxtwayz1mjwJRW4pwJdBXwZFN+DCHHfF1s32ea+nL0K1Us3NN+e6Dr3Y3jXsJ3aiKZ2jZcjTX9FWilvoIBuuOIfdL+L0+iMkFzRiKnQ2Prz9ldmqHeJ8MljIjkruuIOph9+Dg6n4tDs7gUTNh7Ah/0c5Y6LJIxXcf6iNQniCbABmpS4PNYK8liIvmb20ON1Y8I6KS5iIG6Yzj2eGkhiciR+T0UuHmqvRICvujBj65UsdYBgAK2I15X9ZMgmHsE1zAikrEh4UD2778BAFw0vBmnW/DQw3nfW1j74RcQFAoMffUFQMNv/ckxnZsWzyxrBcNr64AsJR6d/TACmvtIHRbJ3APbHkeTKf9D3P5rEPzNaLzmJUDDW0qq2PAIIO/A7wAALyeOfqWKPdZQBQ+NBW/8dBlahRn/HRaOdrVZMKJbe8VlC+YVPIxiaKEAMLMd0CWIv6NuF185IqJ7THYAx91S5WkNGgitSnee8W7sLnE0VCMEeMJlxdM4tHQpAKBxfX+JAyKie9FDdYGr+r0AgNb+4bfoTVSqnjoNn7utQv+RE+HrqoZGxdGM/wbLtERERERERER0z/B1AYtF1YAFIyIiIiIiIiIiEmHBiIiIiIiIiIiIRFgwIiIiIiIiIiIiERaMiIiIiIiIiIhIhAUjIiIiIiIiIiISYcGIiIiIiIiIiIhEWDAiIiIiIiIiIiIRFoyIiIiIiIiIiEiEBSMiIiIiIiIiIhJhwYiIiIiIiIiIiERYMCIiIiIiIiIiIhEWjIiIiIiIiIiISIQFIyIiIiIiIiIiEmHBiIiIiIiIiIiIRFgwIiIiIiIiIiIiERaMiIiIiIiIiIhIhAUjIiIiIiIiIiISYcGIiIiIiIiIiIhEWDAiIiIiIiIiIiIRFoyIiIiIiIiIiEiEBSMiIiIiIiIiIhJhwYiIiIiIiIiIiERYMCIiIiIiIiIiIhEWjIiIiIiIiIiISIQFIyIiIiIiIiIiEmHBiIiIiIiIiIiIRFgwIiIiIiIiIiIiERaMiIiIiIiIiIhIhAUjIiIiIiIiIiISYcGIiIiIiIiIiIhEWDAiIiIiIiIiIiIRFoyIiIiIiIiIiEiEBSMiIiIiIiIiIhJhwYiIiIiIiIiIiERYMCIiIiIiIiIiIhEWjIiIiIiIiIiISIQFIyIiIiIiIiIiEmHBiIiIiIiIiIiIRFgwIiIiIiIiIiIiERaMiIiIiIiIiIhIhAUjIiIiIiIiIiISYcGIiIiIiIiIiIhE1FIHQHQrgiAgNzdX6jAkYTQaUVhYCADIycmBRqOROCLpGAwGKBSKSvVlzjBnAOZMZTFnbmDOVB7z5gbmTeUwZ25gzlQOc+YG5kzlMGfEqpI3jigEQRCqKR6iOyInJwfu7u5Sh0ESy87OhpubW6X6MmcIYM5Q1TFn6HYwb6iqmDNUVcwZuh1VyRtHWDAi2avOKnleXh769euHn3/+Ga6urtVyzDutpsV8p+KV8psV/h3cWcwZ6dW0eIE7EzNzpmpqWsz32ntNTXv9gZoX872WMwD/Du405oz0alq8gDzyxhFOSSPZUygU/7oyWkapVEKlUsHNza3GvIHUtJjlEG915gwgj2uqCsZbdcyZmhUvIH3M93vOADUvZjnEy3uamhWzHOK9399rGG/VMWdqVryAvGPmotdERERERERERCTCghEREREREREREYmwYET3Fa1Wi6effhparVbqUCqtpsVc0+KtjJp2TYxXejXtmmpavEDNjLkiNfF6alrMNS3eW6mJ11PTYq5p8VZGTbsmxiu9mnZNNS1eQN4xc9FrIiIiIiIiIiIS4QgjIiIiIiIiIiISYcGIiIiIiIiIiIhE1FIHQFSRQ4cOYfPmzThz5gwSEhIwfPhwvPLKKzb9jEYjvv76a2zduhX5+fmIjIzEjBkzEBISIuoXGxuLTz75BKdOnYJer0ffvn0xefJkaDQaUb9NmzZhxYoVSE5ORnBwMCZPnowuXbqI+uTl5WHu3Ln47bffYDKZ0KFDB8yYMQM+Pj6ifidPnsT8+fNx8eJFeHp6YtiwYRg/fjwUCkWF117ZWCvr2rVrWLlyJc6cOYPLly8jODgYP/zwg02/u33tgiBg+fLl+PHHH5GVlYWIiAhMnToVzZo1Ex0rNTUVn3zyCQ4fPgy1Wo3u3btjypQpNltPMmeYM2WYM8yZMtWdMwDzhnlzA99rmDNlmDPMmapeO3OGOVPVa78T9zQV4QgjkrWDBw/i0qVLaNWqFQwGg8N+c+bMwcaNGzF58mTMmTMHRqMRkydPRl5enrVPTk4Onn32WZhMJsyZMweTJ0/Gxo0bMXfuXNGxduzYgffffx+9evXC559/jmbNmuE///kPTp8+Leo3c+ZMHD58GDNnzsR7772HuLg4vPTSSzCZTNY+165dw4svvggfHx/MmzcPo0aNwsKFC7Fq1aoKr7uysVbF5cuXceDAAQQGBqJevXp2+0hx7cuXL8fChQsxevRozJs3Dz4+PnjhhRcQHx9v7WMymfDCCy/g6tWrmD17Nl599VUcOnQIs2bNsrkG5gxzBmDOMGfubM4AzBvmTSm+1zBnbsacYc5U9dqZM8yZql77nbinqZBAJGNms9n6//379xc++ugjmz7JyclCu3bthPXr11vbsrKyhM6dOwvLli2zti1ZskTo3LmzkJWVZW1bv3690K5dO+H69evWtsGDBwuvvfaa6BwTJ04UXnzxRevPJ0+eFFq3bi0cPHjQ2hYTEyO0adNG2Llzp7Vt9uzZQv/+/YWSkhJr25dffil069ZNKC4udnjdlY21Km5+Ld966y1h+PDhNn3u9rUXFRUJXbt2Fb788ktrn5KSEqF///7Chx9+aG3btm2b0KZNGyEmJsbadvDgQaF169bC6dOnHV4nc4Y5w5xhztyJnCl/rcwb5g3fa5gzzBnmDHPm7rxu9jBn/t09TUU4wohkTam8dYoeOnQIFosFPXv2tLa5u7ujQ4cOOHDggLXtjz/+QLt27eDu7m5t69WrFywWCw4dOgQAiI+Px9WrV9GrVy/ROXr37o2jR4+ipKTEeiyDwYD27dtb+4SEhCAiIsLmnN26dRMNhezduzdyc3Nx6tQph9dUmVir6lavpRTXfurUKeTn54v+7jQaDbp3725zrPDwcNEw2fbt28Pd3V3UrzLXCTBnKos5cwNzpnLul5ypzLUCzJvKul/yhjnDnCk7FnOGOQMwZ5gz8siZW2HBiGq82NhYeHl5wc3NTdQeEhKCuLg4Ub/y83INBgN8fHwQGxtr7VP23PLHMhqNSExMtPYLDg62mUtbr1496zEKCwuRkpKC4OBgm2MpFAprP0fXdKtYq5sU1+7onPXq1UNycjKKiopE57yZQqFAcHDwbb0ezJnqwZxhzlTV/ZQzZcdk3vx791PeMGeqB3OGOVNVzBnmTFXdLznDghHVeLm5uXYX73Jzc0N2drb155ycHLtzeQ0GA3JycqzHAmBzvLI31LLjVeVY5ftpNBo4OTlZ+9lTmeNXNymuPScnB1qtFjqdzuZYgiBYj5Obm2v3nG5ubrf1ejBnqgdzhjlTVfdTzpQdk3nz791PecOcqR7MGeZMVTFnmDNVdb/kDHdJo7sqLy8PaWlpt+xXp06d217Rnu4teXl5MBqNyM3NtamIFxQUwGQyITY2ljlDVswZuh0Wi+WWOQPw9xPdwPcaqirmDFUVc4akxoIR3VW7d+/G7Nmzb9lv3bp1NkPtHDEYDKIV/cvk5OSI5rG6ubnZ7Zebm2utBJdVYvPy8kTbHJZVYsuO5+bmhpSUlEof62ZGoxFFRUU2w0BvVplYq5sU1+7m5oaSkhIUFxeLKuW5ublQKBQwGAzYvn07kpOTkZycjF27dtmNfdiwYcwZ5gxz5ibMmarlDFBaMNq1a1eFOQPw9xPzhu81N2POMGcqc6ybMWeYM5U51s2YM1W/p6no79jf379K18kpaXRXDRo0CMeOHbvln8q+4QGlczgzMjJshteVn7sZEhJiU5kvG/FUdr6y/5bvFxsbC41Ggzp16lj7xcXFQRAEm35lx3B2doa/v7/NscqeV9E1VibW6ibFtZf99+b50mXHCggIgJOTEwYNGoS+ffsiIiJClCNHjx6Fu7s7nn76aeZMJWOtbswZ5kxVyTVnAKBly5a3zBnmDfOG7zXiYzFnmDPl+zFnmDNlPzNnpLunsfd6CIKAuLi4Kr8eLBhRjdehQwcolUr8+uuv1racnBwcPnwYnTp1srY98MADOHLkiHVuJ1A64kmpVKJDhw4AgMDAQNStWxe//PKL6By7du1C27ZtrUM9H3jgAeTk5ODIkSPWPnFxcbhw4YLNOfft2weTyWRt27lzJwwGA5o3b+7wmioTa3WT4tojIyOh1+uxe/duax+TyYQ9e/bYHOvSpUu4evWqte3IkSPIzs4W9ass5kz1YM4wZ6rqfsoZgHlTXe6nvGHOVA/mDHOmqpgzzJmqul9yhlPSSNaSkpLw999/AwCKioqQkJBg/QdStp2gv78/Bg4ciM8++wxKpRJ+fn5YsmQJXF1dMXToUOuxhg4dirVr12LatGl44okncP36dXz22WcYMmQIfH19rf0mTZqEN954A4GBgWjdujV27dqFM2fO4Ntvv7X2iYyMRMeOHfHuu+9iypQp0Gq1+PrrrxEeHo7u3btb+40bNw7bt2/Ha6+9huHDhyM6OhorV67E5MmTK5xnXNlYq6KoqAi///679XXNz8+3vpatW7eGp6fnXb92nU6HiRMnYtGiRfD09ERYWBh+/PFHZGdnY8yYMdZj9ezZE0uXLsWMGTPw/PPPo6ioCPPnz0fnzp3RtGlT0XUyZ5gzAHOGOXNnc6bs+pg3zBu+1zBnmDPMGeYMc6Ym58ytKITyY6OIZGTz5s1455137D527Ngx6/+XlJTg66+/xtatW5Gfn4/mzZtjxowZNkPuYmJiMGfOHJw8eRJ6vR79+vWz+wa0adMmLF++HMnJyQgODsbzzz+PLl26iPrk5eVh7ty52LNnD8xmM9q3b48ZM2bYvCmdPHkS8+bNw8WLF+Hp6Ynhw4dj/PjxNlsrllfZWCsrMTERAwYMsPvYggUL0KZNG0muXRAELFu2DOvWrUNmZiYiIiIwdepUREZGio51/fp1zJkzB4cPH4ZKpUL37t0xdepUm50JmDPMmTLMGeZMmerOGYB5w7y5ge81zJkyzBnmTFWvnTnDnKnqtd+Je5qKsGBEREREREREREQiXMOIiIiIiIiIiIhEWDAiIiIiIiIiIiIRFoyIiIiIiIiIiEiEBSMiIiIiIiIiIhJhwYiIiIiIiIiIiERYMCIiIiIiIiIiIhEWjIiIiIiIiIiISIQFIyIiIiIiIiIiEmHBiIiIiEgCy5Ytg0KhwG+//SZ1KLLy22+/QaFQYNmyZVKHUu3u5WsjIqJ7DwtGREREJHtXrlzBpEmT0LBhQ7i4uMDT0xONGjXC+PHjsWfPHlHfkJAQNG3a1OGxJkyYAIVCgbS0NLuPnzt3DgqFAgqFAvv373d4nLI+ZX+cnJwQHh6OqVOnIiMj4/YutIrefvttbNq06a6cqzqdOHECb7/9NmJjY6UOhYiIiBxQSx0AERERUUWOHTuGBx98EBqNBuPGjUOTJk1QWFiIS5cuYefOnTAYDOjevXu1nW/x4sUwGAxwdnbGkiVL0KVLF4d9W7RogWnTpgEAMjIysHXrVsybNw+7du3C8ePHodVqHT537NixeOyxxyrscyvvvPMOxo8fj0GDBt32MaRw4sQJvPPOO+jWrRtCQkJEj3Xt2hWFhYXQaDTSBEdEREQAWDAiIiIimXvnnXdQUFCAEydOoHnz5jaPJycnV9u5jEYjVq5cieHDh8Pd3R2LFi3C559/DoPBYLd/nTp1MGbMGOvPL730Eh599FFs2bIFUVFRGD58uMNzqVQqqFSqaou9uuXm5jq87jtJqVTCycnprp+XiIiIxDgljYiIiGTt0qVL8Pb2tlssAoCAgIBqO9fmzZtx/fp1jB8/HhMmTEB+fj7Wrl1bpWP06dMHABAdHV1hP3trGJW1/frrr/j0008RGhoKnU6HiIgILF++3NovNjYWCoUCALB8+XLR1Lib7d69G71794aHhwecnJwQGRmJBQsW2MQSEhKCbt264a+//kKfPn3g7u6OyMhIAKWFo1mzZqF9+/bw8fGBTqdDWFgYXn31VRQUFNgcSxAEfPvtt2jfvj1cXV3h6uqKZs2a4c033wRQOo1u4sSJAIDu3btb454wYQIAx+v85OfnY+bMmdbXJCAgAOPGjUNcXJyo383PX7p0KZo0aQKdTofg4GB88sknFf6dAEBWVhacnJwwZMgQu4/PnDkTCoUCJ06cAAAkJiZi2rRpaNGiBTw9PeHk5ITGjRvj448/htlsvuX5KlrLyt4ILKB01N3gwYOtfx8NGjTA+++/D5PJdMvzERERVRZHGBEREZGshYaG4sKFC9iwYYPDD/Hlmc1mh2sUFRcXO3ze4sWLUa9ePXTp0gUKhQItW7bEkiVL8NRTT1U63kuXLgEAfHx8Kv2c8l577TUUFhbimWeegU6nwzfffIMJEyYgLCwMnTp1gq+vL1auXImxY8eiS5cumDRpks0xFi1ahGeffRYdOnTA66+/Dr1ej127duG5557D5cuXMWfOHFH/q1evokePHhg+fDiGDh2KvLw8AEBCQgK+++47DB06FKNHj4ZarcbevXvxySef4K+//sKOHTtExxk7dixWr16N9u3b4/XXX4eHhwfOnz+PdevW4d1338WQIUOQlJSERYsW4bXXXkOjRo0AlP49O2I0GtGnTx8cOHAAw4YNw7Rp03Dp0iV888032LlzJ44dO4bAwEDRcxYsWICUlBQ8+eST8PDwwKpVq/DKK68gMDAQo0ePdnguDw8PDBgwAFFRUcjIyICXl5f1MYvFgtWrVyMyMhItWrQAAJw6dQobNmzA4MGDERoaCqPRiO3bt+PVV1/FlStXsHDhQofnuh0///wzhgwZgrCwMEybNg1eXl44ePAg3nzzTZw4cQI//vhjtZ6PiIjuYwIRERGRjP3xxx+CRqMRAAjh4eHCxIkTha+//lo4e/as3f7BwcECgFv+SU1NFT0vISFBUKlUwltvvWVtmz9/vgDA7rkACL179xZSU1OF1NRU4eLFi8LcuXMFjUYjuLu7CykpKRVe19KlSwUAwp49e2zaWrRoIRQXF1vb4+PjBa1WKzz22GM2MYwfP97m2ImJiYJOpxNGjRpl89hLL70kKJVK4fLlyzav2bfffmvTv7i4WCgpKbFpnzVrlgBAOHz4sLVt7dq1AgBhzJgxgtlsFvW/+Wd7115mz549AgBh6dKl1rZFixYJAITp06eL+m7ZssV6vvLPr1WrlpCVlWVtz8/PF3x8fIQOHTrYnLO8suN+9dVXovbdu3cLAIT//ve/1raCggLBYrHYHGPMmDGCUqkUEhMTK7y2il6LBx98UAgODrb+XFhYKPj7+wtdunQRjEajqO/cuXMdHoeIiOh2cEoaERERyVrHjh1x/PhxjB8/HtnZ2Vi6dCkmT56Mxo0bo2vXrrhy5YrNc0JCQrBr1y67f3r37m33PMuWLYPFYsG4ceOsbY8//jg0Gg2WLFli9zk7d+6Er68vfH19ERERgalTp6Jx48bYuXMn/Pz8bvuaJ0+eLFoMu06dOoiIiLCOXrqVdevWobi4GE8++STS0tJEfx599FFYLBbs3r1b9BwvLy/rVLGbabVa6wLUJpMJmZmZSEtLQ8+ePQEAhw8ftvZdvXo1AODTTz+FUim+zSz/c1Vs3LgRSqUSM2fOFLX369cPLVq0QFRUFCwWi+ixiRMnwt3d3fqzi4sLOnToUKnXsE+fPvD398eKFStE7StWrIBarcbjjz9ubXN2drZOBSwpKUFGRgbS0tLQp08fWCwWHDt2rMrX68iuXbuQkpKCiRMnIisrS/T32rdvXwClOUlERFQdOCWNiIiIZK9Zs2bWNW3i4uKwd+9efPfdd9i/fz8GDhxosyOZXq+3FjTKW7VqlU2bIAhYsmQJIiMjYbFYROsPderUCStXrsSHH34ItVp869S+fXvMnj0bAKzr5NStW/ffXi7q169v0+bt7W2zXo8j586dAwCHrwEApKSkiH4ODQ11uAj3119/jQULFuDvv/+2KcxkZmZa///SpUuoVasW/P39KxVnZcXExKB27drw9PS0eaxJkyY4ceIE0tLSREU6R69henr6Lc9XVhSaO3cuLl68iIiICOTn52PDhg3o3bu36PpMJhM++ugjrFixAtHR0RAEQXSsm1+ff6vs7/WJJ55w2Kf83ysREdHtYsGIiIiIapTg4GCMGzfOun7PgQMHcOTIEXTu3Pm2j7l3715cvnwZABAeHm63z5YtW2y2r/fx8amwKHO7HBVuyhcjHCnrt2LFCtSqVctun/IFFRcXF7v95s6di2nTpqF379546aWXULt2bWi1WiQkJGDChAk2BSS5+Lc70I0bNw5z587FihUrMHv2bGzYsAF5eXkYP368qN/UqVPxxRdfYOTIkXj99dfh5+cHjUaDP//8E6+88sotX5/yC5XfrPwi1mV/r3PmzLGuoVRe7dq1K3F1REREt8aCEREREdVICoUC7du3x4EDB5CQkPCvjrVkyRLodDqsWLHC7tSpZ555BosXL7YpGMlVWdGrOgpaK1euREhICLZt2yZ6bbZv327TNyIiAlFRUUhJSalwlFFFRRJ76tevj+3btyMrKwseHh6ix86ePQs3N7d/tci4Pc2bN0fz5s2xatUqvPfee1ixYoV1QeybrVy5El27dsX3338var/VLnllyhbVzsjIsHksJibGOh0QuPH3WtEIOiIiourCNYyIiIhI1nbt2mV3u/DCwkLrei2NGze+7eNnZ2dj3bp16N27N0aMGIFhw4bZ/BkwYAC2bduGpKSk2z7PneDq6mq30DBixAjodDq89dZbKCwstHk8Ozu7wt3ibqZSqaBQKESjm8qmYZVXtrbPjBkzbEbW3Px8V1dXAPaLJPYMGjQIFovF5pzbtm3DX3/9hQEDBvyrNZIcGT9+POLi4rBmzRr8+uuvGDlyJJycnER9VCqVzciv/Px8zJs3r1LniIiIAACbNaX+97//ITExUdTWp08f+Pn54aOPPrL72hUWFiI3N7dS5yUiIroVjjAiIiIiWZsyZQrS09MxYMAANGvWDC4uLrh27RrWrFmDixcvYty4cWjWrNltH/9///sfCgsLMXToUId9hg4dimXLlmH58uV49dVXb/tc1a1Dhw7YvXs3Pv74Y9StWxcKhQKPPfYYAgMD8c033+Cpp55Co0aNMHbsWAQHByM1NRWnT5/Gpk2bcPbsWYSEhNzyHMOGDcPMmTPxyCOPYMiQIcjJycGaNWtEI1/KDB8+HCNHjsSKFStw6dIlDBgwAJ6enrh48SJ27NiBM2fOAADatm0LpVKJ999/H5mZmdDr9ahXrx7at29vN4YJEyZg+fLl+PjjjxEbG4uuXbsiOjoaX3/9Nfz9/fHBBx/8q9fRkccffxwzZszA5MmTYbFYbKajAaWvz8KFCzFy5Ej07NkTKSkpWLJkCby9vSt1jgYNGqBnz55YuHAhBEFAixYtcOLECWzcuBFhYWEwGo3Wvnq9HitWrMCgQYPQoEEDPPHEEwgLC0NWVhbOnz+PDRs2YOPGjejWrVt1vQRERHQfY8GIiIiIZG3u3LmIiorC77//jvXr1yMrKwvu7u6IjIzEK6+8ggkTJvyr4y9evBhqtdpmqtHNevXqBYPBgKVLl8qqYPT111/j+eefx/vvv28dWfLYY48BKN0lLCIiAp9++ikWLlyIrKws+Pj4oEGDBnjvvfcQEBBQqXNMnz4dgiBg8eLFePnllxEQEICRI0di4sSJdkd2rVmzBl26dMHixYvx7rvvQqVSoV69ehg+fLi1T926dbFkyRJ8/PHHeO6552A0GjF+/HiHBSONRoMdO3Zg9uzZWLt2LTZs2AAPDw8MHz4cs2fPRlBQUFVfukrx8/PDww8/jC1btiA8PBwdO3a06TN37lwYDAb88MMPiIqKQlBQECZNmoS2bdtWetrYypUr8eKLL2L16tVYuXIlunTpgj179uC5555DbGysqG+fPn1w9OhRfPTRR1i1ahVSU1Ph6emJ0NBQTJ06FZGRkdVx6URERFAIlV09kYiIiIiIiIiI7gtcw4iIiIiIiIiIiERYMCIiIiIiIiIiIhEWjIiIiIiIiIiISIQFIyIiIiIiIiIiEmHBiIiIiIiIiIiIRFgwIiIiIiIiIiIiERaMiIiIiIiIiIhIhAUjIiIiIiIiIiISYcGIiIiIiIiIiIhEWDAiIiIiIiIiIiIRFoyIiIiIiIiIiEiEBSMiIiIiIiIiIhJhwYiIiIiIiIiIiET+H9cYo3yXSO9QAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Tuning LGBM with Optuna"
      ],
      "metadata": {
        "id": "AiPHX8TMQqp0"
      },
      "id": "AiPHX8TMQqp0"
    },
    {
      "cell_type": "code",
      "source": [
        "def objective(trial, data=X,target=y):\n",
        "\n",
        "    params = {\n",
        "                'metric': 'rmse',\n",
        "                'random_state': 22,\n",
        "                'n_estimators': 20000,\n",
        "                'boosting_type': trial.suggest_categorical(\"boosting_type\", [\"gbdt\", \"goss\"]),\n",
        "                'reg_alpha': trial.suggest_loguniform('reg_alpha', 1e-3, 10.0),\n",
        "                'reg_lambda': trial.suggest_loguniform('reg_lambda', 1e-3, 10.0),\n",
        "                'colsample_bytree': trial.suggest_categorical('colsample_bytree', [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]),\n",
        "                'subsample': trial.suggest_categorical('subsample', [0.6, 0.7, 0.85, 1.0]),\n",
        "                'learning_rate': trial.suggest_categorical('learning_rate', [0.005, 0.01, 0.02, 0.03, 0.05, 0.1]),\n",
        "                'max_depth': trial.suggest_int('max_depth', 2, 12, step=1),\n",
        "                'num_leaves' : trial.suggest_int('num_leaves', 13, 148, step=5),\n",
        "                'min_child_samples': trial.suggest_int('min_child_samples', 1, 96, step=5),\n",
        "            }\n",
        "    reg = lgbm.LGBMRegressor(**params)\n",
        "    reg.fit(X_train ,y_train,\n",
        "            eval_set=[(X_test, y_test)],\n",
        "            #categorical_feature=cat_indices,\n",
        "            callbacks=[log_evaluation(period=1000),\n",
        "                       early_stopping(stopping_rounds=50)\n",
        "                      ],\n",
        "           )\n",
        "\n",
        "    y_pred = reg.predict(X_test)\n",
        "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
        "\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "QenYWVysQth-"
      },
      "id": "QenYWVysQth-",
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params_search = True\n",
        "# # Optuna: run study trials\n",
        "\n",
        "if params_search:\n",
        "    study = optuna.create_study(direction='minimize')\n",
        "    study.optimize(objective, n_trials=120)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4oecsElWqGF",
        "outputId": "a42454fa-99db-4ee7-ae84-7772d6eab99f"
      },
      "id": "C4oecsElWqGF",
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:54:54,693] A new study created in memory with name: no-name-eb6d4604-8803-4ca1-83a7-055f87f5b2ee\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[569]\tvalid_0's rmse: 37469.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:54:54,985] Trial 0 finished with value: 37469.83254761123 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.0013527302206814348, 'reg_lambda': 0.08262940130729987, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 10, 'num_leaves': 18, 'min_child_samples': 81}. Best is trial 0 with value: 37469.83254761123.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 33735.1\n",
            "[2000]\tvalid_0's rmse: 32595.3\n",
            "[3000]\tvalid_0's rmse: 32015.8\n",
            "Early stopping, best iteration is:\n",
            "[3194]\tvalid_0's rmse: 31915.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:54:57,743] Trial 1 finished with value: 31915.245718517377 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 4.219924923960138, 'reg_lambda': 4.075462128014512, 'colsample_bytree': 0.7, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 11, 'num_leaves': 33, 'min_child_samples': 41}. Best is trial 1 with value: 31915.245718517377.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 35473.6\n",
            "Early stopping, best iteration is:\n",
            "[1000]\tvalid_0's rmse: 35473.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:54:58,803] Trial 2 finished with value: 35473.581411211926 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.01731772133203689, 'reg_lambda': 7.303543595421454, 'colsample_bytree': 0.5, 'subsample': 0.7, 'learning_rate': 0.005, 'max_depth': 12, 'num_leaves': 108, 'min_child_samples': 11}. Best is trial 1 with value: 31915.245718517377.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 31921.3\n",
            "Early stopping, best iteration is:\n",
            "[1079]\tvalid_0's rmse: 31782.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:54:59,772] Trial 3 finished with value: 31782.43742342515 and parameters: {'boosting_type': 'goss', 'reg_alpha': 9.937853452832918, 'reg_lambda': 0.005736792462315888, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.02, 'max_depth': 10, 'num_leaves': 93, 'min_child_samples': 11}. Best is trial 3 with value: 31782.43742342515.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[883]\tvalid_0's rmse: 32983.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:00,484] Trial 4 finished with value: 32983.05815024558 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 3.17733582908792, 'reg_lambda': 0.1577019409740968, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 148, 'min_child_samples': 41}. Best is trial 3 with value: 31782.43742342515.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:00,905] Trial 5 finished with value: 31680.314798159856 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.003100758182483704, 'reg_lambda': 0.029277980365076194, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 11, 'num_leaves': 123, 'min_child_samples': 16}. Best is trial 5 with value: 31680.314798159856.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[341]\tvalid_0's rmse: 31680.3\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 38464.3\n",
            "[2000]\tvalid_0's rmse: 37419.4\n",
            "[3000]\tvalid_0's rmse: 36425.3\n",
            "[4000]\tvalid_0's rmse: 36019.7\n",
            "[5000]\tvalid_0's rmse: 35755.1\n",
            "[6000]\tvalid_0's rmse: 35350\n",
            "Early stopping, best iteration is:\n",
            "[6561]\tvalid_0's rmse: 35166.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:03,548] Trial 6 finished with value: 35166.23495091295 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 2.4056256989381697, 'reg_lambda': 0.23916802818415075, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 3, 'num_leaves': 138, 'min_child_samples': 71}. Best is trial 5 with value: 31680.314798159856.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:03,732] Trial 7 finished with value: 33204.89757979411 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.021164248256888975, 'reg_lambda': 0.0012201572471750542, 'colsample_bytree': 0.5, 'subsample': 0.7, 'learning_rate': 0.1, 'max_depth': 6, 'num_leaves': 13, 'min_child_samples': 11}. Best is trial 5 with value: 31680.314798159856.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[333]\tvalid_0's rmse: 33204.9\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:04,001] Trial 8 finished with value: 32836.927019211085 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.0033121109667487996, 'reg_lambda': 2.920075813970546, 'colsample_bytree': 0.7, 'subsample': 0.7, 'learning_rate': 0.05, 'max_depth': 5, 'num_leaves': 28, 'min_child_samples': 31}. Best is trial 5 with value: 31680.314798159856.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[706]\tvalid_0's rmse: 32836.9\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 37817.4\n",
            "[2000]\tvalid_0's rmse: 36765.3\n",
            "[3000]\tvalid_0's rmse: 35966.7\n",
            "[4000]\tvalid_0's rmse: 35288.7\n",
            "Early stopping, best iteration is:\n",
            "[4557]\tvalid_0's rmse: 34984.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:05,885] Trial 9 finished with value: 34984.8001380341 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.009318333753746164, 'reg_lambda': 0.613097412260877, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.005, 'max_depth': 6, 'num_leaves': 18, 'min_child_samples': 56}. Best is trial 5 with value: 31680.314798159856.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[572]\tvalid_0's rmse: 29474.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:06,849] Trial 10 finished with value: 29474.6331766052 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.13868168208452053, 'reg_lambda': 0.01883004988578201, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 58, 'min_child_samples': 1}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[637]\tvalid_0's rmse: 29666\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:08,523] Trial 11 finished with value: 29665.957571620143 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.1360015514002235, 'reg_lambda': 0.02205668826458862, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 68, 'min_child_samples': 1}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[562]\tvalid_0's rmse: 29504.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:10,180] Trial 12 finished with value: 29504.123865194142 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.22486067289627334, 'reg_lambda': 0.027059529840937055, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 58, 'min_child_samples': 1}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 35704.5\n",
            "[2000]\tvalid_0's rmse: 34798.7\n",
            "Early stopping, best iteration is:\n",
            "[2001]\tvalid_0's rmse: 34798\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:11,366] Trial 13 finished with value: 34798.0459143028 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.2399666309733721, 'reg_lambda': 0.012250356156366157, 'colsample_bytree': 0.6, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 8, 'num_leaves': 58, 'min_child_samples': 26}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 43575.3\n",
            "[2000]\tvalid_0's rmse: 42261.6\n",
            "Early stopping, best iteration is:\n",
            "[2947]\tvalid_0's rmse: 41532.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:12,203] Trial 14 finished with value: 41532.6014284288 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.3133776452407832, 'reg_lambda': 0.005916025436999573, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 8, 'num_leaves': 48, 'min_child_samples': 96}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 33906.9\n",
            "[2000]\tvalid_0's rmse: 31749.8\n",
            "[3000]\tvalid_0's rmse: 30380.4\n",
            "Early stopping, best iteration is:\n",
            "[3308]\tvalid_0's rmse: 30080.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:13,163] Trial 15 finished with value: 30080.140640383852 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.05375988121379905, 'reg_lambda': 0.05189608395128428, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 2, 'num_leaves': 83, 'min_child_samples': 1}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 35370.4\n",
            "[2000]\tvalid_0's rmse: 34282.3\n",
            "[3000]\tvalid_0's rmse: 33553.9\n",
            "Early stopping, best iteration is:\n",
            "[3710]\tvalid_0's rmse: 33151.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:15,063] Trial 16 finished with value: 33151.43237883186 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.34324227698961735, 'reg_lambda': 0.06438702742037969, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 48, 'min_child_samples': 26}. Best is trial 10 with value: 29474.6331766052.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:15,569] Trial 17 finished with value: 36595.68527793692 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.7014584001332806, 'reg_lambda': 0.010163968870155693, 'colsample_bytree': 0.6, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 8, 'num_leaves': 73, 'min_child_samples': 56}. Best is trial 10 with value: 29474.6331766052.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 37113.5\n",
            "Early stopping, best iteration is:\n",
            "[1427]\tvalid_0's rmse: 36595.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[370]\tvalid_0's rmse: 28818.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:15,867] Trial 18 finished with value: 28818.457194337414 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.0920231077773476, 'reg_lambda': 0.0019787808959599498, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 4, 'num_leaves': 88, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 33573.6\n",
            "Early stopping, best iteration is:\n",
            "[1371]\tvalid_0's rmse: 32984\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:16,526] Trial 19 finished with value: 32984.01107420084 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.07152482823514361, 'reg_lambda': 0.0012649318748360946, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 4, 'num_leaves': 98, 'min_child_samples': 21}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:16,931] Trial 20 finished with value: 34614.52631519228 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.047671053529877376, 'reg_lambda': 0.0025454757449886634, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 5, 'num_leaves': 118, 'min_child_samples': 36}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[833]\tvalid_0's rmse: 34614.5\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:17,388] Trial 21 finished with value: 29912.84455069157 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.11866317528091933, 'reg_lambda': 0.0026320694235826363, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 58, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[194]\tvalid_0's rmse: 29912.8\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[706]\tvalid_0's rmse: 30728.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:18,478] Trial 22 finished with value: 30728.82730546847 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.773121631564466, 'reg_lambda': 0.025548226956555665, 'colsample_bytree': 0.8, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 7, 'num_leaves': 83, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:18,691] Trial 23 finished with value: 37237.38874640466 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.16941047261874745, 'reg_lambda': 0.00967596765338566, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.01, 'max_depth': 2, 'num_leaves': 43, 'min_child_samples': 11}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[570]\tvalid_0's rmse: 37237.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32575.6\n",
            "Early stopping, best iteration is:\n",
            "[1160]\tvalid_0's rmse: 32221.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:19,486] Trial 24 finished with value: 32221.227315022115 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.07737526679340533, 'reg_lambda': 0.004955557791744609, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 10, 'num_leaves': 68, 'min_child_samples': 21}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32569.5\n",
            "Early stopping, best iteration is:\n",
            "[1836]\tvalid_0's rmse: 31614.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:20,454] Trial 25 finished with value: 31614.36756647862 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5940288493983787, 'reg_lambda': 0.023924097318817387, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 4, 'num_leaves': 93, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[889]\tvalid_0's rmse: 33223.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:21,288] Trial 26 finished with value: 33223.701828639234 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.033931145974062, 'reg_lambda': 0.0029729035400941277, 'colsample_bytree': 0.5, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 6, 'num_leaves': 58, 'min_child_samples': 21}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32883.6\n",
            "Early stopping, best iteration is:\n",
            "[1202]\tvalid_0's rmse: 32476.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:22,645] Trial 27 finished with value: 32476.615102121483 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.11818635327486335, 'reg_lambda': 0.015015938618179457, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.02, 'max_depth': 8, 'num_leaves': 78, 'min_child_samples': 16}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:23,243] Trial 28 finished with value: 36254.092813139265 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.21276983606040592, 'reg_lambda': 0.03990098236151685, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 11, 'num_leaves': 33, 'min_child_samples': 51}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 36293.2\n",
            "Early stopping, best iteration is:\n",
            "[954]\tvalid_0's rmse: 36254.1\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:23,558] Trial 29 finished with value: 36913.63863433123 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.08040563188129182, 'reg_lambda': 0.11151986177809006, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 7, 'num_leaves': 103, 'min_child_samples': 71}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[410]\tvalid_0's rmse: 36913.6\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[914]\tvalid_0's rmse: 32140.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:24,904] Trial 30 finished with value: 32140.162926068962 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.03367567726913111, 'reg_lambda': 0.0690092872455433, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 63, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[703]\tvalid_0's rmse: 30273.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:26,239] Trial 31 finished with value: 30273.21984145481 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.13471590326111413, 'reg_lambda': 0.018219894318219396, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 73, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 31970.7\n",
            "Early stopping, best iteration is:\n",
            "[1304]\tvalid_0's rmse: 31774.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:27,689] Trial 32 finished with value: 31774.844187780363 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.1558375272840956, 'reg_lambda': 0.038837615759970356, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 43, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 34220.7\n",
            "[2000]\tvalid_0's rmse: 32771.8\n",
            "Early stopping, best iteration is:\n",
            "[2926]\tvalid_0's rmse: 32103.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:29,714] Trial 33 finished with value: 32103.88364057342 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.41424207978756805, 'reg_lambda': 0.012859874843600906, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 68, 'min_child_samples': 16}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[806]\tvalid_0's rmse: 33247\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:30,889] Trial 34 finished with value: 33246.97833499049 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 1.256748480281642, 'reg_lambda': 0.022315877048218795, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 83, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:31,376] Trial 35 finished with value: 30836.482462946526 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.19605292100181176, 'reg_lambda': 0.037347850388118095, 'colsample_bytree': 0.8, 'subsample': 0.7, 'learning_rate': 0.03, 'max_depth': 10, 'num_leaves': 88, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[184]\tvalid_0's rmse: 30836.5\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[600]\tvalid_0's rmse: 35938.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:32,130] Trial 36 finished with value: 35938.604576111844 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.3643610058318802, 'reg_lambda': 0.00766157806434774, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.005, 'max_depth': 9, 'num_leaves': 53, 'min_child_samples': 11}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 35448.5\n",
            "Early stopping, best iteration is:\n",
            "[1490]\tvalid_0's rmse: 35052.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:32,785] Trial 37 finished with value: 35052.556485125606 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.08987840074684653, 'reg_lambda': 0.017735834804141135, 'colsample_bytree': 0.9, 'subsample': 0.85, 'learning_rate': 0.02, 'max_depth': 11, 'num_leaves': 113, 'min_child_samples': 41}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:33,343] Trial 38 finished with value: 31437.42893966037 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.24541137179517217, 'reg_lambda': 0.0043479793972139025, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.05, 'max_depth': 12, 'num_leaves': 33, 'min_child_samples': 16}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[726]\tvalid_0's rmse: 31437.4\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 34710.1\n",
            "[2000]\tvalid_0's rmse: 33225.7\n",
            "Early stopping, best iteration is:\n",
            "[2230]\tvalid_0's rmse: 33122.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:35,100] Trial 39 finished with value: 33122.52239097015 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 1.3708885454030186, 'reg_lambda': 0.007638455591700616, 'colsample_bytree': 0.8, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 7, 'num_leaves': 128, 'min_child_samples': 31}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:35,574] Trial 40 finished with value: 32247.93863273272 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.022206380235471897, 'reg_lambda': 0.10200241440903661, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.1, 'max_depth': 10, 'num_leaves': 68, 'min_child_samples': 11}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[264]\tvalid_0's rmse: 32247.9\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:36,368] Trial 41 finished with value: 30626.33521872861 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.1377054126793025, 'reg_lambda': 0.001995154078792068, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 58, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[218]\tvalid_0's rmse: 30626.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[702]\tvalid_0's rmse: 31103.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:37,499] Trial 42 finished with value: 31103.156830214215 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.11154343347940968, 'reg_lambda': 0.0036014369486957925, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 8, 'num_leaves': 63, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:38,028] Trial 43 finished with value: 29953.824729002292 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.05572111945424714, 'reg_lambda': 0.0016532564362338297, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[308]\tvalid_0's rmse: 29953.8\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 34080.6\n",
            "[2000]\tvalid_0's rmse: 32617.6\n",
            "[3000]\tvalid_0's rmse: 32017.4\n",
            "Early stopping, best iteration is:\n",
            "[3871]\tvalid_0's rmse: 31612.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:41,333] Trial 44 finished with value: 31612.306363831205 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.10185567266398897, 'reg_lambda': 0.0010478279214322366, 'colsample_bytree': 0.9, 'subsample': 0.85, 'learning_rate': 0.005, 'max_depth': 10, 'num_leaves': 53, 'min_child_samples': 11}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[200]\tvalid_0's rmse: 35012.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:41,639] Trial 45 finished with value: 35012.60674701941 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.2356080335418888, 'reg_lambda': 0.0036726964105304924, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 8, 'num_leaves': 78, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 39645.5\n",
            "[2000]\tvalid_0's rmse: 38442.2\n",
            "[3000]\tvalid_0's rmse: 37861.7\n",
            "Early stopping, best iteration is:\n",
            "[3006]\tvalid_0's rmse: 37858.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:42,621] Trial 46 finished with value: 37858.90470912193 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.1568341054707688, 'reg_lambda': 0.006374505784926569, 'colsample_bytree': 0.6, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 6, 'num_leaves': 88, 'min_child_samples': 71}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32146.1\n",
            "Early stopping, best iteration is:\n",
