{
  "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": "29fd7e8b-46ce-4dae-ac29-7b3dfb4413f7"
      },
      "id": "Gxk7Ljc_AEgq",
      "execution_count": null,
      "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": "7b4156c1-770f-4fbf-cc63-386e5952c1e7"
      },
      "id": "tFbt2zHi4buJ",
      "execution_count": null,
      "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": "902c8726-1a45-4c13-fe15-8d43084b56f5"
      },
      "id": "tZjaFtQeIToL",
      "execution_count": null,
      "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": null,
      "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",
        "\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"
      ]
    },
    {
      "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": null,
      "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": null,
      "id": "d916ab5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d916ab5d",
        "outputId": "f8f37634-eec2-4b68-dcad-18de4ee8db77"
      },
      "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": null,
      "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": null,
      "id": "5bba9f18",
      "metadata": {
        "id": "5bba9f18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "30190870-324e-4fe8-80df-0294d68a1606"
      },
      "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-5cbb9c89-d952-4a80-90bf-e0ba1a61ae8b\">\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",
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              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
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              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ],
      "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": null,
      "id": "213d8d98",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "213d8d98",
        "outputId": "2deb4eb7-04ba-42de-aab8-88c40ae64185"
      },
      "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": null,
      "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": null,
      "id": "e027ed66",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e027ed66",
        "outputId": "a03f9c4a-414e-4bd1-be43-fe9f8f946ac2"
      },
      "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": 186
        }
      ],
      "source": [
        "low_corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "568174fb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "568174fb",
        "outputId": "84023846-e067-4890-bd70-8dbaafcaff37"
      },
      "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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              "      <td>TA</td>\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-b646e03d-0c78-4a17-ba58-dfcb8e52898a 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-690b11af-35ee-44ba-86de-c9c94e94b654 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-690b11af-35ee-44ba-86de-c9c94e94b654');\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": 188
        }
      ],
      "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": null,
      "id": "e761e84e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e761e84e",
        "outputId": "1a9fdea8-8ac8-4e6e-8d11-fe24f33a16bf"
      },
      "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": null,
      "id": "64855097",
      "metadata": {
        "id": "64855097"
      },
      "outputs": [],
      "source": [
        "low_var = variance[(variance) < 1].sort_values(ascending = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32be86a0",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32be86a0",
        "outputId": "28e437e4-b95f-4712-a0d7-36919cb29514"
      },
      "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": 191
        }
      ],
      "source": [
        "low_var"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "e79a1ccd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "e79a1ccd",
        "outputId": "cf97d868-61f9-4122-f086-7b757ef6e1a6"
      },
      "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-da059cd3-adfb-4e69-a680-9a196dc67cdc\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <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-da059cd3-adfb-4e69-a680-9a196dc67cdc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-4311bdff-a251-4c4e-ab74-dcb93bf61447\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4311bdff-a251-4c4e-ab74-dcb93bf61447')\"\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",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4311bdff-a251-4c4e-ab74-dcb93bf61447 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",
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              "\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-da059cd3-adfb-4e69-a680-9a196dc67cdc 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-da059cd3-adfb-4e69-a680-9a196dc67cdc');\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": 193
        }
      ],
      "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": null,
      "id": "9be646b3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9be646b3",
        "outputId": "af2cebd7-0734-4a6b-926b-64c6819f8314"
      },
      "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": null,
      "id": "7aa53645",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aa53645",
        "outputId": "09c721d3-650f-42a2-a093-0cb98eaa73bc"
      },
      "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": 195
        }
      ],
      "source": [
        "high_corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d30f2f6",
      "metadata": {
        "id": "2d30f2f6"
      },
      "outputs": [],
      "source": [
        "drop_hico = ['GarageArea', 'TotRmsAbvGrd', '1stFlrSF', 'GarageYrBlt', 'YearRemodAdd']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "46e4fdc1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46e4fdc1",
        "outputId": "29480098-3e8e-48a3-f059-2862c086ff1f"
      },
      "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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              "      <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",
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              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
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              "      <td>Pave</td>\n",
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              "      <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",
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              "      <td>RFn</td>\n",
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              "      <td>TA</td>\n",
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              "      <th>3</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>Detchd</td>\n",
              "      <td>Unf</td>\n",
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              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Abnorml</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",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 48 columns</p>\n",
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              "\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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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "\n",
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              "\n",
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              "    domScope.querySelector('#df-c6a121ed-e691-4618-a28f-ba0d6ac1ecbc button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-5919d83b-e1b1-47de-bdac-6bb2ed3d042a 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-5919d83b-e1b1-47de-bdac-6bb2ed3d042a');\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": 198
        }
      ],
      "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": null,
      "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": null,
      "id": "6ab315bc",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6ab315bc",
        "outputId": "2d92712f-ac6e-483a-dde1-8947cbb71021"
      },
      "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>OverallQual</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>TotalBsmtSF</th>\n",
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              "      <td>1786</td>\n",
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              "      <td>1145</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ],
      "source": [
        "numerical.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "SEZM6dlIcePb",
        "outputId": "a4802db6-e3d7-4f79-e873-559b57fb4307"
      },
      "id": "SEZM6dlIcePb",
      "execution_count": null,
      "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",
              "2            TA          Y    NaN    NaN         NaN       WD        Normal  \n",
              "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",
              "[1460 rows x 43 columns]"
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              "      <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-4e283dc4-375b-48c9-9894-00fb1f674cb1')\"\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-40bb20fb-e333-452d-9ec7-7b8206d5d63b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-40bb20fb-e333-452d-9ec7-7b8206d5d63b')\"\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-40bb20fb-e333-452d-9ec7-7b8206d5d63b 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-4e283dc4-375b-48c9-9894-00fb1f674cb1 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-4e283dc4-375b-48c9-9894-00fb1f674cb1');\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": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "075dca0e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "075dca0e",
        "outputId": "efdf815b-3a33-4307-fe31-415c8d9ecd8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MasVnrArea'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ],
      "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": null,
      "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": null,
      "id": "87c1a73e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87c1a73e",
        "outputId": "e0d6ed58-6ee2-475a-ccb4-7795de4df5c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MasVnrArea', 'TotalBsmtSF'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ],
      "source": [
        "t_num_na = t_numerical.columns[t_numerical.isnull().any()]\n",
        "t_num_na"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "a003e75b",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a003e75b",
        "outputId": "b9f94bfa-2f71-472c-af53-5a889e9847e5"
      },
      "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]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-58aee406-8cfa-4ad6-83f4-89bf1ff88501\">\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-58aee406-8cfa-4ad6-83f4-89bf1ff88501')\"\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-46148af1-8ea9-4974-bcde-2a9b2609ff30\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-46148af1-8ea9-4974-bcde-2a9b2609ff30')\"\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-46148af1-8ea9-4974-bcde-2a9b2609ff30 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-58aee406-8cfa-4ad6-83f4-89bf1ff88501 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-58aee406-8cfa-4ad6-83f4-89bf1ff88501');\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": 206
        }
      ],
      "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": null,
      "id": "2345bc44",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2345bc44",
        "outputId": "20d8af74-e6cc-4eab-e74c-8363af674f74"
      },
      "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": 207
        }
      ],
      "source": [
        "cat_na = categorical.columns[categorical.isnull().any()]\n",
        "cat_na"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "52ec4ee2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52ec4ee2",
        "outputId": "4696f423-d295-4349-fa15-e6639da9c4cd"
      },
      "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": 209
        }
      ],
      "source": [
        "t_cat_na = t_categorical.columns[t_categorical.isnull().any()]\n",
        "t_cat_na"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "ed9753fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed9753fe",
        "outputId": "96cc6437-69f4-47a2-c48f-f21ae36627a4"
      },
      "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",
              "  <div id=\"df-96bd7c64-0e03-4cf2-a932-51f3bf122d32\">\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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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <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",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96bd7c64-0e03-4cf2-a932-51f3bf122d32')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-946ed043-047c-4dce-bad5-d5841d60dceb')\"\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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              "    domScope.querySelector('#df-946ed043-047c-4dce-bad5-d5841d60dceb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\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-96bd7c64-0e03-4cf2-a932-51f3bf122d32 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-96bd7c64-0e03-4cf2-a932-51f3bf122d32');\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": 211
        }
      ],
      "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": null,
      "id": "68242cc3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68242cc3",
        "outputId": "774bd251-aeb5-4581-f32d-c652872e966a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ],
      "source": [
        "X_start.isnull().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fff1637b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fff1637b",
        "outputId": "205ed77f-32cc-458e-99ed-4329856732e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ],
      "source": [
        "testset.isnull().values.any()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e572f249",
      "metadata": {
        "id": "e572f249"
      },
      "source": [
        "#### Label encoding of categorical data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "eef8457a",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "eef8457a",
        "outputId": "5edf7e2b-3057-412b-c013-9ba6cb54fcce"
      },
      "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-3db42a90-bb30-4675-a22a-498d8874f761')\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ec89121-b7a8-4bbd-af96-8ce8d236da07')\"\n",
              "              title=\"Suggest charts.\"\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-6ec89121-b7a8-4bbd-af96-8ce8d236da07 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-3db42a90-bb30-4675-a22a-498d8874f761 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-3db42a90-bb30-4675-a22a-498d8874f761');\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": 215
        }
      ],
      "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": null,
      "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": null,
      "id": "39405297",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39405297",
        "outputId": "3a738097-aae1-42d2-bd0b-3e5926d9782d"
      },
      "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": 217
        }
      ],
      "source": [
        "high_cat_corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b8d38aa",
      "metadata": {
        "id": "1b8d38aa"
      },
      "outputs": [],
      "source": [
        "drop_hcc = ['GarageCond', 'BsmtQual', 'KitchenQual', 'Exterior2nd']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "6cdfd930",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6cdfd930",
        "outputId": "f850b0a9-3610-440e-90a5-81f6123eb18d"
      },
      "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",
              "3         3       1      1         0            3          0          0   \n",
              "4         3       1      1         0            3          0          2   \n",
              "\n",
              "   LandSlope  Neighborhood  Condition1  ...  FireplaceQu  GarageType  \\\n",
              "0          0             5           2  ...            3           1   \n",
              "1          0            24           1  ...            5           1   \n",
              "2          0             5           2  ...            5           1   \n",
              "3          0             6           2  ...            2           5   \n",
              "4          0            15           2  ...            5           1   \n",
              "\n",
              "   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",
              "2             2           5           2       3      4            1         8   \n",
              "3             3           5           2       3      4            1         8   \n",
              "4             2           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",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f47245d-223e-42ac-81a1-95e51b56fb3f')\"\n",
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              "              style=\"display:none;\">\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58e3b0f6-487e-47ec-b95e-e943050049b7')\"\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",
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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",
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              "  }\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",
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              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-58e3b0f6-487e-47ec-b95e-e943050049b7 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-2f47245d-223e-42ac-81a1-95e51b56fb3f 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-2f47245d-223e-42ac-81a1-95e51b56fb3f');\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": 220
        }
      ],
      "source": [
        "X_start.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e30cfa3b",
      "metadata": {
        "id": "e30cfa3b"
      },
      "source": [
        "#### Normalizing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "ea423b42",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ea423b42",
        "outputId": "fe7f4010-dce8-4b27-9c08-d2d5504f7020"
      },
      "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>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-d11ac138-1526-4717-968b-8d909b532fd4')\"\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-7fa53e72-adfc-4e2b-8e9a-f271999d01d0\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7fa53e72-adfc-4e2b-8e9a-f271999d01d0')\"\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-7fa53e72-adfc-4e2b-8e9a-f271999d01d0 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-d11ac138-1526-4717-968b-8d909b532fd4 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-d11ac138-1526-4717-968b-8d909b532fd4');\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": 222
        }
      ],
      "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": null,
      "id": "66b2d593",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "66b2d593",
        "outputId": "8b5b5016-ec92-4924-fb4e-058cff7a599f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=10, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" 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": 223
        }
      ],
      "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": null,
      "id": "adbbd88b",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "adbbd88b",
        "outputId": "654167d8-5dac-4d92-f144-951c4193efb4"
      },
      "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": null,
      "id": "4a35608d",
      "metadata": {
        "id": "4a35608d"
      },
      "outputs": [],
      "source": [
        "feat = dict(reversed(sorted(zip(model.feature_importances_, X_start.columns.values))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ef61f8c5",
      "metadata": {
        "id": "ef61f8c5"
      },
      "outputs": [],
      "source": [
        "feat10 = [feat[x] for x in list(feat)[:10]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "801cbef5",
      "metadata": {
        "id": "801cbef5"
      },
      "outputs": [],
      "source": [
        "t_drop = [feat[x] for x in list(feat)[10:]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "id": "8e3418e7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e3418e7",
        "outputId": "3b1c855e-46af-489e-ad2c-f040d6150fe8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1459, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ],
      "source": [
        "testset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a091d90",
      "metadata": {
        "id": "6a091d90"
      },
      "outputs": [],
      "source": [
        "X = X_start[feat10].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7c9dcad9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7c9dcad9",
        "outputId": "bde5f0e1-2a4b-4c1a-fd8f-6a78b4ffcc98"
      },
      "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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              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <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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              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-039a4289-d8fe-4b14-8218-4e5493133a75')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-039a4289-d8fe-4b14-8218-4e5493133a75 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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              "      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-1435863f-2e46-44d8-9ed3-d6fe3637df75 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-1435863f-2e46-44d8-9ed3-d6fe3637df75');\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": 231
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a79fe62",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6a79fe62",
        "outputId": "bbdd6e43-b35a-4db2-d75b-e955586dd4e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([208500, 181500, 223500, ..., 266500, 142125, 147500])"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "514a6436",
      "metadata": {
        "id": "514a6436"
      },
      "source": [
        "#### Spiliting the training data for testing purposes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "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": "1f052953-c9c6-4e34-8fb7-2388cde020f1"
      },
      "id": "2Y82Jh1S-T3F",
      "execution_count": null,
      "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-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" 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": 236
        }
      ]
    },
    {
      "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": "9acc389f-37d6-4c42-98c0-a729632261af"
      },
      "id": "7ouVv4khX6xq",
      "execution_count": null,
      "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": "bb26803d-f4b3-45fb-f2a6-461edc5028bd"
      },
      "id": "N334wHmUZy4g",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181370.38356164383"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "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": null,
      "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": 617
        },
        "id": "JSsYo_9JVS0D",
        "outputId": "2d74b324-026d-4965-f9b8-b6a40ce72eec"
      },
      "id": "JSsYo_9JVS0D",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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bWeLh3NzcSgxG3J2SXhERERERqVxmM/y7Hdbscng4ODi4kgMSd6akV0REREREKp+XB3w4y+GhyMjISg5G3JmSXhERERERqXwFZpj0DySl2h3SnF6pSEp6RURERETENcxm+HKuq6MQN6ekV0REREREXMNsgY/+D/LybYrr16/vooDEHSnpFRERERER1zl2En7716YoPz+/hMoi5aekV0REREREXMfDBO/8blN05MgRFwUj7khJr4iIiIiIuI7ZYmxdtGK7qyMRN6WkV0RERKSqiAiF12+HBS/ByR/BMhV6tHRc9783wvI34Oh3kP0zbP8Y3rsHajjY39RkgjHXwe7PjLob3oVbLis9Fi9P2PyBEcMT1zquE1sbfhwFSd9B1k9GDOOGOnevIdXgi5FG/BmTjHtuF1v6ObG1jfgtU6FDQ+euIxcGLw/44A/r21atWrkwGHE3SnqlSjGZTFx11VU2ZTVr1qR169Yuisg58fHxmEwmRowY4epQRETkQta0Djx9A9QNh037Sq/bIRbW74FXf4OHvoIZq+DuXrDsNajma1v31aHw1p3w1wZ45GvYnww/PQ5DLi25/UcGQFSNko+3jYY1bxsf3/kdHvkGfvoH6oSVfZ8mE/zxHAztDh/Phid/gFohsOhlaFTK/qzv3QMFhWW3LxeeAjNMXgqJKQDs3LnTxQGJO/FydQAWi4VNmzaRkJBARkYGfn5+xMbG0rFjR7y9vZ1uJycnh/Xr17N3714yMzPx9vYmNDSUjh07Otzcevv27SQkJJCSkoLFYiEoKIiGDRvSvn17a52ZM2eSmJhY4jXr1q3LwIEDy3fDFWjjxo288MIL/Pvvvxw/fhwvLy/q16/Ptddey4svvki1atVcFltlmTZtGu+99x6bNm0iPT2datWq0axZM+677z7uu+8+V4cnIiJia+HLsPco3P2x4+NrdkHYnXAiA27sBpc0K7mtwePty5Zvg9+ehEEd4ZelRlmdMHjiGvj4/4yEF+DrebD4FRh/F0xebmwbc6aaIfD8zfDmdHjlVvvrmEww8T+w9RD0eh5y8sq8ddvYu8GlzYx7+G25UfbrMqOn+KUhcNv79uf0i4Mr4+Ct6TD2pvJdTy4MZuDzufDSLeTk5Lg6GnEjLk96ly9fTnx8PNHR0bRp04bU1FTi4+M5fvw4AwcOxGQyldlGeno6s2bNIj8/n6ZNmxISEkJeXh4pKSlkZmba1V+0aBE7duwgJiaGxo0bW9tIT0+3qdeuXTuaNbP/ZbNr1y72799PgwYNzvKuz92XX37JI488gslkYuDAgbRt25bMzEzmzZvH+PHjmTJlCvPnzycmJsZlMZ5vw4YN4/vvv6dGjRrccMMNxMbGkpiYyO+//86IESP47bff+OOPP/D09HR1qCIiIs7JOMc/9PceNT5WDzhddm1n8PGGT+fY1v3sT6O3t1sTWLrV9tgbt8O2Q/C/xY6T3n5x0LoB9H/FSHj9fSC3wD55LsngbnDkBEw9Y8Xe5JNG4nv75eDjBXkFp495ecIH98AHs2CXFjhyW2az8XDmmRsJDAx0dTTiRlya9KakpFgT3n79+lnLg4KCWLZsGbt27aJRo0ZltrNw4ULMZjODBw8us3dz69atbN++nZ49e9KkSZNS69arV89h+dq1a/H09HQqtvNh6dKlPPzwwwQFBTF//nzi4uJsjr/88su88MILDBo0iI0bN+LhUfmj2I8fP054ePh5a3/cuHF8//33xMXFsXjxYoKDT89fevfddxk0aBB//vknDz74IF988cV5i0NERMTlwoOMpLBxJLxxhzH8d9Hm08fbxUBGNiQctD1v5Y5Tx2Ntk95OjeCunnDZs2CxOL5m3zbGx9x8WPUWdGxk/HvaCnjwS6OnujTtYmDtbvv2V+6A+/tBkzoQv/90+airITQQxk2BG7qW3rZc2FIy4Nel1L+5m6sjETfi0jm9u3btArCbr9msWTO8vLzYsWNHmW0kJiZy5MgR2rZtS7Vq1TCbzRQUFDisa7FYWL9+PTVq1LAmvHl5eVhK+oFewvXS0tKIjo7Gz8/P6fMq0lNPPUV+fj7vvPOOXcIL8Pzzz3P55ZezefNmJkyYYC0zmUx89dVXdvULCwsJCwuz6xX+66+/uPTSSwkKCsLb25s6derw0EMPkZdnO4SpdevW1KxZkw0bNtC9e3cCAwOpUaOGte2HHnqIli1bUr16dby8vKhRowbXXXcdhw4dOqv7z87O5u2338bX15fp06fbJLwAPj4+/Pbbb4SHhzNhwgQOHjz9S76k+cFTpkzBZDLx+uuvW8uOHz/OPffcQ5MmTayfg4iICO68805Onjx5VrGLiIhUqNrVIfl7OPIt/P2qMQd36HtGL22RyFBISrM/N/GE8bFOqG35R/caQ6P/LWUl3canpo79OtoY4nzjW/DmNLixK8x8puy4I0NPX99hTGfMC65d3RjOPPYnSM8uu225sJ3avihhyxZXRyJuxKU9vceOHcNkMlGrVi2bci8vL8LDwzl27FiZbezfbzwFDAwMZM6cORw4cACLxUJISAjt27e3Dl8GSEtL4+TJk7Rs2ZK1a9eyadMmcnNz8fb2plGjRnTt2rXMecTbtm0DcDjs2RGLxUJubq5TdQF8fX1LHdKdkZHBihUrCAsLY9iwYSXWu//++1myZAmTJ0/mnnvu4cEHH+T111/nhx9+sJvr+ssvv3DixAmGDx9uLfvuu+8YMWIEERERDBs2jPDwcP79918+++wzNm3axJIlS2zayMnJoXfv3rRp04bHH3+cpKQkwEhQv/vuO3r16sVVV11FYGAgq1evZtasWaxdu5Zt27bh7+/v9OcHYNasWaSlpdGnT58Sh5gHBgYyaNAgJkyYwKRJk3jyySfLdQ0wHspMnTqV3r17c/311+Pt7c3ff//N//73PzZv3syaNWvK3aaIiFxEvDyNFYrP5O0Fvt5G7+yZUjJK7lUtTUoG9H0R/LyNHtsbukBgsYfy/j5GL2xxOafK/M9Y9GpYb2PYsqP5wmcqusaqnXDHB8a/p/4LWblGb3OfNjB/Y8nnFw2Htosp7/TxIm/eAbuTjHnI4v7MFtiwl2obDkK7dq6ORtyES5PezMxM/Pz8HM65DAgIICkpicLCwlLnZKalGU8ulyxZQkhICD179sRsNrNx40brsOemTZsCkJqaChjJjNlspl27dgQFBbF//34SEhJITU3l6quvLjHpzMvLY/fu3QQFBVGnTh2n7jEjI4OffvrJqboAt956K0FBQSUeX79+PQUFBTbJvCM9e/YEsPaWR0RE0K1bN5YvX86RI0eIiIiw1v3uu+/w8PDg4Ycftsb82GOP0aRJE9atW4ePz+lfPGPGjOHtt99mypQpDB482OY+7777br799lubOKpVq0ZSUpLdPY0bN46xY8fy1Vdf8eijj5Z6L8WtXbsWwGEv95k6d+7MhAkTWL9+fbnaL9K6dWuSkpLw9bVdAXP48OF8++23zJkzx26laREREatLm8GiVxyX39rdtiz6fthX9sN+O/kFp5PLP9YY/172OhxNM94DZOcZiXZxfqfKsk89nA/yh9dvg/HT4eDx0q+bfSo5/ekf2/JJfxtJ7yVNS096s/PA18GfoX4+tu13aQJ39IA+L57dQwG5MHmYqPfvfhjm6kDEXbh0eHNBQUGJ802LEt2ShioXyc83nlJ6e3tz9dVX07hxY5o2bco111yDj48PK1eutA5fLqqbk5ND3759adu2LbGxsdb5vYmJiRw4cKDEa+3cuZOCggKaNm3q1AJbAP7+/gwYMMDpV1m9nikpxjLupSXGgHV48ZkLeQ0bNoyCggI+++wza9mJEyf4+++/6dixo7XX9JdffiEtLY3bbruNo0ePcvDgQevr5ptvBoze1uLOHBpcxMPDwxprfn4+SUlJHDx4kGuuuQYwFjIrr6KhxdWrVy+1XtHxjIwy5hWVwN/f35rw5uXlceTIEQ4ePMiAAQMAWLx48Vm1e6aUlBSbkQAZGRk2C6rl5eVx/LjtHx7FVxQv/v7IkSM2Q/Z1DV1D16ja18jPL/33nFzANuw1emHPfG3YC3+usy8/klox11y+DQ6nwG2Xny5LPAER1e3rRp4a1nz41JDi0dcaC0j9shQa1DRe9U6tzxEaYLz3PpWoHjb+HiGpWNxHTw2jDi1jEaLEE6ev7zCmU+2/dQf8nQB7kk7HVLQPcWQo1C9lSyW5cJkt5A6Is76tCj/b5cLm0p5eLy+vEpcjLywstNYpTVFy3KhRI5seYV9fXxo0aMCOHTtITU0lNDTUejwgIMBukaomTZqwfft2EhMTiYqKcnitbdu2YTKZrD3HzvDy8ipxQayzERZmzHEpvtJ0ccnJyYBxr0Vuv/12HnvsMX755RdeeuklwFgFOjc3lzvuuMNab9OmTQA8++yzPPvssw7bLz70PCgoiNq1azus++GHH/Lxxx+ze/du69e1SFHve3kUzeEt69yi48WHz5fH2LFj+eGHH6zD5s904oSDuUjlVPT1LFJ8pUIfHx+7BcGKb8FV/P2Zvfi6hq6ha1T9a3h7u/RXsZxPqZn2vZ0nMoyEr7Re0HPl5207rHr9HrjvCmhez3Yxqy5NTh8HYz5wWBBs+dC+zWcHG6+4x43Efc1uo7xusT15i+biHitj7Yv1e6F7c2ProzN/v3ZpDJk5sP3wqZhqQnQt2OtgUcqZzxif49A77I/Jhctkgpb12Ve/GkWPRarCz3a5sLn0N21AQACpqakOhzCXNvS5eBuAwx7SopWci570FH3DO1O3uJSUFI4dO0ZUVJRNIlkWs9lcrn3G/Pz8Sl1tOS4uzqlFvop6Ic8cBu3j48NVV13Fr7/+yrp162jXrh2TJk0iICCAe+65x1qvKLl77LHH6NSpk8P2o6Ojbd4XHwJc5LPPPuM///kPjRs35plnniE6Ohp/f38KCwu54447yrWIWJGivZTLGra8atUqAJtVukvqoS8aBXCm0aNH884779C+fXvuvfde6tevj6+vL/v37+fpp5/G7Oy2DCIiIhWtmq+RLGYX2x/3hq5G4rp61+myGavgvbvhwatO79MLMLKfMYx5mbFeCR/+H0xfadterRD48gH4bgHMWAl7Tm2JNGOlsYXQ3b1hwsLTieu9fY2Pf2043UZEqJGE7zpirCwNMGU53HSJEW/RPr3hQUbZzNWntysa8Zlxr2fq3RoeHQhPTDAW0RL3YrHA44OM5Fekgrg06a1ZsyYHDx7k6NGjNk9TCgoKOH78uFNPWGrVqkVCQoLD/XiLyoqS3LCwMDw9PR3WLRoCW9Lw4q1bjaX8nV3A6swYKnJOb2BgIJ07d2bZsmVMnDjRpof2TF9++SWAzbxbgJEjR/Lrr7/yySefMHr0aDZt2sQ111xjs9VT0T0GBgZy660O9uYrh//97394e3uzevVqm1WWV65cWcpZpbv66qsJCQnhn3/+4eDBgw570jMyMpg5cya+vr42CX1QUJDDlZe3b7dfofK3336jZs2arFy50ubhy8SJE886dhERkTI9e+p3d8v6xsc7esBlzY1/vzrF+Ng4Eua9aAxF3nrI2N+0YyNjj9s9ScZ+tkUOHYf3Z8GT1xvDk1fthOs6w+UtjZWeix7irtttvM7UoKbxcfMBI9EtkpQKr/5m7OE7Z6yRLLeNhvv6wqQlsHrn6bqv32YskHXmvOUpy42h2N89DC3qQXK6kZR7esALP58+98zkuUjRHsSLN8OaXfbH5cJWPQBu7U4Ll07CFHfj0qS3YcOGrFu3jk2bNtkkuFu3bqWgoMBuH9yTJ09iNptt5nJGR0ezbNkydu7cSfv27a2rL2dlZbF3715CQkIICQkBjKHGMTEx7Ny5kz179ths0bPl1LLo9evXt4uzsLCQHTt24O/vX+LQ55IUzektT/2yvP766/Tp04fHHnuMdu3a0apVK5vjr776KosWLaJly5bcfffdNsd69epFgwYN+P333/Hz88NisTBixAibOrfeeitPP/00n376KSNHjrRbtCs9PZ28vDyn9uH18PDAZDLZDGs2m80884wT2xmUwN/fn9GjRzN27FiuvfZaFi1aZPOgID8/n5tuuonk5GRGjhxpnd8M0KBBA5YsWcKOHTusveBZWVl89913dtcpSnTNZrP133l5ebz11ltnHbuIiEiZxg21fT+87+l/FyW9B4/Db/8avZ539TSS2X3H4OPZRp2UYutZPP0/OJFp7IE7rBfsSITb3oef/j6HOCcbw7UfGQDv323MS371N3j517LPNZthwDgYf5fRa+vvYyTjwz46PbRZLj6eHvBQf/DzYd8Zf6uJnCuXJr1hYWG0bNmSzZs3M3fuXKKiojhx4gTx8fFERkbaJb2zZs0iIyPDJknz9fWla9eu/P3330yfPp2mTZtiNpvZsmULZrOZSy+91KaNzp07c+jQIRYsWEDLli0JCgriwIED7N+/n8aNG9vNCQDYu3cvubm5tG3bttShx45U9JxegMsvv5wPPviAUaNG0bFjR66++mratm1LVlYWf/31F2vWrCE6OpqZM2c6jHfw4MG88847fPvtt0RGRtol5dWrV+eTTz7hnnvuoVmzZlx77bU0btyYEydOsH37dhYtWsT3339v14vsyI033sg///xD586duemmm8jPz2f27NnlGvLtyHPPPceOHTv44YcfaNiwIddeey0xMTEkJiby+++/s3//fgYMGMAnn3xic96oUaNYuHAhPXv25PbbbycvL4+pU6c63HN5wIABfPTRR3Tp0oVBgwaRlpbGjBkzyhxyLyIiUqJez5ddx3RD2XWOp8PIz52/rsUCb0w1XuWx71jp8Xwy23iV5u6PjVdxqZlw36fGqzy+X2i8xP1YLPDAlQAOR2aKnC2Xr57RrVs3goKCSEhIYP/+/fj5+dGqVSs6duzo9ArJzZs3x8/Pjw0bNrB69Wrr3r+9e/e2S2IDAwO57rrrWLVqFdu3bycvL4/g4GC6du1K69atHbZfNLS5PAtYnW8PPvgg3bp148UXX+Tvv/9mxowZeHl5ERUVxejRo3nhhRfsJu0Xeeihh3jvvffIzs626wkucvvttxMTE8MLL7zAH3/8wcmTJwkICCAyMpI777yTSy65xKk4R40axcmTJ/nyyy8ZP348AQEBdO/enS+++IK6deue9f0DfP/991xzzTW8//77TJ48mfT0dOs825dffpmxY8fanXPNNdfwxhtv8MEHH/DOO+8QFhbG0KFDueSSSxgyZIhN3XfeeQeLxcLkyZN57bXXCAkJ4aqrruLhhx+mW7du5xS7iIiIiJzBy8OY413XGEl45tQ7kXNlspzNSkIiVdS0adO45ZZbCA8P559//iE2NtbVIYmIlG7OWug/ztVRiIi43rLXoZvRyZSfn2+dtihyrjRFXNzK9ddfz4QJE0hOTqZnz54cPHiw7JNERERExHU8TBAXA11P77ixefNmFwYk7kZJr7idW2+9lby8PPbv31/h86lFREREpIKZtU2RnF9KekVERERExHXCg+Bm28Vnndm6VMRZSnpFRERERMQ1PDzg4f7gazt/V7tlSEVS0isiIiIiIq7hgbF/dDFal0UqkpJeERERERGpfF4exrDmyDBXRyJuTkmviIiIiIhUvgIz/Odqh4eaNWtWycGIO1PSKyIiIiIilcvDBB0bQufGDg8fOnSokgMSd6akV0REREREKpfZAo9fU+Lh9PT0SgxG3J2SXhERERERqVw1g+HGriUe9vPzq8RgxN15uToAERGRi1qXJhT0bY1XSqarIxERqRwmYFhv8PEusUrjxo6HPYucDZPFYrG4OggREZGL2fr164mLi3N1GCIiVYZ+LkpF0vBmERERERERcVtKekVERFwsOjra1SGIiFQptWvXdnUI4kaU9IqIiLhYZqbm84qInMnX19fVIYgbUdIrIiLiYseOHXN1CCIiVcr+/ftdHYK4ESW9IiIiIiIi4ra0erOIiIiLWSwWTCaTq8MQEakysrKyqFatmqvDEDehfXpFRERcLCEhgRYtWjg8lpVvYfYePZ8WqerC/KBXlAZRVpQjR44QGxvr6jDETSjpFRERcbG8vLwSj03cYmHkX+ZKjEZEztaCm8z0aqA/ryvCyZMnXR2CuBE9jhIREXGx4OBgh+UWi4V3V5vRwGeRC8P6bftcHYLb8PHxcXUI4kb0KEpERMTFIiIiHJbP329h+4lKDkZEpApo3ry5q0MQN6KeXhERERfbvn27w/L315jxVDevyAWjevUQV4fgNjZs2ODqEMSNqKdXRESkCtqVauH/doOWsBK5cHh7a0iuSFWknl4REREXi4qKsiv7aK0ZD/XyilxQjh075uoQ3EbNmjVdHYK4ESW9IiIiLpabm2vzPj3PwtebLBSqm1dELlIBAQGuDkHciJJeERERF0tKSrJ5/8NmC1n5LgpGRM5a3bp1XB2C29i7d6+rQxA3oqRXRESkCjFbLLy3WvvyilyIUlNTXR2CiDighaxERERcrHXr1tZ//7XXwq40FwYjImctMzPL1SG4jUaNGrk6BHEj6ukVERFxsR07dlj//e5qbVMkcqHy8lJ/UkVJTk52dQjiRvQ/U0RExMVycnIA2J5iYe4+FwdzEerbwMQL3TxoXxtyC2H+fgujF5nZd/J0nR71TSwa4lliG8/+XchrK06vPBbiC29d7sH1jU1U84aViRaeWGRm3dGzb7MsQ5ub+HGgJxl5FoI+LLQ7/lA7Ew/FeRAbAsnZ8Ms2C2OXmjV/vAJFRdV3dQhuQ0PFpSIp6ZWz9v777/PYY4/x3XffMWzYMFeHIyJywQoKCgLgo3VGL69Wba48A2NNzLjOg7VH4eklZoJ94T/tPfjnVk/a/VBIcrZRL+G4hdv/sE8k72hh4soYD+buO/1FMwF/3OBJ25owfpWZ5Gx4MM6DRUM86TCxkJ2p5W+zLAHeRpKdkef4nDcu9+Cpzh5M3mbmg7UWWoSbeKSdiZbhHlz1m+aQV5Tdu/dAo4auDsMtqNdcKlKV/26yWCxs2rSJhIQEMjIy8PPzIzY2lo4dO+Lt7e1UG+vWrSM5OZnk5GTS09MJDAxk6NChTp27YsUKNmzYgJeXF/fcc8+53MpZKUosnREZGcnhw4fLrPftt9+ydu1axo0bR/Xq1c8xQlu///471157rU2Zp6cnoaGhtGnThhdeeIHLL7+8Qq/prISEBN577z0GDx5Mv3797I7v2bOHl19+mYULF3L06FHMZjPBwcE0bdqUG264webrcMkll7B8+XKH14mOjmbPnj3n7T5ExP3UrVuXk7kWvtE2RZXuzcs92J0Gl04qJP9U7jdzVyFr7/Dk6S4ejF5kFB7Ngh8T7L84L1ziwfYUC6uPnC4b3NTEpXVNDP69kN+2G+f8uq2Q7cM9eelSD277o/xtluW5rh6k58HCAxaua2Q7Pj4iAB7vYOKHzWbuml2U4FrYfsLEx308uTrWwqzd+saTqqVVq1auDkHcSJVPepcvX058fDzR0dG0adOG1NRU4uPjOX78OAMHDsRkKnvi06pVq/D19aVGjRrk5eU5fe3k5GQ2btyIt7c3Fotrfhn07duX8ePH25R98cUX7Ny5k2eeeYbQ0FBree3atZ1q848//mDq1KmMHj26wpPeIl26dGHw4MEAZGVlER8fzx9//EHv3r1ZunQpXbp0OS/XLc2OHTv46quvCA8Pt0t64+Pj6dOnD8nJyfTu3ZuhQ4fi4eHBrl27WLNmDZ9++qnDhw8vvfQS1apVsymrU0fbFYhI+WzdupXFhW3IKXB1JBeXUD9oWcPEWyvN1oQXYOMxSEiBW5qaGL2o5PM7RUDjUBMvLLXtrR3cxMSRTAtTt5/+2yE5G37dZuH2FiZ8PCHPvoO31DZL06g6PNbBxPUzzNzc1P7vom51THh7mvh5q22P7s9bLXzcB25pZlLSW0GCg4NdHYLb2LBhA23btnV1GOImqnTSm5KSYk14z0xSgoKCWLZsGbt27XJqZbdbbrnF+kNo8uTJ5OeXPXnFbDbz999/U79+ffLz8zl27NjZ38g5aNWqld2TrqlTp7Jz507uu+8+oqOjXRJXWVq2bMno0aNtyl566SVefPFFvvnmG5ckvaV56623OHr0KG+++SZPPvmkzTGLxVLiXnEPPvggNWrUqIQIRcSdmS3w3hozSjsql++p6bTZDh42ZOVDqxomaleDpBIW5L2tubEeaPHe2na1TKxNsth9PVcmWri/rQdNQiG+hDV6SmqzNO/39mDhAQuz91gcJr0l3WfRXN4OtbVyWkXx9/dzdQhuw1UdTuKeqvTqzbt27QJst3IAaNasGV5eXjarXZbmbJ66xcfHc+LECS699NJyn+sq//zzDz179iQkJARvb28iIiJ46KGHyMo6/dv6kksuYerUqQDExMRgMpkwmUz897//BWD16tUMHjyY6OhoAgIC8Pb2JioqimeffZbCQuefOjtSt25dAHx9fa1lBQUFvP/++7Ro0YKgoCB8fHyoUaMGPXv2ZPv27TZxm0wm4uPj6d27N0FBQfj7+9OrVy+OHDlCbm4uo0ePpk6dOnh7e1O/fn1++eUX6/nvv/++ddj1G2+8Yb3vol7Z/fv3AzBgwAC7uE0mEzExMed07yIipdlKtM2iSVI5kjLhRI6FS+valof5QYtw4991gxyf62GCIc1MrEi0sCvV9lhkACRm2p9TVFYn0HGSWVqbJRkQa6JfAxOPLyp5Xu62FCN5KH6f3esZcZR0j1J+SUlHy64kTgkPD3d1COJGqnRP77FjxzCZTNSqVcum3MvLi/Dw8PPW+5qens7q1atp3769dXGR8igoKKCgwLkxah4eHvj4+JT7GsX99ddf3HjjjWRnZzNkyBCioqJYuHAhn376KStXrmT58uV4eXkxatQo8vLyWLNmjc3w6F69elnb+eeff+jTpw+xsbFkZWXx119/8dprr7F//34mTpzoVDxZWVkcPHgQgMzMTFasWMHrr7+Or68v9957r7XeJ598wuOPP05MTAwPPPAAgYGBHDhwgGXLlrFz506aNGli0+4111xDVFQUo0aNYtOmTcycOZMrr7yStm3bMn/+fIYMGYLFYuHHH3/ktttuo3PnzsTExNC3b1/uv/9+vvjiC/r06cNVV10FnB4SXr++sdrixx9/zMcff+z04gmJiYnWVVeL1K5d2+n55iIiAJ9v9tECVhXABPiUvBiyjdxCsABfbLDwdBcPXusO324yFrJ663IPazv+Jfw66BNlIiLAxGv/2j8Q9vcy2i+uaPj62bTpiLcHvNfTg883WEg4XnK9dUfh38MWnurswaEMMwv3W2gebuKzvh7kFVpKjEfElTRUXCpSlf4xl5mZiZ+fH56e9r/BAgICSEpKorCw0OHxc/HPP/8QHBxMmzZtzur89evXs3btWqfqRkZGMmjQoLO6TpHc3FzGjh1Leno606dPt/Zo5uXlccMNN/DHH3/w9ddfM3LkSG6++WZ++eUX1qxZ43B49EMPPcTTTz9tM1c6KyuLvn37MmnSJN566y0iIyPLjOnnn3/m559/timrUaMGc+fOtZmfMXv2bCwWC8uXL7d7uOFIz549+fbbbwEoLCxk4MCB/Pnnnxw5coQdO3ZYf0B26NCBO++8kw8//JD33nuPVq1aMWDAAL744gs6depkN/T68ccf548//uCLL77g559/Ji4ujg4dOnDllVfSt29fPDwcD4pw9D0ye/Zsa1ItIlKWrcct/H3k3B9+ClxexhZAZ2r2bQHbUuD5pWZq+MOTnUz8t4vxZ9Gfe8x8s8nCA3EmMkpYCuS2FiYKzBZ+2Wb/pCK74PSQ4jP5eZ0+Xt42HXmso4ka/vDCsrJXX77x90J+udqT764yAiswW3h3tYUe9U00DS3jZHGaM38jiXP27NlDXFycq8MQN1GlhzcXFBSUmGwUJbrO9qg6a+fOnRw4cIDLLrusxGuXpUmTJgwYMMCpV9euXc855v3797N+/XqaN29us3Kyj4+PNYH99ddfnWorODjYmvBmZmZy+PBhUlJSuPTSSzGbzaxevdqpdnr37s2kSZOYNGkS33zzDY899hgmk4lBgwbZtBEYGAjADz/84NTXsmgYNhjfA507dwbgzjvvtHkieNlll+Hn58e2bducirddu3YsWbKEIUOG4Ofnx+LFi3n33Xe58sorqVOnDjNnznR43pdffmm9z0mTJvHzzz/TqVMnp64Jxrz13Nxc6/uMjAzS09Ot7/Py8jh+3PbxfWJiYqnvjxw5YjMPRtfQNXSNqn0NT0s+Js3mrRBbj1sYNrvQqVdihnFOvhnum2umzueFdP+pgCbfFHDVb2ZCfKHQbLFuL3QmPy+4vpGJefssHHUw3zcx0xjiXFxR2eEM+693WW0WF+xjrNj81SYLwT7QINh4BfqAyWT8u+YZ6ywezoDuPxfS+OsCuv9UQL3PC3lqiZn6QbD9RNnXE+cU/d92159XF/M15MJWpXt6vby87IaOFimaX1qRe3jl5OSwfPlymjVrRkRExFm3ExwcXKlDMvbv309ubi7Nmze3OxYdHU1ISEiJCzEVl5KSwhNPPMHs2bM5evSo3SICJ04495sxOjqaW2+91aasb9++XHvttQwfPpwNGzYA8Oyzz7Jq1SrGjBnD888/T5s2bbjyyit56KGHHPb8xsbG2rwPCwsDsBsG7evrS0BAgN0PtNK0atWKn3/+GYvFwp49e5gzZw6//PILf//9NzfddBPx8fF2C6ddf/3157SQVVH8RYoeAhTx8fGxm9NS/Cly8ffFv3d1DV1D16ja12hcw4dBsfn8sUfDm89VUhZ8v/nsPolHs7Ammx4m6FnfxIpEyHSw9uU1DU0E+5r4McFxD+v6oxa61zNhApvHGV0iTWTmWxwmmWW1WVyoHwT5mHiqs4mnOts/pN