            "[1090]\tvalid_0's rmse: 31864.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:43,411] Trial 47 finished with value: 31864.83520272122 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.013334918069030985, 'reg_lambda': 0.002389267145149543, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 73, 'min_child_samples': 16}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[942]\tvalid_0's rmse: 40284.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:43,707] Trial 48 finished with value: 40284.17002602627 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.3926477467823337, 'reg_lambda': 0.0018844768559594105, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.05, 'max_depth': 4, 'num_leaves': 28, 'min_child_samples': 91}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[396]\tvalid_0's rmse: 33293.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:43,977] Trial 49 finished with value: 33293.509845313616 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.06383567720293788, 'reg_lambda': 0.01091132569467609, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.1, 'max_depth': 8, 'num_leaves': 53, 'min_child_samples': 26}. Best is trial 18 with value: 28818.457194337414.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:44,363] Trial 50 finished with value: 30698.6311391978 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.10025624154461797, 'reg_lambda': 0.0042436892293822095, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 3, 'num_leaves': 63, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 30819.5\n",
            "Early stopping, best iteration is:\n",
            "[1090]\tvalid_0's rmse: 30698.6\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:44,703] Trial 51 finished with value: 30601.452652155476 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.04473243993144575, 'reg_lambda': 0.001588278776645622, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[178]\tvalid_0's rmse: 30601.5\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:45,288] Trial 52 finished with value: 31174.47640574992 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.06502052709709606, 'reg_lambda': 0.0016163792092606453, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 38, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[448]\tvalid_0's rmse: 31174.5\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:45,536] Trial 53 finished with value: 29020.13863054379 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.050904845109726746, 'reg_lambda': 0.0026918800514410595, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 8, 'num_leaves': 18, 'min_child_samples': 1}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[223]\tvalid_0's rmse: 29020.1\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:46,258] Trial 54 finished with value: 31372.598765654475 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.03475949028121092, 'reg_lambda': 0.00258009903184286, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 7, 'num_leaves': 98, 'min_child_samples': 11}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[835]\tvalid_0's rmse: 31372.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:47,038] Trial 55 finished with value: 31488.401027683038 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.16611898794814267, 'reg_lambda': 0.005935026872745307, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.02, 'max_depth': 8, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 18 with value: 28818.457194337414.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[853]\tvalid_0's rmse: 31488.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:47,391] Trial 56 finished with value: 28681.350032950784 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.24766481205016327, 'reg_lambda': 0.008890100322137988, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 10, 'num_leaves': 18, 'min_child_samples': 1}. Best is trial 56 with value: 28681.350032950784.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[263]\tvalid_0's rmse: 28681.4\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 35072.6\n",
            "[2000]\tvalid_0's rmse: 33766.5\n",
            "[3000]\tvalid_0's rmse: 32963\n",
            "[4000]\tvalid_0's rmse: 32457.2\n",
            "Early stopping, best iteration is:\n",
            "[4112]\tvalid_0's rmse: 32388.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:50,924] Trial 57 finished with value: 32388.520821805363 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.2382320268325229, 'reg_lambda': 0.014777858000759743, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.005, 'max_depth': 10, 'num_leaves': 13, 'min_child_samples': 16}. Best is trial 56 with value: 28681.350032950784.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 33386.8\n",
            "[2000]\tvalid_0's rmse: 32111.9\n",
            "Early stopping, best iteration is:\n",
            "[2648]\tvalid_0's rmse: 31579.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:52,473] Trial 58 finished with value: 31579.606515889383 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.29889351368408745, 'reg_lambda': 0.029010635209635375, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 5, 'num_leaves': 18, 'min_child_samples': 11}. Best is trial 56 with value: 28681.350032950784.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:53,083] Trial 59 finished with value: 32392.265370856665 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.08461899928909174, 'reg_lambda': 0.009909693074633174, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 7, 'num_leaves': 23, 'min_child_samples': 21}. Best is trial 56 with value: 28681.350032950784.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[925]\tvalid_0's rmse: 32392.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:53,627] Trial 60 finished with value: 29142.328949993764 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5019406441671601, 'reg_lambda': 0.007834292598109931, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 13, 'min_child_samples': 1}. Best is trial 56 with value: 28681.350032950784.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[736]\tvalid_0's rmse: 29142.3\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:55:54,153] Trial 61 finished with value: 29289.57666079378 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.49458308239240184, 'reg_lambda': 0.007862378896532957, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 13, 'min_child_samples': 1}. Best is trial 56 with value: 28681.350032950784.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[729]\tvalid_0's rmse: 29289.6\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 32270.2\n",
            "[2000]\tvalid_0's rmse: 31221.1\n",
            "Early stopping, best iteration is:\n",
            "[2130]\tvalid_0's rmse: 31179\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:55,512] Trial 62 finished with value: 31179.027216093207 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5084497041011936, 'reg_lambda': 0.006992402826870272, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 13, 'min_child_samples': 6}. Best is trial 56 with value: 28681.350032950784.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:56,247] Trial 63 finished with value: 28820.957091620894 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.4698303669510115, 'reg_lambda': 0.00548851412237205, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 56 with value: 28681.350032950784.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[707]\tvalid_0's rmse: 28821\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:57,014] Trial 64 finished with value: 28707.17459080942 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8811325062415712, 'reg_lambda': 0.0048288477719514175, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 56 with value: 28681.350032950784.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[751]\tvalid_0's rmse: 28707.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:57,744] Trial 65 finished with value: 28627.605447660266 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8621929921665014, 'reg_lambda': 0.004969398571272408, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 65 with value: 28627.605447660266.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[687]\tvalid_0's rmse: 28627.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32260.5\n",
            "Early stopping, best iteration is:\n",
            "[1326]\tvalid_0's rmse: 31880\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:55:58,948] Trial 66 finished with value: 31879.990660328873 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.0782557075997787, 'reg_lambda': 0.005044583678222822, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 65 with value: 28627.605447660266.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 38421.1\n",
            "[2000]\tvalid_0's rmse: 37238.6\n",
            "[3000]\tvalid_0's rmse: 36662.9\n",
            "Early stopping, best iteration is:\n",
            "[3074]\tvalid_0's rmse: 36614.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:00,022] Trial 67 finished with value: 36614.94492925784 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.7698049646187829, 'reg_lambda': 0.0032929692127984657, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 18, 'min_child_samples': 61}. Best is trial 65 with value: 28627.605447660266.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 31694\n",
            "Early stopping, best iteration is:\n",
            "[1042]\tvalid_0's rmse: 31646.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:01,312] Trial 68 finished with value: 31646.232509500893 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.9001500659749115, 'reg_lambda': 0.004831035647402106, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 11}. Best is trial 65 with value: 28627.605447660266.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:01,743] Trial 69 finished with value: 30168.39307901296 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.704549550826929, 'reg_lambda': 0.0031972294287954217, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 11, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 65 with value: 28627.605447660266.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[125]\tvalid_0's rmse: 30168.4\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:02,234] Trial 70 finished with value: 35216.01238532882 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.9163399703052431, 'reg_lambda': 0.0013448948284013051, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 65 with value: 28627.605447660266.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[272]\tvalid_0's rmse: 35216\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:02,988] Trial 71 finished with value: 29559.79054755377 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5270222553930455, 'reg_lambda': 0.007221835521988716, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 13, 'min_child_samples': 1}. Best is trial 65 with value: 28627.605447660266.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[633]\tvalid_0's rmse: 29559.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:03,939] Trial 72 finished with value: 28913.72827632021 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5422144950496538, 'reg_lambda': 0.002226665830333725, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 18, 'min_child_samples': 1}. Best is trial 65 with value: 28627.605447660266.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[695]\tvalid_0's rmse: 28913.7\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 32953.5\n",
            "Early stopping, best iteration is:\n",
            "[1516]\tvalid_0's rmse: 32044.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:05,096] Trial 73 finished with value: 32044.753787297355 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5981810043370313, 'reg_lambda': 0.002048688230293973, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 18, 'min_child_samples': 11}. Best is trial 65 with value: 28627.605447660266.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32016.1\n",
            "Early stopping, best iteration is:\n",
            "[1147]\tvalid_0's rmse: 31827.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:06,244] Trial 74 finished with value: 31827.077834948257 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.32632537241830006, 'reg_lambda': 0.0027054317952828256, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 28, 'min_child_samples': 6}. Best is trial 65 with value: 28627.605447660266.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:06,460] Trial 75 finished with value: 28329.71233575474 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.9753312878310171, 'reg_lambda': 0.0037598829121290793, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 12, 'num_leaves': 28, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[123]\tvalid_0's rmse: 28329.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:06,808] Trial 76 finished with value: 32325.805660068367 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.9349830841497166, 'reg_lambda': 0.004035910359090828, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 12, 'num_leaves': 38, 'min_child_samples': 16}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[420]\tvalid_0's rmse: 32325.8\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:06,988] Trial 77 finished with value: 32138.234069022263 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.5491400908654578, 'reg_lambda': 0.0022260697030055615, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 12, 'num_leaves': 18, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[117]\tvalid_0's rmse: 32138.2\n",
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[80]\tvalid_0's rmse: 29235.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:07,177] Trial 78 finished with value: 29235.455939571766 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.2625862332483884, 'reg_lambda': 0.005121966117374902, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[512]\tvalid_0's rmse: 32341.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:07,502] Trial 79 finished with value: 32341.415288298533 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.029980670030737, 'reg_lambda': 0.0011979603787375958, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 5, 'num_leaves': 33, 'min_child_samples': 11}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 35971\n",
            "Early stopping, best iteration is:\n",
            "[1346]\tvalid_0's rmse: 35638.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:08,122] Trial 80 finished with value: 35638.23036174404 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.3000943052749965, 'reg_lambda': 0.0031954853804820734, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 46}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:08,686] Trial 81 finished with value: 28824.575053543602 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.4260540723240683, 'reg_lambda': 0.005705089629276754, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 18, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[628]\tvalid_0's rmse: 28824.6\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:09,398] Trial 82 finished with value: 31334.50560776555 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8572156932621269, 'reg_lambda': 0.0040135110277463686, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 12, 'num_leaves': 38, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[548]\tvalid_0's rmse: 31334.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:10,243] Trial 83 finished with value: 28515.590713484842 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6414925942062846, 'reg_lambda': 0.002732712185422093, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[842]\tvalid_0's rmse: 28515.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[840]\tvalid_0's rmse: 28673.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:11,209] Trial 84 finished with value: 28673.654421635456 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.661084640945519, 'reg_lambda': 0.006225738069187179, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:11,843] Trial 85 finished with value: 29042.352549119114 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.7167023458597223, 'reg_lambda': 0.005634492487613383, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[620]\tvalid_0's rmse: 29042.4\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 31697.6\n",
            "Early stopping, best iteration is:\n",
            "[1348]\tvalid_0's rmse: 31321.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:13,202] Trial 86 finished with value: 31321.5692979308 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.171140235630996, 'reg_lambda': 0.008965749873650982, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 28, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[741]\tvalid_0's rmse: 33256.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:15,021] Trial 87 finished with value: 33256.19543555254 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.4057731515611878, 'reg_lambda': 0.012730112294952432, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 143, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 33997.7\n",
            "[2000]\tvalid_0's rmse: 32671.3\n",
            "Early stopping, best iteration is:\n",
            "[2094]\tvalid_0's rmse: 32593.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:17,417] Trial 88 finished with value: 32593.641245203395 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.2870312275969324, 'reg_lambda': 0.005761040630221352, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 11}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 34803.1\n",
            "[2000]\tvalid_0's rmse: 33566.8\n",
            "[3000]\tvalid_0's rmse: 32815.1\n",
            "[4000]\tvalid_0's rmse: 32308.3\n",
            "Early stopping, best iteration is:\n",
            "[4676]\tvalid_0's rmse: 32043.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:20,641] Trial 89 finished with value: 32043.48964357304 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.5647252699243597, 'reg_lambda': 0.004493682130055773, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.005, 'max_depth': 12, 'num_leaves': 33, 'min_child_samples': 16}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:20,886] Trial 90 finished with value: 29385.848865481763 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6389669984998138, 'reg_lambda': 0.003320164009071443, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.1, 'max_depth': 11, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[115]\tvalid_0's rmse: 29385.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:21,607] Trial 91 finished with value: 28822.93929842243 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8730771594892878, 'reg_lambda': 0.0019371491493387735, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[685]\tvalid_0's rmse: 28822.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:22,308] Trial 92 finished with value: 28636.96438376775 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8017254052007745, 'reg_lambda': 0.0038700510908149448, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[683]\tvalid_0's rmse: 28637\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:22,988] Trial 93 finished with value: 28758.948230413545 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.79731585031644, 'reg_lambda': 0.003798968236363773, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[659]\tvalid_0's rmse: 28758.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 31715.6\n",
            "Early stopping, best iteration is:\n",
            "[1309]\tvalid_0's rmse: 31393.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:24,403] Trial 94 finished with value: 31393.645462886736 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.3160506302815316, 'reg_lambda': 0.0038235006026970026, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 33, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 34332.3\n",
            "[2000]\tvalid_0's rmse: 32993.9\n",
            "Early stopping, best iteration is:\n",
            "[2155]\tvalid_0's rmse: 32859.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:25,259] Trial 95 finished with value: 32859.70318233668 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6561687981228208, 'reg_lambda': 0.002781268662123611, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 3, 'num_leaves': 28, 'min_child_samples': 11}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[801]\tvalid_0's rmse: 32241.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:26,246] Trial 96 finished with value: 32241.850249185067 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.056933662980008, 'reg_lambda': 0.009439937995221286, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:26,956] Trial 97 finished with value: 32568.769399537192 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.7833253602862128, 'reg_lambda': 0.004461081746903504, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.05, 'max_depth': 11, 'num_leaves': 88, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[119]\tvalid_0's rmse: 32568.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[848]\tvalid_0's rmse: 30325\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:31,102] Trial 98 finished with value: 30324.951351259293 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.42949397825632984, 'reg_lambda': 0.0034276477887454095, 'colsample_bytree': 0.6, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 103, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 37020.6\n",
            "[2000]\tvalid_0's rmse: 35938.7\n",
            "Early stopping, best iteration is:\n",
            "[2937]\tvalid_0's rmse: 35225.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:32,500] Trial 99 finished with value: 35225.5511576889 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 1.1181275024979762, 'reg_lambda': 0.006513940925726145, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 48, 'min_child_samples': 76}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 37262\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:32,868] Trial 100 finished with value: 37197.37538734047 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.35138492029108087, 'reg_lambda': 0.0015142346934416888, 'colsample_bytree': 0.7, 'subsample': 0.6, 'learning_rate': 0.005, 'max_depth': 2, 'num_leaves': 13, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[1144]\tvalid_0's rmse: 37197.4\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:33,551] Trial 101 finished with value: 28792.59694388284 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8088634254423261, 'reg_lambda': 0.0020692244571893694, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[678]\tvalid_0's rmse: 28792.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 35930\n",
            "Early stopping, best iteration is:\n",
            "[1827]\tvalid_0's rmse: 35108.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:34,417] Trial 102 finished with value: 35108.2541067293 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.8013771697540658, 'reg_lambda': 0.0024975738948067767, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 18, 'min_child_samples': 36}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:35,236] Trial 103 finished with value: 28906.803783334155 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6904232458834465, 'reg_lambda': 0.0019383882695194659, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[646]\tvalid_0's rmse: 28906.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:35,901] Trial 104 finished with value: 31502.719540256698 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5809856222489129, 'reg_lambda': 0.003023049141015976, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[595]\tvalid_0's rmse: 31502.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:36,510] Trial 105 finished with value: 29187.885186936695 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.3431022023306252, 'reg_lambda': 0.004883161836627712, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[689]\tvalid_0's rmse: 29187.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 38558.9\n",
            "[2000]\tvalid_0's rmse: 37240.8\n",
            "[3000]\tvalid_0's rmse: 36744.7\n",
            "Early stopping, best iteration is:\n",
            "[3186]\tvalid_0's rmse: 36670.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:37,639] Trial 106 finished with value: 36670.47073804907 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.9693839824684788, 'reg_lambda': 0.004035056707245302, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 38, 'min_child_samples': 61}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32139.1\n",
            "Early stopping, best iteration is:\n",
            "[1384]\tvalid_0's rmse: 31771.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:38,757] Trial 107 finished with value: 31771.20647327503 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.48658250603015657, 'reg_lambda': 0.0024266134535450954, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 18, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:38,989] Trial 108 finished with value: 31492.82348124638 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.27103777294203935, 'reg_lambda': 0.0016849722987695855, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.1, 'max_depth': 9, 'num_leaves': 13, 'min_child_samples': 11}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[238]\tvalid_0's rmse: 31492.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:39,710] Trial 109 finished with value: 28674.075987766868 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.20376910281591804, 'reg_lambda': 0.0010608012186752354, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[607]\tvalid_0's rmse: 28674.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:40,289] Trial 110 finished with value: 29592.14877618044 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.19939550878342394, 'reg_lambda': 0.0011434595905431757, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 10, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[389]\tvalid_0's rmse: 29592.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[737]\tvalid_0's rmse: 28815.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:41,554] Trial 111 finished with value: 28815.581271183364 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.37080489308581244, 'reg_lambda': 0.0013793671974874364, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[770]\tvalid_0's rmse: 28838.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:43,008] Trial 112 finished with value: 28838.75583826952 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.37438986223537457, 'reg_lambda': 0.0010162906765952403, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32055.4\n",
            "Early stopping, best iteration is:\n",
            "[1568]\tvalid_0's rmse: 31464.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:44,606] Trial 113 finished with value: 31464.34555579804 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6427572194428545, 'reg_lambda': 0.0016056345774236233, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32292.9\n",
            "Early stopping, best iteration is:\n",
            "[1133]\tvalid_0's rmse: 32170.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:45,761] Trial 114 finished with value: 32170.86858650327 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.18966685273592762, 'reg_lambda': 0.0013156791610653671, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 28, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:46,590] Trial 115 finished with value: 29044.04186138716 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.2720493334796253, 'reg_lambda': 0.0014398866542792074, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[514]\tvalid_0's rmse: 29044\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "[1000]\tvalid_0's rmse: 32886.8\n",
            "Early stopping, best iteration is:\n",
            "[1352]\tvalid_0's rmse: 32388.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:47,585] Trial 116 finished with value: 32388.473400297808 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.32626865795252347, 'reg_lambda': 0.0020320176542573697, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 6, 'num_leaves': 18, 'min_child_samples': 11}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[94]\tvalid_0's rmse: 30117.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:47,888] Trial 117 finished with value: 30117.158432506738 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.85574339345795, 'reg_lambda': 0.0012253043051005817, 'colsample_bytree': 0.6, 'subsample': 0.6, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 93, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "[I 2023-07-23 03:56:48,055] Trial 118 finished with value: 29487.860199840048 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5644291939792292, 'reg_lambda': 0.0028364497359642774, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 11, 'num_leaves': 13, 'min_child_samples': 1}. Best is trial 75 with value: 28329.71233575474.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
            "suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[126]\tvalid_0's rmse: 29487.9\n",
            "Training until validation scores don't improve for 50 rounds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-23 03:56:48,361] Trial 119 finished with value: 35216.03575045633 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 1.4794126017608333, 'reg_lambda': 0.001037225655719393, 'colsample_bytree': 0.9, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 75 with value: 28329.71233575474.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[272]\tvalid_0's rmse: 35216\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Results from Hyperparameters tuning\n",
        "if params_search:\n",
        "    print('Totalnumber of trials: ', len(study.trials))\n",
        "    print(f\"Best RMSE score on validation data: {study.best_value}\")\n",
        "\n",
        "    print(\"-\"*30)\n",
        "    print('Best params:')\n",
        "    print(\"-\"*30)\n",
        "    for param, v in study.best_trial.params.items():\n",
        "        print(f\"{param} :\\t {v}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HLJKgpuW6je",
        "outputId": "f832bb99-9c5e-4406-ea8e-997717f4f50f"
      },
      "id": "-HLJKgpuW6je",
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Totalnumber of trials:  120\n",
            "Best RMSE score on validation data: 28329.71233575474\n",
            "------------------------------\n",
            "Best params:\n",
            "------------------------------\n",
            "boosting_type :\t goss\n",
            "reg_alpha :\t 0.9753312878310171\n",
            "reg_lambda :\t 0.0037598829121290793\n",
            "colsample_bytree :\t 0.9\n",
            "subsample :\t 0.6\n",
            "learning_rate :\t 0.1\n",
            "max_depth :\t 12\n",
            "num_leaves :\t 28\n",
            "min_child_samples :\t 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have a RMSE score on validation data: 28329 which is better than the baseline LGBM model."
      ],
      "metadata": {
        "id": "HRmpLtGCrZCN"
      },
      "id": "HRmpLtGCrZCN"
    },
    {
      "cell_type": "code",
      "source": [
        " # further manually tune params from best params from Optuna\n",
        "params = {\n",
        "             'n_estimators': 20000,\n",
        "             'boosting_type': \"goss\",\n",
        "             'reg_alpha': 0.97,\n",
        "             'reg_lambda': 0.004,\n",
        "             'colsample_bytree': 0.9,\n",
        "             'subsample': 0.6,\n",
        "             'learning_rate': 0.1,\n",
        "             'max_depth': 12,\n",
        "             'num_leaves': 28,\n",
        "             'min_child_samples': 1,\n",
        "          }"
      ],
      "metadata": {
        "id": "cNiStTBnt04R"
      },
      "id": "cNiStTBnt04R",
      "execution_count": 277,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbmreg_optimized = lgbm.LGBMRegressor(**params) # **study.best_trial.params\n",
        "\n",
        "lgbmreg_optimized.fit(X_train, y_train,\n",
        "            eval_set=[(X_test, y_test), (X_train, y_train)],\n",
        "            categorical_feature=cat_indices,\n",
        "            callbacks=[log_evaluation(period=100),\n",
        "                       early_stopping(stopping_rounds=100)\n",
        "                      ],\n",
        "           )\n",
        "\n",
        "# prediction on the test set\n",
        "y_preds = lgbmreg_optimized.predict(X_test)\n",
        "# cross-validation score\n",
        "cv_score = mean_squared_error(y_test, lgbmreg_optimized.predict(X_test), squared=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIjk3zrFvDTh",
        "outputId": "90afaf3f-82df-4740-8ee3-f2e9c35bd0fe"
      },
      "id": "TIjk3zrFvDTh",
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 100 rounds\n",
            "[100]\ttraining's l2: 8.4534e+07\tvalid_0's l2: 8.72746e+08\n",
            "Early stopping, best iteration is:\n",
            "[73]\ttraining's l2: 1.18755e+08\tvalid_0's l2: 8.59695e+08\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using categorical_feature in Dataset.\n",
            "categorical_feature in Dataset is overridden.\n",
            "New categorical_feature is []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98SlsLq5vdDg",
        "outputId": "83ebf9b5-e478-4838-b9cf-117e59de0ddf"
      },
      "id": "98SlsLq5vdDg",
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29320.561800133135"
            ]
          },
          "metadata": {},
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion:**\n",
        "\n",
        "The root mean square error is smaller with the optimized model than the baseline LGBM model, as expected. I was able to lower it down to 29320\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "JLK3gQI_mAUU"
      },
      "id": "JLK3gQI_mAUU"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SHAP for LGBM Optimized"
      ],
      "metadata": {
        "id": "tfnXqVT6i3gN"
      },
      "id": "tfnXqVT6i3gN"
    },
    {
      "cell_type": "code",
      "source": [
        "explainer_lgbm = shap.TreeExplainer(lgbmreg_optimized)\n",
        "shap_interaction_lgbm = explainer_lgbm.shap_interaction_values(X_train)\n",
        "# Get SHAP values\n",
        "shap_values_lgbm = explainer_lgbm(X_train)"
      ],
      "metadata": {
        "id": "FGtn-vFri7lr"
      },
      "id": "FGtn-vFri7lr",
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Waterfall plot for first observation\n",
        "shap.plots.waterfall(shap_values_lgbm[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "BV-yDOO8jXkZ",
        "outputId": "530936c8-7f7e-4d39-d3f7-a0aa58c93003"
      },
      "id": "BV-yDOO8jXkZ",
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Discussion:\n",
        "The TotalBsmtSF has a negative impact according to this model."
      ],
      "metadata": {
        "id": "tt0XJLujozXQ"
      },
      "id": "tt0XJLujozXQ"
    },
    {
      "cell_type": "code",
      "source": [
        "#Mean SHAP\n",
        "shap.plots.bar(shap_values_lgbm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "AfCtqO5_oCXm",
        "outputId": "c7eecbab-0ce6-4ecc-a184-d22f32347a29"
      },
      "id": "AfCtqO5_oCXm",
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_values_lgbm, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "hEwY_qRQoSt8",
        "outputId": "f5b57744-a8dd-42e2-f10e-a4b0beadbfb1"
      },
      "id": "hEwY_qRQoSt8",
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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HePf8pVz2+chaT9ANbjjA/t6vgG6cmFEFdAADo8w8HnppiOD+MoKrq9OVLK1jSFt1LdaW0XVv41hYvxMGP2C21AOs2AhPLz5+5R3u3ZGwZgY0lXKKasw0zDsJC9eawV9D5q40X5W+2wZvfGkG4/PuNwPkM6fAz3vN+XfOgqV/hTNOOby6Avy4G8560AxUAZw28FfUTVXhldvMCyiAyTPhpeXV6/pC8MVP5qumnI3m36OqjD2R84u8ZgD+5c/V0yyqGQQX1vPMiiXfw78XwDv3wIRBZtA88P6KC6gaZi6DiYPg3T/BB1/Dpf82/yY1rdwMc7+C1Y/Vf1wOR4kXhjwAGyougu+wg6ZDsGKUrq+3mAH8zgOw7aA+O6oKr90O8R6Y8ET1OgM7wacPg+v43DE/4QwDxj1mXgyBeS6ePgluPO/E1kPXYcw/zIs+MI//y7fC/51zYutxnMjwmg1rMoH+9u3b8Xg8tGrVqt5lnE4nbdu2Zdu2bXi9XtxuN2PGjOGJJ55g9erVEYHv8uXLCQQCjBkzpmraE088QTgc5o033iAlJaVq+vDhw7n++ut54403uPnmyNvYUVFRPP/881itkYfqmWeeweVyRUwbPXo0V155JXPmzOH+++8/ov3fs2cPwWCQzp07H3K5U089FTDTfI7W3Llza9X9rLPO4tZbb2X+/Plcd911R71tcex9l2UcXZBfQ+igdJMGg3wg3hdkU1osqqGTXhY85LIxZV4y8gp/VR3rEywOsfXdXXT7v44R0wvuXF7dMtxEaLtLKPn3NyRMG378Cpn6VnWQf7I5nCD/UBZ8Z7Z8f7etOsgHKPfDlDfgs0cOf1sPvV0d5EN1kA9mcPSn/8FVZ5kt4q8d2ztFETS9/iC/Zn3uftUM9N9aWTvIr/Te17DyZ7j7tdpBfqWvt8AXP8JZ3Y6+zi9/Uh3kg9lv42DL6hn9Stfh3tfMQD9YY/jeb7bCnC/hd8fxu3MifbKhOsgHM/C//3W4dqiZ+naifPR9dZAPkZ9tW5MJAcVx1GSG1ywrK6vVml4Xj8dTtTzAiBEjsNlsLFq0KGK5xYsXExsby5AhQ6qW/+qrrzjrrLNwOBwUFRVVvdLT08nIyOCbb76pVd6VV15ZK8gHqgJlwzAoKyujqKiI+Ph42rRpw6ZNm45s52vsT0PHoHL/y8uPfnSSyrrrul5V986dOxMVFXVUdT+RCgoKCAQCVe/LysooLS2teh8MBsnPj3zKalZW1iHfZ2dnY9TogNrUythWdOKCWXcgRJv8MjKKvBS57BiKgv0wWnQTSo7sCbpHqmh39bGpPFahzKLjWubRCm01L3iO2+dq69GPatQsbM1C+2VfrcnaL3sj3jf0HQxvPqi1/WAHiinYuddsAbU0gZ/KzAMQClOyt47UrBr863fAzpxDLlP03eaI90d6vgr9tItfJbsIo47PcXhz9d+wsc+7v7qMur6nBWUEs/JP7H7UVY/cErJ/2XnsymhUMrxmQ5rM5VxUVFRVsHsolQFuZUAcGxvL4MGD+eKLL6ouFvbv38/333/PxRdfjM1mXjlnZmai6zrz589n/vz5dW67Zcva+XP1peBs3ryZF198kbVr1+LzRXZYrGs7Dancn4aOQeX+/5rOyN999x0zZ87kxx9/jPgiAxFf5Kbo4NGGDr4wstvttY5NWlraId+npqY26TKGtlKwqRA6ggFwjophgKKwL9ZFuEaurCtUT8tgDUXRnuNZM7pfV93vpfJY+c9uQ/mODce13KPhOt984Ndx+1yd36t2SshvharC8J5Y4qPgtc8iZllG9ol439B30HpBX9h4iOPYpz0JndqY/77hHHj+46Ou9jExrDvYrMT83/nw+MK6746oCs4xA+C9byNTiGpSFOImRg4be6TnK9vo/vDSikPXN8pZ/52nAZ1QkqLN9J4arBf0rV79ZD+3D+9ppuvUCJ7p3hp721QO/vU+rvtRVz16tyO1e2RfwmNxrETT1GQC/Q4dOrBu3Tr27NlTb/qO3+8nMzOT9PR03G531fTRo0fz6aefsnz5csaNG8fixYsxDIPRo0fX2sYFF1wQkc5TU12j6TidtYeRy87O5qabbsLj8fC73/2Otm3b4nQ6URSFf//737UC/8PRqlUr7HY7W7ZsOeRymzebLTE1O+vWN/pOOFz7qaY//vgjkydPJiMjg8mTJ5Oeno7D4UBRFKZMmYJ+JMMpihMiLUrh9VEqf1yhk+OF1tGQ6wPfkT201lRxslcMMGp0tI0KhOi9r4iokIamwPbEKLYnmif20GE0jhRFe9iakULHvTnHtC3F4lDp/6cexLapfacrafpIyt/8CfxHcyB+TaUw8/UPvtFhU4m6vifRt/SpY6Vj6KHLzRbbed+a7xOizM6q32xtuoNKx3lgwZ/h6UXmMJ5g1tUwINoFpQedM7u2ghiXmWZStQ03/Pt6syNhxzRYtwOeWQz+oNm59V//d2R1+uulZufE978xW+wHdjQ7whaVQ6928MYd1cs+exOU+GD2F7W3Y1HNTsUHXxAr1P33iPdAUgzsLzA7SXfNMDscH3wMaj69eUBHePk2c3q7FFj8INz3P/ghszpFx+OE6TdD6xbmsldMM4+f3Qa6BmHd7OD73O8jO/YejXED4YGLYdoCM23n3J6QEmvm/+sGpMTB0r/Ayytg+lKz467FYta1dzt4/Y/gtsPl08x+A1FOmDIRzunx6+rVlHRKh5duNdNk8kuhRxuYc8eJr0e31vDizfDn2Waq2mlt4Y07T3w9jhPJ0W9Ykwn0hw0bxrp165g3bx633357ncssXLiQcDjMsGHDIqYPHjyYuLg4Fi1aVBXot23blu7du1ctk5GRgaIohMNhBg4c+Kvq+umnn+L1epk2bVqtp9sWFxcf1QOtHA4HZ555Jp9++imrVq3ijDPOqHO5efPmATBq1KiqaTExMVVlx8bGVk3ft6/27e2PPvoITdN4+umnI+48+Hy+Jt+a/1t22SkqEzsr5Hoh1WNe3P2Ur/PRDoNdJdCzBazcb/DWZvMCwKrAqYlw3wBY+KPOB9sNwopCuCq4N6h5e7NHdjFRFYGKxYDOeWXku+0UuezYDjN43NCpDTvSkxn+7UZ+7dgZQ/7Vl4wzknG1cNZ7IavYLKSvu57cP3yM94vdGFawB47d0JoRZcU6sA1OJ+by7ngu74piVQlnlRHalAs2FVuPZFS7ihp9AjoSRrvgg/vNvG6rxXwPUFxujpn/3TYzuDqotfSIqYrZgl5frjeYo9Wc3dUcfebui6Cw3Azkd+ZAcgy0TYE+7c1OsooCQ7qa9bao5vuwZo4Yk11oBoPrd5mBSOsW5vbzS81OsqU+84KmcohORYEnroW/XWrm1iceRednjxPeube6PjFusw6F5WagWpOiwOt3mAH0xz+YOda6bgbq4waYQWx2oTkSUokPrhhiBuf/eA+27oczTzWD69F9q/Oi/UGzU2tyHJT5zI69rZKgS7oZkKXGRy5T0zk94LsnzX/nFpst5xmJ1dtum2x2uD1QZO6X027WLzH62OVl//1KMzj3BswLF4CZt0a+/+/v4NErzVx8j9O8iKp5bL/6h1n/KGfz6YRb0w3nwtVnVf89G8tN55vD3zZ2PY4LCfQb0mQC/XHjxvH222/zxhtv0Ldv31qB7ubNm3nuueeIj4/nmmuuiZhntVoZOXIkb731Fh999BG7d+9m8uTJEcvExcVx5plnsmLFCjZu3EiPHpEtB4ZhVOXZN0RV1ap1avrggw/Iz8+vddvtcN18882sWrWKf/zjH8yYMaPWUJfz589n2bJlDBgwIOICozK96Ntvv+W886p79M+ePbtWGZUjEx1c91deeUVa85s4q6qQVqNhu2uiStcad05/1xNeGVl7vSu7QplXZ+OWAFaPhSzFwsT5etUDZC26TlwdreIJ3iCpJX5c4cP/XCgYvzrIP2/m6bQednjfIfupSSQvvpRZs2YBMGF7B8qe+PZX1qAGp4VW+2/HEu+qNcuaFoU1rfadhhMm/qCyYz3m68IB5mt7NvxngRnwux1w14XQsw1c+E/IqqfjdKwb/vcHM7Ad1h2iXGbw+8lG+GGn2Ykzp9hsnbz5/Nqj3LTBbA0/knpDdfDR+qCW5soAvr4HdHmcv/7hXTXrY7fVDvJrsttgbP+656XGm3ccavrH1fVvy2k3X2Ae51HVaStVx6PmMvVpEWu+6lLzAuF4BHhuh/mq7z2Y+1aprmNbX92bC7utaQTXTaUe4oRrMoG+y+Vi2rRp3H777dxxxx2cc8459O3bF4vFwo8//sjixYtxu93861//qhrisqYxY8Ywd+5c/vnPf6KqKhdccEGtZe6//35+//vfc+ONNzJ69Gi6dOmCruvs27ePL774glGjRtUadacuZ555Js888wx//etfufTSS4mOjmb9+vWsWrWKjIwMNK3hnOa6dOzYkUcffZQHHniAyy+/nDFjxlQ9GXfVqlWsWrWKrl278thjj0W0co4YMYLnn3+eRx99lMzMTGJiYli9ejVFRUW1yhg6dChz5szhj3/8I+PHj8dms/HNN9+wbds2GVazGYtyq5zeq/oHN3CXygWvBViaa0E3wGuz4D4o9aDMbuXU4tIj6rHv+7VBlwLpg1oc9erua7of00A/+qbedQb5J4UOqWbKycF2vmiOiFLmM/P9E6LNltYyH2TUPrdit5ljvV/QB/488bhXWwghDpek7jSsyQT6AO3atWPu3Lm8+eabfPrpp6xcuRJd10lNTeWyyy7j6quvrjPIBzjllFPo0KED27dvZ8CAARHDZ1ZKTU1l9uzZvPbaa3z++ecsWbIEu91OSkoKQ4YMiWgNP5SMjAyefvppnnvuOWbNmoWqqpx22mlMnz6dJ554olaP+SMxdOhQ3nrrLV5//XVWrlzJvHnzCAbNoctGjx7NX//614jnBYDZSea///0v06ZNY9asWbhcLs455xweeeSRWmlOvXr14oknnuCll17ixRdfxOFwMGDAAGbMmMGNN9541PUWJxdVURjTFjbsDJAd7eTn5Gh67S/CUtHKn+NxUOCy4ThU2kYdoqMtKFYFI3x06TOqTcXqOvrTkq1LAs6xHfF/ePTDz2JTsbaOIfrWvsT8sV/Dy59sHDYYc9B+xXnMlxBCiGZFMQ7O4RBNTl5eHjfddBMHDhzg2WefpVevXo1dJdEMFPgMxvzpAGV2C/tiXPisFuIDIXxWC4UuG91zSmhVfGQdy3udaueWPjrfPr6J4p0lGEfYTza1fyKj3zz7iNYJhUJVqTvXX389VsXCLtvjR1YwoLaKJv3r67CmH8eHXQkhhDhmAsotdU53GC+c4Jo0XU1gcGDRkKSkJJ5//nkSEhL44x//2OTHuhcnhwSXwpVXJ6BbVLrkldG+sBzFppBc5ufsnXm0LvEd8U3RwX1dtBqaysQlw7luw7gjWtfiUhkwpecRllibYlWJe2JY7RkuC7itZk9ljw37GS3x3NiT2L+cSYv3JpCxZZIE+UIIIZqVJpW6I+qXmprKggULGrsaopmZPNDGrQPi2ZgLaR5o4Yb1m61k5zlon2Hl7sdy0Y6gj/aCT8oZdbYZLB/uzcKkHnG0G5VBl0va4og78hGr6hJ37yCcZ7em6MHPMYIacY+cjWtI/U/dFkIIcfKRHP2GSaAvxG+cqiicVmOwk16nmh1qP/3Gi6ZDgctGUFVJLQ/Us4Vqu7PC5OSFSUmyUrS15LDKH/HKmTjjj/3Qes4B6aQuveKYb1cIIYQ4WUjqjhCiTimJFsptFr7LSCAn5vBG03E5FGKjzdOKJ/XwRqvRAjKsqxBCiKOh1PMSlSTQF0LUqWtHB0qnaHRVISvaSbm14dPFlWNjcDrM5fyFwQaXVx3KYV8QCCGEEOLISKAvhKjXuQPNINxQFPKi6k+viXYr/PfBZCaOqO7MWpJZ1uD24zvG/PpKCiGE+E0yUOp8iWoS6Ash6nV9D5WoisH1d8e5qat77eC+Tl59PI1ObSI70qb0S6Shx+QGikLHqKZCCCF+eyR1pyES6Ash6pURrbD2OitXtDdItRsU1HiYlcMOLz2awpRJSbictU8lzngHw57qf8jtx7SLOuZ1FkIIIYRJRt0RQhxS5wSFORNsgI19OTa27Q7RPsNKq7SGh8Jsf0ErCm4rZv1zW+qcP+TRPse4tkIIIX4rJE2nYRLoCyEOW8sUGy1TbEe0Tp/bu1Ke5WfbB7upzP1xJNg5/6UziGrpPg61FEIIIQRIoC+EOM5Uq8rZT/Rj4J97oAV0GWVHCCGEOEEk0BdCnBDH46FYQgghhKifBPpCCCGEEOKkIzn6DZNRd4QQQgghhGiGpEVfCCGEEEKchKRFvyES6AshhBBCiJOOpO40TFJ3hBBCCCGEaIakRV8IcdQCIZ2/vVHKxs0BUmIUhp5qo1WSlT6nuYiNsTR29YQQQjRj0qLfMAn0hRBHxR82GP6PYqJ2FXFKXhHRwRC/fA77S8pYo0C38RlccE8nFEVOxEIIIURjkEBfCHFU3tykU5wfZPi+HCwGBFUF3aKyoXUq26NcBN/fSasOLnpMaNXYVRVCCCF+kyTQF0IclR9zdRICfiwGLG+dyur0FoQsFjoVlKAqCu/17sry5SGWjDVw2qRVXwghhDjRpDOuEOKotLRolNlt/JwQyxetUglZLFh1nQx/mI5lAdKDYTxBhRv+mdfYVRVCCNEMGSh1vkQ1adEXQhyVwiKNbTFRhIzqk2oLfxC3pkcsV7w7QJlXI8otnXOFEEKIE0kCfSHEYfGHDD7colMa0Jm/1WDBFivYVTbFRZv3BhWFrGgXP+kGXQvLAbBpGq2KivnX5dm07exm7B3tSMxwHbKc3du8vDsji4K8EKf0imLCDWm4o+QiQQghxMGk9b4hEugLIQ7p0106d34SZsMeDUPHPK/aLKAqKKiodtAqR9YxDHbGusko8xMT0uicm48nFAJg98ZSnrppE6ff1Rm73UJZuc4Z/d0kJ5mnoaBf592ndvHjl4UYikLQamHN58Xk7A1w9xMdGmfnhRBCiJOYBPpCiDrtKtR5ZmWAp9boaCgQNkDXwW2vWibJGyTXY8cV1uh3oJhUbxCvVaXcouLy+6uC/Eo2TWfRf3fSIesAjmCQN6xWevyuI8Nv68g7/9nFz18VogCKYeAIhdEVhT3b/ZQWhYmOO7zTlWEYBPICOBLsKBaVQFkYRQG7p3r9QJ6fzLd2EiwIkDYkmcSz02QYUCGEOMlIPn7DJNAXQtTyY7ZG/6fL8IUApxVsClgMsEaeMqJDYYo1KwNzikn2BQHwhHXcYZ2EnPyI7v7lNhvlViun7NqHLRBE1Q1swTA7ntnEO9/n83OWLWLbCmDVdIKKwqbvSkiNNfhlWS4WxaDf1a2Jb+2pXrjALGjVnWvIW7IPvTyM6rKgtYshu0RHNSC5ayxjnu6LGjL45PyPCWWWYw1o7FIgtYVOt3fOxzUg7XgcTiGEEMeBBPoNUwzDMBq7EkKIpsEbMlibbfD4Ui/LfwySbIW8WBc+mxU0HYI6hHWwW0FVSPQGaVXipXOpr9a2Wu3LpoXfT8DtpMRhZ2diAhl5BXTevR9bILKl3wD2tG8FB7WqB6wWMCCqrBxPMIiuKBgKWMIaZ1ycRukBH/sX70ErD6PqBh5/GNUAXYGyaCchu4XKE5yhKjiTnHQamEDBsz/h8OqUxdrwua1YQzptVS89vr6YqDbRx+noCiGEOJaKlD/VOT3OeOIE16TpkkD/GBs7dixpaWnMmDGjsasixBFZskPj2vdCtMwqo2NuMR2KSrDpBmFV4b0urdjmqdGCblSk8SgK7Up99Ckq5+Dusl+7HZy+Yw8DCop4v+cp/BIbhTsYYtymrXTLyq1V/p42LTGs1VvRAa/Djtvnxx4Kmf191eq+AK5SL/G5RSiGga4oWDSDKH8YgHKPHb8r8g5B2GL2Kwg4bHRan015rJ3SuOo0JEU3iCsOENXSjT3NSerQNE65vSsW55F1BNbLgwQ+zURt4cHeP53yL/ZhBDQscXbCOV48w1phibY3vCEhhBCHVKTcV+f0OOPxE1yTpqvZpu4EAgEWLFjAJ598wrZt2ygtLcXlctG6dWv69evHhRdeSNu2bQ97e2PHjsXlcvH2228fv0rXoGkaY8aMITc3l0mTJvH73//+hJQrmq9A2GDhDgN/GMZ2UIhxVLee7y7SGPueRpcDXloVe+lUWIKlog3Aqhvo4YPaAxTFfGk6O2OcWAyDPsXeqtn5FpW9Djtvn9oB3449rGwRb86w23h2QE/uXrmWdsWlKLqBoumouoFfA2+MC0NRUHXdLF9RCFksJBaU4I11V21f1XXi8orNm7aKggoYinlnQAFCttqPCFEw8HnMbWw7LY2owpKqeTZ/GE9ZGAWd8n1evDtLydpQxIE1eQx7axgA4eIARQt3YfjCqLqOvU007mEZZP75O4re3IqS6CTp3GSU576CsI6BgaZY8Rl2NCw48OEkSE5ULOmLL8UzpGW9f6vgih2U/WU5+o58HENbEzXrUhSnrd7lhRBCiLo0yxb9vXv3cuedd7Jz50769OnDoEGDSEpKwuv1smXLFr744gtKSkpYuHAhycnJh7XNww30g8EgiqJgs/26H+Uvv/ySO++8k4yMDAzDYN68edJZUByxVzdpPPClQVEAdAP8mjndrsLSixWGtFKZ87POdYt19IpcR0U3uO77bbQsqU7HealrO3LczsiNqwrYVCj2Q0gjIRQmLRSmzKKyx25Dr/i8JobD5Hsih9Q8c/d+rtq4BQCbP0TGrhI+HtEHn9tRvXlNIyW/kLT9efg9TooTY6rmuUvKic8tiqyPYeDxhlGB0igHAacV3WJBt6igGxgWhaDLad6NAKLzzQuFmIIAcQWBqs2EHApJaiEqOrtjkmjlVLDtKkTXLFX5oAYGAayogAUdF0FsaNgI4iSIUrGcFztZJOAgjIMQdkLY8eOLjiZtygCcQT+KYoDbimv8KSiajvfy2XjXFVBz2DhXUoiYrL9jWFQURUH/fg/hj37G0iUZZWx3FFVB2XkA5n0DKXEYEwaieFwYuo6iqubdl083wXfboG97GH5a9XE7+Lyi61C5jirPVBRCNF2F9bTox0uLfpVm16Lv9/u544472Lt3L08++STDhg2rtUwgEGDOnDkNBs7hcBhN03A4HIdcria7/djckp8/fz4ZGRnceeed3H333axdu5Z+/fod1rrl5eV4aqZZiN+E8qDBsl06n+8xaBOr0DEWrv+o7uv4oA5D3zbA0CriyervgqEqrGifxsQfd+EIayxtn05OrBtCkQ/CwjDMfP2KB2QlaxqbXY7qoTaBOE2j3FH7O6HUqFbYbiUvMRr/QS3WusWCp9yLZlcxjIPKroeig92vERP0U5jkxrCCq6gcRTfwhEKkFpbhDIbIj4tiZ1o8SlAjtkaQD2Dz63TQ9+MiyClle8gmmVKiIjp9mQOLGhioaFgow4EHP1GEq4J8ADdBovBhq+gpoGHFRxSW0jI2TtlAilFEEkXEUAD3BCkjGh/REdsA8OVZ8Cb/nQPF8aiKQYJ2gFiKCWAhjA0NK06KcFJAgES0a9/HZfFh0UIYNgVC5SiAgQWFEIpqVA2VqjhscFoHjO6tYN7XkF8CDgfBgAOjRRz2oa1Ri0ohJQ4uOR0uHBBRN8MfhJc/x9iSjTK8G8oZnSAxGnZkwzurIK8ULugNp3eBQBjiPJBbDAlRYKlIiwqEoNwPCTX6R+QWQ3wUWC2weA0s3QCd0+CCPhDnhqAGUU7wVFyAlvng5U9gbz4M6w6j+tb+gBSVg8MKrjrO6XklZt2sh5mqVVBqlu2QOy1CiKar2QX68+bNIzMzk+uvv77OIB/A4XBw/fXXR0ybPn06M2fO5K233mL+/PksX76cvLw8nn/++cMOsKF2jv51111HVlYWixcvxnrQiCWrV6/m9ttv56677uLKK6+smp6fn8+XX37J73//e84880wSEhKYP39+nfWoLO+uu+7i2WefZePGjcTGxrJgwQIAdu/ezcyZM/n2228pLi6mRYsWDB8+nJtuugmXq7qVNTMzk7lz57Ju3Tqys7PRNI127dpx8cUXM27cuMPef3HihXWDyZ/ovLTBQKsKoA/zRl0917q74zz8Z3A3nKEwfkU182LADPZ1AxyW6pbgWBeuwnLahDQsBPnJYcdQFFyaTktNpzAcZn+N4Mmi6wzevd98oxtYAyECbui1fjP705IpSIojyucHDIrjYrBqGnZ/EEe5j0DFnYGAw0blkP6Vu2D360QXBFENcKLhKS6mONmBblFQDANrSMMeCqMASUVlOAIh9kVH1z4EioIPBy6CqBgkUEQxMRhAAAs6KhZ0FAw0FOyE8eDDgx8LWsUFQfVWnQTRiAwGbRicbnzGtwymPduxE8KLh3LiUQjX8WdRsBYGiaOYImKxYd65UNGwohHCBiio6HjIQSMKRatojQ8ZgAsDLwpBwCCsezCwoRgaqr8c5Zuf4Zufq8sNBLATgNwSlHd2V1dj9udmwL/gzzCgE4bXD20nQ26Bue7T8819j3FCSXn1ev+aX/F5McDtNIP69AR49vewNQsefQ9KvHB2N5gyEe5+FTbthpRYM5jekVPriADmXaXfD4fBp8LvnoOQVl3e/RPgn1eb7wtK4Zr/wpLvwWWHP4yunrdpF1zzNPywE5Jj4clr4dq6fzsA2JcPVz0Fn/8IMW54YCL8aXz9ywshjhsZdadhzS7QX7FiBcBRB6d/+ctfcDgcXHXVVSiKQlJS0q+qz5gxY3j88cdZvXo1Q4YMiZi3aNEiLBYLI0eOrDVd13VGjx6N1Wpl5MiRvPfee9x3331ERUXVKiMnJ4dbbrmF4cOHc8455+D1mrnSP//8M5MmTSI6OpoJEyaQnJzMli1bmDt3LuvXr2fGjBlVFx9r1qxh3bp1DB48mPT0dPx+P8uXL+fvf/87hYWFtS6MRNPx/A8G09cfwww83ahIdjfwVwboYcCiQmkAnLbIdA9VwXDZoNRHRkgjOeyjRFVRFQWfRSUhpKEaAYpsViyGwaU/baN1SRkA1mC4qnXfqum03ptNVDBA2GneBTCAsugoEgMFxOaXoBWVYShgDWtodiuKbuDwhYguCaIYBmXxNqwhA2dZmJDTgmFRqvL4y1xOcjWd1GKz7GhfALfVRdiiEHDZcPpCWDQD1dCJobq/gYUwQSwEsKLV6HJsJUwCXtz48VBz1CGzpd/sFWCG/BY0tIpp5jZDuPGSquzHMAyCOCgnrmJtCwpBjBplKRUXbi78BLARRY0gGrASwkkeKjpGxf2GSAoKFhQMQkSbDzqrqIlODBZKUNAPWqMeOUUw8QnIfBFunoGSW1BjZsXlV0l57fUqs0TL/eb/9xfAZf+uDs7BDJ6/2wbeirssOcVAcX01MT+rM5bBS8vNf9c0bQHcN95spb/nNVi8zpzuDcBj70Of9nDJGXD5NPhxjznvQDFc/xyceSp0SK27zEnTzXqCeXFy3+swoBMM7V5/PYUQopE0u0B/+/bteDweWraM7OimaRqlpaUR05xOJ05nZN5xVFQUzz//fK3W96N1/vnnM23aNBYtWhQR6JeXl/PZZ59xxhlnkJCQELHOggUL6N27N+np6YB5sTBnzhw++ugjLr744lpl7Nu3jwcffLDWxc3DDz9MUlIS//vf/yJSeQYMGMC9997LkiVLGDt2LACjR4+ute0rr7ySSZMm8eqrr3LNNdccs2Mijq2Pdx6HIB/M/xtQ0XRsxnC6YbaiHiRgsxJSwGaA3YAkTadMNQN9gLiwRlxYw6FpOD0etmek4/SW03Zv7dF3HP5gVaCvAKgKYauFQLSHqMJSlIpOumCmGQVcNtz+MCGHGRiHnBByqFgO7kAMEWlEGgrFcS7y0uMwVAVF00nbXcig7J+wUh18FhNNicOBNTLDh3BFIO7CHzHdbLc2MFAIYEOtmKKiEcICGCRjtpJHGSXYCVFCbNX6BgrZtMBVcf/AixMLGi0wHyZmI3Jo0qrjhApoVP/hDv47GRXLRiYGKYCOE0uNi5sG7c2Hn/fC17/UMfMIPo81g/xK3kDtaQ05OMgHCIbNVJ04D3z8Q+35H30PZ3SpDvKrtqXD8vX1B/offV972sc/SKAvRKOQFv2GNLueVmVlZXW2eu/cuZPhw4dHvN55551ay1155ZXHNKCNjY1lyJAhfPnllxEXGp988gl+v58xY8ZELL9+/XoyMzMjpnfu3JnOnTtXpePUVUZlwF5p27ZtbN26lZEjRxIKhSgqKqp69erVC5fLxddff121fM00nkAgQFFRESUlJQwaNIjy8nIyMzN/zWE4ZgoKCggEqgOBsrKyiOMaDAbJz8+PWCcrK+uQ77Ozs6nZJ/1kK6N9HMdOXTFaUDNzqwGsqhlAHaR3Yak5fn3Fex2wGmCpGYAZBu3KzJZvn8fFuk7t8Dlr5+9rdeRIl8dEE+0L0XF3fq3Oo4phVAX5lcJ2tboFuQabVh1YFjld5KXHYlRcuBgWlf1tEsiJjiWMigEUEc1WZwZ6nZ1SFXSUqtb2mnzYKcNFuEbKjgLY8dOBH4ihADOhJkw5cRioFdsx8OEggJMiYsknHh8uyvCgoRBGpZSoirsD1cywPlRVDgQPqlEYqi5e6vphPLIfS8Nhg5aJcFrbI1rvsByjQQdC3VpC24rBFtqn1JrvTYshEGWHGqM5VemQWv93sK4LgA7m9k/2c4mUIWX8mjJE09TsmmijoqIoKyurNb1ly5Y899xzAGzdupWnnnqqzvVbt259zOs0ZswYVqxYwbJly5gwYQJgpufExMRw1llnRSw7f/58rFYrXbp0Yc+e6pam008/nddee42tW7fSqVOniHVatmyJxRIZ6OzcuRMw+x5Mnz69znoVFFTfcvd6vcyYMYNly5aRk1M7H7akpKTWtMZw8N2Pgy/q7HY7iYmJEdPS0tIO+T41NfKH+2Qr497+Kh9s1dhX+2N/bAR18IaAANFhDbemk6MoZqdGQAlr6GGdaE2jTFXxq6oZrBkGnUvL0Q2DgNVCUiBIdNgMNnVFISoQZGdGKqdu341a8fvicznweyLvsvntNty6zqk/7UHTze1GBIP1NCAbCpHLGgZ2v0axzYnfaqMk2oFxcACvKmyNyyBY6qnqbBuw2s2hO5XITsQWNGzoBLDjrBFYG4AfR1UH3JqsKASIxkUZoFJKCkFceCjFWtHqX98OqQTRCZNKEXby0YnCqCgliAUnalX6jUoAgzAGNsx7F1rVnYbKPgY166sedFeiIcpfL4H4KJS/X4GxeB2Kr2YrvA3quOtQp8vOhO3ZsGZ7xapWuOZseOWT6mXs1jovLqukxpkXHDVb7U9pie3DB6vf/+MqGPlI9d2CLi1x3zUOYqPNebfNrF52TD84tyd2Ran7O/jktTDxSQhV1Kl/R7jSPI+f7OcSKUPK+DVlNAbJ0W9Yswv0O3TowLp169i3b19E+o7L5WLgwIEAtYLimg5O5TkWzjjjDOLj41m0aBETJkwgOzubdevWMXHixIhhOL1eL8uXLyccDnPVVVfVua0FCxZw9913N1jnyivzq6++mtNPP73ObcXEVA9X+MADD/DVV18xfvx4+vTpQ2xsLKqqsnLlSubMmYOuH96oJ+LEax2j8PMNFt7fYuALG7isUBpUGN4Gblmu89mehrdBuOKJt7aDvhuGYbbmGwYYBn6LiseigN0CpX7QDAzgB5cLPwq9SstRDAgpCp5wGHvFg7UcgWBkuohhkFHmxXA7+alrB2JLygnZrJRGu/H4g7grWpJ8DjuGAQ5fEHtQI4ANW0ghVONGQHFsFK5AYdXFAphPxs1P9hBTEkCpuKtgKApZLaKwB3U0VaFTdgF72yVFBvuGQWyJt6Kd3qyxVdMJ2SwE3RasAR1VM1AwcBkhDMCLeTfMTrBiBB43YWyohLBEBO3meoVkUEY8IVyEsQM6ARwYhLCi4cZHMdHoNXL0oykkie0Y2NCxYsEP+NGwUe7KQG2XRklREtHZP2HRKy86dCCIio6uqGDoFf0FitGrRvYxUCxB1CuHwLZc+GkPpMXDWV3MoToLy8y/vd0