87wovpO8xcP8O2vZ2pWBP55uHG/OIJ8dqnt6JkZGQAtd3251VlXuPMqWJV4T7kwlale3oDAgLIyclxuIBSaUOfz9batWvJz8+nWbNmpKWlWV9FPZBpaWmnfpiVLj8/n6ysLKdeJSX1rpCfn88tt9zChAkTaNOmDW+++SY//PADkyZNYtiwYYCxqvXZ6ty5MxEREWzcuJHMTGM1j3bt2rFlyxYmTpzIjTfeSGpqKq+88gqxsbH8888/dm2U9PUuaQ7t2az8ZzKZiI2N5cEHH2T+/Plcc8015Obm8sMPP5S7LRERZ9wUkaiEtwoZ3clEnUAT76x2/DtvaHMjeZ22w/EXbcp2CxEBJm5ocnqqULg/3NTUxMxdFofbFZXVZrAPNA0zPoKRoF83vdDutWC/mex8C9dNL+T1FSX/zjZhzF3OzLfw+QYlvRXF07NK/2l9QUlNTXV1COJGqnRPb82aNTl48CBHjx61edpSUFDA8ePHK/wJTHp6OgUFBUyfPt3h8V9++YXQ0FBuuummUtvZsGFDpc7pbdCgAX5+fiQkJNgd27dvH2lpabRr185aVtLexocOHWLp0qV07NiRuXPn2hxbvHjxOcUIRsJc9AAjIyODgABjnFdAQAC33347t99+u3URqrvuuouxY8eycOHCc75ukbMZru7l5UX79u2ZMWOGdYVnEZGK1tI/hRbhdUg4jgY6V7Lbmpu4sYmJJQctZORB3wYmhjTz4KuNZqY6SEBD/aB/jInftlsc9gKDkfQuP2zhu6s8aBFuJjkbHozzwNMELyy1TzCdafP6xiYm9Pdk2OxCvt9sIbsAZuy0j++6RiY6R9gfe7+XB35eRi+0twcMbe5B50i4a7aZA+l2zchZatCggatDcBspKSlERUW5OgxxE1U66W3YsCHr1q1j06ZNNgnu1q1bKSgosBtqevLkScxmM9WrVz+r68XFxdG4cWO78jVr1nDy5El69erl1ErLTZo0cXp49Jnb95yt+vXr07ZtW1asWMHMmTOtSXReXh5vvvkmFouFIUOGWOtXq2ZM8klJSbFZyKpoG5/ivaPbt28v8UFAeSxYsICkpCRq1apF7dq1MZvNJCYmWrcyKoqhffv2+Pj4OD2U2llFQ86Tk+03R5w+fTo9evSwrmZdJDs7m/nz5wOOF60SEakIgYEBPNHRg+F/qsetsm0/YSHMz4OxXU34e8G2E3D/3EK+3Oj48cNNTUz4eJqYVMowZLMFBvxWyPgeHjzazgN/b1h1BIbNLnQ4tNmZNs/VuqMWRnXw4LbmJswWWHnEQp9fLSw6oMcsFWnv3r3QKLbMelK2s11bR8SRKp30hoWF0bJlSzZv3szcuXOJiorixIkTxMfHExkZaZf0zpo1i4yMDEaMGGFTvn37duuw5KLh0kU9sYGBgdb5oEXb1xS3efNm0tPT7eaTlqSy5/T6+vryyiuvcOONN3LjjTdy8803ExUVxaJFi1i+fDkdO3Zk+PDh1vpdunRh4sSJPPDAA9xwww34+vrSo0cPWrRoQefOnVm4cCFXXHEF3bt358CBA0yfPp2QkBCSkpKcjmnz5s28/fbbgPE5T0hI4Pfff8dsNvPGG28AxnDqQYMGkZmZSffu3YmKiiItLY3ff/+dnJwc65DqitKkSRNq1arF5MmTqVWrFjVr1qRWrVoMHTqUjz76iFtuuYVLLrmEDh06EBoaypEjR5g/fz5btmwhJiaG+++/v0LjEREp0qBBA+qZTDy+CNJyy6wuFWjVEej5i/P70H+50cKXG8teeDE111gg6765ZVZ1qs3vN1v4fnPZ1717jpm755R0vvP3KWfHbNZDhIqizgapSFU66QXo1q0bQUFBJCQksH//fvz8/GjVqhUdO3YscZhucdu2bbNbga1oBeHIyEi7RZAuRFdccQWzZs3i+eef5/fffycrK4saNWrw4IMPMn78eJsFv+655x7WrVvH77//zjPPPIPZbObpp5/m9ddf5/vvv2fUqFEsXryYRYsWUbNmTe677z7Cw8PttvkpzYoVK1ixYgVg9N5Wq1aNFi1aMHbsWGtPtJeXF0OGDGHKlClMnTqV9PR0/P39iYmJ4fvvv+fOO++s0M9RREQE77zzDq+88gpvv/02eXl5REZGMnToUEaPHk2dOnVYtWoV69atIz09HW9vb+rUqcOjjz7KK6+8Yh2OLSJS0bZs2UJcXBwPxpl4a6VF83tFLlBBQUGuDsFtbNq0idatW7s6DHETJsvZrPQjIiIiFWb9+vXExcVx4KSF6K8KUWeRyIVpUr9cbm2jh+QVoejnokhF0GB5ERERF6tTpw4A9YNN3NDYhJdzA5lEpIo5cuSIq0NwG8XXWRE5F0p6RUREXOzM6TqjOnhQoJ5eEbnIFd9HV+RcKOkVERFxsUOHDln/fUkdaFNTv6BFLkQlLYoq5bdz505XhyBuRL9TRUREqhCTycTjHTzQ5kUiF57s7CxXhyAiDijpFRERcbHmzZvbvL+lmYkwPxcFIyJn7eTJdFeH4Daio6NdHYK4ESW9IiIiLnbgwAGb975eJh6KM+GpBa1ELijObqcpZcvIyHB1COJGlPSKiIi4mKM/7kbG6Ve0yIUmJiba1SG4jeTkZFeHIG5Ev1FFRERczM/PfixznUATNzXR9kUiF5J9+/a7OgQRcUBJr4iIiIs1atTIYfmojtq+SORCUlhY6OoQ3EZcXJyrQxA3oqRXRETExeLj4x2Wd4k00b4WeKi3V+SCEBAY4OoQ3MbmzZtdHYK4ESW9IiIiVdjjHT0wq7dXpMrzAJpGBLk6DLeRn5/v6hDEjXi5OgAREZGLXURERInHbmpqonGoZyVGIyJnI8QXsvfvAOJcHYpbCAkJcXUI4kaU9IqIiLhYaUmvj6eJzpGVGIyInLX1WseqwtSuXdvVIYgb0fBmEREREZEKEB0d7eoQ3Mb27dtdHYK4ESW9IiIiIiIVIDMz09UhiIgDSnpFRERERCrAsWPHXB2C24iKinJ1COJGlPSKiIiIiEiVkpOT4+oQxI0o6RURERERqQBt27Z1dQhu4+jRo64OQdyIkl4RERERkQqQkJDg6hBExAFtWSQiIiLOu+9TmLHS1VGIVD4PE0x5Ei5rXmKVvLy8SgzIvbVu3drVIYgbUdIrIiIizpu8DNKyXB2FSOXz9ID3Zpaa9AYHB1diQO5t+/btNG9e8udapDw0vFlEREREpCyFZpi+AvaXvEJzREREJQbk3nJzc10dgrgRJb0iIiIiIs4wmeDTOSUe3r59eyUG496CgoJcHYK4ESW9IiIiIiLOKDTDZ39Clnohz7e6deu6OgRxI0p6RUREREScdTILflzi8FBUVFQlB+O+tm7d6uoQxI0o6RURERERcZbJBO/+DhaL3SHNQxWpmpT0ioiIiIg4y2KBrYdgUbzdoaSkJBcE5J7q1avn6hDEjSjpFREREREpj6Lti+S8KSgocHUI4kaU9IqIiIiIlEehGWathj22PbutW7d2UUDu58iRI64OQdyIkl4RERERkfLy8ICPZ9sU7dixw0XBiEhplPSKiIiIiJRXoRm+nAsZ2dainJwcFwbkXlq1auXqEMSNKOkVERERqWgLXwbLVMevvF9t6wb4wXv3wIGvIOcX2PIhjLyy5Lb7tIH5L0HqRDj5I6weDzdfem5tnqlBzZJjH3Jpyed5ecLmD4x6T1zr3LUudJk5MHGx9W1QUJALg3Evu3btcnUI4ka8XB2ASFV0+PBh7r//fmbNmsWePXuIjo4mPT2dRx55hO+//56FCxfSs2dPV4cpIiJV1au/wdfzbMsC/OCLkTB3w+kyDw/483no2BA+mQ07EuHKOPjsfggNhNd/s21jWG/45kH4awM886PR29i0LtSvcfZtlmTSEvi/tbZly7eXXP+RARBVo+Tj7urd340HCiYTdevWdXU0biM7O7vsSiJOuqiSXovFwqZNm0hISCAjIwM/Pz9iY2Pp2LEj3t7eTrWRlZXFmjVr2L9/P9nZ2fj7+xMTE0OHDh3w9fUt9dwtW7bwzz//AHDnnXfi5+dXYW2fDzNmzGD48OF4enqSkJBAWFiYzfEjR47wwAMPMH36dD766CMefvjhSo3vgw8+YNSoUTZlnp6ehIWF0b59e1566SW6dOlyXmPIzc1lypQprF27lkceeYTo6Ojzej0REakiFr4Me4/C3R87Pj5vg33ZbZcbH39ccrrshi5waTO452P4boFR9vmfMHkMjB1sJM7H0ozyBjXhk/vgo/+DUd+WHFt52izN2j22sZamZgg8fzO8OR1eudW5c9yBBdh5xPh6XxHH1q1biYuLc3VUbiEgIMDVIYgbuaiGNy9fvpx///2X0NBQLrnkEmJjY4mPj+fPP//E4mCD8eKys7OZPn0627ZtIzo6mksuuYTo6Gi2bNnCrFmzSl1aPTMzkxUrVpSYXJ9L2+dLv379GDFiBMeOHWPkyJE2xwoKCvjhhx+YNWsWvXr14qGHHqr0+IpceumljB8/nrfeeotnn32Wrl278vfff3PZZZexcuXKs2ozMjKSyZMnk5mZSYMGDUqsl5eXx19//cW7777L3r17z/IORETkojC0uzH/c8YZv5u6tzA+/vyPbd2f/wF/X7i20+mykVcaW+U8/7PxPsD24flZtVmWar7g7UQfyRu3w7ZD8L/FZdd1N9q+6LyIiopydQjiRi6ant6UlBTi4+OJjo6mX79+1vKgoCCWLVvGrl27aNSoUaltrFu3joyMDHr37m1Tt3bt2ixYsICNGzfSvn17h+cuXbqU4OBgQkND2blzZ4W2fb74+/szYsQIVqxYweTJk5k8eTI33XQTYNzPxx9/jL+/PxMmTMBkMlVKTGazmbS0NEJDQ61lcXFxjB492vo+JyeH0aNH88knn/Djjz/SuXPncl/HZDLZ9cSLiIictRrBcEVb+GUpZOWeLvf1hoJCyCv2cLuoToeGp4dJ920DWw/BgPYw/i6oFw4p6fDJHHjhZyh6gF+eNkvzws3w9l1gNsOa3fDsj8aw6uI6NYK7esJlz56O4WJSaIbZ62DHYerVq+fqaNxGQkKCes2lwlw0Pb1Fk+GL75/WrFkzvLy8nFpi/vDhw3h6etKwYUOb8oYNG+Lp6cm2bdscnrdnzx727dtH9+7d8fBw/Ck/27bPt+joaMaMGUPt2rUZOXIkSUlJHD58mLfffpsDBw7wwQcfEBUVRVpaGuPHj6d169ZUq1YNHx8fGjVqxCeffGLTXmpqKu+++y6XXHIJNWvWxMfHh4CAAC699FIWL7Z9Orx69WqaNm1KVFQUU6ZMoXPnzgQFBdkNsy7O19fXWsfHx8da/sEHH2AymXjxxRdt6h8+fJhBgwZhMpmsvbWOyoo7fPgwQ4cO5fvvvwegV69emEwmTCYTw4YNK+MzKyIiF5Uhlxo9psWHC287ZCwA1bWJbXn35sbHumf8zmscCfXD4buH4dv5cONbRrI19iZ4dejZtemI2QJ/roMx38Og1+Cx76BWCMx+DgZ0sK//0b1GMv9vKfN93Z2nsX1RYWGhqyMREQcump7eY8eOYTKZqFWrlk25l5cX4eHhHDt2rMw2CgsL8fLysuvVNJlMeHl5kZ6eTk5Ojk0PYV5eHkuXLqV58+bUqlWLLVu2VFjbjuTm5jo1VBvA29sbT0/PMuv16tWLBx54gJdffpl7772XSy65hNmzZzNw4EDuvvtujh07xujRo5k4cSKtWrXiwQcfxGKxsGDBAh5++GH27NnD22+/DRiJ4tSpUykoKODmm2+mZs2abNu2jT///JM+ffqwcOFCunfvbnP91NRURo4cSceOHXnsscfsfqFkZmZy8OBBzGYzJ0+eZN68efzvf//D39+fO+64w6nPxdmoXr06w4YNIy0tjb///puRI0daH1J07dr1vF1XREQqmZcnhFSzLfP2MnpUw4ut1puS4bi3c2h3OJpm31M66W9jLuy3D8NDX8GOw9AvDh68yjjuf/rhLYF+4OkJT02Et6YZZVP/hbBA+M/V8NpvkJFTvjYdOZAMV71iWzZxMWz5AN65C/5vzenyYb2hdQMYPL70Nt1doRm+nkfSTa2oXbu2q6NxC3Xq1HF1COJGLpqe3szMTPz8/BwmeQEBAeTk5JT5dC40NJTc3FySk5NtypOTk8nNNYYMZWRk2BxbsWIFFoulzCG2Z9O2I7/99hs//PCDUy9Hw6wd8fX1Zfjw4Vx55ZXMmjWL1157jeDgYL7++msApk+fzk8//cTNN9/Mxo0befvtt3nnnXeYOnUqvXv35r333mP37t0ANG7cmD///JOVK1fyySef8OKLLzJp0iTef/99/P39ee211+yun56ezv3338+cOXMYN24cr7/+us3xCRMmUL9+fRo0aEDr1q157LHHyM7OZt68ebRp08apezwb1apVo1+/fsTGxgIwZMgQRo8ezWOPPUa3bt2caiMlJcX69QXja5yenm59n5eXx/Hjx23OSUxMLPX9kSNHbB586Bq6hq6ha5yPa1xULm0Gyd/bvi5tBrd2ty93tHpxTG24pJnRG1potj2WlArXvG4k0H+9AHu/gPF3wiPfGMczztj3NTvP+PjT37Zt/PSPMfe2XWz523TWiQxjUaxm9aBuuFEW5A+v3wbjp8PB46WeflHIyqXalsOA+/w/1zXEXVw0Pb0FBQUlDi0uSoQLCgpK7fls3bo1+/btY/78+XTr1o2wsDBSUlJYvnw5Hh4emM1mmwWnjhw5QkJCAr1797YZZlsRbZekd+/eTi96VdYw4TPVq1ePJ554gg0bNnD48GEmTZpEREQEKSkpzJ8/n/z8fO666y4OHjxoPcfLy4t27dqxYMEC5s2bx4gRI/D29rYu5lVQUEBqaiq5ubnUq1eP6OhoVqxY4fD6Y8aMKTG2fv36WYcTZ2RksHr1aqZOncrAgQNZsGAB7dq1c/o+K1vxr0FgYKDNex8fH8LDw23KIiMjS30fERGha+gauoaucd6vcVHZsBf6vmhb9s4wOHICxs+wLT+San/+0FMjmEpaCfnvLRD7ALSOMhan2rAX6pxau2L74dP1Dp+AJv5GUnumo6dWYg49Y7VbZ9ssjwOnkoSwQDh0HEZfCz5eRjLfoKZxrF746Vga1DRizq/8xThdom4YDe4cALjP/3NXXmP9+vXWEZpV4T7kwnbRJL1eXl7k5Dh+slnUw+vlVfqnIzIykj59+rB06VLmzJkDGMOPmzVrRnZ2Nnv37rUmdIWFhfz999/UrVu3zAWyytt2aYr/p69I9erVo0GDBhw+fNjak3nixAmOHDkCwIABA0o8NykpCYD8/HxmzJjBO++8w8aNG8nKyrKp52hBrBo1alC9evUS227cuDG33np6e4Thw4fTtm1b/vOf/3D//fef9QrOIiIiAKRmwvyNtmUnMiDxhH25I0O7w85EWFHKnFez2UhMi/Rta3ycd0b7a3ZBkzpGT+uepNPlRcnssZPlb7M8YmvbXieqBoQFwZYP7es+O9h4xT1uG4O7Mplg1NXs2b+PJk2alF1fRCrVRZP0BgQEkJqaSmFhoV1vbmlDn4uLjY0lOjqalJQU8vPzqV69Ov7+/kybNg2TyURISAgAmzdvJjU1la5du5KWdnovvLw8Y2jSyZMnycvLIzg4uNxtlyY7O9vpOb0+Pj5lJvrOKLre+++/bzdnGoy5w0UrT8+aNYv77ruPgoIChg8fTsuWLQkMDOT48eN8+umnDhfsqlatml1ZaTw8PIiLiyMiIoJVq1aRmZlJQEBAiStMm81mLTwhIiLnR1wMtKgPL//q/Dk1guGp641k8cwE9ZelxpDq4X3guUlGmckEd/eG4+lGUlzeNoOrQWSokcCfzDpdN7lYAl0nDO7pbZx/5IRR9uH/wfRiD5ZrhcCXDxhDoWeshD1Hnb/vC5mvFwzvS9Y+56aOSdmaN2/u6hDEjVw0SW/NmjU5ePAgR48etRmuUFBQwPHjx8s1hMHDw4MaNU7P2cnKyiI5OZk6depYk8iMjAwsFguzZ8922Mb06dPx8vLinnvuKXfbpZk2bZpTc38BevToQdOmTZ2qW5KQkBBrotukSRP69+9fYt3c3Fz++usvUlNT+fbbb7n77rutx1asWMH7779/TrGc6cxENiMjg4CAAOswlpSUFJu66enpdnOpy6OytmsSEZEL0G2XGx9LGtoMsOgVWL4Ndh6BiOow4gpj0aqrX7VdFGvGSpi3Af57g5GYbtgL13U29uUd8ZntFkXOtnl9F5jwCAz7CL5faJS9dSc0rA3zN8HhFIiuBff3M4ZJ/+eb0+eu2228zlQ0zHnzAdv9iN2Zpwfc1QtCAwlIDii7vjhl//79NG7c2NVhiJu4aJLehg0bsm7dOjZt2mST4G7dupWCggK7IcgnT57EbDaXOqwWjF7OZcuWYbFYbOaONm3a1OFQ482bN5OYmEiPHj3w9fU9q7ZLc77m9JYkPDycPn36MH36dJ577jl69uyJv7+/9bjZbObYsWNUr14dk8lknVd9Zm90VlYWU6ZMYc+ePeccDxgPMpYvX05SUhJ16tSxrqIYERFBSEgI8+bNw2KxYDKZKCwsZMGCBaxfv/6sruXt7W39OhZPpkVE5CJnMsEtlxk9sKXNo12zC266xNhK6GS2scLz2J9shzAXue5NGDfU2AJpWC9je6Lb3odJS86+zeLmroeRV8JD/Y25uamZsGQLjJtin+SKsTjZowMBaNCggYuDcR+ZmZmuDkHcyEWT9IaFhdGyZUs2b97M3LlziYqK4sSJE8THxxMZGWmX9M6aNYuMjAxGjBhhLcvPz2fatGlER0cTHBxMXl4eO3fuJDk5mU6dOtksrR4eHm43QR6Mp1aJiYk0aNDAZvuh8rRdmvM5p9cRk8nE4MGDWb16Nd9++y2xsbFce+21REREkJiYyMaNG1m/fj0JCQlER0dz+eWXM3HiRP7zn/+wevVqqlevzooVK1i2bBl169bl0KFD5Y5h/fr1vP3221gsFrKysti4cSNz587FbDbz1ltvWeu1bNmSnj17MmPGDC677DJ69erF1q1b+euvv6hbt26J+/GWxs/Pj1atWmEymXjuuefYtm0bgYGBdO7cmS5dupS7PRERuUD0er7sOhYL1L+v7HpPTDBezsjMgce+NV4V0eb3C0/38Bb5+R/jdTb2HQPTDWd37oXI0wMub2EMYQe2bNlCXFyca2NyE2d2ooicq4sm6QXo1q0bQUFBJCQksH//fmvC0rFjR6eGqHp4eBAeHs6uXbvIysrCy8uLmjVr0r9/f+rXr39OsZ3Pts+3GjVq8N5779G+fXu+++47Jk2aRFZWFsHBwcTExDB27FhrMn7NNdeQkZHB+++/z/fff4/ZbKZ169Z8/fXXfPLJJ2eV9C5dupSlS5cCRhIeEBBAmzZteOGFF+jXr5+1XkREBK+99hpms5klS5awatUqGjZsyAcffMBvv/12VkkvwM0338yOHTuYPHkyY8eOpbCwkLvuuktJr4iIiLsrNMPj17g6CrfUsGFDV4cgbsRkcXbVIxEREZHqt0NaVtn1RC4GDWrC7s/g1PSto0ePOlzUU8pv/fr16jWXCuN441oRERERESnZqW2KihJeo0iLW4pURUp6RURERETKy8/b2C7qDGczTUscq+x1asS9KekVERERESkPTw+4pw+EaIui88WZrTpFnKWkV0RERESkPArN8MgAu+LmzZu7IBj3dPDgQVeHIG5ESa+IiIiIiLM8PeCKttC0rt2hAwcOuCAgESmLkl4REREREWcVmuHxQQ4PZWRkVHIw7qtZs2auDkHciJJeERERERFnmIDY2tAvzuFhPz+/Sg3HnWlRMKlISnpFRERERJz12CCbbYrO1KhRo0oOxn2lp6e7OgRxI0p6RUREREScUc0X7upV4uH4+PhKDMa9+fr6ujoEcSNaC1xERETKxWIyYfIwuToMkcplscC9V0CQv6sjuSg0adLE1SGIG1HSKyIiIs77dAQnJs0nLDTM1ZGIVL5H7bcpOlNEREQlBeL+Nm3aRFxcnKvDEDehpFdEREScN/Ry9rcIJkx/jIrYUdIrUjVpTq+IiIiUS3R0tKtDEBE3V6tWLVeHIG5ESa+IiIiUS2ZmpqtDEBE3p+2fpCIp6RUREZFyOXbsmKtDEBE3t3//fleHIG5ESa+IiIiIiIi4LZPFYrG4OggRERG5cFgsFkwmbVkkIudPVlYW1apVc3UY4ibU0ysiIiLlkpCQ4OoQRMTNJSUluToEcSPaskhEpCqwWOC7BXAszdWRiMC1naFZvRIP5+XlVWIwInIxSkvT70OpOEp6RUSqgoXxMPwT8PQADRsVVyo0w/q98NPjJVYJDg6uvHhE5KLk7e3t6hDEjWhOr4hIVTBnLfQf5+ooRAyeHrD/S6gT5vCw5tqJiMiFRHN6RURExN7nf5Z4aPv27ZUYiIhcjNavX+/qEMSNKOkVERERW4Vm+Hg25Oa7OhIREZFzpqRXRERE7J3IgF/+cXgoKiqqkoMRkYtNjRo1XB2CuBElvSIiImLPZIJ3fjdWFi8mNzfXBQGJyMUkMDDQ1SGIG1HSKyIiIvYsFti4D5ZttTuk/TNF5Hzbu3evq0MQN6KkV0RERBzz8oD3Z7k6ChERkXOipFdEREQcKzDD1H/hYLJNcevWrV0UkIhcLBo1auTqEMSNKOkVERGRkplM8Okcm6IdO3a4KBgRuVgcP37c1SGIG1HSKyIiIiUrNBtJb/bpxatycnJcGJCIXAxOnDjh6hDEjXi5OgAREbmI9WkDz9wIHWLBwwO2H4a3psOvS0/X8fWGxwbBHT0gupaxlc6ybfDiL7DlwOl6vVvDbZfDZc2hXjgcOQEL4mHsT8a/z2QywYgrYOSV0CgCMnNh7W54ZTIs31Z23COvNK7XpTFE1YQJC+Duj+3rRYTCfwYa9To2giB/6DkWFm8+q0+Xy6RlwU//wD19AAgKCnJxQCLi7jw9PV0dgrgR9fRKhfj8888xmUx88cUXrg5FRC4Uw3rD3OchvwCe+RHGfA9LtkD9Ynsz/jgKXr4FFm2GR7+BL+bC5S1g+etGwlnkzTugZ0uYtsKo9/NSuPkSWPc21K5u2+b4u+DzkbBpHzw+wdiap0kdWPwKdHJiHtlT1xtJ7+YDRvwlaVoHnr4B6oYb17pQmUzwzgzr9kV169Z1cUAi4u60doBUpLPu6bVYLGzatImEhAQyMjLw8/MjNjaWjh074u3tXeb5qamprF27luTkZDIzMzGbzQQGBhIVFUXbtm2pVq2aTf2NGzeyb98+UlNTyc3NxdfXl+rVq9OqVStiYmLOqe3K8sQTT/Duu+/alPn5+REeHk6vXr146aWXiI2NdUls//d//8f06dN56qmnaNiwoc2xzz//nAceeKDEc5ctW3ZO177++uuZPn26U3VvueUWfvrpp3O63rkaPXo0q1evJiEhgaNHjxIeHs6ePXvU8yFypoUvw96jjns/ARrUhE/ug4/+D0Z9W3I7dcLgxm4wfjo8+cPp8r8TjGvc0OX06sKPT4B/Emz3lZ2zDpaMg4f7Gz2+AJ4e8MCVMHkZ3Pnh6bqTl8Gez43e4lU7S7+/HmNh/zHj3+k/llxvzS4Iu9Ponb6xG1zSrPR2qyqLBbYcNB5K9GjJ1q1biYuLc3VUIuLGNm7cSJs2bVwdhriJs056ly9fTnx8PNHR0bRp04bU1FTi4+M5fvw4AwcOxGQylXp+ZmYmWVlZREdHExAQgIeHBykpKSQkJLBr1y5uvPFG/P39rfWPHj1KUFAQ9evXx8/Pj9zcXHbv3s1ff/1Fx44dad++/Vm3XdmGDRtGy5YtASNB/+eff/jf//7HkiVL2LhxIyEhIZUe0/z58/nqq68YMmSIXdJbZNCgQVx++eV25Y0aNaJTp07cfvvt+Pn5lfvaDz30EJdeeqlN2ZgxY4iIiOCJJ56wKW/btm25269o77zzDgEBATRq1IjMzExXhyNyYRp5pZF8Pv+z8T7ADzIdzBMNOvWzOinNtjzx1HDl7LzTZX9vsT//7y1wPB2a1ztd5u0F1XwhKdW27tE0KCy0bbMkRQlvWTLcaO6rlwe8PxN6tHR1JCJyETCbza4OQdzIWSW9KSkp1oS3X79+1vKgoCCWLVvGrl27ylxmvG7dug6HR0VGRjJv3jy2bdtm8xS5b9++dnVbt27N1KlT2bBhA3FxcXh4eJxV25XtiiuuYOjQoTZlffv2Zf78+cyZM4chQ4a4KLLSderUidGjR5d4PDAw8Kza7du3r93Xd8yYMQQHB5d6PVfZuHEjLVu2xMPDg9jYWE6ePOnqkEQuPH3bwNZDMKC9MdS4XjikpMMnc+CFn0/31u46AgeS4YlrYNshWLcH6oTCW3fC7iPw8z+lXyfADwL9IDn9dFlOHvy7HYb1guXbjcS4egCMvQlOZMKXf52/+76QFZhhxirYe5R69eqVXV9E5ByEhYW5OgRxI2c1p3fXrl2A/Vj7Zs2a4eXldU5bGRQlTnl5ZT9p9/DwICAggPz8fKeeBpWn7cpWp04dAJue0pycHB555BHq1q2Lr68v1apVIyoqiiFDhpCdnW2tZzKZuPzyy5k1axYtWrTA19eXsLAw7r//fvLz80lJSeG6664jJCQEX19fOnTowPr1663nX3/99dZh13379sVkMmEymbj11ludjt/RnN6iss8++4xnn32WiIgIvL29iYiIYOzYseV6gnf33XdjMpmYPXu23bHExEQ8PT3p2bMnllN/KBd9TmbMmEHz5s3x9fUlJCSE66+/nsOHD9u1kZqayvDhw4mMjMTb25ugoCAuv/xyli9fble3devW1gcsInKWGkdC/XD47mH4dj7c+BbMXmcknq+e8VCwoNA4lpkDM5+Bg1/ByreMRPaSZ4wFlkoz6mpjIaxfiiXHt78P2w4b84X3fwkb34P2sXDpM7AnqaLv1n14GNsXFRYWujoSEXFz1atXd3UI4kbOqqf32LFjmEwmatWqZduYlxfh4eEcO+bksC+goKDA+kpNTWXFihUA1K9f32H9nJwcLBYLOTk57N69mwMHDlCnTh28vOxvpbxtF5ebm2tNosri7e3t9CpzaWlpHDx4EDB6zf/8809+/fVXmjRpYtPjefvtt/Pbb7/Rq1cvhg8fjsViYefOnaxcuZLs7GybIdp79+7l1ltv5ZprrmHw4MHMnDmTL7/8Ek9PT+bNm0dYWBgPP/ww+/bt46effuK2225j3bp1+Pj48NBDD5GamsqiRYt4+OGHadCgAWA/lDgzM9Mad5HAwMAyfyi999575OTkcPPNNxMQEMD//vc/xo0bR2xsLHfffbdTn7PHHnuMCRMm8PXXX9O/f3+bY5988glms5lhw4bZDKvfs2cPQ4YMYcCAAQwePJilS5cyffp0tm/fzooVK6wPQdLS0oiLi+PIkSP079+fNm3acOzYMSZNmkS/fv2YPXs2l112mVNxilyUvDwhpNhaCd5eRrIZXmyue0qG0Ysb6AeenvDURHhrmnFs6r8QFgj/uRpe++300OATmbB+rzHn9t/t0CgS/nsDTB4NV7wEufmO4+reAl64GX5ZCgvjbY+lZxuLUC3fBvM3QUR1Y8Gp6U9D92eNIdFir9AMn88h6fpm1K5d29XRiIgb2717t9YOkApzVklvZmYmfn5+DpO8gIAAkpKSKCwsdCoJ3Lp1q81CSEFBQfTq1YvIyEiH9X/55Rdyc429Ak0mEzExMSUmJOVtu7jffvuNjIwMp+r26NGDpk2bOlX3wQcf5MEHH7Qp69ixI9OmTSMgIMBa9ueff9K6dWvmzZtXZs/iwYMH+eGHH7j99tsBeO6554iMjOTzzz+nf//+zJw509qGn58f33zzDX/99RcDBw6kb9++zJ49m0WLFnHdddfRp08fh9d48803efPNN23Krr/+eqZOnVpqbDk5Oaxbt47w8HDAmMMbExPDl19+6XTS26ZNG1q3bs38+fNJTk6mRo3Tq7t+//33REZGctNNN9l9Tl5//XWefvppa9ndd9/NhAkTePvtt3nxxRcBGDVqFIcPH+aXX37h+uuvt9YdM2YMLVu25Nlnn2XRokVlzlMXuWhd2gwWveK4/NbutmXR98O+Y8a82UB/+Olv2+M//QP920O7WGPYcXA1+HscjJ8B7/5+ut7qnbB4HNzdGz7/0/7aTevCtKcgfj/c+4ntMU8PmPfiqdWgvz5dPm8jbH4fxlwHT08sxyfgIpOeQ/U5m6FbZ1dHIiIi4pSzGqNZUFBQYhJWlOgWFJSyhcMZoqOjGTBgAP369aN9+/b4+PiUuul9v379GDBgAD169KBevXoUFhaSn+/4KX952y6ud+/eDBgwwKmXs73HYCRZkyZNYtKkSXz22WfcdtttrF+/noEDB3L8+HFrvcDAQA4dOsRff/1VZo9zbGwst9xyi/W9j48Pbdq0wWKx8Oijj9p8vYrmYSckJDgdM8Btt91mjbvo9dRTT5V53k033WRNeAHq1atHVFQUhw8fLvFr58gDDzxAWloa3333nbVswYIFHDx4kIEDB9o8MACoXbs2jz76qE3Za6+9BmAdJm2xWJg6dSpNmjShY8eOHDx40Pry9vamTZs2bNiwgbS0YovoVJCUlBTrQxyAjIwM0tNP9zDl5eXZfE+AMZy7tPdHjhyx+X7RNS6sa1yQNuyFvi/avjbshT/X2ZcfSTXOOXxqISpHi0kBhJ76/3xjV2Ov299X2dZbsgXSMo3Eurh64cZWSGmZMOBV+8WkLm8BrRvYt7kzERIOOW5TbNTp3dFt/w/qGrqGrlE1rnHm7ixV4T7kwnZWPb1eXl4lJo9F83wcDTd2JDAw0DrMNDo6mpiYGKZNm0ZBQQHt2rWzq39mL23Tpk2ZP38+M2bM4Oabb8bX1/ec2i4uIiLCqXsor06dOtnMlx05ciQxMTGMGzeOl19+mQ8++AAwVgm+9957ueqqq6hZsyYdOnTg6quvZvjw4XarJEdERNh9zouGHTdv3tymvGhYenJycrnibtq0abnm+RZxtA1T9erV2bdvHzk5OU5tcQXGqtdjxozh559/5oknnsDDw4MPP/wQLy8vRo4caVe/bt26dttTRUZGEhgYSFJSEvn5+Zw4cYKTJ0+yefNmoqKiHF7XZDKRnZ19XuaWFF+kofhiYD4+PjYPDAC7kQrF3xf/vtU1LqxrXJBSM2H+RtuyExnGCsvFy4us2WXsi1s33HYObZ1Q4+OxUwvEFe2v6+ngQaunhzG0+kxhgTD3BWNodZ8X4cgJ+/NKa9Pb075NOc3DBK0bsDvChyZu+n9Q19A1dI2qcY0DBw5YdzSpCvchF7az6ukNCAggJyfH4UIWpQ19dkZ4eDg1atRgyxYHW0840KRJE7Kzs9mzZ0+Ft52dnU1WVpZTL2d7tkty4403AtgsnDR06FD27dvHe++9R9euXVm7di0PP/wwcXFxdglracOfnX0Acb5U1PX9/f254YYbWL9+PatXr+bkyZPMmTOHdu3aOfUQw5Gip4ItW7a068Uuev34449aQVCkov2y1Pg4/IzpFCaTMVz5eLqRFANsP7Xw3C3FprFc08kYHr3ujJ/91Xzh/56DumEwYJzRc+uItU3brdJoFwtN68C63afL/H2ModLF5yZfrMwWeHwQWWcspigicj4U74kVORdnlY3UrFmTgwcPcvToUZunIAUFBRw/fvycn4wUFBTYDEkoqy5QrvrO1p02bdp5mdPrSNEw3+xif0jUrFmTUaNGMWrUKCwWCw8++CCff/45n332GWPHjj3r6xV3ocxXffzxx5k4cSKff/45rVu3Jjc3l6FDhzpM+g8dOkRWVpZNb29iYiIZGRm0aNECb29vatasSWBgIJmZmQwePNjpXmcROUczVsK8DcaCVDWCjeHQ13U2Fp8a8RnknXqQOHO1MS/3+ZugQc1TC1lFwMP94XAKfDPvdJs/joIuTYyy5vVs9+bNyDGuCbB2N8xdD8N6G3OG566HyFB4ZIAx1/j9WafP69zYmK/84i/w0i+ny6/uCG2jjX97e0GbaHh2sPH+91Wwad/pukXlLU9Ng7mjB1x2agTOq1PO9jPoGmGBMOQyAvbvdXUkIuLmLpS/TeXCcFZJb8OGDVm3bh2bNm2ySXC3bt1KQUGB3R69J0+exGw22wwPLZ6MFDl8+DAnTpywabcoISyekJjNZmuv7ZkrSZen7dL07t3b6R7cc+0J/OGHH4DTQ5ELCws5duyYzXANk8lE165d+fzzz0lJSTmn6xUXFGT0YpR3yHNli4uLo0WLFvzf//0fy5YtIywsjLvuusth3aSkJD788EObhayeeeYZAK688krA6CG//vrrmThxIm+++SbPPfecXTt79+4lOjq64m9G5GJ33ZswbigMudTYM3fbIbjtfZi05HSd/AJjNeWxN8HADnDrZcbKy9NXwjM/2q6yHHdq/tfwvsbrTHuPnk56Aa59A0Zfa/T2XtXOSLL/3gJjfzrdE1yaG7saSXOR9rHGC+Dgcdukd5ztvuw2sV1ISa+HBzw8AHy9rav8i4icL8V3ERE5F2eV9IaFhdGyZUs2b97M3LlziYqK4sSJE8THxxMZGWmX9M6aNYuMjAxGjBhhLfvnn3/IysqiTp06BAYGUlhYSHJyMrt27cLb25uuXbta66alpTFz5kxiY2MJCQnBz8+PzMxMdu7cSVpaGk2aNLFJZMvTdmnO15zev/76y7pX7MmTJ/n333+ZN28eoaGh1qQrPT2dBg0a0LlzZ9q2bUtkZCT79u3j559/plq1agwdOrS0S5Rbz549ARg3bhw7d+4kICCA1q1bl7iSsyuNHDmSRx99lKSkJG655RZCQ0Md1qtXrx4vvvgiq1atokWLFixdupSFCxfSrFkznnzySWu9jz76iOXLl/P888/z559/0qVLF6pVq8a+fftYvHgxDRo0YPHixTb1ixYBS0lJIT8/n9GjR+Pp6UlkZGSF9sCLXLB6PV92ncwceOxb41Wa1Ex4YoLxKk2M/dz+EuXkwbjJxqs0izeD6Qb78rs/Nl7OcHT+hcgEjDQWQtyyZYu2EhGR8yo+Pp5WrVq5OgxxE2c92bJbt24EBQWRkJDA/v378fPzo1WrVnTs2NGp4QgNGzZkx44d7Nixw7ooVmBgIM2bN6dt27Y2E84DAwNp3LgxR44cYe/eveTl5eHj40ONGjVo3769XZJdnrZdYcKECdZ/e3h4EBYWxoABA3j99ddp3bo1ANWqVePOO+9k8eLFTJgwgdzcXKpXr07Xrl15/vnn6dSpU4XG1KNHD/773//yzTff8MILL1BYWMgtt9xSJZPee+65h6effprs7GybBynFxcTE8PHHH/PUU08xc+ZM/P39ueaaa/jkk09svgdCQkLYsGED//3vf5kxYwYrVqzAw8OD8PBw2rRpw7333mvT7oQJE1i7dq1N2ZdffgkYc8yV9IqI2/HygMGXQKTWNxCRynGu6+WInMlkKWsvHJEqJjc3lxo1atCgQQPWr1/vcKEsk8lE9+7dWbJkiYMWRKqgOWuh/zhXRyFSshVvGnOcgaNHj9pMKxIRqWiaXiYVybXL+oqcha+++oqMjAxuvfVWl69MLSLi9jxMxsrWpxJe0AIzInL+1ahRw9UhiBtRxiAXjIkTJ7J9+3Y++OAD6tSpw0MPPeTqkERE3N+pbYrOdOjQIWrWrOmigETkYrBz506tHSAVRkmvXDDGjBlDcnIysbGxfPTRRzargYuIyHlSMxgGd3N1FCIiImdNSa9cMI4cOeJ0XU1VFxGpAB4mY/9iH9stA4u21xMROV80n1cqkoerAxAREZEqysMDRvSzKz5w4IALghGRi0lmZqarQxA3oqRXRERE7Hl5wK2XQe3qdocyMjIqPx4RuagcO3bM1SGIG1HSKyIiIvYKzPCfqx0e8vPzq+RgREREzp7m9IqIiIgtD5OxRVGHhg4PN2rUqJIDEpGLTdu2bV0dgrgR9fSKiIiILbMFHhtU4uH4+PhKDEZELkYJCQmuDkHciJJeERERsRVRHa7v4uooROQilpeX5+oQxI0o6RURqQoaRmAJ8HV1FCKGRweCd8kzoCIiIioxGBG5GAUHB7s6BHEjJos2NBURqRI2Ll9Jm2YtXB2GCAT6lZr0ioicb1lZWVSrVs3VYYib0G80EZEqwuzvA6GBrg5DRETE5bZv305cXJyrwxA3oeHNIiJVRHR0tKtDEBEREXE7SnpFRKqIzMxMV4cgIiJSJURFRbk6BHEjSnpFRKqIY8eOuToEERGRKiE3N9fVIYgbUdIrIiIiIiJVSlJSkqtDEDeipFdEpIpo27atq0MQERERcTvaskhE3FNePhSaXR2FLZMJ/HxKPLxlyxZatNCWRSIiIoWFhXh6ero6DHET2rJIRNzP1oPQcQxkVrH5QJ4esPsziKrp8HBeXl4lByQiIlI17dixg2bNmrk6DHETGt4sIu5n2+Gql/AW+XROiYeCg4MrMRAREZGqKycnx9UhiBtR0isiUlkKzfDZn5DlOCGPiIio5IBERESqpqCgIFeHIG5ESa+ISGU6mQWTljg8tH379koORkREpGqqW7euq0MQN6KkV0SkMplM8M7voDUERURESrR161ZXhyBuREmviEhlslhg6yFYFG93KCoqygUBiYiIiLg3Jb0iIpXN0wPen2VXnJtbRRffEhERqWT16tVzdQjiRpT0iohUtkIzzFwNe5JsipOSkko4QURE5OJSWFjo6hDEjSjpFRFxBQ8TfDzb1VGIiIhUSYmJia4OQdyIkl4REVcoNMOXcyEj21rUunVrFwYkIiIi4p6U9IqIuEpmDvzv9PZFO3bscGEwIiIiVUfLli1dHYK4ESW9IiKu9O7p7YtycnJcHIyIiEjVsGfPHleHIG5ESa+IiKtYgB2JMG8DAEFBQefnOokp8PRE6PU8BA0F0w0Ot0wCwGyGz/+EuMchcCjUvhv6vwLLHOyXmJsPT/0AdYaD/y3Q5Sn4a33psaRmQq1hRgxTljl/D0mpcP9nUPde8BsC0ffD8E/s683bYNxnjbug+u3Q+UmYuMj564iISJWQlZXl6hDEjSjpFZfYu3cvJpOJW2+91dWhiLjWGdsX1a1b9/xcY9theHMaHDoOrRuUXnfM9/DAF0a9d4fBE9fA9sPQYyysLDb8ethH8O5MuO1y+OAe414GvAr/JJTc/vM/QVZe+eI/kAydnoTZ62BkP/h0BNzbF46l2db7fSX0exny8uHFIfDqbeDvA3d+CO/NLN81RUTEpQICAlwdgrgRL1cHUFEsFgubNm0iISGBjIwM/Pz8iI2NpWPHjnh7e5d5fmpqKjt37uTgwYOcPHmSwsJCgoODiYmJoXXr1nZtrF69mrVr1zpsq0uXLrRt29amLCsrizVr1rB//36ys7Px9/cnJiaGDh064Ovre/Y3XgEOHjzIyy+/zIIFC0hMTCQnJ4eAgACio6Pp2bMno0aNIjY21qm29u7dS0xMDFdeeSVz5sw5z5EbNm/eTKtWrQD4+uuvGT58eKVcV6RCFJph9lrYmcjWjCTi4uLK30bPsRBdCyY84vh4h4Zw/HsICzJ6V2/a5rheQSF89icM7gYT/3O6/KZLIPYB+HEJdG5slK3cAT//A+PvhNHXGWV39oRWo+DJH2DZ6/btx+8z2n/+Jnj+Z+fv7/7PwcsTVr0F4aX0hn88GyJDYcHL4HvqZ/b9/aDZIzBhATw2yPlrioiISzVoUMZDWpFycJukd/ny5cTHxxMdHU2bNm1ITU0lPj6e48ePM3DgQEwmU6nnb9u2jS1bttCgQQMaNWqEh4cHhw8fZvXq1ezevZvrrrsOLy/7T1e3bt3w8/OzKatRo4bN++zsbKZPn05WVhbNmzcnNDSUEydOsGXLFhITE7n22msdtl0Zpk6dyh133EFubi6XXHIJ1113HWFhYRw/fpxVq1bx2Wef8euvv7Jr164KfeLWoEEDMjIyKuS+P/jgA/z8/PDx8eGHH37gnnvuKfPrLVKleHgYCduw9uen/SB/5+rlF0B2HtSublteK8SI0d/ndNmU5UbP7oh+p8v8fGB4H3jmR6N3tr7tz0L+8y1c3wW6t3A+9q0HjYcCn44wEt6cPOO63g5+dpzMgtCA0wkvGMlyjfM0bFxERM6bLVu2nN2DYBEH3CLpTUlJsSa8/fqd/gMsKCiIZcuWsWvXLho1alRqG7GxsbRr1w4fn9N/1LVo0YJVq1axbt06tm7dau1NPFN0dHSZ8/DWrVtHRkYGvXv3tomjdu3aLFiwgI0bN9K+/Xn6Y7cUGzZsYOjQoQQEBDB9+nSuuOIKuzqHDh3i+eefL7OttLS0ciXFJpOpQpLo/Px8Jk+eTNeuXWnYsCH/+9//2LFjB02aNHHq/OPHjxMeHn7OcYick0IzfP0X9Uf1K7vu+eTvC10aw4SF0K2JkZymZsIrk41kcsQZPyPW7YYmdSC4mm0bRT3B6/fYJr2Tl8GybZDwIew96nxM8zYaH2tXhz4vwIJNRtJ7RVv47H6jh7tIz1bGMO6xk+CuXmAywaQlsHoX/Dq6XJ8KERERcR9uMad3165dgP0el82aNcPLy8upbUBq1qxpk/AWadiwIQAnTpwo8dy8vDzMZnOJxw8fPoynp6e1rTPb9vT0ZNu2EoYanmejR48mNzeXF1980WHCC8Ycw2+++cYmQe3QoQM1atRg7dq19OrVi8DAQKpXr87Ro87/IVt8Tm9qaio+Pj507NjR4efyoYcewmQyMXnyZJvyKVOmkJqaytChQ3n44YfJzc3liy++KPV6X331FY0aNcLHx4cbbrjBWmfGjBl07NiRgIAAvL29iYqK4sknnyQ/P9+mralTp9KnTx9q166Nj48P1apVIy4ujh9++MHp+xexk5WL96Slro4C/jcKmtaB2z+ABvdD28dh7W5Y+hrERpyul3jCGEpcXFHZ4ZTTZdm5MHoCPHa1bZLqjB2JxscRn4GPF/zyBLxxuzFvuO+LkJV7uu7Ym+DmS+DV36DxQ9DoQXhjGvz2JNzQtXzXFRERl6pTp46rQxA34hY9vceOHcNkMlGrlu0fU15eXoSHh3Ps2LGzbjsjIwMAf3/HwwOnTJlCfn6+9frt2rUjKirKpk5hYSFeXl52Q25NJhNeXl6kp6eTk5NjN0y6uNzcXCyntjYpi7e3N56eniUez8nJYfHixYSHh3PXXXc51Wbx8/v160fz5s0ZNWoUKSkpVKtWjby8ci5Qc0r16tXp3bs38+fPZ9OmTTZzos1mM1OmTKFevXpcffXVNud9+umn1KhRgyFDhhAcHEyTJk2YMWMGr7/+usOHGMuXL2fmzJlcf/313HnnndbP+TvvvMOYMWNo1KgR9957LyEhISxatIjx48ezY8cOfvvtNzw8jGdE33zzDceOHWPQoEFERUVx5MgRfv31V+6++27y8/M1p1jOmu+nf8LTNxlDiUuSXwBpWfZlufmQfNK2PCyw9LYcCfKHlvWhW1Po0xqOpMIbU+G6N+DvV6FGsFEvO892GHERP+/Tx4u8MQ3yC+GZG8sXC0DGqW2cIqrDH8+evp96NeDWd42e3HtPPbTz9TZ6nwd3M5LcwkL48i+4/X346wXo2rT81xcREZfQVDWpSG7R05uZmYmfn5/DJC8gIICcnBwKCwvL3a7ZbGbdunWYTCa74dG+vr40a9aMSy+9lH79+tG5c2fS09OZM2eOXc9taGgoubm5JCcn25QnJyeTm2v0UhQl16X57bff+OGHH5x67dy5s9S2duzYQX5+PvXr1yc4ONjmWFZWFgcPHrR5Fd8/NDMzkz59+rB48WLGjRvHp59+SvXq1cu8h9Lcf//9FBQU8M0339iUz507l6NHj9K/f3+bhw+HDx9m2bJl9O3b13oPd911F7t27eLPP/90eI0DBw7wyy+/MHHiRJ5//nmefPJJEhMT+e9//0u3bt2Ij4/ngw8+4OWXX2bJkiUMGzaMGTNmsGDBAmsbP/74Ixs3buTrr7/m+eef59NPPyU+Pp6AgAA++OCDUnv9i0tJSbF+D4DxfZCenm59n5eXx/Hjx23OSUxMLPX9kSNHbB6OXMzXuKBYwDvpJHnJaaV/rpZuhZrDbF/LthmLShUv32/8zCn+uQJIS7NNkBMTE42FrPq+CCHVSHz2ari+KzxwFcx7EcuuI1jemm6tX+DjiTnbwdcjxxgZUeDtadzH3qMwfjq8OpTE9FSbaxYfQePoa17oc+rn+s2XkpGVdfo+buqGxcuTnIUbTzfw8FfkT10OPz8Ot1wGt/UgceJIo/f5P9+WeI2q+L2ra+gauoaucbFf49ChQ1XqPuTC5hY9vQUFBdZeuOKKEuGCgoJSez4dWb58OUlJSXTq1MkuoSs+lBqgadOmTJ48meXLlxMbG2td8bl169bs27eP+fPn061bN8LCwkhJSWH58uV4eHhgNpspKCgoM57evXs7VQ8gLCys1OMnTxp/8DqaV/vRRx/x9NNP25T9+OOPDB061Kbs2WefLfHzfjYGDRpEaGgof/zxB+PHj7euav3555/j4eHBvffeaxenxWKx6VkdMWIEL7zwAt988w2DBtmv1Nq2bVv69+9vU/b999+Tn5/P7bffbjdEe/DgwUyYMIE//viDvn37Ath8L6SmppKZmYnFYqFt27asW7eO1NTUMj//RYrXCwwMtHnv4+NjN+c4MjKy1PcRERE27y/ma1xQvDzg1u741Aql+F3YfG7aRhu9lmd6YgJEhMKYa23LI6oD9p8rgJAQ24ddkZGRxnzZ+P3w7jDbazaug6l5PZu9er3q1YBDp4cwW6+RuMc4HlXT+Ho89gHUDYeerYjMNRlJ8JFUAEILPI33UTXAw8Px17xoXnDtENv78PTEFB6EX9ap6Qd5+fDNfLyfvM6mdzsyqh70b28sFJaXf8F87+oauoauoWtc7Nc4cOBAlboPubC5RdLr5eVl1xNZpKiHt7yrBK9atYrNmzfTrFkz2rVr59Q5fn5+tGjRgjVr1pCUlES9evUA4z9Nnz59WLp0qXUbH5PJRLNmzcjOzmbv3r1ObatU/D/0uSjqGc3MzLQ7dvPNN1uHaE+aNIlZs2bZ1QkKCqJ+/foVFg8YX6Mbb7yRr7/+mgULFtC/f38yMzOZO3curVq1okOHDta6FouFH374gbp16xIcHMy6deusx9q0acPChQtJSkqidu3aNtdo0KCBXaK+adMmAB588EEefPBBh7GdOUR+69atPPLIIyxbtsxu43STyWTzZFHEaQVmDtzQhjI3aAgNhL5t7csiQ+3Lyysp1fhY6GC0Qn6h0RNcJC4GFsYbKyafuZjViu2nj4PR27wz0djyqLgHvzQ+npgI1UtY2K7DqbUQzkiwASPJTT4JNUOM98fTjfgcxl4AZrPjYyIiUiU1b97c1SGIG3GLpDcgIIDU1FQKCwvtenNLG/pcktWrV7Nu3TqaNGlC9+7dyxVL0UrOxZPw2NhYoqOjSUlJIT8/n+rVq+Pv78+0adMwmUyEhISU2XZ2drbTc3p9fHxKTfQbN26Mt7c3Bw4c4OTJkzZDnGNiYoiJMf5gXb169Vm1f7YefPBBvv76a7777jv69+/PpEmTyM7O5sYbb7T5Gs6fP5/Dhw8Dxr7Ijnz33Xd2PdalzZt+/vnnadasmcNjRcPbMzIyuOyyy8jKymLIkCF06NCB0NBQPDw8eOedd1izZo3TXyMRKw8TdGnCiejQspPe86nJqUVDfl4KV52xovzaXbDtsO3qzYO7wdsz4Mu5p/fpzc2H7xYaK0AX9dCOG2o/1zh+P4z9CZ68zpg7HHBqr/KsXNh/zJg3XDR3uGcrY8ukH5cYc4L9Ts3Vn7DQSGKvOJXo1woxEudpK+DlW8Dn1IPEjGyYuRqa1TVWpxYRkQvCgQMHytx9RcRZbpH01qxZk4MHD3L06FGboQgFBQUcP368XMMTVq9ezdq1a2nSpAk9evQo9yT6tLQ0wPHCVx4eHjZ7+GZlZZGcnEydOnWcSiCnTZvm1NxfgB49etC0acmLtvj5+dGjRw/mzZvH999/zyOPPOJUu+dbu3btaNSoEQsWLCAtLY1vvvmGatWqMWzYMJt6H330EV5eXowbN87h0M3//ve//Pzzz4wZM6bMBx5FiW716tWtq0mXZM6cORw/fpwxY8bw1ltv2Rx79dVXnbhDEQfMFnh8UIXuhW1n3KmVzzefGi42cbGxAjLAczcZHzs0NJLI7xcaPbj94oxVmj/6P2OP3lFnLCTXpQncdAn890c4ehIaRRjn7T0K35wxYuIyB0/qi3p1OzWC6854aLVyB/R6Hl64GV68xSjz9Ybxd8JdH8Hlz8EdPYze4w/+MLZUuuHU+Z6eMPpaeG4SdH0a7uxpJMXfzIeDx+F//zmXz56IiFQyZ//mFXGGWyS9DRs2ZN26dWzatMkmwd26dSsFBQV2T4lOnjyJ2Wy2m6e7Zs0a1q5dS+PGjUtNeIvm4BZfHTgjI4MtW7bg6+trN6y2OIvFwrJly7BYLE4Pn67IOb0Ab7/9Nl26dOHFF1+kWbNmDrctckWv5V133cXYsWP58MMPWblyJX369LFZETstLY05c+bQqlUrxowZ43Be8YIFC5g+fTqrVq2ia9fStyoZNmwYr7zyCh999BFDhw61+9qdOHECDw8PQkJCSkygf//9dzZv3nwWdyuCMTT5ui40MJd/wT2njf3J9v2380//uyjpBZjxtNGD+/NSmLPO2Caoewt45VZoWte2jR8eNdqduAhOZEKbBjDrGbi8ZcXGfmcvo+f2jakw5gcjab7/CnjtdiPZLfLsYIipZSTEL/1q9Dy3aQBTxsCN3So2JhEROa/K2tVEpDzcIukNCwujZcuWbN68mblz5xIVFcWJEyeIj48nMjLSLumdNWsWGRkZjBgxwlq2efNm1qxZQ2BgIHXr1rVb/djf3986Rzc/P5+ffvqJ6Ohoqlevjq+vL2lpaWzdupX8/Hz69Olj03Obn5/PtGnTiI6OJjg4mLy8PHbu3ElycjKdOnVyeh+yipzTC8aiTpMmTeKOO+6gf//+XHLJJXTp0oXQ0FCOHTvG+vXr+fvvv/H39y/3tXfu3OlwfqynpydvvPFGqeeOGDGCF198kddffx2LxcJtt91mc/ybb74hLy+PK6+8ssSFtG6//XamTp3K559/XmbSW79+fd5++21GjRpFy5Ytufrqq4mNjeX48eNs2bKFJUuW8H//93/06dOHXr16ERoayieffEJSUhINGzZk48aN/PHHHzRo0IB9+/aV8ZkRKcbDBP8ZCF6ebFm/ibi4uPK3seiVsutYpjrXlr8vjL3ZeJXFzwfG32W8yqNnK8fxlFQOxmrMt1xWdttDLzdeIiJyQdPQZqlIbpH0AnTr1o2goCASEhLYv38/fn5+tGrVio4dOzo1RLlooaKMjAwWLVpkdzwyMtKa9Hp5eRETE8PRo0fZu3cv+fn5+Pn5UbduXdq2bWu3X7CHhwfh4eHs2rWLrKwsvLy8qFmzJv3796/wxaDK64YbbqBTp0688sorzJ8/n08++YTc3FwCAgJo0KABDzzwAI899hixsbHlanfXrl189tlnduVeXl4899xzpZ5bq1YtLr30UpYsWUKtWrUYPHiwzfGvv/4aDw8Pu9WkzzRgwAD8/f2ZM2eOU8NjHn30UZo3b84rr7zCtGnTyMzMJCgoiLp16zJy5EjrvsHVq1fnzz//5KGHHmLKlCkUFhbSsGFDvvzyS6ZOnaqkV8rPy/P0PrMiIiICQHx8/Nk9CBZxwGTRqjsi4m5mrITrSh9RUCV4ecBdveDrhwA4evSo3UMzERGRi9H69euV9EqFqbhNVkVEpHwKzPCf04tDlXfhPBEREXdV0dP65OKmpFdExBU8PeDyFtD69CZFhw4dcmFAIiIiVYe3t7erQxA3oqRXRMQVCs3w2CBXRyEiIlIlHThwwNUhiBtR0isi4gp1w2BQR5ui5s0d7GkrIiIiIudESa+ISGUzmWDU1bZ7zKKn2iIiIkWaNm3q6hDEjSjpFRGpbL5eMLyvXbEz22uJiIhcDBITE10dgrgRJb0iIpWpaJui0EC7Q35+fi4ISEREpOo5efKkq0MQN6KkV0SkMhWY4dGBDg81atSokoMRERGpmnx9fV0dgrgRJb0iIpXF0wN6tYIW9R0ejo+Pr+SAREREqibN6ZWK5OXqAEREKpz3qQWivDzB5NpQbOQXwuPXuDoKERGRKm/jxo3ExcW5OgxxE0p6RcT9XNWOg09dRT3/EFdHYsvbE/q3K/FwREREJQYjIiIicnFQ0isi7sfDg+RbOlPvAntCrKRXRETEUKtWLVeHIG5Ec3pFxC1FR0e7OgQRERE5S/7+/q4OQdyIkl4RcUuZmZmuDkFERETO0r59+1wdgrgRJb0i4paOHTvm6hBEREREpApQ0isiIiIiIlVK48aNXR2CuBGTxWKxuDoIEZGKZrFYMJmq0n5FIiIi4qy9e/dqfQ6pMOrpFRG3lJCQ4OoQRERE5Cylpqa6OgRxI9qySESct+8ojJsCVWGAiK83fDAcvDwdHs7Ly6vkgERERKSieHt7uzoEcSNKekXEeW/PwPLNfEyerh4kYoECM/RpAzd0dVgjODi4kmMSERGRitKyZUtXhyBuxNV/uYrIBcbiaYKCQhe/zODpAe/9XmKcERERlfhZERERkYq0YcMGV4cgbkRJr4hcmArN8M9W2LDH4eHt27dXckAiIiJSUbTWrlQkJb0iUi4mqtCKyF4e8MEfro5CREREKliNGjVcHYK4ESW9IlI+VSjnpcAM/1sMySftDkVFRbkgIBEREakIQUFBrg5B3IiSXhEplyo33KjQDF/9ZVecm5vrgmBERESkIuzZ43j6ksjZUNIrIhc2swU+/APyC2yKk5KSXBSQiIiIiFQlSnpFpFxMpqo0vvmUI6kwbYWroxAREZEK0rBhQ1eHIG5ESa+IlE8VG90MgIcJ3p1pU9S6dWsXBSMiIiLn6sSJE64OQdyIkl4RKRdLVcx6zRZYsR3W7LIW7dixw4UBiYiIyLlISUlxdQjiRpT0ioh78PKAD2dZ3+bk5LgwGBERETkXnp6erg5B3IiSXhEpF4+qOKcXjO2LJv0DSanAedzqIDEFnp4IvZ6HoKFgugEWxZdcPy8fXpsCzR4BvyFQ+24YOA4OJtvWW7MLrnoZgm8z2u33Eqx3sHLla1Og61NQc5jRXuOHYNQ3cCzNufgzso369e4F35uh+SPw2RzHdf9aD5c9A9VugdA7YPBbsPeoc9cRERE5B5qmJBVJSa+IlIu5qm1ZdCazGb6cC0DdunXPzzW2HYY3p8Gh49C6Qel18wtg4Kvw6m9wVRx8OgKevA4C/CAt63S9tbvgsmdhdxK8cDM8fzPsSIQeY2HbIds21+yGuBh49kb45D64thN8txAueQYyy+jdLiyEK1+Gz/6Emy+F9++BpnXhwS+NZPpMs1bDVeMgNx/euAOeuAYWbzGSYGcTbBERkbO0adMmV4cgbsTLlRe3WCxs2rSJhIQEMjIy8PPzIzY2lo4dO+Lt7e1UG+vWrSM5OZnk5GTS09MJDAxk6NChpZ6zfft2EhISSElJwWKxEBQURMOGDWnfvr21zsyZM0lMTCyxjbp16zJw4EDnbvQ82L17N6+88gpLliwhMTGR3NxcAgICiImJoU+fPjzxxBPn749+F4qPjy/Xk78qt6esnF9mC3z0f/DU9WzdupW4uLjyt9FzLETXggmPOD7eoSEc/x7CgmDKMrhpW8ltvTfTSBT/eRU6Ny653tifwN8Hlr8B4ad6qG+/HJo8DM/8CL89ebrumf8u0q0pDB4PM1fDLZeVfJ2pK2DZNvjmIbinj1H2wFVGD+4rU+DevlCrulH+1ESIrQ1LXwOfUz+PB3WE9mPgjanwzt0lX0dEROQcFRYWujoEcSMuTXqXL19OfHw80dHRtGnThtTUVOLj4zl+/DgDBw50amuUVatW4evrS40aNcjLyyuz/qJFi9ixYwcxMTE0bmz8EZqenk56erpNvXbt2tGsWTO783ft2sX+/ftp0KCMHp7zaOLEidx///3k5eXRvXt3brzxRqpXr86xY8dYtWoVH330ET///DOHDx92WYznS7169Rg/frxN2fTp01m6dCnDhg2jZcuWLors4mGiig5vLnLsJPz2LzQ/T8Obg/ydq2c2wwd/wPWdjYS3oBDyCqCar33dvxPgqnanE16AyDDo0dLocc3IhsBSrhtdy/iYmll6TH9vMT4WT4xvucz4nM1YBfddASnpsOUAjLnudMIL0DYGmteFn5cq6RURkfMqLCzM1SGIG3FZ0puSkmJNePv162ctDwoKYtmyZezatYtGjRqV2c4tt9xCcHAwAJMnTyY/P7/Eulu3bmX79u307NmTJk2alNpuvXr1HJavXbsWT09Pp2I7H5YtW8a9995LUFAQCxYsoGvXrnZ19uzZw8svv1yh101PT8f3/9m777AmrzYM4Pcbwt4IKCgIqIhbEfesWrVarbt11DqqtlVbbe3SWr+2bltbV9XWuuqqe9dV956oKAriBByAIgZlJDnfHynRkACJBYLh/l1XLs3JyXueN2+APDnL1hY2Njb5elxTubm5YdSoUTplUVFROHLkCNq3b49u3bqZKbJipIjnvJBJwE+bUWbHl+aN4/IdIP4hUD0AGDwXWLJPk/RWKwvMGAC89sKIhfRMTU9vdg62mudE3AbqV3xeLgSQ9ESTSEff1cwxtpIBzfP40ic9U1PPJtuv/qxE/EyMJulN//f3aE4xXboD3HsElHLP82UgIiJ6Ge7u/BtD+cdsc3pjYjRbi2QfqhoSEgK5XG70diNZCW9ehBAIDw+Hp6enNuHNyMgwafjr3bt38fjxYwQEBMDOzs7o5+WnL774AhkZGZg5c6bBhBcAAgMDsWjRIp2yHTt2oG3btvDx8YGtrS3s7OxQqVIlzJs3T+/5bdu2hSRJuH37Nt544w24urrC1dUVUVFRAICxY8ciNDQUHh4ekMvlcHNzQ6tWrRARob+YT2ZmJj788EN4enrC2toaZcuWxcyZMzF48GBIkqT3nJiYGHTq1Amenp6Qy+Vwd3dHhw4dcOvWLaNfo1u3bkEul6NVq1YGH+/SpQtkMpm27axYjh49iu7du8PNzQ02NjYIDg7GX3/9ZfAYK1euRGhoKBwcHLTnNW7cOKNjfJUV+SHjagGciYF06pp544j+d3rEz1s0C13N/wBYNAxIywDa/gBcuPm8bsXSwPEozZzbLBmZmm2YACAu27YN95M1C1n5DASafgPcTgRWjARCDH9Zp9OOSq1p60WHInXbKekGuDkCR67o1kt6AlyONRwTERFRPsrKFYjyg9l6ehMSEiBJEry9vXXK5XI5SpQogYSEhHxt7/Hjx0hJSUGVKlVw9uxZXLx4Eenp6bC2tkb58uVRv379POcRX72qmbtnaNizIUIIpKenGx2jra1trkO6FQoFTpw4AU9PzzznLWe3cuVK3LhxA2+88QYCAgKQmJiIdevW4cMPP8TTp0/x6aef6j2nWbNm8PDwwNChQ5Gamgo3NzcAwPz581G1alW8++67KFGiBCIiIrBp0yY0adIEEREROnOJu3fvjk2bNqF69eoYNGgQEhISMHr0aJQqVUqvvcjISDRq1AhKpRKdOnVC+fLlER0djbVr16J+/fq4cOECvLy88jzXsmXLokGDBjh48CDu37+PkiVL6ryGO3fuRM2aNVG1alWd57377ruQyWQYNGgQnjx5gtWrV6NXr15Qq9Xo2bOntt53332H7777DsHBwRgyZAicnJywd+9efP/997h27RqWL1+eZ4xUwOQySDO3A28Y/mJIK1Opu6BUVll6JpCYolvu4QTITPieUPHvolJPngHnfgL8PDX3W1QDyg8Fpm4Elo3QlH3UFvhwPjDwV81CV2o1MH4tcDdZ8/izbFM3PJyA3eOAtEzg3HXNXF2FEVs09WoCfL8aGDBbswhWBV9gVzjw6w7ddmQyYEhrzYJdXy8DBrQAUp4BXyzV9DwbiomIiIioiDJb0puamgo7OzuDe3A5Ojri/v37UKlU+bZHV3JyMgDNt0ZqtRq1atWCs7Mzbt++jcjISCQnJ+PNN9/MMenMyMjA9evX4ezsDF9fX6PaVCgUWLlypdEx9uzZM9dtVsLDw6FUKg0OrVYoFNpzzOLt7a0djjxjxgxt0ppl/PjxCAkJwfTp0w0mveXKlcOePXv0yqOiovSO9ddff+Gdd97BtGnT8MsvvwDQDMXetGkTQkNDcfLkSe217NevH5o2bap33AEDBkCpVOLUqVOoWPH5UM6+ffuibdu2GDNmDH777Te95xnywQcf4PDhw5g9ezZ++OEHbfn8+fPx9OlT9OvXT+85VlZWOH/+POztNXMnR44ciRo1amDUqFHapDcmJgbjx49H8+bNsXfvXu1zf/jhB/To0QMrV67EF198gRo1ahgV56vImLn2ZqdUw3335bznwh65otl6KLujV4FVh3XLbsx7PnfWGFlDgxuFPE94AcDfC2gcomkjywdtgDuJwLRNmmHQABBWTpMAT1gLOGUbWWJjDbT69z32ZhjQsjrQaDTg7aq5n5NS7sDmr4F3ZwCt/50C4eIAzBoIvDdLt53v39Ek/lM3ahauAoDWNYGBLYF5O/VjIiIiykeBgYHmDoEsiNmGNyuVSshy6DXJSo6USmW+tZc11zctLQ2tWrVCjRo1EBQUpJ3fe/fuXdy5cyfH51+7dg1KpRIVK1Y0+kO/vb092rVrZ/QtK9nKycOHmuGETk5Oeo9NnDgRfn5+OrcDBw5oH38xSU1JSUFcXBxSUlJQt25dxMXFITExUe+YY8aMMRhH1rFUKhUSEhIQGxuLRo0awd7eHqdPn9bWyxoa/PHHH+t8edG4cWPUrl1b55gJCQk4ceIEmjRpAkdHR8TGxmpvlSpVQqlSpXDw4MFcX58X9ezZE15eXlixYoVO+dKlS+Hk5IRBgwbpPeejjz7SuQYVK1ZE69atER8fj+PHjwMAFixYAKVSiSFDhujEGBsbi86dO0MIgY0bNxod58OHD3VGAygUCp1F1TIyMpCUlKTznOyrime/f+/ePZ0hyPneRhEf3QwAkEkQNQNw90myTnH287pfygFi17eaXtPd45Cy7lOoq/lrkrvd4/Bs8xd4uukLzeOl3Ay+Vtlp2/DVLMDxzNVO73qoPJ2BRwoAL1yPCb2B+wuRufd/SD44Djg1TdPjCyDB3dpwG/+6F+QG4eMOLD+obSPHa960CjKuzETyvrGaVaXjfn8+Xzj4+Rd6d5MSgQVDgfgFwMHxSDg8DmLHWE3PuEyGhx62r957l22wDbbBNtjGK9PGi2VF4Tzo1Wa2nl65XI60NMPD8bKWKJfL8y+8rKTL0dFRb5Gq4OBgREVF4e7du/D39zf4/KtXr0KSJJ0eyLzI5fIcF8R6GVmr2CkUCr3H3nvvPe386EWLFmH37t06j9+6dQvDhg3DwYMHkZKSovf8hIQEeHp66pRlT0yzrF69Gj/88AOuXr2qt3DYi79Qbt68CQAGt40JCgrSSZDPnDkDIQS2b98OPz8/g+1mHwqfG5lMhh49emDOnDnYt28fXnvtNZw/fx4XL15E9+7dDX7BUKtWLb2ySpUqYcuWLYiMjET9+vVx5YpmjuM777yTY9v37t0zOs7sKxNm/0LDxsYGJUqU0Cnz8fHJ9X72oeP53YaAKPJrWUEtcL93vTxfq5IhQcALsxVcAGDmTsDHHWhVA9nfJTaA3muVnbaNamUBaznsk54CL3xR5uHhAdx7DHhp1iPQuR7uTrB+rTrcsu7vuQCUKQGvJjVzPY9SpUpphjr/O1Q7z2tubweb5i+83/fs1/zbqrp+GyXdgJJu8AI0c473RwD1KsDDX3fEy6vw3mUbbINtsA228eq0ER4erv0cXRTOg15tZkt6HR0dkZycbHAIc25Dn19W1pvdULLj4OAAADnOv3348CESEhLg7+8PR0dHo9tUq9U5JvaG2NnZ5dj7DWiSR7lcjmvX9BfoqVixojYh37dvn14czZs3R2xsLN5++23UrVsXHh4esLKywh9//IF//vnH4F5ohhYJ27lzJ3r27IlSpUph5MiRKF++PBwdHSFJEgYPHgz1vz1Tpsr6dq5Vq1YYMGCAwTqmvPYA8Omnn2Lu3LmYM2cOXnvtNcyYMQNCCHzyyScvFeOLcU6ZMiXH5Lxy5covfXzKJ96uuN84CGb9c+VsD7QL1Ww5dCX2+SJTkbHA0SuaObO5+eswcOoa8ON7z+cSp6ZpEujs2x6tO6bpOQ4r97wsUwnE3ANcHTTbH+Uk4bFm7m71sjpJr0E/bgLuPgJmvZ97PSIiov/olZhORa8MsyW9Xl5eiI2NxYMHD3S+SVEqlUhKSsr3b1eykrzUVP19LLN6TnMaXpzVu2fsAlZZUlNT83VOr5OTE+rVq4cjR45gxYoVRi9mdejQIdy8eRPvv/8+fv/9d53HjJ0jm2XhwoVQq9X4+++/Ub368w/IycnJeq9t1l7G4eHhenNcr1+/rnO/evXqkCQJmZmZOotG/RdBQUFo0KABduzYgeTkZGzcuBHBwcFo2LChwfrnzp1Ds2bNdMoiIzWr2laqVAkAtPOpS5YsmW9xvmpkRf2PkEwChreDo5txK7u/lPFrNP9e+ndKxJ8HgMP/roD8Tffn9Sb2Bv65ALQYB3zcXlM2c5tmIarRXZ/XO3gJ+H4N0LqGZq/e41HAor2avXs/efN5vei7QKv/AW83AkJKa5Lh09eAZQc1841frBv3EKj0MfDea8Di4c/Lm30DNKgIlC8F3EsGftutWQRr6xjdhbqWHdAk002raObv7jkPrD4KvN8K6Nrgv76CREREubLk9VGo8Jkt6S1XrhzOnTuHixcv6iS4V65cMbhYU0pKCtRqtd4CSsaSy+UIDAzEtWvXcOPGDZ3J8ZcvXwYAgz13KpUK0dHRsLe3z3Hoc06y5vSaUj8vU6dOxWuvvYaPP/4YQUFBBrctyr6lTNYw8ezlR44cweHD2RbryUNW73v2Y40cOVKv7J133sGsWbMwc+ZM9OnTR/vcw4cP48yZMzp1S5cujTp16uDQoUPYvn273uumVqsRGxtr8jUYNGgQ+vXrh+7du+PRo0cYOXJkjnV//fVXDBkyRHsdrl69il27dsHX11f7Og8aNAgzZszA+PHj0aVLF70vKRISEuDo6KgdPWCJ1EKYbzEAY8hkwODXUdatAK/B2GxfZi385/n/X0x6K/sBB34AvvxTsxqzTNKs3jytL1D6hWFVpUto9s+dtkmz2nOgNzC+F/BpB0D+woiXMiWArvWBvReBJfs1vbllvYBhbwBjumkS5rzULgesOapJil3sgddrAD/0BIKyrage7As8VAA/rNGs1FzRF5g3BBicRw81ERFRPrh06RKqVMlj/3kiI5kt6fXw8ECVKlVw6dIl7Nq1C/7+/nj06BEiIiLg4+Ojl/Ru3boVCoUCgwcP1imPiorS9tSmpaVBpVLh7NmzADQ9o1l78gLQLtq0d+9eVKlSBc7Ozrhz5w5u376NChUqGNxG5+bNm0hPT0eNGjVyHXpsSH7P6QWAhg0bYsGCBRgyZAgaN26MJk2aoG7dunB1dcWDBw9w9uxZHD16FPb29trtferWrQs/Pz8sWbIET58+RUhICK5evYr169ejbNmyJu2D9s4772DVqlVo164devbsCRsbG+zbtw8xMTF6CWDDhg3RoUMHbNmyBaGhoWjXrh0SEhKwatUqBAUFISYmRmfoypIlS9C4cWN07NgRbdq0Qa1ataBSqXD9+nXs27cPnTp1Mrlnuk+fPhg1ahT27NkDW1tbDBs2LMe6KpUKNWrUQKdOnfDkyRP89ddfyMzMxNSpU7V1KlasiO+++w7ffPMNgoKC0LFjRwQEBODBgwe4dOkSjhw5gjNnzuhth0SFRC4DejcFvN1wOTzc4HzyPO3/Ie86Yr3xxwstB+z+X+51ypUCdhpYRTo7Txdg/ofGtRvgbTjO6f01t7zUrQAcGG9cW0RERPks+7oxRP+F2ZJeAGjQoAGcnZ0RGRmJ27dvw87ODlWrVkVYWJjR4/ivXr2qt7pa1gJJPj4+Okmvk5MTOnXqhFOnTiEqKgoZGRlwcXFB/fr1tYtAZZc1tNmUBawK2rvvvouGDRti/PjxOHDgAE6cOIH09HQ4OjoiMDAQH3/8MT777DPtfrnW1tb4+++/8eGHH2Lr1q1Yt24d/P398fPPP+Ps2bMmJb0dO3bEvHnzMGXKFMyaNQs2NjYICwvDgQMHDG5DtG7dOgwbNgzr1q3Djz/+CF9fX0ycOBHHjx9HTEyMzsICISEhCA8Px5dffom9e/di165dsLa2hqenJ5o3b47+/Y34oJ6NlZUVunXrhnnz5qFly5Zwd3fPse6ff/6J6dOn47fffkNqaioCAgIwe/ZsvWHko0ePRtWqVTFlyhSsWbMGT58+hbOzM/z8/DBs2DAEBASYHOerRCrKy1gp1cDHxo+uICIioqLpZUd3EhkiiexjUokKQb169RAeHg6FQgFra+u8n/AfjBgxAjNmzMDWrVvRvn17vccHDx6M33//HRcvXmQPbV6G/w4xfxekTP2Fz8xOJgH1KgBHJwMAHjx4YNKK30RERFR0pKammryIKVFOivTUPHr1vbiFUZaDBw/i9OnTqFmzZoEnvBkZGVi5ciX8/f0NJrxkuiL7PZlaACM7au9y1UciIqJXV3R0tLlDIAti1uHNZPl+/PFH/PXXX2jZsiW8vb0RGRmJjRs3Qi6XY9KkSQXW7oULF7Bnzx5s3rwZDx48wM8//1xgbVERUcoN6FxPezcuLk47r52IiIiIii8mvVSgGjRogK1bt2LZsmVQKBSwt7dHzZo1MX78eLRo0aLA2t22bRtGjx4NZ2dnvP/++xgxYkSBtVXcFMkeVJmk2a5Hnn97exMREZH5ZG19SZQfOKeXiIw3/HeIebsgKYvYnF4bORC3QLO68b/S09Nha2trxqCIiIjoZcXHx8PX19fcYZCF4JxeIjKJQBH7nkwuA/o000l4AeDOnTtmCoiIiIj+qwcPHpg7BLIgTHqJ6NWmVAOf6C9SlrV/NxEREREVb5zTS0QmkRWlOb1WMqBhRaB6gN5DdnZ2hR8PERER5Yvq1aubOwSyIOzpJSKTqIvSMgAqNTCyg8GHypcvX8jBEBERUX65evWquUMgC8Kkl4heXaU9gI51DD4UERFRyMEQERFRfklPTzd3CGRBmPQSkUkkdRHp6ZVJwIg3AStuU0RERGRpXFxc8q5EZCTO6SUi47WpBfx5wNxRaNjbAANb5fhwqVKlCjEYIiIiyk8+Pj7mDoEsCPfpJSKThIeHo2bNmuYOg4iIiCwYP29QfuLwZiIiIiIiIrJYTHqJyCQBAQHmDoGIiIgsnJ+fn7lDIAvCpJeITJKammruEIiIiMjCZWZmmjsEsiBMeonIJAkJCeYOgYiIiCzcvXv3zB0CWRAmvURERERERGSxuHozEZlECAFJkswdBhEREVkwpVIJuZy7q1L+4DuJqKhRqoDLd8zXvrsT4OeZ48ORkZGoXLlyIQZERERExc21a9cQEhJi7jDIQjDpJSpqvlwKTN9ivvb9PYHrcwErK4MPZ2RkFHJAREREVNykpaWZOwSyIJzTS1TUXL9v3vZvJwLbz+b4sIuLSyEGQ0RERMWRk5OTuUMgC8Kkl4h0WcmAn3PuaS5VqlQhBkNERETFEffppfzEpJeIdKnUwL4I4NJtgw9HRUUVckBERERU3ERGRpo7BLIgTHqJSJ+VDJi53dxREBERERH9Z0x6iUifSg0s2Qs8Uug95O/vb4aAiIiIqDgpXbq0uUMgC8Kkl4gMy1QBf+zRK05PTzdDMERERFScCCHMHQJZECa9RGSYWgC/bNXsG/yC+/fNvLo0ERERWbz4+Hhzh0AWhEkvEeUs7iGw5ZS5oyAiIiIiemlMeokoZwa2L6pWrZqZgiEiIqLionLlyuYOgSwIk14iyplKDRyKBC7c1BZFR0ebLx4iIiIqFm7dumXuEMiCMOklotzJZcCMbdq7aWlpZgyGiIiIioPU1FRzh0AWRG7uAIioiFOqgWUHgCnvAp4ucHZ2Lph27j7UJNcnooHT1wBFGrDve6B5Vf26zccCBy7pl7epCez4Vr/8bAzwv7+Aw1eAtAwgqCQwuDXwcXvN4zcfAIEf5Bzb+62A3z/KPX6pi+HySX2Ar3J4DABe/x+w5wIw9A1g9qDc2yAiIiomHBwczB0CWRAmvRbC09MTZcqUQXh4+Es9v1+/fliyZAlu3LiBgICAXOueOHEC9evXx9ChQzF79uyXau9lmBKjObRs2RJ79+61zCX2lSrg993A110Lbt+8q/HAlA1ABR+gWlng2NXc65cpoUkoX+TroV9vVzjQYSJQKxAY2x1wsgNi7gGxSc/reLkAf36i/9wd54DlB4HWNY07h9drAH2b65bVCsy5/vrjwLEo445NRERUjAQG5vL3k8hEJie9QghcvHgRkZGRUCgUsLOzQ1BQEMLCwmBtbZ3n85OTk3Ht2jXExsYiJSUFKpUKLi4uCAwMRLVq1QweIzk5GSdOnMDdu3ehVqvh6emJ2rVrG/zwrVAocPbsWcTHxyM1NRW2trbw9PREjRo14OPjY+rp5ousZE0mk+Hs2bOoUaOGzuNLly7Fe++9h6+++gqTJk0yS4xEuVILYOY2YNRbuHLlCmrWrGn6MZqPBQK8gcXDDT9euxyQtATwcAbWHgW655H0ujoAfZrlXiflKdB3JtC+NrD2c0CWw4wORzvDx1q8D3BxADqE5d5OlmDfvGPKkpYBfLYY+LIT8O0q455DRERUTFy6dOnlPm8QGWDynN5jx47h+PHjcHd3R8OGDREUFISIiAjs3LnTqB6uq1ev4uLFi3BxcUFoaCjq1asHV1dXnD59Gps2bYJSqdSpn5KSgk2bNuHBgweoUaMG6tWrh8zMTGzfvh2xsbE6dVNTU7F+/Xpcv34dgYGBaNSoESpXroykpCRs3boVt2/fNvV085VarcbHH39cIMe+desWjh8/XiDHJgIA3EsGNp4suOM722sSXlMoVYDiWc6PrzgE3E8GJvTWJLypaYBabdyx7z4E9kUAXeoBdjbGx/QsXZPQ5mXqRk0so94y/thEREREZDKTenofPnyIiIgIBAQEoHXr1tpyZ2dnHD16FDExMShfvnyuxwgKCkKtWrVgY/P8Q2TlypVx6tQpnDt3DleuXEHVqs/n8J08eRIZGRno3LkzPD09AQDBwcFYs2YNjhw5gh49ekCSJABAVFQU0tLS0Lp1a53hr+XKlcNff/2FyMhI+Pv7m3LK+apcuXI4ePAg9uzZg1atWuXrsR0dHfP1eEXJ48ePLfr8XhkyCfh5M8psHmXuSDSi7gKOPYEMJVDSDRjUCvi2B2D9wq+1Pec1PbVxSUCnyUBUvKZX991mwM/9c09mVx3RJKW9mxof0+J9wK87ACGASmWAb7oBvQw8/3YCMHk9sHAYYG9r/PGJiIiKCXON0CTLZFJPb0xMDAD9fTpDQkIgl8uN2srEy8tLJ+HNUq5cOQDAo0ePtGWZmZm4desWfHx8tAkvAFhbWyMkJASPHz9GQkKCTn1Af+K7g4MDJEkyavh1Qfrmm29gY2ODkSNHGlVfCIGffvoJlSpVgp2dHWxtbVGxYkX89ttvenU9PT31hoCo1Wp8/fXX8PX1hbW1NXx8fDBu3DiMGzcOkiRh+/btesd58uQJ3n33XXh4eMDa2hqBgYFYsWJFjjFOnToVZcuWhbW1NTw9PfHRRx/p9dYDwD///IMGDRrAyckJ1tbW8PPzw6hRo6BSqXTqtWzZEpIk4fr162jbti1cXV3h7u6u06tvbIzPnj3DsGHDUKZMGVhbW8PJyQmNGzc22CNuSt3Hjx+jZ8+ecHd3h42NDYKDg7F69eocXyOLoRbAsShI526YOxKgXClgTFdg5afA0o+BehWA8WuBPr/o1ou+q+kNfmuyZpGrdV8AA1oA83YC/fOYj778IODjDrQwcl/ihhWBCb2AjV8Cc4do9jju/Qswd4d+3c8Wa+b6vtPYuGMTEREVM1ZWVuYOgSyIST29CQkJkCQJ3t7eugeRy1GiRAmdBNRUCoUCAGBvb68te/jwIVQqFUqWLKlXPyuGhIQE7f+zFnI6cuSIdth0amoqzp49C7lcjurVq+cZh1qtRkaGEUMT/2VnZ2d03QoVKqB79+5Yvnw5li1bhj59+uRa/91338Xy5ctRp04dfPKJZpGdbdu2YciQIbh37x6+/dbAKrUv+OijjzB//nyUK1cOI0aMgEKhwJw5c+Dm5pbjc7p16wZra2sMGjQIz549w9KlS9G3b1+EhYUhODhYp+727dtx//59vP322/D29sbff/+NuXPnIjY2Fps3b9bW27JlC7p16wa5XK6tu2PHDvz000+IiIjAjh36ScFrr70Gb29vDBs2DCkpKXBycjIpRrVajddffx1HjhxBaGgo+vbti7i4OKxZswbNmzfHP//8g0aNGr1U3datW+PkyZNo0KABWrRogaioKPTt21fv58IiyWWQZm4DXq+Te71MJfD4qX5ZeiaQmKJb7uGU81zbnPwxVPf+u82BwXM1i22NvArUr6gpV6QBT9OBD9oAM9/XlHWpr+kdnr8L+P4doIKv/vGj4oEzMcDIDsbHdiTbfPwBLYDanwOjlwP9Xnveo7vvIrDuOHBistGnS0REVNzExsbqdHoR/RcmfdJMTU2FnZ2dwW9eHB0dkZaWptdzZwy1Wo1z585BkiSd4dFZ+3MZGtqaVfbiHl6+vr5o1KgRUlJSsHXrVixfvhwbN27E48eP0alTJ6N+cO7du4elS5cafTPVlClT4OTkhG+++SbX12rLli1Yvnw5Bg0ahJMnT2LKlCmYMmUKLly4gDp16mDy5MlISUnJ8fkxMTFYuHAhAgICcPHiRUybNg1z587FoUOHEBcXl+PzSpQogYsXL2LKlCmYOXMmFi9eDJVKhWnTpunVvXnzJnbu3ImFCxdi8uTJOHPmDOrWrYstW7Zg7969ADTX9rPPPoNSqcTevXu1dU+fPo2GDRti586dOglylqwh7xMmTMCsWbN0rp0xMS5atAhHjhxBy5Ytcfr0aUycOBFLlizB+vXrkZmZiQ8++OCl6i5cuBAnT55E+/btcfToUYwfPx6rV6/GlClTcOfOnRxf15w8fPgQ6enp2vsKhQKZBnrKiwwBWCU/xb1793Tm8Gc/j2d7zgFe/XRvR68Cqw7rl99OBADcvXtXv7lc2lAoFHjy5In2fsbwNzT/2XNBW5Zp/e+vuJ6Nddvo1QQAkPz3ScNtLD8IAHjaOUy3jYwMJCW9sOqzgbi1922sgWFvAMmpEKc1o2SgVEE59DeoejUG6lTQnseLTGrjX3ldD73Xim2wDbbBNtgG22AbJrVBrzaTenqVSiVkOfR6ZCXCSqXS5OEIx44dw/3791GnTh2dXsisYbKG2nyxvRfZ29vDy8sLpUuXhqurKx4/fowLFy5gx44d6NChg06PoSElSpRAu3btTIrfFKVLl8bAgQMxY8YM/PLLL/jss88M1lu8eDEkScLQoUP1Fuxq27YtTp06hT179qBLF8P7f65duxaZmZl4//33dXrPK1WqhJYtW2Lbtm0Gn/fFF19o50hntWVra6sd2v6iOnXqoHHj58Mz5XI5Ro0ahR49emD58uVo0aIFIiMjER0djWbNmqFevXo6dceMGYP27dtjxYoV6Nixo86xv/vuO4PxGRvjhg0bAACTJ0/WqxsWFoaTJ0/iwYMH8Pb2Nqnuxo0bAQDjx4/XiWnYsGGYPHky7t27l2Pchnh46G6x4+TkBMiL8E5iKjUcR78N11KldIqzn4d9/UrA7nG6z/1sMVDKHfg828JNpdwAGJ678+L1MPhavcCm3L/Pf/g8ibT29wau3tXM+X2xDW9XAICbkAOG2lhxCKhYGg5NdIc229jYoESJEjpl2ePWue+n+bJGevRvTEv3Q37tPrBgqGZvYADas3jyDLj5ADberqa1AaBUHtdD77Uy9TzYBttgG2yDbbCNQm7jxU6vonAe9Goz6dO1XC5HWlqawceyei3lJn5gP3XqFC5duoSQkBDUqlVLrz1A01toTHuRkZE4fPgwunbtqvNm9/Pzw7p163Dy5Em0aNEi13hsbW1RpkwZk87BVN9//z2WL1+OSZMm4aOPPjJY5/r16xBC5LpUe249ttevXwcAg0O6c1tsrEqVKjr37ezs4OTkpDPXOkv24c4AEBam2drlxg3NvM8rV64A0CTb2dWpUweSJOHWrVt6j724mNnLxBgXFwdJkvS2hwI0w8xPnjyJK1euwNvb26S6sbGxkCRJLwYrKyv4+/ubnPS+UiQA5X1wPcgJ+lc+G3cnoFUN/TIfd/3y/HL939fey+V5We1ywO7zQNxDoOILW5zFP9Kvm+VEFHDtrmbo83+O6f6/7WiSbNxO0AzzbjRav+7S/Zrbhi+BTvX0HyciIipG4uLitGv+EP1XJmWojo6OSE5Ohkql0uvNzW3oc05Onz6Nc+fOITg4GE2aNDHYXtaxszM09Dk8PBxubm563+54eHjAzc3NqGEKKpVKZzhEXrIvmmUMFxcXfPLJJxg7dizGjRuXY4InSRKWLl2a42vaoEEDk9vOS06LfRmzHVV+yu11LSoxFkufdsTTZ7lsEVQYUp4CttaaWxYhNAtZAZoFq7L0aKhZJfmPPboLUi3YA8itgOYGfvZWHNL8a2jVZUAzR/h2AuDporkBQMLj54ltlifPgF+2aurUDtKUvdMYqBmof8zOU4B2ocCg1zWLchERERVzLw5HJvqvTEp6vby8EBsbiwcPHuh0+SuVSiQlJZk0DOD06dM4e/YsgoOD0axZM51hjFk8PDxgZWWF+/fv6z324MEDbUxZUlNT4eJioOcGmoTIUI9xdvfv38fWrVuNPQ0MHjzY6Lov+vLLL7FgwQL8+uuvmDRpkt7j/v7+CA8PR/Xq1Y1agCu7wEDNB+sLFy6gQ4cOOo9du3btpWLOLioqSq/s9OnTOu2HhIQA0PTCG6orhEDZsmXzJZ4XlS5dGuHh4Th//ry29zlL1irjWbGZUrdMmTI4f/683obpKpXK7PtAFzgnzVY/jvGmz1022vg1mn8v/dvGnweAw/++d77prvn37HWg53SgZxOgfCngWQaw4QRw5Aow+HUg9IVvhWsFAQNaAgv/0azi3KwKsP8SsOYo8HUXwFf3CzKoVMBfR4D6wZoVog05GQ289i0wrgfwv397g+f8rdnDuEMY4O8F3H2kafN2IvDnx5r5vQAQUkZzMySwJHt4iYiI/mXKYrFEeTEp6S1XrhzOnTuHixcv6iS4V65cgVKp1Bs2m5KSArVarbda8JkzZ3D27FlUqFAhx4QX0PTo+fv74+bNm0hKStKOvc/MzMSVK1fg6uqqk/S6u7vj4cOHuH//vs6Kz/fv38fjx4/h5+eX5zkW9JzeLNbW1vjmm28waNAgzJw5U+/x/v37Y/PmzRg2bBj27dun19t748YNbWJpSLdu3TB27FgsWLAAn376qXZeb2RkJP755598OYdTp07h8OHD2nm9SqUSP/74IwCgV69eADTDmitUqIDDhw/j1KlTqFNHs+qvSqXSJvtZdfNT586dsW3bNowePRo7d+7Uvsd2796N06dPo2rVqtrVlk2p27FjR2zbtg3ffPONzpcjs2fPtuyhzVYyYHBrwNGuQL6k0Bq7Uvf+whfeq1lJb1kvoEllTaJ7L1mzf3ClMsC8IZoYs5s3BPD3BBbtBTac1Dz/5/7AiA76dfdcAO4na7ZDMkWjEM1CXQv2AEkKwNEWqFtBsw+vsVseERERkVaFChz5RPnHpKTXw8MDVapUwaVLl7Br1y74+/vj0aNHiIiIgI+Pj17Su3XrVigUCp3e0EuXLuHMmTNwcnJC6dKl9Xod7e3tdebU1q1bF/Hx8di+fTuqVasGa2trXLlyBampqWjbtq1Owly7dm3s3r0b27dvR6VKlbQLWV2+fBkymQy1a9fO8xwLY05vlgEDBmDGjBmIiIjQe6xTp07o168fFi9ejKCgILRr1w6+vr6Ii4vD+fPncerUKYP74WYpX748+vfvj99//x3VqlVDly5doFAosHr1apQuXRoxMTE5ftlgrICAALRp0wbvvPMOSpYsiW3btuHChQto164dWrZsCUCzCNlPP/2Ebt26oXnz5ujZs6d2y6Jz586hTZs2eotY5Yf+/ftj0aJF2L17N+rWrYvWrVtrtyGytrbGvHnzXqruwIED8fvvv2Pbtm1o2LAhWrZsiaioKGzatAl+fn4vtYLzK0EtgGGaL4MuX76c61zzHO3/Ie86Yn3edQJLAqtHGd+utRwY97bmlpc2tfKOoXlV/Tqv19TcXpYx501ERFSMXLx48eU+bxAZYPIysQ0aNICzszMiIyNx+/Zt2NnZoWrVqggLCzMqicray1ehUGD//v16j/v4+Ogkna6urujYsSNOnjyJ8PBwqFQqeHp64o033tBLTgMCAtCuXTtcuHABV69eRUZGhjaJDQ0NLXJ7fclkMkyYMAGdOnUyOB910aJFaNCgAebOnYs///wT6enpcHFxQVBQEMaNG2fgiLrmzZsHd3d3LF26FD///DM8PT0xdOhQKBQKTJ8+3eBWUKZo164d/P39MXv2bNy9excuLi4YMmQIZs2apVOvQ4cO2Lp1K7799lusWrUK6enpKFmyJD777DNMmTLlP8WQE5lMht27d+Pzzz/Hhg0bMHXqVNja2qJmzZr46aefUL9+/Zeqa2VlhV27duGDDz7Ajh07cPr0aQQEBGDp0qWYP3++ZSa9chnQrjYQUAz2ISYiIiIiiyMJrv5T7HTs2BFbtmxBXFwcfH19zR0OZdd5smZ+aFGy9zvgNc0w3aztm4iIiIgKyt27d7ltEOUbw5vukkVQKBR6ZZcuXcLu3bsREBDAhJfyJv07X/aFVY7/67B4IiIiorzY2tqaOwSyICYPb6ZXx/LlyzFx4kS0adMGZcqUQUxMDDZs2ID09HRMmDDB3OHRq0AI4NMOmuT3X3FxcToLyBERERHlt9u3b+ttQ0r0spj0WrCaNWvC19cXa9asgUKhgLW1NYKDg/HNN9+gW7du5g6PXgUuDjnvV0tERERE9Apg0mvB6tWrh2PHjpk7DHpVWcmAj9oCDrrDiypVqmSmgIiIiKi4CA4ONncIZEE4p5eIDBMC+LCNXrFFrlBNRERERcq9e/fMHQJZEPb0EpE+uQzoWBfw15+7a2iBNCIiIqL8lJKSYu4QyIKwp5eI9CnVwMgOBh+ys7Mr5GCIiIiouLGxsTF3CGRB2NNLRLpkElDFD2gUYvDh8uXLF3JAREREVNxwDRHKT+zpJSJdagF82lFnm6IXRUREFHJAREREVNycP3/e3CGQBWHSS1TUuDqat30PJ+CdxuaNgYiIiIgon3B4M1FRM2MA0LqG+dov6wXY5TyPplSpUoUYDBERERVHXl76i2kSvSxJCCHMHQQREREREVGW5ORkuLm5mTsMshAc3kxEREREREXKzZs3zR0CWRAmvURERERERGSxOLyZiIiIiIiKFIVCAScnJ3OHQRaCPb1ERERERFSkJCYmmjsEsiBMeomIiIiIqEhJTk42dwhkQZj0EhERERFRkSKXc2dVyj+c00v0ipl6UoUtMQXzY2slAXNaWaGKp1QgxyciIiIiKmxMeoleIU8yBHzmqpCaWTDHlwB8WVfCpKZWBdMAERERkRHOnz+PGjVqmDsMshAc3kz0Cll6SeBpASW8ACDnbwQiIiIqAtgvR/mJH3GJXhFqIfDzGbW5wyAiIiIqcCVKlDB3CGRBOEOc6BWx+6ZATLK5oyAiIiIqeC4uLuYOgSwIe3qJXhHTT6thxfWliIiIqBi4ceOGuUMgC8KeXqJXQNRDgV23zB0FEREREdGrhz29RK+AWefYy0tERETFR1BQkLlDIAvCpJeoiHucLvDHRQEVFzEkIiKiYiI5OdncIZAFYdJLVMQtjhBIU5o7CiIiIqLC8/DhQ3OHQBaESS9REZa1TRE7eYmIiKg4kcmYplD+4buJqAjbfl3gVoq5oyAiIiIqXNWrVzd3CGRBuHozFZq7CoEZZ9U4cRc4fU9AkQns6yFDc3/d716eZgosihDYdE3gYqKAIgMo7w4Mri7D4OoSrGTPV3SKVwh8cUCNU/cE4hWAlQwIdgeG1pShbxUJkvS87v+OqPDdMf0+U1srIG1k7j8KpsQEANceCXx1UI1/bgukq4DQksAPjWR4zd+075l+/nebIs7nJSIiouLk4sWLqFatmrnDIAvBpJcKzdWHAlNOClRwB6p5AcfiDde7ngwM/0eNlmUlfFpbBhdbYOdNgY/2qHE8XsKSdlbauonPgNgnAt2CJfi7SMhUAbtvCfTbocbVRxImNrHSO/7cVjI42Ty/b8yqyKbEdCdFoMEKFawk4PM6MjhaA4si1Gi9Vo1/ukto6mfcMsyXEwX23jGqKhEREZFFUalU5g6BLIgkhMjXPiQhBC5evIjIyEgoFArY2dkhKCgIYWFhsLa2zvP5p0+fxtmzZ3MOWJIwaNAgo+rXq1cPNWrUMP0k8kHPnj2xatUqnTI7Ozt4enqiXbt2mDBhAjw9Pc0S29KlS3Hs2DFMmjQJbm5uOo999dVXmDJlSo7P9fX1RVxc3Eu1+yRDIFMFeNhLWHtVje5b1AZ7ehOfCtx/ClTx1E0OB+xQYVGEQPRAK5R3zz1x7LBehX13BB4Pt9L2wmb19CZ8ZAVPB9P2/zElpqF7VPjtgkBEPytU9NCUPc0UCFmogpcDcOZd475r+nC3CgsuCCgLsZfXWgZ8FiZhUlP9LwuIiIiICsutW7dQtmxZc4dBFiLfe3qPHTuGiIgIBAQEoHr16khOTkZERASSkpLQvn17neGmhgQGBsLV1VWvPCkpCRcuXMjxzd+gQQPY2dnplJkrqXzRJ598gjJlykAIgYcPH2Lfvn347bffsG/fPly5csUsk/T//vtvrFq1Cl9++aVe0pulT58+Br8w8PLyeul2nW2MSzQ9HSR4OuiXd64gYVGEQORDkWfSG+AKPL0OZKgA+2wvsQCQki7gbIM8348vE9OhWIFa3tAmvADgYC2hYzkJc8IFoh8JVMgj/kdpAosjCjfhJSIiIioqSpQoYe4QyILka9L78OFDbcLbunVrbbmzszOOHj2KmJgYlC9fPtdjlChRwuCb/O7duwCAkJAQg88LCAiAs7Pzf4i+YPTp0wdhYWHa+0qlEg0aNMDp06dx/vx51KpVy4zR5eyNN95Ar169zB2Gjnupmn897fUTxmeZAqmZgCITOHBHM/+2gS9gb61fN+h3FRSZgKM10Km8hJ+ay1DSMfckND09HZmZmXBycsozpnQV4K77/QsAwOHfgQ5n7ued9C68qJkLTERERFQcXbt2DTVr1jR3GGQh8rWbMSYmBgD0Jp2HhIRALpcjOjr6pY6bmZmJmJgYODo6okyZMjnWy8jIgFqtfqk2CotcLtcm9TY2zyeWJiYmol+/fvD19YWtrS0cHR1RtmxZDBw4UFsnPj4ekiShc+fOWLhwIYKDg2Fra4sSJUrgq6++AgBcv34drVu3houLC2xtbdGoUSPExsZqj9G0aVPtsOvAwEBIkmaxp88++8yk88jIyECDBg0gk8mwc+dOncdmzpwJmUyGt956S6ddSZJw5coVtGjRAn3e7QMAGDR4MI4ePap3fIVCgQ8//BClS5eGtbU1nFw9MGLtdfjapaNOqef1lEolvvvuO5TpORVev6oQ+LsK/XaoYXsvHHMaJgMAnjx5Ak9PT/wx+ycMqyVhfmsZ1naU4f1qElZGqlDq+0j8PPcP7TG/+uorSJKEbdu2YcCAAShZsiQcHBywbNkyAJoE+IsvvkBg+YoY8mcE8PAGPmgfhq1btwLQ9PBeSNAM537R4TjN/bgnub+2KrXAL9ymiIiIiIgoX+RrT29CQgIkSYK3t7duI/8megkJCS913OvXryMzMxNVq1bNcTjw2rVrkZmZqW2/Vq1a8Pf3N+r4KpUKmZmZRtWVJAm2trZGx56QkIDY2FgIIZCQkID169dj3759qFGjBipXrgwAUKvV6N69O/bv34927dqhbt26SE9PR1RUFA4fPqx3zHPnzmHPnj3o3r07evTogQ0bNmDKlCmwtbXFkiVLULZsWXzyySe4fPkyNm7ciK5du+LEiRMAgBEjRiAtLQ2nTp3C6NGj4e7uDgBo0qSJThuPHj3SSZazuLi4wMXFBXJra/yxZBmaNm2GXn0HIPx8OLw8PREefh5jxv0AH79A/LFkOdKUArYvTA9t27atZl5z+wHYAM18jZYtW+LYsWPab/MyMjLw+uuv4/jx4wgLC8N7772HNaIFrrkEIOO3Ljhb9xvUrVsXADBu3DhMnDgRFeu3RuuUEsi0ccW5NF/cfaZC3IOHqBnwfIi7d9RfmNXyC+39rsHAk8t7sdDrNRzMTMbIbOc6bNgwqNVq9OzZEw4ODggJCUFGRgZatWqFI0eOwGfYRoiSlfFW4h8If5SEt956CytWrMCHdbpjS4zA21vUmNBYs5DVr+FqnL6nOe4zZe7vmS0xArGK3OsQERERWbKAgABzh0AWJF+T3tTUVNjZ2cHKSn8RHEdHR9y/fx8qlcrg47m5evUqAKBixYp6j9na2iIkJASlSpWCjY0NHj9+jIsXL2LHjh1o1qyZwedkd+3aNRw4cMCoWJycnEwa9tuuXTu9smbNmmHr1q3a+aSPHj3CyZMn0ahRI2zbti3PY96+fRsHDx5E48aNAQBDhw5FSEgIfvjhB/Tt2xeLFy/W1u3QoQO2bt2Kq1evomLFiujSpQvWrFmDU6dOYdCgQTn+Qhk2bBiGDRumV/7uu+9i6dKlOHhH4LXNZYFRNwEA/ssAQAWgKjDmLhQAvBZoyiL7P7/e5cuXx+7du7EuSmDDFjW++vIrjB/wOoYOHYojR44AAObPn4/jx4+jTZs2+Pvvv/HjKYFrB9V4xy0Sqy9tw+DBtxEeHg4A2LlzJ+zt7RFxaBvk8udv58G7VPjwnMDVWrn3l9a1vo6FD0IQ6V5a7zGlUomrV6/CweH5ZN5Jkybh8OHDePPHfdgqGuOHRjJ802AIHn3ZA7Vq1cKIESMQH98Ds1rI8NUhNUL/1IxRLu8GTGgswxcH1TorRxuyNkpwmyIiIiIq1hQKRY5rzxCZKl+HNyuVyhx7YrMSXaUyj26ubJKTk3Hv3j2ULl0aLi4ueo9Xq1YNTZs2RXBwMAICAlCjRg1069YN9vb2OHbsmFE9uH5+fmjXrp1RtxYtWpgU//jx47FixQqsWLECs2bNQpcuXXDkyBG0bt0aGRkZADTDnO3t7REVFYXTp0/necxq1appE14A8PHxQVBQEIQQ+Oabb3TqNmzYEAAQGRlpUtzDhw/Xxv3iLWsYdEgJCYvayrCwjYTGcYuAvwai1MFvgL8GolP6FixqK9PefF6YBvvdd9/pLB7VrHkzVK5cGcePH4dCoene3LJlCwBg8uTJWHJJ4MuDanxQQ8LyAVVQs2ZNnD9/XjtqwNHREenp6Vi1ahVeXIi8W7CEO0+Ag7FGZI7JsXgq9HvvhwwZopPwCiGwbt06WDcYiG2iEfqUf4p+fvGIjY1FamoqGjVqhHv37iE6OhrDQmW4/6EVjvaywuk+VrgywAqu/zYR7P68jYcPHyI9PV17X6FQoG2ZNLMnvFlz6HO6f+/ePZ3X29B5PHnyfBx3RkYGkpKS2AbbYBtsg22wDbbBNoxqIzExsUidB73iRD5avXq1WLp0qcHHdu/eLebPny+USqVJxzx27JiYP3++iI6ONul5p0+fFvPnzxd37twx6Xn55Z133hEAxKlTp/Qe69+/vwAgfvrpJ23ZtGnThJ2dnQAgSpYsKTp06CAWL14sVCqVtk5cXJwAIDp06KB3zCZNmggAeq/vvHnzBAAxd+5cvdhu3Lihd5wvv/xSABDLly83+lyfPn0qgoODBQBRv359oVarc4wvOTlZCCHEmisqgWmZYt8tlWjfvr0AICIiIoQQQlStWlXIZDKxNjJDWP2YKbpuVArVv8fs3r27ACBOnDghhBBi7969wsvLSwAQTk5OomnTpmLSpElixXmFwLRM8VekSqSkpIgSJUqI0NBQvbjmzp0nMC5OVJr+/LXIeg02b96sd57ujfsKTH4m8O4qAUkS0CwGrXM7ePCgwdep+yalsP85UySn6b8+L8pUqYXvr5kC0wr/Zv1TpvjqgGk/o0RERET57dy5c+YOgSxIvg5vdnR0RHJyssEhzLkNfc6JWq1GdHQ0bG1tERgYaFIsWSs5p6Wl5VlXqVRqe13zIkkS7O3tTYoluzfeeAOLFi3Crl278OmnnwIARo0ahW7dumHlypU4dOgQjh07hi1btuCXX37BiRMndBa9yu01NHXoeH44deoU7ty5A0AzR1ehUPznlbRFYGP0+RtoWkbC8vYyyHLYWqhqveaIjo7G6tWrsWfPHpw6dQpff/01rG9Ug1ShNUJLPn9epq2b3vP3pQcDTt6oYnUcQMDzB7wq4qmN7pZXh+KAR+3mwOrOMSxsC1i/sdxwTFWr6pUdjRNYHy3wYU0Jrra5r9wsl0n4pLYMXx9Uo2gvy0ZERERUMLhyM+WnfE16vby8EBsbiwcPHsDHx0dbrlQqkZSUpFNmjFu3buHZs2eoWrWqycnc48ePAcCoBDUmJqbA5vQakpVgvzjMAtBM2P/666/x9ddfIzMzE++88w7Wr1+PNWvWoHfv3v+pzRcZuzetMZKTk9G/f3+o1WqMHDkSv/zyC3r16qUdopzdqB0PUdbfGZeSNENQll5S45RLK0itamj3YHYPqgER9gsgBLoFS1gTpelEVasFTqmCAZ9q2i9BJhxX40icI9pWGoBW9Qai1jOBXw/dwx2URMWE3Sjv/gYyMjSrYUd02Ij+f6tQzUuCnZVmNeXVGY2A+HA0CY4E8HzIOL6IwKQbj/H2v3dvPRbo8bc1JEkF9bk1yBjwI2Qv7Atd3UtCdS9JW7ftMiU6lpehlCNwKVFg3nmB6l7AxCbGzSh4v5qEb4+A2xYRERFRsXTp0iVUqVLF3GGQhcjXpLdcuXI4d+4cLl68qJPgXrlyBUqlUm+P3pSUFKjV6hwnqWctYJXT3rxqtRpKpVKnFxTQjNu/fPkybG1tUbJkyTzjzprTa4wXF0t6GUIIbNy4EQBQu3ZtAMCzZ8/w7NkzeHh4aOtZW1ujWrVqWL9+PR48ePCf2szO0dERAJCUlPSfVsZTq9UYOHAgrl+/jtmzZ+PDDz/EjRs3sHHjRvz666/46KOP9J6z4I4fcOd5/+WiSwBCNQtmOTlpXtuaLTrjUKYb0tTA0H+y9XU2+B9KOvrCy8sLarUa9VweIibZAwsjBBKeCtjJgUBnB9xZMBCBXgkA3oCNjQ18fX1x59xKHC3xLtZFWyNNCZRxVMH5zG94snE0bH6Zluu53ngs8DgdgNwO6DwTg/YCeKEfdlwDCdW9rHDz5k24lioLHycJs8+p8TANKO0EfBwqYUx9GZxtjPvCwcNewruVJSyOEFByQSsiIiIqZozdWYXIGPma9Hp4eKBKlSq4dOkSdu3aBX9/fzx69AgRERHw8fHRS3q3bt0KhUKBwYMH6x0rNTUVd+7cgZeXl04y+KLMzEysXLkSAQEBcHNzg62tLR4/fowrV64gMzMTLVu2NCpJdXBw0FmwKD8tW7YM+/fvB6BZpfnQoUM4fPgwPD09MWbMGABAdHQ0wsLC0LhxY1SvXh1eXl6IiorCunXr4OTkhLfffjuXFkzXoEEDLFiwAB988AG6desGW1tbNG7cGGFhYdo6f//9N+Lj4/Wea2Njg+HDh0OSJPz666/YsGED2rdvj6FDhwIA/vjjD5w/fx6fffYZWrRoofeFRdnZ5eHl5YXWrVvj9u3bWLt2LYQQ/67crPkS4McPO+BUsyY4fvw46tati1atWuHOnTtYt24dVCoVNh88CEBz/b/qGgYPDw+8WbcuypQpg8TERKxbtw5SfDwGrV2rbXfQoEE4PnAgnh77AUP79MHTp0+xfv16lLK3x5N0A/sDfW6NX/btA9AcANDcXwYxSoaMjAy0bdsW+/btQ+XKldGqVSt4eHjgzqI7qPHRKSQkJCA+Ph4bO/33YeYfh8qw4CK7eomIiKj4cXV1NXcIZEHyNekFNAmVs7MzIiMjcfv2bdjZ2aFq1aoICwszaVhtVFQUhBA59vICml7XwMBAPHjwADdv3kRmZibs7OxQunRp1KhRQ2+/YHOYMWOG9v9WVlbw8PBA165d8fPPP2t7oX19fdGjRw8cP34cJ0+eRHp6Otzd3dGiRQtMmjQJvr6++RpT3759cfLkSaxfvx6jR4+GWq3Gp59+qpP0Llu2DMuWLTP4/I8++giXL1/GmDFj4OPjg5UrV2of8/DwwIIFC9C+fXt06tQJFy5c0OmJ37FjBz788EPMmDEDmZmZqFy5MmbPnq3t9QY0ifWuXbvwxRdfYPPmzZg6dSpsbW0RGhqKn3/+WRunXC5H7969sW3bNqxatQqpqalwdnZGxYoVMXfuXHTo0EF7zH79+uHGjRtYsGABpk2bhhIlSmDAgAHw9/c32COdk6zYJk2ahBUrVuC3336DUqmEu7s7QkJCtMl/fqjmJaFZGeBwHLcvIiIiouLFmNGaRMaShBD8OE0FrmnTpjh06BD4djPNpmtqdNpYeMtZWcuAz8IkTGpa+AuiEREREWUJDw/nYlaUb/J1n14iyl9vBknw+28LYRMRERERFWtMeomKMCuZhBG1Zci/9baJiIiIij5/f39zh0AWhEkvURE3oKoEW442JiIiomIkLS3N3CGQBWHSS4Xi4MGDnM/7ktzsJPSrKkHO7l4iIiIqJvJ7y04q3pj0Er0CPg6Vcb9eIiIiIqKXwKSX6BVQqYSElv6AFXt7iYiIqBioVq2auUMgC8Kkl+gVMbK2jPv1EhERUbEQFRVl7hDIgjDpJXpFvBEkIcDF3FEQERERFbz09HRzh0AWhEkv0StCJkkYGcbti4iIiMjyOTs7mzsEsiBMeoleIf2qSLCTF9zxVeqCOzYRERGRsUqXLm3uEMiCFODHZyLKby62Er6uJ2FjdMFM7pXLgLdD+F0YERERmdeVK1dQs2ZNc4dBFkIS3DyViIiIiIiKkPDwcCa9lG/YpUNEREREREVKmTJlzB0CWRAmvUREREREVKQolUpzh0AWhEkvEREREREVKffu3TN3CGRBmPQSERERERGRxeJCVkREREREVKQolUrI5dxohvIHe3qJiIiIiKhIiYmJMXcIZEH49QkRGe1onMDdVNMHh9TzkVDGWSqAiIiIiMgSPXv2zNwhkAVh0ktERlFkCLRZq4Ii0/TnVvcCzr/HXzdERERkHEdHR3OHQBaEw5uJyChLLwmkvkTCCwCP0vI3FiIiIrJs/v7+5g6BLAiTXiLKk1oI/HxGDa56R0RERIUhMjLS3CGQBWHSS0R52nNL4FqyuaMgIiIiIjIdk14iytP002pYcR0qIiIiKiS+vr7mDoEsCFeWIaJcRT8S2HnT3FEQEREREb0c9vQSUa5mnWUvLxERERWu+Ph4c4dAFoRJLxHlKCVd4I+LAiquYEVERERErygmvUSUo8WXBJ4pzR0FERERFTeVKlUydwhkQZj0EpFBaiHw82luU0RERESF7/bt2+YOgSwIk14iMmjHDYGbKeaOgoiIiIqj1NRUc4dAFoSrNxMZcOaewLijapy+J6DIBIJcgferyzC0pgQr2fNVnQJ+U+KWgcRwSA0J81630t5fHKFG/x1qg23d/dAKpRxzXynq5F2BxRFqnLgncCEBUKoBMUr/x/dOisDCCIFt19WIfgRYyYCqJYBvGsjQqqxp33FlbVPE+bxERERU2Ozt7c0dAlkQJr1E2Zy5J9BwpQoV3IAv68rgYA38fUPgk71qxCRLmNHCSqd+TW/gszDdhDLY3XAS+30jGQJddcvcbPOOaft1NRZcFKjupUnAox4ZrrfpmsCUk2p0Ki/hvSoSlGpg6WU1Xl+jxsI2QP9qxiW+kUkC/3BUEREREZlJuXLlzB0CWRCzJb1CCFy8eBGRkZFQKBSws7NDUFAQwsLCYG1tbdQxzp07h8TERCQmJuLJkydwcnJCr1699OoplUpER0fj9u3bSEpKwrNnz+Dg4ABvb2+EhobC3d1dp35iYiKuXbuGuLg4PHnyBADg6uqK4OBgVKpUCTKZeUaFDxs2DHPmzAEATJs2DaNGjdKrs2TJEvTr1w8A8N5772Hx4sX5HodSqYSXlxdUKhViY2Ph4uJisN7Vq1dRqVIlVK1aFRcuXMj3OLIkJSXBx8cHmZmZ+P777zF27Nj/dLz5FzQ9sgffsYKHvSZ5HVIDaLZKicURAjNa6NYv7SShT2Xj3hNvBEoIK2X6/j8f1pThy7qAvbWEYXtUiHpkuPv1NX8JtwdbwdPheRsf1JBQc6kK3x5VG530zjqrhlwClOzlJSIiIjOIiIhAzZo1zR0GWQizzek9duwYjh8/Dnd3dzRs2BBBQUGIiIjAzp07IYRxn7RPnTqF+Ph4uLi4wNY25+4yhUKBQ4cOIT09HSEhIWjUqBHKlSuH2NhYrFu3Tm8fsPDwcFy9ehWenp6oW7cuwsLCYGtriyNHjmDHjh1Gx1dQrK2tsWLFCoOP/fbbb0Z/afCy5HI5unbtiidPnuCPP/7Isd6MGTMghDD4RUR+mjNnDpRKJby9vbFq1ar/fH1S0gE7K8DNTrfcx1GCfQ5fE2WoBFIzjGv3SYaASm1ajCUdJdhb550sV/GUdBJeALCVS2gXKCH2iabtvCSnCSy6JJjwEhEREZFFMEvS+/DhQ0RERCAgIACtW7dGpUqV0KBBAzRo0ADx8fGIiYkx6jjvvPMO3nvvPbRv3x4ODg451rOzs0OXLl3QsWNHhIaGIiQkBHXr1kXHjh0BAMePH9epX7VqVfTu3RvNmjVD5cqVUbVqVbRr1w7ly5dHbGys2VeTa9y4Mc6fP4+LFy/qlMfFxeHEiRNo2rRpgccwYsQIAMDy5csNPq5Wq7F27Vo4OjpiyJAh+dZuUlKSXtnSpUtRuXJlfPDBB4iMjNS7nqYer7mfhJQMYMguNSKTBG49FpgXrsb6aIGv6+n/yOy9LeDwiwpOM1UI+E2JGWcMz90FgNf+UsFlpgoOM1TouEGF6Bx6bPPbvaeAg1xzy8vCCIF0blNEREREZlSqVClzh0AWxCxJb1ZSW61aNZ3ykJAQyOVyREdHG3WcnIbVZmdnZwdPT0+9cnd3d3h4eODRI90JkqVKlYJcrp8dZM0tyF6/sPXq1QuSJGmHOmeZOXMmAM2wZkO+++47hIaGwsPDA3K5HG5ubmjRogXOnTunV3fZsmWoVq0anJ2dYWNjA09PTzRq1AhHjx4FoPlioFq1ajh37hwiIiL0nr9582YkJCSgRYsW2uHj27dvhyRJGDduHKZNmwZ/f39YW1vD09MTw4YNg1qtmyyWLFkSlStXxqFDhxAWFgYHBwdUrFhRp86RI0cQExOD7t27Y+jQoZDJZHqvSxZJktCyZUusX78eVapUgZ2dHZo0aaJ9fP/+/WjcuDFGNXWHdHQuFoSno/IiFQJ+V2HYP2rMbCHDJ7Vl+Oeff/DGG2/A19cXt09sQ8bWb1By53D0lO+DvwswYp8aXx5Q6bTtIAf6VZEwp5UMG96S4Ys6Ev65JdBwhQp3Ugo28b32SGB9tEDXYN1FuAxRqQV+OcNtioiIiMi8DH0WJ3pZZnk3JSQkQJIkeHt76wYjl6NEiRJISEgolDiEEHj69KnRq8NlLZ1ubP20tDSjY7GxsTF6rnBWArpp0ybMnj1b+0thxYoVqF27NoKCggw+b+7cuahUqRL69OkDT09PXLp0CRs3bsRrr72Gc+fOITAwEACwbds29O3bF35+fhg0aBA8PDwQHx+PQ4cO4cKFC2jYsCEAoH///vj000/x66+/4tdff9VrCwA++OADvThWrlyJR48eoVu3bvDw8MC6deswZ84c+Pr6YvTo0Tp1ExMT0bZtWzRv3hxvvvkmHj9+rPP4zJkzYWtriyFDhsDb2xuNGzfG9u3bkZaWBjs7O6iFQEZW/im3xdWYm+j13gC0b9ceXd7uBbVKhTSlwKq//sL7/d5FqZIl8d67fRDjWw5nH4Yj4Z95CA7yR6We/8PwvWqUcgS2rVqFmJgYtGnTBgH+4Xjo9Azr1m3Byt3zMH7CBDgEf4npZwSGhwqUcdYkmT1CZOgR8jzuThWANgECTVepMOGEWmel5/z0NFOg+xYV7OXA5KZ5v7+2XRe486RAQiEiIiIyWmxsrMFOK6KXYZakNzU1FXZ2drCy0v+g7+joiPv370OlUhl8PD9dvnwZT58+RWhoaJ51MzMzcf78edjY2KBs2bJGHX/p0qVGx/Lmm2/C19fX6PpDhgxB7969sX79evTo0QN79uxBbGwsvvrqqxyfc+XKFbi5uemUbdy4EZ07d8bUqVO1iepff/0FIQS2b9+OKlWq5Hi8QYMGYcyYMdi0aRNmzZqlvV6PHj3Cvn37ULZsWbRt21bveQ8ePEBERATKlCkDABg9ejR8fHywcOFCvaQ3ISEBo0ePxoQJE/SOk5aWhm3btqFRo0baITADBw5E3759sWTJEgwZMgQH7wi8tvrfHuRJCsT9+9z1/94AYOIvKgDdEFh7KSL2rsXMi3ZYf1aN659bYbL1dkyYMAFj3gzG49K9MfQfNc5PmQZPD93XceLEiahYsSLmzJ6NPw5/iZ03gf13BPpUzrlntXEZCfV8gD23CqZfVaUWeGerGpeTgL+7yuDrlPec4J/PcJsiIiIiIrIsZhnerFQqc+zVzEqclMqCnVR47949HD9+HCVKlMhzZTi1Wo29e/fiyZMnaNy4Mezs7HKtn6Vdu3ZG30qUKGFS/D169ICbm5t2Iak5c+bA1dUV/fv3z/E5WQmvSqVCQkICYmNjtcOGXxzinFVv6dKlufZWOzk5oW3btoiPj8fGjRu15XPnzkVmZia6detm8Dq3b99em/ACmi86qlWrhnv37iEzM1OnrqOjI8aMGWOw/YULFyI1NRV9+/bVlr3zzjtwcXHBkiVLAAAhJSQsaivDorYy4K+B8Nj9Of5o87xsUVsZBrudBP4aiO5tGuPhw4eYdSYT9T2fIflBHLp37w4A2LFjBzqWkxCvAJKl53sOpaSkIC4uDo8ePUKDBg1w9+5dyBV3AQAPjejoL2mbiYfPnmeYCoVCu2I4AGRkZOjNO059+lTn/t27d3Xu37t3D0IIDNqlxtYYgdmNn6JRyeeva25tOFsDpq8tbQQh9M4je9w5nUeWhw8fIj09XXvfmNeKbbANtsE22AbbYBuvZhshIc+HyBWF86BXnDCD1atXi6VLlxp8bPfu3WL+/PlCqVSafMzly5cbVffBgwdi0aJFYsWKFUKhUORaV61Wi71794r58+eLs2fPmhRTfhs6dKgAIDZs2CCEEKJ///7Czs5OXL9+Xdjb24sePXoIIYQ4fPiwACDee+89neevX79eVKtWTVhbWwsAOrfKlStr692/f1+UK1dOABB2dnaidu3a4vPPPxfXr1/Xi2nfvn0CgGjbtq22rGLFisLa2lqv/rZt2wQAMWbMGL3jtG/fXgAQKSkp2jJvb28RGBiY4+sRGhoqnJ2dxfHjx8XZs2e1tzZt2gi5XC6uXr2qUx+AaNiwod5xvvjiC93XY5JCoPcynbLXX39dTDmhEpiWKfZfviveeust4eLiovc6AhA//X1FYFqmWHFZlWPsWWovzRTBCzLzrPeiobuVAtNyf86ofZo6v5zOO4YX/XNLc475ffObZ9o5EhERUfF27do1c4dAFsQsw5sdHR2RnJxscAhzbkOf80NiYiK2b98OGxsbvPnmm3B0dMyxrhACBw4cQHR0NEJDQ1GrVi2T2nqarUcuN7a2tiaf84gRI7Bo0SJ07doVz549w4cffphj3QMHDqBbt24oWbIkRowYgQoVKsDR0RGSJGHIkCE63455e3vj6tWr2Lx5M7Zu3Yrjx4/jxx9/xJw5c7B06VJ07dpVW7d58+YoW7YsDhw4gISEBNy5cwdXr15FkyZNtHOEszPlPHPqVY+KisK5c+cghED9+vUN1pkzZw5mzJiR5/Gyzn3YsGFo2LAhvryfhke1OuGntmvhbJUBAPAt44fPrqrhbAP079gMd25eR8d3+qFJWHV4lnCHlZUVFi9ejF179mLJ7ZKwsdLsmZsl4amAV7athLZfV+PMfeDjUN3ymGRNPOXcXq7PddpJNX48LTC6noRPaps2mOM1PwkV3YGoR+BiVkRERGQ2L/bMEv1XZkl6vby8EBsbiwcPHsDHx0dbrlQqkZSUpFOWnxITE7Ft2zZYW1vjzTffhLOzc451sxLeqKgo1KpVC2FhYSa3t2zZMqPrmjqnFwCqV6+OypUr49y5c6hQoQKaNWuWY90FCxZArVZj/fr1OkliamoqevfurVffysoKnTt3RufOnQFotnVq3LgxJkyYoJP0AkDv3r0xceJEzJ8/H9euXQMA9OvXz6RzMdUvv/wCIQTGjh2LkiVL6j0+adIkrF+/HtOnT88zya5cuTIAzQJlPXv2hPqyGn22qzE17S0Mri6DvRyYdUWToH4QEI9516LQt29fvPb5fIw/rkY3fwmBrhJiyiqAEVNwIcUJE5vIUMrxedLacIUKtbwlhJWS4GoLnL0vsDBCwM8ZGJ1tG6SWqzUrb90c/PzH89ZjgT8va9LQ0/c1/44/ppmrXNYFeLeK5hgbotX44qAaFdyBSiUkLLusuyL262UllHTMOZmWJAmfhskwZHfO2y4RERERFTRbW1tzh0AWxCxJb7ly5XDu3DlcvHhRJ8G9cuUKlEolypcvr1M/JSUFarVabxEmU2QlvHK5HG+++Wau2x0JIXDw4EFERUWhZs2aqFOnzku12a5dO6PrmjqnN8vkyZOxZcsWtGzZEpKUczKTlfhl3xbo008/1enlBTSr5b045xYAatWqBRsbG6SkpOgd++OPP8aUKVOwfPly3Lt3D56engYT6fyiVquxZs0alClTBv/73/8Mzhu+evUqZs2ahS1btqBTp065Hq9bt24YMWIEFi5ciI8++gi9KwfA0x6YdEKNaafUSEkXKOeqwrzXbVBXnYR50LxHqnlKqFxCwrLLAg9SVcgs2w2IO48JITH4up7u1kpvh0jYdl1g1y2Bp5mAjyMwqJqEcQ1luSahWW48Fhh7RPfaZd1vVuZ50nv+geZaRj8C3t2un7ju65F3e30qS/j8AJCSkWdYRERERAUiODjY3CGQBTFL0uvh4YEqVarg0qVL2LVrF/z9/fHo0SNERETAx8dHL+ndunUrFAoFBg8erFMeFRUFhUIBQLOSr0qlwtmzZwFoFlnK+mF58uQJtm3bhvT0dFStWhX379/H/fv3dY4VEBAAa2trAMCJEydw9epVlChRAu7u7nr7Bru4uBjsXcwue+JYEDp06IAOHTrkWa9Xr15YunQpunXrhp49e8LOzg779+9HVFSUXo937969ERsbiyZNmiAgIABpaWnYvHkznj17hi5duugdu2TJkmjatCn27dsHQNPLW5Dfzq1fvx6JiYno169fjgui9evXD7NmzcJvv/2WZ9Lr5OSE3377DX369EG1atXQsWNHBAcHI/TxYzhFRWHfvn34au5c9K3RFypVdZQtWxYrV65ERkYGwipXhmt0NNauXQv/0qURExODlu8d12tjfGMrjG9s3Pm92MObpbm/DGJU3kOV/9fICv9rZFw7OXGwlvBBDQk/nRZcxZmIiIjM4uLFi3kuNktkLLPt+tygQQM4OzsjMjISt2/fhp2dHapWrYqwsLBceyxfdPXqVb2V1U6fPg0A8PHx0Ul6s1ZwO3PmjMFj9ezZU5v0Zu0TnJSUpE3kXhQcHGxU0luUtG7dGgsWLMCECRMwe/Zs2NjYIDQ0FP/88w9ef/11nbrvvvsu/vjjD2zevBlPnjyBvb09/Pz8MGPGDAwfPtzg8T/44APs27cPkiTho48+KtBzydoT+J133smxTmhoKEqXLo39+/cjISEBXl5euR6zR48eKFu2LMaOHYu///4bf/31FxwdHeHj44M+ffqgefPmADQ95jt37sSQIUOwfft2bNiwAX5+fpg6dSquXr2KOXPm5Nt5mtNHNWWYdkqVd0UiIiIioiJOEtnHthIRAeiyUYktMYAyH35D+DkDt4eY7Ts2IiIiesXEx8ebvN4NUU7Msk8vERV9I2pb5UvCS0RERGSqnHbwIHoZTHqJyKAmZYAqJYCX2ziJiIiI6OXdvn3b3CGQBWHSS0QGSZKEz8Jk3K+XiIiIiF5pTHqJKEc9K0lw4zZ5REREVMi4ZRHlJya9RJQjO7mEj2pKsOIYZyIiIipE2bcXJfovmPQSUa4+rMkhzkRERFS4Hj9+bO4QyIIw6SWiXJVxltAtWIKcvb1ERERUSKytrc0dAlkQJr1ElKdPQmXcvoiIiIgKTZUqVcwdAlkQJr1ElKcGvkANL0DG3l4iIiIqBOHh4eYOgSwIk14iylPW9kVq9vYSERER0SuGSS8RGaVHRQkl7F7uuf7O+RsLERERWTZPT09zh0AWRBJCsO+GiIwS/UjgUZrpz6voAbjacmw0ERERGSc5ORlubm7mDoMsBJNeIiIiIiIqUsLDw1GzZk1zh0EWgsObiYiIiIiIyGKxp5eIiIiIiIoUhUIBJycnc4dBFoI9vUREREREVKQkJSWZOwSyIEx6iYiIiIioSHn06JG5QyALwqSXiIiIiIiKFCsrK3OHQBaEc3qJiIiIiIjIYrGnl4gKTaMVShyO5fdsRERElLsLFy6YOwSyIEx6iahQKNUCR+OBATtV5g6FiIiIiji1Wm3uEMiCMOklokJxK0Xzr5e9eeMgIiKios/Dw8PcIZAFYdJLRIXi2iPNsObybpKZIyEiIqKizs3NzdwhkAVh0ktEhSL6350H/FzMGwcREREVfdevXzd3CGRBmPQSUaG4lqzp6bViRy8RERERFSImvURUKK4+4qrNREREZJzAwEBzh0AWhEkvERWKK0nmjoCIiIheFSkpKeYOgSwIk14iKnBKtcCdJ+aOgoiIiF4VSUn8tpzyD5NeIipwt1MAFUc3ExERkZEkiYuAUP5h0ktEBS5rESsiIiIiY9SoUcPcIZAFYdJLRAUu+hEgAZDLAIUi1dzhEBERUREXERFh7hDIgsjNHQBRcXBXITDjrBon7gKn7wkoMoF9PWRo7m/4e6ejcQJfHFTh7H3AxQboUVHCxCYyONk8H+qz/7Yar61WG3z+sV5WqO+b97CgPbfUmHBc4GKigFINBLsDw2vJ8G4V3bgepwtMOK7GhmiBWAXgbQ+0KithXEMZ/F3ybudasoD830MqFAoA3KyXiIiIcqZUKs0dAlkQ9vRaCEmS0LJlS3OHUaCGDRsGSZJw4sSJXMty8/PPP0OSJCxdurSgwjTo6kOBKScF4hQC1bxyrxv+QKDlGhWeZgLTm8vwfnUJv10Q6L7ZcIL7caiEP9vJdG7l3fKOafM1NVqvUSNDJfC/BjJMaCyDvRzo+7caP59+3pZaCLy+RoVfwwU6V5Awq4UMPStJWBMl0HCFCk8y8h66HPVQIPPfQ9rZ2eUdHBERERVrbm5u5g6BLEiR7OkVQuDixYuIjIyEQqGAnZ0dgoKCEBYWBmtra6OOkZmZiYiICFy7dg0KhQIymQxubm4ICQlBcHCw2SbHb9myBZMmTUJ0dDSSk5Nhb28PLy8v1KxZE19//TXCwsLMEleWffv2YerUqTh79iwePXoESZLg7e2N+vXrY8SIEWjUqJFZ4zPGpk2bsG3bNowdOxZ+fn7mDgcAULuUhKShVvCwl7D2qhrd4w0nsAAw+pAa7rbA/ret4GKreZ8GuKgxaJcau26q0TpA97uqJqUldKto+vdXs88J+DgBe3tYwVauaWdIDQkhC1VYfEmNkWGaYx6PB07dA2a3lGForeftVHRXY8BONfbc0iTDubny8Pn/HRwcTI6ViIiIihdPT09zh0AWpEj29B47dgzHjx+Hu7s7GjZsiKCgIERERGDnzp0QIu9eJSEE/v77b5w+fRpeXl6oX78+QkNDoVarceDAAZw8ebIQzkLf+PHj0bFjR8TExOCtt97Ct99+i379+qF8+fLYvXs39u/fb5a4sgwfPhwtW7bEkSNH0LRpU4wePRpfffUV6tevjz179qBp06a4ffu2WWPMbvr06Xjy5Anq1KmjLdu9ezd+//13xMfH69UfPnw4njx5gl69ehVmmHC2keBhn/cXLSnpArtvCfSpLGkTXgDoW0WCkzWw+qrh9/+TDAGl2rTFolIyBNxtoU14AUAuk+BpD9jLdesBQMlsuaqPk+Zf+zy+OlOqBW6/sNXew4cPc65MREREBODatWvmDoEsSJHr6X348CEiIiIQEBCA1q1ba8udnZ1x9OhRxMTEoHz58rke48GDB7h37x6qVq2Khg0bassrV66M1atXIzIyEvXq1SuwczBEqVRi8uTJKFGiBM6dOwdfX1+dx9PT05GYmFioMb3oxx9/xOzZs1GlShXs2rVLL77U1FQMHz7cqC8dCpONjQ1sbGyMri+Xy+Hk5FSAEf03FxMBpRoIK6mbINtYSajpDZx7oP/699+hhiITsJKAJmUkTGsmQ1ipvBPs5n4SppwUGHtYhfeqyCBJwIpIgdP3gNUdnn8fFlZSgqM1MPaIGh72QEV3CdeSgS8OqFGnlGZub27upADKovW2ISIiIqJipMj19MbExAAAqlWrplMeEhICuVyO6OjoPI+RkZEBAHB0dNQpt7Kygp2dHeTyws/1ExMTkZqaigoVKugllABga2uL0qVLa++r1Wp8/PHHqFq1Ktzc3CCXy1GiRAl07NgRN2/eNLrdtWvXonbt2nBwcIC1tTXKli2L0aNHQ61+Prw2IyMDEyZMgK2tLTZs2GAwPkdHRyxcuBBly5bVlt29exfdunVDiRIltPF16tRJL76sebRLlizBF198gVKlSsHa2ho+Pj74/vvv9dpSq9UYOXIkvL29YW1tDT8/P0yePNng+WWf0/vmm29izpw5AID69etDkiRIkoR+/frpxJJ9Tm9BnYup7io02aGPk34i6eMoIV7x/L6NlYSuFSTMaCHDpk4yjG8sw8VEgSarVDh3P+8sc2x9GXpUlDDhuECFP1Qov0CFySfUWPeWDF2Cn/9q8HSQ8NebMjxOB1quVqPMfBWa/6WCr5OEvT2sIJflnvRGZ9uuiHN0iIiIKC8BAQHmDoEsSJHr6U1ISNDOI31RViKSkJCQ5zG8vb1hY2OD8+fPw9nZGd7e3lAqlYiKikJiYiIaN25sVCxpaWlGx21jYwOZLOfvEEqWLAk7OztcunQJhw8fzjOGjIwM/PHHH2jatClat24NZ2dnnD17Ftu3b8e5c+dw6dIluLjkvgLu5MmTMXr0aFSoUAGDBw+Gs7Mz9u/fr51TvGbNGgDArl27kJycjObNm6NChQpGne+jR48QFhaGu3fvok2bNggLC8P58+exefNmnDx5EmfOnIGPj4/OcyZOnIi0tDS8/fbbsLe3x7JlyzBu3DhUrFgRb7/9trZe3759sXz5clSuXBn9+/dHUlISfvjhB5QsWTLPuIYNG4aUlBQcOnQII0aM0H6RULt27fw7F0nC+Ck/Ij0tDV3f7g17e3usXLkS436YiMDgSujerZu2qq2VaZurP1M+f152dvLnjwNAw9ISGpZ+XrFjeaBbsITqS1T4+pAaO7oZOMgLbOWa1Zq7BUvoEixBpQZ+u6BGn21q7O4u6az+7OUgoZa3hGGlJVTxBMIfAFNPqtF/hxprOubezrV/tyvKSn0zMjMAcF4vERER5Sw1NZVflFP+EUXM6tWrxdKlSw0+tnv3bjF//nyhVCrzPE58fLxYtWqVmD9/vva2cOFCcePGDaNjefG5ed3i4uLyPN7XX38tAAiZTCbKlSsnunbtKqZOnSqio6P16qrVavH48WO98mnTpgkAYsqUKTrlAESLFi2092/fvi2sra1F06ZNhUql0qnbq1cvIUmSOHr0qBBCiP/9738CgBg2bJhRr4sQQgwePFgAEKNGjdIp//777wUA0adPH23Z9OnTBQBRtmxZ8eTJE235zZs3hVwuF40bN9aWXbhwQUiSJKpWrSrS0tK05YcOHRKSJAkA4vjx49ryoUOHGlWWPZYlS5a8/LkENRWYlmnULTJRrRfDmisqgWmZYt8tlUhKStI5zz/DUwWmZYqDdzTPS09PF4mJiUIIIbpvUopSv2aK+Ph4neNlv99ptULYTM8USpXmGNnbePLkiUhJSRFDdilFjcWZ4lna8zYylGpRYUGmqLXwqbZ+zCO1sJ+eIdZeff4+unv3rlh0USkwLVNsj9E/j6w2hBBi3GGlkP+UITAtU1j/lCkGr40zGHf2+3fv3hVq9fPXL7c2sr9WOR2TbbANtsE22AbbYBuvRhvnzp0rUudBr7Yil/SuWLFCLFu2zOBje/fuFfPnz9d5E+ckISFB7Ny5Uxw7dkxcv35dREZGirVr14oFCxaIO3fuGBXLnTt3jL4ZE5MQQixZskTUqVNH2NraCmg6v4RMJhOtW7fW++HLkpmZKe7fvy/u3LkjIiMjBQDRpUsXnTrZk96sRHbRokV6sW7YsEEAEF999ZUQQoiRI0cKAGL06NFGnYMQQvj5+QlnZ2ehUCj0YnVxcRGlS5fW/vLJSjS//vprveMEBASIgIAA7f0vvvhCABBz5szRq1unTp0CSXpNPhfnkqL9uL/EoosqnVuJtp+KEm0/1SlLTss96c3ucKxaYFqm+CtS/7HGKzJF6NJMvfLsPt+vSUYfG2g7S7pSLeQ/ZYrRB/W/QPr4H6WQ/Zgp0pWa5489pLmflql7vIfPNLGOOZT7l1ALL6i0XwK8mPQSERER5eTFpJfovypyw5vlcnmOw4pVKpW2Tm4ePnyITZs2oUGDBqhcubK2vHz58lizZg0OHjyId955J9fhyABQpkwZE6PPW9++fdG3b18olUqcOHECW7ZswcKFC7Fr1y706dMHf//9t7buvHnz8PPPPyMmJkZ77llSUlKyH1rHpUuXAAD9+/fPsc6DBw8AAK6urgCAJ0+eGH0e9+7dQ2BgoN68ablcDn9/f1y9ehVpaWmwt7fXPmZo6LSrqytiY2O197PmdNeoUUOvbsWKFXHq1CmjYzSWyefy5D66lk1Fv6q6759f7v6D2NhY9Kv600vHUtUTkMuA0/cFeoQ8L89QCYQ/AHpUzHuo9PVkzVBop1zW90p6plkwS2Vg6m+mClALQKUGYAXcfwoIoV8389+3pDLn3ZcAABXcdWM2Zpg6ERERFW+GPgsSvawil/Q6OjoiOTkZKpUKVla6cwVTU1NhZ2enV57dhQsXoFKpEBQUpFOelcRcunQJCoUizzmxT58+NTpuW1vbPOPKHkujRo3QqFEjjBgxAuXKlcOePXvw+PFjuLq6YtGiRfjwww9Rrlw5fPXVVwgMDISDgwNUKhXeffddnYWoDBH/rrI8fvx4vdchS0iIJqvKmu968eJFo+N/Gaa8PkVdQZ2Lq62EVv4Sll0WGNtAwNlGkzD+eVlAkQl0fyHpTXgq4OWgm1CefyCwOUbgjUAJshfmEt9OEXiaCYSU0JR5OwButsCGaIHvGwnYWGnKFRkCW64LhHgA9taasmB3CQICq68K9Kv6/Jgrr2jeY7W8c0/Ey7vr3tesUs7El4iIiHIWGRmp03lF9F8UuaTXy8sLsbGxePDggc7iQUqlEklJSXqLIxmSlawKA9vrZCWLeSWNALBs2TJjw8abb75pcNVjY5QqVQq+vr64du0a7ty5A1dXVyxevBjW1tY4fPgwSpUqpa1rbGIaHBwMQLOxd8+ePXOt27p1a7i5ueH48eO4fv16jknyi3x8fHD37l2kpqbq9JAqlUrcvn0b3t7esLOzMyrWF5UrVw4AcP78eTRq1EjnsatXrxp1jLx68LMrqHPJbvwxzXvuUpLmffnnZYHDcZqybxo8j3lCExkarlCh2SoVBleXIVYh8NNpgdYBEtoGPq/39hY17OWaBa28HYDLSQK/nRdwsAYmN9V9DfpuV+FALCBGaX7krWQSRtWR4ZvDatRfrkLfKjKo1MAfF9WIfQIsa/f8+f2qSvjxNDBktxrnHghUKSHh7AOBBRcEqpQAOlfIPekt6aDZyzdrEa7soxaIiIiIssvajYUoPxS5LYuykp7syd2VK1egVCr19uhNSUlBcnKyTlnWSm/Zk6T09HTcunULtra2efbyAkC7du2MvpUoUSLXYz19+hRr1qwxmIhfvHgRt27dgpOTkzbhzOpJfDE5F0Lgiy++yDNuAHj//fdhbW2NqVOn4tGjR3qPJyUlaYcz29jYYMyYMUhLS0OnTp1w//59g/G///77uHXrFgCgbdu2ePLkid42PVOmTEFKSgpee+01k1YtztK7d29IkoS5c+ciPT1dW3748GGcPn3aqGNk7cObNXw7LwV1LtmNPaLG2CNqrPq3h3RhhNCWvSi0pIQ93a1gLwdG7lfjt/MCA6tKWNtR98e1UwUJic8Epp9W46M9avx1RaBLsITTfaxQqUTe8Y6pL8Py9jJYy4DvjmricLEF1naUoXfl522VsNccs08lCVtiBIbvVWPzNYEB1STsf9tK20ucE0mSEOT6/L6trW2esREREVHxZsxndSJjFbmeXg8PD1SpUgWXLl3Crl274O/vj0ePHiEiIgI+Pj56Se/WrVuhUCgwePBgbVm1atUQHR2NkydP4uHDhyhVqhTS09Nx5coVPH36FI0aNTKqNzA/5/Q+ffoUPXr0gJ+fHxo3bozg4GBIkoQrV65g69atyMzMxFdffQUHB81WLj169MC+ffvQtGlTdO/eHSqVCjt27EBqaqpR7QUGBmLSpEn4/PPPERwcjA4dOiAwMBAJCQm4fPkyDh06hIMHD6JevXoAgFGjRuHWrVuYM2cOgoOD0aZNG1SpUgWAZnjJrl278PjxY3z77bcANNshbd26FdOmTUNERARCQ0Nx4cIFbNmyBT4+Ppg6depLvU7Vq1fH22+/jVWrViE0NBRvvvkmkpKSsHLlSgQEBODGjRt5HuO1117DpEmT8M033yAiIgL29vaoVasWmjVrZrB+QZ1Ldlm9rMZoXEbCkV651/84VIaPQ4373mr/O4aP1auSDL0q5X2M0s4S/mj78kO6K5WQEPlQk+xnfSlBRERElJMXRzoS/VdFLukFgAYNGsDZ2RmRkZG4ffs27OzsULVqVYSFhRnV4+bs7IzOnTvjzJkziI+PR0xMjHaf3/r16yMwMLAQzkKXm5sbpkyZgp07d2L//v3YsGEDMjMz4eLigurVq+Ojjz5C7969tfU/+OADPHr0CL/++it+/PFHODo6olGjRpg7dy7Kli1rVJufffYZKleujAkTJmDt2rV4+vQpnJ2dUaZMGXz00UeoWLGiTv1Zs2ahS5cumDJlijZGmUyGkiVLolWrVvjss8/g7+8PAHB3d8epU6fw8ccfY9++fdi5cydcXFzQoUMHzJgxw6hh6DlZvnw5vL29sWLFCkyfPh2lSpXC2LFjERsbizlz5uT5/Ndffx2jRo3C0qVLMXbsWKhUKrz33ns5Jr0FeS6kUcEdyOoQTkpKAvByUwGIiIioeIiKikLNmjXNHQZZCEkYGm9LRJSPFl5UY+BONawkYGDAA8zvyqSXiIiIchYeHs6kl/JNkZvTS0SWJ2vbIpV4vkUWERERUU6yRhcS5QcmvURU4Mq7Pf+/UqU0WxxERET0anhxQVOi/4pJLxEVuFKOgN2/KwikKoxbjI2IiIiKL0O7iRC9LCa9RFTgsm9bRERERERUWJj0ElGhqPTvVtbe3t7mDYSIiIiKvGrVqpk7BLIgTHqJqFBUcNMsZvXw4UMzR0JERERFXXR0tLlDIAvCpJeICkXWCs5PM1RmjoSIiIiKurS0NHOHQBaESS8RFYry//b03lc6mDkSIiIiKuqcnZ3NHQJZECa9RFQoyrtr/o1PZ9JLREREuStdurS5QyALwqSXiAqFj6PmXynzqXkDISIioiLvypUr5g6BLIjc3AEQUfEgSRJG15PQzOYOAO5fRERERESFQxJCCHMHQUTFR2JiIjw9Pc0dBhERERVh/LxA+YnDm4moUKlUXL2ZiIiIcsfPC5SfmPQSUaG6e/euuUMgIiKiIo6fFyg/MeklIiIiIiIii8U5vURUqDIzM2FtbW3uMIiIiKgI4+cFyk/s6SWiQnXjxg1zh0BERERFHD8vUH5i0ktEherpU+7TS0RERLnj5wXKT0x6iahQOTo6mjsEIiIiKuL4eYHyE+f0ElGhysjIgI2NjbnDICIioiKMnxcoP7Gnl4gK1eXLl80dAhERERVx/LxA+YlJLxEREREREVksJr1EVKh8fX3NHQIREREVcfy8QPmJSS8RFSpJkswdAhERERVx/LxA+YlJLxEVqri4OHOHQEREREUcPy9QfmLSS0RERERERBaLWxYRUaFKT0+Hra2tucMgIiKiIoyfFyg/saeXiArVnTt3zB0CERERFXH8vED5iUkvERUqhUJh7hCIiIioiOPnBcpPTHqJqFDZ2dmZOwQiIiIq4vh5gfIT5/QSUaFSKpWQy+XmDoOIiIiKMH5eoPzEnl4iKlQRERHmDoGIiIiKOH5eoPzEr0+IzESlUiEqKsrcYRS669evczVGIiIiylVR+7wQHBwMKysrc4dBL4lJL5GZREVFoXLlyuYOg4iIiIjycPnyZVSqVMncYdBL4pxeIjMp6j29CoUCdevWxcmTJ+Hk5JQvx7x37x5atGiBvXv3olSpUvlyTMo/BXHNqWjjNS9+eM2LF17v/MOe3lcbk14iMiglJQWurq54/PgxXFxc8uWYsbGx8PPzw507d1CmTJl8OSbln4K45lS08ZoXP7zmxQuvN5EGF7IiIiIiIiIii8Wkl4iIiIiIiCwWk14iMsjW1hbjxo3L15UTXVxc0KxZMw6xKqIK4ppT0cZrXvzwmhcvvN5EGpzTS0RERERERBaLPb1ERERERERksZj0EhERERERkcVi0ktEREREREQWi0kvERERERERWSwmvURF3KRJk9C9e3cEBQVBkiQEBATkWv/vv/9Gy5YtUapUKTg6OqJixYoYNWoU7t+/r1f38ePHGD58OEqXLg07OztUqVIFc+fOhaH17dRqNX7++WeEhITAzs4Ofn5++Oyzz5Cammowju3bt6Nhw4ZwdHSEh4cHunfvjhs3bhise/XqVXTq1Anu7u5wdHREkyZNsHfv3rxfHAtUUNd7//79kCTJ4O3NN980eOyCuoamvO+KA1OuuRAC8+bNQ61atWBvbw83Nze0bdsWx48fN1g/PT0d3377LQIDA2Fra4ty5cph/PjxyMzMNFh/6dKl2mOXLFkS77//PhISEgzWPXHiBFq1agVnZ2e4uLigbdu2CA8PN1g3Pj4effv2hZeXF+zt7REWFoY1a9bk+rpYsoK65jdv3szx57xq1aoGj19Q19HU954li4qKwrfffov69evDy8sLzs7OqFmzJiZMmGDwb2hB/T7l33Eq1gQRFWkAhIeHh2jVqpVwd3cXZcuWzbHub7/9JgCI2rVri+nTp4v58+eL999/X1hZWYnAwEChUCi0ddPT00WdOnWEXC4XI0eOFL/99pvo3LmzACDGjRund+yPP/5YABCdO3cWv/32mxg5cqSQy+XitddeEyqVSqfuunXrhCRJombNmmLOnDli4sSJwtvbW/j4+Ii4uDiduteuXRMeHh7C29tbTJw4UcyZM0fUrFlTyOVysXv37v/02r2KCup679u3TwAQgwcPFn/++afObd++fXrHLqhraOr7rjgw5Zp/8MEHAoBo3ry5mDlzppg8ebIICgoSNjY2Bq/jW2+9JQCIAQMGiN9//10MGDBAABDvvfeeXt3p06cLAKJZs2Zi/vz5YuzYscLR0VFUrlxZ570khBDHjh0Ttra2IigoSEyfPl1Mnz5dBAUFCScnJ3HhwgWduklJSSIwMFA4OjqKsWPHivnz54tmzZoJAGLhwoUv85K98grqmt+4cUP7ezr7z/mWLVv0jl2Q19GU956l+/LLL4WTk5Po1auXmDlzppg7d67o0aOHACCqV68unj59qq1bkL9P+XecijMmvURFXExMjPb/VapUyfXDUXBwsPDx8RHPnj3TKR8zZowAIDZs2KAtmzNnjgAgZs6cqVO3S5cuwtraWty8eVNbFhERISRJEl26dNGpO3PmTAFALF++XFuWkZEhfH19hb+/v3jy5Im2/Ny5c0Imk4lBgwbpHKN79+5CJpOJc+fOacuePHki/P39RXBwsFCr1TmeryUqqOudlfQuWrQozxgK8hqa8r4rLoy95ufOnRMARNu2bXVe00ePHomSJUuKChUq6Hxw3bZtmwAgPv30U53jfPrppwKAOHLkiLYsISFBODg4iDp16gilUqkt37x5swAgJkyYoHOMOnXqCGdnZxEbG6sti42NFc7OzuL111/Xqfv5558LAGLz5s3aMqVSKerUqSM8PDx03mPFRUFd86yk19gvkArqOpry3isOTp06JZKTk/XKs35Xz5o1S1tWUL9P+XecijsmvUSvkLySoFKlSonKlSvrlWf9Ydy1a5e2rFGjRsLBwUEvYTp48KAAIKZMmaIty/rDfPDgQZ26z549Ew4ODuKNN97Qlu3evVsAEN9//71eHC1atBAuLi4iIyNDCCGEQqEQtra2okWLFnp1v//+ewFAnDhxIsfztXT5eb1fTHoVCoXedX9RQV5DU953xVFu1zyrJ3blypV6jw0ePFjvZ7R3794CgLh9+7ZO3du3bwsA4sMPP9SW/f777wKAWLp0qd6xg4KCRKVKlbT3o6OjtT142Q0YMEBIkiTu3r2rLStdurQoV66cXt2lS5cKAOKvv/4yeL7FRX5e8xeT3mfPnonU1NQc2y3I62jKe684u3DhggAghgwZIoQo2N+n/DtOxR3n9BJZkDZt2uDy5cv47LPPEBkZiTt37mD9+vX44Ycf0KxZM7Ro0QKAZl7P2bNnUatWLdjZ2ekco27dupAkCadOndKWnTp1CjKZDHXr1tWpa2dnh5o1a+rVBYAGDRroxVe/fn2kpKQgKioKAHDhwgWkp6fnWPfF45E+Y6/3iz755BM4OTnB3t4ewcHBmDFjht7cr4K6hqa+70hXeno6AMDBwUHvsayyF+d5njp1CqVLl4afn59OXT8/P/j6+pr0c3vlyhUoFAqj6gohcObMGQDA3bt3ERcXp30vZK/74vFIn6nXPMtPP/0EBwcHODo6ws/PD99++632WFkK8jqa8t4rzmJjYwEAJUuWBFCwv0/5d5yKOya9RBZkxowZ6N69O2bMmIHKlSvD398fXbt2xRtvvIHdu3fDysoKAPDo0SM8e/YMpUuX1juGra0tPD09ERcXpy2Lj4+Hp6cnbG1t9eqXLl0aiYmJyMjI0NbNKjdUF4D22KbUJX3GXm8AsLa2RseOHTF16lRsppslkQAAFh1JREFU3rwZ8+bNg5ubG0aMGIEBAwboHLegrqGp7zvSVaVKFQDQWxxGCIEDBw4AAO7cuaMtj4+PN/haA5prk/1nPKvcUF0hhLYOf8YLj6nXXCaToUWLFpg4cSI2btyIBQsWoHLlyvjhhx/w5ptvQqVSaesW5HU05b1XXKlUKvzwww+Qy+Xo1asXgIL9fcq/41Tcyc0dABHlH2tra/j7+6Nz587o0KEDHBwcsHPnTixcuBBWVlb4/fffAQBPnz4FAIN//ADNN79ZdbLq51Y3q46NjU2ux36xbl5xZK9L+oy93gDQqFEjbNq0Sef5gwYNQrt27bB48WK8//77aNSoEQDTrkt+1c2qz+udszfeeAOVK1fGr7/+Cl9fX3Tp0gVPnz7F9OnTERERAQAm/dxmrwsU7jXnz3jeTL3m/v7++Oeff3SOMXDgQAwePBi///47Vq1ahd69e+s8ryCuoynvveJqxIgROHbsGCZOnIiKFSsCKNjfp/w7TsUde3qJLIRarUbbtm1x9OhRrF69Gn379kW3bt3w+++/4/PPP8eCBQuwZ88eAM+HxWUf7pYlLS1NZzidg4NDrnVfPGZux/4vdUmXKdc7JzKZDF9//TUAYNu2bdrygrqGpr7vSJdcLsfff/+NRo0a4csvv0SFChVQo0YNnDt3DpMnTwYAuLi4aOvn9XOb/WccKNxrzp/xvJl6zXMyZswYAIXzc571f/6c52zs2LGYPXs2Bg8erP0dDBTs71P+HafijkkvkYU4fPgwDh06hK5du0KSJJ3HunfvDgDa4XDu7u6wt7c3OOQoPT0diYmJOkOVfH19kZiYaPCPWlxcHDw9PWFjY6Otm1VuqC7wfMiTKXVJlynXOzdZ+4MmJiZqywrqGpr6viN9/v7+2LdvH27duoUDBw4gIiIC58+f1/aohISEaOv6+vrmOKwwLi5O72c8q9xQXUmStHX4M164TLnmOfHz84OVlVWh/Jxn1Tf2vVfc/O9//8P48ePRv39/zJs3T+exgvx9yr/jVNwx6SWyEFl/XF6cs5VFqVTq/CuTyRAaGopz587p/QE8efIkhBAICwvTltWpUwdqtRonT57UqZuWlobw8HC9ugBw7NgxvTiOHz8OFxcXBAcHAwCqVasGW1vbHOsC0Dk2PWfK9c5NdHQ0gOcLqQAFdw1Nfd9Rzvz9/dG0aVPtnM/t27dDJpOhTZs22jp16tRBXFyczpxPQDMHND4+3qSf24oVK8LJycmoupIkoXbt2gAAHx8flC5d2uBiS/wZN40x1zwn169fh0qlMunn/L9cR1Pee8XJ//73P3z33Xd47733sGDBAr0vLAvy9yn/jlOxZ7Z1o4nIZLltbREeHq7d6D5rK4EsH3zwgQAg1q1bpy2bPXt2jvv7yeVycePGDW3ZhQsXct3f788//9SWZWRkCB8fH739/cLDw4VMJhMDBw7UOUa3bt2ETCYT4eHh2rKs/f0qVKhQrPf3y8/rnZiYqHeMtLQ00ahRI70tJQryGpryviuO8tqmypBNmzYJAOK9997TKd+6dWuue6UeOnRIW/bgwQNhb28v6tata3Cf3h9++EHnGGFhYcLZ2VnExcVpy+Li4oSzs7No2bKlTt1Ro0bluL+rm5ubSElJMel8LU1+XnNDP+cqlUq8/fbbBreHKqjraMp7r7j47rvvBADx7rvv6uytnF1B/T7l33Eq7iQhsu1VQURFyp9//olbt24BAGbNmoWMjAx89tlnAICyZcvi3Xff1dbt1q0b1q1bh+rVq6NPnz7ahY22bNmC+vXr4/Dhw9oVfTMyMtCwYUOcP38eH3/8MSpVqoTt27djw4YN+Oabb/DDDz/oxDF8+HDMnj0bnTt3Rrt27RAZGYmZM2eiUaNG2Lt3L2Sy5wNH1qxZg7fffhs1atTAoEGDkJKSgp9//hmSJOHMmTM6Q52uXbuGunXrwtraGiNHjoSLiwt+//13XLx4Edu2bTOqF8OSFNT1rlOnDnx9fVG7dm34+voiPj4ey5YtQ3R0NIYPH46ZM2fqxFFQ19DU911xYMo1HzhwIIQQqFmzJuzt7XH48GEsX74coaGh2L17N1xdXXWO3aFDB2zduhUDBw5EgwYNcOzYMfzxxx/o06cP/vzzT526P/30E0aNGoXmzZujZ8+eiIuLw08//QQ/Pz+cOnVK29MLAEePHsVrr72GMmXKYPjw4drY79+/jyNHjqBGjRrauklJSahduzaSkpLw6aefonTp0li5ciX279+PBQsWYODAgfn7gr4CCuqad+nSBSkpKWjYsCH8/PyQmJiIdevW4cyZM3jrrbewfv16nd/VBXkdTXnvWbo5c+Zg2LBh8Pf3xw8//KBzDQDNSJvXX38dQMH+PuXfcSrWzJtzE1FemjVrJgAYvDVr1kynbnp6upgyZYqoXr26sLOzEzY2NqJChQri66+/FgqFQu/Yjx49EkOHDhU+Pj7CxsZGVKpUScyaNcvgt7JKpVL8+OOPIjg4WNjY2AhfX18xcuRInW+BX7RlyxZRr149YW9vL9zc3ETXrl3FtWvXDNa9fPmy6Nixo3B1dRX29vaiUaNGYvfu3aa/WBagoK735MmTRf369YWnp6eQy+XC1dVVNG/eXKxYsSLHWArqGpryvisOTLnm8+bNE6GhocLFxUXY2dmJqlWrigkTJoinT58aPPazZ8/EmDFjRNmyZYWNjY0IDAwU33//vd7ogCyLFi0S1atXF7a2tsLLy0v0799f3L9/32Ddo0ePihYtWghHR0fh5OQkWrduLc6cOWOwbmxsrOjTp48oUaKEsLW1FbVq1RKrVq0y/kWyMAV1zRcsWCCaNWsmSpYsKaytrYWTk5OoV6+emDNnTo69iwV1HU1971my9957L8frbeiaF9TvU/4dp+KMPb1ERERERERksbiQFREREREREVksJr1ERERERERksZj0EhERERERkcVi0ktEREREREQWi0kvERERERERWSwmvURERERERGSxmPQSERERERGRxWLSS0RERERERBZLbu4AiOjV0q9fPyxZsgQAUKVKFUREROg8rlarMXHiRCxatAi3b9+Gv78/YmJiMHXqVCxcuBCXL1+GTGb6923z5s3DxIkTER0dDVtbW53HfvnlF4wcOVJ7PyEhAZ6eni9xdqbLzMzE+fPn8eDBAyQkJCA9PR3NmjVDxYoV9eo+fvwYp06dwv3795GWlgYnJyeUL18eNWrUgFyu++tYpVLh9OnTiI6ORnp6Ojw8PFCnTh2UKVPmpY5rSpxZEhMTcfr0ady/fx9KpRIuLi6oVKkSqlatmutrEh8fj61btxp87K233kLJkiUBAA8fPsSZM2eQmJiIp0+fQi6Xw93dHTVq1EDZsmVf+nUmIiIiehF7eolIa+fOnZAkKcfb0qVLAQCenp74888/MXnyZL1j/Prrr/j222/RpUsXLFy4EPPnz0dKSgqmTJmCL7/8Ui/h/e677yCTyRAZGal3rAEDBsDKygrbtm1Dv379kJGRgfnz5+vVa9u2Lf7880907tw5n14J46WlpeHs2bNITk6Gh4dHjvUUCgU2bNiABw8eoEqVKmjYsCFKliyJM2fO4J9//tGrv3//fly4cAHly5dHw4YNIZPJ8Pfff+PevXsvdVxj48wSGxuLjRs3Ii0tDbVq1ULDhg3h7+8PhUJh9GtTtWpVvPbaazo3V1dXndgzMzMRHByMhg0bIjQ0FIDmfZj9/WBq/ERERERZ2NNLRFrnz58HAMycORPu7u56j7dp0wZ79+6Fo6Mj+vTpY/AYixYtwuuvv45p06Zpy3755RcolUr07NlTr/6HH36IyZMn45dfftFJaGfNmoVFixZh/PjxaN++PQDgvffew/Tp0zF8+HBIkqStGxISgpCQEFy7dg0bNmx4uZN/SQ4ODujTpw8cHByQkJCQY/vR0dHIyMhAx44dtUlbpUqVIITQ9uZm9WA/ePAAMTExqFevHmrUqAEAqFChAtauXYsTJ07grbfeMvm4xsYJABkZGdi3bx/8/f3x+uuv67zWpihVqhSCgoJyfNzf3x/+/v46ZVWqVMGGDRtw4cIFVKpUSVtuSvxEREREL2LSS0RaFy5cgKurK4YNG/ZSiU5aWhrOnz+P7777Tqd80aJF6NixI+zs7PSe4+3tjd69e+PPP//EhAkT4OnpiQMHDuDTTz9F165dMWbMGG3dHj16YOrUqdi3bx9atGhh+gkWACsrKzg4OORZLyMjAwD06jo4OECSJJ0e8OvXr0OSJJ2kTy6Xo2LFijh16hQUCgWcnJxMOq6xcQLAtWvX8OzZM9SpUweSJCEzMxNyufyl3hMZGRmQy+VGD2mXyWRwdHREQkKCTrkp8RMRERG9iMObiUjr/PnzqFWr1kslNwMHDoS9vT1UKhW++eYbSJKEBg0a4MaNG7hw4QJatWqV43NHjhyJZ8+eYd68ebhz5w569OiBkJAQLF68WKde7dq14eHhgU2bNpkcnyFqtRppaWlG3YQQ/6ktX19fAMCBAweQmJgIhUKBmJgYXL58GVWqVIG1tbW2blJSElxdXWFjY6NzDG9vb+3jL3NcY8XFxcHa2hqpqan466+/sGjRIixatAiHDh2CUqk0+jgHDhzA4sWL8ccff2DLli16iWyWzMxMpKWlISUlBRcuXMCdO3dQunRpk+MmIiIiMoQ9vUQEQNMjd/XqVTRu3BiJiYl6j7u6uuaaQPXu3RvW1taYP38+ZsyYAQ8PD5QtWxZHjx4FAO18TUOqVKmC1q1bY86cOdi4cSMyMzOxceNGbW/mi0JDQ3HkyJGXOEN99+79v727C2mqDeAA/jenbm4ya84cIW3Lr1AzS02pMAp6o5sFXYiUIHVRSBBEiBCFEkS4C7tLr4oMugmMPoREK/ImJDUtN6em05XlzJw4v+bc3gvZweOmzdX7Xtj/d7fnec5zzs528+f5+r7mhkurFRcXIyYmJuR7JSYmIicnB52dnRgeHhbKs7OzkZubK2o7OzsbcFTTVzYzMxNSv8GampqC1+tFU1MTUlNTkZeXh9HRUfT09MDlcuHYsWPrXr9lyxbodDokJiZCKpXC4XCgq6sLT58+hcFg8Ntk7N27d8Ia3rCwMGi1Whw8eDCkZyciIiJajaGXiAAAJpMJi4uLqK2tRW1trV+9xWJBSkrKmtcfPXoULS0tkMvluHTpkjCd9fr16wAAnU637v2vXLmCEydOwG63o7GxEbt27QrYTq/Xo76+PtivtS6VSoWTJ08G1VYmk/32/WJiYqDRaKDT6SCVSjEyMoLOzk7IZDLRjshutxvh4eF+1/vKlpaWQuo3WIuLi3C73di9e7cQPnU6HTweD8xmM3JyckQbUq2WkJCAhIQEUZlOp8Pjx4/R1tbm984zMzOh0+kwOzuLwcFBeL1ev+9IREREFCqGXiICsLyeFwDu378fcGppcnJyUH2kp6eL1m9OTExAIpEEHLVdyTfSp9fr8c8//6zZbuvWrZibm1tzNHQjoqKiAh4B9F8YGBjA27dvUVRUJLwLnU4Hr9eLtrY2JCUlCWueJRJJwNDnK1sZiDfSb7B8xxwlJSWJypOSkmA2mzE2NrZu6A1EqVRCq9ViaGgIHo9H9B+JjY1FbGwsACAlJQUvXrzAy5cvcerUqZA30SIiIiLyYeglIgDL63klEgmKi4v91pJupI/1AutampubcfXqVSQnJ6O/vx9NTU04fvx4wLa+tbV/IgwtLS1hYWEhqLZSqTSk84V9TCYT4uLi/ML/zp070dfXhx8/fggBPDo6WjSF2Wd2dhYAIJfLQ+o3WNHR0ZicnPQb3fZ9DvadrSaXy+HxeOB2u9f9j+n1erS2tmJqakoIw0REREShYuglIgDLo7Q6nS7kwOtwOGCz2ZCZmSkqV6lUcLvdmJ6eDrgmdnBwEEVFRcjOzkZzczNSUlJQU1OzZuidnJxEdHT0H5luPDY29r+t6Z2bmxOOJFrJ4/EAgGijLJVKhdHRUbhcLtHvYbfbhfpQ+g2WWq3G169fMTMzIwqdviAe6rufnp5GeHj4LzfX8m2W5duZmoiIiOh3MPQSEYDl0Jufn/9b1wPAnj17ROVpaWkAgKGhIb86p9MJg8GAiIgINDQ0QKlUoqysDFVVVTCbzaIje3yGhoYClofi/1zTq1Qq8eXLFzgcDlGQ/Pz5M8LCwoQzdoHlkc7u7m6YzWbhnN6lpSVYLBbEx8eLRnU30m+w9Ho9Pnz4AIvFIprq3tvbi7CwMGg0GgDL4dTpdEIqlYqmUM/Nzfm9r4mJCQwPDyMxMVEYpQ/UzuPxoL+/H+Hh4QHPiiYiIiLaKIZeIsL3799ht9uFgBqKrq4uAP6ht6CgAADw/v17UZ3X60VJSQksFgtev34tTMEtKyvD7du3cefOHdTV1fndp6OjA2fOnAn5OVf6U2t6P336BJfLJUw/Hh4eFkZFMzIyEBkZiaysLNhsNjx79gzp6emIiorCyMgIbDYb0tLSRFOW4+Pjodfr0dbWhrm5OSiVSvT19WF6ehqFhYWie2+k32CeEwDi4uKQmpoKi8UCj8cDjUaDb9++YXBwEHv37hX6tNvteP78Ofbt24ecnBzhPi0tLQgPD8f27dshk8kwOTmJ3t5eSCQS5OXlCe1aW1vhcrmg0Wggl8sxOzuLgYEBOBwO5Ofn+40IB/v8RERERCsx9BKREFjHx8fx8OFDv/qsrCy/acurdXd3Y8eOHX4ji3q9HhkZGWhubsa5c+eE8srKSjx58gR1dXWi42nUajXOnj2L+vp63Lp1SzSVt729HT9//oTBYAjpe/5Xuru74XQ6hc9WqxVWqxXA8gZgkZGR0Gg0MBgMaG9vR09PDxYWFhATE4Pc3FxhNHelI0eOQKFQoL+/Hy6XC9u2bcOJEyeEUVafjfQbzHP6HD58GAqFAhaLBVarFQqFAgUFBb/8HwCAVqtFf38/Pn78CJfLBZlMBq1Wi/3794s2wNLr9bBYLDCZTJifn0dkZCTi4uKQl5cHrVYb0nsmIiIiWi3MG8qCLyLaVIxGI8rLy9esf/DgAUpKSgAApaWlePPmjRA2fA4cOACVSoXGxka/62tqanDjxg3Y7XbIZDI0NDTg9OnTuHDhAu7evevX3mQyISMjAzdv3sS1a9eE8oqKCjx69AhWqzXgRlaVlZWoqqrC+Pi431mwRERERPR3Yuglog0pLS3Fq1ev0NHRAYlEEtTuulNTU9Dr9aiursb58+dDuu/CwgK0Wi0qKipw+fJlUd38/DycTieqq6thNBoZeomIiIhIEPr5G0T017LZbFCr1Th06FBQ7ZVKJcrLy2E0GoVdhTfq3r17iIiIwMWLF/3qamtroVarYTQaQ+qbiIiIiDYvjvQS0YaYTCaMjo4CABQKxW/t+Pyn2Gw2WCwW4XNhYeEvj8UhIiIior8DQy8RERERERFtWpzeTERERERERJsWQy8RERERERFtWgy9REREREREtGkx9BIREREREdGmxdBLREREREREmxZDLxEREREREW1aDL1ERERERES0aTH0EhERERER0abF0EtERERERESb1r+zTTUYrul0rQAAAABJRU5ErkJggg==\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": 585