Gbjv06wgPXw79KxobTslA2TcTbn8FVvwIhgoDOsA9o8zOtB6ned84r9T8bKkqtE+Gzfuhe2s4pweENVjwHWQVmkF222T442hzONCuGWaZsz+Ht1aa6ULRLnOI0PgoMzf+iiHm6DvL1pvzh3WHnm0jP8dDusK252Det2Yqz7gB1SPv3HqB2Ql4+QbonA4jeh36rsLY/rDlWVi4xjxWF/Y3L1CEECecBPoNa3Znp3POOYd169Yxb948brvttsauDkBVh9o333yTvXv38vHHH2MYRq20nWXLluH1erntttto1apVre289dZbLF68mD/84Q8NjtNfeWdCVdWqC5z6lJaW8tVXXzFq1CimTJkSMe/bb789nF0UjSzarnBd99onvE8vU3nzZ40/fW6wt0aL/5npsGiiysi3Nb7eqYE/DBbFfFUOZ6MZZkt+5UUAZjttNkCRryoYshgG0aEw2xx2OvoCtPAHiKpxCzjf6cCuaSQFzFZeHXBUpNBoqoJmc5DrNIMuRTfwOh34nNXDH2bsycIaDOOz2VBDkJ8cT0msC7s/QNDpwF3mJWCzYAvrqBVPyQ1bVTSrhcIEF3Z/GFXT0SzmQ77CNgu2oIYtrNEx8wDb2qaYOfq6QUpWEfZQZau6uX/uQIgSi4JhUQm5LVjCOk5vCB2zld1ZGUxXdITVULGgoaJXdI4103IsFa3qIYuV1p9fR7BMRS8NYo22YhT4cJ7XDt0fJn/MWySvz6KYaMJYiaKItq4tGP+9HVLisIzohrJyM+QWYx3Rm9i4GkPpVo6Xn1+KckYXlNVbIM6Nem5PyC2BhWuwZBViGdTZ7FS9Ow/O7WmOpHOw6ZMO78MXHwWz/1B7+pCu9a8zok/1v21WmHhQY0TPtpHB+u2jzdehnN/LfNUnLQFuGVn3vG6tzdfhapsMk0cd/vJCCNFIml2gP27cON59911ef/11unbtWu8Qmyfa6NGjefPNN1m0aBFLly6lTZs2dO8e2Xlr/vz5xMbG1tvx1e/3M3XqVD7//HOGDx9+yPK6dOlChw4deO+995gwYQIZGRkR88PhMOXl5VUt9wAHPzstLy+PefPmHcXeiqbkilMtXHEqlAQMVu836BCn0DHeDGJXX6Py8gaVad9Z+LmwonUkpJuj61SOm19XR0fdAItCB6+fIYUlOA0Dr6qS5XKwODmeLuU+okMaqgEpwQB23UBXq7PHHVWp8pEXJ4aqoBsQW1wGikJcYTHRZV5CNis/n5pBtw17sYU0QnYbIbt5sevzuMwn6Na4G6FXjHCpoBBy2UAzW+JVXccWDKNZVPJj3LTMKSKpoJQyjxOHL0ihy03QYiVKCWIY5ig59igrbYamUpwTwN7CSecHT4OyIIHvDxB7fkvyZvyM/5ssXD0S8Rg+lFc3VAynWZl1r1dl1CutY2m79BLsXRKo64kbFiD1hxvRC7wkT12Add3PWNrGwz0PQ6921Que06PuP7aqmoF7pUtqjBqWEge/O/R5QwghRPPS7AJ9p9PJU089xZ133sm9995L3759GTRoEImJiVWdSpctW4bFYiElpXYHrUMpKiripZdeqnPehRdeeMin7J5yyil07NiROXPmUF5eXutuQ2ZmJhs2bGDs2LH1dgY+66yzsFqtzJ8/v8FAX1EUHn74YW655RauuOIKLrzwQtq3b4/f72fv3r2sWLGCyZMnM3bsWDweD4MGDWLJkiU4HA66detGVlYW77//Pi1btqS4+BDD24mTRoxDYUS72q3+v+tp4Xc9LWzKNbhhicbaHMV8qm0DD812ajrDCoqrTiJuXSfD5+fTlDi+cDmw6AZdS33YDZ0WgSAqUGSzkmu3073I7POhGkaN8W0qtuP1E1dWjm6xYMVMDTJUhQMtYrCdkk7rnQc4kBJrPvEWCNptlMV4iCourxjyxsAe1LBrBmGrBd0wsITCODTzJq+mKGhWhb1J0fgcVqK9QUJAqceNRQPFYUUdfyqtL25LTKoLd7d4LO46vpPnmqmBrZ6N/N7HXduV4MvfECo3sF3VG6VtEqoV1Gg7tg7xhzymldQEN46nLz+sZYUQQoj6NLtAHyAjI4PXX3+dBQsW8MknnzB79mzKyspwuVy0atWKiy66iIsuuoi2bdse0XYLCgp48cUX65w3cODAQwb6YHbKfeqpp1BVlQsuuCBi3vz58wEOeQciJiaGfv368c0335CdnV2r88zBunTpwhtvvMGsWbP44osveO+99/B4PKSlpTF27Fj69+9ftewjjzzCM888w5dffsmiRYto1aoVt956K1arlYceeuiQ5YjmoXsLhW+vtVIaMPjn5xr/XFrR2dCmQuCgcFyB5ECo1gnEoRsk6gY5LhvpxT7sQGaUh5+i3KSENUK6QVhR2RHloW1pGaphYA3ohO3mluzBIDH+AEG32e8k7LChWSwoho5F19jXOoG9GfG0itZIOTORPWsKCW4tJKrMh8cXJGS1mPcIDFD8IVSLBoqCLWzm5XtdNqxxDigwU4/yY9zkx7jBMDilazQthqTR5v864kh2cbSswzpiHdbxqNcXQghxeCRHv2GKcXC+hhDiNy+sGaT/o4zcYq3iiU+W6k65dgudSsooVy2MzS+OOM1qCrzePh2/3YpVN+iZZw7HlmO10C5o5ujHl/k4bUcWUb4g29omkxcfQ5q3lNhyH9aQjit00IgthkGb0xPpMjCOkgMBugxOpOUp1SNAlOeW89a972ApV+hQmkz5hgJCqJSkRBMoDWILaZR7nJTER6HaLUx4rDv7Xt7ILyuqh4brOiaF058683gdTiGEEMdBrvJgndNbGH8/wTVpuppli74Q4texWhQy7/Nw2XshPvopSNhhgxg7GOAOa3TN8RHtD5PtdJLmN0fIMYCNsVH4FRVCOmGbSrFVpcBiISFcfUdAs6psPKV11QVCRisbT/3tVMIhg0cnrqtzZMbxf2qPO7aurHawx9kJnxEkDAy8fnitjurhoM62r/LwFoboODiRmBQn7QaeTae1+eRuKCSpRxyp/X7dE7CFEEKceNKi3zAJ9IUQdXLbVT68wsF/v7Pyp+XmyDUW3SBktbAuPZFOOaUUOx3kO+y4NY0yq4Xcmg/ACutst9uI03VSKsccNwxyWnpYdHssm3eGaJFopWtnc4Qdi0Oh34UpbJq7O2J4Q2e0td4g/3BY7SqnnFM7rS6lbyIpfRt/HGghhBDieJFAXwhxSH/sb+GKriprsw26t1D47KcgN37iIq4shF0z8Fkt+CqeZhuuOf64phMXDBGtKhgYlKoqwVQX82+NISVJJSW1dvB+4U2tCB/wsvnTPDDAGWXh8scOMUyjEEKI3zBp0W+IBPpCiAYlexQu6GCeUK8Z5KBfiwB/el1lf04YpSK414ADjuq0GVtIQ7dZ2Ou0M+VcBwP7uDktTUVVD31invBgF0ontaUsP0hKxyhUi5zIhRBC1CadTBsmgb4Q4oid2sHBWluQIptCoq5jAHlOK0GLWvUU3Q4+L6llPoyMGG4YkYjdrja43UrRSQ6ikxwNLyiEEEKIekmgL4Q4KkVFYXyqwl614kFVYYPOpaWEPHZ2Rrvpn1OANd7O8389siBfCCGEOBzSGbdh8usrhDgqcXUMNZ/lslOuKozfuotOxaX87U8ZOB1ymhFCCCEag/wCCyGOyp1DnRHvFcNgUF4B//fLTgbHhLj6iVNp08ndSLUTQgjR3Bkodb5ENUndEUIclXvPcZHgVpn9XQBVN7joVCv927Shf0cbVulAK4QQQjQ6CfSFEEftd4Mc/G6QdJoVQgjRGKRRqSGSuiOEEEIIIUQzJC36QgghhBDipCP5+A2TFn0hhBBCCCGaIWnRF0IIIYQQJx15Mm7DJNAXQgghhBAnHUndaZik7gghhBBCCNEMSYu+EKLJKPXq/G9hCVmlBgN7uGgX9rHp21IMt5URE5JISLI3dhWFEEI0GdKi3xAJ9IUQTUJmpp+/PbQfJWQQVBS+WO1FDYdwh0LsjrLx7No8nrghljMHehq7qkIIIcRJQVJ3hBBNwvPPHkAJ6liCQYoNg8RgkBRNx2KxMHLvPso1jT+/VkwwJN2vhBBCmDn6db1ENQn0hRCN7qPpmXzgsxNfUEjb/AJahcKkBoIkBENkBELodgcjcvLQfCH2ZYUau7pCCCHESUECfSFEo9qwO8jv1jnoUFhMkj9ATkxMVXuMUfEKWK1YgP5FxWzcXX+gb4R0jLB+AmothBCisRn1vEQ1ydEXQjSqxz4oJTvGRZlV5af4ODr5gySGwtgMA01RUABXMEQs4A5rPDG7hDO7O0mMs1RtQwkZZLwB6257jZDFgvvsVHotGInFYam3XCGEEKK5kxZ9IUSj2qJZsYY0WhoGncIaWC2ELCqaqoKiYCgKXoedgMVCic1GWFVZuKwkYhsp74NjnY0cj4c8t4td3xWxpP3b5G0obKS9EkIIcbxJjn7DJNAXQjSqJLeCxTBI0SpSbgwDu1775muJw87mhDgwDHZ+VwxAsDTE4iu+JPYrlRKng5JoO/kJDorjnOTbbCwd9RGb39xxAvdGCCHEiSKBfsMkdUcI0WgMw2DVHh2VGq0OioIOeEIhYvwBdAWKnS7iS8pom32AnJhoSmw2PvswD3X1bgrXFuB02PC7rHg9dpz+UNVpPmy1se3ur+hyeTsURU7+Qgghflsk0BdCNAp/0GD0C2WUhhWiUChRFFyGgRWwhkO0LiyqCtjjvT48ZV5suk6bgiLyY2P44LUcuuXmE4x2stvlxNB1FLX241OKrQ6K5m8nflzHE7yHQgghji9pwGmIpO4IIU6In/N0PtiskVtuUFquc/G9+/l5azmJYZ3OoTBeq4X9TjtFFpWUcm/E6VsFNFt1u4QrEMAa1igpNbBqUBrrIuC21zncgoHCvvEL+SFpBrse+hajjrQgIYQQojmSFv2D9OvXjzFjxjB16tTGrooQJ43MYjMrsk1s3a0rN34Q4KVN5r8Vw2CIK8DXVje9igrxGwp7XHZyHbaq+RnFbnoHghHbMCpSb8qcDnJjonEEQ2guG16HpWpItaDDhsMXRDEqgnlFIbHMvGiw5XsJTP2MHQ8vJ3ZQEglvTERtm1irrkZeGRwogVPTJN1HCCGaMGm2aVijBPr9+vU77GUXLFhAenr6IZfZv38/H374IUOHDqVLly6/tnq1jB07lqysrKr3iqKQkJBAmzZtmDhxIiNGjDjmZR6uOXPmEB0dzdixY2vN0zSNjz76iPfff5+9e/dSWlpKXFwcrVq1onfv3txwww3Y7XYAPvzwQx566KF6y/noo49ISko6bvshTk7fZelcOE8nu9x8P6wVfDjegseuEAgb/OODMt79PshPrqiqM7JhwBc+J9iD5Nrt2FW1KsgHM6Bf1jKZngVF1Bwc0xoOkxcdxZ7k6s9hidtFnx+3UR7nQVdVVENDVxWsmoEBaKrC/qQYiqKdWHSDjIJCunh3U7pK50C7V2k7bzSu89qC1Uro/Q0Eb38f8kqxEEZpFYdy3elYBrfHmPcDxuodKLEO1KsHol57OkqNOgshhBBNUaME+g8//HDE+++//54PPviA8ePH07t374h58fHxDW5v//79zJw5k/T09OMS6AOkpKRw2223AaDrOgcOHGDhwoU88MAD5OXlcdVVVx2Xchvy5ptvkpaWVmeg/+CDD7Js2TJOO+00rrrqKmJiYsjJyWHz5s3873//4/LLL68K9CtdfvnldO3atda2oqOjj9s+iJNPdrnBRzt1fv+RgVajSeXT3Qan/y+M26Kzfr+OolkJeWygA5oG/hBoBlgUsNvQXA40XYfKdBoFUBT8Vgtb4uNoW1KKT1UYuGkXtnCYj8/oGVEPn8tJfmwM6dm5FCbH4Sn1Yq0YvUcBrJpOUFUpdTkBKHI7Sd1zgHhfMUGc7Bs3nzT2Y6BjJ4ANhRAOwjhgTxn63z/D4DMUdCwEsRGAL7aiP/YR1llXm3cOzugCNrk5KoQQJ5qMsNOwRvl1GjVqVMR7TdP44IMP6NmzZ615TYXH46lVtwkTJjBy5EgWLlzYaIF+fX7++WeWLVvGsGHDePLJJ2vNLyoqIioqqtb0Xr16MXz48BNRRXESMAyDXSWQ6gGn1TyhPrQyzNTVlQscvAJsLDBwhA0CVgtRWhiXL0SB3QbeYPXyBhAI4TQMdjvtENaqt2Gz4NQNDkR7+KRFAgVOGyM/2UC8N0DIWvsBWEG7Fauu0HpHPuWe2qc0a1hHq1xPUdgem0p/31bS2ImNQjRicOGv+rmwEsBHDDrWiuqqGKiEsRLGiZtcLDu2opz9IAC6YqXE3QGlbSLuftEY2SWEWrXC/n8DsZ3ZDgpKIaRBSlxdBxh250JyLLgcsDcPYj0Q7apeZk8exB00TQghhDgMTboZyufz8fLLL7Ns2TIOHDhATEwMAwcO5JZbbiEtLQ2ITDl56KGHqv7dp08fZsyYga7rzJo1i6+//prdu3dTXFxMYmIigwcP5pZbbiEuLu6o6xcTE4PD4cBmi7yFv337dmbMmMGGDRsoKioiJiaGtm3bcs011zB48OCIej///POsX7+e+fPnU1hYSMeOHbnnnnvo0aMHa9eu5fnnn+eXX37B4/FwySWX8Pvf/76qnMoUqKysrIh0qAULFrB79+6IZQ72a/ZbnBjBsMF/VoZYulWjQ6LCfWfZ6ZB4HPvPf78D/r0ADhTDxEH8p89wHlxp4A2DxwpPnK2wYLvBx5kG9Y50oCqAQtCmMHBPAVHBMHs9DuwBnQLNIGi3oLps6BYVZyhMkR6uEWJXCGtEhTS+cTvxqyrxvgAxPjNf31PupyzaXbWoouuckbkOe7aDmGCQHZZEAnbztGYAmkXB7Qvhd1hxhjViy3wkFgTZS2vAIIpSWpIZUQMFsBEgiBUF46BrGRUDBZXqCxPVCOMpz8T4MRvrjwEUwM5ajJfewajZ3hTjgtR4s4DO6dCjDTz1oXkBpCpgUc0LAkWBxCiIdUNmLlQ+X6BrK0iNg/NPg7R4ePMrc3lVgdxi805J+2S47QJQVXhuCQTD0C4ZPlwD5X4Y2Rtm3GJeVNTns03wzGLwB+H6c+DiM6rnLV4L05ea/775fBjVt/7tCCHEcSYt+g1rsoF+OBxm8uTJrF+/nnPPPZerr76a3bt389577/HNN9/wv//9j5SUFHr37s3111/PrFmzIlJ/EhISAAiFQrz++uucc845nH322TidTn766Sfmz5/PDz/8wOzZs2sF6nXRdZ2ioiLAvAORl5fH3LlzKS8vZ8KECVXLFRUVccsttwAwceJEUlNTKSoq4ueff2bTpk1VgX6lZ599Fk3TuPzyywmHw8yePZvJkyfz0EMP8cgjjzB+/HguuOACli1bxosvvkh6enrVnYWHH36YadOmERcXxw033FC1zfj4eDIyMgD45JNPuOCCC4iJiTms4+71eqv2s5LT6cTpdB7W+uLYuWV+gFfWhgFYsQM+3Kyx+U43sc7jcGLbkQ1DHjSDQYBl6yk4vxDveRcDUB6G2z6pDHnrKb/GZENR+Ck5hiE782hVGqQVQUIKfBkXj66aFyt+m5VgjAdK/RF3BhTdIF9VaREKYbdZuP3T78lJiWFPq0TaH8hhN0n4nHbivcVM3PAxp+/+gTISWNjpfPLdUUQVe8EwCDhsGKpC0GHDoul03Z2Ds+LOgYUwKgZlROPHjY4VKyGclFc9cqVSAAcKBlbCqOhYqR4RyMCBgR0LChCucTBUFCyR00p85gvgl/1m8F1JN0CvuHgwDMgrNV81/bTHfK3YWPfxB9i4C+Z/Zwb/dY0uNPsL2HkAvvpH3euv/BmGT62+uFi8Dv73B7hmKCxaA2NqrPfhGvjwzzD68PtcCSGEOLGabKD/4Ycfsn79eq655hr++Mc/Vk0fOHAgd9xxB88++yyPPPIIGRkZDBw4kFmzZtWZ+mO32/noo49qBao9e/bk73//O5999hnnnXdeg/XJzMysldLicDiYMmUK48aNq5q2fv16CgoK+Oc//3lY29U0jVdffbXqYqNdu3bcfffd3HfffcyaNasqX/6iiy5izJgxvPPOO1X7OGrUKF544QUSEhJq7Xe3bt0YMmQIX375JaNGjaJnz550796d7t27M2DAgHoD94P7TwBcd9113H777Q3uizh2ygIG//s+HDEtu9Tg/R/DXN/3OHQCffXT6iC/wq2rl/L3ikC/YbVb+UsdVgIWS9XUcpu1KsivpFe2ZIf1qml9D+QzyBtAVcxwe2/X1sTuUlEBu6YzeuNXXLB9ecR23BRS7HaiWy2UJERhqznyDqBZVPYlxdIhuwCAMDaiKMWFDx9xAASAAC5iyDNz9AEDFSpCfwvluMlHrQjeDWwY1PweVZ5OK1v7G7Gl6VBDiK7cDDlFdacSTV9aHeRXev4jM9B//qPI6YYBL3wsgb4QotFIi37Dmuw4+p9++imqqnL99ddHTB88eDCdO3fmiy++QNf1etaupihKVVCraRqlpaUUFRXRv39/ADZt2nRY9UlPT+e5557jueee49lnn2Xq1Kl0796dxx57jAULFlQtV5n3vmrVKsrKyhrc7sUXXxxxR6HyjkT37t0jOsXabDa6detWlZJzOJ588knuvfdeOnTowNq1a3nllVe46667GDFiBLNnz65znRtvvLFqPytfNS9kGltBQQGBQKDqfVlZGaWl1S2fwWCQ/Pz8iHVqjphU1/vs7GyMGkFhUygjEAxS18BhNeO3Y7ofRu2y1Dqm1auur6IOZbbqnHpXWIsIvivLbVNaXvVWAdoYoFYMa6kAUZqOr8aFqVLHcalc1vyHglHHud9rj7xA0rGg1kjNsRDCTpAg0RVt+gYhzHUclBDLPmw1Eo0Mal9wGRGn1IbPT43BgIi/d8TnqI5DGwwEzc9VXYddN5rtd1DKkDKkjCMrozEY9bxEtSbbor9//35atGhRZ8pJhw4d2LJlC0VFRVUpOoeybNkyZs+ezS+//EI4HNlKWlJSclj1cTqdDBw4MGLayJEjueqqq3jyySc566yziIuLo2/fvowePZoPP/yQJUuW0LVrVwYOHMh5551H+/bta223ZcuWEe8r97euIUVjYmIoLi4+rPoCWK1WLrvsMi677DL8fj+bN29m5cqVvPXWWzz11FMkJSUxcuTIiHU6dOhQaz+bkoP/3gd3KLbb7SQmRo6NXtmfo773qampTa6MxGgHV5xm8HqNVv0kN0zsVv2VPab7ce1QMz/fVz12/fRBkXew+qfAmhwjsgVFofqsGtbN0XTAjHHDOvYaF+MO3aBtUTk746vrMSgrn3Mys5nRpTX5dgeGqpDmjxw/HyDgsOHxm3cctse3o9zmwhPyVc3PcaQTsFbnnRsWtToVpkKsN1DjnZmKAwo6KhbCOGuk5CiAio5eMcCni8Jadao7kK/Z21irY/4JUvPvcvCsAZ3MvgIVIj5Hvx8Oc76EGn83++TRZr+Bm8+HJesiN3bz+c32OyhlSBlSxpGVIZqmJhvoHysrVqzgz3/+M926deOee+4hJSUFu92OruvcfvvtEVewR8pqtTJgwADefPPNiPz7hx56iGuuuYZVq1bx/fffM3v27KrW9MsuuyxiG6pa900Vi6X26CK/htPppFevXvTq1Yu+ffsyefJkFixYUCvQF03HzPEOOiWqfLQlTIdElQeG2olzHafblJ3S4bNH4IkPzIdFXXw63c8fweDvodAPV52q8udBKhtzw/ScFTkUJgpmgB8yzFeFtPIAVsNAx7x16LWpFIYM0rOLONXno01JOWkV6UItAhp5LvO7kOuwk3LQw7KsNUblCVnszO51GYP2riHWX0SuM4W4LBtts3PZlZyEoSpgKBHZREllZWTkF1IZATsIotaIhC1otW4Am2PtmMG+UefNzzAGjhrrGZgJQBpGjYuAqvktYsyRcwwD2qdCfin8sNOc53FCRgIUe838epcNckurc/pddhhyCpQFYERvs1Pu3K8gFDY73uaWmCk37ZJh8igzHeqZxWZn3NZJsPQHKPPDeb3g1cl17EuFs7vBkgfh6UXmRd8N58BVZ5vzLhoA7/8JXqzojHvLCHOaEEI0GkndaUiTDfRbtmzJ6tWrKS0trTWG+44dO/B4PFUjxxzq6ZWLFy/G4XAwffr0iLz0zMzMY1LPyjsEXq83YnrHjh3p2LEj1157LaWlpVx33XU8++yzXHrppcf0aZtHs60ePXoAcODAgWNWD3HsOawKfznHzl/OsTe88LEwoBO8+6eqtxOACadELtKjhZV3L9K4dIFekUZU/QRaDAO8IbCoqFaFvGgHX0fZCSrgCGgEy4OoIR2vaqVDfgnJwVDVdq01Lrg/T45n7L5cXBWtyvl2G+EoD45wGEU30KwWyi0eFp4ygjb7D9Dvlx0YQHSun9bF+egWBVtIJy5Yjp0wNkLEhv0Vt3SVioz7SgYqGnqdgbyBnTJ8xOIlEQclVWlDRsVcUDDcNpQbz0Lp3wa+34H2zU70Tm1RR/dEHdcTtueYgXmMu3YRWYXm0KKt6nkYXW4xlPrMC4OD3XR+3etUuqDPoefX5/xe5qsu4weZLyGEECeFJhvoDx06lJUrV/Lqq69GdARduXIlv/zyCxdccEFVa7jbbf6A1pXWUrlMzXx+wzB4+eWXf3UdA4EAq1atAuCUU06pqkN0dHRES310dDQtW7Zkz549BAKBYzqCjcvlqjP9aPfu3SiKQqtWrWrN++yzzwCz468QR2piFwveO1WW7NT5cLvBK5XdXOwWsKi0dmk8N9LGgoc20yq3lA9bprA+NqYqlI4Lh6uCfAP4NiGWbKuVJF+QPKeNbJeDWe3T6VFcTnRYJ2BRaaOqeHSdaJ+ZI28LBIkqKaPUbuOzHl1otbcI/AYW3XxhGHjCAVSoark3B/6sDNQNylQHNiWAVYMgNmzYsVN9J0FDxUcsoBDETSFtK1J4DNQ7LsA+oZeZrnRWZxRLxd5dNRgLRDzRl86HeLJ3WgMPBGwRa76EEELUIp1xG9ZkA/2xY8eycOFCXnvtNfbv30+fPn3Ys2cP7777LomJiVVPqQUzYPV4PLz77rs4nU6io6NJSEigf//+nHvuuaxYsYJJkyYxevRowuEwn3/+OX6//xCl11ZeXs7ixYsB80IhNzeXJUuWsG/fPsaPH0/r1q0BWLRoEXPmzGHYsGFkZGRgtVpZt24dq1ev5rzzzjvmw1T26NGD+fPn88ILL9CuXTsUReGss85iy5YtTJkyhT59+tC3b1+Sk5Px+Xz8+OOPLFu2DI/Hw4033nhM6yJ+OxxWhXGdLIzrBJN760xaprOvDIa3VnjsbAepHoXhM7qw5KU9qF8XUehysSPKhaEqHLA4eatdS+IDQfa7nGiKap6INJ2Mcj+6RSUmrKMqKuU2lS7FpcSENUrdbkpdLhTDIMrnJ2CzEbJawYDOm3LxOes+nelYCGDFThgFM7N+vzueoig3p/XyEPfkWdjbxoAF/H9dRvDdDehxUbj+NJT4ltGEVu/G2JFPeOkvBJxpuP4xAsfEHifwaAshhBBHp8kG+larlWeffbbqgVmffvop0dHRnHvuudx6660RHUecTiePPvooL7zwAtOmTSMYDNKnTx/69+/PiBEj8Hq9zJkzh//+979ER0dz1llnMXnyZM4999zDrk9OTg5//etfI8ps3749999/f8Q4+n379uWXX37hyy+/JC8vD4vFQnp6OnfccQeXXnrpsTk4Ndx6660UFxfzzjvvUFpaimEYLFiwgD59+vCHP/yBb7/9lgULFlBQUIBhGKSkpDB27FiuvfbaOlv7hThSvVNUvrm6duqL02Nl/B/bMf6PcM7iPP67sIDtip3uaRb6tVd56jsrmhK5nmbA6VkH2FXREcym6cTUfGquomAoCmUuJ3ZfmOSsIjxBP6phYNEin4BbZnMSEzIv6IPYCWHBg4/vU1sRPSid0e+eg2qJbA1y/3sM7n+PiZjmGCp3voQQoimSFv2GKcav6Y0qhBBHIT8rwCUPZrHZE1Vr3rhde/HZbfwSF4dmUeldWFLrVO5VFWLLvfTZuJOC1BhO/X4f9oBG2KoSslvQrAphC8QGAzi1EA4HtJ/Si/J2LYjpGk9ctwZSZoQQQjR5vyjT6pzexbjrBNek6WqyLfpCiOYrMc3BM5MTGfOKj0CNVn2rrnPA7SLH46b/3hzig358LiclbrfZ4RcIKfCjw8Z1m7NwnRKDLSeEz+PAEfBiC+tYwzohuwoOC7tS4ylIiWdc5zCJfxqIDAYnhBDNh7RUN6zJPjBLCNG8desdzdt3xpMYNMe3jw2GGHyggN1RUXhVCw4LGHYbTk2jRWkpHp+P2LJysqwWxm/aRkK3OHrcegoOb4CAtfqErwD2oI7faSVstXDqjr20/efpjbafQgghjg+jYhy1g1+imrToCyEaTf/2Nh735PHZLzpW4JP0FErtNk7PPoBLq87NV4BYnw+LppMYE02C1cIVrw1CUWDfxg7sn7mVwgQNly8MBpRF2/FG2Ygt9jJg1lnYUj2Nto9CCCFEY5EWfSFEo3J3T8AGlNqslNhtAMQFaz8dV1cUcmKiabMnn1F3tUe1KCiqwtB7uxCYnI89rHEg1cOBNA/eKBuKbnD2W0NJHtn6BO+REEKIE0Fa9Bsmgb4QolHFtDKfg2HTdfOhW0Ce01FruVKnk6gCH/1zs2l9SZvImdE6RTf7aYmOpyxELAZDHzqNtLPqeNCUEEII8RshqTtCiEbV46x4Fr64F/w6bcq87Ir2sDEhnthAiISKln1NVSl32NnXJpY/vNWrzidC+zsaDPtlHJTrWGPtx/QJ1EIIIZoe6YzbMGnRF0I0KptdZfIzXQgFQ6T7/LQvKycuFCYnyoPXZsNvt+O32Sh1ODBUhbwi/dDbi3NIkC+EEEIgLfpCiCYgIc1Jl9gwlqwDlKe2ICEcpn1eEcVJcYSsVkrtdsIWC1YrtMqwN3Z1hRBCNAGSj98wadEXQjQJV7zUnzZqiGEbtnDGjzs4bf0e2m7OJt/lJGg1g/zrr0kkJtrS2FUVQgghTgrSoi+EaBLcsTYmfX4u29/fS8HbO4hukcL5N3fhhjYx7NkbpHUrO9FREuQLIYQwSYt+wyTQF0I0KR0mZNBhQkbEtG6nuhqpNkIIIcTJSwJ9IYQQQghx0pFRdxomgb4QQgghhDjpSOpOw6QzrhBCCCGEEM2QtOgLIYQQQoiTjrToN0xa9IUQQgghhGiGJNAXQpwUvL8UUfpdLoYu3a+EEEKYnXHreolqkrojhGjS9IDG2hEfkfNdAYYCjngbZy4fiadLXGNXTQghhGjSpEVfCNGk/XLPt+SuKsDmN7D7DLSsIKvOX0qoKNDYVRNCCNGIDJQ6X6KaBPpCiCbtpzm7sWjVN2NVA4w9Pr5IfovMxzY0Ys2EEEKIpk1Sd4QQTdZPL/2CGq4O8i2GhtMIoWKghVS2/3ktseemNGINhRBCNB5pvW+IBPpCiCYp5A3zwz82kBTwY6CiGAYeI4CTMBY0NFS82ClemQvuxq6tEEKIE03SdBomgb4Qokkq+HQ/8fnleIJByiw2LJqOhwA2dABs6FjRUVvYoLyRKyuEEEI0QRLoCyGapO23fYnfYcNnt1EQ68JQFfK9Ljpn5+HQNACs6BRvKYGWjVxZIYQQJ5wMpdkw6YwrhGhy9LAOe8swNIP8eDeGat6eLXa72JaSGLFs1geZqIEjO90bJT60179Bm/MdRrmM3iOEEKJ5khZ9IUST88n8bEJuF367vda8QrcLA7MLVkC1oG4uoeXfrex5RGtwu8b8tRiPL4Kvt6AaGgZWAvFJ2Jfdidq39bHfESGEEMeN5Og3TAJ9IUSjKysIsmLWbrI2FJPYMYpvVhfTIzoaR6h2a7tV09kXE01BbBRFMVF4yoKkZBWj7qi/Vd8wDIzznkT9ZIN5gYCbMDbslGEvzELr9zfC5/fG6i1C2ZuHck5X+Pc1EOc5jnsthBBCHF+SuvMbsmbNGvr168eHH35YNW3//v3069eP6dOnN2LNxG9ZsVfjnCnZXJmTxF8T2vDe9wF2JrVAVxVcPg1rUKM0Nor9bdLY2zadvBbx7E6Mo9xmQ8egoEUU27qkkLTQILS3DMOIDPi1zdkExryI8skGDMBLLAGicFGAjQAqCja82JauRPnqZ8jMxXjlc4we9zTOARFCCHFYjHpeotpvskX/T3/6E5999hkzZsygV69eteb/8MMP3HTTTQwdOpQnnnjihNZt7NixZGVlVb1XFIWEhATatGnDxIkTGTFixHGvQ2lpKXPmzKFv377069fvuJcnfrte/iHMTfMC6MlJoOmU26y806UdF+/P55eO6fTcmElmcgv0pKiqdUoS47CHdRJyCzGMAGG7jZDDinubjc3t52CNtZFxcQqO7fvQd+dj7MhHxcAB+IlFx46NclRUqsdgVlHQqP6J0DH2FsCtM+Ghy6BFzIk9MEIIIcQx8JsM9P/85z/zww8/MHXqVN58801cLlfVPL/fz9SpU4mLi2PKlCmNUr+UlBRuu+02AHRd58CBAyxcuJAHHniAvLw8rrrqqqPabp8+fVi5ciVW66H/7KWlpcycORNAAn3xqxmGQVkQoh0K5eUaD8wq5qOdGlkxbkqsVqj8POoq+ENowAGbhX2ntuLfQ7szevt+uucVR2yzNNZDanYehqKg6jqapfrmZLg4xL6Xt9OebaioBHEAOn48aJg5/xZCRD5oRQEsQIjKYF9Bx3jhY4wXlqCmxsHMW2GMfB+EEKKp0CVHv0G/yUA/Pj6eKVOmcM899/D0009z3333Vc17+umn2bt3L//617+Ii4