        },
        "id": "U4HyunoGV-Yj",
        "outputId": "43a13a4e-f5f9-4966-8a5e-a8e803aff7db"
      },
      "id": "U4HyunoGV-Yj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 1 Axes>"
            ],
            "image/png": 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\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": 574
        },
        "id": "TFrp6L5Oajkf",
        "outputId": "4c0d69f6-6ca0-407b-cb8e-1ef94ebd2fa2"
      },
      "id": "TFrp6L5Oajkf",
      "execution_count": null,
      "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": 697
        },
        "id": "vqDFIMa_UAVB",
        "outputId": "77559cea-d0db-41ec-b54a-a64ca755cb1c"
      },
      "id": "vqDFIMa_UAVB",
      "execution_count": null,
      "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": 470
        },
        "id": "WIGMO0jjw7iT",
        "outputId": "cbc75c1e-e945-4e67-855e-c874494f551e"
      },
      "id": "WIGMO0jjw7iT",
      "execution_count": null,
      "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": [
        "##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": null,
      "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": "a09cc2fa-e13f-4aaa-c90d-3243b220ef53"
      },
      "id": "6v5xNY0zGzNl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" 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": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_predict = reg_lgbm_baseline.predict(X_test)"
      ],
      "metadata": {
        "id": "6KH3ayV5H1pC"
      },
      "id": "6KH3ayV5H1pC",
      "execution_count": null,
      "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": "acba8e24-05b8-4422-ee48-f621fccee45a"
      },
      "id": "djmKFJ7yQSHq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE test score: 11459\n",
            "MSE test score: 688212425\n",
            "RMSE test score: 26233\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7gvE4wWQf7z",
        "outputId": "cd6af658-0035-4104-aa26-4e9dfba57ee5"
      },
      "id": "K7gvE4wWQf7z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181370.38356164383"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
      ]
    },
    {
      "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": null,
      "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": "f105f112-565f-4beb-d1b9-9cb089565a19"
      },
      "id": "g5mesgOG1sev",
      "execution_count": null,
      "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": "bfed0942-dfc8-4e9f-f708-7a3eb70a849e"
      },
      "id": "YuPkn4ma15D-",
      "execution_count": null,
      "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": "4f6127fe-f97f-42df-e079-0498e1a1b322"
      },
      "id": "9Q3rWf--1-XD",
      "execution_count": null,
      "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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AAreOh3OOgmWbYM4S6FMELoddnqN6QzwJb31lVwSP7w/XjQFX/TVyTSncPMn+rl0+Cn4+FiJxu2IXS8LZwyG7fmSszZXw6jxYthG2Be3vR246BKP2shVBWFwCeen25zDhaDhhwM59DgBzvoW3FtifZTwJpx0BPzvJ/vwbjq1nAUQT9md/xhDonGefhyfes8vy10vh8G7w0XewdKN9vD0KYNYi+PcHdhlHD4DCbMjPgKHd4ZG3YclG6J4HI/vDOSMgww/zlsPCtXB0Hxjaw/7+vb/Irhyfcjj0+nH9nf+xU6wDZHDoq6++moULF/Lmm2/SqVOnNpeJxWKMGjWK/Px8pk+fDsDll1/O1q1bmTlzJprWNHxgKBRizJgxjBgxgoceegiAsrIyJk6cSDKZ5KyzzqK4uJiNGzfy+uuvk52dzQsvvEBaWhrQFOj37t0bt9vNySefjKIoHHPMMXTt2pUrr7ySjIwM+vXrR25uLps2beLNN98kmUzy4osv0rlzU+vc1VdfTWlpKW+99VbjtJbBeTweZ/To0WRlZfH229v/A/jFF19w4403Mn78eP74xz+2ua3m2gr0zzrrLAYOHEivXr3IyMhgzZo1TJ06lbS0NKZMmUJmZmbjsuPHj6ewsJBnnnlmu2U6WJWHLQY+Z7Atume327k6jC9psCHThwV0qo3i0g2+K8y0F7Csdge19yZ0PEmDan+zioBlMWbl1j3Tg76+DM6kzpAV6yjetuvj8v9Q7iwXl341bpfWSQSjfD/4MTLX1t91cKrkvz4B33i7cl1x/buEnvxmTxf1gOAZ3QVTN0h8usme4FDJ/udJ1P5pHkaZXUFTO/gp/PRSnL32QmrflY/bAceuevlWOO9oyLzMDj7F9mkq3DwOHvxp+8te9BBM+Sx1WnGOHdS33GZbFZ+TBtkVkx19phk+O7jfHQ7Vrmi35yfHwKuf29ek9lxwLEz5JZRshV43gt6stjxhhH33Z339k7yz0uCje2BrDYz7MyR28U7lT0fDpJt2vIxlwbF32JW4ln4+Fvp2hBuebX1sTgecNtiuHDRQFbuCMHNhs+U0SG6npUJV7LtfzeVnwJjB8MLHTdPuPBcWroN36rerqvDv6+GnJ+742A4SSeWqNqc7rWfbnP5jdMC06K9Zswa/37/dIB/A4/HQtWtXVq9eTSQSwefzMW7cOO6//34+//zzlDsBs2fPJh6PM25cUyBx//33o+s6L730Eh06NLUknnzyyUycOJGXXnqJa665JmWfaWlpPPHEEzgcqafq0UcfxetNzcEdO3YsF198MZMnT+b222/fpePfuHEjiUSC3r1773C5fv36AXaaz+6aMmVKq7Iff/zxXH/99UybNo0rrrhit7d9MHlxmbXHg3ywG66W5ze1yq3MS6fXtiBYFooF1k48nTbqcjCorJYS02Jren0LraKQ1FTc7bVW7oz6ikbS6WBBv+7kVy/Cpe/bpu94dYLakiAZXXd+PPrI/5Y3BfkASZOqX83BN74XZihB6JlFe76gB4jYh+tTJ+gm1bd/hBVq6kBtbg1T++d55E7atQrUTnn+w91b7x9v2UHYrgZaP0aGCX+fDqMHwtgdjMq2ubJ1kA+tg/yGbbblg2/bL8/uBvmwc0E+2C3tO9vc+L958Pefwq3PpQb5AG98mfq+OgR3vQLrynfvu/fch3awfkT37S/zwkdtB/kAT78PbkfbFZiknhrkgx20Nw/yYftBfsPyLZXXpgb5AH99M/U7YJrw6//CJcfbFQ5xyDtghtcMhUKNrek74vf7G5cHGDNmDE6ns1Ur+MyZM8nIyOC4445rXP6zzz7j+OOPx+12U1NT0/gqKiqiuLiYL79scaEALr744lZBPtAYKFuWRSgUoqamhqysLLp06cJ33323awff7HjaOwcNxx8O736KRUPZTdNsLHvv3r1JS0vbrbLvS1VVVcTj8cb3oVCIYDDY+D6RSFBZmfrHrrS0tM33kT0wwExbKvyt+3CUBjygKDsV5DcwFYVuVamf8/rMPd/B09A0qtP9e3y7OyNRp7f6fMrKylKeQtv8M9dXtb7zoK+uprKyEks37TyqH5HmQX6D+Mqqxn//kN9Hg8bPY3efjxCJU1pZARcft3vr/xh93DQKVJufR3TPpO8dEHblJ2tZdqrNlp27A5lcvhFrVWn7C27P6rId/z7eX7SDnet2mtL+1lZFb1sdZSvWpUza0XUXdu5asn/I8JrtOWCqc2lpaY3B7o40BLgNAXFGRgYjR47kk08+aawsbNmyhW+++YbzzjsPZ30nrZKSEkzTZNq0aUybNq3NbXfs2LHVtOYpOM0tX76cp556iq+//ppoNPXH3NZ22tNwPO2dg4bj/yGdkb/66iueffZZli5dmvJDBlJ+yAeilqMNtawYuVyuVuemsLCwzfcX9VO470uI7eGGxpij9RNoo21MayxPbZRKv4tEs2W8SYPsSIKQu+knmhWO0aNqz+fQK6ZFemTf/0FSHJA3KKvV9IKC1M6RzT9zzyldCf0ttULuOaVr42fuPqYj8Xmb90JpD0xatwyMdbUp0/xjmlogf8jvo0Hj59Gr0M7F31UTT7S3+dQ1UJQFT70H1Xu5L8jBbmDTne3tfh4j+8Fny3Z/H72L7DSX+F5q8dhZAe/OB8Qj+9l56z8dBV+uTJ2Xk273P2jGefpQu0X/zdaNeO1yOeD4/hR2yEyZnPJ5HD8AXvq07fXPGWHn2i/fzvXI57I7eze3vTSrXeF02JWMBpn++s7ozRzRjYKBqX0Jd3TdhZ27logD0wHTot+jRw/C4TAbN27/oTOxWIySkhKKiorw+ZqGBBw7dizxeJzZs2cDdmu+ZVmMHTu21TZOP/10Hn/88TZfd999d6vlPZ7WHdvKysq4+uqrWbFiBf/3f//Hgw8+yGOPPcbjjz9O9+7dMc1d/6F26tQJl8vFypUrd7jc8uXLAVI6625v9B1dbx3BLl26lBtvvJHKykpuvPFG/v73vzeWPSMjY7fKfrDqkanw/nkaJ3UG3x6s8lqqknq71rIwtNSfmmaaZEYT9CqvY3BZLSNLKulYG8Ef1ymsi3LkxipUYHPAbsHPjCQ4alMN2p5usLYs+pVsxrcf/tif+uwxu7yO+/hOrDzHh+6xv/PuYzqS+8zpjfM7vHsBWo/MPVXE/cOpErjjKJyH56PUfzHVTA/Zj5xC1t9PQgnYd4xcwwvpMPMn+C8fCJoCmoL/kgEEfn3U3inXnHvszpZg5wcX1lfSXA57urNFZbYoGx6aCLfUPyzQ44I/XwoVz++d8u0rmmrnrrfXadbjguw0+/y0dHRvOy2kT5F9Lps75XC4sPWAFK28+Rs7v72hA3+GDx76qd0xtfk2PU7oXp+q6nZCQRZcdgLMvgsm39L0OfYosIP/Bsf1g/OPsXO6NRUKM+2gvLnMHdwJHNbDDr4B/G7IaXG3Wqk/1jl3Q//6ik3AZx9HSwEfXHWKfcwA155mH0PDcXbJgwX3wzWn2oGuqsKEo+DuC+Hxq+xjaS7d2/RdbktBJrx0s93pfEcuHwVDWqT25AXsfhbP3QRTbrWHigXwue3PAuCYPjDrLrvcYJ/fK0al7jOrjbv7Deff47Lz/5tTFPuu2Su32n01ALp1sM/Z09fa30WAw7vCS7fs+LgOIhZKmy/R5IBp0R89ejQLFy5k6tSp3HRT2x1gZsyYga7rjB49OmX6yJEjyczM5O233+bss89m5syZdO3alYEDBzYuU1xcjKIo6LrOiBEjflBZP/zwQyKRCA899FCrp9vW1tbu1gOt3G43xx57LB9++CHz5s3jmGPaDoKmTp0KwBlnnNE4LRAINO47I6NpBJLNm1u3JLz77rsYhsEjjzyScuchGo0e8K35e8NxxQqzf2L/DDYGTT7aYFGUpnBkAUxdDX/90mRVjZ21kOmGPK/971O6wOB8hXlbLN5YBZGWdarmla+WFTHLYvCWWvwJnS86Z1Oa4SU9rlPjdZIRTdK1OoKpKKzK9lOS6cUfTzJ0J29V74qrzg/QTYuRWOAnXtOFTZ9uJVK29ztLurKcnPf+qXizdm+Y2pLTvGw4ycOlZ1+Ap2Nmyjw13U2n1deRWFFB9W0fEn1r9/uy7Cla9wyyHz4FqzJK+NXlqNlefBN6U/vXL0gsq8CR78N/YT+0ogBagR/f2b3tyvt99nXO2BpGzXSj1N/dSb9+CFZdHC3fDrLynh9PziOnAOzdEXeKc2DtU/bwkz63/aoM2sGL32MPjTn7Wzt//Lxj7OCsLaoKn/4Jxtxrj5ICdhBZWpO6nNtptzYr2MGb3w1jh9rzpsy1c7QVIC/Dzk0Gu7Jx+wQ7v3pjhT3f77aH1GzI09ZUu6NoXG86rhP6w1drYGX9qDseh/1Db0hXyk6zA8crRtutymAf7wdL7FFUxgy2R575fpMdxDcE1IoCsYSdL/7tethUYZ+bjGYBclK3R6X5dr0ddB/Xf+c+j9wAvPIr+99ba+yguyHo/9f1diu2Ydrldjvt8uWkp+Zld8qFM4+EbbVN5a2tb/1tKGNN2A6oGz7Pr1bZrdSDu8JhXWHROvjXLCjZBod1tkeFGTfUHrY0kYSqkL1tsM/vgtXQowMM6gre+mvA0oebyqcodnlMCz753q58DO3R+vj/+wt48ho7laehQvHUtfDAFfZ3oyFQTvPCJ/fZ29RN+/vQcCylVfDRUnsfXfPtnPpg1P5O7eAubCOPCxY8AB8vtY9twlGpFcDDu8F3zY7NMKEuAvmZ9vySp6G8xi5Pw5Ct5x7d9Hl8v9EeDahXIRzWxV6m+W/uu/X29+awLtA5t+kzO/NI+zfRIdP+vY0aaHcubv5ZHDIkqG/PARPon3322fzvf//jpZdeYujQoa0C3eXLl/P444+TlZXFZZddljLP4XBw2mmn8corr/Duu++yYcMGbrzxxpRlMjMzOfbYY5kzZw5LlizhsMMOS5lvWVZjnn17VFVtXKe5N998k8rKyla3WnfWNddcw7x58/jzn//MM88802qoy2nTpjFr1iyGDx+eUsFoSC+aP38+p5xySuP0F198sdU+GkYmaln2//znPz+q1vy2dEpXuazZiGqXD4DLB6gYpsWWEBSmgaNF69t1R8ALwKoqk5eXW6yutgi4oWMavL0WFm0DrwZuDTaHAcuiQyzBpoCHOo8TXVVIOJyE6v9AR50OygJeDttSRVpCZ+iWWvIj8T3++L+rL8jg7JPTgQCc1NSy9c2Ty1j49x+QDtAOh0/jzP+N2u0gv4HpVBoD3ba4+uTSYfr5JFdUEpm2kpp75mKF9/JdC4WUz8nRN5vCTy9Dy20KeNOuaHrYn//sPju1Wa1D6nGqHocdiDaftjcD/JaaBzINARaApjUNpdmekf0hNNmuFORn2EMmfrMW/jDFDv5vOwtOG2IHsD633QLb3As320FzbsBuMZ/9rR0AnT7EDnjvuche1+tqCuo+WQobKuDUw+1AKxS1h+RsHvgEo/b+O2TaAdnMhZDps1uetRaBn6bZQ3M26JBpB3YteVx28Fu4nZGQnA7oW2y/dlfLlmenA35ybOq07QV4Di21bBktflctW+2P7GW/GgzuBo9t54nQLmfqfnsXpd412F75GspzUTv9Ovwe+9Vcy+9Kg7yM1tMKs1vvI20X+0Epih1Ijxq4/WUajs1J62cwNAT9DZp/HgM626/mmv/mBnaxXy1pWuvvW8vPQvxoHDCBvtfr5aGHHuKmm27i5ptv5sQTT2To0KFomsbSpUuZOXMmPp+PBx98kNzc3Fbrjxs3jilTpvCXv/wFVVU5/fTTWy1z++23c+WVV3LVVVcxduxY+vTpg2mabN68mU8++YQzzjij1ag7bTn22GN59NFH+cMf/sBPfvIT0tPTWbx4MfPmzaO4uBjD2L3RS3r27Ml9993HnXfeyYUXXsi4ceMan4w7b9485s2bR//+/fnrX/+akq4zZswYnnjiCe677z5KSkoIBAJ8/vnn1NTUtNrHqFGjmDx5Mr/4xS8455xzcDqdfPnll6xevTplWE3RRFMVOrVzl75XtsofWtyE+W2LDIqkYbEpCMXpDmastZiyzOStZQbRFsNtdg3Agl/kMf/bKIYJIwblcNGtW4jvof53f7stl8N6tx0YfvfvPd8C7s5x0Xl0Aa50F/0u7kZGt50fZeeHcvbJIePXR+M7pw/BJxdi1iXwndWLuoe/Iv5VKVbCgNgeGG1Ig6Jv/g98TiJvrMA9tADP6C7yULsdURQobnYtP6I7vHVH6jI7Sp1ovm7zgHt76x7fYlz0NG/roC7d2xQoBnw7l0IjhNhvJE2nfQdMoA/QrVs3pkyZwssvv8yHH37I3LlzMU2TgoICLrjgAi699NI2g3yAvn370qNHD9asWcPw4cNThs9sUFBQwIsvvsjzzz/Pxx9/zDvvvIPL5aJDhw4cd9xxKa3hO1JcXMwjjzzC448/zqRJk1BVlcMPP5ynn36a+++/v1Uv/V0xatQoXnnlFV544QXmzp3L1KlTSSTsCG/s2LH84Q9/SHleANidZB5++GEeeughJk2ahNfr5cQTT+Tee+9tleY0ePBg7r//fv71r3/x1FNP4Xa7GT58OM888wxXXdX2eLRiz3BqCt0y7X+f00vhnF4q+jiNG2cZTFkBcQOOLlJ4aayKy6kwcmhTS/AJR/p4f+4PGOqu3hkn+Lcb5AN77Km4ilPBX+Cj/yXd6H9ZDzT3TtwG34ucvbLJfujkxvcN4+5bcZ3IjNXEPt9M8O+79/RcJdtD4ceX4hpo59u6bttLOfJCCCHELjpgHpgltq+iooKrr76a8vJyHnvsMQYPHry/iyT2sVjc5N+v1fLJ/DDB3Yz3Rw7xcMd1bVeUG8y89BNKv6jY4TI7Y/TDw+k+9gekImxHMplk0qRJAEycOLFxVK09ITR5KRVXzoTozld2PGf3osOrE1AcB8y4BkII8aMRV65rc7rbenIfl+TAJX+dDgK5ubk88cQTZGdn84tf/OKAH+te7Hket8oNl2TxysPFvPzQ7vUBCUba74Nx8lNHk9ZpO50od4HqOvguLWkXD6BL+FdkPXgiasd08Dq2e4VUC/0UlVxHwZvnSZAvhBDigHVApe6I7SsoKGD69On7uxjiAJCRruFxQyze/rLNLVvdfpK/K83JBR+exneTVvHlX5bAbvTPVhwK+YO30/HwAKcoChm/HEHGL+2RuSzdJPLWKvSSWryn90Bxa5AwcPaR8aOFEGJ/kxz99kmgL8RByLJaDPGyExI6hCMmfl/7LdADJ/Zi4MRezLziU0rnbtvpfSgOhWPuGowvbx+OArMXKQ4V/zk7NzqOEEIIcaCRQF+Ig1CaTyGe2LVAX1PB7dq11g93+s7lwCuaQqeTChh5zxF4cw+NIF8IIcSBTlr02yPJpUIchE48agdPpNyODnkaDseuXRS7junY/kLA2dNP5JQnjpYgXwghhDiASKAvxEHokjMDnDrSh0Ozn6+i7kT8ftaJbTxSvR3dxxXTceQOHhUP5A3OJrtPGw+jEUIIIfYiC6XNl2giqTtCHIRcToWbr8jmpsuyUIAvF0e594mqHa6zfsuuj5GvKAqnPTeSmjVBgpvC5A7MZOVr61k3cxNG3KTjyHwG39B3N49CCCGE+CEkqG+PBPpCHMS0+qb8o4/wkRWoprpu+3n7ieTuPzIjs0c6mT3sJ9oefk0fDr9GOqgKIYQQBzpJ3RHiEPH7G3K3m8KjKHDKMbue1y+EEEIcqCR1p30S6AtxiOjb3c1/Hyige6emG3XpfpWBvVz87vocDuvj3o+lE0IIIcS+Jqk7QhxCsjMcPPaHAsIRE02zn6grhBBCiB8nCfSFOATtzEOxhBBCCHFok0BfCCGEEEIcdCQfv33S7CeEEEIIIcQhSFr0hRBCCCHEQUha9Nsjgb4QQgghhDjoSOpO+yR1RwghhBBCiEOQtOgL8SOSNCxeXWLw4bwwzs0Rcjp76HJEGif2ctA9y24Z0Q2L2St1dNPilD5O3A5pMRFCCHHgkRb99kmgL8SPxJaQxbEv6pRsSUBSBWcAKhww24JZSS7qp/CX0SqnPxNi2VYTALdLITPfw7GdNSaN1wjIuPxCCCHEQUP+agvxI/HnL0xKNsQgZoBpgdsJqgqKAqrCy8ss+j+RZNk2CxwaeJzEAz62JjTeWA2dH9epjFr7+zCEEEIIsZMk0BfiEJeIm7zx3Fbe+CwCen2g7tRAbXHLU1WI6IDTAZoKHkfKMrUJhTuer6J2fahxmpk0qJ62jooXVqLXxDENixVf1bJwViWhmuQ+ODohhBBCbI+k7ghxiHv132VM/TzK1t4ZQMKeaLXRMm9ZYNgpO/5EEtOtEEVLWeSj5TrPPr2Acb/pTc8xRXw2+A18G2oBKMv08+Wow1AS9jacbpXL/tiD7oen77VjE0II8eMlOfrtk0BfiEOYZVnMnRdiavdiTE0FlwoJE5KmHdRrLW7qOVU6bavhVx8t4LWj+vNpny4ps/MSOp8O7sv8GTpFzy2hZ24ulT26EHK52ZKZTrdgU2t/Mm7y7n82c/3DfffFoQohhBCiBQn0hTiEKYrChkwfRkMKjtcJDgN0E3QDNAUs7NZ8C1AUxi9dgz+pc9LqzawvyGFDRhpYFr2rghxRXoNqwRafl80dctncIRfNsnBZFhpQ53ETiMUb91+xKbY/DlsIIcSPgrTot0cCfSEOcV1yIDcWY5vHb3e8dTpTFzBMMJvefjygK0O2bCM9luCS70qo9rjQNQ1/0gAggoLebBs6oOo6Dsui1ushLR5Hrc8MMtwat160DHfAwfhLO3DMcQEMw2LRwjDl5UkGDPThS9P46uswfp/K8CP9eGRkHyGEEGKPkEBfiEPcV5aHbbn1efJm69z8onCMLV6P3aIPLO2Yz6q8LLpX1lKTk0m6opCoj+vdsQQV6WmttqGrKg7DwFIUkoqK2zJJqCrO2iQ+klAHrz0QYdljIfQlFSRVjc2FufyvQy6mQyFp2cH9C//aSq/NZXTqk8ZJN/ckv2frfQkhhBAgOfo7Q5rOhDjEhBMWE2fo+O5PkP9wgq8CmfYM3URNmq2W9yUMCqLxlGkvDe6DapoUlmxBicUb6gBkV9XhTuittqHUd+7VFYVv8nP4pj6lp7mirRVo80vptr6KIUu3cOonS+mzciOKbuE0TFy6gSMYJ2hpbPqmmtd/vQRDb11eTwl0+YvCe/kvM2vwm1R8uLnN8xCsiPPlKxtZ8NpmwlWJHZ80IYQQBx0Lpc2XaKJYVlvDbwghDnSWZfHCm7W8MyeEbkJGDy+rCwJsqLVYV1O/kIrd4dayIGqQnkiiuDXqPM7GFnwsKAhGKfO6GzYMNTFOKtnE1qwAHqeTQZU15EWipAWjeGoifDa0D0r9xVSxLHy6jmaa+BJJ8sprWdwln8JYgrDLiQJkRGP0W76W3usq8Eebht00FIXJZx9L3OPEF47Sf+V6/JEYukPD0FR6je/I0F/0w1/kQ3WoxINRPuk8mc0ZAQxNRQFU02L0pGPpdFwBimr3S9i4qJo3rl+IEtexFFA8Dg4/owPdR3WgeHgOiqN1G4elmygOFSNp8vHtC1n5SQWKpjDooq4c9fPeKErbfzwSFTG2vrAaM2qQf0E3vD0Ce+LjFUII0Y4a5ddtTs+07t/HJTlwSaC/h40fP57CwkKeeeaZ/V0UcYgq3ZrkodkxZi436LElmDJvWV4aq3PT7RQdi6Z+Sopij7STMAhgUudxpayn6QYGir1eJAGGZY+hb1lMXLWeHsFw47J6NMlrh/chUzcwFKjWNLpEovQKRZqCYbN+H4rdumIoCsd+tYTBK7a2Op5Ph/dhdfdCAArKKgiEY4BFVlUdnlgCV9JCNS28nXzUFWdStjlBNN2L4XTgicbI3lpN3OWgrkMm3jQHvQIWtXO2EPU6qSgIYKoqrniCjmVVdC6twqUbaP1z6PLcSVhJKH1+FbEP1mOVVBLqnE1Fh0xK46nDih7/q94cdmE3FFUhvDlMyczN6GGdQLqD9dfObXw+geJQOHz2aaSPyEd1q43nw0yaVLyyhsiSKjJGFZJ1eufGbVtxHZwqirrzN1gty8KKG6iepuxLSzfqy6BtbzUhhDik1Ci/aXN6pvW3fVySA9chm6Mfj8eZPn06H3zwAatXryYYDOL1euncuTPDhg3jzDPPpGvXrju9vfHjx+P1evnf//639wrdjGEYjBs3jm3btnHttddy5ZVX7pP9igPbspUxrnykmnl+Hzg0Vhdn0zUUo1dNBBUoCMbtQB/soL0h80W1QMN+KJZaHwg21PEVBUu3oL6zrb28HaD2CIZTgnwAxeskaVmUuu3E/Q6RKEPLK3BYFrqqEnG7MTQNh2lhaAoKFp54goqcDHRtGw4jNR0nUn8nQTVNwpnpRDLt8pcX5DBg8Vo8cTutKLoxgrU1QbBfcWMAHfN6KO3cAVCwFPBtDOFatIlsh8qio7oT9dvbjvk8uBNJumypRAHM7yv56ri3MHT7GHSHQjgrj7ihEak1wZMaLH/zh0VE/28WqkNhvT+NSH1FKbMqSkBvaiuxdIvF42YTD5l4OvspPDkX7duNVC6LUBlxg6Lgun8J/UY7yenjITpvM8Fvg+Bzkv3bo/D97kSMiG5XEuqHPjUjSRSX1ngXIjRtNdtunoNeUodrQA4Zw7PQ1pZhfLEeDBPHiE54XroYR5dsrKQBhoniadEBe18LRiHdu3/LIIQQP0KHZIv+pk2buOWWW1i3bh1DhgzhqKOOIjc3l0gkwsqVK/nkk0+oq6tjxowZ5Ofn79Q2dzbQTyQSKIqCs+XIJrvo008/5ZZbbqG4uBjLspg6dep2UwfEoSeatDAt8LvszzyhW8R1+Ouj5fxlqwsr4AFf03esQzDG8E3VbE5zs7BTtt3CbKT+tDXFwjDqp8d1u4W/Pq3FlTSJN4ypb5qNnXaPqKjm3PVbWpXvo4I8NmQEUC2Tc0o24Wx2GTEVhVqvF0sBzTBJOjTimkpeVS1dNlVz+LINVGf4Wdc5n5jbyYruhaAqeBNJPMlkY6fehMNB7rZaeq3clLLvzZ1yiHtdJDwurDZawbsvK8XQVNb2K2ycVrCpktyyGlTTwmXqbO6UQzjNQ3ZZkJyyIBG/g1DAhaUqWEDc78GqPx+eeIKjlq/FadoVFBNYkZ9PnddDzrYI/khqnwULSDY+aMyigBo8JAjhpVwNMNhcTgYhYnjYTEes+q5SCiaJ3ADxSh1nmkr2ERmwrgprYw2Kx4H/0gGYTiehZxalfLYuomQQbNyfgkUCJ/HcbHw1FSi6iWt4IYnjB6EEPPgvHoDic6BleVE8bbT1xJPwr9mwcguMHoh15pGwsRLmfIvy7XoY3gt+cgyE4+B12U9S3p65y7AuewRl3VasvAz4x0SUS47f/vJtMU2ojUDWD+yYvae2I4Q4YFRvp0U/S1r0Gx1yLfqxWIybb76ZTZs28cADDzB69OhWy8TjcSZPntxu4KzrOoZh4Ha7d3r/Lper/YV2wrRp0yguLuaWW27hl7/8JV9//TXDhg3bqXXD4TB+v3+PlEPsOxURizSnxW2fWDy92EI3IdcL3X0mi0sSxNxOMDNAi4M39ae7Nd1DpcfJ0g71+eFtjK7jThgU1kRZ73SgN8vPt6I68WY564plNbb2r0v3k1SUlEDesiz6ReL00GtQdT1lHoBqWfjicZwNZYhDQlXZmpHO6oI8thRmUZGTgVX/+3MaJo5YgpxgKCVodOk6CVfqcVqAw9BRoybuWJy6rICdltRMZX6ApLOpRT5rWx0dtlQ3vk+qDtzhBIHKKPmldoDsi+mkhZOUFvlRFAV3NE7S68bQVLqUVzYG+WB3e+hYW0vI7SLqdeCP6JhKw7GDmdIRTEHFIJ0I6UQImLU4MakimzD+xiDfPjYVrSKEEwf+YIzEJ3X1cxw4Yjqhfy3GRYQMksTwEsduIfcQI44bEwUXcVwk8RDHU1FKsv6dPr8SZf57xPET+cPs+qIpuPtn4uiTiZGdhdNr4a8uQX11LsR1TEB/5EOSagCnGUcjgUIMlRlww7NQHQK3A+uMoVhjj8ZatAnl8I5QW4fyxWqUpI71zteQSNhZZNtq4dJ/Yt36H5TXfw3H9sN8+UvMz0tQeuWjTjwGJd2T8lnyymdw7bNQE4bibJhyCwzqCh8vg/e/tadddhwUZjWtE47ZQ8YGfE3T3vgCrnoKqsJQkAlnD7O/N2cdCWOOSN1nZRDSPfb8Fz6GecvghIEweiC8tQC+3wgj+8H5x0BdFNwO8Lrt8/HoTCivhbOH28s/9T68Og8cKlxwrP0KtHFtDkVh0hxYuhFOHgTnHdN6mcYvigUVdZCTDqoKtWH7d+Or/zsVjNope2le+HIlvPSJfS5G94dpCyDDBxeNhKIcyGxWlpbb+WoV/OcD+3xddSoUZW+/TC2Phfr9CyH2u0OuRX/KlCk8+OCDTJw4kRtuuGGn13v66ad59tlneeWVV5g2bRqzZ8+moqKCJ554gmHDhu10i37LHP0rrriC0tJSZs6cicORGrR8/vnn3HTTTdx6661cfPHFjdMrKys544wzuPLKK5k4cSJnnHEGI0aM4N57793u/m699VYee+wxlixZQkZGBtOnTwdgw4YNPPvss8yfP5/a2lry8vI4+eSTufrqq/F6my7EJSUlTJkyhYULF1JWVoZhGHTr1o3zzjuPs88+e6fPo9h1yystLp1p8PVW8Dsg3HpQG/uPu6I0/b8NzqTRFOAmU8fGb+A2DOLBhB0xuzVwqvhiSQZUBfFaFmvSfGx2OSmIJuiU1HECaZEox24us9uoLYvKtDQiXjsg0wyD3FAoZR+WBZYCKKmt7Zt8XpblZTOgug5Xi8tOTlUNHsvA1FJTZjx1UXqsLm18H0pzE8yyAzgLqMsKtGrVN4G4y4nLMFAMkx4rNuMPpY4qlHSoZFVEG8f7b1Ba4KNDJERuXRgFqPZ7SYvFSTdSR+1JKioxy42pQEluBiG/CxTwRXQyy2OoVtNn1JUy0rGDnwgedOzGgCRaq9Eh3ETRcbYaDk3BRMXCRZxctuJAp44MasjBRZzmA6j5COEh1niOYvixKxwJkqQG0iYKXqqx0DDQyGMNYGHiw6ovp46TCAF8hHAQR6MWCycmrvpyRTHwA040oijNvnj26XVgR54GEEepn2r4sjAiTXellMOLccy/HaWhcrelCoqvrd+Ihf3JNvwOFFK+4GMGw5u/gV89b9+N0E047yj4z432dnr/gqYOK2r99up/aH+5BG6fACXlcMk/YN4Ku9U/zW3fydieTjmwqcq+q3HxSHjxE4g1dTQn4LUrAi1dMRqevhbqU99I6nDkr2FxSdMyR/aEL/5qB/LNzV0GP3vcvtvSMRs658KXq8HlgKtOtitEL39mn6ORfeHD77ZffoDTh8ATV8Otk2DaV/Z2rj3VDu5vf7HZsfhg6T+hOHf724on4dqn7PMAcOnx9nG69nPamDikVSm3tzk92/rrPi7JgeuQa9GfM2cOwG4Hp7///e9xu91ccsklKIpCbu4OLmw7Ydy4cfztb3/j888/57jjjkuZ9/bbb6NpGqeddlqr6aZpMnbsWBwOB6eddhqvv/46v/nNb0hLa33beevWrVx33XWcfPLJnHjiiUQiEQCWLVvGtddeS3p6OhMmTCA/P5+VK1cyZcoUFi9ezDPPPNNY+ViwYAELFy5k5MiRFBUVEYvFmD17Nn/605+orq5m4sSJP+g8iO27ZKbBwvo+qm0G+dAU3G/vLpRlkdSaBQWaktKq7zBNutaFWR/wg8cBTg1cKmlxnXM3bCVQn59/eE2IT/Oz8DWLoUI+L1gmWn1ufUOQD2BoGlGnE2+yKcBxJeLEPa1b8/KCIZbl52A2VFia0R0aptG6zSGW5mZj1zyytgWJ+p3o7qZLluHQUCwLq1nlxwLiLjsFJwmkh6MYWutzZqqKfUehRTmyQ1HyQ019ErLDUeK07txqWComCuVpPkLpTXf8In4narZFZqVdMfATJa0+yDdRsKtONhUTo8W2o4oTVxtNLxYqBhYJ3FSSTwGbCFBJkAxajpIcxdsY6DfXfN8NFCwsNHJYh4kdClto2M85bgjJVXxEsFAxcQAeTNKbzgVeNOoANSXIb00D3EAMCzAiqcduLd6ENX0xynlD7QkPz2waGcp+fHP9P5v9u8F7i+Do36YGy/+bB13y4bPlqetj1pel/rzd8yr86iz42WN2kA9263x1agW2lYZKQCQO//qg9fy2gnyA5z+EXoVw53n2+xkLUssN8NVqeOIduHFs07REEs57AMpq7Pebq+wXQCxh301orr0gH+CdhXDavbBic9N2/jnDHqkr5Vgi8OB0+OfPtr+tv0+D5z5sev/ch9C7CH57bvvlEELsNYdcoL9mzRr8fj8dO3ZMmW4YBsFg6gglHo8Hjye1hSstLY0nnniiVev77jr11FN56KGHePvtt1MC/XA4zEcffcQxxxxDdnbqLdHp06dzxBFHUFRUBNiVhcmTJ/Puu+9y3nnntdrH5s2b+d3vfteqcnPPPfeQm5vLf//735RUnuHDh3PbbbfxzjvvMH78eADGjh3batsXX3wx1157Lc899xyXXXbZHjsnosm2iNUY5O8Wq9noOmqzgFZVwAUYFkO3VNOnOoTXMCmtDjGzk90vRYkbZIQTbEjzk5VIUhSOogBDqupYkhkgodQPoKkqbAyk07O6lq0eN0lICRnrvF6WB9LpWlNLp5o6HLqO7nBhNBv9xQSqvB6ywxFqXE4KYqkt7IFQBFWFhKdZmpxl4UgkqczNZFuHHBTTJL0uhD9iB1CWqtQPr2k2pgElHRpW/XkwVRULhVCaG38ojlZfkTAVCKW7UAyL3IqmYMxUwKfHqfN52JodQLUsCitqccQNKhUfGVYUFQgqbnRLI4KboLd1Wl/cq+EhjhOdzpQ3tiPbrfdNn5GKhYWJTn05VTcJVAqsuu2MAm2PYOShBicVKFh0po5yepGkKU3FQm38Spj1AXvD/ow2tuogXj+/YS8GGkEM0jDxNc6xt+fEIq1F+TQsnI3VgtQStwzJtfr3Ci0rKADWtmbBtbv59WYnbjwvWd962nvfwOryNhZuViGJJiEU27nAeE9595umQH9bXdvLvPNNaqD/7fqmIH9PanbHrJHRRoXtuzbOb3PvLWo97d1vJNAXe5n0XWzPIffArFAo1Gar97p16zj55JNTXq+++mqr5S6++OI9GtBmZGRw3HHH8emnn6ZUND744ANisRjjxo1LWX7x4sWUlJSkTO/duze9e/duTMdpax8NAXuD1atXs2rVKk477TSSySQ1NTWNr8GDB+P1evniiy8al2+exhOPx6mpqaGuro6jjjqKcDhMSUnJDzkNe0xVVRXxeFOQGAqFUs5rIpGgsjL1dntpaekO35eVldE8g21f7iPTDTm7m8pq1Y+qoyqgKHgSOo5Ys1sCisKgbbUMrqjDW/+He3XA39T6rShsTvOwKc1DjdtFldtO1ahwOQlqGnFVJaaqhFSV54/oxd+H9OPRPt2Zm5GW0m5b59BYGfCTEwzh1HUUID0UwplIgGURdDr5Lj+HdR1ysRwa3kSc9JogjqSOO56g06YyAuEI/lAEVywOpoWqG/hDEeIeb2M6j6Wq1GWkk3RomIqCrmlNdRzLsvsGRGIprfQxrxvDqbEtz09thpu6gJuKfD9Jt4OtxZnUZXowVIWo18HWAh/VmX4W9e5EaV4mm/OzWNinM9XpXrZkZbDOnct6LYdtjjT0+gDa2cYDvZxJk5DLQ40rjTI1uz5FpyHQbyqbHepalDoyKHVmEtS8OCz7iNpos7a3TYR81jUG1Q6S5LKuxTL23QQDjURjqo6FhyC0SKtRMEmjdSBsly2e0oegab22/mxo9XcCWi6bWrmhMcy3UGjxEDO3A/XMQUD97+Oak1Mrr+1oMwm1R0FqHnqLctjLdIBMP2bxTuag7wk9CwD7WlJ1bI+2j3OAPQRr47Wkcy7sjaFT2xgNyWrjzmFy3NDGf7d1TYx1zGy97R4Fjf88kK67so+9sw9xYDrkmmjT0tIIhVrfcu3YsSOPP/44AKtWreKf//xnm+t37ty5zek/xLhx45gzZw6zZs1iwoQJgJ2eEwgEOP741BEopk2bhsPhoE+fPmzcuLFx+tFHH83zzz/PqlWr6NWrV8o6HTt2RGuR27xunf3H/+mnn+bpp59us1xVVVWN/45EIjzzzDPMmjWLrVtbNzHX1W2n1Wkfa3n3o2WlzuVykZOTkzKtsLBwh+8LCgpS3u/rffzteJWr3jPbb7NsmZ9vYafoWBaFoRiBWJK+FUHKPC4W5GfgiuuMWl1KzN3Uur7Z72m12TVZfooiccJOBznxBGvSfCnzHaZFIKpTkRvgzOUb0SJRErpBtc/HsoCfOlWlIJkk4nSSEbeDt/TaCJ3WbUM1LUIeF7VH9KajUyUtqdtpGx4nfVZvILMmiKmqhNK9GE4NbziGJxxDw8JQVfSWI7ooCsHMAE7drtC4w3EUBRJuJ55onOzyGtJqI2zrkI3udGAqCq5oAt2lEW0WTBmaiulUWdfXvruh6gbplXUEff6Uc2xqKuuLsnFHk0R9Guk1SeI+lbTyZP1xJgn7HRhOO/hVTYvM6himYge9W51Z6AkHllcnJ5KoT5Vp+gBrFC+goFgmblPHYVmYKKgobaTBWPippiUXUVQSmPX3WZK4qCMTF1FcRGjoEFxHFgGqMXGQxIVGggw2oLXZzt+8uqG0mG6Q2kZUv5zXjWmpqLFw07RWyyUat+agFoN0O9c/1482+SqUjnan2sbfx+zfw0n31OfkN/uFODTQU8utnDHEbvXeWGFPyPTDH38Cs7+zc/fb4nfDVHvUDvXvE+HifzS1Zmf47FF6WlIVGNoDlmywU10AirLs1vaGdLlMn52600aneHLT4Q67ldvlcpE9oIedJ3/Ds037Ls6Bm+3GnsZrSX4m/HYC3NusgcqpNQ2LW5xjpxFV1f/9ywvYefPbSyECu3/BXy6FX//X7sQL0K0DygXHwF/fbFpueE+cN5/Z+LatayJ3XwwfLLU7IwPkZzQeZ8px1Nvf113Zx57fx/4gT8Ft3yEX6Pfo0YOFCxeyefPmlPQdr9fLiBEjAFoFxc21TOXZE4455hiysrJ4++23mTBhAmVlZSxcuJBzzz03ZRjOSCTC7Nmz0XWdSy65pM1tTZ8+nV/+8pftlrmhZn7ppZdy9NFHt7mtQKDpCZ533nknn332Geeccw5DhgwhIyMDVVWZO3cukydPxjR3lHsrfoj/O0zlmCKFWestOvot7v7cYklF6+WcqkLSoingbxa4lqZ7KU33sionnbGrSjn32xKOW7KanEgMXVVZ062ITR3zSE/qhFsEz6amUJ7mJjOeYEF+JnFVq29ZbqKZFt6kzhFbtuJpCLBq6siNZ7I8Pw/L4aQ0I50OoTAO3SSrItR4+U2LJTh5wXK+HdKrMa1GVRTW9ihGM0z8kSimqhD2eUmvDaOZOpnhCKppophmq862imlXirKqg3RaV07c6UR3KBj1IwdlVQfJqg6ypSiXjJoglmJhqU6SDs1eV1GxNDXlD4Tp0HBbJtE28vkNTQOSoCqEMp04DIvybC9ZVUk0w6JwU4So34FD0fGFdRKqg+ap9141THakFgM3CbyNAXQm26i2uhHQm/LpVUyc6JiNj3Fv+ByU+tSZ1pdsAwcWjpQ/dyo6Gia1ZKHXp9U4SGCi4SKO5lXx3Hw0PFKBGY5gOL04k6kNJHHSMHDUVwTqn4KsWTguOxIzrmDNWILiVtHG9kX9w1nQvQArnoSpX0NlCE7sD7e/BNO+hvoeABYuO/+/fzHKkxNxfLMWjugGxw9odVyAPXLNin/CpQ/bqTkFWXD5CfDzsfYIOHe+ZAey5x0Nj15p/zbe+MLuFDvhKMgNwOHd7AD679PsoPiikXBYZ/B74JzhTaPD/ORYO4Cf+TV0yoWxQ+10nmWboE8RrNkKHTLgzCPtDqZbqmDafLtCcfZwO1/+nYXQOc9et7Qanp0F07+CDdvsUWsuHAk3ntH6LsM1Y+zyvvmlHXyfM6LtUWvuuQjGDYPPV8DgbtCvo72O32Ovk9Dt41cVe3tuJ0ydD1VBu9IyaY5dIelZAGePgCtPtss1YUTqdvwe+3x88j0M6AQnDWr782muVxGseMzeP9j7b/NuihB7jgT67TvkAv0TTzyRhQsXMnXq1F0adWdvauhQ+/LLL7Np0ybee+89LMtqlbYza9YsIpEIN9xwA506dWq1nVdeeYWZM2fy85//vN1x+hvuTKiq2ljB2Z5gMMhnn33GGWecwR133JEyb/78+TtziOIH6pej0C/HvmCd2dPi7bUWpWELrwOiusLp3RS6Zihc877BM9+2WLl5C7Sq8F1ugDvnLMRX37ruME16r9nE8uJcoi3HTVcATSHq0JjZOZ8kkB+Kkx1vSgGygDqfkyGlFU1Bfr2u1XUsz8tFURTW5OeSFYvTe1N5q0uvO6kTqAmRcDsJpXkay2w4VOoCTS1FhaWVhH0e1nQpolPpVtKDIeoC6Y3La4Zhb9u0OG7RCtZl2q1Omm6PymJoCqaqUpUTIKuiGs20iHucaEmdpMeF1ZCWZ1pYzmYVCMvCG0viTdayJT+1JcsZb+pobKkKYZcGXgcdq+qI40K1FPJDIfzECWluolrqELvZZg051GJhEanPeVfRKSefDKKEcZNEw4mBnziaQyG/awRzUx0hsomnBfAc0QF/qAL/kjhGyI1Wn1dvAZV0omWruyfXgRkCTzyOYkVxEcNFjOQlozDHD8R/Vi/7qbp/tvPEVYCHZ8Bj72CZJuaoI1CGDMAzpg+qW8N4+zuUogzUMwagOLQ2uifbFLcTLjiqacLU2+Clz+Cvb2FVBFEO6wLXnQxnHmE/EOz4/tvZUjO9iuDLNsbEvmK0/Wrp8jam/fos+9WeHgVwU7O8+FMH26+2FGXDdc0GUuhZmLpup1w7ML/novb3C5CXAVef2v5yw3vZrwbXjGn6tx/4v5NTl79wZNO/t5cvn5+Zuh2AI7rbr12R6YefnbRr6wgh9qpDLtA/++yzee2113jhhRfo379/m+Po7w9jx47l5Zdf5u233+b999+nS5cuDBw4MGWZadOmkZGRsd2Or7FYjLvuuouPP/6Yk08+udX85vr06UOPHj14/fXXmTBhAsXFxSnzdV0nHA43ttwDtBxptaKigqlTp+7G0YofwqkpnN2r7VaK3wxXee47g0TDDZbWmRWkRWONQX6DqNPByjQ3edEkXqdJmc9df1fAnr8+3Wd//gmDcp8LS1EIJHR0RaEy4CbmctC3srZVebQW35lt6X66els8S8KyUHWLw+pHFgmme/nu8K4k3E6wLJxx+yFZBaWVZNSFcSZ1Vro7syUvl8KKSrzhMLrbjWJZaKaJrqoMXbkWgEA8RoXD7hzq0C0cusXGbrl4gvbwmKYCjqRBUlVwRuOYDU+bNcEXTxLzulBNC39dhKSp4YqbpNVGifpcqJaFI6HjbPbEYF9tEr8JnoiOnwTpLfLM0/umU7vOxKrv+Ov3W/iLfMTXR6hV06nTs/EeVUjuZb3Jq4hQ9ZevyIjZKROKWyPrzqPIum24HYQDbd0Y15duJfR/LxH7Zgu1iWySuHGg487QUNOcpF83lMCdTcGd+ckqrOVlqKN74+vVoY0t1vvFOPjFOLvuR8pNCRzXHredlXbCJSPhkpHS7iaEEPvBIRfoezwe/vnPf3LLLbdw2223MXToUI466ihycnIaO5XOmjULTdPo0GEHf/TaUFNTw7/+9a8255155pk7fMpu37596dmzJ5MnTyYcDre621BSUsK3337L+PHjt9sZ+Pjjj8fhcDBt2rR2A31FUbjnnnu47rrruOiiizjzzDPp3r07sViMTZs2MWfOHG688UbGjx+P3+/nqKOO4p133sHtdjNgwABKS0t544036NixI7W1rQM8sX90z1R47zyVu+aZrKq2O6FuiqSGULlRnZhDa2x9/6xHR/5zzCBiLgeaaTKoJoSpKJSn1ad8mWCp0PQULdjmdrCtPsXHkTAZtXkLc3Jz6FZRk/KArIU5mXTevI0jNpQR9HoIZnpJOh12H+H6ZRTDwtFs6Mz0YJRuq0tZMaAzKApdS0rJrGlKGwn77ZSFsM/LuuIi/NEY3mQSRVFIpHswQ3G6lNm5TdlRO4e6zm0fi//C3nQ/vgOL7/7GznBXFDBNtKSBoalopoFi2Td7NYdKz2wIrgyiRnScSRN3rpsz/nMM1WvrqLr6I2pcTurS3CgW5Hlg2NzTqf2igsAR2YRueYfKT5r6uQD0mHQSPQvSqJhTir97OtnH2deY5JYQ5vsbye6URtqJxY0P68u+82gi75VgVETxj+2OthM9sx0DOpD2xa2kAf4FZcQXb8N7bEfcfdvuTKoe3wuO79XmPCGEEIe2Qy7QByguLuaFF15g+vTpfPDBB7z44ouEQiG8Xi+dOnXirLPO4qyzzqJr1667tN2qqiqeeuqpNueNGDFih4E+2J1y//nPf6KqKqeffnrKvGnTpgHs8A5EIBBg2LBhfPnll5SVlbXqPNNSnz59eOmll5g0aRKffPIJr7/+On6/n8LCQsaPH8+RRx7ZuOy9997Lo48+yqeffsrbb79Np06duP7663E4HNx999073I/Yt0Z1Vvmoc1PayTOLTZ5abLKt1sRTG2dt3xxeTxzGBbMXE3M6+NfIw0nUd8Y1VJXFmemcWVLGBz0KCLqbUsDyozGqVA0dJWX0QV1V+TQ3C8O0uP+wPozbuIWiSIzvswLM6ljAVV8sJRCJ49IN+m4sw3BoWJpKSdd83PEkmRUhMmpSOzU2vrcsHM3G4I+7HKzrancSUy2LvOoaNFVh+DmFnHx1Z0xT57k//RfzI1B1CwWFnGiEnGgYrXc2/Z6009R8isXcOxcCdqVXM0wyc93Ej+9G9dJq8nqlc8adfQjkuDCTJuVzSjEiOh1OKcKR5iR/ZAeiR+ex5qpPiC+vIXBcAZ0fOgZ3pzTSB9gdRtM/uBj15g+pfWMNWgc/+fccje9IO7DvdEXPlON1FqWR/dN+rT5LRVPxn7GL6RHNeIcV4B224+uAEEIcqiRHv32H3JNxhRBQFrS46b+1LPumjqUd81rNH7uxnIBu8FWHTGo8Toprw5y0bgvf5QR4v6ig9VCFlmU/bbTh3wqNefOnry+jKjcTgEA4ypHLSjhifRmbO+cRyvCRV1pDwabUlu+KvADfDeqKLxyl9/L1WMCmjh3Y3DEfU1NRTZOcUIjTLy9k4Im5BPLt8eqTySSTJk0id75O7+eSUH+nwNknix7zLsCR3dQxfdlLa1jx8jqMiE63MR054tcDG1vShRBCHPy2Kb9rc3qe9ad9XJID1yHZoi/Ej11BusKrN2Qy4heJVsNyqpZFVjyJ3zA5cWMFeXV1uAw7zSfmcNIrFGGl37e9TYOi4DYM4g6NIzdvawzyAer8Xr7vXsQRa7dQuKmCikQmdQEPAZ8LX8TOZ4+7HKzoWYQnx8Xld3UjOsPN8idXkF9ZjenWiPi9uJNJOvX1c8yFHdssQsVwB6f/8WISX5Tj7puN57DWT7Dud0kP+l3SYzfOnhBCiIOBtOi3TwJ9IQ5hgzpqzA/RGOwrlsWIbTX4mz35MurQcBkGW30evijOY8iWKnpasLphPH3LavWkTM2CX8z7lvL8bNZmpqfMq0z3Yzid+I4t4KyfdcPZK5OP3qqg9MsKcvKcdDi3Cz/v66djYX3a0KDDGXjzADZN28BXi6JUBU2K+2VxwmWpHchbchT48Z7f+wefIyGEEOJQJYG+EIewX14QYMqzMUKqhiupM2FDORnNhshMqiql6emUBmBOpzziDo3lGWlcWFLKMFcdYYfGV+l+yjzuxnUUy8JhWfQoq6a2RZAPkB2NEs3wcM4zw3Fl2+v95NY0oOt2y6n5HHS5qDtddnIkQiGEEKLVsHOilbaeZS6EOET07eJkxv957afHOjS+zU4npqkkFYUSr5upHbL5MC+L1QE/5T43KFDtc/FmQS51TgdZSZ2j6sL4dQPNtHCaJmm6Qe/yarymydbsADmhcOP+fIkEA7aUY/2yKcgXQggh9gZrOy/RRFr0hTjEndDNwR0nWNw3O8b3Pi9rAn7ya8Ns9DV1XC1zO5uujrpJmdvFTLeLHNNkcCjKsEiMLU6NiKLiQ2eQ1+S/pw7HUhSchkG/snIy4gk8hoF2RCHnnL//H40uhBBC/NhJoC/Ej8DtIx385QsnZl2SOLDR20Zru2nSv6yKVVkZWPUPpqpRFByxGJrTSf98J+eM8nLpiX7qQvm8+F6YFRuSZOQ4uOK0AtxxA0O36NbdLaPbCCGE2OukM277JNAX4kcgzaXwu5EO7vlMBUOHZOuL47lfLQO3g8NKq8hHodLlJDeRxGWavN+3iEX35jYG8FkBjZvOD7TYglxOhBBCiAOJ5OgL8SNx13Ea/x6rMb6vk445Wsq8tHiCrfkZnH1zLy4MllKhG4SdGmvTfczsks9TN2RLK70QQogDioXS5ks0kSY4IX4kFEXhZ4dr/OxwDcN08MTnCZ6crxO1FA4r9HDv6GwOL1CJvTKKTv9eyfrVleiDO3DmxHw8Xq39HQghhBDigCKBvhA/QpqqcNOxbm46tnWuvifHzdBfH8bQ/VAuIYQQYudJ6317JHVHCCGEEEKIQ5C06AshhBBCiIOO5OO3T1r0hRBCCCGEOARJi74QQgghhDjoyFNw2yeBvhBCCCGEOOhI6k77JHVHCCGEEEKIQ5C06AshDgiRijjrPijF4dHofkohTp9cnoQQQuyItOi3R/6SCiH2GUM3mfzoRjbMryaQ6WD0RUUMPD6baf/dwsaHviHi9qA7NDo+uZLzXzwWX65nfxdZCCGEOGhJoC+E2CcM0+LXt67Ft6oa1bKorIrz2p9XsXRxIctfWUuoYyEePYnTMFimunj/ibWc/Yf++7vYQgghDlCSo98+CfSFEPvEnCUJ9A1htqb5Cbtc+JJJCoIhVk3bhMvpIDcUarxke5I6i780OXt/FlgIIYQ4yEmgL4TYqyo2RVkwtYyF30cpT0sj6HEDUOtxU+H3kR8MUVRdi9c0U9Zzh2IYuonmkDEDhBBCtCbDa7ZPAn0hxF7zweRS5j23HiwIul0ECwL2DMtCBQxVpTQjQKXPx5CNm3CYFqpu4I7EwbJ48YUyLr68EKcmt2eFEEKIXSWBvhBir3j6jVreeyuIKy8bTXPQ2F5vtc6qTDgdrMvNpUdZOdlba9DqW/eTf/qCP04uQumWxYQrixh0hHTOFUIIYZMc/fZJoC+E2KNqKpK8P6uGxz9Osqkgh4imkaEbDA6G8ZgmhtL2hTnp0FDiemOQD6BZFtmVdVS5nEz58xoKH+vd5rpbv6+lel2YoiFZBAq9jdPLl1Qz/8Gl1KyopVOug34XdiPvgh4oTm3PHrQQQoh9TgL99kmgL4TYI4ykyUv3r2Px1xFU06SyUwHVLicA5ZrKh84A47bV4DItEpqKpTbl3iuWhU/XUQ2z1XadSZ2wx01udR1rHl7CkVPqUCwIW9+Sec1Q3r/0M4LvbsKl6ywJOOh4dleib2+E0hhBp0o0TaPX1hqchsmmt9dQ8eev6f3J2Thyva32JYQQQhxKJNAXQuyQkTR4dGaYd6pdFGap/OpoBwPzW3eQveehzWz7JkpOJIYRj7OlT7eU+UlVZYXfS+9oAgXQAZdp4jRMsmMxHBZUZwXIra4j4XSgOzS80ThbczNRLYuCykrSHlxHlDScGJRf+wFfP7QCfaNOf301uUY5ix0DWDOtvrU+103cpdG1vBZnswpEbFk1W/+xmI73HbUXz5oQQoi9T1r02yOBvhCilemrTf63wsRbFWXt+1uZ06vYnrHB5M3lCWZf6uKIAoXaOGypM7jm2RDmVheHaQ4ygkEimgPVNDHV1ApBhm4H3ArgsgDTojgUBuyKQCgjjSWH9cTQNBIOB5ZlUlxRTreN6+m/dDPVpAMQA8K4CWyo4LvOhQzaXImhQ6m/oGlnioIraeKJ662OL/TF1j19yoQQQogDjgT6LQwbNoxx48Zx11137e+iCLFPmJaF2ixv/h8LDG79qGHQMi80BPn16uIw/JlYfQqOwvCKIEWRBABxVUO1IE3XGVZeyfyCvMb1fIZBfjI16DZVlUqHitcCp2ni0Q0UwGkYeAyDkMuFy7IYu2A+y+maui4a69JySSpO3up6Gj3KN7Q6NgXoQBkhsrHH+QEXcdTPl5L8kwftp8NRi7N278QJIYTYr2R4zfbtl0B/2LBhO73s9OnTKSoq2uEyW7Zs4a233mLUqFH06dPnhxavlfHjx1NaWtr4XlEUsrOz6dKlC+eeey5jxozZ4/vcWZMnTyY9PZ3x48e3mmcYBu+++y5vvPEGmzZtIhgMkpmZSadOnTjiiCP42c9+hsvlAuCtt97i7rvv3u5+3n33XXJzc/facYh97x8LTO773KA2DqdmJXj+fDdfblO5d2aEU1duQdcUvuhVTITWaTo58SR1HicZlkFROA71FYWIy4WuKoQ1je9yUgNoj262usnq1XUCpoWuaTh1vfX8ZJK1eQWYbZQBAEXBkTQwVYUN2R1xR5Mps9OSEQoTlWxxaIT0LDLZRkdKIKqR/H0p8ftmUXbvhRQdU4D5WQla10w8Z/dFcUkbiBBCiIPffvlrds8996S8/+abb3jzzTc555xzOOKII1LmZWW139q2ZcsWnn32WYqKivZKoA/QoUMHbrjhBgBM06S8vJwZM2Zw5513UlFRwSWXXLJX9tuel19+mcLCwjYD/d/97nfMmjWLww8/nEsuuYRAIMDWrVtZvnw5//3vf7nwwgsbA/0GF154If3792+1rfT09L12DGLfe2etya0fmTTkN86sdtPt0SSnLt3EA18swVvf8j560TrumXAcSaU+0LYsSJqETPAHY0R8LmZ3zCbu0siLJTlsW5Dviwp4o2cRESM1bK9yOSh1OShM2Ns2LAt/Mkmt10up04HP4aBvfRpPAwWLnKog5c5M/Mk4YTzN5plEfM7Gd5plklcXYlu6307bMZIMqVptpwlpMep8SToEK1GtCE6SGLiIx/LIvu0V6vDgJImKRcTtImPJz9G3xUmurMJ7YhcctbWwYB1JbxqJoIKmx9CyPKgLV2E9Pw8UFfX60VgXHkNy3ga0frmwvgrzszU4ju6Eeu6QHVYezJIq9I/WoPbOw3FM1937UA0TZn0LFUE4fTDkyG9WCHFok1F32rdfAv0zzjgj5b1hGLz55psMGjSo1bwDhd/vb1W2CRMmcNpppzFjxoz9Fuhvz7Jly5g1axajR4/mgQceaDW/pqaGtLS0VtMHDx7MySefvC+KKPaRjXUWt35o8OVmi6GFCn4nvPG9AZYKmmIH7waEFA1vdQ1PHH8E5QEfmeEY3xXng1sDwwLTgqQBhkVcU4lrKugWCVWBhMkWn5tIoUZRhoegywnR1DQdBVjhc7PB7SQ7qTOqbBt1Ph+1msqCNB+5Lie9Q+GUtnsdlaO/WYuhOMmhDgcGUVw4MfApUdZpmQBYqkJ2TYSONUH6BDeiOpJkxYNoWBiorDW7EI+6+daRjcNKMsyYS5ZVjUYUFRduEo1/LrR4gmDvB1DQ8RAlihOVJH7qcGJSTUdqySWPDaRRhYKCgZ/kH99F/+McQGm2NYX4oxZu5XEcOS6UDDeKzwF1ccyEieJ3kOzcDeasQCOKhYPouKPx/vYE+1x/vgr+/RGW1wXjh8JZQ1GmfALvLcKK6ZinDEU9dSDKva9iLd2MmXQAGooD+P2ZWG9/g1JeBcU5KHeeg3JaakMKoSh8tgy65EO/YojE4dPvoWM2DOyyp7+KQggh9rED+v50NBrl3//+N7NmzaK8vJxAIMCIESO47rrrKCwsBFJTTu6+++7Gfw8ZMoRnnnkG0zSZNGkSX3zxBRs2bKC2tpacnBxGjhzJddddR2Zm5m6XLxAI4Ha7cTqdKdPXrFnDM888w7fffktNTQ2BQICuXbty2WWXMXLkyJRyP/HEEyxevJhp06ZRXV1Nz549+dWvfsVhhx3G119/zRNPPMGKFSvw+/2cf/75XHnllY37aUiBKi0tTUmHmj59Ohs2bEhZpqUfctziwPXtNosHvzIpDUMPv8nGaoP311rolgKaysYqMzUAd2vgqg/kLYvXB/chVj8k5qbcDHCqoCqNqTlEkq13almgKORVBbnku9V80KcT+D329AaKQm5Sp0/UzuW3sJ+Ki6JQ6nJiKQrb3C7ez8/hiNogHsOgQ3WYod+WEHc7MMN2RSGLMFmEAQtVTXDCliWEnR6+ze9C3GlfzmKGnyJjM1p99uYWNY847sai6IqTFepAjjI+BXR0XGiYGKiE8aPjwEUCH7U4SOIkgY6GjkmYNILk0oE1BKhsPJYYASxc9ZUUC7AwUepbmxQSVgbuik1Q0XRKVEAngLp6Jfa9Cw8OwqgzPiE0YzEWDlyEcBGyt7lkLfz5FSyo3ypoT7+N/vT7aCQBFYUEFn503YX1x/dRMHBSjVJSCqd/h+X3oAzpCYO7Qr+OcPO/of4OCy6HXblo+Nw6ZNoVwex0uHYMXDcG1O2kUDWIxOGBqfDxUujfCX47ATrm7HgdIYTYTdKi374DNtDXdZ0bb7yRxYsXc9JJJ3HppZeyYcMGXn/9db788kv++9//0qFDB4444ggmTpzIpEmTUlJ/srOzAUgmk7zwwguceOKJnHDCCXg8Hr7//numTZvGokWLePHFF1sF6m0xTZOamhrAvgNRUVHBlClTCIfDTJgwoXG5mpoarrvuOgDOPfdcCgoKqKmpYdmyZXz33XeNgX6Dxx57DMMwuPDCC9F1nRdffJEbb7yRu+++m3vvvZdzzjmH008/nVmzZvHUU09RVFTUeGfhnnvu4aGHHiIzM5Of/exnjdvMysqiuNjuQPnBBx9w+umnEwgEduq8RyKRxuNs4PF48HjkiaQHug11FiNfNggmAN1kdsxoNtcCTEgYqSvFDdDqgzfTagzy0RTwak0BPvY2UWij95N9V0AxLDpX1WFG9Vat+S4VetQH+WCPm78qK5PCWByn2bTB9T4v631e0pM64zWNT04cBEDJtgLGz/0Kd9JOr0mnDp8RBQOI1VIcquT79G5oShLdcrKRzvgJk0YdNVrrFJawkk4SJ7UUYuJCxyKJkyheQCGJixgeLJz4qcNEI4GfBF4ChIiTTxgFPxUEKUQnDTfxFmfFwqoPyy0cmKioTc8HRgFU4oCBQhILBzpuIuRg4sBBBBeh+j9jSuMWlRYfgIaFRTp21cFCIY6FHwAHQZTm+wzH4NOldqs9LZ5ZkGgxOtHWGvv/W6rhxmdhcyX8+dJW5zLFxf+AafPtf3/4HbyzEJY9Aq72r7FCCCH2vAM20H/rrbdYvHgxl112Gb/4xS8ap48YMYKbb76Zxx57jHvvvZfi4mJGjBjBpEmT2kz9cblcvPvuu60C1UGDBvGnP/2Jjz76iFNOOaXd8pSUlLRKaXG73dxxxx2cffbZjdMWL15MVVUVf/nLX3Zqu4Zh8NxzzzVWNrp168Yvf/lLfvOb3zBp0qTGfPmzzjqLcePG8eqrrzYe4xlnnMGTTz5JdnZ2q+MeMGAAxx13HJ9++ilnnHEGgwYNYuDAgQwcOJDhw4dvN3Bv2X8C4IorruCmm25q91jE/vXC95Yd5AMkWz94CtNsd4iC/LowJ6zciKrCpwO7sCWnRQVRUVJb6gGnZZEbSeC0FJ44chBr/a2/W3mRRMrFxmHZndorXS56hcKUuF2EHU1Pqz22pi4lhWdzXg7z+/XiuG+XUedwkWvEUo7FZel0rKsknSAbfPnUOf2okTjJZB5ZySQ+qih3BEgqdimyrW2EKMKkoY+KghMdJ0mS9dMMHETx4SaKgkUcLzSWSiFMLgpJHMTrg3EHqWM6Nz9PCiYuVGIp50UlidJYQUgATsz6M+Um2KqtSmlR07LvHaSWC9zYQbyKQht3YBrvCeyiJ97dcaC/ubIpyG+wdiu8+w2cOXzX9yeEEO2QFv32tXMfdv/58MMPUVWViRMnpkwfOXIkvXv35pNPPsE02whmWlAUpTGoNQyDYDBITU0NRx55JADffffdTpWnqKiIxx9/nMcff5zHHnuMu+66i4EDB/LXv/6V6dOnNy7XkPc+b948QqFQu9s977zzUu4oNNyRGDhwYEqnWKfTyYABAxpTcnbGAw88wG233UaPHj34+uuv+c9//sOtt97KmDFjePHFF9tc56qrrmo8zoZX84rM/lZVVUU83tRyGgqFCAaDje8TiQSVlZUp6zQfMamt92VlZVjNgteDdR91zdbfrrauifVXgc5Vddz/+kect3AlExas5G8vfEDvzZUp6yoo3P3qPIpDMXz1D6HKjSQaW+VjTgdmG/sIqwrrnA4sC5ym1Rj0J1WFQFJnXHklR9QG6RsKc+q2KnKTrQPULbn2XbqYx9VqHkBFZjpf5fZleXZnatzpaEln45CabgzydPv8pJkRiowqEvhabUOjxR2PxpZ0pXFbDTLZiI9afFSRTikuUj8vB2EaTriFhUYiZb4dbqdewzSSaETbPL7m6zVRaX0ZVxpb/U3aaklv87ZM++pX2e5319rONk3rgPh9yD5kH7KPvbuP/cHazks0OWBb9Lds2UJeXl6bKSc9evRg5cqV1NTUNKbo7MisWbN48cUXWbFiBbqeenu6rq5up8rj8XgYMWJEyrTTTjuNSy65hAceeIDjjz+ezMxMhg4dytixY3nrrbd455136N+/PyNGjOCUU06he/furbbbsWPHlPcNx9vWkKKBQIDa2tqdKi+Aw+Hgggsu4IILLiAWi7F8+XLmzp3LK6+8wj//+U9yc3M57bTTUtbp0aNHq+M8kLT8vFt2KHa5XOTkpOYEN/Tn2N77goKClPcH6z6uPTLA498bhJOAQwWjRdCqaaBadrpOA2d9h1wVxn67Bm+yaZ7LMDnzqxU82PEYe0LCpLgyyFFryli9ditrOufiDEdZl9mUGqMCPsMi4kiN9us8LrLqIiQU8DZP3cdO4/HrOiMqq9FMC0NVCLtdWErqNnJr7N+q10iyMq8T/ZqNmx/TnCwt6IQrZJc/PZbacg7gQeew5Go86PXpLjESpJ735sN4aui4iKFiNobNDUG1nVaTOkKQgwgmNVi40IjiIEwSPyaO+nDe/q+dROXCxI2T1pUzlSQGXuKk4yLSYq4FqCTwYqHiJErrFnoLlSgGfnTSUahBTanA1C+rKvZdnp11tX2Hcrvf3fR0OGMIzFzYNLNzLpw+hEJ3aoXjUP0Nyj5kHz/mfYgD0wEb6O8pc+bM4be//S0DBgzgV7/6FR06dMDlcmGaJjfddFNKDXZXORwOhg8fzssvv5ySf3/33Xdz2WWXMW/ePL755htefPHFxtb0Cy64IGUb6nY6t2ma1ub03eXxeBg8eDCDBw9m6NCh3HjjjUyfPr1VoC8OXt0yFT6+QONv801Kwyo90hSWrYpRtSHK4PIqfLrBa4O6E/E67BF0VMV+6RZoCoFYvNU2A5G4nZuftF83z1wAwNGL1rG6Uw4/nfkNfzn3aGLNArmAaRLRHHYQqSgM3rCNn376PZnRBPP6dGRF/04464N4l67jiSfQaGqXdpgWvliCkM/TGL5mBMOM+H4VNX4fy7t2pCwjg6DbR5eqMoIeH4uKeqJGmn7L8Tb63agYuLEr+g4liMcKUk23xlQZHRUdrb5V3cRNDBMFHUd99nysPn1HaRE42+ykmaaGAwONEAEMHORQQpSM+kx9u6VdwWgjRAdPWhjF4cew0og7NVwVpdiVBHtpBRUXcQzqR9hBx2pMG7KABA5i9WlCauN+GspIph/OPQp+Ogr+PRsWrLE7T6/dCroBJ/SHCUfBs7NgxWbI8NudcX8xttUxt/LKL+HPr8NHS2FAJ/jdeeCW/HwhxN4iqTvtOWAD/Y4dO/L5558TDAZbjeG+du1a/H5/48gxirL9D3rmzJm43W6efvrplLz0kpKSPVLOhjsEkUhqy1vPnj3p2bMnl19+OcFgkCuuuILHHnuMn/zkJzss767anW0ddthhAJSXl++xcogDw9AChf+d2byS6CRY6Wbzcgd5XX38M9vNz6YmeK8Eolaz745h8UXPjgxZtzVle190LoBI/V0wC549ZTAZRpJFvQvpWlZDYXWICV+s4OXjBzS2wPtjCVS/G1NzMHDDNu5/+dPGIP4n85azcvNW3h3Sg6152aiKgj9u57hbqkpS07BUFadpEqgNUVRdxeHr17MurwNzB/Vja3Zm/UOydL7u2ItvC7uDoqDqJkNXraI0K5OE5qTG46HW7Saj/tazVf/fKB7iDo2Ilk73+CoClFBHPlVaB4JuPy6idI1sxMRBEh86DmKaF2+eil5joMeU+rFt/JhoKQG/pSooptW4v6CnE4oBWblBnBPPwBw5CGePAMrS9fbdlRWlMG85TP2icRvK8f1xfHA3jmb9FawFa2HxBvCqKEf1hqowbKlG65AOGypg3hqYvhAiCXCYKB4f1ulDMY4ZjFpRhdq/AIZ2h0++t0fSGd6r6QMe2W/7X6bLRm1/3vakedvvsCuEEGKfOWAD/VGjRjF37lyee+65lI6gc+fOZcWKFZx++umNreE+n51r21ZaS8MyzfP5Lcvi3//+9w8uYzweZ968eQD07du3sQzp6ekpLfXp6el07NiRjRs3Eo/H9+gINl6vt830ow0bNqAoCp06dWo176OPPgLsjr/i0Jee46LvsU23Yd+8yE04YTFxhsFry+vTUjSF+Yd1JicS4+TFa9FMiw8GdeOdAd1TEh6XF2UD9ePu13/HR323gb6bKlnRMQdfNMGTpxxOVihOncfJqd+WtMog71pay+kr1vI/BbIAR8NdNcNANQwMFDqvL8MdT2IBpb4MVncuTtmG7nTQdVUVimKgAhk1UZy6xVEV3/NdZldqnWnUpml0SNYRNQMogImDKA4cepIsXWeBbwS+qEHSp4ArSa/wWnITZagd02DWbzEWbAaHinbWIBSf3S8g8dE6kvM3Q9JAd47COfldlOWb4fCuKC/dDBsrYMUWlJMOI7PXdp7o3bv5LfMz4bv18NlyGNQFjunbanFlWHcY1iztr3kG4Ig+cP6xKP+4PHUdaJ2dP/7ItssjhBAHKemM274DNtAfP348M2bM4Pnnn2fLli0MGTKEjRs38tprr5GTk9P4lFqwA1a/389rr72Gx+MhPT2d7OxsjjzySE466STmzJnDtddey9ixY9F1nY8//phYGzm8OxIOh5k5cyZgVxS2bdvGO++8w+bNmznnnHPo3LkzAG+//TaTJ09m9OjRFBcX43A4WLhwIZ9//jmnnHLKHh+m8rDDDmPatGk8+eSTdOvWDUVROP7441m5ciV33HEHQ4YMYejQoeTn5xONRlm6dCmzZs3C7/dz1VVX7dGyiIOH36XwvwkOysIWLy+zA+0L+zpYcv4AxrzWrJU3nISk1XR31AJfQiejNoqJymf9OjJy2WYKasIEInFuuXQU4frx7H2xZEqQbyp2aomp2hs7bmMpznR/SrlUIFAdxB23O+MqQMjhpi2uhI4nEsdT3xdhS2YWxaEtHLetqYN9Ofmt/gzoOIi6HOhJB9rhOYz45lz4tgSmf2U/KOqCkeBz4+jXkZZco7rhGtWsgvzrU1MX6FkIow9rs7zbNbCLPJxKCCHEXnHABvoOh4PHHnus8YFZH374Ienp6Zx00klcf/31KR1HPB4P9913H08++SQPPfQQiUSCIUOGcOSRRzJmzBgikQiTJ0/m4YcfJj09neOPP54bb7yRk046aafLs3XrVv7whz+k7LN79+7cfvvtKePoDx06lBUrVvDpp59SUVGBpmkUFRVx880385Of/GTPnJxmrr/+empra3n11VcJBoNYlsX06dMZMmQIP//5z5k/fz7Tp0+nqqoKy7Lo0KED48eP5/LLL2+ztV/8uBT4FW4Z1hQKF6YpPHgC3DXPJJTEHkvf1GnIUHFi0iMZowKNDJdF+ZgevFyQRadtdawuzKDGlxqUvzewCycs28TiwV1Z070DimXRsbSCwooKtseVSB1xxxtL4IrFSXiatp1ZESItaFfWg34HoYCbmiwv0VU+1uUXkFsXhJhGDB8eWowPD6zr3YUhvz6Coovrg/ZBXe2XEEKIg4a06LdPsX5Ib1QhxCEpnLCoiMILiw3ummtgWPZDdJ8bq/GT/hqbqk06BFTcToVFy6O89kWcz9YmWVTbIlHHshhTXo3XkTq968ZSXuzVmWG1QQZVNaXcGYpC5tZqcmpSh6bdkp+F27AwVQeB2ggd11eiGRaoJqt6Z9s578BpKxaRdDj4tGd/YpoTfyzG8JXrUZtd5hKKQukNx3Dqowfu6FJCCCHat0J5qM3pfaxb93FJDlwS6Ashdqg8bLGu2uLwAgWPY/utJ1tqDAbdXY3ebCD9NNPk2NoQ3hbDOFYrCtM75eMwTY7bso2edUGcpoXu8eBM6Az9dhXupN0SX5EVoCbDj2ZaDFi6hfTahrQ7i6y+UbwBhW/LslAUgx7bttIpUo0J1PjScCcTJJJedLT6EfGhxuOm8O1z6Hpi6vByQgghDi7LtxPo95VAv9EBm7ojhDgw5PsV8v3t3x4tytR45xcZ/HZSNcsqwW+a+IGoprYK9MOu+iEtVZUPizvwIR3oEI1zWnkVIY+bT0YcRk5NkITTQTDNS+fSct7u3gklHue4kjUkfAqHPz2G3NO7AtDxH19S/dg3KF3SMYryUb7dRGYkijcnQoWZiVFu5x7pDpXsu4+RIF8IIQ4BkrrTPmnRF0LscX/7RznzlsSocjowsegejtE4YKRlsSjDz+LM1GFzuwXDnFJeTdDjBstCtcBSoGN5JZv8XqZ278jMy12s/PwlACZOnJjyVOntsXST0Mx1GNVx0sZ1w5Hj3cNHK4QQYn9Ypvyjzen9rFv2cUkOXNKiL4TY466ZmIP1n0qWfVlNWiiMwzTQvV6iHjcK0CMc4/uAn2T9EJ2qZXFkRQ3+eBJDVYk6HZgKZIYiuE2DgZd14YHRfvJ9Bis/37WyKA6V9DN77PmDFEIIsV9Ji377JNAXQuxxmZkat9+aj67ncd8t3+P/cguKUsu6ToXEXC6yEkkuXbuJdV4Pi7MDjKoKkqaoJDSVtHgCbyKJYhrkd/Yx8bZedCy2R9xJJls/kVYIIYQQbZNAXwix1zgcCn98dACvzO/Bc29WceyCDRiKgoLdit+9uo6wQ6NjJErE6SRan7bjSybxOxR+93DP/X0IQgghDlCSe96+lg+tFEKIPe6C4R7+cUsHNvo8aJbVONxljdPB9xnpqIZBRixGWixGRiyGyzDILXTt51ILIYQQBzcJ9IUQ+0TffA3vhd1ZkJNJucfFkswAaVf04pWr0ukyIA0FcFh2xqVmmJx6fv7+LrIQQogDmIXS5ks0kdQdIcQ+88+LAiw8zs+iUoNjumj0zbfH4jn9rq5Me3oTiz6rweNROOGcIg47Nms/l1YIIYQ4uEmgL4TYp4YUawwp1lKmqZrCOdd34pzrO+2nUgkhhDjYSOt9+yR1RwghhBBCiEOQtOgLIYQQQoiDjoy60z4J9IUQQgghxEFHUnfaJ6k7QgghhBBCHIKkRV8IIYQQQhx0pEW/fdKiL4QQQgghxCFIAn0hxAElXlJH6PMyzKSxv4sihBDiAGZt5yWaSOqOEOKAYFkW349/l8jbJShYmBkeBswZj39I3v4umhBCCHFQkhZ9IcQBYf3D3xF9ex0qJgoWWm2Uhae/s7+LJYQQ4gBlobT5Ek0k0BdCHBDKHv0Wtf4S3fDylgepLovt55IJIYQQBycJ9IUQBwRHWajVNEtR2La0dj+URgghxIFP2c5LNJAcfSHEflOzLsjyB79D/64CgMU9OpIVjtG3rBIAXVExV1TASR32ZzGFEEIcgCRNp30S6Ash9ouv/reJ+X/+nrS6GCs7ZvPEDSOp8XsBGLyhjHumfkxmVYKwKZcpIYQQYndI6o4QYp+L1CRZ9cuv6Ld4E53WbeOt3p0bg3yARZ0LeOPIfmwqyKDuzXX7saRCCCEOVDK8Zvsk0BdC7HObPy6jYFM1wUw3NbkeVhZmt1pmQ1aAylwfsW+38U7xZGJV0ilXCCGE2BUS6Ash9plQzORvb4eY9u9NLBvUgfIOaSQ1jZ7balot272iBmc8yXc9OuGoifL1UW/+8AJ8+B3GOX8nGbgGXbmMZPEtmLOX/fDtCiGE2OdkeM32SaAvhNgnvp5bzcDf13D3lwqODTUogCuuE6iNc/Wcb8gNRQBQLIuj125i2IZSVMCRTLAtPQ1jfYjEptYj8+y0J9/DPPE+lKlfoQXDqFg4Nm/BOu0+zOow+nvL0d/8FiuS2CPHK4QQQuxv0svtR2TBggVce+21/PGPf2T8+PEAbNmyhTPPPJOrrrqKa665Zj+XUBzoausMHA4Fv89uI0gmLWrrDLKzVDbXQX6agtuR2poSXFnL329byXtFxQypCTJi1To8iQQJj4uqDpnU5AZIq43w19c/YnV+JoF4gtxwtHH9wSvWU5GeTtinkVhbt1vltj5fjfWLF1FQsHA3y+HUUI0osd53oVQE7WNCRfE60X52NI5HL0BRpHVICCEORJKP374fZaD/61//mo8++ohnnnmGwYMHt5q/aNEirr76akaNGsX999+/T8s2fvx4SktLG98rikJ2djZdunTh3HPPZcyYMXu9DMFgkMmTJzN06FCGDRu21/cnDmzBhMWfPjN44csE7roEBaE4Q7o58OY5mblUR9EtIsBmtxvVqfDgaU6uO9LJ+u9CKLrB/FPfZ+npQzimdBsOyyKWEcBjQdLrRkvquCJRkk4FnA661AZRDbNx3954kqxwDE/SoC7gYsqdK2CsG61DfIdlthZvxFy2BcXjgGWlGHe8WT+6sgMTJ1b9zUyFJCoq7oqN6KRh4rJv+kaTGI9/gvX6l7gyDBg3BP56GTi0vXaehRBCiD3tRxno//a3v2XRokXcddddvPzyy3i9TaN9xGIx7rrrLjIzM7njjjv2S/k6dOjADTfcAIBpmpSXlzNjxgzuvPNOKioquOSSS3Zru0OGDGHu3Lk4HDv+2IPBIM8++yyABPo/Qk8vMrjvU51Y0uKKwzXeXmUSWxMh3TTZlOahOksjZ2klxaEYA7xuPu6SS53LiakopMcSTHqhgo1/30Z2WQ3uWJz1Q7vQPRgGwJlIklVTi+G0v4OuaDw1m1JVsBQNfzBJ9tYwTsMgggtvIsGCHj1IqCrKzI74T9wMgGWaWG8vJjl1Cfp3lSjrytGqqlENw54PgIGBhhsLAxcWTcG6hQu7DT+OkzosNJKkYeG0FyirhbIQ1oq3Mf/+Dtaw3qgl5eB1ovz6DJQrTwaPCyIxeO1LrC2VcOIglOE99+pnJIQQAkzJx2/XjzLQz8rK4o477uBXv/oVjzzyCL/5zW8a5z3yyCNs2rSJBx98kMzMzH1SHl3XMQwDt9sNgN/v54wzzkhZZsKECZx22mnMmDFjtwN9VVUb9yFEddTi+60GhxVoBDwKsaTFXz/XuXt2EkwLNIVHPrc4uaSC9KQdOA+qqCMQT5Cm2++LQlHGrCnjxYFdQFGo8nuo9rk5vKKWqpwsulZUkZ1MEvJ6APCHwmRURol7XdTlOFAss1W5dM1B7pYaAAw0gqoX01I47ctvMFSV8swAJZU5RCZUkHb5Y0S+D5HAD4CTOE6Mxm01/AnwYqcC6W1c8pT65ZX6f7uoJU4WoGHiwkJFQUVDwVqwBgsnFjrWTa+g3jQJxeWEhAEoKBhYvITeoQPavLtQNm6DUBQicXsPI/tBYVbTzid/Ai98BFnp8H8nQYbPfvUqSi1kaRWsK4ch3e2KhRBCCLETfpSBPsCoUaM444wzeO211xg9ejTDhw9nwYIFvPrqq4wdO5ZRo0axYcMGnn32WebPn09tbS15eXmcfPLJXH311Sl3AUpKSpgyZQoLFy6krKwMwzDo1q0b5513HmeffXbKfp9++mmeffZZXnnlFaZNm8bs2bOpqKjgiSee2GHreSAQwO1243Q6U6aPHz+ewsJCnnnmmZTpbeXjtzWtpYZlAJ599tnGlv3CwkLeeuutnTu5Yr+audbg9k8sauKQ74ONdaCboFsWcR38TlBNi4pw/QqKSbobIkkLwwRcCridoCj0KK9rDPIBPIbZGOQ3yI4l6RCOszXNDuYtReHLTrmM2FzNwl7dSAuFiTudZCcSHL66gqzKCAmXRjjgQXc6ccVTO7+m1bZOy4njwETFZRoUV1XRtWYzNSO+xmNsI0Fu43IqehtnpGnMARMVNSWr00JpsY4CaCQw8KCQqF/fBPT6ikOi/k6ABwsHSkIHDBruHyiAY+tWrB7X05RB2myfuelw4xnw0Xfw0dKmMr48l8ZRoAuzIJYAtwuqQxBPNjscxU4hcmjQqwAuOQH8HghHYdlmGDUALjoO6u+asLYMZi2GJevhvUXgcsBtZ8NPT4RPv4elG+11+ha3ce6EEOLAJSPstO9HG+gD3HbbbXz99dfcc889TJo0iXvvvZf8/Hxuu+02li1bxrXXXkt6ejoTJkwgPz+flStXMmXKFBYvXswzzzzTmAKzYMECFi5cyMiRIykqKiIWizF79mz+9Kc/UV1dzcSJE1vt+/e//z1ut5tLLrkERVHIzW0KVkzTpKamBgDDMKioqGDKlCmEw2EmTJiwV89Jt27duPXWW3nooYcYPXo0o0ePBsDn8+3V/Yo9Y3aJydg3moLKjcH6f5iWHSAC8ZgFzeJGLAgmsANHDXCoUN8B1Z9IDeq3d1FNaKkDeC3Ly2B5fgZ9N9egNnsQVlpxNaMr1+FKGPT/diPV+X7q/G4MBVAUQoE0/HWtO9wqQBgPHuKYKJiml3RqMVHRSOAkikaiPohXW6xrYtVvo+FhKgr2Ld8kbmIUo5LEQzUO7EqHWb8lF3UouKFZZaChImCRqE8DausRLQ1nSml836giCHe90uLoGkpVv3xpdf28MK2YFiR0+7V4PSz+b+r8SXPgT6/Bggdg2nz46WNgtrhzMvExeHY2zFveNO2R/4ObxrbenxBCHKCkM277ftSBfnp6Or///e+58cYbueiii6itreXRRx8lLS2Ne+65h9zcXP773//i9/sb1xk+fDi33XYb77zzTmOr+NixYznvvPNStn3xxRdz7bXX8txzz3HZZZe1yotPS0vjiSeeaDNfvqSkhJNPPjllmtvt5o477mh1h2BPy8nJYdSoUTz00EP07NmzVQqROLDd+VnrVBiAlPi8rUVMwKq/ZDYbZaY03UOP6qZgU9dU/p+9+w6PqmgbOPw7W7Kb3hNCQuhFOiFUEbAgCISOiCKIhSLY62sF26u+foooKqCCCkizQBBElKKgdEFpUgMpBEjvW8/3x5KFZRMCIZDic1/XXrBz5szMLmHz7Jxn5uR46PAznwt8jwV4k+l5Lp1Eb7Vh8dDiYbahcf2ewJr2Del0IAl/WyEnGoaR6e9DWHo2DY6fZEvLRhT4eZNaR0tIag4a+9nxqCqeqiMAt3CunxwCUVCJ5DgaCjERANjQYTsbZjvCeS2Ws3+zo6cIMz6ABSsGOJuvb8eDQkLw4SQqCjY80ZMDGFBLyQJV0KGcbblkJQT5pargWalDJ2HmT/C/792D/GLnB/kAz82HsTeBj2fJ9YUQQlQ7//p99Dt37szgwYPJyspi0KBBdO7cmcOHD3Po0CH69OmDxWIhKyvL+Wjbti2enp5s3rzZ2cb5aTwmk4msrCxycnLo3Lkz+fn5JCQkuPV75513lrootnbt2syYMYMZM2bw4YcfMmXKFFq2bMmbb77J8uXLK/w9qE4yMjIwmc6lduTl5ZGbm+t8bjabSU9Pdznn/F2MSnqempqKqp4LxqpzHyVkvVyeC2LSZH9P/g7zxawo2IFsnQaj1cZfYf4cCPFlfb1QVjaJAA14WG3023mI1xatxctkwXDB1QAAq05LhreBzV2a8U+DKE4HB7CnSV22tmlKw2PJeGbnUeBr4O+OdckJMmJQLYSouQSRh4fLZQiHXPxR0XCaJmRTi0wiySEYLSa0FKHDhHL2RWmwoiUbDXlYMQIKGmxnc/RVVLSY8T2bn685G3prAW0pobr27MODkj9KK/mS8t/H4cxlbEeaVwQpmTX6/4f0IX1IH1evj8ogN8wq2796Rr9Y69at+e6772jdujUAx44dAxz59DNnzizxnIyMDOffCwoKmDVrFmvWrOHUqVNudXNKSEWIjo4udTxGo5FOnTq5lPXp04e77rqL//3vf3Tv3v2aLRSuaoKCglye+/j4uDz38PAgODjYpSwiIuKiz2vVqlVj+hjV3MaLm0oIS88urgUcMemFMbiGszP5qmNm/7xZ/X0BPuzXG9CoEGoy0yCvgKjcQha0qotdc67e4O0H6PvnYQC8i8xkeHlixzUE9ssvwluxk+vjmgqWGhpI9PEUgk9nUpRfAIqC3VMlSM3D82w6jQbVbdh6LFgwuvRiwhsbevScn/uv4kjW8cBI0dkQ3/u8o3Zs6NBRiB0bFvQoZ3fdd7StRz17ZaC453OBvHL2+fnfsjSc+9akUCkXmAd0gL+Ow+6Eko9rFMfPRbF6YdCoFhEa1y8tNen/h/QhfUgfV68PUTVJoF+C4m+1o0aNokuXLiXW8fPzc/79+eefZ+PGjQwePJiYmBj8/f3RaDRs2rSJBQsWYC/h0rnRaLysMel0Ojp27MjXX3/Nnj176NatG0CpN/Ox2dxnU0XN959OGv5Os7P0oIpdBZ0CVhVHUGe1O/5UwSW1XKOABjQ2O3atBiz2s3GqCmY7mGyoioJNgSCzY1bdz2ylQ0IaRwO9iMrModPhZLocTALArkCWpxGbVkNSsDcRmQXo7So2DcTtOoSxlJ/NbE8jiqoSmpZHobcH6YE+FKbr8Dx7p1odNqxonXvgg4q/kk6uGuTWVhG+aMlAwYpydiHtueBcwY5reopC8Yy/DQ352B177JxXQ4/jq4YJBR2ct0XnOcVv6rm0IVclBfzn5eWffU1X/KVAo8Ck22BYV2gWCXe861hwqyiOf1OdFsbdAl2awWNzIC0HGkXA/EdBU9KVCSGEqJpk9r5sEuiXoHi2XaPRuM2sXyg3N5eNGzfSt29ft333t27dWqHjslodedEFBQXOMj8/vxKvGCQnJ5e7H7kTaPWl1SgsitOSbVIx2yDUSyElT+Vknp11iZCcY6dzbYWj2Qof/gnpRSoBBoU2wSoxXjaO5FhYssfmCAo1GseM79mfBz+LlfaZjp81FccXiPBsE+2Onqbz2SAfYHvDOgSYrJzRacnx8iDHU4/eptL2ZDqFYX7kZ+bhlVdEgc+5L7t+2XmgqmhtduqlZqJR4UStAPKNHgSd/XlXACNm8nRGTgb4YfPWEqkWYDuR57I3PoAVDSa8MJKB4/KFhuKAWkVT4i8HPUWA7uy9c3Ox44FrEK7FRAh6Mi/o7fyZ+7P1PT2gayPHc089JGXA4VTHsd5t4K274YUFsPD3c83UDnTcmKtjY9iX6Nid54FbIb8IZv0EGw9AvVC4vxcE+zq6CvZ15NRn5YO3AZLSIToUfM9+kWlZF/a8D0lpEOIH6bkQ5AOeZ7fZvb0rnM6GyGCXqzhCCCFqBgn0S9C0aVMaNmzIN998w5AhQ4iKct12zmq1kp+f75y5B1xy2wDS0tL4/vvvK2xMJpOJ3393BAXNmjVzlkdHR7N27VpOnz5NWFgY4MidW7JkSbn7Kl5zUNIXCFE9+BvOBW21fRRq+2hpXwvOn4n+T+cLz3Js3Xrbl0X8uN8MprMLbjUKQVYrfU+m4WuzY9YobK4dRIq3J375ZjZdV5e1jSKplZ3HKT8frkvJ5Ka9SaxtHsUZHwMoCpE5efQ4fgq7VsM/10XiVViIsciRTOOdW0DwmQy0Njt1TuegOftfKepUNlkGI3YU55aYKpAUFsjJbnpGftAfvzBvit5ZR/Z/NmC3OmbMNVixY6QQDyzo8SH1vC01lbO75liwcf5+9Cp6Z+qNgiPgN2PHiAbV8cUGAwp2LJoAVHshWqwQHgC3tEBZsgnMZ78UPXAzzBxf9j/S14/Dc0NhTyJc39QRoJckxA9eH3XxtrzOBu7+3iUfjzq7q1fkBZfaPfTnjgkhRDUju+6UTQL9EiiKwiuvvMLEiRMZOXIkAwYMoEGDBhQVFZGUlMTatWuZPHkycXFxeHt707lzZ1atWoXBYKBFixacPHmSb7/9lsjISLKzsy+7//z8fFauXAk4vkCcOXOGVatWkZyczODBg13y+2+//XZ++uknHnzwQYYOHYrFYmHlypWXnRp0voCAAOrUqcNPP/1EVFQUQUFBeHp60r1793K3KaqP7+808NVuHX+csNE4SOGeGC2PrVNZtk2HF5DhbcBmVwkqMJNv1HNKr0VvsXI8yA9VUTge5MvLW/+i/YoUUkO8Mag2gvNM/NowkjyDBymeBu7YexjffMdNrFRUfCx2Gh1Pcya3mNGQ7emFh8lKKv4YtBbSI/ww5JvxCC5EubkAz0BHoG588kYMj/fE8vdJLN/sRd3wD+qZbOzeXmi7NsDmY4I3lqLBRvEXHQ/yMQM29CjY0VN49vg5CnYUTJgxYtd6ouveEP1X96JEBqEePgVeHii1z9786suJcCAZagVAkO+lv9mt6joeQgghxFUggX4pmjZtyvz585kzZw6//vor33zzDd7e3kRERBAXF0eHDh2cdV999VU++OADfvvtN3744Qfq1KnDgw8+iE6nY+rUqZfd96lTp3jppZecz41GIw0aNODZZ59120e/bdu2TJkyhc8//5z333+fsLAwhg4dSvPmzZk4cWK5X/+rr77Ku+++y4wZMygqKiIiIkIC/X8Jg07h/vY67m9/7uPh6wHAAD9+T7Dy10kb3erraVnLyLwVOazdmI23UeHmm3057elBtL/CrVM7o9Eo/PLBYf5cmIhdp6C32Vgb5bjq9FaXNoz9+xB1cgtAC3VOZroubVXsaAx2EhqE45NZhL7IjNYCdj2k3Gty3s22mKJR8GhTG482tYFebq/Jvmwntr2JqGhRzi7FNZCHioIFHTq3HX3sqIoO+/DuKEO6YBzQAsXz3M3qlEbhrtU1Gmhep3xvuBBCiHKRHP2yKeqFOSdCCFGBErZnsvq1/XA0iyN+PvzSvB56FG47loSHzY5/dj4ddx45uymaneK59OLFsNvbNKDuwdN42Kxct3UA8Vu+A2Ds2LFud4ou1bfbUIe+jw0Ndjyci21VFHSaTDTPDEN570eUIjP2huFo/m8kdG0CoX5lty2EEKJSbFA+L7G8h3rvNR5J1SUz+kKIq6pebCDjv+/Ksb9yWPrCAUbuOkihtyeqzpFGY/JwpM9onTemUs/uAOrY4LJOSgaZgT7YGvpwQ/MA2FKOQQzpgDLuRjSz1qFgRYv17M75JpQHboI3bodXhkKRBY1P+dPehBBCiKpEAn0hxDVRv7UfTy3vSPL+XHb+nMaRuYcweRkoMupBA8p5u1E6trvEuQQ3x0fPjfc2vLIBzLwPzWvDURf9Acu3g82KMrgDTOztOK7Tgk9J22YKIYSoimpSSkpycjK//vorp0+fZujQoURFRWGz2cjOzsbf3x+ttny/nyTQF0JcU5HX+VJogaRPDqBTC6mfeBptCfea4GxqzekAH5pe50OTexphsbjfHfeyhPqhTO4Nk3tfWTtCCCFEBVBVlSeeeIIPP/wQq9WKoii0atWKqKgo8vLyqFevHq+88gqPPvpoudqXu6MIIa65hq18MNTzwz+9kITaweR5uabLqEC+Xk+mjyeDvuhKz+9vqpyBCiGEqLLUs6u7LnxUJ//73/94//33efLJJ1mzZo3Ldu3+/v4MGTKEb775ptztS6AvhLjmFEXhrqWdifCAhvvSybF5kK/3cN4X1qzR4mWxEZRXiFd9n7KaE0IIIaql2bNnM3r0aN544w3atm3rdrx169YcPHiw3O1LoC+EqBQGTy03HxxEh5W3ED3xOhLDA7GjYEeDzu6Y0bBqNRQWlZTWI4QQ4t9OLeVRnSQmJtK1a9dSj3t7e1/RDUwl0BdCVBpFUQjuVZuQoXU5E+xLpp/rnV1P1ArCJ9KrkkYnhBBCXF1hYWEkJiaWenzHjh0uN0q9XBLoCyEqXXjXMDxsNv66rg57G9fmaJ1QdrSqR2aID1q9fEwJIYRwVxNy9IcMGcInn3zC0aNHnWWK4ngNP/30E3PnzmX48OHlbl9+gwohKp2iUWg1qh6GQjNpQb4k1g7CgkLLoVGVPTQhhBDiqpk6dSoRERG0bduW0aNHoygKb731Ft26deO2226jdevWPPfcc+VuXwJ9IUSV0PmpFsSMrkuwqZCQogLa3RlNlxfbVPawhBBCVFE1YUbf39+fzZs38/TTT5OcnIzRaGTDhg1kZWXx8ssv89tvv+HlVf4UVkU9fx8fIYSowiwWC3PmzAFg7Nix6PX6Sh6REEKIyvKT8kWJ5beqY67xSKoumdEXQgghhBCiBpI74wohhBBCiGpH1VSvNJ2S3HvvvWXWURSFzz77rFztS6AvhBBCCCFEJVi7dq1zl51iNpuNkydPYrPZCA0Nxdvbu5SzyyaBvhBCCCGEqHbU6j+hT0JCQonlFouFmTNnMm3aNNasWVPu9iVHXwghhBBCiCpEr9czefJkbr31ViZPnlzudiTQF0IIIYQQ1Y6qUUp81CRt2rTh119/Lff5krojhKhR7DaVP37O5MCuPMJqe9AzLgTfAPmoE0IIUf2sWbPmivbRl99+QogaZdEnKWxZm+V8/scvWUyd1QS9h1zAFEKImkStAR/rr7zySonlWVlZ/Prrr+zcuZNnn3223O1LoC+EqBGSjhayaskZ9m7NdSkvyLWx8ItTDBxdCz9DzbqkK4QQ/2aqtvp/pk+ZMqXE8sDAQBo2bMgnn3zCAw88UO72JdAXQlR7mWfMTH7tJOu8/biHXLcboH+/qZAHcq083UHhlRvkY08IIUTVYLfbr2r7NeCihxDi327FqixW+QeQ46HDdrYsX68lxceIRaMQll9E3F8nmPlLEWuOXd0PVSGEENeGXaOU+BDnyNSWEKLa+3CHFbPOA0+bDQXYGhnElqgQ7BoFg9VGn0MnqZeVz4i/jzPvp0h6jQ+o7CELIYT4Fzpx4kS5zouOji7XeRLoCyGuiaIDmRTuzcDn+gj0tS6+g4Bqt1P04k/kfLQDG1r8H2qP9yu9Sq1/1KYDHXjZ7GR4evBHdCioKlHZBWjtdn6pH849u46iVUH97TSpo/yp5S2zPkIIUZ1Vx8W49erVc7sT7qWw2WxlVyqBBPpCiKsu6YlNnH53NwAaxUadyfUI9kiHvYlwWwxM6gNarbN+0f/9xtE39mIiEIBTrx6gnlWLz9Qb3No2Fdqwnf3QzPTQsbRFNAarjSF7EwkrMAGQ46FjRZPaxCZn4F9kYdYvhbw0oPzblQkhhBDl8fnnn5cr0C8vCfSFEFdVwZ+nKXr3Z0IowptsAtVT8IGKDQ129Cg/7kX76hKUxJlgNABwavoeTHg621DRcmLaAZqXEOgbPLX4WazE5ORxMsyXfXottx466QzyAfzMVpql5bK0ZTQxyekc2W/m+jZGbopWrukHrhBCiIpTHW+Odc8991zT/iTQF0JcPfmFGG5+mkbkOIts6CkiCDt69ORjIBNbGpi8H4PJt2IYHUtmmvuHt7Ww5C5yi1Q6ZudS22xhb70Q+hxMoWl6rlu9kAITKAq7IgKxa7WsX2Knb6SdH0Z6VNjLFUIIIaoSCfSFEFdPz5fQZp4L8u1oyCMKFUeajg0jJrzxJ4lCO9imb8Qy/XeK9PUxoLo0ZS9l5v1ImhU/qyN3sdmZnBKDfAC9zbHbjsFqp/BsmtDKZA3Prizitd4GdDVgP2YhhPg3UWvQx/amTZvYuXMn2dnZbltuKorCiy++WK52JdC/iJSUFAYMGMADDzzA+PHjK3s4QlQvNhtsP+JSZMEHBRt6ctGTixYzACaM2LUFqDYfTFotyYE+1D+dg3LejvhZPiXPvN85PYPr9Fp8THaan84udTh+Zis+JgsmjYaw3ELSjR7YNApv/Qlf7MrjyJM+eHnUoN8aQgghqryMjAz69evH1q1bUVUVRVFQVcdEV/HfKz3Q3759OxMmTHAp8/DwIDQ0lJiYGEaPHk39+vUroqvLkpKSQnx8PD179qRp06ZuxwYMGFDqua+//jqtWrUqd9/x8fFMnTr1kurGxMQwa9ascvdVEdasWcPvv//OgQMHOHr0KDabjeXLl1O7du1KHZeoBn7bBws3QoA3jOsFdUJg1hr48Ae3qjoKMJyXxlPMQBEeNhP7vBuR6hVMLUsmIWSSjy8qCj7k4WNWsGWaXM7bkWjldKFCLb2OOiYLelV1axvApFH4u1YAWpud7olp7KwdhE1/dvGvVkOqqqXFjCKOPeZZ4vlCCCGqnuqYo3+hp556ir/++osFCxbQqVMnGjRowOrVq6lfvz7vvfcef/zxB6tWrSp3+xU6o9+7d2+uv/56AEwmE4cOHWLZsmWsXbuWhQsXEhERUZHdlSklJYXZs2dTu3Ztt0C/WKdOnejXr59beevWralVqxabNm1Ce95uIJeqXbt2vPLKKy5ln3/+OQkJCW7lQUFBl91+RVuyZAl79+6lcePGREVFcfz48coekqgOPlwJD3167vmb34K95GAbQIu11GMKKpmeOhL8wgE47huKFhWt3Ub9HDt1cs+Q8MgaNDeo2HWOD/fJS/JJ9/Ag0FL6tmM2YEnLuqR7Oxb6/tLo3OeQzmojpNCMqqok+HkxabWFd27S4amv/r88hBCiprPXgI/qlStXMn78eEaMGEF6ejoAGo2GRo0aMWPGDIYMGcKjjz7K119/Xa72KzTQb9asGX379nUpi46O5p133mHt2rXcddddFdldhYiOjnYb8/kMBkO52o2KiiIqKsql7PvvvychIeGi/VWWV155hZCQEHQ6HW+99ZYE+sJd8S42p7Lgre8gMQ1+3Ola5yJB/qWwaPQAGK1mGmenUqsgE19LEQqgAjkrjxGxwZfUgRpu1xawOcmOr9VGqNnCNn8fUgwGQs0W7N5aOqRkYlDhWJCPM8g/n8FixaTXkerric5mp+vx03xmCuKjv6Ger0JqAXho4N6mNl7pqcfXWA03bBZCCFGlZWVl0aJFCwB8fHwAyMvLcx6/9dZbee6558rd/lX/zRUSEgKAXq93lq1YsYLRo0fTs2dPunXrxsCBA3nhhRfIzMx01hk3bhxxcXGkpKTw5JNP0rNnT2688UamTJlCQUEBdrudzz//nAEDBtC1a1fuuusudu3a5Tw/Pj7emU40depUYmNjiY2NZdy4cZc89pSUFGJjY5k5c2aJZb/99hujR4+ma9eu9O7dm/fffx+rtfQZywutW7eO2NhYvvvuuxKP33777QwaNMiZq1X8niQlJfH444/To0cPevTowZNPPklSUpLb+aqqsnTpUkaNGsX111/PDTfcwPjx49m+fbtb3Vq1aqHTyZINUQKzBe6fAQF3Q8AoaDwJZv4EK3eCvezTL1W23pcE7zpo7TZ6Ju+lSfZJ/M4G+QAK0CjnFDk6L6xrAtm0NR+AXL2O72qF8HugPwleRrYF+LLD0xvd2e8cRbqSr8iZ9Od+3q1aDX9GBBGVWwA2lYRc0Odb6P3nCdTZh3nmnoNs+imj4l6sEEKIK6ZqlBIf1Unt2rVJTU0FHJPLYWFh7N6923k8OTn5iraBrtDIrqioiKysLOffjxw5wkcffURAQAA33XQTAD/88ANTpkyhXbt2TJgwAYPBwKlTp9i0aRMZGRkEBgY62yssLGTixInExMQwefJk9u3bx/LlyzGZTAQEBLBnzx5uv/12rFYr8+bN4/HHHyc+Ph5vb2/atWvH2LFjmTNnDoMHD6Zdu3aAe5qM2Wx2jtn5puh0zm9Vpdm0aRNLly5l6NChDBgwgA0bNvDVV1/h6+vLvffee0nv1w033EBwcDDLly9n8ODBLsf+/vtvjh49yoMPPujyD1xYWMj48eNp2bIlkydP5sSJEyxdupS///6b+fPnO79YAbz00kusXr2am2++mbi4OCwWC6tWrWLSpEm8/fbb9OjR45LGKf7l/m85fPbLVe2iQGPkgE8D7BodUblpeFtNJdbToOJtNZPr4UG20QBn/2+cNlywUNeqsi3Un85nsmmYnsuvdUOx6LRgtUOeGfQa8HY9p9BDx5FgX2ebtx4+Se1cx56eHhYbS2aepG5jT6LqSx6/EEKIitG9e3fWrFnD888/D8CIESN4++230Wq12O12pk2bRu/evcvdfoUG+jNnznSZ/QZo0KABn376qTMAXb9+Pd7e3nz88ccuM8gXLuYFx+WM0aNHM3r0aGdZbm4uP//8M82aNWPOnDnONurXr88TTzzBjz/+yNChQ4mKiqJTp07MmTOH1q1bl5ous2zZMpYtW+ZS1rJlS+bOnXvR13r06FEWL17sXKw6dOhQRowYwaJFiy450NfpdAwYMIA5c+Zw9OhRGjRo4DIurVZLXFyc23sycuRInnjiCWdZTEwMTz31FLNmzXJe3lm3bh2rVq3iueeeY8iQIc66d9xxB2PHjuX//u//6N69u9wsSJRt1Z9XvQsvexEdM3dzyK8xGkpP/7EqGjIN3mR7ajGXMlNfzNtuRwU8bXbu3XmU75pFcrpQdaQX2bTgXcJJZ/8/aG12onLdN+4/8GeeBPpCCFFF1ITtNR9//HHWrFmDyWTCYDAwZcoU9u7d69xlp3v37nzwwQflbr9CU3cGDx7MjBkzmDFjBu+99x4PPfQQWVlZPPLII5w8eRJw5B8VFRWxceNGZ0pKabRaLSNGjHApa9u2LaqqMnToUJcvCsUz9omJiZc15h49ejjHXPx46qmnyjyvZ8+eLjvSKIpCbGws6enpFBQUXHL/gwYNQlEUly8bhYWFrFmzhq5duxIaGup2zpgxY1ye33jjjdStW5cNGzY4y1auXIm3tzc9e/YkKyvL+cjLy+OGG24gJSWFEydOXPI4q4qMjAxMpnOzvXl5eeTmnts33Ww2OxezFCv+2SvteWpqqsvPovRxwfP6YVwLChBReIaTXoGYNO5zEBZFw/awhmhRaXU6GS72+aGqGAstzrQfo81Oz2Onz60hMNscM/ultGHTKOTp3b9IBIefuwpQo//NpQ/pQ/qQPi6zD1E+rVq14vHHH3euCQ0MDOTnn38mIyOD7Oxs1q9ff0Wb2VTojH50dDSdOnVyPr/hhhto374999xzD9OnT+e///0vY8eOZefOnTz55JP4+/sTExPD9ddfT69evfD2dp1iCwkJcVsM6+fnB+C27WNxeXZ26ftolyQsLMxlzJcqMjLSrczf3985Bi8vr0tup2PHjqxcuZKHHnoInU7HmjVryM/PZ+DAgW71fX19XdJzitWvX5/169dTWFiIp6cnCQkJ5Ofnc+utt5bad0ZGBnXr1r2kcVYVF6ZeXZhi5eHhQXBwsEvZhf9BLnxeq1Yt6eNifTw31JGPn3Z2W0xFuXiQfQVaZB4mxasWGyKb0yN5Hwb7eWteVNDZrHRP3suSps2ds+8lUhQ2B/jRuKDo3GspMKFR1XM33so1O4L9UC/Qnjfncfa1baobSq/Dqc7ZkPrNPGndye9cezX531z6kD6kD+njMvuoDGoNyErYt28fzZs3dysPCAiokPav+urLli1b4uPj41wAGh0dzZIlS9i6dSvbtm1j586dvPbaa8ycOZPZs2e77FSj0ZR+waG0Y2VdJagoFxvb5Y5h8ODBPPvss2zYsIGbb76ZZcuWERwcTLdu3co9PlVVCQwM5LXXXiu1TsOGDcvdvvgXaRoJ/3wAS/9wBNe3tIZf/oIth+DzX654p51iNjScNoZx64ndpHr5uwb5gB47sWnHUIHm+amMDM5lfbKeQLMNk1bhiLfrl+u8C1J7Mg16PG028s9fdG7QugT5OosNo1alQKPlQKg/uf5GRvsWMDzWg9Yd/NDqqv8vFSGEEFVHy5YtadmyJXfccQe33347jRo1qtD2r8k2KzabDYvF4nzu4eFBt27dnIHsxo0befTRR5k/fz7PPPNMhfVbXfLPe/bsSVBQEMuWLaNhw4bs3r2bMWPGlLgLTm5uLmlpaW6z+seOHSMoKAhPT0f+cJ06dThx4gStWrW65KsLQpQqyBfGnXd16P5ejsfYG+GWqVDouMMtnh7n/n6JVCBTH8CmsE6cNoTQRJ9Mrbyskit7aNh3sx9JcZ58cX8tps7MZO4+BaPdfR/98PM+cywahQ31w7F76GiZkkWuVktUoYlQbPyhC+WUryf+BUV83cXKbb0CsNlV0gsh1MsLRSkpmV8IIURlqwn76H/88ccsXryYl156iRdffJG2bds6g/6KyLq46ttrbt68mcLCQpo1awbgtsMN4Dx2uWk3ZSkOcCu63Yqm0+no378/mzdvZvbs2QAlpu0U++KLL1yer1u3juPHj7vsotOvXz/sdjsffvhhiW1Ibp2oEF2vg9TPYfGTsOZlyFsA6rew8gXo3faSmlAAf0s2uTofxxUDpYg8fcn3rzBunsTeQSGoWsene58WHpwy6J1baTrbVCAxOoglLeqwqnEEn8c04HigN4XeBo7UC6Z7Ti6xOXlE5BVhOTuj3zUzhz63ONLvtBqFMG+l2kwWCCHEv1FN2F5z/Pjx/PLLLyQnJ/P+++/j7e3Ns88+S4MGDejSpQvvv/8+KSkp5W6/Qmf0Dxw4wMqVKwHHQo2jR4/y3XffodPpmDhxIgCTJk3C19eXdu3aER4eTm5uLvHx8SiKUuE3kqpfvz7e3t4sXboUo9GIr68vQUFBdOjQoUL7qQiDBw/mq6++YvXq1cTExBAdHV1ivYCAANauXcuZM2do3769c3vN4OBgxo8f76x3yy23EBcXx+LFizlw4AA33HADAQEBnD59mr/++oukpCSXBcA7d+5k507HzY/2798PwOLFi515effff//VeumiuvPzguFdXctui3E8PIbDRe5aW0yLSous/QSbswgrzKaAcEqah9C2rAXbzj1v0cqLdl+dxN9q4x+fc1euVBUCsgtICfByy+Uv1OtY0aQ2LU9nsyfcn0xPAwabnTkvhUlgL4QQolKEh4czefJkJk+eTHJyMkuWLGHx4sU88cQTPPnkky6ZMZejQgP91atXs3r1asCRw+7v70/nzp255557nHf9GjZsGGvWrOHbb78lOzsbf39/mjZtytNPP01sbGxFDgej0cjrr7/Oxx9/zLvvvovZbCYmJqZKBvp16tQhNjaWbdu2XXQ239PT0/l6PvzwQ1RVpUuXLjz22GNu6Twvv/yy84Zcc+fOxWKxEBwcTLNmzZg0aZJL3W3btjmvJhSbN2+e8+8S6Ity6d4cfvn7kqq2zt5HAQFkegXgUaDhwpC7pJUAgSEe9GqkJ21nPu2zcknwMmJVFJrlF9AxK5dljSM4EeJHVHYBdbPyyTHo2R/qR6aXgd/CA8FDy/Vhdqb31hMeKEG+EEJUJzVhe82SRERE0KJFC6677jr27NlDfn5+udtS1Gu1elWU6eGHH+bvv/9m1apVGI1Gt+Pjxo3j5MmTxMfHV8LohCiHxDPQYKLjRlVlSDVGEP75nSh3dCVD/x80NtdzbCj4mV9jzpw5AIwdOxa9Xk9iqomnHjuO3m4n2Op69cCk1bArPIBOKefuanva28C3zaIwFhTx3RhvbmysRwghRPWzoM7iEsvvTLz9Go/kyqmqyvr161m0aBHfffcdaWlpBAYGMmTIEEaMGMHNN99crnavyWJcUbbExEQ2b97MsGHDSgzyhaiW6oTClrfg5imQVfqMhM3Tk9AdL6FcFw6A6m2EHNf7URTiiV8J5+5KsnHYy0CXrDy3Yx42Ox3PC/IBwvJN3KtmMX1qODptDZ0OEkKIf4GasL3mb7/9xuLFi1m6dCmnT5/Gz8+PQYMGMWLECG655ZYSN2a5HBLoV7I9e/Zw7NgxFi5ciF6vZ9SoUZU9JCEqVkxDx/acw96B3/Y5duZpWx92HHHk79/YAu2y/4DPuTvOKkPbwZxNzud2oKBRybsP2JMKaZJfREn3yS3tV8CgphoJ8oUQQlS6Hj164OPjQ1xcHCNGjKBPnz54eHiUfeIlkkC/ki1dupQffviByMhIXn31VbcbgQlRI4QFwK+vQXa+I9D30EOhCWx2lwC/WMAn/UncmYFu93HsaMj1CqTOZ71LbLp1MyPfn93L3wYlBvznU4GYbgFX8mqEEEJUATVhe80lS5bQr1+/q5bNITn6QogqK2/TSSwnC/C9JQpdgAGLxeKWow8weNQh6p7dv79Qo2C0qyg4gvoLfw+k1/bhqw+q1x2hhRBCuPuq7pISy+8+Pvwaj6Tqkhl9IUSV5XN9RNmVgJ6tjWzeaSfcYsXzvDv1nh/kq8DhQB/a3R7ldr4QQojqpybk6F9tEugLIaq9wUNCOLrlKBkaDUF29x1+TBqF+W3rExSqZ2nnq36fQCGEEKJKkN94QohqL7qRJ3F3hRGqlLyNp09rf96NM7BvrBZPvcwACSFETaAqJT/EOTKjL4SoEW4ZEkq3PkFs+CGdVQvPOMsj6xl58oXactdbIYSoYezyuV4mCfSFEDWG0UtL7+FhtO3iz8G/8giLNNCktbcE+UIIIf6VJHVHCFHjhEcZuKFvME3b+EiQL4QQNVRNSd3JycnhzTffpHfv3rRr146tW7cCkJGRwbvvvsvhw4fL3bbM6AshhBBCCFEJkpKS6NGjB4mJiTRu3JgDBw6Ql+e403tQUBAzZ87k+PHjvP/+++VqXwJ9IYQQQghR7dSE7TWfeuopcnNz2bVrF2FhYYSFhbkcHzRoECtWrCh3+5K6I4QQQgghRCX46aefePjhh2nevHmJqaYNGjQgMTGx3O3LjL4QQgghhKh2asKMfmFhIaGhoaUez83NvaL2ZUZfCCGEEEKIStC8eXN+/fXXUo9///33tGvXrtztS6AvhBBCCCGqnZqw686jjz7KwoULeeutt8jOzgbAbrdz+PBh7r77bv744w8ee+yxcrcvqTtCiCrJbrKybl4yezJ01Gnuw609fTF4VPaohBBCVBWqpppF9SUYNWoUx48f54UXXuD5558HoE+fPqiqikaj4Y033mDQoEHlbl8CfSFElWP++wxvPfYPu6KjARvsz2bD7/m8+UJIZQ9NCCGEqFDPP/88d999N9988w2HDx/GbrfTsGFDhgwZQoMGDa6obQn0hRBVzuZ71/FXi450OHiEG/b+g85mY3vjBmzqGFPZQxNCCFFFVPfFuAUFBdxwww088MADTJgw4YpSdEojgb4Qosr522ykUdJJbt+01Vl2286/2P54LupjCoqHWomjE0IIIa6cl5cXx44du6p3cJfFuEKIKudwSDDNE064lTdNTMb8l28ljEgIIURVo2qUEh/VSZ8+fVi9evVVa18CfSFElbMzuja+RSa3cptWofkvZ2i/MBlVlVl9IYQQ1duLL77IwYMHufvuu9m4cSPJyclkZGS4PcpLUneEEFVKxv5MfKw2cjy9sCoKurMBvQpk+elpeCqViKNpmL7eiceYzpU7WCGEEJWnmufoA7Ro0QKAffv2sWDBglLr2Wy2crUvgb4QosrYvzefqW9lUqvITL6nkc2NGhKVkYFGVUkJDCAkL5t9AfXZE1wf5aND9JNAXwghRDX20ksvXdUcfQn0hRCVLiHdxv/FF5C+4QwWnY4cby8K9I6Pp39qRwBgBzK8vdAWZ+wUwu6ZB2gzvlnlDFoIIUSlqm75+CWZMmXKVW1fcvSFEJVq4bo87vhPKr9uzacILQB2jYY9kaEu9TRwLsg/a9eH+7Dlm9n97Fa+j1nGm2O2s3Kf+RqNXAghhKjaZEZfCHHNbV95mqXz00hS9Pzt54uq11E3rwAPFSw6LV5FJsJyC8psJ1/R8WbsWta3q8+2gc2JTMvhhUFf8P6INjzyasdr8EqEEEJUluq+jz7AK6+8UmYdRVF48cUXy9W+olbRrSumTJnCihUr2L59e7nO3759OxMmTODll18mLi6uzPpxcXFEREQwa9ascvVXHpc7xmstPj6eqVOn8sknnxAbG1vZwxE1xOJXD/LbjiL2BQUQffoUf9aJwqbVolFV2p0+TbPk0/jnFV5SW37phbzfryPbmkQ6ywxmK/+89QyZnbvS5ofhKDq5cCmEEDXRjLarSiyftOu2azyS8tNoSv8dpSgKqqqiKEq5F+OW+zfg9u3biY2NJTY2lu+++67EOrGxsTz66KPl7UIIUYPkF9h59OlEfttuwmow0CS/ELN/oHMRkl1RsFlsbkF+STMRWruNrsd2EZZ5yiXIBzB56Pi2eQf+OZrF4ugFpC/YDSYLJKdDcjqWUwWYj+dcrZcphBBCXDK73e72sFqtHDlyhMcee4zY2FhOnz5d7vYrZKpr1qxZFBUVVURTTi+88AKbNm2q0DaFEJWj0Kzyv8f2470zGavRw1nuZ7VRL7+QiMIiwgtNBOe5f454WKz4FLoG/23PHKB13j8cb6THaLa4neNfUEiPw/sYcnI5wXdNxWq8k7RGj/DCXavp8HwifZ84wcdNlrL0tq85c983FGw/RYFZJSnLXvEvXgghxFVRE26YVRKNRkP9+vV55513aNy4MQ899FC527riHP3mzZuzb98+vv76a8aOHXulzTnpdDp0upq5hMBms2GxuAcnQlQUq8mG3WLHw0d/zfosyjLj4aNDczZVJjepgD8WJvHlPkix6hiyO4ETURFu54UXmSn+WPYs4QPat6iIAoMHDbJO4GMpJCovlei8VAD6HPubbn8d5+fYRs76YVk5dE4+QDiJznZ12Hik9xgWtLreURABW+tH8uZ36+gT2Y6Mr1SuS9lFQP5pdkY35YEmNmLTDtFx+S9o0GK+pzfpuiD0AVpCD+8heVsyezvF0OyhGwjz1eLnoUJGHgT7OvZ1ttuxpeWS5eVDsI+2wt5jIYQQ/y7du3fnmWeeKff5VxxJ33LLLaiqyhdffMHgwYMJCAi4aP19+/bx+eef8+eff1JQUEBERAT9+vVjzJgxLoF9aTn6O3bs4MMPP+TgwYP4+PjQq1cvBg8ezIgRI3jggQcYP368W5/Lly9n3rx5JCYmEhwczPDhwxkzZkyJ4ztw4ADTpk1j79696PV6brjhBh555BGCgoJc6mVlZTFz5kx+/fVX0tPTCQ4Opnv37owfP97lPSjOc58xYwZ///038fHxpKam8sILLxARcS7oudQxrl+/ni+//JKDBw+iKAqNGzdm9OjR9OzZ84rqfvfdd8ybN4+UlBTCw8O5/fbb8fHxKfE9ElXXX98lseLT4/glZ6Gxq9TuFsZtb7bD4OcI+I//dpr9355AtdnRe+soTDcTcp0/7cY2xBjgUWKbpw7msW1xElkpRah2Fa8APS1uDafZTY5dcTKP5hI/cQv5Jx2z8VofPZp8GwEnstkRE83+xvVpn5qCVlUxFpnI9fF2af/80D7b3w97WhoaqyNhp8hDz7HwUCIzM2h+Jpmgohx0mFHPnpfvYeDnxtGEpGaCTku2wcBpxUivux7n4MdP4m1x3F13Z2g0C1t0cek312jg4ZG3YjM43puE0EA6H9exY9az7PNqQXC+ShoGQkil8KkfKCKYSHahx0J9wOu3v+h9LIj9IZGEW/LpePoYTyb8zs8NWzNq1XLqZaeRFliLidcPYlDyXpLDI1jdoj1JgSEE++u5q52eoAAtP25I444fltM5N4WAvq3hsTgwXLsvaEIIUV3VhMW4Zdm+fftF8/jLcsWBvqIoTJ48mUmTJvH555/z+OOPl1p348aNPPXUU9SpU4dRo0bh5+fH33//zcyZMzl48CBvvfXWRfvatWsXkydPxs/PjzFjxuDr68uaNWvYvXt3qed88803ZGRkMGDAAHx9fVm1ahUffPAB4eHh9OnTx6Xu6dOnmThxIjfddBM333wzBw4cYPny5ezfv58vv/wSo9EIQF5eHvfeey+JiYkMGDCAZs2a8c8//7B06VK2bdvGF198gbe3azDz/vvvY7VaGTx4MN7e3tStWxez2XxZY1yyZAlvvfUW9erV4/777wdgxYoVPPnkkzz33HMMGTKkXHUXLFjAu+++S5MmTZg0aRJFRUXMmzePwMDAi/57iKpl95dH+ejrdNomZzrLTm48zab/28dNU9uQsP4Uqx7e5nZe0uY0kv44w7BFN7jdtCMjsYAFD+3CUuSa0nLkjwxMBVba9I8gftwW8k+fS7mx5VmwAYkNgznt7U2+Xs8fUeFsrR3Kdacz8LfZULSOWe7iRUbFVI2GXY0bYUFFZ7OR5eND+38Ocdvh7XjYbdgwYMOAHS1Gcvm0bTeWfT2bXkf+4Yy3N69278On7btg02j4M6w+1ycfJNnHnxtvfwZ7Cb8QnvthE3Wyc3lpcE9SA3zZXLcJP9S+mWbJheSdrZNDIE34Gy8y0HPuSlx4YS7b5r/KEz1G8EH7W0msF8PayGYcn/E4vhbH+9E0M5WvVn7K/7XvzfMtzi4OywQybfyeYMOosbFv9svUzzrjOPbbX7A7Ab4u/XNUCCFEzfHll1+WWJ6VlcWvv/7Kt99+64zjyqNCcmM6depEp06dWLp0KSNHjnSZqS5mMpl49dVXadmyJR9//LFz9n7o0KE0btyY9957z7nAtzTvvvsuiqLw2WefERUVBcDw4cMZN25cqeekpqaydOlS5+z0wIED6d+/P4sWLXIL9JOSknj88ce58847nWUNGjTgvffeY+HChdxzzz0AfPHFF5w4cYJnnnmG4cOHO+s2adKEt99+my+//JKJEye6tF1UVMSCBQucXxYA59WKSxljTk4O06dPJyoqirlz5zrrDhs2jLvuuotp06bRq1cvfH19L6tubm4uH330EfXr1+fzzz93ji8uLo5hw4aV+r6Kquf3xUkYrO6z8kc2nOYmYM/ChFLPTTuQQ+quTCLauV65+nvlKbcgv9if36XQqFOgS5BfTAVsBj0mDx1H/TyxaBxffHdHhHL98RTiDhzDp8hEhq83Z8JDsejPzWAf9fPmoJ8XwWYrnU6mMnb7WjzsrrsNFCle3D14JHf99Se9jxwAIDw/jw9XLSWoMJdQex7D7nuCLKMXvQ/8RY6nF9jsoDuXRlMrN4vh+/9ALQritW/Xc/+9ceisNuqdct2H34aebILwJ8XtdepVO9PXf82u8Lr8FtWUzicOOoP8Yga7lY1RjUt8D/sd2HkuyC+2+HeYfh+E+pd4jhBCiLNqwIR+cWxZkpCQEJ599lleeumlcrdfYfvOPfTQQ1gsFj7++OMSj2/ZsoX09HTi4uLIy8sjKyvL+bj++uuddUqTnp7Ovn376NGjhzPIB0cu/8iRI0s9Ly4uziUFxWg00qpVK06cOOFW19vb2yVwB8cXCW9vb9atW+csW79+PYGBgQwePNil7pAhQwgMDHSpW2zYsGEuQf7ljnHLli0UFhZyxx13uNT18fHhjjvuoKCgwPn+XU7dzZs3U1RUxPDhw13GV9IVj6ogIyMDk8nkfJ6Xl0dubq7zudlsJj093eWckydPXvR5amoq5+8yW137sFpt5BgNXEgf5gmApYRFq+dTbapbm7k5uaXUBrtNpcCWV+IHrU2nxa7TkqfTYjnvkmNwfiF37P4HvyITGiAkN5/opJOgqih2O0e9DKwNDyTJy8juAB967NmBl9X9Blg2RcvKRi2dQf757ti/k6fi7uCUrz8mvZ7lrdoTQQFY7GC2gc2O3mTmj7lTqFPkCN47HEvB02RBsdjQlLDjsIqCidJT2fofcVxVPOof5j5WFE74Bpd4ntZewpcoVQW7YwxV4edK+pA+pA/p41L6EOVz7Ngxt0dCQgLZ2dmcPn2aN954o9T48VJU2GrXZs2a0bt3b3788UfuvvtuGjd2ncE6duwYcPEbA1zshyYlxfELuW7dum7HSiorFhkZ6Vbm7+9PdnZ2iXX1etfcWA8PDyIjI0lOTnYZy3XXXee2WFin0xEdHc2BA+7BR3R09BWNsbj/Bg0auNUtLiuuU5669erVK7VuVXLhWokL1xF4eHgQHOwaVF14henC57Vq1aoRfXQdEsUvP1vJ8jQQUOj4gLZqNfR84joAWo1oQOr2nZQkoL4PETFBKBcshu00tCH/rN6JzeIe/LbpH0ForRDq31yLYz+nltjuhTMJLU6lo70gkPYuMuGfmUmB3c5Pzdq5HKufccFs91mLW7bmoXU7satatFhdjv0ZWcetfo6/D2TZHbP6Nnho+2rqZaeRguNnPN3bE5NNxW62E9+8IUP/PnTea7DiRR7HaUoD/sSI+xWME35BoFE4FF6bma17MP6vDc5jCf4hDDi6i72hUW7nrWjWnpRfA6idm3WuMC4WwgOAqvFzJX1IH9KH9HEpfVSGmpCjrygKoaGheHp6lni8sLCQM2fOXDSOvJgK3dZm4sSJ/PLLL3zwwQdMnz7d5VjxN8VHHnmEJk2alHh+aGhoieVXQqutGjteXOzbWFUZo6jeYh5oxHOeCcxdF8nRXCt1ghTufKwB9Zo50mYa9a4NKuxdchy7TcXDW0tRpmMxbuz4Jm5BPkBoA29GvNuaLV8nkpVSBKqK0VdHyz61aBPn+EXR591YNv5vH/uWHsdmtqP46DlVPxTPE3m0TMtkVf1Iis5+Kc4xuqcWmbRafmxUj+tS3L8s/B5djxZnXPcP3hpRhy9adWT2/FWcIYwIUpwXFXaF12bPBb+wAOplptP00FFSfPwZfHgnj29fjQkjaUShovJV+ybYzY7Z9Tdu7oSH3cTQvQfwJJ9QTqBg45Q2gO+aDuWxfxbjZTt3deRwQBg/tO/KzX653F23iHdD7uXbHZ1pc/IYpz19ydYbGb3/D17660dWNo/ltF8gPr56RsXoCfT35vGwKdy/4ls6ZKfg37c1vCApc0II8W9Rv359vvrqK5e08fMtX76cO++8s9w3zKrQQD8yMpJhw4bx9ddfu+2WU/xNxNPTk06dOl1228XfPo8fP+52rKSy8khOTsZisbjM6pvNZpKTk11mvCMjIzl+/DhWq9VlVt9qtXLixIkSZ+ivVHG60tGjR+nYsaPLseKrJcX9Xk7d4j8TEhLc6h49erQiX4K4yhRFoePd9el4d+l1GvWpTaM+tS+r3ajW/kS1vni+eLenmtPtqeYuZVkphayZfoRJR4+yIiKCM3oPtFodmV6eBBac2xd/R3RtbFotZoOG8LwcTvn4OY9906wV9+z4Ez0WVMCMB0GZdsbscHy+5OKPCQM+5GFFz6DbxxGVn0ZUVgZJAY4ZKa3NxtMr1zLqwDoUrKhnrwDkEkwYieTq9CSEdKBVwWkKjJ70bqLlvnE3ku/ZmwXTtmPcf4Lclo2wxDSmxa5kjvScSHjhEbxPnsLYpzWNHujFMa9zKVOOvbI6OR7J6WD0gOCbGQxMLeG9mxAbDU8/emn/GEIIIZxqwp75agnpouezWCyVu+vOhe677z6WL1/uNqPfpUsXgoKCmDt3Lr169cLf3zVwKCoqwmazue1WUywkJITmzZuzYcMGkpKSnMGs1Wrl66+/rpCx5+fns2TJEpdvVUuWLCE/P99lS8oePXowZ84cvv/+e5cFq99//z2ZmZkuO9pUlE6dOuHp6cmiRYuIi4tzvk/5+fksWrQILy8vOnfuXK66BoOBJUuWMGDAAOeVh1OnTrF69eoKfx3i3yOgtifD32zJcOBNq8qslw6ypkjPH43rEZGR7diZJ8ifk/6+ABR6eDDsaALfNm5Ktl5HZH4hw/cnsD8oivrZJzHarBRhJKDIzJ0HfucEjvRAM0YyMJIY4EuWp5FOx/MY/PdOttZvTAG+3L1hP/VP2fhT34ooYyIpEQFQZMMQ7Ev0m4OoVduLtc2iXBbqFvvPtBtcC+4qvpzd89LehMjKv7QthBA1VXVN3cnJySErK8v5PD09vcS1o1lZWSxcuLDETW4uVYUH+gEBAdx999188sknLuWenp5MnTqVJ598kqFDhzJgwADq1KlDbm4uCQkJrFu3jv/9738X3XXnkUceYdKkSdx3330MGzYMHx8f1qxZg9XqmKG7cGvAyxUVFcXs2bM5cuQI1113Hfv372f58uXUq1ePO+64w1lvzJgx/PLLL7z99tv8888/NG3alH/++Ydly5ZRt25dRo8efUXjKImvry8PP/wwb731Fvfccw/9+/cHHFtmJiYm8txzzzlz6C6nrp+fHxMnTmTatGnce++99O3bl6KiIr799lvq1KnDP//8U+GvRfz76HQKD77RlA0vneJwOiT4+ThuLHWeNqeSOFinKV0ycvE0mYjdfwSD1cbfkXX5J7w2vY7uwt+UQ7oSwBetupCuDWLonwdRcOzyY9ZqWDP9a6bd1JHnew9Fq4dfl3xAm58fwLvluXzT8Gv70oUQQggX7733nnPNqqIoPProozz66KMl1lVVlddee63cfV2VW8+OGjWKpUuXkpaW5lLepUsXvvjiC7744gtWrVpFZmYmfn5+REVFcdddd7kt4L1Q+/bt+eCDD5gxYwZz5szB19eXXr160adPH+655x4MBvcdRy5HWFgYb775JtOmTWP16tXo9Xr69OnDo48+6rJIwsfHh88++8x5w6zly5cTHBzM0KFDGT9+fKlXJa7U8OHDCQkJ4auvvmL27NmAY0vPd955x+0mWJdTd9SoUXh6ejJ//nxmzJhBeHg4o0aNwsfH56KLp4W4XM+MCWTstKziTWXQW614Wiz0+2cX7U6ncLBOUwDqpp7BYD23yNas07MnLJpuiftJCfKi+9/pPHp7DAV6PaO37kUBGqZnk+Lnw5rr6jFuzzoe8Uml+foHHXerFUIIUeNU1xn9W2+9FR8fH1RV5emnn2bkyJHExMS41FEUBW9vb9q3b3/RSfCyKGpZyUHVwC+//MIzzzzD66+/Tu/evSt7OEKIi9iXaGH+2gJOHM7nuqST9F60iaDCfAo9FaYMH4FNq6XdwaME5+S5nBdckEOfIzuZ2649W0Ma0vPQceZe34zIjHxaJWWQ6u/DovbX0SUrhc8+bEmojyxyF0KImuy9Lr+UWP7YHzdf45GU39SpUxk6dCgtW7a8Ku1flRn9q0VVVcxms8vMvdVqZf78+Wi1Wtq3b1+JoxNCXIrmdfS8PsYf8Adqs6SZNxnv/EbDtAz6bNvFutYtSPP3dQv0a+dmYNZomRPTgQMhYYzd8ic9m3gSfVszvtlrI9sCL7bRMSk2BG0NWKAlhBDi4qrrjP75Xn755avafrUK9M1mM3FxcfTp04e6deuSnZ3NmjVrOHToEGPGjCEkJKSyhyiEuEzDn2nGDz2j+b8VZzicreXWH//CaLA7bsR19oKj3l7EvjA/Hut7L7siaqO32gggl1deaIhGozCug/7inQghhBBV2KZNm9i5cyfZ2dnYL7iZoqIovPjii+Vqt1oF+jqdjuuvv54NGzY48//r1q3LM88843ZHWyFE9dGvkxf9OjlufPdM/1RiNx8mI8yLZidPYrCauXXCKHLPu+vvbXsOU7+RAY3M3AshxL9WTZjRz8jIoF+/fmzduhVVVVEUxbnlZvHf/zWBvlarveqXOIQQleuNZTFs9/4dj8IgwotySAnw5/OvlzOnU1uS/P3oeiSRZzetweO7uyp7qEIIIcQVeeqpp/jrr79YsGABnTp1okGDBqxevZr69evz3nvv8ccff7Bq1apyt1+tAn0hRM2n1Wqoo6TwR6MGvNvuejJ8vPGwWhm59U+G/LmHvACFXfeF0rdPw8oeqhBCiEpUE2b0V65cyfjx4xkxYgTp6ekAaDQaGjVqxIwZMxgyZAiPPvpoue8ZVf5bbQkhxFXyT2QkS9u3IsPHsVWtWafji64dOBwSxPaX/DjV3ljJIxRCCFHZVEUp8VGdZGVl0aJFCwDnPY7y8s5tRnHrrbde0Q1MJdAXQlQ5P9/Yk2wvT7fy1bEtsHrLx5YQQoiaoXbt2qSmpgJgMBgICwtj9+7dzuPJyclXdENYSd0RQlQ5cXdHsXKhCavGNajvMKkpOSl7KmlUQgghqhK1BmzI0L17d9asWcPzzz8PwIgRI3j77bfRarXY7XamTZt2RfeIkkBfCFHldO4eSItlyfxVdO4ybJTBztDegcyZU8mDE0IIISrI448/zpo1azCZTBgMBqZMmcLevXudu+x0796dDz74oNztS6AvhKiSZr8eweIfcthywEzz+nruG+IP2Cp7WEIIIaqI6paPX5JWrVrRqlUr5/PAwEB+/vlnsrKy0Gq1+Pr6XlH7EugLIaokT6OGMUMDGHNemcUigb4QQoiaLyAgoELakVVtQgghhBCi2qkJu+4AnDhxggkTJtC0aVOCgoL49ddfAUhLS+Phhx/mzz//LHfbMqMvhBBCCCFEJdi3bx833HADdrudTp06cfjwYaxWKwAhISFs3LiR/Px8Pvvss3K1L4G+EEIIIYSodqrj7P2Fnn76aQICAti8eTOKohAWFuZyvF+/fixatKjc7UvqjhBCCCGEEJXg119/ZeLEiYSGhpa4X350dDTJycnlbl9m9IUQQgghRLVTE2b07XY7Xl5epR4/c+YMBoOh3O3LjL4QomraeQQ++AF2HKnskQghhKiCasJi3JiYGH744YcSj1mtVhYuXEjnzp3L3b7M6AshqhaLFRpOhMT0c2Vjb4LPJ1femIQQQoir4D//+Q/9+/dn4sSJ3HHHHQCcOnWKn3/+mTfeeIP9+/fz4Ycflrt9CfSFEFXLoP+6BvkAc9bC3T2gW7PKGZMQQogqp7rN3pfktttuY+7cuTzyyCPMmjULgFGjRqGqKn5+fnz55Zd079693O1LoC+EqFpWlbJf8OhpcPSTazoUIYQQ4mq7++67GTJkCD/99BOHDx/GbrfTsGFDevfuLXfGFULUIJl5oJZyLCkTbPZrOhwhhBBVl1pNJ/Sfe+457rjjDlq3bu0s8/b2ZvDgwRXelyzGFUJUHW9+c/HjLy+8NuMQQgghrpI333yTPXv2OJ+np6ej1WpZu3Zthfclgb4QourILbroYe2P5b8NuBBCiJqlJuy6U0xVS7ucfWUk0BdCVB11wy5+PDPv2oxDCCGEqAEkR18IUXVoy5iJKWPGXwghxL9HdZ29v5Yk0BdCVB2+xoseVrILrtFAhBBCVHX2ahzoJyQksHPnTgCys7MBOHToEAEBASXWj4mJKVc/inq1koKEEOJynM6C8HsvWkUFZs/sA8DYsWPR6/VXf1xCCCGqpJdv21Zi+dRVHa7xSC6PRqNBueBLiqqqbmXnl9tstnL1JTP6QoiqocXDl1ZPVaEaz+IIIYSoGCrV83fBnDlzrllflx3ob9++nQkTJriUeXh4EBoaSkxMDKNHj6Z+/foVNsBLlZKSQnx8PD179qRp06ZuxwYMGHDR83/44QfCw8Ov5hCFEKV5bzmklb3QVgFCjmeTVi/gqg9JCCGEuBrGjBlzzfoq94x+7969uf766wEwmUwcOnSIZcuWsXbtWhYuXEhERESFDfJSpKSkMHv2bGrXru0W6Bfr1KkT/fr1K/GYv7//1RyeEKI0X6yFx+decvWO3/zDyic6OZ/nbkwh6ZGNJNt02G+rz8Ybr+P7PRY8T+VzLCKA+kFapt6gpU992WRMCCFqElmMW7ZyB/rNmjWjb9++LmXR0dG88847rF27lrvuuuuKB1fRoqOj3cb8b5efn4+3t3dlD0P8W2XlwT0fXtYpEQczATj93l/s+fAfVtavw/x+fcj2MmDVaYmbvZ8+KRnUycpnXduG/NEwgmGJeiJy8mmfkkm+Tku0p40X/lOHiDoXX/x7IVOhjcw0C6ERBrQ6+QUjhBCiaqvQHP2QkBAAlwVyK1asYPHixZw4cQKr1UpwcDCtWrXiiSeeIDAwEIBx48Zx8uRJZs6cybvvvsv27dtRFIUePXrw9NNPYzQamTt3Lt9//z1paWnUr1+fp556irZt2wIQHx/P1KlTAZg6darz7zExMcyaNeuyXsOSJUt46623mDBhAvfff7+z/MyZM4wcOZKAgAC++uorPD09nf3OmDGDXbt2ER8fT3p6OnXr1mXs2LH07t3brf3169fz5ZdfcvDgQRRFoXHjxowePZqePXu61Nu9ezefffYZ//zzD7m5ufj7+9O4cWMeeOABWrVqBcCUKVNYsWIF27dvd+snNjaW/v37M2XKFOBc+tIDDzxA/fr1+fLLLzl27Bi9evVy1tmyZQtffvkle/fuxWw2Ex0dzbBhwxg2bNhlvYdClCqvEGb/DL/tgyMn4a8Tl92EDQ2NH9GQaNlGqM1Ov0wTv7Wox58NHFcR4zs2I15RMJosmPRaVI1jJv9waACHg/0c6T/5JuI/LgJNEXq7SnC+idapWXhZHYudNICicaQK2e0ljyOkto7ohl5kpVnIybRQK8rIoHtqERJhcKmXm21l345cfP11NGvrg6asLUSFEEJcEpnRL1u5A/2ioiKysrKcfz9y5AgfffQRAQEB3HTTTYAj733KlCm0a9eOCRMmYDAYOHXqFJs2bSIjI8MZ6AMUFhYyceJEYmJimDx5Mvv27WP58uWYTCYCAgLYs2cPt99+O1arlXnz5vH4448THx+Pt7c37dq1Y+zYscyZM4fBgwfTrl07AIKCglzGbDabnWM+n1arxdfXF4Dhw4ezdetWZs+eTWxsLG3btsVut/PCCy9QUFDARx99hKenp8v5H3zwAYWFhc6AOD4+nueffx6z2UxcXJyzXvGXiHr16jm/RKxYsYInn3yS5557jiFDhgCOLZcmTZpEcHAwd9xxB0FBQWRkZLBr1y4OHjzoDPTLY8OGDSxatIihQ4cydOhQ52z+t99+y3//+19atWrFvffei6enJ1u2bOHNN98kOTmZRx55pNx9CgHAwRTo8BTkFF5RM3YM+BWZnUuwQvIK+WjWD/T7z0gy/L2dC3WLDCXsyKPRoAJnfIwEFprJ9HIE5UeDfEnx82LYvkRnVdXu2OWnNGkpVtJScs49T81j787DPP9BI0JqOdo9tCef2W8cx2xytFS3iSeTptTDwyBpREIIIa6+cgf6M2fOZObMmS5lDRo04NNPP3XO7K9fvx5vb28+/vhjdLpzXV24mBcgKyuL0aNHM3r0aGdZbm4uP//8M82aNWPOnDnONurXr88TTzzBjz/+yNChQ4mKiqJTp07MmTOH1q1bl5qes2zZMpYtW+ZW3qBBAxYvXux8/uKLL3LXXXfx/PPP8/XXX7N48WJ27NjBU089RZMmTUoc+8KFC/Hx8QFg2LBh3HHHHbz33nv06tULo9FITk4O06dPJyoqirlz57rUveuuu5g2bRq9evXC19eXzZs3U1RUxOuvv07Lli1L/gcopyNHjrBw4UKXBdNpaWm888473Hrrrbz++uvO8uHDh/POO+8wf/585/ssRLm9+PUVB/kA+QS47bPgYbMz/I99zOxziVuqKYozyC+W7O/FKW8D4fmmco9NtcOyL09x39PRACz/MtUZ5AMcP1jItvVZXN87qLQmhBBCXCKZ0S9buaeVBg8ezIwZM5gxYwbvvfceDz30EFlZWTzyyCOcPHkSAB8fH4qKiti4cSNlbdev1WoZMWKES1nbtm1RVZWhQ4e6fFEonrFPTEzkcvTo0cM55vMfL774oks9Pz8/XnvtNdLS0nj44YeZPXs23bt3dxtfsWHDhjkD9+LXPXToUHJyctixYwfgSIspLCzkjjvucKt7xx13UFBQwJYtW5xl4Jh9N5nKH3SUpFu3bm67Iv3888+YzWYGDhxIVlaWy+OGG27AbrezdevWCh1HeWVkZLi8J3l5eeTm5jqfm81m0tPTXc4p/nks7XlqaqrLz6f0cZX62Hd5/19LY6Tk3XliD6dccdtm7ZXPtOdkWADHe3Uq2f3/b3FZpf97SB/Sh/QhfVRgH6JqKveMfnR0NJ06ndv54oYbbqB9+/bcc889TJ8+nf/+97+MHTuWnTt38uSTT+Lv709MTAzXX389vXr1clsAGhISgsHgOsPm5+cHQO3atUssL76T2KUKCwtzGfPFtGnThjFjxvD5558THBzMSy+9VGrdevXquZUVB9PJyckufzZo0MCtbnFZcZ1bb72VlStXMmfOHBYsWECrVq3o3LkzvXv3vuLdjKKjo93KEhISAHjwwQdLPS8jI+OK+q0oF6Zjnf+lCRxbvQYHB7uUXfieXfi8Vq1a0se16KNPO9hz+Tn5F9JjAuxcOE/hUVoyfSn8Cs3keHqce15kJrICrjh0vDEAcLxXjVua2bs91+V4k1aO96jS/z2kD+lD+pA+KrCPyqDKhH6ZKnQxbsuWLfHx8XEuDo2OjmbJkiVs3bqVbdu2sXPnTl577TVmzpzJ7NmzXVJBNJrSZ9JKO3Y1b+prsVjYvHkzADk5OaSmppZ6W+KK5uHhwUcffcSePXvYvHkzO3fudL5nr732GjfeeCNAiXdQA7BaraW2bTS67zJS/D5OnTrVmXZ1ocjIyMt9GUK4evl22HLIsRC3nFTgOE0xa7V42M79/7dqFN4Z0PWi5xpNFkfevqqiUVV6JJzin1B/Un2MhOab6HbijLOP8v7uaNPZl+t7n/vlN3xcBHnZVo4fKkSnV+jRP5iWHXzL2boQQojz2SV1p0wVfmdcm82GxWJxPvfw8KBbt25069YNgI0bN/Loo48yf/58nnnmmQrrt7Sgt7w+/PBD9u3bx8MPP8yXX37Jc889x/z5890W4sK5GfHzHTt2DDgXIBd/qTl69CgdO3a8aN1iLVu2dObop6amctddd/Hxxx87A/3zr2ycfx+A4isDl6pOnToABAQEXPIVDyEum48n/PoapGbC8TPwxz/w2OXdHVABaukPsPGOaDx/8aJeajYmnZY9dUIw6bX4FJpolpRGmp8nCeFBeBWZMVqs9N92kCE7DvF76wbsrxXMb41rsb5xLSZb0wm15pBusnIw1IjW4EkjcwEeeg31mnrRoJkPhQVWThwq5GRiESkJJixmMHopDBoTTq26nnh5azEV2gitbcDoqXUZb0CwnsfebEBWugWjpwajl7bkFyaEEEJcBRUa6G/evJnCwkLatGkDOBapXjgL3qxZM+Dy027K4uXlVWHtbtq0iQULFtC/f3/nnX4fe+wx3n77bV5++WW3+kuXLnXJ08/Ly+Obb77B19eX9u3bA46bdXl6erJo0SLi4uKcqUv5+fksWrQILy8vOnfuDJT8voWHhxMYGOjy+orTcLZu3UqvXr2c5fPmzbus19urVy8++ugjZs6cSfv27d1m/fPy8vDw8MDDw6OUFoS4DLUCHY9OTWDcreA70pGJc4lONQ/A0s3GqM+GcTBL4dYFKkWFduqezuTpbzfS6FQmyYG+zO7VngKjntu2H6RlQQ7mlzpjPwrtAvS8OVSheXNP4NIWmHftVXadiwkILmEHICGEEFdEFuOWrdyB/oEDB1i5ciXgWJRx9OhRvvvuO3Q6HRMnTgRg0qRJ+Pr60q5dO8LDw8nNzSU+Ph5FUSr8xlX169fH29ubpUuXYjQa8fX1JSgoiA4dzu3CceLECeeYL9SxY0dCQkJIS0tjypQp1KlTh6effhpwrD8YOXIkX3/9tTNX/nwBAQGMGTPGuZVmfHw8qampvPDCC86g2dfXl4cffpi33nqLe+65h/79+wOO7TUTExN57rnnnF8UPvvsMzZv3ky3bt2IjIxEVVV+++03EhISXHYl6t27Nx999BGvv/46CQkJ+Pn58ccff5S4hejFhIeH8+yzz/Laa68xfPhw+vbtS0REBJmZmRw+fJj169ezZMkSt7USQlwxLwP8MwOaTLr4Xpbn+fvWc+tcWoTqyHkEjmerFFjDue7d3iTnqpxJt3B3/AFMB7Lwf7Iu/n26ADD8arwGIYQQoooqd6C/evVqVq9eDThy6P39/encuTP33HMPLVq0ABy70axZs4Zvv/3WmV7StGlTnn76aWJjYyvmFZxlNBp5/fXX+fjjj3n33Xcxm83ExMS4BPpbtmxx7mxzoRkzZhAUFMRLL71Efn4+H374ofMqAcDDDz/Mzp07eeONN2jZsqVLms1DDz3Erl27WLJkCRkZGURHR/Paa6/Rp08flz6GDx9OSEgIX331FbNnzwagSZMmvPPOOy43zOrRowdpaWn8/PPPZGRkYDAYqFOnDi+88AIDBw501vPx8eH999/n3XffZc6cOXh6enLTTTfx6quvOtN7LtWAAQOIjo5m3rx5fPvtt+Tm5hIQEEDdunWZOHFilVh0I2qoRhFgXgL6ssNwFciKcL+Tc13/c7M6kb4Kkb4e8FDrihylEEKIKkZm9MumqFdzRWsNV3xn3E8++aTCv7gI8a/TZBIcOllmtS/f6klRgJGxY8e63IVbCCHEv8vTg/4qsfzt72Wip5jcnlEIUTXsmQb+XmVW08jUhBBCCBy77pT0EOdIoC+EqBo89LDqxYtWURUoCHTfIlYIIYQQ7ip8e00hhCi3o6kXPWwP9btGAxFCCFHVyQ2zyiaB/hWIi4tz7rQjhKgArete9LCSd+V3rhVCCFEzqOW+veG/h6TuCCGqDpPl4scvcgdtIYQQQriSGX0hRNURHXrRw+oNza/RQIQQQlR1svC2bDI9JoSoOsICLnrY/sn4azMOIYQQogaQQF8IUbV4l7KrTs8WEBF4bccihBCiylIVpcSHOEcCfSFE1TLjAfcyHyOseP7aj0UIIYSoxiTQF0JULWNuhJkTIMQXvD3g7u6QM7/0mX4hhBD/SjKjXzZZjCuEqHrG3ep4CCGEEKLcJNAXQgghhBDVjl0m78skqTtCCCGEEELUQDKjL4QQQgghqh3Jxy+bBPpCCCGEEKLasSOBflkkdUcIIYQQQogaSGb0hRBCCCFEtSOpO2WTGX0hRLV1Ikel63wr+net1P7Iyg9H7ZU9JCGEEKLKkBl9IUS1tC8Nuiy0oc030yU5HbNOw7DsYHY8oKd5iLayhyeEEOIqk+01yyYz+kKIauc3UxPazodCG+QZPdgaHUpETj4Tf9/HPd9aKnt4QgghRJUgM/pCiGpncWEn0JybyjHpdXzfqgEheYW0+vsMUKfyBieEEOKasEuOfpkk0BdCVDtWteSLkWk+nqQUmq7xaIQQQoiqSQJ9IUS1Y9eUnnX4T7AfqqqiyEyPEELUaLLrTtkkR18IUbNoNEzZZKvsUQghhLjK7ErJD3GOBPpCiGrlqDW4zDr/t1m22RRCCCEkdUcIUa2sNzWDMi7X+mYXAh7XZkBCCCEqhYpM35dFZvSFENVKvt1w0eOK3U6tnIJrNBohhBCi6pIZfSFEteKpmEG9WA2Fkz6e12o4QgghKolsr1k2mdEXQlQ5BVtPkjz0OzIGLcC84ZjLsSz7xYN4VYEsowebk61Xc4hCCCFElVelZvRNJhPLly/nl19+4fDhw+Tm5uLp6Ul0dDSxsbEMGDCAevXqVfYwr4q4uDhOnjx5SXU/+eQTYmNjr/KIhLi2VLudom/3ceLx3/BLTEEHWIDMZfsdWZhGHYYnupAQ0PXiDSkKJqOeFzbCzyOu/riFEEJUDpnRL1uVCfSTkpJ47LHHOHbsGDExMdx5552EhIRQUFDAwYMHWb58OfPmzWPFihWEhYVV9nAr3BNPPEFBwbm84mPHjjFnzhxuvPFGbrzxRpe69evXv9bDE+Kqy77lM0zrjhJY2sdSkZWi13+jxYTGFPj60i0hFYPVxq7awfxRN9x1ga6isD/j2oxbCCGEqKqqRKBfVFTEo48+SlJSEv/73//cAltwzPYvWLCgQm+CY7PZsFgsGI3GCmuzvHr27OnyfPv27cyZM4dGjRrRt2/fyhmUEJfKYgWLDbwcC2VVmx3TIwuxffUHlhwNKioGcjGSjQpY8ESDDSsGzHihoxAVfxS8cSTgl/z/XAEG7f0HjYe/M+8wMqcArV1lY4MIl7op+VfrxQohhKgKZM/8slWJHP3vv/+ehIQE7r777hKDfACDwcDYsWMJDQ11lp05c4b33nuPO++8kxtvvJGuXbsyfPhw5s6di83mesOc+Ph4YmNj2bJlC59++ikDBw6ka9eurFmzBoDNmzfzn//8h4EDB3L99dfTs2dPJk2axI4dO0oczy+//MLIkSPp2rUr/fr1Y9asWWzZsoXY2Fji4+Nd6prNZj7//HNuv/12unbtSs+ePXnsscc4cODAZb1PI0eOpF+/ftjt7nuE//zzz8TGxrJixQrA8UWheCwLFy5kyJAhdO3alSFDhrBw4cIS2z9x4gQvvvgivXv3pnPnzsTFxfH+++9TWFh4WeMU/zJTFkLwGPC9C4a+jXo6m6zgl8mZ8TfWHBV/EgkkCS+yAS25RJFDfXKJwkABCgoFRGHBD9BSWpBfLDLP5PbB1T45rcS6wR9a+SPloit3hRBCiBqrSszor127FoBBgwZd1nmHDh1i3bp19OzZk6ioKKxWK3/88QcffvghycnJPP/8827nvP/++1itVgYPHoy3tzd169YFHF8EsrOz6du3L+Hh4Zw+fZply5bx4IMP8sknn9CuXTtnGz/99BPPP/88UVFRPPDAA2i1WlasWMFvv/3m1p/VauWhhx7ir7/+om/fvtx+++3k5eXx3Xffcd999zF79myaN29+Sa930KBB/O9//2PLli106dLF5diyZcvw8fHhlltucSlftGgR6enpDBkyBC8vL1avXs0777xDTk4O48aNc9bbv38/EyZMwNfXlyFDhhAWFsbBgwdZuHAhu3fvZtasWeh0VeLHRVQlS3+HqYvPPf92M0VHCrFkg4KCHykuYbsGG/4kk4Y3NjzJpAH2y9zv3stsdiuzakr4cqCqZBQpDPjORvIELR5amfoRQoiaxC776JepSkRuR44cwdvbm8jISJdym81Gbm6uS5nRaHSm2sTExLBs2TKXdJ4777yTF198kWXLljF+/HhCQkJczi8qKmLBggVu6TovvPACnp6uu3kMHTqU22+/nTlz5jgDfavVynvvvUdgYCBffPEFfn5+AAwbNoyRI0e6vbZFixaxY8cOPvjgA5fgfNiwYYwYMYJp06Yxa9asS3qf+vbty/Tp01m2bJlLW6mpqWzZsoUhQ4a4va4TJ06wZMkSwsPDAbj99tu57777+Oyzzxg4cKCz/JVXXiEkJIQvv/wSb29v5/kdO3bkqaeeYtWqVcTFxV3SOMW/yMqdbkXW/WlAAHrySvwIVlDxIB8T/tjRX3aXBlM+Zo0Gj/OubP1RN7yEjhy9pxXCjlPQpfZldyWEEKIKU2UxbpmqROpOXl4ePj4+buXHjh3jlltucXksWbLEedxoNDqDfIvFQnZ2NllZWXTp0gW73c6+ffvc2hw2bFiJOfnnB/kFBQVkZWWh1Wpp2bIle/fudR47cOAAZ86coX///s4gH8DLy4shQ4a4tbtq1Srq1avHddddR1ZWlvNhtVrp1KkTu3fvpqio6JLeJ19fX3r16sWGDRvIyspylsfHx2O32xk4cKDbOX369HEG8wB6vZ4777wTm83mvAJx+PBhDh06RJ8+fbBYLC7jbNu2LZ6enmzevPmSxni1ZWRkYDKZnM/z8vJcvgyazWbS09NdzrlwN6MLn6empqKq59I7pI9L76Mg3JcLKcGO/18Xm6m3lSPAL9YhIYF57RqwPSqEv2sFMi+mETujQkutrwEMhaddymrqv4f0IX1IH9JHZfUhqqYqMaPv4+NDXl6eW3lkZCQzZswAHGk606ZNczlutVqZO3cuK1euJDEx0eWHFCAnJ8etzejo6BLHkJSUxIwZM9i8ebPbVYTzrxgkJycDOFN+zldS2bFjxzCZTG4pNefLysqiVq1apR4/3+DBg1mxYgUrV67kzjvvRFVV4uPjadKkCdddd51b/ZJ26GnQoIHLazl2zLFP+cyZM5k5c2aJ/WZkVI0tTIKCglyeX/gF0cPDg+DgYJeyiIiIiz6/8L2XPi69D6/HB8HizXD0lKPA6IH3Z3dRNPxbrPmemPHEA9c1HkX4YsXLMQZyMeMJlxH46212coxGvm95abtPPdxeIaah64x/Tf33kD6kD+lD+qisPiqDLMYtW5UI9Bs2bMjOnTtJTk52Sd/x9PSkU6dOAGi1Wrfz3nvvPRYtWkSvXr249957CQwMRKfTceDAAT744AO3wB8ocTa/oKCABx54gMLCQkaOHEmjRo3w9vZGURTmzp3Ltm3bruj1NWrUiMcee6zU44GBgZfcVps2bWjYsCHLli3jzjvvZOvWraSkpPD000+Xe3zF79OoUaPccv+LnX/1QginUH/Y/S4s+R2yCmBIJ5S6YQSntaBo7JeYv00Dsx0NVlQUzHhRRAgaLOjJxQZ4ko4VLyz4cCkXGXfWrU+az6XtlLWwv8KIZu6fHUIIIcS/QZUI9G+66SZ27tzJ999/z6RJky75vJUrVxITE8N///tfl/LExMTL6n/r1q2cOXOGl156iQEDBrgc+/jjj12e167tSPQ9fvy4WzslldWpU4fMzEw6dOiARlMxmVKDBw/mnXfeYc+ePSxbtgyDwcBtt91WYt3i2frzHT16FMD5par4KodGo3F+sRLikvl4wtibXYoUox7Pr+8D7nMUJDhSZ/Q+RrxPZ2NVDGiCvdCEOWaJ7NlFpDWdBqfK3hMz08voumd+KUL1NkY0M1zWSxFCCFF9yA2zylYlcvQHDRpEvXr1+Oqrr1i3bt0ln6fRaNxm7QsLC1mwYMFl9V98teDCtjZv3syePXtcyq677jpCQkJYsWKFS2pQQUEB3377rVvb/fr1Iz09nfnz55fYd3ly3Pr27YvBYOCrr75i/fr13HTTTfj6uudKA/z444+cOnXK+dxisbBgwQK0Wi3dunUDoGnTpjRs2JBvvvmGpKQktzasVivZ2dmXPU4hnOqFOR4hftC8DrrrwpxBPoDG30hY6rP4zh/mdqp63sPcIZo/mjZBd8H2uRdS7HZ8DfILQAghxL9blZjRNxqNTJs2jccee4ynnnqK9u3b07lzZ4KDg8nPzychIYE1a9ag1WpdFpbefPPNfPvtt/znP/+hY8eOpKenEx8fj7+//2X137ZtW4KDg5k2bRonT550bi25cuVKGjVqxOHDh511dTodjz76KC+88AJjxoxh4MCBaLVaZ7/JyckuOf0jR45ky5YtvP/++2zbto0OHTrg7e1Namoq27Ztw8PDo9S8+NL4+flx0003sWrVKoASF+EWi46O5p577mHo0KF4eXnx448/sm/fPu6//35nTp6iKLzyyitMnDiRkSNHMmDAABo0aEBRURFJSUmsXbuWyZMny6474qrzvLMNhlsbkfv4SkyrDqEJMOIzvT/G25oAji+q6U/vxlrG1TFVUegQKYG+EELUZLK9ZtmqRKAPEBUVxVdffcXy5cv55ZdfmDdvHnl5eXh6elKnTh0GDhzIwIEDqVevnvOcxx9/HG9vb9asWcOGDRsIDw9n8ODBNG/enAcffPCS+/b19eXDDz9k+vTpLFq0CJvNRrNmzXj//fdZtmyZS6APjp1sdDodn376KTNnziQoKIiBAwfSuHFjnnrqKQyGc+kCOp2OadOmsXTpUlauXOkM6kNDQ2nRogX9+/cv1/s1ZMgQVq1aRZ06dWjfvn2p9UaMGEF+fj6LFi0iNTWVWrVq8cQTT7htBdq0aVPmz5/PnDlz+PXXX/nmm2/w9vYmIiKCuLg4OnToUK5xCnG5NCHe+H85vNTjJ+oYLmkF1v/1rBIXLIUQQohKo6glrVgV5TJv3jymTZvGnDlzaNWq1VXta8+ePdxzzz1MmjSJsWPHuh3fvn07EyZM4OWXX5aZeFFjWCwWBsz4kx+tMRevqKqoT5V/C08hhBBV34D7Sl6TufyzOtd4JFVXlZnRr04sFgsajcZlJ6CCggKWLFmCv78/zZo1u+pjWLx4MTqdToJ48a9TqJ7dn19VS1+UK1dzhRCixpPFuGWTQL8ckpOTefjhh7n11lupXbs2aWlp/PDDDyQnJ/Pss8+i11+dmcTCwkJ+/fVXjh49yqpVqxg8eLDbnX+FqOkitRlEZOQTm3ia+Bb1Sgz2ZS5fCCGEkEC/XAICAmjZsiWrVq0iMzMTrVZLo0aNmDx5Mr169bpq/WZmZvL888/j5eXFzTffzMMPP3zV+hKiqupkSCA/sQ4dktJI8fdhR50L7oqrqgxpJrM8QghR08kNs8omgX45BAQE8MYbb1zzfmvXrs327dsvqW5sbOwl1xWiOjEqVrRnlxbdeCSFwyF+ZHueWwBvMFuZftOl3VBLCCGEqMlkWwohRLWzMzIEOxBQZObR3/5mxK7DNDqTBYDJoCfMW6Z5hBCiprOjlPgQ50igL4SodpKCPJkX05jDwX6c9PPiaJAfh0MDANDJZ7wQQggBSOqOEKIaGmbYzsKwLhwMC3A7FuXjXl8IIUTNY5Ndd8okM/pCiGrnRsN+fhmmYtS6H/uin3zwCyGEECCBvhCimrohEvaM1dL67KY7fh4wp4+G7lElRP9CCCFqHLtS8kOcI6k7Qohqq2GAwu4xOlRVRZFLuEIIIYQLCfSFENWeBPlCCPHvY5Mddsokgb4QQgghhKh2bBLnl0ly9IUQQgghhKiBZEZfCCGEEEJUO3ZJ2yyTzOgLIYQQQghRA8mMvhBCCCGEqHbkhlllkxl9IUS1Er0uhwHjE8n0eIXTkW9hPZ5Z2UMSQgghqiQJ9IUQ1Ubh0j20W5h97oMrJY+Meu9W5pCEEEJUEmspD3GOBPpCiGqj6M5vS9w1ueiXI9d8LEIIIURVJ4G+EKLaK/z5UGUPQQghxDVmU5QSH+IcWYwrhKj27PvOVPYQhBBCXGNWienLJDP6Qohqz34iq7KHIIQQQlQ5MqMvhKj21ITsyh6CEEKIa8xa4qotcT6Z0RdCVAv27MLKHoIQQghRrciMvhCiWsgcvbT0g+He124gQgghqgSLTOiXSWb0hRDVgm35wdIP6rXXbiBCCCFENSEz+kKI6i9T0nqEEOLfxiJbaZapys7ojxs3jri4uMoexlW1fft2YmNjiY+Pv2jZxaSkpBAbG8vMmTOv1jCFqPqS8yp7BEIIIUSVUyEz+klJSXzxxRfs3LmT1NRUPDw8CA4OpkWLFsTFxREbG1sR3ZTL6dOn+frrr/njjz9ISUnBYrEQEhJC27ZtiYuLo2PHjpU2tkuVkpJCfHw8PXv2pGnTppU9HCGqILWyByCEEOIas1T2AKqBKw709+3bx7hx49DpdPTr148GDRpgMplITExk8+bNeHl5VVqgv3HjRp5//nnMZjO33HILgwcPxmAwcPLkSdavX8+DDz7ItGnT6NatW6WMryQxMTFs2rQJne7cP01KSgqzZ8+mdu3aboF+REQEmzZtQquVHGVRc9kLzGXWyX1yFb7v3HYNRiOEEEJUD1cc6M+ePZuioiIWLFhAkyZN3I6npaVdaRflcuTIEZ555hn8/f2ZO3cu9evXdzk+YcIEVq1ahcFgqJTxlUaj0VzWmBRFqXKvQQiAfLNjlt1TUck3q/h6O76MFlhUvPSl51WqqgqFFlSdQlqtNyGz7CAfoPD93/F97SYwyv8HIYT4NyiQHP0yXXGgf+LECfz9/UsM8gFCQkKcf//pp59YtWoVBw8eJCMjAy8vL9q2bcuECRNo3LjxJfc3e/Zstm7dSnZ2NqGhodxyyy2MGzcOT09PZ71PPvkEk8nECy+84BbkgyNA7tu3r0uZ1Wpl3rx5/PDDDyQnJ+Pp6Um7du2YMGECjRo1ctZLSUlhwIABPPDAAzRv3pzZs2dz+PBhfH196du3L5MmTXKZkQdYv349s2bNIiEhgcDAQPr370+7du3cxrV9+3YmTJjAyy+/TFxcHPHx8UydOhWAqVOnOv8eExPDrFmzXMYyfvz4a/JaxL/H0n/szN+voqCSmAvHcxw7Waoq7EsvI2FGVdHY7dgVBRQFVBs++WYMFjtWnUKzE2norCrZ3gZansmk8eksPAvMXJd4nJj04+hVG5d+nUrB03oKPEde/otUAK0GNBqw2cHPEzo2Bp0W+sfCuF6OY0IIIaqUQonzy3TFEVxUVBTHjx9n7dq13HTTTRetu3jxYvz9/Rk8eDAhISEkJSXx3Xffcd999zFv3jyio6Mvev7+/fuZMGECvr6+DBkyhLCwMA4ePMjChQvZvXs3s2bNQqfTYTKZ2LRpE+Hh4XTt2vWSX8uLL77ImjVr6NSpE0OHDiU9PZ0lS5YwduxYZs+eTbNmzVzqb9q0iaVLlzJ06FAGDBjAhg0b+Oqrr/D19eXee+911lu3bh1PP/00tWvX5v7770er1RIfH8/GjRvLHFO7du0YO3Ysc+bMYfDgwc4vB0FBQZXyWsS/x+y/7Iz7ye5WfuZSN7hRFOznp5QpCqpWQ0BGISpwxtcHBeiUeobmOblg1FKrIJ/O6cfQqJefc2/BFyjHFUQVsNqBs681Mx9W73L8/YcdcCQV/jfm8tsVQgghKtkVB/r33XcfW7Zs4emnnyY6Opo2bdrQokUL2rdv7zaT/sEHH7jMugP069ePO++8kwULFvDss89etK9XXnmFkJAQvvzyS7y9z90gp2PHjjz11FOsWrWKuLg4EhMTMZvNpV5lKMnmzZtZs2YNvXr14o033kA5ezmoV69e3H333bzzzjt8+umnLuccPXqUxYsXU7t2bQCGDh3KiBEjWLRokTM4ttlsvPPOO/j5+fHFF18QEBDgrHvHHXeUOa6oqCg6derEnDlzaN26tdtViGv5WsS/y/Sd7kH+lcr38sCqLUBncwTyHjYbTTOzncfbHD9SriAfwH61dgv+eDX8d5Rjhl8IIUSVYUam9MtyxdejW7duzbx58+jfvz95eXnEx8fz5ptvMnz4cB544AGSkpKcdYuDfFVVycvLIysri8DAQOrWrcuePXsu2s/hw4c5dOgQffr0wWKxkJWV5Xy0bdsWT09PNm/eDEBenmOrPR8fn0t+HevXrwfg3nvvdQbGAE2aNOGGG25g165dZGZmupzTs2dPZ2AMjnSg2NhY0tPTKSgoABxXIU6dOsWAAQOcQX7x2IYOHXrJ47scV+u1VAUZGRmYTCbn87y8PHJzc53PzWYz6enpLuecPHnyos9TU1MdeeHSh0ubloqP8wFQz/uZ1NvtLuk5Bkv591DQYiq7UnlYbZhNpkr/95A+pA/pQ/qoyn2IqqlCpsAaNWrElClTAMcPz44dO1i2bBl//vknTzzxBPPmzUOv13PgwAE++eQTduzYQWGh6/X/yMjIi/Zx7NgxAGbOnFnqnvEZGRnAuQA/Pz//kl9DSkoKGo2mxHz+Bg0asH79epKTkwkMDLzomP39/QHIzs7Gy8uL5ORkAOrWretWt6S+KsLVei1VwYUpSxd+mSve2vV8ERERF31eq1Yt6aOENu9vpeGpDRUb7XsWWtBbHW2qQL5ezylPI+GFRQAcCa9NaG5OOVpW8eFMxQ30fKN74uHtRbC36/+BmvhvLn1IH9KH9FHePiqFTOiXqcKvdUdERNC/f3/69evH/fffz+7du9m7dy+1atVi3LhxeHt7c99991GvXj2MRiOKovB///d/boH/hYq/aY4aNYouXbqUWMfPzw+AOnXq4OHhwcGDByv2xV1Ac5EFemo50w8qS016LaJiPBGr4KnTMG+fHQVIyYczBRBgAL0GTuRewu71drtjIS6gt9gIyShABUwGLaFZeRQYPPipbm26J6VSLyuPY37h1DGcIcKUgYJ6iZ/hKgbS0HGFd8dVcLwgox6ui3IswI2Lhf8MubJ2hRBCiEpy1bZTURSFli1bsnv3bk6fPs3+/fspKCjg3XffddtXPzs7Gw8Pj4u2V7xQV6PR0KlTp4vWNRgMXH/99axbt47NmzfTuXPnMscbGRmJ3W7n2LFjbjsAFV9NKOuqQ2ntAhw/ftztWHG7ZVEuc/uoq/VaxL+LoihMaqcwqd2lZ/ipqopNdezKY7WrJOVqiPBWMeg0KGixqnqSshWi/BWMuvNnjMKdf7Nmd8OeUYhSaCGz22zILCprpJgIJk0TSOj+ydDkEn62rTbHTjuyNZsQQlRf8hlepivO0d+8eTNWq9WtvKioyJkz36BBA+eM8YWzw999990l5Xk1bdqUhg0b8s0337jk/RezWq1kZ59b1Dd+/HgMBgOvvvoqCQkJJbb5448/sm3bNgB69OgBwJw5c1zGePjwYX799Vfatm3rkupyqa677jrCw8NZvnw5WVlZzvK8vDy++eabS2qjOG3m/Nd3MVfrtQhRFkVR0GkU9FoFT72GxkEafAxa9FoFnVaDUaelUbAGo670D2edvxGP+oHom4cRlvE8YeqrBCU8xsWvHyj4/HT/pQX54FhYK78ghBBC1HBXPKP/7rvvkp2dTffu3WnUqBFGo5FTp07x448/cuLECfr16+cs/+CDD3jppZe4/fbb8fX1Zffu3fz+++9ERUVhs9ku2o+iKLzyyitMnDiRkSNHMmDAABo0aEBRURFJSUmsXbuWyZMnExcXBzjWDbz11ls8//zz3Hnnndxyyy20bNkSg8FAamoqGzZs4ODBg0yfPh2Azp0706tXL3766Sdyc3Pp1q2bc0tKDw8PnnzyyXK9P1qtlscee4z//Oc/jBkzhkGDBqHValm+fDn+/v6kpqaW2Ub9+vXx9vZm6dKlGI1GfH19CQoKokOHDiXWv1qvRYjKoqt78e1kATxvbngNRiKEEEJUH1cc6D/++ONs2LCBXbt2sXbtWvLy8vDx8aFRo0aMGTPGGXhHRUUxffp0ZsyYwZw5c9BoNLRp04aZM2fy9ttvu60AL0nTpk2ZP38+c+bM4ddff+Wbb77B29ubiIgI4uLi3ALfbt26sWTJEr7++mt+//131q1bh9VqJTQ0lDZt2vD444+7pBG9+uqrNG3alBUrVjBt2jQ8PT2JiYlh4sSJLjeZuly33HILGo2GTz/9lFmzZhEUFOS8YdbkyZPLPN9oNPL666/z8ccf8+6772I2m4mJiSk10L+ar0WIqklm54UQ4l9HrsyWSVFlpaUQoho4rbxY+kEfHWG5L1+7wQghhKh0yhNZJZar/xdwTcdRlV21xbhCCCGEEEJcNTKhX6YrXowrhBDXRO2L3MtBLt8KIYQQbiTQF0JUC8F7Hy39oMV95y8hhBA1nVLKQxSTQF8IUS1oAzxLP6jTXruBCCGEENWE5OgLIao/Dwn0hRDiX0cm78skM/pCiOrPIpuHCSGEEBeSGX0hRPUXfJG0HiGEEDWTzOiXSWb0hRDVh0fJH1nej3S5xgMRQgghqj4J9IUQ1YZxziBKStLxmtjpmo9FCCFEZZNdd8oigb4QotrwHN6Sg7d6nwv2jVoCto1HMUgWohBC/OtInF8m+e0ohKhW9g8NYv/QIMaOHYter6/s4QghhBBVlgT6QgghhBCiGpLp+7JI6o4QQgghhBA1kMzoCyGEEEKI6kcm9MskM/pCCCGEEELUQDKjL4QQQgghqh+Z0S+TzOgLIao8m13lna1WblkC6wubVPZwhBBCiGpBZvSFEFWaXVUJf6uIOqfz8LTY2eTbjmWBMYyt7IEJIYSoZDKlXxaZ0RdCVGmP/mCm26EzNM3IJzq3kC4pmdQ9Y+XjPyt7ZEIIIUTVJoG+EKJKW7sxH62qciDYhy1RgZz28qBpeh7v7azskQkhhKhUcmfcMknqjhCiSrPaVX5oUguTXgtAQoAX9TLzyTJV8sCEEEJULkWi+rLIjL4Qoko7FujjDPIBUBQSAr1R1MobkxBCCFEdSKAvhKjSzLoSPqYUBUU+vYQQQoiLkl+VQogqKzHHXuoxD2zXcCRCCCFE9SOBvhCiykorKD3QL8guuoYjEUIIUeXIYtwySaAvhKiymoeU/hGlqvLxJYQQQlyM7LojhKiyCiyUuqtCrqcndlVFI7suCCHEv5R8/pdFpsSEEFXW6Tw7qKVvryMf8UIIIUTpKj3Q3759O7GxscTGxvLWW2+VWCcjI4POnTsTGxvLuHHjrso4nnnmGWJjY/nnn39KraOqKgMGDKBnz54UFV3d/OAxY8YQGxvLK6+8clX7EaIq6/e1Feyl5+kvOywLcoUQ4l9LcvTLVOmBfjGDwcDq1asxm81ux1auXImqqmi12hLOrBgDBw4EID4+vtQ627dvJyUlhVtvvRWj0XjVxnL48GH27t1LVFQUP//8M4WFhVetLyGqsuO5KmhK+ZgyWXlgXt61HZAQQoiqQwL9MlWZQL9nz57k5OSwYcMGt2PLly/n+uuvx8PD46r137lzZ8LDw1m1ahUWi6XEOsuXLwfOfSmoCEVFRVitVpeyZcuW4e3tzauvvkpBQQFr1qy55Pby8/MrbGxCVIakHDuPfJTKqBF/Ep6ei3Je6o6HxcqYX3Yz4+NVvLLoNxrsTWH0w0fYvDELq6X0mX8hhBDi36jKLMZt1qwZR48eJT4+nl69ejnL9+zZw9GjR3nwwQfZtm2byzmbN29m2bJl7Nu3j7S0NPR6PS1atODee++lffv2LnWPHDnCrFmz+Ouvv8jKysLPz4969epx9913061bNzQaDXFxcXz66ads2LCBW265xeX8vLw81q5dS8OGDWnRooWzPDY2lv79+zNkyBA+/PBD9u3bh8FgoGfPnjzxxBN4eXk5606ZMoUVK1awZs0apk+fzqZNm8jMzGTZsmXUrl0bAIvFwqpVq7jpppto1aoVTZs2ZdmyZQwYMMDtPRs3bhwnT57k448/Zvr06Wzfvp2cnBy2b98OQFpaGrNnz2bjxo2kp6cTEBDADTfcwMSJEwkKCnK2c+bMGebNm8e2bds4efIkJpOJyMhI+vXrx913331Vr6SImk1VVZK2ZZB/xkR0l2C8ggzknyni+M8pmH7ag/74SY7618HrUAoFabCxcSN2Noqkx19HyasdSmaIH+p5M/oPrP6TXruPARCZkUvrpNPckrMW7Qc2/tZex2l9KFHqYQJNBXhRhBVP8vHFjgdeFOBtyEYfoJCR5Y9qsuJHBhqjFpPRHzXfilZThNU/EGNsON5NfaF+GGqtAOzbT2D39kEbpEMT2wA6N8VuspHz/h+ox9Pwn9QBTfPIynqbhRDiX0qm78tSZQJ9gAEDBvDee+9x+vRpwsLCAMcselBQEN26dXOrHx8fT3Z2Nn379iU8PJzTp0+zbNkyHnzwQT755BPatWsHQFZWFhMnTgRg6NCh1KpVi6ysLPbv38+ePXucbcfFxfHZZ58RHx/vFuj/9NNPmEymEmfzDx48yGOPPUZcXBy9e/dmx44dLFu2DI1Gw/PPP+9Wf9KkSQQHB3PfffdRWFjo8mVgw4YNZGVl0b9/f+eY3nnnHRISEqhXr55bWwUFBYwfP57WrVvz4IMPkpGRAUBqaipjx47FYrEwcOBAoqKiSExM5JtvvmH79u189dVX+Pj4AHDo0CHWrVtHz549iYqKwmq18scff/Dhhx+SnJxc4msQoiw2i53lD+0gaZvjZ1Jn0NB+VF3+/PQQdqsKaIjOsdP+xBoOalry7l092NCqHgDrrquL0WqnwOPsR5SqogA99xy/oBcNh70a0CZvD21tu9iuduK4tjl6bTKqzUQ2fhT/IrDgic2kI+zUP2gwkk4dCgigXtFuPIvOkE59TtMATmtgpRWflYeJJB4F0OK4/Fn8K8U8tAcHf9JiznWU6D86SpPXmmJ4vt9Vez+FEEKIy1WlAv3bbruN6dOns2LFCu69916Kior46aefGDRoEDqd+1BfeOEFPD09XcqGDh3K7bffzpw5c5yB/u7du8nIyOC///2vy9WCC0VGRhIbG8vmzZtJS0sjJCTEeSw+Ph69Xk/fvn3dzjt06BBz5syhZcuWzjHk5+ezfPlyHnvsMZdAHqBhw4a8+uqrJY5h+fLl1K5dm5iYGAD69OnDtGnTWL58OQ8//LBb/ezsbIYOHcqDDz7oUv72229jtVqZP38+4eHhzvJbbrmFsWPHMn/+fMaPHw9ATEwMy5YtQzlvm8I777yTF198kWXLljF+/HiX90KIS3FoTaozyAewFtnY+ekhVOu5VJwk70j80LGzYYQzyAewazQUeJyXWagoqKqKSa9Fb3NN0TkaUoc2eX+joNDEfpQ8xZdAWy7pBHH+bI8VHSY8ySeEUI6TSQRmvEgnkjASyCKE87MZ8wgmn0B8yHQM4bw+T32Tgplo53MLBk5O3Uq9x28BT0P53jAhhBCXRyb0y1RlcvQBAgIC6N69OytWrABg3bp15OXllZi2ArgE+QUFBWRlZaHVamnZsiV79+51Hiueuf7999/Jy7v44r2BAwdis9mcYwBISEjg77//pnv37gQEBLid06pVK2eQX6xDhw7YbDZSUlLc6o8aNarEvlNTU9m8eTP9+vVzBt0BAQF069aNH374wS2Xv9jdd9/t8jwvL4+NGzfSvXt3DAYDWVlZzkft2rWJiopiy5YtzvpGo9HZn8ViITs7m6ysLLp06YLdbmffvn0l9lsZMjIyMJlMzud5eXnk5uY6n5vNZtLT013OOXny5EWfp6amop6XBy59VEwfmcfc14ucH+QDeBVYUFSF42EBbnXdKArLOjV1KTJ76DgTFgA4/m9oAS+1CBWw455yZkeLHT0a7OhxjNuMF3a0WHEP0E14uZUBFOHtXmYxQEqm83lV+/eQPqQP6UP6uJp9iKqpSs3ogyNV5dFHH2XXrl0sX76cFi1a0KBBgxLrJiUlMWPGDDZv3uzyAwi4zE63b9+efv36ER8fz6pVq2jevDmdOnWiV69ebm3feOON+Pr6Eh8fzz333AM4FscCpX7hiIx0z8319/cHHDPuF6pbt26J7axYsQK73U6bNm1ITEx0lsfGxrJ+/Xo2bdpEjx49XM4JDAzE19fXpSwhIQG73c6yZcucY7/YmK1WK3PnzmXlypUkJia6/GcHyMnJKbGNynD+2gI49yWumIeHB8HBwS5lERERF31eq1Yt6eMq9BEZG8T2z4+eO6go6H10WPLOfWE1e2gBlXZHU7mQ1mbHptWAzQ5ax5zEkm7NSQnyodPBFNJ9PVF9jdz111pAxYKRPPw4Y/SjXmEqOtWCFb1rm1gxko0VvTOI9yILLTYM5GO6IID3JsttXAC+ZJKL63vn62eG+mHleq+gZvybSx/Sh/Tx7+1DVE1VLtDv0qULYWFhzJo1i+3bt/Pss8+WWK+goIAHHniAwsJCRo4cSaNGjfD29kZRFObOneu2cHfq1Kncfffd/P777/z555/MmzePzz//nMcff5wRI0Y46xkMBvr06cOSJUvYvXs3LVu2ZOXKlYSHh9OlS5cSx3KxxaoXBs1AiVtzqqrq3Npz8uTJJba1fPlyt0D/Ytt83nbbbc5c/wsZDOdmL9977//bu/O4qMr9D+CfGfZl2BVUFFBAxAQNA0QEMVF/WQq5dE0FtbTS1FxemtZN7VqWZuJySbuuaHZNXHMLNAR3zC29110xc0PZQfZ5fn945+RxBgEFhfHzfr14wTzzzHOec55zhuc853ueMw/r1q1DeHg4hg0bBltbWxgaGuLcuXNYuHChznUgqkyzAHu88k5zHF+dhvISNew9LNFxtAcOfnUG2dfuw1BdhrZZJ2FoYASPmwqM+jkVy7u1Q6HJg855+f869ygoBSyNAeWDk/cD3s1wwPtB2MzYfQfQ+dJ+lMEM2XDC4SatkG5nj6wzd2CFfORBhXIYAhAwRSHscR3GuI/raAUAsMYd2OEW1DCALW4hE41RAnMoUA4H/AFTFEDgwdVh6bfKDA2/6Ib7cenI/i0PgAJWZrlw2tCv4qlAiYiInoM619E3MDBAz549sWLFCpiYmKB79+4686WmpuLu3bv47LPPtEbav/vuO52fcXd3h7u7O6KiopCXl4fo6GgsWrQI/fv3l10B6N27N9avX4+ff/4Zubm5yMjIwLBhw6CsxX/iv/32G27cuIEBAwbA19dX6/1ffvkFKSkpyMjIqPQs2tnZGQqFAmVlZQgICKh02Tt27MDLL7+MWbNmydIfvqpA9CQCR3qg3WBXFOWWwrrJgxF0l5+7IPfPAqhvZaP0hifKrS1gdaMM3Refh/32FKwK9MVtB2sUG/3v66lMDdy7D9iZAYZ/HYPNsu7i6+3/RKHSDPubBGJ/Mw/43vwToekXYeJtCxNbY9iHNkWpmTmKzmfDwMwQZl3DoDA3QsOjt1FyKRvGzVxQbBYAI5+msM4vgJWVGcqEKQz9mkKZkQ1YmkLhYAVx9S6EygIKdSkUje2gMDdB89FA6dUsiJtZMO7gyk4+EdGzxhj9StW5jj7w4GZWQ0NDNGnSROtykYZmFP3R0ebDhw/jzJkzsrScnByoVCpZR12lUqFJkya4fv06iouLZSPjXl5e8PT0RGJiItLT06FQKCoM26kpW7ZsgYGBgTSi/ihbW1skJSVh+/btiIqKemxZNjY26NixI3799VecPn0abdq0kb0vhEB2dra0HKVSqbUdCwsLsXbt2qdcKyLARGUEE5U8hMbK2QJwtgBe+SuEzG1QC7wBYAaA9AKBQ6fz8dG/7yPNRgWUqoGMQsDCCDA2AMrVcLqajnFd38Gc7wLxWlMVehrqeibmcgAAJrVJREFU7mibAHj0W8S8J3RG3ysASE/raGLzV7qvi87/J0ZutoCb9vFKRERUF9TJjr6Tk5M0I0xF2rZtC3t7e8TExODWrVto2LAhLly4gB07dsDd3R2XLl2S8m7fvh1r165FWFgYnJ2dYWhoiOPHj+PQoUMIDw/XGf7Su3dvzJkzBwcPHoSfnx+cnZ1rfD018vLykJSUhLZt2+rs5ANAu3btYGdnh61bt1ba0QeAjz/+GO+++y6GDx+Onj17omXLllCr1bhx4wZSUlLw2muvSdv41VdfxcaNGzFlyhT4+/sjIyMDP//8s3SfAdGz1tBCgd6BKkQkG0MhBIQwAgpLgfsPHmZnX16Kub8cxNa+HWDhxv2UiOiFpOCQfmXqZEe/KlQqFRYtWoQFCxZg3bp1KC8vh5eXF+bPn48tW7bIOvp+fn44f/489u3bh3v37sHAwACNGzfGRx99hP79++ssXzPVZ3Fxca2P5u/cuRPFxcUICwurMI9SqURoaCg2bdqEU6dO6QzveZiTkxPWrFmDVatWITk5GTt37oSxsTEcHR3RqVMn2TSj48ePh4WFBRITE5GcnAxHR0dERkbC29tba9pOomcp1KoUKXnGgMoQUP31ZGxxX+A3L0+M/7zlYz5NRET0YlMI3mlJRHXU1Sv5aLHeGMJAHpbjmpmH3mf/QMymx5/wEhGR/lLMKNKZLqZVPFHJi4Z3jxFRnXVJaQoDtVorvd2tTFy1Ven4BBERvTAUFfyQhB19IqqzGpkDZQZKQAgohIBJaTlCrt6GZUkZ9rg5Vl4AERHRC6zexugTkf5ztFACCgEoFBAAlELgdyc7pLg5QSHKn3f1iIjoueLwfWXY0SeiOqtUKGSzKhQaG6Lwf3+bGfALnoiI6HHY0SeiOquxpQIQQucUaqVqziNARPRC43hPpRijT0R1m64vciE4SkFERFQJdvSJqI7T0dNXKGBuxKEcIiKix2FHn4jqNLMKhu79G7OjT0T0QuP0mpViR5+I6rRvQ3WlCsS++qxrQkREVL+wo09Eddr77QzR2v7hFIEOBufRhM/LIiIieizez0ZEdd6ZoYa4nKXG0VvlSE+Jg6myHEDL510tIiKiOo0j+kRUL7SwVaKPB/7XyScioheeQqH7hyTs6BMRERER6SGG7hARERFR/cPB+0pxRJ+IiIiISA+xo09EREREpIfY0SciIiIi0kOM0SciIiKi+ocx+pViR5+IiIiI6iH29CvD0B0iIiIiIj3EEX0iIiIiqn84oF8pjugTEREREekhdvSJiIiIiPQQO/pERERERHqIMfpEREREVP8wRr9SHNEnIiIiItJD7OgTEREREekhhu4QERERUf3D0J1KcUSfiIiIiEgPsaNPRERERHpv+vTpsLS0fN7VeKbY0SciIiIi0kOM0SciIiKi+kfBIP3KcESfiIiIiF54p0+fRvfu3WFhYQFra2v07dsXf/zxh/T+O++8g06dOkmv7927B6VSiVdeeUVKy8/Ph5GREdavX/9M614RdvSJiIiIqP5RVPDzBK5fv46QkBBkZGRgzZo1WLx4MY4fP47Q0FDk5eUBAEJCQnD06FEUFRUBAFJSUmBiYoITJ05IeQ4ePIiysjKEhIQ87drVCIbuUL0hhJAOJHoxlZaWorCwEACQm5sLIyOj51wjIiJSqVRQ1PMwmnnz5qG0tBQJCQmws7MDALRr1w7e3t5YuXIlRo8ejZCQEBQXF+PIkSMIDQ1FSkoKIiMjkZCQgAMHDqBHjx5ISUmBp6cnHB0dn/MaPcCOPtUbeXl5sLa2ft7VoDrio48+et5VICIiADk5ObCysnrmyxUTa64bu2/fPnTp0kXq5AOAl5cXfH19sX//fowePRpubm5wdnZGSkqK1NF///33UVhYiOTkZKmjX1dG8wF29KkeUalUyMnJqZWy8/Pz0bNnT2zfvv2Fm3qrvmFb1Q9sp/qB7VQ/1PV2UqlUz7sKTy0rKwtt27bVSnd0dERmZqb0WtPBz83NxalTpxASEoKCggLEx8ejuLgYqampGD58+DOs+eOxo0/1hkKhqLURA6VSCQMDA1hZWdXJL1H6C9uqfmA71Q9sp/qB7VT77OzskJ6erpV+584deHp6Sq9DQkIwfvx47N27Fw4ODvDy8kJBQQEmT56MpKQkFBcXy27Yfd54My4RERERvdCCg4OxZ88eZGVlSWnnz5/H77//juDgYClNM4L/7bffSiE6bdu2hZmZGb766is0bdoUrq6uz7r6FeKIPhERERG9EMrLyxEfH6+VPnbsWKxYsQLdunXDJ598gqKiInz66ado1qwZhgwZIuXz8vJCw4YNkZycjAULFgAADAwM0LFjR+zcuRMDBw58VqtSJezoEwEwNjbG8OHDYWxs/LyrQpVgW9UPbKf6ge1UP7Cdak5RURH69eunlb569WokJydj4sSJGDhwIAwMDBAeHo5vv/1W6x6EkJAQxMfHy266DQ0Nxc6dO+vUjbgAoBBCiOddCSIiIiIiqlmM0SciIiIi0kPs6BMRERER6SHG6JPemD59OrZt26aVvmDBAgQFBUmvS0tLERsbix07dqCgoAA+Pj6YNGmS1l3yaWlpmD17Nn7//XdYWFjgtddew8iRI7Wexrp582bExcXh9u3bcHFxwciRI7Wm1srPz8e3336LvXv3oqysDIGBgZg0aRIcHBxqbgPouaq2B1XPzz//jBkzZmilR0dHY/To0dLrmtzPT506hZiYGFy4cAG2trbo27cvoqOjZU/WFEJg1apVWL9+PbKzs+Hp6Ynx48ejTZs2NbwF6qbr169j9erVOHPmDC5fvgwXFxf89NNPWvnqarvcvXsXs2fPxpEjR2BoaIiwsDCMGzdO76aGrEo7jRgxAsePH9f6bHx8vOz/DtuJagM7+qRXmjRpgpkzZ8rS3NzcZK/nzJmDhIQEjBs3Dg0bNsTy5csxcuRI/PTTT9KXW25uLt5//300a9YMc+bMQXp6OubNm4eioiJMnjxZKuuXX37BF198gWHDhuGVV15BQkICJk6ciKVLl8q+UKdMmYIrV65gypQpMDY2RmxsLMaMGYO4uDgYGvIwrExV24Oe3MKFC2X/3Bs0aCD9XZP7+fXr1zF69GgEBATggw8+wMWLF7Fo0SIYGBhg8ODBUlmrVq3CkiVL8OGHH8LDwwPr16/Hhx9+iB9++AHOzs7PYIs8X5cvX8aBAwfQunVrqNVqqNVqrTx1tV3Kysrw4YcfAgBmzpyJoqIizJ8/H59++iliYmJqcas9e1VpJwDw9fXVepp3o0aNZK/ZTlQrBJGemDZtmujXr99j89y+fVv4+/uLDRs2SGnZ2dkiODhYrFy5Ukpbvny5CA4OFtnZ2VLahg0bhL+/v0hPT5fSIiMjxdSpU2XLGDp0qBg9erT0+tSpU8LPz08cOnRISrt69apo3769SEhIqP6KvoCq2h5UfVu3bhV+fn4iKyurwjw1uZ/PnDlTvP7666KkpERKW7RokejcubMoLi4WQghRVFQkQkJCxKJFi6Q8JSUl4vXXXxezZs164nWtT8rLy6W/K/puq6vtsnPnTtG+fXtx9epVKe3QoUPCz89PnD59ujqboc6rSjsNHz5cjB079rHlsJ2otjBGn14ohw8fhlqtRteuXaU0a2trBAYG4sCBA1LawYMH4e/vD2trayktPDwcarUahw8fBgD8+eef+OOPPxAeHi5bRrdu3XD06FGUlJRIZalUKgQEBEh5XF1d4enpKVsmVawq7UG1o6b384MHD6Jz586ykKtu3bohLy8Pv//+OwDg999/R0FBgew4NTIyQlhY2AtzzCiVj//3XJfb5eDBg/Dw8JCFpQQEBMDa2lrv2q+ydqoqthPVFnb0Sa/8+eefCA0NRWBgIAYNGoS9e/fK3k9LS4OdnR2srKxk6a6urrh27Zos36Mx+yqVCg4ODkhLS5PyaD77aFmlpaW4efOmlM/FxUUWPwk8CCnSlEGPV5X2oKfTv39/+Pv7o3fv3lixYgXKy8sB1Ox+XlhYiDt37sDFxUWrLIVCUemx5ebmhtu3b6OoqOjpVlYP1OV20SzzYQqFAi4uLi/s8Xr8+HEEBwcjKChIZ8w+24lqC4ODSW+0bNkS3t7eaN68OfLz8xEfH4+JEyfiq6++kkY28vLydN5kZGVlhZycHOl1bm6u1gMygAedy9zcXKksAFrlaU4iNOVVpSx6PG7D2uPg4ID33nsPL730EhQKBZKTk/Hdd98hPT0dkydPrtH9XFPWo/mMjIxgamoq5cvNzYWxsTFMTEy0yhJCIC8vD6ampk+76vVaXW6XvLw8ncu0srJ6IY9XPz8/9OzZE82aNcPdu3exZs0ajBw5Et9//z18fHwAsJ2o9rCjT3VWfn4+7t27V2m+Jk2awMjICAMGDJClh4SEYNiwYViyZInsEiYR/aVDhw7o0KGD9DowMBCmpqZYu3Yt3nnnnedYMyL98N5778led+rUCf3798fSpUuxYMGC51QrelGwo0911u7du7Vm0NHl0SnKNJRKJbp06YIFCxagqKgIpqamUKlUyM/P18qbm5sri/+2srLSmS8vL08aMdOMhOTn58umP9OMhGjKs7Kywp07dx5bFj1eVdqDak7Xrl2xevVqnD9/vkb384fLelhpaSmKioqkfFZWVigpKUFxcbFsVDIvLw8KhULnKOSLpi63y+O+Zx0dHZ9shfWImZkZgoODsWfPHimN7US1hR19qrMiIiIQERFRo2W6uroiMzMTubm5sg7io7GKrq6uWjGKmisMmpMKze9H48fT0tJgZGSEJk2aSPlSU1MhhJDFX6alpcHd3b1G109fVaU9qHbU5H5uZmYGR0dHrba8du0ahBBax9a1a9fg6ekpK8vJyemFD9sB6na7uLq64tKlS7KyhBC4du2a7GZT+gvbiWoLb8YlvaVWq7F79240b95c+mILDAyEUqnEr7/+KuXLzc3FkSNH0LFjRyktKCgIqampUkwk8OAKg1KpRGBgIADA2dkZzZo1k43KAEBiYiJeeeUVaVaEoKAg5ObmIjU1Vcpz7do1nD9/XrZMqlhV2oNqTkJCAgwMDNCyZcsa38+DgoKQkpKCsrIy2fJUKhV8fX0BAD4+PrCwsMDu3bulPGVlZUhKSuIx8z91uV2CgoJw8eJF/PHHH1JaamoqcnJy2H54cFPtvn374O3tLaWxnai2cESf9MKtW7cwbdo0dO/eHU2bNkVubi42bNiAs2fPYvbs2VI+R0dH9O7dG/Pnz4dSqZQemGVpaYk+ffpI+fr06YN169ZhwoQJGDZsGNLT0zF//ny8+eabsgcJjRgxAn//+9/h7OwMPz8/JCYm4syZM/jXv/4l5fHx8UGHDh3w+eefY9y4cdKDUDw8PBAWFvZsNlA9V9X2oOr78MMP0b59e2nUMCUlBZs2bcLf/vY3KSSkJvfzqKgo7Nq1C1OnTkW/fv1w6dIlrF69WvaUYxMTEwwdOhTff/89bG1t4e7ujvXr1yMnJweDBg16hlvn+SkqKsL+/fsBPPh+KygokDpufn5+sLW1rbPt0rVrV6xYsQKTJk3CqFGjUFRUhJiYGAQHB+Oll156FpvvmamsndLS0hAXF4ewsDA0btxYuhk3IyMDX331lVQO24lqi0IIIZ53JYieVk5ODmbMmIHz588jMzMTRkZGaNWqFYYMGSK70RAASkpKEBsbix07dqCgoAC+vr6YNGmSVgjI1atXMWfOHJw6dQoWFhbo2bOn7MtUY/PmzVi1apX0CPpRo0ZV+Aj6pKQklJeXIyAgAJMmTWIntRqq2h5UPd988w0OHjyIO3fuQAiBZs2aISIiAm+99ZYshKAm9/NTp05h3rx5uHDhAmxtbdGvXz9ER0fLlieEwMqVKxEfH4+srCx4enpi/Pjx0iwl+u7mzZvo1auXzvcWL16M9u3bA6i77ZKeno45c+bgyJEjMDAwQFhYGMaPH69z1rP6rLJ2cnR0xOzZs3HhwgXk5OTAzMwMPj4+GD58uFZnmu1EtYEdfSIiIiIiPcQYfSIiIiIiPcSOPhERERGRHmJHn4iIiIhID7GjT0RERESkh9jRJyIiIiLSQ+zoExERERHpIXb0iYiIiIj0EDv6RERERER6iB19InrhDRkyRPZUyefpzJkzMDQ0RGJiopS2d+9eKBQKrFy58vlVjOqElStXQqFQYO/evU/0ee5Lup08eRJKpRLJycnPuypENYodfSI9deXKFYwYMQJeXl4wNzeHra0tWrVqhejoaCQlJcnyurq6aj2O/WGajvC9e/d0vn/27FkoFAooFArs27evwnI0eTQ/pqam8PDwwPjx45GZmflkK6pnxo8fj44dOyI8PPx5V+WZSEtLw/Tp03Hy5MnnXRV6RrKzszF9+vQnPll5Uo/b19q2bYuIiAhMmDABQohnWi+i2mT4vCtARDXvt99+Q2hoKIyMjBAVFYXWrVujsLAQFy9eREJCAlQqFcLCwmpsecuWLYNKpYKZmRmWL1+OTp06VZi3bdu2mDBhAgAgMzMTO3bswLx585CYmIhjx47B2Ni4xupV3xw6dAiJiYnYvHmzLD0kJASFhYUwMjJ6PhWrRWlpaZgxYwZcXV3Rtm3b510degays7MxY8YMAEDnzp2f2XIr29c++ugjhIaGYseOHejZs+czqxdRbWJHn0gPzZgxA/fv38fJkyfh6+ur9f7t27drbFmlpaVYvXo1+vXrB2tra3z//fdYsGABVCqVzvxNmjTBoEGDpNdjxozBG2+8gW3btmHLli3o169fjdWtvomNjYWDgwNee+01WbpSqYSpqelzqhXRi6FTp05wdXXF4sWL2dEnvcHQHSI9dPHiRdjb2+vs5AOAk5NTjS3r559/Rnp6OqKjozFkyBAUFBRg3bp11Sqje/fuAIBLly5VmOe7776DQqHA1q1btd5Tq9VwdnaWjdIlJCTgrbfeQvPmzWFmZgYbGxt069atyjG4nTt3hqurq1Z6WloaFAoFpk+fLksXQuC7776Dn58fzM3NYWlpibCwMK0wqYqUlZVh8+bN6Nq1q9bIva646ofTYmNj0bJlS5iamqJNmzbYtm0bAOD06dPo0aMHrKysYG9vjzFjxqC0tFTnel65cgW9e/eGtbU1rKysEBkZiStXrsjyqtVqfPHFFwgJCYGTkxOMjY3RrFkzfPDBB8jIyNC5Xhs2bEDnzp1hY2MDc3NztGzZEmPGjEFJSQlWrlwpXVkaOnSoFNJVlVHetLQ0DB48GI6OjjAxMUGLFi0wdepU3L9/X5Zv+vTpUCgUOH/+PKZOnQpnZ2eYmJjA19cXO3bsqHQ5wF9x8Xv27MHnn38OFxcXmJmZISAgAIcPHwYAJCcnIzg4GBYWFmjUqBH+8Y9/6Cxr8+bN6NixIywsLGBpaYmOHTtiy5YtOvP+61//gpeXF0xMTODu7o6YmJgKw0pycnIwefJkuLu7w8TEBA0aNMCAAQO02rC6qrqdH3efi0KhwJAhQwA82G/d3NwAPBiQ0LS55lh7+Pj68ccf4ePjA1NTUzRr1gzTp09HWVmZrOyqHqdV2dcUCgW6d++OXbt2IT8/v5pbiqhu4og+kR5q0aIFzp8/j40bN+LNN9+s0mfKy8srjMEvLi6u8HPLli2Dm5sbOnXqBIVCgXbt2mH58uV49913q1zfixcvAgAcHBwqzPO3v/0N48aNQ1xcHHr16iV7b8+ePbhx44YUEgQ8+MeemZmJqKgoODs748aNG1i6dCleffVVJCUlPTa86EkMHjwYP/74I/r27YuhQ4eiuLgYP/zwA8LDw7Fx40atOj/q2LFjyM/Ph7+/f7WW+89//hNZWVl49913YWpqigULFiAyMhLr16/H8OHDMWDAAERERCAhIQELFy5Ew4YN8emnn8rKKCgoQOfOnREQEIBZs2bh4sWLiI2NxeHDh3HixAnpxLCkpARz5sxBnz590Lt3b1hYWODo0aNYtmwZ9u/frxV69cknn+DLL7+Et7c3xo0bh0aNGuHy5cvYsGEDPv/8c4SEhGDq1Kn48ssvMWLECKlNHB0dH7vO165dg7+/P3JycjBy5Eh4eHhg7969mDVrFg4cOIA9e/bA0FD+7y06OhpGRkaYOHEiSkpKEBMTg4iICFy4cEFnR1GXjz/+GOXl5Rg7dixKSkowd+5cdOvWDXFxcXjnnXcwYsQIDBw4ED/99BM+++wzuLm5ya5excbGYtSoUfDy8sJnn30G4MF+GhERgSVLlmDEiBFS3piYGIwbNw6+vr748ssvcf/+fXzzzTdo2LChVr1ycnIQFBSEP/74A8OGDUPr1q1x69YtxMbGIiAgAL/99htcXFyqtI5Pu50r06pVK8ybNw/jxo1DZGSk9P1kaWkpy7d161ZcuXIFo0aNgpOTE7Zu3YoZM2bg2rVrWLFiRbXXpar7WocOHbBkyRLs378fPXr0qPZyiOocQUR65+DBg8LIyEgAEB4eHmLo0KEiNjZW/Pe//9WZ38XFRQCo9Ofu3buyz924cUMYGBiIadOmSWkxMTECgM5lARDdunUTd+/eFXfv3hUXLlwQ3377rTAyMhLW1tbizp07j12vvn37ChMTE5GZmSlLHzRokDA0NJR9Pj8/X+vzt2/fFvb29uL//u//ZOnR0dHi0a/D0NBQ4eLiolXG1atXBQDZOm/cuFEAEEuWLJHlLS0tFX5+fsLV1VWo1erHrtvy5csFALFlyxat95KSkgQAsWLFCq20xo0bi+zsbCn91KlTAoBQKBRiw4YNsnJefvll4eTkpLWeAMTYsWNl6Zp1eu+996Q0tVot7t+/r1W/pUuXCgBi3bp1UtqRI0cEABEWFiYKCwtl+dVqtbQ9dK1bZd5++20BQGzfvl2WPnHiRAFALF26VEqbNm2aACB69uwpa4PU1FQBQHz88ceVLm/FihUCgGjXrp0oLi6W0rds2SIACENDQ3H06FEpvbi4WDg5OYnAwEApLTMzU1hYWIgWLVqInJwcKT0nJ0c0b95cWFpaiqysLCGEEFlZWcLc3Fy0atVKFBQUSHmvX78uLCwsBACRlJQkpY8ZM0aYmpqKkydPyuqdlpYmVCqViI6OltKqs72rs511HUMaAGR10HUMPfqeUqkUx44dk9LVarWIiIgQAMShQ4ek9Oocp1VZ93379gkA4ptvvqkwD1F9wtAdIj3UoUMHHDt2DNHR0cjJycGKFSswcuRIeHt7IyQkROflfFdXVyQmJur86datm87lrFy5Emq1GlFRUVLawIEDYWRkhOXLl+v8TEJCAho0aIAGDRrA09MT48ePh7e3NxISEnSOVj4sOjoaxcXFstCg/Px8bNq0CT169JB93sLCQpYnIyMDBgYGCAgIwJEjRx67nOpas2YNVCoVIiIicO/ePeknOzsbb7zxBtLS0qSrFhW5e/cuAMDOzq5ayx4yZAisra2l1z4+PrCyskLjxo21ruYEBwfj9u3bOsMSPv74Y9nryMhItGzZUnZjsEKhgJmZGYAHV4Cys7Nx7949dOnSBQBk2/WHH34AAMyaNUvr/gJN2MSTUKvV2Lp1K9q1a6d1L8OUKVOgVCqxadMmrc+NHTtWtsxXXnkFlpaWlbbLwz744APZFQvNqHBAQADat28vpRsbG8Pf319WdmJiIgoKCjBmzBhYWVlJ6VZWVhgzZgzy8/Oxe/duAA+Okfv372PUqFEwNzeX8jo7O2PgwIGyOgkh8MMPPyAkJARNmjSR7X8WFhYIDAxEQkJClddR40m3c00JDw/Hyy+/LL1WKBSYNGkSANTqcu3t7QEA6enptbYMomeJoTtEeqpNmzZSTPe1a9eQnJyMpUuXYt++fejdu7dWmIWFhQW6du2qs6w1a9ZopQkhsHz5cvj4+ECtVsvi6zt27IjVq1dj1qxZWpf2AwICMHPmTACAiYkJXFxc0KxZsyqtk6YzHxcXh/fffx/AgxjwgoIC2ckGAFy+fBmffPIJfvnlF2RnZ8veq+k588+ePYu8vLzHhpzcuXMHnp6eFb6vqZOo5tR+zZs310qztbVF06ZNdaYDQEZGhixUwsbGRud9G61atcLmzZtRUFAgnTj99NNPmDt3Lk6cOKEV75+VlSX9ffHiRSgUigrvE3lSd+/eRX5+Plq3bq31np2dHRo1aqTzRFbXdrK3t6/w3gJdHi1Dsz01MeePvvdw2VevXgUAnfXWpGnqrfnt5eWlldfb21v2+u7du8jIyJBOoHVRKqs/pvek27mmtGrVSitNs+61uVzN8VdXnqtB9LTY0Sd6Abi4uCAqKgqDBw9Gp06dcODAAaSmpiI4OPiJy0xOTsbly5cBAB4eHjrzbNu2DREREbI0BweHCk8oKmNoaIi3334bMTExuHTpEtzd3REXFwdbW1tZDHx+fj5CQkJQUFCAjz76CG3atIFKpYJSqcSsWbPw66+/Vrqsiv7RP3ozIPCgc9CgQQOsXbu2wvIe95wCAFInrbrPEzAwMKhWOlD9kwmNjRs34q233oK/vz/mz5+Ppk2bwtTUFOXl5ejRowfUarUs/9OM3Ne0irZHdbbFk2zr2qapf9euXTF58uTnVo/qHC91ebma46+ikyai+oYdfaIXiEKhQEBAAA4cOIAbN248VVnLly+HiYkJ4uLidI4Yvvfee1i2bJlWR/9pRUdHIyYmBnFxcRg+fDj27t2LESNGwMTERMqzZ88e3Lx5E8uXL8fQoUNln3/0RtSK2NnZ4dixY1rpukYTPTw8cOHCBQQGBmrdVFhVmhOB6oSS1JTs7Gzcvn1ba1T/7NmzaNiwoTSav3r1apiamiIpKUkWUnLu3DmtMj09PbFz506cOnXqsTcYV/dEoEGDBlCpVPjPf/6j9V5WVhZu3bpVJ+fj11wN+M9//oNXX31V9t5///tfWR7N73PnzlWYV6NBgwawsbFBbm7uE59A61Ld7awJOcvMzJSFn+k6XqrS5mfPntVKe3Q7aZZb1eO0KsvVXJms7MScqL5gjD6RHkpMTNQ5olVYWCjF6z4aAlAdOTk5iI+PR7du3dC/f3/07dtX66dXr17YuXMnbt269cTL0aVt27bw8fHBmjVrsHr1aqjVakRHR8vyaEZYHx2tTUhIqHJ8vqenJ/Ly8pCamiqlqdVqzJs3TytvVFQU1Go1pkyZorOsO3fuVLq8du3awcrKSpqu8Vn76quvZK83bdqE8+fPy07UDAwMoFAoZCP3QggpFOthb7/9NgBg6tSpKCkp0Xpf0zaaE6OqXslQKpV44403cOLECezatUtrHdRqNSIjI6tU1rMUHh4OCwsLLFy4EHl5eVJ6Xl4eFi5cCEtLS+lpyOHh4TAzM8M///lP2TSWf/75p9ZVI6VSiYEDByI1NRXx8fE6l/0k8ebV3c6asDTNfQYac+fO1Sq7Km2emJiI48ePS6+FEJg9ezYAyPbJ6hynVVnu4cOHYWhoiI4dO1aYh6g+4Yg+kR4aN24cMjIy0KtXL7Rp0wbm5ua4fv061q5diwsXLiAqKgpt2rR54vJ//PFHFBYWok+fPhXm6dOnD1auXIlVq1Zp3ej5tKKjozFhwgR8/fXX8PT0RGBgoOz94OBgODk5YcKECUhLS4OzszNOnjyJ1atXo02bNjh9+nSlyxgxYgTmzp2LyMhIjB07FsbGxoiPj9d5AqWZUnPRokU4fvw4Xn/9dTg4OODPP//EoUOHcOnSpUrjig0MDPDmm29i8+bNKC4ull2hqG0ODg7YuHEjbt68ic6dO0vTazo6OsqeF9C3b19s2LABXbp0QVRUFEpLS7F582atOdUBwN/fH5MnT8bXX3+Nl19+GW+99RacnJxw9epVxMfHIzU1FTY2NvD29oZKpUJsbCzMzc1hY2ODhg0bSjf46vLll18iMTERERERGDlyJNzd3ZGSkoJ169YhJCRE68SvLrCxscHs2bMxatQoBAQESPPKr1y5EpcuXcKSJUukm6ptbW3xj3/8AxMnTkRQUBCioqJw//59LF68GB4eHjhx4oSs7C+++AIHDhxA//790b9/fwQGBsLY2BjXrl3Djh074OfnJ3sGQ1VVZzsPGDAAU6dOxYgRI3Du3DnY2dlh165dOqfstbe3h7u7O/7973+jRYsWcHR0hIWFBd544w0pj6+vL7p06YJRo0ahUaNG2LJlC3bv3o3BgwejQ4cOUr7qHKeV7WtCCOzatQs9evR44itzRHXOc5nrh4hq1S+//CJGjhwpfHx8hL29vTAwMBB2dnaic+fOYtmyZaK8vFyW38XFRbRu3brC8jRT52mm12zfvr0wNDTUmubyYUVFRUKlUglPT08pDf+b5vBp3b59WxgaGgoAYubMmTrznDp1SnTv3l3Y2NgIS0tLERoaKlJSUnROA1jR1IDbt28Xvr6+wtjYWDRq1EhMmjRJnDt3rsKpAePi4kRwcLBQqVTCxMREuLi4iMjISPHvf/+7SuulmZIyPj5elv646TV1TRXo4uIiQkNDtdI1U01evXpVStNMT3j58mXRq1cvoVKphKWlpejVq5e4ePGiVhnff/+9aNWqlTAxMRFOTk5i+PDhIiMjQ2sKRY21a9eKoKAgYWlpKczNzUXLli3F2LFjZdNUbt++XbRr106YmJgIADrr/qgrV66IQYMGiQYNGggjIyPh5uYmpkyZIpuOsqJ1rmw7PUozvebDU1pqVLTeFe1TGzduFB06dBDm5ubC3NxcdOjQQWzatEnnchcvXiw8PT2FsbGxaNGihZg3b540DeujdSkoKBCff/65eOmll4SpqamwtLQUXl5e4t133xWHDx+W8lV3OtOqbmchhDh8+LAICgoSJiYmwt7eXgwfPlxkZWXp3EZHjhwRQUFBwtzcXACQpsh8eFrMtWvXijZt2ghjY2Ph7Ows/v73v4uSkhKt5VbnOH3cvrZ3714BQGzbtq1K24aoPlAI8YR3ZRERUY3r0aMHCgoKsG/fvmeyvM6dOyMtLQ1paWnPZHlEj5OWlgY3NzdMmzZN6+nTtS0yMhLXr1/H0aNH68xN5ERPizH6RER1yNy5c3Ho0KEnmvuciJ7MiRMnsGXLFsydO5edfNIrjNEnIqpDWrduXetTEhKRXLt27bSmhyXSBxzRJyIiIiLSQ4zRJyIiIiLSQxzRJyIiIiLSQ+zoExERERHpIXb0iYiIiIj0EDv6RERERER6iB19IiIiIiI9xI4+EREREZEeYkefiIiIiEgPsaNPRERERKSH2NEnIiIiItJD/w8tta1XSLrcdQAAAABJRU5ErkJggg==\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": 714
        },
        "id": "53n5XJ9p2Emp",
        "outputId": "8b35e3f9-f9b9-4a66-e0fe-db0fab61bf58"
      },
      "id": "53n5XJ9p2Emp",
      "execution_count": null,
      "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 1150x660 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Tuning 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": null,
      "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": "4b62124e-67f1-4150-b6b5-5ff4bb972562"
      },
      "id": "C4oecsElWqGF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:15,987] A new study created in memory with name: no-name-7ee8cb69-df61-422c-a7a1-1222587d226f\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: 34610.7\n",
            "[2000]\tvalid_0's rmse: 33190.5\n",
            "Early stopping, best iteration is:\n",
            "[2603]\tvalid_0's rmse: 32526\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:17,275] Trial 0 finished with value: 32525.95127113584 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.564753421299634, 'reg_lambda': 6.127269055702681, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 2, 'num_leaves': 23, 'min_child_samples': 26}. Best is trial 0 with value: 32525.95127113584.\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: 25145.9\n",
            "[2000]\tvalid_0's rmse: 21710.9\n",
            "[3000]\tvalid_0's rmse: 19783\n",
            "[4000]\tvalid_0's rmse: 18562\n",
            "[5000]\tvalid_0's rmse: 17771.3\n",
            "[6000]\tvalid_0's rmse: 17359\n",
            "[7000]\tvalid_0's rmse: 17046.3\n",
            "[8000]\tvalid_0's rmse: 16821.6\n",
            "Early stopping, best iteration is:\n",
            "[8445]\tvalid_0's rmse: 16733.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:25,109] Trial 1 finished with value: 16733.697042790067 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.0792484856047635, 'reg_lambda': 0.5511270098802705, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 103, 'min_child_samples': 11}. Best is trial 1 with value: 16733.697042790067.\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: 34059.3\n",
            "[2000]\tvalid_0's rmse: 32055.6\n",
            "[3000]\tvalid_0's rmse: 30689.2\n",
            "[4000]\tvalid_0's rmse: 29559.7\n",
            "[5000]\tvalid_0's rmse: 28638.6\n",
            "[6000]\tvalid_0's rmse: 27908.7\n",
            "[7000]\tvalid_0's rmse: 27325.4\n",
            "[8000]\tvalid_0's rmse: 26800.2\n",
            "[9000]\tvalid_0's rmse: 26388.4\n",
            "[10000]\tvalid_0's rmse: 25927.8\n",
            "[11000]\tvalid_0's rmse: 25444.2\n",
            "[12000]\tvalid_0's rmse: 25029.6\n",
            "[13000]\tvalid_0's rmse: 24661.2\n",
            "[14000]\tvalid_0's rmse: 24363.7\n",
            "[15000]\tvalid_0's rmse: 24068.2\n",
            "[16000]\tvalid_0's rmse: 23800.9\n",
            "[17000]\tvalid_0's rmse: 23570.2\n",
            "[18000]\tvalid_0's rmse: 23287.1\n",
            "[19000]\tvalid_0's rmse: 23046.3\n",
            "[20000]\tvalid_0's rmse: 22861.9\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[20000]\tvalid_0's rmse: 22861.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:36,504] Trial 2 finished with value: 22861.947255775216 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 2.143484619426666, 'reg_lambda': 0.017296058368244424, 'colsample_bytree': 0.6, 'subsample': 0.7, 'learning_rate': 0.005, 'max_depth': 8, 'num_leaves': 58, 'min_child_samples': 46}. Best is trial 1 with value: 16733.697042790067.\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: 26981.3\n",
            "[2000]\tvalid_0's rmse: 24377.5\n",
            "[3000]\tvalid_0's rmse: 22819.4\n",
            "Early stopping, best iteration is:\n",
            "[3255]\tvalid_0's rmse: 22476.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:38,064] Trial 3 finished with value: 22476.68720082341 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.04114065600099953, 'reg_lambda': 0.277067753964267, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 6, 'num_leaves': 98, 'min_child_samples': 26}. Best is trial 1 with value: 16733.697042790067.\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: 40013.3\n",
            "[2000]\tvalid_0's rmse: 38498.5\n",
            "[3000]\tvalid_0's rmse: 37592.1\n",
            "[4000]\tvalid_0's rmse: 37016.1\n",
            "[5000]\tvalid_0's rmse: 36514.2\n",
            "[6000]\tvalid_0's rmse: 36151.6\n",
            "[7000]\tvalid_0's rmse: 35852.2\n",
            "[8000]\tvalid_0's rmse: 35624.6\n",
            "[9000]\tvalid_0's rmse: 35413.3\n",
            "[10000]\tvalid_0's rmse: 35224.7\n",
            "[11000]\tvalid_0's rmse: 35062.9\n",
            "[12000]\tvalid_0's rmse: 34888.4\n",
            "Early stopping, best iteration is:\n",
            "[12818]\tvalid_0's rmse: 34759.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:42,554] Trial 4 finished with value: 34759.43082616638 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.5679052633045572, 'reg_lambda': 0.009644831124326583, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 12, 'num_leaves': 13, 'min_child_samples': 71}. Best is trial 1 with value: 16733.697042790067.\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: 30429.9\n",
            "[2000]\tvalid_0's rmse: 27934.8\n",
            "[3000]\tvalid_0's rmse: 26173.2\n",
            "[4000]\tvalid_0's rmse: 25045.4\n",
            "[5000]\tvalid_0's rmse: 23950.2\n",
            "[6000]\tvalid_0's rmse: 22852.7\n",
            "[7000]\tvalid_0's rmse: 22188.7\n",
            "Early stopping, best iteration is:\n",
            "[7891]\tvalid_0's rmse: 21615.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:47,747] Trial 5 finished with value: 21615.910899622057 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 7.566372450248474, 'reg_lambda': 0.006372790886012054, 'colsample_bytree': 0.8, 'subsample': 0.6, 'learning_rate': 0.005, 'max_depth': 5, 'num_leaves': 53, 'min_child_samples': 16}. Best is trial 1 with value: 16733.697042790067.\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: 31543.5\n",
            "Early stopping, best iteration is:\n",
            "[1671]\tvalid_0's rmse: 30192.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:48,358] Trial 6 finished with value: 30192.489818872462 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.0032475876290340047, 'reg_lambda': 0.005315941039419465, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 4, 'num_leaves': 13, 'min_child_samples': 41}. Best is trial 1 with value: 16733.697042790067.\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: 39331.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:48,848] Trial 7 finished with value: 37895.06702403795 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.028730033286312403, 'reg_lambda': 0.06634074375782298, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 83, 'min_child_samples': 96}. Best is trial 1 with value: 16733.697042790067.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[1944]\tvalid_0's rmse: 37895.1\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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1000]\tvalid_0's rmse: 20902.5\n",
            "[2000]\tvalid_0's rmse: 17835.2\n",
            "[3000]\tvalid_0's rmse: 16501.1\n",
            "[4000]\tvalid_0's rmse: 15968\n",
            "[5000]\tvalid_0's rmse: 15770.4\n",
            "Early stopping, best iteration is:\n",
            "[4988]\tvalid_0's rmse: 15768\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:58:55,568] Trial 8 finished with value: 15767.962510758935 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.0038310691556454795, 'reg_lambda': 2.045898765281297, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 103, 'min_child_samples': 6}. Best is trial 8 with value: 15767.962510758935.\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: 33672.8\n",
            "[2000]\tvalid_0's rmse: 32105.8\n",
            "[3000]\tvalid_0's rmse: 31077.5\n",
            "[4000]\tvalid_0's rmse: 30285.2\n",
            "[5000]\tvalid_0's rmse: 29544.5\n",
            "[6000]\tvalid_0's rmse: 28768.6\n",
            "[7000]\tvalid_0's rmse: 28018.8\n",
            "[8000]\tvalid_0's rmse: 27413.8\n",
            "[9000]\tvalid_0's rmse: 26952.9\n",