s7IfUJh8NomobD4QDA4/EwatSoiGUmTJjAyJEjWbhw4VEH+qqqVpUhxInw8g9h/rBMxxsy36sYxPpsuNwWSmxWQImMt+1WCITZGO2iLNZNyGLBZ7XU2m6Ct5QR+7+nxObiS1d3ynQ3CcVeAFqQRSr7sKBVlOlGQUXHVmMLtbdp0iv+b94AVswaYmQXoYz9B9x0PgzvCeMHQs165RTBE/Pg+x3m9MJy6N0O/nk1JEYf4VETQgghjo3fZKAPMHToUEaNGsW7777LsGHDGDBgAGvWrOGdd95h9OjRDB06lN27dzNz5ky+/fZbiouLadGiBcOHD+emm26KuAuQmZnJ3LlzWbduHdnZ2WiaRrt27bj44osZN25cRLnTp09n5syZvPXWW8yfP5/ly5eTl5fH888/f8jW85iYGBwOBzabLWL62LFjSUtLY8aMGRHT16xZw6RJk/jb3/7G2LFj6512sMplAGbOnFnVsp+WlhaR2y+at6/3G/xwwOCMlgoto+DvqzXe/gV8IeicAN2T4Py2ChM6q/zja53XfjQIaTCsNXRLhMXbDX7IMigOAFYFrJDg9TN8ezYOTWd1yyRwHdQSowAVw2iW2qwEKgLpb9MS6ZFbhFMzg3BV1zl9+48AxIR8nLvtB3aorVANgyiKyGA3OgrFJBHAg4UwLsqxEIaK8XrCOLDjiyjeOMT4DVXTZyw1XwBW1Qzqgxroeu2V1myDmcvAokCMG9wO8IfM9QzMdUIauOwwfpCZIhTWYNFaSImDC/pEXkwcyqZd8NVm6NkGzjjl8NYRQoiTnIy607DfbKAPcO+997J27VoefvhhZs2axSOPPEJycjL33nsvP//8M5MmTSI6OpoJEyaQnJzMli1bmDt3LuvXr2fGjBlVKTBr1qxh3bp1DB48mPT0dPx+P8uXL+fvf/87hYWFXH/99bXK/stf/oLD4eCqq65CURSSkpKq5um6TlFREQCappGXl8fcuXMpLy9nwoQJx/WYtGvXjrvuuotp06YxbNgwhg0bBoDb7T6u5Yqm47blGs//UN2dyapAuEbvpm+yzdfLmwzcVg1vuHre/zbpRKS6U/Fvi0JBlIvcKBf99+eTXuJlW/xBLd2KArqOJxTmgsx9KBaVNRkt2Bkfw8zTOnLGngMM/yUTeyDAmuT2lFic9MzJxKmFSNZzKSGBluwBoJBUfJh59SHMUXYSyEElVPE8XSsBXNjxoVB5+1cBHBU/GwHq7tJV+aNiQFg3Xw3RDLOFv7Cex/cWe+GFj2DmUlBVCFYc0IGd4NOHwdXAXbh/zYN7/1f9/vfDzTQjIYRo5qTjbcN+04F+dHQ0f/nLX5g8eTJXXHEFxcXFPPPMM0RFRfHwww+TlJTE//73Pzye6iH2BgwYwL333suSJUuqWsVHjx7NxRdfHLHtK6+8kkmTJvHqq69yzTXX1MqLj4qK4vnnn68zXz4zM5Phw4dHTHM4HEyZMqXWHYJjLTExkaFDhzJt2jQ6duxYK4VING8/5xsRQT5EBvkHqxnkA2awftCoN2iGOb6XovB16yRKUVibHG8up9RojTEM3OV+bt/wC3FBM9enb04+753alrVpLcgoKMbp8wMQstr4MaUNnpCf9gU5bE7MoF3BAZy6Dx0VH5EXEQYqAZy48GLBjx0vlW1BOioaNmzUHMrTDvjr2OMagf6xFtapTh0CvtkKr39upgvVp6gc/jI3ctpLy2HyBXBau2NfRyGEECeV3/zwmoMGDWL8+PEUFRUxbtw4Bg0axLZt29i6dSsjR44kFApRVFRU9erVqxcul4uvv/66ahs103gCgQBFRUWUlJQwaNAgysvLyczMrFXulVdeWW+n2PT0dJ577jmee+45nn32WaZOnUr37t157LHHWLBgwTE/BieTgoICAoHqgKysrIzS0tKq98FgkPz8/Ih1ao5iVNf77OzsiCEZf8tlfL29gF9FOfRtVL+hsCY5wQyxw0bVRUGLMh/R5X76FBVXBflhVSWsqozesodJ67dxanZere3tjUlipyeFMpsbv8uKl9iKELx2IG4hjBUvdkqAcMUoOxo+YrASPGhpldp5/McxyK9H2botEe8P/pvnrfsZ/AfXHfhlv7l+E/lcSRlShpTR/MtoDAZKnS9R7Tfdol+pZ8+efPDBB/Ts2ROAnTt3AmY+fX3jyxcUVAdEXq+XGTNmsGzZMnJycmotW1JSUmta69b1P4XT6XQycODAiGkjR47kqquu4sknn+Sss846YR2Fm5qEhISI91FRURHv7XY7iYmJEdPS0tIO+T41NVXKqHBhjwScqzT8B7fUH66DW/PBjI8rLgBsQY1g5UlYNyBooOgGN/2wjSKnjTyHDV1R2NEikQKPmS6WUFZOcmk5mqVyVJxqpRYXW2NaAhBWLRjY8BKPi1J8xFYtZyGEmwKsB62vV+Tr66gVOfyVeUdqxatyVObG+eGIGndGxPuD/+ZJZ/WG9ATYX+MCzW6Fs7uZ6zeRz5WUIWVIGc2/DNE0SaBfh8qr2quvvprTTz+9zmViYqrH1X7ggQf46quvGD9+PH369CE2NhZVVVm5ciVz5sxBr6OjntPpPKI6Wa1WBgwYwJtvvsmmTZsYPHgwYI6zXxdN045o+0IAJLoU3hilctsnOtnl0CoaHCpsK669rEWB89rA8t0HpapbFDNdp+Z7AMMgzetnV407YAAt/AH2x0QTtFqI8/vZHxtDQVR1ulxBdBQxZV7KPS6c/gBqxfdT0Q3UgFG17VR/ZbCr4sSLBZ0gDmwEiKaoahSeSCoWgviJwk1ujXC+MuA/gQF+73Zm59vvtpkddO8dByP7HHodqwXevhuufRp25EByLDz9O7MzrxBCNHPSet8wCfTrUNnarqpqrZb1g5WWlvLVV18xatSoWuPuf/vtt8e0XuGw2czq9XqrpsXExNR5x2Dfvn1HXU59Fw/it2FCZ5WxHRSyy6FlNKiKwld7dd7bomNTFa7pCjarQkaUQpRdwRvUef0ng5/yYVC6yrBWcKBcZ8Vug6c+LGNXTEW+fFhnl81htuRXjK5j1zR6FJbhdZjj2xd5POh1pMYURbnpsCUHe3mYqLIAqm6gahphu4on7KdT2T7iQ9WdXUN40FGIohgXZfXuq4GCDV+dN3sNFBTq6myrmAF2n3Zw9RD4bjt8uxW2ZZn7VtH5mNbJcNapcOUQ6NUO8krMoTZ14P3VsDXLnD6oM8RHVQfnWQXmKD2ew2wMOPNU2Poc7CuA1DiwyWldCCGESX4R6tClSxc6dOjAe++9x4QJE8jIyIiYHw6HKS8vr2q5ByJy2wDy8vKYN2/eMatTIBBg1apVAJxySvXwea1bt2bFihUcOHCA5ORkwMyde+edd466rMo+B3VdQIjfBptFoVWNh8EOzlAZnFF3lx63XeXmXpHTUqNUeqbAzb3i+NPnOkt2GFhUC7tUoDhIQFe48PvtpLusFNZovQfQldrluP1BFN2gdWYRFk2PCMw7sodYqj+rBqChoKDiJwYnZeZY+Fio+XgVAwjjREVDpXauUs3A3wCUFvGQ+aw5TObRSI6r/vdth+jknpZQ/7z6qCq0Smp4OSGEaEZk1J2GSaBfB0VRePjhh7nlllu44ooruPDCC2nfvj1+v5+9e/eyYsUKJk+ezNixY/F4PAwaNIglS5bgcDjo1q0bWVlZvP/++7Rs2ZLi4jpyHhpQXl7O4sWLAfMCIjc3lyVLlrBv3z7Gjx8fkd9/6aWXsnTpUm699VYmTpxIKBRi8eLFR5waVFNcXBytWrVi6dKlZGRkkJCQgMvl4qyzzjrqbYrfJpdN4Znh1Z1aw7qFHw/YWPF1OctL4onfsL9WoG8xDJSQRqiiZdoRDNE2+wDO8hAWzUA9qPU9k3TaKDoOI0ix241ugaxYN/EBjda5OYQwsKFUtM/bq557q+HAQMWCDwNbRQt+9c9G5eOyDKcT9R9XwKThZkqNEEIIcZKQQL8eXbp04Y033mDWrFl88cUXvPfee3g8HtLS0hg7diz9+/evWvaRRx7hmWee4csvv2TRokW0atWKW2+9FavVykMPPXTEZefk5PDXv/616r3T6aR9+/bcf//9tcbR79WrF1OnTuWVV17hv//9L8nJyUycOJGuXbtyyy23HPX+P/LII0ybNo3nnnsOv99PWlqaBPriV7OqCqelKpw2Lpo7x0WT723FzXftxeqrzp+3aRop2cW02Z2LN8qKSw9g03Q0C9SVkalhIdOZxp7UBJz9UinXLbQblkK337XD5rRgrN9FcMDjWIJ+FDR07OgVI+qohCq2p6PjRiGAgo6BitExHeWlG1HP7nJiDo4QQogjIjn6DVOMg3NOhBDiBPL7df7xWhHfri3HU+bDadPR/QpXz/sWi2bwfYcUvj01nZSScsZ+tQVPqHaazZ7WsZS2tHDj56NrPT0aIDz1Q/SHFh08FQMVHbXiv0EsmGPZK4Paoix7EKKO/s6YEEKI4+tz5ZU6p59t3HCCa9J0SYu+EKJROZ0qD9+cgK7HUx4ChxW6/aOIWz8oY8bQAcw+p0fVsl+3b8kTr32CvcYwP4ZiEJ9fjvfy+tssLHcOx1i0EWPN7oopGgZhdBxYOsVi+79BKBf2NTuzuh1Hn4cvhBBCNCES6AshmgRVVYiuiK9fui6Wna/G8c7gUyOWWd8+hc9Oa0OvfbmkZRexIyWesALObj6UDoE6tmpSYl1Yv/0zxtc7MTZnweJ1KPsKUX5/NsoNZx/P3RJCCHGcNKeUlH379vHFF19w4MABJk6cSEZGBpqmUVxcTGxsLBbLwQ9xPDwS6AshmpyzW6u8de1pBOoYKnJfWizpeoDNLeL528QhXJG5k6Gnf9ngNhVFQTm9PZzeHq4/83hUWwghhDgihmFw99138+yzzxIOh1EUhR49epCRkUFZWRlt27bl4Ycf5o477jiq7dc9Xp4QQjSyix84BVWLHMfeFtboklcIhsHORPPJt1rr2LpWF0II0cwZFYMtH/w6mTz55JP897//5Z577mHZsmURw7XHxsYyYcIE3nvvvaPevgT6QogmyWJViQ2FwDDMJ9+WlPP71RuJCYRwFQd5b0AXrJrOLZdIoC+EEOLkNHPmTK699lr+8Y9/0KtXr1rze/bsyZYtW456+xLoCyGarPtODZlJmAbEl/ggoBMOQ256LCk+H08s+YIzu8jY9kII8Vtk1PM6mezZs4czzjij3vkej+dXPcBUcvSFEE3WfVcksOvCtXzbMhlPMEyMxSAc48QKPPf6Rwx8c1hjV1EIIYQ4asnJyezZs6fe+WvXro14UOqRkhZ9IUSTdsOVLbh8wzbG/rKLJJ85so4lrHHaO8OJHdmmkWsnhBCisTSHHP0JEybw4osvsmPHjqppimLuw9KlS3n11Ve55JJLjnr7EugLIZq0fpe3Jq2Vw8zVBxRdp2N3NynDMxq5ZkIIIcSv89BDD5GWlkavXr249tprURSFxx9/nMGDB3PBBRfQs2dPpkyZctTbl9QdIUSTd+Wc09n50T72fZtHxsAk2pzfsrGrJIQQopGdbK33dYmNjeXrr7/m3//+N++++y5Op5PPP/+cDh068Le//Y17770Xl8t11NtXjJrj+AghxEkmFAoxa9YsAK6//npsNlsj10gIIcSJsFR5rc7p5xvXneCaNF2SuiOEEEIIIUQzJKk7QgghhBDipGOoJ3/qzg033NDgMoqi8PLLLx/V9iXQF0IIIYQQohGsWLGiapSdSpqmkZWVhaZptGjRAo/Hc9Tbl0BfCCGEEEKcdIyTv0GfzMzMOqeHQiGmT5/OU089xbJly456+5KjL4QQQgghRBNis9mYPHky559/PpMnTz7q7UigL4QQQgghTjqGqtT5ak5OO+00vvjii6NeX1J3hBDN1opPi3hlSRmO8hBj+jkZf31aY1dJCCGEOGzLli3D7XYf9foS6AshmqUZi0r42yI/Zxd68YTCzF0aYtO6MqY81RGLpXm1+AghxG+R0QzyUh5++OE6pxcVFfHFF1+wbt067r///qPevgT6Qohm6YFlfi7PycdS8d4ZDLE/CzZ+U0KvM2IbtW5CCCF+PaMZNNpMnTq1zunx8fF06NCBF198kRtvvPGoty+BvhCi2QlpBh1K/FVBPmA+KF1VWbeqWAJ9IYQQTYKu68d1+xLoCyGandxyg4RQqNZ0h6azPiinPSGEaA70Ztbx9niQXzwhRNPzzRaYsRRaxMItI6BN8hGtfs5jBZzn9deanu+04U49+gePCCGEEL/G7t27j2q91q1bH9V6EugLIZqWZxbBH2o86vvpRbDpKWifelirb8oKM3B7XkTaTqXv0+KJz9LQdQNVWoKEEOKkdjJ2xm3btm2tJ+EeDk3Tjqo8CfSFEE2HYUQG+QC+IDw4B+bcdVibuP/NEjqG6z4hxnmDfJWSzJQX83ns1qRfW1shhBDiiLzyyitHFegfLQn0hRBNx5ApdU//6PvD3sQX2QoZqooFA5tuVE3PjHUTEwyjGAZP5Hu4tyRMYoycAoUQ4mR1Mj4c6//+7/9OaHnyKyeEaBo2ZcLKX+qeV+zFCIYJf7YDxW3Dcmb9tz5LNYWPWsSRmxRFC02jXWE5PquFH1tEk+wNYqgqBIO8u0Hl5sFyChRCCNF8ya+cEKLxPbsYbn+p3tmligsct6MRBSjQMoboVbdhaR0fsdyfPzOHKdvlckG5xgFdp2swhDsU5rIfiwirCjsTojCiHTz4kZffn26Xh2cJIcRJymhGp++VK1eybt06iouLaw25qSgKf/nLX45qu4phGEbDi/027d+/nwsvvJAbb7yRm2++ubGrI0Tz9OlGOOdvh7WoAYRx4SUJHQdGagyu5y/k9dyvAfjDnisIeGvk5ye6wG52y3WENS78eS9LOqVRpisoZUFigyHuHeFmygVH/3hxIYQQjWNewpw6p48ruPIE1+ToFRQUMHr0aL799lsMw0BRFCpD88p/K4rSuJ1x16xZw6RJkyKm2e12WrRoQZ8+fbj22mtp167dsSjqiOzfv58PP/yQoUOH0qVLl1rzLrzwwnrXffTRR+nRo8dRl/3hhx/y0EMPHdayffr0YcaMGUdd1rGwbNkyVq1axebNm9mxYweaprFgwQLS09MbtV7iN2DyzMNeVAF8FpWl6b1ILS+lW/Ze/BNmE//nDArbOgn6a5wI3daqIB+gfUEZUcEwV2zcxc9uN6viYyiyWHlgeZDFX3t588ZoWmU4juGOCSGEOJ5Oxhz9g917771s2LCBOXPmMHDgQNq3b8/HH39Mu3bt+M9//sPq1atZsmTJUW//mKbujBgxgjPPPBOAQCDA1q1bmT9/PitWrGDu3LmkpaUdy+IatH//fmbOnEl6enqtQL/SwIEDGT16dK3pPXv2JDU1lZUrV2Kx1DVQ36H17t2bhx9+OGLaK6+8QmZmZq3pCQkJR7z9Y+2dd97hxx9/pFOnTmRkZLBr167GrpJo7j7ZAJOmw7asI1otRivn7Jxv2OnqgqZYsBkag/+dxeNTB5gnfd1ANQw6lvmIKyljT5wHi65z/vbsqm30LSnjlDIvS1oksM/lYGXATr/Xwvxyl40410k4XpsQQvwG6Sd/nM/ixYu5+eabueyyy8jPzwdAVVU6duzIc889x4QJE7jjjjt48803j2r7xzTQP+WUUxg1alTEtNatW/Ovf/2LFStWcNVVVx3L4o6J1q1b16pzTQ7H0bXwZWRkkJGRETFt3rx5ZGZmHrK8xvLwww+TlJSE1Wrl8ccfl0BfRNI0KPFBfNShl/MHQdPB4zTfl/shrEGpDwIhCIbg4bdh7qpfVZ24YCldgtUXCK6gxpCZOfx3vAGGwbicPFr5gwCcvjefLHft77FH17noQD7roj1gU9niTqDX0z4u6Wvntm4GbdPsv6qOQgghREOKioro1q0bAFFR5m9sWVlZ1fzzzz+fKVPqGZHuMBz3zrhJSeZY1TabrWrawoULefvtt9m9ezfhcJjExER69OjB3XffTXy82bnupptuIisri+nTpzNt2jTWrFmDoiicffbZ/OlPf8LpdPLqq68yb9488vLyaNeuHffeey+9evUCIlNnHnrooap/H0maTF05+jWnde3alZkzZ7Jt2zaio6MZNWoUt912G1br4R3WTz/9lHvvvZcHHniA8ePH15p/6aWXEgwG+eCDD1AUpeqYvPDCC0ybNo21a9cC0L9/f+64445aFxaGYfDee+8xb948du7ciaqqdO3alRtvvJF+/fpFLJuaengPIxK/QXO/grtmQVYh9GkP//sDdDvoCX26Dne/Ci8uhVAYxg8EVYF3Vptj4x8FDRUdK5pqpuw49GDVvAAxtZY/LdtssW/jC1QF+ZXcms6OWDfti70R022GwcAS84Tar6CE1RmJFM8s4qmwhi3Gyj1/b0tKS0nnEUKIpqg5pO6kp6eTXfH75XA4SE5OZv369Vx00UUA7Nu371eNu39MA32/309RUVHVv7dv387zzz9PXFwc55xzDgCLFi1i6tSp9O7dm0mTJuFwOMjJyWHlypUUFBRUBfoAPp+PW265hT59+jB58mR++uknFixYQCAQIC4ujk2bNnHppZcSDoeZPXs2d911Fx9++CEej4fevXtz/fXXM2vWLMaPH0/v3r2B2mkywWCwqs5VB8Vqrbqqqs/KlSt59913mThxIhdeeCGff/45r7/+OtHR0dxwww2HdbyGDBlCYmIiCxYsqBXob9y4kR07dnDrrbdG/IF9Ph8333wz3bt3Z/LkyezevZt3332XjRs38sYbb1RdWAH89a9/5eOPP+bcc89l7Nix/D979x0eRfEGcPx7d+m9kQAJIfTeQ4cAShMIvStVaaIIqFiwgL3wE0QBASVIU6qGIEgvAoYqIL2GkhBCem93+/sj5uC4CwkhpPF+nuceuNnZmdnN5fLu7MxsRkYGW7ZsYeLEiXz11Ve0a9cuT+0UT7HrETDs26xeeYDjV2HQ/+D0t4b5ftoJczbde7/u78eqNozK3KEiChoSzc257uFAhdRbtI34Gw06wq1cKfNAMB9jYwuAXWYm0eZmOGVkkj0IxzEtgwPebvjEJZPTwByNAr5hMVhps1Y7yIjPZOEn1/lgQfXHOhYhhBAiJ35+fmzfvp3p06cDMGjQIL766is0Gg06nY45c+bQpUuXfJdfoIH+woULWbhwoUFa5cqV+fHHH/UB6J49e7C1tWXBggUGPd8PTuaFrNsZw4cPZ/jw4fq0hIQEduzYQc2aNQkICNCXUalSJV5//XX+/PNP+vXrh5eXF82bNycgIID69evnOFwmMDCQwMBAg7S6deuydOnShx7r1atXWbNmjX6yar9+/Rg0aBCrV6/Oc6BvZmZGz549CQgI4OrVq1SuXNmgXRqNBn9/f6NzMmTIEF5//XV9WuPGjXnzzTdZtGiR/vbO7t272bJlC++++y59+/bV5x08eDCjRo3if//7H35+foX6dDZRAu04dS/Iz3bmJty4C95l7qVtOV5gVcbhSjj3fhfs0jLwikzkSrlKZKjMyFSbUT06CrgX6CvAgRr1sNDqOGtvyxkHO2wztTx3NxrPtHRC7a2JsLXi13retAu5i2dCism6s4P8bNERGSQlZGJrLysRCyFEcVMaltecOnUq27dvJy0tDUtLS2bMmMGZM2f0y2n6+fnx3Xff5bv8Ap111qdPH+bNm8e8efOYPXs2r776KrGxsbz22mvcvp01ntbOzo7U1FT2799Pbit7ajQaBg0aZJDWsGFDFEWhX79+BhcK2T32N2/efKQ2t2vXTt/m7Nebb76Z637t27c3WJFGpVLh6+tLVFQUycnJD9nTUO/evVGpVAYXGykpKWzfvp1WrVpRpkwZo31GjBhh8L5Dhw5UrFiRvXv36tM2b96Mra0t7du3JzY2Vv9KTEykbdu2hIWFcePGjTy3s7iIjo4mLS1N/z4xMZGEhAT9+/T0dP1klmzZn72c3oeHhxt8FqWOe6JMPDlWsbcGNwfDOiq5G+XLrwSMJ6fbp2Qdxw27CqSo7XFNSzTYrgJirKzJUKtQ/rt4TTLT8GcZF0IcbVhfpwJx1hbctbNmfZ0K3HTI23KaZhYqrKw1xebnIXVIHVKH1FFc6xD5U69ePaZOnaqfE+rs7MyOHTuIjo4mLi6OPXv2PNZiNgXaTeXt7U3z5s3179u2bUuTJk0YOXIkc+fO5fPPP2fUqFEcP36cN954A0dHRxo3bkzr1q3p1KkTtra2BuW5ubkZTYZ1cMgKMB5c9jE7PS4u7pHa7O7ubtDmvPL09DRKc3R01LfBxiZvgYSnpyfNmjVj8+bNvPrqq5iZmbF9+3aSkpL047PuZ29vbzA8J1ulSpXYs2cPKSkpWFtbExISQlJSEp07d86x7ujoaCpWrJindhYXDw69enCIlYWFBa6urgZpD/6CPPj+wfkJUsc9rv3bQfcD8McxfZrq4yHw3+RWfR1T/GHNQbj13xe/rVVW9J2YyqOyxPhCOdX83leVmc70WsI+t66gNGxgkJZopuHvcs764B9AUak4Uc6JCvH36slUqTjj7ohrcipeCffa/NwgdzRmKjQUj5+H1CF1SB1SR3GtoygopWBUwtmzZ6ldu7ZRupOTU4GU/8TvR9etWxc7OzuOHj0KZF0MrF27lsOHD3PkyBGOHz/OJ598wsKFC1m8eLHBhFK1OucbDjltK6znfz2sbY/ahj59+vD222+zd+9enn32WQIDA3F1daVNmzb5bp+iKDg7O/PJJ5/kmKdKlSr5Ll88JVQq2PhO1tCc86HwbH1oaOKZGF5ucObbrLH5qenQr2VWoP/rfjh8KWvfqP96g9QquBqRY5Wu3CaK8iSTdeGsU0GYqz0oCnYpadgmZqCQVfz9ouxMf51FmOi91973+3vK3ZH9FcvgmpJOm9vpaKrZ4Wmvpt9AVypWkwdpCSGEeHLq1q1L3bp1GTx4MAMHDqRq1aoFWn6hDDzVarVkZGTo31tYWNCmTRt9ILt//34mT57MypUreeuttwqs3pIy/rx9+/a4uLgQGBhIlSpVOHnyJCNGjDC5ek9CQgKRkZFGvfrXrl3DxcUFa2trACpUqMCNGzeoV69enu8uCGGSWg3dfbNeD+NgA6OfNUyb1MN03rM3oe+XcCHMuDp01OAo8biSghXx1pY0SbiMTUwysRaO2KcqRkG+DghuXRVXbQZRmnsrfGGlgcR0cLQyyF874t6dv/oRcZSLS6aqrcLMxaaftyGEEKL4KQ3r6C9YsIA1a9bwwQcf8P7779OwYUN90F8Qoy6e+JNhgoODSUlJoWbNmgBGK9wA+m2POuwmN9kBbkGXW9DMzMzo0aMHwcHBLF6c9ZRQU8N2sv38888G73fv3s3169cNVtHp3r07Op2O77//3mQZMrZOFKnaFeD895C4Cso6Gm1WoeBIJGW5RfXkK1RODMEuFcrHZ2CXkTX5Nh0zFLL+f7SpJ77Vr2TNG9aowMES3G3A2ZpnImPpdPk2ZRNSKJuQQqdLt6kelWBQn311O96ZW7C9KEIIIZ4sRa0y+SpJxo0bx86dOwkNDeXbb7/F1taWt99+m8qVK9OyZUu+/fZbwsKMO8XyqkB79M+fP8/mzZuBrIkaV69e5bfffsPMzIwJEyYAMHHiROzt7WnUqBEeHh4kJCQQFBSESqUq8AdJVapUCVtbW9atW4eVlRX29va4uLjQtGnTAq2nIPTp04fly5ezdetWGjdujLe3t8l8Tk5O7Nq1i7t379KkSRP98pqurq76tf4BOnbsiL+/P2vWrOH8+fO0bdsWJycnIiIiOHXqFLdu3TKYAHz8+HGOH89aOeXcuXMArFmzRj8u76WXXnpShy6eZrZW8NdnUG3iQ7OlY8kl6uNAHPYkokVNKlZYq5O51KoBIQPjcLFIIcXcDBwsqR2fRLMLUdikZ2D+30i62nfj9eVlkvXlpwBuTZx4/93ypqoVQgghCoWHhwevvPIKr7zyCqGhoaxdu5Y1a9bw+uuv88YbbxiMjHkUBRrob926la1btwJZY9gdHR1p0aIFI0eO1D/1q3///mzfvp0NGzYQFxeHo6MjNWrUYNq0aUYPcXpcVlZWfPrpp/oHTKWnp9O4ceNiGehXqFABX19fjhw58tDefGtra/3xfP/99yiKQsuWLZkyZYrRcJ4PP/wQX19ffvvtN5YuXUpGRgaurq7UrFmTiRMNA6sjR47o7yZkW7Fihf7/EuiLJ6ZqOZjsD3OCcsxiQRquhBFFOeJxRKsBZVgNKr/emJY17DgfEABANU8zYqLT6HQl/KFVqoDg8s5sfNcV93LyQCwhhCiJSsPymqaUK1eOOnXqUKtWLU6fPk1SUlK+y1IphTV7VeRq0qRJ/Pvvv2zZsgUrKyuj7dlPxg0KyjkgEqLEqj8F/r1ucpPOwoy7cyZz7ogWM62O6lPq4t4wa8WHjIwMAv4L9P0HjmTItDs0jIo3WU62EEcbkp6rwPYBsj6+EEKUVKsqrDGZPvTmwEJuyeNTFIU9e/awevVqfvvtNyIjI3F2dqZv374MGjSIZ599NvdCTJC/csXEzZs3CQ4Opn///iaDfCFKvZWTs4J9E9Q+ZfCY0AqPCQ8vws1WRVUr08tvZouxNGdXZQ/+ebaUdgUJIcRTojQsr/nXX3+xZs0a1q1bR0REBA4ODvTu3ZtBgwbRsWNHkwuzPAoJ9IvY6dOnuXbtGr/++ivm5ua88MILRd0kIYpGvYrwwUD4yEQPjU/eH8hVra0zYb8mGa3MA1lj8pc1rkznClDFRZPvpgohhBAFoV27dtjZ2eHv78+gQYPo2rUrFhYWBVb+E191RzzcunXr+Oijj0hKSuLjjz82ehCYEE+VmYOhalnj9Jc65bmIsc/ZowB3bIzH3idYmNErJIytg6SPQwghSjqdyvSrJFm7di0RERGsXLmSnj17FmiQD9KjX+RmzJjBjBkz8pR30aJFT7YxQhQHp+fAgFnw5z9gZwXv9IMBrfK8u7OdmnBPe/4o686wE1ex0mZNQ1KABDMNzW3yt3KBEEIIUdD69ev3RMuXQF8IUbxYWsDGdx+riHene/Lv/2L1QT5krbTjmZyGg4v9YzZQCCFEcVAaxug/aTJ0RwhR6jT00ND6eoTJbao0XSG3RgghhCga0qMvhCiV7DJNB/RlvWTdfCGEKA1K6zr6BUl69IUQpVLZCsYBvUoNXQaVKYLWCCGEKGg6lcrkS9wjgb4QolQa9UYF7J3uLaFp56jh3W+rYmsnNzKFEEI8HeQvnhCiVPLwsmTm4hqEXkvF1l6Di3vBLlkmhBCiaJWWoTvx8fHMnz+f3bt3ExERwcKFC2nWrBnR0dEsXbqUnj17UrVq1XyVLYG+EKLUUqtVVKhiXdTNEEIIIUy6desW7dq14+bNm1SrVo3z58+TmJgIgIuLCwsXLuT69et8++23+SpfAn0hhBBCCFHilIblNd98800SEhI4ceIE7u7uuLsbPgm+d+/ebNq0Kd/lyxh9IYQQQgghisC2bduYNGkStWvXRmXiwqVy5crcvHkz3+VLj74QQgghhChxSkOPfkpKCmXK5LwaXEJCwmOVLz36QgghhBBCFIHatWuzb9++HLf//vvvNGrUKN/lS6AvhBBCCCFKHEVl+lWSTJ48mV9//ZUvv/ySuLg4AHQ6HZcvX2bYsGH8/fffTJkyJd/ly9AdIUTJERkP43+AMzfB3xdmDAZz6a8QQoinkaIuYVG9CS+88ALXr1/nvffeY/r06QB07doVRVFQq9V89tln9O7dO9/lS6AvhCgZ0tKh3GjI1GW9Px8KGw7BufwtOSaEEEIUB9OnT2fYsGGsX7+ey5cvo9PpqFKlCn379qVy5cqPVbYE+kKIkqHPl/eC/GxXwmHjkaJpjxBCiCJV0ifjJicn07ZtW8aMGcP48eMfa4hOTuSetxCiZNh5ymSyempAITdECCGEeHw2NjZcu3bN5LKaBUUCfSFEyZCuNZmsC40t3HYIIYQoFhS1yuSrJOnatStbt259YuVLoC+EKNHUWtMXAEIIIURx9/7773Px4kWGDRvG/v37CQ0NJTo62uiVXzJGXwhRoqkBs9RMMq3k60wIIZ4qJXyMPkCdOnUAOHv2LKtWrcoxnzafnVryl1EIUaKpgNYrT7P3xYZF3RQhhBDikXzwwQdPdIy+BPpCiOJPUR662eNqXCE1RAghRHFR0sbjmzJjxownWr6M0RdCFH+xSQ/dnG5R8r/shRBCiIImPfpCiOJv+oqHbra/m1xIDRFCCFFclPR19AE++uijXPOoVCref//9fJUvgb4Qovj7ccdDN1tlgDo9s5AaI4QQojhQVCV/YMrDhu6oVCoURSncQP/o0aOMHz/eIM3CwoIyZcrQuHFjhg8fTqVKlfLVmMcRFhZGUFAQ7du3p0aNGkbbevbs+dD9//jjDzw8PJ5kE4UQ+ZGQAhm6h2ZRAWUvx+rfR6UoXIwBM5WCtZmKumVKfq+PEEKI0kenM/77ptPpuH79OvPmzWPfvn1s2bIl3+Xnu0e/S5cutG7dGoC0tDQuXbpEYGAgu3bt4tdff6VcuXL5blR+hIWFsXjxYsqXL28U6Gdr3rw53bt3N7nN0dHxSTZPCJFfH6/JNYsCrAhrw5DP0rBOS2DAofPUuhXJ8cplWd+8FuXiE5n5nDWDOjk98eYKIYQoHKVhMq4parWaSpUqMWvWLJ5//nleffXVhy69+TD5DvRr1qxJt27dDNK8vb2ZNWsWu3bt4vnnn89v0U+Mt7e3UZufdklJSdja2hZ1M4QwdicGDp6HrwNzzfrJs31Z3awVAN8GbKXh9TsApJlruObuzJ56lfh2zQ02BISTamFBhJ0VWo0KZxuY0lRNjbr2eFexQq1WPdFlzoQQQohH4efnx1tvvZXv/Qt0jL6bmxsA5ubm+rRNmzaxZs0abty4QWZmJq6urtSrV4/XX38dZ2dnAMaOHcvt27dZuHAh33zzDUePHkWlUtGuXTumTZuGlZUVS5cu5ffffycyMpJKlSrx5ptv0rBhQwCCgoKYOXMmADNnztT/v3HjxixatOiRjmHt2rV8+eWXjB8/npdeekmffvfuXYYMGYKTkxPLly/H2tpaX++8efM4ceIEQUFBREVFUbFiRUaNGkWXLl2Myt+zZw/Lli3j4sWLqFQqqlWrxvDhw2nfvr1BvpMnT/LTTz9x4cIFEhIScHR0pFq1aowZM4Z69eoBWeO6Nm3axNGjR43q8fX1pUePHvqxX9nDl8aMGUOlSpVYtmwZ165do1OnTvo8hw4dYtmyZZw5c4b09HS8vb3p378//fv3f6RzKESu/jwOi7bD7Ri4GQmR8VnpWh1kPnyYzoMSzSz4qGPWZ7RuSLg+yP/GvwUr/err810v40Szu6EoaVq8E1JQA7ftrBj2rweRVy3wSEzimat3cE9KQ2MGGjMVFava0GukB16VrAvksIUQQhSc0jAZNzdHjx5Frc7/XIR8B/qpqanExsbq/3/lyhXmz5+Pk5MTzzzzDJA17n3GjBk0atSI8ePHY2lpyZ07dzhw4ADR0dH6QB8gJSWFCRMm0LhxY1555RXOnj3Lxo0bSUtLw8nJidOnTzNw4EAyMzNZsWIFU6dOJSgoCFtbWxo1asSoUaMICAigT58+NGrUCAAXFxeDNqenp+vbfD+NRoO9vT0AAwYM4PDhwyxevBhfX18aNmyITqfjvffeIzk5mfnz52NtbfhH/7vvviMlJUUfEAcFBTF9+nTS09Px9/fX58u+iPDx8dFfRGzatIk33niDd999l759+wIQEhLCxIkTcXV1ZfDgwbi4uBAdHc2JEye4ePGiPtDPj71797J69Wr69etHv3799L35GzZs4PPPP6devXqMHj0aa2trDh06xBdffEFoaCivvfZavusUwsCmo9Dz81zXxs+rM2W9yDTL+iqz1GZdJMTaWLK6VR2DfGEuDjiHnyTG0x0NkKmCoJqepJpn7XvHzpqNNT0ZdfwqZII2U+HS6STmvHOVt76pSpnylgXSXiGEECLbsmXLTKbHxsayb98+NmzYYNDx/KjyHegvXLiQhQsXGqRVrlyZH3/8Ud+zv2fPHmxtbVmwYAFmZveqenAyL2Qd0PDhwxk+fLg+LSEhgR07dlCzZk0CAgL0ZVSqVInXX3+dP//8k379+uHl5UXz5s0JCAigfv36OQ7PCQwMJDDQeBhA5cqVWbPm3jjg999/n+eff57p06fzyy+/sGbNGo4dO8abb75J9erVTbb9119/xc7ODoD+/fszePBgZs+eTadOnbCysiI+Pp65c+fi5eXF0qVLDfI+//zzzJkzh06dOmFvb09wcDCpqal8+umn1K1b1/QPIJ+uXLnCr7/+ajBhOjIyklmzZtG5c2c+/fRTffqAAQOYNWsWK1eu1J9nIR7bvC0FFuQD/Fuuov7/J3zKcsvFHkWlItNMY5TXPDUdq9R0Uq0siLc01wf52ZIszLlja0X5xFR9WmYGHNodS4/nZbK+EEIUK6WgQ3/kyJE5bnNzc+Ptt9/mgw8+yHf5+b4X0KdPH+bNm8e8efOYPXs2r776KrGxsbz22mvcvn0bADs7O1JTU9m/fz9KLn/YNRoNgwYNMkhr2LAhiqLQr18/gwuF7B77mzdvPlKb27Vrp2/z/a8HlyxycHDgk08+ITIykkmTJrF48WL8/PyM2petf//++sA9+7j79etHfHw8x44dA7KGxaSkpDB48GCjvIMHDyY5OZlDhw7p0yCr9z0tLe2RjjE3bdq0MVoVaceOHaSnp9OrVy9iY2MNXm3btkWn03H48OECbUd+RUdHG5yTxMREEhIS9O/T09OJiooy2Cf785jT+/DwcIPPp9TxZOtIS06hoCjA4hYd9e+1GjUTxnVHp4IaoZEGeW1T02l18Saq/9rvkGZiOU5FwS7DOD0jXVtqfx5Sh9QhdUgdBVGHyJ9r164ZvUJCQoiLiyMiIoLPPvsMKyurfJef7x59b29vmjdvrn/ftm1bmjRpwsiRI5k7dy6ff/45o0aN4vjx47zxxhs4OjrSuHFjWrduTadOnYwmgLq5uWFpaXhr3MHBAYDy5cubTI+Le7TH3ru7uxu0+WEaNGjAiBEjWLJkCa6urg+9mvLx8TFKyw6mQ0NDDf6tXLmyUd7stOw8nTt3ZvPmzQQEBLBq1Srq1atHixYt6NKly2OvZuTt7W2UFhISAsDLL7+c437R0dGPVW9BeXA41v0XTZC11Kurq6tB2oPn7MH3ZcuWlToKsQ7LV3rAvvMUBJ1KxQ0nN4O0MBcHIhxtmfXzNr7o04ZjVcpRJTyGKZuCiXewI8U663vGTFGocTeeC2Uc9PvWjYgzugBQq6HFsy64uhp+0ZaWn4fUIXVIHVJHQdRRFErDGH2VSkWZMmWMhoVnS0lJ4e7duybjt7wo0Mm4devWxc7OTj851Nvbm7Vr13L48GGOHDnC8ePH+eSTT1i4cCGLFy82GArysIkGOW3L7S7B48jIyCA4OBiA+Ph4wsPDcXJyemL13c/CwoL58+dz+vRpgoODOX78uP6cffLJJ3To0AEgx9VBMjNzfnCQqavC7PM4c+ZM/bCrB3l6ej7qYQhh2oBWsPp1+GEr3ImD21EQnwKoQKfL6qbPI42i8P3vS+g/bCr89/vgmJRC46vhaBSFuUv+BLLG7N/wcmdfsypAVhUqoMvl21SNSuCunSVlE1LxiU0CQKUGjVpFWW9L+r5YjnLe+e9NEUIIIXJSqVIlli9fztChQ01u37hxI0OHDkWr1ear/AJ/Mq5WqyUjI0P/3sLCgjZt2tCmTRsA9u/fz+TJk1m5cuVjLRf0oIJeEu/777/n7NmzTJo0iWXLlvHuu++ycuVKk1dc2T3i97t27RpwL0DOvqi5evUqzZo1e2jebHXr1tWP0Q8PD+f5559nwYIF+kD//jsb9z8HIPvOQF5VqFABACcnpzzf8RDisQxsnfV6GEWBO7FQ7sWHZuv37yG+WLOBd/v3QadRk2hlyZ7aFWl/9jo6Feyp48P3XZuSZmlOi9BoyiSnYZ6hJd7GjHLe1nw3zBUHF3O0GTocXMyxsjYe2y+EEKL4KQ3r6OfWaZ2RkVE0q+6YEhwcTEpKCg0aNACyJqk+2Ates2ZN4NGH3eTGxsamwMo9cOAAq1atokePHvon/U6ZMoWvvvqKDz/80Cj/unXrDMbpJyYmsn79euzt7WnSpAmQ9bAua2trVq9ejb+/v37oUlJSEqtXr8bGxoYWLVoAps+bh4cHzs7OBseXfRvn8OHDdOrUSZ++YsWKRzreTp06MX/+fBYuXEiTJk2Mev0TExOxsLDAwsLikcoV4rGoVFDWGf43Al7/+aFZ3zq6mjKpCczv0h3XlBT+ruHJ/K6+ZGrUmGVkkuhqy6CG5nzzjAM25iX/D4MQQoiSO3QnPj7eYBXIqKgobty4YZQve7GXxxm2ne9A//z582zevBnImpRx9epVfvvtN8zMzJgwYQIAEydOxN7enkaNGuHh4UFCQgJBQUGoVKoCf3BVpUqVsLW1Zd26dVhZWWFvb4+LiwtNmzbV57lx44a+zQ9q1qwZbm5uREZGMmPGDCpUqMC0adOArPkHQ4YM4ZdfftGPlb+fk5MTI0aM0C+lGRQURHh4OO+9954+aLa3t2fSpEl8+eWXjBw5kh49egBZy2vevHmTd999V3+h8NNPPxEcHEybNm3w9PREURT++usvQkJCDFYl6tKlC/Pnz+fTTz8lJCQEBwcH/v77b5NLiD6Mh4cHb7/9Np988gkDBgygW7dulCtXjpiYGC5fvsyePXtYu3at0VwJIQrFK91yDfQBfBrf4O/P3fTP8dApCmqVCkVR5CFYQgghio3Zs2fz0UcfAVkjUiZPnszkyZNN5lUUhU8++STfdeU70N+6dStbt24FssbQOzo60qJFC0aOHEmdOlnrV/fv35/t27ezYcMG/fCSGjVqMG3aNHx9ffPdaFOsrKz49NNPWbBgAd988w3p6ek0btzYINA/dOiQfmWbB82bNw8XFxc++OADkpKS+P777/V3CQAmTZrE8ePH+eyzz6hbt67BMJtXX32VEydOsHbtWqKjo/H29uaTTz6ha9euBnUMGDAANzc3li9fzuLFiwGoXr06s2bNMnhgVrt27YiMjGTHjh1ER0djaWlJhQoVeO+99+jVq5c+n52dHd9++y3ffPMNAQEBWFtb88wzz/Dxxx/rh/fkVc+ePfH29mbFihVs2LCBhIQEnJycqFixIhMmTCgWk27EU8rCHFzsIDoxxywKcKVZOdrel6b+L7iXIF8IIUqnktqj37lzZ+zs7FAUhWnTpjFkyBAaN25skEelUmFra0uTJk0eK2ZWKU9yRmspl/1k3B9++KHAL1yEEPeJSwKnYTlu1gI/LezKqFGjDJ7MLYQQovSa3XKnyfQpfz9byC3Jv5kzZ9KvX78Cf25StgKfjCuEEAXO0fahmxOcZf6IEEI8bUpqj/79TM39LEgS6Ashir9clhVLsZFAXwghRMl14MABjh8/TlxcHDqdzmCbSqUyerhrXkmgL4Qo/jQPX/LyRmOPQmqIEEKI4qI09OhHR0fTvXt3Dh8+rF88IntUveq+BSXyG+jnf2FOgb+/P0ePHpXx+UI8aQ/0bhhsAk72qFZ4bRFCCCEKyJtvvsmpU6dYtWoVV69eRVEUtm7dysWLFxk/fjwNGzYkLCws3+VLoC+EKP4e8rCQkt+fI4QQIj8UlcrkqyTZvHkz48aNY9CgQdjb2wNZq1lWrVqVefPm4ePjk+PSm3khgb4QQgghhChxSkOgHxsbq1+W/v4Hr2br3Lmzfjn7/JBAXwhRMthZmkzW+ZQp5IYIIYQQBaN8+fKEh4cDYGlpibu7OydPntRvDw0NfaznwUigL4QoGV7rYTJZd/CzQm6IEEKI4kBRq0y+ShI/Pz+2b9+ufz9o0CC++uorPv30Uz7++GPmzJnzyA9BvZ+suiOEKBne7gsLt0LkfU/IHdoW3ByKrk1CCCHEY5g6dSrbt28nLS0NS0tLZsyYwZkzZ/Sr7Pj5+fHdd9/lu3wJ9IUQJYOdNdxYDD/vhjM34cVnoWFlyMgo6pYJIYQoAiVtPL4p9erVo169evr3zs7O7Nixg9jYWDQajX6Cbn5JoC+EKDmsLWF816JuhRBCCPFEOTk5FUg5MkZfCCGEEEKUOKVh1R2AGzduMH78eGrUqIGLiwv79u0DIDIykkmTJvHPP//ku2zp0RdCCCGEEKIInD17lrZt26LT6WjevDmXL18mMzMTADc3N/bv309SUhI//fRTvsqXQF8IIYQQQpQ4JbH3/kHTpk3DycmJ4OBgVCoV7u7uBtu7d+/O6tWr812+DN0RQgghhBCiCOzbt48JEyZQpkwZk+vle3t7Exoamu/ypUdfCCGEEEKUOKWhR1+n02FjY5Pj9rt372JpafqBkXkhPfpCiBJHl6nj4J93+HhhOEtO6MhQNEXdJCGEEIWsNEzGbdy4MX/88YfJbZmZmfz666+0aNEi3+VLoC+EKFFuHYviu8ZbWPm/a2T+fIbbk/5iVlgP0hS5QSmEEKJkeeedd/jzzz+ZMGECp0+fBuDOnTvs2LGDzp07c+7cOd5+++18l69SFEUpqMYKIcSTFHs0gl9fOMC+2lV4c+tOXJJTAAhxcWbRlPosfasZ5ubmRdxKIYQQheGTTsEm09/bnv8e8KKwfPlyXnvtNeLi4lAUBZVKhaIoODg4sGDBAoYMGZLvsqULTAhRYuzsvY19bZvR/2CwPsgH8ImOoe2GMHirCBsnhBBC5MOwYcPo27cv27Zt4/Lly+h0OqpUqUKXLl3kybhCiKfD3ZNR3PRwwv1OLHVuhRttrxdypwhaJYQQoqgoJWs4vt67777L4MGDqV+/vj7N1taWPn36FHhdMkZfCFEirJp7GdtYLV33nSdVsTDabqNLKoJWCSGEEI/miy++0I/HB4iKikKj0bBr164Cr0sCfSFEiRCcaYfX9TgAIlSOpJA1Fl8Bbtk7onZKK8LWCSGEKGylYdWdbE9qyqwM3RFCFHu3LiZgEZaKh+4uFZQwVCiEqsoShjtalZrbNvbYlVNRu6gbKoQQQhQjEugLIYq92W+e4b09W/FRbunTHJUEMrAkTu3AZed6VDufwLY287hWpzrdp7WkUk3nImyxEEKIJ62k9t4XJgn0hRDFXqt/z1E+03iyrYYM7LUpjLywE2slCQ6DcvgQyw9fIOr38fhWtS6C1gohhCgMuhIc6IeEhHD8+HEA4uKyhqVeunQJJycnk/kbN26cr3pkHX0hRLGWnpjB5pq/8FzoftQYfl3pUKHFAhuiDNIVFN4e9hJfLutYmE0VQghRiD587ojJ9JlbmhZySx6NWq1G9cBFSvb6+Q/KTtdqtfmqq9j26M+YMYNNmzZx9OjRfO1/9OhRxo8fz4cffoi/v3+u+f39/SlXrhyLFi3KV3358ahtLGxBQUHMnDmTH374AV9f36JujnhKrfbbTrKDE0qo8TYdGtRkGqWrUNFrxw6Iag6uj7cGsRBCiOJJoWT26AcEBBRaXfkO9LODVIDp06ebXPvT19eXNm3aMGfOnHw3UAjxdNJpFT7te4Ra1xLx4Q6p2JJkpyHB0gYAi8x0POKSgayVd+7/uk/FkgScuV7pHTLmvETV0Q0Lvf1CCCGEKSNGjCi0ugqkR3/RokU899xzWFlZFURxALz33nu88847BVaeEKLk+PtALItmXMIpMwNrbRr1Ei8T4WLPzG4jSTfLWlZTpdPR62gwTW6do3r8XX2gn4mGK9TCKcKGitootC9+ReZz8zEr54RWp6BRl8weICGEEIZkMm7uHjvQr127NmfPnuWXX35h1KhRBdEmAMzMzDAzK7Yjix6LVqslIyOjqJshRLE0anYEv4Rbkda8DgD7fcqxcMtN/q5SRx/kAyhqNfur12bo2Y2oyOrVVwAztFTjDDqtBgXQoDC712984NeTREcbzFUK07zjGNPdjZ/PQnUXFYPrlM7vGiGEEE+3x/7r1rFjRxRF4eeff6ZPnz45zhbOdvbsWZYsWcI///xDcnIy5cqVo3v37owYMcIgsM9pjP6xY8f4/vvvuXjxInZ2dnTq1Ik+ffowaNAgxowZw7hx44zq3LhxIytWrODmzZu4uroyYMCAHG+bnD9/njlz5nDmzBnMzc1p27Ytr732Gi4uLgb5YmNjWbhwIfv27SMqKgpXV1f8/PwYN26cwTnIHuc+b948/v33X4KCgggPD+e9996jXLlyj9zGPXv2sGzZMi5evIhKpaJatWoMHz6c9u3bP1be3377jRUrVhAWFoaHhwcDBw7Ezs7O5DkqTXQ6hUMX0omI1dGqtgVlHDVF3aRiJTU2net772Bpb06Ftu5ozA2fsRd9OYE7J6Kx1iloEjJwalEG+zpORF9O4Ma+O2QkZ1K2sQteLcuYnGSUHJ/J32vCCDt0l6TbiZzDhs1VK5Fmd++74KhnWd7t9jyV4+OMG6jKGrITTRkiqIAWM2yJw5OrWJGqz+acFE+iuTkkppNhacant12xeG4XlplaJnZvxRBLKyx0mVhrM+l67jiatAz+qNEIewcL3LTJJNrY0LG2DW28NaRkQisvFbXLyPMGhRCiKEmPfu4eO9BXqVS88sorTJw4kSVLljB16tQc8+7fv58333yTChUq8MILL+Dg4MC///7LwoULuXjxIl9++eVD6zpx4gSvvPIKDg4OjBgxAnt7e7Zv387Jkydz3Gf9+vVER0fTs2dP7O3t2bJlC9999x0eHh507drVIG9ERAQTJkzgmWee4dlnn+X8+fNs3LiRc+fOsWzZMv3QpMTEREaPHs3Nmzfp2bMnNWvW5MKFC6xbt44jR47w888/Y2tra1D2t99+S2ZmJn369MHW1paKFSuSnp7+SG1cu3YtX375JT4+Prz00ksAbNq0iTfeeIN3332Xvn375ivvqlWr+Oabb6hevToTJ04kNTWVFStW4Oxcutchz8hUePn7WI5eyrq7YmEGs8Y40rauZRG3rHi4cyqGP8b8TXpi1mRX1xoO9Py5NZb2Wb3q//x4iUOzz+EckYJjzL2n0poNrsTlf2IMyvJs4Ua3H1oYXCjcOJtIwFsXsIhLwikqlnhnB8qTSISdjVFboizN8VGbG6V7RMWQhC23qaxPS8KJMCpTkQv6tOsOLpChy3qTnAE6hbldmuP37zUSNBaACpekRPYvnkGVmIis47dxoN3AtzhRzgs0Flz+R+GHf+5N/P3yGQ3TWsmdACGEEMVXgfyVat68Oc2bN2fdunUMGTLEoKc6W1paGh9//DF169ZlwYIF+t77fv36Ua1aNWbPns3Ro0cfurrLN998g0ql4qeffsLLywuAAQMGMHbs2Bz3CQ8PZ926dfre6V69etGjRw9Wr15tFOjfunWLqVOnMnToUH1a5cqVmT17Nr/++isjR44E4Oeff+bGjRu89dZbDBgwQJ+3evXqfPXVVyxbtowJEyYYlJ2amsqqVasM5jFk363ISxvj4+OZO3cuXl5eLF26VJ+3f//+PP/888yZM4dOnTphb2//SHkTEhKYP38+lSpVYsmSJfr2+fv7079//xzPa2mw7XiaPsgHSM+EbzYkSqD/n0Ozz+mDfICoC/GcXRNCoxerkRKTxpHvL2CWrjUI8rUaFSHHo+GBXpbQ4Eiubb9N1W6e+rRtP91Em6lgF5dIprkZikaNVXIqlaJjuebiZLB/+eRUfMLv4mBnwx0nB3QqFa7xiThHJ3LGrja2iYZtT8TRYIJuvOUD6+mna4lysOY333vP0n39wB/6IB/AIzmeGX8HMqTHeLDUgcawB/+DvVpebKjB1UZ6lIQQoihIj37uCuze86uvvkpGRgYLFiwwuf3QoUNERUXh7+9PYmIisbGx+lfr1q31eXISFRXF2bNnadeunT7Ih6yx/EOGDMlxP39/f4MhKFZWVtSrV48bN24Y5bW1tTUI3CHrQsLW1pbdu3fr0/bs2YOzs7PRSkN9+/bF2dnZIG+2/v375zhZOS9tPHToECkpKQwePNggr52dHYMHDyY5OVl//h4lb3BwMKmpqQwYMMCgfabueBQH0dHRpKXdCywTExNJSEjQv09PTycqynBN9du3b5t8H3LHeFnG6xFaoqKiCqyObOHh4dz/yIqCPI4nVUdsyAPRM3D77F0URSHhVjK6DB3m6TqD7ZnmaqMgP1vMtQSDOiJvZQ2t0Wi1ZJqZkfpfIP3C8XOUScxaTUetKDSKjqNaTDxmOh2OScmkpKcTq9VinZLKSR9PAuvde4iIVqViV/WK/NSqHofLVwIgSWPJilrNDBtjadzHUfNumHFa9H/nw8TTRtK0cC323obS8DOXOqQOqUPqyG8dongqsPvONWvWpEuXLvz5558MGzaMatWqGWy/du0aAB999FGOZTzsQxMWlvVHuGLFikbbTKVl8/T0NEpzdHTUP4Xswbzm5obDAywsLPD09CQ09N4i3mFhYdSqVctosrCZmRne3t6cP3/eqGxvb+/HamN2/ZUrVzbKm52WnSc/eX18fHLMW5w8OFfiwXkEFhYWuLq6GqQ9eIcp+32zGhb8+Geywbam1cxxdTUcsvQ4dWQrW7bsEzuOJ1WHZ3M3LgXdMthetYM3KpUK1xoOWLlYkKbVoVOB+r+/D+ZpWjQ6Ba2JlW28WpQxqKNyQwdO74shxcYaq5RUomxcsErPwCM2kRnbD3Ld1YnIsm5YoyJDoyHJzIyjZd1ocicSM52O4x5uWKakcaJiWRpcc6NOeCSvDujMvmpZ3wezaUWHa+f43+pdmGc+0B5z4z6OXZXr0OPiPwZpO71rgVoFGuPjKWMD9dzvpZeGn7nUIXVIHVJHfusoCop06OeqQAeYTpgwgZ07d/Ldd98xd+5cg23ZV4qvvfYa1atXN7l/mTJlCrI5AGg0xWNy5cOWHi0ubXzaNK1uwfjutizdlkRqBtSsYMb7zzsUdbOKjVZv1iExPIXbR6JQm6moPciHat2zLko1Fho6fd2EXdP/ITI5E9e7KWgyFGw8bWj1Sg2OrgkhJTJrDoraQk2T8dUp18Twj8Jz47yJvZNG6DkdoOAVFc/6JjXwP3UZVUoa3tFxBNfw4mTFsqSYafA/fpl2t8LvtS8sghNujry+dwtnnD1JMS+rD/Kz7a5Ui8BGoaxc9QfLm9Thl+Z1SHawBnMNZBp203/XvDMNw0IY+u9B1IrCH5XrM8OvD9haGN2lqOAAP/c0x9JM/soIIURR0cnQnVwVaKDv6elJ//79+eWXX4xWy8nu0ba2tqZ58+aPXHb21ef169eNtplKy4/Q0FAyMjIMevXT09MJDQ016PH29PTk+vXrZGZmGvTqZ2ZmcuPGDZM99I8re7jS1atXadbMcBhC9t2S7HofJW/2vyEhIUZ5r169WpCHUCyN62bLC89Yk5CiUNZZLrjuZ+1qSa+lrUmKSMXMSoOlg+HdLs8WZXh+WycSw1OwdjQnIzIN6wo2qDRqao2vQWJ4Ckqmgo2bJea2xl819i7mjJ1Tm/jIdHSZOm4djyZ5XxIbGlbjhQOniHF2wNzMgjgrCwBsMo0f/+2ZlEqz2zdoevsGC+v5mTyOP2tWo8fJG/Q9fYFGt2+zqlVDzpdzIdrGVh/AaxQdPauo2PXOONaGDsXFRkXNWk7sq6YhDRVnoxTK2qppVwHuJquo4ICsxy+EEKLYK/AlI1588UU2btxo1KPfsmVLXFxcWLp0KZ06dcLR0dFge2pqKlqt1mi1mmxubm7Url2bvXv3cuvWLX0wm5mZyS+//FIgbU9KSmLt2rUGk3HXrl1LUlKSwZKU7dq1IyAggN9//91gwurvv/9OTEyMwYo2BaV58+ZYW1uzevVq/P399ecpKSmJ1atXY2NjQ4sWLfKV19LSkrVr19KzZ0/9nYc7d+6wdevWAj+O4sjWSo1twT3rrdSxdc/55Kg1Khw8s1bJMbczN5meGwe3rEDeqVt56naDzstucPiAQrqZGdWiEzlXJpkwBxsSrIxX3bHKyBpDqgK6XjvLNF1nlAcC8Evl3Fj5y0iCYzSoLtzC0t2BDf2caFfZjLhUBQVwsrp/H+PvoBb3XbvbWuTpsIQQQjxhMhk3dwUe6Ds5OTFs2DB++OEHg3Rra2tmzpzJG2+8Qb9+/ejZsycVKlQgISGBkJAQdu/ezddff/3QVXdee+01Jk6cyIsvvkj//v2xs7Nj+/btZGZmTao0tU73o/Dy8mLx4sVcuXKFWrVqce7cOTZu3IiPjw+DBw/W5xsxYgQ7d+7kq6++4sKFC9SoUYMLFy4QGBhIxYoVGT58+GO1wxR7e3smTZrEl19+yciRI+nRoweQtWTmzZs3effdd/Vj6B4lr4ODAxMmTGDOnDmMHj2abt26kZqayoYNG6hQoQIXLlww3SAhnpDWw71p+XwF1o88wN4ULb3Ph6LOSGT8nm38UbslalXW+Hod0Pf0X6jQoqChbGICU3YdYvazzfVf/jbpaWzkEK379fivdMO5Q45W8kdCCCFE6fVEFoF+4YUXWLduHZGRkQbpLVu25Oeff+bnn39my5YtxMTE4ODggJeXF88//7zRBN4HNWnShO+++4558+YREBCAvb09nTp1omvXrowcORJLy8dbFtHd3Z0vvviCOXPmsHXrVszNzenatSuTJ0/G2vre8nx2dnb89NNP+gdmbdy4EVdXV/r168e4ceNyvCvxuAYMGICbmxvLly9n8eLFQNaSnrNmzTJ6CNaj5H3hhRewtrZm5cqVzJs3Dw8PD1544QXs7OweOnlaiCdFrVExYHkbHBZf58TK6/Q+sQ/PuChGHNnGRXdPMs1U1LlzFefUBBQgA2vS0TD60Cm6XThHYP2qxKkd6Gp9idabC/7CWwghRNGTHv3cqZT711MqoXbu3Mlbb73Fp59+SpcuXYq6OUKIAnSsze9UP7BXvya+BUmo/lvv8t5a+ZmgTmS7T2PKXLUnEwsuetsw4lRfcMzbECIhhBAly7Tep0ymf/V7/UJuSfFVoh7rqCgK6enpBj33mZmZrFy5Eo1GQ5MmTYqwdUKIJ6HxX72ItPgb68x0fVo6FoRTnlRsMSeN8HLWfNvlOZKTMuiVfoF/fZz4qHWiBPlCCFGKyao7uStRgX56ejr+/v507dqVihUrEhcXx/bt27l06RIjRozAzc2tqJsohChgKpWKi2Uq0OD2FQC0mBNGBdLJmiScgSUOEXDe0ZaBV3bi4BDBJ3ZmOH/2alE2WwghhChyJSrQNzMzo3Xr1uzdu1c//r9ixYq89dZbRk+0FUKUHtG9mxK3JBTHtFRiNA6kaw1XAjLX6vg46E+6fVAVq0GDwEqWxhFCiNJOHpiVuxIV6Gs0Gj788MOiboYQopB1mt2Ub0/FMyT4AJbaDO4fnZ/tjosNmqEtwdx4GU4hhBClj4JE+rkxfg68EEIUM1aWaqycMrlp5/7fuPxMg+1/Va5AkkYCfCGEEOJ+JapHXwjx9Lqus8bOoyw26em4pCSjQscZd3cWtWzMv54eDAr/t6ibKIQQohDJZNzcSaAvhCgR7rg7E6OxI8PMjEwU/qxZiZPlPPCJiWPqX0cp6xkGtC/qZgohhBDFhgT6QogSoVcPdwaFlOG0VxlaXAnFSg2VkhMZeuI8zcPOE19ZW9RNFEIIUYjkgVm5kzH6QogSYWD/ckxOD+WUtwfzOjbltpsNU3ftoVPIP3gn3OVGK4eibqIQQghRrEiPvhCixJj9bkU+Sc5k8eQTeO84SZXYO2htrNg91pkUJ5mMK4QQTxPp0c+dBPpCiBLF1saMyYt8UbSNUdK1aM1gT0BAUTdLCCGEKHYk0BdClEgqjRqVtRptRkZRN0UIIUQR0EmHfq5kjL4QQgghhBClkPToCyGEEEKIEkfG6OdOAn0hhBBCCFHi6JBAPzcydEcIIYQQQohSSHr0hRClilanAKBRS0+PEEKUZjJ0J3cS6AshSgWtoqLVL3A0QosK6FsNVvtrJOAXQgjx1JKhO0KIUmFGQm+ORmQF9Qqw/hJM2a0t2kYJIYR4YnQq0y9xjwT6QohSIQIno7QfThR6M4QQQohiQ4buCCFKvP+G5RvJyCFdCCFEyaeTMfq5kh59IUSJp6CCHJZZuxmvK9zGCCGEEMWEBPpCiBJPhULWyHxjL22VQF8IIUojRaUy+RL3yNAdIUSJp8uO8018v5+OLOzWCCGEKAwy8TZ30qMvhCjxVqa0gBx6cezMC7kxQgghRDEhPfpCiBLvb221HLdpZYVNIYQolZQc5maJe6RHXwhR8iSlQkiE/q2iaHLMeiWhMBokhBBCFD8S6AshSpbxC8BuKFQaD7aD4e8LoECDq2FF3TIhhBCFSKdSmXyJeyTQF0KUHKO+hYXb771PTkfb8SNaXbyF3/kbRdcuIYQQohgqVmP009LS2LhxIzt37uTy5cskJCRgbW2Nt7c3vr6+9OzZEx8fn6Ju5hPh7+/P7du385T3hx9+wNfX9wm3SIhi5tgVWLrXIEkBUhULWl68yU03xxx3zdTqMNNIv4YQQpQm0nufu2IT6N+6dYspU6Zw7do1GjduzNChQ3FzcyM5OZmLFy+yceNGVqxYwaZNm3B3dy/q5ha4119/neTkZP37a9euERAQQIcOHejQoYNB3kqVKhV284QofFEJoNNBmf8C+FZvG2VRAY6ZyaSZm1HrVs7raL6zT8fXHSTQF0II8XQpFoF+amoqkydP5tatW3z99ddGgS1k9favWrUKVQFevWm1WjIyMrCysiqwMvOrffv2Bu+PHj1KQEAAVatWpVu3bkXTKCEK2j9X4ehlSEwBC3M4FQJBR+FuPGTm8GArMw1k5rx0zlUXT05X9qTB9QiaXAnjWJXyRnkW/ZXK1x3s0KZpUQAzy5wn7wohhCgZZB393BWLQP/3338nJCSEUaNGmQzyASwtLRk1apRB2t27d1mxYgVHjhzh9u3bpKWl4enpSffu3Rk2bBgazb0/5kFBQcycOZN58+bx77//EhQURHh4OO+99x7+/v4EBwcTGBjI2bNniYyMxNzcnDp16jB69GiaNGli1J6dO3fy448/cv36dZydnenVqxcNGjRg4sSJfPjhh/j7++vzpqens2LFCv78809u3bqFhYUFjRo1Yty4cdSsWTPP52nIkCHEx8cTFBSEWm3YO7ljxw7efvttZsyYQY8ePTh69Cjjx4/nww8/JCkpiTVr1hAeHk7ZsmUZOHAggwcPNir/xo0bLF68mMOHDxMXF0eZMmXo2LEjY8eOxdraOs/tFMLIgXPQ9SNITHv0fe8L8iPNnXDLiNW/P+5Zk0+eeZEKMclcK++Og06DWUYmmWYag3X1422sGPzeTbr+dgxFp1CllzetZzZEIwG/EEKIUqxYBPq7du0CoHfv3o+036VLl9i9ezft27fHy8uLzMxM/v77b77//ntCQ0OZPn260T7ffvstmZmZ9OnTB1tbWypWrAhkXQjExcXRrVs3PDw8iIiIIDAwkJdffpkffviBRo0a6cvYtm0b06dPx8vLizFjxqDRaNi0aRN//fWXUX2ZmZm8+uqrnDp1im7dujFw4EASExP57bffePHFF1m8eDG1a9fO0/H27t2br7/+mkOHDtGyZUuDbYGBgdjZ2dGxY0eD9NWrVxMVFUXfvn2xsbFh69atzJo1i/j4eMaOHavPd+7cOcaPH4+9vT19+/bF3d2dixcv8uuvv3Ly5EkWLVqEmVmx+LiIkiY1Hfw/y1+Qf58Yc0dOOdflmYj9+rQ/K7ala/ApNDqFTLWaFW3rkWlu+nO6xrEszdTmOKalcWnddezK29B4Uq3HapMQQoiio5N19HNVLCK3K1euYGtri6enp0G6VqslIcFwEWwrKyv9UJvGjRsTGBhoMJxn6NChvP/++wQGBjJu3Djc3NwM9k9NTWXVqlVGw3Xee+89o17rfv36MXDgQAICAvSBfmZmJrNnz8bZ2Zmff/4ZBwcHAPr378+QIUOMjm316tUcO3aM7777ziA479+/P4MGDWLOnDksWrQoT+epW7duzJ07l8DAQIOywsPDOXToEH379jU6rhs3brB27Vo8PDwAGDhwIC+++CI//fQTvXr10qd/9NFHuLm5sWzZMmxtbfX7N2vWjDfffJMtW7YY3KUQIs+OXIaYpMcu5opdRbySb6OQNTY/RW1JmYgU1ErWdjOdjqH7/+WfSuVIsrIw2l9RqTjrVYaWl24BcHNPuAT6QghRgikyGTdXxWJ2WmJiInZ2dkbp165do2PHjgavtWvX6rdbWVnpg/yMjAzi4uKIjY2lZcuW6HQ6zp49a1Rm//79TY7Jvz/IT05OJjY2Fo1GQ926dTlz5ox+2/nz57l79y49evTQB/kANjY29O3b16jcLVu24OPjQ61atYiNjdW/MjMzad68OSdPniQ1NTVP58ne3p5OnTqxd+9eYmNj9elBQUHodDp69epltE/Xrl31wTyAubk5Q4cORavV6u9AXL58mUuXLtG1a1cyMjIM2tmwYUOsra0JDg7OUxuftOjoaNLS7vUMJyYmGlwMpqenExUVZbDPg6sZPfg+PDwcRVGkjidUR4x9wfQnVEy6RfXEK/r+m2hLZ32Qn81Cq8MrKj7HMtwS7k14t/O00f+/uJwrqUPqkDqkjpJahyieikWPvp2dHYmJiUbpnp6ezJs3D8gapjNnzhyD7ZmZmSxdupTNmzdz8+ZNgw8pQHy88R98b29vk224desW8+bNIzg42Oguwv13DEJDQwH0Q37uZyrt2rVrpKWlGQ2puV9sbCxly5bNcfv9+vTpw6ZNm9i8eTNDhw5FURSCgoKoXr06tWoZ906aWqGncuXKBsdy7do1ABYuXMjChQtN1hsdHZ2n9j1pLi4uBu8fvEC0sLDA1dXVIK1cuXIPff/guZc6CrYOi4bV4ZXn4PstPA63dMPPoEt6DGpFi051b5x9hkZNqIu9yf1rhd6lWnhWGRb25jSaeG9+THE5V1KH1CF1SB0ltY6iIJNxc1csAv0qVapw/PhxQkNDDYbvWFtb07x5cwCDibXZZs+ezerVq+nUqROjR4/G2dkZMzMzzp8/z3fffWcU+AMme/OTk5MZM2YMKSkpDBkyhKpVq2Jra4tKpWLp0qUcOXLksY6vatWqTJkyJcftzs7OeS6rQYMGVKlShcDAQIYOHcrhw4cJCwtj2rRp+W5f9nl64YUXjMb+Z7v/7oUQj+y7MdChLkz7GULugo6s+4la49/RnDz4fW6tTaN55DEOuTVBp9KgA1a3rEOitaWJvRWOvu/C9aYNUTIVKnXzxNqt6FfbEkIIIZ6kYhHoP/PMMxw/fpzff/+diRMn5nm/zZs307hxYz7//HOD9Js3bz5S/YcPH+bu3bt88MEH9OzZ02DbggULDN6XL5+1dN/169eNyjGVVqFCBWJiYmjatKnRSjn51adPH2bNmsXp06cJDAzE0tKS5557zmTe7N76+129ehVAf1GVfZdDrVbrL6yEKHB9W2a9Hib74lyng4MXwM4KGlaCu3HgMdooe+34izhlRDG38VBSzM04WKOCyWKfq6jCxsWSWkMrP+5RCCGEKCbkgVm5KxZj9Hv37o2Pjw/Lly9n9+7ded5PrVYb9dqnpKSwatWqR6o/+27Bg2UFBwdz+vRpg7RatWrh5ubGpk2bDIYGJScns2HDBqOyu3fvTlRUFCtXrjRZd37GuHXr1g1LS0uWL1/Onj17eOaZZ7C3Nz1c4c8//+TOnTv69xkZGaxatQqNRkObNm0AqFGjBlWqVGH9+vXcunXLqIzMzEzi4uIeuZ1CPDKVKuul0UDb2tCoctZ7dyf4YIDJXayUVP7XoxVzu7ckPYcVd/5netVeIYQQolQrFj36VlZWzJkzhylTpvDmm2/SpEkTWrRogaurK0lJSYSEhLB9+3Y0Go3BxNJnn32WDRs28M4779CsWTOioqIICgrC0dHxkepv2LAhrq6uzJkzh9u3b+uXlty8eTNVq1bl8uXL+rxmZmZMnjyZ9957jxEjRtCrVy80Go2+3tDQUIMx/UOGDOHQoUN8++23HDlyhKZNm2Jra0t4eDhHjhzBwsIix3HxOXFwcOCZZ55hy5asMc+mJuFm8/b2ZuTIkfTr1w8bGxv+/PNPzp49y0svvaQfk6dSqfjoo4+YMGECQ4YMoWfPnlSuXJnU1FRu3brFrl27eOWVV2TVHVG03ukHswIhOd0g2T41GbWJYXr387IvFn0aQgghCpAsr5m7YhHoA3h5ebF8+XI2btzIzp07WbFiBYmJiVhbW1OhQgV69epFr1698PHx0e8zdepUbG1t2b59O3v37sXDw4M+ffpQu3ZtXn755TzXbW9vz/fff8/cuXNZvXo1Wq2WmjVr8u233xIYGGgQ6EPWSjZmZmb8+OOPLFy4EBcXF3r16kW1atV48803sbS8N0bYzMyMOXPmsG7dOjZv3qwP6suUKUOdOnXo0aNHvs5X37592bJlCxUqVDD5QK9sgwYNIikpidWrV+sfmPX6668bLQVao0YNVq5cSUBAAPv27WP9+vXY2tpSrlw5/P39adq0ab7aKUSBsbKA6OVQ61W4FpGVpgLd0lfRhj38y97eUgJ9IYQQTx+VYmrGqsiXFStWMGfOHAICAqhXr94Trev06dOMHDmSiRMnGj0xGDB4Mq70xItS5/JtuBwOrWuSYWWGxRwMnoT7IOWNYtOnIYQQooD0fNH0nMyNP5mer/U0kr9++ZCRkYFarTZYCSg5OZm1a9fi6OhIzZo1H7J3wVizZg1mZmYSxIunU9VyWS+AjAxQZT9Gy5h54bVKCCFEIZLJuLmTQD8fQkNDmTRpEp07d6Z8+fJERkbyxx9/EBoayttvv425+ZMJLVJSUti3bx9Xr15ly5Yt9OnTx+jJv0I8jWxJIQlbk9us5FtOCCHEU0r+BOaDk5MTdevWZcuWLcTExKDRaKhatSqvvPIKnTp1emL1xsTEMH36dGxsbHj22WeZNGnSE6tLiJJkvPVu/pfSHVO9+uYyPF8IIUoleWBW7iTQzwcnJyc+++yzQq+3fPnyHD16NE95fX1985xXiJKuunkEpJjeVtmpUJsihBBCFBsS6AshSrXZz0iXjxBClEayvGbu5Ka2EKJUa+OlyT2TEEIIUQpJj74QosTTKipMjc+Xvh4hhCi9tLLqTq6kR18IUeJpVApqtEbptV2LoDFCCCFEMSGBvhCiVHjRag9w7/l/FmrY2l+G7QghRGmlU5l+iXtk6I4QolTwtbjOhy/AojMq3K1hRF01dhbyjS+EEOLpJYG+EKLUcLeFma2lF18IIZ4GWpmJlSsJ9IUQQgghRImjlTg/VzJGXwghhBBCiFJIevSFEEIIIUSJo5PlNXMlPfpCCCGEEEKUQtKjL4QQQgghShx5YFbupEdfCFGixf0vGP8JN+k17iZRHl+TGRZf1E0SQgghigUJ9IUQJVbs6tNo39mGRgcqQB2TQpTn1yiKkuu+QgghSrbMHF7iHgn0hRAlVsLwdUarKKuAxM/2FkVzhBBCiGJFAn0hRIllnq41mZ4y71Aht0QIIURh06pUJl/iHpmMK4QofWKSi7oFQgghnrBMielzJT36QogSK8cvMJ2uMJshhBBCFEvSoy+EKH3MNEXdAiGEEE9YptEsLfEg6dEXQpRISqbp8fkApDxkmxBCCPGUkB59IUSJlLY3JOeNsrqmEEKUehnSoZ8r6dEXQpRIuuikom6CEEIIUaxJj74QokRKO3zrodsVRUEly6wJIUSplSHf8bkqtj36Y8eOxd/fv6ib8UQdPXoUX19fgoKCHpr2MGFhYfj6+rJw4cIn1UwhiqWM//390O0S5AshhHjaFUiP/q1bt/j55585fvw44eHhWFhY4OrqSp06dfD398fX17cgqsmXiIgIfvnlF/7++2/CwsLIyMjAzc2Nhg0b4u/vT7NmzYqsbXkVFhZGUFAQ7du3p0aNGkXdHCGKBxmHL4QQT7WMom5ACfDYgf7Zs2cZO3YsZmZmdO/encqVK5OWlsbNmzcJDg7GxsamyAL9/fv3M336dNLT0+nYsSN9+vTB0tKS27dvs2fPHl5++WXmzJlDmzZtiqR9pjRu3JgDBw5gZnbvRxMWFsbixYspX768UaBfrlw5Dhw4gEYjywkKIYQQQoh7HjvQX7x4MampqaxatYrq1asbbY+MjHzcKvLlypUrvPXWWzg6OrJ06VIqVapksH38+PFs2bIFS0vLImlfTtRq9SO1SaVSFbtjEOJR6BQFFXA7Vsu1WDA3h2ZeOX813V1/gcPvHsXD2Y0KMZG5r6J8OxqSUiElHRxtwMMZLM0L8AiEEEIUhWQZopmrxw70b9y4gaOjo8kgH8DNzU3//23btrFlyxYuXrxIdHQ0NjY2NGzYkPHjx1OtWrU817d48WIOHz5MXFwcZcqUoWPHjowdOxZra2t9vh9++IG0tDTee+89oyAfsgLkbt26GaRlZmayYsUK/vjjD0JDQ7G2tqZRo0aMHz+eqlWr6vOFhYXRs2dPxowZQ+3atVm8eDGXL1/G3t6ebt26MXHiRIMeeYA9e/awaNEiQkJCcHZ2pkePHjRq1MioXUePHmX8+PF8+OGH+Pv7ExQUxMyZMwGYOXOm/v+NGzdm0aJFBm0ZN25coRyLEPdLTFf45qjCgTAdmVoIT4bUTGjiAbXdVBwLh3J24GalMPcfSMq+16o8MPYm+wtbUUDJABV4qdJxj0qm0tUwXt69H8/IBC7beaJJhbQMS5Kwwo5UozYpKCSpXsCW5Ec/IBsLSE4HFVC9PGyannWBMCsQ/r0BbWrCFH+wlgtsIYQoSikS5+fqsSM4Ly8vrl+/zq5du3jmmWcemnfNmjU4OjrSp08f3NzcuHXrFr/99hsvvvgiK1aswNvb+6H7nzt3jvHjx2Nvb0/fvn1xd3fn4sWL/Prrr5w8eZJFixZhZmZGWloaBw4cwMPDg1atWuX5WN5//322b99O8+bN6devH1FRUaxdu5ZRo0axePFiatasaZD/wIEDrFu3jn79+tGzZ0/27t3L8uXLsbe3Z/To0fp8u3fvZtq0aZQvX56XXnoJjUZDUFAQ+/fvz7VNjRo1YtSoUQQEBNCnTx/9xYGLi0uRHIsQD+oTqGPHdeMB81fjgIsPGUifU0+MSkV2N/0tLLnlaslxV2d2167Ebx//gmWCDoAUrLlGeWpyHXMMH5ClQoUVafk5nKwgH7LmAFwIgzqvQZWycO6/VX62HIdDlyDwnfyVL4QQQhSSxw70X3zxRQ4dOsS0adPw9vamQYMG1KlThyZNmhj1pH/33XcGve4A3bt3Z+jQoaxatYq33377oXV99NFHuLm5sWzZMmxtbfXpzZo1480332TLli34+/tz8+ZN0tPTc7zLYEpwcDDbt2+nU6dOfPbZZ/oVOzp16sSwYcOYNWsWP/74o8E+V69eZc2aNZQvXx6Afv36MWjQIFavXq0PjrVaLbNmzcLBwYGff/4ZJycnfd7Bgwfn2i4vLy+aN29OQEAA9evXN7oLUZjHIsSDzkUpJoP8JyFNbUaa1spgqI6CmjhscSPeKL8WczQUwBNy0zPvBfnZNh6BkAjwcX/88oUQQuRLeu6DN596j728Zv369VmxYgU9evQgMTGRoKAgvvjiCwYMGMCYMWO4deveH8jsIF9RFBITE4mNjcXZ2ZmKFSty+vTph9Zz+fJlLl26RNeuXcnIyCA2Nlb/atiwIdbW1gQHBwOQmJgIgJ2dXZ6PY8+ePQCMHj3aYFm+6tWr07ZtW06cOEFMTIzBPu3bt9cHxpA1HMjX15eoqCiSk7OGDJw7d447d+7Qs2dPfZCf3bZ+/frluX2P4kkdS3EQHR1NWtq9ntrExEQSEhL079PT04mKijLY5/bt2w99Hx4ejnLfMBKpI+91RMbEUVjSzTToTHxjadCZyK1gRvoTbU9EWPH7eUgdUofUIXUUVR2ieCqQwddVq1ZlxowZQNaH59ixYwQGBvLPP//w+uuvs2LFCszNzTl//jw//PADx44dIyUlxaAMT0/Ph9Zx7do1ABYuXJjjmvHR0dHAvQA/KSnvT84MCwtDrVabHM9fuXJl9uzZQ2hoKM7Ozg9ts6OjIwBxcXHY2NgQGhoKQMWKFY3ymqqrIDypYykOHhyy9ODFXPbSrvcrV67cQ9+XLVtW6shnHW2rOtG0bCZHwnnizHVabM2TSE27dzfPknQcMf17rjZ5AZAPasDTDW7et7BAuzq4t2pgkK04/DykDqlD6pA6iqqOIiEd+rkq8FmW5cqVo0ePHnTv3p2XXnqJkydPcubMGcqWLcvYsWOxtbXlxRdfxMfHBysrK1QqFf/73/+MAv8HZV9pvvDCC7Rs2dJkHgcHBwAqVKiAhYUFFy9eLNiDe4BanfMNEeXBiYbFXGk6FlG4/uirYcZBHQdCFXQK3E2GDB3UdoUaLipO3lUoZwv2FrDuAqRlx9+mPlf3T8hVqXBNT8MxKY0Kt6N4bftflE2LI5ZMFNRYk4YL8ahNLKivAPHY40CC0bZcadSg/a+R7g6w8Z2slXpmrIZTIdC2NswY9OjlCiGEEIXsiS2nolKpqFu3LidPniQiIoJz586RnJzMN998Y7SuflxcHBYWFg8tL3uirlqtpnnz5g/Na2lpSevWrdm9ezfBwcG0aNEi1/Z6enqi0+m4du2a0QpA2XcTcrvrkFO5ANevXzfall1ubh71CZ9P6liEMKWMjYp5HfP2HIcV3Y3TFEUhQ6twO1HhSLjCxRh4qb4Gd1s1WV9RtoALkPVZvrXwOIlTtmOfkoRZDk/NUqHgoPycdcFw7Q5cCAUPJyjjAK72YGOlv5jIs6Wv5j2vEEKIJ0+W18zVY4/RDw4OJjMz0yg9NTVVP2a+cuXK+h7jB3uHf/vttzyN86pRowZVqlRh/fr1BuP+s2VmZhIXd2+88Lhx47C0tOTjjz8mJCTEZJl//vknR44cAaBdu3YABAQEGLTx8uXL7Nu3j4YNGxoMdcmrWrVq4eHhwcaNG4mNjdWnJyYmsn79+jyVkT1s5v7je5gndSxCPAkqlQoLMzUVnTT0r2nGuy3N/gvyTfMa15iayW9h/tBH4+qyC4fKZeG5JtC4ClQokxXkZ28TQgghSrHH7tH/5ptviIuLw8/Pj6pVq2JlZcWdO3f4888/uXHjBt27d9enf/fdd3zwwQcMHDgQe3t7Tp48ycGDB/Hy8kKrffjqGCqVio8++ogJEyYwZMgQevbsSeXKlUlNTeXWrVvs2rWLV155BX9/fyBr3sCXX37J9OnTGTp0KB07dqRu3bpYWloSHh7O3r17uXjxInPnzgWgRYsWdOrUiW3btpGQkECbNm30S1JaWFjwxhtv5Ov8aDQapkyZwjvvvMOIESPo3bs3Go2GjRs34ujoSHh47oObK1WqhK2tLevWrcPKygp7e3tcXFxo2rSpyfxP6liEKClUD70IEEIIIZ4Ojx3oT506lb1793LixAl27dpFYmIidnZ2VK1alREjRugDby8vL+bOncu8efMICAhArVbToEEDFi5cyFdffWU0A9yUGjVqsHLlSgICAti3bx/r16/H1taWcuXK4e/vbxT4tmnThrVr1/LLL79w8OBBdu/eTWZmJmXKlKFBgwZMnTrVYBjRxx9/TI0aNdi0aRNz5szB2tqaxo0bM2HCBIOHTD2qjh07olar+fHHH1m0aBEuLi76B2a98sorue5vZWXFp59+yoIFC/jmm29IT0+ncePGOQb6T/JYhCgJJMwXQoingNyZzZVKkZmWQogSKO5/+0h9Y3uOiy64Kx8XanuEEEIULtXrsSbTlf85FWo7irPHHqMvhBBFwcLLUVZWE0KIp5kqh5fQk0BfCFEiqcrZF3UThBBCiGLtiS2vKYQQT5JlC++cN0qPjhBCPAXkyz430qMvhCiRVBYP6aeQmUdCCCGE9OgLIYQQQogSSDr0cyU9+kKI0ke6MIQQQgj5cyiEKIWcrIq6BUIIIZ406dHPlfToCyFKLguN6eSRjQu5IUIIIUTxI4G+EKLEctg10mS645ddCrchQgghioAspJ8bCfSFECWWVWsfrBb1QKv+b6EdOwucr05BpZavNiGEKPUkzs+VjNEXQpRo1iMb86tyEoBRo0Zhbm5exC0SQgghigcJ9IUQQgghRAkk3fe5kfvbQgghhBBClELSoy+EEEIIIUoe6dDPlfToCyGEEEIIUQpJj74QQgghhCh5pEc/VxLoCyFKnIiIdN4PTGR/ug1daqrx0llip04r6mYJIYQQxYoE+kKIEuXAjii6/W1NvJU9qFScPa6AMoQPbH8r6qYJIYQoVNKlnxsJ9IUQJYZOpzBlYzrx3o5YZGZS404MdcOiiLGx4uvK/rxX1A0UQgghihGZjCuEKDHSkrWEOtoCkG5mxr+eZVjdpDrOKWmMOXSGYStl+I4QQjw15Mm4uZJAXwhRYljbmRFnrjFI06nVbGhQhfJxSRz6J7GIWiaEEKLQqVSmX0JPAn0hRImSYmlulJZmbsZ1Z3vQKUXQIiGEEKJ4kkBfCFFiJKYreMYnmdx2tqwLt53tCrlFQgghRPElgb4QosSwNQfn5HTUWp3RtsMVy2KhkVu2QgghRDYJ9IUQJYZKpeKqsx1OqelG21LMzUi1kIXEhBDiqSGTcXMlgb4QokRxSEghzsp4nD4qUGdmFn6DhBBCiGJKAn0hRImRrlWoe+0uWlOrKqhUpGVIV44QQjw9pEs/N3KfWwhRYlT7NpXQWt5Zq+uY6KZwUDIKv1FCCCFEMVXkPfpHjx7F19cXX19fvvzyS5N5oqOjadGiBb6+vowdO/aJtOOtt97C19eXCxcu5JhHURR69uxJ+/btSU1NfSLtyDZixAh8fX356KOPnmg9QpQUJyO0xN9NQ6tRQ4bpZTSbhkez+Jk/C7llQgghioR06OeqyAP9bJaWlmzdupX0dONJdps3b0ZRFDQajYk9C0avXr0ACAoKyjHP0aNHCQsLo3PnzlhZWT2xtly+fJkzZ87g5eXFjh07SElJeWJ1CVFS7N16h/jsNfQtTXwXKAq+N+8SqbPl5oKThds4IYQQhU8C/VwVm0C/ffv2xMfHs3fvXqNtGzdupHXr1lhYWDyx+lu0aIGHhwdbtmwhI8P07f+NGzcC9y4KCkJqaiqZD0wgDAwMxNbWlo8//pjk5GS2b9+e5/KSkkyvMS5ESZZ45S6Be8JxTU8HMxWojb/JXROSSUlPo97VS2S8vowoswmkzt5RBK0VQgghiodiM0a/Zs2aXL16laCgIDp16qRPP336NFevXuXll1/myJEjBvsEBwcTGBjI2bNniYyMxNzcnDp16jB69GiaNGlikPfKlSssWrSIU6dOERsbi4ODAz4+PgwbNow2bdqgVqvx9/fnxx9/ZO/evXTs2NFg/8TERHbt2kWVKlWoU6eOPt3X15cePXrQt29fvv/+e86ePYulpSXt27fn9ddfx8bGRp93xowZbNq0ie3btzN37lwOHDhATEwMgYGBlC9fHoCMjAy2bNnCM888Q7169ahRowaBgYH07NnT6JyNHTuW27dvs2DBAubOncvRo0eJj4/n6NGjAERGRrJ48WL2799PVFQUTk5OtG3blgkTJuDi4qIv5+7du6xYsYIjR45w+/Zt0tLS8PT0pHv37gwbNuyJ3kkR4n6KTuHWoUhibyRxbes1/opTc8y5LGfLuRNXuSZlE1KJ1+lIM7FvrK01bvEZfPRcK7b9eBAnXTTK1PlETf2NGMpgRSJO3EWFQqqFA1pzGxRrc6xbemLVtx63zidxJ8IC9/puVBpbC7WNiZV9xKNLSIHNx8DaAp5rDOY5/NkJi4YdJ8HHHfzqmM4jhBAGpPs+N8Um0Afo2bMns2fPJiIiAnd3dyCrF93FxYU2bdoY5Q8KCiIuLo5u3brh4eFBREQEgYGBvPzyy/zwww80atQIgNjYWCZMmABAv379KFu2LLGxsZw7d47Tp0/ry/b39+enn34iKCjIKNDftm0baWlpJnvzL168yJQpU/D396dLly4cO3aMwMBA1Go106dPN8o/ceJEXF1defHFF0lJSTG4GNi7dy+xsbH06NFD36ZZs2YREhKCj4+PUVnJycmMGzeO+vXr8/LLLxMdHQ1AeHg4o0aNIiMjg169euHl5cXNmzdZv349R48eZfny5djZZT1F9NKlS+zevZv27dvj5eVFZmYmf//9N99//z2hoaEmj0GIgpaZqiVo3CHC/8n6DMdZWbCtZT1Oe7qh+68HP8TSAhTT4/Nt07Vs862NjVbLKt/mPH/4COFURUMmNkRTjmuoUNChwiw9A8v0MEgCgs6x5YTCdfsKQDocSqTS3NO0394NyypOhXPwpdWZG9DhA7gbn/W+lhfs+wTcHAzz/RYMg7+B9P/ubvr7wm9vgXQyCCHEYylWgf5zzz3H3Llz2bRpE6NHjyY1NZVt27bRu3dvzMyMm/ree+9hbW1tkNavXz8GDhxIQECAPtA/efIk0dHRfP755wZ3Cx7k6emJr68vwcHBREZG4ubmpt8WFBSEubk53bp1M9rv0qVLBAQEULduXX0bkpKS2LhxI1OmTDEI5AGqVKnCxx9/bLINGzdupHz58jRu3BiArl27MmfOHDZu3MikSZOM8sfFxdGvXz9efvllg/SvvvqKzMxMVq5ciYeHhz69Y8eOjBo1ipUrVzJu3DgAGjduTGBgIKr7liwcOnQo77//PoGBgYwbN87gXAjxJFzcdEsf5AOc8nInysFGH+TrmVha0yJTS+W7CQBoNRo21+jA80cPoNVZoELRB/kAahTMSUchqy8ozMbjvyD/nuuWNoTNOEyl5Z0L9BifOu//ci/IBzh3C+b+AR8NuZemKDB5yb0gHyDoKGw+Dv5NC6+tQoiSRzr0c1VsxugDODk54efnx6ZNmwDYvXs3iYmJJoetAAZBfnJyMrGxsWg0GurWrcuZM2f027J7rg8ePEhiYuJD29CrVy+0Wq2+DQAhISH8+++/+Pn54eTkZLRPvXr19EF+tqZNm6LVagkLCzPK/8ILL5isOzw8nODgYLp3764Pup2cnGjTpg1//PGH0Vj+bMOGDTN4n5iYyP79+/Hz88PS0pLY2Fj9q3z58nh5eXHo0CF9fisrK319GRkZxMXFERsbS8uWLdHpdJw9e9ZkvUUhOjqatLR7AzcSExNJSEjQv09PTycqKspgn9u3bz/0fXh4OMp9vcRSR9HUEXPV8Hcz1cqSu7a5T3o3y9RRISoJu3StPi3OxpYTnnWwIRZLkvRBfjY1CulkzflJMLczKlOnVhN7+l67i9u5KjF1nL2FkbM3DetITIUbkUbZMk+FFJ/jkDqkDqkj1zpE8VSsevQha6jK5MmTOXHiBBs3bqROnTpUrlzZZN5bt24xb948goODDT6AgEHvdJMmTejevTtBQUFs2bKF2rVr07x5czp16mRUdocOHbC3tycoKIiRI0cCWZNjgRwvODw9PY3SHB0dgawe9wdVrFjRZDmbNm1Cp9PRoEEDbt68qU/39fVlz549HDhwgHbt2hns4+zsjL29vUFaSEgIOp2OwMBAfdsf1ubMzEyWLl3K5s2buXnzpsEvO0B8fPyDuxeZ++cWwL2LuGwWFha4uroapJUrV+6h78uWLSt1FIM6PJu5cWrFNX16xbsx2KRnkm728OEbmWZqrng4YKbEUyYx6w+TXWoaV50r0/DmNsKpgg4V6vuCfS1qzMiadO+ZdBu1okWnulePdVo6ZXt45+s4spX0n0eB1PFMXbgQarCdDvUM67C3Bt8qcPSKQTazTg2Lz3FIHVKH1JFrHaJ4KnaBfsuWLXF3d2fRokUcPXqUt99+22S+5ORkxowZQ0pKCkOGDKFq1arY2tqiUqlYunSp0cTdmTNnMmzYMA4ePMg///zDihUrWLJkCVOnTmXQoEH6fJaWlnTt2pW1a9dy8uRJ6taty+bNm/Hw8KBly5Ym2/KwyaoPBs2AyaU5FUXRL+35yiuvmCxr48aNRoH+w5b5fO655/Rj/R9kaWmp///s2bNZvXo1nTp1YvTo0Tg7O2NmZsb58+f57rvvTB6DEAXNp70HDUdW5tTKEHQZOhxT0qgaEcvRiu4mh+s8KMLekjKJaagUBd8boVhlZAX9WiwIpypluYIaHVrUJOGIAzEogF1mMs/c+osDZVuQYm6FbUoaData4v6eDBt5bJ8MhYu3YecpUKthWDsYZ2I41NJXof/XcD4UbCzh/QHQrFrht1cIIUqZYhfoazQaunfvTkBAAJaWlnTp0sVkvsOHD3P37l0++OADo572BQsWmNynatWqVK1aleHDh5OQkMCIESP4/vvvGThwoMEdgF69erF27VqCgoKIj48nKiqK0aNHo1Y/uZFOR48eJTQ0lCFDhtCgQQOj7Vu3bmXfvn1ERUXlehXt5eWFSqUiMzOT5s2b51r35s2bady4MZ9//rlB+v13FYQoDC2n1qbxS9VIS8hAExvH4PMxfBRjx5YwDcnmZjglpZJobUmypfGKOHHWFpz0dGTCvn9wTUqm9c2D3FWVRafSkmTpTFiZpihqDdYdfHCpriHzTgLaFjUxr1ueqhVdqRIWQzJWWNhZYF7eeDiPyAcXe9gxA25FgqU5lHE0na+ON5ydC1fvgLtjVi+/EELkRsbo56rYBfqQNZnVzMwMT09Po9tF2bJ70R/sbQ4ODub06dMGaXFxcdjb2xsE6vb29nh6enLz5k3S0tIMesZr1qxJ9erV2b59OxEREahUqhyH7RSUwMBANBqNvkf9Qc7OzuzevZs//viD4cOHP7QsJycnWrduza5du/j333+pV6+ewXZFUYiNjdXXo1arjc5jSkoKq1atesyjEuLRWTqYY+lgDp421KxTjuxPof9nkdxO1HLMyfR3gk6tIsHagmQ0dDi3h5DKjWlzaDhqK9Nfc2oMvwBV1a2xLdAjEXpeeZjMr1JBlbK55xNCCJFnxTLQL1u2rH5FmJw0bNgQV1dX5syZw+3bt3F3d+fixYts3ryZqlWrcvnyZX3eP/74g1WrVtGhQwe8vLwwMzPj+PHj/P3333Tq1Mnk8JdevXrx9ddfc/DgQZo0aYKXl1eBH2e2hIQEdu/eTcOGDU0G+QCNGjXCxcWFjRs35hroA7z99tu89NJLjBkzhu7du1OjRg10Oh2hoaHs27ePbt266c/xs88+y4YNG3jnnXdo1qwZUVFRBAUF6ecZCFEcDOtqz5vrcnlKtKJwzcOW6nc/xtqyWK01IIQQoqDlYVjn065YBvp5YW9vz/fff8/cuXNZvXo1Wq2WmjVr8u233xIYGGgQ6Ddp0oQLFy7w119/ERkZiUajoXz58kyePJmBAweaLD97qc+0tLQn3pu/ZcsW0tLS6NChQ4551Go17dq147fffuPkyZMmh/fcr2zZsqxYsYKff/6ZvXv3smXLFiwsLPDw8KBt27YGy4xOnToVW1tbtm/fzt69e/Hw8KBPnz7Url3baNlOIYpKi6oWJGpSsUtLJdHygYtzRQFFYV5AID3frS9BvhBCCAGoFJlpKYQoIYauTWHCm9/QeewUUi0sDLa1OXOdMpGJbAh6+EWwEEKI0kE1M9VkuvJh7kszPy1KbI++EOLps7K/FXEv3CLTxEpXVzycOVbOxcReQgghSiUZuZMrub8thCg50jPZUqMunS6eMdp0282BDBvpxRFCCCGySaAvhCgxVJbmXHbz4Kx7OTQ6rdF2C61xmhBCiNJKlcNLZJNAXwhRoqRYunDdxQ2t2nj4Ttm4pCJokRBCCFE8yRh9IUSJkaFVWNmwYY5Lqt11ti/cBgkhhCg60nmfK+nRF0KUGFoFbjjn/NTaBBNPzBVCCCGeVhLoCyFKDCsz1cMfkKKS1YKFEEKIbDJ0RwhRYqRrHxLIKwreOXf2CyGEKG1k6E6upEdfCFFiWGhU2GhMBPuKgn1CPLOfkW99IYQQIpsE+kKIEuWfkWaouS/YVxScdfG8WW47/pWLrl1CCCFEcSNDd4QQJUp1FzXaN9Ssu6jlaozC8Fqw8df1Rd0sIYQQotiRQF8IUSL1r561jn5GRkYRt0QIIUSReNjiDAKQoTtCCCGEEEKUStKjL4QQQgghSh7p0M+V9OgLIYQQQghRCkmgL4QQQgghRCkkgb4QQgghhBClkIzRF0IIIYQQJY+M0c+VBPpCCCGEEKIEkkg/NzJ0RwghhBBCiFJIevSFEEIIIUTJIx36uZIefSGEEEIIIUohCfSFEEIIIYQohSTQF0IIIYQQohSSMfpCCCGEEKLkkTH6uZIefSGEEEIIIUohCfSFEEIIIYQohWTojhBCCCGEKHlk6E6upEdfCCGEEEKIUkgCfSGEEEIIUerNmDEDOzu7om5GoZJAXwghhBBCiFJIxugLIYQQQoiSRyWD9HMjPfpCCCGEEOKp9++//9KlSxdsbW1xdHSkf//+3LhxQ7/9xRdfpG3btvr3kZGRqNVqmjZtqk9LTEzE3NyctWvXFmrbcyKBvhBCCCGEKHlUObzy4ebNm/j5+REVFcWKFSv44YcfOH78OO3atSMhIQEAPz8/jhw5QmpqKgD79u3D0tKSf/75R5/n4MGDZGZm4ufn97hHVyBk6I4oMRRF0f8iCZEtIyODlJQUAOLj4zE3Ny/iFgkhxNPF3t4eVQkfRjN79mwyMjLYtm0bLi4uADRq1IjatWuzdOlSXn31Vfz8/EhLS+PQoUO0a9eOffv20adPH7Zt28aBAwfo2rUr+/bto3r16nh4eBTxEWWRQF+UGAkJCTg6OhZ1M0QxNnny5KJughBCPHXi4uJwcHAo9HqVNwoujP3rr7945pln9EE+QM2aNWnQoAH79+/n1VdfpVKlSnh5ebFv3z59oD9+/HhSUlLYu3evPtAvLr35IIG+KEHs7e2Ji4sr6mYUucTERLp3784ff/zx1C0TlhM5J4bkfBiS82FIzochOR+G8nM+7O3tn3CrnryYmBgaNmxolO7h4UF0dLT+fXaAHx8fz8mTJ/Hz8yMpKYl169aRlpbG4cOHGTNmTCG2/OEk0BclhkqlKpIeg+JGrVaj0WhwcHCQP0r/kXNiSM6HITkfhuR8GJLzYehpPR8uLi5EREQYpd+5c4fq1avr3/v5+TF16lT27NmDm5sbNWvWJCkpibfeeovdu3eTlpZmMGG3qMlkXCGEEEII8VRr06YNO3fuJCYmRp924cIFTp06RZs2bfRp2T3433zzjX6ITsOGDbG2tuaLL76gQoUK+Pj4FHbzcyQ9+kIIIYQQ4qmg1WpZt26dUfprr71GQEAAnTt3Zvr06aSmpvLee+/h7e3NyJEj9flq1qyJu7s7e/fuZe7cuQBoNBpat27Nli1beP755wvrUPJEAn0hShgLCwvGjBmDhYVFUTel2JBzYkjOhyE5H4bkfBiS82GotJ+P1NRUBgwYYJS+fPly9u7dyxtvvMHzzz+PRqOhU6dOfPPNN0ZzEPz8/Fi3bp3BpNt27dqxZcuWYjURF0ClKIpS1I0QQgghhBBCFCwZoy+EEEIIIUQpJIG+EEIIIYQQpZCM0ReiCMyYMYNNmzYZpc+dO5dWrVrp32dkZDB//nw2b95MUlIS9evXZ9q0aUYz+kNCQvjqq684deoUtra2dOvWjZdfftnoKbG///47y5YtIzw8nIoVK/Lyyy8bLQOWmJjIN998w549e8jMzKRFixZMmzYNNze3gjsBBSSvx11cBQUFMXPmTKP0ESNG8Oqrr+rfF+TP7eTJk8yZM4eLFy/i7OxM//79GTFihMFTLRVF4eeff2bt2rXExsZSvXp1pk6dSr169Qr0+G/evMny5cs5ffo0V65coWLFiqxZs8YoX3E9/rt37/LVV19x6NAhzMzM6NChA1OmTMn3koR5OR9jx47l+PHjRvuuW7fO4HuhpJ+PHTt2sHnzZs6fP098fDze3t4MGjSInj17GrTtafls5OV8PC2fDfGIFCFEofvwww+Vnj17KqdOnTJ4JSQkGOT79NNPlXbt2im///67cvDgQeWll15SnnvuOYN8cXFxSpcuXZQxY8YoBw8eVH7//XelXbt2yhdffGFQ1p9//qn4+voq8+fPV44cOaJ8+umnSrNmzZRTp04Z5HvllVeUbt26Kdu2bVP27NmjDBw4UBkyZIiSkZHx5E5IPuT1uIuzjRs3Kk2aNFEOHjxo8Dm4ffu2Pk9B/txu3LihtG3bVnnjjTeUQ4cOKStWrFBatGihLFu2zKCsgIAApUWLFsqKFSuUQ4cOKW+88Ybi5+en3Lx5s0CPf/fu3Uq3bt2UN998Uxk0aJAyYMAAozzF9fgzMjKUgQMHKgMHDlT27t2rbN26VenWrZvy2muvPdHzMWbMGGX06NFG3x2pqaml6nyMHDlSeeedd5StW7cqhw8fVr777juladOmysKFC/V5nqbPRl7Ox9Py2RCPRgJ9IYrAhx9+aPKP+P3Cw8OVZs2aKevXr9enxcbGKm3atFGWLl2qT1uyZInSpk0bJTY2Vp+2fv16pVmzZkpERIQ+rU+fPsq7775rUMeoUaOUV199Vf/+5MmTSpMmTZS///5bn3bt2jXF19dX2bZt26Mf6BOU1+MuzrID/ZiYmBzzFOTP7ZNPPlF69OihpKen69O+//57pX379kpaWpqiKIqSmpqq+Pn5Kd9//70+T3p6utKjRw/l888/z/exmqLVavX/z+l3orge/5YtWxRfX1/l2rVr+rS///5badKkifLvv/8+ymnQy8v5GDNmTK4BUmk4H6Z+Jz755BPFz89Pf56eps9GXs7H0/LZEI9GxugLUUwFBwej0+no2LGjPs3R0ZEWLVpw4MABfdrBgwdp1qwZjo6O+rROnTqh0+kIDg4G4NatW9y4cYNOnToZ1NG5c2eOHDlCenq6vix7e3uaN2+uz+Pj40P16tUN6iwO8nLcJV1B/9wOHjxI+/btDYY2de7cmYSEBE6dOgXAqVOnSEpKMvjcmZub06FDhwL/DKjVD/8TVJyP/+DBg1SrVs1gSETz5s1xdHTM93nK7XzkVWk4H05OTkZpNWrUICkpiZSUlKfus5Hb+cir0nI+RN5JoC9EEbl16xbt2rWjRYsWvPDCC+zZs8dge0hICC4uLjg4OBik+/j4cP36dYN8D47Zt7e3x83NjZCQEH2e7H0fLCsjI4OwsDB9vooVKxqMwQSoVKmSvoziIi/HXVIMHDiQZs2a0atXLwICAtBqtUDB/txSUlK4c+cOFStWNCpLpVLl+lmpVKkS4eHhpKamPt7BPoLifPzZdd5PpVJRsWLFJ/75O378OG3atKFVq1Ymx2WX1vNx4sQJ3N3dsbW1lc8Ghucj29P62RA5k8m4QhSBGjVqULt2bSpXrkxiYiLr1q3jjTfe4IsvvtD3jiQkJJicqOTg4EBcXJz+fXx8vNHDPCAr6I2Pj9eXBRiVl30RkV1eXsoqLkpSW3Pi5ubGuHHjqFu3LiqVir1797JgwQIiIiJ46623CvTnll3Wg/nMzc2xsrLS54uPj8fCwgJLS0ujshRFISEhASsrq8c99DwpzsefkJBgsk4HB4cn+vlr0qQJ3bt3x9vbm7t377JixQpefvllFi1aRP369fXHUNrOx4kTJ9i2bRuTJ082aP/T+tl48HzA0/vZEA8ngb4QBSAxMZHIyMhc83l6emJubs6QIUMM0v38/Bg9ejQLFy40uA0qSreWLVvSsmVL/fsWLVpgZWXFqlWrePHFF4uwZaK4GjdunMH7tm3bMnDgQH788Ufmzp1bRK16su7cucM777yDr68vgwcPLurmFLmczsfT+NkQuZNAX4gCsGPHDj755JNc8z24zFk2tVrNM888w9y5c0lNTcXKygp7e3sSExON8sbHxxuMS3dwcDCZLyEhQd+7ld2bkpiYaLCEWnZvSnZ5Dg4O3Llz56FlFRd5Oe6SqGPHjixfvpwLFy4U6M/t/rLul5GRQWpqqj6fg4MD6enppKWlGfTUJSQkoFKpTPbMPSnF+fgf9vvp4eGRvwPOB2tra9q0acPOnTv1aaXpfCQkJDBp0iQcHR356quv9PMYntbPRk7nw5TS/tkQeSNj9IUoAL179+bo0aO5vkwF+Tnx8fEhOjra6Nbmg+MdfXx8jMY5Zt9hyK4v+98H84WEhGBubo6np6c+3/Xr11EUxSjfo7S9MOTluEu6gvy5WVtb4+HhYVRW9n4PflbunweSXVbZsmULbdjO/W0pjsdv6vOnKArXr18v8s9faTkfqampTJ48mcTERObOnWswTOdp/Gw87HzkVWk6HyJvJNAXohjQ6XTs2LGDypUr678cW7RogVqtZteuXfp88fHxHDp0iNatW+vTWrVqxeHDh/XjKiHrDoNaraZFixYAeHl54e3tbdCzA7B9+3aaNm2qX1mhVatWxMfHc/jwYX2e69evc+HCBYM6i4O8HHdJtG3bNjQaDTVq1Cjwn1urVq3Yt28fmZmZBvXZ29vToEEDAOrXr4+trS07duzQ58nMzGT37t2F/hkozsffqlUrLl26xI0bN/Rphw8fJi4urlDPU0pKCn/99Re1a9c2aFtJPx+ZmZm88847hISE8N133+Hu7m6w/Wn7bOR2PkwprZ8N8Whk6I4Qhez27dt8+OGHdOnShQoVKhAfH8/69es5d+4cX331lT6fh4cHvXr14ttvv0WtVuPu7s6SJUuws7OjX79++nz9+vVj9erVvP7664wePZqIiAi+/fZb+vbtS5kyZfT5xo4dy/vvv4+XlxdNmjRh+/btnD59msWLF+vz1K9fn5YtW/LRRx8xZcoULCwsmD9/PtWqVaNDhw6Fc4LyKK/HXZy98sor+Pr6UrVqVQD27dvHb7/9xuDBg/XDEQry5zZ8+HD+/PNP3n33XQYMGMDly5dZvny5wdOELS0tGTVqFIsWLcLZ2ZmqVauydu1a4uLieOGFFwr0+FNTU9m/fz+Q9XuRlJSkDxqaNGmCs7NzsT3+jh07EhAQwLRp05g4cSKpqanMmTOHNm3aULdu3SdyPkJCQli2bBkdOnSgfPny+gmXUVFRfPHFF6XqfHz55Zf89ddfTJ48maSkJP7991/9tho1amBhYfFUfTZyOx9nzpx5aj4b4tGolAfv3wghnqi4uDhmzpzJhQsXiI6OxtzcnFq1ajFy5EiDiZkA6enpzJ8/n82bN5OUlESDBg2YNm2a0e3Oa9eu8fXXX3Py5ElsbW3p3r27wRdytt9//52ff/5Z/7j4iRMn5vi4+N27d6PVamnevDnTpk0rlsFzXo+7uJo1axYHDx7kzp07KIqCt7c3vXv3ZtCgQQbL3xXkz+3kyZPMnj1b/1j7AQMGmHys/dKlS1m3bh0xMTH6x9pnr9xRUMLCwujZs6fJbT/88AO+vr7F+vgjIiL4+uuvOXToEBqNhg4dOjB16tR8DanIy/nw8PDgq6++4uLFi8TFxWFtbU39+vUZM2aMUcBU0s+Hv78/t2/fNrlt48aNlC9fHnh6Phu5nQ+tVvvUfDbEo5FAXwghhBBCiFJIxugLIYQQQghRCkmgL4QQQgghRCkkgb4QQgghhBClkAT6QgghhBBClEIS6AshhBBCCFEKSaAvhBBCCCFEKSSBvhBCCCGEEKWQBPpCCCGEEEKUQhLoCyGeeiNHjjR42mNROn36NGZmZmzfvl2ftmfPHlQqFUuXLi26holiYenSpahUKvbs2ZOv/eWzZNqJEydQq9Xs3bu3qJsiRIGSQF+IUurq1auMHTuWmjVrYmNjg7OzM7Vq1WLEiBHs3r3bIK+Pj4/RY9Lvlx0IR0ZGmtx+7tw5VCoVKpWKv/76K8dysvNkv6ysrKhWrRpTp04lOjo6fwdaykydOpXWrVvTqVOnom5KoQgJCWHGjBmcOHGiqJsiCklsbCwzZszI98VKfj3ss9awYUN69+7N66+/jqIohdouIZ4ks6JugBCi4B09epR27dphbm7O8OHDqVOnDikpKVy6dIlt27Zhb29Phw4dCqy+n376CXt7e6ytrVmyZAlt27bNMW/Dhg15/fXXAYiOjmbz5s3Mnj2b7du3c+zYMSwsLAqsXSXN33//zfbt2/n9998N0v38/EhJScHc3LxoGvYEhYSEMHPmTHx8fGjYsGFRN0cUgtjYWGbOnAlA+/btC63e3D5rkydPpl27dmzevJnu3bsXWruEeJIk0BeiFJo5cybJycmcOHGCBg0aGG0PDw8vsLoyMjJYvnw5AwYMwNHRkUWLFjF37lzs7e1N5vf09OSFF17Qv580aRL+/v5s2rSJwMBABgwYUGBtK2nmz5+Pm5sb3bp1M0hXq9VYWVkVUauEeDq0bdsWHx8ffvjhBwn0RakhQ3eEKIUuXbqEq6urySAfoGzZsgVWV1BQEBEREYwYMYKRI0eSlJTE6tWrH6mMLl26AHD58uUc8yxYsACVSsXGjRuNtul0Ory8vAx66bZt28agQYOoXLky1tbWODk50blz5zyPwW3fvj0+Pj5G6SEhIahUKmbMmGGQrigKCxYsoEmTJtjY2GBnZ0eHDh2MhknlJDMzk99//52OHTsa9dybGld9f9r8+fOpUaMGVlZW1KtXj02bNgHw77//0rVrVxwcHHB1dWXSpElkZGSYPM6rV6/Sq1cvHB0dcXBwoE+fPly9etUgr06n49NPP8XPz4+yZctiYWGBt7c3EyZMICoqyuRxrV+/nvbt2+Pk5ISNjQ01atRg0qRJpKens3TpUv2dpVGjRumHdOWllzckJIRhw4bh4eGBpaUlVapU4d133yU5Odkg34wZM1CpVFy4cIF3330XLy8vLC0tadCgAZs3b861Hrg3Ln7nzp189NFHVKxYEWtra5o3b05wcDAAe/fupU2bNtja2lKuXDk+/vhjk2X9/vvvtG7dGltbW+zs7GjdujWBgYEm8y5evJiaNWtiaWlJ1apVmTNnTo7DSuLi4njrrbeoWrUqlpaWlClThiFDhhj9DB9VXs/zw+a5qFQqRo4cCWR9bitVqgRkdUhk/8yzf9fu//365ZdfqF+/PlZWVnh7ezNjxgwyMzMNys7r72lePmsqlYouXbrw559/kpiY+IhnSojiSXr0hSiFqlSpwoULF9iwYQN9+/bN0z5arTbHMfhpaWk57vfTTz9RqVIl2rZti0qlolGjRixZsoSXXnopz+29dOkSAG5ubjnmGTx4MFOmTGHZsmX07NnTYNvOnTsJDQ3VDwmCrD/s0dHRDB8+HC8vL0JDQ/nxxx959tln2b1790OHF+XHsGHD+OWXX+jfvz+jRo0iLS2NlStX0qlTJzZs2GDU5gcdO3aMxMREmjVr9kj1zps3j5iYGF566SWsrKyYO3cuffr0Ye3atYwZM4YhQ4bQu3dvtm3bxnfffYe7uzvvvfeeQRlJSUm0b9+e5s2b8/nnn3Pp0iXmz59PcHAw//zzj/7CMD09na+//pp+/frRq1cvbG1tOXLkCD/99BP79+83Gno1ffp0PvvsM2rXrs2UKVMoV64cV65cYf369Xz00Uf4+fnx7rvv8tlnnzF27Fj9z8TDw+Ohx3z9+nWaNWtGXFwcL7/8MtWqVWPPnj18/vnnHDhwgJ07d2JmZvjnbcSIEZibm/PGG2+Qnp7OnDlz6N27NxcvXjQZKJry9ttvo9Vqee2110hPT+d///sfnTt3ZtmyZbz44ouMHTuW559/njVr1vDBBx9QqVIlg7tX8+fPZ+LEidSsWZMPPvgAyPqc9u7dm4ULFzJ27Fh93jlz5jBlyhQaNGjAZ599RnJyMrNmzcLd3d2oXXFxcbRq1YobN24wevRo6tSpw+3bt5k/fz7Nmzfn6NGjVKxYMU/H+LjnOTe1atVi9uzZTJkyhT59+ui/n+zs7Azybdy4katXrzJx4kTKli3Lxo0bmTlzJtevXycgIOCRjyWvn7WWLVuycOFC9u/fT9euXR+5HiGKHUUIUeocPHhQMTc3VwClWrVqyqhRo5T58+crZ8+eNZm/YsWKCpDr6+7duwb7hYaGKhqNRvnwww/1aXPmzFEAk3UBSufOnZW7d+8qd+/eVS5evKh88803irm5ueLo6KjcuXPnocfVv39/xdLSUomOjjZIf+GFFxQzMzOD/RMTE432Dw8PV1xdXZXnnnvOIH3EiBHKg1+H7dq1UypWrGhUxrVr1xTA4Jg3bNigAMrChQsN8mZkZChNmjRRfHx8FJ1O99BjW7JkiQIogYGBRtt2796tAEpAQIBRWvny5ZXY2Fh9+smTJxVAUalUyvr16w3Kady4sVK2bFmj4wSU1157zSA9+5jGjRunT9PpdEpycrJR+3788UcFUFavXq1PO3TokAIoHTp0UFJSUgzy63Q6/fkwdWy5GTp0qAIof/zxh0H6G2+8oQDKjz/+qE/78MMPFUDp3r27wc/g8OHDCqC8/fbbudYXEBCgAEqjRo2UtLQ0fXpgYKACKGZmZsqRI0f06WlpaUrZsmWVFi1a6NOio6MVW1tbpUqVKkpcXJw+PS4uTqlcubJiZ2enxMTEKIqiKDExMYqNjY1Sq1YtJSkpSZ/35s2biq2trQIou3fv1qdPmjRJsbKyUk6cOGHQ7pCQEMXe3l4ZMWKEPu1RzvejnGdTv0PZAIM2mPodenCbWq1Wjh07pk/X6XRK7969FUD5+++/9emP8nual2P/66+/FECZNWtWjnmEKElk6I4QpVDLli05duwYI0aMIC4ujoCAAF5++WVq166Nn5+fydv5Pj4+bN++3eSrc+fOJutZunQpOp2O4cOH69Oef/55zM3NWbJkicl9tm3bRpkyZShTpgzVq1dn6tSp1K5dm23btpnsrbzfiBEjSEtLMxgalJiYyG+//UbXrl0N9re1tTXIExUVhUajoXnz5hw6dOih9TyqFStWYG9vT+/evYmMjNS/YmNj8ff3JyQkRH/XIid3794FwMXF5ZHqHjlyJI6Ojvr39evXx8HBgfLlyxvdzWnTpg3h4eEmhyW8/fbbBu/79OlDjRo1DCYGq1QqrK2tgaw7QLGxsURGRvLMM88AGJzXlStXAvD5558bzS/IHjaRHzqdjo0bN9KoUSOjuQzvvPMOarWa3377zWi/1157zaDOpk2bYmdnl+vP5X4TJkwwuGOR3SvcvHlzfH199ekWFhY0a9bMoOzt27eTlJTEpEmTcHBw0Kc7ODgwadIkEhMT2bFjB5D1O5KcnMzEiROxsbHR5/Xy8uL55583aJOiKKxcuRI/Pz88PT0NPn+2tra0aNGCbdu25fkYs+X3PBeUTp060bhxY/17lUrFtGnTAJ5ova6urgBEREQ8sTqEKEwydEeIUqpevXr6Md3Xr19n7969/Pjjj/z111/06tXLaJiFra0tHTt2NFnWihUrjNIURWHJkiXUr18fnU5nML6+devWLF++nM8//9zo1n7z5s355JNPALC0tKRixYp4e3vn6Ziyg/lly5Yxfvx4IGsMeFJSksHFBsCVK1eYPn06W7duJTY21mBbQa+Zf+7cORISEh465OTOnTtUr149x+3ZbVIecWm/ypUrG6U5OztToUIFk+kAUVFRBkMlnJycTM7bqFWrFr///jtJSUn6C6c1a9bwv//9j3/++cdovH9MTIz+/5cuXUKlUuU4TyS/7t69S2JiInXq1DHa5uLiQrly5UxeyJo6T66urjnOLTDlwTKyz2f2mPMHt91f9rVr1wBMtjs7Lbvd2f/WrFnTKG/t2rUN3t+9e5eoqCj9BbQpavWj9+nl9zwXlFq1ahmlZR/7k6w3iQTAVQAACMtJREFU+/evuDxXQ4jHJYG+EE+BihUrMnz4cIYNG0bbtm05cOAAhw8fpk2bNvkuc+/evVy5cgWAatWqmcyzadMmevfubZDm5uaW4wVFbszMzBg6dChz5szh8uXLVK1alWXLluHs7GwwBj4xMRE/Pz+SkpKYPHky9erVw97eHrVazeeff86uXbtyrSunP/QPTgaErOCgTJkyrFq1KsfyHvacAkAfpD3q8wQ0Gs0jpcOjX0xk27BhA4MGDaJZs2Z8++23VKhQASsrK7RaLV27dkWn0xnkf5ye+4KW0/l4lHORn3P9pGW3v2PHjrz11ltF1o5H+X0pzvVm//7ldNEkREkjgb4QTxGVSkXz5s05cOAAoaGhj1XWkiVLsLS0ZNmyZSZ7DMeNG8dPP/1kFOg/rhEjRjBnzhyWLVvGmDFj2LNnD2PHjsXS0lKfZ+fOnYSFhbFkyRJGjRplsP+DE1Fz4uLiwrFjx4zSTfUmVqtWjYsXL9KiRQujSYV5lX0h8ChDSQpKbGws4eHhRr36586dw93dXd+bv3z5cqysrNi9e7fBkJLz588blVm9enW2bNnCyZMnHzrB+FEvBMqUKYO9vT1nzpwx2hYTE8Pt27eL5Xr82XcDzpw5w7PPPmuw7ezZswZ5sv89///27i6kqfCPA/g3ta3mFsuXVBBEtPVi06woXZIiWF7kaClGb/MmvdALCaNQiKA3UhheWGFBYBlmsHyBLMsuwgjNQIsusrJcKL1cWJSYZbTf/+Z/RnMzp/3rb+P7AS989nCenWfnwHOe85zv6e+fsq4iPDwcer0enz9/nvUFtDcz7WdlydmHDx/clp95O198+c2fPn3qUTa5n5R2fT1PfWlXuTM53YU50b+Ca/SJ/FBHR4fXGa3x8XHXet3JSwBm4tOnT7Db7di8eTPy8/ORl5fn8Wc2m3Hz5k28fft21u14s3r1aiQmJuLy5cuor6+H0+lEQUGBWx1lhnXybO3t27d9Xp9vMBgwOjqKnp4eV5nT6UR1dbVHXavVCqfTifLycq/bev/+/bTtJScnY9GiRa64xr/t1KlTbv83Nzfj2bNnbhdqgYGBmDdvntvMvYi4lmL9bNeuXQCAiooKTExMeHyu/DbKhZGvdzICAgKQk5ODvr4+tLe3e+yD0+mExWLxaVt/U1ZWFoKDg1FTU4PR0VFX+ejoKGpqaqDVal1vQ87KysLChQtx5swZtxjL4eFhj7tGAQEB2L17N3p6emC32722PZv15jPtZ2VZmvKcgcJms3ls25ffvKOjA729va7/RQRVVVUA4HZMzuQ89aXd7u5uBAUFYePGjVPWIfqXcEafyA/t378fIyMjMJvNMBqN0Gg0GBoaQkNDA54/fw6r1Qqj0Tjr7V+5cgXj4+PIzc2dsk5ubi7q6upw8eJFjwc9f1dBQQHKyspQWVkJg8GAlJQUt8/T0tIQGRmJsrIyOBwOREdH49GjR6ivr4fRaMSTJ0+mbaOoqAg2mw0WiwWlpaVQqVSw2+1eL6CUSM3Tp0+jt7cXW7duRVhYGIaHh9HV1YWBgYFp1xUHBgZi+/btaGlpwbdv39zuUPxpYWFhaGpqwps3b5CRkeGK14yIiHB7X0BeXh6uXbuGzMxMWK1WfP/+HS0tLR6Z6gCwfv16HDp0CJWVlVizZg127NiByMhIDA4Owm63o6enB3q9HitXroROp8PZs2eh0Wig1+uxZMkS1wO+3pw8eRIdHR3Ytm0biouLER8fj87OTly9ehWbNm3yuPCbC/R6PaqqqlBSUoINGza4cuXr6uowMDCAc+fOuR6qXrx4MY4dO4YDBw7AZDLBarXiy5cvqK2txdKlS9HX1+e27RMnTuD+/fvIz89Hfn4+UlJSoFKp8Pr1a9y4cQNr1651eweDr2bSzzt37kRFRQWKiorQ39+PkJAQtLe3e43sDQ0NRXx8PBobGxEXF4eIiAgEBwcjJyfHVScpKQmZmZkoKSlBVFQUWltbcefOHezduxepqamuejM5T6c71kQE7e3tyM7OnvWdOaI55/+S9UNEf9StW7ekuLhYEhMTJTQ0VAIDAyUkJEQyMjLkwoUL8uPHD7f6MTExkpCQMOX2lOg8JV5z3bp1EhQU5BFz+bOvX7+KTqcTg8HgKsN/Yw5/17t37yQoKEgAyPHjx73Wefz4sWzZskX0er1otVpJT0+Xzs5OrzGAU0UDtrW1SVJSkqhUKomKipKDBw9Kf3//lNGAly5dkrS0NNHpdKJWqyUmJkYsFos0Njb6tF9KJKXdbncr/1W8preowJiYGElPT/coV6ImBwcHXWVKPOHLly/FbDaLTqcTrVYrZrNZXrx44bGN8+fPy4oVK0StVktkZKQUFhbKyMiIR4SioqGhQUwmk2i1WtFoNLJs2TIpLS11i6lsa2uT5ORkUavVAsDrd5/s1atXsmfPHgkPD5f58+dLbGyslJeXu8VRTrXP0/XTZEq85s+Rloqp9nuqY6qpqUlSU1NFo9GIRqOR1NRUaW5u9tpubW2tGAwGUalUEhcXJ9XV1a4Y1snfZWxsTI4ePSqrVq2SBQsWiFarleXLl8u+ffuku7vbVW+mcaa+9rOISHd3t5hMJlGr1RIaGiqFhYXy8eNHr3304MEDMZlMotFoBIArIvPnWMyGhgYxGo2iUqkkOjpaDh8+LBMTEx7tzuQ8/dWxdvfuXQEg169f96lviP4F80Rm+VQWERH9z2VnZ2NsbAz37t37K+1lZGTA4XDA4XD8lfaIfsXhcCA2NhZHjhzxePv0n2axWDA0NISHDx/OmYfIiX4X1+gTEc0hNpsNXV1ds8o+J6LZ6evrQ2trK2w2Gwf55Fe4Rp+IaA5JSEj445GEROQuOTnZIx6WyB9wRp+IiIiIyA9xjT4RERERkR/ijD4RERERkR/iQJ+IiIiIyA9xoE9ERERE5Ic40CciIiIi8kMc6BMRERER+SEO9ImIiIiI/BAH+kREREREfogDfSIiIiIiP8SBPhERERGRH/oPDPCTVYiJWxMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display summary plot\n",
        "shap.summary_plot(shap_interaction_lgbm, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "mHXiCos4oeVW",
        "outputId": "dd691548-905a-4f73-da50-6da6e220d8b1"
      },
      "id": "mHXiCos4oeVW",
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1150x660 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Pickle the models for Steamlit"
      ],
      "metadata": {
        "id": "Ub2QmTb9YOm9"
      },
      "id": "Ub2QmTb9YOm9"
    },
    {
      "cell_type": "code",
      "source": [
        "# Save LGBM baseline model\n",
        "pickle.dump(reg_lgbm_baseline, open('lgbm_base.pkl', 'wb'))\n",
        "\n",
        "# Save LightGBM model optimized with Optuna\n",
        "pickle.dump(lgbmreg_optimized, open('lgbm_optimized.pkl', 'wb'))\n",
        "\n",
        "# Save XGBoost baseline model\n",
        "pickle.dump(xgb_model, open('xgb_base.pkl', 'wb'))\n",
        "\n",
        "# Save XGBoost model optimized with Optuna\n",
        "pickle.dump(xgb_optimized, open('xgb_optimized.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "UL0nntirX9xy"
      },
      "id": "UL0nntirX9xy",
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**References:**\n",
        "\n",
        "1. https://towardsdatascience.com/analysing-interactions-with-shap-8c4a2bc11c2a\n",
        "\n",
        "2. https://towardsdatascience.com/introduction-to-shap-with-python-d27edc23c454\n",
        "\n",
        "3. https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/\n",
        "\n",
        "4. https://www.kaggle.com/code/rnepal2/lightgbm-optuna-housing-prices-regression/notebook\n",
        "\n",
        "5. https://www.kaggle.com/code/rnepal2/lightgbm-optuna-housing-prices-regression/notebook\n",
        "\n",
        "6. https://practicaldatascience.co.uk/machine-learning/how-to-tune-an-xgbregressor-model-with-optuna"
      ],
      "metadata": {
        "id": "TZ4Ci-YXOSl6"
      },
      "id": "TZ4Ci-YXOSl6"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.12"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "8Vjfbt-sDp13",
        "ce7d0a75",
        "43ab061c",
        "42da68a9",
        "4d3cd6a1",
        "f1827825",
        "58ba1209",
        "d02aa749",
        "08eb4efb",
        "e572f249",
        "8c19de74",
        "5KMnVh6V-UZw"
      ]
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}