            "[10000]\tvalid_0's rmse: 26581.4\n",
            "[11000]\tvalid_0's rmse: 26234.8\n",
            "[12000]\tvalid_0's rmse: 25655\n",
            "[13000]\tvalid_0's rmse: 25297.9\n",
            "[14000]\tvalid_0's rmse: 24870.1\n",
            "[15000]\tvalid_0's rmse: 24305.2\n",
            "Early stopping, best iteration is:\n",
            "[15629]\tvalid_0's rmse: 24116.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:02,074] Trial 9 finished with value: 24116.355391728423 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.14139812256617787, 'reg_lambda': 0.07456234059519377, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.005, 'max_depth': 4, 'num_leaves': 28, 'min_child_samples': 31}. Best is trial 8 with value: 15767.962510758935.\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: 34233.2\n",
            "[2000]\tvalid_0's rmse: 32326\n",
            "[3000]\tvalid_0's rmse: 31048.3\n",
            "[4000]\tvalid_0's rmse: 29858.8\n",
            "[5000]\tvalid_0's rmse: 29031.8\n",
            "[6000]\tvalid_0's rmse: 28312.3\n",
            "[7000]\tvalid_0's rmse: 27746.5\n",
            "[8000]\tvalid_0's rmse: 27275.8\n",
            "[9000]\tvalid_0's rmse: 26762.5\n",
            "[10000]\tvalid_0's rmse: 26350.1\n",
            "[11000]\tvalid_0's rmse: 26031.7\n",
            "[12000]\tvalid_0's rmse: 25669.7\n",
            "[13000]\tvalid_0's rmse: 25430.8\n",
            "[14000]\tvalid_0's rmse: 25132.7\n",
            "[15000]\tvalid_0's rmse: 24856.6\n",
            "[16000]\tvalid_0's rmse: 24622.4\n",
            "[17000]\tvalid_0's rmse: 24432.1\n",
            "[18000]\tvalid_0's rmse: 24266\n",
            "Early stopping, best iteration is:\n",
            "[18186]\tvalid_0's rmse: 24234.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:13,266] Trial 10 finished with value: 24234.182975557454 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.001972076419062882, 'reg_lambda': 9.280577705471206, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 143, 'min_child_samples': 66}. Best is trial 8 with value: 15767.962510758935.\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: 15972.4\n",
            "Early stopping, best iteration is:\n",
            "[1869]\tvalid_0's rmse: 15583.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:17,524] Trial 11 finished with value: 15583.874488583177 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.35636332930573894, 'reg_lambda': 1.143511745084067, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 118, 'min_child_samples': 1}. Best is trial 11 with value: 15583.874488583177.\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: 14271.9\n",
            "[2000]\tvalid_0's rmse: 13714.9\n",
            "Early stopping, best iteration is:\n",
            "[2215]\tvalid_0's rmse: 13700.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:22,966] Trial 12 finished with value: 13700.511683372875 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.2777676675717941, 'reg_lambda': 1.500203026036375, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 128, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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",
            "[842]\tvalid_0's rmse: 17099.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:25,318] Trial 13 finished with value: 17099.22660266075 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.351202305860911, 'reg_lambda': 1.2976656556731612, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.03, 'max_depth': 9, 'num_leaves': 138, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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",
            "[283]\tvalid_0's rmse: 17121.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:26,288] Trial 14 finished with value: 17121.451607792624 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.3692059645367899, 'reg_lambda': 0.3584159615961434, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 10, 'num_leaves': 123, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 37068\n",
            "[2000]\tvalid_0's rmse: 35506.6\n",
            "[3000]\tvalid_0's rmse: 34741.9\n",
            "[4000]\tvalid_0's rmse: 34198.2\n",
            "Early stopping, best iteration is:\n",
            "[4882]\tvalid_0's rmse: 33763.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:27,946] Trial 15 finished with value: 33763.478053455714 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.5515619282672644, 'reg_lambda': 0.0016761317898506614, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 7, 'num_leaves': 123, 'min_child_samples': 61}. Best is trial 12 with value: 13700.511683372875.\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: 29404.6\n",
            "[2000]\tvalid_0's rmse: 26687.4\n",
            "[3000]\tvalid_0's rmse: 24773.6\n",
            "[4000]\tvalid_0's rmse: 23348.7\n",
            "[5000]\tvalid_0's rmse: 22338.9\n",
            "[6000]\tvalid_0's rmse: 21483.3\n",
            "Early stopping, best iteration is:\n",
            "[6142]\tvalid_0's rmse: 21382\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:32,198] Trial 16 finished with value: 21381.975476159558 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.20663098000307636, 'reg_lambda': 2.529514421535878, 'colsample_bytree': 0.7, 'subsample': 0.85, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 118, 'min_child_samples': 16}. Best is trial 12 with value: 13700.511683372875.\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: 41067.9\n",
            "[2000]\tvalid_0's rmse: 39502.1\n",
            "[3000]\tvalid_0's rmse: 38646.1\n",
            "Early stopping, best iteration is:\n",
            "[3113]\tvalid_0's rmse: 38552.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:33,148] Trial 17 finished with value: 38552.51268774739 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.0837874742484967, 'reg_lambda': 1.341237275720056, 'colsample_bytree': 0.9, 'subsample': 0.7, 'learning_rate': 0.02, 'max_depth': 9, 'num_leaves': 148, 'min_child_samples': 96}. Best is trial 12 with value: 13700.511683372875.\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: 28259.1\n",
            "[2000]\tvalid_0's rmse: 25718.2\n",
            "[3000]\tvalid_0's rmse: 24501.9\n",
            "Early stopping, best iteration is:\n",
            "[3296]\tvalid_0's rmse: 24179.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:34,523] Trial 18 finished with value: 24179.443247891377 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.9016040424509133, 'reg_lambda': 0.1388016883617899, 'colsample_bytree': 0.5, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 11, 'num_leaves': 83, 'min_child_samples': 81}. Best is trial 12 with value: 13700.511683372875.\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: 31373.2\n",
            "[2000]\tvalid_0's rmse: 29458.6\n",
            "Early stopping, best iteration is:\n",
            "[2508]\tvalid_0's rmse: 28789.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:35,708] Trial 19 finished with value: 28789.912920157432 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.07432537303094319, 'reg_lambda': 0.8034452235561255, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.03, 'max_depth': 8, 'num_leaves': 63, 'min_child_samples': 36}. Best is trial 12 with value: 13700.511683372875.\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: 36199.8\n",
            "[2000]\tvalid_0's rmse: 34773\n",
            "[3000]\tvalid_0's rmse: 33973.9\n",
            "[4000]\tvalid_0's rmse: 33471.6\n",
            "Early stopping, best iteration is:\n",
            "[4607]\tvalid_0's rmse: 33180.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:38,319] Trial 20 finished with value: 33180.52519874304 and parameters: {'boosting_type': 'goss', 'reg_alpha': 9.62811306825525, 'reg_lambda': 3.855172999861669, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 133, 'min_child_samples': 56}. Best is trial 12 with value: 13700.511683372875.\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: 20993.8\n",
            "[2000]\tvalid_0's rmse: 17897.6\n",
            "[3000]\tvalid_0's rmse: 16648.7\n",
            "[4000]\tvalid_0's rmse: 16178.1\n",
            "[5000]\tvalid_0's rmse: 15955.1\n",
            "Early stopping, best iteration is:\n",
            "[5693]\tvalid_0's rmse: 15873\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:46,688] Trial 21 finished with value: 15872.981364314686 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.013399251134420608, 'reg_lambda': 2.3065845861353775, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 11, 'num_leaves': 103, 'min_child_samples': 6}. Best is trial 12 with value: 13700.511683372875.\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: 28996\n",
            "[2000]\tvalid_0's rmse: 25816.5\n",
            "[3000]\tvalid_0's rmse: 23928.5\n",
            "[4000]\tvalid_0's rmse: 22518.6\n",
            "[5000]\tvalid_0's rmse: 21449.4\n",
            "[6000]\tvalid_0's rmse: 20553.5\n",
            "[7000]\tvalid_0's rmse: 19887.7\n",
            "[8000]\tvalid_0's rmse: 19408.6\n",
            "[9000]\tvalid_0's rmse: 18926.5\n",
            "[10000]\tvalid_0's rmse: 18576.5\n",
            "Early stopping, best iteration is:\n",
            "[10530]\tvalid_0's rmse: 18396.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:55,322] Trial 22 finished with value: 18396.389533948455 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.0011234483893170773, 'reg_lambda': 3.814740162314544, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 12, 'num_leaves': 113, 'min_child_samples': 16}. Best is trial 12 with value: 13700.511683372875.\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: 15706.4\n",
            "Early stopping, best iteration is:\n",
            "[1936]\tvalid_0's rmse: 15204.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 03:59:59,061] Trial 23 finished with value: 15204.63872894407 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.23467304302819325, 'reg_lambda': 1.2096817139684517, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 93, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 30089.8\n",
            "[2000]\tvalid_0's rmse: 27427.5\n",
            "[3000]\tvalid_0's rmse: 25739.4\n",
            "[4000]\tvalid_0's rmse: 24517.2\n",
            "[5000]\tvalid_0's rmse: 23474.5\n",
            "[6000]\tvalid_0's rmse: 22642.7\n",
            "[7000]\tvalid_0's rmse: 22027.1\n",
            "Early stopping, best iteration is:\n",
            "[6970]\tvalid_0's rmse: 22020.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:03,834] Trial 24 finished with value: 22020.475501179248 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.24744780940500602, 'reg_lambda': 0.7681843921192294, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 73, 'min_child_samples': 21}. Best is trial 12 with value: 13700.511683372875.\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: 20777.1\n",
            "[2000]\tvalid_0's rmse: 17532.8\n",
            "[3000]\tvalid_0's rmse: 16479.5\n",
            "[4000]\tvalid_0's rmse: 16102.6\n",
            "Early stopping, best iteration is:\n",
            "[4077]\tvalid_0's rmse: 16085\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:09,732] Trial 25 finished with value: 16085.027909511144 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.45336892103282045, 'reg_lambda': 8.56663656806255, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 7, 'num_leaves': 93, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 24413.5\n",
            "Early stopping, best iteration is:\n",
            "[1285]\tvalid_0's rmse: 23980.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:11,371] Trial 26 finished with value: 23980.663136200623 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.16630783228010712, 'reg_lambda': 1.1414348524733784, 'colsample_bytree': 0.9, 'subsample': 0.85, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 128, 'min_child_samples': 11}. Best is trial 12 with value: 13700.511683372875.\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: 22565.3\n",
            "[2000]\tvalid_0's rmse: 19636.5\n",
            "[3000]\tvalid_0's rmse: 18471\n",
            "[4000]\tvalid_0's rmse: 17974.9\n",
            "[5000]\tvalid_0's rmse: 17671.7\n",
            "Early stopping, best iteration is:\n",
            "[5561]\tvalid_0's rmse: 17541\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:18,049] Trial 27 finished with value: 17540.95792626229 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6915093576666373, 'reg_lambda': 0.4908603731661763, 'colsample_bytree': 0.5, 'subsample': 0.7, 'learning_rate': 0.03, 'max_depth': 10, 'num_leaves': 113, 'min_child_samples': 11}. Best is trial 12 with value: 13700.511683372875.\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: 30745.8\n",
            "[2000]\tvalid_0's rmse: 28438.3\n",
            "[3000]\tvalid_0's rmse: 26843.6\n",
            "[4000]\tvalid_0's rmse: 25676.3\n",
            "Early stopping, best iteration is:\n",
            "[3964]\tvalid_0's rmse: 25636.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:20,461] Trial 28 finished with value: 25636.734928223577 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.1072693632339167, 'reg_lambda': 0.17361633332812135, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.02, 'max_depth': 8, 'num_leaves': 88, 'min_child_samples': 26}. Best is trial 12 with value: 13700.511683372875.\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 2023-07-22 04:00:20,693] Trial 29 finished with value: 31348.839487652614 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.031865143805996, 'reg_lambda': 4.971690349405795, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 2, 'num_leaves': 73, 'min_child_samples': 21}. Best is trial 12 with value: 13700.511683372875.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 50 rounds\n",
            "Early stopping, best iteration is:\n",
            "[583]\tvalid_0's rmse: 31348.8\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",
            "[1000]\tvalid_0's rmse: 16749.3\n",
            "[2000]\tvalid_0's rmse: 15196.1\n",
            "[3000]\tvalid_0's rmse: 14893.1\n",
            "Early stopping, best iteration is:\n",
            "[2988]\tvalid_0's rmse: 14887.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:23,657] Trial 30 finished with value: 14887.723102039105 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.3066683703883992, 'reg_lambda': 5.327799169383905, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 6, 'num_leaves': 113, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 18425.8\n",
            "[2000]\tvalid_0's rmse: 16214\n",
            "Early stopping, best iteration is:\n",
            "[2280]\tvalid_0's rmse: 15936.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:25,439] Trial 31 finished with value: 15936.347503657315 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.2267980679571584, 'reg_lambda': 5.543961854186508, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 5, 'num_leaves': 113, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 20055.5\n",
            "[2000]\tvalid_0's rmse: 17579\n",
            "[3000]\tvalid_0's rmse: 16790\n",
            "Early stopping, best iteration is:\n",
            "[3432]\tvalid_0's rmse: 16638\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:28,429] Trial 32 finished with value: 16637.951964218584 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.730796630868802, 'reg_lambda': 2.3358802353332004, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 6, 'num_leaves': 133, 'min_child_samples': 6}. Best is trial 12 with value: 13700.511683372875.\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: 25326.6\n",
            "[2000]\tvalid_0's rmse: 21985.5\n",
            "[3000]\tvalid_0's rmse: 20127\n",
            "[4000]\tvalid_0's rmse: 19078.5\n",
            "[5000]\tvalid_0's rmse: 18468.9\n",
            "[6000]\tvalid_0's rmse: 18072.2\n",
            "Early stopping, best iteration is:\n",
            "[6281]\tvalid_0's rmse: 17968.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:34,096] Trial 33 finished with value: 17968.675735914443 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.28080888338647714, 'reg_lambda': 9.838555453595697, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 6, 'num_leaves': 108, 'min_child_samples': 11}. Best is trial 12 with value: 13700.511683372875.\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: 21294\n",
            "[2000]\tvalid_0's rmse: 18481.5\n",
            "[3000]\tvalid_0's rmse: 17436.7\n",
            "[4000]\tvalid_0's rmse: 16956.8\n",
            "Early stopping, best iteration is:\n",
            "[4405]\tvalid_0's rmse: 16856.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:39,999] Trial 34 finished with value: 16856.050947488693 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.9237213706843868, 'reg_lambda': 0.7777352221625428, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.01, 'max_depth': 10, 'num_leaves': 93, 'min_child_samples': 6}. Best is trial 12 with value: 13700.511683372875.\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: 29768.7\n",
            "[2000]\tvalid_0's rmse: 27236.7\n",
            "[3000]\tvalid_0's rmse: 25571.1\n",
            "[4000]\tvalid_0's rmse: 24287.5\n",
            "[5000]\tvalid_0's rmse: 23238.3\n",
            "[6000]\tvalid_0's rmse: 22480\n",
            "[7000]\tvalid_0's rmse: 21742.4\n",
            "[8000]\tvalid_0's rmse: 21222.5\n",
            "Early stopping, best iteration is:\n",
            "[8292]\tvalid_0's rmse: 21054.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:45,859] Trial 35 finished with value: 21054.217026467057 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.1214444340406515, 'reg_lambda': 4.228294549064427, 'colsample_bytree': 0.6, 'subsample': 0.6, 'learning_rate': 0.02, 'max_depth': 7, 'num_leaves': 123, 'min_child_samples': 21}. Best is trial 12 with value: 13700.511683372875.\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: 29702.1\n",
            "[2000]\tvalid_0's rmse: 27320.9\n",
            "[3000]\tvalid_0's rmse: 25740.1\n",
            "[4000]\tvalid_0's rmse: 25096.6\n",
            "[5000]\tvalid_0's rmse: 24373.9\n",
            "Early stopping, best iteration is:\n",
            "[5809]\tvalid_0's rmse: 23908.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:49,330] Trial 36 finished with value: 23908.10026391802 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.5174416074273249, 'reg_lambda': 0.40143032932640144, 'colsample_bytree': 0.8, 'subsample': 0.7, 'learning_rate': 0.01, 'max_depth': 9, 'num_leaves': 98, 'min_child_samples': 31}. Best is trial 12 with value: 13700.511683372875.\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: 31579\n",
            "[2000]\tvalid_0's rmse: 29865.7\n",
            "Early stopping, best iteration is:\n",
            "[1988]\tvalid_0's rmse: 29831.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:50,169] Trial 37 finished with value: 29831.9374262178 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.5302274766779993, 'reg_lambda': 1.6152696937464888, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 138, 'min_child_samples': 46}. Best is trial 12 with value: 13700.511683372875.\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: 18961.2\n",
            "[2000]\tvalid_0's rmse: 16739.4\n",
            "[3000]\tvalid_0's rmse: 15891.5\n",
            "[4000]\tvalid_0's rmse: 15429.4\n",
            "Early stopping, best iteration is:\n",
            "[3974]\tvalid_0's rmse: 15420.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:00:54,276] Trial 38 finished with value: 15420.141862430997 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.921381224894363, 'reg_lambda': 0.6270606886261528, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 6, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 30777.1\n",
            "[2000]\tvalid_0's rmse: 28547.1\n",
            "[3000]\tvalid_0's rmse: 26832.1\n",
            "[4000]\tvalid_0's rmse: 25647.7\n",
            "[5000]\tvalid_0's rmse: 24656.9\n",
            "[6000]\tvalid_0's rmse: 23884\n",
            "[7000]\tvalid_0's rmse: 23180\n",
            "[8000]\tvalid_0's rmse: 22553.3\n",
            "[9000]\tvalid_0's rmse: 22047\n",
            "[10000]\tvalid_0's rmse: 21605.9\n",
            "[11000]\tvalid_0's rmse: 21196.3\n",
            "[12000]\tvalid_0's rmse: 20799.8\n",
            "[13000]\tvalid_0's rmse: 20453.4\n",
            "[14000]\tvalid_0's rmse: 20199.6\n",
            "[15000]\tvalid_0's rmse: 19917.3\n",
            "Early stopping, best iteration is:\n",
            "[15502]\tvalid_0's rmse: 19780.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:06,057] Trial 39 finished with value: 19780.192486782533 and parameters: {'boosting_type': 'goss', 'reg_alpha': 5.638216355455647, 'reg_lambda': 0.25688110051100055, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 5, 'num_leaves': 38, 'min_child_samples': 11}. Best is trial 12 with value: 13700.511683372875.\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: 30362.6\n",
            "[2000]\tvalid_0's rmse: 27788.7\n",
            "[3000]\tvalid_0's rmse: 25639.5\n",
            "[4000]\tvalid_0's rmse: 24167.7\n",
            "[5000]\tvalid_0's rmse: 23176.4\n",
            "[6000]\tvalid_0's rmse: 22324.3\n",
            "[7000]\tvalid_0's rmse: 21624.4\n",
            "[8000]\tvalid_0's rmse: 21075.1\n",
            "[9000]\tvalid_0's rmse: 20625.5\n",
            "[10000]\tvalid_0's rmse: 20030.6\n",
            "[11000]\tvalid_0's rmse: 19668.7\n",
            "[12000]\tvalid_0's rmse: 19420.2\n",
            "[13000]\tvalid_0's rmse: 19051.6\n",
            "Early stopping, best iteration is:\n",
            "[13731]\tvalid_0's rmse: 18818.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:14,549] Trial 40 finished with value: 18818.360916457965 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 1.9204977158153966, 'reg_lambda': 3.0026057734231384, 'colsample_bytree': 0.8, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 6, 'num_leaves': 38, 'min_child_samples': 16}. Best is trial 12 with value: 13700.511683372875.\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: 17591.6\n",
            "[2000]\tvalid_0's rmse: 15200.3\n",
            "[3000]\tvalid_0's rmse: 14384\n",
            "Early stopping, best iteration is:\n",
            "[3653]\tvalid_0's rmse: 14143.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:18,991] Trial 41 finished with value: 14143.812565094271 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.326105973951393, 'reg_lambda': 1.0335524772719313, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 48, 'min_child_samples': 1}. Best is trial 12 with value: 13700.511683372875.\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: 27400.7\n",
            "[2000]\tvalid_0's rmse: 24409.4\n",
            "[3000]\tvalid_0's rmse: 22497.1\n",
            "[4000]\tvalid_0's rmse: 21143.7\n",
            "[5000]\tvalid_0's rmse: 20163.9\n",
            "[6000]\tvalid_0's rmse: 19409\n",
            "[7000]\tvalid_0's rmse: 18868.1\n",
            "[8000]\tvalid_0's rmse: 18404.8\n",
            "[9000]\tvalid_0's rmse: 18095\n",
            "[10000]\tvalid_0's rmse: 17804.6\n",
            "Early stopping, best iteration is:\n",
            "[10141]\tvalid_0's rmse: 17773.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:26,840] Trial 42 finished with value: 17773.09104554403 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.1791076325219063, 'reg_lambda': 0.644108798770552, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 5, 'num_leaves': 48, 'min_child_samples': 6}. Best is trial 12 with value: 13700.511683372875.\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: 17966.3\n",
            "[2000]\tvalid_0's rmse: 14921.2\n",
            "[3000]\tvalid_0's rmse: 13808.3\n",
            "[4000]\tvalid_0's rmse: 13368.4\n",
            "[5000]\tvalid_0's rmse: 13125.7\n",
            "Early stopping, best iteration is:\n",
            "[5136]\tvalid_0's rmse: 13109.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:32,805] Trial 43 finished with value: 13109.071116685189 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.5930536115453737, 'reg_lambda': 1.6801211565263818, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 43 with value: 13109.071116685189.\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: 28691.2\n",
            "[2000]\tvalid_0's rmse: 26040.4\n",
            "[3000]\tvalid_0's rmse: 24007.5\n",
            "[4000]\tvalid_0's rmse: 22670.8\n",
            "[5000]\tvalid_0's rmse: 21609.1\n",
            "[6000]\tvalid_0's rmse: 20716.2\n",
            "[7000]\tvalid_0's rmse: 20033.7\n",
            "[8000]\tvalid_0's rmse: 19510.5\n",
            "[9000]\tvalid_0's rmse: 19062\n",
            "[10000]\tvalid_0's rmse: 18664.1\n",
            "[11000]\tvalid_0's rmse: 18352.9\n",
            "[12000]\tvalid_0's rmse: 18128.2\n",
            "[13000]\tvalid_0's rmse: 17889.1\n",
            "Early stopping, best iteration is:\n",
            "[13206]\tvalid_0's rmse: 17842.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:44,094] Trial 44 finished with value: 17842.711914495554 and parameters: {'boosting_type': 'goss', 'reg_alpha': 5.71867586490075, 'reg_lambda': 1.7930303187702383, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 23, 'min_child_samples': 11}. Best is trial 43 with value: 13109.071116685189.\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: 23896.4\n",
            "[2000]\tvalid_0's rmse: 19399.8\n",
            "[3000]\tvalid_0's rmse: 17474.9\n",
            "[4000]\tvalid_0's rmse: 16496.3\n",
            "[5000]\tvalid_0's rmse: 15997.5\n",
            "[6000]\tvalid_0's rmse: 15782.5\n",
            "Early stopping, best iteration is:\n",
            "[5957]\tvalid_0's rmse: 15782\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:01:54,362] Trial 45 finished with value: 15782.033308897691 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.8691463665886774, 'reg_lambda': 6.804722279298265, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 8, 'num_leaves': 63, 'min_child_samples': 1}. Best is trial 43 with value: 13109.071116685189.\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: 28964.9\n",
            "[2000]\tvalid_0's rmse: 26582.3\n",
            "[3000]\tvalid_0's rmse: 24756.7\n",
            "[4000]\tvalid_0's rmse: 23447.9\n",
            "[5000]\tvalid_0's rmse: 22497.6\n",
            "[6000]\tvalid_0's rmse: 21704.3\n",
            "[7000]\tvalid_0's rmse: 21074.2\n",
            "[8000]\tvalid_0's rmse: 20503\n",
            "[9000]\tvalid_0's rmse: 19979.2\n",
            "[10000]\tvalid_0's rmse: 19500.7\n",
            "[11000]\tvalid_0's rmse: 19130.7\n",
            "[12000]\tvalid_0's rmse: 18767.9\n",
            "[13000]\tvalid_0's rmse: 18475.9\n",
            "Early stopping, best iteration is:\n",
            "[13644]\tvalid_0's rmse: 18323.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:02,104] Trial 46 finished with value: 18323.44938566317 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.6634522760314338, 'reg_lambda': 3.4083732615762092, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 4, 'num_leaves': 73, 'min_child_samples': 6}. Best is trial 43 with value: 13109.071116685189.\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: 23489.6\n",
            "[2000]\tvalid_0's rmse: 20516.6\n",
            "[3000]\tvalid_0's rmse: 19407.3\n",
            "Early stopping, best iteration is:\n",
            "[2980]\tvalid_0's rmse: 19385.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:04,563] Trial 47 finished with value: 19385.20667922463 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.5608147255818459, 'reg_lambda': 1.0293665478979879, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 28, 'min_child_samples': 16}. Best is trial 43 with value: 13109.071116685189.\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: 32433.7\n",
            "[2000]\tvalid_0's rmse: 30687.2\n",
            "[3000]\tvalid_0's rmse: 29147\n",
            "[4000]\tvalid_0's rmse: 28267.6\n",
            "[5000]\tvalid_0's rmse: 27499.2\n",
            "[6000]\tvalid_0's rmse: 26688.4\n",
            "[7000]\tvalid_0's rmse: 26029.9\n",
            "[8000]\tvalid_0's rmse: 25466.4\n",
            "[9000]\tvalid_0's rmse: 24896.8\n",
            "[10000]\tvalid_0's rmse: 24393.7\n",
            "[11000]\tvalid_0's rmse: 23936\n",
            "[12000]\tvalid_0's rmse: 23556.9\n",
            "[13000]\tvalid_0's rmse: 23264.5\n",
            "[14000]\tvalid_0's rmse: 23007.9\n",
            "Early stopping, best iteration is:\n",
            "[14379]\tvalid_0's rmse: 22917.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:12,167] Trial 48 finished with value: 22917.720644554716 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 0.9429315404528533, 'reg_lambda': 1.7348126148743765, 'colsample_bytree': 0.5, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 58, 'min_child_samples': 26}. Best is trial 43 with value: 13109.071116685189.\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: 21128.2\n",
            "[2000]\tvalid_0's rmse: 18404.2\n",
            "Early stopping, best iteration is:\n",
            "[2209]\tvalid_0's rmse: 18203.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:13,069] Trial 49 finished with value: 18203.150141061255 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.744424095770271, 'reg_lambda': 6.549147985802539, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 3, 'num_leaves': 13, 'min_child_samples': 6}. Best is trial 43 with value: 13109.071116685189.\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: 36987.4\n",
            "[2000]\tvalid_0's rmse: 35725.2\n",
            "[3000]\tvalid_0's rmse: 35031\n",
            "Early stopping, best iteration is:\n",
            "[2983]\tvalid_0's rmse: 35018.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:14,028] Trial 50 finished with value: 35018.329163069924 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.4080839944644246, 'reg_lambda': 2.6614930265208554, 'colsample_bytree': 0.8, 'subsample': 0.85, 'learning_rate': 0.03, 'max_depth': 6, 'num_leaves': 53, 'min_child_samples': 81}. Best is trial 43 with value: 13109.071116685189.\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: 19882.2\n",
            "[2000]\tvalid_0's rmse: 17807\n",
            "[3000]\tvalid_0's rmse: 17056.8\n",
            "[4000]\tvalid_0's rmse: 16596.9\n",
            "Early stopping, best iteration is:\n",
            "[4281]\tvalid_0's rmse: 16511.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:19,295] Trial 51 finished with value: 16511.08344552754 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.7949561046291511, 'reg_lambda': 0.5258819352454711, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 6, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 43 with value: 13109.071116685189.\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: 18100.2\n",
            "[2000]\tvalid_0's rmse: 15208.2\n",
            "[3000]\tvalid_0's rmse: 14162.4\n",
            "[4000]\tvalid_0's rmse: 13665.9\n",
            "[5000]\tvalid_0's rmse: 13400.9\n",
            "Early stopping, best iteration is:\n",
            "[5720]\tvalid_0's rmse: 13266.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:26,002] Trial 52 finished with value: 13266.070766054665 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.45553070348005, 'reg_lambda': 1.0490125970648665, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 43 with value: 13109.071116685189.\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: 25012.2\n",
            "[2000]\tvalid_0's rmse: 21339.3\n",
            "[3000]\tvalid_0's rmse: 19339.6\n",
            "[4000]\tvalid_0's rmse: 18225.3\n",
            "[5000]\tvalid_0's rmse: 17427.3\n",
            "[6000]\tvalid_0's rmse: 16947.3\n",
            "[7000]\tvalid_0's rmse: 16601.2\n",
            "[8000]\tvalid_0's rmse: 16342.1\n",
            "[9000]\tvalid_0's rmse: 16155.2\n",
            "Early stopping, best iteration is:\n",
            "[9720]\tvalid_0's rmse: 16052.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:37,071] Trial 53 finished with value: 16052.42643480233 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.9329843206448483, 'reg_lambda': 1.086713027838019, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 8, 'num_leaves': 28, 'min_child_samples': 6}. Best is trial 43 with value: 13109.071116685189.\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: 17975.3\n",
            "[2000]\tvalid_0's rmse: 14923.9\n",
            "[3000]\tvalid_0's rmse: 13794.1\n",
            "[4000]\tvalid_0's rmse: 13309.9\n",
            "[5000]\tvalid_0's rmse: 13020\n",
            "Early stopping, best iteration is:\n",
            "[5300]\tvalid_0's rmse: 12986.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:43,148] Trial 54 finished with value: 12986.354549574013 and parameters: {'boosting_type': 'goss', 'reg_alpha': 7.318949963289874, 'reg_lambda': 1.5036377932297724, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 54 with value: 12986.354549574013.\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: 30333.5\n",
            "[2000]\tvalid_0's rmse: 27926.9\n",
            "[3000]\tvalid_0's rmse: 26018.4\n",
            "[4000]\tvalid_0's rmse: 24635.7\n",
            "[5000]\tvalid_0's rmse: 23469.7\n",
            "[6000]\tvalid_0's rmse: 22587.6\n",
            "[7000]\tvalid_0's rmse: 21823.8\n",
            "[8000]\tvalid_0's rmse: 21255.3\n",
            "[9000]\tvalid_0's rmse: 20763.8\n",
            "[10000]\tvalid_0's rmse: 20292.3\n",
            "[11000]\tvalid_0's rmse: 19857.1\n",
            "[12000]\tvalid_0's rmse: 19531.8\n",
            "[13000]\tvalid_0's rmse: 19237.7\n",
            "[14000]\tvalid_0's rmse: 19024.6\n",
            "[15000]\tvalid_0's rmse: 18834.3\n",
            "[16000]\tvalid_0's rmse: 18662.8\n",
            "[17000]\tvalid_0's rmse: 18502.1\n",
            "[18000]\tvalid_0's rmse: 18335.3\n",
            "Early stopping, best iteration is:\n",
            "[18228]\tvalid_0's rmse: 18309.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:02:57,810] Trial 55 finished with value: 18309.8737616633 and parameters: {'boosting_type': 'goss', 'reg_alpha': 7.4185286325449775, 'reg_lambda': 1.7434919143548975, 'colsample_bytree': 0.6, 'subsample': 0.7, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 11}. Best is trial 54 with value: 12986.354549574013.\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: 26813.5\n",
            "[2000]\tvalid_0's rmse: 23346.3\n",
            "[3000]\tvalid_0's rmse: 21359.1\n",
            "[4000]\tvalid_0's rmse: 20040.7\n",
            "[5000]\tvalid_0's rmse: 19112.5\n",
            "[6000]\tvalid_0's rmse: 18420.7\n",
            "[7000]\tvalid_0's rmse: 17908.3\n",
            "[8000]\tvalid_0's rmse: 17530.9\n",
            "[9000]\tvalid_0's rmse: 17216.6\n",
            "[10000]\tvalid_0's rmse: 17014.7\n",
            "Early stopping, best iteration is:\n",
            "[10785]\tvalid_0's rmse: 16859.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:08,761] Trial 56 finished with value: 16859.804174042016 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.1958637385354205, 'reg_lambda': 2.880040556471665, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 8, 'num_leaves': 18, 'min_child_samples': 6}. Best is trial 54 with value: 12986.354549574013.\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: 31803.4\n",
            "[2000]\tvalid_0's rmse: 30004.7\n",
            "[3000]\tvalid_0's rmse: 28595.1\n",
            "[4000]\tvalid_0's rmse: 27519.7\n",
            "[5000]\tvalid_0's rmse: 26640.8\n",
            "[6000]\tvalid_0's rmse: 25860\n",
            "[7000]\tvalid_0's rmse: 25185.3\n",
            "[8000]\tvalid_0's rmse: 24586\n",
            "[9000]\tvalid_0's rmse: 24032.6\n",
            "[10000]\tvalid_0's rmse: 23597.9\n",
            "[11000]\tvalid_0's rmse: 23139.5\n",
            "[12000]\tvalid_0's rmse: 22720.1\n",
            "[13000]\tvalid_0's rmse: 22309.3\n",
            "[14000]\tvalid_0's rmse: 21967.2\n",
            "[15000]\tvalid_0's rmse: 21651.6\n",
            "[16000]\tvalid_0's rmse: 21321.4\n",
            "[17000]\tvalid_0's rmse: 21062.9\n",
            "[18000]\tvalid_0's rmse: 20807.2\n",
            "[19000]\tvalid_0's rmse: 20578.7\n",
            "[20000]\tvalid_0's rmse: 20333.3\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[20000]\tvalid_0's rmse: 20333.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:22,896] Trial 57 finished with value: 20333.302914437973 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.627044774877573, 'reg_lambda': 2.0503447389387497, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 5, 'num_leaves': 48, 'min_child_samples': 16}. Best is trial 54 with value: 12986.354549574013.\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: 22091.6\n",
            "[2000]\tvalid_0's rmse: 17974.3\n",
            "[3000]\tvalid_0's rmse: 16145.4\n",
            "[4000]\tvalid_0's rmse: 15218.6\n",
            "[5000]\tvalid_0's rmse: 14708.6\n",
            "[6000]\tvalid_0's rmse: 14402.1\n",
            "[7000]\tvalid_0's rmse: 14266.7\n",
            "Early stopping, best iteration is:\n",
            "[7035]\tvalid_0's rmse: 14261.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:31,905] Trial 58 finished with value: 14261.78242240327 and parameters: {'boosting_type': 'goss', 'reg_alpha': 7.213673638759569, 'reg_lambda': 4.140812913371344, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 54 with value: 12986.354549574013.\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: 26107\n",
            "[2000]\tvalid_0's rmse: 24807.6\n",
            "Early stopping, best iteration is:\n",
            "[2550]\tvalid_0's rmse: 24372.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:33,578] Trial 59 finished with value: 24372.21512450622 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 8.908723789238318, 'reg_lambda': 0.8798105572742232, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 11}. Best is trial 54 with value: 12986.354549574013.\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: 37522.4\n",
            "[2000]\tvalid_0's rmse: 35979.5\n",
            "[3000]\tvalid_0's rmse: 35169.2\n",
            "[4000]\tvalid_0's rmse: 34574.8\n",
            "[5000]\tvalid_0's rmse: 34106.5\n",
            "Early stopping, best iteration is:\n",
            "[5115]\tvalid_0's rmse: 34062.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:35,546] Trial 60 finished with value: 34062.726170610615 and parameters: {'boosting_type': 'goss', 'reg_alpha': 6.478143687539045, 'reg_lambda': 1.457962594892291, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 8, 'num_leaves': 18, 'min_child_samples': 51}. Best is trial 54 with value: 12986.354549574013.\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: 22789.4\n",
            "[2000]\tvalid_0's rmse: 18988.8\n",
            "[3000]\tvalid_0's rmse: 17043.7\n",
            "[4000]\tvalid_0's rmse: 15982.1\n",
            "[5000]\tvalid_0's rmse: 15373.2\n",
            "[6000]\tvalid_0's rmse: 15054.4\n",
            "Early stopping, best iteration is:\n",
            "[6795]\tvalid_0's rmse: 14902.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:47,927] Trial 61 finished with value: 14902.628236314007 and parameters: {'boosting_type': 'goss', 'reg_alpha': 9.526973637121873, 'reg_lambda': 4.244337369789318, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 6, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 54 with value: 12986.354549574013.\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: 21467.9\n",
            "[2000]\tvalid_0's rmse: 17505.6\n",
            "[3000]\tvalid_0's rmse: 15852.3\n",
            "[4000]\tvalid_0's rmse: 15090.1\n",
            "[5000]\tvalid_0's rmse: 14659.1\n",
            "[6000]\tvalid_0's rmse: 14405.3\n",
            "[7000]\tvalid_0's rmse: 14255.2\n",
            "Early stopping, best iteration is:\n",
            "[6997]\tvalid_0's rmse: 14253.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:03:57,068] Trial 62 finished with value: 14253.759096315824 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.5758004350054593, 'reg_lambda': 3.4230087443504305, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 54 with value: 12986.354549574013.\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: 21034.8\n",
            "[2000]\tvalid_0's rmse: 17177.9\n",
            "[3000]\tvalid_0's rmse: 15539.7\n",
            "[4000]\tvalid_0's rmse: 14769.4\n",
            "[5000]\tvalid_0's rmse: 14344.2\n",
            "Early stopping, best iteration is:\n",
            "[5717]\tvalid_0's rmse: 14161\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:03,047] Trial 63 finished with value: 14161.047049119454 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.773383652594923, 'reg_lambda': 3.2565445728047053, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 54 with value: 12986.354549574013.\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: 25731.5\n",
            "[2000]\tvalid_0's rmse: 22101.3\n",
            "[3000]\tvalid_0's rmse: 20186.9\n",
            "[4000]\tvalid_0's rmse: 18858.1\n",
            "[5000]\tvalid_0's rmse: 17967.3\n",
            "[6000]\tvalid_0's rmse: 17360.9\n",
            "[7000]\tvalid_0's rmse: 16943.1\n",
            "[8000]\tvalid_0's rmse: 16623.2\n",
            "[9000]\tvalid_0's rmse: 16376.9\n",
            "[10000]\tvalid_0's rmse: 16190.8\n",
            "[11000]\tvalid_0's rmse: 16037.8\n",
            "Early stopping, best iteration is:\n",
            "[10994]\tvalid_0's rmse: 16035.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:13,605] Trial 64 finished with value: 16035.163796281078 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.3558910702291462, 'reg_lambda': 2.1953801731163587, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 8, 'num_leaves': 23, 'min_child_samples': 6}. Best is trial 54 with value: 12986.354549574013.\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: 27599.8\n",
            "[2000]\tvalid_0's rmse: 24465.3\n",
            "[3000]\tvalid_0's rmse: 22440.7\n",
            "[4000]\tvalid_0's rmse: 21133.2\n",
            "[5000]\tvalid_0's rmse: 20177.3\n",
            "[6000]\tvalid_0's rmse: 19445.9\n",
            "[7000]\tvalid_0's rmse: 18986.3\n",
            "[8000]\tvalid_0's rmse: 18615.1\n",
            "[9000]\tvalid_0's rmse: 18337.4\n",
            "[10000]\tvalid_0's rmse: 18105.7\n",
            "[11000]\tvalid_0's rmse: 17927.1\n",
            "Early stopping, best iteration is:\n",
            "[11853]\tvalid_0's rmse: 17787.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:24,225] Trial 65 finished with value: 17787.141695913902 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.284779170565813, 'reg_lambda': 1.3239228219523695, 'colsample_bytree': 0.5, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 43, 'min_child_samples': 6}. Best is trial 54 with value: 12986.354549574013.\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: 28338.2\n",
            "[2000]\tvalid_0's rmse: 25759.2\n",
            "[3000]\tvalid_0's rmse: 23825.4\n",
            "[4000]\tvalid_0's rmse: 22484.6\n",
            "[5000]\tvalid_0's rmse: 21374.7\n",
            "[6000]\tvalid_0's rmse: 20501.7\n",
            "[7000]\tvalid_0's rmse: 19851.9\n",
            "[8000]\tvalid_0's rmse: 19341.3\n",
            "[9000]\tvalid_0's rmse: 18891.4\n",
            "[10000]\tvalid_0's rmse: 18514.3\n",
            "[11000]\tvalid_0's rmse: 18217.8\n",
            "[12000]\tvalid_0's rmse: 17954.2\n",
            "[13000]\tvalid_0's rmse: 17714.7\n",
            "[14000]\tvalid_0's rmse: 17534.2\n",
            "[15000]\tvalid_0's rmse: 17376.6\n",
            "Early stopping, best iteration is:\n",
            "[15949]\tvalid_0's rmse: 17230.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:38,509] Trial 66 finished with value: 17230.420994518357 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.687237551908612, 'reg_lambda': 2.83795055097684, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.005, 'max_depth': 7, 'num_leaves': 53, 'min_child_samples': 11}. Best is trial 54 with value: 12986.354549574013.\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",
            "[877]\tvalid_0's rmse: 12338.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:39,466] Trial 67 finished with value: 12338.665498601415 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.9731274536451826, 'reg_lambda': 0.8825276525195174, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 6, 'num_leaves': 48, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 21880.9\n",
            "[2000]\tvalid_0's rmse: 19073.5\n",
            "[3000]\tvalid_0's rmse: 17845.4\n",
            "Early stopping, best iteration is:\n",
            "[3422]\tvalid_0's rmse: 17456.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:41,592] Trial 68 finished with value: 17456.901073411995 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.7826710595470407, 'reg_lambda': 0.6509085899326545, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 6, 'num_leaves': 63, 'min_child_samples': 16}. Best is trial 67 with value: 12338.665498601415.\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: 25496.5\n",
            "[2000]\tvalid_0's rmse: 22670\n",
            "[3000]\tvalid_0's rmse: 21193.4\n",
            "Early stopping, best iteration is:\n",
            "[3367]\tvalid_0's rmse: 20718.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:43,369] Trial 69 finished with value: 20718.932586886942 and parameters: {'boosting_type': 'goss', 'reg_alpha': 5.955294804486291, 'reg_lambda': 1.1388104725308077, 'colsample_bytree': 1.0, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 5, 'num_leaves': 48, 'min_child_samples': 21}. Best is trial 67 with value: 12338.665498601415.\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: 34801.7\n",
            "Early stopping, best iteration is:\n",
            "[1476]\tvalid_0's rmse: 34032\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:44,106] Trial 70 finished with value: 34031.97969349016 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.4899426464630114, 'reg_lambda': 0.9027579381519146, 'colsample_bytree': 1.0, 'subsample': 0.7, 'learning_rate': 0.05, 'max_depth': 4, 'num_leaves': 58, 'min_child_samples': 71}. Best is trial 67 with value: 12338.665498601415.\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",
            "[694]\tvalid_0's rmse: 13090.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:45,462] Trial 71 finished with value: 13090.774448039061 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.733691367541995, 'reg_lambda': 1.3956913047401724, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[488]\tvalid_0's rmse: 12826.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:46,586] Trial 72 finished with value: 12826.36258629206 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.861750080901028, 'reg_lambda': 1.5821754824917593, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 16456.9\n",
            "Early stopping, best iteration is:\n",
            "[1052]\tvalid_0's rmse: 16374.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:47,866] Trial 73 finished with value: 16374.396548074903 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.4161348712898483, 'reg_lambda': 1.5338188417206544, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 43, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[878]\tvalid_0's rmse: 13011.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:49,023] Trial 74 finished with value: 13011.947296576307 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.185374536077573, 'reg_lambda': 0.8342080623903309, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 18479.1\n",
            "[2000]\tvalid_0's rmse: 16987.8\n",
            "Early stopping, best iteration is:\n",
            "[2572]\tvalid_0's rmse: 16705.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:51,129] Trial 75 finished with value: 16705.144817718956 and parameters: {'boosting_type': 'goss', 'reg_alpha': 9.945767692636815, 'reg_lambda': 0.4316709555447753, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 38, 'min_child_samples': 11}. Best is trial 67 with value: 12338.665498601415.\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: 19496.5\n",
            "[2000]\tvalid_0's rmse: 18707.8\n",
            "Early stopping, best iteration is:\n",
            "[2364]\tvalid_0's rmse: 18640.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:52,455] Trial 76 finished with value: 18640.915427648844 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 2.4323028513117695, 'reg_lambda': 0.7349312567495908, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 12, 'num_leaves': 18, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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 2023-07-22 04:04:53,259] Trial 77 finished with value: 15254.106734050369 and parameters: {'boosting_type': 'goss', 'reg_alpha': 5.471725795569687, 'reg_lambda': 0.5304507295084386, 'colsample_bytree': 0.8, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 23, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[808]\tvalid_0's rmse: 15254.1\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",
            "[1000]\tvalid_0's rmse: 30270\n",
            "Early stopping, best iteration is:\n",
            "[1366]\tvalid_0's rmse: 29400\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:53,942] Trial 78 finished with value: 29399.96653064652 and parameters: {'boosting_type': 'goss', 'reg_alpha': 7.6002637382744, 'reg_lambda': 2.173026098480888, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 11, 'num_leaves': 28, 'min_child_samples': 36}. Best is trial 67 with value: 12338.665498601415.\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 2023-07-22 04:04:54,811] Trial 79 finished with value: 12764.419416597855 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.8184660932012047, 'reg_lambda': 1.3275421321069067, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 53, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[500]\tvalid_0's rmse: 12764.4\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: 17435.3\n",
            "Early stopping, best iteration is:\n",
            "[1441]\tvalid_0's rmse: 17219.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:56,572] Trial 80 finished with value: 17219.341930681734 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.4997164771748697, 'reg_lambda': 0.8315067279228323, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 58, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[450]\tvalid_0's rmse: 13878.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:57,684] Trial 81 finished with value: 13878.321478758528 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.9855388749037834, 'reg_lambda': 1.3998781166215162, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[574]\tvalid_0's rmse: 13018.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:04:59,318] Trial 82 finished with value: 13018.491432061495 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.94242828917807, 'reg_lambda': 1.8412291905394365, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 53, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 17152.7\n",
            "Early stopping, best iteration is:\n",
            "[1790]\tvalid_0's rmse: 16800.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:01,724] Trial 83 finished with value: 16800.2440644177 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.5970498277849288, 'reg_lambda': 1.8802340417291137, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 53, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[573]\tvalid_0's rmse: 13363.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:02,798] Trial 84 finished with value: 13363.25509010882 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.0292512879634708, 'reg_lambda': 1.256409705132436, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 68, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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 2023-07-22 04:05:03,442] Trial 85 finished with value: 16854.392955407824 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.1414711805083635, 'reg_lambda': 0.3411280376083147, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 48, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[380]\tvalid_0's rmse: 16854.4\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: 19098.4\n",
            "[2000]\tvalid_0's rmse: 17186.6\n",
            "Early stopping, best iteration is:\n",
            "[2742]\tvalid_0's rmse: 16786.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:05,792] Trial 86 finished with value: 16786.603508102035 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.262188692093602, 'reg_lambda': 0.9947342839624924, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 43, 'min_child_samples': 11}. Best is trial 67 with value: 12338.665498601415.\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: 15739.3\n",
            "Early stopping, best iteration is:\n",
            "[1263]\tvalid_0's rmse: 15517.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:07,635] Trial 87 finished with value: 15517.175921445412 and parameters: {'boosting_type': 'goss', 'reg_alpha': 0.9011573776871208, 'reg_lambda': 2.289519521737294, 'colsample_bytree': 0.9, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 43, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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: 22285.1\n",
            "[2000]\tvalid_0's rmse: 19446.7\n",
            "Early stopping, best iteration is:\n",
            "[2326]\tvalid_0's rmse: 18947.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:09,301] Trial 88 finished with value: 18947.374256509163 and parameters: {'boosting_type': 'goss', 'reg_alpha': 5.267696461949616, 'reg_lambda': 0.6108120122042315, 'colsample_bytree': 0.7, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 28, 'min_child_samples': 16}. Best is trial 67 with value: 12338.665498601415.\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: 16262\n",
            "Early stopping, best iteration is:\n",
            "[1506]\tvalid_0's rmse: 15826.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:10,907] Trial 89 finished with value: 15826.69096642663 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 1.5043606709930664, 'reg_lambda': 1.7783780113866117, 'colsample_bytree': 0.5, 'subsample': 0.7, 'learning_rate': 0.03, 'max_depth': 6, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[744]\tvalid_0's rmse: 16733.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:12,151] Trial 90 finished with value: 16733.40997361516 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.9127746514124975, 'reg_lambda': 0.8078746175753906, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 10, 'num_leaves': 53, 'min_child_samples': 11}. Best is trial 67 with value: 12338.665498601415.\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",
            "[587]\tvalid_0's rmse: 13168.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:13,762] Trial 91 finished with value: 13168.44247491491 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.1094347373887947, 'reg_lambda': 1.3706287310338237, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 68, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 37083.7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:14,358] Trial 92 finished with value: 35690.55710661 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.145935946075254, 'reg_lambda': 1.5222269029168443, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 11, 'num_leaves': 78, 'min_child_samples': 91}. Best is trial 67 with value: 12338.665498601415.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2000]\tvalid_0's rmse: 35710.6\n",
            "Early stopping, best iteration is:\n",
            "[1981]\tvalid_0's rmse: 35690.6\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: 15685.8\n",
            "Early stopping, best iteration is:\n",
            "[1454]\tvalid_0's rmse: 15469.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:16,112] Trial 93 finished with value: 15469.515535924053 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.9439199019986164, 'reg_lambda': 1.2622929208184979, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 63, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[571]\tvalid_0's rmse: 13950.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:17,062] Trial 94 finished with value: 13950.604068247323 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.1925981455524886, 'reg_lambda': 0.9893404276484257, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 48, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 16120.5\n",
            "Early stopping, best iteration is:\n",
            "[1453]\tvalid_0's rmse: 15680\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:18,512] Trial 95 finished with value: 15680.017149203857 and parameters: {'boosting_type': 'goss', 'reg_alpha': 6.718122952904974, 'reg_lambda': 2.4872667424259234, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 68, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[467]\tvalid_0's rmse: 14064\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:19,351] Trial 96 finished with value: 14063.995080212288 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.744918048239693, 'reg_lambda': 1.867566700163619, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 58, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 18577.1\n",
            "[2000]\tvalid_0's rmse: 16943.7\n",
            "Early stopping, best iteration is:\n",
            "[2487]\tvalid_0's rmse: 16703.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:21,511] Trial 97 finished with value: 16703.33688552741 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.8108720956579643, 'reg_lambda': 0.698206377272884, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 53, 'min_child_samples': 11}. Best is trial 67 with value: 12338.665498601415.\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: 16227.5\n",
            "Early stopping, best iteration is:\n",
            "[1392]\tvalid_0's rmse: 15957.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:23,189] Trial 98 finished with value: 15957.20185781716 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.826324277107948, 'reg_lambda': 1.2319038414535577, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 33, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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: 14596.7\n",
            "Early stopping, best iteration is:\n",
            "[1328]\tvalid_0's rmse: 14450.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:25,924] Trial 99 finished with value: 14450.757464602806 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.559697674752237, 'reg_lambda': 0.9118523160855287, 'colsample_bytree': 0.8, 'subsample': 1.0, 'learning_rate': 0.03, 'max_depth': 10, 'num_leaves': 38, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 19333.8\n",
            "Early stopping, best iteration is:\n",
            "[1445]\tvalid_0's rmse: 18617.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:27,246] Trial 100 finished with value: 18617.56358522466 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.0280216038984706, 'reg_lambda': 1.584901205637044, 'colsample_bytree': 0.6, 'subsample': 1.0, 'learning_rate': 0.1, 'max_depth': 6, 'num_leaves': 28, 'min_child_samples': 11}. Best is trial 67 with value: 12338.665498601415.\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",
            "[487]\tvalid_0's rmse: 13014.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:28,219] Trial 101 finished with value: 13014.408006485426 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.0547063086074537, 'reg_lambda': 1.1910490915227714, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 68, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[821]\tvalid_0's rmse: 13318.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:29,825] Trial 102 finished with value: 13318.240658321121 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.9737712560791465, 'reg_lambda': 2.5429584964544785, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 83, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[458]\tvalid_0's rmse: 13828.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:30,661] Trial 103 finished with value: 13828.15120659474 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.3178231934026627, 'reg_lambda': 1.1290799298943348, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 68, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 15822.3\n",
            "Early stopping, best iteration is:\n",
            "[1317]\tvalid_0's rmse: 15635.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:33,362] Trial 104 finished with value: 15635.947449874888 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.236672076515621, 'reg_lambda': 2.0249762227632617, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 78, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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: 17377.3\n",
            "Early stopping, best iteration is:\n",
            "[1314]\tvalid_0's rmse: 17053.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:35,077] Trial 105 finished with value: 17053.420612365848 and parameters: {'boosting_type': 'goss', 'reg_alpha': 5.412526743838737, 'reg_lambda': 1.4978446066889382, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 43, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[502]\tvalid_0's rmse: 14619\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:35,928] Trial 106 finished with value: 14618.97616528577 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.7426495035481342, 'reg_lambda': 0.7146138673074688, 'colsample_bytree': 0.9, 'subsample': 0.85, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 48, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 15862.7\n",
            "Early stopping, best iteration is:\n",
            "[1525]\tvalid_0's rmse: 15474.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:38,278] Trial 107 finished with value: 15474.120726117588 and parameters: {'boosting_type': 'goss', 'reg_alpha': 7.8329629311889555, 'reg_lambda': 0.9902794962121558, 'colsample_bytree': 0.7, 'subsample': 0.7, 'learning_rate': 0.02, 'max_depth': 9, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 16532.7\n",
            "Early stopping, best iteration is:\n",
            "[1453]\tvalid_0's rmse: 16201.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:40,465] Trial 108 finished with value: 16201.463189578317 and parameters: {'boosting_type': 'goss', 'reg_alpha': 6.551453440780365, 'reg_lambda': 0.5632748561181921, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 38, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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: 22277.3\n",
            "Early stopping, best iteration is:\n",
            "[1008]\tvalid_0's rmse: 22271\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:41,134] Trial 109 finished with value: 22271.009322806563 and parameters: {'boosting_type': 'gbdt', 'reg_alpha': 3.756251483309493, 'reg_lambda': 1.387200017888328, 'colsample_bytree': 0.5, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 7, 'num_leaves': 88, 'min_child_samples': 11}. Best is trial 67 with value: 12338.665498601415.\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",
            "[508]\tvalid_0's rmse: 14040.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:42,034] Trial 110 finished with value: 14040.6087636808 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.7554038389920024, 'reg_lambda': 1.938353184996722, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 8, 'num_leaves': 58, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[743]\tvalid_0's rmse: 13688.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:43,492] Trial 111 finished with value: 13688.94419343175 and parameters: {'boosting_type': 'goss', 'reg_alpha': 3.1925445555713914, 'reg_lambda': 2.476343350043407, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 83, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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",
            "[583]\tvalid_0's rmse: 13948.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:44,728] Trial 112 finished with value: 13948.153373495079 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.553299279936993, 'reg_lambda': 2.740484341602376, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 88, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 16501.5\n",
            "Early stopping, best iteration is:\n",
            "[1300]\tvalid_0's rmse: 16270.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:46,308] Trial 113 finished with value: 16270.132495358783 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.4067902489394406, 'reg_lambda': 3.2593374613471595, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 78, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[696]\tvalid_0's rmse: 13468.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:47,439] Trial 114 finished with value: 13468.115221700853 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.057763397638192, 'reg_lambda': 1.1659001548100527, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 43, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 33216.4\n",
            "[2000]\tvalid_0's rmse: 31869.5\n",
            "Early stopping, best iteration is:\n",
            "[2167]\tvalid_0's rmse: 31594.4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:48,290] Trial 115 finished with value: 31594.377153533154 and parameters: {'boosting_type': 'goss', 'reg_alpha': 2.959371217419176, 'reg_lambda': 1.664159584087141, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 9, 'num_leaves': 73, 'min_child_samples': 56}. Best is trial 67 with value: 12338.665498601415.\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: 16446.4\n",
            "Early stopping, best iteration is:\n",
            "[1446]\tvalid_0's rmse: 16162.9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:50,232] Trial 116 finished with value: 16162.926122305818 and parameters: {'boosting_type': 'goss', 'reg_alpha': 6.0149575542475375, 'reg_lambda': 2.2240511949050115, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 10, 'num_leaves': 63, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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",
            "[766]\tvalid_0's rmse: 13832.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:51,655] Trial 117 finished with value: 13832.207364260754 and parameters: {'boosting_type': 'goss', 'reg_alpha': 4.3696768283025795, 'reg_lambda': 0.8017829028153463, 'colsample_bytree': 1.0, 'subsample': 1.0, 'learning_rate': 0.05, 'max_depth': 6, 'num_leaves': 53, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\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: 17750.8\n",
            "Early stopping, best iteration is:\n",
            "[1838]\tvalid_0's rmse: 16639.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[I 2023-07-22 04:05:54,342] Trial 118 finished with value: 16639.528145635086 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.665028396738309, 'reg_lambda': 4.4235323394526995, 'colsample_bytree': 0.9, 'subsample': 1.0, 'learning_rate': 0.03, 'max_depth': 8, 'num_leaves': 68, 'min_child_samples': 6}. Best is trial 67 with value: 12338.665498601415.\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 2023-07-22 04:05:54,960] Trial 119 finished with value: 15349.593667412657 and parameters: {'boosting_type': 'goss', 'reg_alpha': 1.291761917884168, 'reg_lambda': 2.8661602819597265, 'colsample_bytree': 1.0, 'subsample': 0.6, 'learning_rate': 0.1, 'max_depth': 7, 'num_leaves': 33, 'min_child_samples': 1}. Best is trial 67 with value: 12338.665498601415.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early stopping, best iteration is:\n",
            "[447]\tvalid_0's rmse: 15349.6\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": "cd1f63a7-247c-49fd-9941-1327e86154c9"
      },
      "id": "-HLJKgpuW6je",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Totalnumber of trials:  120\n",
            "Best RMSE score on validation data: 12338.665498601415\n",
            "------------------------------\n",
            "Best params:\n",
            "------------------------------\n",
            "boosting_type :\t goss\n",
            "reg_alpha :\t 3.9731274536451826\n",
            "reg_lambda :\t 0.8825276525195174\n",
            "colsample_bytree :\t 1.0\n",
            "subsample :\t 1.0\n",
            "learning_rate :\t 0.05\n",
            "max_depth :\t 6\n",
            "num_leaves :\t 48\n",
            "min_child_samples :\t 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have a pretty good RMSE score on validation data: 12338.665498601415"
      ],
      "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': 3.97,\n",
        "             'reg_lambda': 0.88,\n",
        "             'colsample_bytree': 1.0,\n",
        "             'subsample': 1.0,\n",
        "             'learning_rate': 0.05,\n",
        "             'max_depth': 6,\n",
        "             'num_leaves': 48,\n",
        "             'min_child_samples': 1,\n",
        "          }"
      ],
      "metadata": {
        "id": "cNiStTBnt04R"
      },
      "id": "cNiStTBnt04R",
      "execution_count": null,
      "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": "ba865800-36b2-4555-cdf6-c662228ae27f"
      },
      "id": "TIjk3zrFvDTh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using categorical_feature in Dataset.\n",
            "categorical_feature in Dataset is overridden.\n",
            "New categorical_feature is []\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 100 rounds\n",
            "[100]\ttraining's l2: 1.68637e+08\tvalid_0's l2: 3.50524e+08\n",
            "[200]\ttraining's l2: 8.36041e+07\tvalid_0's l2: 2.55989e+08\n",
            "[300]\ttraining's l2: 5.30458e+07\tvalid_0's l2: 2.30302e+08\n",
            "[400]\ttraining's l2: 3.58467e+07\tvalid_0's l2: 2.12857e+08\n",
            "[500]\ttraining's l2: 2.53169e+07\tvalid_0's l2: 2.06067e+08\n",
            "[600]\ttraining's l2: 1.80813e+07\tvalid_0's l2: 2.01463e+08\n",
            "[700]\ttraining's l2: 1.27702e+07\tvalid_0's l2: 1.99342e+08\n",
            "[800]\ttraining's l2: 9.36061e+06\tvalid_0's l2: 1.97024e+08\n",
            "[900]\ttraining's l2: 7.02391e+06\tvalid_0's l2: 1.93047e+08\n",
            "[1000]\ttraining's l2: 5.26416e+06\tvalid_0's l2: 1.9145e+08\n",
            "[1100]\ttraining's l2: 4.04002e+06\tvalid_0's l2: 1.90823e+08\n",
            "[1200]\ttraining's l2: 3.23886e+06\tvalid_0's l2: 1.90553e+08\n",
            "Early stopping, best iteration is:\n",
            "[1197]\ttraining's l2: 3.26136e+06\tvalid_0's l2: 1.9042e+08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98SlsLq5vdDg",
        "outputId": "ccf40de3-720d-49eb-8560-dad53d2cdfa9"
      },
      "id": "98SlsLq5vdDg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13799.282803291926"
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ]
    },
    {
      "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 13800.\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": null,
      "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": "e90b3f24-c8bf-4f3e-f507-ff3248f7a166"
      },
      "id": "BV-yDOO8jXkZ",
      "execution_count": null,
      "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": "befba05a-77a9-471d-daba-acb52add402c"
      },
      "id": "AfCtqO5_oCXm",
      "execution_count": null,
      "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": "d0ee48eb-e3fe-4358-c3fa-273637e5f6c5"
      },
      "id": "hEwY_qRQoSt8",
      "execution_count": null,
      "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, X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "mHXiCos4oeVW",
        "outputId": "a036e3e8-1645-4e94-9657-b54fbbf27962"
      },
      "id": "mHXiCos4oeVW",
      "execution_count": null,
      "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 via Optuna\n",
        "pickle.dump(lgbmreg_optimized, open('lgbm_optimized.pkl', 'wb'))\n"
      ],
      "metadata": {
        "id": "UL0nntirX9xy"
      },
      "id": "UL0nntirX9xy",
      "execution_count": null,
      "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"
      ],
      "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": [],
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        "d02aa749",
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        "e572f249",
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  "nbformat": 4,
  "nbformat_minor": 5
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